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The Dean’s Office The Education Department is a core unit within the faculty, responsible for planning, organizing, and overseeing educational activities. It works closely with academic staff to design and update course curricula, coordinate class schedules, and enhance the overall quality of teaching. The department aims to provide a supportive environment for effective learning and the academic development of students. It also plays a key role in academic advising, addressing educational concerns, and organizing consultation sessions. By applying modern teaching methods and responding to current educational needs, the Education Department strives to improve the learning process and contribute to student success.
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Baarslag, T. ,
Elfrink, T. ,
Nassiri-mofakham, F. ,
Koça, T. ,
Kaisers, M. ,
Aydogan, R. Publication Date: 0
pp. 390-397
This study presents Bargaining Chips: a framework for one-to-many concurrent composite negotiations, where multiple deals can be reached and combined. Our framework is designed to mirror the salient aspects of real-life procurement and trading scenarios, in which a buyer seeks to acquire a number of items from different sellers at the same time. To do so, the buyer needs to successfully perform multiple concurrent bilateral negotiations as well as coordinate the composite outcome resulting from each interdependent negotiation. This paper contributes to the state of the art by: (1) presenting a model and test-bed for addressing such challenges; (2) by proposing a new, asynchronous interaction protocol for coordinating concurrent negotiation threads; and (3) by providing classes of multi-deal coordinators that are able to navigate this new one-to-many multi-deal setting. We show that Bargaining Chips can be used to evaluate general asynchronous negotiation and coordination strategies in a setting that generalizes over a number of existing negotiation approaches. © 2021 Owner/Author.
Publication Date: 0
pp. 148-152
The Internet of Things (IoT) enables smart Things to communicate via the Internet. Things are growing in number, and their need for multiple resources in a complementary manner engenders serious problems in resource allocation. Combinatorial Auctions (CA) are the optimal market mechanism for allocating such indivisible bundles. Since the abundance of bundles in the IoT market makes it impossible to bid on all bundles, Things express their preferences on some (and not all) bundles to make the winner determination amenable. We address the winner determination problem by proposing an allocation mechanism based on social choice methods, which operates on the number of requested resources, the number of bundles, the offered price, and the preferred weight of each bundle. These methods include Borda, Copeland, Average without Misery, Least Misery, and Hare. Finally, we demonstrate the evaluation of these methods in terms of execution time and envy-freeness among the Things. © 2023 IEEE.
For a long time, culture has been an influencing parameter in negotiations. Growth of international trades and business competitions has increased the importance of negotiations among countries and different cultures. Developing new technologies, particularly the use of artificial intelligence in electronic trading areas, has provided us with the application of intelligent agents to resolve challenges in e- negotiations. In this study, a model is developed and implemented to arm intelligent agents with time-sensitivity cultural parameter in negotiations in electronic commerce context. The seller's proposals are offered based on the estimated value of the buyers' time-sensitivity in delivering the products. It starts from the highest price which satisfies the buyer's time sensitivity. The simulations are based on the Salacuse's Cultural dataset related to five countries, Finland, Mexico, Turkey, India, and the United States of America. The negotiation algorithms were implemented in Java platform and MySQL database for both cases of with and without cultural differences in time sensitivity. The evaluation shows that the cultural-based model starts the negotiation from an offer close to the buyer's desire. This yields less number of rounds and total negotiation time period. The simulation results also show that the buyer's budget as an economic factor can be effective in the negotiation outcomes in some cases. © 2014 IEEE.
Publication Date: 0
pp. 197-201
Automated negotiating agents are usually designed and implemented in a general way so that they can negotiate successfully in front of a vast variety of opponents. In the real world, most opponents are single-peaked. Gaussian agents that use such distribution function to rate the negotiation items are important sorts of such opponents. Modeling the opponents is of great importance since it enables us to adjust our next decisions accordingly. This can bring us short-time compromises, ideal eventual utility, more satisfaction, and so on. In negotiating with Gaussian opponents, the estimation of the opponent's peak point is the core. In this regard, we have paid particular attention to how accurate the existing automated agents attended in Automated Negotiating Agents Competition (ANAC) during 2010-2019 can model Gaussian bidders and showed the result of the experiments. © 2021 IEEE.
Feedback is an essential component of learning, as it helps students identify their strengths and weaknesses, and improve their performance. However, the students may not be able to understand how their work has been judged. One way to address this issue is to let the students assess and comment on the work of their peers, Peer Assessment (PA). PA has benefits such as enhancing learning outcomes, developing self-assess and critical thinking skills, and fostering collaboration. However, PA also poses some challenges such as ensuring fairness, anonymity, and reliability. In this study, we designed and implemented an anonymous electronic PA for a few classes participating EU-Iran STEM/UNITEL project. We used several digital tools to simulate a double-blind PA process, and a rubric based on the students' own criteria and weights. The grades collected from 70 students represent positive feedback. We discuss the functionality, advantages, and limitations of our approach. © 2024 IEEE.
Delay and capacity (throughput) are two important parameters to route data packets in Mobile Ad Hoc Networks (MANETs). In this paper, a new connectionless routing algorithm has been proposed to overcome the performance limit. The proposed algorithm is an extension of dynamic virtual route (DVR) algorithm. Mobility degree of nodes' neighborhood is used to calculate two mobility metrics. Mobility metrics are utilized to establish a more stable route between source and destination. Simulation study shows that the proposed algorithm can improve the network throughput and decrease average end-to-end delay significantly. © 2014 IEEE.
The 13th automated negotiation competition was held in 2 leagues (ANL2022 and SCML2022) in conjunction with the 31st IJCAI conference. The ANL for 2022 is bilateral negotiation under the SOAP protocol. Agents are allowed to learn from their previous negotiations. The agents could have 3 main BOA components: a Bidding strategy that decides which bid and when must be sent to the opponent, an Opponent model that tries to model the opponent's preferences, and an Acceptance strategy that decides whether to accept the opponent's offer or not. This paper explains our LuckyAgent2022's BOA components and its learning methods over negotiation sessions. To improve its utility over sessions, we propose SLM, a LSN Stop-Learning mechanism, to prevent overfitting by adapting it to a multi-armed bandit problem. It finds the best value for variables of a time-dependent bidding strategy for the opponent. © 2022 IEEE.
Publication Date: 0
pp. 31-34
In any negotiation, one of the most important parts of the negotiator's task is deciding whether or not to accept the opponent's offer. Actually, the most challenging thing is answering this question: which offer and when must be accepted? A wide range of simple to sophisticated acceptance strategies have been proposed: simple acceptance strategies which have the constant threshold value and sophisticated strategies that notice both utility and time in order to determine acceptance thresholds. This study introduces a novel statistical acceptance strategy with considering the similarity between the opponent's offer and our previous offers, which is combined with existing usual acceptance strategies. Experiments show our strategy has advantages against the state-of-the-art acceptance strategies. © 2020 IEEE.
Taghiyarrenani, Zahra ,
Fanian, A. ,
Mahdavi, E. ,
Mirzaei, A. ,
Farsi, Hamed Publication Date: 0
In the past decades, machine learning based intrusion detection systems have been developed. This paper discloses a new aspect of machine learning based intrusion detection systems. The proposed method detects normal and anomaly behaviors in the desired network where there are not any labeled samples as training dataset. That is while a plenty of labeled samples may exist in another network that is different from the desired network. Because of the difference between two networks, their samples produce in different manners. So, direct utilizing of labeled samples of a different network as training samples does not provide acceptable accuracy to detect anomaly behaviors in the desired network. In this paper, we propose a transfer learning based intrusion detection method which transfers knowledge between the networks and eliminates the problem of providing training samples that is a costly procedure. Comparing the experimental results with the results of a basic machine learning method (SVM) and also baseline method(DAMA) shows the effectiveness of the proposed method for transferring knowledge for intrusion detection systems. © 2018 IEEE.
Mala, H. ,
Dakhil-alian, M. ,
Brenjkoub, M. Publication Date: 2006
2pp. 3304-3308
Proxy signature schemes allow a proxy signer to generate a proxy signature on behalf of an original signer. In this paper we propose an Identity-based proxy signature scheme from bilinear pairings. In comparison with the Xu et al's scheme, our scheme is more efficient in computation and requires fewer pairing operations especially in verification phase. © 2006 IEEE.
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