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24th IEEE International Conference on Communication Technology, ICCT 2024

作者： Xie, Qian Wang, Yong Ding, Jianyang Guo, Zihan Niu, Jiajun Xidian University State Key Laboratory of Integrated Services Networks Xi'an710061 China Jiangnan University School of Artificial Intelligence and Computer Science Wuxi214000 China

ISBN: (纸本)9798350363760

The predistortion models for radio frequency (RF) power amplifiers (PAs), such as convolutional neural networks (CNNs) and deep neural networks (DNNs), has shown a significant improvement in predistortion performance. However, digital predistortion (DPD) models with neural network structures encounter challenges related to high computational complexity and prolonged training duration. In this study, we propose a DPD model utilizing a low complexity convolutional neural network (LCCNN). The proposed model simplifies the traditional CNN structure, employing an augmented real-valued cross-memorized terms (ARCT) matrix as input layer, and configuring convolutional layer to extract nonlinear features through macro convolutional kernels. Furthermore, migration learning and pruning operations are implemented to expedite model training and parameter reduction, thereby decreasing training duration and floating-point operations (FLOPs). The experiment results based on a 160 MHz Doherty PA indicate that the proposed model effectively reduces computational complexity while maintaining required predistortion performance. © 2024 IEEE.

关键词： Convolutional neural networks

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SPAFuzz: Web Security Fuzz Testing Tool Based on Fuzz Testing and Genetic Algorithm 4

SPAFuzz: Web Security Fuzz Testing Tool Based on Fuzz Testin...

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4th International Conference on Consumer Electronics and computer Engineering, ICCECE 2024

作者： Lin, Jiang Dong, Weiyu School of Computer Science and Artificial Intelligence Zhengzhou University Zhengzhou China Information Engineering University State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

ISBN: (纸本)9798350384062

Fuzzing is one of the most effective methods currently available for detecting web vulnerabilities. However, traditional fuzzing suffers from issues such as low coverage and limited diversity in test cases. To address these issues, we propose a way to generate test cases based on dynamic feature libraries and attack models by analysing the characteristics of SQL and XSS. Additionally, we introduce a genetic algorithm to guide the population's evolution by monitoring the state changes of the web application. Furthermore, we improved the selection phase of the genetic algorithm to optimally deform multiple iterations for use cases with low fitness values in order to preserve the population diversity to expand the attack capability of the test cases and based on this the SPAFuzz fuzzy testing tool was implemented. Experimental results show that SPAFuzz is able to detect more network vulnerabilities than Sqlmap and Powerfuzzer. © 2024 IEEE.

关键词： Genetic algorithms

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通智测试——基于动态具身物理社会交互环境的通用人工智能测试

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Engineering 2024年第3期34卷 12-22页

作者： Yujia Peng Jiaheng Han Zhenliang Zhang Lifeng Fan Tengyu Liu Siyuan Qi Xue Feng Yuxi Ma Yizhou Wang Song-Chun Zhu National Key Laboratory of General Artificial Intelligence Beijing Institute for General Artificial IntelligenceBeijing 100086China Institute for Artificial Intelligence Peking UniversityBeijing 100871China Beijing Key Laboratory of Behavior and Mental Health School of Psychological and Cognitive SciencesPeking UniversityBeijing 100871China School of Intelligence Science and Technology Peking UniversityBeijing 100871China School of Computer Science Peking UniversityBeijing 100871China

The release of the generative pre-trained transformer(GPT)series has brought artificial general intelligence(AGI)to the forefront of the artificial intelligence(AI)field once ***,the questions of how to define and evaluate AGI remain *** perspective article proposes that the evaluation of AGI should be rooted in dynamic embodied physical and social interactions(DEPSI).More specifically,we propose five critical characteristics to be considered as AGI benchmarks and suggest the Tong test as an AGI evaluation *** Tong test describes a value-and ability-oriented testing system that delineates five levels of AGI milestones through a virtual environment with DEPSI,allowing for infinite task *** contrast the Tong test with classical AI testing systems in terms of various aspects and propose a systematic evaluation system to promote standardized,quantitative,and objective benchmarks and evaluation of AGI.

关键词： artificial general intelligence artificial intelligence benchmark artificial intelligence evaluation Embodied artificial intelligence Value alignment Turing test Causality

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An Infrared and Visible Image Fusion Approach of Self-calibrated Residual Networks and Feature Embedding

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Recent Advances in computer science and Communications 2023年第2期16卷 2-13页

作者： Dai, Jinpeng Luo, Zhongqiang Li, Chengjie School of Automation and Information Engineering Sichuan University of Science and Engineering Yibin644000 China Artificial Intelligence Key Laboratory of Sichuan University of Science and Engineering Yibin644000 China School of Computer Science and Technology Southwest Minzu University Chengdu610041 China

Background: The fusion of infrared images and visible images has been a hot topic in the field of image fusion. In the process of image fusion, different methods of feature extraction and processing will directly affect the fusion performance. Objectives: Low resolution (small size) of high-level features will lead to the loss of spatial information. On the other side, the low-level features are not significant due to their insufficient filtering of background and noise. Methods: In order to solve the problem of insufficient feature utilization in existing methods, a new fusion approach (SC-Fuse) based on self-calibrated residual networks (SCNet) and feature embedding has been proposed. The method improves the quality of image fusion from two aspects: feature extraction and feature processing. Results: First, self-calibrated modules are applied to the field of image fusion for the first time, which enlarged the receptive field to make feature maps contain more information. Second, we use ZCA (Zero-phase Component Analysis) and l1-norm to process features, and propose a feature embedding operation to realize the complementarity of feature information at different levels. Conclusion: Finally, a suitable strategy is given to reconstruct the fused image. After ablation experiments and comparison with other representative algorithms, the results show the effectiveness and superiority of SC-Fuse. © 2023 Bentham science Publishers.

关键词： Image reconstruction

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Chess Equilibrium Puzzles

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Mathematics Magazine 2023年第4期96卷 391-398页

作者： Demaine, Erik D. Liu, Quanquan C. Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology Cambridge 02139 MA United States Northwestern University Evanston 60208 IL United States

Summary: What happens when the only goal in a chess game is to capture at least one piece of the opposite side? Can both sides live peacefully in an equilibrium where neither can capture the other’s pieces? In this short paper, we develop a new set of puzzles which we call chess equilibrium puzzles on this premise. We explain the rules of the game, analyze puzzles that have obvious and generalizable solutions, and provide several interesting puzzles for the reader to solve (solutions are provided at the end). Our puzzles provide an exciting twist to the realm of traditional chess puzzles. ©, Mathematical Association of America.

关键词： 00A08

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Provably Efficient Exploration in Quantum Reinforcement Learning with Logarithmic Worst-Case Regret 41

Provably Efficient Exploration in Quantum Reinforcement Lear...

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41st International Conference on Machine Learning, ICML 2024

作者： Zhong, Han Hu, Jiachen Xue, Yecheng Li, Tongyang Wang, Liwei Center for Data Science Peking University China School of Computer Science Peking University China Center on Frontiers of Computing Studies Peking University China National Key Laboratory of General Artificial Intelligence School of Intelligence Science and Technology Peking University China

While quantum reinforcement learning (RL) has attracted a surge of attention recently, its theoretical understanding is *** particular, it remains elusive how to design provably efficient quantum RL algorithms that can address the exploration-exploitation *** this end, we propose a novel UCRL-style algorithm that takes advantage of quantum computing for tabular Markov decision processes (MDPs) with S states, A actions, and horizon H, and establish an O(poly(S, A, H, log T)) worst-case regret for it, where T is the number of ***, we extend our results to quantum RL with linear function approximation, which is capable of handling problems with large state ***, we develop a quantum algorithm based on value target regression (VTR) for linear mixture MDPs with d-dimensional linear representation and prove that it enjoys O(poly(d, H, log T)) *** algorithms are variants of UCRL/UCRL-VTR algorithms in classical RL, which also leverage a novel combination of lazy updating mechanisms and quantum estimation *** is the key to breaking the Ω(√T)-regret barrier in classical *** the best of our knowledge, this is the first work studying the online exploration in quantum RL with provable logarithmic worst-case regret. Copyright 2024 by the author(s)

关键词： Quantum computers

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Enhancing the capabilities of Llama2 in biomedical applications 3

Enhancing the capabilities of Llama2 in biomedical applicati...

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

作者： Ren, Yuankai Xie, Zhenping School of Artificial Intelligence and Computer Science Jiangnan University Wuxi China Jiangsu Key University Laboratory of Software and Media Technology under Human-Computer Cooperation Jiangnan University Jiangsu Wuxi China

ISBN: (纸本)9781510681316

This study is dedicated to the development and evaluation of a large language model specifically optimized for the biomedical domain, using the sophisticated Llama2 architecture as its foundation. By delving into the vast corpus of biomedical literature, which includes medical research papers, news reports, drug labels, and case studies, we have curated a comprehensive training dataset comprising 10 billion tokens aimed at addressing the deficiencies of existing language models in specialized medical knowledge. During the model training phase, we initially conducted pre-training using data from the biomedical domain, followed by supervised training of a biomedical question-answering model utilizing LoRA technology. In the evaluation phase, the model's performance underwent rigorous testing with challenging datasets, including the Chinese National Medical Licensing Examination, the United States Pharmacist Licensing Examination, and the PubMedQA question-answering dataset. These tests served as a robust indicator of the model's practical applicability in real-world medical professional scenarios. Experimental results suggest that our model performs exceptionally well in various specific biomedical tasks and surpasses the well-known ChatGPT-3.5 model in key performance metrics, affirming the effectiveness of domain-optimized large language models. Despite the exemplary performance, the model still lags behind the latest ChatGPT-4, which indicates potential for improvement in understanding depth, reasoning capabilities, and knowledge updating. Overall, this research provides a potent tool for natural language processing tasks in the biomedical field and lays the groundwork for the future development of more specialized and precise large language models. © 2024 SPIE.

关键词： Statistical tests

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ESC-Eval: Evaluating Emotion Support Conversations in Large Language Models

ESC-Eval: Evaluating Emotion Support Conversations in Large ...

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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Zhao, Haiquan Li, Lingyu Chen, Shisong Kong, Shuqi Wang, Jiaan Huang, Kexin Gu, Tianle Wang, Yixu Wang, Jian Liang, Dandan Li, Zhixu Teng, Yan Xiao, Yanghua Wang, Yingchun Shanghai Artificial Intelligence Laboratory China School of Computer Science Fudan University China School of Information Renmin University of China China Renmin University of China China

ISBN: (纸本)9798891761643

Emotion Support Conversation (ESC) is a crucial application, which aims to reduce human stress, offer emotional guidance, and ultimately enhance human mental and physical well-being. With the advancement of Large Language Models (LLMs), many researchers have employed LLMs as the ESC models. However, the evaluation of these LLM-based ESCs remains uncertain. Inspired by the awesome development of role-playing agents, we propose an ESC Evaluation framework (i.e., ESC-Eval), which uses a role-playing agent to interact with ESC models, followed by a manual evaluation of the interactive dialogues. In detail, we first reorganize 2,801 role-playing cards from seven existing datasets to define the roles of the role-playing agent. Second, we train a specific role-playing model - ESC-Role to mimic the behavior of a real person experiencing distress. Third, through ESC-Role and organized role cards, we systematically conduct experiments using 14 LLMs as the ESC models, including general AI-assistant LLMs (e.g., ChatGPT) and ESC-oriented LLMs (e.g., ExTES-Llama). We conduct comprehensive human annotations on interactive multi-turn dialogues of different ESC models. The results show that ESC-oriented LLMs exhibit superior ESC abilities compared to general AI-assistant LLMs, but there is still a gap behind human performance. Moreover, to automate the evaluation of future ESC models, we developed ESC-RANK, which trained on the annotated data, achieving a scoring performance surpassing 35 points of GPT-4. Our data and code are available at https://***/haidequanbu/ESC-Eval. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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3D printed-electrospun PCL/hydroxyapatite/MWCNTs scaffolds for the repair of subchondral bone

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Regenerative Biomaterials 2023年第1期10卷 294-305页

作者： Yanyan Cao Lei Sun Zixian Liu Zhizhong Shen Wendan Jia Peiyi Hou Shengbo Sang College of Information Science and Engineering Hebei North UniversityZhangjiakou 075000China Shanxi Key Laboratory of Micro Nano Sensors&Artificial Intelligence Perception College of Information and ComputerTaiyuan University of TechnologyTaiyuan 030024China Key Lab of Advanced Transducers and Intelligent Control System of the Ministry of Education Taiyuan University of TechnologyTaiyuan 030024China Shanxi Research Institute of 6D Artificial Intelligence Biomedical Science Taiyuan 030031China

Osteochondral defect caused by trauma or osteoarthritis exhibits a major challenge in clinical treatment with limited symptomatic effects at *** regeneration and remodeling of subchondral bone play a positive effect on cartilage regeneration and further promotes the repair of osteochondral *** use of the strengths of each preparation method,the combination of 3D printing and electrospinning is a promising method for designing and constructing multi-scale scaffolds that mimic the complexity and hierarchical structure of subchondral bone at the microscale and nanoscale,*** this study,the 3D printed-electrospun poly(ɛ-caprolactone)/nano-hydroxyapatites/multi-walled carbon nanotubes(PCL/nHA/MWCNTs)scaffolds were successfully constructed by the combination of electrospinning and layer-by-layer 3D *** resulting dual-scale scaffold consisted of a dense layer of disordered nanospun fibers and a porous microscale 3D scaffold layer to support and promote the ingrowth of subchondral ***,the biomimetic PCL/nHA/MWCNTs scaffolds enhanced cell seeding efficiency and allowed for higher cell-cell interactions that supported the adhesion,proliferation,activity,morphology and subsequently improved the osteogenic differentiation of bone marrow mesenchymal stem cells in ***,this study elucidates that the construction of 3D printed-electrospun PCL/nHA/MWCNTs scaffolds provides an alternative strategy for the regeneration of subchondral bone and lays a foundation for subsequent in vivo studies.

关键词： additive manufacturing 3D printing electrospinning subchondral bone

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Attention-stacking adaptive fuzzy neural networks for autism diagnosis

Attention-stacking adaptive fuzzy neural networks for autism...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Zhang, Shuzhan Xiao, Lei Huang, Hui Hu, Zhongyi Wenzhou University Institute of Intelligent Information System Wenzhou University Key Laboratory of Intelligent Image Processing and Analysis College of Computer Science and Artificial Intelligence Wenzhou China

ISBN: (纸本)9798350386226

The current diagnostic methods for Autism Spectrum Disorder (ASD) based on Resting-State Functional Magnetic Resonance Imaging (rs-fMRI) face two significant challenges. Firstly, the functional connectivity networks (FCNs) of the brain are subject to uncertainty due to noise interference during the rs-fMRI acquisition process. Secondly, variations in equipment parameters across different imaging sites lead to diverse distributions of collected features. To address these issues, this paper introduces a novel Multi-Task Stacked Adaptive Fuzzy Neural Network (AT-STTSK) specifically designed for Autism Spectrum Disorder (ASD) classification. The proposed network seamlessly integrates a Multi-Output TSK (MO-TSK) fuzzy system, a Stacked Autoencoder (SAE), and a Transformer. This integration aims to reduce the impact of uncertainty factors, enhance the model's interpretability, and leverage the multi-head attention mechanisms to capture both local and global information, thus strengthening feature learning. Additionally, we explore the utilization of a Contrastive Learning (CL) strategy to further optimize the model. By incorporating these advancements, our AT-STTSK network offers a promising approach for improving the accuracy and reliability of ASD diagnosis based on rs-fMRI data. The validity of the method in this paper is verified on ABIDE dataset, and the result shows that the accuracy rate can reach 85.7% at most. © 2024 IEEE.

关键词： Diseases

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