Reviewerly/exHarmony huggingface dataset:
Effective hybrid video denoising and blending framework for Internet of Remote Things (IoRT) environments

Authors:
raw_author_name: B. Aruna Devi
raw_affiliation_string: Department of Electronics and Communication Engineering, Dr. N.G.P. Institute of Technology, Coimbatore, India
raw_author_name: Mani Deepak Choudhry
raw_affiliation_string: Department of Information Technology, KGiSL Institute of Technology, Coimbatore, India

Abstract: The Internet of Remote Things (IoRT) has emerged as a transformative paradigm, merging IoT capabilities with remote technologies. IoRT environments, featuring interconnected sensors and robots, face challenges like sensor noise and low-light conditions, compromising video stream quality. This paper proposes a Hybrid Video Denoising and Blending Framework to address IoRT video data shortcomings. Leveraging spatial and temporal domain denoising techniques, the framework effectively removes noise while preserving crucial details. The inclusion of advanced blending algorithms facilitates seamless fusion of data from multiple sources, enhancing decision-making in real-world scenarios. The framework adopts a dynamic weighted averaging approach and an optimal sensor selection mechanism to intelligently choose informative data sources, improving blended output quality. Extensive experiments with a diverse IoRT dataset showcase the framework's superiority over state-of-the-art techniques, offering significant enhancements in video quality, noise reduction, and data fusion accuracy. Applications like surveillance, autonomous remotes, and industrial automation can benefit from the framework's ability to provide clearer, more reliable visual information. In conclusion, this research introduces a pioneering approach to mitigate video noise and enhance data fusion in IoRT, showcasing promising results and paving the way for further research in the integration of Remotes and IoT.
A Study of a blockchain-based judicial evidence preservation scheme

Authors:
raw_author_name: Shuaiqi Liu
raw_affiliation_string: Industry College of Blockchain Technology, Chengdu University of Information Technology, Sichuan, China
raw_author_name: Qingxiao Zheng
raw_affiliation_string: Industry College of Blockchain Technology, Chengdu University of Information Technology, Sichuan, China

Abstract: To address the challenges of low credibility, difficult data sharing, and regulatory supervision issues involving electronic evidence storage in the judicial preservation process, this paper proposes a blockchain-based judicial evidence preservation scheme. The scheme utilizes the characteristics of blockchain's immutability to achieve credible forensics of electronic evidence on the chain and employs the decentralized storage of the interplanetary file system for secure and efficient off-chain storage. Simultaneously, it resolves the problem of declining throughput due to limited block capacity. Additionally, it leverages smart contract technology to encompass major aspects of the judicial process, including user case registration, authority management, judicial evidence uploading and downloading, case data sharing, partial disclosure of case information, and regulatory review. Simulation experiments demonstrate that the scheme significantly improves throughput and stability. Performance tests indicate that the transfer speed of the interplanetary file system can meet the data-sharing needs among judicial organizations.
“Journey of Finding the Best Query”: Understanding the User Experience of AI Image Generation System

Authors:
raw_author_name: Soomin Kim
raw_affiliation_string: Seoul National University, Seoul, Korea
raw_author_name: Jinsu Eun
raw_affiliation_string: Seoul National University, Seoul, Korea
raw_author_name: Changhoon Oh
raw_affiliation_string: Yonsei University, Seoul, Korea
raw_author_name: Joonhwan Lee
raw_affiliation_string: Seoul National University, Seoul, Korea

Abstract: With the advancement of AI, even people without professional experience can create artworks using AI-based image generation systems like DALL-E 2. However, little is known about how users interact with these new AI algorithms, much less how AI-infused systems can be designed. We explore the user experience of these new technologies and their potential to foster creativity. A user study was carried out where 13 participants executed tasks of creating artworks using DALL-E 2 alongside in-depth interviews related to their experience. The results showed that users had ambivalent opinions regarding the algorithm's performance. When users were informed of the system's capabilities, they subsequently utilized more specific prompts to generate the intended output. Users also optimized their prompts (the queries they entered to create artworks) based on how algorithms worked to achieve their desired outcome. The users wanted a two-way interaction where AI explained the outcome and accepted feedback rather than simply accepting unilateral instructions. We discuss the implications for designing interfaces that maximize creativity while providing comfort for the users.
A shape-aware enhancement Vision Transformer for building extraction from remote sensing imagery

Authors:
raw_author_name: Xiaoyan Tang
raw_affiliation_string: College of Civil Engineering and Architecture, Xinjiang University, Urumqi, Xinjiang, China
raw_author_name: Haibin Shang
raw_affiliation_string: College of Civil Engineering and Architecture, Xinjiang University, Urumqi, Xinjiang, China

Abstract: Convolutional neural networks (CNN) have been developed for several years in the field of extracting buildings from remote sensing images. Vision Transformer (ViT) has recently demonstrated superior performance over CNN, thanks to its ability to model long-range dependencies through self-attention mechanisms. However, most existing ViT models lack shape information enhancement for the building objects, resulting in insufficient fine-grained segmentation. To address this limitation, we construct an efficient dual-path ViT framework for building segmentation, termed shape-aware enhancement Vision Transformer (SAEViT). Our approach incorporates shape-aware enhancement module (SAEM) that perceives and enhances the shape features of buildings using multi-shapes of convolutional kernels. We also introduce multi-pooling channel attention (MPCA) to exploit channel-wise information without squeezing the channel dimension. Furthermore, we propose a progressive aggregation upsampling model (PAUM) in the decoder to aggregate multilevel features using a progressive upsampling methodology, coupled with the utilization of the soft-pool algorithm operating on the channel axis. We evaluate our model on three public building datasets. The experimental results show that SAEViT obtains a significant improvement on various datasets, confirming its efficacy. Compared with several state-of-the-art models, SAEViT achieves a comprehensive transcendence in overall performance.
StegoBackoff: Creating a Covert Channel in Smart Grids Using the Backoff Procedure of IEEE 802.11 Networks

Authors:
raw_author_name: Geovani Teca
raw_affiliation_string: Institute of Telecommunications, AGH University of Krakow, al. Mickiewicza 30, 30-059 Krakow, Poland
raw_author_name: Marek Natkaniec
raw_affiliation_string: Institute of Telecommunications, AGH University of Krakow, al. Mickiewicza 30, 30-059 Krakow, Poland

Abstract: A smart grid constitutes an electrical infrastructure that integrates communication technologies to optimize electricity production, distribution, and consumption. Within the smart grid, IEEE 802.11 networks play a crucial role in facilitating communication between smart meters and data collectors, operating within a shared transmission medium. However, a notable challenge arises due to the lack of certainty regarding the genuine identity of data recipients. In response, we present a solution—a novel covert channel leveraging the IEEE 802.11 backoff procedure—to transmit data that requires special protection. Implemented using the ns-3 simulator, our covert channel achieved a throughput of 140,000 bps when single covert station realized transmission in the wireless channel, and 880 bps in a populated environment characterized by high traffic volumes. This performance metric shows that our mechanism is better than other covert channels, where the performance in saturated conditions usually does not exceed several hundred bps. This covert channel represents a new approach to fortifying data integrity and privacy within smart grid communication.
Fear of Missing Out: Constrained Trial of Blockchain in Supply Chain

Authors:
raw_author_name: Roland Kromes
raw_affiliation_string: Department of Intelligent Systems, Delft University of Technology, P.O. Box 5031, 2600 GA Delft, The Netherlands
raw_author_name: Tianyu Li
raw_affiliation_string: Department of Intelligent Systems, Delft University of Technology, P.O. Box 5031, 2600 GA Delft, The Netherlands
raw_author_name: Zekeriya Erkin
raw_affiliation_string: Department of Intelligent Systems, Delft University of Technology, P.O. Box 5031, 2600 GA Delft, The Netherlands

Abstract: Blockchain’s potential to revolutionize supply chain and logistics with transparency and equitable stakeholder engagement is significant. However, challenges like scalability, privacy, and interoperability persist. This study explores the scarcity of real-world blockchain implementations in supply chain and logistics since we have not witnessed many real-world deployments of blockchain-based solutions in the field. Puzzled by this, we integrate technology, user experience, and operational efficiency to illuminate the complex landscape of blockchain integration. We present blockchain-based solutions in three use cases, comparing them with alternative designs and analyzing them in terms of technical, economic, and operational aspects. Insights from a tailored questionnaire of 50 questions addressed to practitioners and experts offer crucial perspectives on blockchain adoption. One of the key findings from our work shows that half of the companies interviewed agree that they will miss the potential for competitive advantage if they do not invest in blockchain technology, and 61% of the companies surveyed claimed that their customers ask for more transparency in supply chain-related transactions. However, only one-third of the companies were aware of the main features of blockchain technology, which shows a lack of knowledge among the companies that may lead to a weaker blockchain adaption in supply chain use cases. Our readers should note that our study is specifically contextualized in a Netherlands-funded national project. We hope that researchers as well as stakeholders in supply chain and logistics can benefit from the insights of our work.
Quantum Tomography: From Markovianity to Non-Markovianity

Authors:
raw_author_name: Zetong Li
raw_affiliation_string: Quantum Information Center, Southeast University, Nanjing 210096, China
raw_author_name: Xutao Yu
raw_affiliation_string: Quantum Information Center, Southeast University, Nanjing 210096, China
raw_author_name: Zaichen Zhang
raw_affiliation_string: Quantum Information Center, Southeast University, Nanjing 210096, China

Abstract: The engineering of quantum computers requires the reliable characterization of qubits, quantum operations, and even the entire hardware. Quantum tomography is an indispensable framework in quantum characterization, verification, and validation (QCVV), which has been widely accepted by researchers. According to the tomographic target, quantum tomography can be categorized into quantum state tomography (QST), quantum process tomography (QPT), gate set tomography (GST), process tensor tomography (PTT), and instrument set tomography (IST). Standard quantum tomography toolkits generally consist of basic linear inverse methods and statistical maximum likelihood estimation (MLE)-based methods. Furthermore, the performance of standard methods, including effectiveness and efficiency, has been further developed by exploiting Bayesian estimation, neural networks, matrix completion techniques, etc. In this review, we introduce the fundamental quantum tomography techniques, including QST, QPT, GST, PTT, and IST. We first introduce the details of basic linear inverse methods. Then, the framework of MLE methods with constraints is summarized. Finally, we briefly introduce recent further research in developing the performance of tomography, utilizing some symmetry properties of the target. This review provides a primary getting-start in developing quantum tomography, which promotes quantum computer development.
Forensic Audio and Voice Analysis: TV Series Reinforce False Popular Beliefs

Authors:
raw_author_name: Emmanuel Ferragne
raw_affiliation_string: Laboratoire CLILLAC-ARP, UFR d’Études Anglophones, Faculté Sociétés et Humanités, Université Paris Cité, 75013 Paris, France
raw_author_name: Jean-François Bonastre
raw_affiliation_string: Laboratoire Informatique d’Avignon, Avignon Université, 84000 Avignon, France
raw_author_name: Corinne Fredouille
raw_affiliation_string: Laboratoire Informatique d’Avignon, Avignon Université, 84000 Avignon, France

Abstract: People’s perception of forensic evidence is greatly influenced by crime TV series. The analysis of the human voice is no exception. However, unlike fingerprints—with which fiction and popular beliefs draw an incorrect parallel—the human voice varies according to many factors, can be altered deliberately, and its potential uniqueness has yet to be proven. Starting with a cursory examination of landmarks in forensic voice analysis that exemplify how the voiceprint fallacy came about and why people think they can recognize people’s voices, we then provide a thorough inspection of over 100 excerpts from TV series. Through this analysis, we seek to characterize the narrative and aesthetic processes that fashion our perception of scientific evidence when it comes to identifying somebody based on voice analysis. These processes converge to exaggerate the reliability of forensic voice analysis. We complement our examination with plausibility ratings of a subset of excerpts. We claim that these biased representations have led to a situation where, even today, one of the main challenges faced by forensic voice specialists is to convince trial jurors, judges, lawyers, and police officers that forensic voice comparison can by no means give the sort of straightforward answers that fingerprints or DNA permit.
More than malware: unmasking the hidden risk of cybersecurity regulations

Authors:
raw_author_name: Mazaher Kianpour
raw_affiliation_string: Cybersecurity Unit, RISE Research Institutes of Sweden, Stockholm, Sweden
raw_author_name: Shahid Raza
raw_affiliation_string: Cybersecurity Unit, RISE Research Institutes of Sweden, Stockholm, Sweden

Abstract: Abstract Cybersecurity investments are made within a complex and ever-evolving environment, where regulatory changes represent a significant risk factor. While cybersecurity regulations aim to minimize cyber risks and enhance protection, the uncertainty arising from frequent changes or new regulations can significantly impact organizational response strategies. This paper explores the determinants and implications of regulatory risks associated with cybersecurity, aiming to provide a deeper understanding of how these risks influence strategic decision-making. The study delves into the suggestion of preventive and mitigative controls that enable businesses to adapt to and mitigate potential disruptions caused by regulatory changes, thereby preserving their established cybersecurity practices. Another key contribution of this study is the introduction of a stochastic econometric model that illustrates how regulatory risks and uncertainties can affect investment behaviors, often prompting a “wait-and-see” stance. This model synthesizes the complex relationship among investment choices, regulatory changes, and cybersecurity risks, providing insights into the dynamic nature of cybersecurity investment strategies. The research findings offer valuable guidance for risk management and strategic planning in cybersecurity investments. By comprehensively understanding the drivers and impacts of regulatory risks, businesses and policymakers can develop more effective risk evaluation and management approaches. This is essential for sustaining a strong cybersecurity posture while navigating the changing regulatory environment.
Randomized Block Kaczmarz Methods for Inner Inverses of a Matrix

Authors:
raw_author_name: Lili Xing
raw_affiliation_string: College of Science, China University of Petroleum, Qingdao 266580, China
raw_author_name: Wendi Bao
raw_affiliation_string: College of Science, China University of Petroleum, Qingdao 266580, China
raw_author_name: Ying Lv
raw_affiliation_string: College of Science, China University of Petroleum, Qingdao 266580, China
raw_author_name: Zhiwei Guo
raw_affiliation_string: College of Science, China University of Petroleum, Qingdao 266580, China
raw_author_name: Weiguo Li
raw_affiliation_string: College of Science, China University of Petroleum, Qingdao 266580, China

Abstract: In this paper, two randomized block Kaczmarz methods to compute inner inverses of any rectangular matrix A are presented. These are iterative methods without matrix multiplications and their convergence is proved. The numerical results show that the proposed methods are more efficient than iterative methods involving matrix multiplications for the high-dimensional matrix.
Research on Path Planning Method of Solid Backfilling and Pushing Mechanism Based on Adaptive Genetic Particle Swarm Optimization

Authors:
raw_author_name: Lei Bo
raw_affiliation_string: School of Artificial Intelligence, China University of Mining and Technology (Beijing), Beijing 100083, China
raw_author_name: Yang Liu
raw_affiliation_string: School of Mechanical Electronic & Information Engineering, China University of Mining and Technology (Beijing), Beijing 100083, China
raw_author_name: Shangqing Yang
raw_affiliation_string: School of Artificial Intelligence, China University of Mining and Technology (Beijing), Beijing 100083, China
raw_author_name: Yanwen Wang
raw_affiliation_string: School of Mechanical Electronic & Information Engineering, China University of Mining and Technology (Beijing), Beijing 100083, China
raw_author_name: Yiying Wang
raw_affiliation_string: School of Mechanical and Equipment Engineering, Hebei University of Engineering, Handan 056038, China
raw_author_name: Xuanrui Zhang
raw_affiliation_string: School of Electrical and Electronic Engineering, Guangdong Technology College, Zhaoqing 526100, China

Abstract: This paper investigates the path planning problem of the coal mine solid-filling and pushing mechanism and proposes a hybrid improved adaptive genetic particle swarm algorithm (AGAPSO). To enhance the efficiency and accuracy of path planning, the algorithm combines a particle swarm optimization algorithm (PSO) and a genetic algorithm (GA), introducing the sharing mechanism and local search capability of the particle swarm optimization algorithm. The path planning of the pushing mechanism for the solid-filling scenario is optimized by dynamically adjusting the algorithm parameters to accommodate different search environments. Subsequently, the proposed algorithm’s effectiveness in the filling equipment path planning problem is experimentally verified using a simulation model of the established filling equipment path planning scenario. The experimental findings indicate that the improved hybrid algorithm converges three times faster than the original algorithm. Furthermore, it demonstrates approximately 92% and 94% better stability and average performance, respectively, than the original algorithm. Additionally, AGAPSO achieves a 27.59% and 19.16% improvement in path length and material usage optimization compared to the GA and GAPSO algorithms, showcasing superior efficiency and adaptability. Therefore, the AGAPSO method offers significant advantages in the path planning of the coal mine solid-filling and pushing mechanism, which is crucial for enhancing the filling effect and efficiency.
Granite Exposure Mapping Through Sentinel‐2 Visible and Short Wave Infrared Bands

Authors:
raw_author_name: Nazir Jan
raw_affiliation_string: Department of Computer Systems Engineering Faculty of Electrical and Computer Engineering University of Engineering and Technology Peshawar Peshawar Pakistan
raw_author_name: Nasru Minallah
raw_affiliation_string: Department of Computer Systems Engineering Faculty of Electrical and Computer Engineering University of Engineering and Technology Peshawar Peshawar Pakistan
raw_author_name: Neelam Gohar
raw_affiliation_string: Department of Computer Science Shaheed Benazir Bhutto Women University Peshawar Pakistan
raw_author_name: Naveed Jan
raw_affiliation_string: Department of Electronics Engineering Technology University of Technology Nowshera Nowshera Pakistan
raw_author_name: Mohammad Alibakhshikenari
raw_affiliation_string: Department of Signal Theory and Communications Universidad Carlos III de Madrid  Leganés Spain

Abstract: Abstract Nonmetallic minerals like granite and limestone have calcite and biotitic ingredients as their major part which exhibit wonderful absorption features in the visible and short wave range of the electromagnetic spectrum. This research puts emphasis on delineating granite and limestone deposits of the Mardan district through the latest multispectral Landsat‐9 and Sentinel‐2 sensors of which the latter provided 94% mapping accuracy in delineating granites (biotitic bearing minerals) and limestone (calcite‐bearing minerals). The Image processing techniques of minimum noise fraction, which is double cascaded principal components analysis and pixel purity index algorithms proved helpful to bring significant improvements in classification results and in the reduction of noise and data size. The outcomes of the research study show that supervised machine learning algorithms are impactful to map such minerals provided that the data must be well organized and limited in size. The results achieved were verified through validation steps like, (a) Independent reference data of high‐resolution Google Earth maps and (b) Ground survey reports. Arc GIS 10.2 and Envi 5.3 software suite were used to create (a) ground truth points at random for accuracy assessment (b) portraying study area maps (c) Image Processing and Preprocessing tools and (d) implementation of machine learning algorithms. Access to the data and software suite is being provided for open research work.