Terrorist Networks (TNs) and Organized Crime (OC) are nowadays an increasing threat in the modern society. Due to the strong adoption of the IT technology, an emergent phenomenon is represented by the exploitation of social media, such as Twitter, Facebook, YouTube to disseminate and promote illegal activities, recruit terrorists and establish collaborations. The traditional approaches and countermeasures against cyber-crimes result inadequate in the cyber-space. In this context, the paper proposes an engineering method, centered on three main phases, to support the analysis of suspicious users on social media related to OC and TNs. It is based on the exploitation and extension of social network analysis approaches combined with well-known clustering techniques and association rules. It aims to identify similarities as well as groups of users associated to specific illegal activities such as drugs, weapons and human trafficking. Moreover, it supports the identification process of leaders in groups and mediators between them. A software, which enables the automatic execution of the proposed method, is developed and experimented on the Twitter social media. The results show both the identification of groups of users related to OC and TNs along with their intra-group activities as well as inter-group relationships through potential mediators.

The exploitation of Internet technology represents for terrorists and criminals a convenient means for establishing and advertising illegal activities. Especially, social networks facilitate new collaborations as well as the spreading of information with a lower risk of being exposed and fetched. Indeed, due to the increasing number of social media and the huge amount of data continuously generated from them, the discovering process of cyber-criminals is a hard task to be performed by the Law Enforcement Agencies and Police Forces if only based on traditional approaches. It becomes even harder if the heterogeneous nature of data, due to multi-cultural aspects, such as the variety of languages, is considered during the searching process. As a consequence, the adoption of a computer-based approach represents a viable solution. In particular, this paper aims at supporting the automatic identification process of potential online suspicious users, who act on social media. A methodological process, centered on the combination of well-known text analysis techniques by considering multi-language aspects, is proposed. In addition, an evaluation approach, based on the exploitation of different qualitative evaluation criteria, is employed to assess the level of suspiciousness of the identified users. Finally, a software tool that supports the execution of the proposed process is developed and its experimentation is shown through a case study on Twitter.

Cone beam computed tomography (CBCT) can provide enhanced information for diagnosing dental diseases when compared to 2D radiographs. However, other nonionizing imaging modes such as near-infrared transillumination may have clinical potential to supplement radiographic methods. In a study of 13 extracted human teeth, we compared clinical features between 2D radiograph, CBCT, and near-infrared transillumination imaging. We found that near-infrared imaging independently, and in some cases exclusively, was successful in identifying early demineralization and shallow enamel features, while radiography was optimal for capturing deeper decay and developed caries. We report that near-infrared transillumination imaging is well-suited for rapid screening of patients for detection of early problem sites and as a preliminary assessment tool to guide administration of radiographs.

Road intersections are considered to be serious bottlenecks in urban transportation. More than 44% of all reported crashes in U.S. Occur within intersection areas, which in turn lead to 8,500 fatalities and approximately 1 million injuries every year. Furthermore, because traffic traveling in one direction is generally stopped at busy intersections to allow traffic to flow in another direction, an intersection creates traffic congestion and frustration. The impact of road intersections on traffic delays leads to enormous waste of human and natural resources. According to the 2011 Urban Mobility Report, the delay endured by the average commuter was 34 hours, which costs in aggregate more than $100 billion each year in the U.S. With the advances in Cyber-Physical Systems (CPS), autonomous driving as a part of Intelligent Transportation Systems (ITS) is likely to be at the heart of urban transportation in the future. Autonomous vehicles have been demonstrated successfully at the DARPA Urban Challenge. General Motors' Electrical-Networked Vehicle, CMU's autonomous vehicle and Google's car are just a few other recently unveiled examples. Therefore, it is critical to address safety and throughput concerns as one of the main challenges for autonomous driving through intersections. In this paper, we propose a spatio-temporal technique called the Ballroom Intersection Protocol (BRIP) to manage the safe and efficient passage of autonomous vehicles through intersections. To achieve high throughput at intersections, BRIP aims to maximize the utilization of the capacity of the intersection area by increasing parallelism. By enforcing a synchronized arrival of autonomous vehicles at intersections, BRIP allows vehicles approaching from all directions to simultaneously and continuously cross without stopping behind or inside the intersection area. Our simulation results show that we are able to avoid collisions and increase the throughput of the intersections by up to 96.24% compared to common signalized intersections. Under BRIP, the optimal intersection capacity utilization of 100% is achievable in certain cases.

MOTIVATION: Imputation using external reference panels (e.g. 1000 Genomes) is a widely used approach for increasing power in genome-wide association studies and meta-analysis. Existing hidden Markov models (HMM)-based imputation approaches require individual-level genotypes. Here, we develop a new method for Gaussian imputation from summary association statistics, a type of data that is becoming widely available. RESULTS: In simulations using 1000 Genomes (1000G) data, this method recovers 84% (54%) of the effective sample size for common (>5%) and low-frequency (1-5%) variants [increasing to 87% (60%) when summary linkage disequilibrium information is available from target samples] versus the gold standard of 89% (67%) for HMM-based imputation, which cannot be applied to summary statistics. Our approach accounts for the limited sample size of the reference panel, a crucial step to eliminate false-positive associations, and it is computationally very fast. As an empirical demonstration, we apply our method to seven case-control phenotypes from the Wellcome Trust Case Control Consortium (WTCCC) data and a study of height in the British 1958 birth cohort (1958BC). Gaussian imputation from summary statistics recovers 95% (105%) of the effective sample size (as quantified by the ratio of [Formula: see text] association statistics) compared with HMM-based imputation from individual-level genotypes at the 227 (176) published single nucleotide polymorphisms (SNPs) in the WTCCC (1958BC height) data. In addition, for publicly available summary statistics from large meta-analyses of four lipid traits, we publicly release imputed summary statistics at 1000G SNPs, which could not have been obtained using previously published methods, and demonstrate their accuracy by masking subsets of the data. We show that 1000G imputation using our approach increases the magnitude and statistical evidence of enrichment at genic versus non-genic loci for these traits, as compared with an analysis without 1000G imputation. Thus, imputation of summary statistics will be a valuable tool in future functional enrichment analyses. AVAILABILITY AND IMPLEMENTATION: Publicly available software package available at http://bogdan.bioinformatics.ucla.edu/software/. CONTACT: [email protected] or [email protected] SUPPLEMENTARY INFORMATION: Supplementary materials are available at Bioinformatics online.

Recent trends towards increasing complexity in distributed embedded real-time systems pose challenges in designing and implementing a reliable system such as a self-driving car. The conventional way of improving reliability is to use redundant hardware to replicate the whole (sub)system. Although hardware replication has been widely deployed in hard real-time systems such as avionics, space shuttles and nuclear power plants, it is significantly less attractive to many applications because the amount of necessary hardware multiplies as the size of the system increases. The growing needs of flexible system design are also not consistent with hardware replication techniques. To address the needs of dependability through redundancy operating in real-time, we propose a layer called SAFER(System-level Architecture for Failure Evasion in Real-time applications) to incorporate configurable task-level fault-tolerance features to tolerate fail-stop processor and task failures for distributed embedded real-time systems. To detect such failures, SAFER monitors the health status and state information of each task and broadcasts the information. When a failure is detected using either time-based failure detection or event-based failure detection, SAFER reconfigures the system to retain the functionality of the whole system. We provide a formal analysis of the worst-case timing behaviors of SAFER features. We also describe the modeling of a system equipped with SAFER to analyze timing characteristics through a model-based design tool called SysWeaver. SAFER has been implemented on Ubuntu 10.04 LTS and deployed on Boss, an award-winning autonomous vehicle developed at Carnegie Mellon University. We show various measurements using simulation scenarios used during the 2007 DARPA Urban Challenge. Finally, we present a case study of failure recovery by SAFER when node failures are injected.

As Electronic Control Units (ECUs) and embedded software functions within an automobile keep increasing in number, the scale and complexity of automotive embedded systems is growing at a very rapid pace. Hence, the automotive industry has been developing the Automotive Open System Architecture (AUTOSAR) to harness the reusability of common interfaces to communication buses, real-time operating systems and services. These common interfaces foster ease of adoption, interoperability, maintainability, predictability, and analyzability. However, realizing such standards also requires strong support from end-to-end design tool chains. In this paper, we describe some key analytical components that together characterize the end-to-end timing properties of hierarchical bus structures composed of FlexRay, CANbus and LINbus. Our analysis shows that the practical constraints imposed by standards such as AUTOSAR can lead to higher levels of schedulable resource utilization. This reduces both the overall component count and cost, while facilitating easy enhancements. Our analytical results show (a) how a schedulable utilization of 100% can be obtained for time-triggered FlexRay static segments under AUTOSAR compliance, (b) average-case schedulable utilization of 87% for the event-triggered CAN bus, and (c) similarities between LINbus and FlexRay analyses. We generalize the analytical results from different bus technologies, by exploiting their common underlying structure to enable an integrated end-to-end timing analysis of hierarchical heterogeneous networks. These together yield an end-to-end framework to analyze heterogeneously networked AUTOSAR-compliant automotive systems.

Genome wide association (GWA) studies, which test for association between common genetic markers and a disease phenotype, have shown varying degrees of success. While many factors could potentially confound GWA studies, we focus on the possibility that multiple, rare variants (RVs) may act in concert to influence disease etiology. Here, we describe an algorithm for RV analysis, RareCover. The algorithm combines a disparate collection of RVs with low effect and modest penetrance. Further, it does not require the rare variants be adjacent in location. Extensive simulations over a range of assumed penetrance and population attributable risk (PAR) values illustrate the power of our approach over other published methods, including the collapsing and weighted-collapsing strategies. To showcase the method, we apply RareCover to re-sequencing data from a cohort of 289 individuals at the extremes of Body Mass Index distribution (NCT00263042). Individual samples were re-sequenced at two genes, FAAH and MGLL, known to be involved in endocannabinoid metabolism (187Kbp for 148 obese and 150 controls). The RareCover analysis identifies exactly one significantly associated region in each gene, each about 5 Kbp in the upstream regulatory regions. The data suggests that the RVs help disrupt the expression of the two genes, leading to lowered metabolism of the corresponding cannabinoids. Overall, our results point to the power of including RVs in measuring genetic associations.