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تعداد مقالات یافته شده: 33
ردیف عنوان نوع
1 ConTrib: Maintaining fairness in decentralized big tech alternatives by accounting work
ConTrib: حفظ انصاف در جایگزین های غیرمتمرکز فناوری بزرگ با کار حسابداری-2021
‘‘Big Tech’’ companies provide digital services used by billions of people. Recent developments, however, have shown that these companies often abuse their unprecedented market dominance for selfish interests. Meanwhile, decentralized applications without central authority are gaining traction. Decentralized applications critically depend on its users working together. Ensuring that users do not consume too many resources without reciprocating is a crucial requirement for the sustainability of such applications. We present ConTrib, a universal mechanism to maintain fairness in decentralized applications by accounting the work performed by peers. In ConTrib, participants maintain a personal ledger with tamper-evident records. A record describes some work performed by a peer and links to other records. Fraud in ConTrib occurs when a peer illegitimately modifies one of the records in its personal ledger. This is detected through the continuous exchange of random records between peers and by verifying the consistency of incoming records against known ones. Our simple fraud detection algorithm is highly scalable, tolerates significant packet loss, and exhibits relatively low fraud detection times. We experimentally show that fraud is detected within seconds and with low bandwidth requirements. To demonstrate the applicability of our work, we deploy ConTrib in the Tribler file-sharing application and successfully address free-riding behaviour. This two-year trial has resulted in over 160 million records, created by more than 94’000 users.
keywords: عادلانه بودن | برنامه های کاربردی غیر متمرکز | حسابداری | رایگان RiderPrevention | Fairness | Decentralizedapplications | Accountingmechanism | Free-riderprevention
مقاله انگلیسی
2 Hybridization of an interactive fuzzy methodology with a lexicographic min-max approach for optimizing a multi-period multi-product multi-echelon sustainable closed-loop supply chain network
ترکیبی از یک روش فازی تعاملی با رویکرد حداکثر واژه نامه نگاری برای بهینه سازی یک شبکه زنجیره تامین پایدار حلقه بسته چند محصولی چند دوره ای-2021
Here, a fuzzy multi-period multi-echelon multi-objective mixed-integer non-linear programming (MOMINLP) model is considered for a sustainable multi-product multi-site multi-distribution multi-customer supply chain in forward flow having multi-centers for collecting, checking, repairing and decomposing and multi-disposal cen- ters in the reverse flow. Minimizing the total cost of the closed-loop supply chain (CLSC), elevating the customer satisfaction degrees, minimizing the total waiting time, minimizing the manufacturing site greenhouse gases and minimizing the CO2 emissions from vehicles are considered as the objective functions. Furthermore, integration of strategic decisions of flow allocations and vehicle routing with tactical and operational decisions such as production and workforce planning and improving upon customer satisfaction are considered. Also, we are concerned with the stability of the model and the accuracy of the obtained solution. We consider uncertainty and propose an appropriate method to develop a fair optimization of the distribution of raw materials and products to the supply chain participants. The presented solution method initially considers an adaptation of the lexico- graphic min–max fairness approach to finding a short delay for the delivery time of all the existing flows between every two consecutive echelons of the CLSC network. Then, a service quality measure is introduced to evaluate the uncertain delivery time considering the delay unpleasantness measure (DUM) index and measure the delay unpleasantness of all the existing delivery time delays. The model is converted to an auxiliary crisp MOMINLP problem by taking appropriate strategies, and a novel interactive fuzzy approach is proposed to find a compromised solution. The effectiveness of the algorithm is illustrated through a generated case study. The validity of the proposed method is confirmed by comparing the obtained results with the ones obtained by some other valid approaches, making use of distance and dispersion measure functions. Computational results show the proposed fuzzy method to be more efficient than other approaches. The encouraging results provide motivations for the use of our proposed fuzzy approach to solving other kinds of multi-objective mixed-integer models.
Keywords: Closed-loop supply chain | Fuzzy optimization | Lexicographic min–max fairness | Multi-objective mixed-integer non-linear programming | Uncertainty | Compromised solution
مقاله انگلیسی
3 Coordinating a closed loop supply chain with fairness concern by a constant wholesale price contract
هماهنگی یک زنجیره تامین حلقه بسته با رعایت انصاف توسط یک قرارداد ثابت قیمت عمده فروشی-2021
The literature on closed loop supply chains (CLSCs) has ignored advantageous inequality aversion while modelling the fairness concern of channel partners and demonstrated that coordinating a decentralised channel requires complex price contracts. In this paper, we show that a constant wholesale price contract can coordinate a decentralised channel in a manufacturer-led CLSC if the retailer’s advantageous inequality aversion is sufficiently strong. The result is valid for a range of equitable shares of the channel profit, such that the allocated share of the manufacturer is larger than that of the retailer, and the retailer’s share is greater than a minimum threshold. Used product collection rate and channel profit are higher when the retailer is inequality averse compared to when she is a profit maximiser. The results are independent of whether the end-of-use products are collected by the manufacturer or the retailer. We also show that the collection rate is higher, and both channel partners are better-off, under the manufacturer collection model. To obtain these results, we solve multistage sequential move games under the two collection models. We apply Karush–Kuhn–Tucker conditions for constrained optimisation, to determine the boundaries for the existence of the subgame perfect Nash equilibrium.
Keywords: Pricing | Channel coordination | Fairness | Inequality aversion | Wholesale price contract
مقاله انگلیسی
4 Decision-making and coordination of green closed-loop supply chain with fairness concern
تصمیم گیری و هماهنگی زنجیره تامین حلقه بسته سبز با رعایت انصاف-2021
This paper considers a green closed-supply chain consisting of a manufacturer and a retailer. A Stack- elberg game model of centralized decision-making and decentralized decision-making with manufac- turer’s fairness concern was constructed based on the consideration of retailer’s sales effort. The decision-making of supply chain members under the above two situations and their reasons are analyzed in depth. According to the model, a green closed-loop supply chain with profit sharing contract coordination fairness is designed. Finally, the correctness of the model is verified by numerical simu- lation. We generate our findings from three aspects, as follows: when the manufacturer has fair concern behavior, it is not conducive to the environmental performance of green products, resulting in waste of resources, but also forcing retailers to reduce sales efforts and increase the retail price of products. Finally, the benefits of green closed-loop supply chain are seriously damaged. The profit-sharing contract could improve the relationship between members of the supply chain to achieve sustainable economic and environmental development.© 2021 Elsevier Ltd. All rights reserved.
Keywords: Green closed-loop supply chain | Fairness concern | Decision model | Profit-sharing contract
مقاله انگلیسی
5 Takeovers, shareholder litigation, and the free-riding problem
تصرفات ، دعاوی سهامداران و مشکل آزاد سواری-2020
When shareholders of a target firm expect a value improving takeover to be successful, they are individually better off not tendering their shares to the buyer and the takeover potentially fails. Squeeze-out procedures can overcome this free-riding dilemma by allowing a buyer to enforce a payout of minority shareholders and seize complete control of the target firm. However, it is often argued that shareholder litigation restores the free-riding dilemma. Applying a sequential takeover game, we examine the two standard legal remedies of shareholders, the ‘action of avoidance’ and the judicial ‘price fairness review’ and demonstrate that it is not shareholder litigation that brings back the free-riding dilemma, but rather the strategic gambling of buyers for lower prices and flaws in the design and application of squeezeout laws. We also analyze a favorable change in jurisdiction of the German Federal Court and provide implications for legal policy.
Keywords: Squeeze-out | Appraisals | Entire fairness | Judicial review | Takeover bids
مقاله انگلیسی
6 Trustworthy AI in the Age of Pervasive Computing and Big Data
هوش مصنوعی قابل اعتماد در عصر محاسبات فراگیر و داده های بزرگ-2020
The era of pervasive computing has resulted in countless devices that continuously monitor users and their environment, generating an abundance of user behavioural data. Such data may support improving the quality of service, but may also lead to adverse usages such as surveillance and advertisement. In parallel, Artificial Intelligence (AI) systems are being applied to sensitive fields such as healthcare, justice, or human resources, raising multiple concerns on the trustworthiness of such systems. Trust in AI systems is thus intrinsically linked to ethics, including the ethics of algorithms, the ethics of data, or the ethics of practice. In this paper, we formalise the requirements of trustworthy AI systems through an ethics perspective. We specifically focus on the aspects that can be integrated into the design and development of AI systems. After discussing the state of research and the remaining challenges, we show how a concrete use-case in smart cities can benefit from these methods.
Index Terms: Artificial Intelligence | Pervasive Computing | Ethics | Data Fusion | Transparency | Privacy | Fairness | Accountability | Federated Learning
مقاله انگلیسی
7 Managing bottleneck congestion with incentives
مدیریت ازدحام گلوگاه با مشوق ها-2020
Incentive-Based Traffic Demand Management (IBTDM) is a strategy that adopts incentives to demotivate driving trips, or to redistribute demand across space and time. In this pa- per, we demonstrate the effectiveness of an IBTDM strategy that provides incentives to shift the commuting public’s departure times so that the queueing delay is reduced. Based on Vickrey’s bottleneck model, this paper considers the impact of incentive budget and market penetration rate on the optimal incentive profile for both homogeneous and het- erogeneous commuters. The resulting departure pattern created by the optimal incentive profile achieves Pareto Optimality. The results indicate that an optimal incentive profile is “U-shape”during the morning peak with a limited budget. Additionally, we find that the marginal benefit of incentive is diminishing. Lastly, although Pareto improvement is achieved, commuters with higher values of time are found to benefit more under the op- timal incentive design. It is also discovered that the incentive provider should promote IBTDM to the two ends of the income level of the commuters to achieve the lowest total system travel time under an insufficient marketing budget.
Keywords: Traffic demand management | Bottleneck model | Incentives | Point-queue model | Departure time | Equity
مقاله انگلیسی
8 Decision-making by machines: Is the ‘Law of Everything’ enough?
تصمیم گیری توسط ماشین ها: آیا "قانون همه چیز" کافی است؟-2020
Machines have moved from supporting decision-making processes of humans to making decisions for humans. This shift has been accompanied by concerns regarding the impact of decisions made by algorithms on individuals and society. Unsurprisingly, the delegation of important decisions to machines has therefore triggered a debate on how to regulate the automated decision-making practices. In Europe, policymakers have attempted to address these concerns through a combination of individual rights and due processes established in data protection law, which relies on other statutes, e.g., anti-discrimination law and restricting trade secret laws, to achieve certain goals. This article adds to the literature by disentangling the challenges arising from automated decision-making systems and focusing on ones arising without malevolence but merely as unwanted side-effects of increased automation. Such side-effects include ones arising from the internal processes leading to a decision, the impacts of decisions, as well as the responsibility for decisions and have consequences on an individual and societal level. Upon this basis the article discusses the redress mechanisms provided in data protection law. It shows that the approaches within data protection law complement one another, but do not fully remedy the identified side- effects. This is particularly true for side-effects that lead to systemic societal shifts. To that end, new paradigms to guide future policymaking discourse are being explored.
Keywords: Automated decision-making | Artificial intelligence | Data protection | Transparency | Fairness | Due process
مقاله انگلیسی
9 Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI
هوش مصنوعی قابل توضیح (XAI): مفاهیم ، طبقه بندی ها ، فرصت ها و چالش ها در برابر هوش مصنوعی مسئول-2020
In the last few years, Artificial Intelligence (AI) has achieved a notable momentum that, if harnessed appropriately, may deliver the best of expectations over many application sectors across the field. For this to occur shortly in Machine Learning, the entire community stands in front of the barrier of explainability, an inherent problem of the latest techniques brought by sub-symbolism (e.g. ensembles or Deep Neural Networks) that were not present in the last hype of AI (namely, expert systems and rule based models). Paradigms underlying this problem fall within the so-called eXplainable AI (XAI) field, which is widely acknowledged as a crucial feature for the practical deployment of AI models. The overview presented in this article examines the existing literature and contributions already done in the field of XAI, including a prospect toward what is yet to be reached. For this purpose we summarize previous efforts made to define explainability in Machine Learning, establishing a novel definition of explainable Machine Learning that covers such prior conceptual propositions with a major focus on the audience for which the explainability is sought. Departing from this definition, we propose and discuss about a taxonomy of recent contributions related to the explainability of different Machine Learning models, including those aimed at explaining Deep Learning methods for which a second dedicated taxonomy is built and examined in detail. This critical literature analysis serves as the motivating background for a series of challenges faced by XAI, such as the interesting crossroads of data fusion and explainability. Our prospects lead toward the concept of Responsible Artificial Intelligence , namely, a methodology for the large-scale implementation of AI methods in real organizations with fairness, model explainability and accountability at its core. Our ultimate goal is to provide newcomers to the field of XAI with a thorough taxonomy that can serve as reference material in order to stimulate future research advances, but also to encourage experts and professionals from other disciplines to embrace the benefits of AI in their activity sectors, without any prior bias for its lack of interpretability.
Keywords: Explainable Artificial Intelligence | Machine Learning | Deep Learning | Data Fusion | Interpretability | Comprehensibility | Transparency | Privacy | Fairness | Accountability | Responsible Artificial Intelligence
مقاله انگلیسی
10 Finding appropriate settings for fairness and engagement in a newly designed game through self-playing AI program: A case study using Japanese crossword game MyoGo Renju’
یافتن تنظیمات مناسب برای انصاف و تعامل در یک بازی تازه طراحی شده از طریق برنامه هوش مصنوعی خود-بازی: یک مطالعه موردی با استفاده از بازی جدول کلمات متقاطع ژاپنی "MyoGo Renju"-2020
This paper explores an innovative way to find the comfortable settings of a newly designed game under development using a computer program. A Japanese crossword game ‘MyoGo Renju’ has been chosen as a benchmark for this research, whereas some important aspects such as fairness and engagement are evaluated to find the most optimum settings for the player. The game ‘MyoGo Renju’ can be played by Japanese language learners with competitive purposes as well as educational purposes. An artificial intelligence program of the ‘MyoGo Renju’ has been developed and various parameters had been evaluated such as board size, word length, bonus point rule, block system, weighted score system, and round mode. The experiment is performed using selfplaying Myogo AI where some interesting results have been demonstrated where the 5 × 5 board with the minimum word length of 3-g, 3 number of blocks, and 15 Hiragana characters chosen in a single round is expected to provide the best-expected fairness and engagement to the overall game experiences. The limitations and future works of the research are also discussed.
Keywords: Crossword puzzle game | Comfortable settings | Game refinement theory | Game design | Fairness
مقاله انگلیسی
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