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2nd International Symposium on Sensor Technology and Control, ISSTC 2023

作者： Fang, Chenyu Fu, Tianli Shi, Yao Changzhou No.1 High School Changzhou China Wuhan University of Technology School of Mechanical and Electronic Engineering Wuhan China Southwest University College of Computer and Information Science College of Software Chongqing China

ISBN: (纸本)9798350314465

Flexible capacitive pressure sensors have shown excellent application potential in human-machine interactions, wearable electronic devices, biological and medical, and electronic skin. Compared with piezoresistive and piezoelectric pressure sensors, capacitive pressure sensors with advantages such as high flexibility, high stability, low consumption, and simple construction. Because of these advantages, flexible capacitive pressure sensors have become the key research direction of pressure sensors in recent years. This paper introduces the working principle of flexible capacitive pressure sensors and discusses numerous research on improving the performance of flexible capacitive pressure sensors. Then two methods of improving the dielectric layer structures to improve the sensitivity of the sensor and some related works are reviewed. Finally, the flexible capacitive pressure sensors' huge potential for advances in human-machine interactions areas such as health care and motion monitoring is also discussed. The paper concludes with an overview of future research directions of improving the performance of flexible capacitive pressure sensors. © 2023 IEEE.

关键词： Pressure sensors

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Deep Learning-Based Approaches for Accurate Brain Tumor Detection in MRI Images

Deep Learning-Based Approaches for Accurate Brain Tumor Dete...

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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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AI-Driven Strategic Foresight: Anticipating Future Trends and Modelling Business Strategies

AI-Driven Strategic Foresight: Anticipating Future Trends an...

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

作者： Qader Ismail Alnajem, Ahmed Abdel Ibrahim, Amer Abdulrahman, Mayasa M. Abdulrazak Al-Kubaisi, Abdulrazak A. Tawfeq, Jamal Fadhil Khishe, Mohammad Saeed, Ali Baghdad College of Economic Sciences Business Administration Department Iraq College of Information Technology Department of Computer Science and Software Engineering United Arab Emirates College of Engineering Computer Engineering Department University of Baghdad Baghdad Iraq Medical Technical College Department of Medical Instrumentation Technical Engineering Al-Farahidi University Baghdad00965 Iraq Yuan Ze University Innovation Center for Artificial Intelligence Applications Taiwan Jadara University Jadara Research Center Jordan Northern Technical University Computer Center Ninevah Iraq

ISBN: (纸本)9798350369106

In a generation characterized with the aid of speedy technological advancements, socio-monetary shifts, and environmental challenges, groups need to undertake proactive strategies to stay aggressive and resilient. Strategic foresight, which entails expecting destiny tendencies and modeling enterprise techniques, accordingly, emerges as a vital device in navigating this uncertainty. The trouble this have a look at addresses is the growing trouble groups face in predicting and making ready for destiny disruptions the use of conventional forecasting techniques. The number one goals of this have a look at are to discover the important thing methodologies and gear of strategic foresight, display how those techniques may be implemented to assume destiny tendencies, and offer insights on integrating foresight into enterprise approach formulation. This has a look at contributes to the present frame of information with the aid of using providing a complete evaluation of strategic foresight techniques, which includes environmental scanning, situation planning, the Delphi method, fashion evaluation, SWOT and PESTLE evaluation, and era street mapping. Research findings show that forward-looking strategies provide an organization with the ability to identify potential risks and opportunities, develop adaptive strategies, and achieve long-term growth Specifically, strategic organizations using anticipation reports a 30% increase in the ability to anticipate market fluctuations, a 25% improvement in strategic haste and a 20% reduction in unexpected disruptions, of consumer goods etc. Case studies of various industries demonstrate the practical application and usefulness of forecasting in real-world situations The study concludes that incorporating forward-looking strategies into business planning does not that it not only prepares organizations for future challenges but empowers them to shape their future and ensure sustainable competitive advantage. © 2024 IEEE.

关键词： Competition

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KAnoCLIP: Zero-Shot Anomaly Detection through Knowledge-Driven Prompt Learning and Enhanced Cross-Modal Integration

KAnoCLIP: Zero-Shot Anomaly Detection through Knowledge-Driv...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Chengyuan Li Suyang Zhou Jieping Kong Lei Qi Hui Xue College of Software Engineering Southeast University Nanjing China School of Computer Science and Engineering Southeast University Nanjing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Zero-shot anomaly detection (ZSAD) identifies anomalies without needing training samples from the target dataset, essential for scenarios with privacy concerns or limited data. Vision-language models like CLIP show potential in ZSAD but have limitations: relying on manually crafted fixed textual descriptions or anomaly prompts is time-consuming and prone to semantic ambiguity, and CLIP struggles with pixel-level anomaly segmentation, focusing more on global semantics than local details. To address these limitations, We introduce KAnoCLIP, a novel ZSAD framework that leverages vision-language models. KAnoCLIP combines general knowledge from a Large Language Model (GPT-3.5) and fine-grained, image-specific knowledge from a Visual Question Answering system (Llama3) via Knowledge-Driven Prompt Learning (KnPL). KnPL uses a knowledge-driven (KD) loss function to create learnable anomaly prompts, removing the need for fixed text prompts and enhancing generalization. KAnoCLIP includes the CLIP visual encoder with V-V attention (CLIP-VV), Bi-Directional Cross-Attention for Multi-Level Cross-Modal Interaction (Bi-CMCI), and Conv-Adapter. These components preserve local visual semantics, improve local cross-modal fusion, and align global visual features with textual information, enhancing pixel-level anomaly detection. KAnoCLIP achieves state-of-the-art performance in ZSAD across 12 industrial and medical datasets, demonstrating superior generalization compared to existing methods.

关键词： Training Visualization Image segmentation Large language models Semantics Signal processing Question answering (information retrieval) Speech processing Anomaly detection Biomedical imaging

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Drowsy Driving Detection System Using Face Detection 3

Drowsy Driving Detection System Using Face Detection

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3rd International Conference on Technological Advancements in Computational sciences, ICTACS 2023

作者： Mirabdullayev, Ismoiljon Ahamed Ayoobkhan, Mohamed Uvaze Jasmine Hashana, A.M. Khan Subair Ali, Liayakath Ali Software Engineering New Uzbekistan University Tashkent Uzbekistan Computer Science and Engineering Francis Xavier Engineering College Tirunelveli India School of Information Technology Digital Media and Mass Communication Tmc Institute in Tashkent Tashkent Uzbekistan

ISBN: (纸本)9798350342338

Drowsy driving poses a major threat to road safety, demanding effective solutions. To address this issue, this paper introduces a cutting-edge drowsy driving detection system that leverages advanced face detection algorithms to analyze real-time facial features. By closely monitoring eye movements and mouth patterns, the system is capable of accurately identifying early signs of fatigue. In response, timely warnings are promptly issued to the driver through various means, including audible alarms, visual cues, or even physical interventions. Although the system holds great potential in enhancing road safety, there are still challenges to overcome, particularly regarding accuracy and adaptability. However, ongoing research endeavors aim to refine the algorithms and integrate additional physiological and behavioral measures, with the goal of further improving the system's capabilities. By preventing accidents caused by drowsiness, this innovative system demonstrates a promising pathway towards bolstering road safety. By addressing the critical issue of drowsy driving, it contributes significantly to the overall safety of drivers and passengers alike, making our roads a safer place for everyone. © 2023 IEEE.

关键词： allowed distraction time braking distance deep learning driver's fatigue level Drowsy driving face analysis reaction distance

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Efficient Privacy-Preserving Multi-Functional Data Aggregation Scheme for Multi-Tier IoT System 28

Efficient Privacy-Preserving Multi-Functional Data Aggregati...

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28th IEEE Symposium on computers and Communications, ISCC 2023

作者： Tao, Yunting Kong, Fanyu Shi, Yuliang Yu, Jia Zhang, Hanlin Liu, Huiyi School of Software Shandong University China College of Computer Science and Technology Qingdao University China College of Information Engineering Binzhou Polytechnic China Shandong Sansec Information Technology Co. Ltd China

ISBN: (纸本)9798350300482

The proliferation of Internet of Things (IoT) devices has led to the generation of massive amounts of data that require efficient aggregation for analysis and decision-making. However, multi-tier IoT systems, which involve multiple layers of devices and gateways, face more complex security challenges in data aggregation compared to ordinary IoT systems. In this paper, we propose an efficient privacy-preserving multi-functional data aggregation scheme for multi-tier IoT architecture. The scheme supports privacy-preserving calculation of mean, variance, and anomaly proportion. The scheme uses the Paillier cryptosystem and the BLS algorithm for encryption and signature, and uses blinding techniques to keep the size of the IoT system secret. In order to make the Paillier algorithm more suitable for the IoT scenario, we also improve its efficiency of encryption and decryption. The performance evaluation shows that the scheme improves encryption efficiency by 43.7% and decryption efficiency by 45% compared to the existing scheme. © 2023 IEEE.

关键词： Internet of things

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SemLog: A Semantics-based Approach for Anomaly Detection in Big Data System Logs 29

SemLog: A Semantics-based Approach for Anomaly Detection in ...

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29th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2023

作者： Tan, Xiaoman Han, Ningning Lu, Siyang Chen, Wu Wang, Dongdong College of Computer and Information Science College of Software Southwest University Chongqing China School of Computer and Information Technology Beijing Jiaotong University Beijing China Environmental and Construction Engineering University of Central Florida Department of Civil OrlandoFL United States

ISBN: (纸本)9798350330717

Syslog-based anomaly detection is crucial for protecting the systems from malicious attacks or malfunctions. System logs are semi-structured text messages printed by logging statements to record the system's run-time status, involving rich semantic information. However, the existing BERT-based log anomaly detection method is based on the log key sequence, does not consider the semantics of the log data, and discards the variable part, resulting in a high rate of missed detection. In this paper, we propose SemLog, a self-supervised framework for log anomaly detection based on BERT. By incorporating log semantics and variables and employing multi-feature fusion, we mitigate the independent assumption issue in the Masked Language Modeling model. The experimental results on three benchmarks show that SemLog achieves high performance compared with the state-of-the-art approaches for anomaly detection. © 2023 IEEE.

关键词： BERT log anomaly detection log semantics log variables multi-feature fusion

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Covert Communication in Underlay Multi-Antenna Cognitive Radio Networks

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IEEE Transactions on Cognitive Communications and Networking 2024年第3期11卷 1493-1507页

作者： Fan, Hongbin Yang, Weiwei Shi, Hui Pan, Gaofeng Si, Jiangbo Wang, Yutao Rong, Chuanzhen Army Engineering University of PLA College of Communications Engineering Nanjing210007 China Xiangnan University College of Computer and Artificial Intelligence Chenzhou423000 China Guilin University of Electronic Technology Guangxi Key Laboratory of Trusted Software Guilin541004 China Beijing Institute of Technology School of Cyberspace Science and Technology Beijing100081 China Xidian University State Key Laboratory of Integrated Services Networks Xi'an710100 China

In this paper, we investigated a multi-antenna covert communication in underlay cognitive radio networks (UCRNs), by considering both cases that Willie has the perfect and statistical channel state information (CSI) of the link from the secondary transmitter (ST) to the primary receiver (PR), respectively. Specifically, the optimal antenna of the secondary receiver (SR) is selected to receive the private information from ST, and then one of the remaining antennas is randomly selected to transmit jamming signals. The closed-form expressions of Willie's detection error probability, the average minimum detection error probability, the transmission outage probability and the maximum effective covert rate under both cases are derived. The maximum effective covert rate is obtained by optimising the maximum jamming power of SR. The numerical results show that the combined effect of the maximum interference tolerance of PR and the maximum interference power of SR provides the positive effective covert rate between ST and SR. It is further found that the maximum effective covert rate between ST and SR when Willie knows the perfect CSI of the ST-SR link is greater than that when Willie only knows the statistical CSI of the ST-SR link. © 2015 IEEE.

关键词： Jamming

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Fast Omni-Directional Image Super-Resolution: Adapting the Implicit Image Function with Pixel and Semantic-Wise Spherical Geometric Priors

arXiv

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arXiv 2025年

作者： Shen, Xuelin Wang, Yitong Zheng, Silin Xiao, Kang Yang, Wenhan Wang, Xu College of Computer Science and Software Engineering Shenzhen University China China Peng Cheng Laboratory China

In the context of Omni-Directional Image (ODI) Super-Resolution (SR), the unique challenge arises from the nonuniform oversampling characteristics caused by EquiRectangular Projection (ERP). Considerable efforts in designing complex spherical convolutions or polyhedron reprojection offer significant performance improvements but at the expense of cumbersome processing procedures and slower inference speeds. Under these circumstances, this paper proposes a new ODI-SR model characterized by its capacity to perform Fast and Arbitrary-scale ODI-SR processes, denoted as FAOR. The key innovation lies in adapting the implicit image function from the planar image domain to the ERP image domain by incorporating spherical geometric priors at both the latent representation and image reconstruction stages, in a low-overhead manner. Specifically, at the latent representation stage, we adopt a pair of pixel-wise and semantic-wise sphere-to-planar distortion maps to perform affine transformations on the latent representation, thereby incorporating it with spherical properties. Moreover, during the image reconstruction stage, we introduce a geodesic-based resampling strategy, aligning the implicit image function with spherical geometrics without introducing additional parameters. As a result, the proposed FAOR outperforms the state-of-the-art ODI-SR models with a much faster inference speed. Extensive experimental results and ablation studies have demonstrated the effectiveness of our design. Copyright © 2025, The Authors. All rights reserved.

关键词： Spheres

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Mutation Testing Techniques for Android Applications: A Comparative Study 2

Mutation Testing Techniques for Android Applications: A Comp...

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2nd International Conference on Multidisciplinary engineering and Applied science, ICMEAS 2023

作者： Salihu, Ibrahim Anka Usman, Asma'U Eke, Ndukwe-Oke Ibrahim, Rosziati Kusharki, Muhammad Bello Nile University of Nigeria Department Software Engineering Abuja Nigeria Abdu-Gusau Polytehcnic Department Computer Science Talata Mafara Nigeria Nile Univeristy of Nigeria Department of Computer Science Abuja Nigeria Universiti Tun Hussein Onn Malaysia Department Software Engineering Batu Pahat Malaysia National Defence College Department of Information and Communication Technology Abuja Nigeria

ISBN: (纸本)9798350358834

Mobile applications are becoming increasingly used to achieve various computing needs. Hence, it is essential to guarantee quality of the applications. software testing has been the main activity for ensuring the quality of software, however, it is a critical and costly activity. Furthermore, code coverage is commonly used as metric for verifying the effectiveness of testing technique. However, numerous researchers and experts claimed that considering code coverage only to ascertain the testing quality is not enough to ensure quality of apps. In view of this, mutation testing has been introduced to complement the procedure by assuring that apps behave as expected and are released free of faults. Several techniques/tools were proposed based on different syntax and mutation operators. This study presents a comparative study of six (6) mutation techniques/tools for Android applications to highlight their strengths and weakness which will give an insight to researchers on the directions for further research. © 2023 IEEE.

关键词： software testing

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