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Efficient Progressive Image Compression with Variance-Aware Masking

Efficient Progressive Image Compression with Variance-Aware ...

引用

2025 IEEE/CVF Winter Conference on Applications of computer Vision, WACV 2025

作者： Presta, Alberto Tartaglione, Enzo Fiandrotti, Attilio Grangetto, Marco Cosman, Pamela University of Turin Italy Ltci Télécom Paris Institut Polytechnique de Paris France Uc San Diego Dept. of Electrical and Computer Engineering CA United States

ISBN: (纸本)9798331510831

Learned progressive image compression is gaining momentum as it allows improved image reconstruction as more bits are decoded at the receiver. We propose a progressive image compression method in which an image is first represented as a pair of base-quality and top-quality latent representations. Next, a residual latent representation is encoded as the element-wise difference between the top and base representations. Our scheme enables progressive image compression with element-wise granularity by introducing a masking system that ranks each element of the residual latent representation from most to least important, dividing it into complementary components, which can be transmitted separately to the decoder in order to obtain different reconstruction quality. The masking system does not add further parameters or complexity. At the receiver, any elements of the top latent representation excluded from the transmitted components can be independently replaced with the mean predicted by the hyperprior architecture, ensuring reliable reconstructions at any intermediate quality level. We also in-troduced Rate Enhancement Modules (REMs), which refine the estimation of entropy parameters using already decoded components. We obtain results competitive with state-of-the-art competitors, while significantly reducing computational complexity, decoding time, and number of parameters. © 2025 IEEE.

关键词： Image compression

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Study of Privacy in Federated Reservoir Computing

Study of Privacy in Federated Reservoir Computing

引用

Annual Conference on Information sciences and Systems (CISS)

作者： Christopher C. Zawacki Eyad H. Abed Dept. of Electrical and Computer Engineering University of Maryland College Park MD USA

Federated Reservoir Computing allows multiple parties to collaboratively train a reservoir-based model while providing privacy guarantees for participating clients. One such proposed method is Incremental Federated Learning [1]. This method trains Echo State Networks (ESN) within a federated framework by exploiting an algebraic decomposition of the objective function. Incremental Federated Learning relies on the complexity of the reservoir to ensure the privacy of client data. In this work, we present an example showing that, in actuality, client data may be reconstructed by an adversarial agent with access to the suggested update step in Incremental Federated Learning. We demonstrate that client input data can be extracted from the transferred matrices, suggesting the need for additional privacy safeguards when extending Reservoir Computing to federated environments.

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TRUSTCACHE: Trust-based Content Caching in Information-Centric Networks

TRUSTCACHE: Trust-based Content Caching in Information-Centr...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Sean Tsikteris Aisha B Rahman Md Sadman Siraj Panagiotis Charatsaris Eirini Eleni Tsiropoulou Symeon Papavassiliou Institute of Communication and Computer Systems School of Electrical and Computer Engineering National Technical University of Athens Athens Greece Dept. of Electrical and Computer Engineering University of New Mexico Albuquerque NM USA

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

The Information-Centric Networking (ICN) paradigm has reshaped the modern network architectures and promises efficient content delivery to the end-users. This paper introduces TRUSTCACHE, a novel framework enabling the content caching within ICNs and focusing on the trust-based ICN selection and optimal cache memory allocation to the Content Providers (CPs). The TRUSTCACHE framework incorporates the Optimistic Q-learning with Upper Confidence Bound reinforcement learning algorithm that enables the CPs to autonomously select ICNs based on their cache memory availability and trust levels. Also, TRUSTCACHE enables the CPs to jointly consider the reliability of the ICNs and their cache memory availability by integrating a novel trust model. Furthermore, TRUSTCACHE leverages the multilateral bargaining principles in order to ensure the optimal cache memory allocation among the CPs, in terms of aligning with their profit margin characteristics. Simulation-based experiments validate TRUSTCACHE’s operational efficiency across diverse CP profit margin profiles and highlight its superiority over alternative models lacking trust-based ICN selection or employing proportional fairness strategies for cache memory allocation.

关键词： Q-learning Cache memory Information-centric networking Focusing Resource management Reliability Global communication Game theory

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MIMO/RIS Communication via Beam Networks: Three Case Studies

MIMO/RIS Communication via Beam Networks: Three Case Studies

引用

IEEE International Conference on Communications (ICC)

作者： Youjian Eugene Liu Mahesh K. Varanasi Ibrahim Khalife Ali Abbasi Dept. Electrical Computer and Energy Engineering University of Colorado Boulder Colorado USA

ISBN: (数字)9781728190549

ISBN: (纸本)9781728190556

In MIMO communications, it is expensive and some-times impossible to obtain timely channel state information at transmitter (CSIT) when the number of antennas is large. Furthermore, if the carrier frequency is high, the variation of the phases of the channel state information is significant with small movement in the channels. On the other hand, spatial signal path directions and the amplitudes of path gains vary slowly. We propose a beam network methodology for multi-antenna communication using such partial CSIT as directions and amplitudes instead of the conventional approach that employs full CSIT. Three representative problems are presented to demonstrate the simplicity and performance of the methodology. These include the single-user MIMO channel, a MIMO interference channel, and a multicast network via a reconfigurable intelligent surface (RIS). In particular, we see that the usual high complexity of RIS state design can be simplified to choosing states to connect an incoming beam to outgoing beams. 1

关键词： Transmitting antennas Reconfigurable intelligent surfaces Complexity theory Interference channels Finite element analysis MIMO communication Channel state information

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Cybersecurity for Industrial IoT: A Review on Vulnerabilities and Attacks

Cybersecurity for Industrial IoT: A Review on Vulnerabilitie...

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International Conference on Electronics, Materials engineering and Nano-Technology (IEMENTech)

作者： Reetu Kumari Sunil Kumar Amandeep Kamlesh Dutta Aparna Kumari Dept. of Computer Science & Engineering Guru Jambheshwar University of Science and Technology Hisar (Haryana) India Dept. of AI & Data Science Guru Jambheshwar University of Science and Technology Hisar Haryana India Dept. of Computer Science & Engineering National Institute of Technology Hamirpur Himachal Pardesh India Dept. of Computer Science & Engineering Institute of Technology Nirma University Ahmedabad Gujarat India

ISBN: (数字)9798331520762

ISBN: (纸本)9798331520779

Internet has evolved from a network of connecting people to a network of connecting things, leading to a more complex and sophisticated network of Industrial Things, known as Industrial IoT (IIoT) today. This evolution has revolutionized the traditional Industrial system into real-time smarter systems that enhanced overall efficiency and productivity. Despite its benefits, the interconnected nature of IIoT, expanded the attack surface, exposing the systems to new crucial and complex cybersecurity threats. Several real time attacks on various industries as mentioned in this paper and the increasing cybersecurity cost over the years, clearly states the need of robust security solutions. To deeply understand the cybersecurity aspect, we provided a comprehensive study of attacks and vulnerabilities in this paper. In this paper, we did a indept. study of various IIoT related severe attacks and aspects related to these attacks, like vulnerabilities, impacts, potential solutions etc. We also provided future research directions to help the research community, to work upon and develop more robust and efficient cybersecurity solutions.

关键词： Industries Productivity Reviews Prevention and mitigation Intrusion detection Interconnected systems Real-time systems Internet computer security Industrial Internet of Things

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Emergency control measures against long term voltage instability

Emergency control measures against long term voltage instabi...

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17th International Conference on Developments in Power System Protection (DPSP 2024)

作者： Vassiliki Alexopoulou Vassilis C. Nikolaidis Dept. of Electrical & Computer Engineering Democritus University of Thrace (DUTH) Xanthi Greece

ISBN: (数字)9781837240852

System collapse may be caused by undesired protection relay operations during voltage instability. In this paper, the analysis of different emergency control measures is presented as means to prevent voltage collapse when long term voltage instability is evolving in the system. Being the first line of defence after such unexpected relay trip, the emergency control of load tap changers (LTC) of power transformers during undervoltage conditions is investigated. Alternatively, load shedding is applied to avoid undesired Zone 3 distance relay trips. Sensitivities of Zone 3 trip margin to load powers are calculated to determine the most efficient load shedding actions to prevent undesired operation of critical relays. The effectiveness of the proposed emergency control measures is demonstrated though simulation results for the Hellenic Interconnected System model.

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An Efficient Explainable AI Method Combining CNN and SVM for Corn Leaf Disease Detection and Visualization

An Efficient Explainable AI Method Combining CNN and SVM for...

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International Conference on computer and Information Technology (ICCIT)

作者： Hasibul Islam Peyal Md. Nazrul Islam Mondal Shafiun Miraz Dept. of Computer Science & Engineering Rajshahi University of Engineering & Technology Rajshahi Bangladesh

ISBN: (数字)9798331519094

ISBN: (纸本)9798331519100

Agriculture plays a crucial role in Bangladesh's economy, with corn serving as a key crop. Plant diseases pose significant threats to agricultural productivity and economic stability worldwide. Effective monitoring and prediction are essential to mitigate these risks, as the prevalence of infectious diseases severely disrupts agricultural production. This study focuses on diagnosing agricultural diseases and pests affecting corn stalks using convolutional neural networks (CNN) combined with an SVM classifier. The proposed model achieves a classification accuracy of 98.34%, comparable to transfer learning models such as VGG-16 (97.42%) and VGG-19 (97.13%), while utilizing approximately 0.95 million parameters—about 145 times and 151 times fewer than VGG-16 and VGG-19, respectively. The model exhibits outstanding performance, with accuracy, recall, and F1 scores nearing 97% and an exceptional Area Under Curve (AUC) score of 99%. Furthermore, the model's compact design requires minimal disk space (approximately 4 MB) and features a significantly reduced parameter count. To enhance interpretability, the study integrates explainable AI techniques, including Gradient Weighted Class Activation Mapping (Grad-CAM), Shapley Additive Explanations (SHAP), and Local Interpretable Model-Agnostic Explanations (LIME). These methods provide visual explanations through heatmaps, highlighting regions critical for classification, thereby facilitating a clearer understanding of the disease diagnosis process.

关键词： Support vector machines Visualization Plant diseases Accuracy Explainable AI Computational modeling Transfer learning Agriculture Stability analysis Convolutional neural networks

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Descriptor Feature Map for Classification

Descriptor Feature Map for Classification

引用

Cyber science and Technology Congress (CyberSciTech), IEEE

作者： Jongchae Lee Minseung Kim Kanghyun Jo Dept. of Electrical Electronic and Computer Engineering University of Ulsan Ulsan Republic of Korea

ISBN: (数字)9798331532093

ISBN: (纸本)9798331532109

This paper introduces a method to enhance ResNet18 by incorporating a Descriptor Feature Map(DFM). The approach uses the SIFT algorithm, which is mathematically validated for low-level feature extraction, to generate descriptors. These descriptors are then integrated into the model training. The Descriptor Feature Map, containing keypoints and descriptor information from SIFT, is combined with the original images as inputs to the ResNet18 model. Two separate networks are employed, one for the original images and one for the Descriptor Feature Map. The outputs of these networks are concatenated to improve performance. Experimental results show that the proposed model achieves approximately 16% higher classification accuracy on CIFAR-10 and 11% higher on CIFAR-100 compared to the baseline ResNet18. This method enhances both the interpretability and performance of CNN models.

关键词： Training Accuracy Feature extraction Mathematical models

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Sentiment Analysis of Reddit Posts Using the BERT Model in Peer-to-Peer Networks 12

Sentiment Analysis of Reddit Posts Using the BERT Model in P...

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12th International Conference on Intelligent Systems and Embedded Design, ISED 2024

作者： Dash, Prasant Kumar Singh, Tithi Panda, Rabi Shankar Budhiraja, Ritika Das, Chinmoyee Swain, Rajshree C.V Raman Global University Dept. of Computer Science and Engineering Odisha Bhubaneshwar India Dr.Akhilesh Das Gupta Institute of Professional Studies Dept. of Computer Science and Engineering New Delhi India Indian Institute of Technology Gandhinagar Dept. of Cognitive & Brain Sciences Gujarat Gandhinagar India

ISBN: (纸本)9798331522612

Sentiment analysis, a key area in Natural Language Processing (NLP), involves categorizing text data based on its emotional tone-positive, negative, or neutral. With the growing reliance on online interactions, understanding sentiments expressed in text is vital for assessing user opinions, behaviours, and engagement. In peer-to-peer (P2P) networks, where content sharing and decentralized user interaction dominate, sentiment analysis can uncover critical insights into digital relationships and collaborative tendencies. This paper explores sentiment analysis within P2P platforms using the BERT (Bidirectional Encoder Representations from Transformers) algorithm, a state-of-the-art NLP model. Unlike traditional methods, BERT effectively captures contextual and nuanced sentiments, enabling more accurate classification. The methodology includes preprocessing data, extracting embeddings using BERT, and employing fine-tuned models for sentiment categorization. Dimensionality reduction and visualization techniques further reveal patterns, sentiment clusters, and alignment between emotional tones in user interactions. Results demonstrate that BERT-powered sentiment analysis identifies content trends, emotional polarities, and behavioural dynamics in decentralized environments. The research also addresses challenges such as handling diverse content and biases in sentiment interpretation. This study highlights the growing need for advanced sentiment analysis techniques to enhance content profiling, trend forecasting, and user understanding on decentralized platforms, offering valuable implications for businesses and researchers. © 2024 IEEE.

关键词： Emotion Recognition

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Optical Mark Recognition with Object Detection and Clustering

Optical Mark Recognition with Object Detection and Clusterin...

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International Conference on electrical engineering and Information & Communication Technology (ICEEICT)

作者： Kaifa Tabassum Zhalok Rahman Dept. of Computer Science and Engineering Shahjalal University of Science and Technology Dhaka Bangladesh

ISBN: (数字)9798350385779

ISBN: (纸本)9798350385786

OMR, or optical mark recognition, is the automated analysis of human-marked documents. Presently, many competitive examinations rely on multiple-choice questions, and the answers to these questions are recorded in OMR sheets. In this study, we present an automated answer detection system from an OMR sheet image using computer vision techniques. Typically, evaluating an OMR sheet from an image involves scanning, extracting data, and interpreting the results through various image processing techniques. However, traditional approaches have limitations. They cannot detect empty responses or recognize multiple choices properly without a specified layout or total number of questions stated. We took a unique approach by employing an object detection model, YOLOv8n, to detect and classify OMR marks. To address overlapping bounding boxes, we implemented the DBSCAN clustering algorithm to effectively group the columns and determine question order. Our system is designed to work with multiple layouts, as it can detect columns through clustering. Furthermore, it can identify unanswered questions as well as multiple marked options. To train and evaluate our model, we created a custom dataset with five different OMR sheet layouts. We achieved a remarkable precision of 96.5%, a recall of 99.8%, and an mAP (mean average precision) of 97.3%. These results demonstrate the efficacy of our approach to OMR sheet analysis.

关键词： Integrated optics Image recognition Codes Text analysis Optical recording Layout Clustering algorithms

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