We also applied BIGnav to a realistic application and showed that users can navigate to highly probable points of interest on a map with only a few steps . We then discuss the tradeoffs of BIGnav -- including efficiency vs. increased cognitive load -- and its application to other interaction tasks . The module/system is set to have structural complexity when they are tightly couple and has more number of dynamic interconnections Environment Complexity : The interacting feature module set to have environment complexity when the behavior of the module/system depends on the configuration and its operation and rules of the environment . Use case complexity : The interacting feature module set to have use case complexity when a change affects the behavior of software from different end user perspective . Users are also looking for emotional satisfaction from using and interacting with the products . With powerful interactive features technology can potentially deliver more and more technology products and interactive computing systems are developed toward satisfying users both efficiently and emotionally . This article reviews different emotions incorporated into usability and design for technology products and interactive computing systems ; and discusses the importance of emotional usability and the need for it . In this study we examine the design goals that engineers apply in order to construct an ideal-as-possible cooling jacket geometry and use a broad range of visualization tools in order to analyze , explore , and present the visualization of heat transfer . We systematically employ direct , geometric , and texture-based flow visualization techniques as well as automatic feature extraction and interactive feature-based methodology – all from the field of visual computing . We discuss the relative advantages and disadvantages of these approaches as well as the challenges , both technical and perceptual with this application . Users can use displays projected on tables and walls as a spatiallycontinuous extension of their portable computers . Using aninteraction technique called hyperdragging , users can transferinformation from one computer to another , by only knowing thephysical relationship between them . We also provide a mechanism forattaching digital data to physical objects , such as a videotape ora document folder , to link physical and digital spaces . Adolescent mental health can not be ignored , and psychological pressure is one of the prominent problems of current teenagers . Micro-blog , as the most important information exchange and broadcast tool in the current society , is becoming an important channel for teenagers ' information acquisition , inter-interaction , self-expression , emotion release due to its unique equality , freedom , fragmentation , individuality characteristics . This poster envisions a micro-blog platform , aiming to ( 1 ) sense psychological pressures through teenagers ' tweets , and ( 2 ) assist teenagers to release their stress through micro-blog . Motor-disabled end users have successfully driven a telepresence robot in a complex environment using a Brain-Computer Interface ( BCI ) . However , to facilitate the interaction aspect that underpins the notion of telepresence , users must be able to voluntarily and reliably stop the robot at any moment , not just drive from point to point . In this work , we propose to exploit the user 's residual muscular activity to provide a fast and reliable control channel , which can start/stop the telepresence robot at any moment . Current works solutions focus on separate model between the resident , activities and interactions . Some study use data association and extra auxiliary of graphical nodes to model human tracking information in an environment and some produce separate framework to incorporate the auxiliary for interaction feature model . Thus , recognizing the activities and which resident perform the activity at the same time in the smart home are vital for the smart home development and future applications . Features about complex network based on the two constructing methods are discussed and analyzed . To study interaction feature and model , this paper also presents member similar model and correlation model . Our experimental results show that BBS member networks based threshold have features both of small network and free-Scale network . We demonstrate empirical results on three datasets . This paper focuses on recognizing the human interaction relative to human emotion , and addresses the problem of interaction features representation . We propose a two-layer feature description structure that exploits the representation of spatio-temporal motion features and context features hierarchically . Despite of the significance of deep models , they ignore the fine - grained ( matching signals between words and classes ) classification clues since their classifications mainly rely on the text - level representations . To address this problem , we introduce the interaction mechanism to incorporate word - level matching signals into the text classification task . In particular , we design a novel framework , EXplicit interAction Model ( dubbed as EXAM ) , equipped with the interaction mechanism . Based upon the interaction mechanism , we devise an EXplicit interAction Model ( dubbed as EXAM ) . Specifically , the proposed framework consists of three main components : word - level encoder , interaction layer , and aggregation layer . The word - level encoder projects the textual contents into the word - level representations . By taking advantage of the statistical distributions of the user ’s behaviour , it is also possible to provide for efficient verification of service software and certification of service quality from the feature interaction point of view . The paper discusses general aspects of the feature interaction problem . The principles of statistical testing and statistical usage modelling of services are outlined . Mechanisms have been developed to allow features to be independently created and added freely to the model . This allows decisions on feature interaction and resolution to be based on the actual results of service operation and animation rather than on preconceived rules on how services might be expected to interact . The underlying technology behind these projects is the standard specification language SDL , with its powerful commercial tool support . For certain systems ( safely featured parameterised systems ) our generated specifications are suitable for conversion to abstract specifications representing any size of network . This enables feature interaction analysis to be carried out , via model checking and induction , for systems of any number of featured components . In addition , we show how , for any balanced network of components , by using a graphical representation of the features and the process communication structure , a group of permutations of the underlying state space of the generated specification can be determined easily . To maintain validation of models , it must track and check each modeling operation in feature modeling systems . The ways used at large about feature interaction checking can not be used for product models with complicated topology and lots of features . Improving feature interaction checking efficiency is one of the most challenge questions in the researching on feature modeling technology . Ideally , a feature interaction ( FI ) approach for scenario models should require minimal additional specification effort and allow for incremental definition of new scenarios . Furthermore , the scenario specification model and the validation model should be clearly separated . In this paper we propose a framework of building communication features as web widgets and compose these widgets to better serve users with highly customizable features , friendly user interfaces , and easier integration with other web services . We address several challenges of the widgetizing process , including feature interaction handling , inter-widget communication , integrating phone , desktop and network communication resources . We also present the implementation of our framework . The main contribution of the framework is that it allows us to model aspects of privacy and related system concerns ( such as security and scalability ) in a more comprehensive manner than the dataflow diagrams traditionally used for privacy analysis . The feature interaction perspective taken in the chapter allows us to reason about conflicts between a service user ’s model of how the service works and its actual implementation . In our modeling framework such conflicts can be modeled in terms of goal conflicts and service deployment . Objective : This study aims at surveying existing research on feature interaction in SPL engineering in order to identify common practices and research trends . Method : A systematic mapping study was conducted with a set of seven research questions , in which the 35 studies found are mainly classified regarding the feature interaction solution presented : detection , resolution and general analysis . Results : 43 % of the papers deal with feature interaction at early phases of a software lifecycle . In this paper , we propose an unbounded model checking method for feature interaction verification for telecommunication systems . Unbounded model checking is a SAT-based verification method and has attracted recent attention as a powerful approach . subsubsection : FM Component The FM component is a factorization machine , which is proposed in to learn feature interactions for recommendation . Besides a linear ( order - 1 ) interactions among features , FM models pairwise ( order - 2 ) feature interactions as inner product of respective feature latent vectors . Furthermore , they are mostly restricted to data clustering and the resulting dimensions can not be interactively refined . In this paper we propose a technique and an architectural solution that is based on an interaction concept for creating OLAP dimensions on subsets of the data dynamically , triggered interactively by the user , based on arbitrary multi-dimensional grouping mechanisms . This approach allows combining the advantages of both , OLAP exploration and interactive multidimensional analysis . There are a control device ( 15 ) for a field device ( 20 ) , a system ( 1 ; 2 ; 3 ) and a method for operation of a field device ( 20 ) is provided . The control device ( 15 ) comprises a communication means ( 21 ) for communication with an external hand-held controller ( 10 ) for operation of the field device ( 20 ) is suitable , and an interaction means ( 22 ) for interacting with the handheld terminal ( 10 ) using the communication device ( 21 ) , wherein the interaction means ( 22 ) is configured to request an input in the manual control unit ( 10 ) at a start-up of the field device ( 20 ) and / or for providing data ( 23A ) of the field device ( 20 ) for the manual control unit ( 10 ) ( in response to an input in the manual control unit ( 10 ) or a request of the manual control device 10 ) . The discussion is initiated by describing possible threats that the system faces , as well as the specific security services that have to be provided in order to mitigate them . Next , a set of five primal security functions that a federated satellite system has to implement is defined , and an interaction state model is propose to characterize the security state when two federates are interacting with each other . Differences in these security states are then used to define the interaction state machine , which is a transition state diagram that can be used to rapidly identify which security functionality has to be provided in order to securitize an unreliable interaction . The work is concluded in this chapter by presenting an overview and the implications of the Interaction-Value model for theory , and whether the findings can be applied , replicated or transferred elsewhere . Concluding comments are also presented regarding health informatics and information provision in general . This nonparametric estimator leads to aninteresting analogy between learning andinteracting particles in a potential field . Itturns out that learning by second orderstatistics is a special case of thisinteraction model for learning . We investigatethe mathematical properties of thisnonparametric entropy estimator , provide batchand stochastic gradient expressions foroff-line and on-line adaptation , and illustratethe performance of the corresponding algorithmsin examples of supervised and unsupervisedtraining , including time-series prediction andICA . Each facet model is written with reference to a domain that defines a vocabulary and semantics for the model definition . To model interaction between specifications from different domains , Rosetta provides an interaction definition mechanism based on institution theory . The Rosetta model-centered specification approach allows systems designers to specify many domains of interest from many perspectives and supports predictive design analysis at the systems-level . A simulation of benzene .. Ar2 clusters has been performed using molecular dynamics software . Details on development tools , interaction formulation and calculation parameters are given . Estimates of macroscopic properties as well as elements for understanding the dynamics of the systems are discussed . An information-cognitive psychology study of child-tablet computer interaction . Using Luhmann 's communication framework we examine the interaction implications that arise when kindergarten-grade 2 students use mathematics applications on four types of tablet computers . We asked a ) what content is communicated between the child and the tablet computer , b ) how is content communicated , c ) how engaged are children in the tablet-child interaction , and d ) what factors influence this engagement . We study interactions among players in cooperative games . We propose a new interaction index called Shapley-Taylor interaction index . It decomposes the value of the game into terms that model the interactions between subsets of players , analogous to how the Taylor series represents a function in terms of its derivatives of various orders . Product information is extracted from STEP file with down-top patten extracting method , and an intelligent feature recognition methodology by using a sturcture of relative elements list is proposed . Based on this methodology , a product feature model is established and this model can be ammended through the change of key elements of features . This paper presents data from a case study of a student ( Mary ) who solved quadratic contextual problems . Mary ’s schemes , constructed from quadratically related quantities and patterns of additive rates , fostered the development of an iterative , summative conceptualisation of quadratics in contrast to the product view . Findings support the use of contextual problems to motivate students to think reflectively and mathematically . A cost estimating model in the product design phase based on CAD/CAPP integration is proposed . The model includes CAD feature recognition and extraction , product feature modeling and process planning . A process planning based cost estimation system ( PPBCES ) based on the model is developed . Many NLP tasks involve sentence-level annotation yet the relevant information is not encoded at sentence level but at some relevant parts of the sentence . Such tasks include but are not limited to : sentiment expression annotation , product feature annotation , and template annotation for Q&A systems . However , annotation of the full corpus sentence by sentence is resource intensive . In the proposed modeling method , result of human intent analysis is applied at situation based generation of features in the product structure . The new method of product feature definition and the demanded new features are explained in this paper . In order to easy implementation of the proposed modeling in large modeling systems , the method of the feature driven product definition is the same as in the current leading industrial product lifecycle management systems . Automated computerized machine vision inspection , techniques can be used as a tooling for automated shape inspection in an advanced CIM environment so that real time , reliable , non-contact and non-destructive , and 100 % quality measure activities can be achieved fruitfully . This paper develops a generic hardware system , software architecture and a technology-mix for product feature identification and measurement analysis of various machined parts/products . The feature analysis seeks to identify inherent characteristics of feature of an object found within the image frame . Deep Crossing , Wide & Deep Learning and YouTube Recommendation CTR model extend LS - PLM and FM by replacing the transformation function with complex MLP network , which enhances the model capability greatly . PNN tries to capture high - order feature interactions by involving a product layer after embedding layer . DeepFM imposes a factorization machines as ” wide ” module in Wide & Deep with no need of feature engineering . The problem of visual fine - grained categorization can be extremely challenging due to the subtle differences in the appearance of certain parts across related categories . In contrast to basic - level recognition , fine - grained categorization aims to distinguish between different breeds or species or product models , and often requires distinctions that must be conditioned on the object pose for reliable identification . Facial recognition is the classic case of fine - grained recognition , and it is noteworthy that the best facial recognition methods jointly discover facial landmarks and extract features from those locations . The focus of our research is on techniques for automatically constructing anomaly detection models that are capable of detecting new ( or unseen ) attacks . We introduce a new data mining method that performs " cross-feature analysis " to capture the inter-feature correlation patterns in normal traffic . These patterns can be used as normal profiles to detect deviation ( or anomalies ) caused by attacks . In particular , a new interactive attention is proposed to bridge two tasks for achieving information complementarity between different tasks , which effectively leverages the boundary information for offering a strong cue to better segmentation prediction . At last , a cross feature fusion module aims to selectively aggregate multi-level features from the whole encoder network . By cascaded three modules , richer context and fine-grain features of each stage are encoded . This design problem can be improved by refactoring on architectural level . This paper offered an automatic software architecture refactoring method based on the existing analysis of crosscutting feature . At first , this method analyzes crosscutting features based on traceability between features and components . Common feature fusion strategies ( addition or concatenation ) ignore these differences and may cause suboptimal solutions . In this paper , we propose the F3Net to solve above problem , which mainly consists of cross feature module ( CFM ) and cascaded feedback decoder ( CFD ) trained by minimizing a new pixel position aware loss ( PPA ) . Specifically , CFM aims to selectively aggregate multi-level features . The system is new of its kind in this area , where the new algorithm either feature extraction or classifier is easily adaptable to the system and starts working accordingly . Testing the proposed feature extraction method by cross-classifiers or validate the new classification technique using cross-feature extraction mechanisms are the key factors of this newly designed system . Moreover , an easy guide and understandable GUI facilitates the users comfortable to utilize this system for personal use only . These techniques use binarization , thinning and features extraction algorithms which are computational methods that can be applied to digital image processing used in scientific research and security issues . This paper presents a comparative analysis of four thresholding techniques ( Niblack , Bernsen , Fisher , Fuzzy ) , two thinning techniques ( Stentiford and Holt ) and a feature extraction ( Cross Number ) technique to evaluate the best performance of the algorithms in fingerprint images . To develop this project a set of 160 fingerprint images was used in experiments and analysis . The improvement of segmentation algorithm and the optimization of feature space are the key factors of improving the accuracy of tree-crown information extraction , and are also the urgent problems of tree-crown information extraction using high resolution images . In the present study , the spectral threshold method was used on the first-class segmentation of QuickBird multi-spectral image to obtain vegetation regions . In the proposed approach , the model of four-layer feed forward BP(back-propagation ) neural network is given , by making use of the real human body data , the training samples of the neck , bust , waist and hip are obtained . The weight and the feature curve modeling of the neck , bust , waist and hip can be got respectively after training . Finally , the error analysis is done and the results show that the method can approach the real human body feature curve . This paper presents a new unified feature parametrization approach for monocular SLAM . The parametrization is based on the parallax angle and can reliably represent both nearby and distant features , as well as features in the direction of camera motion and features observed only once . For fingerprint matching , we propose a single hidden layer neural network ( SLFN ) , which combines a powerful extreme learning machine ( ELM ) and a well generalized resilient propagation ( RPROP ) algorithm . The proposed fingerprint matching system comprises the following steps : fingerprint pre-processing/enhancement , ROI extraction , invariant LDP feature extraction , and matching using proposed hybrid classifier . The experimental result shows that the matching accuracy of the proposed system is improved as compare to ELM for lower values of hidden nodes , and other distance based matching approaches proposed in the literature . Analysis is further performed on how errors in feature detection affect the resulting pose determination . The method resulted in the detection of facial pose from 30 to -30 degrees with an average error of 4.28 degrees for the Frontal Position Model and 5.79 degrees for the Side Position Model with correct feature detection . The Intel(R ) Streaming SIMD Extensions ( SSE ) technology was employed to enhance the performance of floating point operations . In this study , an efficient , singular value , decomposition-based feature extraction method for image splicing detection is presented . Kernel Principal Component Analysis is also applied as classifier feature preprocessor to improve the classification process ; and finally , support vector machine is used to distinguish the authenticated and spliced images . The results show a detection accuracy of 98.78 % for the proposed method with only 50-dimensional feature vector . Implementations of signal processing tasks such as feature extraction , learning , or recognition can especially benefit from bio-inspired computational models . In this thesis , we present a bio-inspired artificial olfactory system , which consists of an analog feature extraction front end that operates efficiently in the low-precision regime and a spike pattern classifier that exploits the randomness in circuits . The analog front end implements a novel trainable feature extraction algorithm for metal-oxide gas sensor arrays . -Mobile wireless devices play important roles in our daily life , e.g. , users often use such devices to take pictures and share with friends via opportunistic peer-to-peer links , which however are intermittent in nature , and hence require the store andforward feature proposed in Delay Tolerant Networks to provide useful data sharing opportunities . Moreover , mobile devices may not be willing to forward data items to other devices due to the limited resources . The proposed algorithm consists of two levels such as the face detection and the face tracking . First , the face detection used the eight-wavelet feature models which ate very simple . Each feature model applied to variable size and position , and then create initial feature set . In Experiment 2 we returned to the typical sequence and restored the large partial repetition costs . Experiment 3 confirmed these findings , which have implications for interpreting partial repetition costs and for feature integration theories in general . ( PsycINFO Database Record ( c ) 2020 APA , all rights reserved ) . Instead of using the gated hyperbolic tangent activation , we use weight normalization followed by ReLU to reduce computation . We also replaced feature concatenation with element - wise multiplication to combine the features from text and visual modalities when computing the top - down attention . To compute the question representation , we used 300 GloVe vectors to initialize the word embeddings and then passed it to a GRU network and a question attention module to extract attentive text features . Prior to the renaissance of convolutional neural network ( CNN ) , numerous of machine learning algorithms are applied to face detection . The pioneering work of Viola - Jones utilizes Adaboost with Haar - like feature to train a cascade model to detect face and get a real - time performance . Also , deformable part models ( DPM ) is employed for face detection with remarkable performance . In our work we focus on improving image set embedding model , which maps an image set to an representation with fixed dimension so that image sets with different number of images are comparable with each other . Let and denote representation of and . is determined by all elements in , therefore it can be denoted as The is produced by a feature extraction process , containing traditional hand - craft feature extractors or convolutional neural network . is an aggregative function , which maps a variable - length input set to a representation of fixed dimension . GMemN2N selects the supporting sentence in the same way as MemN2N , but uses the gate to selectively add the the question to control the influence of the question information in finding the supporting sentence in the next step . DMN and DMN + use output feature map based on various relatedness such as absolute difference , as well as inner product , to understand the relation between sentence and question at various points . The more difficult the task , the more complex the output feature map and the generalization component to get the correct answer . The authors report promising performance improvement during training with better convergence compared to U - Net , but no benefit was observed when using a summation of features during the testing phase [ reference ] . However , this concept proved that feature summation impacts the performance of a network . The importance of skipped connections for biomedical image segmentation tasks have been empirically evaluated with U - Net and residual networks [ Cross - bigrams : for every pair of bigrams across the premise and hypothesis which share a POS tag on the second word , an indicator feature over the two bigrams . We report results in Table [ reference ] , along with ablation studies for removing the cross - bigram features ( leaving only the cross - unigram feature ) and for removing all lexicalized features . On our large corpus in particular , there is a substantial jump in accuracy from using lexicalized features , and another from using the very sparse cross - bigram features . We further propose a Feature Weighting sub - Net ( FWN ) to learn the weights of global representations and local representations on different parts . Therefore , more reasonable feature fusion is conducted to facilitate feature similarity measurement . Some more detailed descriptions to our local representation generation are illustrated in Fig . Template adaptation is an example of transfer learning , where the target domain is defined by the set of media of a subject in a template . In general , transfer learning includes a source domain for feature encoding of subjects trained offline , and a specific target domain with limited available observations of new subjects . In the case of template adaptation , the source domain may be a deep convolutional network trained offline to predict subject identity , and the target domain is the set of media in templates of never before seen subjects . Geometric and appearance - based features tend to capture different representations of facial expressions , hence work well together . Consequently , improvements for either of these feature types will be beneficial for facial expression recognition systems . In this study , we proposed geometric features derived from landmark pairs , including many non - descriptive and redundant ones . Main Results Table [ reference ] lists the test - set accuracies of our best parsing models , compared to other state - of - the - art parsers from the literature . It is clear that our parsers are very competitive , despite using very simple parsing architectures and minimal feature extractors . When not using external embeddings , the first - order graph - based parser with 2 features outperforms all other systems that are not using external resources , including the third - order TurboParser .
We also applied BIGnav to a realistic application and showed that users can navigate to highly probable points of interest on a map with only a few steps . We then discuss the tradeoffs of BIGnav -- including efficiency vs. increased cognitive load -- and its application to other interaction tasks .
The module/system is set to have structural complexity when they are tightly couple and has more number of dynamic interconnections Environment Complexity : The interacting feature module set to have environment complexity when the behavior of the module/system depends on the configuration and its operation and rules of the environment . Use case complexity : The interacting feature module set to have use case complexity when a change affects the behavior of software from different end user perspective .
Users are also looking for emotional satisfaction from using and interacting with the products . With powerful interactive features technology can potentially deliver more and more technology products and interactive computing systems are developed toward satisfying users both efficiently and emotionally . This article reviews different emotions incorporated into usability and design for technology products and interactive computing systems ; and discusses the importance of emotional usability and the need for it .
In this study we examine the design goals that engineers apply in order to construct an ideal-as-possible cooling jacket geometry and use a broad range of visualization tools in order to analyze , explore , and present the visualization of heat transfer . We systematically employ direct , geometric , and texture-based flow visualization techniques as well as automatic feature extraction and interactive feature-based methodology – all from the field of visual computing . We discuss the relative advantages and disadvantages of these approaches as well as the challenges , both technical and perceptual with this application .
Users can use displays projected on tables and walls as a spatiallycontinuous extension of their portable computers . Using aninteraction technique called hyperdragging , users can transferinformation from one computer to another , by only knowing thephysical relationship between them . We also provide a mechanism forattaching digital data to physical objects , such as a videotape ora document folder , to link physical and digital spaces .
Adolescent mental health can not be ignored , and psychological pressure is one of the prominent problems of current teenagers . Micro-blog , as the most important information exchange and broadcast tool in the current society , is becoming an important channel for teenagers ' information acquisition , inter-interaction , self-expression , emotion release due to its unique equality , freedom , fragmentation , individuality characteristics . This poster envisions a micro-blog platform , aiming to ( 1 ) sense psychological pressures through teenagers ' tweets , and ( 2 ) assist teenagers to release their stress through micro-blog .
Motor-disabled end users have successfully driven a telepresence robot in a complex environment using a Brain-Computer Interface ( BCI ) . However , to facilitate the interaction aspect that underpins the notion of telepresence , users must be able to voluntarily and reliably stop the robot at any moment , not just drive from point to point . In this work , we propose to exploit the user 's residual muscular activity to provide a fast and reliable control channel , which can start/stop the telepresence robot at any moment .
Current works solutions focus on separate model between the resident , activities and interactions . Some study use data association and extra auxiliary of graphical nodes to model human tracking information in an environment and some produce separate framework to incorporate the auxiliary for interaction feature model . Thus , recognizing the activities and which resident perform the activity at the same time in the smart home are vital for the smart home development and future applications .
Features about complex network based on the two constructing methods are discussed and analyzed . To study interaction feature and model , this paper also presents member similar model and correlation model . Our experimental results show that BBS member networks based threshold have features both of small network and free-Scale network .
We demonstrate empirical results on three datasets . This paper focuses on recognizing the human interaction relative to human emotion , and addresses the problem of interaction features representation . We propose a two-layer feature description structure that exploits the representation of spatio-temporal motion features and context features hierarchically .
Despite of the significance of deep models , they ignore the fine - grained ( matching signals between words and classes ) classification clues since their classifications mainly rely on the text - level representations . To address this problem , we introduce the interaction mechanism to incorporate word - level matching signals into the text classification task . In particular , we design a novel framework , EXplicit interAction Model ( dubbed as EXAM ) , equipped with the interaction mechanism .
Based upon the interaction mechanism , we devise an EXplicit interAction Model ( dubbed as EXAM ) . Specifically , the proposed framework consists of three main components : word - level encoder , interaction layer , and aggregation layer . The word - level encoder projects the textual contents into the word - level representations .
By taking advantage of the statistical distributions of the user ’s behaviour , it is also possible to provide for efficient verification of service software and certification of service quality from the feature interaction point of view . The paper discusses general aspects of the feature interaction problem . The principles of statistical testing and statistical usage modelling of services are outlined .
Mechanisms have been developed to allow features to be independently created and added freely to the model . This allows decisions on feature interaction and resolution to be based on the actual results of service operation and animation rather than on preconceived rules on how services might be expected to interact . The underlying technology behind these projects is the standard specification language SDL , with its powerful commercial tool support .
For certain systems ( safely featured parameterised systems ) our generated specifications are suitable for conversion to abstract specifications representing any size of network . This enables feature interaction analysis to be carried out , via model checking and induction , for systems of any number of featured components . In addition , we show how , for any balanced network of components , by using a graphical representation of the features and the process communication structure , a group of permutations of the underlying state space of the generated specification can be determined easily .
To maintain validation of models , it must track and check each modeling operation in feature modeling systems . The ways used at large about feature interaction checking can not be used for product models with complicated topology and lots of features . Improving feature interaction checking efficiency is one of the most challenge questions in the researching on feature modeling technology .
Ideally , a feature interaction ( FI ) approach for scenario models should require minimal additional specification effort and allow for incremental definition of new scenarios . Furthermore , the scenario specification model and the validation model should be clearly separated .
In this paper we propose a framework of building communication features as web widgets and compose these widgets to better serve users with highly customizable features , friendly user interfaces , and easier integration with other web services . We address several challenges of the widgetizing process , including feature interaction handling , inter-widget communication , integrating phone , desktop and network communication resources . We also present the implementation of our framework .
The main contribution of the framework is that it allows us to model aspects of privacy and related system concerns ( such as security and scalability ) in a more comprehensive manner than the dataflow diagrams traditionally used for privacy analysis . The feature interaction perspective taken in the chapter allows us to reason about conflicts between a service user ’s model of how the service works and its actual implementation . In our modeling framework such conflicts can be modeled in terms of goal conflicts and service deployment .
Objective : This study aims at surveying existing research on feature interaction in SPL engineering in order to identify common practices and research trends . Method : A systematic mapping study was conducted with a set of seven research questions , in which the 35 studies found are mainly classified regarding the feature interaction solution presented : detection , resolution and general analysis . Results : 43 % of the papers deal with feature interaction at early phases of a software lifecycle .
In this paper , we propose an unbounded model checking method for feature interaction verification for telecommunication systems . Unbounded model checking is a SAT-based verification method and has attracted recent attention as a powerful approach .
subsubsection : FM Component The FM component is a factorization machine , which is proposed in to learn feature interactions for recommendation . Besides a linear ( order - 1 ) interactions among features , FM models pairwise ( order - 2 ) feature interactions as inner product of respective feature latent vectors .
Furthermore , they are mostly restricted to data clustering and the resulting dimensions can not be interactively refined . In this paper we propose a technique and an architectural solution that is based on an interaction concept for creating OLAP dimensions on subsets of the data dynamically , triggered interactively by the user , based on arbitrary multi-dimensional grouping mechanisms . This approach allows combining the advantages of both , OLAP exploration and interactive multidimensional analysis .
There are a control device ( 15 ) for a field device ( 20 ) , a system ( 1 ; 2 ; 3 ) and a method for operation of a field device ( 20 ) is provided . The control device ( 15 ) comprises a communication means ( 21 ) for communication with an external hand-held controller ( 10 ) for operation of the field device ( 20 ) is suitable , and an interaction means ( 22 ) for interacting with the handheld terminal ( 10 ) using the communication device ( 21 ) , wherein the interaction means ( 22 ) is configured to request an input in the manual control unit ( 10 ) at a start-up of the field device ( 20 ) and / or for providing data ( 23A ) of the field device ( 20 ) for the manual control unit ( 10 ) ( in response to an input in the manual control unit ( 10 ) or a request of the manual control device 10 ) .
The discussion is initiated by describing possible threats that the system faces , as well as the specific security services that have to be provided in order to mitigate them . Next , a set of five primal security functions that a federated satellite system has to implement is defined , and an interaction state model is propose to characterize the security state when two federates are interacting with each other . Differences in these security states are then used to define the interaction state machine , which is a transition state diagram that can be used to rapidly identify which security functionality has to be provided in order to securitize an unreliable interaction .
The work is concluded in this chapter by presenting an overview and the implications of the Interaction-Value model for theory , and whether the findings can be applied , replicated or transferred elsewhere . Concluding comments are also presented regarding health informatics and information provision in general .
This nonparametric estimator leads to aninteresting analogy between learning andinteracting particles in a potential field . Itturns out that learning by second orderstatistics is a special case of thisinteraction model for learning . We investigatethe mathematical properties of thisnonparametric entropy estimator , provide batchand stochastic gradient expressions foroff-line and on-line adaptation , and illustratethe performance of the corresponding algorithmsin examples of supervised and unsupervisedtraining , including time-series prediction andICA .
Each facet model is written with reference to a domain that defines a vocabulary and semantics for the model definition . To model interaction between specifications from different domains , Rosetta provides an interaction definition mechanism based on institution theory . The Rosetta model-centered specification approach allows systems designers to specify many domains of interest from many perspectives and supports predictive design analysis at the systems-level .
A simulation of benzene .. Ar2 clusters has been performed using molecular dynamics software . Details on development tools , interaction formulation and calculation parameters are given . Estimates of macroscopic properties as well as elements for understanding the dynamics of the systems are discussed .
An information-cognitive psychology study of child-tablet computer interaction . Using Luhmann 's communication framework we examine the interaction implications that arise when kindergarten-grade 2 students use mathematics applications on four types of tablet computers . We asked a ) what content is communicated between the child and the tablet computer , b ) how is content communicated , c ) how engaged are children in the tablet-child interaction , and d ) what factors influence this engagement .
We study interactions among players in cooperative games . We propose a new interaction index called Shapley-Taylor interaction index . It decomposes the value of the game into terms that model the interactions between subsets of players , analogous to how the Taylor series represents a function in terms of its derivatives of various orders .
Product information is extracted from STEP file with down-top patten extracting method , and an intelligent feature recognition methodology by using a sturcture of relative elements list is proposed . Based on this methodology , a product feature model is established and this model can be ammended through the change of key elements of features .
This paper presents data from a case study of a student ( Mary ) who solved quadratic contextual problems . Mary ’s schemes , constructed from quadratically related quantities and patterns of additive rates , fostered the development of an iterative , summative conceptualisation of quadratics in contrast to the product view . Findings support the use of contextual problems to motivate students to think reflectively and mathematically .
A cost estimating model in the product design phase based on CAD/CAPP integration is proposed . The model includes CAD feature recognition and extraction , product feature modeling and process planning . A process planning based cost estimation system ( PPBCES ) based on the model is developed .
Many NLP tasks involve sentence-level annotation yet the relevant information is not encoded at sentence level but at some relevant parts of the sentence . Such tasks include but are not limited to : sentiment expression annotation , product feature annotation , and template annotation for Q&A systems . However , annotation of the full corpus sentence by sentence is resource intensive .
In the proposed modeling method , result of human intent analysis is applied at situation based generation of features in the product structure . The new method of product feature definition and the demanded new features are explained in this paper . In order to easy implementation of the proposed modeling in large modeling systems , the method of the feature driven product definition is the same as in the current leading industrial product lifecycle management systems .
Automated computerized machine vision inspection , techniques can be used as a tooling for automated shape inspection in an advanced CIM environment so that real time , reliable , non-contact and non-destructive , and 100 % quality measure activities can be achieved fruitfully . This paper develops a generic hardware system , software architecture and a technology-mix for product feature identification and measurement analysis of various machined parts/products . The feature analysis seeks to identify inherent characteristics of feature of an object found within the image frame .
Deep Crossing , Wide & Deep Learning and YouTube Recommendation CTR model extend LS - PLM and FM by replacing the transformation function with complex MLP network , which enhances the model capability greatly . PNN tries to capture high - order feature interactions by involving a product layer after embedding layer . DeepFM imposes a factorization machines as ” wide ” module in Wide & Deep with no need of feature engineering .
The problem of visual fine - grained categorization can be extremely challenging due to the subtle differences in the appearance of certain parts across related categories . In contrast to basic - level recognition , fine - grained categorization aims to distinguish between different breeds or species or product models , and often requires distinctions that must be conditioned on the object pose for reliable identification . Facial recognition is the classic case of fine - grained recognition , and it is noteworthy that the best facial recognition methods jointly discover facial landmarks and extract features from those locations .
The focus of our research is on techniques for automatically constructing anomaly detection models that are capable of detecting new ( or unseen ) attacks . We introduce a new data mining method that performs " cross-feature analysis " to capture the inter-feature correlation patterns in normal traffic . These patterns can be used as normal profiles to detect deviation ( or anomalies ) caused by attacks .
In particular , a new interactive attention is proposed to bridge two tasks for achieving information complementarity between different tasks , which effectively leverages the boundary information for offering a strong cue to better segmentation prediction . At last , a cross feature fusion module aims to selectively aggregate multi-level features from the whole encoder network . By cascaded three modules , richer context and fine-grain features of each stage are encoded .
This design problem can be improved by refactoring on architectural level . This paper offered an automatic software architecture refactoring method based on the existing analysis of crosscutting feature . At first , this method analyzes crosscutting features based on traceability between features and components .
Common feature fusion strategies ( addition or concatenation ) ignore these differences and may cause suboptimal solutions . In this paper , we propose the F3Net to solve above problem , which mainly consists of cross feature module ( CFM ) and cascaded feedback decoder ( CFD ) trained by minimizing a new pixel position aware loss ( PPA ) . Specifically , CFM aims to selectively aggregate multi-level features .
The system is new of its kind in this area , where the new algorithm either feature extraction or classifier is easily adaptable to the system and starts working accordingly . Testing the proposed feature extraction method by cross-classifiers or validate the new classification technique using cross-feature extraction mechanisms are the key factors of this newly designed system . Moreover , an easy guide and understandable GUI facilitates the users comfortable to utilize this system for personal use only .
These techniques use binarization , thinning and features extraction algorithms which are computational methods that can be applied to digital image processing used in scientific research and security issues . This paper presents a comparative analysis of four thresholding techniques ( Niblack , Bernsen , Fisher , Fuzzy ) , two thinning techniques ( Stentiford and Holt ) and a feature extraction ( Cross Number ) technique to evaluate the best performance of the algorithms in fingerprint images . To develop this project a set of 160 fingerprint images was used in experiments and analysis .
The improvement of segmentation algorithm and the optimization of feature space are the key factors of improving the accuracy of tree-crown information extraction , and are also the urgent problems of tree-crown information extraction using high resolution images . In the present study , the spectral threshold method was used on the first-class segmentation of QuickBird multi-spectral image to obtain vegetation regions .
In the proposed approach , the model of four-layer feed forward BP(back-propagation ) neural network is given , by making use of the real human body data , the training samples of the neck , bust , waist and hip are obtained . The weight and the feature curve modeling of the neck , bust , waist and hip can be got respectively after training . Finally , the error analysis is done and the results show that the method can approach the real human body feature curve .
This paper presents a new unified feature parametrization approach for monocular SLAM . The parametrization is based on the parallax angle and can reliably represent both nearby and distant features , as well as features in the direction of camera motion and features observed only once .
For fingerprint matching , we propose a single hidden layer neural network ( SLFN ) , which combines a powerful extreme learning machine ( ELM ) and a well generalized resilient propagation ( RPROP ) algorithm . The proposed fingerprint matching system comprises the following steps : fingerprint pre-processing/enhancement , ROI extraction , invariant LDP feature extraction , and matching using proposed hybrid classifier . The experimental result shows that the matching accuracy of the proposed system is improved as compare to ELM for lower values of hidden nodes , and other distance based matching approaches proposed in the literature .
Analysis is further performed on how errors in feature detection affect the resulting pose determination . The method resulted in the detection of facial pose from 30 to -30 degrees with an average error of 4.28 degrees for the Frontal Position Model and 5.79 degrees for the Side Position Model with correct feature detection . The Intel(R ) Streaming SIMD Extensions ( SSE ) technology was employed to enhance the performance of floating point operations .
In this study , an efficient , singular value , decomposition-based feature extraction method for image splicing detection is presented . Kernel Principal Component Analysis is also applied as classifier feature preprocessor to improve the classification process ; and finally , support vector machine is used to distinguish the authenticated and spliced images . The results show a detection accuracy of 98.78 % for the proposed method with only 50-dimensional feature vector .
Implementations of signal processing tasks such as feature extraction , learning , or recognition can especially benefit from bio-inspired computational models . In this thesis , we present a bio-inspired artificial olfactory system , which consists of an analog feature extraction front end that operates efficiently in the low-precision regime and a spike pattern classifier that exploits the randomness in circuits . The analog front end implements a novel trainable feature extraction algorithm for metal-oxide gas sensor arrays .
-Mobile wireless devices play important roles in our daily life , e.g. , users often use such devices to take pictures and share with friends via opportunistic peer-to-peer links , which however are intermittent in nature , and hence require the store andforward feature proposed in Delay Tolerant Networks to provide useful data sharing opportunities . Moreover , mobile devices may not be willing to forward data items to other devices due to the limited resources .
The proposed algorithm consists of two levels such as the face detection and the face tracking . First , the face detection used the eight-wavelet feature models which ate very simple . Each feature model applied to variable size and position , and then create initial feature set .
In Experiment 2 we returned to the typical sequence and restored the large partial repetition costs . Experiment 3 confirmed these findings , which have implications for interpreting partial repetition costs and for feature integration theories in general . ( PsycINFO Database Record ( c ) 2020 APA , all rights reserved ) .
Instead of using the gated hyperbolic tangent activation , we use weight normalization followed by ReLU to reduce computation . We also replaced feature concatenation with element - wise multiplication to combine the features from text and visual modalities when computing the top - down attention . To compute the question representation , we used 300 GloVe vectors to initialize the word embeddings and then passed it to a GRU network and a question attention module to extract attentive text features .
Prior to the renaissance of convolutional neural network ( CNN ) , numerous of machine learning algorithms are applied to face detection . The pioneering work of Viola - Jones utilizes Adaboost with Haar - like feature to train a cascade model to detect face and get a real - time performance . Also , deformable part models ( DPM ) is employed for face detection with remarkable performance .
In our work we focus on improving image set embedding model , which maps an image set to an representation with fixed dimension so that image sets with different number of images are comparable with each other . Let and denote representation of and . is determined by all elements in , therefore it can be denoted as The is produced by a feature extraction process , containing traditional hand - craft feature extractors or convolutional neural network . is an aggregative function , which maps a variable - length input set to a representation of fixed dimension .
GMemN2N selects the supporting sentence in the same way as MemN2N , but uses the gate to selectively add the the question to control the influence of the question information in finding the supporting sentence in the next step . DMN and DMN + use output feature map based on various relatedness such as absolute difference , as well as inner product , to understand the relation between sentence and question at various points . The more difficult the task , the more complex the output feature map and the generalization component to get the correct answer .
The authors report promising performance improvement during training with better convergence compared to U - Net , but no benefit was observed when using a summation of features during the testing phase [ reference ] . However , this concept proved that feature summation impacts the performance of a network . The importance of skipped connections for biomedical image segmentation tasks have been empirically evaluated with U - Net and residual networks [
Cross - bigrams : for every pair of bigrams across the premise and hypothesis which share a POS tag on the second word , an indicator feature over the two bigrams . We report results in Table [ reference ] , along with ablation studies for removing the cross - bigram features ( leaving only the cross - unigram feature ) and for removing all lexicalized features . On our large corpus in particular , there is a substantial jump in accuracy from using lexicalized features , and another from using the very sparse cross - bigram features .
We further propose a Feature Weighting sub - Net ( FWN ) to learn the weights of global representations and local representations on different parts . Therefore , more reasonable feature fusion is conducted to facilitate feature similarity measurement . Some more detailed descriptions to our local representation generation are illustrated in Fig .
Template adaptation is an example of transfer learning , where the target domain is defined by the set of media of a subject in a template . In general , transfer learning includes a source domain for feature encoding of subjects trained offline , and a specific target domain with limited available observations of new subjects . In the case of template adaptation , the source domain may be a deep convolutional network trained offline to predict subject identity , and the target domain is the set of media in templates of never before seen subjects .
Geometric and appearance - based features tend to capture different representations of facial expressions , hence work well together . Consequently , improvements for either of these feature types will be beneficial for facial expression recognition systems . In this study , we proposed geometric features derived from landmark pairs , including many non - descriptive and redundant ones .
Main Results Table [ reference ] lists the test - set accuracies of our best parsing models , compared to other state - of - the - art parsers from the literature . It is clear that our parsers are very competitive , despite using very simple parsing architectures and minimal feature extractors . When not using external embeddings , the first - order graph - based parser with 2 features outperforms all other systems that are not using external resources , including the third - order TurboParser .
BIGnav: Bayesian Information Gain for Guiding Multiscale Navigation Neural network based regression test selection The Importance of Emotional Usability Effective Visualization of Heat Transfer Augmented surfaces: a spatially continuous work space for hybrid computing environments Towards a micro-blog platform for sensing and easing adolescent psychological pressures A hybrid BCI for enhanced control of a telepresence robot Modeling activity recognition of multi resident using label combination of multi label classification in smart home Research on BBS Complex Online Network and Members Interactive Characteristics Affective interaction recognition using spatio-temporal features and context Explicit Interaction Model towards Text Classification A Statistical Approach to Feature Interaction Service Validation and Testing A template-based approach for the generation of abstractable and reducible models of featured networks Strategy of Feature Interaction Based on the Sufficiency Principle Feature Interactions in Aspect-Oriented Scenario Models Widgetizing Communication Services Modeling Method for Assessing Privacy Technologies Feature interaction in software product line engineering: A systematic mapping study Feature Interaction Verification Using Unbounded Model Checking with Interpolation DeepFM: A Factorization-Machine based Neural Network for CTR Prediction Augmenting OLAP exploration with dynamic advanced analytics Control device for field device and system and method for operation of a field device Architecting Information Security Services for Federated Satellite Systems Implications for Theory, Practice and the Future Beyond second-order statistics for learning: A pairwise interaction model for entropy estimation Heterogeneous Modeling Support for Embedded Systems Design A Molecular Dynamics Study of the Benzene... Ar2 Complexes Intuitive or Idiomatic? An information-cognitive psychology study of child-tablet computer interaction A New Interaction Index inspired by the Taylor Series Research on Product Feature Model Based on STEP Exchange File Rates of Change and an Iterative Conception of Quadratics A cost estimation model of product based on CAD/CAPP integration Selective Annotation of Sentence Parts: Identification of Relevant Sub-sentential Units Elevated level design intent and behavior driven feature definition for product modeling Dimentional inspection and feature recognition through machine vision system Deep Interest Network for Click-Through Rate Prediction Part-Based R-CNNs for Fine-Grained Category Detection Cross-feature analysis for detecting ad-hoc routing anomalies Boundary-aware Context Neural Network for Medical Image Segmentation Crosscutting Feature Analysis-based Automatic Software Architecture Refactoring Method F3Net: Fusion, Feedback and Focus for Salient Object Detection RaFaReS: Range image-based 3D face recognition system by surface descriptor Techniques of Binarization, Thinning and Feature Extraction Applied to a Fingerprint System [Tree-crown information extraction of farmland returned to forests using QuickBird image based on object-oriented approach]. Human Body Feature Curve Generating Method Based on Neural Network for 3D Human Body Modelling Parallax angle parametrization for monocular SLAM Fingerprint Matching Using Rotational Invariant Image Based Descriptor and Machine Learning Techniques Efficient Human Facial Pose Estimation Image splicing detection using singular value decomposition Bio-Inspired Artificial Olfactory System Routing in Socially Selfish Delay Tolerant Network Real-Time Face Detection and Tracking Using the AdaBoost Algorithm Does feature-based attention play a role in the episodic retrieval of event files? Pythia v0.1: the Winning Entry to the VQA Challenge 2018 Face Attention Network: An Effective Face Detector for the Occluded Faces Quality Aware Network for Set to Set Recognition Finding ReMO (Related Memory Object): A Simple Neural Architecture for Text based Reasoning Recurrent Residual Convolutional Neural Network based on U-Net (R2U-Net) for Medical Image Segmentation A large annotated corpus for learning natural language inference Pose-Driven Deep Convolutional Model for Person Re-identification Template adaptation for face verification and identification Greedy search for descriptive spatial face features Simple and Accurate Dependency Parsing Using Bidirectional LSTM Feature Representations
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CHI CHI UbiComp (Adjunct Publication) AAAI FIW ICFI ICC IJCAI EDBT EMSOFT International Conference on Computational Science (3) ASIST ALR@COLING IECON KDD ECCV ICDCS AAAI ICRA ICETET ICC ArXiv ArXiv 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) ArXiv ArXiv EMNLP 2017 IEEE International Conference on Computer Vision (ICCV) Image Vis. Comput. 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Transactions of the Association for Computational Linguistics
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