ALBERT

Description: Vehicular traffic re-routing is the key to provide better traffic mobility. However, taking into account just traffic-related information to recommend better routes for each vehicle is far from achieving the desired requirements of proper transportation management. In this way, context-aware and multi-objective re-routing approaches will play an important role in traffic management. Yet, most procedures are deterministic and cannot support the strict requirements of traffic management applications, since many vehicles potentially will take the same route, consequently degrading overall traffic efficiency. So, we propose an efficient algorithm named as Better Safe Than Sorry (BSTS), based on Pareto-efficiency. Simulation results have shown that our proposal provides a better trade-off between mobility and safety than state-of-the-art approaches and also avoids the problem of potentially creating different congestion spots.

Description: 〈h3〉Abstract〈/h3〉 〈p〉The viral marketing is a relatively new form of marketing that exploits social networks to promote a brand, a product, etc. The idea behind it is to find a set of influencers on the network that can trigger a large cascade of propagation and adoptions. In this paper, we will introduce an evidential opinion-based influence maximization model for viral marketing. Besides, our approach tackles three opinion-based scenarios for viral marketing in the real world. The first scenario concerns influencers who have a positive opinion about the product. The second scenario deals with influencers who have a positive opinion about the product and produces effects on users who also have a positive opinion. The third scenario involves influence users who have a positive opinion about the product and produce effects on the negative opinion of other users concerning the product in question. Next, we proposed six influence measures, two for each scenario. We also use an influence maximization model that the set of detected influencers for each scenario. Finally, we show the performance of the proposed model with each influence measure through some experiments conducted on a generated dataset and a real-world dataset collected from Twitter.〈/p〉

Description: Due to the increasing popularity of recent advanced features and context-awareness in smart mobile phones, the contextual data relevant to users’ diverse activities with their phones are recorded through the d...

Description: 〈h3〉Abstract〈/h3〉 〈p〉In recent years, notable number of research studies have been conducted on the analysis of diffusion process in complex networks. One fundamental problem in this domain is to find the most influential spreader nodes. For achieving a successful spreading process, nodes having high spreading ability should be selected as spreaders. Many centrality measures have been proposed for determining and ranking the significance of nodes and detecting the best spreaders. The majority of proposed centrality measures require network global information which leads to high time complexity. Moreover, with the advent of large-scale complex networks, there is a critical need for improving accurate measures through using nodes’ local information. On the other hand, most of the formerly proposed centrality measures have attempted to select core nodes as spreaders but global bridge nodes have the highest spreading ability since they are located among several giant communities of the network. In this study, a new local and parameter-free centrality measure is proposed which is aimed at finding global bridge nodes in the network. Hence, two new local metrics, namely 〈em〉edge ratio and neighborhood diversity〈/em〉, are firstly defined which are used in the proposed method. Considering edge ratio of neighbors ensures that the selected node be not in the periphery location of the network. Furthermore, a node with high neighborhood diversity is likely a connector between some modules (dense parts) of the network. Therefore, a node with a high edge ratio and more diverse neighborhood has high spreading ability. The major merits of the proposed measure are near-linear time complexity, using local information and being parameter-free. For evaluating the proposed method, we conducted experiments on real-world networks. The results of comparing the proposed centrality measure with other measures in terms of epidemic models (SIR and SI), Kendall’s tau correlation coefficient and Rank-Frequency measures indicated that the proposed method outperforms the other compared centrality measures.〈/p〉

Description: 〈h3〉Abstract〈/h3〉 〈p〉Current developments in information and electronic technologies have pushed a tremendous amount of applications to meet the demands of personal computing services. Various kinds of smart devices have been launched and applied in our daily lives to provide services for individuals; however, the existing computing frameworks including local silo-based and cloud-based architectures, are not quite fit for personal computing services. Meanwhile, personal computing applications exhibit special features, they are latency-sensitive, energy efficient, highly reliable, mobile, etc, which further indicates that a new computing architecture is urgently needed to support such services. Thanks to the emerging edge computing paradigm, we were inspired to apply the distributed cooperative computing idea at the data source, which perfectly solves issues occurring among existing computing paradigms while meeting the requirements of personal computing services. Therefore, we explore personal computing services utilizing the edge computing paradigm, discuss the overall edge-based system architecture for personal computing services, and design the conceptual framework for an edge-based personal computing system. We analyze the functionalities in detail. To validate the feasibility of the proposed architecture, a fall detection application is simulated in our preliminary evaluation as an example service in which three Support Vector Machine based fall detection algorithms with different kernel functions are implemented. Experimental results show edge computing architecture can improve the performance of the system in terms of total latency, with about 22.75% reduction on average in the case of applying 4G at the second hop even when the data and computing stream of the application is small.〈/p〉

Description: 〈h3〉Abstract〈/h3〉 〈p〉Communications among IoT devices and backend servers are among the most critical aspects of IoT system, as it not only governs hardware requirement and system performance but also impacts security and privacy of the system. Multi-access edge computing (MEC) is an enabler for service development in 5G. Furthermore, MEC will reduce IoT system communication cost such as latency and bandwidth thanks to its ubiquitously nearby processing and storage capability. Also, MEC will provide opportunities to enhance IoT system’s network privacy, which cannot be solved by IoT system itself easily. In this paper, we present MEC enhanced mobility support system for IoT system: with MEC it can be implemented with low operation cost and overhead and can protect IoT system’s user from being exploited due to the exposed network location of data uploading and the control signals.〈/p〉

Description: 〈h3〉Abstract〈/h3〉 〈p〉Detecting and tracking events from logging data is a critical element for security and system administrators and thus attracts more and more research efforts. However, there exists a major limitation in current processes of Event Logging analysis, related to the verbosity and language-dependence of messages produced by many logging systems. In this paper, a novel methodology was proposed to tackle this limitation by analysing event messages through a Natural Language Processing task in order to annotate them with semantic metadata. These metadata are further used to enable semantic searches or domain ontology population that help administrator to filter only relevant event and to correlate them for a prompt and efficient response and incident analysis.〈/p〉

Description: 〈h3〉Abstract〈/h3〉 〈p〉Network virtualization is widely considered as a mainstay for overcoming the Internet’s ossification problem, and virtual network embedding (VNE) is a critical issue. Over recent years, growing energy costs and increased ecological awareness have stimulated the interest in reducing energy consumption by Internet service providers (ISP). Dependability is also an important requirement, as it involves metrics such as reliability and availability, which directly impact quality of service (QoS). Prior works on virtual network embedding have focused mainly on maximizing revenue for Internet service providers (ISPs), and they did not consider energy consumption and dependability metrics jointly in the mapping. This paper presents an energy-efficient mapping of dependable virtual networks. The approach considers a problem formulation that concomitantly takes into account energy consumption and availability constraints for virtual network embedding problem, and an algorithm based on Greedy Randomized Adaptive Search Procedure (GRASP) metaheuristic is adopted. The algorithm utilizes a sensitivity analysis based on availability importance to achieve the QoS required by each virtual network, and models based on reliability block diagrams (RBD) and stochastic Petri nets (SPN) are utilized to estimate availability. Results demonstrate the feasibility of the proposed approach, and they show the trade-off between availability, energy consumption, cost and revenue.〈/p〉

Description: 〈h3〉Abstract〈/h3〉 〈p〉Thinning algorithms have been widely applied in many applications such as computer graphics, computer vision and medical imaging. The performances of thinning algorithms are hampered by the increase of image’s size and image’s content. By employing graphics processing units (GPUs), the calculation of thinning algorithms can be accelerated. In this paper, we present GPU parallel versions of well-known binary thinning algorithms namely the ZS algorithm, the GH algorithm, the AW algorithm and the hybrid algorithm belonging to different classes for an efficient implementation using CUDA as a parallel programming model. These thinning algorithms are then evaluated and compared according to quality and runtime measures. The obtained results show that the GPU multithreaded implementations of the selected algorithms present high computational speed outperforming the central processing unit sequential implementations achieving the best average speedup when executing the AW algorithm.〈/p〉

Description: 〈h3〉Abstract〈/h3〉 〈p〉This study elucidates the dynamic behaviour of the two competing mutually exclusive epidemic (meme) spreading model with the alert of memes over multiplex social networks. Each meme spreads over a distinct contact networks 〈span〉 〈span〉\((CN_1,CN_2)\)〈/span〉 〈/span〉 of an undirected multiplex social network. The behavioural responses of agents (alerts) to the spread of competing memes is disseminated through information dissemination network (IDN). Here, IDN has the same nodes but different links with respect to the respective 〈span〉 〈span〉\(CN_i(i=1,2)\)〈/span〉 〈/span〉. The analytical treatment of this model is analysed through the mean field approximation of the epidemic process. Also, it has been shown through numerical illustrations that a node in the alert state is less probable to become infected than a node in the susceptible state. Moreover, co-existence of both the memes, the survival threshold, the absolute dominance threshold of the two competitive memes and the alert threshold for minimizing the severity of meme spread are analytically explored and numerically illustrated.〈/p〉

Description: 〈h3〉Abstract〈/h3〉 〈p〉Recently, the Internet of things (IoT) has received a lot of attention from both industry and academia. A reliable and secure IoT connection and communication is essential for the proper working of the IoT network as a whole. One of the ways to achieve robust security in an IoT network is to enable and build trusted communication among the things (nodes). In this area, the existing IoT literature faces many critical issues, such as the lack of intelligent cluster-based trust approaches for IoT networks and the detection of attacks on the IoT trust system from malicious nodes, such as bad service providers. The existing literature either does not address these issues or only addresses them partially. Our proposed solution can firstly detect on-off attacks using the proposed fuzzy-logic based approach, and it can detect contradictory behaviour attacks and other malicious nodes. Secondly, we develop a fuzzy logic-based approach to detect malicious nodes involved in bad service provisioning. Finally, to maintain the security of the IoT network, we develop a secure messaging system that enables secure communication between nodes. This messaging system uses hexadecimal values with a structure similar to serial communication. We carried out extensive experimentation under varying network sizes to validate the working of our proposed solution and also to test the efficiency of the proposed methods in relation to various types of malicious behavior. The experiment results demonstrate the effectiveness of our approach under various conditions.〈/p〉

Description: 〈h3〉Abstract〈/h3〉 〈p〉Individuals who suffer from different sleep breathing disorders suffer from a wide range of serious health problems. Unfortunately, the rate of diagnosis is very low, and the existing breathing monitoring techniques are expensive, uncomfortable and time- and labor-intensive. The gold standard PSG is invasive, costly, technically complex and time-consuming. Toward developing a non-contact sleep breathing monitoring system, this study presents a motion-based computer vision approach that aims to detect breathing movements of the sleeping patient from infrared videos and map them into a waveform. The proposed waveform illustrates that each type of breathing difficulty has a specific pattern and hence can be easily distinguished. This facilitates identifying only suspicious periods during which physiological signals will be scored, instead of analyzing the whole signals of 8 h of sleep.〈/p〉

Description: 〈h3〉Abstract〈/h3〉 〈p〉This research has been investigating an automatic and online crowd anomaly detection model by exploring a novel compound image descriptor generated from live video streams. A dual-channel convolutional neural network (DCCNN) has been set up for efficiently processing scene-related and motion-related crowd information inherited from raw frames and the compound descriptor instances. The novelty of the work stemmed from the creation of the spatio-temporal cuboids in online (or near real-time) manner through dynamically extracting local feature tracklets within the temporal space while handling the foreground region-of-interests (i.e., moving targets) through the exploration of Gaussian Mixture Model in the spatial space. Hence, the extracted foreground blocks can effectively eliminate irrelevant backgrounds and noises from the live streams for reducing the computational costs in the subsequent detecting phases. The devised compound feature descriptor, named as spatio-temporal feature descriptor (STFD), is capable of characterizing the crowd attributes through the measures such as collectiveness, stability, conflict and density in each online generated spatio-temporal cuboid. A STFD instance registers not only the dynamic variation of the targeted crowd over time based on local feature tracklets, but also the interaction information of neighborhoods within a crowd, e.g., the interaction force through the K-nearest neighbor (K-NN) analysis. The DCCNN developed in this research enables online identification of suspicious crowd behaviors based on analyzing the live-feed images and their STFD instances. The proposed model has been developed and evaluated against benchmarking techniques and databases. Experimental results have shown substantial improvements in terms of detection accuracy and efficiency for online crowd abnormal behavior identification. 〈/p〉

Description: 〈h3〉Abstract〈/h3〉 〈p〉Identification and classification of extremist-related tweets is a hot issue. Extremist gangs have been involved in using social media sites like Facebook and Twitter for propagating their ideology and recruitment of individuals. This work aims at proposing a terrorism-related content analysis framework with the focus on classifying tweets into extremist and non-extremist classes. Based on user-generated social media posts on Twitter, we develop a tweet classification system using deep learning-based sentiment analysis techniques to classify the tweets as extremist or non-extremist. The experimental results are encouraging and provide a gateway for future researchers.〈/p〉

Description: 〈h3〉Abstract〈/h3〉 〈p〉Automatically and efficiently detecting abnormal events that occur in dynamic surveillance video is one of the important tasks in real-time video streaming analysis. However, in some typical application scenarios with narrow areas, the perspective distortion caused by the large depth-of-field has a tremendous negative impact on the accuracy of detection, thereby increasing the difficulty of identifying abnormal behavior. Taking the real-time violence occurring in the metro platform as an example, the article introduces a more effective algorithm for detecting abnormal behaviors in narrow areas with perspective distortion. The algorithm firstly uses the adaptive transformation mechanism to make up for the distorting effect in the region of interest extraction. Then, an improved pyramid L–K optical flow method with perspective weight and disorder coefficient is proposed to extract the abnormal behavior feature occurred in historical moving images. The side-by-side comparison of the experimental results proves that this algorithm can effectively compensate for the distortion effect and obviously improve the accuracy of abnormal behavior detection in narrow area scenes.〈/p〉

Description: 〈h3〉Abstract〈/h3〉 〈p〉The imbalance energy consumption and high data traffic at intermediate nodes degrade the network performance. In this paper, we propose: energy grade and balance load distribution corona, EG without corona and DA without corona based schemes to distribute data traffic across the network nodes for efficient energy consumption. The dynamic adjustment of transmission range in first scheme helps in reducing data load. Additionally, the transmission range is purely based on distance, energy and data load of the receiver node for achieving maximum network lifetime. Second scheme divides a data packet into three fractions; small, medium and large for transmitting from various paths to evenly distribute the data load on the network nodes. In third scheme, depth adjustment of void node is performed to resume network operations, whereas, the load distribution and transmission range mechanisms are the same. The extensive simulations are carried out to show the effectiveness of proposed schemes in terms of PDR, energy consumption, and load distribution against the baseline scheme.〈/p〉

Description: 〈h3〉Abstract〈/h3〉 〈p〉O2O commerce is a new business model combining online shopping and offline transactions. While many reports indicate the large potential size of the O2O market, little is known about users’ continuance intention to use. This study applies an expectation confirmation model (ECM) that incorporates perceived hedonic benefits, product information intensity and transaction costs as belief-related constructs to predict users’ continuance intention to use O2O apps. The proposed model was empirically evaluated using survey data collected from 333 users concerning their perceptions of O2O apps. The results indicated that confirmation of O2O app usage experience was positively related to both perceived benefits such as utilitarian and hedonic benefits and satisfaction. Perceived benefits, satisfaction and transaction costs were found to have a direct impact on continuance intention. Specifically, there was a significant difference between task-oriented O2O apps users and entertainment-oriented O2O apps users. The results may provide further insights into O2O app marketing strategies.〈/p〉

Description: 〈h3〉Abstract〈/h3〉 〈p〉We propose a data-driven approach to quantify the uncertainty of models constructed by kernel methods. Our approach minimizes the needed distributional assumptions, hence, instead of working with, for example, Gaussian processes or exponential families, it only requires knowledge about some mild regularity of the measurement noise, such as it is being symmetric or exchangeable. We show, by building on recent results from finite-sample system identification, that by perturbing the residuals in the gradient of the objective function, information can be extracted about the amount of uncertainty our model has. Particularly, we provide an algorithm to build exact, non-asymptotically guaranteed, distribution-free confidence regions for ideal, noise-free representations of the function we try to estimate. For the typical convex quadratic problems and symmetric noises, the regions are star convex centered around a given nominal estimate, and have efficient ellipsoidal outer approximations. Finally, we illustrate the ideas on typical kernel methods, such as LS-SVC, KRR, 〈span〉 〈span〉\(\varepsilon \)〈/span〉 〈/span〉-SVR and kernelized LASSO.〈/p〉

Description: 〈h3〉Abstract〈/h3〉 〈p〉In this paper, an early prediction of vehicle trajectories and turning movements are investigated using traffic cameras. A vision-based tracking system is developed to monitor intersection videos and collect vehicle trajectories with their labels known as turning movements. Firstly, two intersection videos are monitored for 2 h, and collected trajectories with their labels are used to train deep neural networks and obtain the turning models for the prediction task. Deep neural networks are further investigated on a third intersection with different video settings. The future 2 s evaluation of trajectories shows the success of long short-term memory networks to early predict the turning movements with more than 92% accuracy.〈/p〉

Description: 〈h3〉Abstract〈/h3〉 〈p〉To understand how anatomy and physiology allow an organism to perform its function, it is important to know how information that is transmitted by spikes in the brain is received and encoded. A natural question is whether the spike rate alone encodes the information about a stimulus (〈em〉rate code〈/em〉), or additional information is contained in the temporal pattern of the spikes (〈em〉temporal code〈/em〉). Here we address this question using data from the cat Lateral Geniculate Nucleus (LGN), which is the visual portion of the thalamus, through which visual information from the retina is communicated to the visual cortex. We analyzed the responses of LGN neurons to spatially homogeneous spots of various sizes with temporally random luminance modulation. We compared the Firing Rate with the Shannon Information Transmission Rate , which quantifies the information contained in the temporal relationships between spikes. We found that the behavior of these two rates can differ quantitatively. This suggests that the energy used for spiking does not translate directly into the information to be transmitted. We also compared Firing Rates with Information Rates for X-ON and X-OFF cells. We found that, for X-ON cells the Firing Rate and Information Rate often behave in a completely different way, while for X-OFF cells these rates are much more highly correlated. Our results suggest that for X-ON cells a more efficient 〈em〉“temporal code”〈/em〉 is employed, while for X-OFF cells a straightforward 〈em〉“rate code”〈/em〉 is used, which is more reliable and is correlated with energy consumption. 〈/p〉

Description: Recommender systems have become a popular and effective way to quickly discover new service items that are probably preferred by prospective users. Through analyzing the historical service usage data produced ...

Description: 〈h3〉Abstract〈/h3〉 〈p〉Active learning algorithms propose what data should be labeled given a pool of unlabeled data. Instead of selecting randomly what data to annotate, active learning strategies aim to select data so as to get a good predictive model with as little labeled samples as possible. Single-shot batch active learners select all samples to be labeled in a single step, before any labels are observed. We study single-shot active learners that minimize generalization bounds to select a representative sample, such as the maximum mean discrepancy (MMD) active learner. We prove that a related bound, the discrepancy, provides a tighter worst-case bound. We study these bounds probabilistically, which inspires us to introduce a novel bound, the nuclear discrepancy (ND). The ND bound is tighter for the expected loss under optimistic probabilistic assumptions. Our experiments show that the MMD active learner performs better than the discrepancy in terms of the mean squared error, indicating that tighter worst case bounds do not imply better active learning performance. The proposed active learner improves significantly upon the MMD and discrepancy in the realizable setting and a similar trend is observed in the agnostic setting, showing the benefits of a probabilistic approach to active learning. Our study highlights that assumptions underlying generalization bounds can be equally important as bound-tightness, when it comes to active learning performance. Code for reproducing our experimental results can be found at 〈a href="https://github.com/tomviering/NuclearDiscrepancy"〉https://github.com/tomviering/NuclearDiscrepancy〈/a〉.〈/p〉

Description: Future large-scale network function virtualization (NFV) environments will be based on hundreds or even thousands of NFV infrastructure installations, the so called points of presence (PoP). All their resource...

Description: Precise location information will play an important role in 5G networks, their applications and services, especially in indoor environments. Ultra-wideband (UWB) technology offers exceptional temporal resoluti...

Description: 〈h3〉Abstract〈/h3〉 〈p〉We introduce 〈span〉CaDET〈/span〉, an algorithm for 〈em〉parametric Conditional Density Estimation〈/em〉 (CDE) based on decision trees and random forests. 〈span〉CaDET〈/span〉 uses the 〈em〉empirical cross entropy〈/em〉 impurity criterion for tree growth, which incentivizes splits that improve predictive accuracy more than the regression criteria or estimated mean-integrated-square-error used in previous works. 〈span〉CaDET〈/span〉 also admits more efficient training and query procedures than existing tree-based CDE approaches, and stores only a bounded amount of information at each tree leaf, by using 〈em〉sufficient statistics〈/em〉 for all computations. Previous tree-based CDE techniques produce complicated uninterpretable distribution objects, whereas 〈span〉CaDET〈/span〉 may be instantiated with easily interpretable distribution families, making every part of the model easy to understand. Our experimental evaluation on real datasets shows that 〈span〉CaDET〈/span〉 usually learns more accurate, smaller, and more interpretable models, and is less prone to overfitting than existing tree-based CDE approaches.〈/p〉

Description: 〈h3〉Abstract〈/h3〉 〈p〉We introduce a new unsupervised learning problem: clustering wide-sense stationary ergodic stochastic processes. A covariance-based dissimilarity measure together with asymptotically consistent algorithms is designed for clustering offline and online datasets, respectively. We also suggest a formal criterion on the efficiency of dissimilarity measures, and discuss an approach to improve the efficiency of our clustering algorithms, when they are applied to cluster particular type of processes, such as self-similar processes with wide-sense stationary ergodic increments. Clustering synthetic data and real-world data are provided as examples of applications.〈/p〉

Description: 〈h3〉Abstract〈/h3〉 〈p〉Optimization over low rank matrices has broad applications in machine learning. For large-scale problems, an attractive heuristic is to factorize the low rank matrix to a product of two much smaller matrices. In this paper, we study the nonconvex problem 〈span〉 〈span〉\(\min _{\mathbf {U}\in \mathbb {R}^{n\times r}} g(\mathbf {U})=f(\mathbf {U}\mathbf {U}^T)\)〈/span〉 〈/span〉 under the assumptions that 〈span〉 〈span〉\(f(\mathbf {X})\)〈/span〉 〈/span〉 is restricted 〈span〉 〈span〉\(\mu \)〈/span〉 〈/span〉-strongly convex and 〈em〉L〈/em〉-smooth on the set 〈span〉 〈span〉\(\{\mathbf {X}:\mathbf {X}\succeq 0,\text{ rank }(\mathbf {X})\le r\}\)〈/span〉 〈/span〉. We propose an accelerated gradient method with alternating constraint that operates directly on the 〈span〉 〈span〉\(\mathbf {U}\)〈/span〉 〈/span〉 factors and show that the method has local linear convergence rate with the optimal dependence on the condition number of 〈span〉 〈span〉\(\sqrt{L/\mu }\)〈/span〉 〈/span〉. Globally, our method converges to the critical point with zero gradient from any initializer. Our method also applies to the problem with the asymmetric factorization of 〈span〉 〈span〉\(\mathbf {X}={\widetilde{\mathbf {U}}}{\widetilde{\mathbf {V}}}^T\)〈/span〉 〈/span〉 and the same convergence result can be obtained. Extensive experimental results verify the advantage of our method.〈/p〉

Description: 〈span〉 〈h3〉Abstract〈/h3〉 〈p〉Solidification process of lead–bismuth eutectic (LBE) is one of the key phenomena to prevent flow channel blockage accident in an LBE-cooled accelerator-driven system. However, the solidification of liquid metal cannot be observed optically and it is difficult to detect noninvasively. In this study, the one-dimensional solidification process of the LBE was visualized by pulsed neutron transmission imaging. Neutrons have higher transmittivity to the LBE than X-ray and neutron transmission spectrum of the LBE sample can be obtained by pulsed neutron imaging technique. The solid and liquid phases of the LBE were identified during the solidification process by the presence or absence of Bragg edge in the measured neutron transmission spectrum, and the transient behavior of the solid–liquid interface could be visualized. In addition, the characteristic spatial distribution of the crystalline structure was found in Bragg-edge transmission image after the solidification.〈/p〉 〈/span〉 〈span〉 〈h3〉Graphic abstract〈/h3〉 〈p〉 〈span〉 〈span〉 〈img alt="" src="https://static-content.springer.com/image/MediaObjects/12650_2019_572_Figa_HTML.png"〉 〈/span〉 〈/span〉 〈/p〉 〈/span〉

Description: 〈h3〉Abstract〈/h3〉 〈p〉Health care professionals regularly require access to information systems throughout their daily work. However, existing smart devices like smartphones and tablets are difficult to use at the point of care, because health care professionals require both hands during their work. Following a design science research approach including ethnographic fieldwork and prototype tests with focus groups, we find that Augmented Reality smart glass applications offer potential for service innovation in the health care sector. Our smart glass prototype supports health care professionals during wound treatment by allowing them to document procedures hands-free while they perform them. Furthermore, we investigate the use of audio based and physical interaction with the smart glasses in a within-subjects design experiment.〈/p〉

Description: Deep neural networks have become a standard framework for image analytics. Besides the traditional applications, such as object classification and detection, the latest studies have started to expand the scope...

Description: 〈h3〉Abstract〈/h3〉 〈p〉Knowledge discovery and information extraction of large and complex datasets has attracted great attention in wide-ranging areas from statistics and biology to medicine. Tools from machine learning, data mining, and neurocomputing have been extensively explored and utilized to accomplish such compelling data analytics tasks. However, for time-series data presenting active dynamic characteristics, many of the state-of-the-art techniques may not perform well in capturing the inherited temporal structures in these data. In this paper, integrating the Koopman operator and linear dynamical systems theory with support vector machines, we develop a novel dynamic data mining framework to construct low-dimensional linear models that approximate the nonlinear flow of high-dimensional time-series data generated by unknown nonlinear dynamical systems. This framework then immediately enables pattern recognition, e.g., classification, of complex time-series data to distinguish their dynamic behaviors by using the trajectories generated by the reduced linear systems. Moreover, we demonstrate the applicability and efficiency of this framework through the problems of time-series classification in bioinformatics and healthcare, including cognitive classification and seizure detection with fMRI and EEG data, respectively. The developed Koopman dynamic learning framework then lays a solid foundation for effective dynamic data mining and promises a mathematically justified method for extracting the dynamics and significant temporal structures of nonlinear dynamical systems. 〈/p〉

Description: 〈h3〉Abstract〈/h3〉 〈p〉Identifying at-risk students is one of the most important issues in online education. During different stages of a semester, students display various online learning behaviors. Therefore, we propose a phased prediction model to predict at-risk students at different stages of a semester. We analyze students’ individual characteristics and online learning behaviors, extract features that are closely related to their learning performance, and propose combined feature sets based on a time window constraint strategy and a learning time threshold constraint strategy. The results of our experiments show that the precision of the proposed model in different phases is from 90.4 to 93.6%. 〈/p〉

Description: This paper describes several aspects of the physical layer and over the air interface of Loon. Loon utilizes stratospheric balloon-based high-altitude platforms (HAPs) that use Long-Term Evolution (LTE) to con...

Description: 〈h3〉Abstract〈/h3〉 〈p〉Characterization of oil palm plantation is a crucial step toward many geographical based management strategies, ranging from determining regional planting and appropriate species to irrigation and logistics planning. Accurate and most updated plantation identification enables well informed and effective measures for such schemes. This paper proposes a computerized method for detecting oil-palm plantation from remotely sensed imagery. Unlike other existing approaches, where imaging features were retrieved from spectral data and then trained with a machine learning box for region of interest extraction, this paper employed 2-stage detection. Firstly, a deep learning network was employed to determine a presence of oil-palm plantation in a generic Google satellite image. With irrelevant samples being disregarded and thus the problem space being so contained, the images with detected oil-palm had their plantation delineated at higher accuracy by using a support vector machine, based on Gabor texture descriptor. The proposed coupled detection-delineation was benchmarked against different feature descriptors and state-of-the-art supervised and unsupervised machine learning techniques. The validation was made by comparing the extraction results with those ground surveyed by an authority. It was shown in the experiments that it could detect and delineate the plantations with an accuracy of 92.29% and precision, recall and Kappa of 91.16%, 84.97%, and 0.81, respectively.〈/p〉

Description: 〈h3〉Abstract〈/h3〉 〈p〉Minimizing the empirical risk is a popular training strategy, but for learning tasks where the data may be noisy or heavy-tailed, one may require many observations in order to generalize well. To achieve better performance under less stringent requirements, we introduce a procedure which constructs a robust approximation of the risk gradient for use in an iterative learning routine. Using high-probability bounds on the excess risk of this algorithm, we show that our update does not deviate far from the ideal gradient-based update. Empirical tests using both controlled simulations and real-world benchmark data show that in diverse settings, the proposed procedure can learn more efficiently, using less resources (iterations and observations) while generalizing better.〈/p〉

Description: 〈h3〉Abstract〈/h3〉 〈p〉〈em〉Neutral program variants〈/em〉 are alternative implementations of a program, yet equivalent with respect to the test suite. Techniques such as approximate computing or genetic improvement share the intuition that potential for enhancements lies in these acceptable behavioral differences (e.g., enhanced performance or reliability). Yet, the automatic synthesis of neutral program variants, through 〈em〉program transformations〈/em〉 remains a key challenge. This work aims at characterizing 〈em〉plastic code regions〈/em〉 in Java programs, i.e., the code regions that are modifiable while maintaining functional correctness, according to a test suite. Our empirical study relies on automatic variations of 6 real-world Java programs. First, we transform these programs with three state-of-the-art program transformations: add, replace and delete statements. We get a pool of 23,445 neutral variants, from which we gather the following novel insights: developers naturally write code that supports fine-grain behavioral changes; statement deletion is a surprisingly effective program transformation; high-level design decisions, such as the choice of a data structure, are natural points that can evolve while keeping functionality. Second, we design 3 novel program transformations, targeted at specific plastic regions. New experiments reveal that respectively 60%, 58% and 73% of the synthesized variants (175,688 in total) are neutral and exhibit execution traces that are different from the original.〈/p〉

Description: Sequence of graph snapshots have been commonly utilized in literature to represent changes in a dynamic graph. This approach may be suitable for small-size and slowly evolving graphs; however, it is associated...

Description: 〈h3〉Abstract〈/h3〉 〈p〉This study adopted a sentiment word database to extract sentiment-related data from microblog posts. These data were then used to investigate the effect of different types of sentiment-related words on product recommendations. The results indicate that posts containing strong sentiments received more clicks than posts containing neutral sentiments. Posts containing more than one positive sentiment word generate more effective recommendations than posts containing only one positive sentiment word. This study also demonstrated that posts with a negative polarity classification received more clicks than those with a positive polarity classification. Additionally, the microblog posts containing implicit sentiment words received more clicks than those containing explicit sentiment words. The findings presented here could assist product or service marketers who use Plurk or similar microblogging platforms better focus their limited financial resources on potential online customers to achieve maximum sale revenue.〈/p〉

Description: 〈h3〉Abstract〈/h3〉 〈p〉We present 〈span〉Pirasa〈/span〉: an agent-based simulation environment for studying how autonomous agents can best interact with each other to exchange goods in e-commerce marketplaces. A marketplace in 〈span〉Pirasa〈/span〉 enables agents to enact buyer or seller roles and select from sales, auction, and negotiation protocols to achieve the individual goals of their users. An agent’s strategy to maximize its utility in the marketplace is guided by its user’s preferences and constraints such as ‘maximum price’ and ‘deadline’, as well as an agent’s personality attributes, e.g., how ‘eager’ or ‘late’ the agent can be for exchanging goods and whether the agent is a ‘spender’ or ‘saver’ in an exchange. To guide the agent’s actions selected by a strategy, we use the notion of electronic contracts formulated as regulatory norms. In this context, we present how 〈span〉Pirasa〈/span〉 is organized with regards to seller processes for goods submission, the inclusion of buyer preferences, and the management of transactions through specialized broker agents. Using randomized simulations, we demonstrate how a buyer agent can strategically select the most suitable protocol to satisfy its user’s preferences, goals and constraints in dynamically changing market settings. The generated simulation data can be leveraged by researchers to analyze agent behaviors, and develop additional strategies.〈/p〉

Description: 〈h3〉Abstract〈/h3〉 〈p〉In recent years, as a novel financing mechanism, crowdfunding has gained popularity worldwide. In China, Crowdfunding started in 2011 and has experienced extremely fast growth since then. Several studies have identified success factors for crowdfunding. While these studies help infer the dynamics of crowdfunding, such dynamics have not been sufficiently examined, particularly by large-scale empirical studies. In addition, the relationship between crowdfunding market characteristics and project funding success remains fuzzy. In this research, we first empirically assessed the effect of crowdfunding success factors using a large sample (〈em〉N =〈/em〉 5128) collected from 〈a href="http://taobao.com/"〉Taobao.com〈/a〉, a leading crowdfunding platform in China. Built upon these factors, we conducted a multi-period, multi-party simulation study that evaluate crowdfund raisers’ project positioning decisions and investors’ project funding decision in a massive market. In the simulation study, we varied market characteristics such as the number of projects, number of investors per project, entrepreneurs’ moral risk, and ratio of community over financial benefits, to observe their effect on participants’ decisions and consequently, project funding success rate. Results reveal that dynamics of a crowdfunding market are rather complex. These insights contribute to a more in-depth theoretical understanding of crowdfunding, as well as provide useful guidance for crowdfund raisers, investors, and administrators.〈/p〉

Description: 〈h3〉Abstract〈/h3〉 〈p〉Digital watermarking is increasingly being used in the copyright authentication of vector maps to prevent misuse and illegal distribution. However, it is difficult to avoid influences on watermarks when undergoing spacial projection transformations, vector data compression, and other common geographic information system (GIS) operations. In this paper, a highly robust copyright-authentication method is proposed for digital vector maps. First, we discuss the descriptive approaches to the geometric characterization of map elements by building the graphical complexity index. Next, using the Moran’s I Coefficient (MC), a type of spatial autocorrelation index (SAI), we effectively integrate the spatial topological information and the spatial geometric shape information into the meaningless zero-watermark, which is robust against common GIS operations. Finally, we generate a meaningful zero-watermark by using the exclusive OR (XOR) process to combine the meaningless zero watermark and useful copyright information, which not only embeds the copyright information but also effectively avoids authentication conflict. These results demonstrate that this method is robust to translation, scaling, compression, rotation and edit operations (robustness is 80%). The robustness suggests that this method can readily support business applications for copyright certification of digital vector maps.〈/p〉

Description: 〈h3〉Abstract〈/h3〉 〈p〉Today, as organizations constantly adjust their activities to meet ever-changing circumstances, continuous business transformation is taking place. However, planning and steering this transformation can be a daunting task as complexity has been built into the organization over the years. Enterprise Architecture (EA) has been widely adapted as a planning and governance approach to manage the complexity and constant change, and to align the organization toward a common goal. This article studies the EA benefit-realization process by clarifying how EA benefits are realized. Specifically, the focus is on the strategies, resources, and practices which the EA benefits stem from. The findings, derived from an in-depth case study, show that the EA benefit-realization process constitutes a long, intertwined chain of activities. Organizations benefit from EA through various means: from the initiation, when comprehensive understanding starts to form, until years later, when measurable outcomes such as cost savings materialize. Suggestions on what to incorporate into EA programs are presented.〈/p〉

Description: 〈h3〉Abstract〈/h3〉 〈p〉Density estimation is a versatile technique underlying many data mining tasks and techniques, ranging from exploration and presentation of static data, to probabilistic classification, or identifying changes or irregularities in streaming data. With the pervasiveness of embedded systems and digitisation, this latter type of streaming and evolving data becomes more important. Nevertheless, research in density estimation has so far focused on stationary data, leaving the task of of extrapolating and predicting density at time points outside a training window an open problem. For this task, temporal density extrapolation (TDX) is proposed. This novel method models and predicts gradual monotonous changes in a distribution. It is based on the expansion of basis functions, whose weights are modelled as functions of compositional data over time by using an isometric log-ratio transformation. Extrapolated density estimates are then obtained by extrapolating the weights to the requested time point, and querying the density from the basis functions with back-transformed weights. Our approach aims for broad applicability by neither being restricted to a specific parametric distribution, nor relying on cluster structure in the data. It requires only two additional extrapolation-specific parameters, for which reasonable defaults exist. Experimental evaluation on various data streams, synthetic as well as from the real-world domains of credit scoring and environmental health, shows that the model manages to capture monotonous drift patterns accurately and better than existing methods. Thereby, it requires not more than 1.5 times the run time of a corresponding static density estimation approach.〈/p〉

Description: 〈h3〉Abstract〈/h3〉 〈p〉The covariance matrix adaptation evolution strategy (CMA-ES) is an efficient derivative-free optimization algorithm. It optimizes a black-box objective function over a well-defined parameter space in which feature functions are often defined manually. Therefore, the performance of those techniques strongly depends on the quality of the chosen features or the underlying parametric function space. Hence, enabling CMA-ES to optimize on a more complex and general function class has long been desired. In this paper, we consider 〈em〉modeling〈/em〉 the input spaces in black-box optimization 〈em〉non-parametrically〈/em〉 in reproducing kernel Hilbert spaces (RKHS). This modeling leads to a 〈em〉functional optimisation〈/em〉 problem whose domain is a RKHS function space that enables optimisation in a very rich function class. We propose CMA-ES-RKHS, a generalized CMA-ES framework that is able to carry out black-box functional optimisation in RKHS. A search distribution on non-parametric function spaces, represented as a Gaussian process, is adapted by updating both its mean function and covariance operator. Adaptive and sparse representation of the mean function and the covariance operator can be retained for efficient computation in the updates and evaluations of CMA-ES-RKHS by resorting to sparsification. We will also show how to apply our new black-box framework to search for an optimum policy in reinforcement learning in which policies are represented as functions in a RKHS. CMA-ES-RKHS is evaluated on two functional optimization problems and two bench-marking reinforcement learning domains.〈/p〉

Description: 〈h3〉Abstract〈/h3〉 〈p〉While recent research has highlighted the importance of identifying diverse aspects of user preferences in terms of the quality of recommendations, most of the widely used performance measures tend to consider the accuracy of the recommendations and ignore other important aspects such as preference for diversity in recommendations. This is despite the emerging consensus that improving the diversity in recommendations allows users to discover a wider variety of items and encourage them to extend their range of interests in domains such as books, movies, and music. By proposing a novel diversity evaluation metric, this paper aims to address the problem of measuring the diversity with respect to the distribution of preferences for diversity among recommendation system users. We perform several experiments in order to provide a better understanding of the diversity preferences of users and present the results of diversity evaluations of several recommendation methods. These experiments highlight the accuracy–diversity trade-off and show that higher accuracy does not lead to higher performance in terms of the diversity of the recommendations and that the users’ preferred level of diversity should be considered when designing and evaluating recommender systems. This paper also proposes our Diversity Adjustment algorithm that modifies the diversity of recommendations to suit each user’s preferences while preserving the accuracy. Our experiments suggest that diversifying the recommendations without considering the user’s preferences can lead to a dramatic decline in accuracy, while adjusting the diversity based on users’ diversity needs can support recommender systems in maintaining overall accuracy.〈/p〉

Description: 〈h3〉Abstract〈/h3〉 〈p〉Start-ups successfully deliver creative results, and their success highlights the need for corporate engagement with innovation (Weiblen and Chesbrough in California Management Review 57(2): 66–90, 〈span〉2015〈/span〉). To assess insights on corporate high-involvement innovation activities, we developed and applied an intrapreneurship model within a multinational financial services corporation in Germany. Successful implementation requires that individuals have the autonomy to act as corporate entrepreneurs (i.e., intrapreneurs). To this end, we introduced a model for implementing and analysing intrapreneurship so corporations could develop a start-up culture. The corporation studied hosted an innovation competition called the Appathon, where competing teams developed app prototypes, and the winning team received funding for continued development. Afterwards, we completed qualitative and quantitative research by interviewing and surveying the competition participants to examine practical intrapreneurship and corporate and individual innovation. Our findings highlight the challenges surrounding the development of high-involvement innovation management routines (Bessant in 〈em〉High involvement innovation〈/em〉, 〈span〉2003〈/span〉).〈/p〉

Description: 〈h3〉Abstract〈/h3〉 〈p〉While new ways of doing research and innovation such as open science, Science 2.0, open innovation, user innovation or crowdsourcing have been intensively discussed in the past few years, little systematic analysis and exploration of their wider positive, but also negative effects for economy, society and environment has been conducted. Based on the findings from three European foresight projects we discuss critical aspects of changing research and innovation patterns and their challenges for innovation policy and management.〈/p〉

Description: 〈h3〉Abstract〈/h3〉 〈p〉We introduce the speculate-correct method to derive error bounds for local classifiers. Using it, we show that 〈em〉k〈/em〉-nearest neighbor classifiers, in spite of their famously fractured decision boundaries, have exponential error bounds with 〈span〉 〈span〉\(\hbox {O} \left( \sqrt{(k + \ln n)/n} \right) \)〈/span〉 〈/span〉 range around an estimate of generalization error for 〈em〉n〈/em〉 in-sample examples. 〈/p〉

Description: 〈h3〉Abstract〈/h3〉 〈p〉Online to offline (O2O) is defined as e-commerce that combines searching and booking products or services online, and consumption in brick-and-mortar stores. Both China and other countries have witnessed the rise of O2O e-commerce platforms in recent years. This paper explores this rapid development from the supply side of O2O e-commerce platforms. Drawing upon the utilities roles of digital platforms and resource dependence theory, we propose and validate hypotheses on the relationships between platform choice and suppliers’ efficiency, using cinemas as an example. The findings support the significant and positive impact of platform choice on cinemas’ efficiency. Furthermore, we find that the more platforms a cinema chooses to link with, the higher efficiency the cinema will achieve. Our empirical results partially support that vertical integration has a negative impact on the relationship between platform choice and suppliers’ efficiency. This study enhances the understanding of the utilities roles of digital platforms and interaction between platform ecosystems and vertical integration.〈/p〉

Description: 〈h3〉Abstract〈/h3〉 〈p〉Smart city development is gaining considerable recognition in the systematic literature and international policies throughout the world. The study aims to identify the key barriers of smart cities from a review of existing literature and views of experts in this area. This work further makes an attempt on the prioritisation of barriers to recognise the most important barrier category and ranking of specific barriers within the categories to the development of smart cities in India. Through the existing literature, this work explored 31 barriers of smart cities development and divided them into six categories. This research work employed fuzzy Analytic Hierarchy Process (AHP) technique to prioritise the selected barriers. Findings reveal that ‘Governance’ is documented as the most significant category of barriers for smart city development followed by ‘Economic; ‘Technology’; ‘Social’; ‘Environmental’ and ‘Legal and Ethical’. In this work, authors also performed sensitivity analysis to validate the findings of study. This research is useful to the government and policymakers for eradicating the potential interferences in smart city development initiatives in developing countries like India.〈/p〉

Description: This article reviews some integrators particularly suitable for the numerical resolution of differential equations on a large time interval. Symplectic integrators are presented. Their stability on exponential...

Description: 〈h3〉Abstract〈/h3〉 〈p〉This paper presents the transcendental Bernstein series (TBS) as a generalization of the classical Bernstein polynomials for solving the variable-order space–time fractional telegraph equation (V-STFTE). An approximation method using optimization techniques and the TBS is introduced. The solution of the problem under consideration is expanded in terms of TBS with unknown free coefficients and control parameters. The new corresponding operational matrices of variable-order fractional derivatives, in the Caputo type, are derived. The proposed approach reduces the V-STFTE to a system of algebraic equations and, subsequently, to find the free coefficients and control parameters using the Lagrange multipliers technique. The convergence analysis of the method is guranteed by means of a new theorem concerning the TBS. The experimental results confirm the high accuracy and computational efficiency of the TBS method.〈/p〉

Description: 〈h3〉Abstract〈/h3〉 〈p〉We consider a general load balancing problem on parallel machines. Our machine environment in particular generalizes the standard models of identical machines, and the model of uniformly related machines, as well as machines with a constant number of types, and machines with activation costs. The objective functions that we consider contain in particular the makespan objective and the minimization of the 〈span〉 〈span〉\(\ell _p\)〈/span〉 〈/span〉-norm of the vector of loads of the machines, and each case allows the possibility of job rejection. We consider this general model and design an efficient polynomial time approximation scheme (EPTAS) that applies for all its previously-studied special cases. This EPTAS improves the current best approximation scheme for some of these cases where only a polynomial time approximation scheme was known into an EPTAS.〈/p〉

Description: 〈h3〉Abstract〈/h3〉 〈p〉We address the problem of calibrating an embedded depth camera network designed for people tracking purposes. In our system, the nodes of the network are responsible for detecting the people moving in their view, and sending the observations to a centralized server for data fusion and tracking. We employ a plan-view approach where the depth camera views are transformed to top-view height maps where people are observed. As the server transforms the observations to a global plan-view coordinate system, accurate geometric calibration of the sensors has to be performed. Our main contribution is an auto-calibration method for such depth camera networks. In our approach, the sensor network topology and the initial 2D rigid transformations that map the observations to the global frame are determined using observations only. To distribute the errors in the initial calibration, the transformation parameters and the estimated positions of people are refined using a global optimization routine. To overcome inaccurate depth camera parameters, we re-calibrate the sensors using more flexible transformations, and experiment with similarity, affine, homography and thin-plate spline mappings. We evaluate the robustness, accuracy and precision of the approach using several real-life data sets, and compare the results to a checkerboard-based calibration method as well as to the ground truth trajectories collected with a mobile robot.〈/p〉

Description: 〈h3〉Abstract〈/h3〉 〈p〉Graticule intersections in topographic maps are usually considered to be suitable candidates for reference points in geometric calibration because the corresponding geographical information can be directly retrieved from the maps or derived from sheet numbers. Previous research on automatic corner point detection relies on the assumption that scanned maps are not rotated, which is rarely practical. To address this issue, a semantic segmentation approach for accurate graticule intersection localization is proposed in this paper. A fully convolutional network is utilized to provide pixel level information about the locations of specific rectangular objects at the corners of map frames by dense classification in regions of interest. The globally optimal segmentation of the foreground rotated object is obtained by the graph cuts technique. The bounding box of the rotated object is further retrieved with the minimum-area enclosing rectangle algorithm. Finally, the coordinates of graticule intersections are derived in accordance with the positions of the sliding windows and the relative locations of the vertices of the objects. The proposed method reduces the average localization error to 1.5 pixels, which is 32.4% lower than that of the baseline model. The standard deviation of localization error is 0.91 pixels, which aligns with an average of 52% improvements to the baseline model in the location variance metric.〈/p〉

Description: 〈h3〉Abstract〈/h3〉 〈p〉Bayesian matrix factorization (BMF) is a powerful tool for producing low-rank representations of matrices and for predicting missing values and providing confidence intervals. Scaling up the posterior inference for massive-scale matrices is challenging and requires distributing both data and computation over many workers, making communication the main computational bottleneck. Embarrassingly parallel inference would remove the communication needed, by using completely independent computations on different data subsets, but it suffers from the inherent unidentifiability of BMF solutions. We introduce a hierarchical decomposition of the joint posterior distribution, which couples the subset inferences, allowing for embarrassingly parallel computations in a sequence of at most three stages. Using an efficient approximate implementation, we show improvements empirically on both real and simulated data. Our distributed approach is able to achieve a speed-up of almost an order of magnitude over the full posterior, with a negligible effect on predictive accuracy. Our method outperforms state-of-the-art embarrassingly parallel MCMC methods in accuracy, and achieves results competitive to other available distributed and parallel implementations of BMF.〈/p〉

Description: 〈h3〉Abstract〈/h3〉 〈p〉In this paper, the wake modes behind a circular cylinder under streamwisely forcing oscillating motion are studied at Reynolds number 〈em〉Re〈/em〉 = 360–460 which are observed by laser-induced fluorescence flow visualization technique. The forcing frequency 〈span〉〈span〉\(f_{\mathrm{e}}\)〈/span〉〈/span〉 ranges from 0 to 6.85 〈span〉〈span〉\(f_{\mathrm{s}}\)〈/span〉〈/span〉 , where 〈span〉〈span〉\(f_{\mathrm{s}}\)〈/span〉〈/span〉 is the vortex shedding frequency behind a stationary cylinder, and the forcing amplitude 〈span〉〈span〉\(A/d = 0.2, 0.5, 1.0\)〈/span〉〈/span〉, where 〈em〉d〈/em〉 is the cylinder diameter. Both time-invariant and linearly ramping 〈span〉〈span〉\(f_{\mathrm{e}}\)〈/span〉〈/span〉 are investigated. Following our previous modal notation, the following conclusions can be drawn: firstly, three rarely reported modes in numerical studies, C-I, C-II and S-III, are now confirmed in experiments (though with differentiable appearance in their far wake behaviour) at higher 〈em〉A〈/em〉 and/or 〈span〉〈span〉\(f_{\mathrm{e}}\)〈/span〉〈/span〉 ranges and the envelope lines of S modes and C modes, yielded from a vortex circulation model, are shown to be dependent on the peak relative velocity of the free stream to the cylinder surface. So is the occurrence of the S-II mode. Secondly, near the demarcation of A modes and S-I mode, wake mode undergoes constant transition in a stochastic manner at fixed 〈em〉Re〈/em〉, 〈em〉A〈/em〉 and 〈span〉〈span〉\(f_{\mathrm{e}}\)〈/span〉〈/span〉. Thirdly, a typical hysteretic effect can be observed when the oscillation frequency of the cylinder ramps up and down in a linear way, and the extent of delay is dependent on the ramping rate 〈em〉k〈/em〉. Finally, mode switching during frequency ramping obeys a unidirectional order. During 〈span〉〈span〉\(k〈0\)〈/span〉〈/span〉 (ramp-down), when S-I (Type-II) mode switches to A-IV mode, or A-IV to A-III, the flow structure downstream is affected by the upstream and the entire wake flow eventually switched, which is classified as slow switches. In contrast, during 〈span〉〈span〉\(k〉0\)〈/span〉〈/span〉 (ramp-up), a clear and abrupt switch can be observed in the wake when A-IV or A-III switch to S-I (Type-II) modes, which are jump switches.〈/p〉 〈span〉 〈h3〉Graphical abstract〈/h3〉 〈p〉 〈span〉 〈span〉 〈img alt="" src="https://static-content.springer.com/image/MediaObjects/12650_2019_554_Figa_HTML.jpg"〉 〈/span〉 〈/span〉 〈/p〉 〈/span〉

Description: Routing in mobile ad hoc networks is based on the cooperation of the network’s nodes. The presence of selfish nodes that do not cooperate in this task drastically reduces the number of delivered packets. In or...

Description: 〈h3〉Abstract〈/h3〉 〈p〉Over the past few years, there has been a rapid increase of data originating from evolving networks such as social networks, sensor networks and others. A major challenge that arises when handling such networks and their respective graphs is the ability to issue a historical query on their data, that is, a query that is concerned with the state of the graph at previous time instances. While there has been a number of works that index the historical data in a time-centric manner (i.e. according to the time instance an update event occurs), in this work, we focus on the less-explored vertex-centric storage approach (i.e. according to the entity in which an update event occurs). We demonstrate that the design choices for a vertex-centric model are not trivial, by proposing two different modelling and storage models that leverage NoSQL technology and investigating their tradeoffs. More specifically, we experimentally evaluate the two models and show that under certain cases, their relative performance can differ by several times. Finally, we provide evidence that simple baseline and non-NoSQL solutions are slower by up to an order of magnitude.〈/p〉

Description: 〈h3〉Abstract〈/h3〉 〈p〉We propose a new topology-aware point set registration algorithm which can cope with multi-part articulated and non-rigid deformations. Point set registration is formulated as a maximum likelihood (ML) estimation problem where two topologically complementary constraints are jointly optimized in a probabilistic framework. The first is coherent point drift that keeps the overall spatial connectivity and associativity by moving the point set collectively and coherently. The second is local linear embedding that preserves the local topological structure during registration. Hence, the new algorithm is called global–local topology preservation (GLTP). Without any pre-segmentation and correspondence initialization, GLTP is particularly useful and effective in dealing with complex shape matching with non-coherent and non-rigid local deformations at different parts of a point set. We have derived the expectation maximization algorithm for the ML optimization constrained with both regularization terms. Experimental results on a large set of 2D and 3D examples show the advantages and robustness of GLTP over existing algorithms in the presence of outliers, noise and missing data, especially in the case of articulated non-rigid transformations.〈/p〉

Description: In this paper we develop a methodology, based on Mutual Information and Transfer of Entropy, that allows to identify, quantify and map on a network the synchronization and anticipation relationships between fi...

Description: Composite structures are convenient structural solutions for many engineering fields, but their design is challenging and may lead to oversizing due to the significant amount of uncertainties concerning the cu...

Description: 〈span〉 〈h3〉Abstract〈/h3〉 〈p〉Scientific visualizations offer domain experts the ability to explore data visually and interactively to gain insights from data. Most visualization systems focus on functionality and scalability. However, we believe that with the advent of faster rendering techniques and higher-speed networks, accessibility for every device should also be a goal for scientific visualizations. In this paper, we propose a novel scientific visualization system, Voxer, which provides ubiquitous visualizations by decoupling user interfaces from system space. In Voxer, we encapsulate the data processing and rendering functionality as a web service and design a module-based user interface for domain experts to create and customize different visualization pipelines in response to their specific requirements. These configured visualizations can be shared with the public through embedding visualizations on the web and interactively rendering on the server. Use cases and benchmarks are used to demonstrate how our system can help domain experts easily create and customize visualizations and improve visualizations accessibility.〈/p〉 〈/span〉 〈span〉 〈h3〉Graphic abstract〈/h3〉 〈p〉 〈span〉 〈span〉 〈img alt="" src="https://static-content.springer.com/image/MediaObjects/12650_2019_595_Figa_HTML.png"〉 〈/span〉 〈/span〉 〈/p〉 〈/span〉

Description: 〈span〉 〈h3〉Abstract〈/h3〉 〈p〉In the field of forest pest and disease research, researchers have combined the experience and data accumulated over many years and conducted long-term and systematic observations of the research object; they have used regression analysis to determine the factors that affect the occurrence of pests and diseases. This traditional approach is time-consuming and highly dependent on expert experience. In this paper, we propose a multicombination multivariable linear regression model to quantitatively describe the multiple linear combinations of relationship between multiple independent variables and a single dependent variable. Based on this model and a data flow model combined with statistical principles and visualization techniques, we propose a multicombination multivariable linear regression visual analysis method to assist researchers in quickly assessing the correlations between the disease indexes of forest diseases and pests and the factors that may affect the pest and disease occurrences. Based on this approach, a multicombination multivariable linear regression visual analysis system was designed and implemented, and the cases of a given forest pest and disease data set were analyzed. It is shown that the multicombination multivariable linear regression visual analysis method can effectively assist researchers in quickly understanding pest and disease data, determining impact factors, and finding relevant laws. 〈/p〉 〈/span〉 〈span〉 〈h3〉Graphic abstract〈/h3〉 〈p〉 〈span〉 〈span〉 〈img alt="" src="https://static-content.springer.com/image/MediaObjects/12650_2019_593_Figa_HTML.png"〉 〈/span〉 〈/span〉 〈/p〉 〈/span〉

Description: 〈h3〉Abstract〈/h3〉 〈p〉For branding campaigns, the demand-side platforms (DSPs) in real-time bidding (RTB) usually need to win as many impressions as possible to inform most audiences about the product messages under constraints on budgets, campaign lifetimes and budget spending plans. In this paper, we propose a novel bidding strategy by introducing the concept of expected win rate. With the proposed expected win rate-based bidding strategy, the DSP can dynamically adjust the expected win rate for each incoming bid request based on factors such as the predicted number of bid requests in the near future, the remaining budget and the remaining lifetime of the campaign. The experimental results show that the proposed bidding strategy has a lower cost per thousand impressions and cost per clicks than existing pacing model-based bidding strategies for branding campaigns with the same budgets and budget spending plans.〈/p〉

Description: 〈h3〉Zusammenfassung〈/h3〉 〈p〉Datenverarbeitung ist ein integraler Bestandteil polizeilicher Tätigkeit. Ihre Rechtmäßigkeit beschäftigt immer wieder deutsche Gerichte. Dabei steht regelmäßig die Frage im Vordergrund, ob eine ausreichende gesetzliche Erlaubnis für die Datenverarbeitung vorliegt. Ein effektiver Schutz natürlicher Personen bei der Verarbeitung personenbezogener Daten setzt aber auch technische und organisatorische Maßnahmen voraus, um sicherzustellen, dass die gesetzlichen Vorgaben eingehalten und Betroffenenrechte geschützt werden. Dies betrifft unter anderem die Einbindung des behördlichen Datenschutzbeauftragten, die Zusammenarbeit mit der Aufsichtsbehörde, das Führen eines Verarbeitungsverzeichnisses, die Durchführung einer Datenschutz-Folgenabschätzung, die Gewährleistung von Datensicherheit, die Beachtung von Protokollierungspflichten, die Vornahme von Maßnahmen zum Datenschutz durch Technikgestaltung und schließlich auch die Einhaltung von Benachrichtigungspflichten bei Verletzung des Schutzes personenbezogener Daten. 〈/p〉

Description: 〈h3〉Zusammenfassung〈/h3〉 〈p〉Mit der seit dem 25.05.2018 geltenden Datenschutzgrundverordnung (DS-GVO) hat der europäische Gesetzgeber neben den Bußgeldvorschriften auch zivilrechtliche Anspruchsgrundlagen normiert. Gemäß Art. 288 Abs. 2 AEUV gelten diese unmittelbar und sind gegenüber nationalen Rechtsvorschriften der Mitgliedstaaten vorranging anwendbar. Im Folgenden sollen der in Art. 82 Abs. 1 DS-GVO geregelte Ersatz des immateriellen Schadens und dessen Voraussetzungen genauer beleuchtet werden. Die praktische Anwendung wird anhand zweier Beispiele aus der Rechtsprechung illustriert und kritisch gewürdigt. 〈/p〉

Description: 〈span〉 〈h3〉Abstract〈/h3〉 〈p〉Guided wave tomography is a very attractive technique for online nondestructive evaluation and structural health monitoring. The reconstruction algorithm for probabilistic inspection of damage (RAPID) is an effective tomography algorithm for detecting, locating, and imaging defects. Conventional tomography imaging techniques are difficult to quantify defects in structures with curved surfaces like aircraft. It is not a complete tool for evaluation of a damaged area on a curved surface, because of insufficient guidelines for shape factor (Bata) and curvature. Probabilistic inspection of damage is used to construct tomographic images of hole defects in a 30 mm diameter and thickness 5 mm carbon steel plate and curved surface specimen. Imaging is completed using an array of 16 transducers. It is shown that defect location can be accurately determined on plate and curved surfaces. The work presented here introduces a calculation for the shape factor for evaluation of the damaged area, as well as a variable 〈span〉 〈span〉\(\beta\)〈/span〉 〈/span〉 parameter technique to correct a damaged shape. An experiment is performed using the guided wave pitch-catch method to find the length of the damage on a ray path. Also, we perform research in modeling simulation and an experiment for comparison with a suggested inspection method and verify its validity. In the plate and curved surface images resulting from the advanced RAPID algorithm, the defect area and shape showed good agreement. Quantitative imaging techniques on the surface can be applied to real-time defects imaging of aircraft component monitoring.〈/p〉 〈/span〉 〈span〉 〈h3〉Graphic abstract〈/h3〉 〈p〉 〈span〉 〈span〉 〈img alt="" src="https://static-content.springer.com/image/MediaObjects/12650_2019_589_Figa_HTML.png"〉 〈/span〉 〈/span〉 〈/p〉 〈/span〉

Description: 〈h3〉Abstract〈/h3〉 〈p〉We introduce a dynamical spatio-temporal model formalized as a recurrent neural network for modeling time series of spatial processes, i.e., series of observations sharing temporal and spatial dependencies. The model learns these dependencies through a structured latent dynamical component, while a decoder predicts the observations from the latent representations. We consider several variants of this model, corresponding to different prior hypothesis about the spatial relations between the series. The model is used for the tasks of forecasting and data imputation. It is evaluated and compared to state-of-the-art baselines, on a variety of forecasting and imputation problems representative of different application areas: epidemiology, geo-spatial statistics, and car traffic prediction. The experiments also show that this approach is able to learn relevant spatial relations without prior information.〈/p〉

Description: 〈h3〉Zusammenfassung〈/h3〉 〈p〉Obwohl das Internet der Dinge (IoT) die Voraussetzung für smarte Anwendungen schafft, die signifikante Vorteile gegenüber traditionellen Anwendungen bieten, stellt die zunehmende Verbreitung von IoT-fähigen Geräten auch eine immense Gefährdung der Privatheit dar. IoT-Anwendungen sammeln eine Vielzahl an Daten und senden diese zur Verarbeitung an ein Back-End. Hierbei werden umfangreiche Erkenntnisse über den Nutzer1 gewonnen. Erst dieses Wissen ermöglicht die Servicevielfalt, die IoT-Anwendungen bieten. Der Nutzer muss daher einen Kompromiss aus Servicequalität und Datenschutz treffen. Heutige Datenschutzansätze berücksichtigen dies unzureichend und sind dadurch häufig zu restriktiv. Daher stellen wir neue Konzepte zum Schutz privater Daten für das IoT vor. Diese berücksichtigen die speziellen Eigenschaften von IoT-Zeitreihendaten. So kann die Privatheit des Nutzers gewährleistet werden, ohne die Servicequalität unnötig einzuschränken. 〈/p〉

Description: 〈h3〉Abstract〈/h3〉 〈p〉Several large uncertain knowledge bases (KBs) are available on the Web where facts are associated with a certainty degree. When querying these uncertain KBs, users seek high-quality results, i.e., results that have a certainty degree greater than a given threshold 〈span〉 〈span〉\(\alpha \)〈/span〉 〈/span〉. However, as they usually have only a partial knowledge of the KB contents, their queries may be 〈em〉failing〈/em〉 i.e., they return no result for the desired certainty level. To prevent this frustrating situation, instead of returning an empty set of answers, our approach explains the reasons of the failure with a set of 〈span〉 〈span〉\(\alpha \)〈/span〉 〈/span〉minimal failing subqueries (〈span〉 〈span〉\(\alpha \)〈/span〉 〈/span〉MFSs) and computes alternative relaxed queries, called 〈span〉 〈span〉\(\alpha \)〈/span〉 〈/span〉ma〈span〉X〈/span〉imal succeeding subqueries (〈span〉 〈span〉\(\alpha \)〈/span〉 〈/span〉XSSs), that are as close as possible to the initial failing query. Moreover, as the user may not always be able to provide an appropriate threshold 〈span〉 〈span〉\(\alpha \)〈/span〉 〈/span〉, we propose three algorithms to compute the 〈span〉 〈span〉\(\alpha \)〈/span〉 〈/span〉MFSs and 〈span〉 〈span〉\(\alpha \)〈/span〉 〈/span〉XSSs for other thresholds, which also constitutes a relevant feedback for the user. Multiple experiments with the WatDiv benchmark show the relevance of our algorithms compared to a baseline method.〈/p〉

Description: 〈h3〉Zusammenfassung〈/h3〉 〈p〉Die DSGVO schreibt Verantwortlichen vor, personenbezogene Daten nach Erreichung des Zwecks ihrer Verarbeitung zu löschen. Einer Löschpflicht stehen häufig zahlreiche (spezial-) gesetzliche Aufbewahrungspflichten gegenüber. In der betrieblichen Praxis ist die E-Mail eines der wichtigsten Kommunikationsmittel, das gezielte Bewerten und Löschen von tausenden E-Mails stellt jedoch eine komplexe Herausforderung dar. Der Beitrag diskutiert verschiedene Lösungsmöglichkeiten, diese Herausforderung zu meistern. 〈/p〉

Description: 〈span〉 〈h3〉Abstract〈/h3〉 〈p〉Multivariate visualization for atmospheric pollution is a challenging research topic. Appropriate algorithms and data structures based on modern graphics hardware are used to obtain high performance. 3D visualization of the atmospheric wind field and pollutant concentrations can easily result in visual perception problems such as occlusion and cluttering even artifacts. To solve the above issues, a 〈em〉K〈/em〉-means clustering technique is used in combination with a similarity metric between streamlines based on an iterative closest point method to cluster the initial streamlines. A small set of streamlines is then selected to represent the prominent structure of the wind field. The proper illumination model and the depth sorting method reduce the inter-occlusion between streamlines and isosurfaces to show much clearer wind field pattern and important features effectively. The atmospheric pollution data set is employed to evaluate the proposed algorithm framework.〈/p〉 〈/span〉 〈span〉 〈h3〉Graphic abstract〈/h3〉 〈p〉 〈span〉 〈span〉 〈img alt="" src="https://static-content.springer.com/image/MediaObjects/12650_2019_588_Figa_HTML.png"〉 〈/span〉 〈/span〉 〈/p〉 〈/span〉

Description: 〈h3〉Abstract〈/h3〉 〈p〉Energy consumption, execution time, and availability are common terms in discussions on application development for mobile devices. Mobile applications executing in a mobile cloud computing (MCC) environment must consider several issues, such as Internet connections problems and CPU performance. Misconceptions during the design phase can have a significant impact on costs and time-to-market, or even make the application development unfeasible. Anticipating the best configuration for each type of application is a challenge that many developers are not prepared to tackle. In this work, we propose models to rapidly estimate execution time, availability, and energy consumption of mobile applications executing in an MCC environment. We defined a methodology to create and validate Deterministic and Stochastic Petri net (DSPN) models to evaluate these three critical metrics. The DSPNs results were compared with results obtained through experiments performed on a testbed environment. We analyzed an image processing application, regarding connections type (WLAN, WiFi, and 3G), servers type (MCC or cloudlet), and functionalities performance. Our numerical analyses indicate, for instance, that the use of a cloudlet significantly improves performance and energy efficiency. Besides, the baseline scenario took us one month to implement, while modeling and evaluation the three scenarios required less than one day. In this way, our DSPN models represent a powerful tool for mobile developers to plan efficient and cost-effective mobile applications. They allow rapidly assess execution time, availability, and energy consumption metrics to improve the quality of mobile applications.〈/p〉

Description: 〈h3〉Abstract〈/h3〉 〈p〉Given a contact network and coarse-grained diagnostic information such as electronic Healthcare Reimbursement Claims (〈span〉eHRC〈/span〉) data, can we develop efficient intervention policies from data to control an epidemic? Immunization is an important problem in multiple areas, especially epidemiology and public health. However, most existing studies rely on assuming prior epidemiological models to develop pre-emptive strategies, which may fail to adapt to the change in new epidemiological patterns and the availability of rich data such as 〈span〉eHRC〈/span〉. In practice, disease spread is usually complicated, hence assuming an underlying model may deviate from true spreading patterns, leading to possibly inaccurate interventions. Additionally, the abundance of health care surveillance data (such as 〈span〉eHRC〈/span〉) makes it possible to study data-driven strategies without too many restrictive assumptions. Hence, such a data-driven intervention approach can help public-health experts take more practical decisions. In this paper, we take into account propagation log and contact networks for controlling propagation. Different from previous model-based approaches, our solutions are solely data driven in a sense that we develop immunization strategies directly from the network and 〈span〉eHRC〈/span〉 without assuming classical epidemiological models. In particular, we formulate the novel and challenging 〈em〉data-driven immunization〈/em〉 problem. To solve it, we first propose an efficient sampling approach to align surveillance data with contact networks, then develop an efficient algorithm with the provably approximate guarantee for immunization. Finally, we show the effectiveness and scalability of our methods via extensive experiments on multiple datasets, and conduct case studies on nation-wide real medical surveillance data.〈/p〉

Description: 〈span〉 〈h3〉Abstract〈/h3〉 〈p〉Learning more about human mobility is crucial for official decision makers and urban planners. Mobility datasets characterize human daily travel behaviors. Most current researches only studied human dynamics from one kind of mobility dataset. However, people may use different means of transportation to different places for different purposes. Besides, the spatial distributions of different types of point of interests (POIs) reflect the land-use types. How to jointly analyze the multi-source mobility datasets and POI information is a great challenge. In this paper, we adopt multi-source datasets, including taxi dataset, public bicycle system dataset and POI dataset, and propose a visual analytics methodology to explore human mobility dynamics insightfully. Two region–feature–time tensors are constructed first, and a tensor decomposition method is employed to classify the mobility patterns automatically. Then, a new POI–mobility glyph is designed to visualize multi-source datasets in a compact manner. Several interactive visual views are also designed to visualize the spatiotemporal patterns from global, regional and locational perspectives. Case studies based on real-world datasets demonstrate the effectiveness of our method, which supports the visual reasoning of trip purposes and mixed urban functions.〈/p〉 〈/span〉 〈span〉 〈h3〉Graphic abstract〈/h3〉 〈p〉 〈span〉 〈span〉 〈img alt="" src="https://static-content.springer.com/image/MediaObjects/12650_2019_594_Figa_HTML.png"〉 〈/span〉 〈/span〉 〈/p〉 〈/span〉

Description: 〈h3〉Abstract〈/h3〉 〈p〉A key challenge for document clustering consists in finding a proper similarity measure for text documents that enables the generation of cohesive groups. Measures based on the classic bag-of-words model take into account solely the presence (and frequency) of words in documents. In doing so, semantically similar documents which use different vocabularies may end up in different clusters. For this reason, semantic similarity measures that use external knowledge, such as word 〈em〉n〈/em〉-gram corpora or thesauri, have been proposed in the literature. In this paper, the Frequency Google Tri-gram Measure is proposed to assess similarity between documents based on the frequencies of terms in the compared documents as well as the Google 〈em〉n〈/em〉-gram corpus as an additional semantic similarity source. Clustering algorithms are applied to several real datasets in order to experimentally evaluate the quality of the clusters obtained with the proposed measure and compare it with a number of state-of-the-art measures from the literature. The experimental results demonstrate that the proposed measure improves significantly the quality of document clustering, based on statistical tests. We further demonstrate that clustering results combining bag-of-words and semantic similarity are superior to those obtained with either approach independently.〈/p〉

Description: 〈h3〉Abstract〈/h3〉 〈p〉Classification consists of extracting a classifier from large datasets. A dataset is imbalanced if it contains more instances in one class compared to the others. An imbalanced dataset contains majority instances and minority ones. It is worth noting that classical learning algorithms have a bias toward majority instances. If classification is applied to imbalanced datasets, it is called partial classification. Its approaches are generally based on sampling methods or algorithmic methods. In this paper, we propose a new hybrid approach using a three-phase-rule-based extraction process. Initially, the first classifier is extracted; it contains classification rules representing only majority instances. Then, we delete the majority instances, which are well classified by these rules, to produce a balanced dataset. The deleted majority instances are replaced by the extracted classification rules, which prevent any information loss. Subsequently, our algorithm is applied to the obtained balanced dataset to produce the second classifier which contains rules that represent both majority and minority instances. Finally, we add the rules of the first classifier to the second classifier to obtain the final classifier, which will be post-processed. Our approach has been tested on several imbalanced binary datasets. The obtained results show its efficiency compared to other results.〈/p〉

Description: 〈h3〉Abstract〈/h3〉 〈p〉In this paper, we consider the problem of anonymization on directed networks. Although there are several anonymization methods for networks, most of them have explicitly been designed to work with undirected networks and they cannot be straightforwardly applied when they are directed. Moreover, ignoring the direction of the edges causes important information loss on the anonymized networks in the best case. In the worst case, the direction of the edges may be used for reidentification, if it is not considered in the anonymization process. Here, we propose two different models for 〈em〉k〈/em〉-degree anonymity on directed networks, and we also present algorithms to fulfill these 〈em〉k〈/em〉-degree anonymity models. Given a network 〈em〉G〈/em〉, we construct a 〈em〉k〈/em〉-degree anonymous network by the minimum number of edge additions. Our algorithms use multivariate micro-aggregation to anonymize the degree sequence, and then, they modify the graph structure to meet the 〈em〉k〈/em〉-degree anonymous sequence. We apply our algorithms to several real datasets and demonstrate their efficiency and practical utility.〈/p〉

Description: 〈span〉 〈h3〉Abstract〈/h3〉 〈p〉Temporal ranking ensembles indicate time-evolving multivariate rankings. Such data can be commonly found in our daily life, for example, different rankings of universities (QS, ARWU, THE, and USNews) over year and those of NBA players over season. Effective analysis and tracking of rankings allow users to gain insights into the overall ranking change over time and seek the explanation for the change. This paper introduces a novel visual analytics approach for characterizing and visualizing the uncertainty, dynamics, and differences of ranking ensemble data. A novel visual design is proposed to characterize the evolution pattern, distribution, and uncertainty of a large number of temporal ranking ensembles. The evolutionary ranking ensembles are progressively explored, tracked, and compared by means of an intuitive visualization system. Two case studies and a task-driven user study conducted on real datasets demonstrate the effectiveness and feasibility of the implemented system.〈/p〉 〈/span〉 〈span〉 〈h3〉Graphic abstract〈/h3〉 〈p〉 〈span〉 〈span〉 〈img alt="" src="https://static-content.springer.com/image/MediaObjects/12650_2019_598_Figa_HTML.png"〉 〈/span〉 〈/span〉 〈/p〉 〈/span〉

Description: 〈span〉 〈h3〉Abstract〈/h3〉 〈p〉Traffic zone is a geographical unit which is proposed to reduce the complexity of traffic control and management systems in modern urban road networks. Traffic zone could be generated by different division approaches (e.g., rule-based approaches, data-driven approaches), which may lead to different diverse division results and characteristics. The traditional division approaches usually rely on human experience or static constraints (e.g., political boundaries or natural environment) while neglecting the traveling pattern among different regions. Data-driven approaches are also utilized in recent years; however, domain knowledge and rules are not well integrated and represented in such systems. This paper proposes a new approach that both combines patterns that is hidden in the data and user-defined rules. It not only fully employs the traffic hot spots and traveling patterns among different regions, but also considers the realistic constraints such as road network. We also design TZVis, a visual analysis system that fully integrates with the division approach. The system allows users to generate multiple traffic zone division schemes under different requirements, analyze the schemes in multiple views and select an optimal traffic zone scheme. It provides a map overview to show the traffic zone division result, a matrix view to show the traffic relationship of zones, a parallel coordinates plot for displaying the specific traffic records, a LineUp view to display all the division results and a radar chart to show the features of each zones. We further test our division approach and demonstrate the usefulness of our system based on the shared bicycle dataset.〈/p〉 〈/span〉 〈span〉 〈h3〉Graphic abstract〈/h3〉 〈p〉 〈span〉 〈span〉 〈img alt="" src="https://static-content.springer.com/image/MediaObjects/12650_2019_600_Figa_HTML.png"〉 〈/span〉 〈/span〉 〈/p〉 〈/span〉

Description: 〈h3〉Abstract〈/h3〉 〈p〉Searchable encryption (SE) is a cryptographic technique that allows the users to perform search over outsourced encrypted data in cloud servers while preserving the privacy of data and search query. In order to preserve privacy and security, data owners use various encryption schemes to encrypt their data and indexes. Due to the vulnerabilities in the encryption schemes adopted by SE schemes, information leakages are possible from the encrypted data. The cloud servers use various information disclosure attacks that exploit these leakages to infer plaintext information. In this paper, we analyze the existing SE approaches with respect to their security and precision goals and possible attacks on them. We also identify the root causes of these attacks and their remedies. An experimental study is also performed to investigate the susceptibility of SE schemes against attacks and countermeasures to prevent them.〈/p〉

Description: 〈span〉 〈h3〉Abstract〈/h3〉 〈p〉Electron Microscopy Data Bank (EMDB) provides biological researchers with a platform for uploading and downloading data obtained mainly by electron microscopy. With the continuous expansion of the total amount of data stored in EMDB in recent years, its influence has also been continuously improved. In order to make it easier for domain scientists to make better use of these data, we analyzed the EMDB meta-information structure and developed a visual analytics system, called VASEM, to provide convenient data exploration, searching, and analysis tools. Domain scientists can use our system to interactively filter massive EMDB data into relatively small subset for analysis and obtain subset depiction from visualization results. A modified parallel coordinates plot suitable for large-scale multi-dimensional ranking data visualization is also proposed for EM data subset depiction tasks. We present two usage scenarios to describe how the proposed system achieves the design goals.〈/p〉 〈/span〉 〈span〉 〈h3〉Graphic abstract〈/h3〉 〈p〉 〈span〉 〈span〉 〈img alt="" src="https://static-content.springer.com/image/MediaObjects/12650_2019_597_Figa_HTML.png"〉 〈/span〉 〈/span〉 〈/p〉 〈/span〉

Description: 〈h3〉Abstract〈/h3〉 〈p〉Similarity join on time series databases is an essential operation for data analysis applications. Due to the curse of dimensionality, it is not suitable to use traditional index techniques, such as R-tree and kd-tree. In the paper, a dynamic segment index (i.e., DSTree) is utilized to reduce the huge comparison cost on the similarity join on time series databases. However, the DSTree is designed for similarity search and only supports bound estimations between a time series and a batch of time series in a DSTree node. To make the DSTree suitable for the similarity join on time series databases, it is necessary to have tight bounds for the nodes to achieve a better pruning power, where the biggest challenge is that the DSTree nodes may have different segmentations. To solve the problem aforementioned, a segmentation alignment and synopsis evaluation method is proposed to support the estimation of DSTree nodes to significantly reduce the time cost by pruning unnecessary comparisons. Moreover, to make our approach I/O efficient, a caching strategy is proposed by taking advantage of both graph partitioning and the locality of the DSTree index. The efficiency and effectiveness of the proposed approaches are verified by experiments on real-life datasets.〈/p〉