2021 IEEE 19th World Symposium on Applied Machine Intelligence and Informatics (SAMI)最新文献_第2页

Application of Reinforcement Learning to a Mining System 强化学习在采矿系统中的应用

2021 IEEE 19th World Symposium on Applied Machine Intelligence and Informatics (SAMI) Pub Date : 2021-01-21 DOI: 10.1109/SAMI50585.2021.9378663

Aline Xavier Fidencio, T. Glasmachers, Daniel Naro

{"title":"Application of Reinforcement Learning to a Mining System","authors":"Aline Xavier Fidencio, T. Glasmachers, Daniel Naro","doi":"10.1109/SAMI50585.2021.9378663","DOIUrl":"https://doi.org/10.1109/SAMI50585.2021.9378663","url":null,"abstract":"Automation techniques have been widely applied in different industry segments, among others, to increase both productivity and safety. In the mining industry, with the usage of such systems, the operator can be removed from hazardous environments without compromising task execution and it is possible to achieve more efficient and standardized operation. In this work a study case on the application of machine learning algorithms to a mining system example is presented, in which reinforcement learning algorithms were used to solve a control problem. As an example, a machine chain consisting of a Bucket Wheel Excavator, a Belt Wagon and a Hopper Car was used. This system has two material transfer points that need to remain aligned during operation in order to allow continuous material flow. To keep the alignment, the controller makes use of seven degrees of freedom given by slewing, luffing and crawler drives. Experimental tests were done in a simulated environment with two state-of-the-art algorithms, namely Proximal Policy Optimization (PPO) and Soft Actor-Critic (SAC). The trained agents were evaluated in terms of episode return and length, as well as alignment quality and action values used. Results show that, for the given task, the PPO agent performs quantitatively and qualitatively better than the SAC agent. However, none of the agents were able to completely solve the proposed testing task.","PeriodicalId":402414,"journal":{"name":"2021 IEEE 19th World Symposium on Applied Machine Intelligence and Informatics (SAMI)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122013456","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

AI supported Corporate Governance 人工智能支持的企业管治

2021 IEEE 19th World Symposium on Applied Machine Intelligence and Informatics (SAMI) Pub Date : 2021-01-21 DOI: 10.1109/SAMI50585.2021.9378679

K. Szenes, Bence Tureczki

引用次数: 0

Lightweight Deep Learning Model for Detection of Copy-Move Image Forgery with Post-Processed Attacks 带有后处理攻击的复制-移动图像伪造检测轻量级深度学习模型

2021 IEEE 19th World Symposium on Applied Machine Intelligence and Informatics (SAMI) Pub Date : 2021-01-21 DOI: 10.1109/SAMI50585.2021.9378690

Muhammad Naveed Abbas, M. S. Ansari, M. Asghar, N. Kanwal, Terry O'Neill, Brian Lee

{"title":"Lightweight Deep Learning Model for Detection of Copy-Move Image Forgery with Post-Processed Attacks","authors":"Muhammad Naveed Abbas, M. S. Ansari, M. Asghar, N. Kanwal, Terry O'Neill, Brian Lee","doi":"10.1109/SAMI50585.2021.9378690","DOIUrl":"https://doi.org/10.1109/SAMI50585.2021.9378690","url":null,"abstract":"As digital image forgery can be alarmingly detrimental, therefore, an insight into detection and classification of tampered digital images is of paramount importance. Without undermining the significance of other image forgery types, copy-move can be regarded as one of the most commonly used forgeries due to its ease of implementation. To counter the rapidly complicating forgery methods due to easily accessible technologically advanced tools, passive image forensic methods have also undergone massive evolution. Presently, deep learning based techniques are regarded as state-of-the-art for image processing/image forgery detection and classification due to their enhanced accuracy and automatic feature extraction capabilities. But the existing deep learning based techniques are time and resource-intensive as well. To cater for these solutions with complexities as stated, this research focuses on experimentation using two state-of-the-art deep learning models; SmallerVGGNet (inspired from VGGNet) and MobileNetV2. These two models are time and resource friendly deep learning frameworks for digital image forgery detection on embedded devices. After rigorous analysis, the study considers a suitably modified version of MobileNetV2 to be more effective on copy-move forgery detection which also caters for inconsistencies executed post-forgery including visual-appearance related such as brightness change, blurring and noise adding and geometric transformations such as cropping and rotation. The experimental results demonstrate that the proposed MobileNetV2 based model shows 84% True Positive Rate (TPR) and 14.35% False Positive Rate (FPR) for the detection of digital image forgery post-processed with the said multiple attacks.","PeriodicalId":402414,"journal":{"name":"2021 IEEE 19th World Symposium on Applied Machine Intelligence and Informatics (SAMI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124963128","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: 14

Multi-Class Detection of Laparoscopic Instruments for the Intelligent Box-Trainer System Using Faster R-CNN Architecture 基于更快R-CNN架构的智能训练箱系统腹腔镜器械多类检测

2021 IEEE 19th World Symposium on Applied Machine Intelligence and Informatics (SAMI) Pub Date : 2021-01-21 DOI: 10.1109/SAMI50585.2021.9378617

F. Fathabadi, J. Grantner, Saad A. Shebrain, I. Abdel-Qader

{"title":"Multi-Class Detection of Laparoscopic Instruments for the Intelligent Box-Trainer System Using Faster R-CNN Architecture","authors":"F. Fathabadi, J. Grantner, Saad A. Shebrain, I. Abdel-Qader","doi":"10.1109/SAMI50585.2021.9378617","DOIUrl":"https://doi.org/10.1109/SAMI50585.2021.9378617","url":null,"abstract":"Laparoscopic Surgical Box-Trainer devices have been used by surgery residents to learn specific skills not traditionally taught to surgeons. Assessment of performance, however, is crude, frequently focusing on speed alone or subjective observations. For a better, objective assessment, the residents' efficiency should be recorded and have the process be tracked and have a system in place to provide consistent automated assessment and analysis. In this paper, we propose a novel framework for the detection and recognition of multi-class laparoscopic instruments for our Intelligent Box-Trainer System. This framework is based upon the Faster R-CNN architecture and RESNet-50 for an open-source module with our custom dataset (AR-Set). Despite a relatively limited number of training examples, experimental results have proved that our approach is effective for locating regions of interest and detecting multi-class instruments. This research is a cooperation between the Department of Electrical and Computer Engineering and the Department of Surgery of the Homer Stryker M.D. School of Medicine, at WMU.","PeriodicalId":402414,"journal":{"name":"2021 IEEE 19th World Symposium on Applied Machine Intelligence and Informatics (SAMI)","volume":"313 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127567932","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: 12

The Virtual Classroom: A New Cyber Physical System 虚拟教室:一种新的网络物理系统

2021 IEEE 19th World Symposium on Applied Machine Intelligence and Informatics (SAMI) Pub Date : 2021-01-21 DOI: 10.1109/SAMI50585.2021.9378678

J. Olszewska

引用次数: 5

Leveraging Phone Numbers for Spam detection in Online Social Networks 利用电话号码来检测在线社交网络中的垃圾邮件

2021 IEEE 19th World Symposium on Applied Machine Intelligence and Informatics (SAMI) Pub Date : 2021-01-21 DOI: 10.1109/SAMI50585.2021.9378644

R. Jere, Anant Pandey, Manvi Singh, Mandar Ganjapurkar

{"title":"Leveraging Phone Numbers for Spam detection in Online Social Networks","authors":"R. Jere, Anant Pandey, Manvi Singh, Mandar Ganjapurkar","doi":"10.1109/SAMI50585.2021.9378644","DOIUrl":"https://doi.org/10.1109/SAMI50585.2021.9378644","url":null,"abstract":"Online Social Networks (OSNs) are platforms that have gained immense traction from society today. Social media has reshaped our social world and has been playing a pivotal role in sculpting our personal and professional goals. While it provides invaluable information to millions of individuals daily, it has also become one of the most popular places for spam campaigns. In this paper, we design an algorithm for the recognition of spam campaigns, specifically focusing on a phone-numbers based approach. We build a system for spam campaign recognition with an emphasis on phone numbers in the light of the malicious activity that is vandalizing our online experience. This research focuses on data extracted from monitoring the following social networking channels: Tumblr, Twitter, and Flickr. The paper serves as an analytical lens for spam posts accumulated over four months. Regular expressions are used for data cleaning to identify posts containing phone numbers. We collected over 18 million spam posts and filtered the spam-containing posts using regular expressions. Next, we used a Bayesian Model called Latent Dirichlet Allocation (LDA) to perform a statistical model for detecting the category of the posts. We further use the bag-of-words and the tf-idf means to this data and apply cosine similarity for the similarity measure.","PeriodicalId":402414,"journal":{"name":"2021 IEEE 19th World Symposium on Applied Machine Intelligence and Informatics (SAMI)","volume":"142 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116584640","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

Curiosity-Driven Reinforced Learning of Undesired Actions in Autonomous Intelligent Agents 自主智能体中非期望行为的好奇心驱动强化学习

2021 IEEE 19th World Symposium on Applied Machine Intelligence and Informatics (SAMI) Pub Date : 2021-01-21 DOI: 10.1109/SAMI50585.2021.9378666

Christopher Rosser, Khalid H. Abed

引用次数: 2

Semantic Reasoning from Model-Agnostic Explanations 基于模型不可知论解释的语义推理

2021 IEEE 19th World Symposium on Applied Machine Intelligence and Informatics (SAMI) Pub Date : 2021-01-21 DOI: 10.1109/SAMI50585.2021.9378668

Timen Stepisnik Perdih, N. Lavrač, Blaž Škrlj

{"title":"Semantic Reasoning from Model-Agnostic Explanations","authors":"Timen Stepisnik Perdih, N. Lavrač, Blaž Škrlj","doi":"10.1109/SAMI50585.2021.9378668","DOIUrl":"https://doi.org/10.1109/SAMI50585.2021.9378668","url":null,"abstract":"With the wide adoption of black-box models, instance-based post hoc explanation tools, such as LIME and SHAP became increasingly popular. These tools produce explanations, pinpointing contributions of key features associated with a given prediction. However, the obtained explanations remain at the raw feature level and are not necessarily understandable by a human expert without extensive domain knowledge. We propose ReEx (Reasoning with Explanations), a method applicable to explanations generated by arbitrary instance-level explainers, such as SHAP. By using background knowledge in the form of on-tologies, ReEx generalizes instance explanations in a least general generalization-like manner. The resulting symbolic descriptions are specific for individual classes and offer generalizations based on the explainer's output. The derived semantic explanations are potentially more informative, as they describe the key attributes in the context of more general background knowledge, e.g., at the biological process level. We showcase ReEx's performance on nine biological data sets, showing that compact, semantic explanations can be obtained and are more informative than generic ontology mappings that link terms directly to feature names. ReEx is offered as a simple-to-use Python library and is compatible with tools such as SHAP and similar. To our knowledge, this is one of the first methods that directly couples semantic reasoning with contemporary model explanation methods.","PeriodicalId":402414,"journal":{"name":"2021 IEEE 19th World Symposium on Applied Machine Intelligence and Informatics (SAMI)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115884363","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

Resilience Interpretations of Small and Medium-sized Enterprises and its Analytical Approaches - Literature Review 中小企业弹性的解释及其分析方法——文献综述

2021 IEEE 19th World Symposium on Applied Machine Intelligence and Informatics (SAMI) Pub Date : 2021-01-21 DOI: 10.1109/SAMI50585.2021.9378637

Ferenc Tolner, G. Eigner, Balázs Barta

引用次数: 0

Clothoid-based Trajectory Following Approach for Self-driving vehicles 基于clothoid的自动驾驶车辆轨迹跟踪方法

2021 IEEE 19th World Symposium on Applied Machine Intelligence and Informatics (SAMI) Pub Date : 2021-01-21 DOI: 10.1109/SAMI50585.2021.9378664

Ernő Horváth, C. Pozna

引用次数: 1