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Omni-layer Intelligent Systems (COINS), International Conference on

作者： Mahmood Fazlali Amirhossein Alihoseini Ebrahim Zarei Myasar Tabani Seyedali Pourmoafi Cybersecurity and Computing Systems Research Group University of Hertfordshire Hatfiled UK Dept. of data and Computer Sciensce Faculty of Mathematical sciences Shahid Beheshti University Tehran Iran Dep. of Computer Science Khansar Campus University Of Isfahan Isfahan Iran

Image encryption is a reliable method for securely transmitting images over a network. The time required to encrypt and decrypt an image in online applications is also very important. Although cellular automata cryptography is an appropriate technique for parallelizing and accelerating cryptographic methods, its capacity cannot be demonstrated only in multi-core platforms. Thus, it is needed to parallelize cellular automata cryptography on Graphic Processor Units (GPUs) in order to significantly decrease the encryption/decryption time. In this paper, we propose a new parallel algorithm for two-dimensional cellular automata cryptography that is implemented on GPU. The proposed algorithm uses multiple threads at once to accelerate the bit-level permutation and substitution operations by taking into account the capacity of cellular automata in parallel processing. According to the study experimental findings, the proposed algorithm performs faster on GPU compared to a multicore platform while maintaining the same level of security in comparison to the serial algorithm.

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FGSNet: A Finer-Grained Siamese Network for Industrial Few-Shot Anomaly Detection

FGSNet: A Finer-Grained Siamese Network for Industrial Few-S...

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IEEE International Conference on Systems, Man and Cybernetics

作者： DeLong Han Luo Xu MingLe Zhou Gang Li Min Li Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China Faculty of Data Science City University of Macau Macau China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

Image anomaly detection usually relies on a large set of training samples and abnormal samples are relatively rare and challenging to obtain in daily industrial scenarios. In the scenario of Few-Shot Anomaly Detection (FSAD), how to better utilize the fine-grained features of the few images is a key issue. To solve these problems, a Finer-Grained Siamese Network (FGSNet) is proposed in this paper. FGSNet considers the setting of Few-Shot Anomaly Detection (FSAD) and consists of two stages. The first stage is responsible for extracting finer-grained features and roughly aligning the image features, while incorporating an efficient Fine-grained Feature Fusion Module $(\mathbf{F}^{3}\mathbf{M})$ to enhance feature representation. Meanwhile, we design a Deep-supervised Loss to improve the level of fine-grained information extraction. The second is primarily used to refine and denoise the fused features, followed by detailed alignment operations for subsequent feature distribution modeling. Compared to most existing FASD models that use single-class training and single-model evaluation, FGSNet is more suitable for existing industrial scenarios. Extensive experiments are conducted on the publicly available MVTec AD and MPDD datasets in this paper. The results show that, for 2, 4 and 8-shot cases, FGSNet can increase the average Image-AUROC on MVTec AD by 0.87%, 1.27% and 1.33% respectively, compared to RegAD.

关键词： Training Feature extraction Information retrieval Anomaly detection Cybernetics

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Anomaly-Free Prior Guided Knowledge Distillation for Industrial Anomaly Detection

Anomaly-Free Prior Guided Knowledge Distillation for Industr...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Gang Li Tianjiao Chen Min Li Delong Han MingLe Zhou Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China Faculty of Data Science City University of Macau Macau China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

In industrial manufacturing, visual anomaly detection is critical for maintaining product quality by detecting and preventing production anomalies. Anomaly detection methods based on knowledge distillation demonstrate promising performance in addressing the unpredictability and diversity of anomalies. However, they suffer from a lack of effective guidance from anomaly-free priors when handling anomalous features and underutilize multi-scale features during the segmentation scoring stage, yielding suboptimal detection results. To alleviate these issues, we propose an Anomaly-free Prior Guided knowledge distillation (APG) for industrial anomaly detection. Firstly, it filters the abnormal features by training the de-noising target network with knowledge distillation structure. Concurrently, we propose the Prior Perception Propagation Module (P 3 M), which extracts more efficient anomaly-free features by imposing constraints on anomalous features. Secondly, we propose the Multi-scale Prior Guided Fusion Module (MPGFM) to improve anomaly detection accuracy by utilizing anomaly-free features from the target network as priors to guide the generation and fusion of cross-scale differential features. Finally, the Global Perception Enhancement Module (GPEM) is proposed to construct an anomaly scoring network, leveraging comprehensive scene features to enhance the detection and localization performance of numerous small-target anomalies in industrial manufacturing. Extensive experiments on the MVTecAD and BTAD datasets show that the proposed method demonstrates a consistent and significant outperformance against competing methods.

关键词： Training Location awareness Visualization Noise reduction Production Feature extraction Product design Manufacturing Quality assessment Anomaly detection

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Fast Intra Mode Prediction Algorithms for SCBS in VVC SCC

Fast Intra Mode Prediction Algorithms for SCBS in VVC SCC

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

作者： Dayong Wang Yishen Deng Weisheng Li Xin Lu Frederic Dufaux Bo Hang Ce Zhu Key Laboratory of Big Data Intelligent Computing Chongqing University of Posts and Telecommunications Guangxi Key Laboratory of Machine Vision and Intelligent Control Wuzhou University Chongqing Key Laboratory of Image Cognition Chongqing University of Posts and Telecommunications Faculty of Computing Engineering and Media (CEM) De Montfort University Université Paris-Saclay CNRS CentraleSupélec Laboratoire des Signaux et systèmes The Computer School Hubei University of Arts and Science School of Information and Communication Engineering University of Electronic Science and Technology of China

Versatile Video Coding (VVC) now supports Screen Content Coding (SCC) by integrating two efficient coding modes: Intra Block Copy (IBC) and Palette (PLT). However, the numerous modes and the Quad-Tree Plus Multi-Type Tree (QTMT) structure inherent to VVC contribute to a very high coding complexity. To effectively reduce the computational complexity of VVC SCC, we propose a fast Intra mode prediction algorithm for VVC SCC. More specifically, we first use the difference of minimum Sum of Absolute Transformed Differences (SATD) value of four Directional Modes (DMs) of Intra and the SATD value of the IBC-merge mode to determine whether to early skip Intra checking. Subsequently, we use a decision tree to determine whether to early terminate the checking after block differential pulse coded modulation (BDPCM). Finally, we employ a decision tree to determine whether to early skip multiple transform selection (MTS) and low frequency non-separable transform (LFNST) checking. The results demonstrate that our algorithm achieves an average encoding time reduction of 34.34% with a negligible Bjøntegaard delta bitrate increase of 0.46%.

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MemADet: A Representative Memory Bank Approach for Industrial Image Anomaly Detection

MemADet: A Representative Memory Bank Approach for Industria...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Min Li Jinghui He Zuobin Ying Gang Li Mingle Zhou Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China Faculty of Data Science City University of Macau Macau China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

In the field of industrial production, anomaly detection is crucial for ensuring product quality and maintaining production efficiency. With the continuous advancement of computer vision technology, it has shown tremendous potential in industrial applications. However, the scarcity of labeled anomaly samples in real-world operating environments poses significant challenges for traditional anomaly detection techniques. To address this, we propose MemADet, a novel anomaly detection model that employs an unsupervised approach and leverages a representative memory bank. It employs a dynamic decision mechanism to control the representativeness of the features stored in the memory bank, and employs a weighted anomaly score calculation mechanism to further enhance the performance of image anomaly detection. Our evaluations indicate that MemADet performs robustly in industrial image anomaly detection across three datasets, with particularly no-table detection accuracy on the MVTec AD dataset. Its efficacy is further validated by competitive results on two additional datasets, highlighting its consistent and effective performance in various settings.

关键词： Accuracy Computational modeling Memory management Production Feature extraction Real-time systems Product design Quality assessment Reliability Anomaly detection

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Provably secure fog-based authentication protocol for VANETs

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Computer Networks 2024年 246卷

作者： Awais, Syed Muhammad Yucheng, Wu Mahmood, Khalid Badar, Hafiz Muhammad Sanaullah Kharel, Rupak Das, Ashok Kumar School of Microelectronics and Communication Engineering Chongqing University Chongqing China Graduate School of Intelligent Data Science National Yunlin University of Science and Technology Yunlin Douliu64002 Taiwan Department of Emerging Computing Technologies Emerson University Multan Pakistan School of Computing and Engineering University of Huddersfield HuddersfieldHD1 3DH United Kingdom The Faculty of Electrical and Electronics Engineering Ton Duc Thang University Ho Chi Minh City Viet Nam Center for Security Theory and Algorithmic Research International Institute of Information Technology Hyderabad500 032 India

The integration of fog computing and vehicular ad-hoc networks (VANETs) has become increasingly important due to advancements in cloud computing, Internet of Things (IoT) technologies, and intelligent transportation systems. However, ensuring secure communication in fog-based VANETs poses a significant challenge. To address this challenge, we propose a novel authenticated key agreement protocol that achieves mutual authentication, generates a secure session key for confidential communication, and ensures privacy protection without relying on bilinear pairing. We provide rigorous security proofs for our protocol, specifically tailored for fog-based VANETs, demonstrating its ability to meet their stringent security requirements. Simulations using NS3 allow for a comprehensive performance evaluation, providing insights into the protocol's efficiency and scalability. Additionally, our protocol is highly efficient, featuring low computation and communication costs, thereby offering a practical and scalable solution for secure communication in fog-based VANETs. © 2024

关键词： Fog

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Channel and space-based joint rate allocation algorithm

Channel and space-based joint rate allocation algorithm

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

作者： Dayong Wang Chao Yuan Yu Sun Xin Lu Hui Guo Frederic Dufaux Ce Zhu Key Laboratory of Big Data Intelligent Computing Chongqing University of Posts and Telecommunications Guangxi Key Laboratory of Machine Vision and Intelligent Control Wuzhou University Chongqing Key Laboratory of Image Cognition Chongqing University of Posts and Telecommunications China Department of Computer Science University of Central Arkansas Faculty of Computing Engineering and Media (CEM) De Montfort University UK Université Paris-Saclay CNRS CentraleSupélec Laboratoire Des Signaux et Systèmes France School of Information and Communication Engineering University of Electronic Science and Technology of China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Rate control is a critical component for image and video compression Particularly under limited network bandwidth conditions, bitrate control is essential to ensure efficient image transmission by effectively allocation channel resources. In this research, since both Channel and Spatial have relationship with rate allocation, we first propose a joint Channel-wise and Spatial-wise Quantization scheme to determine optimal quantization parameters. Subsequently, we develop a quantization step estimation network to obtain parameters to efficiently allocate rate according to target rate. Experiments demonstrate that our algorithm significantly improve compressed image quality with minimal bitrate distortion and achieve accurate rate control with nearly 3% average bitrate error.

关键词： Image quality Quantization (signal) Image coding Bit rate Signal processing algorithms Estimation Rate-distortion Video compression Resource management Speech processing

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Real-Time Traffic Flow Optimization using Adaptive IoT and data Analytics: A Novel DeepStreamNet Model

Real-Time Traffic Flow Optimization using Adaptive IoT and D...

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Sustainable Expert Systems (ICSES), International Conference on

作者： V.S. Saranya Garimidi Subbarao Dega Balakotaiah Maloth Bhavsingh K. Suresh Babu Srinivasa Rao Dhanikonda Department of Computing Technologies School of Computing SRM Institute of Science and Technology Chennai Department o Computer Science and Engineering (AIDS) Pace Institute of Technology and Sciences Department of Computer Science and Engineering Vignan's Founadation for Science Technology & Research (Deemed to be University) Department of Computer Science and Engineering Ashok: Womens Engineering College Kurnool Andhra Pradesh Department of Computer Science and Engineering Narasaraopeta Engineering College(Autonomous) Narasaraopet A.P Department of Artificial Intelligence and Data Science Faculty of Science and Technology (IcfaiTech) ICFAI Foundation for Higher Education (Deemed to Be University) Hyderabad Telangana India

ISBN: (数字)9798331540364

ISBN: (纸本)9798331540371

Thisstudy introduces the DeepStreamNet model, an advanced framework for enhancing real-time traffic management in urban environments using adaptive IoT and sophisticated big data analytics. Central to our approach is the integration of convolutional neural networks (CNNs), recurrent neural networks (RNNs), and long short-term memory (LSTM) units, which synergize with IoT devices to facilitate dynamic adaptation to fluctuating traffic conditions. The model excels in operational performance, achieving a notable accuracy of 94.7% in congestion prediction with an exceptionally low latency of only 45 ms per decision cycle and the capability to process up to 50,000 data points per second. These technical achievements have marked significant improvements over traditional traffic management systems, enhancing traffic flow efficiency and reducing congestion effectively. The results underscore the transformative potential of the DeepStreamNet model in urban traffic management and public safety, offering actionable insights for intelligent transportation systems and setting the stage for more informed urban planning. Looking forward, we suggest extending the application of our model to areas such as logistics and broader smart city initiatives, highlighting its adaptability and broad utility in various urban contexts

关键词： Adaptation models Analytical models Accuracy Smart cities Urban planning data models Real-time systems Internet of Things Optimization Context modeling

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Building a data Lake for Power BI in the Cloud: A Review on Utilizing Cloud Storage Services for Large datasets

Building a Data Lake for Power BI in the Cloud: A Review on ...

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Artificial Intelligence in Education and Industry 4.0 (IDICAIEI), DMIHER International Conference on

作者： Viraj Vijay Gurbade Prateek Verma Swapnil Gundewar Vijendra Singh Bramhe Department of Artificial Intelligence and Data Science Faculty of Engineering and Technology Datta Meghe Institute of Higher Education & Research (DU) Wardha Maharashtra India Department of Artificial Intelligence and Machine Learning Faculty of Engineering and Technology Datta Meghe Institute of Higher Education & Research (DU) Wardha Maharashtra India School of Computing Science and Engineering VIT Bhopal University Sehore Madhya Pradesh India

ISBN: (数字)9798331528713

ISBN: (纸本)9798331528720

A data lake is a centralized repository where you may store both structured and unstructured data at any scale. Unlike typical data warehouses, which need data structure for storage, data lakes allow you to store raw data in its natural state. This adaptability makes data lakes suitable for storing large volumes of various data kinds, including photographs, videos, log files, sensor data, and others. When integrating a data lake with Power Business Intelligence (BI), organizations can harness the power of both technologies to gain valuable insights from their data. In the realm of data-driven decision-making, organizations face the challenge of managing and extracting insights from vast and diverse datasets, a task conventional on-premises storage solutions often struggle with due to the exponential growth of data. Thus, the adoption of more scalable and flexible alternatives becomes imperative. Cloud-based data lakes emerge as transformative solutions, offering unparalleled scalability, accessibility, cost-effectiveness, and advanced security protocols. This paper discuss the concept of constructing a cloud-based data lake optimized for Power BI, leveraging leading cloud storage services like Azure data Lake Storage (ADLS) and Amazon S3. By seamlessly integrating Power BI with the cloud data lake, organizations can overcome traditional storage limitations and reap numerous benefits. The practical applications in advanced analytics, customer insights, IoT data management, and data discovery underscore the real-world utility of cloud data lakes. The integration of Power BI with a cloud data lake augments efficient data analysis and self-service analytics potential, offering organizations a comprehensive solution for deriving actionable insights from their data assets.

关键词： Cloud computing Scalability Decision making Buildings Organizations Big data applications Security data mining Business intelligence Self-service

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Emotion Classification Through EEG Signals: The EmoSense Model

Emotion Classification Through EEG Signals: The EmoSense Mod...

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computing Innovation, Intelligence, Technologies and Education (CIITE), International Conference on

作者： Yi Ken Chuah Chean Khim Toa Sim Kuan Goh Chow-Khuen Chan Hiroki Takada School of Electrical Engineering and Artificial Intelligence Xiamen University Malaysia Selangor Malaysia School of Computing and Data Science Xiamen University Malaysia Selangor Malaysia Department of Biomedical Engineering Faculty of Engineering Universiti Malaya Malaysia Department of Human & Artificial Intelligent Systems Graduate School of Engineering University of Fukui Fukui Japan

ISBN: (数字)9798350377286

ISBN: (纸本)9798350377293

In the Emotion research, there has been extensive work on emotion classification using electroencephalogram (EEG) signals. Despite most studies having achieved high accuracy, there is a potential for improvement in the model, particularly in training time reduction to optimize the modeling efficiency. Hence, our study aims to develop an efficient EEG-based emotion classification model that achieves high accuracy with a reduced number of training epochs. The proposed model, named EmoSense, employs a hybrid deep learning neural network architecture, which comprises a combination of Convolutional Neural Network (CNN), Deep Autoencoder (DAE), Bidirectional Long Short-Term Memory (BiLSTM), and an Attention mechanism. EEG features are first extracted using Differential Entropy (DE) on the EEG channels, which are then structured to form a four-dimensional (4D) representation of features with temporal, spatial, and frequency information. Subsequently, the extracted features will be used as input to the proposed model for training. Experimental evaluation of the publicly available DEAP dataset demonstrated that the proposed model achieved an accuracy of 94.11% with only 35 epochs. This performance is comparable with the result of the Cascaded Convolutional Recurrent Neural Networks model developed by Meng et al., which reported an accuracy of 94.64% with 100 epochs. The primary reason for the comparison with Meng et al. is that our overall process, including feature extraction and formation, is similar to their study, with the only difference being the deep learning architecture. Our finding indicates that the EmoSense model is not only able to achieve comparable accuracy but also reduces the number of training epochs, demonstrating the potential to learn the EEG features in a shorter time and lowering the computational cost. This study will benefit EEG-based emotion recognition to be more accessible and practical for real-world applications.

关键词： Training Emotion recognition Accuracy Attention mechanisms Computational modeling Brain modeling Feature extraction Electroencephalography Convolutional neural networks Tuning

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