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2024 International Conference on Decision Aid sciences and applications, DASA 2024

作者： Noori, Mohammed Ahmed, Mohsin Ibrahim, Amer Saeed, Ali Khishe, Mohammad Mahfuri, Mahmoud Al-ma'Moon University College Baghdad Iraq College of Information Technology Department of Computer Science and Software Engineering United Arab Emirates Northern Technical University Computer Center Ninevah Iraq Yuan Ze University Innovation Center for Artificial Intelligence Applications Taiwan Jadara Research Center Jadara University Jordan International University for Science and Technology Computer and Information Technology Department Syria

ISBN: (纸本)9798350369106

Brain tumor classification is considered one of the major tasks in medical image analysis, where correct and timely diagnosis could be achieved to serve as the key to effective treatment. This research paper proposes a deep learning-based approach for automatic classification of brain tumors from MRI images by fine tuning the ResNet50V2 CNN model. The dataset is made up of four classes, namely: Glioma Tumor, Meningioma Tumor, No Tumor, and Pituitary Tumor, totaling 2,844 images. We have done some strategy data augmentation and class weighting in order to prevent class imbalance, which ensures good performance in all tumor classes. Pre-processing and augmentation of the input images are done via rotation, shifting, zooming, and flipping. It yielded 95.29% on the validation set, with the major highlights of performance metrics for the minority class being class precision equal to 0.97 and class recall equal to 0.92, hence proving the efficiency of performed class balancing. The range of F1-scores, from 0.93 to 0.97, means fantastic predictive capability across all tumor types. Other techniques were also used to make this model optimal, such as early stopping, learning rate reduction, and checkpointing. The high performance of this model shows great promise in helping clinicians toward the right diagnosis of brain tumors in an effective way. Future efforts will involve incorporating larger datasets, exploring advanced augmentation techniques to enhance further model generalizability. Model explainability tools such as Grad-CAM will be used to extract insight into the model's decision-making process. This enhances the clinical interpretability of the results. © 2024 IEEE.

关键词： Convolutional neural networks

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FreqSense: Universal and Low-Latency Adversarial Example Detection for Speaker Recognition with Interpretability in Frequency Domain

FreqSense: Universal and Low-Latency Adversarial Example Det...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Huang, Yihuan Li, Yuanzhe Ren, Yanzhen Tu, Weiping Yang, Yuhong Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education China School of Cyber Science and Engineering Wuhan University China National Engineering Research Center for Multimedia Software School of Computer Science Wuhan University China

ISBN: (纸本)9798350368741

Speaker recognition (SR) systems are particularly vulnerable to adversarial example (AE) attacks. To mitigate these attacks, AE detection systems are typically integrated into SR systems. To overcome the limitations of low detection accuracy, poor generalization, and high latency in existing schemes, this paper proposes FreqSense, an AE detection scheme based on frequency distribution features. FreqSense detects a variety of unknown AE attacks with low latency, and provides interpretability in its detection process. The basic idea of FreqSense is that AE typically introduce carefully designed noise in specific frequency bands that are associated with highly distinctive speaker identities. Therefore, leveraging the distributional variations in these frequency bands can effectively distinguish between AE and benign audio. FreqSense models frequency distribution features by integrating time-frequency transformation technology with a self-attention mechanism and employs a neural network-based classifier to distinguish between AE and benign audio. Experimental results show that FreqSense achieves an overall detection accuracy of 99.2%, surpassing state-of-the-art (SOTA) schemes by 27.2%. When confronting unknown AE attacks, FreqSense achieves a detection accuracy of 98.3% with a latency of just 0.0014 seconds. © 2025 IEEE.

关键词： Adversarial Attack Adversarial Example Detection Speaker Recognition

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Skull ethnic classification by combining skull auxiliary image with deep learning

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Quantitative Biology 2022年第4期10卷 381-389页

作者： Huijie Sun Junli Zhao Chengyuan Wang Yi Li Niankai Zhang Mingquan Zhou College of Computer Science and Technology Qingdao UniversityQingdao 266071China Engineering Research Center of Virtual Reality and Applications Ministry of EducationBeijing 100875China

Background:China is a multi-ethnic *** is of great significance for the skull identification to realize the skull ethnic classification through computers,which can promote the development of forensic anthropology and accelerate the exploration of national ***:In this paper,the 3D skull model is transformed into 2D auxiliary image including curvature,depth and elevation information,and then the deep learning method of the 2D auxiliary image is used for ethnic *** construct a convolution neural network structure inspired by VGGNet16 which has achieved excellent performance on image *** order to optimize the network,Adam algorithm is adopted to avoid falling into local minimum,and to ensure the stability of the algorithm with regularization ***:Experiments on 400 skull models have been conducted for ethnic classification by our *** set different learning rates to compare the performance of the model,the highest accuracy of ethnic classification is 98.75%,which have better performance than other five classical neural network ***:Deep learning based on skull auxiliary image for skull ethnic classification is an automatic and effective method with great application significance.

关键词： skull auxiliary image deep learning skull ethnic classification convolutional neural network

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Ultra Dense Satellite-Enabled 6G Networks:Resource Optimization and Interference Management

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China Communications 2023年第10期20卷 262-275页

作者： Xiangnan Liu Haijun Zhang Min Sheng Wei Li Saba Al-Rubaye Keping Long School of Computer&Communication Engineering University of Science and Technology BeijingBeijing 100083China Beijing Engineering and Technology Research Center for Convergence Networks and Ubiquitous Services University of Science and Technology BeijingBeijing 100083China School of Telecommunications Engineering Xidian UniversityXi’an 710071China State Radio Monitoring Center of China Beijing 100037China School of Aerospace Transport and Manufacturing Cranfield UniversityMK430ALUK

With the evolution of the sixth generation(6G)mobile communication technology,ample attention has gone to the integrated terrestrial-satellite *** paper notes that four typical application scenarios of integrated terrestrial-satellite networks are integrated into ultra dense satellite-enabled 6G networks *** the subchannel and power allocation schemes for the downlink of the ultra dense satellite-enabled 6G heterogeneous networks are *** mobile edge computing(SMEC)with edge caching in three-layer heterogeneous networks serves to reduce the link traffic of ***,a scheme for interference management is presented,involving quality-of-service(QoS)and co-tier/cross-tier interference *** simulation results show that the proposed schemes can significantly increase the total capacity of ultra dense satellite-enabled 6G heterogeneous networks.

关键词： satellite-enabled 6G networks network architecture resource optimization interference management

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Effective Integration of Text Diffusion and Pre-Trained Language Models with Linguistic Easy-First Schedule 30

Effective Integration of Text Diffusion and Pre-Trained Lang...

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Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Ou, Yimin Jian, Ping School of Computer Science and Technology Beijing Institute of Technology Beijing China Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications Beijing Institute of Technology Beijing China

ISBN: (纸本)9782493814104

Diffusion models have become a powerful generative modeling paradigm, achieving great success in continuous data patterns. However, the discrete nature of text data results in compatibility issues between continuous diffusion models (CDMs) and pre-trained language models (PLMs). That is, the performance of diffusion models even degrades when combined with PLMs. To alleviate this issue, we propose to utilize a pre-trained decoder to convert the denoised embedding vectors into natural language instead of using the widely used rounding operation. In this way, CDMs can be more effectively combined with PLMs. Additionally, considering that existing noise schedules in text diffusion models do not take into account the linguistic differences among tokens, which violates the easy-first policy for text generation, we propose a linguistic easy-first schedule that incorporates the measure of word importance, conforming to easy-first-generation linguistic features and bringing about improved generation quality. Experiment results on the E2E dataset and five controllable tasks show that our approach can combine the merits of CDMs and PLMs, significantly outperforming other diffusion-based models. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Diffusion

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Improving Implicit Discourse Relation Recognition with Semantics Confrontation 30

Improving Implicit Discourse Relation Recognition with Seman...

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Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Cai, Mingyang Yang, Zhen Jian, Ping School of Computer Science and Technology Beijing Institute of Technology Beijing China Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications Beijing Institute of Technology Beijing China

ISBN: (纸本)9782493814104

Implicit Discourse Relation Recognition (IDRR), which infers discourse logical relations without explicit connectives, is one of the most challenging tasks in natural language processing (NLP). Recently, pre-trained language models (PLMs) have yielded impressive results across numerous NLP tasks, but their performance still remains unsatisfactory in IDRR. We argue that prior studies have not fully harnessed the potential of PLMs, thereby resulting in a mixture of logical semantics, which determine the logical relations between discourse arguments, and general semantics, which encapsulate the non-logical contextual aspects (detailed in Sec.1). Such a mixture would inevitably compromise the logic reasoning ability of PLMs. Therefore, we propose a novel method that trains the PLMs through two semantics enhancers to implicitly differentiate logical and general semantics, ultimately achieving logical semantics enhancement. Due to the characteristic of PLM in word representation learning, these two semantics enhancers will inherently confront with each other, facilitating an augmentation of logical semantics by disentangling them from general semantics. The experimental results on PDTB 2.0 dataset show that the confrontation approach exceeds our baseline by 3.81% F1 score, and the effectiveness of the semantics confrontation method is validated by comprehensive ablation experiments. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Semantics

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Differential and TriPhase Adaptive Learning-Based Privacy-Preserving Model for Medical Data in Cloud Environment

IEEE Networking Letters

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IEEE Networking Letters 2022年第4期4卷 217-221页

作者： Gupta, Rishabh Saxena, Deepika Gupta, Ishu Singh, Ashutosh Kumar National Institute of Technology Kurukshetra Department of Computer Applications Kurukshetra136119 India National Sun Yat-sen University Cloud Computing Research Center Department of Computer Science and Engineering Kaohsiung804 Taiwan

This letter proposes a novel secure data protection model for privacy preservation in the cloud environment by partitioning, sanitizing, and analyzing the data effectively to improve the model's privacy. Several experiments and comparisons of the proposed model with existing works indicate that it protects data with high accuracy, precision, recall, and F1-score up to 87.03%, 84.87%, 87.03%, and 85.00%, for diverse datasets with a relative improvement up to 15.89%, 27.73%, 15.89%, and 21.44%, respectively. © 2019 IEEE.

关键词： Deep neural networks

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Prediction of water quality parameters by IoT and machine learning

Prediction of water quality parameters by IoT and machine le...

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2023 International Conference on computer Communication and Informatics, ICCCI 2023

作者： Kolli, Srinivas Ranjani, M. Kavitha, P. Daniel, D. Arul Pon Chandramauli, Awadhesh VNR Vignana Jyothi Institute of Engineering & Technology Department of Information Technology Hyderabad India Computer Applications Jawaharlal Nehru College for Women Ulundurpet India Muthayammal Center for Advanced Research Muthayammal College of Arts & Science India Loyola College of Arts & Science Department of Computer Science and Applications India Uttaranchal Institute of Technology Uttaranchal University Department of Civil Engineering Dehrradun India

ISBN: (纸本)9798350348217

Increases in domestical applications and other applications drive the need for better clean Water Management in India. What makes up the 'chemical, physical, and biological' quality of freshwater There are three stages in the conventional method of monitoring water quality: sample, analysis, and inquiry. The scientists do these by hand. This method needs to be foolproof and predict a water supply's quality. They're Using the development of wireless sensor technologies, has also been some investigation into the possibility of monitoring water quality with wireless sensors put in water and communicating with farmers through brief messages regarding water. There has also been a study into the possibility of employing machine learning algorithms to analyze water quality. In light of recent advances in Machine Communication, which allow for devices to communicate with one another and smart ly analyze the data they collect. We have developed an 'smart water quality monitoring system by using wireless sensonrs' for residential areas that rely on water storage tanks. A pH sensor and a total dissolved solids meter are used in this system to provide accurate readings of the water's pH and dissolved solids levels. K-Means clustering, a machine learning technique, has been used to forecast water quality using a training data set consisting of various water samples. This system is now in its prototype stage, used with low-cost embedded devices like the Arduino Uno and Raspberry Pi3. © 2023 IEEE.

关键词： Water quality

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Decentralized Application Identification via Burst Feature Aggregation 26

Decentralized Application Identification via Burst Feature A...

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

作者： Yang, Chen Wang, Can Zhang, Weidong Zhang, Huiyi Wu, Xuangou Anhui University of Technology School of Computer Science and Technology Ma'Anshan China Anhui Engineering Research Center for Intelligent Applications and Security of Industrial Internet Ma'Anshan China

ISBN: (纸本)9798350331684

With the development of blockchain technology, de-centralized applications (DApps) are increasingly being developed and deployed on blockchain platforms. However, the complex data validation mechanism and strict encryption protocol settings of blockchain often lead to sparse traffic behavior of DApps. This sparsity poses a challenge for existing encrypted traffic identification methods to extract distinguishable DApps traffic features. In this study, we propose a novel approach for identifying DApps traffic features by observing the differences in burst timing features of DApps. We introduce a continuous burst feature matrix (CBFM) method based on burst feature aggregation that can aggregate sparse features and express the burst timing differences of DApps encrypted traffic. Additionally, we design a deep learning classifier to automatically extract the features contained in the CBFM. Our experimental results on real datasets demonstrate that the proposed CBFM method achieves a classification accuracy of 94%, outperforming state-of-the-art methods. © 2023 IEEE.

关键词： Blockchain

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Recyclability and Self-Healing of Dynamic Cross-Linked Polyimide with Mechanical/Electrical Damage

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Energy & Environmental Materials 2023年第1期6卷 401-407页

作者： Baoquan Wan Ming-Sheng Zheng Xing Yang Xiaodi Dong Yuchao Li Yiu-Wing Mai George Chen Jun-Wei Zha School of Chemistry and Biological Engineering University of Science and Technology BeijingBeijing 100083China Beijing Advanced Innovation Center for Materials Genome Engineering University of Science and Technology Beijing 100083China Shunde Graduate School of University of Science and Technology Beijing Foshan 528300China School of Materials Science and Engineering Liaocheng UniversityLiaocheng 252059China Centre for Advanced Materials Technology(CAMT) School of AerospaceMechanical and Mechatronic EngineeringThe University of SydneySydney New South Wales 2006Australia Department of Electronics and Computer Science University of SouthamptonSouthampton SO171BJUK

Recyclability and self-healing are two most critical factors in developing sustainable polymers to deal with environmental pollution and resource *** this work,a dynamic cross-linked polyimide insulation film with full closed-loop recyclability is successfully prepared,which also possesses good self-healing ability after being mechanical/electrical damaged depending on the Schiff base dynamic covalent *** recycled and self-healed polyimide film still maintain its good tensile strength(r t)>60 MPa with Young’s modulus(E)>4 GPa,high thermal stability with glass transition temperature(T g)>220℃,and outstanding insulation property with breakdown strength(E 0)>358 kV mm^(-1),making it a very promising low energy consumption and high temperature resistant insulation *** strategy of using Schiff base dynamic covalent bonds for reversible repairing the structure of high T g polyimides promotes the wider application of such sustainable and recyclable material in the field of electrical power and micro-electronics.

关键词： electrical damage polyimide recyclability self-healing

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