Reviewerly/exHarmony huggingface dataset:
A STUDENTS SILENCE IN SPEAKING CLASS: FACTORS AND SOLUTIONS:

Speaking is one of four skills that learned by the students in learning English as a foreign language. Through this skill they are supposed to be able to communicate and to express their idea, feelings, and thought orally in English. Students silence is a phenomenon that often occurs in speaking class where the students are not actively engage in speaking activity. It also happened to the eleventh grade students at SMA N.1 and SMA N.2 Tembilahan Kota. This problem motivated the researchers to investigate what factors causing the students to be silent in speaking class and what are the teachers’ efforts to cope with it. There were 144 students of eleventh grade and 5 English teachers as the subject of this research. The instruments were questionnaires, 15 closed-ended questions to the students and 10 open-ended questions for the teachers. The results shows that psychological factor is the dominant factor that make mostly students performed passively in speaking activity with percentage 42%, linguistic factor 39%, and socio-cultural 19%. While the efforts of the teachers to cope with students’ silence are implementing various strategies and teaching techniques, using audio visual media, and personal approach to help students’ difficulties in Speaking.
PENINGKATAN KEMAMPUAN MENULIS PERMULAAN DENGAN MENGGUNAKAN METODE STRUKTUR ANALITIK SINTETIK (SAS) PADA PEMBELAJARAN BAHASA INDONESIA SISWA KELAS I UPT. SD NEGERI 20 BARINGIN

The low ability of students to write at the beginning of the class, especially in Indonesian subjects, is still experienced by students of class I UPT. SD Negeri 20 Baringin because the teacher has not used a suitable method in writing, namely teaching writing only by asking students to follow the teacher's writing on the blackboard without any writing guidance from the teacher himself. Therefore, many students have difficulty in writing and students seem inactive in learning. The formulation of the problems in this study are, (1) How is the teacher's ability to manage learning by applying the Synthetic Analytical Structure (SAS) method in the writing material of the UPT class I students. SD Negeri 20 Baringin? (2) How do students' activities through the Synthetic Analytical Structures (SAS) method in the writing material of class I UPT students. SD Negeri 20 Baringin (3) How to improve students' initial writing skills through the Synthetic Analytical Structures (SAS) method in writing material for class I UPT students. SD Negeri 20 Baringin ?. The research method used was classroom action research (PTK). The research subjects were students of class I UPT. SD Negeri 20 Baringin, totaling 32 people. The ability to write at the beginning of the students had an increase in each indicator, namely, the indicator of neatness of writing in the first cycle with a percentage of 62.5% and increased in the second cycle with a percentage of 84.4%, the indicator of word completeness in the first cycle with a percentage of 34.4% and increased in the cycle. II with a percentage of 81.3% and indicators of word structure in the sentence in the first cycle with a percentage of 62.5% and increasing in the second cycle with a percentage of 87.5%. Overall the results of the research which lasted for the second cycle showed that the Synthetic Analytical Structures method could be applied to the pre-writing material. This is indicated by the ability of students to write at the beginning of each cycle, which has increased and is strengthened by the results of the research, the ability of teachers to increase in each cycle, namely in cycle I with a percentage of 75% and increasing in cycle II with a percentage of 96.15%, as well as student activities which also increased. increased in each cycle, namely in the first cycle with a percentage of 64.28% and increased in the second cycle with a percentage of 85.71%.
Elsa: Energy-based learning for semi-supervised anomaly detection

Anomaly detection aims at identifying deviant instances from the normal data distribution. Many advances have been made in the field, including the innovative use of unsupervised contrastive learning. However, existing methods generally assume clean training data and are limited when the data contain unknown anomalies. This paper presents Elsa, a novel semi-supervised anomaly detection approach that unifies the concept of energy-based models with unsupervised contrastive learning. Elsa instills robustness against any data contamination by a carefully designed fine-tuning step based on the new energy function that forces the normal data to be divided into classes of prototypes. Experiments on multiple contamination scenarios show the proposed model achieves SOTA performance. Extensive analyses also verify the contribution of each component in the proposed model. Beyond the experiments, we also offer a theoretical interpretation of why contrastive learning alone cannot detect anomalies under data contamination.
ИССЛЕДОВАНИЕ ЧУВСТВИТЕЛЬНОСТИ К ВНЕШНИМ ПАРАМЕТРАМ ПРОЦЕССА ОБУЧЕНИЯ С ПОМОЩЬЮ ЭРГОНОМИКИ В ПРИОБРЕТЕНИИ ЗНАНИЙ

This research work is devoted to the development and creation of a mathematical model of the learning process based on ergonomics, to study the quality of education in the acquisition of knowledge. And also one of the types of qualitative indicators of the theory of sensitivity and roughness to the used parameters of different types of technical means is proposed, the process, which is described by a system of first-order differential equations, and its function is determined using partial derivatives
A junction for single-photon detection

Quantum DevicesJosephson junctions are simple superconducting devices comprising an insulator or semiconductor separating two superconducting regions. They form the workhorse of superconducting technologies and are exquisitely sensitive to magnetic field. One long-sought proposal has been to use these devices to detect light. Walsh et al. have realized a photosensitive Josephson junction based on graphene that is capable of sensing single infrared photons. Such a photosensitive Josephson junction is expected to operate as a high-speed, low-power consumption optical interconnect for communication between superconducting-based supercomputers and quantum computers.Science , this issue p. [409][1] [1]: /lookup/doi/10.1126/science.abf5539
Arduino based Weather Monitoring and Forecasting System using SARIMA Time-Series Forecasting

Arduino is an open-source electronics platform based on easy-to-use hardware and software. A DHT-11 temperature and humidity sensing module, monitors and stores the data onto a computer every ten minutes. A program is written in Python, which stores the minimum and maximum for the day. In addition to the data obtained from the DHT-11 sensor module, historical data for 4 years is used in order to account for seasonality trends in the weather, the two are combined into a single dataset. Using SARIMA Time Series Forecasting Model, the forecasts are generated.
Understanding Chinese Video and Language via Contrastive Multimodal Pre-Training

The pre-trained neural models have recently achieved impressive performances in understanding multimodal content. However, it is still very challenging to pre-train neural models for video and language understanding, especially for Chinese video-language data, due to the following reasons. Firstly, existing video-language pre-training algorithms mainly focus on the co-occurrence of words and video frames, but ignore other valuable semantic and structure information of video-language content, e.g., sequential order and spatiotemporal relationships. Secondly, there exist conflicts between video sentence alignment and other proxy tasks. Thirdly, there is a lack of large-scale and high-quality Chinese video-language datasets (e.g., including 10 million unique videos), which are the fundamental success conditions for pre-training techniques. In this work, we propose a novel video-language understanding framework named VICTOR, which stands for VIdeo-language understanding via Contrastive mulTimOdal pRe-training. Besides general proxy tasks such as masked language modeling, VICTOR constructs several novel proxy tasks under the contrastive learning paradigm, making the model be more robust and able to capture more complex multimodal semantic and structural relationships from different perspectives. VICTOR is trained on a large-scale Chinese video-language dataset, including over 10 million complete videos with corresponding high-quality textual descriptions. We apply the pre-trained VICTOR model to a series of downstream applications and demonstrate its superior performances, comparing against the state-of-the-art pre-training methods such as VideoBERT and UniVL. The codes and trained checkpoints will be publicly available to nourish further developments of the research community.
Pharmacy Inventory System Design Using Agile Methods

Currently, the information system has been used by many companies / agencies in Indonesia. All activities will be properly managed by the system, so that they will be more effective and efficient. And now, pharmacies also need systems that can control data properly. Every day there are people who need medicine, therefore pharmacies can be used as a profitable business ingredient. The pharmacy business must have a good system, so that the business can move forward and get a large turnover. The purpose of this study is to assist the pharmacy in processing data on incoming and outgoing stock data so that it will make it easier for the warehouse admin to provide the information needed. In developing this system the researchers used the Agile Software Development method where this method is used to develop systems in the short term that require rapid adaptation and development to changes in any form. The result of this research is the development of sInventory information system at pharmacies that can display drug data, drug stock, sales, expenses, purchase of goods, profit and loss, reports in real time.Keyword : Information Systems, Pharmacy, Agile Method
Rancang Bangun Sistem Piket Kepegawaian Berbasis Web di PT Perusahaan Gas Negara Tbk

PT Perusahaan Gas Negara TBK or abbreviated as PGN is Badan Usaha Milik Negara (BUMN) which is engaged in the transmission and distribution of natural gas. In this company, there must be several systems in various operations to support employees, but it is different from picket scheduling. Scheduling pickets for national holidays or joint leave is still done manually, whereas for picket scheduling and implementation can sometimes change according to the needs and conditions of employees who are offered picket on that day. This results in different payments at the time of payment for employees who are picket. The purpose of making this system is to increase the efficiency and accuracy of picket data. The system is created using the waterfall method. The results obtained are reports on the implementation of pickets for payment of employee salary pickets on a certain day for operational safety related to picket and approval of employee picket data in payment of picket wages. System testing is done by testing the blackbox and User Acceptance Test (UAT) from the user's side. From the results of testing on the user's side reached 81.7% it can be concluded that the user strongly agrees that this system can improve the efficiency of employee picket data and increase the level of difficulty in paying employee pickets.
HiCOMEX: Facial Action Unit Recognition Based on Hierarchy Intensity Distribution and COMEX Relation Learning

The detection of facial action units (AUs) has been studied as it has the competition due to the wide-ranging applications thereof. In this paper, we propose a novel framework for the AU detection from a single input image by grasping the \textbf{c}o-\textbf{o}ccurrence and \textbf{m}utual \textbf{ex}clusion (COMEX) as well as the intensity distribution among AUs. Our algorithm uses facial landmarks to detect the features of local AUs. The features are input to a bidirectional long short-term memory (BiLSTM) layer for learning the intensity distribution. Afterwards, the new AU feature continuously passed through a self-attention encoding layer and a continuous-state modern Hopfield layer for learning the COMEX relationships. Our experiments on the challenging BP4D and DISFA benchmarks without any external data or pre-trained models yield F1-scores of 63.7\% and 61.8\% respectively, which shows our proposed networks can lead to performance improvement in the AU detection task.
Aceptación y confianza de Bitcoin en México

Bitcoin is part of the so-called crypto-currencies and can be defined as a universal decentralized public electronic payment system, based on Blockchain technology. The problem it faces is trust in its use; therefore, the objective of this research is to analyze the factors that influence the adoption and use of Bitcoin in Mexico. For this purpose, the UTAUT2 model integrated with the trust variable is used. The model was empirically tested by applying a survey of 106 questionnaires, under the partial least square’s method, with structural equation models (PLS-SEM). The main results indicate that performance expectation, hedonistic motivation, habit and its decentralization, are the main factors that influence confidence for the adoption and use of Bitcoin. The results illustrate to companies and any interested party the advantages of using Bitcoin.
Algorithmic Speech and Freedom of Expression

Algorithms have become increasingly common, and with this development, so have algorithms that approximate human speech. This has introduced new issues with which courts and legislators will have to grapple. Courts in the United States have found that search engine results are a form of speech that is protected by the Constitution, and cases in Europe concerning liability for autocomplete suggestions have led to varied results. Beyond these instances, insight into how courts handle algorithmic speech are few and far between.By focusing on three categories of algorithmic speech, defined as curated production, interactive/responsive production, and semi-autonomous production, this Article analyzes these various forms of algorithmic speech within the international framework for freedom of expression. After a brief introduction of that framework and a look towards approaches to algorithmic speech in the United States, the Article then examines whether the creators or controllers of different forms of algorithms should be considered content providers or mere intermediaries, the determination of which ultimately has implications for liability, which is also explored. The Article then looks at possible interferences with algorithmic speech, and how such interferences may be examined under the three-part test-particular attention is paid to the balancing of rights and interests at play-in order to answer the question of the extent to which algorithmic speech is worthy of protection under international standards of freedom of expression. Finally, other relevant issues surrounding algorithmic speech are discussed that will have an impact going forward, many of which involve questions of policy and societal values that accompany granting algorithmic speech protection.
Educational Machine Learning Modules for Undergraduates in Cybersecurity

Machine learning resources have developed by leaps and bounds in recent years and have become pervasive in many fields, including the field of Cybersecurity. As such, it is important that machine learning applications be taught at the undergraduate level in the context of Cybersecurity to provide students with a competitive advantage that will be useful in industry. We are seeking to provide versatile module-based solutions that can either be integrated into existing security courses or stand alone. Several of these modules are currently in development, including a convolutional neural network (CNN) implementation for the classification of malware samples, an adversarial attack on that model in a later module by use of the fast gradient sign method (FGSM), followed by an adversarial training lab in order to harden machine learning models against such adversarial attacks. Students will use virtualization with Oracle’s VirtualBox to sandbox their experiments in a Linux environment. They will make use of Python to train and test their models, making use of the Keras, Tensorflow, and Scikit-learn libraries. These modules are intended to make applied machine learning knowledge accessible to undergraduates sooner than grad school, but may be used at higher levels or even by themselves. They are self-contained with the lectures and lab materials required for students to succeed as they gain valuable knowledge for their careers in Cybersecurity.
Preserving Earlier Knowledge in Continual Learning with the Help of All Previous Feature Extractors

Continual learning of new knowledge over time is one desirable capability for intelligent systems to recognize more and more classes of objects. Without or with very limited amount of old data stored, an intelligent system often catastrophically forgets previously learned old knowledge when learning new knowledge. Recently, various approaches have been proposed to alleviate the catastrophic forgetting issue. However, old knowledge learned earlier is commonly less preserved than that learned more recently. In order to reduce the forgetting of particularly earlier learned old knowledge and improve the overall continual learning performance, we propose a simple yet effective fusion mechanism by including all the previously learned feature extractors into the intelligent model. In addition, a new feature extractor is included to the model when learning a new set of classes each time, and a feature extractor pruning is also applied to prevent the whole model size from growing rapidly. Experiments on multiple classification tasks show that the proposed approach can effectively reduce the forgetting of old knowledge, achieving state-of-the-art continual learning performance.
PENGEMBANGAN MULTIMEDIA PEMBELAJARAN MENGGUNAKAN APLIKASI ADOBE CAPTIVATE 9 DALAM PENANAMAN NILAI KEMANDIRIAN ANAK USIA DINI

AbstractMultimedia learning is a medium that is developed using a computer that involves several elements of media that exist both audio, visual and audio visual equipped with a controller that can be operated by the user or teacher, so users can choose what they want for the next process. The research aims to produce as a learning media that fits the criteria of the feasibility of the media, material and practicality. The application that I use in making this learning multimedia is Adobe Captivate 9 application. This type of research is development research known as Research & Development (R&D). The development model used is the development model proposed by the modified Borg and Gall. This research was conducted at the Islamic Kindergarten Khaira Ummah. The product of the development was assessed by one material expert and two media experts as well as practicality tests on two teachers at the Khaira Ummah Islamic Kindergarten. The research subjects were 13 young children. Documentation data collection tools and validity sheets. The product validity test results show that the product is suitable for use, for the material aspects the average validator is 4.44 in the very good category. For aspects of the media are in the category of very valid with an average of 4.67, and product trials for aspects of product practicality are categorized practically with an average number of 4.50. Then it can be concluded that this learning media product is suitable for use in the learning process. Keywords : Development, Multimedia Learning, Adobe Captivate 9, Early Childhood.
Survey on Vector Quantization for Image Compression

— This paper presents a hybrid (loss less and lossy) technique for image vector quantization. The codebook is generated in two steps and first step is training set is sorted based on the magnitudes of the training vectors and step 2 is from the sorted list, training vector from every nth position is selected to for the code vectors. Followed by that, centroid computation with clustering is done by repeated iterations to improve the optimality of the codebook. The code book thus generated is compressed (Iossy) to reduce the memory needed to store the codebook with the slight degradation in the quality of the reconstructed image. The future wireless networks, such as Centralized Radio Access Network (C-RAN), c will requirement to deliver data rate about 100 times to 1000 times the current 4G technology. For C-RAN based network layout, there is a pressing The future wireless networks, such as Centralized Radio Access Network (C-RAN), will need to deliver data rate about 100 times to 1000 times the ongoing 4G technology. Compression of CPRI data is one of the prospect l enhancements. We introduce a vector quantization used compression algorithm for CPRI links, utilizing Lloyd algorithm.
Consistent Density Estimation Under Discrete Mixture Models

This work considers a problem of estimating a mixing probability density $f$ in the setting of discrete mixture models. The paper consists of three parts. The first part focuses on the construction of an $L_1$ consistent estimator of $f$. In particular, under the assumptions that the probability measure $μ$ of the observation is atomic, and the map from $f$ to $μ$ is bijective, it is shown that there exists an estimator $f_n$ such that for every density $f$ $\lim_{n\to \infty} \mathbb{E} \left[ \int |f_n -f | \right]=0$. The second part discusses the implementation details. Specifically, it is shown that the consistency for every $f$ can be attained with a computationally feasible estimator. The third part, as a study case, considers a Poisson mixture model. In particular, it is shown that in the Poisson noise setting, the bijection condition holds and, hence, estimation can be performed consistently for every $f$.
Readiness of UIN Maulana Malik Ibrahim Malang in Implementing Smart Islamic University

The success of UIN Maulana Malik Ibrahim Malang in utilizing Information and Communication Technology (ICT) with the term smart Islamic university as a competitive effort and competitiveness in essence lies in a number of indicators such as techno ware, info ware, orgaware and human ware.Efforts to achieve the goals of using a smart campus require a careful planning scheme and thorough need analysis.Many universities fail to implement a smart university due to several things, such as poor planning, unprepared experts, inadequate infrastructure, unavailable initial development costs and inconsistent policies.This study aims to see the readiness of UIN Maulana Malik Ibrahim Malang in implementing a smart campus so that it can be used as a reference in developing the use of ICT on campus.This research uses descriptive quantitative research, namely research that describes an object as it is with the research stages including pre-survey, literature study, questionnaire distribution, data analysis and empirical findings.Data obtained by using a research questionnaire.The results of research conducted at UIN Maulana Malik Ibrahim Malang showed that the development of a cybercampus on the ICT Use component which includes the dimensions of need and harmony as well as the dimensions of process and governance has entered a less prepared stage.The ICT Readiness component which covers the dimensions of technology resources in the category is almost successful.The ICT Capability component which includes the community dimension has not been successful.The ICT impact component has entered the almost successful category.Overall, the cyber campus development component was categorized as nearly successful.