Pub Date : 2018-07-01DOI: 10.1109/ICCIA.2018.00052
B. Handoko, S. Ariyanto, D. Warganegara
The purpose of this study is to determine the perception of financial auditor about the use of computer assisted audit techniques (CAATs) in their daily process of work. This research is a quantitative research uses, which use primary data by distributing questionnaires to the respondent. The respondents are financial auditor who worked in public accounting firm in Jakarta Special Region of Indonesia. This study tested the hypotheses between variables by using path analysis, while the independent variables in this study are Performance Expectancy, Effort Expectancy, Social Influence and Facilitating Condition. Intervening variable is Behavioral Intention and dependent variable is Use Behavior. The results of this research indicate that Performance Expectancy has significant impact on Behavioral Intention. Both Effort Expectancy and Social Influence do not have significant impact on Behavioral Intention. Facilitating Condition and Behavioral Intention have significant impact on Use Behavior
{"title":"Perception of Financial Auditor on Usage of Computer Assisted Audit Techniques","authors":"B. Handoko, S. Ariyanto, D. Warganegara","doi":"10.1109/ICCIA.2018.00052","DOIUrl":"https://doi.org/10.1109/ICCIA.2018.00052","url":null,"abstract":"The purpose of this study is to determine the perception of financial auditor about the use of computer assisted audit techniques (CAATs) in their daily process of work. This research is a quantitative research uses, which use primary data by distributing questionnaires to the respondent. The respondents are financial auditor who worked in public accounting firm in Jakarta Special Region of Indonesia. This study tested the hypotheses between variables by using path analysis, while the independent variables in this study are Performance Expectancy, Effort Expectancy, Social Influence and Facilitating Condition. Intervening variable is Behavioral Intention and dependent variable is Use Behavior. The results of this research indicate that Performance Expectancy has significant impact on Behavioral Intention. Both Effort Expectancy and Social Influence do not have significant impact on Behavioral Intention. Facilitating Condition and Behavioral Intention have significant impact on Use Behavior","PeriodicalId":297098,"journal":{"name":"2018 3rd International Conference on Computational Intelligence and Applications (ICCIA)","volume":"12 4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128644120","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}
Pub Date : 2018-07-01DOI: 10.1109/ICCIA.2018.00045
Y. Nozaki, M. Yoshikawa
Energy harvesting has been attracted attention for Internet of Things. From the viewpoint of security, data encryption for energy harvester is very important. On the other hand, the threat of side-channel attacks for a cryptographic circuit, which reveal the secret key using the physical information such as power consumption or electromagnetic wave, is pointed out. This study proposes a new tamper resistance evaluation method to secure energy harvester in the future. In experiments, an actual energy harvester, which consists of a TWELITE microcontroller and a silicon based solar cell, was used, and a lightweight cipher TWINE was implemented as an encryption technique. Then, experimental results by the proposed method revealed the 7 partial keys out of 8 with 900 electromagnetic waveforms, and the tamper resistance was successfully evaluated.
{"title":"Tamper Resistance Evaluation Method for Energy Harvester","authors":"Y. Nozaki, M. Yoshikawa","doi":"10.1109/ICCIA.2018.00045","DOIUrl":"https://doi.org/10.1109/ICCIA.2018.00045","url":null,"abstract":"Energy harvesting has been attracted attention for Internet of Things. From the viewpoint of security, data encryption for energy harvester is very important. On the other hand, the threat of side-channel attacks for a cryptographic circuit, which reveal the secret key using the physical information such as power consumption or electromagnetic wave, is pointed out. This study proposes a new tamper resistance evaluation method to secure energy harvester in the future. In experiments, an actual energy harvester, which consists of a TWELITE microcontroller and a silicon based solar cell, was used, and a lightweight cipher TWINE was implemented as an encryption technique. Then, experimental results by the proposed method revealed the 7 partial keys out of 8 with 900 electromagnetic waveforms, and the tamper resistance was successfully evaluated.","PeriodicalId":297098,"journal":{"name":"2018 3rd International Conference on Computational Intelligence and Applications (ICCIA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132597266","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}
Pub Date : 2018-07-01DOI: 10.1109/ICCIA.2018.00019
Zhenyu Zhao, Mandie Liu, Anirban Deb
During the rapid development cycle for Internet products (websites and mobile apps), new features are developed and rolled out to users constantly. Features with code defects or design flaws can cause outages and significant degradation of user experience. The traditional method of code review and change management can be time-consuming and error-prone. In order to make the feature rollout process safe and fast, this paper proposes a methodology for rolling out features in an automated way using an adaptive experimental design. Under this framework, a feature is gradually ramped up from a small proportion of users to a larger population based on real-time evaluation of the performance of important metrics. If there are any regression detected during the ramp-up step, the ramp-up process stops and the feature developer is alerted. There are two main algorithm components powering this framework: 1) a continuous monitoring algorithm - using a variant of the sequential probability ratio test (SPRT) to monitor the feature performance metrics and alert feature developers when a metric degradation is detected, 2) an automated ramp-up algorithm - deciding when and how to ramp up to the next stage with larger sample size. This paper presents one monitoring algorithm and three ramping up algorithms including time-based, power-based, and risk-based (a Bayesian approach) schedules. These algorithms are evaluated and compared on both simulated data and real data. There are three benefits provided by this framework for feature rollout: 1) for defective features, it can detect the regression early and reduce negative effect, 2) for healthy features, it rolls out the feature quickly, 3) it reduces the need for manual intervention via the automation of the feature rollout process.
{"title":"Safely and Quickly Deploying New Features with a Staged Rollout Framework Using Sequential Test and Adaptive Experimental Design","authors":"Zhenyu Zhao, Mandie Liu, Anirban Deb","doi":"10.1109/ICCIA.2018.00019","DOIUrl":"https://doi.org/10.1109/ICCIA.2018.00019","url":null,"abstract":"During the rapid development cycle for Internet products (websites and mobile apps), new features are developed and rolled out to users constantly. Features with code defects or design flaws can cause outages and significant degradation of user experience. The traditional method of code review and change management can be time-consuming and error-prone. In order to make the feature rollout process safe and fast, this paper proposes a methodology for rolling out features in an automated way using an adaptive experimental design. Under this framework, a feature is gradually ramped up from a small proportion of users to a larger population based on real-time evaluation of the performance of important metrics. If there are any regression detected during the ramp-up step, the ramp-up process stops and the feature developer is alerted. There are two main algorithm components powering this framework: 1) a continuous monitoring algorithm - using a variant of the sequential probability ratio test (SPRT) to monitor the feature performance metrics and alert feature developers when a metric degradation is detected, 2) an automated ramp-up algorithm - deciding when and how to ramp up to the next stage with larger sample size. This paper presents one monitoring algorithm and three ramping up algorithms including time-based, power-based, and risk-based (a Bayesian approach) schedules. These algorithms are evaluated and compared on both simulated data and real data. There are three benefits provided by this framework for feature rollout: 1) for defective features, it can detect the regression early and reduce negative effect, 2) for healthy features, it rolls out the feature quickly, 3) it reduces the need for manual intervention via the automation of the feature rollout process.","PeriodicalId":297098,"journal":{"name":"2018 3rd International Conference on Computational Intelligence and Applications (ICCIA)","volume":"112 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130811078","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}
Pub Date : 2018-07-01DOI: 10.1109/iccia.2018.00006
{"title":"ICCIA 2018 Conference Organization","authors":"","doi":"10.1109/iccia.2018.00006","DOIUrl":"https://doi.org/10.1109/iccia.2018.00006","url":null,"abstract":"","PeriodicalId":297098,"journal":{"name":"2018 3rd International Conference on Computational Intelligence and Applications (ICCIA)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116942321","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}
Pub Date : 2018-07-01DOI: 10.1109/iccia.2018.00042
L. Ke, Xu Wu, He Jin, Cao Lucheng
At present, the protection methods of Yunnan Cangyuan cliff paintings are mainly based on storage and shape protection. Because of the long history and various natural factors, the protection of Cangyuan cliff painting can not be well protected and stored; moreover, as a result of the reality factors that Cangyuan cliff painting has been damaged by many tourists, its integrity has not been as good as before, which has led the relevant departments to protect the cultural heritage. Many scenic spots of Cangyuan cliff painting have not been opened to the public, which has great influence on their transmission and inheritance. In this article, we used digital means to collect the original material of cliff paintings. First of all, we model it in three-dimensional and summarize the common patterns of cliff paintings. Then we used the restoration techniques to repair the missing part of Cangyuan cliff painting. We have not only refined the complex images and culture connotation of Cangyuan cliff painting, but also use the three-dimensional model to establish the virtual exhibition hall. Finally, we achieve the perfect combination of traditional culture and modern technology.
{"title":"Research on the Protection and Communication Strategy of Cangyuan Cliff Painting Based on Virtual Reality Technology","authors":"L. Ke, Xu Wu, He Jin, Cao Lucheng","doi":"10.1109/iccia.2018.00042","DOIUrl":"https://doi.org/10.1109/iccia.2018.00042","url":null,"abstract":"At present, the protection methods of Yunnan Cangyuan cliff paintings are mainly based on storage and shape protection. Because of the long history and various natural factors, the protection of Cangyuan cliff painting can not be well protected and stored; moreover, as a result of the reality factors that Cangyuan cliff painting has been damaged by many tourists, its integrity has not been as good as before, which has led the relevant departments to protect the cultural heritage. Many scenic spots of Cangyuan cliff painting have not been opened to the public, which has great influence on their transmission and inheritance. In this article, we used digital means to collect the original material of cliff paintings. First of all, we model it in three-dimensional and summarize the common patterns of cliff paintings. Then we used the restoration techniques to repair the missing part of Cangyuan cliff painting. We have not only refined the complex images and culture connotation of Cangyuan cliff painting, but also use the three-dimensional model to establish the virtual exhibition hall. Finally, we achieve the perfect combination of traditional culture and modern technology.","PeriodicalId":297098,"journal":{"name":"2018 3rd International Conference on Computational Intelligence and Applications (ICCIA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128448807","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}
Pub Date : 2018-07-01DOI: 10.1109/ICCIA.2018.00050
Rongjie Wang
Aiming to source number estimation, the recovery of mixing matrix and source signal under underdetermined case, we propose a method of underdetermined blind source separation with an unknown number of sources. Firstly, we introduced an algorithm based on S transform and fuzzy c-means clustering technique to estimate number of sources and mixing mixtures. Then sources are represented as null space form and the source signals are recovered by using an algorithm based on Maximum Likelihood. The simulation results show that the proposed method can separate sources of any distribution, and it has superior evaluation performance to the conventional methods.
{"title":"A Method of Underdetermined Blind Source Separation with an Unknown Number of Sources","authors":"Rongjie Wang","doi":"10.1109/ICCIA.2018.00050","DOIUrl":"https://doi.org/10.1109/ICCIA.2018.00050","url":null,"abstract":"Aiming to source number estimation, the recovery of mixing matrix and source signal under underdetermined case, we propose a method of underdetermined blind source separation with an unknown number of sources. Firstly, we introduced an algorithm based on S transform and fuzzy c-means clustering technique to estimate number of sources and mixing mixtures. Then sources are represented as null space form and the source signals are recovered by using an algorithm based on Maximum Likelihood. The simulation results show that the proposed method can separate sources of any distribution, and it has superior evaluation performance to the conventional methods.","PeriodicalId":297098,"journal":{"name":"2018 3rd International Conference on Computational Intelligence and Applications (ICCIA)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128645287","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}
Pub Date : 2018-07-01DOI: 10.1109/ICCIA.2018.00058
Zheng Gong, Yujiao Fu, Xiangdong Su, Heng Xu
Representation learning is an essential process in the text similarity task. The methods based on neural variational inference first learn the semantic representation of the texts, then measure the similarity of these texts by calculating the cosine similarity of their representations. However, it is not generally desirable that using the neural network simply to learn semantic representation as it cannot capture the rich semantic information completely. Considering that the similarity of context information reflects the similarity of text pairs in most cases, we integrate the topic information into a stacked variational autoencoder in process of text representation learning. The improved text representations are used in text similarity calculation. Experiment result shows that our approach obtains the state-of-art performance.
{"title":"Deep Variation Autoencoder with Topic Information for Text Similarity","authors":"Zheng Gong, Yujiao Fu, Xiangdong Su, Heng Xu","doi":"10.1109/ICCIA.2018.00058","DOIUrl":"https://doi.org/10.1109/ICCIA.2018.00058","url":null,"abstract":"Representation learning is an essential process in the text similarity task. The methods based on neural variational inference first learn the semantic representation of the texts, then measure the similarity of these texts by calculating the cosine similarity of their representations. However, it is not generally desirable that using the neural network simply to learn semantic representation as it cannot capture the rich semantic information completely. Considering that the similarity of context information reflects the similarity of text pairs in most cases, we integrate the topic information into a stacked variational autoencoder in process of text representation learning. The improved text representations are used in text similarity calculation. Experiment result shows that our approach obtains the state-of-art performance.","PeriodicalId":297098,"journal":{"name":"2018 3rd International Conference on Computational Intelligence and Applications (ICCIA)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115849214","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}
Pub Date : 2018-07-01DOI: 10.1109/ICCIA.2018.00054
Lei Bao, Yang Le
In order to analysis maritime traffic data from Automatic Identification System,this paper present a big data framework based on SpatialHadoop. This framework extend the data type, storage, computing and operation layer of traditional Hadoop to incorporate maritime location data. In storage layer, it introduce a two-layer spatial index structure which can establish R-tree or R+-tree spatial index on Hadoop Distributed File System(HDFS) storage. And it add two new components in Mapreduce programming,which make it fitful for parallel computing on maritime spatial data. Based on these function provided, we can build up various spatial analysis operation on big maritime location data, and support various spatial statistical or spatial data mining applications
{"title":"A Spatial Big Data Framework for Maritime Traffic Data","authors":"Lei Bao, Yang Le","doi":"10.1109/ICCIA.2018.00054","DOIUrl":"https://doi.org/10.1109/ICCIA.2018.00054","url":null,"abstract":"In order to analysis maritime traffic data from Automatic Identification System,this paper present a big data framework based on SpatialHadoop. This framework extend the data type, storage, computing and operation layer of traditional Hadoop to incorporate maritime location data. In storage layer, it introduce a two-layer spatial index structure which can establish R-tree or R+-tree spatial index on Hadoop Distributed File System(HDFS) storage. And it add two new components in Mapreduce programming,which make it fitful for parallel computing on maritime spatial data. Based on these function provided, we can build up various spatial analysis operation on big maritime location data, and support various spatial statistical or spatial data mining applications","PeriodicalId":297098,"journal":{"name":"2018 3rd International Conference on Computational Intelligence and Applications (ICCIA)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124499845","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}
Pub Date : 2018-07-01DOI: 10.1109/ICCIA.2018.00032
Dan Li, S. Bass, S. Hurley
The electrocardiogram (ECG) is the main source of heartbeat analysis. However, analyzing the ECG alone can be problematic because ECG data can be noisy. This research analyzes the associations between ECG and a variety of biomedical signals, and uses these associations to detect heartbeat locations with a higher accuracy than just analyzing ECG alone.
{"title":"Locating Heartbeats from Electrocardiograms and Other Correlated Signals","authors":"Dan Li, S. Bass, S. Hurley","doi":"10.1109/ICCIA.2018.00032","DOIUrl":"https://doi.org/10.1109/ICCIA.2018.00032","url":null,"abstract":"The electrocardiogram (ECG) is the main source of heartbeat analysis. However, analyzing the ECG alone can be problematic because ECG data can be noisy. This research analyzes the associations between ECG and a variety of biomedical signals, and uses these associations to detect heartbeat locations with a higher accuracy than just analyzing ECG alone.","PeriodicalId":297098,"journal":{"name":"2018 3rd International Conference on Computational Intelligence and Applications (ICCIA)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114195849","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}
Pub Date : 2018-07-01DOI: 10.1109/iccia.2018.00020
Wang Haixin, Haixin Wang
Solar energy is a new energy, which is not only perennial but also obtainable to every strata of the world. The use of solar photovoltaic systems (SPV) is the process of converting solar energy into electricity. Photovoltaic modules are mounted on the wings of solar unmanned aerial vehicles. In this paper, a new MPPT controller is proposed to predict the voltage to obtain the maximum power from the solar panel. The proposed MPPT controller is based on mind evolution algorithm (MEA) optimized back propagation neural network (BPNN). Firstly, the mind evolution algorithm model is constructed based on topology of BP Neural Network. Then, it is used to obtain the optimal solutions, which is regarded as initial weights and threshold value of BP Neural Network. Finally, the simulation experiment is carried out by using MATLAB software. The prediction results of the BP neural network optimized by the mind evolution algorithm are compared.
{"title":"The Research of Voltage Prediction of Solar UAV Panel by Improved Mind Evolutionary Algorithm","authors":"Wang Haixin, Haixin Wang","doi":"10.1109/iccia.2018.00020","DOIUrl":"https://doi.org/10.1109/iccia.2018.00020","url":null,"abstract":"Solar energy is a new energy, which is not only perennial but also obtainable to every strata of the world. The use of solar photovoltaic systems (SPV) is the process of converting solar energy into electricity. Photovoltaic modules are mounted on the wings of solar unmanned aerial vehicles. In this paper, a new MPPT controller is proposed to predict the voltage to obtain the maximum power from the solar panel. The proposed MPPT controller is based on mind evolution algorithm (MEA) optimized back propagation neural network (BPNN). Firstly, the mind evolution algorithm model is constructed based on topology of BP Neural Network. Then, it is used to obtain the optimal solutions, which is regarded as initial weights and threshold value of BP Neural Network. Finally, the simulation experiment is carried out by using MATLAB software. The prediction results of the BP neural network optimized by the mind evolution algorithm are compared.","PeriodicalId":297098,"journal":{"name":"2018 3rd International Conference on Computational Intelligence and Applications (ICCIA)","volume":"184 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133947722","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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