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

{"title":"Heterogeneous Parallel Island Models","authors":"L. A. D. Silveira, J. L. Soncco-Álvarez, T. Lima, M. Ayala-Rincón","doi":"10.1109/SSCI50451.2021.9659938","DOIUrl":"https://doi.org/10.1109/SSCI50451.2021.9659938","url":null,"abstract":"Homogeneous Parallel Island Models (HoPIMs) run the same bio-inspired algorithm (BA) in all islands. Several communication topologies and migration policies have been fine-tuned in such models, speeding up and providing better quality solutions than sequential BAs for different case studies. This work selects four HoPIMs that successfully ran a genetic algorithm (GA) in all their islands. Furthermore, it proposes and studies the performance of heterogeneous versions of such models (HePIMs) that run four different BAs in their islands, namely, GA, double-point crossover GA, Differential Evolution, and Particle Swarm Optimization. HePIMs aim to maintain population diversity covering the space of solutions and reducing the overlap between islands. The NP-hard evolutionary reversal distance problem is addressed with HePIMs verifying their ability to compute accurate solutions and outperforming HoPIMs.","PeriodicalId":255763,"journal":{"name":"2021 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":"254 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":"132429872","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}

{"title":"Phishing Detection Using URL-based XAI Techniques","authors":"P. R. G. Hernandes, Camila P. Floret, Katia F. Cardozo De Almeida, Vinícius Camargo Da Silva, J. Papa, K. Costa","doi":"10.1109/SSCI50451.2021.9659981","DOIUrl":"https://doi.org/10.1109/SSCI50451.2021.9659981","url":null,"abstract":"The Internet has been growing exponentially and expanding facilities, such as payments and online purchases. Likewise, the number of criminals using electronic devices to commit theft or hijacking of information has increased. Many scams still require interaction with the victim, who in many cases is persuaded to access a malicious link sent by email, which is classified as phishing. This technique is one of the biggest threats for users and one of the most efficient for criminals. Several studies show different sorts of protection using Artificial Intelligence, which despite being very efficient, do not describe the reasons for categorizing them or using a URL as phishing. This paper focuses on detecting phishing using explainable techniques, i.e., Local Interpretable Model-Agnostic Explanations and Explainable Boosting Machine, to lighten up new advances and future works in the area.","PeriodicalId":255763,"journal":{"name":"2021 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":"5 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":"133269219","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}

{"title":"Compressing Interpretable Representations of Piecewise Linear Neural Networks using Neuro-Fuzzy Models","authors":"L. Glass, Wael Hilali, O. Nelles","doi":"10.1109/SSCI50451.2021.9659976","DOIUrl":"https://doi.org/10.1109/SSCI50451.2021.9659976","url":null,"abstract":"We present Rectified Linear Unit based Local Linear Model Tree (ReLUMoT). A model that bridges the gap between Piecewise Linear Neural Networks (PLNN) and Local Model Networks (LMN) like those resulting from the LoLiMoT algorithm. Essentially, we perform the input space partitioning of LoLiMoT by training a PLNN and extracting its linear regions. These become the input space partitions of ReLUMoT. From the perspective of PLNNs our approach compresses and smoothens low-dimensional models, while making them interpretable. From the perspective of LoLiMoT, our approach replaces the incremental and heuristic input space partitioning scheme with gradient-based training of a neural network, which is considerably more flexible.","PeriodicalId":255763,"journal":{"name":"2021 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":"4 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":"132653522","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}

{"title":"Multi-stage Deep Learning Technique with a Cascaded Classifier for Turn Lanes Recognition","authors":"Pubudu Sanjeewani, B. Verma, J. Affum","doi":"10.1109/SSCI50451.2021.9659973","DOIUrl":"https://doi.org/10.1109/SSCI50451.2021.9659973","url":null,"abstract":"The accurate recognition of road markings such as lanes and turn arrows is required in many applications including autonomous vehicles. Nevertheless, studies on road markings detection are commonly found in literature, detection and classification of turn lane arrows has not gained much attention. Most of the research which exists on the detection and classification of turn lane arrows have many limitations including low accuracy. Therefore, a novel technique based on two novel concepts for improving the performance of the detection and classification of turn lane arrows is proposed in this paper. Firstly, pixel-wise segmentation of all turn lane arrows into one class instead of each turn lane arrow in a separate class is proposed. Secondly, a novel cascaded classifier that evolves its weights so that it can identify turn lane arrows is proposed. Three turn lane road markings named left turn lane, right turn lane and Continuous Central Turning Lane (CCTL) are evaluated using a real-world roadside image dataset created by video data including all state roads in Queensland provided by our industry partners. The comparative analysis of the experimental results demonstrated outstanding results in terms of accuracy.","PeriodicalId":255763,"journal":{"name":"2021 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":"8 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":"132694929","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}

{"title":"Monitoring Activities of Daily Living with a Mobile App and Bluetooth Beacons","authors":"Wenbing Zhao, Jack Perish","doi":"10.1109/SSCI50451.2021.9659964","DOIUrl":"https://doi.org/10.1109/SSCI50451.2021.9659964","url":null,"abstract":"In this paper, we present a preliminary study on the monitoring of activities of daily living (ADL) with a mobile app. We rely on a set of Bluetooth beacons deployed around the household to perform indoor localization. The mobile app also tracks the instrumental ADL (IADL) in terms of the app usage, such as apps for social networking, financial management, and personal entertainment. Furthermore, the mobile app collects information regarding the level of physical activities and the environment such as light, temperature, air pressure using the built-in sensors of the smartphone. The latter could help infer the living conditions of the individual, even though the information is not directly about ADL. Tracking ADL in any method will inevitably intrude on the user's privacy. Our mobile app informs the user exactly what information we collect and all the data are stored locally on the smartphone. The user can view the report of the individual's ADL, and has the choice of deleting some or all data. Finally, we propose a feature extraction model for temporal ADL data and demonstrate how the features can be used to classify different behavioral patterns.","PeriodicalId":255763,"journal":{"name":"2021 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":"92 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":"133926011","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}

{"title":"Second-order Time Delay Reservoir Computing for Nonlinear Time Series Problems","authors":"Xinming Shi, Jiashi Gao, Leandro L. Minku, James J. Q. Yu, Xin Yao","doi":"10.1109/SSCI50451.2021.9659913","DOIUrl":"https://doi.org/10.1109/SSCI50451.2021.9659913","url":null,"abstract":"Time Delay Reservoir (TDR) can exhibit effects of high dimensionality and short-term memory based on delay differential equations (DDEs), as well as having hardware-friendly characteristics. However, the predictive performance and memory capacity of the standard TDRs are still limited, and dependent on the hyperparameter of the oscillation function. In this paper, we first analyze these limitations and their corresponding reasons. We find that the reasons for such limitations are fused by two aspects, which are the trade-off between the strength of self-feedback and neighboring-feedback caused by neuron separation, as well as the unsuitable order setting of the nonlinear function in DDE. Therefore, we propose a new form of TDR with second-order time delay to overcome such limitations, incurring a more flexible time-multiplexing. Moreover, a parameter-free nonlinear function is introduced to substitute the classic Mackey-Glass oscillator, which alleviates the problem of parameter dependency. Our experiments show that the proposed approach achieves better predictive performance and memory capacity compared with the standard TDR. Our proposed model also outperforms six other existing approaches on both time series prediction and recognition tasks.","PeriodicalId":255763,"journal":{"name":"2021 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":"17 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":"115972615","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}

{"title":"Hybrid Flowshop Scheduling using Leaders and Followers: An Implementation with Iterated Greedy and Genetic Algorithm","authors":"Tsung-Su Yeh, T. Chiang","doi":"10.1109/SSCI50451.2021.9660090","DOIUrl":"https://doi.org/10.1109/SSCI50451.2021.9660090","url":null,"abstract":"A hybrid flow shop is a kind of flow shop where multiple machines are available at some stages. This paper addresses the hybrid flow shop scheduling problem (HFSP) with identical parallel machines. We propose an algorithm based on the framework of Leaders and Followers (LaF), a recent metaheuristic that searches by two populations. We apply iterated greedy (IG) to the leader population for exploitation and genetic algorithm (GA) to the follower population for exploration. Investigations on the parameter setting and technical details of the algorithm are made by experiments using 240 public problem instances. Performance comparison with two recent algorithms verifies the solution quality and computational efficiency of the proposed algorithm.","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":"115566127","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}

{"title":"Machine Learning for Stock Prediction Based on Fundamental Analysis","authors":"Yuxuan Huang, Luiz Fernando Capretz, D. Ho","doi":"10.1109/SSCI50451.2021.9660134","DOIUrl":"https://doi.org/10.1109/SSCI50451.2021.9660134","url":null,"abstract":"Application of machine learning for stock prediction is attracting a lot of attention in recent years. A large amount of research has been conducted in this area and multiple existing results have shown that machine learning methods could be successfully used toward stock predicting using stocks' historical data. Most of these existing approaches have focused on short term prediction using stocks' historical price and technical indicators. In this paper, we prepared 22 years' worth of stock quarterly financial data and investigated three machine learning algorithms: Feed-forward Neural Network (FNN), Random Forest (RF) and Adaptive Neural Fuzzy Inference System (ANFIS) for stock prediction based on fundamental analysis. In addition, we applied RF based feature selection and bootstrap aggregation in order to improve model performance and aggregate predictions from different models. Our results show that RF model achieves the best prediction results, and feature selection is able to improve test performance of FNN and ANFIS. Moreover, the aggregated model outperforms all baseline models as well as the benchmark DJIA index by an acceptable margin for the test period. Our findings demonstrate that machine learning models could be used to aid fundamental analysts with decision-making regarding stock investment.","PeriodicalId":255763,"journal":{"name":"2021 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":"119 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":"116317314","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}

{"title":"Dynamic Context in Graph Neural Networks for Item Recommendation","authors":"Asma Sattar, D. Bacciu","doi":"10.1109/SSCI50451.2021.9659550","DOIUrl":"https://doi.org/10.1109/SSCI50451.2021.9659550","url":null,"abstract":"Graph neural networks allow to build recommendation systems which can straightforwardly take into account relational knowledge concerning multiple types of interactions, such as user-item relationships, but also interactions between users and within items. Graph-based approaches in the literature consider such interactions to be static, independent of the surroundings. In this paper, we put forward a novel approach to graph-based item recommendation built on the foundational idea that relational knowledge is characterized by a dynamic nature of the user and its surroundings. We claim that being able to capture such dynamic knowledge allows to build richer contexts upon which more precise recommendations can be built, e.g., taking into account current location, weather conditions, and user mood. The paper provides recipes to build and integrate dynamic user and item contexts in existing item recommendation tasks. We also introduce a novel Dynamic Context-aware Graph Neural Network (DCGNN) that can effectively leverage the knowledge of surroundings to learn the context-aware recommendation behaviour of users. The empirical analysis shows how our model outperforms static state-of-the-art approaches on four movie and travel recommendation benchmarks.","PeriodicalId":255763,"journal":{"name":"2021 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":"45 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":"114758417","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}

{"title":"Selection-based Per-Instance Heuristic Generation for Protein Structure Prediction of 2D HP Model","authors":"Mustafa Misir","doi":"10.1109/SSCI50451.2021.9660025","DOIUrl":"https://doi.org/10.1109/SSCI50451.2021.9660025","url":null,"abstract":"The present study aims at generating heuristics for Protein Structure Prediction represented in the 2D HP model. Protein Structure Prediction is about determining the 3-dimensional form of a protein from a given amino acid sequence. The resulting structure directly relates to the functionalities of the protein. There are a wide range of algorithms to address Protein Structure Prediction as an optimization problem. Being said that there is no an ultimate algorithm that can effectively solve PSP under varying experimental settings. Hyper-heuristics can offer a solution as high-level, problem-independent search and optimization strategies. Selection Hyper-heuristics operate on given heuristic sets that directly work on the solution space. One group of Selection Hyper-heuristics focus on automatically specify the best heuristics on-the-fly. Yet, the candidate heuristics tend to be decided, preferably a domain expert. Generation Hyper-heuristics approach differently as aiming to generate such heuristics automatically. This work introduces a automated heuristic generation strategy supporting Selection Hyper-heuristics. The generation task is formulated as a selection problem, disclosing the best expected heuristic specifically f or a given problem instance. The heuristic generation process is established as a parameter configuration problem. T he corresponding system is devised by initially generating a training data alongside with a set of basic features characterizing the Protein Structure Prediction problem instances. The data is generated discretizing the parameter configuration space o f a single heuristic. The resulting data is used to predict the best configuration of a specific heuristic used in a heuristic set under Selection Hyper-heuristics. The prediction is performed separately for each instance rather than using one setting for all the instances. The empirical analysis showed that the proposed idea offers both better and robust performance on 22 PSP instances compared to the one-for-all heuristic sets. Additional analysis linked to the selection method, ALORS, revealed insights on what makes the PSP instances hard / easy while providing dis/-similarity analysis between the candidate configurations.","PeriodicalId":255763,"journal":{"name":"2021 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":"28 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":"115305720","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}