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Exam paper generation is an indispensable part of teaching. Existing methods focus on the use of question extraction algorithms with labels for each question provided. Obviously, manual labeling is inefficient and cannot avoid label bias. Furthermore, the quality of the exam papers generated by the existing methods is not guaranteed. To address these problems, we propose a novel approach to generating exam papers based on prediction of exam performance. As such, we update the quality of the initially generated questions one by using dynamic programming, as well as in batches by using genetic algorithms. We performed the prediction task by using Deep Knowledge Tracing. Our approach considered the skill weight, difficulty, and distribution of exam scores. By comparisons, experimental results indicate that our approach performed better than the two baselines. Furthermore, it can generate exam papers with adaptive difficulties closely to the expected levels, and the related student exam scores will be guaranteed to be relatively reasonable distribution. In addition, our approach was evaluated in a real learning scenarios and shows advantages.

In this survey, a systematic literature review of the state-of-the-art on emotion expression recognition from facial images is presented. The paper has as main objective arise the most commonly used strategies employed to interpret and recognize facial emotion expressions, published over the past few years. For this purpose, a total of 51 papers were analyzed over the literature totaling 94 distinct methods, collected from well-established scientific databases (ACM Digital Library, IEEE Xplore, Science Direct and Scopus), whose works were categorized according to its main construction concept. From the analyzed works, it was possible to categorize them into two main trends: classical and those approaches specifically designed by the use of neural networks . The obtained statistical analysis demonstrated a marginally better recognition precision for the classical approaches when faced to neural networks counterpart, but with a reduced capacity of generalization . Additionally, the present study verified the most popular datasets for facial expression and emotion recognition showing the pros and cons each and, thereby, demonstrating a real demand for reliable data-sources regarding artificial and natural experimental environments.

Surrogate-assisted evolutionary algorithms (SAEAs), which use efficient surrogate models or meta-models to approximate the fitness function in evolutionary algorithms (EAs), are effective and popular methods for solving computationally expensive optimization problems. During the past decades, a number of SAEAs have been proposed by combining different surrogate models and EAs. This paper dedicates to providing a more systematical review and comprehensive empirical study of surrogate models used in single-objective SAEAs. A new taxonomy of surrogate models in SAEAs for single-objective optimization is introduced in this paper. Surrogate models are classified into two major categories: absolute fitness models, which directly approximate the fitness function values of candidate solutions, and relative fitness models, which estimates the relative rank or preference of candidates rather than their fitness values. Then, the characteristics of different models are analyzed and compared by conducting a series of experiments in terms of time complexity (execution time), model accuracy, parameter influence, and the overall performance when used in EAs. The empirical results are helpful for researchers to select suitable surrogate models when designing SAEAs. Open research questions and future work are discussed at the end of the paper.

Knowledge graphs (KGs) are one of the most common frameworks for knowledge representation. However, they suffer from a severe scalability problem that hinders their usage. KG embedding aims to provide a solution to this issue. Nonetheless, general approaches are incapable of representing and reasoning about information not previously contained in the graph. This paper proposes to leverage semantic and ontological information for a significant benefit of knowledge graph completion, focusing on triple classification. The goal of this task is to determine whether a given fact holds. Furthermore, this paper also considers the classification of facts that include entities that have not been seen during training, denoted out-of-knowledge-base or OOKB entities. An incremental method is presented, composed of six stages. Although the proposal can be applied to any KG embedding model, this work focuses on its application for semantic matching models, such as ComplEx and DistMult. Compared to other approaches, our proposal is model-agnostic, computationally inexpensive, and does not require retraining. The results show that triple classification accuracy scales up to 15% with the proposed approach, as well as accelerating the convergence of the model to its optimal solution. Furthermore, facts containing OOKB entities can be classified with a reasonable accuracy.

Experton theory, a generalization of probabilistic set theory, that is of great usefulness to group decision analysis, was first proposed as a means of improving the processing and analysis of opinions issued by experts. This theory produces an information-fusion mathematical object , the experton, which can be used in predictive problems to justify decisions based on well-constructed reasoning. The aim of this paper is to present an aggregative method of several expertons, with the idea that some of the groups of experts involved in producing these expertons may have more influence than others in the decision-making process. In this article, we carry out an aggregation analysis of expertons, not experts, which culminates in the creation of a new mathematical object. This object, which is called the weighted average multiexperton, is coherent with an experton-type object created from a weighting of the initial data provided by all experts. Since the aggregation method presented has been devised to represent the decision-maker’s attitude regarding the importance of different groups of experts, this approach represents a new tool for dealing with group decision-making problems. Additionally, the study presents some properties of the new object. Finally, the paper ends with an application for business decision-making.

Acute hypotensive episode (AHE) is a common serious postoperative complication in ICU, which may raise multiple system failure (especially of cardiac and respiratory kinds), and even cause death. Timely and effective clinical intervention is obviously vital to the saving of patients. AHE detection involves physiological time-series monitoring, processing and prediction technologies, which can offer insights to neuroscientists, biologists, and even provide support for clinicians. This paper presents a hybrid artificial intelligence model combined with CEEMDAN (complete ensemble empirical mode decomposition with adaptive noise, a typical method for physiological signal decomposition), deep learning, multiple gene expression programming and fuzzy expert system for AHE detection. In this paper, the physiological signal is selected from a benchmark dataset, for example MIMIC-II (Multiparameter Intelligent Monitoring in Intensive Care II), which collects large scale real patients’ data for clinical research. In the hybrid model, a typical signal decomposition method is employed for AHE signal processing, and an autoencoder based deep neural network is established for feature extraction. Finally, a reliable and explainable classifier is presented by fusing gene expression programming and the fuzzy method. Experimental results based on real data set demonstrate that the proposed method outperforms state-of-the-art AHE detection methods by achieving the prediction accuracy of 88.14% in 2866 records.

Highlights • The minimum cost consensus model is proposed to coordinate the opinions of experts. • A new method of LSGDM clustering is proposed to improve CRP efficiency. • A consensus index CI reflecting the harmony degree between experts is devised. • A robust counterpart of robust optimization consensus is implemented., Recently, large-scale group decision making (LSGDM) in social network comes into being. In the practical consensus of LSGDM, the unit adjustment cost of experts is difficult to obtain and may be uncertain. Therefore, the purpose of this paper is to propose a consensus model based on robust optimization. This paper focuses on LSGDM, considering the social relationship between experts. In the presented model, an expert clustering method, combining trust degree and relationship strength, is used to classify experts with similar opinions into subgroups. A consensus index, reflecting the harmony degree between experts, is devised to measure the consensus level among experts. Then, a minimum cost model based on robust optimization is proposed to solve the robust optimization consensus problem. Subsequently, a detailed consensus feedback adjustment is presented. Finally, a case study and comparative analysis are provided to verify the validity and advantage of the proposed method.

Considering that most popular models solving aspect-level sentiment classification problems mainly focus on designing complicated neural networks to scale the importance of each word in the sentence, this paper addresses this problem from the view of semantic space. Motivated by the fact that the senses of a word can be sophisticatedly embedded into the semantic space using a distributed representation, this paper hypothesizes that each sense of a word can be represented by one or more specific dimensions, and thus the target of aspect-level sentiment classification can be simplified into searching the related dimensions for the aspects and sentiments concerned. Particularly, an Attention Vector (ATV) based on attention mechanism is designed for each aspect in terms of a specific task, which involves two sub-vectors, i.e., a Dimension Attention Vector (DATV) and a Sentiment Attention Vector (SATV). The DATV determines the significances of different dimensions based on their correlations with an aspect; and the SATV allocates weights for the attributes of words, which are decided by sentiment polarities and part-of-speech (PoS) tagging. Given a sub-dataset related to an aspect, the ATV will be optimized by an Artificial Bee Colony (ABC) algorithm with a Support Vector Machine (SVM) classifier, the objective of which is to maximize classification accuracy. Intrinsically, the DATV can reduce the ambiguity existing in polysemy, meanwhile, the SATV is an auxiliary means for the optimization of the DATV, which can help eliminate the misunderstandings caused by antonyms. Then, the optimized DATV will be applied on a Convolutional Neural Network (CNN) model via simply scaling the pretrained word embeddings as inputs (named as ATV-CNN model). Experimental results show that the ATV-CNN model can have substantial advantages when compared with state-of-the-art models.

Bot detection is a fundamental and crucial task for tracing and mitigating cyber threats in the Internet. This paper aims to address two major limitations of current bot detection systems. First, existing flow-based bot detection approaches ignore structural information of botnets, which lead to false detection. Second, they cannot identify the interactive behavioral patterns among heterogeneous botnet objects. In this paper, we propose a novel bot detection framework, namely Bot-AHGCN, which models fine-grained network flow objects (e.g., IP, response) as a multi-attributed heterogeneous graph and transforms bot detection problem into a semi-supervised node classification task on the graph. Particularly, we first build a multi-attributed heterogeneous information network (AHIN) to model the interdependent relationships among botnet objects. Second, we present a weight-learning based node embedding method, which learns the interactive behavioral patterns among bots and integrates them into weighted similarity graphs. Finally, we perform graph convolution on the learned similarity graphs to characterize more comprehensive and discriminative features of bots, and feed them into a forward neural network to identify bots. The overall experimental results on two real-world datasets confirm that Bot-AHGCN outperforms the existing state-of-the-art approaches, and presents better interpretability by introducing meaningful meta-paths and meta-graphs.

Association Rule Mining (ARM) is a significant task for discovering frequent patterns in data mining. It has achieved great success in a plethora of applications such as market basket, computer networks, recommendation systems, and healthcare. In the past few years, evolutionary computation-based ARM has emerged as one of the most popular research areas for addressing the high computation time of traditional ARM. Although numerous papers have been published, there is no comprehensive analysis of existing evolutionary ARM methodologies. In this paper, we review emerging research of evolutionary computation for ARM. We discuss the applications on evolutionary computations for different types of ARM approaches including numerical rules, fuzzy rules, high-utility itemsets, class association rules, and rare association rules. Evolutionary ARM algorithms were classified into four main groups in terms of the evolutionary approach, including evolution-based, swarm intelligence-based, physics-inspired, and hybrid approaches. Furthermore, we discuss the remaining challenges of evolutionary ARM and discuss its applications and future topics.

Nowadays, computer science has the ability of assisting experts in different application areas. Recently there has been increasing attention in the health area, and there exist different approaches based on artificial intelligence that have been proposed in the diagnosis of several kinds of diseases. In particular, fuzzy systems have been successfully used as Diagnosis Systems, in this way helping doctors to realize a faster and more accurate diagnosis. However, with the emergence of Type-2 Fuzzy Systems, there have been important improvements in handling the uncertainty with respect to traditional Fuzzy Systems (now called Type-1 Fuzzy Systems) in different kinds of problems. In the present paper, a new approach to Fuzzy Diagnosis based on Type-2 Fuzzy Systems is proposed and compared with respect to Type-1 Fuzzy Systems on a set of diagnosis problems, in order to evaluate the relevance of the uncertainty handling in this kind of problems. On the other hand, the paper is also aiming at observing the accuracy behavior in Fuzzy Diagnosis Systems by changing the uncertainty level in the models. Finally, a comparison of Interval Type-2 Fuzzy Systems with respect to General Type-2 Fuzzy Systems for a set of diagnosis problems is presented.

Cyber code analysis is fundamental to malware detection and vulnerability discovery for defending cyber attacks. Traditional approaches resorting to manually defined rules are gradually replaced by automated approaches empowered by machine learning. This revolution is accelerated by big code from open source projects which support machine learning models with outstanding performance. In the context of a data-driven paradigm, this paper reviews recent analytic research on cyber code of malicious and common software by using a set of common concepts of similarity, correlation and collective indication. Sharing security goals in recognizing anomalous code that may be malicious or vulnerable. The ability to do so is not determined in isolation, rather drawn for code correlation and context awareness. This paper demonstrates a new research methodology of data driven cyber security (DDCS) and its application in cyber code analysis. The framework of the DDCS methodology consists of three components, i.e., cyber security data processing, cyber security feature engineering, and cyber security modeling. Some challenging issues are suggested to direct the future research.

Class imbalanced datasets are common across different domains including health, security, banking and others. A typical supervised learning algorithm tends to be biased towards the majority class when dealing with imbalanced datasets. The learning task becomes more challenging when there is also an overlap of instances from different classes. In this paper, we propose an undersampling framework for handling class imbalance in binary datasets by removing potential overlapped data points. Our methods are designed to identify and eliminate majority class instances from the overlapping region. Accurate identification and elimination of these instances maximise the visibility of the minority class instances and at the same time minimises excessive elimination of data, which reduces information loss. Four methods based on neighbourhood searching with different criteria to identify potential overlapped instances are proposed in this paper. Extensive experiments using simulated and real-world datasets were carried out. Results show comparable performance with state-of-the-art methods across different common metrics with exceptional and statistically significant improvements in sensitivity.

A kernel-based one-class classifier is mainly used for outlier or novelty detection. Kernel ridge regression (KRR) based methods have received quite a lot of attention in recent years due to its non-iterative approach of learning. In this paper, KRR-based one-class classifier (KOC) has been extended for learning using privileged information (LUPI) framework. LUPI-based KOC method is referred to as KOC+ in this paper. This privileged information is available as feature/features of the dataset, but only during training (not during testing). KOC+ utilizes privileged features information differently compared to other features information. It uses this information in KOC+ by the help of so-called correction function . This information helps KOC+ in achieving better generalization performance . Existing and proposed classifiers are evaluated on the datasets taken from UCI machine learning repository and MNIST dataset. Moreover, experimental results exhibit that KOC+ outperforms KOC and other LUPI-based state-of-the-art one-class classifiers. Source code of this paper is provided on the corresponding author’s GitHub homepage: https://github.com/Chandan-IITI/KOCPlus_or_OCKELMPlus_or_OCLSSVMPlus

Internet of Things (IoT) is expanding rapidly and so is the number of devices, sensors and actuators joining this world. IoT devices are an important part of the data collection process in Big Data systems, so by protecting them we support and improve the security of the whole system. ZigBee is a secure communication system for the underlying Internet of Things (IoT) infrastructure. Even though ZigBee has a strong security stack built on a variety of secret keys, ZigBee devices are vulnerable to the side-channel and key extraction attacks. Due to the low cost and limited resources, most ZigBee devices store their secret keys in plaintext. In this paper, we focus on protecting the storage of ZigBee secret keys and show how Physical Unclonable Functions (PUFs) can help the ZigBee devices to be robust tamper-resistant against the physical attacks. The proposed schemes include PUF-based key storage protection and key generation. The experiments in this paper were done using SRAM-PUF. Furthermore, two algorithms were proposed to overcome the defects in the randomness of keys generated using SRAM-PUF and, at the same time, to increase the reliability of these keys. We were able to significantly improve the hardware security of ZEDs by protecting their keying materials using costless, high secure, random, stable and volatile PUF-based secret keys.

This paper proposes a novel top-down approach to rule-based fuzzy systems, one that begins with the product—an equation—and then addresses the unique features of the product, without requiring the reader to know anything about fuzzy sets and systems. The "products" are adaptive variable-structure basis function expansions , where "adaptive variable-structure" means that different subsets of its basis functions are active (non-zero) in different regions of the state space, something that occurs automatically by virtue of the structure of the basis functions, so that the products can be said to "adapt" to locations in the state space. These products are novel candidates for machine learning . Unique features of all products are: (1) Number of basis functions is no longer a variable and is established locally through type-1 or type-2 uncertainty partitioning of each variable; (2) Both coarse and fine sculpting of the state space are achieved, and are described in terms of first- and second-order partitions of the state space, respectively; and (3) Linguistic interpretability is obtained, which may be of value to an end-user. Learning about rule-based fuzzy systems can be greatly compressed by using the top-down approach that is described in this paper.

The big data samples are important source for analytics. However, its relevant/irrelevant information, unspecified variables/scales, noise/null, and so forth impose huge challenges on the analysis of relevance, feature, cause, and evaluation. This paper proposes an evidential analytics to disclose buried information in big data samples. Technically, it models memberships composed of relevant preference and replaces data with these priors. Its operations include generating analytics baselines, reducing variables, identifying sparse features, and inducing rules by taking advantage of evidence. In illustration, a case study of semiconductor manufacturing in UCI secom is presented. It discloses relevant signals, key factors, variables’ thresholds, sparse characteristics, and causal effect of damages buried in normal samples. The contribution of this paper not only contains these achievements but provides priori data for inference.

The choice of the most promising algorithm to model and predict a particular phenomenon is one of the most prominent activities of the temporal data forecasting. Forecasting (or prediction), similarly to other data mining tasks , uses empirical evidence to select the most suitable model for a problem at hand since no modeling method can be considered as the best. However, according to our systematic literature review of the last decade, few scientific publications rigorously expose the benefits and limitations of the most popular algorithms for time series prediction. At the same time, there is a limited performance record of these models when applied to complex and highly nonlinear data. In this paper, we present one of the most extensive, impartial and comprehensible experimental evaluations ever done in the time series prediction field. From 95 datasets, we evaluate eleven predictors, seven parametric and four non-parametric, employing two multi-step-ahead projection strategies and four performance evaluation measures. We report many lessons learned and recommendations concerning the advantages, drawbacks, and the best conditions for the use of each model. The results show that SARIMA is the only statistical method able to outperform, but without a statistical difference, the following machine learning algorithms : ANN , SVM , and k NN-TSPI. However, such forecasting accuracy comes at the expense of a larger number of parameters. The evaluated datasets, as well detailed results achieved by different indexes as MSE , Theil’s U coefficient, POCID, and a recently-proposed multi-criteria performance measure are available online in our repository. Such repository is another contribution of this paper since other researchers can replicate our results and evaluate their methods more rigorously. The findings of this study will impact further research on this topic since they provide a broad insight into models selection, parameters setting, evaluation measures, and experimental setup.

With the continued growth of interoperable software developed for Internet of Things (IoT), there is a growing demand to predict software defect at various testing and operational phases. This paper solves the software defect prediction problem by proposing a novel model with the help of a local tangent space alignment support vector machine (LTSA-SVM) algorithm. The model employs the SVM algorithm as the basic classifier of software defect distribution prediction model. Then, the model parameters are optimized by combining a grid search method and ten-fold cross validation. In the traditional dimensionality reduction algorithms, data loss caused by the poor attributes of data nonlinearity reduces the accuracy of SVM. Aiming at this problem, this paper uses a LTSA algorithm to extract the intrinsic structure of low-dimensional data and performs effective dimension reduction. The SVM algorithm is trained by the reduced dimension data. Finally, the feasibility of the prediction model is verified. Compared with the single SVM and the LLE-SVM prediction algorithm, the prediction model in this paper improves the prediction accuracy and F-measure by 1–4%.

Belief rules extend traditional IF-THEN rules to represent vagueness, incompleteness, and nonlinear causal relationships by assigning belief degrees to singletons or the universe of all possible values that the consequents of rules can take. First, this paper extends belief rules by assigning belief degrees to the subsets of all possible values that the consequents of rules with interval-valued rule weights can take. Then, this paper proposes a Valuation-Based System (VBS) approach for the modeling and risk assessment of extended belief rule-based expert systems. Finally, the proposed VBS approach is applied to two use cases for evaluating the occurrence probabilities of accidents: one is a car equipped with Automated Speed Control (ASC) using values from experts, and the other is hazardous material (hazmat) transportation accidents using real statistical data.

During the past decade, graph-based learning methods have proved to be an effective tool to make full use of both labeled and unlabeled data samples to improve learning performance. These methods try to discover the intrinsic structures and discriminative information embedded in the data, by building one or more graphs to model the relationship among the data samples. Consequently, how to build an effective graph is the core problem. In this paper we introduce a novel graph-based classification method, called Supervised clustering-based Regularized Least Squares Classification (SuperRLSC), in which local and global graphs of the data are built by supervised clustering. The motivation is that supervised clustering may discover more actual data structures compared to unsupervised clustering. In our algorithm, we firstly employ supervised k-means to partition the whole training dataset into several meaningful clusters in order to discover the intrinsic and discriminative structures. We then use the discovered structures to build local and global graphs of the data. The local graph reveals local geometric and discriminative structures, while the global graph reveals global discriminative information. Finally a hybrid local/global graph-based regularization term is embedded into supervised classification (i.e., RLSC in this paper). To validate the effectiveness of our algorithm, a series of experiments are performed on several UCI benchmark datasets. The results show that our algorithm can achieve better or at least comparable performance to the other graph-based algorithms and the traditional state-of-the-art supervised classification methods.

Social networking services allow users to adopt and spread information via diffusion actions, e.g., share, retweet, and reply. Real applications such as viral marketing and trending topic detection rely on information diffusion. Given past items with diffusion records on a social network, this paper aims at forecasting who will participate in the diffusion of a new item c (we use hashtags in the paper) with its k earliest adopters, without using content and profile information, i.e., finding which users will adopt c in the future. We define the Diffusion Participation Forecasting (DPF) problem, which is challenging since all users except for early adopters can be the candidates, comparing to existing studies that predict which one-layer followers will adopt a new hashtag given past diffusion observations with content and profile info. To solve the DFP problem, we propose an Adoption-based Participation Ranking (APR) model, which aims to rank the actual participants in reality at higher positions. The first is to estimate the adoption probability of a new hashtag for each user while the second is a random walk-based model that incorporates nodes with higher adoption probability values and early adopters to generate the forecasted participants. Experiments conducted on Twitter exhibit that our model can significantly outperform several competing methods in terms of Precision and Recall. Moreover, we demonstrate that an accurate DPF can be applied for effective targeted marketing using influence maximization and boosting the accuracy of popularity prediction in social media.

Deep Neural Networks for image/video classification have obtained much success in various computer vision applications. Existing deep learning algorithms are widely used on RGB images or video data. Meanwhile, with the development of low-cost RGB-D sensors (such as Microsoft Kinect and Xtion Pro-Live), high-quality RGB-D data can be easily acquired and used to enhance computer vision algorithms [14]. It would be interesting to investigate how deep learning can be employed for extracting and fusing features from RGB-D data. In this paper, after briefly reviewing the basic concepts of RGB-D information and four prevalent deep learning models (i.e., Deep Belief Networks (DBNs), Stacked Denoising Auto-Encoders (SDAE), Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) Neural Networks), we conduct extensive experiments on five popular RGB-D datasets including three image datasets and two video datasets. We then present a detailed analysis about the comparison between the learned feature representations from the four deep learning models. In addition, a few suggestions on how to adjust hyper-parameters for learning deep neural networks are made in this paper. According to the extensive experimental results, we believe that this evaluation will provide insights and a deeper understanding of different deep learning algorithms for RGB-D feature extraction and fusion.

We confirm that agent-ants can learn only via the pheromone trail.It is better to use both the heuristic function and the pheromone trail.The ACDT alg. makes it possible to optimize the construction of decision trees based on a heuristic.The pheromone map clearly present a data set as the solution space and pheromone trail values.Analysis of pheromone maps shows that the ACDT alg. searches different subspaces of the solution space. This paper considers the topic of cooperation between agent ants in an Ant Colony Optimization (ACO) algorithm that is used to construct decision trees (Ant Colony Decision Tree or ACDT). To follow a suitable methodology, the paper presents a formal definition of the ACDT algorithm with a focus on the influence that Ant Colony Optimization algorithms have on the obtained results. The aim of this paper is to provide the rationale for using swarm intelligence (i.e., ACO) in the process of constructing decision trees. Many experiments were conducted to provide a solid justification. These experiments tested cooperation between agent ants in ant colony algorithms with different ACO performance scenarios: the application of only a pheromone trail, the application of only a heuristic function, the application of both components, and the application of neither component. Additionally, different values of the pheromone trail were tested at various stages of the algorithm's operation and pheromone representations were presented. The experiments were conducted on 30 publicly available data sets; all observations were preceded by statistical tests. This paper confirms that it is reasonable to use the pheromone trail and balanced heuristics. Moreover, we found that, for the ACDT algorithm, good results can also be obtained without heuristics.

In this paper we propose a design for the kNN rule classifier based on GPU devices.This design overcomes the dependency between dataset size and GPU memory required.An efficient CPU-GPU communication scheme is designed.The carefully designed kernels for each step of the computation improve the run-time performance.An efficient selection algorithm replaces the sorting algorithm traditionally used to find the neighborhood. The k nearest neighbor (kNN) rule is one of the most used techniques in data mining and pattern recognition due to its simplicity and low identification error. However, the computational effort it requires is directly related to the dataset sizes, hence delivering a poor performance on large datasets. The use of graphics processing units (GPU) has improved the run-time performance of the kNN rule but the computational requirements of current approaches limit this performance as the dataset size increases.In this paper, we propose a new scalable and memory efficient design for a GPU-based kNN rule, called GPU-SME-kNN, that breaks the dependency between dataset size and memory footprint while delivering high performance. An experimental study of GPU-SME-kNN is presented showing a high performance, even in cases that other methods cannot address, while the computational requirements are suitable for most commercial GPU devices. Our design has also been applied to kNN-based lazy learning algorithms reducing run-times in a significant way.

Paper presents the work on development and validation of spatio-temporal clustering technique called ST-OPTICS.Emphasis has also been laid down on validation using performance indices.Capable to handle all types of dimensions i.e. spatial, non-spatial and temporal.It gives micro level clusters, agglomeration has been done for better visualization and interpretation.Results (average runtime & quality of clusters) found to be better than existing technique. Spatio-temporal data mining (STDM) is a process of the extraction of implicit knowledge, spatial and temporal relationships, or other patterns not explicitly stored in spatio-temporal databases. As data are growing not only from static view point, but they also evolve spatially and temporally which is dynamic in nature that is the reason why this field is now becoming very important field of research. In addition, spatio-temporal (ST) data tend to be highly auto-correlated, which leads to failure of assumption of independence, which is there in Gaussian distribution model. Vital issues in spatio temporal clustering technique for earth observation data is to obtain clusters of, good quality, arbitrary shape, problem of nested clustering and their validation. The present paper addresses these issues and proposes their solutions. In this direction, an attempt has been made to develop a clustering algorithm named as "Spatio-Temporal - Ordering Points to Identify Clustering Structure (ST-OPTICS)" which is modified version of existing density based technique - "Ordering Points to Identify Clustering Structure (OPTICS)". Experimental work carried out is analysed and found that quality of clusters obtained and run time efficiency are much better than existing technique i.e. ST-DBSCAN. In order to improve the visualization and the interpretation of obtained micro level clusters, sincere effort has been put in to merge the obtained clusters using agglomerative approach. Performance evaluation is done in both ways i.e. qualitatively and quantitatively for cross validating the results. Results show performance improvement of proposed ST-OPTICS clustering technique compared to ST-DBSCAN algorithm.

Privacy and accountability are two important issues in mobile e-commerce. In this paper, we formalized the definition and security model of identity-based plaintext-checkable encryption (IBPCE) schemes, and proposed an IBPCE scheme. Furthermore, we designed an accountable mobile e-commerce (AMEC) scheme. This scheme can provide the following features: (1) users can register to the e-commerce system by using their mobile identities, such as mobile phone numbers; (2) the transactions between a buyer and a vendor are encrypted; (3) if there is a dispute, an offline adjudicator can identify who is dishonest by checking the encrypted transactions. Hence, our scheme is an ideal primitive to balance the need for privacy and accountability. In mobile e-commerce systems, users conduct transactions using wireless or Internet-based devices, such as mobile phones and tablets. It is different from traditional e-commerce systems relying on workstations or desktops, which is usually used in a fixed location. Recently, privacy and accountability have become users' primary concerns in mobile e-commerce applications. In this paper, a novel mobile e-commerce scheme is developed to address the fundamental requirements. We first propose an identity-based plaintext-checkable encryption (IBPCE) scheme where anyone can check whether a ciphertext is the encryption of a plaintext under a specific identity without knowing the secret key. Furthermore, the proposed IBPCE scheme is incorporated into the mobile e-commerce scenario, which results in an accountable mobile e-commerce (AMEC) scheme. Our proposed AMEC scheme has several superior features: (1) Users can register to the e-commerce system by using their mobile identities, such as mobile phone numbers; (2) The transactions between a buyer and a vendor are encrypted; (3) If there is a dispute, an offline adjudicator can identify who is dishonest by checking the encrypted transactions. We evaluate the proposed scheme and confirm that the new scheme can effectively balance the need for privacy and accountability.

Many real-life problems can be described as unbalanced, where the number of instances belonging to one of the classes is much larger than the numbers in other classes. Examples are spam detection, credit card fraud detection or medical diagnosis. Ensembles of classifiers have acquired popularity in this kind of problems for their ability to obtain better results than individual classifiers. The most commonly used techniques by those ensembles especially designed to deal with imbalanced problems are for example Re-weighting, Oversampling and Undersampling. Other techniques, originally intended to increase the ensemble diversity, have not been systematically studied for their effect on imbalanced problems. Among these are Random Oracles, Disturbing Neighbors, Random Feature Weights or Rotation Forest. This paper presents an overview and an experimental study of various ensemble-based methods for imbalanced problems, the methods have been tested in its original form and in conjunction with several diversity-increasing techniques, using 84 imbalanced data sets from two well known repositories. This paper shows that these diversity-increasing techniques significantly improve the performance of ensemble methods for imbalanced problems and provides some ideas about when it is more convenient to use these diversifying techniques.

With an advancement of wireless communication technology, wireless sensor network (WSN) has emerged as one of the most powerful technologies which can be used in various applications, such as military surveillance, environment monitoring, industrial control, and medical monitoring. WSNs are vulnerable to large collection of attacks than traditional networks because they transmit data using a wireless channel and are deployed in unattended environments. So, in this environment, how to ensure secure communications between different communication parties becomes a challenging issue with respect to the constraints of energy consumption, and large overhead generated during various operations performed. In this direction, the mutual authentication and key agreement (MAAKA) scheme attracts much attention in recent years. In literature, MAAKA schemes were presented in last several years. However, most of these schemes cannot satisfy security requirements in WSNs. Recently, Xue et al. proposed a temporal-credential-based MAAKA scheme for WSNs and proved that it could withstand various types of attacks. However, this paper points out that Xue et al.'s MAAKA scheme is vulnerable to the off-line password guessing attack, the user impersonation attack, the sensor node impersonation attack and the modification attack. Moreover, this paper also points out that Xue et al.'s MAAKA scheme cannot provide user anonymity. To overcome weaknesses in Xue et al.'s MAAKA scheme, this paper proposes a new temporal-credential-based MAAKA scheme for WSNs. Security analysis shows the proposed MAAKA scheme could overcome the weaknesses in Xue et al.'s MAAKA scheme. Performance analysis shows the proposed MAAKA scheme has better performance than the existing benchmarked schemes in literature. Therefore, the proposed MAAKA scheme is more suitable for providing security for various applications in WSNs.

One of the most challenging limitations for the elderly and/or the visually impaired people is the inability of self-navigation in unfamiliar indoor environments. Numerous existing indoor navigation systems have been proposed in order to solve such problems. However, these papers do not adequately examine the effectiveness of the systems in real time trials and are faced with a variety of technical and usability problems. In our paper we present a unique and elegant architecture of an indoor navigation system as an integrated and tested system in real time. This solution enables the localization of the user, as well as the navigation considering scenarios that the user might follow and suggests guidelines for self- navigation. To evaluate the feasibility of the proposed architecture we carried out experimental scenarios to prove that the system proposed is suitable enough to guide elderly people through an unknown built environment.

Selecting the most appropriate algorithm to use when attempting to solve a black-box continuous optimization problem is a challenging task. Such problems typically lack algebraic expressions, it is not possible to calculate derivative information, and the problem may exhibit uncertainty or noise. In many cases, the input and output variables are analyzed without considering the internal details of the problem. Algorithm selection requires expert knowledge of search algorithm efficacy and skills in algorithm engineering and statistics. Even with the necessary knowledge and skills, success is not guaranteed.In this paper, we present a survey of methods for algorithm selection in the black-box continuous optimization domain. We start the review by presenting Rice's (1976) selection framework. We describe each of the four component spaces - problem, algorithm, performance and characteristic - in terms of requirements for black-box continuous optimization problems. This is followed by an examination of exploratory landscape analysis methods that can be used to effectively extract the problem characteristics. Subsequently, we propose a classification of the landscape analysis methods based on their order, neighborhood structure and computational complexity. We then discuss applications of the algorithm selection framework and the relationship between it and algorithm portfolios, hybrid meta-heuristics, and hyper-heuristics. The paper concludes with the identification of key challenges and proposes future research directions.

The unprecedented explosion of real-life big data sets have sparked a lot of research interests in data mining in recent years. Many of these big data sets are generated in network environment and are characterized by a dauntingly large size and high dimensionality which pose great challenges for detecting useful knowledge and patterns, such as network traffic anomalies, from them. In this paper, we study the problem of anomaly detection in big network connection data sets and propose an outlier detection technique, called Adaptive Stream Projected Outlier deTector (A-SPOT), to detect anomalies from large data sets using a novel adaptive subspace analysis approach. A case study of A-SPOT is conducted in this paper by deploying it to the 1999 KDD CUP anomaly detection application. Innovative approaches for training data generation, anomaly classification and false positive reduction are proposed in this paper as well to better tailor A-SPOT to deal with the case study. Experimental results demonstrate that A-SPOT is effective and efficient in detecting anomalies from network data sets and outperforms existing detection methods.

This paper presents a novel approach to the detection and recognition of faulty audio signalling devices as part of an automated industrial manufacturing quality assurance process. The proposed system outperforms other well-established automated systems based on mel-frequency cepstrum coefficients (MFCC) and multi-layer perceptron (MLP). It uses both unlabelled sound data and labelled historical data acquired from human experts in detecting faulty signalling devices. The unlabelled data is used to train a deep neural network generative model to create multiple levels of hierarchical feature extractors which are used to train an MLP classifier, with the intent to model the human reasoning and judging processes in respect to sound classification. This paper presents the results of real world experiments based on data pertaining to the audio signalling quality assurance process for car instrument cluster manufacturing. These results show that the proposed system is able to successfully classify speakers into two groups: "Good" and "No good" depending on the part quality. The proposed system proves to be capable enough to eliminate the need for a manual inspection within the manufacturing process and is shown to be able to diagnose a fault with a high degree of accuracy. This work can be extended to other areas of automotive inspection where there is a need for a robust solution to sound detection and where an output signal is represented by a complex and changing frequency spectrum even with significant environmental noise.

Nowadays, every public transportation company uses Automatic Vehicle Location (AVL) systems to track the services provided by each vehicle. Such information can be used to improve operational planning. This paper describes an AVL-based evaluation framework to test whether the actual Schedule Plan fits, in terms of days covered by each schedule, the network’s operational conditions. Firstly, clustering is employed to group days with similar profiles in terms of travel times (this is done for each different route). Secondly, consensus clustering is used to obtain a unique set of clusters for all routes. Finally, a set of rules about the groups content is drawn based on appropriate decision variables. Each group will correspond to a different schedule and the rules identify the days covered by each schedule. This methodology is simultaneously an evaluator of the schedules that are offered by the company (regarding its coverage) and an advisor on possible changes to such offer. It was tested by using data collected for one year in a company running in Porto, Portugal. The results are sound. The main contribution of this paper is that it proposes a way to combine Machine Learning techniques to add a novel dimension to the Schedule Plan evaluation methods: the day coverage. Such approach meets no parallel in the current literature.

The paper offers a new methodology for modelling, recognition and understanding of electroencephalography (EEG) spatio-temporal data measuring complex cognitive brain processes during mental tasks. The key element is that mental tasks are performed through complex spatio-temporal brain processes and they can be better understood only if we model properly the spatio-/spectro temporal data that measures these processes. The proposed methodology is based on a recently proposed novel spiking neural network architecture, called NeuCube as a general framework for spatio-temporal brain data modelling. The methodology is demonstrated on benchmark cognitive EEG data. The new approach leads to a faster data processing, improved accuracy of the EEG data classification and improved understanding of this data and the cognitive processes that generated it. The paper concluded that the new methodology is worth exploring further on other spatio-temporal data, measuring complex cognitive brain processes, aiming at using this method for the development of the next generation of brain-computer interfaces and systems for early diagnosis of degenerative brain disease, such as Alzheimer's Disease (AD), and for personalised neuro-rehabilitation systems.

The selection of a subset of discriminative features for semantic recognition is crucial to making multimedia analysis more interpretable. This paper proposes a model of spatial path coding (SPC) that uses a supervised technique to select sparse features. SPC is a regularized penalty that encodes the spatial correlations of features obtained by the spatial pyramid model. In SPC, each feature dimension is considered as a vertex in a direct acyclic graph (DAG), and the spatial correlations among features are considered as directed edges associated with predefined weights. Thus, the process of supervised feature selection can be directly formulated to solve a path selection problem with minimum cost. Experiments are conducted to evaluate the performance of supervised feature selection with SPC for the tasks of scene classification and action recognition using four benchmark datasets. The results show that SPC can be used to automatically select a subgraph of the DAG with a small number of discriminative features for a certain category. In addition, the method proposed in this paper shows better performance in terms of classification and recognition accuracy as compared with state-of-the-art algorithms.

The cloud computing data center has numerous hosts as well as application requests. It needs to allocate resources dynamically according to the varied demands of users. It should not only provide QoS such as short response time and high throughput but also achieve efficient power consumption. This paper first puts forward a reconfiguration framework based on a request prediction, which anticipates the application request volume in advance. To determine the objective of relatively optimal configuration, it can work out the allocation scheme which can improve the resource utilization ratio as well as lower energy consumption. In addition, a concept of Utility Ratio Matrix (URM) is put forward to represent allocations of hosts and Virtual Machines (VMs), and a reconfiguration algorithm based on request prediction is also presented. The algorithm will predict the application requests so as to work out the allocation scheme in advance. The algorithm can separate the reconfiguration computing from the real allocation so that it can avoid a time delay between the reconfiguration result and the varied demands, and can also reduce the energy consumption in data center. The corresponding analysis and experimental results indicate the feasibility of the reconfiguration algorithm in this paper.

Network traffic monitoring and analysis-related research has struggled to scale for massive amounts of data in real time. Some of the vertical scaling solutions provide good implementation of signature based detection. Unfortunately these approaches treat network flows across different subnets and cannot apply anomaly-based classification if attacks originate from multiple machines at a lower speed, like the scenario of Peer-to-Peer Botnets. In this paper the authors build up on the progress of open source tools like Hadoop, Hive and Mahout to provide a scalable implementation of quasi-real-time intrusion detection system. The implementation is used to detect Peer-to-Peer Botnet attacks using machine learning approach. The contributions of this paper are as follows: (1) Building a distributed framework using Hive for sniffing and processing network traces enabling extraction of dynamic network features; (2) Using the parallel processing power of Mahout to build Random Forest based Decision Tree model which is applied to the problem of Peer-to-Peer Botnet detection in quasi-real-time. The implementation setup and performance metrics are presented as initial observations and future extensions are proposed.

With the proliferation of social media, the number of active web-users is rapidly increasing these days. They create and maintain their personal web-profiles, and use them to interact with others in the cyber-space. Currently two major problems are being faced to automatically identify these web-users and correlate their web-profiles. First is the presence of namesakes on the Web, and the second is the use of alias names. In this paper, we propose a context-based text mining approach to discover alias names for all the namesakes sharing a common name on the Web, and leave the task of selecting the namesake of interest on part of the user. The proposed method employs a search-engine API to retrieve relevant webpages for a given name. The retrieved webpages are modeled into a graph, and a clustering algorithm is applied to disambiguate the webpages. Thereafter each obtained cluster standing for a namesake is mined for alias identification following a text pattern based statistical technique. The existing research works do not consider the presence of namesakes on the Web to mine aliases, which is impractical. The novelty of the proposed approach lies in discovering this drawback of existing works. Additionally the contribution includes the disambiguation technique that does not need to have a pre-determined number of clusters to be generated and the light-weight text pattern based alias mining technique. The number of clusters in the proposed method is rather determined dynamically by the inflation parameter, the pre-determination of which is comparatively much easier. Experimental results on different components demonstrate the robustness of the proposed alias mining approach. This paper also brings forth the significance of alias mining to the problem of suspect monitoring and tracking on the Web.

The objective of this paper is to present a multi-attribute decision model for network intrusion response. The proposed technique uses a weighted linear combination (WLC) model to standardize the different attributes to a common numeric range before applying a genetic algorithm to determine the appropriate alternative. The model is characterized by a new implementation of individual structure based on the response-resource matrix and a fitness function based on cost-benefit approach. The decision model parameters are represented by vectors modeling the different attributes that are respectively: financial cost, reputation loss, and processing resources. The paper presents a new approach for handling a multi-attribute decision problem in intrusion response context which has not been addressed in previous studies. Finally, this paper will discuss the findings of simulation experiments and will conclude by discussing the effectiveness of the proposed approach.

Understanding the difficulty of a dataset is of primary importance when it comes to testing and evaluating fingerprint recognition systems or algorithms because the evaluation result is dependent on the dataset. The difficulty exhibited in this paper represents how difficult it is to achieve better recognition accuracy within the specific dataset. Proposed in this paper is a general framework for assessing the level of difficulty of fingerprint datasets based on quantitative measurements of not only the sample quality of individual fingerprints but also the relative differences between genuine pairs, such as common area and deformation. The experimental results over various datasets demonstrate that the proposed method can predict the level of difficulty of fingerprint datasets which coincide with the equal error rates produced by four comparison algorithms. The proposed method is independent of comparison algorithms and can be performed automatically.

One of the most popular tools for mining data streams are decision trees. In this paper we propose a new algorithm, which is based on the commonly known CART algorithm. The most important task in constructing decision trees for data streams is to determine the best attribute to make a split in the considered node. To solve this problem we apply the Gaussian approximation. The presented algorithm allows to obtain high accuracy of classification, with a short processing time. The main result of this paper is the theorem showing that the best attribute computed in considered node according to the available data sample is the same, with some high probability, as the attribute derived from the whole data stream.

Cloud computing emerges as a new computing paradigm that aims to provide reliable, customized and quality of service guaranteed computation environments for cloud users. Applications and databases are moved to the large centralized data centers, called cloud. Due to resource virtualization, global replication and migration, the physical absence of data and machine in the cloud, the stored data in the cloud and the computation results may not be well managed and fully trusted by the cloud users. Most of the previous work on the cloud security focuses on the storage security rather than taking the computation security into consideration together. In this paper, we propose a privacy cheating discouragement and secure computation auditing protocol, or SecCloud, which is a first protocol bridging secure storage and secure computation auditing in cloud and achieving privacy cheating discouragement by designated verifier signature, batch verification and probabilistic sampling techniques. The detailed analysis is given to obtain an optimal sampling size to minimize the cost. Another major contribution of this paper is that we build a practical secure-aware cloud computing experimental environment, or SecHDFS, as a test bed to implement SecCloud. Further experimental results have demonstrated the effectiveness and efficiency of the proposed SecCloud.

Cloud computing has been rapidly adopted over the last few years. However, techniques on Web services, one of the most important enabling technologies for cloud computing, are still not mature yet. In this paper, we propose a novel approach that supports dependable development of Web services. Our approach includes a new Web service model that separates service behaviors into operational and control behaviors. The coordination of operational and control behaviors at runtime is facilitated by conversational messages. We also propose an automated service verification approach based on symbolic model checking. In particular, our approach extracts the checking properties, in the form of temporal logic formulas, from control behaviors, and automatically verifies the properties in operational behaviors using the NuSMV model checker. The approach presented in this paper has been implemented using a number of state-of-the-art technologies. We conducted a number of experiments to study the performance of our proposed approach in detecting design problems in services. The results show that our automated approach can successfully detect service design problems. Our system offers a set of tools assisting service developers in specifying, debugging, and monitoring service behaviors.

In this position paper, we are discussing achievements and open issues in the interpretability of evolving fuzzy systems (EFS). In addition to pure on-line complexity reduction approaches, which can be an important direction for increasing the transparency of the evolved fuzzy systems, we examine the state-of-the-art and provide further investigations and concepts regarding the following interpretability aspects: distinguishability, simplicity, consistency, coverage and completeness, feature importance levels, rule importance levels and interpretation of consequents. These are well-known and widely accepted criteria for the interpretability of expert-based and standard data-driven fuzzy systems in batch mode. So far, most have been investigated only rudimentarily in the context of evolving fuzzy systems, trained incrementally from data streams: EFS have focussed mainly on precise modeling, aiming for models of high predictive quality. Only in a few cases, the integration of complexity reduction steps has been handled. This paper thus seeks to close this gap by pointing out new ways of making EFS more transparent and interpretable within the scope of the criteria mentioned above. The role of knowledge expansion, a peculiar concept in EFS, will be also addressed. One key requirement in our investigations is the availability of all concepts for on-line usage, which means they should be incremental or at least allow fast processing.

In engineering situations, we usually have a large amount of prior knowledge that needs to be taken into account when processing data. Traditionally, the Bayesian approach is used to process data in the presence of prior knowledge. Sometimes, when we apply the traditional Bayesian techniques to engineering data, we get inconsistencies between the data and prior knowledge. These inconsistencies are usually caused by the fact that in the traditional approach, we assume that we know the exact sample values, that the prior distribution is exactly known, etc. In reality, the data is imprecise due to measurement errors, the prior knowledge is only approximately known, etc. So, a natural way to deal with the seemingly inconsistent information is to take this imprecision into account in the Bayesian approach - e.g., by using fuzzy techniques. In this paper, we describe several possible scenarios for fuzzifying the Bayesian approach. Particular attention is paid to the interaction between the estimated imprecise parameters. In this paper, to implement the corresponding fuzzy versions of the Bayesian formulas, we use straightforward computations of the related expression - which makes our computations reasonably time-consuming. Computations in the traditional (non-fuzzy) Bayesian approach are much faster - because they use algorithmically efficient reformulations of the Bayesian formulas. We expect that similar reformulations of the fuzzy Bayesian formulas will also drastically decrease the computation time and thus, enhance the practical use of the proposed methods.

This paper develops and presents a novel technique for missing data estimation using a combination of dynamic programming, neural networks and genetic algorithms (GA) on suitable subsets of the input data. The method proposed here is well suited for decision making processes and uses the concept of optimality and the Bellman's equation to estimate the missing data. The proposed approach is applied to an HIV/AIDS database and the results shows that the proposed method significantly outperforms a similar method where dynamic programming is not used. This paper also suggests a different way of formulating a missing data problem such that the dynamic programming is applicable to estimate the missing data.

The paper deals with the problem of learning the control of Multi-Component Robotic Systems (MCRSs) applying Multi-Agent Reinforcement Learning (MARL) algorithms. Modeling Linked MCRS usually leads to over-constrained environments, posing great difficulties for efficient learning with conventional single and multi-agent reinforcement algorithms. In this paper, we propose a hybrid learning algorithm composed of a modified Q-Learning algorithm embedding an Undesired State-Action Prediction (USAP) module trained by a supervised learning approach which learns a model predicting undesired transitions to states breaking physical constraints. The USAP module's output is used by the Q-Learning algorithm to prevent these undesired transitions, therefore boosting learning efficiency. This hybrid approach is extended to the multi-agent case embedding the USAP module in Distributed Round-Robin Q-Learning (D-RR-QL), which requires very little communications among agents. We present results of computational experiments conducted in the classical multi-agent taxi scheduling task and a hose transportation task. Results show a considerable learning gain both in time and accuracy, compared to the state-of-the-art Distributed Q-Learning approach in the deterministic taxi scheduling task. In the hose transportation task, USAP module introduces a significant improvement in learning convergence speed.

This paper introduces token-passing based multi-point relays (TOSS) data dissemination scheme in mobile Peer-to-Peer (P2P) environments. TOSS is designed to support reliability and efficiency of the peers to disseminate messages to other peers in mobile P2P networks. The TOSS model incorporates location-based scheduling technique as a means to determine the most optimal scheduling for the peers to receive messages whilst taking into account the location and mobility of the peers. The novelties of this paper are summarized as follows; (i) it presents an efficient scheduling approach to disseminate messages; (ii) it formulates location-based scheduling schemes for constructing the order of the delivery; (iii) it experimentally evaluates the performance of the proposed approach and compares them against the most closely relevant prior arts (e.g. pure flooding and Trustworthiness -based Broadcast (TBB) scheme) in respect to the transmission cost, computational cost and message deliverable performance. The experiments yield a promising result for TOSS based on the metrics of studies.

Ambient Intelligence (AmI) systems require the integration of complex and innovative solutions. In this sense, agents and multi-agent systems have characteristics such as autonomy, reasoning, reactivity, social abilities and pro-activity which make them appropriate for developing distributed systems based on Ambient Intelligence. In addition, the use of context-aware technologies is an essential aspect in these developments in order to perceive stimuli from the context and react to it autonomously. This paper presents the integration of the Hardware-Embedded Reactive Agents (HERA) Platform into the Flexible and User Services Oriented Multi-agent Architecture (FUSION@), a multi-agent architecture for developing AmI systems that integrates intelligent agents with a service-oriented architecture approach. Because of this integration, FUSION@ has the ability to manage both software and hardware agents by using self-adaptable heterogeneous wireless sensor networks. Preliminary results presented in this paper demonstrate the feasibility of FUSION@ as a future alternative for developing Ambient Intelligence systems where users and systems can use both software and hardware agents in a transparent way, achieving a higher level of ubiquitous computing and communication.