Through direct communication, device-to-device (D2D) technology can increase the overall throughput, enhance the coverage, and reduce the power consumption of cellular communications. Security will be of paramount importance in 5G, because 5G devices will directly affect our safety, such as by steering self-driving vehicles and controlling health care applications. 5G will be supporting millions of existing devices without adequate built-in security, as well as new devices whose extreme computing power will make them attractive targets for hackers.This paper presents a survey of the literature on security problems relating to D2D communications in mobile 5G networks. Issues include eavesdropping, jamming, primary user emulation attack, and injecting attack. Because multipath routing emerges as a strategy that can help combat many security problems, particularly eavesdropping, the paper contains an extensive discussion of the security implications of multipath routing. Finally, the paper describes results of a simulation that tests three path selection techniques inspired by the literature. The simulation reveals that routing information through interference disjoint paths most effectively inhibits eavesdropping.

With the growing ubiquity of data in network form, clustering in the context of a network, represented as a graph, has become increasingly important. Clustering is a very useful data exploratory machine learning tool that allows us to make better sense of heterogeneous data by grouping data with similar attributes based on some criteria. This paper investigates the application of a novel graph theoretic clustering method, Node-Based Resilience clustering (NBR-Clust), to address the heterogeneity of Autism Spectrum Disorder (ASD) and identify meaningful subgroups. The hypothesis is that analysis of these subgroups would reveal relevant biomarkers that would provide a better understanding of ASD phenotypic heterogeneity useful for further ASD studies. We address appropriate graph constructions suited for representing the ASD phenotype data. The sample population is drawn from a very large rigorous dataset: Simons Simplex Collection (SSC). Analysis of the results performed using graph quality measures, internal cluster validation measures, and clinical analysis outcome demonstrate the potential usefulness of resilience measure clustering for biomedical datasets. We also conduct feature extraction analysis to characterize relevant biomarkers that delineate the resulting subgroups. The optimal results obtained favored predominantly a 5-cluster configuration.

Many algorithms require doing a large number of betweenness centrality calculations quickly, and accommodating this need is an active open research area. There are many different ideas and approaches to speeding up these calculations, and it is difficult to know which approach will work best in practical situations. The current study attempts to judge performance of betweenness centrality approximation algorithms by running them under conditions that practitioners are likely to experience. For several approaches to approximation, we run two tests, clustering and immunization, on identical hardware, along with a process to determine appropriate parameters. This allows an across-the-board comparison of techniques based on the dimensions of speed and accuracy of results. Overall, the speed of betweenness centrality can be reduced several orders of magnitude by using approximation algorithms. We find that the speeds of individual algorithms can vary widely based on input parameters. The fastest algorithms utilize parallelism, either in the form of multi-core processing or GPUs. Interestingly, getting fast results does not require an expensive GPU. The methodology presented here can guide the selection of a betweenness centrality approximation algorithm depending on a practitioner’s needs and can also be used to compare new methods to existing ones.

Performing network-based analysis on medical and biological data makes a wide variety of machine learning tools available. Clustering, which can be used for classification, presents opportunities for identifying hard-to-reach groups for the development of customized health interventions. Due to a desire to convert abundant DNA gene co-expression data into networks, many graph inference methods have been developed. Likewise there are many clustering and classification tools. This paper presents a comparison of techniques for graph inference and clustering, using different numbers of features, in order to select the best tuple of graph inference method, clustering method and number of features according to a particular phenotype. An extensive machine learning based analysis of the REGARDS dataset is conducted, evaluating the CoNet and K-Nearest Neighbors (KNN) network inference methods, along with the Louvain, Leiden and NBR-Clust clustering techniques. Results from analysis involving five internal cluster evaluation indices show the traditional KNN inference method and NBR-Clust and Louvain clustering produce the most promising clusters with medical phenotype data. It is also shown that visualization can aid in interpreting the clusters, and that the clusters produced can identify meaningful groups indicating customized interventions.

Networks science techniques are frequently used to provide meaningful insights into the populations underlying medical and social data. This paper examines SATHCAP, a dataset related to HIV and drug use in three US cities. In particular, we use network measures such as betweenness centrality, closeness centrality, and eigenvector centrality to find central, important nodes in a network derived from SATHCAP data. We evaluate the attributes of these important nodes and create an exceptionality score based on the number of nodes that share a particular attribute. This score, along with the underlying network itself, is used to reveal insight into the attributes of groups that can be effectively targeted to slow the spread of disease. Our research confirms a known connection between homelessness and HIV, as well as drug abuse and HIV, and shows support for the theory that individuals without easy access to transportation are more likely to be central to the spread of HIV in urban, high risk populations.

Peer Instruction (PI) as defined by Mazur, and variations on this pedagogic technique, have been in use in computing courses for about a decade. Despite dozens of educational research publications documenting positive learning effects, improved retention, student acceptance, and effectiveness for large classes; PI does not appear to be widely adopted for computing courses. This paper reports on a three-way investigation into this apparent contradiction. First, the authors reflect on their own adoption, practice, experience, and abandonment of the use of PI in computing courses. Second, we surveyed the literature regarding the use of PI in computing courses and present a summary of the research findings, variations, and extensions to PI used in computing courses. Third, a survey of computing instructors was conducted to gauge the attitude toward PI in computing courses. To add context, this report considers publications documenting usage of PI in STEM courses, and the adoption of other pedagogic techniques in computing. Particular effort was made to identify the reasons computing instructors don't adopt PI. This report also includes advice to instructors considering adopting PI in computing courses.