Search Results (34)

Fully decoupled radio access networks (FD-RANs) achieve spectral efficiency and coverage flexibility for 6G via independent uplink (UL) and downlink (DL) base station operation, yet dynamic user mobility brings critical challenges to joint user association and resource allocation. Asymmetric interference and heterogeneous base station capacities cause persistent network unfairness, while uncoordinated mobility management triggers ping-pong handovers and heavy handover overheads. To resolve these intertwined problems, we propose a fully decoupled, mobility-resilient and fairness-guaranteed framework, which integrates short-term congestion pricing with the long-term Jain fairness index for equitable resource distribution and introduces a composite handover penalty with a strict physical hysteresis margin to block invalid handovers. We formulate the optimization problem as a novel Sliding-Window Hysteresis-Integrated Fairness Two-Layer Multi-Armed Bandit (SHIFT-MAB) model, embedding an exponentially weighted moving average (EWMA) sliding-window mechanism to track real-time channel fluctuations efficiently. Theoretical analysis confirms the model’s decoupling optimality, sublinear regret bound and fairness convergence. Extensive simulations show that SHIFT-MAB effectively suppresses invalid handovers, ensures high network fairness, optimizes system utility and achieves a superior handover–throughput trade-off. Full article

To address the inefficiency and unfairness of traditional manual scrap sorting, we propose the application of 3D vision technology for grading in this work. The multi-view 3D reconstruction algorithm achieves an accuracy within 1 mm in both synthetic and real scrap scenes. This level of accuracy meets the requirements for scrap grading. Subsequently, an automated processing workflow in a non-overlapping scrap scenario is investigated, in which a pipeline based on the multi-view reconstruction integrating point cloud segmentation technique is proposed. Four-point cloud clustering segmentation methods, including Euclidean clustering, Kmeans, DBSCAN and Region Grow, are compared, and it is found that the Euclidean-clustering-based point cloud segmentation algorithm provides the best overall trade-off, achieving an mIoU score of 99.35%, while the thickness measurement error is less than 0.5 mm. The workflow suggests improved robustness and reliability compared to using a single 2D image for thickness inference. These results indicate that 3D vision may provide a valuable basis for the future development of scrap grading systems. Full article

Asymmetries in data distributions and performance across subgroups can induce systematic unfairness in real-world systems. A variety of previous studies have significantly ameliorated the fairness of deep learning models; however, most of them necessarily require additional labels for sensitive attributes, (i.e., ethnicity and gender). Since sensitive attributes often correspond to personal information, collecting such labels can be restricted and may raise privacy concerns. Although recent work has sought to address these issues by training a model without sensitive attribute labels, we point out that it has limitations, as it assumes specific characteristics of sensitive attributes and is validated in simplistic, constrained environments. Therefore, we propose an Unsupervised Fairness-aware Framework (UFF) that trains a fair classification model without pre-defining the characteristics of the sensitive attributes. It includes branches that capture various types of biases and eliminates them through adversarial training. In various scenarios on benchmark datasets, (i.e., CelebA and UTK Face) for facial attribute classification, the proposed method significantly enhances fairness without assuming specific characteristics of sensitive attributes. Moreover, we introduce g-FAT, which is a new metric to measure generalized trade-off performances between classification accuracy and fairness. For example, on CelebA, ours reduces EO from 11.8 to 7.6 for malignant bias and from 15.6 to 9.6 for benign bias, while improving g-FAT from 80.7 to 84.9 and from 79.0 to 85.2, respectively. In terms of g-FAT, our method achieves the highest trade-off performance among the compared methods on the benchmarks. Full article

Few-Shot Text Classification (FSTC) aims to classify text accurately into predefined categories using minimal training samples. Recently, prompt-tuning-based methods have achieved promising results by constructing verbalizers that map input data to the label space, thereby maximizing the utilization of pre-trained model features. However, existing verbalizer construction methods often rely on external knowledge bases, which require complex noise filtering and manual refinement, making the process time-consuming and labor-intensive, while approaches based on pre-trained language models (PLMs) frequently overlook inherent prediction biases. Furthermore, conventional data augmentation methods focus on modifying input instances while overlooking the integral role of label semantics in prompt tuning. This disconnection often leads to a trade-off where increased sample diversity comes at the cost of semantic consistency, resulting in marginal improvements. To address these limitations, this paper first proposes a novel Bayesian Mutual Information-based method that optimizes label mapping to retain general PLM features while reducing reliance on irrelevant or unfair attributes to mitigate latent biases. Based on this method, we propose two synergistic generators that synthesize semantically consistent samples by integrating label word information from the verbalizer to effectively enrich data distribution and alleviate sparsity. To guarantee the reliability of the augmented set, we propose a Low-Entropy Selector that serves as a semantic filter, retaining only high-confidence samples to safeguard the model against ambiguous supervision signals. Furthermore, we propose a Difficulty-Aware Adversarial Training framework that fosters generalized feature learning, enabling the model to withstand subtle input perturbations. Extensive experiments demonstrate that our approach outperforms state-of-the-art methods on most few-shot and full-data splits, with F1 score improvements of up to +2.8% on the standard AG’s News benchmark and +1.0% on the challenging DBPedia benchmark. Full article

This paper proposes a risk-based, multi-objective approach to identify a solution, referred to as the fairness improvement plan, that enhances the fairness of photovoltaic (PV) curtailment, primarily applied to mitigate overvoltage issues in both balanced and unbalanced low-voltage distribution networks with high PV penetration. The proposed approach considers the uncertainty of loads, PV generation, and slack bus voltage. Relative Distance Measure (RDM) interval arithmetic is employed to represent these uncertainties while accounting for correlations among uncertain quantities, and the Pareto Simulated Annealing (PSA) method is used to generate a set of efficient fairness improvement plans. The Hurwicz criterion for measuring risk, which accounts for a decision maker’s risk preference, is incorporated in the interval TOPSIS technique to identify the fairness improvement plan, selected from a set of efficient plans, that minimizes the risk of financial losses and the risk of unfairness of PV’s active power curtailment. The numerical results obtained show that the proposed approach improves the insight and the understanding of the fairness improvement planning under uncertainty. They also highlight the effectiveness of incorporating decision makers’ risk preferences and their trade-off preferences between fairness and cost in developing the optimal fairness improvement plan under uncertainty in low-voltage distribution networks with high PV penetration. Full article

Artificial intelligence (AI) chatbots are now embedded across therapeutic contexts, from the United Kingdom’s National Health Service (NHS) Talking Therapies to widely used platforms like ChatGPT. Whether welcomed or not, these systems are increasingly used for both patient care and everyday support, sometimes even replacing human contact. Their capacity to convey empathy strongly influences how people experience and benefit from them. However, current systems often create an “AI empathy gap”, where interactions feel impersonal and superficial compared to those with human practitioners. This paper, presented as a critical narrative review, cautiously challenges the prevailing narrative that empathy is a uniquely human skill that AI cannot replicate. We argue this belief can stem from an unfair comparison: evaluating generic AIs against an idealised human practitioner. We reframe capabilities seen as exclusively human, such as building bonds through long-term memory and personalisation, not as insurmountable barriers but as concrete design targets. We also discuss the critical architectural and privacy trade-offs between cloud and on-device (edge) solutions. Accordingly, we propose a conceptual framework to meet these targets. It integrates three key technologies: Retrieval-Augmented Generation (RAG) for long-term memory; feedback-driven adaptation for real-time emotional tuning; and lightweight adapter modules for personalised conversational styles. This framework provides a path toward systems that users perceive as genuinely empathic, rather than ones that merely mimic supportive language. While AI cannot experience emotional empathy, it can model cognitive empathy and simulate affective and compassionate responses in coordinated ways at the behavioural level. However, because these systems lack conscious, autonomous ‘helping’ intentions, these design advancements must be considered alongside careful ethical and regulatory safeguards. Full article

This study innovatively moves beyond traditional mono-method research by employing an integrated approach that synergizes corpus linguistics and text mining. Through sentiment, thematic, and collocational analyses, it critically examines the representation of China’s image in European media coverage of the China–EU electric vehicle dispute. Initially, sentiment analysis of news reports concerning EU tariffs on Chinese electric vehicles was conducted. Subsequently, four key themes emerged from analyzing a corpus consisting of 202 news articles: “market reaction,”; “trade war,” “China’s response,” and “dialogue and negotiation.” Finally, collocation analysis of the keywords “China” and “Beijing” reveals four main images of China in European media: China is framed as the unfair-subsidy provider, threatener, negotiator, and defender. The key conclusion is that European media coverage is characterized by discursive ambivalence, simultaneously portraying China as both a threat and a partner. These findings are significant as they illuminate how media discourse serves as a key arena where the economic and political complexities of the China–EU trade conflict are negotiated, legitimized, and managed. Full article

The vehicle-to-vehicle (V2V) charging mode of charging stations solves the problem of users being unable to charge immediately due to the absence of charging piles during peak charging times. However, in blockchain-based V2V power transactions, attackers collect private information such as the payment address and transaction amount of electric vehicle owners through ledger information. This makes the relationship between electric vehicle owners and the charging behavior the object of inference attacks, resulting in user privacy disclosure and unfair trading. To solve these problems, we propose a communication scheme with privacy protection in V2V power transactions based on a linkable ring signature. We use a linkable ring signature algorithm to sign EV account addresses and payment information, ensuring the non-traceability of V2V transactions. In addition, we design a stealth address algorithm to avoid inferential attacks in V2V power transactions due to the exposure of the actual account address. The theoretical analysis proves the scheme’s security, and the experiment shows that the scheme has lower computing costs, so it is more suitable for V2V scenarios with limited computing resources. Full article

Following the economic shocks of recent decades, characterized by the destabilization of markets and pressure on national economies, protectionist policies have seen a significant increase. Thus, anti-dumping has become a convenient and frequently used tool in the political game of trade. In the context of the transition toward a climate-neutral economy, anti-dumping measures have become a topic of great interest due to their indirect effects on CO₂ emissions. Often used to protect domestic industries from unfair trade practices, these measures influence trade and the geographical redistribution of production, contributing to the phenomenon of “carbon leakage”. By transferring emissions from countries with strict climate regulations to economies with more permissive standards, anti-dumping measures can undermine global efforts to reduce emissions. Trade policies are becoming, in this context, an important tool in regulating international trade. Consequently, the objective of this paper is to analyze the impacts of anti-dumping measures, primary energy consumption, and urbanization on CO₂ emissions in OECD countries for the period 2000–2021. The methodology used is based on dynamic A.R.D.L. models using panel data. Our results suggest that anti-dumping measures and primary energy consumption influence CO₂ emissions and are statistically significant, at least at the 10% level. The results of this study are useful to both policymakers and environmental authorities in developing trade policies that support both economic development and emission-reduction targets. Full article

This article starts by providing an updated literature review and the EU legislative framework concerning reducing carbon emissions in the maritime industry as part of the European Green Deal (EGD). It specifically examines the EU Emission Trading System (ETS) tax regime. This document then analyses the current factors influencing ships’ decisions to avoid stopping at hub ports and going to neighbouring Mediterranean countries, such as North Africa and Turkey. In the discussion section, this study presents various suggestions for updating EU laws or expediting the collection and analysis of data to prompt the Commission to take appropriate actions to prevent unfair competition between EU and non-EU ports. This study focuses on identifying the most effective solutions within the EU legislative framework to address the need for the Commission to take legitimate action to prevent ships from bypassing EU hub ports. These solutions can be further developed alongside initiatives at the International Maritime Organization (IMO), and certain provisions can be adjusted at the EU level. The IMO’s call for a carbon fee on bunkering exacerbates the existing challenges. Preventive measures must be implemented to control the diversion of shipping traffic from EU hub ports, ensure fair treatment of EU ports involved in transhipment, and prevent carbon leakage. Moreover, the recent Houthi attacks in the Red Sea have significantly increased shipping costs on the route around the Cape of Good Hope to Europe, necessitating increased allowances for traffic to and from Europe. Full article

P2P power trading is necessary for efficiently using consumer electricity not subject to FIT. However, the execution rules for P2P power trading do not include restrictions on voltage, and there is a trade-off between the activation of the P2P power trading market through the mass introduction of PV and the optimization of the voltage of the power distribution system. In addition, there is a tendency for output curtailment to be biased toward consumers connected to the end of the grid. Since consumers cannot choose the interconnection location, there are concerns about unfairness. In this study, we investigate a new P2P model that includes voltage constraints for the execution rules of P2P power trading to avoid voltage deviation while ensuring benefits and fairness for the participants. In the proposed model, to increase the incentive to participate in the P2P power trading market, we consider compensating consumers who receive output curtailment signals due to voltage constraints. In addition, the profit is secured by differentiating the compensation cost unit price depending on the contract’s availability. A case study was conducted on this model using the IEEE 33 bus system. The results show that the proposed model is superior. Full article

In the European agri-food sector, operators with substantial bargaining power often engage in unfair trading practices (UTPs). Our paper aims to empirically examine the occurrence of UTPs and their influence on the performance of cooperatives. To fulfill the goal of our paper, we collected responses from 109 cooperatives in Greece after the transposition of a specialized EU Directive (i.e., Directive (EU) 2019/633). We found that, on average, the sampled cooperatives encountered three prohibited (“black”) UTPs, while all reported at least one prohibited UTP. Moreover, the two most commonly reported practices (i.e., “unduly late payments” and “buyers’ demand that suppliers pay for the deterioration or loss of products that occurred after ownership transfer”) exerted a significant negative influence on cooperative performance, even in the presence of a proficient Board of Directors. Consequently, policymakers may need to pay more attention to UTPs and ensure that the national enforcement authorities are well-equipped to act rapidly and effectively against offenders. Full article

As the demand and diversity of digital content increase, consumers now have simple and easy access to digital content through Over-the-Top (OTT) services. However, the rights of copyright holders remain unsecured due to issues with illegal copying and distribution of digital content, along with unclear practices in copyright royalty settlements and distributions. In response, this paper proposes an automated OTT service copyright distribution management system using the Open Digital Rights Language (ODRL) to safeguard the rights of copyright holders in the OTT service field. The proposed system ensures that the rights to exercise copyright transactions and agreements, such as trading of copyright, can only be carried out when all copyright holders of a single digital content agree based on the Threshold Schnorr Digital Signature. This approach takes into account multiple joint copyright holders, thereby safeguarding their rights. Furthermore, it ensures fair and transparent distribution of copyright royalties based on the ratio information outlined in ODRL. From the user’s perspective, the system not only provides services proactively based on the rights information specified in ODRL, but also employs zero-knowledge proof technology to handle sensitive information in OTT service copyright distribution, thereby addressing existing privacy concerns. This approach not only considers joint copyright holders, but also demonstrates its effectiveness in resolving prevalent issues in current OTT services, such as illegal digital content replication and distribution, and the unfair settlement and distribution of copyright royalties. Applying this proposed system to the existing OTT services and digital content market is expected to lead to the revitalization of the digital content trading market and the establishment of an OTT service environment that guarantees both vitality and reliability. Full article

The rapid progression of Decentralized Finance (DeFi) has established Decentralized Exchanges (DEX) as critical elements in the financial landscape. Nevertheless, the open and transparent nature of DEX makes them susceptible to strategic manipulations, especially the sandwich attack. During such maneuvers, ill-intentioned actors exploit price slippage by positioning their transactions strategically around a target’s order to reap unfair profits. This paper introduces a ground-breaking framework rooted in mechanism design game theory to lessen the impact of sandwich attacks. The framework delineates the precise strategy of the sandwich attack and its repercussions, shedding light on the tactical aspects and utility functions pertinent to both the attackers and the ordinary traders, subsequently referred to as workers. The discussion extends to defining utility functions for both the market and the workers, emphasizing the benefits of liquidity provision for the market and the potential profits and losses for the workers. The proposal encourages adopting a market-centric mechanism design grounded in game theory, wherein the market, operating as the designer, creates rules to maximize its utility while considering the workers’ utility. Through a meticulous analysis of this game-theoretic approach, the study identifies optimum strategies for all the involved parties, demonstrating that these strategies can reach a balanced state. Further, this study presents a comparative view against existing research, highlighting the limitations of contemporary solutions and asserting the effectiveness of the proposed model in protecting the interests of both the market and the workers. Ultimately, this research furnishes stakeholders with new perspectives and instruments to thwart sandwich attacks and lays a foundation for creating resilient and fair decentralized trading infrastructures. Full article

Great efforts are constantly being made by industry-specific coffee agencies to standardize the certification of coffee quality. In consequence, international trade requires quick and reliable analyses because of their high cost, the risk of misclassification, the difficulty of large-scale analysis, and, most importantly, the subjectivity generated by tasters. A powerful analytical method that can be used to accurately evaluate and identify coffee varieties is Laser-Induced Breakdown Spectroscopy (LIBS). In this study, it provided a quick, cost-effective, and residue-free method commonly used in laboratories for direct analysis, determining multi-elemental composition, and exploring the organic composition of roasted coffee. The mineral composition of eight varieties of pure roasted coffee was determined using a pulsed nanosecond laser produced from a Nd:YAG laser at 1064 nm. The most important spectral variables for coffee variety identification were sequestered using LIBS coupled with a chemometric-tool-based principal component analysis (PCA). The nine main wavelengths chosen corresponded to the elements of C(I), Mg(II, I), Ca(II), Fe(I), K(I), H(I), and O(I), in addition to the CN group. The overall findings indicated that using LIBS to identify coffee varieties is feasible based on a simple, quick, and eco-friendly strategy without the requirement for complex preparation or wasting time in preparation. Such studies can help to protect the coffee market and businesses by certifying product quality. Using LIBS and full statistical illustrations with PCA, the prevention of unfair competition, protection of consumers, and determination of coffee quality can be achieved. Full article