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44th ACM/IEEE International Conference on Software engineering, ICSE 2024

作者： Li, Zhen Wang, Ning Zou, Deqing Li, Yating Zhang, Ruqian Xu, Shouhuai Zhang, Chao Jin, Hai Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security Cluster and Grid Computing Lab National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hong Kong Jin YinHu Laboratory Wuhan China School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China University of Colorado Colorado Springs Department of Computer Science Colorado Springs Colorado United States Institute for Network Sciences and Cyberspace Tsinghua University Beijing China School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9798400702174

Software vulnerabilities are a major cyber threat and it is important to detect them. One important approach to detecting vulnerabilities is to use deep learning while treating a program function as a whole, known as function-level vulnerability detectors. However, the limitation of this approach is not understood. In this paper, we investigate its limitation in detecting one class of vulnerabilities known as inter-procedural vulnerabilities, where the to-be-patched statements and the vulnerability-triggering statements belong to different functions. For this purpose, we create the first Inter -Procedural Vulnerability Dataset (InterPVD) based on C/C++ open-source software, and we propose a tool dubbed VulTrigger for identifying vulnerability-triggering statements across functions. Experimental results show that VulTrigger can effectively identify vulnerability-triggering statements and inter-procedural vulnerabilities. Our findings include: (i) inter-procedural vulnerabilities are prevalent with an average of 2.8 inter-procedural layers;and (ii) function-level vulner-ability detectors are much less effective in detecting to-be-patched functions of inter-procedural vulnerabilities than detecting their counterparts of intra-procedural vulnerabilities. © 2024 ACM.

关键词： Open source software

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Geometry-calibrated DRO: combating over-pessimism with free energy implications 24

Geometry-calibrated DRO: combating over-pessimism with free ...

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Proceedings of the 41st International Conference on Machine Learning

作者： Jiashuo Liu Jiayun Wu Tianyu Wang Hao Zou Bo Li Peng Cui Department of Computer Science and Technology Tsinghua University Department of Industrial Engineering and Operations Research Columbia University Zhongguancun Lab School of Economics and Management Tsinghua University

Machine learning algorithms minimizing average risk are susceptible to distributional shifts. Distributionally Robust Optimization (DRO) addresses this issue by optimizing the worst-case risk within an uncertainty set. However, DRO suffers from over-pessimism, leading to low-confidence predictions, poor parameter estimations as well as poor generalization. In this work, we conduct a theoretical analysis of a probable root cause of over-pessimism: excessive focus on noisy samples. To alleviate the impact of noise, we incorporate data geometry into calibration terms in DRO, resulting in our novel Geometry-Calibrated DRO (GCDRO) for regression. We establish the connection between our risk objective and the Helmholtz free energy in statistical physics, and this free-energy-based risk can extend to standard DRO methods. Leveraging gradient flow in Wasserstein space, we develop an approximate minimax optimization algorithm with a bounded error ratio and elucidate how our approach mitigates noisy sample effects. Comprehensive experiments confirm GCDRO's superiority over conventional DRO methods

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Pineapple Quality Assessment: Integrating Maturity and Size Classification Through Advanced Neural Networks and Color-Based Analysis

SSRN

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SSRN 2024年

作者： Kalabarige, Lakshmana Rao Ramana, A. Venkata Rao, Routhu Srinivasa Kumar, Jonnalagadda Suresh AI Research Lab Department of Computer Science and Engineering GMR Institute of Technology Andhra Pradesh Rajam532127 India Department of Computer Science and Engineering GMR Institute of Technology Andhra Pradesh Rajam532127 India Department of Computer Science and Engineering Gandhi Institute of Technology and Management Andhra Pradesh Visakhapatnam530045 India

The technology driven of pineapple size and maturity grading is a useful marketing strategy to enhance proper utilization and increase of profits. This comprehensive paper introduces an advanced methodology for pineapple quality assessment for initial ripe and unripe classification, followed by a detailed categorization within the ripe class based on model predicted value. Through image resizing, and augmentation, the original dataset undergoes meticulous processing. Transfer learning models such as AlexNet, VGG16, and InceptionV3 classify pineapples into two primary categories—ripe and unripe. Subsequently, within the ripe category, the model further categorizes pineapples into partially ripened, fully ripened, and over-ripened stages based on predicted values. The predicted category of pineapple compared with Hue, Saturation, and Value (HSV) approach for maturity level conformation. Additionally, a Color-based size classification through HSV adopted. The InceptionV3 outperformed all other models with 93% accuracy on large dataset with clutter pineapple images. The methodology, demonstrating superior accuracy and granularity, provides valuable insights for supply chain management. © 2024, The Authors. All rights reserved.

关键词： Grading

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Correction to:Determining Learning Style Preferences of Learners

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Journal of computer Science research 2022年第1期4卷 20-21页

作者： Sushil Shrestha Manish Pokharel Digital Learning Research Lab Department of Computer Science and EngineeringKathmandu UniversityNepal

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Evaluation of the Performance of Wearables’ Inertial Sensors for the Diagnosis of Resting Tremor in Parkinson’s Disease

Evaluation of the Performance of Wearables’ Inertial Sensor...

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17th International Joint Conference on Biomedical engineering Systems and Technologies, BIOSTEC 2024

作者： Polvorinos-Fernández, Carlos Sigcha, Luis de Pablo, Laura Pereira Borzí, Luigi Cardoso, Paulo Costa, Nelson Costa, Susana López, Juan Manuel de Arcas, Guillermo Pavón, Ignacio Mechanical Engineering Department ETSI Industriales Universidad Politécnica de Madrid Spain Group University of Limerick LimerickV94 T9PX Ireland ALGORITMI Research Center School of Engineering University of Minho Guimarães Portugal PolitoBIOMed Lab–Biomedical Engineering Lab Politecnico di Torino Turin10129 Italy ANTHEA Lab–Data Analytics and Technologies for Health Lab Department of Control and Computer Engineering Politecnico di Torino Turin10129 Italy Physical Electronics Electrical Engineering and Applied Physics Department ETS de Ingeniería y Sistemas de Telecomunicación Universidad Politécnica de Madrid Spain

Currently, objective monitoring of resting tremor in Parkinson’s disease (PD) involves wearable devices and machine learning. Smartwatches may present an affordable method for remote and unintrusive tremor monitoring. However, the development of optimized systems is necessary to perform accurate monitoring in free-living settings. In this study, the potential of inertial sensors to detect resting tremors is evaluated. A smartwatch was placed on the wrist of six subjects with PD during the execution of MDS-UPDRS motor tasks. Data were collected over eight weeks from triaxial accelerometer and gyroscope simultaneously and used to implement machine learning algorithms to detect resting tremor. The best performance (accuracy 97.0% in tremor detection) was achieved using accelerometer data analysed with a Random Forest classifier, while the gyroscope showed lower performance (93.0%). The results indicates that the use of the accelerometer in commercial smartwatches can offer effective results for detecting resting tremors, while reducing computational workload. These results show opportunities for the development of robust free-living tremor monitoring systems using commodity devices and algorithms using a single sensor. © 2024 by SCITEPRESS–Science and Technology Publications, Lda.

关键词： Gyroscopes

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Wireless Federated Learning With Hybrid Local and Centralized Training: A Latency Minimization Design

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IEEE Journal on Selected Topics in Signal Processing 2023年第1期17卷 248-263页

作者： Huang, Ning Dai, Minghui Wu, Yuan Quek, Tony Q. S. Shen, Xuemin State Key Lab of Internet of Things for Smart City China University of Macau Department of Computer and Information Science China University of Macau State Key Laboratory of Internet of Things for Smart City The Department of Computer and Information Science China Zhuhai Um Science and Technology Research Institute Zhuhai519031 China Singapore University of Technology and Design Information Systems Technology and Design Pillar 487372 Singapore National Cheng Kung University Taiwan University of Waterloo Department of Electrical and Computer Engineering WaterlooONN2L 3G1 Canada

Wireless federated learning (FL) is a collaborative machine learning (ML) framework in which wireless client-devices independently train their ML models and send the locally trained models to the FL server for aggregation. In this paper, we consider the coexistence of privacy-sensitive client-devices and privacy-insensitive yet computing-resource constrained client-devices, and propose an FL framework with a hybrid centralized training and local training. Specifically, the privacy-sensitive client-devices perform local ML model training and send their local models to the FL server. Each privacy-insensitive client-device can have two options, i.e., (i) conducting a local training and then sending its local model to the FL server, and (ii) directly sending its local data to the FL server for the centralized training. The FL server, after collecting the data from the privacy-insensitive client-devices (which choose to upload the local data), conducts a centralized training with the received datasets. The global model is then generated by aggregating (i) the local models uploaded by the client-devices and (ii) the model trained by the FL server centrally. Focusing on this hybrid FL framework, we firstly analyze its convergence feature with respect to the client-devices' selections of local training or centralized training. We then formulate a joint optimization of client-devices' selections of the local training or centralized training, the FL training configuration (i.e., the number of the local iterations and the number of the global iterations), and the bandwidth allocations to the client-devices, with the objective of minimizing the overall latency for reaching the FL convergence. Despite the non-convexity of the joint optimization problem, we identify its layered structure and propose an efficient algorithm to solve it. Numerical results demonstrate the advantage of our proposed FL framework with the hybrid local and centralized training as well as our proposed alg

关键词： Information management

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FPGA Acceleration of Rotation in Homomorphic Encryption Using Dynamic Data Layout

FPGA Acceleration of Rotation in Homomorphic Encryption Usin...

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International Conference on Field Programmable Logic and Applications

作者： Yang Yang Weihang Long Rajgopal Kannan Viktor K. Prasanna Department of Electrical and Computer Engineering University of Southern California Department of Electronic Engineering Tsinghua University DEVCOM Army Research Lab Los Angeles CA

Homomorphic Encryption (HE) is a promising technique to guarantee the security and privacy of Machine Learning (ML) applications in the cloud. Rotation is a key operation in HE ML; however, the high computational complexity and memory bandwidth requirements severely limit its performance. This work proposes a low-latency HE rotation accelerator targeting HBM-enabled FPGAs. First, we identify memory inefficiencies due to the access patterns of various sub-routines in rotation. We propose a dynamic data layout technique that converts large stride memory accesses to unit stride accesses to improve the bandwidth utilization. We leverage this technique to develop an FPGA accelerator that supports rotation for various HE parameter settings. The accelerator utilizes an optimized dataflow and an architecture specially designed to perform the dynamic data layout. We evaluate the accelerator using AMD U280 FPGA. Our design achieves up to 2.1 x speedup compared with two commonly used static layout approaches and up to 1.47x speedup compared with state-of-the-art GPU implementation across various rotation benchmarks.

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scNovel: a scalable deep learning-based network for novel rare cell discovery in single-cell transcriptomics

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BRIEFINGS IN BIOINFORMATICS 2024年第3期25卷 bbae112-bbae112页

作者： Zheng, Chuanyang Wang, Yixuan Cheng, Yuqi Wang, Xuesong Wei, Hongxin King, Irwin Li, Yu Department of Computer Science and Engineering CUHK Hong Kong SAR China College of Computing Georgia Institute of Technology Atlanta GA USA MLR Lab Southern University of Science and Technology The CUHK Shenzhen Research Institute Hi-Tech Park Nanshan Shenzhen 518057 China Institute for Medical Enginering and Science Massachusetts Institute of Technology Cambridge MA USA Wyss Institute for Biologically Inspired Engineering Harvard University Boston MA USA Broad Institute of MIT and Harvard Cambridge MA USA

Single-cell RNA sequencing has achieved massive success in biological research fields. Discovering novel cell types from single-cell transcriptomics has been demonstrated to be essential in the field of biomedicine, yet is time-consuming and needs prior knowledge. With the unprecedented boom in cell atlases, auto-annotation tools have become more prevalent due to their speed, accuracy and user-friendly features. However, existing tools have mostly focused on general cell-type annotation and have not adequately addressed the challenge of discovering novel rare cell types. In this work, we introduce scNovel, a powerful deep learning-based neural network that specifically focuses on novel rare cell discovery. By testing our model on diverse datasets with different scales, protocols and degrees of imbalance, we demonstrate that scNovel significantly outperforms previous state-of-the-art novel cell detection models, reaching the most AUROC performance(the only one method whose averaged AUROC results are above 94%, up to 16.26% more comparing to the second-best method). We validate scNovel's performance on a million-scale dataset to illustrate the scalability of scNovel further. Applying scNovel on a clinical COVID-19 dataset, three potential novel subtypes of Macrophages are identified, where the COVID-related differential genes are also detected to have consistent expression patterns through deeper analysis. We believe that our proposed pipeline will be an important tool for high-throughput clinical data in a wide range of applications.

关键词： novel rare cell discovery single-cell analysis neural network

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CHAN: Cross-Modal Hybrid Attention Network for Temporal Language Grounding in Videos

CHAN: Cross-Modal Hybrid Attention Network for Temporal Lang...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Wen Wang Ling Zhong Guang Gao Minhong Wan Jason Gu Zhejiang Lab Research Center for Intelligent Robotics China Department of Electrical and Computer Engineering Dalhousie University Canada

The goal of temporal language grounding (TLG) task is to temporally localize the most semantically matched video segment with respect to a given sentence query in an untrimmed video. How to effectively incorporate the cross-modal interactions between video and language is the key to improve grounding performance. Previous approaches focus on learning correlations by computing the attention matrix between each frame-word pair, while ignoring the global semantics conditioned on one modality for better associating the complex video contents and sentence query of the target modality. In this paper, we propose a novel Cross-modal Hybrid Attention Network, which integrates two parallel attention fusion modules to exploit the semantics of each modality and interactions in cross modalities. One is Intra-Modal Attention Fusion, which utilizes gated self-attention to capture the frame-by-frame and word-by-word relations conditioned on the other modality. The other is Inter-Modal Attention Fusion, which utilizes query and key features derived from different modalities to calculate the co-attention weights and further promote inter-modal fusion. Experimental results show that our CHAN significantly outperforms several existing state-of-the-arts on three challenging datasets (ActivityNet Captions, Charades-STA and TACOS), demonstrating the effectiveness of our proposed method.

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Joint Self-Supervised Image-Volume Representation Learning with Intra-inter Contrastive Clustering 37

Joint Self-Supervised Image-Volume Representation Learning w...

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37th AAAI Conference on Artificial Intelligence, AAAI 2023

作者： Nguyen, Duy M.H. Nguyen, Hoang Mai, Truong T.N. Cao, Tri Nguyen, Binh T. Ho, Nhat Swoboda, Paul Albarqouni, Shadi Xie, Pengtao Sonntag, Daniel Department of Computer Science University of Stuttgart Germany AISIA Lab. University of Science - VNU HCM Viet Nam Department of Multimedia Engineering Dongguk University Korea Republic of Department of Statistics and Data Sciences University of Texas Austin United States Max Planck Institute for Informatics Germany Helmholtz AI Helmholtz Munich Germany Clinic for Diagnostic and Interventional Radiology University of Bonn Germany Department of Electrical and Computer Engineering University of California San Diego United States Department of Computer Science Oldenburg University Germany German Research Center for Artificial Intelligence Germany

ISBN: (纸本)9781577358800

Collecting large-scale medical datasets with fully annotated samples for training of deep networks is prohibitively expensive, especially for 3D volume data. Recent breakthroughs in self-supervised learning (SSL) offer the ability to overcome the lack of labeled training samples by learning feature representations from unlabeled data. However, most current SSL techniques in the medical field have been designed for either 2D images or 3D volumes. In practice, this restricts the capability to fully leverage unlabeled data from numerous sources, which may include both 2D and 3D data. Additionally, the use of these pre-trained networks is constrained to downstream tasks with compatible data dimensions. In this paper, we propose a novel framework for unsupervised joint learning on 2D and 3D data modalities. Given a set of 2D images or 2D slices extracted from 3D volumes, we construct an SSL task based on a 2D contrastive clustering problem for distinct classes. The 3D volumes are exploited by computing vectored embedding at each slice and then assembling a holistic feature through deformable self-attention mechanisms in Transformer, allowing incorporating long-range dependencies between slices inside 3D volumes. These holistic features are further utilized to define a novel 3D clustering agreement-based SSL task and masking embedding prediction inspired by pre-trained language models. Experiments on downstream tasks, such as 3D brain segmentation, lung nodule detection, 3D heart structures segmentation, and abnormal chest X-ray detection, demonstrate the effectiveness of our joint 2D and 3D SSL approach. We improve plain 2D Deep-ClusterV2 and SwAV by a significant margin and also surpass various modern 2D and 3D SSL approaches. Copyright © 2023, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Embeddings

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