Reviewerly/exHarmony huggingface dataset:
Rank Your Summaries: Enhancing Bengali Text Summarization via Ranking-based Approach

With the increasing need for text summarization techniques that are both efficient and accurate, it becomes crucial to explore avenues that enhance the quality and precision of pre-trained models specifically tailored for summarizing Bengali texts. When it comes to text summarization tasks, there are numerous pre-trained transformer models at one's disposal. Consequently, it becomes quite a challenge to discern the most informative and relevant summary for a given text among the various options generated by these pre-trained summarization models. This paper aims to identify the most accurate and informative summary for a given text by utilizing a simple but effective ranking-based approach that compares the output of four different pre-trained Bengali text summarization models. The process begins by carrying out preprocessing of the input text that involves eliminating unnecessary elements such as special characters and punctuation marks. Next, we utilize four pre-trained summarization models to generate summaries, followed by applying a text ranking algorithm to identify the most suitable summary. Ultimately, the summary with the highest ranking score is chosen as the final one. To evaluate the effectiveness of this approach, the generated summaries are compared against human-annotated summaries using standard NLG metrics such as BLEU, ROUGE, BERTScore, WIL, WER, and METEOR. Experimental results suggest that by leveraging the strengths of each pre-trained transformer model and combining them using a ranking-based approach, our methodology significantly improves the accuracy and effectiveness of the Bengali text summarization.
Towards spoken dialect identification of Irish

The Irish language is rich in its diversity of dialects and accents. This compounds the difficulty of creating a speech recognition system for the low-resource language, as such a system must contend with a high degree of variability with limited corpora. A recent study investigating dialect bias in Irish ASR found that balanced training corpora gave rise to unequal dialect performance, with performance for the Ulster dialect being consistently worse than for the Connacht or Munster dialects. Motivated by this, the present experiments investigate spoken dialect identification of Irish, with a view to incorporating such a system into the speech recognition pipeline. Two acoustic classification models are tested, XLS-R and ECAPA-TDNN, in conjunction with a text-based classifier using a pretrained Irish-language BERT model. The ECAPA-TDNN, particularly a model pretrained for language identification on the VoxLingua107 dataset, performed best overall, with an accuracy of 73%. This was further improved to 76% by fusing the model's outputs with the text-based model. The Ulster dialect was most accurately identified, with an accuracy of 94%, however the model struggled to disambiguate between the Connacht and Munster dialects, suggesting a more nuanced approach may be necessary to robustly distinguish between the dialects of Irish.
Atlas-Based Interpretable Age Prediction In Whole-Body MR Images

Age prediction is an important part of medical assessments and research. It can aid in detecting diseases as well as abnormal ageing by highlighting the discrepancy between chronological and biological age. To gain a comprehensive understanding of age-related changes observed in various body parts, we investigate them on a larger scale by using whole-body 3D images. We utilise the Grad-CAM interpretability method to determine the body areas most predictive of a person's age. We expand our analysis beyond individual subjects by employing registration techniques to generate population-wide interpretability maps. Our findings reveal three primary areas of interest: the spine, the autochthonous back muscles, and the cardiac region, which exhibits the highest importance.
Population Expansion for Training Language Models with Private Federated Learning

Federated learning (FL) combined with differential privacy (DP) offers machine learning (ML) training with distributed devices and with a formal privacy guarantee. With a large population of devices, FL with DP produces a performant model in a timely manner. However, for applications with a smaller population, not only does the model utility degrade as the DP noise is inversely proportional to population, but also the training latency increases since waiting for enough clients to become available from a smaller pool is slower. In this work, we thus propose expanding the population based on domain adaptation techniques to speed up the training and improves the final model quality when training with small populations. We empirically demonstrate that our techniques can improve the utility by 13% to 30% on real-world language modeling datasets.
A testing-based approach to assess the clusterability of categorical data

The objective of clusterability evaluation is to check whether a clustering structure exists within the data set. As a crucial yet often-overlooked issue in cluster analysis, it is essential to conduct such a test before applying any clustering algorithm. If a data set is unclusterable, any subsequent clustering analysis would not yield valid results. Despite its importance, the majority of existing studies focus on numerical data, leaving the clusterability evaluation issue for categorical data as an open problem. Here we present TestCat, a testing-based approach to assess the clusterability of categorical data in terms of an analytical $p$-value. The key idea underlying TestCat is that clusterable categorical data possess many strongly correlated attribute pairs and hence the sum of chi-squared statistics of all attribute pairs is employed as the test statistic for $p$-value calculation. We apply our method to a set of benchmark categorical data sets, showing that TestCat outperforms those solutions based on existing clusterability evaluation methods for numeric data. To the best of our knowledge, our work provides the first way to effectively recognize the clusterability of categorical data in a statistically sound manner.
Study of Various Dominations in Bipolar Intuitionistic Anti Fuzzy Graphs

Bipolar intuitionistic fuzzy graphs (BIFG) with perfectly connected domination are introduced in this study. The study also discusses the bipolar intuitionistic antifuzzy graphs (BIAFG) domination concepts. Additionally, the topic of 2-domination in BIAFG are explored. we developed the secure edge dominating set and its domination number in BIAFG also some results are derived with suitable examples.
PseudoCal: A Source-Free Approach to Unsupervised Uncertainty Calibration in Domain Adaptation

Unsupervised domain adaptation (UDA) has witnessed remarkable advancements in improving the accuracy of models for unlabeled target domains. However, the calibration of predictive uncertainty in the target domain, a crucial aspect of the safe deployment of UDA models, has received limited attention. The conventional in-domain calibration method, \textit{temperature scaling} (TempScal), encounters challenges due to domain distribution shifts and the absence of labeled target domain data. Recent approaches have employed importance-weighting techniques to estimate the target-optimal temperature based on re-weighted labeled source data. Nonetheless, these methods require source data and suffer from unreliable density estimates under severe domain shifts, rendering them unsuitable for source-free UDA settings. To overcome these limitations, we propose PseudoCal, a source-free calibration method that exclusively relies on unlabeled target data. Unlike previous approaches that treat UDA calibration as a \textit{covariate shift} problem, we consider it as an unsupervised calibration problem specific to the target domain. Motivated by the factorization of the negative log-likelihood (NLL) objective in TempScal, we generate a labeled pseudo-target set that captures the structure of the real target. By doing so, we transform the unsupervised calibration problem into a supervised one, enabling us to effectively address it using widely-used in-domain methods like TempScal. Finally, we thoroughly evaluate the calibration performance of PseudoCal by conducting extensive experiments on 10 UDA methods, considering both traditional UDA settings and recent source-free UDA scenarios. The experimental results consistently demonstrate the superior performance of PseudoCal, exhibiting significantly reduced calibration error compared to existing calibration methods.
CRIMINAL LAW AGAINST FRAUD USING ANDROID PACKAGE APPLICATIONS VIA WHATSAPP MESSAGES

Online fraud using the Android Package application via Whatsapp messages is one type of cybercrime with the sniffing method. Normative-Empirical Legal Research was chosen as a method, in order to obtain normative legal provisions and their application to each particular legal event. Legal arrangements for personal data theft (identity theft) using the Android Package application via Whatsapp messages are generally contained in the PDP Law and the ITE Law, where law enforcement cannot be done optimally. Increase Cyber Patrol and police capacity in monitoring websites selling personal data. Implement standardized data security governance protocols for institutions that manage large amounts of personal data.
Unsupervised Readability Assessment via Learning from Weak Readability Signals

Unsupervised readability assessment aims to evaluate the reading difficulty of text without any manually-labeled data for model training. This is a challenging task because the absence of labeled data makes it difficult for the model to understand what readability is. In this paper, we propose a novel framework to Learn a neural model from Weak Readability Signals (LWRS). Instead of relying on labeled data, LWRS utilizes a set of heuristic signals that specialize in describing text readability from different aspects to guide the model in outputting readability scores for ranking. Specifically, to effectively use multiple heuristic weak signals for model training, we build a multi-signal learning model that ranks the unlabeled texts from multiple readability-related aspects based on intra- and inter-signal learning. We also adopt the pairwise ranking paradigm to reduce the cascade coupling among partial-order pairs. Furthermore, we propose identifying the most representative signal based on the batch-level consensus distribution of all signals. This strategy helps identify the predicted signal that is most correlated with readability in the absence of ground-truth labels. We conduct experiments on three public readability assessment datasets. The experimental results demonstrate that our LWRS outperforms each heuristic signal and their combinations significantly, and can even perform comparably with some supervised methods. Additionally, our LWRS trained on one dataset can be effectively transferred to other datasets, including those in other languages, which indicates its good generalization and potential for wide application.
Learning Curves for Noisy Heterogeneous Feature-Subsampled Ridge Ensembles.

Feature bagging is a well-established ensembling method which aims to reduce prediction variance by combining predictions of many estimators trained on subsets or projections of features. Here, we develop a theory of feature-bagging in noisy least-squares ridge ensembles and simplify the resulting learning curves in the special case of equicorrelated data. Using analytical learning curves, we demonstrate that subsampling shifts the double-descent peak of a linear predictor. This leads us to introduce heterogeneous feature ensembling, with estimators built on varying numbers of feature dimensions, as a computationally efficient method to mitigate double-descent. Then, we compare the performance of a feature-subsampling ensemble to a single linear predictor, describing a trade-off between noise amplification due to subsampling and noise reduction due to ensembling. Our qualitative insights carry over to linear classifiers applied to image classification tasks with realistic datasets constructed using a state-of-the-art deep learning feature map.
Assessing Adoption Challenges of Blockchain Technology in Agri-Food Supply Chain

The Agri-Food Supply Chain (AFSC) faces several barriers in developing economies, hindering the adoption of blockchain technology. However, the adoption of these technologies can transform traditional supply chains by incorporating transparency, traceability, transaction records, and enhanced security. This paper aims to evaluate the barriers impacting the adoption of blockchain in the AFSC and provide insights to improve performance and facilitate their effective implementation in emerging economies. Therefore, a comprehensive literature survey was conducted to identify the barriers, which were further validated through experts’ opinions. The integrated interpretive structural modeling (ISM) and decision-making trial and evaluation laboratory (DEMATEL) technique were then employed to develop a structural model and determine the cause–effect relationships between the identified barriers. The study revealed several key barriers affecting the adoption of blockchain technology in the AFSC of developing economies. These barriers were analyzed using the ISM and DEMATEL technique, which provided insights into their interdependencies and impact on each other. The results offer a comprehensive understanding of the barriers and their causal relationships. This study provides unique insights for the agri-food sector to improve performance by addressing the identified barriers. The results can guide agri-food managers, blockchain technology service providers, and the government in formulating strategies and policies to effectively adopt blockchain technologies in the AFSC.
Framework for Assessing Trust in the Use of Blockchain Technology in Agrifood Supply Chains

The advent of Industry 4.0 (I4.0) technologies has revolutionized production environments with their application in supply chains, particularly within the agrifood sector. One notable I4.0 technology is blockchain, which holds significant potential for traceability in agrifood supply chains. However, there are concerns regarding digital trust among the actors involved in adopting this technology. The concept of digital trust, essential for successful implementation, remains underexplored. This research aims to propose a framework for evaluating digital trust in the context of blockchain technology to foster a secure and reliable information sharing environment among all stakeholders within the agrifood supply chain to build confidence in security based on data permissions for user identity. To accomplish this, an extensive literature review was conducted to identify the factors affecting stakeholders’ expectations and trust in using blockchain technology in agrifood supply chains. The literature review will enhance the knowledge about these different factors affecting digital trust under four key dimensions, that are, security/privacy, data control, accountability, and benefit/value. These factors are then ranked using a multi criteria decision-making technique, enabling the development of a framework for industries and government organizations. This framework addresses the use of blockchain technology for traceability in agrifood supply chains while ensuring the trust of actors utilizing this technology. In regions facing war-like situations, such as Ukraine, it becomes crucial to evaluate the factors that can enhance food safety in agrifood supply chains, mitigate food waste and fraud risks, and maintain the supply chain sustainability by exploring alternative food supplies from reliable partners. The situation is the same all over the globe, in which supply chain risks include frauds and lack of transparency. This study outlines the managerial implications and suggests future research directions to develop a model for assessing digital trust. This model aims to foster information sharing among actors, considering aspects, such as willingness, vulnerability acceptance, shared values, security, identifiability, and digital trust. Smart contracts can be added to the model that removes the need for a third party, warranting more trust. The deployment of this model on a private or public blockchain can enhance transparency, traceability, and address food safety concerns within transactions by addressing issues of security and reliability, accountability, and oversight with an inclusive, ethical, and responsible use.
Manuscripts and Philological Studies in Indonesia

This essay is based on the fact that Indonesia is a country with abundant presence of ancient manuscripts. The manuscript is an ancient cultural heritage that contains various things about life in the past. Research on a manuscript aims to determine the history and culture of a nation and then to find the relevance of it with the present live. Furthermore, it can also be used in the future. Nowadays, the manuscript is one of the ten objects of Indonesian cultural advancement that has been set in the constitution, Article 5 Law Number 5 of 2017 concerning the Advancement of Culture. Therefore, it deserves to get more attention from many parties.
MMEAD: MS MARCO Entity Annotations and Disambiguations

MMEAD, or MS MARCO Entity Annotations and Disambiguations, is a resource for entity links for the MS MARCO datasets. We specify a format to store and share links for both document and passage collections of MS MARCO. Following this specification, we release entity links to Wikipedia for documents and passages in both MS MARCO collections (v1 and v2). Entity links have been produced by the REL and BLINK systems. MMEAD is an easy-to-install Python package, allowing users to load the link data and entity embeddings effortlessly. Using MMEAD takes only a few lines of code. Finally, we show how MMEAD can be used for IR research that uses entity information. We show how to improve recall@1000 and MRR@10 on more complex queries on the MS MARCO v1 passage dataset by using this resource. We also demonstrate how entity expansions can be used for interactive search applications.
A Study of the Effect of the Dongsasub on the Enhancement of Self-Esteem and Feeling of Happiness of Middle-aged Female Buddhists

본 연구는 동사섭 삶의 오요(五要)와 수심(修心) 프로그램이 중년여성 불자의 자아존중감과 행복감 증진에 효과가 있는가를 검증하고자 하였다. 연구참여자는 김해시 소재의 한 사찰의 중년여성 불자를 대상으로 하였다. 참여자는 모두 16명으로 동사섭 프로그램을 1·2차로 두 번, 프로그램을 진행하였다. 모두 그 효과가 있을 것이라는 가설을 정하였다. 1차 프로그램은 주 1회 8회기를 실행하였고, 2차 프로그램은 직장인이 많아서 주말에 실행하였다. 각 회기는 90분이며 프로그램의 내용은 모두 같다. 연구방법은 프로그램 실행 전·후로 자아존중감과 행복감 척도를 설문 조사하여 증감의 효과를 비교 분석하였다. 자료 통계 분석은 SPSS 22를 사용하고, 전후의 대응 표본 t-test를 통해 각각 비교하였다. 그리고 각 회기 후 기록한 개개인의 명상일지나 수행 인터뷰·집단상담 나누기를 통하여, 자아존중감과 행복감의 변화가 있는지를 관찰하였다. 프로그램을 실행한 결과, 참여자 모두가 자아존중감과 행복감이 높아지는 긍정적인 변화가 나타났다. 추수 검사에서도 일상생활에서 긍정적인 영향이 지속된다는 점을 알게 되었다. 동사섭 프로그램을 통해서 중년여성 불자의 정서적인 위기감을 극복할 수 있다. 자신의 정체성이나 가치관을 정립하여 자아존중감을 증진하여 행복의 실질적인 도움을 제공할 수 있을 것으로 기대한다. This study attempted to verify whether the five-essentials of life and depth programs of Dongsasub are effective in improving the self-esteem and feeling of happiness of middle-aged women. Participants in the study were middle-aged female Buddhists at a temple in Gimhae-si. There were a total of 16 participants, and the program was conducted twice in the first and second rounds. All hypothesized that it would work. The first program ran eight sessions once a week, and the second program ran on weekends because there were many workers. Each session is 90 minutes long and the contents of the program are all the same. As for the research method, the effects of increase and decrease were compared and analyzed by surveying the scale of self-esteem and feeling of happiness before and after the program execution. Data statistical analysis was performed using SPSS 22, and compared through the corresponding sample t-test before and after. In addition, through individual meditation journal, performance interviews, and group counseling recorded after each session, it was observed whether there was a change in self-esteem and happiness. As a result of implementing the program, there was a positive change in which all participants increased their self-esteem and happiness. The follow-up test also found that positive effects persist in daily life. Through the Dongsasub program, the emotional crisis of middle-aged women can be overcome. It is expected that it will be able to provide practical help for happiness by establishing one's identity or values to promote self-esteem.
PENGARUH PENGGUNAAN MEDIA FILM ANIMASI KARTUN TERHADAP KEMAMPUAN MENYIMAK MATA PELAJARAN BAHASA INDONESIA SISWA KELAS IV SD NEGERI 2 TANJUNG KERANG

ABSTRACT&#x0D; This study aims to determine whether there is an influence of animated cartoon film media on the ability to listen to Indonesian language subjects in class IV students at SD Negeri 2 Tanjung Kerang. The research method used in this research is an experimental method with the form of Pre-Experimental Designs and using the One Group Pretest-Posttest Design. The research sample was 22 students of class IV A. The instruments in this study were pre-test and post-test. The data analysis technique in this study was to analyze the data by means of a paired sample t-test using IBM SPSS Statistics version 26. The results of this study indicated that the posttest average value was greater than the pretest average value (81.50 &gt; 63.27 ). Then the value is greater than (14.029 &gt; 2.080), with a sig (2 t-tailed) value less than 0.05 of 0.000 &lt;0.05 with the results of the hypothesis = rejected and = accepted. So it can be concluded that this study shows that the use of animated cartoon film media has an effect on the ability to listen to Indonesian language subjects in class IV SD Negeri 2 Tanjung Kerang.
Deep ANN-based Touch-less 3D Pad for Digit Recognition

The Covid-19 pandemic has changed the way humans interact with their environment. Common touch surfaces such as elevator switches and ATM switches are hazardous to touch as they are used by countless people every day, increasing the chance of getting infected. So, a need for touch-less interaction with machines arises. In this paper, we propose a method of recognizing the ten decimal digits (0-9) by writing the digits in the air near a sensing printed circuit board using a human hand. We captured the movement of the hand by a sensor based on projective capacitance and classified it into digits using an Artificial Neural Network. Our method does not use pictures, which significantly reduces the computational requirements and preserves users' privacy. Thus, the proposed method can be easily implemented in public places.
A Scenario-Based Functional Testing Approach to Improving DNN Performance

This paper proposes a scenario-based functional testing approach for enhancing the performance of machine learning (ML) applications. The proposed method is an iterative process that starts with testing the ML model on various scenarios to identify areas of weakness. It follows by a further testing on the suspected weak scenarios and statistically evaluate the model's performance on the scenarios to confirm the diagnosis. Once the diagnosis of weak scenarios is confirmed by test results, the treatment of the model is performed by retraining the model using a transfer learning technique with the original model as the base and applying a set of training data specifically targeting the treated scenarios plus a subset of training data selected at random from the original train dataset to prevent the so-call catastrophic forgetting effect. Finally, after the treatment, the model is assessed and evaluated again by testing on the treated scenarios as well as other scenarios to check if the treatment is effective and no side effect caused. The paper reports a case study with a real ML deep neural network (DNN) model, which is the perception system of an autonomous racing car. It is demonstrated that the method is effective in the sense that DNN model's performance can be improved. It provides an efficient method of enhancing ML model's performance with much less human and compute resource than retrain from scratch.