Reviewerly/exHarmony huggingface dataset:
Disentangling Semantics and Syntax in Sentence Embeddings with Pre-trained Language Models

Pre-trained language models have achieved huge success on a wide range of NLP tasks. However, contextual representations from pre-trained models contain entangled semantic and syntactic information, and therefore cannot be directly used to derive useful semantic sentence embeddings for some tasks. Paraphrase pairs offer an effective way of learning the distinction between semantics and syntax, as they naturally share semantics and often vary in syntax. In this work, we present ParaBART, a semantic sentence embedding model that learns to disentangle semantics and syntax in sentence embeddings obtained by pre-trained language models. ParaBART is trained to perform syntax-guided paraphrasing, based on a source sentence that shares semantics with the target paraphrase, and a parse tree that specifies the target syntax. In this way, ParaBART learns disentangled semantic and syntactic representations from their respective inputs with separate encoders. Experiments in English show that ParaBART outperforms state-of-the-art sentence embedding models on unsupervised semantic similarity tasks. Additionally, we show that our approach can effectively remove syntactic information from semantic sentence embeddings, leading to better robustness against syntactic variation on downstream semantic tasks.
An efficient framework for accelerating Needleman-Wunsch algorithm using GPU

The Needleman-Wunsch algorithm is considered the benchmark for global alignment, this work proposes a new implementation for the parallel NW algorithm over the graphical processing unit (GPU). Focusing on enhancing the second phase of the algorithm (The Fill) the most time demanding phase. The idea of filling a percentage of the matrix is presented which guarantees a decrease in execution time, the key was to find the minimum needed percentage to be filled while ensuring the same result as filling the whole matrix of the algorithm. Experiments show the effectiveness of the proposed model in execution time when compared with the sequential algorithm.
FORMATION OF SOCIAL ACTIVITY OF STUDENT YOUTH BY MEANS OF STUDENT SELF-GOVERNMENT IN THE COURSE OF PROJECT ACTIVITY

&#x0D; The article considers the essence and ways of solving an important task of a modern secondary school - the formation of social activity of student youth. Possibilities of student self-government in the context of solving this problem are analyzed. One of the ways of social activation of schoolchildren is considered - their involvement in work on various projects, during the implementation of which students learn to identify socially significant problems and make fruitful efforts to solve them.&#x0D; The purpose of the article is to substantiate the feasibility of using student government projects to form the social activity of student youth.&#x0D; The results of the research are given, which give grounds to consider student self-government as an actual and perspective means of activating schoolchildren as members of society, young citizens of Ukraine. One of the activities of student government, which is comprehensive and contributes to its effectiveness and influence in the school and out-of-school environment, is a project activity, which today has a wider scope and deserves further study, in particular in the context of solving educational problems of secondary school .
Attritable multi-agent learning

Autonomous systems will operate in highly contested environments in which it must be assumed that adversaries are equally capable, agile and informed. To achieve and sustain dominant performance in such environments, autonomous systems must be able to adapt through online machine learning while managing and tolerating attrition - that is, improve their performance quickly, even over the duration of a single engagement with principled asset losses. However, there are novel challenges to adapting effectively in such environments. We present an approach that leverages several recent innovations in reinforcement learning, distributed computing and trusted consensus algorithms such as Blockchain. We note that multi-agent systems operating in contested environments must leverage their redundancy for learning while also remaining resilient with respect to component failures and com- promises. In particular, to enable and accelerate learning, such systems will have to allow some number of components to operate sub-optimally to achieve the right exploration-exploitation balance needed for rapid and effective learning. At the same time that some number of components are possibly being sacrificed due to sub-optimal performance, the underlying mission of the system must be maintained. This leads to challenges in distributed trusted computing such as Byzantine agreement problems. Simulations demonstrating these various tradeoffs using epidemiological models are presented.
Measuring what Really Matters: Optimizing Neural Networks for TinyML

With the surge of inexpensive computational and memory resources, neural networks (NNs) have experienced an unprecedented growth in architectural and computational complexity. Introducing NNs to resource-constrained devices enables cost-efficient deployments, widespread availability, and the preservation of sensitive data. This work addresses the challenges of bringing Machine Learning to MCUs, where we focus on the ubiquitous ARM Cortex-M architecture. The detailed effects and trade-offs that optimization methods, software frameworks, and MCU hardware architecture have on key performance metrics such as inference latency and energy consumption have not been previously studied in depth for state-of-the-art frameworks such as TensorFlow Lite Micro. We find that empirical investigations which measure the perceptible metrics - performance as experienced by the user - are indispensable, as the impact of specialized instructions and layer types can be subtle. To this end, we propose an implementation-aware design as a cost-effective method for verification and benchmarking. Employing our developed toolchain, we demonstrate how existing NN deployments on resource-constrained devices can be improved by systematically optimizing NNs to their targeted application scenario.
Continuance Intention in Blockchain-Enabled Supply Chain Applications: Modelling the Moderating Effect of Supply Chain Stakeholders Trust

If blockchain technologies are emerging as important game changers in the supply chain, this is largely attributed to their high operational and strategic business value. While this high potential is acknowledged by the practitioner’s literature, very few empirical studies have been conducted on the factors explaining the adoption, use and continuance of blockchain-enabled supply chain applications. To fill this knowledge gap, this study extended the expectation-confirmation model (ECM) by integrating the supply chain stake-holders trust to analyze to the continuance intention in blockchain-enabled supply chain applications. The proposed model was tested and supported by the data collected among 344 supply chain professionals in India. The paper ends up with the formulation of important implications for practice and research.
Ontological and Aesthetic Qualities of a Singing Sound

Aim: to reveal the existential qualities of a singing sound associated with a person’s spiritual and creative potential.&#x0D; Methods: Unlike any other voice manifestation of a person, a singing sound always represents the ideal area of a person’s existence and thus reveals the connection with the individual’s mental and creative potential. To reveal these relations, the authors used Heidegger’s philosophical aesthetic approach and Sintsov’s theory of the ‘inexpressible’ in art and culture, which examine the ontological foundations of artistic phenomena.&#x0D; Results: the expression of certain continual mental processes that are not directly related to the production and formation of certain meanings should be the initial task of a singing sound. A singing sound can be viewed as a manifestation of artwork (Heidegger) since in its ‘material component’, it expresses the ‘essence’ and ‘truth’ of man as a creator, acts as a source of man’s multiple relations with the world (‘being-in-the-world’), is characterized as ‘composite’ due to having an idealistic-materialistic nature, and contains in itself a reason for self-movement. The source of the entelechy of singing sound is ‘inexpressible’ and contains all stages of its being (‘pre-sounding – sounding – post-sounding’).&#x0D; Conclusions: the revealed qualities of a singing sound, artistry and the connection with the ‘inexpressible’, allow defining singing sound as an existential phenomenon.
Improving Student’s Vocabulary Mastery Through English Newspapaper Article At Eight Grade Of Mts Al-Munawwarah Medang Deras

This research was aimed to know and to describe how the Englishnewspaper articles can improve the students’ vocabulary mastery by usingEnglish newspaper articles at eighth grade of MTs Al-Munawwarah in20120/2021 academic year. The method used in this study was Classroom ActionResearch (CAR). The subject of this research consisted of 30 students of VIII-1.The research conducted in two cycles, cycle I and cycly II. The data weregathered through qualitative and quantitative data. The qualitative data weregained by analysing the interview, observation result, diary notes anddocumentation. The quantitative data were obtained from the students’ vocabularyscore of pre-test, post-test I, and post-test II. The result of this research, most ofthe students achieved a good score at the end of every cycle. The passing grade ofEnglish lesson was 75. The mean of the pre-test was 42,66. The mean of the firstcycle was 70,50. The mean of second cycle was 86,33. It was indicated that thescores and the mean in second cycle were better than the first cycle. Thepercentage of students who got point 75or more also grew up.. It means that therewas an improving about 43,33%. The post-test of cycle II, students who got point75or more there were 30 students (100%) and the improving was about 56,67%.For the total improving of the students’ score from pre-test to post-test of cycle IIwas 100%. From this result, English newspaper article could improve students’vocabulary mastery.
Hubungan Screen Time Dengan Status Gizi Pada Anak Usia 6 – 18 Tahun: A Narative Review

Introduction: The prevalence of obesity in children and adolescents has increased rapidly in the last few decades. Obesity that occurs during adolescence can have an impact on increasing the morbidity of non-communicable diseases, so it is necessary to prevent obesity from an early age by controlling obesity risk factors. Several studies have shown that adolescents with high screen time duration can exacerbate the risk of being overweight / obesity due to lack of physical activity and tend to choose high-calorie foods. Objective: The purpose of this study was to analyze the correlation between screen time and nutritional status in children aged 6-18 years. Methods: This research study was a narrative review through journal searches using the PubMed journal site. The selected journals were in English, in full text format and publication for the last 10 years starting in January 2011. The selected study was cross-sectional, providing screen time data and data on children's nutritional status, and data on the relation between screen time and nutritional status of children aged 6-18 years. Result: The results of the research from the five journals showed that there was a significant correlation between screen time and nutritional status in children aged 6-18 years. This was evidenced by the value of p ≤0.05. Conclusion: Screen time activity in children aged 6 - 18 years for > 2 hours / day increases the risk of overweight / obesity. Suggestion: It was expected that children and adolescents can set a screen time duration of less than 2 hours and maintain normal nutritional status by adjusting diet and physical activity. The role of parents was very necessary in directing children and adolescents in the use of time in front of the screen both for doing assignments and just for entertainment.
Incorporating unlabeled data into distributionally-robust learning

We study a robust alternative to empirical risk minimization called distributionally robust learning (DRL), in which one learns to perform against an adversary who can choose the data distribution from a specified set of distributions. We illustrate a problem with current DRL formulations, which rely on an overly broad definition of allowed distributions for the adversary, leading to learned classifiers that are unable to predict with any confidence. We propose a solution that incorporates unlabeled data into the DRL problem to further constrain the adversary. We show that this new formulation is tractable for stochastic gradient-based optimization and yields a computable guarantee on the future performance of the learned classifier, analogous to -- but tighter than -- guarantees from conventional DRL. We examine the performance of this new formulation on 14 real datasets and find that it often yields effective classifiers with nontrivial performance guarantees in situations where conventional DRL produces neither. Inspired by these results, we extend our DRL formulation to active learning with a novel, distributionally-robust version of the standard model-change heuristic. Our active learning algorithm often achieves superior learning performance to the original heuristic on real datasets.
Recursive Estimation of State-Space Noise Covariance Matrix by Approximate Variational Bayes

This working paper considers state-space models where the variance of the observation is known but the covariance matrix of the state process is unknown and potentially time-varying. We propose an adaptive algorithm to estimate jointly the state and the covariance matrix of the state process, relying on Variational Bayes and second-order Taylor approximations.
A Result Review Analysis of on Distributed De-Duplication System using File Level and Block Level

As the world moves to digital storage for archival purposes, there is an increasing demand for systems that can provide secure data storage in a cost-effective manner. Now days with the huge increasing of population and the using of technology, it leads to many problems. The growth in technology is increasing the amount of storage or communication and technique devices. By the unpredictable development of digital data, DE-duplication techniques are broadly engaged to backup data and decrease network and storage transparency by notice and eradicate redundancy among data. As an alternative of maintaining multiple data copies with the same content, DE duplication reducing redundant data by maintaining only single copy and referring other redundant data to that copy. By identifying common chunks of data both within and between files and storing them only once, DE duplication can yield cost savings by increasing the utility of a given amount of storage. DE duplication has inward much concentration from both academic world and industry since it can really recover storage utilization and keep storage space, particularly for the applications with high DE duplication ratio such as archival storage systems.I.
Document-level grammatical error correction

Document-level context can provide valuable information in grammatical error correction (GEC), which is crucial for correcting certain errors and resolving inconsistencies. In this paper, we investigate context-aware approaches and propose document-level GEC systems. Additionally, we employ a three-step training strategy to benefit from both sentence-level and document-level data. Our system outperforms previous document-level and all other NMT-based single-model systems, achieving state of the art on a common test set.
The Problem of the Formation of Value-Sense Orientations of Future Preschool Educators in Pedagogical Theory and Practice

The article is devoted to the study of the problem of the formation of value-sense orientations of future kindergarten educators in pedagogical theory and practice. The essence of the leading values, which should be inherent in future teachers in general and future kindergarten educators, in particular, within the framework of various approaches, namely: historical and cultural, competence-based, integrated, has been determined. It was determined that from the point of view of the historical and cultural approach, classical teachers attributed a number of general cultural values to the most important values of the educator (Person, goodness, beauty, love, tenderness, sincerity, justice, truth, etc.). Representatives of the competence-based approach to the leading values of the educator include those that characterize him as a professional educator (love for children, justice, tolerance, responsibility, flexibility, creativity, self-development, self-education). Within the framework of the integrated approach, which the author adheres to, the personality of the future educator is considered holistically, and the values that should be inherent in him are interpreted as a complex of universal, professional and individual life values.
IMPLEMENTATION OF INTERSUBJECT COMMUNICATIONS IN THE PROCESS OF STUDYING «MUSIC INFORMATICS»

The article sets the task to consider the interdisciplinary sphere of professional activity related to the creation and use of specialized musical software and hardware – music informatics – which requires knowledge and skills both in the music field and in the field of computer science.The issue of interdisciplinary links and their implementation in the educational process in higher education institutions has been raised in the article. The content of the concept of information competence as a component of professional competence and information culture has been consider.The author argues that the use of intersubject communications contributes to the formation of practical skills and abilities to apply knowledge in one discipline during the study of others.It has been analyzed the importance of pedagogy of cooperation, which uses a variety of forms of learning: group work, collective creative work, work in microgroups, work in variable groups;The result of a scientific article is the conclusion about the feasibility of the high-quality implementation of interdisciplinary connections, the spread of interdisciplinary relations within not only the artistic-aesthetic and related humanitarian cycles in the classes on “Music Informatics”, but also with other, so to speak, deleted subjects; the use of art both as a means of developing special artistic abilities and thinking, and as a universal way of stimulating the creative potential of an individual.Among the prospects for research in this direction, the author identifies a purely practical aspect, in particular, the creation of methodological materials, manuals, the development and implementation of a system that is based on an information technology basis and which takes into account the orientation of students in the use of computer technology in professional and musical pedagogical activities in a subtle combination with other disciplines, the establishment and strengthening of the relationships between them.
StegColNet: Steganalysis Based on an Ensemble Colorspace Approach

Image steganography refers to the process of hiding information inside images. Steganalysis is the process of detecting a steganographic image. We introduce a steganalysis approach that uses an ensemble color space model to obtain a weighted concatenated feature activation map. The concatenated map helps to obtain certain features explicit to each color space. We use a levy-flight grey wolf optimization strategy to reduce the number of features selected in the map. We then use these features to classify the image into one of two classes: whether the given image has secret information stored or not. Extensive experiments have been done on a large scale dataset extracted from the Bossbase dataset. Also, we show that the model can be transferred to different datasets and perform extensive experiments on a mixture of datasets. Our results show that the proposed approach outperforms the recent state of the art deep learning steganalytical approaches by 2.32% on average for 0.2 bits per channel (bpc) and 1.87% on average for 0.4 bpc.
KUALITAS PELAYANAN DAN PROMOSI MEMPENGARUHI KEPUTUSAN MAHASISWA KULIAH DI STIE BENTARA PERSADA BATAM

The study of the research that the researchers did was the influence of Service Quality and Promotion on Student Decisions. Lecture at STIE Bentara Persada Batam. With the object of reseach on STIE Bentara Persada Batam students. The purpose of this study was to determine the effect of Service Quality and Promotion on Student Decision Lecture at STIE Bentara Persada Batam.&#x0D; This study used a sample of 70 students at STIE Bentara Persada Batam with the chosen sample technique being saturated sampling. Data collection techniques in this study used a questionnaire. The results showed that, Service Quality and Promotion partially had a positive and significant effect on Student Decision Lecture at STIE Bentara Persada Batam.&#x0D; The regression analysis results obtained a coefficient of determination (R2) of 0.175which means that student decisions areinfluenced by Service Quality and Promotion by 17.5%, while the remaining 82.5% is influenced by other variables not examined inthis study.
Layer Reduction: Accelerating Conformer-Based Self-Supervised Model via Layer Consistency

Transformer-based self-supervised models are trained as feature extractors and have empowered many downstream speech tasks to achieve state-of-the-art performance. However, both the training and inference process of these models may encounter prohibitively high computational cost and large parameter budget. Although Parameter Sharing Strategy (PSS) proposed in ALBERT paves the way for parameter reduction, the computation required remains the same. Interestingly, we found in experiments that distributions of feature embeddings from different Transformer layers are similar when PSS is integrated: a property termed as Layer Consistency (LC) in this paper. Given this similarity of feature distributions, we assume that feature embeddings from different layers would have similar representing power. In this work, Layer Consistency enables us to adopt Transformer-based models in a more efficient manner: the number of Conformer layers in each training iteration could be uniformly sampled and Shallow Layer Inference (SLI) could be applied to reduce the number of layers in inference stage. In experiments, our models are trained with LibriSpeech dataset and then evaluated on both phone classification and Speech Recognition tasks. We experimentally achieve 7.8X parameter reduction, 41.9% training speedup and 37.7% inference speedup while maintaining comparable performance with conventional BERT-like self-supervised methods.