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arXiv 2024年

作者： Chen, Peilin Fang, Xiaohan Wang, Meng Wang, Shiqi Ma, Siwei Department of Computer Science City University of Hong Kong Hong Kong The School of Data Science Lingnan University Hong Kong National Engineering Research Center of Visual Technology School of Computer Science Peking University Beijing100871 China

The Human visual System (HVS), with its intricate sophistication, is capable of achieving ultra-compact information compression for visual signals. This remarkable ability is coupled with high generalization capability and energy efficiency. By contrast, the state-of-the-art Versatile Video Coding (VVC) standard achieves a compression ratio of around 1,000 times for raw visual data. This notable disparity motivates the research community to draw inspiration to effectively handle the immense volume of visual data in a green way. Therefore, this paper provides a survey of how visual data can be efficiently represented for green multimedia, in particular when the ultimate task is knowledge extraction instead of visual signal reconstruction. We introduce recent research efforts that promote green, sustainable, and efficient multimedia in this field. Moreover, we discuss how the deep understanding of the HVS can benefit the research community, and envision the development of future green multimedia technologies. © 2024, CC BY.

关键词： Green development

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Process Mining Approach to Analyze Engagement in Campaign Activities 9

Process Mining Approach to Analyze Engagement in Campaign Ac...

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9th IEEE Information Technology International Seminar, ITIS 2023

作者： Nugroho, Aryo Ambarwati, Awalludiyah Faza, Arsi Raniah Falah, Ardi Ilham Putra, Oddy Virgantara Widiastuti, Ika Universitas Narotama Department of Information System Surabaya Indonesia UPN Veteran Jawa Timur Department of Accounting Surabaya Indonesia AONO Research Center Department of Soft Computing Surabaya Indonesia Universitas Darussalam Gontor Department of Informatics Ponorogo Indonesia Ewha Womans University Department of Computer Science & Engineering. Seoul Korea Republic of

ISBN: (纸本)9798350306835

Campaign strategies are evolving because of developments in communication technologies. Campaign activities have become more attractive to the younger generation with the existence of social media. There is much research related to campaigns, but it focuses on data collection and content. This research tries to look at it from the other side, namely from the perspective of the campaign team. How can teams improve performance and ensure that strategies are executed correctly? The analysis was carried out using process mining. The methodology used is the Project Mining Methodology, supported by data, identification, conformity checking, and quality improvement. The data used was taken from a Facebook group that was specifically created and operated during the 100 days of the official campaign. Interactions occurred with 2215 members with 975 active members, of which there were 357 posts, 8755 reactions, 1275 comments, and 280 shares. The results obtained from this process show weekly posting patterns and responses. Furthermore, we can see the involvement of key figures in the activities of the campaign group. © 2023 IEEE.

关键词： Social networking (online)

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Towards Holistic Analysis of Wireless Capsule Endoscopic Videos: A Taxonomy-Driven Machine Learning Framework for Clinically Comprehensive WCE Frame Level Analysis

Towards Holistic Analysis of Wireless Capsule Endoscopic Vid...

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Electrics and computer (INTCEC), Interdisciplinary Conference on

作者： Haroon Wahab Rita Goel Maida Alamgir Irfan Mehmood Khan Muhammad Hassan Ugail Centre for Visual Computing & Intelligent Systems School of Computer Science AI and Electronics University of Bradford Bradford England UK Department of Applied Artificial Intelligence Visual Analytics Knowledge Laboratory (VIS2KNOW) Sungkyunkwan University Seoul Republic of Korea

ISBN: (数字)9798350349450

ISBN: (纸本)9798350349467

Manual perusal of capsule endoscopic videos is laborious due to their length, redundancy, and poor quality frames, risking misdetection. Limited representation of diverse anomalies and subtle lesions led to less generalized approaches targeting only individual or few anomalous findings. To deploy automated analysis in clinical settings effectively, a holistic, end-to-end approach is crucial, distinguishing between frame-level analysis and disease-level diagnosis. This study proposes a holistic framework for frame-level analysis based on a taxonomy of findings, leveraging clinical consensus. Clinical knowledge implicit in the taxonomy can enhance computer Aided Diagnostics (CAD) systems. Convolutional Neural Networks (CNNs) trained at appropriate granularity levels in the taxonomy show improved performance metrics. Validation on the Kvasir-capsule dataset demonstrates a 2-4% performance boost. Taxonomy-based inference scores effectively resolve mimicked anomalies without additional learning.

关键词： Training Wireless communication Measurement Taxonomy Redundancy Merging Manuals

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Testing GPU Numerics: Finding Numerical Differences Between NVIDIA and AMD GPUs

Testing GPU Numerics: Finding Numerical Differences Between ...

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High Performance computing, Networking, Storage and Analysis, SC-W: Workshops of the International Conference for

作者： Anwar Hossain Zahid Ignacio Laguna Wei Le Department of Computer Science Iowa State University Ames IA Center for Applied Scientific Computing Lawrence Livermore National Laboratory Livermore CA

ISBN: (数字)9798350355543

ISBN: (纸本)9798350355550

As scientific codes are ported between GPU platforms, continuous testing is required to ensure numerical robustness and identify potential numerical differences between platforms. Compiler-induced numerical differences can occur when a program is compiled and run on different GPUs and compilers, and the numerical outcomes are different for the same input. We present a study of compiler-induced numerical differences between NVIDIA and AMD GPUs, two widely used GPUs in HPC clusters. Our approach uses a random program generator (Varity) to generate thousands of short numerical tests in CUDA and HIP, and their inputs; then, we use differential testing to check if the program produced a numerical inconsistency when run on NVIDIA and AMD GPUs, using the same compiler optimization level. We also use the AMD’s HIPIFY tool to convert CUDA tests into HIP tests and test if there are numerical inconsistencies induced by HIPIFY. In our study, we generated more than 600,000 tests and found subtle numerical differences occurring between the two classes of GPUs. We found that some of the differences come from (1) math library calls, (2) differences in floating-point precision (FP64 versus FP32), and (3) converting code to HIP with HIPIFY.

关键词： Codes High performance computing Graphics processing units Production Numerical simulation Robustness Libraries Hip Optimization Testing

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KAN-AD: Time Series Anomaly Detection with Kolmogorov-Arnold Networks

arXiv

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arXiv 2024年

作者： Zhou, Quan Pei, Changhua Zhang, Haiming Xie, Gaogang Li, Jianhui Sun, Fei Han, Jing Gao, Zhengwei Pei, Dan Computer Network Information Center Chinese Academy of Science China Institution of Computing Technology Chinese Academy of Science China ZTE China Computer Science Department Tsinghua University China University of Chinese Academy of Sciences China

Time series anomaly detection (TSAD) has become an essential component of large-scale cloud services and web systems because it can promptly identify anomalies, providing early warnings to prevent greater losses. Deep learning-based forecasting methods have become very popular in TSAD due to their powerful learning capabilities. However, accurate predictions don't necessarily lead to better anomaly detection. Due to the common occurrence of noise, i.e., local peaks and drops in time series, existing black-box learning methods can easily learn these unintended patterns, significantly affecting anomaly detection performance. Kolmogorov-Arnold Networks (KAN) offers a potential solution by decomposing complex temporal sequences into a combination of multiple univariate functions, making the training process more controllable. However, KAN optimizes univariate functions using spline functions, which are also susceptible to the influence of local anomalies. To address this issue, we present KAN-AD, which leverages the Fourier series to emphasize global temporal patterns, thereby mitigating the influence of local peaks and drops. KAN-AD improves both effectiveness and efficiency by transforming the existing black-box learning approach into learning the weights preceding univariate functions. Experimental results show that, compared to the current state-ofthe-art, we achieved an accuracy increase of 15% while boosting inference speed by 55 times. © 2024, CC BY-NC-SA.

关键词： Time series

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Sparse in Space and Time: Audio-visual Synchronisation with Trainable Selectors 33

Sparse in Space and Time: Audio-visual Synchronisation with ...

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33rd British Machine Vision Conference Proceedings, BMVC 2022

作者： Iashin, Vladimir Xie, Weidi Rahtu, Esa Zisserman, Andrew Computing Sciences Tampere University Tampere Finland Coop. Medianet Innovation Center Shanghai Jiao Tong University Shanghai China Visual Geometry Group Department of Engineering Science University of Oxford Oxford United Kingdom

The objective of this paper is audio-visual synchronisation of general videos 'in the wild'. For such videos, the events that may be harnessed for synchronisation cues may be spatially small and may occur only infrequently during a many seconds-long video clip, i.e. the synchronisation signal is 'sparse in space and time'. This contrasts with the case of synchronising videos of talking heads, where audio-visual correspondence is dense in both time and space. We make four contributions: (i) in order to handle longer temporal sequences required for sparse synchronisation signals, we design a multi-modal transformer model that employs 'selectors' to distil the long audio and visual streams into small sequences that are then used to predict the temporal offset between streams. (ii) We identify artefacts that can arise from the compression codecs used for audio and video and can be used by audio-visual models in training to artificially solve the synchronisation task. (iii) We curate a dataset with only sparse in time and space synchronisation signals;and (iv) the effectiveness of the proposed model is shown on both dense and sparse datasets quantitatively and qualitatively. © 2022. The copyright of this document resides with its authors. It may be distributed unchanged freely in print or electronic forms.

关键词： Synchronization

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A Lightweight Security Scheme for Failure Detection in Microservices IoT-Edge Networks 14th

A Lightweight Security Scheme for Failure Detection in Micro...

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14th International Conference on Sensing Technology, ICST 2022

作者： Sodhro, Ali Hassan Lakhan, Abdullah Pirbhulal, Sandeep Groenli, Tor Morten Abie, Habtamu Department of Computer Science Kristianstad University Kristianstad291 88 Sweden Department of Technology Mobile Technology Lab Kristiania University College Oslo Norway Norwegian Computing Center Oslo0373 Norway

ISBN: (纸本)9783030988852

Nowadays, microservices-based applications such as E-Business, E-Healthcare, 3D-Gaming, and Augmented Reality have latterly drawn attention in the research area. The microservices enabled applications are different from traditional monolithic applications with high demand of security and fault detection, therefore, a lightweight secure and failure detection enabled schemes widely required for the new applications. This paper proposes a new lightweight microservices mobile cloud (Mob-Cloud) framework that replaces the heavyweight virtual machine (VM) based on mobile cloud computing (MCC). The study devises MFHE (Modified Fully Homomorphism Encryption) and WATFA (Workload Assignment Transient Fault Aware) schemes to deal with security and failures. Simulation results show that the proposals are practical for the considered problem. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Cryptography

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Intelligent Energy Consumption For Smart Homes Using Fused Machine-Learning Technique

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computers, Materials & Continua 2023年第1期74卷 2261-2278页

作者： Hanadi AlZaabi Khaled Shaalan Taher M.Ghazal Muhammad A.Khan Sagheer Abbas Beenu Mago Mohsen A.A.Tomh Munir Ahmad Faculty of Engineering and IT The British University in DubaiUnited Arab Emirates Center for Cyber Security Faculty of Information Science and TechnologyUniversity Kebangsaan Malaysia(UKM)Bangi43600SelangorMalaysia School of Information Technology Skyline University CollegeUniversity City SharjahSharjah1797United Arab Emirates Riphah School of Computing&Innovation Faculty of ComputingRiphah International University Lahore CampusLahore54000Pakistan Pattern Recognition and Machine Learning Lab. Department of SoftwareGachon UniversitySeongnamGyeonggido13120Korea Faculty of Computer Science NCBA&ELahore54660Pakistan

Energy is essential to practically all exercises and is imperative for the development of personal ***,valuable energy has been in great demand for many years,especially for using smart homes and structures,as individuals quickly improve their way of life depending on current ***,there is a shortage of energy,as the energy required is higher than that *** new plans are being designed to meet the consumer’s energy *** many regions,energy utilization in the housing area is 30%–40%.The growth of smart homes has raised the requirement for intelligence in applications such as asset management,energy-efficient automation,security,and healthcare monitoring to learn about residents’actions and forecast their future *** overcome the challenges of energy consumption optimization,in this study,we apply an energy management *** fusion has recently attracted much energy efficiency in buildings,where numerous types of information are *** proposed research developed a data fusion model to predict energy consumption for accuracy and miss *** results of the proposed approach are compared with those of the previously published techniques and found that the prediction accuracy of the proposed method is 92%,which is higher than the previously published approaches.

关键词： Energy consumption intelligent machine learning technique smart homes prediction

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(Image Present) ForgerySleuth: Empowering Multimodal Large Language Models for Image Manipulation Detection

arXiv

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arXiv 2024年

作者： Sun, Zhihao Jiang, Haoran Chen, Haoran Cao, Yixin Qiu, Xipeng Wu, Zuxuan Jiang, Yu-Gang Shanghai Key Lab of Intell. Info. Processing School of CS Fudan University China Shanghai Collaborative Innovation Center of Intelligent Visual Computing China School of Computer Science Fudan University China

(Image Present) Multimodal large language models have unlocked new possibilities for various multimodal tasks. However, their potential in image manipulation detection remains unexplored. When directly applied to the IMD task, M-LLMs often produce reasoning texts that suffer from hallucinations and overthinking. To address this, in this work, we propose ForgerySleuth, which leverages M-LLMs to perform comprehensive clue fusion and generate segmentation outputs indicating specific regions that are tampered with. Moreover, we construct the ForgeryAnalysis dataset through the Chain-of-Clues prompt, which includes analysis and reasoning text to upgrade the image manipulation detection task. A data engine is also introduced to build a larger-scale dataset for the pre-training phase. Our extensive experiments demonstrate the effectiveness of ForgeryAnalysis and show that ForgerySleuth significantly outperforms existing methods in generalization, robustness, and explainability. The resource is available at https://***/sunzhihao18/ForgerySleuth. Copyright © 2024, The Authors. All rights reserved.

关键词： Large datasets

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A Survey on Video Diffusion Models

arXiv

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arXiv 2023年

作者： Xing, Zhen Feng, Qijun Chen, Haoran Dai, Qi Hu, Han Xu, Hang Wu, Zuxuan Jiang, Yu-Gang Shanghai Collaborative Innovation Center of Intelligent Visual Computing School of Computer Science Fudan University China Microsoft Research Asia China Noah's Ark Lab China

The recent wave of AI-generated content (AIGC) has witnessed substantial success in computer vision, with the diffusion model playing a crucial role in this achievement. Due to their impressive generative capabilities, diffusion models are gradually superseding methods based on GANs and auto-regressive Transformers, demonstrating exceptional performance not only in image generation and editing, but also in the realm of video-related research. However, existing surveys mainly focus on diffusion models in the context of image generation, with few up-to-date reviews on their application in the video domain. To address this gap, this paper presents a comprehensive review of video diffusion models in the AIGC era. Specifically, we begin with a concise introduction to the fundamentals and evolution of diffusion models. Subsequently, we present an overview of research on diffusion models in the video domain, categorizing the work into three key areas: video generation, video editing, and other video understanding tasks. We conduct a thorough review of the literature in these three key areas, including further categorization and practical contributions in the field. Finally, we discuss the challenges faced by research in this domain and outline potential future developmental trends. A comprehensive list of video diffusion models studied in this survey is available at https://***/ChenHsing/Awesome-Video-Diffusion-Models. Copyright © 2023, The Authors. All rights reserved.

关键词： Video analysis

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