Query: IoT-Based Health Monitoring System for Active and Assisted Living

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ECG signal analysis and arrhythmia detection on IoT wearable medical devices <=SEP=> Healthcare is one of the most rapidly expanding application areas of the Internet of Things (IoT) technology. IoT devices can be used to enable remote health monitoring of patients with chronic diseases such as cardiovascular diseases (CVD). In this paper we develop an algorithm for ECG analysis and classification for heartbeat diagnosis, and implement it on an IoT-based embedded platform. This algorithm is our proposal for a wearable ECG diagnosis device, suitable for 24-hour continuous monitoring of the patient. We use Discrete Wavelet Transform (DWT) for the ECG analysis, and a Support Vector Machine (SVM) classifier. The best classification accuracy achieved is 98.9%, for a feature vector of size 18, and 2493 support vectors. Different implementations of the algorithm on the Galileo board, help demonstrate that the computational cost is such, that the ECG analysis and classification can be performed in real-time.

Ubiquitous Data Accessing Method in IoT-Based Information System for Emergency Medical Services <=SEP=> The rapid development of Internet of things (IoT) technology makes it possible for connecting various smart objects together through the Internet and providing more data interoperability methods for application purpose. Recent research shows more potential applications of IoT in information intensive industrial sectors such as healthcare services. However, the diversity of the objects in IoT causes the heterogeneity problem of the data format in IoT platform. Meanwhile, the use of IoT technology in applications has spurred the increase of real-time data, which makes the information storage and accessing more difficult and challenging. In this research, first a semantic data model is proposed to store and interpret IoT data. Then a resource-based data accessing method (UDA-IoT) is designed to acquire and process IoT data ubiquitously to improve the accessibility to IoT data resources. Finally, we present an IoT-based system for emergency medical services to demonstrate how to collect, integrate, and interoperate IoT data flexibly in order to provide support to emergency medical services. The result shows that the resource-based IoT data accessing method is effective in a distributed heterogeneous data environment for supporting data accessing timely and ubiquitously in a cloud and mobile computing platform.

The impact of the MIT-BIH Arrhythmia Database <=SEP=> The MIT-BIH Arrhythmia Database was the first generally available set of standard test material for evaluation of arrhythmia detectors, and it has been used for that purpose as well as for basic research into cardiac dynamics at about 500 sites worldwide since 1980. It has lived a far longer life than any of its creators ever expected. Together with the American Heart Association Database, it played an interesting role in stimulating manufacturers of arrhythmia analyzers to compete on the basis of objectively measurable performance, and much of the current appreciation of the value of common databases, both for basic research and for medical device development and evaluation, can be attributed to this experience. In this article, we briefly review the history of the database, describe its contents, discuss what we have learned about database design and construction, and take a look at some of the later projects that have been stimulated by both the successes and the limitations of the MIT-BIH Arrhythmia Database.

LIBSVM: a library for support vector machines <=SEP=> 

PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals. <=SEP=> The newly inaugurated Research Resource for Complex Physiologic Signals, which was created under the auspices of the National Center for Research Resources of the National Institutes of Health, is intended to stimulate current research and new investigations in the study of cardiovascular and other complex biomedical signals. The resource has 3 interdependent components. PhysioBank is a large and growing archive of well-characterized digital recordings of physiological signals and related data for use by the biomedical research community. It currently includes databases of multiparameter cardiopulmonary, neural, and other biomedical signals from healthy subjects and from patients with a variety of conditions with major public health implications, including life-threatening arrhythmias, congestive heart failure, sleep apnea, neurological disorders, and aging. PhysioToolkit is a library of open-source software for physiological signal processing and analysis, the detection of physiologically significant events using both classic techniques and novel methods based on statistical physics and nonlinear dynamics, the interactive display and characterization of signals, the creation of new databases, the simulation of physiological and other signals, the quantitative evaluation and comparison of analysis methods, and the analysis of nonstationary processes. PhysioNet is an on-line forum for the dissemination and exchange of recorded biomedical signals and open-source software for analyzing them. It provides facilities for the cooperative analysis of data and the evaluation of proposed new algorithms. In addition to providing free electronic access to PhysioBank data and PhysioToolkit software via the World Wide Web (http://www.physionet. org), PhysioNet offers services and training via on-line tutorials to assist users with varying levels of expertise.

A One-Stage Correction of the Blepharophimosis Syndrome Using a Standard Combination of Surgical Techniques <=SEP=> The aim of this study was to evaluate the efficacy of a one-stage treatment for the blepharophimosis-ptosis-epicanthus inversus syndrome (BPES) using a combination of standard surgical techniques. This is a retrospective interventional case series study of 21 BPES patients with a 1-year minimum follow-up period. The one-stage intervention combined three different surgical procedures in the following order: Z-epicanthoplasty for the epicanthus, transnasal wiring of the medial canthal ligaments for the telecanthus, and a bilateral fascia lata sling for ptosis correction. Preoperative and postoperative measurements of the horizontal lid fissure length (HFL), vertical lid fissure width (VFW), nasal intercanthal distance (ICD), and the ratio between the intercanthal distance and the horizontal fissure length (ICD/HFL) were analyzed using Student’s t test for paired variables. The mean preoperative measurements were 4.95 ± 1.13 mm for the VFW, 20.90 ± 2.14 mm for the HFL, 42.45 ± 2.19 mm for the ICD, and 2.04 ± 0.14 mm for the ICD/HFL ratio. The mean postoperative measurements were 7.93 ± 1.02 mm for the VFW, 26.36 ± 1.40 mm for the HFL, 32.07 ± 1.96 mm for the ICD, and 1.23 ± 0.09 mm for the ICD/HFL ratio. All these values and their differences were statistically significant (P < 0.0001). All of the patients developed symmetric postoperative inferior version lagophthalmus, a complication that tended to decrease over time. One-stage correction of BPES is safe and efficient with the surgical techniques described.

Time and cognitive load in working memory. <=SEP=> According to the time-based resource-sharing model (P. Barrouillet, S. Bernardin, & V. Camos, 2004), the cognitive load a given task involves is a function of the proportion of time during which it captures attention, thus impeding other attention-demanding processes. Accordingly, the present study demonstrates that the disruptive effect on concurrent maintenance of memory retrievals and response selections increases with their duration. Moreover, the effect on recall performance of concurrent activities does not go beyond their duration insofar as the processes are attention demanding. Finally, these effects are not modality specific, as spatial processing was found to disrupt verbal maintenance. These results suggest a sequential and time-based function of working memory in which processing and storage rely on a single and general purpose attentional resource needed to run executive processes devoted to constructing, maintaining, and modifying ephemeral representations.

Supervised LogEuclidean Metric Learning for Symmetric Positive Definite Matrices <=SEP=> Metric learning has been shown to be highly effective to improve the performance of nearest neighbor classification. In this paper, we address the problem of metric learning for symmetric positive definite (SPD) matrices such as covariance matrices, which arise in many real-world applications. Naively using standard Mahalanobis metric learning methods under the Euclidean geometry for SPD matrices is not appropriate, because the difference of SPD matrices can be a non-SPD matrix and thus the obtained solution can be uninterpretable. To cope with this problem, we propose to use a properly parameterized LogEuclidean distance and optimize the metric with respect to kernel-target alignment, which is a supervised criterion for kernel learning. Then the resulting non-trivial optimization problem is solved by utilizing the Riemannian geometry. Finally, we experimentally demonstrate the usefulness of our LogEuclidean metric learning algorithm on real-world classification tasks for EEG signals and texture patches.

Visualizing Scholarly Publications and Citations to Enhance Author Profiles <=SEP=> With data on scholarly publications becoming more abundant and accessible, there exist new opportunities for using this information to provide rich author profiles to display and explore scholarly work. We present a pair of linked visualizations connected to the Microsoft Academic Graph that can be used to explore the publications and citations of individual authors. We provide an online application with which a user can manage collections of papers and generate these visualizations.

Three Models for the Description of Language <=SEP=> The grammar of a language is a device that describes the structure of that language. The grammar is comprised of a set of rules whose goal is twofold: first these rules can be used to create sentences of the associated language and only these, and second they can be used to classify whether a given sentence is an element of the language or not. The goal of a linguist is to discover grammars that are simple and yet are able to fully span the language. In [1] Chomsky describes three possible options of increasing complexity for English grammars: Finite-state, Phrase Structure and Transformational. This paper briefly present these three grammars and summarizes Chomsky’s analysis and results which state that finite-state grammars are inadequate because they fail to span all possible sentences of the English language, and phrase structure grammar is overly complex.

SemEval-2017 Task 7: Detection and Interpretation of English Puns <=SEP=> Apun is a form of wordplay in which aword suggests two or more meanings by exploiting polysemy, homonymy, or phonological similarity to another word, for an intended humorous or rhetorical effect. Though a recurrent and expected feature in many discourse types, puns stymie traditional approaches to computational lexical semantics because they violate their one-sense-percontext assumption. This paper describes the first competitive evaluation for the automatic detection, location, and interpretation of puns. We describe the motivation for these tasks, the evaluation methods, and the manually annotated data set. Finally, we present an overview and discussion of the participating systems’ methodologies, resources, and results.

An objective and subjective study of the role of semantics and prosodic features in building corpora for emotional TTS <=SEP=> Building a text corpus suitable to be used in corpus-based speech synthesis is a time-consuming process that usually requires some human intervention to select the desired phonetic content and the necessary variety of prosodic contexts. If an emotional text-to-speech (TTS) system is desired, the complexity of the corpus generation process increases. This paper presents a study aiming to validate or reject the use of a semantically neutral text corpus for the recording of both neutral and emotional (acted) speech. The use of this kind of texts would eliminate the need to include semantically emotional texts into the corpus. The study has been performed for Basque language. It has been made by performing subjective and objective comparisons between the prosodic characteristics of recorded emotional speech using both semantically neutral and emotional texts. At the same time, the performed experiments allow for an evaluation of the capability of prosody to carry emotional information in Basque language. Prosody manipulation is the most common processing tool used in concatenative TTS. Experiments of automatic recognition of the emotions considered in this paper (the "Big Six emotions") show that prosody is an important emotional indicator, but cannot be the only manipulated parameter in an emotional TTS system-at least not for all the emotions. Resynthesis experiments transferring prosody from emotional to neutral speech have also been performed. They corroborate the results and support the use of a neutral-semantic-content text in databases for emotional speech synthesis

Stakeholder engagement, social auditing and corporate sustainability <=SEP=> Purpose – To identify the applicability of social auditing as an approach of engaging stakeholders in assessing and reporting on corporate sustainability and its performance. Design/methodology/approach – Drawing upon the framework of AA1000 and the social auditing studies, this paper links stakeholder engagement, social auditing and corporate sustainability with a view to applying dialogue-based social auditing to address corporate sustainability. Findings – This paper identifies a “match” between corporate sustainability and social auditing, as both aim at improving the social, environmental and economic performance of an organisation, considering the well-being of a wider range of stakeholders and requiring the engagement of stakeholders in the process. This paper suggests that social auditing through engaging stakeholders via dialogue could be applied to build trusts, identify commitment and promote co-operation amongst stakeholders and corporations. Research limitations/implications – This research requires further empirical research into the practicality of social auditing in addressing corporate sustainability and the determination of the limitations of dialogue-based social auditing. Practical implications – Social auditing has been identified as a useful mechanism of balancing differing interests among stakeholders and corporations in a democratic business society. The application of social auditing in developing and achieving corporate sustainability has apparently practical implications. Originality/value – This paper examines the applicability of dialogue-based social auditing in helping business to move towards sustainability. Social auditing as a process of assessing and reporting on corporate social and environmental performance through engaging stakeholders via dialogue could be applied to build trusts, identify commitment and promote cooperation amongst stakeholders and corporations.

Using Semantic Web Technologies for Exploratory OLAP: A Survey <=SEP=> This paper describes the convergence of some of the most influential technologies in the last few years, namely data warehousing (DW), on-line analytical processing (OLAP), and the Semantic Web (SW). OLAP is used by enterprises to derive important business-critical knowledge from data inside the company. However, the most interesting OLAP queries can no longer be answered on internal data alone, external data must also be discovered (most often on the web), acquired, integrated, and (analytically) queried, resulting in a new type of OLAP, exploratory OLAP. When using external data, an important issue is knowing the precise semantics of the data. Here, SW technologies come to the rescue, as they allow semantics (ranging from very simple to very complex) to be specified for web-available resources. SW technologies do not only support capturing the “passive” semantics, but also support active inference and reasoning on the data. The paper first presents a characterization of DW/OLAP environments, followed by an introduction to the relevant SW foundation concepts. Then, it describes the relationship of multidimensional (MD) models and SW technologies, including the relationship between MD models and SW formalisms. Next, the paper goes on to survey the use of SW technologies for data modeling and data provisioning, including semantic data annotation and semantic-aware extract, transform, and load (ETL) processes. Finally, all the findings are discussed and a number of directions for future research are outlined, including SW support for intelligent MD querying, using SW technologies for providing context to data warehouses, and scalability issues.

Do cheerfulness , exhilaration , and humor production moderate pain tolerance ? A FACS study <=SEP=> Prior studies have shown that watching a funny film leads to an increase in pain tolerance. The present study aimed at separating three factors considered potentially essential (mood, behavior, and cognition related to humor) and examined whether they are responsible for this effect. Furthermore, the study examined whether trait cheerfulness and trait seriousness, as measured by the State-TraitCheerfulness-Inventory (STCI; Ruch et al. 1996), moderate changes in pain tolerance. Fifty-sixty female subjects were assigned randomly to three groups, each having a different task to pursue while watching a funny film: (1) get into a cheerful mood without smiling or laughing (”Cheerfulness”); (2) smile and laugh extensively (”Exhilaration”); and (3) produce a humorous commentary to the film (”Humor production”). Pain tolerance was measured using the cold pressor test before, immediately after, and twenty minutes after the film. Results indicated that pain tolerance increased for participants from before to after watching the funny film and remained high for the twenty minutes. This effect was moderated by facial but not verbal indicators of enjoyment of humor. Participants low in trait seriousness had an overall higher pain tolerance. Subjects with a high score in trait cheerfulness showed an increase in pain tolerance after producing humor while watching the film whereas subjects low in trait cheerfulness showed a similar increase after smiling and laughter during the film. DOI: https://doi.org/10.1515/humr.2004.009 Posted at the Zurich Open Repository and Archive, University of Zurich ZORA URL: https://doi.org/10.5167/uzh-77579 Originally published at: Zweyer, K; Velker, B; Ruch, Willibald (2004). Do cheerfulness, exhilaration, and humor production moderate pain tolerance? A FACS study. HUMOR: International Journal of Humor Research, 17(12):85-119. DOI: https://doi.org/10.1515/humr.2004.009 Do cheerfulness, exhilaration, and humor production moderate pain tolerance? A FACS study KAREN ZWEYER, BARBARA VELKER, and WILLIBALD RUCH

A sneak peek into digital innovations and wearable sensors for cardiac monitoring <=SEP=> Many mobile phone or tablet applications have been designed to control cardiovascular risk factors (obesity, smoking, sedentary lifestyle, diabetes and hypertension) or to optimize treatment adherence. Some have been shown to be useful but the long-term benefits remain to be demonstrated. Digital stethoscopes make easier the interpretation of abnormal heart sounds, and the development of pocket-sized echo machines may quickly and significantly expand the use of ultrasounds. Daily home monitoring of pulmonary artery pressures with wireless implantable sensors has been shown to be associated with a significant decrease in hospital readmissions for heart failure. There are more and more non-invasive, wireless, and wearable sensors designed to monitor heart rate, heart rate variability, respiratory rate, arterial oxygen saturation, and thoracic fluid content. They have the potential to change the way we monitor and treat patients with cardiovascular diseases in the hospital and beyond. Some may have the ability to improve quality of care, decrease the number of medical visits and hospitalization, and ultimately health care costs. Validation and outcome studies are needed to clarify, among the growing number of digital innovations and wearable sensors, which tools have real clinical value.

Applications of Data Mining Techniques in TelecomChurn Prediction <=SEP=> In this competitive world, business becomes highly saturated. Especially, the field of telecommunication faces complex challenges due to a number of vibrant competitive service providers. Therefore it has become very difficult for them to retain existing customers. Since the cost of acquiring new customers is much higher than the cost of retaining the existing customers, it is the time for the telecom industries to take necessary steps to retain the customers to stabilize their market value. This paper explores the application of data mining techniques in predicting likely churners and the impact of attribute selection on identifying the churn. It also compares the efficiency of Decision tree and Neural Network classifiers and lists their performances.

Learning from Corrupted Binary Labels via Class-Probability Estimation <=SEP=> Many supervised learning problems involve learning from samples whose labels are corrupted in some way. For example, each label may be flipped with some constant probability (learning with label noise), or one may have a pool of unlabelled samples in lieu of negative samples (learning from positive and unlabelled data). This paper uses class-probability estimation to study these and other corruption processes belonging to the mutually contaminated distributions framework (Scott et al., 2013), with three conclusions. First, one can optimise balanced error and AUC without knowledge of the corruption parameters. Second, given estimates of the corruption parameters, one can minimise a range of classification risks. Third, one can estimate corruption parameters via a class-probability estimator (e.g. kernel logistic regression) trained solely on corrupted data. Experiments on label noise tasks corroborate our analysis. 1. Learning from corrupted binary labels In many practical scenarios involving learning from binary labels, one observes samples whose labels are corrupted versions of the actual ground truth. For example, in learning from class-conditional label noise (CCN learning), the labels are flipped with some constant probability (Angluin & Laird, 1988). In positive and unlabelled learning (PU learning), we have access to some positive samples, but in lieu of negative samples only have a pool of samples whose label is unknown (Denis, 1998). More generally, suppose there is a notional clean distribution D over instances and labels. We say a problem involves learning from corrupted Proceedings of the 32 International Conference on Machine Learning, Lille, France, 2015. JMLR: W&CP volume 37. Copyright 2015 by the author(s). binary labels if we observe training samples drawn from some corrupted distribution Dcorr such that the observed labels do not represent those we would observe under D. A fundamental question is whether one can minimise a given performance measure with respect to D, given access only to samples from Dcorr. Intuitively, in general this requires knowledge of the parameters of the corruption process that determines Dcorr. This yields two further questions: are there measures for which knowledge of these corruption parameters is unnecessary, and for other measures, can we estimate these parameters? In this paper, we consider corruption problems belonging to the mutually contaminated distributions framework (Scott et al., 2013). We then study the above questions through the lens of class-probability estimation, with three conclusions. First, optimising balanced error (BER) as-is on corrupted data equivalently optimises BER on clean data, and similarly for the area under the ROC curve (AUC). That is, these measures can be optimised without knowledge of the corruption process parameters; further, we present evidence that these are essentially the only measures with this property. Second, given estimates of the corruption parameters, a range of classification measures can be minimised by thresholding corrupted class-probabilities. Third, under some assumptions, these corruption parameters may be estimated from the range of the corrupted class-probabilities. For all points above, observe that learning requires only corrupted data. Further, corrupted class-probability estimation can be seen as treating the observed samples as if they were uncorrupted. Thus, our analysis gives justification (under some assumptions) for this apparent heuristic in problems such as CCN and PU learning. While some of our results are known for the special cases of CCN and PU learning, our interest is in determining to what extent they generalise to other label corruption problems. This is a step towards a unified treatment of these problems. We now fix notation and formalise the problem. Learning from Corrupted Binary Labels via Class-Probability Estimation 2. Background and problem setup Fix an instance space X. We denote byD some distribution over X × {±1}, with (X,Y) ∼ D a pair of random variables. Any D may be expressed via the class-conditional distributions (P,Q) = (P(X | Y = 1),P(X | Y = −1)) and base rate π = P(Y = 1), or equivalently via marginal distribution M = P(X) and class-probability function η : x 7→ P(Y = 1 | X = x). When referring to these constituent distributions, we write D as DP,Q,π or DM,η. 2.1. Classifiers, scorers, and risks A classifier is any function f : X→ {±1}. A scorer is any function s : X → R. Many learning methods (e.g. SVMs) output a scorer, from which a classifier is formed by thresholding about some t ∈ R. We denote the resulting classifier by thresh(s, t) : x 7→ sign(s(x)− t). The false positive and false negative rates of a classifier f are denoted FPR(f),FNR(f), and are defined by P X∼Q (f(X) = 1) and P X∼P (f(X) = −1) respectively. Given a function Ψ: [0, 1] → [0, 1], a classification performance measure ClassΨ : {±1} → [0, 1] assesses the performance of a classifier f via (Narasimhan et al., 2014) ClassΨ(f) = Ψ(FPR (f),FNR(f), π). A canonical example is the misclassification error, where Ψ: (u, v, p) 7→ p · v+ (1− p) · u. Given a scorer s, we use ClassΨ(s; t) to refer to Class D Ψ(thresh(s, t)). The Ψ-classification regret of a classifier f : X→ {±1} is regretΨ(f) = Class D Ψ(f)− inf g : X→{±1} ClassΨ(g). A loss is any function ` : {±1} ×R→ R+. Given a distribution D, the `-risk of a scorer s is defined as L` (s) = E (X,Y)∼D [`(Y, s(X))] . (1) The `-regret of a scorer, regret` , is as per the Ψ-regret. We say ` is strictly proper composite (Reid & Williamson, 2010) if argmins L` (s) is some strictly monotone transformation ψ of η, i.e. we can recover class-probabilities from the optimal prediction via the link function ψ. We call class-probability estimation (CPE) the task of minimising Equation 1 for some strictly proper composite `. The conditional Bayes-risk of a strictly proper composite ` is L` : η 7→ η`1(ψ(η)) + (1 − η)`−1(ψ(η)). We call ` strongly proper composite with modulus λ if L` is λstrongly concave (Agarwal, 2014). Canonical examples of such losses are the logistic and exponential loss, as used in logistic regression and AdaBoost respectively. Quantity Clean Corrupted Joint distribution D Corr(D,α, β, πcorr) or Dcorr Class-conditionals P,Q Pcorr, Qcorr Base rate π πcorr Class-probability η ηcorr Ψ-optimal threshold tΨ t D corr,Ψ Table 1. Common quantities on clean and corrupted distributions. 2.2. Learning from contaminated distributions Suppose DP,Q,π is some “clean” distribution where performance will be assessed. (We do not assume that D is separable.) In MC learning (Scott et al., 2013), we observe samples from some corrupted distribution Corr(D,α, β, πcorr) over X × {±1}, for some unknown noise parameters α, β ∈ [0, 1] with α + β < 1; where the parameters are clear from context, we occasionally refer to the corrupted distribution as Dcorr. The corrupted classconditional distributions Pcorr, Qcorr are Pcorr = (1− α) · P + α ·Q Qcorr = β · P + (1− β) ·Q, (2) and the corrupted base rate πcorr in general has no relation to the clean base rate π. (If α+β = 1, then Pcorr = Qcorr, making learning impossible, whereas if α+ β > 1, we can swap Pcorr, Qcorr.) Table 1 summarises common quantities on the clean and corrupted distributions. From (2), we see that none of Pcorr, Qcorr or πcorr contain any information about π in general. Thus, estimating π from Corr(D,α, β, πcorr) is impossible in general. The parameters α, β are also non-identifiable, but can be estimated under some assumptions on D (Scott et al., 2013). 2.3. Special cases of MC learning Two special cases of MC learning are notable. In learning from class-conditional label noise (CCN learning) (Angluin & Laird, 1988), positive samples have labels flipped with probability ρ+, and negative samples with probability ρ−. This can be shown to reduce to MC learning with α = π−1 corr · (1− π) · ρ− , β = (1− πcorr) · π · ρ+, (3) and the corrupted base rate πcorr = (1−ρ+)·π+ρ−·(1−π). (See Appendix C for details.) In learning from positive and unlabelled data (PU learning) (Denis, 1998), one has access to unlabelled samples in lieu of negative samples. There are two subtly different settings: in the case-controlled setting (Ward et al., 2009), the unlabelled samples are drawn from the marginal distribution M , corresponding to MC learning with α = 0, β = π, Learning from Corrupted Binary Labels via Class-Probability Estimation and πcorr arbitrary. In the censoring setting (Elkan & Noto, 2008), observations are drawn from D followed by a label censoring procedure. This is in fact a special of CCN (and hence MC) learning with ρ− = 0. 3. BER and AUC are immune to corruption We first show that optimising balanced error and AUC on corrupted data is equivalent to doing so on clean data. Thus, with a suitably rich function class, one can optimise balanced error and AUC from corrupted data without knowledge of the corruption process parameters. 3.1. BER minimisation is immune to label corruption The balanced error (BER) (Brodersen et al., 2010) of a classifier is simply the mean of the per-class error rates, BER(f) = FPR(f) + FNR(f) 2 . This is a popular measure in imbalanced learning problems (Cheng et al., 2002; Guyon et al., 2004) as it penalises sacrificing accuracy on the rare class in favour of accuracy on the dominant class. The negation of the BER is also known as the AM (arithmetic mean) metric (Menon et al., 2013). The BER-optimal classifier thresholds the class-probability function at the base rate (Menon et al., 2013), so that: argmin f : X→{±1} BER(f) = thresh(η, π) (4) argmin f : X→{±1} BERcorr(f) = thresh(ηcorr, πcorr), (5) where ηcorr denotes the corrupted class-probability function. As Equation 4 depends on π, it may appear that one must know π to minimise the clean BER from corrupted data. Surprisingly, the BER-optimal classifiers in Equations 4 and 5 coincide. This is because of the following relationship between

Habits — A Repeat Performance <=SEP=> Habits are response dispositions that are activated automatically by the context cues that co-occurred with responses during past performance. Experiencesampling diary studies indicate that much of everyday action is characterized by habitual repetition. We consider various mechanisms that could underlie the habitual control of action, and we conclude that direct cuing and motivated contexts best account for the characteristic features of habit responding—in particular, for the rigid repetition of action that can be initiated without intention and that runs to completion with minimal conscious control. We explain the utility of contemporary habit research for issues central to psychology, especially for behavior prediction, behavior change, and self-regulation. KEYWORDS—habit; automaticity; motivation; goals; behavior change; behavior prediction; self-regulation From self-help guru Anthony Robbins to the religion of Zen Buddhism, received wisdom exhorts people to be mindful, deliberative, and conscious in all they do. In contrast, contemporary research in psychology shows that it is actually people’s unthinking routines—or habits—that form the bedrock of everyday life. Without habits, people would be doomed to plan, consciously guide, and monitor every action, from making that first cup of coffee in the morning to sequencing the finger movements in a Chopin piano concerto. But what is a habit? The cognitive revolution radically reshaped the behaviorist view that habits rely on simple stimulus– response associations devoid of mental representation. Emerging is a more nuanced construct that includes roles for consciousness, goals, and motivational states. Fundamental questions persist, however, especially when comparing evidence across neuropsychology, animal-learning, and social-cognition literatures. Data from these fields support three views of habit, which we term the direct-context-cuing, implicit-goal, and motivated-context models. In this article, we consider these models and explain the relevance for psychology of a reinvigorated habit construct. HABITS AFTER BEHAVIORISM Within current theorizing, habits are automated response dispositions that are cued by aspects of the performance context (i.e., environment, preceding actions). They are learned through a process in which repetition incrementally tunes cognitive processors in procedural memory (i.e., the memory system that supports the minimally conscious control of skilled action). The relatively primitive associative learning that promotes habits is shared in some form across mammalian species. Our own interest in habits has been fueled by the recognition that much of everyday action is characterized by repetition. In experience-sampling diary studies using both student and community samples, approximately 45% of everyday behaviors tended to be repeated in the same location almost every day (Quinn & Wood, 2005; Wood, Quinn, & Kashy, 2002). In these studies, people reported a heterogeneous set of actions that varied in habit strength, including reading the newspaper, exercising, and eating fast food. Although a consensual perspective on habit mechanisms has yet to develop, common to all views is the idea that many behavioral sequences (e.g., one’s morning coffee-making routine) are performed repeatedly in similar contexts. When responses and features of context occur in contiguity, the potential exists for associations to form between them, such that contexts come to cue responses. In what follows, we outline three views of habitual control that build on this understanding. Direct Context Cuing According to the direct-context-cuing model, repeated coactivation forges direct links in memory between context and response representations. Once these links are formed via associative learning, merely perceiving a context triggers associated responses. Supporting evidence comes from research in which merely activating a construct, such as the elderly stereotype, influences the performance of relevant behaviors, such as a slow speed of walking (e.g., Bargh, Chen, & Burrows, 1996). Address correspondence to David Neal, Department of Psychology, Box 90085, Duke University, Durham, NC 27708; e-mail: dneal@ duke.edu. CURRENT DIRECTIONS IN PSYCHOLOGICAL SCIENCE 198 Volume 15—Number 4 Copyright r 2006 Association for Psychological Science Readers might wonder if it is realistic that contexts cue responses through this simple mechanism in the absence of an implicit or explicit goal. The answer is not clear, given that social-cognition research has thus far demonstrated only a limited version of direct-cuing effects. For example, activating the elderly stereotype influences walking speed, but it remains to be demonstrated whether such activation can initiate walking itself. However, the direct cuing of repeated action by contexts is suggested by myriad findings in cognitive neuroscience that reveal reduced involvement of goal-related neural structures, such as the prefrontal cortex, when behaviors have come under habitual control (see Daw, Niv, & Dayan, 2005). Furthermore, animal-learning research using a clever paradigm in which reinforcers are devalued suggests direct control by context. When rats initially perform an instrumental behavior (e.g., pressing a bar for a food pellet), they appear to be guided by specific goal expectations; they cease the behavior if the reward is devalued (e.g., by pairing it with a toxin; Dickinson & Balleine, 1995). In contrast, when rats have extensively repeated a behavior, their responses appear to be cued directly by contextual stimuli (e.g., the bar); reward devaluation has little impact on continued performance. These data are commonly interpreted as indicating that habit formation involves a shift to direct context cuing. Implicit Goals Associative learning explains not only the direct binding of contexts and actions but also the binding of contexts and goals. In implicit-goal models, habits develop when people repeatedly pursue a goal via a specific behavior in a given context. An indirect association then forms between the context and behavior within the broader goal system. In support, Aarts and Dijksterhuis (2000) found in several experiments that the automatic activation of habitual responses (e.g., bicycle riding) only occurs when a relevant goal has first been made accessible (e.g., the thought of attending class). These studies did not measure people’s real-world behavior, however, but focused instead on judgments about behavior. It remains to be seen whether such judgments tap the cognitive processes that actually drive behavior. In addition, there is good reason to think that habit performance itself does not depend on goal activation. Goaldriven responses tend to be dynamic and flexible, as evidenced by people sometimes substituting behaviors that serve a common goal. In contrast, habits emerge in a rigid pattern such that, for example, a habitual runner is unlikely to substitute a cycling class for running. Thus, although implicit goals provide potentially powerful guides to action, they do not plausibly explain the context cuing of habits. Motivated Contexts In another framework for understanding context-cued responses, contexts can acquire diffuse motivational value when they have preceded rewards in the past. When contexts predict rewards in this way, they energize associated responses without activating specific goals. Evidence of the motivating quality of contexts comes from animal studies of the neurotransmitters that mediate reward learning. For example, when monkeys first learn that a feature of the environment (e.g., a light) predicts a reward (e.g., a drop of juice) when a response is made (e.g., a lever press), neurotransmitter activity (i.e., dopamine release) occurs just after the reward (see Schultz, Dayan, & Montague, 1997). After repeated practice, the animal reaches for the lever when the light is illuminated. Furthermore, the neurotransmitter response is no longer elicited by the juice but instead by the light. In this way, environmental cues can acquire motivational value. Reward-predicting environments are thought to signal the cached (or long-run future) value of an action without signaling a specific outcome (e.g., juice; Daw et al., 2005). This diffuse motivation may explain the rigid nature of context cuing, given that cached values do not convey a specific desired outcome that could be met by substitutable means. Contributing further to the rigidity of habits, neural evidence indicates that, with repetition, whole sequences of responses become chunked or integrated in memory with the contexts that predict them (Barnes, Kubota, Hu, Jin, & Graybiel, 2005). Chunked responses are cued and implemented as a unit, consistent with the idea that habits require limited conscious control to proceed to completion. This quality of habitual responding is frustratingly evident when, for example, trying to fix a well-practiced but badly executed golf swing or dance-step sequence. As yet, the motivated-context idea has been tested primarily with animals. Its promise as a model of human habits comes from evidence that reward-related neurotransmitter systems are shared across species (e.g., in humans, dopamine is elicited by monetary reward). Multiple Habit Mechanisms The high degree of repetition in daily life observed in the diary research of Wood et al. (2002) is likely to be a product of multiple habit-control mechanisms that draw, in various cases, on direct context associations as well as on diffuse motivations. Although we consider implicit goals to be an implausible mediator of habitual behavior, they undoubtedly contribute to some types of repetition. Whether habits are cued directly or are diffusely motivated, they are triggered automatically by contexts and performed in a relatively rigid way. These features of responding have important implications for theories of behavior prediction, behavior cha

Intelligent criminal identification system <=SEP=> In many countries the amount of crime incidents that is reported per day is increasing dramatically. Concerning about Sri Lanka, The department of Police is the major organization of preventing crimes. In general, Sri Lankan police stations utilize paper-based information storing systems and they don't employ computer based applications up to a great extent. Due to this utilization of paper-based systems police officers have to spend a lot of time as well as man power to analyze existing crime information and to identify suspects for crime incidents. So the requirement of an efficient way for crime investigation has arisen. Data mining practices is one aspect of crime investigation, for which numerous technique are available. This paper highlights the use of data mining techniques, clustering and classification for effective investigation of crimes. Further the paper aims to identify suspects by analyzing existing evidences in situations where any witness or forensic clues are not present.

Intelligent Tutoring Systems and Learning Outcomes : A Meta-Analysis <=SEP=> Intelligent Tutoring Systems (ITS) are computer programs that model learners’ psychological states to provide individualized instruction. They have been developed for diverse subject areas (e.g., algebra, medicine, law, reading) to help learners acquire domain-specific, cognitive and metacognitive knowledge. A meta-analysis was conducted on research that compared the outcomes from students learning from ITS to those learning from non-ITS learning environments. The meta-analysis examined how effect sizes varied with type of ITS, type of comparison treatment received by learners, type of learning outcome, whether knowledge to be learned was procedural or declarative, and other factors. After a search of major bibliographic databases, 107 effect sizes involving 14,321 participants were extracted and analyzed. The use of ITS was associated with greater achievement in comparison with teacher-led, large-group instruction (g .42), non-ITS computer-based instruction (g .57), and textbooks or workbooks (g .35). There was no significant difference between learning from ITS and learning from individualized human tutoring (g –.11) or small-group instruction (g .05). Significant, positive mean effect sizes were found regardless of whether the ITS was used as the principal means of instruction, a supplement to teacher-led instruction, an integral component of teacher-led instruction, or an aid to homework. Significant, positive effect sizes were found at all levels of education, in almost all subject domains evaluated, and whether or not the ITS provided feedback or modeled student misconceptions. The claim that ITS are relatively effective tools for learning is consistent with our analysis of potential publication bias.

Kalman filtering over a packet-delaying network: A probabilistic approach <=SEP=> In this paper, we consider Kalman filtering over a packet-delaying network. Given the probability distribution of the delay, we can characterize the filter performance via a probabilistic approach. We assume that the estimator maintains a buffer of length D so that at each time k, the estimator is able to retrieve all available data packets up to time k−D+1. Both the cases of sensorwith andwithout necessary computation capability for filter updates are considered. When the sensor has no computation capability, for a givenD, we give lower and upper bounds on the probability forwhich the estimation error covariance is within a prescribed bound. When the sensor has computation capability, we show that the previously derived lower and upper bounds are equal to each other. An approach for determining the minimum buffer length for a required performance in probability is given and an evaluation on the number of expected filter updates is provided. Examples are provided to demonstrate the theory developed in the paper. © 2009 Elsevier Ltd. All rights reserved.

SCNN: An accelerator for compressed-sparse convolutional neural networks <=SEP=> Convolutional Neural Networks (CNNs) have emerged as a fundamental technology for machine learning. High performance and extreme energy efficiency are critical for deployments of CNNs, especially in mobile platforms such as autonomous vehicles, cameras, and electronic personal assistants. This paper introduces the Sparse CNN (SCNN) accelerator architecture, which improves performance and energy efficiency by exploiting the zero-valued weights that stem from network pruning during training and zero-valued activations that arise from the common ReLU operator. Specifically, SCNN employs a novel dataflow that enables maintaining the sparse weights and activations in a compressed encoding, which eliminates unnecessary data transfers and reduces storage requirements. Furthermore, the SCNN dataflow facilitates efficient delivery of those weights and activations to a multiplier array, where they are extensively reused; product accumulation is performed in a novel accumulator array. On contemporary neural networks, SCNN can improve both performance and energy by a factor of 2.7x and 2.3x, respectively, over a comparably provisioned dense CNN accelerator.

Hidden Community Detection in Social Networks <=SEP=> We introduce a new paradigm that is important for community detection in the realm of network analysis. Networks contain a set of strong, dominant communities, which interfere with the detection of weak, natural community structure. When most of the members of the weak communities also belong to stronger communities, they are extremely hard to be uncovered. We call the weak communities the hidden community structure. We present a novel approach called HICODE (HIdden COmmunity DEtection) that identies the hidden community structure as well as the dominant community structure. By weakening the strength of the dominant structure, one can uncover the hidden structure beneath. Likewise, by reducing the strength of the hidden structure, one can more accurately identify the dominant structure. In this way, HICODE tackles both tasks simultaneously. Extensive experiments on real-world networks demonstrate that HICODE outperforms several state-of-the-art community detection methods in uncovering both the dominant and the hidden structure. For example in the Facebook university social networks, we nd multiple non-redundant sets of communities that are strongly associated with residential hall, year of registration or career position of the faculties or students, while the state-of-the-art algorithms mainly locate the dominant ground truth category. Due to the diculty of labeling all ground truth communities in real-world datasets, HICODE provides a promising approach to pinpoint the existing latent communities and uncover communities for which there is no ground truth. Finding this unknown structure is an extremely important community detection problem.

HiStory: a hierarchical storyboard interface design for video browsing on mobile devices <=SEP=> This paper presents an interactive thumbnail-based video browser for mobile devices such as smartphones featuring a touch screen. Developed as part of on-going research and supported by user studies, it introduces HiStory, a hierarchical storyboard design offering an interface metaphor that is familiar and intuitive yet supports fast and effective completion of Known-Item-Search tasks by rapidly providing an overview of a video's content with varying degrees of granularity.

Idiopathic isolated clitoromegaly: A report of two cases <=SEP=> BACKGROUND: Clitoromegaly is a frequent congenital malformation, but acquired clitoral enlargement is relatively rare. METHODS: Two acquired clitoromegaly cases treated in Ataturk Training Hospital, Izmir, Turkey are presented. RESULTS: History from both patients revealed clitoromegaly over the last three years. Neither gynecological nor systemic abnormalities were detected in either patient. Karyotype analyses and hormonal tests were normal. Abdominal and gynaecological ultrasound did not show any cystic lesion or other abnormal finding. Computerized tomography scan of the adrenal glands was normal. Clitoroplasty with preservation of neurovascular pedicles was performed for the treatment of clitoromegaly. CONCLUSION: The patients were diagnosed as "idiopathic isolated" clitoromegaly. To the best of our knowledge, there has been no detailed report about idiopathic clitoromegaly in the literature.

On the design and implementation of linear differential microphone arrays. <=SEP=> Differential microphone array (DMA), a particular kind of sensor array that is responsive to the differential sound pressure field, has a broad range of applications in sound recording, noise reduction, signal separation, dereverberation, etc. Traditionally, an Nth-order DMA is formed by combining, in a linear manner, the outputs of a number of DMAs up to (including) the order of N - 1. This method, though simple and easy to implement, suffers from a number of drawbacks and practical limitations. This paper presents an approach to the design of linear DMAs. The proposed technique first transforms the microphone array signals into the short-time Fourier transform (STFT) domain and then converts the DMA beamforming design to simple linear systems to solve. It is shown that this approach is much more flexible as compared to the traditional methods in the design of different directivity patterns. Methods are also presented to deal with the white noise amplification problem that is considered to be the biggest hurdle for DMAs, particularly higher-order implementations.

Data-free parameter pruning for Deep Neural Networks <=SEP=> Deep Neural nets (NNs) with millions of parameters are at the heart of many stateof-the-art computer vision systems today. However, recent works have shown that much smaller models can achieve similar levels of performance. In this work, we address the problem of pruning parameters in a trained NN model. Instead of removing individual weights one at a time as done in previous works, we remove one neuron at a time. We show how similar neurons are redundant, and propose a systematic way to remove them. Our experiments in pruning the densely connected layers show that we can remove upto 85% of the total parameters in an MNIST-trained network, and about 35% for AlexNet without significantly affecting performance. Our method can be applied on top of most networks with a fully connected layer to give a smaller network.

An 82.4% efficiency package-bondwire-based four-phase fully integrated buck converter with flying capacitor for area reduction <=SEP=> Multi-phase converters have become a topic of great interest due to the high output power capacity and output ripple cancellation effect. They are even more beneficial to nowadays high-frequency fully integrated converters with output capacitor integrated on-chip. As one of the dominant chip area consumers, reducing the size of the on-chip decoupling capacitors directly leads to cost down. It is reported that a 5× capacitor area reduction can be achieved with a four-phase converter compared to a single-phase one [1]. However, the penalty is obvious. Every extra phase comes with an inductor, which is also counted as cost and becomes more dominant with increase in the number of phases.

Object Detection in High-Resolution Remote Sensing Images Using Rotation Invariant Parts Based Model <=SEP=> In this letter, we propose a rotation invariant parts-based model to detect objects with complex shape in high-resolution remote sensing images. Specifically, the geospatial objects with complex shape are firstly divided into several main parts, and the structure information among parts is described and regulated in polar coordinates to achieve the rotation invariance on configuration. Meanwhile, the pose variance of each part relative to the object is also defined in our model. In encoding the features of the rotated parts and objects, a new rotation invariant feature is proposed by extending histogram oriented gradients. During the final detection step, a clustering method is introduced to locate the parts in objects, and that method can also be used to fuse the detection results. By this way, an efficient detection model is constructed and the experimental results demonstrate the robustness and precision of our proposed detection model.