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32nd ACM International Conference on Multimedia, MM 2024

作者： Zhu, Hongze Xie, Guoyang Hou, Chengbin Dai, Tao Gao, Can Wang, Jinbao Shen, Linlin National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China Department of Computer Science City University of Hong Kong Hong Kong Hong Kong Department of Intelligent Manufacturing CATL Ningde China Fuzhou Fuyao Institute for Advanced Study Fuyao University of Science and Technology Fuzhou China College of Computer Science and Software Engineering Shenzhen University Shenzhen China Guangdong Provincial Key Laboratory of Intelligent Information Processing Shenzhen China Shenzhen University Shenzhen China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China

ISBN: (纸本)9798400706868

High-resolution point clouds (HRPCD) anomaly detection (AD) plays a critical role in precision machining and high-end equipment manufacturing. Despite considerable 3D-AD methods that have been proposed recently, they still cannot meet the requirements of the HRPCD-AD task. There are several challenges: i) It is difficult to directly capture HRPCD information due to large amounts of points at the sample level;ii) The advanced transformer-based methods usually obtain anisotropic features, leading to degradation of the representation;iii) The proportion of abnormal areas is very small, which makes it difficult to characterize. To address these challenges, we propose a novel group-level feature-based network, called Group3AD, which has a significantly efficient representation ability. First, we design an Intercluster Uniformity Network (IUN) to present the mapping of different groups in the feature space as several clusters, and obtain a more uniform distribution between clusters representing different parts of the point clouds in the feature space. Then, an Intracluster Alignment Network (IAN) is designed to encourage groups within the cluster to be distributed tightly in the feature space. In addition, we propose an Adaptive Group-Center Selection (AGCS) based on geometric information to improve the pixel density of potential anomalous regions during inference. The experimental results verify the effectiveness of our proposed Group3AD, which surpasses Reg3D-AD by the margin of 5% in terms of object-level AUROC on Real3D-AD. We provide the code and supplementary information on our website: https://***/M-3LAB/Group3AD. © 2024 ACM.

关键词： Contrastive Learning

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StraightLine: An End-to-End Resource-Aware Scheduler for Machine Learning Application Requests

StraightLine: An End-to-End Resource-Aware Scheduler for Mac...

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Artificial Intelligence, Internet of Things and Cloud Computing Technology (AIoTC), International Conference on

作者： Cheng-Wei Ching Boyuan Guan Hailu Xu Liting Hu Department of Computer Science and Engineering University of California Santa Cruz Santa Cruz CA USA Geographic Information System (GIS) Center of Florida International University Miami FL USA Department of Computer Engineering and Computer Science California State University Long Beach CA USA

ISBN: (数字)9798331530280

ISBN: (纸本)9798331530297

The life cycle of machine learning (ML) applications consists of two stages: model development and model deployment. However, traditional ML systems (e.g., training-specific or inference-specific systems) focus on one particular stage or phase of the life cycle of ML applications. These systems often aim at optimizing model training or accelerating model inference, and they frequently assume homogeneous infrastructure, which may not always reflect real-world scenarios that include cloud data centers, local servers, containers, and serverless platforms. We present StraightLine, an end-to-end resource-aware scheduler that schedules the optimal resources (e.g., container, virtual machine, or serverless) for different ML application requests in a hybrid infrastructure. The key innovation is an empirical dynamic placing algorithm that intelligently places requests based on their unique characteristics (e.g., request frequency, input data size, and data distribution). In contrast to existing ML systems, StraightLine offers end-to-end resource-aware placement, thereby it can significantly reduce response time and failure rate for model deployment when facing different computing resources in the hybrid infrastructure.

关键词： Training Cloud computing Technological innovation Schedules Heuristic algorithms Machine learning Containers Data models Time factors Servers

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Optimal Deep Hybrid Boltzmann Machine Based Arabic Corpus Classification Model

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computer systems science & engineering 2023年第9期46卷 2755-2772页

作者： Mesfer Al Duhayyim Badriyya B.Al-onazi Mohamed K.Nour Ayman Yafoz Amal S.Mehanna Ishfaq Yaseen Amgad Atta Abdelmageed Gouse Pasha Mohammed Department of Computer Science College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAl-Kharj16273Saudi Arabia Department of Language Preparation Arabic Language Teaching InstitutePrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversityMakkah 24211Saudi Arabia Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddahSaudi Arabia Department of Digital Media Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11845Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Natural Language Processing(NLP)for the Arabic language has gained much significance in recent *** most commonly-utilized NLP task is the‘Text Classification’*** main intention is to apply the Machine Learning(ML)approaches for automatically classifying the textual files into one or more pre-defined *** ML approaches,the first and foremost crucial step is identifying an appropriate large dataset to test and train the *** of the trending ML techniques,i.e.,Deep Learning(DL)technique needs huge volumes of different types of datasets for training to yield the best *** current study designs a new Dice Optimization with a Deep Hybrid Boltzmann Machinebased Arabic Corpus Classification(DODHBM-ACC)model in this *** presented DODHBM-ACC model primarily relies upon different stages of pre-processing and the word2vec word embedding *** Arabic text classification,the DHBM technique is *** technique is a hybrid version of the Deep Boltzmann Machine(DBM)and Deep Belief Network(DBN).It has the advantage of learning the decisive intention of the classification *** adjust the hyperparameters of the DHBM technique,the Dice Optimization Algorithm(DOA)is exploited in this *** experimental analysis was conducted to establish the superior performance of the proposed DODHBM-ACC *** outcomes inferred the better performance of the proposed DODHBM-ACC model over other recent approaches.

关键词： Arabic corpus text classification machine learning deep learning dice optimization

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Imaging flow cytometry with a real-time throughput beyond 1,000,000 events per second

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Light(science & Applications) 2025年第4期14卷 931-947页

作者： Jiehua Zhou Liye Mei Mingjie Yu Xiao Ma Dan Hou Zhuo Yin Xun Liu Yan Ding Kaining Yang Ruidong Xiao Xiandan Yuan Yueyun Weng Mengping Long Taobo Hu Jinxuan Hou Yu Xu Liang Tao Sisi Mei Hui Shen Yaxiaer Yalikun Fuling Zhou Liang Wang Du Wang Sheng Liu Cheng Lei The Institute of Technological Sciences Wuhan UniversityWuhan 430072China School of Computer Science Hubei University of TechnologyWuhan 430068China Division of Materials Science Nara Institute of Science and TechnologyTakayama-cho 8916-5Japan School of Science Hubei University of TechnologyWuhan 430068China Department of Pathology Peking University Cancer HospitalBeijing 100142China Department of Breast Surgery Peking University People’s HospitalBeijing 100044China Department of Thyroid and Breast Surgery Zhongnan HospitalWuhan UniversityWuhan 430071China Department of Radiation and Medical Oncology Zhongnan HospitalWuhan UniversityWuhan 430071China People’s Hospital of Anshun City Guizhou Province Anshun 561000China Department of Hematology Zhongnan HospitalWuhan UniversityWuhan 430071China National Engineering Laboratory for Next Generation Internet Access System School of Optics and Electronic InformationHuazhong University of Science and TechnologyWuhan 430074China Suzhou Institute of Wuhan University Suzhou 215000China Shenzhen Institute of Wuhan University Shenzhen 518057China

Imaging flow cytometry(IFC)combines the imaging capabilities of microscopy with the high throughput of flow cytometry,offering a promising solution for high-precision and high-throughput cell analysis in fields such as biomedicine,green energy,and environmental ***,due to limitations in imaging framerate and realtime data processing,the real-time throughput of existing IFC systems has been restricted to approximately 1000-10,000 events per second(eps),which is insufficient for large-scale cell *** this work,we demonstrate IFC with real-time throughput exceeding 1,000,000 eps by integrating optical time-stretch(OTS)imaging,microfluidic-based cell manipulation,and online image *** flowing at speeds up to 15 m/s are clearly imaged with a spatial resolution of 780 nm,and images of each individual cell are captured,stored,and *** capabilities and performance of our system are validated through the identification of malignancies in clinical colorectal *** work sets a new record for throughput in imaging flow cytometry,and we believe it has the potential to revolutionize cell analysis by enabling highly efficient,accurate,and intelligent measurement.

关键词： real time data processing flow cytometryoffering spatial resolution imaging flow cytometry ifc combines high throughput cell analysis cell analysis imaging flow cytometry optical time stretch imaging

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Deep learning-based point cloud upsampling: a review of recent trends

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JMST Advances 2023年第4期5卷 105-111页

作者： Kwon, Soonjo Hur, Ji-Hyeon Kim, Hyungki Department of Mechanical System Engineering Kumoh National Institute of Technology Gumi Korea Department of Computer Science and Artificial Intelligence/CAIIT Jeonbuk National University Jeonju Korea

Point clouds are acquired primarily using 3D scanners and are used for product inspection and reverse engineering. The quality of the point cloud varies depending on the scanning environment and scanner specifications. The quality of the point cloud has a significant impact on the accuracy of automatic or manual modeling. In response, various point cloud post-processing technologies are being developed. Point cloud upsampling is a technique to improve the resolution of point clouds, and the purpose of upsampling is to generate additional points to express the target object more accurately and in higher detail. This technology is important in areas where high-resolution 3D representation is required, and approaches based on deep learning have been recently gaining attention. Deep learning-based point cloud upsampling research can be classified as surface consolidation or edge consolidation research depending on the target regions to be consolidated, and as supervised or self-supervised learning depending on the type of learning approaches. This study examines the latest research trends in deep learning-based point cloud sampling, analyzes the issues and limitations of each research category, and proposes future research directions.

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Jigsaw-Based Benchmarking for Learning Robotic Manipulation 8

Jigsaw-Based Benchmarking for Learning Robotic Manipulation

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8th IEEE International Conference on Advanced Robotics and Mechatronics, ICARM 2023

作者： Liu, Xiaobo Wan, Fang Ge, Sheng Wang, Haokun Sun, Haoran Song, Chaoyang Southern University of Science and Technology Department of Mechanical and Energy Engineering Guangdong Shenzhen518055 China School of Design Southern University of Science and Technology Guangdong Shenzhen518055 China Hong Kong University of Science and Technology Robotics & Autonomous Systems Thrust System Hub Hong Kong Hong Kong The University of Hong Kong Department of Computer Science Hong Kong Hong Kong

ISBN: (纸本)9798350300178

Benchmarking provides experimental evidence of the scientific baseline to enhance the progression of fundamental research, which is also applicable to robotics. In this paper, we propose a method to benchmark metrics of robotic manipulation, which addresses the spatial-temporal reasoning skills for robot learning with the jigsaw game. In particular, our approach exploits a simple set of jigsaw pieces by designing a structured protocol, which can be highly customizable according to a wide range of task specifications. Researchers can selectively adopt the proposed protocol to benchmark their research outputs, on a comparable scale in the functional, task, and system-level of details. The purpose is to provide a potential look-up table for learning-based robot manipulation, commonly available in other engineering disciplines, to facilitate the adoption of robotics through calculated, empirical, and systematic experimental evidence. © 2023 IEEE.

关键词： Benchmarking

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Multi-secret Sharing Scheme for Zero Trust Environment 14

Multi-secret Sharing Scheme for Zero Trust Environment

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14th IEEE International Conference on Software engineering and Service science, ICSESS 2023

作者： Zhou, Qin Yang, Rumiao Zhou, Hao Cai, Lei Yongyou Network Technology Co. Ltd Beijing China School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China North China Institute of Computing Technology The 4th System Department Beijing China

ISBN: (纸本)9798350336269

The zero-trust concept of 'never trust, always verify' injected new vitality into the development of network security. However, the related research on zero trust is still preliminary, and the research on secure data transmission is relatively less. How to achieve secure data transmission in zero trust environment is a problem to be solved. Aiming the data transmission scenario of private data secret sharing among agents, this paper proposed a secure data transmission scheme based on multi-secret sharing to solve the problems of identity comparison confirmation of participants and adjust the access structure according to the security value of trust evaluation in existing secret sharing schemes. The proposed scheme adopts the secret sharing design without the trusted third party, which avoids the security risk of trusted third party disclosure in the traditional secret sharing scheme. Secondly, in the system parameter setting phase, the identity of participants and zero-trust identity database are compared to combine the characteristics of zero-trust 'identity authentication', to improve the security of secret sharing. Finally, during secret distribution, zero-trust trust evaluation security values are used to adjust the access structure based on zero-trust 'trust evaluation' to ensure secure data transmission. © 2023 IEEE.

关键词： Data communication systems

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Initial Study of Oil Palm Crop Condition Detection Through Drone Imagery: A YOLO Approach

Initial Study of Oil Palm Crop Condition Detection Through D...

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International Conference on Informatics, Multimedia, Cyber and Information system (ICIMCIS)

作者： Winata Nugraha Ermatita Deris Stiawan Faculty of Computer Science Doctoral Program Faculty of Engineering Universitas Sriwijaya Palembang Indonesia Department of Information System Universitas Sriwijaya Palembang Indonesia Faculty of Computer Science Universitas Sriwijaya Palembang Indonesia

ISBN: (数字)9798331533243

ISBN: (纸本)9798331533250

Oil palm plantations in Indonesia still have many challenges, especially in terms of monitoring and mapping technology. One of the important aspects in the monitoring stage of oil palm plantations is monitoring the productivity of oil palm plants. To identify the oil palm plants, many previous studies have utilized image analysis to solve the problem. The problem is that poor image conditions can affect the architecture model to be built.[1] So in this research we do tune mapping on the image to produce a quality image. Each image data that has been improved will be feature extracted using ResNet and VGG to produce the best model. Furthermore, the classification model development process is carried out using CNN architecture as the model architecture. We also provide innovation by using object detection technology. Which uses artificial neural networks. Here we train the model using aerial video datasets of oil palm plants obtained from drones. Furthermore, to evaluate the performance of the above technique, it is proposed to use the mapping model, precision and accuracy evaluation obtained in the test phase[2]

关键词： YOLO Productivity Technological innovation Oils Multimedia systems Plantations Feature extraction Informatics Monitoring Drones

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ISAC-Assisted Wireless Rechargeable Sensor Networks with Multiple Mobile Charging Vehicles

arXiv

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arXiv 2024年

作者： Qaisar, Muhammad Umar Farooq Yuan, Weijie Bellavista, Paolo Han, Guangjie Ahmed, Adeel The School of System Design and Intelligent Manufacturing Southern University of Science and Technology Shenzhen China The Department of Computer Science and Engineering University of Bologna Bologna Italy The Department of Internet of Things Engineering Hohai University Changzhou China The School of Computer Science and Technology University of Science and Technology of China Hefei China

As IoT-based wireless sensor networks (WSNs) become more prevalent, the issue of energy shortages becomes more pressing. One potential solution is the use of wireless power transfer (WPT) technology, which is the key to building a new shape of wireless rechargeable sensor networks (WRSNs). However, efficient charging and scheduling are critical for WRSNs to function properly. Motivated by the fact that probabilistic techniques can help enhance the effectiveness of charging scheduling for WRSNs, this article addresses the aforementioned issue and proposes a novel ISAC-assisted WRSN protocol. In particular, our proposed protocol considers several factors to balance the charging load on each mobile charging vehicle (MCV), uses an efficient charging factor strategy to partially charge network devices, and employs the ISAC concept to reduce the traveling cost of each MCV and prevent charging conflicts. Simulation results demonstrate that this protocol outperforms other classic, cutting-edge protocols in multiple areas. © 2024, CC BY.

关键词： Internet of things

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Language-Queried Target Sound Extraction Without Parallel Training Data

Language-Queried Target Sound Extraction Without Parallel Tr...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Ma, Hao Peng, Zhiyuan Li, Xu Li, Yukai Shao, Mingjie Kong, Qiuqiang Liu, Ju School of Information Science and Engineering Shandong University Qingdao China Department of Computer Science North Carolina State University NC United States ARC Lab Tencent PCG China Key Laboratory of System and Control AMSS Chinese Academy of Sciences Beijing China The Chinese University of Hong Kong Hong Kong

ISBN: (纸本)9798350368741

Language-queried target sound extraction (TSE) aims to extract specific sounds from mixtures based on language queries. Traditional fully-supervised training schemes require extensively annotated parallel audio-text data, which are labor-intensive. We introduce a parallel-data-free training scheme, requiring only unlabelled audio clips for TSE model training by utilizing the contrastive language-audio pre-trained model (CLAP). In a vanilla parallel-data-free training stage, target audio is encoded using the pre-trained CLAP audio encoder to form a condition embedding, while during testing, user language queries are encoded by CLAP text encoder as the condition embedding. This vanilla approach assumes perfect alignment between text and audio embeddings, which is unrealistic. Two major challenges arise from training-testing mismatch: the persistent modality gap between text and audio and the risk of overfitting due to the exposure of rich acoustic details in target audio embedding during training. To address this, we propose a retrieval-augmented strategy. Specifically, we create an embedding cache using audio captions generated by a large language model (LLM). During training, target audio embeddings retrieve text embeddings from this cache to use as condition embeddings, ensuring consistent modalities between training and testing and eliminating information leakage. Extensive experiment results show that our retrieval-augmented approach achieves consistent and notable performance improvements over existing state-of-the-art with better generalizability. © 2025 IEEE.

关键词： contrastive language-audio pre-training retrieval-augmented strategy target sound extraction uni-modal training for multi-modal tasks

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