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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Bi, Guanqun Cao, Yanan Li, Piji Xie, Yuqiang Fang, Fang Lin, Zheng Chinese Academy of Sciences Institute of Information Engineering Beijing China University of Chinese Academy of Sciences School of Cyber Security Beijing China Nanjing University of Aeronautics and Astronautics College of Computer Science and Technology Nanjing China

ISBN: (纸本)9781728163277

Empathy is a key ability for a human-like dialogue system. Inspired by social psychology, empathy includes both affective and cognitive aspects. Previous works on this topic have merely focused on recognizing emotions or modeling cognition with commonsense knowledge. Nevertheless, the generated results of these works still have a big gap with human-like empathetic responses. In this paper, we propose Seri, a SkEtching-Reasoning-Integrating framework for empathetic response generation. In particular, we define an empathy planner to capture and reason about multi-source information that considers cognition and affection. Further, we introduce a dynamic integrator module that allows the model dynamically select the appropriate information to generate empathetic responses. Experimental results on EmpatheticDialogue show that our method outperforms competitive baselines and generates responses with higher diversity and cognitive empathy levels. © 2023 IEEE.

关键词： Dialogue System Empathetic Response Generation Empathy Natural Language Generation

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An Interpretable CNN for the Segmentation of the Left Ventricle in Cardiac MRI by Real-Time Visualization

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computer Modeling in engineering & Sciences 2023年第5期135卷 1571-1587页

作者： Jun Liu Geng Yuan Changdi Yang Houbing Song Liang Luo Robotics Institute School of Computer ScienceCarnegie Mellon UniversityPittsburghPA15217USA Department of Electrical&Computer Engineering College of EngineeringNortheastern UniversityBostonMA02115USA Security and Optimization for Networked Globe Laboratory(SONG Lab) Embry-Riddle Aeronautical UniversityDaytona BeachFL32114USA School of Computer Science and Engineering University of Electronic Science and Technology of ChinaChengdu610054China

The interpretability of deep learning models has emerged as a compelling area in artificial intelligence *** safety criteria for medical imaging are highly stringent,and models are required for an ***,existing convolutional neural network solutions for left ventricular segmentation are viewed in terms of inputs and ***,the interpretability of CNNs has come into the *** medical imaging data are limited,many methods to fine-tune medical imaging models that are popular in transfer models have been built using massive public Image Net datasets by the transfer learning ***,this generates many unreliable parameters and makes it difficult to generate plausible explanations from these *** this study,we trained from scratch rather than relying on transfer learning,creating a novel interpretable approach for autonomously segmenting the left ventricle with a cardiac *** enhanced GPU training system implemented interpretable global average pooling for graphics using deep *** deep learning tasks were *** included data management,neural network architecture,and *** system monitored and analyzed the gradient changes of different layers with dynamic visualizations in real-time and selected the optimal deployment *** results demonstrated that the proposed method was feasible and efficient:the Dice coefficient reached 94.48%,and the accuracy reached 99.7%.It was found that no current transfer learning models could perform comparably to the ImageNet transfer learning *** model is lightweight and more convenient to deploy on mobile devices than transfer learning models.

关键词： Interpretable graphics training visualization image segmentation left ventricle CNNs global average pooling

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Evaluation of Adaptive Synthetic Resampling Technique for Imbalanced Breast Cancer Identification

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Procedia computer Science 2024年 235卷 1000-1007页

作者： Tsehay Admassu Assegie Ayodeji Olalekan Salau Kanimozhi Sampath Rajkumar Govindarajan Sangeetha Murugan B. Lakshmi Department of Computer Science College of Engineering and Technology Injibara University Injibara Ethiopia Department of Electrical/Electronics and Computer Engineering Afe Babalola University Ado-Ekiti Nigeria Department of Computer Science & Engineering (Cyber Security) Madanapalle Institute of Technology and Science Madanapalle Andhra Pradesh India Department of Computer Science & Engineering (Data Science) Madanapalle Institute of Technology and Science Madanapalle Andhra Pradesh India Department of Computer Science & Engineering Madanapalle Institute of Technology and Science Madanapalle Andhra Pradesh India Department of Computer Science & Technology Madanapalle Institute of Technology and Science Madanapalle Andhra Pradesh India

As one of the most common types of cancer among women, breast cancer is a serious health concern worldwide. Early detection is crucial for successful treatment and improved survival rates. However, detecting breast cancer is challenging due to imbalanced classification, where the minority class (cancerous) is ominously smaller than the majority class (non-cancerous). In this paper, we explore the use of logistic regression (LR) and the adaptive synthetic resampling (ADASYN) technique to address imbalanced classification in breast cancer detection. To that end, we collected the Wisconsin Breast Cancer dataset, which contains 569 instances. The dataset is imbalanced, with 212 malignant (cancerous) cases and 357 benign (non-cancerous) cases. Then, we trained support vector machine, LR, K-nearest neighbor, gradient, and adaptive boosting on the imbalanced dataset. Finally, we trained these algorithms on resampled data with the ADASYN oversampling and we evaluated their performance using cross-validation score with 5-folds. The results of the experiment showed that using ADASYN with LR significantly improved the performance the LR model. The LR model achieves 99.46% accuracy on breast cancer diagnosis. Moreover, the confusion matrix shows that among the 188 samples, the model misclassified one cancerous instance. Thus, we concluded that the proposed model is effective for breast cancer diagnosis.

关键词： Breast cancer detection imbalanced classification machine learning Adaptive synthetic oversampling

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Optimizing Convolutional Neural Network Hyperparameters for MNIST Image Classification: Insights and Implications 2

Optimizing Convolutional Neural Network Hyperparameters for ...

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2nd International Conference on Advances in Computation, Communication and Information technology, ICAICCIT 2024

作者： Dar, Eman Rana, Khadija Yousaf, Hoor Fatima Asif, Talha Hussain, Muhammad Zunnurain Ijaz, Hussain Hasan, Muhammad Zulkifl Dept. of Computer Engineering Information Technology University Lahore Pakistan Bahria University Lahore Campus Pakistan NCBA&E DHA Campus Pakistan Dept. of Computer Science Bahria University Lahore Campus Pakistan Cloud Engineer Information Technology University Pakistan Researcher Cyber Security Center Universitie Putra Malaysia Malaysia

ISBN: (纸本)9798331541217

The recognition of handwritten digits has been among the most enduring fundamental problems explored in the field of machine learning and computer vision. The objective of this work is to design a state-of-art Convolutional Neural Network (CNN) for the image classification on the MNIST dataset to resolve issues related to data pre-processing and parameter tuning effectively. The work starts with standardizing the image data and then using one-hot encoding for the models that will improve the generalization and representation. The CNN structure followed in this work includes the convolutional layer, batch normalization, pooling layers, and fully connected layers with an efficient integration of features. To measure such performance indicators as accuracy, the given dataset is divided for training and validation. The results are also preprocessed to control for overfitting and convergence using back propagation with early stop to stop training when significant accuracy improvement is not observed and learning rate decay to encourage the model to search for better weights in the final stages of training. The model's performance is tested and evaluated by using different epoch intervals and learning rates, and it is shown how critical it is to select the correct default hyperparameters for achieving efficiency. This paper also presents the approach for the practical application of the preprocessing of images and the systematic assessment in enhancing CNN-based handwritten digit recognition. © 2024 IEEE.

关键词： Convolutional neural networks

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Quantum Assisted Scheduling Algorithm for Federated Learning in Distributed Networks 32

Quantum Assisted Scheduling Algorithm for Federated Learning...

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32nd International Conference on computer Communications and Networks, ICCCN 2023

作者： Wei, Xinliang Fan, Lei Guo, Yuanxiong Gong, Yanmin Han, Zhu Wang, Yu Temple University Department of Computer and Information Sciences PhiladelphiaPA United States University of Houston Department of Engineering Technology HoustonTX United States University of Texas at San Antonio Department of Information Systems and Cyber Security San AntonioTX United States University of Texas at San Antonio Department of Electrical and Computer Engineering San AntonioTX United States University of Houston Department of Electrical and Computer Engineering HoustonTX United States

ISBN: (纸本)9798350336184

The scheduling problem for federated learning (FL) with multiple models in a distributed network is challenging, as it involves NP-hard mixed-integer nonlinear programming. Moreover, it requires optimal participant selection and learning rate determination among multiple FL models to avoid high training costs and resource competition. To overcome those chal-lenges, in literature the Benders' decomposition algorithm (BD) can deal with mixed integer problems, however, it still suffers from limited scalability. To address this issue, in this paper, we present the Hybrid Quantum-Classical Benders' Decomposition (HQCBD) algorithm, which combines the power of quantum and classical computing to solve the joint participant selection and learning scheduling problem in multi-model FL. HQCBD decomposes the optimization problem into a master problem with binary variables and small subproblems with continuous variables. This collaboration maximizes the potential of both quantum and classical computing, and optimizes the complex joint optimization problem. Simulation on the commercial D-Wave quantum annealing machine demonstrates the effectiveness and robustness of the proposed method, with up to 18% improvement of iterations and 81% improvement of computation time over BD algorithm on classical CPUs even at small scales. © 2023 IEEE.

关键词： Scheduling algorithms

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Advancing Cattle Health: AI-Driven Innovations in Lameness Detection and Management 14

Advancing Cattle Health: AI-Driven Innovations in Lameness D...

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2024 IEEE 14th Annual Computing and Communication Workshop and Conference, CCWC 2024

作者： Roopaei, Mehdi Bergmann, Collin Azemi, Asad Hardyman, Krista Hampton, James University of Wisconsin-Platteville Electrical and Computer Engineering Department PlattevilleWI United States University of Wisconsin-Platteville Cyber Security and Criminal Justice Department PlattevilleWI United States University of Wisconsin-Platteville Animal Dairy and Veterinary Science Department PlattevilleWI United States

ISBN: (纸本)9798350360134

In this study, we present a novel approach to monitoring the health and mobility of dairy cattle through advanced image processing and machine learning techniques. By analyzing sequential frames captured from video footage, we extract key biomechanical features that indicate the presence of lameness in cattle. Our methodology employs a robust statistical model to quantify deviations in movement patterns, as evidenced by the featured graphical plots and mathematical equations. These deviations are then correlated with lameness indicators, offering a predictive tool for early intervention. The application of our system promises to improve animal welfare and farm management by providing farmers with actionable insights derived from precise, non-invasive monitoring technology. © 2024 IEEE.

关键词： Feature extraction

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'Optimal Control for Virus Spread in Network: A Mathematical Model with Saturated Incidence Rate' 4

'Optimal Control for Virus Spread in Network: A Mathematical...

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4th International Conference on Innovative Research in Applied Science, engineering and technology, IRASET 2024

作者： Baba, Bashir Abdullahi Faruk, Nasir Garba, Salisu Sule Lamido University Department Of Cyber Security Jigawa State Kafin Hausa Nigeria Sule Lamido University Department Of Information Technology Jigawa State Kafin Hausa Nigeria Sule Lamido University Department Of Software Engineering Jigawa State Kafin Hausa Nigeria

ISBN: (纸本)9798350309508

This study explores dynamic analysis and optimal control strategies for modeling the propagation of computer viruses in point-to-group information networks, addressing the significant threat they pose in the modern technological landscape. Grounded in the susceptible-exposed-infected-recovered (SEIR) framework, our mathematical model incorporates a saturated incidence rate to capture the evolving dynamics of virus spread and administrator responses. Our optimal control analysis focuses on three control laws corresponding to antivirus measures targeting susceptible, exposed, and infected populations, with an objective to minimize virus prevalence and associated control costs. Leveraging Pontryagin's Maximum Principle, we derive necessary conditions for optimal control, offering guidance on antivirus measure implementation to mitigate virus impact. Numerical simulations was carried out in other to validate our model and strategies, offering practical insights for network managers to bolster cyber security and minimize economic losses from viral outbreaks. The simulation result shows that by using the optimal control strategies in the model, the population of the infected and exposed computers will reduce to its possible minimal value at a minimum cost. This work establishes a robust framework for studying optimal control of computer virus spread in dynamic networks, merging mathematical modeling with actionable intervention strategies. © 2024 IEEE.

关键词： Losses

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DIG: Improved DINO for Graffiti Detection

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IEEE Transactions on Systems, Man, and cybernetics: Systems 2025年第5期55卷 3557-3569页

作者： Wang, Bingshu Mao, Qianchen Liu, Aifei Chen, Long Chen, C. L. Philip Northwestern Polytechnical University School of Software Xi’an710129 China Shenzhen University Guangdong Key Laboratory of Intelligent Information Processing Shenzhen Key Laboratory of Media Security Shenzhen518060 China University of Macao Department of Computer and Information Science China South China University of Technology School of Computer Science and Engineering Guangzhou510641 China

Graffiti detection is essential in historic building protection and urban neighborhood management. Graffiti detection has made significant progress in recent years based on the development of deep learning. However, small-scale graffiti, interference from the background, and the false detection of word parts in graffiti make it a challenging problem. This article proposes a Transformer-based high-precision graffiti detection method, namely DIG. Precisely, it consists of three modules: 1) Spatial query selection (SQS), scale-aware IoU loss (SIL);2) Denoising Task with binary contrastive denoising (BCDN);and 3) IoU-guided box denoising (IBD) modules. To detect small-scale graffiti, this paper proposes SIL to help the loss function to perceive small-scale graffiti and large-scale graffiti fairly. To reduce the false detection of word parts, this article presents the SQS module, which integrates spatial information into the query selection process of the Encoder to filter out falsely detected bounding boxes within the graffiti. To reduce the interference from the background, this article introduces a denoising task with BCDN and IBD modules, improving the model’s ability to distinguish graffiti from the background and accurately select appropriate bounding boxes. A large number of experimental results on the STORM dataset show that our method achieves state-of-the-art results with an AP50 of 87.9%. Moreover, DIG achieved competitive results on the FineFM dataset for mask detection. This indicates that DIG can also be conveniently transferred to detect other scenarios. © 2013 IEEE.

关键词： Historic preservation

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FAMC: Fair and Publicly Auditable Multi-Party Computation with Cheater Detection 26th

FAMC: Fair and Publicly Auditable Multi-Party Computation w...

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26th International Conference on Information and Communications security, ICICS 2024

作者： Li, Yong Feng, Yueyang Chen, Xi Zhang, Jian Li, Ruxian Lv, Kewei Zhang, Dawei School of Electronic and Information Engineering Beijing Jiaotong University Beijing100044 China Linklogis Shenzhen518063 China State Key Lab of Information Security Institute of Information Engineering Chinese Academy of Sciences Beijing100193 China School of Cyber Security University of Chinese Academy of Sciences Beijing101400 China School of Computer Science and Technology Beijing Jiaotong University Beijing100044 China

ISBN: (纸本)9789819788002

Secure multi-party computation (MPC) protocols do not completely prevent malicious parties from cheating. Though numerous researches on cheating detection are proposed, most cheater detection works do not guarantee fairness of the protocol. That is, if the corrupted parties try to violate fairness, the output of the computation can only be obtained by themselves, but not by the honest parties. In this paper, based on the SPDZ framework, we propose a fair secure multi-party computation protocol with honest-majority by using identifiable secret sharing. This protocol can identify corrupted parties during the execution process and prevent them from undermining the fairness of the protocol. In addition, the protocol satisfies publicly auditability, which allows any third party to verify the correctness of the protocol’s output results. The security of the protocol was proved in the universal composability framework. The protocol maintains high online efficiency when no cheating happens. And if corrupted parties try to violate the fairness, honest parties need to expend additional computation to regain the output results of the protocol, but the cost remains within a reasonable range. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Authentication Protocol

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AI Driven Soil Monitoring and Crop Recommendation using Machine Learning Algorithm 15

AI Driven Soil Monitoring and Crop Recommendation using Mach...

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15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Shenbagavadivu, S. Susee, S.K. Chidhambarajan, B. Kumar, Senthil M. Department of Information Technology SRM Valliammai Engineering College Kattankulathur Tamil Nadu Chengalpattu District India Department of Electronics and Communication Engineering SRM Madurai College for Engineering and Technology Tamil Nadu Madurai India SRM Valliammai Engineering College Kattankulathur Tamil Nādu Chengalpattu District India Department of Cyber Security SRM Valliammai Engineering College Kattankulathur Tamil Nadu Chengalpattu District India

ISBN: (纸本)9798331300579

Farmers, notably in India, face challenges such as inadequate expertise in crop selection and crop failures due to *** Learning's untapped potential in agriculture, limited by data quality and processing constraints, presents an opportunity for *** objective is to give an open-source online application is developed using Flask to aid farmers in informed *** Precision Agriculture principles to recommend crops based on soil types, features, and data-driven insights to boost *** various methods based on a machine learning-based ensemble model is employed to suggest suitable crops based on soil data, focusing on accuracy and *** outcome of the developed system shows promise in enhancing input-output efficiency, improving decision-making, and reducing unsuitable crop choices, thereby boosting agricultural productivity and positively impacting India's *** approach demonstrates practical potential in resolving farming challenges by leveraging machine learning for crop recommendations, potentially revolutionizing agriculture in India and beyond. © Grenze Scientific Society, 2024.

关键词： Adversarial machine learning

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