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检索条件"任意字段=International Conference on Information Technology - Coding and Computing"

共 193417 条记录，以下是421-430 订阅

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Tell Codec What Worth Compressing: Semantically Disentangled Image coding for Machine with LMMs

Tell Codec What Worth Compressing: Semantically Disentangled...

引用

2024 conference on Visual Communications and Image Processing

作者： Liu, Jinming Wei, Yuntao Lin, Junyan Zhao, Shengyang Sun, Heming Chen, Zhibo Zeng, Wenjun Jin, Xin Shanghai Jiao Tong Univ Shanghai Peoples R China Ningbo Inst Digital Twin Eastern Inst Technol Ningbo Peoples R China Univ Sci & Technol China Hefei Peoples R China Yokohama Natl Univ Yokohama Kanagawa Japan

ISBN: (纸本)9798331529543;9798331529550

We present a new image compression paradigm to achieve "intelligently coding for machine" by cleverly leveraging the common sense of Large Multimodal Models (LMMs). We are motivated by the evidence that large language/multimodal models are powerful general-purpose semantics predictors for understanding the real world. Different from traditional image compression typically optimized for human eyes, the image coding for machines (ICM) framework we focus on requires the compressed bitstream to more comply with different downstream intelligent analysis tasks. To this end, we employ LMM to tell codec what to compress: 1) first utilize the powerful semantic understanding capability of LMMs w.r.t object grounding, identification, and importance ranking via prompts, to disentangle image content before compression, 2) and then based on these semantic priors we accordingly encode and transmit objects of the image in order with a structured bitstream. In this way, diverse vision benchmarks including image classification, object detection, instance segmentation, etc., can be well supported with such a semantically structured bitstream. We dub our method "SDComp" for "Semantically Disentangled Compression", and compare it with state-of-the-art codecs on a wide variety of different vision tasks. SDComp codec leads to more flexible reconstruction results, promised decoded visual quality, and a more generic/satisfactory intelligent task-supporting ability.

关键词： Image coding for Machine Large Multimodal Model Semantically Structured Bitstream

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A Literature Review on AWS-Based Cloud computing: A Case in South Korea

A Literature Review on AWS-Based Cloud Computing: A Case in ...

引用

IEEE international conference on Big Data and Smart computing (BigComp)

作者： Lee, Byeongcheon Oh, Jinyeong Shon, Woojin Moon, Jihoon Soonchunhyang Univ Dept AI & Big Data Asan South Korea

ISBN: (纸本)9781665475785

A collection of information technology (IT) services known as "cloud computing" is offered to customers across a network on a subscription basis with the flexibility to scale up or down based on their service needs. The emergence of cloud computing technology, which has tremendous advantages, is one of the major advancements in recent times. Many computers and servers are specifically devoted to meeting the demands of businesses in a cloud computing system for internal communications. Users can access their services via an internet connection. Registered users have remote access to both hardware and software, thanks to the cloud service, which has made essential adjustments to how information is stored and made accessible. This paper investigates the use of Amazon Web Services (AWS) for big data processing and analytics in South Korea. We collected several domestic journal and conference papers that studied local cloud services based on AWS to introduce distributed systems and cloud computing technologies. This study can provide researchers with a compact version of the extensive AWS-based data processing literature and potential future insights. It can also provide stakeholders tailored services, information about cutting-edge solutions that can influence academics, and details about current research needs.

关键词： Amazon Web Services (AWS) big data processing cloud computing distributed system

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IoT Sensor Data Stream Compression with Hybrid Compression Algorithms 15

IoT Sensor Data Stream Compression with Hybrid Compression A...

引用

15th international conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Garikipati, Karthik Muppala, Tejaswi Vinitha Chowdary, A. Sahay, Apurvanand Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350370249

The recent world advancement in technology especially in the use of IoT devices has posed some serious complications in terms of how effectively we can convey data, this is normally a result of the limited bandwidth and power capacity. In this paper, we look into the need for efficient data compression in IoT devices and come up with a new approach to compress the data stream needed for IoT sensors. In this paper, we propose to integrate Dynamic Huffman coding with Run Length Encoding smoothly as one novel network coding strategy to decrease the load carried by the communication channels and at the same time ensure the originality of the transmitted data. The proposed method is based on the use of RLE to remove a lot of redundant data and dynamic Huffman coding for further data compression. When compared with other central algorithms like dynamic Huffman, static Huffman, RLE, LZW, ZLIB, LZMA, and BZ2 this hybrid approach provided more efficient compression ratios and consumed less power. Compression can be considered an essential paradigm for IoT devices because it can minimize the restrictions associated with the prevention of limited bandwidths and power sources that are characteristic of such devices, which results in improved data sending and a longer lifespan of devices. As substantiated by the findings from our experiments, this approach holds a high degree of effectiveness and, therefore, is suitable for the IoT domain with a scarcity of resources in particular. The contributions of this research support the promotion of compression strategies while considering the IoT-specific needs thus enhancing standard system outcomes and durability. © 2024 IEEE.

关键词： Bandwidth Constraints BZ2 Data Compression Dynamic Huffman coding Hybrid Techniques Internet of Things (IoT) IoT Sensors LZMA LZW Power Constraints RLE Run-Length Encoding Static Huffman coding ZLIB

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Quantum computing application opportunities in military scenarios

Quantum computing application opportunities in military scen...

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international conference on Military Communications and information Systems (ICMCIS)

作者： Sliwa, Joanna Wrona, Konrad NATO Cyber Secur Ctr The Hague Netherlands

ISBN: (纸本)9798350343854

Quantum information Science (QIS) is potentially a game-changing technology for the future military operational environment. Being significantly dependent on the capabilities of current quantum computers, it is still very immature;however, evolving rapidly and increasing the technical and knowledge potential of industrial and state actor players that invest in it. Investing in this technology can lead the NATO Alliance to gain quantum superiority and outperform its adversaries in technological development in diverse military domains. This article presents possible applications of QIS in the military domain in the form of use cases. We have formulated these use cases after analysis of today's capabilities of quantum computing technology and its prospects for the future of the quantum information domain. Our article concludes with initial recommendations for the early adoption of quantum technology. This paper was presented at the NATO Science and technology Organization Symposium (ICMCIS) organized by the information Systems technology (IST) Panel, IST-200 RSY - the ICMCIS, held in Skopje, North Macedonia, 16-17 May 2023.

关键词： quantum computing quantum information science quantum computer NISQ FTQC military domain

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Energy Consumption Minimization Scheme with Dynamic Computational Windows for UAV-Assisted Edge computing 5

Energy Consumption Minimization Scheme with Dynamic Computat...

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5th international conference on Intelligent computing and Human-Computer Interaction, ICHCI 2024

作者： Li, Zhiqi Bai, Wenle School of Information Science and Technology North China University of Technology Beijing China

ISBN: (纸本)9798350368284

As unmanned aerial vehicles (UAVs) demonstrate significant potential for edge computing, their capacity to rapidly deploy computing services at user locations offers considerable advantages. However, their limited operational time poses a significant challenge for current UAV-Assisted edge computing solutions. This paper addresses the optimal scheduling of UAV computing resources and introduces a computing resource scheduling scheme based on dynamic computational windows allocation (CRS-DW). Simulation results demonstrate that the CRS-DW algorithm significantly outperforms existing benchmark algorithms in performance. © 2024 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Predictive coding for Decision Transformer

Predictive Coding for Decision Transformer

引用

2024 international conference on Intelligent Robots and Systems

作者： Luu, Tung M. Lee, Donghoon Yoo, Chang D. KAIST Korea Adv Inst Sci & Technol Sch Elect Engn Daejeon South Korea

ISBN: (纸本)9798350377712;9798350377705

Recent work in offline reinforcement learning (RL) has demonstrated the effectiveness of formulating decision-making as return-conditioned supervised learning. Notably, the decision transformer (DT) architecture has shown promise across various domains. However, despite its initial success, DTs have underperformed on several challenging datasets in goal-conditioned RL. This limitation stems from the inefficiency of return conditioning for guiding policy learning, particularly in unstructured and suboptimal datasets, resulting in DTs failing to effectively learn temporal compositionality. Moreover, this problem might be further exacerbated in long-horizon sparse-reward tasks. To address this challenge, we propose the Predictive coding for Decision Transformer (PCDT) framework, which leverages generalized future conditioning to enhance DT methods. PCDT utilizes an architecture that extends the DT framework, conditioned on predictive codings, enabling decision-making based on both past and future factors, thereby improving generalization. Through extensive experiments on eight datasets from the AntMaze and FrankaKitchen environments, our proposed method achieves performance on par with or surpassing existing popular value-based and transformer-based methods in offline goal-conditioned RL. Furthermore, we also evaluate our method on a goal-reaching task with a physical robot.

关键词： Self-supervised learning

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Analysis of Developing IoT and Cloud computing Based Smart Campuses and its Applications 3

Analysis of Developing IoT and Cloud Computing Based Smart C...

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international conference on Advances in computing, Communication and Applied Informatics (ACCAI)

作者： Hussain, Karrar Parandhaman, Vishnu P. Rohini, V. Ravichandran, Arivumalar Bhat, Subrahmanya K. M. Murugesan, G. CVR Coll Engn ECE Hyderabad Telangana India Paypal Los Angeles CA USA SA Engn Coll Avadi Poonamallee Rd Chennai India Great Lakes Inst Management Fac Analyt Dr Bala V Balachandar CampusEast Coast Rd Manamai 603102 Tamil Nadu India Goa Univ Dept Commerce & Management VVMs Shree Damodar Coll Commerce & Econ Margao 403601 Goa India Vinayaka Missions Res Fdn Deemed Be Univ Dept Management Studies Vinayaka Missions Kirupananda Variyar Engn Coll Salem India

ISBN: (纸本)9798350389432;9798350389449;9798350389456

The advancement of computer technology has led to the adoption of new management models by schools, improving management efficiency, and a shift from digital to smart campus construction. A crucial phase in the creation of campus information is the digital campus. There have been several successes in the creation of India smart campuses. India education system has benefited considerably from the contributions of numerous scientists, technologists, educators, and pertinent national policies. As a result, the country's educational system now uses far more information technology. The creation of new smart campuses is crucial to India educational landscape as we approach the era of big data, cloud computing, and artificial intelligence. This article first explains cloud computing and related technologies, then examines the NEC smart campus service platform. Using this information as a foundation, it develops a cloud computing-based smart campus service platform that will aid in the growth of smart campuses in India.

关键词： Artificial intelligence Cloud computing Internet of things Smart campus

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A High Capacity Reversible Data Hiding in Encrypted Images Using Multi-MSB Prediction and Huffman coding 1st

A High Capacity Reversible Data Hiding in Encrypted Images U...

引用

1st international conference on Security and information Technologies with AI, Internet computing, and Big data Applications (SITAIBA)

作者： Lin, Li-Chiun Huang, Cheng-Ta Wang, Shiuh-Jeng Cent Police Univ Dept Informat Management Informat Cryptol Construct Lab Taoyuan Taiwan Yuan Ze Univ Int Bachelor Program Informat Taoyuan Taiwan Yuan Ze Univ Dept Informat Management Taoyuan Taiwan Cent Police Univ Dept Informat Management Taoyuan Taiwan

ISBN: (纸本)9783031054914;9783031054907

The prosperity of cloud services stimulates the study of reversible data hiding in encrypted images, for which researchers dedicate to find better methods to adopt the new assumption that the ones who provide the cover images, a.k.a. the Content-owner, are no longer the same as the ones who hide the secret message into them, a.k.a. the Data-hider, and the Data-hider might not always be allowed to know the actual content of cover image the Content-owner provided, while the one who receives the stego image must be able to restore the exact cover image after extracting the secret. A method of such reversible data hiding in encrypted images has been proposed and we propose improvements to enhance its performance and make it more generalized and stable. Experimental results show that our proposal improves its performance even in the worst-case scenario.

关键词： Image enhancement

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A Cloud-Edge Collaborative computing Framework for Model Partitioning Based on Genetic Algorithm and Reinforcement Learning 21

A Cloud-Edge Collaborative Computing Framework for Model Par...

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21st international Computer conference on Wavelet Active Media technology and information Processing, ICCWAMTIP 2024

作者： Xiabing, Wang School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu Sichuan611731 China

ISBN: (纸本)9798331519254

Deep Neural Networks are rapidly attracting widespread attention as a key technology for AI applications in 5G era. However, running DNN-based computation tasks on mobile devices is challenging due to limited computational resources and conventional cloud-based computation may lead to cloud overloading. In this paper, we introduce a novel cloud-edge collaborative computing framework to optimize task allocation between cloud and edge. The framework is based on model partitioning and explores different partitioning strategies to adapt to different edge cluster environments through genetic algorithms and reinforcement learning. It achieves flexible allocation of edge resources and effective balance of computing demands. The experiment show that the proposed method effectively reduces the cloud load and the combination of genetic algorithm and reinforcement learning greatly improves the efficiency of finding the optimal solution. © 2024 IEEE.

关键词： Cloud-edge collaborative computing Genetic algorithm Reinforcement learning

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Context Aware Resource Discovery in Fog computing: Agent Approach 2

Context Aware Resource Discovery in Fog Computing: Agent App...

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2nd IEEE international conference on Advances in information technology, ICAIT 2024

作者： Kumbar, Basavaraj G. Bhajantri, Lokesh B. Department of Computer Science and Engineering BGMIT Mudhol VTU Karnataka Belagavi India Department of Information Science and Engineering Basaveshwar Engineering College Karnataka Bagalkote India

ISBN: (纸本)9798350383867

Fog computing offers advantages like low latency and distributed processing at the network edge. Resource discovery in heterogeneous and distributed fog nodes remain a critical research problem. While traditional approaches have been explored extensively, recent advancements in machine learning bid new prospects. This paper proposes an agent-based approach for context-aware resource discovery in fog computing that leverages machine learning. The agents autonomously discover resources while considering the context of the request and the fog networks. Machine learning techniques are employed to predict resource availability and optimize resource selection. This approach aims to improve resource utilization, reduce latency, and enhance the efficiency of fog computing networks. © 2024 IEEE.

关键词： Adversarial machine learning

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