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Data classification using machine learning approach 3rd

Data classification using machine learning approach

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3rd international symposium on Intelligent Systems Technologies and applications, ISTA 2017

作者： Pandey, Shekhar Supriya, M. Shrivastava, Abhilash Department of Computer Science and Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Amrita University Bengaluru India

ISBN: (纸本)9783319683843

Currently, Internet has numerous effects on our everyday lifecycle. Its significance as an intermediate for commercial transactions will develop exponentially throughout the next years. In terms of the engaged marketplace volume, the business to business region will hereby be the supreme exciting area. As the extensive usage of electronic business transactions increase, great volume of products information gets generated and managing such large information automatically becomes a challenging task. The accurate classification of such products to each of the existing classes also becomes an additional multifarious task. The catalog classification is an essential part for operative electronic business applications and classical machine learning problems. This paper presents a supervised Multinomial Naïve Bayes Classifier machine learning algorithm to classify product listings to anonymous marketplaces. If the existing products are classified under the master taxonomy, the task is to automatically categorize a new product into one of the existing categories. Our algorithm approach proposes a method to accurately classify the existing millions of products. © Springer international Publishing AG 2018.

关键词： Learning algorithms

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Differentially Private Auctions for Private Data Crowdsourcing

Differentially Private Auctions for Private Data Crowdsourci...

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IEEE international Conference on Big Data and Cloud computing (BdCloud)

作者： Mingyu Shi Yu Qiao Xinbo Wang Electronic information engineering college Zhengzhou university of light industry Department of Computer Science and Technology Nanjing University HSBC Business School Peking University

ISBN: (数字)9781728143286

ISBN: (纸本)9781728143293

The success of machine learning and deep learning depends on the large amount of for training. Data acquisition is an indispensable event for data science. In this paper, we study a scenario where a data broker wantes to buy private data from a population to estimate some statistics and resell the results to the data users in order to obtain profits. It's necessary to compensate owners of private data for their loss of privacy. In our setting, we assume that sellers are selfish agents who would like to maximize his or her utilities, thus we need to design truthful mechanisms. In our work, we will study a more generalized optimization goal for budget free crowdsoucer, profit maximization. The utility of the buyer is a function of the accuracy of the expected statistic. We will use a Profit-Extract algorithm for the auction, and a Laplace mechanism will be used for privacy protection. Our proposed designed mechanism has properties of individual rationality, truthfulness, computational efficiency and also has a performance lowerbound. Furthermore, we will design an online auction for our setting.

关键词： Privacy Data integration Differential privacy Procurement Cost accounting Optimization

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Structure Maintaining Discriminant Maps (SMDM) for Grassmann manifold dimensionality reduction with applications to the Image Set Classification 16

Structure Maintaining Discriminant Maps (SMDM) for Grassmann...

引用

16th international symposium on distributed computing and applications to business, engineering and science (DCABES)

作者： Wang, Rui Wu, Xiao-Jun Jiangnan Univ Sch Internet Things Engn JNU Wuxi Peoples R China

ISBN: (纸本)9781538621622

For the image-set based classification, a considerable advance has been made by representing original image sets on Grassmann manifold. In order to extend the advantages of the Euclidean based dimensionality reduction methods to the Grassmann Manifold, several methods have been suggested recently to jointly perform dimensionality reduction and metric learning on Grassmann manifold and they have achieved good results in some computer vision tasks. Nevertheless, when handling the classification tasks on the complicated datasets, the learned features do not exhibit enough discriminatory ability and the data distribution of the resulted Grassmann manifold also be ignored which may lead to overfitting. To overcome the two problems, we propose a new method named Structure Maintaining Discriminant Maps (SMDM) for manifold dimensionality reduction problems. As to SMDM, we mainly design a new discriminant function for metric learning. We make experiments on two tasks: face recognition and object categorization to evaluate the proposed method, the achieved better results compared with the state-of-the-art methods, showing the feasibility and effectiveness of the proposed algorithm.

关键词： Grassmann Manifold Dimensionality Reduction Discriminant Function Overfitting Data Distribution

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Review of Social Networking applications for Infectious Disease Management Systems in Saudi Arabia: Current Status & Future Prospects 16

Review of Social Networking Applications for Infectious Dise...

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16th international symposium on distributed computing and applications to business, engineering and science (DCABES)

作者： Alanezi, Fahad Alshammary, Miznah Bedfordshire Univ Dept Comp Sci London England Kings Coll London Dept Comp Sci London England

ISBN: (纸本)9781538621622

Internet Technologies are widely used in healthcare services and information management. Various applications have been proved to be highly beneficial in delivering healthcare services, information and disease management. Social Networking is one of the best internet technologies that is being used in various business and management areas, and it has been revolutionary in enhancing the various operations through effective interactions between the various users. This paper focuses on reviewing the social networking, its architecture, and the prospects of using it for Infectious Disease Management (IDM) in Saudi Arabia. Methods: Review of Various literature sources and Social Networking applications for Healthcare/IDM Management. Results: The Study has found that Social Networking can be an effective approach for integrating it with the healthcare/IDM Systems as it would enable effective user interactions, which could enhance behavioral aspects in delivering/receiving healthcare services. However, there has been no study identified that has used social networking for IDM in Saudi Arabia. This paper suggests the wide scope for future research in integrating the social networking concept across various healthcare systems in Saudi Arabia.

关键词： Web infectious disease management social networking for healthcare infectious disease e-health Kingdom of Saudi Arabia Social Networking Infectious Disease Management Facebook Internet

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Hotspots Analysis and Its applications in Vocational Education with CiteSpace

Hotspots Analysis and Its Applications in Vocational Educati...

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IEEE international symposium on Information (IT) in Medicine and Education, ITME

作者： Shuang Ma Xiaomei Yu Qi Chen School of Information Science and Engineering Shandong Normal University Jinan China Shandong Provincial Key Laboratory of Distributed Computing Software Jinan China

Vocational education is an important part in modern education and its teaching methods appear distinguishing characters. In recent years, the research on teaching models in vocational education has attracted great attention and obtained significant achievements. In this paper, the knowledge mapping software of CiteSpace is introduced to bibliometric analyze and study the hotspots in vocational education based on the data from the China National Knowledge Infrastructure (CNKI). With the visualizations on knowledge maps including institutional cooperation, author cooperation, keyword co-occurrence and time zone map, the development veins of vocational education is explored, the frontiers and research focus on vocational education are achieved. Finally, the research results are applied to practical teaching mode in the course of Python Programming. The results of the teaching practice show that studying passion is promoted and the effect of teaching are improved in the class with novel teaching mode.

关键词： Education Software Tools Data visualization Visualization Veins Market research

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The research of domain ontology recommendation method with its applications in requirement traceability 16

The research of domain ontology recommendation method with i...

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16th international symposium on distributed computing and applications to business, engineering and science (DCABES)

作者： Dong Huaqiang Liu Hongxing Xie Songyu Feng Yuqing Wuhan Univ Technol Sch Comp Sci & Technol Wuhan Hubei Peoples R China SDIC Chongqing GUOYUAN Port Grp Co Ltd Chongqing Peoples R China Wuhan Social Work Profess Coll Wuhan Hubei Peoples R China

ISBN: (纸本)9781538621622

Ontology can specify the concepts and their relationships. If ontology was used as the bridge to map between the source and target artifacts, it can help to solve the keyword matching problem regarding the requirement traceability in the semantic level. A domain ontology recommendation method has been proposed. Using this method, the lexical semantic representation list of the dependency syntax types can be produced at first using Stanford Parser. Then, the terms as well as their relations can be extracted using the clear algorithm and rule-based matching algorithm. As follows, these terms and their relations can be transformed into domain concepts and their relations, obtaining the domain ontology recommendation. Finally, the domain ontology that is suitable for the requirement traceability can be gained through the processes of selection and revision by the domain experts. The experimental results demonstrate that if the proposed domain ontology was applied to requirement traceability, it can effectively improve the accuracy of the requirement traceability, compared with the vector space model and the requirement traceability method based on HOWNET.

关键词： Domain Ontology Requirement Traceability Dependency Parsing Stanford Parser

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The effect on network flows-based features and training set size on malware detection 17

The effect on network flows-based features and training set ...

引用

17th IEEE international symposium on Network computing and applications, NCA 2018

作者： Hernandez Jimenez, Jarilyn M. Goseva-Popstojanova, Katerina West Virginia University Lane Department of Computer Science and Electrical Engineering MorgantownWV26506 United States

ISBN: (纸本)9781538676592

Although network flows have been used in areas such as network traffic analysis and botnet detection, not many works have used network flows-based features for malware detection. This paper is focused on malware detection based on using features extracted from the network traffic and system logs. We evaluated the performance of four supervised machine learning algorithms (i.e., J48, Random Forest, Naive Bayes, and PART) for malware detection and identified the best learner. Furthermore, we used feature selection based on information gain to identify the smallest number of features needed for classification. In addition, we experimented with training sets of different sizes. The main findings include: (1) Adding network flows-based features improved significantly the performance of malware detection. (2) J48 and PART were the best performing learners, with the highest F-score and G-score values. (3) Using J48, the top five features ranked by information gain attained the same performance as when using all 88 features. In the case of PART, the top fourteen features ranked by information gain led to the same performance as when all 88 features were used. None of the system logs-based features were included in these two models. (4) The classification performance when training on 75% of the data was comparable to training on 90% of the data. As little as 25% of the data can be used for training at an expense of somewhat higher, but not very significant performance degradation (i.e., less than 7% for F-score and 6% for G-score compared to when 90% of the data were used for training). © 2018 IEEE.

关键词： Classification (of information)

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Allocate-on-use space complexity of shared-memory algorithms 32

Allocate-on-use space complexity of shared-memory algorithms

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32nd international symposium on distributed computing, DISC 2018

作者： Aspnes, James Haeupler, Bernhard Tong, Alexander Woelfel, Philipp Yale University Department of Computer Science New HavenCT United States Carnegie Mellon School of Computer Science PittsburghPA United States University of Calgary Department of Computer Science CalgaryAB Canada

ISBN: (纸本)9783959770927

Many fundamental problems in shared-memory distributed computing, including mutual exclusion [8], consensus [18], and implementations of many sequential objects [14], are known to require linear space in the worst case. However, these lower bounds all work by constructing particular executions for any given algorithm that may be both very long and very improbable. The significance of these bounds is justified by an assumption that any space that is used in some execution must be allocated for all executions. This assumption is not consistent with the storage allocation mechanisms of actual practical systems. We consider the consequences of adopting a per-execution approach to space complexity, where an object only counts toward the space complexity of an execution if it is used in that execution. This allows us to show that many known randomized algorithms for fundamental problems in shared-memory distributed computing have expected space complexity much lower than the worst-case lower bounds, and that many algorithms that are adaptive in time complexity can also be made adaptive in space complexity. For the specific problem of mutual exclusion, we develop a new algorithm that illustrates an apparent trade-off between low expected space complexity and low expected RMR complexity. Whether this trade-off is necessary is an open problem. For some applications, it may be helpful to pay only for objects that are updated, as opposed to those that are merely read. We give a data structure that requires no space to represent objects that are not updated at the cost of a small overhead on those that are. © James Aspnes, Bernhard Haeupler, Alexander Tong, and Philipp Woelfel.

关键词： Memory architecture

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Design and Implementation of Personalized Teaching System for Online Learning

Design and Implementation of Personalized Teaching System fo...

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IEEE international symposium on Information (IT) in Medicine and Education, ITME

作者： Qi Chen Xiaomei Yu Qian Chu Shuang Ma School of Information Science and Engineering Shandong Normal University Jinan China Shandong Provincial Key Laboratory of Distributed Computing Software Jinan China

With the development and popularization of the Internet and multimedia technology, online learning has become a trend in education. Based on the analysis of the current situation of personalized teaching in present education platforms, we found that online learning, as an independent learning method, has some disadvantages. For example, with insufficient learning environment provided, learning enthusiasm is poor and some students would not participate in courses actively. In this paper, we design and implement a personalized teaching system for online learning, which is suitable for blended learning. In online learning, personalized courses are recommended according to each student's interests and needs. Moreover, discussion areas are set up for communication among teachers and students. In the personalized teaching system, the students' learning status and activity levels are recorded, along with the questions raised in the discussion area. Finally, the system is applied in the course of python programming and the experimental results show that the personalized teaching is achieved and diversified assessment can be realized.

关键词： Education Data collection Browsers business Registers Password Internet

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Docker's Security Analysis of Using Control Group to Enhance Container Resistance to Pressure

Docker's Security Analysis of Using Control Group to Enhance...

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IEEE international symposium on Information (IT) in Medicine and Education, ITME

作者： Tianshuo Yang Zhongxuan Luo Zheliang Shen Yican Zhong Xin Huang Department of Computer Science and Software Engineering Xi’an Jiaotong-Liverpool University Suzhou China

Docker is a container technology to create lightweight virtual system framework in the cloud computing environment. Massive users exploit it on systems of Linux, Mac, and Windows to simplify configuration or test large-scale operations and isolate applications. However, considering the security of Docker container, distributed Denial of Service (DDoS) attacks have been a severe problem which needs to be solved. Therefore, this paper aims to analyze the compressive ability of Docker container and reduce the influence of DDoS by using Control group (Cgroup). Furthermore, an experiment will be designed to detect the effects of Cgroup under three kinds of pressure: run out Central Process Unit (CPU), run out bandwidth and DDoS attack. In addition, limiting CPU, limiting Network (Net) I/O and limiting both of them will be considered as the method to use Cgroup to restrict containers' resources. In a result, it is shown that the attacks would be limited in a certain scope after restricting the resources of containers by Cgroup. Therefore, the method of imposing restrictions on CPU and Net I/O resources of Docker containers by using Cgroup can effectively reduce the impact of DDoS attacks.

关键词： Containers Central Processing Unit Linux Denial-of-service attack Computer crime Kernel

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