Gremlin: an interactive visualization model for analyzing genomic rearrangements

In this work we present, apply, and evaluate a novel, interactive visualization model for comparative analysis of structural variants and rearrangements in human and cancer genomes, with emphasis on data integration and uncertainty visualization. To support both global trend analysis and local feature detection, this model enables explorations continuously scaled from the high-level, complete genome perspective, down to the low-level, structural rearrangement view, while preserving global context at all times. We have implemented these techniques in Gremlin, a genomic rearrangement explorer with multi-scale, linked interactions, which we apply to four human cancer genome data sets for evaluation. Using an insight-based evaluation methodology, we compare Gremlin to Circos, the state-of-the-art in genomic rearrangement visualization, through a small user study with computational biologists working in rearrangement analysis. Results from user study evaluations demonstrate that this visualization model enables more total insights, more insights per minute, and more complex insights than the current state-of-the-art for visual analysis and exploration of genome rearrangements.     

Cryptanalysis of the application secure alternative to SNMP (APSSNMP)

The APSSNMP network management protocol was proposed as a secure alternative to the Simple Network Management Protocol (SNMP) used in the TCP/IP suite. The designers claimed that it resists meet-in-the-middle (MITM) attacks. In this paper, we present an advanced MITM attack on the APSSNMP and hence show that it fails to achieve its design objective of providing better security against MITM attacks. Since the APSSNMP has been presented in several journals, and IEEE and IASTED conferences, our result is important to highlight to network implementers and users of distributed systems that the APSSNMP is not secure for practical deployment.     

A computational model for developing semantic web-based educational systems

Recently, some initiatives to start the so-called semantic web-based educational systems (SWBES) have emerged in the field of artificial intelligence in education (AIED). The main idea is to incorporate semantic web resources to the design of AIED systems aiming to update their architectures to provide more adaptability, robustness and richer learning environments. However, the construction of such systems is highly complex and faces several challenges in terms of software engineering and artificial intelligence aspects. This paper presents a computational model for developing SWBES focusing on the problem of how to make the development easier and more useful for both developers and authors. In order to illustrate the features of the proposed model, a case study is presented. Furthermore, a discussion about the results regarding the computational model construction is available.     

Skill Memory in Biped Locomotion

Robots must be able to adapt their motor behavior to unexpected situations in order to safely move among humans. A necessary step is to be able to predict failures, which result in behavior abnormalities and may cause irrecoverable damage to the robot and its surroundings, i.e. humans. In this paper we build a predictive model of sensor traces that enables early failure detection by means of a skill memory. Specifically, we propose an architecture based on a biped locomotion solution with improved robustness due to sensory feedback, and extend the concept of Associative Skill Memories (ASM) to periodic movements by introducing several mechanisms into the training workflow, such as linear interpolation and regression into a Dynamical Motion Primitive (DMP) system such that representation becomes time invariant and easily parameterizable. The failure detection mechanism applies statistical tests to determine the optimal operating conditions. Both training and failure testing were conducted on a DARwIn-OP inside a simulation environment to assess and validate the failure detection system proposed. Results show that the system performance in terms of the compromise between sensitivity and specificity is similar with and without the proposed mechanism, while achieving a significant data size reduction due to the periodic approach taken.     

Relational Learning with GPUs: Accelerating Rule Coverage

Relational learning algorithms mine complex databases for interesting patterns. Usually, the search space of patterns grows very quickly with the increase in data size, making it impractical to solve important problems. In this work we present the design of a relational learning system, that takes advantage of graphics processing units (GPUs) to perform the most time consuming function of the learner, rule coverage. To evaluate performance, we use four applications: a widely used relational learning benchmark for predicting carcinogenesis in rodents, an application in chemo-informatics, an application in opinion mining, and an application in mining health record data. We compare results using a single and multiple CPUs in a multicore host and using the GPU version. Results show that the GPU version of the learner is up to eight times faster than the best CPU version.     

Visually impaired people and the emerging connected TV: a comparative study of TV and Web applications’ accessibility

With the TV signal digitization and the current market growth of connected TVs, the authors envision the appearance of accessibility barriers to visually impaired persons. The paper addresses the following hypothesis: (1) visually impaired users want to extend their TV usage to explore new TV features; (2) TV applications are in less conformance with accessibility guidelines compared to their Desktop versions. Additionally, the authors wanted to assess whether guideline conformance reflected real TV accessibility problems experienced by users. The methods used for this study included surveys aimed at characterizing the interest of the visually impaired population regarding the use of TV, and specifically of Web applications on TV, an automated accessibility evaluation to compare TV and Desktop versions of the same Web application, to understand their conformance with accessibility guidelines, and a user study where participants with visual impairments were asked to perform some tasks on both versions. From the survey, we confirmed that people with visual disabilities are interested in extra features on their TV. Results from the automated accessibility evaluation show that TV applications are in a significantly better level of conformance with accessibility guidelines. The user study has illustrated that users were unable to complete any task using the TV versions of the applications. The results from these studies demonstrated that the new features that come with connected TVs still have a long way to go in order to be accessible by all. Furthermore, they lead us to concur with other works that automated evaluations are not enough to assess the accessibility of a Web page.     

Fast computation of minimal elementary decompositions of metabolic flux vectors

The concept of elementary flux vector is valuable in a number of applications of metabolic engineering. For instance, in metabolic flux analysis, each admissible flux vector can be expressed as a non-negative linear combination of a small number of elementary flux vectors. However a critical issue concerns the total number of elementary flux vectors which may be huge because it combinatorially increases with the size of the metabolic network. In this paper we present a fast algorithm that randomly computes a decomposition of admissible flux vectors in a minimal number of elementary flux vectors without explicitly enumerating all of them.     

The State of the Art in Topology‐Based Visualization of Unsteady Flow

Vector fields are a common concept for the representation of many different kinds of flow phenomena in science and engineering. Methods based on vector field topology are known for their convenience for visualizing and analysing steady flows, but a counterpart for unsteady flows is still missing. However, a lot of good and relevant work aiming at such a solution is available. We give an overview of previous research leading towards topology‐based and topology‐inspired visualization of unsteady flow, pointing out the different approaches and methodologies involved as well as their relation to each other, taking classical (i.e. steady) vector field topology as our starting point. Particularly, we focus on Lagrangian methods, space–time domain approaches, local methods and stochastic and multifield approaches. Furthermore, we illustrate our review with practical examples for the different approaches.     

Distributed data clustering in sensor networks

Low overhead analysis of large distributed data sets is necessary for current data centers and for future sensor networks. In such systems, each node holds some data value, e.g., a local sensor read, and a concise picture of the global system state needs to be obtained. In resource-constrained environments like sensor networks, this needs to be done without collecting all the data at any location, i.e., in a distributed manner. To this end, we address the distributed clustering problem, in which numerous interconnected nodes compute a clustering of their data, i.e., partition these values into multiple clusters, and describe each cluster concisely. We present a generic algorithm that solves the distributed clustering problem and may be implemented in various topologies, using different clustering types. For example, the generic algorithm can be instantiated to cluster values according to distance, targeting the same problem as the famous k-means clustering algorithm. However, the distance criterion is often not sufficient to provide good clustering results. We present an instantiation of the generic algorithm that describes the values as a Gaussian Mixture (a set of weighted normal distributions), and uses machine learning tools for clustering decisions. Simulations show the robustness, speed and scalability of this algorithm. We prove that any implementation of the generic algorithm converges over any connected topology, clustering criterion and cluster representation, in fully asynchronous settings.