ALBERT

Description: We examine a distributed detection problem in a wireless sensor network, where sensor nodes collaborate to detect a Gaussian signal with an unknown change of power, i.e., a scale parameter. Due to power/bandwidth constraints, we consider the case where each sensor quantizes its observation into a binary digit. The binary data are then transmitted through error-prone wireless links to a fusion center, where a generalized likelihood ratio test (GLRT) detector is employed to perform a global decision. We study the design of a binary quantizer based on an asymptotic analysis of the GLRT. Interestingly, the quantization threshold of the quantizer is independent of the unknown scale parameter. Numerical results are included to illustrate the performance of the proposed quantizer and GLRT in binary symmetric channels (BSCs).

Description: More and more hybrid electric vehicles are driven since they offer such advantages as energy savings and better active safety performance. Hybrid vehicles have two or more power driving systems and frequently switch working condition, so controlling stability is very important. In this work, a two-stage Kalman algorithm method is used to fuse data in hybrid vehicle stability testing. First, the RT3102 navigation system and Dewetron system are introduced. Second, a modeling of data fusion is proposed based on the Kalman filter. Then, this modeling is simulated and tested on a sample vehicle, using Carsim and Simulink software to test the results. The results showed the merits of this modeling.

Description: Background: With the introduction and implementation of a variety of government programs and policies to encourage adoption of electronic medical records (EMRs), EMRs are being increasingly adopted in North America. We sought to evaluate the completeness of a variety of EMR fields to determine if family physicians were comprehensively using their EMRs and the suitability of use of the data for secondary purposes in Ontario, Canada. Methods: We examined EMR data from a convenience sample of family physicians distributed throughout Ontario within the Electronic Medical Record Administrative data Linked Database (EMRALD) as extracted in the summer of 2012. We identified all physicians with at least one year of EMR use. Measures were developed and rates of physician documentation of clinical encounters, electronic prescriptions, laboratory tests, blood pressure and weight, referrals, consultation letters, and all fields in the cumulative patient profile were calculated as a function of physician and patient time since starting on the EMR. Results: Of the 167 physicians with at least one year of EMR use, we identified 186,237 patients. Overall, the fields with the highest level of completeness were for visit documentations and prescriptions (〉70 %). Improvements were observed with increasing trends of completeness overtime for almost all EMR fields according to increasing physician time on EMR. Assessment of the influence of patient time on EMR demonstrated an increasing likelihood of the population of EMR fields overtime, with the largest improvements occurring between the first and second years. Conclusions: All of the data fields examined appear to be reasonably complete within the first year of adoption with the biggest increase occurring the first to second year. Using all of the basic functions of the EMR appears to be occurring in the current environment of EMR adoption in Ontario. Thus the data appears to be suitable for secondary use.

Description: Background: Recognising the limitations of a paper-based approach to documenting vital sign observations and responding to national clinical guidelines, we have explored the use of an electronic solution that could improve the quality and safety of patient care. We have developed a system for recording vital sign observations at the bedside, automatically calculating an Early Warning Score, and saving data such that it is accessible to all relevant clinicians within a hospital trust. We have studied current clinical practice of using paper observation charts, and attempted to streamline the process. We describe our user-focussed design process, and present the key design decisions prior to describing the system in greater detail. Results: The system has been deployed in three pilot clinical areas over a period of 9 months. During this time, vital sign observations were recorded electronically using our system. Analysis of the number of observations recorded (21,316 observations) and the number of active users (111 users) confirmed that the system is being used for routine clinical observations. Feedback from clinical end-users was collected to assess user acceptance of the system. This resulted in a System Usability Scale score of 77.8, indicating high user acceptability. Conclusions: Our system has been successfully piloted, and is in the process of full implementation throughout adult inpatient clinical areas in the Oxford University Hospitals. Whilst our results demonstrate qualitative acceptance of the system, its quantitative effect on clinical care is yet to be evaluated.

Description: Currently deep learning has made great breakthroughs in visual and speech processing, mainly because it draws lessons from the hierarchical mode that brain deals with images and speech. In the field of NLP, a topic model is one of the important ways for modeling documents. Topic models are built on a generative model that clearly does not match the way humans write. In this paper, we propose Event Model, which is unsupervised and based on the language processing mechanism of neurolinguistics, to model documents. In Event Model, documents are descriptions of concrete or abstract events seen, heard, or sensed by people and words are objects in the events. Event Model has two stages: word learning and dimensionality reduction. Word learning is to learn semantics of words based on deep learning. Dimensionality reduction is the process that representing a document as a low dimensional vector by a linear mode that is completely different from topic models. Event Model achieves state-of-the-art results on document retrieval tasks.

Description: Background: According to the World Health Organization 130–150 million (according to WHO) of people globally are chronically infected with hepatitis C virus. The virus is responsible for chronic hepatitis that ultimately may cause liver cirrhosis and death. The disease is progressive, however antiviral treatment may slow down or stop its development. Therefore, it is important to estimate the severity of liver fibrosis for diagnostic, therapeutic and prognostic purposes.Liver biopsy provides a high accuracy diagnosis, however it is painful and invasive procedure. Recently, we witness an outburst of non-invasive tests (biological and physical ones) aiming to define severity of liver fibrosis, but commonly used FibroTest®, according to an independent research, in some cases may have accuracy lower than 50 %. In this paper a data mining and classification technique is proposed to determine the stage of liver fibrosis using easily accessible laboratory data. Methods: Research was carried out on archival records of routine laboratory blood tests (morphology, coagulation, biochemistry, protein electrophoresis) and histopathology records of liver biopsy as a reference value. As a result, the granular model was proposed, that contains a series of intervals representing influence of separate blood attributes on liver fibrosis stage. The model determines final diagnosis for a patient using aggregation method and voting procedure. The proposed solution is robust to missing or corrupted data. Results: The results were obtained on data from 290 patients with hepatitis C virus collected over 6 years. The model has been validated using training and test data. The overall accuracy of the solution is equal to 67.9 %. The intermediate liver fibrosis stages are hard to distinguish, due to effectiveness of biopsy itself. Additionally, the method was verified against dataset obtained from 365 patients with liver disease of various etiologies. The model proved to be robust to new data. What is worth mentioning, the error rate in misclassification of the first stage and the last stage is below 6.5 % for all analyzed datasets. Conclusions: The proposed system supports the physician and defines the stage of liver fibrosis in chronic hepatitis C. The biggest advantage of the solution is a human-centric approach using intervals, which can be verified by a specialist, before giving the final decision. Moreover, it is robust to missing data. The system can be used as a powerful support tool for diagnosis in real treatment.

Description: Given a database table with records that can be ranked, an interesting problem is to identify selection conditions for the table, which are qualified by an input record and render its ranking as high as possible among the qualifying tuples. In this paper, we study this standing maximization problem, which finds application in object promotion and characterization. After showing the hardness of the problem, we propose greedy methods, which are experimentally shown to achieve high accuracy compared to exhaustive enumeration, while scaling very well to the problem input size. Our contributions include a linear-time algorithm for determining the optimal selection range for an ordinal attribute and techniques for choosing and prioritizing the most promising selection predicates to apply. Experiments on real datasets confirm the effectiveness and efficiency of our techniques.

Description: Some fairly recent research has focused on providing XACML-based solutions for dynamic privacy policy management. In this regard, a number of works have provided enhancements to the performance of XACML policy enforcement point (PEP) component, but very few have focused on enhancing the accuracy of that component. This paper improves the accuracy of an XACML PEP by filling some gaps in the existing works. In particular, dynamically incorporating user access context into the privacy policy decision, and its enforcement. We provide an XACML-based implementation of a dynamic privacy policy management framework and an evaluation of the applicability of our system in comparison to some of the existing approaches.

Description: This paper first introduces pattern aided regression (PXR) models, a new type of regression models designed to represent accurate and interpretable prediction models. This was motivated by two observations: (1) Regression modeling applications often involve complex diverse predictor-response relationships , which occur when the optimal regression models (of given regression model type) fitting two or more distinct logical groups of data are highly different. (2) State-of-the-art regression methods are often unable to adequately model such relationships. This paper defines PXR models using several patterns and local regression models, which respectively serve as logical and behavioral characterizations of distinct predictor-response relationships. The paper also introduces a contrast pattern aided regression (CPXR) method, to build accurate PXR models. In experiments, the PXR models built by CPXR are very accurate in general, often outperforming state-of-the-art regression methods by big margins. Usually using (a) around seven simple patterns and (b) linear local regression models, those PXR models are easy to interpret; in fact, their complexity is just a bit higher than that of (piecewise) linear regression models and is significantly lower than that of traditional ensemble based regression models. CPXR is especially effective for high-dimensional data. The paper also discusses how to use CPXR methodology for analyzing prediction models and correcting their prediction errors.

Description: We analyze models for predicting the probability of a strikeout for a batter/pitcher matchup in baseball using player descriptors that can be estimated accurately from small samples. We start with the log5 model which has been used extensively for describing matchups in sports. Log5 is a special case of a logit model and we use constrained logistic regression over nearly one million matchup observations to assess the use of the log5 explanatory variables for this application. We also show that a batter/pitcher ground ball rate interaction variable is significant for the prediction of strikeout probability and we provide physical justification for the inclusion of this variable in the model. We quantify the differences among the models and show that batters control the majority of the variance in predicted strikeout rate.

Description: Background: Health decision-making requires evidence from high-quality data. As one example, the Discharge Abstract Database (DAD) compiles data from the majority of Canadian hospitals to form one of the most comprehensive and highly regarded administrative health databases available for health research, internationally. However, despite the success of this and other administrative health data resources, little is known about their history or the factors that have led to their success. The purpose of this paper is to provide an historical overview of Canadian administrative health data for health research to contribute to the institutional memory of this field. Methods: We conducted a qualitative content analysis of approximately 20 key sources to construct an historical narrative of administrative health data in Canada. Specifically, we searched for content related to key events, individuals, challenges, and successes in this field over time. Results: In Canada, administrative health data for health research has developed in tangent with provincial research centres. Interestingly, the lessons learned from this history align with the original recommendations of the 1964 Royal Commission on Health Services: (1) standardization, and (2) centralization of data resources, that is (3) facilitated through governmental financial support. Conclusions: The overview history provided here illustrates the need for longstanding partnerships between government and academia, for classification, terminology and standardization are time-consuming and ever-evolving processes. This paper will be of interest to those who work with administrative health data, and also for countries that are looking to build or improve upon their use of administrative health data for decision-making.

Description: Community detection in a complex network is an important problem of much interest in recent years. In general, a community detection algorithm chooses an objective function and captures the communities of the network by optimizing the objective function, and then, one uses various heuristics to solve the optimization problem to extract the interesting communities for the user. In this article, we demonstrate the procedure to transform a graph into points of a metric space and develop the methods of community detection with the help of a metric defined for a pair of points. We have also studied and analyzed the community structure of the network therein. The results obtained with our approach are very competitive with most of the well-known algorithms in the literature, and this is justified over the large collection of datasets. On the other hand, it can be observed that time taken by our algorithm is quite less compared to other methods and justifies the theoretical findings.

Description: A three-step iterative method with fifth-order convergence as a new modification of Newton’s method was presented. This method is for finding multiple roots of nonlinear equation with unknown multiplicity m whose multiplicity m is the highest multiplicity. Its order of convergence is analyzed and proved. Results for some numerical examples show the efficiency of the new method.

Description: Background: There is general consensus that appropriate development and use of information and communication technologies (ICT) are crucial in the delivery of effective primary care (PC). Several countries are defining policies to support and promote a structural change of the health care system through the introduction of ICT. This study analyses the state of development of basic ICT in PC systems of 31 European countries with the aim to describe the extent of, and main purposes for, computer use by General Practitioners (GPs) across Europe. Additionally, trends over time have been analysed. Methods: Descriptive statistical analysis was performed on data from the QUALICOPC (Quality and Costs of Primary Care in Europe) survey, to describe the geographic differences in the general use of computer, and in specific computerized clinical functions for different health-related purposes such as prescribing, medication checking, generating health records and research for medical information on the Internet. Results: While all the countries have achieved a near-universal adoption of a computer in their primary care practices, with only a few countries near or under the boundary of 90 %, the computerisation of primary care clinical functions presents a wide variability of adoption within and among countries and, in several cases (such as in the southern and central-eastern Europe), a large room for improvement. Conclusions: At European level, more efforts could be done to support southern and central-eastern Europe in closing the gap in adoption and use of ICT in PC. In particular, more attention seems to be need on the current usages of the computer in PC, by focusing policies and actions on the improvement of the appropriate usages that can impact on quality and costs of PC and can facilitate an interconnected health care system. However, policies and investments seem necessary but not sufficient to achieve these goals. Organizational, behavioural and also networking aspects should be taken in consideration.

Description: Background: Bangladesh is facing serious shortage of trained health professionals. In the pluralistic healthcare system of Bangladesh, formal health care providers constitute only 5 % of the total workforce; the rest are informal health care providers. Information Communication Technologies (ICTs) are increasingly seen as a powerful tool for linking the community with formal healthcare providers. Our study assesses an intervention that linked village doctors (a cadre of informal health care providers practising modern medicine) to formal doctors through call centres from the perspective of the village doctors who participated in the intervention. Methods: The study was conducted in Chakaria, a remote rural area in south-eastern Bangladesh during April–May 2013. Twelve village doctors were selected purposively from a pool of 55 village doctors who participated in the mobile health (mHealth) intervention. In depth interviews were conducted to collect data. The data were manually analysed using themes that emerged.ResultThe village doctors talked about both business benefits (access to formal doctors, getting support for decision making, and being entitled to call trained doctors) and personal benefits (both financial and non-financial). Some of the major barriers mentioned were technical problems related to accessing the call centre, charging consultation fees, and unfamiliarity with the call centre physicians. Conclusion: Village doctors saw many benefits to having a business relationship with the trained doctors that the mHealth intervention provided. mHealth through call centres has the potential to ensure consultation services to populations through existing informal healthcare providers in settings with a shortage of qualified healthcare providers.

Description: Background: Choosing the most appropriate family physician (FP) for the individual, plays a fundamental role in primary care. The aim of this study is to determine the selection criteria for the patients in choosing their family doctors and priority ranking of these criteria by using the multi-criteria decision-making method of the Analytic Hierarchy Process (AHP) model. Methods: The study was planned and conducted in two phases. In the first phase, factors affecting the patients’ decisions were revealed with a qualitative research. In the next phase, the priorities of FP selection criteria were determined by using AHP model. Criteria were compared in pairs. 96 patient were asked to fill the information forms which contains comparison scores in the Family Health Centres. Results: According to the analysis of focus group discussions FP selection criteria were congregated in to five groups: Individual Characteristics, Patient-Doctor relationship, Professional characteristics, the Setting, and Ethical Characteristics.For each of the 96 participants, comparison matrixes were formed based on the scores of their information forms. Of these, models of only 5 (5.2 %) of the participants were consistent, in other words, they have been able to score consistent ranking. The consistency ratios (CR) were found to be smaller than 0.10. Therefore the comparison matrix of this new model, which was formed based on the medians of scores only given by these 5 participants, was consistent (CR = 0.06 

Description: Over the past decade or so, several research groups have addressed the problem of multi-label classification where each example can belong to more than one class at the same time. A common approach, called  Binary Relevance (BR) , addresses this problem by inducing a separate classifier for each class. Research has shown that this framework can be improved if mutual class dependence is exploited: an example that belongs to class $X$ is likely to belong also to class $Y$ ; conversely, belonging to $X$ can make an example less likely to belong to $Z$ . Several works sought to model this information by using the vector of class labels as additional example attributes. To fill the unknown values of these attributes during prediction, existing methods resort to using outputs of other classifiers, and this makes them prone to errors. This is where our paper wants to contribute. We identified two potential ways to prune unnecessary dependencies and to reduce error-propagation in our new classifier-stacking technique, which is named PruDent . Experimental results indicate that the classification performance of PruDent compares favorably with that of other state-of-the-art approaches over a broad range of testbeds. Mor- over, its computational costs grow only linearly in the number of classes.

Description: This work deals with the problem of producing a fast and accurate data classification, learning it from a possibly small set of records that are already classified. The proposed approach is based on the framework of the so-called Logical Analysis of Data (LAD), but enriched with information obtained from statistical considerations on the data. A number of discrete optimization problems are solved in the different steps of the procedure, but their computational demand can be controlled. The accuracy of the proposed approach is compared to that of the standard LAD algorithm, of support vector machines and of label propagation algorithm on publicly available datasets of the UCI repository. Encouraging results are obtained and discussed.

Description: A new graph based constrained semi-supervised learning (G-CSSL) framework is proposed. Pairwise constraints (PC) are used to specify the types (intra- or inter-class) of points with labels. Since the number of labeled data is typically small in SSL setting, the core idea of this framework is to create and enrich the PC sets using the propagated soft labels from both labeled and unlabeled data by special label propagation (SLP), and hence obtaining more supervised information for delivering enhanced performance. We also propose a Two-stage Sparse Coding, termed TSC, for achieving adaptive neighborhood for SLP. The first stage aims at correcting the possible corruptions in data and training an informative dictionary, and the second stage focuses on sparse coding. To deliver enhanced inter-class separation and intra-class compactness, we also present a mixed soft-similarity measure to evaluate the similarity/dissimilarity of constrained pairs using the sparse codes and outputted probabilistic values by SLP. Simulations on the synthetic and real datasets demonstrated the validity of our algorithms for data representation and image recognition, compared with other related state-of-the-art graph based semi-supervised techniques.

Description: In large databases, the amount and the complexity of the data calls for data summarization techniques. Such summaries are used to assist fast approximate query answering or query optimization. Histograms are a prominent class of model-free data summaries and are widely used in database systems. So-called self-tuning histograms look at query-execution results to refine themselves. An assumption with such histograms, which has not been questioned so far, is that they can learn the dataset from scratch, that is—starting with an empty bucket configuration. We show that this is not the case. Self-tuning methods are very sensitive to the initial configuration. Three major problems stem from this. Traditional self-tuning is unable to learn projections of multi-dimensional data, is sensitive to the order of queries, and reaches only local optima with high estimation errors. We show how to improve a self-tuning method significantly by starting with a carefully chosen initial configuration. We propose initialization by dense subspace clusters in projections of the data, which improves both accuracy and robustness of self-tuning. Our experiments on different datasets show that the error rate is typically halved compared to the uninitialized version.

Description: Recently, two ideas have been explored that lead to more accurate algorithms for time-series classification (TSC). First, it has been shown that the simplest way to gain improvement on TSC problems is to transform into an alternative data space where discriminatory features are more easily detected. Second, it was demonstrated that with a single data representation, improved accuracy can be achieved through simple ensemble schemes. We combine these two principles to test the hypothesis that forming a collective of ensembles of classifiers on different data transformations improves the accuracy of time-series classification. The collective contains classifiers constructed in the time, frequency, change, and shapelet transformation domains. For the time domain, we use a set of elastic distance measures. For the other domains, we use a range of standard classifiers. Through extensive experimentation on 72 datasets, including all of the 46 UCR datasets, we demonstrate that the simple collective formed by including all classifiers in one ensemble is significantly more accurate than any of its components and any other previously published TSC algorithm. We investigate alternative hierarchical collective structures and demonstrate the utility of the approach on a new problem involving classifying Caenorhabditis elegans mutant types.

Description: In real-world graphs such as social networks, Semantic Web and biological networks, each vertex usually contains rich information, which can be modeled by a set of tokens or elements. In this paper, we study a subgraph matching with set similarity (SMS $^2$ ) query over a large graph database, which retrieves subgraphs that are structurally isomorphic to the query graph, and meanwhile satisfy the condition of vertex pair matching with the (dynamic) weighted set similarity. To efficiently process the SMS $^2$ query, this paper designs a novel lattice-based index for data graph, and lightweight signatures for both query vertices and data vertices. Based on the index and signatures, we propose an efficient two-phase pruning strategy including set similarity pruning and structure-based pruning, which exploits the unique features of both (dynamic) weighted set similarity and graph topology. We also propose an efficient dominating-set-based subgraph matching algorithm guided by a dominating set selection algorithm to achieve better query performance. Extensive experiments on both real and synthetic datasets demonstrate that our method outperforms state-of-the-art methods by an order of magnitude.

Description: Data imputation aims at filling in missing attribute values in databases. Most existing imputation methods to string attribute values are inferring-based approaches, which usually fail to reach a high imputation recall by just inferring missing values from the complete part of the data set. Recently, some retrieving-based methods are proposed to retrieve missing values from external resources such as the World Wide Web, which tend to reach a much higher imputation recall, but inevitably bring a large overhead by issuing a large number of search queries. In this paper, we investigate the interaction between the inferring-based methods and the retrieving-based methods. We show that retrieving a small number of selected missing values can greatly improve the imputation recall of the inferring-based methods. With this intuition, we propose an inTeractive Retrieving-Inferring data imPutation approach (TRIP), which performs retrieving and inferring alternately in filling in missing attribute values in a data set. To ensure the high recall at the minimum cost, TRIP faces a challenge of selecting the least number of missing values for retrieving to maximize the number of inferable values. Our proposed solution is able to identify an optimal retrieving-inferring scheduling scheme in deterministic data imputation, and the optimality of the generated scheme is theoretically analyzed with proofs. We also analyze with an example that the optimal scheme is not feasible to be achieved in $tau$ -constrained stochastic data imputation ( $tau$ -SDI), but still, our proposed solution identifies an expected-optimal scheme in $tau$ -SDI. Extensive experiments on four data collections show that TRIP retrieves on average 20 percent missing values and achieves the same high recall that was reached by the retrieving-based approach.

Description: Visual classification has attracted considerable research interests in the past decades. In this paper, a novel $ell _1$ -hypergraph model for visual classification is proposed. Hypergraph learning, as a natural extension of graph model, has been widely used in many machine learning tasks. In previous work, hypergraph is usually constructed by attribute-based or neighborhood-based methods. That is, a hyperedge is generated by connecting a set of samples sharing a same feature attribute or in a neighborhood. However, these methods are unable to explore feature space globally or sensitive to noises. To address these problems, we propose a novel hypergraph construction approach that leverages sparse representation to generate hyperedges and learns the relationship among hyperedges and their vertices. First, for each sample, a hyperedge is generated by regarding it as the centroid and linking it as well as its nearest neighbors. Then, the sparse representation method is applied to represent the centroid vertex by other vertices within the same hyperedge. The vertices with zero coefficients are removed from the hyperedge. Finally, the representation coefficients are used to define the incidence relation between the hyperedge and the vertices. In our approach, we also optimize the hyperedge weights to modulate the effects of different hyperedges. We leverage the prior knowledge on the hyperedges so that the hyperedges sharing more vertices can have closer weights, where a graph Laplacian is used to regularize the optimization of the weights. Our approach is named $ell _1$ -hypergraph since the $ell _1$ sparse representation is employed in the hypergraph construction process. The method is evaluated on various visual classification tasks, and it demonstrates promising performance.

Description: In this paper, we present three improvements to a three-point third order variant of Newton’s method derived from the Simpson rule. The first one is a fifth order method using the same number of functional evaluations as the third order method, the second one is a four-point 10th order method and the last one is a five-point 20th order method. In terms of computational point of view, our methods require four evaluations (one function and three first derivatives) to get fifth order, five evaluations (two functions and three derivatives) to get 10th order and six evaluations (three functions and three derivatives) to get 20th order. Hence, these methods have efficiency indexes of 1.495, 1.585 and 1.648, respectively which are better than the efficiency index of 1.316 of the third order method. We test the methods through some numerical experiments which show that the 20th order method is very efficient.

Description: Robust small target detection of low signal-to-noise ratio (SNR) is very important in infrared search and track applications for self-defense or attacks. Due to the complex background, current algorithms have some unsolved issues with false alarm rate. In order to reduce the false alarm rate, an infrared small target detection algorithm based on saliency detection and support vector machine was proposed. Firstly, we detect salient regions that may contain targets with phase spectrum Fourier transform (PFT) approach. Then, target recognition was performed in the salient regions. Experimental results show the proposed algorithm has ideal robustness and efficiency for real infrared small target detection applications.

Description: In dynamic propagation environments, beamforming algorithms may suffer from strong interference, steering vector mismatches, a low convergence speed and a high computational complexity. Reduced-rank signal processing techniques provide a way to address the problems mentioned above. This paper presents a low-complexity robust data-dependent dimensionality reduction based on an iterative optimization with steering vector perturbation (IOVP) algorithm for reduced-rank beamforming and steering vector estimation. The proposed robust optimization procedure jointly adjusts the parameters of a rank reduction matrix and an adaptive beamformer. The optimized rank reduction matrix projects the received signal vector onto a subspace with lower dimension. The beamformer/steering vector optimization is then performed in a reduced dimension subspace. We devise efficient stochastic gradient and recursive least-squares algorithms for implementing the proposed robust IOVP design. The proposed robust IOVP beamforming algorithms result in a faster convergence speed and an improved performance. Simulation results show that the proposed IOVP algorithms outperform some existing full-rank and reduced-rank algorithms with a comparable complexity.

Description: Recently, wireless sensor networks (WSNs) have drawn great interest due to their outstanding monitoring and management potential in medical, environmental and industrial applications. Most of the applications that employ WSNs demand all of the sensor nodes to run on a common time scale, a requirement that highlights the importance of clock synchronization. The clock synchronization problem in WSNs is inherently related to parameter estimation. The accuracy of clock synchronization algorithms depends essentially on the statistical properties of the parameter estimation algorithms. Recently, studies dedicated to the estimation of synchronization parameters, such as clock offset and skew, have begun to emerge in the literature. The aim of this article is to provide an overview of the state-of-the-art clock synchronization algorithms for WSNs from a statistical signal processing point of view. This article focuses on describing the key features of the class of clock synchronization algorithms that exploit the traditional two-way message (signal) exchange mechanism. Upon introducing the two-way message exchange mechanism, the main clock offset estimation algorithms for pairwise synchronization of sensor nodes are first reviewed, and their performance is compared. The class of fully-distributed clock offset estimation algorithms for network-wide synchronization is then surveyed. The paper concludes with a list of open research problems pertaining to clock synchronization of WSNs.

Description: We consider the problem of adaptively routing a fleet of cooperative vehicles within a road network in the presence of uncertain and dynamic congestion conditions. To tackle this problem, we first propose a Gaussian process dynamic congestion model that can effectively characterize both the dynamics and the uncertainty of congestion conditions. Our model is efficient and thus facilitates real-time adaptive routing in the face of uncertainty. Using this congestion model, we develop efficient algorithms for non-myopic adaptive routing to minimize the collective travel time of all vehicles in the system. A key property of our approach is the ability to efficiently reason about the long-term value of exploration, which enables collectively balancing the exploration/exploitation trade-off for entire fleets of vehicles. Our approach is validated by traffic data from two large Asian cities. Our congestion model is shown to be effective in modeling dynamic congestion conditions. Our routing algorithms also generate significantly faster routes compared to standard baselines, and achieve near-optimal performance compared to an omniscient routing algorithm. We also present the results from a preliminary field study, which showcases the efficacy of our approach.

Description: Betweenness centrality is a classic measure that quantifies the importance of a graph element (vertex or edge) according to the fraction of shortest paths passing through it. This measure is notoriously expensive to compute, and the best known algorithm runs in $mathcal {O}(nm)$ time. The problems of efficiency and scalability are exacerbated in a dynamic setting, where the input is an evolving graph seen edge by edge, and the goal is to keep the betweenness centrality up to date. In this paper, we propose the first truly scalable algorithm for online computation of betweenness centrality of both vertices and edges in an evolving graph where new edges are added and existing edges are removed. Our algorithm is carefully engineered with out-of-core techniques and tailored for modern parallel stream processing engines that run on clusters of shared-nothing commodity hardware. Hence, it is amenable to real-world deployment. We experiment on graphs that are two orders of magnitude larger than previous studies. Our method is able to keep the betweenness centrality measures up-to-date online, i.e., the time to update the measures is smaller than the inter-arrival time between two consecutive updates.

Description: Phase change memory (PCM) is non-volatile memory that is byte-addressable. It is two to four times denser than DRAM, orders of magnitude better than NAND Flash memory in read latency, and 10 times better than NAND Flash memory in write endurance. However, it still limits the number of write operations to at most $10^6$ times per PCM cell. To extend its lifetime, it is necessary to evenly distribute write operations over all the memory cells. Up to now, the $mathrm{B^{+}}$ -Tree index structure has been used to quickly locate a search key in a relational database management system (RDBMS). All the record keys in each node are sorted and packed upon insertion in, and deletion from, the $mathrm{B^{+}}$ -Tree. In addition, a counter keeps track of the number of valid keys in the $mathrm{B^{+}}$ -Tree. Consequently, a $mathrm{B^{+}}$ -Tree algorithm results in a large number of write operations, which deteriorates the endurance of PCM. This restricts the usage of PCM on a database server and deteriorates performance of database servers. In this paper, we propose a novel PCM-aware $math- m{B^{+}}$ -Tree index structure, called $mathrm{PB^{+}}$ -Tree, to provide wear-leveling in PCM. According to our experiment results, $mathrm{PB^{+}}$ -Tree is much faster than the existing $mathrm{B^{+}}$ -Tree algorithms for PCM and NAND Flash memory with versatile workloads. More importantly, our scheme also greatly reduces the number of write operations compared to other $mathrm{B^{+}}$ -Tree algorithms. All of these results suggest that $mathrm{PB^{+}}$ -Tree is the $mathrm{B^{+}}$ -Tree algorithm best fitted to PCM.

Description: Background: Non-adherence to Antiretroviral Treatment (ART) is strongly associated with virologic rebound and drug resistance. Studies have shown that the most frequently mentioned reason for missing ART doses is the forgetfulness of patients to take their medications on time. Therefore using communication devices as reminder tools, for example alarms, pagers, text messages and telephone calls could improve adherence to ART. The aim of this study is to measure access to cellphones, willingness to receive text message medication reminders and to identify associated factors of ART patients at the University of Gondar Hospital, in North West Ethiopia. Methods: An institution based cross sectional quantitative study was conducted among 423 patients on ART during April 2014. Data were collected using structured interviewer-administered questionnaires. Data entry and analysis were done using Epi-Info version 7 and SPSS version 20 respectively. Descriptive statistics and multivariable logistic regression analysis were used to describe the characteristic of the sample and identify factors associated with the willingness to receive text message medication reminders. Results: A total of 415 (98 % response rate) respondents participated in the interview. The majority of respondents 316 (76.1 %) owned a cellphone, and 161(50.9 %) were willing to receive text message medication reminders. Positively associated factors to the willingness were the following: Younger age group (AOR = 5.18, 95 % CI: [1.69, 15.94]), having secondary or higher education (AOR = 4.61, 95 % CI: [1.33, 16.01]), using internet (AOR = 3.94, 95 % CI: [1.67, 9.31]), not disclosing HIV status to anyone other than HCP (Health Care Provider) (AOR = 3.03, 95 % CI: [1.20, 7.61]), availability of radio in dwelling (AOR = 2.74 95 % CI: [1.27, 5.88]), not answering unknown calls (AOR = 2.67, 95 % CI: [1.34, 5.32]), use of cellphone alarm as medication reminder (AOR = 2.22, 95%CI [1.09, 4.52]), and forgetting to take medications (AOR = 2.13, 95 % CI: [1.14, 3.96]). Conclusions: A high proportion of respondents have a cell phone and are willing to use it as medication reminders. Age, educational status and using internet were the main factors that are significantly associated with the willingness of patients to receive text message medication reminders.

Description: The $k$ nearest neighbor ( $k$ NN) search on road networks is an important function in web mapping services. These services are now dealing with rapidly arriving queries, that are issued by a massive amount of users. While overlay graph-based indices can answer shortest path queries efficiently, there have been no studies on utilizing such indices to answer $k$ NN queries efficiently. In this paper, we fill this research gap and present two efficient $k$ NN search solutions on overlay graph-based indices. Experimental results show that our solutions offer very low query latency (0.1 ms) and require only small index sizes, even for 10-million-node networks.

Description: Measuring semantic similarity between two terms is essential for a variety of text analytics and understanding applications. Currently, there are two main approaches for this task, namely the knowledge based and the corpus based approaches. However, existing approaches are more suitable for semantic similarity between words rather than the more general multi-word expressions (MWEs), and they do not scale very well. Contrary to these existing techniques, we propose an efficient and effective approach for semantic similarity using a large scale semantic network. This semantic network is automatically acquired from billions of web documents. It consists of millions of concepts, which explicitly model the context of semantic relationships. In this paper, we first show how to map two terms into the concept space, and compare their similarity there. Then, we introduce a clustering approach to orthogonalize the concept space in order to improve the accuracy of the similarity measure. Finally, we conduct extensive studies to demonstrate that our approach can accurately compute the semantic similarity between terms of MWEs and with ambiguity, and significantly outperforms 12 competing methods under Pearson Correlation Coefficient. Meanwhile, our approach is much more efficient than all competing algorithms, and can be used to compute semantic similarity in a large scale.

Description: Given a spatio-temporal network, a source, a destination, and a desired departure time interval, the All-departure-time Lagrangian Shortest Paths (ALSP) problem determines a set which includes the shortest path for every departure time in the given interval. ALSP is important for critical societal applications such as eco-routing. However, ALSP is computationally challenging due to the non-stationary ranking of the candidate paths across distinct departure-times. Current related work for reducing the redundant work, across consecutive departure-times sharing a common solution, exploits only partial information e.g., the earliest feasible arrival time of a path. In contrast, our approach uses all available information, e.g., the entire time series of arrival times for all departure-times. This allows elimination of all knowable redundant computation based on complete information available at hand. We operationalize this idea through the concept of critical-time-points (CTP), i.e., departure-times before which ranking among candidate paths cannot change. In our preliminary work, we proposed a CTP based forward search strategy. In this paper, we propose a CTP based temporal bi-directional search for the ALSP problem via a novel impromptu rendezvous termination condition. Theoretical and experimental analysis show that the proposed approach outperforms the related work approaches particularly when there are few critical-time-points.

Description: Computing connected components is a core operation on graph data. Since billion-scale graphs cannot be resident in memory of a single server, several approaches based on distributed machines have recently been proposed. The representative methods are $mathsf{Hashhbox{-}Tohbox{-}Min}$ and $mathsf{PowerGraph}$ . $mathsf{Hashhbox{-}Tohbox{-}Min}$ is the state-of-the art disk-based distributed method which minimizes the number of MapReduce rounds. $mathsf{PowerGraph}$ is the-state-of-the-art in-memory distributed system, which is typically faster than the disk-based distributed one, however, requires a lot of machines for handling billion-scale graphs. In this paper, we propose an I/O efficient parallel algorithm for billion-scale graphs in a single PC. We first propose the Disk-based Sequential access-oriented Parallel processing  (DSP) model that exploits sequential disk access in terms of disk I/Os and parallel processing in terms of computation. We then propose an ultra-fast disk-based parallel algorithm for computing connected components, $mathsf{DSPhbox{-}CC}$ , which largely improves the performance through sequential disk scan and page-level cache-conscious parallel processing . Extensive experimental results show that $mathsf{DSPhbox{-}CC}$ 1) computes connected components in billion-scale graphs using the limited memory size whereas in-memory algorithms can only support medium-sized graphs with the same memory size, and 2) significantly outperforms all distributed competitors as well as a representative disk-based parallel method.

Description: Answering why-not questions in databases is promised to have wide application prospect in many areas and thereby, has attracted recent attention in the database research community. This paper addresses the problem of answering these so-called why-not questions in similar graph matching for graph databases. Given a set of answer graphs of an initial query graph $q$ and a set of missing ( why-not ) graphs, we aim to modify $q$ into a new query graph $q^*$ such that the missing graphs are included in the new answer set of $q^*$ . We present an approximate solution to address the above as the optimal solution is NP-hard to compute. In our approach, we first compute the bounded search space and the distance to be minimized for $q^*$ . Then, we present a two-phase algorithm to find the new query $q^*$ . In the first phase, we generate a set of candidate edges to be added/deleted into/from the initial query $q$ within the bounded search space and in the second phase, we select a subset of candidate edges generated in the first phase to minimize the distance for $q^*$ . We also demonstrate the effectiveness and efficiency of our approach by conducting extensive experiments on two real datasets.

Description: How has the interdisciplinary data mining field been practiced in Network and Systems Management (NSM)? In Science and Technology, there is a wide use of data mining in areas like bioinformatics, genetics, Web, and, more recently, astroinformatics. However, the application in NSM has been limited and inconsiderable. In this article, we provide an account of how data mining has been applied in managing networks and systems for the past four decades, presumably since its birth. We look into the field’s applications in the key NSM activities—discovery, monitoring, analysis, reporting, and domain knowledge acquisition. In the end, we discuss our perspective on the issues that are considered critical for the effective application of data mining in the modern systems which are characterized by heterogeneity and high dynamism.

Description: With the rapid development of location-aware mobile devices, ubiquitous Internet access and social computing technologies, lots of users’ personal information, such as location data and social data, has been readily accessible from various mobile platforms and online social networks. The convergence of these two types of data, known as geo-social data , has enabled collaborative spatial computing that explicitly combines both location and social factors to answer useful geo-social queries for either business or social good. In this paper, we study a new type of Geo-Social K-Cover Group (GSKCG) queries that, given a set of query points and a social network, retrieves a minimum user group in which each user is socially related to at least $k$ other users and the users’ associated regions (e.g., familiar regions or service regions) can jointly cover all the query points. Albeit its practical usefulness, the GSKCG query problem is NP-complete. We consequently explore a set of effective pruning strategies to derive an efficient algorithm for finding the optimal solution. Moreover, we design a novel index structure tailored to our problem to further accelerate query processing. Extensive experiments demonstrate that our algorithm achieves desirable performance on real-life datasets.

Description: High utility sequential pattern mining has been considered as an important research problem and a number of relevant algorithms have been proposed for this topic. The main challenge of high utility sequential pattern mining is that, the search space is large and the efficiency of the solutions is directly affected by the degree at which they can eliminate the candidate patterns. Therefore, the efficiency of any high utility sequential pattern mining solution depends on its ability to reduce this big search space, and as a result, lower the computational complexity of calculating the utilities of the candidate patterns. In this paper, we propose efficient data structures and pruning technique which is based on Cumulated Rest of Match (CRoM) based upper bound. CRoM, by defining a tighter upper bound on the utility of the candidates, allows more conservative pruning before candidate pattern generation in comparison to the existing techniques. In addition, we have developed an efficient algorithm, High Utility Sequential Pattern Extraction (HuspExt), which calculates the utilities of the child patterns based on that of the parents’. Substantial experiments on both synthetic and real datasets from different domains show that, the proposed solution efficiently discovers high utility sequential patterns from large scale datasets with different data characteristics, under low utility thresholds.

Description: Ordinal classification with a monotonicity constraint is a kind of classification tasks, in which the objects with better attribute values should not be assigned to a worse decision class. Several learning algorithms have been proposed to handle this kind of tasks in recent years. The rank entropy-based monotonic decision tree is very representative thanks to its better robustness and generalization. Ensemble learning is an effective strategy to significantly improve the generalization ability of machine learning systems. The objective of this work is to develop a method of fusing monotonic decision trees. In order to achieve this goal, we take two factors into account: attribute reduction and fusing principle. Through introducing variable dominance rough sets, we firstly propose an attribute reduction approach with rank-preservation for learning base classifiers, which can effectively avoid overfitting and improve classification performance. Then, we establish a fusing principe based on maximal probability through combining the base classifiers, which is used to further improve generalization ability of the learning system. The experimental analysis shows that the proposed fusing method can significantly improve classification performance of the learning system constructed by monotonic decision trees.

Description: Influence maximization, defined as finding a small subset of nodes that maximizes spread of influence in social networks, is NP-hard under both Independent Cascade (IC) and Linear Threshold (LT) models, where many greedy-based algorithms have been proposed with the best approximation guarantee. However, existing greedy-based algorithms are inefficient on large networks, as it demands heavy Monte-Carlo simulations of the spread functions for each node at the initial step [7] . In this paper, we establish new upper bounds to significantly reduce the number of Monte-Carlo simulations in greedy-based algorithms, especially at the initial step. We theoretically prove that the bound is tight and convergent when the summation of weights towards (or from) each node is less than 1. Based on the bound, we propose a new Upper Bound based Lazy Forward algorithm ( UBLF in short) for discovering the top-k influential nodes in social networks. We test and compare UBLF with prior greedy algorithms, especially CELF [30] . Experimental results show that UBLF reduces more than 95 percent Monte-Carlo simulations of CELF and achieves about $2hbox{-}10$ times speedup when the seed set is small.

Description: Feature selection has been an important research topic in data mining, because the real data sets often have high-dimensional features, such as the bioinformatics and text mining applications. Many existing filter feature selection methods rank features by optimizing certain feature ranking criterions, such that correlated features often have similar rankings. These correlated features are redundant and don’t provide large mutual information to help data mining. Thus, when we select a limited number of features, we hope to select the top non-redundant features such that the useful mutual information can be maximized. In previous research, Ding et al. recognized this important issue and proposed the minimum Redundancy Maximum Relevance Feature Selection (mRMR) model to minimize the redundancy between sequentially selected features. However, this method used the greedy search, thus the global feature redundancy wasn’t considered and the results are not optimal. In this paper, we propose a new feature selection framework to globally minimize the feature redundancy with maximizing the given feature ranking scores, which can come from any supervised or unsupervised methods. Our new model has no parameter so that it is especially suitable for practical data mining application. Experimental results on benchmark data sets show that the proposed method consistently improves the feature selection results compared to the original methods. Meanwhile, we introduce a new unsupervised global and local discriminative feature selection method which can be unified with the global feature redundancy minimization framework and shows superior performance.

Description: Multimedia information retrieval usually involves two key modules including effective feature representation and ranking model construction. Most existing approaches are incapable of well modeling the inherent correlations and interactions between them, resulting in the loss of the latent consensus structure information. To alleviate this problem, we propose a learning to rank approach that simultaneously obtains a set of deep linear features and constructs structure-aware ranking models in a joint learning framework. Specifically, the deep linear feature learning corresponds to a series of matrix factorization tasks in a hierarchical manner, while the learning-to-rank part concentrates on building a ranking model that effectively encodes the intrinsic ranking information by structural SVM learning. Through a joint learning mechanism, the two parts are mutually reinforced in our approach, and meanwhile their underlying interaction relationships are implicitly reflected by solving an alternating optimization problem. Due to the intrinsic correlations among different queries (i.e., similar queries for similar ranking lists), we further formulate the learning-to-rank problem as a multi-task problem, which is associated with a set of mutually related query-specific learning-to-rank subproblems. For computational efficiency and scalability, we design a MapReduce-based parallelization approach to speed up the learning processes. Experimental results demonstrate the efficiency, effectiveness, and scalability of the proposed approach in multimedia information retrieval.

Description: In a peer-to-peer system, a node should estimate reputation of other peers not only on the basis of its own interaction, but also on the basis of expression of other nodes. Reputation aggregation mechanism implements strategy for achieving this. Reputation aggregation in peer to peer networks is generally a very time and resource consuming process. Moreover, most of the methods consider that a node will have the same reputation after aggregation with all the nodes in the network, which is not true. This paper proposes a reputation aggregation algorithm that uses a variant of gossip algorithm called differential gossip. In this paper, estimate of reputation is considered to be having two parts, one common component which is same with every node, and the other one is the information received from immediate neighbours based on the neighbours’ direct interaction with the node. The differential gossip is fast and requires a lesser amount of resources. This mechanism allows computation of independent reputation value by every node, of every other node in the network. The differential gossip trust has been investigated for a power law network formed using preferential attachment (PA) Model. The reputation computed using differential gossip trust shows good amount of immunity to the collusion. We have verified the performance of the algorithm on the power law networks with sizes ranging from 100 nodes to 50,000 nodes.

Description: In this paper, the limitation that is prominent in most existing works of change-point detection methods is addressed by proposing a nonparametric, computationally efficient method. The limitation is that most works assume that each data point observed at each time step is a single multi-dimensional vector. However, there are many situations where this does not hold. Therefore, a setting where each observation is a collection of random variables, which we call a bag of data, is considered. After estimating the underlying distribution behind each bag of data and embedding those distributions in a metric space, the change-point score is derived by evaluating how the sequence of distributions is fluctuating in the metric space using a distance-based information estimator. Also, a procedure that adaptively determines when to raise alerts is incorporated by calculating the confidence interval of the change-point score at each time step. This avoids raising false alarms in highly noisy situations and enables detecting changes of various magnitudes. A number of experimental studies and numerical examples are provided to demonstrate the generality and the effectiveness of our approach with both synthetic and real datasets.

Description: In many applications, top- k query is an important operation to return a set of interesting points in a potentially huge data space. It is analyzed in this paper that the existing algorithms cannot process top- k query on massive data efficiently. This paper proposes a novel table-scan-based T2S algorithm to efficiently compute top- k results on massive data. T2S first constructs the presorted table, whose tuples are arranged in the order of the round-robin retrieval on the sorted lists. T2S maintains only fixed number of tuples to compute results. The early termination checking for T2S is presented in this paper, along with the analysis of scan depth. The selective retrieval is devised to skip the tuples in the presorted table which are not top- k results. The theoretical analysis proves that selective retrieval can reduce the number of the retrieved tuples significantly. The construction and incremental-update/batch-processing methods for the used structures are proposed in this paper. The extensive experimental results, conducted on synthetic and real-life data sets, show that T2S has a significant advantage over the existing algorithms.

Description: We witness an unprecedented proliferation of knowledge graphs that record millions of entities and their relationships. While knowledge graphs are structure-flexible and content-rich, they are difficult to use. The challenge lies in the gap between their overwhelming complexity and the limited database knowledge of non-professional users. If writing structured queries over “simple” tables is difficult, complex graphs are only harder to query. As an initial step toward improving the usability of knowledge graphs, we propose to query such data by example entity tuples, without requiring users to form complex graph queries. Our system, Graph Query By Example ( $mathsf {GQBE}$ ), automatically discovers a weighted hidden maximum query graph based on input query tuples, to capture a user’s query intent. It then efficiently finds and ranks the top approximate matching answer graphs and answer tuples. We conducted experiments and user studies on the large Freebase and DBpedia datasets and observed appealing accuracy and efficiency. Our system provides a complementary approach to the existing keyword-based methods, facilitating user-friendly graph querying. To the best of our knowledge, there was no such proposal in the past in the context of graphs.

Description: This paper explores combinatorial optimization for problems of max-weight graph matching on multi-partite graphs, which arise in integrating multiple data sources. In the most common two-source case, it is often desirable for the final matching to be one-to-one; the database and statistical record linkage communities accomplish this by weighted bipartite graph matching on similarity scores. Such matchings are intuitively appealing: they leverage a natural global property of many real-world entity stores—that of being nearly deduped—and are known to provide significant improvements to precision and recall. Unfortunately, unlike the bipartite case, exact max-weight matching on multi-partite graphs is known to be NP-hard. Our two-fold algorithmic contributions approximate multi-partite max-weight matching: our first algorithm borrows optimization techniques common to Bayesian probabilistic inference; our second is a greedy approximation algorithm. In addition to a theoretical guarantee on the latter, we present comparisons on a real-world entity resolution problem from Bing significantly larger than typically found in the literature, on publication data, and on a series of synthetic problems. Our results quantify significant improvements due to exploiting multiple sources, which are made possible by global one-to-one constraints linking otherwise independent matching sub-problems. We also discover that our algorithms are complementary: one being much more robust under noise, and the other being simple to implement and very fast to run.

Description: Internet users can be classified as two types: (a) active users —actively contribute to blogs, publish opinions, write comments in Youtube, tweet messages, etc. and (b) passive consumers who only consume Internet information without contributing to it. While the majority of current social media research deals with active user analysis, there is very little work in understanding the dynamics of passive consumers and their influence. Our global scale Internet measurement of user access patterns of a diverse set of Internet media services indicates conclusively that majority of consumers are passive. In this paper, we develop a spatio-temporal mathematical model and the corresponding stochastic analysis to understand the passive consumer dynamics. Both discrete and continuous time analysis are presented. We also show how the analysis can be used to identify spatial points of influence, i.e., spatial locations that have maximum expertise or influence on a topic. The analysis takes into account, the initial level of consumption at each spatial location and the influence of different passive consumers at different geographic locations on each other. The effect of information noise is taken into account to derive fundamental limits of passive information consumption. Theoretical results are verified using real Internet measurement data. The large scale data have been made available for other researchers.

Description: The objective of the paper is a contribution to data mining within the framework of the observational calculus, through introducing ǵeneralized quantifiers related to copulas. Fitting copulas to multidimensional data is an increasingly important method for analyzing dependencies, and the proposed quantifiers of observational calculus assess the results of estimating the structure of joint distributions of continuous variables by means of hierarchical Archimedean copulas. To this end, the existing theory of hierarchical Archimedean copulas has been slightly extended in the paper: It has been proven that sufficient conditions for the function defining a hierarchical Archimedean copula to be indeed a copula, which have so far been rigorously established only for the special case of fully nested Archimedean copulas, hold in general. These conditions allow us to define three new generalized quantifiers, which are then thoroughly validated on four benchmark data sets and one data set from a real-world application. The paper concludes by comparing the proposed quantifiers to a more traditional approach—maximum weight spanning trees.

Description: In this work, we develop an approach for the safe distribution and parallel execution of data-centric workflows over the publish/subscribe abstraction. In essence, we design a unique representation of data-centric workflows, specifically designed to exploit the loosely coupled and distributed nature of publish/subscribe systems. Furthermore, we argue for the practicality and expressiveness of our approach by mapping a standard and industry-strength data-centric workflow model, namely, IBM Business Artifacts with Guard-Stage-Milestone (GSM), into the publish/subscribe abstraction. In short, the contributions of this work are three-fold: (1) mapping of data-centric workflows into publish/subscribe to achieve distributed and parallel execution; (2) detailed theoretical analysis of the mapping; and (3) formulation of the complexity of the optimal workflow distribution over the publish/subscribe abstraction as an NP-hard problem.

Description: In this paper we investigate some parallel variants of Broyden’s method and, for the basic variant, we present its convergence properties. The main result is that the behavior of the considered parallel Broyden’s variants is comparable with the classical parallel Newton method, and significantly better than the parallel Cimmino method, both for linear and nonlinear cases. The considered variants are also compared with two more recently proposed parallel Broyden’s method. Some numerical experiments are presented to illustrate the advantages and limits of the proposed algorithms.

Description: Background: Prenatal screening tests for Down syndrome (DS) are routine in many developed countries and new tests are rapidly becoming available. Decisions about prenatal screening are increasingly complex with each successive test, and pregnant women need information about risks and benefits as well as clarity about their values. Decision aids (DAs) can help healthcare providers support women in this decision. Using an environmental scan, we aimed to identify publicly available DAs focusing on prenatal screening/diagnosis for Down syndrome that provide effective support for decision making. Methods: Data sources searched were the Decision Aids Library Inventory (DALI) of the Ottawa Patient Decision Aids Research Group at the Ottawa Health Research Institute; Google searches on the internet; professional organizations, academic institutions and other experts in the field; and references in existing systematic reviews on DAs. Eligible DAs targeted pregnant women, focused on prenatal screening and/or diagnosis, applied to tests for fetal abnormalities or aneuploidies, and were in French, English, Spanish or Portuguese. Pairs of reviewers independently identified eligible DAs and extracted characteristics including the presence of practical decision support tools and features to aid comprehension. They then performed quality assessment using the 16 minimum standards established by the International Patient Decision Aids Standards (IPDASi v4.0). Results: Of 543 potentially eligible DAs (512 in DALI, 27 from experts, and four on the internet), 23 were eligible and 20 were available for data extraction. DAs were developed from 1996 to 2013 in six countries (UK, USA, Canada, Australia, Sweden, and France). Five DAs were for prenatal screening, three for prenatal diagnosis and 12 for both). Eight contained values clarification methods (personal worksheets). The 20 DAs scored a median of 10/16 (range 6–15) on the 16 IPDAS minimum standards.DiscussionNone of the 20 included DAs met all 16 IPDAS minimum standards, and few included practical decision support tools or aids to comprehension. Conclusions: Our results indicate there is a need for DAs that effectively support decision making regarding prenatal testing for Down syndrome, especially in light of the recently available non-invasive prenatal screening tests.

Description: The sign least mean square with reweighted L1-norm constraint (SLMS-RL1) algorithm is an attractive sparse channel estimation method among Gaussian mixture model (GMM) based algorithms for use in impulsive noise environments. The channel sparsity can be exploited by SLMS-RL1 algorithm based on appropriate reweighted factor, which is one of key parameters to adjust the sparse constraint for SLMS-RL1 algorithm. However, to the best of the authors’ knowledge, a reweighted factor selection scheme has not been developed. This paper proposes a Monte-Carlo (MC) based reweighted factor selection method to further strengthen the performance of SLMS-RL1 algorithm. To validate the performance of SLMS-RL1 using the proposed reweighted factor, simulations results are provided to demonstrate that convergence speed can be reduced by increasing the channel sparsity, while the steady-state MSE performance only slightly changes with different GMM impulsive-noise strengths.

Description: Background: A plethora of publicly available biomedical resources do currently exist and are constantly increasing at a fast rate. In parallel, specialized repositories are been developed, indexing numerous clinical and biomedical tools. The main drawback of such repositories is the difficulty in locating appropriate resources for a clinical or biomedical decision task, especially for non-Information Technology expert users. In parallel, although NLP research in the clinical domain has been active since the 1960s, progress in the development of NLP applications has been slow and lags behind progress in the general NLP domain.The aim of the present study is to investigate the use of semantics for biomedical resources annotation with domain specific ontologies and exploit Natural Language Processing methods in empowering the non-Information Technology expert users to efficiently search for biomedical resources using natural language. Methods: A Natural Language Processing engine which can “translate” free text into targeted queries, automatically transforming a clinical research question into a request description that contains only terms of ontologies, has been implemented. The implementation is based on information extraction techniques for text in natural language, guided by integrated ontologies. Furthermore, knowledge from robust text mining methods has been incorporated to map descriptions into suitable domain ontologies in order to ensure that the biomedical resources descriptions are domain oriented and enhance the accuracy of services discovery. The framework is freely available as a web application at (http://calchas.ics.forth.gr/). Results: For our experiments, a range of clinical questions were established based on descriptions of clinical trials from the ClinicalTrials.gov registry as well as recommendations from clinicians. Domain experts manually identified the available tools in a tools repository which are suitable for addressing the clinical questions at hand, either individually or as a set of tools forming a computational pipeline. The results were compared with those obtained from an automated discovery of candidate biomedical tools. For the evaluation of the results, precision and recall measurements were used. Our results indicate that the proposed framework has a high precision and low recall, implying that the system returns essentially more relevant results than irrelevant. Conclusions: There are adequate biomedical ontologies already available, sufficiency of existing NLP tools and quality of biomedical annotation systems for the implementation of a biomedical resources discovery framework, based on the semantic annotation of resources and the use on NLP techniques. The results of the present study demonstrate the clinical utility of the application of the proposed framework which aims to bridge the gap between clinical question in natural language and efficient dynamic biomedical resources discovery.

Description: Der Gerichtshof der Europäischen Union hat mit Urteil vom 6. Oktober 2015 die Entscheidung der Kommission vom 26.7.2000 (2000/520/EG) zur Angemessenheit des von den Grundsätzen des „sicheren Hafens“ und der diesbezüglichen „Häufig gestellten Fragen“ (FAQ) gewährleisteten Schutzes, vorgelegt vom Handelsministerium der USA, für ungültig erklärt. 1 Der folgende Beitrag erläutert die Hintergründe der Entscheidung, fasst deren wesentliche Punkte noch einmal zusammen und gibt einen ersten Ausblick, welche Konsequenzen aus der Entscheidung zu ziehen sind.

Description: Datenschutz und Datensicherheit bilden die Eckpfeiler im Umgang mit personenbezogenen Daten. Auch wenn viele Unternehmen hinsichtlich dieser Themen sensibilisiert sind, ist die unfreiwillige Preisgabe von personenbezogenen Daten an Dritte nicht völlig ausgeschlossen. Der folgende Beitrag befasst sich daher mit der Frage der Bußgeldbewehrtheit von Datenpannen und geht dabei insbesondere auf den Begriff der Verarbeitung i. S. v. § 3 Abs. 4 BDSG ein.

Description: Nachdem die Kommission und das Parlament bereits ihre Entwürfe der Datenschutz-Grundverordnung veröffentlicht hatten, folgte im Juni 2015 der Entwurf des Rates der Europäischen Union, wobei sich immer mehr abzeichnet, wie die Reform des europäischen Datenschutzrechts am Ende aussehen wird. So muss insbesondere bei der Durchsetzung und Sanktionierung mit erheblichen praxisrelevanten Veränderungen gerechnet werden.

Description: We present a local convergence analysis of an eighth order three step methodin order to approximate a locally unique solution of nonlinear equation in a Banach spacesetting. In an earlier study by Sharma and Arora (2015), the order of convergence wasshown using Taylor series expansions and hypotheses up to the fourth order derivative oreven higher of the function involved which restrict the applicability of the proposed scheme.However, only first order derivative appears in the proposed scheme. In order to overcomethis problem, we proposed the hypotheses up to only the first order derivative. In this way,we not only expand the applicability of the methods but also propose convergence domain.Finally, where earlier studies cannot be applied, a variety of concrete numerical examplesare proposed to obtain the solutions of nonlinear equations. Our study does not exhibit thistype of problem/restriction.

Description: Lung cancer continues to rank as the leading cause of cancer deaths worldwide. One of the most promising techniques for early detection of cancerous cells relies on sputum cell analysis. This was the motivation behind the design and the development of a new computer aided diagnosis (CAD) system for early detection of lung cancer based on the analysis of sputum color images. The proposed CAD system encompasses four main processing steps. First is the preprocessing step which utilizes a Bayesian classification method using histogram analysis. Then, in the second step, mean shift segmentation is applied to segment the nuclei from the cytoplasm. The third step is the feature analysis. In this step, geometric and chromatic features are extracted from the nucleus region. These features are used in the diagnostic process of the sputum images. Finally, the diagnosis is completed using an artificial neural network and support vector machine (SVM) for classifying the cells into benign or malignant. The performance of the system was analyzed based on different criteria such as sensitivity, specificity and accuracy. The evaluation was carried out using Receiver Operating Characteristic (ROC) curve. The experimental results demonstrate the efficiency of the SVM classifier over other classifiers, with 97% sensitivity and accuracy as well as a significant reduction in the number of false positive and false negative rates.

Description: Near-infrared spectroscopy (NIRS) enables the non-invasive measurement of changes in hemodynamics and oxygenation in tissue. Changes in light-coupling due to movement of the subject can cause movement artifacts (MAs) in the recorded signals. Several methods have been developed so far that facilitate the detection and reduction of MAs in the data. However, due to fixed parameter values (e.g., global threshold) none of these methods are perfectly suitable for long-term (i.e., hours) recordings or were not time-effective when applied to large datasets. We aimed to overcome these limitations by automation, i.e., data adaptive thresholding specifically designed for long-term measurements, and by introducing a stable long-term signal reconstruction. Our new technique (“acceleration-based movement artifact reduction algorithm”, AMARA) is based on combining two methods: the “movement artifact reduction algorithm” (MARA, Scholkmann et al. Phys. Meas. 2010, 31, 649–662), and the “accelerometer-based motion artifact removal” (ABAMAR, Virtanen et al. J. Biomed. Opt. 2011, 16, 087005). We describe AMARA in detail and report about successful validation of the algorithm using empirical NIRS data, measured over the prefrontal cortex in adolescents during sleep. In addition, we compared the performance of AMARA to that of MARA and ABAMAR based on validation data.

Description: Der datenschutzrechtliche Zweckbindungsgrundsatz wurde vor einiger Zeit 1 in den Medien hitzig 2 diskutiert. Anlass gab die Veröffentlichung des aktuellen Ratsentwurfs der Datenschutzgrundverordnung durch European Digital Rights. 3 Unter anderen berichtete der Tagesspiegel, dass durch den aktuellen Entwurf das Zweckbindungsprinzip ausgehebelt werden würde. 4 Der folgende Beitrag möchte mit Blick auf die jeweils verfassungsrechtliche Bedeutung den gesetzgeberischen Spielraum bei der Umsetzung des Zweckbindungsprinzips beleuchten.

Description: Im Zusammenhang mit einer vom Bundesministerium für Gesundheit propagierten „Entbürokratisierung in der Pflege“ mit dem Ziel des „Abbaus von Bürokratie in der Pflegedokumentation“ könnten die auch in Pflegeeinrichtungen nicht mehr wegzudenkenden IT-Systeme eine große Rolle spielen. Bei der Umstellung auf bzw. der Einführung von digitalisierter Pflegedokumentation bestehen aber gleichwohl datenschutzrechtliche Aspekte, die nicht unberücksichtigt bleiben dürfen. Dies gilt insbesondere vor dem Hintergrund, dass es sich bei den in der Pflegedokumentation enthaltenen Informationen vielfach um Gesundheitsdaten der Pflegebedürftigen handelt. 1

Description: This paper focuses on the parameter identification problem for Wiener nonlinear dynamic systems with moving average noises. In order to improve the convergence rate, the gradient-based iterative algorithm is presented by replacing the unmeasurable variables with their corresponding iterative estimates, and to compute iteratively the noise estimates based on the obtained parameter estimates. The simulation results show that the proposed algorithm can effectively estimate the parameters of Wiener systems with moving average noises.

Description: Background: Although evidence has suggested that computerized drug-drug interaction alert systems may reduce the occurrence of drug-drug interactions, the numerous reminders and alerts generated by such systems could represent an excessive burden for clinicians, resulting in a high override rate of not only unimportant, but also important alerts. Methods: We analyzed physicians’ responses to alerts of relative contraindications and contraindications for coadministration in a computerized drug-drug interaction alert system at Hokkaido University Hospital. In this system, the physician must enter a password to override an alert and continue an order. All of the drug-drug interaction alerts generated between December 2011 and November 2012 at Hokkaido University Hospital were included in this study. Results: The system generated a total of 170 alerts of relative contraindications and contraindication for coadministration; 59 (34.7 %) of the corresponding orders were cancelled after the alert was accepted, and 111 (65.3 %) were overridden. The most frequent contraindication alert was for the combination of 3-hydroxy-3-methylglutaryl–coenzyme A reductase inhibitors and fibrates. No incidents involving drug-drug interactions were reported among patients who were prescribed contraindicated drug pairs after an override. Conclusions: Although computerized drug-drug interaction alert systems that require password overrides appear useful for promoting medication safety, having to enter passwords to override alerts may represent an excessive burden for the prescribing physician. Therefore, both patient safety and physicians’ workloads should be taken into consideration in future designs of computerized drug-drug interaction alert systems.

Description: Größter Innovationstreiber moderner vernetzter Fahrzeuge ist die Informationstechnologie. Jedoch ergeben sich hierdurch auch neue Bedrohungen für IT-Sicherheit und Datenschutz. In diesem Artikel wird ein Überblick über vernetzte Fahrzeuge, mögliche Anwendungen, die benötigten Daten und die sich ergebenden Bedrohungen gegeben. Weiterhin werden Herausforderungen für die aktuelle Forschung identifiziert.

Description: Aus der Vernetzung des Autos ergeben sich vielfältige Datenschutzfragen. Inwieweit lassen sich die Erfahrungen mit Internet und Smartphones übertragen?

Description: Background: When new pharmaceutical products appear on the market, physicians need to know whether they are likely to be useful in their practices. Physicians currently obtain most of their information about the market release and properties of new drugs from pharmaceutical industry representatives. However, the official information contained in the summary of product characteristics (SPCs) and evaluation reports from health agencies, provide a more complete view of the potential value of new drugs, although they can be long and difficult to read. The main objective of this work was to design a prototype computer program to facilitate the objective appraisal of the potential value of a new pharmaceutical product by physicians. This prototype is based on the modeling of pharmaceutical innovations described in a previous paper. Methods: The interface was designed to allow physicians to develop a rapid understanding of the value of a new drug for their practices. We selected five new pharmaceutical products, to illustrate the function of this prototype. We considered only the texts supplied by national or international drug agencies at the time of market release. The perceived usability of the prototype was evaluated qualitatively, except for the System Usability Scale (SUS) score evaluation, by 10 physicians differing in age and medical background. Results: The display is based on the various axes of the conceptual model of pharmaceutical innovations. The user can select three levels of detail when consulting this information (highly synthetic, synthetic and detailed). Tables provide a comparison of the properties of the new pharmaceutical product with those of existing drugs, if available for the same indication, in terms of efficacy, safety and ease of use.The interface was highly appreciated by evaluators, who found it easy to understand and suggested no other additions of important, internationally valid information. The mean System Usability Scale score for the 10 physicians was 82, corresponding to a “good” user interface. Conclusions: This work led us to propose the selection, grouping, and mode of presentation for various types of knowledge on pharmaceutical innovations in a way that was appreciated by evaluators. It provides physicians with readily accessible objective information about new drugs.

Description: Der „Pay-as-you-drive“-Tarif hat Einzug in den deutschen Versicherungsmarkt gehalten. Mit ihm kommt es zu einer neuen Dimension der Datenerhebung im Alltagsleben. Dadurch stellen sich zahlreiche Fragen rund um das Recht der Datenverarbeitung, die für die Zukunft reichlich Diskussionsstoff beinhalten.

Description: Facebook hat wieder einmal seine Bedingungen und Richtlinien geändert–und ist offensichtlich mächtig stolz darauf. Grund genug, Facebook auf diesem Wege einmal eine kurze Rückmeldung zur neuen Datenrichtlinie zu geben.

Description: In this paper, the dynamical behavior of different optimal iterative schemes for solving nonlinear equations with increasing order, is studied. The tendency of the complexity of the Julia set is analyzed and referred to the fractal dimension. In fact, this fractal dimension can be shown to be a powerful tool to compare iterative schemes that estimate the solution of a nonlinear equation. Based on the box-counting algorithm, several iterative derivative-free methods of different convergence orders are compared.

Description: Background: Clinical trials apply standards approved by regulatory agencies for Electronic Data Capture (EDC). Operational Data Model (ODM) from Clinical Data Interchange Standards Consortium (CDISC) is commonly used. Electronic Health Record (EHR) systems for patient care predominantly apply HL7 standards, specifically Clinical Document Architecture (CDA). In recent years more and more patient data is processed in electronic form. Results: An open source reference implementation was designed and implemented to convert forms between ODM and CDA format. There are limitations of this conversion method due to different scope and design of ODM and CDA. Specifically, CDA has a multi-level hierarchical structure and CDA nodes can contain both XML values and XML attributes. Conclusions: Automated transformation of ODM files to CDA and vice versa is technically feasible in principle.

Description: Background: An academic, community medicine partnership was established to build a phenotype-to-outcome model targeting chronic pain. This model will be used to drive clinical decision support for pain medicine in the community setting. The first step in this effort is an examination of the electronic health records (EHR) from clinics that treat chronic pain. The biopsychosocial components provided by both patients and care providers must be of sufficient scope to populate the spectrum of patient types, treatment modalities, and possible outcomes. Methods: The patient health records from a large Midwest pain medicine practice (Michigan Pain Consultants, PC) contains physician notes, administrative codes, and patient-reported outcomes (PRO) on over 30,000 patients during the study period spanning 2010 to mid-2014. The PRO consists of a regularly administered Pain Health Assessment (PHA), a biopsychosocial, demographic, and symptomology questionnaire containing 163 items, which is completed approximately every six months with a compliance rate of over 95 %. The biopsychosocial items (74 items with Likert scales of 0–10) were examined by exploratory factor analysis and descriptive statistics to determine the number of independent constructs available for phenotypes and outcomes. Pain outcomes were examined both in the aggregate and the mean of longitudinal changes in each patient. Results: Exploratory factor analysis of the intake PHA revealed 15 orthogonal factors representing pain levels; physical, social, and emotional functions; the effects of pain on these functions; vitality and health; and measures of outcomes and satisfaction. Seven items were independent of the factors, offering unique information. As an exemplar of outcomes from the follow-up PHAs, patients reported approximately 60 % relief in their pain. When examined in the aggregate, patients showed both a decrease in pain levels and an increase in coping skills with an increased number of visits. When examined individually, 80-85 % of patients presenting with the highest pain levels reported improvement by approximately two points on an 11-point pain scale. Conclusions: We conclude that the data available in a community practice can be a rich source of biopsychosocial information relevant to the phenotypes of chronic pain. It is anticipated that phenotype linkages to best treatments and outcomes can be constructed from this set of records.

Description: Health information is increasingly being digitally stored and exchanged. The public is regularly collecting and storing health-related data on their own electronic devices and in the cloud. Diabetes prevention...

Description: Electronic medical records (EMR) offer a major potential for secondary use of data for research which can improve the safety, quality and efficiency of healthcare. They also enable the measurement of disease b...

Description: Clustering is a fundamental task in data mining. Affinity propagation clustering (APC) is an effective and efficient clustering technique that has been applied in various domains. APC iteratively propagates information between affinity samples, updates the responsibility matrix and availability matrix, and employs these matrices to choose cluster centers (or exemplars) of respective clusters. However, since it mainly uses negative Euclidean distance between exemplars and samples as the similarity between them, it is difficult to identify clusters with complex structure. Therefore, the performance of APC deteriorates on samples distributed with complex structure. To mitigate this problem, we propose an improved APC based on a path-based similarity (APC-PS). APC-PS firstly utilizes negative Euclidean distance to find exemplars of clusters. Then, it employs the path-based similarity to measure the similarity between exemplars and samples, and to explore the underlying structure of clusters. Next, it assigns non-exemplar samples to their respective clusters via that similarity. Our empirical study on synthetic and UCI datasets shows that the proposed APC-PS significantly outperforms original APC and other related approaches.

Description: Graph-based semi-supervised classification uses a graph to capture the relationship between samples and exploits label propagation techniques on the graph to predict the labels of unlabeled samples. However, it is difficult to construct a graph that faithfully describes the relationship between high-dimensional samples. Recently, low-rank representation has been introduced to construct a graph, which can preserve the global structure of high-dimensional samples and help to train accurate transductive classifiers. In this paper, we take advantage of low-rank representation for graph construction and propose an inductive semi-supervised classifier called Semi-Supervised Classification based on Low-Rank Representation (SSC-LRR). SSC-LRR first utilizes a linearized alternating direction method with adaptive penalty to compute the coefficient matrix of low-rank representation of samples. Then, the coefficient matrix is adopted to define a graph. Finally, SSC-LRR incorporates this graph into a graph-based semi-supervised linear classifier to classify unlabeled samples. Experiments are conducted on four widely used facial datasets to validate the effectiveness of the proposed SSC-LRR and the results demonstrate that SSC-LRR achieves higher accuracy than other related methods.

Description: This research proposes a two-stage user-based collaborative filtering process using an artificial immune system for the prediction of student grades, along with a filter for professor ratings in the course recommendation for college students. We test for cosine similarity and Karl Pearson (KP) correlation in affinity calculations for clustering and prediction. This research uses student information and professor information datasets of Yuan Ze University from the years 2005–2009 for the purpose of testing and training. The mean average error and confusion matrix analysis form the testing parameters. A minimum professor rating was tested to check the results, and observed that the recommendation systems herein provide highly accurate results for students with higher mean grades.

Description: Epileptic seizure is a serious health problem in the world and there is a huge population suffering from it every year. If an algorithm could automatically detect seizures and deliver the patient therapy or no...

Description: Learning deep representations of clinical events based on their distributions in electronic health records has been shown to allow for subsequent training of higher-performing predictive models compared to the...

Description: Glands are vital structures found throughout the human body and their structure and function are affected by many diseases. The ability to segment and detect glands among other types of tissues is important fo...

Description: People want to live independently, but too often disabilities or advanced age robs them of the ability to do the necessary activities of daily living (ADLs). Finding relationships between electromyograms measu...