Query: Discovering Event Evolution Graphs From News Corpora

Relevant passages:
Temporal Summaries of News Topics <=SEP=> We discuss technology to help a person monitor changes in news coverage over time. We define temporal summaries of news stories as extracting a single sentence from each event within a news topic, where the stories are presented one at a time and sentences from a story must be ranked before the next story can be considered. We explain a method for evaluation, and describe an evaluation corpus that we have built. We also propose several methods for constructing temporal summaries and evaluate their effectiveness in comparison to degenerate cases. We show that simple approaches are effective, but that the problem is far from solved.

A probabilistic model for retrospective news event detection <=SEP=> Retrospective news event detection (RED) is defined as the discovery of previously unidentified events in historical news corpus. Although both the contents and time information of news articles are helpful to RED, most researches focus on the utilization of the contents of news articles. Few research works have been carried out on finding better usages of time information. In this paper, we do some explorations on both directions based on the following two characteristics of news articles. On the one hand, news articles are always aroused by events; on the other hand, similar articles reporting the same event often redundantly appear on many news sources. The former hints a generative model of news articles, and the latter provides data enriched environments to perform RED. With consideration of these characteristics, we propose a probabilistic model to incorporate both content and time information in a unified framework. This model gives new representations of both news articles and news events. Furthermore, based on this approach, we build an interactive RED system, HISCOVERY, which provides additional functions to present events, Photo Story and Chronicle.

Discovering evolutionary theme patterns from text: an exploration of temporal text mining <=SEP=> Temporal Text Mining (TTM) is concerned with discovering temporal patterns in text information collected over time. Since most text information bears some time stamps, TTM has many applications in multiple domains, such as summarizing events in news articles and revealing research trends in scientific literature. In this paper, we study a particular TTM task -- discovering and summarizing the evolutionary patterns of themes in a text stream. We define this new text mining problem and present general probabilistic methods for solving this problem through (1) discovering latent themes from text; (2) constructing an evolution graph of themes; and (3) analyzing life cycles of themes. Evaluation of the proposed methods on two different domains (i.e., news articles and literature) shows that the proposed methods can discover interesting evolutionary theme patterns effectively.

Text classification and named entities for new event detection <=SEP=> New Event Detection is a challenging task that still offers scope for great improvement after years of effort. In this paper we show how performance on New Event Detection (NED) can be improved by the use of text classification techniques as well as by using named entities in a new way. We explore modifications to the document representation in a vector space-based NED system. We also show that addressing named entities preferentially is useful only in certain situations. A combination of all the above results in a multi-stage NED system that performs much better than baseline single-stage NED systems.

A cross-collection mixture model for comparative text mining <=SEP=> In this paper, we define and study a novel text mining problem, which we refer to as Comparative Text Mining (CTM). Given a set of comparable text collections, the task of comparative text mining is to discover any latent common themes across all collections as well as summarize the similarity and differences of these collections along each common theme. This general problem subsumes many interesting applications, including business intelligence and opinion summarization. We propose a generative probabilistic mixture model for comparative text mining. The model simultaneously performs cross-collection clustering and within-collection clustering, and can be applied to an arbitrary set of comparable text collections. The model can be estimated efficiently using the Expectation-Maximization (EM) algorithm. We evaluate the model on two different text data sets (i.e., a news article data set and a laptop review data set), and compare it with a baseline clustering method also based on a mixture model. Experiment results show that the model is quite effective in discovering the latent common themes across collections and performs significantly better than our baseline mixture model.

Analysis of first-order anti-aliasing integration sampler <=SEP=> Performance of the first-order anti-aliasing integration sampler used in software-defined radio (SDR) receivers is analyzed versus all practical nonidealities. The nonidealities that are considered in this paper are transconductor finite output resistance, switch resistance, nonzero rise and fall times of the sampling clock, charge injection, clock jitter, and noise. It is proved that the filter is quite robust to all of these nonidealities except for transconductor finite output resistance. Furthermore, linearity and noise performances are all limited to design of a low-noise and highly linear transconductor.

If You Measure It, Can You Improve It?: Exploring The Value of Energy Disaggregation <=SEP=> Over the past few years, dozens of new techniques have been proposed for more accurate energy disaggregation, but the jury is still out on whether these techniques can actually save energy and, if so, whether higher accuracy translates into higher energy savings. In this paper, we explore both of these questions. First, we develop new techniques that use disaggregated power data to provide actionable feedback to residential users. We evaluate these techniques using power traces from 240 homes and find that they can detect homes that need feedback with as much as 84% accuracy. Second, we evaluate whether existing energy disaggregation techniques provide power traces with sufficient fidelity to support the feedback techniques that we created and whether more accurate disaggregation results translate into more energy savings for the users. Results show that feedback accuracy is very low even while disaggregation accuracy is high. These results indicate a need to revisit the metrics by which disaggregation is evaluated.

Supervised Word Sense Disambiguation using Python <=SEP=> In this paper, we discuss the problem of Word Sense Disambigu ation (WSD) and one approach to solving the lexical sample problem. We use training and te st data fromSENSEVAL-3 and implement methods based on Naı̈ve Bayes calculations, cosi ne comparison of word-frequency vectors, decision lists, and Latent Semantic Analysis. We a lso implement a simple classifier combination system that combines these classifiers into one WSD module. We then prove the effectiveness of our WSD module by participating in the M ultilingual Chinese-English Lexical Sample Task from SemEval-2007.

Replicability Analysis for Natural Language Processing: Testing Significance with Multiple Datasets <=SEP=> With the ever growing amount of textual data from a large variety of languages, domains, and genres, it has become standard to evaluate NLP algorithms on multiple datasets in order to ensure a consistent performance across heterogeneous setups. However, such multiple comparisons pose significant challenges to traditional statistical analysis methods in NLP and can lead to erroneous conclusions. In this paper we propose a Replicability Analysis framework for a statistically sound analysis of multiple comparisons between algorithms for NLP tasks. We discuss the theoretical advantages of this framework over the current, statistically unjustified, practice in the NLP literature, and demonstrate its empirical value across four applications: multi-domain dependency parsing, multilingual POS tagging, cross-domain sentiment classification and word similarity prediction.

Speech Recognition Engineering Issues in Speech to Speech Translation System Design for Low Resource Languages and Domains <=SEP=> Engineering automatic speech recognition (ASR) for speech to speech (S2S) translation systems, especially targeting languages and domains that do not have readily available spoken language resources, is immensely challenging due to a number of reasons. In addition to contending with the conventional data-hungry speech acoustic and language modeling needs, these designs have to accommodate varying requirements imposed by the domain needs and characteristics, target device and usage modality (such as phrase-based, or spontaneous free form interactions, with or without visual feedback) and huge spoken language variability arising due to socio-linguistic and cultural differences of the users. This paper, using case studies of creating speech translation systems between English and languages such as Pashto and Farsi, describes some of the practical issues and the solutions that were developed for multilingual ASR development. These include novel acoustic and language modeling strategies such as language adaptive recognition, active-learning based language modeling, class-based language models that can better exploit resource poor language data, efficient search strategies, including N-best and confidence generation to aid multiple hypotheses translation, use of dialog information and clever interface choices to facilitate ASR, and audio interface design for meeting both usability and robustness requirements

Effects of prior hamstring strain injury on strength, flexibility, and running mechanics. <=SEP=> BACKGROUND
Previous studies have shown evidence of residual scar tissue at the musculotendon junction following a hamstring strain injury, which could influence re-injury risk. The purpose of this study was to investigate whether bilateral differences in strength, neuromuscular patterns, and musculotendon kinematics during sprinting are present in individuals with a history of unilateral hamstring injury, and whether such differences are linked to the presence of scar tissue.


METHODS
Eighteen subjects with a previous hamstring injury (>5 months prior) participated in a magnetic resonance (MR) imaging exam, isokinetic strength testing, and a biomechanical assessment of treadmill sprinting. Bilateral comparisons were made for peak knee flexion torque, angle of peak torque, and the hamstrings:quadriceps strength ratio, as well as muscle activations and peak hamstring stretch during sprinting. MR images were used to measure the volumes of the proximal tendon/aponeurosis of the biceps femoris, with asymmetries considered indicative of scar tissue.


FINDINGS
A significantly enlarged proximal biceps femoris tendon volume was measured on the side of prior injury. However, no significant differences between the previously injured and uninjured limbs were found in strength measures, peak hamstring stretch, or muscle activation patterns. Further, the degree of asymmetry in tendon volume was not correlated to any of the functional measures.


INTERPRETATION
Injury-induced changes in morphology do not seem discernable from strength measures, running kinematics, or muscle activation patterns. Further research is warranted to ascertain whether residual scarring alters localized musculotendon mechanics in a way that may contribute to the high rates of muscle re-injury that are observed clinically.

Gray-level grouping (GLG): an automatic method for optimized image contrast enhancement - part II: the variations <=SEP=> This is Part II of the paper, "Gray-Level Grouping (GLG): an Automatic Method for Optimized Image Contrast Enhancement". Part I of this paper introduced a new automatic contrast enhancement technique: gray-level grouping (GLG). GLG is a general and powerful technique, which can be conveniently applied to a broad variety of low-contrast images and outperforms conventional contrast enhancement techniques. However, the basic GLG method still has limitations and cannot enhance certain classes of low-contrast images well, e.g., images with a noisy background. The basic GLG also cannot fulfill certain special application purposes, e.g., enhancing only part of an image which corresponds to a certain segment of the image histogram. In order to break through these limitations, this paper introduces an extension of the basic GLG algorithm, selective gray-level grouping (SGLG), which groups the histogram components in different segments of the grayscale using different criteria and, hence, is able to enhance different parts of the histogram to various extents. This paper also introduces two new preprocessing methods to eliminate background noise in noisy low-contrast images so that such images can be properly enhanced by the (S)GLG technique. The extension of (S)GLG to color images is also discussed in this paper. SGLG and its variations extend the capability of the basic GLG to a larger variety of low-contrast images, and can fulfill special application requirements. SGLG and its variations not only produce results superior to conventional contrast enhancement techniques, but are also fully automatic under most circumstances, and are applicable to a broad variety of images.

Affective Computing: A Review <=SEP=> Affective computing is currently one of the most active research topics, furthermore, having increasingly intensive attention. This strong interest is driven by a wide spectrum of promising applications in many areas such as virtual reality, smart surveillance, perceptual interface, etc. Affective computing concerns multidisciplinary knowledge background such as psychology, cognitive, physiology and computer sciences. The paper is emphasized on the several issues involved implicitly in the whole interactive feedback loop. Various methods for each issue are discussed in order to examine the state of the art. Finally, some research challenges and future directions are also discussed.

Advances in Instance Selection for Instance-Based Learning Algorithms <=SEP=> The basic nearest neighbour classifier suffers from the indiscriminate storage of all presented training instances. With a large database of instances classification response time can be slow. When noisy instances are present classification accuracy can suffer. Drawing on the large body of relevant work carried out in the past 30 years, we review the principle approaches to solving these problems. By deleting instances, both problems can be alleviated, but the criterion used is typically assumed to be all encompassing and effective over many domains. We argue against this position and introduce an algorithm that rivals the most successful existing algorithm. When evaluated on 30 different problems, neither algorithm consistently outperforms the other: consistency is very hard. To achieve the best results, we need to develop mechanisms that provide insights into the structure of class definitions. We discuss the possibility of these mechanisms and propose some initial measures that could be useful for the data miner.

Effects of sleep deprivation on performance: a meta-analysis. <=SEP=> To quantitatively describe the effects of sleep loss, we used meta-analysis, a technique relatively new to the sleep research field, to mathematically summarize data from 19 original research studies. Results of our analysis of 143 study coefficients and a total sample size of 1.932 suggest that overall sleep deprivation strongly impairs human functioning. Moreover, we found that mood is more affected by sleep deprivation than either cognitive or motor performance and that partial sleep deprivation has a more profound effect on functioning than either long-term or short-term sleep deprivation. In general, these results indicate that the effects of sleep deprivation may be underestimated in some narrative reviews, particularly those concerning the effects of partial sleep deprivation.

An investigation of the impacts of some external contextual factors on ERP systems success assessment: a case of firms in Baltic-Nordic region <=SEP=> Enterprise Resource Planning (ERP) systems are among the largest information systems (IS) investments made by firms, and the use of such systems is spreading globally. Many researchers have discussed their adoption and implementation, few have investigated the impact of external contextual factors on the success of such technologies in adopting firms. This study aims to fill this gap in research by examining the effects of three external contextual factors, i.e., industry type, industry climate, and national economic climate on ERP success assessment. We obtained data from Estonia and Finland and our analysis shows that industry and national economic climates have significant relationships with ERP success.

An Open Smart City IoT Test Bed: Street Light Poles as Smart City Spines: Poster Abstract <=SEP=> Street light poles will be a key enabler for a smart city's hardware infrastructure, thanks to their ubiquity throughout the city as well as access to power. We propose an IoT test bed around light poles for the city, with a modular hardware and software architecture to enable experimentation with various technologies.

RFBoost: An improved multi-label boosting algorithm and its application to text categorisation <=SEP=> The AdaBoost.MH boosting algorithm is considered to be one of the most accurate algorithms for multilabel classification. AdaBoost.MH works by iteratively building a committee of weak hypotheses of decision stumps. In each round of AdaBoost.MH learning, all features are examined, but only one feature is used to build a new weak hypothesis. This learning mechanism may entail a high degree of computational time complexity, particularly in the case of a large-scale dataset. This paper describes a way to manage the learning complexity and improve the classification performance of AdaBoost.MH. We propose an improved version of AdaBoost.MH, called RFBoost . The weak learning in RFBoost is based on filtering a small fixed number of ranked features in each boosting round rather than using all features, as AdaBoost.MH does. We propose two methods for ranking the features: One Boosting Round and Labeled Latent Dirichlet Allocation (LLDA), a supervised topic model based on Gibbs sampling. Additionally, we investigate the use of LLDA as a feature selection method for reducing the feature space based on the maximal conditional probabilities of words across labels. Our experimental results on eight well-known benchmarks for multi-label text categorisation show that RFBoost is significantly more efficient and effective than the baseline algorithms. Moreover, the LLDA-based feature ranking yields the best performance for RFBoost. © 2016 Elsevier B.V. All rights reserved.

Novel use of electronic whiteboard in the operating room increases surgical team compliance with pre-incision safety practices. <=SEP=> BACKGROUND
Despite evidence that use of a checklist during the pre-incision time out improves patient morbidity and mortality, compliance with performing the required elements of the checklist has been low. In an effort to improve compliance, a standardized time out interactive Electronic Checklist System [iECS] was implemented in all hospital operating room (OR) suites at 1 institution. The purpose of this 12-month prospective observational study was to assess whether an iECS in the OR improves and sustains improved surgical team compliance with the pre-incision time out.


METHODS
Direct observational analyses of preprocedural time outs were performed on 80 cases 1 month before, and 1 and 9 months after implementation of the iECS, for a total of 240 observed cases. Three observers, who achieved high interrater reliability (kappa = 0.83), recorded a compliance score (yes, 1; no, 0) on each element of the time out. An element was scored as compliant if it was clearly verbalized by the surgical team.


RESULTS
Pre-intervention observations indicated that surgical staff verbally communicated the core elements of the time out procedure 49.7 ± 12.9% of the time. After implementation of the iECS, direct observation of 80 surgical cases at 1 and 9 months indicated that surgical staff verbally communicated the core elements of the time out procedure 81.6 ± 11.4% and 85.8 ± 6.8% of the time, respectively, resulting in a statistically significant (P < .0001) increase in time out procedural compliance.


CONCLUSION
Implementation of a standardized, iECS can dramatically increase compliance with preprocedural time outs in the OR, an important and necessary step in improving patient outcomes and reducing preventable complications and deaths.

Self-Adaptive Skin Segmentation in Color Images <=SEP=> In this paper, we present a new method for skin detection and segmentation, relying on spatial analysis of skin-tone pixels. Our contribution lies in introducing self-adaptive seeds, from which the skin probability is propagated using the distance transform. The seeds are determined from a local skin color model that is learned on-line from a presented image, without requiring any additional information. This is in contrast to the existing methods that need a skin sample for the adaptation, e.g., acquired using a face detector. In our experimental study, we obtained F-score of over 0.85 for the ECU benchmark, and this is highly competitive compared with several state-of-the-art methods.

Mobile Robot Control on a Reference Path <=SEP=> In this paper a control design of a nonholonomic mobile robot with a differential drive is presented. On the basis of robot kinematics equations a robot control is designed where the robot is controlled to follow the arbitrary path reference with a predefined velocity profile. The designed control algorithm proved stable and robust to the errors in robot initial positions, to input and output noises and to other disturbances. The obtained control law is demonstrated on a simple trajectory example, however, for a more general applicability a time-optimal motion planning algorithm considering acceleration constraints is presented as well

Dynamically pre-trained deep recurrent neural networks using environmental monitoring data for predicting PM2.5 <=SEP=> Fine particulate matter ( $$\hbox {PM}_{2.5}$$ PM 2.5 ) has a considerable impact on human health, the environment and climate change. It is estimated that with better predictions, US$9 billion can be saved over a 10-year period in the USA (State of the science fact sheet air quality. http://www.noaa.gov/factsheets/new , 2012). Therefore, it is crucial to keep developing models and systems that can accurately predict the concentration of major air pollutants. In this paper, our target is to predict $$\hbox {PM}_{2.5}$$ PM 2.5 concentration in Japan using environmental monitoring data obtained from physical sensors with improved accuracy over the currently employed prediction models. To do so, we propose a deep recurrent neural network (DRNN) that is enhanced with a novel pre-training method using auto-encoder especially designed for time series prediction. Additionally, sensors selection is performed within DRNN without harming the accuracy of the predictions by taking advantage of the sparsity found in the network. The numerical experiments show that DRNN with our proposed pre-training method is superior than when using a canonical and a state-of-the-art auto-encoder training method when applied to time series prediction. The experiments confirm that when compared against the $$\hbox {PM}_{2.5}$$ PM 2.5 prediction system VENUS (National Institute for Environmental Studies. Visual Atmospheric Environment Utility System. http://envgis5.nies.go.jp/osenyosoku/ , 2014), our technique improves the accuracy of $$\hbox {PM}_{2.5}$$ PM 2.5 concentration level predictions that are being reported in Japan.

An investigation of factors associated with the health and well-being of HIV-infected or HIV-affected older people in rural South Africa <=SEP=> BACKGROUND
Despite the severe impact of HIV in sub-Saharan Africa, the health of older people aged 50+ is often overlooked owing to the dearth of data on the direct and indirect effects of HIV on older people's health status and well-being. The aim of this study was to examine correlates of health and well-being of HIV-infected older people relative to HIV-affected people in rural South Africa, defined as participants with an HIV-infected or death of an adult child due to HIV-related cause.


METHODS
Data were collected within the Africa Centre surveillance area using instruments adapted from the World Health Organization (WHO) Study on global AGEing and adult health (SAGE). A stratified random sample of 422 people aged 50+ participated. We compared the health correlates of HIV-infected to HIV-affected participants using ordered logistic regressions. Health status was measured using three instruments: disability index, quality of life and composite health score.


RESULTS
Median age of the sample was 60 years (range 50-94). Women HIV-infected (aOR 0.15, 95% confidence interval (CI) 0.08-0.29) and HIV-affected (aOR 0.20, 95% CI 0.08-0.50), were significantly less likely than men to be in good functional ability. Women's adjusted odds of being in good overall health state were similarly lower than men's; while income and household wealth status were stronger correlates of quality of life. HIV-infected participants reported better functional ability, quality of life and overall health state than HIV-affected participants.


DISCUSSION AND CONCLUSIONS
The enhanced healthcare received as part of anti-retroviral treatment as well as the considerable resources devoted to HIV care appear to benefit the overall well-being of HIV-infected older people; whereas similar resources have not been devoted to the general health needs of HIV uninfected older people. Given increasing numbers of older people, policy and programme interventions are urgently needed to holistically meet the health and well-being needs of older people beyond the HIV-related care system.

A Machine Learning Approach to Anomaly Detection <=SEP=> Much of the intrusion detection research focuses on signature (misuse) detection, where models are built to recognize known attacks. However, signature detection, by its nature, cannot detect novel attacks. Anomaly detection focuses on modeling the normal behavior and identifying significant deviations, which could be novel attacks. In this paper we explore two machine learning methods that can construct anomaly detection models from past behavior. The first method is a rule learning algorithm that characterizes normal behavior in the absence of labeled attack data. The second method uses a clustering algorithm to identify outliers.

Construction and Evaluation of a User Experience Questionnaire <=SEP=> An end-user questionnaire to measure user experience quickly in a simple and immediate way while covering a preferably comprehensive impression of the product user experience was the goal of the reported construction process. An empirical approach for the item selection was used to ensure practical relevance of items. Usability experts collected terms and statements on user experience and usability, including ‘hard’ as well as ‘soft’ aspects. These statements were consolidated and transformed into a first questionnaire version containing 80 bipolar items. It was used to measure the user experience of software products in several empirical studies. Data were subjected to a factor analysis which resulted in the construction of a 26 item questionnaire including the six factors Attractiveness, Perspicuity, Efficiency, Dependability, Stimulation, and Novelty. Studies conducted for the original German questionnaire and an English version indicate a satisfactory level of reliability and construct validity.

Transformational change and business process reengineering (BPR): Lessons from the British and Dutch public sector <=SEP=> Available online 18 May 2011

Clustering of multivariate time series data using particle swarm optimization <=SEP=> Particle swarm optimization (PSO) is a practical and effective optimization approach that has been applied recently for data clustering in many applications. While various non-evolutionary optimization and clustering algorithms have been applied for clustering multivariate time series in some applications such as customer segmentation, they usually provide poor results due to their dependency on the initial values and their poor performance in manipulating multiple objectives. In this paper, a particle swarm optimization algorithm is proposed for clustering multivariate time series data. Since the time series data sometimes do not have the same length and they usually have missing data, the regular Euclidean distance and dynamic time warping can not be applied for such data to measure the similarity. Therefore, a hybrid similarity measure based on principal component analysis and Mahalanobis distance is applied in order to handle such limitations. The comparison between the results of the proposed method with the similar ones in the literature shows the superiority of the proposed method.

Nonlinear Dynamics of Spring Softening and Hardening in Folded-MEMS Comb Drive Resonators <=SEP=> This paper studies analytically and numerically the spring softening and hardening phenomena that occur in electrostatically actuated microelectromechanical systems comb drive resonators utilizing folded suspension beams. An analytical expression for the electrostatic force generated between the combs of the rotor and the stator is derived and takes into account both the transverse and longitudinal capacitances present. After formulating the problem, the resulting stiff differential equations are solved analytically using the method of multiple scales, and a closed-form solution is obtained. Furthermore, the nonlinear boundary value problem that describes the dynamics of inextensional spring beams is solved using straightforward perturbation to obtain the linear and nonlinear spring constants of the beam. The analytical solution is verified numerically using a Matlab/Simulink environment, and the results from both analyses exhibit excellent agreement. Stability analysis based on phase plane trajectory is also presented and fully explains previously reported empirical results that lacked sufficient theoretical description. Finally, the proposed solutions are, once again, verified with previously published measurement results. The closed-form solutions provided are easy to apply and enable predicting the actual behavior of resonators and gyroscopes with similar structures.

Automatic Detection and Classification of Brain Hemorrhages <=SEP=> Computer-aided diagnosis systems have been the focus of many research endeavors. They are based on the idea of processing and analyzing images of different parts of the human body for a quick and accurate diagnosis. In this paper, the aforementioned approach is followed to detect whether a brain hemorrhage exists or not in a Computed Topography (CT) scans of the brain. Moreover, the type of the hemorrhage is identified. The implemented system consists of several stages that include image preprocessing, image segmentation, feature extraction, and classification. The results of the conducted experiments are very promising. A recognition rate of 100% is attained for detecting whether a brain hemorrhage exists or not. For the hemorrhage type classification, more than 92% accuracy is achieved. Key–Words:brain hemorrhage, brain ct scans, machine learning, image processing, image segmentation

Analysis of a linear series-fed rectangular microstrip antenna array <=SEP=> A new method is proposed to analyze a linear series-fed microstrip antenna array which maintains accuracy while not requiring a full-wave analysis of the entire structure. The method accounts for mutual coupling between patches as well as the patch excitations by the feed lines.