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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Zhou, Kaiwen Chen, Zhilin Liu, Guochen Chen, Zhitang The Chinese University of Hong Kong Department of Computer Science and Engineering Hong Kong Huawei Noah's Ark Lab Canada

ISBN: (纸本)9781728163277

Precoding design is essential for massive multi-user multiple-input multiple-output (MU-MIMO) systems, which aims at maximizing the weighted sum-rate (WSR). This problem is known to be NP-hard, and iterative algorithms are typically used to approximately solve it. The weighted minimum mean-squared error (WMMSE) algorithm is a popular solver for WSR maximization, which efficiently finds a local maxima of WSR. In this work, we introduce a novel extrapolation technique to further accelerate WMMSE. This technique is inspired by the momentum technique in convex optimization, and can be interpreted as an accelerated second-order method. The merits of the proposed extrapolation technique are (i) lightweight, as it almost does not increase the iteration complexity, (ii) generic, since it works in various settings such as the sum power constraint or per-antenna power constraint cases and coordinated multi-point joint transmission networks, and (iii) effective, that our simulation results show it significantly accelerates the convergence of WMMSE in the high channel correlation regime. © 2023 IEEE.

关键词： Convex optimization

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Collaborative image compression and classification with multi-task learning for visual Internet of Things

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Chinese Journal of Aeronautics 2022年第5期35卷 390-399页

作者： Bing DU Yiping DUAN Hang ZHANG Xiaoming TAO Yue WU Congchong RU School of Computer and Communication Engineering University of Science and Technology BeijingBeijing 100083China Department of Electronic Engineering Tsinghua UniversityBeijing 100084China School of Computer Science and Technology Xidian UniversityXian 710071China Smart Network Computing Lab Cross-strait Tsinghua Research InstitutionBeijing 100084China

Widespread deployment of the Internet of Things(Io T)has changed the way that network services are developed,deployed,and *** onboard advanced Io T devices are equipped with visual sensors that form the so-called visual Io ***,the sender would compress images,and then through the communication network,the receiver would decode images,and then analyze the images for ***,image compression and semantic inference are generally conducted separately,and thus,current compression algorithms cannot be transplanted for the use of semantic inference directly.A collaborative image compression and classification framework for visual Io T applications is proposed,which combines image compression with semantic inference by using multi-task *** particular,the multi-task Generative Adversarial Networks(GANs)are described,which include encoder,quantizer,generator,discriminator,and classifier to conduct simultaneously image compression and *** key to the proposed framework is the quantized latent representation used for compression and *** with perceptual quality can achieve low bitrate compression and reduce the amount of data *** addition,the design in which two tasks share the same feature can greatly reduce computing resources,which is especially applicable for environments with extremely limited *** extensive experiments,the collaborative compression and classification framework is effective and useful for visual IoT applications.

关键词： Deep learning Generative Adversarial Network(GAN) Image classification Image compression Internet of Things

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Safety Helmet Detection of Workers in Construction Site using YOLOv8 26

Safety Helmet Detection of Workers in Construction Site usin...

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26th International Conference on computer and Information Technology, ICCIT 2023

作者： Mahmud, Syed Shakil Islam, Md. Ashraful Ritu, Khandaker Jannatul Hasan, Mahmudul Kobayashi, Yoshinori Mohibullah, Md Comilla University Department of Computer Science and Engineering Cumilla Bangladesh Saitama University Interactive Systems Lab. Saitama Japan

ISBN: (纸本)9798350359015

The safety of construction workers is a paramount concern in the modern construction industry. A significant proportion of injuries and fatalities on construction sites are attributed to a lack of adherence to safety regulations, often resulting from the non-utilization of Personal Protective Equipment (PPE) by workers. Additionally, effective monitoring of construction areas by site supervisors can be challenging due to the vastness and complexity of construction sites. Manual monitoring, while a crucial aspect of construction safety management, is often hindered by time constraints and associated costs. To address these challenges, the application of computer vision and Convolutional Neural Network(CNN) techniques has led to the development of automated helmet and jacket detection systems. This research employs the YOLOv8 object detection algorithm to explore the efficacy of safety helmet detection, aiming to enhance the accuracy of automated safety helmet detection systems. The YOLOv8 object detection algorithm is a robust tool for recognizing objects in images. Our dataset comprises a total of 4,200 images, including 4,000 images of hard hats and jackets obtained from Kaggle [25], supplemented by 200 images depicting foggy, rainy, and nighttime conditions from our custom collection sourced from the internet. The dataset was meticulously divided into training, testing, and validation sets, following a ratio of 60%, 20%, and 20%, respectively. The experimental results revealed that the YOLOv8m architecture achieved an accuracy of 92%, demonstrating its effectiveness in detecting safety helmets under various lighting conditions, including low-light environments. This performance highlights the potential of the proposed approach for real-world applications in construction site safety monitoring. This developed model primarily focuses on detecting helmets and jackets in real-time in adverse weather conditions such as rainy, foggy, and low-light environments. It trigg

关键词： and jacket computer vision helmet helmet detection image processing object detection openCV YOLOv8

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Classification of Lung Nodule from CT and PET/CT Images Using Artificial Neural Network

Classification of Lung Nodule from CT and PET/CT Images Usin...

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International Conference on Machine Vision and Augmented Intelligence, MAI 2022

作者： Hansdah, Malho Singh, Koushlendra Kumar Machine Vision and Intelligence Lab Department of Computer Science and Engineering National Institute of Technology Jamshedpur Jharkhand Jamshedpur831014 India

ISBN: (纸本)9789819901883

This work aims to design and develop an artificial neural network (ANN) architecture for the classification of cancerous tissue in the lung. A sequential model is used for the machine learning process. ReLU and Sigmoid activation functions have been used to supply weights to the model. The present work encompasses detecting and classifying the tumor cells into four categories. The four types of lung cancer nodules are adenocarcinoma, squamous-cell carcinoma, large-cell carcinoma, and small-cell carcinoma. Computed tomography (CT) and Positron emission tomography (PET) scan DICOM images are used for the classification. The proposed approach has been validated with the subset of the original dataset. A total of 6500 images have been taken in the experiment. The approach is to feed the CT scan images into ANNs and classify the image as the correct type. The dataset is provided by The Cancer Imaging Archive (TCIA). The dataset is titled "A Large-Scale CT and PET/CT Dataset for Lung Cancer Diagnosis." The tumor cells are classified using the ANN architecture with 99.6% of validation accuracy and 4.35% loss. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Neural networks

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GoT: Effective Graph-of-Thought Reasoning in Language Models

GoT: Effective Graph-of-Thought Reasoning in Language Models

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2024 Findings of the Association for Computational Linguistics: NAACL 2024

作者： Yao, Yao Li, Zuchao Zhao, Hai Department of Computer Science and Engineering Shanghai Jiao Tong University China MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University China National Engineering Research Center for Multimedia Software School of Computer Science Wuhan University Wuhan430072 China

ISBN: (纸本)9798891761193

With the widespread use of language models (LMs) in NLP tasks, researchers have discovered the potential of Chain-of-thought (CoT) to assist LMs in accomplishing complex reasoning tasks by generating intermediate steps. However, human thought processes are often non-linear, rather than simply sequential chains of thoughts. Therefore, we propose Graph-of-Thought (GoT) reasoning, which models human thought processes not only as a chain but also as a graph. By representing thought units as nodes and connections between them as edges, our approach captures the non-sequential nature of human thinking and allows for a more realistic modeling of thought processes. GoT adopts a two-stage framework with an additional GoT encoder for thought graph representation and fuses the graph representation with the original input representation through a gated fusion mechanism. We evaluate GoT's performance on a text-only reasoning task (AQUA-RAT) and a multimodal reasoning task (scienceQA). Our model achieves significant improvement over the strong CoT baseline on the AQUA-RAT test set and boosts accuracy from 85.19% to 87.59% using the T5-base model over the state-of-the-art Multimodal-CoT (Zhang et al., 2023) on the scienceQA test set. Our code is publicly available at https://***/Zoeyyao27/Graphof-Thought. © 2024 Association for Computational Linguistics.

关键词： Rats

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Where Are the Constants? New Insights on the Role of Round Constant Addition in the SymSum Distinguisher 1

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25th International Symposium on Stabilization, Safety, and Security of Distributed Systems, SSS 2023

作者： Suryawanshi, Sahiba Saha, Dhiman de.ci.phe.red Lab Department of Computer Science & Engineering Indian Institute of Technology Bhilai492015 India

ISBN: (数字)9783031442742

ISBN: (纸本)9783031442735

The current work makes a systematic attempt to describe the effect of the relative order of round constant (RCon) addition in the round function of an SPN cipher on its algebraic structure. The observations are applied to the SymSum distinguisher, introduced by Saha et al. in FSE 2017 which is one of the best distinguishers on the SHA3 hash function reported in literature. Results show that certain ordering (referred to as Type-LCN) of RCon makes the distinguisher less effective but it still works with some limitations. Results in the form of new SymSum distinguishers are reported on concrete Type-LCN constructions - NIST LWC competition finalist Xoodyak-Hash and its internal permutation Xoodoo. New linear structures are also reported on Xoodoo that augment the distinguisher to penetrate more rounds. Final results include SymSum distinguishers on 7 rounds of Xoodoo and 5 rounds of Xoodyak-Hash with complexity 2128 and 232, respectively. All practical distinguishers have been verified. The characterization encompassing the algebraic structure and effect of RCon provided by the current work improves the understanding of SymSum in general and constitutes one of the first such result on Xoodyak-Hash and Xoodoo. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Hash functions

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Edge Metric Dimension of Honeycomb and Hexagonal Networks for IoT

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computers, Materials & Continua 2022年第5期71卷 2683-2695页

作者： Sohail Abbas Zahid Raza Nida Siddiqui Faheem Khan Taegkeun Whangbo Department of Computer Science College of Computing and InformaticsUniversity of SharjahSharjah27272UAE Department of Mathematics College of SciencesUniversity of SharjahSharjah27272UAE Artificial Intelligence Lab Department of Computer EngineeringGachon UniversitySeongnam13557Korea

Wireless Sensor Network(WSN)is considered to be one of the fundamental technologies employed in the Internet of things(IoT);hence,enabling diverse applications for carrying out real-time *** navigation in such networks was the main motivation for the introduction of the concept of landmarks.A robot can identify its own location by sending signals to obtain the distances between itself and the *** networks to be a type of graph,this concept was redefined as metric dimension of a graph which is the minimum number of nodes needed to identify all the nodes of the *** idea was extended to the concept of edge metric dimension of a graph G,which is the minimum number of nodes needed in a graph to uniquely identify each edge of the *** plane networks can be easily constructed by repeating regular *** design is of extreme importance as it yields high overall performance;hence,it can be used in various networking and IoT *** honeycomb and the hexagonal networks are two such popular mesh-derived parallel *** this paper,it is proved that the minimum landmarks required for the honeycomb network HC(n),and the hexagonal network HX(n)are 3 and 6 *** bounds for the landmarks required for the hex-derived network HDN1(n)are also proposed.

关键词： Edge metric dimension internet of things wireless sensor network honeycomb network hexagonal network hex-derived networks

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Automated Maternal Fetal Ultrasound Image Identification Using a Hybrid Vision Transformer Model 27th

Automated Maternal Fetal Ultrasound Image Identification Usi...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Krishna, Thunakala Bala Poreddy, Ajay Kumar Reddy Sindhu, Kolla Gnapika Kokil, Priyanka Lab Department of Electronics and Communication Engineering Indian Institute of Information Technology Design and Manufacturing Kancheepuram Chennai600127 India Amrita School of Computing Department of Computer Science and Engineering Amrita Vishwa Vidyapeetham Tamil Nadu Chennai India

ISBN: (纸本)9783031781940

Ultrasound (US) technology has revolutionized prenatal care by offering noninvasive, real-time visualization of maternal-fetal anatomy. The accurate classification of maternal-fetal US planes is a critical segment of effective prenatal diagnosis. However, the inherent inter-class variance among different fetal US images presents a significant hurdle, making fetal anatomy detection a laborious and time-consuming task, even for experienced sonographers. This paper proposes a novel approach using a Hybrid Vision Transformer (H-ViT) for automated fetal anatomical plane classification to address these challenges. The proposed method utilizes hierarchical features extracted from DenseNet-121, which are then inputted into the vision transformer to analyze complex spatial relationships and patterns within fetal US images. By incorporating both global and local features, the proposed method enhances feature discriminability, thus alleviating low inter-class variance. The effectiveness of the H-ViT is evaluated using the largest publicly available maternal-fetal US image dataset. The experimental results rigorously demonstrate the superiority of our approach, achieving an accuracy of 96.60% compared to other state-of-the-art techniques. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Convolutional neural networks

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Machine Learning-Based Malware Detection and Classification in Encrypted TLS Traffic 1

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Second International Conference on Security, Privacy and Data Analytics, ISPDA 2022

作者： Kashyap, Himanshu Pais, Alwyn Roshan Kondaiah, Cheemaladinne Information Security Research Lab Department of Computer Science and Engineering National Institute of Technology Karnataka Karnataka Surathkal575025 India

ISBN: (数字)9789819935697

ISBN: (纸本)9789819935680

Malware has become a significant threat to Internet users in the modern digital era. Malware spreads quickly and poses a significant threat to cyber security. As a result, network security measures play an important role in countering these cyber threats. Existing malware detection techniques are unable to detect them effectively. A novel Ensemble Machine Learning (ML)-based malware detection technique from Transport Layer Security (TLS)-encrypted traffic without decryption is proposed in this paper. The features are extracted from TLS traffic. Based on the extracted features, malware detection is performed using Ensemble ML algorithms. The benign and malware file datasets are created using features extracted from TLS traffic. According to the experimental results, the 65 new extracted features perform well in detecting malware from encrypted traffic. The proposed method achieves an accuracy of 99.85% for random forest and 97.43% for multiclass classification for identifying malware families. The ensemble model achieved an accuracy of 99.74% for binary classification and 97.45% for multiclass classification. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Cryptography

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Joint Design of Probabilistic Constellation Shaping and Precoding for Multi-user VLC Systems

Joint Design of Probabilistic Constellation Shaping and Prec...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Nguyen, Thang K. Pham, Thanh V. Le, Hoang D. Nguyen, Chuyen T. Pham, Anh T. The University of Aizu Computer Communications Lab. Japan Shizuoka University Department of Mathematical and Systems Engineering Japan Hanoi University of Science & Technology School of Electrical and Electronic Engineering Viet Nam

ISBN: (纸本)9798350351255

This paper proposes a joint design of probabilistic constellation shaping (PCS) and precoding to enhance the sum-rate performance of multi-user visible light communications (VLC) broadcast channels subject to signal amplitude constraint. In the proposed design, the transmission probabilities of bipolar M-pulse amplitude modulation (M-PAM) symbols for each user and the transmit precoding matrix are jointly optimized to improve the sum-rate performance. The joint design problem is shown to be a complex non-convex problem due to the non-convexity of the objective function. To tackle the problem, the firefly algorithm (FA), a nature-inspired heuristic optimization approach, is employed to solve a local optima to the original non-convex optimization problem. The FA-based approach, however, suffers from high computational complexity. Therefore, we propose a low-complexity design based on zero-forcing (ZF) precoding, which is solved using an alternating optimization (AO) approach. Simulation results reveal that the proposed joint design with PCS significantly improves the sum-rate performance compared to the conventional design with uniform signaling. Some insights into the optimal symbol distributions of the two joint design approaches are also provided. © 2024 IEEE.

关键词： Convex optimization

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