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检索条件"任意字段=2023 IEEE Asia-Pacific Conference on Computer Science and Data Engineering, CSDE 2023"

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Using Graph Convolution Network for Predicting Performance of Automatically Generated Convolution Neural Networks

Using Graph Convolution Network for Predicting Performance o...

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2019 ieee asia-pacific conference on computer science and data engineering, csde 2019

作者： Zhang, Enzhi Harada, Tomohiro Thawonmas, Ruck Ritsumeikan University Graduate School of Information Science and Engineering Kusatsu Shiga Japan Ritsumeikan University College of Information Science and Engineering Kusatsu Shiga Japan

ISBN: (纸本)9781728163031

In this paper, we propose a model using a graph convolution network for predicting the accuracy of the automatically generated convolution neural network (CNN). In recent years, there have been many excellent results in the automatic designing of deep learning network structures and their hyperparameters. These works mainly use evolutionary algorithms and reinforcement learning methods. But to verify the accuracy of newly generated neural network samples, it requires a lot of computational time and large scale of hardware. Therefore, how to reduce training time and improve the utilization of samples has become an important problem of automatic design for deep learning networks. On the other hand, the effectiveness of the graph convolution network (GCN) has been shown in processing graph data on the tasks of graph node classification, link prediction, and clustering. We are considering using GCN to predict the accuracy of the automatically designed neural networks. We compare GCN, a multi-layer neural network(NN), and CNN on the accuracy prediction task of automatically generated CNN for image classification tasks on the CIFAR-10 dataset. Our experiment demonstrates the advantages of GCN on small datasets compared to the multi-layer neural network and CNN, while GCN achieves the equivalent accuracy to the other methods on large datasets. © 2019 ieee.

关键词： Convolution

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Partnerships with Governments to Implement In-Country science Programmes in the South pacific Region

Partnerships with Governments to Implement In-Country Scienc...

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2019 ieee asia-pacific conference on computer science and data engineering, csde 2019

作者： Sharma, Bibhya Prasad, Avinesh Narayan, Swasti Kumar, Bijeta Singh, Vineet Nusair, Salsabil Khan, Gavin University of South School of Computing Information and Mathematical Sciences Pacific Suva Fiji University of South Faculty of Science Technology and Environment Pacific Suva Fiji

ISBN: (纸本)9781728163031

This paper essays the design of a new cohort-based model to deliver science programmes for high school graduates and teachers teaching science in the high schools in the South pacific. The aim is to increase the number of skilled science teachers and ultimately the number of science graduates qualified to work in not only the public-private sectors but also find solutions to unique problems faced in the South pacific. Sustaining such programmes depends heavily on the culminated tripartite efforts between students, respective regional countries and higher education institutes. The University of the South pacific offers selected undergraduate degree programmes through its science Teachers Accelerated Programme (STAP) and the In-country science Programme (ISP) in its member countries. The government bodies decide upon the discipline combinations of programme based on their countries' priority areas. The first STAP launched in Samoa in 2014 had 60 science teachers enrolled in Bachelor of science with 5 different combination of majors. The partnership achieved well and in turn paved the way for STAP offers in Samoa (second cohort), Tonga and Vanuatu. Ideally, science students would travel to the main USP Campus in Fiji for further studies;however, with programmes like STAP and ISP, the university is able to deliver science to its various member countries at much lower costs. This paper will highlight the design and implementation of the new cohort based science model, together with the successes and opportunities for the future. © 2019 ieee.

关键词： Mergers and acquisitions

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A Prototype of a Secured File Storing and Sharing System for Cloud Storage Infrastructure

A Prototype of a Secured File Storing and Sharing System for...

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2019 ieee asia-pacific conference on computer science and data engineering, csde 2019

作者： Patwary, Md. Shahadul Alam Palit, Rajesh North South University Electrical and Computer Engineering Dhaka Bangladesh

ISBN: (纸本)9781728163031

With the increased popularity of third-party cloud storage services, privacy has become a major concern. As cloud storage services can be availed at a minimum price now-a-days, it comes with a greater cost, vulnerability of being accessed by anyone else. To ensure reliability, most of the cloud service providers keep multiple copies of files. This becomes a serious privacy issue when the replicated copies remain in the server even after the owner has deleted the original file. To solve this problem, several models such as FADE, MADS, SFADE+ etc. were proposed that ensures assured file deletion but has limitations of their own. We have designed a system using the existing cryptographic approaches which ensures assured deletion of files resolving the limitations that remained un-patched by the previous models. The primary focus of our proposed model is to ensure user privacy and security through a user-friendly system which is computationally less intensive and does not rely upon any trusted entity. For factual exhibition, we have implemented a prototype named CryptedCloud which assures file deletion without relying upon any trusted body with no compromise to the performance and user-friendliness. © 2019 ieee.

关键词： Cloud storage

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Optimized RTPS platform for IIoT based smart farming systems

Optimized RTPS platform for IIoT based smart farming systems

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2019 ieee asia-pacific conference on computer science and data engineering, csde 2019

作者： Almadani, Basem Mostafa, Saud Mohammad King Fahd University of Petroleum and Minerals Computer Engineering Department Dhahran Saudi Arabia

ISBN: (纸本)9781728163031

With the ever-growing population, food and water demands have risen exponentially. Almost 80-85% of food is produced by agriculture but the rise in demand needs to be met by an increase in food production. The agriculture sector tends to tackle this issue by incorporating the Industrial Internet of Things (IIoT) and cloud technologies to improve productivity and reduce wastage of resources needed for it. The prime target of incorporation of these technologies would be to facilitate farmers to grow crops and raise livestock with ease and lower cost with increasing profits. Smart farming systems can comprise of one or more sub-systems that are automated using IIoT technology and processing is done either in local server or cloud. Most researchers work on individual sub-system and improve the performance providing local optimization. These sub-systems can have different vendors and integration is quite difficult at times. Therefore, we propose to use data Distribution Service (DDS) middleware to integrate these heterogeneous sub-systems and provide high performance with low latency system. In this paper, we introduce a novel approach to tackle the issue of heterogeneity and provide global optimization. A suitable mathematical model for our proposed system is given and experiments are conducted to show the efficiency of the system in terms of throughput and delay. © 2019 ieee.

关键词： Agriculture

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Sliding Window-based Regularly Frequent Patterns Mining over Sensor data Streams

Sliding Window-based Regularly Frequent Patterns Mining over...

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2019 ieee asia-pacific conference on computer science and data engineering, csde 2019

作者： Rashid, Md Mamunur Kamruzzaman, Joarder Wasimi, Saleh CQ University School of Engineering and Technolog Melbourne Australia Federation University School of Science Engineering and Technology Churchill Australia

ISBN: (纸本)9781728163031

WSNs generate a large amount of data in the form of data stream;and mining these streams with well used support metric-based sensor association rule mechanism can result in extracting interesting knowledge. Support metric-based sensor association use occurrence frequency of pattern as criteria, but the occurrence frequency of a pattern may not be an appropriate criterion for finding significant patterns. However, temporal regularity in occurrence behavior can be considered as another important measure for assessing the importance of patterns in WSNs. A frequent pattern that occurs after regular intervals in WSNs called as regularly frequent sensor patterns. Even though mining regularly frequent sensor patterns from sensor data stream is extremely required in real-time applications, no such algorithm has been proposed yet. Therefore, in this paper we propose a novel tree structure, called regular frequent sensor pattern stream tree (RFSPS-tree) and a new technique, called regularly frequent sensor pattern mining of data stream (RFSPMS), using sliding window-based regularly frequent sensor pattern mining for WSNs. By capturing the useful knowledge of the data stream into an RFSPS-tree, our RFSPMS algorithm can mine associated sensor patterns in the current window with frequent pattern (FP)-growth like pattern-growth method. Extensive experimental analyses show that our technique is very efficient in discovering regularly frequent sensor patterns over sensor data stream. © 2019 ieee.

关键词： Association rules

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Advanced data Balancing Method with SVM Decision Boundary and Bagging

Advanced Data Balancing Method with SVM Decision Boundary an...

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2019 ieee asia-pacific conference on computer science and data engineering, csde 2019

作者： Arafat, Md. Yasir Hoquef, Sabera Xuf, Shuxiang Farid, Dewan Md. United International University United City Department of Computer Science Engineering Madani Avenue Badda Dhaka1212 Bangladesh University of Tasmania School of Engineering and ICT Faculty of Science Engineering and Technology Tasmania Australia

ISBN: (纸本)9781728163031

In the field of data mining applications, the most challenging research work is to classify imbalanced data in supervised learning. From the beginning of the machine learning era, a huge number of researches has done by inventing a lot of successful data balancing methods, however, these are not yet fully compatible to learn from imbalanced data. The key reason is data is producing in every moment and randomly by various sources, like humans, machines, sensors, robots, and so on. To date, the existing machine learning algorithms mostly biased to the majority class instances and neglecting the minority class instances, therefore they have a crucial effect on the prediction capacity and exactness on the final results. To deal with imbalanced data the most popular classifiers are under-sampling and over-sampling, cost-sensitive learning and ensemble learning. In this paper, we have presented a novel data balancing method by employing clustering technique with SVM especially decision boundary instances for creating balanced data. The proposed approach selects the most informative majority class and minority class support vectors nearest to the decision boundary or hyperplane, and finally, combines them to establishes a novel method. We have experimented with the performance of our proposed technique with some popular single classifiers such as C4.5 Decision Tree (DT) and Naive Bayes (NB) classifier and ensemble classifiers such as Random Forest and AdaBoost on thirteen standard real-life imbalanced datasets. The experimental result shows apparently, that the proposed idea generates improved accuracy when classifying both the minority and majority class instances compared to other existing methods. © 2019 ieee.

关键词： Support vector machines

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Predicting Media Literacy Level of Secondary School Students in Fiji

Predicting Media Literacy Level of Secondary School Students...

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2019 ieee asia-pacific conference on computer science and data engineering, csde 2019

作者： Reddy, Pritika Chaudhary, Kaylash Sharma, Bibhya Fiji National University Department of Computing Science and Information Systems Suva Fiji University of South Pacific School of Computing Information and Mathematical Sciences Suva Fiji

ISBN: (纸本)9781728163031

The digital revolution has set a platform for all the information and means of communication to be digitised, thus creating a digital media society. This explosion of digital media requires individuals to have a set of skills and knowledge to survive in this lifelong digital media society. In such a context, many countries around the world are now leveraging on Media Literacy to enhance the necessary skills of individuals and improve upon responsible media engagement. Therefore, predicting media literacy of students is essential so that suitable interventions can be put in place. This paper presents an analysis of Media Literacy status of Year 12 and Year 13 students at randomly selected secondary schools in Fiji, and it presents a set of predictive models using classification techniques. A quantitative study using a reliable survey was conducted to determine the Media Literacy of students using a Likert scale of 1-5. The analysis for this study was using the R software whereby classification algorithms such as Random Forest Classifiers, Decision Trees and Support Vector Machine Algorithm (SVM) were used to build the predictive models. These models will be used to derive appropriate interventions to improve Media Literacy of students. The baseline data from the study provide information on media literacy of Fijian students. The paper concludes with the important attributes that contribute towards an individual's competency on media literacy. © 2019 ieee.

关键词： Decision trees

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Self-motivated Information Sharing in Communities for Promoting Regional Revitalization

Self-motivated Information Sharing in Communities for Promot...

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2019 ieee asia-pacific conference on computer science and data engineering, csde 2019

作者： Shioya, Ryo Tanaka, Mizuki Shiozu, Yurika Yonezaki, Katsuhiko Tanev, Ivan Shimohara, Katsunori Doshisha University Graduate School of Science and Engineering Kyoto Japan Kyoto Sangyo University Faculty of Economics Kyoto Japan Taisho University Department of Regional Development Tokyo Japan

ISBN: (纸本)9781728163031

A community is a system composed of 'Hito,' 'Mono,' and 'Koto,' and their relationships, and it cannot be established without the voluntary involvement of people. In order to revitalize the local community, we have worked on a mechanism through which residents can be involved in creating better and richer relationships. For this purpose, we have built a system for collecting, quantifying, and visualizing relationships created by residents in their daily lives, and the system has been in operation in the field so far. In addition, we have introduced a mechanism for people to transmit and share awareness about regional factors and problems. As a result, we can confirm the behavior modification of residents through the said mechanism. In this study, we propose a new mechanism to promote self-motivated information sharing, which results in more effective behavior modification among people in a community. The mechanism incorporates peoples' self-centered behavior, such as walking and exercise, into an information-sharing mechanism based on the map application, with the aim to investigate whether people's self-centered behavior can have altruistic effects. © 2019 ieee.

关键词： Information analysis

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A Framework for Sedimentological data Structure Extraction in Gold Mining Facies Delineation

A Framework for Sedimentological Data Structure Extraction i...

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2019 ieee asia-pacific conference on computer science and data engineering, csde 2019

作者： Khoza, Phumlani Nhlanganiso University of Witwatersrand Scilinx Research School of Computer Science Applied Mathematics Johannesburg South Africa

ISBN: (纸本)9781728163031

Quartz-pebble conglomerate (reef) horizons of the Witwatersrand Basin are the most important sources of gold (Au) in South Africa. Concentration of gold to economic ore-grades in the Witwatersrand-type gold deposits has been linked to sedimentary processes having operated as principal driving forces. Therefore, in Witwatersrand-type gold deposits, sedimentological data are often utilized to make qualitative assessment of in-situ gold-grade. This paper presents a framework that prescribes a normative and principled analysis of sedimentological data in order to quantify in-situ gold-grades. Its significance is the provision of a scalable process for extracting correlational structures, regardless of the attribute value-types. The framework applies network analysis, and a bespoke clustering algorithm to support visual analysis of spatial correlation between sedimentological data samples. A key result is the preservation of the semantic interpretation of the resulting cluster assignments via a topologically invariant transformation. This is achieved by applying non-uniform weighting to the attributes to emphasize the influence of non-numeric attributes in cluster assignment. Additionally, sedimentological attributes that are strongly correlated with gold-grade are identified by adopting total variance reduction from decision tree regression as a guiding principle. Using this approach, and data collected from a mining company located in the Witwatersrand Basin, spatial correlation structures defined over sedimentological data attributes are reported. As a byproduct, observations that lack spatial correlation with the rest of the data are identified. These isolated observations can inform quality assurance and quality control departments about potential irregularities in the reporting of sedimentological data within exploration and mining companies. © 2019 ieee.

关键词： Quality control

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Incremental Improvement for Sub-optimal Euclidean TSP Paths Generated by Traditional Heuristics

Incremental Improvement for Sub-optimal Euclidean TSP Paths ...

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computer science and data engineering (csde), ieee asia-pacific conference on

作者： Andre L.C. Barczak Erik T. Barczak Napoleon H. Reyes SNCS Massey University Auckland New Zealand Computational Engineering Technische Universität Darmstadt Darmstadt Germany

This paper proposes and tests three simple algorithms that are capable of rapidly improving non-optimal paths for the Euclidean Travelling Salesman Problem (TSP).The ETSP is a special case of the more general TSP, but it still plays a very important part of planning and scheduling. For example, it can be used to optimise CNC tool paths. There is no known polynomial solution for the ETSP (nor for any of the other TSP problems), so various approximation heuristics were proposed over the years. Many advancements were made in the past 10 years. For example, CONCORDE [1] and LKH [2] are two of the most important publicly available implementations that are able to find optimal solutions for graphs up to a few thousand nodes. However, the runtime to find the optimal solution is very long for large graphs. In the case where the application can afford an approximate solution, it is convenient to improve one of the classical heuristic solutions, namely, Nearest neighbour, Greedy and Insertion. The cheapest of the improvement heuristics is 2-opt, followed by a generalisation of the former, *** objective of the three proposed algorithms is to find an improved non-optimal suitable solution in a relatively short time, at least much shorter than the well-known 2-opt or k-opt improvement algorithms. This is of course a compromise, as the improvements are much further away from the optimal path. In many cases k-opt approaches can find optimal solutions, but the runtime can be very long. The three proposed algorithms improve an existing heuristic solution in polynomial time. The first algorithm unravels crossing edges in an sub-optimal path. The second algorithm moves nodes that are closer to edges, improving the path. The third algorithm uses a K-nodes optimiser to stretch small parts of the path. The experiments were carried out using the TSPLIB instances [3] for the instances that are Euclidean (and therefore, symmetric). The results show that one can quickly improve paths o

关键词： computer science Runtime Heuristic algorithms conferences Tools data engineering Approximation algorithms

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