Open AccessArticle
#europehappinessmap: A Framework for Multi-Lingual Sentiment Analysis via Social Media Big Data (A Twitter Case Study)
Information 2018, 9(5), 102; doi:10.3390/info9050102 (registering DOI) -
Abstract
The growth and popularity of social media platforms have generated a new social interaction environment thus a new collaboration and communication network among individuals. These platforms own tremendous amount of data about users’ behaviors and sentiments since people create, share or exchange their
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The growth and popularity of social media platforms have generated a new social interaction environment thus a new collaboration and communication network among individuals. These platforms own tremendous amount of data about users’ behaviors and sentiments since people create, share or exchange their information, ideas, pictures or video using them. One of these popular platforms is Twitter, which via its voluntary information sharing structure, provides researchers data potential of benefit for their studies. Based on Twitter data, in this study a multilingual sentiment detection framework is proposed to compute European Gross National Happiness (GNH). This framework consists of a novel data collection, filtering and sampling method, and a newly constructed multilingual sentiment detection algorithm for social media big data, and tested with nine European countries (United Kingdom, Germany, Sweden, Turkey, Portugal, The Netherlands, Italy, France and Spain) and their national languages over a six year period. The reliability of the data is checked with peak/troughs comparison for special days from Wikipedia news lists. The validity is checked with a group of correlation analyses with OECD Life Satisfaction survey reports’, Euro-Dollar and other currency exchanges, and national stock market time series data. After validity and reliability confirmations, the European GNH map is drawn for six years. The main problem addressed is to propose a novel multilingual social media sentiment analysis framework for calculating GNH for countries and change the way of OECD type organizations’ survey and interview methodology. Also, it is believed that this framework can serve more detailed results (e.g., daily or hourly sentiments of society in different languages). Full article
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Open AccessArticle
Genetic Algorithm with an Improved Initial Population Technique for Automatic Clustering of Low-Dimensional Data
Information 2018, 9(4), 101; doi:10.3390/info9040101 (registering DOI) -
Abstract
K-means clustering is an important and popular technique in data mining. Unfortunately, for any given dataset (not knowledge-base), it is very difficult for a user to estimate the proper number of clusters in advance, and it also has the tendency of trapping in
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K-means clustering is an important and popular technique in data mining. Unfortunately, for any given dataset (not knowledge-base), it is very difficult for a user to estimate the proper number of clusters in advance, and it also has the tendency of trapping in local optimum when the initial seeds are randomly chosen. The genetic algorithms (GAs) are usually used to determine the number of clusters automatically and to capture an optimal solution as the initial seeds of K-means clustering or K-means clustering results. However, they typically choose the genes of chromosomes randomly, which results in poor clustering results, whereas a generally selected initial population can improve the final clustering results. Hence, some GA-based techniques carefully select a high-quality initial population with a high complexity. This paper proposed an adaptive GA (AGA) with an improved initial population for K-means clustering (SeedClust). In SeedClust, which is an improved density estimation method and the improved K-means++ are presented to capture higher quality initial seeds and generate the initial population with low complexity, and the adaptive crossover and mutation probability is designed and is then used for premature convergence and to maintain the population diversity, respectively, which can automatically determine the proper number of clusters and capture an improved initial solution. Finally, the best chromosomes (centers) are obtained and are then fed into the K-means as initial seeds to generate even higher quality clustering results by allowing the initial seeds to readjust as needed. Experimental results based on low-dimensional taxi GPS (Global Position System) data sets demonstrate that SeedClust has a higher performance and effectiveness. Full article
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Open AccessArticle
Analysis of Document Pre-Processing Effects in Text and Opinion Mining
Information 2018, 9(4), 100; doi:10.3390/info9040100 -
Abstract
Typically, textual information is available as unstructured data, which require processing so that data mining algorithms can handle such data; this processing is known as the pre-processing step in the overall text mining process. This paper aims at analyzing the strong impact that
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Typically, textual information is available as unstructured data, which require processing so that data mining algorithms can handle such data; this processing is known as the pre-processing step in the overall text mining process. This paper aims at analyzing the strong impact that the pre-processing step has on most mining tasks. Therefore, we propose a methodology to vary distinct combinations of pre-processing steps and to analyze which pre-processing combination allows high precision. In order to show different combinations of pre-processing methods, experiments were performed by comparing some combinations such as stemming, term weighting, term elimination based on low frequency cut and stop words elimination. These combinations were applied in text and opinion mining tasks, from which correct classification rates were computed to highlight the strong impact of the pre-processing combinations. Additionally, we provide graphical representations from each pre-processing combination to show how visual approaches are useful to show the processing effects on document similarities and group formation (i.e., cohesion and separation). Full article
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Open AccessEssay
The Singularity Isn’t Simple! (However We Look at It) A Random Walk between Science Fiction and Science Fact
Information 2018, 9(4), 99; doi:10.3390/info9040099 -
Abstract
It seems to be accepted that intelligenceartificial or otherwise—and ‘the singularity’ are inseparable concepts: ‘The singularity’ will apparently arise from AI reaching a, supposedly particular, but actually poorly-defined, level of sophistication; and an empowered combination of hardware and software will take
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It seems to be accepted that intelligenceartificial or otherwise—and ‘the singularity’ are inseparable concepts: ‘The singularity’ will apparently arise from AI reaching a, supposedly particular, but actually poorly-defined, level of sophistication; and an empowered combination of hardware and software will take it from there (and take over from us). However, such wisdom and debate are simplistic in a number of ways: firstly, this is a poor definition of the singularity; secondly, it muddles various notions of intelligence; thirdly, competing arguments are rarely based on shared axioms, so are frequently pointless; fourthly, our models for trying to discuss these concepts at all are often inconsistent; and finally, our attempts at describing any ‘post-singularity’ world are almost always limited by anthropomorphism. In all of these respects, professional ‘futurists’ often appear as confused as storytellers who, through freer licence, may conceivably have the clearer view: perhaps then, that becomes a reasonable place to start. There is no attempt in this paper to propose, or evaluate, any research hypothesis; rather simply to challenge conventions. Using examples from science fiction to illustrate various assumptions behind the AI/singularity debate, this essay seeks to encourage discussion on a number of possible futures based on different underlying metaphysical philosophies. Although properly grounded in science, it eventually looks beyond the technology for answers and, ultimately, beyond the Earth itself. Full article
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Open AccessArticle
Chinese Knowledge Base Question Answering by Attention-Based Multi-Granularity Model
Information 2018, 9(4), 98; doi:10.3390/info9040098 -
Abstract
Chinese knowledge base question answering (KBQA) is designed to answer the questions with the facts contained in a knowledge base. This task can be divided into two subtasks: topic entity extraction and relation selection. During the topic entity extraction stage, an entity extraction
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Chinese knowledge base question answering (KBQA) is designed to answer the questions with the facts contained in a knowledge base. This task can be divided into two subtasks: topic entity extraction and relation selection. During the topic entity extraction stage, an entity extraction model is built to locate topic entities in questions. The Levenshtein Ratio entity linker is proposed to conduct effective entity linking. All the relevant subject-predicate-object (SPO) triples to topic entity are searched from the knowledge base as candidates. In relation selection, an attention-based multi-granularity interaction model (ABMGIM) is proposed. Two main contributions are as follows. First, a multi-granularity approach for text embedding is proposed. A nested character-level and word-level approach is used to concatenate the pre-trained embedding of a character with corresponding embedding on word-level. Second, we apply a hierarchical matching model for question representation in relation selection tasks, and attention mechanisms are imported for a fine-grained alignment between characters for relation selection. Experimental results show that our model achieves a competitive performance on the public dataset, which demonstrates its effectiveness. Full article
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Open AccessArticle
Scene Semantic Recognition Based on Probability Topic Model
Information 2018, 9(4), 97; doi:10.3390/info9040097 -
Abstract
In recent years, scene semantic recognition has become the most exciting and fastest growing research topic. Lots of scene semantic analysis methods thus have been proposed for better scene content interpretation. By using latent Dirichlet allocation (LDA) to deduce the effective topic features,
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In recent years, scene semantic recognition has become the most exciting and fastest growing research topic. Lots of scene semantic analysis methods thus have been proposed for better scene content interpretation. By using latent Dirichlet allocation (LDA) to deduce the effective topic features, the accuracy of image semantic recognition has been significantly improved. Besides, the method of extracting deep features by layer-by-layer iterative computation using convolutional neural networks (CNNs) has achieved great success in image recognition. The paper proposes a method called DF-LDA, which is a hybrid supervised–unsupervised method combined CNNs with LDA to extract image topics. This method uses CNNs to explore visual features that are more suitable for scene images, and group the features of salient semantics into visual topics through topic models. In contrast to the LDA as a tool for simply extracting image semantics, our approach achieves better performance on three datasets that contain various scene categories. Full article
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Open AccessArticle
Hierarchical Guidance Strategy and Exemplar-Based Image Inpainting
Information 2018, 9(4), 96; doi:10.3390/info9040096 -
Abstract
To solve the issue that it is difficult to maintain the consistency of linear structures when filling large regions by the exemplar-based technique, a hierarchical guidance strategy and exemplar-based image inpainting technique is proposed. The inpainting process is as follows: (i) the multi-layer
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To solve the issue that it is difficult to maintain the consistency of linear structures when filling large regions by the exemplar-based technique, a hierarchical guidance strategy and exemplar-based image inpainting technique is proposed. The inpainting process is as follows: (i) the multi-layer resolution images are firstly acquired through decomposing of the pyramid on the target image; (ii) the top-layer inpainted image, the beginning of the inpainting from the top layer, is generated by the exemplar-based technique; (iii) there is a combined result between the next layer of the top image and the up-sampling output on the top-layer inpainted image, and the target regions are filled with information as guidance data; (iv) this process is repeated until the inpainting of all layers have been completed. Our results were compared to those obtained by existing techniques, and our proposed technique maintained the consistency of linear structures in a visually plausible way. Objectively, we choose SSIM (structural similarity index measurement) and PSNR (peak signal-to-noise ratio) as the measurement indices. Since the values of SSIM are well reflected when compared with other techniques, our technique clearly demonstrated that our approach is better able to maintain the consistency of linear structures. The core of our algorithm is to fill large regions whether they are synthesis images or real-scene photographs. It is easy to apply in practice, with the goal of having plausible inpainted image. Full article
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Open AccessArticle
CSI Frequency Domain Fingerprint-Based Passive Indoor Human Detection
Information 2018, 9(4), 95; doi:10.3390/info9040095 -
Abstract
Passive indoor personnel detection technology is now a hot topic. Existing methods have been greatly influenced by environmental changes, and there are problems with the accuracy and robustness of detection. Passive personnel detection based on Wi-Fi not only solves the above problems, but
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Passive indoor personnel detection technology is now a hot topic. Existing methods have been greatly influenced by environmental changes, and there are problems with the accuracy and robustness of detection. Passive personnel detection based on Wi-Fi not only solves the above problems, but also has the advantages of being low cost and easy to implement, and can be better applied to elderly care and safety monitoring. In this paper, we propose a passive indoor personnel detection method based on Wi-Fi, which we call FDF-PIHD (Frequency Domain Fingerprint-based Passive Indoor Human Detection). Through this method, fine-grained physical layer Channel State Information (CSI) can be extracted to generate feature fingerprints so as to help determine the state in the scene by matching online fingerprints with offline fingerprints. In order to improve accuracy, we combine the detection results of three receiving antennas to obtain the final test result. The experimental results show that the detection rates of our proposed scheme all reach above 90%, no matter whether the scene is human-free, stationary or a moving human presence. In addition, it can not only detect whether there is a target indoors, but also determine the current state of the target. Full article
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Open AccessArticle
An Ensemble of Condition Based Classifiers for Device Independent Detailed Human Activity Recognition Using Smartphones
Information 2018, 9(4), 94; doi:10.3390/info9040094 -
Abstract
Human activity recognition is increasingly used for medical, surveillance and entertainment applications. For better monitoring, these applications require identification of detailed activity like sitting on chair/floor, brisk/slow walking, running, etc. This paper proposes a ubiquitous solution to detailed activity recognition through the
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Human activity recognition is increasingly used for medical, surveillance and entertainment applications. For better monitoring, these applications require identification of detailed activity like sitting on chair/floor, brisk/slow walking, running, etc. This paper proposes a ubiquitous solution to detailed activity recognition through the use of smartphone sensors. Use of smartphones for activity recognition poses challenges such as device independence and various usage behavior in terms of where the smartphone is kept. Only a few works address one or more of these challenges. Consequently, in this paper, we present a detailed activity recognition framework for identifying both static and dynamic activities addressing the above-mentioned challenges. The framework supports cases where (i) dataset contains data from accelerometer; and the (ii) dataset contains data from both accelerometer and gyroscope sensor of smartphones. The framework forms an ensemble of the condition based classifiers to address the variance due to different hardware configuration and usage behavior in terms of where the smartphone is kept (right pants pocket, shirt pockets or right hand). The framework is implemented and tested on real data set collected from 10 users with five different device configurations. It is observed that, with our proposed approach, 94% recognition accuracy can be achieved. Full article
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Open AccessArticle
Robust Eye Blink Detection Based on Eye Landmarks and Savitzky–Golay Filtering
Information 2018, 9(4), 93; doi:10.3390/info9040093 -
Abstract
A new technique to detect eye blinks is proposed based on automatic tracking of facial landmarks to localise the eyes and eyelid contours. Automatic facial landmarks detectors are trained on an in-the-wild dataset and shows an outstanding robustness to varying lighting conditions, facial
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A new technique to detect eye blinks is proposed based on automatic tracking of facial landmarks to localise the eyes and eyelid contours. Automatic facial landmarks detectors are trained on an in-the-wild dataset and shows an outstanding robustness to varying lighting conditions, facial expressions, and head orientation. The proposed technique estimates the facial landmark positions and extracts the vertical distance between eyelids for each video frame. Next, a Savitzky–Golay (SG) filter is employed to smooth the obtained signal while keeping the peak information to detect eye blinks. Finally, eye blinks are detected as sharp peaks and a finite state machine is used to check for false blink and true blink cases based on their duration. The efficiency of the proposed technique is shown to outperform the state-of-the-art methods on three standard datasets. Full article
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Open AccessArticle
An Architecture to Manage Incoming Traffic of Inter-Domain Routing Using OpenFlow Networks
Information 2018, 9(4), 92; doi:10.3390/info9040092 -
Abstract
The Border Gateway Protocol (BGP) is the current state-of-the-art inter-domain routing between Autonomous Systems (ASes). Although BGP has different mechanisms to manage outbound traffic in an AS domain, it lacks an efficient tool for inbound traffic control from transit ASes such as Internet
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The Border Gateway Protocol (BGP) is the current state-of-the-art inter-domain routing between Autonomous Systems (ASes). Although BGP has different mechanisms to manage outbound traffic in an AS domain, it lacks an efficient tool for inbound traffic control from transit ASes such as Internet Service Providers (ISPs). For inter-domain routing, the BGP’s destination-based forwarding paradigm limits the granularity of distributing the network traffic among the multiple paths of the current Internet topology. Thus, this work offered a new architecture to manage incoming traffic in the inter-domain using OpenFlow networks. The architecture explored direct inter-domain communication to exchange control information and the functionalities of the OpenFlow protocol. Based on the achieved results of the size of exchanging messages, the proposed architecture is not only scalable, but also capable of performing load balancing for inbound traffic using different strategies. Full article
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Open AccessArticle
Substantially Evolutionary Theorizing in Designing Software-Intensive Systems
Information 2018, 9(4), 91; doi:10.3390/info9040091 -
Abstract
Useful inheritances from scientific experience open perspective ways for increasing the degree of success in designing of systems with software. One such way is a search and build applied theory that takes into account the nature of design and the specificity of software
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Useful inheritances from scientific experience open perspective ways for increasing the degree of success in designing of systems with software. One such way is a search and build applied theory that takes into account the nature of design and the specificity of software engineering. This paper presents a substantially evolutionary approach to creating the project theories, the application of which leads to positive effects that are traditionally expected from theorizing. Any implementation of the approach is based on a reflection by designers of an operational space of designing onto a semantic memory of a question-answer type. One of the results of such reflection is a system of question-answer nets, the nodes of which register facts of interactions of designers with accessible experience. A set of such facts is used by designers for creating and using the theory that belongs to the new subclass of Grounded Theories. This sub-class is oriented on organizationally behavioral features of a project’s work based on design thinking, automated mental imagination, and thought experimenting that facilitate increasing the degree of controlled intellectualization in the design process and, correspondingly, increasing the degree of success in the development of software-intensive systems. Full article
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Open AccessArticle
A Hybrid Information Mining Approach for Knowledge Discovery in Cardiovascular Disease (CVD)
Information 2018, 9(4), 90; doi:10.3390/info9040090 -
Abstract
The healthcare ambit is usually perceived as “information rich” yet “knowledge poor”. Nowadays, an unprecedented effort is underway to increase the use of business intelligence techniques to solve this problem. Heart disease (HD) is a major cause of mortality
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The healthcare ambit is usually perceived as “information rich” yet “knowledge poor”. Nowadays, an unprecedented effort is underway to increase the use of business intelligence techniques to solve this problem. Heart disease (HD) is a major cause of mortality in modern society. This paper analyzes the risk factors that have been identified in cardiovascular disease (CVD) surveillance systems. The Heart Care study identifies attributes related to CVD risk (gender, age, smoking habit, etc.) and other dependent variables that include a specific form of CVD (diabetes, hypertension, cardiac disease, etc.). In this paper, we combine Clustering, Association Rules, and Neural Networks for the assessment of heart-event-related risk factors, targeting the reduction of CVD risk. With the use of the K-means algorithm, significant groups of patients are found. Then, the Apriori algorithm is applied in order to understand the kinds of relations between the attributes within the dataset, first looking within the whole dataset and then refining the results through the subsets defined by the clusters. Finally, both results allow us to better define patients’ characteristics in order to make predictions about CVD risk with a Multilayer Perceptron Neural Network. The results obtained with the hybrid information mining approach indicate that it is an effective strategy for knowledge discovery concerning chronic diseases, particularly for CVD risk. Full article
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Open AccessEditorial
Editorial for the Special Issue on “Wireless Energy Harvesting for Future Wireless Communications”
Information 2018, 9(4), 89; doi:10.3390/info9040089 -
Open AccessArticle
Hesitant Neutrosophic Linguistic Sets and Their Application in Multiple Attribute Decision Making
Information 2018, 9(4), 88; doi:10.3390/info9040088 -
Abstract
In this paper, the hesitant neutrosophic linguistic set is first defined by extending a hesitant fuzzy set to accommodate linguistic terms and neutrosophic fuzzy values. Some operational laws are defined for hesitant neutrosophic linguistic fuzzy information. Several distance measures have been defined including
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In this paper, the hesitant neutrosophic linguistic set is first defined by extending a hesitant fuzzy set to accommodate linguistic terms and neutrosophic fuzzy values. Some operational laws are defined for hesitant neutrosophic linguistic fuzzy information. Several distance measures have been defined including generalized hesitant neutrosophic linguistic distance, generalized hesitant neutrosophic linguistic Hausdorff distance, and generalized hesitant neutrosophic linguistic hybrid distance. Some hesitant neutrosophic fuzzy linguistic aggregation operators based on the Choquet integral have been defined. A new multiple attribute decision making method for hesitant neutrosophic fuzzy linguistic information has been developed based on TOPSIS. In order to illustrate the feasibility and practical advantages of the new algorithm, we use it to select a company to invest. The new method is then compared with other methods. Full article
Open AccessArticle
Auction-Based Cloud Service Pricing and Penalty with Availability on Demand
Information 2018, 9(4), 87; doi:10.3390/info9040087 -
Abstract
Availability is one of the main concerns of cloud users, and cloud providers always try to provide higher availability to improve user satisfaction. However, higher availability results in higher provider costs and lower social welfare. In this paper, taking into account both the
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Availability is one of the main concerns of cloud users, and cloud providers always try to provide higher availability to improve user satisfaction. However, higher availability results in higher provider costs and lower social welfare. In this paper, taking into account both the users’ valuation and desired availability, we design resource allocation, pricing and penalty mechanisms with availability on demand. Considering two scenarios: public availability in which the desired availabilities of all users are public information, and private availability in which the desired availabilities are private information of users, and, analyzing the possible behaviours of users, we design a truthful deterministic mechanism with 2-approximation in public availability scenario and a universal truthful mechanism with 11+γ approximation in private availability scenario, where γ is the backup ratio of resources with the highest availability. The experiment results show that our mechanisms significantly improve the social welfare compared to the mechanism without considering availability demand of users. Full article
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Open AccessArticle
An Improved Two-Way Security Authentication Protocol for RFID System
Information 2018, 9(4), 86; doi:10.3390/info9040086 -
Abstract
This paper proposes an improved two-way security authentication protocol to improve the security level of Radio Frequency Identification (RFID) system. In the proposed protocol, tags calculate hash value, which is divided into two parts. The left half is used to verify the identity
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This paper proposes an improved two-way security authentication protocol to improve the security level of Radio Frequency Identification (RFID) system. In the proposed protocol, tags calculate hash value, which is divided into two parts. The left half is used to verify the identity of the tags, and the right half is used to verify the identity of the reader, which will reduce the tag’s computation and storage. By updating the tag’s secret key value and random number, the protocol can prevent most attacks existing in RFID systems such as data privacy, replay attack, fake attack, position tracking and asynchronous attack. The correctness of the protocol is proved by using Burrows-Abadi-Needham (BAN) logic analysis. The evaluation results show that the scalability of the protocol proposed is achieved with acceptable response time limits. The simulation results indicate that the protocol has significant advantages on performance efficiency for many tags, which provides a reliable approach for RFID system application in practice. Full article
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Open AccessArticle
Location Regularization-Based POI Recommendation in Location-Based Social Networks
Information 2018, 9(4), 85; doi:10.3390/info9040085 -
Abstract
POI (point-of-interest) recommendation as one of the efficient information filtering techniques has been widely utilized in helping people find places they are likely to visit, and many related methods have been proposed. Although the methods that exploit geographical information for POI recommendation have
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POI (point-of-interest) recommendation as one of the efficient information filtering techniques has been widely utilized in helping people find places they are likely to visit, and many related methods have been proposed. Although the methods that exploit geographical information for POI recommendation have been studied, few of these studies have addressed the implicit feedback problem. In fact, in most location-based social networks, the user’s negative preferences are not explicitly observable. Consequently, it is inappropriate to treat POI recommendation as traditional recommendation problem. Moreover, previous studies mainly explore the geographical information from a user perspective and the methods that model them from a location perspective are not well explored. Hence, this work concentrates on exploiting the geographical characteristics from a location perspective for implicit feedback, where a neighborhood aware Bayesian personalized ranking method (NBPR) is proposed. To be specific, the weighted Bayesian framework that was proposed for personalized ranking is first introduced as our basic POI recommendation method. To exploit the geographical characteristics from a location perspective, we then constrain the ranking loss by using a regularization term derived from locations, and assume nearest neighboring POIs are more inclined to be visited by similar users. Finally, several experiments are conducted on two real-world social networks to evaluate the NBPR method, where we can find that our NBPR method has better performance than other related recommendation algorithms. This result also demonstrates the effectiveness of our method with neighborhood information and the importance of the geographical characteristics. Full article
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Open AccessArticle
Hybrid Destination-Based Jamming and Opportunistic Scheduling with Optimal Power Allocation to Secure Multiuser Untrusted Relay Networks
Information 2018, 9(4), 84; doi:10.3390/info9040084 -
Abstract
In this paper, we investigate secure communication for a dual-hop multiuser relay network, where a source communication with N (N1) destinations via an untrusted variable gains relay. To exploit multiuser diversity while protecting source’s confidential message, we first propose
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In this paper, we investigate secure communication for a dual-hop multiuser relay network, where a source communication with N (N1) destinations via an untrusted variable gains relay. To exploit multiuser diversity while protecting source’s confidential message, we first propose a joint destination-based jamming and opportunistic scheduling (DJOS) scheme. Then, we derive closed-form approximated and asymptotic expressions of the secrecy outage probability (SOP) for the considered system with DJOS. Furthermore, we determine an asymptotical optimal power allocation (OPA), which minimizes the asymptotic SOP, to further improve the secrecy performance. Our analytical results show that the achievable secrecy diversity order in terms of SOP with fixed power allocation is min(1,N2), whereas, with OPA, the achievable secrecy diversity order can be improved up to min(1,2NN+2). This interesting result reveals that OPA can improve the secrecy diversity order of the single-user network. This is intuitive since full diversity order of 1 cannot be achieved when N=1, thus leaving some space for OPA to improve the diversity order. Nevertheless, for N2, the effect of OPA is to increase the secrecy array gain rather than the secrecy diversity order since full diversity order 1 has been achieved by the OS scheme. Finally, simulation results are presented to validate our analysis. Full article
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Open AccessFeature PaperArticle
Thinking in Patterns and the Pattern of Human Thought as Contrasted with AI Data Processing
Information 2018, 9(4), 83; doi:10.3390/info9040083 -
Abstract
We propose that the ability of humans to identify and create patterns led to the unique aspects of human cognition and culture as a complex emergent dynamic system consisting of the following human traits: patterning, social organization beyond that of the nuclear family
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We propose that the ability of humans to identify and create patterns led to the unique aspects of human cognition and culture as a complex emergent dynamic system consisting of the following human traits: patterning, social organization beyond that of the nuclear family that emerged with the control of fire, rudimentary set theory or categorization and spoken language that co-emerged, the ability to deal with information overload, conceptualization, imagination, abductive reasoning, invention, art, religion, mathematics and science. These traits are interrelated as they all involve the ability to flexibly manipulate information from our environments via pattern restructuring. We argue that the human mind is the emergent product of a shift from external percept-based processing to a concept and language-based form of cognition based on patterning. In this article, we describe the evolution of human cognition and culture, describing the unique patterns of human thought and how we, humans, think in terms of patterns. Full article
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