2020 IEEE Third International Conference on Artificial Intelligence and Knowledge Engineering (AIKE)最新文献_第3页

Toward Explanation-Centered Story Generation 走向以解释为中心的故事生成

2020 IEEE Third International Conference on Artificial Intelligence and Knowledge Engineering (AIKE) Pub Date : 2020-12-01 DOI: 10.1109/AIKE48582.2020.00046

Jumpei Ono, Miku Kawai, Takashi Ogata

引用次数: 0

Intrusion detection algorithm in Smart Environments featuring activity footprints approach 基于活动足迹方法的智能环境入侵检测算法

2020 IEEE Third International Conference on Artificial Intelligence and Knowledge Engineering (AIKE) Pub Date : 2020-12-01 DOI: 10.1109/AIKE48582.2020.00027

Agostino Forestiero

{"title":"Intrusion detection algorithm in Smart Environments featuring activity footprints approach","authors":"Agostino Forestiero","doi":"10.1109/AIKE48582.2020.00027","DOIUrl":"https://doi.org/10.1109/AIKE48582.2020.00027","url":null,"abstract":"Discovering anomalous data or behaviors is fundamental to obtain critical security information such as intrusion detections, faults and system failures. The limited resources, like computing and storage, make conventional techniques to design Intrusion Detection Systems (IDS) not particularly suitable for smart environments. This paper proposes a novel multiagent algorithm leveraging on devices activity footprints for intrusion detection in Internet of Things environment. Smart objects are mapped with real-valued vectors obtained through the IoT2Vec model, a word embedding technique able to capture the semantic context of device activities and represent these ones in dense vectors. The vectors are assigned to agents, which are spread onto a 2D virtual space, where they move following the rules of a bio-inspired model, the flocking model. A similarity function, applied to the associated vectors, drives the agents for a selective application of the movement rules. The outcome is the emergence of agent groups aggregated on the basis of the activities of their associated devices. Thus, it is possible to easily individuate isolated agents (i.e. devices with dissimilar activity from all), representing potential intruders or with anomalous behaviors to be monitored. Preliminary results confirm the validity of the approach.","PeriodicalId":370671,"journal":{"name":"2020 IEEE Third International Conference on Artificial Intelligence and Knowledge Engineering (AIKE)","volume":"33 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":"116747532","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

The Cyber Security of Battery Energy Storage Systems and Adoption of Data-driven Methods 电池储能系统的网络安全与数据驱动方法的采用

2020 IEEE Third International Conference on Artificial Intelligence and Knowledge Engineering (AIKE) Pub Date : 2020-12-01 DOI: 10.1109/AIKE48582.2020.00037

N. Kharlamova, S. Hashemi, C. Træholt

{"title":"The Cyber Security of Battery Energy Storage Systems and Adoption of Data-driven Methods","authors":"N. Kharlamova, S. Hashemi, C. Træholt","doi":"10.1109/AIKE48582.2020.00037","DOIUrl":"https://doi.org/10.1109/AIKE48582.2020.00037","url":null,"abstract":"Battery energy storage systems (BESSs) are becoming a crucial part of electric grids due to their important roles in renewable energy sources (RES) integration in energy systems. Cyber-secure operation of BESS in renewable energy systems is significant, since it is susceptible to cyber threats and its potential failure may result in economical and physical damage to both the BESS and the system. However, there is a lack of comprehensive study on the attack detection methods for industrial BESSs. This paper reviews the state-of-the-art work in the area of BESS cyber threats, investigates how to detect cyberattackes in the operation stage. We address the problem of enhancing the communication channels' integrity can by implementing blockchain in the design stage of BESS, combined with applying artificial intelligence (AI) and machine learning (ML) methods for false data injection attack (FDIA) detection in the BESS operation stage. The focus is on the application of ML and AI methods for FDIA detection on different system layers. Based on our analysis, data-driven approaches such as clustering and artificial-neutral-network-based state estimation (SE) forecast are recommended for the implementation in BESSs.","PeriodicalId":370671,"journal":{"name":"2020 IEEE Third International Conference on Artificial Intelligence and Knowledge Engineering (AIKE)","volume":"152 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":"116659573","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

Method of Applying Df-pn Algorithm to On-the-fly Controller Synthesis Df-pn算法在动态控制器综合中的应用方法

2020 IEEE Third International Conference on Artificial Intelligence and Knowledge Engineering (AIKE) Pub Date : 2020-12-01 DOI: 10.1109/AIKE48582.2020.00033

Kengo Kuwana, K. Tei, Y. Fukazawa, S. Honiden

{"title":"Method of Applying Df-pn Algorithm to On-the-fly Controller Synthesis","authors":"Kengo Kuwana, K. Tei, Y. Fukazawa, S. Honiden","doi":"10.1109/AIKE48582.2020.00033","DOIUrl":"https://doi.org/10.1109/AIKE48582.2020.00033","url":null,"abstract":"Discrete controller synthesis is a method that involves using game theory to automatically generate a controller for achieving a system goal. This method is used in artificial intelligence for planning self-adaptive systems, in which it is necessary to shorten the time taken to generate a plan. Discrete controller synthesis generates a controller from an environment model and requirement model. The environment model represents the behavior of the system’s external environment as a finite state machine and is often constructed by parallel composition, which causes a state explosion. As a result, a controller cannot be synthesized within a realistic amount of memory or time. An on-the-fly method called directed controller synthesis (DCS) was developed by Daniel Ciolek. DCS partially expands and checks the environment model during exploration to avoid the state explosion caused by parallel composition. DCS uses a best-first search algorithm and has open lists, which drastically increases the size of the open list when searching for a large-scale problem and lowers search efficiency. Therefore, we propose a method of applying the df-pn algorithm, which is used when playing shogi (Japanese chess) on a computer, particularly tsume-shogi (a type of shogi problem). This algorithm is an iterative deepening depth-first search algorithm that does not have an open list but uses a hash table to store search history. Through experiments comparing our method with DCS, we were able to attain faster controller synthesis with our method than with DCS for large-scale problems.","PeriodicalId":370671,"journal":{"name":"2020 IEEE Third International Conference on Artificial Intelligence and Knowledge Engineering (AIKE)","volume":"96 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":"122519176","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

Real-time Restoration of Quality Distortions in Mobile Images using Deep Learning 利用深度学习实时恢复移动图像中的高质量失真

2020 IEEE Third International Conference on Artificial Intelligence and Knowledge Engineering (AIKE) Pub Date : 2020-12-01 DOI: 10.1109/AIKE48582.2020.00025

T. Koçak, Cagkan Ciloglu

引用次数: 0

Analysis of Permission Selection Techniques in Machine Learning-based Malicious App Detection 基于机器学习的恶意应用程序检测中的权限选择技术分析

2020 IEEE Third International Conference on Artificial Intelligence and Knowledge Engineering (AIKE) Pub Date : 2020-12-01 DOI: 10.1109/AIKE48582.2020.00021

Jihyeon Park, Munyeong Kang, Seong-je Cho, Hyoil Han, Kyoungwon Suh

引用次数: 4

A Predictive Model of Cost Growth in Construction Projects Using Feature Selection 基于特征选择的建设项目成本增长预测模型

2020 IEEE Third International Conference on Artificial Intelligence and Knowledge Engineering (AIKE) Pub Date : 2020-12-01 DOI: 10.1109/AIKE48582.2020.00029

Negar Tajziyehchi, Mohammad Moshirpour, George Jergeas, F. Sadeghpour

{"title":"A Predictive Model of Cost Growth in Construction Projects Using Feature Selection","authors":"Negar Tajziyehchi, Mohammad Moshirpour, George Jergeas, F. Sadeghpour","doi":"10.1109/AIKE48582.2020.00029","DOIUrl":"https://doi.org/10.1109/AIKE48582.2020.00029","url":null,"abstract":"The construction industry spends billions of dollars on large-scale projects annually. These projects typically experience cost overruns. To solve this issue, it is essential to identify the key factors that contribute to project cost growth. The data provided by the Construction Owners Association of Alberta (COAA) and the Construction Industry Institute (CII) was used in this study. This data shows that Alberta’s average cost growth is much higher than similar projects in the United States, and it is therefore desirable to improve Alberta’s project performance. There are 139 samples for Alberta projects, and the nature of the data is high dimensional, making it difficult to extract useful information from the data for cost growth prediction. The use of dimensionality reduction techniques, such as feature selection, contribute to identifying the most important features that impact cost growth. This study identified 16 out of 281 significant features, selected in two steps. Initially, 21 features were selected by LASSO. The R2 score and RMSE are calculated for five different models in three train and test split models. Random forest had the highest score, using more than 80 percent of the data for training. The permutation importance of each feature is calculated using random forest, and 16 variables are extracted. These features are applied as an input for five machine learning algorithms to evaluate the variables’ predictive ability.","PeriodicalId":370671,"journal":{"name":"2020 IEEE Third International Conference on Artificial Intelligence and Knowledge Engineering (AIKE)","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":"133840198","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 New Adaptive Bidirectional Region-of-Interest Detection Method for Intelligent Traffic Video Analysis 一种用于智能交通视频分析的自适应双向兴趣区域检测方法

2020 IEEE Third International Conference on Artificial Intelligence and Knowledge Engineering (AIKE) Pub Date : 2020-12-01 DOI: 10.1109/AIKE48582.2020.00012

Hadi Ghahremannezhad, Hang Shi, Chengjun Liu

引用次数: 2

Automatic Calibration of Forest Fire Weather Index For Independent Customizable Regions Based on Historical Records 基于历史记录的独立可定制区域林火天气指数自动定标

2020 IEEE Third International Conference on Artificial Intelligence and Knowledge Engineering (AIKE) Pub Date : 2020-12-01 DOI: 10.1109/AIKE48582.2020.00011

J. S. Junior, J. Paulo, Jérôme Mendes, D. Alves, L. Ribeiro

{"title":"Automatic Calibration of Forest Fire Weather Index For Independent Customizable Regions Based on Historical Records","authors":"J. S. Junior, J. Paulo, Jérôme Mendes, D. Alves, L. Ribeiro","doi":"10.1109/AIKE48582.2020.00011","DOIUrl":"https://doi.org/10.1109/AIKE48582.2020.00011","url":null,"abstract":"Wildfire Decision Support Systems are critical tools for civil protection authorities in the management of all wildfire stages, including prevention. To timely act and apply the necessary preventive measures to reduce the fire danger in wildfires, many proposed calibration studies of the Canadian Forest Fire Weather Index System (CFFWIS) have been performed mainly based on techniques that still depend on manual and empirical analysis, being limited to exploiting a few regions. This paper proposes a methodology for automatic calibration of the CFFWIS to obtain a fire danger measurement that best suits the specific characteristics of a given region. The proposed methodology, applied to 769 regions from Europe, is based on the k-means clustering technique to automatically identify patterns in the data sets composed of elements of the CFFWIS and wildfire records. The results of the automatic calibration of the CFFWIS on each of the 769 regions reinforce the versatility of the proposed methodology, which can be adapted to different regions.","PeriodicalId":370671,"journal":{"name":"2020 IEEE Third International Conference on Artificial Intelligence and Knowledge Engineering (AIKE)","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":"121949956","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: 6

Hey ML, what can you do for me? 嘿，ML，你能为我做什么?

2020 IEEE Third International Conference on Artificial Intelligence and Knowledge Engineering (AIKE) Pub Date : 2020-12-01 DOI: 10.1109/AIKE48582.2020.00023

Javier Pastorino, A. Biswas

{"title":"Hey ML, what can you do for me?","authors":"Javier Pastorino, A. Biswas","doi":"10.1109/AIKE48582.2020.00023","DOIUrl":"https://doi.org/10.1109/AIKE48582.2020.00023","url":null,"abstract":"Machine learning (ML) algorithms are data-driven and given a goal task and a prior experience dataset relevant to the task, one can attempt to solve the task using ML seeking to achieve high accuracy. There is usually a big gap in the understanding between an ML experts and the dataset providers due to limited expertise in cross disciplines. Narrowing down a suitable set of problems to solve using ML is possibly the most ambiguous yet important agenda for data providers to consider before initiating collaborations with ML experts. We proposed an ML-fueled pipeline to identify potential problems (i.e., the tasks) so data providers can, with ease, explore potential problem areas to investigate with ML. The autonomous pipeline integrates information theory and graph-based unsupervised learning paradigms in order to generate a ranked retrieval of top-k problems for the given dataset for a successful ML based collaboration. We conducted experiments on diverse real-world and well-known datasets, and from a supervised learning standpoint, the proposed pipeline achieved 72% top-5 task retrieval accuracy on an average, which surpasses the retrieval performance for the same paradigm using the popular exploratory data analysis tools. Detailed experiment results with our source codes are available at: https://github.com/jpastorino/heyml.","PeriodicalId":370671,"journal":{"name":"2020 IEEE Third International Conference on Artificial Intelligence and Knowledge Engineering (AIKE)","volume":"1074 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":"132882704","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