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30th IEEE Real-Time and Embedded Technology and Applications Symposium, RTAS 2024

作者： Kang, Woosung Lee, Jinkyu Lee, Youngmoon Oh, Sangeun Lee, Kilho Chwa, Hoon Sung DGIST Dept. of Electrical Engineering and Computer Science Korea Republic of Dept. of Computer Science and Engineering Korea Republic of Hanyang University Dept. of Robotics Korea Republic of Ajou University Department of Software and Computer Engineering Korea Republic of School of AI Convergence Soongsil University Korea Republic of

ISBN: (纸本)9798350358414

The increasing complexity and memory demands of Deep Neural Networks (DNNs) for real-Time systems pose new significant challenges, one of which is the GPU memory capacity bottleneck, where the limited physical memory inside GPUs impedes the deployment of sophisticated DNN models. This paper presents, to the best of our knowledge, the first study of addressing the GPU memory bottleneck issues, while simultaneously ensuring the timely inference of multiple DNN tasks. We propose RT-Swap, a real-Time memory management framework, that enables transparent and efficient swap scheduling of memory objects, employing the relatively larger CPU memory to extend the available GPU memory capacity, without compromising timing guarantees. We have implemented RT-Swap on top of representative machine-learning frameworks, demonstrating its effectiveness in making significantly more DNN task sets schedulable at least 72% over existing approaches even when the task sets demand up to 96.2% more memory than the GPU's physical capacity. © 2024 IEEE.

关键词： Deep neural networks

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Deep Semantic and Attentive Network for Unsupervised Video Summarization

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ACM Transactions on Multimedia Computing, Communications and Applications 2022年第2期18卷 1-21页

作者： Zhong, Sheng-Hua Lin, Jingxu Lu, Jianglin Fares, Ahmed Ren, Tongwei College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Department of Electrical Engineering Computer Systems Engineering Program Faculty of Engineering at Shoubra Benha University Cairo Egypt State Key Laboratory for Novel Software Technology Nanjing University Nanjing210023 China

With the rapid growth of video data, video summarization is a promising approach to shorten a lengthy video into a compact version. Although supervised summarization approaches have achieved state-of-the-art performance, they require frame-level annotated labels. Such an annotation process is time-consuming and tedious. In this article, we propose a novel deep summarization framework named Deep Semantic and Attentive Network for Video Summarization (DSAVS) that can select the most semantically representative summary by minimizing the distance between video representation and text representation without any frame-level labels. Another challenge associated with video summarization tasks mainly originates from the difficulty of considering temporal information over a long time. Long Short-Term Memory (LSTM) performs well for temporal dependencies modeling but does not work well with long video clips. Therefore, we introduce a self-attention mechanism into our summarization framework to capture the long-range temporal dependencies among the frames. Extensive experiments on two popular benchmark datasets, i.e., SumMe and TVSum, show that our proposed framework outperforms other state-of-the-art unsupervised approaches and even most supervised methods. © 2022 Association for Computing Machinery.

关键词： Semantics

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Stock Price Prediction using CuDNNLSTM and multiple CNN layers

Stock Price Prediction using CuDNNLSTM and multiple CNN laye...

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2023 IEEE International Conference on Artificial Intelligence, Blockchain, and Internet of Things, AIBThings 2023

作者： Kanwal, Anika Chandrasekaran, Siva Akhunzada, Adnan Swinburne University of Technology Department of Computer Science and Software Engineering Melbourne Australia University of Doha for Science and Technology Department of Computing and Information Technology Doha Qatar

ISBN: (纸本)9798350322347

Academic and financial sectors are interested in research areas that focus on understanding the patterns of financial activities and predicting their future changes. The daily movement of financial data involves complex, insufficient, and ambiguous information, making its prediction an extremely difficult endeavour. The issues of forecasting and analysing financial data is therefore complex and time-dependent. Deep neural networks (DNNs) can be used to solve nonlinear problems more effectively than conventional machine learning techniques. This study proposes an intelligent and optimal model CuDNNLSTM-Multi(1dCNN) for stock market price prediction using a hybrid of CUDA based Long Short Term Memory (LSTM) and Multiple One Dimensional Convolution Neural Networks (CNNs). A variety of models, including Multiple CuDNNLSTM-CNN, Multi-CuDNNLSTM, Multi (1dCNN), are used to compare the prediction performance of the proposed model. In terms of RMSE, MAE, and MAPE, the results show that the suggested model outperforms all hybrid and individual models. © 2023 IEEE.

关键词： Forecasting

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A Provably Secure Lightweight Key Agreement Protocol for Wireless Body Area Networks in Healthcare System

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IEEE Transactions on Industrial Informatics 2023年第2期19卷 1683-1690页

作者： Zia, Maryam Obaidat, Mohammad S. Mahmood, Khalid Shamshad, Salman Saleem, Muhammad Asad Chaudhry, Shehzad Ashraf Comsats University Islamabad Department of Computer Science Sahiwal57000 Pakistan University of Jordan King Abdullah Ii School of Information Technology Amman11942 Jordan University of Science and Technology Beijing School of Computer and Communication Engineering Beijing100083 China Amity University UP Noida201301 India National Yunlin University of Science and Technology Future Technology Research Center Yunlin64002 Taiwan The University of Lahore Department of Software Engineering Lahore54590 Pakistan Abu Dhabi University Department of Computer Science and Information Technology College of Engineering Abu Dhabi United Arab Emirates Nisantasi University Department of Computer Engineering Faculty of Engineering and Architecture Istanbul34398 Turkey

Wireless Body Area Network (WBAN) is a vital application of the Internet of Things (IoT) that plays a significant role in gathering a patient's healthcare information. This collected data helps special professionals like doctors or physicians analyze patients' health status to cure different diseases. However, collecting such information from an insecure channel can be threatening due to the potential security threats. Therefore, it is crucial to secure this sensitive information. This article proposes a secure and lightweight authentication protocol for WBAN. The devised protocol is scalable, secure, and lightweight compared to various relevant competing protocols. The informal security analysis shows that the designed protocol is lightweight, secure, and efficient in resisting various major attacks. The performance analysis demonstrates our protocol's supremacy over various competing protocols in terms of computation and communication costs, inducing efficiency of 20.3% and 12.3%, respectively. Moreover, the practical performance of the designed protocol from the network point of view is measured using the widely recognized NS3 simulation tool. © 2005-2012 IEEE.

关键词： Authentication

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Artificial Intelligence-based Speech Signal for COVID-19 Diagnostics 6

Artificial Intelligence-based Speech Signal for COVID-19 Dia...

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6th International Conference on Future Networks and Distributed Systems, ICFNDS 2022

作者： Alfaidi, Aseel Alshahrani, Abdullah Aljohani, Maha Department of Computer Science and Artificial Intelligence College of Computer Science and Engineering University of Jeddah Saudi Arabia Department of Software Engineering College of Computer Science and Engineering University of Jeddah Saudi Arabia

ISBN: (纸本)9781450399050

The speech signal has numerous features that represent the characteristics of a specific language and recognize emotions. It also contains information that can be used to identify the mental, psychological, and physical states of the speaker. Recently, the acoustic analysis of speech signals offers a practical, automated, and scalable method for medical diagnosis and monitoring symptoms of many diseases. In this paper, we explore the deep acoustic features from confirmed positive and negative cases of COVID-19 and compare the performance of the acoustic features and COVID-19 symptoms in terms of their ability to diagnose COVID-19. The proposed methodology consists of the pre-trained Visual Geometry Group (VGG-16) model based on Mel spectrogram images to extract deep audio features. In addition to the K-means algorithm that determines effective features, followed by a Genetic Algorithm-Support Vector Machine (GA-SVM) classifier to classify cases. The experimental findings indicate the proposed methodology's capability to classify COVID-19 and NOT COVID-19 from acoustic features compared to COVID-19 symptoms, achieving an accuracy of 97%. The experimental results show that the proposed method remarkably improves the accuracy of COVID-19 detection over the handcrafted features used in previous studies. © 2022 ACM.

关键词： COVID-19

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Sand Cat Swarm Optimization with Deep Transfer Learning for Skin Cancer Classification

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computer Systems science & engineering 2023年第11期47卷 2079-2095页

作者： C.S.S.Anupama Saud Yonbawi G.Jose Moses E.Laxmi Lydia Seifedine Kadry Jungeun Kim Department of Electronics and Instrumentation Engineering V.R.Siddhartha Engineering CollegeVijayawada520007India Department of Software Engineering College of Computer Science and EngineeringUniversity of JeddahJeddahSaudi Arabia Department of Computer Science and Engineering University Institute of Engineering and Technology(UIET)Guru Nanak UniversityHyderabadIndia Department of Computer Science and Engineering Vignan’s Institute of Information TechnologyVisakhapatnam530049India Department of Applied Data Science Noroff University CollegeKristiansandNorway Artificial Intelligence Research Center(AIRC) College of Engineering and Information TechnologyAjman UniversityAjmanUnited Arab Emirates Department of Electrical and Computer Engineering Lebanese American UniversityByblosLebanon Department of Software Kongju National UniversityCheonan31080Korea

Skin cancer is one of the most dangerous *** of the high melanoma death rate,skin cancer is divided into non-melanoma and *** dermatologist finds it difficult to identify skin cancer from dermoscopy images of skin ***,pathology and biopsy examinations are required for cancer *** studies have formulated computer-based systems for detecting skin cancer from skin lesion *** recent advancements in hardware and software technologies,deep learning(DL)has developed as a potential technique for feature ***,this study develops a new sand cat swarm optimization with a deep transfer learning method for skin cancer detection and classification(SCSODTL-SCC)*** major intention of the SCSODTL-SCC model lies in the recognition and classification of different types of skin cancer on dermoscopic ***,Dull razor approach-related hair removal and median filtering-based noise elimination are ***,the U2Net segmentation approach is employed for detecting infected lesion regions in dermoscopic ***,the NASNetLarge-based feature extractor with a hybrid deep belief network(DBN)model is used for ***,the classification performance can be improved by the SCSO algorithm for the hyperparameter tuning process,showing the novelty of the *** simulation values of the SCSODTL-SCC model are scrutinized on the benchmark skin lesion *** comparative results assured that the SCSODTL-SCC model had shown maximum skin cancer classification performance in different measures.

关键词： Deep learning skin cancer dermoscopic images sand cat swarm optimization machine learning

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Intelligent-Based Extraction of Business Capabilities from Company Descriptions 7

Intelligent-Based Extraction of Business Capabilities from C...

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7th International Conference on Information Technology, Information Systems and Electrical engineering, ICITISEE 2023

作者： Fahmi, Faisal Hamid, Siti Hafizah Ab Yuadi, Imam Airlangga University Faculty of Political and Social Science Department of Library and Information Science Indonesia Universiti Malaya Faculty of Computer Science & Information Technology Department of Software Engineering Malaysia

ISBN: (纸本)9798350382266

In the era of Big Data, Information can be generated, extracted, and utilized in diverse ways. In business, information about business capabilities can be a crucial aspect in understanding the strengths and competencies of organizations. This Information can empower stakeholders to make informed decisions regarding partnerships, acquisitions, and market positioning. Additionally, by comparing capabilities across companies and industries, organizations can identify their competitive advantages, industry trends, potential disruptions, and effective resource allocations, leading to improved strategies and better market opportunities. Traditional methods for extracting business capabilities primarily rely on keyword matching or rule-based techniques, which often fail to capture the complex relationships between words in a sentence. This paper presents a comprehensive study on extracting business capabilities using dependency parsing techniques in natural language processing (NLP) to overcome limitations of the traditional methods and improve the accuracy of business capability extraction. Dependency parsing utilizes pre-trained language models to analyze the grammatical structure of sentences, identifying the dependencies between words and capturing their relationships. The feasibility and effectiveness of the proposed approach is demonstrated through experiments conducted on more than 40K real-world company descriptions crawled from Wikipedia. © 2023 IEEE.

关键词： Competition

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Grey Level Texture Features for Segmentation of Chromogenic Dye RNAscope from Breast Cancer Tissue

Grey Level Texture Features for Segmentation of Chromogeni...

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International Conference on Medical Imaging and computer-Aided Diagnosis, MICAD 2023

作者： Davidson, Andrew Morley-Bunker, Arthur Wiggins, George Walker, Logan Harris, Gavin Mukundan, Ramakrishnan Department of Computer Science and Software Engineering University of Canterbury Christchurch New Zealand Department of Pathology and Biomedical Science University of Otago Christchurch New Zealand Canterbury Health Laboratories Christchurch New Zealand

ISBN: (纸本)9789819713349

Chromogenic RNAscope dye and haematoxylin staining of cancer tissue facilitates diagnosis of the cancer type and subsequent treatment, and fits well into existing pathology workflows. However, manual quantification of the RNAscope transcripts (dots), which signify gene expression, is prohibitively time consuming. In addition, there is a lack of verified supporting methods for quantification and analysis. This paper investigates the usefulness of gray level texture features for automatically segmenting and classifying the positions of RNAscope transcripts from breast cancer tissue. Feature analysis showed that a small set of gray level features, including Gray Level Dependence Matrix and Neighbouring Gray Tone Difference Matrix features, were well suited for the task. The automated method performed similarly to expert annotators at identifying the positions of RNAscope transcripts, with an F1-score of 0.571 compared to the expert inter-rater F1-score of 0.596. These results demonstrate the potential of gray level texture features for automated quantification of RNAscope in the pathology workflow. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Image segmentation

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Impact of Reenactment Programs on Young Generation 2nd

Impact of Reenactment Programs on Young Generation

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2nd International Conference on Ubiquitous Security, UbiSec 2022

作者： Fatima, Anam Iqbal, Muhammad Waseem Aqeel, Muhammad Rana, Toqir A. Chen, Shuhong Arif, Muhammad Department of Computer Science Superior University Lahore Lahore54000 Pakistan Department of Software Engineering Superior University Lahore Lahore54000 Pakistan Department of Computer Science and IT The University of Lahore Lahore54000 Pakistan School of Computer Science and Cyber Engineering Guangzhou University Guangzhou510006 China

ISBN: (纸本)9789819902712

The influence of reenactment-based violence programs on young people is the subject of this survey-based research project. A sample of 200 respondents (100 respondents were male and 100 were female) was gathered by the distribution of a questionnaire, and their responses were examined for the study. The data was broken down into five categories: violence, instigation, language, seduction, and fear. It was intriguing to learn that a lot of teenagers believe that violence and filthy language are permissible in the reenactment program circumstances. A considerable number of young people believe that drinking and smoking are wrong and that lying in some situations is unacceptable. A significant majority of adolescent respondents considered the representation of love, relationship, flirting, seduction, physical contact, and rape attractive in various situations of reenactment crime shows. Following the violence and crimes depicted in the reenactment shows, some young viewers believed that society was unsafe for them or others. The results are shown that 53% of respondents think that the use of violence and abusive language is justified. On the other hand, only 21% of respondents believe that criminal activity and violence are justified. 53% of those polled stated that watching the shows had made them fearful. The findings demonstrate that young viewers are afraid of violence and criminality when they see it on television. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Crime

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Sequential Image Storytelling Model Based on Transformer Attention Pooling 38

Sequential Image Storytelling Model Based on Transformer Att...

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38th International Conference on Image and Vision Computing New Zealand, IVCNZ 2023

作者： Malakan, Zainy M. Hassan, Ghulam Mubashar Mian, Ajmal The University of Western Australia Department of Computer Science and Software Engineering Perth Australia Umm Al-Qura University Department of Data Science Makkah Saudi Arabia

ISBN: (纸本)9798350370515

The Visual Storytelling Task (VST) extends beyond describing a single image, such as image captioning, to sequential image descriptions in the form of a coherent story. However, such descriptions present challenges in handling varying language styles, relational role-modeling, consistency, and events not evident in individual images. A common limitation in existing approaches is their inability to fully describe relations and visual changes, leading to stories devoid of linguistic cohesion between multiple sentences. To address this challenge, we introduce a novel framework, Sequential Image Storytelling Model (SISM), based on the Transformer architecture. Our model contextualizes input images by dividing them into multiple 16 × 16 patches and associates them with language content using the encoder-decoder technique. It incorporates cross-attention and attention pooling to identify the most relevant relationships between input images. Our proposed model achieves state-of-the-art performance on the recently published SSID dataset and performs competitively on VIST dataset, achieving the top score on BLEU-1 metric. © 2023 IEEE.

关键词： Modeling languages

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