Reviewerly/exHarmony huggingface dataset:
Causal maps in the analysis and unsupervised assessment of the development of expert knowledge: Quantification of the learning effects for knowledge management purposes

Authors:
raw_author_name: Mahinda Mailagaha Kumbure
raw_affiliation_string: Business School, LUT University, Yliopistonkatu 34, 53850 Lappeenranta, Finland
raw_author_name: Anssi Tarkiainen
raw_affiliation_string: Business School, LUT University, Yliopistonkatu 34, 53850 Lappeenranta, Finland
raw_author_name: Jan Stoklasa
raw_affiliation_string: Business School, LUT University, Yliopistonkatu 34, 53850 Lappeenranta, Finland; Department of Economic and Managerial Studies, Faculty of Arts, Palacký University, 77180 Olomouc, Czech Republic
raw_author_name: Pasi Luukka
raw_affiliation_string: Business School, LUT University, Yliopistonkatu 34, 53850 Lappeenranta, Finland
raw_author_name: Ari Jantunen
raw_affiliation_string: Business School, LUT University, Yliopistonkatu 34, 53850 Lappeenranta, Finland

Abstract: This study proposes an application of cognitive maps in the representation of cognitive structures of the experts and assessment of their development/modification as a result of a (computer or expert system-assisted) learning process. It strives to identify information needed for the guidance of the process of creation and management of expert knowledge by formal modeling tools. Changes in experts’ cognitive structures are assumed to stem from individual and collaborative (group-level) learning. The novel approach to assessing the outcomes of learning reflected as changes in the cognitive structures of experts or groups of experts, modeled by cognitive maps, does not assume any correct or desired outcome of the learning process to be known in advance. Instead, it identifies and analyses the changes in (or robustness of) the constituents of the cognitive maps from different points of view and allows for quantifying and visualizing the actual effect of the learning. The proposed methodology can identify changes in cognitive diversity, causal structures in terms of causal relations and concepts, and the perceived importance of strategic issues over the learning period. It can also detect which cause–effect relationships have appeared/disappeared considering the pre-/post-mapping design. Thus it provides an exploratory account on the changes in the cognitive structures of the expert(s) as a result of learning. The applicability of the proposed methods is illustrated in the assessment of the learning outcomes of a group of 71 graduate students who participated in an eight-week business simulation task. The results of the empirical analysis confirm the viability of the proposed methodology and indicate that the students’ understanding of the utilized concepts and associated relationships in the decision-making process improved throughout the learning activity, ultimately showing that the course learning has considerably improved students’ perception and knowledge. Based on the results, it can be concluded that the proposed approach has the potential to be effective in assessing learning outcomes in a teaching-learning activities.
A nonlinear African vulture optimization algorithm combining Henon chaotic mapping theory and reverse learning competition strategy

Authors:
raw_author_name: Patrick Siarry
raw_affiliation_string: Laboratoire Images, Signaux et Systèmes Intelligents; SIMO
raw_author_name: Xinhua Liu
raw_affiliation_string: School of Mechatronic Engineering, China University of Mining and Technology, Xuzhou, China
raw_author_name: Grzegorz Królczyk
raw_affiliation_string: Department of Manufacturing Engineering and Automation Products, Opole University of Technology, 45-758 Opole, Poland
raw_author_name: Dezheng Hua
raw_affiliation_string: School of Mechatronic Engineering, China University of Mining and Technology, Xuzhou, China
raw_author_name: Frantisek Brumercik
raw_affiliation_string: Department of Design and Machine Elements, Faculty of Mechanical Engineering, University of Zilina, Univerzitna 1, 010 26 Zilina, Slovakia
raw_author_name: Zhixiong Li
raw_affiliation_string: Department of Manufacturing Engineering and Automation Products, Opole University of Technology, Opole 45-758, Poland; Donghai Laboratory, Zhoushan, Zhejiang 316021, China

Abstract: As a new intelligent optimization algorithm, the African vultures optimization algorithm (AVOA) has been widely used in various fields today. However, when solving complex multimodal problems, the AVOA still has some shortcomings, such as low searching accuracy, deficiency on the search capability and tendency to fall into local optimum. In order to alleviate the main shortcomings of the AVOA, a nonlinear African vulture optimization algorithm combining Henon chaotic mapping theory and reverse learning competition strategy (HWEAVOA) is proposed. Firstly, the Henon chaotic mapping theory and elite population strategy are proposed to improve the randomness and diversity of the vulture's initial population; Furthermore, the nonlinear adaptive incremental inertial weight factor is introduced in the location update phase to rationally balance the exploration and exploitation abilities, and avoid individual falling into a local optimum; The reverse learning competition strategy is designed to expand the discovery fields for the optimal solution and strengthen the ability to jump out of the local optimal solution. HWEAVOA and other advanced comparison algorithms are used to solve classical and CEC2022 test functions. Compared with other algorithms, the convergence curves of the HWEAVOA drop faster and the line bodies are smoother. These experimental results show the proposed HWEAVOA is ranked first in all test functions, which is superior to the comparison algorithms in convergence speed, optimization ability, and solution stability. Meanwhile, HWEAVOA has reached the general level in the algorithm complexity, and its overall performance is competitive in the swarm intelligence algorithms.
Human–machine Teaming with Small Unmanned Aerial Systems in a MAPE-K Environment

Authors:
raw_author_name: Jane Cleland-Huang
raw_affiliation_string: Computer Science and Engineering, University of Notre Dame, IN, USA
raw_author_name: Sebastian Zudaire
raw_affiliation_string: Instituto Balseiro – Universidad Nacional de Cuyo, Argentina
raw_author_name: Muhammed Tawfiq Chowdhury
raw_affiliation_string: Computer Science and Engineering, University of Notre Dame, IN, USA
raw_author_name: Ankit Agrawal
raw_affiliation_string: Department of Computer Science, Saint Louis University, MO, USA
raw_author_name: Michael Vierhauser
raw_affiliation_string: LIT Secure and Correct Systems Lab, Johannes Kepler University Linz, Austria

Abstract: The Human Machine Teaming (HMT) paradigm focuses on supporting partnerships between humans and autonomous machines. HMT describes requirements for transparency, augmented cognition, and coordination that enable far richer partnerships than those found in typical human-on-the-loop and human-in-the-loop systems. Autonomous, self-adaptive systems in domains such as autonomous driving, robotics, and Cyber-Physical Systems, are often implemented using the MAPE-K feedback loop as the primary reference model. However, while MAPE-K enables fully autonomous behavior, it does not explicitly address the interactions that occur between humans and autonomous machines as intended by HMT. In this article, we, therefore, present the MAPE-K HMT framework, which utilizes runtime models to augment the monitoring, analysis, planning, and execution phases of the MAPE-K loop to support HMT despite the different operational cadences of humans and machines. We draw on examples from our own emergency response system of interactive, autonomous, small unmanned aerial systems to illustrate the application of MAPE-K HMT in both a simulated and physical environment, and we discuss how the various HMT models are connected and can be integrated into a MAPE-K solution.
Fractional-Order Echo State Network Backstepping Control of Fractional-Order Nonlinear Systems

Authors:
raw_author_name: Heng Liu
raw_affiliation_string: School of Mathematics and Physics, Guangxi Minzu University, Nanning, China
raw_author_name: Jinde Cao
raw_affiliation_string: School of Mathematics, Southeast University, Nanjing, China
raw_author_name: Yongping Pan
raw_affiliation_string: School of Advanced Manufacturing, Sun Yat-sen University, Shenzhen, China

Abstract: Classical backstepping control of fractional-order nonlinear systems (FONSs) needs to calculate fractional derivatives of virtual control inputs recursively, which usually results in the “explosion of complexity” problem. This article proposes a command-filtering adaptive control scheme based on fractional-order echo state networks (FOESNs) for strict-feedback uncertain FONSs. Finite-time command filters are introduced to estimate instead of calculating the complicated fractional-order derivatives of virtual control inputs, which not only has the advantages of the conventional command-filters, but also guarantees the finite-time convergent property. An FOESN with stronger approximation ability is presented to approach uncertainties. Compared with conventional adaptive neural network or fuzzy control, the proposed method with similar computational complexity has more design freedom and better approximation effect. The effectiveness of the proposed control framework is verified by two simulation examples.
Updatable Siamese tracker with two-stage one-shot learning

Authors:
raw_author_name: Xinglong Sun
raw_affiliation_string: Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China
raw_author_name: Haijiang Sun
raw_affiliation_string: Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China
raw_author_name: Jianan Li
raw_affiliation_string: School of Optics and Photonics, Image Engineering and Video Technology Lab, Beijing Institute of Technology, Beijing 100081, China

Abstract: Offline-trained Siamese networks have realized very promising tracking precision and efficiency. However, the performance is still limited by the drawbacks in online update. Traditional strategies cannot tackle the irregular variations of object and the sampling noise, so it is quite risky to adopt them to update Siamese trackers. In this paper, we present a two-stage one-shot learner by exploring the learning scheme of Siamese network, which reveals there are two key issues during online update, i.e., feature fusion and feature comparison. Based on this finding, we propose an updatable Siamese tracker by introducing two independent transformers (SiamTOL). Concretely, a Cross-aware transformer is designed to combine the features of the initial and the dynamic templates, while a Decoder-favored transformer is exploited to compare the fusing template and the search region. By combining these transformers, our tracker is able to adequately model the feature dependencies between multi-frame object samples. Extensive experimental results on several popular benchmarks well manifest that the proposed approach achieves the leading performance, and outperforms other state-of-the-art trackers.
Overview of modulation techniques for spatially structured-light 3D imaging

Authors:
raw_author_name: Tao Yang
raw_affiliation_string: Guangdong-Hong Kong-Macao Joint Laboratory of Human–Machine Intelligence Synergy Systems, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China; The Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, 999077, Hong Kong, China
raw_author_name: Feifei Gu
raw_affiliation_string: Guangdong Laboratory of Artificial Intelligence and Digital Economy, Shenzhen 518052, China; Guangdong-Hong Kong-Macao Joint Laboratory of Human–Machine Intelligence Synergy Systems, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China; The Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, 999077, Hong Kong, China

Abstract: The modulation and projection of patterns are the cornerstones of spatially structured-light three-dimensional (3D) imaging. However, an overview of this field is lacking. This paper comprehensively reviews the modulation techniques for spatially structured-light 3D imaging. First, the frameworks and the state-of-the-art status of all the mainstream methods are introduced. Then, side-by-side comparisons are performed from different perspectives to illustrate their respective characteristics. In particular, experiments to compare the shape reconstruction quality of different methods were conducted. Beyond academic research, the applications and off-the-shelf products in the industry are reviewed. Finally, our views on the demands and trends in the field are presented.
Product consumptions meet reviews: Inferring consumer preferences by an explainable machine learning approach

Authors:
raw_author_name: Fan Zhou
raw_affiliation_string: School of Management, Hefei University of Technology, Hefei, Anhui 230009, China
raw_author_name: Yuanchun Jiang
raw_affiliation_string: Key Laboratory of Philosophy and Social Sciences for Cyberspace Behaviour and Management, Hefei, Anhui 230009, China; School of Management, Hefei University of Technology, Hefei, Anhui 230009, China
raw_author_name: Yang Qian
raw_affiliation_string: Intelligent Interconnected Systems Laboratory of Anhui Province (Hefei University of Technology), Hefei, Anhui 230009, China
raw_author_name: Yezheng Liu
raw_affiliation_string: National Engineering Laboratory for Big Data Distribution and Exchange Technologies, Shanghai 200436, China; School of Management, Hefei University of Technology, Hefei, Anhui 230009, China
raw_author_name: Yidong Chai
raw_affiliation_string: Key Laboratory of Philosophy and Social Sciences for Cyberspace Behaviour and Management, Hefei, Anhui 230009, China; School of Management, Hefei University of Technology, Hefei, Anhui 230009, China

Abstract: Inferring consumers' preferences provides a better understanding of their purchase behavior, which is very important for business success, e.g., recommendation systems and targeted advertising. In this paper, we propose an explainable machine learning approach, namely Multi-view Latent Dirichlet Allocation (MVLDA), to infer and interpret consumer preferences. In the proposed model, we assume that there exists a downstream relationship between consumers' motivations and their purchase behaviors. We model this downstream relationship by linking two types of topics (i.e., textual topics for motivations and product-related topics for purchase behaviors), to quantify and explain consumers' choices. We validate our modeling framework using a real-world dataset collected from the online retailer Amazon. The experimental results show that the proposed model identifies a set of high-quality textual topics but also interprets its effect on consumer choices based on product-related topics. In addition, we demonstrate that the proposed model quantifies the consumer's preferences. The proposed model yields interesting insights about user preferences, and provides several important managerial implications, e.g., e-commerce platforms, brand managers, and marketers.
MMDS: A secure and verifiable multimedia data search scheme for cloud-assisted edge computing

Authors:
raw_author_name: Shiwen Zhang
raw_affiliation_string: Advanced Cryptography and System Security Key Laboratory of Sichuan Province, China; School of Computer Science and Engineering, Hunan University of Science and Technology, XiangTan, Hunan, China
raw_author_name: Jiayi He
raw_affiliation_string: School of Computer Science and Engineering, Hunan University of Science and Technology, XiangTan, Hunan, China
raw_author_name: Wei Liang
raw_affiliation_string: School of Computer Science and Engineering, Hunan University of Science and Technology, XiangTan, Hunan, China
raw_author_name: Keqin Li
raw_affiliation_string: Department of Computer Science, State University of New York, New Paltz, NY 12561, USA

Abstract: With the rapid development and popularity of Internet of Things (IoT) technology, IoT devices can generate MultiMedia Big Data (MMBD) as multimedia devices. MMBD is often encrypted and stored on an untrusted cloud server. Searchable encryption is an effective way which can finish a controllable keyword search of ciphertext. However, there are two limitations to these existing works. On the one hand, it is hard to independently protect the data privacy of each IoT device. On the other hand, when faced with an untrusted cloud server, it is difficult to implement more flexible fine-grained verification for search results. To overcome these limitations, we propose a secure and verifiable MultiMedia Data Search (MMDS) scheme for cloud-assisted edge computing. To protect the data of IoT devices independently, we designed a secure, flexible, and efficient keyword search mechanism based on bilinear pairings. To achieve a more flexible and practical search result verification mechanism, we design a fine-grained verification algorithm combining blockchain and hashing techniques. We have conducted performance evaluations and security proof and analysis for MMDS scheme. Finally, we applied MMDS scheme to solve real problems in intelligent multimedia systems. Security proof and analysis prove the security of MMDS. Performance analyses and evaluations further confirm that MMDS is efficient and feasible for practical applications.
Self-distillation and self-supervision for partial label learning

Authors:
raw_author_name: Xiaotong Yu
raw_affiliation_string: Key Laboratory of Big Data Mining and Knowledge Management, Chinese Academy of Sciences, Beijing, 100190, China; Research Center on Fictitious Economy and Data Science, Chinese Academy of Sciences, Beijing, 100190, China; School of Mathematical Sciences, University of Chinese Academy of Sciences, Beijing, 100049, China
raw_author_name: Shiding Sun
raw_affiliation_string: Key Laboratory of Big Data Mining and Knowledge Management, Chinese Academy of Sciences, Beijing, 100190, China; Research Center on Fictitious Economy and Data Science, Chinese Academy of Sciences, Beijing, 100190, China; School of Mathematical Sciences, University of Chinese Academy of Sciences, Beijing, 100049, China
raw_author_name: Yingjie Tian
raw_affiliation_string: Key Laboratory of Big Data Mining and Knowledge Management, Chinese Academy of Sciences, Beijing, 100190, China; MOE Social Science Laboratory of Digital Economic Forecasts and Policy Simulation at UCAS, Beijing, 100190, China; Research Center on Fictitious Economy and Data Science, Chinese Academy of Sciences, Beijing, 100190, China; School of Economics and Management, University of Chinese Academy of Sciences, Beijing, 100190, China

Abstract: As a main branch of weakly supervised learning paradigm, partial label learning (PLL) copes with the situation where each sample corresponds to ambiguous candidate labels containing the unknown true label. The primary difficulty of PLL lies in label ambiguities, most existing researches focus on individual instance knowledge while ignore the importance of cross-sample knowledge. To circumvent this difficulty, an innovative multi-task framework is proposed in this work to integrate self-supervision and self-distillation to tackle PLL problem. Specifically, in the self-distillation task, cross-sample knowledge in the same batch is utilized to refine ensembled soft targets to supervise the distillation operation without using multiple networks. The auxiliary self-supervised task of recognizing rotation transformations of images provides more supervisory signal for feature learning. Overall, training supervision is constructed not only from the input data itself but also from other instances within the same batch. Empirical results on benchmark datasets reveal that this method is effective in learning from partially labeled data.
A hybrid framework using explainable AI (XAI) in cyber-risk management for defence and recovery against phishing attacks

Authors:
raw_author_name: Baidyanath Biswas
raw_affiliation_string: Trinity Business School, Trinity College Dublin, Ireland
raw_author_name: Arunabha Mukhopadhyay
raw_affiliation_string: IT and Systems Area, Indian Institute of Management, Lucknow, India
raw_author_name: Ajay Kumar
raw_affiliation_string: EMLYON Business School, Ecully, France
raw_author_name: Dursun Delen
raw_affiliation_string: Center for Health Systems Innovation, Department of Management Science and Information Systems, Spears School of Business, Oklahoma State University, Tulsa, Oklahoma, USA; Department of Industrial Engineering, Faculty of Engineering and Natural Sciences, Istinye University, Istanbul, Turkey

Abstract: Phishing and social engineering contribute to various cyber incidents such as data breaches and ransomware attacks, financial frauds, and denial of service attacks. Often, phishers discuss these attack vectors in dark forums. Further, the probability of phishing attacks and the subsequent loss suffered by the firm are highly correlated. In this context, we propose a hybrid framework using explainable AI techniques to assess cyber-risks generated from correlated phishing attacks. The first phase computes the probability of expert phishers within a community of similar attackers with varying expertise. The second phase calculates the probability of phishing attacks upon a firm even after it has invested in IT security and adopted regulatory steps. The third phase categorises phishing and genuine URLs using various machine-learning-based classifiers. Next, it estimates the joint distribution of phishing attacks using an exponential-beta distribution and quantifies the expected loss using Archimedean Copula. Finally, we offer recommendations for firms through the computation of optimal investments in cyber-insurance versus IT security. First, based on the risk attitude of a firm, it can use this explainable-AI (XAI) framework to optimally invest in building security into its enterprise architecture and plan for cyber-risk mitigation strategies. Second, we identify a long-tail phenomenon demonstrated by the losses suffered during most cyber-attacks, which are not one-off incidents and are correlated. Third, contrary to the belief that cyber-insurance markets are ineffective, it can guide financial firms to design realistic cyber-insurance products.