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Fine-Grained Software Vulnerability Detection via Neural Architecture Search 28th

Fine-Grained Software Vulnerability Detection via Neural Ar...

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28th International Conference on Database Systems for Advanced Applications, DASFAA 2023

作者： Du, Qianjin Kuang, Xiaohui Li, Xiang Zhao, Gang The Department of Computer Science and Technology Tsinghua University Beijing100084 China National Key Laboratory of Science and Technology on Information System Security Beijing100084 China

ISBN: (纸本)9783031306778

Vulnerability detection methods based on the deep learning have achieved remarkable performance improvements compared to traditional methods. Current deep learning-based detectors mostly use a single RNN or its variants (i.e., LSTM or GRU) to detect vulnerabilities. However, vulnerability detection is a multi-domain problem. Different types of vulnerabilities have different characteristics. Using a single neural network cannot perform well in all types of vulnerability detection tasks. Manually designing a matching neural network for each type of vulnerability detection task not only requires a lot of trials and computational resources but also highly relies on the knowledge and design experience of experts. To address this issue, in this paper, we propose a novel fine-grained vulnerability detection framework named A-DARTS, which is capable of searching for well-performed neural network architectures for different vulnerability detection tasks by introducing neural network architecture search (NAS) techniques. Specifically, we design a more efficient search space to ensure superior neural network architectures can be found by the search algorithm. Besides, we propose an adaptive differentiable search algorithm to search for superior neural network architectures. Experimental results show that searched models consistently outperform all baseline models and achieve significant performance improvements. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Long short-term memory

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40th computer Graphics International Conference, CGI 2023

40th Computer Graphics International Conference, CGI 2023

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40th computer Graphics International Conference, CGI 2023

ISBN: (纸本)9783031500688

The proceedings contain 31 papers. The special focus in this conference is on computer Graphics. The topics include: Automated Marker-Less Patient-to-Preoperative Medical Image Registration Approach Using RGB-D Images and Facial Landmarks for Potential Use in Computed-Aided Surgical Navigation of the Paranasal Sinus;challenges and Constraints in Deformation-Based Medical Mesh Representation;LS-Net: COVID-19 Lesion Segmentation from CT Image via Diffusion Probabilistic Model;intersection of Conic Sections Using Geometric Algebra;a Multi-dimensional Unified Concavity and Convexity Detection Method Based on Geometric Algebra;splossoms: Spherical Blossoms;a Hybrid Supervised Fusion Deep Learning Framework for Microscope Multi-Focus Images;scaleNet: Rethinking Feature Interaction from a Scale-Wise Perspective for Medical Image Segmentation;large Language Model for Geometric Algebra: A Preliminary Attempt;dynamic Ball B-Spline Curves;Game Physics Engine Using Optimised Geometric Algebra RISC-V Vector Extensions Code Using Fourier Series Data;quadratic Phase Quaternion Domain Fourier Transform;MSINET: Multi-scale Interconnection Network for Medical Image Segmentation;CASCO: A Contactless Cough Screening System Based on Audio Signal Processing;a Novel Neighbor Aggregation Function for Medical Point Cloud Analysis;cup-Disk Ratio Segmentation Joint with Key Retinal Vascular Information Under Diagnostic and Screening Scenarios;FLAME-Based Multi-view 3D Face Reconstruction;paraxial Geometric Optics in 3D Through Point-Based Geometric Algebra;Camera Motion Correction with PGA;weyl Calculus Perspective on the Discrete Stokes’ Formula in Octonions;schatten Capped p Regularization for Robust Principle Component Analysis;algorithmic Computation of Multivector Inverses and Characteristic Polynomials in Non-degenerate Clifford Algebras;on Singular Value Decomposition and Polar Decomposition in Geometric Algebras;sparse Graph Hashing with Spectral Regression;a Crowd Behavior Analysis M

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Fog-Assisted Smart Healthcare Prediction System for Diabetics Patients 1

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Emergent Converging Technologies and Biomedical Systems Select Proceedings of the 2nd International Conference, ETBS 2022

作者： Sekhar Tripathy, Subhranshu Bhusan Panda, Shashi Pati, Abhilash Rath, Mamata Tripathy, Niva Sahu, Premananda Odisha Bhubaneswar India Department of Electronics and Telecommunication Engineering Raajdhani Engineering College Odisha Bhubaneswar India Department of Computer Science and Engineering Siksha ‘O’ Anusandhan University Odisha Bhubaneswar India Department of Computer Science and Engineering DRIEMS Autonomous College Odisha Cuttack India Department of Computer Science and Engineering SRMIST Delhi-NCR Campus UP Ghaziabad India

ISBN: (数字)9789819922710

ISBN: (纸本)9789819922703

Accurately, forecasting human diseases continues to be a challenging issue in the search for better and more crucial studies. Diabetic multifunctional sickness is a potentially dangerous condition that afflicts people all around the entire globe. Among the important parts of the human body it affects are the heart, neuropathy, retinopathy, and nephropathy. A healthcare monitoring recommendation system accurately detects and also suggests diabetes complications using adaptive machine learning methods and data fusion techniques on medical management datasets. Recently, various machine learning approaches and algorithms for predicting diabetic illness have been created. Even so, a lot of systems are unable to handle the massive volume of multi-feature raw data on the diabetes mellitus condition. An intelligent diabetic health management strategy is created using deep machine learning and data fusion technologies. By combining the data, we can reduce the unnecessary burden on organization computing power and increase the effectiveness of the suggested method, allowing us to predict and advise this life-threatening condition more accurately. Finally, the adaptive prediction system is assessed, and a machine learning model for diabetes diagnosis that is effective and up-to-date is trained. The suggested approach attained the maximum level of accuracy against standard advanced classification algorithms. The strategy we provide is therefore more effective in predicting and advising multimodal diabetic illness. Our proposed system’s improved disease diagnosis performance warrants its inclusion in automated diabetes patient diagnostic and recommendation systems. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Diseases

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Learning Asynchronous Common and Individual Functional Brain Network for AD Diagnosis 26th

Learning Asynchronous Common and Individual Functional Brain...

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26th International Conference on Medical Image Computing and computer-Assisted Intervention (MICCAI)

作者： Tang, Xiang Zhang, Xiaocai Liu, Mengting Zhang, Jianjia Sun Yat Sen Univ Sch Biomed Engn Shenzhen Campus Shenzhen 518107 Peoples R China ASTAR Inst High Performance Comp Singapore 138632 Singapore

ISBN: (纸本)9783031439926;9783031439933

Construction and analysis of functional brain network (FBN) with rs-fMRI is a promising method to diagnose functional brain diseases. Traditional methods usually construct FBNs at the individual level for feature extraction and classification. There are several issues with these approaches. Firstly, due to the unpredictable interferences of noises and artifacts in rs-fMRI, these individual-level FBNs have large variability, leading to instability and unsatisfactory diagnosis accuracy. Secondly, the construction and analysis of FBNs are conducted in two successive steps without negotiation with or joint alignment for the target task. In this case, the two steps may not cooperate well. To address these issues, we propose to learn common and individual FBNs adaptively within the Transformer framework. The common FBN is shared, and it would regularize the FBN construction as prior knowledge, alleviating the variability and enabling the network to focus on these disease-specific individual functional connectivities (FCs). Both the common and individual FBNs are built by specially designed modules, whose parameters are jointly optimized with the rest of the network for FBN analysis in an end-to-end manner, improving the flexibility and discriminability of the model. Another limitation of the current methods is that the FCs are only measured with synchronous rs-fMRI signals of brain regions and ignore their possible asynchronous functional interactions. To better capture the actual FCs, the rs-fMRI signals are divided into short segments to enable modeling cross-spatiotemporal interactions. The superior performance of the proposed method is consistently demonstrated in early AD diagnosis tasks on ADNI2 and ADNI3 data sets.

关键词： Functional brain network Alzheimer's disease rs-fMRI computer-aided diagnosis Transformer

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A Deep Meta-model for Environmental Sound Recognition 1

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3rd International Conference on Data science, Machine Learning and Applications, ICDSMLA 2021

作者： Arun, K.S. Department of Computer Science and Engineering Amal Jyothi College of Engineering Kottayam India

ISBN: (数字)9789811959363

ISBN: (纸本)9789811959356

Nowadays, sound serves as a crucial factor in all facets of human life. Staring from automating personal security systems to critical surveillance systems, sound is an indispensable component. The practical implementation of the present day automatic sound recognition systems in real-life settings is inadmissible due to their poor detection accuracy. However, deep learning-based systems overcome the incompetence of the traditional machine learning-based models, and it can be used to develop automatic sound classification systems. This work proposes a deep meta-model for categorizing environmental sounds on the basis of the spectrogram images generated from these sounds. In the proposed approach, spectrogram images of environmental sounds are used to train five different deep learning models, and the predictions from these base models are then stacked using the proposed deep meta-model. Experimental results on two benchmark datasets such as ESC-50 and UrbanSound 8K demonstrate the fact that the proposed deep meta-model is a promising alternative to the conventional approaches for environmental sound recognition. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Spectrographs

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40th computer Graphics International Conference, CGI 2023

40th Computer Graphics International Conference, CGI 2023

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40th computer Graphics International Conference, CGI 2023

ISBN: (纸本)9783031500718

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6th Chinese Conference on Pattern Recognition and computer Vision, PRCV 2023

6th Chinese Conference on Pattern Recognition and Computer V...

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6th Chinese Conference on Pattern Recognition and computer Vision, PRCV 2023

ISBN: (纸本)9789819984282

The proceedings contain 522 papers. The special focus in this conference is on Pattern Recognition and computer Vision. The topics include: Image Priors Assisted Pre-training for Point Cloud Shape Analysis;AMM-GAN: Attribute-Matching Memory for Person Text-to-Image Generation;recFormer: Recurrent Multi-modal Transformer with History-Aware Contrastive Learning for Visual Dialog;KV Inversion: KV Embeddings Learning for Text-Conditioned Real Image Action Editing;enhancing Text-Image Person Retrieval Through Nuances Varied Sample;unsupervised Prototype Adapter for Vision-Language Models;Multimodal Causal Relations Enhanced CLIP for Image-to-Text Retrieval;exploring Cross-Modal Inconsistency in Entities and Emotions for Multimodal Fake News Detection;deep Consistency Preserving Network for Unsupervised Cross-Modal Hashing;multi-scale Dilated Attention Graph Convolutional Network for Skeleton-Based Action Recognition;learning Adapters for Text-Guided Portrait Stylization with Pretrained Diffusion Models;edgeFusion: Infrared and Visible Image Fusion Algorithm in Low Light;an Efficient Momentum Framework for Face-Voice Association Learning;multi-modal Instance Refinement for Cross-Domain Action Recognition;modality Interference Decoupling and Representation Alignment for Caricature-Visual Face Recognition;plugging Stylized Controls in Open-Stylized Image Captioning;MGT: Modality-Guided Transformer for Infrared and Visible Image Fusion;multimodal Rumor Detection by Using Additive Angular Margin with Class-Aware Attention for Hard Samples;an Effective Dynamic Reweighting Method for Unbiased Scene Graph Generation;multi-modal Graph and Sequence Fusion Learning for Recommendation;Auto-Learning-GCN: An Ingenious Framework for Skeleton-Based Action Recognition;co-attention Guided Local-Global Feature Fusion for Aspect-Level Multimodal Sentiment Analysis;discovering Multimodal Hierarchical Structures with Graph Neural Networks for Multi-modal and Multi-hop Question Answering;enhan

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Using Ensemble Convolutional Neural Network to Detect Deepfakes Using Periocular Data

Using Ensemble Convolutional Neural Network to Detect Deepfa...

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Intelligent Systems Conference (IntelliSys)

作者： Johnson, David Yuan, Xiaohong Roy, Kaushik North Carolina Agr & Technol State Univ Greensboro NC 27411 USA

ISBN: (纸本)9783031477232;9783031477249

Deepfakes are manipulated or altered images, or video, that are created using deep learning models with high levels of photorealism. A popular method of deepfake creation is using convolutional neural networks (CNN). Deepfakes created using CNN comparatively show high qualities of realism, yet oftentimes leave artifacts and distortions in the generated media that can be detected using machine learning and deep learning algorithms. In recent years, there has been an influx of periocular image and video data because of the increased usage of face masks. By wearing masks, much of what is used for facial recognition is hidden, leaving only the periocular region visible to an observer. This loss of vital information leads to easier misidentification of media, allowing deepfakes to less likely be identified as fake. In this work, feature extraction methods, such as Scale-Invariant Feature Transform (SIFT), Histogram of Oriented Gradients (HOG), and CNN, are used to train an ensemble deep learning model to detect deepfakes in videos on a frame-by-frame level based on the periocular region. Our proposed model is able to distinguish original and manipulated images with averaged accuracy of 98.9 percent, which is an improvement to previous works by combining SIFT and HOG for deepfake detection in convolutional neural networks.

关键词： Deepfake detection CNN Deep neural network computer vision Scale invariant feature transform Histogram of oriented gradients

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Implicit Sentiment Extraction Using Structure Generation with Sentiment Instructor Prompt Template 9th

Implicit Sentiment Extraction Using Structure Generation wit...

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19th International Conference on Advanced Data Mining and Applications, ADMA 2023

作者： Liu, YuXuan Zhong, Jiang College of Computer Science ChongQing University ChongQing China

ISBN: (纸本)9783031466731

Aspect-Category-Opinion-Sentiment quadruple extraction (ACOS) is the novel and challenging sentiment analysis task, which aims to analyze the full range of emotional causes. Existing approaches focus on solving explicit sentiment, but struggle with analyzing implicit sentiment reviews. In this paper, to address the issue, we propose SI-TS, a framework that takes implicit sentiment extraction into account. Specifically, we design target structure (TS) to capture implicit sentiment by converting sentiment elements into a structured format. Furthermore, to adaptively generate appropriate TS according to different sentiment scenarios, we design an prompt template based sentiment instructor(SI). It assists the framework in effectively extracting implicit sentiment elements from the reviews. Extensive experiments were conducted on two widely used ACOS benchmarks, and improvements in F1 values were observed. Specifically, we achieved a 1.05% and 1.28% improvement in F1 values for Laptop-ACOS and Restaurant-ACOS, respectively. Notably, significant results were achieved in extracting implicit sentiment. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Extraction

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Routing Protocol Based on Mission-Oriented Opportunistic Networks 22nd

Routing Protocol Based on Mission-Oriented Opportunistic Net...

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22nd International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2022

作者： Cui, Yahui Zhou, Xinlian Liang, Wei Li, Kuan-Ching School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan China

ISBN: (纸本)9783031226762

Mission-oriented opportunistic networks are a new form of network. Its application background is to complete specific tasks. Its prominent feature is Mission-oriented mobility. This paper, based on the goal-driven mobility model of human nodes in the established Mission-oriented opportunistic networks, proposes a routing protocol based on multi-dimensional trust evaluation parameters (MTEPRP). Firstly, contact intimacy, delivery credibility and location intimacy are selected by the routing protocol as the direct-trusting multi-dimensional trust evaluation parameters to calculate the direct trust value of the nodes, and the weight coefficient of the direct trust value is adjusted by grey relational analysis algorithm. Then, contact intimacy and delivery credibility are selected to calculate the recommended-trusting multi-dimensional trust evaluation parameters, which uses the collaborative computing technology and joint multi-nodes to calculate the recommendation trust values. Finally, the total trust values of nodes are calculated by fusing direct trust values and recommendation trust values. This paper transforms a routing problem for dynamic moving and unstructured space is transformed into a routing problem for static and structured multi-dimensional trust evaluation parameter space by adopting the data forwarding strategy of comparing multi-dimensional trust evaluation parameters. Experimental results show that compared with Epidemic and Prophet routing protocol, the MTEPRP proposed in this paper has better performance in network transmission success rate, data transmission delay and routing overhead. © 2023, Springer Nature Switzerland AG.

关键词： Routing protocols

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