Reviewerly/exHarmony huggingface dataset:
IMPACT ONLINE LEARNING POLICY DROPS SEMESTER GRADES OF STUDENTS OF FISIP IN CENDERAWASIH UNIVERSITY YEAR 2020-2021

This study aims to investigate the impact online learning policy, issued Ministry of Education and Culture Republic of Indonesia, followed Rector of Cendrawasih University and Dean of Faculty of Social and Political Sciences. Policy impact drops semester grades students from seven program studies in Faculty of Social and Political Sciences: Governance Science, Public Administration, Business Administration, International Relations, Anthropology, Welfare, and Library. To analyze problem, Merilee S. Grindle’s policy theory was used. Qualitative research method. The study was conducted in Faculty of Social and Political Sciences of Cenderawasih University year 2020-2021. Study involves 20 respondents: 8 are lecturers of Faculty of Social and Political Sciences and 12 are students of Faculty of Social and Political Sciences. Qualitative analyzed method was used; data collection methods interview, observation, documentation have been analyzed according to the reality. The result shows that the second semester grades of students of batch 2021 drastically drop due to internet trouble, unavailable facilities, infrastructures, and server building for Wi-Fi in Faculty of Social and Political Sciences, Cenderawasih University, districts in Papua Province, and districts in West Papua Province. Recommendation for Dean of Faculty of Social and Political Sciences of Cenderawasih University, Rector of Cenderawasih University, Ministry of Education and Culture Republic of Indonesia is to allow face to face learning with number of students grouped into several daily shifts and good implementation of covid-19 health protocols in Faculty of Social and Political Sciences of Cenderawasih University.
Experimental Realization of Non-Adiabatic Holonomic Quantum Gates

We present the experimental realization of non-adiabatic holonomic quantum gates, paving the way towards noise-resilient quantum computing in integrated quantum optics. Their topologically protected functionalities are realized solely by means of non-Abelian geometric phases.
One-Stage Arbitrary-Oriented Object Detection With Prediction Decoupling

Arbitrary-oriented objects widely appear in natural scenes, remote sensing images, text images, etc., and thus arbitrary-oriented object detection has received considerable attention. Arbitrary-oriented object detection is a comprehensive task including multi sub-tasks which are boundary regression, classification and orientation prediction. However, since there is feature misalignment among these sub-tasks, the performance of one-stage detectors without fine-tuning process is greatly limited. Thus, in this study, we explore feature misalignment among sub-tasks in one-stage arbitrary-oriented object detection and find that there are relevance and conflicts among the features of category prediction, foreground score prediction, orientation prediction and boundary prediction. In order to alleviate conflicts and extract especial features for each sub-task, we propose a decoupling detection head composed of three branches respectively for object classification, orientation prediction, boundary regression and foreground score prediction. Especially, boundary regression and foreground score prediction share one branch due to their relevance and feature similarity. What&#x2019;s more, in order to construct an effective detection head for our decoupling model, we design two decoupling detection heads with different network depths. Experiments indicate that appropriate network depth can improve detection performance, and the proposed detection model achieves 68.91% mAP with single-scale testing on the challenging public dataset DOTA-v1.5. Compared with previous competitive method, our model gains 2.05% mAP.
Research on the Practice of &amp;quot;Web Design and Production&amp;quot; Course in Higher Vocational Colleges

The basic educational process of higher vocational college education is to teach young people with development of their morality, intelligence, physique, critical thinking and labor, with certain theoretical and practical knowledge.The research focuses on the study from traditional teaching to independent practical learning.The main change possibility is about teacher and student's cooperation which guides methodology and a project-based learning opportunity.The paper presents teaching and learning possibility based on an application of web page design and production course.We, beginning with the studies existed in web design and production course, introduce the application-based course, and develop the application the web design and production course teaching method.Through application-based project courses, students are able learn basic operations related computer software and then practice designing and producing digital products.The paper can show the practical teaching method in the course of web design and production has been effective in promoting learners, their interest in learning and improving their professional core qualities.
Preface: ICCET 2022

Welcome to the conference proceedings of ICCET 2022, the 5th International Conference on Communication Engineering and Technology. Since the continued COVID-19 pandemic made our original plan to host ICCET 2022 in Shanghai infeasible, we followed the footsteps of ICCET 2021 and made this year&#x0027;s conference a virtual event held during February 25&#x2013;27, 2022 to ensure attendees&#x0027; safety. We are glad to see that our researchers involved in related filed remain active and productive in this challenging year.
Revisiting Historical Bar Graphics on Epidemics in the Era of R ggplot2

This study is motivated by an article published in a local history magazine on "Pandemics in the History". That article was also motivated by a government report involving several statistical graphics which were drawn by hand in 1938 and used to summarize official statistics on epidemics occurred between the years 1923 and 1937. Due to the aesthetic information design available on these historical graphs, in this study, we would like to investigate how graphical elements of the graphs such as titles, axis lines, axis tick marks, tick mark labels, colors, and data values are presented on these graphics and how to reproduce these historical graphics via well-known data visualization package [ggplot2](https://CRAN.R-project.org/package=ggplot2) in our era.
Turn Tree into Graph: Automatic Code Review via Simplified AST Driven Graph Convolutional Network

Automatic code review (ACR), which can relieve the costs of manual inspection, is an indispensable and essential task in software engineering. To deal with ACR, existing work is to serialize the abstract syntax tree (AST). However, making sense of the whole AST with sequence encoding approach is a daunting task, mostly due to some redundant nodes in AST hinder the transmission of node information. Not to mention that the serialized representation is inadequate to grasp the information of tree structure in AST. In this paper, we first present a new large-scale Apache Automatic Code Review (AACR) dataset for ACR task since there is still no publicly available dataset in this task. The release of this dataset would push forward the research in this field. Based on it, we propose a novel Simplified AST based Graph Convolutional Network (SimAST-GCN) to deal with ACR task. Concretely, to improve the efficiency of node information dissemination, we first simplify the AST of code by deleting the redundant nodes that do not contain connection attributes, and thus deriving a Simplified AST. Then, we construct a relation graph for each code based on the Simplified AST to properly embody the relations among code fragments of the tree structure into the graph. Subsequently, in the light of the merit of graph structure, we explore a graph convolution networks architecture that follows an attention mechanism to leverage the crucial implications of code fragments to derive code representations. Finally, we exploit a simple but effective subtraction operation in the representations between the original and revised code, enabling the revised difference to be preferably learned for deciding the results of ACR. Experimental results on the AACR dataset illustrate that our proposed model outperforms the state-of-the-art methods.
360 Depth Estimation in the Wild -- The Depth360 Dataset and the SegFuse Network

Single-view depth estimation from omnidirectional images has gained popularity with its wide range of applications such as autonomous driving and scene reconstruction. Although data-driven learning-based methods demonstrate significant potential in this field, scarce training data and ineffective 360 estimation algorithms are still two key limitations hindering accurate estimation across diverse domains. In this work, we first establish a large-scale dataset with varied settings called Depth360 to tackle the training data problem. This is achieved by exploring the use of a plenteous source of data, 360 videos from the internet, using a test-time training method that leverages unique information in each omnidirectional sequence. With novel geometric and temporal constraints, our method generates consistent and convincing depth samples to facilitate single-view estimation. We then propose an end-to-end two-branch multi-task learning network, SegFuse, that mimics the human eye to effectively learn from the dataset and estimate high-quality depth maps from diverse monocular RGB images. With a peripheral branch that uses equirectangular projection for depth estimation and a foveal branch that uses cubemap projection for semantic segmentation, our method predicts consistent global depth while maintaining sharp details at local regions. Experimental results show favorable performance against the state-of-the-art methods.
Job Characteristics and Employee Work Attitude: A Theoretical Literature

This study theoretically examined the relationship between job characteristics and employee work attitude. The desk research method of inquiring was adopted in this study. In this manner research method is concern with gathering secondary data and basing conclusion on the retrieved information. A detailed empirical review was completed in the study based on the review, it was observed that the job characteristics in relation to autonomy, feedback and task significance relates significantly with employee work attitude. Hence, the research found that the degree to which an organization is able to design the characteristics of employee job to enhance the degree of work autonomy, feedback and task significance will help boost the employees’ level of commitment and satisfaction within the company. According to the study, allowing employees to complete their task independently of their boss will help them feel more committed to the company and raise their sense of job autonomy.
AN ANALYSIS OF WRITTEN FEEDBACK ON STUDENTS’ WRITING GIVEN BY TEACHERS AT MAN KOTO BARU PADANG PANJANG

Abstract&#x0D; This research was based on the phenomenon that the student’s writing ability was still low. It was assumed that there were two main factors influencing the students’ writing. One of them was about facilitating the students with appropriate written feedback. In line with that, this research was a kind of descriptive study, which was aimed at figuring out the focuses of written feedback given and the techniques used by the teachers in giving the written feedback on students’ academic writing, in this case,the students were asked to write a paragraph, at MAN Koto Baru Padang Panjang. Participants of the research were all English teachers there. Further more, the data were collected by using two instruments, documents, and interviews. The findings showed that, firstly, almost all of the written feedback items were provided to help students with their grammatical mistakes or forms of the writing, it was proven by the percentage that 99.66% of the written feedback items were focused on forms, the most dominant one is on S-V agreement and the least dominant one deals with the usage of adverb, where as only 0.34% of the written feedback items were focused on contents. Secondly, there were two written feedback techniques applied by theEnglish teachers, they were written commentary and minimal marking techniques.
FASFA: A Novel Next-Generation Backpropagation Optimizer

This paper introduces the fast adaptive stochastic function accelerator (FASFA) for gradient-based optimization of stochastic objective functions. It works based on Nesterov-enhanced first and second momentum estimates. The method is simple and effective during implementation because it has intuitive/familiar hyperparameterization. The training dynamics can be progressive or conservative depending on the decay rate sum. It works well with a low learning rate and mini batch size. Experiments and statistics showed convincing evidence that FASFA could be an ideal candidate for optimizing stochastic objective functions, particularly those generated by multilayer perceptrons with convolution and dropout layers. In addition, the convergence properties and regret bound provide results aligning with the online convex optimization framework. In a first of its kind, FASFA addresses the growing need for diverse optimizers by pro-viding next-generation training dynamics for artificial intelligence algorithms. Future experiments could modify FASFA based on the infinity norm. FASFA: A Novel Next-Generation Backpropagation Optimizer
ANALISA MANAJEMEN SISTEM INFORMASI KOPERASI SIMPAN PINJAM BERBASIS MULTI USER DENGAN METODE RND

Information technology that is used properly will have a good influence on the company, among others, can increase productivity, work efficiency and of course very useful for institutions such as Koperasi Kawi Mandiri Sentosa in facing competitive business competition. The determination and speed of data processing from Koperasi Kawi Mandiri Sentosa, the presentation of fast, precise and accurate information will make it easier for companies to make the right decisions as well.&#x0D; Koperasi Kawi Mandiri Sentosa has a variety of fund products which include Voluntary Deposits and Time Deposits. As well as credit products which include Monthly Loans and Term Loans, while the procedures to be processed consist of the savings section, and the credit loan section.&#x0D; If there is a deposit transaction, it can be recorded in the customer's proof of deposit and at the same time the data can be saved to the computer automatically. Likewise, if there is a loan transaction from a customer, the data on the computer can be known with certainty and accuracy. The program that will be developed by the author is a multiuser-based savings and loan accounting information system, the software used is Microsoft Visual Basic 6.0
Adaptive Boosting for Domain Adaptation: Towards Robust Predictions in Scene Segmentation

Domain adaptation is to transfer the shared knowledge learned from the source domain to a new environment, i.e., target domain. One common practice is to train the model on both labeled source-domain data and unlabeled target-domain data. Yet the learned models are usually biased due to the strong supervision of the source domain. Most researchers adopt the early-stopping strategy to prevent over-fitting, but when to stop training remains a challenging problem since the lack of the target-domain validation set. In this paper, we propose one efficient bootstrapping method, called Adaboost Student, explicitly learning complementary models during training and liberating users from empirical early stopping. Adaboost Student combines the deep model learning with the conventional training strategy, i.e., adaptive boosting, and enables interactions between learned models and the data sampler. We adopt one adaptive data sampler to progressively facilitate learning on hard samples and aggregate "weak" models to prevent over-fitting. Extensive experiments show that (1) Without the need to worry about the stopping time, AdaBoost Student provides one robust solution by efficient complementary model learning during training. (2) AdaBoost Student is orthogonal to most domain adaptation methods, which can be combined with existing approaches to further improve the state-of-the-art performance. We have achieved competitive results on three widely-used scene segmentation domain adaptation benchmarks.
Biological Anthropology and Ergonomics: A review on Indian scenario

In this review paper, the main objective was to try to find studies relating to augmented reality and virtual reality and the use of ergonomics and anthropology for the betterment of human life by reviewing articles incorporating studies related to ergonomics, human-machine interaction, and increment in functional perspective by using artificial means like artificial neural networks. These works suggest several directions for ergonomics research in India, including the use of virtual and augmented reality. This area is comparatively new while keeping the Indian population in consideration and the use of this methodology, which needs further exploration and research application.
Factorial, Integers, and Multinomials for Algorithms

This paper presents an innovative theorem using factorials, integers, and multinomials. The theorems and its results can be used as an application in computing and cryptography to develop algorithms like RSA algorithm and Elliptic Curve Cryptography.
Some shortened codes from linear codes constructed by defining sets

<p style='text-indent:20px;'>The puncturing and shortening technologies are two important approaches to construct new linear codes. It is noticed that many works on punctured linear codes have been done in the past few decades, but till now little research on the shortening technique has been done and there are only a handful references on shortened linear codes. In this paper, we use the shortening technology to obtain some shortened codes from linear codes constructed by certain special defining sets, and explicitly determine their parameters. Some of those shortened codes are optimal or almost optimal.
Single Camera Face Position-Invariant Driver’s Gaze Zone Classifier Based on Frame-Sequence Recognition Using 3D Convolutional Neural Networks

Estimating the driver’s gaze in a natural real-world setting can be problematic for different challenging scenario conditions. For example, faces will undergo facial occlusions, illumination, or various face positions while driving. In this effort, we aim to reduce misclassifications in driving situations when the driver has different face distances regarding the camera. Three-dimensional Convolutional Neural Networks (CNN) models can make a spatio-temporal driver’s representation that extracts features encoded in multiple adjacent frames that can describe motions. This characteristic may help ease the deficiencies of a per-frame recognition system due to the lack of context information. For example, the front, navigator, right window, left window, back mirror, and speed meter are part of the known common areas to be checked by drivers. Based on this, we implement and evaluate a model that is able to detect the head direction toward these regions having various distances from the camera. In our evaluation, the 2D CNN model had a mean average recall of 74.96% across the three models, whereas the 3D CNN model had a mean average recall of 87.02%. This result show that our proposed 3D CNN-based approach outperforms a 2D CNN per-frame recognition approach in driving situations when the driver’s face has different distances from the camera.
Enhancing Cyber-attacks Awareness via Mobile Gamification Techniques

Nowadays, the changes in online behavior make data privacy extremely relevant which requires a suitable approach for raising cyber-attacks awareness. Every generation is constantly relying on the cyber world to run their life and they often are lacking in knowledge on security, which exposes them to attackers. Gamification techniques is chosen to be implemented in this research as the use of gamification are increasingly used in education to improve learner’s motivation and engagement. This research introduces a cyber-attacks awareness via mobile gamification techniques known as CYBERPOLY. This quiz-based game combined with the board game concept assists in increasing cyber-attacks awareness and reduces the incidents report and victim risk of any cyber traps. CYBERPOLY promotes Naqli (Revealed knowledge) and Aqli (Rational knowledge) (iNAQ) via board game where users can learn a few Arabic terms that are related to Muslim life such as Tawakkal (hibah, infaq, waqaf, qard, sedeqah), Ta’alim, Iktikaf, Muhasabah. CYBERPOLY offers password complexity indicator feature while offering portability, accessibility, off-line service (anytime, anywhere) and fun learning. It provides four levels of interactive quiz which are beginner, moderate, advanced, and professional that implement the Bloom’s Taxonomy keyword for more effective learning process. The user will be rewarded with score by earning their own money via security awareness quiz that will be used later as their initial salary to begin the board game. The game will end when the player’s money balance reached negative. Thus, this game is mainly developed to educate netizen about cyber-attacks in fun and interactive. Based on the end user feedbacks of functional testing and security authentication, the results show that CYBERPOLY has huge potential as an education tool on cultivating cyber-attacks awareness for digital society. For future planning, the additional scope can be expanded by creating more quiz questions according to specific cyberattack topics, add multiplayer game mode, made available for the iOS platform and save the game result for monitoring the learning performance of each user.
Mechanisms for ensuring the accessibility of higher education in the Russian Federation

At the moment, education is the most important factor in the economic development of any country, therefore in Russia guided by the interests of strategic development it special attention is paid to it both by society and by the state. This approach enables the country's economy to adapt to global trends without losing the quality of education. High-quality education testifies to the compliance of the existing system of education with the needs of a person, his expectations in terms of obtaining professional competencies, as well as purposeful development of the individual [1]. The article describes the results of studying the practice of implementing the state policy of the Russian Federation in terms of ensuring the availability of higher education based on the data of the Report "On the results of the activities of the Ministry of Science and Higher Education of the Russian Federation for 2020 and tasks for 2021", the results of the implementation of the state policy of the Russian Federation are given in the field of higher education for the period 2018–2021 and its features revealed in the context of the COVID-19 pandemic.
Balanced Feature Fusion for Grouped 3D Pose Estimation

3D human pose estimation by grouping human body joints according to anatomical relationship is currently a popular and effective method. For grouped pose estimation, fusing features of different groups together effectively is the key step to ensure the integrity of whole body pose prediction. However, the existing methods for feature fusion between groups require a large number of network parameters, and thus are often computational expensive. In this paper, we propose a simple yet efficient feature fusion method that can improve the accuracy of pose esti- mation while require fewer parameters and less calculations. Experiments have shown that our proposed network outperforms previous state-of-the-art results on Human3.6M dataset.