Reviewerly/exHarmony huggingface dataset:
Optimalisasi Gerakan Literasi Sekolah (GLS) melalui Program Inovatif

OPTIMIZING THE SCHOOL LITERACY MOVEMENT THROUGH INNOVATIVE PROGRAMS.The government initiated the School Literacy Movement program as an effort to foster a culture of student literacy so that schools must have special programs to optimize the School Literacy Movement. The objectives of implementing this service are 1) Knowing the implementation of the School Literacy Movement at SDN Prawit 1 Surakarta, and 2) Disseminating innovative programs to optimize it. The method of implementing the service includes: (1) the approach method carried out starting from the survey, licensing, and providing motivation for teachers who will take part in the training (2) the method of implementing the program, including the preliminary stage, the stage of socialization and hearings, the implementation stage, and the monitoring and evaluation stage. evaluation. The target of the service is the 11 teachers of SDN Prawit 1 Surakarta. The expected result is the implementation of innovative programs for School Literacy Movement, namely the habit of reading 15 minutes (one child book, diary book, and reading award), library empowerment (mandatory hours of library visits, library ambassadors, library usage guidance, book info), literacy tree, making wall magazines, and posters (posting posters in strategic places and poster competitions between classes).
Academic Writing Workshop at One Junior High School in Merangin

The purposes of this community service program were to motivate the teachers to publish and to share our experiences in academic writing, especially writing academic journal article(s). The participants of this workshop were 51 teachers in one of junior high schools (SMP) in Merangin. The workshop was done synchronously and divided into several sessions, including material presentation (concept of academic writing, steps in writing the paper academically, and component of academic writing especially journal article); question and answer; practice (analyzing the paper and formulating the problems); and feedback survey sessions. Based on the result of this program, it is recommended to have similar programs to motivate SMP teachers publish their articles.
Prototype Application for Data Processing Teacher Performance Assessment at Christian Elementary School Imanuel Nunu

The Performance every employee appraisal is a method used to assess the work performance of an employee whether he or she has achieved the work target assigned.Teacher performance appraisal at Imanuel Nunu Christian Elementary School is currently still being carried out manually in the sense that the preparation of employee performance appraisal reports still uses word processing applications (Microsoft word and excel) resulting in inefficient and ineffective time and a greater risk of data loss. The purpose that the authors take from this research is to build a Data Processing Application for Teacher Performance Assessment at Imanuel Nunu Christian Elementary School using computerized visual basic 6.0. The Teacher Performance Assessment that was made was a Data Processing Application for Teacher Performance Assessment at Imanuel Nunu Christian Elementary School by using the Visual Basic 6.0 programming language and Microsoft Office Access as databases. The results obtained are a prototype of the teacher performance assessment report at the Imanuel Nunu Christian Elementary School
Web-Based Student Violation Monitoring Information System Design at SMK Gandasari

SMK Gandasari is one of the vocational high schools in Tangerang City. The purpose of the school authority is to improve student discipline related to increasing student discipline at school, namely by attendance in class or in attendance calls. Parents are still very concerned about the condition of the children at school, whether they really behave well or not, and parents cannot do direct monitoring but have to wait when the report cards are distributed. In monitoring violations, Gandasari Vocational School students still use paper media, namely by being recorded in a book, this makes information about student behavior slow to reach parents. To overcome this, a student monitoring information system is needed at the Gandasari Vocational School to communicate information to parents of students that are directly related to the condition of their students at school through computerized data processing using a web-based system. To analyze the problem data and the needs of a student monitoring information system using the waterfall method, with stages of needs analysis, design, code generation, system design with Unified Modeling Language (UML) Diagrams, programs and testing. The waterfall method has the main stages of the waterfall model that reflect basic development activities. There are 5 (five) stages in the Waterfall method, namely requirements analysis and definition, system and software design, implementation and unit testing, integration and system testing, and operation and maintenance. The result of this research is a website for student violation information systems as a solution to existing problems
Addressing the Responsibility Gap in Data Protection by Design: Towards a More Future-oriented, Relational, and Distributed Approach

This paper explores the extent to which technology providers are responsible to end users for embedding data protection rules in the AI systems they design and develop, so as to safeguard the fundamental rights to privacy and data protection. The main argument set forth is that a relational rationale, requiring a broader range of actors in the supply chain to share legal responsibility for Data Protection by Design (DPbD) is better suited to address infringements to these fundamental rights than the current model that assigns responsibility mainly to the data controller or data processor. Reconceptualizing the law in a more future-oriented, relational, and distributed way would make it possible to adapt legal rules &ndash; including those within the GDPR and the continuously evolving EU acquis &ndash; to the complex reality of technology development, at least partly addressing the responsibility gap in DPbD. A future-oriented conception of responsibility would require technology providers to adopt more proactive approaches to DPbD, even where they are unlikely to qualify as a controller. A relational approach to DPbD would require technology providers to bear greater responsibilities to those individuals or groups that are affected by their design choices. A distributed approach to DPbD would allow for downstream actors in the supply chain to bear part of the legal responsibility for DPbD by relying on legal requirements that are applicable to various actors in the supply chain supporting DPbD such as those found in contract law, liability law, and the emerging EU acquis governing AI, data, and information security.
Detecting and approximating decision boundaries in low dimensional spaces

A method for detecting and approximating fault lines or surfaces, respectively, or decision curves in two and three dimensions with guaranteed accuracy is presented. Reformulated as a classification problem, our method starts from a set of scattered points along with the corresponding classification algorithm to construct a representation of a decision curve by points with prescribed maximal distance to the true decision curve. Hereby, our algorithm ensures that the representing point set covers the decision curve in its entire extent and features local refinement based on the geometric properties of the decision curve. We demonstrate applications of our method to problems related to the detection of faults, to Multi-Criteria Decision Aid and, in combination with Kirsch's factorization method, to solving an inverse acoustic scattering problem. In all applications we considered in this work, our method requires significantly less pointwise classifications than previously employed algorithms.
Networked Virtual Reality and Enhanced Sensing for Remote Classes and Presentations

Networked virtual reality is gaining recognition as a way to conduct remote classes or meetings when in-person meetings are difficult or risky. This chapter summarizes our ongoing work to develop and assess VR techniques for remote education. We first present two case studies of remote teaching in VR: a classroom-embedded virtual field trip of an energy center guided by a remote teacher, and a remote university class conducted for several weeks in a social VR tool. We then summarize our ongoing research to enhance remote educational VR interfaces using enhanced sensing, for example, to visualize or detect student attention based on eye-tracked gaze. Finally, we identify several practical considerations that will need to be addressed for the long-term success of educational deployments of virtual reality. This can help educators, researchers, and VR developers make informed decisions about how to best use VR technology for designing and deploying educational VR in everyday contexts such as schools and homes.
PELATIHAN PENULISAN KARYA SASTRA BERGENRE CERITA PENDEK UNTUK MENINGKATKAN MINAT LITERASI MAHASISWA

This community service aims to increase literacy interest in students in the Surabaya and Probolinggo areas through short story writing training. The urgency of doing this community service activity is because students' interest in literacy activities is still low. The method used in this training is a tutorial method and online mentoring by utilizing the Zoom meeting application. During the training, participants received literature introduction materials, basic materials for writing short story genres, short story writing practices, and work discussions. The results of this community service activity are the ability to write short stories and the formation of student literacy interests, especially in reading literary works of the short story genre. Keywords: writing workshop, short story, literation, student
META-ANALISIS DETERMINAN PERDARAHAN POSTPARTUM

&lt;em&gt;Postpartum haemorrhage is the most common cause of all deaths from obstetric haemorrhage. Postpartum haemorrhage is bleeding that exceeds 500 ml after the baby is born in vaginal delivery and exceeds 1000 ml in cesarean section or bleeding that is more than normal which has caused changes in vital signs. The purpose of this study was to determine the determinants of postpartum hemorrhage from national and international research publications with meta-analyses. Identify study articles by searching through Pubmed, Springer Link, Google Scholar database searches on the internet. Assessment of the quality of the studies included in this study were selected in advance, the selected journals were publication journals that were relevant to the research question, original research (original), indexed Scopus Q1, Q2, Q3, Q4 and indexed Sinta (S1, S2). The research study used was an observational cohort, case-control and cross-sectional design study. Statistical analysis using the fixed effect model or random effect model to see the estimated specific effect and the combined determinants of postpartum hemorrhage and sensitivity test to see the stability of the variables to changes. Data analysis software uses RevMan 5.4. The results showed that there were 13 countries out of 20 selected journals, the estimation of the determinant effect of Postpartum Hemorrhage in Asia from the meta analysis obtained age had an OR of 2.52 (95% CI: 1.83-3.47). Parity has an OR of 2.54 (95% CI: 1.22-5.31). Anemia has an OR of 3.43 (95% CI: 1.65-7.11). History of childbirth has an OR of 3.40 (95% CI: 2.62-4.42). Stress has an OR of 2.06 (95% CI: 0.46-9.31). Education has an OR of 1.27 (95% CI: 0.23-6.94). Lifestyle has an OR of 1.48 (95% CI: 0.85-2.22). It was concluded that the determinant of postpartum hemorrhage in Asia was anemia with the highest OR of 3.43. It is hoped that this can be used as a guideline for stakeholders in the health sector in determining program priorities to prevent and control postpartum hemorrhage problems. The results of the research can directly provide knowledge to the public about the determinants of postpartum hemorrhage and how to manage pressure to control and avoid postpartum hemorrhage&lt;/em&gt;
CRT-101.04 An Assessment of the Leaman Score Inter-Observer Reproducibility in an Independent Core Lab

This article investigates the use of statistical mapping techniques for the conversion of articulatory movements into audible speech with no restriction on the vocabulary, in the context of a silent speech interface driven by ultrasound and video imaging. As a baseline, we first evaluated the GMM-based mapping considering dynamic features, proposed by Toda et al. (2007) for voice conversion. Then, we proposed a ‘phonetically-informed’ version of this technique, based on full-covariance HMM. This approach aims (1) at modeling explicitly the articulatory timing for each phonetic class, and (2) at exploiting linguistic knowledge to regularize the problem of silent speech conversion. Both techniques were compared on continuous speech, for two French speakers (one male, one female). For modal speech, the HMM-based technique showed a lower spectral distortion (objective evaluation). However, perceptual tests (transcription and XAB discrimination tests) showed a better intelligibility of the GMM-based technique, probably related to its less fluctuant quality. For silent speech, a perceptual identification test revealed a better segmental intelligibility for the HMM-based technique on consonants.
Chinese hackers use phishing tactics for espionage

Headline INT: Chinese hackers use phishing for espionage
Distributed Online Multi-Label Learning with Privacy Protection in Internet of Things

With the widespread use of end devices, online multi-label learning has become popular as the data generated by users using the Internet of Things devices have become huge and rapidly updated. However, in many scenarios, the user data are often generated in a geographically distributed manner that is often inefficient and difficult to centralize for training machine learning models. At the same time, current mainstream distributed learning algorithms always require a centralized server to aggregate data from distributed nodes, which inevitably causes risks to the privacy of users. To overcome this issue, we propose a distributed approach for multi-label classification, which trains the models in distributed computing nodes without sharing the source data from each node. In our proposed method, each node trains its model with its local online data while it also learns from the neighbour nodes without transferring the training data. As a result, our proposed method achieved the online distributed approach for multi-label classification without losing performance when taking existing centralized algorithms as a reference. Experiments show that our algorithm outperforms the centralized online multi-label classification algorithm in F1 score, being 0.0776 higher in macro F1 score and 0.1471 higher for micro F1 score on average. However, for the Hamming loss, both algorithms beat each other on some datasets, and our proposed algorithm loses 0.005 compared to the centralized approach on average, which can be neglected. Furthermore, the size of the network and the degree of connectivity are not factors that affect the performance of this distributed online multi-label learning algorithm.
Vernadsky's Concept of the Noosphere in a Global Management Environment: Searching for Ways to Exit Global Crises

Institute of Mediamatics, Faculty of Social and Economic Sciences, Comenius University Mlynske luhy 4, 821 05 Bratislava, Slovakia
Automation: A New Dawn

Since 2016 Case IH’s magnum tractor autonomous concept vehicle has been celebrated as a signpost to the future, but with passing years some began to doubt the vision. Now, as the oem launches an industy-first autonomous spreader, a new chapter in real-world autonomy has finally begun
Off-Highway Telematics: Over the Air in Norway

A complete telematics package from Bosch Rexroth with over-the-air services has enabled drilling rig manufacturer Qmatec to install a nationwide remote service, aiding transparency, productivity and environmental credentials
Electroencephalogram-based Stress Detection using Extreme Learning Machine

The detection of stress is important because it contributes to diverse pathophysiological changes including sudden death. Various techniques have been used to evaluate stress in terms of questionnaire or by quantifying the changes of physiological signals. Electroencephalogram signals are highly useful in measuring human stress. Therefore, to solve and detect stress problem, this work had extracted electroencephalogram features of theta, alpha, and beta bands in the frequency domain by wavelet packet transform because these bands are concerned with stress. In this research four features have been supplied to extreme learning machine which gave accuracy of 98.56% of detecting stress from normal state based on db4 with an average sensitivity of 92.52% and specificity of 95.88%. This research studied the stress on 15 students due to mathematical exercises in a noisy environment with different stimulus.
Automatic Reward System Based on Public Blockchains

We ropose a blockchain-based automatic reward mechanism. Since it is based on public blockchains, codes deployed in smart contracts are public and form a tamper-proof ledger. Rewards are recorded in a smart contract. If anyone has doubts about the reward accumulated, he/she can appeal through smart contracts without third-party mediation, and obtain indemnity in a short time. There is no need to establish and run a customer service center with human employees. Moreover, the traditional third-party trust is no longer needed because we employ public blockchains as the core of the trust. An application of rewarding photographers in a running race is used as an example to illustrate the proposed scheme. We solve problems that public blockchains are slow and their miner fees are high. The feasibility of the proposed application has been demonstrated through pilot implementation and testing.
A Study on the Effects of TikTok Characteristics on Perceived Pleasure and Satisfaction: Focusing on Chinese Students in Korea

본 연구는 추천 알고리즘을 기반한 틱톡을 중심으로 선호적합성, 콘텐츠의 다양성, 영상 품질, 준사회적 상호작용, 편리성 그리고 프라이버시 우려가 인지된 즐거움과 사용 만족도에 어떠한 영향을 미치는지 실증적으로 분석하였다. 이를 위해 295명의 중국 유학생을 대상으로 설문 데이터를 수집하여 분석한 결과, 틱톡의 선호적합성, 콘텐츠 다양성, 영상품질, 준사회적 상호작용, 편리성, 인지된 즐거움을 거쳐 사용 만족도에 정적인 영향을 미치는 것으로 나타났다. 한편 틱톡에 대한 프라이버시 우려가 인지된 즐거움과 만족도에 영향을 미치지 않는 것으로 확인되었다. 기존에 많이 도입되고 있는 AI 기반의 동영상 서비스가 가져오는 효용을 이용자 측면에서 확인하였다는 점에서 동영상 플랫폼의 효과적 전략 수립에 도움을 주며 AI 기술이 동영상 플랫폼의 만족도에 미치는 영향을 실증 데이터를 통해 확인하여 기존 연구를 확장하였다는 점에서 학술적 및 실무적 의의를 가진다. This study empirically analyzed how preference suitability, content diversity, video quality, parasocial interaction, convenience, and privacy concerns affect perceived enjoyment and satisfaction with use, focusing on TikTok based on a recommendation algorithm. To this end, we collected and analyzed survey data from 295 Chinese students. The results showed that TikTok preference, content diversity, video quality, parasocial interaction, convenience, and perceived pleasure positively impact usage satisfaction. At the same time, it was confirmed that privacy concerns about TikTok did not affect perceived enjoyment and satisfaction. Acknowledging the utility of AI-based video services that have been widely introduced from the user"s perspective can help establish effective strategies for video platforms and confirm the impact of AI technology on video satisfaction Build a platform with empirical data, extending existing research, It has academic and practical significance.
Tuning spectral properties of individual and multiple quantum emitters in noisy environments

A quantum emitter in a dynamic environment may have its energy levels drift uncontrollably in time with the fluctuating bath. This can result in an emission and/or absorption spectrum that is spread over a broad range of frequencies and presents a challenging hurdle for various applications. We consider a quantum emitter in an environment that alters the energy levels so that the emission frequency is represented by a Gaussian random distribution around a given mean value with given standard deviation and correlation time. We study the emission spectrum of this system when it is placed under the influence of a periodic sequence of finite-width $\ensuremath{\pi}$ pulses. We show that this external field protocol can effectively overcome spectral diffusion in this system by refocusing the bulk of the emission spectrum onto the pulse carrier frequency. We further consider two such emitters in different noisy environments and find that the two-photon interference operation can be made efficient by the sequence of finite-width pulses applied on both systems. Finally, we show that an ensemble of nominally similar emitters, each with its different environment, and thus randomly shifted emission frequency, can have its overall emission spectrum that would otherwise be inhomogeneously broadened according to the random distribution, refocused onto a lineshape with a well-defined central peak that has the linewidth of an individual isolated non-noisy emitter. These results demonstrate for this specific model of noisy environments, the protection of spectral properties by an external control protocol here represented by a periodic sequence of finite-width pulses.
ANALISIS SENTIMEN PENERAPAN PPKM PADA TWITTER MENGGUNAKAN NAIVE BAYES CLASSIFIER DENGAN SELEKSI FITUR CHI-SQUARE

Dissemination of information related to the implementation of PPKM takes place very quickly, especially on social media networks. Positive and negative news certainly has an impact on public opinion or sentiment on the implementation of PPKM. In this study, the data used is public opinion on Twitter social media with the keyword "PPKM" from November 2, 2021 to November 8, 2021 and obtained data as many as 12,616 tweets which then deleted duplicate data to become 6,465 data. Data classification was performed using Naïve Bayes with Chi-Square feature selection and the data were classified into positive and negative classes. The results of the classification performance using Nave Bayes with Chi-Square feature selection obtained an accuracy of 80% which means that the Nave Bayes classification model with Chi-Square feature selection is quite effective in classifying public opinion on the implementation of PPKM. Keywords: PPKM, Twitter, Sentyment Analysis, Naïve Bayes Classifier, Featured Selection Chi-Square.