Showing papers in "Artificial Intelligence Review in 2015"

Vision based hand gesture recognition for human computer interaction: a survey

Abstract: As computers become more pervasive in society, facilitating natural human---computer interaction (HCI) will have a positive impact on their use. Hence, there has been growing interest in the development of new approaches and technologies for bridging the human---computer barrier. The ultimate aim is to bring HCI to a regime where interactions with computers will be as natural as an interaction between humans, and to this end, incorporating gestures in HCI is an important research area. Gestures have long been considered as an interaction technique that can potentially deliver more natural, creative and intuitive methods for communicating with our computers. This paper provides an analysis of comparative surveys done in this area. The use of hand gestures as a natural interface serves as a motivating force for research in gesture taxonomies, its representations and recognition techniques, software platforms and frameworks which is discussed briefly in this paper. It focuses on the three main phases of hand gesture recognition i.e. detection, tracking and recognition. Different application which employs hand gestures for efficient interaction has been discussed under core and advanced application domains. This paper also provides an analysis of existing literature related to gesture recognition systems for human computer interaction by categorizing it under different key parameters. It further discusses the advances that are needed to further improvise the present hand gesture recognition systems for future perspective that can be widely used for efficient human computer interaction. The main goal of this survey is to provide researchers in the field of gesture based HCI with a summary of progress achieved to date and to help identify areas where further research is needed.

Visual simultaneous localization and mapping: a survey

Abstract: Visual SLAM (simultaneous localization and mapping) refers to the problem of using images, as the only source of external information, in order to establish the position of a robot, a vehicle, or a moving camera in an environment, and at the same time, construct a representation of the explored zone. SLAM is an essential task for the autonomy of a robot. Nowadays, the problem of SLAM is considered solved when range sensors such as lasers or sonar are used to built 2D maps of small static environments. However SLAM for dynamic, complex and large scale environments, using vision as the sole external sensor, is an active area of research. The computer vision techniques employed in visual SLAM, such as detection, description and matching of salient features, image recognition and retrieval, among others, are still susceptible of improvement. The objective of this article is to provide new researchers in the field of visual SLAM a brief and comprehensible review of the state-of-the-art.

Extreme learning machine: algorithm, theory and applications

Abstract: Extreme learning machine (ELM) is a new learning algorithm for the single hidden layer feedforward neural networks Compared with the conventional neural network learning algorithm it overcomes the slow training speed and over-fitting problems ELM is based on empirical risk minimization theory and its learning process needs only a single iteration The algorithm avoids multiple iterations and local minimization It has been used in various fields and applications because of better generalization ability, robustness, and controllability and fast learning rate In this paper, we make a review of ELM latest research progress about the algorithms, theory and applications It first analyzes the theory and the algorithm ideas of ELM, then tracking describes the latest progress of ELM in recent years, including the model and specific applications of ELM, finally points out the research and development prospects of ELM in the future

Features and classifiers for emotion recognition from speech: a survey from 2000 to 2011

Abstract: Speaker emotion recognition is achieved through processing methods that include isolation of the speech signal and extraction of selected features for the final classification. In terms of acoustics, speech processing techniques offer extremely valuable paralinguistic information derived mainly from prosodic and spectral features. In some cases, the process is assisted by speech recognition systems, which contribute to the classification using linguistic information. Both frameworks deal with a very challenging problem, as emotional states do not have clear-cut boundaries and often differ from person to person. In this article, research papers that investigate emotion recognition from audio channels are surveyed and classified, based mostly on extracted and selected features and their classification methodology. Important topics from different classification techniques, such as databases available for experimentation, appropriate feature extraction and selection methods, classifiers and performance issues are discussed, with emphasis on research published in the last decade. This survey also provides a discussion on open trends, along with directions for future research on this topic.

Metalearning: a survey of trends and technologies

Abstract: Metalearning attracted considerable interest in the machine learning community in the last years. Yet, some disagreement remains on what does or what does not constitute a metalearning problem and in which contexts the term is used in. This survey aims at giving an all-encompassing overview of the research directions pursued under the umbrella of metalearning, reconciling different definitions given in scientific literature, listing the choices involved when designing a metalearning system and identifying some of the future research challenges in this domain.

A review on particle swarm optimization algorithm and its variants to clustering high-dimensional data

Abstract: Data clustering is one of the most popular techniques in data mining. It is a process of partitioning an unlabeled dataset into groups, where each group contains objects which are similar to each other with respect to a certain similarity measure and different from those of other groups. Clustering high-dimensional data is the cluster analysis of data which have anywhere from a few dozen to many thousands of dimensions. Such high-dimensional data spaces are often encountered in areas such as medicine, bioinformatics, biology, recommendation systems and the clustering of text documents. Many algorithms for large data sets have been proposed in the literature using different techniques. However, conventional algorithms have some shortcomings such as the slowness of their convergence and their sensitivity to initialization values. Particle Swarm Optimization (PSO) is a population-based globalized search algorithm that uses the principles of the social behavior of swarms. PSO produces better results in complicated and multi-peak problems. This paper presents a literature survey on the PSO algorithm and its variants to clustering high-dimensional data. An attempt is made to provide a guide for the researchers who are working in the area of PSO and high-dimensional data clustering.

Recommender systems in e-learning environments: a survey of the state-of-the-art and possible extensions

Abstract: With the development of sophisticated e-learning environments, personalization is becoming an important feature in e-learning systems due to the differences in background, goals, capabilities and personalities of the large numbers of learners. Personalization can achieve using different type of recommendation techniques. This paper presents an overview of the most important requirements and challenges for designing a recommender system in e-learning environments. The aim of this paper is to present the various limitations of the current generation of recommendation techniques and possible extensions with model for tagging activities and tag-based recommender systems, which can apply to e-learning environments in order to provide better recommendation capabilities.

Particle swarm optimisation for discrete optimisation problems: a review

Abstract: In many optimisation problems, all or some of decision variables are discrete. Solving such problems are more challenging than those problems with pure continuous variables. Among various optimisation techniques, particle swarm optimisation (PSO) has demonstrated more promising performance in tackling discrete optimisation problems. In PSO, basic variants are merely applicable to continuous problems. So, appropriate strategies should be adopted for enabling them to be applicable to discrete problems. This paper analyses all strategies adopted in PSO for tackling discrete problems and discusses thoroughly about pros and cons of each strategy.

Fuzzy logic for modeling machining process: a review

Abstract: The application of artificial intelligence (AI) techniques in modeling of machining process has been investigated by many researchers. Fuzzy logic (FL) as a well-known AI technique is effectively used in modeling of machining processes such as to predict the surface roughness and to control the cutting force in various machining processes. This paper is started with the introduction to definition of FL and machining process, and their relation. This paper then presents five types of analysis conducted on FL techniques used in machining process. FL was considered for prediction, selection, monitoring, control and optimization of machining process. Literature showed that milling contributed the highest number of machining operation that was modeled using FL. In terms of machining performance, surface roughness was mostly studied with FL model. In terms of fuzzy components, center of gravity method was mostly used to perform defuzzification, and triangular was mostly considered to perform membership function. The reviews extend the analysis on the abilities, limitations and effectual modifications of FL in modeling based on the comments from previous works that conduct experiment using FL in the modeling and review by few authors. The analysis leads the author to conclude that FL is the most popular AI techniques used in modeling of machining process.

Rough set-based approaches for discretization: a compact review

Abstract: The extraction of knowledge from a huge volume of data using rough set methods requires the transformation of continuous value attributes to discrete intervals. This paper presents a systematic study of the rough set-based discretization (RSBD) techniques found in the literature and categorizes them into a taxonomy. In the literature, no review is solely based on RSBD. Only a few rough set discretizers have been studied, while many new developments have been overlooked and need to be highlighted. Therefore, this study presents a formal taxonomy that provides a useful roadmap for new researchers in the area of RSBD. The review also elaborates the process of RSBD with the help of a case study. The study of the existing literature focuses on the techniques adapted in each article, the comparison of these with other similar approaches, the number of discrete intervals they produce as output, their effects on classification and the application of these techniques in a domain. The techniques adopted in each article have been considered as the foundation for the taxonomy. Moreover, a detailed analysis of the existing discretization techniques has been conducted while keeping the concept of RSBD applications in mind. The findings are summarized and presented in this paper.

Automatic detection of learning styles: state of the art

Abstract: A learning style describes the attitudes and behaviors, which determine an individual's preferred way of learning. Learning styles are particularly important in educational settings since they may help students and tutors become more self-aware of their strengths and weaknesses as learners. The traditional way to identify learning styles is using a test or questionnaire. Despite being reliable, these instruments present some problems that hinder the learning style identification. Some of these problems include students' lack of motivation to fill out a questionnaire and lack of self-awareness of their learning preferences. Thus, over the last years, several approaches have been proposed for automatically detecting learning styles, which aim to solve these problems. In this work, we review and analyze current trends in the field of automatic detection of learning styles. We present the results of our analysis and discuss some limitations, implications and research gaps that can be helpful to researchers working in the field of learning styles.

Dealing with the evaluation of supervised classification algorithms

Abstract: Performance assessment of a learning method related to its prediction ability on independent data is extremely important in supervised classification. This process provides the information to evaluate the quality of a classification model and to choose the most appropriate technique to solve the specific supervised classification problem at hand. This paper aims to review the most important aspects of the evaluation process of supervised classification algorithms. Thus the overall evaluation process is put in perspective to lead the reader to a deep understanding of it. Additionally, different recommendations about their use and limitations as well as a critical view of the reviewed methods are presented according to the specific characteristics of the supervised classification problem scenario.

Application of reinforcement learning to routing in distributed wireless networks: a review

Abstract: The dynamicity of distributed wireless networks caused by node mobility, dynamic network topology, and others has been a major challenge to routing in such networks. In the traditional routing schemes, routing decisions of a wireless node may solely depend on a predefined set of routing policies, which may only be suitable for a certain network circumstances. Reinforcement Learning (RL) has been shown to address this routing challenge by enabling wireless nodes to observe and gather information from their dynamic local operating environment, learn, and make efficient routing decisions on the fly. In this article, we focus on the application of the traditional, as well as the enhanced, RL models, to routing in wireless networks. The routing challenges associated with different types of distributed wireless networks, and the advantages brought about by the application of RL to routing are identified. In general, three types of RL models have been applied to routing schemes in order to improve network performance, namely Q-routing, multi-agent reinforcement learning, and partially observable Markov decision process. We provide an extensive review on new features in RL-based routing, and how various routing challenges and problems have been approached using RL. We also present a real hardware implementation of a RL-based routing scheme. Subsequently, we present performance enhancements achieved by the RL-based routing schemes. Finally, we discuss various open issues related to RL-based routing schemes in distributed wireless networks, which help to explore new research directions in this area. Discussions in this article are presented in a tutorial manner in order to establish a foundation for further research in this field.

Characteristics and classification of outlier detection techniques for wireless sensor networks in harsh environments: a survey

Abstract: Wireless sensor networks (WSNs) have received considerable attention for multiple types of applications. In particular, outlier detection in WSNs has been an area of vast interest. Outlier detection becomes even more important for the applications involving harsh environments, however, it has not received extensive treatment in the literature. The identification of outliers in WSNs can be used for filtration of false data, find faulty nodes and discover events of interest. This paper presents a survey of the essential characteristics for the analysis of outlier detection techniques in harsh environments. These characteristics include, input data type, spatio-temporal and attribute correlations, user specified thresholds, outlier types(local and global), type of approach(distributed/centralized), outlier identification(event or error), outlier degree, outlier score, susceptibility to dynamic topology, non-stationarity and inhomogeneity. Moreover, the prioritization of various characteristics has been discussed for outlier detection techniques in harsh environments. The paper also gives a brief overview of the classification strategies for outlier detection techniques in WSNs and discusses the feasibility of various types of techniques for WSNs deployed in harsh environments.

Supervised feature selection: A tutorial

Abstract: Supervised feature selection research has a long history. Its popularity exploded in the past 30 years due to the advance ofinformation technology and the need to analyze high-dimensional data sets. Research papers published during these yearswere mostly from the machine learning and artificial intelligence community. The emphasis was largely on improving modelaccuracy using empirical methods; whereas the issue of feature relevance was somewhat overlooked. Feature selection methodswere loosely classified as filters, wrappers, and embedded methods with little attention paid to their intricate details. This paperprovides a tutorial of supervised feature selection, on the basis of reviewing frequently cited papers in this area and a numberof classical publications from the statistics community. The objective of feature selection (either to improve model predictiveaccuracy or to determine relevance for hypothesis generation) is presented and discussed in details. Various supervised featureselection methods are classified using a detailed taxonomy. Guidelines for using feature selection methods in practice areprovided based on a comprehensive review of the performance of these methods. Issues that require further attention are alsodiscussed.

Computational irony: A survey and new perspectives

Abstract: Irony is a fundamental rhetorical device It is a uniquely human mode of communication, curious in that the speaker says something other than what he or she intends Recently, computationally detecting irony has attracted attention from the natural language processing (NLP) and machine learning (ML) communities While some progress has been made toward this end, I argue that current machine learning methods rely too heavily on shallow, unstructured, syntactic modeling of text to consistently discern ironic intent Irony detection is an interesting machine learning problem because, in contrast to most text classification tasks, it requires a semantics that cannot be inferred directly from word counts over documents alone To support this position, I survey the large body of existing philosophical/literary work investigating ironic communication I then survey more recent computational efforts to operationalize irony detection in the fields of NLP and ML I identify the disparities of the latter with respect to the former Specifically, I highlight a major conceptual problem in all existing computational models of irony: none maintain an explicit model of the speaker/environment I argue that without such an internal model of the speaker, irony detection is hopeless, as this model is necessary to represent expectations, which play a key role in ironic communication I sketch possible means of embedding such models into computational approaches to irony detection In particular, I introduce the pragmatic context model, which looks to operationalize computationally existing theories of irony This work is a step toward unifying work on irony from literary, empirical and philosophical perspectives with modern computational models

A survey of task-oriented crowdsourcing

Abstract: Since the advent of artificial intelligence, researchers have been trying to create machines that emulate human behaviour. Back in the 1960s however, Licklider (IRE Trans Hum Factors Electron 4---11, 1960) believed that machines and computers were just part of a scale in which computers were on one side and humans on the other (human computation). After almost a decade of active research into human computation and crowdsourcing, this paper presents a survey of crowdsourcing human computation systems, with the focus being on solving micro-tasks and complex tasks. An analysis of the current state of the art is performed from a technical standpoint, which includes a systematized description of the terminologies used by crowdsourcing platforms and the relationships between each term. Furthermore, the similarities between task-oriented crowdsourcing platforms are described and presented in a process diagram according to a proposed classification. Using this analysis as a stepping stone, this paper concludes with a discussion of challenges and possible future research directions.

Research on data stream clustering algorithms

Abstract: Data stream is a potentially massive, continuous, rapid sequence of data information. It has aroused great concern and research upsurge in the field of data mining. Clustering is an effective tool of data mining, so data stream clustering will undoubtedly become the focus of the study in data stream mining. In view of the characteristic of the high dimension, dynamic, real-time, many effective data stream clustering algorithms have been proposed. In addition, data stream information are not deterministic and always exist outliers and contain noises, so developing effective data stream clustering algorithm is crucial. This paper reviews the development and trend of data stream clustering and analyzes typical data stream clustering algorithms proposed in recent years, such as Birch algorithm, Local Search algorithm, Stream algorithm and CluStream algorithm. We also summarize the latest research achievements in this field and introduce some new strategies to deal with outliers and noise data. At last, we put forward the focal points and difficulties of future research for data stream clustering.

3-D face recognition: features, databases, algorithms and challenges

Abstract: Face recognition is being widely accepted as a biometric technique because of its non-intrusive nature. Despite extensive research on 2-D face recognition, it suffers from poor recognition rate due to pose, illumination, expression, ageing, makeup variations and occlusions. In recent years, the research focus has shifted toward face recognition using 3-D facial surface and shape which represent more discriminating features by the virtue of increased dimensionality. This paper presents an extensive survey of recent 3-D face recognition techniques in terms of feature detection, classifiers as well as published algorithms that address expression and occlusion variation challenges followed by our critical comments on the published work. It also summarizes remarkable 3-D face databases and their features used for performance evaluation. Finally we suggest vital steps of a robust 3-D face recognition system based on the surveyed work and identify a few possible directions for research in this area.

Learning content design and learner adaptation for adaptive e-learning environment: a survey

Abstract: This paper presents a survey about learning content designs and various adaptation levels, in order to adapt the learners' necessities in e-learning environment. Normally, learners have different learning styles, cognitive traits, learning goals and varying progress of their learning over period of time, which affects the learner's performance while providing the same bundle of course to all learners. Hence, there is a need to create adaptive e-learning environment to offer appropriate learning content to all individuals. In general, the adaptation can be done based on learners' characteristics. Here, we explore the adaptation that can be done, not only based on learner context parameters but also on the learning content (learning object) and the configuration of e-learning environment. In this paper, we provide a detail review about the various levels of adaptation, learning object design and process for learning content design, learner context parameters, and models/components of e-learning; moreover, we analyze and portray the associations among the components, necessary to achieve the well-defined adaptation in e-learning environment.

An overview of kernel alignment and its applications

Abstract: The success of kernel methods is very much dependent on the choice of kernel. Kernel design and learning a kernel from the data require evaluation measures to assess the quality of the kernel. In recent years, the notion of kernel alignment, which measures the degree of agreement between a kernel and a learning task, is widely used for kernel selection due to its effectiveness and low computational complexity. In this paper, we present an overview of the research progress of kernel alignment and its applications. We introduce the basic idea of kernel alignment and its theoretical properties, as well as the extensions and improvements for specific learning problems. The typical applications, including kernel parameter tuning, multiple kernel learning, spectral kernel learning and feature selection and extraction, are reviewed in the context of classification framework. The relationship between kernel alignment and other evaluation measures is also explored. Finally, concluding remarks and future directions are presented.

One-class support vector machines: analysis of outlier detection for wireless sensor networks in harsh environments

Abstract: Machine learning, like its various applications, has received a great interest in outlier detection in Wireless Sensor Networks. Support Vector Machines (SVM) are a special type of Machine learning techniques which are computationally inexpensive and provide a sparse solution. This work presents a detailed analysis of various formulations of one-class SVMs, like, hyper-plane, hyper-sphere, quarter-sphere and hyper-ellipsoidal. These formulations are used to separate the normal data from anomalous data. Various techniques based on these formulations have been analyzed in terms of a number of characteristics for harsh environments. These characteristics include input data type, spatio-temporal and attribute correlations, user specified thresholds, outlier types, outlier identification(event/error), outlier degree, susceptibility to dynamic topology, non-stationarity and inhomogeneity. A tabular description of improvement and feasibility of various techniques for deployment in the harsh environments has also been presented.

Nature inspired feature selection meta-heuristics

Abstract: Many strategies have been exploited for the task of feature selection, in an effort to identify more compact and better quality feature subsets. A number of evaluation metrics have been developed recently that can judge the quality of a given feature subset as a whole, rather than assessing the qualities of individual features. Effective techniques of stochastic nature have also emerged, allowing good quality solutions to be discovered without resorting to exhaustive search. This paper provides a comprehensive review of the most recent methods for feature selection that originated from nature inspired meta-heuristics, where the more classic approaches such as genetic algorithms and ant colony optimisation are also included for comparison. A good number of the reviewed methodologies have been significantly modified in the present, in order to systematically support generic subset-based evaluators and higher dimensional problems. Such modifications are carried out because the original studies either work exclusively with certain subset evaluators (e.g., rough set-based methods), or are limited to specific problem domains. A total of ten different algorithms are examined, and their mechanisms and work flows are summarised in an unified manner. The performance of the reviewed approaches are compared using high dimensional, real-valued benchmark data sets. The selected feature subsets are also used to build classification models, in an effort to further validate their efficacies.

A study of the dynamic features of recommender systems

Abstract: The extensive usage of internet is fundamentally changing the way we live and communicate. Consequently, the requirements of users while browsing internet are changing drastically. Recommender Systems (RSs) provide a technology that helps users in finding relevant contents on internet. Revolutionary innovations in the field of internet and their consequent effects on users have activated the research in the area of recommender systems. This paper presents issues related to the changing needs of user requirements as well as changes in the systems' contents. The RSs involving said issues are termed as Dynamic Recommender Systems (DRSs). The paper first defines the concept of DRS and explores the various parameters that contribute in developing a DRS. The paper also discusses the scope of contributions in this field and concludes citing in possible extensions that can improve the dynamic qualities of recommendation systems in future.

Review of state of the art for metaheuristic techniques in Academic Scheduling Problems

Abstract: The Academic Scheduling Problems have drawn great interest from many researchers of various fields, such as operational research and artificial intelligence. Despite the long history of literature, the problem still remains as an interesting research topic as new and emerging metaheuristic techniques continue to exhibit promising results. This paper surveys the properties of the Academic Scheduling Problems, such as the complexity of the problem and the constraints involved and addresses the various metaheuristic techniques and strategies used in solving them. The survey in this paper presents the aspects of solution quality in terms of computational speed, feasibility and optimality of a solution.

Assessing methodologies for intelligent bankruptcy prediction

Abstract: Bankruptcy prediction is one of the most important business decision-making problems. Intelligent techniques have been employed in order to develop models capable of predicting business failure cases. The present article provides a systematic literature review of the field. As opposed to previous reviews which concentrate on the classification methods, this study adopts a much broader approach to the bankruptcy prediction problem. The survey is articulated around six major axes which cover all the range of issues related to bankruptcy prediction. These axes are the definition of main research objectives, the employed classification methods, performance metrics issues, the input data and data sets, feature selection and input vectors and finally, the interpretation of the models and the extraction of domain knowledge. The findings and employed methodologies of the collected papers are categorized, presented and assessed according to these axes. The ultimate goal is to detect weaknesses and omissions and to highlight research opportunities. We hope that future researchers will find this survey useful in their attempt to orientate their efforts and to locate interesting topics for further research.

RETRACTED ARTICLE: From the human visual system to the computational models of visual attention: a survey

Abstract: This article has been retracted by the authors. The article included text and ideas taken by the first author, without acknowledgement, from the following published article: “State-ofthe-art in visual attention modeling”, Ali Borji, Laurent Itti, IEEE Transactions on Pattern Analysis and Machine Intelligence 35(1) (2013) 185–207, published online 05/04/12. Most notably: • In Sect. 3.1 (Biological plausible methods) the following paragraphs or sentences largely derive from the Borji and Itti article: “Rosenholtz (1999), Rosenholtz et al. (2004) designed a model ...”; “In Gu et al. (2005), a saliency map ... ”; “Le Meur et al. (2006) proposed ...”; “Kootstra et al. (2008) developed ...”; “Marat et al. (2009) proposed ...”; “Chikkerur et al. (2010) proposed ...”; and “Murray et al. (2011) introduced ...”. • In Sect. 3.2 (Computational methods) the following paragraphs or sentences largely derive from the Borji and Itti article: “Salah et al. (2002) proposed ...”; “Ramstrom and Christensen (2002) introduced ...”; “In Rao et al. (2002) and Rao (2005), they proposed ...”; “Jodogne andPiater (2007) introduced ...”; “Boccignone (2008) presented ...”; “Rosin (2009) proposed ...”; “Mahadevan and Vasconcelos (2010) presented ...”; and “Wang et al. (2011) introduced ...”. • In Sect. 3.3 (Hybrid methods) the following paragraphs or sentences largely derive from theBorji and Itti article: “Lee andYu (1999) proposed ...”; “Peters et al. (2005), Peters and Itti (2007a,b, 2008) trained ...”; “Weights between two nodes ...”; “The model consists of a nonlinear ...”; “Zhang et al. (2007, 2008) proposed ...”; “Pang et al. (2008) presented ...”; “Zhang et al. (2009) extended ...”; and “Li et al. (2010a) presented ...”. • Section 6 (Discussion) largely derives from, or summarizes ideas presented in, Sects. 2.1, 2.2, 2.4, 2.6 and 3.1–3.8 of the Borji and Itti article. The first author apologizes for his action.

Adaptive systems: a content analysis on technical side for e-learning environments

Abstract: Adaptive systems refer to autonomous interactive systems that adjust their behavior and functionality to environmental changes. In e-learning context, adaptive e-learning systems (AESs) adapt their services to users interests, knowledge and goals. In order to investigate the trend of researches in the field of adaptation in e-learning systems, a comprehensive survey of research papers in this context is presented. In this regard, 190 research papers, published between 2000 and 2012, from 45 journals are reviewed and analyzed. The basic contributions of the paper are manifold. First, it provides classifications of research papers from two different points of view: the adaptive technologies utilized in research papers in order to provide adaptation services for AESs and the application fields of research papers in AESs as research goals. Second, it presents statistical analyses on adaptive technologies and application fields. The analyses are carried out based on publication year of papers, the publication year versus adaptive technologies, the publication year versus application fields and adaptive technologies versus application fields. Third, the open problems, current state and prospective direction of researches in AESs are discussed. Finally, the paper suggests what adaptive technology might be the best choice for ongoing researches in each application field.

Comparison of machine learning techniques for target detection

Abstract: This paper focuses on machine learning techniques for real-time detection. Although many supervised learning techniques have been described in the literature, no technique always performs best. Several comparative studies are available, but have not always been performed carefully, leading to invalid conclusions. Since benchmarking all techniques is a tremendous task, literature has been used to limit the available options, selecting the two most promising techniques (AdaBoost and SVM), out of 11 different Machine Learning techniques. Based on a thorough comparison using 2 datasets and simulating noise in the feature set as well as in the labeling, AdaBoost is concluded to be the best machine learning technique for real-time target detection as its performance is comparable to SVM, its detection time is one or multiple orders of magnitude faster, its inherent feature selection eliminates this as a separate task, while it is more straightforward to use (only three coupled parameters to tune) and has a lower training time.

A systematic review of argumentation techniques for multi-agent systems research

Abstract: The ability to build arguments that express thoughts is crucial for intelligent interactions among human beings. Thus, argumentation techniques have been applied for years in fields, such as rhetoric or artificial intelligence. More specifically, the agents paradigm fits into the use of these types of techniques because agents shape a society in which they interact to make arrangements or to decide future actions. Those interactions can be modelled using argumentation techniques. Therefore, the application of those techniques in multi-agent systems is an interesting research field. However, no systematic review has been conducted previously, to the best of the authors' knowledge, to provide an overview of argumentation techniques for multi-agent systems. This paper presents a systematic review of argumentation techniques for multi-agent systems research. The period of time that is included in this review is from 1998 to 2014. The objective of this review is to obtain an overview of the existing approaches and to study their impact on research and practice. The research method has been defined to identify relevant studies based on a predefined search strategy, and it is clearly defined to facilitate the reading of this paper. All of the included studies in this review have been analysed from two different points of view: the Application view and the Multi-Agent System view. A comprehensive analysis of the extracted data is provided in the paper, which is based on a set of research questions that are defined. The results of this review reveal suggestions for further research and practice. The argumentation technology is actually in a phase of internal enhancement and exploration. Moreover, the research interest in this topic has increased in the last years. Furthermore, several interesting findings are presented in the paper.