Special Issue "Recent Advances in Social Data and Artificial Intelligence 2019"

A special issue of Symmetry (ISSN 2073-8994). This special issue belongs to the section "Computer and Engineer Science and Symmetry".

Deadline for manuscript submissions: 31 August 2020.

Special Issue Editors

Prof. Dr. Hari Mohan Srivastava
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Guest Editor
Department of Mathematics and Statistics, University of Victoria, Victoria, BC V8W 3R4, Canada
Interests: real and complex analysis; fractional calculus and its applications; integral equations and transforms; higher transcendental functions and their applications; q-series and q-polynomials; analytic number theory; analytic and geometric Inequalities; probability and statistics; inventory modelling and optimization
Special Issues and Collections in MDPI journals
Dr. Gautam Srivastava
Website
Guest Editor
Department of Mathematics & Computer Science, Brandon University, Brandon, MB R7A 6A9, Canada
Interests: Blockchain Technology; Cryptography; Big Data; Data Mining; Social Networks; Security and Privacy; Anonymity and Graphs
Special Issues and Collections in MDPI journals
Prof. Vijay Mago
Website
Guest Editor
Lakehead University, Canada
Interests: Social data analysis; Artificial Intelligence; Big data; Health Informatics; Medical decision making

Special Issue Information

Dear Colleagues,

The importance and usefulness of subjects and topics involving social data and artificial intelligence are becoming widely recognized.

In this Special Issue, we cordially invite and welcome review, expository, and original research articles dealing with the recent advances in the subjects of social data and artificial intelligence, and potentially their links to Cyberspace.

Cyberspace, the seamless integration of physical, social, and mental spaces, is an integral part of our society, ranging from learning and entertainment to business and cultural activities, and so on. However, there are a number of pressing challenges associated with cyberspace. For example, how do we strike a balance between the need for strong cybersecurity and preserving the privacy of ordinary citizens?

This Special Issue has emerged from the International Conference on Social Data and Artificial Intelligence (SDAI 2020) held in Toronto, Canada on 26–27 May 2020 and the IEEE Cyber Science and Technology Congress (CyberSciTech 2020) which will also be held in Canada (CyberSciTech 2020, Calgary, Canada, 22–26 June 2020).

To address the challenges described for both conferences, there is a need to establish new science and research portfolios that incorporate social data and artificial intelligence alone or in combination with cyber-physical, cyber-social, cyber-intelligent, and cyber-life technologies in a cohesive and efficient manner.

Prof. H. M. Srivastava
Prof. Gautam Srivastava
Prof. Vijay Mago
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Symmetry is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Social data inadequacies and inconsistencies
  • Predictive models of social behaviors
  • Infrastructure and architecture for testing social theories
  • Data collection and analysis platforms
  • Relevance of IoT for social science theories
  • Building capacity to continuously collect data across a range of social media networks
  • Designing efficient parsers to deal with noisy social media data-sets for real-time tracking of health issues, diseases, and wellness
  • Designing tools to map and measure the effectiveness of health campaigns by healthcare organizations
  • Cross-validating the predictive models of social media data-sets with ground truth data
  • Developing frameworks and algorithms to perform real-time analysis of social media data-sets
  • Cyberspace theory and technology
  • Cyber social computing and networks
  • Cyber life and wellbeing
  • Cyber intelligence and cognitive science

Published Papers (15 papers)

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Research

Open AccessArticle
Blockchain Paradigm for Healthcare: Performance Evaluation
Symmetry 2020, 12(8), 1200; https://doi.org/10.3390/sym12081200 - 22 Jul 2020
Abstract
Electronic health records (EHRs) have become a popular method to store and manage patients’ data in hospitals. Sharing these records makes the current healthcare data management system more accurate and cost-efficient. Currently, EHRs are stored using the client/server architecture by which each hospital [...] Read more.
Electronic health records (EHRs) have become a popular method to store and manage patients’ data in hospitals. Sharing these records makes the current healthcare data management system more accurate and cost-efficient. Currently, EHRs are stored using the client/server architecture by which each hospital retains the stewardship of the patients’ data. The records of a patient are scattered among different hospitals using heterogeneous database servers. These limitations constitute a burden towards a personalized healthcare, when it comes to offering a cohesive view and a shared, secure and private access to patients’ health history for multiple allied professionals and the patients. The data availability, privacy and security characteristics of the blockchain have a propitious future in the healthcare presenting solutions to the complexity, confidentiality, integrity, interoperability and privacy issues of the current client/server architecture-based EHR management system. This paper analyzes and compares the performance of the blockchain and the client/server paradigms. The results reveal that notable performance can be achieved using blockchain in a patient-centric approach. In addition, the immutable and valid patients’ data in the blockchain can aid allied health professionals in better prognosis and diagnosis support through machine learning and artificial intelligence. Full article
(This article belongs to the Special Issue Recent Advances in Social Data and Artificial Intelligence 2019)
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Open AccessArticle
A Photo Post Recommendation System Based on Topic Model for Improving Facebook Fan Page Engagement
Symmetry 2020, 12(7), 1105; https://doi.org/10.3390/sym12071105 - 02 Jul 2020
Abstract
Digital advertising on social media officially surpassed traditional advertising and became the largest marketing media in many countries. However, how to maximize the value of the overall marketing budget is one of the most concerning issues of all enterprises. The content of the [...] Read more.
Digital advertising on social media officially surpassed traditional advertising and became the largest marketing media in many countries. However, how to maximize the value of the overall marketing budget is one of the most concerning issues of all enterprises. The content of the Facebook photo post needs to be analyzed effectively so that the social media companies and managers can concentrate on handling their fan pages. This research aimed to use text mining techniques to find the audience accurately. Therefore, we built a topic model recommendation system (TMRS) to analyze Facebook posts by sorting the target posts according to the recommended scores. The TMRS includes six stages, such as data preprocessing, Chinese word segmentation, word refinement, TF-IDF word vector conversion, creating model via Latent Semantic Indexing (LSI), or Latent Dirichlet Allocation (LDA), and calculating the recommendation score. In addition to automatically selecting posts to create advertisements, this model is more effective in using marketing budgets and getting more engagements. Based on the recommendation results, it is verified that the TMRS can increase the engagement rate compared to the traditional engagement rate recommended method (ERRM). Ultimately, advertisers can have the chance to create ads for the post with potentially high engagements under a limited budget. Full article
(This article belongs to the Special Issue Recent Advances in Social Data and Artificial Intelligence 2019)
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Open AccessArticle
Learning Context-Aware Outfit Recommendation
Symmetry 2020, 12(6), 873; https://doi.org/10.3390/sym12060873 - 26 May 2020
Abstract
With the rapid development and increasing popularity of online shopping for fashion products, fashion recommendation plays an important role in daily online shopping scenes. Fashion is not only a commodity that is bought and sold but is also a visual language of sign, [...] Read more.
With the rapid development and increasing popularity of online shopping for fashion products, fashion recommendation plays an important role in daily online shopping scenes. Fashion is not only a commodity that is bought and sold but is also a visual language of sign, a nonverbal communication medium that exists between the wearers and viewers in a community. The key to fashion recommendation is to capture the semantics behind customers’ fit feedback as well as fashion visual style. Existing methods have been developed with the item similarity demonstrated by user interactions like ratings and purchases. By identifying user interests, it is efficient to deliver marketing messages to the right customers. Since the style of clothing contains rich visual information such as color and shape, and the shape has symmetrical structure and asymmetrical structure, and users with different backgrounds have different feelings on clothes, therefore affecting their way of dress. In this paper, we propose a new method to model user preference jointly with user review information and image region-level features to make more accurate recommendations. Specifically, the proposed method is based on scene images to learn the compatibility from fashion or interior design images. Extensive experiments have been conducted on several large-scale real-world datasets consisting of millions of users/items and hundreds of millions of interactions. Extensive experiments indicate that the proposed method effectively improves the performance of items prediction as well as of outfits matching. Full article
(This article belongs to the Special Issue Recent Advances in Social Data and Artificial Intelligence 2019)
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Open AccessFeature PaperArticle
A Two-Tier Partition Algorithm for the Optimization of the Large-Scale Simulation of Information Diffusion in Social Networks
Symmetry 2020, 12(5), 843; https://doi.org/10.3390/sym12050843 - 21 May 2020
Abstract
As online social networks play a more and more important role in public opinion, the large-scale simulation of social networks has been focused on by many scientists from sociology, communication, informatics, and so on. It is a good way to study real information [...] Read more.
As online social networks play a more and more important role in public opinion, the large-scale simulation of social networks has been focused on by many scientists from sociology, communication, informatics, and so on. It is a good way to study real information diffusion in a symmetrical simulation world by agent-based modeling and simulation (ABMS), which is considered an effective solution by scholars from computational sociology. However, on the one hand, classical ABMS tools such as NetLogo cannot support the simulation of more than thousands of agents. On the other hand, big data platforms such as Hadoop and Spark used to study big datasets do not provide optimization for the simulation of large-scale social networks. A two-tier partition algorithm for the optimization of large-scale simulation of social networks is proposed in this paper. First, the simulation kernel of ABMS for information diffusion is implemented based on the Spark platform. Both the data structure and the scheduling mechanism are implemented by Resilient Distributed Data (RDD) to simulate the millions of agents. Second, a two-tier partition algorithm is implemented by community detection and graph cut. Community detection is used to find the partition of high interactions in the social network. A graph cut is used to achieve the goal of load balance. Finally, with the support of the dataset recorded from Twitter, a series of experiments are used to testify the performance of the two-tier partition algorithm in both the communication cost and load balance. Full article
(This article belongs to the Special Issue Recent Advances in Social Data and Artificial Intelligence 2019)
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Open AccessArticle
A New LSB Attack on Special-Structured RSA Primes
Symmetry 2020, 12(5), 838; https://doi.org/10.3390/sym12050838 - 20 May 2020
Abstract
Asymmetric key cryptosystem is a vital element in securing our communication in cyberspace. It encrypts our transmitting data and authenticates the originality and integrity of the data. The Rivest–Shamir–Adleman (RSA) cryptosystem is highly regarded as one of the most deployed public-key cryptosystem today. [...] Read more.
Asymmetric key cryptosystem is a vital element in securing our communication in cyberspace. It encrypts our transmitting data and authenticates the originality and integrity of the data. The Rivest–Shamir–Adleman (RSA) cryptosystem is highly regarded as one of the most deployed public-key cryptosystem today. Previous attacks on the cryptosystem focus on the effort to weaken the hardness of integer factorization problem, embedded in the RSA modulus, N = p q . The adversary used several assumptions to enable the attacks. For examples, p and q which satisfy Pollard’s weak primes structures and partial knowledge of least significant bits (LSBs) of p and q can cause N to be factored in polynomial time, thus breaking the security of RSA. In this paper, we heavily utilized both assumptions. First, we assume that p and q satisfy specific structures where p = a m + r p and q = b m + r q for a , b are positive integers and m is a positive even number. Second, we assume that the bits of r p and r q are the known LSBs of p and q respectively. In our analysis, we have successfully factored N in polynomial time using both assumptions. We also counted the number of primes that are affected by our attack. Based on the result, it may poses a great danger to the users of RSA if no countermeasure being developed to resist our attack. Full article
(This article belongs to the Special Issue Recent Advances in Social Data and Artificial Intelligence 2019)
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Open AccessArticle
Pancreatic Cancer Early Detection Using Twin Support Vector Machine Based on Kernel
Symmetry 2020, 12(4), 667; https://doi.org/10.3390/sym12040667 - 23 Apr 2020
Abstract
Early detection of pancreatic cancer is difficult, and thus many cases of pancreatic cancer are diagnosed late. When pancreatic cancer is detected, the cancer is usually well developed. Machine learning is an approach that is part of artificial intelligence and can detect pancreatic [...] Read more.
Early detection of pancreatic cancer is difficult, and thus many cases of pancreatic cancer are diagnosed late. When pancreatic cancer is detected, the cancer is usually well developed. Machine learning is an approach that is part of artificial intelligence and can detect pancreatic cancer early. This paper proposes a machine learning approach with the twin support vector machine (TWSVM) method as a new approach to detecting pancreatic cancer early. TWSVM aims to find two symmetry planes such that each plane has a distance close to one data class and as far as possible from another data class. TWSVM is fast in building a model and has good generalizations. However, TWSVM requires kernel functions to operate in the feature space. The kernel functions commonly used are the linear kernel, polynomial kernel, and radial basis function (RBF) kernel. This paper uses the TWSVM method with these kernels and compares the best kernel for use by TWSVM to detect pancreatic cancer early. In this paper, the TWSVM model with each kernel is evaluated using a 10-fold cross validation. The results obtained are that TWSVM based on the kernel is able to detect pancreatic cancer with good performance. However, the best kernel obtained is the RBF kernel, which produces an accuracy of 98%, a sensitivity of 97%, a specificity of 100%, and a running time of around 1.3408 s. Full article
(This article belongs to the Special Issue Recent Advances in Social Data and Artificial Intelligence 2019)
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Open AccessFeature PaperArticle
Symmetry Analysis in Analyzing Cognitive and Emotional Attitudes for Tourism Consumers by Applying Artificial Intelligence Python Technology
Symmetry 2020, 12(4), 606; https://doi.org/10.3390/sym12040606 - 11 Apr 2020
Abstract
Symmetries play very important roles in the analysis of cognitive and emotional attitudes. The analysis with Python technology, including optimized artificial intelligence technology, is designed on the basis of symmetry principles. Destination image perception as a branch of destination image research is of [...] Read more.
Symmetries play very important roles in the analysis of cognitive and emotional attitudes. The analysis with Python technology, including optimized artificial intelligence technology, is designed on the basis of symmetry principles. Destination image perception as a branch of destination image research is of great significance to tourists’ decision-making and destination image building. Ice-snow tourism is a hot topic nowadays, and research on perceptions of images of ice-snow tourism has become a focus. In this paper, python programming was used to crawl online travel journals and reviews about Jilin province’s ice-snow tourism on the Internet to analyze the frequency of frequently used words, their classification, word cloud and co-occurrence network, and other aspects of image perception, and proceed to the emotional perception of and emotional attitude to the emotional images and an overall image analysis. The study found that: (1) Perceptions of images of ice-snow tourism can be divided into five categories: tourism attractions, tourism activities, tourism facilities, tourism features and the tourism service environment. The frequency of tourism attractions is the highest, followed by tourism facilities and the tourism service environment. “Changbai Mountain” and “rime” are the core words, that is, tourists are most impressed by the scenic spot and landscape of “Changbai Mountain and rime.” (2) Positive emotional expressions accounted for 67.23% of perceptions of images of ice-snow tourism. Tourists gave a positive evaluation for Changbai Mountain, the snow landscape of Tianchi and skiing facilities. Meanwhile, passive emotional expressions accounted for 21.07% and tourists gave passive evaluations for travel, transportation, accommodation and catering. (3) Tourists spoke highly of overall images of ice-snow tourism in Jilin Province but few were willing to revisit. In the conclusion, strategies are put forward to improve image perceptions of ice-snow tourism and promote the sustainable development of ice and snow tourism. Full article
(This article belongs to the Special Issue Recent Advances in Social Data and Artificial Intelligence 2019)
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Open AccessArticle
Analysis of Structural Changes in Financial Datasets Using the Breakpoint Test and the Markov Switching Model
Symmetry 2020, 12(3), 401; https://doi.org/10.3390/sym12030401 - 04 Mar 2020
Abstract
The price movements of commodities are determined by changes in the expectations about future economic variables. Crude oil price is non-stationary, highly volatile, and unstructured in nature, which makes it very difficult to predict over short-to-medium time horizons. Some analysts have indicated that [...] Read more.
The price movements of commodities are determined by changes in the expectations about future economic variables. Crude oil price is non-stationary, highly volatile, and unstructured in nature, which makes it very difficult to predict over short-to-medium time horizons. Some analysts have indicated that the difficulty in forecasting the crude oil price is due to the fact that economic models cannot consistently show evidence of a strong connection between commodities and economic fundamentals, and, as a result, regarded the idea that economic fundamentals help predict price values as random luck. This study aimed to overcome the limitations of the economic models through the detection of structural changes as well as breaks in the data, using a breakpoint test. The Markov switching model is used to address the price patterns that led to a different market state. The results show that there are several changes as well as breaks in the estimated model. Moreover, there is an asymmetric correlation between the crude oil price and the GDP. Full article
(This article belongs to the Special Issue Recent Advances in Social Data and Artificial Intelligence 2019)
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Open AccessArticle
Building a Speech and Text Corpus of Turkish: Large Corpus Collection with Initial Speech Recognition Results
Symmetry 2020, 12(2), 290; https://doi.org/10.3390/sym12020290 - 17 Feb 2020
Cited by 1
Abstract
To build automatic speech recognition (ASR) systems with a low word error rate (WER), a large speech and text corpus is needed. Corpus preparation is the first step required for developing an ASR system for a language with few argument speech documents available. [...] Read more.
To build automatic speech recognition (ASR) systems with a low word error rate (WER), a large speech and text corpus is needed. Corpus preparation is the first step required for developing an ASR system for a language with few argument speech documents available. Turkish is a language with limited resources for ASR. Therefore, development of a symmetric Turkish transcribed speech corpus according to the high resources languages corpora is crucial for improving and promoting Turkish speech recognition activities. In this study, we constructed a viable alternative to classical transcribed corpus preparation techniques for collecting Turkish speech data. In the presented approach, three different methods were used. In the first step, subtitles, which are mainly supplied for people with hearing difficulties, were used as transcriptions for the speech utterances obtained from movies. In the second step, data were collected via a mobile application. In the third step, a transfer learning approach to the Grand National Assembly of Turkey session records (videotext) was used. We also provide the initial speech recognition results of artificial neural network and Gaussian mixture-model-based acoustic models for Turkish. For training models, the newly collected corpus and other existing corpora published by the Linguistic Data Consortium were used. In light of the test results of the other existing corpora, the current study showed the relative contribution of corpus variability in a symmetric speech recognition task. The decrease in WER after including the new corpus was more evident with increased verified data size, compensating for the status of Turkish as a low resource language. For further studies, the importance of the corpus and language model in the success of the Turkish ASR system is shown. Full article
(This article belongs to the Special Issue Recent Advances in Social Data and Artificial Intelligence 2019)
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Open AccessArticle
A Feasible Community Detection Algorithm for Multilayer Networks
Symmetry 2020, 12(2), 223; https://doi.org/10.3390/sym12020223 - 02 Feb 2020
Abstract
As a more complicated network model, multilayer networks provide a better perspective for describing the multiple interactions among social networks in real life. Different from conventional community detection algorithms, the algorithms for multilayer networks can identify the underlying structures that contain various intralayer [...] Read more.
As a more complicated network model, multilayer networks provide a better perspective for describing the multiple interactions among social networks in real life. Different from conventional community detection algorithms, the algorithms for multilayer networks can identify the underlying structures that contain various intralayer and interlayer relationships, which is of significance and remains a challenge. In this paper, aiming at the instability of the label propagation algorithm (LPA), an improved label propagation algorithm based on the SH-index (SH-LPA) is proposed. By analyzing the characteristics and deficiencies of the H-index, the SH-index is presented as an index to evaluate the importance of nodes, and the stability of the SH-LPA algorithm is verified by a series of experiments. Afterward, considering the deficiency of the existing multilayer network aggregation model, we propose an improved multilayer network aggregation model that merges two networks into a weighted single-layer network. Finally, considering the influence of the SH-index and the weight of the edge of the weighted network, a community detection algorithm (MSH-LPA) suitable for multilayer networks is exhibited in terms of the SH-LPA algorithm, and the superiority of the mentioned algorithm is verified by experimental analysis. Full article
(This article belongs to the Special Issue Recent Advances in Social Data and Artificial Intelligence 2019)
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Open AccessArticle
A Feasible Temporal Links Prediction Framework Combining with Improved Gravity Model
Symmetry 2020, 12(1), 100; https://doi.org/10.3390/sym12010100 - 05 Jan 2020
Abstract
Social network analysis is a multidisciplinary study covering informatics, mathematics, sociology, management, psychology, etc. Link prediction, as one of the fundamental studies with a variety of applications, has attracted increasing focus from scientific society. Traditional research based on graph theory has made numerous [...] Read more.
Social network analysis is a multidisciplinary study covering informatics, mathematics, sociology, management, psychology, etc. Link prediction, as one of the fundamental studies with a variety of applications, has attracted increasing focus from scientific society. Traditional research based on graph theory has made numerous achievements, whereas suffering from incapability of dealing with dynamic behaviors and low predicting accuracy. Aiming at addressing the problem, this paper employs a diagonally symmetrical supra-adjacency matrix to represent the dynamic social networks, and proposes a temporal links prediction framework combining with an improved gravity model. Extensive experiments on several real-world datasets verified the superiority on competitors, which benefits recommending friends in social networks. It is of remarkable significance in revealing the evolutions in temporal networks and promoting considerable commercial interest for social applications. Full article
(This article belongs to the Special Issue Recent Advances in Social Data and Artificial Intelligence 2019)
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Open AccessFeature PaperArticle
Learning Large Margin Multiple Granularity Features with an Improved Siamese Network for Person Re-Identification
Symmetry 2020, 12(1), 92; https://doi.org/10.3390/sym12010092 - 03 Jan 2020
Cited by 2
Abstract
Person re-identification (Re-ID) is a non-overlapping multi-camera retrieval task to match different images of the same person, and it has become a hot research topic in many fields, such as surveillance security, criminal investigation, and video analysis. As one kind of important architecture [...] Read more.
Person re-identification (Re-ID) is a non-overlapping multi-camera retrieval task to match different images of the same person, and it has become a hot research topic in many fields, such as surveillance security, criminal investigation, and video analysis. As one kind of important architecture for person re-identification, Siamese networks usually adopt standard softmax loss function, and they can only obtain the global features of person images, ignoring the local features and the large margin for classification. In this paper, we design a novel symmetric Siamese network model named Siamese Multiple Granularity Network (SMGN), which can jointly learn the large margin multiple granularity features and similarity metrics for person re-identification. Firstly, two branches for global and local feature extraction are designed in the backbone of the proposed SMGN model, and the extracted features are concatenated together as multiple granularity features of person images. Then, to enhance their discriminating ability, the multiple channel weighted fusion (MCWF) loss function is constructed for the SMGN model, which includes the verification loss and identification loss of the training image pair. Extensive comparative experiments on four benchmark datasets (CUHK01, CUHK03, Market-1501 and DukeMTMC-reID) show the effectiveness of our proposed method and its performance outperforms many state-of-the-art methods. Full article
(This article belongs to the Special Issue Recent Advances in Social Data and Artificial Intelligence 2019)
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Open AccessArticle
Micro-Distortion Detection of Lidar Scanning Signals Based on Geometric Analysis
Symmetry 2019, 11(12), 1471; https://doi.org/10.3390/sym11121471 - 03 Dec 2019
Abstract
When detecting micro-distortion of lidar scanning signals, current hardwires and algorithms have low compatibility, resulting in slow detection speed, high energy consumption, and poor performance against interference. A geometric statistics-based micro-distortion detection technology for lidar scanning signals was proposed. The proposed method built [...] Read more.
When detecting micro-distortion of lidar scanning signals, current hardwires and algorithms have low compatibility, resulting in slow detection speed, high energy consumption, and poor performance against interference. A geometric statistics-based micro-distortion detection technology for lidar scanning signals was proposed. The proposed method built the overall framework of the technology, used TCD1209DG (made by TOSHIBA, Tokyo, Japan) to implement a linear array CCD (charge-coupled device) module for photoelectric conversion, signal charge storage, and transfer. Chip FPGA was used as the core component of the signal processing module for signal preprocessing of TCD1209DG output. Signal transmission units were designed with chip C8051, FT232, and RS-485 to perform lossless signal transmission between the host and any slave. The signal distortion feature matching algorithm based on geometric statistics was adopted. Micro-distortion detection of lidar scanning signals was achieved by extracting, counting, and matching the distorted signals. The correction of distorted signals was implemented with the proposed method. Experimental results showed that the proposed method had faster detection speed, lower detection energy consumption, and stronger anti-interference ability, which effectively improved micro-distortion correction. Full article
(This article belongs to the Special Issue Recent Advances in Social Data and Artificial Intelligence 2019)
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Open AccessArticle
Algorithm for Detecting Communities in Complex Networks Based on Hadoop
Symmetry 2019, 11(11), 1382; https://doi.org/10.3390/sym11111382 - 07 Nov 2019
Abstract
With the explosive growth of the scale of complex networks, the existing community detection algorithms are unable to meet the needs of rapid analysis of the community structure in complex networks. A new algorithm for detecting communities in complex networks based on the [...] Read more.
With the explosive growth of the scale of complex networks, the existing community detection algorithms are unable to meet the needs of rapid analysis of the community structure in complex networks. A new algorithm for detecting communities in complex networks based on the Hadoop platform (called Community Detection on Hadoop (CDOH)) is proposed in this paper. Based on the basic idea of modularity increment, our algorithm implements parallel merging and accomplishes a fast and accurate detection of the community structure in complex networks. Our extensive experimental results on three real datasets of complex networks demonstrate that the CDOH algorithm can improve the efficiency of the current memory-based community detection algorithms significantly without affecting the accuracy of the community detection. Full article
(This article belongs to the Special Issue Recent Advances in Social Data and Artificial Intelligence 2019)
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Open AccessArticle
Centrality Metrics’ Performance Comparisons on Stock Market Datasets
Symmetry 2019, 11(7), 916; https://doi.org/10.3390/sym11070916 - 15 Jul 2019
Cited by 4
Abstract
The stock market is an essential sub-sector in the financial area. Both understanding and evaluating the mountains of collected stock data has become a challenge in relevant fields. Data visualisation techniques can offer a practical and engaging method to show the processed data [...] Read more.
The stock market is an essential sub-sector in the financial area. Both understanding and evaluating the mountains of collected stock data has become a challenge in relevant fields. Data visualisation techniques can offer a practical and engaging method to show the processed data in a meaningful way, with centrality measurements representing the significant variables in a network, through exploring the aspects of the exact definition of the metric. Here, in this study, we conducted an approach that combines data processing, graph visualisation and social network analysis methods, to develop deeper insights of complex stock data, with the ultimate aim of drawing the correct conclusions with the finalised graph models. We addressed the performance of centrality metrics methods such as betweenness, closeness, eigenvector, PageRank and weighted degree measurements, drawing comparisons between the experiments’ results and the actual top 300 shares in the Australian Stock Market. The outcomes showed consistent results. Although, in our experiments, the results of the top 300 stocks from those five centrality measurements’ rankings did not match the top 300 shares given by the ASX (Australian Securities Exchange) entirely, in which the weighted degree and PageRank metrics performed better than other three measurements such as betweenness, closeness and eigenvector. Potential reasons may include that we did not take into account the factor of stock’s market capitalisation in the methodology. This study only considers the stock price’s changing rates among every two shares and provides a relevant static pattern at this stage. Further research will include looking at cycles and symmetry in the stock market over chosen trading days, and these may assist stakeholder in grasping deep insights of those stocks. Full article
(This article belongs to the Special Issue Recent Advances in Social Data and Artificial Intelligence 2019)
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