Image segmentation through combined methods: watershed transform, unsupervised distance learning and normalized cut

Research on image processing has shown that combining segmentation methods may lead to a solid approach to extract semantic information from different sort of images. Within this context, the Normalized Cut (NCut) is usually used as a final partitioning tool for graphs modeled in some chosen method. This work explores the Watershed Transform as a modeling tool, using different criteria of the hierarchical Watershed to convert an image into an adjacency graph. The Watershed is combined with an unsupervised distance learning step that redistributes the graph weights and redefines the Similarity matrix, before the final segmentation step using NCut. Adopting the Berkeley Segmentation Data Set and Benchmark as a background, our goal is to compare the results obtained for this method with previous work to validate its performance.

Intelligent tutoring system for distance education

This study aims to develop and implement a tool called intelligent tutoring system in an online course to help a formative evaluation in order to improve student learning. According to Bloom et al. (1971,117) formative evaluation is a systematic evaluation to improve the process of teaching and learning. The intelligent tutoring system may provide a timely and high quality feedback that not only informs the correctness of the solution to the problem, but also informs students about the accuracy of the response relative to their current knowledge about the solution. Constructive and supportive feedback should be given to students to reveal the right and wrong answers immediately after taking the test. Feedback about the right answers is a form to reinforce positive behaviors. Identifying possible errors and relating them to the instructional material may help student to strengthen the content under consideration. The remedial suggestion should be given in each answer with detaileddescription with regards the materials and instructional procedures before taking next step. The main idea is to inform students about what they have learned and what they still have to learn. The open-source LMS called Moodle was extended to accomplish the formative evaluation, high-quality feedback, and the communal knowledge presented here with a short online financial math course that is being offered at a large University in Brazil. The preliminary results shows that the intelligent tutoring system using high quality feedback helped students to improve their knowledge about the solution to the problems based on the errors of their past cohorts. The results and suggestion for future work are presented and discussed.

CID: an efficient complexity-invariant distance for time series

The ubiquity of time series data across almost all human endeavors has produced a great interest in time series data mining in the last decade. While dozens of classification algorithms have been applied to time series, recent empirical evidence strongly suggests that simple nearest neighbor classification is exceptionally difficult to beat. The choice of distance measure used by the nearest neighbor algorithm is important, and depends on the invariances required by the domain. For example, motion capture data typically requires invariance to warping, and cardiology data requires invariance to the baseline (the mean value). Similarly, recent work suggests that for time series clustering, the choice of clustering algorithm is much less important than the choice of distance measure used.In this work we make a somewhat surprising claim. There is an invariance that the community seems to have missed, complexity invariance. Intuitively, the problem is that in many domains the different classes may have different complexities, and pairs of complex objects, even those which subjectively may seem very similar to the human eye, tend to be further apart under current distance measures than pairs of simple objects. This fact introduces errors in nearest neighbor classification, where some complex objects may be incorrectly assigned to a simpler class. Similarly, for clustering this effect can introduce errors by “suggesting” to the clustering algorithm that subjectively similar, but complex objects belong in a sparser and larger diameter cluster than is truly warranted.We introduce the first complexity-invariant distance measure for time series, and show that it generally produces significant improvements in classification and clustering accuracy. We further show that this improvement does not compromise efficiency, since we can lower bound the measure and use a modification of triangular inequality, thus making use of most existing indexing and data mining algorithms. We evaluate our ideas with the largest and most comprehensive set of time series mining experiments ever attempted in a single work, and show that complexity-invariant distance measures can produce improvements in classification and clustering in the vast majority of cases.

Relationships between running economy and mechanics in middle-distance runners

Running economy (RE), i.e. the oxygen consumption at a given submaximal speed, is an important determinant of endurance running performance. So far, investigators have widely attempted to individuate the factors affecting RE in competitive athletes, focusing mainly on the relationships between RE and running biomechanics. However, the current results are inconsistent and a clear mechanical profile of an economic runner has not been yet established. The present work aimed to better understand how the running technique influences RE in sub-elite middle-distance runners by investigating the biomechanical parameters acting on RE and the underlying mechanisms. Special emphasis was given to accounting for intra-individual variability in RE at different speeds and to assessing track running rather than treadmill running. In Study One, a factor analysis was used to reduce the 30 considered mechanical parameters to few global descriptors of the running mechanics. Then, a biomechanical comparison between economic and non economic runners and a multiple regression analysis (with RE as criterion variable and mechanical indices as independent variables) were performed. It was found that a better RE was associated to higher knee and ankle flexion in the support phase, and that the combination of seven individuated mechanical measures explains ∼72% of the variability in RE. In Study Two, a mathematical model predicting RE a priori from the rate of force production, originally developed and used in the field of comparative biology, was adapted and tested in competitive athletes. The model showed a very good fit (R2=0.86). In conclusion, the results of this dissertation suggest that the very complex interrelationships among the mechanical parameters affecting RE may be successfully dealt with through multivariate statistical analyses and the application of theoretical mathematical models. Thanks to these results, coaches are provided with useful tools to assess the biomechanical profile of their athletes. Thus, individual weaknesses in the running technique may be identified and removed, with the ultimate goal to improve RE.

Dynamic distance analysis : new geometric data structures and algorithms for the real-time calculation of tolerance violating regions in the digital mockup process

In vielen Industriezweigen, zum Beispiel in der Automobilindustrie, werden Digitale Versuchsmodelle (Digital MockUps) eingesetzt, um die Konstruktion und die Funktion eines Produkts am virtuellen Prototypen zu überprüfen. Ein Anwendungsfall ist dabei die Überprüfung von Sicherheitsabständen einzelner Bauteile, die sogenannte Abstandsanalyse. Ingenieure ermitteln dabei für bestimmte Bauteile, ob diese in ihrer Ruhelage sowie während einer Bewegung einen vorgegeben Sicherheitsabstand zu den umgebenden Bauteilen einhalten. Unterschreiten Bauteile den Sicherheitsabstand, so muss deren Form oder Lage verändert werden. Dazu ist es wichtig, die Bereiche der Bauteile, welche den Sicherhabstand verletzen, genau zu kennen. rnrnIn dieser Arbeit präsentieren wir eine Lösung zur Echtzeitberechnung aller den Sicherheitsabstand unterschreitenden Bereiche zwischen zwei geometrischen Objekten. Die Objekte sind dabei jeweils als Menge von Primitiven (z.B. Dreiecken) gegeben. Für jeden Zeitpunkt, in dem eine Transformation auf eines der Objekte angewendet wird, berechnen wir die Menge aller den Sicherheitsabstand unterschreitenden Primitive und bezeichnen diese als die Menge aller toleranzverletzenden Primitive. Wir präsentieren in dieser Arbeit eine ganzheitliche Lösung, welche sich in die folgenden drei großen Themengebiete unterteilen lässt.rnrnIm ersten Teil dieser Arbeit untersuchen wir Algorithmen, die für zwei Dreiecke überprüfen, ob diese toleranzverletzend sind. Hierfür präsentieren wir verschiedene Ansätze für Dreiecks-Dreiecks Toleranztests und zeigen, dass spezielle Toleranztests deutlich performanter sind als bisher verwendete Abstandsberechnungen. Im Fokus unserer Arbeit steht dabei die Entwicklung eines neuartigen Toleranztests, welcher im Dualraum arbeitet. In all unseren Benchmarks zur Berechnung aller toleranzverletzenden Primitive beweist sich unser Ansatz im dualen Raum immer als der Performanteste.rnrnDer zweite Teil dieser Arbeit befasst sich mit Datenstrukturen und Algorithmen zur Echtzeitberechnung aller toleranzverletzenden Primitive zwischen zwei geometrischen Objekten. Wir entwickeln eine kombinierte Datenstruktur, die sich aus einer flachen hierarchischen Datenstruktur und mehreren Uniform Grids zusammensetzt. Um effiziente Laufzeiten zu gewährleisten ist es vor allem wichtig, den geforderten Sicherheitsabstand sinnvoll im Design der Datenstrukturen und der Anfragealgorithmen zu beachten. Wir präsentieren hierzu Lösungen, die die Menge der zu testenden Paare von Primitiven schnell bestimmen. Darüber hinaus entwickeln wir Strategien, wie Primitive als toleranzverletzend erkannt werden können, ohne einen aufwändigen Primitiv-Primitiv Toleranztest zu berechnen. In unseren Benchmarks zeigen wir, dass wir mit unseren Lösungen in der Lage sind, in Echtzeit alle toleranzverletzenden Primitive zwischen zwei komplexen geometrischen Objekten, bestehend aus jeweils vielen hunderttausend Primitiven, zu berechnen. rnrnIm dritten Teil präsentieren wir eine neuartige, speicheroptimierte Datenstruktur zur Verwaltung der Zellinhalte der zuvor verwendeten Uniform Grids. Wir bezeichnen diese Datenstruktur als Shrubs. Bisherige Ansätze zur Speicheroptimierung von Uniform Grids beziehen sich vor allem auf Hashing Methoden. Diese reduzieren aber nicht den Speicherverbrauch der Zellinhalte. In unserem Anwendungsfall haben benachbarte Zellen oft ähnliche Inhalte. Unser Ansatz ist in der Lage, den Speicherbedarf der Zellinhalte eines Uniform Grids, basierend auf den redundanten Zellinhalten, verlustlos auf ein fünftel der bisherigen Größe zu komprimieren und zur Laufzeit zu dekomprimieren.rnrnAbschießend zeigen wir, wie unsere Lösung zur Berechnung aller toleranzverletzenden Primitive Anwendung in der Praxis finden kann. Neben der reinen Abstandsanalyse zeigen wir Anwendungen für verschiedene Problemstellungen der Pfadplanung.

Treating intermittent claudication with Tibetan medicine Padma 28: does it work?

Herbal drugs are being increasingly used in medical practice, often without appropriate scrutiny of their safety and efficacy. The medicinal product Padma 28 is a fixed combination with Tibetan origin, used in Europe since the 1960s for the symptomatic treatment of circulatory disorders, including those of peripheral arterial occlusive disease (PAOD). We have conducted an analysis of all available data on this herbal drug from published literature as well as from original data we obtained from contacting the authors of published papers, reports and the manufacturer. A total of 19 trials have reported on 2084 patients to date, 444 of whom were in six controlled clinical studies on PAOD. A meta-analysis of five trials showed Padma 28 to increase walking distance by >100m in 18.2% of the patients with verum, versus 2.1% with placebo (P<0.001; odds ratio: 10 [95% CI 3.03, 33.33]; RR: 0.12; number needed to treat=6.2). The safety profile appears to be favourable. Available evidence shows that Padma 28 provides significant relief from PAOD-related symptoms (i.e. walking distance), probably of the same order of magnitude as other employed medications. However, larger confirmatory RCTs are desirable.

Distance education in transition

In his most recent publication, Against the Tide: Critics of Digitalisation, Otto Peters brings together some of the most formidable and critical voices and compelling perspectives on the potential hazards of digitalization. The viewpoints presented range from personal, anthropological, and pedagogical to more scientific and technical, and arise from multiple disciplines. Peters has long been a respected scholar in the field of distance education, and while Peters’ earlier work has advocated the affordances of digitalization, this latest book is an abrupt shift to the darker side of digitalization. The assembly of critics Peters has gathered come from around the world and different walks of life: journalists, educators, scientists, philosophers, lawyers, mathematicians, and computer scientists, to name a few. Their one shared bond is a deep-seated belief that digitalization will have a profound and lasting impact on humankind – and not only in positive ways. ...

The long arm of time pressure at work: Cognitive failure and commuting near-accidents

This study tests whether cognitive failures mediate effects of work-related time pressure and time control on commuting accidents and near-accidents. Participants were 83 employees (56% female) who each commuted between their regular place of residence and place of work using vehicles. The Workplace Cognitive Failure Scale (WCFS) asked for the frequency of failure in memory function, failure in attention regulation, and failure in action execution. Time pressure and time control at work were assessed by the Instrument for Stress Oriented Task Analysis (ISTA). Commuting accidents in the last 12 months were reported by 10% of participants, and half of the sample reported commuting near-accidents in the last 4 weeks. Cognitive failure significantly mediated the influence of time pressure at work on near-accidents even when age, gender, neuroticism, conscientiousness, commuting duration, commuting distance, and time pressure during commuting were controlled for. Time control was negatively related to cognitive failure and neuroticism, but no association with commuting accidents or near-accidents was found. Time pressure at work is likely to increase cognitive load. Time pressure might, therefore, increase cognitive failures during work and also during commuting. Hence, time pressure at work can decrease commuting safety. The result suggests a reduction of time pressure at work should improve commuting safety.

Effect of substrate-target distance and sputtering pressure in the synthesis of AlN thin films

In this work, we analyze the influence of the processing pressure and the substrate–target distance on the synthesis by reactive sputtering of c-axis oriented polycrystalline aluminum nitride thin films deposited on Si(100) wafers. The crystalline quality of AlN has been characterized by high-resolution X-ray diffraction (HR-XRD). The films exhibited a very high degree of c-axis orientation especially when a low process pressure was used. After growth, residual stress measurements obtained indirectly from radius of curvature measurements of the wafer prior and after deposition are also provided. Two different techniques are used to determine the curvature—an optically levered laser beam and a method based on X-ray diffraction. There is a transition from compressive to tensile stress at a processing pressure around 2 mTorr. The transition occurs at different pressures for thin films of different thickness. The degree of c-axis orientation was not affected by the target–substrate distance as it was varied in between 30 and 70 mm.

Multi-distributed wireless sensors for monitoring a long distance transport in a reefer container

The study of the temperature gradients in cold stores and containers is a critical issue in the food industry for the quality assurance of products during transport and for minimising losses. This work presents an analysis of the temperatures during the refrigerated transport of 4,320 kg of blueberries in a reefer (set point temperature at ?1ºC) on a container ship from Montevideo (Uruguay) to Verona (Italy). The monitoring was performed by using semi-passive RFID loggers (TurboTag cards). The objective was to carry out a multi-distributed supervision using low-cost, wireless and autonomous sensors for the characterisation of the distribution and spatial gradients of temperatures during a long distance transport. Data analysis shows spatial (phase space) and temporal sequencing diagrams and reveals a significant heterogeneity of temperature at different locations in the container, which highlights the ineffectiveness of a temperature control system based on a single sensor, as is usually done.

Web-based distance continuing education: a new way of thinking for students and instructors

As people have more difficulty taking time away from work to attend conferences and workshops, the idea of offering courses via the Web has become more desirable. Addressing a need voiced by Medical Library Association membership, the authors developed a Web-based continuing-education course on the subject of the librarian's role in evidence-based medicine. The aim of the course was to provide medical librarians with a well-constructed, content-rich learning experience available to them at their convenience via the Web. This paper includes a discussion of the considerations that need to be taken into account when developing Web-based courses, the issues that arise when the information delivery changes from face-to-face to online, the changing role of the instructor, and the pros and cons of offering Web-based versus traditional courses. The results of the beta test and future plans for the course are also discussed.

Computer Networks Virtualization with GNS3: Evaluating a solution to optimize resources and achieve a distance learning

Designing educational resources allow students to modify their learning process. In particular, on-line and downloadable educational resources have been successfully used in engineering education the last years [1]. Usually, these resources are free and accessible from web. In addition, they are designed and developed by lecturers and used by their students. But, they are rarely developed by students in order to be used by other students. In this work-in-progress, lecturers and students are working together to implement educational resources, which can be used by students to improve the learning process of computer networks subject in engineering studies. In particular, network topologies to model LAN (Local Area Network) and MAN (Metropolitan Area Network) are virtualized in order to simulate the behavior of the links and nodes when they are interconnected with different physical and logical design.

Convergence analysis and validation of low cost distance metrics for computational cost reduction of the Iterative Closest Point algorithm

The Iterative Closest Point algorithm (ICP) is commonly used in engineering applications to solve the rigid registration problem of partially overlapped point sets which are pre-aligned with a coarse estimate of their relative positions. This iterative algorithm is applied in many areas such as the medicine for volumetric reconstruction of tomography data, in robotics to reconstruct surfaces or scenes using range sensor information, in industrial systems for quality control of manufactured objects or even in biology to study the structure and folding of proteins. One of the algorithm’s main problems is its high computational complexity (quadratic in the number of points with the non-optimized original variant) in a context where high density point sets, acquired by high resolution scanners, are processed. Many variants have been proposed in the literature whose goal is the performance improvement either by reducing the number of points or the required iterations or even enhancing the complexity of the most expensive phase: the closest neighbor search. In spite of decreasing its complexity, some of the variants tend to have a negative impact on the final registration precision or the convergence domain thus limiting the possible application scenarios. The goal of this work is the improvement of the algorithm’s computational cost so that a wider range of computationally demanding problems from among the ones described before can be addressed. For that purpose, an experimental and mathematical convergence analysis and validation of point-to-point distance metrics has been performed taking into account those distances with lower computational cost than the Euclidean one, which is used as the de facto standard for the algorithm’s implementations in the literature. In that analysis, the functioning of the algorithm in diverse topological spaces, characterized by different metrics, has been studied to check the convergence, efficacy and cost of the method in order to determine the one which offers the best results. Given that the distance calculation represents a significant part of the whole set of computations performed by the algorithm, it is expected that any reduction of that operation affects significantly and positively the overall performance of the method. As a result, a performance improvement has been achieved by the application of those reduced cost metrics whose quality in terms of convergence and error has been analyzed and validated experimentally as comparable with respect to the Euclidean distance using a heterogeneous set of objects, scenarios and initial situations.

Using multiple landscape genetic approaches to test the validity of genetic clusters in a species characterized by an isolation-by-distance pattern

Bayesian clustering methods are typically used to identify barriers to gene flow, but they are prone to deduce artificial subdivisions in a study population characterized by an isolation-by-distance pattern (IbD). Here we analysed the landscape genetic structure of a population of wild boars (Sus scrofa) from south-western Germany. Two clustering methods inferred the presence of the same genetic discontinuity. However, the population in question was characterized by a strong IbD pattern. While landscape-resistance modelling failed to identify landscape features that influenced wild boar movement, partial Mantel tests and multiple regression of distance matrices (MRDMs) suggested that the empirically inferred clusters were separated by a genuine barrier. When simulating random lines bisecting the study area, 60% of the unique barriers represented, according to partial Mantel tests and MRDMs, significant obstacles to gene flow. By contrast, the random-lines simulation showed that the boundaries of the inferred empirical clusters corresponded to the most important genetic discontinuity in the study area. Given the degree of habitat fragmentation separating the two empirical partitions, it is likely that the clustering programs correctly identified a barrier to gene flow. The differing results between the work published here and other studies suggest that it will be very difficult to draw general conclusions about habitat permeability in wild boar from individual studies.