Information, Volume 9, Issue 3 (March 2018) – 23 articles

Selection of appropriate subcontractors for outsourcing is very important for the success of construction projects. This can improve the overall quality of projects and promote the qualification and reputation of the main contractors. The evaluation of subcontractors can be made by some experts or decision-makers with respect to some criteria. If this process is done in different time periods, it can be defined as a dynamic multi-criteria group decision-making (MCGDM) problem. In this study, we propose a new fuzzy dynamic MCGDM approach based on the EDAS (Evaluation based on Distance from Average Solution) method for subcontractor evaluation. In the procedure of the proposed approach, the sets of alternatives, criteria and decision-makers can be changed at different time periods. Also, the proposed approach gives more weight to newer decision information for aggregating the overall performance of alternatives. A numerical example is used to illustrate the proposed approach and show the application of it in subcontractor evaluation. The results demonstrate that the proposed approach is efficient and useful in real-world decision-making problems. Full article

Multiplayer online battle arena is a genre of online games that has become extremely popular. Due to their success, these games also drew the attention of our research community, because they provide a wealth of information about human online interactions and behaviors. A crucial problem is the extraction of activity patterns that characterize this type of data, in an interpretable way. Here, we leverage the Non-negative Tensor Factorization to detect hidden correlated behaviors of playing in a well-known game: League of Legends. To this aim, we collect the entire gaming history of a group of about 1000 players, which accounts for roughly 100K matches. By applying our framework we are able to separate players into different groups. We show that each group exhibits similar features and playing strategies, as well as similar temporal trajectories, i.e., behavioral progressions over the course of their gaming history. We surprisingly discover that playing strategies are stable over time and we provide an explanation for this observation. Full article

Throughout history, storytelling has been an effective way of conveying information and knowledge. In the field of visualization, storytelling is rapidly gaining momentum and evolving cutting-edge techniques that enhance understanding. Many communities have commented on the importance of storytelling in data visualization. Storytellers tend to be integrating complex visualizations into their narratives in growing numbers. In this paper, we present a survey of storytelling literature in visualization and present an overview of the common and important elements in storytelling visualization. We also describe the challenges in this field as well as a novel classification of the literature on storytelling in visualization. Our classification scheme highlights the open and unsolved problems in this field as well as the more mature storytelling sub-fields. The benefits offer a concise overview and a starting point into this rapidly evolving research trend and provide a deeper understanding of this topic. Full article

In this paper, we investigate distributed state estimation for multi-agent networks with random communication, where the state is constrained by an inequality. In order to deal with the problem of environmental/communication uncertainties and to save energy, we introduce two random schemes, including a random sleep scheme and an event-triggered scheme. With the help of Kalman-consensus filter and projection on the constrained set, we propose two random distributed estimation algorithm. The estimation of each agent is achieved by projecting the consensus estimate, which is obtained by virtue of random exchange information with its neighbors. The estimation error is shown to be bounded with probability one when the agents randomly take the measurement or communicate with their neighbors. We show the stability of proposed algorithm based on Lyapunov method and projection and demonstrate their effectiveness via numerical simulations. Full article

In order to solve the problem of tag collision in Radio Frequency Identification (RFID) system, the paper proposes a Multi-Bit Identification Collision Tree (MICT) algorithm based on a collision tree. The algorithm uses an array scheme to mark the collision bits in the identification process, and determines the collision information according to the first few bits of the tag, which can effectively reduce the number of recognitions and the amount of communication data. The testing results show that the proposed algorithm reduces the time complexity by about 38% and the communication complexity by about 27% compared to existing collision-tree-based algorithms. Through theoretical analysis and experimental evaluation, the MICT algorithm has obvious advantages in terms of time and communication complexity compared to the other typical algorithms. The algorithm can be applied to the field of RFID-related systems to significantly improve the system efficiency. Full article

Network coding approaches typically consider an unrestricted recoding of coded packets in the relay nodes to increase performance. However, this can expose the system to pollution attacks that cannot be detected during transmission, until the receivers attempt to recover the data. To prevent these attacks while allowing for the benefits of coding in mesh networks, the cache coding protocol was proposed. This protocol only allows recoding at the relays when the relay has received enough coded packets to decode an entire generation of packets. At that point, the relay node recodes and signs the recoded packets with its own private key, allowing the system to detect and minimize the effect of pollution attacks and making the relays accountable for changes on the data. This paper analyzes the delay performance of cache coding to understand the security-performance trade-off of this scheme. We introduce an analytical model for the case of two relays in an erasure channel relying on an absorbing Markov chain and an approximate model to estimate the performance in terms of the number of transmissions before successfully decoding at the receiver. We confirm our analysis using simulation results. We show that cache coding can overcome the security issues of unrestricted recoding with only a moderate decrease in system performance. Full article

Accurate visual tracking is a challenging research topic in the field of computer vision. The challenge emanates from various issues, such as target deformation, background clutter, scale variations, and occlusion. In this setting, discriminative correlation filter (DCF)-based trackers have demonstrated excellent performance in terms of speed. However, existing correlation filter-based trackers cannot handle major changes in appearance due to severe occlusions, which eventually result in the development of a bounding box for target drift tracking. In this study, we use a set of DCFs called discriminative correlation filter bank (DCFB) for visual tracking to address the key causes of object occlusion and drift in a tracking-by-detection framework. In this work, we treat thxe current location of the target frame as the center, extract several samples around the target, and perform online learning of DCFB. The sliding window then extracts numerous samples within a large radius of the area where the object in the next frame is previously located. These samples are used for the DCFB to perform correlation operation in the Fourier domain to estimate the location of the new object; the coordinates of the largest correlation scores indicate the position of the new target. The DCFB is updated according to the location of the new target. Experimental results on the quantitative and qualitative evaluations on the challenging benchmark sequences show that the proposed framework improves tracking performance compared with several state-of-the-art trackers. Full article

Nowadays, wireless power transfer is ubiquitously used in wireless rechargeable sensor networks (WSNs). Currently, the energy limitation is a grave concern issue for WSNs. However, lifetime enhancement of sensor networks is a challenging task need to be resolved. For addressing this issue, a wireless charging vehicle is an emerging technology to expand the overall network efficiency. The present study focuses on the enhancement of overall network lifetime of the rechargeable wireless sensor network. To resolve the issues mentioned above, we propose swarm intelligence based hard clustering approach using fireworks algorithm with the adaptive transfer function (FWA-ATF). In this work, the virtual clustering method has been applied in the routing process which utilizes the firework optimization algorithm. Still now, an FWA-ATF algorithm yet not applied by any researcher for RWSN. Furthermore, the validation study of the proposed method using the artificial neural network (ANN) backpropagation algorithm incorporated in the present study. Different algorithms are applied to evaluate the performance of proposed technique that gives the best results in this mechanism. Numerical results indicate that our method outperforms existing methods and yield performance up to 80% regarding energy consumption and vacation time of wireless charging vehicle. Full article

When we couple the rise in video streaming with the growing number of portable devices (smart phones, tablets, laptops), we see an ever-increasing demand for high-definition video online while on the move. Wireless networks are inherently characterised by restricted shared bandwidth and relatively high error loss rates, thus presenting a challenge for the efficient delivery of high quality video. Additionally, mobile devices can support/demand a range of video resolutions and qualities. This demand for mobile streaming highlights the need for adaptive video streaming schemes that can adjust to available bandwidth and heterogeneity, and can provide a graceful changes in video quality, all while respecting viewing satisfaction. In this context, the use of well-known scalable/layered media streaming techniques, commonly known as scalable video coding (SVC), is an attractive solution. SVC encodes a number of video quality levels within a single media stream. This has been shown to be an especially effective and efficient solution, but it fares badly in the presence of datagram losses. While multiple description coding (MDC) can reduce the effects of packet loss on scalable video delivery, the increased delivery cost is counterproductive for constrained networks. This situation is accentuated in cases where only the lower quality level is required. In this paper, we assess these issues and propose a new approach called Streaming Classes (SC) through which we can define a key set of quality levels, each of which can be delivered in a self-contained manner. This facilitates efficient delivery, yielding reduced transmission byte-cost for devices requiring lower quality, relative to MDC and Adaptive Layer Distribution (ALD) (42% and 76% respective reduction for layer 2), while also maintaining high levels of consistent quality. We also illustrate how selective packetisation technique can further reduce the effects of packet loss on viewable quality by leveraging the increase in the number of frames per group of pictures (GOP), while offering a means of reducing overall error correction and by providing equality of data in every packet transmitted per GOP. Full article

The conventional channel estimation schemes for filter bank multicarrier with offset quadrature amplitude modulation (FBMC/OQAM) systems are mainly based on preamble methods. However, the utilization of preamble for channel estimation decreases the system’s spectrum efficiency. In this paper, we propose a modified subspace blind channel estimation method for FBMC/OQAM systems. The proposed method distinguishes itself from previously preamble based methods by utilizing spatial diversity technique to introduce data redundancy for blind channel estimation, which leads to high spectral utilization. Thus, the proposed method can provide significant root mean square error (RMSE) performance improvement compared to conventional preamble based methods at high SNRs. Simulation results verify the validity of the proposed method in FBMC/OQAM systems. Full article

The ecological approach studied in this paper is a new level of information studies. It allows for achieving a better understanding of information processes in society as well as more efficient creation of information processing systems. At first, in Section 2, we describe and analyze ecological studies in different areas ranging from biology to technology to sociology to knowledge and information. Then, in Section 3, we present elements of general ecology building methodological and philosophical foundation for information ecology. In Section 4 and Section 5, we elaborate a concise definition of information ecology and further develop information ecology as a methodological base for information studies in general based on the concepts and principles of the general theory of information. Full article

Recurrent neural networks (RNN) are efficient in modeling sequences for generation and classification, but their training is obstructed by the vanishing and exploding gradient issues. In this paper, we reformulate the RNN unit to learn the residual functions with reference to the hidden state instead of conventional gated mechanisms such as long short-term memory (LSTM) and the gated recurrent unit (GRU). The residual structure has two main highlights: firstly, it solves the gradient vanishing and exploding issues for large time-distributed scales; secondly, the residual structure promotes the optimizations for backward updates. In the experiments, we apply language modeling, emotion classification and polyphonic modeling to evaluate our layer compared with LSTM and GRU layers. The results show that our layer gives state-of-the-art performance, outperforms LSTM and GRU layers in terms of speed, and supports an accuracy competitive with that of the other methods. Full article

During the decision-making process, evaluation information may be given in different formats based on the decision makers’ research fields or personal customs. To address the situation that alternatives are evaluated by both intuitionistic fuzzy preference relations (IFPRs) and intuitionistic multiplicative preference relations (IMPRs), a new priority approach based on a net flow score function is proposed. First, the two preference relations above are transformed into the corresponding interval-valued fuzzy preference relations (IVFPRs) and interval-valued multiplicative preference relations (IVMPRs), respectively. Second, the net flow score functions of individual IFPRs and IMPRs are obtained. Third, according to information theory, a mean deviation maximization model is constructed to compute the weights of decision-makers objectively. Finally, the collective net flow score of each alternative is obtained to determine the ranking result. The proposed method is certified to be simple, valid, and practical with three examples. Full article

Increasingly, malicious Android apps use various methods to steal private user data without their knowledge. Detecting the leakage of private data is the focus of mobile information security. An initial investigation found that none of the existing security analysis systems can track the flow of information through Unix domain sockets to detect the leakage of private data through such sockets, which can result in zero-day exploits in the information security field. In this paper, we conduct the first systematic study on Unix domain sockets as applied in Android apps. Then, we identify scenarios in which such apps can leak private data through Unix domain sockets, which the existing dynamic taint analysis systems do not catch. Based on these insights, we propose and implement JDroid, a taint analysis system that can track information flows through Unix domain sockets effectively to detect such privacy leaks. Full article

In the next future the IoT system will introduce extraordinary changes in our daily life. We will communicate with our domestic appliances to inform them about our preferences and goals and they will develop initiative and autonomy to be put at our service. But are we sure that we can afford all the automation they could offer? Are we able to manage it? Is it compatible with our cognitive attitudes and our actual and real goals? In this paper, we face the question of the IoT from the point of view of the user. We start analyzing which reasons undermine the acceptance of IoT systems and then we propose a possible solution. The first contribution of this work is the level characterization of the autonomy a user can grant to an IoT device. The second contribution is a theoretical model to deal with users and to stimulate users’ acceptance. By the means of simulation, we show how the model works and we prove that it leads the system to an optimal solution. Full article

This article illustrates the progresses made in representing a multilingual and multi-alphabetical Old Occitan medico-botanical lexicon in the context of the project Dictionnaire de Termes Médico-botaniques de l’Ancien Occitan (DiTMAO). The chosen lexical model of reference is lemon, which has been extended accordingly to some specific linguistic and lexical features of the lexicon. In particular, issues and solutions about the modeling of multilingual and multiword phenomena are discussed, as the way they are managed through LexO, a web editor developed in the context of the project. Full article

Electricity load forecasting plays a paramount role in capacity planning, scheduling, and the operation of power systems. Reliable and accurate planning and prediction of electricity load are therefore vital. In this study, a novel approach for forecasting monthly electricity demands by wavelet transform and a neuro-fuzzy system is proposed. Firstly, the most appropriate inputs are selected and a dataset is constructed. Then, Haar wavelet transform is utilized to decompose the load data and eliminate noise. In the model, a hierarchical adaptive neuro-fuzzy inference system (HANFIS) is suggested to solve the curse-of-dimensionality problem. Several heuristic algorithms including Gravitational Search Algorithm (GSA), Cuckoo Optimization Algorithm (COA), and Cuckoo Search (CS) are utilized to optimize the clustering parameters which help form the rule base, and adaptive neuro-fuzzy inference system (ANFIS) optimize the parameters in the antecedent and consequent parts of each sub-model. The proposed approach was applied to forecast the electricity load of Hanoi, Vietnam. The constructed models have shown high forecasting performances based on the performance indices calculated. The results demonstrate the validity of the approach. The obtained results were also compared with those of several other well-known methods including autoregressive integrated moving average (ARIMA) and multiple linear regression (MLR). In our study, the wavelet CS-HANFIS model outperformed the others and provided more accurate forecasting. Full article

In the last few years, the number of private cars has expanding quickly in China, more and more parents use cars to escort their children to school, thus cause serious traffic congestions near school in many cities. In this paper, we developed an agent-based model (ABM) of the parents’ choice of escort mode. The core of this model is an escort mode choice motivation adjustment function that combines distance, traffic safety and social influence. We also used ABM to exhibit the emergent decoy effect phenomenon, which is a dynamic phenomenon that the introduction of a decoy to the choice-set could increase the share of other alternatives. The model reveals the parents’ inner psychological mechanism when facing competing escort mode choice in transportation system. The simulation results show that the proportion of parents to choose bus escort was 62.45% without the decoy effect was introduced, while the proportion of parents to choose bus escort increased to 74.29% with the decoy effect was entry. The use of the ABM method gives the potential to cope with the dynamic changes in studying parent escort mode choice behavior. Full article

Over a period of seven months (August 2017–February 2018), the Special Issue dedicated to “Neutrosophic Information Theory and Applications” by the “Information” journal (ISSN 2078-2489), located in Basel, Switzerland, was a success. The Guest Editors, Prof. Dr. Florentin Smarandache from the University of New Mexico (USA) and Prof. Dr. Jun Ye from the Shaoxing University (China), were happy to select—helped by a team of neutrosophic reviewers from around the world, and by the “Information” journal editors themselves—and publish twelve important neutrosophic papers, authored by 27 authors and coauthors. There were a variety of neutrosophic topics studied and used by the authors and coauthors in Multi-Criteria (or Multi-Attribute and/or Group) Decision-Making, including Cross Entropy-Based MAGDM, Neutrosophic Hesitant Fuzzy Prioritized Aggregation Operators, Biparametric Distance Measures, Pattern Recognition and Medical Diagnosis, Intuitionistic Neutrosophic Graph, NC-TODIM-Based MAGDM, Neutrosophic Cubic Set, VIKOR Method, Neutrosophic Multiple Attribute Group Decision-Making, Competition Graphs, Intuitionistic Neutrosophic Environment, Neutrosophic Commutative N-Ideals, Neutrosophic N-Structures Applied to BCK/BCI-Algebras, Neutrosophic Similarity Score, Weighted Histogram, Robust Mean-Shift Tracking, and Linguistic Neutrosophic Cubic Numbers. Full article

In this paper, we propose a bimodal 3D facial recognition method aimed at increasing the recognition rate and reducing the effect of illumination, pose, expression, ages, and occlusion on facial recognition. There are two features extracted from the multiscale sub-blocks in both the 3D mode depth map and 2D mode intensity map, which are the local gradient pattern (LGP) feature and the weighted histogram of gradient orientation (WHGO) feature. LGP and WHGO features are cascaded to form the 3D facial feature vector LGP-WHGO, and are further trained and identified by the support vector machine (SVM). Experiments on the CASIA database, FRGC v2.0 database, and Bosphorus database show that, the proposed method can efficiently extract the structure information and texture information of the facial image, and have a robustness to illumination, expression, occlusion and pose. Full article

After many decades of flourishing computer science it is now rather evident that in a world dominated by different kinds of digital information, both applications and people are forced to seek new, innovative structures and forms of data management and organization. Following this blunt observation, researchers in informatics have strived over the recent years to tackle the non-unique and rather evolving notion of context, which aids significantly the data disambiguation process. Motivated by this environment, this work attempts to summarize and organize in a researcher-friendly tabular manner important or pioneer related research works deriving from diverse computational intelligence domains: Initially, we discuss the influence of context with respect to traditional low-level multimedia content analysis and search, and retrieval tasks and then we advance to the fields of overall computational context-awareness and the so-called human-generated contextual elements. In an effort to provide meaningful information to fellow researchers, this brief survey focuses on the impact of context in modern and popular computing undertakings of our era. More specifically, we focus to the presentation of a short review of visual context modeling methods, followed by the depiction of context-awareness in modern computing. Works dealing with the interpretation of context by human-generated interactions are also discussed herein, as the particular domain gains an ever-increasing proportion of related research nowadays. We then conclude the paper by providing a short discussion on (i) the motivation behind the included context type categorization into three main pillars; (ii) the findings and conclusions of the survey for each context category; and (iii) a couple of brief advices derived from the survey for both interested developers and fellow researchers. Full article

When considering today’s uncertain atmosphere, many people and organizations believe that strategy has lost its meaning and position. When future is predictable, common approaches for strategic planning are applicable; nonetheless, vague circumstances require different methods. Accordingly, a new approach that is compatible with uncertainty and unstable conditions is necessary. Fuzzy logic is a worldview compatible with today complicated requirements. Regarding today’s uncertain and vague atmosphere, there is an absolute requirement to fuzzify the tools and strategic planning models, especially for dynamic and unclear environment. In this research, an extended version of Strengths, Weaknesses, Opportunities and Threats (SWOT) fuzzy approach has been presented for strategic planning based on fuzzy logic. It has solved the traditional strategic planning key problems like internal and external factors in imprecision and ambiguous environment. The model has been performed in an information technology corporation to demonstrate the capabilities in real world cases. Full article