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Belief Combination of Classifiers for Incomplete Data

IEEE/CAA Journal of Automatica Sinica 2022年第4期9卷 652-667页

作者： Zuowei Zhang Songtao Ye Yiru Zhang Weiping Ding Hao Wang IEEE the Research Center for Optical Fiber Sensing Zhejiang Laboratory the School of Automation Northwestern Polytechnical University the School of Cyber Engineering Xidian University the Department of Computer Science CY Cergy Paris Université the School of Information Science and Technology Nantong University the School with of Cyber Engineering Xidian University the Department of Computer Science Norwegian University of Science and Technology

Data with missing values,or incomplete information,brings some challenges to the development of classification,as the incompleteness may significantly affect the performance of *** this paper,we handle missing values in both training and test sets with uncertainty and imprecision reasoning by proposing a new belief combination of classifier(BCC)method based on the evidence *** proposed BCC method aims to improve the classification performance of incomplete data by characterizing the uncertainty and imprecision brought by *** BCC,different attributes are regarded as independent sources,and the collection of each attribute is considered as a ***,multiple classifiers are trained with each subset independently and allow each observed attribute to provide a sub-classification result for the query ***,these sub-classification results with different weights(discounting factors)are used to provide supplementary information to jointly determine the final classes of query *** weights consist of two aspects:global and *** global weight calculated by an optimization function is employed to represent the reliability of each classifier,and the local weight obtained by mining attribute distribution characteristics is used to quantify the importance of observed attributes to the pattern *** comparative experiments including seven methods on twelve datasets are executed,demonstrating the out-performance of BCC over all baseline methods in terms of accuracy,precision,recall,F1 measure,with pertinent computational costs.

关键词： Classifier fusion classification evidence theory incomplete data missing values

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Image Classification Using Federated Learning 6th

Image Classification Using Federated Learning

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6th International Conference on Recent Trends in Image Processing and Pattern Recognition, RTIP2R 2023

作者： Byeon, Haewon Ullah, Ajdar Syed, Ziauddin Siddiqui, Alighazi Aftab, Aasif Sarfaraz, Mohd Department of Digital Anti-Aging Healthcare Inje University Gimhae50834 Korea Republic of School of Mechanical and Manufacturing Engineering National University of Science and Technology Islamabad44000 Pakistan Department of Computer Science College of Computer Science and Information Technology Jazan University Jazan45142 Saudi Arabia Department of Information Technology and Security College of Computer Science and Information Technology Jazan University Jazan45142 Saudi Arabia

ISBN: (纸本)9783031530814

Multiple users can jointly train a universal model using federated learning, a revolutionary AI approach, without having to reveal their personal data. With this strategy, anonymity is maintained while a secure learning environment is guaranteed. In contrast to the Workplace 31 dataset, this study presents a brand-new dataset made up of 23,326 photos obtained from eight different corporate sources and painstakingly organised into 31 classifications. It is noteworthy that this dataset is the first image classification dataset created specifically for federated learning. Along with the dataset, we also suggest the revolutionary federated learning algorithms Handled Cyclic and Handled Star. A cycle progression is established via Handled cycle, whereby a user gets weights from a previous user, adjusts them through localised training, and then transmits them to the following user. The essential job of image categorization within the context of computer vision is considerably improved by this novel approach, which highlights the development of federated learning. Traditional centralised deep learning model training requires centralised access to enormous datasets, which raises questions about data security and privacy. Federated learning addresses these issues by facilitating dispersed device collaboration for model training while restricting raw data exposure. This paper highlights the crucial role that federated learning has had in furthering AI research by giving an overview of its use in the context of image categorization. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Image classification

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An Efficient Design of Multiplier for Using in Nano-Scale IoT Systems Using Atomic Silicon

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IEEE Internet of Things Journal 2023年第16期10卷 14908-14909页

作者： Ahmadpour, Seyed-Sajad Heidari, Arash Navimpour, Nima Jafari Asadi, Mohammad-Ali Yalcin, Senay Kadir Has University Department of Computer Engineering Istanbul34083 Turkey Islamic Azad University Tabriz Branch Department of Computer Engineering Tabriz*** Iran Kadir Has University Faculty of Engineering and Natural Sciences Department of Computer Engineering Istanbul34083 Turkey National Yunlin University of Science and Technology Future Technology Research Center Douliou64002 Taiwan Nisantasi University Department of Computer Engineering Istanbul34485 Turkey

Because of recent technological developments, such as Internet of Things (IoT) devices, power consumption has become a major issue. Atomic silicon quantum dot (ASiQD) is one of the most impressive technologies for developing low-power processing circuits, which are critical for efficient transmission and power management in micro IoT devices. On the other hand, multipliers are essential computational circuits used in a wide range of digital circuits. Therefore, the multiplier design with a low occupied area and low energy consumption is the most critical expected goal in designing any micro IoT circuits. This article introduces a low-power atomic silicon-based multiplier circuit for effective power management in the micro IoT. Based on this design, a 4× 4 -bit multiplier array with low power consumption and size is presented. The suggested circuit is also designed and validated using the SiQAD simulation tool. The proposed ASiQD-based circuit significantly reduces energy consumption and area consumed in the micro IoT compared to most recent designs. © 2014 IEEE.

关键词： Semiconductor quantum dots

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Dual-Path Spectrogram Refinement Network for Robust Speaker Verification

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Journal of Shanghai Jiaotong University (science) 2025年 1-9页

作者： Wang, Zonghui Fang, Zhihua He, Liang School of Computer Science and Technology Xinjiang University Urumqi830017 China School of Intelligence Science and Technology Xinjiang University Urumqi830017 China Xinjiang Key Laboratory of Signal Detection and Processing Urumqi830017 China Department of Electronic Engineering Beijing National Research Center for Information Science and Technology Tsinghua University Beijing100084 China

The accuracy and reliability of automatic speaker verification (ASV) face significant challenges in noisy environments. In recent years, joint training of speech enhancement front-end and ASV back-end has been widely applied to improve the robustness of ASV systems. Traditional joint training directly uses the enhanced speech features as the input of the back-end. However, the diversity of noise types and noise intensities will excessively suppress the enhanced features, resulting in speech distortion or residual noise. To alleviate this problem, we propose a dual-path spectrogram refinement network that enables the enhanced features to learn supplementary information from the separated noise features. In addition, we incorporate coordinate attention into the overall joint architecture to capture more comprehensive frequency and temporal information of the speaker from different spatial positions. We conduct extensive experiments on the VoxCeleb1 test set, the out-of-domain noise test set, and the VOiCES corpus. The experimental results demonstrate that our proposed method significantly improves the accuracy and robustness of speaker verification systems in both clean and noisy environments. (Figure presented.) © Shanghai Jiao Tong University 2025.

关键词： Spectrographs

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Anomalous Sound Detection Using Time-Frequency Feature and Mixbatch

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Journal of Shanghai Jiaotong University (science) 2025年 1-8页

作者： Huang, Shun Zhang, Yunxiang Fang, Zhihua Tang, Minrui Xu, Ruifeng He, Liang School of Computer Science and Technology Xinjiang University Urumqi830017 China Xinjiang Key Laboratory of Signal Detection and Processing Urumqi830017 China School of Intelligence Science and Technology Xinjiang University Urumqi830017 China Department of Electronic Engineering Beijing National Research Center for Information Science and Technology Tsinghua University Beijing100084 China

The sound emitted by machines under abnormal working conditions exhibits various frequency patterns. Currently, the most advanced anomalous sound detection (ASD) approach is to apply a multi-head self-attention mechanism to the Log-Mel spectrogram for automatic frequency pattern analysis. However, the Log-Mel spectrogram may filter out high-frequency components of abnormal sounds;thus the use of self-attention mechanisms on Log-Mel spectrogram seems to have certain limitations. In this paper, we construct a simple convolutional neural network to extract comprehensive frequency features from raw audio to complement spectral-temporal information fusion. The parameters of this neural network are continuously updated during the training process to extract better frequency features for downstream classification neural networks in ASD. Additionally, a method for data augmentation in the batch dimension was developed to help the classification model learn both types of comprehensive features simultaneously. The proposed method achieved an AUC of 94.13% and a pAUC of 89.09% on the DCASE 2020 Challenge Task 2 dataset. Even when using only the features proposed in this paper, an AUC of 84.62% was achieved. (Figure presented.). © Shanghai Jiao Tong University 2025.

关键词： Spectrographs

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Improving ECAPA-TDNN Performance with Coordinate Attention

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Journal of Shanghai Jiaotong University (science) 2024年 1-7页

作者： Liu, Shuanghong Song, Zhida He, Liang School of Computer Science and Technology Xinjiang University Urumqi830017 China Xinjiang Key Laboratory of Signal Detection and Processing Urumqi830017 China Department of Electronic Engineering and Beijing National Research Center for Information Science and Technology Tsinghua University Beijing100084 China

The current mainstream networks, such as squeeze and excitation residual neural network (SE-ResNet) and emphasized channel attention, propagation and aggregation based time delay neural network (ECAPA-TDNN), enhance the capability of speaker embedding extractors to extract more discriminative speaker embeddings by incorporating squeeze and excitation (SE) attention within the convolutional blocks. However, the SE attention focuses solely on encoding inter-channel information, overlooking the importance of spatial positional information and time-frequency information, which are crucial for the model’s performance. In this paper, we first experimentally compare the effectiveness of several mainstream attention mechanisms in the computer vision domain for the ECAPA-TDNN model. Next, we focus on the substantial improvements that coordinate attention (CA) brings to the ECAPA-TDNN model. The introduction of CA can help the model embed time-frequency information into the channel representation. Even without using AS-Norm, our proposed model achieves relative reductions of about 5.3% equal error rate (EER) and 5.5% minimum detection cost function (minDCF) on both the Voxceleb-O and Voxceleb-H test sets compared to the ECAPA-TDNN baseline model. In addition, the EER is relatively reduced by 9.46% on the CN-Celeb1 test set. This result strongly demonstrates that the CA module can effectively improve the generalization ability of the ECAPA-TDNN model. © Shanghai Jiao Tong University 2024.

关键词： Neural network models

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Efficient Convolutional Neural Network Based Side Channel Attacks Based on AES Cryptography 21

Efficient Convolutional Neural Network Based Side Channel At...

引用

21st IEEE Student Conference on Research and Development, SCOReD 2023

作者： Ahmed, Amjed Abbas Hasan, Mohammad Kamrul Aman, Azana Hafizah Abdulkadir, Rabiu Aliyu Islam, Shayla Farhan, Basim Abdulkareem Center for Cyber Security Faculty of Information Science and Technology Bangi43600 Malaysia Department of Computer Techniques Engineering Baghdad10011 Iraq Institute of Computer Science and Digital Innovation UCSI University Kuala Lumpur56000 Malaysia

ISBN: (纸本)9798350318821

In recent years, profiled side-channel attacks have emerged as a particularly potent kind of side-channel attack that can circumvent cryptographic equipment's security. Convolutional neural networks (CNNs) have been widely used as deep learning infrastructure for attacks in recent research that has examined a new kind of profiled attack based on deep learning. The design of CNNs will have a significant impact on attack effectiveness. However, image recognition fields are frequently the foundation of the CNN architecture currently used for profiled attacks. Additionally, it is still challenging to choose the proper parameters and CNN infrastructure concerning adaptation to profiled assault types. In the current study, an effective CNN-based profiled attack was suggested that can be used against masking-protected cryptographic devices. The Grey Wolf Optimization (GWO) approach obtains the CNN architecture parameters proposed in this research. The characteristics of intriguing areas on the power trace determine these parameters. The proposed attacks were tested using experiments over a tracing set gathered from an intelligent card Atmega8515 processing the ASCAD public dataset. © 2023 IEEE.

关键词： Side channel attack

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Performance Analysis of Energy and Secure Efficient Routing for Wireless Sensor Networks

Performance Analysis of Energy and Secure Efficient Routing ...

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

作者： Ahmed, Amjed Abbas Hussein, Zainab Mohammed Najim, Ali Hamza Ali Alheeti, Khattab M. Mohd Satar, Nurhizam Safie Abdallah Hashim, Aisha Hassan Universiti Kebangsaan Malaysia Center for Cyber Security Faculty of Information Science and Technology Bangi Malaysia Department of Computer Techniques Engineering Baghdad Iraq Baghdad Iraq Department of Computer Technical Engineering Al-Diwaniyah Iraq College of Computer and Information Technology Computer Networking Systems Department University of Anbar Anbar Iraq Center for Cyber Security Faculty of Information Science and Technology Bangi43600 Malaysia International Islamic University Malaysia Faculty of Engineering Department of Electrical and Computer Engineering Selangor Malaysia

ISBN: (纸本)9798350369106

Wireless Sensor Networks (WSNs) are essential for various applications, but their architecture makes them vulnerable to attacks. While traditional security methods like authentication and encryption offer some protection, they often fall short against specific node attacks. This paper introduces the Secure Energy Aware Routing (SEAR) strategy, which uses trust-based assessments to exclude malicious nodes while optimizing energy consumption. SEAR factors in node trust, hop time, and residual energy to ensure reliable data transmission through trustworthy nodes. MATLAB simulations on networks of varying sizes (100-500) compare SEAR with existing methods (EAD and EASER), demonstrating SEAR's superior performance. Results show significant improvements in packet delivery ratio (13-16%), network throughput (19-24 %), energy consumption (32-36%), packet loss (16-190/0), and end-to-end latency (21-26%), proving SEAR's effectiveness in enhancing WSN security, energy efficiency, and performance. © 2024 IEEE.

关键词： MATLAB

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A Review of Key Elements for Underwater Wireless Sensor Networks

A Review of Key Elements for Underwater Wireless Sensor Netw...

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

作者： Ahmed, Amjed Abbas Najim, Ali Hamza Al-Dulaimi, Aymen Mohammed Ali Alheeti, Khattab M. Mohd Satar, Nurhizam Safie Aliesawi, Salah A. Universiti Kebangsaan Malaysia Center for Cyber Security Faculty of Information Science and Technology Bangi Malaysia Department of Computer Techniques Engineering Baghdad Iraq Department of Computer Technical Engineering Al-Diwaniyah Iraq College of Engineering Techniques Department of Communication Technical Engineering Al-Farahidi University Baghdad Iraq College of Computer and Information Technology Computer Networking Systems Department University of Anbar Anbar Iraq Center for Cyber Security Faculty of Information Science and Technology Bangi43600 Malaysia College of Computer and Information Technology Department of Computer Networking Systems University of Anbar Anbar Iraq

ISBN: (纸本)9798350369106

Underwater Wireless Sensor Networks (UWSNs) are emerging and have huge prospects since they have various applications in oceanography, environmental monitoring, and underwater communication. These networks contain low-cost submerged sensors that are mostly connected by way of acoustic media. Sensors are relatively new to underwater research, and thanks to recent technological development, they can now function at different depths. In this article, the author explores what UWSNs must do to operate smoothly by exploring the basic features that they must meet for further development. Real-time Underwater communication remains an open problem due to underwater acoustic channels important for undersea data acquisition, undersea navigation, and undersea remote sensing. To mitigate such challenges and in light of the use of new technologies, researchers seek to improve the reliability and reliability of UWSNs. Finally, there is great potential with UWSNs for investigating and exploiting the underwater environment by improving scenarios of different domains in line with subaqueous data and communications. © 2024 IEEE.

关键词： Wireless sensor networks

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Chain of Condition: Construct, Verify and Solve Conditions for Conditional Question Answering

Chain of Condition: Construct, Verify and Solve Conditions f...

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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Lin, Jiuheng Lai, Yuxuan Feng, Yansong Wangxuan Institute of Computer Technology Peking University China State Key Laboratory of General Artificial Intelligence Peking University China Department of Computer Science The Open University of China China Engineering Research Center of Integration and Application of Digital Learning Technology Ministry of Education China

ISBN: (纸本)9798891761681

Conditional question answering (CQA) is an important task that aims to find probable answers and identify missing conditions. Existing approaches struggle with CQA due to two challenges: (1) precisely identifying necessary conditions and the logical relationship, and (2) verifying conditions to detect any that are missing. In this paper, we propose a novel prompting approach, Chain of condition, by first identifying all conditions and constructing their logical relationships explicitly according to the document, then verifying whether these conditions are satisfied, finally solving the logical expression to indicate any missing conditions and generating the answer accordingly. Experiments on two CQA benchmark datasets show our chain of condition outperforms existing prompting baselines, establishing a new state of the art. Furthermore, with only a few examples, our method can facilitate GPT-3.5-Turbo or GPT-4 to outperform all existing supervised models. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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