Hasil untuk "Regulation of industry, trade, and commerce. Occupational law"

Basma Belmoukari, Jean-François Audy, Pascal Forget et al.

<i>Background</i>: Port digital transition is central to competitiveness and sustainability, yet existing frameworks devoted to such transition toward smart port are descriptive, technology-centered, or weak on data governance. This study designs and empirically refines a comprehensive and novel ten-step roadmap relative to existing Port/Industry 4.0 models, synthesized from 14 partial frameworks that each cover only subsets of the transition, by considering data governance and consolidating cost, time, and impact in the selection step. <i>Methods</i>: We synthesized recent Industry 4.0 and smart port-related frameworks into a normalized sequence of steps embedded in the so-called roadmap, then examined it in an exploratory case of a technology deployment project in a Canadian port using stakeholder interviews and project documents. Evidence was coded with a step-aligned scheme, and stakeholder feedback and implementation observations assessed whether each step’s outcomes were met. <i>Results</i>: The sequence proved useful yet revealed four recurrent hurdles: limited maturity assessment, uneven stakeholder engagement, ad hoc technology selection and integration, and under-specified data governance. The refined roadmap adds a diagnostic maturity step with target-state setting and gap analysis, a criteria-based selection worksheet, staged deployment with checkpoints, and compact indicators of transformation performance, such as reduced logistics delays, improved energy efficiency, and technology adoption. <i>Conclusions</i>: The work couples theory-grounded synthesis with empirical validation and provides decision support to both ports and public authorities to prioritize investments, align stakeholders, propose successful policies and digitalization supporting programs, and monitor outcomes, while specifying reusable steps and indicators for multi-port testing and standardized metrics.

Mengxiang Chen, Zhouwei Zhai, Jin Li

Modern e-commerce search is evolving to resolve complex user intents. While Large Language Models (LLMs) offer strong reasoning, existing LLM-based paradigms face a fundamental blindness-latency dilemma: query rewriting is agnostic to retrieval capabilities and real-time inventory, yielding invalid plans; conversely, deep search agents rely on iterative tool calls and reflection, incurring seconds of latency incompatible with industrial sub-second budgets. To resolve this conflict, we propose Environment-Aware Search Planning (EASP), reformulating search planning as a dynamic reasoning process grounded in environmental reality. EASP introduces a Probe-then-Plan mechanism: a lightweight Retrieval Probe exposes the retrieval snapshot, enabling the Planner to diagnose execution gaps and generate grounded search plans. The methodology comprises three stages: (1) Offline Data Synthesis: A Teacher Agent synthesizes diverse, execution-validated plans by diagnosing the probed environment. (2) Planner Training and Alignment: The Planner is initialized via Supervised Fine-Tuning (SFT) to internalize diagnostic capabilities, then aligned with business outcomes (conversion rate) via Reinforcement Learning (RL). (3) Adaptive Online Serving: A complexity-aware routing mechanism selectively activates planning for complex queries, ensuring optimal resource allocation. Extensive offline evaluations and online A/B testing on JD.com demonstrate that EASP significantly improves relevant recall and achieves substantial lifts in UCVR and GMV. EASP has been successfully deployed in JD.com's AI-Search system.

Haoyan Lin, Jie Huang

The output regulation problem for unknown linear systems has been studied using state-based and output-based internal model approaches in the special case with no disturbances. This paper further investigates the output regulation problem for unknown linear systems using a data-driven output-based approach via value iteration. For this purpose, we first develop a novel output-feedback control law that does not explicitly rely on the observer gain to solve the output regulation problem. We then show that the data-driven approach for designing an output-feedback control law for the given plant can be reduced to the data-driven design of a state-feedback control law for a well-defined augmented auxiliary system. As a result, we develop a systematic data-driven approach to solve the output regulation problem for unknown linear systems via value iteration. Finally, we establish a relation between the data-driven state-feedback control law and the data-driven output-feedback control law in the LQR sense.

Mohamed Karim Hajji, Asma Fekih, Alperen Bal et al.

<i>Background</i>: This study deploys the Lean Six Sigma DMAIC framework to achieve systemic optimization of the school subscription process in Tunisia’s public transport service, a critical administrative operation affecting efficiency and customer satisfaction across the urban mobility network. <i>Methods</i>: Beyond conventional applications, the research integrates advanced analytical and process engineering tools, including capability indices, measurement system analysis (MSA), variance decomposition, and root-cause prioritization through Pareto–ANOVA integration, supported by a structured control plan aligned with ISO 9001:2015 and ISO 31000:2018 risk-management standards. <i>Results</i>: Quantitative diagnosis revealed severe process instability and nonconformities in information flow, workload balancing, and suboptimal resource allocation that constrained effective capacity utilization. Corrective interventions were modeled and validated through statistical control and real-time performance dashboards to institutionalize improvements and sustain process stability. The implemented actions led to a 37.5% reduction in cycle time, an 80% decrease in process errors, a 38.5% increase in customer satisfaction, and a 38.9% improvement in throughput. <i>Conclusions</i>: This study contributes theoretically by positioning Lean Six Sigma as a data-centric governance framework for stochastic capacity optimization and process redesign in public service systems, and practically by providing a replicable, evidence-based roadmap for operational excellence in governmental organizations within developing economies.

Mohammad Mahdi Fatahian, Ftemeh Ghanad

In recent years, assets have emerged that can be used digitally in various fields, including artistic, historical, cultural, and sports. The value of these assets in common practice usually creates a legal relationship between the digital asset and its owner, The owner can personalize his right and tokenized into a tradable and transferable token. These tokens are called Non-Fungible Tokens (NFTs). On the one hand, a market has formed for these assets, and there is supply and demand for them. However, the rules governing this market are not the same as those of traditional markets or even other digital assets. NFTs are traded under specific conditions on the blockchain; Non-Fungible Token contracts, executed on the blockchain, are smart contracts. Therefore, in addition to examining the legal nature of Non-Fungible Token contracts, we must separately examine the characteristics and effects of the smart contract transferring the tokens. Studies show how NFTs have emerged and gained popularity, contracts have been formed in this field, and people engage in transactions of this nature globally. Additionally, research has been written on NFTs' economic, technical, and copyright aspects. Given the particularities of NFT smart contracts and the differences with smart contracts of other tokens, this article seeks to structure and explain the nature, effects, and provisions of Non-Fungible Token contracts.1. Introduction Non-fungible tokens (NFTs) are digital codes that encode an external asset (e.g., digital artwork) and uniquely personalize it to its creator. Each non-fungible token has three essential elements: 1. non-fungibility, 2. proof of ownership, and 3. a unique identification code. Non-fungibility means that the NFT cannot be divided into smaller parts or converted into other tokens; therefore, non-fungible tokens are always a single, fixed digital asset. Proof of ownership means that whoever holds the token is considered the owner (whether the creator or the transferee) of the digital asset that is attributed to the asset. Also, each non-fungible token is a unique identification code that authenticates the property owned by it. Each asset owner can mint a unique token for their work, meaning they can encode an image of their asset using blockchain network technologies and turn it into a token, and transfer the created token to others on that blockchain network. Non-fungible token contracts (smart contracts) are different from traditional contracts and electronic contracts. Smart contracts are similar to electronic contracts in that they are concluded on the Internet, but the provisions of electronic contracts are the same as those of traditional contracts that appear in a new format and are not different from them in terms of substance; but smart contracts are basically programmed codes that will be executed when the conditions specified for them are met; therefore, they are different from electronic contracts in terms of both nature and format and content. 2. Methodology Numerous studies have been conducted in the legal, economic, technical, and other characteristics and features of digital currencies, especially Bitcoin. Also, smart contracts have been examined in various works and adapted to domestic law, but what has been neglected and not addressed much in legal works in Persian is non-fungible tokens (NFTs). The neglect of this part of digital assets, which is different from other digital currencies such as Bitcoin and Tether, is due to the lack of widespread use and transfer of these in Iran, the legislator's inattention to these newly created assets and the approval of related regulations, and the lack of knowledge of the scientific community about the nature of non-fungible tokens and the contract related to them. Although the non-fungible token contract is a type of smart contract, it has features that do not exist in other smart contracts and are specific to NFTs, which will be effective in the works resulting from the contract. In other words, the token creator can configure the smart contract to produce different outcomes and effects. This article, using a descriptive-analytical method, seeks to identify the nature of the non-fungible token contract and its related effects so that during judicial proceedings, the judge has sufficient knowledge of the specific aspects and characteristics of these contracts. Also, if the legislator wants to enact regulations and laws in this field, he will take the necessary measures considering the special conditions that non-fungible tokens and related contracts have. 3. Results and Discussion Non-fungible tokens, as emerging digital assets, are different from property in the traditional implication in Iranian law. Digital assets do not have the characteristics of property in the traditional sense, but this difference does not prevent us from considering non-fungible tokens as property and considering contracts related to them invalid due to the lack of property as the subject of the contract. NFTs are cryptographic tokens backed by a valuable digital asset, such as artwork, music, film, computer games, etc., that is unique and has no equal. They are created on the blockchain network under a unique identifier to strengthen the rights of the creator of the valuable asset and protect it from infringement. In the legal doctrine, the two components of utility and assignability are considered to be signs of property. If the aforementioned components are present, the legal relationship with the property is of ownership, and the person can transfer his rights to others. Non-fungible tokens are digital assets that people consider beneficial and useful. Also, this asset is unique and basically cannot be made available to everyone; therefore, by applying the criteria and elements stated for assets to non-fungible tokens, there is no doubt that it has value. Blockchain uses smart contract tools and algorithms to transfer all digital assets, including NFTs and other tokens. To trade and transfer non-fungible tokens, a smart contract must be created on the blockchain network. In creating this contract, the digital asset owner must specify the number of non-fungible tokens and the value of each. Smart contracts are written in code language and concluded with a digital signature, but in many other respects, such as the need to have basic conditions for the validity of transactions, the characteristics of the transaction, and other matters, they are the same as traditional contracts, and the legal rules related to contracts will also be current and enforceable in smart contracts. There are limitations to the payment of compensation in NFT contracts. Since the contract of non-fungible tokens is concluded and executed on the blockchain platform, all its features and related matters must be carried out with the blockchain network. Therefore, in the payment of NFT compensation, common currencies (rials, dollars, yen, euros, pounds, etc.) cannot be used, but what is paid in exchange for the NFT must be usable on the blockchain network. Only digital currencies that exist on the blockchain network and the NFT platform (Ethereum, Trum, Tether, etc.) can be used as a means of payment in the contract of non-fungible tokens. The subject of the contract for non-fungible tokens is not the same thing, but the granting of a digital certificate of the work, which is a digital asset; and digital assets are assets that exist only in digital form and the right to use them is specific, meaning that the digital asset can be used within a specific scope; for example, non-fungible tokens are used within the scope of the blockchain and the right to use them is limited to material and moral benefits from NFTs. In a non-fungible token contract, what is transferred to the transferee is one of the rights arising from the external asset, namely the digital certificate and license of the original asset and the material use and enjoyment of it, while the attribution of the original asset remains with the creator and creator of the NFT, and if it is intellectual property, its intellectual property rights belong to him in any case; therefore, in a non-fungible token contract, ownership of the original asset is not transferred. Such contracts can be considered as indefinite contracts by referring to Article 10 of the Civil Code. A contract whose subject and content is permission to materially exploit an NFT will result in a personalized certificate and license for the property that has been transformed into a visual work and created by the owner of the work, and the ownership right to the original property or asset to which the token is attributed will remain with the owner and creator of the token and will not be transferred to the transferee through a non-fungible token contract. Non-fungible token contracts, due to their smart nature, are self-executing and are executed as soon as they are concluded, and the obligations of the parties end with them. The transfer of a non-fungible token to the transferee does not transfer the copyright, publication, reproduction, promotion, and other rights related to the original property and asset, if it is intellectual property, to him. Rather, all these rights remain with the owner of the original property, that is, the creator of the non-fungible token, and even after the token is transferred to another person, he can exercise the rights he has. Of course, it is worth noting that smart contracts have the ability to specify various conditions that if a right to the original property is revocable in a self-executing smart contract, the contract can revoke or transfer some of these rights from the creator of the token in favor of the transferees. 4. Conclusions and Future Research In this article, focusing on non-fungible tokens, the nature, effects, and provisions of the NFT transfer contract on the block chain were examined concerning the existing Iranian law. The non-fungible token contract, which is a type of smart contract, is recognizable and valid in contract law due to its characteristics of a valid contract in Iranian law. This contract, which falls under the scope of indefinite contracts and Article 10 of the Civil Code, transfers a visual work (NFT) on the blockchain from one person to another. What is transferred in this contract is not the original property to which the NFT is attributed but rather a unique digital code that shows itself in the blockchain network in the form of a visual work. Of course, the creator of the non-fungible token smart contract can program it in such a way that it cannot be transferred to the next person, and this type of transfer is done for charity, support of the work, and other similar purposes. The most important difference between a non-fungible token contract and a contract for other tokens, such as Bitcoin, is that this contract can be designed in various forms and for various purposes.

Sepehr Abrishami, Reshma Jayaram

<i>Background</i>: Urban disasters expose persistent gaps in the operational picture and timely decision-making for response teams, which require user-centred systems that connect analysis to action. This study proposes and formatively validates an integrated framework that couples digital twins and augmented reality for humanitarian logistics. <i>Methods</i>: A mixed methods design combined a structured literature synthesis with a practitioner survey across architecture, engineering, planning, BIM, and construction to assess perceived value and adoption conditions. <i>Results</i>: Findings indicate that practitioners prioritised digital twins for enhancing situational awareness (71.4%) and augmented reality for providing real-time information overlays (64.3%). A majority judged that integrating these technologies would yield substantial improvements in disaster response (67.9%), despite implementation challenges. <i>Conclusions</i>: The framework links live state estimation and short-horizon simulation to role-specific, in-scene AR cues, with the aim of reducing decision latency and improving coordination. Adoption depends primarily on human and organisational factors, including user accessibility, preparation needs, and clear governance. These results suggest a viable pathway to operationalise the bridge between analysis and field action and outline priorities for pilot evaluation.

Lorenzo Schiavon, Mattia Stival, Angela Andreella et al.

Behavioral risk factors, i.e., smoking, poor nutrition, alcohol misuse, and physical inactivity (SNAP), are leading contributors to chronic diseases and healthcare costs worldwide. Their prevalence is shaped %not only by demographic characteristics %but and also by contextual ones such as socioeconomic and occupational environments. In this study, we leverage data from the Italian health and behavioral surveillance system PASSI to model SNAP behaviors through a Bayesian framework that integrates textual information on occupations. We use Structural Topic Modeling (STM) to cluster free-text job descriptions into latent occupational groups, which inform mixture weights in a multivariate ordered probit model. Covariate effects are allowed to vary across occupational clusters and evolve over time. To enhance interpretability and variable selection, we impose non-local spike-and-slab priors on regression coefficients. Finally, an online learning algorithm based on sequential Monte Carlo enables efficient updating as new data become available. This dynamic, scalable, and interpretable approach permits observing how occupational contexts modulate the impact of socio-demographic factors on health behaviors, providing valuable insights for targeted public health interventions.

Abderahman Rejeb, Karim Rejeb, Imen Zrelli

<i>Background</i>: Effective humanitarian logistics (HL) is essential in disaster response. The “Internet of Things” (IoT) holds potential to enhance the efficiency and efficacy of HL, yet adoption is slowed by numerous barriers. <i>Methods</i>: This study employs interpretive structural modeling (ISM) and decision-making trial and evaluation laboratory (DEMATEL) to explore and classify barriers to IoT integration in HL. <i>Results</i>: A total of 12 barriers were identified, classified, and ranked according to their driving power and dependence. Key barriers include lack of standardization, organizational resistance, data quality issues, and legal challenges. <i>Conclusions</i>: Overcoming these barriers could significantly improve relief operations, reduce errors, and enhance decision-making processes in HL. This investigation is the first of its kind into IoT barriers in HL, laying the groundwork for further research and providing valuable insights for HL managers.

Roberto Montemanni, Derek H. Smith

<i>Background:</i> The Clustered Orienteering Problem is an optimization problem faced in last-mile logistics. The aim is, given an available time window, to visit vertices and to collect as much profit as possible in the given time. The vertices to visit have to be selected among a set of service requests. In particular, the vertices belong to clusters, the profits are associated with clusters, and the price relative to a cluster is collected only if all the vertices of a cluster are visited. Any solving methods providing better solutions also imply a new step towards sustainable logistics since companies can rely on more efficient delivery patterns, which, in turn, are associated with an improved urban environment with benefits both to the population and the administration thanks to an optimized and controlled last-mile delivery flow. <i>Methods:</i> In this paper, we propose a constraint programming model for the problem, and we empirically evaluate the potential of the new model by solving it with out-of-the-box software. <i>Results:</i> The results indicate that, when compared to the exact methods currently available in the literature, the new approach proposed stands out. Moreover, when comparing the quality of the heuristic solutions retrieved by the new model with those found by tailored methods, a good performance can be observed. In more detail, many new best-known upper bounds for the cost of the optimal solutions are reported, and several instances are solved to optimality for the first time. <i>Conclusions:</i> The paper provides a new practical and easy-to-implement tool to effectively deal with an optimization problem commonly faced in last-mile logistics.

Zahra Shayesteh Majd, Jalil Ghanavati, Javad hoseinzadeh

There is no doubt that the right to access information is one of the most basic rights of every buyer. This doubles the necessity of the current research in relation to the examination of contracts in this field. The leading article was written with the aim of analyzing the effect of the principle of justice and good faith on the provision of information and warnings in contract law. Therefore, the obligation to provide necessary information is one of the traditional rights of every consumer based on the principle of justice and good faith, which is certainly considered one of the duties of the strong party of the contract. This research has been compiled by content analysis method and in a library form and by referring to the regulations. The results of the research indicate that there is no definition of good faith and contractual justice in the laws of Iran. Also, the lack of strict regulations on the advertising of goods can create many problems in the direction of protection from the weak side of the contract. After conducting the research, it is concluded that in commercial dealings and providing information and warnings about contractual substitutes, it is appropriate to accept the theory of pure responsibility in Iranian law and take a big step in promoting the rights of consumers and all-round compliance. Justice in contracts should be removed.

Jukka Ruohonen

The short paper discusses algorithmic fairness by focusing on non-discrimination and a few important laws in the European Union (EU). In addition to the EU laws addressing discrimination explicitly, the discussion is based on the EU's recently enacted regulation for artificial intelligence (AI) and the older General Data Protection Regulation (GDPR). Through a theoretical scenario analysis, on one hand, the paper demonstrates that correcting discriminatory biases in AI systems can be legally done under the EU regulations. On the other hand, the scenarios also illustrate some practical scenarios from which legal non-compliance may follow. With these scenarios and the accompanying discussion, the paper contributes to the algorithmic fairness research with a few legal insights, enlarging and strengthening also the growing research domain of compliance in AI engineering.

Duo Xu, Jenna Karcheski, Chi-Yan Law et al.

Accurately measuring magnetic field strength in the interstellar medium, including giant molecular clouds (GMCs), remains a significant challenge. We present a machine learning approach using Denoising Diffusion Probabilistic Models (DDPMs) to estimate magnetic field strength from synthetic observables such as column density, dust continuum polarization vector orientation angles, and line-of-sight (LOS) nonthermal velocity dispersion. We trained three versions of the DDPM model: the 1-channel DDPM (using only column density), the 2-channel DDPM (incorporating both column density and polarization angles), and the 3-channel DDPM (which combines column density, polarization angles, and LOS nonthermal velocity dispersion). We assessed the models on both synthetic test samples and new simulation data that were outside the training set's distribution. The 3-channel DDPM consistently outperformed both the other DDPM variants and the power-law fitting approach based on column density alone, demonstrating its robustness in handling previously unseen data. Additionally, we compared the performance of the Davis-Chandrasekhar-Fermi (DCF) methods, both classical and modified, to the DDPM predictions. The classical DCF method overestimated the magnetic field strength by approximately an order of magnitude. Although the modified DCF method showed improvement over the classical version, it still fell short of the precision achieved by the 3-channel DDPM.

Mehdi Abdolmaleki, Amir Reza Mamdoohi, Mohammadamin Emami

Planning for rail transportation requires an assessment of past performance to identify and address weaknesses and gaps. Efficiency assessment is one of the tools for evaluating performance. In this study, the efficiency of Iran's rail freight transportation during the period April 1982-April 2022 (due to the use of the Solar Hijri calendar, the year starts in April) was evaluated using the data envelopment analysis (DEA) method, a non-parametric approach in operations research and economics. One of the policies in Iran's rail transportation sector in recent years has been privatization and the government's withdrawal from the management of the rail transportation system in order to improve performance and enhance efficiency. To examine the impact of privatization on Iran's rail freight transportation performance, the study time period has been divided into two distinct time periods: before privatization and after privatization. According to the results, privatization has had a positive impact on Iran's rail freight transportation performance and has led to improvements in efficiency in this sector. Based on the DEA-CCR model, the average efficiency score of Iran's rail freight transportation in the pre-privatization years was 0.893, and after privatization, it was 0.922. Furthermore, based on the DEA-BCC model, the average efficiency score before privatization was 0.953, and after privatization, it was 0.984.

Seyede Zohre Ahmadi SheykhShabani, Seyed Mohammad Mousavi Gazafroodi

Active suspensions that combine conventional mechanical structures with advanced electronics, sensors, and controllers have enabled the development of railway vehicles that can meet the new demands for higher speed, improved ride comfort, and stricter safety standards. Nevertheless, these aspects are affected by low track quality or high train speed.  Therefore, it is crucial to regulate the vibration of the vehicle's suspension by using advanced control and automation techniques that can optimize the performance of a rail car suspension system. A method to improve these factors under such operating conditions is active suspension control. Active suspension enables designers to achieve a comfort level that is impossible with passive suspension elements. This work introduces the mathematical model of a two-degree-of-freedom system and the implementation of a robust artificial neural network control system for the active suspension system of a rail car. The control system that is proposed comprises a robust controller, a NARMA-L2 controller, which is a type of neural network controller that can be used to control nonlinear systems, and a model neural network of the rail car's suspension system. A standard PID controller is also used for comparison to control the railway vehicle's suspension system. The simulation results indicate that the proposed control system has enhanced efficiency and a better outcome at adjusting to random track disturbances for the railway vehicle's suspension.

Catalina Goanta, Nikolaos Aletras, Ilias Chalkidis et al.

The scientific innovation in Natural Language Processing (NLP) and more broadly in artificial intelligence (AI) is at its fastest pace to date. As large language models (LLMs) unleash a new era of automation, important debates emerge regarding the benefits and risks of their development, deployment and use. Currently, these debates have been dominated by often polarized narratives mainly led by the AI Safety and AI Ethics movements. This polarization, often amplified by social media, is swaying political agendas on AI regulation and governance and posing issues of regulatory capture. Capture occurs when the regulator advances the interests of the industry it is supposed to regulate, or of special interest groups rather than pursuing the general public interest. Meanwhile in NLP research, attention has been increasingly paid to the discussion of regulating risks and harms. This often happens without systematic methodologies or sufficient rooting in the disciplines that inspire an extended scope of NLP research, jeopardizing the scientific integrity of these endeavors. Regulation studies are a rich source of knowledge on how to systematically deal with risk and uncertainty, as well as with scientific evidence, to evaluate and compare regulatory options. This resource has largely remained untapped so far. In this paper, we argue how NLP research on these topics can benefit from proximity to regulatory studies and adjacent fields. We do so by discussing basic tenets of regulation, and risk and uncertainty, and by highlighting the shortcomings of current NLP discussions dealing with risk assessment. Finally, we advocate for the development of a new multidisciplinary research space on regulation and NLP (RegNLP), focused on connecting scientific knowledge to regulatory processes based on systematic methodologies.

Sami Barrit, Alexandre Niset

Occupational stress among health workers is a pervasive issue that affects individual well-being, patient care quality, and healthcare systems' sustainability. Current time-tracking solutions are mostly employer-driven, neglecting the unique requirements of health workers. In turn, we propose an open and decentralized worker-centered solution that leverages machine intelligence for occupational health and safety monitoring. Its robust technological stack, including blockchain technology and machine learning, ensures compliance with legal frameworks for data protection and working time regulations, while a decentralized autonomous organization bolsters distributed governance. To tackle implementation challenges, we employ a scalable, interoperable, and modular architecture while engaging diverse stakeholders through open beta testing and pilot programs. By bridging an unaddressed technological gap in healthcare, this approach offers a unique opportunity to incentivize user adoption and align stakeholders' interests. We aim to empower health workers to take control of their time, valorize their work, and safeguard their health while enhancing the care of their patients.

Dong Quan Ngoc Nguyen

In this paper, we establish an explicit higher reciprocity law for the polynomial ring over a nonprincipal ultraproduct of finite fields. Such an ultraproduct can be taken over the same finite field, which allows to recover the classical higher reciprocity law for the polynomial ring $\mathbb{F}_q[t]$ over a finite field $\mathbb{F}_q$ that is due to Dedekind, Kühne, Artin, and Schmidt. On the other hand, when the ultraproduct is taken over finite fields of unbounded cardinalities, we obtain an explicit higher reciprocity law for the polynomial ring over an infinite field in both characteristics $0$ and $p >0$ for some prime $p$. We then use the higher reciprocity law to prove an analogue of the Grunwald--Wang theorem for such a polynomial ring in both characteristics $0$ and $p > 0$ for some prime $p$.

François Terrier

The explosion in the performance of Machine Learning (ML) and the potential of its applications are strongly encouraging us to consider its use in industrial systems, including for critical functions such as decision-making in autonomous systems. While the AI community is well aware of the need to ensure the trustworthiness of AI-based applications, it is still leaving too much to one side the issue of safety and its corollary, regulation and standards, without which it is not possible to certify any level of safety, whether the systems are slightly or very critical.The process of developing and qualifying safety-critical software and systems in regulated industries such as aerospace, nuclear power stations, railways or automotive industry has long been well rationalized and mastered. They use well-defined standards, regulatory frameworks and processes, as well as formal techniques to assess and demonstrate the quality and safety of the systems and software they develop. However, the low level of formalization of specifications and the uncertainties and opacity of machine learning-based components make it difficult to validate and verify them using most traditional critical systems engineering methods. This raises the question of qualification standards, and therefore of regulations adapted to AI. With the AI Act, the European Commission has laid the foundations for moving forward and building solid approaches to the integration of AI-based applications that are safe, trustworthy and respect European ethical values. The question then becomes "How can we rise to the challenge of certification and propose methods and tools for trusted artificial intelligence?"

Gordon Owusu Boateng, Guisong Liu

The industrial Internet of Things (IIoT) and network slicing (NS) paradigms have been envisioned as key enablers for flexible and intelligent manufacturing in the industry 4.0, where a myriad of interconnected machines, sensors, and devices of diversified quality of service (QoS) requirements coexist. To optimize network resource usage, stakeholders in the IIoT network are encouraged to take pragmatic steps towards resource sharing. However, resource sharing is only attractive if the entities involved are able to settle on a fair exchange of resource for remuneration in a win-win situation. In this paper, we design an economic model that analyzes the multilateral strategic trading interactions between sliced tenants in IIoT networks. We formulate the resource pricing and purchasing problem of the seller and buyer tenants as a cooperative Stackelberg game. Particularly, the cooperative game enforces collaboration among the buyer tenants by coalition formation in order to strengthen their position in resource price negotiations as opposed to acting individually, while the Stackelberg game determines the optimal policy optimization of the seller tenants and buyer tenant coalitions. To achieve a Stackelberg equilibrium (SE), a multi-agent deep reinforcement learning (MADRL) method is developed to make flexible pricing and purchasing decisions without prior knowledge of the environment. Simulation results and analysis prove that the proposed method achieves convergence and is superior to other baselines, in terms of utility maximization.

Tongxin Hu, Zhuang Li, Xin Jin et al.

Annually, e-commerce platforms incur substantial financial losses due to trademark infringements, making it crucial to identify and mitigate potential legal risks tied to merchant information registered to the platforms. However, the absence of high-quality datasets hampers research in this area. To address this gap, our study introduces TMID, a novel dataset to detect trademark infringement in merchant registrations. This is a real-world dataset sourced directly from Alipay, one of the world's largest e-commerce and digital payment platforms. As infringement detection is a legal reasoning task requiring an understanding of the contexts and legal rules, we offer a thorough collection of legal rules and merchant and trademark-related contextual information with annotations from legal experts. We ensure the data quality by performing an extensive statistical analysis. Furthermore, we conduct an empirical study on this dataset to highlight its value and the key challenges. Through this study, we aim to contribute valuable resources to advance research into legal compliance related to trademark infringement within the e-commerce sphere. The dataset is available at https://github.com/emnlpTMID/emnlpTMID.github.io .