Reviewerly/exHarmony huggingface dataset:
ReviVal: Towards Automatically Evaluating the Informativeness of Peer Reviews

The peer-review process is currently under stress due to the increasingly large number of submissions to top-tier venues, especially in Artificial Intelligence (AI) and Machine Learning (ML). Consequently, the quality of peer reviews is under question, and dissatisfaction among authors is not uncommon but rather prominent. In this work, we propose "ReviVal" (expanded as "REVIew eVALuation"), a system to automatically grade a peer-review report for its informativeness. We define review informativeness in terms of its Exhaustiveness and Strength, where Exhaustiveness signifies how exhaustively the review covers the different sections and qualitative aspects1 of the paper and Strength signifies how sure the reviewer is of their evaluation. We train ReviVal, a multitask deep network for review informativeness prediction on the publicly available peer reviews, which we curate from the openreview2 platform. We annotate the review sentence(s) with labels for (a) which sections and (b) what quality aspects of the paper those refer. We automatically annotate our data with the reviewer’s sentiment intensity to capture the reviewer’s conviction. Our approach significantly outperforms several intuitive baselines for this novel task. To the best of our knowledge, our work is a first-of-its-kind to automatically estimate the informativeness of a peer review report.
A Comprehensive Review of Arabic Question Answering Datasets

The research community has shown significant interest in Question Answering (QA) due to the strong relevance of QA applications. In recent years, there has been a significant increase in the availability of publicly accessible datasets aimed at advancing research in Arabic QA systems. This survey aims to identify, summarize, and analyze current Arabic QA datasets, such as Monolingual, Multilingual, and Cross-lingual. Our research surveys the existing datasets and provides a comprehensive and multi-faceted classification. Furthermore, this study aims to guide research in Arabic QA by providing the latest updates about the state-of-the-art in this field and identifying shortcomings in the current datasets to develop more substantial and improved collections. Finally, we discuss the existing challenges in Arabic QA datasets and highlight their potential benefits for future research.
Cybersecurity and Data Privacy in Digital Finance

The digital finance industry has long faced challenges related to cybersecurity and data privacy. To address these issues, various security technologies have been adopted.
Comparing Age Estimation with CNN and EfficientNetV2B1

Age estimation from facial images is a challenging topic in computer vision since it can automatically label the human face with an exact age according to various physical or biological characteristics, such as facial structure, spots, and wrinkles. Additionally, it has substantial applications in many fields, including healthcare, security, entertainment, and education. There are a lot of techniques to estimate age, but the most popular one is the convolutional neural network (CNN), which offers high accuracy but needs a lot of training time and much more labeled data to achieve it. Another popular technique is transfer learning with feature extraction, which can provide higher accuracy with faster training time because it uses a pre-trained model rather than building one from scratch but has limited flexibility and a substantial risk of overfitting. Due to the advantages and disadvantages of both methods, this study analyzes and compares the effectiveness of transfer learning and CNN methods for age estimation from facial images. Using R-squared and RMSE as test metrics, the results show that the CNN method offers a better process and accuracy than the transfer learning method, with a higher R-squared value and a lower RMSE value.
Remote Deployment of a JADE Agent in Docker

This work presents a technical introduction into the implementation process of a containerized multi-agent system. More specifically, the JADE framework is used as a middleware for the development of software agents. With the goal of achieving high modularity and enhancing usability, the system will be containerized in Docker. To model an application-oriented scenario, the containerized agent-system is deployed on a headless remote server. The goal of this paper is to provide a comprehensive solution to help overcome the technical difficulties encountered in accessing the graphical user interface of agents from an end device. A procedural guide to the implementation process is provided, including the preparation of the JADE-based multi-agent system, creation of Docker containers, and deployment of the containerized multi-agent system on a remote server.
Assessment of Society Based on the Analysis of the Behavior of Citizens on the Platform of Electronic Demography

Due to the fact that documents collected in various registries (medical certificates, court documents, fines, tax payments, etc.) as well as demographic indicators create big data, it is impossible today to determine human behavior by conventional statistical or empirical methods. The proposed approach to assessing the behavior of citizens on the e-demography platform consists of five steps: a collection of documents, data pre-processing, description of the data, clustering of documents, and assessment of the behavior of citizens by conducting sentiment analysis. Such an approach can be critical in studying society, identifying social and economic problems, and ensuring transparency of relations between citizens and authorities in the e-government environment.
Cultural Heritage Preservation and Manuscript Conservation: Safeguarding the Laklak Batak Manuscript of Batak Culture in Indonesia

The Laklak Batak Manuscript, an invaluable repository of Batak culture and history, stands as a testament to the enduring significance of cultural heritage preservation. This research endeavors to safeguard the manuscript from the environmental, physical, and neglectful threats it faces, while also celebrating its profound cultural and historical significance. The research methodology encompasses a multidisciplinary approach, combining skilled conservation, climate-controlled storage, digitization, and active community engagement. Collaborations with experts, institutions, and local communities play an essential role in these preservation efforts, transforming the manuscript's condition from vulnerability to resilience. The current condition of the manuscript reflects meticulous restoration and an optimal storage environment, ensuring its structural integrity and visual quality. Ongoing preservation initiatives continue to enhance access, awareness, and appreciation of this living cultural treasure. In conclusion, the Laklak Batak Manuscript is not just a relic; it is a bridge connecting the past to the future, facilitating an enriched understanding of Batak culture and history. This research serves as a model for the preservation of cultural heritage, underscoring the importance of safeguarding linguistic, artistic, and historical legacies that enrich our collective human experience.
Group Recommender Applications

In this chapter, we present an overview of different group recommender applications. We organize this overview into the application domains of music, movies and TV programs, travel destinations and events, news and web pages, healthy living, software engineering, and domain-independent recommenders. Each application is analyzed with regard to the characteristics of group recommenders as introduced in Chap. 2 .
PoQuAD - The Polish Question Answering Dataset - Description and Analysis

This paper showcases PoQuAD — a SQuAD-like contribution to building Question Answering tools for Polish. It largely follows the usual Machine Reading Comprehension format, but a few innovations are added, the key ones being: lower density of annotation, an abstractive answer layer, and the inclusion of polar questions. Additionally, just like in SQuAD 2.0, ‘impossible questions’ are included, to ensure that the models can learn to abstain from answering in situations of insufficient information. The dataset consists of 70k question-answer pairs with contexts from Polish Wikipedia. Some linguistic analysis of the data is provided and discussed, alongside experiments on baseline performance of Question Answering models. The baseline results are compared against a human, and few-shot GPT baseline, and analyzed with respect to different factors which might affect the difficulty of the task. Both human and machine performance is slightly lower than the figures listed for other similar datasets. It is argued that this is because the alterations with respect to SQuAD make the task more challenging. Additionally, the subtask of recognizing impossible questions remains difficult for Machine Reading Comprehension models. Finally, the robustness of models is estimated by a selection of adversarial attacks.
Traffic Monitoring and Violation Detection Using Deep Learning

The traffic density on roads has been increasing rapidly for the past few decades, which has in turn been reflected in the increase in traffic violations and accidents. Official reports from various governments and private entities bolster the fact that, indeed, the current methods for traffic monitoring are inept to deal with the huge traffic density [1, 2]. These methods, which traditionally included the deployment of traffic police personnel at a select few junctions where the traffic density is high, ignore the majority of the other roads. Traffic monitoring systems that exploit image processing, computer vision and deep learning techniques thus come out to be a viable and optimal solution to monitor traffic and detect violations. These systems can easily be integrated with the architecture of law enforcement to penalize violators in real time. The proposed method—which utilizes YOLOv3 and SORT—is effective and accurate in detecting several violations like—over-speeding, wrong-way driving, signal jumping, driving without helmet and triple seat violation. It also helps to keep track of the count of vehicles, their types and also the number of axels for multi-axle vehicles, thus, asserting itself as a novel and indigenous solution to a widely recognized problem.
Harnessing the VQE to Simulate Quantum Chemistry in an Undergraduate Project: Properties of Hydrogen, Oxygen and Water Molecules

With the advent of convenient toolkits like Qiskit now allowing for the simulation of quantum information processing architectures from the ground up, it has now become likelier than ever to consider the possibility of widely adopting quantum computational methods towards research-led teaching of techniques in undergraduate-level quantum chemistry. To that end, we show how elementary formulations of the Variational Quantum Eigensolver (VQE) can be harnessed for computing key chemical properties of molecules, namely their ground states, excited states, dissociation energies, and the Potential Energy Surface. We conclude with a discussion of the potential for adopting our methods towards both the dissemination of wider applications of quantum mechanics and the teaching of quantum computational methods for scientific computing in undergraduate curricula for STEM, and beyond.
History of the William G. Syrotuck Symposium on Search Theory and Practice

The Syrotuck Symposium began in 1996 to: “Foster the intellectual and scientific development of SAR methods and techniques, to promote new ideas, and to encourage development of new techniques and the practical use of technology.”
Big Data Analysis: How Public Response to Poverty Eradication Programs in Indonesia during COVID-19 Pandemic?

Poverty eradication is the main focus of the first Sustainable Development Goals (SDGs), namely 'No Poverty'. Nationally, poverty eradication is also implied in the 2020-2024 National Medium-Term Development Plan (RPJMN) on the first and second agendas. These are economic resilience and reducing disparities between regions. Various government programs are implemented to reduce poverty. The benefits obtained by the target community need to be measured to reflect the program's success and for government evaluation. Big data through social media analysis can be empowered to gain public perceptions of poverty eradication programs. Therefore, in this study, the Twitter social media analysis approach will be used to obtain public responses regarding various poverty eradication programs in Indonesia during the COVID-19 pandemic, especially the Family Hope Program (PKH or Program Keluarga Harapan), Cash Intensive Work (PKT or Padat Karya Tunai), Direct Cash Assistance (BLT or Bantuan Langsung Tunai), and Pre-Employment Cards (Kartu Prakerja). Those data were analyzed using the text mining method. The results of this study indicate that the public response generally accepts and supports the four poverty eradication programs in Indonesia. Those analysis results can be used as evaluation material and input for the Government to make the following policy.
Comparing Artificial Intelligence Classification Models to Improve an Image Comparison System with User Inputs

Abstract Data science techniques have increased in popularity over the last decades due to its numerous applications when handling complex data, but also due to its high precision. In particular, Machine (ML) and Deep Learning (DL) systems have been explored in many unique applications, owing to their high precision, flexible customization, and strong adaptability. Our research focuses on a previously described image detection system and analyses the application of a user feedback system to improve the accuracy of the comparison formula. Due to the non-traditional requirements of our system, we intended to assess the performance of multiple AI techniques and find the most suitable model to analyze our data and implement possible improvements. The study focuses on a set of test data, using the test results collected for one particular image cluster. We researched some of the previous solutions on similar topics and compared multiple ML methods to find the most suitable model for our results. Artificial Neural networks and binary decision trees were among the better performing models tested. Reinforcement and Deep Learning methods could be the focus of future studies, once more varied data are collected, with bigger comparison weight diversity.
Some Logics in the Vicinity of Interpretability Logics

In this paper we shall define semantically some families of propositional modal logics related to the interpretability logic \(\mathbf{IL}\). We will introduce the logics \(\mathbf{BIL}\) and \(\mathbf{BIL}^{+}\) in the propositional language with a modal operator \(\square\) and a binary operator \(\Rightarrow\) such that \(\mathbf{BIL}\subseteq\mathbf{BIL}^{+}\subseteq\mathbf{IL}\). The logic \(\mathbf{BIL}\) is generated by the relational structures \(\left&lt;X,R,N\right&gt;\), called basic frames, where \(\left&lt;X,R\right&gt;\) is a Kripke frame and \(\left&lt;X,N\right&gt;\) is a neighborhood frame. We will prove that the logic \(\mathbf{BIL}^{+}\) is generated by the basic frames where the binary relation \(R\) is definable by the neighborhood relation \(N\) and, therefore, the neighborhood semantics is suitable to study the logic \(\mathbf{BIL}^{+}\) and its extensions. We shall also study some axiomatic extensions of \(\mathsf{\mathbf{BIL}}\) and we will prove that these extensions are sound and complete with respect to a certain classes of basic frames.
Snowball effect of COVID-19: Stroke Care Underutilization (P1-1.Virtual)

Sunday, April 24May 3, 2022Free AccessSnowball effect of COVID-19: Stroke Care Underutilization (P1-1.Virtual)Abhilash Thatikala, Hafsa Bareen Syeda, Shubham Biyani, Naren Raj Arulprakash, Khushboo Verma, Sen Sheng, Mudassar Kamran, Vishank Shah, Sisira Yadala, Sanjeeva Onteddu, and Krishna NalleballeAuthors Info & AffiliationsMay 3, 2022 issue98 (18_supplement)https://doi.org/10.1212/WNL.98.18_supplement.2879 Letters to the Editor
HeFUN: Homomorphic Encryption for Unconstrained Secure Neural Network Inference

Homomorphic encryption (HE) has emerged as a pivotal technology for secure neural network inference (SNNI), offering privacy-preserving computations on encrypted data. Despite active developments in this field, HE-based SNNI frameworks are impeded by three inherent limitations. Firstly, they cannot evaluate non-linear functions such as ReLU, the most widely adopted activation function in neural networks. Secondly, the permitted number of homomorphic operations on ciphertexts is bounded, consequently limiting the depth of neural networks that can be evaluated. Thirdly, the computational overhead associated with HE is prohibitively high, particularly for deep neural networks. In this paper, we introduce a novel paradigm designed to address the three limitations of HE-based SNNI. Our approach is an interactive approach that is solely based on HE, called iLHE. Utilizing the idea of iLHE, we present two protocols: ReLU, which facilitates the direct evaluation of the ReLU function on encrypted data, tackling the first limitation, and HeRefresh, which extends the feasible depth of neural network computations and mitigates the computational overhead, thereby addressing the second and third limitations. Based on HeReLU and HeRefresh protocols, we build a new framework for SNNI, named HeFUN. We prove that our protocols and the HeFUN framework are secure in the semi-honest security model. Empirical evaluations demonstrate that HeFUN surpasses current HE-based SNNI frameworks in multiple aspects, including security, accuracy, the number of communication rounds, and inference latency. Specifically, for a convolutional neural network with four layers on the MNIST dataset, HeFUN achieves 99.16% accuracy with an inference latency of 1.501 s, surpassing the popular HE-based framework CryptoNets proposed by Gilad-Bachrach, which achieves 98.52% accuracy with an inference latency of 3.479 s.
Analisis Sentimen Pergerakan Harga Saham Sebuah Perusahaan di Media Sosial Twitter

Twitter has become an essential platform for traders and stock investors worldwide, including major countries like America. Traders rely on Twitter to gather information, similar to how they use Bloomberg terminals. While Twitter provides valuable insights, it also contains negative elements such as false information. The sentiment surrounding stocks on Twitter has been growing, and this study aims to analyze the sentiment of Telkom Indonesia's stock price based on tweets. The research involved several stages. First, data was collected from Twitter and labeled manually into positive, neutral, and negative sentiments. The data then underwent pre-processing, including cleaning and dividing it into training and testing datasets using K-Fold Cross Validation. The data was further weighted using the TF-IDF method, and a training process was conducted to develop a model. The final stage involved testing the accuracy of the model. The study successfully implemented the Multinomial Naïve Bayes (MNB) method, achieving an accuracy of 89.0%. The tweet classification results revealed that out of 1000 tweets, 76.5% were classified as positive, 14.3% as negative, and 9.2% as neutral.
ZeroShape: Regression-based Zero-shot Shape Reconstruction

We study the problem of single-image zero-shot 3D shape reconstruction. Recent works learn zero-shot shape reconstruction through generative modeling of 3D assets, but these models are computationally expensive at train and inference time. In contrast, the traditional approach to this problem is regression-based, where deterministic models are trained to directly regress the object shape. Such regression methods possess much higher computational efficiency than generative methods. This raises a natural question: is generative modeling necessary for high performance, or conversely, are regression-based approaches still competitive? To answer this, we design a strong regression-based model, called ZeroShape, based on the converging findings in this field and a novel insight. We also curate a large real-world evaluation benchmark, with objects from three different real-world 3D datasets. This evaluation benchmark is more diverse and an order of magnitude larger than what prior works use to quantitatively evaluate their models, aiming at reducing the evaluation variance in our field. We show that ZeroShape not only achieves superior performance over state-of-the-art methods, but also demonstrates significantly higher computational and data efficiency.
Toward Leveraging Augmented Reality (AR) for Enhancing Remote Intergenerational Communication in Cooking Scenarios

The close connection between food culture and family relationship has always been regarded as an important link to maintain family harmony and intergenerational(IG) communication. Through cooking and eating together, family members inherit family customs and culture, and enhance mutual feelings and understanding. However, with the development changes in family structure, there is an urgent need to find new approaches for IG communication between the younger and older generations. In this context, this study will explore how modern technological means such as remote home intelligent control and augmented reality (AR) technology, combined with the characteristics of food culture, can be used to innovate and strengthen IG communication between family members. This study explored the challenges inherent in remote integrated circuits in the context of collaborative cooking. Building on this, we use participatory workshops to explore design proposals to address challenges. Our comprehensive exploratory study consists of two parts, the first of which is an observation of 12 pairs of remote IG participants cooking to summarize the challenges they face. We then had 12 pairs IG participants engage in a participatory workshop, with semi-structured interviews and discussions to summarize the design recommendations. Our findings reveal potential ways to enhance remote integrated circuits through collaborative cooking, suggest the feasibility and versatility of AR in this regard, and outline design recommendations for AR based assistive functions in remote cooking scenarios.