Reviewerly/exHarmony huggingface dataset:
Utilizing QR Codes for Smart and Low-cost Student Attendance Acquisition and Monitoring System in Eastern Visayas State University, Philippines

SDG 4 that pertains to quality education can be initialize through schools and universities for direct impact. Thus, one way to have quality education is to ensure that students go to school and receive learning from the academe. However, absenteeism has a significant impact on their academic achievement of every student since those who attend school consistently are more likely to thrive academically. With that, this study aims to address absenteeism using a smart attendance acquisition and monitoring system. The first objective is to design and build a prototype of the Student Attendance Acquisition System (SAAS) utilizing QR code technology and the Raspberry Pi. Then, the establishment of web server for the Attendance Monitoring System (AMS) that can store, retrieve, and display attendance data can be employed. Finally, the constructed system's efficiency must be able to test for reliability and identify the top absentees among the participants. Thus, using the QR code technology, there were no problem in the system after testing to 10 BSECE students over five consecutive days. The prototype is highly efficient and has a strong potential for EVSU attendance system improvement, which might reduce absenteeism and increase student performance in the BSECE department and to the university.
Readwell: A Book Recommender Android Application with a Point-of-Sales System

The COVID-19 pandemic has given people much free time. With this, the researchers want to encourage these people to read instead of scrolling through social media. A barrier to reading for many people is not knowing what to read and disinterest in popular books that they would find when they search online. The existing websites that encourage book reading rely on social networking for their recommendations, while the collaborative filtering algorithms applied to books do not exist in the mobile application form. Readwell is a book recommender Android app with a Point-of-Sales System created using Java, Python, and SQLite databases. The information regarding the books was web scraped from the Goodreads website. It aims to apply the more efficient collaborative filtering algorithm to an accessible mobile application that allows users to directly buy the books they are interested in, thus encouraging the reading and buying of books. The researchers created unit test cases to validate the different functionalities of the application.
Abnormal Behavior Detection using Object Detection and Tracking

Video surveillance and computer vision have been integrating rapidly. A full-pledged detection surveillance system, requiring proprietary hardware and systems, is uncommon in a regular household or small business. This study aims to bring person tracking, running detection, and fall detection into a highly modifiable package to make computer vision more accessible. The system utilizes the person detection capabilities of YOLOv4 in combination with the DeepSORT tracking algorithm to track people in a frame and derive useful metrics that would aid in detecting running and falling behavior. The accuracy tests on the system showed 78% on running detection and 80% on fall detection. FPS testing shows that the system could run at an average of 7.4 frames per second on entry-level hardware. The system utilizes a web-based approach with an API and an included web UI to show the customizability of the project.
Social Distancing Monitoring and Contact Tracing on CCTV Footage

Social distancing is one of the most effective measures to prevent the spread of the COVID-19 disease. Most methods of enforcing this in the Philippines resort to manual methods. As such, a video-based social distancing monitoring tool can help ensure constant enforcement of social distancing due to the availability and up-time of CCTV cameras in various areas. This can be achieved by using object detection and tracking techniques. Object detection can be used to detect people within an area, and tracking can be used to watch people who get into close contact with one another. Contact tracing can also be performed by processing the social distancing measurements and tracking information. This information can be stored to keep a record of who has a high risk of infection based on who they came into contact with and for how long. We introduce a social distancing monitoring and contact tracing framework using the EfficientDet object detector and DeepSORT tracker. This framework is used to monitor social distancing violations and keep a record of violations associated to the tracked people.
Integrating Virtual Class Unila with Online Multimedia Platforms in Case-Based and Project-Based Undergraduate ESL Classes

Implementing learning management system and multimedia platforms has been common practice in this era of online-learning.This study investigates the integration of Virtual Class Unila, an online learning management system, and some multimedia platforms such as YouTube, Quizziz, HTML5, and Google Meet in undergraduate English as a Second Language classes.The design of this study is classroom action research where the primary objective is to find out which multimedia platforms might be suitable to be integrated with Virtual Class Unila in order to improve students' learning experience.The participants of this study are 100 undergraduate students of Pre-Intermediate Reading Classes in English Education Department, Lampung University.The participants will be involved in several activities with multiple platforms applied in order to support the learning process.The participants then assessed and interviewed to see whether each platform is able to provide satisfying learning experience.This research is targeted to become reliable guidance for the development of Virtual Class Unila in the future.
A Practical Approach to Visualizing Different Phases of Project Management Using MSProject

This chapter discusses the problems of project preparation, project planning, project execution, and monitoring of a real-world project. Emphasis is placed on MS Project – a software product designed to create plans, projects, and schedules as well as control and management of their implementation. It is discussed in a practical manner its ability to understand how much time and work developers are investing in a software project. As a result, the project management actions helped the project be delivered on time without compromising any time, budget, or resource constraints. The authors considered different scenarios related to project risk. They used the project crashing technique, and the time of the project was significantly reduced allowing the project to finish on time. The relations between the activities, the resources, and the time for their implementation are defined. The effectiveness of using different types of calendars—standard and continuous—specific to the continuous mining activities is evaluated.
Mechanism for Crawling, Filtering, and Presenting Opinionated Content on Online Products to the Customers

There is a large amount of data available on the web in form of opinions, which need to be accessed for mining opinions. This is an ever-growing field that brings together the reviews, blogs, discussions on forums, Twitter, microblogs, and social networks. A user may be looking for opinions on some commodity or product for making decision regarding purchase for which there is the need of a system based on question answering. This gives rise to a question answering (QA) system. This system works on all the aspects of question answering along with the mining of opinions. The chapter discusses all the modules of the question answering system along with how the opinions are mined. The details of implementation along with the performance analysis of the proposed system are given in the chapter. On performance evaluation, a high value of opinion accuracy has been found that shows that the system performs well.
Visualisation of Blockchain Concepts

A blockchain is a growing list of documents, called blocks, that can be cryptographically related together. A cryptographic hash of the previous block, a timestamp, and transaction data are all protected in every block. The project's purpose is to supply a web app that demonstrates blockchain fundamentals that include transaction verification and hash generation. The reason of blockchain generation is to provide a chain of sequentially ordered data blocks that are stored in a decentralised manner through all community contributors (nodes). A block of facts is formed while the nodes solution, a complex mathematical assignment (evidence of labour) that is stipulated via a consensus strategy, is cryptographically blanketed. Because everything that happens on blockchain is encrypted, there's no doubt that it provides next-level encryption. Similarly, tampering with the data on the blockchain is impossible. To be safe, you can check file signatures on all nodes across all ledgers in the network to make sure they haven't changed.
AI in Gaming and Entertainment

The chapter describes the understanding and the application of the concept of the various AI algorithms that also include reinforcement learning algorithms, which is a great contribution to the decision-making of the AI agents/AI bots. The game consists of player and opponent AI competing with each other in which the goal is to kill the opponent. The game is basically an Action RPG game (real-time combat between the player and AI agents). Wherever the performer has the ability to focus and see on his own stats and the stats of the character, accordingly he can determine the health, mana, strength, XP, and other abilities. The AI bots on the other hand are programmed to follow the player, and depending on the state that it is in and the player's last attack, it makes decisions accordingly and tries to defeat the player. Application of the various AI algorithms on the software Unreal Engine are achieved by C++ programming and blueprint/visual scripting. The game contains player and adversary AI competing with each other in which the goal is to slay the opponent.
Analysis Method of Flow Density Based on YOLOv4 Multi-feature Fusion

In order to solve the problem of low precision in calculating the pedestrian flow density of train carriages under complex background and mutual occlusion scenes, a method based on YOLOv4 multi-feature fusion model is proposed to estimate the pedestrian flow density. Firstly, the data set is labeled with head and body respectively to generate the head set and body set. Then use these two data sets to train YOLOv4 multi-feature fusion models YOLO-body and YOLO-head respectively. Finally, the two models are used to reason on the same test data set, and their output results are fused with maximum values. The results show that the method based on YOLOv4 multi-feature fusion model improves the accuracy by 8% compared with the original object detection method and the density map regression method while maintaining better robustness, and can better adapt to the scene of multi-occlusions carriage flow density detection.
Research on Improved Image Classification Algorithm Based on Darknet53 Model

An improved Image classification algorithm based on Darknet53 model is proposed in this paper to solve the problem that a large amount of the same gradient information is repeatedly used to update the weights of different dense layers in the Darknet53 network in the process of back propagation. Firstly, the residual block in the original network is replaced by THE CSP module, referring to the idea of cutting off the gradient flow in CSPnet to prevent too much repeated gradient information. Second, the Mish activation function is used to replace the Leaky ReLU function, which can transmit information more smoothly and achieve better accuracy and generalization. Finally, a SPP module is added to the end of the original network structure to solve the multi-scale problem of the main part of image classification. The experimental results show that the improved Darknet has achieved better performance, and the accuracy is improved by 1.3% compared with the original network; At the same time, compared with resnet50 and resnet101, the improved Darknet has better effect on image classification.
A Method of UAV Cluster Track Planning Based on Improved PSO Algorithm

Aiming at the problem that the complex constraints of UAV cluster offline path planning are easy to lead to no solution in calculation, a method of UAV cluster offline path planning based on improved particle swarm optimization (PSO) is proposed. The method first constructs a cost function that combines UAV energy consumption , high threat, cluster collision avoidance and communication restrictions. Then, the acceleration coefficient and the maximum speed in the PSO algorithm are adaptively changed linearly to obtain the global optimal solution with fewer iterations, and the mutation strategy is updated for the cost function value of particles, so that the influence of beneficial particles is strengthened and the influence of unrelated particles is weakened. The final simulation results show that compared with the traditional PSO algorithm, this method can effectively reduce the number of algorithm iterations and improve the success rate of UAV cluster offline path planning.
Mapping Crypto: Analysis of Cryptocurrency Controversies Based on Latour’s Approach

Crypto market is characterized by high volatility, an abundance of fraud and asymmetric information. Despite the declared advantages: decentralisation, independence, security and transparency, cryptocurrency is perhaps one of the most striking manifestations of “risk society”. The discussion about cryptocurrencies still has a variety of debated aspects and a plurality of positions. However, the proponents of all these positions, while speaking theoretically, do not disclose the motivation of the actual actors of this market – the users of cryptocurrencies. Based on Latour’s idea of “following the actors”, we map the crypto community controversies and trace their connections with discourse, technologies and other actors in the cryptocurrency field, using the InfraNodus tool to analyze the crypto discussion on Twitter.
Data Encoding for Social Media: Comparing Twitter, Reddit, and Telegram

Social networking platforms have become a major source of data for most textual machine learning models. Applications of encodings in earlier language models, as well as advancements in model reusage, have opened new possibilities for case studies with limited or unsupervised data. In this paper, the authors test whether semantic similarity of large-scale data from three platforms allow for applying the same transfer-learning language models on data from various social media. For this, the authors perform a comparative case study to outline linguistic differences and measure similarity for deep neural encodings for the case data. In particular, semantic similarity is evaluated using traditional text similarity metrics, structure metrics of the corpora, and RUBERT encodings that provide general semantic characteristics of the text data in three datasets. We show that the platforms are similar in semantic terms well enough for the transfer learning models to be applied, by both linguistic metrics and semantic encodings. We also demonstrate, however, that, despite the difference in the average text length, Twitter is more similar to Reddit than to Telegram by linguistic metrics, which hints to the idea of ‘platformization’ of social media speech. We conclude by stating the speech factors that may lead to platform dissimilarity.
Three Legal Views of a Software: Source Code, Derivative Work, and Work for Hire

As the digital landscape continues to evolve, intellectual property law plays an increasingly significant role in regulation of websites, databases, and computer software. This article critically examines key is-sues in the regulation of computer software in Russia, highlighting potential shortcomings in the legisla-tion and its alignment with legal practice and technological advances. Specifically, the analysis focuses on three core areas: 1) the legal definition of computer software and its source code, 2) the legal protection of computer software, and 3) the regulation of software made for hire. We will focus in particular on source code as the primary component of a computer software.While this article provides valuable insights into the aforementioned aspects of software regulation in Russia, it does not purport to address all related legal issues. In particular, it excludes discussions on un-protected elements of computer programs, software alienation, protection of software-generated audio-visual images, and indirect copying of source code. It also does not address the legal issues of "import substitution" of software in Russia, such as the legal status of Russian software and compulsory licensing of foreign software.
Benchmarking Parallelism in Unikernels

Virtualisation technologies are widely used in Cloud computing infrastructures, because they can be provisioned cheaply and quickly to meet demand. The common approaches are either to package a Operating System (OS) as a Virtual Machine, or to containerise software with an OS kernel. An emerging alternative are unikernels, which are customised kernels to support just one application. Unikernels are lightweight and an applications has sole use of the kernel, which offers potential for fast, resource efficient and secure execution. For these reasons, unikernels may be idea for parallel computing in the Cloud. However, the parallel performance of unikernel-based Cloud applications has not been extensively studied. This paper presents an evaluation of the OSv unikernel using a parallelised Mandelbrot benchmark, comparing with Docker and a monolithic VM for runtime, parallel speedups and boot-up time. OSv has the fastest boot-up time, and is comparable with the parallel speedups of Docker and the monolithic VM.
ParaGnosis: A Tool for Parallel Knowledge Compilation

ParaGnosis ( https://doi.org/10.5281/zenodo.7312034 , https://zenodo.org/badge/latestdoi/560170574 , Alternative url: https://github.com/gisodal/paragnosis , Demo url: https://github.com/gisodal/paragnosis/blob/main/DEMO.md ) is an open-source tool that supports inference queries on Bayesian networks through weighted model counting. In the knowledge compilation step, the input Bayesian network is encoded as propositional logic and then compiled into a knowledge base in decision diagram representation. The tool supports various diagram formats, including the Weighted-Positive Binary Decision Diagram (WPBDD) which can concisely represent discrete probability distributions. Once compiled, the probabilistic knowledge base can be queried in the inference step. To efficiently implement both steps, ParaGnosis uses simulated annealing to split the knowledge base into a number of partitions. This further reduces the decision diagram size and crucially enables parallelism in both the compilation and the inference steps. Experiments demonstrate that this partitioned approach, in combination with the WPBDD representation, can outperform other approaches in the knowledge compilation step, at the cost of slightly more expensive inference queries. Additionally, the tool can attain 15-fold parallel speedups using 64 cores.
Hetero-Vis: A Framework for Latency Optimized Heterogeneous Deployment of Convolutional Neural Networks

Convolutional Neural Network (CNN) models often comprise multiple layers varying in compute requirements. For deployment, a number of hardware accelerators are available that have subtle differences in compute architectures within the same family of platforms. A component (a set of layers) of a CNN model may perceive different performance in different compute architectures. Optimal mapping of the components of a CNN model across a given heterogeneous architecture can leverage underlying different compute architectures to deliver minimum inference latency $$^3$$ . In this paper, we present an optimal partitioning approach to map a CNN model across heterogeneous architectures by leveraging a repository of performance-measurement-benchmark (PerfLib) for different accelerators. Our proposed framework, Hetero-vis, decides optimal partitions and mapping of a CNN network across different accelerators to minimize the inference latency. Our experiments reveal up to 1.43 $$\times $$ better performance with grouped layer deployment of CNN models on heterogeneous hardware compared to the entire model deployed on a single accelerator. $$^1$$ Inference latency and latency terms are used interchangeably in the rest of the paper.
Enhancing Recommendation Capabilities Using Multi-Head Attention-Based Federated Knowledge Distillation

As the internet and mobile computing have advanced, recommendation algorithms are used to manage large amounts of data. However, traditional recommendation systems usually require collecting user data on a central server, which may expose user privacy. Furthermore, data and models from different organizations may be proprietary and cannot be shared directly, leading to data isolation. To address these challenges, we propose a method that combines federated learning (FL) with the recommendation system using a federated knowledge distillation algorithm based on a multi-head attention mechanism. In the proposed approach, knowledge distillation is introduced on the basis of FL to induce the training of the local network and facilitate knowledge transfer. Further, to address the non-independent identical distribution of training samples in FL, Wasserstein distance and regularization terms are incorporated into the objective function of federated knowledge distillation to reduce the distribution difference between server and client networks. A multi-head attention mechanism is used to enhance user encoding information. A combined adaptive learning rate is adopted to further improve the convergence. Compared to the benchmark model, this approach demonstrates significant improvements, with accuracy enhanced up to 10%, model training time shortened by approximately 45%, and average error and NDCG values decreased by 10%.
Geodesic Affinity Propagation Clustering Based on Angle-Based Outlier Factor

The affinity propagation (AP) clustering algorithm has received a lot of attention over the past few years. AP is efficient and insensitive to initialization, and generates clustering results with lower error and in less time. However, there are still two key limitations: AP-related algorithms cannot identify outliers in clusters. And they are usually not ideal for processing nonlinear data. To address the above issues, we propose a geodesic affinity propagation clustering algorithm based on angle-based outlier factor (ABOF-GAP). First, outliers are identified according to the value of angle-based outlier factor. Besides, Euclidean distance is replaced with geodesic distance to measure similarity. Experiments on synthetic data and real data illustrate the effectiveness of the ABOF-GAP algorithm.