Neuro-fuzzy knowledge processing in intelligent learning environments for improved student diagnosis

In this paper, a neural network implementation for a fuzzy logic-based model of the diagnostic process is proposed as a means to achieve accurate student diagnosis and updates of the student model in Intelligent Learning Environments. The neuro-fuzzy synergy allows the diagnostic model to some extent "imitate" teachers in diagnosing students' characteristics, and equips the intelligent learning environment with reasoning capabilities that can be further used to drive pedagogical decisions depending on the student learning style. The neuro-fuzzy implementation helps to encode both structured and non-structured teachers' knowledge: when teachers' reasoning is available and well defined, it can be encoded in the form of fuzzy rules; when teachers' reasoning is not well defined but is available through practical examples illustrating their experience, then the networks can be trained to represent this experience. The proposed approach has been tested in diagnosing aspects of student's learning style in a discovery-learning environment that aims to help students to construct the concepts of vectors in physics and mathematics. The diagnosis outcomes of the model have been compared against the recommendations of a group of five experienced teachers, and the results produced by two alternative soft computing methods. The results of our pilot study show that the neuro-fuzzy model successfully manages the inherent uncertainty of the diagnostic process; especially for marginal cases, i.e. where it is very difficult, even for human tutors, to diagnose and accurately evaluate students by directly synthesizing subjective and, some times, conflicting judgments

Deep Learning Based Malware Classification Using Deep Residual Network

The traditional malware detection approaches rely heavily on feature extraction procedure, in this paper we proposed a deep learning-based malware classification model by using a 18-layers deep residual network. Our model uses the raw bytecodes data of malware samples, converting the bytecodes to 3-channel RGB images and then applying the deep learning techniques to classify the malwares. Our experiment results show that the deep residual network model achieved an average accuracy of 86.54% by 5-fold cross validation. Comparing to the traditional methods for malware classification, our deep residual network model greatly simplify the malware detection and classification procedures, it achieved a very good classification accuracy as well. The dataset we used in this paper for training and testing is Malimg dataset, one of the biggest malware datasets released by vision research lab of UCSB

Proceedings, MSVSCC 2019

Old Dominion University Department of Modeling, Simulation & Visualization Engineering (MSVE) and the Virginia Modeling, Analysis and Simulation Center (VMASC) held the 13th annual Modeling, Simulation & Visualization (MSV) Student Capstone Conference on April 18, 2019. The Conference featured student research and student projects that are central to MSV. Also participating in the conference were faculty members who volunteered their time to impart direct support to their students’ research, facilitated the various conference tracks, served as judges for each of the tracks, and provided overall assistance to the conference. Appreciating the purpose of the conference and working in a cohesive, collaborative effort, resulted in a successful symposium for everyone involved. These proceedings feature the works that were presented at the conference. Capstone Conference Chair: Dr. Yuzhong Shen Capstone Conference Student Chair: Daniel Pere

Aerospace Medicine and Biology: A continuing bibliography with indexes (supplement 314)

This bibliography lists 139 reports, articles, and other documents introduced into the NASA scientific and technical information system in August, 1988

UNDERSTANDING THE ROLE OF OVERT AND COVERT ONLINE COMMUNICATION IN INFORMATION OPERATIONS

This thesis combines regression, sentiment, and social network analysis to explore how Russian online media agencies, both overt and covert, affect online communication on Twitter when North Atlantic Treaty Organization (NATO) exercises occur. It explores the relations between the average sentiment of tweets and the activities of Russia’s overt and covert online media agencies. The data source for this research is the Naval Postgraduate School’s licensed Twitter archive and open-source information about the NATO exercises timeline. Publicly available lexicons of positive and negative terms helped to measure the sentiment in tweets. The thesis finds that Russia’s covert media agencies, such as the Internet Research Agency, have a great impact on and likelihood for changing the sentiment of network users about NATO than do the overt Russian media outlets. The sentiment during NATO exercises becomes more negative as the activity of Russian media organizations, whether covert or overt, increases. These conclusions suggest that close tracking and examination of the activities of Russia’s online media agencies provide the necessary base for detecting ongoing information operations. Further refining of the analytical methods can deliver a more comprehensive outcome. These refinements could employ machine learning or natural language processing algorithms that can increase the precision of the sentiment measurement probability and timely identification of trolls’ accounts.Podpolkovnik, Bulgarian Air ForceApproved for public release. Distribution is unlimited

NASA Space Human Factors Program

This booklet briefly and succinctly treats 23 topics of particular interest to the NASA Space Human Factors Program. Most articles are by different authors who are mainly NASA Johnson or NASA Ames personnel. Representative topics covered include mental workload and performance in space, light effects on Circadian rhythms, human sleep, human reasoning, microgravity effects and automation and crew performance

eWOM for public institutions: application to the case of the Portuguese Army

Social media platforms provide easy access to the public opinion (called electronic word-of-mouth), which can be collected and analyzed to extract knowledge about the reputation of an organization. Monitoring this reputation in the public sector may bring several benefits for its institutions, especially in supporting decision-making and developing marketing campaigns. Thus, to offer a solution aimed at the needs of this sector, the goal of this research was to develop a methodology capable of extracting relevant information about eWOM in social media, using text mining and natural language processing techniques. Our goal was achieved through a methodology capable of handling the small amount of information regarding public state organizations in social media. Additionally, our work was validated using the context of the Portuguese Army and revealed the potential to provide indicators of institutional reputation. Our results present one of the first cases of the application of this type of techniques to an Army organization and to understand its negative reputation among the population.info:eu-repo/semantics/acceptedVersio