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

作者： Zhang, Xuanchang Zhang, Zhuosheng Zhao, Hai College of Zhiyuan Shanghai Jiao Tong University China School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University China Department of Computer Science and Engineering Shanghai Jiao Tong University China Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai Jiao Tong University China Shanghai Key Laboratory of Trusted Data Circulation and Governance in Web3 China

ISBN: (纸本)9798891761643

Despite the rapid progress of large language models (LLMs), their task performance remains sensitive to prompt design. Recent studies have explored leveraging the LLM itself as an optimizer to identify optimal prompts that maximize task accuracy. However, when evaluating prompts, such approaches heavily rely on elusive manually annotated gold labels to calculate task accuracy for each candidate prompt, which hinders its generality. To overcome the limitation, this work proposes GLaPE, a gold label-agnostic prompt evaluation method to alleviate dependence on gold labels. GLaPE is composed of two critical aspects: self-consistency evaluation of a single prompt and mutual-consistency refinement across multiple prompts. Experimental results on 8 widely-recognized reasoning tasks demonstrate that GLaPE can produce more effective prompts, achieving performance comparable to those derived from manually annotated gold labels. Analysis shows that GLaPE provides reliable evaluations aligned with accuracy, even in the absence of gold labels. Code is publicly available at https://***/thunderous77/GLaPE. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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AMSER: Accelerate Mobile Speech Emotion Recognition with Signal Compression

AMSER: Accelerate Mobile Speech Emotion Recognition with Sig...

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

作者： Yu Lu Ran Wang Dian Ding Han Zhang Liyun Zhang Lanqing Yang Yi-Chao Chen Guangtao Xue Department of Computer Science and Engineering Shanghai Jiao Tong University China Shanghai Key Laboratory of Trusted Data Circulation and Governance and Web3

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Speech-based interaction systems are widely used in mobile devices like smartphones. With advances in deep neural networks, tasks such as speech emotion recognition (SER) enhance these systems’ user-friendliness. However, deploying SER models on mobile devices is challenging due to their complexity and computational demands. While pruning can reduce complexity, it often compromises accuracy, and hardware accelerators like FPGAs are difficult to integrate into mobile devices. This paper proposes AMSER, a real-time speech emotion recognition framework using signal compression and task offloading. AMSER utilizes logarithmic Mel-filter bank coefficients (Fbank) and singular value decomposition (SVD) for feature extraction and compression. The compressed signal is only 6.25% of the original size, achieving 2.24x faster transfer rates and 55.35% energy savings compared to raw audio transmission. Despite the compression, the features preserve key audio information for text and emotion recognition, performed server-side. Experiments show a WER of 4.68% (Librispeech), 10.69% (CommonVoice), and 69.83% emotion recognition accuracy (IEMOCAP).

关键词： Emotion recognition Accuracy Computational modeling Speech recognition Artificial neural networks Speech enhancement Real-time systems Complexity theory Smart phones Singular value decomposition

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DASIV: Directional Acoustic Sensing based Intelligent Vehicle Interaction System

DASIV: Directional Acoustic Sensing based Intelligent Vehicl...

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IEEE International Conference on Performance, Computing and Communications (IPCCC)

作者： Dinghua Zhao Juntao Zhou Dian Ding Yu Lu Yijie Li Hang Yang Yi-Chao Chen Guangtao Xue Department of Computer Science and Engineering Shanghai Jiao Tong University China Shanghai Key Laboratory of Trusted Data Circulation Governance and Web3

ISBN: (数字)9798350367942

ISBN: (纸本)9798350367959

With the increase in motor vehicles, more convenient and accurate interactions are expected while retaining a high standard of safe driving. However, complex and dynamic vehicle environments challenge sensing tasks such as breathing monitor and hand gesture recognition. In this paper, we propose DASIV, which utilizes the highly directional nature of ultrasonic signals to achieve fine-grained directional acoustic sensing in vehicle environments. Due to air nonlinearity, the system enables synchronized directional acoustic communication to transmit information (e.g., navigation) to the driver without affecting other passengers. By optimizing the frequency of the Frequency Modulated Continuous Wave (FMCW) signals, DASIV avoids mutual interference between the sensing and communication signals and achieves breathing detection and hand gesture recognition for the driver. Specifically, the system extracts breathing-induced weak thoracic bullying through the signal phase, captures and analyses breathing patterns using bandpass and Gaussian filters, and develops a breathing model. Then, the system defines 10 interaction hand gestures to meet daily interaction needs, uses spectral features to mine complex and fast hand movement features, and proposes a hand gesture recognition model. Extensive experiments in real environments show that DASIV achieves high-precision breathing monitor (Pearson correlation coefficient of 0.89) and hand gesture recognition (Precision of 91.7%).

关键词： Hands Frequency modulation Navigation Gesture recognition Acoustics Sensors Synchronization Vehicle dynamics Monitoring Standards

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Machine Learning Techniques for Autism Spectrum Disorder: current trends and future directions

Machine Learning Techniques for Autism Spectrum Disorder: cu...

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Innovative Trends in Information Technology (ICITIIT), International Conference on

作者： Kainat Khan Rahul Katarya Department of Computer Science & Engineering Big Data Analytics and Web Intelligence Laboratory Delhi Technological University New Delhi India

ASD or autism spectrum disorder is a critical neuro-developmental disorder that hinders an individual's capability of social communication and interaction. This disorder has acquired considerable attention and importance due to its ubiquity among individuals covering all the countries worldwide. Individuals with ASD struggles in daily life activities. Detection of autism with the help of medical tests is a tedious and very costly task. However, detection and care of ASD still remains unfamiliar due to inadequate awareness, knowledge among the society, limited number of diagnostic devices and limited verbal therapy services for ASD patients. This paper investigates and displays reviews of various machine learning approaches on extracting useful data associated with distinctive characteristics of ASD such as brain functioning, hyperactivitperactivity, language disability, etc. Current researches reveal that analysis of biological traits by employing machine learning techniques have helped in the progress of early detection of ASD. ABIDE dataset is very much explored for the research in ASD. Additionally, numerous studies for the advancement of tools are still in progression. The presented research work can remarkably aid future studies on machine learning for ASD.

关键词： Autism Medical treatment Machine learning Market research Medical tests Biology data mining

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Empirical Analysis of Nature-Inspired Algorithms for Autism Spectrum Disorder Detection Using 3D Video dataset

arXiv

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arXiv 2025年

作者： Panchal, Aneesh Khan, Kainat Katarya, Rahul Department of Computational and Data Sciences Indian Institute of Science Karnataka Bangalore560012 India Big Data Analytics and Web Intelligence Laboratory Department of Computer Science & Engineering Delhi Technological University Delhi110042 India

Autism Spectrum Disorder (ASD) is a chronic neurodevelopmental disorder symptoms of which includes repetitive behaviour and lack of social and communication skills. Even though these symptoms can be seen very clearly in social but a large number of individuals with ASD remains undiagnosed. In this paper, we worked on a methodology for detection of ASD from 3-dimensional walking video dataset, utilizing supervised machine learning (ML) classification algorithms and using nature-inspired optimization algorithms for feature extraction from dataset. The proposed methodology involve the classification of ASD using supervised ML classification algorithm and extracting important and relevant features from the dataset using nature-inspired optimization algorithms. We also included the ranking coefficients for finding the initial leading particle. This selection of particle significantly reduces the computation time and hence, improving the total efficiency and accuracy for the ASD detection. To evaluate the efficiency of the proposed methodology, we deployed various combination of classification algorithm and nature-inspired algorithms resulting in an outstanding classification accuracy of 100% using random forest classification algorithm and gravitational search algorithm for feature selection. The application of the proposed methodology with different datasets would enhance the robustness and generalizability of the proposed methodology. Due to high accuracy and less total computation time, the proposed methodology will offer a significant contribution to the medical and academia fields, providing a foundation for future research and advancements in ASD diagnosis. Copyright © 2025, The Authors. All rights reserved.

关键词： Diseases

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Are LLMs Aware that Some Questions are not Open-ended?

arXiv

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arXiv 2024年

作者： Yang, Dongjie Zhao, Hai Department of Computer Science and Engineering Shanghai Jiao Tong University Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai Key Laboratory of Trusted Data Circulation and Governance in Web3 China

Large Language Models (LLMs) have shown the impressive capability of answering questions in a wide range of scenarios. However, when LLMs face different types of questions, it is worth exploring whether LLMs are aware that some questions have limited answers and need to respond more deterministically but some do not. We refer to this as question awareness of LLMs. The lack of question awareness in LLMs leads to two phenomena that LLMs are: (1) too casual to answer non-open-ended questions or (2) too boring to answer open-ended questions. In this paper, we first evaluate the question awareness in LLMs. The experimental results show that LLMs have the issues of lacking awareness of questions in certain domains, e.g. factual knowledge, resulting in hallucinations during the generation. To mitigate these, we propose a method called Question Awareness Temperature Sampling (QuATS). This method enhances the question awareness of LLMs by adaptively adjusting the output distributions based on question features. The automatic adjustment in QuATS eliminates the need for manual temperature tuning in text generation and consistently improves model performance in various benchmarks. Copyright © 2024, The Authors. All rights reserved.

关键词： Natural language processing systems

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SCANS: Mitigating the Exaggerated Safety for LLMs via Safety-Conscious Activation Steering 39

SCANS: Mitigating the Exaggerated Safety for LLMs via Safety...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Cao, Zouying Yang, Yifei Zhao, Hai Department of Computer Science and Engineering Shanghai Jiao Tong University Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai Jiao Tong University Shanghai Key Laboratory of Trusted Data Circulation and Governance in Web3 China

ISBN: (纸本)157735897X

Safety alignment is indispensable for Large Language Models (LLMs) to defend threats from malicious instructions. However, recent researches reveal safety-aligned LLMs tend to reject benign queries due to the exaggerated safety issue, limiting their helpfulness. In this paper, we propose a Safety-Conscious Activation Steering (SCANS) method to mitigate the exaggerated safety concerns in aligned LLMs. First, SCANS extracts the refusal steering vectors within the activation space and utilizes vocabulary projection to anchor some specific safety-critical layers which influence model refusal behavior. Second, by tracking the hidden state transition, SCANS identifies the steering direction and steers the model behavior accordingly, achieving a balance between exaggerated safety and adequate safety. Experiments show that SCANS achieves new state-of-the-art performance on XSTest and OKTest benchmarks, without impairing their defense capability against harmful queries and maintaining almost unchanged model capability. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

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A Secure Device Management Scheme with Audio-Based Location Distinction in IoT

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Computer Modeling in engineering & Sciences 2024年第1期138卷 939-956页

作者： Haifeng Lin Xiangfeng Liu Chen Chen Zhibo Liu Dexin Zhao Yiwen Zhang Weizhuang Li Mingsheng Cao College of Economics and Management Nanjing University of Aeronautics and AstronauticsNanjing211106China Chengdu Aircraft Industrial(Group)Co. Ltd.Chengdu610073China Academy of Military Sciences of PLA Beijing100091China School of Information and Software Engineering University of Electronic Science and Technology of ChinaChengdu610054China Ningbo Web King Technology Joint Stock Co. Ltd.Ningbo315000China The Network and Data Security Key Laboratory of Sichuan Province University of Electronic Science and Technology of ChinaChengdu610054China

Identifying a device and detecting a change in its position is critical for secure devices management in the Internet of Things(IoT).In this paper,a device management system is proposed to track the devices by using audio-based location distinction *** the proposed scheme,traditional cryptographic techniques,such as symmetric encryption algorithm,RSA-based signcryption scheme,and audio-based secure transmission,are utilized to provide authentication,non-repudiation,and confidentiality in the information interaction of the management ***,an audio-based location distinction method is designed to detect the position change of the ***,the audio frequency response(AFR)of several frequency points is utilized as a device *** device signature has the features as follows.(1)Hardware Signature:different pairs of speaker and microphone have different signatures;(2)Distance Signature:in the same direction,the signatures are different at different distances;and(3)Direction Signature:at the same distance,the signatures are different in different *** on the features above,amovement detection algorithmfor device identification and location distinction is ***,a secure communication protocol is also proposed by using traditional cryptographic techniques to provide integrity,authentication,and non-repudiation in the process of information interaction between devices,Access Points(APs),and *** experiments are conducted to evaluate the performance of the proposed *** experimental results show that the proposedmethod has a good performance in accuracy and energy consumption.

关键词： Acoustic hardware fingerprinting device management IoT location distinction

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SCANS: Mitigating the Exaggerated Safety for LLMs via Safety-Conscious Activation Steering

arXiv

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arXiv 2024年

Safety alignment is indispensable for Large Language Models (LLMs) to defend threats from malicious instructions. However, recent researches reveal safety-aligned LLMs prone to reject benign queries due to the exaggerated safety issue, limiting their helpfulness. In this paper, we propose a Safety-Conscious Activation Steering (SCANS) method to mitigate the exaggerated safety concerns in aligned LLMs. First, SCANS extracts the refusal steering vectors within the activation space and utilizes vocabulary projection to anchor some specific safety-critical layers which influence model refusal behavior. Second, by tracking the hidden state transition, SCANS identifies the steering direction and steers the model behavior accordingly, achieving a balance between exaggerated safety and adequate safety. Experiments show that SCANS achieves new state-of-the-art performance on XSTest and OKTest benchmarks, without impairing their defense capability against harmful queries and maintaining almost unchanged model capability. Copyright © 2024, The Authors. All rights reserved.

关键词： Benchmarking

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Front-running Attacks in Hash-Based Transaction Sharding Blockchains 23

Front-running Attacks in Hash-Based Transaction Sharding Blo...

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23rd IEEE International Conference on Trust, Security and Privacy in Computing and Communications, TrustCom 2024

作者： Wang, Yusen Lou, Jiong Wang, Zihan Li, Jie Shanghai Jiao Tong University Department of Computer Science and Engineering China Blockchain Advanced Research Center China Shanghai Key Laboratory of Trusted Data Circulation and Governance and Web3 China

Sharding is one of the prominent solutions to solve the scalability problem of traditional blockchains. By dividing the blockchain network into independent shards, transactions in different shards can be executed in parallel, improving the throughput of the blockchain. However, sharding also brings security issues. In a hash-based transaction sharding system, the output shard of a transaction is determined by the hash value of the transaction. Unfortunately, we show that this hashbased transaction assignment strategy can be easily exploited by attackers to conduct front-running attacks. Attackers can take advantage of the execution differences between different shards and the extra processing time of cross-shard transactions to make the attacker's transaction executed and committed before the victim's transaction, thereby obtaining the benefits that originally belonged to the victim. We also propose a flooding front-running attack, which introduces a single-shard flooding attack to enhance the front-running attack. Specifically, injecting a large number of junk transactions into the shard where the victim's transaction is located can significantly extend the execution time of the victim's transaction. We demonstrate the feasibility and practical effects of these two attacks through experiments on RapidChain, which show that a single-shard flooding attack can increase the success rate of a front-running attack by 12%. Finally, we discuss two possible mitigation measures and the cost of two proposed attacks. © 2024 IEEE.

关键词： blockchain flooding attack front-running attack sharding

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