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7th International Conference on Algorithms, Computing and Artificial Intelligence, ACAI 2024

作者： Aksoy, Meltem Tsishetska, Veronika Computer Science Research Center Trustworthy Data Science and Security Technical University Dortmund Dortmund Germany Computer Science Technical University Dortmund Dortmund Germany

ISBN: (纸本)9798331529314

This study examines the adherence of ChatGPT (GPT-3.S and GPT-4) to fairness principles, specifically proportionality and equality, in negotiation scenarios. Three distinct negotiation contexts were explored: work-study program funding, company sale proceeds division, and employee bonus allocation. The models were prompted to prioritize proportionality, equality, or no specific fairness principle to assess how they respond in different ethical frameworks. A combination of qualitative and quantitative methods, including dialogue analysis, sentiment tracking, and fairness scoring, was used to evaluate their ne-gotiation behaviors. Results indicate that both models tend to favor proportionality by default, with GPT-4 showing greater adaptability compared to GPT-3.S. When explicitly directed to prioritize equality, the models followed these instructions but maintained a largely assertive negotiation style with limited engagement in dynamic exchanges. Sentiment analysis revealed that both models adopted increasingly positive tones as nego-tiations progressed. However, both versions were susceptible to prompt manipulation, potentially compromising fairness in some outcomes. This research highlights the potential of LLMs like ChatGPT in automating fair negotiations, though improvements in consistency, adaptability, and safeguards against manipulation are necessary for more robust, ethical applications in real-world scenarios. © 2024 IEEE.

关键词： Economic and social effects

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A blockchain-enabled wiki framework based on participatory design

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Digital Communications and Networks 2022年第6期8卷 995-1004页

作者： Zheng Xu Chaofan Liu Peng Zhang Tun Lu Ning Gu School of Computer Science Fudan UniversityShanghaiChina Shanghai Key Laboratory of Data Science Fudan UniversityShanghaiChina

The 5G mobile Internet facilitates contents generation for online communities and platforms through human-to-human ***,a well-known online community,uses wiki technology to build an encyclopedia through collective intelligence and *** wiki systems adopt a centralized implementation,and while existing studies have optimized the efficiency of the wiki systems'centralized implementation,these systems still suffer from a lot of problems,for example,opacity and *** the years,blockchain has brought a flurry of fervour and decentralization to the system architecture,meanwhile giving users a sense of trust and ***,an innovative blockchain-enabled wiki framework called DecWiki is proposed to build one transparent,truthful,collaborative and autonomous *** several participatory design iterations,we present DecWiki's detailed architecture and its implementation in the form of a smart contract and use the interplanetary file system to complement the big data ***,we use the trusted execution environment to secure sensitive information in the wireless ***,the system overhead and the acceptance of the prototype are *** experiments present its significant performance.

关键词： Blockchain Wiki Participatory design Trusted execution envir onment Smart contract

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Automated Theorem Provers Help Improve Large Language Model Reasoning 25th

Automated Theorem Provers Help Improve Large Language Model ...

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25th Conference on Logic for Programming, Artificial Intelligence and Reasoning, LPAR 2024

作者： McGinness, Lachlan Baumgartner, Peter School of Computer Science Australian National University and Data61 CSIRO Australia Data61 CSIRO and School of Computer Science Australian National University Australia

In this paper we demonstrate how logic programming systems and Automated first-order logic Theorem Provers (ATPs) can improve the accuracy of Large Language Models (LLMs) for logical reasoning tasks where the baseline performance is given by direct LLM solutions. We first evaluate LLM reasoning on steamroller problems using the PRON-TOQA benchmark. We show how accuracy can be improved with a neuro-symbolic architecture where the LLM acts solely as a front-end for translating a given problem into a formal logic language and an automated reasoning engine is called for solving it. However, this approach critically hinges on the correctness of the LLM translation. To assess this translation correctness, we secondly define a framework of syntactic and semantic error categories. We implemented the framework and used it to identify errors that LLMs make in the benchmark domain. Based on these findings, we thirdly extended our method with capabilities for automatically correcting syntactic and semantic errors. For semantic error correction we integrate first-order logic ATPs, which is our main and novel contribution. We demonstrate that this approach reduces semantic errors significantly and further increases the accurracy of LLM logical reasoning. © 2024, EasyChair. All rights reserved.

关键词： Semantics

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Cost-Efficient Edge Caching for NOMA-Enabled IoT Services

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China Communications 2024年第8期21卷 182-191页

作者： Chen Ying Xing Hua Ma Zhuo Chen Xin Huang Jiwei School of Computer Science Beijing Information Science and Technology UniversityBeijing 100101China Beijing Key Laboratory of Petroleum Data Mining China University of PetroleumBeijing 102249China

Mobile edge computing(MEC)is a promising paradigm by deploying edge servers(nodes)with computation and storage capacity close to IoT *** Providers can cache data in edge servers and provide services for IoT devices,which effectively reduces the delay for acquiring *** the increasing number of IoT devices requesting for services,the spectrum resources are generally *** order to effectively meet the challenge of limited spectrum resources,the Non-Orthogonal Multiple Access(NOMA)is proposed to improve the transmission *** this paper,we consider the caching scenario in a NOMA-enabled MEC *** the devices compete for the limited resources and tend to minimize their own *** formulate the caching problem,and the goal is to minimize the delay cost for each individual device subject to resource *** reformulate the optimization as a non-cooperative game *** prove the existence of Nash equilibrium(NE)solution in the game ***,we design the Game-based Cost-Efficient Edge Caching Algorithm(GCECA)to solve the *** effectiveness of our GCECA algorithm is validated by both parameter analysis and comparison experiments.

关键词： caching cost Internet of Things mobile edge computing non-orthogonal multiple access

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data Aggregation Point Placement and Subnetwork Optimization for Smart Grids

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computers, Materials & Continua 2025年第4期83卷 407-434页

作者： Tien-Wen Sung Wei Li Chao-Yang Lee Yuzhen Chen Qingjun Fang Fujian Provincial Key Laboratory of Big Data Mining and Applications College of Computer Science and MathematicsFujian University of TechnologyFuzhou350118China Department of Computer Science and Information Engineering National Yunlin University of Science and TechnologyYunlin640301TaiwanChina

To transmit customer power data collected by smart meters(SMs)to utility companies,data must first be transmitted to the corresponding data aggregation point(DAP)of the *** number of DAPs installed and the installation location greatly impact the whole *** the traditional DAP placement algorithm,the number of DAPs must be set in advance,but determining the best number of DAPs is difficult,which undoubtedly reduces the overall performance of the ***,the excessive gap between the loads of different DAPs is also an important factor affecting the quality of the *** address the above problems,this paper proposes a DAP placement algorithm,APSSA,based on the improved affinity propagation(AP)algorithm and sparrow search(SSA)algorithm,which can select the appropriate number of DAPs to be installed and the corresponding installation locations according to the number of SMs and their distribution locations in different *** algorithm adds an allocation mechanism to optimize the subnetwork in the *** is evaluated under three different areas and compared with other DAP placement *** experimental results validated that the method in this paper can reduce the network cost,shorten the average transmission distance,and reduce the load gap.

关键词： Smart grid data aggregation point placement network cost average transmission distance load gap

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Heterogeneous graph neural network based on dual-view graph structure augmentation 3

Heterogeneous graph neural network based on dual-view graph ...

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3rd International Symposium on computer Applications and Information Systems, ISCAIS 2024

作者： Chen, Jinjie College of Computer and Data Science Fuzhou University Fuzhou350108 China

ISBN: (纸本)9781510681316

Heterogeneous Graph Neural Networks (HGNNs) have emerged as powerful tools for handling heterogeneous graphs. However, current HGNNs often rely on meta-paths or intricate aggregation operations. In response, we introduce a heterogeneous graph neural network based on dual-view graph structure augmentation, which consists of three aggregation processes. By leveraging both node feature information and graph topology structure information, our method selecting homogeneous neighbors for nodes and constructing homogeneous views. Subsequently, it learns node representations through aggregation on these views and the original graph. Through extensive experiments on three widely used real-world heterogeneous graphs, our method demonstrates its simplicity and effectiveness, and outperforms the most of existing models in the task of heterogeneous graph node classification. © 2024 SPIE.

关键词： Graph neural networks

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Diffusion Generation of Lace Texture Images with Self-Attention Mechanism Introduced in Higher Resolution Layers 24

Diffusion Generation of Lace Texture Images with Self-Attent...

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2024 7th International Conference on computer Information science and Artificial Intelligence, CISAI 2024

作者： Deng, Kun Zhong, Shangping Chen, Kaizhi College of Computer and Data Science Fuzhou University Fujian Fuzhou China

ISBN: (纸本)9798400707254

Artificial design of lace images requires a lot of time and effort, and creativity is limited. Therefore, using generative models to assist in design becomes a good choice. However, the current lace generative models based on GAN (e.g. Stylegan2, etc.) have shortcomings in diversity and other aspects. Although current denoising diffusion models are able to generate more diverse images, the generated images suffer from texture loss and blurring. This article analyzes IDDPM (Improved Denoising Diffusion Probabilistic Models) and finds that its self-attention mechanism only focuses on the high-frequency components in the low-resolution layers (8x8, 16x16) of lace images. However, these high-frequency information are not sufficient to support good up-sampling effects, resulting in poor generation effect. In this paper, the self-attention mechanism is applied to the higher resolution layers (32x32, 64x64) to further enhance the high-frequency components in the high-resolution feature maps in the skip connections. In addition, to improve the up-sampling feature fusion effect of the model backbone network, this paper adopts quadratic linear interpolation as the up-sampling method. The analysis and experimental results show that the improved model generates lace images with rich details, maintaining symmetry and periodicity;in terms of diversity, the enhancement is obvious compared with the GAN method. © 2024 Copyright held by the owner/author(s).

关键词： Image texture

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Artificial Intelligence Support for the Prompt Identification and Understanding of the Broader Spectrum of Autism in Children 5

Artificial Intelligence Support for the Prompt Identificatio...

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5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Julaiha, A.G. Noorul Hemamalini, S. Preiya, V. Sathya Sathyamoorthy, K. Priyanka, V. Rajalakshmi Institute of Technology Centre for Data Science Department of Computer Science & Engineering Chennai India Panimalar Engineering College Department of Artificial Intelligence & Data Science Chennai India Panimalar Engineering College Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331518097

This research work presents a novel language intervention system for Tamil-speaking children with autism spectrum disorder (ASD). The system satisfies the considerable requirement for tools aimed at one more section of population that has actually been forgotten by mainstream economics. Traditional language learning applications often lack specificity for diverse linguistic contexts, focusing on general learning rather than the unique challenges faced by ASD children. The proposed solution leverages Deep Learning (DL) modules to transform partial spoken Tamil words into complete, child-friendly vocabulary. This system goes beyond simple word completion;it incorporates interactive features to engage children with ASD and promote active learning. Preliminary evaluations yielded promising results, suggesting the system's potential in enhancing communication skills among Tamil-speaking children with ASD. This paper research study reviews the limitations of existing language learning tools and the rationale behind employing a DL-based approach. We discuss the specific DL architecture (e.g., Recurrent Neural Networks) envisioned for the system and how it will be trained on a corpus of Tamil speech data. This study also investigate the potential advantages beyond vocabulary learning, such as improved sentence formulation and the development of practical language abilities. Future ambitions include broadening the system's scope to include more languages, refining the DL model through ongoing training with enriched datasets, and conducting extensive user research with varied ASD populations. Validation across demographics is critical to ensuring the system's efficiency and generality. This research has the potential to bridge the language acquisition gap for Tamil-speaking children with ASD, empowering them to develop crucial communication skills and fostering their social inclusion. © 2024 IEEE.

关键词： Recurrent neural networks

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GeoBeeDashboard: An Intelligent Geospatial Platform for Enhancing Waste Management in Tangerang City 13

GeoBeeDashboard: An Intelligent Geospatial Platform for Enha...

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13th IEEE International Conference on Communication, Networks and Satellite, COMNETSAT 2024

作者： Muljana, Richie Deffansyah, Syauqi Akmal Andrew, Hari Gunawan, Alexander Agung Santoso Irwansyah, Edy Pramudya, Fabian Surya Ario, Muhamad Keenan Syahputra, Muhammad Edo Achmad, Said School of Computer Science Bina Nusantara University Data Science Program Computer Science Department Jakarta11480 Indonesia Bina Nusantara University School of Computer Science Computer Science Department Jakarta11480 Indonesia Bina Nusantara University School of Computer Science Mathematics Department Jakarta11480 Indonesia

ISBN: (纸本)9798350368086

In this study, we created an Intelligent Geospatial Platform called GeoBeeDashboard. The GeoBeeDashboard combines advanced geospatial technologies and artificial intelligence (AI), allowing real-time data visualization and optimized decision-making across multiple sectors. The system architecture consists of a ***-based front end, a FastAPI-driven back end, and PostGIS for geospatial data management. One of the GeoBeeDashboard's primary use cases is addressing waste management issues in Tangerang City, Indonesia. As rapid urbanization increases waste generation, effective management becomes critical. The dashboard identifies critical trends and inefficiencies in Tangerang's districts by K-Means clustering waste management data from 2020 to 2023. Our findings identify distinct clusters, highlighting areas with effective waste management and those that require targeted interventions. The benefits that local government can get from GeoBeeDashboard by utilising insights based on clustering analysis of data per each district in Tangerang to optimise waste collection, allocate resources efficiently, and improve the policy-making process for each district in Tangerang. The GeoBeeDashboard improves local government capabilities by providing a scalable model for other cities facing similar urban challenges. In conclusion, GeoBeeDashboard provides local governments with a powerful tool for addressing waste management inefficiencies and fostering sustainable urban development. © 2024 IEEE.

关键词： Urban planning

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Proximal Policy Optimization-Based Intrusion Detection Model for Satellite Networks 5

Proximal Policy Optimization-Based Intrusion Detection Model...

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5th International Symposium on computer Engineering and Intelligent Communications, ISCEIC 2024

作者： Liang, Shuaishuai Feng, Huibin Wu, Jiawei He, Canfei College of Computer and Data Science Fuzhou University Fuzhou China School of Computer and Big Data Minjiang University Fuzhou China

ISBN: (纸本)9798331518677

As satellite network communication systems become an increasingly pivotal role in modern life, The routine maintenance of satellite networks is challenging due to limited resources and their susceptibility to interference. Satellite networks are more reliant on interruption detection systems than terrestrial networks. The importance of interruption detection systems in satellite networks is becoming increasingly evident. In this paper, we propose a novel intrusion detection model based on Proximal Policy Optimization (PPO). This method interacts with the environment to learn and optimize detection strategies, allowing for dynamic adaptation to environmental changes and attack patterns. We evaluated our model on benchmark datasets and compared its performance metrics against existing models. The results demonstrate that our proposed model significantly enhances overall accuracy and precision, and outperforms baseline models in terms of training convergence speed. © 2024 IEEE.

关键词： Network intrusion

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