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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 a very recently presented paper, Turky and Abdullah 5 proposed a novel multi-population harmony search with external archive (MHSA-ExtArchive) for dynamic optimization problems. In the experimental results, the authors claimed that their approach could outperform several state-of-the-art algorithms. They also showed the superiority of their method by means of numerical experiments on Moving Peaks Benchmark (MPB). Despite the interesting idea of applying multi-population scheme on harmony search and using a new type of external archive for dealing with dynamic problems, we believe that there are two very important shortcomings in the result analysis, which we point out in this short note. The main motivation of the present note is to contribute toward preventing the same mistakes from happening by the other researchers.

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.

As a new type of network, the types of attack in the Internet of Vehicles (IoV) are constantly emerging and changing. Consequently, the machine learning based intrusion detection model has to update to cope with new attacks. However, existing machine learning based IoV intrusion detection schemes require large amounts of labeled data to complete model updates. For new attacks, the IoV cloud is also difficult to identify in time, which requires a lot of labor and time cost in IoV. To solve above issue, this paper employs transfer learning and proposes two model update schemes based on whether the IoV cloud can timely provide a small amount of labeled data for a new attack. The first one is the cloud-assisted update scheme where the IoV cloud can provide a small amount of data. And the second one is the local update scheme where the IoV cloud cannot provide any labeled data timely. In this paper, the local update scheme obtains pseudo label of the unlabeled data in new attacks via pre-classifies and uses the pseudo-labeled data for multiple rounds of transfer learning. Then the vehicle can complete the update without obtaining any labeled data through the IoV cloud. The experimental results show that compared with the existing method, our two schemes have improved the detection accuracy by at least 23%.

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.

This paper addresses the access control issue for the comprehensive and distributed Internet of Things (IoT) environments. The typical eXtensible Access Control Markup Language (XACML) which implements the sophisticated access conditions in XML files, is widely used to guarantee access control decisions for the distributed IoT environments. To the best of our knowledge, the typical XACML-based access control schemes never consider the authentication run-time parameters. Moreover, the access control schemes that are mainly based on the typical XACML cannot secure themselves against some kinds of attacks, such as the Masquerade attack. Also, those schemes are not secure in opposition to Man-in-the-Middle (MITM) attack. Therefore, this paper proposes an adaptive XACML scheme that extends the typical XACML by integrating an access code generation and verification schemes for heterogeneous distributed IoT environments. Our adaptive XACML scheme considers some sensitive authentication run-time parameters before authorizing the user. Moreover, our scheme is proven secure against the Masquerade attack and MITM attack through hashing the generated access code using Message Digest Algorithm-5 (MD5) and Secure Hash Algorithm-1 (SHA-1) Checksum Utility. The experimental analysis of many different configurations supports the efficacy and efficiency of our adaptive XACML. It also shows exceptional compatibility and performance with different implementations. The processing time comparison between our adaptive XACML and Typical XACML, has shown that there is a low time overhead when using our adaptive XACML. This processing time overhead is nothing compared to the extra features that have been achieved in excess of the typical XACML and the security against Masquerade and MITM attacks. Therefore, our adaptive XACML scheme has the capability to be applied in various distinct distributed environments not only IoT.

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.

Encryption is one of the most straightforward methods for ensuring the confidentiality of outsourced data on the cloud. However, encryption makes queries more difficult to perform. In recent years, new encryption schemes for facilitating queries have been proposed. However, for these schemes, some cannot support the scenario of multiple users, some are inefficient, and some are not sufficiently flexible (users must always ask the data owner for the tokens that are used for searching ciphertexts on the cloud). In this paper, we propose a scheme that supports efficient and flexible range search over ciphertexts in the scenario of multiple users. In our scheme, we construct an Encrypted Interval Tree (EIT) as the index for ciphertexts. The data owner outsources the EIT and ciphertexts to the cloud, and later distributes secret parameters (search keys, navigation paths and signatures) to users. By utilizing these secret parameters, users can generate tokens for the queried ranges without communication with the data owner and subsequently use the tokens to perform range search over ciphertexts on the cloud. Moreover, the signature technique is adopted in our scheme. Thus, the cloud can authenticate the identifiers of users and determine the legality of tokens. In this paper, we implement our scheme and conduct extensive experiments. The experimental results demonstrate the efficiency of our scheme. Finally, we analyze the security of our scheme.

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.

Clustering analysis is a fundamental technique in machine learning, which is also widely used in information granulation. Multiple clustering systems granulate a data set into multiple granular structures. Therefore, clustering ensemble can serve as an important branch of multigranulation information fusion. Many approaches have been proposed to solve the clustering ensemble problem. This paper focuses on the direct approaches which involve two steps: finding cluster correspondence and utilizing a fusion strategy to produce a final result. The existing direct approaches mainly discuss the process of finding cluster correspondence, while the fusing process is simply done by voting. In this paper, we mainly focus on the fusing process and propose a Dempster-Shafer evidence theory-based clustering ensemble algorithm. The advantage of the algorithm is that the information of an object's surrounding cluster structure is taken into consideration by using its neighbors to describe it. First, we find neighbors of each object and generate its label probability outputs in every base partition. Second, these label probability outputs are integrated based on DS theory. Theoretically, our method is superior to other voting methods. Besides, several experiments show that the proposed algorithm is statistically better than seven other clustering ensemble methods.

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.

Fuzzy numbers are ranked using Dempster-Shafer theory (DST) with fuzzy targets.Fuzzy targets are used to reflect human viewpoints and avoid biases in fuzzy ranking.Fuzzy numbers are ranked with different viewpoints and with a combined viewpoint.Transferable Belief Model is adopted to elaborate DST.Benchmark examples are adopted for experimental studies and positive results are obtained. In this paper, an extended ranking method for fuzzy numbers, which is a synthesis of fuzzy targets and the Dempster-Shafer Theory (DST) of evidence, is devised. The use of fuzzy targets to reflect human viewpoints in fuzzy ranking is not new. However, different fuzzy targets can lead to contradictory fuzzy ranking results; making it difficult to reach a final decision. In this paper, the results from different viewpoints are treated as different sources of evidence, and Murphy's combination rule is used to aggregate the fuzzy ranking results. DST allows fuzzy numbers to be compared and ranked while preserving their uncertain and imprecise characteristics. In addition, a hybrid method consisting of fuzzy targets and DST with the Transferable Belief Model is formulated, which fulfils a number of important ordering properties. A series of empirical experiments with benchmark examples has been conducted and the experimental results clearly indicate the usefulness of the proposed method.

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.

Semi-supervised dimensionality reduction is one of the important topics in pattern recognition and machine learning. During the past decade, Laplacian Regularized Least Square (LapRLS) and Semi-supervised Discriminant Analysis (SDA) are the two widely-used semi-supervised dimensionality reduction methods. In this paper, we show that SDA and LapRLS can be unified into a constrained manifold regularized least square framework. The manifold term, however, cannot fully utilize the underlying discriminative information. We thus introduce a new and effective semi-supervised dimensionality reduction method, called Learning from Local and Global Information (LLGDI), to solve the problem. The proposed LLGDI method adopts a set of local classification functions to preserve both local geometrical and discriminative information of dataset. It also adopts a global classification function to preserve the global discriminative information, and an uncorrelated constraint to calculate the projection matrix for simultaneously solving regression and dimensionality reduction problem. As a result, the LLGDI method is able to preserve local discriminative, manifold information as well as the global discriminative information. Theoretical analysis and extensive simulations presented in the paper show the effectiveness of the LLGDI algorithm. The results also demonstrate LLGDI can achieve superior performance compared with other existing methods.

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.

The task of visual categorization becomes very challenging as the number of samples or classes increases. This is mainly due to large memory requirements or a crowded semantic space, which causes class separation difficulties. In this paper, a new approach called OPrDuM2 (online primal-dual multiple kernel multiple class) is proposed. This new algorithm can significantly reduce the complexity of visual categorization. Under this approach, an elegant heterogeneous feature fusion machine is used to consolidate complementary class-discriminative information, and online learning is applied to deal with large sample sizes. To be specific, a data-dependent multi-kernel machine that fuses multiple heterogeneous features in a nonlinear manner is defined by extending the multi-kernel learning model, and multivariate hinge loss and conservative updating rules are used to increase the sample-level sparsity in the model. Given the intrinsic hierarchical structure of samples, features, and classes, an innovative triple mixed-norm regularizer with strong convexity is utilized to facilitate optimization. An iterative solution is derived using the language of duality, leading to a low regret bound of O T , where T is the number of online iterations. This paper details a proof of the algorithm's convergence, and describes extensive visual data classification experiments that demonstrate the effectiveness and robustness of this new approach.

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.

We proposed a novel kernel-based fuzzy clustering for Geo-Demographic Analysis.It relied on Gaussian kernel function, updated elements and standardized weights.The algorithm was both theoretical and experimental validated.Its clustering quality was better than those of other relevant algorithms.Some properties of solutions were examined. Geo-Demographic Analysis (GDA) is a major concentration of various interdisciplinary researches and has been used in many decision-making processes regarding the provision and distribution of products and services in society. Machine learning methods namely Principal Component Analysis, Self-Organizing Map, K-Means, fuzzy clustering and fuzzy geographically weighted clustering were proposed to enhance the quality of GDA. Among them, the state-of-the-art method - Modified Intuitionistic Possibilistic Fuzzy Geographically Weighted Clustering (MIPFGWC) has some drawbacks such as: (i) using the Euclidean similarity measure often results in high error rate and sensitivity to noises and outliers; (ii) updating the membership matrix by the Spatial Interaction - Modification Model (SIM2) model leads to new centers not being "geographically aware". In this paper, we present a novel fuzzy clustering algorithm named as Kernel Fuzzy Geographically Clustering (KFGC) that utilizes both the kernel similarity function and the new update mechanism of the SIM2 model to remedy the disadvantages of MIPFGWC. Some supported properties and theorems of KFGC are also examined in the paper. Specifically, the differences between solutions of KFGC and those of MIPFGWC and of some variants of KFGC are theoretically validated. Lastly, experimental analysis is performed to compare the performance of KFGC with those of the relevant algorithms in terms of clustering quality.

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.

We investigate four factors for recommending a potential high-revenue location.The OFF-ON model obtains the relation between the drop-off and the get-on locations.The ON-OFF model estimates the fare for a trip from the recommended location.Results show the revenue on weekdays is better than that on weekends.Distance to the next cruising location and waiting time are important features. The taxi fleet management systems based on GPS have become an important tool for taxi businesses. Such systems can be used not only for fleet management, but also to provide useful information for taxi drivers to increase their profits by mining historical GPS trajectories. In this paper, we propose a taxi recommender system for determining the next cruising location, which could be a value-added module in fleet management systems. In the literature, three factors have been considered in different studies to address a similar objective: distance between the current location and the recommended location, waiting time for the next passengers, and expected fare for the trip. In this paper, in addition to these factors, we consider one key factor based on driver experience: what is the most likely location to pick up passengers, given the current passenger drop off location. A location-to-location graph model, referred to as an OFF-ON model, is adopted to capture the relation between the passenger drop-off location and the next passenger get-on location. We also adopt an ON-OFF model to estimate the expected fare for a trip that begins at a recommended location. A real-world dataset from CRAWDAD is used to evaluate the proposed system. A simulator that simulates the cruising behavior of taxies in the dataset and a virtual taxi that cruises based on our recommender system is developed. Our simulation results indicate that although the statistics of the historical data may be different from real-time passenger requests, our recommender system is still effective in terms of recommending more profitable cruising locations.

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.

Two-stage process of creating coalitions in a dispersed decision system was applied.Three types of relationships were used - friendship, conflict and neutrality.Initial clusters are formed in the first stage - groups of classifiers in friendship.In the second stage (a negotiation stage) neutral classifiers play a crucial role.New approach of generating clusters has been compared to approach proposed in 22. The issues that are related to the process of global decision-making on the basis of knowledge which is stored in a dispersed form (several local knowledge bases or classifiers) are discussed in this paper. In a decision-making system, which is described in the paper, the classification process of the test object starts with an investigation of how particular classifiers classify a test object. We describe the views of classifiers by using probability vectors over decision classes. In the system, the process of combining classifiers in coalitions is very important. Negotiation is used in the clustering process. We define three types of relations between classifiers: friendship, conflict and neutrality. The clustering process consists of two stages. In the first step, the initial groups are created. These groups contain classifiers that are in a friendship relation. In the second stage, classifiers which are in neutrality relation are attached to the existing groups. In this paper, a formal description of the clustering process is presented and mathematical properties of functions, which are used, are described. For every cluster, we find a kind of combined information. Finally, we classify the given test object by voting among clusters, using the combined information from each of the clusters.In the paper a new way of creating clusters (with a negotiation stage) is compared to the approach presented in the paper (Przybyla-Kasperek and Wakulicz-Deja, 2014) 23 (without negotiations). There are significant differences between the clusters that are generated using these two approaches, which are shown in the paper. In the new approach, the clusters are more complex and better reconstruct and illustrate the views of the classifiers on the classification.

The work described in this paper is part of the development of a framework to support the joint execution of cooperative missions by a group of robotic vehicles, in a simulated, augmented or real environment. Such framework brings forward the need for formal languages in which to specify the group of vehicles that compose a team; the scenario in which they will operate; and the definition of the mission to be performed (surveillance, pollution detection, search and rescue operations, and so on). This paper presents the Mission Description Language (MDL), a XML dialect that describes the specifics of a mission to be performed by a team of autonomous vehicles. The MDL definition decomposes a mission into a number of possibly dependent phases and uses high-level concepts that facilitate the specification of a mission by both experts and non-experts. The dialect was defined using XML Schema, with all the required information being integrated in the definition. An interface was developed and incorporated into the framework, as to allow for the creation and edition of XML files following the defined schema. Once the information is correctly specified, it can be used in the framework, thus facilitating the process of mission specification and deployment. A survey answered by both practitioners and researchers shows that the degree of satisfaction with MDL is elevated (the overall evaluation of MDL achieved a 3.97 score (out of 5), with 82.05% of the answers being equal to or above 4); also, the usability of the interface was evaluated, having achieved a score of 82.9 in the SUS scale. These results imply that MDL is flexible enough to represent several types of missions, through a user-friendly interface.

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.

This paper focuses on credibilistic clustering approach. A data clustering method partitions unlabeled data sets into clusters and labels them for various goals such as computer vision and pattern recognition. There are different models for objective function-based fuzzy clustering such as Fuzzy C-Means (FCM), Possibilistic C-Mean (PCM) and their combinations. Credibilistic clustering is a new approach in this field. In this paper, a new credibilistic clustering model is introduced in which credibility measure is applied instead of possibility measure in possibilistic clustering. Also, in objective function, the separation of clusters is considered in addition to the compactness within clusters. The steps of clustering are designed based on this approach. Finally, the main issues about model are discussed, and the results of computational experiments are presented to show the efficiency of the proposed model.