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A High Resolution Convolutional Neural Network with Squeeze and Excitation Module for Automatic Modulation Classification

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China Communications 2024年第10期21卷 132-147页

作者： Duan Ruifeng Zhao Yuanlin Zhang Haiyan Li Xinze Cheng Peng Li Yonghui School of Information Science and Technology Bejing Forestry UniversityBeijing 100083China Engineering Research Center for Forestry-Oriented Intelligent Information Processing of National Forestry and Grassland Administration Beijing 100083China Department of Computer Science and Information Technology La Trobe UniversityMelbourneVIC 3086Australia School of Electrical and Information Engineering the University of SydneySydneyNSW 2006Australia

Automatic modulation classification(AMC) technology is one of the cutting-edge technologies in cognitive radio communications. AMC based on deep learning has recently attracted much attention due to its superior performances in classification accuracy and robustness. In this paper, we propose a novel, high resolution and multi-scale feature fusion convolutional neural network model with a squeeze-excitation block, referred to as HRSENet,to classify different kinds of modulation *** proposed model establishes a parallel computing mechanism of multi-resolution feature maps through the multi-layer convolution operation, which effectively reduces the information loss caused by downsampling convolution. Moreover, through dense skipconnecting at the same resolution and up-sampling or down-sampling connection at different resolutions, the low resolution representation of the deep feature maps and the high resolution representation of the shallow feature maps are simultaneously extracted and fully integrated, which is benificial to mine signal multilevel features. Finally, the feature squeeze and excitation module embedded in the decoder is used to adjust the response weights between channels, further improving classification accuracy of proposed *** proposed HRSENet significantly outperforms existing methods in terms of classification accuracy on the public dataset “Over the Air” in signal-to-noise(SNR) ranging from-2dB to 20dB. The classification accuracy in the proposed model achieves 85.36% and97.30% at 4dB and 10dB, respectively, with the improvement by 9.71% and 5.82% compared to ***, the model also has a moderate computation complexity compared with several state-of-the-art methods.

关键词： automatic modulation classification deep learning feature squeeze-and-excitation high-resolution multi-scale

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A Fast and More Accurate Seed-and-Extension Density-Based Clustering Algorithm

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IEEE Transactions on Knowledge and Data engineering 2023年第6期35卷 5458-5471页

作者： Tung, Ming-Hao Chen, Yi-Ping Phoebe Liu, Chen-Yu Liao, Chung-Shou Research and Development Micron Technology Inc Hsinchu Taiwan Department of Computer Science and Information Technology La Trobe University Melbourne Australia Dept. Industrial Engineering and Engineering Management National Tsing Hua University Hsinchu Taiwan Industrial Engineering and Engineering Management National Tsing Hua University Hsinchu Taiwan

Clustering algorithms have been widely studied in many scientific areas, such as data mining, knowledge discovery, bioinformatics and machine learning. A density-based clustering algorithm, called density peaks (DP), which was proposed by Rodriguez and laio, outperforms almost all other approaches. Although the DP algorithm performs well in many cases, there is still room for improvement in the precision of its output clusters as well as the quality of the selected centers. In this study, we propose a more accurate clustering algorithm, seed-and-extension-based density peaks (SDP). SDP selects the centers that hold the features of their clusters while building a spanning forest, and meanwhile, constructs the output clusters in a seed-and-extension manner. Experiment results demonstrate the effectiveness of SDP, especially when dealing with clusters with relatively high densities. Precisely, we show that SDP is more accurate than the DP algorithm as well as other state-of-the-art clustering approaches concerning the quality of both output clusters and cluster centers while maintaining similar running time of the DP algorithm, particularly for a variety of time-series data. Moreover, SDP outperforms DP in the dynamic model in which data point insertion and deletion are allowed. From a practical perspective, the proposed SDP algorithm is obviously helpful to many application problems. © 1989-2012 IEEE.

关键词： Heuristic algorithms

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CSFIR: Leveraging Code-Specific Features to Augment Information Retrieval in Low-Resource Code Datasets

CSFIR: Leveraging Code-Specific Features to Augment Informat...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Tong, Zhenyu Luo, Chenxi Luo, Tiejian School of Computer Science and Technology University of Chinese Academy of Sciences Beijing100049 China School of Computing Engineering and Mathematics La Trobe University MelbourneVIC3086 Australia

ISBN: (纸本)9781665410205

From search engines like Google to advanced applications such as Retrieval Augmented Generation integrating large language Model (LLM), Information Retrieval (IR) serves a crucial role. To facilitate the development of increasingly large and complex code program projects, researchers introduce IR systems into the code domain. Unfortunately, although IR systems achieve significant success in retrieving natural language corpus and query, they face challenges when tasked with retrieving corpus consisting of code sequences. Primarily, in practical applications, most code sequences lack corresponding natural language annotations, which are known as low-resource scenarios, hindering the training of neural network-based retriever in IR systems. Furthermore, modern IR systems often overlook the structural features of code sequences, which may be beneficial for understanding these sequences. Additionally, the length of most code sequences exceeds that of equivalent natural language expressions, complicating the processing of relationships between code and natural language sequences. To address these challenges, we propose a novel IR system CSFIR, which leverages Code-Speclflc Features to augment IR. For the prevalent issue of unlabeled code sequences in low-resource scenarios, we employ a supervised fine-tuned LLM as a generator to generate natural language queries for unlabeled code sequence. Subsequently, we extract structural features from the abstract syntax tree of code sequence using graph convolution networks and integrate these features to enhance the original retriever. Finally, given the adverse effects of lengthy code sequences on generators, we propose a subtree segmentation algorithm, which reduces the length of code sequences without compromising their original meaning, thereby enhancing the quality of queries generated by the generator. We conduct comparative experiments to ascertain the efficacy of our method. Regarding Recall@100, our CSFIR system improves f

关键词： Query languages

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Optimizing QoE in IoT-Based Video Streaming through Deep Learning Algorithms 6

Optimizing QoE in IoT-Based Video Streaming through Deep Lea...

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6th International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2024

作者： Darwich, Mahmoud Khalil, Kasem Bayoumi, Magdy University of Mount Union Department of Mathematics and Computer Science AllianceOH44601 United States University of Mississippi Department of Electrical and Computer Engineering MS38677 United States University of Louisiana Department of Electrical and Computer Engineering LafayetteLA70504 United States

ISBN: (纸本)9798350344349

The demand for video streaming services over Internet of Things (IoT) networks has surged, yet maintaining a high Quality of Experience (QoE) remains challenging due to network heterogeneity and resource constraints. This paper presents an innovative deep learning-based approach to optimize QoE in IoT-based video streaming. Leveraging convolutional neural networks (CNN) and long short-Term memory (LSTM) networks, the proposed framework dynamically adapts video streaming parameters in real-Time, such as bit rate, frame rate, and resolution, based on the existing network conditions and device capabilities. Through extensive simulations and real-world deployments, our approach exhibited a 24.5% reduction in re-buffering ratio, a 12.5% increase in average bitrate, and a 15% improvement in video quality measured by SSIM, compared to conventional methods. Furthermore, it reduced network bandwidth consumption by up to 150% without compromising on video quality. These results underscore the effectiveness of applying deep learning algorithms to optimize video streaming in complex IoT environments. © 2024 IEEE.

关键词： Video streaming

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End-to-End Secure Video Streaming Using Homomorphic Encryption Techniques 2

End-to-End Secure Video Streaming Using Homomorphic Encrypti...

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2nd International Conference on Artificial Intelligence, Blockchain, and Internet of Things, AIBThings 2024

作者： Darwich, Mahmoud Khalil, Kasem Bayoumi, Magdy University of Mount Union Department of Mathematics and Computer Science AllianceOH44601 United States University of Mississippi University Department of Electrical and Computer Engineering MS38677 United States University of Louisiana Department of Electrical and Computer Engineering LafayetteLA70504 United States

ISBN: (纸本)9798331529529

In the rapidly evolving landscape of digital media, video streaming has become a ubiquitous method of content delivery. However, the increasing sophistication of cyber threats poses significant risks to the security and privacy of streamed content. Traditional encryption methods, while effective in many scenarios, often fall short in providing comprehensive protection without compromising performance and user experience. This paper introduces a novel approach to achieving end-to-end secure video streaming by leveraging homomorphic encryption techniques. Homomorphic encryption allows for computations to be performed on encrypted data without needing to decrypt it first, thereby maintaining data privacy and security throughout the streaming process. Our proposed method integrates homomorphic encryption into the video streaming pipeline, ensuring that video data remains secure from the point of origin to the end user. We discuss the theoretical foundations of homomorphic encryption, detail the implementation of our approach, and evaluate its performance against conventional encryption methods. Experimental results demonstrate that our technique provides robust security with minimal impact on streaming quality and latency, making it a viable solution for secure video streaming in various applications. © 2024 IEEE.

关键词： Video streaming

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Adaptive Multi-Path Video Streaming Using AI-Driven Edge Computing for Enhanced Quality of Experience (QoE) 2

Adaptive Multi-Path Video Streaming Using AI-Driven Edge Com...

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2nd International Conference on Artificial Intelligence, Blockchain, and Internet of Things, AIBThings 2024

作者： Darwich, Mahmoud Khalil, Kasem Bayoumi, Magdy University of Mount Union Department of Mathematics and Computer Science AllianceOH44601 United States University of Mississippi University Department of Electrical and Computer Engineering MS38677 United States University of Louisiana at Lafayette Department of Electrical and Computer Engineering LA70504 United States

ISBN: (纸本)9798331529529

With the increasing demand for high-quality video streaming services, there is a need to enhance the Quality of Experience (QoE) for users, especially in environments with variable network conditions. This research paper proposes a novel approach leveraging AI-driven edge computing to implement adaptive multi-path video streaming. The proposed method uses machine learning algorithms to dynamically select the optimal streaming paths and adjust video quality in real-time based on network conditions and user preferences. By utilizing edge computing resources, this approach aims to reduce latency, improve bandwidth utilization, and provide a more reliable and high-quality video streaming experience. © 2024 IEEE.

关键词： Video streaming

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ChatGPT and Generative AI Guidelines for Addressing Academic Integrity and Augmenting Pre-Existing Chatbots

ChatGPT and Generative AI Guidelines for Addressing Academic...

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2023 IEEE International Conference on Industrial Technology, ICIT 2023

作者： De Silva, Daswin Mills, Nishan El-Ayoubi, Mona Manic, Milos Alahakoon, Damminda Centre for Data Analytics and Cognition La Trobe University Melbourne Australia Education Services La Trobe University Melbourne Australia Virginia Commonwealth University Department of Computer Science Richmond United States

ISBN: (纸本)9798350336504

Chat Generative Pretrained Transformer (Chat-GPT) and related Generative AI models are leading a paradigm shift in the acceptance and application of Artificial Intelligence (AI) across all disciplines and industry sectors. Despite the criticisms of an 'intelligence without knowledge or reasoning or the notions of truth', ChatGPT is highly effective at human-like conversation with seemingly sophisticated and useful responses to questions, summarization, classification, extraction and generation tasks. Unlike similar large AI models in the modalities of image, audio and video, text-based conversation is straightforward and familiar to a large audience of regular users of the Internet and smartphone applications. This is further accentuated by the large-scale adoption of 'standard' chatbot technologies for trivial conversations in task-specific automation, across every industry sector. This rare combination of highly effective human-like conversation, familiarity of foundational technology and versatility of intelligent application, has led to several challenges and opportunities in leveraging generative AI. A primary challenge is its impact on the academic integrity of scholarly work, where AI-generated content can be useful and detrimental in both teaching and research. On the other hand, ChatGPT presents a unique opportunity in augmenting preexisting ('standard') chatbots with human-like conversation for advanced intelligent automation, across all application domains. Although diametrically opposed, the challenge of addressing academic integrity and the opportunity of augmenting pre-existing chatbots are grounded in the conversational AI capabilities of ChatGPT and similar generative AI models. In this paper, we investigate these formative capabilities and present guidelines for leveraging ChatGPT and similar generative AI models. © 2023 IEEE.

关键词： Automation

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Group activity recognition using unreliable tracked pose

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Neural Computing and Applications 2024年第36期36卷 23131-23148页

作者： Thilakarathne, Haritha Nibali, Aiden He, Zhen Morgan, Stuart Department of Computer Science & amp Information Technology La Trobe University Plenty Road & Kingsbury Dr BundooraVIC3086 Australia Sport Strategy & amp Investment Australian Institute of Sport Leverrier St BruceACT2617 Australia

Group activity recognition in video is a complex task due to the need for a model to recognise the actions of all individuals in the video and their complex interactions. Recent studies propose that optimal performance is achieved by individually tracking each person and subsequently inputting the sequence of poses or cropped images/optical flow into a model. This helps the model to recognise what actions each person is performing before they are merged to arrive at the group action class. However, all previous models rely highly on high-quality tracking and have only been evaluated using ground truth tracking information. In practice, achieving highly reliable tracking information for all individuals in a group activity video is almost impossible. We introduce an innovative deep learning-based group activity recognition approach called Rendered Pose-based Group Activity Recognition System (RePGARS), designed to tolerate unreliable tracking and pose information. Experimental results confirm that RePGARS outperforms all existing group activity recognition algorithms tested, which do not use ground truth detection and tracking information. © The Author(s) 2024.

关键词： Active learning

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Orchestrating the Resilience of Cloud Microservices Using Task-Based Reliability and Dynamic Costing

Orchestrating the Resilience of Cloud Microservices Using Ta...

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2022 IEEE Asia-Pacific Conference on computer science and Data engineering, CSDE 2022

作者： O'Neill, Vyas Soh, Ben La Trobe University Department of Computer Science and Information Technology Melbourne Australia

ISBN: (纸本)9781665453059

In recent years, cloud computing has grown in popularity as enterprises realize the technical and economic benefits of managing resources using an on-demand model. With this growth come user expectations of quality of service, availability and reliability. As the trend in cloud systems architecture moves from monolithic systems to microservices, opportunities exist to develop granular solutions for improving the reliability of the overall system. In this paper we propose an approach to the resilience of microservices-based cloud systems using task-based reliability, quantifying of the reliability requirement with respect to the marginal cost of additional redundancy and the cost of failure. Our approach enables the cloud orchestrator to manage replication of microservice containers to reach an optimal, cost-effective level of redundancy. Empirical testing showed our approach achieved a lower overall cost in comparison with a fixed-reliability approach under an exponential failure function. © 2022 IEEE.

关键词： Cloud computing

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Knowledge and Perceptions of Hospital Care Staff towards Medical Internet of Things and the Role of Awareness Videos: A Quasi Experimental Study

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Advances in Internet of Things 2023年第4期13卷 119-143页

作者： Abdulaziz Alomari Ben Soh Department of Computer Science and IT La Trobe University Bundoora Australia

Introduction: The healthcare industry continues to adopt and integrate smart technology into its operations. However, the adoption of the eHealth solutions has not been smooth in the Kingdom of Saudi Arabia (KSA) due to negative beliefs about the technology, lack of awareness and motivation and resistance to change. Thus, this study was developed to investigate the knowledge and perceptions of hospital care staff towards the Medical Internet of Things and to explore the role of awareness videos in changing negative perceptions. Methods: One group pre-test post-test quasi-experimental study design was incorporated, and 116 participants from Ministry of Health hospitals in Riyadh, KSA, were included. A series of four videos were developed to observe their influence on the knowledge and perceptions of mIoT. Results: The findings showed that participants had more knowledge about the individual components of mIoT (particularly wearable devices) compared to the processes or functions of mIoT. Similarly, just over half (56.0%) of the individuals think that the current systems in the hospital are enough to deliver mIoT. However, 90% think mIoT is the future of digital health. Similarly, PE, SI, BI, EE and CESE were considered facilitators and PTA and CC were considered grave barriers to mIoT adoption. The awareness videos positively influenced knowledge and perceptions of PE, EE, CESE and SI. Conclusion: The study concludes that hospital staff in Riyadh (excluding doctors) possess basic mIoT knowledge, consider various adoption factors as enablers, and awareness video can play a critical role in effectively introducing the technology to the hospital care staff.

关键词： Internet of Things Healthcare Professionals eHealth Knowledge Awareness Intervention Experimental Study

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