Even if a significant amount of machine and process data is available , one of the common problems of these data is the lack of annotations describing the machine status or maintenance history . For this reason , companies have limited options to analyse manufacturing data , despite the capability of advanced machine learning techniques in supporting the identification of failure symptoms in order to optimize scheduling of maintenance operations . Moreover , each machine generates highly heterogeneous data , making it difficult to integrate all the information to provide data-driven decision support for predictive maintenance . This work utilizes high resolution images in order to improve the classification accuracy on low resolution images . The approach is based on the machine learning paradigm called LUPI - “ Learning Using Privileged Information ” . In this contribution , the LUPI paradigm is demonstrated on images from the Caltech 101 dataset . A novel method employing machine-based learning to identify messages related to other messages is described and evaluated . This technique may enable an analyst to identify and correlate a small number of related messages from a large sample of individual messages . This methodology explained that machine learning for bridge seismic tasks had two aspects . The first was the collection of considerable bridge design data and set up data sets , and the second was data reduction , including raw data processing and debugging or developing a reasonable machine learning algorithm model . This study also discussed the shortcomings of existing performance-based probabilistic seismic design and evaluation methods that are currently used in analyzing bridges in China . Each submission was rigorously reviewed by at least two experts in the related fields based on the criteria of originality , significance , quality , and clarity . Eventually , eight papers were accepted for the special issue , spanning a variety of topics in terms ofmachine learning and its utilization for analyzingmotion includingbiological trajectorymatching . The first contribution , an invited survey by Metaxas , introduces the concept and applications of Nonverbal Communication Computing and studies its corresponding motion analysis methods , including face tracking , expression recognition , body reconstruction , and group activity analysis . This leads tragedy the shop dealers in tragedy to for verifying whether there isgenuine client doing shopping on their own credit card or not . Using Machine learning in , a strategy for information investigation that iteratively gain from information and enables PCs to discover shrouded bits of knowledge without being any express customized . Calculation starts by extricating information and obscure intriguing examples essential on the grounds that as models are presented to new information , it can freely adjust . The problem is especially important if a study involves comparisons of multiple datasets , where each is derived from different open-access publications where data are represented in an aggregated form . This chapter describes the problem of machine learning of models from aggregated data as compared to traditional learning from individual examples . It presents a method of rule induction from such data as well as an application of this method to constructing of the predictive models for diagnosing liver complications of the metabolic syndrome – one of the most common chronic diseases in humans . In this paper , we propose a virtuous circle based knowledge convergence and extension model to accommodate the above intrinsic requirements . The proposed model exploits 1 ) the advanced information during preprocessing phase got by analyzing the high volume data of various IoE devices on real-time and 2 ) gives machine learning based learning models and their results during learning model phase to support the decision making accurately . These self-learning and learning results can be generated , converged , inferenced , and expanded with new knowledge of data processing and learning . TermPicker uses such SLPs and generates a ranked list of vocabulary terms for reuse . The lists of recommended terms are ordered by a ranking model which is computed using the machine learning approach Learning To Rank ( L2R ) . TermPicker is evaluated based on the recommendation quality that is measured using the Mean Average Precision ( MAP ) and the Mean Reciprocal Rank at the first five positions ( MRR@5 ) . In this paper , we present WeMonet , a design prototype supporting citizens engaging in participatory street art creation via human-AI collaboration . Citizens ' sketches are synthesized , enhanced to be more vivid through machine machine learning algorithms , and projected on a screen , forming a participatory artwork . WeMonet aims to promote citizens ' engagement in creative practices and hence the city 's creativity . Most classical techniques do not make effective use of the larger amounts of data that are now available . The time is ripe to apply more powerful and flexible machine learning methods to these problems , assuming we can find models with suitable inductive biases . The symmetries of atomic systems suggest neural networks that operate on graph structured data and are invariant to graph isomorphism might also be appropriate for molecules . RNNs , in fact , are more and more often employed in speech recognition , due to their capabilities to properly manage time contexts and capture long - term speech modulations . In the machine learning community , the research of novel and powerful RNN models is a very active research topic . General - purpose RNNs such as Long Short Term Memories ( LSTMs ) have been the subject of several studies and modifications over the past years . For each unit , we only see their response to one of the possible actions - the one they had actually received . This is close to what is known in the machine learning literature as “ learning from logged bandit feedback ” , with the distinction that we do not have access to the model generating the action . Our work differs from much work in causal inference in that we focus on the individual - level causal effect ( also known as “ c - specific treatment effects ” ) , rather that the average or population level . One approach to the problem of estimating the function is by learning the two functions and using samples from . This is similar to a standard machine learning problem of learning from finite samples . However , there is an additional source of variance at work here : For example , if mostly rich patients received treatment , and mostly poor patients received treatment , we might have an unreliable estimation of for poor patients . The bound is expressed in terms of ( 1 ) the expected loss of the model when learning the observed outcomes as a function of and , denoted , standing for “ Factual ” ; ( 2 ) an Integral Probability Metric ( IPM ) distance between the distribution of treated and control units . The term is the classic machine learning generalization - error , and in turn can be upper bounded using the empirical error and model complexity terms , applying standard machine learning theory . subsection : Problem setup A wide variety of applications rely on text generation , including machine translation , video / text summarization , question answering , among others . From a machine learning perspective , text generation is the problem of predicting a syntactically and semantically correct sequence of consecutive words given some context . For instance , given an image , generate an appropriate caption or given a sentence in English language , translate it into French . Also , ordinal regression models are common choices for text message advertising and various recommender systems . While the field of machine learning field developed many powerful algorithms for predictive modeling , most algorithms were designed for classification tasks . About ten years ago , Li and Lin proposed a general framework for ordinal regression via extended binary classification , which has become the standard choice for extending state - of - the - art machine learning algorithms for ordinal regression tasks . When attempting to prove properties such as memory safety ( i.e. , that there are no null pointer dereferences in a program ) , a core problem is to find mathematical descriptions of the data structures used in a program . Following , we have phrased this as a machine learning problem where we will learn to map from a set of input graphs , representing the state of memory , to a logical description of the data structures that have been instantiated . Whereas relied on a large amount of hand - engineering of features , we show that the system can be replaced with a GGS - NN at no cost in accuracy . However , bottom - up discriminative models are focused on learning useful representations from data , being incapable of capturing the data distribution . Learning top - down generative models that can explain complex data distribution is a long - standing problem in machine learning research . The expressive power of deep neural networks makes them natural candidates for generative models , and several recent works have shown promising results . By their very nature , these tumors can appear anywhere in the brain and have almost any kind of shape , size , and contrast . These reasons motivate our exploration of a machine learning solution that exploits a flexible , high capacity DNN while being extremely efficient . Here , we give a description of different model choices that we ’ ve found to be necessary for obtaining competitive performance . Artificial intelligence is increasingly becoming a part of everyday life . This raises the question whether clinical neurology can benefit from these novel methods to increase diagnostic accuracy . Affective Computing is a new Artificial Intelligence area that deals with the possibility of making computers able to recognize human emotions in different ways . This paper represents a study about the integration of this new area in the intelligent tutoring system . Swarm Intelligence is an artificial intelligence discipline which is concerned with the design of intelligent multi-agent systems by taking inspiration from the collective behaviours of social insects and other animal societies . They are characterized by a decentralized way of working that mimics the behaviour of the swarm . The application of artificial intelligence will become more and more extensive during the development of computer network technology . Related Concepts of Artificial Intelligence Artificial intelligence mainly refers to the use of computing technology in order to achieve human thinking and behavior and achieve the intelligent computer technology through the simulation of computer technology . In order to realize the simulation of artificial intelligence , we need to start from many aspects . In this situation , this research work propose at using ANFIS ( Adaptive Neuro Fuzzy Inference System ) to assess the present condition of daily water consumption and predict the future trend in the urban environment . In the field of water resources and hydrology , Artificial Intelligence system approaches such as fuzzy logic ( FL ) , Artificial Neural Network ( ANN ) , etc . have been used successfully for nonlinear and non-stationary time series modeling . For complex system , both FL and ANN techniques are combined together so as the individual strength can be exploited in a better manner for the construction of powerful hybrid system . Artificial neural networks ( ANNs ) are a form of artificial computer intelligence which are the mathematical algorithms , generated by computers . They are the digitized model of biological brain and can detect complex nonlinear relationships between dependent as well as independent variables in a data where human brain may fail to detect . Initially , we show a Multi-Agent system called SMAGS , which has the ability of integrating different information systems and manage the information inherent to the project of interest in real time , as well as acting as a “ Virtual Manager ” . Next , we included new Artificial Intelligence ( IA ) techniques with semantic interpretation and metrical testing to supply the user with a CASE ( Computer Aided Software Engineering ) tool with the ability of generating intelligent computerized information systems , which can learn from information from intelligent searches in ontology servers , case bases , specialist in the area , or even from fragmented data from the Internet , with the purpose of enhancing decision taking . User Manual and Implementation Notes . This document briefly describes FrameLib , a C++ library to manipulate " frames " as they are used in the Artificial Intelligence world . The library provides a general framework ( ! ) for creating new frames ( ! ! ) Visual question - answering tasks provide a testbed to cultivate the synergistic proposals which handle multidisciplinary problems of vision , language and integrated reasoning . So , the visual question - answering tasks let the studies in artificial intelligence go beyond narrow tasks . Furthermore , it may help to solve the real world problems which need the integrated reasoning of vision and language . Recent developments in Artificial Intelligence ( AI ) have resulted in breakthroughs in applications such as computer vision , natural language processing , robotics , and data mining . These breakthroughs have been optimally utilized in various military applications such as surveillance , reconnaissance , threat evaluation , underwater mine warfare , cyber security , intelligence analysis , command and control as well as military education and training . In this paper , various ethical issues related with the implementation of AI/AS are demonstrated with different perspectives . The ongoing developments in the area of AI/AS are critically evaluated and related advantages and serious concerns of the society are discussed . Various global initiatives and legal amendments across the globe to limit the excessive usage of AI/AS are being examined with critical assessments . Causal rules are characterized as consisting of a diachronic component and a synchronic component which distinguishes our formulation of causal rules from that of other research . A classic problem , the spatial movement to goal problem , is used to illustrate the power of causal learning in vastly reducing the problem solving search space involved , and this is contrasted with the traditional AI A * algorithm which requires a huge search space . As a result , the method is scalable to real world situations . Combination of AI/OR methods is gaining a great deal of attention because many combinatorial problems especially in planning and scheduling areas can be solved by means of combined AI/OR techniques . Many of these combinatorial problems can be expressed in a natural way as a Constraint Satisfaction Problem ( CSP ) . To help bridge this gap , we created the ' AI Driving Olympics ' ( AI-DO ) , a competition with the objective of evaluating the state of the art in machine learning and artificial intelligence for mobile robotics . Based on the simple and well specified autonomous driving and navigation environment called ' Duckietown ' , AI-DO includes a series of tasks of increasing complexity -- from simple lane-following to fleet management . For each task , we provide tools for competitors to use in the form of simulators , logs , code templates , baseline implementations and low-cost access to robotic hardware . An integration architecture tactic to guard AI-first components . Intelligent services provide the power of AI to developers via simple RESTful API endpoints , abstracting away many complexities of machine learning . The development of AI can not be separated from the Industrial Revolution 4.0 . AI itself is the senses cognitive and machine learning process in problem- solving . AI systems modeled after the way that human brains operate ; taking in information and activating neurons that themselves are not smart , but combine to create probabilistic models that mimic our pattern-recognizing intelligence . We also apply AI-enabled emotional communication to emotional social robots to provide users with personalized service emotion . Then the system architecture for the AI-enabled emotion communication is introduced in detail , and the no-tag learning model of dataset labeling and processing as well as the AI algorithm model for emotion recognition are elaborated in detail , and experiments are done to verify the interactive delay in the AI-enabled emotion communication system and accuracy of emotion recognition . Since the cloud computing became widely available , lots of previously algorithms and systems previously thought of as very time consuming became instantly viable . For robotics and AI especially this means that if the power behind the cloud could be harnessed , it would be possible to build smaller , more battery effective robots because there would be no need to have a powerful computer on board , but the brain of the robot can be in the cloud . The idea of remote brain is not a new one , though . A layer of artificial intelligence ( AI ) techniques built around this core automates the high-level stages of the identification process that are normally performed by a human expert . The AI layer accomplishes this by selecting and applying appropriate methods from the SID library and performing qualitative , symbolic , algebraic , and geometric reasoning on the user 's inputs . For each supported domain ( e.g. , mechanics ) , the program uses a few powerful encoded rules ( e.g. , σF=0 ) to combine hypotheses into models . In 2009 , Pereira presented a survey paper to look into impact of data in fields such as natural language processing and computer vision . They argued unlike physics , areas in AI are more likely to see an impact using more data - driven approaches . Another related work is the empirical study by Torralba and Efros that highlighted the dataset biases in current computer vision approaches and how it impacts future research . What makes for a compelling " AI - complete " task ? We believe that in order to spawn the next generation of AI algorithms , an ideal task should ( i ) require multi - modal knowledge beyond a single sub - domain ( such as CV ) and ( ii ) have a well - defined quantitative evaluation metric to track progress . For some tasks , such as image captioning , automatic evaluation is still a difficult and open research problem [ reference ] , [ reference ] , In this paper , we tackle the problem of joint angles and time delays estimation ( JADE ) in a non-data aided ( NDA ) scenario where the transmitted signal is unknown at the receiver . We do so by applying the maximum likelihood ( ML ) in order to obtain the best performance achievable . The importance sampling ( IS ) technique is used to reduce the multidimensionality of the maximization problem without recurring to an iterative option . In this paper we define the concept of the Machine Learning Morphism ( MLM ) as a fundamental building block to express operations performed in machine learning such as data preprocessing , feature extraction , and model training . Inspired by statistical learning , MLMs are morphisms whose parameters are minimized via a risk function . In this paper , a maximum likelihood based beamforming ( MLBB ) algorithm that uses a two-element array is proposed to reject a follower jamming signal and carry out symbol detection in slow FH/MFSK systems over quasi-static flat fading channels . Deploying a received signal model that takes care of flat fading , the proposed scheme first uses a ML-based approach to obtain a ML estimate of the ratio of jamming fading gains . Based on this ML estimate , a simple beamforming structure is employed to place a null toward the follower jamming source and the symbol detection is then performed by the ML technique . This work focuses on the problem of multi-label learning with missing labels ( MLML ) , which aims to label each test instance with multiple class labels given training instances that have an incomplete/partial set of these labels ( i.e. some of their labels are missing ) . To handle missing labels , we propose a unified model of label dependencies by constructing a mixed graph , which jointly incorporates ( i ) instance-level similarity and class co-occurrence as undirected edges and ( ii ) semantic label hierarchy as directed edges . We propose an ML algorithm for the estimation of synchronization impairments and channel together , using grid search method . To reduce the complexity of the joint grid search in ML algorithm , a Modified ML ( MML ) algorithm with multiple one-dimensional searches is also proposed . Further , a Stage-wise ML ( SML ) algorithm using existing algorithms , which estimate fewer number of parameters , is also proposed . Usually , models are trained on corpora , that is to say on literary works often “ available on the internet � ? . The question thus becomes the following : is the act of training a model for ML purposes a copyright relevant activity ? The answer to this question is not only relevant in terms of copyright law and theory , but more broadly in terms of innovation policy as it has the potential to determine who has to ask whom for what permission in order to perform ML functions . An attempt is made to apply ideas about algebraic specification in the context of a programming language . Standard ML with modules is extended by allowing axioms in module interface specifications and in place of code . The resulting specification language , called Extended ML , is given a semantics based on the primitive specification-building operations of the kernel algebraic specification language ASL . When examined , many issues and questions arose , both technically and conceptually . We share our experience and our reluctances , being aware of the fact that more advanced colleagues ( using SIARDS or ADDML , for instance ) may regard all as these primitive solutions . But , in the same time , we are convinced we are not the only ones lacking proper skills and expertise and our examples may be an example for other colleagues . The current security protocol which is used for present IoT system is not enough to secure all the layer of cellular IoT and also the communication overhead is much high . The proposed Multi-level Authentication and Key Agreement Protocol ( ML-AKA ) focus on threats generated by all the layer of the IoT starting from application to the core network layer . This proposed protocol is authenticated and validated by AVISPA tool and ProVerif Automatic Cryptographic Protocol Verifier tool and the simulation result shows that ML-AKA protocol is safe from attacks and comparison done after testing , the ML-AKA protocol takes less communication overhead than the others existing protocols . As the notion of many IoT applications is changing from reactive to proactive where complex events can be predicted before they actually happen , solutions based on CEP required an extension to address this issue . To this end , this work proposes a proactive method based on CEP and machine learning ( ML ) where historical data is exploited using ML part and combined with real-time flow of CEP to provide the basis for predictive event processing . Second , systems based on CEP deploy static rules and there is no means to update the rules according to the current context automatically . subsection : Architecture - I ( Arc - I ) Architecture - I ( Arc - I ) , as illustrated in Figure [ reference ] , takes a conventional approach : It first finds the representation of each sentence , and then compares the representation for the two sentences with a multi - layer perceptron ( MLP ) . It is essentially the Siamese architecture introduced in , which has been applied to different tasks as a nonlinear similarity function . These differ , as some classes contain fewer than 50 test samples . Most researchers in ML use average overall accuracy . saturates at around 100 samples . We also evaluate the vertex embeddings through a multi - label classification task . We choose 7 popular conferences including AAAI , CIKM , ICML , KDD , NIPS , SIGIR , and WWW as the classification categories . Authors publishing in the conferences or papers published in the conferences are assumed to belong to the categories corresponding to the conferences . subsubsection : Comparison of Metric Learning Algorithms We evaluated the proposed XQDA algorithm and several metric learning algorithms , including Euclidean distance , Mahalanobis distance trained with genuine pairs , LMNN v2.5 , ITML , KISSME , and RLDA , with the same LOMO feature . For the compared algorithms , PCA was first applied to reduce the feature dimensionality to 100.The proposed XQDA algorithm and RLDA also learned a 100 - dimensional subspace . We adopted state - of - art graphs kernels for comparison namely : Random Walk ( RW ) , Shortest Path Kernel ( SP ) , Graphlet Kernel ( GK ) , Weisfeiler - Lehman Sub - tree Kernel ( WL ) , Deep Graph Kernels ( DGK ) and Multiscale Laplacian Graph Kernels ( MLK ) . Baselines Settings : It is important to note that there are non - trivial gaps between our definition of and other definitions employed by previous work . Adding MLP in does not seem to help , yielding slightly worse result than without MLP . On the one hand , several of us voiced the opinion that language development is no longer a topic of AI research and that AI researchers , just as everybody else in computer science , use some more or less mainstream language which is convenient for a given area of research . On the other hand , it became immediately clear that everybody has strong feelings or at least a qualified opinion about the languages which he or she uses to create AI programs . Because we all are interested in the question which language has what advantages in the context of our area of research , we are hoping that the readers of this journal are also interested in this topic . We implemented the proposed mechanism on the real StarCraft : BroodWar binary code . We compared the performance of the modified AI code , and the results show that the new AI code outperforms the existing artificial intelligence of the game . Discussions are also provided of the two major AI programming languages , Lisp and Prolog , including their strengths and weaknesses . The remainder of the book is devoted to a review of some of the existing AI software products for microcomputers , such as natural language query systems , decision support systems , expert system development shells , and AI programming languages . I found the chapter on expert system shells to be worth the price of the book . It follows that some of the systems that perform learning essentially by adjusting coefficients may find it difficult to fit into our schemes . On the contrary , all AI oriented programs should fit in . unless we have made mistakes with our description . Artificial as it looks , this effort of characterization addresses two deep concerns . * note that Prolog ’s performance drops to one tenth of the performance of procedural languages ( such as C and Pascal ) when executed by a generalpurpose computer . This fact basically explains why expert systems and other AI-application programs implemented in Prolog run very slowly on traditional general-purpose computers . Researchers traced the sources of Prolog ’s inefficiency and slow execution on these machines to two algorithms : unification and backtracking . Rigorous evaluation of ML techniques compared to traditional methods or other AI methods will be required to validate the algorithms developed with consideration of database limitations and potential learning errors . Demonstration of value on processes and outcomes will be necessary to support the use of ML as a feature in monitoring system development : Future research is needed to evaluate all AI based programs before clinical implementation in non-research settings . Games have been used as an effective way to measure the advancement of artificial intelligence . Chess , Atari2600 and Go , are some of the most mediatic demonstrations where AI computer programs defeated human players . In this paper we add the popular Flappy Bird game in the list of games to quantify the performance of an AI player . The platform of cloud computing now provides a means of implementing a kind of ubiquitous awareness unknown to humans before now and a means of augmenting human intelligence . The level of awareness held by Google is explored and some recent developments in the uses of AI programmes for social media are covered . The misinterpretation of these developments is explored and a solution proposed . Abstract Prolog is one of the most important candidates to build expert systems and AI-related programs and has potential applications in embedded systems . However , Prolog is not suitable to develop many kinds of components , such as data acquisition and task scheduling , which are also crucial . This paper discusses the strengths and weakness of using Ada as an AI language and provides a preliminary analysis of factors needed for the development of criteria for the integration of these two families of languages and the environments in which they are implemented . The intent for developing such criteria is to have a logical rationale that may be used to guide the development of Ada tools and methodology to support KBS requirements , and to identify those AI technology components that may most readily and effectively be deployed in Ada versus those best left in a functional language . With the ultra - realism of current generation consoles , each console represents a natural stepping stone toward general real - world competency . Our hope is that by using the methodology advocated in this paper , we can work in a bottom - up fashion towards developing more sophisticated AI technology while still maintaining empirical rigor . section : Conclusion
Even if a significant amount of machine and process data is available , one of the common problems of these data is the lack of annotations describing the machine status or maintenance history . For this reason , companies have limited options to analyse manufacturing data , despite the capability of advanced machine learning techniques in supporting the identification of failure symptoms in order to optimize scheduling of maintenance operations . Moreover , each machine generates highly heterogeneous data , making it difficult to integrate all the information to provide data-driven decision support for predictive maintenance .
This work utilizes high resolution images in order to improve the classification accuracy on low resolution images . The approach is based on the machine learning paradigm called LUPI - “ Learning Using Privileged Information ” . In this contribution , the LUPI paradigm is demonstrated on images from the Caltech 101 dataset .
A novel method employing machine-based learning to identify messages related to other messages is described and evaluated . This technique may enable an analyst to identify and correlate a small number of related messages from a large sample of individual messages .
This methodology explained that machine learning for bridge seismic tasks had two aspects . The first was the collection of considerable bridge design data and set up data sets , and the second was data reduction , including raw data processing and debugging or developing a reasonable machine learning algorithm model . This study also discussed the shortcomings of existing performance-based probabilistic seismic design and evaluation methods that are currently used in analyzing bridges in China .
Each submission was rigorously reviewed by at least two experts in the related fields based on the criteria of originality , significance , quality , and clarity . Eventually , eight papers were accepted for the special issue , spanning a variety of topics in terms ofmachine learning and its utilization for analyzingmotion includingbiological trajectorymatching . The first contribution , an invited survey by Metaxas , introduces the concept and applications of Nonverbal Communication Computing and studies its corresponding motion analysis methods , including face tracking , expression recognition , body reconstruction , and group activity analysis .
This leads tragedy the shop dealers in tragedy to for verifying whether there isgenuine client doing shopping on their own credit card or not . Using Machine learning in , a strategy for information investigation that iteratively gain from information and enables PCs to discover shrouded bits of knowledge without being any express customized . Calculation starts by extricating information and obscure intriguing examples essential on the grounds that as models are presented to new information , it can freely adjust .
The problem is especially important if a study involves comparisons of multiple datasets , where each is derived from different open-access publications where data are represented in an aggregated form . This chapter describes the problem of machine learning of models from aggregated data as compared to traditional learning from individual examples . It presents a method of rule induction from such data as well as an application of this method to constructing of the predictive models for diagnosing liver complications of the metabolic syndrome – one of the most common chronic diseases in humans .
In this paper , we propose a virtuous circle based knowledge convergence and extension model to accommodate the above intrinsic requirements . The proposed model exploits 1 ) the advanced information during preprocessing phase got by analyzing the high volume data of various IoE devices on real-time and 2 ) gives machine learning based learning models and their results during learning model phase to support the decision making accurately . These self-learning and learning results can be generated , converged , inferenced , and expanded with new knowledge of data processing and learning .
TermPicker uses such SLPs and generates a ranked list of vocabulary terms for reuse . The lists of recommended terms are ordered by a ranking model which is computed using the machine learning approach Learning To Rank ( L2R ) . TermPicker is evaluated based on the recommendation quality that is measured using the Mean Average Precision ( MAP ) and the Mean Reciprocal Rank at the first five positions ( MRR@5 ) .
In this paper , we present WeMonet , a design prototype supporting citizens engaging in participatory street art creation via human-AI collaboration . Citizens ' sketches are synthesized , enhanced to be more vivid through machine machine learning algorithms , and projected on a screen , forming a participatory artwork . WeMonet aims to promote citizens ' engagement in creative practices and hence the city 's creativity .
Most classical techniques do not make effective use of the larger amounts of data that are now available . The time is ripe to apply more powerful and flexible machine learning methods to these problems , assuming we can find models with suitable inductive biases . The symmetries of atomic systems suggest neural networks that operate on graph structured data and are invariant to graph isomorphism might also be appropriate for molecules .
RNNs , in fact , are more and more often employed in speech recognition , due to their capabilities to properly manage time contexts and capture long - term speech modulations . In the machine learning community , the research of novel and powerful RNN models is a very active research topic . General - purpose RNNs such as Long Short Term Memories ( LSTMs ) have been the subject of several studies and modifications over the past years .
For each unit , we only see their response to one of the possible actions - the one they had actually received . This is close to what is known in the machine learning literature as “ learning from logged bandit feedback ” , with the distinction that we do not have access to the model generating the action . Our work differs from much work in causal inference in that we focus on the individual - level causal effect ( also known as “ c - specific treatment effects ” ) , rather that the average or population level .
One approach to the problem of estimating the function is by learning the two functions and using samples from . This is similar to a standard machine learning problem of learning from finite samples . However , there is an additional source of variance at work here : For example , if mostly rich patients received treatment , and mostly poor patients received treatment , we might have an unreliable estimation of for poor patients .
The bound is expressed in terms of ( 1 ) the expected loss of the model when learning the observed outcomes as a function of and , denoted , standing for “ Factual ” ; ( 2 ) an Integral Probability Metric ( IPM ) distance between the distribution of treated and control units . The term is the classic machine learning generalization - error , and in turn can be upper bounded using the empirical error and model complexity terms , applying standard machine learning theory . subsection : Problem setup
A wide variety of applications rely on text generation , including machine translation , video / text summarization , question answering , among others . From a machine learning perspective , text generation is the problem of predicting a syntactically and semantically correct sequence of consecutive words given some context . For instance , given an image , generate an appropriate caption or given a sentence in English language , translate it into French .
Also , ordinal regression models are common choices for text message advertising and various recommender systems . While the field of machine learning field developed many powerful algorithms for predictive modeling , most algorithms were designed for classification tasks . About ten years ago , Li and Lin proposed a general framework for ordinal regression via extended binary classification , which has become the standard choice for extending state - of - the - art machine learning algorithms for ordinal regression tasks .
When attempting to prove properties such as memory safety ( i.e. , that there are no null pointer dereferences in a program ) , a core problem is to find mathematical descriptions of the data structures used in a program . Following , we have phrased this as a machine learning problem where we will learn to map from a set of input graphs , representing the state of memory , to a logical description of the data structures that have been instantiated . Whereas relied on a large amount of hand - engineering of features , we show that the system can be replaced with a GGS - NN at no cost in accuracy .
However , bottom - up discriminative models are focused on learning useful representations from data , being incapable of capturing the data distribution . Learning top - down generative models that can explain complex data distribution is a long - standing problem in machine learning research . The expressive power of deep neural networks makes them natural candidates for generative models , and several recent works have shown promising results .
By their very nature , these tumors can appear anywhere in the brain and have almost any kind of shape , size , and contrast . These reasons motivate our exploration of a machine learning solution that exploits a flexible , high capacity DNN while being extremely efficient . Here , we give a description of different model choices that we ’ ve found to be necessary for obtaining competitive performance .
Artificial intelligence is increasingly becoming a part of everyday life . This raises the question whether clinical neurology can benefit from these novel methods to increase diagnostic accuracy .
Affective Computing is a new Artificial Intelligence area that deals with the possibility of making computers able to recognize human emotions in different ways . This paper represents a study about the integration of this new area in the intelligent tutoring system .
Swarm Intelligence is an artificial intelligence discipline which is concerned with the design of intelligent multi-agent systems by taking inspiration from the collective behaviours of social insects and other animal societies . They are characterized by a decentralized way of working that mimics the behaviour of the swarm .
The application of artificial intelligence will become more and more extensive during the development of computer network technology . Related Concepts of Artificial Intelligence Artificial intelligence mainly refers to the use of computing technology in order to achieve human thinking and behavior and achieve the intelligent computer technology through the simulation of computer technology . In order to realize the simulation of artificial intelligence , we need to start from many aspects .
In this situation , this research work propose at using ANFIS ( Adaptive Neuro Fuzzy Inference System ) to assess the present condition of daily water consumption and predict the future trend in the urban environment . In the field of water resources and hydrology , Artificial Intelligence system approaches such as fuzzy logic ( FL ) , Artificial Neural Network ( ANN ) , etc . have been used successfully for nonlinear and non-stationary time series modeling . For complex system , both FL and ANN techniques are combined together so as the individual strength can be exploited in a better manner for the construction of powerful hybrid system .
Artificial neural networks ( ANNs ) are a form of artificial computer intelligence which are the mathematical algorithms , generated by computers . They are the digitized model of biological brain and can detect complex nonlinear relationships between dependent as well as independent variables in a data where human brain may fail to detect .
Initially , we show a Multi-Agent system called SMAGS , which has the ability of integrating different information systems and manage the information inherent to the project of interest in real time , as well as acting as a “ Virtual Manager ” . Next , we included new Artificial Intelligence ( IA ) techniques with semantic interpretation and metrical testing to supply the user with a CASE ( Computer Aided Software Engineering ) tool with the ability of generating intelligent computerized information systems , which can learn from information from intelligent searches in ontology servers , case bases , specialist in the area , or even from fragmented data from the Internet , with the purpose of enhancing decision taking .
User Manual and Implementation Notes . This document briefly describes FrameLib , a C++ library to manipulate " frames " as they are used in the Artificial Intelligence world . The library provides a general framework ( ! ) for creating new frames ( ! ! )
Visual question - answering tasks provide a testbed to cultivate the synergistic proposals which handle multidisciplinary problems of vision , language and integrated reasoning . So , the visual question - answering tasks let the studies in artificial intelligence go beyond narrow tasks . Furthermore , it may help to solve the real world problems which need the integrated reasoning of vision and language .
Recent developments in Artificial Intelligence ( AI ) have resulted in breakthroughs in applications such as computer vision , natural language processing , robotics , and data mining . These breakthroughs have been optimally utilized in various military applications such as surveillance , reconnaissance , threat evaluation , underwater mine warfare , cyber security , intelligence analysis , command and control as well as military education and training .
In this paper , various ethical issues related with the implementation of AI/AS are demonstrated with different perspectives . The ongoing developments in the area of AI/AS are critically evaluated and related advantages and serious concerns of the society are discussed . Various global initiatives and legal amendments across the globe to limit the excessive usage of AI/AS are being examined with critical assessments .
Causal rules are characterized as consisting of a diachronic component and a synchronic component which distinguishes our formulation of causal rules from that of other research . A classic problem , the spatial movement to goal problem , is used to illustrate the power of causal learning in vastly reducing the problem solving search space involved , and this is contrasted with the traditional AI A * algorithm which requires a huge search space . As a result , the method is scalable to real world situations .
Combination of AI/OR methods is gaining a great deal of attention because many combinatorial problems especially in planning and scheduling areas can be solved by means of combined AI/OR techniques . Many of these combinatorial problems can be expressed in a natural way as a Constraint Satisfaction Problem ( CSP ) .
To help bridge this gap , we created the ' AI Driving Olympics ' ( AI-DO ) , a competition with the objective of evaluating the state of the art in machine learning and artificial intelligence for mobile robotics . Based on the simple and well specified autonomous driving and navigation environment called ' Duckietown ' , AI-DO includes a series of tasks of increasing complexity -- from simple lane-following to fleet management . For each task , we provide tools for competitors to use in the form of simulators , logs , code templates , baseline implementations and low-cost access to robotic hardware .
An integration architecture tactic to guard AI-first components . Intelligent services provide the power of AI to developers via simple RESTful API endpoints , abstracting away many complexities of machine learning .
The development of AI can not be separated from the Industrial Revolution 4.0 . AI itself is the senses cognitive and machine learning process in problem- solving . AI systems modeled after the way that human brains operate ; taking in information and activating neurons that themselves are not smart , but combine to create probabilistic models that mimic our pattern-recognizing intelligence .
We also apply AI-enabled emotional communication to emotional social robots to provide users with personalized service emotion . Then the system architecture for the AI-enabled emotion communication is introduced in detail , and the no-tag learning model of dataset labeling and processing as well as the AI algorithm model for emotion recognition are elaborated in detail , and experiments are done to verify the interactive delay in the AI-enabled emotion communication system and accuracy of emotion recognition .
Since the cloud computing became widely available , lots of previously algorithms and systems previously thought of as very time consuming became instantly viable . For robotics and AI especially this means that if the power behind the cloud could be harnessed , it would be possible to build smaller , more battery effective robots because there would be no need to have a powerful computer on board , but the brain of the robot can be in the cloud . The idea of remote brain is not a new one , though .
A layer of artificial intelligence ( AI ) techniques built around this core automates the high-level stages of the identification process that are normally performed by a human expert . The AI layer accomplishes this by selecting and applying appropriate methods from the SID library and performing qualitative , symbolic , algebraic , and geometric reasoning on the user 's inputs . For each supported domain ( e.g. , mechanics ) , the program uses a few powerful encoded rules ( e.g. , σF=0 ) to combine hypotheses into models .
In 2009 , Pereira presented a survey paper to look into impact of data in fields such as natural language processing and computer vision . They argued unlike physics , areas in AI are more likely to see an impact using more data - driven approaches . Another related work is the empirical study by Torralba and Efros that highlighted the dataset biases in current computer vision approaches and how it impacts future research .
What makes for a compelling " AI - complete " task ? We believe that in order to spawn the next generation of AI algorithms , an ideal task should ( i ) require multi - modal knowledge beyond a single sub - domain ( such as CV ) and ( ii ) have a well - defined quantitative evaluation metric to track progress . For some tasks , such as image captioning , automatic evaluation is still a difficult and open research problem [ reference ] , [ reference ] ,
In this paper , we tackle the problem of joint angles and time delays estimation ( JADE ) in a non-data aided ( NDA ) scenario where the transmitted signal is unknown at the receiver . We do so by applying the maximum likelihood ( ML ) in order to obtain the best performance achievable . The importance sampling ( IS ) technique is used to reduce the multidimensionality of the maximization problem without recurring to an iterative option .
In this paper we define the concept of the Machine Learning Morphism ( MLM ) as a fundamental building block to express operations performed in machine learning such as data preprocessing , feature extraction , and model training . Inspired by statistical learning , MLMs are morphisms whose parameters are minimized via a risk function .
In this paper , a maximum likelihood based beamforming ( MLBB ) algorithm that uses a two-element array is proposed to reject a follower jamming signal and carry out symbol detection in slow FH/MFSK systems over quasi-static flat fading channels . Deploying a received signal model that takes care of flat fading , the proposed scheme first uses a ML-based approach to obtain a ML estimate of the ratio of jamming fading gains . Based on this ML estimate , a simple beamforming structure is employed to place a null toward the follower jamming source and the symbol detection is then performed by the ML technique .
This work focuses on the problem of multi-label learning with missing labels ( MLML ) , which aims to label each test instance with multiple class labels given training instances that have an incomplete/partial set of these labels ( i.e. some of their labels are missing ) . To handle missing labels , we propose a unified model of label dependencies by constructing a mixed graph , which jointly incorporates ( i ) instance-level similarity and class co-occurrence as undirected edges and ( ii ) semantic label hierarchy as directed edges .
We propose an ML algorithm for the estimation of synchronization impairments and channel together , using grid search method . To reduce the complexity of the joint grid search in ML algorithm , a Modified ML ( MML ) algorithm with multiple one-dimensional searches is also proposed . Further , a Stage-wise ML ( SML ) algorithm using existing algorithms , which estimate fewer number of parameters , is also proposed .
Usually , models are trained on corpora , that is to say on literary works often “ available on the internet � ? . The question thus becomes the following : is the act of training a model for ML purposes a copyright relevant activity ? The answer to this question is not only relevant in terms of copyright law and theory , but more broadly in terms of innovation policy as it has the potential to determine who has to ask whom for what permission in order to perform ML functions .
An attempt is made to apply ideas about algebraic specification in the context of a programming language . Standard ML with modules is extended by allowing axioms in module interface specifications and in place of code . The resulting specification language , called Extended ML , is given a semantics based on the primitive specification-building operations of the kernel algebraic specification language ASL .
When examined , many issues and questions arose , both technically and conceptually . We share our experience and our reluctances , being aware of the fact that more advanced colleagues ( using SIARDS or ADDML , for instance ) may regard all as these primitive solutions . But , in the same time , we are convinced we are not the only ones lacking proper skills and expertise and our examples may be an example for other colleagues .
The current security protocol which is used for present IoT system is not enough to secure all the layer of cellular IoT and also the communication overhead is much high . The proposed Multi-level Authentication and Key Agreement Protocol ( ML-AKA ) focus on threats generated by all the layer of the IoT starting from application to the core network layer . This proposed protocol is authenticated and validated by AVISPA tool and ProVerif Automatic Cryptographic Protocol Verifier tool and the simulation result shows that ML-AKA protocol is safe from attacks and comparison done after testing , the ML-AKA protocol takes less communication overhead than the others existing protocols .
As the notion of many IoT applications is changing from reactive to proactive where complex events can be predicted before they actually happen , solutions based on CEP required an extension to address this issue . To this end , this work proposes a proactive method based on CEP and machine learning ( ML ) where historical data is exploited using ML part and combined with real-time flow of CEP to provide the basis for predictive event processing . Second , systems based on CEP deploy static rules and there is no means to update the rules according to the current context automatically .
subsection : Architecture - I ( Arc - I ) Architecture - I ( Arc - I ) , as illustrated in Figure [ reference ] , takes a conventional approach : It first finds the representation of each sentence , and then compares the representation for the two sentences with a multi - layer perceptron ( MLP ) . It is essentially the Siamese architecture introduced in , which has been applied to different tasks as a nonlinear similarity function .
These differ , as some classes contain fewer than 50 test samples . Most researchers in ML use average overall accuracy . saturates at around 100 samples .
We also evaluate the vertex embeddings through a multi - label classification task . We choose 7 popular conferences including AAAI , CIKM , ICML , KDD , NIPS , SIGIR , and WWW as the classification categories . Authors publishing in the conferences or papers published in the conferences are assumed to belong to the categories corresponding to the conferences .
subsubsection : Comparison of Metric Learning Algorithms We evaluated the proposed XQDA algorithm and several metric learning algorithms , including Euclidean distance , Mahalanobis distance trained with genuine pairs , LMNN v2.5 , ITML , KISSME , and RLDA , with the same LOMO feature . For the compared algorithms , PCA was first applied to reduce the feature dimensionality to 100.The proposed XQDA algorithm and RLDA also learned a 100 - dimensional subspace .
We adopted state - of - art graphs kernels for comparison namely : Random Walk ( RW ) , Shortest Path Kernel ( SP ) , Graphlet Kernel ( GK ) , Weisfeiler - Lehman Sub - tree Kernel ( WL ) , Deep Graph Kernels ( DGK ) and Multiscale Laplacian Graph Kernels ( MLK ) . Baselines Settings :
It is important to note that there are non - trivial gaps between our definition of and other definitions employed by previous work . Adding MLP in does not seem to help , yielding slightly worse result than without MLP .
On the one hand , several of us voiced the opinion that language development is no longer a topic of AI research and that AI researchers , just as everybody else in computer science , use some more or less mainstream language which is convenient for a given area of research . On the other hand , it became immediately clear that everybody has strong feelings or at least a qualified opinion about the languages which he or she uses to create AI programs . Because we all are interested in the question which language has what advantages in the context of our area of research , we are hoping that the readers of this journal are also interested in this topic .
We implemented the proposed mechanism on the real StarCraft : BroodWar binary code . We compared the performance of the modified AI code , and the results show that the new AI code outperforms the existing artificial intelligence of the game .
Discussions are also provided of the two major AI programming languages , Lisp and Prolog , including their strengths and weaknesses . The remainder of the book is devoted to a review of some of the existing AI software products for microcomputers , such as natural language query systems , decision support systems , expert system development shells , and AI programming languages . I found the chapter on expert system shells to be worth the price of the book .
It follows that some of the systems that perform learning essentially by adjusting coefficients may find it difficult to fit into our schemes . On the contrary , all AI oriented programs should fit in . unless we have made mistakes with our description . Artificial as it looks , this effort of characterization addresses two deep concerns .
* note that Prolog ’s performance drops to one tenth of the performance of procedural languages ( such as C and Pascal ) when executed by a generalpurpose computer . This fact basically explains why expert systems and other AI-application programs implemented in Prolog run very slowly on traditional general-purpose computers . Researchers traced the sources of Prolog ’s inefficiency and slow execution on these machines to two algorithms : unification and backtracking .
Rigorous evaluation of ML techniques compared to traditional methods or other AI methods will be required to validate the algorithms developed with consideration of database limitations and potential learning errors . Demonstration of value on processes and outcomes will be necessary to support the use of ML as a feature in monitoring system development : Future research is needed to evaluate all AI based programs before clinical implementation in non-research settings .
Games have been used as an effective way to measure the advancement of artificial intelligence . Chess , Atari2600 and Go , are some of the most mediatic demonstrations where AI computer programs defeated human players . In this paper we add the popular Flappy Bird game in the list of games to quantify the performance of an AI player .
The platform of cloud computing now provides a means of implementing a kind of ubiquitous awareness unknown to humans before now and a means of augmenting human intelligence . The level of awareness held by Google is explored and some recent developments in the uses of AI programmes for social media are covered . The misinterpretation of these developments is explored and a solution proposed .
Abstract Prolog is one of the most important candidates to build expert systems and AI-related programs and has potential applications in embedded systems . However , Prolog is not suitable to develop many kinds of components , such as data acquisition and task scheduling , which are also crucial .
This paper discusses the strengths and weakness of using Ada as an AI language and provides a preliminary analysis of factors needed for the development of criteria for the integration of these two families of languages and the environments in which they are implemented . The intent for developing such criteria is to have a logical rationale that may be used to guide the development of Ada tools and methodology to support KBS requirements , and to identify those AI technology components that may most readily and effectively be deployed in Ada versus those best left in a functional language .
With the ultra - realism of current generation consoles , each console represents a natural stepping stone toward general real - world competency . Our hope is that by using the methodology advocated in this paper , we can work in a bottom - up fashion towards developing more sophisticated AI technology while still maintaining empirical rigor . section : Conclusion
A Hybrid Machine Learning Approach for Predictive Maintenance in Smart Factories of the Future Machine learning approach to fusion of high and low resolution imagery for improved target classification Analysis of Multichannel Internet Communication Seismic Design and Evaluation Methods for Small-to-Medium-Span Highway Girder Bridges Based on Machine Learning and Earthquake Damage Experience Machine learning in motion analysis: New advances Eshopping Scam Identification using Machine Learning Model Learning from Published Aggregated Data A Study on the Virtuous Circle Self-Learning Methods for Knowledge Enhancement TermPicker: Enabling the Reuse of Vocabulary Terms by Exploiting Data from the Linked Open Data Cloud Empowering a Creative City: Engage Citizens in Creating Street Art through Human-AI Collaboration Neural Message Passing for Quantum Chemistry Light Gated Recurrent Units for Speech Recognition Estimating individual treatment effect: generalization bounds and algorithms Sequence Level Training with Recurrent Neural Networks Consistent Rank Logits for Ordinal Regression with Convolutional Neural Networks Gated Graph Sequence Neural Networks Stacked Generative Adversarial Networks Brain tumor segmentation with Deep Neural Networks Machine learning in neurology: what neurologists can learn from machines and vice versa Agent-Based Framework for Affective Intelligent Tutoring Systems A Survey Paper on Particle Swarm Optimization based Routing Protocols in Mobile Ad-Hoc Networks Discussion on the Application of Artificial Intelligence in Computer Network Technology Urban Water Consumption Estimation Using Artificial Intelligence Techniques Artificial neural networks: Current applications in modern medicine Enhancing Managing Decisions with Artificial Intelligence A C++ Frame Library : User Manual and Implementation Notes Multimodal Residual Learning for Visual QA On an attempt to explore challenges for Artificial Intelligence and Machine Learning in Indian Military and Defence Sector and Studying the Possible Inter-relationship amongst them using ISM Methodology Ethical Issues and Related Considerations Involved with Artificial Intelligence and Autonomous Systems Rapid Unsupervised Effective Causal Learning Realizing a Global Hyperpolyhedron Constraint via LP Techniques The AI Driving Olympics at NeurIPS 2018 Beware the evolving 'intelligent' web service! An integration architecture tactic to guard AI-first components Keynote Speech 1 : Ai For 4ir: Sofware Engineering Challenges AI-Enabled Emotion Communication Cloud robotics: Current trends and possible use as a service Automatic construction of accurate models of physical systems Revisiting Unreasonable Effectiveness of Data in Deep Learning Era VQA: Visual Question Answering Blind Maximum Likelihood Jade in Multipath Environement Using Importance Sampling Designing machine learning workflows with an application to topological data analysis ML-based beamforming for follower jamming rejection in slow FH/MFSK systems ML-MG: Multi-label Learning with Missing Labels Using a Mixed Graph Maximum Likelihood Algorithms for Joint Estimation of Synchronization Impairments and Channel in MIMO-OFDM System Artificial Intelligence, Machine Learning and EU Copyright Law: Who Owns AI? Program specification and development in standard ML Besides Standards and Automation: an Experience with Census Databases LTE based C-IoT Network Security Issues and Protocols Extracting knowledge from raw IoT data streams Convolutional Neural Network Architectures for Matching Natural Language Sentences Discriminative Unsupervised Feature Learning with Convolutional Neural Networks LINE: Large-scale Information Network Embedding Person re-identification by Local Maximal Occurrence representation and metric learning Graph Capsule Convolutional Neural Networks Bidirectional Attention Flow for Machine Comprehension What Language Do You Use to Create Your AI Programs and Why? Adaptive Strategy Decision Mechanism for StarCraft AI Artificial Intelligence For Microcomputers Characterising Machine Learning Programs: A European Compilation A Parallel Unification Applying machine learning to continuously monitored physiological data Reinforcement Learning and Neuroevolution in Flappy Bird Game Is Google Self-aware? An IPC-based Prolog design pattern for integrating backward chaining inference into applications or embedded systems Ada in AI or AI in Ada. On developing a rationale for integration The Arcade Learning Environment: An Evaluation Platform for General Agents (Extended Abstract)
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APMS (2) Learning Structure and Schemas from Documents CHI Extended Abstracts ICML IEEE Transactions on Emerging Topics in Computational Intelligence ICML ICLR ArXiv ICLR 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Medical Image Anal. Intelligent Tutoring Systems NIPS PuK 2017 IEEE International Conference on Computer Vision (ICCV) 2015 IEEE International Conference on Computer Vision (ICCV) CAMSAP SIP ICCV POPL NIPS NIPS WWW '15 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) ArXiv ICLR Artificial Intelligence IbPRIA (1) IJCAI
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