2018 IEEE 16th International Conference on Software Engineering Research, Management and Applications (SERA)最新文献

{"title":"A General Framework and Maturity Model for Bioinformatics Pipeline Development","authors":"Ethan W. Chen","doi":"10.1109/SERA.2018.8477200","DOIUrl":"https://doi.org/10.1109/SERA.2018.8477200","url":null,"abstract":"Bioinformatics pipelines are often constructed in chaotic, ad hoc environments. This ad hoc construction results in problems with quality control, pipeline maintainability, and code reusability. The application of process control to bioinformatics pipeline creation can address these issues, increasing the lifespan of pipelines and decreasing the amount of time and energy spent on recreating defunct pipelines. The description of a bioinformatics pipeline development lifecycle and maturity model presented here lays the groundwork of a defined process for pipeline development. As pipelines are tools used to conduct research, the decrease of resources used in pipeline creation can result in more resources dedicated to research productivity.","PeriodicalId":161568,"journal":{"name":"2018 IEEE 16th International Conference on Software Engineering Research, Management and Applications (SERA)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128694035","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}

{"title":"Solar Power and Electricity Market in Saudi Arabia","authors":"Fathe Jeribi, Sungchul Hong","doi":"10.1109/SERA.2018.8477217","DOIUrl":"https://doi.org/10.1109/SERA.2018.8477217","url":null,"abstract":"Electricity is essential in daily life and it is difficult to imagine a life without it. When its supply is not sufficient, people's daily activities can be interrupted. Small sellers with batteries can form an electricity market and they can supply a supplement amount of electricity that is needed during a peak consumption time instead of building a large-scale power plant. As a case study, Saudi Arabia's electricity market is studied. The people in Saudi Arabia, especially in hot regions such as Jazan (or Jizan), experience the interruption of the electricity especially during the summer season for two reasons [1]. The first reason is Jazan has hot weather and whenever the hot weather increases, the demand of the electricity will increase. The second reason is there are few power plants that can provide enough electricity for people. This paper will explore the possibility of generating electricity using numerous solar panels with batteries by simulation.","PeriodicalId":161568,"journal":{"name":"2018 IEEE 16th International Conference on Software Engineering Research, Management and Applications (SERA)","volume":"63 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122261040","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}

{"title":"Transfer Learning with Ensemble of Multiple Feature Representations","authors":"Hang Zhao, Qing Liu, Yun Yang","doi":"10.1109/SERA.2018.8477189","DOIUrl":"https://doi.org/10.1109/SERA.2018.8477189","url":null,"abstract":"Supervised learning algorithms are to discover the hidden patterns of the statistics, assuming that the training data and the test data are from the same distribution. There are two challenges in the traditional supervised machine learning. One is that the test data distribution always differs largely from the training data distribution in the real world, while another is that there is usually very few labeled data to train a machine learning model. In such cases, transfer learning, which emphasizes the transfer of the previous knowledge from different but related domains and tasks, is recommended to deal with these problems. Traditional transfer learning methods care more about the data itself rather than the task. In fact, there is no one universal feature representation can perfectly benefit the model training work. But different feature representations can discover some independent latent knowledge from the original data. In this paper, we propose an instance-based transfer learning method, which is a weighted ensemble transfer learning framework with multiple feature representations. In our work, mutual information is applied as the smart weighting schema to measure the weight of each feature representation. Extensive experiments have been conducted on three facial expression recognition data sets: JAFFE, KDEF and FERG-DB. The experimental results demonstrate that our approach achieves better performance than the traditional transfer learning method and the non-transfer learning method.","PeriodicalId":161568,"journal":{"name":"2018 IEEE 16th International Conference on Software Engineering Research, Management and Applications (SERA)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127937168","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}

{"title":"Mulesoft – Salesforce Integration Using Batch Processing","authors":"Manvi Seth","doi":"10.1145/3265007.3265013","DOIUrl":"https://doi.org/10.1145/3265007.3265013","url":null,"abstract":"Mulesoft has the capability to process messages in batches. It splits the large messages into individual records that are processed asynchronously within batch jobs. Batch Processing can be used for integrating large or small datasets and process the records in parallel. Further, one can set or remove variables on individual records so that during batch processing, Mule can route or otherwise act upon records in a batch according to a record variable. With the batch approach, large volumes of incoming data from any upstream system can be extracted, transformed, and loaded (ETL) into any destination system in real time. In this paper upstream system used is Oracle database and destination system used profoundly is Salesforce. Salesforce is a cloud computing platform which stores data in the form of data objects. This paper identifies challenges that are encountered when upstream systems have complex data storage formats and hence the conversions that are necessary to perform efficacious data transfers are discussed. To help provide a deeper insight, this paper discusses many components that are very specific to batch processing and can be used to implement business logic along with some general scenarios that form the basis of any batch flow. Uses Cases wherein up to 52 million records were retrieved from database, transformed and upserted successfully to Salesforce along with appropriate error handling mechanisms are discussed. Also, the recent news of Salesforce acquiring Mulesoft opens up vast opportunities to integrate data with Salesforce using powerful Mulesoft capabilities like the Batch Processing.","PeriodicalId":161568,"journal":{"name":"2018 IEEE 16th International Conference on Software Engineering Research, Management and Applications (SERA)","volume":"137 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128309037","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}

{"title":"Message from General Chair","authors":"Benjamin C. Lee","doi":"10.1109/ISPASS.2015.7095776","DOIUrl":"https://doi.org/10.1109/ISPASS.2015.7095776","url":null,"abstract":"Welcome to SERA 2018. Welcome to Kunming, China.","PeriodicalId":161568,"journal":{"name":"2018 IEEE 16th International Conference on Software Engineering Research, Management and Applications (SERA)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116022361","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}

{"title":"A Research and Strategy of Objection Detection on Remote Sensing Image","authors":"Yanmei Fu, Fengge Wu, Junsuo Zhao","doi":"10.1109/SERA.2018.8477209","DOIUrl":"https://doi.org/10.1109/SERA.2018.8477209","url":null,"abstract":"Data acquisition from satellite is a challenging task due to the limitation of ground station resource and data transmission capacity. Considering that most of the raw data downloaded to the ground are useless, it is worthy to directly get the results by automatic detection on orbit and only transfer the images that include the target objects, which can filter the useless data efficiently. On orbit automatic detection, satellite computing resources need to be considered, so a smaller and faster model needs to be built. Though enormous object detection methods have been proposed and several application have emerged, a detailed survey on different models about detection accuracy and detection speed as well as memory cost is still lacking. This paper aims to provide a survey on the recent object detection researches and make a strategy to detect on orbit. To further compare the performance among different methods, we conduct an experiment in the same real dataset and compare them from accuracy, speed and memory cost. Following the experiment result, a feasible strategy of object detection for the TZ-1 satellite on-orbit which has a low memory dependency, fast speed and comparable accuracy adapt to its computing resources is proposed.","PeriodicalId":161568,"journal":{"name":"2018 IEEE 16th International Conference on Software Engineering Research, Management and Applications (SERA)","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122874449","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}

{"title":"Quantitative Modeling and Analytical Calculation of Elasticity in Cloud Computing","authors":"Keqin Li","doi":"10.1109/SERA.2018.8477227","DOIUrl":"https://doi.org/10.1109/SERA.2018.8477227","url":null,"abstract":"Elasticity is a fundamental feature of cloud computing and can be considered as a great advantage and a key benefit of cloud computing. Our research makes the following significant contributions. First, we present a new, quantitative, and formal definition of elasticity in cloud computing, i.e., the probability that the computing resources provided by a cloud platform match the current workload. Our definition is applicable to any cloud platform and can be easily measured and monitored. Furthermore, we develop an analytical model to study elasticity by treating a cloud platform as a queueing system, and use a continuous-time Markov chain (CTMC) model to precisely calculate the elasticity value of a cloud platform by using an analytical and numerical method based on just a few parameters, namely, the task arrival rate, the service rate, the virtual machine start-up and shut-down rates. In addition, we formally define auto-scaling schemes and point out that our model and method can be easily extended to handle arbitrarily sophisticated scaling schemes. Second, we apply our model and method to predict many other important properties of an elastic cloud computing system, such as average task response time, throughput, quality of service, average number of VMs, average number of busy VMs, utilization, cost, cost-performance ratio, productivity, and scalability. In fact, from a cloud consumer's point of view, these performance and cost metrics are even more important than the elasticity metric. Ourperformance and cost guarantee using the results developed in this talk. On the other hand, a cloud service provider can optimize its elastic scaling scheme to deliver the best cost-performance ratio. study in this talk has two significance. On one hand, a cloud service provider can predict its To the best of our knowledge, this is the first work that analytically and comprehensively studies elasticity, performance, and cost in cloud computing. Our model and method significantly contribute to the understanding of cloud elasticity and management of elastic cloud computing systems.","PeriodicalId":161568,"journal":{"name":"2018 IEEE 16th International Conference on Software Engineering Research, Management and Applications (SERA)","volume":"101 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132054158","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}

{"title":"Improvement of TF-IDF Algorithm Based on Knowledge Graph","authors":"Yanpeng Wang, Dehai Zhang, Ye Yuan, Qing Liu, Yun Yang","doi":"10.1109/SERA.2018.8477196","DOIUrl":"https://doi.org/10.1109/SERA.2018.8477196","url":null,"abstract":"The TF-IDF algorithm is commonly used for text information retrieval and data mining. The traditional TF-IDF algorithm does not consider the domain characteristics of the article, and does not consider the distribution ratio. Currently, the solution proposed by many scholars only solves the problems of distribution ratio and the like, and does not solve the problem that the domain keywords have unreasonable weights. The problem has led to the use of domain-specific applications where relevant keywords in some areas have not been given appropriate weights. This paper proposes an improved method based on domain knowledge graph. This method will mainly consider the application of the legal field, and use the legal knowledge graph to make improvements to the TF-IDF algorithm, so as to achieve the reasonable weight assigned to the domain-related keywords in text feature extraction. Experiments show that this method can effectively improving the accuracy of the extraction.","PeriodicalId":161568,"journal":{"name":"2018 IEEE 16th International Conference on Software Engineering Research, Management and Applications (SERA)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128689300","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}