Reviewerly/exHarmony huggingface dataset:
Dark Light Image-Enhancement Method Based on Multiple Self-Encoding Prior Collaborative Constraints

Authors:
raw_author_name: Lei Guan
raw_affiliation_string: University of Chinese Academy of Sciences, Beijing 100049, China; Xi’an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi’an 710119, China
raw_author_name: Jijiang Huang
raw_affiliation_string: Xi’an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi’an 710119, China
raw_author_name: Weining Chen
raw_affiliation_string: Xi’an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi’an 710119, China

Abstract: The purpose of dark image enhancement is to restore dark images to visual images under normal lighting conditions. Due to the ill-posedness of the enhancement process, previous enhancement algorithms often have overexposure, underexposure, noise increases and artifacts when dealing with complex and changeable images, and the robustness is poor. This article proposes a new enhancement approach consisting in constructing a dim light enhancement network with more robustness and rich detail features through the collaborative constraint of multiple self-coding priors (CCMP). Specifically, our model consists of two prior modules and an enhancement module. The former learns the feature distribution of the dark light image under normal exposure as an a priori term of the enhancement process through multiple specific autoencoders, implicitly measures the enhancement quality and drives the network to approach the truth value. The latter fits the curve mapping of the enhancement process as a fidelity term to restore global illumination and local details. Through experiments, we concluded that the new method proposed in this article can achieve more excellent quantitative and qualitative results, improve detail contrast, reduce artifacts and noise, and is suitable for dark light enhancement in multiple scenes.
Research on course teaching evaluation and academic early warning based on data mining

Authors:
raw_author_name: Xiaocheng Cai
raw_affiliation_string: Chongqing College of Architecture and Technology (China)
raw_author_name: Jing Hu
raw_affiliation_string: Chongqing College of Architecture and Technology (China)

Abstract: Based on the development requirements of modern vocational education and the development trend of information-based teaching, the purpose of the paper is to build a three-dimensional and diversified teaching evaluation method by introducing data mining technology, use appropriate data mining algorithms to summarize the knowledge as well as master rules, predict the learning status of students, timely carry out early warning and teaching intervention, and explore a teaching evaluation and academic early warning method suitable for vocational education courses through practical classroom practice so as to improve curriculum mastery level of the students and optimize the quality of personnel training technical skills.
Song Emotion Intelligence Analysis for Psychological Stress Relief

Authors:
raw_author_name: Xiaoyu Huang
raw_affiliation_string: Zhengzhou Shengda College of Economic and Trade Management, China

Abstract: In today's digital and networked era, multimedia content such as images, audio, and video have become an important part of the data transmitted on the Internet's information superhighway. How to manage the stress in the college student population is directly related to the future life and development of college students. In this article, based on domestic and international data mining technology, the authors designed an intelligent analysis system for college students' mental health, pre-processed the data, then analysed these data in detail by using the outlier analysis algorithm in clustering algorithm, and finally mined the intrinsic connection between the psychological problems and attributes by using the Apriori association rule algorithm, so as to provide the decision makers with a reliable basis. This study puts forward reasonable solutions and suggestions for the problems existing in the psychological level of contemporary college students.
Unsupervised Graph Anomaly Detection with Discriminative Embedding Similarity for Viscoelastic Sandwich Cylindrical Structures

Authors:
raw_author_name: Rujie Hou
raw_affiliation_string: State Key Laboratory for Manufacturing and Systems Engineering, Xi’an Jiaotong University, Xi’an 710049, PR China
raw_author_name: Jinglong Chen
raw_affiliation_string: State Key Laboratory for Manufacturing and Systems Engineering, Xi’an Jiaotong University, Xi’an 710049, PR China

Abstract: In the detection of slipping anomalies in viscoelastic sandwich cylindrical structures (VSCS), conventional methods may encounter challenges due to the extremely rare and weak nature of slipping signals. This study focuses on normal signals and introduces unsupervised graph representation learning (UGRL) with discriminative embedding similarity for VSCS's detection. UGRL involves data preprocessing, model embedding, and matrix reconstructing. Association graphs are constructed based on sample similarities for yielding adjacency and attribute matrices. Subsequently, the matrices undergo embedding and reconstruction via various network modules to enhance graph data characterization. Detection indicators are derived by calculating embedding similarities and reconstruction errors, and thresholds are constructed using these indicators to enable efficient anomaly detection. The experiments in VSCS slipping dataset effectively indicate the superiority of the proposed method.
Artificial Intelligence and the Modelling of Teachers Competencies

Authors:
raw_author_name: Ramona Simut
raw_author_name: Alina Badulescu

Abstract: This research explores educators' perceptions of their role in shaping competencies and also presents the key challenges and measures perceived by educators in the AI system development landscape.The study employs a partial least squarestructural equation modelling (PLS-SEM) as the primary analytical tool.A set of hypotheses was derived from the literature, formulated and tested to substantiate the relationship between educators' attitudes and competencies.The findings highlight a significant positive relationship between teachers' attitudes toward AI and competence development, with the most pronounced impact on educational management competencies.Thus, teachers' positive attitudes toward AI statistically significant influence cognitive, fundamental and educational management competencies, underscoring the critical role of instilling positive attitude towards AI among educators to foster the development of multifaceted competencies.Furthermore, the research highlights key challenges to integrating AI into education, including the imperative for professional development for educators and ensuring equitable access to educational resources and technology.From a practical point of view, the study advocates for initiatives aimed at bridging the digital divide and integrating AI education into curricula as indispensable policy measures.The originality of our research lies in the unitary approach of the relationship between positive attitudes of teachers toward artificial intelligence and a series of competencies, such as cognitive, fundamental and educational management.Although the study provides valuable information, it has certain limitations.The findings are based on educators' perceptions and attitudes, which may vary among individuals, the study scope is limited to a specific region, and the generalisability of findings to other settings may require further investigation.This research contributes to the existing literature by exploring the relationship between teachers' attitudes toward AI and the development of their skills, with the help of an exploratory model using structural equations, a model that has the teachers in the foreground.
Adversarial attacks and defenses in explainable artificial intelligence: A survey

Authors:
raw_author_name: Hubert Baniecki
raw_affiliation_string: University of Warsaw, Warsaw, Poland
raw_author_name: Przemyslaw Biecek
raw_affiliation_string: University of Warsaw, Warsaw, Poland; Warsaw University of Technology, Warsaw, Poland

Abstract: Explainable artificial intelligence (XAI) methods are portrayed as a remedy for debugging and trusting statistical and deep learning models, as well as interpreting their predictions. However, recent advances in adversarial machine learning (AdvML) highlight the limitations and vulnerabilities of state-of-the-art explanation methods, putting their security and trustworthiness into question. The possibility of manipulating, fooling or fairwashing evidence of the model’s reasoning has detrimental consequences when applied in high-stakes decision-making and knowledge discovery. This survey provides a comprehensive overview of research concerning adversarial attacks on explanations of machine learning models, as well as fairness metrics. We introduce a unified notation and taxonomy of methods facilitating a common ground for researchers and practitioners from the intersecting research fields of AdvML and XAI. We discuss how to defend against attacks and design robust interpretation methods. We contribute a list of existing insecurities in XAI and outline the emerging research directions in adversarial XAI (AdvXAI). Future work should address improving explanation methods and evaluation protocols to take into account the reported safety issues.
In Conversation with Sir David Spiegelhalter and Professor Sylvia Richardson

Authors:
raw_author_name: Bhramar Mukherjee
raw_affiliation_string: Bhramar Mukherjee is a Distinguished University Professor and current Chair, Department of Biostatistics University of Michigan, Ann Arbor, MI 48109-2029, United States

Abstract: Sir David Spiegelhalter and Professor Sylvia Richardson are two eminent statisticians of our time who have made pioneering contributions to Statistics and Data Science with careers spanning over more than four decades. They have a long and celebrated legacy built through foundational research in Bayesian statistics, impactful collaborations, steadfast professional service and superb scientific communications. They have won many prestigious awards and recognitions throughout their distinguished careers. During my sabbatical in 2022 at the University of Cambridge I had the honor of sitting down for a conversation with these two remarkable individuals. We discussed early career influences and digressions, research philosophy, role of mentors and advice for the future generation. We hope this conversation with David and Sylvia will inspire many future statisticians.
Emerging Directions in Bayesian Computation

Authors:
raw_author_name: Steven Winter
raw_affiliation_string: Steven Winter is Ph.D. Student, Department of Statistical Science, Duke University, Durham, North Carolina 27710, USA
raw_author_name: Trevor Campbell
raw_affiliation_string: Trevor Campbell is Associate Professor, Department of Statistics, University of British Columbia, Vancouver, British Columbia, Canada V6T 1Z4
raw_author_name: Lizhen Lin
raw_affiliation_string: Lizhen Lin is Robert and Sara Lumpkins Associate Professor, Department of Applied and Computational Mathematics and Statistics, University of Notre Dame, Notre Dame, Indiana 46556, USA
raw_author_name: Sanvesh Srivastava
raw_affiliation_string: Sanvesh Srivastava is Associate Professor, Department of Statistics and Actuarial Science, University of Iowa, Iowa City, Iowa 52242, USA
raw_author_name: David B. Dunson
raw_affiliation_string: David B. Dunson is Arts and Sciences Distinguished Professor, Departments of Statistical Science and Mathematics, Duke University, Durham, North Carolina 27710, USA

Abstract: Bayesian models are powerful tools for studying complex data, allowing the analyst to encode rich hierarchical dependencies and leverage prior information. Most importantly, they facilitate a complete characterization of uncertainty through the posterior distribution. Practical posterior computation is commonly performed via MCMC, which can be computationally infeasible for high-dimensional models with many observations. In this article, we discuss the potential to improve posterior computation using ideas from machine learning. Concrete directions are explored in vignettes on normalizing flows, statistical properties of variational approximations, Bayesian coresets and distributed Bayesian inference.
Outlier detection using flexible categorisation and interrogative agendas

Authors:
raw_author_name: Krishna Manoorkar
raw_affiliation_string: KPMG, Amstelveen, The Netherlands; School of Business and Economics, Vrije Universiteit, Amsterdam, The Netherlands
raw_author_name: Alessandra Palmigiano
raw_affiliation_string: Department of Mathematics and Applied Mathematics, University of Johannesburg, South Africa; School of Business and Economics, Vrije Universiteit, Amsterdam, The Netherlands
raw_author_name: Mattia Panettiere
raw_affiliation_string: KPMG, Amstelveen, The Netherlands; School of Business and Economics, Vrije Universiteit, Amsterdam, The Netherlands
raw_author_name: Apostolos Tzimoulis
raw_affiliation_string: School of Business and Economics, Vrije Universiteit, Amsterdam, The Netherlands
raw_author_name: Nachoem Wijnberg
raw_affiliation_string: College of Business and Economics, University of Johannesburg, South Africa; Faculty of Economics and Business, University of Amsterdam, The Netherlands

Abstract: Categorization is one of the basic tasks in machine learning and data analysis. Building on formal concept analysis (FCA), the starting point of the present work is that different ways to categorize a given set of objects exist, which depend on the choice of the sets of features used to classify them, and different such sets of features may yield better or worse categorizations, relative to the task at hand. In their turn, the (a priori) choice of a particular set of features over another might be subjective and express a certain epistemic stance (e.g. interests, relevance, preferences) of an agent or a group of agents, namely, their interrogative agenda. In the present paper, we represent interrogative agendas as sets of features, and explore and compare different ways to categorize objects w.r.t. different sets of features (agendas). We first develop a simple unsupervised FCA-based algorithm for outlier detection which uses categorizations arising from different agendas. We then present a supervised meta-learning algorithm to learn suitable (fuzzy) agendas for categorization as sets of features with different weights or masses. We combine this meta-learning algorithm with the unsupervised outlier detection algorithm to obtain a supervised outlier detection algorithm. We show that these algorithms perform at par with commonly used algorithms for outlier detection on commonly used datasets in outlier detection. These algorithms provide both local and global explanations of their results.
Quantum Annealing-driven Branch and Bound for the single machine total weighted number of tardy jobs scheduling problem

Authors:
raw_author_name: Wojciech Bożejko
raw_affiliation_string: Department of Control Systems and Mechatronics, Wrocław University of Science and Technology, Janiszewskiego 11/17, 50-372 Wrocław, Poland
raw_author_name: Jarosław Pempera
raw_affiliation_string: Department of Control Systems and Mechatronics, Wrocław University of Science and Technology, Janiszewskiego 11/17, 50-372 Wrocław, Poland
raw_author_name: Mariusz Uchroński
raw_affiliation_string: Wroclaw Centre for Networking and Supercomputing, Wybrzeże Wyspiańskiego 27, 50-370 Wrocław, Poland
raw_author_name: Mieczysław Wodecki
raw_affiliation_string: Department of Telecommunications and Teleinformatics, Wrocław University of Science and Technology, Wybrzeże Wyspiańskiego 27, 50-370 Wrocław, Poland

Abstract: In the paper we present a new approach to solving NP-hard problems of discrete optimization adapted to the architecture of quantum processors (QPU, Quantum Processor Unit) implementing hardware quantum annealing. This approach is based on the use of the quantum annealing metaheuristic in the exact branch and bound algorithm to compute the lower and upper bounds of the objective function. To determine the lower bound, a new method of defining the Lagrange function for the dual problem (the generalized discrete knapsack problem) was used, the value of which is calculated on the QPU of a quantum machine. In turn, to determine the upper bound, we formulate an appropriate task in the form of binary quadratic programming with constraints. Despite the fact that the results generated by the quantum machine are probabilistic, the hybrid method of algorithm construction proposed in the paper, using alternately a CPU and QPU, guarantees the optimal solution. As a case study we consider the NP-hard single machine scheduling problem with minimizing the weighted number of tardy jobs. The performed computational experiments showed that optimal solutions were already obtained in the root of the solution tree, and the values of the lower and upper bounds differ by only a few percent.
Feature selection based on dataset variance optimization using Hybrid Sine Cosine – Firehawk Algorithm (HSCFHA)

Authors:
raw_author_name: Syed Kumayl Raza Moosavi
raw_affiliation_string: National University of Sciences and Technology (NUST), Islamabad, Pakistan
raw_author_name: Ahsan Saadat
raw_affiliation_string: National University of Sciences and Technology (NUST), Islamabad, Pakistan
raw_author_name: Zainab Abaid
raw_affiliation_string: FAST School of Computing, National University of Computer and Emerging Sciences, Islamabad, Pakistan
raw_author_name: Wei Ni
raw_affiliation_string: Data61, Commonwealth Scientific and Industrial Research Organisation, Sydney, Australia
raw_author_name: Mohsen Guizani
raw_affiliation_string: Mohamed Bin Zayed University of Artificial Intelligence, Abu Dhabi, United Arab Emirates

Abstract: Feature selection plays a pivotal role in preprocessing data for machine learning (ML) models. It entails choosing a subset of pertinent features to enhance the model’s accuracy and minimize overfitting. Wrapper methods based on metaheuristics are one approach to feature selection, leveraging the predictive accuracy of a learning algorithm to form a condensed set of features. Traditionally, this method uses K-Nearest Neighbour (KNN) for maximizing accuracy as its cost function. However, this approach often yields less than optimal results in large sample spaces and demands considerable computational resources. To circumvent the shortcomings of this approach, this work proposes a novel metaheuristic algorithm, termed the Hybrid Sine Cosine Firehawk Algorithm. Furthermore, a novel feature selection technique is designed that uses this hybrid algorithm to eliminate insignificant and redundant features by incorporating the minimization of dataset variance in the cost function. Additionally, the hybridization of multiple metaheuristic algorithms produces the best features of each algorithm to improve the exploration ability. The proposed technique is tested on 22 University of California Irvine datasets containing low, medium and high dimensional datasets and compared to the traditional KNN-based approach. The technique is also compared with other state-of-the-art metaheuristic techniques, namely Particle Swarm Optimizer, Grey Wolf Optimizer, Whale Optimization Algorithm, Hybrid Ant Colony Optimizer and Improved Binary Bat Algorithm. The results show significant improvements over previous techniques in terms of minimal loss in essential data while reducing the size of the raw data in considerably less time, as well as a well-balanced confusion matrix.
Hybrid deep learning model using SPCAGAN augmentation for insider threat analysis

Authors:
raw_author_name: Atul Sajjanhar
raw_affiliation_string: School of Information Technology, Deakin University, Geelong, VIC 3217, Australia
raw_author_name: Yong Xiang
raw_affiliation_string: School of Information Technology, Deakin University, Geelong, VIC 3217, Australia

Abstract: Cyberattacks from within an organization’s trusted entities are known as insider threats. Anomaly detection using deep learning requires comprehensive data, but insider threat data is not readily available due to confidentiality concerns of organizations. Therefore, there arises demand to generate synthetic data to explore enhanced approaches for threat analysis. We propose a linear manifold learning-based generative adversarial network, SPCAGAN, that takes input from heterogeneous data sources and adds a novel loss function to train the generator to produce high-quality data that closely resembles the original data distribution. Furthermore, we introduce a deep learning-based hybrid model for insider threat analysis. We provide extensive experiments for data synthesis, anomaly detection, adversarial robustness, and synthetic data quality analysis using benchmark datasets. In this context, empirical comparisons show that GAN-based oversampling is competitive with numerous typical oversampling regimes. For synthetic data generation, our SPCAGAN model overcame the problem of mode collapse and converged faster than previous GAN models. Results demonstrate that our proposed approach has a lower error, is more accurate, and generates substantially superior synthetic insider threat data than previous models.
VFL-Chain: Bulletproofing Federated Learning in the V2X environments

Authors:
raw_author_name: Abla Smahi
raw_affiliation_string: Peking University, Shenzhen Graduate School, Shenzhen, 518055, Guangdong Province, China
raw_author_name: Hui Li
raw_affiliation_string: Peking University, Shenzhen Graduate School, Shenzhen, 518055, Guangdong Province, China; Peng Cheng Laboratory, Shenzhen, 518055, Guangdong Province, China
raw_author_name: Wang Han
raw_affiliation_string: Peking University, Shenzhen Graduate School, Shenzhen, 518055, Guangdong Province, China
raw_author_name: Ahmed Ameen Fateh
raw_affiliation_string: Shenzhen Children’s hospital, Shenzhen, 518055, Guangdong Province, China

Abstract: Federated Learning (FL) has gained significant traction as a promising approach to enable collaborative machine learning (ML) while safeguarding data privacy across diverse applications, with the Vehicle-to-Everything (V2X) environment being a notable use case. However, conventional FL systems remain susceptible to model poisoning attacks, wherein malicious participants can introduce inaccurate or deliberately misleading local model updates to the central aggregator. In the context of V2X, such attacks could spawn catastrophic outcomes and jeopardize safety-critical applications. To tackle this pressing concern, we introduce a privacy-preserving and verifiable FL framework, dubbed VFL-Chain, designed to facilitate secure and efficient collaboration among intelligent connected vehicles (ICVs). VFL-Chain aspires to deliver a privacy-centric, verifiable FL experience complemented by a robust incentive mechanism tailored for ICVs. Our proposed solution leverages Bulletproofs to enable efficient verification of model update integrity, which are assimilated within smart contracts on an underlying permissioned blockchain. Furthermore, VFL-Chain presents a fair incentive mechanism that rewards honest participation, fostering a resilient and efficient FL implementation. We conduct an exhaustive security analysis and performance assessment of our proposed system, which underscores its efficacy in countering data poisoning attacks and augmenting the accuracy of FL.