Information, Volume 6, Issue 3 (September 2015) – 20 articles , Pages 287-575

An adapted ant colony algorithm is proposed to adapt e-content to learner’s profile. The pertinence of proposed units keeps learners motivated. A model of categorization of course’s units is presented. Two learning paths are discussed based on a predefined graph. In addition, the ant algorithm is simulated on the proposed model. The adapted algorithm requires a definition of a new pheromone which is a parameter responsible for defining whether the unit is in the right pedagogical sequence or in the wrong one. Moreover, it influences the calculation of quantity of pheromone deposited on each arc. Accordingly, results show that there are positive differences in learner’s passages to propose the suitable units depending on the sequence and the number of successes. The proposed units do not depend on the change of number of units around 10 to 30 units in the algorithm process. Full article

A software product line is a complex system the aim of which is to provide a platform dedicated to large reuse. It necessitates a great investment. Thus, its ability to cope with customers’ ever-changing requirements is among its key success factors. Great effort has been made to deal with the software product line evolution. In our previous works, we carried out a classification of these works to provide an overview of the used techniques. We also identified the following key challenges of software product lines evolution: the ability to predict future changes, the ability to define the impact of a change easily and the improvement in understanding the change. We have already tackled the second and the third challenges. The objective of this paper is to deal with the first challenge. We use the cladistics classification which was used in biology to understand the evolution of organisms sharing the same ancestor and their process of descent at the aim of predicting their future changes. By analogy, we consider a population of applications for media management on mobile devices derived from the same platform and we use cladistics to construct their evolutionary tree. We conducted an analysis to show how to identify the evolution trends of the case study products and to predict future changes. Full article

With the rapid development of M2M wireless network, damages caused by malicious worms are getting more and more serious. The main goal of this paper is to explore the influences of removable devices on the interaction dynamics between malicious worms and benign worms by using a mathematical model. The model takes two important network environment factors into consideration: benign worms and the influences of removable devices. Besides, the model’s basic reproduction number is obtained, along with the correct control conditions of the local and global asymptotical stability of the worm-free equilibrium. Simulation results show that the effectiveness of our proposed model in terms of reflecting the influences of removable devices on the interaction dynamics of an anti-treat model. Based on numerical analyses and simulations, effective methods are proposed to contain the propagation of malicious worms by using anti-worms. Full article

The collection of massive Global Positioning System (GPS) data from travel surveys has increased exponentially worldwide since the 1990s. A number of methods, which range from rule-based to advanced classification approaches, have been applied to detect travel modes from GPS positioning data collected in travel surveys based on GPS-enabled smartphones or dedicated GPS devices. Among these approaches, neural networks (NNs) are widely adopted because they can extract subtle information from training data that cannot be directly obtained by human or other analysis techniques. However, traditional NNs, which are generally trained by back-propagation algorithms, are likely to be trapped in local optimum. Therefore, particle swarm optimization (PSO) is introduced to train the NNs. The resulting PSO-NNs are employed to distinguish among four travel modes (walk, bike, bus, and car) with GPS positioning data collected through a smartphone-based travel survey. As a result, 95.81% of samples are correctly flagged for the training set, while 94.44% are correctly identified for the test set. Results from this study indicate that smartphone-based travel surveys provide an opportunity to supplement traditional travel surveys. Full article

Feature extraction and classification are two key steps for activity recognition in a smart home environment. In this work, we used three methods for feature extraction: Principal Component Analysis (PCA), Independent Component Analysis (ICA), and Linear Discriminant Analysis (LDA). The new features selected by each method are then used as the inputs for a Weighted Support Vector Machines (WSVM) classifier. This classifier is used to handle the problem of imbalanced activity data from the sensor readings. The experiments were implemented on multiple real-world datasets with Conditional Random Fields (CRF), standard Support Vector Machines (SVM), Weighted SVM, and combined methods PCA+WSVM, ICA+WSVM, and LDA+WSVM showed that LDA+WSVM had a higher recognition rate than other methods for activity recognition. Full article

This paper presents a new biometric score fusion approach in an identification system using the upper integral with respect to Sugeno’s fuzzy measure. First, the proposed method considers each individual matcher as a fuzzy set in order to handle uncertainty and imperfection in matching scores. Then, the corresponding fuzzy entropy estimates the reliability of the information provided by each biometric matcher. Next, the fuzzy densities are generated based on rank information and training accuracy. Finally, the results are aggregated using the upper fuzzy integral. Experimental results compared with other fusion methods demonstrate the good performance of the proposed approach. Full article

In the existing attribute-based encryption (ABE) scheme, the authority (i.e., private key generator (PKG)) is able to calculate and issue any user’s private key, which makes it completely trusted, which severely influences the applications of the ABE scheme. To mitigate this problem, we propose the black box traceable ciphertext policy attribute-based encryption (T-CP-ABE) scheme in which if the PKG re-distributes the users’ private keys for malicious uses, it might be caught and sued. We provide a construction to realize the T-CP-ABE scheme in a black box model. Our scheme is based on the decisional bilinear Diffie-Hellman (DBDH) assumption in the standard model. In our scheme, we employ a pair (ID, S) to identify a user, where ID denotes the identity of a user and S denotes the attribute set associated with her. Full article

Taking into consideration both shipping and pipeline transport, this paper first analysed the risk factors for different modes of crude oil import transportation. Then, based on the minimum of both transportation cost and overall risk, a multi-objective programming model was established to optimize the transportation network of crude oil import, and the genetic algorithm and ant colony algorithm were employed to solve the problem. The optimized result shows that VLCC (Very Large Crude Carrier) is superior in long distance sea transportation, whereas pipeline transport is more secure than sea transport. Finally, this paper provides related safeguard suggestions on crude oil import transportation. Full article

This article discusses the problem of user identification and psychophysiological state assessment while writing a signature using a graphics tablet. The solution of the problem includes the creation of templates containing handwriting signature features simultaneously with the hidden registration of physiological parameters of a person being tested. Heart rate variability description in the different time points is used as a physiological parameter. As a result, a signature template is automatically generated for psychophysiological states of an identified person. The problem of user identification and psychophysiological state assessment is solved depending on the registered value of a physiological parameter. Full article

Digital publishing resources contain a lot of useful and authoritative knowledge. It may be necessary to reorganize the resources by concepts and recommend the related concepts for e-learning. A recommender system is presented in this paper based on the semantic relatedness of concepts computed by texts from digital publishing resources. Firstly, concepts are extracted from encyclopedias. Information in digital publishing resources is then reorganized by concepts. Secondly, concept vectors are generated by skip-gram model and semantic relatedness between concepts is measured according to the concept vectors. As a result, the related concepts and associated information can be recommended to users by the semantic relatedness for learning or reading. History data or users’ preferences data are not needed for recommendation in a specific domain. The technique may not be language-specific. The method shows potential usability for e-learning in a specific domain. Full article

In this paper a Sliding Mode Control scheme (SMC) applied to the Permanent Magnet Synchronous Motor (PMSM) speed control is designed and improved. A Fuzzy logic algorithm is added to mitigate chattering caused by discontinuous term in steady states, and to ensure good performances of the controller in transient states. The proposed Fuzzy-SMC performance is tested in simulation and experimental results are obtained using eZdsp F28335. Full article

The connection between semantic information and evolution has gained growing attention recently. Evolution in this contribution—as in others before—we consider as being driven by information. Semantic information, as we consider it, is based on energy. It follows syntactic and semantic rules. We assume syntax, semantics and pragmatics to be structural features of information in biological evolution. These features started to evolve with the very beginning of life and have become more complex and richer in the course of the unfolding biological evolution across all species. We argue that semantic information is an exclusive feature of biological evolution. We present an information model covering this which to a certain degree—it does not cover quantitative aspects—complements Shannon’s information theory and opens novel views on informational based processes in nature. Full article

In next generation wireless networks, the most tempting feature is the ability of the user to move smoothly over different access networks regardless of the network access technology. In this paper we study the benefit of Multiple Attribute Decision Making (MADM) strategies for network selection. We compare three of these methods naming Simple Additive Weighting (SAW), Multiplicative Exponential Weighting (MEW) and Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) in a realtime ns-3 simulation. Analytic Hierarchy Process (AHP) provides the weights of attributes which allow the comparison in different types of applications. Therefore, we propose a performance evaluation model with a reconfiguration of AHP parameters used in the literature. Simulation results show that the proposed parameters provide an improvement of Delay and offer allowable Packet loss in different types of applications. Full article

One of the most important roles in the machine learning area is to classify, and neural networks are very important classifiers. However, traditional neural networks cannot identify intervals, let alone classify them. To improve their identification ability, we propose a neural network-based interval matcher in our paper. After summarizing the theoretical construction of the model, we take a simple and a practical weather forecasting experiment, which show that the recognizer accuracy reaches 100% and that is promising. Full article

The protocols for controlled remote state preparation of a single qubit and a general two-qubit state are presented in this paper. The general pure three-qubit states are chosen as shared quantum channel, which are not Local operations and classical communication (LOCC) equivalent to the mostly used GHz state. This is the first time that general pure three-qubit states have been introduced to complete remote state preparation. The probability of successful preparation is presented. Moreover, in some special cases, the successful probability could reach a unit value. Full article

In the field of information theory, statistics and other application areas, the information-theoretic divergences are used widely. To meet the requirement of metric properties, we introduce a class of new metrics based on triangular discrimination which are bounded. Moreover, we obtain some sharp inequalities for the triangular discrimination and other information-theoretic divergences. Their asymptotic approximation properties are also involved. Full article

Automated forward vehicle detection is an integral component of many advanced driver-assistance systems. The method based on multi-visual information fusion, with its exclusive advantages, has become one of the important topics in this research field. During the whole detection process, there are two key points that should to be resolved. One is to find the robust features for identification and the other is to apply an efficient algorithm for training the model designed with multi-information. This paper presents an adaptive SVM (Support Vector Machine) model to detect vehicle with range estimation using an on-board camera. Due to the extrinsic factors such as shadows and illumination, we pay more attention to enhancing the system with several robust features extracted from a real driving environment. Then, with the introduction of an improved genetic algorithm, the features are fused efficiently by the proposed SVM model. In order to apply the model in the forward collision warning system, longitudinal distance information is provided simultaneously. The proposed method is successfully implemented on a test car and evaluation experimental results show reliability in terms of both the detection rate and potential effectiveness in a real-driving environment. Full article

In this paper I give a new information-theoretic analysis of the formalisms and interpretations of quantum mechanics (QM) in general, and of two mainstream interpretations of quantum mechanics in particular: The Copenhagen interpretation and David Bohm’s interpretation of quantum mechanics. Adopting Juan G. Roederer’s reading of the notion of pragmatic information, I argue that pragmatic information is not applicable to the Copenhagen interpretation since the interpretation is primarily concerned with epistemology rather than ontology. However it perfectly fits Bohm’s ontological interpretation of quantum mechanics in the realms of biotic and artificial systems. Viewing Bohm’s interpretation of QM in the context of pragmatic information imposes serious limitations to the qualitative aspect of such an interpretation, making his extension of the notion active information to every level of reality illegitimate. Such limitations lead to the idea that, contrary to Bohm’s claim, mind is not a more subtle aspect of reality via the quantum potential as active information, but the quantum potential as it affects particles in the double-slit experiment represents the non-algorithmic aspect of the mind as a genuine information processing system. This will provide an information-based ground, firstly, for refreshing our views on quantum interpretations and secondly, for a novel qualitative theory of the relationship of mind and matter in which mind-like properties are exclusive attributes of living systems. To this end, I will also take an information-theoretic approach to the notion of intentionality as interpreted by John Searle. Full article

In order to improve the accuracy and real-time of all kinds of information in the cash business, and solve the problem which accuracy and stability is not high of the data linkage between cash inventory forecasting and cash management information in the commercial bank, a hybrid learning algorithm is proposed based on adaptive population activity particle swarm optimization (APAPSO) algorithm combined with the least squares method (LMS) to optimize the adaptive network-based fuzzy inference system (ANFIS) model parameters. Through the introduction of metric function of population diversity to ensure the diversity of population and adaptive changes in inertia weight and learning factors, the optimization ability of the particle swarm optimization (PSO) algorithm is improved, which avoids the premature convergence problem of the PSO algorithm. The simulation comparison experiments are carried out with BP-LMS algorithm and standard PSO-LMS by adopting real commercial banks’ cash flow data to verify the effectiveness of the proposed time series prediction of bank cash flow based on improved PSO-ANFIS optimization method. Simulation results show that the optimization speed is faster and the prediction accuracy is higher. Full article

Owing to the robustness of large sparse corruptions and the discrimination of class labels, sparse signal representation has been one of the most advanced techniques in the fields of pattern classification, computer vision, machine learning and so on. This paper investigates the problem of robust face classification when a test sample has missing values. Firstly, we propose a classification method based on the incomplete sparse representation. This representation is boiled down to an l₁ minimization problem and an alternating direction method of multipliers is employed to solve it. Then, we provide a convergent analysis and a model extension on incomplete sparse representation. Finally, we conduct experiments on two real-world face datasets and compare the proposed method with the nearest neighbor classifier and the sparse representation-based classification. The experimental results demonstrate that the proposed method has the superiority in classification accuracy, completion of the missing entries and recovery of noise. Full article