# Report of the 2017 IEEE Cyber Science and Technology Congress
## 4 Cyber Science and Fundamentals  
This session has seven papers on a variety of topics related to cyber science. [PERSON] et al. [19] surveyed the publications on cyberspace between the period of 1989 and 2016 and showed that the field is matured. Two papers focused on using neural networks for knowledge discovery [20; 21]. Two other papers investigated recommendation models [22; 23]. One paper reviewed the current approaches on data-intensive computing [24]. The remaining paper presented an interesting visual analysis solution for massive open online courses (MOOCs) [25]. We also include one paper from the short paper session because it is also related to knowledge discovery [26].  
In [19], [PERSON] et al. analyzed the literature on the topics of cyberspace based on data obtained from Web of Science during the period between 1989 and 2016. They observed that 1999 had the most number of publications, and, in 2016, the field has matured.  
In [20], [PERSON] et al. proposed a semantic neuron network based associative memory model to assign semantics on lower-level text-mining result. They performed chunking mechanisms on matrices to merge and decompose correlated matrices. The goal of their study is to make their associative memory model to be self-organizable and self-evolvable. In [26], [PERSON] and [PERSON] introduced a framework to identify relationship between organizational data and organizational goals based on ontology. They implemented their framework in a higher education institution in Australia.  
In [21], [PERSON] and [PERSON] proposed to derive rules from the layers closer to the output layer of a deep multilayer perceptron neural network. Their hypothesis is that such layers could learn more abstract and therefore simpler rules. The validated their hypothesis using several public datasets. Their work is important for knowledge discovery using machine learning.  
In [22], [PERSON] et al. presented their work aiming to improve the quality of making recommendations to online users based on their behaviors. They defined the preference on two-item sets with different types. The first item set has the same-type feedback relationship, and the second item set is a mixed-type set. Based on this assumption, they introduced a novel algorithm called Bayesian Personalized Ranking over Mixed-Type Item-sets to make better recommendations, and validated their approach with data collected from a healthcare website and a mobile e-commerce application.  
In [23], [PERSON] et al. studied the issue of diversity when making recommendations with a novel ranking model. The model takes into account factorized category features of items for diversity of the recommendation. They also devised a scoring formula based on both relevancy and diversity of the recommendation results.  
In [24], [PERSON] and [PERSON] provided a review of the state of the art approaches to enhancing the performance of data-intensive computing by considering the semantics of the computing. They gave an overview of current programming models and technologies for data-intensive computing. They identified four types of performance defects as part of their survey, and outlined future research challenges and opportunities.  
In [25], [PERSON] et al. presented a visual analysis solution to help instructors of MOOCs to know better the progress made by students and gain insight on how well an individual student is teaching.  
Based on relevant data collected in the courses, their solution could present several visual views for the instructors, such as activeness calendar view and progress distribution view.