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International Conference on Software engineering (ICSE)

作者： Maolin Sun Yibiao Yang Ming Wen Yongcong Wang Yuming Zhou Hai Jin School of Cyber Science and Engineering Huazhong University of Science and Technology China Hubei Key Laboratory of Distributed System Security Services Computing Technology and System Lab Cluster and Grid Computing Lab Hubei Engineering Research Center on Big Data Security National Engineering Research Center for Big Data Technology and System State Key Laboratory for Novel Software Technology Nanjing University China School of Computer Science and Technology Huazhong University of Science and Technology China

SMT solvers check the satisfiability of logic formulas over first-order theories, which have been utilized in a rich number of critical applications, such as software verification, test case generation, and program synthesis. Bugs hidden in SMT solvers would severely mislead those applications and further cause severe consequences. Therefore, ensuring the reliability and robustness of SMT solvers is of critical importance. Although many approaches have been proposed to test SMT solvers, it is still a challenge to discover bugs effectively. To tackle such a challenge, we conduct an empirical study on the historical bug-triggering formulas in SMT solvers' bug tracking systems. We observe that the historical bug-triggering formulas contain valuable skeletons (i.e., core structures of formulas) as well as associated atomic formulas which can cast significant impacts on formulas' ability in triggering bugs. Therefore, we propose a novel approach that utilizes the skeletons extracted from the historical bug-triggering formulas and enumerates atomic formulas under the guidance of association rules derived from historical formulas. In this study, we realized our approach as a practical fuzzing tool HistFuzz and conducted extensive testing on the well-known SMT solvers Z3 and cvc5. To date, HistFuzz has found 111 confirmed new bugs for Z3 and cvc5, of which 108 have been fixed by the developers. More notably, out of the confirmed bugs, 23 are soundness bugs and invalid model bugs found in the solvers' default mode, which are essential for SMT solvers. In addition, our experiments also demonstrate that HistFuzz outperforms the state-of-the-art SMT solver fuzzers in terms of achieved code coverage and effectiveness.

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Study of the response of 10B-doped MCP to wide-energy range neutrons from eV to MeV

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Nuclear Science and Techniques 2024年第8期35卷 132-141页

作者： Qiang Li Li‑Jiao Wang Xiang‑Biao Qiu Jing‑Wen Li Wei Xu Tao Li Ze‑Bin Lin Chang‑Jun Ning Yong‑Hao Chen Rui‑Rui Fan Kang Sun Jing‑Yu Tang Rong Zhang Han‑Tao Jing Bo Mei Qi An Hao‑Fan Bai Jiang‑Bo Bai Jie Bao Ping Cao Qi‑Ping Chen Zhen Chen Zeng‑Qi Cui An‑Chuan Fan Chang‑Qing Feng Fan‑Zhen Feng Ke‑Qing Gao Min‑Hao Gu Chang‑Cai Han Zi‑Jie Han Guo‑Zhu He Yong‑Cheng He Yang Hong Yi‑Wei Hu Han‑Xiong Huang Wei‑Hua Jia Hao‑Yu Jiang Wei Jiang Zhi‑Jie Jiang Zheng‑Yao Jin Ling Kang Bo Li Chao Li Gong Li Jia‑Wen Li Xiao Li Yang Li Jie Liu Rong Liu Shu‑Bin Liu Guang‑Yuan Luan Bin‑Bin Qi Jie Ren Zhi‑Zhou Ren Xi‑Chao Ruan Zhao‑Hui Song Zhi‑Xin Tan Sheng‑Da Tang Peng‑Cheng Wang Zhao‑Hui Wang Zhong‑Wei Wen Xiao‑Guang Wu Xuan Wu Li‑Kun Xie Yi‑Wei Yang Han Yi Yong‑Ji Yu Guo‑Hui Zhang Lin‑Hao Zhang Mo‑Han Zhang Qi‑Wei Zhang Xian‑Peng Zhang Yu‑Liang Zhang Yue Zhang Zhi‑Yong Zhang Mao‑Yuan Zhao Lu‑Ping Zhou Zhi‑Hao Zhou Ke‑Jun Zhu Institute of High Energy Physics Chinese Academy of Sciences(CAS)Beijing 100049China Spallation Neutron Source Science Center Dongguan 523803China State Key Laboratory of Particle Detection and Electronics Institute of High Energy PhysicsChinese Academy of SciencesBeijing 100049China Dongguan Songshan Lake Future School Dongguan 523808China North Night Vision Science&Technology(Nanjing)Research Institute Co.Ltd. Nanjing 211106China Sino‑French Institute of Nuclear Engineering and Technology Sun Yat-sen UniversityZhuhai 519082China School of Nuclear Science and Technology University of Science and Technology of ChinaHefei 230027China State Key Laboratory of Particle Detection and Electronics University of Science and Technology of ChinaHefei 230026China Department of Modern Physics University of Science and Technology of ChinaHefei 230026China State Key Laboratory of Nuclear Physics and Technology School of PhysicsPeking UniversityBeijing 100871China Institute of Nuclear Physics and Chemistry China Academy of Engineering PhysicsMianyang 621900China Key Laboratory of Nuclear Data China Institute of Atomic EnergyBeijing 102413China USTC Archaeometry Lab University of Science and Technology of ChinaHefei 230026China Northwest Institute of Nuclear Technology Xi’an 710024China University of Chinese Academy of Sciences Beijing 100049China

Neutron-sensitive microchannel plates(nMCPs)have applications in neutron detection,including energy spectrum measurements,neutron-induced cross sections,and neutron imaging.10B-doped MCPs(B-MCPs)have attracted significant attention owing to their potential for exhibiting a high neutron detection efficiency over a large neutron energy *** spatial and temporal resolutions are useful for neutron energy-resolved ***,their practical applications still face many technical *** this study,a B-MCP with 10 mol%10B was tested for its response to wide-energy neutrons from eV to MeV at the Back-n white neutron source at the China Spallation Neutron *** neutron detection efficiency was calibrated at 1 eV,which is approximately 300 times that of an ordinary MCP and indicates the success of 10 B *** factors that caused the reduction in the detection efficiency were simulated and *** neutron energy spectrum obtained using B-MCP was compared with that obtained by other measurement methods,and showed very good consistency for neutron energies below tens of *** response is more complicated at higher neutron energy,at which point the elastic and nonelastic reactions of all nuclides of B-MCP gradually become *** is beneficial for the detection of neutrons,as it compensates for the detection efficiency of B-MCP for high-energy neutrons.

关键词： Neutron radiation image 10B-doped MCP Neutron response Wide-energy range neutrons

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Mitigating label-Noise for Facial Expression Recognition in the Wild

Mitigating Label-Noise for Facial Expression Recognition in ...

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2022 IEEE International Conference on Multimedia and Expo, ICME 2022

作者： Yan, Huan Gu, Yu Zhang, Xiang Wang, Yantong Ji, Yusheng Ren, Fuji School of Computer and Information Hefei University of Technology S2AC Lab China Hefei University of Technology Key Laboratory of Knowledge Engineering with Big Data China Anhui Province Key Laboratory of Cyberspace Security Situation Awareness and Evaluation China National Institute of Informatics Tokushima University Japan

ISBN: (数字)9781665485630

ISBN: (纸本)9781665485630

label-noise constitutes a major challenge for facial expression recognition in the wild due to the ambiguity of facial expressions worsened by low-quality images. To deal with this problem, we propose a simple but effective label-noise Robust Network (LRN) which explores the inter-class correlations for mitigating ambiguity that usually happens between morphologically similar classes. Specifically, LRN leverages a multivariate normal distribution to model such correlations at the final hidden layer of the neural network to suppress the heteroscedastic uncertainty caused by inter-class label noise. Furthermore, LRN utilizes a confidence-based label-free loss to extract compact intra-class feature representations under label noise while preserving the intrinsic inter-class relationships. Experiments on three in-the-wild facial expression datasets demonstrates the superiority of our method. © 2022 IEEE.

关键词： Face recognition

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Attention-Constrained Inference For Robust Decoder-Only Text-to-Speech

Attention-Constrained Inference For Robust Decoder-Only Text...

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IEEE Spoken Language Technology Workshop

作者： Hankun Wang Chenpeng Du Yiwei Guo Shuai Wang Xie Chen Kai Yu Department of Computer Science and Engineering MoE Key Lab of Artificial Intelligence AI Institute X-LANCE Lab Shanghai Jiao Tong University Shanghai China Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Shenzhen China

ISBN: (数字)9798350392258

ISBN: (纸本)9798350392265

Recent popular decoder-only text-to-speech models are known for their ability of generating natural-sounding speech. However, such models sometimes suffer from word skipping and repeating due to the lack of explicit monotonic alignment constraints. In this paper, we notice from the attention maps that some particular attention heads of the decoder-only model indicate the alignments between speech and text. We call the attention maps of those heads Alignment-Emerged Attention Maps (AEAMs). Based on this discovery, we propose a novel inference method without altering the training process, named Attention-Constrained Inference (ACI), to facilitate monotonic synthesis. It first identifies AEAMs using the Attention Sweeping algorithm and then applies constraining masks on AEAMs. Our experimental results on decoderonly TTS model VALL-E show that the WER of synthesized speech is reduced by up to 20.5% relatively with ACI while the naturalness and speaker similarity are comparable.

关键词： Training Conferences Robustness Inference algorithms Text to speech Optimization

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GraphBEV: Towards Robust BEV Feature Alignment for Multi-Modal 3D Object Detection

arXiv

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arXiv 2024年

作者： Song, Ziying Yang, Lei Xu, Shaoqing Liu, Lin Xu, Dongyang Jia, Caiyan Jia, Feiyang Wang, Li School of Computer and Information Technology Beijing Jiaotong University China Beijing Key Lab of Traffic Data Analysis and Mining China School of Vehicle and Mobility Tsinghua University China Department of Electrome chanical Engineering University of Macau China School of Mechanical Engineering Beijing Institute of Technology China

Integrating LiDAR and camera information into Bird’s-Eye-View (BEV) representation has emerged as a crucial aspect of 3D object detection in autonomous driving. However, existing methods are susceptible to the inaccurate calibration relationship between LiDAR and the camera sensor. Such inaccuracies result in errors in depth estimation for the camera branch, ultimately causing misalignment between LiDAR and camera BEV features. In this work, we propose a robust fusion framework called GraphBEV. Addressing errors caused by inaccurate point cloud projection, we introduce a LocalAlign module that employs neighbor-aware depth features via Graph matching. Additionally, we propose a GlobalAlign module to rectify the misalignment between LiDAR and camera BEV features. Our GraphBEV framework achieves state-of-the-art performance, with an mAP of 70.1%, surpassing BEVFusion by 1.6% on the nuScnes validation set. Importantly, our GraphBEV outperforms BEVFusion by 8.3% under conditions with misalignment noise. Copyright © 2024, The Authors. All rights reserved.

关键词： Object detection

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Prototype and Instance Contrastive Learning for Unsupervised Domain Adaptation in Speaker Verification

Prototype and Instance Contrastive Learning for Unsupervised...

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International Symposium on Chinese Spoken Language Processing

作者： Wen Huang Bing Han Zhengyang Chen Shuai Wang Yanmin Qian Department of Computer Science and Engineering Auditory Cognition and Computational Acoustics Lab MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University Shanghai Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Shenzhen

ISBN: (数字)9798331516826

ISBN: (纸本)9798331516833

Speaker verification system trained on one domain usually suffers performance degradation when applied to another domain. To address this challenge, researchers commonly use feature distribution matching-based methods in unsupervised domain adaptation scenarios where some unlabeled target domain data is available. However, these methods often have limited performance improvement and lack generalization in various mismatch situations. In this paper, we propose Prototype and Instance Contrastive Learning (PICL), a novel method for unsupervised domain adaptation in speaker verification through dual-level contrastive learning. For prototype contrastive learning, we generate pseudo labels via clustering to create dynamically updated prototype representations, aligning instances with their corresponding class or cluster prototypes. For instance contrastive learning, we minimize the distance between different views or augmentations of the same instance, ensuring robust and invariant representations resilient to variations like noise. This dual-level approach provides both high-level and low-level supervision, leading to improved generalization and robustness of the speaker verification model. Unlike previous studies that only evaluated mismatches in one situation, we have conducted relevant explorations on various datasets and achieved state-of-the-art performance currently, which also proves the generalization of our method.

关键词： Degradation Adaptation models Noise Prototypes Contrastive learning Robustness

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Flow-TSVAD: Target-Speaker Voice Activity Detection via Latent Flow Matching for Speaker Diarization

Flow-TSVAD: Target-Speaker Voice Activity Detection via Late...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Zhengyang Chen Bing Han Shuai Wang Yidi Jiang Yanmin Qian Department of Computer Science and Engineering Auditory Cognition and Computational Acoustics Lab MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University Shanghai China Shenzhen Research Institute of Big Data Shenzhen China School of Data Science The Chinese University of Hong Kong Shenzhen China National University of Singapore Singapore

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Speaker diarization is typically considered as a discriminative task, using discriminative approaches to produce fixed diarization results. In this paper, we explore for the first time the use of neural network-based generative methods for speaker diarization. We implement a Flow-Matching (FM) based generative algorithm within the sequenceto-sequence target speaker voice activity detection (Seq2Seq-TSVAD) diarization system. Our experiments reveal that applying the generative method directly to the original binary label sequence space of the TS-VAD output is ineffective. To address this issue, we propose mapping the binary label sequence into a dense latent space before applying the generative algorithm, and our proposed Flow-TSVAD method can significantly outperform the traditional Seq2Seq-TSVAD system. Additionally, we observe that the FM algorithm converges rapidly during the inference stage, only requiring two inference steps to achieve promising results. Moreover, as a generative model, Flow-TSVAD allows for sampling different diarization results by running the model multiple times, so the ensemble system combining the results from various sampling instances can further boost the diarization performance.

关键词： Voice activity detection Frequency modulation Computational modeling Signal processing algorithms Signal processing Inference algorithms Acoustics Computational efficiency

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ATTENTION-CONSTRAINED INFERENCE FOR ROBUST DECODER-ONLY TEXT-TO-SPEECH

arXiv

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arXiv 2024年

作者： Wang, Hankun Du, Chenpeng Guo, Yiwei Wang, Shuai Chen, Xie Yu, Kai MoE Key Lab of Artificial Intelligence AI Institute X-LANCE Lab Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Shenzhen China

Recent popular decoder-only text-to-speech models are known for their ability of generating natural-sounding speech. However, such models sometimes suffer from word skipping and repeating due to the lack of explicit monotonic alignment constraints. In this paper, we notice from the attention maps that some particular attention heads of the decoder-only model indicate the alignments between speech and text. We call the attention maps of those heads Alignment-Emerged Attention Maps (AEAMs). Based on this discovery, we propose a novel inference method without altering the training process, named Attention-Constrained Inference (ACI), to facilitate monotonic synthesis. It first identifies AEAMs using the Attention Sweeping algorithm and then applies constraining masks on AEAMs. Our experimental results on decoder-only TTS model VALL-E show that the WER of synthesized speech is reduced by up to 20.5% relatively with ACI while the naturalness and speaker similarity are comparable. Copyright © 2024, The Authors. All rights reserved.

关键词： Decoding

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Effective Concurrency Testing for Go via Directional Primitive-Constrained Interleaving Exploration

Effective Concurrency Testing for Go via Directional Primiti...

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IEEE International Conference on Automated Software engineering (ASE)

作者： Zongze Jiang Ming Wen Yixin Yang Chao Peng Ping Yang Hai Jin School of Cyber Science and Engineering Huazhong University of Science and Technology China Hubei Key Laboratory of Distributed System Security Services Computing Technology and System Lab Cluster and Grid Computing Lab Hubei Engineering Research Center on Big Data Security National Engineering Research Center for Big Data Technology and System ByteDance Beijing China School of Computer Science and Technology Huazhong University of Science and Technology China

The Go language (Go/Golang) has been attracting increasing attention from the industry over recent years due to its strong concurrency support and ease of deployment. This programming language encourages developers to use channel-based concurrency, which simplifies the development of concurrent programs. Unfortunately, it also introduces new concurrency problems that differ from those caused by the mechanism of shared memory concurrency. However, there are only few works that aim to detect such Go-specific concurrency issues. Even state-of-the-art testing tools will miss critical concurrent bugs that require fine-grained and effective interleaving exploration. This paper presents GoPie, a novel testing approach for detecting Go concurrency bugs through primitive-constrained interleaving exploration. GoPie utilizes execution histories to identify new interleavings instead of relying on exhaustive exploration or random scheduling. To evaluate its performance, we applied GoPie to existing benchmarks and large-scale open-source projects. Results show that GoPie can effectively explore concurrent interleavings and detect significantly more bugs in the benchmark. Furthermore, it uncovered 11 unique previously unknown concurrent bugs, and 9 of which have been confirmed.

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Flickr30K-CFQ: A Compact and Fragmented Query dataset for Text-image Retrieval

arXiv

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arXiv 2024年

作者： Liu, Haoyu Song, Yaoxian Wang, Xuwu Zhu, Xiangru Li, Zhixu Song, Wei Li, Tiefeng Research Center for Intelligent Robotics Zhejiang Lab China Department of Engineering Mechanics Center for X-Mechanics Zhejiang University China Shanghai Key Laboratory of Data Science School of Computer Science Fudan University China

With the explosive growth of multi-modal information on the Internet, unimodal search cannot satisfy the requirement of Internet applications. Text-image retrieval research is needed to realize high-quality and efficient retrieval between different modalities. Existing text-image retrieval research is mostly based on general vision-language datasets (e.g. MS-COCO, Flickr30K), in which the query utterance is rigid and unnatural (i.e. verbosity and formality). To overcome the shortcoming, we construct a new Compact and Fragmented Query challenge dataset (named Flickr30K-CFQ) to model text-image retrieval task considering multiple query content and style, including compact and fine-grained entity-relation corpus. We propose a novel LLM-based Query-enhanced method using prompt engineering based on LLM. Experiments show that our proposed Flickr30-CFQ reveals the insufficiency of existing vision-language datasets in realistic text-image tasks. Our LLM-based Query-enhanced method applied on different existing text-image retrieval models improves query understanding performance both on public dataset and our challenge set Flickr30-CFQ with over 0.9% and 2.4% respectively. Our project can be available anonymously in https://***/view/Flickr30K-cfq. Copyright © 2024, The Authors. All rights reserved.

关键词： Image enhancement

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