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Proceedings of the 55th Annual IEEE/ACM International Symposium on Microarchitecture

作者： Xueliang Li Zhuobin Shi Junyang Chen Yepang Liu National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China College of Computer Science and Software Engineering Shenzhen University China Research Institute of Trustworthy Autonomous Systems Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation and Department of Computer Science and Engineering Southern University of Science and Technology China

ISBN: (纸本)9781665462723

In the age of AI, mobile architectures such as smartphones are still "cold machines"; machines do not feel. If the architecture is able to feel users' feelings and runtime user experience (UX), it will accordingly adapt performance/energy to find the optimal system-operating state that consumes the least energy to satisfy users. In this paper, we will utilize users' facial expressions (FEs) to learn their runtime UX. We know that FEs are the natural and direct way for humans to convey their emotions and feelings. Our study reveals that FEs also reflect UX. Our research for the first time quantifies the link between FEs and UX. Leveraging this link, the architecture will be able to use the front camera to see FEs and feel users' UX. Based on UX, the architecture can appropriately provision computing resources. We propose Vi-energy system to realize the above idea. Our evaluation shows that Vi-energy reduces energy consumption by 52.9% at maximum and secures UX.

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Reconciling Selective Logging and Hardware Persistent Memory Transaction

Reconciling Selective Logging and Hardware Persistent Memory...

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IEEE Symposium on High-Performance Computer Architecture

作者： Chencheng Ye Yuanchao Xu Xipeng Shen Yan Sha Xiaofei Liao Hai Jin Yan Solihin National Engineering Research Center for Big Data Technology and System/Services Computing Technology and System Lab/Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China North Carolina State University Raleigh North Carolina USA University of Central Florida Florida USA

Log creation, maintenance, and its persist ordering are known to be performance bottlenecks for durable transactions on persistent memory. Existing hardware persistent memory transactions overlook an important opportunity for improving performance: some persistent data is algorithmically redundant such that it can be recovered from other data, removing the need for logging such data. The paper presents an ISA extension that enables selective logging for hardware persistent memory transactions for the first time. The ISA extension features two novel components: fine-grain logging and lazy persistency. Fine-grain logging allows hardware to log updates on data in the granularity of words without lengthening the critical path of data accesses. Lazy persistency allows updated data to remain in the cache after the transaction commits. Together, the new hardware persistent memory transaction outperforms the state-of-the-art hardware counterpart by 1.8× on average.

关键词： Computer architecture Maintenance engineering Hardware Optimization IEEE transactions

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MFCLIP: Multi-modal Fine-grained CLIP for Generalizable Diffusion Face Forgery Detection

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IEEE Transactions on Information Forensics and Security 2025年 20卷 5888-5903页

作者： Zhang, Yaning Wang, Tianyi Yu, Zitong Gao, Zan Shen, Linlin Chen, Shengyong Qilu University of Technology Shandong Academy of Sciences Faculty of Computer Science and Technology Jinan 250014 China National University of Singapore School of Computing 21 Lower Kent Ridge Rd 119077 Singapore Great Bay University School of Computing and Information Technology Dongguan 523000 China Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen 518060 China Qilu University of Technology (Shandong Academy of Sciences) Shandong Artificial Intelligence Institute Jinan 250014 China Tianjin University of Technology Key Laboratory of Computer Vision and System Ministry of Education Tianjin 300384 China Shenzhen University Computer Vision Institute College of Computer Science and Software Engineering Shenzhen 518060 China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen 518129 China Shenzhen University Guangdong Key Laboratory of Intelligent Information Processing Shenzhen 518060 China

The rapid development of photo-realistic face generation methods has raised significant concerns in society and academia, highlighting the urgent need for robust and generalizable face forgery detection (FFD) techniques. Although existing approaches mainly capture face forgery patterns using image modality, other modalities like fine-grained noises and texts are not fully explored, which limits the generalization capability of the model. In addition, most FFD methods tend to identify facial images generated by GAN, but struggle to detect unseen diffusion-synthesized ones. To address the limitations, we aim to leverage the cutting-edge foundation model, contrastive language-image pre-training (CLIP), to achieve generalizable diffusion face forgery detection (DFFD). In this paper, we propose a novel multi-modal fine-grained CLIP (MFCLIP) model, which mines comprehensive and fine-grained forgery traces across image-noise modalities via language-guided face forgery representation learning, to facilitate the advancement of DFFD. Specifically, we devise a fine-grained language encoder (FLE) that extracts fine global language features from hierarchical text prompts. We design a multi-modal vision encoder (MVE) to capture global image forgery embeddings as well as fine-grained noise forgery patterns extracted from the richest patch, and integrate them to mine general visual forgery traces. Moreover, we build an innovative plug-and-play sample pair attention (SPA) method to emphasize relevant negative pairs and suppress irrelevant ones, allowing cross-modality sample pairs to conduct more flexible alignment. Extensive experiments and visualizations show that our model outperforms the state of the arts on different settings like cross-generator, cross-forgery, and cross-dataset evaluations. © 2005-2012 IEEE.

关键词： CLIP Diffusion face forgery detection Image-noise fusion Sample pair attention Transformer

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UniFace: Unified Cross-Entropy Loss for Deep Face Recognition

UniFace: Unified Cross-Entropy Loss for Deep Face Recognitio...

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International Conference on Computer Vision (ICCV)

作者： Jiancan Zhou Xi Jia Qiufu Li Linlin Shen Jinming Duan National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Computer Vision Institute Shenzhen University Aqara Lumi United Technology Co. Ltd. School of Computer Science University of Birmingham UK Alan Turing Institute UK

As a widely used loss function in deep face recognition, the softmax loss cannot guarantee that the minimum positive sample-to-class similarity is larger than the maximum negative sample-to-class similarity. As a result, no unified threshold is available to separate positive sample-to-class pairs from negative sample-to-class pairs. To bridge this gap, we design a UCE (Unified Cross-Entropy) loss for face recognition model training, which is built on the vital constraint that all the positive sample-to-class similarities shall be larger than the negative ones. Our UCE loss can be integrated with margins for a further performance boost. The face recognition model trained with the proposed UCE loss, UniFace, was intensively evaluated using a number of popular public datasets like MFR, IJB-C, LFW, CFP-FP, AgeDB, and MegaFace. Experimental results show that our approach outperforms SOTA methods like SphereFace, CosFace, ArcFace, Partial FC, etc. Especially, till the submission of this work (Mar. 8, 2023), the proposed UniFace achieves the highest TAR@MR-All on the academic track of the MFR-ongoing challenge. $\color{Blue}{\mathbf{Code}}$ is publicly available.

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Iterative Refinement of Project-Level Code Context for Precise Code Generation with Compiler Feedback

arXiv

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arXiv 2024年

作者： Bi, Zhangqian Wan, Yao Wang, Zheng Zhang, Hongyu Guan, Batu Lu, Fangxin Zhang, Zili Sui, Yulei Jin, Hai Shi, Xuanhua Huazhong University of Science and Technology China University of Leeds United Kingdom Chongqing University China Shanghai Jiao Tong University China University of New South Wales Australia National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China

Large Language Models (LLMs) have shown remarkable progress in automated code generation. Yet, LLM-generated code may contain errors in API usage, class, data structure, or missing project-specific information. As much of this project-specific context cannot fit into the prompts of LLMs, we must find ways to allow the model to explore the project-level code context. We present COCOGEN, a new code generation approach that uses compiler feedback to improve the LLM-generated code. COCOGEN first leverages static analysis to identify mismatches between the generated code and the project’s context. It then iteratively aligns and fixes the identified errors using information extracted from the code repository. We integrate COCOGEN with two representative LLMs, i.e., GPT-3.5-Turbo and Code Llama (13B), and apply it to Python code generation. Experimental results show that COCOGEN significantly improves the vanilla LLMs by over 80% in generating code dependent on the project context and consistently outperforms the existing retrieval-based code generation baselines. © 2024, CC BY-NC-SA.

关键词： Static analysis

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Service Decomposition method for multimodal data exchange scenarios 3

Service Decomposition method for multimodal data exchange sc...

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3rd International Conference on Computer Science and Management technology, ICCSMT 2022

作者： Jiang, Deyou Huang, Lei Tian, Hanyu Xie, Yujie Li, Xiaoming Beijing Jiaotong University School of Economics and Management Beijing China Beijing Jingwei Information Technologies Limited Company National Engineering Laboratory of Comprehensive Transportation Big Data Application Technology Beijing China Institute of Computing Technologies China Academy of Railway Sciences Beijing China

ISBN: (纸本)9798350320404

Influenced by the complexity of multimodal data exchange scenarios and their differences in data flow graph construction, the objective basis of existing service decomposition methods is not sufficient, and the granularity of the results is too large and difficult to be decomposed. To address this problem, this paper presents a service decomposition method for multimodal data exchange scenarios. This method reconstructs the differentially constructed data flow diagram by unified rules at the stage of requirement analysis and design, and then completes the fine-grained division of microservices around documents based on service-decomposed rules. The case study shows that the method is more effective than the existing method in the multimodal data exchange scenario. © 2022 IEEE.

关键词： Electronic data interchange

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STSE-xLSTM: A Deep Learning Framework for Automated Seizure Detection in Long Video Sequences Using Spatio-Temporal and Attention Mechanisms 10

STSE-xLSTM: A Deep Learning Framework for Automated Seizure ...

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10th International Conference on Computer and Communications, ICCC 2024

作者： Ding, Lihui Wang, Hongliang Fu, Lijun Shenyang Institute of Computing Technology University of Chinese Academy of Sciences Shenyang China Liaoning Province Human-Computer Interaction System Engineering Research Center Based on Digital Twin Shenyang China Shandong University Laboratory of Big Data and Artificial Intelligence Technology Beijing China

A detailed analysis of seizure semiology, the symptoms and signs that occur during a seizure, is crucial for the management of epilepsy patients. The inter-rater reliability of qualitative visual analysis is often low, highlighting the need for automatic and quantitative analysis of video-recorded seizures for objective assessment. To enhance evaluation accuracy and consistency, computer-aided video analysis of seizures has emerged as a natural direction of development. In the field of medical applications, deep learning and computer vision methods have driven significant advancements. However, analyzing video data presents challenges due to high dimensionality, variations in lighting, posture, and video quality. Additionally, the duration of seizures can range from seconds to minutes. To effectively extract features from seizures, we propose a novel deep learning model, STSE-xLSTM, designed to learn deep representations of arbitrarily long seizure videos. This model integrates a Spatio-Temporal Convolutional Network (STCNN) for feature extraction, a Squeeze-and-Excitation (SE) attention mechanism to enhance relevant features, and an extended Long Short-Term Memory network (xLSTM) to process long video sequences. The SE mechanism adaptively recalibrates channel weights, improving recognition of minority classes and reducing information redundancy. The xLSTM introduces scalar and matrix memory units, optimizing memory capacity and multi-scale feature extraction, thereby enhancing detection accuracy. When evaluated on the GESTURES dataset, our model outperformed state-of-the-art epilepsy detection methods in classification performance. Key contributions include the integration of STCNN, SE attention mechanism, and xLSTM to address data imbalance issues and improve the handling of complex sequential data in epilepsy detection. © 2024 IEEE.

关键词： Video analysis

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Metadata-Driven Federated Learning of Connectional Brain Templates in Non-IID Multi-Domain Scenarios

arXiv

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arXiv 2024年

作者： Chen, Geng Wang, Qingyue Rekik, Islem National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology School of Computer Science and Engineering Northwestern Polytechnical University China BASIRA Lab Imperial-X Department of Computing Imperial College London United Kingdom

A connectional brain template (CBT) is a holistic representation of a population of multi-view brain connectivity graphs, encoding shared patterns and normalizing typical variations across individuals. The federation of CBT learning allows for an inclusive estimation of representative center of multi-domain brain connectivity datasets in a fully data-preserving manner. However, existing methods overlook the non-independent and identically distributed (non-IDD) issue stemming from multidomain brain connectivity heterogeneity, in which data domains are drawn from different hospitals and imaging modalities. This non-IDD issue degrades the centrality of locally learned CBT from multiple decentralized datasets (i.e., domains/hospitals), eventually leading to a limited representation ability. To overcome this limitation, we unprecedentedly propose a metadata-driven federated learning framework, called MetaFedCBT, for cross-domain CBT learning. Given the data drawn from a specific domain (i.e., hospital), our model aims to learn a metadata in a fully supervised manner by introducing a local client-based regressor network. The generated meta-data is forced to meet the statistical attributes (e.g., mean) of other domains, while preserving their privacy. Our supervised meta-data generation approach boosts the unsupervised learning of a more centered and representative and holistic CBT of a particular brain state across diverse domains. As the federated learning progresses over multiple rounds, the learned metadata and associated generated connectivities are are continuously updated to better approximate the target domain information. MetaFedCBT overcomes the non-IID issue of existing methods by generating informative brain connectivities for privacy-preserving holistic CBT learning with the guidance using metadata. Extensive experiments on multi-view morphological brain networks of normal and patient subjects demonstrate that our MetaFedCBT is a superior federated CBT learnin

关键词： Metadata

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STSE-xLSTM: A Deep Learning Framework for Automated Seizure Detection in Long Video Sequences Using Spatio-Temporal and Attention Mechanisms

STSE-xLSTM: A Deep Learning Framework for Automated Seizure ...

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International Conference on Computer and Communications (ICCC)

作者： Lihui Ding Hongliang Wang Lijun Fu Shenyang Institute of Computing Technology University of Chinese Academy of Sciences Shenyang China Liaoning Province Human-Computer Interaction System Engineering Research Center Based on Digital Twin Shenyang China Laboratory of Big Data and Artificial Intelligence Technology Shandong University Beijing China

ISBN: (数字)9798331507077

ISBN: (纸本)9798331507084

关键词： Deep learning Adaptation models Visualization Attention mechanisms Accuracy Video sequences Epilepsy Feature extraction Reliability Long short term memory

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Neural TTS-Based Dynamic data Augmentation for Improved Speech Separation

IEEE Transactions on Audio, Speech and Language Processing

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IEEE Transactions on Audio, Speech and Language Processing 2025年 33卷 2457-2470页

作者： Kai Wang Cuicui Zhu Lili Yin Sheng Li Madina Mansurova Hao Huang School of Computer Science and Technology Xinjiang University Urumqi China Joint International Research Laboratory of Silk Road Multilingual Cognitive Computing Urumqi China Xinjiang Key Laboratory of Multilingual Information Technology Urumqi China School of Engineering Institute of Science Tokyo Kanagawa Japan Department of Artificial Intelligence and Big Data Al-Farabi Kazakh National University Almaty Kazakhstan

Text-to-speech (TTS) synthetic data augmentation has been widely used in various speech processing tasks, but its effectiveness in speech separation remains understudied. In this paper, we present SpeakerAugment+ (SA+), a neural TTS-based dynamic data augmentation framework for speech separation. The SA+ framework consists of two modules: speaker module, which learns a Gaussian Mixture Model (GMM) to characterize the distribution over speaker embeddings in training data and samples unseen speaker embeddings during inference; speech module, which conditions speech synthesis using speaker embeddings with controllable speaker parameters. SA+ incorporates three augmentation techniques: speaker generation, parameter manipulation and utterance generation, enhancing speaker and utterance diversity from different perspectives. Following the SA+ framework, we design FS2-SA+ and Matcha-SA+, which are based on FastSpeech 2 and Matcha-TTS, respectively. We evaluate SA+ across multiple separation models and datasets, and the results demonstrate a substantial improvement in speech separation performance. Matcha-SA+ generates higher-quality speech and achieves better separation performance in intra-corpus tests. Conversely, FS2-SA+ supports a broad range of speaker parameter adjustments, leading to better generalization. Besides, the relationship between harmonicity and speech separation is a widely researched topic, and our findings indicate that speech lacking an explicit harmonic structure, when generated by neural TTS, can function as augmented data to improve speech separation. This research underscores the effectiveness of neural TTS-based data augmentation in speech separation tasks. We hope that our work can offer insights for future studies investigating data augmentation strategies within speech separation.

关键词： data augmentation Training Speech synthesis Speech recognition Speech processing Training data Vocoders Speech enhancement Harmonic analysis Optimization

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