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IEEE Conference on computer Communications Workshops, INFOCOM Wksps

作者： Chong Chen Ying Gao Siquan Huang Xingfu Yan School of Computer Science and Technology South China University of Technology Guangzhou China Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application China School of Computer Science South China Normal University Guangzhou China

Malicious falsification of medical data destroys the training process of the medical-aided diagnosis models and causes serious damage to Healthcare IoMT Systems. To solve this unsupervised problem, this paper finds a robust data filtering method for various data poisoning attacks. First, we adapt the federated learning framework to project all of the clients' data features into the public subspace domain, allowing unified feature mapping to be established while their data remains stored locally. Then we adopt the federated clustering to re-group their features to clarify the poisoned data. The federated clustering is based on the consistent association of data and its semantics. Finally, we do the data sanitization with a simple yet efficient strategy. Extensive experiments are conducted to evaluate the accuracy and efficacy of the proposed defense method against data poisoning attacks.

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Design and Implementation of Handwritten Digit Recognition Based on K-Nearest Neighbor Algorithm 10th

Design and Implementation of Handwritten Digit Recognition B...

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10th International Conference on computer Engineering and Networks, CENet 2020

作者： Wang, Ying Liu, Qingyun Sun, Yaqi Zhang, Feng Zhu, Yining College of Computer Science and Technology Hengyang Normal University Hengyang421002 China Hunan Provincial Key Laboratory of Intelligent Information Processing and Application Hengyang421002 China

ISBN: (纸本)9789811584619

In this paper, a handwritten digit recognition system based on K-nearest neighbor algorithm is designed and implemented. By analyzing the application principle and steps of k-nearest neighbor algorithm in machine learning classifier, we design handwritten digit recognition in Python language. In order to improve the accuracy and accuracy of handwritten character recognition, the method of smooth denoising and character segmentation methods are used to preprocess the data, extract the structure and statistical features. The experiments had shown that the accuracy of the algorithm for handwritten character recognition can reach over 97%. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Machine learning

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Research on Differential Power Analysis of Lightweight Block Cipher LED 10th

Research on Differential Power Analysis of Lightweight Block...

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10th International Conference on computer Engineering and Networks, CENet 2020

作者： Zou, Yi Li, Lang Zhao, Hui-huang Jiao, Ge College of Computer Science and Technology Hengyang Normal University Hengyang421002 China Human Provincial Key Laboratory of Intelligent Information Processing and Application Hengyang421002 China

ISBN: (纸本)9789811584619

LED algorithm is a new lightweight encryption algorithm proposed in CHES 2011, which is used for IOT to protect the communication security of RFID tags and smart cards. It has been found that it is possible to retrieve secret key of algorithm by appropriate analysis of the devicess power consumption, and the differential power analysis is the most powerful attack. This paper proposed a method of differential power analysis on LED algorithm. Combined with hardware circuit, it designed and implemented differential power analysis of the LED encryption system. The experimental results show that method which is proposed in this paper was cracked the 64 bits key of LED. Thus, without protection, the cipher LED would be difficult to resist differential power analysis. and according this, We provide a general analysis method of differential power analysis on other lightweight cryptographic algorithms. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Light emitting diodes

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ALOHA Anti-collision Algorithm for Frame Idle Slot Removal and Collision Reduction

ALOHA Anti-collision Algorithm for Frame Idle Slot Removal a...

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作者： Deng, Hong-wei Zeng, Wei Yao, Ming Kong, Jia-li College of Computer Science and Technology Hengyang Normal University Hengyang421002 China Hunan Provincial Key Laboratory of Intelligent Information Processing and Application Hengyang421002 China

In this study, we propose an ALOHA anti-collision algorithm for frame idle slot removal and collision reduction—called the Cancel Idle and Reduce Collision Framed Slotted Aloha (CIRC-FSA) algorithm—to address the collision problems of tags in mobile scenarios. The proposed algorithm is based on the radio frequency identification grouped adaptive allocating slots (GAAS) anti-collision algorithm with self-adaptive packet allocation. A combined chaotic mapping pseudo-random generator is used, the tags are initially grouped, and the frame length L is set to be much larger than the number of grouped tags X (L >> X). Furthermore, incorporating the GAAS protocol enhances the algorithm to skip idle and collision slots and improve the efficiency of tag identification. The results of simulation analysis indicate that, compared with the free framed slotted ALOHA (FSA), grouped dynamic framed slotted ALOHA (GDFSA), and cancel idle framed slotted ALOHA (CIFSA) algorithms, the proposed CIRC-FSA algorithm results in a lower tag loss rate, higher throughput, and higher recognition efficiency. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Radio frequency identification (RFID)

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Design of a Real-Time and Reliable Multi-machine System 10th

Design of a Real-Time and Reliable Multi-machine System

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10th International Conference on computer Engineering and Networks, CENet 2020

作者： Zhang, Jian Li, Lang Li, Qiuping Zhao, Junxia Liang, Xiaoman College of Computer Science and Technology Hengyang Normal University Hengyang421002 China Hunan Provincial Key Laboratory of Intelligent Information Processing and Application Hengyang421002 China

ISBN: (纸本)9789811584619

An IoT (Internet of Things) system is a multi-machine system composed of a master controller and multiple slave controllers. In some automatic control systems such as petroleum and electric power, the main control uses UART (Universal Asynchronous Receiver/Transmitter) to communicate with the slave controller. However, the communication rate of the UART interface is limited and the delay time is long, which affects the systems response to external events. In order to improve the response speed of the system to external events, this paper designs a multi-machine system based on SPI (Serial Peripheral Interface) interface. First, we use the SPI interface of the microcontroller to build a communication network. The circuit structure of the system is simple, and the hardware resource consumption is low. Then, for the problem that the SPI interface cannot detect communication errors and there is no response mechanism, we designed a communication protocol to make up for these defects from the software level. Finally, we use STM32F429IGT6 as the master controller and STM32F103C8T6 as the slave controller to build the experimental platform. The experimental results show that the system is safe and reliable, and can meet the requirements of real-time applications. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Controllers

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An Optimized Scheme of Information Hiding-Based Visual Secret Sharing 9th

An Optimized Scheme of Information Hiding-Based Visual Secre...

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9th International Conference on computer Engineering and Networks, CENet2019

作者： Zou, Yi Li, Lang Jiao, Ge College of Computer Science and Technology Hengyang Normal University Hengyang421002 China Hunan Provincial Key Laboratory of Intelligent Information Processing and Application Hengyang421002 China

ISBN: (纸本)9789811537523

Information hiding-based visual secret sharing (IH-VSS) uses the information hiding techniques to embed secret data into images. In the IH-VSS scheme, hidden images are divided into n shadows under the guidance and constraints of some predetermined methods, and only a certain number or all of the credible shadows work together to recover hidden information. Based on (2, 2)-threshold IH-VSS scheme, this paper proposes an optimized IH-VSS scheme, it can randomly locate the hidden position of the secret data. We set a random number and use it as the initial hiding position, then combined with the length of the secret data, assigns the address, and completes the hiding of the secret data. The experimental results show that under the same embedding capacity, the peak signal-to-noise ratio (PSNR) of the optimized scheme is higher than other similar schemes, which is high to 55.41 dB. Moreover, the difference between the two shadows is very small. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Signal to noise ratio

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Gender Identification for Coloring Black and White Portrait with cGan 10th

Gender Identification for Coloring Black and White Portrait ...

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10th International Conference on computer Engineering and Networks, CENet 2020

作者： Liu, Qingyun Lin, Mugang Sun, Yaqi Zhang, Feng College of Computer Science and Technology Hengyang Normal University Hengyang421002 China Hunan Provincial Key Laboratory of Intelligent Information Processing and Application Hengyang421002 China

ISBN: (纸本)9789811584619

This paper proposes a method to color black & white portrait images with gender recognition model and condition generation network (cGan). As a cGan model essentially, a method named pix2pix interprets image style transformation as a translation process from input pixels to target pixels. Lots of input to target image pairs should be prepared for model training for different application scenarios. When pix2pix is applied to color black & white portraits, there are obvious differences in color and saturation between portraits of different genders while the ability of gender recognition isnt owned by pix2pix in essence. Therefore, a gender recognition model is added to the network to produce more realistic and gender specific color portraits. The experiments show that our work has achieved better results than some other common methods. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Color

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Analysis and Design of Image Encryption Algorithms Based on Interlaced Chaos 10th

Analysis and Design of Image Encryption Algorithms Based on ...

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10th International Conference on computer Engineering and Networks, CENet 2020

作者： Li, Kangman Li, Qiuping College of Computer Science and Technology Hengyang Normal University Hengyang421002 China Hunan Provincial Key Laboratory of Intelligent Information Processing and Application Hengyang421002 China

ISBN: (纸本)9789811584619

For chaotic encryption algorithm, the algorithm of generating chaotic sequence is complex, and the data is float, which directly affects the speed of encryption. In this paper, an interlaced chaotic encryption algorithm is proposed, In this algorithm, the pixel interval is encrypted by chaotic sequence, and the chaotic sequences of other pixels are obtained by XOR the chaotic values of the front and back points, Experiments show that the algorithm can encrypt and decrypt images quickly and has good effect. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Cryptography

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ConsistencyDet: A Robust Object Detector with a Denoising Paradigm of Consistency Model

arXiv

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arXiv 2024年

作者： Jiang, Lifan Wang, Zhihui Wang, Changmiao Li, Ming Leng, Jiaxu Wu, Xindong the College of Computer Science and Engineering Shandong University of Science and Technology Qingdao266510 China Shenzhen Research Institute of Big Data Shenzhen518172 China Key Laboratory of Intelligent Education Technology and Application Zhejiang Normal University Jinhua321004 China the Key Laboratory of Image Cognition Chongqing University of Posts and Telecommunications Chongqing400065 China Jiangsu Key Laboratory of Image and Video Understanding for Social Safety Nanjing University of Science and Technology Nanjing210094 China the Key Laboratory of Knowledge Engineering with Big Data the Ministry of Education of China China School of Computer Science and Information Technology Hefei University of Technology Hefei230009 China

Object detection, a quintessential task in the realm of perceptual computing, can be tackled using a generative methodology. In the present study, we introduce a novel framework designed to articulate object detection as a denoising diffusion process, which operates on the perturbed bounding boxes of annotated entities. This framework, termed ConsistencyDet, leverages an innovative denoising concept known as the Consistency Model. The hallmark of this model is its self-consistency feature, which empowers the model to map distorted information from any temporal stage back to its pristine state, thereby realizing a "one-step denoising" mechanism. Such an attribute markedly elevates the operational efficiency of the model, setting it apart from the conventional Diffusion Model. Throughout the training phase, ConsistencyDet initiates the diffusion sequence with noise-infused boxes derived from the ground-truth annotations and conditions the model to perform the denoising task. Subsequently, in the inference stage, the model employs a denoising sampling strategy that commences with bounding boxes randomly sampled from a normal distribution. Through iterative refinement, the model transforms an assortment of arbitrarily generated boxes into definitive detections. Comprehensive evaluations employing standard benchmarks, such as MS-COCO and LVIS, corroborate that ConsistencyDet surpasses other leading-edge detectors in performance metrics. Our code is available at https://***/Tankowa/ConsistencyDet. Copyright © 2024, The Authors. All rights reserved.

关键词： Object detection

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Adaptive Ensemble Model for Ultra-short-term Wind Power Forecasting 11

Adaptive Ensemble Model for Ultra-short-term Wind Power Fore...

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11th IEEE Data Driven Control and Learning Systems Conference, DDCLS 2022

作者： Li, Yunlong Jin, Huaiping Jin, Huaikang Cao, Yundong Kunming University of Science and Technology Faculty of Information Engineering and Automation Department of Automation Kunming650500 China Yunnan Key Laboratory of Computer Technologies Application Kunming650500 China Huaneng Renewables Co. Ltd. Yunnan Branch Kunming650000 China

ISBN: (数字)9781665496759

ISBN: (纸本)9781665496759

Stable and reliable wind power forecasting is of great significance for grid dispatching. However, wind power has strong nonlinear characteristics. Its amount of historical data is very large, and traditional machine learning methods cannot fit the nonlinear relationship well. Furthermore, owing to the seasonality and periodicity of the wind power, the performance of the offline model inevitably deteriorates. To address these problems, this study proposes an adaptive ensemble model for ultra-short-term wind power forecasting. First, a long short-term memory (LSTM) network is established with historical wind power data for an entire year to ensure a strong generalization performance. Second, local weighted partial least squares (LWPLS) were used to obtain LSTM network prediction errors in real time by adaptive modeling and further improving its prediction accuracy. In addition, just-in-time learning (JITL) is used to ensure the adaptability of forecasting using LWPLS. Finally, to fully integrate the prediction advantages of different prediction models, a random forest (RF) was used to transform the determination of the ensemble weight into a three-classification problem. The adaptive fusion of the three different forecasting mechanisms is realized by weighting through the posterior probability of classification. Finally, the effectiveness and superiority of the proposed method are verified in a practical case. © 2022 IEEE.

关键词： Error correction

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