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Topic-aware cosine graph convolutional neural network for short text classification

Soft Computing 2024年第13-14期28卷 8119-8132页

作者： Min, Changrong Chu, Yonghe Lin, Hongfei Wang, Bolin Yang, Liang Xu, Bo Criminal Investigation Police University of China No.83 Tawan St Liaoning Shenyang China School of Computer Science and Technology Dalian University of Technology No. 2 Linggong Road Liaoning Dalian116024 China School of Information Science and Engineering Henan University of Technology No. 100 Lianhua Street Henan Zhengzhou450001 China

Graph Convolutional Network (GCN) has been extensively studied in the task of short text classification (STC), utilizing global graphs that incorporate texts at different levels of granularity to learn text embeddings. However, the GCN-based methods only focus on the alignment between ground-truth labels and predicted labels, overlooking the geometric structure implicitly encoded by the graph. To address this limitation, we propose a novel GCN-based method that is entitled Topic-aware Cosine GCN (ToCo-GCN) for the STC. The ToCo-GCN defines and captures underlying geometric structures of short texts from different categories in the cosine space. Specifically, the ToCo-GCN regards the within-class and between-class geometric structures as constraint, aiming to learn both representative and discriminative short text representations. Moreover, to mitigate the inherent sparsity problem of short texts, the ToCo-GCN augment the text graph with latent topics. Experimental results on 8 STC datasets demonstrate that the ToCo-GCN is superior to state-of-the-art baselines in terms of Accuracy and Macro-F1 score. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Classification (of information)

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Multi-modal Dynamic Information Selection Pyramid Network for Alzheimer’s Disease Classification 14th

Multi-modal Dynamic Information Selection Pyramid Network fo...

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

作者： Ma, Yuanmin Chen, Yuan Liu, Yuqing Chen, Jie Jiang, Bo School of Artificial Intelligence Anhui University Hefei China School of Internet Anhui University Hefei China Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei China School of Electronic and Information Engineering Anhui Jianzhu University Hefei China School of Computer Science and Technology Anhui University Hefei China

ISBN: (纸本)9789819628841

Alzheimer’s disease (AD) is a common and dangerous disorder that primarily impacts older adults, early detection is crucial, and diagnostic tools like PET and MRI have an impact on offering detailed anatomical and metabolic insights, respectively. However, traditional methods usually directly concatenate the two modal data by channel, which fails to fully utilize the complementary information provided by MRI and PET data. Hence, this paper introduces a new multi-modal dynamic information selection framework to enhance the accuracy of AD classification. It includes three main modules: a dual ResNet50-based feature extraction module;a modal fusion module containing a feature pyramid for finer detail extraction and a cross-attention mechanism to integrate modal information;a dynamic information selection module that evaluates the data content across modalities to optimize decision-making. The outcome on the ADNI dataset confirm the efficiency of the proposed approach. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Diagnosis

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Empirical Research on the Application of a Structure-Based Software Reliability Model

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IEEE/CAA Journal of Automatica Sinica 2021年第6期8卷 1153-1162页

作者： Jie Zhang Yang Lu Ke Shi Chong Xu School of Computer and Information Anhui Normal UniversityWuhu 241003China School of Computer Science and Information Engineering Hefei University of TechnologyHefei 230601China

Reliability engineering implemented early in the development process has a significant impact on improving software *** can assist in the design of architecture and guide later testing,which is beyond the scope of traditional reliability analysis *** reliability models work for this,but most of them remain tested in only simulation case studies due to lack of actual *** we use software metrics for reliability modeling which are collected from source codes of post *** the proposed strategy,redundant metric elements are filtered out and the rest are aggregated to represent the module *** further propose a framework to automatically apply the module value and calculate overall reliability by introducing formal *** experimental results from an actual project show that reliability analysis at the design and development stage can be close to the validity of analysis at the test stage through reasonable application of metric *** study also demonstrates that the proposed methods have good applicability.

关键词： Algebraic method reliability evaluation software metrics software reliability

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AKGIN: An API Knowledge Graph and Intent Network based Mashup-Oriented API Recommendation Method 26

AKGIN: An API Knowledge Graph and Intent Network based Mashu...

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26th International Conference on computer Supported Cooperative Work in Design, CSCWD 2023

作者： Yu, Changyong Hu, Rong Wang, Bangjun Hunan University of Science and Technology Computing and Novel Software Technology Hnust School of Computer Science and Engineering Xiangtan China

ISBN: (纸本)9798350331684

With the continuous development of the Web API ecosystem, mashup-oriented API recommendation gets a lot of attention. Collaborative filtering, deep learning and their combination based methods are recently proposed for API recommendation. However, the recommendation results often perform poorly when data is sparse. Therefore, we propose an API knowledge graph (AKG) and intent network based mashup-oriented API recommendation method, whose main target is to recruit the semantic representation ability to improve the precision of API recommendation. Firstly, Latent Dirichlet Allocation (LDA) is used to extract API topics, which are important entities in the AKG. Then, the mashup intention is modeled based on the mashup-API interactions. The high-order relationship paths are recursively aggregated into the representation vector of Mashup and API. Finally, the inner product of their representations present the probability of the API recommendation for the mashup. The experimental results on real-world datasets show that our proposed method outperforms several baseline methods in mashup-oriented API recommendations. © 2023 IEEE.

关键词： Knowledge graph

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A multi-strategy integrated improved YOLOv5 model and its application in target detection 4

A multi-strategy integrated improved YOLOv5 model and its ap...

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4th IEEE International Conference on Electronic technology, Communication and Information, ICETCI 2024

作者： Li, Jia Liu, Ziyan Song, Yuxin Harbin Engineering University College of Computer Science and Technology Harbin China Hunan University of Technology and Business College of Intelligent Engineering and Intelligent Manufacturing Changsha China Jiangnan University School of Artificial Intelligence and Computer Science Wuxi China

ISBN: (纸本)9798350361643

Deep learning techniques have the potential to significantly improve target detection speed and detection accuracy in autonomous driving. Most existing target detection algorithms have poor real-time performance and poor accuracy. Moreover, the algorithm is difficult to be deployed. Based on analysis of the existing problems of target detection in autonomous driving, this paper puts forward an improved YOLOv5 algorithm. First, the extract feature network of YOLOv5 is replaced with the FasterNet-T0 model to reduce model parameters. Next, in the fusion part (Neck) of the network, the attention mechanism CBAM is introduced to improve the accuracy of the target detection. Then, the Slim-Neck framework is presented to improve the computational efficiency of the model. Finally, considering that CIoU may be replaced by Inner-IoU to improve the accuracy and generalization of the model, in order to verify the effect of the improved model, this paper uses the improved YOLOv5 algorithm model to extract the specific categories from VOC2007 dataset and DOTA dataset. After experimentation. The results show that the model we made, the improved YOLOv5 algorithm model is capable of getting a 0.848 and 0.748mAP @ 0.5 of the VOC2007 dataset and DOTA dataset for the specific category extraction, which verifies its effectiveness. Compared with other algorithms, the improved YOLOv5s algorithm is more competitive. On the VOC2007 data set, mAP live/voc, 0.5 increased by 5.1% for a specific category acquisition, and mAP live/voc, 0.5:0.95 increased by 6.4% relative to the original YOLOv5s algorithm, and the number of parameters reduced by 39.2% from 7035811 to 4276490. On the DOTA dataset, mAP@0.5 increased by 0.9% for a specific category acquisition, and mAP@ 0.5:0.95 increased by 0.4%, which demonstrates that this method improves the accuracy and efficiency of vehicle detection to some extent. In this study, DOTA dataset was used as an experimental dataset to verify the performance of YOL

关键词： Autonomous vehicles

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Randomized-enhanced Magnitude Channel Pruning for Radar Jamming Detection 7

Randomized-enhanced Magnitude Channel Pruning for Radar Jamm...

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7th IEEE International Conference on Electronic Information and Communication technology, ICEICT 2024

作者： Ni, Xun Wu, Hao Zhu, Xuan Shi, Zhaopeng Huazhong University of Science and Technology School of Computer Science and Technology Wuhan China Naval University of Engineering National Key Laboratory of Electromagnetic Energy Wuhan China Wuhan University School of Electrical Engineering and Automation Wuhan China Huazhong University of Science and Technology School of Electronic Information and Communications Wuhan China

ISBN: (纸本)9798350384437

With the rapid development of radar jamming technology, traditional detection techniques face significant limitations in complex electromagnetic environments. Recent interference detection methods based on deep learning struggle with processing speed and real-time performance. To address this, we propose a new pruning technique called randomized-enhanced magnitude channel pruning (RMP) to achieve faster detection speeds and more lightweight models. This study evaluates the performance of RMP compared to random channel pruning and magnitude channel pruning in radar jamming detection. To verify RMP's generalization, we also tested it on the VOC2007 [16] object detection task. Results show that RMP consistently reduces mAP@50 loss more effectively than other methods. In one iteration pruning, RMP reduced mAP@50 loss by an average of 0.8% and 1% in radar jamming detection, and by 0.4% and 3.1% in the VOC2007 dataset, compared to random and magnitude pruning, respectively. In multi-pass pruning, RMP reduced mAP@50 loss by an average of 0.3% in radar jamming detection, and by 0.7% and 1.6% in the VOC2007 dataset, compared to random and magnitude pruning, respectively. These findings confirm RMP's effectiveness in maintaining model accuracy and its practical value in fast-response scenarios like radar jamming detection. © 2024 IEEE.

关键词： Jamming

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Malicious Traffic Detection with Noise Labels Based on Cross-Modal Consistency

IEEE Networking Letters

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IEEE Networking Letters 2024年第2期6卷 148-151页

作者： Yuan, Qingjun Niu, Weina Wang, Yongjuan Gou, Gaopeng Lu, Bin Henan Key Laboratory of Network Cryptography Technology Zhengzhou450001 China Institute for Cyber Security School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China Shenzhen518110 China Institute of Information Engineering Chinese Academy of Sciences Beijing100093 China

To train robust malicious traffic identification models under noisy labeled datasets, a number of learning with noise labels approaches have been introduced, among which parallel training methods have been proved to be effective. Parallel training methods tend to select samples with disagreement to mitigate the risk of self-control degradation. However, it also introduces noisy knowledge into training. In this letter, we try to avoid introducing noisy knowledge by enhancing the consistency of the representations of parallel networks. Meanwhile, the two networks are heterogeneous and introduce information from different modalities, thus mitigating the risk of self-control degradation from multiple perspectives. © 2019 IEEE.

关键词： Feature extraction

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Quantum Computing Circuit Design: A Tutorial 15

Quantum Computing Circuit Design: A Tutorial

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15th International Conference on Advanced Scientific Computing, ICASC 2024

作者： Abdul Wahid, Seham Al Asad, Arghavan Kaur, Rupinder Mohammadi, Farah Toronto Metropolitan University Electrical and Computer Engineering Department Toronto Canada School of Computer Science and Technology Algoma University Brampton Canada

ISBN: (纸本)9798350356526

Recent advancements in computing speed and capacity of Artificial Intelligence (AI) algorithms have reached a saturation level in performance due to the continuous application of Moore's law which resulted in the memory wall phenomenon. Quantum computing (QC), based on the principles of quantum mechanics which include superposition and entanglement, has the potential to contribute to the advancements of AI by providing exponential speedup. Quantum computers use quantum bits, also known as qubits, as the metric to demonstrate the power of quantum technology. Qubits, the fundamental unit of quantum information can exist in two states, 0 and 1, simultaneously, leveraging the principle of superposition. Quantum gates are the building blocks of quantum circuits, operating on qubits to transform one quantum state into another, and are comparative to classical logic gates. Designing and simulating quantum circuits can be completed using the Qiskit library design by IBM. Qiskit is a cloud-based, open access platform to real quantum computers with 5 to 16 qubits. This paper offers a straightforward tutorial on implementing one, two and three-qubit quantum gates, as well as designing quantum circuits using the Qiskit library, illustrated with an example of a quantum half adder implementation. © 2024 IEEE.

关键词： Qubits

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Real-time Obesity and Heart Disease Monitoring with IoT and Deep Learning

Real-time Obesity and Heart Disease Monitoring with IoT and ...

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2024 International Conference on Smart Devices, ICSD 2024

作者： Singh, Jagendra Govindaraju, P. Kumar, Vinish Yamsani, Nagendar Umamaheswari, J. Munagala, Madhu Bennett University School of Computer Science Engineering & Technology Greater Noida India K S R Institute for Engineering and Technology Department of Electronics and Communication Engineering Tiruchengode India Raj Kumar Goel Institute of Technology Department of Computer Science Engineering Ghaziabad India SR University School of Computer Science and Artificial Intelligence Warangal India Brainware University Department of Computer Science Kolkata India Koneru Lakshmaiah Education Foundation Department of of English Vaddeswaram India

ISBN: (纸本)9798350394412

The accelerating spread of junk food consumption is progressively putting the younger generations at risk of obesity. The concept of health prioritization is deeply rooted in the development of nurturing and general welfare in society. The job has further become more complex by the glaring statistics of people across the globe who are fighting obesity. The World Health Organization (WHO) puts a staggering estimate that approximately 1 billion people across the globe suffer from obesity-related issues, a statistic heavy in terms of impact primarily because obesity greatly affects one's health but also holds fatal risks. Diagnosing the early stages of obesity can just be simple and prevent one from being obese;however, several obstacles stand in the way of its early diagnosis. Symptoms or signs that one is in the trajectory of suffering from obesity generally go unnoticed by the individual, and the availability of specialists to diagnose such risks persistently on time is not always possible. In this regard, the current paper shall showcase an application hitherto unknown using cloud-based diagnosis, in collaboration with machine learning through Secure Access Internet of Things to predict obesity-related risks without having to depend on glaring physical parameter indicators - in this regard, redefining how such information could be sourced even without the need of regularly visiting health professional people. To ensure the best possible health predictability from this machine learning, we gathered and plotted the datasets consisting of obese and overweight people. Other than machine learning, we use a Secure Access IoT sensor network. This allows for real-time monitoring and diagnosis, making early detection achievable. Consequently, this system paves the way for mitigating and substantially reducing the complications associated with obesity. © 2024 IEEE.

关键词： Nutrition

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Hybrid Arithmetic-Rider Optimization algorithm as new intelligent model for travelling salesman problem

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International Journal of Information technology (Singapore) 2024年 1-18页

作者： Krishna, Madugula Murali Majhi, Santosh Kumar Panda, Nibedan Department of Computer Science and Engineering Veer Surendra Sai University of Technology Odisha Burla 768018 India Department of Computer Science and Information Technology Guru Ghasidas Viswavidyalaya C.G. Bilaspur India School of Computer Engineering KIIT Deemed to Be University Odisha Bhubaneswar 751024 India

Travelling Salesman Problem (TSP) is one of the significant NP-hard benchmark problems for performing discrete optimization. In recent times, determining the optimal route mechanism has been implemented and ensured as an important mechanism for solving practical applications. In real-time applications, energy-saving, reaching the destination at the assigned time, and identifying the shortest route is very essential. Route determination is researched by different scientists and engineers. Numerous research is done on this problem, and it is still challenging. Therefore, meta-heuristics are involved in this problem as it is inspired by the biological species. This article constitutes the continuation of the work on adapting the TSP using the hybridized algorithm named Arithmetic-ROA (A-ROA). The ultimate aim of the proposed TSP is to reduce the distance travelled by the salesman while focusing on the entire city. To attain this optimal solution, the novel hybrid A-ROA optimizes the number of the city to be travelled. The results acquired by the new hybrid heuristic are compared with other heuristic algorithms. The computational results confirm that the developed algorithm obtained better results when compared with existing algorithms. The developed optimization algorithm also provides enhanced performance within a realistic amount of computational time. © The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Arithmetic Optimization Algorithm Arithmetic-Rider Optimization Algorithm Benchmark Datasets Meta-heuristic Algorithms Rider Optimization Algorithm Travelling Salesman Problem

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