Reviewerly/exHarmony huggingface dataset:
Private Anomaly Detection of Student Health Conditions based on Wearable Sensors in Mobile Cloud Computing

Abstract With the continuous spread of COVID-19 virus, how to guarantee the healthy living of people especially the students who are of relative weak physique is becoming a key research issue of significant values. Specifically, precise recognition of the anomaly in student health conditions is beneficial to the quick discovery of potential patients. However, there are so many students in each school that the education managers cannot know about the health conditions of students in a real-time manner and accurately recognize the possible anomaly among students quickly. Fortunately, the quick development of mobile cloud computing technologies and wearable sensors has provided a promising way to monitor the real-time health conditions of students and find out the anomalies timely. However, two challenges are present in the above anomaly detection issue. First, the health data monitored by massive wearable sensors are often massive and updated frequently, which probably leads to high sensor-cloud transmission cost for anomaly detection. Second, the health data of students are often sensitive enough, which probably impedes the integration of health data in cloud environment even renders the health data-based anomaly detection infeasible. In view of these challenges, we propose a time-efficient and privacy-aware anomaly detection solution for students with wearable sensors in mobile cloud computing environment. At last, we validate the effectiveness and efficiency of our work via a set of simulated experiments.
Japanese Translation Based on Bilingual Bases from Intercultural Communication

Japanese, as a global language, can aid cultural exchanges and improve mutual understanding. When translating news, the translator should focus on the sentence from the standpoint of cross-cultural communication, so that the translation is more accurate and natural. Only the relationship between Japanese and Chinese can help translate more news reports, spread foreign information, and allow them to learn more about foreign events. This paper analyzes corpus using corpus means, fully exploiting the technical advantages of corpus in terms of words, vocabulary, text correspondence, semantic rhyme characteristics, and so on, breaking through traditional translation research with a large number of corpus statistics data. The frequency and richness of vocabulary selection are not as good as in the original text due to the language of language and translation language, but the numerical difference is not large, and the high-frequency vocabulary is essentially consistent, ensuring the core content of the original text. The original sentence will be resolved according to the text content in the sentence and discourse. The Chinese translation day’s original vocabulary is 358 times, and the table’s high-frequency modal words are 1274 times. As a result, the statement with the highest frequency “should” account for 33% of the high-frequency voices used. 332 Japanese original text, the highest frequency verb, a total of 247 times. Make the original text’s and translation’s language characteristics more convincing, as well as the translation and translation strategy selection.
PENGEMBANGAN MODUL DASHBOARD ADMIN “EPAKAN” DENGAN IMPLEMENTASI USABILITY TESTING

The creation of application is very easy for farmers and non-farmers to buy and sell livestock products. Along with the creation of the application and the existence of a business process in it is needed a system that can be a connection between the application and its users. The presence of a dashboard system is expected to help admin performance in handling some business activities in ePakan. In its development the dashboard system was built using the PHP programming language with Laravel Framework. The methods used for system development use Extreme Programming. The features in this ePakan dashboard include, payment confirmation, account verification, balance disbursement, transaction logging. Based on The results of the dashboard testing the ePakan works as needed and based on testing user satisfaction usability, the dashboard system gets an average value of 83% which means it is very good.
Book Metadata for an Open Access World

The advantages and requirements of digital aggregation often run into a wall with scientific book publications. Standard metadata formats such as MARC and ONIX were developed to serve a different purpose – describing books for library cataloguing or sales, but not originally intended as a primary source for indexing in a digital research environment. Consequentially, book records tend to lack in detail required for proper description in an electronic research landscape. Recognized essentials in journal publishing such as a link to the (single digital) publisher’s version of record, DOIs registered with a rich, reusable metadata set listing keywords and subjects, affiliations or biographical information, machine-readable licenses, ORCID and FundRef IDs are seldomly available when it comes to books. With a grant from the German Ministry for Education and Research ScienceOpen has just launched the BookMetaHub, freely accessible to publishers around the world, that will provide them with the means to maintain metadata more easily and support them to better integrate books and chapters into to the digital knowledge graph.
Toward Zero Oracle Word Error Rate on the Switchboard Benchmark

The "Switchboard benchmark" is a very well-known test set in automatic speech recognition (ASR) research, establishing record-setting performance for systems that claim human-level transcription accuracy. This work highlights lesser-known practical considerations of this evaluation, demonstrating major improvements in word error rate (WER) by correcting the reference transcriptions and deviating from the official scoring methodology. In this more detailed and reproducible scheme, even commercial ASR systems can score below 5% WER and the established record for a research system is lowered to 2.3%. An alternative metric of transcript precision is proposed, which does not penalize deletions and appears to be more discriminating for human vs. machine performance. While commercial ASR systems are still below this threshold, a research system is shown to clearly surpass the accuracy of commercial human speech recognition. This work also explores using standardized scoring tools to compute oracle WER by selecting the best among a list of alternatives. A phrase alternatives representation is compared to utterance-level N-best lists and word-level data structures; using dense lattices and adding out-of-vocabulary words, this achieves an oracle WER of 0.18%.
LEARNING PROBLEMS DURING QUARANTINE MEASURES IN THE COURSE "SCIENTIFIC RESEARCH METHODOLOGY"

The use of problem-based learning in higher education institutions during quarantine restrictions involves the creation and development of problem situations with their subsequent solution by students in group cooperation. This approach involves working out a high level of self-organization in students, and from the teacher – work to improve methodological skills in the new educational environment. This determined the relevance of the presented research. The conditions created by the COVID-19 pandemic required from all participants in the educational process significant changes in the organization, technical and methodological support of quality educational process, where priority was given to work with generalized knowledge and basic principles of problem solving. The subject of the research is problembased learning as an interactive pedagogical technology that is able to provide quality higher professional education in conditions of quarantine restrictions during the teaching of the course "Research Methodology". The research methodology is based on an integrated approach. Descriptive method, as well as analysis and synthesis are used for the analysis of theoretical and methodological investigations. The paper also uses comparative-historical, comparative methods. In addition, the consideration of research issues is based on an interdisciplinary approach. The result of the study is the establishment of evolutionary changes in the problembased approach used in the course "Methodology of Scientific Research" under quarantine restrictions. Using a problem-based approach in this course is a comprehensive approach to online learning. This will allow students to effectively use research methods, support research activity, independence of thinking and activities in conditions of quarantine restrictions.
THE EFFECT OF AMOUNT OF DATA ON RESULTS OF ACCURACY VALUE OF C4.5 ALGORITHM ON STUDENT ACHIEVEMENT INDEX DATA

Of the many academic data, data in the form of an achievement index needs to be used in-depth so that it does not become a display of numbers and information only. This achievement index evaluation data reflects the educational process students and teaching staff carries out in an educational process. This study aims to measure the accuracy of data mining processing based on differences in test data by analyzing the C4.5 algorithm using RapidMiner as a data processing tool and determining the decisions students can make and academic institutions in developing study strategies and educational curricula to be maximized. The data processing is carried out by classifying the student achievement index data at a private university using data analysis test equipment. The data source comes from kaggle.com, which consists of 1687 data that have been processed and processed. The conclusion from the results of this study is that the amount of data turns out to have a significant influence on the accuracy value of the C4.5 algorithm, where an accuracy rate of 91.69% is obtained from the test results of 1687 data with four main attributes, namely IPK1, IPK2, IPK3, IPK4 and correctly or not as a label.
A Path Planning Algorithm for AUV Using Fuzzy Logic and Artificial Potential Field

An autonomous underwater vehicle (AUV) must move from a start point to a target point without colliding with other vehicles or obstacles to execute various missions. Path planning is a crucial function that causes AUVs to find the shortest and safest path without human intervention. This paper presents a path planning algorithm that adopts a safety distance to keep the vehicle as safe as possible while avoiding collisions with obstacles. The algorithm uses a combination of fuzzy logic (FL) and an artificial potential field (APF), which together overcome the shortcomings of global path planning. The proposed algorithm simultaneously considers the shortest path to the target position while maintaining a safe distance from obstacles. The effectiveness of the proposed algorithm is demonstrated with computer simulations.
Regularization Penalty Optimization for Addressing Data Quality Variance in OoD Algorithms

Due to the poor generalization performance of traditional empirical risk minimization (ERM) in the case of distributional shift, Out-of-Distribution (OoD) generalization algorithms receive increasing attention. However, OoD generalization algorithms overlook the great variance in the quality of training data, which significantly compromises the accuracy of these methods. In this paper, we theoretically reveal the relationship between training data quality and algorithm performance, and analyze the optimal regularization scheme for Lipschitz regularized invariant risk minimization. A novel algorithm is proposed based on the theoretical results to alleviate the influence of low quality data at both the sample level and the domain level. The experiments on both the regression and classification benchmarks validate the effectiveness of our method with statistical significance.
Cross-codex Learning for Reliable Large Scale Scribe Identification in Medieval Manuscripts

Abstract Historic scribe identification is a substantial task for obtaining information about the past. Uni-form script styles, such as the Carolingian minuscule, make it a difficult task for classification to focuse on meaningful features. Therefore, we demonstrate in this paper the importance of cross-codex training data for CNN based text-independent off-line scribe identification, to overcome codex dependent overfitting. We report three main findings: First, we found that preprocessing with masked grayscale images instead of RGB images increased the F1-score of the classification results drastically. Second, we trained different neural networks on our complex data, validating time and accuracy differences in order to define the most reliable net-work architecture. With AlexNet, the network with the best trade-off between F1-score and time, we achieved for individual classes F1-scores of up to 0,96 on line level and up to 1.0 on page level in classification. Third, we could replicate the finding that the CNN output can be further improved by implementing a reject option, giving more stable results. We present the results on our large scale open source dataset – the Codex Claustroneoburgensis database (CCl-DB) – containing a significant number of writings from different scribes in several codices. We demonstrate for the first time on a dataset with such a variety of codices that paleographic decisions can be reproduced automatically and precisely with CNNs. This gives manifold new and fast possibilities for paleographers to gain insights into unlabeled material, but also to develop further hypotheses.
Validation of the Results of Linking Speaking Test of IELTS to China’s Standards of English Language Ability

This study, taking into consideration of teachers&amp;rsquo; as well as students&amp;rsquo; judgments, explored the generalizability and consistency of the results of linking speaking test of IELTS to China&amp;rsquo;s Standards of English Language Ability (CSE). Nine college English teachers and 81 undergraduate students judged the degree of congruence between the IELTS speaking test and 72 relevant CSE descriptors to generate evidence of generalizability; 2 teachers judged the CSE levels of 11 videos of IELTS speaking test and 113 students assessed themselves based on the self-assessment scale, to provide data for exploring consistency from three perspectives: consistency of teachers&amp;rsquo; judgments, consistency between teachers&amp;rsquo; judgments and empirical scores, and consistency between students&amp;rsquo; actual ability and the ability demonstrated in the self-assessment scale. The evidence showed that the linking results performed well both in generalizability and consistency from the perspective of both teachers and students, but students rated comparatively low than teachers both in the two facets. However, considering that certain descriptors are relatively abstract and separate from daily life situations in IELTS speaking test, it is understandable that students rate low in generalizability; and considering students&amp;#39; insufficient self-assessment ability, their relative low recognition in consistency also get explained. In general, therefore, the linking results show good generalizability and consistency.&amp;nbsp;
Phishing Detection

The phishing email is one of the significant threats in the world today and has caused tremendous financial losses. Phishing is a type of social engineering attack often used to steal user data, including login credentials and credit card numbers. It occurs when an attacker, masquerading as a trusted entity, dupes a victim into opening an email, instant message, or text message. Although the methods of confrontation are continually being updated, the results of those methods are not very satisfactory at present. Moreover, phishing emails are growing at an alarming rate in recent years. Therefore, more effective phishing detection technology is needed to curb the threat of phishing emails. So There are many ways to detect these phishing mails nowadays using Machine Learning. so using the phishing mail detector where these links could be tested and then predicted and to detect whether it is a spam or not. Keywords: Phishing mail, Social engineering, Machine learning
DA-UNet: Deep supervision and atrous inception-based U-Net combining CRF for automatic liver segmentation from CT

Abstract Due to low contrast and the blurred boundary between liver tissue and neighboring organs sharing similar intensity values, the problem of liver segmentation from CT images has not yet achieved satisfactory performance and remains a challenge. To alleviate these problems, we introduce deep supervision (DS) and atrous inception (AI) technologies with conditional random field (CRF) and propose three major improvements that are experimentally shown to have substantive and practical value. First, we replace the encoder's standard convolution with the residual block. Residual block can increase the depth of the network. Second, we provide AI module to connect the encoder and decoder. AI allows us to obtain multi-scale features. Third, we incorporate the DS mechanism into the decoder. This helps to make full use of information of the shallow layers. In addition, we employ the Tversky loss function to balance the segmented and non-segmented regions and perform the further refinement with a dense CRF. Finally, we extensively validate the proposed method on three public databases: LiTS17, 3DIRCADb, and SLiver07. Compared to the state-of-the-art (SOTA) methods, the proposed method achieved increased segmentation accuracy for the livers with low contrast and the fuzzy boundary between liver tissue and neighboring organs and is, therefore, more suited for automatic segmentation of these livers.
PAC-Net: A Model Pruning Approach to Inductive Transfer Learning

Inductive transfer learning aims to learn from a small amount of training data for the target task by utilizing a pre-trained model from the source task. Most strategies that involve large-scale deep learning models adopt initialization with the pre-trained model and fine-tuning for the target task. However, when using over-parameterized models, we can often prune the model without sacrificing the accuracy of the source task. This motivates us to adopt model pruning for transfer learning with deep learning models. In this paper, we propose PAC-Net, a simple yet effective approach for transfer learning based on pruning. PAC-Net consists of three steps: Prune, Allocate, and Calibrate (PAC). The main idea behind these steps is to identify essential weights for the source task, fine-tune on the source task by updating the essential weights, and then calibrate on the target task by updating the remaining redundant weights. Under the various and extensive set of inductive transfer learning experiments, we show that our method achieves state-of-the-art performance by a large margin.
Pelatihan Pemanfaatan Microsoft Power Point sebagai Video Pembelajaran Matematika untuk Membantu Pembelajaran Secara Daring bagi Guru-Guru MGMP Matematika SMP/MTs Kota Pekanbaru

This community service was carried out at SMP Negeri 42 Pekanbaru City with the participants of the activity is MGMP Mathematics teachers for SMP/MTS Pekanbaru City. This activity is motivated by the fact that there has never been any socialization or training on the use of Microsoft Power Point to help online learning for Mathematics MGMP teachers. Microsoft power point is only used for presentations at seminars or delivering material face-to-face in front of the class. Therefore, it is necessary to hold a training activity on the use of Microsoft power point to help online learning for MGMP Mathematics teachers at SMP/MTs Pekanbaru City. This activity aims to provide material about the ideal slide size for online delivery, criteria for teaching material slides, and video making guidelines using Microsoft power point. The results of community service show that most of the training participants can receive the training materials well and can use Microsoft power point to make online learning videos.
PEDAGOGICAL TECHNOLOGIES OF FORMATION OF SOCIALLY SUCCESSFUL PERSONALITY OF PRIMARY AND SECONDARY SCHOOL PUPILS

The article is dedicated to the problem of pedagogical technologies implemented in general secondary education institutions in the practice of forming a socially successful personality of a pupil. The objectives of the research are to summarize data on this problem, highlight the pedagogical potential of the most effective technologies and their successful implementation in working with primary and secondary school pupils. The research applied theoretical and methodological approaches in the formation of socially successful personality – axiological, acmeological, environmental, competence, design and technological, as well as the following methods – theoretical (analysis, comparison and reinterpretation of data) and empirical (event analysis). The author clarifies the essence of the concepts of "social successfulness", "socially successful personality of a pupil", "technology of forming socially successful personality of a pupil", revealed the pedagogical potential of art technologies in the formation of socially successful personality of a pupil, in particular, theater technology, outlined the defining characteristics of training, play, project technologies, "case-study" technologies and the experience of implementation of these technologies is presented. The proposed research highlights the importance of partnership between general secondary education institutions and families of pupils, focuses on the qualities and competencies of pupil's necessary for social success, which are developed in educational institutions using these pedagogical technologies. Based on the obtained results, the important role of pedagogical technologies in the formation of socially successful personality of primary and secondary school pupils is proved.
Constrained Quantum Optimization for Extractive Summarization on a Trapped-ion Quantum Computer

Realizing the potential of near-term quantum computers to solve industry-relevant constrained-optimization problems is a promising path to quantum advantage. In this work, we consider the extractive summarization constrained-optimization problem and demonstrate the largest-to-date execution of a quantum optimization algorithm that natively preserves constraints on quantum hardware. We report results with the Quantum Alternating Operator Ansatz algorithm with a Hamming-weight-preserving XY mixer (XY-QAOA) on trapped-ion quantum computer. We successfully execute XY-QAOA circuits that restrict the quantum evolution to the in-constraint subspace, using up to 20 qubits and a two-qubit gate depth of up to 159. We demonstrate the necessity of directly encoding the constraints into the quantum circuit by showing the trade-off between the in-constraint probability and the quality of the solution that is implicit if unconstrained quantum optimization methods are used. We show that this trade-off makes choosing good parameters difficult in general. We compare XY-QAOA to the Layer Variational Quantum Eigensolver algorithm, which has a highly expressive constant-depth circuit, and the Quantum Approximate Optimization Algorithm. We discuss the respective trade-offs of the algorithms and implications for their execution on near-term quantum hardware.