How Members of Covert Networks Conceal the Identities of Their Leaders

Centrality measures are the most commonly advocated social network analysis tools for identifying leaders of covert organizations. While the literature has predominantly focused on studying the effectiveness of existing centrality measures or developing new ones, we study the problem from the opposite perspective, by focusing on how a group of leaders can avoid being identified by centrality measures as key members of a covert network. More specifically, we analyze the problem of choosing a set of edges to be added to a network to decrease the leaders’ ranking according to three fundamental centrality measures, namely, degree, closeness, and betweenness. We prove that this problem is NP-complete for each measure. Moreover, we study how the leaders can construct a network from scratch, designed specifically to keep them hidden from centrality measures. We identify a network structure that not only guarantees to hide the leaders to a certain extent but also allows them to spread their influence across the network.

SCALPEL: Exploring the Limits of Tag-enforced Compartmentalization

We present Secure Compartments Automatically Learned and Protected by Execution using Lightweight metadata (SCALPEL), a tool for automatically deriving compartmentalization policies and lowering them to a tagged architecture for hardware-accelerated enforcement. SCALPEL allows a designer to explore high-quality points in the privilege-reduction vs. performance overhead tradeoff space using analysis tools and a detailed knowledge of the target architecture to make best use of the available hardware. SCALPEL automatically implements hundreds of compartmentalization strategies across the privilege-performance tradeoff space, all without manual tagging or code restructuring. SCALPEL uses two novel optimizations for achieving highly performant policies: the first is an algorithm for packing policies into working sets of rules for favorable rule cache characteristics, and the second is a rule prefetching system that allows it to exploit the highly predictable nature of compartmentalization rules. To create policies, SCALPEL introduces a quantitative privilege metric (the Overprivilege Ratio) that is used to drive its algorithmic compartment generation. We implement SCALPEL on a FreeRTOS stack and target a tag-extended RISC-V core. Our results show that SCALPEL-created policies can reduce overprivilege by orders of magnitude with hundreds of logical compartments while imposing low overheads (<5%).

Critical assessment of pan-genomics of metagenome-assembled genomes

Background: Large scale metagenome assembly and binning to generate metagenome-assembled genomes (MAGs) has become possible in the past five years. As a result, millions of MAGs have been produced and increasingly included in pan-genomics workflow. However, pan-genome analyses of MAGs may suffer from the known issues with MAGs: fragmentation, incompleteness, and contamination, due to mis-assembly and mis-binning. Here, we conducted a critical assessment of including MAGs in pan-genome analysis, by comparing pan-genome analysis results of complete bacterial genomes and simulated MAGs. Results: We found that incompleteness led to more significant core gene loss than fragmentation. Contamination had little effect on core genome size but had major influence on accessory genomes. The core gene loss remained when using different pan-genome analysis tools and when using a mixture of MAGs and complete genomes. Importantly, the core gene loss was partially alleviated by lowering the core gene threshold and using gene prediction algorithms that consider fragmented genes, but to a less degree when incompleteness was higher than 5%. The core gene loss also led to incorrect pan-genome functional predictions and inaccurate phylogenetic trees. Conclusions: We conclude that lowering core gene threshold and predicting genes in metagenome mode (as Anvio does with Prodigal) are necessary in pan-genome analysis of MAGs to alleviate the accuracy loss. Better quality control of MAGs and development of new pan-genome analysis tools specifically designed for MAGs are needed in future studies.

A Practical Guide to Lesion Symptom Mapping

The purpose of this guide is to provide a detailed overview of everything researchers need to think about and do when conducting lesion symptom mapping (LSM) analysis. This guide includes step-by-step instructions for data collection, lesion delineation, lesion normalisation, LSM, secondary analyses, results interpretation, and write-up. All original scripts and analysis tools referenced in this guide are openly availible on the Open Science Framework.

Time-space-direction Extension TRIZ Innovation Model for Product Innovation

In order to solve the problem that college students are prone to thinking set and direction deviation in the process of innovation practice. TRIZ (Latin abbreviation of "Teoriya Resheniya Izoblatelskikh Zadatch", which means theory of the solution of innovative problems) is extended and matched with the meta conditional features or quantities of the three dimensions of TSD (time, space and direction) of the problem. In this paper, TRIZ-TSD extension problem solving model is proposed to find compatible solutions. TRIZ-TSD extension problem solving model expands the available resources of the original TRIZ analysis tools, strengthens the interaction between the analysis tools, and makes it more suitable for beginners to use in practical innovation. Taking the university student innovation award-winning project "the intelligent wall planting system" as an example, the basic process and practical effect of TRIZ-TSD fusion innovation model in solving specific problems are verified. The basic principle and thinking mode of this method is not only limited to the practice of College Students’ innovation projects, but also has certain reference value for solving problems in other fields.