检索结果-内蒙古大学图书馆

Preserving privacy information flow security in composite service evolution

Frontiers of Information Technology & Electronic engineering 2018年第5期19卷 626-638页

作者： Huan-feng PENG Zhi-qiu HUANG Lin-yuan LIU Yong LI Da-juan FAN Yu-qing WANG College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics College of Computer Engineering Nanjing Institute of Technology College of Technology Nanjing Audit University Software Engineering Research Unit University of Oulu

After a composite service is deployed, user privacy requirements and trust levels of component services are subject to variation. When the changes occur, it is critical to preserve privacy information flow security. We propose an approach to preserve privacy information flow security in composite service evolution. First, a privacy data item dependency analysis method based on a Petri net model is presented. Then the set of privacy data items collected by each component service is derived through a privacy data item dependency graph, and the security scope of each component service is calculated. Finally, the evolution operations that preserve privacy information flow security are defined. By applying these evolution operations, the re-verification process is avoided and the evolution efficiency is improved. To illustrate the effectiveness of our approach, a case study is presented. The experimental results indicate that our approach has high evolution efficiency and can greatly reduce the cost of evolution compared with re-verifying the entire composite service.

关键词： Composite service Privacy information flow security Service evolution Petri net

来源：评论

学校读者我要写书评

暂无评论

Machine Learning Classifiers for Asthma Disease Prediction: A Practical Illustration

Machine Learning Classifiers for Asthma Disease Prediction: ...

引用

International Conference on Wavelet Active Media Technology and Information Processing (ICWAMTIP)

作者： WASIF AKBAR WEI-PING WU MUHAMMAD FAHEEM MUHAMMAD ASIM SALEEM NOORBAKHSH AMIRI GOLILARZ AMIN UL HAQ School of Computer Science and Engineering University of Electronic Sciences and Technology of China (UESTC) Department of Computer Science National College of Businees Administration & Economics (NCBAE) School of Information and Software Engineering University of Electronic Sciences and Technology of China (UESTC)

ISBN: (数字)9781728142425

ISBN: (纸本)9781728142432

Asthma is an inflammatory and airway-induced lung disease that causes breathlessness, wheezing and often life-threatening attacks. It is necessary for patients with asthma to predict severe exacerbation caused by uncontrolled asthma. So, there is a need for a framework that can predict the likelihood of asthma in a patient with maximum accuracy. In this paper, an effective mechanism for asthma disease prediction has been used by mining the data containing patients' previous health records. Four machine learning classification algorithms named as Naïve Bayes, J48, Random Forest and Random tree are used in these experiments to predict asthma disease at an early stage. The performance of all four algorithms is evaluated on various measures. Accuracy is measured over instances that are classified correctly and incorrectly. After experiments, results show that Naïve Bayes approaches 98% with the highest accuracy.

关键词： Diseases Lung Vegetation Medical diagnostic imaging Forestry Decision trees

来源：评论

学校读者我要写书评

暂无评论

Ubiquitous acoustic sensing on commodity IoT devices: A survey

arXiv

引用

arXiv 2019年

作者： Cai, Chao Zheng, Rong Luo, Jun The College of Life Science and Technology Huazhong University of Science and Technology The Department of Computing and Software McMaster University Canada The School of Computer Science and Engineering Nanyang Technological University Singapore Singapore

With the proliferation of Internet-of-Things devices, acoustic sensing attracts much attention in recent years. It exploits acoustic transceivers such as microphones and speakers beyond their primary functions, namely recording and playing, to enable novel applications and new user experiences. In this paper, we present the first systematic survey of recent advances in active acoustic sensing using commodity hardware with a frequency range below 24 kHz. We propose a general framework that categorizes main building blocks of acoustic sensing systems. This framework encompasses three layers, i.e., physical layer, core technique layer, and application layer. The physical layer includes basic hardware components, acoustic platforms as well as the air-borne and structure-borne channel characteristics. The core technique layer encompasses key mechanisms to generate acoustic signals (waveforms) and to extract useful temporal, spatial and spectral information from received signals. The application layer builds upon the functions offered by the core techniques to realize different acoustic sensing applications. We highlight unique challenges due to the limitations of physical devices and acoustic channels and how they are mitigated or overcame by core processing techniques and application-specific solutions. Finally, research opportunities and future directions are discussed to spawn further in-depth investigation on acoustic sensing. Copyright © 2019, The Authors. All rights reserved.

关键词： Antennas

来源：评论

学校读者我要写书评

暂无评论

Joint Subcarrier Pairing and Power Allocation for Achieving Energy-Efficient Decode-and-Forward Relay Networks

Joint Subcarrier Pairing and Power Allocation for Achieving ...

引用

IEEE Vehicular Technology Conference

作者： Keshav Singh Meng-Lin Ku Chih-Min Yu School of Engineering University of Edinburgh Edinburgh UK Department of Communication Engineering National Central University Taoyuan Taiwan College of Computer Science and Software Zhaoqing University China

In this paper, subcarrier pairing and power allocation are jointly investigated to maximize the energy efficiency (EE) of a dual-hop multicarrier decode-and-forward (DF) relay network. The optimization problem is formulated as a ratio of the spectrum efficiency (SE) over the entire power consumption of the network subject to total power and subcarrier pairing constraints. A near-optimal iterative scheme is proposed to perform the subcarrier pairing and power allocation for achieving the maximum EE of the network. A two-step suboptimal resource allocation scheme is further proposed to reduce the complexity, in which the subcarrier pairing is first performed by considering the channel quality of the source-to-relay (SR) and relay-to-destination (RD) links, followed by an energy-efficient power allocation scheme to maximize the EE. Numerical results are presented to confirm the effectiveness of the proposed schemes and to demonstrate the tradeoff between EE and SE performances.

关键词： Resource management Relay networks (telecommunications) Optimization Power dissipation Complexity theory Power demand

来源：评论

学校读者我要写书评

暂无评论

Location-centered house price prediction: A multi-task learning approach

arXiv

引用

arXiv 2019年

作者： Gao, Guangliang Bao, Zhifeng Cao, Jie Qin, A.K. Sellis, Timos Wu, Zhiang School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China School of Science Computer Science and Information Technology RMIT University MelbourneVIC3000 Australia School of Information Engineering Nanjing University of Finance and Economics Nanjing China College of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China Department of Computer Science and Software Engineering Swinburne University of Technology MelbourneVIC3122 Australia

Accurate house prediction is of great significance to various real estate stakeholders such as house owners, buyers, investors, and agents. We propose a location-centered prediction framework that differs from existing work in terms of data profiling and prediction model. Regarding data profiling, we make an important observation as follows-besides the in-house features such as floor area, the location plays a critical role in house price prediction. Unfortunately, existing work either overlooked it or had a coarse grained measurement of locations. Thereby, we define and capture a fine-grained location profile powered by a diverse range of location data sources, such as transportation profile (e.g., distance to nearest train station, door-to-door travel time by public transportation), education profile (e.g., school zones and ranking), suburb profile based on census data, facility profile (e.g., nearby GPs, hospitals, supermarkets). Regarding the choice of prediction model, we observe that a variety of approaches either consider the entire data for modeling, or split the entire house data and model each partition independently. However, such modeling ignores the relatedness between partitions, and for all prediction scenarios, there may not be sufficient training samples per partition for the latter approach. We address this problem by conducting a careful study of exploiting the Multi-Task Learning (MTL) model. Specifically, we map the strategies for splitting the entire house data to the ways the tasks are defined in MTL, and each partition obtained is aligned with a task. Furthermore, we select specific MTL-based methods with different regularization terms to capture and exploit the relatedness between tasks. Based on real-world house transaction data collected in Melbourne, Australia from Jan. 2015 to Jan. 2018, these transactions are recorded daily for three years, including house sales price and various house features. We design extensive experimental evaluati

关键词： Population statistics

来源：评论

学校读者我要写书评

暂无评论

A New Atmospheric Dataset for GIIRS Sampled from ERA5 Using Shannon Entropy Method

引用

IOP Conference Series: Earth and Environmental science 2021年第1期668卷

作者： Qi Zhang Yi Yu Weimin Zhang College of Meteorology and Oceanography National University of Defense Technology Changsha 410073 China College of Computer Science National University of Defense Technology Changsha 410073 China Key Laboratory of Software Engineering for Complex Systems Changsha 410073 China

Geostationary Interferometric Infrared Sounder (GIIRS) on board the new Chinese geostationary meteorological satellite Fengyun-4A is aimed to obtain three-dimensional atmospheric information regularly at the regional scale. Since the existing atmospheric datasets are global covered and they are not representative of the GIIRS detection area, a diverse and local representative atmospheric dataset is required for GIIRS in training transmittance parameterization scheme of fast radiative transfer model and retrieving atmospheric profiles. The new atmospheric dataset for GIIRS application in this paper is sampled from the European Centre for Medium-Range Weather Forecasts (ECMWF) fifth-generation reanalysis (ERA5) 2019 dataset using Shannon entropy sampling method. The new dataset demonstrates the feature of uniform distribution within multiple atmospheric variables' (i.e. atmospheric temperature, specific humidity, 2 meter temperature) variation ranges and GIIRS's spatial coverage. To address the issue derived from the climate difference and the varied vertical pressure structure of regions with great differences in topographic height, the study area is divided into six categories regarding of topographic height categories where atmospheric dataset are sampled separately. Seasonal variation is also taken into account in the process of sampling. Compared with the initial randomly sampled dataset, the atmospheric and 2 meter temperature in the new dataset demonstrate a higher level of uniformity within their variation ranges. The statistical results indicate that the differences between samples in the new dataset are enlarged, meanwhile, the new dataset well retains the statistical characteristics of the original ERA5 dataset. The new dataset has a potential in the study of the utility of GIIRS data in the numerical weather forecast.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Predicting software Perfection Through Advanced Models to Uncover and Prevent Defects

引用

IET software 2025年第1期2025卷

作者： Tariq Shahzad Sunawar Khan Tehseen Mazhar Wasim Ahmad Khmaies Ouahada Habib Hamam Department of Electrical and Electronic Engineering Science University of Johannesburg Johannesburg 2006 South Africa uj.ac.za Department of Software Engineering Islamia University of Bahawalpur Bahawalnagar Campus Bahawalnagar 62300 Pakistan iub.edu.pk School of Computer Science National College of Business Administration and Economics Lahore 54000 Pakistan ncbae.edu.pk Department of Computer Science School Education Department Government of Punjab Layyah 31200 Pakistan punjab.gov.pk Department of Computing School of Arts and Creative Technology University of Greater Manchester Manchester BL15AB UK Faculty of Engineering Université de Moncton Moncton NB E1A3E9 Canada umoncton.ca International Institute of Technology and Management (IITG) Avenue Grandes Ecoles Libreville BP 1989 Gabon Bridges for Academic Excellence - Spectrum Centre-ville Tunis Tunisia

software defect prediction is a critical task in software engineering, enabling organizations to proactively identify and address potential issues in software systems, thereby improving quality and reducing costs. In this study, we evaluated and compared various machine learning models, including logistic regression (LR), random forest (RF), support vector machines (SVMs), convolutional neural networks (CNNs), and eXtreme Gradient Boosting (XGBoost), for software defect prediction using a combination of diverse datasets. The models were trained and tested on preprocessed and feature-selected data, followed by optimization through hyperparameter tuning. Performance evaluation metrics were employed to analyze the results comprehensively, including classification reports, confusion matrices, receiver operating characteristic–area under the curve (ROC-AUC) curves, precision–recall curves, and cumulative gain charts. The results revealed that XGBoost consistently outperformed other models, achieving the highest accuracy, precision, recall, and AUC scores across all metrics. This indicates its robustness and suitability for predicting software defects in real-world applications.

关键词： ensemble learning hyperparameter optimization machine learning models NLP techniques software defect prediction text mining

来源：评论

学校读者我要写书评

暂无评论

Cell quantization tool for anterior chamber OCT images

引用

Transactions of the Korean Institute of Electrical Engineers 2020年第7期69卷 1073-1080页

作者： Lee, Yeongseop Kang, Taeseen Han, Yongseop Kim, Jinhyun Kim, Kyong Hoon Lee, Seongjin Dept. of Aerospace and Software Engineering Gyeongsang National University Korea Republic of Dept. of Informatics Gyeongsang National University Korea Republic of Dept of Ophthalmology Gyeongsang National University Changwon Hospital Changwon Korea Republic of Dept of Ophthalmology College of Medicine Gyeongsang National University Jinju Korea Republic of Dept of Information and Communication Engineering Gyeongsang University Korea Republic of School of Computer Science and Engineering Kyungpook National University Korea Republic of

In general, ophthalmologists visually grade the state of a patient by counting the cells within the anterior chamber OCT image. The manual cell counting method is highly inaccurate and spends a lot of time to determine the progress of the patient. In this work, we develop a new tool to count cells in anterior chamber OCT images to aid doctors in analyzing the state of patients. We exploit image processing to remove noises from images, segment the anterior chamber, and quantize the cells in OCT images. We also provide statistics to aid the doctors in determining the progress of the patients. © Korean Institute of Electrical Engineers

关键词： Cells

来源：评论

学校读者我要写书评

暂无评论

Representation of a convolutional neuronal network as a high level paralel composition applied to the recognition of DNA sequences 31

Representation of a convolutional neuronal network as a high...

引用

31st European Modeling and Simulation Symposium, EMSS 2019

作者： Rossainz-López, M. Zúñiga-Herrera, S. Capel-Tuñón, M. Pineda-Torres, I. Faculty of Computer Science Autonomous University of Puebla San Claudio Avenue and South 14th Street San Manuel Puebla Puebla72000 Mexico Software Engineering Department College of Informatics and Telecommunications ETSIIT University of Granada Daniel Saucedo Aranda s/n Granada18071 Spain

ISBN: (纸本)9788885741263

This work proposes the use of Structured Parallel Programming using the process communication pattern called Pipeline in its version of High-Level Parallel Composition (HLPC) to implement a process composition that represents a convolutional neuronal network or CNN and that is used to solve a specific problem of DNA sequences. The HLPC Pipeline-CNN is then shown, which represents the implementation of a convolutional neural network making use of the three types of parallel objects that make up an HLPC: A manager object, one or more stage objects and a collector object. The manager object represents the HLPC itself and makes an encapsulated abstraction out of it that hides the internal structure, the stage objects are objects of a specific purpose, in charge of encapsulating an client-server type interface that settles down between the manager and the slave-objects and the collector object that is an object in charge of storing the results received from the stage objects to which is connected. To show the usefulness and performance of the HLPC Pipeline-CNN implemented, it was used in the recognition of DNA sequences from a database with 4 types of hepatitis C virus (type 1, 2, 3 and 6). The results of this classification were obtained in terms of percentages of training precision and validation precision, as well as performance results in terms of speedup from 1000 to 4000 training steps with 2, 4, 8, 16 and 32 exclusive processors in one parallel machine of up to 64 processors with shared-distributed memory. © 2019 Dime Universita di Genova, DIMEG University of Calabria.

关键词： Managers

来源：评论

学校读者我要写书评

暂无评论

SAFA: A semi-asynchronous protocol for fast federated learning with low overhead

arXiv

引用

arXiv 2019年

作者： Wu, Wentai He, Ligang Lin, Weiwei Mao, Rui Maple, Carsten Jarvis, Stephen Department of Computer Science University of Warwick United Kingdom School of Computer Science and Technology South China University of Technology China College of Computer Science and Software Engineering Shenzhen University China Warwick Manufacturer Group University of Warwick United Kingdom

Federated learnin (FL) has attracted increasin attention as a promising approach to driving a vast number of end devices with artificial intelligence. However, it is very challenging to guarantee the efficiency of FL considering the unreliable nature of end devices while the cost of device-server communication cannot be neglected. In this paper, we propose SAFA, a semi-asynchronous FL protocol, to address the problems in federated learning such as low round efficiency and poor convergence rate in extreme conditions (e.g., clients dropping offline frequently). We introduce novel designs in the steps of model distribution, client selection and global aggregation to mitigate the impacts of stragglers, crashes and model staleness in order to boost efficiency and improve the quality of the global model. We have conducted extensive experiments with typical machine learning tasks. The results demonstrate that the proposed protocol is effective in terms of shortening federated round duration, reducing local resource wastage, and improving the accuracy of the global model at an acceptable communication cost. © 2019, CC BY-NC-ND.

关键词： Machine learning

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：