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12th International Conference on Learning Representations, ICLR 2024

作者： Messaoud, Safa Mokeddem, Billel Xue, Zhenghai Pang, Linsey An, Bo Chen, Haipeng Chawla, Sanjay Qatar Computing Research Institute Hamad Bin Khalifa University Qatar School of Computer Science and Engineering Nanyang Technological University Singapore SalesForce United States Skywork AI Data Science William & Mary United States

Learning expressive stochastic policies instead of deterministic ones has been proposed to achieve better stability, sample complexity, and robustness. Notably, in Maximum Entropy Reinforcement Learning (MaxEnt RL), the policy is modeled as an expressive Energy-Based Model (EBM) over the Q-values. However, this formulation requires the estimation of the entropy of such EBMs, which is an open problem. To address this, previous MaxEnt RL methods either implicitly estimate the entropy, resulting in high computational complexity and variance (SQL), or follow a variational inference procedure that fits simplified actor distributions (e.g., Gaussian) for tractability (SAC). We propose Stein Soft Actor-Critic (S2AC), a MaxEnt RL algorithm that learns expressive policies without compromising efficiency. Specifically, S2AC uses parameterized Stein Variational Gradient Descent (SVGD) as the underlying policy. We derive a closed-form expression of the entropy of such policies. Our formula is computationally efficient and only depends on first-order derivatives and vector products. Empirical results show that S2AC yields more optimal solutions to the MaxEnt objective than SQL and SAC in the multi-goal environment, and outperforms SAC and SQL on the MuJoCo benchmark. Our code is available at: https://***/SafaMessaoud/S2AC-Energy-Based-RL-with-Stein-Soft-Actor-Critic. © 2024 12th International Conference on Learning Representations, ICLR 2024. All rights reserved.

关键词： Reinforcement learning

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AsymRnR: Video Diffusion Transformers Acceleration with Asymmetric Reduction and Restoration

arXiv

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arXiv 2024年

作者： Sun, Wenhao Tu, Rong-Cheng Liao, Jingyi Jin, Zhao Tao, Dacheng College of Computing and Data Science Nanyang Technological University Singapore Singapore

Diffusion Transformers (DiTs) have proven effective in generating high-quality videos but are hindered by high computational costs. Existing video DiT sampling acceleration methods often rely on costly fine-tuning or exhibit limited generalization capabilities. We propose Asymmetric Reduction and Restoration (AsymRnR), a training-free and model-agnostic method to accelerate video DiTs. It builds on the observation that redundancies of feature tokens in DiTs vary significantly across different model blocks, denoising steps, and feature types. Our AsymRnR asymmetrically reduces redundant tokens in the attention operation, achieving acceleration with negligible degradation in output quality and, in some cases, even improving it. We also tailored a reduction sched- © 2024, CC BY.

关键词： Acceleration

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A Dual-Port, and Single-Element Planar Inverted-F Antenna for Current 4G/5G Portable Applications 21

A Dual-Port, and Single-Element Planar Inverted-F Antenna fo...

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21st International Multi-Conference on Systems, Signals and Devices, SSD 2024

作者： Alja'Afreh, Saqer S. Altakhaineh, Amjaad T. Al-Shamaileh, Mallak H. Matarneh, Aser M Daoud, Omar R. Al-Khawaldah I, Mohammad School of Engineering and Computing American International University Electrical and Computing Engineering Al-Jahra Kuwait Pioneers International Academy Data Science and Machine Learning Dept. Amman Jordan Al-Ahliyya Amman University Communications and Computer Engineering Amman Jordan

ISBN: (纸本)9798350374131

A first-hand, low-profile, two-port planar inverted-F antenna is proposed for current 4G/5G sub-3 GHz hand-portable multiple-input and multiple-output (MIMO) applications. The design features a profile of about 5 mm and dual-coplanar feeds, which are isolated using only a modified top plate, while the shared ground plane is not perturbed as in previous designs. An open-edge slot on the top of the PIFA provides a 3λ/4 current path. The proposed antenna covers approximately a 15% impedance bandwidth fraction in the range of 2.45-2.85 GHz. The simulated results, including scattering parameters, far-field radiation patterns, and the envelope correlation coefficient, all exhibit excellent features. © 2024 IEEE.

关键词： Antenna feeders

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Deep Convolutional Neural Network for Enhancing Traffic Sign Recognition Developed on Yolo V5

Deep Convolutional Neural Network for Enhancing Traffic Sign...

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Advancements in Smart, Secure and Intelligent computing (ASSIC), International Conference on

作者： Aofan Liu Yutong Liu Saif Kifah School of Computing and Data Science Xiamen University Malaysia Sepang Malaysia

In today's era, deep learning neural networks with multiple hidden layers have been widely used in many fields. The deep learning method has more powerful features that enhance the method's performance by a learning process. With the development of the logistics industry and the prevalence of autonomous driving, traffic sign recognition has gained rising attention. This study uses a YOLO CNN to classify traffic signs. To improve model performance, we used MSRCR image augmentation during preprocessing. In the improvement phase, we used YOLOv5 to automate traffic sign categorization and improved training methods and network architecture. GTSRB and CCTSDB were used to assess the proposed technique. The experimental results show that the YOLOv5 model outperforms other methods. It has a 99.8% accuracy rate in the GTSRB dataset and 98.4% precision in the CCTSDB.

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data Processing Model of Students' Evaluation of Teaching Based on Outlier Detection 5

Data Processing Model of Students' Evaluation of Teaching Ba...

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5th IEEE Eurasian Conference on Educational Innovation, ECEI 2022

作者： Qu, Ying Guo, Fei Hebei University of Science and Technology School of Economics and Management Data Science and Intelligent Computing Research Center Hebei China

ISBN: (纸本)9781665433181

The problem of low credibility of students in teaching evaluation is caused by ignoring non-teaching factors such as students' emotions, class culture, and course nature in the existing data of university students' teaching evaluation. A data processing model of students' teaching evaluation is constructed based on outlier detection. The outliers of the original data are screened and optimized based on students' emotional differences. Based on the analysis of non-teaching factors such as class culture and curriculum characteristics, the abnormal data of students' evaluation of teaching are processed. This improves the credibility of students' evaluation of teaching data to a certain extent. For the same sample data, the optimized data processing model of students' evaluation of teaching shows realistic evaluation results. The guidance of students' evaluation of teaching needs to be strengthened based on the original intention of teaching quality evaluation. It is suggested that evaluation models are scientifically selected, and the evaluation results are rationally used. © 2022 IEEE.

关键词： Students

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Towards Responsible and Ethical AI Chatbot in Education: A Guideline for Developers

Towards Responsible and Ethical AI Chatbot in Education: A G...

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Robotics, Automation and Artificial Intelligence (RAAI), IEEE International Conference on

作者： Jiayun Mo Ong Chin Ann College of Computing and Data Science Nanyang Technological University Singapore Singapore

ISBN: (数字)9798331520038

ISBN: (纸本)9798331520045

With the expansion of generative Artificial Intelligence (AI) and the penetration of AI chatbots into every aspect of people's daily lives, discussions and issues related to AI ethics and responsibility began to arise simultaneously. A large portion of AI users remain ignorant of such ethical discussions, while a significant percentage of developers lack precautionary measures to prevent irresponsible AI behaviors. An increasing number of companies and organizations are taking action to address the problems. Despite the efforts, prominent issues still prevail. Most existing solutions are too general and theoretical; few are narrowed down to examine the specific application scenario of AI chatbots in education. In response, this study aims to address the arising concerns and the lack of a satisfying solution. After conducting an extensive literature review and performing a detailed analysis of survey data, this study develops a comprehensive guideline about AI ethics and responsible AI, which specifically targets undergraduate developers and guides them during their development of AI chatbots used in education. This guideline successfully bridges the knowledge gap, raises awareness about AI ethics, and acts as the first guideline for undergraduate students, influencing future development practices.

关键词： Surveys Ethics Systematics Generative AI Educational robots Education Chatbots Artificial intelligence Guidelines Systematic literature review

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A Local Regression-Based SLA-Aware Energy-Efficient Virtual Machine Consolidation Algorithm in data Centers 9

A Local Regression-Based SLA-Aware Energy-Efficient Virtual ...

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9th International Conference on Big data computing and Communications, BigCom 2023

作者： Wu, Xiaodong Wang, Ronghai Lin, Guoxin Quanzhou Normal University School of Mathmatics and Computer Science China Fujian Province University Fujian Provincial Key Laboratory of Data-Intensive Computing Key Laboratory of Intelligent Computing and Information Processing Fujian China Provincial Big Data Research Institute of Intelligent Manufacturing Quanzhou China

ISBN: (纸本)9798350331240

Virtual Machine Consolidation (VMC) is an effective means to improve the resource utilization and reduce energy consumption of data centers. However, the VMC optimization that only considers energy consumption may lead to SLA (Service-Level Agreements) violations and thus fail to guarantee the QoS (Quality of Service) provided. Therefore, it is necessary to consider both energy saving and SLA violations in the process of VMC. In this paper, we propose an energy and SLA-aware VMC algorithm that considers both energy saving and SLA violation optimization in data centers. The proposed algorithm uses the local regression method to predict the utilization of virtual machines, based on which it determines whether the host will be overloaded, so as to make the decision of virtual machine migrations. The proposed VMC algorithm is evaluated with CloudSim toolkit. Simulation results demonstrate that our algorithm has good energy saving performance and can effectively reduce SLA violation in cloud data center. Considering energy consumption and SLA violation, the ESV can be reduced by 7.57% on average and 16.36% at most compared with other algorithms. © 2023 IEEE.

关键词： Virtual machine

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Deep Neural Network Approach on Phishing Websites Detection, Identification and Proactive Prevention 8

Deep Neural Network Approach on Phishing Websites Detection,...

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8th IEEE International Conference on Computational System and Information Technology for Sustainable Solutions, CSITSS 2024

作者： Soundarya, K. Siddaraju, H.S. Tekchandani, Hitesh Bhardwaj, Navodit Rani, Pooja Joshi, Pallavi Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science Mysuru India Koneru Lakshmaiah Education Foundation Artificial Intelligence and Data Science Hyderabad India Researcher at Food and Drug Administration MD United States

ISBN: (纸本)9798331505462

Phishing is a prevalent type of cyberattack that involves posing as a trustworthy source in an email message to fraudulently attempt to get personal information such as usernames, passwords, and bank account information. Strong detection and prevention methods are required to address the phishing issue. The objective is to develop efficient algorithms that use machine learning (ML) and data mining methods to accurately detect and categorize phishing URLs. This initiative aims to support ongoing initiatives to strengthen cybersecurity and shield consumers from the risks associated with phishing scams. It can be observed from the results that the Deep neural network model gives 95% of accuracy and 96% of precision for classification and detection of phishing websites. © 2024 IEEE.

关键词： Phishing

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Towards Efficient Discrete Controller Synthesis: Semantics-Aware Stepwise Policy Design via LLM

Towards Efficient Discrete Controller Synthesis: Semantics-A...

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2024 IEEE International Conference on Consumer Electronics-Asia, ICCE-Asia 2024

作者： Ishimizu, Yusei Li, Jialong Yamauchi, Takuto Chen, Sinan Cai, Jinyu Hirano, Takanori Tei, Kenji School of Computing Tokyo Institute of Technology Tokyo Japan Waseda University Department of Computer Science and Engineering Tokyo Japan Kobe University Center of Mathematical and Data Sciences Kobe Japan

ISBN: (纸本)9798331530839

Discrete Controller Synthesis (DCS) is an automated generation technique to generate controllers from environment models and requirements. The time and space complexities of DCS are critical because they directly govern the feasibility of using DCS for real-time generation of controllers to cope with environmental changes. Stepwise DCS is considered a promising approach to prevent the construction of ineffective state spaces in the subsequent analysis of the full controller by analyzing sub-controllers that meet part of the requirements at an early stage. However, constructing a stepwise policy is a challenge, as the current methods often require domain experts to manually build it by understanding and reasoning the semantics of each sub-environment model and sub-requirement. To reduce the effort of manual construction, this paper introduces a method that leverages large language models (LLMs) to automate the generation of stepwise policies by utilizing the LLM's ability to understand and reason about the semantics of state machines. The evaluation using GPT-4 on a drone control scenario demonstrates the effectiveness of our method, with an average reduction of 82.2% in states and 91.2% in transitions. © 2024 IEEE.

关键词： Control system synthesis

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Enhanced Multi-Objective Optimization Approach for Low-Interference UAV Communication Based on Collaborative Beamforming 10

Enhanced Multi-Objective Optimization Approach for Low-Inter...

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10th IEEE Smart World Congress, SWC 2024

作者： Zhang, Chao Kang, Hui Sun, Geng Li, Jiahui Wang, Boxiong Xu, Bo Jilin University College of Computer Science and Technology Changchun130012 China Nanyang Technological University Singapore College of Computing and Data Science 639798 Singapore Hainan University College of Information and Communication Engineering Haikou570228 China

ISBN: (纸本)9798331520861

Unmanned aerial vehicles (UAVs) are widely used in the field of wireless communications due to their inherent advantages such as easy deployment and low cost. Employing collaborative beamforming techniques to form a UAV-based virtual antenna array (UVAA) is a promising solution to achieve remote and low-cost communications. Owing to the nature of electromagnetic waves, UVAA may cause interference to unaffiliated users when deployed in densely populated areas. To address this issue, UAVs must identify the most suitable positions to achieve optimal beam patterns. However, considering their limited energy capacity and the increased power consumption associated with excessive flight, a multi-objective optimization analysis is necessary to balance these factors. Thus, we formulate a low-interference communication multi-objective problem (LIMOP), aiming to maximize transmission rate, minimize interference, and reduce energy consumption through the optimization of UAV positions, excitation currents, and the selection of target BS. Given the complexity of LIMOP, we introduce multiple improvement strategies to propose an improved swarm intelligence method. According to simulation results, the proposed IMOAHA is effective in addressing the formulated LIMOP and is superior to other algorithms. © 2024 IEEE.

关键词： Multiobjective optimization

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