Reviewerly/exHarmony huggingface dataset:
Application E-Learning Based on Cloud Computing at the Faculty of Engineering, Batam University

The development of information technology and the increasingly widespread use of the internet has changed the learning process in higher education, especially during the COVID-19 pandemic, many higher education are conducting distance learning processes by implementing cloud computing-based e-learning. This is also in line with the government's recommendation that we apply health protocols to break the chain of the Covid-19 virus, such as studying and working from home. Batam University, in this case, the engineering faculty, includes those implementing cloud computing-based e-learning for distance learning The purpose of this research is to see how far the implementation of e-learning based on cloud computing according to users, in this case, are lecturers and students of the engineering faculty of Batam University, knowing the readiness of lecturers and students towards the process and products of e-learning itself and satisfaction from the lecturers and students. at the University of Batam at the engineering faculty during the e-learning implementation process. This research method uses a combination of quantitative and qualitative descriptive methods by distributing questionnaires to lecturers and students in the university environment at the engineering faculty which consists of 4 (four) study programs namely mechanical engineering, electrical engineering, civil engineering, and information systems After processing the data using the validity test, reliability test and qualitative analysis, it was obtained that the lecturer respondents agreed with the implementation of cloud computing-based e-learning by 41%, with an average Respondent Achievement Level (TCR) of lecturers of 81.82% in good category. Student respondents' answers strongly agree with the implementation of e-learning based on cloud computing of 44 with an average Student Achievement Level of Respondents (TCR) of 80.47% in good category.
Preface

On behalf of the Steering Committee, I have the pleasure of welcoming you to the 4 th International Conference on Research and Learning of Physics (ICRLP 2021), which will be held online, in Padang, West Sumatra, Indonesia on September 1 st – 2 nd , 2021. Due to the impact of COVID-19, many communities from around the world are under strict health measures and strict travel restrictions, and participants of the 4th International Conference on Research and Learning of Physics (ICRLP 2021) are also facing the difficulties of travel restrictions. Considering the situation in order to make academic communication on schedule, ICRLP 2021 has been held online using a video conferencing tool. This 4 th ICRLP is organized by the Department of Physics, Universitas Negeri Padang. The theme of the conference is "Flexibility and Innovation in Physics and Physics Education Research in a New Normal and Society 5.0 Era". This topic was brought up because research must continue to be conducted and developed even if the Covid-19 pandemic is not over. As a result, one solution to addressing the limitations of this new normal era is the implementation of research that is flexible while still offering novelty or innovation in the field. Furthermore, as the times change, we are expected to be adaptable, and a concept of a human-centered and technology-based society known as the era of society 5.0 has begun to be applied. I would like to express my sincere appreciation to six distinguished experts as our keynote speakers, all the participants, financial sponsors, exhibitors, supporting organizations and all the committee members who has made 4th ICRLP successful. With these strong support, we are sure 4th ICRLP will be beneficial to all the participants. Finally, I would like to thank the members of reviewers for their kind assistance in reviewing the papers. Dr. Riri Jonuarti, M. Si General Chair of ICRLP 2021 List of Organizing Committee and Editorial Team are available in this pdf.
The Effect of Variation Calcination Temperature of Polystyrene/Tetrahydrofuran (PS/THF) Hydrophobic Layers on Contact Angle and The Compressive Strength of Cement Mortar

Abstract Infrastructure development in Indonesia continues to increase, this can be seen from the increasing number of buildings and housing. The house is an important building for humans, one of the important parts of the house is the walls made of bricks and covered with plaster. Its function, which can be used as plaster on walls, makes the authors use mortar as a sample in this study. The drawback is that the existing mortar is easy to grab because it absorbs water. Therefore, the author wants to make a waterproof mortar by providing a hydrophobic layer. This study aims to obtain the optimum value of the contact angle and compressive strength by varying the temperature as the independent variable used, namely 30°C, 60°C, 90°C, 120°C and 150°C. The results showed that the optimal contact angle was 120.7053° with a compressive strength of 258.19 kg/cm 2 . The temperature calcination relationship with the contact angle and compressive strength of mortar, is the higher the temperature. The temperature relationship with the contact angle and compressive strength of cement mortar is higher than the temperature.
Multi-hop Knowledge Reasoning with Deep Reinforcement Learning

Multi-hop reasoning is an effective and explainable approach to predicting missing facts in Knowledge Graphs (KGs). It usually adopts the Reinforcement Learning (RL) framework and searches over the KG to find an evidential path. However, there are few RL based reasoning methods in Knowledge Forests (KFs), the existing approach is usually to transfer multi-hop reasoning methods in the KG to the KF. However, this way often leads to a long reasoning path with a lot of redundant information. And the existing reasoning methods tend to capture vector representations from local structures, which will lead to get meaningless paths. In order to solve these two problems, we present, in this paper, a deep reinforcement learning based model named by MultiKR for KFs and KGs, which reduces redundant information in the path by learning global vector representations. In addition, we construct a novel reward function that guides the agent's choice of actions by considering accuracy, diversity and rationality to get valid and short path. We test our model on two KF datasets, that is, COMPUTER SCIENCE (CS) and MEDICINE (MED), and two KG datasets FB15K-237 and NELL-995. Extensive experiments show that our model is effective and competitive with many current state-of-the-art methods, and also performs well in practice.
Detecting Runtime Exceptions by Deep Code Representation Learning with Attention-Based Graph Neural Networks

Uncaught runtime exceptions have been recognized as one of the commonest root causes of real-life exception bugs in Java applications. However, existing runtime exception detection techniques rely on symbolic execution or random testing, which may suffer the scalability or coverage problem. Rule-based bug detectors (e.g., SpotBugs) provide limited rule support for runtime exceptions. Inspired by the recent successes in applying deep learning to bug detection, we propose a deep learning-based technique, named Drex, to identify not only the types of runtime exceptions that a method might signal but also the statement scopes that might signal the detected runtime exceptions. It is realized by graph-based code representation learning with (i) a lightweight analysis to construct a joint graph of CFG, DFG and AST for each method without requiring a build environment so as to comprehensively characterize statement syntax and semantics and (ii) an attention-based graph neural network to learn statement embeddings in order to distinguish different types of potentially signaled runtime exceptions with interpretability. Our evaluation on 54,255 methods with caught runtime exceptions and 54,255 methods without caught runtime exceptions from 5,996 GitHub Java projects has indicated that Drex improves baseline approaches by up to 18.2&#x0025; in exact accuracy and 41.6&#x0025; in F1-score. Drex detects 20 new uncaught runtime exceptions in 13 real-life pro-jects, 7 of them have been fixed, while none of them is detected by rule-based bug detectors (i.e., SpotBugs and PMD).
Book Review: The Electronic Resources Troubleshooting Guide

The discovery and access of electronic resources is full of challenges. Complicated relationships between vendors, integrated library systems, link resolvers, and knowledge bases both guarantee the occurrence of patron access problems and obscure the root cause. In The Electronic Resources Troubleshooting Guide, the authors provide frameworks for solving any technological problem and then break down the components of electronic resources (e-resources) access. This book is ideal for those who have found themselves in a position where they need to develop familiarity with the problems that may occur with e-resources, as well as manager who seek to formalize e-resources troubleshooting policies.
Presence Stickers: A Seamlessly Integrated Smart Living System at a Solitary Elderly Home

This research develops Presence Stickers, the capacitive sensing module that can be easily affixed to the existing space element surfaces (such as a wall, door, stairs), and daily objects or home furniture (such as a chair, cabinet, table, sofa, etc.).These 30*30 cm Presence Stickers can actively sense people's physical behaviors and body movements in spaces.From the preliminary analysis of observing the 80-year-old elderly subject's daily activities, the movement trajectory of the 'Move-Stop' pattern is found.There will be a Touch (T) and Touchless (TL) relationship between the body and the space elements or objects.Furthermore, the touchless, or non-contact, intimate relationship can also be divided into two types: 1.The body that 'Passes by' (P) the spatial elements or objects, and 2. The body that 'Stays' (S) in front of the object and performs activities.These three types of the intimate relationship between bodies and objects, i.e., T, TL-P, and TL-S, were used as the main sensing conditions to develop the Presence Stickers sensor module.We affixed 8 Presence Stickers on 9 objects in six spaces and finally, the life pattern can be analyzed and the sensors provide the customized intelligent application function for the elderly.
A memetic algorithm based on edge-state learning for max-cut

Max-cut is one of the most classic NP-hard combinatorial optimization problems. The symmetry nature of it leads to special difficulty in extracting meaningful configuration information for learning; none of the state-of-the-art algorithms has employed any learning operators. This paper proposes an original learning method for max-cut, namely post-flip edge-state learning (PF-ESL). Different from previous algorithms, PF-ESL regards edge-states (cut or not cut) rather than vertex-positions as the critical information of a configuration, and extracts their statistics over a population for learning. It is based on following observations. 1) Edges are the only factors considered by the objective function. 2) Edge-states keep invariant when rotating a local configuration to its symmetry position, but vertex-positions do not. These suggest that edge-states contain more meaningful information about a configuration than vertex-positions do. It is impossible to set the state of an edge without influencing some other edges’ states due to their dependencies. Therefore, instead of setting edge-states directly, PF-ESL samples the flips on vertices. Flips on vertices are sampled according to their capacities in increasing the similarity on edge-states between the given solution and a population. PF-ESL is employed in an EDA (Estimation of Distribution Algorithm) perturbation operator and a path-relinking operator. Experimental results show that our algorithm is competitive, and show that edge-state learning is value-added for both the two operators. The main contributions of this paper are as follows. Firstly, previous state-of-the-art evolutionary algorithms for max-cut focus on vertex positions in their evolutionary operation, this paper proposes a new and more reasonable perspective suggesting that edge-states are the critical information of divided graphs rather than vertex positions, and introduces a novel method to measure and utilize their similarities based on it. Such a perspective is fundamental to learning based algorithms design for max-cut and other graph partitioning problems, and can shed lights on future researches. Furthermore, since max-cut is one of the most classic and fundamental NP hard problems, many real-world problems involve dividing graph data into different parts to optimize certain functions, this new perspective may inspire related or similar problems. Secondly, besides the original edge-states based perspective, and the post-flip edge-states learning (PFESL) operator based on it, our memetic algorithm also incorporates a novel evolutionary framework which alternates between EDA based Iterated Tabu search (ITS) and path relinking based genetic algorithm. Finally, the proposed algorithm provides competitive results on two mostly used benchmark sets and and improves the best-known results of 6 most challenging instances. • An original edge-state perspective for design learning operators for max-cut. • A novel learning operator to learn edge states. • A new evolutionary framework for max-cut. • It provides competitive results on two mostly used benchmark sets.
Augmented Reality Implementation for Fault Diagnosis on Robotic Welding Cell

Industry 4.0 driven by digitalization and smart manufacturing involves the digital transformation of industries from automated to intelligent ones. Industrial robots and augmented reality which overlays digital information in the real world play a key role in achieving this. In this context, a robotic welding cell (RWC) is considered, which consists of two robots for material handling and welding process and also consists of various safety systems, namely emergency buttons, safety interlocks, and light curtains. The end-users take quick action using safety systems to rectify errors involved in the process and they may not be able to identify the faults occurring in RWC safety systems which require fault diagnosis as the solution. The main objective is to identify the critical systems and faults of safety systems such as the breakdown of interlock and diagnose using AR implementation by monitoring real-time IoT data and training the user to correct the faults. The 3D model of RWC is designed through Creo Parametric and then the animation sequences of the 3D model are created using Creo Illustrate to train the user to identify safety systems errors. Thingworx is used to create an IoT-powered augmented reality and connect real-time data as indicated in tower light, and finally, the AR experience is created and published through Vuforia Studio, which can be experienced by a mobile app called Vuforia view, which overlays digital visualization. This method allows enhanced user experience, reduces downtime of the system, lowers production losses, and improves real-time capabilities and system monitoring.
Penerapan Model Pembelajaran Quiz Team Untuk Meningkatkan Motivasi Dan Hasil Belajar Materi Pemerintah Kabupaten Dan Kota Pada Siswa Kelas IV SD 4 Kaliwungu

The data obtained during the observations were carried out for students and 4th grade students of SD 4 Kaliwungu Kudus district and city education material was still low, some helped students by 35% and completeness of student learning outcomes by 29%. This is caused by the teacher still using conventional methods, students are interested in low learning. Based on the description above, the authors are interested in clearing up the research on "Application of Team Quiz Learning Models to Increase Motivation and Learning Outcomes. From research conducted and based on results and supported by data, conclusions can be drawn from the application of valuable Student Learning Quiz models to 90% and completeness of learning outcomes to 86%.
DIGITAL IMAGE ENCRYPTION USING RSA AND LFSR

In this world of hasty evolution of exchanging digital data, data protection is essential to keep the data safe from the unauthorized parities. With the broad use of digital images of various fields, it is important to preserve the confidentiality for the data of an image from any unauthorized access. In this paper, the keys are generated using a random number generator which is based on Linear Feedback Shift Register (LFSR). The encryption/decryption is based on Rivest–Shamir–Adleman (RSA) with the random key generator.
Tangible Data Visualization of Physical Activity for Children and Adolescents: A Qualitative Study of Temporal Transition of Experiences

Children and adolescents in the UK are increasingly at risk of significant health problems due to physical inactivity. While activity trackers and fitness applications have focused on addressing this problem in youth, poor wear-time compliance and usability and accessibility issues have been frequently reported in the literature as barriers to engagement. Physicalization of data offers an alternative approach to engage with physical activity (PA). In this paper, we present the results of a seven-week qualitative study with 97 primary and secondary school children (8-14 years old). We took a temporal approach to collect children’s and adolescents’ perspectives in short video interviews as they received 3D-printed models representing their faded-weekly PA levels. Our findings showed that children’s and adolescents’ emotional engagement with the models remained high throughout the study, while their reflection on the models and their knowledge of what constitutes PA and its different types evolved over time. The findings from this temporal study suggest that tangible data visualization of PA evokes a number of experiences such as embodied reflection, active learning, emotions, and temporality of PA experience. Therefore, we argue that the motivational impact of regular tangible visualizations as a form of feedback should be considered alongside wearable trackers in addressing childhood inactivity.
Reflections on the 2020 Nashville Christmas Day bombing.

On Christmas Day, 2020, the detonation of an explosive device in Nashville, Tennessee resulted in many thousands of homes and businesses losing access to telecommunications services. While pre-established business continuity and disaster recovery plans helped many businesses and organisations recover from the impact and outages, many still suffered extended downtimes due to the lack of redundancies in place. Indeed, for organisations without a backup telecommunication provider, the effect of the bombing was dramatic, with hospitals, urgent care facilities, restaurants and other businesses all suffering major disruption. This paper examines how the various organisations responded and recovered from the incident, and considers whether legislation is required to ensure that telecommunications providers have sufficient redundancies in place to avoid such extended outages from happening again.
Seven Grade Students' Vocabulary Mastery Through the Use of a Vocabularies List in Online Learning

The goal of this study is to see how the vocabularies list technique can help students in class VII C at MTs Khazanah Kebajikan improve their vocabulary mastery. The goal of this study was to use the "Vocabularies List" technique to help students in class VII C improve their vocabulary mastery. Mixed Method research is used in this type of study, with quantitative data coming in the form of a percentage of students' vocabulary test scores, and qualitative data coming in the form of observation and direct and online interviews. The students' vocabulary mastery improved as a result of the vocabularies list technique, according to the findings. Students can understand and identify the meanings of English words. Students can gradually add new vocabulary, which will help them improve their English skills. The vocabulary arrangement is tailored to the student's material and designed to be as appealing as possible so that students are interested in learning it.
Curvature Injected Adaptive Momentum Optimizer for Convolutional Neural  Networks

In this paper, we propose a new approach, hereafter referred as AdaInject, for the gradient descent optimizers by injecting the curvature information with adaptive momentum. Specifically, the curvature information is used as a weight to inject the second order moment in the update rule. The curvature information is captured through the short-term parameter history. The AdaInject approach boosts the parameter update by exploiting the curvature information. The proposed approach is generic in nature and can be integrated with any existing adaptive momentum stochastic gradient descent optimizers. The effectiveness of the AdaInject optimizer is tested using a theoretical analysis as well as through toy examples. We also show the convergence property of the proposed injection based optimizer. Further, we depict the efficacy of the AdaInject approach through extensive experiments in conjunction with the state-of-the-art optimizers, i.e., AdamInject, diffGradInject, RadamInject, and AdaBeliefInject on four benchmark datasets. Different CNN models are used in the experiments. A highest improvement in the top-1 classification error rate of $16.54\%$ is observed using diffGradInject optimizer with ResNeXt29 model over the CIFAR10 dataset. Overall, we observe very promising performance improvement of existing optimizers with the proposed AdaInject approach.
Workshop And Motivation For Improving Student Skills Through The Information And Communications Technology

In the digital age, the role of information technology is needed to face competition in the community. Information and communication technology is an important element in contributing to changes that are fundamental to the structure of operations and management of organizations, education, transportation, health, and research. The internet is like two sides of a coin, the content offered is positive and negative, both are very dependent on the behavior of its users. The ease of access to the internet is increasingly being felt by the public with increasingly cheap hardware such as tablets and laptops as well as wider connection support. Various efforts to stem negative information continue to be pursued by various elements of society, but it is not effective if the user behavior is not changed. Teenagers are among the most vulnerable in the misuse of advances in internet technology, so it needs serious efforts to provide the right knowledge and skills in utilizing these advancements. By conducting workshops and motivation to improve the abilities and skills of Girimarto 1 High School students, it is hoped that school students can face the development of the digital era more readily. The results of this training gained a high level of satisfaction with the material that had been carried out.
Unambiguous discrimination of coherent states

Coherent states of the quantum electromagnetic field, the quantum description of ideal laser light, are a prime candidate as information carriers for optical communications. A large body of literature exists on quantum-limited parameter estimation and discrimination for coherent states. However, very little is known about practical realizations of receivers for unambiguous state discrimination (USD) of coherent states. Here we fill this gap and establish a theory of unambiguous discrimination of coherent states, with receivers that are allowed to employ: passive multimode linear optics, phase-space displacements, un-excited auxiliary input modes, and on-off photon detection. Our results indicate that these currently-available optical components are near optimal for unambiguous discrimination of multiple coherent states in a constellation.