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BOFSanitizer: Efficient locator and detector for buffer overflow vulnerability 23

BOFSanitizer: Efficient locator and detector for buffer over...

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23rd IEEE International conference on high performance Computing and Communications, 7th IEEE International conference on Data Science and Systems, 19th IEEE International conference on Smart City and 7th IEEE International conference on Dependability in Sensor, Cloud and Big Data Systems and Applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Wang, Wenzhi Fan, Meng Yu, Aimin Meng, Dan Institute of Information Engineering Chinese Academy of Sciences China School of Cyber Security University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781665494571

Buffer overflow is one of the most serious and common vulnerability that using memory beyond the allocated memory. It can cause many severe threats, including system crash, arbitrary code execution and data corruption. Buffer overflow vulnerability has two main characteristics, one is that buffer overflow vulnerability may not be discovered after its path has been executed. The other is that not every buffer overflow vulnerability could cause crash. Therefore, existing program analysis tools have more or less deficiencies in detecting buffer overflow vulnerability. This paper presents a BOF-Sanitizer method for buffer overflow vulnerability detection. We proposed a pre-process method that combine vulnerability metric and rank, which can effectively avoid the exploration of unnecessary paths and quickly locate potential bug positions. We present a custom memory model of concolic execution and a boundary detection engine based on a low fat pointer, which not only ensure that the executed path is safe, but also effectively reduces the system performance overhead through the real time interacting with the analysis engine and solver in the process of dynamic detection. We implement a prototype system of BOFSanitizer to detect buffer overflow in different application, the result show that BOFSanitizer accurately discovers 88% buffer overflow target points and the detection rate improve over 50% with compare to simple combination of existing program analysis tools. © 2021 IEEE.

关键词： Measurement Smart cities System performance computer bugs Prototypes Buffer overflows Detectors

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Towards Optimized Streaming Tensor Completion on multiple GPUs 24

Towards Optimized Streaming Tensor Completion on multiple GP...

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24th IEEE International conference on high performance Computing and Communications, 8th IEEE International conference on Data Science and Systems, 20th IEEE International conference on Smart City and 8th IEEE International conference on Dependability in Sensor, Cloud and Big Data Systems and Application, HPCC/DSS/SmartCity/DependSys 2022

作者： Hao, Jiwei Yang, Hailong Sun, Qingxiao Zhang, Huaitao Luan, Zhongzhi Qian, Depei School of Computer Science and Engineering Beihang University Beijing100191 China School of Computer Science and Technology Xi'an Jiaotong University Shaanxi710049 China

ISBN: (纸本)9798350319934

Tensor completion is a prevailing method for predicting the unobserved or missing data in incomplete tensors. In many real-world scenarios, incomplete tensors can grow in multiple modes with streaming fashion. Such tensors are generally transformed into a dynamically changing tensor sequence known as Multi-Aspect Streaming Tensor (MAST) sequence. However, existing optimization techniques for a single static tensor completion cannot scale well to large streaming tensor sequences with dynamically growing properties. In this paper, we develop an efficient streaming tensor completion library cuSTC on multiple graphics processing units (GPUs). cuSTC adopts a novel TB-COO storage format with tiling and bit mapping data structure. In addition, cuSTC implements a two-level tensor partition scheme to accelerate the prevalent dynamic alternating least squares (ALS) algorithm for MAST sequence. Finally, cuSTC designs a warp-level reduction scheme to reduce atomic operations in tensor decomposition. We compare cuSTC with SPLATT tensor completion library and achieve 48.69×, 24.22×, 4.54 ×, 3.58×, 12.26 × performance speedup on five real-world datasets. © 2022 IEEE.

关键词： Tensors

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Independent and Collaborative Demosaicking Neural Networks 5

Independent and Collaborative Demosaicking Neural Networks

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5th ACM International conference on Multimedia in Asia, MMAsia 2023

作者： Niu, Yan Zhang, Lixue Li, Chenlai State Key Laboratory of Symbol Computation and Knowledge Engineering Ministry of Education College of Computer Science and Technology Jilin University Jilin Changchun China College of Computer Science and Technology Jilin University Changchun China School of Artificial Intelligence Shenzhen Polytechnic University Shenzhen China

ISBN: (纸本)9798400702051

Existing demosaicking neural models generally reconstruct the red, green and blue channels by one unified network. This has the advantage of allowing the RGB channels to share latent features. However, it is unnoticed that the training samples are severely unbalanced among the three channels. As a consequence, a unified model trained to fit the red or blue sample distributions is prone to under-fit the green samples. In this paper, we demonstrate the existence of such conflicts, and analyze the reason behind this phenomenon. To solve this disadvantage, we decouple the traditional three-in-one demosaicking to three independent but collaborative sub-tasks, respecting the distribution of samples in each spectrum. We show that, by our decoupling strategy, a very simple architecture can achieve high accuracy with high efficiency. For substantiation, we construct a model that has merely 1.3M parameters in total, and compares its accuracy to state of the art methods of similar or larger model size, across a wide variety of benchmark datasets. The proposed independent and collaborative architecture gains performance improvement over comparable models in both accuracy and inference time. © 2023 Copyright held by the owner/author(s).

关键词： Network architecture

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Development of Methods for Analyzing the Process of Operator Training When Working with a CNC System Using Components of Analytical Systems

Development of Methods for Analyzing the Process of Operator...

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2022 International Russian Automation conference, RusAutoCon 2022

作者： Tuktamysheva, Liliia Kovalev, Ilya Nezmetdinov, Ramil Msut 'STANKIN' Department of Computer Control Systems Moscow Russia

ISBN: (数字)9781665466554

ISBN: (纸本)9781665466554

The article discusses the trend of introduction of high-performance CNC equipment at Russian enterprises. It is planned to introduce various equipment, primarily domestic, in order to ensure uninterrupted performance with consistently high product quality. Providing enterprises with modern CNC equipment, introducing new technologies, increasing production volumes, expanding the range of manufactured products becomes a necessity. All these factors actualize the problem of staffing modern enterprises. When analyzing existing approaches to training specialists to work with CNC systems, a number of shortcomings were identified. A method of analyzing the results of the learning process is formulated, which will allow displaying data in analytical systems in accordance with pre-configured formats to improve the quality of educational courses. © 2022 IEEE.

关键词： Personnel training

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An Unsupervised Short- and Long-Term Mask Representation for Multivariate Time Series Anomaly Detection 29th

An Unsupervised Short- and Long-Term Mask Representation for...

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29th International conference on Neural Information Processing

作者： Miao, Qiucheng Xu, Chuanfu Zhan, Jun Zhu, Dong Wu, Chengkun Natl Univ Def Technol Inst Quantum Informat Coll Comp Sci & Technol Changsha Hunan Peoples R China Natl Univ Def Technol State Key Lab High Performance Comp Coll Comp Sci & Technol Changsha Hunan Peoples R China

ISBN: (纸本)9789819916443;9789819916450

Anomaly detection of multivariate time series is meaningful for system behavior monitoring. This paper proposes an anomaly detection method based on unsupervised Short- and Long-term Mask Representation learning(SLMR). The main idea is to extract short-term local dependency patterns and long-term global trend patterns by using multi-scale residual dilated convolution and Gated Recurrent Unit(GRU) respectively. Furthermore, our approach can comprehend temporal contexts and feature correlations by combining spatial-temporal masked self-supervised representation learning and sequence split. It considers the importance of features is different, and we introduce the attention mechanism to adjust the contribution of each feature. Finally, a forecasting-based model and a reconstruction-based model are integrated to focus on single timestamp prediction and latent representation of time series. Experiments show that the performance of our method outperforms other state-of-the-art models on three real-world datasets. Further analysis shows that our method is good at anomaly localization.

关键词： Anomaly detection Multivariate time series Representation learning Multi-scale convolution

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A Novel Lightweight Convolutional Neural Network for EEG-Based Miniature-Event-Related Potentials

A Novel Lightweight Convolutional Neural Network for EEG-Bas...

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IEEE International conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA)

作者： Liu, Chang Zhong, Wenxiao Di, Yang Zhao, Liang An, Xingwei Tianjin Univ Acad Med Engn & Translat Med Tianjin Peoples R China Tianjin Univ Acad Med Engn & Translat Med Tianjin Key Lab Brain Sci & Neuroengn Tianjin Peoples R China Tianjin Univ Haihe Lab Brain Comp Interact & Human Machine Int Tianjin Peoples R China Tianjin Univ State Key Lab Adv Med Mat & Devices Tianjin Peoples R China

ISBN: (纸本)9798350322996;9798350323009

Miniature asymmetric visual evoked potential (aVEP) induced by very small lateral visual stimuli is a comfortable stimulation paradigm. It could only induce a miniature potential about 0.5 mu V in amplitude, which could be challenging to detect. Most decoding methods based on miniature aVEP are machine learning approaches, and deep learning methods have not yet been introduced. Therefore, this paper introduces deep learning techniques for the first time and designs a lightweight convolutional neural network. Specifically, depthwise convolution is employed to reduce the number of network parameters and extract temporal and spatial features from EEG signals. The model achieves an average classification accuracy of 78.48% on the miniature aVEP dataset, which is 1.14% higher than the previous state-of-the-art model, DCPM. Through comparison with other deep learning models in terms of parameter count, computational complexity, and classification accuracy, it is evident that the proposed model surpasses them in all these aspects The results suggest that the CNN model proposed in this study achieves high classification accuracy while demanding fewer computational resources. Although the network model we proposed is very simple, it can achieve good results in processing miniature aVEP. In future research, we will continue to improve this lightweight network to enhance its decoding performance for miniature aVEP.

关键词： Brain-computer interface (BCI) asymmetric visual evoked potential (aVEP) lightweight network convolutional neural networks (CNN)

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A Secure and Reliable Construction Scheme for Enterprise Value Chain Networks 24

A Secure and Reliable Construction Scheme for Enterprise Val...

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作者： Qin, Chaoxia Guo, Bing Zhao, Yuting Shen, Yan College of Computer Science Sichuan University Chengdu China School of Computer Science Chengdu University of Information Technology Chengdu China

ISBN: (纸本)9798350319934

A value chain optimizes the economic benefit of an enterprise alliance by integrating the cooperation relationships among the upstream and downstream companies. Nevertheless, the absence of information security mechanisms within the value chain limits the horizontal cooperation between competing enterprises. The paper proposes a scheme for building an enterprise value chain network based on an alliance blockchain. In the scheme, a distributed authentication technology and cross-chain transaction technology are introduced into the value chain, which enhances the security and reliability of value chain information and facilitates horizontal cooperation among companies. Using Hyperledger Fabric platform, we simulate and extensively evaluate an enterprise value chain network. Experimental results demonstrate the feasibility of the proposed scheme, which can serve as a guide for the construction of enterprise value chain networks in the real world. © 2022 IEEE.

关键词： Blockchain

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Spatio-temporal Graph Learning on Adaptive Mined Key Frames for high-performance Multi-Object Tracking 14th

Spatio-temporal Graph Learning on Adaptive Mined Key Frames...

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14th International conference on Brain Inspired Cognitive Systems, BICS 2024

作者： Wang, Futian Liu, Fengxiang Wang, Xiao School of Computer Science and Technology Anhui University Hefei China Anhui Provincial Key Laboratory of Multimodal Cognitive Computation Hefei China

ISBN: (纸本)9789819628841

In this work, we propose a novel adaptive key frame mining strategy to guide multi-object tracking, which combines the advantages of short-term and long-term correlation to capture both the structured spatial relationships between different objects as well as the temporal relationships of objects in different frames. We designed the KFE (Key Frame Extraction) module to adaptively perform video segmentation using the reinforcement learning KFE module, which guides the tracker to mine the intrinsic logic of the video to make the correlation effect more significant. In video, mutual occlusion between objects frequently occurs, so the interactions between objects within frames cannot be ignored. Most of the current graph-based work utilizes the features of inter-frame objects for mutual fusion. Based on this, we design an IFF (intra-frame feature fusion) module that passes information between the target and the surrounding objects through GCN to make the target recognizable, thus solving the problem of tracking loss and similar appearance due to object occlusion. Our proposed tracker utilizes both long and short trajectories and takes into account the spatial relationship between objects while leveraging the intrinsic logic of the video clips, thus further improving the performance. It achieves impressive results: 68.6 HOTA, 81.0 IDF1, 66.6 AssA, and 893 IDS on the MOT17 dataset, proving its effectiveness and accuracy. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Reinforcement learning

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A Multi-Scale Temporal Data Fusion Model Based on Deep Neural Network

A Multi-Scale Temporal Data Fusion Model Based on Deep Neura...

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2022 IEEE Asia-Pacific conference on Image Processing, Electronics and computers, IPEC 2022

作者： Wei, Xianglong Liu, Huiyi Chen, Zhongshuo Hohai University School of Computer and Information Nanjing China

ISBN: (纸本)9781665409025

Aiming at the poor performance of traditional data fusion algorithms for high-dimension, high-noise and multi-scale temporal data fusion, a data fusion model based on deep neural network (DDB model) is proposed in this paper. DAE is used to denoise and reconstruct the input data. Secondly, deep convolutional neural network (DCNN) is used to extract spatial and short-term features of the data. The bidirectional long and short-term memory network (BLSTM) is introduced to further extract the long-term time features of the data. Finally, a fully connected network is used to fuse the temporal features of different scales. Experimental results on GE data set show that the fusion performance of this model is superior to BPNN, DCNN and CNN-LSTM data fusion models. © 2022 IEEE.

关键词： Deep neural networks

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DrugProtKGE: Weakly Supervised Knowledge Graph Embedding for highly-Effective Drug-Protein Interaction Representation

DrugProtKGE: Weakly Supervised Knowledge Graph Embedding for...

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2023 IEEE International conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Qiu, Yanlong Wang, Siqi Yang, Xi Qiu, Xinyuan Wu, Chengkun Cui, Yingbo Yang, Canqun National University of Defense Technology Institute for Quantum Information State Key Laboratory of High-Performance Computing College of Computer Science Hunan Changsha410073 China National Supercomputer Center in Tianjin Tianjin300457 China National University of Defense Technology National Key Laboratory of Parallel and Distributed Computing College of Computer Science Hunan Changsha410073 China National University of Defense Technology Department of Biology and Chemistry College of Science Hunan Changsha410073 China

ISBN: (纸本)9798350337488

With the exponential growth of biomedical knowledge in unstructured text repositories such as PubMed, it is imminent to establish a knowledge graph-style, efficient searchable and targeted database that can support the need of information retrieval from researchers and clinicians. To mine knowledge from graph databases, most previous methods view a triple in a graph (see Fig. 1) as the basic processing unit and embed the triplet element (i.e. drugs/chemicals, proteins/genes and their interaction) as separated embedding matrices, which cannot capture the semantic correlation among triple elements. To remedy the loss of semantic correlation caused by disjoint embeddings, we propose a novel approach to learn triple embeddings by combining entities and interactions into a unified representation. Furthermore, traditional methods usually learn triple embeddings from scratch, which cannot take advantage of the rich domain knowledge embedded in pre-trained models, and is also another significant reason for the fact that they cannot distinguish the differences implied by the same entity in the multi-interaction triples. In this paper, we propose a novel fine-tuning based approach to learn better triple embeddings by creating weakly supervised signals from pre-trained knowledge graph embeddings. The method automatically samples triples from knowledge graphs and estimates their pairwise similarity from pre-trained embedding models. The triples are then fed pairwise into a Siamese-like neural architecture, where the triple representation is fine-tuned in the manner bootstrapped by triple similarity scores. Finally, we demonstrate that triple embeddings learned with our method can be readily applied to several downstream applications (e.g. triple classification and triple clustering). We evaluated the proposed method on two open-source drug-protein knowledge graphs constructed from PubMed abstracts, as provided by BioCreative. Our method achieves consistent improvement in both t

关键词： Drug-Protein Interaction Knowledge Graph Embedding Triple Embedding Weakly Supervised Learning

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