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arXiv 2023年

作者： Yajadda, Seyed Hassan Safaei, Farshad Faculty of Computer Science and Engineering Shahid Beheshti University Tehran Iran

Despite technological advancements, the significance of interdisciplinary subjects like complex networks has grown. Exploring communication within these networks is crucial, with traffic becoming a key concern due to the expanding population and increased need for connections. Congestion tends to originate in specific network areas but quickly proliferates throughout. Consequently, understanding the transition from a flow-free state to a congested state is vital. Numerous studies have delved into comprehending the emergence and control of congestion in complex networks, falling into three general categories: soft strategies, hard strategies, and resource allocation strategies. This article introduces a routing algorithm leveraging reinforcement learning to address two primary objectives: congestion control and optimizing path length based on the shortest path algorithm, ultimately enhancing network throughput compared to previous methods. Notably, the proposed method proves effective not only in Barabási-Albert scale-free networks but also in other network models such as Watts-Strogatz (small-world) and Erdös-Rényi (random network). Simulation experiment results demonstrate that, across various traffic scenarios and network topologies, the proposed method can enhance efficiency criteria by up to 30% while reducing maximum node congestion by five times. © 2023, CC BY-NC-SA.

关键词： Complex networks

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Implementing a Serious Traffic Game to Improve Children’s Road Awareness Skills

Implementing a Serious Traffic Game to Improve Children’s R...

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Electrical and Information Technology (IEIT), International Conference on

作者： Azadeh Nahvi Maedeh Mosharraf Computer Engineering Student Shahid Beheshti University Tehran Iran Faculty of Computer Science and Engineering Shahid Beheshti University Tehran Iran

To ensure children's safety in streets, it is crucial to teach them the basic rules of traffic. Given the risks associated with training in the real world and the ineffectiveness of traditional education in these situations, it is essential to teach road awareness through simulations and games to young traffic users. Children can be immersed in the virtual environment and feel the sense of presence in the real environment. Children can experience many risky scenarios in the virtual world and learn how to avoid them. This paper introduces a serious game to educate children about traffic laws. Seven crucial traffic regulations for children when crossing the street are presented by two scenarios. The learning process is carried out during playing in three stages: pretest, test, and posttest. Children receive feedback regarding the suitability of their traffic behavior using the coins they acquire during the game. Both of the game's scenarios were found to be effective at helping players understand traffic laws, according to the pretest and posttest results.

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Identifying Children's Personality Styles through Drawing Analysis using Machine Learning

Identifying Children's Personality Styles through Drawing An...

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Conference on Information and Knowledge Technology

作者： Maedeh Mosharraf Faezeh Banabazi Faculty of Computer Science and Engineering Shahid Beheshti University Tehran Iran Computer Engineering student Shahid Beheshti University Tehran Iran

The impact of personality styles on individuals' behavior and choices is undeniable. With the advancements in datamining and machine learning techniques, various methods have been proposed to automatically identify personality styles by processing extensive data and exploring some human behavioral patterns. In this article, we examine children's personality styles by analyzing their drawings using some machine learning techniques. We employ the Murphy-Meisgeier Type Indicator for Children (MMTIC) questionnaire results to train k-nearest neighbors, random forest, and decision tree classifiers. Although we focus on a limited set of drawing features in this study, the results obtained are remarkably significant. We believe that the findings of this study will have a profound impact on early identification of children's personality styles and their decision-making throughout their lives.

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Validity and Reliability of Persian Version of Cyber-Bullying /Victimization Experience Questionnaire (CBVEQ) Among Iranian Adolescents

International Journal of Bullying Prevention

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International Journal of Bullying Prevention 2024年 1-12页

作者： Nooripour, Roghieh Hosseinian, Simin Ghanbari, Nikzad Wisniewski, Pamela Sikström, Sverker Department of Counseling Faculty of Education and Psychology Alzahra University Tehran Iran Faculty of Education and Psychology Shahid Beheshti University (SBU) Tehran Iran Department of Computer Science Faculty Fellow Flowers Family in Engineering Vanderbilt University Nashville United States Cognitive Psychology Chair of the Cognitive Division Department of Psychology Lund University Lund Sweden

This study aimed to measure validity and reliability of the Persian version of the Cyber-Bullying/Victimization Experience Questionnaire (CBVEQ) among Iranian adolescents. This cross-sectional validation study was conducted on Iranian adolescents between January 2022 and September 2022. Online questionnaires were completed by a total of 1439 Iranian high school students, with an age range of 14–18 years (M = 15.93 years, SD = 1.43). Participants completed Cyber-Bullying/Victimization Experience Questionnaire (CBVEQ), Online Disinhibition, Sensation Seeking, Empathy, and School Connectedness questionnaires. The validity and reliability of the CBVEQ were assessed through measures including Cronbach's alpha, construct validity via Confirmatory Factor Analysis (CFA), and evaluations of divergent and convergent validity. The CFA indicated that a two-factor model provides a good fit for the data: (_sbX² = 3011.31 (p < 0.01);SRMR = 0.06;CFI = 0.96;NFI = 0.96;IFI = 0.96;PNFI = 0.86;GFI = 0.83;RMSEA = 0.088). Cronbach’s alpha coefficient for the self-escapism was 0.89. There was a significant positive relationship between CBVEQ with online disinhibition (r = 0.47;P < 0.01) and sensation seeking (r = 0.44;P < 0.01). The finding showed significant negative relationship between CBVEQ score with school connectedness (SC) (r = -0.35;P < 0.01), and empathy (r = -0.42;P < 0.01). In addition, the findings have shown a relationship between CBVEQ and maternal educational status. Iranian researchers and specialists can use this scale for research or assessment purposes in Iranian adolescents to expand the existing knowledge about preventing and reducing cyberbullying. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Adolescent Cyber Bullying Cyber Victimization Measurement Validity

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Formality Style Transfer in Persian

arXiv

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arXiv 2024年

作者： Falakaflaki, Parastoo Shamsfard, Mehrnoush Faculty of Computer Science and Engineering Shahid Beheshti University Tehran Iran School of Electronic Engineering and Computer Science Queen Mary University of London United Kingdom

This study explores the formality style transfer in Persian, particularly relevant in the face of the increasing prevalence of informal language on digital platforms, which poses challenges for existing Natural Language Processing (NLP) tools. The aim is to transform informal text into formal while retaining the original meaning, addressing both lexical and syntactic differences. We introduce a novel model, Fa-BERT2BERT, based on the Fa-BERT architecture, incorporating consistency learning and gradient-based dynamic weighting. This approach improves the model's understanding of syntactic variations, balancing loss components effectively during training. Our evaluation of Fa-BERT2BERT against existing methods employs new metrics designed to accurately measure syntactic and stylistic changes. Results demonstrate our model's superior performance over traditional techniques across various metrics, including BLEU, BERT score, Rouge-l, and proposed metrics underscoring its ability to adeptly navigate the complexities of Persian language style transfer. This study significantly contributes to Persian language processing by enhancing the accuracy and functionality of NLP models and thereby supports the development of more efficient and reliable NLP applications, capable of handling language style transformation effectively, thereby streamlining content moderation, enhancing data mining results, and facilitating cross-cultural communication. © 2024, CC BY-SA.

关键词： Syntactics

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ChatGPT: Applications, Opportunities, and Threats

ChatGPT: Applications, Opportunities, and Threats

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2023 Systems and Information engineering Design Symposium, SIEDS 2023

作者： Bahrini, Aram Khamoshifar, Mohammadsadra Abbasimehr, Hossein Riggs, Robert J. Esmaeili, Maryam Majdabadkohne, Rastin Mastali Pasehvar, Morteza University of Virginia Department of Systems and Information Engineering CharlottesvilleVA United States Iran University of Science and Technology School of Computer Engineering Tehran Iran Azarbaijan Shahid Madani University Faculty of Information Technology and Computer Engineering Tabriz Iran Alzahra University Faculty of Engineering Department of Industrial Engineering Tehran Iran The American University Faculty of Medical Sciences Department of Medicine Managua Nicaragua Islamic Azad University Faculty of Engineering Department of Industrial Engineering Tehran Iran

ISBN: (纸本)9798350300642

Developed by OpenAI, ChatGPT (Conditional Generative Pre-trained Transformer) is an artificial intelligence technology that is fine-tuned using supervised machine learning and reinforcement learning techniques, allowing a computer to generate natural language conversation fully autonomously. ChatGPT is built on the transformer architecture and trained on millions of conversations from various sources. The system combines the power of pre-trained deep learning models with a programmability layer to provide a strong base for generating natural language conversations. In this study, after reviewing the existing literature, we examine the applications, opportunities, and threats of ChatGPT in 10 main domains, providing detailed examples for the business and industry as well as education. We also conducted an experimental study, checking the effectiveness and comparing the performances of GPT-3.5 and GPT-4, and found that the latter performs significantly better. Despite its exceptional ability to generate natural-sounding responses, the authors believe that ChatGPT does not possess the same level of understanding, empathy, and creativity as a human and cannot fully replace them in most situations. © 2023 IEEE.

关键词： Deep learning

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Deepfake Detection of Occluded Images Using a Patch-based Approach

arXiv

引用

arXiv 2023年

作者： Soleimani, Mahsa Nazari, Ali Moghaddam, Mohsen Ebrahimi Faculty of Computer Science and Engineering Shahid Beheshti University Evin Tehran*** Iran

DeepFake involves the use of deep learning and artificial intelligence techniques to produce or change video and image contents typically generated by GANs. Moreover, it can be misused and leads to fictitious news, ethical and financial crimes, and also affects the performance of facial recognition systems. Thus, detection of real or fake images is significant specially to authenticate originality of people’s images or videos. One of the most important challenges in this topic is obstruction that decreases the system precision. In this study, we present a deep learning approach using the entire face and face patches to distinguish real/fake images in the presence of obstruction with a three-path decision: first entire-face reasoning, second a decision based on the concatenation of feature vectors of face patches, and third a majority vote decision based on these features. To test our approach, new datasets including real and fake images are created. For producing fake images, StyleGAN and StyleGAN2 are trained by FFHQ images and also StarGAN and PGGAN are trained by CelebA images. The CelebA and FFHQ datasets are used as real images. The proposed approach reaches higher results in early epochs than other methods and increases the SoTA results by 0.4%-7.9% in the different built data-sets. Also, we have shown in experimental results that weighing the patches may improve accuracy. © 2023, CC BY-NC-SA.

关键词： Face recognition

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Towards Study of Research Topics Evolution in Artificial Intelligence based on Topic Embedding 11

Towards Study of Research Topics Evolution in Artificial Int...

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11th International Conference on computer engineering and Knowledge, ICCKE 2021

作者： Harikandeh, Seyyed Reza Taher Aliakbary, Sadegh Taheri, Soroush Shahid Beheshti University Faculty of Computer Science and Engineering Tehran Iran

ISBN: (纸本)9781665402088

Artificial Intelligence (AI) is one of the hottest trending research topics of computer science. As it is a fast-growing domain, analyzing its changes during different periods will help researchers analyze its evolution in order to develop their career path according to possible changes in the future. In this paper, we investigate how AI subfields are becoming closer or further from each other. This research also offers a general methodology for studying the relationships and interactions of research topics over time. In this regard, we adopt a topic embedding approach to examine how various sub-fields of AI evolve over time. Topic embedding is a method to convert topics to meaningful mathematical models so that their latent relationships remain. Besides being computationally efficient, this method has proven the ability to reveal significant patterns regarding the relationships between sub-domains. © 2021 IEEE.

关键词： Embeddings

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A Novel Approach for Mining Team Leaders in Community Question Answering 21

A Novel Approach for Mining Team Leaders in Community Questi...

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11th ACM SIGIR International Conference on Theory of Information Retrieval, ICTIR 2021

作者： Entezari Maleki, Sepideh Neshati, Mahmood Faculty of Computer Science and Engineering Shahid Beheshti University Tehran Iran

ISBN: (纸本)9781450386111

Expert finding is the task of ranking experts for a given topic. This task can be utilized for finding suitable people for a job. The problem of finding a team leader for coaching a team is a practical and industrial issue that can be addressed by employing the expert finding task. On the other hand, this is a challenging problem because for the position of team leader, having knowledge in the required skills solely is not enough. A suitable leader should have abilities, including communication, mentoring, solving problems, etc. In this paper, we proposed a model to address this problem. Our proposed model ranks candidates for a given team. In this model, team members vote for candidates based on the centrality metrics. Our experiments on two real-world and large datasets collected from Stackoverflow indicated that our proposed method outperforms the baseline. © 2021 ACM.

关键词： Large dataset

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Adversarial Weakly Supervised Domain Adaptation for Few Shot Sentiment Analysis 7

Adversarial Weakly Supervised Domain Adaptation for Few Shot...

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7th International Conference on Web Research, ICWR 2021

作者： Taher, Seyyed Ehsan Shamsfard, Mehrnoush Shahid Beheshti University Faculty of Computer Science and Engineering Tehran Iran

ISBN: (纸本)9781665404266

The ability of deep neural networks to generate state-of-the-art results on many NLP problems has been apparent to everyone for some years now. However, when there is not enough labeled data or the test dataset has domain shift, these networks face many challenges and results are getting *** this article, we present a method for adapting the domain from formal to colloquial (in sentiment classification). Our method uses two approaches, adversarial training and weak supervision, and only needs a few shots of labeled *** the first stage, we label a crawled dataset (containing colloquial and formal sentences) with weakly supervised sentiment tags using a sentiment vocabulary network. Then we fine-tune a pre-trained model with adversarial training on this weak dataset to generate domain-independent representations. In the last stage, we train the above fine-tuned neural network with just 50 samples of data (formal domain) and test it on *** results show that our method outperforms the state-of-the-art model (Pars BERT) on the same data with 15% higher F1 measure. © 2021 IEEE.

关键词： Sentiment analysis

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