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[EN] Malleability is a property of certain applications (or tasks) that, given an external request or autonomously, can accommodate a dynamic modification of the degree of parallelism being exploited at runtime. Malleability improves resource usage (core occupation) on modern multicore architectures for applications that exhibit irregular and divergent execution paths and heavily depend on the underlying library performance to attain high performance. The integration of malleability within high-performance instances of the Basic Linear Algebra Subprograms (BLAS) is nonexistent, and, in addition, it is difficult to attain given the rigidity of current application programming interfaces (APIs). In this paper, we overcome these issues presenting the integration of a malleability mechanism within BLIS, a high-performance and portable framework to implement BLAS-like operations. For this purpose, we leverage low-level (yet simple) APIs to integrate on-demand malleability across all Level-3 BLAS routines, and we demonstrate the performance benefits of this approach by means of a higher-level dense matrix operation: the LU factorization with partial pivoting and look-ahead

[EN] In this paper we present a smartphone-based real-time video overtaking architecture for vehicular networks. The developed application aims to prevent head-on collisions that might occur due to attempts to overtake when the view of the driver is obstructed by the presence of a larger vehicle ahead. Under such conditions, the driver does not have a clear view of the road ahead and of any vehicles that might be approaching from the opposite direction, resulting in a high probability of accident occurrence. Our application relies on the use of a dashboard-mounted smartphone with the back camera facing the windshield, and having the screen towards the driver. A video is streamed from the vehicle ahead to the vehicle behind automatically, where it is displayed so that the driver can decide if it is safe to overtake. One of the major challenges is the way to pick the right video source and destination among vehicles in close proximity, depending on their relative position on the road. For this purpose, we have focused on two different methods: one relying solely on GPS data, and the other involving the use of the camera and vehicle heading information. Our experiments show that the faster method, using just the location information, is prone to errors due to GPS inaccuracies. A second method that depends on data fusion from the optical sensor and GPS, although accurate over short distances, becomes more computationally intensive, and its performance significantly depends on the quality of the camera.

[EN] In this paper, we investigate to what extent the conclusion of the Lebesgue dominated convergence theorem holds if the assumption of dominance is dropped. Specifically, we study both topological and algebraic genericity of the family of all null sequences of functions that, being continuous on a locally compact space and integrable with respect to a given Borel measure in it, are not controlled by an integrable function.

[EN] Let sigma = {sigma(i) : i is an element of I} be a partition of the set P of all prime numbers. A subgroup X of a finite group G is called sigma-subnormal in G if there is a chain of subgroups X = X-0 subset of X-1 subset of center dot center dot center dot subset of X-n = G where for every j = 1,..., n the subgroup X j-1 is normal in X j or X j /CoreX j ( X j-1) is a si -group for some i. I. In the special case that s is the partition of P into sets containing exactly one prime each, the sigma-subnormality reduces to the familiar case of subnormality. In this paper some sigma-subnormality criteria for subgroups of s-soluble groups, or groups in which every chief factor is a sigma(i)-group, for some sigma(i) sigma s, are showed.

[EN] KM3NeT is a research infrastructure located in the Mediterranean Sea, that will consist of two deep-sea Cherenkov neutrino detectors. With one detector (ARCA), the KM3NeT Collaboration aims at identifying and studying TeV¿PeV astrophysical neutrino sources. With the other detector (ORCA), the neutrino mass ordering will be determined by studying GeV-scale atmospheric neutrino oscillations. The first KM3NeT detection units were deployed at the Italian and French sites between 2015 and 2017. In this paper, a description of the detector is presented, together with a summary of the procedures used to calibrate the detector in-situ. Finally, the measurement of the atmospheric muon flux between 2232¿3386 m seawater depth is obtained.

[EN] Continuous monitoring of breathing frequency (f(B)) could foster early prediction of adverse clinical effects and exacerbation of medical conditions. Current solutions are invasive or obtrusive and thus not suitable for prolonged monitoring outside the clinical setting. Previous studies demonstrated the feasibility of deriving f(B) by measuring inclination changes due to breathing using accelerometers or inertial measurement units (IMU). Nevertheless, few studies faced the problem of motion artifacts that limit the use of IMU-based systems for continuous monitoring. Moreover, few attempts have been made to move towards real portability and wearability of such devices. This paper proposes a wearable IMU-based device that communicates via Bluetooth with a smartphone, uploading data on a web server to allow remote monitoring. Two IMU units are placed on thorax and abdomen to record breathing-related movements, while a third IMU unit records body/trunk motion and is used as reference. The performance of the proposed system was evaluated in terms of long-acquisition-platform reliability showing good performances in terms of duration and data loss amount. The device was preliminarily tested in terms of accuracy in breathing temporal parameter measurement, in static condition, during postural changes, and during slight indoor activities showing favorable comparison against the reference methods (mean error breathing frequency < 5%). Graphical abstract Proof of concept of a wearable, wireless, modular respiratory Holter based on inertial measurement units (IMUS) for the continuous breathing pattern monitoring through the detection of chest wall breathing-related movements.

[EN] Modeling the execution time of the sparse matrix-vector multiplication (SpMV) on a current CPU architecture is especially complex due to (i) irregular memory accesses; (ii) indirect memory referencing; and (iii) low arithmetic intensity. While analytical models may yield accurate estimates for the total number of cache hits/misses, they often fail to predict accurately the total execution time. In this paper, we depart from the analytic approach to instead leverage convolutional neural networks (CNNs) in order to provide an effective estimation of the performance of the SpMV operation. For this purpose, we present a high-level abstraction of the sparsity pattern of the problem matrix and propose a blockwise strategy to feed the CNN models by blocks of nonzero elements. The experimental evaluation on a representative subset of the matrices from the SuiteSparse Matrix collection demonstrates the robustness of the CNN models for predicting the SpMV performance on an Intel Haswell core. Furthermore, we show how to generalize the network models to other target architectures to estimate the performance of SpMV on an ARM A57 core

[EN] In this paper we present a Silverman-Ho algorithm-based method to obtain a realization of a polynomial matrix. This method provides the final formulation of a minimal realization directly from a full rank factorization of a specific given matrix. Also, some classical problems in control theory such as model reduction in singular systems or the positive realization problem in standard systems are solved with this method.

[EN] Total profit is one of the most important factors to be considered from the perspective of resource providers. In this paper, an original MapReduce workflow scheduling with deadline and data locality is proposed to maximize total profit of resource providers. A new workflow conversion based on dynamic programming and ChainMap/ChainReduce is designed to decrease transmission times among MapReduce jobs of workflows. A new deadline division considering execution time, float time and job level is proposed to obtain better deadlines of MapReduce jobs in workflows. With the adapted replica strategy in MapReduce workflow, a new task scheduling is proposed to improve data locality which assigns tasks to servers with the earliest completion time in order to ensure resource providers obtain more profit. Experimental results show that the proposed heuristic results in larger total profit than other adopted algorithms.

[EN] A subgroup H of a finite group G is called P-subnormal in G if either H = G or it is connected to G by a chain of subgroups of prime indices. In this paper, some structural results of finite groups which are factorised as the product of two P-subnormal subgroups is showed.

[EN] Border tracking in binary images is an important kernel for many applications. There are very efficient sequential algorithms, most notably, the algorithm proposed by Suzuki et al., which has been implemented for CPUs in well-known libraries. However, under some circumstances, it would be advantageous to perform the border tracking in GPUs as efficiently as possible. In this paper, we propose a parallel version of the Suzuki algorithm that is designed to be executed in GPUs and implemented in CUDA. The proposed algorithm is based on splitting the image into small rectangles. Then, a thread is launched for each rectangle, which tracks the borders in its associated rectangle. The final step is to perform the connection of the borders belonging to several rectangles. The parallel algorithm has been compared with a state-of-the-art sequential CPU version, using two different CPUs and two different GPUs for the evaluation. The computing times obtained show that in these experiments with the GPUs and CPUs that we had available, the proposed parallel algorithm running in the fastest GPU is more than 10 times faster than the sequential CPU routine running in the fastest CPU.

[EN] The supply chain management requires increasingly proposals for the production programming planning that brings together its special singularities. Solving coexisting products and alternative processes or by-products must be allowed by the mathematical programming models. The generic materials and operations planning (GMOP) formulation allows operating with different materials and process lists. The paper presents a procedure to solve the versatile GMOP model by the Lagrange Relaxation. The subgradient update method of the lagrangian multiplier is compared with a linear update method. Obtaining lower bound faster compared to the linear method is allowed by the subgradient method, but the linear method provides better solutions after certain iterations.

This paper has integrated new and past data to elucidate how lipid, protein and glycogen metabolism contribute to generating the ATP required by the southern hemisphere lamprey Geotria australis during its ~ 13–15 months, non-trophic upstream spawning migration. Energy is required for maintenance, swimming, the development of gonads and secondary sexual characters and spawning and post-spawning activities. Plasma and muscle metabolites were measured in animals subjected to an exercise-recovery regime at the commencement and completion of the spawning run. The present study demonstrated the following. At all stages of the migration, plasma glucose and glycerol concentrations increased during exercise and then declined, whereas plasma FFAs exhibited the reverse trend. During exercise and recovery, alanine declined and ammonia increased in the plasma of early migrants, while the opposite occurred in mature males. Following exercise, muscle alanine rose and then declined in early migrants, but declined and then rose in mature males. The composite data emphasise that, while the same catabolic processes are employed by both sexes early in the migration, when animals are immature, they differ markedly between the sexes as they mature and then spawn, reflecting their different demands. Energy is supplied predominantly via anaerobic metabolism in early migrants, but by anaerobic and aerobic metabolism in prespawning females and by aerobic metabolism in mature males and spent females. Although proteolysis is limited early in the migration, it is employed extensively during maturation and particularly by females, which undergo a substantial reduction in length in the lead-up to spawning.

[EN] Optimization of lead placement and interventricular delay settings in patients under cardiac resynchronization therapy is a complex task that might benefit from prior information based on models. Biophysical models can be used to predict the sequence of electrical heart activation in a patient given a set of parameters which should be personalized to the patient. In this paper, we use electroanatomical maps to personalize the endocardial activation of the right ventricle, and the different tissue conductivities in a pig model with left bundle branch block, to reproduce personalized biventricular activations. Following, we tested the personalized heart model by virtually simulating cardiac resynchronization therapy

This paper has integrated new and past data to elucidate how lipid, protein and glycogen metabolism contribute to generating the ATP required by the southern hemisphere lamprey Geotria australis during its ~ 13–15 months, non-trophic upstream spawning migration. Energy is required for maintenance, swimming, the development of gonads and secondary sexual characters and spawning and post-spawning activities. Plasma and muscle metabolites were measured in animals subjected to an exercise-recovery regime at the commencement and completion of the spawning run. The present study demonstrated the following. At all stages of the migration, plasma glucose and glycerol concentrations increased during exercise and then declined, whereas plasma FFAs exhibited the reverse trend. During exercise and recovery, alanine declined and ammonia increased in the plasma of early migrants, while the opposite occurred in mature males. Following exercise, muscle alanine rose and then declined in early migrants, but declined and then rose in mature males. The composite data emphasise that, while the same catabolic processes are employed by both sexes early in the migration, when animals are immature, they differ markedly between the sexes as they mature and then spawn, reflecting their different demands. Energy is supplied predominantly via anaerobic metabolism in early migrants, but by anaerobic and aerobic metabolism in prespawning females and by aerobic metabolism in mature males and spent females. Although proteolysis is limited early in the migration, it is employed extensively during maturation and particularly by females, which undergo a substantial reduction in length in the lead-up to spawning.

KM3NeT is a research infrastructure located in the Mediterranean Sea, that will consist of two deep-sea Cherenkov neutrino detectors. With one detector (ARCA), the KM3NeT Collaboration aims at identifying and studying TeV–PeV astrophysical neutrino sources. With the other detector (ORCA), the neutrino mass ordering will be determined by studying GeV-scale atmospheric neutrino oscillations. The first KM3NeT detection units were deployed at the Italian and French sites between 2015 and 2017. In this paper, a description of the detector is presented, together with a summary of the procedures used to calibrate the detector in-situ. Finally, the measurement of the atmospheric muon flux between 2232–3386 m seawater depth is obtained., Peer Reviewed, Postprint (published version)

Generative adversarial networks (GANs) have been promising for many computer vision problems due to their powerful capabilities to enhance the data for training and test. In this paper, we leveraged GANs and proposed a new architecture with a cascaded Single Shot Detector (SSD) for pedestrian detection at distance, which is yet a challenge due to the varied sizes of pedestrians in videos at distance. To overcome the low-resolution issues in pedestrian detection at distance, DCGAN is employed to improve the resolution first to reconstruct more discriminative features for a SSD to detect objects in images or videos. A crucial advantage of our method is that it learns a multi-scale metric to distinguish multiple objects at different distances under one image, while DCGAN serves as an encoder-decoder platform to generate parts of an image that contain better discriminative information. To measure the effectiveness of our proposed method, experiments were carried out on the Canadian Institute for Advanced Research (CIFAR) dataset, and it was demonstrated that the proposed new architecture achieved a much better detection rate, particularly on vehicles and pedestrians at distance, making it highly suitable for smart cities applications that need to discover key objects or pedestrians at distance.

The UN Sustainable Development Goals (SDGs) are a vision for achieving a sustainable future. Reliable, timely, comprehensive, and consistent data are critical for measuring progress towards, and ultimately achieving, the SDGs. Data from citizen science represent one new source of data that could be used for SDG reporting and monitoring. However, information is still lacking regarding the current and potential contributions of citizen science to the SDG indicator framework. Through a systematic review of the metadata and work plans of the 244 SDG indicators, as well as the identification of past and ongoing citizen science initiatives that could directly or indirectly provide data for these indicators, this paper presents an overview of where citizen science is already contributing and could contribute data to the SDG indicator framework. The results demonstrate that citizen science is “already contributing” to the monitoring of 5 SDG indicators, and that citizen science “could contribute” to 76 indicators, which, together, equates to around 33%. Our analysis also shows that the greatest inputs from citizen science to the SDG framework relate to SDG 15 Life on Land, SDG 11 Sustainable Cities and Communities, SDG 3 Good Health and Wellbeing, and SDG 6 Clean Water and Sanitation. Realizing the full potential of citizen science requires demonstrating its value in the global data ecosystem, building partnerships around citizen science data to accelerate SDG progress, and leveraging investments to enhance its use and impact.

The UN Sustainable Development Goals (SDGs) are a vision for achieving a sustainable future. Reliable, timely, comprehensive, and consistent data are critical for measuring progress towards, and ultimately achieving, the SDGs. Data from citizen science represent one new source of data that could be used for SDG reporting and monitoring. However, information is still lacking regarding the current and potential contributions of citizen science to the SDG indicator framework. Through a systematic review of the metadata and work plans of the 244 SDG indicators, as well as the identification of past and ongoing citizen science initiatives that could directly or indirectly provide data for these indicators, this paper presents an overview of where citizen science is already contributing and could contribute data to the SDG indicator framework. The results demonstrate that citizen science is “already contributing” to the monitoring of 5 SDG indicators, and that citizen science “could contribute” to 76 indicators, which, together, equates to around 33%. Our analysis also shows that the greatest inputs from citizen science to the SDG framework relate to SDG 15 Life on Land, SDG 11 Sustainable Cities and Communities, SDG 3 Good Health and Wellbeing, and SDG 6 Clean Water and Sanitation. Realizing the full potential of citizen science requires demonstrating its value in the global data ecosystem, building partnerships around citizen science data to accelerate SDG progress, and leveraging investments to enhance its use and impact.

The UN Sustainable Development Goals (SDGs) are a vision for achieving a sustainable future. Reliable, timely, comprehensive, and consistent data are critical for measuring progress towards, and ultimately achieving, the SDGs. Data from citizen science represent one new source of data that could be used for SDG reporting and monitoring. However, information is still lacking regarding the current and potential contributions of citizen science to the SDG indicator framework. Through a systematic review of the metadata and work plans of the 244 SDG indicators, as well as the identification of past and ongoing citizen science initiatives that could directly or indirectly provide data for these indicators, this paper presents an overview of where citizen science is already contributing and could contribute data to the SDG indicator framework. The results demonstrate that citizen science is “already contributing” to the monitoring of 5 SDG indicators, and that citizen science “could contribute” to 76 indicators, which, together, equates to around 33%. Our analysis also shows that the greatest inputs from citizen science to the SDG framework relate to SDG 15 Life on Land, SDG 11 Sustainable Cities and Communities, SDG 3 Good Health and Wellbeing, and SDG 6 Clean Water and Sanitation. Realizing the full potential of citizen science requires demonstrating its value in the global data ecosystem, building partnerships around citizen science data to accelerate SDG progress, and leveraging investments to enhance its use and impact.

The UN Sustainable Development Goals (SDGs) are a vision for achieving a sustainable future. Reliable, timely, comprehensive, and consistent data are critical for measuring progress towards, and ultimately achieving, the SDGs. Data from citizen science represent one new source of data that could be used for SDG reporting and monitoring. However, information is still lacking regarding the current and potential contributions of citizen science to the SDG indicator framework. Through a systematic review of the metadata and work plans of the 244 SDG indicators, as well as the identification of past and ongoing citizen science initiatives that could directly or indirectly provide data for these indicators, this paper presents an overview of where citizen science is already contributing and could contribute data to the SDG indicator framework. The results demonstrate that citizen science is “already contributing” to the monitoring of 5 SDG indicators, and that citizen science “could contribute” to 76 indicators, which, together, equates to around 33%. Our analysis also shows that the greatest inputs from citizen science to the SDG framework relate to SDG 15 Life on Land, SDG 11 Sustainable Cities and Communities, SDG 3 Good Health and Wellbeing, and SDG 6 Clean Water and Sanitation. Realizing the full potential of citizen science requires demonstrating its value in the global data ecosystem, building partnerships around citizen science data to accelerate SDG progress, and leveraging investments to enhance its use and impact.

The UN Sustainable Development Goals (SDGs) are a vision for achieving a sustainable future. Reliable, timely, comprehensive, and consistent data are critical for measuring progress towards, and ultimately achieving, the SDGs. Data from citizen science represent one new source of data that could be used for SDG reporting and monitoring. However, information is still lacking regarding the current and potential contributions of citizen science to the SDG indicator framework. Through a systematic review of the metadata and work plans of the 244 SDG indicators, as well as the identification of past and ongoing citizen science initiatives that could directly or indirectly provide data for these indicators, this paper presents an overview of where citizen science is already contributing and could contribute data to the SDG indicator framework. The results demonstrate that citizen science is “already contributing” to the monitoring of 5 SDG indicators, and that citizen science “could contribute” to 76 indicators, which, together, equates to around 33%. Our analysis also shows that the greatest inputs from citizen science to the SDG framework relate to SDG 15 Life on Land, SDG 11 Sustainable Cities and Communities, SDG 3 Good Health and Wellbeing, and SDG 6 Clean Water and Sanitation. Realizing the full potential of citizen science requires demonstrating its value in the global data ecosystem, building partnerships around citizen science data to accelerate SDG progress, and leveraging investments to enhance its use and impact.

In fields such as space, aeronautics, mobility, telecommunications, banking, or defense, systems are becoming increasingly complex, consisting of a network of interrelated and interacting components, with significant heterogeneity and diverse lifecycles. The need for continuous changes in product, process and organization in order to keep manufacturing business competitiveness, make these segments seek to apply methods to innovate and develop their complex products and services with lower costs, improved productivity and quality, and in less lead-time. This paper presents the integrated and concurrent development of a complex product and their performing organizations with System Concurrent Engineering approach that performs stakeholder analysis, requirements analysis, functional analysis and implementation architecture analysis, simultaneously, for the product, its life cycle processes and their performing organization at all levels of the product hierarchy. In order to demonstrate the System Concurrent Engineering approach and the methodology, this paper shows a Satellite Container (Mechanical Ground Support Equipments MGSE) as example of a complex systems development., isbn: 978-1-4471-4425-0 978-1-4471-4426-7, Pages: 1057-1068

[EN] Childhood obesity is a significant health problem in current societies that is increasing at an alarming way among population of all ages. To date, studies on the effectiveness of treatments for childhood obesity in the medium and long term suggest a moderate effect on weight loss and maintenance, which has led to suggestions that early interventions have a preventive nature on adult obesity. The long-term recovery of the weight lost is often associated with a lack of adherence to recommendations for changing life habits. Then, obesity becomes a chronic problem, difficult to approach, and the main difficulty lies in promoting and ensuring adherence to a change in lifestyle. A system known as ETIOBE has been developed to improve the treatment adherence, to promote the mechanisms of self-control in patients and to prevent relapses. An important part of the ETIOBE system is the ubiquitous monitoring platform since it enables the clinician to obtain relevant information from patients (contextual, physiological and psychological), which enables treatment customization and adaptation, depending on the patient's evolution. The aim of this paper is to describe the monitoring platform which is intended to establish a sensor network whose focus is the obese children under clinical treatment, and the various elements that compose it: electronic PDA records to establish diet habits, HAS: home ambulatory system (data integration of biomedical devices; blood pressure to study hypertension; pulse oximeter to detect Sleep Disorders; and electronic t-shirt to detect physical activity). This paper presents the first validations of the electronic PDA records and the electronic t-shirt. These validations suggest that the monitoring platform can help to achieve the goals previously mentioned, by offering constant support and increasing motivation to change.

[EN] Childhood obesity is a significant health problem in current societies that is increasing at an alarming way among population of all ages. To date, studies on the effectiveness of treatments for childhood obesity in the medium and long term suggest a moderate effect on weight loss and maintenance, which has led to suggestions that early interventions have a preventive nature on adult obesity. The long-term recovery of the weight lost is often associated with a lack of adherence to recommendations for changing life habits. Then, obesity becomes a chronic problem, difficult to approach, and the main difficulty lies in promoting and ensuring adherence to a change in lifestyle. A system known as ETIOBE has been developed to improve the treatment adherence, to promote the mechanisms of self-control in patients and to prevent relapses. An important part of the ETIOBE system is the ubiquitous monitoring platform since it enables the clinician to obtain relevant information from patients (contextual, physiological and psychological), which enables treatment customization and adaptation, depending on the patient's evolution. The aim of this paper is to describe the monitoring platform which is intended to establish a sensor network whose focus is the obese children under clinical treatment, and the various elements that compose it: electronic PDA records to establish diet habits, HAS: home ambulatory system (data integration of biomedical devices; blood pressure to study hypertension; pulse oximeter to detect Sleep Disorders; and electronic t-shirt to detect physical activity). This paper presents the first validations of the electronic PDA records and the electronic t-shirt. These validations suggest that the monitoring platform can help to achieve the goals previously mentioned, by offering constant support and increasing motivation to change.

When a user performs a search on a search engine, the query reflects a particular interest of the user. The interest may either span a short session of a few minutes, or a long period of time like months or years. In the latter, the user may perform searching related to a particular interest from time to time, making the queries related to that interest sporadically distributed in the search log. Identification of these topically related queries is very meaningful, since it can help the search engine better understand the user's interest and hence deliver better results to the user. In this paper, we propose a method to aggregate topically related queries into interests regardless of where the queries appear in the search log. It first identifies sets of continuous topically-related queries called CTQs and then clusters similar CTQs together to form interests. In order to identify the CTQs accurately, we propose the Pattern-Concept-Time-Based (PCTB) method that utilizes query reformulation patterns, concepts behind the queries and the user's continuous searching behavior to compute the similarity between two queries. To evaluate the effectiveness of our approach, we employ the AOL search log as our test dataset and develop a search middleware on top of Google for extracting concepts related to the queries. Experimental results show that our method can obtain a high precision and recall on identifying CTQs, which in turn improves the performance of interest identification. © Springer-Verlag 2013.

Nonnegative matrix factorization (NMF) aims to decompose a given data matrix X into the product of two lower-rank nonnegative factor matrices UV T. Graph regularized NMF (GNMF) is a recently proposed NMF method that preserves the geometric structure of X during such decomposition. Although GNMF has been widely used in computer vision and data mining, its multiplicative update rule (MUR) based solver suffers from both slow convergence and non-stationarity problems. In this paper, we propose a new efficient GNMF solver called rank-one residue approximation (RRA). Different from MUR, which updates both factor matrices (U and V) as a whole in each iteration round, RRA updates each of their columns by approximating the residue matrix by their outer product. Since each column of both factor matrices is updated optimally in an analytic formulation, RRA is theoretical and empirically proven to converge rapidly to a stationary point. Moreover, since RRA needs neither extra computational cost nor parametric tuning, it enjoys a similar simplicity to MUR but performs much faster. Experimental results on real-world datasets show that RRA is much more efficient than MUR for GNMF. To confirm the stationarity of the solution obtained by RRA, we conduct clustering experiments on real-world image datasets by comparing with the representative solvers such as MUR and NeNMF for GNMF. The experimental results confirm the effectiveness of RRA. © 2013 Springer-Verlag.

Search engine query log is a valuable information source to analyze the users' interests and preferences. In existing work, click graph is intensively utilized to analyze the information in query log. However, click graph is usually plagued by low information coverage, failure of capturing the diverse types of co-occurrence and the incapability of discovering the latent semantics in data. In this paper, we go beyond click graph and analyze query log through the new perspective of probabilistic topic modeling. In order to systematically explore the potential assumptions of the latent structure of the log data, we propose three different topic models. The first model, the Meta-word Model (MWM), unifies the co-occurrence of query terms and URLs by the meta-word occurrence. The second model, the Term-URL Model (TUM), captures the characteristics of query terms and URLs separately. The third model, the Clickthrough Model (CTM), captures the clicking behavior explicitly and models the ternary relation between search queries, query terms and URLs. We evaluate the three proposed models against several strong baselines on a real-life query log. The experimental results show that the proposed models demonstrate significantly improved performance with respect to different quantitative metrics and also in applications such as date prediction, community discovery and URL annotation. © Springer-Verlag 2013.

Ranking documents in terms of their relevance to a given query is fundamental to many real-life applications such as document retrieval and recommendation systems. Extensive studies in this area have focused on developing efficient ranking models. While ranking models are usually trained based on given training datasets, besides model training algorithms, the quality of the document features selected for model training also plays a very important aspect on the model performance. The main objective of this paper is to present an approach to discover “significant” document features for learning to rank (LTR) problem. We conduct a systematic exploration of frequent pattern-based ranking. First, we formally analyze the effectiveness of frequent patterns for ranking. Combined features, which constitute a large portion of frequent patterns, perform better than single features in terms of capturing rich underlying semantics of the documents and hence provide good feature candidates for ranking. Based on our analysis, we propose a new ranking approach called FP-Rank. Essentially, FP-Rank adopts frequent pattern mining algorithms to mine frequent patterns, and then a new pattern selection algorithm is adopted to select a set of patterns with high overall significance and low redundancy. Our experiments on the real datasets confirm that, by incorporating effective frequent patterns to train a ranking model, such as RankSVM, the performance of the ranking model can be substantially improved.

Side information is highly useful in the learning of a nonparametric kernel matrix. However, this often leads to an expensive semidefinite program (SDP). In recent years, a number of dedicated solvers have been proposed. Though much better than off-the-shelf SDP solvers, they still cannot scale to large data sets. In this paper, we propose a novel solver based on the alternating direction method of multipliers (ADMM). The key idea is to use a low-rank decomposition of the kernel matrix Z = XTY , with the constraint that X = Y . The resultant optimization problem, though non-convex, has favorable convergence properties and can be efficiently solved without requiring eigen-decomposition in each iteration. Experimental results on a number of real-world data sets demonstrate that the proposed method is as accurate as directly solving the SDP, but can be one to two orders of magnitude faster. © Springer-Verlag 2013.

In this paper we present a novel two-stage framework for extracting what we define as a quasi-regular structure in facade images. A quasi-regular structure is an irregular rectangular grid representing the placements of repetitive structural architecture objects, e.g., windows, in a facade. Such a structure generalizes a perfect lattice structure generated by the 2D symmetry groups, studied by the previous work. First, we propose to formulate the quasi-regular structure detection in an object-oriented Marked Point Process framework by treating the architectural elements as objects. This leads to an initial quasi-regular structure map which serves as an indicator map of potential object locations. Then, we propose a regularization scheme to recover the complete quasi-regular structures from the initial incomplete structure. This stage takes advantage of the intrinsic low rank constraint of the quasi-regular structure representing a regularized facade. By applying such a regularization, the complete quasi-regular facade structure is obtained. We have extensively tested our method on a large variety of facade images, and demonstrated both the effectiveness and the robustness of our two-stage framework. © 2013 Springer-Verlag.

Previous papers show discordant patterns of monthly and seasonal differences in the frequency of multiple sclerosis relapses. Attacks are more often reported in spring and summer, but there are many variations, mainly as to summer peaks. This paper, an MSBase collaboration substudy, reports multiple series of relapses from 1980 to 2010, comparing ultradecennal trends of seasonal frequency of attacks in different countries. The MSBase international database was searched for relapses in series recording patient histories from 1980 up to 2010. The number of relapses by month was stratified by decade (1981-1990, 1991-2000, 2001-2010). Positive spring versus summer peaks were compared by odds ratios; different series were compared by weighted odds ratio (Peto OR). Decade comparison of the 1990s versus 2000s shows inversion of spring-summer peak (2000s = March; 1990s = July), significant in the whole group (Peto odds ratio = 1.31, CI = 1.10-1.56, p = 0.003) and in Salerno series (OR = 1.97, CI = 1.14-1.40). The global significance persisted also excluding Salerno series (Peto odds ratio = 1.25, CI = 1.04-1.50, p = 0.002). Multicentric data confirm a summer peak of relapses in the 1991-2000 decade, significantly different from the spring peak of 2001-2010. Seasonal frequency of relapses shows long-term variations, so that other factors such as viral epidemics might have more relevance than ultraviolet exposure.

In this paper, we evaluate the performances of state-of-the-art higher-order masking schemes for the AES. Doing so, we pay a particular attention to the comparison between specialized solutions introduced exclusively as countermeasures against side-channel analysis, and a recent proposal by Roche and Prouff exploiting MultiParty Computation (MPC) techniques. We show that the additional security features this latter scheme provides (e.g. its glitch-freeness) comes at the cost of large performance overheads. We then study how exploiting standard optimization techniques from the MPC literature can be used to reduce this gap. In particular, we show that “packed secret sharing” based on a modified multiplication algorithm can speed up MPC-based masking when the order of the masking scheme increases. Eventually, we discuss the randomness requirements of masked implementations. For this purpose, we first show with information theoretic arguments that the security guarantees of masking are only preserved if this randomness is uniform, and analyze the consequences of a deviation from this requirement. We then conclude the paper by including the cost of randomness generation in our performance evaluations. These results should help actual designers to choose a masking scheme based on security and performance constraints.

Nonnegative matrix factorization (NMF) aims to decompose a given data matrix X into the product of two lower-rank nonnegative factor matrices UV T. Graph regularized NMF (GNMF) is a recently proposed NMF method that preserves the geometric structure of X during such decomposition. Although GNMF has been widely used in computer vision and data mining, its multiplicative update rule (MUR) based solver suffers from both slow convergence and non-stationarity problems. In this paper, we propose a new efficient GNMF solver called rank-one residue approximation (RRA). Different from MUR, which updates both factor matrices (U and V) as a whole in each iteration round, RRA updates each of their columns by approximating the residue matrix by their outer product. Since each column of both factor matrices is updated optimally in an analytic formulation, RRA is theoretical and empirically proven to converge rapidly to a stationary point. Moreover, since RRA needs neither extra computational cost nor parametric tuning, it enjoys a similar simplicity to MUR but performs much faster. Experimental results on real-world datasets show that RRA is much more efficient than MUR for GNMF. To confirm the stationarity of the solution obtained by RRA, we conduct clustering experiments on real-world image datasets by comparing with the representative solvers such as MUR and NeNMF for GNMF. The experimental results confirm the effectiveness of RRA. © 2013 Springer-Verlag.

Search engine query log is a valuable information source to analyze the users' interests and preferences. In existing work, click graph is intensively utilized to analyze the information in query log. However, click graph is usually plagued by low information coverage, failure of capturing the diverse types of co-occurrence and the incapability of discovering the latent semantics in data. In this paper, we go beyond click graph and analyze query log through the new perspective of probabilistic topic modeling. In order to systematically explore the potential assumptions of the latent structure of the log data, we propose three different topic models. The first model, the Meta-word Model (MWM), unifies the co-occurrence of query terms and URLs by the meta-word occurrence. The second model, the Term-URL Model (TUM), captures the characteristics of query terms and URLs separately. The third model, the Clickthrough Model (CTM), captures the clicking behavior explicitly and models the ternary relation between search queries, query terms and URLs. We evaluate the three proposed models against several strong baselines on a real-life query log. The experimental results show that the proposed models demonstrate significantly improved performance with respect to different quantitative metrics and also in applications such as date prediction, community discovery and URL annotation. © Springer-Verlag 2013.

Side information is highly useful in the learning of a nonparametric kernel matrix. However, this often leads to an expensive semidefinite program (SDP). In recent years, a number of dedicated solvers have been proposed. Though much better than off-the-shelf SDP solvers, they still cannot scale to large data sets. In this paper, we propose a novel solver based on the alternating direction method of multipliers (ADMM). The key idea is to use a low-rank decomposition of the kernel matrix Z = XTY , with the constraint that X = Y . The resultant optimization problem, though non-convex, has favorable convergence properties and can be efficiently solved without requiring eigen-decomposition in each iteration. Experimental results on a number of real-world data sets demonstrate that the proposed method is as accurate as directly solving the SDP, but can be one to two orders of magnitude faster. © Springer-Verlag 2013.

This paper considers minimax regret 1-sink location problems in dynamic path networks. A dynamic path network consists of an undirected path with positive edge lengths and constant edge capacity and the vertex supply which is nonnegative value, called weight, is unknown but only the interval of weight is known. A particular assignment of weight to each vertex is called a scenario. Under any scenario, the cost of a sink is defined as the minimum time to complete evacuation for all weights (evacuees), and the regret of a sink location x is defined as the cost of x minus the cost of an optimal sink. Then, the problem is to find a point as a sink such that the maximum regret for all possible scenarios is minimized. We present an O(n log2 n) time algorithm for minimax regret 1-sink location problems in dynamic path networks, where n is the number of vertices in the network. © Springer-Verlag Berlin Heidelberg 2013.

In this paper we present a novel two-stage framework for extracting what we define as a quasi-regular structure in facade images. A quasi-regular structure is an irregular rectangular grid representing the placements of repetitive structural architecture objects, e.g., windows, in a facade. Such a structure generalizes a perfect lattice structure generated by the 2D symmetry groups, studied by the previous work. First, we propose to formulate the quasi-regular structure detection in an object-oriented Marked Point Process framework by treating the architectural elements as objects. This leads to an initial quasi-regular structure map which serves as an indicator map of potential object locations. Then, we propose a regularization scheme to recover the complete quasi-regular structures from the initial incomplete structure. This stage takes advantage of the intrinsic low rank constraint of the quasi-regular structure representing a regularized facade. By applying such a regularization, the complete quasi-regular facade structure is obtained. We have extensively tested our method on a large variety of facade images, and demonstrated both the effectiveness and the robustness of our two-stage framework. © 2013 Springer-Verlag.

When a user performs a search on a search engine, the query reflects a particular interest of the user. The interest may either span a short session of a few minutes, or a long period of time like months or years. In the latter, the user may perform searching related to a particular interest from time to time, making the queries related to that interest sporadically distributed in the search log. Identification of these topically related queries is very meaningful, since it can help the search engine better understand the user's interest and hence deliver better results to the user. In this paper, we propose a method to aggregate topically related queries into interests regardless of where the queries appear in the search log. It first identifies sets of continuous topically-related queries called CTQs and then clusters similar CTQs together to form interests. In order to identify the CTQs accurately, we propose the Pattern-Concept-Time-Based (PCTB) method that utilizes query reformulation patterns, concepts behind the queries and the user's continuous searching behavior to compute the similarity between two queries. To evaluate the effectiveness of our approach, we employ the AOL search log as our test dataset and develop a search middleware on top of Google for extracting concepts related to the queries. Experimental results show that our method can obtain a high precision and recall on identifying CTQs, which in turn improves the performance of interest identification. © Springer-Verlag 2013.

Mathematics in schools exists substantially as pedagogical material crafted for supposed modes of apprehension. But of course such apprehension depends on how we understand mathematical objects and how we understand human subjects. The apprehension of mathematical objects is examined through sessions with student teachers researching their own spatial awareness from a pedagogical point of view. The paper is guided by the recent work of Alain Badiou whose philosophical model develops a Lacanian conception of human subjectivity and defines a new conception of objectivity. In this model the conception of subjectivity comprises a refusal to allow humans to settle on certain self-images that have fuelled psychology and set the ways in which humans are seen as apprehending the mathematically defined world. The assertion of an object, meanwhile, is associated with finding a place for it in a given supposed world, and it may reconfigure that world. The composite model understands learning as shared participation in renewal where there is a mutual dependency between the growth of human subjects and of mathematical objects. Renewal is referenced to a diversity of ever shifting discursive parameters that enable learning through negotiating the spaces within which we operate and the objects those spaces allow. Learning to teach then comprises developing sensitivity towards the discursive spaces that allow others to build objects. The paper provides examples from teacher education activities centred in addressing these concerns.

As a society we are now more connected than ever before. Citizens interact with each other and form virtual communities based on a common interest or being involved in certain cultural, political, intellectual, or other issues. Volunteered Geographic Information (VGI) is generated when citizens annotate content from social media and smart devices. OpenStreetMap is a famous example of a VGI project with a very large community of contributors. This chapter examines this community and investigates the types of contributors and interactions amongst members of OSM. Our results show that there are very small groups of individuals creating and editing over 85 % of all OSM objects in three casestudy cities. Editing and contribution behaviour is mostly steady and consistent over time except during months where OSM ‘mapping parties’ occur or when freely available spatial data is bulk imported into OSM. The paper also provides results of analysis into the social interaction between contributors to OSM where we show that a very small number of contributors are actively editing and maintaining the data submitted by other contributors. This indicates that most contributors work exclusively on their own data and rarely edit the work of others. Overall this paper will be of interest to LBS practitioners considering using OSM as a source of spatial data for LBS applications.

In fields such as space, aeronautics, mobility, telecommunications, banking, or defense, systems are becoming increasingly complex, consisting of a network of interrelated and interacting components, with significant heterogeneity and diverse lifecycles. The need for continuous changes in product, process and organization in order to keep manufacturing business competitiveness, make these segments seek to apply methods to innovate and develop their complex products and services with lower costs, improved productivity and quality, and in less lead-time. This paper presents the integrated and concurrent development of a complex product and their performing organizations with System Concurrent Engineering approach that performs stakeholder analysis, requirements analysis, functional analysis and implementation architecture analysis, simultaneously, for the product, its life cycle processes and their performing organization at all levels of the product hierarchy. In order to demonstrate the System Concurrent Engineering approach and the methodology, this paper shows a Satellite Container (Mechanical Ground Support Equipments MGSE) as example of a complex systems development., Pages: 1057-1068

In fields such as space, aeronautics, mobility, telecommunications, banking, or defense, systems are becoming increasingly complex, consisting of a network of interrelated and interacting components, with significant heterogeneity and diverse lifecycles. The need for continuous changes in product, process and organization in order to keep manufacturing business competitiveness, make these segments seek to apply methods to innovate and develop their complex products and services with lower costs, improved productivity and quality, and in less lead-time. This paper presents the integrated and concurrent development of a complex product and their performing organizations with System Concurrent Engineering approach that performs stakeholder analysis, requirements analysis, functional analysis and implementation architecture analysis, simultaneously, for the product, its life cycle processes and their performing organization at all levels of the product hierarchy. In order to demonstrate the System Concurrent Engineering approach and the methodology, this paper shows a Satellite Container (Mechanical Ground Support Equipments MGSE) as example of a complex systems development., Pages: 1057-1068

In fields such as space, aeronautics, mobility, telecommunications, banking, or defense, systems are becoming increasingly complex, consisting of a network of interrelated and interacting components, with significant heterogeneity and diverse lifecycles. The need for continuous changes in product, process and organization in order to keep manufacturing business competitiveness, make these segments seek to apply methods to innovate and develop their complex products and services with lower costs, improved productivity and quality, and in less lead-time. This paper presents the integrated and concurrent development of a complex product and their performing organizations with System Concurrent Engineering approach that performs stakeholder analysis, requirements analysis, functional analysis and implementation architecture analysis, simultaneously, for the product, its life cycle processes and their performing organization at all levels of the product hierarchy. In order to demonstrate the System Concurrent Engineering approach and the methodology, this paper shows a Satellite Container (Mechanical Ground Support Equipments MGSE) as example of a complex systems development., isbn: 978-1-4471-4425-0 978-1-4471-4426-7, Pages: 1057-1068

This paper presents a new form of consensus that allows nodes to agree locally on the extent of crashed regions in networks of arbitrary size. One key property of our algorithm is that it shows local complexity, i.e. its cost is independent of the size of the complete system, and only depends on the shape and extent of the crashed region to be agreed upon. In this paper, we motivate the need for such an algorithm, formally define this new consensus problem, propose a fault-tolerant solution, and prove its correctness.

This paper presents an overview of a pilot implementation of the ISO/IEC 29110 standard, with a group of very small software development companies based in Ireland. This paper may serve as guidance for both researchers and practitioners wishing to understand the issues of process standards adoption by very small companies and ISO/IEC 29110 in particular.

In recent years there has been a rapid increase in demand for e-learning systems. The software development process plays a crucial role in the design and development of a high-quality e-learning system. However, to date, there is no comprehensive comparative study of open source software (OSS) development process for different OS e-learning systems. This hinders the development of a generalized OSS development process, a key requisite for rapidly developing high-quality OS e-learning systems. This paper provides a full analysis of different existing and successful OS e-learning software systems and the best practices followed in the e-learning development. In particular, this paper investigates the software development activities of Moodle, Dokeos and ILIAS. An activity flow representation that describes their current development practices is constructed individually for all three OS e-learning systems. Further, a comprehensive comparative analysis is carried out that leads to an explicit identification of various development stages of the three OS e-learning systems.

In fields such as space, aeronautics, mobility, telecommunications, banking, or defense, systems are becoming increasingly complex, consisting of a network of interrelated and interacting components, with significant heterogeneity and diverse lifecycles. The need for continuous changes in product, process and organization in order to keep manufacturing business competitiveness, make these segments seek to apply methods to innovate and develop their complex products and services with lower costs, improved productivity and quality, and in less lead-time. This paper presents the integrated and concurrent development of a complex product and their performing organizations with System Concurrent Engineering approach that performs stakeholder analysis, requirements analysis, functional analysis and implementation architecture analysis, simultaneously, for the product, its life cycle processes and their performing organization at all levels of the product hierarchy. In order to demonstrate the System Concurrent Engineering approach and the methodology, this paper shows a Satellite Container (Mechanical Ground Support Equipments MGSE) as example of a complex systems development., isbn: 978-1-4471-4425-0 978-1-4471-4426-7, Pages: 1057-1068

As a society we are now more connected than ever before. Citizens interact with each other and form virtual communities based on a common interest or being involved in certain cultural, political, intellectual, or other issues. Volunteered Geographic Information (VGI) is generated when citizens annotate content from social media and smart devices. OpenStreetMap is a famous example of a VGI project with a very large community of contributors. This chapter examines this community and investigates the types of contributors and interactions amongst members of OSM. Our results show that there are very small groups of individuals creating and editing over 85 % of all OSM objects in three casestudy cities. Editing and contribution behaviour is mostly steady and consistent over time except during months where OSM ‘mapping parties’ occur or when freely available spatial data is bulk imported into OSM. The paper also provides results of analysis into the social interaction between contributors to OSM where we show that a very small number of contributors are actively editing and maintaining the data submitted by other contributors. This indicates that most contributors work exclusively on their own data and rarely edit the work of others. Overall this paper will be of interest to LBS practitioners considering using OSM as a source of spatial data for LBS applications.

As a society we are now more connected than ever before. Citizens interact with each other and form virtual communities based on a common interest or being involved in certain cultural, political, intellectual, or other issues. Volunteered Geographic Information (VGI) is generated when citizens annotate content from social media and smart devices. OpenStreetMap is a famous example of a VGI project with a very large community of contributors. This chapter examines this community and investigates the types of contributors and interactions amongst members of OSM. Our results show that there are very small groups of individuals creating and editing over 85 % of all OSM objects in three casestudy cities. Editing and contribution behaviour is mostly steady and consistent over time except during months where OSM ‘mapping parties’ occur or when freely available spatial data is bulk imported into OSM. The paper also provides results of analysis into the social interaction between contributors to OSM where we show that a very small number of contributors are actively editing and maintaining the data submitted by other contributors. This indicates that most contributors work exclusively on their own data and rarely edit the work of others. Overall this paper will be of interest to LBS practitioners considering using OSM as a source of spatial data for LBS applications.