[go: up one dir, main page]
Next Issue
Volume 9, January
Previous Issue
Volume 8, September
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
6.9
2.4
Journals
Information
Volume 8
Issue 4
share announcement

Information, Volume 8, Issue 4 (December 2017) – 49 articles

  • Issues are regarded as officially published after their release is announced to the table of contents alert mailing list.
  • You may sign up for e-mail alerts to receive table of contents of newly released issues.
  • PDF is the official format for papers published in both, html and pdf forms. To view the papers in pdf format, click on the "PDF Full-text" link, and use the free Adobe Reader to open them.
Order results
Result details
Section
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
1191 KiB  
Article
Uncertain Production Scheduling Based on Fuzzy Theory Considering Utility and Production Rate
by Yue Wang, Xin Jin, Lei Xie, Yanhui Zhang and Shan Lu
Information 2017, 8(4), 158; https://doi.org/10.3390/info8040158 - 18 Dec 2017
Cited by 2 | Viewed by 3783
Abstract
Handling uncertainty in an appropriate manner during the real operation of a cyber-physical system (CPS) is critical. Uncertain production scheduling as a part of CPS uncertainty issues should attract more attention. In this paper, a Mixed Integer Nonlinear Programming (MINLP) uncertain model for [...] Read more.
Handling uncertainty in an appropriate manner during the real operation of a cyber-physical system (CPS) is critical. Uncertain production scheduling as a part of CPS uncertainty issues should attract more attention. In this paper, a Mixed Integer Nonlinear Programming (MINLP) uncertain model for batch process is formulated based on a unit-specific event-based continuous-time modeling method. Utility uncertainty and uncertain relationship between production rate and utility supply are described by fuzzy theory. The uncertain scheduling model is converted into deterministic model by mathematical method. Through one numerical example, the accuracy and practicability of the proposed model is proved. Fuzzy scheduling model can supply valuable decision options for enterprise managers to make decision more accurate and practical. The impact and selection of some key parameters of fuzzy scheduling model are elaborated. Full article
(This article belongs to the Special Issue Smart Sensing, Information Security and Control of Industrial Cyber-Physical Systems)
►▼ Show Figures

Figure 1

Figure 1
<p>Triangular possibility distribution of fuzzy number.</p> Full article ">Figure 2
<p>Triangular possibility distribution of fuzzy number.</p> Full article ">Figure 3
<p>State-task network representation.</p> Full article ">Figure 4
<p>Gantt chart for the deterministic model.</p> Full article ">Figure 5
<p>Gantt chart for the fuzzy model.</p> Full article ">
807 KiB  
Article
Some New Biparametric Distance Measures on Single-Valued Neutrosophic Sets with Applications to Pattern Recognition and Medical Diagnosis
by Harish Garg and Nancy
Information 2017, 8(4), 162; https://doi.org/10.3390/info8040162 - 15 Dec 2017
Cited by 47 | Viewed by 4610
Abstract
Single-valued neutrosophic sets (SVNSs) handling the uncertainties characterized by truth, indeterminacy, and falsity membership degrees, are a more flexible way to capture uncertainty. In this paper, some new types of distance measures, overcoming the shortcomings of the existing measures, for SVNSs with two [...] Read more.
Single-valued neutrosophic sets (SVNSs) handling the uncertainties characterized by truth, indeterminacy, and falsity membership degrees, are a more flexible way to capture uncertainty. In this paper, some new types of distance measures, overcoming the shortcomings of the existing measures, for SVNSs with two parameters are proposed along with their proofs. The various desirable relations between the proposed measures have also been derived. A comparison between the proposed and the existing measures has been performed in terms of counter-intuitive cases for showing its validity. The proposed measures have been illustrated with case studies of pattern recognition as well as medical diagnoses, along with the effect of the different parameters on the ordering of the objects. Full article
(This article belongs to the Special Issue Neutrosophic Information Theory and Applications)
181 KiB  
Article
Can Computers Become Conscious, an Essential Condition for the Singularity?
by Robert K. Logan
Information 2017, 8(4), 161; https://doi.org/10.3390/info8040161 - 9 Dec 2017
Cited by 11 | Viewed by 7334
Abstract
Given that consciousness is an essential ingredient for achieving Singularity, the notion that an Artificial General Intelligence device can exceed the intelligence of a human, namely, the question of whether a computer can achieve consciousness, is explored. Given that consciousness is being aware [...] Read more.
Given that consciousness is an essential ingredient for achieving Singularity, the notion that an Artificial General Intelligence device can exceed the intelligence of a human, namely, the question of whether a computer can achieve consciousness, is explored. Given that consciousness is being aware of one’s perceptions and/or of one’s thoughts, it is claimed that computers cannot experience consciousness. Given that it has no sensorium, it cannot have perceptions. In terms of being aware of its thoughts it is argued that being aware of one’s thoughts is basically listening to one’s own internal speech. A computer has no emotions, and hence, no desire to communicate, and without the ability, and/or desire to communicate, it has no internal voice to listen to and hence cannot be aware of its thoughts. In fact, it has no thoughts, because it has no sense of self and thinking is about preserving one’s self. Emotions have a positive effect on the reasoning powers of humans, and therefore, the computer’s lack of emotions is another reason for why computers could never achieve the level of intelligence that a human can, at least, at the current level of the development of computer technology. Full article
(This article belongs to the Special Issue AI AND THE SINGULARITY: A FALLACY OR A GREAT OPPORTUNITY?)
396 KiB  
Article
Individual Differences, Self-Efficacy, and Chinese Scientists’ Industry Engagement
by Zhiyan Zhao and Jianfeng Cai
Information 2017, 8(4), 160; https://doi.org/10.3390/info8040160 - 8 Dec 2017
Cited by 1 | Viewed by 4034
Abstract
Research indicates that non-commercial and informal university–industry interactions, which are defined as academic engagement, account for a larger part and play a more important role than commercialization in academic knowledge transfer in China. This paper aims to explore the effect of Chinese scientists’ [...] Read more.
Research indicates that non-commercial and informal university–industry interactions, which are defined as academic engagement, account for a larger part and play a more important role than commercialization in academic knowledge transfer in China. This paper aims to explore the effect of Chinese scientists’ individual differences on academic engagement via social cognitive theory. This study attempts to provide an interpretation of how individual differences affect Chinese academics’ industrial engagement through self-efficacy. Based on data collection from Chinese universities, these analysis results show that gender, academic rank, industry connections, and previous industrial experience are significantly associated with Chinese scientists’ industry engagement. Furthermore, a scientist’s self-efficacy in industry collaborations is also influenced by these four individual factors. The mediating effects of self-efficacy on the relationship between individual differences and academic engagement are confirmed by empirical analysis results. Implications, limitations, and future research directions are discussed at the end of this paper. Full article
►▼ Show Figures

Figure 1

Figure 1
<p>Theoretical framework.</p> Full article ">
1876 KiB  
Article
sCwc/sLcc: Highly Scalable Feature Selection Algorithms
by Kilho Shin, Tetsuji Kuboyama, Takako Hashimoto and Dave Shepard
Information 2017, 8(4), 159; https://doi.org/10.3390/info8040159 - 6 Dec 2017
Cited by 8 | Viewed by 5242
Abstract
Feature selection is a useful tool for identifying which features, or attributes, of a dataset cause or explain the phenomena that the dataset describes, and improving the efficiency and accuracy of learning algorithms for discovering such phenomena. Consequently, feature selection has been studied [...] Read more.
Feature selection is a useful tool for identifying which features, or attributes, of a dataset cause or explain the phenomena that the dataset describes, and improving the efficiency and accuracy of learning algorithms for discovering such phenomena. Consequently, feature selection has been studied intensively in machine learning research. However, while feature selection algorithms that exhibit excellent accuracy have been developed, they are seldom used for analysis of high-dimensional data because high-dimensional data usually include too many instances and features, which make traditional feature selection algorithms inefficient. To eliminate this limitation, we tried to improve the run-time performance of two of the most accurate feature selection algorithms known in the literature. The result is two accurate and fast algorithms, namely sCwc and sLcc. Multiple experiments with real social media datasets have demonstrated that our algorithms improve the performance of their original algorithms remarkably. For example, we have two datasets, one with 15,568 instances and 15,741 features, and another with 200,569 instances and 99,672 features. sCwc performed feature selection on these datasets in 1.4 seconds and in 405 seconds, respectively. In addition, sLcc has turned out to be as fast as sCwc on average. This is a remarkable improvement because it is estimated that the original algorithms would need several hours to dozens of days to process the same datasets. In addition, we introduce a fast implementation of our algorithms: sCwc does not require any adjusting parameter, while sLcc requires a threshold parameter, which we can use to control the number of features that the algorithm selects. Full article
(This article belongs to the Special Issue Feature Selection for High-Dimensional Data)
►▼ Show Figures

Figure 1

Figure 1
<p>Clustering of twitter data. (<b>a</b>) tweets between 2:00 p.m. and 3:00 p.m. of 11 March. The quake hit Japan at 2:46 p.m., and 97,977 authors who posted 351,491 tweets in total are plotted; (<b>b</b>) tweets between 3:00 p.m. and 4:00 p.m. of 11 March. Furthermore, 161,853 authors who posted 978,155 tweets in total are plotted.</p> Full article ">Figure 2
<p>An example result of feature selection by C<span class="html-small-caps">wc</span>: Word (Score, Rank). Scores and ranks are measured by the symmetrical uncertainty. The Japanese words in this figure are translated as “emergency” (9), “networks” (11), “utilize” (13”, “favor” (15), “bath” (18), “great tsunami warning” (19), “place” (24), “phone” (26), “evacuation” (28), “absolute” (32), “all” (34), “possible” (37), “information” (39), “like” (40), “preparation” (41), “Miyagi” (42), “possibility” (45), “thing” (52), “Hanshin Great Quake” (55), “notification” (62), “over” (63), “disaster mail telephone” (65), “friend” (71), “as if” (72), “coast” (73), “safety” (74), “tsunami” (75), “Chu-Etsu Quake” (106), “television” (112), “Ibaraki” (115), “shock of earthquake” (119), “worry” (125), “Mr.”, “Mrs.” or “Ms.” (141), “earthquake intensity” (146) and “seem” (167). The numbers within parentheses indicate the ranks of the words.</p> Full article ">Figure 3
<p>Progress of feature selection.</p> Full article ">Figure 4
<p>A relationship between <math display="inline"> <semantics> <mrow> <msub> <mi>N</mi> <mi>F</mi> </msub> <msub> <mi>N</mi> <mi>I</mi> </msub> <mrow> <mo>(</mo> <mo form="prefix">log</mo> <msub> <mi>N</mi> <mi>F</mi> </msub> <mo>+</mo> <mo form="prefix">log</mo> <msub> <mi>N</mi> <mi>I</mi> </msub> <mo>)</mo> </mrow> </mrow> </semantics> </math> (the <span class="html-italic">x</span>-axis) and run-time of <span class="html-small-caps">s</span>C<span class="html-small-caps">wc</span> (the <span class="html-italic">y</span>-axis).</p> Full article ">Figure 5
<p>The fifteen datasets used in the experiment. The blue plots (<span style="color:blue">•</span>) represent the five datasets used in the feature selection challenge of NIPS 2003 [<a href="#B14-information-08-00159" class="html-bibr">14</a>], while the red plots (<span style="color:red">•</span>) do those used in the challenge of WCCI 2006 [<a href="#B15-information-08-00159" class="html-bibr">15</a>]. The other five are retrieved from the USI repository [<a href="#B16-information-08-00159" class="html-bibr">16</a>].</p> Full article ">Figure 6
<p>Comparison in accuracy.</p> Full article ">Figure 6 Cont.
<p>Comparison in accuracy.</p> Full article ">Figure 7
<p>Ranking.</p> Full article ">Figure 8
<p>The Area Under an Receiver Operating Characteristic Curve (AUC-ROC) scores and numbers of features selected by <span class="html-small-caps">s</span>L<span class="html-small-caps">cc</span> changing <math display="inline"> <semantics> <mi>δ</mi> </semantics> </math> from 0.0 to 0.02 at interval of 0.002. The lines and plots in blue represent feature numbers, while those in orange do AUC-ROC scores.</p> Full article ">Figure 9
<p>Relation between the run-time of <span class="html-small-caps">s</span>L<span class="html-small-caps">cc</span> and the value of <math display="inline"> <semantics> <mi>δ</mi> </semantics> </math>. The <span class="html-italic">x</span>-axis represents the value of <math display="inline"> <semantics> <mi>δ</mi> </semantics> </math>, while the <span class="html-italic">y</span>-axis does the run-time of <span class="html-small-caps">s</span>L<span class="html-small-caps">cc</span> in milliseconds.</p> Full article ">Figure 10
<p>The effects of different values upon the threshold parameter <math display="inline"> <semantics> <mi>δ</mi> </semantics> </math>. The <span class="html-italic">x</span>-axis represents the value of <math display="inline"> <semantics> <mi>δ</mi> </semantics> </math>, while the <span class="html-italic">y</span>-axis represents (<b>a</b>) the number of feature selected by and (<b>b</b>) the run-time of <span class="html-small-caps">s</span>L<span class="html-small-caps">cc</span>. The orange lines indicate the corresponding values by <span class="html-small-caps">s</span>C<span class="html-small-caps">wc</span>.</p> Full article ">Figure 11
<p>An example of data structure.</p> Full article ">Figure 12
<p>Evaluating <math display="inline"> <semantics> <mrow> <mi>Bn</mi> <mo>(</mo> <msub> <mi>F</mi> <mn>5</mn> </msub> <mo>,</mo> <msub> <mi>F</mi> <mn>3</mn> </msub> <mo>,</mo> <msub> <mi>F</mi> <mn>2</mn> </msub> <mo>,</mo> <msub> <mi>F</mi> <mn>9</mn> </msub> <mo>,</mo> <msub> <mi>F</mi> <mn>8</mn> </msub> <mo>)</mo> </mrow> </semantics> </math>.</p> Full article ">Figure 13
<p>Eliminating <math display="inline"> <semantics> <msub> <mi>F</mi> <mn>6</mn> </msub> </semantics> </math> and sorting with respect to the value of <math display="inline"> <semantics> <msub> <mi>F</mi> <mn>7</mn> </msub> </semantics> </math>.</p> Full article ">Figure 14
<p>An example of data structure.</p> Full article ">
603 KiB  
Article
Bidirectional Long Short-Term Memory Network with a Conditional Random Field Layer for Uyghur Part-Of-Speech Tagging
by Maihemuti Maimaiti, Aishan Wumaier, Kahaerjiang Abiderexiti and Tuergen Yibulayin
Information 2017, 8(4), 157; https://doi.org/10.3390/info8040157 - 30 Nov 2017
Cited by 21 | Viewed by 6686
Abstract
Uyghur is an agglutinative and a morphologically rich language; natural language processing tasks in Uyghur can be a challenge. Word morphology is important in Uyghur part-of-speech (POS) tagging. However, POS tagging performance suffers from error propagation of morphological analyzers. To address this problem, [...] Read more.
Uyghur is an agglutinative and a morphologically rich language; natural language processing tasks in Uyghur can be a challenge. Word morphology is important in Uyghur part-of-speech (POS) tagging. However, POS tagging performance suffers from error propagation of morphological analyzers. To address this problem, we propose a few models for POS tagging: conditional random fields (CRF), long short-term memory (LSTM), bidirectional LSTM networks (BI-LSTM), LSTM networks with a CRF layer, and BI-LSTM networks with a CRF layer. These models do not depend on stemming and word disambiguation for Uyghur and combine hand-crafted features with neural network models. State-of-the-art performance on Uyghur POS tagging is achieved on test data sets using the proposed approach: 98.41% accuracy on 15 labels and 95.74% accuracy on 64 labels, which are 2.71% and 4% improvements, respectively, over the CRF model results. Using engineered features, our model achieves further improvements of 0.2% (15 labels) and 0.48% (64 labels). The results indicate that the proposed method could be an effective approach for POS tagging in other morphologically rich languages. Full article
(This article belongs to the Section Artificial Intelligence)
►▼ Show Figures

Figure 1

Figure 1
<p>Simple LSTM model.</p> Full article ">Figure 2
<p>BI-LSTM-CRF model.</p> Full article ">
266 KiB  
Article
The Emperor of Strong AI Has No Clothes: Limits to Artificial Intelligence
by Adriana Braga and Robert K. Logan
Information 2017, 8(4), 156; https://doi.org/10.3390/info8040156 - 27 Nov 2017
Cited by 60 | Viewed by 24676
Abstract
Making use of the techniques of media ecology we argue that the premise of the technological Singularity based on the notion computers will one day be smarter that their human creators is false. We also analyze the comments of other critics of the [...] Read more.
Making use of the techniques of media ecology we argue that the premise of the technological Singularity based on the notion computers will one day be smarter that their human creators is false. We also analyze the comments of other critics of the Singularity, as well supporters of this notion. The notion of intelligence that advocates of the technological singularity promote does not take into account the full dimension of human intelligence. They treat artificial intelligence as a figure without a ground. Human intelligence as we will show is not based solely on logical operations and computation, but also includes a long list of other characteristics that are unique to humans, which is the ground that supporters of the Singularity ignore. The list includes curiosity, imagination, intuition, emotions, passion, desires, pleasure, aesthetics, joy, purpose, objectives, goals, telos, values, morality, experience, wisdom, judgment, and even humor. Full article
(This article belongs to the Special Issue AI AND THE SINGULARITY: A FALLACY OR A GREAT OPPORTUNITY?)
3095 KiB  
Article
Face Classification Using Color Information
by Atul Sajjanhar and Ahmed Abdulateef Mohammed
Information 2017, 8(4), 155; https://doi.org/10.3390/info8040155 - 26 Nov 2017
Cited by 3 | Viewed by 5223
Abstract
Color models are widely used in image recognition because they represent significant information. On the other hand, texture analysis techniques have been extensively used for facial feature extraction. In this paper; we extract discriminative features related to facial attributes by utilizing different color [...] Read more.
Color models are widely used in image recognition because they represent significant information. On the other hand, texture analysis techniques have been extensively used for facial feature extraction. In this paper; we extract discriminative features related to facial attributes by utilizing different color models and texture analysis techniques. Specifically, we propose novel methods for texture analysis to improve classification performance of race and gender. The proposed methods for texture analysis are based on Local Binary Pattern and its derivatives. These texture analysis methods are evaluated for six color models (hue, saturation and intensity value (HSV); L*a*b*; RGB; YCbCr; YIQ; YUV) to investigate the effect of each color model. Further, we configure two combinations of color channels to represent color information suitable for gender and race classification of face images. We perform experiments on publicly available face databases. Experimental results show that the proposed approaches are effective for the classification of gender and race. Full article
(This article belongs to the Section Information and Communications Technology)
►▼ Show Figures

Figure 1

Figure 1
<p>Face image in: (<b>a</b>) Cartesian coordinates; (<b>b</b>) polar coordinates.</p> Full article ">Figure 2
<p>(<b>a</b>) RGB face image; (<b>b</b>) Y channel; (<b>c</b>) Cb channel; (<b>d</b>) Cr channel.</p> Full article ">Figure 3
<p>(<b>a</b>) Polar image of Cr color channel of <a href="#information-08-00155-f002" class="html-fig">Figure 2</a>a; (<b>b</b>) Local Binary Patterns (LBP) (<span class="html-italic">P</span> = 8, <span class="html-italic">R</span> = 1); (<b>c</b>) LBP (<span class="html-italic">P</span> = 8, <span class="html-italic">R</span> = 2), and; (<b>d</b>) LBP (<span class="html-italic">P</span> = 8, <span class="html-italic">R</span> = 3).</p> Full article ">Figure 4
<p>Gender classification using SVM (FERET database).</p> Full article ">Figure 5
<p>Gender classification using KNN (FERET database).</p> Full article ">Figure 6
<p>Race classification using SVM (FERET database).</p> Full article ">Figure 7
<p>Race classification using KNN (FERET database).</p> Full article ">Figure 8
<p>Gender classification using KNN (PICS database).</p> Full article ">Figure 9
<p>Gender classification using SVM (PICS database).</p> Full article ">
680 KiB  
Article
Certain Concepts in Intuitionistic Neutrosophic Graph Structures
by Muhammad Akram and Muzzamal Sitara
Information 2017, 8(4), 154; https://doi.org/10.3390/info8040154 - 25 Nov 2017
Cited by 11 | Viewed by 4907
Abstract
A graph structure is a generalization of simple graphs. Graph structures are very useful tools for the study of different domains of computational intelligence and computer science. In this research paper, we introduce certain notions of intuitionistic neutrosophic graph structures. We illustrate these [...] Read more.
A graph structure is a generalization of simple graphs. Graph structures are very useful tools for the study of different domains of computational intelligence and computer science. In this research paper, we introduce certain notions of intuitionistic neutrosophic graph structures. We illustrate these notions by several examples. We investigate some related properties of intuitionistic neutrosophic graph structures. We also present an application of intuitionistic neutrosophic graph structures. Full article
(This article belongs to the Special Issue Neutrosophic Information Theory and Applications)
►▼ Show Figures

Figure 1

Figure 1
<p>An intuitionistic neutrosophic graph structure.</p> Full article ">Figure 2
<p>IN subgraph structure.</p> Full article ">Figure 3
<p>An IN induced-subgraph structure.</p> Full article ">Figure 4
<p>An IN spanning-subgraph structure.</p> Full article ">Figure 5
<p>A strong INGS.</p> Full article ">Figure 6
<p>A complete INGS.</p> Full article ">Figure 7
<p>An INGS <math display="inline"> <semantics> <mrow> <msub> <mover accent="true"> <mi>G</mi> <mo>ˇ</mo> </mover> <mi>i</mi> </msub> <mo>=</mo> <mo stretchy="false">(</mo> <mi>O</mi> <mo>,</mo> <msub> <mi>O</mi> <mn>1</mn> </msub> <mo>,</mo> <msub> <mi>O</mi> <mn>2</mn> </msub> <mo stretchy="false">)</mo> </mrow> </semantics> </math>.</p> Full article ">Figure 8
<p>An IN fuzzy <span class="html-italic">O</span><sub>1</sub>-tree.</p> Full article ">Figure 9
<p>Two isomorphic INGSs.</p> Full article ">Figure 10
<p>An INGS <math display="inline"> <semantics> <mrow> <msub> <mover accent="true"> <mi>G</mi> <mo>ˇ</mo> </mover> <mrow> <mi>i</mi> <mn>1</mn> </mrow> </msub> </mrow> </semantics> </math>.</p> Full article ">Figure 11
<p>An INGS <math display="inline"> <semantics> <mrow> <msub> <mover accent="true"> <mi>G</mi> <mo>ˇ</mo> </mover> <mrow> <mi>i</mi> <mn>2</mn> </mrow> </msub> </mrow> </semantics> </math>.</p> Full article ">Figure 12
<p>INGSs <math display="inline"> <semantics> <mrow> <msub> <mover accent="true"> <mi>G</mi> <mo>ˇ</mo> </mover> <mi>i</mi> </msub> </mrow> </semantics> </math>, <math display="inline"> <semantics> <mrow> <msubsup> <mover accent="true"> <mi>G</mi> <mo>ˇ</mo> </mover> <mi>i</mi> <mrow> <mi>ψ</mi> <mi>c</mi> </mrow> </msubsup> </mrow> </semantics> </math>.</p> Full article ">Figure 13
<p>Totally-strong self-complementary INGS.</p> Full article ">Figure 14
<p>INGS identifying crucial interdependence relation between any two provinces.</p> Full article ">
2663 KiB  
Article
A Routing Protocol Based on Received Signal Strength for Underwater Wireless Sensor Networks (UWSNs)
by Meiju Li, Xiujuan Du, Kejun Huang, Senlin Hou and Xiuxiu Liu
Information 2017, 8(4), 153; https://doi.org/10.3390/info8040153 - 24 Nov 2017
Cited by 11 | Viewed by 4742
Abstract
Underwater wireless sensor networks (UWSNs) are featured by long propagation delay, limited energy, narrow bandwidth, high BER (Bit Error Rate) and variable topology structure. These features make it very difficult to design a short delay and high energy-efficiency routing protocol for UWSNs. In [...] Read more.
Underwater wireless sensor networks (UWSNs) are featured by long propagation delay, limited energy, narrow bandwidth, high BER (Bit Error Rate) and variable topology structure. These features make it very difficult to design a short delay and high energy-efficiency routing protocol for UWSNs. In this paper, a routing protocol independent of location information is proposed based on received signal strength (RSS), which is called RRSS. In RRSS, a sensor node firstly establishes a vector from the node to a sink node; the length of the vector indicates the RSS of the beacon signal (RSSB) from the sink node. A node selects the next-hop along the vector according to RSSB and the RSS of a hello packet (RSSH). The node nearer to the vector has higher priority to be a candidate next-hop. To avoid data packets being delivered to the neighbor nodes in a void area, a void-avoiding algorithm is introduced. In addition, residual energy is considered when selecting the next-hop. Meanwhile, we establish mathematic models to analyze the robustness and energy efficiency of RRSS. Lastly, we conduct extensive simulations, and the simulation results show RRSS can save energy consumption and decrease end-to-end delay. Full article
►▼ Show Figures

Figure 1

Figure 1
<p>Topology 1.</p> Full article ">Figure 2
<p>Network structure.</p> Full article ">Figure 3
<p>Topology 2.</p> Full article ">Figure 4
<p>Topology 3.</p> Full article ">Figure 5
<p>Topology 4.</p> Full article ">Figure 6
<p>Topology 5.</p> Full article ">Figure 7
<p>Void-avoiding algorithm.</p> Full article ">Figure 8
<p>Candidate area.</p> Full article ">Figure 9
<p>Volume of the candidate area.</p> Full article ">Figure 10
<p>Topology 6.</p> Full article ">Figure 11
<p>Delivery ratio vs. time interval of the beacon signal.</p> Full article ">Figure 12
<p>Effect of data rate. (<b>a</b>) Delivery ratio vs. the number of nodes; (<b>b</b>) end-to-end delay vs. the number of nodes; (<b>c</b>) energy consumption vs. the number of nodes.</p> Full article ">Figure 13
<p>Performance comparison. (<b>a</b>) Delivery ratio vs. the number of nodes; (<b>b</b>) delay vs. the number of nodes; (<b>c</b>) energy consumption vs. the number of nodes.</p> Full article ">Figure 13 Cont.
<p>Performance comparison. (<b>a</b>) Delivery ratio vs. the number of nodes; (<b>b</b>) delay vs. the number of nodes; (<b>c</b>) energy consumption vs. the number of nodes.</p> Full article ">
640 KiB  
Article
Ensemble of Filter-Based Rankers to Guide an Epsilon-Greedy Swarm Optimizer for High-Dimensional Feature Subset Selection
by Mohammad Bagher Dowlatshahi, Vali Derhami and Hossein Nezamabadi-pour
Information 2017, 8(4), 152; https://doi.org/10.3390/info8040152 - 22 Nov 2017
Cited by 36 | Viewed by 5154
Abstract
The main purpose of feature subset selection is to remove irrelevant and redundant features from data, so that learning algorithms can be trained by a subset of relevant features. So far, many algorithms have been developed for the feature subset selection, and most [...] Read more.
The main purpose of feature subset selection is to remove irrelevant and redundant features from data, so that learning algorithms can be trained by a subset of relevant features. So far, many algorithms have been developed for the feature subset selection, and most of these algorithms suffer from two major problems in solving high-dimensional datasets: First, some of these algorithms search in a high-dimensional feature space without any domain knowledge about the feature importance. Second, most of these algorithms are originally designed for continuous optimization problems, but feature selection is a binary optimization problem. To overcome the mentioned weaknesses, we propose a novel hybrid filter-wrapper algorithm, called Ensemble of Filter-based Rankers to guide an Epsilon-greedy Swarm Optimizer (EFR-ESO), for solving high-dimensional feature subset selection. The Epsilon-greedy Swarm Optimizer (ESO) is a novel binary swarm intelligence algorithm introduced in this paper as a novel wrapper. In the proposed EFR-ESO, we extract the knowledge about the feature importance by the ensemble of filter-based rankers and then use this knowledge to weight the feature probabilities in the ESO. Experiments on 14 high-dimensional datasets indicate that the proposed algorithm has excellent performance in terms of both the error rate of the classification and minimizing the number of features. Full article
(This article belongs to the Special Issue Feature Selection for High-Dimensional Data)
►▼ Show Figures

Figure 1

Figure 1
<p>Flowchart of the ensemble of feature rankers to calculate the <math display="inline"> <semantics> <mrow> <msub> <mi>ϵ</mi> <mn>2</mn> </msub> </mrow> </semantics> </math> vector.</p> Full article ">Figure 2
<p>System architecture of the proposed EFR-ESO algorithm for feature subset selection.</p> Full article ">
5039 KiB  
Article
A New Anomaly Detection System for School Electricity Consumption Data
by Wenqiang Cui and Hao Wang
Information 2017, 8(4), 151; https://doi.org/10.3390/info8040151 - 20 Nov 2017
Cited by 27 | Viewed by 5990
Abstract
Anomaly detection has been widely used in a variety of research and application domains, such as network intrusion detection, insurance/credit card fraud detection, health-care informatics, industrial damage detection, image processing and novel topic detection in text mining. In this paper, we focus on [...] Read more.
Anomaly detection has been widely used in a variety of research and application domains, such as network intrusion detection, insurance/credit card fraud detection, health-care informatics, industrial damage detection, image processing and novel topic detection in text mining. In this paper, we focus on remote facilities management that identifies anomalous events in buildings by detecting anomalies in building electricity consumption data. We investigated five models within electricity consumption data from different schools to detect anomalies in the data. Furthermore, we proposed a hybrid model that combines polynomial regression and Gaussian distribution, which detects anomalies in the data with 0 false negative and an average precision higher than 91%. Based on the proposed model, we developed a data detection and visualization system for a facilities management company to detect and visualize anomalies in school electricity consumption data. The system is tested and evaluated by facilities managers. According to the evaluation, our system has improved the efficiency of facilities managers to identify anomalies in the data. Full article
(This article belongs to the Special Issue Supporting Technologies and Enablers for Big Data)
►▼ Show Figures

Figure 1

Figure 1
<p>One week’s electricity consumption data of a school. The <span class="html-italic">x</span>-axis is the electricity consumption (kWh). The <span class="html-italic">y</span>-axis is the time. Adapted from [<a href="#B8-information-08-00151" class="html-bibr">8</a>].</p> Full article ">Figure 2
<p>Comparison of the 30th week’s electricity consumption data and the predicted value. The <span class="html-italic">y</span>-axis is the electricity consumption data (kWh). The <span class="html-italic">x</span>-axis is the time. Adapted from [<a href="#B8-information-08-00151" class="html-bibr">8</a>].</p> Full article ">Figure 3
<p>Comparison of one week’s electricity consumption data and the predicted value. Adapted from [<a href="#B8-information-08-00151" class="html-bibr">8</a>].</p> Full article ">Figure 4
<p>Polynomial regression model fitted to one school’s electricity consumption data for the first time slot through the year 2011. The <span class="html-italic">x</span>-axis is the week number from 1 to 52. The <span class="html-italic">y</span>-axis is the electricity consumption data (kWh). Adapted from [<a href="#B8-information-08-00151" class="html-bibr">8</a>].</p> Full article ">Figure 5
<p>Predicted values a week. Adapted from [<a href="#B8-information-08-00151" class="html-bibr">8</a>].</p> Full article ">Figure 6
<p>Detecting anomalies in Saturday and Sunday. Adapted from [<a href="#B8-information-08-00151" class="html-bibr">8</a>].</p> Full article ">Figure 7
<p>Gaussian kernel distribution of the first time slot’s electricity consumption data. The <span class="html-italic">x</span>-axis is the electricity consumption data. The <span class="html-italic">y</span>-axis is the probability.</p> Full article ">Figure 8
<p>Anomaly detection in weekly data by the Gaussian kernel distribution model. Adapted from [<a href="#B8-information-08-00151" class="html-bibr">8</a>].</p> Full article ">Figure 9
<p>Low precision of the Gaussian kernel distribution model. Adapted from [<a href="#B8-information-08-00151" class="html-bibr">8</a>].</p> Full article ">Figure 10
<p>Gaussian distribution of the first time slot’s electricity consumption data. Adapted from [<a href="#B8-information-08-00151" class="html-bibr">8</a>].</p> Full article ">Figure 11
<p>Anomaly detection based on the Gaussian distribution model. The <span class="html-italic">x</span>-axis is the time. The <span class="html-italic">y</span>-axis is the electricity consumption data. Adapted from [<a href="#B8-information-08-00151" class="html-bibr">8</a>].</p> Full article ">Figure 12
<p>Anomaly detection on normal weekly data based on Gaussian distribution model. Adapted from [<a href="#B8-information-08-00151" class="html-bibr">8</a>].</p> Full article ">Figure 13
<p>Data detection process of the system.</p> Full article ">Figure 14
<p>The line chart of the 5th week’s electricity consumption data of a school [<a href="#B8-information-08-00151" class="html-bibr">8</a>].</p> Full article ">Figure 15
<p>The heat map of the 5th week’s electricity consumption data of a school [<a href="#B8-information-08-00151" class="html-bibr">8</a>].</p> Full article ">Figure 16
<p>The precision of anomaly detection result for three schools’ weekly data of the year 2012.</p> Full article ">Figure 17
<p>The statistical result of the questionnaire.</p> Full article ">
531 KiB  
Article
Investigating the Statistical Distribution of Learning Coverage in MOOCs
by Xiu Li, Chang Men, Zhihui Du, Jason Liu, Manli Li and Xiaolei Zhang
Information 2017, 8(4), 150; https://doi.org/10.3390/info8040150 - 20 Nov 2017
Cited by 2 | Viewed by 5844
Abstract
Learners participating in Massive Open Online Courses (MOOC) have a wide range of backgrounds and motivations. Many MOOC learners enroll in the courses to take a brief look; only a few go through the entire content, and even fewer are able to eventually [...] Read more.
Learners participating in Massive Open Online Courses (MOOC) have a wide range of backgrounds and motivations. Many MOOC learners enroll in the courses to take a brief look; only a few go through the entire content, and even fewer are able to eventually obtain a certificate. We discovered this phenomenon after having examined 92 courses on both xuetangX and edX platforms. More specifically, we found that the learning coverage in many courses—one of the metrics used to estimate the learners’ active engagement with the online courses—observes a Zipf distribution. We apply the maximum likelihood estimation method to fit the Zipf’s law and test our hypothesis using a chi-square test. In the xuetangX dataset, the learning coverage in 53 of 76 courses fits Zipf’s law, but in all of 16 courses on the edX platform, the learning coverage rejects the Zipf’s law. The result from our study is expected to bring insight to the unique learning behavior on MOOC. Full article
(This article belongs to the Special Issue Supporting Technologies and Enablers for Big Data)
►▼ Show Figures

Figure 1

Figure 1
<p>Registrants and participants distribution: (<b>a</b>) Registrants distribution; (<b>b</b>) Participants distribution.</p> Full article ">Figure 2
<p>Courses across disciplines.</p> Full article ">Figure 3
<p>Histogram of the learning coverage of a course.</p> Full article ">Figure 4
<p>Linear regression results of six courses.</p> Full article ">Figure 5
<p>Linear regression results: (<b>a</b>) The exponent parameter <math display="inline"> <semantics> <mi>α</mi> </semantics> </math>; (<b>b</b>) The R-squared values.</p> Full article ">Figure 6
<p>Maximum likelihood estimation (MLE) fitting results. (<b>a</b>) Medical Parasitology in the xuetangX dataset; (<b>b</b>) Mechanics Review in the edX dataset.</p> Full article ">Figure 7
<p>Number of participants for courses fitting and rejecting Zipf in the xuetangX dataset.</p> Full article ">
1647 KiB  
Article
NC-TODIM-Based MAGDM under a Neutrosophic Cubic Set Environment
by Surapati Pramanik, Shyamal Dalapati, Shariful Alam and Tapan Kumar Roy
Information 2017, 8(4), 149; https://doi.org/10.3390/info8040149 - 18 Nov 2017
Cited by 31 | Viewed by 4805
Abstract
A neutrosophic cubic set is the hybridization of the concept of a neutrosophic set and an interval neutrosophic set. A neutrosophic cubic set has the capacity to express the hybrid information of both the interval neutrosophic set and the single valued neutrosophic set [...] Read more.
A neutrosophic cubic set is the hybridization of the concept of a neutrosophic set and an interval neutrosophic set. A neutrosophic cubic set has the capacity to express the hybrid information of both the interval neutrosophic set and the single valued neutrosophic set simultaneously. As newly defined, little research on the operations and applications of neutrosophic cubic sets has been reported in the current literature. In the present paper, we propose the score and accuracy functions for neutrosophic cubic sets and prove their basic properties. We also develop a strategy for ranking of neutrosophic cubic numbers based on the score and accuracy functions. We firstly develop a TODIM (Tomada de decisao interativa e multicritévio) in the neutrosophic cubic set (NC) environment, which we call the NC-TODIM. We establish a new NC-TODIM strategy for solving multi attribute group decision making (MAGDM) in neutrosophic cubic set environment. We illustrate the proposed NC-TODIM strategy for solving a multi attribute group decision making problem to show the applicability and effectiveness of the developed strategy. We also conduct sensitivity analysis to show the impact of ranking order of the alternatives for different values of the attenuation factor of losses for multi-attribute group decision making strategies. Full article
(This article belongs to the Special Issue Neutrosophic Information Theory and Applications)
►▼ Show Figures

Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Evolution of the neutrosophic cubic set.</p> Full article ">Figure 2
<p>A flow chart of the proposed neutrosophic cubic set (NC)-TODIM strategy.</p> Full article ">Figure 3
<p>Global values of the alternatives for different values of attenuation factor <math display="inline"> <semantics> <mi mathvariant="sans-serif">α</mi> </semantics> </math> = 0.5, 1, 1.5, 2, 3.</p> Full article ">Figure 4
<p>Ranking of the alternatives for <math display="inline"> <semantics> <mi mathvariant="sans-serif">α</mi> </semantics> </math> = 0.5, 1, 1.5, 2, 3.</p> Full article ">
291 KiB  
Article
Source Code Documentation Generation Using Program Execution
by Matúš Sulír and Jaroslav Porubän
Information 2017, 8(4), 148; https://doi.org/10.3390/info8040148 - 17 Nov 2017
Cited by 7 | Viewed by 4948
Abstract
Automated source code documentation approaches often describe methods in abstract terms, using the words contained in the static source code or code excerpts from repositories. In this paper, we describe DynamiDoc: a simple automated documentation generator based on dynamic analysis. Our representation-based approach [...] Read more.
Automated source code documentation approaches often describe methods in abstract terms, using the words contained in the static source code or code excerpts from repositories. In this paper, we describe DynamiDoc: a simple automated documentation generator based on dynamic analysis. Our representation-based approach traces the program being executed and records string representations of concrete argument values, a return value and a target object state before and after each method execution. Then, for each method, it generates documentation sentences with examples, such as “When called on [3, 1.2] with element = 3, the object changed to [1.2]”. Advantages and shortcomings of the approach are listed. We also found out that the generated sentences are substantially shorter than the methods they describe. According to our small-scale study, the majority of objects in the generated documentation have their string representations overridden, which further confirms the potential usefulness of our approach. Finally, we propose an alternative, variable-based approach that describes the values of individual member variables, rather than the state of an object as a whole. Full article
(This article belongs to the Special Issue Special Issues on Languages Processing)
►▼ Show Figures

Figure 1

Figure 1
<p>Length of a documentation sentence (histogram).</p> Full article ">Figure 2
<p>Length of a documentation sentence divided by the length of the method it describes (histogram).</p> Full article ">
7230 KiB  
Article
Land Cover Classification from Multispectral Data Using Computational Intelligence Tools: A Comparative Study
by André Mora, Tiago M. A. Santos, Szymon Łukasik, João M. N. Silva, António J. Falcão, José M. Fonseca and Rita A. Ribeiro
Information 2017, 8(4), 147; https://doi.org/10.3390/info8040147 - 15 Nov 2017
Cited by 26 | Viewed by 6448
Abstract
This article discusses how computational intelligence techniques are applied to fuse spectral images into a higher level image of land cover distribution for remote sensing, specifically for satellite image classification. We compare a fuzzy-inference method with two other computational intelligence methods, decision trees [...] Read more.
This article discusses how computational intelligence techniques are applied to fuse spectral images into a higher level image of land cover distribution for remote sensing, specifically for satellite image classification. We compare a fuzzy-inference method with two other computational intelligence methods, decision trees and neural networks, using a case study of land cover classification from satellite images. Further, an unsupervised approach based on k-means clustering has been also taken into consideration for comparison. The fuzzy-inference method includes training the classifier with a fuzzy-fusion technique and then performing land cover classification using reinforcement aggregation operators. To assess the robustness of the four methods, a comparative study including three years of land cover maps for the district of Mandimba, Niassa province, Mozambique, was undertaken. Our results show that the fuzzy-fusion method performs similarly to decision trees, achieving reliable classifications; neural networks suffer from overfitting; while k-means clustering constitutes a promising technique to identify land cover types from unknown areas. Full article
(This article belongs to the Special Issue Fuzzy Logic for Image Processing)
►▼ Show Figures

Figure 1

Figure 1
<p>(<b>a</b>) Landsat 5 image taken in 1989 over the district of Mandimba (RGB-Bands 743) and (<b>b</b>) in black, the mask of the Mandimba district and areas not covered by clouds in the 3 images.</p> Full article ">Figure 2
<p>Example of histograms and unimodal and bimodal membership function fits for classes Waterbodies and Forests and Woodlands.</p> Full article ">Figure 3
<p>(<b>a</b>) Land cover ground-truth classification as published by Temudo et al. [<a href="#B2-information-08-00147" class="html-bibr">2</a>]; (<b>b</b>) Fuzzy-Fusion (FF)-Uninorm classification; (<b>c</b>) FF-Uninorm classification after applying a mean filtering.</p> Full article ">Figure 4
<p>Land cover areas distribution along the three-year study for the four classification methods: (<b>a</b>) FF-Uninorm; (<b>b</b>) Decision Tree; (<b>c</b>) Artificial Neural Network; (<b>d</b>) k-means clustering.</p> Full article ">Figure 4 Cont.
<p>Land cover areas distribution along the three-year study for the four classification methods: (<b>a</b>) FF-Uninorm; (<b>b</b>) Decision Tree; (<b>c</b>) Artificial Neural Network; (<b>d</b>) k-means clustering.</p> Full article ">Figure 5
<p>Land cover classification results using FF-Uninorm, decision trees, artificial neural networks and k-means clustering.</p> Full article ">Figure 6
<p>Detail of the land cover classification using FF-Uninorm and k-means, where a road is clearly seen and correctly identified by both methods.</p> Full article ">Figure 7
<p>(<b>Top row</b>) Detail on the Grassland and Shrubland classification comparison between FF-Uninorm and DT. (<b>Bottom row</b>) Correct and detailed classification obtained by FF-Uninorm and DT.</p> Full article ">
151 KiB  
Editorial
Editorial of the Special Issue “Intelligent Transportation Systems”
by Muhammad Alam and Joaquim Ferreira
Information 2017, 8(4), 146; https://doi.org/10.3390/info8040146 - 12 Nov 2017
Cited by 2 | Viewed by 3746
Abstract
Transportation systems are very important in modern life; therefore, massive research efforts have been devoted to this field of study in the recent past. Effective vehicular connectivity techniques can significantly enhance efficiency of travel, reduce traffic incidents and improve safety, and alleviate the [...] Read more.
Transportation systems are very important in modern life; therefore, massive research efforts have been devoted to this field of study in the recent past. Effective vehicular connectivity techniques can significantly enhance efficiency of travel, reduce traffic incidents and improve safety, and alleviate the impact of congestion, constituting the so-called Intelligent Transportation Systems (ITS) experience.[...] Full article
(This article belongs to the Special Issue Intelligent Transportation Systems)
4005 KiB  
Article
End-to-End Delay Model for Train Messaging over Public Land Mobile Networks
by Franco Mazzenga, Romeo Giuliano and Alessandro Vizzarri
Information 2017, 8(4), 145; https://doi.org/10.3390/info8040145 - 11 Nov 2017
Viewed by 4741
Abstract
Modern train control systems rely on a dedicated radio network for train to ground communications. A number of possible alternatives have been analysed to adopt the European Rail Traffic Management System/European Train Control System (ERTMS/ETCS) control system on local/regional lines to improve transport [...] Read more.
Modern train control systems rely on a dedicated radio network for train to ground communications. A number of possible alternatives have been analysed to adopt the European Rail Traffic Management System/European Train Control System (ERTMS/ETCS) control system on local/regional lines to improve transport capacity. Among them, a communication system based on public networks (cellular&satellite) provides an interesting, effective and alternative solution to proprietary and expensive radio networks. To analyse performance of this solution, it is necessary to model the end-to-end delay and message loss to fully characterize the message transfer process from train to ground and vice versa. Starting from the results of a railway test campaign over a 300 km railway line for a cumulative 12,000 traveled km in 21 days, in this paper, we derive a statistical model for the end-to-end delay required for delivering messages. In particular, we propose a two states model allowing for reproducing the main behavioral characteristics of the end-to-end delay as observed experimentally. Model formulation has been derived after deep analysis of the recorded experimental data. When it is applied to model a realistic scenario, it allows for explicitly accounting for radio coverage characteristics, the received power level, the handover points along the line and for the serving radio technology. As an example, the proposed model is used to generate the end-to-end delay profile in a realistic scenario. Full article
►▼ Show Figures

Figure 1

Figure 1
<p>Test trial: (<b>a</b>) schematic diagram of the onboard architecture of the measurement system; (<b>b</b>) GPS and radio antennas on the train roof.</p> Full article ">Figure 2
<p>Delay components contributing to the overall end-to-end (E2E) delay between the transmitted and received messages—EVC to RBC link.</p> Full article ">Figure 3
<p>Example of the received power (left <span class="html-italic">y</span>−axis) and E2E delay (right <span class="html-italic">y</span>−axis) as a function of the travel time in (<b>a</b>). Corresponding zoomings are in (<b>b</b>,<b>c</b>). The red line indicates the duration of the time interval the train uses the radio technology as in the corresponding label (i.e., Wideband Code Division Multiple Access (WCDMA) 2100, WCDMA 900, GSM 900) for train to ground communications; the black line is the received power level measured by the on board terminal; the blue line is the measured E2E delay.</p> Full article ">Figure 4
<p>E2E delay as a function of the message IDs. Each message is 500 bytes long; messages are generated at a constant rate of 5 msg/s i.e., one message every 200 ms.</p> Full article ">Figure 5
<p>Examples of saw-tooth like behavior for the message generation rate of 5 msg/s.</p> Full article ">Figure 6
<p>Time-based representation of buffering (state Q) and direct transmission (state G) conditions in the channel.</p> Full article ">Figure 7
<p>Two states diagram of the proposed E2E delay model.</p> Full article ">Figure 8
<p>Principle scheme for the usage of the proposed model for performance assessment.</p> Full article ">Figure 9
<p>Probability Density Functions of the random variables <math display="inline"> <semantics> <msub> <mi>X</mi> <mn>0</mn> </msub> </semantics> </math> and <math display="inline"> <semantics> <msub> <mi>X</mi> <mi>H</mi> </msub> </semantics> </math> obtained from experimental data ranked by technology and the received power interval: <math display="inline"> <semantics> <msub> <mi>X</mi> <mn>0</mn> </msub> </semantics> </math> and <math display="inline"> <semantics> <msub> <mi>X</mi> <mi>H</mi> </msub> </semantics> </math> for WCDMA 2100 (<b>a</b>,<b>b</b>); <math display="inline"> <semantics> <msub> <mi>X</mi> <mn>0</mn> </msub> </semantics> </math> and <math display="inline"> <semantics> <msub> <mi>X</mi> <mi>H</mi> </msub> </semantics> </math> for WCDMA 900 (<b>c</b>,<b>d</b>); <math display="inline"> <semantics> <msub> <mi>X</mi> <mn>0</mn> </msub> </semantics> </math> and <math display="inline"> <semantics> <msub> <mi>X</mi> <mi>H</mi> </msub> </semantics> </math> for GSM 900 (<b>e</b>,<b>f</b>).</p> Full article ">Figure 10
<p>Probability Density Functions of the random variable <math display="inline"> <semantics> <msub> <mi>X</mi> <mn>1</mn> </msub> </semantics> </math> obtained from experimental data ranked by technology and the received power interval: WCDMA 2100 (<b>a</b>), WCDMA 900 (<b>b</b>), GSM 900 (<b>c</b>).</p> Full article ">Figure 11
<p>Example of E2E delay generation based on a given received power profile and for a single technology (i.e., WCDMA 2100).</p> Full article ">Figure 12
<p>Comparison of E2E delay Cumulative Density Function generated from the model with the corresponding experimental Cumulative Density Function: Two different measured samples on the same railway section (WCDMA 2100 technology only).</p> Full article ">Figure 13
<p>Comparison of E2E delay CDF generated from the model after collecting E2E delays of each generated samples: Expanded time scale up to 2 s, two different experimental samples of the same railway section (WCDMA 2100 technology only).</p> Full article ">Figure 14
<p>Comparison of E2E delay CDF generated from the model with the corresponding experimental CDF: Two different measured samples on the same railway section (all available radio access technologies).</p> Full article ">Figure 15
<p>Comparison of E2E delay CDF generated from the model after collecting E2E delays of each generated sample: Expanded time scale up to 2 s, two different experimental samples of the same railway section (all available radio access technologies).</p> Full article ">
267 KiB  
Article
VIKOR Method for Interval Neutrosophic Multiple Attribute Group Decision-Making
by Yu-Han Huang, Gui-Wu Wei and Cun Wei
Information 2017, 8(4), 144; https://doi.org/10.3390/info8040144 - 10 Nov 2017
Cited by 55 | Viewed by 5286
Abstract
In this paper, we will extend the VIKOR (VIsekriterijumska optimizacija i KOmpromisno Resenje) method to multiple attribute group decision-making (MAGDM) with interval neutrosophic numbers (INNs). Firstly, the basic concepts of INNs are briefly presented. The method first aggregates all individual decision-makers’ assessment information [...] Read more.
In this paper, we will extend the VIKOR (VIsekriterijumska optimizacija i KOmpromisno Resenje) method to multiple attribute group decision-making (MAGDM) with interval neutrosophic numbers (INNs). Firstly, the basic concepts of INNs are briefly presented. The method first aggregates all individual decision-makers’ assessment information based on an interval neutrosophic weighted averaging (INWA) operator, and then employs the extended classical VIKOR method to solve MAGDM problems with INNs. The validity and stability of this method are verified by example analysis and sensitivity analysis, and its superiority is illustrated by a comparison with the existing methods. Full article
(This article belongs to the Special Issue Neutrosophic Information Theory and Applications)
6481 KiB  
Article
The Impact of Message Replication on the Performance of Opportunistic Networks for Sensed Data Collection
by Tekenate E. Amah, Maznah Kamat, Kamalrulnizam Abu Bakar, Syed Othmawi Abd Rahman, Muhammad Hafiz Mohammed, Aliyu M. Abali, Waldir Moreira and Antonio Oliveira-Jr
Information 2017, 8(4), 143; https://doi.org/10.3390/info8040143 - 9 Nov 2017
Cited by 5 | Viewed by 5127
Abstract
Opportunistic networks (OppNets) provide a scalable solution for collecting delay‑tolerant data from sensors for their respective gateways. Portable handheld user devices contribute significantly to the scalability of OppNets since their number increases according to user population and they closely follow human movement patterns. [...] Read more.
Opportunistic networks (OppNets) provide a scalable solution for collecting delay‑tolerant data from sensors for their respective gateways. Portable handheld user devices contribute significantly to the scalability of OppNets since their number increases according to user population and they closely follow human movement patterns. Hence, OppNets for sensed data collection are characterised by high node population and degrees of spatial locality inherent to user movement. We study the impact of these characteristics on the performance of existing OppNet message replication techniques. Our findings reveal that the existing replication techniques are not specifically designed to cope with these characteristics. This raises concerns regarding excessive message transmission overhead and throughput degradations due to resource constraints and technological limitations associated with portable handheld user devices. Based on concepts derived from the study, we suggest design guidelines to augment existing message replication techniques. We also follow our design guidelines to propose a message replication technique, namely Locality Aware Replication (LARep). Simulation results show that LARep achieves better network performance under high node population and degrees of spatial locality as compared with existing techniques. Full article
►▼ Show Figures

Figure 1

Figure 1
<p>A sample opportunistic network (OppNet) consisting of 11 nodes.</p> Full article ">Figure 2
<p>Taxonomy of message replication techniques in OppNets.</p> Full article ">Figure 3
<p>Movement scenarios used for investigating the impact of increasing node population and spatial locality on the performance of OppNet replication techniques (partitioned by a grid, the small, medium and large scenario consists of 1 region, 4 regions and 16 regions, which also corresponds to low, medium and high degree of spatial locality, respectively).</p> Full article ">Figure 4
<p>Results when PRoPHET utilizes single-copy replication (SC) and fixed-quota replication (FQ) under the large scenario: (<b>a</b>) without external messages (<b>b</b>) in the presence of external messages, i.e., messages generated by other applications and routed using PRoPHET without a replication technique (for external messages, a randomly selected source node generates a message between 10 KB and 100 KB to a randomly selected destination node at every 1 to 5 min interval).</p> Full article ">Figure 5
<p>Transmission overhead under different node populations in the small scenario with PRoPHET (using no replication technique).</p> Full article ">Figure 6
<p>PRoPHET’s throughput (in %) without summary vector exchange and with summary vector exchange in the small scenario.</p> Full article ">Figure 7
<p>The number of messages replicated per hour with n-Epidemic (<span class="html-italic">n</span> = 3) under the (<b>a</b>) small (<b>b</b>) medium and (<b>c</b>) large scenario.</p> Full article ">Figure 8
<p>Average difference between the sending nodes’ and receiving nodes’ forwarding utilities for messages traversing 0, 1, 2 and 3 regions, under (<b>a</b>) low (<b>b</b>) medium and (<b>c</b>) high degree of spatial locality, with PRoPHET using fixed-quota replication (L = 8).</p> Full article ">Figure 8 Cont.
<p>Average difference between the sending nodes’ and receiving nodes’ forwarding utilities for messages traversing 0, 1, 2 and 3 regions, under (<b>a</b>) low (<b>b</b>) medium and (<b>c</b>) high degree of spatial locality, with PRoPHET using fixed-quota replication (L = 8).</p> Full article ">Figure 9
<p>Average hop count and percentage of remaining TTL of delivered messages traversing 0, 1, 2 and 3 regions under high locality with PRoPHET.</p> Full article ">Figure 10
<p>Percentage of successful delivery for messages traversing 0, 1, 2 and 3 regions under high degree of spatial locality with SnF (L = 8).</p> Full article ">Figure 11
<p>Functional block diagram of Locality Aware Replication (LARep).</p> Full article ">Figure 12
<p>Structure of the location table.</p> Full article ">Figure 13
<p>Map of a fictional city.</p> Full article ">Figure 14
<p>The Skudai simulation area (map data provided by OpenStreetMap, 2015).</p> Full article ">Figure 15
<p>Transmission overhead in the Skudai scenario.</p> Full article ">Figure 16
<p>Average energy consumption (in Joules) in the Skudai scenario.</p> Full article ">Figure 17
<p>Throughput (in %) in the Skudai scenario.</p> Full article ">Figure 18
<p>Average delivery delay (in hours) in the Skudai scenario.</p> Full article ">Figure 19
<p>Number of failed transmissions (aka. aborted messages in ONE simulator) in the Skudai scenario.</p> Full article ">Figure 20
<p>Multiple deliveries of the same message and its impact on average delivery delay.</p> Full article ">Figure 21
<p>Throughput and average delivery delay under increasing data traffic in the Helsinki scenario.</p> Full article ">Figure 22
<p>Transmission overhead and average energy consumption under increasing data traffic in the Helsinki scenario.</p> Full article ">
2062 KiB  
Article
Arabic Handwritten Digit Recognition Based on Restricted Boltzmann Machine and Convolutional Neural Networks
by Ali A. Alani
Information 2017, 8(4), 142; https://doi.org/10.3390/info8040142 - 9 Nov 2017
Cited by 49 | Viewed by 10753
Abstract
Handwritten digit recognition is an open problem in computer vision and pattern recognition, and solving this problem has elicited increasing interest. The main challenge of this problem is the design of an efficient method that can recognize the handwritten digits that are submitted [...] Read more.
Handwritten digit recognition is an open problem in computer vision and pattern recognition, and solving this problem has elicited increasing interest. The main challenge of this problem is the design of an efficient method that can recognize the handwritten digits that are submitted by the user via digital devices. Numerous studies have been proposed in the past and in recent years to improve handwritten digit recognition in various languages. Research on handwritten digit recognition in Arabic is limited. At present, deep learning algorithms are extremely popular in computer vision and are used to solve and address important problems, such as image classification, natural language processing, and speech recognition, to provide computers with sensory capabilities that reach the ability of humans. In this study, we propose a new approach for Arabic handwritten digit recognition by use of restricted Boltzmann machine (RBM) and convolutional neural network (CNN) deep learning algorithms. In particular, we propose an Arabic handwritten digit recognition approach that works in two phases. First, we use the RBM, which is a deep learning technique that can extract highly useful features from raw data, and which has been utilized in several classification problems as a feature extraction technique in the feature extraction phase. Then, the extracted features are fed to an efficient CNN architecture with a deep supervised learning architecture for the training and testing process. In the experiment, we used the CMATERDB 3.3.1 Arabic handwritten digit dataset for training and testing the proposed method. Experimental results show that the proposed method significantly improves the accuracy rate, with accuracy reaching 98.59%. Finally, comparison of our results with those of other studies on the CMATERDB 3.3.1 Arabic handwritten digit dataset shows that our approach achieves the highest accuracy rate. Full article
►▼ Show Figures

Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Data flow of the proposed method.</p> Full article ">Figure 2
<p>Restricted Boltzmann machine.</p> Full article ">Figure 3
<p>RBM feature map.</p> Full article ">Figure 4
<p>Convolutional neural network (C: convolutional layer, S: sub sampling layer, FC: fully connected layer, F: filters, K: kernels, MP: MaxPooling).</p> Full article ">Figure 5
<p>Final structure of the proposed RBM–CNN model (C: convolutional layer, S: sub sampling layer, FC: fully connected layer, F: filters, K: kernels, MP: MaxPooling).</p> Full article ">Figure 6
<p>(<b>a</b>) Accuracy on the CMATERDB 3.3.1 dataset; (<b>b</b>) Training error rates of RBM–CNN on the CMATERDB 3.3.1 dataset.</p> Full article ">Figure 7
<p>Confusion matrix of RNM–CNN on the CMATERDB dataset.</p> Full article ">
998 KiB  
Article
Rate Optimization of Two-Way Relaying with Wireless Information and Power Transfer
by Thinh Phu Do, Yeonjin Jeong and Yun Hee Kim
Information 2017, 8(4), 141; https://doi.org/10.3390/info8040141 - 8 Nov 2017
Cited by 3 | Viewed by 3859
Abstract
We consider the simultaneous wireless information and power transfer in two-phase decode-and-forward two-way relaying networks, where a relay harvests the energy from the signal to be relayed through either power splitting or time splitting. Here, we formulate the resource allocation problems optimizing the [...] Read more.
We consider the simultaneous wireless information and power transfer in two-phase decode-and-forward two-way relaying networks, where a relay harvests the energy from the signal to be relayed through either power splitting or time splitting. Here, we formulate the resource allocation problems optimizing the time-phase and signal splitting ratios to maximize the sum rate of the two communicating devices. The joint optimization problems are shown to be convex for both the power splitting and time splitting approaches after some transformation if required to be solvable with an existing solver. To lower the computational complexity, we also present the suboptimal methods optimizing the splitting ratio for the fixed time-phase and derive a closed-form solution for the suboptimal method based on the power splitting. The results demonstrate that the power splitting approaches outperform their time splitting counterparts and the suboptimal power splitting approach provides a performance close to the optimal one while reducing the complexity significantly. Full article
(This article belongs to the Special Issue Wireless Energy Harvesting for Future Wireless Communications)
►▼ Show Figures

Figure 1

Figure 1
<p>DF-TWR network: (<b>a</b>) System configuration (<b>b</b>) DF-TWR with PS-SWIPT (<b>c</b>) DF-TWR with TS-SWIPT.</p> Full article ">Figure 2
<p>Optimized TP and PS ratios for the PS-SWIPT: (<b>a</b>) <math display="inline"> <semantics> <mrow> <mi mathvariant="sans-serif">P</mi> <mo>=</mo> <mn>10</mn> </mrow> </semantics> </math> dBm; (<b>b</b>) <math display="inline"> <semantics> <mrow> <mi mathvariant="sans-serif">P</mi> <mo>=</mo> <mn>20</mn> </mrow> </semantics> </math> dBm; (<b>c</b>) <math display="inline"> <semantics> <mrow> <mi mathvariant="sans-serif">P</mi> <mo>=</mo> <mn>30</mn> </mrow> </semantics> </math> dBm.</p> Full article ">Figure 3
<p>Optimized TP and TS ratios for the TS-SWIPT: (<b>a</b>) <math display="inline"> <semantics> <mrow> <mi mathvariant="sans-serif">P</mi> <mo>=</mo> <mn>10</mn> </mrow> </semantics> </math> dBm; (<b>b</b>) <math display="inline"> <semantics> <mrow> <mi mathvariant="sans-serif">P</mi> <mo>=</mo> <mn>20</mn> </mrow> </semantics> </math> dBm; (<b>c</b>) P = 30 dBm.</p> Full article ">Figure 4
<p>Sum rate of the PS-SWIPT with different resource allocation methods as a function of <math display="inline"> <semantics> <mrow> <msub> <mi>d</mi> <mn>1</mn> </msub> <mo>/</mo> <mi>D</mi> </mrow> </semantics> </math> when <math display="inline"> <semantics> <mrow> <mi mathvariant="sans-serif">P</mi> <mo>=</mo> <mn>20</mn> </mrow> </semantics> </math> dBm and <math display="inline"> <semantics> <mrow> <mi>ν</mi> <mo>=</mo> <mn>3</mn> </mrow> </semantics> </math>.</p> Full article ">Figure 5
<p>Sum rate of the TS-SWIPT with different resource allocation methods as a function of <math display="inline"> <semantics> <mrow> <msub> <mi>d</mi> <mn>1</mn> </msub> <mo>/</mo> <mi>D</mi> </mrow> </semantics> </math> when <math display="inline"> <semantics> <mrow> <mi mathvariant="sans-serif">P</mi> <mo>=</mo> <mn>20</mn> </mrow> </semantics> </math> dBm and <math display="inline"> <semantics> <mrow> <mi>ν</mi> <mo>=</mo> <mn>3</mn> </mrow> </semantics> </math>.</p> Full article ">Figure 6
<p>Sum rate of the PS-SWIPT and the TS-SWIPT as a function of <math display="inline"> <semantics> <mrow> <msub> <mi>d</mi> <mn>1</mn> </msub> <mo>/</mo> <mi>D</mi> </mrow> </semantics> </math> when <math display="inline"> <semantics> <mrow> <mi mathvariant="sans-serif">P</mi> <mo>=</mo> <mn>20</mn> </mrow> </semantics> </math> dBm.</p> Full article ">Figure 7
<p>Sum rate of the PS-SWIPT and the TS-SWIPT as a function of <math display="inline"> <semantics> <mi mathvariant="sans-serif">P</mi> </semantics> </math> dBm when <math display="inline"> <semantics> <mrow> <mi>ν</mi> <mo>=</mo> <mn>3</mn> </mrow> </semantics> </math> and <math display="inline"> <semantics> <mrow> <msub> <mi>d</mi> <mn>1</mn> </msub> <mo>/</mo> <mi>D</mi> <mo>=</mo> <mn>0.5</mn> </mrow> </semantics> </math>.</p> Full article ">
928 KiB  
Article
An Opportunistic Routing for Data Forwarding Based on Vehicle Mobility Association in Vehicular Ad Hoc Networks
by Leilei Wang, Zhigang Chen and Jia Wu
Information 2017, 8(4), 140; https://doi.org/10.3390/info8040140 - 7 Nov 2017
Cited by 12 | Viewed by 6717
Abstract
Vehicular ad hoc networks (VANETs) have emerged as a new powerful technology for data transmission between vehicles. Efficient data transmission accompanied with low data delay plays an important role in selecting the ideal data forwarding path in VANETs. This paper proposes a new [...] Read more.
Vehicular ad hoc networks (VANETs) have emerged as a new powerful technology for data transmission between vehicles. Efficient data transmission accompanied with low data delay plays an important role in selecting the ideal data forwarding path in VANETs. This paper proposes a new opportunity routing protocol for data forwarding based on vehicle mobility association (OVMA). With assistance from the vehicle mobility association, data can be forwarded without passing through many extra intermediate nodes. Besides, each vehicle carries the only replica information to record its associated vehicle information, so the routing decision can adapt to the vehicle densities. Simulation results show that the OVMA protocol can extend the network lifetime, improve the performance of data delivery ratio, and reduce the data delay and routing overhead when compared to the other well-known routing protocols. Full article
(This article belongs to the Section Information Applications)
►▼ Show Figures

Figure 1

Figure 1
<p>Generalized architecture in vehicular ad hoc networks (VANETs).</p> Full article ">Figure 2
<p>The difference between Euclidean distance and cosine similarity.</p> Full article ">Figure 3
<p>Brief introduction of OVMA (Opportunistic Routing for Data Forwarding Based on Vehicle Mobility Association).</p> Full article ">Figure 4
<p>Network lifetimes vs. value of <math display="inline"> <semantics> <mi>α</mi> </semantics> </math>.</p> Full article ">Figure 5
<p>Data forwarding ratio vs. value of <math display="inline"> <semantics> <mi>α</mi> </semantics> </math>.</p> Full article ">Figure 6
<p>Routing overhead vs. value of <math display="inline"> <semantics> <mi>α</mi> </semantics> </math>.</p> Full article ">Figure 7
<p>Data forwarding delay vs. value of <math display="inline"> <semantics> <mi>α</mi> </semantics> </math>.</p> Full article ">Figure 8
<p>Data delivery ratio vs. number of vehicles for different algorithms. GPSR: Greedy Perimeter Stateless Routing; OLSR: Optimized Link State Routing; D-ODMRO: destination driven on demand multicast routing protocol.</p> Full article ">Figure 9
<p>Routing overhead vs. number of vehicles for different algorithms.</p> Full article ">Figure 10
<p>Data delivery delay vs. number of vehicles for different algorithms.</p> Full article ">Figure 11
<p>Routing overhead vs. data rate with different numbers of vehicles (50, 150, 250, 350, 450).</p> Full article ">
234 KiB  
Article
Structural and Symbolic Information in the Context of the General Theory of Information
by Mark Burgin and Rainer Feistel
Information 2017, 8(4), 139; https://doi.org/10.3390/info8040139 - 6 Nov 2017
Cited by 9 | Viewed by 8476
Abstract
The general theory of information, which includes syntactic, semantic, pragmatic, and many other special theories of information, provides theoretical and practical tools for discerning a very large diversity of different kinds, types, and classes of information. Some of these kinds, types, and classes [...] Read more.
The general theory of information, which includes syntactic, semantic, pragmatic, and many other special theories of information, provides theoretical and practical tools for discerning a very large diversity of different kinds, types, and classes of information. Some of these kinds, types, and classes are more important and some are less important. Two basic classes are formed by structural and symbolic information. While structural information is intrinsically imbedded in the structure of the corresponding object or domain, symbolic information is represented by symbols, the meaning of which is subject to arbitrary conventions between people. As a result, symbolic information exists only in the context of life, including technical and theoretical constructs created by humans. Structural information is related to any objects, systems, and processes regardless of the existence or presence of life. In this paper, properties of structural and symbolic information are explored in the formal framework of the general theory of information developed by Burgin because this theory offers more powerful instruments for this inquiry. Structural information is further differentiated into inherent, descriptive, and constructive types. Properties of correctness and uniqueness of these types are investigated. In addition, predictive power of symbolic information accumulated in the course of natural evolution is considered. The phenomenon of ritualization is described as a general transition process from structural to symbolic information. Full article
(This article belongs to the Special Issue Selected Papers from FIS 2017: Structure, Dynamics, Complexity in the Informational Nature)
1124 KiB  
Article
MR Brain Image Segmentation: A Framework to Compare Different Clustering Techniques
by Laura Caponetti, Giovanna Castellano and Vito Corsini
Information 2017, 8(4), 138; https://doi.org/10.3390/info8040138 - 4 Nov 2017
Cited by 22 | Viewed by 5933
Abstract
In Magnetic Resonance (MR) brain image analysis, segmentation is commonly used for detecting, measuring and analyzing the main anatomical structures of the brain and eventually identifying pathological regions. Brain image segmentation is of fundamental importance since it helps clinicians and researchers to concentrate [...] Read more.
In Magnetic Resonance (MR) brain image analysis, segmentation is commonly used for detecting, measuring and analyzing the main anatomical structures of the brain and eventually identifying pathological regions. Brain image segmentation is of fundamental importance since it helps clinicians and researchers to concentrate on specific regions of the brain in order to analyze them. However, segmentation of brain images is a difficult task due to high similarities and correlations of intensity among different regions of the brain image. Among various methods proposed in the literature, clustering algorithms prove to be successful tools for image segmentation. In this paper, we present a framework for image segmentation that is devoted to support the expert in identifying different brain regions for further analysis. The framework includes different clustering methods to perform segmentation of MR images. Furthermore, it enables easy comparison of different segmentation results by providing a quantitative evaluation using an entropy-based measure as well as other measures commonly used to evaluate segmentation results. To show the potential of the framework, the implemented clustering methods are compared on simulated T1-weighted MR brain images from the Internet Brain Segmentation Repository (IBSR database) provided with ground truth segmentation. Full article
(This article belongs to the Special Issue Fuzzy Logic for Image Processing)
►▼ Show Figures

Figure 1

Figure 1
<p>Graphical interface for the selection of Spatial FCM (SFCM) parameters.</p> Full article ">Figure 2
<p>An example of macro in the developed framework.</p> Full article ">Figure 3
<p>(<b>a</b>) Original image of Subject 202-3, Slice 20 and (<b>b</b>) its ground truth; (<b>c</b>) original image of Subject 205-3, Slice 20 and (<b>d</b>) its ground truth.</p> Full article ">Figure 4
<p>Average trend of the entropy-based measure. The dotted line refers to the average value computed on the ground truth segmented images.</p> Full article ">Figure 4 Cont.
<p>Average trend of the entropy-based measure. The dotted line refers to the average value computed on the ground truth segmented images.</p> Full article ">Figure 5
<p>Entropy-based evaluation measure for the clustering algorithms on Image 202-3 and on Image 205-3 using the optimal parameter setting.</p> Full article ">Figure 5 Cont.
<p>Entropy-based evaluation measure for the clustering algorithms on Image 202-3 and on Image 205-3 using the optimal parameter setting.</p> Full article ">Figure 6
<p>Best segmentation of Image 202-3 using K-means (<b>a</b>) FCM (<b>b</b>) SFCM (<b>c</b>) and KFCM (<b>d</b>).</p> Full article ">Figure 7
<p>Best segmentation of Image 205-3 using K-means (<b>a</b>) FCM (<b>b</b>) SFCM (<b>c</b>) and KFCM (<b>d</b>).</p> Full article ">Figure 8
<p>The 202-3 (<b>a</b>) and 205-3 (<b>b</b>) images with added noise and the pre-processed images using the median filter (<b>c</b>,<b>d</b>).</p> Full article ">Figure 9
<p>Segmentation of the noised Image 202-3 using K-means (<b>a</b>) FCM (<b>b</b>) SFCM (<b>c</b>) and KFCM (<b>d</b>).</p> Full article ">Figure 10
<p>Segmentation of the noised Image 205-3 using K-means (<b>a</b>) FCM (<b>b</b>) SFCM (<b>c</b>) and KFCM (<b>d</b>).</p> Full article ">
956 KiB  
Article
A Distributed Ledger for Supply Chain Physical Distribution Visibility
by Haoyan Wu, Zhijie Li, Brian King, Zina Ben Miled, John Wassick and Jeffrey Tazelaar
Information 2017, 8(4), 137; https://doi.org/10.3390/info8040137 - 2 Nov 2017
Cited by 178 | Viewed by 15014
Abstract
Supply chains (SC) span many geographies, modes and industries and involve several phases where data flows in both directions from suppliers, manufacturers, distributors, retailers, to customers. This data flow is necessary to support critical business decisions that may impact product cost and market [...] Read more.
Supply chains (SC) span many geographies, modes and industries and involve several phases where data flows in both directions from suppliers, manufacturers, distributors, retailers, to customers. This data flow is necessary to support critical business decisions that may impact product cost and market share. Current SC information systems are unable to provide validated, pseudo real-time shipment tracking during the distribution phase. This information is available from a single source, often the carrier, and is shared with other stakeholders on an as-needed basis. This paper introduces an independent, crowd-validated, online shipment tracking framework that complements current enterprise-based SC management solutions. The proposed framework consists of a set of private distributed ledgers and a single blockchain public ledger. Each private ledger allows the private sharing of custody events among the trading partners in a given shipment. Privacy is necessary, for example, when trading high-end products or chemical and pharmaceutical products. The second type of ledger is a blockchain public ledger. It consists of the hash code of each private event in addition to monitoring events. The latter provide an independently validated immutable record of the pseudo real-time geolocation status of the shipment from a large number of sources using commuters-sourcing. Full article
(This article belongs to the Special Issue Selected Papers from the “Integrating Process-oriented, Event-based and Data-driven Systems (ICDCS-PED 2017)”)
►▼ Show Figures

Figure 1

Figure 1
<p>Current supply chain operating networks (SCONs).</p> Full article ">Figure 2
<p>Interactions among the participants in the proposed framework.</p> Full article ">Figure 3
<p>Posting of events to the private and public ledgers.</p> Full article ">Figure 4
<p>Private genesis and custody events data structure.</p> Full article ">Figure 5
<p>Public events data structure.</p> Full article ">Figure 6
<p>Blocks data structure.</p> Full article ">Figure 7
<p>Private event process.</p> Full article ">Figure 8
<p>Public event process.</p> Full article ">Figure 9
<p>Build block process.</p> Full article ">Figure 10
<p>PrivateEvents collection of node A after event 1.</p> Full article ">Figure 11
<p>tempPublicEvent collection of node A after event 1.</p> Full article ">Figure 12
<p>tempPublicEvent collection of node A after event 2.</p> Full article ">Figure 13
<p>New document in tempPublicEvent collection of node A after event 3.</p> Full article ">Figure 14
<p>Candidate block in node A.</p> Full article ">
12469 KiB  
Article
Enhancement of Low Contrast Images Based on Effective Space Combined with Pixel Learning
by Gengfei Li, Guiju Li and Guangliang Han
Information 2017, 8(4), 135; https://doi.org/10.3390/info8040135 - 1 Nov 2017
Cited by 4 | Viewed by 5983
Abstract
Images captured in bad conditions often suffer from low contrast. In this paper, we proposed a simple, but efficient linear restoration model to enhance the low contrast images. The model’s design is based on the effective space of the 3D surface graph of [...] Read more.
Images captured in bad conditions often suffer from low contrast. In this paper, we proposed a simple, but efficient linear restoration model to enhance the low contrast images. The model’s design is based on the effective space of the 3D surface graph of the image. Effective space is defined as the minimum space containing the 3D surface graph of the image, and the proportion of the pixel value in the effective space is considered to reflect the details of images. The bright channel prior and the dark channel prior are used to estimate the effective space, however, they may cause block artifacts. We designed the pixel learning to solve this problem. Pixel learning takes the input image as the training example and the low frequency component of input as the label to learn (pixel by pixel) based on the look-up table model. The proposed method is very fast and can restore a high-quality image with fine details. The experimental results on a variety of images captured in bad conditions, such as nonuniform light, night, hazy and underwater, demonstrate the effectiveness and efficiency of the proposed method. Full article
►▼ Show Figures

Figure 1

Figure 1
<p>Images and the projection of their 3D surface graphs on the <span class="html-italic">x-z</span> plane. In the 2D projection, the <span class="html-italic">x</span>-axis is the image width, and the <span class="html-italic">y</span>-axis is the pixel value: (<b>a</b>) the clear images; (<b>b</b>) the low illumination images; (<b>c</b>) the hazy images; (<b>d</b>) the underwater images.</p> Full article ">Figure 1 Cont.
<p>Images and the projection of their 3D surface graphs on the <span class="html-italic">x-z</span> plane. In the 2D projection, the <span class="html-italic">x</span>-axis is the image width, and the <span class="html-italic">y</span>-axis is the pixel value: (<b>a</b>) the clear images; (<b>b</b>) the low illumination images; (<b>c</b>) the hazy images; (<b>d</b>) the underwater images.</p> Full article ">Figure 2
<p>Comparison of α fusion and guided filter: (<b>a</b>) the α fusion, with <span class="html-italic">thred</span>1 = 10, the radius of the mean filter is 40; (<b>b</b>) the guided filter.</p> Full article ">Figure 3
<p>Comparison of refinement: (<b>a</b>) the pixel learning, with <span class="html-italic">thred</span>1 = 10; (<b>b</b>) the α fusion, with <span class="html-italic">thred</span>1 = 10; (<b>c</b>) the guided filter.</p> Full article ">Figure 4
<p>Comparison of refinement from different labels: (<b>a</b>) the label is <b>Ibm</b>, with <span class="html-italic">thred</span>1 = 10; (<b>b</b>) the label is <b>Ib</b>, with <span class="html-italic">thred</span>1 = 10.</p> Full article ">Figure 5
<p>Comparison of refinement: (<b>a</b>) the pixel learning, where <span class="html-italic">thred</span>1 = 10, <span class="html-italic">thred</span>2 = 20; (<b>b</b>) the guided filter.</p> Full article ">Figure 6
<p>Overenhancement and truncation: (<b>a</b>) the original image; (<b>b</b>) the result without truncation by <span class="html-italic">t</span><b><sub>D</sub></b> = 255, <span class="html-italic">t</span><b><sub>U</sub></b> = 0; (<b>c</b>) the result with truncation by <span class="html-italic">t</span><b><sub>D</sub></b> = 150, <span class="html-italic">t</span><b><sub>U</sub></b> = 70.</p> Full article ">Figure 7
<p>Color casts: (<b>a</b>) the original image needs white balance; (<b>b</b>) the result without white balance; (<b>c</b>) the result with white balance.</p> Full article ">Figure 8
<p>Flow diagram of the proposed algorithm: for the gray scale, <span class="html-italic">bright<sub>p</sub></span> and <span class="html-italic">dark<sub>p</sub></span> should be skipped, which means the input gray image <b>I</b> = <b>Ip<sub>U</sub></b> = <b>Ip<sub>D</sub></b>.</p> Full article ">Figure 9
<p>Results with different <span class="html-italic">thred</span>1: (<b>a</b>) the original hazy image; (<b>b</b>) <span class="html-italic">thred</span>1 = 1; (<b>c</b>) <span class="html-italic">thred</span>1 = 10; (<b>d</b>) <span class="html-italic">thred</span>1 = 20.</p> Full article ">Figure 10
<p>Results of enhancement for color images: (<b>a</b>) the original hazy image; (<b>b</b>) our results.</p> Full article ">Figure 11
<p>Results of enhancement for infrared images (14 bits): <span class="html-italic">thred</span>1 = 800, <span class="html-italic">thred</span>2 = 1600, <span class="html-italic">tD</span> = 4000, <span class="html-italic">tU</span> = 14,000.</p> Full article ">Figure 12
<p>Comparison of haze removal: (<b>a</b>) input image; and results from using the methods by (<b>b</b>) Zhang’s [<a href="#B28-information-08-00135" class="html-bibr">28</a>]; (<b>c</b>) He’s [<a href="#B23-information-08-00135" class="html-bibr">23</a>]; (<b>d</b>) Kim’s [<a href="#B29-information-08-00135" class="html-bibr">29</a>]; (<b>e</b>) Tan’s [<a href="#B21-information-08-00135" class="html-bibr">21</a>]; (<b>f</b>) ours; our parameters were set as: for <b>D</b> <span class="html-italic">thred</span>1 = 1, for <b>U</b> <span class="html-italic">thred</span>1 <span class="html-italic">=</span> 20.</p> Full article ">Figure 12 Cont.
<p>Comparison of haze removal: (<b>a</b>) input image; and results from using the methods by (<b>b</b>) Zhang’s [<a href="#B28-information-08-00135" class="html-bibr">28</a>]; (<b>c</b>) He’s [<a href="#B23-information-08-00135" class="html-bibr">23</a>]; (<b>d</b>) Kim’s [<a href="#B29-information-08-00135" class="html-bibr">29</a>]; (<b>e</b>) Tan’s [<a href="#B21-information-08-00135" class="html-bibr">21</a>]; (<b>f</b>) ours; our parameters were set as: for <b>D</b> <span class="html-italic">thred</span>1 = 1, for <b>U</b> <span class="html-italic">thred</span>1 <span class="html-italic">=</span> 20.</p> Full article ">Figure 13
<p>Comparison of the nonuniform illumination image: (<b>a</b>) input image; and results from using the methods by (<b>b</b>) multiscale retina-cortex (Retinex) (MSR) [<a href="#B5-information-08-00135" class="html-bibr">5</a>]; (<b>c</b>) Wang’s [<a href="#B10-information-08-00135" class="html-bibr">10</a>]; (<b>d</b>) ours.</p> Full article ">Figure 14
<p>Comparison of the approaches for the nighttime image: (<b>a</b>) input image; and the results from using the methods by (<b>b</b>) MSR [<a href="#B5-information-08-00135" class="html-bibr">5</a>]; (<b>c</b>) Lin’s [<a href="#B11-information-08-00135" class="html-bibr">11</a>]; (<b>d</b>) ours.</p> Full article ">Figure 15
<p>Comparison of the approaches for the underwater image: (<b>a</b>) input image; and the results form using the methods by (<b>b</b>) He’s [<a href="#B23-information-08-00135" class="html-bibr">23</a>]; (<b>c</b>) MSR [<a href="#B5-information-08-00135" class="html-bibr">5</a>]; (<b>d</b>) Ancuti’s [<a href="#B32-information-08-00135" class="html-bibr">32</a>]; (<b>e</b>) Zhang’s [<a href="#B8-information-08-00135" class="html-bibr">8</a>]; (<b>f</b>) ours.</p> Full article ">Figure 16
<p>Comparison of the approaches for the underwater image: (<b>a</b>) input image; and the results from using the methods by (<b>b</b>) He’s [<a href="#B23-information-08-00135" class="html-bibr">23</a>]; (<b>c</b>) Li’s [<a href="#B30-information-08-00135" class="html-bibr">30</a>]; (<b>d</b>) Lin’s [<a href="#B11-information-08-00135" class="html-bibr">11</a>]; (<b>e</b>) Ancuti’s [<a href="#B32-information-08-00135" class="html-bibr">32</a>]; (<b>f</b>) ours.</p> Full article ">
501 KiB  
Article
Fuzzy Extractor and Elliptic Curve Based Efficient User Authentication Protocol for Wireless Sensor Networks and Internet of Things
by Anup Kumar Maurya and V. N. Sastry
Information 2017, 8(4), 136; https://doi.org/10.3390/info8040136 - 30 Oct 2017
Cited by 30 | Viewed by 6269
Abstract
To improve the quality of service and reduce the possibility of security attacks, a secure and efficient user authentication mechanism is required for Wireless Sensor Networks (WSNs) and the Internet of Things (IoT). Session key establishment between the sensor node and the user [...] Read more.
To improve the quality of service and reduce the possibility of security attacks, a secure and efficient user authentication mechanism is required for Wireless Sensor Networks (WSNs) and the Internet of Things (IoT). Session key establishment between the sensor node and the user is also required for secure communication. In this paper, we perform the security analysis of A.K.Das’s user authentication scheme (given in 2015), Choi et al.’s scheme (given in 2016), and Park et al.’s scheme (given in 2016). The security analysis shows that their schemes are vulnerable to various attacks like user impersonation attack, sensor node impersonation attack and attacks based on legitimate users. Based on the cryptanalysis of these existing protocols, we propose a secure and efficient authenticated session key establishment protocol which ensures various security features and overcomes the drawbacks of existing protocols. The formal and informal security analysis indicates that the proposed protocol withstands the various security vulnerabilities involved in WSNs. The automated validation using AVISPA and Scyther tool ensures the absence of security attacks in our scheme. The logical verification using the Burrows-Abadi-Needham (BAN) logic confirms the correctness of the proposed protocol. Finally, the comparative analysis based on computational overhead and security features of other existing protocol indicate that the proposed user authentication system is secure and efficient. In future, we intend to implement the proposed protocol in real-world applications of WSNs and IoT. Full article
(This article belongs to the Section Information and Communications Technology)
►▼ Show Figures

Figure 1

Figure 1
<p>Wireless body area network (WBAN).</p> Full article ">Figure 2
<p>Security verification result obtained using Scyther tool.</p> Full article ">Figure 3
<p>AVISPA Architecture.</p> Full article ">
602 KiB  
Review
Feature Encodings and Poolings for Action and Event Recognition: A Comprehensive Survey
by Changyu Liu, Qian Zhang, Bin Lu and Cong Li
Information 2017, 8(4), 134; https://doi.org/10.3390/info8040134 - 29 Oct 2017
Cited by 2 | Viewed by 4763
Abstract
Action and event recognition in multimedia collections is relevant to progress in cross-disciplinary research areas including computer vision, computational optimization, statistical learning, and nonlinear dynamics. Over the past two decades, action and event recognition has evolved from earlier intervening strategies under controlled environments [...] Read more.
Action and event recognition in multimedia collections is relevant to progress in cross-disciplinary research areas including computer vision, computational optimization, statistical learning, and nonlinear dynamics. Over the past two decades, action and event recognition has evolved from earlier intervening strategies under controlled environments to recent automatic solutions under dynamic environments, resulting in an imperative requirement to effectively organize spatiotemporal deep features. Consequently, resorting to feature encodings and poolings for action and event recognition in complex multimedia collections is an inevitable trend. The purpose of this paper is to offer a comprehensive survey on the most popular feature encoding and pooling approaches in action and event recognition in recent years by summarizing systematically both underlying theoretical principles and original experimental conclusions of those approaches based on an approach-based taxonomy, so as to provide impetus for future relevant studies. Full article
(This article belongs to the Section Review)
►▼ Show Figures

Figure 1

Figure 1
<p>The hierarchical feature encoding and pooling taxonomy of the paper.</p> Full article ">
1119 KiB  
Article
Bi-Objective Economic Dispatch of Micro Energy Internet Incorporating Energy Router
by Tian Li, Yongqian Li and Anqiang Lv
Information 2017, 8(4), 133; https://doi.org/10.3390/info8040133 - 26 Oct 2017
Cited by 1 | Viewed by 3996
Abstract
Integration of different energy networks will increase additional flexibility to system operation. The key component in such a coupled infrastructure is the energy router, which plays an important role in energy transition and storage to smoothing the prediction error both in renewables and [...] Read more.
Integration of different energy networks will increase additional flexibility to system operation. The key component in such a coupled infrastructure is the energy router, which plays an important role in energy transition and storage to smoothing the prediction error both in renewables and load. The router has the multi-carrier energy generation capability, and builds physical linkages among the power network, heat network, and other networks in the micro energy internet. The economic dispatch problem of the micro energy internet is formulated as a bi-objective optimization problem. Golden section search method is adopted to locate a compromising solution in the sense of Nash Bargaining. Case studies on a typical test system verify the effectiveness of the proposed bi-objective dispatch model and solution method. Full article
(This article belongs to the Section Information Applications)
►▼ Show Figures

Figure 1

Figure 1
<p>Illustration of energy router.</p> Full article ">Figure 2
<p>Configuration of the studied micro energy internet.</p> Full article ">Figure 3
<p>Time-of-use power price.</p> Full article ">Figure 4
<p>The forecasted power load, heat demand, and predicted wind power output.</p> Full article ">Figure 5
<p>Pareto front and balancing solution for BOED(Equation (35)).</p> Full article ">Figure 6
<p>Power load and active power distribution under the Nash balancing point.</p> Full article ">Figure 7
<p>State of the charge of thermal and electric power of the energy router.</p> Full article ">Figure 8
<p>Renewable energy consumption and curtailment.</p> Full article ">
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-49
Previous Issue
Next Issue
Back to TopTop