Ground-penetrating radar (GPR) facilitates the detection and localization of subsurface structural anomalies in critical transport infrastructure (e.g., tunnels), better informing targeted maintenance strategies. However, conventional fixed-directional systems suffer from limited coverage—especially of less-accessible structural aspects (e.g., crowns)—alongside the unclear visual output of anomaly spatial profiles, both for physical and simulated datasets. To tackle these limitations, new hybrid-rotational GPR utilizes novel 360° orientable air-launched antennas to increase subsurface coverage, principally in tunnels. Prototype systems currently lack a versatile workflow to generate practical visual output for surveyors. This study develops a versatile visualization workflow based on entirely open-access tools, returning 3-D spatial profiles of subsurface anomalies in: 1) simulated; 2) fixed-directional; and 3) hybrid-rotational GPR datasets. Work includes the development of two unique hybrid-rotational GPR systems, for laboratory and in-field data collection, respectively. Following initial 3-D grid alignment and smoothing, data undergo 3-D Stolt migration, normalization, and proximal clustering via Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN). This returns segmented point subsets associated with suspected structural anomalies. Finally, 3-D convex hulls are recovered using the QuickHull method. Detection and localization performance is first appraised through numerical simulation in open-source software gprMax. Practical laboratory experimentation follows, with both commercial fixed-directional systems and developed hybrid-rotational GPR, before in-field demonstration on a large-scale, tunnel subsurface analog. In each experiment, all targets were successfully identified within returned 3-D visualizations of hybrid-rotational GPR datasets. Moreover, the spatial profiles were consistently observed to be accurately localized within decimeter-length scales of known target locations. Overall, the advances presented in this work both facilitate and demonstrate the significant practical potential of new hybrid-rotational GPR technology as a basis for future subsurface surveying of critical transport infrastructure.

This demo introduces an automated collaborative requirements engineering tool, called TestMEReq, which is used to promote effective communication and collaboration between client-stakeholders and requirements engineers for better requirements validation. Our tool is augmented with real time communication and collaboration support to allow multiple stakeholders to collaboratively validate the same set of requirements. We have conducted a user study focusing on validating requirements using TestMEReq with a few groups of requirements engineers and client stakeholders. The study shows that our automated tool support is able to assist requirements engineers to effectively communicate with client-stakeholders to better validate the requirements virtually in real time. (Demo video: https://www.youtube.com/watch?v=7sWLOx-N4Jo).

Requirements validation is a crucial process to determine whether client-stakeholders' needs and expectations of a product are sufficiently correct and complete. Various requirements validation techniques have been used to evaluate the correctness and quality of requirements, but most of these techniques are tedious, expensive and time consuming. Accordingly, most project members are reluctant to invest their time and efforts in the requirements validation process. Moreover, automated tool supports that promote effective collaboration between the client-stakeholders and the engineers are still lacking. In this paper, we describe a novel approach that combines prototyping and test-based requirements techniques to improve the requirements validation process and promote better communication and collaboration between requirements engineers and client-stakeholders. To justify the potential of this prototype tool, we also present three types of evaluation conducted on the prototpye tool, which are the usability survey, 3-tool comparison analysis and expert reviews.

The Combined Fragments of UML Sequence Diagram permit various types of control flow among messages (e.g., interleaving and branching) to express an aggregation of multiple traces encompassing complex and concurrent behaviors. However, Combined Fragments increase the difficulty of Sequence Diagram comprehension and analysis. To alleviate this problem, we introduce an approach to formally describe Sequence Diagrams with Combined Fragments in terms of the input language of the model checker NuSMV. This approach permits the verification of desired properties against Sequence Diagrams.

Today's techniques for controlling access to software artifacts are limited to restricting access to whole files and directories. But when a company's access control policy does not match a project's existing physical modularization, these techniques require either an all-or-nothing approach or re-modularization of the files and directories. The increased maintenance overhead this brings to project administration can lead to unimplemented or insufficient developer access control and an increased risk of insider security incidents (e.g., theft of intellectual property). We have created a tool (GitBAC) to provide access control of software artifacts using a crosscutting concern instead of artifact modularization. Our method provides fine-grained access control of artifacts and accommodates flexible access control policies.

Finding the location of binding sites in DNA is a difficult problem. Although the location of some binding sites have been experimentally identified, other parts of the genome may or may not contain binding sites. This poses problems with negative data in a trainable classifier. Here we show that using randomized negative data gives a large boost in classifier performance when compared to the original labeled data.

The identification of cis-regulatory binding sites in DNA in multicellular eukaryotes is a particularly difficult problem in computational biology. To obtain a full understanding of the complex machinery embodied in genetic regulatory networks it is necessary to know both the identity of the regulatory transcription factors together with the location of their binding sites in the genome. We show that using an SVM together with data sampling, to integrate the results of individual algorithms specialised for the prediction of binding site locations, can produce significant improvements upon the original algorithms applied to the mouse genome. These results make more tractable the expensive experimental procedure of actually verifying the predictions.

Currently the best algorithms for transcription factor binding site prediction are severely limited in accuracy. There is good reason to believe that predictions from these different classes of algorithms could he used in conjunction to improve the quality of predictions. In this paper, we apply single layer networks, rules sets and support vector machines on predictions from 12 key real valued algorithms. Furthermore, we use a window of consecutive results in the input vector in order to contextualise the neighbouring results. We improve the classification result with the aid of under- and over- sampling techniques. We find that support vector machines outperform each of the original individual algorithms and the other classifiers employed in this work. In particular they have a better tradeoff between recall and precision.