2020 IEEE Symposium Series on Computational Intelligence (SSCI)最新文献_第7页

AiCE: automating horizon scanning for the detection of emerging technologies AiCE:自动水平扫描，用于检测新兴技术

2020 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2020-12-01 DOI: 10.1109/SSCI47803.2020.9308128

D. Bongiorno, Nivedita Prakasan, Jordan Truswell, Michael Posadowski, James Walsh

引用次数: 1

Lifelike Neuromorphic Learning Networks (LNLN) 仿生神经形态学习网络(LNLN)

2020 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2020-12-01 DOI: 10.1109/SSCI47803.2020.9308601

Aishwarya Asesh

{"title":"Lifelike Neuromorphic Learning Networks (LNLN)","authors":"Aishwarya Asesh","doi":"10.1109/SSCI47803.2020.9308601","DOIUrl":"https://doi.org/10.1109/SSCI47803.2020.9308601","url":null,"abstract":"Artificial Neural Network (ANN) has been known and used extensively to solve the demanding tasks of Machine Learning (ML) and Artificial Intelligence (AI). These networks have proven to be exceedingly successful with challenging tasks but only at the cost of doing massive amounts of computations. Spiking Neural Network (SNN) are known to be able to perform the same tasks but potentially with less power and computations. The proposed research develops an application on Spiking Neural Networks Simulators using various algorithms and input encoding to achieve accuracy that is at par with Analog Artificial Neural Network (AANN). Backpropagation approach is used on a pre-trained neural network and it is converted to SNN for rate coding. To add further Spike-timing-dependent plasticity (STDP) is used for training a rate encoded network. Using the above settings significant accuracy is achieved proving its uniqueness amongst the state-of-the-art algorithms. A detailed profiling of current literature is included. These findings underlie a huge potential and may locate the stage for further thrilling novel advances that drives key applications in neuromorphic engineering.","PeriodicalId":413489,"journal":{"name":"2020 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":"26 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":"125316162","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

Intrusion Detection with Interpretable Rules Generated Using the Tsetlin Machine 使用Tsetlin机器生成可解释规则的入侵检测

2020 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2020-12-01 DOI: 10.1109/SSCI47803.2020.9308206

Kuruge Darshana Abeyrathna, H. S. G. Pussewalage, S. Ranasinghe, V. Oleshchuk, Ole-Christoffer Granmo

{"title":"Intrusion Detection with Interpretable Rules Generated Using the Tsetlin Machine","authors":"Kuruge Darshana Abeyrathna, H. S. G. Pussewalage, S. Ranasinghe, V. Oleshchuk, Ole-Christoffer Granmo","doi":"10.1109/SSCI47803.2020.9308206","DOIUrl":"https://doi.org/10.1109/SSCI47803.2020.9308206","url":null,"abstract":"The rapid deployment in information and communication technologies and internet-based services have made anomaly based network intrusion detection ever so important for safeguarding systems from novel attack vectors. To this date, various machine learning mechanisms have been considered to build intrusion detection systems. However, achieving an acceptable level of classification accuracy while preserving the interpretability of the classification has always been a challenge. In this paper, we propose an efficient anomaly based intrusion detection mechanism based on the Tsetlin Machine (TM). We have evaluated the proposed mechanism over the Knowledge Discovery and Data Mining 1999 (KDD’99) dataset and the experimental results demonstrate that the proposed TM based approach is capable of achieving superior classification performance in comparison to several simple Multi-Layered Artificial Neural Networks, Support Vector Machines, Decision Trees, Random Forest, and K-Nearest Neighbor machine learning algorithms while preserving the interpretability.","PeriodicalId":413489,"journal":{"name":"2020 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":"17 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":"125374342","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 Simple 3D-Only Evolutionary Bipedal System with Albatross Morphology for Increased Performance 一个简单的3d进化双足系统与信天翁形态提高性能

2020 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2020-12-01 DOI: 10.1109/SSCI47803.2020.9308500

Ben Jackson, A. Channon

{"title":"A Simple 3D-Only Evolutionary Bipedal System with Albatross Morphology for Increased Performance","authors":"Ben Jackson, A. Channon","doi":"10.1109/SSCI47803.2020.9308500","DOIUrl":"https://doi.org/10.1109/SSCI47803.2020.9308500","url":null,"abstract":"Bipedal walking is a difficult behaviour to encode into an evolutionary neural network, particularly in three-dimensional environments. Agents must be constantly maintaining balance alongside their primary objectives. Here we re-implement a simple evolutionary bipedal system, achieving high fitness and stepping gaits in 3D without the preliminary 2D bootstrapping process required by the original work. This high-performing system, with its deliberately simple neurocontroller, provides an excellent foundation for the community to use for the evolution or learning of more complex behaviours in bipeds. We also investigate the effects of modified morphology with the system, significantly improving agent fitness by evolving networks alongside morphologies resembling a baby albatross. The agents with albatross morphologies travel up to three times further than default agents. We then test incrementally evolving agent morphology via the simultaneous evolution of a separate morphological genotype. We initialised this genotype either alongside a high-performing controller or from a completely random point in both fitness landscapes. Agents evolved from this random initialisation travel up to four times further than default agents. One randomly initialised incremental morphology also achieves gaits with significantly higher upper body and swing knee controller input weights than the default.","PeriodicalId":413489,"journal":{"name":"2020 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":"36 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":"125543410","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

PSO-assisted Lyapunov control design for quantum systems 量子系统的pso辅助Lyapunov控制设计

2020 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2020-12-01 DOI: 10.1109/SSCI47803.2020.9308347

Xiaoke Guan, S. Kuang, D. Dong

引用次数: 1

The Adversarial UFP/UFN Attack: A New Threat to ML-based Fake News Detection Systems? 对抗性UFP/UFN攻击:对基于ml的假新闻检测系统的新威胁?

2020 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2020-12-01 DOI: 10.1109/SSCI47803.2020.9308298

Brandon Brown, Alexicia Richardson, Marcellus Smith, Gerry V. Dozier, Michael C. King

引用次数: 2

Optimally designed Variational Autoencoders for Efficient Wind Characteristics Modelling 优化设计的变分自编码器的有效风特征建模

2020 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2020-12-01 DOI: 10.1109/SSCI47803.2020.9308245

S. Miriyala, S. Chowdhury, NagaSree Keerthi Pujari, K. Mitra

{"title":"Optimally designed Variational Autoencoders for Efficient Wind Characteristics Modelling","authors":"S. Miriyala, S. Chowdhury, NagaSree Keerthi Pujari, K. Mitra","doi":"10.1109/SSCI47803.2020.9308245","DOIUrl":"https://doi.org/10.1109/SSCI47803.2020.9308245","url":null,"abstract":"Wind energy is increasingly applied as a large scale clean energy generating alternative to fossil fuels. However, limited amount of real wind data results in inaccurate construction of Wind Frequency Maps (WFMs), which model the stochastic nature of wind. The inaccuracies in WFMs may lead to over or under estimation of wind power eventually causing significant losses to wind-farmers. Hence, to resolve this crisis, deep generative models such as convolutional Variational Autoencoders (VAEs) are implemented in this work to enable accurate construction of WFMs from limited amount of real wind characteristics data. However, the heuristics based estimation of hyper-parameters in VAEs decrease their efficiency. Thus, in this work, a novel multi-objective evolutionary neural architecture search (NAS) strategy is devised for simultaneously estimating the optimal number of convolutional and feedforward layers, number of filters/nodes in each layer, filter size, pooling option and nonlinear activation choice in VAEs. The proposed framework is designed to balance the conflicting objectives of generalizability and parsimony in VAEs, thereby reducing the chances of their over-fitting. The optimally designed VAE (with 92% accuracy) is used to generate new wind frequency scenarios for accurate construction of WFM. Additionally, the effect of number of new scenarios required for accurate WFM construction is also studied while performing the comparison with an ideal case. It was found that WFM constructed with original limited data resulted in 9% deficit in energy calculation from a single wind turbine, justifying the need for generative models such as VAEs for accurate wind characteristics modelling.","PeriodicalId":413489,"journal":{"name":"2020 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":"45 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":"122545210","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

What Is the Optimal Annealing Schedule in Quantum Annealing 量子退火的最优退火程序是什么

2020 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2020-12-01 DOI: 10.1109/SSCI47803.2020.9308407

Oscar Galindo, V. Kreinovich

{"title":"What Is the Optimal Annealing Schedule in Quantum Annealing","authors":"Oscar Galindo, V. Kreinovich","doi":"10.1109/SSCI47803.2020.9308407","DOIUrl":"https://doi.org/10.1109/SSCI47803.2020.9308407","url":null,"abstract":"In many real-life situations in engineering (and in other disciplines), we need to solve an optimization problem: we want an optimal design, we want an optimal control, etc. One of the main problems in optimization is avoiding local maxima (or minima). One of the techniques that helps with solving this problem is annealing: whenever we find ourselves in a possibly local maximum, we jump out with some probability and continue search for the true optimum. A natural way to organize such a probabilistic perturbation of the deterministic optimization is to use quantum effects. It turns out that often, quantum annealing works much better than non-quantum one. Quantum annealing is the main technique behind the only commercially available computational devices that use quantum effects-D-Wave computers. The efficiency of quantum annealing depends on the proper selection of the annealing schedule, i.e., schedule that describes how the perturbations decrease with time. Empirically, it has been found that two schedules work best: power law and exponential ones. In this paper, we provide a theoretical explanation for these empirical successes, by proving that these two schedules are indeed optimal (in some reasonable sense).","PeriodicalId":413489,"journal":{"name":"2020 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":"4 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":"123015192","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: 7

A bilingual cognitive robot that learns like a toddler 一个像学步儿童一样学习的双语认知机器人

2020 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2020-12-01 DOI: 10.1109/SSCI47803.2020.9308575

Ioanna Giorgi, A. Cangelosi, G. Masala

引用次数: 0

Enhanced Sampling of Nucleic Acids’ Structures Using Deep-Learning-Derived Biasing Forces 利用深度学习衍生的偏置力增强核酸结构的采样

2020 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2020-12-01 DOI: 10.1109/SSCI47803.2020.9308559

E. Salawu

{"title":"Enhanced Sampling of Nucleic Acids’ Structures Using Deep-Learning-Derived Biasing Forces","authors":"E. Salawu","doi":"10.1109/SSCI47803.2020.9308559","DOIUrl":"https://doi.org/10.1109/SSCI47803.2020.9308559","url":null,"abstract":"The conformation spaces (CS) of macromolecules and their associated dynamics are of vital importance in the understanding of many biochemical functions as well as diseases and in the developments of drugs for curing or managing disease conditions. While the exploration of the CS is generally easier for molecules with fewer atoms (such as ligands and short peptides), achieving the same for larger molecules (such as nucleic acids and proteins) beyond a narrow local equilibrium is non-trivial and sometimes computationally prohibitive. In this work, we present Deep Enhanced Sampling of Nucleic Acids’ Structures Using Deep-Learning-Derived Biasing Forces (DESNA, pronounced DES-na), that combines variational autoencoder, a special deep neural network (DNN), and molecular dynamics (MD) simulations to create a robust technique for enhanced sampling, in which DNN-learned latent space is used for inferring appropriate biasing potentials for guiding the MD simulations. The results obtained show that DESNA performs better than conventional MD simulations and efficiently samples wider CS than conventional MD simulations even when DESNA is allowed to run for as short as 10% of the length of conventional MD simulations. This suggests that DESNA is at least 10 times more efficient that conventional MD simulations in its sampling of CS of molecules.","PeriodicalId":413489,"journal":{"name":"2020 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":"24 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":"123862247","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