Reviewerly/exHarmony huggingface dataset:
Neurodynamics for Equality-Constrained Time-Variant Nonlinear Optimization Using Discretization

Authors:
raw_author_name: Yang Shi Wangrong Sheng
raw_affiliation_string: Jiangsu Province Engineering Research Center of Knowledge Management and Intelligent Service, Yangzhou University, Yangzhou, China; School of Information Engineering, Yangzhou University, Yangzhou, China
raw_author_name: Shuai Li
raw_affiliation_string: Faculty of Information Technology and Electrical Engineering, University of Oulu, Pentti Kaiteran katu 1, Oulu, Finland; Technology Research Center of Finland (VTT), Kaitovayla 11, Oulu, Finland
raw_author_name: Bin Li
raw_affiliation_string: Jiangsu Province Engineering Research Center of Knowledge Management and Intelligent Service, Yangzhou University, Yangzhou, China; School of Information Engineering, Yangzhou University, Yangzhou, China
raw_author_name: and Xiaobing Sun
raw_affiliation_string: Jiangsu Province Engineering Research Center of Knowledge Management and Intelligent Service, Yangzhou University, Yangzhou, China; School of Information Engineering, Yangzhou University, Yangzhou, China

Abstract: Time-variant problems are widespread in science and engineering, and discrete-time recurrent neurodynamics (DTRN) method has been proved to be an effective way to deal with a variety of discrete time-variant problems. However, this DTRN method is usually based on the study of continuous time-variant problems and lacks a direct study of discrete time-variant problems. To solve the above problem, based on a pioneering direct discretization technique, we study and develop a new DTRN method to solve equality-constrained discrete time-variant nonlinear optimization (EC-DTVNO) problem. Specifically, firstly, to solve the EC-DTVNO problem, the recent method widely used by researchers is Lagrange multiplier method. By introducing Lagrange multiplier to construct Lagrange function, the objective function and equality constraint are integrated into a discrete time-variant nonlinear system. Then, the corresponding error function is defined, and the corresponding DTRN method for solving the EC-DTVNO problem can be obtained by direct discretization technique. Thereafter, this DTRN method is analyzed theoretically and its convergence is proved. In addition, numerical experiments and application experiments further confirm the effectiveness and superiority of DTRN method.
An advanced data fabric architecture leveraging homomorphic encryption and federated learning

Authors:
raw_author_name: A.B.M. Muntasir Rahman
raw_affiliation_string: Department of Computer Science and Engineering, School of Data and Sciences, BRAC University, 66 Mohakhali, Dhaka, 1212, Bangladesh
raw_author_name: Md. Golam Rabiul Alam
raw_affiliation_string: Department of Computer Science and Engineering, School of Data and Sciences, BRAC University, 66 Mohakhali, Dhaka, 1212, Bangladesh
raw_author_name: Mohammad Mehedi Hassan
raw_affiliation_string: Department of Information Systems, College of Computer and Information Sciences, King Saud University, Riyadh, 11543, Saudi Arabia
raw_author_name: Michele Ianni
raw_affiliation_string: Department of Informatics, Modeling, Electronics, and Systems, University of Calabria, Rende, CS, 87036, Italy
raw_author_name: Giancarlo Fortino
raw_affiliation_string: Department of Informatics, Modeling, Electronics, and Systems, University of Calabria, Rende, CS, 87036, Italy

Abstract: Data fabric is an automated and AI-driven data fusion approach to accomplish data management unification without moving data to a centralized location for solving complex data problems. In a Federated learning architecture, the global model is trained based on the learned parameters of several local models that eliminate the necessity of moving data to a centralized repository for machine learning. This paper introduces a secure approach for medical image analysis using federated learning and partially homomorphic encryption within a distributed data fabric architecture. With this method, multiple users or clients (hospitals/medical data centers) can collaborate in training a machine-learning model without exchanging raw data. The approach complies with laws and regulations such as HIPAA and GDPR, ensuring the privacy and security of the data. The study demonstrates the method’s effectiveness through a case study on pituitary tumor classification, achieving a significant accuracy of 83.31%. However, the primary focus of the study is using the data fabric architecture to securely store and analyze medical images while complying with HIPAA and GDPR regulations. The results highlight the potential of these techniques to be applied to other privacy-sensitive domains and contribute to the growing body of research on secure and privacy-preserving machine learning.
The role of ethical climates in employee information security policy violations

Authors:
raw_author_name: Adel Yazdanmehr
raw_affiliation_string: Paul H. Chook Department of Information Systems and Statistics, Zicklin School of Business, Baruch College, City University of New York, New York, NY, United States
raw_author_name: Muhammad Jawad
raw_affiliation_string: Department of Information, Risk, and Operations Management, McCombs School of Business, The University of Texas at Austin, Austin, TX, United States
raw_author_name: Raquel Benbunan-Fich
raw_affiliation_string: Paul H. Chook Department of Information Systems and Statistics, Zicklin School of Business, Baruch College, City University of New York, New York, NY, United States
raw_author_name: Jingguo Wang
raw_affiliation_string: Department of Information Systems and Operations Management, College of Business, The University of Texas at Arlington, Arlington, TX, United States

Abstract: In the context of information security, the organizational social environment plays an important role in deterring employee violations of the Information Security Policy (ISP). As extant research has not yet determined the role ethical climates may play, this study seeks to understand their influence on employee ISP violations. To this end, we classified ISPs into two major categories: those related to communal information technology (IT) resources in the organization, such as databases or software applications, and those related to connective IT resources, such as email or Internet access. Then, we explored how rules-, performance-, and friendship-oriented organizational ethical climates affect ISP violations. Based on a survey of 177 professionals employed in the United States, we found that rules-oriented and friendship-oriented ethical climates deter ISP violations, whereas a performance-oriented one does not. Moreover, rules- and friendship-oriented ethical climates lead to fewer communal ISP violations, while performance-oriented ethical climates lead to more connectivity ISP violations. These findings underscore the importance of considering the type of IT resources that an ISP aims to protect as well as the ethical climate of an organization in order to better understand the causes of ISP violations.
Keyword-enhanced recommender system based on inductive graph matrix completion

Authors:
raw_author_name: Donghee Han
raw_affiliation_string: Graduate School of Data Science, KAIST, Daejeon 34141, Republic of Korea
raw_author_name: Daehee Kim
raw_affiliation_string: Graduate School of Data Science, KAIST, Daejeon 34141, Republic of Korea
raw_author_name: Keejun Han
raw_affiliation_string: Division of Computer Engineering, Hansung University, Seoul 02876, Republic of Korea
raw_author_name: Mun Yong Yi
raw_affiliation_string: Department of Industrial and Systems Engineering, KAIST, Daejeon 34141, Republic of Korea

Abstract: Going beyond the user–item rating information, recent studies have utilized additional information to improve the performance of recommender systems. Graph neural network (GNN) based approaches are among the most common. However, existing models that utilize text data require a lot of computing resources and have a complex structure that makes them difficult to utilize in real-world applications. In this research, we propose a new method, keyword-enhanced graph matrix completion (KGMC), which utilizes keyword sharing relationships in user–item graphs. Our model has a simpler structure and requires less computing resources than existing models that utilize text data, but it has the advantage of cross-domain transferability while providing an intuitive understanding of the inference results. KGMC consists of three steps: (1) keyword extraction from the review text, (2) subgraph extraction and keyword-enhanced subgraph construction, and (3) GNN-based rating prediction. We have conducted extensive experiments over eight benchmark datasets to examine the relative superiority of the proposed KGMC method, compared to state-of-the-art baselines. Additional experiments and case studies have been also conducted to demonstrate the transferability as well as keyword-based explainability of KGMC. Our findings highlight the practical advantages of our model for recommender systems and support its effectiveness in inductive graph-based link prediction.
Multi-level graph contrastive learning

Authors:
raw_author_name: Jianhua Tao
raw_affiliation_string: Beijing National Research Center for Information Science and Technology, Tsinghua University, Beijing, China

Abstract: Graph representation learning has attracted a surge of interest recently, which targets learning discriminant representation for each node in the graph. Most of these representation methods focus on supervised learning and heavily depend on label information. However, annotating graphs are expensive in the real-world, especially in specialized domains (i.e. biology), as it requires the annotators with the domain knowledge to label the graph. To approach this problem, self-supervised learning provides a feasible solution for graph representation learning. In this paper, we propose a Multi-Level Graph Contrastive Learning (MLGCL) framework for learning robust representation of graph data by contrasting space views of graphs. Specifically, we introduce a novel contrastive view — space view. The original graph is a first-order approximation structure in the topological space where nodes are linked by feature similarity, relationship, etc. While the k-nearest neighbor (kNN) graph with community structure generated by encoding features preserves high-order proximity in feature space, it not only provides a complementary graph to the original graph from the feature space view but also is suitable for GNNs encoder. Furthermore, we develop a multi-level contrastive mode to preserve the local similarity and semantic similarity of graph-structured data simultaneously. Extensive experiments indicate MLGCL achieves promising results compared with the existing state-of-the-art graph representation learning methods on seven node classification datasets and three graph classification datasets.
Deep feature extraction with tri-channel textual feature map for text classification

Authors:
raw_author_name: Kunyan Li
raw_affiliation_string: Central China Normal University, NO.152, Luoyu Road, Hongshan District, Wuhan, 430079, Hubei, China

Abstract: The complexity and diversity of texts make it difficult for shallow text classification models to capture deeper text features. Therefore, this paper takes advantage of the BiLSTM-CNN hybrid network based on the self-attention mechanism to extract text features, and constructs a new text feature representation similar to RGB-channel images, that is, tri-channel text feature maps. Drawing on the effectiveness of ResNet and SA-Net in deep networks, we designed a deep feature extraction network to capture deeper features in the text. We use the tri-channel text feature map as the input to the deep feature extraction network, and propose the Deeper Feature Text Classification (DFTC) end-to-end text classification model. Experiments prove that the DFTC model is competitive compared to the most advanced methods on five challenging text classification datasets.
Virtual quantum resource distillation: General framework and applications

Authors:
raw_author_name: Ryuji Takagi
raw_affiliation_string: Department of Basic Science, The University of Tokyo, Tokyo 153-8902, Japan; Nanyang Quantum Hub, School of Physical and Mathematical Sciences, Nanyang Technological University, Singapore 637371, Singapore
raw_author_name: Xiao Yuan
raw_affiliation_string: Center on Frontiers of Computing Studies, Peking University, Beijing 100871, China
raw_author_name: Bartosz Regula
raw_affiliation_string: Department of Physics, Graduate School of Science, The University of Tokyo, Bunkyo-ku, Tokyo 113-0033, Japan; Mathematical Quantum Information RIKEN Hakubi Research Team, RIKEN Cluster for Pioneering Research, and RIKEN Center for Quantum Computing, Wako, Saitama 351-0198, Japan
raw_author_name: Mile Gu
raw_affiliation_string: CNRS-UNS-NUS-NTU International Joint Research Unit, UMI No. 3654, Singapore 117543, Singapore; Centre for Quantum Technologies, National University of Singapore, 3 Science Drive 2, Singapore 117543, Singapore; Nanyang Quantum Hub, School of Physical and Mathematical Sciences, Nanyang Technological University, Singapore 637371, Singapore

Abstract: We develop the general framework of virtual resource distillation, an alternative distillation strategy proposed in Phys. Rev. Lett. 132, 050203 (2024), which extends conventional quantum resource distillation by integrating the power of classical postprocessing. The framework presented here is applicable not only to quantum states, but also to dynamical quantum objects such as quantum channels and higher-order processes. We provide a general characterization and benchmarks for the performance of virtual resource distillation in the form of computable semidefinite programs as well as several operationally motivated quantities. We apply our general framework to various concrete settings of interest, including standard resource theories such as entanglement, coherence, and magic, as well as settings involving dynamical resources such as quantum memory, quantum communication, and non-Markovian dynamics. The framework of probabilistic distillation is also discussed.
Application of a reconfigurable all-optical activation unit based on optical injection into a bistable Fabry–Perot laser in multilayer perceptron neural networks

Authors:
raw_author_name: Jasna Crnjanski
raw_affiliation_string: University of Belgrade School of Electrical Engineeringhttps://ror.org/02qsmb048, Bulevar kralja Aleksandra 73, Belgrade 11120, Serbia
raw_author_name: Isidora Teofilovic
raw_affiliation_string: Department of Electrical and Photonics Engineering, Technical University of Denmark, Kongens Lyngby 2800, Denmark
raw_author_name: Marko Krstic
raw_affiliation_string: University of Belgrade School of Electrical Engineeringhttps://ror.org/02qsmb048, Bulevar kralja Aleksandra 73, Belgrade 11120, Serbia
raw_author_name: Dejan Gvozdic
raw_affiliation_string: University of Belgrade School of Electrical Engineeringhttps://ror.org/02qsmb048, Bulevar kralja Aleksandra 73, Belgrade 11120, Serbia

Abstract: In this Letter, we theoretically investigate the application of a bistable Fabry-Perot semiconductor laser under optical injection as an all-optical activation unit for multilayer perceptron optical neural networks. The proposed device is programmed to provide reconfigurable sigmoid-like activation functions with adjustable thresholds and saturation points and benchmarked on machine learning image recognition problems. Due to the reconfigurability of the activation unit, the accuracy can be increased by up to 2% simply by adjusting the control parameter of the activation unit to suit the specific problem. For a simple two-layer perceptron neural network, we achieve inference accuracies of up to 95% and 85%, for the MNIST and Fashion-MNIST datasets, respectively.
Development of a Digital Twin Prototype for Industrial Manufacturing Monitoring System Using IoT and Augmented Reality

Authors:
raw_author_name: None Dony Novaliendry
raw_author_name: None Novi Febrianti
raw_author_name: None Doni Tri Putra Yanto
raw_author_name: None Fadhillah Majid Saragih
raw_author_name: None Wan Mohd Yusof Rahiman

Abstract: The world is currently abuzz with the rapid development of technology in the era of Industrial Revolution 4.0. Various technological advancements are facilitating progress and accelerating the development of industrial technology. This evolution has led to automation in production processes, transitioning toward digitalization. With the implementation of sensors that provide real-time data, production processes can now be monitored remotely. However, direct monitoring is still necessary at times to periodically check the condition of each operating machine. Therefore, there is a need for technology that can monitor production processes and reduce high maintenance costs. Currently, numerous new technologies are emerging to enhance the performance and efficiency of production processes in various industries. One such technology is the digital twin. A digital twin is a visual representation that offers insights into the continuous operations of a system. This research focuses on an industrial manufacturing monitoring system that integrates the Internet of Things (IoT) and augmented reality (AR) technologies. The system is composed of an application and a prototype machine in the form of a conveyor, which can simulate a digital twin of the prototype machine. It also transmits sensor data and error notifications to the application in real time. The designed system can serve as a prototype for implementing digital twin technology, combining IoT and AR. This makes it possible to apply the technology to machinery and production tools in various industrial sectors.
Fundamental Components and Principles of Supervised Machine Learning Workflows with Numerical and Categorical Data

Authors:
raw_author_name: Styliani I. Kampezidou
raw_affiliation_string: Aerospace Systems Design Laboratory, School of Aerospace Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA
raw_author_name: Archana Tikayat Ray
raw_affiliation_string: AI Fusion Technologies, Toronto, ON M5V 3Z5, Canada
raw_author_name: Anirudh Prabhakara Bhat
raw_affiliation_string: Amazon, Toronto, ON M5H 4A9, Canada
raw_author_name: Olivia J. Pinon Fischer
raw_affiliation_string: Aerospace Systems Design Laboratory, School of Aerospace Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA
raw_author_name: Dimitri N. Mavris
raw_affiliation_string: Aerospace Systems Design Laboratory, School of Aerospace Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA

Abstract: This paper offers a comprehensive examination of the process involved in developing and automating supervised end-to-end machine learning workflows for forecasting and classification purposes. It offers a complete overview of the components (i.e., feature engineering and model selection), principles (i.e., bias–variance decomposition, model complexity, overfitting, model sensitivity to feature assumptions and scaling, and output interpretability), models (i.e., neural networks and regression models), methods (i.e., cross-validation and data augmentation), metrics (i.e., Mean Squared Error and F1-score) and tools that rule most supervised learning applications with numerical and categorical data, as well as their integration, automation, and deployment. The end goal and contribution of this paper is the education and guidance of the non-AI expert academic community regarding complete and rigorous machine learning workflows and data science practices, from problem scoping to design and state-of-the-art automation tools, including basic principles and reasoning in the choice of methods. The paper delves into the critical stages of supervised machine learning workflow development, many of which are often omitted by researchers, and covers foundational concepts essential for understanding and optimizing a functional machine learning workflow, thereby offering a holistic view of task-specific application development for applied researchers who are non-AI experts. This paper may be of significant value to academic researchers developing and prototyping machine learning workflows for their own research or as customer-tailored solutions for government and industry partners.
Unsupervised clustering of catalog-driven features for characterizing temporal evolution of labquake stress

Authors:
raw_author_name: Sadegh Karimpouli
raw_affiliation_string: Helmholtz Centre Potsdam, GFZ German Research Centre for Geosciences , Potsdam , Germany
raw_author_name: Grzegorz Kwiatek
raw_affiliation_string: Helmholtz Centre Potsdam, GFZ German Research Centre for Geosciences , Potsdam , Germany
raw_author_name: Patricia Martínez-Garzón
raw_affiliation_string: Helmholtz Centre Potsdam, GFZ German Research Centre for Geosciences , Potsdam , Germany
raw_author_name: Georg Dresen
raw_affiliation_string: Helmholtz Centre Potsdam, GFZ German Research Centre for Geosciences , Potsdam , Germany; Institute of Earth and Environmental Sciences, Universität Potsdam , Potsdam , Germany
raw_author_name: Marco Bohnhoff
raw_affiliation_string: Department of Earth Sciences, Free University Berlin , Berlin , Germany; Helmholtz Centre Potsdam, GFZ German Research Centre for Geosciences , Potsdam , Germany

Abstract: Summary Earthquake forecasting poses significant challenges, especially due to the elusive nature of stress states in fault systems. To tackle this problem, we employ features derived from seismic catalogs obtained from acoustic emission (AE) signals recorded during triaxial stick-slip experiments on natural fractures in three Westerly granite samples. We extracted 47 physically explainable features from AE data that described spatio-temporal evolution of stress and damage in the vicinity of the fault surface. These features are then subjected to unsupervised clustering using the K-means method, revealing three distinct stages with a proper agreement with the temporal evolution of stress. The recovered stages correspond to the mechanical behavior of the rock, characterized as initial stable (elastic) deformation, followed by a transitional stage leading to an unstable deformation prior to failure. Notably, AE rate, clustering-localization features, fractal dimension, b-value, interevent time distribution, and correlation integral are identified as significant features for the unsupervised clustering. The systematically evolving stages can provide valuable insights for characterizing preparatory processes preceding earthquake events associated with geothermal activities and waste-water injections. In order to address the upscaling issue, we propose to use the most important features and, in case of normalization challenge, removing non-universal features, such as AE rate. Our findings hold promise for advancing earthquake prediction methodologies based on laboratory experiments and catalog-driven features.