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arXiv 2023年

作者： Qi, Houyi Liwang, Minghui Wang, Xianbin Li, Li Gong, Wei Jin, Jian Jiao, Zhenzhen The School of Informatics Xiamen University Fujian China The Department of Electrical and Computer Engineering Western University ON Canada The Department of Control Science and Engineering Tongji University Shanghai China The Shanghai Research Institute for Intelligent Autonomous Systems Tongji University Shanghai China The Research Institute of Industrial Internet of Things CAICT China The iF-Labs Beijing Teleinfo Technology Co. Ltd. CAICT China

Cloud-aided mobile edge networks (CAMENs) allow edge servers (ESs) to purchase resources from remote cloud servers (CSs), while overcoming resource shortage when handling computation-intensive tasks of mobile users (MUs). Conventional trading mechanisms (e.g., onsite trading) confront many challenges, including decision-making overhead (e.g., latency) and potential trading failures. This paper investigates a series of cross-layer matching mechanisms to achieve stable and cost-effective resource provisioning across different layers (i.e., MUs, ESs, CSs), seamlessly integrated into a novel hybrid paradigm that incorporates futures and spot trading. In futures trading, we explore an overbooking-driven aforehand cross-layer matching (OA-CLM) mechanism, facilitating two future contract types: contract between MUs and ESs, and contract between ESs and CSs, while assessing potential risks under historical statistical analysis. In spot trading, we design two backup plans respond to current network/market conditions: determination on contractual MUs that should switch to local processing from edge/cloud services;and an onsite cross-layer matching (OS-CLM) mechanism that engages participants in real-time practical transactions. We next show that our matching mechanisms theoretically satisfy stability, individual rationality, competitive equilibrium, and weak Pareto optimality. Comprehensive simulations in real-world and numerical network settings confirm the corresponding efficacy, while revealing remarkable improvements in time/energy efficiency and social welfare. Copyright © 2023, The Authors. All rights reserved.

关键词： Decision making

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Accuracy of the TOPSIS Method with Different Input Data 26

Accuracy of the TOPSIS Method with Different Input Data

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26th International Conference on Knowledge-Based and intelligent Information and Engineering systems, KES 2022

作者： Król, Robert Wieckowski, Jakub Watróbski, Jaroslaw Doctoral School of University of Szczecin ul. Mickiewicza 16 Szczecin70-383 Poland Research Team on Intelligent Decision Support Systems Department of Artificial Intelligence Methods and Applied Mathematics Faculty of Computer Science and Information Technology West Pomeranian University of Technology Szczecin ul. Zolnierska 49 Szczecin71-210 Poland Institute of Management University of Szczecin ul. Cukrowa 8 Szczecin71-004 Poland

Using fuzzy logic in multi-criteria problems allows real-world problems to be modeled with greater accuracy. In addition, it is possible to solve problems in which all data are incomplete. The difficulty may be the choice of an appropriate technique leading to a ranking calculation. Methods fully integrated with fuzzy logic can be used. It is also possible to convert fuzzy data to crisp values and then apply traditional MCDA methods. In this paper, the performance of the TOPSIS method in a crisp and fuzzy environment was analyzed. The approaches of operating fully on uncertain data when calculating preference values were compared with converting data to crisp values using selected membership functions. The research has shown that significant differences and discrepancies in how the alternatives are classified are noticeable in the rankings. © 2022 The Authors. Published by Elsevier B.V.

关键词： Fuzzy logic

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Towards robust results in Multi-Criteria Decision Analysis: ranking reversal free methods case study 26

Towards robust results in Multi-Criteria Decision Analysis: ...

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26th International Conference on Knowledge-Based and intelligent Information and Engineering systems, KES 2022

作者： Wieckowski, Jakub Król, Robert Watróbski, Jaroslaw Research Team on Intelligent Decision Support Systems Department of Artificial Intelligence Methods and Applied Mathematics Faculty of Computer Science and Information Technology West Pomeranian University of Technology Szczecin ul. Zolnierska 49 Szczecin71-210 Poland Doctoral School of University of Szczecin ul. Mickiewicza 16 Szczecin70-383 Poland Institute of Management University of Szczecin ul. Cukrowa 8 Szczecin71-004 Poland

The practical capabilities of decision support systems are beneficial in many complex problems. They aim to equip the decision-maker with knowledge about the preferred choices from the set under consideration. Often, there are minor differences between the alternatives analyzed, making it necessary to determine whether changes in the parameters characterizing them can influence their final position. For this purpose, a sensitivity analysis of the solutions can be used to examine the resistance of the results to changes. In this paper, we address the problem of performing the sensitivity analysis using MCDA methods. Most studies focus on testing the sensitivity of rankings of popular methods. However, their main disadvantage is their susceptibility to the phenomenon of ranking reversal. It may distort the results obtained in a sensitivity analysis. Therefore, it is worth using methods resistant to the ranking reversal phenomenon for this purpose. The SPOTIS and COMET methods were used in a theoretical multi-criteria problem, where the calculated results were then subjected to sensitivity analysis. The study showed that the SPOTIS method tends to be more robust to the computed results. © 2022 The Authors. Published by Elsevier B.V.

关键词： Sensitivity analysis

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Adaptive Three Layer Hybrid Reconfigurable intelligent Surface for 6G Wireless Communication: Trade-Offs and Performance

Adaptive Three Layer Hybrid Reconfigurable Intelligent Surfa...

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IEEE Asia Pacific Conference on Wireless and Mobile

作者： Rashed Hasan Ratul Muhammad Iqbal Tabinda Ashraf Jen-Yi Pan Yi-Han Wang Shao-Yu Lien Department of Electrical and Electronic Engineering Islamic University of Technology (IUT) Dhaka Bangladesh Department of Communication Engineering National Chung Cheng University (CCU) Chiayi Taiwan Department of Computer Science and Information Engineering National Chung Cheng University (CCU) Chiayi Taiwan Institute of Intelligent Systems College of Artificial Intelligence National Yang Ming Chiao Tung University (NYCU) Tainan Taiwan

A potential candidate technology for the development of future 6G networks has been recognized as Reconfigurable intelligent Surface (RIS). However, due to the variation in radio link quality, traditional passive RISs only accomplish a minimal signal gain in situations with strong direct links between user equipment (UE) and base station (BS). In order to get over this fundamental restriction of smaller gain, the idea of active RISs might be a suitable solution. In contrast to current passive RIS, which simply reflects and directs signals without any additional amplification, active RISs have the ability to enhance reflected signals by the incorporation of amplifiers inside its elements. However, with additional amplifiers, apart from the relatively complex attributes of RIS-assisted arrangements, the additional energy consumption of such technologies is often disregarded. So, there might be a tradeoff between the additional energy consumption for the RIS technologies and the overall gain acquired by deploying this potential advancement. The objective of this work is to provide a primary idea of a three-layer hybrid RIS-assisted configuration that is responsive to both active and passive RIS, as well as an additional dormant or inactive state. The single RIS structure should be capable of adjusting its overall configuration in response to fluctuations in transmit power and radio link quality. Furthermore, our fabricated passive RIS-assisted structure verifies a portion of the proposed idea, with simulations highlighting its advantages over standalone passive or active RIS-assisted technologies.

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ItD: Large Language Models Can Teach Themselves Induction through Deduction

arXiv

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arXiv 2024年

作者： Sun, Wangtao Xu, Haotian Yu, Xuanqing Chen, Pei He, Shizhu Zhao, Jun Liu, Kang The Laboratory of Cognition and Decision Intelligence for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China CAS Engineering Laboratory for Intelligent Industrial Vision Institute of Automation Chinese Academy of Sciences Beijing China Department of Computer Science and Engineering Texas A&M University United States Shanghai Artificial Intelligence Laboratory China Xiaohongshu Inc China

Although Large Language Models (LLMs) are showing impressive performance on a wide range of Natural Language Processing tasks, researchers have found that they still have limited ability to conduct induction. Recent works mainly adopt "post processes" paradigms to improve the performance of LLMs on induction (e.g., the hypothesis search & refinement methods), but their performance is still constrained by the inherent inductive capability of the LLMs. In this paper, we propose a novel framework, Induction through Deduction (ItD), to enable the LLMs to teach themselves induction through deduction. The ItD framework is composed of two main components: a Deductive Data Generation module to generate induction data and a Naive Bayesian Induction module to optimize the fine-tuning and decoding of LLMs. Our empirical results showcase the effectiveness of ItD on two induction benchmarks, achieving relative performance improvement of 36% and 10% compared with previous state-of-the-art, respectively. Our ablation study verifies the effectiveness of two key modules of ItD. We also verify the effectiveness of ItD across different LLMs and deductors. The data and code of this paper can be found at https://***/r/ItD-E844. Copyright © 2024, The Authors. All rights reserved.

关键词： Benchmarking

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ScaleFusionNet: Transformer-Guided Multi-Scale Feature Fusion for Skin Lesion Segmentation

arXiv

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arXiv 2025年

作者： Qamar, Saqib Qadri, Syed Furqan Alroobaea, Roobaea Alsafyani, Majed Baqasah, Abdullah M. Department of Intelligent Systems KTH Royal Institute of Technology Stockholm10044 Sweden Department of Computing and IT Sohar University Sohar311 Oman BGI Research Hanzhou310030 China Department of Computer Science College of Computers and Information Technology Taif University P. O. Box 11099 Taif21944 Saudi Arabia Department of Information Technology College of Computers and Information Technology Taif University P. O. Box 11099 Taif21974 Saudi Arabia

Melanoma is a malignant tumor originating from skin cell lesions. Accurate and efficient segmentation of skin lesions is essential for quantitative medical analysis but remains challenging. To address this, we propose ScaleFusionNet, a segmentation model that integrates Cross-Attention Transformer Module (CATM) and AdaptiveFusionBlock to enhance feature extraction and fusion. The model employs a hybrid architecture encoder that effectively captures both local and global features. We introduce CATM, which utilizes Swin Transformer Blocks and Cross Attention Fusion (CAF) to adaptively refine encoder-decoder feature fusion, reducing semantic gaps and improving segmentation accuracy. Additionally, the AdaptiveFusionBlock is improved by integrating adaptive multi-scale fusion, where Swin Transformer-based attention complements deformable convolution-based multi-scale feature extraction. This enhancement refines lesion boundaries and preserves fine-grained details. ScaleFusionNet achieves Dice scores of 92.94% and 91.65% on ISIC-2016 and ISIC-2018 datasets, respectively, demonstrating its effectiveness in skin lesion analysis. Our code implementation is publicly available at GitHub. © 2025, CC BY.

关键词： Semantic Segmentation

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Long and Short-Term Constraints Driven Safe Reinforcement Learning for Autonomous Driving

arXiv

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arXiv 2024年

作者： Hu, Xuemin Chen, Pan Wen, Yijun Tang, Bo Chen, Long School of Artificial Intelligence Hubei University Wuhan430062 China Key Laboratory of Intelligent Sensing System and Security Hubei University Ministry of Education Hubei Wuhan430062 China Department of Electrical and Computer Engineering Worcester Polytechnic Institute WorcesterMA01609 United States State Key Laboratory of Multimodal Artificial Intelligence Systems State Key Laboratory of Management and Control for Complex Systems Chinese Academy of Sciences Beijing100190 China WAYTOUS Inc. Beijing100083 China Shenzhen518107 China Institute of Artificial Intelligence and Robotics Xi'an Jiaotong University Xi’an710049 China

Reinforcement learning (RL) has been widely used in decision-making and control tasks, but the risk is very high for the agent in the training process due to the requirements of interaction with the environment, which seriously limits its industrial applications such as autonomous driving systems. Safe RL methods are developed to handle this issue by constraining the expected safety violation costs as a training objective, but the occurring probability of an unsafe state is still high, which is unacceptable in autonomous driving tasks. Moreover, these methods are difficult to achieve a balance between the cost and return expectations, which leads to learning performance degradation for the algorithms. In this paper, we propose a novel algorithm based on the long and short-term constraints (LSTC) for safe RL. The short-term constraint aims to enhance the shortterm state safety that the vehicle explores, while the long-term constraint enhances the overall safety of the vehicle throughout the decision-making process, both of which are jointly used to enhance the vehicle safety in the training process. In addition, we develop a safe RL method with dual-constraint optimization based on the Lagrange multiplier to optimize the training process for end-to-end autonomous driving. Comprehensive experiments were conducted on the MetaDrive simulator. Experimental results demonstrate that the proposed method achieves higher safety in continuous state and action tasks, and exhibits higher exploration performance in long-distance decision-making tasks compared with state-of-the-art methods. Copyright © 2024, The Authors. All rights reserved.

关键词： Lagrange multipliers

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Towards concept map based free student answer assessment 32

Towards concept map based free student answer assessment

引用

32nd International Florida Artificial Intelligence Research Society Conference, FLAIRS 2019

作者： Maharjan, Nabin Rus, Vasile Department of Computer Science Institute for Intelligent Systems United States

ISBN: (纸本)9781577358053

We propose a concept map based approach to assessing freely generated student responses. The proposed approach is based on a novel automated tuple extraction system, DT-OpenIE, for automatically extracting concept maps from student responses. The DT-OpenIE system is significantly better, for assessment purposes, in terms of concept map quality than state-of-the-art open information extraction (IE) systems such as Ollie or Stanford as evidenced by our experimental results. The concept map based approach can not only generate a holistic score assessing the accuracy of a student response but also enable diagnostic feedback. © 2019, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Students

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Exploiting Rank-Based Filter Pruning for Real-Time Uav Tracking

SSRN

引用

SSRN 2022年

作者： Wang, Xucheng Zeng, Dan Zhao, Qijun Li, Shuiwang Guangxi Key Laboratory of Embedded Technology and Intelligent System Guilin University of Technology Guilin541001 China Research Institute of Trustworthy Autonomous Systems Department of computer science and engineering Southern University of Science and Technology Shenzhen518000 China Sichuan University Chengdu610000 China

UAV tracking is an emerging task and has wide potential applications in such as agriculture, navigation, entertainment and public security. However, the limitations of computing resources, battery capacity, and maximum load of UAV hinder the deployment of DL-based tracking algorithms on UAV. In contrast to deep learning trackers, discriminative correlation filters (DCF)-based trackers stand out in the UAV tracking community because of their high efficiency. However, their precision is usually much lower than trackers based on deep learning. Model compression is a promising way to narrow the gap (i.e., effciency, precision) between DCF- and deep learning- based trackers, which has not caught much attention in UAV tracking community. In this paper, We propose the P-SiamFC++ tracker, which is the first to use rank-based filter pruning to compress the SiamFC++ model, achieving a remarkable balance between efficiency and precision. Our method is general and may encourage further studies on UAV tracking with model compression in the future. Extensive experiments on four UAV benchmarks, including UAV123@10fps, DTB70, UAVDT and Vistrone2018, show that P-SiamFC++ tracker significantly outperforms state-of-the-art UAV tracking methods. © 2022, The Authors. All rights reserved.

关键词： Unmanned aerial vehicles (UAV)

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Keep CALM and improve visual feature attribution

arXiv

引用

arXiv 2021年

作者： Kim, Jae Myung Choe, Junsuk Akata, Zeynep Oh, Seong Joon University of Tübingen Department of Computer Science and Engineering Sogang University Max Planck Institute for Intelligent Systems Max Planck Institute for Informatics NAVER AI Lab

The class activation mapping, or CAM, has been the cornerstone of feature attribution methods for multiple vision tasks. Its simplicity and effectiveness have led to wide applications in the explanation of visual predictions and weakly-supervised localization tasks. However, CAM has its own shortcomings. The computation of attribution maps relies on ad-hoc calibration steps that are not part of the training computational graph, making it difficult for us to understand the real meaning of the attribution values. In this paper, we improve CAM by explicitly incorporating a latent variable encoding the location of the cue for recognition in the formulation, thereby subsuming the attribution map into the training computational graph. The resulting model, class activation latent mapping, or CALM, is trained with the expectation-maximization algorithm. Our experiments show that CALM identifies discriminative attributes for image classifiers more accurately than CAM and other visual attribution baselines. CALM also shows performance improvements over prior arts on the weakly-supervised object localization benchmarks. Our code is available at https://***/naver-ai/calm. Copyright © 2021, The Authors. All rights reserved.

关键词： Image segmentation

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