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Advancements in Animal Tracking: Assessing Deep Learning Algorithms

Advancements in Animal Tracking: Assessing Deep Learning Alg...

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International Conference on Computing and Networking Technology (ICCNT)

作者： Tahmina Akter Avishek Majumder Tanjim Mahmud Israt Binteh Habib Mohammad Shahadat Hossain Karl Andersson Dept. of Computer Science and Engineering Port City International University Chittagong Bangladesh Dept. of Computer Science and Engineering Rangamati Science and Technology University Rangamati Bangladesh Dept. of Computer Science and Engineering International Islamic University Chittagong Chittagong Bangladesh Dept. of Computer Science and Engineering University of Chittagong Chittagong Bangladesh Cybersecurity Laboratory Luleå University of Technology Luleå Sweden

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

In the rapidly advancing field of computer vision, object detection has become crucial for various applications, including animal tracking, face detection, and surveillance systems. This study investigates the efficacy of contemporary object detection methodologies by evaluating the performance of the You Only Look Once (YOLO) models and TensorFlow Model Zoo architectures for animal tracking. YOLO models, known for their ability to process entire images in real-time and predict bounding boxes and class probabilities simultaneously, offer significant advantages over traditional methods such as Convolutional Neural Networks (CNNs) and Fast R-CNNs. This paper compares the performance of YOLOv5 and YOLOv7, alongside TensorFlow-based models like Faster R-CNN ResNetv152 and SSD ResNet101, using a dataset of animal images. Our findings reveal that YOLOv5 outperforms other models with a mean average precision (mAP) of 9 7.5%, demonstrating superior accuracy and efficiency in object detection tasks. YOLOv7 also shows strong performance with an mAP of 96.7%, while TensorFlow Model Zoo’s Faster R-CNN and SSD models lag behind with mAPs of 81.9% and 81.6%, respectively. The results highlight the significant advancements in deep learning and object detection algorithms, particularly the advantages of YOLO’s architecture in handling complex detection tasks in real-world scenarios.

关键词： YOLO Deep learning Animals Computational modeling Surveillance computer architecture Predictive models Real-time systems Face detection Convolutional neural networks

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An Information-Theoretic Framework for Unifying Active Learning Problems 35

An Information-Theoretic Framework for Unifying Active Learn...

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35th AAAI Conference on Artificial Intelligence, AAAI 2021

作者： Nguyen, Quoc Phong Hsiang Low, Bryan Kian Jaillet, Patrick Dept. of Computer Science National University of Singapore Singapore Singapore Dept. of Electrical Engineering and Computer Science MIT United States

ISBN: (纸本)9781713835974

This paper presents an information-theoretic framework for unifying active learning problems: level set estimation (LSE), Bayesian optimization (BO), and their generalized variant. We first introduce a novel active learning criterion that subsumes an existing LSE algorithm and achieves state-of-the-art performance in LSE problems with a continuous input domain. Then, by exploiting the relationship between LSE and BO, we design a competitive information-theoretic acquisition function for BO that has interesting connections to upper confidence bound and max-value entropy search (MES). The latter connection reveals a drawback of MES which has important implications on not only MES but also on other MES-based acquisition functions. Finally, our unifying information-theoretic framework can be applied to solve a generalized problem of LSE and BO involving multiple level sets in a data-efficient manner. We empirically evaluate the performance of our proposed algorithms using synthetic benchmark functions, a real-world dataset, and in hyperparameter tuning of machine learning models. © 2021, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Benchmarking

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Differentially Private Federated Bayesian Optimization with Distributed Exploration 35

Differentially Private Federated Bayesian Optimization with ...

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35th Conference on Neural Information Processing Systems, NeurIPS 2021

作者： Dai, Zhongxiang Hsiang Low, Bryan Kian Jaillet, Patrick Dept. of Computer Science National University of Singapore Singapore Singapore Dept. of Electrical Engineering and Computer Science MIT United States

ISBN: (纸本)9781713845393

Bayesian optimization (BO) has recently been extended to the federated learning (FL) setting by the federated Thompson sampling (FTS) algorithm, which has promising applications such as federated hyperparameter tuning. However, FTS is not equipped with a rigorous privacy guarantee which is an important consideration in FL. Recent works have incorporated differential privacy (DP) into the training of deep neural networks through a general framework for adding DP to iterative algorithms. Following this general DP framework, our work here integrates DP into FTS to preserve user-level privacy. We also leverage the ability of this general DP framework to handle different parameter vectors, as well as the technique of local modeling for BO, to further improve the utility of our algorithm through distributed exploration (DE). The resulting differentially private FTS with DE (DP-FTS-DE) algorithm is endowed with theoretical guarantees for both the privacy and utility and is amenable to interesting theoretical insights about the privacy-utility trade-off. We also use real-world experiments to show that DP-FTS-DE achieves high utility (competitive performance) with a strong privacy guarantee (small privacy loss) and induces a trade-off between privacy and utility. © 2021 Neural information processing systems foundation. All rights reserved.

关键词： Deep neural networks

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IFA in Named Data Networking (NDN) : Case Study on INHERENT Topology 9

IFA in Named Data Networking (NDN) : Case Study on INHERENT ...

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9th International Conference on Wireless and Telematics, ICWT 2023

作者： Tiarama, Ribka Gumelar, Agil Fuad Rayhan, Muhammad Nur Pirusita, Nauroha Shofiyyah Jupriyadi Ahdan, Syaiful Syambas, Nana Rachmana Bandung Institute of Technology Dept. of Telecommunication Engineering Bandung40132 Indonesia Universitas Teknokrat Indonesia Faculty of Engineering & Computer Science Lampung35132 Indonesia

ISBN: (纸本)9798350305029

Named Data Networking (NDN) is part of an NSF research project that started in 2010 and was created to be implemented as a future Internet architecture. NDN as a network service has evolved from an Internet host-based packet delivery model to an Internet content-based packet delivery model. Attackers design various attacks for NDN networks by exploiting the interaction between interest packets and data packets. IFA is one type of DDoS attack on NDN networks. IFA attackers would overload intermediate routing nodes by requesting a large number of fake Interest packets. In this paper, the interest-flooding attack model is tested by varying the attack time and location of the attacker. The results show that IFA attacks are more effective when the consumer is closer to the producer due to the increased latency resulting from the attack. © 2023 IEEE.

关键词： Floods

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Drishyam: An Image is Worth a Data Prefetcher 23

Drishyam: An Image is Worth a Data Prefetcher

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Proceedings of the 32nd International Conference on Parallel Architectures and Compilation Techniques

作者： Shubdeep Mohapatra Biswabandan Panda Dept. of Computer Science and Engineering BITS Pilani Goa India Dept. name of Computer Science and Engineering Indian Institute of Technology Bombay Mumbai India

ISBN: (纸本)9798350342543

Hardware prefetching is a latency-hiding technique that hides the costly off-chip DRAM accesses. Although hardware prefetching is an extensively researched topic with many state-of-the-art data prefetchers pushing the performance limits, prefetching for irregular applications with hard-to-predict access patterns is still a challenging problem to solve. The usage of neural networks for hardware prefetching is a promising direction, especially for predicting irregular memory access patterns. This paper presents Drishyam, a novel hardware prefetcher based on computer vision algorithms that use images to learn memory access patterns and predict future memory accesses with high accuracy and coverage. For hardware prefetching, an image is a graphical representation of memory accesses observed over time. For a sequence of memory addresses, Drishyam creates images that predict the future addresses by predicting the future OS page and a cache line offset within the OS page. Drishyam outperforms Voyager, the state-of-the-art machine learning (ML) based prefetcher, for a set of irregular benchmarks by an average of 4.7% with an average prefetch accuracy and prefetch coverage of 89.5% and 66.6%, respectively. In terms of training time, Drishyam outperforms Voyager by 225.5%.

关键词： Cache

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Automated Bengali Sign Language Character Classification with Deep Learning Techniques

Automated Bengali Sign Language Character Classification wit...

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International Conference on Computing and Networking Technology (ICCNT)

作者： Tahmina Akter Tanjim Mahmud Tikle Barua Sultana Rokeya Naher Mohammad Shahadat Hossain Karl Andersson Dept. of Computer Science and Engineering Port City International University Chittagong Bangladesh Dept. of Computer Science and Engineering Rangamati Science and Technology University Rangamati Bangladesh Dept. of Computer Science and Engineering University of Information Technology and Sciences Dhaka Bangladesh Dept. of Computer Science and Engineering University of Chittagong Chittagong Bangladesh Cybersecurity Laboratory Luleå University of Technology Luleå Sweden

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Recognition of Bengali sign language characters is crucial for facilitating communication for the deaf and hard-of-hearing population in Bengali-speaking regions, which encompass approximately 430 million people worldwide. Despite the significant number of individuals requiring this support, research on Bengali sign language character recognition remains underdeveloped. This article presents a novel approach to categorize Bengali sign language characters using the Ishara-Lipi dataset, based on convolutional neural networks (CNNs) and pretrained models. We evaluated our approach using metrics such as accuracy, precision, recall, F1-score, and confusion matrices. Our findings indicate that the CNN model achieved the highest performance with an accuracy of 98%, followed by VGG19 with $\mathbf{94\%}$ and ResNet variants achieving around $\mathbf{88\%}$. The proposed model demonstrates robust and efficient classification capabilities, significantly bridging the gap in existing literature. This study holds substantial promise for enhancing assistive technology, thereby improving social inclusion and quality of life for Bengalispeaking deaf and hard-of-hearing individuals.

关键词： Measurement Deep learning Sign language Accuracy Assistive technologies Convolutional neural networks Character recognition

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CNN Autoencoder Resizer: A Power-Efficient LoS/NLoS Detector in MIMO-enabled UAV Networks

CNN Autoencoder Resizer: A Power-Efficient LoS/NLoS Detector...

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IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC)

作者： Azim Akhtarshenas Navid Ayoobi David Lopez-Perez Ramin Toosi Matin Amoozadeh dept. of Telecommunication Engineering Universitat Politècnica de València Valencia Spain dept. of Computer Science University of Houston Houston USA dept. of Electrical and Computer Engineering University of Tehran Tehran Iran

ISBN: (数字)9798350362244

ISBN: (纸本)9798350362251

Optimizing the design, performance, and resource efficiency of wireless networks (WNs) necessitates the ability to discern Line of Sight (LoS) and Non-Line of Sight (NLoS) scenarios across diverse applications and environments. Unmanned Aerial Vehicles (UAVs) exhibit significant potential in this regard due to their rapid mobility, aerial capabilities, and payload characteristics. Particularly, UAVs can serve as vital nonterrestrial base stations (NTBS) in the event of terrestrial base station (TBS) failures or downtime. In this paper, we propose CNN autoencoder resizer (CAR) as a framework that improves the accuracy of LoS/NLoS detection without demanding extra power consumption. Our proposed method increases the mean accuracy of detecting LoS/NLoS signals from 66% to 86%, while maintaining consistent power consumption levels. In addition, the resolution provided by CAR shows that it can be employed as a preprocessing tool in other methods to enhance the quality of signals.

关键词： Base stations Power demand Accuracy Wireless networks Autoencoders Autonomous aerial vehicles Automobiles Signal resolution Payloads Land mobile radio

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Demystifying PDFs: Emerging AI for text generation and operation on PDF

Demystifying PDFs: Emerging AI for text generation and opera...

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Innovation in Technology (ASIANCON), Asian Conference on

作者： Ashish Sharma Mannu Baveja Suhaj Kanikicherla Devansh Kaushik Yashuman Sharma Jessica Jessica Dept of Electrical Engineering Chandigarh University Mohali India Dept. of Computer Science Chandigarh University Mohali India

ISBN: (数字)9798350354218

ISBN: (纸本)9798350354225

AI is considered as most disruptive language and revolutionized various sectors with its ability analyze data with its large language model. incapability of other AI to read PDF and accept prompt and do text generation using their large language models .The work is based on proposing a new technique to integrate it into current AI. It will eliminate the need manual work of copy pasting Portable Document Format data to AI for prompt . In this can rely on Technologies Like Optical character recognition for reading images to text but challenging part is Portable Document Format to image and then image to text. this work is advancement of Optical character recognition which can eliminate the drawback of already existing technology.

关键词： Integrated optics Technological innovation Analytical models Large language models Optical character recognition Manuals Portable document format Optical imaging

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On Managing Test-Time, Power, and Layer Assignment in 3D SoCs with Built-In-Self-Repair Modules

On Managing Test-Time, Power, and Layer Assignment in 3D SoC...

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International Conference on VLSI Design

作者： Sabyasachee Banerjee Subhashis Majumder Bhargab B. Bhattacharya Dept. of Computer Science and Engineering Heritage Institute of Technology Kolkata India Dept. of Computer Science and Engineering Indian Institute of Technology Kharagpur Kharagpur India

Although built-in self-repair (BISR) techniques have been widely used to improve memory yield, their applications to the testing of 3D systems-on-chip (SoC) remained primarily unexplored. In this manuscript, we present a multi-stage approach to implement BISR in 3D SoCs with an aim to (i) reduce test time by proposing a test scheduling technique satisfying given power constraints, (ii) reduce the number of BISR modules, and (iii) to place BISR circuitry in suitable layers for facilitating thermal dissipation. Experimental results on several SoC benchmarks show that our approach reduces both test time as well as the cost of BISR architecture in most cases.

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DNN Object Detection in High-Resolution Multispectral Satellite Imagery with Varied Bit-dept. and Spectral Bands

DNN Object Detection in High-Resolution Multispectral Satell...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Trevor M. Bajkowski Curt H. Davis Grant J. Scott Dept. of Electrical Engineering and Computer Science Center for Geospatial Intelligence University of Missouri Columbia Missouri USA

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

While the availability of multispectral imagery (MSI) is increasing, there is still much to be learned regarding the spectral sensitivity of deep neural networks (DNNs). This work presents and analyzes DNNs trained to perform object detection in high-resolution multispectral, satellite imagery. These DNNs are first trained and evaluated on variations of the xView dataset with varied bit-dept.s and spectral band inclusion. Inference is also performed on test imagery with different bit-dept.s or with missing spectral information in order to analyze their importance for object detection. Initial experiments show an MSI-trained DNN had an average optimal F1-score 3.7 points higher than the best performing RGB network. Additional experiments suggest significant DNN sensitivity to changes in bit-dept. and an even greater sensitivity to the loss of any given spectral band. The results given here highlight the need for future research regarding spectral biases of DNNs for object detection in high-resolution MSI.

关键词： Training Sensitivity Image color analysis Artificial neural networks Object detection Metadata Transformers

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