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Information, Volume 9, Issue 10 (October 2018)

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Open AccessArticle Group Buying-Based Data Transmission in Flying Ad-Hoc Networks: A Coalition Game Approach
Information 2018, 9(10), 253; https://doi.org/10.3390/info9100253
Received: 13 September 2018 / Revised: 4 October 2018 / Accepted: 11 October 2018 / Published: 15 October 2018
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Abstract
In scenarios such as natural disasters and military strikes, it is common for unmanned aerial vehicles (UAVs) to form groups to execute reconnaissance and surveillance. To ensure the effectiveness of UAV communications, repeated resource acquisition issues and transmission mechanism designs need to be
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In scenarios such as natural disasters and military strikes, it is common for unmanned aerial vehicles (UAVs) to form groups to execute reconnaissance and surveillance. To ensure the effectiveness of UAV communications, repeated resource acquisition issues and transmission mechanism designs need to be addressed urgently. Since large-scale UAVs will generate high transmission overhead due to the overlapping resource requirements, in this paper, we propose a resource allocation optimization method based on distributed data content in a Flying Ad-hoc network (FANET). The resource allocation problem with the goal of throughput maximization is constructed as a coalition game framework. Firstly, a data transmission mechanism is designed for UAVs to execute information interaction within the coalitions. Secondly, a novel mechanism of coalition selection based on group-buying is investigated for UAV coalitions to acquire data from the central UAV. The data transmission and coalition selection problem are modeled as coalition graph game and coalition formation game, respectively. Through the design of the utility function, we prove that both games have stable solutions. We also prove the convergence of the proposed approach with coalition order and Pareto order. Based on simulation results, coalition order based coalition selection algorithm (CO-CSA) and Pareto order based coalition selection algorithm (PO-CSA) are proposed to explore the stable coalition partition of system model. CO-CSA and PO-CSA can achieve higher data throughput than the contrast onetime coalition selection algorithm (Onetime-CSA) (at least increased by 34.5% and 16.9%, respectively). Besides, although PO-CSA has relatively lower throughput gain, its convergence times is on average 50.9% less than that of CO-CSA, which means that the algorithm choice is scenario-dependent. Full article
(This article belongs to the Section Information and Communications Technology)
Open AccessArticle Integration of Context Information through Probabilistic Ontological Knowledge into Image Classification
Information 2018, 9(10), 252; https://doi.org/10.3390/info9100252
Received: 26 August 2018 / Revised: 21 September 2018 / Accepted: 9 October 2018 / Published: 12 October 2018
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Abstract
The use of ontological knowledge to improve classification results is a promising line of research. The availability of a probabilistic ontology raises the possibility of combining the probabilities coming from the ontology with the ones produced by a multi-class classifier that detects particular
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The use of ontological knowledge to improve classification results is a promising line of research. The availability of a probabilistic ontology raises the possibility of combining the probabilities coming from the ontology with the ones produced by a multi-class classifier that detects particular objects in an image. This combination not only provides the relations existing between the different segments, but can also improve the classification accuracy. In fact, it is known that the contextual information can often give information that suggests the correct class. This paper proposes a possible model that implements this integration, and the experimental assessment shows the effectiveness of the integration, especially when the classifier’s accuracy is relatively low. To assess the performance of the proposed model, we designed and implemented a simulated classifier that allows a priori decisions of its performance with sufficient precision. Full article
(This article belongs to the Special Issue Advanced Learning Methods for Complex Data)
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Open AccessArticle What Smart Campuses Can Teach Us about Smart Cities: User Experiences and Open Data
Information 2018, 9(10), 251; https://doi.org/10.3390/info9100251
Received: 2 September 2018 / Revised: 4 October 2018 / Accepted: 6 October 2018 / Published: 12 October 2018
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Abstract
Universities, like cities, have embraced novel technologies and data-based solutions to improve their campuses with ‘smart’ becoming a welcomed concept. Campuses in many ways are small-scale cities. They increasingly seek to address similar challenges and to deliver improved experiences to their users. How
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Universities, like cities, have embraced novel technologies and data-based solutions to improve their campuses with ‘smart’ becoming a welcomed concept. Campuses in many ways are small-scale cities. They increasingly seek to address similar challenges and to deliver improved experiences to their users. How can data be used in making this vision a reality? What can we learn from smart campuses that can be scaled up to smart cities? A short research study was conducted over a three-month period at a public university in the United Kingdom, employing stakeholder interviews and user surveys, which aimed to gain insight into these questions. Based on the study, the authors suggest that making data publicly available could bring many benefits to different groups of stakeholders and campus users. These benefits come with risks and challenges, such as data privacy and protection and infrastructure hurdles. However, if these challenges can be overcome, then open data could contribute significantly to improving campuses and user experiences, and potentially set an example for smart cities. Full article
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Open AccessArticle Robotic Choreography Inspired by the Method of Human Dance Creation
Information 2018, 9(10), 250; https://doi.org/10.3390/info9100250
Received: 1 September 2018 / Revised: 16 September 2018 / Accepted: 9 October 2018 / Published: 10 October 2018
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Abstract
In general, human dance is created by the imagination and innovativeness of human dancers, which in turn provides an inspiration for robotic choreography generation. This paper proposes a novel mechanism for a humanoid robot to create good choreography autonomously with the imagination of
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In general, human dance is created by the imagination and innovativeness of human dancers, which in turn provides an inspiration for robotic choreography generation. This paper proposes a novel mechanism for a humanoid robot to create good choreography autonomously with the imagination of human dance. Such a mechanism combines innovativeness with the characteristic preservation of human dance, and enables a humanoid robot to present the characteristics of “imitation, memory, imagination, process and combination”. The proposed mechanism has been implemented on a real humanoid robot, NAO, to verify its feasibility and performance. Experimental results are presented to demonstrate good performance of the proposed mechanism. Full article
(This article belongs to the Section Artificial Intelligence)
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Open AccessArticle A Theory of Physically Embodied and Causally Effective Agency
Information 2018, 9(10), 249; https://doi.org/10.3390/info9100249
Received: 31 July 2018 / Revised: 18 September 2018 / Accepted: 28 September 2018 / Published: 6 October 2018
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Abstract
Causality is fundamental to agency. Intelligent agents learn about causal relationships by interacting with their environments and use their causal knowledge to choose actions intended to bring about desired outcomes. This paper considers a causal question that is central to the very meaning
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Causality is fundamental to agency. Intelligent agents learn about causal relationships by interacting with their environments and use their causal knowledge to choose actions intended to bring about desired outcomes. This paper considers a causal question that is central to the very meaning of agency, that of how a physically embodied agent effects intentional action in the world. The prevailing assumption is that both biological and computer agents are automatons whose decisions are determined by the physical processes operating in their information processing apparatus. As an alternative hypothesis, this paper presents a mathematical model of causally efficacious agency. The model is based on Stapp’s theory of efficacious choice in physically embodied agents. Stapp’s theory builds on a realistic interpretation of von Neumann’s mathematical formalization of quantum theory. Because it is consistent with the well-established precepts of quantum theory, Stapp’s theory has been dismissed as metaphysical and unfalsifiable. However, if taken seriously as a model of efficacious choice in biological agents, the theory does have empirically testable implications. This paper formulates Stapp’s theory as an interventionist causal theory in which interventions are ascribed to agents and can have macroscopically distinguishable effects in the world. Empirically testable implications of the theory are discussed and a path toward scientific evaluation is proposed. Implications for artificial intelligence are considered. Full article
(This article belongs to the Special Issue Probabilistic Causal Modelling in Intelligent Systems)
Open AccessArticle Analysis of the Risk Management Process on the Development of the Public Sector Information Technology Master Plan
Information 2018, 9(10), 248; https://doi.org/10.3390/info9100248
Received: 11 September 2018 / Revised: 26 September 2018 / Accepted: 1 October 2018 / Published: 4 October 2018
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Abstract
The Information and Communication Technology Master Plan—ICTMP—is an important tool for the achievement of the strategic business objectives of public and private organizations. In the public sector, these objectives are closely related to the provision of benefits to society. Information and Communication Technology
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The Information and Communication Technology Master Plan—ICTMP—is an important tool for the achievement of the strategic business objectives of public and private organizations. In the public sector, these objectives are closely related to the provision of benefits to society. Information and Communication Technology (ICT) actions are present in all organizational processes and involves size-able budgets. The risks inherent in the planning of ICT actions need to be considered for ICT to add value to the business and to maximize the return on investment to the population. In this context, this work intends to examine the use of risk management processes in the development of ICTMPs in the Brazilian public sector. Full article
Open AccessArticle Dynamic Handwriting Analysis for Supporting Earlier Parkinson’s Disease Diagnosis
Information 2018, 9(10), 247; https://doi.org/10.3390/info9100247
Received: 15 September 2018 / Revised: 25 September 2018 / Accepted: 28 September 2018 / Published: 3 October 2018
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Abstract
Machine learning techniques are tailored to build intelligent systems to support clinicians at the point of care. In particular, they can complement standard clinical evaluations for the assessment of early signs and manifestations of Parkinson’s disease (PD). Patients suffering from PD typically exhibit
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Machine learning techniques are tailored to build intelligent systems to support clinicians at the point of care. In particular, they can complement standard clinical evaluations for the assessment of early signs and manifestations of Parkinson’s disease (PD). Patients suffering from PD typically exhibit impairments of previously learned motor skills, such as handwriting. Therefore, handwriting can be considered a powerful marker to develop automatized diagnostic tools. In this paper, we investigated if and to which extent dynamic features of the handwriting process can support PD diagnosis at earlier stages. To this end, a subset of the publicly available PaHaW dataset has been used, including those patients showing only early to mild degree of disease severity. We developed a classification framework based on different classifiers and an ensemble scheme. Some encouraging results have been obtained; in particular, good specificity performances have been observed. This indicates that a handwriting-based decision support tool could be used to administer screening tests useful for ruling in disease. Full article
(This article belongs to the Special Issue eHealth and Artificial Intelligence)
Open AccessArticle A Hybrid PAPR Reduction Method Based on SLM and Multi-Data Block PTS for FBMC/OQAM Systems
Information 2018, 9(10), 246; https://doi.org/10.3390/info9100246
Received: 12 September 2018 / Revised: 26 September 2018 / Accepted: 29 September 2018 / Published: 1 October 2018
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Abstract
The filter bank multicarrier employing offset quadrature amplitude modulation (FBMC/OQAM) is a candidate transmission scheme for 5G wireless communication systems. However, it has a high peak-to-average power ratio (PAPR). Due to the nature of overlapped signal structure of FBMC/OQAM, conventional PAPR reduction schemes
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The filter bank multicarrier employing offset quadrature amplitude modulation (FBMC/OQAM) is a candidate transmission scheme for 5G wireless communication systems. However, it has a high peak-to-average power ratio (PAPR). Due to the nature of overlapped signal structure of FBMC/OQAM, conventional PAPR reduction schemes cannot work effectively. A hybrid PAPR reduction scheme based on selective mapping (SLM) and multi data block partial transmit sequence (M-PTS) methods is proposed for FBMC/OQAM signals in this paper. Different from the simple SLM-PTS method, the proposed hybrid algorithm takes into account the overlapping effect of multiple adjacent data blocks on its PTS process. From simulation results, it can be obtained that the proposed method can offer a significant PAPR reduction performance improvement compared with the SLM, PTS and SLM-PTS methods. The proposed method can effectively reduce the PAPR in FBMC/OQAM systems. Full article
(This article belongs to the Section Information and Communications Technology)
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Open AccessArticle Performance Analysis of Honeypot with Petri Nets
Information 2018, 9(10), 245; https://doi.org/10.3390/info9100245
Received: 11 September 2018 / Revised: 21 September 2018 / Accepted: 26 September 2018 / Published: 30 September 2018
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Abstract
As one of the active defense technologies, the honeypot deceives the latent intruders to interact with the imitated systems or networks deployed with security mechanisms. Its modeling and performance analysis have not been well studied. In this paper, we propose a honeypot performance
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As one of the active defense technologies, the honeypot deceives the latent intruders to interact with the imitated systems or networks deployed with security mechanisms. Its modeling and performance analysis have not been well studied. In this paper, we propose a honeypot performance evaluation scheme based on Stochastic Petri Nets (SPN). We firstly set up performance evaluation models for three types of defense scenarios (i.e., firewall; firewall and Intrusion Detection System (IDS); firewall, IDS and honeypot) based on SPN. We then theoretically analyze the SPN models by constructing Markov Chains (MC), which are isomorphic to the models. With the steady state probabilities based on the MC, the system performance evaluation is done with theoretical inference. Finally, we implement the proposed three SPN models on the PIPE platform. Five parameters are applied to compare and evaluate the performance of the proposed SPN models. The analysis of the probability and delay of three scenarios shows that the simulation results validate the effectiveness in security enhancement of the honeypot under the SPN models. Full article
(This article belongs to the Section Information Theory and Methodology)
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Open AccessFeature PaperArticle Countering Superintelligence Misinformation
Information 2018, 9(10), 244; https://doi.org/10.3390/info9100244
Received: 9 September 2018 / Revised: 25 September 2018 / Accepted: 26 September 2018 / Published: 30 September 2018
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Abstract
Superintelligence is a potential type of future artificial intelligence (AI) that is significantly more intelligent than humans in all major respects. If built, superintelligence could be a transformative event, with potential consequences that are massively beneficial or catastrophic. Meanwhile, the prospect of superintelligence
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Superintelligence is a potential type of future artificial intelligence (AI) that is significantly more intelligent than humans in all major respects. If built, superintelligence could be a transformative event, with potential consequences that are massively beneficial or catastrophic. Meanwhile, the prospect of superintelligence is the subject of major ongoing debate, which includes a significant amount of misinformation. Superintelligence misinformation is potentially dangerous, ultimately leading bad decisions by the would-be developers of superintelligence and those who influence them. This paper surveys strategies to counter superintelligence misinformation. Two types of strategies are examined: strategies to prevent the spread of superintelligence misinformation and strategies to correct it after it has spread. In general, misinformation can be difficult to correct, suggesting a high value of strategies to prevent it. This paper is the first extended study of superintelligence misinformation. It draws heavily on the study of misinformation in psychology, political science, and related fields, especially misinformation about global warming. The strategies proposed can be applied to lay public attention to superintelligence, AI education programs, and efforts to build expert consensus. Full article
(This article belongs to the Special Issue AI AND THE SINGULARITY: A FALLACY OR A GREAT OPPORTUNITY?)
Open AccessArticle PIF and ReCiF: Efficient Interest-Packet Forwarding Mechanisms for Named-Data Wireless Mesh Networks
Information 2018, 9(10), 243; https://doi.org/10.3390/info9100243
Received: 31 August 2018 / Revised: 20 September 2018 / Accepted: 26 September 2018 / Published: 29 September 2018
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Abstract
In this paper, we propose three mechanisms to reduce the broadcast storm problem in wireless mesh networks based on the Named-Data Network (NDN) architecture. The goal of our mechanisms is to reduce the number of content requests forwarded by nodes and consequently, increase
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In this paper, we propose three mechanisms to reduce the broadcast storm problem in wireless mesh networks based on the Named-Data Network (NDN) architecture. The goal of our mechanisms is to reduce the number of content requests forwarded by nodes and consequently, increase the network efficiency. The first proposed mechanism, called Probabilistic Interest Forwarding (PIF), randomly forwards content requests. The second mechanism, called Retransmission-Counter-based Forwarding (ReCIF), decides to forward content requests based on the number of retransmissions by adding a counter to the header of requests. The third mechanism, called ReCIF+PIF, combines the features of PIF and ReCIF to suppress content requests. We compare the performance of our mechanisms with both the NDN default forwarding mechanism and the Listen First Broadcast Later (LFBL) mechanism. Our proposals outperform the default NDN forwarding mechanism by up to 21% regarding the data delivery rate in dense networks and provide a 25% lower delivery delay than the default NDN. Our mechanisms accomplish this performance by only reducing the number of content requests forwarded by nodes. One of our mechanisms, PIF, outperforms LFBL regarding the data delivery rate and delivery delay by up to 263% and 55%, respectively, for high network contention levels. Full article
(This article belongs to the Special Issue Information-Centric Networking)
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Open AccessArticle Multi-User Searchable Symmetric Encryption with Dynamic Updates for Cloud Computing
Information 2018, 9(10), 242; https://doi.org/10.3390/info9100242
Received: 2 September 2018 / Revised: 25 September 2018 / Accepted: 26 September 2018 / Published: 28 September 2018
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Abstract
With the advent of cloud computing, more and more users begin to outsource encrypted files to cloud servers to provide convenient access and obtain security guarantees. Searchable encryption (SE) allows a user to search the encrypted files without leaking information related to the
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With the advent of cloud computing, more and more users begin to outsource encrypted files to cloud servers to provide convenient access and obtain security guarantees. Searchable encryption (SE) allows a user to search the encrypted files without leaking information related to the contents of the files. Searchable symmetric encryption (SSE) is an important branch of SE. Most of the existing SSE schemes considered single-user settings, which cannot meet the requirements for data sharing. In this work, we propose a multi-user searchable symmetric encryption scheme with dynamic updates. This scheme is applicable to the usage scenario where one data owner encrypts sensitive files and shares them among multiple users, and it allows secure and efficient searches/updates. We use key distribution and re-encryption to achieve multi-user access while avoiding a series of issues caused by key sharing. Our scheme is constructed based on the index structure where a bit matrix is combined with two static hash tables, pseudorandom functions and hash functions. Our scheme is proven secure in the random oracle model. Full article
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Open AccessArticle Correlation Tracking via Self-Adaptive Fusion of Multiple Features
Information 2018, 9(10), 241; https://doi.org/10.3390/info9100241
Received: 13 August 2018 / Revised: 12 September 2018 / Accepted: 21 September 2018 / Published: 27 September 2018
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Abstract
Correlation filter (CF) based tracking algorithms have shown excellent performance in comparison to most state-of-the-art algorithms on the object tracking benchmark (OTB). Nonetheless, most CF based tracking algorithms only consider limited single channel feature, and the tracking model always updated from frame-by-frame. It
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Correlation filter (CF) based tracking algorithms have shown excellent performance in comparison to most state-of-the-art algorithms on the object tracking benchmark (OTB). Nonetheless, most CF based tracking algorithms only consider limited single channel feature, and the tracking model always updated from frame-by-frame. It will generate some erroneous information when the target objects undergo sophisticated scenario changes, such as background clutter, occlusion, out-of-view, and so forth. Long-term accumulation of erroneous model updating will cause tracking drift. In order to address problems that are mentioned above, in this paper, we propose a robust multi-scale correlation filter tracking algorithm via self-adaptive fusion of multiple features. First, we fuse powerful multiple features including histogram of oriented gradients (HOG), color name (CN), and histogram of local intensities (HI) in the response layer. The weights assigned according to the proportion of response scores that are generated by each feature, which achieve self-adaptive fusion of multiple features for preferable feature representation. In the meantime the efficient model update strategy is proposed, which is performed by exploiting a pre-defined response threshold as discriminative condition for updating tracking model. In addition, we introduce an accurate multi-scale estimation method integrate with the model update strategy, which further improves the scale variation adaptability. Both qualitative and quantitative evaluations on challenging video sequences demonstrate that the proposed tracker performs superiorly against the state-of-the-art CF based methods. Full article
(This article belongs to the Section Information Processes)
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Open AccessArticle Development of an ANFIS Model for the Optimization of a Queuing System in Warehouses
Information 2018, 9(10), 240; https://doi.org/10.3390/info9100240
Received: 3 September 2018 / Revised: 19 September 2018 / Accepted: 21 September 2018 / Published: 22 September 2018
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Abstract
Queuing systems (QS) represent everyday life in all business and economic systems. On the one hand, and there is a tendency for their time and cost optimization, but on the other hand, they have not been sufficiently explored. This especially applies to logistics
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Queuing systems (QS) represent everyday life in all business and economic systems. On the one hand, and there is a tendency for their time and cost optimization, but on the other hand, they have not been sufficiently explored. This especially applies to logistics systems, where a large number of transportation and storage units appear. Therefore, the aim of this paper is to develop an ANFIS (Adaptive neuro-fuzzy inference system) model in a warehouse system with two servers for defining QS optimization parameters. The research was conducted in a company for the manufacturing of brown paper located in the territory of Bosnia and Herzegovina, which represents a significant share of the total export production of the country. In this paper, the optimization criterion is the time spent in the system, which is important both from the aspect of all customers of the system, and from that of the owner of the company. The time criterion directly affects the efficiency of the system, but also the overall costs that this system causes. The developed ANFIS model was compared with a mathematical model through a sensitivity analysis. The mathematical model showed outstanding results, which justifies its development and application. Full article
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