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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Zhaohui Yang Mingzhe Chen Yuchen Liu Zhaoyang Zhang College of Information Science and Electronic Engineering Zhejiang University Hangzhou China Department of Electrical and Computer Engineering and Institute for Data Science and Computing University of Miami Coral Gables FL USA Department of Computer Science North Carolina State University Raleigh NC USA

In this paper, the problem of low-latency communication and computation resource allocation for digital twin (DT) over wireless networks is investigated. In the considered model, multiple physical devices in the physical network (PN) needs to frequently offload the computation task related data to the digital network twin (DNT), which is generated and controlled by the central server. Due to limited energy budget of the physical devices, both computation accuracy and wireless transmission power must be considered during the DT procedure. This joint communication and computation problem is formulated as an optimization problem whose goal is to minimize the overall transmission delay of the system under total PN energy and DNT model accuracy constraints. To solve this problem, an alternating algorithm with iteratively solving device scheduling, power control, and data offloading subproblems. For the device scheduling subproblem, the optimal solution is obtained in closed form through the dual method. Numerical results verify that the proposed algorithm can reduce the transmission delay of the system by up to 51.2% compared to the conventional schemes.

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Rethinking the Role of Structural Information: How It Enhances Code Representation Learning?

Rethinking the Role of Structural Information: How It Enhanc...

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International Joint Conference on Neural Networks (IJCNN)

作者： Qiushi Sun Nuo Chen Jianing Wang Xiaoli Li Department of Mathematics National University of Singapore Singapore School of Data Science and Engineering East China Normal University Shanghai China Institute for Infocomm Research Agency for Science Technology and Research Singapore

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Code pre-trained models (CodePTMs) have recently exhibited remarkable accomplishments in the realm of software engineering. However, there are still limited advancements in understanding the inner mechanism of these models, as well as their sensitivity to samples of varying quality. Codes have a more rigid and structured syntax compared to natural languages; hence, leveraging and understanding structural information becomes essential for analyzing, interpreting, and utilizing CodePTMs. While previous studies have verified models’ ability to acquire knowledge from code structure through techniques such as attention analysis and probing tasks, the specific roles it plays in downstream tasks have yet to be explored. In this work, we propose a set of novel and practical methods for probing and exploiting the structural information within the code. In particular, dataflow perturbation experiments are first employed to explore the sensitivity of models with varying levels of structural information when confronted with input changes. Based on our findings, structure-aware exemplars selection strategies are proposed for both code generation and understanding, aiming to recover the model performance at minimal cost under perturbed conditions. Moreover, efficient fine-tuning can be achieved by utilizing exemplars instead of full fine-tuning.

关键词： Representation learning Training Solid modeling Codes Sensitivity Costs Perturbation methods

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Efficient Real-Time Okra Stage Identification using YOLOV8

Efficient Real-Time Okra Stage Identification using YOLOV8

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IEEE International Conference on Electronics, computing and Communication Technologies (CONECCT)

作者： Nikhilesh Kumar B Ferbin FJ Shoba Sivapatham Asutosh Kar Krithiga R School of Mechanical Engineering Vellore Institute of Technology Chennai India Center for Advanced Data Science Vellore Institute of Technology Chennai India Electronics and Communication Engineering National Institute of Technology Jalandhar Punjab School of Computer Science Vellore Institute of Technology Chennai India

ISBN: (数字)9798350385922

ISBN: (纸本)9798350385939

In modern agriculture, crop growth monitoring is a crucial component, as it offers intuitive information about the health and growth of the plant, assisting farmers and other agricultural specialists. This systematic growth monitoring is necessary for crop health and agricultural productivity. We preferred the YOLOv8, which utilizes machine learning and offers efficient plant analysis in agriculture. This preferred method predicts bounding boxes and the probability of each possible class, allowing it to achieve exceptional detection speed without trading off accuracy. Pre-processing was done on the created, "Okra-dataset" to standardize the image to a fixed resolution and enhance our dataset’s strength. We tested our work using different models: YOLOv8s, YOLOv8m, YOLOv8l, and YOLOv8x. Our test revealed that YOLOv8x achieved the highest mean average precision (mAP) of 82.9%. The implementation of research indicates that YOLOv8x is a good tool for agricultural applications, which can be very helpful to farmers.

关键词： Deep learning Agricultural robots Computational modeling Plants (biology) Crops Prediction algorithms Real-time systems

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Incremental Reliability Assessment of Large-Scale Software via Theoretical Structure Reduction

Incremental Reliability Assessment of Large-Scale Software v...

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18th International Conference on Software Technologies, ICSOFT 2023

作者： Liu, Wenjing Xu, Zhiwei Liu, Limin Gong, Yunzhan College of Data Science and Application Inner Mongolia University of Technology Hohhot100080 China State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing100876 China Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China

ISBN: (纸本)9789897586651

Problems of software quality assurance and behavior prediction of large-scale software systems have high importance due to the fact that software systems are getting more prevalent in almost all areas of human activities, and always include an large number of modules. To continuously offer significant changes or major improvements over the existing system, software upgrading is inevitable. This involves additional difficulty to assess reliability and guarantee the quality assurance of the large-scale system. The existing reliability assessment methods cannot continuously yet effectively assess the software reliability because the program structure of the software is not taken into account to drive the assessment process. Thus, it is highly desired to estimate the software reliability in an incremental way. This paper incorporates theoretical sequentialization and reduction of the program structure into sampling-based software reliability evaluation. Specifically, we leverage importance sampling to evaluate reliability rates of sequence structures, branch structures and loop structures in the software, as well as transition probabilities among these structures. In addition, we sequentialize program structures to support the aggregation of reliability assessment results corresponding to different structures. Finally, a real-world case study is provided as a practical application of the proposed incremental assessment model. © 2023 by SCITEPRESS-science and Technology Publications, Lda.

关键词： Software quality

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Enhancing Cloud Security with AI: Developing Robust, Scalable Solutions for Threat Mitigation and data Protection in Cloud Platforms 5

Enhancing Cloud Security with AI: Developing Robust, Scalabl...

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5th International Conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Abusalih Rahumath Ali, AbuFaizur Rahman Saravanan, K. Abirami, B.Bala Pandi, V.Samuthira Aroulanandam, Vijay Vasanth Parthiban, S. Enterprise Solution Architect Tekskills Inc. PrincetonNJ08540 United States R.M.K Engineering College Department of Information Technology Tamil Nadu Kavaraipettai India Panimalar Engineering College Department of Artificial Intelligence and Data Science India Centre for Advanced Wireless Integrated Technology Chennai Institute of Technology Tamil Nadu Chennai India School of Computing SRM Institute of Science & Technology Networking and Communications Department Tamil Nadu Kattankulathur India SIMATS Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of CSE Chennai Thandalam India

ISBN: (纸本)9798331530013

As cloud computing continues to gain popularity, guaranteeing the security of cloud environments has become an issue of the utmost importance for businesses all over the world. With a particular emphasis on the development of solutions that are both robust and scalable for the purpose of threat mitigation and data protection, this study investigates the application of artificial intelligence (AI) in the process of improving cloud security. Security frameworks that are driven by artificial intelligence are able to proactively discover, evaluate, and respond to new threats in real time. This is accomplished by employing machine learning algorithms and advanced analytics. With the purpose of demonstrating the efficacy of various artificial intelligence approaches in tackling common vulnerabilities such as data breaches, unauthorized access, and distributed denial of service attacks, we investigate a variety of AI methodologies, such as anomaly detection, behavior analysis, and automated incident response. In addition, we analyze the significance of a multilayered security approach, which involves integrating artificial intelligence tools with conventional security measures in order to develop an all-encompassing defense plan. The purpose of this article is to present case studies that illustrate successful implementations of artificial intelligence-enhanced security solutions on cloud platforms. These case studies demonstrate the influence that these solutions have on increasing security posture and operational efficiency. Ultimately, the findings of this study highlight the significant role that artificial intelligence plays in the transformation of cloud security practices. This paves the way for cloud infrastructures that are more robust and secure in the face of changing cyber threats. © 2024 IEEE.

关键词： Cloud platforms

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Separate and Integrate Different Level Reasoning for Event Causality Identification

Separate and Integrate Different Level Reasoning for Event C...

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International Joint Conference on Neural Networks (IJCNN)

作者： Yuchang Deng Wenti Huang Tingxuan Chen Jun Long School of Software Xinjiang University Urumqi China School of Computer Science and Engineering Hunan University of Science and Technology Changsha China School of Computer Science and Engineering Central South University Changsha China Big Data Institute Central South University Changsha China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Document-level event causality identification (DECI) aims to discern the causal relationship between two event mentions within a document, which is a more challenging task compared to sentence-level event causality identification. However, the conventional DECI model applies the same method to all pairs of events without considering the distinctions and connections between intra-sentence and inter-sentence event pairs. This paper introduces a novel Separate and Integrate Different Level reasoning model (SIDL) to extract and fuse text features of events. Specifically, we separately encode intra-sentence and inter-sentence event pairs and leverage attention from pre-trained language models to identify the relevant context for aiding in the determination of causality. Additionally, we propose a causal reasoning strategy that leverages intra-sentence causal relationships to assist in the inference of inter-sentence causal relationships. We combine different features of event pairs by discriminating the characters and effects of intra-sentence and inter-sentence reasoning. Experimental results show that our approach achieves superior performance compared to previous models in two benchmarks EventStoryLine and CausalTimeBank.

关键词： Knowledge engineering Correlation Fuses Neural networks Cause effect analysis Benchmark testing Feature extraction

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A Hybrid Neural Coding Approach for Pattern Recognition with Spiking Neural Networks

arXiv

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arXiv 2023年

作者： Chen, Xinyi Yang, Qu Wu, Jibin Li, Haizhou Tan, Kay Chen Department of Computing The Hong Kong Polytechnic University Hong Kong Department of Electrical and Computer Engineering National University of Singapore Singapore Shenzhen Research Institute of Big Data School of Data Science The Chinese University of Hong Kong Shenzhen [CUHK- Shenzhen China

Recently, brain-inspired spiking neural networks (SNNs) have demonstrated promising capabilities in solving pattern recognition tasks. However, these SNNs are grounded on homogeneous neurons that utilize a uniform neural coding for information representation. Given that each neural coding scheme possesses its own merits and drawbacks, these SNNs encounter challenges in achieving optimal performance such as accuracy, response time, efficiency, and robustness, all of which are crucial for practical applications. In this study, we argue that SNN architectures should be holistically designed to incorporate heterogeneous coding schemes. As an initial exploration in this direction, we propose a hybrid neural coding and learning framework, which encompasses a neural coding zoo with diverse neural coding schemes discovered in neuroscience. Additionally, it incorporates a flexible neural coding assignment strategy to accommodate task-specific requirements, along with novel layer-wise learning methods to effectively implement hybrid coding SNNs. We demonstrate the superiority of the proposed framework on image classification and sound localization tasks. Specifically, the proposed hybrid coding SNNs achieve comparable accuracy to state-of-the-art SNNs, while exhibiting significantly reduced inference latency and energy consumption, as well as high noise robustness. This study yields valuable insights into hybrid neural coding designs, paving the way for developing high-performance neuromorphic systems. Copyright © 2023, The Authors. All rights reserved.

关键词： Pattern recognition

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An Enhanced Convolutional Neural Network Based on L1 Regularization for Segmentation of Coronary Arteries in X-Ray Angiograms

An Enhanced Convolutional Neural Network Based on L1 Regular...

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International Conference on Advancements in Computational sciences (ICACS)

作者： Fazli Wahid Yingliang Ma Ramla Sheikh Muhammad Aamir School of Computing Sciences University of East Anglia Norwich United Kingdom Department of Information Technology University of Haripur Khyber Pakhtunkhwa Pakistan Data Science Research Centre Colloge of Science and Engineering University of Derby Derby United Kingdom

The automatic segmentation of coronary arteries in X-Ray angiograms has a significant role in identification and detection of various abnormalities in vessels which can be adequately helpful in computer aided diagnosis (CAD) for supporting in analyzing the coronary heart diseases. Previously many machine learning (ML) algorithms and deep learning (DL) models have been applied for this purpose but all of them are suffered from some serious drawbacks when applied in their standardized working formats which leads to low performance of these models. Therefore, in this work an enhanced convolutional neural network named eCNN which is based on L1 regularization has been applied for automatic segmentation of coronary arteries. It has been observed that incorporating L1 regularization with different coefficient values has distinct effects on the working mechanism of CNN architecture resulting in improving its performance. In the proposed method, firstly, Gaussian Matched Filters (GMF) have been applied to enhance the quality of X-Ray angiograms. In segmentation stages, the eCNN has been applied for the segmentation. In this stage, dual L1 regularization (coefficient: 0.001 for convolutional and 0.01 for dense layers) improves model interpretability and generalization, facilitating effective segmentation of arteries. Finally, standard performance evaluation parameters have been used to evaluate the performance of the proposed model and compared with state-of-the-art techniques.

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Frequency-aware Graph Signal Processing for Collaborative Filtering 25

Frequency-aware Graph Signal Processing for Collaborative Fi...

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Companion Proceedings of the ACM on Web Conference 2025

作者： Jiafeng Xia Dongsheng Li Hansu Gu Tun Lu Peng Zhang Li Shang Ning Gu Fudan University Shanghai China and School of Computer Science and Shanghai Key Laboratory of Data Science Fudan University Shanghai China Microsoft Research Asia Shanghai China *** Seattle USA Fudan University Shanghai China Fudan Institute on Aging MOE Laboratory for National Development and Intelligent Governance and Shanghai Institute of Intelligent Electronics & Systems Fudan University Shanghai China and School of Computer Science and Shanghai Key Laboratory of Data Science Fudan University Shanghai China

ISBN: (纸本)9798400713316

Graph Signal Processing (GSP) based recommendation algorithms have recently attracted lots of attention for high efficiency. However, these methods failed to utilize user/item unique characteristics, as well as user and item high-order neighborhood information when modeling user preference, leading to sub-optimal performance. To this end, we propose a frequency-aware graph signal processing method (FaGSP) for collaborative filtering (CF). Firstly, we design a Cascaded Filter Module to capture both unique and common user/item characteristics to more accurately model user preference. Then, we devise a Parallel Filter Module to fully utilize high-order neighborhood information of users/items for more accurate user preference modeling. Finally, we combine these modules via a linear model to further improve recommendation accuracy. Extensive experiments demonstrate the superiority of our method from prediction accuracy and training efficiency.

关键词： collaborative filtering

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Rethinking the visual cues in audio-visual speaker extraction

arXiv

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arXiv 2023年

作者： Li, Junjie Ge, Meng Pan, Zexu Cao, Rui Wang, Longbiao Dang, Jianwu Zhang, Shiliang Tianjin Key Laboratory of Cognitive Computing and Application College of Intelligence and Computing Tianjin University Tianjin China Department of Electrical and Computer Engineering National University of Singapore Singapore Institute of Data Science National University of Singapore Singapore Shenzhen Research Institute of Big Data Shenzhen China

The Audio-Visual Speaker Extraction (AVSE) algorithm employs parallel video recording to leverage two visual cues, namely speaker identity and synchronization, to enhance performance compared to audio-only algorithms. However, the visual front-end in AVSE is often derived from a pre-trained model or end-to-end trained, making it unclear which visual cue contributes more to the speaker extraction performance. This raises the question of how to better utilize visual cues. To address this issue, we propose two training strategies that decouple the learning of the two visual cues. Our experimental results demonstrate that both visual cues are useful, with the synchronization cue having a higher impact. We introduce a more explainable model, the Decoupled Audio-Visual Speaker Extraction (DAVSE) model, which leverages both visual cues. Copyright © 2023, The Authors. All rights reserved.

关键词： Extraction

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