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57th Annual IEEE/ACM International Symposium on Microarchitecture, MICRO 2024

作者： Wang, Zongwu Liu, Fangxin Yang, Ning Huang, Shiyuan Li, Haomin Jiang, Li Shanghai Jiao Tong University Department of Computer Science and Engineering China Shanghai Qi Zhi Institute China AI Institute Shanghai Jiao Tong University MoE Key Lab of Artificial Intelligence China

ISBN: (纸本)9798350350579

Brain-inspired spiking neural networks (SNNs) are considered energy-efficient alternatives to conventional deep neural networks (DNNs). By adopting event-driven information processing, SNNs can significantly reduce the computational demands associated with DNNs, while still achieving comparable performance. However, current SNNs primarily prioritize high accuracy by constructing complex neuron models that generate sparse spikes. Unfortunately, this approach results in low energy efficiency and high latency, posing a significant challenge for deploying SNNs at the edge. Furthermore, the dominant computation in SNNs, which involves spike-wise Accumulate-Compare operations, is well-suited for Computing-in-Memory (CIM) architectures. However, exploiting high parallel processing and spike sparsity in CIM-based SNN accelerators is challenging due to the irregularity and time dependency of spikes. To address these limitations, the paper proposes COMPASS, a SRAM-based CIM architecture for efficient SNNs. We first introduce an efficient method to exploit irregular sparsity for both input spikes (explicit) and output spikes (implicit). This is achieved through a speculation mechanism that exploit dynamic spike patterns, enabling lean hardware for sparsity utilization. Additionally, the CIM architecture is carefully modified to facilitate dynamic spike pattern generation and exploitation with minimal overhead. Moreover, we design an adaptive dataflow with temporal spike representation tailored for input/output spikes, reducing memory footprint and enabling parallel execution. Comprehensive evaluation results demonstrate that COMPASS can achieve 26.7x end-to-end speedup over recent SNN accelerators hardware implementation with up to 386.7x less energy per inference. © 2024 IEEE.

关键词： Compasses (magnetic)

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SWING: Weakly Supervised Table Question Answering with Self-training via Reinforcement Learning

SWING: Weakly Supervised Table Question Answering with Self-...

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2025 IEEE International Conference on Big Data and Smart Computing, BigComp 2025

作者： Cho, Sanghyun Kim, Hye-Lynn Lee, Jung-Hun Kwon, Hyuk-Chul Pusan National University Department of Information Convergence Engineering Busan Korea Republic of R&D Center AI LAB HDC LABS Seoul Korea Republic of Dong-Eui University Department of Artificial Intelligence Busan Korea Republic of Pusan National University Department of Computer Science and Engineering Busan Korea Republic of

Previous works with auto-regressive pre-trained language model achieved state-of-art results in semantic parsing tasks. On the other hand, these models generate answers directly, it is difficult to get selected cells for generated answers and answers that require numerical reasoning. In this paper, we present a self-training framework to generate logical forms with weakly supervised question answering. Self-training framework composed of the training procedure and pseudo label correction procedure. In the training procedure, we train models to generate column and row positions of answer cells with supervised training data whose tagged answer cells are a subset of the input table cells. In the pseudo label correction procedure, we correct the tagged dataset for training the model. Furthermore, to enhance the correcting accuracy, we employed reinforcement learning, incorporating a reward function that yields a greater reward as the computed correct answer approaches the benchmark correct answer. Ultimately, we observed an improvement in accuracy from 47.1 to 54.06, a 6.89 increase, using the tapex-base model. Furthermore, we managed to outperform the existing performance, achieving 64.17, using the OmniTab model. Thus, our proposed method, SWING, a self-learning table parsing framework, has shown improved performance in numerical reasoning cases, and can predict rationale cells that allow auto-regressive PLM to compute answers without the need for logical form. © 2025 IEEE.

关键词： Reinforcement learning

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Vision Transformer Based Automated Model for Enhancing Lung Cancer Classification

Vision Transformer Based Automated Model for Enhancing Lung ...

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2024 IEEE International Conference on Imaging Systems and Techniques, IST 2024

作者： Akbari, Akbar Sheikh Kumar, Arvind Reddy, B Ramachandra Singh, Koushlendra Kumar Takei, Masahiro Leeds Beckett University United Kingdom National Institute of Technology Machine Vision and Intelligence Lab Jharkhand Jamshedpur India Computer Science and Engineering National Institute of Technology Jharkhand Jamshedpur India Chiba University Department of Mechanical Engineering Chiba Japan

ISBN: (纸本)9798350378214

Lung cancer is one of the leading causes of cancer related mortality. The early detection and classification of the cancers tissues will reduce the mortalities rate. The present research focus on the development of automated classification model for lung and colon cancers tissues based on the histopathology images. The present work encompasses a vision transformer (ViT) based model to enhance diagnostic accuracy of lung cancers tissues. The proposed model utilizes the self-attention mechanism of ViT to focus on essential features present in histopathologicals images. The proposed model has been validated using two different dataset namely LC25000 & IQ-OTH/NCCD with 25000 & 1096 images respectively. The performance of proposed model is compared with traditional convolutional neural network (CNN) model and it has been observed the based model outforms better in terms of accuracy which - 98.80% & 99.09% respectively for datasets. © 2024 IEEE.

关键词： Lung cancer

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New In-Air Signature Datasets

New In-Air Signature Datasets

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2023 International Symposium on Networks, computers and Communications, ISNCC 2023

作者： Alobaidi, Abdulazeez R. Dhieb, Thameur Nuimi, Zeina N. Hamdani, Tarek M. Wali, Ali Alimi, Adel M. University of Sfax REGIM-Lab.: REsearch Groups in Intelligent Machines Enis Tunisia Imam ja'Afar Al-Sadiq University Faculty of Information Technology Department of Computer Techniques Engineering Iraq Higher Institute of Business Administration University of Gafsa Tunisia College of Engineering Department of Electrical Engineering University of Misan Iraq Higher Institute of Computer Science Mahdia University of Monastir Tunisia Higher Institute of Computer Science and Multimedia of Sfax University of Sfax Tunisia University of Johannesburg Faculty of Engineering and the Built Environment Department of Electrical and Electronic Engineering Science South Africa

ISBN: (纸本)9798350335590

Compared to traditional biometric systems, in-air signatures are considered more robust and secure than classical pen paper. A few datasets capturing in-air signatures have been introduced, utilizing various devices such as the Leap Motion and the Microsoft Kinect sensor camera. However, these devices are not exempt from shortcomings and exhibit certain limitations. The expenses associated with their implementation and the requirement for technical proficiency in operating them present notable challenges for in-air signature analysis. Additionally, users may encounter difficulties in adapting their finger movements to fit within the device's limited field of view, particularly if they lack familiarity with these devices. To address these concerns, this paper proposes the creation of three in-air signature datasets using solely the camera of a laptop or a smartphone, eliminating the need for any additional specialized equipment. Our datasets were collected in three ways. The first is the In-Air Signature dataset (IAS dataset) and the second is the In-Air Signature dataset using a transparent Glass Plate (IASGP dataset) while the third is the In-Air Signature dataset using Smart Phone (IASSP dataset). Forty volunteers participated in the construction of these datasets. Their ages ranged from 21 to 40 years. Each volunteer signs in the air five signatures and imitates five signatures of five other volunteers. Our in-air signatures datasets are publicly available and can be used for various research tasks like in-air signature verification and identification. © 2023 IEEE.

关键词： Cameras

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Eyes Closed, Safety On: Protecting Multimodal LLMs via Image-to-Text Transformation

arXiv

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arXiv 2024年

作者： Gou, Yunhao Chen, Kai Liu, Zhili Hong, Lanqing Xu, Hang Li, Zhenguo Yeung, Dit-Yan Kwok, James T. Zhang, Yu Department of Computer Science and Engineering Southern University of Science and Technology China Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong Huawei Noah's Ark Lab Hong Kong

Multimodal large language models (MLLMs) have shown impressive reasoning abilities. However, they are also more vulnerable to jailbreak attacks than their LLM predecessors. Although still capable of detecting the unsafe responses, we observe that safety mechanisms of the pre-aligned LLMs in MLLMs can be easily bypassed with the introduction of image features. To construct robust MLLMs, we propose ECSO (Eyes Closed, Safety On), a novel training-free protecting approach that exploits the inherent safety awareness of MLLMs, and generates safer responses via adaptively transforming unsafe images into texts to activate the intrinsic safety mechanism of pre-aligned LLMs in MLLMs. Experiments on five state-of-the-art (SoTA) MLLMs demonstrate that ECSO enhances model safety significantly (e.g., 37.6% improvement on the MM-SafetyBench (SD+OCR) and 71.3% on VLSafe with LLaVA-1.5-7B), while consistently maintaining utility results on common MLLM benchmarks. Furthermore, we show that ECSO can be used as a data engine to generate supervised-finetuning (SFT) data for MLLM alignment without extra human intervention. © 2024, CC BY.

关键词： Eye protection

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SAM struggles in concealed scenes—empirical study on “Segment Anything”

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science China(Information sciences) 2023年第12期66卷 278-280页

作者： Ge-Peng JI Deng-Ping FAN Peng XU Bowen ZHOU Ming-Ming CHENG Luc VAN GOOL School of Computing Australian National University Computer Vision Lab(CVL) ETH Zurich Department of Electronic Engineering Tsinghua University College of Computer Science Nankai University

Large models open up new opportunities for artificial intelligence. In the past few months, there has been a boom in training foundation models on the vast linguistic corpus to produce amazing applications, e.g., Chat GPT, *** natural language processing and multimodal learning communities have been revolutionized. Large models' capacity for generalization and emergent makes it easy for users to believe that large models can solve anything.

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MSIsensor-RNA:Microsatellite Instability Detection for Bulk and Single-cell Gene Expression Data

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Genomics, Proteomics & Bioinformatics 2024年第3期22卷 85-90页

作者： Peng Jia Xuanhao Yang Xiaofei Yang Tingjie Wang Yu Xu Kai Ye Department of Gynecology and Obstetrics Center for Mathematical MedicalThe First Affiliated Hospital of Xi’an Jiaotong UniversityXi’an 710061China MOE Key Lab for Intelligent Networks&Networks Security Faculty of Electronic and Information EngineeringXi’an Jiaotong UniversityXi’an 710049China School of Automation Science and Engineering Faculty of Electronic and Information EngineeringXi’an Jiaotong UniversityXi’an 710049China School of Computer Science and Technology Faculty of Electronic and Information EngineeringXi’an Jiaotong UniversityXi’an 710049China Genome Institute The First Affiliated Hospital of Xi’an Jiaotong UniversityXi’an 710061China School of Life Science and Technology Xi’an Jiaotong UniversityXi’an 710049China

Microsatellite instability(MSI)is an indispensable biomarker in cancer ***,MSI scoring methods by high-throughput omics methods have gained popularity and demonstrated better performance than the gold standard method for MSI ***,the MSI detection method on expression data,especially single-cell expression data,is still lacking,limiting the scope of clinical application and prohibiting the investigation of MSI at a single-cell ***,we developed MSIsensor-RNA,an accurate,robust,adaptable,and standalone software to detect MSI status based on expression values of MSI-associated *** demonstrated the favorable performance and promise of MSIsensor-RNA in both bulk and single-cell gene expression data in multiplatform technologies including RNA sequencing(RNA-seq),microarray,and single-cell ***-RNA is a versatile,efficient,and robust method for MSI status detection from both bulk and single-cell gene expression data in clinical studies and ***-RNA is available at https://***/xjtu-omics/msisensor-rna.

关键词： Microsatellite instability Gene expression Single-cell RNA-seq RNA-seq Microarray

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Advswap: Covert Adversarial Perturbation with High Frequency Info-Swapping for Autonomous Driving Perception 27

Advswap: Covert Adversarial Perturbation with High Frequency...

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27th IEEE International Conference on Intelligent Transportation Systems, ITSC 2024

作者： Huang, Yuanhao Zhang, Qinfan Xing, Jiandong Cheng, Mengyue Yu, Haiyang Ren, Yilong Xiong, Xiao School of Transportation Science and Engineering Beihang University Beijing100191 China State Key Lab of Intelligent Transportation System Beijing100191 China Zhongguancun Laboratory Beijing100094 China University of Alberta Department of Electrical and Computer Engineering Edmonton Canada

ISBN: (纸本)9798331505929

Perception module of Autonomous vehicles (AVs) are increasingly susceptible to be attacked, which exploit vulnerabilities in neural networks through adversarial inputs, thereby compromising the AI safety. Some researches focus on creating covert adversarial samples, but existing global noise techniques are detectable and difficult to deceive the human visual system. This paper introduces a novel adversarial attack method, AdvSwap, which creatively utilizes wavelet-based highfrequency information swapping to generate covert adversarial samples and fool the camera. AdvSwap employs invertible neural network for selective high-frequency information swapping, preserving both forward propagation and data integrity. The scheme effectively removes the original label data and incorporates the guidance image data, producing concealed and robust adversarial samples. Experimental evaluations and comparisons on the GTSRB and nuScenes datasets demonstrate that AdvSwap can make concealed attacks on common traffic targets. The generates adversarial samples are also difficult to perceive by humans and algorithms. Meanwhile, the method has strong attacking robustness and attacking transferability. © 2024 IEEE.

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Assessment of Serum Creatinine and Serum Sodium Prognostic Potential in Heart Failure Patients Using Machine Learning 46

Assessment of Serum Creatinine and Serum Sodium Prognostic P...

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46th Annual International Conference of the IEEE engineering in Medicine and Biology Society, EMBC 2024

作者： Alyounis, Sona Khandoker, Ahsan Stefanini, Cesare Hadjileontiadis, Leontios Scuola Superiore Sant'Anna Creative Engineering Design Lab The BioRobotics Institute Pontedera Italy Khalifa University of Science and Technology Healthcare Engineering Innovation Center Department of Biomedical Engineering P.O. Box 127788 Abu Dhabi United Arab Emirates Aristotle University of Thessaloniki Department of Electrical and Computer Engineering Thessaloniki Greece

ISBN: (纸本)9798350371499

Heart failure (HF) is the leading etiology for hospital admissions and ranks among the foremost contributors to mortality. This complex clinical syndrome with various phenotypes is categorized by left ventricle ejection fraction levels (LVEF), namely preserved (HFpEF), mid-range (HFmEF), and reduced (HFrEF). This study investigates the prognostic impact of serum creatinine and serum sodium levels in HF patients across these three classes using machine learning tools. A comprehensive dataset of HF patients' medical records including serum sodium and serum creatinine was utilized. Machine learning regression models were employed to predict the LVEF levels. Additionally, classification models were implemented to categorize patients into HFpEF, HFmEF, and HFrEF classes. Regression analyses revealed the predictive capabilities of serum sodium and serum creatinine in estimating the progression of HF severity. Furthermore, classification models successfully differentiated between the three EF classes, providing valuable insights into the classification patterns of HF patients based on these biomarkers. The results demonstrated the significance of serum sodium serum creatinine as prognostic markers in HF, and this contributes to a more nuanced approach to HF management, paving the way for targeted interventions and improved patient outcomes. Moreover, this study highlights the potential of machine learning techniques to enhance risk stratification and classification in HF patients, enabling personalized prognostication and treatment strategies. © 2024 IEEE.

关键词： Creatinine

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A Requirements-Driven Platform for Validating Field Operations of Small Uncrewed Aerial Vehicles 31

A Requirements-Driven Platform for Validating Field Operatio...

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31st IEEE International Requirements engineering Conference, RE 2023

作者： Agrawal, Ankit Zhang, Bohan Shivalingaiah, Yashaswini Vierhauser, Michael Cleland-Huang, Jane Saint Louis University Department of Computer Science St LouisMO United States Johannes Kepler University Linz Lit Secure and Correct Systems Lab Linz Austria University of Notre Dame Computer Science and Engineering Notre DameIN United States

ISBN: (纸本)9798350326895

Flight-time failures of small Uncrewed Aerial Systems (sUAS) can have a severe impact on people or the environment. Therefore, sUAS applications must be thoroughly evaluated and tested to ensure their adherence to specified requirements, and safe behavior under real-world conditions, such as poor weather, wireless interference, and satellite failure. However, current simulation environments for autonomous vehicles, including sUAS, provide limited support for validating their behavior in diverse environmental contexts and moreover, lack a test harness to facilitate structured testing based on system-level requirements. We address these shortcomings by eliciting and specifying requirements for an sUAS testing and simulation platform, and developing and deploying it. The constructed platform, DroneReq Validator (DRV), allows sUAS developers to define the operating context, configure multi-sUAS mission requirements, specify safety properties, and deploy their own custom sUAS applications in a high-fidelity 3D environment. The DRV Monitoring system collects runtime data from sUAS and the environment, analyzes compliance with safety properties, and captures violations. We report on two case studies in which we used our platform prior to real-world sUAS deployments, in order to evaluate sUAS mission behavior in various environmental contexts. Furthermore, we conducted a study with developers and found that DRV simplifies the process of specifying requirements-driven test scenarios and analyzing acceptance test results. © 2023 IEEE.

关键词： Acceptance tests

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