Reviewerly/exHarmony huggingface dataset:
Handwritten Tamil character recognition

This paper focuses on recognizing Tamil characters that are handwritten and displaying their digital variant. Tamil is a regional language and one of the classical languages of India. Tamil literature has a significance of over four thousands years and many of those literatures are yet to be digitized. The main objective of this paper is to convert old handwritten Tamil documents or scriptures into their digital form so that they can be preserved over a long period of time. Character recognition generally can be implemented by using either CNN or OCR methodologies. In this paper we will be using Optical Character Recognition commonly known as OCR for implementation. It is a technique which involves four modules in order to recognize the characters.
Violin Tools

Harmonic created by holding down one finger (thus "shortening" the string) and putting another finger down lightly ,on the violin one, one usually holds down the first finger and touches the fourth lightly to the string "czardas" by Monti has asection with all artificial harmonic, does the "pe loc" , movement from Bartok's.
SEER: Unsupervised and sample-efficient environment specialization of image descriptors

Image descriptor based place recognition is an important means for loop-closure detection in SLAM.The currently best performing image descriptors for this task are trained on large training datasets with the goal to be applicable in many different environments.In particular, they are not optimized for a specific environment, e.g. the city of Oxford.However, we argue that for place recognition, there is always a specific environment -not necessarily geographically defined, but specified by the particular set of descriptors in the database.In this paper, we propose SEER, a simple and efficient algorithm that can learn to create better descriptors for a specific environment from such a potentially very small set of database descriptors.The new descriptors are better in the sense that they will be more suited for image retrieval on these database descriptors.SEER stands for Sparse Exemplar Ensemble Representations.Both sparsity and ensemble representations are necessary components of the proposed approach.This is evaluated on a large variety of standard place recognition datasets where SEER considerably outperforms existing methods.It does not require any label information and is applicable in online place recognition scenarios.Open source code is available.
Evaluation of Sigmoid and ReLU Activation Functions Using Asymptotic Method

The performance of two algorithms may be compared using an asymptotic technique in algorithm analysis, which focuses largely on the growth rate as the number of inputs grows.Sigmoid activation and ReLU activation functions are widely employed in ANNs (Yingying, 2020), and each has advantages and disadvantages that should be considered when designing ANN solutions for a given issue.This study aimed to compare the performance of sigmoid activation and ReLU activation function during training using an asymptotic approach.The work focuses on training time complexity as the basis of comparison of the two activation functions using an asymptotic approach.The result derived from this study showed that sigmoid activation function takes more computation time in performing forward path, loss computation and backward propagation than ReLU activation functions.The computation cost will become significant when dealing with deep neural networks with hundreds to thousands of neurons.Overall, the training time for ReLU based Neural network will be better than that of sigmoid based one.Sigmoid have higher computational cost compared to ReLU but the two algorithms have a linear growth rate.
The effect of the Perkins and Blythe model on acquiring Islamic concepts for second-grade intermediate students in the subject of the Holy Qur’an and Islamic Education

Holy Qur'an and Islamic education for the first intermediate grade, prepared by the researcher himself because there was no ready test in the subject of the Holy Qur'an and Islamic education.For the second intermediate grade, its validity and reliability were verified, and its stability value was (0,83).After the completion of the research tools, The current research aims to identify the effect of the Perkins and Blythe model on the acquisition of Islamic concepts by second-grade intermediate students in the subject of the Holy Qur'an and Islamic education in the academic year (2021)(2022).The researcher used the experimental design of the control and the experimental groups with a post-test for both.The experimental group studied according to the Perkins and Blythe model, while the control group studied according to the usual method.The research sample consisted of (51) female students, chosen by the researcher intentionally with (25) female students in the experimental group and (26) female students in the control group.The researcher equalized both groups in some variables, including age calculated in months, the intelligence test, the parents' academic achievement level, the grade of the Holy Qur'an and Islamic education for the first intermediate grade, and the general average for the first intermediate grade.Achieving the aim of the research required constructing a test for acquiring Islamic concepts in the subject of the he
Design and Development of Student Achievement Management System Based on JSP

With the extensive use of computers, it has become more and more common to use computers instead of manual management.Compared with manual management, it is fast, accurate and convenient.Through the comprehensive management of students, we can know the daily performance of students in school, students' grades, etc., which is convenient for teachers to solve the problems existing in students' daily study in time.In order to realize the efficient management of school performance management, a set of student performance management system is designed.Firstly, the requirements of performance management are analyzed, then the conceptual model of the system is designed according to the analysis results, and then the logical structure and physical structure of the database are designed.On this basis, the student achievement management information system is designed with MySQL as the database management system and MyEclipse as the development tool.The system has realized the functions of course management, teacher management, student management, score registration, query and analysis, etc.The system interface is friendly, simple and practical, and it is convenient for teachers to grasp students' daily learning situation in time.The functions such as timely entry and viewing of examination results can improve the efficiency of teachers' daily educational work, and make the school develop rapidly with the help of computer technology.
Implementation Of Hospital Management Information System (SIMRS) At Royal Prima Hospital

The health information system is part of the national health system (SKN), therefore the health system in Indonesia and health information are needed by hospitals. This concept is needed for the development of a hospital management information system (SIMRS), a puskesmas information system (SIP) and a clinicalinformation system (Handayani, 2020). The purpose of this study was to determine the analysis of theapplication of hospital management information systems (SIMRS) at RSU Royal Prima. The method in this research is qualitative. The population in this study amounted to 10 respondents. The results of the study show that SIMR is very influential on patient care, HR can use the SIMRS application and there is management support in theuse of SIMRS at Royal Prima Hospital Medan. Based on the results of research on the analysis of the application of hospital management information systems (SIMRS) at Royal Prima Hospital, it can be concluded that the quality of the system is still not good in the quality of the system in the field because the internet network problem is not good, the quality of system information has been very helpful for service, the quality of service is very good. , HR is different, new employees are the same as the old ones, if new employees haven't, so new employees should want to learn with old employees, and old employees are proficient in using SIMRS, management supports it because this is indeed a program from management, SIMRS Royal Prima Medan is there The advantages are in patient care and the quality of information systems, all service data at RSU Royal Prima Medan can be stored properly and nicely. It is hoped that the hospital will pay more attention to and improve the quality of the SIMRS internet network at RSU Royal Prima and provide training to all employees, especially for new employees on how to use SIMRS so that services at RSU Royal Prima Medan runsmoothly and well.
Confidence Calibration for Intent Detection via Hyperspherical Space and Rebalanced Accuracy-Uncertainty Loss

Data-driven methods have achieved notable performance on intent detection, which is a task to comprehend user queries. Nonetheless, they are controversial for over-confident predictions. In some scenarios, users do not only care about the accuracy but also the confidence of model. Unfortunately, mainstream neural networks are poorly calibrated, with a large gap between accuracy and confidence. To handle this problem defined as confidence calibration, we propose a model using the hyperspherical space and rebalanced accuracy-uncertainty loss. Specifically, we project the label vector onto hyperspherical space uniformly to generate a dense label representation matrix, which mitigates over-confident predictions due to overfitting sparse one-hot label matrix. Besides, we rebalance samples of different accuracy and uncertainty to better guide model training. Experiments on the open datasets verify that our model outperforms the existing calibration methods and achieves a significant improvement on the calibration metric.
Equivalence in Argumentation Frameworks with a Claim-Centric View – Classical Results with Novel Ingredients

A common feature of non-monotonic logics is that the classical notion of equivalence does not preserve the intended meaning in light of additional information. Consequently, the term strong equivalence was coined in the literature and thoroughly investigated. In the present paper, the knowledge representation formalism under consideration are claim-augmented argumentation frameworks (CAFs) which provide a formal basis to analyze conclusion-oriented problems in argumentation by adapting a claim-focused perspective. CAFs extend Dung AFs by associating a claim to each argument representing its conclusion. In this paper, we investigate both ordinary and strong equivalence in CAFs. Thereby, we take the fact into account that one might either be interested in the actual arguments or their claims only. The former point of view naturally yields an extension of strong equivalence for AFs to the claim-based setting while the latter gives rise to a novel equivalence notion which is genuine for CAFs. We tailor, examine and compare these notions and obtain a comprehensive study of this matter for CAFs. We conclude by investigating the computational complexity of naturally arising decision problems.
Mentoring in professional training of international students

Introduction. Mentoring is one of the major instruments of international students’ adaptation to getting education in Russian universities. Finding out the attitude of international students to various forms of mentoring will help their professional growth. The object of the research is to find out the opinion of Turkman students about mentors and mentees and to define the main ways to ensure individualized psychological pedagogical support of international students in Russian universities. Materials and Methods. The total number of Turkman respondents of Ryazan State University named after S. A. Yesenin who took part in the survey was 55. Empirical methods included online opinion poll of international students with the help of cloud technologies (Google Forms), participant observation method, interview. Results of the Study. The data obtained show positive attitude of Turkman students towards mentoring. To wit, 41 survey participants (75 %) gave an answer that a mentor is a university lecturer or teacher sharing their knowledge and experience with their students and supervising the whole process of learning. 8 people (15 %) consider their groupmates to be mentors, which shows potential readiness of some Turkman students for budding. Over 50 % of those who took part in the poll believe that mentors must communicate with their relatives or official representatives as it ensures their feeling of security. 45 international students (82 %) said that there should be one mentor who will supervise them during the whole period of university education, with only 10 people (18 %) considering that mentors should be different at each stage of their professional growth and development. Mentoring serves the purpose of individualized professional training of international students. Conclusion. The results of the research can profit educators to work out the strategy of further development of various forms of mentoring (group, individual, virtual) while giving professional training to international students. Besides, they will help international graduates find gainful employment. Another application of the results obtained is doing graduation research work in the form of Startup.
Conditional Collaborative Filtering Process for Top-K Recommender System (Student Abstract)

Conditional neural process (CNP) has been extensively applied into data analyzing tasks due to its excellent ability to make accurate predictions for incomplete data points. However, in literature there are only few works that studied the CNPin recommendation systems. In this work, we propose CCFP, which is a collaborative filtering method that differs from other CF models by incorporating CNP into encoder-decoder architecture. By analyzing the complete user-item interaction data, our model fits a global representation that can better rep-resenting the features of users and items. CCFP can significantly improve the recommendation performance compared to baselines by predicting items for the target users with their incomplete observation data.
Gender as a Correlate of Senior School Students’ Reading Comprehension Achievement in English in Ebonyi State

The study considered the correlation between gender and senior school students' reading comprehension achievement in English in Ebonyi State of Nigeria.The design of the study was a correlational design, which correlated gender with senior school students' achievement in reading comprehension.The population of the study was 33,986, being the total number of senior school 2 students in Ebonyi State public schools.The sample size was 396 students which was arrived at, using the Taro Yamane formulae for determining sample size.Instrument for data collection was a reading comprehension test adapted from the government prescribed English language reading text.While research questions were answered using coefficient of correlation, hypotheses were tested at an alpha level of 0.05 of significance, using step-wise linear regression.Items whose "T" were less than significance of "T" were accepted, while items whose "T" were more than significance of "T" were rejected.Result showed that there was significant relationship between male students' attitude and achievement.Result equally showed that there was a significant relationship between female students' attitude and their achievement in reading comprehension.Based on these findings, recommendations were made among which was that since attitude to learning made a lot of difference in academic achievement, senior students' attitude to learning must be monitored by teachers if their achievement would be improved.
Analysis of Aristotle's rhetoric philosophy in improving public speaking ability and its relevance to the process of learning activities

The purpose of writing this article is to determine the relationship between Aristotle's Philosophy of Rhetoric in improving public speaking skills as well its relevance to learning activities in the classroom, such as in writing articles using the library research method with the source of books and articles.Rhetoric is one way used to express opinions in public.In teaching, rhetoric needs good communications skills so that the message can be conveyed to the intended person.The rhetoric was first stated by Aristotle, the Greek Philosopher, by basing it on three aspects, including logic, ethics, and emotions.The ability in the barology also began to be used in classroom learning activities.An educator certainly must have the appropriate sentence to convey the content of the material to students to avoid misconception.Likewise, as a student, this rhetoric ability can be used to express his/her opinion about a matter based on applied ethics.
Learning Contrastive Multi-View Graphs for Recommendation (Student Abstract)

This paper exploits self-supervised learning (SSL) to learn more accurate and robust representations from the user-item interaction graph. Particularly, we propose a novel SSL model that effectively leverages contrastive multi-view learning and pseudo-siamese network to construct a pre-training and post-training framework. Moreover, we present three graph augmentation techniques during the pre-training stage and explore the effects of combining different augmentations, which allow us to learn general and robust representations for the GNN-based recommendation. Simple experimental evaluations on real-world datasets show that the proposed solution significantly improves the recommendation accuracy, especially for sparse data, and is also noise resistant.
What Can We Learn Even from the Weakest? Learning Sketches for Programmatic Strategies

In this paper we show that behavioral cloning can be used to learn effective sketches of programmatic strategies. We show that even the sketches learned by cloning the behavior of weak players can help the synthesis of programmatic strategies. This is because even weak players can provide helpful information, e.g., that a player must choose an action in their turn of the game. If behavioral cloning is not employed, the synthesizer needs to learn even the most basic information by playing the game, which can be computationally expensive. We demonstrate empirically the advantages of our sketch-learning approach with simulated annealing and UCT synthesizers. We evaluate our synthesizers in the games of Can't Stop and MicroRTS. The sketch-based synthesizers are able to learn stronger programmatic strategies than their original counterparts. Our synthesizers generate strategies of Can't Stop that defeat a traditional programmatic strategy for the game. They also synthesize strategies that defeat the best performing method from the latest MicroRTS competition.
An Ontological Approach towards Automatic Creation of Infographics from Formal Text (Student Abstract)

Infographics deal with representing data or information visually in a perceptually compelling manner. Recently, infographics have gained widespread popularity, giving rise to automated infographics synthesis from texts. Our research follows an ontological approach to automatically extract the necessary indicators from an input sentence and synthesize an infographic corresponding to it. This work includes (1) the creation of a dataset, (2) an end-to-end domain-agnostic framework, and (3) demonstrating the application of the proposed framework. The results demonstrate our framework's ability to extract the necessary textual cues from real-world textual descriptions (from various domains) and synthesize meaningful infographics.
Bounding Quality in Diverse Planning

Diverse planning is an important problem in automated planning with many real world applications. Recently, diverse planning has seen renewed interest, with work that defines a taxonomy of computational problems with respect to both plan quality and solution diversity. However, despite the recent advances in diverse planning, the variety of approaches and the number of available planners are still quite limited, even nonexistent for several computational problems. In this work, we aim to extend the portfolio of planners for various computational problems in diverse planning. To that end, we introduce a novel approach to finding solutions for three computational problems within diverse planning and present planners for these three problems. For one of these problems, our approach is the first one that is able to provide solutions to the problem. For another, we show that top-k and top quality planners can provide, albeit naive, solutions to the problem and we extend these planners to improve the diversity of the solution. Finally, for the third problem, we show that some existing diverse planners already provide solutions to that problem. We suggest another approach and empirically show it to compare favorably with these existing planners.
Harvest – a System for Creating Structured Rate Filing Data from Filing PDFs

We present a machine-learning-guided process that can efficiently extract factor tables from unstructured rate filing documents. Our approach combines multiple deep-learning-based models that work in tandem to create structured representations of tabular data present in unstructured documents such as pdf files. This process combines CNN's to detect tables, language-based models to extract table metadata and conventional computer vision techniques to improve the accuracy of tabular data on the machine-learning side. The extracted tabular data is validated through an intuitive user interface. This process, which we call Harvest, significantly reduces the time needed to extract tabular information from PDF files, enabling analysis of such data at a speed and scale that was previously unattainable.
Structure Learning-Based Task Decomposition for Reinforcement Learning in Non-stationary Environments

Reinforcement learning (RL) agents empowered by deep neural networks have been considered a feasible solution to automate control functions in a cyber-physical system. In this work, we consider an RL-based agent and address the issue of learning via continual interaction with a time-varying dynamic system modeled as a non-stationary Markov decision process (MDP). We view such a non-stationary MDP as a time series of conventional MDPs that can be parameterized by hidden variables. To infer the hidden parameters, we present a task decomposition method that exploits CycleGAN-based structure learning. This method enables the separation of time-variant tasks from a non-stationary MDP, establishing the task decomposition embedding specific to time-varying information. To mitigate the adverse effect due to inherent noises of task embedding, we also leverage continual learning on sequential tasks by adapting the orthogonal gradient descent scheme with a sliding window. Through various experiments, we demonstrate that our approach renders the RL agent adaptable to time-varying dynamic environment conditions, outperforming other methods including state-of-the-art non-stationary MDP algorithms.
NeuralArTS: Structuring Neural Architecture Search with Type Theory (Student Abstract)

Neural Architecture Search (NAS) algorithms automate the task of finding optimal deep learning architectures given an initial search space of possible operations. Developing these search spaces is usually a manual affair with pre-optimized search spaces being more efficient, rather than searching from scratch. In this paper we present a new framework called Neural Architecture Type System (NeuralArTS) that categorizes the infinite set of network operations in a structured type system. We further demonstrate how NeuralArTS can be applied to convolutional layers and propose several future directions.