Reviewerly/exHarmony huggingface dataset:
Abnormal Notion of Depicting Encrypted Result—Dynamic Usage of Multi-key Encryption Model (DUMKE)

Cryptography is a robust security mechanism of converting the secret data into secured format to control unauthorized access, updating, and so on. Due to digitization, all business activities happened through online. So security or trust on transaction is most essential. So it is focused on to increase the strength of security by newly designed encryption mechanisms called dynamic usage of multi-key encryption. Before encryption, input data are compressed by the method of compression technique using matrix mathematical model (MMM). Compressed data are utilized by DUMKE. This mechanism is applied to three public-key algorithms for encryption. The final phase of the system is an outcome of DUMKE which is displayed as image.
Visual Tracking with Attentional Convolutional Siamese Networks

Recently Siamese trackers have drawn great attention due to their considerable accuracy and speed. To further improve the discriminability of Siamese networks for visual tracking, some deeper networks, such as VGG and ResNet, are exploited as backbone. However, high-level semantic information reduces the location discrimination. In this paper, we propose a novel Attentional Convolutional Siamese Networks for visual tracking (ACST), to improve the classical AlexNet by fusing spatial and channel attentions during feature learning. Moreover, a response-based weighted sampling strategy during training is proposed to strengthen the discrimination power to distinguish two objects with the similar attributes. With the efficiency of cross-correlation operator, our tracker can be trained end-to-end while running in real-time at inference phase. We validate our tracker through extensive experiments on OTB2013 and OTB2015, and results show that the proposed tracker obtains great improvements over the other Siamese trackers.
The data mining analysis to determine the priorities of families who receiving assistance

Abstract Government has made efforts to improve the level of social life as an object of national development either by providing direct cash assistance or a program to create jobs. Fund distributed to the society is not small, so the distribution must be done properly. The data mining is a method of extracting data to find the hidden patterns of the data. In this research, the data are processed by using K-Means and C4.5 algorithm. The aim of this research is to classify the society into three clusters based on economic conditions using the K-means algorithm, the three clusters are pre-prosperous families, pre-prosperous families 1 and pre-prosperous families 2. Moreover, the C4.5 algorithm is used to make a decision tree model in determining which priority family gets assistance.
A Cloud Computing Adoption Model for the South African Police Services (SAPS)

Record keeping and safety of dockets in the police stations in South Africa has been identified and reported as a barrier or challenge in finalising some of the court cases. Criminal records are physically stored in hard copies on one site, whereby they cannot be accessible electronically in other locations such as courts, law firms and other police stations. This form of record keeping exposes the criminal records to many risks such as dockets being misplaced, lost or stolen, as they rotate from one unit to another within the station. This leads to withdrawal of cases by the state without proper implementation of justice. An alternative way of storing and sharing information, which eliminates the shortfalls of the current system is needed. Embracing cloud computing for storage of these records can address such challenges. Cloud computing will enable SAPS to store, share and retrieve files from various locations, where sharing of resources is crucial for quick and effective decision-making. Therefore, this study investigates a cloud computing adoption model, which can introduce the benefits of cloud computing in storing, sharing and managing criminal records within the South African Police Service. An interpretive paradigm was adopted for this research, embracing the qualitative research approach.
Sentiment Analysis of Social Media Users Using Naïve Bayes, Decision Tree, Random Forest Algorithm: A Case Study of Draft Law on the Elimination of Sexual Violence (RUU PKS)

In this digitalization era, it made it easy for the people of Indonesia to deliver ideas, opinions to carry out campaigns. One real example is regarding the existence of Draft Law on the Elimination of Sexual Violence (RUU PKS) with the aim of creating a new paradigm that ensures the community is free from sexual violence. The draft initiated by the National Commission on Violence Against Women, known as KOMNAS Perempuan, succeeded in becoming a conversation with social media users. Twitter is one of the social media that is a means of delivering public opinion regarding the case. One of the uses of this research is to find out the tendency of Twitter users to comment on regarding the existence of Draft Law on the Elimination of Sexual Violence (RUU PKS) by conducting sentiment analysis. The stages of this research are carried out by collecting data. The data that has been collected will go through the pre-processing, processing stage which will produce data in the positive, negative and neutral categories then testing using confession matrix. From the results of accuracy in the order of naive bayes, decision tree and random forest, the accuracy is 83.94%, 75.31 and 75.72%. Based on this, it can be concluded that the sentiment analysis of this case gets the best results using the Naive Bayes algorithm. As well as based on the results obtained that campaigning through social media has an effect, in this case for the campaign to support the RUU PKS itself produces more positive sentiments.
Research on Image Recognition Method Based on Deep Learning

Abstract With the rise of artificial intelligence technology, computer vision, image processing, image recognition, voice recognition and other technologies have gradually entered the public’s vision. This paper focuses on image recognition. This paper discusses the original image acquisition, image information preprocessing, image feature extraction, pattern classification and recognition results. Analyzes VGG-19 network, VGG improved has realized the image quick recognition, studies input and output points, the number of neurons by convolution neural network, and finally concludes that the deep learning method can shorten the training time, improve the identification results, also the requirement for images in the image library is greatly reduced, the demand for hardware is not too high.
Design of High Performance Server for Shipboard Common Computing Applications

Analyze the advantages of the Shipboard “Common computing” application model, and analyze the server design requirements and design ideas for “common computing” in combination with the vessel application environment. On this basis, introduce the design of the server structure, hardware topology, computing blade and cloud operating system. Finally, performance testing is performed. The physical performance of the server compute blade and the virtual machine performance of the server are tested and compared with the current shipboard computer. The results show that the server has outstanding computing performance, and the performance of virtual machine is better than current shipboard computer. In terms of real-time performance, the virtual machine is equivalent to the current shipboard computer. The virtual machine can realize the function of the physical machine on ships.
Anonymity and Pseudonymity

Anonymous authorship can be defined as encompassing any publication that appears without the author’s name printed either on the title page or in any other paratext such as a preface or dedication. Pseudonymous authorship, it follows, is a form of anonymity; the author’s name is concealed, while a false name is presented to the public. Anonymity, in this definition, is constituted formally by the absence of a name, or the presence of a false name, in the medium of publication. Friends, family, colleagues, editors, and publishers, naturally, are frequently aware of the identity of an anonymous author (but they are also frequently kept in the dark). The anonymity of a publication, as I define it, is not affected by whether a few people are in on the secret, or whether the identity of the author is in fact an open secret to many. For example, consider a first edition that has been published anonymously while the second edition has been signed; the author is now known, but the first edition continues to be an anonymous publication.
Towards Reasoning About Product Lines with Design Choices

While designing changes to Software Product Lines (SPLs), engineers may need to consider many alternative SPL designs. In the absence of enough information to pick an appropriate SPL design, they face design-time uncertainty about how to make the appropriate design choices. The combination of the two dimensions (variability and design choices) leads to Software Product Lines with Design Choices (SPLDCs). We propose Tyson, an Alloy-based domain-specific language for modelling SPLDCs and reasoning about their structural properties. We illustrate the applicability and feasibility of Tyson with a worked example, showing the kind of nuanced feedback necessary for meaningful analysis of SPLs with design choices.
Tourism Recommendation System Based on User Reviews

Recently, recommendation systems have become an active topic. Studies indicated that existing tourism recommendation systems provide misleading recommendations that do not actually meet tourist's expectations. One of the main reasons for this problem is that most of these systems neglect previous user reviews. This paper proposes a tourism recommendation system with the integration of the user review element. Based on three factors- number of reviews, rating, and sentiment, the user reviews are analyzed and then used in the recommendation of hotels for tourists.
Noise Reduction with Fuzzy Inference Based on Generalized Mean and Singleton Input–Output Rules: Toward Fuzzy Rule Learning in a Unified Inference Platform

A method is proposed for reducing noise in learning data based on fuzzy inference methods called α-GEMII (α-level-set and generalized-mean-based inference with the proof of two-sided symmetry of consequences) and α-GEMINAS (α-level-set and generalized-mean-based inference with fuzzy rule interpolation at an infinite number of activating points). It is particularly effective for reducing noise in randomly sampled data given by singleton input–output pairs for fuzzy rule optimization. In the proposed method, α-GEMII and α-GEMINAS are performed with singleton input–output rules and facts defined by fuzzy sets (non-singletons). The rules are initially set by directly using the input–output pairs of the learning data. They are arranged with the facts and consequences deduced by α-GEMII and α-GEMINAS. This process reduces noise to some extent and transforms the randomly sampled data into regularly sampled data for iteratively reducing noise at a later stage. The width of the regular sampling interval can be determined with tolerance so as to satisfy application-specific requirements. Then, the singleton input–output rules are updated with consequences obtained in iteratively performing α-GEMINAS for noise reduction. The noise reduction in each iteration is a deterministic process, and thus the proposed method is expected to improve the noise robustness in fuzzy rule optimization, relying less on trial-and-error-based progress. Simulation results demonstrate that noise is properly reduced in each iteration and the deviation in the learning data is suppressed considerably.
Obtaining Managerial Skills in Virtual Reality

Virtual worlds are very versatile environments. It isn’t a didactic device narrowly focused on one or a few uses, but it allows a wide range of activities, teaching forms and methods. In the paper’s introductory part, virtual worlds are defined in the conceptual and content areas. Further, their technological concept is described. Using the available knowledge, didactic-technological aspects have been defined to accelerate the use options of these virtual worlds in the role of the educational environment. The research aim is to describe the benefits and drawbacks of using virtual reality in management teaching and to answer the research question RQ: Are managerial skills training in a simulated environment more effective than other teaching methods, thanks to clarity? In the paper’s application part, a research survey on respondents groups A and B is described, whose aim was to compare the deployment of virtual reality in teaching managerial skills of bachelor students with the traditional way of teaching. Subsequently, a questionnaire was carried out by all respondents, where 7 questions were placed with the possibility of evaluation on a scale of 1–5 points where 5 points are the highest ranking. The basic questionnaire file included all respondents. The Student’s T Test was used. Conclusions and recommendations were formed, based on the synthesis of the acquired knowledge.
The Legalization of the Security Protection, Circulation and Application of Biological Big Data in China: Current Status and Outlook

Biotechnology Law ReportVol. 38, No. 6 Original ArticlesThe Legalization of the Security Protection, Circulation and Application of Biological Big Data in China: Current Status and OutlookBy Boyun Ding and Junhua ChenBy Boyun DingDr. Ding: E-mail Address: Cathy.ju@126.comBoyun Ding, PhD, Department of Neurosurgery, Zhujiang Hospital, Southern Medical University, Guangzhou, China.Search for more papers by this author and Junhua ChenDr. Chen: E-mail Address: 997292075@qq.comJunhua Chen, PhD, Intellectual Property Law, Wuhan University School of Law, Wuhan, China.Search for more papers by this authorPublished Online:9 Dec 2019https://doi.org/10.1089/blr.2019.29146.bdAboutSectionsView articleView Full TextPDF/EPUB Permissions & CitationsPermissionsDownload CitationsTrack CitationsAdd to favorites Back To Publication ShareShare onFacebookTwitterLinked InRedditEmail View articleFiguresReferencesRelatedDetails Volume 38Issue 6Dec 2019 InformationCopyright 2019, Mary Ann Liebert, Inc., publishersTo cite this article:By Boyun Ding and Junhua Chen.The Legalization of the Security Protection, Circulation and Application of Biological Big Data in China: Current Status and Outlook.Biotechnology Law Report.Dec 2019.386-394.http://doi.org/10.1089/blr.2019.29146.bdPublished in Volume: 38 Issue 6: December 9, 2019Online Ahead of Print:November 22, 2019KeywordsBig Databiological big dataChina legalizationcurrent statusoutlookPDF download
System Performance Measurement Using Web Server Log Files Method and Sink’s Seven Performance Criteria in Multichannel System Architecture

Abstract The purpose of system of updating population data with a multichannel architecture helps to simplify and streamline population data service procedures. This study builds a model of performance measurement in system processes using a multichannel system. The research method uses quantitative methods. Performance is seen in terms of productivity, effectiveness, efficiency, quality, quality, innovation, quality of work life and profitability in processes and sub-processes in multichannel systems. The results of this study get a critical area; namely the achievement of outputs that are not in line with the very poor product achievement standards, an increase in RnD is not optimal, employee trainees are not in accordance with the target, and work accidents are high. This paper uses multichannel performance measurement by determining key performance indicators (KPI) and performing performance measurements by recording processes on a multi-channel system with the Seven Sink Criteria method to produce standard performance measurements with the system automatically. In conclusion we can measure performance in SIAK using a multi-channel system, by calculating activity in the system. Even though there are two KPIs that cannot be measured by the system, they can still be combined using a weighted average value. Then we get a value that can be compared with the performance of the service unit to determine the performance conditions.
Exemplar-Based Pseudo-Viewpoint Rotation for White-Cane User Recognition from a 2D Human Pose Sequence

In recent years, various facilities are equipped to support visually impaired people, but accidents caused by visual disabilities still occur. In this paper, to support the visually-impaired people in a public space, we aim to classify whether a pedestrian image sequence obtained by a surveillance camera is a white-cane user or not from the temporal transition of a human pose represented as 2D coordinates. However, since the appearance of the 2D pose varies largely depending on the viewpoint of the pose, it is difficult to classify them. So, in this paper, we propose a method to rotate the viewpoint of a pose from various pseudo-viewpoints based on a pair of 2D poses simultaneously observed and classify the sequence by multiple classifiers corresponding to each viewpoint. Viewpoint rotation makes it possible to obtain pseudo-poses seen from various pseudo-viewpoints, extract richer pose features, and recognize white-cane users more accurately. Through an experiment, we confirmed that the proposed method improves the recognition rate by 12% compared to the method not employing viewpoint rotation.
Flow Analysis and Its Applications for Equipment Design

An approach based on flow analysis of matter, energy and information is discussed. The cognitive approach matter-energy-information allows to analyze complex systems in different modes, including dynamic situations. It is shown that flow diagrams are universal, common to many complex technical systems including electronic equipment design and technologies, communication systems, and etc. Restrictions of the proposed method is discussed. It is remarkable that visual analytics of dynamic process is available using weighted directed graphs. Based on the available experience, a classification of the concepts of flow analysis of systems is proposed. The chapter discusses flow analysis practical applications. Finally, conclusions are derived.
Understanding and Modeling Visitor Behaviours for Enhancing Personalized Cultural Experiences

Nowadays, there is an increasing interest in using adaptive technologies in cultural heritage sites to personalize and enhance the user's visit experience. However, personalizing the cultural experiences is still a challenging task that requires a deep knowledge of those user aspects that influence the visit. In order to facilitate the learning process during the visit, adaptive systems should consider differences between individuals for personalizing access to cultural heritage collections. This article calls into question the role that technologies can play both to enhance a user's visit experience and to attract new audiences through personalized interactions with cultural objects. It addresses a specific understanding of visitors' needs and behaviours by means of empirical data collected through a survey questionnaire. Knowing the main factors underlying visitors' styles it allowed formalization of this knowledge into a user model ontology which collects the main visitors' characteristics in the use of cultural heritage contexts.
Hierarchical Salient Object Detection Network with Dense Connections

Fully convolutional neural networks (FCNs) have shown outstanding performance in many dense labeling tasks. FCN-like salient object detection models haven mostly developed lately. In the work, we propose a novel pixel-wise salient object detection network based on FCN by aggregating multi-level feature maps. Our model first makes a coarse prediction by automatically learning various saliency cues, including color and texture contrast, shapes and objectness. Then a densely connected feature extraction block is adopted to further extract rich features at each resolution. Moreover, skip-layer structure is introduced for providing a better feature representation and helping shallow side outputs locate salient objects. In addition, a weighted-fusion module is utilized to combine multi-level features. Finally, a fully connected CRF model can be optimally incorporated to improve spatial coherence and contour localization in the fused saliency map. The whole architecture works in a coarse to fine manner. Evaluations on five benchmark datasets and comparisons with 10 state-of-the-art algorithms demonstrate the robustness and efficiency of our proposed model.
Evaluating graphical manipulations in automatically laid out LineSets

This paper presents an empirical study to determine whether alterations to graphical features (colour and size) of automatically generated LineSets improve task performance. LineSets are used to visualise sets and networks. The increasingly common nature of such data suggests that having effective visualisations is important. Unlike many approaches to set and network visualisation, which often use concave or convex shapes to represent sets alongside graphs, LineSets use lines overlaid on a graph. LineSets have been shown to be advantageous over shape-based approaches. However, the graphical properties of LineSets have not been fully explored. Our results suggest that automatically drawn LineSets can be significantly improved for certain tasks through the considered use of colour alongside size variations applied to their graphical elements. In particular, we show that perceptually distinguishable colours, lines of varying width, and nodes of varying diameter lead to improved task performance in automatically laid-out LineSets.
Multilayer Control for Coordinating Three - Wheeled Omnidirectional Mobile Robots

Operating multiple robots in an environment presents a challenge on how to coordinate each robot to be able to work together while still reaching their maximum performance. To do so, the right control system needs to be designed. On this challenge, we take the RoboCup Middle Size League environment as a case study as it requires both individual robot's performance as well as good coordination to do well in this setting. Our goal is to design a control system that enables the robots to coordinate, avoid obstacles and collisions, as well as performing velocity tracking to control each of the robot's movement. We developed a communication framework using socket.io to manage high-level coordination. While we used an A*-based path generating an algorithm to handle each robot's navigation. To enable velocity tracking, we used a cascade PID algorithm with acceleration feed-forward. The resulting control system can successfully coordinate and navigate each robot safely. The velocity tracking algorithm is also able to successfully achieve a velocity error of about 1.5% and a distance error of about 2%.