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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Miao, Yuchun Zhang, Sen Ding, Liang Bao, Rong Zhang, Lefei Tao, Dacheng National Engineering Research Center for Multimedia Software School of Computer Science Wuhan University China The University of Sydney Australia Fudan University China Nanyang Technological University Singapore

Despite the success of reinforcement learning from human feedback (RLHF) in aligning language models with human values, reward hacking, also termed reward overoptimization, remains a critical challenge. This issue primarily arises from reward misgeneralization, where reward models (RMs) compute reward using spurious features that are irrelevant to human preferences. In this work, we tackle this problem from an information-theoretic perspective and propose a framework for reward modeling, namely InfoRM, by introducing a variational information bottleneck objective to filter out irrelevant information. Notably, we further identify a correlation between overoptimization and outliers in the IB latent space of InfoRM, establishing it as a promising tool for detecting reward overoptimization. Inspired by this finding, we propose the Cluster Separation Index (CSI), which quantifies deviations in the IB latent space, as an indicator of reward overoptimization to facilitate the development of online mitigation strategies. Extensive experiments on a wide range of settings and RM scales (70M, 440M, 1.4B, and 7B) demonstrate the effectiveness of InfoRM. Further analyses reveal that InfoRM's overoptimization detection mechanism is not only effective but also robust across a broad range of datasets, signifying a notable advancement in the field of RLHF. Code is available at: https://***/miaoyuchun/InfoRM. © 2024 Neural information processing systems foundation. All rights reserved.

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Bond Energy Assists Accurate Molecule Property Prediction

Bond Energy Assists Accurate Molecule Property Prediction

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2022 International Conference on Electrical, Electronics and Information engineering, EEIE 2022

作者： Yang, Xiaoting Du, Wenjie School of Computer Science and Technology University of Science and Technology of China China School of Software Engineering University of Science and Technology of China China

Molecule property prediction is a fundamental problem in many fields. To accurate and rapid prediction of molecules properties, molecule characterization and representation are key operations in the pretreatment stage. Generally, we represent molecule as graph based on different features such as element type, bond type, etc. The features we select could have the ability of enough representation and discrimination. However, in terms of bond embedding, one-hot coding is the most common processing method in the current research which means the features we used are discrete and could not distinguish different single or double bonds in a molecule. Here we add predicted bond energy feature as an extra chemical bond descriptor and compare three popular GNN models on two different datasets. The experiment shows that supplying additional unusual bond features - bond energy will improve the model performance significantly. The PDN model with bond energy has best performance among three model. Finally, we discuss the experimental result. © Published under licence by IOP Publishing Ltd.

关键词： Molecules

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A Machine Learning Approach for Expression Detection in Healthcare Monitoring Systems

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computers, Materials & Continua 2021年第5期67卷 2123-2139页

作者： Muhammad Kashif Ayyaz Hussain Asim Munir Abdul Basit Siddiqui AaqifAfzaal Abbasi Muhammad Aakif Arif Jamal Malik Fayez Eid Alazemi Oh-Young Song Department of Computer Science&Software Engineering International Islamic UniversityIslamabad44000Pakistan Department of Computer Science Quaid-i-Azam UniversityIslamabad44000Pakistan Department of Computer Science Capital University of Science&TechnologyIslamabad44000Pakistan Department of Software Engineering Foundation UniversityIslamabad44000Pakistan Department of Computer Science Abdul Wali Khan UniversityMardan23200Pakistan Department of Computer Science and Information Systems College of Business StudiesPAAET12062Kuwait Department of Software Sejong UniversitySeoul05006Korea

Expression detection plays a vital role to determine the patient’s condition in healthcare *** helps the monitoring teams to respond swiftly in case of *** to the lack of suitable methods,results are often compromised in an unconstrained environment because of pose,scale,occlusion and illumination variations in the image of the face of the patient.A novel patch-based multiple local binary patterns(LBP)feature extraction technique is proposed for analyzing human behavior using facial expression *** consists of three-patch[TPLBP]and four-patch LBPs[FPLBP]based feature engineering *** representation is encoded from local patch statistics using these *** and FPLBP capture information that is encoded to find likenesses between adjacent patches of pixels by using short bit strings contrary to pixel-based *** images are transformed into the frequency domain using a discrete cosine transform(DCT).Most discriminant features extracted from coded DCT images are combined to generate a feature *** vector machine(SVM),k-nearest neighbor(KNN),and Naïve Bayes(NB)are used for the classification of facial expressions using selected *** experimentation is performed to analyze human behavior by considering standard extended Cohn Kanade(CK+)and Oulu–CASIA *** demonstrate that the proposed methodology outperforms the other techniques used for comparison.

关键词： Detection expressions gestures analytics pain patch-based local binary descriptor discrete cosine transform healthcare

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Surrogate-Assisted Flip for Evolutionary High-Dimensional Multiobjective Feature Selection 13

Surrogate-Assisted Flip for Evolutionary High-Dimensional Mu...

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13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Yang, Qi-Te Luo, Liu-Yue Chen, Chun-Hua Li, Jian-Yu Zhong, Jing-Hui Zhang, Jun Zhan, Zhi-Hui School of Computer Science and Engineering South China University of Technology Guangzhou510006 China School of Software Engineering South China University of Technology Guangzhou510006 China College of Artificial Intelligence Nankai University Tianjin300350 China

ISBN: (纸本)9798350308365

Feature selection (FS), which aims to minimize the classification error and the number of selected features, can essentially be modeled as a multiobjective optimization problem. To deal with such multiobjective FS (MOFS) problems, many multiobjective evolutionary algorithms (MOEAs) have been proposed. These MOEAs can find multiple optimal solutions, whereas substantial computational resources are required for fitness evaluations (FEs). More seriously, due to the high dimensionality and sparsity of FS problems, many FEs will be spent on unpromising solutions, resulting in a meaningless loss of computational resources. In this paper, two innovations are made so as to improve the performance of MOEAs for MOFS. First, we propose a surrogate-assisted flip (SF) strategy for MOEAs to reduce the FE waste on potentially unpromising solutions and improve search efficiency. This SF strategy is free of FE consumption and theoretically can be embedded in any MOEA to deal with MOFS problems. The experimental results show that this SF can improve the performance of different MOEAs, especially in reducing the number of selected features. Second, based on SF, we propose a more efficient SF -assisted MOEA for dealing with high-dimensional MOFS problems. The proposed algorithm divides the whole search space into different subspaces based on redundant feature subsets clustering to achieve parallel search, so as to reduce the search difficulty. The experimental results show that this algorithm is even more competitive than other SF -assisted MOEAs. © 2024 IEEE.

关键词： Optimization algorithms

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Static Eccentricity Fault Analysis in Three-Phase Induction Motors Using Current Signal 2

Static Eccentricity Fault Analysis in Three-Phase Induction ...

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2nd Edition IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural engineering, MetroXRAINE 2023

作者： Refaat, Shady S. Al-Shemmery, Ahmed Abdulmawjood, Kais Kameli, Sayed Mohammad Abuelrub, Abdelaziz Texas A&m University at Qatar Electrical and Computer Engineering Department Doha Qatar School of Physics Engineering and Computer Science University of Hertfordshire Hatfield United Kingdom Ontario Tech University Department of Electrical Computer and Software Engineering Canada

ISBN: (纸本)9798350300802

Preventing mechanical faults in motors is often impossible, early detection of air gap eccentricity faults in induction motors is critical in preventing damage to the machine. Therefore, designing a reliable, effective fault detection system can help to improve its operation. This paper presents an analysis of static eccentricity fault for induction motors. This paper proposes the use of the empirical mode decomposition (EMD) followed by the implementation of wavelet packet decomposition (WPD) on current signals to extract and identify static eccentricity fault frequency signatures. The accuracy in fault detection and diagnosis of the effects of static airgap eccentricity using stator current based monitoring with WPD based on EMD is experimentally verified. © 2023 IEEE.

关键词： Airgap eccentricity fault empirical mode decomposition induction machine wavelet packet decomposition

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Multi-branch Person Re-identification Net 2

Multi-branch Person Re-identification Net

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2nd International Conference on Image Processing, computer Vision and Machine Learning, ICICML 2023

作者： Chen, Zhihao Ge, Yiyuan Zhang, Ji Gao, Xiang Beijing Information Science and Technology University Computer School Beijing China Jilin University Department of Software Engineering Changchun China Beijing Information Science and Technology University School of Instrument Science and Opto-Electronics Engineering Beijing China Xihua University School of Mechanical Science and Engineering Chengdu China

ISBN: (纸本)9798350331417

Person ReID is a complex and important problem within the domain of computer vision. Its objective is to identify and match individuals in video sequences, often captured from different camera viewpoints. By comparing visual characteristics, poses, and clothing of pedestrians, Person ReID enables the tracking and recognition of the same person across various scenarios. This paper introduces a Multi-Branches Person Re-identification Net (MPRN), a novel framework for person re-identification. MPRN leverages OSNet as the backbone and establishes a multi-branch structure on top of it, effectively integrating both local and global features. Experimental results demonstrate that the combination of OSNet and the multi-branch structure yields highly competitive results. Specifically, it achieves a mean average precision (mAP) of 90.5% and a rank-1 accuracy of 96% on the Market-1501 dataset, as well as an mAP of 85.5% and a rank-1 accuracy of 87.7% on the CHUK03-L dataset. © 2023 IEEE.

关键词： Feature Extraction Multi-Branch Structure Person Re-identification

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OpenCML: An Open Customizable Modeling Language for Directed Acyclic Graphs 22

OpenCML: An Open Customizable Modeling Language for Directed...

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22nd IEEE International Symposium on Parallel and Distributed Processing with Applications, ISPA 2024

作者： Wei, Zhenjie Xing, Weiwei Liu, Weibin Zou, Zhiyuan Chen, Genxiang Beijing Jiaotong University School of Software Engineering Beijing China Beijing Jiaotong University Institute of Information Science Beijing China Beijing Information Science and Technology University Computer School Beijing China Minzu University of China College of Science Beijing China

ISBN: (纸本)9798331509712

Directed Acyclic Graphs (DAGs) are widely utilized across various domains for tasks such as graph algorithms, data flow analysis, program optimization, and machine learning. Representing DAGs using General-purpose Programming Languages (GPLs) or Data Serialization Formats (DSFs) can lead to complex and obscure expressions, making it challenging to comprehend and manage the codebase. Domain-Specific Languages (DSLs) offer a more tailored approach, but come with limitations and development overhead. This paper introduces the Open Customizable Modeling Language (OpenCML), a universal DAG modeling language specification that aims to provide a standardized and customizable framework for modeling and scripting DAGs. Evaluations demonstrate that OpenCML offers expressive power, customizability, and interoperability, simplifying the learning process and providing a powerful solution for DAG modeling and scripting. © 2024 IEEE.

关键词： Data flow analysis

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A comprehensive survey on 6G-security: physical connection and service layers

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Discover Internet of Things 2025年第1期5卷 1-30页

作者： Saeed, Mamoon M. Saeed, Rashid A. Hasan, Mohammad Kamrul Ali, Elmustafa Sayed Mazha, Tehseen Shahzad, Tariq Khan, Sunawar Hamam, Habib Sana00967 Yemen Department of Computer Engineering College of Computers and Information Technology Taif University P.O. Box 11099 Taif21944 Saudi Arabia Department of Electronics Engineering College of Electronics Engineering Sudan University of Science and Technology Omdurman Sudan Bangi43600 Malaysia Department of Electrical and Electronic Engineering Faculty of Engineering Red Sea University Port Sudan33312 Sudan School of Computer Science National College of Business Administration and Economics Lahore54000 Pakistan Faculty of Engineering Uni de Moncton MonctonNBE1A3E9 Canada Department of Computer Engineering COMSATS University Islamabad Sahiwal Campus Sahiwal57000 Pakistan Department of Computer Science and Software Engineering International Islamic University Islamabad44000 Pakistan School of Electrical Engineering University of Johannesburg Johannesburg2006 South Africa BP 1989 Av. Grandes Ecoles Libreville Gabon Bridges for Academic Excellence-Spectrum Tunis Tunisia Department of Computer Science School Education Department Government of Punjab Layyah31200 Pakistan Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura140401 India

After adopting 5G technology, businesses and academia have started working on sixth-generation wireless networking (6G) technologies. Mobile communications options are expected to expand in areas where previous generations could not do so. 6G networks are anticipated to be constructed using various diverse technologies. These encompass diverse cutting-edge advancements, such as distributed ledger systems like blockchain, visible light communications (VLC), post-quantum cryptography, edge computing, molecular communication, THz, and other advances. These advances necessitate a reassessment of previous security strategies from a security perspective. In the future, networks must adhere to stricter criteria for authentication, encryption, access control, connectivity, and detection of harmful activities. Ensuring privacy and dependability necessitates the implementation of supplementary security protocols. The essay explores the primary concerns and challenges related to the security of the 6G network. This paper describes the improvements in security in communications from 1G through 6G. This paper divides security in the sixth generation into three layers: physical, connection, and service. Each layer-by-layer discusses the standard technologies and security issues for each technology proposed in each sixth-generation security layer. All proposed solutions for each of the three layers are discussed in Sixth Generation Security. It also reviews all proposed solutions for each layer, indicating the proposed solution and its limitations. © The Author(s) 2025.

关键词： 5G mobile communication systems

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A Neurodynamic-diversity-based Spiking Network model for Text Classification

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IEEE Transactions on Cognitive and Developmental Systems 2024年

作者： Liu, Yuguo Chen, Wenyu Liu, Hanwen Zhang, Yun Zhang, Malu Qu, Hong University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu610054 China Southwest Petroleum University School of Computer Science and Software Engineering Chengdu610500 China

Language models have been undergoing rapid growth and remarkable success, while requiring massive computing resource. The brain-inspired spiking neural networks(SNNs), with advantages of better biological interpretability and less energy consumption, provides a likely alternative to process language tasks in a more sustainable way. However, there are still major difficulties in representing and processing text information with SNN-based models. Comprehensively exploring the neurodynamic diversity, we propose a spiking neural network model that could taking respective advantages from both integrator and resonator neurons to address text classification tasks. With collaborations of these two dynamically different spiking neurons, our network model outperforms previous SNN-based text classification model in an all-round way with much less time steps, and even shows some extent of potential to reach the performance of a topologically equivalent artificial neural network. © 2016 IEEE.

关键词： Neural network models

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GIRP: Energy-Efficient QoS-Oriented Microservice Resource Provisioning via Multi-Objective Multi-Task Reinforcement Learning

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IEEE Transactions on Mobile Computing 2025年第7期24卷 5793-5807页

作者： Yuan, Honggang Wang, Ting Fu, Min Shi, Yuanming East China Normal University MoE Engineering Research Center of Software/Hardware Co-design Technology and Application Shanghai Key Laboratory of Trustworthy Computing Shanghai200062 China National University of Singapore Department of Electrical and Computer Engineering 117583 Singapore ShanghaiTech University School of Information Science and Technology Shanghai201210 China

Microservice architecture has revolutionized web service development by facilitating loosely coupled and independently developable components distributed as containers or virtual machines. While existing studies emphasize end-to-end latency, this paper investigates energy-efficient quality-of-service (QoS)-oriented microservice provisioning, focusing on both QoS satisfaction and power consumption (PC) conservation. We propose the Green and Intelligent Resource Provision (GIRP) architecture, integrating a data-driven energy-latency-aware resource allocation and scheduling manager to balance latency and PC. To reconcile the trade-offs involved, a dual-objective optimization problem is formulated to minimize latency and energy use by selecting proper servers, allocating CPU cores, and determining service replicas. To address challenges with discrete variables, dual objectives, and implicit mappings, we leverage a model-free deep deterministic policy gradient-based reinforcement learning algorithm. Specifically, we develop a multi-task agent via the Multi-gate Mixture-of-Experts model to simultaneously make two separate actions regarding CPU core numbers and service replica numbers, followed by a single-task agent to determine service scheduling. Extensive experiments on the DeathStarBenchmark testbed validate GIRP's effectiveness, demonstrating approximately 52% resource savings and a 43% reduction in PC compared to leading methods like Sinan, Firm, and heuristic-based algorithms. These results highlight GIRP's capability to optimize microservice orchestration by balancing end-to-end latency and power efficiency. © 2025 IEEE. All rights reserved.

关键词： Quality of service

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