Reusing Historical testcases play a crucial role in ensuring software testing quality. However, the diversity of historical testcases limits their potential uses. As a result, large amounts of human effort is required to write testcases for complex functional testings. In this paper, an effective framework is proposed to integrate and retrieve historical testcase bases with semantic analysis technologies. Firstly, semantic similarity is calculated to integrate the metadata of the inputted semi-structured testcases. Then, testcases are clustered by using similarity measures to eliminate heterogeneity existed in the contents of the testcases. The clustering results are added to the testcases as semantic annotations for the later semantic query. Using the semantic query interface, testers can easily obtain useful testcases without ambiguity. Finally, a case study demonstrates the effectiveness and scalability of this method for testcases retrieval for bank information systems testing.
{"title":"Semantic Annotation and Retrieval Approach for Historical Testcases","authors":"Jieqiong Hu, Zhiqing Chen, Hongming Cai, Xinyu Liu, Xiang Fei, Lihong Jiang","doi":"10.1109/ICEBE.2017.18","DOIUrl":"https://doi.org/10.1109/ICEBE.2017.18","url":null,"abstract":"Reusing Historical testcases play a crucial role in ensuring software testing quality. However, the diversity of historical testcases limits their potential uses. As a result, large amounts of human effort is required to write testcases for complex functional testings. In this paper, an effective framework is proposed to integrate and retrieve historical testcase bases with semantic analysis technologies. Firstly, semantic similarity is calculated to integrate the metadata of the inputted semi-structured testcases. Then, testcases are clustered by using similarity measures to eliminate heterogeneity existed in the contents of the testcases. The clustering results are added to the testcases as semantic annotations for the later semantic query. Using the semantic query interface, testers can easily obtain useful testcases without ambiguity. Finally, a case study demonstrates the effectiveness and scalability of this method for testcases retrieval for bank information systems testing.","PeriodicalId":347774,"journal":{"name":"2017 IEEE 14th International Conference on e-Business Engineering (ICEBE)","volume":"72 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130507814","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}
Very few ecommerce participants are observed to be satisfied with ecommerce-raised business expenses, profit sharing, fake products, or user privacy. In this article, a new ecommerce concept, i.e., Balanced Commerce, is proposed to address the concerns through innovative trading paradigms and principles. The balanced ecommerce promotes direct trades with no intermediary merchants, public and sharing resources and services, and smart broker-based business activities to assure the fairness and reduce business expenses. To implement the principles and features of the balanced ecommerce, a reference model has been developed. To identify how balanced an ecommerce system is, a balanced indicator and associated algorithms have been developed. Based on the reference model and identified features, a balanced ecommerce model, i.e., Individual - Individual (I2I), has been developed. An I2I ecommerce system is featured with an individual-oriented cloud browser to support independent trading, and a public creditworthiness cloud to provide basic and tracing data of individuals and commodities, along with smart brokering services. A number of I2I ecommerce systems have been developed and some put into practice. Three of them are described to testify the values and feasibility of the balanced ecommerce.
{"title":"I2I: A Balanced Ecommerce Model with Creditworthiness Cloud","authors":"Yinsheng Li, Shuai Xue, X. Liang, Xiao Zhu","doi":"10.1109/ICEBE.2017.31","DOIUrl":"https://doi.org/10.1109/ICEBE.2017.31","url":null,"abstract":"Very few ecommerce participants are observed to be satisfied with ecommerce-raised business expenses, profit sharing, fake products, or user privacy. In this article, a new ecommerce concept, i.e., Balanced Commerce, is proposed to address the concerns through innovative trading paradigms and principles. The balanced ecommerce promotes direct trades with no intermediary merchants, public and sharing resources and services, and smart broker-based business activities to assure the fairness and reduce business expenses. To implement the principles and features of the balanced ecommerce, a reference model has been developed. To identify how balanced an ecommerce system is, a balanced indicator and associated algorithms have been developed. Based on the reference model and identified features, a balanced ecommerce model, i.e., Individual - Individual (I2I), has been developed. An I2I ecommerce system is featured with an individual-oriented cloud browser to support independent trading, and a public creditworthiness cloud to provide basic and tracing data of individuals and commodities, along with smart brokering services. A number of I2I ecommerce systems have been developed and some put into practice. Three of them are described to testify the values and feasibility of the balanced ecommerce.","PeriodicalId":347774,"journal":{"name":"2017 IEEE 14th International Conference on e-Business Engineering (ICEBE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131233201","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}
Jingmin Xu, Pengfei Chen, L. Yang, F. Meng, Ping Wang
Recently, as the evolution of application's development and management paradigms, the deployment declaration becomes a standard interface connecting application developers and Cloud platforms. Kuberenetes is such a system for automating deployment, scaling, and management of micro-service based applications. However, managing and operating such a cloud benefit with additional complexities from the declarative deployment. This paper proposes a log model based problem diagnosis tool for declaratively-deployed cloud applications with the full lifecycle Kubernetes logs. With the runtime logs and deployment declarations, we can pinpoint the root causes in terms of abnormal declarative items and log entries. The advantage of this approach is that we provide a precise log model of a normal deployment to help diagnose problems. The experimental results show that our approach can find out the anomalies of some real-world Kubernetes problems, some of which have been confirmed as bugs. Within the given fault types, our approach can pinpoint the root causes at 91% in Precision and at 92% in Recall.
{"title":"LogDC: Problem Diagnosis for Declartively-Deployed Cloud Applications with Log","authors":"Jingmin Xu, Pengfei Chen, L. Yang, F. Meng, Ping Wang","doi":"10.1109/ICEBE.2017.52","DOIUrl":"https://doi.org/10.1109/ICEBE.2017.52","url":null,"abstract":"Recently, as the evolution of application's development and management paradigms, the deployment declaration becomes a standard interface connecting application developers and Cloud platforms. Kuberenetes is such a system for automating deployment, scaling, and management of micro-service based applications. However, managing and operating such a cloud benefit with additional complexities from the declarative deployment. This paper proposes a log model based problem diagnosis tool for declaratively-deployed cloud applications with the full lifecycle Kubernetes logs. With the runtime logs and deployment declarations, we can pinpoint the root causes in terms of abnormal declarative items and log entries. The advantage of this approach is that we provide a precise log model of a normal deployment to help diagnose problems. The experimental results show that our approach can find out the anomalies of some real-world Kubernetes problems, some of which have been confirmed as bugs. Within the given fault types, our approach can pinpoint the root causes at 91% in Precision and at 92% in Recall.","PeriodicalId":347774,"journal":{"name":"2017 IEEE 14th International Conference on e-Business Engineering (ICEBE)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125435434","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}
Pricing Asian Option is imperative to researchers, analysts, traders and any other related experts involved in the option trading markets and the academic field. Not only trading highly affected by the accuracy of the price of Asian options but also portfolios that involve hedging of commodity. Several attempts have been made to model the Asian option prices with closed-form over the past twenty years such as the Kemna-Vorst Model and Levy Approximation. Although today the two closed-form models are still widely used, their accuracy and reliability are called into question. The reason is simple; the Kemna-Vorst model is derived with an assumption of geometric mean of the stocks. In practice, Average Priced Options are mostly arithmetic and thus always have a volatility high than the volatility of a geometric mean making the Asian options always underpriced. On the other hand, the Levy Approximation using Monte Carlo Simulation as a benchmark, do not perform well when the product of the sigma (volatility) and square root maturity of the underlying is larger than 0.2. When the maturity of the option enlarges, the performance of the Levy Approximation largely deteriorates. If the closed-form models could be improved, higher frequency trading of Asian option will become possible. Moreover, building neural networks for different contracts of Asian Options allows reuse of computed prices and large-scale portfolio management that involves many contracts. In this thesis, we use Neural Network to fill the gap between the price of a closed-form model and that of an Asian option. The significance of this method answers two interesting questions. First, could an Asian option trader with a systematic behavior in pricing learned from previous quotes improve his pricing or trading performance in the future? Second, will a training set of previous data help to improve the performance of a financial model? We perform two simulation experiments and show that the performance of the closed-form model is significantly improved. Moreover, we extend the learning process to real data quote. The use of Neural Network highly improves the accuracy of the traditional closed-form model. The model's original price is not so much accurate as what we estimate using Neural network and could not capture the high volatility effectively; still, it provides a relative reasonable fit to the problem (Especially the Levy Model). The analysis shows that the Neural Network Algorithms we used affect the results significantly.
{"title":"Application of Machine Learning: An Analysis of Asian Options Pricing Using Neural Network","authors":"Z. Fang, K. M. George","doi":"10.1109/ICEBE.2017.30","DOIUrl":"https://doi.org/10.1109/ICEBE.2017.30","url":null,"abstract":"Pricing Asian Option is imperative to researchers, analysts, traders and any other related experts involved in the option trading markets and the academic field. Not only trading highly affected by the accuracy of the price of Asian options but also portfolios that involve hedging of commodity. Several attempts have been made to model the Asian option prices with closed-form over the past twenty years such as the Kemna-Vorst Model and Levy Approximation. Although today the two closed-form models are still widely used, their accuracy and reliability are called into question. The reason is simple; the Kemna-Vorst model is derived with an assumption of geometric mean of the stocks. In practice, Average Priced Options are mostly arithmetic and thus always have a volatility high than the volatility of a geometric mean making the Asian options always underpriced. On the other hand, the Levy Approximation using Monte Carlo Simulation as a benchmark, do not perform well when the product of the sigma (volatility) and square root maturity of the underlying is larger than 0.2. When the maturity of the option enlarges, the performance of the Levy Approximation largely deteriorates. If the closed-form models could be improved, higher frequency trading of Asian option will become possible. Moreover, building neural networks for different contracts of Asian Options allows reuse of computed prices and large-scale portfolio management that involves many contracts. In this thesis, we use Neural Network to fill the gap between the price of a closed-form model and that of an Asian option. The significance of this method answers two interesting questions. First, could an Asian option trader with a systematic behavior in pricing learned from previous quotes improve his pricing or trading performance in the future? Second, will a training set of previous data help to improve the performance of a financial model? We perform two simulation experiments and show that the performance of the closed-form model is significantly improved. Moreover, we extend the learning process to real data quote. The use of Neural Network highly improves the accuracy of the traditional closed-form model. The model's original price is not so much accurate as what we estimate using Neural network and could not capture the high volatility effectively; still, it provides a relative reasonable fit to the problem (Especially the Levy Model). The analysis shows that the Neural Network Algorithms we used affect the results significantly.","PeriodicalId":347774,"journal":{"name":"2017 IEEE 14th International Conference on e-Business Engineering (ICEBE)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122140648","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}
Szu-Yin Lin, C. Chiang, Zih-Siang Hung, Yu-Hui Zou
With the advent of the big data era, dynamic and real-time data have increased in both volume and varieties. It is a difficult task to achieve an accurate prediction results to rapidly dynamic changing data. The stacked auto-encoder is a neural network approach in machine learning for feature extraction. It attempts to model high-level abstractions and dimension reduction in data by using multiple processing layers. However, some of the common issues may occur during the implementation process of deep learning or neural network, such as input data having over-complicated dimension, and unable to execute in a dynamic environment. Therefore, it will be helpful if we combine dynamic data-driven concept with stacked auto-encoder neural network to obtain the dynamic data correlation or relationship between prediction results and actual data in a dynamic environment. This study applies the concept of dynamic data-driven to obtain the correlations between the prediction goals and numbers of different combination results. The methods of association analysis, sequence analysis, and stacked auto-encoder neural network are applied to design a dynamic data-driven system based on deep learning.
{"title":"A Dynamic Data-Driven Fine-Tuning Approach for Stacked Auto-Encoder Neural Network","authors":"Szu-Yin Lin, C. Chiang, Zih-Siang Hung, Yu-Hui Zou","doi":"10.1109/ICEBE.2017.43","DOIUrl":"https://doi.org/10.1109/ICEBE.2017.43","url":null,"abstract":"With the advent of the big data era, dynamic and real-time data have increased in both volume and varieties. It is a difficult task to achieve an accurate prediction results to rapidly dynamic changing data. The stacked auto-encoder is a neural network approach in machine learning for feature extraction. It attempts to model high-level abstractions and dimension reduction in data by using multiple processing layers. However, some of the common issues may occur during the implementation process of deep learning or neural network, such as input data having over-complicated dimension, and unable to execute in a dynamic environment. Therefore, it will be helpful if we combine dynamic data-driven concept with stacked auto-encoder neural network to obtain the dynamic data correlation or relationship between prediction results and actual data in a dynamic environment. This study applies the concept of dynamic data-driven to obtain the correlations between the prediction goals and numbers of different combination results. The methods of association analysis, sequence analysis, and stacked auto-encoder neural network are applied to design a dynamic data-driven system based on deep learning.","PeriodicalId":347774,"journal":{"name":"2017 IEEE 14th International Conference on e-Business Engineering (ICEBE)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114646677","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}
Numerous crime-related security concerns exist in e-commerce transactions recently. User authentication for mobile payment has numerous approaches including face recognition, iris scan, and fingerprint scan to identify user's true identity by comparing the biometric features of users with patterns in the signature database. Existing studies on the face recognition problem focus mainly on the static analysis to determine the face recognition precision by examining the facial features of images with different facial expressions for users rather than the dynamic aspects where images were are often vague affected by lighting changes with different poses. Because the lighting, facial expressions, and facial details varied in the face recognition process. Consequently, it limits the effectiveness of scheme with which to determine the true identity. Accordingly, this study focused on a face recognition process under the situation of vague facial features using deep reinforcement learning (DRL) approach with convolutional neuron networks (CNNs) thru facial feature extraction, transformation, and comparison to determine the user identity for mobile payment. Specifically, the proposed authentication scheme uses back propagation algorithm to effectively improve the accuracy of face recognition using feed-forward network architecture for CNNs. Overall, the proposed scheme provided a higher precision of face recognition (100% at gamma correction γlocated in [0.5, 1.6]) compared with the average precision for face image (approximately 99.5% at normal lighting γ=1) of the existing CNN schemes with ImageNet 2012 Challenge training data set.
{"title":"A Face-Recognition Approach Using Deep Reinforcement Learning Approach for User Authentication","authors":"Ping Wang, Wen-Hui Lin, K. Chao, Chi-Chun Lo","doi":"10.1109/ICEBE.2017.36","DOIUrl":"https://doi.org/10.1109/ICEBE.2017.36","url":null,"abstract":"Numerous crime-related security concerns exist in e-commerce transactions recently. User authentication for mobile payment has numerous approaches including face recognition, iris scan, and fingerprint scan to identify user's true identity by comparing the biometric features of users with patterns in the signature database. Existing studies on the face recognition problem focus mainly on the static analysis to determine the face recognition precision by examining the facial features of images with different facial expressions for users rather than the dynamic aspects where images were are often vague affected by lighting changes with different poses. Because the lighting, facial expressions, and facial details varied in the face recognition process. Consequently, it limits the effectiveness of scheme with which to determine the true identity. Accordingly, this study focused on a face recognition process under the situation of vague facial features using deep reinforcement learning (DRL) approach with convolutional neuron networks (CNNs) thru facial feature extraction, transformation, and comparison to determine the user identity for mobile payment. Specifically, the proposed authentication scheme uses back propagation algorithm to effectively improve the accuracy of face recognition using feed-forward network architecture for CNNs. Overall, the proposed scheme provided a higher precision of face recognition (100% at gamma correction γlocated in [0.5, 1.6]) compared with the average precision for face image (approximately 99.5% at normal lighting γ=1) of the existing CNN schemes with ImageNet 2012 Challenge training data set.","PeriodicalId":347774,"journal":{"name":"2017 IEEE 14th International Conference on e-Business Engineering (ICEBE)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127086426","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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