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2022 IEEE Delhi Section Conference, DELCON 2022

作者： Sharma, Tripti Prasad, Sanjeev Kumar Sharma, Vedangi Inderprastha Engineering College Department of Computer Science-Data Science Ghaziabad India Galgotias University School of Computer Science and Engineering Greater Noida India Galgotias University School of Computing Science and Engineering U.P. Greater Noida India

ISBN: (纸本)9781665458832

Nowadays, video calling is very much on demand, and it is working well in 4G and 5G networks. It is a time for 3D calling, virtual reality live streaming and holographic communication;Accessing this type of data requires ultrahigh data rates and traffic volumes, so there is a need to speed up the network and increase bandwidth. On the other hand, a system has to maintain reliability, security, availability and flexibility in a wireless network. The network should be extended to the needs of human life such as entertainment, healthcare, smart cities, and transportation expected to enhance service quality with a high-end user experience. To establish this network, the telecommunications infrastructure must upgrade its unmatched service level requirements. The two major disruptive emerging components in this modern Internet-enabled era of technology are both the blockchain and the Internet of Things (IoT). In research and industry, IoT has experienced exponential growth, although it suffers from several limitations, such as poor interoperability, decentralization, privacy, and security vulnerabilities. 6G applications can be based on telecommunications networks and IoT, therefore, the network must ensure strong connectivity between nodes and also ensure the privacy and security of user data. Blockchain is the appropriate solution for these issues. Blockchain is a distributed ledger technology and it enables many industries to exchange data over the Internet between trusted parties without using a central server. The blockchain facilitates verifiable, transparent and secure digital asset transactions with proof of right and ownership. There is a need to identify the role of blockchain in 6G networks. This paper explores the challenges and opportunities related to blockchain for 6G networks, future application possibilities and upcoming research directions. © 2022 IEEE.

关键词： Internet of things

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Can Language Models Serve as Temporal Knowledge Bases?

Can Language Models Serve as Temporal Knowledge Bases?

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2022 Findings of the Association for Computational Linguistics: EMNLP 2022

作者： Zhao, Ruilin Zhao, Feng Xu, Guandong Zhang, Sixiao Jin, Hai National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology China Data Science and Machine Intelligence Lab University of Technology Sydney Sydney Australia

Recent progress regarding the use of language models (LMs) as knowledge bases (KBs) has shown that language models can act as structured knowledge bases for storing relational facts. However, most existing works only considered the LM-as-KB paradigm in a static setting, which ignores the analysis of temporal dynamics of world knowledge. Furthermore, a basic function of KBs, i.e., the ability to store conflicting information (i.e., 1-N, N-1 and N-M relations), is underexplored. In this paper, we formulate two practical requirements for treating LMs as temporal KBs: (i) the capacity to store temporally-scoped knowledge that contains conflicting information and (ii) the ability to use stored knowledge for temporally-scoped knowledge queries. We introduce a new dataset called LAMA-TK which is aimed at probing temporally-scoped knowledge, and investigate the two above requirements to explore the LM-as-KB paradigm in the temporal domain. On the one hand, experiments show that LMs can memorize millions of temporally-scoped facts with relatively high accuracy and transfer stored knowledge to temporal knowledge queries, thereby expanding the LM-as-KB paradigm to the temporal domain. On the other hand, we show that memorizing conflicting information, which has been neglected by previous works, is still challenging for LMs and hinders the memorization of other unrelated one-to-one relationships. © 2022 Association for Computational Linguistics.

关键词： Computational linguistics

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AccelES: Accelerating Top-K SpMV for Embedding Similarity via Low-bit Pruning

AccelES: Accelerating Top-K SpMV for Embedding Similarity vi...

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IEEE Symposium on High-Performance Computer Architecture

作者： Jiaqi Zhai Xuanhua Shi Kaiyi Huang Chencheng Ye Weifang Hu Bingsheng He Hai Jin National Engineering Research Center for Big Data Technology and System/Services Computing Technology and System Lab/Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China School of Computing National University of Singapore Singapore

ISBN: (数字)9798331506476

ISBN: (纸本)9798331506483

In the realm of recommendation systems, achieving real-time performance in embedding similarity tasks is often hindered by the limitations of traditional Top-K sparse matrix-vector multiplication (SpMV) methods, which suffer from high latency due to inefficient memory access patterns. This paper identifies these critical gaps and introduces AccelES, a novel approach that significantly enhances the efficiency of Top-K SpMV. Our method employs a two-stage calculation scheme: the first stage utilizes a compact, low-bit dataset to quickly identify the most relevant entries, while the second stage performs full-precision calculations solely on this pruned subset, thereby minimizing computational overhead. Furthermore, AccelES incorporates innovative matrix representations, Ultra-CSR and Random-CSR, which optimize memory bandwidth utilization. Experimental results demonstrate that AccelES accelerates performance, surpassing state-of-the-art FPGA, GPU, and CPU solutions by factors of 3.4×, 2.5×, and 153.3×, respectively, under controlled conditions. These advancements not only enhance processing speed but also significantly improve real-time performance in recommendation systems, establishing AccelES as a pivotal contribution to the field of Top-K sparse matrix-vector multiplication.

关键词： Quantization (signal) High dimensional data Memory management Graphics processing units Bandwidth Real-time systems Sparse matrices Recommender systems Field programmable gate arrays Optimization

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Monochromatic Image Dehazing Using Enhanced Feature Extraction Techniques in Deep Learning

Monochromatic Image Dehazing Using Enhanced Feature Extracti...

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Artificial Intelligence and Knowledge Discovery in Concurrent engineering (ICECONF), International Conference on

作者： Nisarg Doshi Sagar Bhavsar D Rajeswari R Srinivasan Department of Data Science and Business Systems School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Department of Computing Technologies School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur

Images photographed in foggy weather usually have poor visibility. To mitigate this problem researchers have come up with various image dehazing techniques. Now, more than ever, high-quality images that can be used to glean maximum information from autonomous systems are in high demand. This research work uses different Deep Learning (DL) architectures to draw out essential details from the picture and localize the information recovered to reduce the haze from the picture. The paper investigates to remove the hazes from the dehazed images using DL techniques. The first task of this proposed work attempts three pre-processing techniques namely, air light estimation, contextual regularization and boundary constraint. The second task of this work is to identify the suitable DL model to extract clear images from dehazed images. Evaluation metrics are PSNR value and SSIM value are used to estimate the values of dehazed images compared with clear images. Experimental results proves that AOD-Net outperforms good result with respect to PSNR value.

关键词： Deep learning Measurement Autonomous systems Atmospheric modeling Estimation Feature extraction Knowledge discovery

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High-Quality dataset-Sharing and Trade Based on A Performance-Oriented Directed Graph Neural Network

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IEEE Transactions on Automation Science and engineering 2025年 22卷 15576-15587页

作者： Zeng, Yingyan Zhou, Xiaona Chilukuri, Premith Kumar Lourentzou, Ismini Jin, Ran University of Cincinnati Department of Mechanical and Materials Engineering CincinnatiOH45242 United States University of Illinois Urbana-Champaign Siebel School of Computing and Data Science ChampaignIL61820 United States University of Illinois Urbana-Champaign School of Information Sciences ChampaignIL61820 United States Virginia Tech Department of Computer Science BlacksburgVA24060 United States Grado Department of Industrial and Systems Engineering BlacksburgVA24060 United States

The advancement of Artificial Intelligence (AI) models heavily relies on large high-quality datasets. However, in advanced manufacturing, collecting such data is time-consuming and labor-intensive for a single enterprise. Hence, it is important to establish a context-aware and privacy-preserving data sharing system to share small-but-high-quality datasets between trusted stakeholders. Existing data sharing approaches have explored privacy-preserving data distillation methods and focused on valuating individual samples tied to a specific AI model, limiting their flexibility across data modalities, AI tasks, and dataset ownership. In this work, we propose a performance-oriented representation learning (PORL) framework in a Directed Graph Neural Network (DiGNN). PORL distills raw datasets into privacy-preserving proxy datasets for sharing and learns compact meta data representations for each stakeholder locally. The meta data will then be used in DiGNN to forecast the AI model performance and guide the sharing via graph-level supervised learning. The effectiveness of the PORL-DiGNN is validated by two case studies: data sharing in the semiconducting manufacturing network between similar processes to create similar quality defect models;and data sharing in the design and manufacturing network of Microbial Fuel Cell anodes between upstream (design) and downstream (Additive Manufacturing) stages to create distinct but related AI models. © 2004-2012 IEEE.

关键词： data flow graphs

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Violence Detection System using MobileNetV2

Violence Detection System using MobileNetV2

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Applied Artificial Intelligence and computing (ICAAIC), International Conference on

作者： Rahul Kumar Aniket Gupta D Rajeswari Department of Data Science and Business Systems School of computing College of Engineering and technology SRM Institute of Science and Technology Kattankulathur Chennai India

ISBN: (数字)9798350375190

ISBN: (纸本)9798350375206

The number of cases of violence and fights has been increasing around the world. With the use of CCTV, such incidents can be recorded but the detection of Violence is a major issue around the globe, and it plays a vital role in the safety and security of the area. It helps in effective surveillance and law enforcement. The effectiveness of any violence detection system depends on the speed and accuracy with which it detects the events and how it performs over different types of videos of varied sizes and formats. In the proposed algorithm, the dataset used contains videos of different types and scenarios to help the model understand and train better. To improve the overall effectiveness of the model, Lightweight CNN MobileNetV2 has been used along with that Bidirectional LSTM (BiLSTM) has been used. The accuracy rate of the proposed model is 98%.

关键词： Analytical models Accuracy Computational modeling Surveillance Bidirectional control Feature extraction Security

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Analysis Comparison of Hard and Soft Voting Ensemble Model for Sentiment Analysis on IMDB Movie Reviews

Analysis Comparison of Hard and Soft Voting Ensemble Model f...

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International Conference on Informatics and computing (ICIC)

作者： Nur Ghaniaviyanto Ramadhan Gita Fadila Fitriana Data Science School of Computing Telkom University Bandung Indonesia Software Engineering Faculty of Informatics Telkom University Purwokerto Purwokerto Indonesia

ISBN: (数字)9798331517601

ISBN: (纸本)9798331517618

In the context of movie reviews, sentiment analysis plays a crucial role as these reviews often capture the emotions and opinions of viewers regarding the film. With the growing number of reviews across platforms like social media and movie review sites, it becomes feasible to automatically classify them into positive or negative sentiments. However, manually analyzing a dataset of 50,000 reviews is impractical. Therefore, this study focuses on conducting sentiment analysis on the IMDB movie review dataset, consisting of 50,000 entries, using an ensemble voting model. The machine learning models applied in this ensemble include Random Forest (RF) and Adaptive Boosting (AdaBoost). Two test scenarios were employed: the first compared the soft and hard voting approaches, while the second examined the impact of different training-test data splits. The findings indicate that the soft voting ensemble (SVE) model with an 80%-20% training-testing split achieved an accuracy of 88%, an F1-score of 88%, and an AUC of 0.94, which is 4% higher than the hard voting model.

关键词： Radio frequency Sentiment analysis Analytical models Adaptation models Accuracy Reviews Social networking (online) Motion pictures Ensemble learning Random forests

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Analysis of Labanotation generation based on motion capture data 5

Analysis of Labanotation generation based on motion capture ...

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5th International Conference on Artificial Intelligence and Computer Science, AICS 2023

作者： Chen, Boran Wang, Jiaji Miao, Zhenjiang Li, Jianpo Yang, Shengchun Jilin Electric Power Big Data Intelligent Processing Engineering Technology Research Center School of Computing Northeast Electric Power University Jilin Jilin132012 China Institute of Information Science School of Computer and Information Technology Beijing Jiaotong University Beijing100044 China

ISBN: (纸本)9781510668621

Labanotation is an approach to record human movement with different distributions in space that have different representations of symbols. Motion capture is a technique that records the trajectory of an object in the time domain by tracking the motion of key points, observed using different sensors, and recorded in files. This paper is to manually mark the Labanotation based on 3D human motion capture data, to divide the 3D human motion into gaits and poses, and to record and represent different human movements and positions in space in the notation. This is an exploration of motion capture data to generate Labanotation, while also contributing to the preservation of folk-art forms. © 2023 SPIE.

关键词： Time domain analysis

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Optimised Design of a Current Mirror in 150 nm GaAs Technology

Optimised Design of a Current Mirror in 150 nm GaAs Technolo...

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computing, Networking, Telecommunications & engineering Sciences Applications (CoNTESA), International Conference on

作者： Lua Ying Qian Chia Chao Kang Tan Jian Ding Mohammad Arif Sobhan Bhuiyan Khairun Nisa Minhad Mahdi H. Miraz Electrical and Electronics Engineering Xiamen University Malaysia Selangor Malaysia School of Computing and Data Science Xiamen University Malaysia Selangor Malaysia

ISBN: (数字)9798331534530

ISBN: (纸本)9798331534547

The Current Mirror (CM) is a basic building block commonly used in analogue and mixed-signal integrated circuits. Its significance lies in its ability to replicate and precisely regulate the current, making it crucial in various applications such as amplifiers, filters and data converters. Recently, there has been a growing need for smaller and more energy-efficient Radio Frequency (RF) devices due to the advancements in wireless communication, the Internet of Things (IoT) and portable electronics. This research aims to propose an improved and optimised CM design focusing on compactness and energy-efficient operation. Through a comprehensive methodology involving transistor sizing, biasing techniques, load resistance selection, frequency response stabilisation and noise analysis, the proposed high swing CM design achieves a gain of at least 6.005 dB, a reduced power consumption of 91.17 mW, a wide bandwidth of 22.60 kHz and improved linearity as well as accuracy through precise current matching and minimised mismatch. This optimised CM design will further boost the realisation of compact and lower power RF devices, contributing to the advancement of analogue circuit design techniques and enhancing system performance, accuracy and reliability.

关键词： Radio frequency Current mirrors Accuracy Power demand Gallium arsenide Bandwidth Energy efficiency Transistors Internet of Things Gain

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Selective State Space Model for Monaural Speech Enhancement

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IEEE Transactions on Consumer Electronics 2025年

作者： Chen, Moran Zhang, Qiquan Wang, Mingjiang Zhang, Xiangyu Liu, Hexin Ambikairaiah, Eliathamby Chen, Deying Harbin Institute of Technology School of Electronic and Information Engineering Shenzhen518055 China The University of New South Wales School of Electrical Engineering and Telecommunications Sydney2052 Australia Nanyang Technological University College of Computing and Data Science Singapore Singapore

Voice user interfaces (VUIs) have facilitated the efficient interactions between humans and machines through spoken commands. Since real-word acoustic scenes are complex, speech enhancement plays a critical role for robust VUI. Transformer and its variants, such as Conformer, have demonstrated cutting-edge results in speech enhancement. However, both of them suffers from the quadratic computational complexity with respect to the sequence length, which hampers their ability to handle long sequences. Recently a novel State Space Model called Mamba, which shows strong capability to handle long sequences with linear complexity, offers a solution to address this challenge. In this paper, we propose a novel hybrid convolution-Mamba backbone, denoted as MambaDC, for speech enhancement. Our MambaDC marries the benefits of convolutional networks to model the local interactions and Mamba's ability for modeling long-range global dependencies. We conduct comprehensive experiments within both basic and state-of-the-art (SoTA) speech enhancement frameworks, on two commonly used training targets. The results demonstrate that MambaDC outperforms Transformer, Conformer, and the standard Mamba across all training targets. Built upon the current advanced framework, the use of MambaDC backbone showcases superior results compared to existing SoTA systems. This sets the stage for efficient long-range global modeling in speech enhancement. © 2025 IEEE.

关键词： State space methods

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