2021 IEEE Symposium Series on Computational Intelligence (SSCI)最新文献

Automated Behavior-based Malice Scoring of Ransomware Using Genetic Programming 基于自动行为的基于遗传编程的勒索软件恶意评分

2021 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2021-12-05 DOI: 10.1109/SSCI50451.2021.9660009

Muhammad Shabbir Abbasi, Harith Al-Sahaf, I. Welch

引用次数: 3

Classification of Artificial and Real Objects Using Faster Region-Based Convolutional Neural Networks 基于更快区域的卷积神经网络的人工和真实物体分类

2021 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2021-12-05 DOI: 10.1109/SSCI50451.2021.9660105

Ritvik Sai Teegavarapu, Debojit Biswas

{"title":"Classification of Artificial and Real Objects Using Faster Region-Based Convolutional Neural Networks","authors":"Ritvik Sai Teegavarapu, Debojit Biswas","doi":"10.1109/SSCI50451.2021.9660105","DOIUrl":"https://doi.org/10.1109/SSCI50451.2021.9660105","url":null,"abstract":"Object detection and classification tasks can be addressed effectively using machine learning (ML) methods that use convolutional neural networks (CNNs) and region-based convolutional neural networks (R-CNNs). In this study, the ability of R-CNNs to distinguish between digital images of artificial and real objects is evaluated. A single-shot detection (SSD) network is also developed to serve as a baseline approach and for comparative evaluation. Experiments are designed using several images of real and artificial leaves as inputs to the R-CNNs that are trained and tested with different proposal areas of the images. The performances of R-CNNs and SSDs are evaluated using mean average precision (mAP) measure. Results from this study indicate that trained R-CNN s perform well in classification of real and artificial leaves and are robust in performance against changes in many of the experimental factors including minimal training data and resolution of the images. R-CNNs have also performed better than SSDs in the classification tasks with higher values of mAP. The performance of R-CNNs is affected by the proposal area, or the number of subsections the R-CNNs utilizes to determine distinct characteristics of the objects (i.e., leaves) presented. Results based on limited experiments from this study indicate the R-CNNs and their variants are ideally suited for object classification tasks with numerous real-world applications.","PeriodicalId":255763,"journal":{"name":"2021 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123080652","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

Intelligent Strategies to Combine Move Heuristics in Selection Hyper-heuristics for Real-World Fibre Network Design Optimisation 结合移动启发式选择的智能策略-现实世界光纤网络设计优化的超启发式

2021 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2021-12-05 DOI: 10.1109/SSCI50451.2021.9659994

Anil Arpaci, Jun Chen, J. Drake, Tim Glover

{"title":"Intelligent Strategies to Combine Move Heuristics in Selection Hyper-heuristics for Real-World Fibre Network Design Optimisation","authors":"Anil Arpaci, Jun Chen, J. Drake, Tim Glover","doi":"10.1109/SSCI50451.2021.9659994","DOIUrl":"https://doi.org/10.1109/SSCI50451.2021.9659994","url":null,"abstract":"Increasing competition in today's telecommunication industry drives the need for more cost effective services. In order to reduce the cost of designing a fibre network with low capital expenditure, automation and optimisation of network design has become crucial. British Telecom's network design software, BT NetDesign, has been developed for the purpose of network design and optimisation using a rich set of network/graph-based heuristics and the simulated annealing (SA) search method. Although NetDesign provides several different ways of navigating the search space via different move heuristics, the existing search method (SA) does not consistently reach the near-global optimum as the size of network increases. To deal with larger networks, this study utilises an intelligent approach based on the well-known Luby sequence to combine move heuristics, using two separate learning schemes: frequency based and bigram statistics. These two strategies are rigorously evaluated on network instances of different sizes. Experimental results on real-world case studies indicate that a bigram scheme with a longer warm-up period to learn heuristic combinations can reach high quality solutions for large networks.","PeriodicalId":255763,"journal":{"name":"2021 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125322672","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

Scalable embedding of multiple perspectives for indefinite life-science data analysis 无限生命科学数据分析的多视角可扩展嵌入

2021 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2021-12-05 DOI: 10.1109/SSCI50451.2021.9659914

Maximilian Münch, Simon Heilig, Philipp Väth, Frank-Michael Schleif

{"title":"Scalable embedding of multiple perspectives for indefinite life-science data analysis","authors":"Maximilian Münch, Simon Heilig, Philipp Väth, Frank-Michael Schleif","doi":"10.1109/SSCI50451.2021.9659914","DOIUrl":"https://doi.org/10.1109/SSCI50451.2021.9659914","url":null,"abstract":"Life science data analysis frequently encounters particular challenges that cannot be solved with classical techniques from data analytics or machine learning domains. The complex inherent structure of the data and especially the encoding in non-standard ways, e.g., as genome- or protein-sequences, graph structure or histograms, often limit the development of appropriate classification models. To address these limitations, the application of domain-specific expert similarity measures has gained a lot of attention in the past. However, the use of such expert measures suffers from two major drawbacks: (a) there is not one outstanding similarity measure that guarantees success in all application scenarios, and (b) such similarity functions often lead to indefinite data that cannot be processed by classical machine learning methods. In order to tackle both of these limitations, this paper presents a method to embed indefinite life science data with various similarity measures at the same time into a complex-valued vector space. We test our approach on various life science data sets and evaluate the performance against other competitive methods to show its efficiency.","PeriodicalId":255763,"journal":{"name":"2021 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116165849","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

Discrete Collective Estimation in Swarm Robotics with Ranked Voting Systems 基于排序投票系统的群体机器人离散集体估计

2021 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2021-12-05 DOI: 10.1109/SSCI50451.2021.9659868

Qihao Shan, Alexander Heck, Sanaz Mostaghim

引用次数: 2

A Study of Transfer Learning in a Generation Constructive Hyper-Heuristic for One Dimensional Bin Packing 一维装箱生成超启发式迁移学习研究

2021 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2021-12-05 DOI: 10.1109/SSCI50451.2021.9660092

Darius Scheepers, N. Pillay

{"title":"A Study of Transfer Learning in a Generation Constructive Hyper-Heuristic for One Dimensional Bin Packing","authors":"Darius Scheepers, N. Pillay","doi":"10.1109/SSCI50451.2021.9660092","DOIUrl":"https://doi.org/10.1109/SSCI50451.2021.9660092","url":null,"abstract":"The research presented in this paper investigates the use of transfer learning in a genetic programming generation constructive hyper-heuristic for discrete optimisation, namely, the one dimensional bin packing problem (1BPP). The source hyper-heuristic solves easy and medium problem instances from the Scholl benchmark set and the target hyper-heuristic solves the hard problem instances in the same benchmark set. Performance is assessed in terms of objective value, i.e. the number of bins, computational effort and generality of the hyper-heuristic. This study firstly compares the performance of two transfer learning approaches previously shown to be effective for generation constructive hyper-heuristics, for the one dimensional bin packing problem. Both these approaches performed better than not using transfer learning, with the approach transferring the best elements from each generation of the source hyper-heuristic to the target hyper-heuristic (TL2) producing the best results. The study then investigated transferring knowledge on an area of the search space rather than a point in the search space. Three approaches were developed and evaluated for this purpose. Two of these approaches were able to improve the performance of TL2 on three of the ten problem instances with respect to objective value.","PeriodicalId":255763,"journal":{"name":"2021 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114145836","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

AHPSO: Altruistic Heterogeneous Particle Swarm Optimisation Algorithm for Global Optimisation 全局优化的利他异构粒子群优化算法

2021 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2021-12-05 DOI: 10.1109/SSCI50451.2021.9660149

Fevzi Tugrul Varna, P. Husbands

引用次数: 2

Information sharing in multi-agent search and task allocation problems 多智能体搜索与任务分配问题中的信息共享

2021 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2021-12-05 DOI: 10.1109/SSCI50451.2021.9660121

Mathias Minos-Stensrud, H. Moen, Jan Dyre Bjerknes

引用次数: 0

Investigating Neural Network Architectures, Techniques, and Datasets for Autonomous Navigation in Simulation 研究自主导航仿真中的神经网络架构、技术和数据集

2021 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2021-12-05 DOI: 10.1109/SSCI50451.2021.9659907

Oliver Chang, Christiana Marchese, Jared Mejia, A. Clark

{"title":"Investigating Neural Network Architectures, Techniques, and Datasets for Autonomous Navigation in Simulation","authors":"Oliver Chang, Christiana Marchese, Jared Mejia, A. Clark","doi":"10.1109/SSCI50451.2021.9659907","DOIUrl":"https://doi.org/10.1109/SSCI50451.2021.9659907","url":null,"abstract":"Neural networks (NNs) are becoming an increasingly important part of mobile robot control systems. Compared with traditional methods, NNs (and other data-driven techniques) produce comparable-if not better-results while requiring less engineering knowhow. Training NNs, however, still requires exploration of a significant number of architectural, optimization, and evaluation options. In this study, we build a simulation environment, generate three image datasets using distinct techniques, train 652 models (including replicates) using a variety of architectures and paradigms (e.g., classification, regression, etc.), and evaluate the navigation ability of the model in simulation. Our goal is to explore a large number of model possibilities so that we can select the most promising for future study with a physical device. Training datasets leading to the best performing models were those that included a significant amount of noise from seemingly inefficient actions. The most promising models explicitly incorporated “memory” wherein previous actions were included as an input in the next step. Such models performed as good or better than conventional convolutional NNs, recurrent NNs, and custom architectures including two camera frames. Although trained models perform well in an environment matching the distribution of the training dataset, they fail when the simulation environment is altered in a seemingly insignificant manner. In robotics research it is often taken for granted that a model with good validation characteristics will perform well on the underlying task, but the results presented here show that there can often be a loose relationship between validation metrics and performance.","PeriodicalId":255763,"journal":{"name":"2021 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129520504","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

Online Microgrid Energy Management Based on Safe Deep Reinforcement Learning 基于安全深度强化学习的微电网在线能量管理

2021 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2021-12-05 DOI: 10.1109/SSCI50451.2021.9659545

Hepeng Li, Zhenhua Wang, Lusi Li, Haibo He

{"title":"Online Microgrid Energy Management Based on Safe Deep Reinforcement Learning","authors":"Hepeng Li, Zhenhua Wang, Lusi Li, Haibo He","doi":"10.1109/SSCI50451.2021.9659545","DOIUrl":"https://doi.org/10.1109/SSCI50451.2021.9659545","url":null,"abstract":"Microgrids provide power systems with an effective manner to integrate distributed energy resources, increase power supply reliability, and reduce operational cost. However, intermittent renewable energy resources (RESs) makes it challenging to operate a microgrid safely and economically based on forecasting. To overcome this issue, we develop an online energy management approach for efficient microgrid operation using safe deep reinforcement learning (SDRL). By considering uncertainties and AC power flow, the proposed method formulates online microgrid energy management as a constrained Markov decision process (CMDP). The objective is to find a safety-guaranteed scheduling policy to minimize the total operational cost. To achieve this, we use a SDRL method to learn a neural network-based policy based on constrained policy optimization (CPO). Different from tradition DRL methods that allow an agent to freely explore any behavior during training, the proposed method limits the exploration to safe policies that satisfy AC power flow constraints during training. The proposed method is model-free and does not require predictive information or explicit model of the microgrid. The proposed method is trained and tested on a medium voltage distribution network with real-world power grid data from California Independent Operator (CAISO). Simulation results verify the effectiveness and superiority of proposed method over traditional DRL approaches.","PeriodicalId":255763,"journal":{"name":"2021 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":"96 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128761988","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: 5