Reviewerly/exHarmony huggingface dataset:
Document Image Cleaning using Budget-Aware Black-Box Approximation

Recent work has shown that by approximating the behaviour of a non-differentiable black-box function using a neural network, the black-box can be integrated into a differentiable training pipeline for end-to-end training. This methodology is termed "differentiable bypass,'' and a successful application of this method involves training a document preprocessor to improve the performance of a black-box OCR engine. However, a good approximation of an OCR engine requires querying it for all samples throughout the training process, which can be computationally and financially expensive. Several zeroth-order optimization (ZO) algorithms have been proposed in black-box attack literature to find adversarial examples for a black-box model by computing its gradient in a query-efficient manner. However, the query complexity and convergence rate of such algorithms makes them infeasible for our problem. In this work, we propose two sample selection algorithms to train an OCR preprocessor with less than 10% of the original system's OCR engine queries, resulting in more than 60% reduction of the total training time without significant loss of accuracy. We also show an improvement of 4% in the word-level accuracy of a commercial OCR engine with only 2.5% of the total queries and a 32x reduction in monetary cost. Further, we propose a simple ranking technique to prune 30% of the document images from the training dataset without affecting the system's performance.
Abstractive Text Summarization for Resumes With Cutting Edge NLP Transformers and LSTM

Text summarization is a fundamental task in natural language processing that aims to condense large amounts of textual information into concise and coherent summaries. With the exponential growth of content and the need to extract key information efficiently, text summarization has gained significant attention in recent years. In this study, LSTM and pre-trained T5, Pegasus, BART and BART-Large model performances were evaluated on the open source dataset (Xsum, CNN/Daily Mail, Amazon Fine Food Review and News Summary) and the prepared resume dataset. This resume dataset consists of many information such as language, education, experience, personal information, skills, and this data includes 75 resumes. The primary objective of this research was to classify resume text. Various techniques such as LSTM, pre-trained models, and fine-tuned models were assessed using a dataset of resumes. The BART-Large model fine-tuned with the resume dataset gave the best performance.
Automated Automotive Radar Calibration With Intelligent Vehicles

While automotive radar sensors are widely adopted and have been used for automatic cruise control and collision avoidance tasks, their application outside of vehicles is still limited. As they have the ability to resolve multiple targets in 3D space, radars can also be used for improving environment perception. This application, however, requires a precise calibration, which is usually a time-consuming and labor-intensive task. We, therefore, present an approach for automated and geo-referenced extrinsic calibration of automotive radar sensors that is based on a novel hypothesis filtering scheme. Our method does not require external modifications of a vehicle and instead uses the location data obtained from automated vehicles. This location data is then combined with filtered sensor data to create calibration hypotheses. Subsequent filtering and optimization recovers the correct calibration. Our evaluation on data from a real testing site shows that our method can correctly calibrate infrastructure sensors in an automated manner, thus enabling cooperative driving scenarios.
Implementation of School Improvement Plan (SIP) on the School Performance: Basis for Enhancement Program

A School Improvement Plan (SIP) is a strategic plan that addresses the priority improvement areas of the school.It is a collection of different intervention program and activities that addresses the school problems and issues.Every school has its own version of SIP that is tailored to its specific needs for improvement.However, SIP is completely inadequate for school improvement.If there is no action or proper implementation, it will simply be a plan.Philippines has been closed for almost two years due to pandemic and most learners experienced learning deprivation.Schools crafted their SIP and developed programs to solve this issue.The purpose of this study was to determine the relationship between School Improvement Plan Enhancement on the School Performance in the Division of Laguna.To gather information about the relationship between independent and dependent variables, the researcher used a descriptive research design.The survey questionnaire, which served as the primary data collection tool, was distributed to 150 randomly selected elementary teachers from 25 elementary schools in the Division of Laguna who participated in the study.The study found out that the implementation of SIP has no significant relationship to the school performance in the Division of Laguna.However, the data revealed that the level of teachers' involvement as regards crafting of school improvement plan, decision-making, implementation of school programs and projects, and monitoring and evaluation marked as very high.It is also discovered that the level of external stakeholder's participation as regards financial support, services, and training is also marked as very high.The level of school performance development in the Division of Laguna relative to enrollment rate shows that there was an increase in the enrolment rate, graduation rate, and proficiency rates for every subject among the responses.The evidence showed that teachers' involvement and stakeholders' participation in the crafting and implementation of SIP is very evident in the school.Based on the findings and conclusions drawn, the researcher recommends that the Department of Education may provide training to school personnel as well as representatives from its stakeholders for effective school improvement planning yearly, and provide the necessary technical assistance to the school for effective implementation of SIP, all schools may form a school improvement committee, which will begin the process of developing a school improvement plan in which the major priorities will be addressed and given priority based on the availability of resources, school heads may develop plans and strategies to attract stakeholders to extend appropriate financial support and generate income-generating projects to finance the school projects and programs, and all schools may encourage practical involvement of all stakeholders by raising awareness of the importance of SIP implementation.
PEDAGOGICAL TECHNOLOGY OF VISUALIZATION IN EDUCATION

Visualization is the basis of any modern information project. Today, without appropriate graphic images, it is impossible to present the results of scientific research, technical developments, a successful educational process, etc. Visualization technology includes the following parts: methodical methods of including visual models in the educational process; systematic use of visual models of one type or their combinations in the educational process; training students in the techniques of rational processing of information and its cognitive and graphic representation. The article describes the relevance of using visualization tools of educational materials from foreign literature in the conditions of online education. The most popular tools for creating visualized content are considered. Among them: metaphorical schemes, infographics, interactive poster, crosses, word clouds. The main features of each of the software tools are described and examples of their use in educational activities are given. Active means of visualization should be understood not just as illustrative material, but as a system for transmitting visual information that allows any subject of interaction to manage information. The use of various visualization methods makes it possible to diversify learning in foreign literature lessons. Thus, the visualization of educational information allows you to solve a number of pedagogical tasks: to ensure the intensification of learning; to activate educational and cognitive activities; formation and development of critical and visual thinking, visual perception; figurative representation of knowledge and educational activities; knowledge transfer and pattern recognition; improvement of visual literacy and visual culture, etc. It has been established that visualization is an assistant during online learning in working on a work, in developing imagination and fantasy, memorizing important facts from the theory and history of literature, and most importantly, in expressing one's own «I». In the course of preparation and processing of visualization samples, first of all, the skills to critically analyze the educational material, to highlight the main points in it, to translate it into a visual plan and to aesthetically shape one's thinking-image are developed.
WORKSHOP FOR MAKING INTERACTIVE PRESENTATION MATERIALS FOR WEST JAKARTA TEACHER WORK GROUP (Kelompok Kerja Guru Bahasa Inggris)

The COVID-19 pandemic has lasted 2 years which has resulted in the rapid development of the world of information technology, especially in the world of education. Online and hybrid learning have been carried out, especially in primary school education. In 2021, the design of the tutoring application concept has been carried out to be used by the English Teacher Working Group (Kelompok Kerja Guru-KKG) at the elementary level. This application can be used if teachers can arrange material in digital form in the form of subject matter, quiz questions, puzzles, and audio-video material. Teachers as pioneers of educational success must continue to be able to innovate in preparing interesting and interactive presentation materials to reduce the level of saturation of elementary school students. Therefore, training in making interactive materials for the English KKG is very necessary. The method of implementing this activity is to provide 2 online and hybrid trainings. The training was conducted for 2 types of software commonly used to create presentation materials, namely Microsoft PowerPoint and Google Slides. Based on the evaluation form filled out by participants from the English KKG, the result of this activity was that 100% of the participants understood how to access PowerPoint from Office 365 and understood how to use Morph transitions and animation features. 100% of participants can also know how to access Google Slides and know how to make classes and assignments using Google Classroom.
Bring Your Own Data! Self-Supervised Evaluation for Large Language Models

With the rise of Large Language Models (LLMs) and their ubiquitous deployment in diverse domains, measuring language model behavior on realistic data is imperative. For example, a company deploying a client-facing chatbot must ensure that the model will not respond to client requests with profanity. Current evaluations approach this problem using small, domain-specific datasets with human-curated labels. These evaluation sets are often sampled from a narrow and simplified distribution, and data sources can unknowingly be leaked into the training set which can lead to misleading evaluations. To bypass these drawbacks, we propose a framework for self-supervised evaluation of LLMs by analyzing their sensitivity or invariance to transformations on the input text. Self-supervised evaluation can directly monitor LLM behavior on datasets collected in the wild or streamed during live model deployment. We demonstrate self-supervised evaluation strategies for measuring closed-book knowledge, toxicity, and long-range context dependence, in addition to sensitivity to grammatical structure and tokenization errors. When comparisons to similar human-labeled benchmarks are available, we find strong correlations between self-supervised and human-supervised evaluations. The self-supervised paradigm complements current evaluation strategies that rely on labeled data.
FORMATION OF SAFETY CULTURE FOR BACHELOR DEGREE ACQUISITIONS IN INSTITUTIONS OF HIGHER EDUCATION

The article is devoted to the justification of the need for the formation of a safety culture and ways to implement the task in higher education institutions. Important features of the formation of a safety culture among students of higher education as a means of preventing loss of health and life, both their own and those around them, are outlined. The theoretical aspects of the use of inter-subject and inter-thematic connections in the process of mastering professional disciplines, the discipline of free choice “safety culture” and with the application of soft skills for the formation of a safety culture in higher education graduates of the “bachelor” degree are considered.The current state of security depends on changes in technology caused by scientific and technical progress, the introduction of informatization and the influence of anthropogenic environmental factors, which predicts the emergence of new threats to human health and life. The priority in the development of the state and society is to find ways to solve the problem of preventing diseases and fatal consequences among the citizens of the state. And the concepts at the state level are built in such a way that they include the creation of a safe environment in all spheres of human life, socio-political, spiritual, cultural, household and material-production.To implement the given problem, it is necessary to train new specialists, which can be done in pedagogical institutions of higher education. After all, the educational process in pedagogical institutions of higher education, in addition to the formation of professional competences, allows to focus attention on the process of formation of safety competence.Therefore, the training of future specialists in the higher education system, in addition to acquiring knowledge, developing skills, mastering new technologies and innovations for the formation of professional competences, involves the wide use of pedagogical technologies in the educational process of higher education institutions, which makes it possible to combine the fundamentals of pedagogical disciplines and professional disciplines, in particular, safety cycle (life safety and labor protection) with innovative thinking and a practice-oriented, research approach to solving certain educational problems.&#x0D; Keywords: bachelor; safety culture; soft skills; educational process; competence approach; institution of higher education; interdisciplinary connection; interdisciplinary connection.
Unsupervised detection of contrast enhanced highlight landmarks

Abstract In the field of landmark detection based on deep learning, most of the research utilise convolutional neural networks to represent landmarks, and rarely adopt Transformer to represent and encode landmarks. Meanwhile, many works focus on modifying the network structure to improve network performance, and there is little research on the distribution of landmarks. In this article,the authors propose an unsupervised model to extract landmarks of objects in images. First, Transformer structure is combined with the convolutional neural network structure to represent and encode the landmarks; next, positive and negative sample pairs between landmarks are constructed, so that the semantically consistent landmarks on the image are pulled closer in the feature space and the semantically inconsistent landmarks are pushed farther in the feature space; then the authors concentrate their attention on the most active points to distinguish the landmarks of an object from the background; finally, based on the new contrastive loss, the network reconstructs the image by the landmarks of the object that are continuously learnt during training. Experiments show that the proposed model achieves better performance than other unsupervised methods on the CelebA, Annotated Facial Landmarks in the Wild, 300W datasets.
Explainable Lifelong Stream Learning Based on "Glocal" Pairwise Fusion

Real-time on-device continual learning applications are used on mobile phones, consumer robots, and smart appliances. Such devices have limited processing and memory storage capabilities, whereas continual learning acquires data over a long period of time. By necessity, lifelong learning algorithms have to be able to operate under such constraints while delivering good performance. This study presents the Explainable Lifelong Learning (ExLL) model, which incorporates several important traits: 1) learning to learn, in a single pass, from streaming data with scarce examples and resources; 2) a self-organizing prototype-based architecture that expands as needed and clusters streaming data into separable groups by similarity and preserves data against catastrophic forgetting; 3) an interpretable architecture to convert the clusters into explainable IF-THEN rules as well as to justify model predictions in terms of what is similar and dissimilar to the inference; and 4) inferences at the global and local level using a pairwise decision fusion process to enhance the accuracy of the inference, hence ``Glocal Pairwise Fusion.'' We compare ExLL against contemporary online learning algorithms for image recognition, using OpenLoris, F-SIOL-310, and Places datasets to evaluate several continual learning scenarios for video streams, low-sample learning, ability to scale, and imbalanced data streams. The algorithms are evaluated for their performance in accuracy, number of parameters, and experiment runtime requirements. ExLL outperforms all algorithms for accuracy in the majority of the tested scenarios.
Long-range Language Modeling with Self-retrieval

Retrieval-augmented language models (LMs) have received much attention recently. However, typically the retriever is not trained jointly as a native component of the LM, but added to an already-pretrained LM, which limits the ability of the LM and the retriever to adapt to one another. In this work, we propose the Retrieval-Pretrained Transformer (RPT), an architecture and training procedure for jointly training a retrieval-augmented LM from scratch for the task of modeling long texts. Given a recently generated text chunk in a long document, the LM computes query representations, which are then used to retrieve earlier chunks in the document, located potentially tens of thousands of tokens before. Information from retrieved chunks is fused into the LM representations to predict the next target chunk. We train the retriever component with a semantic objective, where the goal is to retrieve chunks that increase the probability of the next chunk, according to a reference LM. We evaluate RPT on four long-range language modeling tasks, spanning books, code, and mathematical writing, and demonstrate that RPT improves retrieval quality and subsequently perplexity across the board compared to strong baselines.
Brand Name Language: Is it True That Foreign Brand Names are More Qualified, Luxurious and Modern?

The purpose of this study is to see whether product brand names that use foreign languages, especially English, are perceived as higher quality, more luxurious, and more modern than product brand names that use local languages by consumers in developing countries. The population in this study were residents of Banten Province, Indonesia. Data collection using a questionnaire distributed via google form and collected as many as 275 respondents' answers. The analysis method uses paired sample mean difference test. The results showed that foreign brand names that use foreign languages are perceived as higher quality, more luxurious, and more modern than brand names that use local languages. Therefore, giving product brand names whose markets are consumers in developing countries, using foreign language brand names, especially English, will be preferred by consumers.
Pedagogical Conditions and Structural and Functional Model of Development of Socio-Cultural Competence of Future Foreign Language Bachelors Based on Interdisciplinary Integration

The newest educational space is characterized by a focus on a competency-based approach to training qualified specialists, which indicates that they have mastered not only theoretical but also practical competencies in the context of interdisciplinary integration. The purpose of the study is to identify and substantiate the pedagogical conditions for the formation of socio-cultural competence of future philologists on the basis of interdisciplinary integration with the subsequent definition of a structural and functional model. In the course of the study, general scientific research methods were used, in particular, methods of analysis and synthesis to develop a theoretical framework, as well as modeling and systematization to develop a structural and functional model. The article highlights the pedagogical conditions and structural components of the structural-functional model of the formation of socio-cultural competence of future bachelors of foreign languages on the basis of interdisciplinary integration. The author's own interpretation of the concept of "pedagogical conditions" is proposed, which includes ensuring students' motivation to master the socio-cultural context of a foreign language and the culture of another people; actualization of interdisciplinary socio-cultural information in the content of professional disciplines; expanding the range of socio-cultural skills of future bachelors of foreign languages through the use of interdisciplinary communicative and cognitive cases; pedagogical support and organization of extracurricular cultural training of future bachelors of foreign languages. It has been found that the structural-functional model is characterized by integrity, openness, invariance, variability and mobility. The practical significance of the research lies in further empirical study of the functioning of the structural-functional model of formation of socio-cultural competence of future bachelors of foreign languages.
TEXT SUMMARIZATION IN MONGOLIAN LANGUAGE

Textual information in this new era, it is difficult to manually extract the summary of a large data different areas of social communication accumulates the enormous amounts of data. Therefore, it is important to develop methods for searching and absorbing relevant information, selecting important sentences, paragraphs from large texts, to summarize texts by finding topics of the text and frequency based clustering of sentences. In this paper, the author presents some ideas on using mathematical models in presenting the source text into a shorter version with semantics, graph-based approach for text summarization in Mongolian language.