Reviewerly/exHarmony huggingface dataset:
The complementarity of offshore wind and floating photovoltaics in the Belgian North Sea, an analysis up to 2100

The combination of offshore wind with floating photovoltaics (PV) presents a major opportunity to scale up renewable energy offshore. As offshore grid development is a substantial cost driver for marine renewables, making optimal use of grid connections is most desirable. The complementarity of wind and solar resources can increase common transmission loading, thereby reducing grid costs per kWh. To fully assess the benefits of solar-wind hybridization, temporal resource complementarity must be evaluated on different timescales. In this work, the complementarity of offshore wind and solar energy resources is investigated for the Belgian North Sea using Kendall's τ. As climate change will affect the behavior of renewable energy resources, the analysis is extended up until 2100 for the climate representative concentration pathways 4.5 and 8.5. Significant solar-wind complementarity is found on monthly and weekly timescales, and to a lesser extent on daily, hourly and 10-minute timescales. Moreover, this complementarity is maintained under climate change. This study therefore identifies solar-wind hybridization as a sustainable option to reduce offshore grid costs per kWh.
Artificial Intelligence and Intellectual Property: Navigating the Complexities of Cyber Law

Abstract This article provides an extensive analysis of the relationship between Artificial Intelligence (AI) and Intellectual Property (IP) within the framework of Cyber Law. It examines the current state of AI and IP, emphasizing the legal and regulatory implications of their intersection. A comprehensive literature review and legal analysis, coupled with a comparative study of AI and IP regulatory frameworks, informs the discussion. We explore the privacy and data protection issues in AI, the evolving nature of Intellectual Property rights in the AI era, and the liability concerns related to AI applications. A comparative analysis of AI and IP regulation across various jurisdictions provides insights into the effectiveness of international frameworks and the challenges posed by cross-border regulation. The article further discusses the need for flexible and adaptive regulatory approaches that balance risk management with innovation promotion. We then highlight emerging legal and regulatory issues in AI and IP and offer recommendations for policymakers, regulators, and AI developers. Finally, the article encapsulates the key findings and their implications for AI and IP regulation, outlining future challenges and opportunities in this domain.
CMU-Net: A Strong ConvMixer-based Medical Ultrasound Image Segmentation Network

U-Net and its extensions have achieved great success in medical image segmentation. However, due to the inherent local characteristics of ordinary convolution operations, U-Net encoder cannot effectively extract global context information. In addition, simple skip connections cannot capture salient features. In this work, we propose a fully convolutional segmentation network (CMU-Net) which incorporates hybrid convolutions and multi-scale attention gate. The ConvMixer module extracts global context information by mixing features at distant spatial locations. Moreover, the multi-scale attention gate emphasizes valuable features and achieves efficient skip connections. We evaluate the proposed method using both breast ultrasound datasets and a thyroid ultrasound image dataset; and CMU-Net achieves average Intersection over Union (IoU) values of 73.27% and 84.75%, and F1 scores of 84.16% and 91.71%. The code is available at https://github.com/FengheTan9/CMU-Net.
A new conservative chaotic system and its application in image encryption

A new n-dimensional conservative chaotic model is proposed to quickly realise a conservative chaotic system. The validity of this model is verified by generating a five-dimensional conservative chaotic system as an example. This system is dynamically analysed from the perspective of Lyapunov exponents (LEs), complexity and phase diagram, which prove its good chaotic properties. Compared with existing systems, the proposed system has a simple structure, large LEs, continuous chaotic ranges and high complexity. Additionally, the National Institute of Standards and Technology verified that the proposed system demonstrates good randomness and is implemented on a digital signal processor hardware platform. Subsequently, a new image encryption scheme that uses true random numbers, lattice-based public key cryptography and dynamic P- and S-boxes is presented based on the new system. The results show that the algorithm demonstrates the capability of the cryptosystem to achieve quality encryption while resisting quantum computer, statistical, differential and cutting attacks compared with other encryption algorithms.
Classification of Breast Cancer in Mri with Multimodal Fusion

Magnetic resonance imaging (MRI) is the recommended imaging modality in the diagnosis of breast cancer. However, each MRI scan comprises dozens of volumes for the radiologist to inspect, each providing its own set of information on the tissues being scanned. This paper proposes a multimodal framework that processes all the available MRI data in order to reach a diagnosis, instead of relying on a single volume, mimicking the radiologists’ workflow. The framework comprises a 3D convolutional neural network for each modality, whose predictions are then combined using a late fusion strategy based on Dempster-Shafer theory. Results highlight the most relevant modalities required to obtain accurate diagnosis, in agreement with clinical practice. They also show that combining multiple modalities leads to better overall results than their individual counterparts.
Quantum utility – definition and assessment of a practical quantum advantage

Several benchmarks have been proposed to holistically measure quantum computing performance.While some have focused on the end user's perspective (e.g., in application-oriented benchmarks), the real industrial value taking into account the physical footprint of the quantum processor are not discussed.Different use-cases come with different requirements for size, weight, power consumption, or data privacy while demanding to surpass certain thresholds of fidelity, speed, problem size, or precision.This paper aims to incorporate these characteristics into a concept coined quantum utility, which demonstrates the effectiveness and practicality of quantum computers for various applications where quantum advantage -defined as either being faster, more accurate, or demanding less energyis achieved over a classical machine of similar size, weight, and cost.To successively pursue quantum utility, a level-based classification scheme -constituted as application readiness levels (ARLs) -as well as extended classification labels are introduced.These are demonstratively applied to different quantum applications from the fields of quantum chemistry, quantum simulation, quantum machine learning, and data analysis followed by a brief discussion.
Few-shot time-series anomaly detection with unsupervised domain adaptation

Anomaly detection for time-series data is crucial in the management of systems for streaming applications, computational services, and cloud platforms. The majority of current few-shot learning (FSL) approaches are supposed to discover the remarkably low fraction of anomaly samples in a large number of time-series samples. Furthermore, due to the tremendous effort required to label data, most time-series datasets lack data labels, necessitating unsupervised domain adaptation (UDA) methods. Therefore, time-series anomaly detection is a problem that combines the aforementioned two difficulties, termed FS-UDA. To solve the problem, we propose a Few-Shot time-series Anomaly Detection framework with unsupervised domAin adaPTation (FS-ADAPT), which consists of two modules: a dueling triplet network to address the constraints of unsupervised target information, and an incremental adaptation module for addressing the limitations of few anomaly samples in an online scenario. The dueling triplet network is adversarially trained with augmented data and unlabeled target samples to learn a classifier. The incremental adaptation module fully exploits both the critical anomaly samples and the freshest normal samples to keep the classifier up to date. Extensive experiments on five real-world time-series datasets are conducted to assess FS-ADAPT, which outperforms the state-of-the-art FSL and UDA based time-series classification models, as well as their naive combinations.
Facial Emotion Recognition for Photo and Video Surveillance Based on Machine Learning and Visual Analytics

Modern video surveillance systems mainly rely on human operators to monitor and interpret the behavior of individuals in real time, which may lead to severe delays in responding to an emergency. Therefore, there is a need for continued research into the designing of interpretable and more transparent emotion recognition models that can effectively detect emotions in safety video surveillance systems. This study proposes a novel technique incorporating a straightforward model for detecting sudden changes in a person’s emotional state using low-resolution photos and video frames from surveillance cameras. The proposed technique includes a method of the geometric interpretation of facial areas to extract features of facial expression, the method of hyperplane classification for identifying emotional states in the feature vector space, and the principles of visual analytics and “human in the loop” to obtain transparent and interpretable classifiers. The experimental testing using the developed software prototype validates the scientific claims of the proposed technique. Its implementation improves the reliability of abnormal behavior detection via facial expressions by 0.91–2.20%, depending on different emotions and environmental conditions. Moreover, it decreases the error probability in identifying sudden emotional shifts by 0.23–2.21% compared to existing counterparts. Future research will aim to improve the approach quantitatively and address the limitations discussed in this paper.
Challenges and Opportunities for Legal Practice and the Legal Profession in the Cyber Age

Abstract This article examines the challenges and opportunities faced by legal professionals in the digital age, focusing on the impact of technological innovations, cyber-security and data protection issues, and ethical considerations in legal practice. The rapid evolution of technology has transformed the legal landscape, with artificial intelligence and machine learning streamlining legal services, and block-chain and smart contracts reshaping the way legal professionals conduct their work. However, these advancements also raise concerns surrounding data privacy, security, and ethical obligations. Legal professionals must navigate complex data protection regulations, such as the GDPR and the California Consumer Privacy Act, to ensure compliance and protect their clients' data. The article discusses the importance of data privacy and security in legal practice and the measures that legal professionals need to take to comply with these regulations. Furthermore, the article analyzes the balance between technology adoption and ethical obligations in the legal profession, exploring the challenges of maintaining the attorney-client relationship and ensuring access to justice in a digital world. The article provides a comprehensive analysis of the challenges and opportunities presented by technological advancements in legal practice, proposing potential improvements and recommendations for legal professionals and regulatory bodies to navigate the digital landscape effectively. It also highlights the implications for policymakers and the future of the legal profession, suggesting future research directions in the field of legal practice and the legal profession in the cyber age.
Global Transfer of Bitcoins from One Party to Another

We have been hearing about Bitcoins for several years not only in the news but also in TV series. The problem is that in most cases, especially in TV series, what Bitcoins really are and what we can do with them is distorted. It is not controlled by any authority, it is not stored in banks, it is not traceable and, in many cases, especially in the early days, it is associated with illegal activities related to drug and arms trade. But if we dig a little deeper into what this new currency actually means, we can see that it could become a widely used currency by users in the near future. Bitcoins is the thing that used by any person or organization without any restriction and this currency may be used as a currency officially. To understand transferring bitcoins among parties globally, it can be essential to clarify what a bitcoin is itself.
Toward Legal Recognition of Artificial Intelligence Proposals for Limited Subject of law Status

This paper explores the challenge of legally recognizing Artificial Intelligence (AI) as subjects of law with limited rights. The rapid evolution of AI technology has outpaced the development of legal systems, resulting in ambiguities in liability, ownership, and agency, as well as ethical and practical challenges. To address these issues, this study employs a multidisciplinary approach, including comparative legal analysis, review of case law and academic literature, and consideration of ethical, technical, and practical aspects of AI. The proposed solutions involve the establishment of an AI-specific legal framework that recognizes AI as a subject of law with limited rights and the introduction of ethical guidelines for AI development and use. However, the implementation of these solutions faces potential objections and requires careful planning, stakeholder engagement, and adaptability. This research underscores the importance of integrating AI into our legal system and invites further exploration and dialogue.
Feasibility Analysis for Sybil Attacks in Shard-Based Permissionless Blockchains

Committee-based permissionless blockchain approaches overcome single leader consensus protocols’ scalability issues by partitioning the outstanding transaction set into shards and selecting multiple committees to process these transactions in parallel. However, by design, shard-based blockchain solutions are vulnerable to Sybil attacks. An adversary with enough computational/hash power can easily manipulate the consensus protocol by generating multiple valid node identifiers/IDs (i.e., multiple Sybil committee members).Despite the straightforward nature of these attacks, they have not been systematically investigated. This article fills this research gap by analyzing Sybil attacks in shard-based consensus of proof-of-work blockchain systems. Specifically, we provide a detailed analysis for Elastico, one of the prominent shard-based blockchain models. We show that the proof-of-work technique used for ID generation in the initial phase of such protocols is vulnerable to Sybil attacks when an adversary (could be a group of colluding nodes) possesses enough hash power. We analytically derive conditions for two different Sybil attacks and perform numerical simulations to validate our theoretical results under various parameters. Further, we utilize the BlockSim simulator to validate our mathematical computation, and results confirm the correctness of the analysis.
A Comprehensive Analysis of Ecological Law in the Cyber Era

This article provides a comprehensive analysis of the intersection between ecological law and the cyber era. With the rapid advancement of technology, the enforcement and regulation of ecological laws have seen significant changes. This paper explores these transformations, particularly focusing on privacy and data protection issues, the effectiveness of international regulatory frameworks, and the challenges of cross-border regulation. Furthermore, the study delves into the need for an adaptive regulatory framework that balances technological progress and ecological conservation. It concludes with a discussion on the future of ecological law in the cyber era, shedding light on emerging legal and regulatory challenges and providing recommendations for policymakers, regulators, and digital platform developers. The research methodology includes a thorough literature review, legal analysis, and comparative studies of various ecological law frameworks across different jurisdictions. The findings of this study underscore the urgency of addressing the complexities of ecological law in the digital age and the importance of proactive regulation for a sustainable future.
A novel enhanced hybrid clinical decision support system for accurate breast cancer prediction

Feature selection is one of the crucial data preprocessing techniques for improving the performance of machine learning (ML) models. Recently, metaheuristic feature selection algorithms have become popular because they select optimal features for ML problems. This paper presents three feature selection strategies based on metaheuristic algorithms: Bacterial Foraging (BFOA), Emperor Penguin (EPO), and a hybrid (hBFEPO) combining BFOA and EPO. The baseline algorithms have been investigated for feature selection in other ML tasks, but not for breast cancer classification. A hybrid of these two has been used for the first time. These strategies were initially tested on the COVID-19 dataset. After achieving satisfactory results, these strategies are evaluated on the WDBC Breast Cancer dataset. The performance of our models on WDBC is compared with recent eighteen state-of-the-art studies. The results indicate that the hBFEPO model outperforms other models, achieving 100% precision and specificity, 98.49% accuracy, 95.43% sensitivity, a 95.99% F1-score, and a 99.60% AUC.
Levarging Blockchain for Transparency in Agriculture Supply Chain Management Using IoT and Machine Learning

India’s agriculture industry is diversified and has the capacity to feed the second-largest population in the world. Despite of its significance the existing agriculture methodology suffers due to various issues like no proper supply chain management and the transparency in the data. Thus, blockchain Technology has greatly increased in popularity across a variety of industries due to its distinctive qualities of immutability, transparency, and security. The work is concerned with remarkable growth of the agriculture sector with the help of blockchain technology. Blockchain technology can assist in lowering the number of middlemen in the supply chain, which will lower transaction costs and boost farmer profit margins. Blockchain technology can also help reduce fraud, improve food safety and quality management, and guarantee fair prices for farmers. It involves the use of IOT sensors to generate data from the crops like climatic changes, soil moisture etc. The generated data is analyzed by using machine learning algorithms like image processing for crop disease prediction. The organized content is subsequently saved as a block in the blockchain ledger. A smart contract is made between the stakeholders involved which leads to the transparency of the transaction and this promotes a good growth in the supply chain management. It also makes it easy for the farmers to claim their insurance as each data is stored in an immutable and transparent ledger. Thus, the proposed methodology of combining blockchain with agriculture boost the efficiency and potential of the agriculture sector.
Transformer for object detection: Review and benchmark

Object detection is a crucial task in computer vision (CV). With the rapid advancement of Transformer-based models in natural language processing (NLP) and various visual tasks, Transformer structures are becoming increasingly prevalent in CV tasks. In recent years, numerous Transformer-based object detectors have been proposed, achieving performance comparable to mainstream convolutional neural network-based (CNN-based) approaches. To provide researchers with a comprehensive understanding of the development, advantages, disadvantages, and future potential of Transformer-based object detectors in Artificial Intelligence (AI), this paper systematically reviews the mainstream methods and analyzes the limitations and challenges encountered in their current applications, while also offering insights into future research directions. We have reviewed a large number of papers, selected the most prominent Transformer detection methods, and divided them into Transformer Neck and Transformer Backbone categories for introduction and comparative analysis. Furthermore, we have constructed a benchmark using the COCO2017 dataset to evaluate different object detection algorithms. Finally, we summarize the challenges and prospects in this field.
Predictive GAN-Powered Multi-Objective Optimization for Hybrid Federated Split Learning

As an edge intelligence algorithm for multi-device collaborative training, federated learning (FL) can protect data privacy but increase the computing load of wireless devices. In contrast, split learning (SL) can reduce the computing load of devices by model splitting and assignment. To take advantage of FL and SL, we propose a hybrid federated split learning (HFSL) framework for wireless networks in this paper, which combines the multi-worker collaborative training of FL and the flexible splitting of SL. To reduce the computational idleness in model splitting, we design a parallel computing scheme for model splitting without label sharing and conduct a theoretical analysis of the impact of the delayed gradient on the convergence. Aiming to obtain the trade-off between the training time and energy consumption, we model the joint optimization problem of splitting decisions, the bandwidth, and computing resources as a multi-objective problem. As such, we propose a predictive generative adversarial network (GAN)-powered multi-objective optimization algorithm to obtain the Pareto front of the problem, which utilizes the discriminator to guide the training of the generator to predict promising solutions. Experimental results demonstrate that the proposed algorithm outperforms the considered baselines in finding Pareto optimal solutions, and the solutions obtained from the proposed HFSL framework can dominate the solution of FL.