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SroX: Accelerating Hidden Weight Creation in Neural Networks via Spectral Relaxation and X-Iteration 2

SroX: Accelerating Hidden Weight Creation in Neural Networks...

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2nd International Conference on Advanced Computing and Communication Technologies, ICACCTech 2024

作者： Agarwal, Harshit Middha, Parth Thakur, Ayush Singh, Sofia Saraswat, Shipra Bhatia, Neha Amity University Dept. of Computer Science & Engineering Noida India Amity Institute of Information Technology Amity university Noida India Panipat Institute of Engineering & Technology Dept. of Information Technology Panipat India

ISBN: (纸本)9798331519056

This paper proposes SroX, a novel approach for accelerating Hidden Weight creation in neural networks, which is a critical component of neural network training that can significantly impact the overall efficiency of the training process. SroX leverages spectral relaxation and X-Iteration to reduce the computational time required for Hidden Weight creation, enabling faster training times without sacrificing accuracy. Our approach addresses the long-standing challenge of efficient Hidden Weight creation, which has been a major bottleneck in neural network training. We demonstrate the efficacy of SroX through extensive experiments on benchmark datasets, including MNIST, CIFAR-10, and ImageNet, where SroX outperforms existing methods, including Random Projection and Gradient-Based Optimization, in terms of training time and accuracy. The proposed approach provides a promising solution for large-scale neural network applications, where efficient training is crucial, and has the potential to revolutionize the field of neural network research and applications, driving innovation and breakthroughs in areas such as computer vision, natural language processing, and beyond. By providing a faster and more efficient way to create Hidden Weights, SroX can enable researchers and practitioners to tackle complex problems with unprecedented speed and efficiency, leading to significant advancements in the field of artificial intelligence. © 2024 IEEE.

关键词： Accelerated Training Hidden Weights Neural Networks Spectral Relaxation X-Iteration

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Design and Development of Lemmatizer for KannadaVachana Sahithya - A Hybrid Approach 4

Design and Development of Lemmatizer for KannadaVachana Sahi...

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4th International Conference on Multimedia Processing, Communication and information Technology, MPCIT 2024

作者： Lavanya, C.B. Nagendra Swamy, H.S. Pavan Kumar, M.P. University of Mysore Dept. of Studies in Computer Science Mysore India J.N.N College of Engineering Dept. of Information Science & Engg. Shimoga India

ISBN: (纸本)9798350375466

This paper presents a hybrid approach for the design and development of Lemmatizer for Kannada Vachana Sahithya. Kannada has a rich literary history and is officially recognized as one of the India's classical languages. The existing work on Kannada Morphological Analysis and Lemmatization is conducted on Kannada nouns and verbs from 'Kannada Shabdha Kosha' and Kannada text from Government Documents, Circulars and news from All India Radio. In this paper, a hybrid lemmatizer for Kannada Vachana Sahithya is designed and evaluated on an annotated dataset of 5,735 words. In 5735 words of KVS, 80% (4588) are considered for training and 20% (1147) words are used as testing data. The experimental results with the hybrid lemmatizer demonstrate that the proposed model achieves approximately 90% accuracy across various types of KVS input text. © 2024 IEEE.

关键词： Natural language processing systems

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Enhancing Online Learning Monitoring with Novel Image Recognition Method Using Dlib for Eye Feature Detection 12

Enhancing Online Learning Monitoring with Novel Image Recogn...

引用

12th International Conference on Awareness science and Technology, iCAST 2023

作者： Yeh, ChunHsiu Shen, Wei-Cheng Chi, Hung-Yu Lin, Chin-En Chen, Jong-Shin Dept. of Information Engineering and Computer Science Feng Chia University Taichung Taiwan College of Computer Science Jimei University Xiamen China Dept. of Information Communication and Engineering ChaoYang University of Technology Taichung Taiwan

ISBN: (纸本)9798350324693

In the post-pandemic era, online learning has become a prevalent mode of education. To ensure effective monitoring of learners' engagement in online courses, this study proposes a novel method for real-time monitoring of learners' learning focus. It is divided into three main components: The first component is eye detection. We utilize Dlib technology and leverage pre-trained facial landmark detection to accurately identify the positions of the eyes. Through image processing and the application of appropriate thresholds (thresholds set above 190), this enables precise outlining of eye contours and recognition of eye movement characteristics, even during rapid eye movements or from varying angles. The second component is gaze estimation. By using adaptive thresholds for pupil segmentation and applying erosion and dilation to reduce image noise and enhance essential features, we can effectively determine pupil positions within images taken under varying photographic resolutions and lighting conditions. Angle calculation is subsequently employed to determine whether learners are concentrating on online learning courses. The third component involves an anti-cheating mechanism. By utilizing pupil-centered corneal reflex (PCCR) technology to address potential cheating behaviors of learners, an anti-cheating mechanism is proposed to detect and counter fraudulent tactics such as photos and makeup. Experimental results demonstrate that this study is capable of effectively monitoring learners' learning status and accurately determining whether learners exhibit appropriate learning attitudes and behaviors. This empowers a comprehensive assessment of learners' learning outcomes during the period of online learning. © 2023 IEEE.

关键词： Eye movements

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On Min-Max Graph Balancing with Strict Negative Correlation Constraints 34

On Min-Max Graph Balancing with Strict Negative Correlation ...

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34th International Symposium on Algorithms and Computation, ISAAC 2023

作者： Kuo, Ting-Yu Chen, Yu-Han Frosini, Andrea Hsieh, Sun-Yuan Tsai, Shi-Chun Kao, Mong-Jen Dept. of Computer Science National Yang-Ming Chiao-Tung University Hsinchu Taiwan Dept. of Computer Science and Information Engineering National Cheng-Kung University Tainan Taiwan Dept. of Mathematics and Informatics University of Florence Italy Dept. of Computer Science and Information Engineering National Chi-Nan University Puli Taiwan

ISBN: (纸本)9783959772891

We consider the min-max graph balancing problem with strict negative correlation (SNC) constraints. The graph balancing problem arises as an equivalent formulation of the classic unrelated machine scheduling problem, where we are given a hypergraph G = (V, E) with vertex-dependent edge weight function p: E × V 7→ Z≥0 that represents the processing time of the edges (jobs). The SNC constraints, which are given as edge subsets C1, C2, . . ., Ck, require that the edges in the same subset cannot be assigned to the same vertex at the same time. Under these constraints, the goal is to compute an edge orientation (assignment) that minimizes the maximum workload of the vertices. In this paper, we conduct a general study on the approximability of this problem. First, we show that, in the presence of SNC constraints, the case with maxe∈E |e| = maxi |Ci| = 2 is the only case for which approximation solutions can be obtained. Further generalization on either direction, e.g., maxe∈E |e| or maxi |Ci|, will directly make computing a feasible solution an NP-complete problem to solve. Then, we present a 2-approximation algorithm for the case with maxe∈E |e| = maxi |Ci| = 2, based on a set of structural simplifications and a tailored assignment LP for this problem. We note that our approach is general and can be applied to similar settings, e.g., scheduling with SNC constraints to minimize the weighted completion time, to obtain similar approximation guarantees. Further cases are discussed to describe the landscape of the approximability of this prbolem. For the case with |V | ≤ 2, which is already known to be NP-hard, we present a fully-polynomial time approximation scheme (FPTAS). On the other hand, we show that the problem is at least as hard as vertex cover to approximate when |V | ≥ 3. © Ting-Yu Kuo, Yu-Han Chen, Andrea Frosini, Sun-Yuan Hsieh, Shi-Chun Tsai, and Mong-Jen Kao;licensed under Creative Commons License CC-BY 4.0.

关键词： Computational complexity

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Detect Bangladeshi Mango Leaf Diseases Using Lightweight Convolutional Neural Network 3

Detect Bangladeshi Mango Leaf Diseases Using Lightweight Con...

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3rd International Conference on Electrical, computer and Communication engineering, ECCE 2023

作者： Mahbub, Nosin Ibna Naznin, Feroza Hasan, Md Imran Shifat, Syed Mahfuzur Rahman Hossain, Md. Alamgir Islam, Md Zahidul Islamic University Dept. of Information and Communication Technology Kushtia7003 Bangladesh Green University Dept. of Computer Science and Engineering Dhaka Bangladesh Islamic University Dept. of Computer Science and Engineering Kushtia7003 Bangladesh University of Global Village Dept. of Computer Science and Engineering Barishal Bangladesh

ISBN: (纸本)9798350345360

This research concentrates on the diagnosis of common mango leaf diseases in Bangladesh using image processing via deep learning. Mango production could be raised by at least 28% globally if the crop could be safeguarded from a variety of diseases. However, without the assistance of an expert, it is challenging for the farmer to detect the disease at the appropriate time. Few studies have been conducted to identify the mango leaf disease present in Bangladesh. So far, no study has been done to identify the seven distinct mango leaf diseases reported in Bangladesh. We proposed a lightweight convolutional neural network (LCNN) in this paper to accurately classify seven distinct mango leaf diseases as well as normal mango leaf. To assess the proposed LCNN model, performance is compared to several pre-trained models such as VGG16, Resnet50, Resnet101, and Xception, and it is found that LCNN achieves the highest testing accuracy (98%). © 2023 IEEE.

关键词： Convolutional neural networks

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A novel approach incorporating feature extraction followed by YOLOv7 for quality enhancement of mangoes in Bangladesh 14

A novel approach incorporating feature extraction followed b...

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14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Kundu, Debashis Rahaman, Md. Mushfiqur Sakib, Md. Nazmus Abdullah Al Sohan, Md. Faruk Fardaush Mithu, Jannatul Hossain, Afzal Aktar, Nishita American International University- Bangladesh Dept. Management Information Systems Dhaka Bangladesh Daffodil Institute of It Dept. of Computer Science and Engineering Dhaka Bangladesh World University of Bangladesh Dept. of Computer Science and Engineering Dhaka Bangladesh American International University- Bangladesh Dept. of Computer Science Dhaka Bangladesh North South University Dept. of Electrical and Computer Engineering Dhaka Bangladesh Savar Dept. of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350335095

Qualitative approach for automated grading and quality assessment of fruits, machine learning techniques are crucial in agricultural applications. Automation enhances a nation's agricultural quality, production, and economic prosperity. Fruit quality grading, particularly the surface fault identification of a fruit, is a crucial indicator in the export market. This is particularly important for mangoes, which are quite well-liked inBangladesh. On the other hand, the physical grading of mangoes is a procedure that is labor-intensive, prone to error, and very subjective. In this paper, we proposed a YOLOv7 integrated Discrete wave transformation computer vision system. The proposed model includes support vector machine (SVM) and decision tree for the classification of high-quality mangoes. The results of the experiments show that the proposed solution obtained 96.25% accuracy when the system was trained and tested using a publicly accessible mango database. © 2023 IEEE.

关键词： Support vector machines

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Unified Object Detection Model for Image Style Convertible Modalities

Unified Object Detection Model for Image Style Convertible M...

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2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023

作者： Li, Mu-Chuan Chen, Xiu-Zhi Chen, Yen-Lin National Taipei University of Technology Dept. of Computer Science & Information Engineering Taipei Taiwan

ISBN: (纸本)9798350324174

Object detection is an important task in computer vision. In earlier studies, object detection mostly works on single-modal. But recently, in order to improve detection efficiency, researchers start adding more modals for improving the robustness of detection result. In this paper, we assume that all input modals which can convert to image style are able to complete object detection task through a unified model. We choose YOLOv7 as our baseline model and use RGB and infrared images from the public dataset Camel to test our hypothesis. Based on our experiment results, training RGB and IR images together obtains better performance than training individually, which shows that all image style modalities are possibly handled by a unified model and even achieve better results. © 2023 IEEE.

关键词： Object detection

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Latency of spatial audio plugins: a comparative study

Latency of spatial audio plugins: a comparative study

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2023 Immersive and 3D Audio: from Architecture to Automotive, I3DA 2023

作者： Tomasetti, Matteo Farina, Angelo Turchet, Luca University of Trento Dept. of Information Engineering and Computer Science Trento Italy University of Parma Dept. of Engineering and Architecture Parma Italy

ISBN: (纸本)9798350311044

The use of spatial audio plugins (SAPs) with Ambisonics processing and binaural rendering has become widespread in the last decade, thanks to their increased accessibility and usability. SAPs are particularly relevant in scenarios involving real-time music playing with headphones, such as networked music performance and individual recreational music-making using backing tracks. However, a crucial issue that has been largely overlooked thus far is the measurement of the processing latency introduced by currently available SAPs. Identifying which SAPs are the fastest is essential to enable designers, musicians, and researchers to create time-sensitive applications involving 3D audio. To bridge this gap, we compared nine systems formed by different SAPs that enable 3D audio management. We measured the latency of each system throughout the third-order Ambisonics plugins pipeline: encoding, room simulation, sound scene rotation, and binaural decoding. In particular, the measurements were performed utilizing different buffer sizes. Results showed that to achieve a minimization of the latency, it is necessary to use a combination of different SAPs from different systems. Based on our measurements, we propose two spatial audio systems that mix different SAPs. Considering a sampling rate of 48 kHz, a Dell Alienware x15 R2 laptop running the Windows 10 operating system, and an RME Fireface UFX sound card, the two systems achieved an overall latency of 0.33 ms and 0.94 ms respectively. © 2023 IEEE.

关键词： Audio systems

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An Advanced MIMO Multicarrier Framework for NTN-Based 5G Broadcast/Multicast Transmissions 11

An Advanced MIMO Multicarrier Framework for NTN-Based 5G Bro...

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11th IEEE International Workshop on Metrology for AeroSpace, MetroAeroSpace 2024

作者： Rahman, Talha Faizur Marojevic, Vuk Sacchi, Claudio Mississippi State University Dept. of Electrical and Computer Engineering Mississippi StateMS United States University of Trento Dept. of Information Engineering and Computer Science Trento Italy The University of New Mexico Dept. of Electrical Engineering AlbuquerqueNM United States

ISBN: (纸本)9798350385045

Satellites are teaming up with 5G, forming a non-terrestrial network (NTN), to support broadband applications over wide coverage areas. However, low latency and high reliability are the challenges in NTN which is addressed through the use of low-earth orbit (LEO) satellites. High transmission efficiency can be accomplished by employing multiple-input multiple-output (MIMO) antenna systems and multicarrier wave-forms. Conventional multicarrier techniques, such as Orthogonal Frequency Division Multiplexing (OFDM) and its variants, are sensitive to the nonlinear distortion of high power amplifiers (HPAs) and high Doppler environments. Therefore, we consider Constant Envelope OFDM (CE-OFDM) assisted with Space-Time Shift Keying (STSK) and Low-Density Parity Check (LDPC) encoding for 5G broadcast/multicast systems based on LEO satellites working in the L-band. We propose a low complexity receiver based on A Posteriori Probability (APP) STSK decoding integrated with LDPC decoding, in which we derive the APP of LDPC coded bits for the STSK-CE-OFDM transmission. We analyze the computational complexity of the proposed receiver scheme and conclude that it is effective in counteracting HPA nonlinearity and high Doppler effects inherent to LEO satellites. Numerical results show that the proposed framework outper-forms conventional multicarrier techniques with a gain as high as 16 dB in the presence of amplifier nonlinearity. © 2024 IEEE.

关键词： Orthogonal frequency division multiplexing

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Dynamic Asset Allocation Using Trading Agent

Dynamic Asset Allocation Using Trading Agent

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2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023

作者： You, Shingchern D. Liu, Chien-Hung Yen, Ching-Cheng National Taipei Universiyt of Technology Dept. of Computer Science and Information Engineering Taipei Taiwan

ISBN: (纸本)9798350324174

We study the use of PPO (Proximal Policy Optimization) algorithm for trading ETFs belonging to different asset classes. The studied asset classes include common stocks, bonds, REIT (Real Estate Investment Trust), gold, and future contracts on agricultural commodities. When properly training the PPO agent with the proposed ratio allocation strategy, the agent outperforms the static, periodical rebalancing approach. © 2023 IEEE.

关键词： Investments

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