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Proceedings of the 2023 7th International Conference on computer science and Artificial Intelligence

作者： Abdulrahman Hamman Adama Chukkol Senlin Luo Haruna Yunusa Abdulganiyu Abdu Yusuf Abulfatahi Mohammed Information and Electronics Department of Information and Electronics. Beijing Institute of Technology Beijing China China Information and Electronics Information System and Security & Countermeasures Experimental Center Beijing Institute of Technology Beijing China China Automation Science and Electrical Engineering School of Automation Science and Electrical Engineering Beihang University Beijing China China Computer Science School of Computer Science and Technology Beijing Institute of Technology Beijing China China Information Technology School of Management and Information Technology Modibbo Adama University Yola Nigeria Nigeria

ISBN: (纸本)9798400708688

Identifying vulnerabilities in binary code with precision is a critical area of study in the field of software security. Most existing techniques fail to pinpoint the vulnerability's exact location, due to the extensive labelled data requirement and lack of targeted approach to improve on the problematic code. In this paper, we introduce VulPin, a system specifically designed to pinpoint vulnerabilities in binary code. This framework uses a hierarchical attention network that analyze program slices along with a vulnerability position matrix. Experimental outcomes indicate that our framework not only outperforms existing approaches but also pinpoints vulnerabilities more rapidly with a high Accuracy and a very low rate of false positives.

关键词： Binary codes KEYWORDS Program slice Neural networks Vulnerability detection Vulnerability pinpoint

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Detection of Ballastless Track Interlayer Gap Based on Vehicle's Multivariate Dynamic Response and Deep Learning

SSRN

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SSRN 2024年

作者： Miao, Shuaijie Gao, Liang Xin, Tao Nian, Fanjun Zhong, Yanglong School of Civil Engineering Beijing Jiaotong University Beijing100044 China Frontiers Science Center for Smart High-speed Railway System Beijing Jiaotong University Beijing100044 China School of Computer and Information Technology Beijing Jiaotong University Beijing100044 China

To adapt to the rapid detection of interlayer damage in ballastless track structures of high-speed railways, a cement asphalt mortar (CAM) gap localization and damage degree classification scheme based on multivariate data fusion and deep learning is proposed. Based on vertical axle box acceleration (VABA) and vertical wheel-rail force (VWRF) data, the variation patterns of multi-dynamic response data of vehicles under the edge type and internal type interlayer gaps are analyzed. An improved ensemble local mean decomposition algorithm (IELMD) is designed to achieve data denoising and preliminary enhancement preprocessing of weak damage features for VABA and VWRF under interlayer gaps. The measured and simulated VABA and VWRF constituted multiple dynamic response data sets for interlayer gap detection. An interlayer gap detection model: DTA-Tcnformer is constructed, integrating a dual temporal convolutional network with an attention mechanism, transformer architecture, and gate control mechanism. Multiple dynamic response data are input into the model to locate and classify 24 cases of the two interlayer gap types. The influence of damage cases and vehicle speeds on the location effect is analyzed. The learning ability of the proposed model on the gap features is visualized. The misidentification and omission of the gap cases are calculated, and the comprehensive recognition accuracy is 92.48%. The recognition performance of the proposed model is compared and verified. © 2024, The Authors. All rights reserved.

关键词： Dynamic response

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Seastar: A Cache-Efficient and Load-Balanced Key-Value Store on Disaggregated Memory

Seastar: A Cache-Efficient and Load-Balanced Key-Value Store...

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IEEE International Conference on Cluster Computing

作者： Jingwen Du Fang Wang Dan Feng Dexin Zeng Sheng Yi Wuhan National Laboratory for Optoelectronics Key Laboratory of Information Storage System Engineering Research Center of Data Storage Systems and Technology Ministry of Education of China China School of Computer Science and Technology Huazhong University of Science and Technology China Shenzhen Huazhong University of Science and Technology Research Institute China

ISBN: (数字)9798350358711

ISBN: (纸本)9798350358728

In modern datacenters, memory disaggregation un-packs monolithic servers to build independent network-connected compute and memory pools, greatly improving resource uti-lization. Existing memory-disaggregated key-value stores adopt ownership sharing or ownership partitioning for request scheduling and they all show poor performance and scalability. The former generates multiple cache misses and multiple network round trips due to poor cache locality, while the latter fails to fully use resources on all Compute Nodes (CNs) due to load imbalance. In this paper, we propose Seastar, a fast and scalable key-value store that achieves high cache locality as well as load balancing. To enable high concurrency and ensure linearizability, Seastar exploits a self-verification version chain to provide out-of-place atomic updates and ensure consistent reads. To improve cache locality, Seastar leverages a pair-based ownership assigning scheme to assign the processing permission of requests corre-sponding to each key to a CN pair through two independent hash functions. To ensure load balancing among CNs, Seastar proposes a decentralized power-of-two-choices routing scheme to route the requests to the less-loaded CNs in CN pairs for handling. Experimental results demonstrate that Seastar improves the throughput by up to 6.7x and significantly reduces the latency compared with state-of-the-art memory-disaggregated key-value stores.

关键词： Concurrent computing Hash functions Protocols Processor scheduling Scalability Cluster computing Load management Throughput Routing Servers

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Unaligned RGB Guided Hyperspectral Image Super-Resolution with Spatial-Spectral Concordance

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International Journal of computer Vision 2025年 1-21页

作者： Zhang, Yingkai Lai, Zeqiang Zhang, Tao Fu, Ying Zhou, Chenghu School of Computer Science and Technology Beijing Institute of Technology Beijing China School of Communication Engineering Hangzhou Dianzi University Hangzhou China State Key Laboratory of Resources and Environmental Information System Institute of Geographic Sciences and Natural Resources Research Chinese Academy of Sciences Beijing China College of Resources and Environment University of Chinese Academy of Sciences Beijing China

Hyperspectral images (HSIs) super-resolution (SR) aims to improve the spatial resolution, yet its performance is often limited at high-resolution ratios. The recent adoption of high-resolution reference images for super-resolution is driven by the poor spatial detail found in low-resolution HSIs, presenting it as a favorable method. However, these approaches cannot effectively utilize information from the reference image, due to the inaccuracy of alignment and its inadequate interaction between alignment and fusion modules. In this paper, we introduce a Spatial-Spectral Concordance Hyperspectral Super-Resolution (SSC-HSR) framework for unaligned reference RGB guided HSI SR to address the issues of inaccurate alignment and poor interactivity of the previous approaches. Specifically, to ensure spatial concordance, i.e., align images more accurately across resolutions and refine textures, we construct a Two-Stage Image Alignment (TSIA) with a synthetic generation pipeline in the image alignment module, where the fine-tuned optical flow model can produce a more accurate optical flow in the first stage and warp model can refine damaged textures in the second stage. To enhance the interaction between alignment and fusion modules and ensure spectral concordance during reconstruction, we propose a Feature Aggregation (FA) module and an Attention Fusion (AF) module. In the feature aggregation module, we introduce an Iterative Deformable Feature Aggregation (IDFA) block to achieve significant feature matching and texture aggregation with the fusion multi-scale results guidance, iteratively generating learnable offset. Besides, we introduce two basic spectral-wise attention blocks in the attention fusion module to model the inter-spectra interactions. Extensive experiments on three natural or remote-sensing datasets show that our method outperforms state-of-the-art approaches on both quantitative and qualitative evaluations. Our code is publicly available to the community ( ht

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Egcnet: Edge-Guided Context Real-Time Semantic Segmentation Network

SSRN

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SSRN 2023年

作者： Yu, Kaili Chen, Zhenxue Ye, Mengting Guo, Yixin Wu, Q.M. Jonathan School of Control Science and Engineering Shandong University Jinan250061 China School of Control Science and Engineering Shandong University Engineering Research Center of Intelligent Unmanned System Ministry of Education Jinan250061 China Department of Electrical and Computer Engineering University of Windsor WindsorONN9B 3P4 Canada

Semantic segmentation is widely needed in many application areas. Driven by the development of deep learning, semantic segmentation methods have made significant progress in accuracy, robustness, and speed. Based on the short-term dense concatenate network, this paper proposes an edge-guided context real-time semantic segmentation network (EGCNet), which further improves the detail branch, extracts edge information from feature maps, and enhances the network’s ability to extract edge information through edge-assisted training. Because the top-level feature map contains minimal detailed information, the network only extracts the boundary of the low-level feature map such that the useful information is utilized as much as possible while reducing the computational load. In addition, we improve the attention-refining module by embedding position information, allowing it to capture the target structure more accurately. EGCNet achieves 71 FPS and 78.6\% mIoU on the Cityscapes dataset at a low computational cost, making it an efficient real-time semantic segmentation network. © 2023, The Authors. All rights reserved.

关键词： Semantic Segmentation

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GraphInstruct: Empowering Large Language Models with Graph Understanding and Reasoning Capability

arXiv

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arXiv 2024年

作者： Luo, Zihan Song, Xiran Huang, Hong Lian, Jianxun Zhang, Chenhao Jiang, Jinqi Xie, Xing Huazhong University of Science and Technology Wuhan China Microsoft Research Asia Beijing China The National Engineering Research Center for Big Data Technology and System Service Computing Technology and Systems Laboratory Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology China

Evaluating and enhancing the general capabilities of large language models (LLMs) has been an important research topic. Graph is a common data structure in the real world, and understanding graph data is a crucial part for advancing general intelligence. To evaluate and enhance the graph understanding abilities of LLMs, in this paper, we propose a benchmark named GraphInstruct, which comprehensively includes 21 classical graph reasoning tasks, providing diverse graph generation pipelines and detailed reasoning steps. Based on GraphInstruct, we further construct GraphLM through efficient instruction-tuning, which shows prominent graph understanding capability. In order to enhance the LLM with graph reasoning capability as well, we propose a step mask training strategy, and construct a model named GraphLM+. As one of the pioneering efforts to enhance the graph understanding and reasoning abilities of LLMs, extensive experiments have demonstrated the superiority of GraphLM and GraphLM+ over other LLMs. We look forward to more researchers exploring the potential of LLMs in the graph data mining domain through GraphInstruct. Our code for generating GraphInstruct is released publicly at: https://***/CGCL-codes/GraphInstruct. Copyright © 2024, The Authors. All rights reserved.

关键词： Computational linguistics

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Utilizing Artificial Neural Networks and Multivariate Patient Data for Anemia Detection using WEKA based Approach for Diagnosis

Utilizing Artificial Neural Networks and Multivariate Patien...

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Automation, Computing and Renewable systems (ICACRS), International Conference on

作者： Prof Neeraj Bhargava Pramod Singh Rathore Parul Vaishnav Manish Rai Professor School of Engineering & System Sciences MDS University Ajmer India Department of Computer and Communication Engineering Manipal University Jaipur India M.Tech Research Scholar MDS University Ajmer Department of Computer Science and Engineering (AI& ML) Manipal University Jaipur India

This research study describes a method that uses neural networks to figure out if a woman has anemia. An artificial neural network is a type of computer network that is based on the real neural networks that make up the structure of the human brain. In this paper, the WEKA tool is used for the classification process carried out using an ANN structure, which is referred to as one of the data mining approaches and is based on multiple perceptions. The network has seven inputs (RBC, HCT, MCV, MCH, MCHC, RDW, and WBC) and an output (decision). Simulations on 567 patients demonstrate that the Artificial Neural Network (ANN) accurately diagnose the disease. This study investigates the utilization of Artificial Neural Network (ANN)-supervised machine learning techniques for predicting anemia by using the data from Kaggle.

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Advancements in Chatbot Technology: Enhancing User Experience through AI and NL

Advancements in Chatbot Technology: Enhancing User Experienc...

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Artificial Intelligence and Quantum Computation-Based Sensor Application (ICAIQSA), International Conference on

作者： Yojna Arora Ajeet Singh Abhay Sharma Nitin Aggarwal Nidhi Bansal Department of Computer Science & Engineering Sharda University Greater Noida India Department of Computer Science & Engineering Moradabad Institute of Technology Moradabad Uttar Pradesh India Department of IoT & IS School of Computing and Intelligent System Manipal University Jaipur Jaipur Rajasthan Supervisor Oracle Development Database Admin Data Science PA USA Symbiosis Institute of Technology Nagpur Campus Symbiosis International (Deemed University) Pune India

ISBN: (数字)9798331517953

ISBN: (纸本)9798331517960

A chatbot is an AI-powered software or application designed to communicate with people. This technology can perform a variety of tasks, including providing instant responses and answers to users, delivering information, assisting with purchasing products, and enhancing customer service. In my paper, I present the fundamental principles and essential concepts of computing related to chatbots and its applications various sectors like Education, E commerce, Entertainment, Healthcare, Telecommunication, Customer Service etc. The paper also mentions detailed literature explain the contribution by various researchers in this fields. Architecture and step wise approach of creating a chatbot is presented lately.

关键词： Quantum computing Customer services Education Medical services computer architecture Chatbots User experience Software Artificial intelligence systematic literature review

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Optical Evanescent Field for Direct Observation of Sub-100nm Particle Motion on Glass Surface

Optical Evanescent Field for Direct Observation of Sub-100nm...

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Robotics, engineering, science, and Technology (RESTCON), International Conference on

作者： Panart Khajornrungruang Thitipat Permpatdechakul Keisuke Suzuki Aran Blattler Yutaka Terayama Daiki Goto Pipat Phaisalpanumas Natthaphon Bun-Athuek Amornphun Phunopas Dept. of Intelligent and Control System Faculty of Computer Science and Systems Engineering Mechanical Science and Technology Division Kyushu Institute of Technology Iizuka-Shi Fukuoka Japan Dept. of Interdisciplinary Informatics Graduate School of Computer Science and Systems Engineering Mechanical Information Science and Technology Division Kyushu Institute of Technology Iizuka-Shi Fukuoka Japan Dept. of Production and Robotics Engineering Faculty of Engineering King Mongkut’s University of Technology North Bangkok Bangsue Bangkok Thailand Dept. of Intelligent and Control System Graduate School of Computer Science and Systems Engineering Mechanical Science and Technology Course Kyushu Institute of Technology Iizuka-Shi Fukuoka Japan Dept. of Industiral Engineering Faculty of Engineering King Mongkut’s University of Technology North Bangkok Bangsue Bangkok Thailand Dept. of Mechanical Engineering Technology College of Industrial Technology King Mongkut’s University of Technology North Bangkok Bangsue Bangkok Thailand

To directly investigate the dynamic nanoscale phenomenon on the surface being processed in wet conditions such as precision polishing, and cleaning in semiconductor industrial, an optical method for visualization and observation of each sub-100 nm sized particle that is moving on an interface such as a silica glass surface by applying an evanescent field have been proposing. Subsequently, we developed an experimental apparatus equipped with an optical microscopy system for verifying the moving particle observation method in a laboratory scale. This article introduces some experimentally direct observation results of duplicated wet processes.

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Automatic Mail Reception system for Patients to Identify Health Conditions 4

Automatic Mail Reception System for Patients to Identify Hea...

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4th International Conference on Inventive Research in Computing Applications, ICIRCA 2022

作者： Prema, S. Harshith, N. Kousar, Heena Malini, K.V. Meenatchi, S. Kumar, M Jogendra Mahendra Engineering College Department of Information Technology Tamil Nadu Namakkal637503 India Jss Academy of Higher Education & Research Department of Health System Management Studies Karnataka Mysuru570004 India East Point College of Engineering and Technology Department of Computer Science and Engineering Karnataka Bengaluru560049 India Sri Sairam College of Engineering Department of Electrical and Electronics Engineering Bengaluru562106 India School of Information Technology and Engineering Vellore Institute of Technology Tamil Nadu Vellore632014 India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Andhra Pradesh Vaddeswaram522502 India

ISBN: (数字)9781665497077

ISBN: (纸本)9781665497077

Predicting and detecting human health conditions in the early stages is a crucial thing to be considered in the medical field. Lung tumour has been one of the most often common malignancies in the past few decades, as well as the high incidence of melanoma-related fatality globally. As a cost-effective alternative, computer-aided diagnostic systems can combine complicated features and identify a patient's probability of getting a lung tumour, reducing the need for unneeded and costly medical treatments. The computing profession is completely digitizing everything, and the healthcare profession is following suit with image identification and data analytics. As a modern solution to predict the presence of tumours in scan images aConvolutional Neural Network (CNN),Deep Learning (DL) model is proposed in this study. The dataset required for this study is collected from the Kaggle website and pre-processed using various pre-processing methods to make it compatible with the DL model. After pre-processing the data, the dataset is divided into a training set, testing set and validation set. The CNN model is trained using the training set, validated using the validation set and tested for efficiency using the testing set. After training and testing the CNN model a user-friendly webpage is created to make this CNN model easily accessible to the individuals. The webpage is designed in such a way that when the patients upload their lung CT scan image and enter the necessary contact details requested by the webpage, the scan is analysed and evaluated by the trained CNN model and the result is sent as an email to the user. © 2022 IEEE.

关键词： Tumors

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