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IEEE International Conference on Multimedia and Expo (ICME)

作者： Ying Ren Kailai Shen Zhe Ye Diqun Yan Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo China Juphoon System Software Co. Ltd Ningbo China Key Laboratory of Computing Power Network and Information Security Ministry of Education Qilu University of Technology (Shandong Academy of Sciences) China

ISBN: (数字)9798350390155

ISBN: (纸本)9798350390162

Non-Intrusive speech quality assessment (NISQA) has gained significant attention for predicting speech’s mean opinion score (MOS) without requiring the reference speech. Researchers have gradually started to apply NISQA to various practical scenarios. However, little attention has been paid to the security of NISQA models. Backdoor attacks represent the most serious threat to deep neural networks (DNNs) due to the fact that backdoors possess a very high attack success rate once embedded. However, existing backdoor attacks assume that the attacker actively feeds samples containing triggers into the model during the inference phase. This is not adapted to the specific scenario of NISQA. And current backdoor attacks on regression tasks lack an objective metric to measure the attack performance. To address these issues, we propose a novel backdoor triggering approach (EventTrojan) that utilizes an event during the usage of the NISQA model as a trigger. Moreover, we innovatively provide an objective metric for backdoor attacks on regression tasks. Extensive experiments on four benchmark datasets demonstrate the effectiveness of the EventTrojan attack. Besides, it also has good resistance to several defense methods.

关键词： Measurement Resistance Adaptation models Multimedia systems Current measurement Artificial neural networks Benchmark testing

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RSGPT: A Remote Sensing Vision Language Model and Benchmark

arXiv

引用

arXiv 2023年

作者： Hu, Yuan Yuan, Jianlong Wen, Congcong Lu, Xiaonan Li, Xiang The DAMO Academy Alibaba Group Beijing China The Department of Electrical and Computer Engineering New York University Abu Dhabi Abu Dhabi United Arab Emirates The University of Chinese Academy of Sciences Beijing China The King Abdullah University of Science and Technology Jeddah Saudi Arabia

The emergence of large-scale Large Language Models (LLMs), with GPT-4 as a prominent example, has significantly propelled the rapid advancement of Artificial General Intelligence (AGI) and sparked the revolution of Artificial Intelligence 2.0. In the realm of remote sensing, there is a growing interest in developing large vision language models (VLMs) specifically tailored for data analysis in this domain. However, current research predominantly revolves around visual recognition tasks, lacking comprehensive, large-scale image-text datasets that are aligned and suitable for training large VLMs, which poses significant challenges to effectively training such models for remote sensing applications. In computer vision, recent research has demonstrated that fine-tuning large vision language models on small-scale, high-quality datasets can yield impressive performance in visual and language understanding. These results are comparable to state-of-the-art VLMs trained from scratch on massive amounts of data, such as GPT-4. Inspired by this captivating idea, in this work, we build a high-quality Remote Sensing Image Captioning dataset (RSICap) that facilitates the development of large VLMs in the remote sensing field. Unlike previous remote sensing datasets that either employ model-generated captions or short descriptions, RSICap comprises 2,585 human-annotated captions with rich and high-quality information. This dataset offers detailed descriptions for each image, encompassing scene descriptions (e.g., residential area, airport, or farmland) as well as object information (e.g., color, shape, quantity, absolute position, etc). To facilitate the evaluation of VLMs in the field of remote sensing, we also provide a benchmark evaluation dataset called RSIEval. This dataset consists of human-annotated captions and visual question-answer pairs, allowing for a comprehensive assessment of VLMs in the context of remote sensing. We are actively engaged in expanding the scale of thes

关键词： Remote sensing

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Enhancing Mobile Robot Speed Control: PID Controller Optimization with Bio-Inspired Algorithms

Enhancing Mobile Robot Speed Control: PID Controller Optimiz...

引用

Expert Clouds and Applications (ICOECA), International Conference on

作者： Sangeeta Singh Uma HR Bhargav H K Sujata Arya Praveen Singh Natrayan L Department of Electrical Engineering JSS Academy of Technical Education Noida Uttar Pradesh India Department of Computer Science and Engineering BGS Institute of Technology Adichunchanagiri University Mandya Karnataka India Department of Computer Science and Engineering Basaveshwar Engineering College Bagalkot Karnataka India Faculty of Law Symbiosis Law School (SLS) Symbiosis International (Deemed University) (SIU) Viman Nagar Pune Maharashtra India Department of Management Studies Graphic Era Deemed to be University Dehradun Uttarakhand India Department of Mechanical Engineering Saveetha School of Engineering SIMATS Chennai Tamil Nadu India

ISBN: (数字)9798350385793

ISBN: (纸本)9798350385809

This research study presents an investigation into the optimization of PID controllers using bio-inspired algorithms for controlling the speed of mobile robots. The study begins by identifying a mathematical model that accurately represents the dynamics of the DC motor used in robots. Three different tuning methods, namely Ziegler-Nichols (ZN), Flower Pollination Algorithm (FPA), and Fish Swarm Optimization (FSO) are then utilized to design the PID controllers. The primary objective is to enhance the speed control performance of mobile robots. To evaluate the effectiveness of the optimization methods, the set speed is given to each of the three controllers, and their performance is compared. The results demonstrate that the optimization techniques outperform the conventional ZN method in achieving the desired speed control. Among the two bio-inspired algorithms, FPA yields exceptional outcomes in terms of time characteristics. The comparison of time characteristics serves as a benchmark for evaluating the performance of the controllers. It reveals that bio-inspired optimization methods, especially FPA, significantly improve the speed control of mobile robots. The time characteristics, such as rise time, settling time, overshoot, peak time, and peak value, illustrate the superior performance achieved through the optimization process.

关键词： Accuracy Uncertainty Heuristic algorithms Velocity control Optimization methods Robot sensing systems Stability analysis

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Implementation of the Optical Lock through Visible Light Communication using AES

Implementation of the Optical Lock through Visible Light Com...

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Advances in Signal Processing, Power, Communication, and Computing (ASPCC), IEEE International Conference on

作者： K.P. Swain Sumant Kumar Mohapatra Puspanjali Mallik R. K. Dash Rakesh Kumar Lenka Department of ETC Trident Academy of Technology Bhubaneswar India Dept. of CSE Shailabala Women’s College Cuttack India Dept. of Electrical Electronics and Communication Engineering Galgotia University Dept. of Computer Science Central University of Odisha Sunabeda Koraput

ISBN: (数字)9798350355093

ISBN: (纸本)9798350355109

This study presents the development and implementation of a novel optical lock using visible light communication (VLC), leveraging a custom-built mobile application and flash LED for secure information transmission. The system utilizes an Android application with a user-friendly interface to input and encrypt messages using the AES-128 bit algorithm and Base64 encoding. The encrypted message is transmitted via a smartphone’s flash LED as an OOK signal, received by a specially designed PCB composed of a photodiode and a servo motor. At the heart of the receiving mechanism is a Raspberry Pi, which is responsible for decrypting the message and executing a comparison check with a pre-stored message to determine system unlocking. This innovative approach combines the convenience of mobile technology with advanced encryption, offering a robust solution in optical locking systems.

关键词： Heart Signal processing algorithms Information processing Light emitting diodes Mobile applications Encryption Photodiodes Optical signal processing Servomotors Visible light communication

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Identification of Autism spectrum disorder based on a novel feature selection method and Variational Autoencoder

arXiv

引用

arXiv 2022年

作者： Zhang, Fangyu Wei, Yanjie Liu, Jin Wang, Yanlin Xi, Wenhui Pan, Yi College of Engineering Southern University of Science and Technology Shenzhen518055 China Centre for High Performance Computing Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China College of Computer Science and Control Engineering Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China School of Information Science and Engineering Central South University Changsha410083 China

The development of noninvasive brain imaging such as resting-state functional magnetic resonance imaging (rs-fMRI) and its combination with AI algorithm provides a promising solution for the early diagnosis of Autism spectrum disorder (ASD). However, the performance of the current ASD classification based on rs-fMRI still needs to be improved. This paper introduces a classification framework to aid ASD diagnosis based on rs-fMRI. In the framework, we proposed a novel filter feature selection method based on the difference between step distribution curves (DSDC) to select remarkable functional connectivities (FCs) and utilized a multilayer perceptron (MLP) which was pretrained by a simplified Variational Autoencoder (VAE) for classification. We also designed a pipeline consisting of a normalization procedure and a modified hyperbolic tangent (tanh) activation function to replace the original tanh function, further improving the model accuracy. Our model was evaluated by 10 times 10-fold cross-validation and achieved an average accuracy of 78.12%, outperforming the state-of-the-art methods reported on the same dataset. Given the importance of sensitivity and specificity in disease diagnosis, two constraints were designed in our model which can improve the model's sensitivity and specificity by up to 9.32% and 10.21%, respectively. The added constraints allow our model to handle different application scenarios and can be used broadly. Copyright © 2022, The Authors. All rights reserved.

关键词： Feature extraction

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FedTC: Enabling Communication-Efficient Federated Learning via Transform Coding

FedTC: Enabling Communication-Efficient Federated Learning v...

引用

IEEE Annual Joint Conference: INFOCOM, IEEE computer and Communications Societies

作者： Yixuan Guan Xuefeng Liu Jianwei Niu Tao Ren State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University Beijing China Zhongguancun Laboratory Beijing China Zhengzhou University Research Institute of Industrial Technology School of Information Engineering Zhengzhou University Zhengzhou China State Key Laboratory of Intelligent Game Institute of Software Chinese Academy of Sciences Beijing China

ISBN: (数字)9798350383508

ISBN: (纸本)9798350383515

Federated learning (FL) enables distributed training via periodically synchronizing model updates among participants. Communication overhead becomes a dominant constraint of FL since participating clients usually suffer from limited bandwidth. To tackle this issue, top-k based gradient compression techniques are broadly explored in FL context, manifesting powerful capabilities in reducing gradient volumes via picking significant entries. However, previous studies are primarily conducted on the raw gradients where massive spatial redundancies exist and positions of non-zero (top-k) entries vary greatly between gradients, which both impede the achievement of deeper compressions. Top-k may also degrade the performance of trained models due to biased gradient estimations. Targeting the above issues, we propose FedTC, a novel transform coding based compression framework. FedTC transforms gradients into a new domain with more compact energy distributions, which facilitates reducing spatial redundancies and biases in subsequent sparsification. Furthermore, non-zero entries across clients from different rounds become highly aligned in the transform domain, motivating us to partition the gradients into smaller entry blocks with various alignment levels to better exploit these alignments. Lastly, positions and values of non-zero entries are independently compressed in a block-wise manner with our customized designs, through which a higher compression ratio is achieved. Theoretical analysis and extensive experiments consistently demonstrate the effectiveness of our approach.

关键词： Training Quantization (signal) Costs Federated learning Redundancy Transform coding Estimation

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M3OFF: Module-Compositional Model-Free Computation Offloading in Multi-Environment MEC

M3OFF: Module-Compositional Model-Free Computation Offloadin...

引用

IEEE Annual Joint Conference: INFOCOM, IEEE computer and Communications Societies

作者： Tao Ren Zheyuan Hu Jianwei Niu Weikun Feng Hang He State Key Laboratory of Intelligent Game Institute of Software Chinese Academy of Sciences Beijing China State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University Beijing China Zhongguancun Laboratory Beijing China Zhengzhou University Research Institute of Industrial Technology School of Information Engineering Zhengzhou University Zhengzhou China

ISBN: (数字)9798350383508

ISBN: (纸本)9798350383515

Computation offloading is one of the key issues in mobile edge computing (MEC) that alleviates the tension between user equipment's limited capabilities and mobile application's high requirements. To achieve model-free computation offloading when reliable MEC dynamics are unavailable, deep reinforcement learning (DRL) has become a popular methodology. However, most existing DRL-based offloading approaches are developed for a single MEC environment, with invariant system bandwidth, edge capability, task types, etc., while realistic MEC scenarios tend to be of high diversity. Unfortunately, in multi-MEC environments, DRL-based offloading faces at least two challenges, learning inefficiency and interference of offloading experiences. To address the challenges, we propose a DRL-based Multi-environmental Module-compositional Modelfree computation OFFloading (M 3 OFF) framework. M 3 OFF generates offloading policies using module composition instead of a single DRL network so that learning efficiency could be improved by reusing the same modules and learning interference could be reduced by composing different modules. Furthermore, we design multiple module composition-specific training methods for M 3 OFF, including alternate modules-and-composer updates to improve training stability, loss-regularization to avoid module degeneration, and module-dropout to mitigate overfitting. Extensive experimental results on both simulation and testbed demonstrate that M 3 OFF outperforms the performances of most state-of-the-arts in multi-MEC and reaches close to single-MEC.

关键词： Training Visualization Multi-access edge computing Computational modeling Interference Bandwidth Stability analysis

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Exploring Intra- and Inter-Video Relation for Surgical Semantic Scene Segmentation

arXiv

引用

arXiv 2022年

作者： Jin, Yueming Yu, Yang Chen, Cheng Zhao, Zixu Heng, Pheng-Ann Stoyanov, Danail The Department of Computer Science University College London United Kingdom The Department of Computer Science and Engineering The Chinese University of Hong Kong HKSAR China Guangdong Provincial Key Laboratory of Computer Vision and Virtual Reality Technology Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China

Automatic surgical scene segmentation is fundamental for facilitating cognitive intelligence in the modern operating theatre. Previous works rely on conventional aggregation modules (e.g., dilated convolution, convolutional LSTM), which only make use of the local context. In this paper, we propose a novel framework STswinCL that explores the complementary intra- and inter-video relations to boost segmentation performance, by progressively capturing the global context. We firstly develop a hierarchy Transformer to capture intra-video relation that includes richer spatial and temporal cues from neighbor pixels and previous frames. A joint space-time window shift scheme is proposed to efficiently aggregate these two cues into each pixel embedding. Then, we explore inter-video relation via pixel-to-pixel contrastive learning, which well structures the global embedding space. A multi-source contrast training objective is developed to group the pixel embeddings across videos with the ground-truth guidance, which is crucial for learning the global property of the whole data. We extensively validate our approach on two public surgical video benchmarks, including EndoVis18 Challenge and CaDIS dataset. Experimental results demonstrate the promising performance of our method, which consistently exceeds previous state-of-the-art approaches. Code is available at https://***/YuemingJin/STswinCL. Copyright © 2022, The Authors. All rights reserved.

关键词： Pixels

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Management of a Microgrid using Deep Learning Techniques 5

Management of a Microgrid using Deep Learning Techniques

引用

5th International Conference on Contemporary Computing and Informatics, IC3I 2022

作者： Panduro-Ramirez, Jeidy Joshi, Kapil Chakravarthi, M. Kalyan Singh, Prabhdeep Bind, Naveen Kumar Banik, Ayan Universidad Tecnológica Del Perú Peru Uttaranchal Institute of Technology Uttaranchal University Uttarakhand Dehradun India VIT - AP University School of Electronics Engineering Andhra Pradesh Amaravati India Graphic Era Deemed to Be University Department of Computer Science & Engineering Uttarakhand Dehradun India JSS Academy of Technical Education Department of Electrical Engineering Uttar Pradesh Noida India West Bengal Kolkata India

ISBN: (纸本)9798350398267

Our power generation grid is experiencing a dramatic modernization as a result of recent developments in smart-grid advancements. By offering a flexible method of sort of technology solar and wind power (RES) into the electricity system, microgrid (MG) plays a significant role in the modernity. Spread RES, like solar and wind, can, nevertheless, be very essentially random and occasional. It is challenging to effectively execute an MG because of these erratic resources, state of charge, and the like but on the sides of the market. This paper addressed this issue and presented a novel strategy for the real-time timetabling of an MG taking into account the adequacy of renewables, energy cost, and utility grid confusion. The suggested solution is studying and therefore does not involve an accurate model of the confusion, in contrast to traditional model-based methods that call for an indicator to estimate the confusion. The goal of the MG power process is to reduce the daily operational expenses, and it is specifically modelled as a Markov Decision Process (MDP). To resolve the MDP, a deep encouragement learning (DRL) strategy has been developed. The deep Q-network (DQN) algorithm is used to find the human brain in the DRL framework, which uses a deep fnn network to measure the ideal response role. The suggested method directly generates real-time creation schedule using the MG's current state as inputs. Furthermore, case experiments are performed to show the usefulness of the suggested strategy using total power data from the Cal Organization For Standardization (CAISO). © 2022 IEEE.

关键词： Markov processes

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An Efficient Intra-Inter Pixel Encryption Scheme to Secure Healthcare Images for an Iot Environment

SSRN

引用

SSRN 2023年

作者： Dash, Sachikanta Padhy, Sasmita Devi, S. Anjali Sharna, Savyasachi Patro, K. Abhimanyu Kumar Department of Computer Science and Engineering GIET University Odisha Gunupur765022 India School of Computing Science and Engineering VIT Bhopal University Kothrikalan Madhya Pradesh Sehore466114 India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Guntur Andhra Pradesh Vaddeswaram522502 India Department of Information Technology Lalit Narayan Mishra College of Business Management Bihar Muzaffarpur842001 India Department of Mechatronics Manipal Institute of Technology Manipal Academy of Higher Education Karnataka Manipal576104 India

Digital images are being frequently used for diagnosis in clinics today. Diagnostic images with identifying patient data are stored and transmitted across open networks. Security measures are therefore becoming more and more crucial to safeguard the confidentiality of sensitive patient data. In medical imaging, adjacent pixels show a strong degree of connection. This paper presents an efficient intra-inter bit-level pixel operation-based image encryption method that eliminates pixel correlation and provides strong security. In this method, after gathering medical image data from IoT sensors, the intra bit-level pixel permutation and diffusion operations are executed separately and in parallel in each of the bit planes, and the inter bit-level pixel permutation and diffusion operations are then performed in the concatenated bit-planes. The bit-level operation not only permutes but also diffuses the pixels at the same time. As a result, the security of the algorithm is increased by executing several pixel permutations and diffusions. In addition to this, the approach is effective due to the parallel execution of bit-plane permutation and diffusion. The simulation results and the security analysis indicate the superior performance of the proposed algorithm. © 2023, The Authors. All rights reserved.

关键词： Pixels

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