Query: Detecting Significant Locations from Raw GPS Data Using Random Space Partitioning

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MapReduce: Simplified Data Processing on Large Clusters <=SEP=> MapReduce is a programming model and an associated implementation for processing and generating large datasets that is amenable to a broad variety of real-world tasks. Users specify the computation in terms of a map and a reduce function, and the underlying runtime system automatically parallelizes the computation across large-scale clusters of machines, handles machine failures, and schedules inter-machine communication to make efficient use of the network and disks. Programmers find the system easy to use: more than ten thousand distinct MapReduce programs have been implemented internally at Google over the past four years, and an average of one hundred thousand MapReduce jobs are executed on Google's clusters every day, processing a total of more than twenty petabytes of data per day.

Mining interesting locations and travel sequences from GPS trajectories <=SEP=> The increasing availability of GPS-enabled devices is changing the way people interact with the Web, and brings us a large amount of GPS trajectories representing people's location histories. In this paper, based on multiple users' GPS trajectories, we aim to mine interesting locations and classical travel sequences in a given geospatial region. Here, interesting locations mean the culturally important places, such as Tiananmen Square in Beijing, and frequented public areas, like shopping malls and restaurants, etc. Such information can help users understand surrounding locations, and would enable travel recommendation. In this work, we first model multiple individuals' location histories with a tree-based hierarchical graph (TBHG). Second, based on the TBHG, we propose a HITS (Hypertext Induced Topic Search)-based inference model, which regards an individual's access on a location as a directed link from the user to that location. This model infers the interest of a location by taking into account the following three factors. 1) The interest of a location depends on not only the number of users visiting this location but also these users' travel experiences. 2) Users' travel experiences and location interests have a mutual reinforcement relationship. 3) The interest of a location and the travel experience of a user are relative values and are region-related. Third, we mine the classical travel sequences among locations considering the interests of these locations and users' travel experiences. We evaluated our system using a large GPS dataset collected by 107 users over a period of one year in the real world. As a result, our HITS-based inference model outperformed baseline approaches like rank-by-count and rank-by-frequency. Meanwhile, when considering the users' travel experiences and location interests, we achieved a better performance beyond baselines, such as rank-by-count and rank-by-interest, etc.

Similarity estimation techniques from rounding algorithms <=SEP=> (MATH) A locality sensitive hashing scheme is a distribution on a family $\F$ of hash functions operating on a collection of objects, such that for two objects <i>x,y</i>, <b>Pr</b><sub><i>h</i></sub>εF[<i>h</i>(<i>x</i>) = <i>h</i>(<i>y</i>)] = sim(<i>x,y</i>), where <i>sim</i>(<i>x,y</i>) ε [0,1] is some similarity function defined on the collection of objects. Such a scheme leads to a compact representation of objects so that similarity of objects can be estimated from their compact sketches, and also leads to efficient algorithms for approximate nearest neighbor search and clustering. Min-wise independent permutations provide an elegant construction of such a locality sensitive hashing scheme for a collection of subsets with the set similarity measure <i>sim</i>(<i>A,B</i>) = \frac{|A &Pgr; B|}{|A &Pgr B|}.(MATH) We show that rounding algorithms for LPs and SDPs used in the context of approximation algorithms can be viewed as locality sensitive hashing schemes for several interesting collections of objects. Based on this insight, we construct new locality sensitive hashing schemes for:<ol><li>A collection of vectors with the distance between → \over <i>u</i> and → \over <i>v</i> measured by Ø(→ \over <i>u</i>, → \over <i>v</i>)/π, where Ø(→ \over <i>u</i>, → \over <i>v</i>) is the angle between → \over <i>u</i>) and → \over <i>v</i>). This yields a sketching scheme for estimating the cosine similarity measure between two vectors, as well as a simple alternative to minwise independent permutations for estimating set similarity.</li><li>A collection of distributions on <i>n</i> points in a metric space, with distance between distributions measured by the Earth Mover Distance (<b>EMD</b>), (a popular distance measure in graphics and vision). Our hash functions map distributions to points in the metric space such that, for distributions <i>P</i> and <i>Q</i>, <b>EMD</b>(<i>P,Q</i>) &xie; <b>E</b><sub>hε\F</sub> [<i>d</i>(<i>h</i>(<i>P</i>),<i>h</i>(<i>Q</i>))] &xie; <i>O</i>(log <i>n</i> log log <i>n</i>). <b>EMD</b>(<i>P, Q</i>).</li></ol>.

Building personal maps from GPS data. <=SEP=> In this article we discuss an assisted cognition information technology system that can learn personal maps customized for each user and infer his daily activities and movements from raw GPS data. The system uses discriminative and generative models for different parts of this task. A discriminative relational Markov network is used to extract significant places and label them; a generative dynamic Bayesian network is used to learn transportation routines, and infer goals and potential user errors at real time. We focus on the basic structures of the models and briefly discuss the inference and learning techniques. Experiments show that our system is able to accurately extract and label places, predict the goals of a person, and recognize situations in which the user makes mistakes, such as taking a wrong bus.

A Metric for Distributions with Applications to Image Databases <=SEP=> Proceedings of the 1998 IEEE International Conference on Computer Vision, Bombay, India We introduce a new distance between two distributions that we call the Earth Mover’s Distance (EMD), which reflects the minimal amount of work that must be performed to transform one distribution into the other by moving “distribution mass” around. This is a special case of the transportation problem from linear optimization, for which efficient algorithms are available. The EMD also allows for partial matching. When used to compare distributions that have the same overall mass, the EMD is a true metric, and has easy-to-compute lower bounds. In this paper we focus on applications to image databases, especially color and texture. We use the EMD to exhibit the structure of color-distribution and texture spaces by means of MultiDimensional Scaling displays. We also propose a novel approach to the problem of navigating through a collection of color images, which leads to a new paradigm for image database search.

Case study of the Miner Botnet <=SEP=> Malware and botnets are one of the most serious threats to today's Internet security. In this paper, we characterise the so-called &Miner Botnet”. It received major media attention after massive distributed denial of service attacks against a wide range of German and Russian websites, mainly during August and September 2011. We use our insights on this botnet to outline current botnet-related money-making concepts and to show that multiple activities of this botnet are actually centred on the virtual anonymised currency Bitcoin, thus justifying the name. Furthermore, we provide a binary-level analysis of the malware's design and components to illustrate the modularity of the previously mentioned concepts. We give an overview of the structure of the command-and-control protocol as well as of the botnet's architecture. Both centralised as well as distributed infrastructure aspects realised through peer-to-peer are present to run the botnet, the latter for increasing its resiliency. Finally, we provide the results of our ongoing tracking efforts that started in September 2011, focusing on the development of the botnet's size and geographic distribution. In addition we point out the challenge that is generally connected with size measurements of botnets due to the reachability of individual nodes and the persistence of IP addresses over time.

Design and implementation of fine pitch COB LED display <=SEP=> LED displays are widely used in advertisement, transportation, and commercial signages. Conventional LED display modules consist of a LED array assembly and a driving circuit board, inside an external housing. The dot pitch of conventional LED displays is typically 2.0 mm or above, which is relatively coarse. In some applications, such as the screen of personal mobile LED devices, a higher resolution with the dot pitch smaller than <;1.5 mm is needed due to the short viewing distance. Therefore, technologies for implementing fine pitch LED displays are in demand. This paper introduces a new type of LED display with the chip-on-board (COB) technology. The dot pitch of this new type of COB LED display may be reduced to 0.5 mm. The configurations for monochromatic and tricolored display modules are discussed. The reliability studies on the COB LED display are also presented. The results of ball bond shear, wire pull, junction temperature and thermal cycling tests indicate that the fine pitch COB LED display is feasible and reliable.

Logic Programming for Software-Defined Networks <=SEP=> 

Deploying Dense Networks for Maximal Energy Efficiency: Small Cells Meet Massive MIMO <=SEP=> What would a cellular network designed for maximal energy efficiency look like? To answer this fundamental question, tools from stochastic geometry are used in this paper to model future cellular networks and obtain a new lower bound on the average uplink spectral efficiency. This enables us to formulate a tractable uplink energy efficiency (EE) maximization problem and solve it analytically with respect to the density of base stations (BSs), the transmit power levels, the number of BS antennas and users per cell, and the pilot reuse factor. The closed-form expressions obtained from this general EE maximization framework provide valuable insights on the interplay between the optimization variables, hardware characteristics, and propagation environment. Small cells are proved to give high EE, but the EE improvement saturates quickly with the BS density. Interestingly, the maximal EE is achieved by also equipping the BSs with multiple antennas and operate in a “massive MIMO” fashion, where the array gain from coherent detection mitigates interference and the multiplexing of many users reduces the energy cost per user.

Automatic Room Detection and Room Labeling from Architectural Floor Plans <=SEP=> This paper presents an automatic system for analyzing and labeling architectural floor plans. In order to detect the locations of the rooms, the proposed systems extracts both, structural and semantic information from given floor plans. Furthermore, OCR is applied on the text layer to retrieve the meaningful room labeling. Finally, a novel post-processing is proposed to split rooms into several sub-regions if several semantic rooms share the same physical room. Our fully automatic system is evaluated on a publicly available dataset of architectural floor plans. In our experiments, we could clearly outperform other state-of-the-art approaches for room detection.

A Computational Semantic Analysis of Noun Compounds in Dutch <=SEP=> .........................................................................................................................................1 TABLE OF CONTENTS .........................................................................................................................2 0. PREFACE.............................................................................................................................................5

Multi-User Energy Consumption Monitoring and Anomaly Detection with Partial Context Information <=SEP=> Anomaly detection is an important problem in building energy management in order to identify energy theft and inefficiencies. However, it is hard to differentiate actual anomalies from the genuine changes in energy consumption due to seasonal variations and changes in personal settings such as holidays. One of the important drawbacks of existing anomaly detection algorithms is that various unknown context variables, such as seasonal variations, can affect the energy consumption of users in ways that appear anomalous to existing time series based anomaly detection algorithms.
 In this paper, we present a system for monitoring the energy consumption of multiple users within a neighborhood and a novel algorithm for detecting anomalies by combining data from multiple users. For each user, the neighborhood is defined as the set of all other users that have similar characteristics (function, location or demography), and are therefore likely to react and consume energy in the similar way in response to the external conditions. The neighborhood can be predefined based on prior customer information, or can be identified through an analysis of historical energy consumption. The proposed algorithm works as a two-step process. In the first step, the algorithm periodically computes an anomaly score for each user by just considering their own energy consumption and variations in the consumption of the past. In the second step, the anomaly score for a user is adjusted by analyzing the energy consumption data in the neighborhood. The collation of data within the neighborhood allows the proposed algorithm to differentiate between these genuine effects and real anomalous behavior of users. Unlike multivariate time series anomaly detection algorithms, the proposed algorithm can identify specific users that are exhibiting anomalous behavior. The capabilities of the algorithm are demonstrated using several year-long real-world data sets, for commercial as well as residential consumers.

Image-based phenotyping of plant disease symptoms <=SEP=> Plant diseases cause significant reductions in agricultural productivity worldwide. Disease symptoms have deleterious effects on the growth and development of crop plants, limiting yields and making agricultural products unfit for consumption. For many plant-pathogen systems, we lack knowledge of the physiological mechanisms that link pathogen infection and the production of disease symptoms in the host. A variety of quantitative high-throughput image-based methods for phenotyping plant growth and development are currently being developed. These methods range from detailed analysis of a single plant over time to broad assessment of the crop canopy for thousands of plants in a field and employ a wide variety of imaging technologies. Application of these methods to the study of plant disease offers the ability to study quantitatively how host physiology is altered by pathogen infection. These approaches have the potential to provide insight into the physiological mechanisms underlying disease symptom development. Furthermore, imaging techniques that detect the electromagnetic spectrum outside of visible light allow us to quantify disease symptoms that are not visible by eye, increasing the range of symptoms we can observe and potentially allowing for earlier and more thorough symptom detection. In this review, we summarize current progress in plant disease phenotyping and suggest future directions that will accelerate the development of resistant crop varieties.

The adoption of hyped technologies: a qualitative study <=SEP=> The introduction of new consumer technology is often greeted with declarations that the way people conduct their lives will be changed instantly. In some cases, this might create hype surrounding a specific technology. This article investigates the adoption of hyped technology, a special case that is absent in the adoption literature. The study employs a consumer research perspective, specifically the theory of consumption values (TCV), to understand the underlying motives for adopting the technology. In its original form, TCV entails five values that influence consumer behavior: functional, social, epistemic, emotional and conditional. The values catch the intrinsic and extrinsic motives influencing behavior. Using a qualitative approach that includes three focus groups and 60 one-on-one interviews, the results of the study show that emotional, epistemic and social values influence the adoption of hyped technologies. Contrary to expectations, functional value, which is similar to the widely used information system constructs of perceived usefulness and relative advantage, has little impact on the adoption of technologies that are surrounded with significant hype. Using the findings of the study, this article proposes a model for investigating and understanding the adoption of hyped technologies. This article contributes to the literature by (1) focusing on the phenomenon of hyped technology, (2) introducing TCV, a consumer research-based theoretical framework, to enhance the understanding of technology adoption, and (3) proposing a parsimonious model explaining the adoption of hyped technology.

A Thin Shell Volume for Modeling Human Hair <=SEP=> Hair-to-hair interaction is often ignored in human hair modeling, due to its computational and algorithmic complexity. In this paper, we present our experimental approach to simulate the complex behavior of long human hair, taking into account hairto-hair interactions. We describe a thin shell volume (TSV) model for enhancing hair realism by simulating complex hair-hair interaction. The TSV is a thin bounding volume that encloses a given hair surface. The TSV method enables virtual hair combing for surface-model hairstyles. Combing produces the effects of hair-hair interaction that occur in real human hair. Any hair model based on a surface representation can benefit from this approach. The TSV is presented mainly as a tool to modeling human hair, but this model also gives rise to global hair animation control.

Combination therapy : Synergism between natural plant extracts and antibiotics against infectious diseases <=SEP=> Antibiotics are one of the most important weapons in fighting bacterial infections and have greatly benefited the health‐related quality of human life since their introduction. However, over the past few decades these health benefits are under threat as many commonly used antibiotics have become less and less effective against certain illnesses not only because many of them produce toxic reactions but also due to emergence of drug resistant bacteria. Resistance development is an even bigger problem since the bacterial resistance is often not restricted to the specific antibiotic prescribed, but generally extends to other compounds of the same class. Bacterial resistance and its rapid increase is a major concern of global public health and is emerging as one of the most significant challenges to human health. Treating bacterial infections by antibiotics is beneficial but their indiscriminate use has led to an alarming resistance among microorganisms as well as led to re-emergence of old infectious diseases. One approach to treat infectious diseases is the use of plant extracts individually and /or as an alternative approach is the use of combination of antibiotics with plant extracts. This latter approach i.e. combination therapy or synergistic therapy; against resistant microorganisms may lead to new ways of treating infectious diseases and probably this represents a potential area for further future investigations. Combination therapy is helpful and useful for patients with serious infections caused by drug resistant pathogens. The present review describes in detail, the observed synergy between natural extracts and standard antibiotics combating bacterial and fungal infections. The mode of action of combination therapy significantly differs from that of the same drugs acting individually; therefore the selection of an appropriate combination is crucial and essential which requires understanding the potential interaction between the plant extracts and antimicrobial agents.

Social ties and mental health <=SEP=> It is generally agreed that social ties play a beneficial role in the maintenance of psychological well-being. In this targeted review, we highlight four sets of insights that emerge from the literature on social ties and mental health outcomes (defined as stress reactions, psychological well-being, and psychological distress, including depressive symptoms and anxiety). First, the pathways by which social networks and social supports influence mental health can be described by two alternative (although not mutually exclusive) causal models—the main effect model and the stress-buffering model. Second, the protective effects of social ties on mental health are not uniform across groups in society. Gender differences in support derived from social network participation may partly account for the higher prevalence of psychological distress among women compared to men. Social connections may paradoxically increase levels of mental illness symptoms among women with low resources, especially if such connections entail role strain associated with obligations to provide social support to others. Third, egocentric networks are nested within a broader structure of social relationships. The notion of social capital embraces the embeddedness of individual social ties within the broader social structure. Fourth, despite some successes reported in social support interventions to enhance mental health, further work is needed to deepen our understanding of the design, timing, and dose of interventions that work, as well as the characteristics of individuals who benefit the most.

Contingency planning over probabilistic hybrid obstacle predictions for autonomous road vehicles <=SEP=> This paper presents a novel optimization based path planner that can simultaneously plan multiple contingency paths to account for the uncertain actions of dynamic obstacles. This planner addresses the particular problem of collision avoidance for autonomous road vehicles which are required to safely interact with other vehicles with unknown intentions. The presented path planner utilizes an efficient spline based trajectory representation and fast but accurate collision probability approximations to enable the simultaneous optimization of multiple contingency paths.

The inhibitors of apoptosis (IAPs) as cancer targets <=SEP=> Apoptosis has been accepted as a fundamental component in the pathogenesis of cancer, in addition to other human diseases including neurodegeneration, coronary disease and diabetes. The origin of cancer involves deregulated cellular proliferation and the suppression of apoptotic processes, ultimately leading to tumor establishment and growth. Several lines of evidence point toward the IAP family of proteins playing a role in oncogenesis, via their effective suppression of apoptosis. The central mechanisms of IAP apoptotic suppression appear to be through direct caspase and pro-caspase inhibition (primarily caspase 3 and 7) and modulation of, and by, the transcription factor NF-kappaB. Thus, when the IAPs are over-expressed or over-active, as is the case in many cancers, cells are no longer able to die in a physiologically programmed fashion and become increasingly resistant to standard chemo- and radiation therapies. To date several approaches have been taken to target and eliminate IAP function in an attempt to re-establish sensitivity, reduce toxicity, and improve efficacy of cancer treatment. In this review, we address IAP proteins as therapeutic targets for the treatment of cancer and emphasize the importance of novel therapeutic approaches for cancer therapy. Novel targets of IAP function are being identified and include gene therapy strategies and small molecule inhibitors that are based on endogenous IAP antagonists. As well, molecular mechanistic approaches, such as RNAi to deplete IAP expression, are in development.

Large-Scale Analysis of Soccer Matches Using Spatiotemporal Tracking Data <=SEP=> Although the collection of player and ball tracking data is fast becoming the norm in professional sports, large-scale mining of such spatiotemporal data has yet to surface. In this paper, given an entire season's worth of player and ball tracking data from a professional soccer league (≈400,000,000 data points), we present a method which can conduct both individual player and team analysis. Due to the dynamic, continuous and multi-player nature of team sports like soccer, a major issue is aligning player positions over time. We present a "role-based" representation that dynamically updates each player's relative role at each frame and demonstrate how this captures the short-term context to enable both individual player and team analysis. We discover role directly from data by utilizing a minimum entropy data partitioning method and show how this can be used to accurately detect and visualize formations, as well as analyze individual player behavior.

Motor skill acquisition. <=SEP=> INTRODUCTION ..................................................................................... 213 A GENERAL LAW OF MOTOR LEARNING ................................................. 215 Motor Learning as a Power Law .............................................................. 216 The Power Law--A Special Case of Motor Learning? .................................... 217 WHAT IS LEARNED WITH PRACTICE ....................................................... 219 One-To-One R call-Recognition Processes .................................................. 219 Schema Representation of Action .............................................................. 221 Problems with Prescriptive Accounts of Motor Learning ................................. 223 Motor Skill Acquisition as a Search Strategy ............................................... 224 INFORMATION A D MOTOR SKILL ACQUISITION ..................................... 225 Information as a Prescription .................................................................. 226 Information as Feedback ........................................................................ 227 Information to Channel the Search ............................................................ 231 CONCLUDING REMARKS ........................................................................ 233

Bayesian Estimation of a Stochastic Volatility Model Using Option and Spot Prices : Application of a Bivariate Kalman Filter <=SEP=> In this paper Bayesian methods are applied to a stochastic volatility model using both the prices of the asset and the prices of options written on the asset. Posterior densities for all model parameters, latent volatilities and the market price of volatility risk are produced via a hybrid Markov Chain Monte Carlo sampling algorithm. Candidate draws for the unobserved volatilities are obtained using the Kalman filter and smoother to a linearization of a statespace representation of the model. The method is illustrated using the Heston (1993) stochastic volatility model applied to Australian News Corporation spot and option price data. Alternative models nested in the Heston framework are ranked via Bayes Factors and via fit, predictive and hedging performance.

Genetic and Stress-Induced Loss of NG2 Glia Triggers Emergence of Depressive-like Behaviors through Reduced Secretion of FGF2 <=SEP=> NG2-expressing glia (NG2 glia) are a uniformly distributed and mitotically active pool of cells in the central nervous system (CNS). In addition to serving as progenitors of myelinating oligodendrocytes, NG2 glia might also fulfill physiological roles in CNS homeostasis, although the mechanistic nature of such roles remains unclear. Here, we report that ablation of NG2 glia in the prefrontal cortex (PFC) of the adult brain causes deficits in excitatory glutamatergic neurotransmission and astrocytic extracellular glutamate uptake and induces depressive-like behaviors in mice. We show in parallel that chronic social stress causes NG2 glia density to decrease in areas critical to Major Depressive Disorder (MDD) pathophysiology at the time of symptom emergence in stress-susceptible mice. Finally, we demonstrate that loss of NG2 glial secretion of fibroblast growth factor 2 (FGF2) suffices to induce the same behavioral deficits. Our findings outline a pathway and role for NG2 glia in CNS homeostasis and mood disorders.

Substrate Integrated Waveguide Quasi-Elliptic Filter Using Slot-Coupled and Microstrip-Line Cross-Coupled Structures <=SEP=> This paper proposes a quasi-elliptic filter with slot coupling and nonadjacent cross coupling based on the substrate integrated waveguide (SIW) cavity. The slots etched on the top metal plane of SIW cavity are used to produce electrical coupling, and the cross coupling is realized by the microstrip transmission line above the SIW cavity. The coupling strength is mainly controlled by the width and height of the slot. The length of the open-ended microstrip line controls the sign of the cross coupling. The cross coupling with different signs are used in the filter to produce a pair of transmission zeros (TZs) at both sides of the passband. In order to prove the validity, a fourth-order SIW quasi-elliptic filter with TZsat both sides of the passband is fabricated in a two-layer printed circuit board. The measured insertion loss at a center frequency of 3.7 GHz is 1.1 dB. The return loss within the passband is below -18 dB with a fractional bandwidth of 16%. The measured results are in good agreement with the simulated results.

A distributed ADMM approach for mobile data offloading in software defined network <=SEP=> Mobile data offloading has been introduced to alleviate the congestion of cellular networks and to improve the quality of service for mobile end users. This paper presents a distributed mechanism for mobile data offloading in software defined network (SDN) at the network edge. In SDN, the data traffic of base stations (BSs) can be dynamically offloaded to access points (APs), which is enabled by the SDN controller. The SDN controller formulates a revenue maximization problem to optimize the data offloading decision, and solves the problem in a fully distributed fashion. The proposed mechanism is based on the proximal Jacobian multi-block alternating direction method of multipliers (ADMM). BSs and APs perform the offloading decision update concurrently, and are coordinated by the SDN controller through dual variables to reach a consensus on the offloading demand and supply. Numerical simulations validate the effectiveness of the proposed algorithm.

Comparing Twitter and Traditional Media Using Topic Models <=SEP=> Twitter as a new form of social media can potentially contain much useful information, but content analysis on Twitter has not been well studied. In particular, it is not clear whether as an information source Twitter can be simply regarded as a faster news feed that covers mostly the same information as traditional news media. In This paper we empirically compare the content of Twitter with a traditional news medium, New York Times, using unsupervised topic modeling. We use a Twitter-LDA model to discover topics from a representative sample of the entire Twitter. We then use text mining techniques to compare these Twitter topics with topics from New York Times, taking into consideration topic categories and types. We also study the relation between the proportions of opinionated tweets and retweets and topic categories and types. Our comparisons show interesting and useful findings for downstream IR or DM applications.

A Sequential Importance Sampling Algorithm for Generating Random Graphs with Prescribed Degrees <=SEP=> Random graphs with a given degree sequence are a useful model capturing several features absent in the classical Erdős-Rényi model, such as dependent edges and non-binomial degrees. In this paper, we use a characterization due to Erdős and Gallai to develop a sequential algorithm for generating a random labeled graph with a given degree sequence. The algorithm is easy to implement and allows surprisingly efficient sequential importance sampling. Applications are given, including simulating a biological network and estimating the number of graphs with a given degree sequence.

Immersive Analytics for Clinical Neuroscience <=SEP=> This paper introduces NeuroCave, an open-source immersive analytics software tool for exploring and making sense of both structural and functional connectomes describing the interconnectivity between brain regions. The software supports visual analysis tasks for clinical neuroscientists, facilitating explorations of connectome datasets used in comparative group studies. Researchers can enter and leave virtual reality as desired in order to visualize and reason about connectome datasets from a range of different perspectives.

An Improved Topology of SEPIC Converter With Reduced Output Voltage Ripple <=SEP=> An improved version of a single-ended primary inductor converter (SEPIC) is presented. The converter consists of a conventional SEPIC converter plus an additional high-frequency transformer and diode to maintain a freewheeling mode of the DC inductor currents during the switch on state. The voltage conversion ratio characteristics and semiconductor device voltage and current stresses are characterized. The main advantages of this converter are the continuous output current, smaller output voltage ripple, and lower semiconductors current stress compared with the conventional SEPIC converter. The design and simulation of the concept is verified by an experiment with a 48-V input and 12-V/3.75-A output converter.

A new computing environment for collective privacy protection from constrained healthcare devices to IoT cloud services <=SEP=> The Internet of healthcare things is essentially a new model that changes the way of the delivery and management of healthcare services. It utilizes digital sensors and cloud computing to present a quality healthcare service outside of the classical hospital environment. This resulted in the emergence of a new class of online web 4.0 services, which are termed “cloud healthcare services”. Cloud healthcare services offer a straightforward opportunity for patients to communicate with healthcare professionals and utilize their personal IoHT devices to obtain timely and accurate medical guidance and decisions. The personal IoHT devices integrate sensed health data at a central cloud healthcare service to extract useful health insights for wellness and preventive care strategies. However, the present practices for cloud healthcare services rely on a centralized approach, where patients’ health data are collected and stored on servers, located at remote locations, which might be functioning under data privacy laws somewhat different from the ones applied where the service is running. Promoting a privacy respecting cloud services encourages patients to actively participate in these healthcare services and to routinely provide an accurate and precious health data about themselves. With the emergence of fog computing paradigm, privacy protection can now be enforced at the edge of the patient’s network regardless of the location of service providers. In this paper, a framework for cloud healthcare recommender service is presented. We depicted the personal gateways at the patients’ side act as intermediate nodes (called fog nodes) between IoHT devices and cloud healthcare services. A fog-based middleware will be hosted on these fog nodes for an efficient aggregation of patients generated health data while maintaining the privacy and the confidentiality of their health profiles. The proposed middleware executes a two-stage concealment process that utilizes the hierarchical nature of IoHT devices. This will unburden the constrained IoHT devices from performing intensive privacy preserving processes. At that, the patients will be empowered with a tool to control the privacy of their health data by enabling them to release their health data in a concealed form. The further processing at the cloud healthcare service continues over the concealed data by applying the proposed protocols. The proposed solution was integrated into a scenario related to preserving the privacy of the patients’ health data when utilized by a cloud healthcare recommender service to generate health insights. Our approach induces a straightforward solution with accurate results, which are beneficial to both patients and service providers.