Reviewerly/exHarmony huggingface dataset:
Hardware-Aware Evolutionary Explainable Filter Pruning for Convolutional Neural Networks

Authors:
raw_author_name: Christian Heidorn
raw_author_name: Muhammad Sabih
raw_author_name: Nicolai Meyerhöfer
raw_author_name: Christian Schinabeck
raw_author_name: Jürgen Teich
raw_author_name: Frank Hannig

Abstract: Abstract Filter pruning of convolutional neural networks (CNNs) is a common technique to effectively reduce the memory footprint, the number of arithmetic operations, and, consequently, inference time. Recent pruning approaches also consider the targeted device (i.e., graphics processing units) for CNN deployment to reduce the actual inference time. However, simple metrics, such as the $$\ell ^1$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mi>ℓ</mml:mi> <mml:mn>1</mml:mn> </mml:msup> </mml:math> -norm, are used for deciding which filters to prune. In this work, we propose a hardware-aware technique to explore the vast multi-objective design space of possible filter pruning configurations. Our approach incorporates not only the targeted device but also techniques from explainable artificial intelligence for ranking and deciding which filters to prune. For each layer, the number of filters to be pruned is optimized with the objective of minimizing the inference time and the error rate of the CNN. Experimental results show that our approach can speed up inference time by 1.40× and 1.30× for VGG-16 on the CIFAR-10 dataset and ResNet-18 on the ILSVRC-2012 dataset, respectively, compared to the state-of-the-art ABCPruner.
Identifying galaxy cluster mergers with deep neural networks using idealized compton-y and X-ray maps

Authors:
raw_author_name: Yvette C Perrott
raw_affiliation_string: School of Chemical and Physical Sciences, Victoria University of Wellington , PO Box 600, Wellington 6140, New Zealand
raw_author_name: Weiguang Cui
raw_affiliation_string: Institute for Astronomy, University of Edinburgh , Royal Observatory, Edinburgh EH9 3HJ, UK

Abstract: Abstract We present a novel approach to identify galaxy clusters that are undergoing a merger using a deep learning approach. This paper uses massive galaxy clusters spanning 0 ≤ z ≤ 2 from The Three Hundred project, a suite of hydrodynamic re-simulations of 324 large galaxy clusters. Mock, idealised Compton-y and X-ray maps were constructed for the sample, capturing them out to a radius of 2R200. The idealised nature of these maps mean they do not consider observational effects such as foreground or background astrophysical objects, any spatial resolution limits or restriction on X-ray energy bands. Half of the maps belong to a merging population as defined by a mass increase ΔM/M ≥ 0.75, and the other half serve as a control, relaxed population. We employ a convolutional neural network architecture and train the model to classify clusters into one of the groups. A best-performing model was able to correctly distinguish between the two populations with a balanced accuracy (BA) and recall of 0.77, ROC-AUC of 0.85, PR-AUC of 0.55 and F1 score of 0.53. Using a multichannel model relative to a single channel model, we obtain a 3% improvement in BA score, and a 6% improvement in F1 score. We use a saliency interpretation approach to discern the regions most important to each classification decision. By analysing radially binned saliency values we find a preference to utilise regions out to larger distances for mergers with respect to non-mergers, greater than ∼1.2R200 and ∼0.7R200 for SZ and X-ray respectively.
Fairness-Aware Graph Neural Networks: A Survey

Authors:
raw_author_name: Ryan A. Rossi
raw_affiliation_string: Adobe Research, San Jose, United States
raw_author_name: Namyong Park
raw_affiliation_string: Carnegie Mellon University, Pittsburgh, United States
raw_author_name: Puja Trivedi
raw_affiliation_string: University of Michigan, Ann Arbor, United States
raw_author_name: Yu Wang
raw_affiliation_string: Vanderbilt University, Nashville, United States
raw_author_name: Tong Yu
raw_affiliation_string: Adobe Research, San Jose, United States
raw_author_name: Sungchul Kim
raw_affiliation_string: Adobe Research, San Jose, United States
raw_author_name: Franck Dernoncourt
raw_affiliation_string: Adobe Research, Seattle, United States
raw_author_name: Nesreen K. Ahmed
raw_affiliation_string: Intel Labs, Santa Clara, United States

Abstract: Graph Neural Networks (GNNs) have become increasingly important due to their representational power and state-of-the-art predictive performance on many fundamental learning tasks. Despite this success, GNNs suffer from fairness issues that arise as a result of the underlying graph data and the fundamental aggregation mechanism that lies at the heart of the large class of GNN models. In this article, we examine and categorize fairness techniques for improving the fairness of GNNs. We categorize these techniques by whether they focus on improving fairness in the pre-processing, in-processing (during training), or post-processing phases. Furthermore, we discuss how such techniques can be used together whenever appropriate, and highlight the advantages and intuition as well. We also introduce an intuitive taxonomy for fairness evaluation metrics including graph-level fairness, neighborhood-level fairness, embedding-level fairness, and prediction-level fairness metrics. In addition, graph datasets that are useful for benchmarking the fairness of GNN models are summarized succinctly. Finally, we highlight key open problems and challenges that remain to be addressed.
Geochemical Characteristics of Trace Elements of Hot Springs in the Xianshuihe–Xiaojiang Fault Zone

Authors:
raw_author_name: Daqiong Qiu
raw_affiliation_string: Hetian Seismic Station of Earthquake Agency of Xinjiang Uygur Autonomous Region, Hetian 848000, China
raw_author_name: Jianguo Du
raw_affiliation_string: School of Earth Science and Resources, China University of Geosciences (Beijing), Beijing 100083, China; United Laboratory of High-Pressure Physics and Earthquake Science, Institute of Earthquake Forecasting, China Earthquake Administration, Beijing 100036, China
raw_author_name: Yuwen Wang
raw_affiliation_string: United Laboratory of High-Pressure Physics and Earthquake Science, Institute of Earthquake Forecasting, China Earthquake Administration, Beijing 100036, China
raw_author_name: Jingchao Li
raw_affiliation_string: United Laboratory of High-Pressure Physics and Earthquake Science, Institute of Earthquake Forecasting, China Earthquake Administration, Beijing 100036, China
raw_author_name: Ying Li
raw_affiliation_string: School of Earth Science and Resources, China University of Geosciences (Beijing), Beijing 100083, China; United Laboratory of High-Pressure Physics and Earthquake Science, Institute of Earthquake Forecasting, China Earthquake Administration, Beijing 100036, China
raw_author_name: Feng Zhang
raw_affiliation_string: Hetian Seismic Station of Earthquake Agency of Xinjiang Uygur Autonomous Region, Hetian 848000, China

Abstract: Trace elements in hot-spring waters are indicators for tracing hydrochemical processes, such as the deep circulation of geothermal water, the degree of water–rock interaction, redox reactions, the contamination of shallow water by thermal water, etc. In this study, 74 hot springs in the Xianshuihe–Xiaojiang Fault Zone (XSHF-XJF) were chosen for an investigation of the geochemical characteristics of trace elements using classic geochemical tools and multivariate statistical analysis. The results indicated (1) the hot-spring waters were mainly derived from atmospheric precipitation as indicated by δD and δ18O values that generally increased with decreasing elevations; (2) the high concentrations of B, As, Fe, and Mn in the waters, as well as the values of the Water Quality Index (WQI), indicated that the water quality was spatially heterogeneous and the hot-spring waters are not entirely suitable for drinking; and (3) B/Cl ratios showed that hot springs have different reservoir sources in the XSHF and XJF, respectively. The leaching of surrounding rock during water circulation contributed predominantly to the trace elements of the study’s waters. Diversity of lithology was the main factor affecting their concentrations. In addition, deep circulation controlled by the fault could influence trace-element enrichment. Our results offer a guide for the exploration and use of geothermal resources.
Sign-to-Text Translation from Panamanian Sign Language to Spanish in Continuous Capture Mode with Deep Neural Networks

Authors:
raw_author_name: Alvaro A. Teran-Quezada
raw_affiliation_string: Facultad de Ingeniería de Sistemas Computacionales, Universidad Tecnológica de Panamá (UTP), El Dorado, Panama City P.O. Box 0819-07289, Panama
raw_author_name: Victor Lopez-Cabrera
raw_affiliation_string: Facultad de Ingeniería de Sistemas Computacionales, Universidad Tecnológica de Panamá (UTP), El Dorado, Panama City P.O. Box 0819-07289, Panama
raw_author_name: Jose Carlos Rangel
raw_affiliation_string: Centro de Estudios Multidisciplinarios en Ciencias, Ingeniería y Tecnología (CEMCIT AIP), Universidad Tecnológica de Panamá (UTP), El Dorado, Panama City P.O. Box 0819-07289, Panama; Facultad de Ingeniería de Sistemas Computacionales, Universidad Tecnológica de Panamá (UTP), El Dorado, Panama City P.O. Box 0819-07289, Panama; Sistema Nacional de Investigación, SENACYT, Edificio 205, Ciudad del Saber, Clayton, Balboa, Panama City P.O. Box 0816-02852, Panama
raw_author_name: Javier E. Sanchez-Galan
raw_affiliation_string: Centro de Estudios Multidisciplinarios en Ciencias, Ingeniería y Tecnología (CEMCIT AIP), Universidad Tecnológica de Panamá (UTP), El Dorado, Panama City P.O. Box 0819-07289, Panama; Facultad de Ingeniería de Sistemas Computacionales, Universidad Tecnológica de Panamá (UTP), El Dorado, Panama City P.O. Box 0819-07289, Panama; Sistema Nacional de Investigación, SENACYT, Edificio 205, Ciudad del Saber, Clayton, Balboa, Panama City P.O. Box 0816-02852, Panama

Abstract: Convolutional neural networks (CNN) have provided great advances for the task of sign language recognition (SLR). However, recurrent neural networks (RNN) in the form of long–short-term memory (LSTM) have become a means for providing solutions to problems involving sequential data. This research proposes the development of a sign language translation system that converts Panamanian Sign Language (PSL) signs into text in Spanish using an LSTM model that, among many things, makes it possible to work with non-static signs (as sequential data). The deep learning model presented focuses on action detection, in this case, the execution of the signs. This involves processing in a precise manner the frames in which a sign language gesture is made. The proposal is a holistic solution that considers, in addition to the seeking of the hands of the speaker, the face and pose determinants. These were added due to the fact that when communicating through sign languages, other visual characteristics matter beyond hand gestures. For the training of this system, a data set of 330 videos (of 30 frames each) for five possible classes (different signs considered) was created. The model was tested having an accuracy of 98.8%, making this a valuable base system for effective communication between PSL users and Spanish speakers. In conclusion, this work provides an improvement of the state of the art for PSL–Spanish translation by using the possibilities of translatable signs via deep learning.
New Algorithm for Evaluation of Online Courses Based on Quality Matters Rubric Using Fuzzy Soft Expert Sets

Authors:
raw_author_name: Anas AbuKaraki
raw_author_name: Tawfiq Alrawashdeh
raw_author_name: Ibrahim Alkore Alshalabi
raw_author_name: Moha’med Al-Jaafreh
raw_author_name: Malek Zakarya Alksasbeh
raw_affiliation_string: Computer Information Systems Department, College of Information Technology, Al-Hussein Bin Talal University, Ma’an, Jordan
raw_author_name: Abdulhameed Alenezi
raw_author_name: Mohammed Al-Kaseasbeh

Abstract: The field of instructional technology has experienced significant growth in recent times. Due to the rapid shift towards online courses, the technology-based learning system is facing challenges in ensuring quality and assurance. The aim of this study was to develop online course evaluation tools by proposing a new algorithm to assess the success of the provided online courses and address quality assurance issues. The proposed algorithm is based on quality matters (QM) attributes and the use of fuzzy soft expert sets (FSESs). One key advantage of the proposed algorithm is that it incorporates experts’ opinions, which significantly contributes to achieving the study objective. The proposed algorithm was successfully implemented using the ASP.NET programming language. It resulted in the development of an EOC-FSES prototype system. The experimental evaluation of the prototype system confirms that it requires low effort and achieves high levels of performance, satisfaction, and behavioral intention to use. This paper includes several recommendations and suggestions.
Virtual reality natural experiences for mental health: comparing the effects between different immersion levels

Authors:
raw_author_name: Wen-Hsin Huang
raw_affiliation_string: Humboldt Park Health, Chicago, IL, USA

Abstract: Abstract Virtual nature is an innovative approach for promoting mental health. The purpose of this study was to compare the effects on mental health outcomes between two immersion levels of virtual reality natural experiences. The study design was a cluster trial. Healthy adults were allocated to two experimental groups. Identical pre-recorded 360° videos of natural scenes and sounds were played on the two virtual reality devices, one with a higher immersive level via a head-mounted display and the other one with a lower immersive level via a smartphone. The intervention was conducted for 30 min per session, once a week for 12 weeks. Data were collected by self-reported questionnaires at the baseline and post-intervention. In total, 54 participants completed the interventions. A significantly greater effect was revealed on improving happiness, self-rated health, and physical, mental, social, and environmental quality of life, and ameliorating distress, depression, and somatization in participants who experienced the higher immersive level compared to participants who experienced the lower immersive level. Virtual reality natural experiences with high immersion are recommended to promote mental health.
A Comprehensive Exploration of Outlier Detection in Unstructured Data for Enhanced Business Intelligence Using Machine Learning

Authors:
raw_author_name: None Md Salim Chowdhury
raw_affiliation_string: College of Graduate and Professional Studies Trine University, USA
raw_author_name: None Malay Sarkar
raw_affiliation_string: Department of Management Science and Quantitative Methods, Gannon University, USA
raw_author_name: None Md Al-Imran
raw_affiliation_string: College of Graduate and Professional Studies Trine University, USA
raw_author_name: None Md Ariful Islam Sarkar
raw_affiliation_string: Department of Business Administration Stamford University, Dhaka, Bangladesh

Abstract: Due to the rapid growth of online data, it is evident that social informatics faces a significant obstacle. The task of effectively utilizing this abundance of information for business intelligence purposes and extracting valuable insights from it across diverse and heterogeneous platforms presents a daunting challenge. Coordinating AI with business knowledge stands apart as an essential worry in the ongoing scene. Customarily, exceptions were many times excused as boisterous information, bringing about the deficiency of relevant data. This paper highlights the need to rethink how outliers are handled and shed light on the primary research challenges in this mining subfield. It presents a thorough scientific categorization of different Business Knowledge strategies and diagrams their ongoing application areas. Also, the paper talks about future exploration bearings and proposals to overcome any barrier concerning oddities in information examination, consequently empowering more successful business methodologies. This work plans to improve the usage of tremendous web-based information hotspots for better business insight results.
Simulation of robot arm system control using fuzzy logic

Authors:
raw_author_name: Sariman Sariman
raw_affiliation_string: Teknik Elektro Universitas Sriwijaya
raw_author_name: Zainuddin Nawawi
raw_affiliation_string: Teknik Elektro Universitas Sriwijaya
raw_author_name: Tresna Dewi
raw_affiliation_string: Teknik Elektro Politeknik Negeri Sriwijaya

Abstract: This study explains a robot arm system control simulation using a fuzzy logic controller. The robot arm is a nonlinear system and is not easily controlled using a conventional control system since the mathematical model of the system is not easy to obtain. Considering these problems, this final project presents a simulation of a fuzzy logic controller on a robot arm system. The aim of this control is to move the robot arm at an angular speed thus the robot gets the desired torque or the right position. Determining fuzzy rules greatly influences system stability. This control network produces output in the form of torque while the robot arm system calculates the next torque from the robot. The best output results were found on the triangular curve at response 2 because of the difference in values 0.01 on the final angle obtained.
Calculation and Analysis of Key Parameters of Underwater Optical Imaging System

Authors:
raw_author_name: Ying Liu
raw_affiliation_string: Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences (CIOMP), Changchun 130033, China
raw_author_name: Chen Yu
raw_affiliation_string: Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences (CIOMP), Changchun 130033, China; University of Chinese Academy of Sciences, Beijing 100049, China
raw_author_name: Linhan Li
raw_affiliation_string: Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences (CIOMP), Changchun 130033, China; University of Chinese Academy of Sciences, Beijing 100049, China
raw_author_name: Jie Du
raw_affiliation_string: Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences (CIOMP), Changchun 130033, China
raw_author_name: Junlin Ma
raw_affiliation_string: Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences (CIOMP), Changchun 130033, China
raw_author_name: Xinyue Liu
raw_affiliation_string: Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences (CIOMP), Changchun 130033, China

Abstract: When photographing objects underwater, it is important to utilize an optical window to isolate the imaging device from the water. The properties of the entire imaging system will change, and the imaging quality will decrease due to the refraction impact of the water and the window. The theoretical calculation method for air imaging is no longer relevant in this context. To analyze the unique rule, this research derives the formulas for key parameters of underwater imaging systems under paraxial circumstances. First, the optical window is modeled, then the formula for the optical window’s focal length in the underwater environment is derived, and the change rule for the focal length of various window forms underwater is condensed. For the ideal imaging system using a domed optical window, the equivalent two-optical group model of the imaging system is established, and the formula for calculating the focal length, working distance, and depth of field of the underwater imaging system is derived through paraxial ray tracing. The accuracy of the formula is verified through the comparative analysis of the formula calculation results and the Zemax modeling simulation results. It provides an important theoretical basis for the in-depth study of underwater imaging technology.
Document Storage and Verification System Using Blockchain Technology

Authors:
raw_author_name: None Gaurav Shende
raw_affiliation_string: Smt. Kashibai Navale College of Engineering, Pune, India
raw_author_name: None Sudhir Patil
raw_affiliation_string: Smt. Kashibai Navale College of Engineering, Pune, India
raw_author_name: None Mohit Pawar
raw_affiliation_string: Smt. Kashibai Navale College of Engineering, Pune, India
raw_author_name: None Prof. Supriya Jagtap
raw_affiliation_string: Smt. Kashibai Navale College of Engineering, Pune, India

Abstract: A banking or identity provider can set up a customer identification data verification process between reliant parties with the use of the electronic know your customer (e-KYC) system. Due to its high degree of accessibility and availability and its efficient resource usage, the majority of banks choose to implement their e-KYC system on the cloud. All of the KYC procedures used by banks rely on encryption, which is a cumbersome process that may cause consumer data to be disclosed to unaffiliated financial institutions. Blockchain technology can be used to increase the efficiency of this system because it can automate a lot of human labor and is impervious to attacks of all kinds. The distributed ledger and immutable blockchain block make the perfect addition to the KYC process. The use of smart contacts can automate the identification of fraud. Any kind of KYC can be used to store information related to KYC identity. Consequently, financial institutions can establish a shared private blockchain on their premises for the purpose of document validation. This allows the user to maintain control over their private documents while also simplifying the process for banks to obtain the records needed for compliance
Digital Renaissance in Education: Unveiling the Transformative Potential of Digitization in Educational Institutions

Authors:
raw_author_name: George Bucăţa
raw_affiliation_string: 1 “Nicolae Bălcescu” Land Forces Academy , Sibiu , Romania

Abstract: Abstract This paper examines the ongoing trend of digitization within educational institutions, analyzing the potential opportunities that lie ahead. As educational landscapes evolve, embracing digitization offers institutions a pathway to unlock a myriad of prospects. This research explores the current state of digitization in educational settings, highlighting the opportunities that arise from the integration of digital technologies. The driving forces behind significant changes in higher education include escalating operating costs, the emergence of micro and nano degrees, the surge in online learning, the influx of digitally-native students, and the overarching wave of digital transformation. Students are increasingly leveraging digital technologies to showcase a diverse array of tools, integrating structured learning environments into the fabric of higher education teaching. This research provides a comprehensive overview of digital transformation in higher education, evaluating the potential of digitalization and forecasting its prospects in the near future. Key concepts such as digital transformation, virtual reality (VR) technology, artificial intelligence (AI), and Technology, Entertainment, Design (TED) courses are examined as practical and indispensable elements shaping the future of education. Through a meticulous analysis, it becomes evident that digitalization marks only the initial phase in a broader process wherein institutions harness new technologies not merely to replicate old tasks but to redefine educational paradigms. Furthermore, the paper emphasizes that digital transformation necessitates the infusion of new management practices capable of harnessing the full potential of information and communication technologies (ICT). The journey towards the digital transformation of educational institutions involves a profound shift in mindset, encompassing innovative approaches to teaching, learning, and administration. As institutions embrace this transformative journey, they position themselves to create enhanced value for students and forge dynamic collaborations with partners in the evolving landscape of higher education.