2021 IEEE Symposium Series on Computational Intelligence (SSCI)最新文献_第10页

Feature Selection for Fake News Classification 假新闻分类的特征选择

2021 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2021-12-05 DOI: 10.1109/SSCI50451.2021.9660080

Simen Sverdrup-Thygeson, P. Haddow

{"title":"Feature Selection for Fake News Classification","authors":"Simen Sverdrup-Thygeson, P. Haddow","doi":"10.1109/SSCI50451.2021.9660080","DOIUrl":"https://doi.org/10.1109/SSCI50451.2021.9660080","url":null,"abstract":"An explosive growth of misleading and untrustworthy news articles has been observed over the last years. These news articles are often referred to as fake news and have been found to severely impact fair elections and democratic values. Computational Intelligence models may be applied to the classification of news articles, assuming that an efficient feature set is available as input to the model. However, the selection of appropriate feature sets is an open question for such high-dimensional tasks. A further challenge is the general applicability of feature selection strategies, where testing on a single dataset may convey misleading results. The work herein evaluates a wide-range of potential news article features resulting in twenty-five potential features. Feature selection, based on a combination of feature scoring, feature ranking and mutual information is then applied, evaluated on multiple datasets: Kaggle, Liar and FakeNewsNet. An Artificial Immune System model is applied in the feature ranking and as the classification model. The accuracy obtained is compared to state of the art fake news classification models, highlighting that the approach shows promise in terms of accuracy despite the small feature sets provided for classification.","PeriodicalId":255763,"journal":{"name":"2021 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":"9 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":"129341214","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

Diabetes Prediction Using Quantum Neurons with Preprocessing Based on Hypercomplex Numbers 基于超复数预处理的量子神经元糖尿病预测

2021 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2021-12-05 DOI: 10.1109/SSCI50451.2021.9660028

Cláudio A. Monteiro, F. M. P. Neto

{"title":"Diabetes Prediction Using Quantum Neurons with Preprocessing Based on Hypercomplex Numbers","authors":"Cláudio A. Monteiro, F. M. P. Neto","doi":"10.1109/SSCI50451.2021.9660028","DOIUrl":"https://doi.org/10.1109/SSCI50451.2021.9660028","url":null,"abstract":"The use of properties that are intrinsic to quantum mechanics has made it possible to build quantum algorithms with greater efficiency than classical algorithms to solve problems whose classically efficient solution either does not exist or is not known. There are quantum neurons that can carry an exponential amount of information to a linear number of quantum information units (qubits) using the quantum property of superposition. In this paper, we compare the performance of three of these quantum neuron models applied to the diabetes classification problem. We also propose the use of different data preprocessing strategies. Quantum neurons were simulated using the IBM Qiskit tool. We compare the preprocessing approaches applied to two toy problems (1) simulating the XOR operator and (2) solving a generic nonlinear problem. The results of the experiments shows that a single quantum neuron is capable of achieving an accuracy rate of 100% in the XOR problem and an accuracy rate of 100% in a non-linear dataset, demonstrating that the quantum neurons with real weights are capable of modeling non-linearly separable problems. In the problem of diagnosing diabetes, quantum neurons achieved an accuracy rate of 76% and AUC-ROC of 88%, while its classic version, the perceptron, reached only 63% accuracy and the artificial neural network reached 80% AUC-ROC. These results indicate that a single quantum neuron performs better than its classical version and even the artificial neural network for AUC-ROC, demonstrating potential for use in healthcare applications in the near future.","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":"129475554","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

A Statistical Analysis of Performance in the 2021 CEC-GECCO-PESGM Competition on Evolutionary Computation in the Energy Domain 2021年cecc - gecco - pesgm能源领域进化计算竞赛中性能的统计分析

2021 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2021-12-05 DOI: 10.1109/SSCI50451.2021.9660117

F. Lezama, J. Soares, B. Canizes, Z. Vale

{"title":"A Statistical Analysis of Performance in the 2021 CEC-GECCO-PESGM Competition on Evolutionary Computation in the Energy Domain","authors":"F. Lezama, J. Soares, B. Canizes, Z. Vale","doi":"10.1109/SSCI50451.2021.9660117","DOIUrl":"https://doi.org/10.1109/SSCI50451.2021.9660117","url":null,"abstract":"Evolutionary algorithms (EAs) have emerged as an efficient alternative to deal with real-world applications with high complexity. However, due to the stochastic nature of the results obtained using EAs, the design of benchmarks and competitions where such approaches can be evaluated and compared is attracting attention in the field. In the energy domain, the “2021 CEC-GECCO-PESGM Competition on Evolutionary Computation in the Energy Domain: Smart Grid Applications” provides a platform to test and compare new EAs to solve complex problems in the field. However, the metric used to rank the algorithms is based solely on the mean fitness value (related to the objective function value only), which does not give statistical significance to the performance of the algorithms. Thus, this paper presents a statistical analysis using the Wilcoxon pair-wise comparison to study the performance of algorithms with statistical grounds. Results suggest that, for track 1 of the competition, only the winner approach (first place) is significantly different and superior to the other algorithms; in contrast, the second place is already statistically comparable to some other contestants. For track 2, all the winner approaches (first, second, and third) are statistically different from each other and the rest of the contestants. This type of analysis is important to have a deeper understanding of the stochastic performance of algorithms.","PeriodicalId":255763,"journal":{"name":"2021 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":"36 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":"124856917","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

Cooperative Optimization Strategy for Distributed Energy Resource System using Multi-Agent Reinforcement Learning 基于多智能体强化学习的分布式能源系统协同优化策略

2021 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2021-12-05 DOI: 10.1109/SSCI50451.2021.9659540

Zhaoyang Liu, Tianchun Xiang, Tianhao Wang, C. Mu

引用次数: 1

Road-network aware Dynamic Workload Balancing Technique for Real-time Route Generation in On-Demand Public Transit 基于路网感知的按需公共交通实时路线生成动态负载均衡技术

2021 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2021-12-05 DOI: 10.1109/SSCI50451.2021.9659934

Thilina Perera, L. Wijerathna, Deshya Wijesundera, T. Srikanthan

{"title":"Road-network aware Dynamic Workload Balancing Technique for Real-time Route Generation in On-Demand Public Transit","authors":"Thilina Perera, L. Wijerathna, Deshya Wijesundera, T. Srikanthan","doi":"10.1109/SSCI50451.2021.9659934","DOIUrl":"https://doi.org/10.1109/SSCI50451.2021.9659934","url":null,"abstract":"On-demand public transit systems require real-time computation of routes to ensure a user-friendly responsive service while also minimizing the vehicle miles traveled (VMT) of the fleet for increasing the profits of an operator. To ensure responsiveness, heuristic algorithms that rapidly generate near-optimal solutions are preferred over time-consuming exact computations. In order to further ensure the scalability of heuristic algorithms, especially to solve large problems, parallel computing techniques need to distribute the workload evenly across several partitions, while keeping passengers on similar routes with less detour in a single partition to reduce the VMT. However, existing works ignore these factors when partitioning the workload. This work proposes a road-network aware tree partitioning algorithm that not only considers the shortest path based routes but also the workloads to create balanced partitions in real-time. Experimental results on a real road-network show that the proposed algorithm outperforms a well-known unsupervised learning algorithm in terms of quality of results and runtime.","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":"126227968","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

Comparing Autonomic Physiological and Electroencephalography Features for VR Sickness Detection Using Predictive Models 利用预测模型比较VR疾病检测的自主生理学和脑电图特征

2021 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2021-12-05 DOI: 10.1109/SSCI50451.2021.9660126

Gang Li, Ogechi Onuoha, Mark Mcgill, S. Brewster, C. Chen, F. Pollick

引用次数: 4

An Adaptive Evolutionary Algorithm for Bi- Level Multi-objective VRPs with Real-Time Traffic Conditions 实时交通条件下双级多目标vrp的自适应进化算法

2021 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2021-12-05 DOI: 10.1109/SSCI50451.2021.9659933

Baojian Chen, Changhe Li, Sanyou Zeng, Shengxiang Yang, Michalis Mavrovouniotis

引用次数: 0

Privacy-Preserving Online Mirror Descent for Federated Learning with Single-Sided Trust 单面信任联邦学习的在线镜像下降保护隐私

2021 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2021-12-05 DOI: 10.1109/SSCI50451.2021.9659544

O. Odeyomi, G. Záruba

{"title":"Privacy-Preserving Online Mirror Descent for Federated Learning with Single-Sided Trust","authors":"O. Odeyomi, G. Záruba","doi":"10.1109/SSCI50451.2021.9659544","DOIUrl":"https://doi.org/10.1109/SSCI50451.2021.9659544","url":null,"abstract":"This paper discusses how clients in a federated learning system can collaborate with privacy guarantee in a fully decentralized setting without a central server. Most existing work includes a central server that aggregates the local updates from the clients and coordinates the training. Thus, the setting in this existing work is prone to communication and computational bottlenecks, especially when large number of clients are involved. Also, most existing federated learning algorithms do not cater for situations where the data distribution is time-varying such as in real-time traffic monitoring. To address these problems, this paper proposes a differentially-private online mirror descent algorithm. To provide additional privacy to the loss gradients of the clients, local differential privacy is introduced. Simulation results are based on a proposed differentially-private exponential gradient algorithm, which is a variant of differentially-private online mirror descent algorithm with entropic regularizer. The simulation shows that all the clients can converge to the global optimal vector over time. The regret bound of the proposed differentially-private exponential gradient algorithm is compared with the regret bounds of some state-of-the-art online federated learning algorithms found in the literature.","PeriodicalId":255763,"journal":{"name":"2021 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":"7 7","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120891697","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

CLC: Noisy Label Correction via Curriculum Learning 通过课程学习来纠正噪音标签

2021 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2021-12-05 DOI: 10.1109/SSCI50451.2021.9660078

Jaeyoon Lee, Hyuntak Lim, Ki-Seok Chung

引用次数: 2

Moonwalkers: Evolving Robots for Locomotion in a Moon-like Environment 月球漫步者:在类似月球的环境中运动的进化机器人

2021 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2021-12-05 DOI: 10.1109/SSCI50451.2021.9660029

Koen Van Der Pool, A. Eiben

引用次数: 1