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

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Detecting Proper Mask Usage with Soft Attention 用软注意检测口罩的正确使用
2020 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2020-12-01 DOI: 10.1109/SSCI47803.2020.9308430
Thomas Truong, Dhyey Lalseta, Ryan Ittyipe, S. Yanushkevich
{"title":"Detecting Proper Mask Usage with Soft Attention","authors":"Thomas Truong, Dhyey Lalseta, Ryan Ittyipe, S. Yanushkevich","doi":"10.1109/SSCI47803.2020.9308430","DOIUrl":"https://doi.org/10.1109/SSCI47803.2020.9308430","url":null,"abstract":"Proper mask usage in public areas has been shown to be critical in the efforts to reduce infection spread in circumstances such as the COVID-19 pandemic. In this paper, we propose mask usage detection approach based on deep learning: a Mask Regional-Convolutional Neural Network (Mask R-CNN) that provides segmentation of faces and masks, and another CNN using a novel Soft Attention unit to detect the correctness of the mask usage. We also provide a small instance segmented subset of the Masked Faces (MAFA) dataset for instance segmentation problems. We use the Mask R-CNN to provide instance segmentations of faces and face masks to the visual relationship detection CNN and predict improperly and properly worn face masks. Various CNN architectures such as ResNet50 were tested and compared to determine its effectiveness for the above task. We evaluate the CNN architectures on accuracy, precision, recall, and specificity of detecting properly worn masks. The best performing network was determined to be the ResNet50V2 architecture with 76.27% accuracy, 84.76% precision, 74.38% recall, and 79.20% specificity.","PeriodicalId":413489,"journal":{"name":"2020 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":"91 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":"131557188","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 Memetic Algorithm for Evolving Deep Convolutional Neural Network in Image Classification 基于模因算法的深度卷积神经网络图像分类
2020 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2020-12-01 DOI: 10.1109/SSCI47803.2020.9308162
Junwei Dong, Liangjie Zhang, Boyu Hou, Liang Feng
{"title":"A Memetic Algorithm for Evolving Deep Convolutional Neural Network in Image Classification","authors":"Junwei Dong, Liangjie Zhang, Boyu Hou, Liang Feng","doi":"10.1109/SSCI47803.2020.9308162","DOIUrl":"https://doi.org/10.1109/SSCI47803.2020.9308162","url":null,"abstract":"As evolutionary algorithms (EAs) are robust to the problem formulation and easy to use, there is a growing interest in designing EAs for automated neural architecture search in recent years. In particular, EvoCNN is a recently proposed evolutionary algorithm to automate the configuration of a deep Convolutional Neural Network (CNN) for image classification. Its efficacy has been confirmed against 22 existing algorithms for CNN configuration, on the widely used image classification tasks. However, despite the success enjoyed by this method, we note that there are several limitations existed in this method. For example, only chain structured network is considered for evolution. Further, there are many decision variables, which is computational expensive. In this paper, we embark a study on evolutionary neural architecture search by proposing a memetic algorithm (MA), with the aim of addressing the problems mentioned above. Particularly, first of all, besides evolving the chain structured network, local search is designed for multibranch network search. Next, to reduce the network parameters for optimization, we focus on the architecture search only on the convolutional layers. Moreover, based on a recent hypothesis in the literature, the network evaluation is conducted based on only the early training process in our proposed MA. To confirm the efficacy of the proposed method, comprehensive empirical studies are conducted against EvoCNN for NAS, on the commonly used image classification benchmarks.","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":"130831418","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 Spiking Neural Network Based Auto-encoder for Anomaly Detection in Streaming Data 基于脉冲神经网络的自编码器在流数据异常检测中的应用
2020 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2020-12-01 DOI: 10.1109/SSCI47803.2020.9308187
Peter G. Stratton, Andrew Wabnitz, T. J. Hamilton
{"title":"A Spiking Neural Network Based Auto-encoder for Anomaly Detection in Streaming Data","authors":"Peter G. Stratton, Andrew Wabnitz, T. J. Hamilton","doi":"10.1109/SSCI47803.2020.9308187","DOIUrl":"https://doi.org/10.1109/SSCI47803.2020.9308187","url":null,"abstract":"Anomaly Detection (AD) is useful for a range of applications including cyber security, health analytics, robotics, defense and big data. Automating the detection of anomalies is necessary to deal with large volumes of data and to satisfy real time processing constraints. Current Machine Learning (ML) methods have had some success in the automated detection of anomalies, but no ideal ML solutions have been found for any domain. Spiking Neural Networks (SNNs), an emerging ML technique, have the potential to do AD well, especially for Edge applications where it needs to be low power, readily adaptable, autonomous and reliable. Here we investigate SNNs doing anomaly detection on streams of text. We show that SNNs are well suited for detecting anomalous character sequences, that they can learn rapidly, and that there are many optimizations to the SNN architecture and training that can improve AD performance.","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":"131148123","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
We Are Not Pontius Pilate: Acknowledging Ethics and Policy 我们不是彼拉多:承认伦理与政策
2020 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2020-12-01 DOI: 10.1109/SSCI47803.2020.9308312
J. Hughes, W. Hannah, P. Kikkert, B. MacKenzie, W. Ashlock, S. Houghten, D. Ashlock, Matthew Stoodley, Michael Dubé, Rachel Brown, Amanda Saunders
{"title":"We Are Not Pontius Pilate: Acknowledging Ethics and Policy","authors":"J. Hughes, W. Hannah, P. Kikkert, B. MacKenzie, W. Ashlock, S. Houghten, D. Ashlock, Matthew Stoodley, Michael Dubé, Rachel Brown, Amanda Saunders","doi":"10.1109/SSCI47803.2020.9308312","DOIUrl":"https://doi.org/10.1109/SSCI47803.2020.9308312","url":null,"abstract":"A new AI system is being developed to optimize vaccination strategies based on the structure and shape of a community’s social contact network. The technology is minimally constrained and not bound by preconceived notions or human biases. With this come novel outside the box strategies; however, the system is only capable of optimizing what it is instructed to optimize, and does not consider any ethical or political concerns. With the growing concern for systematic discrimination as a result of artificial intelligence, we acknowledge a number of relevant issues that may arise as a consequence of our new technology and categorize them into three classes. We also introduce four normative ethical approaches that are used as a framework for decision-making. Despite the focus on vaccination strategies, our goal is to improve the discussions surrounding public concern and trust over artificial intelligence and demonstrate that artificial intelligence practitioners are addressing these concerns.","PeriodicalId":413489,"journal":{"name":"2020 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":"37 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":"131162210","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: 0
Energy-Efficient Cluster-based Routing Protocol in Internet of Things Using Swarm Intelligence 基于群智能的物联网节能集群路由协议
2020 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2020-12-01 DOI: 10.1109/SSCI47803.2020.9308609
Sankar Sennan, S. Ramasubbareddy, Fang Chen, A. Gandomi
{"title":"Energy-Efficient Cluster-based Routing Protocol in Internet of Things Using Swarm Intelligence","authors":"Sankar Sennan, S. Ramasubbareddy, Fang Chen, A. Gandomi","doi":"10.1109/SSCI47803.2020.9308609","DOIUrl":"https://doi.org/10.1109/SSCI47803.2020.9308609","url":null,"abstract":"Energy conservation is a difficult challenge, because the Internet of Things (IoT) connects limited resource devices. Clustering is an efficient method for energy saving in network nodes. The existing clustering algorithms have problems with the short lifespan of a network, an unbalanced load among the network nodes and increased end-to-end delays. This paper proposes a new Cluster Head (CH) selection and cluster formation algorithm to overcome these issues. The process has two phases. First, the CH is selected using a Swarm Intelligence Algorithm called Sailfish optimization Algorithm (SOA). Second, the cluster is formed by the Euclidean distance. The simulation is conducted using the NS2 simulator. The efficacy of the SOA is compared to Improved Ant Bee Colony optimization-based Clustering (IABCOCT), Enhanced Particle Swarm optimization Technique (EPSOCT) and Hierarchical Clustering-based CH Election (HCCHE). The final results of the simulation show that the proposed SOA improves network life and decreases node-to-sink delays.","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":"131204166","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: 9
A Study on Parameter Sensitivity Analysis of the Virus Spread Optimization 病毒传播优化的参数敏感性分析研究
2020 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2020-12-01 DOI: 10.1109/SSCI47803.2020.9308167
Zhixi Li, V. Tam, K. Yeung
{"title":"A Study on Parameter Sensitivity Analysis of the Virus Spread Optimization","authors":"Zhixi Li, V. Tam, K. Yeung","doi":"10.1109/SSCI47803.2020.9308167","DOIUrl":"https://doi.org/10.1109/SSCI47803.2020.9308167","url":null,"abstract":"The virus spread optimization (VSO) is a radically new metaheuristic optimization algorithm to mimic the viral behavior and spread of viruses for continuous optimization. Due to the multiple search strategies design, the VSO achieves an excellent performance on a series of well-known benchmark functions in terms of the solution quality, convergence rate and stability. Yet the number of control parameters involved in the VSO algorithm is relatively larger than those of other popular metaheuristics such as genetic algorithm (GA) and particle swarm optimization (PSO). Besides, there is rarely any study on the possible impact of such parameters on the performance of the VSO as based on the default parameter settings when compared to those of other metaheuristics. In this work, the parameter sensitivity of the VSO is carefully examined by performing a suite of experiments. More importantly, the rules of thumb for the parameter tuning of the VSO is also considered. Essentially, this work reveals the impact of the parameters contributing to the success of the VSO to boost the research of this promising optimization algorithm.","PeriodicalId":413489,"journal":{"name":"2020 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":"2 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":"132947934","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
Autonomous Target Allocation Recommendations 自主目标分配建议
2020 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2020-12-01 DOI: 10.1109/SSCI47803.2020.9308399
L. Marsh, Madeleine Cochrane, R. Lodge, B. Sims, Jason M. Traish, Richard Y. D. Xu
{"title":"Autonomous Target Allocation Recommendations","authors":"L. Marsh, Madeleine Cochrane, R. Lodge, B. Sims, Jason M. Traish, Richard Y. D. Xu","doi":"10.1109/SSCI47803.2020.9308399","DOIUrl":"https://doi.org/10.1109/SSCI47803.2020.9308399","url":null,"abstract":"We consider the problem of land vehicles under attack from a number of unmanned aerial systems. As the number of unmanned aerial systems increase, it may become difficult for human operators to coordinate actions across vehicles in a timely manner. In this paper, we study a number of algorithms designed to recommend actions to operators that will maximise the survivability of the vehicle fleet. We present a comparison of several assignment approaches including evolutionary strategies, genetic algorithms, multi-armed bandits, probability trees and basic heuristics. The performance of these algorithms is analysed across six different simulated scenarios. Our findings indicate that while there was no single best approach, Evolution Strategies, Ensemble and Genetic Algorithms were the strongest performers. It was also seen that a number of heuristic algorithms and the multi-armed bandits approach offered reliable performance in a number of scenarios without the need for any training.","PeriodicalId":413489,"journal":{"name":"2020 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":"124 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":"133105238","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
Vision-based Vehicle Detection and Distance Estimation 基于视觉的车辆检测与距离估计
2020 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2020-12-01 DOI: 10.1109/SSCI47803.2020.9308364
Donghao Qiao, F. Zulkernine
{"title":"Vision-based Vehicle Detection and Distance Estimation","authors":"Donghao Qiao, F. Zulkernine","doi":"10.1109/SSCI47803.2020.9308364","DOIUrl":"https://doi.org/10.1109/SSCI47803.2020.9308364","url":null,"abstract":"Real-time vehicle detection is one of the most important topics under the Autonomous Vehicles (AVs) research paradigm and traffic surveillance. Detecting vehicles and estimating their distances are essential to ensure that the vehicles can keep a safe distance and run safely on the roads. The technology can also be utilized to determine traffic flow and estimate vehicle speed. In this paper, we apply two different deep learning models and compare their performances in detecting vehicles such as cars and trucks for deployment on the self-driving cars to ensure road safety. Our models are based on YOLOv4 and Faster R-CNN which are efficient and accurate in object detection within a given distance. We also propose a vision-based distance estimation algorithm to estimate other vehicles’ distances. In detecting vehicles within 100 meters, the two variations of our models, YOLOv4 and Faster R-CNN, achieved 99.16% and 95.47% mean precision, and 79.36% and 85.54% Fl-measure respectively on a two-way road. The detection speed is 68 fps and 14 fps for YOLOv4 and Faster R-CNN respectively.","PeriodicalId":413489,"journal":{"name":"2020 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":"44 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":"133203948","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 Survey on Ethical Principles of AI and Implementations 人工智能伦理原则及其实现综述
2020 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2020-12-01 DOI: 10.1109/SSCI47803.2020.9308437
Jianlong Zhou, Fang Chen, Adam Berry, M. Reed, Shujia Zhang, Siobhan Savage
{"title":"A Survey on Ethical Principles of AI and Implementations","authors":"Jianlong Zhou, Fang Chen, Adam Berry, M. Reed, Shujia Zhang, Siobhan Savage","doi":"10.1109/SSCI47803.2020.9308437","DOIUrl":"https://doi.org/10.1109/SSCI47803.2020.9308437","url":null,"abstract":"AI has powerful capabilities in prediction, automation, planning, targeting, and personalisation. Generally, it is assumed that AI can enable machines to exhibit human-like intelligence, and is claimed to benefit to different areas of our lives. Since AI is fueled by data and is a distinct form of autonomous and self-learning agency, we are seeing increasing ethical concerns related to AI uses. In order to mitigate various ethical concerns, national and international organisations including governmental organisations, private sectors as well as research institutes have made extensive efforts by drafting ethical principles of AI, and having active discussions on ethics of AI within and beyond the AI community. This paper investigates these efforts with a focus on the identification of fundamental ethical principles of AI and their implementations. The review found that there is a convergence around limited principles and the most prevalent principles are transparency, justice and fairness, responsibility, non-maleficence, and privacy. The investigation suggests that ethical principles need to be combined with every stages of the AI lifecycle in the implementation to ensure that the AI system is designed, implemented and deployed in an ethical manner. Similar to ethical framework used in biomedical and clinical research, this paper suggests checklist-style questionnaires as benchmarks for the implementation of ethical principles of AI.","PeriodicalId":413489,"journal":{"name":"2020 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":"240 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":"131908017","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: 21
A Comparison of Genetic & Swarm Intelligence-Based Feature Selection Algorithms for Author Identification 基于遗传和群体智能的特征选择算法在作者识别中的比较
2020 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2020-12-01 DOI: 10.1109/SSCI47803.2020.9308343
Steve Halladay, Gerry V. Dozier
{"title":"A Comparison of Genetic & Swarm Intelligence-Based Feature Selection Algorithms for Author Identification","authors":"Steve Halladay, Gerry V. Dozier","doi":"10.1109/SSCI47803.2020.9308343","DOIUrl":"https://doi.org/10.1109/SSCI47803.2020.9308343","url":null,"abstract":"Researchers are moving beyond stylometric features to improve author identification systems. They are exploring non-traditional and hybrid feature sets that include areas like sentiment analysis and topic models. This feature set exploration leads to the concern of determining which features are best suited for which systems and datasets. In this paper, we compare Genetic Search and a number of Swarm Intelligence (SI) methods for feature selection. In addition to Genetic Search methods, we compare SI methods including Artificial Bee Colony, Ant System optimization, Glowworm Swarm optimization and Particle Swarm optimization for feature selection.","PeriodicalId":413489,"journal":{"name":"2020 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":"3 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":"133753770","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: 0
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