Reviewerly/exHarmony huggingface dataset:
A Model for Teaching English Translation in Universities Based on the Random Matrix Optimization Bell Translation Model

This paper adopts the random matrix optimization fusion of Bell’s translation model to conduct in-depth research and analysis on the teaching model of English translation in colleges and universities. Bell’s translation model theory explicates the mental model of the translation process to a certain extent based on psycholinguistic research results. Using the results of random matrix theory on the eigenvalues of the sample covariance matrix, the energy of each subspace is estimated, and the estimated energy is then used to construct the subspace-weighting matrix. The statistical properties of the sample covariance matrix eigenvectors were analyzed using the first-order perturbation approximation, and then asymptotic results from random matrix theory on the projection of the sample covariance matrix signal subspaces to the real signal parametrization were used to obtain the weighting matrix based on the random matrix eigenvectors. The analysis shows that the teacher can help students understand the teaching content and master the knowledge points by using guided meta-discoursal to indicate the logical relationships inherent in linguistic expressions and make the discourse transitions natural, coherent, and organized. The use of interactive meta-discoursal attracts students’ attention and treats them as participants in the discourse, making them feel a sense of belonging and satisfying their need for psychological belonging, which is beneficial to the students’ main position in the classroom. In teaching methods, there is a principle of comprehensible input, which is to provide students with a lot of interesting and understandable listening and reading input. In the translation classroom, the translation teachers code-switched infrequently and mainly used three types of code-switching: inter-sentence code-switching, intra-sentence code-switching, and additional code-switching to carry out translation teaching. Among them, intra-sentence code-switching accounts for 57.46%, inter-sentence code-switching accounts for 41.53%, and the least frequent is additional code-switching, accounting for 1.01%.
On The Euclidean Algorithm: Rhythm Without Recursion

A modified form of Euclid's algorithm has gained popularity among musical composers following Toussaint's 2005 survey of so-called Euclidean rhythms in world music. We offer a method to easily calculate Euclid's algorithm by hand as a modification of Bresenham's line-drawing algorithm. Notably, this modified algorithm is a non-recursive matrix construction, using only modular arithmetic and combinatorics. This construction does not outperform the traditional divide-with-remainder method; it is presented for combinatorial interest and ease of hand computation.
Comparative analysis of the Chinese and Russian digital educational environment in high education (using the example of Moscow Pedagogical State University and Beijing State Pedagogical University)

The subject of the study is the digital educational environments of Chinese and Russian universities. The object of the research is the elements of the digital educational environment of the modern educational space. The author attempts to compare digital educational environments in Russia and China. For this purpose, several elements of the digital educational environment are identified and compared. The author also pays attention to the concept of digitalization of the educational environment and the description of its important aspects. The author clearly shows how the principles of the digital educational environment are implemented in the practice of Chinese and Russian universities, and also draws conclusions at the end of the article. The novelty of the study lies in the fact that the Chinese and Russian digital educational environments are being studied for the first time in a comparative aspect. The main conclusions of the study are: 1. the digital educational environment plays an important role for Chinese and Russian universities, which is manifested in the development of digital technologies, resources and other important elements of the environment; 2. in Russian and Chinese universities, the development of the digital educational environment has been going on for several decades and is contradictory, on the one hand, positive results have been achieved, on the other hand, there are areas that still need to be improved and adjusted.
Multi-agent blind quantum computation without universal cluster states

Blind quantum computation (BQC) protocols enable quantum algorithms to be executed on third-party quantum agents while keeping the data and algorithm confidential. The previous proposals for measurement-based BQC require preparing a highly entangled cluster state. In this paper, we show that such a requirement is not necessary. Our protocol only requires pre-shared bell pairs between delegated quantum agents, and there is no requirement for any classical or quantum information exchange between agents during the execution. Our proposal requires fewer quantum resources than previous proposals by eliminating the need for a universal cluster state.
PENERAPAN APLIKASI SIMPEG BERBASIS E-GOVERNMENT DALAM PENGELOLAAN DATA INFORMASI KEPEGAWAIAN (Studi pada Badan Kepegawaian Daerah, Pendidikan dan Pelatihan Kota Bnjarmasin)

The existence of staffing management information systems in an institution is to facilitate thefilling or updating of staffing information data quickly, this can facilitate the process ofincreasing the rank of employees. assist the leadership in determining the employee who ismutated and promoted his position. Therefore, the purpose of this research is to find out theapplication of the Staffing Management Information System Application at the RegionalStaffing, Education and Training Agency of Banjarmasin City which is analyzed using threeelements of successful e-government development. The study used a qualitative descriptiveapproach.The data was collected by conducting interviews with three informants. The resultsof the study showed that from the support element can be known to have been agreed on thee-government framework both by the leadership and subordinates, has been allocatedfinancial human resources, time, and information, the availability of infrastructure in the formof computer networks, servers, internet networks, computers, computers, superstructures,namely legal institutions, and applications Staffing management information systems havebeen socialized to all employees. From the capacity element, it can be known that theavailability of financial resources is still lacking, technological infrastructure has not reached50% but existing human resources are already compete.
Supermodular f-divergences and bounds on lossy compression and  generalization error with mutual f-information

In this paper, we introduce super-modular $\mf$-divergences and provide three applications for them: (i) we introduce Sanov's upper bound on the tail probability of sum of independent random variables based on super-modular $\mf$-divergence and show that our generalized Sanov's bound strictly improves over ordinary one, (ii) we consider the lossy compression problem which studies the set of achievable rates for a given distortion and code length. We extend the rate-distortion function using mutual $\mf$-information and provide new and strictly better bounds on achievable rates in the finite blocklength regime using super-modular $\mf$-divergences, and (iii) we provide a connection between the generalization error of algorithms with bounded input/output mutual $\mf$-information and a generalized rate-distortion problem. This connection allows us to bound the generalization error of learning algorithms using lower bounds on the rate-distortion function. Our bound is based on a new lower bound on the rate-distortion function that (for some examples) strictly improves over previously best-known bounds. Moreover, super-modular $\mf$-divergences are utilized to reduce the dimension of the problem and obtain single-letter bounds.
Are We There Yet? A Decision Framework for Replacing Term Based Retrieval with Dense Retrieval Systems

Recently, several dense retrieval (DR) models have demonstrated competitive performance to term-based retrieval that are ubiquitous in search systems. In contrast to term-based matching, DR projects queries and documents into a dense vector space and retrieves results via (approximate) nearest neighbor search. Deploying a new system, such as DR, inevitably involves tradeoffs in aspects of its performance. Established retrieval systems running at scale are usually well understood in terms of effectiveness and costs, such as query latency, indexing throughput, or storage requirements. In this work, we propose a framework with a set of criteria that go beyond simple effectiveness measures to thoroughly compare two retrieval systems with the explicit goal of assessing the readiness of one system to replace the other. This includes careful tradeoff considerations between effectiveness and various cost factors. Furthermore, we describe guardrail criteria, since even a system that is better on average may have systematic failures on a minority of queries. The guardrails check for failures on certain query characteristics and novel failure types that are only possible in dense retrieval systems. We demonstrate our decision framework on a Web ranking scenario. In that scenario, state-of-the-art DR models have surprisingly strong results, not only on average performance but passing an extensive set of guardrail tests, showing robustness on different query characteristics, lexical matching, generalization, and number of regressions. It is impossible to predict whether DR will become ubiquitous in the future, but one way this is possible is through repeated applications of decision processes such as the one presented here.
Application of Mobile Information System in Quality Education of Research Activities

In order to solve the problems of uneven local educational resources, imperfect comprehensive practice systems, and insufficient teachers in rural schools, this paper proposes a research activity based on mobile information system. The research activities proposed in this paper take STEAM (the acronym of Science, Technology, Engineering, Art, and Mathematics) as the educational concept, carry out local research-based learning (research-based learning, hereinafter referred to as “research-based learning”), and create STEAM research activities suitable for local teaching in combination with the requirements of national education reform to improve students’ scientific literacy such as innovation, scientific, and technological level, and independent practice and exploration. The results are as follows: 92% of the students in the experimental group think that STEAM research and learning activities are very interesting and recognize the research and learning process; STEAM research activities have a strong role in promoting the cultivation of students’ three abilities and the training of core skills, and the scores are increased by about 50%; the excellent, good, qualified, and unqualified students in the experimental group accounted for 15%, 73%, 12%, and 0%, respectively, while the control group accounted for 6%, 55%, 32%, and 7%, respectively. Through the participation of STEAM research activities, the students’ learning attitude has been greatly improved. The practice of STEAM research activities proposed in this paper in the research and implementation institute has achieved remarkable results, which provides experience for Western schools in the follow-up stage of compulsory education to carry out STEAM research activities.
Generalized Beliefs for Cooperative AI

Self-play is a common paradigm for constructing solutions in Markov games that can yield optimal policies in collaborative settings. However, these policies often adopt highly-specialized conventions that make playing with a novel partner difficult. To address this, recent approaches rely on encoding symmetry and convention-awareness into policy training, but these require strong environmental assumptions and can complicate policy training. We therefore propose moving the learning of conventions to the belief space. Specifically, we propose a belief learning model that can maintain beliefs over rollouts of policies not seen at training time, and can thus decode and adapt to novel conventions at test time. We show how to leverage this model for both search and training of a best response over various pools of policies to greatly improve ad-hoc teamplay. We also show how our setup promotes explainability and interpretability of nuanced agent conventions.
Interlingual Errors in Indonesian EFL learners' Pronunciation: From Minimal Pairs to Speaking Ability

The majority of previous research observed phonemes produced individually to examine errors. As a result, based on the concept of minimum pairings, this study offered a novel way of assessing interlingual errors based on the concept of minimal pairings. This study examines the interlingual errors in consonant minimal pairs to see the correlation between the errors and the speaking ability that happened to high school students. There are several consonants which are being used such as /v/, /θ̠/, /ð̠/, /ʃ/, /t̠ʃ/, /ʤ/, /f/, /t/, /d/, /z/, and /s/ as indicators to find the interlingual errors. This study is descriptive qualitative research that examines the interlingual errors produced by high school students when utilizing English minimal pairs of consonants. All of the consonants acted as the test tool for the 19 selected students in the form of 10 minimal pairs. The research found that students did have difficulties when they were pronouncing the consonant minimal pairs such as /d/ &amp; /ð/, /f/ &amp; /v/, and /ʃ/ &amp; /t̠ʃ/. Thus, the difficulties that occurred in the students’ pronunciation also generate speaking ability between students based on the interlingual errors. The findings showed that high school students mostly struggled with their pronunciation when uttering consonants that do not exist in the Indonesian language. In conclusion, interlingual errors play an important role as a method in teaching a foreign language to see the consonant errors happen in high school students when pronouncing the English language.
The Development of Telegram Bot Api to Maximize The Dissemination Process of Islamic Knowledge in 4.0 Era

Information technology is developing quickly in this era as if the world is in our hands. All forms of information can be accessed quickly, including Islamic information by utilizing internet technology. One of well-known medium to spread information is social media. Telegram, as one of the social media that has various beneficial features and widely used by people, has the potential to be a medium for disseminating Islamic knowledge. To improve the dissemination process of Islamic knowledge, we used one of the services found on Telegram namely chatbot. By utilizing Telegram Bot API, we make a bot that can automatically provide output according to the command given by the user in this case, giving various Islamic knowledges that users need such as Tafsir, hadith, daily prayer, and even Islamic history with a reliable source. Based on the testing result, the bot is showing a good result because it’s displaying the expected output and all features are working fine as they should be. We hoped that this bot can maximize the dissemination process of Islamic knowledge in Indonesia and reach wider audience coverage.
On the problems in preparing students of the specialty “Physical Education” to participate in the WorldSkills Russia professional skills competition

Introduction. The introduction of the competence “Physical Culture, sports and fitness” into the competence for participation in the WorldSkills Russia championship necessitated the analysis of conditions and generalization of the accumulated experience of preparing students for this event. The theoretical analysis of the sources confirmed the insufficiency of research on the identification and interpretation of problems related to the target, content, methodological, organizational, resource readiness of secondary vocational education organizations to participate in the championship. The purpose of the article is to identify problems in preparing students of the Department of Physical Culture of Belgorod Pedagogical College to participate in the competition of professional skills according to WorldSkills Russia standards. Materials and methods: a theoretical comprehensive analysis of literature, standards of the Federal State Educational Standard of Secondary Vocational Education and WorldSkills Russia; generalization of the experience of the teaching staff of Belgorod Pedagogical College (25 people); interpretation of the results of the activity of focus groups from among the representatives of scientific and teaching staff of Valuisky and Starooskolsky pedagogical colleges and the Faculty of Physical Culture of Belgorod State National Research University. As a result, the stages of work of the teaching staff are described, the changes in the modules of curricula for the specialty "Physical Education" are listed, the problems of compliance with modern requirements of WorldSkills Russia are identified: insufficient preparedness of the teaching staff of a professional educational organization; insufficient material and technical support of professional educational organizations; expensive certified sports equipment and inventory, which must be updated almost every year; constantly changing criteria for evaluating competitive tasks. Conclusions. WorldSkills Russia for the specialty "Physical Education" significantly expands the volume of educational material, helps to successfully solve the problems of improving the quality of training specialists, improves professional skills, develops professional and creative thinking of students, contributes to the formation of experience of creative activity in the professional sphere in accordance with the challenges of our time.
Which one is more toxic? Findings from Jigsaw Rate Severity of Toxic Comments

The proliferation of online hate speech has necessitated the creation of algorithms which can detect toxicity. Most of the past research focuses on this detection as a classification task, but assigning an absolute toxicity label is often tricky. Hence, few of the past works transform the same task into a regression. This paper shows the comparative evaluation of different transformers and traditional machine learning models on a recently released toxicity severity measurement dataset by Jigsaw. We further demonstrate the issues with the model predictions using explainability analysis.
Adversarial Example Detection in Deployed Tree Ensembles

Tree ensembles are powerful models that are widely used. However, they are susceptible to adversarial examples, which are examples that purposely constructed to elicit a misprediction from the model. This can degrade performance and erode a user's trust in the model. Typically, approaches try to alleviate this problem by verifying how robust a learned ensemble is or robustifying the learning process. We take an alternative approach and attempt to detect adversarial examples in a post-deployment setting. We present a novel method for this task that works by analyzing an unseen example's output configuration, which is the set of predictions made by an ensemble's constituent trees. Our approach works with any additive tree ensemble and does not require training a separate model. We evaluate our approach on three different tree ensemble learners. We empirically show that our method is currently the best adversarial detection method for tree ensembles.
Multi Visual Modality Fall Detection Dataset

Falls are one of the leading cause of injury-related deaths among the elderly worldwide. Effective detection of falls can reduce the risk of complications and injuries. Fall detection can be performed using wearable devices or ambient sensors; these methods may struggle with user compliance issues or false alarms. Video cameras provide a passive alternative; however, regular RGB cameras are impacted by changing lighting conditions and privacy concerns. From a machine learning perspective, developing an effective fall detection system is challenging because of the rarity and variability of falls. Many existing fall detection datasets lack important real-world considerations, such as varied lighting, continuous activities of daily living (ADLs), and camera placement. The lack of these considerations makes it difficult to develop predictive models that can operate effectively in the real world. To address these limitations, we introduce a novel multi-modality dataset (MUVIM) that contains four visual modalities: infra-red, depth, RGB and thermal cameras. These modalities offer benefits such as obfuscated facial features and improved performance in low-light conditions. We formulated fall detection as an anomaly detection problem, in which a customized spatio-temporal convolutional autoencoder was trained only on ADLs so that a fall would increase the reconstruction error. Our results showed that infra-red cameras provided the highest level of performance (AUC ROC=0.94), followed by thermal (AUC ROC=0.87), depth (AUC ROC=0.86) and RGB (AUC ROC=0.83). This research provides a unique opportunity to analyze the utility of camera modalities in detecting falls in a home setting while balancing performance, passiveness, and privacy.
Facial Landmark Detection with Sentiment Recognition

Abstract: Facial landmark detection is gaining more importance and popularity as its requirement and applications increase tremendously. Facial landmark detection has always been an exciting field of research cause of many potential applications encompassing facial recognition, sentiment recognition, and facial expression analysis. Innumerous datasets are extracted, cleaned, and explored for training Facial Landmark detection at various angles and setups, resulting in successful working in occult faces and multiple facial mapping in real-time. Facial Landmark detection, coupled with one of the robust applications of sentiment recognition, becomes an excellent tool for the user with the potential to add many applications further in the future.
Sentiment Analysis of Public Opinion Covid-19 Vaccine Using Naïve Bayes and Random Forest Methods

The emergence of COVID-19 or 2019 coronavirus disease has been reported as a problem with a new type of disease caused by SARS-Voc 2. It has spread to 223 countries and 25 areas around the world, including Indonesia. COVID-19 has deeply affected many aspects of our lives, the environment, mental health and the economy. Twitter is one of the media outlets that is busy discussing news regarding the COVID-19 vaccine. Covid-19 has been a major impact. The Government has implemented policies such as large-scale social restrictions to address the spread of COVID-19. The elevated spread of COVID-19 has prompted the Government of Indonesia to encourage the production of a COVID-19 vaccine. The provision of the COVID-19 vaccine has become a boon and a boon to the people of Indonesia. A lot of people don't want to be vaccinated because the news of the impact of vaccination is spreading on social media, even if the news isn't necessarily real. The Government is looking for ways to continue vaccinating the community, including by collaborating with community leaders, influencers and others. The purpose of this study is to identify the community response to the vaccine so that the right strategy can be used. The results of this study yielded 89.79% for Naïve Bayes and 84.62% for Random Forest. Indonesians are giving positive responses to the administration of the COVID-19 vaccine.