Crowd Opinion Mining And Scoring

A system and method are disclosed for mining and rating one or more crowd opinions. The system uses a machine learning approach for crowd opinion mining and scoring. The machine learning algorithm creates and updates concepts of the target query in the server by simultaneously mining the web to update opinion scores. A search interface is provided to find concepts and opinions on the targets. Based on the search, the system retrieves the target-related opinions from the server. The system sends crowd-sourced opinions or answers to users. Crowd knowledge is utilized to find opinions and the scores are displayed in a central place. Biasing of community-based opinions is mitigated

MOVING: A User-Centric Platform for Online Literacy Training and Learning

Part of the Progress in IS book series (PROIS)In this paper, we present an overview of the MOVING platform, a user-driven approach that enables young researchers, decision makers, and public administrators to use machine learning and data mining tools to search, organize, and manage large-scale information sources on the web such as scientific publications, videos of research talks, and social media. In order to provide a concise overview of the platform, we focus on its front end, which is the MOVING web application. By presenting the main components of the web application, we illustrate what functionalities and capabilities the platform offer its end-users, rather than delving into the data analysis and machine learning technologies that make these functionalities possible

Pelatihan Implementasi Machine Learning pada Bidang Pendidikan

Machine learning is a machine that can learn like humans. Machine learning (ML) technology was developed so that machines can learn by themselves without direction from the user. Machine learning consists of various disciplines such as statistics, mathematics and data mining so that machines can learn by analyzing data patterns without the need to be explicitly reprogrammed. Making machine learning applications is not easy because you have to have good understanding of methods and programming skills. Therefore, this service uses a solution to improve the abilities of the participants, namely a training approach by presenting material and demonstrating the use of machine learning in midwifery education. The activity was carried out on April 21 2021 online via the Zoom Meeting application with student participants. Based on the results of the material presentation session and hands-on practice using the Python programming language at Google Colab, it showed that the participants looked enthusiastic in following the material. Not only that, the participants know various machine learning methods and can apply them in completing a case study and building web applications with Flask tools

Detecting Hacker Threats: Performance of Word and Sentence Embedding Models in Identifying Hacker Communications

Abstract—Cyber security is striving to find new forms of protection against hacker attacks. An emerging approach nowadays is the investigation of security-related messages exchanged on deep/dark web and even surface web channels. This approach can be supported by the use of supervised machine learning models and text mining techniques. In our work, we compare a variety of machine learning algorithms, text representations and dimension reduction approaches for the detection accuracies of software-vulnerability-related communications. Given the imbalanced nature of the three public datasets used, we investigate appropriate sampling approaches to boost detection accuracies of our models. In addition, we examine how feature reduction techniques such as Document Frequency Reduction, Chi-square and Singular Value Decomposition (SVD) can be used to reduce the number of features of the model without impacting the detection performance. We conclude that: (1) a Support Vector Machine (SVM) algorithm used with traditional Bag of Words achieved highest accuracies (2) The increase of the minority class with Random Oversampling technique improves the detection performance of the model by 5% on average, and (3) The number of features of the model can be reduced by up to 10% without affecting the detection performance. Also, we have provided the labelled dataset used in this work for further research. These findings can be used to support Cyber Security Threat Intelligence (CTI) with respect to the use of text mining techniques for detecting security-related communication

Eavesdropping Hackers: Detecting Software Vulnerability Communication on Social Media Using Text Mining

Abstract—Cyber security is striving to find new forms of protection against hacker attacks. An emerging approach nowadays is the investigation of security-related messages exchanged on Deep/Dark Web and even Surface Web channels. This approach can be supported by the use of supervised machine learning models and text mining techniques. In our work, we compare a variety of machine learning algorithms, text representations and dimension reduction approaches for the detection accuracies of software-vulnerability-related communications. Given the imbalanced nature of the three public datasets used, we investigate appropriate sampling approaches to boost detection accuracies of our models. In addition, we examine how feature reduction techniques, such as Document Frequency Reduction, Chi-square and Singular Value Decomposition (SVD) can be used to reduce the number of features of the model without impacting the detection performance. We conclude that: (1) a Support Vector Machine (SVM) algorithm used with traditional Bag of Words achieved highest accuracies (2) The increase of the minority class with Random Oversampling technique improves the detection performance of the model by 5% on average, and (3) The number of features of the model can be reduced by up to 10% without affecting the detection performance. Also, we have provided the labelled dataset used in this work for further research. These findings can be used to support Cyber Security Threat Intelligence (CTI) with respect to the use of text mining techniques for detecting security-related communicatio

Evolution and scientific visualization of Machine learning field

[EN] This article provides a retrospective and understanding of the development of automatic learning methods. The beginnings are visualized as a discipline within Computer Sciences in the subcategory of Artificial Intelligence, its development and the current transfer of knowledge to other areas of Engineering and its industrial applications. Based on the publications about machine learning and its application contained in the Web of Science database, records from 1986 to 2017 are downloaded. After a description of the technological profile, a new approach is introduced to the classification of a discipline based on the year of appearance of those terms that define it. Mining of technological texts and network theory has been applied to extract the terms and interpret their evolution. They are the those that define the stages of emergence, development and maturation of the discipline Machine learning. The novelty of this approach lies in the technical nature of applied research in Machine Learning, which aims to be a guide for the development of future engineering applications and to make technology transfer toindustry visible.Río-Belver, R.; Garechana, G.; Bildosola, I.; Zarrabeitia, E. (2018). Evolution and scientific visualization of Machine learning field. En 2nd International Conference on Advanced Reserach Methods and Analytics (CARMA 2018). Editorial Universitat Politècnica de València. 115-123. https://doi.org/10.4995/CARMA2018.2018.8329OCS11512