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14th International Conference on computing Communication and Networking Technologies, ICCCNT 2023

作者： Mishra, Priti Khare, Sangita Singh, Tripty Nair, Rekha R Sharma, Mansi Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India Dayananda Sagar University School of Engineering Department of Computer Applications Bengaluru India Cloud Computing Sw Research Section Future Computing Research Division Korea Republic of

ISBN: (纸本)9798350335095

Breast cancer is currently one of the deadliest types of cancer, and the death rate has considerably grown as a result of a lack of knowledge about the disease, its symptoms, and prevention techniques. Therefore, early detection at a nearly stage is crucial and vital in order to limit the progression of cancer. Malignant and benign breast cancer are the two further subtypes. In order to categorise the different forms of breast cancer, an automated system with logistic regression and neural network is proposed. The classification of the breast cancer data utilises the DNN with various levels of processing. The Digital Database for Screening Mammography (DDMS) dataset was used in the proposed study on the Kaggle platform. The dataset was divided into various train-test splits. On the basis of accuracy, ROC AUC cuve the system's performance is evaluated. The outcome demonstrates that proposed-2 outperforms in a comparable sense with a training accuracy of 98.99% and testing accuracy of 98.83%. © 2023 IEEE.

关键词： Diseases

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GearDB: A GC-free Key-Value Store on HM-SMR Drives with Gear Compaction 23

GearDB: A GC-free Key-Value Store on HM-SMR Drives with Gear...

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2023 ACM Turing Award Celebration Conference, CHINA 2023

作者： Yao, Ting Wan, Jiguang Huang, Ping Zhang, Yiwen Xie, Changsheng He, Xubin Huawei Cloud Computing Technologies Co. Ltd. China School of Computer Science and Technology Huazhong University of Science and Technology China College of Science and Technology Temple University United States

No abstract available.

ISBN: (纸本)9798400702334

No abstract available.

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AI-Driven Solutions for cloud Security Implementing Intelligent Threat Detection and Mitigation Strategies

AI-Driven Solutions for Cloud Security Implementing Intellig...

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2024 IEEE International Conference on Intelligent computing and Emerging Communication Technologies, ICEC 2024

作者： Yasani, Rajashekar Reddy Prasad, Putalpattu Muni Srinivas, Pattlola Reddy, N V Raja Sekhar Jawarkar, Parag Raghunath, Vedaprada Cloud Security Cloud Computing Cyber Security India University of Rome Tor Vergata Department of Industrial Engineering Italy Department of Computer Science and Engineering Telangana India Mlr Institute of Technology Department of It India Shri Ramdeobaba College of Engineering and Management Electronics Engineering Nagpur India Visveswaraya Technological University India

ISBN: (纸本)9798331508432

Adapting threat detection and response procedures is essential for businesses to stay ahead of the constantly developing cyberattack landscape. Researching cutting-edge tools like XDR, SIEM, SOAR, and NDR - which shed light on the dynamic realm of detection and response systems - is crucial. Other areas of investigation include real-time monitoring and network forensics. Security and threat detection have both been greatly enhanced since businesses began storing their apps and data in the cloud. In order to safeguard vulnerable network infrastructures in cloud settings against advanced attacks, traditional security procedures must be revised. Artificial intelligence (AI) is used to learn more about this problem so that threat reaction and identification can be done more quickly and accurately. Artificial intelligence has changed cloud security and threat identification, as this study shows. Because cyberattacks on cloud infrastructures and service providers are becoming more common, strong security steps that are also easy to use are still needed. This piece will talk about how cloud security and AI operations work together, with a focus on how this collaboration speeds up the time it takes to respond to incidents. The paper will also show how this relationship helps a company improve its defences and lessen the impact of security events. Organisations that want to keep up with the constantly changing danger landscape need to use cloud security and AI and understand how the two work together. This is because it is necessary to keep a security stance that is both flexible and strong. © 2024 IEEE.

关键词： cloud security

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Plausible Heterogeneous Graph k-Anonymization for Social Networks

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Tsinghua science and Technology 2022年第6期27卷 912-924页

作者： Kaiyang Li Ling Tian Xu Zheng Bei Hui School of Computer Science and Engineering University of Electronic Science and Technology of ChinaChengdu 611731China School of Information and Software Engineering University of Electronic Science and Technology of ChinaChengdu 610054China Trusted Cloud Computing and Big Data Key Laboratory of Sichuan Province Chengdu 610000China

The inefficient utilization of ubiquitous graph data with combinatorial structures necessitates graph embedding methods,aiming at learning a continuous vector space for the graph which is amenable to be adopted in traditional machine learning algorithms in favor of vector *** embedding methods build an important bridge between social network analysis and data analytics as social networks naturally generate an unprecedented volume of graph data *** social network data not only bring benefit for public health,disaster response,commercial promotion,and many other applications,but also give birth to threats that jeopardize each individual’s privacy and ***,most existing works in publishing social graph embedding data only focus on preserving social graph structure with less attention paid to the privacy issues inherited from social *** be specific,attackers can infer the presence of a sensitive relationship between two individuals by training a predictive model with the exposed social network *** this paper,we propose a novel link-privacy preserved graph embedding framework using adversarial learning,which can reduce adversary’s prediction accuracy on sensitive links while persevering sufficient non-sensitive information such as graph topology and node attributes in graph *** experiments are conducted to evaluate the proposed framework using ground truth social network datasets.

关键词： social network graph embedding privacy preservation adversarial learning

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Three-Stage Progressive Pre-Analysis Framework for VMAF Controllable Image Coding

Three-Stage Progressive Pre-Analysis Framework for VMAF Cont...

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Data Compression Conference (DCC)

作者： Shaokang Wang Guoqing Xiang Wenzhao Li Mingyuan Yang Fan Yang Shanghang Zhang Xiaodong Xie Huizhu Jia School of Computer Science Peking University Beijing China Alibaba Cloud Computing Beijing China

ISBN: (数字)9798331534714

ISBN: (纸本)9798331534721

To achieve controllable subjective quality in image coding, this paper proposes a Video Multi-method Assessment Fusion (VMAF)-oriented image coding pre-analysis algorithm, enabling the adaptive derivation of quantization parameters corresponding to a specified quality target. First, a $Q-\mathcal{V}$ model is constructed to describe the relationship between encoding quantization and VMAF distortion. Then, a Three-stage Progressive Control (TPC) algorithm, shown in Fig. 1(a), is designed to adapt quantization parameters using the discovered $Q-\mathcal{V}$ model. The first two stages, based on lightweight feature extraction, iteratively fit the distortion metrics intrinsically calculated by VMAF to predict the VMAF value for a given sample under specified distortion conditions. The final stage fits the $Q-\mathcal{V}$ model parameters using multi-point VMAF distortion data and outputs the corresponding quantization step for encoder control. A two-pass refinement algorithm, depicted in Fig. 1(b), further adjusts the quantization parameters based on the first encoding pass, improving quality control accuracy and framework robustness. Experiments on four datasets show that the quality control error remains below 1.293% for various VMAF targets, and the two-pass refinement reduces it further to 0.710%, outperforming existing methods.

关键词： Measurement Adaptation models Quantization (signal) Image coding Quality control Distortion Prediction algorithms Feature extraction Encoding Robustness

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DAG-aware harmonizing job scheduling and data caching for disaggregated analytics frameworks

DAG-aware harmonizing job scheduling and data caching for di...

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作者： Tong, Yulai Liu, Jiazhen Wang, Hua He, Mingjian Zhou, Ke He, Rongfeng Zhang, Qin Wang, Cheng School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China Wuhan National Laboratory for Optoelectronics Huazhong University of Science and Technology Wuhan430074 China Huawei Cloud Computing Technologies Co. Ltd Chengdu611730 China

Modern data analytics frameworks often integrate with external storage services, which can lead to storage bottlenecks. Existing caching and prefetching solutions utilize high-level information from data analytics frameworks to forecast future data accesses. They employ these predictions to prefetch data into the cache and manage the cache contents. However, this approach overlooks a fundamental opportunity: rather than caching data given a prediction of job execution, influencing the job execution order can enable more effective caching and prefetching. With this key insight, we introduce a novel system called TRIPOD, designed to synchronize job scheduling and data caching for analytics frameworks. Leveraging the higher-level information provided by analytics frameworks, TRIPOD explores the best-suited job execution order, guided by developed heuristics, to facilitate prefetching and caching. To fully exploit the potential of TRIPOD, we also introduce a novel caching strategy named CAP. This strategy not only acknowledges the job scheduling order but also offers fine-grained control over object prefetching and eviction. Our evaluation, conducted using standard analytic benchmarks (TPC-H and TPC-DS), demonstrates that TRIPOD achieves a speedup of up to 1.7x on state-of-the-art approaches. Moreover, when employing CAP to make caching decisions, the performance can further be improved (as much as 1.5x). © 2024

关键词： Forecasting

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Enhancing Energy Efficiency with a Dynamic Trust Measurement Scheme in Power Distribution Network

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computers, Materials & Continua 2024年第3期78卷 3909-3927页

作者： Yilei Wang Xin Sun Guiping Zheng Ahmar Rashid Sami Ullah Hisham Alasmary Muhammad Waqas Zhejiang Electric-Power Corporation Research Institute Zhejiang310014China Faculty of Information Technology Beijing University of TechnologyBeijing100124China Beijing Trusty Cloud Technology Co. Ltd.Beijing100022China Department of Computer Science and Engineering Ghulam Ishaq Khan Institute of Engineering Sciences and EngineeringTopi23640Pakistan Department of Computer Science Shaheed Benazir Bhutto UniversitySheringalUpper Dir18050Pakistan Department of Computer Science College of Computer ScienceKing Khalid UniversityAbha61421Saudi Arabia School of Computing and Mathematical Science Faculty of Engineering and ScienceUniversity of GreenwichLondonSE109LSUK School of Engineering Edith Cowan UniversityPerth6027Australia

The application of Intelligent Internet of Things(IIoT)in constructing distribution station areas strongly supports platform transformation,upgrade,and intelligent *** sensing layer of IIoT comprises the edge convergence layer and the end sensing layer,with the former using intelligent fusion terminals for real-time data collection and ***,the influx of multiple low-voltage in the smart grid raises higher demands for the performance,energy efficiency,and response speed of the substation fusion ***,it brings significant security risks to the entire distribution substation,posing a major challenge to the smart *** response to these challenges,a proposed dynamic and energy-efficient trust measurement scheme for smart grids aims to address these *** scheme begins by establishing a hierarchical trust measurement model,elucidating the trust relationships among smart IoT *** then incorporates multidimensional measurement factors,encompassing static environmental factors,dynamic behaviors,and energy *** comprehensive approach reduces the impact of subjective factors on trust ***,the scheme incorporates a detection process designed for identifying malicious low-voltage end sensing units,ensuring the prompt identification and elimination of any malicious ***,in turn,enhances the security and reliability of the smart grid *** effectiveness of the proposed scheme in pinpointing malicious nodes has been demonstrated through simulation ***,the scheme outperforms established trust metric models in terms of energy efficiency,showcasing its significant contribution to the field.

关键词： IIoT trusted measure energy efficient

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Introducing on-chain graph data to consortium blockchain for commercial transactions

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Frontiers of computer science 2024年第2期18卷 255-257页

作者： Pengyi ZHANG Yuchen YUAN Jie SONG Yu GU Qiang QU Yongjie BAI Software College Northeastern UniversityShenyang 110819China School of Computer Science and Engineering Northeastern UniversityShenyang 110819China Shenzhen Institute of Advanced Technology Chinese Academy of SciencesShenzhen 518055China Blockchain Lab Huawei Cloud Computing Technologies Co.Ltd.Shenzhen 518101China

1 Introduction A blockchain can be referred to as a distributed ledger[1].Nowadays,according to our previous work[2],the graphrelated blockchain grows efficiently in both academic studies and commercial *** data,algor... 详细信息

关键词： transactions ledge commercial

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Efficient Quality Controllable Neural Image Compression based on QD-Model

Efficient Quality Controllable Neural Image Compression base...

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

作者： Shaokang Wang Guoqing Xiang Jinchang Xu Shanghang Zhang Xiaodong Xie School of Computer Science Peking University Beijing China Alibaba Cloud Computing Beijing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Neural image compression has achieved significant advancements, consistently outperforming traditional codecs in terms of performance. However, research on quality control algorithms for neural image compression is still lacking. In this paper, we propose a framework designed to control the quality of compressed images through a one-pass pre-analysis. First, we construct a foundational relationship between the quantization factor and compression distortion, utilizing variable rate neural image compression as the basis for quality control. Second, we introduce the image Content-Compression features-based Distortion Estimation Network (C 2 DEN) to efficiently fit the sample-adaptive Quantization-Distortion (QD) model. Leveraging the QD model, we convert the target quality into a quantization factor to control the compression model, enabling quality-controllable compression of samples. Experimental results show that the average quality errors on four different datasets are only 0.89%, 1.79%, 1.73%, and 1.61%. Compared with existing control methods, our method reduces the algorithm time complexity by 98.58%, 98.52%, 98.85%, and 98.50% while ensuring accuracy, which further demonstrates the superiority of our method.

关键词： Image coding Quantization (signal) Acoustic distortion Signal processing algorithms Rate-distortion Estimation Quality control Rate distortion theory Time complexity Speech processing

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Effective Integration of Text Diffusion and Pre-Trained Language Models with Linguistic Easy-First Schedule 30

Effective Integration of Text Diffusion and Pre-Trained Lang...

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Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Ou, Yimin Jian, Ping School of Computer Science and Technology Beijing Institute of Technology Beijing China Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications Beijing Institute of Technology Beijing China

ISBN: (纸本)9782493814104

Diffusion models have become a powerful generative modeling paradigm, achieving great success in continuous data patterns. However, the discrete nature of text data results in compatibility issues between continuous diffusion models (CDMs) and pre-trained language models (PLMs). That is, the performance of diffusion models even degrades when combined with PLMs. To alleviate this issue, we propose to utilize a pre-trained decoder to convert the denoised embedding vectors into natural language instead of using the widely used rounding operation. In this way, CDMs can be more effectively combined with PLMs. Additionally, considering that existing noise schedules in text diffusion models do not take into account the linguistic differences among tokens, which violates the easy-first policy for text generation, we propose a linguistic easy-first schedule that incorporates the measure of word importance, conforming to easy-first-generation linguistic features and bringing about improved generation quality. Experiment results on the E2E dataset and five controllable tasks show that our approach can combine the merits of CDMs and PLMs, significantly outperforming other diffusion-based models. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Diffusion

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