Predicting dependencies using domain-based coupling

Software dependencies play a vital role in programme comprehension, change impact analysis and other software maintenance activities. Traditionally, these activities are supported by source code analysis; however, the source code is sometimes inaccessible or difficult to analyse, as in hybrid systems composed of source code in multiple languages using various paradigms (e.g. object-oriented programming and relational databases). Moreover, not all stakeholders have adequate knowledge to perform such analyses. For example, non-technical domain experts and consultants raise most maintenance requests; however, they cannot predict the cost and impact of the requested changes without the support of the developers. We propose a novel approach to predicting software dependencies by exploiting the coupling present in domain-level information. Our approach is independent of the software implementation; hence, it can be used to approximate architectural dependencies without access to the source code or the database. As such, it can be applied to hybrid systems with heterogeneous source code or legacy systems with missing source code. In addition, this approach is based solely on information visible and understandable to domain users; therefore, it can be efficiently used by domain experts without the support of software developers. We evaluate our approach with a case study on a large-scale enterprise system, in which we demonstrate how up to 65 of the source code dependencies and 77% of the database dependencies are predicted solely based on domain information.

Enterprise Integration of Smart Objects using Semantic Service Descriptions

Integrating physical objects (smart objects) and enterprise IT systems is still a labor intensive, mainly manual task done by domain experts. On one hand, enterprise IT backend systems are based on service oriented architectures (SOA) and driven by business rule engines or business process execution engines. Smart objects on the other hand are often programmed at very low levels. In this paper we describe an approach that makes the integration of smart objects with such backends systems easier. We introduce semantic endpoint descriptions based on Linked USDL. Furthermore, we show how different communication patterns can be integrated into these endpoint descriptions. The strength of our endpoint descriptions is that they can be used to automatically create REST or SOAP endpoints for enterprise systems, even if which they are not able to talk to the smart objects directly. We evaluate our proposed solution with CoAP, UDP and 6LoWPAN, as we anticipate the industry converge towards these standards. Nonetheless, our approach also allows easy integration with backend systems, even if no standardized protocol is used.

Femur Specific Polyaffine Model to Regularize the Log-Domain Demons Registration

Osteoarticular allograft transplantation is a popular treatment method in wide surgical resections with large defects. For this reason hospitals are building bone data banks. Performing the optimal allograft selection on bone banks is crucial to the surgical outcome and patient recovery. However, current approaches are very time consuming hindering an efficient selection. We present an automatic method based on registration of femur bones to overcome this limitation. We introduce a new regularization term for the log-domain demons algorithm. This term replaces the standard Gaussian smoothing with a femur specific polyaffine model. The polyaffine femur model is constructed with two affine (femoral head and condyles) and one rigid (shaft) transformation. Our main contribution in this paper is to show that the demons algorithm can be improved in specific cases with an appropriate model. We are not trying to find the most optimal polyaffine model of the femur, but the simplest model with a minimal number of parameters. There is no need to optimize for different number of regions, boundaries and choice of weights, since this fine tuning will be done automatically by a final demons relaxation step with Gaussian smoothing. The newly developed synthesis approach provides a clear anatomically motivated modeling contribution through the specific three component transformation model, and clearly shows a performance improvement (in terms of anatomical meaningful correspondences) on 146 CT images of femurs compared to a standard multiresolution demons. In addition, this simple model improves the robustness of the demons while preserving its accuracy. The ground truth are manual measurements performed by medical experts.

Decision making in beach volleyball defense: Crucial factors derived from interviews with top-level experts

Objectives: In fast ball sports like beach volleyball, decision-making skills are a determining factor for excellent performance. The current investigation aimed to identify factors that influence the decisionmaking process in top-level beach volleyball defense in order to find relevant aspects for further research. For this reason, focused interviews with top players in international beach volleyball were conducted and analyzed with respect to decision-making characteristics. Design: Nineteen world-tour beach volleyball defense players, including seven Olympic or world champions, were interviewed, focusing on decision-making factors, gaze behavior, and interactions between the two. Methods: Verbal data were analyzed by inductive content analysis according to Mayring (2008). This approach allows categories to emerge from the interview material itself instead of forcing data into preset classifications and theoretical concepts. Results: The data analysis showed that, for top-level beach volleyball defense, decision making depends on opponent specifics, external context, situational context, opponent's movements, and intuition. Information on gaze patterns and visual cues revealed general tendencies indicating optimal gaze strategies that support excellent decision making. Furthermore, the analysis highlighted interactions between gaze behavior, visual information, and domain-specific knowledge. Conclusions: The present findings provide information on visual perception, domain-specific knowledge, and interactions between the two that are relevant for decision making in top-level beach volleyball defense. The results can be used to inform sports practice and to further untangle relevant mechanisms underlying decision making in complex game situations.

Urinary diversion: how experts divert

OBJECTIVE To determine the rates of the available urinary diversion options for patients treated with radical cystectomy for bladder cancer in different settings (pioneering institutions, leading urologic oncology centers, and population based). METHODS Population-based data from the literature included all patients (n = 7608) treated in Sweden during the period 1964-2008, from Germany (n = 14,200) for the years 2008 and 2011, US patients (identified from National Inpatient Sample during 1998-2005, 35,370 patients and 2001-2008, 55,187 patients), and from Medicare (n = 22,600) for the years 1992, 1995, 1998, and 2001. After the International Consultation on Urologic Diseases-European Association of Urology International Consultation on Bladder Cancer 2012, the urinary diversion committee members disclosed data from their home institutions (n = 15,867), including the pioneering institutions and the leading urologic oncology centers. They are the coauthors of this report. RESULTS The receipt of continent urinary diversion in Sweden and the United States is <15%, whereas in the German high-volume setting, 30% of patients receive a neobladder. At leading urologic oncology centers, this rate is also 30%. At pioneering institutions up to 75% of patients receive an orthotopic reconstruction. Anal diversion is <1%. Continent cutaneous diversion is the second choice. CONCLUSION Enormous variations in urinary diversion exist for >2 decades. Increased attention in expanding the use of continent reconstruction may help to reduce these disparities for patients undergoing radical cystectomy for bladder cancer. Continent reconstruction should not be the exclusive domain of cystectomy centers. Efforts to increase rates of this complex reconstruction must concentrate on better definition of the quality-of-life impact, technique dissemination, and the centralization of radical cystectomy.

Differentiation of Diabetic Macular Edema From Pseudophakic Cystoid Macular Edema by Spectral-Domain Optical Coherence Tomography.

PURPOSE: To differentiate diabetic macular edema (DME) from pseudophakic cystoid macular edema (PCME) based solely on spectral-domain optical coherence tomography (SD-OCT). METHODS: This cross-sectional study included 134 participants: 49 with PCME, 60 with DME, and 25 with diabetic retinopathy (DR) and ME after cataract surgery. First, two unmasked experts classified the 25 DR patients after cataract surgery as either DME, PCME, or mixed-pattern based on SD-OCT and color-fundus photography. Then all 134 patients were divided into two datasets and graded by two masked readers according to a standardized reading-protocol. Accuracy of the masked readers to differentiate the diseases based on SD-OCT parameters was tested. Parallel to the masked readers, a computer-based algorithm was established using support vector machine (SVM) classifiers to automatically differentiate disease entities. RESULTS: The masked readers assigned 92.5% SD-OCT images to the correct clinical diagnose. The classifier-accuracy trained and tested on dataset 1 was 95.8%. The classifier-accuracy trained on dataset 1 and tested on dataset 2 to differentiate PCME from DME was 90.2%. The classifier-accuracy trained and tested on dataset 2 to differentiate all three diseases was 85.5%. In particular, higher central-retinal thickness/retinal-volume ratio, absence of an epiretinal-membrane, and solely inner nuclear layer (INL)-cysts indicated PCME, whereas higher outer nuclear layer (ONL)/INL ratio, the absence of subretinal fluid, presence of hard exudates, microaneurysms, and ganglion cell layer and/or retinal nerve fiber layer cysts strongly favored DME in this model. CONCLUSIONS: Based on the evaluation of SD-OCT, PCME can be differentiated from DME by masked reader evaluation, and by automated analysis, even in DR patients with ME after cataract surgery. The automated classifier may help to independently differentiate these two disease entities and is made publicly available.