Reviewerly/exHarmony huggingface dataset:
Deep Regression Tracking with Graph Attention

Deep regression tracking is one of the fastest tracking algorithms due to its simplicity of framework, and therefore it is suitable for practical applications. However, most regression-based trackers do not make good balance between tracking speed and accuracy. Besides, the features of two adjacent frames are simply concatenated together without feature fusion operations, which limits the tracking accuracy. To address the above issues, we present a deep regression network with graph attention for visual tracking, which makes a trade-off between tracking speed and accuracy. To be specific, we introduce the graph attention network into the deep regression tracking framework to establish part-to-part correspondence between the two adjacent frames, and propagate the target information from the previous feature to the current feature. Experiments on challenging benchmarks including GOT-10k, OTB-100 demonstrate the competitive performance of the proposed algorithm in comparison with the state-of-the-art trackers.
Challenges in addressing low student performance

This article presents basic ideas for improving student achievement. Basic guidelines for motivating students, teachers and parents are proposed. Specific educational products resulting from these activities are presented. In the article, we present opportunities implemented in the STE(A)M center of High School Sava Dobroplodni. The learning material is "translated" by the teachers so that it can meet the students' interests and activities. We jointly implement numerous projects aimed at in-depth understanding of the educational material. We believe that students learn willingly when the material is close in subject matter and content.
An End-to-End Framework for Repairing Potentially Vulnerable Source Code

Nowadays, program development is getting easier and easier as the various IDE tools provide advice on what to write in the program. But it is not enough to implement a solution to a problem; it is also important that the non-functional properties, like the quality or security of the code, are appropriate in all aspects. One of the most widely used techniques to ensure quality is testing. If the tests fail, one can fix the code immediately. However, security issues are unexpected cases when implementing the program, which is why we do not write tests for them in advance. In many cases, security-relevant bugs can not only cause financial loss but also put human lives at risk, so detecting and fixing them is an important step for the reliability and quality of the program. The tool presented in this paper aims to generate automatic code repairs to potential vulnerabilities in the program. By integrating the recommended fixes, one can easily harden the security of their program early in the development process. A case study on six open-source Java subject systems showed that we were able to generate viable repair patches for 57 out of the 81 detected security issues (70%). For certain types (e.g., revealing private references of mutable objects), our tool reached close to perfect performance.
A IQ Correlation and Long-term Features Neural Network Classifier for Automatic Modulation Classification

Automatic modulation classification is the process of determining the modulation scheme of an intercepted signal with no a prior information. Its main advantage is that no communication overhead needs to be allocated for control information to inform the receiver about changes in a transmitted signal's modulation scheme. Recently, deep learning has been applied to automatic modulation classification tasks, but most deep learning classifiers have the difficulty balancing weight and efficiency. According to the characteristics of in-phase (I) and quadrature (Q) signals, a IQ correlation and long-term temporal features neural network classifier is proposed. It first extracts IQ correlation features and short-term temporal features by different directional convolutional filters, and then extracts long-term temporal features by the Long Short-Term Memory unit, and finally uses only one full connected layer for classification. Experiment results in the standard dataset RML2016.10a show that our proposed is more efficient and lightweight compared with the state-of-the-art deep learning classifiers.
Literasi Kearsipan Keluarga Bagi Siswa-Siswi Sekolah Calon Ayah Ibu

The neatness of family archives is still underestimated by the community. A well-managed family archive can provide convenience in finding the family data needed. Negligence in tidying the archives is due to a lack of knowledge and awareness of the community in family archives. This community service provides literacy training on family archives to school of future father and mother’s students. The debriefing was carried out for one month with two stages of implementation. The first stage uses the workshop method, where the service team provides materials and training on procedures for storing archives manually and digitally. Second, using the mentoring method, where participants carry out practice and are directly accompanied by a service team. Of the 19 students who attended the debriefing, 100% stated that the service was very useful and felt that they had an awareness of the family archive. 63% of participants continued to the mentoring stage and 18% of participants were able to complete manual and digital family file fireplaces properly. The process of tidying up family archives requires sufficient time and knowledge so that in the future participants can be given more time so that they can tidy up archives to the maximum.
Accurate All-round 3D Measurement from Trinocular 360-degree Stereo Cameras via Geometric Optimization of Image Texture and Color

Cameras are often used for Three-dimensional (3D) measurement in robotics and other applications.3D reconstruction with cameras is useful in applications such as mapping at disaster sites that are inaccessible to humans and infrastructure inspections.Robots can move around in such locations and capture 3D data from multiple points of view.However, such data obtained from a moving robot needs to be integrated to obtain the reconstruction of the entire environment.This process is tedious, time consuming, and inaccurate.For this reason, we focus on a method that can accurately measure the environment in all directions at once.
Inverse Kinematics Selection Algorithm for 6-DOF Manipulator Based on Neural Network to Pass Through the Singular Point

NOMENCLATURE= position and posture vectors of the robot's end-effector =vector related with each joint angle of the robot = Jacobi matrix of partial derivatives of the robot's p osition and posture at each joint angle = rotation matrix between each coordinate system co nfigured for each link 223
Segmentation for Grasping: An Approach toward Autonomous Table Clearing

Autonomous clearing of food trays and crockery at hawker centres involve robotic tasks such as item recognition, grasp point estimation and grasp execution.Assuming there are no dynamic obstacles in the manipulator workspace, we focus on the machine intelligence required for the first two tasks above.Our problem statement is as follows: Given a RGBD view of a cluttered scene consisting of one or more known objects at rest on a flat surface, we seek a way to determine a feasible grasp pose for each of these objects.
THE REFORMING OF THE SPHERE FOR HIGHER EDUCATION IN RUSSIA: GENESIS, FEATURES, AND IMPACT ON SOCIAL AND LABOR RELATIONS AT THE UNIVERSITY

The main stages, directions, and programs of reforming the sphere of higher education in Russia and their impact on changing the system of social and labor relations in universities are discussed in this article. The characteristic of the main categories of the institutions of higher education is given: the goals of creation, similarities and differences, the role of implementation programs of academic leadership. The authors have highlighted the features of social and labor relations in the organizations of higher education: in the sphere of attraction, employment, organization, and motivation of scientific and pedagogical workers.
Bimbingan dan Penyuluhan Inovasi Media Pembelajaran untuk Guru-Guru SMP Se-Komisariat Telukjambe Karawang

The purpose of this service activity is to provide motivation with the aim of relying on boring, tedious, and frightening learning methods through the total use of internet-based learning media. The technique of enforcing network provider is withinside the shape of Work Shop Guidance and Counseling on Learning Media Innovations for Teachers on the Telukjambe Karawang Commissariat. The topics of the provider had been the lecturers of the Public and Private Middle Schools of the Telukjambe Commissariat, Karawang Regency. The provider tool used is a questionnaire approximately motivation in enforcing using net media as an innovation in gaining knowledge of. The effects of this provider had been obtained, namely (1) The belief of instructors in developing gaining knowledge of media turned into commonly very enthusiastic, however wished the guide of centers and infrastructure, (2) The motivation for innovation of instructors in developing gaining knowledge of media turned into very enthusiastic, (3) The motivation to put into effect the coaching and gaining knowledge of method via gaining knowledge of media improvements, it seems that nearly 1/2 of of them said that they had been prepared to perform improvements withinside the gaining knowledge of method, and (4) the incentive for gaining knowledge of improvements to be made into medical writings, evidently this nevertheless has a tendency to now no longer but provide an positive signal.
Penggunaan media flashcard pada pembelajaran kosakata bahasa inggris di sekolah dasar

English as an international language has an important role in communication in the international sphere. This research is motivated by the lack of motivation to learn English in elementary school students and the difficulty of learning English vocabulary. The purpose of this study is to describe the use of flashcard media in learning English vocabulary. In this study, a qualitative descriptive method was used with a sample of 28 students at Public Elementary School 3 Banjarwangi, Garut, West Java. Data collection was carried out using observation techniques, interviews, and a literature review. From this research, data was generated that the use of flashcard media in learning professional vocabulary could increase students' motivation to learn and improve their understanding of professional vocabulary material. Nonetheless, learning motivation and increased understanding of English vocabulary needs to be in line with interesting and effective learning strategies. Keyword: flashcards, vocabulary, English language, Elementary school
Data-Driven Learning Tasks and Involvement Load Hypothesis

Despite the increasing research on the benefits of using corpora in language teaching and learning, Data-Driven Learning (henceforth, DDL) research has been criticized for its lack of contribution to second language theories. This paper intends to address this gap by examining the assumptions of Involvement Load Hypothesis (ILH) using two DDL tasks with different cognitive loads. Learners were assigned to one of two conditions: reading only or translation. Based on ILH, translation is more effective than reading in learning vocabulary, as it induces more cognitive involvement (Laufer &amp; Hulstjin, 2001). The two groups received a pretest to ensure their unfamiliarity with six target words. Each group underwent one instructional session under one of the two conditions. After the session, students took three immediate post tests on the six target items: active recall of form, passive recall of meaning, and production. Contrary to the expectations of ILH, the results of the immediate post tests showed no statistically significant difference in the mean of vocabulary knowledge between the two groups. In addition, in the delayed test, the reading-only group showed statistically higher scores in the active recall of form than their translation peers. The findings highlight some important theoretical and pedagogical implications for using DDL tasks, particularly for EFL vocabulary learning.
New Approach of Covid-19 Prevention by Implemented Combination of Decision Support System Algorithm

Indonesia and Malaysia from 2020 to 2021 were exposed to COVID-19 pandemic. Both countries implemented a policy of restricting entry areas based on almost the same criteria, In Indonesia namely as PPKM which applying some level of exposure to those infected with covid-19. The determination of this level was all based on the growth in numbers exposed to covid-19, but on pandemic cases, the number of people who do not suffer from COVID-19 disease but have the same symptoms as the symptoms of COVID-19 also need to be considered as the pandemic agent to their environment. We named it as Precaution Covid-19 Pandemic (PCP) Level. The current level of the COVID-19 pandemic has not been fully determined by this idea. So, the idea of this research is to determine the pre-pandemic or precaution level of covid-19 in an area interfere by surrounding area. PCP level was not based on the growth of those infected with the covid-19 disease, but influenced by the number of patients whose have the symptoms similar to the dominant symptoms of the covid-19. The PCP Level determination can be used for precaution policy and support the previous Level Pandemic Methods. To accomplish this idea, three algorithms are used, they are K-Mean algorithm as a pattern clustering and the AHP algorithm as a level determination of the Covid-19 pandemic, While the relationship of candidate symptom pairs to Covid-19 transmission is carried out using the Naïve Bayes algorithm. The results of this study show that the combination of the three proposed algorithms provides and using data symptoms closely to dominant covid-19 symptoms can give an alternative for precaution level of covid-19 pandemic. The model for determining Covid-19 transmission based on four candidate symptoms has 89% precision and 85% accuracy.
The Importance of Digital Learning and Teaching in the Era of COVID-19

We are pleased to publish the second regular issue (Volume 12, Issue 2) of &lt;em&gt;Higher Learning Research Communications (HLRC)&lt;/em&gt; for 2022. This year, we published two regular issues and a special issue, &lt;em&gt;Education Technologies and COVID-19: Experiences and Lessons Learned&lt;/em&gt;. COVID-19 and the implications of the switch to emergency online teaching continue to capture the interest of researchers. In the present issue, some of the manuscripts focus directly on COVID-19; still others examine different aspects of digital teaching and learning. As is typical with the HLRC, articles represent the voices of academics from various parts of the globe, demonstrating the importance and reach of digital education. The focus of the HLRC is on digital teaching and learning, higher education and the public good, and the preparation of students in key 21st-century employability skills, and the present issue reflects each of these focus areas. Summaries of the manuscripts for this issue are provided below.
Unsupervised Learning: Clustering Algorithms

Most unsupervised learning performs clustering. A well-known exception is autoencoder neural networks, which learn how to code the input data into a (typically) lower-dimensional representation. However, although autoencoders are normally categorized under unsupervised learning, they use the input data itself as the expected output, and therefore can also be regarded as supervised learning. We do not cover autoencoders or any other unsupervised method whose goal is not to split the data into different groups.
Ensemble Methods

Ensemble ML methods build predictive models by inducing several different predictors, called base predictors or base learners. Typically, a base predictor is a very simple model that is not meant to work on its own. The predictions of such simple models are normally not much better than random guesses, which is why they are called weak learners. It is through the aggregation of the different answers from the various base learners that ensemble learning builds strong learners that produce accurate predictions.
Optimization Problems and Local Search

This chapter introduces optimization problems, one of the largest classes of complex tasks where intelligent systems have become a reality in the last few years. Then, this chapter tackles the first type of algorithms that can be used to approach optimization problems: local search algorithms.
Decision Tree Learning

Given a training set, Decision Trees (DTs) [Quinlan, 1986] are predictive models represented as trees where each vertex represents a feature, or attribute, and each edge represents a possible value of that attribute. Leaves contain target values and a path from the root to a leaf allows us to make a prediction. Although DTs can be used for a wide variety of tasks [Rokach and Maimon, 2014], we will focus only on classification and regression.
Introduction to Machine Learning

As already discussed in Chap. 1 , Machine Learning (ML) (Mitchell, 1997; Shalev-Shwartz and Ben-David, 2014) is a field of study whose objective is to program computers to automatically learn to solve a problem, or accomplish a task. ML is useful when manually programming a computer to carry out a task is either impractical or infeasible. Typical cases are either problems that are so complex that they are beyond human capabilities, like those characterized by vast amounts of data, or tasks that living beings perform routinely, but our introspection on how we do it is not sufficiently elaborated to allow us to extract a well-defined algorithm, for instance driving, speech recognition, image understanding or client categorization. Other tasks where ML is useful are those where adaptability to changes in the environment is a necessary requirement, for instance time series forecasting, handwritten text decoding or spam detection
Genetic Algorithms

Genetic Algorithms (GAs) [Holland, 1975, Goldberg, 1989] are a commonly known method belonging to the field of Evolutionary Computation (EC) [Eiben and Smith, 2015].
Implementation of One Data-based Lecturer Profile Information System for Key Performance Indicator Monitoring

The Key Performance Indicators (KPI) released by the Indonesian Ministry of Education, Culture, Research, and Technology in 2021 are a new performance indicator for state universities that are concerned with output-based. It consists of 8 indicators that focus on the quality of university graduates, the quality of the lecturer, the quality of the curriculum, and learning. These papers focus on developing an integrated lecturer information profile with the university's one data database and the ministry's integrated human resource information system. The information focused on KPI 3 and KPI 4 to measure lecturer quality. The development process is presented using extreme programming (XP) methodology. The results show that all user stories are fulfilled and tested successfully.
The Application of Multimedia and Human Body Digital Modeling Algorithm Technology in College Physical Guiding

Using interviews and digital modeling of the human body to investigate the digital application of professional physical education in colleges and universities in Heilongjiang Province, analyze and explore the current problems in the digital application of physical education in colleges and universities. On this basis, it is proposed to strengthen the construction and improvement of the digital teaching system of college physical education, computer multimedia technology, multimedia courseware library, and address the problems of incomplete or incomplete facilities, inadequate development of operating platforms, and generally low teacher application capabilities. Strategic suggestions for optimizing the teaching of physical education courses in colleges and universities under the multimedia technology environment, and the efficiency increased by 7.28%.
Ad Fraud Detector for Mobile Applications

Ad fraud happens in cost per click ad networks where publishers charge advertisers for each click. Ad fraud is posing the huge loss to the mobile advertising industry. The conventional technologies use ensemble machine learning methods, neglecting the cost of incorrect classification for a fraud publisher is higher than a normal publisher. An effective classification model for variable ad fraud is proposed in this paper. Cost-sensitive Back Propagation Neural Network is combined with the novel Artificial Bee Colony algorithm in this research (CSBPNN-ABC). Feature selection is synchronously optimized with BPNN connection weights by ABC to reduce the interaction between features and weights. Cost Parameters are added to BPNN by correcting the error function. Experiments on real world click data in mobile advertising show that its superior classification performance compared with the state-of-the-art technology.
Enhancing Designer Knowledge to Dialogue Management: A Comparison between Supervised and Reinforcement Learning Approaches

Task-oriented dialogue systems are complex natural language applications employed in various fields such as health care, sales assistance, and digital customer servicing. Although the literature suggests several approaches to managing this type of dialogue system, only a few of them compares the performance of different techniques. From this perspective, in this paper we present a comparison between supervised learning, using the transformer architecture, and reinforcement learning using two flavors of Deep Q-Learning (DQN) algorithms. Our experiments use the MultiWOZ dataset and a real-world digital customer service dataset, from which we show that integrating expert pre-defined rules with DQN allows outperforming supervised approaches. Additionally, we also propose a method to make better usage of the designer knowledge by improving how interactions collected in warm-up are used in training phase. Our results indicate a reduction in training time by preserving the designer’s knowledge, expressed as pre-defined rules in memory during the initial steps of the DQN training procedure.
Textual Enhancement on Students’ Reading Comprehension

Textual enhancement is a treatment to modify the appearance of a text (bold, underline, capitalize, italicize, etc.) in certain parts that are considered important.This recent paper aimed at reviewing the relationship between the action of implementing textual enhancement and the student's reading comprehension.In this paper, the writers tried to analyze and explain points related and their contribution to the student's reading comprehension.Several studies have shown that by modifying the appearance of texts, readers will find it easier to recognize, mark, and realize important things that need to be known from the text [1][2][3][4][5].Related to scanning and skimming in reading, this treatment will give more impact on noticing important messages conveyed by the text.Once the reader can recognize and identify the important things in a text, the reader will understand the overall content of the text more easily.
Website-Based Information System Design at the TEFA Tax Center Politeknik Negeri Jember

The objective is to design a website-based information system at the TEFA Tax Center at Politeknik Negeri Jember.This research was conducted for approximately eight months.This was research qualitative research.The data used in this study were primary and secondary data obtained from interviews, field observations, data analysis, and documentation of supporting data for the TEFA Tax Center business process.This research resulted in creating a website-based information system design to be used in the TEFA Tax Center pilot.This website
Acknowledgment to the Reviewers of Journal of Cybersecurity and Privacy in 2022

High-quality academic publishing is built on rigorous peer review [...]