Reviewerly/exHarmony huggingface dataset:
PERANCANGAN SISTEM INFORMASI GRAFIK BARBER JOHNSON DALAM MENGUKUR EFISIENSI RUMAH SAKIT MENGGUNAKAN MICROSOFT VISUAL STUDIO 2013

The purpose of this research is to design an information systems johson barber graphs using Microsoft visual studio 2013 at RSUD Sumedang. The research method used qualitative descriptive method with design model, while data collection techniques with observation, interview and literature study. Based on research done, information systems Johnson barber graphs is still manually. Therefore the new system created with Microsoft Visual Studio 2013, using My Sql database, using the software development waterfall model, so the user in this case is the officer, just simply input data in the form that has been provided and the data can be directly in the process so get desired results. The suggestions provided are: 1) making computerized Johnson barber graphs information systems, which can reduce weaknesses during implementation of the manual systems, and 2) planning maintenance and care sustainable development of the system.&#x0D; Keyword: Information Systems, Johnson Barber Graphs, Microsoft Visual Studio 2013
Application of the Learning Cycle Model with Video Media in Improving the Quality of Learning IPS For Students

Improving the quality of social studies learning consists of improving teacher skills, student activities, and learning outcomes. Data on student learning outcomes showed as many as 21 students (70%) of the 30 students did not complete the KKM (64). This happens because the teacher has not been optimal in using learning models and media, causing student activity and learning outcomes to be low. Therefore, it is necessary to improve the quality of social studies learning through the application of the Learning Cycle model with video media. The purpose of this study was to improve the quality of social studies learning through the Learning Cycle model with video media for fifth grade students at SDN 32 Sungai Jaring. This study used classroom action research which was carried out in three cycles, each cycle consisting of one meeting with four stages, namely planning, implementation, observation, and reflection. The results of the reflection as planning for the next cycle. The subjects of this study were teachers and 30 fifth grade students at SDN 32 Sungai Jaring. Data collection techniques using test and non-test techniques were analyzed with descriptive and qualitative descriptive statistical techniques. The results showed that the teacher's skills in the first cycle obtained good criteria, the second cycle obtained very good requirements, and the third cycle obtained very good requirements. Student activities in cycle I got good criteria, cycle II got good criteria, and cycle III got very good criteria. Student learning outcomes in cycle I obtained sufficient criteria, cycle II obtained good criteria, and cycle III obtained excellent criteria. The conclusion of this research is the Learning Cycle model with video media can improve the quality of social studies learning for class V SDN 32 Sungai Jaring.
Automatic Context Pattern Generation for Entity Set Expansion

Entity Set Expansion (ESE) is a valuable task that aims to find entities of the target semantic class described by given seed entities. Various Natural Language Processing (NLP) and Information Retrieval (IR) downstream applications have benefited from ESE due to its ability to discover knowledge. Although existing corpus-based ESE methods have achieved great progress, they still rely on corpora with high-quality entity information annotated, because most of them need to obtain the context patterns through the position of the entity in a sentence. Therefore, the quality of the given corpora and their entity annotation has become the bottleneck that limits the performance of such methods. To overcome this dilemma and make the ESE models free from the dependence on entity annotation, our work aims to explore a new ESE paradigm, namely corpus-independent ESE. Specifically, we devise a context pattern generation module that utilizes autoregressive language models (e.g., GPT-2) to automatically generate high-quality context patterns for entities. In addition, we propose the GAPA, a novel ESE framework that leverages the aforementioned <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">G</b> ener <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">A</b> ted <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">PA</b> tterns to expand target entities. Extensive experiments and detailed analyses on three widely used datasets demonstrate the effectiveness of our method. All the codes of our experiments are available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/geekjuruo/GAPA</uri> .
A didactic tool for updating the teaching-learning process of English as a foreign language

The teaching-learning process is a binomial in constant change due to its actors and contexts. Therefore, this process requires an update to respond to students’ needs according to their learning styles. Given this situation, the objective of this study is to know if TikTok, as a didactic tool, can contribute to said educational update and develop oral skills since it centers on communicative competence. Regarding the methodology, the descriptive quantitative approach took place on a 50-student sample enrolled in an extracurricular course. The findings provide contributions of a theoretical nature since they characterize TikTok as a didactic tool to update the English teaching-learning process with an innovative, participatory, and collaborative methodology with motivational features and ubiquitous learning. Besides, it contributes to oral skills development through the construction of knowledge, interpersonal relationships, discursive and sociocultural skills development, and the promotion of positive attitudes towards work (commitment).
Design and manufacture of a splint prototype for the upper extremities of the human body

The article presented below shows the process carried out for the elaboration of a splint prototype and the techniques used for its elaboration, the design is carried out with the solidworks software, the designs with the most optimal forms that confer a greater resistance are analyzed. to the prototype, the configuration of the laminating software and the correct orientation for its manufacture are shown. A review of the optimal materials for its manufacture is made, such as PLA, PETG and ABS. For the printing of the prototype, an Ender 3 pro model 3D printing machine will be used, while the Simplify 3D program will be used for the configuration of the printer, which will allow us to configure temperature parameters, such as the extruder temperature, temperature of bed, layer height, printing speed, filling percentage among others.
Data security and protection in blockchain-based local energy markets for smart cities

In the last years, the improvements in distributed ledger technologies brought disruptive improvements in the management of distributed energy resources. The availability of public, immutable and trustless ledgers allowed for the creation of decentralized energy exchange platforms, potentially held in an autonomous way by smart distributed devices. However, the vast amount of data exchanged in these processes, and its potentially infinite lifetime once shared on distributed ledger technologies, posed different privacy and information security issues. In this view, this paper proposes a methodology for performing autonomous, decentralized and secure local energy markets, which does not disclose sensitive information about the user or the market itself. The proposed methodology has been applied on a Hardware in the Loop experimental setup and proved to provide a privacy-safe and fair way to manage a distributed resources in an energy community and/or smart city context.
Blockchain Oriented Software Engineering for Dapp smart contracts in smart energy markets

Transmission and Distribution Systems Operators are facing the need for new market tools able to evaluate the potential of flexibility contracting, with smart and decentralized energy production, consumption, and exchange. The blockchain technology, has been proposed in platforms and projects for energy trading, given that BC platforms natively support not only decentralization but also transparency, privacy, smart grid management, token trading, pseudo-anonymity, and other desirable features of a smart and decentralized market. However, BC software projects are complex, may be large, and often exposed to critical failures. To reduce the chance of such failures and to improve the quality of the software development process, this paper introduces software development methodologies for blockchain applications specific to the decentralized energy market. By using Blockchain Oriented Software Engineering (BOSE) and Agile Block-Chain Dapp Engineering (ABCDE), a novel decentralized application based on an energy token is proposed, defined by a smart contract on the ERC721 standard, that can be refilled, resold, emptied, and traded even without any energy content. According to the ABCDE methodology, the system has been divided into two subsystems, the BC and the application levels, and determined the actors and the related use case diagrams.
EyeCoD: Eye Tracking System Acceleration via FlatCam-Based Algorithm and Hardware Co-Design

Eye tracking has become an essential human–machine interaction modality in virtual reality (VR) and augmented reality (AR) applications requiring high throughput (e.g., more than 240 frames per second), small form factor, and enhanced visual privacy. Existing eye tracking systems have adopted bulky, lens-based cameras, and thus suffer from both a large form factor and high communication cost between the camera and back-end processor. This work presents a camera, algorithm, and accelerator co-designed lensless eye tracking system dubbed EyeCoD, which, to the best of our knowledge, is the first to provide a general, front-end eye tracking solution for AR/VR while satisfying the requirements for both high throughput and smaller form factor. Specifically, EyeCoD integrates system-, algorithm-, and accelerator-level techniques to boost system efficiency without sacrificing eye tracking accuracy. We believe that our EyeCoD system will pave the way for next-generation eye tracking solutions in VR/AR and shed light on future innovations for intelligent imaging systems.
Communication-Efficient Coded Computing for Distributed Multi-Task Learning

Distributed multi-task learning (MTL) can jointly learn multiple models and achieve better generalization performance by exploiting relevant information between the tasks. However, distributed MTL suffers from communication bottlenecks, in particular for large-scale learning with a massive number of tasks. This paper considers distributed MTL systems where distributed workers wish to learn different models orchestrated by a central server. To mitigate communication bottlenecks both in the uplink and downlink, we propose coded computing schemes for flexible and fixed data placements, respectively. Our schemes can significantly reduce communication loads by exploiting workers’ local information and creating multicast opportunities for both the server and workers. Moreover, we establish information-theoretic lower bounds on the optimal downlink and uplink communication loads, and prove the approximate optimality of the proposed schemes. For flexible data placement, our scheme achieves the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">optimal</i> downlink communication load, and the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">order optimal</i> uplink communication load that is smaller than 2 times of the information-theoretic optimum. For fixed data placement, the gaps between our communication load and the optimum are within the minimum computation load among all workers, regardless of the number of workers. Experiments demonstrate that our schemes can significantly speed up the training process compared to the traditional approach.
Magnetic Model Calibration and Distortion Compensation for Electromagnetic Tracking in a Clinical Environment

Electromagnetic tracking (EMT) of surgical tools is used for image-guided navigation in minimally invasive interventions. Calibration of the tracker position and orientation is a fundamental task to correct systematic errors. This article explores the effect of metallic distortion on the open hardware Anser EMT system and describes a newly developed approach for volume calibration of the magnetic field model. The technique is also applied to effectively map the field in the presence of magnetic shields that can be used to reduce electromagnetic distortions passively. Unlike calibration techniques commonly found in literature, which aim to correct the tracking position error, our method can reconstruct an analytical magnetic field model starting from a reduced amount of scattered data. This way, a faster convergence of the solver algorithm is obtained, and EMT position and orientation errors are inherently corrected. The new modelling technique was applied to generate a new, undistorted magnetic field model for Anser EMT, to demonstrate the effectiveness of passive shielding, and to compensate for the distortion introduced by a commercial fluoroscopy C-arm. Furthermore, the concept of a real-time distortion compensation technique is expressed based on the modelling method presented in this article. For all the experiments, sub-millimeter errors were obtained after calibration, which may meet the needs of most endoscopic and surgical navigation tasks.
Cosine Similarity Based Group Movie Recommendation Scheme Considering Privacy of Users

The system that can recommend movies for individuals based on their interest has received high popularity. The schemes fail to provide recommendations for a family with people involving different age groups and different interests. Therefore a content based filtering scheme with cosine similarity based recommendation, considering interest of a group of people is proposed here. The system also ensures privacy of the users by generating hash code of the interest provided by the group. The proposed scheme performs well with a prediction accuracy of 83% for a maximum of four users with TMDB database.
Analysis on Classification of Handwritten Devanagari Characters Using Deep Learning Models

Handwritten Character Recognition in Indian scripts is a complex problem compared to printed characters. This paper concerns the handwritten isolated Devanagari characters, mainly consonants of 36 classes. The dataset for Devanagari Characters available at UCI library is chosen for this work which consists of 72,000 images for classification. CNN, Alexnet, LeNet and modified LeNet, are used for classification of this dataset. While CNN achieved an accuracy of 98.63% for the training data and 97.71% for unseen data, Alexnet has shown an accuracy of 98.71% accuracy for unseen data and 96.78% for training data. Experiments on LeNet and Modified LeNet have compromised the results with 87.76% maximum. Deep Learning algorithms were analysed with different training and testing data split (80:20, 70:30 and 90:10) with varying combinations of epochs (40, 50, 150).
Summarising a Twitter Feed Using Weighted Frequency

Abstract Data is growing exponentially every day, with 500 million tweets sent on Twitter alone (Desjardins 2021). Twitter feeds are long, take time to understand, are multilingual, and have multimedia. This makes it difficult to analyse in its raw form so the data needs to be extracted, cleaned, and structured, to be able to be used in research. This paper proposes summarising twitter feeds as a manner of structuring them. The objectives we sought to achieve are: (1) Use the Twitter API to retrieve tweets successfully, (2) Efficiently detect the language of text, and tokenize it to then analyse their content (in its language), (3) Use live tweets as the input instead of a database of tweets, (4) Create the interface as a plugin to make it accessible for computer scientists, and others, alike. We also aimed to test whether using weighted frequency to construct summaries of tweets would be successful, and by conducting a survey to test our results, we have found that our program is seen to be useful, accessible, and efficient at giving summarizations of twitter accounts. Weighted frequency also proved to be good at summarising text of any language, inputted.
Migrating Human Capital: A Case Study of Indian Migrants in the GCC Countries

How has the migration of human capital gained importance in this age of globalization especially when the mode of production has changed considerably after the industrial revolution? It has been profoundly considered that global production depends upon human capital, technology and other factors. Countries are investing heavily in human capital and technology to ensure long-term sustainability. The Gulf Cooperation Council countries, as one of the vital production regions of the world, are also shifting their development approach towards diversification of the economy. Until recently, human capital has long been neglected among the GCC countries as their economy was assorted towards oil and gas-based production only. However, the migration of skilled labour force from India and other South Asian countries has played an important role in the accumulation of human capital in the region. The present study tries to decipher the potential and volume of the human capital of the skilled and non-skilled workers who migrated from India to the GCC countries. It also analyses the role of the National Education Policy of India (NEP 2019) along with the resulting accumulation of human capital in upcoming years. The GCC countries are also exploring different avenues to enhance the skills of their respective citizens to compete for the challenges in a rapidly changing world. Some instances include increasing the share of budgetary allocation on research and development, education, training and, skill-oriented curriculum, etc. The study emphasizes the historical inventory of human resources, professionalism and skill transfer. Data about this research work has been collected from published reports, government publications research journals, etc. and demonstrates the result by applying relevant statistical tools.
Scalability of Gaussian Processes Using Asynchronous Tasks: A Comparison Between HPX and PETSc

Gaussian processes are a widely used alternative to neural networks for non-linear system identification. The method requires computing the inversion of a large covariance matrix. In this work, we introduce our new task-based asynchronous implementation, focusing on its most popular solver, the Cholesky decomposition. Our implementation is based on HPX, utilizing its asynchronous many-task runtime system. We can therefore investigate its scaling on multi-core hardware and for GPU offloading. Furthermore, we compare our HPX implementation against a high-level reference implementation based on PETSc. We demonstrate that the HPX implementation’s performance is directly tied to the chosen tile size. Compared to the PETSc reference, our task-based implementation is faster in the entire node-level strong scaling experiment on EPYC ROME, showing better parallel efficiency.
Research on the Visualization Method of Weibo User Sentiment Analysis Based on IP Affiliation and Comment Content

Aiming at the problems of huge number of comments in Weibo, complex data format, inconsistent emotion model, vague emotion identification and lack of effective spatial location expression, a visual research method of emotion analysis of Weibo users based on IP affiliation and comment content is proposed in combination with the open IP affiliation function of Weibo in 2022. The proposed method uses crawler to obtain Weibo comment data, and uses manual tagging and machine learning methods to segment words and analyze emotions of comment content; Finally, two methods are proposed to form maps by combining spatio-temporal data such as time and coordinates. Experimental results show that this method is feasible and effective for the visualization of emotion analysis of Weibo users, and can achieve better visualization results.
Scheduling Many-Task Applications on Multi-clouds and Hybrid Clouds

A centralized scheduler can become a bottleneck for placing the tasks of a many-task application on heterogeneous cloud resources. We have previously demonstrated that a decentralized vector scheduling approach based on performance measurements can be used successfully for this task placement scenario. We then extended this approach to task placement based on latency measurements. Each node collects the performance measurements from its neighbors on an overlay graph, measures the communication latency, and then makes local decisions on where to move tasks. Our recent experiments in CloudLab with nodes allocated on multiple cloud sites demonstrate that using latency in our vector scheduling approach results in better performance and resource utilization. While our algorithm for configuring the overlay graph based on latency measurements was beneficial with simulated communication delays, it was not beneficial in the multi-cloud environment.
Fast Mining Prevalent Co-location Patterns Over Dense Spatial Datasets

Traditional prevalent co-location pattern mining (PCPM) methods generate complete table instances (TIs) of all candidates, which is both time and space consuming. Existing Apriori-like methods focus on improving the efficiency of TI generation, while existing Clique-based methods still contain many repeated traversing processes, and therefore neither of them can very well detect the overlap in TIs. To address these challenges, this paper first proposes the concept of extended maximal cliques (EMCs) to detect instance overlap situations, and designs a hash-based storage structure SSHT to reduce mining consumption. Second, a novel approach PCPM-EMC is introduced to detect all prevalent co-location patterns (PCPs), which uses the proposed P-BKp,d algorithm to generate EMCs, and adopts a bidirectional pruning strategy for PCP detection. Lastly, extensive experiments on both real-world and synthetic datasets show that the proposed approach is efficient, and reducing more over 80% space consumption and more than 50% time consumption than existing methods, especially in dense datasets.
A Deep-Learning Based Method for Real-Time Insulator Detection in Power System

By using adapted YOLOv3 depth network model, an object detection algorithm based on deep learning module is proposed to solve the accuracy problem of insulator detection in aerial images. In order to solve the problems of wrong detection and missing detection of the target, the detection effect of various deep learning algorithms on insulators in complex environments is analyzed, and ResNet-18 is taken as the backbone network structure of the insulator detection model for the power grid patrol inspection. First, the target to be tested is decomposed into multiple component sub targets with intersection, and then it is detected. Then, using the features and meanings of the intersection areas between the components, they are aggregated and redefined, and then a multi-scale feature pyramid is constructed and formed by merging multi-level labels, which is incorporated into the backbone network, effectively using the multi-target multi-level labels to solve problems such as missed detection and error detection, so that the detected target areas are more accurate and meet the real-time requirements. The experimental results show that, compared with R-CNN and Faster R-CNN, the proposed network has better recognition accuracy and detection speed, and significantly improves the accuracy of target detection.