Reviewerly/exHarmony huggingface dataset:
Implementation of the Ant Colony System Algorithm in the Lecture Scheduling Process

Scheduling lectures using the ant algorithm is able to provide alternative solutions for scheduling lectures that want a certain time optimally. In this research, the method used is literature study. This literature study was carried out by reading books and literature related to scheduling and ant algorithms. In addition to using literature studies, researchers also use field studies. This is intended to get a true picture of the state of the institution by observing directly at the institution, by means of observation or data collection methods by conducting interviews on the parts involved in the process of preparing the class schedule. From the results of the analysis obtained: scheduling lectures using the ant algorithm is able to provide solutions in scheduling. The ant algorithm can be applied to create a class schedule with optimal results, the ant algorithm is able to provide solutions in the preparation of a lecture schedule that requires a certain time.
Development of A Single-Axis Solar Tracker in Malaysia

The performance of solar photovoltaic systems can be improved if solar modules are kept perpendicular to the direction of solar radiation. Therefore, an accurate solar tracker system is important to continuously orientate solar modules to be always perpendicular to the solar radiation throughout the day. This paper presents the development and testing of a selfadjusting single-axis solar tracking system using two photovoltaic cells as photosensors. A prototype of the single-axis solar tracking system is built and tested based on continuous tracking method to the sun’s position throughout the day. An Arduino UNO microcontroller, a servo motor, and photovoltaic cells are selected as the components of the prototype. As this is an active tracking system, the orientation of the tracker receiver surface depends on the feedback received from the photosensors. Outdoor tests were carried out under clear skies at Kota Damansara, Malaysia (3.1467512 N, 101.5740615 E). The tracking inaccuracy is less than 5% with the maximum being 4.12%.
The Second Round of Information Security Challenges at TJX Companies

This descriptive case study summarizes TJX Companies (TJX), highlighting the considerable success its off-price retailing business has experienced in the United States and abroad. TJX traces its roots to small-town Massachusetts as far back as the early 20th century through its precursor company, Zayre Corporation. With over 4,500 stores globally, TJX is renowned as a dominant off-price retail business giant, positioned in the top 300 in the Fortune Global 500 annual rankings of the worldâ€™s largest companies with over $40B in sales and a market value of over $62B. TJXâ€™s resilience and sustainability result from its sophisticated value proposition comprised of its business model flexibility and opportunistic purchasing. Despite their financial performance, business niche dominance, and growth and expansion prospects, an unexpected ethical dilemma was recently uncovered. Based on UpGuard's third-party report, it was discovered that despite the purported recovery from a 2007 TJX data breach debacle and supposed enhancements in its digital infrastructure, there are still significant issues related to TJXâ€™s network security. It appears as though TJX, despite having a previous opportunity to reconcile, is still, even today, unable to provide adequate customer data protection. Thus, it is recommended that TJX configures its Domain Name System Security Extensions (DNSSEC) and bolster the security of its digital transactions by implementing point-to-point encryption (P2PE) and tokenization, payment card industry (PCI) validated P2PE solutions from its store chains to the banks and PCI-compliant firewalls. Additionally, they should revise their current business model to integrate consumer information protection into its key activities and include a reliable and secure digital infrastructure as a critical resource for the business. This case study will identify best-practices that organizational leaders in a number of industries might adopt and apply within their companies to benefit from the many lessons learned after studying TJX's many challenges and successes.
Method for detecting curvilinear trajectories of air targets using parametric transformations

For effective detection of maneuvering targets in conditions of low values of the signal-to-noise ratio, block methods of trajectory processing based on parametric transformations are used. The use of parametric transformations increases the resistance of the trajectory detection algorithm to an increase in the number of false marks and the omission of true ones in the analyzed sample, but is limited by the choice of a certain type of target movement model. The purpose of the article is to expand the possibilities for detecting target trajectories using parametric transformations by developing a new technique that solves the problem of detecting trajectories of air targets moving with lateral accelerations or maneuvering simultaneously in two planes. The paper proposes a method for restoring curvilinear trajectories of air objects (in the form of an ellipse) using the parametric Radon transform. The structure of the methodology is presented in the form of successive stages: formation of a sample of true and false marks for n surveys, parametric conversion of marks, clustering of selected groups, inter-survey identification of marks and making a decision on target detection and its trajectory. A variant of the parametric transformation for the selection of ellipses with various parameters and criteria for choosing the optimal values of the parameters that affect the operation of the technique are proposed. Estimates of the quality of the method are obtained based on the results of a statistical experiment, confirming the high capabilities of detecting target trajectories.
A Survey on Covid-19 Face-Mask Detection Techniques

This survey primarily focuses on 9 research papers which revolve around Covid-19 Face Mask Prediction. Since Covid has affected our lives a lot in the past year, the primary motive for this survey was to applaud the work done by various people to identify the current scenario and bring valuable solutions to this problem. In this way, even we could contribute to society's betterment. The face mask has proved to be a lifesaver throughout this pandemic, where various countries have made wearing masks compulsory. The rise of the Covid-19 pandemic has led to significant betterment in computer vision, face mask detection and Image processing. Many studies have been done in the face detection or facial expression recognition domain, but there are hardly any papers describing face mask detection. The highest training accuracy was obtained in the case of MTCNN along with FaceNet which was 100% [1], and highest test accuracy was achieved by Facemasknet Architecture which was 98.6%. [2]. This paper offers a concise description and summary of previous studies in the subject detection of face masks, as well as a comparative overview of works in this domain, outlining their gaps and areas for potential development.
Source Code Library (SCL): Application Support Learning Software Development: Source Code Library (SCL): Application Support Learning Software Development

Learning for software developers enthusiasts both students and faculty has its own problems in learning a programming language. During this practical learning has been widely used by online media. However, problems and obstacles at the time of writing the program code and error code appear an obstacle in developing software for students. This study developed a learning support applications software development so that it becomes a learning solution in software development. In a study using the stages are divided into three tahapyaitu; data collection of pre-development, development and implementation, data collection and post-development, sedangkantahap software development using linear sequential model is often called the waterfall model.
Specimen Data Refinery: Project Overview and Update

Consortium of European Taxonomic Facilities (CETAF) - Joint Spring 2021 meeting of the Information Science & Technology Commission and Digitisation Working GroupDate: 26 April 2021Location: Online (Zoom)An overview of the Specimen Data Refinery (SDR) project (workpackage) and progress update within the SYNTHESYS+ project. The SDR aims to develop a cloud-based platform of tools to processing natural history specimen images and their labels en masse in order to extract essential data efficiently and effectively
From Detail to Context: Modeling Distributed Practice Intensity and Timing by Multiresolution Signal Analysis

The distributed practice effect suggests that students retain learning content better when they pace their practice over time. The key factors are practice dosage (intensity) and timing (when to practice and how in between). Inspired by the thriving development of image recognition, this study adopts one of the successful techniques, multiresolution analysis (MRA), to model distributed and spaced practice (SP). We consider a sequence of practice sessions as a signal of the student's learning strategy. Then, we apply the stationary wavelet transform (SWT) to extract practice patterns spaced by three periods: small, medium, large. The result reveals a positive correlation between the small-spaced practice and the exam grade. The benchmark against baseline feature models shows that the SP patterns significantly improve the goodness-of-fit and complements the baseline models. This work successfully demonstrates 1) the use of MRA in modeling sequential patterns by event intensity and event timing; 2) the MRA approach can be used as an alternative method to improve existing student models of practice effort.
L2ight: Enabling On-Chip Learning for Optical Neural Networks via  Efficient in-situ Subspace Optimization

Silicon-photonics-based optical neural network (ONN) is a promising hardware platform that could represent a paradigm shift in efficient AI with its CMOS-compatibility, flexibility, ultra-low execution latency, and high energy efficiency. In-situ training on the online programmable photonic chips is appealing but still encounters challenging issues in on-chip implementability, scalability, and efficiency. In this work, we propose a closed-loop ONN on-chip learning framework L2ight to enable scalable ONN mapping and efficient in-situ learning. L2ight adopts a three-stage learning flow that first calibrates the complicated photonic circuit states under challenging physical constraints, then performs photonic core mapping via combined analytical solving and zeroth-order optimization. A subspace learning procedure with multi-level sparsity is integrated into L2ight to enable in-situ gradient evaluation and fast adaptation, unleashing the power of optics for real on-chip intelligence. Extensive experiments demonstrate our proposed L2ight outperforms prior ONN training protocols with 3-order-of-magnitude higher scalability and over 30X better efficiency, when benchmarked on various models and learning tasks. This synergistic framework is the first scalable on-chip learning solution that pushes this emerging field from intractable to scalable and further to efficient for next-generation self-learnable photonic neural chips. From a co-design perspective, L2ight also provides essential insights for hardware-restricted unitary subspace optimization and efficient sparse training. We open-source our framework at https://github.com/JeremieMelo/L2ight.
Non-existence results for vectorial bent functions with Dillon exponent

We prove new non-existence results for vectorial monomial Dillon type bent functions mapping the field of order 22m to the field of order 2m/3. When m is odd and m>3 we show that there are no such functions. When m is even we derive a condition for the bent coefficient. The latter result allows us to find examples of bent functions with m=6 in a simple way.
Transformer-based Language Models for Semantic Search and Mobile Applications Retrieval

Search engines are being extensively used by Mobile App Stores, where millions of users world-wide use them every day.However, some stores still resort to simple lexical-based search engines, despite the recent advances in Machine Learning, Information Retrieval, and Natural Language Processing, which allow for richer semantic strategies.This work proposes an approach for semantic search of mobile applications that relies on transformer-based language models, fine-tuned with the existing textual information about known mobile applications.Our approach relies solely on the application name and on the unstructured textual information contained in its description.A dataset of about 500 thousand mobile apps was extended in the scope of this work with a test set, and all the available textual data was used to fine-tune our neural language models.We have evaluated our models using a public dataset that includes information about 43 thousand applications, and 56 manually annotated non-exact queries.The results show that our model surpasses the performance of all the other retrieval strategies reported in the literature.Tests with users have confirmed the performance of our semantic search approach, when compared with an existing deployed solution.
Klasifikasi Tingkat Kesegaran Cumi - Cumi Berdasarkan Fitur Tekstur dan Warna dengan Menggunakan Metode Support Vector Machine

Marine animals are very susceptible to decay. The traditional method that is often used to distinguish the freshness of squid by local people is from the body color and smell of the squid. This method is very simple but has many shortcomings in distinguishing freshness from squid. The drawback of this method lies in the understanding and level of accuracy of each person who is different. So it is necessary to create a system that can distinguish the freshness level of squid automatically only from the image of the squid. In this study, a system model was developed that can classify the freshness level of squid using the Support Vector Mahine (SVM) method. The GLCM and histogram methods as well as the HSI color space are used for texture and color feature extraction. This study uses three types of classification. The total data used in this study are 360 body images of squid that have been cropped and resized by 128 x 128 pixels for the treatment type class. The total data for the freshness class with three types of classes is 495 and the total data for the freshness class with two types of classes is 330. In this study, the process of cropping, augmentation, resizing and conversion of color space in the dataset was carried out. The distribution of training data and test data is 70:30. The highest accuracy obtained is 67.75%.
Aspect-Based Sentiment Analysis and Singer Name Entity Recognition using Parameter Generation Network Based Transfer Learning

When we are interested in a certain domain, we can collect and analyze data from the Internet. The newly collected data is not labeled, so the use of labeled data is hoped to be helpful to the new data. We perform name entity recognition (NER) and aspect-based sentiment analysis (ABSA) in multi-task learning, and combine parameter generation network and DANN architecture to build the model. In the NER task, the data is labeled with Tie, Break, and the task weight is adjusted according to the loss change rate of each task using Dynamic Weight Average (DWA). This study used two different source domain data sets. The experimental results show that Tie, Break can improve the results of the model; DWA can have better performance in the results; the combination of parameter generation network and gradient reversal layer can be used for every good learning in different domain.
NEED ANALYSIS OF INSTRUCTIONAL MEDIA FOR DEAD AND MENTALLY RETARDED AT SLBN LENGAYANG

This study aims to analyze students need in the first class at SLBN students in development of learning media on the material for the theme of myself with sub-theme of part of body. This research is descriptive qualitative. Needs analysis is done based on the results of interviews, questionnaire and direct observation. The purpose of this study is to analyze the need for multimedia-based learning media software in the form of applications on android. The application is used to assist teachers in delivering learning materials for the introduction of part of body in the first class at SLBN Lengayang consisting of deaf and mentally retarded students. To collect the data, the researchers used questionnaires, interviews and observations. The results of the needs analysis obtained are: 1) The learning media used is still a presentation media 2) There is still a lack of attractiveness and student activity in teaching and learning activities.
Edu2Com: an anytime algorithm to form student teams in companies

Research supported by projects AI4EU (H2020-825619),Wenet (H2020-FETPROACT-2018-01), LOGISTAR (H2020-769142), and 2019DI17.
Interactions in the educommunicative ecosystem and the COVID-19 pandemic: the case of the Educational Science School of the UATx

After few months since the confinement caused by the COVID-19 pandemic began, and once the school year ended; It is possible to examine the situation in higher education due to the tensions caused by the new environment in which teachers and students have been forced to work; the students in particular, have faced a series of environments that have impacted their training process.Therefore, the present study seeks to identify the conditions in which the students faced and valued the experience of a non-face-to-face education, based on the interactions between the environments that make up the educommunicative ecosystem, from the communication models proposed by Kaplun (1998), and from the dialogical perspective based on the inheritance of C. Freinet and P. Freire.
Discourse Markers in Journalism: A Case in Compostela Valley State College-Maragusan

This study mainly explored the use of Discourse Markers (DMs) in journalistic writings and the factors that prompted the BSED English students of CVSC Maragusan to commit errors. This study employed the qualitative research. Ten (10) informants were part of the writing assessment and phone interview and were chosen through purposive sampling. For ethical considerations, this study underwent an ethics review before the data were gathered. Furthermore, to adhere to the Data Privacy Act, the identity of the respondents was held confidential. The Coding and thematic analysis were used in data analysis. The study's findings revealed that students have limited knowledge about DMs and their functions in basic writing. Additionally, the limited knowledge about DMs are the factors that prompted the respondents to commit errors in using the DMs in their journalistic writings. These findings imply that students should be taught more about the DMs, their types, and their proper usage in writing. With this, the incorporation of the DMs in the discussion should be given more attention, especially in writing class.
Software development projects: A process of collecting and marking data

During the recent years, software projects have tendency to be more complicated and sophisticated. Project Effort and timing preliminary estimation has low accuracy and IT-industry forces to use agile-methodologies to correct risks of resource overrun. Last inventions of software – neural networks and machine learning models in combination with classic methods like COCOMO offer help to estimate project’s parameters, but these models can solve only the tasks after continuous learning on big datasets.One of the main problems in current project estimation research area is lack of available big highquality datasets of real software projects. All current existed datasets have low relevance, tight range of project features or are not available for wide network of researchers. This article describes new project’s data mapping and collecting algorithm, divided to 3 different scenarios. it offers possibility to collect project data without complicated features or spending of additional resources. Next important part is idea of central open-source repository for project’s data collecting. Implementation of this idea in project team and research community could seriously improve the quality of project’s estimation, accelerate continuous delivery of project results, improve research quality and extend research community.
Rancang Bangun Low-Budget Autonomous Quadcopter

Quadcopter is an unstable flying vehicle. The flight controller regulates the rotational speed of the four motors, so that each propeller produces the lift required for the quadcopter to fly stable. It is interesting to know whether the use of generic quadcopter components obtained in the market can be mutually compatible. A stable and controllable quadcopter is a requirement for a quadcopter to fly autonomously. This research focusses on design a low-budget autonomous quadcopter. The research method consists of three main stages. First, the design and build phase of the quadcopter. The main frame and quadcopter arms are designed with a diagonal distance between the rotors of 45 cm. The basic components of the quadcopter are selected which are mutually compatible and assembled on the frame and arms. The flight controller is connected to the GPS, transmitter, and receiver. The autonomous system also involves a ground control system in the form of a Mission Planner. Second, the flight test stage is setting the PID constant so that the quadcopter can fly stable. PID tuning produces proportional constants of 0.088, integrals of 0.016, and derivatives of 0.008. Third, the autonomous flight test stage. The success of autonomous flying is known by analyzing the difference in input waypoint coordinates through the Mission Planner against the results of low-budget GPS receiver readings on autonomous flying missions. GPS accuracy was analyzed by calculating the 2DRMS and CEP values. The best value of 2DRMS = 1.56 meters and CEP = 0.64 meter occurred at Waypoint #2.
Pemanfaatan Software Desain untuk Pembuatan Media Promosi UMKM Kota Medan

Promotion media is a means used in supporting promotion activities and the introduction of products or services to the community, so that when doing promotion media can increase the income of a product or service. The purpose of this Scientific Writing is to increase knowledge for people MSMEs of Medan City to create various designs such as banners, pamphlets, logos, and brochures using Adobe Photoshop CS6 Software. From the results of the analysis that the people of MSMEs of Medan City can be given design training to be able to make their own all promotions of products and services using Adobe Photoshop CS6 Software, because their use is easy to understand for beginners and can be developed more optimally according to ability.