Enterprise Resource Planning (ERP) is a popular business management tool used by almost all companies these days to organize their business. In-spite of the challenges faced by ERP; before, during and after its implementation into the Enterprise, it fetches greater profits to the organization. This paper deals with the challenges faced by ERP with a complete literature overview of the challenges from earlier authors. Then after a brief visit of these factors, a very essential topic to the Enterprises i.e., Costs are discussed. The costs that are incurred in the project, some unknown or hidden costs are dealt with. A solution is proposed to solve this cost problem of ERP and to improve the profit margins to the companies. The solution is Cloud ERP. The latter part deals with the benefits of Cloud ERP in general and with respect to costs along with the concerns of cloud ERP, the major issue among all the concerns and few proposed solutions of solving this problem in the cloud ERP.
{"title":"Integrating Cloud Computing to Solve ERP Cost Challenge","authors":"Amal Alhosban, Anvitha Akurathi","doi":"10.5121/CSIT.2019.90917","DOIUrl":"https://doi.org/10.5121/CSIT.2019.90917","url":null,"abstract":"Enterprise Resource Planning (ERP) is a popular business management tool used by almost all companies these days to organize their business. In-spite of the challenges faced by ERP; before, during and after its implementation into the Enterprise, it fetches greater profits to the organization. This paper deals with the challenges faced by ERP with a complete literature overview of the challenges from earlier authors. Then after a brief visit of these factors, a very essential topic to the Enterprises i.e., Costs are discussed. The costs that are incurred in the project, some unknown or hidden costs are dealt with. A solution is proposed to solve this cost problem of ERP and to improve the profit margins to the companies. The solution is Cloud ERP. The latter part deals with the benefits of Cloud ERP in general and with respect to costs along with the concerns of cloud ERP, the major issue among all the concerns and few proposed solutions of solving this problem in the cloud ERP.","PeriodicalId":248929,"journal":{"name":"9th International Conference on Computer Science, Engineering and Applications (CCSEA 2019)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116944166","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
F. Haque, Weijia Zhou, Jun-Shuo Ng, A. Abdelgawad, K. Yelamarthi, Frank Walsh
Internet of Things is the next big thing, as almost everything developed now has an extensive use of data which is then used to get the daily statistics and usage of every individual. The work mainly consists of constructing a screen where the parking space will be shown, and a camera module will be set up, and PIR (Passive Infrared Sensor) will be at the entrance to detect the entrance of a car or any vehicle eligible to park at the lot. The vehicle will be scanned for its registration number in to provide a check whether the vehicle is registered to park or not. This also acts as the security of the parking lot. Moreover, a viable sensor will be placed at each parking slot through which the vacancy of each parking slot will be shown to determine the exact spot available to the user. In order to surpass the project completion, we will be using Raspberry Pi 3 with camera module mounted on it and by using Tensorflow, Node-Red we would be able to identify the car and the license number and also infrared sensor to detect the parking availability which would be displayed on the screen.
{"title":"IoT -Based Approach to Monitor Parking Space in Cities","authors":"F. Haque, Weijia Zhou, Jun-Shuo Ng, A. Abdelgawad, K. Yelamarthi, Frank Walsh","doi":"10.5121/CSIT.2019.90907","DOIUrl":"https://doi.org/10.5121/CSIT.2019.90907","url":null,"abstract":"Internet of Things is the next big thing, as almost everything developed now has an extensive use of data which is then used to get the daily statistics and usage of every individual. The work mainly consists of constructing a screen where the parking space will be shown, and a camera module will be set up, and PIR (Passive Infrared Sensor) will be at the entrance to detect the entrance of a car or any vehicle eligible to park at the lot. The vehicle will be scanned for its registration number in to provide a check whether the vehicle is registered to park or not. This also acts as the security of the parking lot. Moreover, a viable sensor will be placed at each parking slot through which the vacancy of each parking slot will be shown to determine the exact spot available to the user. In order to surpass the project completion, we will be using Raspberry Pi 3 with camera module mounted on it and by using Tensorflow, Node-Red we would be able to identify the car and the license number and also infrared sensor to detect the parking availability which would be displayed on the screen.","PeriodicalId":248929,"journal":{"name":"9th International Conference on Computer Science, Engineering and Applications (CCSEA 2019)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131244877","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Organizations maintain different databases to store and process big data which is huge in volume and have different data models. Querying and analysing big data for insight is critical for business. The data warehouses built should be able to meet the ever growing demand of data. With new requirements it is important to have near real times response from the big data gathered. All the data cannot be fit in to one particular database “One Size Does Not Fit All” since data originating from sources have different formats. The main focus of our research is to find an adequate solution using optimized data created by data engineers to improve the performance of query execution in a big data ecosystem.
{"title":"Query Performance Optimization in Databases for Big Data","authors":"M. Muniswamaiah, T. Agerwala, C. Tappert","doi":"10.5121/CSIT.2019.90908","DOIUrl":"https://doi.org/10.5121/CSIT.2019.90908","url":null,"abstract":"Organizations maintain different databases to store and process big data which is huge in volume and have different data models. Querying and analysing big data for insight is critical for business. The data warehouses built should be able to meet the ever growing demand of data. With new requirements it is important to have near real times response from the big data gathered. All the data cannot be fit in to one particular database “One Size Does Not Fit All” since data originating from sources have different formats. The main focus of our research is to find an adequate solution using optimized data created by data engineers to improve the performance of query execution in a big data ecosystem.","PeriodicalId":248929,"journal":{"name":"9th International Conference on Computer Science, Engineering and Applications (CCSEA 2019)","volume":"76 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128718793","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Di Liu, X. Hou, Yan-Bo Liu, Lei Liu, Yan-Cheng Wang
Object detection typically requires a large amount of data to ensure detection accuracy. However, it is often impossible to ensure sufficient data in practice. This paper presents a new data augmentation method based on pixel-level image blend and domain adaptation. This method consists of two steps: 1.Image blend using a labeled dataset as object instances and an unlabeled dataset as background images.2. Domain adaptation based on Cycle Generative Adversarial Networks (Cycle GAN).A neural network will be trained to transform samples from step 1 to approximate the original dataset. Statistical consistency between new dataset generated by different data augmentation methods and original dataset will be measured by metrics such as generator loss and hellinger distance. Furthermore, a detection/segmentation network for diabetic retinopathy based on Mask R-CNN will be built and trained by the generated dataset. The effect of data augmentation method on the detection accuracy will be presented.
{"title":"Data Augmentation Based on Pixel-level Image Blend and Domain Adaptation","authors":"Di Liu, X. Hou, Yan-Bo Liu, Lei Liu, Yan-Cheng Wang","doi":"10.5121/CSIT.2019.90923","DOIUrl":"https://doi.org/10.5121/CSIT.2019.90923","url":null,"abstract":"Object detection typically requires a large amount of data to ensure detection accuracy. However, it is often impossible to ensure sufficient data in practice. This paper presents a new data augmentation method based on pixel-level image blend and domain adaptation. This method consists of two steps: 1.Image blend using a labeled dataset as object instances and an unlabeled dataset as background images.2. Domain adaptation based on Cycle Generative Adversarial Networks (Cycle GAN).A neural network will be trained to transform samples from step 1 to approximate the original dataset. Statistical consistency between new dataset generated by different data augmentation methods and original dataset will be measured by metrics such as generator loss and hellinger distance. Furthermore, a detection/segmentation network for diabetic retinopathy based on Mask R-CNN will be built and trained by the generated dataset. The effect of data augmentation method on the detection accuracy will be presented.","PeriodicalId":248929,"journal":{"name":"9th International Conference on Computer Science, Engineering and Applications (CCSEA 2019)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121571094","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
M. Muniswamaiah, Dr.Tilak Agerwala, Dr.Charles Tappert
Big Data applications are used for decision making process for gaining useful insights hidden from large volume of data. They make use of cloud computing infrastructure for massive scale and complex computation which eliminates the need to maintain dedicated hardware and software resources. The relationship between big data and cloud computing is presented with focus on challenges and issues in data storage with different formats, data transformation techniques applied, data quality and business challenges associated with it. Also, some good practices which helps in big data analysis has been listed
{"title":"Challenges of Big Data Applications in Cloud Computing","authors":"M. Muniswamaiah, Dr.Tilak Agerwala, Dr.Charles Tappert","doi":"10.5121/CSIT.2019.90918","DOIUrl":"https://doi.org/10.5121/CSIT.2019.90918","url":null,"abstract":"Big Data applications are used for decision making process for gaining useful insights hidden from large volume of data. They make use of cloud computing infrastructure for massive scale and complex computation which eliminates the need to maintain dedicated hardware and software resources. The relationship between big data and cloud computing is presented with focus on challenges and issues in data storage with different formats, data transformation techniques applied, data quality and business challenges associated with it. Also, some good practices which helps in big data analysis has been listed","PeriodicalId":248929,"journal":{"name":"9th International Conference on Computer Science, Engineering and Applications (CCSEA 2019)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133045358","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
If a computational system is to be successful, it must have an impressive user interface endowed with appealing usability features for providing exceptional user experience. User interface engineering requires an innovative approach because it is one of the most challenging areas given the diversity of knowledge, ideas, skills and creativity needed for building smart interfaces in order to succeed in today’s rapidly paced and tough, competitive marketplace.Modern engineering aspects including analytical, intuitive, user experience, artistic, technical, graphical, mathematical, psychological and programming models need to be considered in the development process of a user interface. This paper critically examines some of the past practices and recommends a set of principles for designing alluring user interfaces.It also demonstrates how UML use case diagrams can be improved by naturally relating use cases to user interface elements. The improved design constructs of an enhanced UML view are presented with examples for highlighting and clarifying important user interface engineering issues.
{"title":"An Innovative Approach to User Interface Engineering","authors":"P. Dey, B. Sinha, M. Amin, H. Badkoobehi","doi":"10.5121/CSIT.2019.90926","DOIUrl":"https://doi.org/10.5121/CSIT.2019.90926","url":null,"abstract":"If a computational system is to be successful, it must have an impressive user interface endowed with appealing usability features for providing exceptional user experience. User interface engineering requires an innovative approach because it is one of the most challenging areas given the diversity of knowledge, ideas, skills and creativity needed for building smart interfaces in order to succeed in today’s rapidly paced and tough, competitive marketplace.Modern engineering aspects including analytical, intuitive, user experience, artistic, technical, graphical, mathematical, psychological and programming models need to be considered in the development process of a user interface. This paper critically examines some of the past practices and recommends a set of principles for designing alluring user interfaces.It also demonstrates how UML use case diagrams can be improved by naturally relating use cases to user interface elements. The improved design constructs of an enhanced UML view are presented with examples for highlighting and clarifying important user interface engineering issues.","PeriodicalId":248929,"journal":{"name":"9th International Conference on Computer Science, Engineering and Applications (CCSEA 2019)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114189219","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Anomaly detection is vital for automated data analysis, with specific applications spanning almost every domain. In this paper, we propose a hybrid supervised learning of anomaly detection using frequent itemset mining and random forest with an ensemble probabilistic voting method, which outperforms the alternative supervised learning methods through the commonly used measures for anomaly detection: accuracy, true positive rate (i.e. recall) and false positive rate. To justify our claim, a benchmark dataset is used to evaluate the efficiency of the proposed approach, where the results illustrate its benefits.
{"title":"Ensemble learning using frequent itemset mining for anomaly detection","authors":"Saeid Soheily-Khah, Yiming Wu","doi":"10.5121/csit.2019.90931","DOIUrl":"https://doi.org/10.5121/csit.2019.90931","url":null,"abstract":"Anomaly detection is vital for automated data analysis, with specific applications spanning almost every domain. In this paper, we propose a hybrid supervised learning of anomaly detection using frequent itemset mining and random forest with an ensemble probabilistic voting method, which outperforms the alternative supervised learning methods through the commonly used measures for anomaly detection: accuracy, true positive rate (i.e. recall) and false positive rate. To justify our claim, a benchmark dataset is used to evaluate the efficiency of the proposed approach, where the results illustrate its benefits.","PeriodicalId":248929,"journal":{"name":"9th International Conference on Computer Science, Engineering and Applications (CCSEA 2019)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114456839","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We designed and implemented an efficient tough random symmetric 3-SAT generator and propose two deterministic algorithms that efficiently generate 3-SAT instances with a unique solution. We quantify the first algorithms hardness in terms of CPU time, numbers of restarts, decisions, propagations, conflicts and conflicted literals that occur when a solver tries to solve 3-SAT instances. In this experiment, the clause variable ratio was chosen to be around the conventional critical phase transition number 4.24. The experiment shows that instances generated by our generator are significantly harder than instances generated by the Tough K-SAT generator. The two deterministic algorithms generate 3-SAT instances with the number of clauses scaling as 4n, where n is the number of variables, and (n+6), respectively. By combining these two algorithms along with a simple padding algorithm, we prove a hybrid algorithm that can generate n-variable instances with the number of clauses that scale between (n+6) and 7n(n-1)(n-2). Overall, all proposed SAT generators seek to explore unique difficult to solve SAT problems.
{"title":"Efficient Tough Random Symmetric 3-SAT Generator","authors":"R. Amador, Chen-Fu Chiang, Chang-Yu Hsieh","doi":"10.5121/CSIT.2019.90904","DOIUrl":"https://doi.org/10.5121/CSIT.2019.90904","url":null,"abstract":"We designed and implemented an efficient tough random symmetric 3-SAT generator and propose two deterministic algorithms that efficiently generate 3-SAT instances with a unique solution. We quantify the first algorithms hardness in terms of CPU time, numbers of restarts, decisions, propagations, conflicts and conflicted literals that occur when a solver tries to solve 3-SAT instances. In this experiment, the clause variable ratio was chosen to be around the conventional critical phase transition number 4.24. The experiment shows that instances generated by our generator are significantly harder than instances generated by the Tough K-SAT generator. The two deterministic algorithms generate 3-SAT instances with the number of clauses scaling as 4n, where n is the number of variables, and (n+6), respectively. By combining these two algorithms along with a simple padding algorithm, we prove a hybrid algorithm that can generate n-variable instances with the number of clauses that scale between (n+6) and 7n(n-1)(n-2). Overall, all proposed SAT generators seek to explore unique difficult to solve SAT problems.","PeriodicalId":248929,"journal":{"name":"9th International Conference on Computer Science, Engineering and Applications (CCSEA 2019)","volume":"126 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116092385","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Many applications in mesh processing require the detection of feature lines. Feature lines convey the inherent features of the shape. Existing techniques to find feature lines in discrete surfaces are relied on user-specified thresholds, inaccurate and time-consuming. We use an automatic approximation technique to estimate the optimal threshold for detecting feature lines. Some examples are presented to show our method is effective, which leads to improve the feature lines visualization.
{"title":"Automatic Extraction of Feature Lines on 3D Surface","authors":"Zhihong Mao, Ruichao Wang, Yu-lin Zhou","doi":"10.5121/CSIT.2019.90901","DOIUrl":"https://doi.org/10.5121/CSIT.2019.90901","url":null,"abstract":"Many applications in mesh processing require the detection of feature lines. Feature lines convey the inherent features of the shape. Existing techniques to find feature lines in discrete surfaces are relied on user-specified thresholds, inaccurate and time-consuming. We use an automatic approximation technique to estimate the optimal threshold for detecting feature lines. Some examples are presented to show our method is effective, which leads to improve the feature lines visualization.","PeriodicalId":248929,"journal":{"name":"9th International Conference on Computer Science, Engineering and Applications (CCSEA 2019)","volume":"157 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123472479","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The JONSWAP spectrum sea surface is mainly determined by parameters such as the wind speed, the fetch length and the peak enhancement factor. In view of the study of electromagnetic scattering from JONSWAP spectrum sea surface, we need to determine the above parameters. In this paper, we use the double summation model to generate the multi-directional irregular rough JONSWAP sea surface and analyze the distribution concentration parameter and the peak enhancement factor’s influence on the rough sea surface model, then using physical optics method to analysis the JONSWAP spectrum sea surface’s average backward scattering coefficient change with the different distribution concentration parameters and the peak enhancement factors, the simulation results show that the peak enhancement factor influence on the ocean surface of the average backward scattering coefficient is less than 1 dB, but the distribution concentration parameter influence on the JONSWAP surface of the average backward scattering coefficient is more than 5 dB. Therefore, when we study the electromagnetic scattering of the JONSWAP spectral sea surface, the peak enhancement factor can be taken as the mean value but the distribution concentration parameter have to be determined by the wave growth state.
{"title":"Sea Surface Electromagnetic Scattering Characteristics of JONSWAP Spectrum Influenced by its Parameters","authors":"X. Mi, Xiaobing Wang, Xinyi He, F. Dai","doi":"10.5121/CSIT.2019.90921","DOIUrl":"https://doi.org/10.5121/CSIT.2019.90921","url":null,"abstract":"The JONSWAP spectrum sea surface is mainly determined by parameters such as the wind speed, the fetch length and the peak enhancement factor. In view of the study of electromagnetic scattering from JONSWAP spectrum sea surface, we need to determine the above parameters. In this paper, we use the double summation model to generate the multi-directional irregular rough JONSWAP sea surface and analyze the distribution concentration parameter and the peak enhancement factor’s influence on the rough sea surface model, then using physical optics method to analysis the JONSWAP spectrum sea surface’s average backward scattering coefficient change with the different distribution concentration parameters and the peak enhancement factors, the simulation results show that the peak enhancement factor influence on the ocean surface of the average backward scattering coefficient is less than 1 dB, but the distribution concentration parameter influence on the JONSWAP surface of the average backward scattering coefficient is more than 5 dB. Therefore, when we study the electromagnetic scattering of the JONSWAP spectral sea surface, the peak enhancement factor can be taken as the mean value but the distribution concentration parameter have to be determined by the wave growth state.","PeriodicalId":248929,"journal":{"name":"9th International Conference on Computer Science, Engineering and Applications (CCSEA 2019)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125141895","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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