Reviewerly/exHarmony huggingface dataset:
Full Duplex Smart System for Deaf &amp; Dumb and Normal People

Ability to express yourself to the events that occurs in your surroundings is one of the priceless gift that we have. Normal human being have the ability to see, listen, talk and express themselves but there are few people who are unable to communicate and are not able to express themselves. Moreover it is also difficult for them to understand what normal people are saying to them and for normal people too to commute with them. Therefore to bridge the gap between these two we have created a full duplex smart communication system that less the communication and its gap between the normal people and the people who are unable to communicate. Our system focuses on Deaf & Dumb (D&D) people. Using our approach we have achieved an accuracy and precision of 95% and 85.5%. This paper has two major contributions. At start, uses the Leap Motion Device for hand gesture recognition. This will track data that will be used for training Pakistani Sign Language (PSL). In the second part, when normal people is trying to communicate with them, they can do it without learning a sign language. We have implemented this by using our Android Application that converts speech to text language that can be understood by the deprived one easily.
Edge Detection using Ant Colony Optimization under Novel Intensity Mapping Function and Weighted Adaptive Threshold

Edge detection is a crucial phenomenon in image segmentation. In general, kernel based methods like Sobel, Canny, Roberts etc. are used which are based on first and second derivatives pixels intensity. However, these methods fail to find all the true edges. Moreover, number of falsely detected edges is much more than true edges. This happens due to a fixed threshold used in these methods. To reduce falsely detected edges, a method which can dynamically adjust its threshold is desirable. Artificial and swarm intelligence based methods are capable to handle minute details. In this work, Ant Colony Optimization (ACO) based method is detailed for edge detection. In this method, a novel function is used to capture intensity variation in a particular image. In learning based method adjustment of threshold is also necessary to obtain good results. In this work, we have considered weighted average for threshold update in contrast to earlier method where simple average is taken. Finally, the performance evaluation and comparison is made in terms of Peak-Signal-to Noise Ratio (PSNR), accuracy and F-Score and usefulness of proposed method is shown.
Implementation of VR-based personal basketball team-practice equipment

In basketball, the next motion can be instantaneously determined with an accurate understanding of the surrounding conditions. Therefore, it is necessary to develop the ability to judge a situation accurately and perform appropriate actions. Development of such decision-making ability in individual practice can lead to higher efficiency. The purpose of this study is to construct an environment closely resembling interpersonal practice by arranging players in a virtual space using VR technology. As this device is used for practice, the sense of distance from the VR player is important. Hence, this study measures the error between VR players and users.
Blockchain Technology for Healthcare Cloud-Based Data Privacy and Security

Recently, there has been a growing demand for nursing staff to provide control to patients in their personal health records. But security concerns regarding the way in which various healthcare providers exchange healthcare information have led to such systems not being connected. The ability to exchange data security is important so that new, unified integrated care services can be delivered to patients. Thanks to its decentralized nature, blockchain technology is an appropriate driving force for the much-needed transition to integrated care, providing new insights and addressing some of the key challenges in many healthcare areas. It is worth mentioning that health data and services are gradually transferred to the cloud for the practicality showed in the real-time accessibility to a full patient medical history and savings showed in managing health data and healthcare economics. Yet, in a growing cloud-based setting, applying traditional cryptographic primitives and access control patterns to solve privacy and security issues is challenging. In the proximal future, the development of quantum computing may cast doubt on the impregnability of blockchain. This chapter explores the opportunities to apply the blockchain technique to safeguard health data hosted on the cloud. It also highlights the pragmatic obstacles and additionally needed investigations.
Analisis Pengaruh Teks Preprocessing Terhadap Deteksi Plagiarisme Pada Dokumen Tugas Akhir

Final Project Report at a university has the potential for plagiarism. To detect possible plagiarism, String Similarity can be used. Text preprocessing is needed to process words which can make String Similarity results inaccurate. The value of the distribution of the results of the similarity that is getting higher shows the level of accuracy is also getting higher. Reports that contain many words can make it difficult to find plagiarism recommendations. In this study, we try to divide the report into each chapter to provide more detailed recommendation material. By using text preprocessing and comparison methods in the same chapter, can determine the characteristics of each chapter. The discovery of the characteristics of each chapter can be used as plagiarism recommendation material in more detail than a full text report. The experiment was a comparison of the results of cosine similarity between the same chapters and full text, then combined with preprocessing stopword removal and stemming. The experimental results show that the use of preprocessing stopword removal and stemming can produce the highest distribution value and the similarity ratio in each chapter can show its characteristics. Words that represent the characteristics of a chapter can potentially become a stopword.
Analysis of In text Citation Patterns in Local Journals for Ranking Scientific Documents

In-text citations have been put forward as a new way to overcome the bias inherent in bibliographic citation analysis. In-text citation patterns have been used as the basis for citation analysis previously, but all the evidence has come from international journals. However, many countries have more local journals than international journals. This paper uses in-text citation analysis to examine local journals in Indonesia. The paper aims to determine the location-based citation pattern in the text and its effect on the articles’ and authors’ rankings. We collected articles from seven food science journals and then parsed these articles to detect the citations and their locations within the text. Pre-processing included normalizing section names, developing a database, and matching citation identities. The rankings were based on sections and then evaluated using the Spearman rank correlation in the final step. The results revealed that Indonesian journals did not exhibit the same patterns as international journals. There were differences in the section locations of the highest percentages of citations, the distributions of publication years, and the ranking methods. The correlations between sections indicated that the citations in the results and discussion section should be give the highest weight, followed by those in the method section, while the lowest weight should be assigned to citations in the introduction. These results need to be strengthened with further research using more extensive data and fields. Other findings, such as nonstandard and inconsistent citations, made developing an automatic citation detection system for local journals challenging.
Exploiting Machine Learning For Improving In-Memory Execution of Data-Intensive Workflows on Parallel Machines

Workflows are largely used to orchestrate complex sets of operations required to handle and process huge amounts of data. Parallel processing is often vital to reduce execution time when complex data-intensive workflows must be run efficiently, and at the same time, in-memory processing can bring important benefits to accelerate execution. However, optimization techniques are necessary to fully exploit in-memory processing, avoiding performance drops due to memory saturation events. This paper proposed a novel solution, called the Intelligent In-memory Workflow Manager (IIWM), for optimizing the in-memory execution of data-intensive workflows on parallel machines. IIWM is based on two complementary strategies: (1) a machine learning strategy for predicting the memory occupancy and execution time of workflow tasks; (2) a scheduling strategy that allocates tasks to a computing node, taking into account the (predicted) memory occupancy and execution time of each task and the memory available on that node. The effectiveness of the machine learning-based predictor and the scheduling strategy were demonstrated experimentally using as a testbed, Spark, a high-performance Big Data processing framework that exploits in-memory computing to speed up the execution of large-scale applications. In particular, two synthetic workflows were prepared for testing the robustness of the IIWM in scenarios characterized by a high level of parallelism and a limited amount of memory reserved for execution. Furthermore, a real data analysis workflow was used as a case study, for better assessing the benefits of the proposed approach. Thanks to high accuracy in predicting resources used at runtime, the IIWM was able to avoid disk writes caused by memory saturation, outperforming a traditional strategy in which only dependencies among tasks are taken into account. Specifically, the IIWM achieved up to a 31% and a 40% reduction of makespan and a performance improvement up to 1.45× and 1.66× on the synthetic workflows and the real case study, respectively.
A Federated Content Distribution System to Build Health Data Synchronization Services

In organizational environments, such as in hospitals, data have to be processed, preserved, and shared with other organizations in a cost-efficient manner. Moreover, organizations have to accomplish different mandatory non-functional requirements imposed by the laws, protocols, and norms of each country. In this context, this paper presents a Federated Content Distribution System to build infrastructure-agnostic health data synchronization services. In this federation, each hospital manages local and federated services based on a pub/sub model. The local services manage users and contents (i.e., medical imagery) inside the hospital, whereas federated services allow the cooperation of different hospitals sharing resources and data. Data preparation schemes were implemented to add non-functional requirements to data. Moreover, data published in the content distribution system are automatically synchronized to all users subscribed to the catalog where the content was published.
Analysis on Development Tendency of Computer Vision and Graphics based on Bibliometrics

Abstract In this paper, the bibliography included in Web of Science is taken as the data sample. This paper analyzes articles and reviews published from 2010 to 2020 in the field of computer vision and graphics to reveal the major countries or regions. We use leiden-type community algorithm to identify research topics. Besides, we explore the research fronts in the field of computer vision and graphics based on highly cited papers. The results show that the research on computer vision and graphics in the United States started earlier. The United States has always been in the leading position. China has developed rapidly in recent years. The United Kingdom, South Korea and India are also prominent. Deep learning, face recognition, image retrieval and volume rendering are the hottest research topics in the field of computer vision and graphics. Convolutional neural network and deep learning are the most emerging research fronts in the field of computer vision and graphics.
XGBoost Algorithm under Differential Privacy Protection

Privacy protection is a hot research topic in information security field. An improved XGBoost algorithm is proposed to protect the privacy in classification tasks. By combining with differential privacy protection, the XGBoost can improve the classification accuracy while protecting privacy information. When using CART regression tree to build a single decision tree, noise is added according to Laplace mechanism. Compared with random forest algorithm, this algorithm can reduce computation cost and prevent overfitting to a certain extent. The experimental results show that the proposed algorithm is more effective than other traditional algorithms while protecting the privacy information in training data.
The Effect of the Positive Psychology Interaction Group Program on Anger And Hope In Elderly Individuals

Introduction: This study aimed to evaluate the predictive role of the Geriatric Nutrition Risk Index in long-term mortality of very elderly patients with ST-segment elevation myocardial infarction and to compare it with the other known objective nutritional indices, namely the Prognostic Nutritional Index and Controlling Nutritional Status. Materials and Methods:A total of 212 eligible patients, aged 80 years or older, who were hospitalized with a diagnosis of ST-segment elevation myocardial infarction and underwent primary percutaneous coronary intervention were included in the study.Baseline patient characteristics, echocardiographic assessments, laboratory findings, and nutritional indices were assessed.Results: During the median follow-up period of 34 months, 60 (28.3%) allcause mortalities were identified, and event-free cumulative rates were 46.3%, 81.4%, and 90.1% for Geriatric Nutrition Risk Index <100.5, 100.5-112.6,and >112.6,respectively (log-rank test, p < 0.0001).Receiver operating characteristic curve comparison analysis revealed that the Geriatric Nutrition Risk Index was a better predictor than the Controlling Nutritional Status, Prognostic Nutritional Index, Body Mass Index, and serum albumin (p < 0.001 for each pairwise comparison of Receiver operating characteristic curves). Conclusion:It is important to evaluate malnutrition that is known to be associated with mortality in very elderly patients with ST-segment elevation myocardial infarction, who are more fragile than young people, and Geriatric Nutrition Risk Index -a simple and easy-to-calculate index-can be a guide in this regard.
Simplified Hardware Implementation of Memoryless Dot Product for Neural Network Inference

In this paper a simplified hardware implementation of a dot product arithmetic operation with constant coefficients is presented. The proposed methodology exploits a combination of distributed arithmetic and common subexpression sharing techniques. An algorithm is introduced for identifying the common sub partial sums systematically. Subsequently, a hardware architecture is proposed and the obtained circuits are synthesized in a 90-nm 1.0 V CMOS standard-cell library using Synopsys Design Compiler. Comparisons reveal significant reduction of 52% and 23% in area and power respectively for 1.5 ns delay over a regular dot product constant multiplier.
Hybrid Pyramid Convolutional Network for Multiscale Face Detection

Face detection remains a challenging problem due to the high variability of scale and occlusion despite the strong representational power of deep convolutional neural networks and their implicit robustness. To handle hard face detection under extreme circumstances especially tiny faces detection, in this paper, we proposed a multiscale Hybrid Pyramid Convolutional Network (HPCNet), which is a one-stage fully convolutional network. Our HPCNet consists of three newly presented modules: firstly, we designed the Hybrid Dilated Convolution (HDC) module to replace the fully connected layers in VGG16, which enlarges receptive field and reduces its loss of local information; secondly, we constructed the Hybrid Feature Pyramid (HFP) to combine semantic information from higher layers together with details from lower layers; and thirdly, to deal with the problem of occlusion and blurring effectively, we introduced Context Information Extractor (CIE) in HPCNet. In addition, we presented an improved Online Hard Example Mining (OHEM) strategy, which can enhance the average precision of face detection by balancing the number of positive and negative samples. Our method has achieved an accuracy of 0.933, 0.924, and 0.848 on the Easy, Medium, and Hard subset of WIDER FACE, respectively, which surpasses most of the advanced algorithms.
Tensor-Based GAN to Defense Adversarial Attacks for Cyber-Physical-Social System

In recent years, considerable achievements made by scientists in artificial intelligence have provided strong support for the development of Cyber-Physical-Social-Systems. Meanwhile, deep computation to realize AI is widely used in various applications, such as object classification. However, in classification application, the nice perturbations carefully designed added to the input data will make the classification label wrong, and the difference between the adversarial sample and the original one is hard to detect for humans. Hence, this paper proposed a defense mechanism which was tensor-based deep convolution generation adversarial network model to resist the possible attacks of classifiers in CPSSs. The method could play a good defense effect of the application, because of the reconstruction method using a complete Tensor-DCGAN model. The decoder with the same structure as the discriminator was used to decode the classified data, and the generator was used to generate a data which was the most similar to the data to be classified. The experimental results shown that our improved method not only preserved the advantages of the original model, but also reduced the parameters required by the original methods, and be more importantly, it made up the shortcomings of the original of color images and network data.
Face recognition based on full convolutional neural network based on transfer learning model

Deep learning has achieved a great success in face recognition (FR), however, little work has been done to apply deep learning for face photo-sketch recognition. This paper proposes an adaptive scale local binary pattern extraction method for optical face features. The extracted features are classified by Gaussian process. The most authoritative optical face test set LFW is used to train the trained model. Test, the test accuracy is 98.7%. Finally, the face features extracted by this method and the face features extracted from the convolutional neural network method are adapted to sketch faces through transfer learning, and the results of the adaptation are compared and analyzed. Finally, the paper tested the open-source sketch face data set CUHK Face Sketch database(CUFS) using the multimedia experiment of the Chinese University of Hong Kong. The test result was 97.4%. The result was compared with the test results of traditional sketch face recognition methods. It was found that the method recognized High efficiency, it is worth promoting.
Experimental Induction of Micro- and Macrosomatognosia: A Virtual Hand Illusion Study

Although body size misperceptions are known to occur under various neurological and psychiatric conditions, their neurocognitive underpinnings are not yet resolved. Accordingly, it would be beneficial to have an experimental paradigm, by which aberrant body misperceptions could be induced and systematically investigated. Expanding upon the “Virtual Hand Illusion” (VHI), this study aimed to design such a paradigm. Using a body-motion tracking system, we translated the participant's real hand position and movements to an embodiable, but resizable virtual hand model that we presented to the participants via a head-mounted display. The virtual hand's size was then systematically shrunk and enlarged in five different conditions (i.e., −50%, −25%, 0%, +25% or +50% rescaling). Applying this VHI derivate on n = 35 healthy participants, we investigated (1) if participants experience Sense of Ownership (SoO) and Sense of Agency (SoA) over a virtual hand that significantly deviates in size from their own hand, and (2) if by such size-deviant VHI induction, a change in their own hand size perception is also induced. Virtual hand embodiment was explicitly and implicitly assessed by means of self-report and EDA analysis. Questionnaire results revealed a stable SoA across all hand size conditions, while SoO parametrically decreased according to the hand scaling factor in either direction. Hand size perception, in turn, adapted according to the hand-scaling factor. In conclusion, the present study provides an important step toward an experimental paradigm that can induce and investigate aberrant body-size misperceptions.
Generating a dataset for learning setplays from demonstration

Abstract Coordination is an important requirement for most Multiagent Systems. A setplay is a particular instance of a coordinated plan for multi-robot systems in collective sports. Setplays are usually designed by robotics specialists using some existing tools, like the SPlanner, or by hand-coding. This work presents recent improvements to the Strategy Planner (SPlanner) and its corresponding FCPortugal Setplays Framework (FSF) to provide sophisticated setplays. This toolkit is useful to design strategic plans for robotic soccer teams as a particular case of Multi-Agent Systems (MASs). The new enhancements enable more realistic setplays, including, but not limited to, the definition of better pass strategies and defensive setplays. The enhanced tool is used to populate a dataset with demonstrations made by soccer experts and used in a Learning from Demonstration (LfD) approach to allow robotic soccer teams to learn new setplays. A new demonstration mode in the RoboCup Soccer Simulation 3D (SSIM3D) viewer RoboViz was also introduced to integrate this tool with SPlanner. Domain experts can use this set of tools to capture a specific scene in a game in RoboViz and use it as an initial step for a new setplay recommendation in SPlanner. The resulting dataset is organized into fuzzy clusters to be used in a reinforcement learning strategy. This paper describes the whole process. Article Highlights This paper’s main contribution is generating a dataset of setplays to support learning from demonstration in robotic soccer. A set of new features were added to the Strategic Planner(SPlanner) to enable the design of more realistic setplays. The official RoboCup viewer (Roboviz) was integrated with SPlanner using a new demonstration mode .
An Interactive Virtual Reality Physics Instructional Environment based on Vygotskian Educational Theory

Physics Education is a popular application for Virtual Reality (VR), with many simulations being available for students to manipulate objects in virtual space. However, most of these applications take a Piagetian approach to learning, instructing first in a traditional way and then exposing students to VR in a sandbox environment without the instructor present. This paper proposes a design for a VR physics simulation that takes a Vygotskian approach using Zone of Proximal Development (ZPD) and scaffolding to teaching physics, allowing the students and instructor to collaborate in virtual space. Using gravity and circular motion as a case study, this paper discusses a design to facilitate this learning comprising a virtual universe with control of planet placement and size, external satellite placement and size, as well as gravitational forces acting on these objects, along with a design-based research (DBR) research design to evaluate both usability and learning for the intervention.
SAVSNET research

Carmen Tamayo, a member of the SAVSNET-Agile team at the University of Bristol tells us about the recent 2020 outbreak of prolific vomiting in UK dogs investigated by SAVSNET-Agile. Here we can read about the challenges faced, the lessons learned, and future SAVSNET work.