2020 IEEE Symposium Series on Computational Intelligence (SSCI)最新文献

Low-Rank Temporal Attention-Augmented Bilinear Network for financial time-series forecasting 低秩时间注意增强双线性网络用于金融时间序列预测

2020 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2020-12-01 DOI: 10.1109/SSCI47803.2020.9308440

M. Shabani, Alexandros Iosifidis

引用次数: 3

Towards Potential of N-back Task as Protocol and EEGNet for the EEG-based Biometric 论N-back任务作为脑电图生物识别协议和脑电图网络的潜力

2020 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2020-12-01 DOI: 10.1109/SSCI47803.2020.9308487

Nima Salimi, M. Barlow, E. Lakshika

{"title":"Towards Potential of N-back Task as Protocol and EEGNet for the EEG-based Biometric","authors":"Nima Salimi, M. Barlow, E. Lakshika","doi":"10.1109/SSCI47803.2020.9308487","DOIUrl":"https://doi.org/10.1109/SSCI47803.2020.9308487","url":null,"abstract":"Electroencephalogram (EEG) has emerged as a biometric trait potentially with more security benefits compared to its conventional competitors such as fingerprint, iris scan, voice recognition, and face detection. However, there is still a long way to go to make EEG biometrics practical in real-world environments. One of the challenges of the EEG-based biometric systems is time efficiency. The protocols that can evoke individualdependent EEG patterns are usually time consuming. The signal-to-noise ratio (SNR) of the EEG signal is also low, which means a large number of epochs/trials (i.e. long acquisition time) are required to achieve a high accuracy recognition system. In this study we propose an EEG-based biometric model that could achieve high identification accuracy with data instances as short as only 1.1s (single epoch instances). In our biometric model, we propose a new protocol called the N-back task which is based on human working memory. As the nature of working memory is very short, it would be possible to elicit individual-dependent EEG responses within a very short period of time. The single epoch classification was achieved applying a deep neural network called EEGNet. Using 1.1s data instances, the proposed model could identify a pool of 26 subjects with the mean accuracy of 0.95, where recognition rate for majority of subjects was ≥0.99. Different components of this identity recognition model, from the proposed protocol to the classification algorithm, can be a line of research for the future of EEG biometric.","PeriodicalId":413489,"journal":{"name":"2020 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":"13 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":"115317977","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: 3

AugMapping: Accurate and Efficient Inference with Deep Double-Threshold Spiking Neural Networks AugMapping:深度双阈值峰值神经网络的准确高效推理

2020 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2020-12-01 DOI: 10.1109/SSCI47803.2020.9308402

Chenxiang Ma, Qiang Yu

{"title":"AugMapping: Accurate and Efficient Inference with Deep Double-Threshold Spiking Neural Networks","authors":"Chenxiang Ma, Qiang Yu","doi":"10.1109/SSCI47803.2020.9308402","DOIUrl":"https://doi.org/10.1109/SSCI47803.2020.9308402","url":null,"abstract":"Spiking neural networks (SNNs) are regarded as one of the promising candidates to overcome the high energy costs of artificial neural networks (ANNs), but the accuracy gap between them is still large on practical tasks. A straightforward yet effective conversion scheme was developed recently to narrow this gap by mapping a trained ANN to an SNN. However, current conversion methods require a relatively large number of time steps and spikes, alleviating the advantages of spike-based computation. In this paper, we propose a new augmented spiking neuron model composed of a double-threshold firing scheme, and it is advanced with the ability to process and elicit augmented spikes whose strength is used to carry the number of typical allor-nothing spikes firing at one time step. Based on this model, a new conversion method called AugMapping is developed. We examine the performance of our methods with both MNIST and CIFAR10 datasets. Our results highlight that the as-proposed methods, as benchmarked to other baselines, are advantageous to accurate and efficient computation with SNNs. Therefore, our work contributes to improving the performance of spike-based computation, which would be of great merit to neuromorphic computing.","PeriodicalId":413489,"journal":{"name":"2020 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":"11 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":"115371180","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

Optimizing the Energy Consumption of Neural Networks 优化神经网络的能量消耗

2020 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2020-12-01 DOI: 10.1109/SSCI47803.2020.9308576

Jan Linus Steuler, Markus Beck, Benjamin N. Passow, Michael Guckert

引用次数: 0

Quantifying Sustainability in a System of Coupled Tipping Elements 耦合引爆要素系统的可持续性量化

2020 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2020-12-01 DOI: 10.1109/SSCI47803.2020.9308385

Jan T. Kim, D. Polani

{"title":"Quantifying Sustainability in a System of Coupled Tipping Elements","authors":"Jan T. Kim, D. Polani","doi":"10.1109/SSCI47803.2020.9308385","DOIUrl":"https://doi.org/10.1109/SSCI47803.2020.9308385","url":null,"abstract":"Characterising sustainability has become a core challenge when trying to understand the interplay between global economical and ecological dynamics and both their mutual dependence as well as their competing requirements. Identifying and understanding warning signs that would indicate where when a system gets irrevocably out of control before this happens would be a critical tool in being able to attain a viable long-term strategy that takes the needs of both economy and ecology into account.We here explore a route towards such quantities. In the last years, the concept of empowerment has been investigated as a measure of control of an actor over its environment, i.e. the potential impact that an actor can have on its environment; in addition an extension had been proposed towards a concept of sustainable empowerment which, in addition, limits oneself only to control strategies which can be undone.We investigate both concepts inside a framework of systems of coupled elements endowed with a dynamics governed by cubic differential equations, which have been established as simple but powerful models to study sustainability. In this framework, we illustrate how the dynamical properties of such a system affect empowerment and sustainable empowerment. The results suggest that these quantities can provide relevant indicators for desirable strategies to guiding such systems under sustainability considerations.","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":"125201780","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

Two-stage Unsupervised Approach for Combating Social Spammers 打击社会垃圾邮件制造者的两阶段无监督方法

2020 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2020-12-01 DOI: 10.1109/SSCI47803.2020.9308315

D. Koggalahewa, Yue Xu, Ernest Foo

引用次数: 2

Who is physically active? Classification and Analysis of Physical Activity using NHANES data 谁是体育活动者?使用NHANES数据对体育活动进行分类和分析

2020 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2020-12-01 DOI: 10.1109/SSCI47803.2020.9308353

Khyoi Nu, Tahar Touati, Srushti Buddhadev, R. Sun, M. Smuck, I. H. J. Song

引用次数: 1

Visualizing Parameter Adaptation in Differential Evolution with Expected Fitness Improvement 具有期望适应度改进的差分进化中参数自适应的可视化

2020 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2020-12-01 DOI: 10.1109/SSCI47803.2020.9308467

V. Stanovov, S. Akhmedova, E. Semenkin

引用次数: 1

Improved nondominated sorting genetic algorithm-II for bi-objective flexible job-shop scheduling problem 双目标柔性作业车间调度问题的改进非支配排序遗传算法ii

2020 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2020-12-01 DOI: 10.1109/SSCI47803.2020.9308210

Shu Luo, Linxuan Zhang, Yushun Fan

{"title":"Improved nondominated sorting genetic algorithm-II for bi-objective flexible job-shop scheduling problem","authors":"Shu Luo, Linxuan Zhang, Yushun Fan","doi":"10.1109/SSCI47803.2020.9308210","DOIUrl":"https://doi.org/10.1109/SSCI47803.2020.9308210","url":null,"abstract":"In modern manufacturing industry, not only production efficiency, but also fairness in wages should be emphasized so as to motivate the staff. To this end, this paper addresses the multi-objective flexible job shop scheduling problem (MOFJSP) aiming at minimizing the makespan and maximum wage gap among workers simultaneously. An improved nondominated sorting genetic algorithm-II (INSGAII) is developed. The novelties are mainly presented as follows. First, a probability-based extended precedence preservative crossover operator (PEPPX) is designed for accelerating the convergence rate. Second, in order to solve the multi-modal problem, a “pruning-regenerating” mechanism (PRM) is developed to remove the redundant solutions in objective space and reinsert new solutions. Specifically, a novel metric called “encoding distance” is proposed to measure the diversity of solutions with exactly the same objective function values in chromosome encoding. Meanwhile, a critical path based variable neighborhood search (VNS) is designed to regenerate new solutions replacing the removed ones. Numerical experiments on a wide set of well-known benchmarks have confirmed the effectiveness and efficiency of the proposed INSGA-II.","PeriodicalId":413489,"journal":{"name":"2020 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":"49 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":"126845595","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

A dynamic data-driven model for optimizing waste collection 一个动态数据驱动的废物收集优化模型

2020 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2020-12-01 DOI: 10.1109/SSCI47803.2020.9308221

P. Sarvari, Issam Abdeldjalil Ikhelef, S. Faye, D. Khadraoui

{"title":"A dynamic data-driven model for optimizing waste collection","authors":"P. Sarvari, Issam Abdeldjalil Ikhelef, S. Faye, D. Khadraoui","doi":"10.1109/SSCI47803.2020.9308221","DOIUrl":"https://doi.org/10.1109/SSCI47803.2020.9308221","url":null,"abstract":"Commercial waste collection activities are critical from environmental, societal, as well as economic perspectives. Logistic activities carried out in any large or small human settlement, must be efficient by passing through obstacles while optimizing rare and valuable resource usages. With the advent of the Internet of Things and smart waste management ideas, the concept of static waste collection resource optimization and more specifically vehicle routing problem are being exposed to a fortunate mutation. This study introduces a dynamic waste collection optimization model and its solution for a unique type of waste collection problem. Unlike public waste collection, which is made up of homogeneous customers, commercial waste collection has to consider other factors, relating to the quality or time of service, while considering the socio-economic characteristics of the customers. Moreover, the paper has completed a comprehensive literature review over the waste collection filed to emphasize the singularity of the problem and the proposed mathematical model. The data-driven model proposed in this paper targets the optimization of costs in the embedded solver with invoking real-time data generated by filllevel sensors integrated into waste containers. The outputs of the model are dynamic and time-wise vehicle routing chains for efficient waste collection under the field official guidelines, constraints, and priorities. In order to scrutinize the scalability, applicability and validity of the proposed model, a real-life network in Luxembourg with multiple vehicles, stops, as well as a depot and a disposal site has been considered. The partnership with a waste management company, called Polygone, benchmarking results with real data conclude the merits, excellence, and findings of the paper.","PeriodicalId":413489,"journal":{"name":"2020 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":"15 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":"114930446","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