2020 IEEE Symposium Series on Computational Intelligence (SSCI)最新文献_第8页

Can an AI perform market manipulation at its own discretion? – A genetic algorithm learns in an artificial market simulation – 人工智能可以自行操纵市场吗?遗传算法在人工市场模拟中学习

2020 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2020-12-01 DOI: 10.1109/SSCI47803.2020.9308349

T. Mizuta

引用次数: 5

D-MAENS2: A Self-adaptive D-MAENS Algorithm with Better Decision Diversity D-MAENS2:一种具有更好决策多样性的自适应D-MAENS算法

2020 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2020-12-01 DOI: 10.1109/SSCI47803.2020.9308250

Qingquan Zhang, Feng Wu, Yang Tao, Jiyuan Pei, Jialin Liu, X. Yao

引用次数: 1

The Crucial Role of Sensitive Attributes in Fair Classification 敏感属性在公平分类中的关键作用

2020 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2020-12-01 DOI: 10.1109/SSCI47803.2020.9308585

M. Haeri, K. Zweig

{"title":"The Crucial Role of Sensitive Attributes in Fair Classification","authors":"M. Haeri, K. Zweig","doi":"10.1109/SSCI47803.2020.9308585","DOIUrl":"https://doi.org/10.1109/SSCI47803.2020.9308585","url":null,"abstract":"In many countries, it is illegal to make certain decisions based on sensitive attributes such as gender or race. This is because historically, sensitive attributes of individuals were exploited to abuse the rights of individuals, leading to unfair decisions. This view is extended to algorithmic decision-making systems (ADMs) where similar to humans, ADMs should not use sensitive attributes for input. We reject the extension of law from humans to machines, since contrary to humans, algorithms are explicit in their decisions, and the fairness of their decision can be studied independently of their input. The main purpose of this paper is to study and discuss the importance of using sensitive attributes in fair classification systems. Specifically, we suggest two statistical tests on the training dataset, to evaluate whether using sensitive attributes may have an impact on the quality and fairness of prospective classification algorithms. These statistical tests compare the distribution and data complexity of the training dataset between groups identified by the same value for sensitive attributes (e.g., men vs. women). We evaluated our fairness tests on several datasets. It was shown that, the removal of sensitive attributes may result in the decrease of the fairness of ADMs. The results were confirmed by designing and implementing simple classifiers on each dataset (with and without the sensitive attributes). Therefore, the use of sensitive attributes must be evaluated per dataset and algorithm, and ignoring them blindly may result in unfair ADMs.","PeriodicalId":413489,"journal":{"name":"2020 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116783307","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}

引用次数: 4

A Rule and Graph-Based Approach for Targeted Identity Resolution on Policing Data 基于规则和图的警务数据目标身份解析方法

2020 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2020-12-01 DOI: 10.1109/SSCI47803.2020.9308182

Michael Phillips, Mohammad Hossein Amirhosseini, H. Kazemian

{"title":"A Rule and Graph-Based Approach for Targeted Identity Resolution on Policing Data","authors":"Michael Phillips, Mohammad Hossein Amirhosseini, H. Kazemian","doi":"10.1109/SSCI47803.2020.9308182","DOIUrl":"https://doi.org/10.1109/SSCI47803.2020.9308182","url":null,"abstract":"In criminal records, intentional manipulation of data is prevalent to create ambiguous identity and mislead authorities. Registering data electronically can result in misspelled data, variations in naming order, case sensitive data and inconsistencies in abbreviations and terminology. Therefore, trying to obtain the true identity (or identities) of a suspect can be a challenge for law enforcement agencies. We have developed a targeted approach to identity resolution which uses a rule-based scoring system on physical and official identity attributes and a graph-based analysis on social identity attributes to interrogate policing data and resolve whether a specific target is using multiple identities. The approach has been tested on an anonymized policing dataset, used in the SPIRIT project, funded by the European Union’s Horizon 2020. The dataset contains four ‘known’ identities using a total of five false identities. 23 targets were inputted into the methodology with no knowledge of how many or which had false identities. The rule-based scoring system ranked four of the five false identities with the joint highest score for the relevant target name with the remaining false identity holding the joint second highest score for its target. Moreover, when using graph analysis, 51 suspected false identities were found for the 23 targets with four of the five false identities linked through the crimes they had been involved in. Therefore, an identity resolution approach using both a rule-based scoring system and graph analysis, could be effective in facilitating the investigation process for law enforcement agencies and assisting them in finding criminals using false identities.","PeriodicalId":413489,"journal":{"name":"2020 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122701248","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}

引用次数: 1

Predictive Modeling of Sports-Related Concussions using Clinical Assessment Metrics 使用临床评估指标的运动相关脑震荡预测模型

2020 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2020-12-01 DOI: 10.1109/SSCI47803.2020.9308473

Sujit Subhash, Tayo Obafemi-Ajayi, Dennis Goodman, D. Wunsch, G. Olbricht

引用次数: 2

Association Rule Mining Based Algorithm for Recovery of Silent Data Corruption in Convolutional Neural Network Data Storage 基于关联规则挖掘的卷积神经网络数据存储中静默数据损坏恢复算法

2020 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2020-12-01 DOI: 10.1109/SSCI47803.2020.9308545

M. Ramzanpour, Simone A. Ludwig

{"title":"Association Rule Mining Based Algorithm for Recovery of Silent Data Corruption in Convolutional Neural Network Data Storage","authors":"M. Ramzanpour, Simone A. Ludwig","doi":"10.1109/SSCI47803.2020.9308545","DOIUrl":"https://doi.org/10.1109/SSCI47803.2020.9308545","url":null,"abstract":"Embedded systems are finding their way into almost every aspects of our daily life from mp3 players and console games to the mobile phones. Different Artificial Intelligence (AI) based applications are commonly utilized in embedded systems from which computer vision based approaches are included. The demand for higher accuracy in computer vision applications is associated with the increased complexity of convolutional neural networks and the storage requirement for saving pre-trained networks. Different factors can lead to the data corruption in the storage units of the embedded systems, which can result in drastic failures due to the propagation of the errors. Hence, the development of software-based algorithms for the detection and recovery of data corruption is crucial for improvement and failure-prevention of embedded systems. This paper proposes a new algorithm for the recovery of the data in the case of single event upset (SEU) error. The association rule mining based algorithm will be used to find the probability of the corruption in each of the bits. The recovery algorithm was tested on four different pre-trained ResNet (ResNet32 and ResNet110 at two different accuracy levels each) and the best recovery rate of 66% was found in the most complex scenario, i.e., random bit corruption. However, for the special cases of SEU errors, e.g. error in the frequently repeated bits, the recovery rate was found to be perfect with a value of 100%.","PeriodicalId":413489,"journal":{"name":"2020 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127759051","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}

引用次数: 1

NDBIris with Better Unlinkability NDBIris具有更好的不可链接性

2020 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2020-12-01 DOI: 10.1109/SSCI47803.2020.9308596

Dongdong Zhao, Xiaoyan Zhou, Jianwen Xiang, Wenjian Luo

{"title":"NDBIris with Better Unlinkability","authors":"Dongdong Zhao, Xiaoyan Zhou, Jianwen Xiang, Wenjian Luo","doi":"10.1109/SSCI47803.2020.9308596","DOIUrl":"https://doi.org/10.1109/SSCI47803.2020.9308596","url":null,"abstract":"Iris recognition is one of the mainstream biometric recognition methods. Protecting iris data to prevent personal privacy leakage is significant to the popularity of iris recognition. Negative database is a new type of privacy protection technique. We proposed a promising method (called NDBIris) of iris template protection based on negative databases in previous work. However, its unlinkability is vulnerable under typical parameter settings (e.g. p1=0.8$,p_{2}$=0.14) and it does not protect the privacy of real-time iris data from users for recognition. This paper proposes an improved version called NDBIris-II to achieve better unlinkability and protect the real-time iris data. Specifically, a noninvertible transform using local sorting is performed before converting iris data into negative databases. Moreover, a method for estimating the similarity between iris data from negative databases is proposed to support effective iris recognition. Finally, an iris template in the form of negative database is generated for each iris data, and it is stored and used during iris recognition instead of raw iris data for privacy protection. Experimental results on iris database CASIA-IrisV3-Interval demonstrate that the proposed method could maintain recognition performance while achieving better unlinkability and protecting real-time iris data.","PeriodicalId":413489,"journal":{"name":"2020 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134319476","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}

引用次数: 1

Island-based Modified Harmony Search Algorithm with Neighboring Heuristics Methods for Flow Shop Scheduling with Blocking 具有阻塞的流水车间调度的基于邻近启发式的改进海岛和谐搜索算法

2020 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2020-12-01 DOI: 10.1109/SSCI47803.2020.9308556

Iyad Abu Doush, M. Al-Betar, M. Awadallah, Abdelaziz I. Hammouri, Mohammed El-Abd

{"title":"Island-based Modified Harmony Search Algorithm with Neighboring Heuristics Methods for Flow Shop Scheduling with Blocking","authors":"Iyad Abu Doush, M. Al-Betar, M. Awadallah, Abdelaziz I. Hammouri, Mohammed El-Abd","doi":"10.1109/SSCI47803.2020.9308556","DOIUrl":"https://doi.org/10.1109/SSCI47803.2020.9308556","url":null,"abstract":"The flow shop scheduling with blocking involves assigning several jobs to machines with minimum time complexity. This problem can be considered as a combinatorial optimization problem that does not have an algorithmic solution. Recently, the modified harmony search algorithm with neighboring heuristics methods (MHSNH) is proposed to tackle this problem. In this paper, the flow shop scheduling with blocking problem is tackled using the island harmony search version of MHSNH. Recently, a new version of harmony search algorithm (iHS) is proposed for global optimization problems. In i HS, the population stored in the harmony memory is divided into a set of sub-populations called islands. After a predefined number of iterations, some of the migrant individuals determined by migration rate are exchanged between islands following a migration topology to control the population diversity. In order to evaluate the island modified harmony search algorithm with neighboring heuristics methods (iMHSNH), a de facto standard job scheduling dataset, Taillard’s benchmark, is used. The proposed algorithm is compared to a number of well-established methods in terms of the mean total flow time and the average relative percentage deviation. The proposed method outperforms other comparative algorithms. Finally, the proposed algorithm is compared against MHSNH in terms of locating multiple optimal solutions, which has not been studied before in the literature.","PeriodicalId":413489,"journal":{"name":"2020 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":"125 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134381877","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}

引用次数: 2

BCINet: An Optimized Convolutional Neural Network for EEG-Based Brain-Computer Interface Applications 基于脑电图的脑机接口应用的优化卷积神经网络

2020 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2020-12-01 DOI: 10.1109/SSCI47803.2020.9308292

Avinash Kumar Singh, Xian Tao

引用次数: 2

A3DQN: Adaptive Anderson Acceleration for Deep Q-Networks 深度q -网络的自适应安德森加速

2020 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2020-12-01 DOI: 10.1109/SSCI47803.2020.9308288

Melike Ermis, Insoon Yang

引用次数: 8