Reviewerly/exHarmony huggingface dataset:
An FPGA Implementation of a Gaussian Process Based Predictor for Sequential Time Series Data

Gaussian Process Regression (GPR) is one of popular supervised learning techniques that map input data sequence into the continuous outputs with Gaussian probability distribution. Since GPR can quantify uncertainty in the predicted values unlike other machine learning techniques, it is becoming used to predict behaviors of mission critical applications such as satellites or gas turbines. However, its computation requirement is very high due to the extensive use of matrix operations. In this paper, we focus on the mean value prediction part as a major time-consuming one of Gaussian process regression and attempt to implement a custom hardware accelerator on FPGAs using high-level synthesis technique. Assuming the target dataset is onedimensional time series data, we design the accelerator specific to the single feature. Following the reference design using Python’s scikit-learn, we prepare a straightforward implementation in C++, and optimize it for an FPGA with high-level synthesis technique. From the results of implementation on the Ultra96 MPSoC board, we find that our accelerator can achieve a 250x speedup and 177 times less energy compared with the reference Python code for the CPU on the target board.
Anonymity in Questions and Answers about Health

Social Question &amp; Answer (Q&amp;A) sites are a unique source of health information that draw from personal, rather than professional experience. When people ask or answer questions about health using these sites, they may do so using their real name, or another type of identity such as pseudonymity (e.g., a username or nickname) or anonymity. People’s behavior may differ when they have a choice about which type of identity they choose, especially the context of different levels of sensitivity of content (e.g., health vs. non- health). In this work, we explore the relationship between different types of identity (e.g., anonymity and pseudonymity) and several Q&amp;A metrics of user behavior on Yahoo Answers in the context of health and non-health content using path analysis. We find that health-related questions are more likely to be asked and answered anonymously. We also find that anonymous answers have more upvotes and downvotes than pseudonymous answers indicating more engagement. We conclude by suggesting that health Q&amp;A sites and other online health communities may improve the quality of discussion by providing anonymity features and implementing moderation mechanisms.
A Probe into the Integration of Computer Technology and the Development of Aerobics with the Goal of “Two in One”

Abstract With the rapid development of computer technology, the application of computer technology in every corner of people’s production and life has become a general trend. At present, in order to achieve national fitness, improve the physical quality of the Chinese people and strengthen their physique, China is paying more and more attention to aerobics. As computer technology can effectively improve the shortcomings of traditional aerobics training methods, it is very necessary to apply computer technology in the development of aerobics. This study discusses the development process and characteristics of Chinese aerobics, the basic information of computer-aided instruction system and the way of applying computer technology in aerobics training, which provides a boost for realizing the goal of “two in one”.
Semi-supervised subject recognition in low-modal sensor data

Subject Recognition (SR) refers to the task of identifying persons performing activities in a smart environment using the data captured by the sensors installed in it. The existing literature mainly concentrates on supervised SR using the sensor data captured through multiple modalities. However, majority of the real-life sensor datasets are not annotated with the subjects performing the activities, which creates a scarcity of labeled data samples for this task. Issues of privacy and high manual annotation costs further complicate the problem of less labeled data. In addition to this problem, most of the datasets are of low modalities. Hence, the challenge lies in developing semi-supervised frameworks that are suitable for low-modal sensor data with sparse or no labels. Towards this, we initially perform benchmark experiments to analyze the factors of modality and amount of labeled data in the context of SR. Then, we propose semi-supervised frameworks for SR on the data collected by low-modal ubiquitous and visual sensors. In particular, we propose a clustering-based pseudo label generation algorithm to facilitate the training process in a semi-supervised domain for ubiquitous data. On the other hand, we propose Transfer Learning and Data Augmentation (TLDA) framework to perform SR on visual data in semi-supervised domain. To validate our proposed frameworks, we perform experiments on three real-world datasets, namely Smartphone, OPPORTUNITY, and UTD-MHAD dataset to achieve an accuracy of around 77%, 98%, and 91% respectively. Finally, we also provide an analysis on the aspect of merging modalities to propose a new research dimension for SR.
DC Side Voltage Monitoring Model of Transformer based on Pattern Recognition

Aiming at the problem that the transformer DC side voltage is affected by the voltage quality during the monitoring process, its monitoring performance is greatly reduced. In order to improve the monitoring effect of the transformer DC side voltage, a transformer DC side voltage monitoring model based on pattern recognition is designed. Pattern recognition based on the RMS method using DC voltage transformer discrete signal processing using a pure sinusoidal AC signal with the average RMS mathematical relationship, by calculating the average of the AC voltage, indirectly, of the DC voltage transformer Valid values. From the OPP-CI model that ignores the influence of the ZIB node and the OPP-CI model that considers the influence of the ZIB node, pattern recognition is used to establish the transformer DC side voltage OPP-CI model, and the transformer DC side voltage monitoring process design is combined to realize the transformer DC Monitoring of side voltage. The experimental results show that the error of the monitoring results of the proposed model is relatively low, indicating that the method can improve the monitoring effect of the transformer DC side voltage.
Theme Evolution of Researches on Knowledge Graphs Based on Visualization Analyses of Data

Abstract The knowledge graph, as a significant branch of Library and Information Science, has also become a vital part of Computer Science. In a bid to reveal the research trend and hot topics of the knowledge graph domain, this paper collected latest data about literature on knowledge graphs from SCI-Expanded and EI Compendex database. This paper took advantage of Citespace for visualization analyses of data, which discerned the research topics, hot issues and theme changes of the knowledge graph domain over time. Through analyses, we conclude that the knowledge graph has developed greatly in both Library and Information Science and Computer Science. In Library and Information Science domain, researches focus on the issues like the plotting of knowledge graphs and visualization of knowledge. In Computer Science domain, hot issues are the techniques of constructing knowledge graphs, which include entity recognition, knowledge integration, etc., and their applications in Semantic Web. Among all the research topics, the domain knowledge graph is one of the paramount research trends. Compared with previous researches, this paper keeps up with the times and can reveal the theme evolution of researches on knowledge graphs more precisely.
Fitting Regression Lines to Scatterplots: The Role of Perceptual Heuristics

Relations in data are described and analyzed by fitting a regression line to the data. The graphical equivalent is a line fit to a scatterplot, typically via the least squares fit. How do people visually fit a line or recognize the best fitting line for a scatterplot? The present research proposes that people use a set of perceptual heuristics – point capture, early point, balancing points and balancing distances. Experiments 1 and 2A found that participants with no training in regression drew a line and chose the best fitting line, respectively, using those heuristics, especially balancing points. The application of the heuristics was sensitive to the structure of the graphs and influenced the error in drawing the regression line. Participants in Experiment 2B who had received formal training in regression were less likely to apply the heuristics and more likely to choose a line based on the least squares fit. The discussion focuses on the value of cognitive/perceptual analyses of graph reading and proposes methods to help people overcome biases produced by the application of these perceptual heuristics.
The Implementation of Fuzzy-C Means to Categorize Poverty Data in Riau Province

Abstract Riau Province was the 13th in line as the poverty contributor in Indonesia. It was because of the fund from government was not properly distributed. Therefore, an identification system to distribute the poverty funding was needed in order to reach the citizens in poverty. Based on the available parameter, it could accelerate the eradication of poverty. Poverty caused by various factors, i.e. educational background, living environment, job and health. One of the solutions to distribute the fund properly was by categorizing the poverty data. This research used Fuzzy Clustering Means (FCM) method because every area has the possibility to become part of each cluster with different membership level between score 0 and 1. The recommended parameter was w = 2, Max_iter = 100, ε = 0.1, P0 = 10.000.0000, t = 1, C = 3 with Partition Coefficient Index (PCI) validity score 0.47. The web built based-system uses programming language PHP with Framework YII 2 Advanced. All clustered data was analyzed based on poverty variable level that was dominant in range of regency and District.
RAASID: A Multipurpose Crowd Sensing Smart System With Sentimental Analysis

Disruptive technologies have evolved dramatically rendering our world increasingly connected and introducing many business opportunities. The use of AI in various businesses has increased in unprecedented rate. In this paper, we design and implement RAASID, a video based facial and emotional recognition smart system that aims at automating the attendance taking procedure in institutes by autonomously marking the attendance of individuals in real-time with no direct physical interaction. In addition, RAASID simultaneously detect and analyze individuals' facial expressions in order to identify the current emotional state of the students at regular time points. The proposed solution guarantees the highest level of student's discipline in the classroom while simultaneously monitoring the student's emotional state regularly. Upon experimentation, the accuracy of the system scored 80% in differentiating students and classifying emotions during various tested classes. The proposed system can be applied to large scale classrooms or conference events with further enhancements.
Towards improving edge quality using combinatorial optimization and a novel skeletonize algorithm

Object detection and image segmentation of regions of interest provide the foundation for numerous pipelines across disciplines. Robust and accurate computer vision methods are needed to properly solve image-based tasks. Multiple algorithms have been developed to solely detect edges in images. Constrained to the problem of creating a thin, one-pixel wide, edge from a predicted object boundary, we require an algorithm that removes pixels while preserving the topology. Thanks to skeletonize algorithms, an object boundary is transformed into an edge; contrasting uncertainty with exact positions.To extract edges from boundaries generated from different algorithms, we present a computational pipeline that relies on: a novel skeletonize algorithm, a non-exhaustive discrete parameter search to find the optimal parameter combination of a specific post-processing pipeline, and an extensive evaluation using three data sets from the medical and natural image domains (kidney boundaries, NYU-Depth V2, BSDS 500). While the skeletonize algorithm was compared to classical topological skeletons, the validity of our post-processing algorithm was evaluated by integrating the original post-processing methods from six different works.Using the state of the art metrics, precision and recall based Signed Distance Error (SDE) and the Intersection over Union bounding box (IOU-box), our results indicate that the SDE metric for these edges is improved up to 2.3 times.Our work provides guidance for parameter tuning and algorithm selection in the post-processing of predicted object boundaries.
Discussion on Network Security of Information Retrieval Technology in Network Communication

Abstract Network communication has become an important tool to ensure the efficient operation of modern society. The retrieval of network communication information is mainly based on the annotation and extraction of information characteristics in network communication, so as to carry out retrieval matching. The key of its application is to organize the information organically. Network communication information retrieval is not only a fast and effective way to obtain the required information, but also has important research value and role for the security of network communication. Based on this, this paper first analyses the information retrieval technology and network security technology in network communication, and then gives the practical application and development trend of the retrieval technology in network communication.
CODOC: A Future of Documentation

Advancement of technology caters men comfort and convenience. New technologies are changing the world but there are few things which are still used as the way they were being used since the day they were invented. Paper is the most important medium to store data is still used in the same way. New technologies comes with new responsibilities, and now a days everyone is concerned about security of their data. Cryptography is the necessity now a days, advanced encryption standard (AES) being the most feasible algorithm for encryption and decryption, and itâ€™s best to use for securing data over open network or on a standalone device. Cipher and inverse cipher are the two components generated from advanced encryption standard. Crypted Organization of Document (CODOC) is the solution for all of these problems.
Weakly Supervised Bounding Box Extraction for Unlabeled Data in Table Detection

The organization and presentation of data in tabular format became an essential strategy of scientific communication and remains fundamental to the transmission of knowledge today. The use of automated detection to identify typographical elements such as tables and diagrams in digitized historical print offers a promising approach for future research. Most of the table detection tasks are using existing off-the-shelf methods for their detection algorithm. However, datasets that are used for evaluation are not challenging enough due to the lack of quantity and diversity. To have a better comparison between proposed methods we introduce the NAS dataset in this paper for historical digitized images. Tables in historic scientific documents vary widely in their characteristics. They also appear alongside visually similar items, such as maps, diagrams, and illustrations. We address these challenges with a multi-phase procedure, outlined in this article, evaluated using two datasets, ECCO (https://www.gale.com/primary-sources/eighteenth-century-collections-online) and NAS (https://beta.synchromedia.ca/vok-visibility-of-knowledge). In our approach, we utilized the Gabor filter [] to prepare our dataset for algorithmic detection with Faster-RCNN []. This method detects tables against all categories of visual information. Due to the limitation in labeled data, particularly for object detection, we developed a new method, namely, weakly supervision bounding box extraction, to extract bounding boxes automatically for our training set in an innovative way. Then a pseudo-labeling technique is used to create a more general model, via a three-step process of bounding box extraction and labeling.
ORM Diagram as an Intermediate Model for OWL Ontology Engineering: Protégé ORM Plugin Implementation

Abstract OWL2, a widely-used ontology-representation language, is poorly perceived by humans because OWL2 statements have a low level of abstraction. To solve this issue, various OWL2 ontology editors are used, which allow to group statements and represent them using some visual notation. ORM-diagram is a good candidate for an intermediate model for authoring and understanding of OWL2-ontology as Object-Role Modelling notation supports visually distinguishable constructs, has the high expressive capabilities, and implements the node-link paradigm and the attribute-free approach A Protégé plugin allowing to create an ORM2-diagram using the live error checking approach was implemented. The plugin allows us to form a valid object-oriented diagram model in computer memory using a widely known ontology authoring tool Protégé.
CYBER CRIME IN PAKISTAN; DETECTION AND PUNISHMENT MECHANISM

“Cyber Crime in Pakistan; Detection and Punishment Mechanism” addresses improvement of public health and safety policies by focusing on enhancing knowledge about cybercrime, women victimization, the pattern of time spent on the internet, sexual harassment and cyber- bullying and the effect of socio- demographic factors on cybercrime. A quantitative, self-selected research study designed by the researcher and utilizing a voluntary, anonymous internet survey consisting of open and closed-ended questions targeted students attending large universities in Pakistan (N=400), based on Routine Activity Theory (RAT). The results were analyzed through SPSS via directing descriptive statistics, Cronbach’s alpha, and regression analysis to confirm the validity and internal consistency of data and verification of the hypotheses. Results depicted women represent the largest group impacted by cyber abuse. Single women, young adults, and employed students demonstrate increased rates of victimization. Frequent usage of social media may account for increased victimization for women. Time spent online and deficient knowledge of cyber protection measures are positively correlated with digital victimization. Respondents report on inadequate effective and affordable cyber protection and ineffective responses by agencies to cybercrime. Based on the results garnered, and supported by Public Policy Theory, Cybersecurity policies have been proposed to Pakistan’s government.
The Relevance of Reading Teaching Material Using New Technology

Abstract This research was conducted to find a clear picture of typical reading teaching materials and to find out what principles are in determining teaching material, and in what form teaching material is manifested. In the process of making teaching materials very much related to several principles, among others; the principle of response, cohesion and stability. affective curriculum must be responsive to the needs of stakeholders, market needs, and ongoing research. Cohesion shows that teaching is made according to what has been learned and what will be learned by students. And stability is a technique of testing teaching material in the language learning process. Based on expert opinion that the embodiment of teaching material is print and electronic media. the use of electronic media has advantages. In accordance with the description above that learning by e-learning can use teaching materials / materials independently, can determine learning schedules, can increase active student participation, can improve the quality of learning materials and exercises. In the end, in determining the relevant teaching material has to do with several principles, among others; the principle of response, cohesion and stability. In the realization of this teaching material can be through print media such as books, and through electronic media such as; Computer Based Training (CBT), Web Based Training (WBT), Internet (Interconnection Networking), etc.
Multi-Precision Integer Arithmetic Processing Using Arithmetic Circuit Homomorphic Encryption

This is an extension of ongoing research on Arithmetic Circuit using Fully Homomorphic Encryption using TFHE (Fast Fully Homomorphic Encryption over Torus). Just like most Homomorphic Encryption methods, the current implementation is limited to 32bit computation. Thus this was an issue that this paper attempts to resolve. This paper will focus on creating a set of arithmetic operation, namely- Addition, Subtraction and Multiplication using the logic gates provided by the library to a dynamic range to at least 128 bits. This paper will focus on creating a set of arithmetic operation, namely- Addition, Subtraction and Multiplication using the logic gates provided by the library to a dynamic range to at least 128 bits.
The advantages and disadvantages of agricultural economic management information

Abstract Agriculture is an important part of economic management. In recent years, due to the popularization of the Internet, the adjustment of agricultural industry structure and the information management of agricultural industry have been accelerated. The wide application of information technology in rural economic management can effectively promote the development of rural economy. Because the development of my country’s agricultural information industry started late, and its application is relatively late. On this basis, this article studies the advantages and disadvantages of agricultural economic management information, such as modern production factors and agricultural economic information management by means of agricultural data collection. Taking the agricultural economic management informationization as the experimental group and traditional agricultural economic management as the control group, the application research is carried out. Through the use case test of the agricultural informationization platform data collection system, it is shown that information technology can effectively improve the efficiency of agricultural production. Experimental results show that information technology can effectively make up for the lack of economic information management in agricultural production and life. In addition, the government should increase capital investment in personnel training to promote agricultural economic management information.
IMPEDIMENTS TO INVESTIGATING CYBERCRIMES: COMPARISON OF SOME COMMON LAW COUNTRIES AND UZBEKISTAN

In this paper, the author endeavors to identify systemic impediments that obstruct police investigations, prosecutions, and digital forensics interrogations of cybercrimes in common law and civil law system countries, especially in Uzbekistan. The author offers grounded, extensional proposals for further development of Uzbek legislation based on learning several local and international research, papers of specialized authors.