Reviewerly/exHarmony huggingface dataset:
Enhancing Zero Trust Models in the Financial Industry through Blockchain Integration: A Proposed Framework

Authors:
raw_author_name: Clement Daah
raw_affiliation_string: School of Computer Science, Artificial Intelligence and Electronics, Faculty of Engineering and Digital Technologies, University of Bradford, Bradford BD7 1DP, UK
raw_author_name: Amna Qureshi
raw_affiliation_string: School of Computer Science, Artificial Intelligence and Electronics, Faculty of Engineering and Digital Technologies, University of Bradford, Bradford BD7 1DP, UK
raw_author_name: Irfan Awan
raw_affiliation_string: School of Computer Science, Artificial Intelligence and Electronics, Faculty of Engineering and Digital Technologies, University of Bradford, Bradford BD7 1DP, UK
raw_author_name: Savas Konur
raw_affiliation_string: School of Computer Science, Artificial Intelligence and Electronics, Faculty of Engineering and Digital Technologies, University of Bradford, Bradford BD7 1DP, UK

Abstract: As financial institutions navigate an increasingly complex cyber threat landscape and regulatory ecosystem, there is a pressing need for a robust and adaptive security architecture. This paper introduces a comprehensive, Zero Trust model-based framework specifically tailored for the finance industry. It encompasses identity and access management (IAM), data protection, and device and network security and introduces trust through blockchain technology. This study provides a literature review of existing Zero Trust paradigms and contrasts them with cybersecurity solutions currently relevant to financial settings. The research adopts a mixed methods approach, combining extensive qualitative analysis through a literature review and assessment of security assumptions, threat modelling, and implementation strategies with quantitative evaluation using a prototype banking application for vulnerability scanning, security testing, and performance testing. The IAM component ensures robust authentication and authorisation processes, while device and network security measures protect against both internal and external threats. Data protection mechanisms maintain the confidentiality and integrity of sensitive information. Additionally, the blockchain-based trust component serves as an innovative layer to enhance security measures, offering both tamper-proof verification and increased integrity. Through analysis of potential threats and experimental evaluation of the Zero Trust model’s performance, the proposed framework offers financial institutions a comprehensive security architecture capable of effectively mitigating cyber threats and fostering enhanced consumer trust.
Conditional Proxy Re-Encryption-Based Key Sharing Mechanism for Clustered Federated Learning

Authors:
raw_author_name: Yongjing Zhang
raw_affiliation_string: College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
raw_author_name: Zhouyang Zhang
raw_affiliation_string: Research Center for Basic Theories of Intelligent Computing, Research Institute of Basic Theories, Zhejiang Laboratory, Hangzhou 311100, China
raw_author_name: Shan Ji
raw_affiliation_string: College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
raw_author_name: Shenqing Wang
raw_affiliation_string: College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China

Abstract: The need of data owners for privacy protection has given rise to collaborative learning, and data-related issues heterogeneity faced by federated learning has further given rise to clustered federated learning; whereas the traditional privacy-preserving scheme of federated learning using homomorphic encryption alone fails to fulfill the privacy protection demands of clustered federated learning. To address these issues, this research provides an effective and safeguarded answer for sharing homomorphic encryption keys among clusters in clustered federated learning grounded in conditional representative broadcast re-encryption. This method constructs a key sharing mechanism. By combining the functions of the bilinear pairwise accumulator and specific conditional proxy broadcast re-ciphering, the mechanism can verify the integrity of homomorphic encryption keys stored on cloud servers. In addition, the solution enables key management centers to grant secure and controlled access to re-encrypted homomorphic encryption keys to third parties without disclosing the sensitive information contained therein. The scheme achieves this by implementing a sophisticated access tree-based mechanism that enables the cloud server to convert forwarded ciphertexts into completely new ciphertexts customized specifically for a given group of users. By effectively utilizing conditional restrictions, the scheme achieves fine-grained access control to protect the privacy of shared content. Finally, this paper showcases the scheme’s security against selective ciphertext attacks without relying on random prediction.
Adaptive Influence Maximization: Adaptability via Nonadaptability

Authors:
raw_author_name: Hongmin W. Du
raw_affiliation_string: Accounting and Information Systems Department, Rutgers University, Piscataway, New Jersey 08854;
raw_author_name: Zhao Zhang
raw_affiliation_string: School of Mathematical Sciences, Zhejiang Normal University, Jinhua, Zhejiang 321004, China

Abstract: Adaptive influence maximization is an important research problem in computational social networks, which is also a typical problem in the study of adaptive processing of information and adaptive construction of objects. In this paper, we propose a new method that reduces the adaptive influence maximization problem into a nonadaptive one in a different social network, so that an adaptive optimization can be solved by those methods for nonadaptive optimization. In addition, we provide a new approximation algorithm for the submodular maximization problem with a knapsack constraint, which runs in [Formula: see text] time and has performance ratio [Formula: see text], where n is the number of nodes in the network. The ratio is better than the best known previous one with the same running time. History: Accepted by Erwin Pesch, Area Editor for Heuristic Search &amp; Approximation Algorithms. Funding: This research is supported in part by the National Natural Science Foundation of China [Grant U20A2068].
A Multi-Task Learning and Knowledge Selection Strategy for Environment-Induced Color-Distorted Image Restoration

Authors:
raw_author_name: Yuan Ding
raw_affiliation_string: School of Electronics and Information Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China
raw_author_name: Kaijun Wu
raw_affiliation_string: School of Electronics and Information Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China

Abstract: Existing methods for restoring color-distorted images in specific environments typically focus on a singular type of distortion, making it challenging to generalize their application across various types of color-distorted images. If it were possible to leverage the intrinsic connections between different types of color-distorted images and coordinate their interactions during model training, it would simultaneously enhance generalization, address potential overfitting and underfitting issues during data fitting, and consequently lead to a positive performance boost. In this paper, our approach primarily addresses three distinct types of color-distorted images, namely dust-laden images, hazy images, and underwater images. By thoroughly exploiting the unique characteristics and interrelationships of these types, we achieve the objective of multitask processing. Within this endeavor, identifying appropriate correlations is pivotal. To this end, we propose a knowledge selection and allocation strategy that optimally distributes the features and correlations acquired by the network from the images to different tasks, enabling a more refined task differentiation. Moreover, given the challenge of difficult dataset pairing, we employ unsupervised learning techniques and introduce novel Transformer blocks, feedforward networks, and hybrid modules to enhance context relevance. Through extensive experimentation, we demonstrate that our proposed method significantly enhances the performance of color-distorted image restoration.
Multimodal meta-learning through meta-learned task representations

Authors:
raw_author_name: Anna Vettoruzzo
raw_affiliation_string: Center for Applied Intelligent Systems Research (CAISR), Halmstad University, Halmstad, Sweden
raw_author_name: Mohamed-Rafik Bouguelia
raw_affiliation_string: Center for Applied Intelligent Systems Research (CAISR), Halmstad University, Halmstad, Sweden
raw_author_name: Thorsteinn Rögnvaldsson
raw_affiliation_string: Center for Applied Intelligent Systems Research (CAISR), Halmstad University, Halmstad, Sweden

Abstract: Abstract Few-shot meta-learning involves training a model on multiple tasks to enable it to efficiently adapt to new, previously unseen tasks with only a limited number of samples. However, current meta-learning methods assume that all tasks are closely related and belong to a common domain, whereas in practice, tasks can be highly diverse and originate from multiple domains, resulting in a multimodal task distribution. This poses a challenge for existing methods as they struggle to learn a shared representation that can be easily adapted to all tasks within the distribution. To address this challenge, we propose a meta-learning framework that can handle multimodal task distributions by conditioning the model on the current task, resulting in a faster adaptation. Our proposed method learns to encode each task and generate task embeddings that modulate the model’s activations. The resulting modulated model become specialized for the current task and leads to more effective adaptation. Our framework is designed to work in a realistic setting where the mode from which a task is sampled is unknown. Nonetheless, we also explore the possibility of incorporating auxiliary information, such as the task-mode-label, to further enhance the performance of our method if such information is available. We evaluate our proposed framework on various few-shot regression and image classification tasks, demonstrating its superiority over other state-of-the-art meta-learning methods. The results highlight the benefits of learning to embed task-specific information in the model to guide the adaptation when tasks are sampled from a multimodal distribution.
A Segmented Hybrid Algorithm for Beam Shaping Combining Iterative and Simulated Annealing Approaches

Authors:
raw_author_name: Xiaoyu Zhang
raw_affiliation_string: School of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China
raw_author_name: Qi Zhang
raw_affiliation_string: School of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China

Abstract: In recent years, laser technology has made significant advancements, yet there are specific requirements for the energy concentration and uniformity of lasers in various fields, such as optical communication, laser processing, 3D printing, etc. Beam shaping technology enables the transformation of ordinary Gaussian-distributed laser beams into square or circular flat-top uniform beams. Currently, LCOS-based beam shaping algorithms do not adequately meet these requirements, and most of these algorithms do not simultaneously consider the impact of phase quantization and zero-padding, leading to a decrease in the practicality of phase holograms. To address these issues, this paper proposes a novel segmented beam shaping algorithm that combines iterative and simulated annealing approaches. This paper validated the reliability of the proposed algorithm through numerical simulations. Compared to other algorithms, the proposed algorithm can effectively reduce the root mean square error by an average of nearly 37% and decrease the uniformity error by almost 39% without a significant decrease in diffraction efficiency.
Remote Sensing for Maritime Monitoring and Vessel Identification

Authors:
raw_author_name: Emanuele Salerno
raw_affiliation_string: Institute of Information Science and Technologies, National Research Council of Italy, 56124 Pisa, Italy
raw_author_name: Angelica Lo Duca
raw_affiliation_string: Institute of Informatics and Telematics, National Research Council of Italy, 56124 Pisa, Italy

Abstract: According to the statistics published by the United Nations Conference on Trade and Development [1], the total fleet worldwide consisted of more than 2 [...]
Unbiased, Effective, and Efficient Distillation from Heterogeneous Models for Recommender Systems

Authors:
raw_author_name: SeongKu Kang
raw_affiliation_string: UIUC, Urbana, United States
raw_author_name: Wonbin Kweon
raw_affiliation_string: POSTECH, Pohang, South Korea
raw_author_name: Dongha Lee
raw_affiliation_string: Yonsei University, Seoul, South Korea
raw_author_name: Jianxun Lian
raw_affiliation_string: Microsoft Research Asia, Beijing, China
raw_author_name: Xing Xie
raw_affiliation_string: Microsoft Research Asia, Beijing, China
raw_author_name: Hwanjo Yu
raw_affiliation_string: POSTECH, Pohang, South Korea

Abstract: In recent years, recommender systems have achieved remarkable performance by using ensembles of heterogeneous models. However, this approach is costly due to the resources and inference latency proportional to the number of models, creating a bottleneck for production. Our work aims to transfer the ensemble knowledge of heterogeneous teachers to a lightweight student model using knowledge distillation (KD), reducing inference costs while maintaining high accuracy. We find that the efficacy of distillation decreases when transferring knowledge from heterogeneous teachers. To address this, we propose a new KD framework, named HetComp, that guides the student model by transferring easy-to-hard sequences of knowledge generated from teachers’ trajectories. HetComp uses dynamic knowledge construction to provide progressively difficult ranking knowledge and adaptive knowledge transfer to gradually transfer finer-grained ranking information. Although HetComp improves accuracy, it exacerbates popularity bias, resulting in a high popularity lift. To mitigate this issue, we introduce two strategies that leverage models’ disagreement knowledge (i.e., dissensus) for heterogeneous comparison. Our experiments demonstrate that HetComp significantly enhances distillation quality and the student model’s generalization capabilities. Furthermore, we provide extensive experimental results supporting the effectiveness of our dissensus-based debiasing techniques in mitigating the popularity lift caused by HetComp.
Blockchain consensus mechanisms comparison in fog computing: A systematic review

Authors:
raw_author_name: Yehia Ibrahim Alzoubi
raw_affiliation_string: College of Business Administration, American University of the Middle East, Kuwait
raw_author_name: Alok Mishra
raw_affiliation_string: Faculty of Engineering, Norwegian University of Science and Technology (NTNU), Norway

Abstract: Numerous consensus mechanisms have been suggested to cater to the specific characteristics of fog computing. To comprehensively understand their unique features, performance, and applications in fog computing, it is crucial to conduct a systematic analysis of these mechanisms. For this study, 79 relevant articles were carefully selected based on predefined criteria. Among these articles, 35 employed work-proof-based consensus mechanisms, 24 utilized voting-based mechanisms, and 22 adopted capability-based mechanisms. Among the 26 identified consensus mechanisms, proof of work remains the most prevalent one. It’s important to note that the scope of this paper is limited to the research available in the predominant databases at the time of writing. Future research may expand to include additional databases and more recent literature in this domain.
Improving classification accuracy of fine-tuned CNN models: Impact of hyperparameter optimization

Authors:
raw_author_name: Mikolaj Wojciuk
raw_affiliation_string: Faculty of Electrical Engineering, Warsaw University of Technology, Warsaw, Poland
raw_author_name: Zaneta Swiderska-Chadaj
raw_affiliation_string: Faculty of Electrical Engineering, Warsaw University of Technology, Warsaw, Poland
raw_author_name: Krzysztof Siwek
raw_affiliation_string: Faculty of Electrical Engineering, Warsaw University of Technology, Warsaw, Poland
raw_author_name: Arkadiusz Gertych
raw_affiliation_string: Department of Surgery, Department of Pathology and Laboratory Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA; Faculty of Biomedical Engineering, Silesian University of Technology, Zabrze, Poland

Abstract: The immense popularity of convolutional neural network (CNN) models has sparked a growing interest in optimizing their hyperparameters. Discovering the ideal values for hyperparameters to achieve optimal CNN training is a complex and time-consuming task, often requiring repetitive numerical experiments. As a result, significant attention is currently being devoted to developing methods aimed at tailoring hyperparameters for specific CNN models and classification tasks. While existing optimization methods often yield favorable image classification results, they do not provide guidance on which hyperparameters are worth optimizing, the appropriate value ranges for those hyperparameters, or whether it is reasonable to use a subset of training data for the optimization process. This work is focused on the optimization of hyperparameters during transfer learning, with the goal of investigating how different optimization methods and hyperparameter selections impact the performance of fine-tuned models. In our experiments, we assessed the importance of various hyperparameters and identified the ranges within which optimal CNN training can be achieved. Additionally, we compared four hyperparameter optimization methods-grid search, random search, Bayesian optimization, and the Asynchronous Successive Halving Algorithm (ASHA). We also explored the feasibility of fine-tuning hyperparameters using a subset of the training data. By optimizing the hyperparameters, we observed an improvement in CNN classification accuracy of up to 6%. Furthermore, we found that achieving a balance in class distribution within the subset of data used for parameter optimization is crucial in establishing the optimal set of hyperparameters for CNN training. The results we obtained demonstrate that hyperparameter optimization is highly dependent on the specific task and dataset at hand.
Systematic Literature Review on Hybrid Robotic Vehicles

Authors:
raw_author_name: Diogo F. Gomes
raw_affiliation_string: Research Center for Systems and Technologies (SYSTEC-DIGI2), ARISE & ECE Department, Faculdade de Engenharia, Universidade do Porto, Rua Dr. Roberto Frias, 4200-465 Porto, Portugal
raw_author_name: Vítor H. Pinto
raw_affiliation_string: Research Center for Systems and Technologies (SYSTEC-DIGI2), ARISE & ECE Department, Faculdade de Engenharia, Universidade do Porto, Rua Dr. Roberto Frias, 4200-465 Porto, Portugal

Abstract: Autonomous vehicles are a continuously rising technology in several industry sectors. Examples of these technologies lie in the advances in self-driving cars and can be linked to extraterrestrial exploration, such as NASA’s Mars Exploration Rovers. These systems present a leading methodology allowing for increased task performance and capabilities, which are no longer limited to active human support. However, these robotic systems may vary in shape, size, locomotion capabilities, and applications. As such, this report presents a systematic literature review (SLR) regarding hybrid autonomous robotic vehicles focusing on leg–wheel locomotion. During this systematic review of the literature, a considerable number of articles were extracted from four different databases. After the selection process, a filtered sample was reviewed. A brief description of each document can be found throughout this report.
Audio-visual saliency prediction with multisensory perception and integration

Authors:
raw_author_name: Jiawei Xie
raw_affiliation_string: Shanghai Institute for Advanced Communication and Data Science, School of Communication and Information Engineering, Shanghai University, Shanghai 200444, China
raw_author_name: Zhi Liu
raw_affiliation_string: Wenzhou Institute of Shanghai University, Wenzhou 325000, China
raw_author_name: Gongyang Li
raw_affiliation_string: Wenzhou Institute of Shanghai University, Wenzhou 325000, China
raw_author_name: Yingjie Song
raw_affiliation_string: Shanghai Institute for Advanced Communication and Data Science, School of Communication and Information Engineering, Shanghai University, Shanghai 200444, China

Abstract: Audio-visual saliency prediction (AVSP) is a task that aims to model human attention patterns in the perception of auditory and visual scenes. Given the challenges associated with perceiving and combining multi-modal saliency features from videos, this paper presents a multi-sensory framework for AVSP. This framework is designed to extract audio, motion and image saliency features and integrate them effectively, which can then serve as a general architecture for the AVSP task. To obtain multi-sensory information, we develop a three-stream encoder that extracts audio, motion and image saliency features. In particular, we utilize a pre-trained encoder with knowledge related to image saliency to extract saliency features for each frame. The image saliency features are then incorporated with motion features using a spatial attention module. For motion features, 3D convolutional neural networks (CNNs) like S3D are commonly used in AVSP models. However, these networks are unable to effectively capture the global motion relationship in videos. To tackle this problem, we incorporate Transformer- and MLP-based motion encoders into the AVSP models. To learn joint audio-visual representations, an audio-visual fusion block is exploited to enhance the correlation between audio and visual motion features under the supervision of a cosine similarity loss in a self-supervised manner. Finally, a multi-stage decoder integrates audio, motion and image saliency features to generate the final saliency map. We evaluate our methods on six audio-visual eye-tracking datasets. Experimental results demonstrate that our method achieves compelling performance compared to the state-of-the-art methods. The source code will be available at https://github.com/oraclefina/MSPI.