Pub Date : 2018-07-01DOI: 10.1109/ICCIA.2018.00046
Y. Nozaki, M. Yoshikawa
Artificial intelligence technology such as neural network (NN) is widely used in intelligence module for Internet of Things (IoT). On the other hand, the risk of illegal attacks for IoT devices is pointed out; therefore, security countermeasures such as an authentication are very important. In the field of hardware security, the physical unclonable functions (PUFs) have been attracted attention as authentication techniques to prevent the semiconductor counterfeits. However, implementation of the dedicated hardware for both of NN and PUF increases circuit area. Therefore, this study proposes a new area constraint aware PUF for intelligence module. The proposed PUF utilizes the propagation delay time from input layer to output layer of NN. To share component for operation, the proposed PUF reduces the circuit area. Experiments using a field programmable gate array evaluate circuit area and PUF performance. In the result of circuit area, the proposed PUF was smaller than the conventional PUFs was showed. Then, in the PUF performance evaluation, for steadiness, diffuseness, and uniqueness, favorable results were obtained.
{"title":"Area Constraint Aware Physical Unclonable Function for Intelligence Module","authors":"Y. Nozaki, M. Yoshikawa","doi":"10.1109/ICCIA.2018.00046","DOIUrl":"https://doi.org/10.1109/ICCIA.2018.00046","url":null,"abstract":"Artificial intelligence technology such as neural network (NN) is widely used in intelligence module for Internet of Things (IoT). On the other hand, the risk of illegal attacks for IoT devices is pointed out; therefore, security countermeasures such as an authentication are very important. In the field of hardware security, the physical unclonable functions (PUFs) have been attracted attention as authentication techniques to prevent the semiconductor counterfeits. However, implementation of the dedicated hardware for both of NN and PUF increases circuit area. Therefore, this study proposes a new area constraint aware PUF for intelligence module. The proposed PUF utilizes the propagation delay time from input layer to output layer of NN. To share component for operation, the proposed PUF reduces the circuit area. Experiments using a field programmable gate array evaluate circuit area and PUF performance. In the result of circuit area, the proposed PUF was smaller than the conventional PUFs was showed. Then, in the PUF performance evaluation, for steadiness, diffuseness, and uniqueness, favorable results were obtained.","PeriodicalId":297098,"journal":{"name":"2018 3rd International Conference on Computational Intelligence and Applications (ICCIA)","volume":"122 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":"115089759","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.2018.00018
Xiangguang Dai, Yingji Cui, Zheng Chen, Yi Yang
As Internet expanding into offline, the traditional retail industry began to use the personalized recommendation algorithm to increase user stickiness, conversion and business income. Without considering the data segmentation problem, traditional recommendation algorithm did not perform well in the traditional business data. Accordingly, we considered the interest spread characteristic of retail industry behavior, adopted the method of complex network to construct a personalized recommendation algorithm using the segmentation data set. By using a real sales dataset of a large supermarket, we provided an evaluation of our algorithm. The results show that our algorithm have much better performance in accuracy and recall than the traditional ones, but with the disadvantage of being less coverage.
{"title":"A Network-Based Recommendation Algorithm","authors":"Xiangguang Dai, Yingji Cui, Zheng Chen, Yi Yang","doi":"10.1109/iccia.2018.00018","DOIUrl":"https://doi.org/10.1109/iccia.2018.00018","url":null,"abstract":"As Internet expanding into offline, the traditional retail industry began to use the personalized recommendation algorithm to increase user stickiness, conversion and business income. Without considering the data segmentation problem, traditional recommendation algorithm did not perform well in the traditional business data. Accordingly, we considered the interest spread characteristic of retail industry behavior, adopted the method of complex network to construct a personalized recommendation algorithm using the segmentation data set. By using a real sales dataset of a large supermarket, we provided an evaluation of our algorithm. The results show that our algorithm have much better performance in accuracy and recall than the traditional ones, but with the disadvantage of being less coverage.","PeriodicalId":297098,"journal":{"name":"2018 3rd International Conference on Computational Intelligence and Applications (ICCIA)","volume":"23 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":"126445480","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.2018.00021
Ben-Da Zhou, Zhao Pan, Zhang Jinbo
Based on the analysis of local monotonic of modularity function, this paper designs a fast and effective mutation operator, and then proposes an improved Estimation of Distribution Algorithm (EDA) for solving community detection problem. The proposed algorithm is tested on basic network and big scale complex network. Experimental results show that this algorithm can get 0.419 8 for the average Q function while running 100 times, has better performance than Girvan-Newman(GN) algorithm, Fast Newman (FN) algorithm and Tasgin Genetic Algorithm (TGA).
{"title":"The Improved Estimation of Distribution Algorithms for Community Detection","authors":"Ben-Da Zhou, Zhao Pan, Zhang Jinbo","doi":"10.1109/iccia.2018.00021","DOIUrl":"https://doi.org/10.1109/iccia.2018.00021","url":null,"abstract":"Based on the analysis of local monotonic of modularity function, this paper designs a fast and effective mutation operator, and then proposes an improved Estimation of Distribution Algorithm (EDA) for solving community detection problem. The proposed algorithm is tested on basic network and big scale complex network. Experimental results show that this algorithm can get 0.419 8 for the average Q function while running 100 times, has better performance than Girvan-Newman(GN) algorithm, Fast Newman (FN) algorithm and Tasgin Genetic Algorithm (TGA).","PeriodicalId":297098,"journal":{"name":"2018 3rd International Conference on Computational Intelligence and Applications (ICCIA)","volume":"18 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":"121453694","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.2018.00002
{"title":"Title Page iii","authors":"","doi":"10.1109/iccia.2018.00002","DOIUrl":"https://doi.org/10.1109/iccia.2018.00002","url":null,"abstract":"","PeriodicalId":297098,"journal":{"name":"2018 3rd International Conference on Computational Intelligence and Applications (ICCIA)","volume":"212 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":"124156705","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.2018.00005
{"title":"ICCIA 2018 Preface","authors":"","doi":"10.1109/iccia.2018.00005","DOIUrl":"https://doi.org/10.1109/iccia.2018.00005","url":null,"abstract":"","PeriodicalId":297098,"journal":{"name":"2018 3rd International Conference on Computational Intelligence and Applications (ICCIA)","volume":"45 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":"130011052","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.2018.00001
{"title":"Title Page i","authors":"","doi":"10.1109/iccia.2018.00001","DOIUrl":"https://doi.org/10.1109/iccia.2018.00001","url":null,"abstract":"","PeriodicalId":297098,"journal":{"name":"2018 3rd International Conference on Computational Intelligence and Applications (ICCIA)","volume":"30 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":"116005772","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.2018.00016
Feng-Cheng Yang, Ren-Fu Li
This paper presents a novel heuristic optimization algorithm for discrete optimization problems, the Bandwidth Restricted Transmission-Simulated Optimization Algorithm (BRT-S). This algorithm imitates the intellective behaviors of human in managing network transmission. BRT-S is a constructive heuristic algorithm whose optimization procedures simulate processes of data transferring and management operations over the network. A population of solution agents mimicking message transmitters on networks is deployed to quest for optimal solutions. The algorithm however restricts the resource utilized in solution search mimicking the bandwidth resource is limited in network transmission. As a result, agents must compete with others to obtain solution construction resources. Due to the mimicked bandwidth restriction, not every agent is able to complete a solution construction. Only constructed solutions are subject to objective value evaluations. In each evolution iteration, bandwidth resources are separately modulated by conducting bandwidth deterioration, enhancement, or depletion, on the basis of the solution qualities obtained. To illustrate the operation procedures of the algorithm, a BRT-S computation model for solving the Traveling Salesman Problem is presented and the solving system is implemented for benchmark testing. Numerical results of the tests indicate that given similar computation resources, the algorithm generates better solutions than other meta heuristic algorithms, such as ACO and GA.
{"title":"A Human Intelligence Inspired Meta Heuristic Optimization Algorithm for TSPs","authors":"Feng-Cheng Yang, Ren-Fu Li","doi":"10.1109/ICCIA.2018.00016","DOIUrl":"https://doi.org/10.1109/ICCIA.2018.00016","url":null,"abstract":"This paper presents a novel heuristic optimization algorithm for discrete optimization problems, the Bandwidth Restricted Transmission-Simulated Optimization Algorithm (BRT-S). This algorithm imitates the intellective behaviors of human in managing network transmission. BRT-S is a constructive heuristic algorithm whose optimization procedures simulate processes of data transferring and management operations over the network. A population of solution agents mimicking message transmitters on networks is deployed to quest for optimal solutions. The algorithm however restricts the resource utilized in solution search mimicking the bandwidth resource is limited in network transmission. As a result, agents must compete with others to obtain solution construction resources. Due to the mimicked bandwidth restriction, not every agent is able to complete a solution construction. Only constructed solutions are subject to objective value evaluations. In each evolution iteration, bandwidth resources are separately modulated by conducting bandwidth deterioration, enhancement, or depletion, on the basis of the solution qualities obtained. To illustrate the operation procedures of the algorithm, a BRT-S computation model for solving the Traveling Salesman Problem is presented and the solving system is implemented for benchmark testing. Numerical results of the tests indicate that given similar computation resources, the algorithm generates better solutions than other meta heuristic algorithms, such as ACO and GA.","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":"129388668","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.2018.00047
Taichi Umeda, Y. Nozaki, M. Yoshikawa
Recently, the damage caused by semiconductor counterfeit has become a serious problem in our lives. Physical Unclonable Function (PUF) has attracted attention as a countermeasure method. In the countermeasures, a ring oscillator (RO) PUF is one of the most popular PUFs. Regarding tamper resistance of RO PUFs, Genetic Programming (GP) based attacks have been proposed. However, the GP based attacks only apply the basic genetic strategy. To evaluate tamper resistance of RO PUF accurately, improvement of GP based attack for RO PUF is important. Therefore, this study proposes an accurate attack which is based on GP using dynamic adaptive mutation.
{"title":"Dynamic Adaptive Mutation Based Genetic Programming for Ring Oscillator PUF","authors":"Taichi Umeda, Y. Nozaki, M. Yoshikawa","doi":"10.1109/ICCIA.2018.00047","DOIUrl":"https://doi.org/10.1109/ICCIA.2018.00047","url":null,"abstract":"Recently, the damage caused by semiconductor counterfeit has become a serious problem in our lives. Physical Unclonable Function (PUF) has attracted attention as a countermeasure method. In the countermeasures, a ring oscillator (RO) PUF is one of the most popular PUFs. Regarding tamper resistance of RO PUFs, Genetic Programming (GP) based attacks have been proposed. However, the GP based attacks only apply the basic genetic strategy. To evaluate tamper resistance of RO PUF accurately, improvement of GP based attack for RO PUF is important. Therefore, this study proposes an accurate attack which is based on GP using dynamic adaptive mutation.","PeriodicalId":297098,"journal":{"name":"2018 3rd International Conference on Computational Intelligence and Applications (ICCIA)","volume":"20 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":"130617608","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.2018.00011
Vidya Diantorio Putri, K. Komarudin, A. R. Destyanto
Mass Rapid Transit (MRT) Jakarta is one of the new urban transportation in Greater Jakarta area which will be operated in early 2019. The development period has been started in 2010. A research has been conducted to determine its train specification, including train set and car number in 2010. Time revealed that the based data for that research, forecasted number of Jakarta population, is not fits the actual number. This research goal is to determine the train specification, which are the number of MRT Jakarta train set and number of car for each train set to reach its headway target by considering MRT Jakarta daily passenger target based on actual number of Jakarta population. The researcher uses ProModel 7.5 as the tool to simulate 12 optional policy. These 12 optional policies is made of combined three control variable, which are train set, car number, and headway. Researcher use the number of passenger compared to MRT Jakarta daily passenger target as the indicator to choose the best policy. Based on the result of this research, the best train specification policy that could reach the 5 minutes headway MRT Jakarta target is 7 train set and 6 cars for each set.
{"title":"The Determination of MRT (Mass Rapid Transit) Jakarta Train Specification to Reach Headway Target by Using ProModel","authors":"Vidya Diantorio Putri, K. Komarudin, A. R. Destyanto","doi":"10.1109/ICCIA.2018.00011","DOIUrl":"https://doi.org/10.1109/ICCIA.2018.00011","url":null,"abstract":"Mass Rapid Transit (MRT) Jakarta is one of the new urban transportation in Greater Jakarta area which will be operated in early 2019. The development period has been started in 2010. A research has been conducted to determine its train specification, including train set and car number in 2010. Time revealed that the based data for that research, forecasted number of Jakarta population, is not fits the actual number. This research goal is to determine the train specification, which are the number of MRT Jakarta train set and number of car for each train set to reach its headway target by considering MRT Jakarta daily passenger target based on actual number of Jakarta population. The researcher uses ProModel 7.5 as the tool to simulate 12 optional policy. These 12 optional policies is made of combined three control variable, which are train set, car number, and headway. Researcher use the number of passenger compared to MRT Jakarta daily passenger target as the indicator to choose the best policy. Based on the result of this research, the best train specification policy that could reach the 5 minutes headway MRT Jakarta target is 7 train set and 6 cars for each set.","PeriodicalId":297098,"journal":{"name":"2018 3rd International Conference on Computational Intelligence and Applications (ICCIA)","volume":"36 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":"127032989","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.2018.00030
Crino Shin, J. Yun, Seunghyun Jeong, Yongsik Jin
This paper presents a Remaining Useful Life (RUL) prediction method for a speed reducer based on denoising au- to-encoder (DAE). Constructing the efficiency map of the re- ducer is an important process for predicting the life span. However, due to the situational constraints that occur, un- measured intervals hinder the completion of the efficiency map. to solve this problem, we propose a method that can pre- dict and reconstruct an unmeasured interval effectively and reliably by using DAE. In addition, we examine the applicabil- ity of the proposed algorithm through experiments that assume various situations.
{"title":"Predicting Unmeasured Region of the Efficiency Map of a Speed Reducer Using a Denoising Auto-Encoder","authors":"Crino Shin, J. Yun, Seunghyun Jeong, Yongsik Jin","doi":"10.1109/ICCIA.2018.00030","DOIUrl":"https://doi.org/10.1109/ICCIA.2018.00030","url":null,"abstract":"This paper presents a Remaining Useful Life (RUL) prediction method for a speed reducer based on denoising au- to-encoder (DAE). Constructing the efficiency map of the re- ducer is an important process for predicting the life span. However, due to the situational constraints that occur, un- measured intervals hinder the completion of the efficiency map. to solve this problem, we propose a method that can pre- dict and reconstruct an unmeasured interval effectively and reliably by using DAE. In addition, we examine the applicabil- ity of the proposed algorithm through experiments that assume various situations.","PeriodicalId":297098,"journal":{"name":"2018 3rd International Conference on Computational Intelligence and Applications (ICCIA)","volume":"84 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":"126019877","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：