Reviewerly/exHarmony huggingface dataset:
Digital transformation and integration of artificial intelligence in financial institutions 

Authors:
raw_author_name: Allam Hamdan
raw_affiliation_string: Department of Accounting and Economics, College of Business and Finance, Ahlia University, Manama, Bahrain
raw_author_name: Haneen Mohammad Shoaib
raw_affiliation_string: College of Business Administration, University of Business and Technology, Jeddah, Saudi Arabia

Abstract: Purpose Integrating artificial intelligence (AI) into various industries, including the financial sector, has transformed them. This paper aims to examine the influence of integrating AI, including machine learning, process automation, predictive analytics and chatbots, on financial institutions and explores its various aspects and areas. The study aims to determine the impact of AI integration on financial services, products and customer experience. Design/methodology/approach The research study uses quantitative and qualitative methods, as well as secondary data analysis. It investigates four AI subfields: machine learning, process automation, predictive analytics and chatbots. Findings The research findings indicate that integrating AI, particularly in machine learning and chatbot subfields, holds promise and high strategic potential for financial institutions. These subfields can contribute significantly to enhancing financial services and customer experience. However, the significance of predictive analytics integration and process automation is relatively lower. Although these subfields retain their usefulness, they might necessitate alternative workflows and tools that incorporate human involvement. Overall, AI integration minimizes human interactions and errors in financial institutions. Originality/value The research study contributes original insights by exploring the specific subfields of AI within the financial industry and assessing their strategic significance. It provides recommendations for financial institutions to adopt AI integration partially in multiple phases, measure and evaluate the impact of the transformation and structure internal units and expertise to strategize adoption and change.
Economics of incident response panels in cyber insurance

Authors:
raw_author_name: Daniel Arce
raw_affiliation_string: Economics Program, The University of Texas at Dallas, USA
raw_author_name: Daniel W. Woods
raw_affiliation_string: School of Informatics, University of Edinburgh, United Kingdom
raw_author_name: Rainer Böhme
raw_affiliation_string: Department of Computer Science, University of Innsbruck, Austria

Abstract: Cyber insurance is becoming a popular cyber risk management tool. Beyond pure financial risk transfer, prior theoretical works anticipated that cyber insurance would influence the mitigation measures employed by policyholders, such as by excluding losses caused by security mismanagement or by offering premium discounts for security controls. Empirical literature has shown cyber insurance is ineffective at influencing pre-breach security levels; however, it has also identified how insurers indemnify the cost of a team of post-breach providers with expertise spanning legal, technical, and communications. Our work models the peculiarities of the institution, the panel, that triages incidents and assigns firms. In particular, we model the incomplete aspect of this contract in which policyholders may be assigned a less efficient firm, which can be interpreted as a bait and switch. At the same time, our context for the bait and switch is business-to-business (B2B) and differs from the usual understanding of the phenomenon as an upsell. Consequently, new managerial implications arise on the insurer-side of the market. We characterise the conditions under which policyholders accept their insurer's hotline recommendation for incident response under the incomplete contract. We additionally show how panels can mitigate the adverse selection problem with respect to policyholders' losses by including providers of differentiated efficiency.
A systematic literature review of knowledge graph construction and application in education

Authors:
raw_author_name: Bilal Abu-Salih
raw_affiliation_string: King Abdullah II School of Information Technology, The University of Jordan, Amman, Jordan

Abstract: In the dynamic landscape of modern education, the search for improved pedagogical methods, enriched learning experiences, and empowered educators remains a perpetual pursuit. In recent years, a remarkable technological innovation has asserted its dominance in education: Knowledge Graphs (KGs). These structured representations of knowledge are increasingly proving to be indispensable tools, fostering advancements driven by the growing recognition of their essential role in enriching personalised learning, curriculum design, concept mapping, and educational content recommendation systems. In this paper, a systematic literature review (SLR) has been conducted to comprehensively examine KG construction methodologies and their applications across five key domains in education. In each examined study, we highlight the specific KG functionalities, knowledge extraction techniques, knowledge base characteristics, resource requirements, evaluation criteria, and limitations. This paper distinguishes itself by offering a broad overview of KGs in education, analyzing state-of-the-art methodologies, and identifying research gaps and limitations, paving the way for future advancements.
Teaching with Augmented Reality Using Tablets, Both as a Tool and an Object of Learning

Authors:
raw_author_name: Sabrina Syskowski
raw_affiliation_string: Chair of Science Education, Thurgau University of Education, 8280 Kreuzlingen, Switzerland; Department of Chemistry, Institute for Science Education, University of Konstanz, 78464 Konstanz, Germany
raw_author_name: Canan Kanbur
raw_affiliation_string: Chair of Science Education, Thurgau University of Education, 8280 Kreuzlingen, Switzerland; Department of Chemistry, Institute for Science Education, University of Konstanz, 78464 Konstanz, Germany
raw_author_name: Ingo Eilks
raw_affiliation_string: Department of Biology and Chemistry, Institute for Science Education, University Bremen, 28359 Bremen, Germany; Department of Mathematics and Natural Sciences, Universitas Negeri Malang, 65145 Malang, Indonesia
raw_author_name: Johannes Huwer
raw_affiliation_string: Chair of Science Education, Thurgau University of Education, 8280 Kreuzlingen, Switzerland; Department of Chemistry, Institute for Science Education, University of Konstanz, 78464 Konstanz, Germany

Abstract: In this paper, we present a novel approach to utilizing tablets in chemistry education. In the context of education for sustainable development, we utilize tablets as objects of learning to address the lithium-ion battery. In addition, we used tablets as learning tools by making use of augmented reality technology. This way, we have created an innovative digital learning scenario that corresponds to a 3-h laboratory. Evaluation took place during the implementation of nonformal student laboratories. Results show positive effects in interest to working with tablets as a tool and an object of learning.
Overview of temporal action detection based on deep learning

Authors:
raw_author_name: Kai Hu
raw_affiliation_string: School of Automation, Nanjing University of Information Science and Technology, No.219, Ningliu Road, Nanjing, 210044, Jiangsu, China
raw_author_name: Chaowen Shen
raw_affiliation_string: School of Automation, Nanjing University of Information Science and Technology, No.219, Ningliu Road, Nanjing, 210044, Jiangsu, China
raw_author_name: Tianyan Wang
raw_affiliation_string: School of Automation, Nanjing University of Information Science and Technology, No.219, Ningliu Road, Nanjing, 210044, Jiangsu, China
raw_author_name: Keer Xu
raw_affiliation_string: School of Automation, Nanjing University of Information Science and Technology, No.219, Ningliu Road, Nanjing, 210044, Jiangsu, China
raw_author_name: Qingfeng Xia
raw_affiliation_string: School of Automation, Wuxi University, No.333, Xishan Road, Wuxi, 214105, Jiangsu, China
raw_author_name: Min Xia
raw_affiliation_string: School of Automation, Nanjing University of Information Science and Technology, No.219, Ningliu Road, Nanjing, 210044, Jiangsu, China
raw_author_name: Chengxue Cai
raw_affiliation_string: Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology (CICAEET), Nanjing University of Information Science and Technology, No.219, Ningliu Road, Nanjing, 210044, Jiangsu, China

Abstract: Abstract Temporal Action Detection (TAD) aims to accurately capture each action interval in an untrimmed video and to understand human actions. This paper comprehensively surveys the state-of-the-art techniques and models used for TAD task. Firstly, it conducts comprehensive research on this field through Citespace and comprehensively introduce relevant dataset. Secondly, it summarizes three types of methods, i.e., anchor-based, boundary-based, and query-based, from the design method level. Thirdly, it summarizes three types of supervised learning methods from the level of learning methods, i.e., fully supervised, weakly supervised, and unsupervised. Finally, this paper explores the current problems, and proposes prospects in TAD task.
A Semantic Tree-Based Fast-Moving Object Trajectory Tracking Algorithm for Table Tennis

Authors:
raw_author_name: Tianjian Zou
raw_affiliation_string: Beijing University of Posts and Telecommunications, China
raw_author_name: Dazhuang Sun
raw_affiliation_string: Beijing University of Posts and Telecommunications, China
raw_author_name: Yu Yang
raw_affiliation_string: Beijing Sport University, China
raw_author_name: Jun Liu
raw_affiliation_string: Beijing University of Posts and Telecommunications, China

Abstract: Table tennis is a popular sport around the world. A key technology in table tennis education and analysis system is reconstructing the trajectory of the fast-moving ball from videos. Typically the table tennis ball is too small and barely visible in the video, making it difficult to be recognized directly by detection models like YOLO. However, table tennis balls usually has obvious motion features, which are usually not found in similar false targets. It inspired the authors to first find all candidate targets and then use the motion features of table tennis ball to select them out. In this article, the authors propose a tree-based algorithm named T-FORT to track the ball and reconstruct its trajectory. Specifically, they consider all the possible objects in a tree-framework, and identify the real target by integrating visual features and moving patterns. The authors conduct a set of experiments on three datasets to evaluate the effectiveness and performance of the proposed algorithm. The experimental results show that the proposed method is more precise than existing algorithms, and is robust in various scenarios.
Classification Performance Analysis of Decision Tree-Based Algorithms with Noisy Class Variable

Authors:
raw_author_name: Abdulmajeed Atiah Alharbi
raw_affiliation_string: Department of Mathematics, College of Science, Taibah University, Al-Madinah Al-Munawara, Saudi Arabia

Abstract: Class noise is a common issue that affects the performance of classification techniques on real-world data sets. Class noise appears when a class variable in data sets has incorrect class labels. In the case of noisy data, the robustness of classification techniques against noise could be more important than the performance results on noise-free data sets. The decision tree method is one of the most popular techniques for classification tasks. The C4.5, CART, and random forest (RF) algorithms are considered to be three of the most used algorithms in decision trees. The aim of this paper is to reach conclusions on which decision tree algorithm is better to use for building decision trees in terms of its performance and robustness against class noise. In order to achieve this aim, we study and compare the performance of the models when applied to class variables with noise. The results obtained indicate that the RF algorithm is more robust to data sets with noisy class variable than other algorithms.
Rate distortion optimization for adaptive gradient quantization in federated learning

Authors:
raw_author_name: Kaixuan Xie
raw_affiliation_string: China Mobile Research Institute, Beijing 100053, China
raw_author_name: Wenqiang Luo
raw_affiliation_string: School of Information Science and Engineering, Southeast University, Nanjing, 210096, China
raw_author_name: Yinfei Xu
raw_affiliation_string: School of Information Science and Engineering, Southeast University, Nanjing, 210096, China
raw_author_name: Lun Xin
raw_affiliation_string: China Mobile Research Institute, Beijing 100053, China
raw_author_name: Tiecheng Song
raw_affiliation_string: School of Information Science and Engineering, Southeast University, Nanjing, 210096, China
raw_author_name: Jing Hu
raw_affiliation_string: School of Information Science and Engineering, Southeast University, Nanjing, 210096, China

Abstract: Federated Learning (FL) is an emerging machine learning framework designed to preserve privacy. However, the continuous updating of model parameters over uplink channels with limited throughput leads to a huge communication overload, which is a major challenge for FL. To address this issue, we propose an adaptive gradient quantization approach that enhances communication efficiency. Aiming to minimize the total communication costs, we consider both the correlation of gradients between local clients and the correlation of gradients between communication rounds, namely, in the time and space dimensions. The compression strategy is based on rate distortion theory, which allows us to find an optimal quantization strategy for the gradients. To further reduce the computational complexity, we introduce the Kalman filter into the proposed approach. Finally, numerical results demonstrate the effectiveness and robustness of the proposed rate-distortion optimization adaptive gradient quantization approach in significantly reducing the communication costs when compared to other quantization methods.
Property probes: Live exploration of program analysis results

Authors:
raw_author_name: Anton Risberg Alaküla
raw_affiliation_string: Lund University, Ole Römers Väg 3, 221 00, Lund, Sweden
raw_author_name: Görel Hedin
raw_affiliation_string: Lund University, Ole Römers Väg 3, 221 00, Lund, Sweden
raw_author_name: Niklas Fors
raw_affiliation_string: Lund University, Ole Römers Väg 3, 221 00, Lund, Sweden
raw_author_name: Adrian Pop
raw_affiliation_string: Linköping University, Mäster Mattias väg, 581 83, Linköping, Sweden

Abstract: We present property probes, a mechanism for helping a developer explore partial program analysis results in terms of the source program interactively while the program is edited. A node locator data structure is introduced that maps between source code spans and program representation nodes, and that helps identify probed nodes in a robust way, after modifications to the source code. We have developed a client–server based tool CodeProber supporting property probes, and argue that it is very helpful in debugging and understanding program analyses. We have evaluated our tool on several languages and analyses, including a full Java compiler and a tool for intraprocedural dataflow analysis. Our performance results show that the probe overhead is negligible even when analyzing large projects.
Distributed Learning in the IoT–Edge–Cloud Continuum

Authors:
raw_author_name: Krisjanis Nesenbergs
raw_affiliation_string: Institute of Electronics and Computer Science, LV-1006 Riga, Latvia
raw_author_name: Leo Selavo
raw_affiliation_string: Institute of Electronics and Computer Science, LV-1006 Riga, Latvia

Abstract: The goal of the IoT–Edge–Cloud Continuum approach is to distribute computation and data loads across multiple types of devices taking advantage of the different strengths of each, such as proximity to the data source, data access, or computing power, while mitigating potential weaknesses. Most current machine learning operations are currently concentrated on remote high-performance computing devices, such as the cloud, which leads to challenges related to latency, privacy, and other inefficiencies. Distributed learning approaches can address these issues by enabling the distribution of machine learning operations throughout the IoT–Edge–Cloud Continuum by incorporating Edge and even IoT layers into machine learning operations more directly. Approaches like transfer learning could help to transfer the knowledge from more performant IoT–Edge–Cloud Continuum layers to more resource-constrained devices, e.g., IoT. The implementation of these methods in machine learning operations, including the related data handling security and privacy approaches, is challenging and actively being researched. In this article the distributed learning and transfer learning domains are researched, focusing on security, robustness, and privacy aspects, and their potential usage in the IoT–Edge–Cloud Continuum, including research on tools to use for implementing these methods. To achieve this, we have reviewed 145 sources and described the relevant methods as well as their relevant attack vectors and provided suggestions on mitigation.
A soft voting ensemble learning approach for credit card fraud detection

Authors:
raw_author_name: Peal Mazumder
raw_affiliation_string: Department of Computer Science and Telecommunication Engineering, Noakhali Science and Technology University, Noakhali-3814, Bangladesh

Abstract: With the advancement of e-commerce and modern technological development, credit cards are widely used for both online and offline purchases, which has increased the number of daily fraudulent transactions. Many organizations and financial institutions worldwide lose billions of dollars annually because of credit card fraud. Due to the global distribution of both legitimate and fraudulent transactions, it is difficult to discern between the two. Furthermore, because only a small proportion of transactions are fraudulent, there is a problem of class imbalance. Hence, an effective fraud-detection methodology is required to sustain the reliability of the payment system. Machine learning has recently emerged as a viable substitute for identifying this type of fraud. However, ML approaches have difficulty identifying fraud with high prediction accuracy, while also decreasing misclassification costs due to the size of the imbalanced data. In this research, a soft voting ensemble learning approach for detecting credit card fraud on imbalanced data is proposed. To do this, the proposed approach is evaluated and compared with numerous sophisticated sampling techniques (i.e., oversampling, undersampling, and hybrid sampling) to overcome the class imbalance problem. We develop several credit card fraud classifiers, including ensemble classifiers, with and without sampling techniques. According to the experimental results, the proposed soft-voting approach outperforms individual classifiers. With a false negative rate (FNR) of 0.0306, it achieves a precision of 0.9870, recall of 0.9694, f1-score of 0.8764, and AUROC of 0.9936.
Sublinear Scaling in Non-Markovian Open Quantum Systems Simulations

Authors:
raw_author_name: Moritz Cygorek
raw_affiliation_string: SUPA, Institute of Photonics and Quantum Sciences, Heriot-Watt University, Edinburgh EH14 4AS, United Kingdom
raw_author_name: Jonathan Keeling
raw_affiliation_string: SUPA, School of Physics and Astronomy, University of St Andrews, St Andrews KY16 9SS, United Kingdom
raw_author_name: Erik M. Gauger
raw_affiliation_string: SUPA, Institute of Photonics and Quantum Sciences, Heriot-Watt University, Edinburgh EH14 4AS, United Kingdom

Abstract: An exact algorithm to calculate process tensors---compact representations of environmental influences---provides a scaling advantage over previous algorithms and enables tackling problems in open quantum systems that are currently out of reach.
Efficient color image steganography based on new adapted chaotic dynamical system with discrete orthogonal moment transforms

Authors:
raw_author_name: Mohamed Yamni
raw_affiliation_string: Dhar El Mahrez Faculty of Science, Sidi Mohamed Ben Abdellah-Fez University, Fez, Morocco
raw_author_name: Achraf Daoui
raw_affiliation_string: National School of Applied Sciences, Sidi Mohamed Ben Abdellah-Fez University, Fez, Morocco
raw_author_name: Ahmed A. Abd El-Latif
raw_affiliation_string: Center of Excellence in Quantum and Intelligent Computing, Prince Sultan University, Riyadh 11586, Saudi Arabia; Department of Mathematics and Computer Science, Faculty of Science, Menoufia University, Shebin El-Koom 32511, Egypt

Abstract: In today's rapidly evolving digital landscape, safeguarding the secure transmission of sensitive information is paramount. Traditional steganography methods, while effective at data concealment, have grown vulnerable to brute force attacks in our age of powerful computing. This paper addresses the pressing need to bolster data transmission security. To address the challenges posed by limited key space and security concerns associated with 1-D chaotic maps in data transmission, we introduce a novel 1-D chaotic map, the Multi-Parameter Chebyshev Map (MCM). The MCM boasts four control parameters, each with a broad chaotic range and high chaos level, significantly enhancing security. Furthermore, we present an innovative steganography technique for color images that leverages the MCM in conjunction with discrete moment transforms. This method chaotically embeds the secret message within the moment coefficients of a cover color image using MCM and quantization rules. The expansive chaotic range and high chaos level of the MCM substantially reinforce the security of this approach. The considered moment transforms include Tchebichef, Krawtchouk, Hahn, Charlier, Meixner, Shmaliy, Racah, and Dual-Hahn. The performance of the proposed steganography method is thoroughly evaluated and compared with other state-of-the-art methods in terms of hiding capacity and image quality in both spatial and transform domains. The quantization of moment coefficients demonstrates its superior performance. This method holds crucial implications for practical applications requiring secure communication of color images, such as military and medical contexts. The combination of MCM and moment transforms in this method presents a promising avenue for future research in the field of information hiding.