Reviewerly/exHarmony huggingface dataset:
Successive data injection in conditional quantum GAN applied to time series anomaly detection

Authors:
raw_author_name: Benjamin Kalfon
raw_affiliation_string: Department of Computer and Software Engineering Polytechnique Montréal  Montréal, Quebec Canada
raw_author_name: Soumaya Cherkaoui
raw_affiliation_string: Department of Computer and Software Engineering Polytechnique Montréal  Montréal, Quebec Canada
raw_author_name: Jean‐Frédéric Laprade
raw_affiliation_string: Institut Quantique Université de Sherbrooke  Sherbrooke, Quebec Canada
raw_author_name: Ola Ahmad
raw_affiliation_string: Thales Digital Solutions  Montréal, Quebec Canada
raw_author_name: Shengrui Wang
raw_affiliation_string: Department of Computer Science Université de Sherbrooke  Sherbrooke, Quebec Canada; Institut Quantique Université de Sherbrooke  Sherbrooke, Quebec Canada

Abstract: Abstract Classical GAN architectures have shown interesting results for solving anomaly detection problems in general and for time series anomalies in particular, such as those arising in communication networks. In recent years, several quantum GAN (QGAN) architectures have been proposed in the literature. When detecting anomalies in time series using QGANs, huge challenges arise due to the limited number of qubits compared to the size of the data. To address these challenges, a new high‐dimensional encoding approach, named Successive Data Injection (SuDaI) is proposed. In this approach, SuDaI explores a larger portion of the quantum state, compared to the conventional angle encoding method used predominantly in the literature. This is achieved through repeated data injections into the quantum state. SuDaI encoding allows the authors to adapt the QGAN for anomaly detection with network data of a much higher dimensionality than with the existing known QGANs implementations. In addition, SuDaI encoding applies to other types of high‐dimensional time series and can be used in contexts beyond anomaly detection and QGANs, opening up therefore multiple fields of application.
Human-Centered Interaction in Virtual Worlds: A New Era of Generative Artificial Intelligence and Metaverse

Authors:
raw_author_name: Le Wang
raw_affiliation_string: Department of Information System, City University of Hong Kong, Kowloon, Hong Kong
raw_author_name: Keng Leng Siau
raw_affiliation_string: Department of Information System, City University of Hong Kong, Kowloon, Hong Kong

Abstract: The metaverse has emerged as an exciting new paradigm for human-computer interaction (HCI) and virtual collaboration. This paper presents a comprehensive review of the metaverse to address the gap in the existing literature where there is a lack of a survey that reviews the nature of the metaverse and its building blocks from a human-centric perspective. We first synthesize a definition of the metaverse from existing literature and delineate key affordances. We then introduce a detailed framework encompassing the metaverse’s nature, infrastructure technologies, and input/output technologies that facilitate multi-sensory HCI, alongside applications across diverse domains. The components within this framework are explained in depth, offering insights into the metaverse’s nature and the readiness level of current technologies. Based on this comprehensive analysis, we outline major open challenges and propose promising directions demanding further exploration and investigation. By clarifying the vision for the metaverse and characterizing the building blocks required to realize it, this review provides essential insights and serves as an invaluable resource for metaverse developers and researchers working to advance this transformative new medium.
Bringing digital competence to the disciplines

Authors:
raw_author_name: Francesco Caviglia
raw_affiliation_string: Department of Curriculum Studies, Danish School of Education, Aarhus University, Aarhus, Denmark
raw_author_name: Mette Alma Kjærsholm Boie
raw_affiliation_string: Department of Curriculum Studies, Danish School of Education, Aarhus University, Aarhus, Denmark
raw_author_name: Christian Dalsgaard
raw_affiliation_string: Department of Curriculum Studies, Danish School of Education, Aarhus University, Aarhus, Denmark
raw_author_name: Alex Young Pedersen
raw_affiliation_string: Department of Curriculum Studies, Danish School of Education, Aarhus University, Aarhus, Denmark

Abstract: How can upper secondary teachers accommodate the increasing demand for integrating digital competence in their disciplines? Research on digital competence typically operates independently from specific disciplines and may be difficult to translate into teaching practices. Based on a three-year action research project, the paper presents the results of a study of teacher’s understanding of digital technologies in connection to their disciplines. The study involves two rounds of interviews with 40 teachers. From the interviews, different perspectives emerge with 1) technology-driven, 2) pedagogy-driven, and 3) discipline-specific use of digital technologies. By further exploring discipline-specific use of digital technologies, digital competence is seen as expanding the disciplines where technology directly impacts and transforms subject areas of disciplines. Further, drawing from teacher experiences from completed courses, the paper develops a framework for accommodating the interplay between digital competence and the disciplines. The framework presents boundary concepts that are meant to bridge the gap between teaching practices within the disciplines and contributions from research. The framework is intended for teachers in developing their disciplines from digital competence. Finally, the paper discusses how expanding the disciplines through the lens of digital competence may call for moving across and possibly beyond the disciplines.
Real‐time route planning for low observable <scp>unmanned combat aerial vehicle</scp>

Authors:
raw_author_name: Yuanchao Yang
raw_affiliation_string: School of Electronics and Information Xi'an Polytechnic University  Xi'an China

Abstract: Abstract The next generation of low observable (LO) unmanned combat aerial vehicle (UCAV) with highly autonomy to implement a penetration mission requires advanced methods for flyable and safe route planning (i.e., respecting physical capability of vehicle and threat coverage by hostile air defense radars) at a real‐time manner. Currently, the main challenge of real‐time route planning for LO UCAV is to achieve computationally efficiency under dynamic (pop‐up/moving) threats by air defense radars. In this paper, a real‐time planning paradigm in compliance with complex penetration requirements is proposed, and a complete modeling of route planning for LO UCAV's penetration as an optimal control problem is designed. The paper at first devises a direct method to transform the optimal control problem into a nonlinear programming (NLP) problem and then solves the formulated NLP problem under a moving planning horizon. The proposed method can give computationally efficient route planning results for LO UCAV's penetration under multiple kinds of radar threats. Numerical test results based on F‐16 uninhabited platform demonstrate the effectiveness of the proposed method.
Multi‐exposure embeddings for graph learning: Towards high dynamic range image saliency prediction

Authors:
raw_author_name: Jun Xing
raw_affiliation_string: Shenzhen Academy of Inspection and Quarantine Shenzhen China
raw_author_name: Qiudan Zhang
raw_affiliation_string: Department of Computer Science and Software Engineering Shenzhen University Shenzhen China
raw_author_name: Xuelin Shen
raw_affiliation_string: Guangdong Laboratory of Artificial Intelligence and Digital Economy Shenzhen China
raw_author_name: Xu Wang
raw_affiliation_string: Department of Computer Science and Software Engineering Shenzhen University Shenzhen China

Abstract: Abstract Identifying saliency in high dynamic range (HDR) images is a fundamentally important issue in HDR imaging, and plays critical roles towards comprehensive scene understanding. Most of existing studies leverage hand‐crafted features for HDR image saliency prediction, lacking the capabilities of fully exploiting the characteristics of HDR image (i.e. wider luminance range and richer colour gamut). Here, systematical studies are carried out on HDR image saliency prediction by proposing a new framework to single out the contributions from multi‐exposure images. Specifically, inspired by the mechanism of HDR imaging, the method first utilizes graph neural networks to model the relations among multi‐exposure images and the tone‐mapped image obtained from an HDR image, enabling more discriminative saliency‐related feature representations. Subsequently, the saliency features driven by global semantic knowledge are aggregated from the tone‐mapped image through enhancing global context‐aware semantic information. Finally, a fusion module is designed to integrate saliency‐oriented feature representations originated from multi‐exposure images and the tone‐mapped image, producing the saliency maps of HDR images. Moreover, a new challenging HDR eye fixation database (HDR‐EYEFix) is created, expecting to further contribute the research on HDR image saliency prediction. Experiment results show that the method obtains superior performance compared to the state‐of‐the‐art methods.
Video quality adaptation using CNN and RNN models for cost-effective and scalable video streaming Services

Authors:
raw_author_name: Mahmoud Darwich
raw_affiliation_string: University of Mount Union, Alliance, OH, 6601, USA
raw_author_name: Magdy Bayoumi
raw_affiliation_string: University of Louisiana at Lafayette, Lafayette, 70504, USA

Abstract: Abstract Video streaming services require adaptive bit rate strategies that optimize video quality based on network conditions and user preferences to provide a cost-effective and scalable solution. In this manuscript, we present a novel architecture that utilizes a combination of Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) to extract features from the video stream and predict optimal bit rates for each frame. The CNN feature extractor extracts relevant features from the input video stream, which are then passed on to the RNN predictor, where the optimal bit rate is predicted to ensure a personalized viewing experience tailored to the current network conditions and specific user preferences. Our experimental results demonstrate that the proposed architecture achieves significant improvements in video quality while minimizing bandwidth usage and providing a better user experience. Specifically, our results show a 37.1% improvement in average bit rate, indicating that the proposed architecture optimizes the video quality and reduces bandwidth usage by 37.1% on average. We also observed a 16.6% improvement in Quality of Experience (QoE), meaning that users will perceive the video quality as better. Additionally, rebuffering was reduced by 87.5%, indicating that users enjoy smoother video playback without interruptions. Importantly, our architecture is optimized for Dynamic Adaptive Streaming over HTTP (DASH), addressing the need for efficient streaming over the most widely used protocol in the industry.
Blockchain for finance: A survey

Authors:
raw_author_name: Zhenguang Liu
raw_affiliation_string: School of Cyber Science and Technology Zhejiang University Hangzhou China; ZJU‐Hangzhou Global Scientific and Technological Innovation Center Hangzhou China
raw_author_name: Butian Huang
raw_affiliation_string: Hangzhou Yunphant Network Technology Co. Ltd. Hangzhou China
raw_author_name: Huayun Tang
raw_affiliation_string: Institute of Chinese Finance Studies Southwestern University of Finance and Economics Chengdu China
raw_author_name: Erwu Liu
raw_affiliation_string: College of Electronics and Information Engineering Tongji University Shanghai China

Abstract: Abstract As an innovative technology for enhancing authenticity, security, and risk management, blockchain is being widely adopted in trade and finance systems. The unique capabilities of blockchain, such as immutability and transparency, enable new business models of distributed data storage, point‐to‐point transactions, and decentralized autonomous organizations. Here, the authors focus on blockchain‐based securities trading, in which blockchain technology plays a vital role in financial services as it ultimately lifts trust and frees the need for third‐party verification by using consensus‐based verification. The 12 most popular blockchain platforms are investigated and 6 platforms that are related to finance are elaborated on, seeking to provide a panorama of securities trading practices. Meanwhile, this survey provides a comprehensive summary of blockchain‐based securities trading applications. Numerous practical applications of blockchain‐based securities trading are gathered and they are categorized into four distinct categories. For each category, a typical example is introduced and how blockchain contributes to solving the key problems faced by FinTech companies and researchers explained. Finally, interesting observations are provided ranging from mainstream blockchain‐based financial institutions to security issues of decentralized finance applications, aiming to picture the current blockchain ecosystem in finance.
Enhanced Heterogeneous Graph Attention Network with a Novel Multilabel Focal Loss for Document-Level Relation Extraction

Authors:
raw_author_name: Bowen Shi
raw_affiliation_string: School of Journalism, Communication University of China, Beijing 100024, China

Abstract: Recent years have seen a rise in interest in document-level relation extraction, which is defined as extracting all relations between entities in multiple sentences of a document. Typically, there are multiple mentions corresponding to a single entity in this context. Previous research predominantly employed a holistic representation for each entity to predict relations, but this approach often overlooks valuable information contained in fine-grained entity mentions. We contend that relation prediction and inference should be grounded in specific entity mentions rather than abstract entity concepts. To address this, our paper proposes a two-stage mention-level framework based on an enhanced heterogeneous graph attention network for document-level relation extraction. Our framework employs two different strategies to model intra-sentential and inter-sentential relations between fine-grained entity mentions, yielding local mention representations for intra-sentential relation prediction and global mention representations for inter-sentential relation prediction. For inter-sentential relation prediction and inference, we propose an enhanced heterogeneous graph attention network to better model the long-distance semantic relationships and design an entity-coreference path-based inference strategy to conduct relation inference. Moreover, we introduce a novel cross-entropy-based multilabel focal loss function to address the class imbalance problem and multilabel prediction simultaneously. Comprehensive experiments have been conducted to verify the effectiveness of our framework. Experimental results show that our approach significantly outperforms the existing methods.
DeforestVis: Behaviour Analysis of Machine Learning Models with Surrogate Decision Stumps

Authors:
raw_author_name: Angelos Chatzimparmpas
raw_affiliation_string: Department of Computer Science Northwestern University Evanston USA
raw_author_name: Rafeal M. Martins
raw_affiliation_string: Department of Computer Science and Media Technology Linnaeus University Växjö Sweden
raw_author_name: Alexandru C. Telea
raw_affiliation_string: Department of Information and Computing Sciences Utrecht University Utrecht The Netherlands
raw_author_name: Andreas Kerren
raw_affiliation_string: Department of Computer Science and Media Technology Linnaeus University Växjö Sweden; Department of Science and Technology Linköping University Norrköping Sweden

Abstract: Abstract As the complexity of machine learning (ML) models increases and their application in different (and critical) domains grows, there is a strong demand for more interpretable and trustworthy ML. A direct, model‐agnostic, way to interpret such models is to train surrogate models—such as rule sets and decision trees—that sufficiently approximate the original ones while being simpler and easier‐to‐explain. Yet, rule sets can become very lengthy, with many if–else statements, and decision tree depth grows rapidly when accurately emulating complex ML models. In such cases, both approaches can fail to meet their core goal—providing users with model interpretability. To tackle this, we propose DeforestVis, a visual analytics tool that offers summarization of the behaviour of complex ML models by providing surrogate decision stumps (one‐level decision trees) generated with the Adaptive Boosting (AdaBoost) technique. DeforestVis helps users to explore the complexity versus fidelity trade‐off by incrementally generating more stumps, creating attribute‐based explanations with weighted stumps to justify decision making, and analysing the impact of rule overriding on training instance allocation between one or more stumps. An independent test set allows users to monitor the effectiveness of manual rule changes and form hypotheses based on case‐by‐case analyses. We show the applicability and usefulness of DeforestVis with two use cases and expert interviews with data analysts and model developers.
High capacity reversible data hiding in radiographic images with optimal bit allocation

Authors:
raw_author_name: Davide Cavagnino
raw_affiliation_string: Dipartimento di Informatica, Università di Torino, Corso Svizzera 185, Torino, 10149, Italy
raw_author_name: Alessandro Druetto
raw_affiliation_string: Dipartimento di Informatica, Università di Torino, Corso Svizzera 185, Torino, 10149, Italy
raw_author_name: Marco Grangetto
raw_affiliation_string: Dipartimento di Informatica, Università di Torino, Corso Svizzera 185, Torino, 10149, Italy
raw_author_name: Maurizio Lucenteforte
raw_affiliation_string: Dipartimento di Informatica, Università di Torino, Corso Svizzera 185, Torino, 10149, Italy

Abstract: Abstract This paper extends and improves the performance of a digital reversible watermarking algorithm based on histogram shifting presented in previous works. The considered algorithm exploits the property of image histograms of some kinds of medical images which present many contiguous 0-runs, i.e., a comb structure in the gray level frequencies. In particular, radiographic images exhibit this structure after contrast enhancement during the acquisition process. The previous work suggested performing gray-level histogram shifting according to a local optimization technique. In this paper, we apply combinatorial optimization techniques to entire blocks of contiguous 0-runs using a non-linear objective function transformed to fit a linear optimization algorithm. The obtained results show a meaningful improvement in the payload capacity of the original data-hiding method. A mild Peak Signal-to-Noise Ratio (PSNR) reduction is still acceptable for a qualitative preview of the images, which can be completely restored to their original cover form thanks to the reversibility of the method.
Modeling digital skills beyond the IT workforce: construct definition, measurement and impact on digitalization value

Authors:
raw_author_name: Stan Karanasios

Abstract: Purpose Information technology (IT) personnels’ technical, business and behavioral skills are critical enablers for generating IT value. In an increasingly digitalized working environment where non-IT employees participate in digital innovations, a focus on IT personnels’ skills only doesn’t meet researchers’ need for a framework to study digital skills and managers’ need to address digital skills challenges across an enterprise’s workforce. Nevertheless, the digital skills topic is complicated by conceptual ambiguity and a lack of theoretically derived and empirically validated model. The purpose of this study is to address this problem. Design/methodology/approach Theoretically, this study draws on human capital (HC) and resource-based view (RBV) theories. Empirically, it follows mixed method combining interviews and a survey. Findings The digital skills construct is a multidimensional second order reflective construct. While its development is influenced by an organization’s commitment and exposure to digitalization, it influences the value organizations obtain from digitalization. Research limitations/implications This study conceptualizes the digital skills construct, identifying technology agnostic subdimensions that are meaningful beyond a particular digital domain [information and communication technology (ICT), information, Internet, Inter of Things (IoT)] and establishing a valid measure. Other researchers can improve both the indicators of the existing four conceptually distinct and managerially recognizable workplace digital skills dimensions as well as testing new ones. Practical implications Managers can use the instrument to assess the extent to which their non-IT workforces are equipped with digital skills and get strategic insights for specific interventions such as upskilling or buying in skills. Originality/value The main theoretical contribution of the paper is the conceptualization and validation of the digital skills construct for the non-IT workforce. Furthermore, we provide a theoretical framework to explain the factors that could influence the development of digital skills and demonstrate the impact that digital skills have on selected digitalization value indicators. This contribution provides the foundation for investigating the drivers, outcomes and the relationship of digital skills to other constructs such as digital transformation, innovation and firm performance.
A framework for evaluating clinical artificial intelligence systems without ground-truth annotations

Authors:
raw_author_name: Dani Kiyasseh
raw_affiliation_string: Cedars-Sinai Medical Center, Los Angeles, CA, USA
raw_author_name: Aaron Cohen
raw_affiliation_string: Flatiron Health, New York City, NY, USA
raw_author_name: Chengsheng Jiang
raw_affiliation_string: Flatiron Health, New York City, NY, USA
raw_author_name: Nicholas Altieri
raw_affiliation_string: Flatiron Health, New York City, NY, USA

Abstract: A clinical artificial intelligence (AI) system is often validated on data withheld during its development. This provides an estimate of its performance upon future deployment on data in the wild; those currently unseen but are expected to be encountered in a clinical setting. However, estimating performance on data in the wild is complicated by distribution shift between data in the wild and withheld data and the absence of ground-truth annotations. Here, we introduce SUDO, a framework for evaluating AI systems on data in the wild. Through experiments on AI systems developed for dermatology images, histopathology patches, and clinical notes, we show that SUDO can identify unreliable predictions, inform the selection of models, and allow for the previously out-of-reach assessment of algorithmic bias for data in the wild without ground-truth annotations. These capabilities can contribute to the deployment of trustworthy and ethical AI systems in medicine.