title: A Situation Awareness-Based Framework for Design and Evaluation of Explainable AI

text: Recent advances in artificial intelligence (AI) have drawn attention to the need for AI systems to be understandable to human users. The explainable AI (XAI) literature aims to enhance human understanding and human-AI team performance by providing users with necessary information about AI system behavior. Simultaneously, the human factors literature has long addressed important considerations that contribute to human performance, including how to determine human informational needs. Drawing from the human factors literature, we propose a three-level framework for the development and evaluation of explanations about AI system behavior. Our proposed levels of XAI are based on the informational needs of human users, which can be determined using the levels of situation awareness (SA) framework from the human factors literature. Based on our levels of XAI framework, we also propose a method for assessing the effectiveness of XAI systems.


title: fuzzy-rough-learn 0.1: A Python Library for Machine Learning with Fuzzy Rough Sets

text: We present fuzzy-rough-learn, the first Python library of fuzzy rough set machine learning algorithms. It contains three algorithms previously implemented in R and Java, as well as two new algorithms from the recent literature. We briefly discuss the use cases of fuzzy-rough-learn and the design philosophy guiding its development, before providing an overview of the included algorithms and their parameters.


title: Efficient Context-Sensitive CFI Enforcement Through a Hardware Monitor

text: Recent works on Control-Flow Integrity (CFI) have mainly focused on Context-Sensitive CFI policies to provide higher security guarantees. They utilize a debugging hardware feature in modern Intel CPUs, Processor Trace (PT), to efficiently collect runtime contextual information. These PT-based CFI mechanisms offload the processing of the collected PT trace and CFI enforcement onto idle cores. However, a processor does not always have idle cores due to the commonly-used multi-threaded applications such as web browsers. In fact, dedicating one or more cores for CFI enforcement reduces the number of available cores for running user programs. Our evaluation with a state-of-the-art CFI mechanism ([Formula: see text]CFI) shows that the performance overhead of a CFI mechanism can substantially increase (up to 652% on a single-core processor) when there is no idle core for CFI enforcement. To improve the performance of [Formula: see text]CFI, we propose to leverage a hardware monitor that unlike PT does not incur trace processing overhead. We show that the hardware monitor can be used to efficiently collect program traces (<1% overhead) in their original forms and apply [Formula: see text]CFI. We prototype the hardware-monitor based [Formula: see text]CFI on a single-core RISC-V processor. Our analysis show that hardware-monitor based [Formula: see text]CFI incurs, on average, 43% (up to 277%) performance overhead.


title: The Reduct of a Fuzzy [Formula: see text]-Covering

text: This paper points some mistakes of three algorithms of updating the reduct in fuzzy [Formula: see text]-covering via matrix approaches while adding and deleting some objects of the universe, and gives corrections of these mistakes. Moreover, we study the reduct of a fuzzy [Formula: see text]-covering while adding and deleting objects further.


title: Three-Way Decision for Handling Uncertainty in Machine Learning: A Narrative Review

text: In this work we introduce a framework, based on three-way decision (TWD) and the trisecting-acting-outcome model, to handle uncertainty in Machine Learning (ML). We distinguish between handling uncertainty affecting the input of ML models, when TWD is used to identify and properly take into account the uncertain instances; and handling the uncertainty lying in the output, where TWD is used to allow the ML model to abstain. We then present a narrative review of the state of the art of applications of TWD in regard to the different areas of concern identified by the framework, and in so doing, we will highlight both the points of strength of the three-way methodology, and the opportunities for further research.


title: On Positive-Correlation-Promoting Reducts

text: We introduce a new rough-set-inspired binary feature selection framework, whereby it is preferred to choose attributes which let us distinguish between objects (cases, rows, examples) having different decision values according to the following mechanism: for objects u1 and u2 with decision values [Formula: see text] and [Formula: see text], it is preferred to select attributes a such that [Formula: see text] and [Formula: see text], with the secondary option – if the first one is impossible – to select a such that [Formula: see text] and [Formula: see text]. We discuss the background for this approach, originally inspired by the needs of the genetic data analysis. We show how to derive the sets of such attributes – called positive-correlation-promoting reducts (PCP reducts in short) – using standard calculations over appropriately modified rough-set-based discernibility matrices. The proposed framework is implemented within the RoughSets R package which is widely used for the data exploration and knowledge discovery purposes.


title: In-Time Explainability in Multi-Agent Systems: Challenges, Opportunities, and Roadmap

text: In the race for automation, distributed systems are required to perform increasingly complex reasoning to deal with dynamic tasks, often not controlled by humans. On the one hand, systems dealing with strict-timing constraints in safety-critical applications mainly focused on predictability, leaving little room for complex planning and decision-making processes. Indeed, real-time techniques are very efficient in predetermined, constrained, and controlled scenarios. Nevertheless, they lack the necessary flexibility to operate in evolving settings, where the software needs to adapt to the changes of the environment. On the other hand, Intelligent Systems (IS) increasingly adopted Machine Learning (ML) techniques (e.g., subsymbolic predictors such as Neural Networks). The seminal application of those IS started in zero-risk domains producing revolutionary results. However, the ever-increasing exploitation of ML-based approaches generated opaque systems, which are nowadays no longer socially acceptable—calling for eXplainable AI (XAI). Such a problem is exacerbated when IS tend to approach safety-critical scenarios. This paper highlights the need for on-time explainability. In particular, it proposes to embrace the Real-Time Beliefs Desires Intentions (RT-BDI) framework as an enabler of eXplainable Multi-Agent Systems (XMAS) in time-critical XAI.


title: Linguistic Summaries Generation with Hybridization Method Based on Rough and Fuzzy Sets

text: In this paper authors propose a new algorithm for linguistic data summarization based on hybridization of rough sets and fuzzy sets techniques. The new algorithm applies rough sets theory for feature selection in early stages of linguistic summaries’ generation. The rough sets theory was used to reduce on significant way, the amount on summaries obtained by others algorithms. The algorithm combines lower approximation, k grade dependency and fuzzy sets to get linguistic summaries. The results of proposed algorithm are compared with association rules approach. In order to validate the algorithm proposed, authors apply both qualitative and quantitative methods. Authors used two databases in order to validate the algorithm; theses databases belong to “Repository of Project Management Research”. The first database is associated to personality traits and human performance in software projects. The second database is associated to analysis of revenue assurance in different organization. Considering quantitative approach, the algorithm proposed, obtains better results than the algorithm based on association rules; while regards execution time, the best algorithm was the algorithm based on association rules, because rough sets theory was high time-consuming technique.


title: Object Oriented Protoconcepts and Logics for Double and Pure Double Boolean Algebras

text: The notion of a protoconcept in the framework of Yao’s object oriented concepts is proposed. Approximations by object oriented concepts are defined and these ‘object oriented protoconcepts’ are characterized using them. It is shown that the object oriented protoconcepts form a double Boolean algebra, and any double Boolean algebra is quasi-embedded in an algebra of protoconcepts. A logic DBL for the class of double Boolean algebras is proposed along with an extension PDBL for the class of pure double Boolean algebras. Utilizing the representation result for (pure) double Boolean algebras, it is established that DBL (PDBL) is sound and complete with respect to a semantics based on object oriented protoconcepts (semiconcepts).


title: Detecting Overlapping Communities Using Distributed Neighbourhood Threshold in Social Networks

text: In this work, we have proposed a simple overlapping community detection algorithm based on a distributed neighbourhood threshold method (DNTM). DNTM uses pre-partitioned disjoint communities and then analyzes the neighbourhood distribution of boundary nodes in disjoint communities to detect overlapping communities. It is a form of seed-based global method since boundary nodes are considered as seeds and become the starting point for detecting overlapping communities. Threshold value for each boundary node is used as minimum influence by the neighbours of a node in order to determine its belongingness to any community. The effectiveness of the DNTM algorithm has been demonstrated by testing on fifteen real-world datasets and compared with seven overlapping community detection algorithms. DNTM outperforms comparable algorithms with 10 out of 15 datasets and gives comparable results for the remaining 5 datasets in terms of the extended modularity [Formula: see text] measure. Experiments with various disjoint algorithms on 15 datasets reveal that DNTM with tolerance community detection (TCD) as a preprocessing algorithm gives the best result.


title: Agent-Based Explanations in AI: Towards an Abstract Framework

text: Recently, the eXplainable AI (XAI) research community has focused on developing methods making Machine Learning (ML) predictors more interpretable and explainable. Unfortunately, researchers are struggling to converge towards an unambiguous definition of notions such as interpretation, or, explanation—which are often (and mistakenly) used interchangeably. Furthermore, despite the sound metaphors that Multi-Agent System (MAS) could easily provide to address such a challenge, and agent-oriented perspective on the topic is still missing. Thus, this paper proposes an abstract and formal framework for XAI-based MAS, reconciling notions, and results from the literature.


title: Distributed Heterogeneous N-Variant Execution

text: N-Variant Execution (NVX) systems utilize artificial diversity techniques to enhance software security. The general idea is to run multiple different variants of the same program alongside each other while monitoring their diverging behavior on a malicious input. Existing NVX systems execute diversified program variants on a single host. This means the level of inter-variant diversity will be limited to what a single platform can offer, without costly emulation. This paper presents DMON , a novel distributed NVX design that executes native program variants across multiple heterogeneous hosts. Our approach greatly increases the level of diversity between the simultaneously running variants that can be supported, encompassing different ISAs and ABIs. Our evaluation shows that DMON can provide comparable performance to traditional, non-distributed NVX systems, while enhancing security.


title: Web Runner 2049: Evaluating Third-Party Anti-bot Services

text: Given the ever-increasing number of malicious bots scouring the web, many websites are turning to specialized services that advertise their ability to detect bots and block them. In this paper, we investigate the design and implementation details of commercial anti-bot services in an effort to understand how they operate and whether they can effectively identify and block malicious bots in practice. We analyze the JavaScript code which their clients need to include in their websites and perform a set of gray box and black box analyses of their proprietary back-end logic, by simulating bots utilizing well-known automation tools and popular browsers. On the positive side, our results show that by relying on browser fingerprinting, more than 75% of protected websites in our dataset, successfully defend against attacks by basic bots built with Python scripts or PhantomJS. At the same time, by using less popular browsers in terms of automation (e.g., Safari on Mac and Chrome on Android) attackers can successfully bypass the protection of up to 82% of protected websites. Our findings show that the majority of protected websites are prone to bot attacks and the existing anti-bot solutions cannot substantially limit the ability of determined attackers. We have responsibly disclosed our findings with the anti-bot service providers.


title: It Never Rains but It Pours: Analyzing and Detecting Fake Removal Information Advertisement Sites

text: Fake antivirus (AV) software is a serious threat on the Internet to make users install malware and expose their personal information. Fake removal information advertisement (FRAD) sites, which introduce fake removal information for cyber threats, have emerged as platforms for distributing fake AV software. Although FRAD sites seriously threaten users who have been suffering from cyber threats and need information for removing them, little attention has been given to revealing these sites. In this paper, we propose a system to automatically crawl the web and identify FRAD sites. To shed light on the pervasiveness of this type of attack, we performed a comprehensive analysis of both passively and actively collected data. Our system collected 2,913 FRAD sites in 31 languages, which have 73.5 million visits per month in total. We show that FRAD sites occupy search results when users search for cyber threats, thus preventing the users from obtaining the correct information.


title: On the Behavior of Fuzzy Grey Cognitive Maps

text: Fuzzy Cognitive Maps (FCMs) are recurrent neural networks made up of well-defined neurons and causal relations. Fuzzy Grey Cognitive Maps (FGCMs) are an extension of FCMs, intended to surpass the intrinsic uncertainties modeling real-world problems by means of Grey theory. Despite the rising number of studies about FGCM-based models, little has been investigated with regard to the convergence of such networks. In this paper, we build a mathematical basis to uncover the behavior FGCM-based models equipped with transfer F-functions. To do so, we propose sufficient conditions for the existence and unicity of fixed-point attractors. Also, the results reported in the literature on the convergence of FGCMs, are compared with ours. Furthermore, we elucidate the reach and depth of our findings, especially and not exclusive to the prediction of FCMs’ behavior.


title: Understanding Android VoIP Security: A System-Level Vulnerability Assessment

text: VoIP is a class of new technologies that deliver voice calls over the packet-switched networks, which surpasses the legacy circuit-switched telecom telephony. Android provides the native support of VoIP, including the recent VoLTE and VoWiFi standards. While prior works have analyzed the weaknesses of VoIP network infrastructure and the privacy concerns of third-party VoIP apps, no efforts were attempted to investigate the (in)security of Android’s VoIP integration at the system level. In this paper, we first demystify Android VoIP’s protocol stack and all its four attack surfaces. We then propose a novel vulnerability assessment approach that assembles on-device Intent/API fuzzing, network-side packet fuzzing, and targeted code auditing. By testing Android from version 7.0 to the recent 9.0, we have discovered 8 zero-day Android VoIP vulnerabilities, all of which were confirmed by Google with bug bounty awards. The security consequences are serious, including denying voice calls, caller ID spoofing, unauthorized call operations, and remote code execution. To mitigate these vulnerabilities and further improve Android VoIP security, we uncover a new root cause that requires developers’ attention during their design and implementation.


title: Concept Analysis Using Quantitative Structured Three-Way Rough Set Approximations

text: One important topic of concept analysis is to learn an intension of a concept through a given extension. In the case where an exact intension cannot be formulated due to limited information, rough set theory introduces approximations to roughly learn the intension. Pawlak originally proposes a qualitative formulation of approximations which allows no error in the learned intension. Various quantitative formulations have been studied as generalizations, most of which use probabilistic measures. In contrast, non-probabilistic formulations have not been fully investigated. On the other hand, three-way approximations and structured approximations have been proposed to emphasize the semantics of approximations for the purpose of learning and interpreting intension. To combine the benefits of these two directions of generalizations, this paper investigates quantitative structured three-way approximations based on both probabilistic and non-probabilistic measures in the context of both complete and incomplete information.


title: Rough Net Approach for Community Detection Analysis in Complex Networks

text: Rough set theory has many interesting applications in circumstances characterized by vagueness. In this paper, the applications of rough set theory in community detection analysis are discussed based on the Rough Net definition. We will focus the application of Rough Net on community detection validity in both monoplex and multiplex networks. Also, the topological evolution estimation between adjacent layers in dynamic networks is discussed and a new community interaction visualization approach combining both complex network representation and Rough Net definition is adopted to interpret the community structure. We provide some examples that illustrate how the Rough Net definition can be used to analyze the properties of the community structure in real-world networks, including dynamic networks.


title: Fuzzy FCA Attribute Reduction Properties in Rough Set Theory

text: Formal concept analysis and rough set theory are two of the most important mathematical tools for the treatment of information collected on relational systems. In particular, the idea of reducing the size of a database is widely studied in both theories separately. There are some papers that studied the reduction of a formal context by means of reducts from rough set. In this paper, we are focused in the reduction obtained in an information system considering the FCA reduction mechanism.


title: Automated CPE Labeling of CVE Summaries with Machine Learning

text: Open Source Security and Dependency Vulnerability Management (DVM) has become a more vital part of the software security stack in recent years as modern software tend to be more dependent on open source libraries. The largest open source of vulnerabilities is the National Vulnerability Database (NVD), which supplies developers with machine-readable vulnerabilities. However, sometimes Common Vulnerabilities and Exposures (CVE) have not been labeled with a Common Platform Enumeration (CPE) -version, -product and -vendor. This makes it very hard to automatically discover these vulnerabilities from import statements in dependency files. We, therefore, propose an automatic process of matching CVE summaries with CPEs through the machine learning task called Named Entity Recognition (NER). Our proposed model achieves an F-measure of 0.86 with a precision of 0.857 and a recall of 0.865, outperforming previous research for automated CPE-labeling of CVEs.


title: Multi-granularity Complex Network Representation Learning

text: Network representation learning aims to learn the low dimensional vector of the nodes in a network while maintaining the inherent properties of the original information. Existing algorithms focus on the single coarse-grained topology of nodes or text information alone, which cannot describe complex information networks. However, node structure and attribution are interdependent, indecomposable. Therefore, it is essential to learn the representation of node based on both the topological structure and node additional attributes. In this paper, we propose a multi-granularity complex network representation learning model (MNRL), which integrates topological structure and additional information at the same time, and presents these fused information learning into the same granularity semantic space that through fine-to-coarse to refine the complex network. Experiments show that our method can not only capture indecomposable multi-granularity information, but also retain various potential similarities of both topology and node attributes. It has achieved effective results in the downstream work of node classification and the link prediction on real-world datasets.


title: Explainable Agents for Less Bias in Human-Agent Decision Making

text: As autonomous agents become more self-governing, ubiquitous and sophisticated, it is vital that humans should have effective interactions with them. Agents often use Machine Learning (ML) for acquiring expertise, but traditional ML methods produce opaque results which are difficult to interpret. Hence, these autonomous agents should be able to explain their behaviour and decisions before they can be trusted by humans. This paper focuses on analyzing the human understanding of the explainable agents behaviour. It conducts a preliminary human-agent interaction study to investigate the effect of explanations on the introduced bias in human-agent decision making for the human participants. We test the hypothesis where different explanation types are used to detect the bias introduced in the autonomous agents decisions. We present three user groups: Agents without explanation, and explainable agents using two different algorithms which automatically generate different explanations for agent actions. Quantitative analysis of three user groups (n = 20, 25, 20) in which users detect the bias in agents’ decisions for each explanation type for 15 test data cases is conducted for three different explanations types. Although the interaction study does not give significant findings, but it shows the notable differences between the explanation based recommendations and non-XAI recommendations in human-agent decision making.


title: Decision Theory Meets Explainable AI

text: Explainability has been a core research topic in AI for decades and therefore it is surprising that the current concept of Explainable AI (XAI) seems to have been launched as late as 2016. This is a problem with current XAI research because it tends to ignore existing knowledge and wisdom gathered over decades or even centuries by other relevant domains. This paper presents the notion of Contextual Importance and Utility (CIU), which is based on known notions and methods of Decision Theory. CIU extends the notions of importance and utility for the non-linear models of AI systems and notably those produced by Machine Learning methods. CIU provides a universal and model-agnostic foundation for XAI.


title: Towards Student Centric Rough Concept Inventories

text: In the context of education research, a concept inventory is an instrument (that consists of a number of multiple-choice questions) designed to test the understanding of concepts (and possibly the reasons for failure to understand) by learners. Subject to a few caveats they are known to be somewhat effective in non student centric learning environments. In this research the issue of adapting the subject/concept-specific instruments to make room for diverse response patterns (including vague ones) is explored in some detail by the present author. It is shown that higher granular operator spaces (or partial algebras) with additional temporal and key operators are well suited for handling them. An improved version of concept inventory called rough concept inventory that can handle vague subjective responses is also proposed in this research.


title: HGAR: Hybrid Granular Algorithm for Rating Recommendation

text: Recommendation algorithms based on collaborative filtering show products which people might like and play an important role in personalized service. Nevertheless, the most of them just adopt explicit information feedback and achieve low recommendation accuracy. In recent years, deep learning methods utilize non-linear network framework to receive feature representation of massive data, which can obtain implicit information feedback. Therefore, many algorithms are designed based on deep learning to improve recommendation effects. Even so, the results are unsatisfactory. The reason is that they never consider explicit information feedback. In this paper, we propose a Hybrid Granular Algorithm for Rating Recommendation (HGAR), which is based on granulation computing. The core idea is to explore the multi-granularity of interaction information for both explicit and implicit feedback to predict the users ratings. Thus, we used Singular Value Decomposition model to get explicit information and implicit information can be received by multi-layer perception of deep learning. In addition, we fused the two part information when the two models are jointly trained. Therefore, HGAR can explore the multi-granularity of interaction information which learned explicit interaction information and mined implicit information in different information granular level. Experiment results show that HGAR significantly improved recommendation accuracy compared with different recommendation models including collaborative filtering and deep learning methods.


title: Towards the Role of Theory of Mind in Explanation

text: Theory of Mind is commonly defined as the ability to attribute mental states (e.g., beliefs, goals) to oneself, and to others. A large body of previous work—from the social sciences to artificial intelligence—has observed that Theory of Mind capabilities are central to providing an explanation to another agent or when explaining that agent’s behaviour. In this paper, we build and expand upon previous work by providing an account of explanation in terms of the beliefs of agents and the mechanism by which agents revise their beliefs given possible explanations. We further identify a set of desiderata for explanations that utilize Theory of Mind. These desiderata inform our belief-based account of explanation.


title: On the Security of Application Installers and Online Software Repositories

text: The security of application installers is often overlooked, but the security risks associated to these pieces of code are not negligible. Online public repositories have been one of the most popular ways for end users to obtain software, but there is a lack of systematic security evaluation of popular public repositories. In this paper, we bridge this gap by analyzing five popular software repositories. We focus on their software updating dynamics, as well as the presence of traces of vulnerable and/or trojanized applications among the top-100 most downloaded Windows programs on each of the evaluated repositories. We analyzed 2,935 unique programs collected in a period of 144 consecutive days. Our results show that: (i) the repositories frequently exhibit rank changes due to applications fast climbing toward the first positions; (ii) the repositories often update their payloads, which may cause the distribution of distinct binaries for the same intended application (binaries for the same applications may also be different in each repository); (iii) the installers are composed by multiple components and often download payloads from the Internet to complete their installation steps, posing new risks for users (we demonstrate that some installers are vulnerable to content tampering through man-in-the-middle attacks); (iv) the ever-changing nature of repositories and installers makes them prone to abuse, as we observed that 30% of all applications were reported malicious by at least one AV.


title: Comparative Approaches to Granularity in General Rough Sets

text: A number of nonequivalent perspectives on granular computing are known in the literature, and many are in states of continuous development. Further related concepts of granules and granulations may be incompatible in many senses. This expository paper is intended to explain basic aspects of these from a critical perspective, their range of applications and provide directions relative to general rough sets and related formal approaches to vagueness. General granular principles related to knowledge are also mentioned.


title: Functional Extensions of Knowledge Representation in General Rough Sets

text: A number of low and high-level models of general rough sets can be used to represent knowledge. Often binary relations between attributes or collections thereof have deeper properties related to decisions, inference or vision that can be expressed in ternary functional relationships (or groupoid operations) – this is investigated from a minimalist perspective in this research by the present author. General approximation spaces and reflexive up-directed versions thereof are used by her as the basic frameworks. Related semantic models are invented and an interpretation is proposed in this research. Further granular operator spaces and variants are shown to be representable as partial algebras through the method. An analogous representation for all covering spaces does not necessarily hold. Applications to education research contexts that possibly presume a distributed cognition perspective are also outlined.


title: Short Paper - Taming the Shape Shifter: Detecting Anti-fingerprinting Browsers

text: When it comes to leaked credentials and credit card information, we observe the development and use of anti-fingerprinting browsers by malicious actors. These tools are carefully designed to evade detection, often by mimicking the browsing environment of the victim whose credentials were stolen. Even though these tools are popular in the underground markets, they have not received enough attention by researchers. In this paper, we report on the first evaluation of four underground, commercial, and research anti-fingerprinting browsers and highlight their high success rate in bypassing browser fingerprinting. Despite their success against well-known fingerprinting methods and libraries, we show that even slightest variation in the simulated fingerprint compared to the real ones can give away the presence of anti-fingerprinting tools. As a result, we provide techniques and fingerprint-based signatures that can be used to detect the current generation of anti-fingerprinting browsers.


title: Rough Forgetting

text: Recent work in the area of Knowledge Representation and Reasoning has focused on modification and optimization of knowledge bases (KB) through the use of forgetting operators of the form [Formula: see text], where [Formula: see text] is a set of relations in the language signature used to specify the KB. The result of this operation is a new KB where the relations in [Formula: see text] are removed from the KB in a principled manner resulting in a more efficient representation of the KB for different purposes. The forgetting operator is also reflected semantically in terms of the relation between the original models of the KB and the models for the revised KB after forgetting. In this paper, we first develop a rough reasoning framework where our KB’s consist of rough formulas with a semantics based on a generalization of Kleene algebras. Using intuitions from the classical case, we then define a forgetting operator that can be applied to rough KBs removing rough relations. A constructive basis for generating a new KB as the result of applying the forgetting operator to a rough KB is specified using second-order quantifier elimination techniques. We show the application of this technique with some practical examples.


title: HAEPG: An Automatic Multi-hop Exploitation Generation Framework

text: Automatic exploit generation for heap vulnerabilities is an open challenge. Current studies require a sensitive pointer on the heap to hijack the control flow and pay little attention to vulnerabilities with limited capabilities. In this paper, we propose HAEPG, an automatic exploit framework that can utilize known exploitation techniques to guide exploit generation. We implemented a prototype of HAEPG based on the symbolic execution engine S2E [15] and provided four exploitation techniques for it as prior knowledge. HAEPG takes crashing inputs, programs, and prior knowledge as input, and generates exploits for vulnerabilities with limited capabilities by abusing heap allocator’s internal functionalities. We evaluated HAEPG with 24 CTF programs, and the results show that HAEPG is able to accurately reason about the type of vulnerability for 21 (87.5%) of them, and generate exploits that spawn a shell for 16 (66.7%) of them. All the exploits could bypass NX [25] and Full RELRO [28] security mechanisms.


title: Algebraic Structure of Adjoint Triples Generating a Weak Negation on the Unit Interval

text: This paper presents a theoretical research about the relationship between weak negations and adjoint negations. Adjoint negations are a generalization of residuated negations built from the implications of an adjoint triple. Specifically, this work shows how to build adjoint triples on the unit interval such that their adjoint negations coincide with a given weak negation. Moreover, the algebraic structure formed by these adjoint triples is also investigated.


title: Engaging the Media: Telling Our Operations Research Stories to the Public

text: Academic research occasionally captures the attention of the media. When this happens, there is a small window of opportunity to disseminate the real-world impact of our research and the value of our operations research and analytics expertise to the public. To do so, we must package our messages for public consumption. In this article, I summarize principles for interacting with the media, describe what various media interactions are like, and offer tips for capitalizing on one’s expertise. Finally, I reflect on what we have to offer to journalists and the value of telling our stories to the public.


title: Seeing and Viewing Through a Postdigital Pandemic: Shifting from Physical Proximity to Scopic Mediation

text: This paper addresses a particular area of concern regarding our habits that pertains to embodied experience and education through the Covid-19 pandemic: the shift from comfort to discomfort regarding face-to-face social interaction within the same physical space. To explore this transition, I use the related concepts of seeing and viewing from Isaac Asimov’s novel, The Naked Sun (1957), which are useful tools for investigating a probable collateral effect of rapid social distancing for the sake of avoiding contagion and includes replacing physically proximate interaction and procedures with a scopic mediation. Seeing and viewing provide concepts for understanding how values change in the midst of fears concerning contagion through physical contact that are mollified through the use of technology analogous to video conferencing. Postdigital education and the concepts of we-think, we-learn, and we-act provide critical tools for helping us understand this transition of perspective regarding educational and social practices in the midst of a pandemic.


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title: Pressure Support Ventilation (PSV) versus Neurally Adjusted Ventilatory Assist (NAVA) in difficult to wean pediatric ARDS patients: a physiologic crossover study

text: BACKGROUND: Neurally adjusted ventilatory assist (NAVA) is an innovative mode for assisted ventilation that improves patient-ventilator interaction in children. The aim of this study was to assess the effects of patient-ventilator interaction comparing NAVA with pressure support ventilation (PSV) in patients difficult to wean from mechanical ventilation after moderate pediatric acute respiratory distress syndrome (PARDS). METHODS: In this physiological crossover study, 12 patients admitted in the Pediatric Intensive Care Unit (PICU) with moderate PARDS failing up to 3 spontaneous breathing trials in less than 7 days, were enrolled. Patients underwent three study conditions lasting 1 h each: PSV1, NAVA and PSV2. RESULTS: The Asynchrony Index (AI) was significantly reduced during the NAVA trial compared to both the PSV1 and PSV2 trials (p = 0.001). During the NAVA trial, the inspiratory and expiratory trigger delays were significantly shorter compared to those obtained during PSV1 and PSV2 trials (Delay(trinsp)p < 0.001, Delay(trexp)p = 0.013). These results explain the significantly longer Time(sync) observed during the NAVA trial (p < 0.001). In terms of gas exchanges, PaO(2) value significantly improved in the NAVA trial with respect to the PSV trials (p < 0.02). The PaO(2)/FiO(2) ratio showed a significant improvement during the NAVA trial compared to both the PSV1 and PSV2 trials (p = 0.004). CONCLUSIONS: In this specific PICU population, presenting difficulty in weaning after PARDS, NAVA was associated with a reduction of the AI and a significant improvement in oxygenation compared to PSV mode. TRIAL REGISTRATION: ClinicalTrial.gov Identifier: NCT04360590 “Retrospectively registered”.


title: Software-Illustrated Explanations of Econometrics Contributions by CR Rao for his 100-th Birthday

text: Since the vast array of scientific contributions by Dr. C. R. Rao are difficult to summarize in a short paper, we focus on (1) concepts relevant for applied statisticians including econometricians, (2) using the R software to explain the concepts to be better understood by Economics students who may lack sufficient background in mathematical statistics, (3) topics that are covered in publicly and freely available R packages. The range of R packages influenced by Rao’s research is impressive. Dr. Rao will be 100 years old in 2020, and it is high time the Economics profession recognizes and honors his foundational contributions to econometrics.


Prolonged shedding of severe acute respiratory coronavirus virus 2 (SARS-CoV-2) RNA among patients with coronavirus disease 2019 (COVID-19)


title: Impact of lockdown on air quality in India during COVID-19 pandemic

text: First time in India, total lockdown was announced on 22 March 2020 to stop the spread of COVID-19 and the lockdown was extended for 21 days on 24 March 2020 in the first phase. During the total lockdown, most of the sources for poor air quality were stopped in India. In this paper, we present an analysis of air quality (particulate matter-PM(2.5), Air Quality Index, and tropospheric NO(2)) over India using ground and satellite observations. A pronounced decline in PM(2.5) and AQI (Air Quality Index) is observed over Delhi, Mumbai, Hyderabad, Kolkata, and Chennai and also a declining trend was observed in tropospheric NO(2) concentration during the lockdown period in 2020 compared with the same period in the year 2019. During the total lockdown period, the air quality has improved significantly which provides an important information to the cities’ administration to develop rules and regulations on how they can improve air quality.


title: How green is green enough? The changing landscape of financing a sustainable European economy

text: Changing narratives surrounding the climate and environmental crisis have shaped the degree of ambition in the approach of EU policy-makers towards the sustainability transition and sustainable finance. This paper presents the evolution of EU sustainable finance policies between 2015 and 2019, focusing on the EU Taxonomy, arguing that as European narratives have shifted towards highlighting the severity of the climate and environmental crises, EU sustainable finance policies have also accelerated. The paper considers the IPCC SR1.5, published in October 2018, as one catalyst for this shift in narratives and the subsequently stronger policy responses, culminating in the European Green Deal.


title: Expert consensus on protocol of rehabilitation for COVID‐19 patients using framework and approaches of WHO International Family Classifications

text: Coronavirus disease 2019 (COVID‐19) has widely spread all over the world and the numbers of patients and deaths are increasing. According to the epidemiology, virology, and clinical practice, there are varying degrees of changes in patients, involving the human body structure and function and the activity and participation. Based on the World Health Organization (WHO) International Classification of Functioning, Disability and Health (ICF) and its biopsychosocial model of functioning, we use the WHO Family of International Classifications (WHO‐FICs) framework to form an expert consensus on the COVID‐19 rehabilitation program, focusing on the diagnosis and evaluation of disease and functioning, and service delivery of rehabilitation, and to establish a standard rehabilitation framework, terminology system, and evaluation and intervention systems based the WHO‐FICs.


title: Tourism and the Highlands: A cross-sectional study on trauma and orthopaedic service use by tourists in 2017

text: BACKGROUND: NHS Highland covers a wide geographical region encompassing the Isle of Skye, and Ben Nevis. Tourism is a significant contributor to the local economy and in 2017 the Highlands welcomed 534,000 visitors. Health services across the region treat tourists in addition to the local population. We investigated how many tourists accessed the Trauma and Orthopaedic (T&O) Department at Raigmore Hospital, Inverness. METHODS: We conducted a cross-sectional study with data collected over one year (2017). The number of tourists referred to T&O including patient demographics, country of origin, type of injury and their clinical outcome were recorded. A freedom of information (FOI) request to NHS Highland was sought to investigate associated costs incurred by tourists. RESULTS: 376 tourists accessed T&O services in 2017. Country of origin: 47 (12.5%) Scotland; 177 (47.1%) rest of UK; 74 (19.7%) EU; 45 (12.0%) non-EU. Highest referral month August (61), lowest referral month November (8). Injuries: 224 (59.6%) fracture; 62 (16.5%) soft tissue injury; 20 (5.3%) laceration. Commonest sites of injury were ankle, distal radius and finger. Outcomes: 28 (7.4%) Virtual clinic; 137 (36.4%) hospital admission; 193 (51.3%) advice to referring team and discharge; 13 (3.5%) direct discharge by T&O; 4 (1.1%) missing. No. of trauma cases booked: tourists 133 (9%), local population 1415 (91%). CONCLUSIONS: Tourists account for fewer than ten percent of the T&O surgical workload over one year with common injuries being fractures affecting the extremities. Seasonal variation was observed with more referrals occurring in the summer months. Just under half of tourists originated from outside the UK and EU. Health boards should consider increasing resources over the summer months to deal with expected increases in tourist numbers and should be able to recover the cost of treatment from the patient or their travel insurance companies directly at point of care.


Critical care nursing workforce: Global imperatives, innovations and future-proofing – A call for papers


A proof of concept study: Esophagogastroduodenoscopy is an aerosol-generating procedure and continuous oral suction during the procedure reduces the amount of aerosol generated


Supporting a Work-Life Balance for Radiology Resident Parents


title: Down Under: Shark attack

text: Op het moment gelden ook in Australië forse restricties om de COVID-19 besmettingen onder controle te houden, al is het hier zo dunbevolkt dat het even zoeken is naar iemand die je op een afstand van 1,5 meter kunt houden. Hier kennen we andere gevaren.


title: Online University Teaching During and After the Covid-19 Crisis: Refocusing Teacher Presence and Learning Activity

text: The Covid-19 pandemic has raised significant challenges for the higher education community worldwide. A particular challenge has been the urgent and unexpected request for previously face-to-face university courses to be taught online. Online teaching and learning imply a certain pedagogical content knowledge (PCK), mainly related to designing and organising for better learning experiences and creating distinctive learning environments, with the help of digital technologies. With this article, we provide some expert insights into this online-learning-related PCK, with the goal of helping non-expert university teachers (i.e. those who have little experience with online learning) to navigate in these challenging times. Our findings point at the design of learning activities with certain characteristics, the combination of three types of presence (social, cognitive and facilitatory) and the need for adapting assessment to the new learning requirements. We end with a reflection on how responding to a crisis (as best we can) may precipitate enhanced teaching and learning practices in the postdigital era.


IR voices about COVID 19


Dexamethasone to the rescue


Operating Room Protocols in OMFS During Corona virus (Covid-19) Pandemic


Hydroxychloroquine – An Antimalarial to Fight COVID-19?


α-Ketoamide Inhibitors of SARS-CoV-2 Main Protease


Azithromycin – An Antibiotic to Fight COVID-19?


Non-neoplastic diseases of the testis


COVID-19 in Adult Patients with Hematological Disease: Analysis of Clinical Characteristics and Outcomes


title: High-resolution CT features of COVID-19 pneumonia in confirmed cases

text: BACKGROUND: Coronavirus (COVID-19) pneumonia emerged in Wuhan, China, in December 2019. It was highly contagious spreading all over the world, with a rapid increase in the number of deaths. COVID-19 is characterized by fever, fatigue, dry cough, and dyspnea with variable chest imaging features which have been detected. In our study, we shared our experience of CT findings in proven cases of COVID-19 to recognize the different CT patterns to help in proper and accurate diagnosis. RESULTS: The most common CT features detected in COVID-19 cases were ground glass patches (93.3%) followed by subpleural linear abnormality (53.3%), air bronchogram (23.3%), and consolidation patches (23.3%), as well as bronchial wall thickening (16.7%), crazy paving pattern (13.3%), and discrete nodules surrounded by ground glass appearance (10%). Only one case had pleural effusion (3.3%). No cavitary lesions or specific lymph nodes were detected in any of the examined patients. The lung lesions showed typical diffuse, basal, and subpleural involvement with less affection of the upper lobes. CONCLUSION: CT imaging findings of COVID-19 can help in early and accurate diagnosis of COVID-19 and proper assessment of the severity of the disease.


title: Seuchengeschichte in der deutschsprachigen Urologie

text: Pandemics are relevant for many fields of medicine from microbiology to economics and epidemiology. Many medical specialties which developed during the 19th century, e. g., urology, have had much impact on diagnostics and therapy, such as during the treatment of tuberculosis and sexually transmitted diseases. For some of them, including urology, treatment of, for example, sexually transmitted diseases, was constitutional and differed between countries.


title: Flexibler Entscheidungsalgorithmus zum innerklinischen stationären Patient*innen-Management unter wechselnden Anforderungen der SARS-CoV-2-Pandemie

text: After the initial fulminant outbreak, the SARS-CoV‑2 pandemic has now taken a more protracted course which, nevertheless, challenges hospitals in returning to a “normal” mode and in preparing for a worst-case scenario of a second wave. Not only the organization of the first contact with the patient and the admission in the emergency department but also the admission as an in-patient and the subsequent management requires both flexibility and clear directions of action for the medical personnel involved. The aim of the algorithm was to develop a structured, easy to implement and easy to follow guideline while simultaneously preserving resources. The algorithm covers some key points of decision making such as clinical signs, first contact, admission for in-patient treatment, consequences of swab and computed tomography (CT) results, and allocation and isolation measures within the hospital. The algorithm is not intended to guide diagnostics, decisions and treatment in the narrower medical sense but to provide more general instructions for the management of in-patients considering specific aspects of SARS-CoV‑2.


title: CT chest of COVID-19 patients: what should a radiologist know?

text: BACKGROUND: The aim of current work is to review the CT findings of COVID-19 in a pictorial study to help the radiologists to be familiar to imaging findings of COVID-19. MAIN BODY: Coronavirus disease 2019 (COVID-19) is a pandemic highly infectious disease which is first reported in December 2019 in Wuhan, China, and then had its outbreak leads to a global public health emergency. Real-time reverse transcription polymerase chain reaction (RT-PCR) of viral nucleic acid is considered as the reference standard for COVID-19 diagnosis; however, recent studies showed the importance of CT chest in the diagnosis of COVID-19 with high sensitivity. The CT hallmarks of COVID-19 were bilateral peripheral ground-glass opacities, consolidation with the advance of the disease more consolidation is there with linear opacities and crazy-paving pattern as well as halo and reverse halo sign. CONCLUSION: Early identification of COVID-19 cases is vital. The radiologist should be familiar with the possible findings. Further future studies with pathological correlation will help for more understanding of the imaging findings and its value in assessing of prognosis.


title: Transaction Management

text: In the first step of the E2E M&A Process Design, the Embedded M&A Strategy aligned with the overarching corporate and SBU strategies was defined. Based on this framework of the Embedded M&A Strategy a distinguished shortlist of suitable M&A targets with a strong financial, strategic, Business and Culture Design fit have been distilled. The Transaction Management, as the follow-on module of the E2E M&A Process Design, is focused on a specific transaction with a selected target company or merger partner. Core parts of the Transaction Management are the valuation of the target company (Standalone Value) and the potential synergies (Integrated Value), the Due Diligence of the target company which should identify the risks and upsides of the potential transaction, as well as the blending of the Standalone Business and Culture Designs and the Redrafting of the Joint Culture and Business Designs according to the Due Diligence outcomes. Supplementary parts of the Transaction Management as the negotiation of a share or asset purchase agreement, the acquisition financing, and the Purchase Price Allocation (PPA), will be not discussed in detail. (These parts will be incorporated in the second edition).


Increasing Immunity to Fight against Novel COVID-19: Noninvasive Public Health Approach


Zukunft des ASCO Annual Meetings


title: Enhancing the antimalarial activity of artesunate

text: The global challenge to the treatment of malaria is mainly the occurrence of resistance of malaria parasites to conventionally used antimalarials. Artesunate, a semisynthetic artemisinin compound, and other artemisinin derivatives are currently used in combination with selected active antimalarial drugs in order to prevent or delay the emergence of resistance to artemisinin derivatives. Several methods, such as preparation of hybrid compounds, combination therapy, chemical modification and the use of synthetic materials to enhance solubility and delivery of artesunate, have been employed over the years to improve the antimalarial activity of artesunate. Each of these methods has advantages it bestows on the efficacy of artesunate. This review discussed the various methods employed in enhancing the antimalarial activity of artesunate and delaying the emergence of resistance of parasite to it.


Infection as a trigger for cardiovascular disease


After the pandemic: the role of science in the future of the countries


title: Hand-held point of care ultrasound probes – the new generation of POCUS

text: Recent advances in ultrasound technology have made ultrasound equipment more versatile, portable and accessible than ever. Modern hand-held, ultra-portable ultrasound devices have been developed by multiple companies and are contributing to make bedside ultrasound evaluation a practice available to all physicians. The significance of making point-of-care ultrasound a common practice that all physicians can eventually use in the evaluation of their patients is changing the way medicine is practiced, allowing physicians to quickly obtain valuable information to complement the traditional physical exam. Despite the proven benefits of using bedside ultrasound imaging as a part of the patient evaluation and for procedure guidance, adoption of this technology is still not widespread among anesthesiology clinicians, nor is there uniform teaching of ultrasound skills to anesthesia residents and faculty. Among obstacles that have been identified as precluding achievement of the goal of widespread utilization of POCUS among anesthesia professionals and trainees is the availability of equipment for all physicians when it is needed and lack of instructor supervision for trainees who desire to use ultrasound but do not always have an instructor knowledgeable in POCUS with them when an US exam is warranted. We analyze the characteristics, advantages and limitations of available ultra-portable, hand-held ultrasound devices, with a focus on the Butterfly IQ pocket-probe, which is available at our institution, and how some of its features, like the capacity to emulate multiple transducers and its cloud-sharing and teleguidance technology, may contribute to increase the availability and utilization of POCUS by anesthesia clinicians.


COVID-19 and the Radiology Match: A Residency Program's Survival Guide to the Virtual Interview Season


title: Biological Biosensors for Monitoring and Diagnosis

text: Quantification and detection of various contaminants in the ecosystem have become critically important in the past few decades due to their exhaustive use in soil and aquatic ecosystems. The contamination by both organic and inorganic contaminants in the ecosystem has drawn attention due to their persistence, biological accumulation, and toxicity. Organic contaminants reach the air, water, food, soil, and other systems through drift mechanism and have detrimental effect on various life systems after entering the food chain, thus interfering the normal biological process of the ecosystem. Inorganic contaminants have less solubility, primarily get adsorbed, and accumulate on lower sediments. The sources of both organic and inorganic contaminants include anthropogenic activities which dispose industrial and sewage effluent directly into water bodies. Most of the contaminants are very much toxic and have tumorigenic, carcinogenic, and mutagenic effect on various life-forms. Biosensors have various prospective and existing applications in the detection of these compounds in the environment by transducing a signal. It also has immense applications in the detection of different contaminants in the food industry, environmental monitoring, disease diagnosis, etc. where reliable and precise analyses are required. This chapter points out a comprehensive glimpse on different biosensors and their characteristics, operating principles, and their designs, based on transduction types and biological components. Efforts have been made to summarize various applications of biosensors in food industry, environmental monitoring, drug delivery systems, and clinical diagnostics etc.


title: Acute Stroke Management During the COVID-19 Pandemic: Does Confinement Impact Eligibility for Endovascular Therapy?

text: During the coronavirus disease 2019 (COVID-19) pandemic, the World Health Organization recommended measures to mitigate the outbreak such as social distancing and confinement. Since these measures have been put in place, anecdotal reports describe a decrease in the number of endovascular therapy (EVT) treatments for acute ischemic stroke due to large vessel occlusion. The purpose of our study was to determine the effect on EVT for patients with acute ischemic stroke during the COVID-19 confinement. In this retrospective, observational study, data were collected from November 1, 2019, to April 15, 2020, at 17 stroke centers in countries where confinement measures have been in place since March 2020 for the COVID-19 pandemic (Switzerland, Italy, France, Spain, Portugal, Germany, Canada, and United States). This study included 1600 patients treated by EVT for acute ischemic stroke. Date of EVT and symptom onset-to-groin puncture time were collected. Mean number of EVTs performed per hospital per 2-week interval and mean stroke onset-to-groin puncture time were calculated before confinement measures and after confinement measures. Distributions (non-normal) between the 2 groups (before COVID-19 confinement versus after COVID-19 confinement) were compared using 2-sample Wilcoxon rank-sum test. The results show a significant decrease in mean number of EVTs performed per hospital per 2-week interval between before COVID-19 confinement (9.0 [95% CI, 7.8–10.1]) and after COVID-19 confinement (6.1 [95% CI, 4.5–7.7]), (P<0.001). In addition, there is a significant increase in mean stroke onset-to-groin puncture time (P<0.001), between before COVID-19 confinement (300.3 minutes [95% CI, 285.3–315.4]) and after COVID-19 confinement (354.5 minutes [95% CI, 316.2–392.7]). Our preliminary analysis indicates a 32% reduction in EVT procedures and an estimated 54-minute increase in symptom onset-to-groin puncture time after confinement measures for COVID-19 pandemic were put into place.


title: Breast Feeding in Suspected or Confirmed Cases of COVID 19–a New Perspective

text: The encounter with the rampant novel Corona virus infection has led the healthcare system across the world to update and modify its tools to fight this pandemic. Pregnancy, childbirth and breast feeding are a set of special situations to be dealt in women afflicted with Covid-19. Currently there is no universal consensus on managing the issue of breast feeding with rooming-in of the neonates in women with suspected or confirmed Covid-19. Literature is still evolving with contradictory guidelines from various authorities across the globe. This review intends to analyse the available evidence on managing breast feeding in such women and to derive a practically plausible approach in handling such situations.


Impact of SARS CoV-2 in Hemoglobinopathies with Immune Disfunction and Epidemiology. A Protective Mechanism from Beta Chain Hemoglobin Defects?


Focusing On A Unique Innate Memory Cell Population Of Natural Killer Cells In The Fight Against COVID-19: Harnessing The Ubiquity Of Cytomegalovirus Exposure


title: Children in Coronaviruses’ Wonderland: What Clinicians Need to Know

text: Human coronaviruses (HCoVs) commonly cause mild upper-respiratory tract illnesses but can lead to more severe and diffusive diseases. A variety of signs and symptoms may be present, and infections can range in severity from the common cold and sore throat to more serious laryngeal or tracheal infections, bronchitis, and pneumonia. Among the seven coronaviruses that affect humans (SARS)-CoV, the Middle East respiratory syndrome (MERS)-CoV, and the most recent coronavirus disease 2019 (COVID-19) represent potential life-threatening diseases worldwide. In adults, they may cause severe pneumonia that evolves in respiratory distress syndrome and multiorgan failure with a high mortality rate. Children appear to be less susceptible to develop severe clinical disease and present usually with mild and aspecific symptoms similar to other respiratory infections typical of childhood. However, some children, such as infants, adolescents, or those with underlying diseases may be more at-risk categories and require greater caution from clinicians. Available data on pediatric coronavirus infections are rare and scattered in the literature. The purpose of this review is to provide to clinicians a complete and updated panel useful to recognize and characterize the broad spectrum of clinical manifestations of coronavirus infections in the pediatric age.


Management of Pediatric Rheumatological Diseases During the Outbreak of COVID-19: Our Experience


Pidotimod in Paucisymptomatic SARS-CoV2 Infected Patients


Severe Autoimmune Hemolytic Anemia in COVID-19 İnfection, Safely Treated with Steroids


Spontaneous and Severe Haematomas in Patients with COVID-19 on Low- Molecular-Weight Heparin for Paroxysmal Atrial Fibrillation


title: Kawasaki Disease as the Immune-Mediated Echo of a Viral Infection

text: Although the etiology of Kawasaki disease (KD) remains elusive, the available evidence indicates that the primum movens may be a dysregulated immune response to various microbial agents, leading to cytokine cascade and endothelial cell activation in patients with KD. Documented infections by different viruses in many individual cases have been largely reported and are discussed herein, but attempts to demonstrate their causative role in the distinctive KD scenario and KD epidemiological features have been disappointing. To date, no definite link has been irrefutably found between a single infection and KD.


title: Preliminary Data on COVID-19 in Patients with Hemoglobinopathies: A Multicentre ICET-A Study

text: OBJECTIVES: This study aims to investigate, retrospectively, the epidemiological and clinical characteristics, laboratory results, radiologic findings, and outcomes of COVID-19 in patients with transfusion-dependent β thalassemia major (TM), β-thalassemia intermedia (TI) and sickle cell disease (SCD). DESIGN: A total of 17 Centers, from 10 countries, following 9,499 patients with hemoglobinopathies, participated in the survey. MAIN OUTCOME DATA: Clinical, laboratory, and radiologic findings and outcomes of patients with COVID-19 were collected from medical records and summarized. RESULTS: A total of 13 patients, 7 with TM, 3 with TI, and 3 with SCD, with confirmed COVID-19, were identified in 6 Centers from different countries. The overall mean age of patients was 33.7±12.3 years (range:13–66); 9/13 (69.2%) patients were females. Six patients had pneumonia, and 4 needed oxygen therapy. Increased C-reactive protein (6/10), high serum lactate dehydrogenase (LDH; 6/10), and erythrocyte sedimentation rate (ESR; 6/10) were the most common laboratory findings. 6/10 patients had an exacerbation of anemia (2 with SCD). In the majority of patients, the course of COVID-19 was moderate (6/10) and severe in 3/10 patients. A 30-year-old female with TM, developed a critical SARS-CoV-2 infection, followed by death in an Intensive Care Unit. In one Center (Oman), the majority of suspected cases were observed in patients with SCD between the age of 21 and 40 years. A rapid clinical improvement of tachypnea/dyspnea and oxygen saturation was observed, after red blood cell exchange transfusion, in a young girl with SCD and worsening of anemia (Hb level from 9.2 g/dl to 6.1g/dl). CONCLUSIONS: The data presented in this survey permit an early assessment of the clinical characteristics of COVID 19 in different countries. 70% of symptomatic patients with COVID- 19 required hospitalization. The presence of associated co-morbidities can aggravate the severity of COVID- 19, leading to a poorer prognosis irrespective of age.


A Socioeconomic Paradox in the COVID-19 Pandemic in Italy: a Call to Study Determinants of Disease Severity in High and Low-Income Countries


Guidance for Facing Dilemmas of Hematopoietic Stem Cell Transplant Clinicians in the COVID-19 Pandemic: An Iranian Consensus


title: Successful Outcomes of Severe COVID-19 in Patient with Chronic Lymphocytic Leukemia: Diagnostic Challenges in Immunocompromised Hosts

text: The emergence and spread of 2019 novel coronavirus have led to an unprecedented public health crisis around the globe, threatening the lives of millions of people. We report a severe case of COVID-19 in a patient with chronic lymphocytic leukemia and describe primarily the clinical presentation and the challenges encountered in the COVID-19 diagnosis, treatment, and specimens sampling pitfalls. This case highlights the importance of a comprehensive diagnostic approach of pneumonia in immunocompromised hosts, including timely and safe bronchoscopy, because of the broad differential diagnosis, more challenging with the current outbreak of COVID-19.


Ozone Alerts and Respiratory Emergencies: The Environmental Protection Agency’s Potential Biological Pathways for Respiratory Effects


Air pollution in Africa


Personal care


BM1: Events Guide


New products and investments target the laundry sector


Integrating Biodiversity Infrastructure into Pathogen Discovery and Mitigation of Emerging Infectious Diseases


On the use of lymphocyte to neutrophil ratios in laboratory medicine


title: Impact of the Coronavirus Infection in Pregnancy: A Preliminary Study of 141 Patients

text: BACKGROUND: The novel coronavirus disease (COVID-19) is the most challenging health crisis that we are facing today. Against the backdrop of this pandemic, it becomes imperative to study the effects of this infection on pregnancy and its outcome. Hence, the present study was undertaken to evaluate the effects of COVID-19 infection on the maternal morbidity and mortality, the course of labour as well as the neonatal outcome. MATERIALS AND METHODS: A total of 977 pregnant women were included in the study, from 1st April to 15th May 2020 at a tertiary care hospital. There were 141 women who tested COVID positive and remaining 836 patients were included in the COVID negative group. Findings were compared in both the groups. RESULTS: The incidence of COVID positive pregnant women was found to be 14.43%. More patients delivered by LSCS in the COVID positive and the COVID negative group (50%) as compared to COVID negative group (47%), (p > 0.05). Low APGAR score (0-3) was observed in 2(1.52%) neonates of COVID positive mothers and in 15 (1.91%) neonates of COVID negative mothers. Overall most of the babies were healthy. Out of all babies tested, 3 were detected positive initially which were retested on day 5 and were found to be negative. CONCLUSION: There is no significant effect of COVID infection on maternal and foetal outcome in pregnancy and there is no evidence of vertical transmission of the COVID-19 infection but long-term follow-up of these babies is recommended.


Prepare for Post-COVID-Pandemic Polio Problems


Theme: Immunization


title: Guillain-Barré syndrome and SARS-CoV-2

text: Since January 2020, after Chinese health authorities identified a new type of coronavirus (SARS-CoV-2), the virus has spread throughout China and consecutively throughout the whole world. The most common symptoms include fever and respiratory tract symptoms. Nevertheless, some patients show less common symptoms such as gastrointestinal or neurological manifestations. This article presents the case of a 65-years old man who was presumptively infected with SARS-CoV-2 during his ski vacation in Austria in March 2020 and acutely presented with typical symptoms of Guillain-Barré syndrome.


title: Security of passenger transport in the Baltic Sea in the context of foreign terrorist fighters

text: The Baltic Sea basin is one of the busiest areas in Europe in terms of the passenger transport and is considered as a domestic sea of the European Union, very important for the development and prosperity of all citizens. A high number of ferries connections along with exclusive cruise ships, visiting the Baltic countries, make the sea very busy. At the same time the security of lines of communications has become the highest priority, especially that a possibility of terrorist attacks still poses a serious threat. Although terrorism has been being fought intensively since 11th September, terrorist organizations such as Al-Qaeda or Islamic State of Iraq and the Levant, are still able to attack. The article considers the threat of organizing a terrorist attack posed by Foreign Terrorist Fighters returning home to the Baltic countries. Due to the fairly high number of Foreign Terrorist Fighters in some countries around the Baltic Sea, very intense passenger traffic in the area, and the appeals which have been proclaimed by Al-Qaeda and Islamic State leaders, there is a possibility of a terrorist attack occurrence in the near future. Cited facts lead to the conclusion that some precautions should be taken both in terms of technical and organizational measures to make passenger traffic secure.


title: Ozone (O3) and SARS-CoV-2: Physiological Bases and Their Therapeutic Possibilities According to COVID-19 Evolutionary Stage

text: To date, there is no definitive treatment for the new SARS-CoV-2 pandemic. Three evolutionary stages in SARS-CoV-2 infection are recognized (early infection, pulmonary phase, and systemic hyper inflammation), with characteristic clinical signs and symptoms. There are 80 international experimental trials underway seeking effective treatment for the COVID-19 pandemic. Of these, there are only three that consider ozone therapy (major auto hemotherapy) as an alternative option. There is no study that evaluates rectal ozone insufflation, despite being a safe, cheap, risk-free technique. That technique is a systemic route of ozone administration (95–96%) and that could be extrapolated to the use of SARS-CoV-2, given the excellent results observed in the management of Ebola. Ozone has four proven biological properties that could allow its use as an alternative therapy in the different phases of SARS-CoV-2 infection. Ozone could inactivate the virus by direct (O3) or indirect oxidation (ROS and LOPs) and could stimulate the cellular and humoral immune systems, being useful in the early COVID-19 infection phase (stages 1 and 2a). Ozone improves gas exchange, reduces inflammation, and modulates the antioxidant system, so it would be useful in the hyper inflammation or “cytokine storm” phase, and in the hypoxemia and/or multi-organ failure phase (stage 2b and stage 3). Given the current pandemic, it is urgent to carry out an experimental study that confirms or rules out the biological properties of ozone and thus allows it to be an alternative or compassionate therapy for the effective management of SARS-Cov-2 infection. The Ethical Committee at our Hospital has authorized the use of this technique for compassionate management of SARS-CoV-2 infection, considering the four biological Ozone properties exposed previously.


Role of frailty in COVID-19 patients


Coronavirus and surgery


title: Management of Pregnant Women in Times of Covid-19: A Review of Current Literature

text: COVID-19 is a Public Health Emergency of International Concern. Its impact on pregnant women is not yet clear owing to limited data and the knowledge is evolving in several aspects. Based on the available evidences, various clinical guidelines for management of COVID-19 have been formulated. This article intends to compile and summarise guidelines from esteemed organisations, along with their implication in the Indian scenario, and offers an easy tool for clinicians managing pregnant women in times of COVID-19.


Mother and Son Suicide Pact Due to COVID-19-Related Online Learning Issues in Bangladesh: An Unusual Case Report


title: Changes in provider Fidelity after introducing a new model of intervention

text: Given the impact of implementation fidelity on community-based outcomes, it is important to understand how fidelity may change over time as providers learn an intervention. Attachment and Biobehavioral Catch-up is an evidence-based early intervention that assesses fidelity during weekly supervision. Providers are first trained in the infant model, with toddler model training considered to be a separate, specialized opportunity. The current study examined changes in fidelity, measured by “in-the-moment” commenting, as providers moved from infant to toddler certification. An initial drop, with a subsequent increase, in commenting fidelity over the training year was expected. Results were consistent with our hypotheses, demonstrating a main effect of time, with most indices of commenting data initially decreasing and then increasing. These findings are consistent with research suggesting that fluctuation in fidelity is typical within community dissemination and suggests that ongoing supervision after the initial training is useful in facilitating successful skill development.


title: Does behavior simulation based on augmented reality improve moral imagination?

text: Innovative learning techniques are needed, to match the expectations of the current audience to improve business ethics education for the twenty-first century. One of the innovative technologies which is believed to have a big effect on a bachelor’s degree is augmented reality (AR). Incorporating AR into the modern education system is expected to produce optimal augmentation in the learning and teaching environment. This will have a large impact on increasing students’ moral imagination. This study examines the use of AR-based behavior simulation, as an innovative technique for learning ethics, to improve moral imagination. This study employs a 3 × 2 experiment method, three training modes (AR-based behavioral simulation, paper-based-behavioral simulation, and no training) and two times (time one and time two) between- and within-subject factorial design. The subjects are 147 students on a business ethics course. The result of this study reveals that the use of AR-based behavior simulation can improve moral imagination.


title: Histogram analysis for bedside respiratory monitoring in not critically ill preterm neonates: a proposal for a new way to look at the monitoring data

text: Despite robust evidence in favour of maintaining optimal oxygen saturation targets in the preterm infants, the titration of oxygen is largely dependent on manual observations and transcription. Similarly, notwithstanding the gaining popularity of non-invasive modalities like high-flow nasal therapy, the practices of weaning and escalating support are largely individualized and based on point of care observations. These are often erroneous and lack objectivity. Histogram analysis from patient monitors is an easy and objective way of quantifying vital parameters and their trends. We review the technology and evidence available behind this practice. Conclusions: Though there are no randomized controlled trials on this practice solely, we identify several quality improvement studies implementing this into practice with benefit. We also cite studies which have implemented histogram analysis in methodology, thus concluding that this is a useful clinical tool worth incorporating into clinical practice to reduce manual errors and bring more objectivity into decisions.


title: Designing dynamic reverse logistics network for post-sale service

text: The paper addresses the problem of designing a multi-country production–distribution network that also provides services such as repairs and remanufacturing. The proposed work concentrates primarily on post-sale service provided by the firm under warranty returns. The proposed model assumes that existing warehouses can also serve as collection centres or repair centres for reverse logistics. In addition, the model also explores the possibility of establishing a new facility. Hybrid facilities are considered because of their huge cost-cutting potential due to equipment sharing and space sharing. The capacity of hybrid facilities can be expanded to a predefined limit to process returned products without hampering forward logistics operations. However, if a product cannot be repaired at the warehouse, it is transported to the plant for remanufacturing. The model optimizes the overall configuration and operation cost of the production–distribution network. The production–distribution model developed in the paper is a mixed-integer nonlinear program (MINLP) that is later transformed to a mixed-integer linear program to reduce the solution time. The usefulness of the model is illustrated using a randomly generated dataset. The model identifies (a) the optimal locations/allocations of the existing/new facilities, (b) the distribution of returned products for refurbishing and remanufacturing, and (c) the capacity expansion of the existing plants and warehouses to facilitate remanufacturing and repair services.


title: A brief review on lavatory cleaning devices and their feasibility in public toilets in developing countries

text: The purpose of this article is twofold, one is to provide a brief review on various lavatory cleaning devices while the other is to study the feasibility of using these devices in public toilets in developing countries. The article presents a literature review on various lavatory cleaning devices, focusing on various designs available as patent documents and commercially available toilet cleaning robots. The issues that prevent these devices from being deployed in multi-lavatory public toilet complexes in developing countries are discussed. Design considerations of a simple, cost-effective, semi-automated lavatory cleaning robot that may serve as a solution to these issues are also covered in this article.


Highlights of the issue 7, 2020


title: Object Detector Combination for Increasing Accuracy and Detecting More Overlapping Objects

text: Object detection is considered as the cornerstone of many modern applications such as Drone vision and Self-driven cars. Object detectors, mainly those which are based on Convolutional Neural Net-works (CNNs) have received great attention from many researchers because they were able to yield remarkable results. However, most of them fail when it comes to detecting overlapping and small objects in images. There are two families of detectors: the first family detects more objects but with imprecise bounding boxes, while those of the second family do the opposite. In this paper, we propose a solution to this problem by combining the two families, in a way similar to classifier combination. Our solution has been validated through the combination of two famous detectors, Faster R-CNN which detects more objects and YOLO which produces accurate bounding boxes. However, it is more general and it can be applied to other detectors. The evaluation of our method has been applied to the PASCAL VOC dataset and it gave promising results.


title: Vine Disease Detection by Deep Learning Method Combined with 3D Depth Information

text: Vine disease detection (VDD) is an important asset to predict a probable contagion of virus or fungi. Diseases that spreads through the vineyard has a huge economic impact, therefore it is considered as a challenge for viticulture. Automatic detection and mapping of vine disease in earlier stage can help to limit its impact and reduces the use of chemicals. This study deals with the problem of locating symptomatic areas in images from an unmanned aerial vehicle (UAV) using the visible and infrared domains. This paper, proposes a new method, based on segmentation by a convolutional neuron network SegNet and a depth map (DM), to delineate the asymptomatic regions in the vine canopy. The results obtained showed that SegNet combined with the depth information give better accuracy than a SegNet segmentation alone.


title: Classification Results for Hyperovals of Generalized Quadrangles

text: A hyperoval of a point-line geometry is a nonempty set of points meeting each line in either 0 or 2 points. We discuss a combination of theoretical and practical techniques that are helpful for classifying hyperovals of generalized quadrangles. These techniques are based on the connection between hyperovals, even sets and pseudo-embeddings of point-line geometries.


title: The Program Generation in the Software Package QextNewEdition

text: This paper is devoted to the program Generation which is a self-containing console application for classification of linear codes. It can be used for codes over fields with [Formula: see text] elements and with wide-range parameters. The base of the implemented algorithm is the concept of canonical augmentation.


title: Image-Based Place Recognition Using Semantic Segmentation and Inpainting to Remove Dynamic Objects

text: Place recognition is an important step in intelligent driving, allowing the vehicle recognize where it is to plan its route. Obtaining distinguishable features can ensure the success of image-based place recognition. However, generating robust features across drastically appearance changing images is still a challenging problem. Deep features are frequently chosen instead of local features in the tasks of place recognition following the development of convolutional neural networks. But even the deep features generated by powerful neural models can cause unsatisfactory recognition results. This is perhaps due to a lack of information selecting process. The technology of semantic segmentation allows recognizing and classifying image information. Semantic segmentation followed by image inpainting provide a possibility of detecting, deleting and reconstructing annoying information. This paper proves that dynamic information present in images such as vehicles and pedestrians damages the performance of place recognition and proposes a feature extraction system that includes a step to decrease the presence of dynamic information of an image. This system is composed of two stages: 1) dynamic objects detection and removing, 2) image inpainting to reconstruct the background of removed regions. Objects detection and removing consists of deleting unstable objects recognized by semantic segmentation method from images. Image inpainting and reconstructing deals with generating inpaint-images by repairing missing regions through image inpainting method. The robustness of the proposed approach is evaluated by comparing to the non-selecting deep feature based place recognition approaches over three datasets.


title: Web Based Notebooks for Teaching, an Experience at Universidad de Zaragoza

text: Since 2012, web based notebooks have been used as interface for computer algebra systems in teaching mathematics courses at Universidad de Zaragoza. We present an overview of the experience, detailing the advantages and problems that have been noticed during this time.


title: Mathematical World Knowledge Contained in the Multilingual Wikipedia Project

text: The purpose of this project is to test and evaluate an approach for Formula Concept Discovery (FCD). FCD aims at retrieving a formula concept (in the form of a Wikidata item) together with its defining formula within documents, in this case 100 English Wikipedia articles. To correctly identify the defining formula of a Wikipedia article, this approach searches for shared formulae across Wikipedia articles available in different languages. The formula shared in the most languages is then assumed to be the defining formula. The results show that neither this approach alone nor a combination with an existing approach that considers the order of the formulae inside an article leads to satisfying results. It is thus concluded that the number of times a formula is shared across a Wikipedia article in different languages is not a good indicator to determine the defining formula with the current approach. Consequently, several ideas for further research are proposed which could improve the results.


title: Multispectral Dynamic Codebook and Fusion Strategy for Moving Objects Detection

text: The Codebook model is one of the popular real-time models for background subtraction to detect moving objects. In this paper, we propose two techniques to adapt the original Codebook algorithm to multispectral images: dynamic mechanism and fusion strategy. For each channel, only absolute spectral value is used to calculate the spectral similarity between the current frame pixel and reference average value in the matching process, which can simplify the matching equations. Besides, the deciding boundaries are obtained based on statistical information extracted from the data and always adjusting themselves to the scene changes. Results demonstrate that with the proposed techniques, we can acquire a comparable accuracy with other methods using the same multispectral dataset for background subtraction.


title: Variable Ordering Selection for Cylindrical Algebraic Decomposition with Artificial Neural Networks

text: Cylindrical algebraic decomposition (CAD) is a fundamental tool in computational real algebraic geometry. Previous studies have shown that machine learning (ML) based approaches may outperform traditional heuristic ones on selecting the best variable ordering when the number of variables [Formula: see text]. One main challenge for handling the general case is the exponential explosion of number of different orderings when n increases. In this paper, we propose an iterative method for generating candidate variable orderings and an ML approach for selecting the best ordering from them via learning neural network classifiers. Experimentations show that this approach outperforms heuristic ones for [Formula: see text].


title: Evaluating Fractional Derivatives of the Riemann Zeta Function

text: We present a method for evaluating the reverse Grünwald-Letnikov fractional derivatives of the Riemann Zeta function [Formula: see text] and use it to explore the location of zeros of integral and fractional derivatives on the left half plane.


title: Classifying Simplicial Dissections of Convex Polyhedra with Symmetry

text: A convex polyhedron is the convex hull of a finite set of points in [Formula: see text] A triangulation of a convex polyhedron is a decomposition into a finite number of 3-simplices such that any two intersect in a common face or are disjoint. A simplicial dissection is a decomposition into a finite number of 3-simplices such that no two share an interior point. We present an algorithm to classify the simplicial dissections of a regular polyhedron under the symmetry group of the prolyhedron.


title: A Dataset to Support Sexist Content Detection in Arabic Text

text: Social media have become a viral source of information. This huge amount of data offers an opportunity to study the feelings and opinions of the crowds toward any subject using Sentiment Analysis, which is a struggling area for Arabic Language. In this research, we present our approach to build a thematic training set by combining manual and automatic annotation of Arabic texts addressing Discrimination and Violence Against Women.


title: Classification of Linear Codes by Extending Their Residuals

text: An approach for classification of linear codes with given parameters starting from their proper residual codes or subcodes is presented. The base of the algorithm is the concept of canonical augmentation which is important for parallel implementations. The algorithms are implemented in the programs LengthExtension and DimExtension of the package QextNewEdition. As an application, the nonexistence of binary [41, 14, 14] codes is proved.


title: Archiving and Referencing Source Code with Software Heritage

text: Software, and software source code in particular, is widely used in modern research. It must be properly archived, referenced, described and cited in order to build a stable and long lasting corpus of scientific knowledge. In this article we show how the Software Heritage universal source code archive provides a means to fully address the first two concerns, by archiving seamlessly all publicly available software source code, and by providing intrinsic persistent identifiers that allow to reference it at various granularities in a way that is at the same time convenient and effective. We call upon the research community to adopt widely this approach.


title: Chordality Preserving Incremental Triangular Decomposition and Its Implementation

text: In this paper, we first prove that the incremental algorithm for computing triangular decompositions proposed by Chen and Moreno Maza in ISSAC’ 2011 in its original form preserves chordality, which is an important property on sparsity of variables. On the other hand, we find that the current implementation in Triangularize command of the RegularChains library in Maple may not always respect chordality due to the use of some simplification operations. Experimentation show that modifying these operations, together with some other optimizations, brings significant speedups for some super sparse polynomial systems.


title: A Numerical Approach for Computing Euler Characteristics of Affine Varieties

text: We develop a numerical nonlinear algebra approach for computing the Euler characteristic of an affine variety. Our approach is to relate Euler characteristics of a smooth affine variety with the number of critical points using Morse theory. In general, we stratify a variety into the union of smooth affine varieties to obtain results on singular varieties.


title: Phase Portraits of Bi-dimensional Zeta Values

text: In this extended abstract, we present how to compute and visualize phase portraits of bi-dimensional Zeta Values. Such technology is useful to explore bi-dimensional Zeta Values and in long-term quest to discover a 2D-Riemann hypothesis. To reach this goal, we need two preliminary steps: [Formula: see text] the notion of phase portraits and a general tool to visualize phase portrait based on interactive Jupyter widgets. [Formula: see text] the ability to compute numerical approximations of bi-dimensional Zeta values, using mpmath, a Python library for arbitrary-precision floating-point arithmetic. To this end, we develop a theory to numerically compute double sums and produce the first algorithm to compute bi-dimensional Zeta Values with complex parameters.


title: DermoNet: A Computer-Aided Diagnosis System for Dermoscopic Disease Recognition

text: The research of skin lesion diseases is currently one of the hottest topics in the medical research fields, and has gained a lot of attention on the last few years. However, the existing skin lesion methods are mainly relying on conventional Convolutional Neural Network (CNN) and the performance of skin lesion recognition is far from satisfactory. Therefore, to overcome the aforementioned drawbacks of traditional methods, we propose a novel Computer-Aided Diagnosis (CAD) system, named DermoNet, based on Multi-Scale Feature Level (MSFL) blocks and Multi-Level Feature Fusion (MLFF). Further, the DermoNet approach yields a significant enhancement in terms of dealing with the challenge of small training data sizes in the dermoscopic domain and avoiding high similarity between classes and overfitting issue. Extensive experiments are conducted on the public dermoscopic dataset, and the results demonstrate that DermoNet outperforms the state-of-the-art approaches. Hence, DermoNet can achieve an excellent diagnostic efficiency in the auxiliary diagnosis of skin lesions.


title: The GAP Package LiePRing

text: A symbolic Lie p-ring defines a family of Lie rings with [Formula: see text] elements for infinitely many different primes p and a fixed positive integer n. Symbolic Lie p-rings are used to describe the classification of isomorphism types of nilpotent Lie rings of order [Formula: see text] for all primes p and all [Formula: see text]. This classification is available as the LiePRing package of the computer algebra system GAP. We give a brief description of this package, including an approach towards computing the automorphism group of a symbolic Lie p-ring.


title: Markov Transition Matrix Analysis of Mathematical Expression Input Models

text: Computer software interfaces for mathematics collaboration and problem solving rely, as all interfaces do, on user identification and recognition of symbols (via icons and other contextual widgets). In this paper we examine the results of a short study which examined users interacting with mathematics software (Mathematics Classroom Communicator, MC[Formula: see text]) designed for education, real-time communication and collaboration. Videos were recorded of 14 users working through seven comprehensive problems in the MC[Formula: see text] interface. Extensive second-by-second coding was completed of the user’s actions and status throughout their work, and a set of transition matrices were tabulated, estimating transition probabilities between symbols, operators and other aspects of mathematical expressions. We discuss the results of these matrices, and their implications in the translation of abstract mathematical concepts into software interfaces, and further conclude with a brief discussion of suggestions for mathematical software interface design. This study also has applications in mathematical software usability and accessibility.


title: Considerably Improving Clustering Algorithms Using UMAP Dimensionality Reduction Technique: A Comparative Study

text: Dimensionality reduction is widely used in machine learning and big data analytics since it helps to analyze and to visualize large, high-dimensional datasets. In particular, it can considerably help to perform tasks like data clustering and classification. Recently, embedding methods have emerged as a promising direction for improving clustering accuracy. They can preserve the local structure and simultaneously reveal the global structure of data, thereby reasonably improving clustering performance. In this paper, we investigate how to improve the performance of several clustering algorithms using one of the most successful embedding techniques: Uniform Manifold Approximation and Projection or UMAP. This technique has recently been proposed as a manifold learning technique for dimensionality reduction. It is based on Riemannian geometry and algebraic topology. Our main hypothesis is that UMAP would permit to find the best clusterable embedding manifold, and therefore, we applied it as a preprocessing step before performing clustering. We compare the results of many well-known clustering algorithms such ask-means, HDBSCAN, GMM and Agglomerative Hierarchical Clustering when they operate on the low-dimension feature space yielded by UMAP. A series of experiments on several image datasets demonstrate that the proposed method allows each of the clustering algorithms studied to improve its performance on each dataset considered. Based on Accuracy measure, the improvement can reach a remarkable rate of 60%.


title: Real Tropical Hyperfaces by Patchworking in polymake

text: An implementation of Viro’s patchworking in polymake is presented, and a census of Betti numbers of real tropical surfaces serves as a showcase. The latter is relevant in the context of Hilbert’s 16th Problem.


title: A Flow-Based Programming Environment for Geometrical Construction

text: In this article, we show a flow-based programming environment for interactive geometry software. Flow-based programming is one of the programming paradigms. All of the processes and data are represented as nodes, and we connect processes and data with edges. We call the figure with nodes and edges graph because the figure looks like a planar graph. There is a lot of software implementing flow-based programming. However, there are few mathematical software based on a flow-based programming environment. So, we develop experimental interactive geometry software to generate kaleidoscope patterns based on flow-based programming. The software shows us some advantages of flow-based programming. First, it is easy to understand the procedure of construction. Second, flow-based programming is flexible. Third, flow-based programming has high extensibility. We seek possibilities of practical use of the geometrical construction software with flow-based programming.


title: A Bottom-Up Approach for Pig Skeleton Extraction Using RGB Data

text: Animal behavior analysis is a crucial task for the industrial farming. In an indoor farm setting, extracting Key joints of animals is essential for tracking the animal for a longer period of time. In this paper, we proposed a deep network that exploits transfer learning to train the network for the pig skeleton extraction in an end to end fashion. The backbone of the architecture is based on an hourglass stacked dense-net. In order to train the network, keyframes are selected from the test data using K-mean sampler. In total, 9 Keypoints are annotated that gives a brief detailed behavior analysis in the farm setting. Extensive experiments are conducted and the quantitative results show that the network has the potential of increasing the tracking performance by a substantial margin.


title: Isomorphism and Invariants of Parallelisms of Projective Spaces

text: We consider the computer-aided constructive classification of parallelisms with predefined automorphism groups in small finite projective spaces. The usage of a backtrack search algorithm makes it very important to filter away equivalent partial solutions as soon as possible and to use a fast method for checking for isomorphism of any two parallelisms. The rejection of most of the equivalent solutions can be done by a test which uses the normalizer of the predefined automorphism group. We consider the applicability and effectiveness of such a test, and present sensitive invariants of resolutions of Steiner 2-designs. They can be used to facilitate any type of test for isomorphism of parallelisms.