Query: Earth Mover's Distance Pooling over Siamese LSTMs for Automatic Short Answer Grading

Relevant passages:
Sinkhorn Distances: Lightspeed Computation of Optimal Transport <=SEP=> Optimal transport distances are a fundamental family of dis tances for probability measures and histograms of features. Despite their appeali ng theoretical properties, excellent performance in retrieval tasks and intuiti ve formulation, their computation involves the resolution of a linear program whose c ost an quickly become prohibitive whenever the size of the support of these me asur s or the histograms’ dimension exceeds a few hundred. We propose in this work a new family of optimal transport distances that look at transport probl ems from a maximumentropy perspective. We smooth the classic optimal transpo rt pr blem with an entropic regularization term, and show that the resulting o ptimum is also a distance which can be computed through Sinkhorn’s matrix scali ng algorithm at a speed that is several orders of magnitude faster than that of transport solvers. We also show that this regularized distance improves upon clas si optimal transport distances on the MNIST classification problem.

Supervised Word Mover's Distance <=SEP=> Recently, a new document metric called the word mover’s distance (WMD) has been proposed with unprecedented results on kNN-based document classification. The WMD elevates high-quality word embeddings to a document metric by formulating the distance between two documents as an optimal transport problem between the embedded words. However, the document distances are entirely unsupervised and lack a mechanism to incorporate supervision when available. In this paper we propose an efficient technique to learn a supervised metric, which we call the Supervised-WMD (S-WMD) metric. The supervised training minimizes the stochastic leave-one-out nearest neighbor classification error on a perdocument level by updating an affine transformation of the underlying word embedding space and a word-imporance weight vector. As the gradient of the original WMD distance would result in an inefficient nested optimization problem, we provide an arbitrarily close approximation that results in a practical and efficient update rule. We evaluate S-WMD on eight real-world text classification tasks on which it consistently outperforms almost all of our 26 competitive baselines.

Siamese Recurrent Architectures for Learning Sentence Similarity <=SEP=> We present a siamese adaptation of the Long Short-Term Memory (LSTM) network for labeled data comprised of pairs of variable-length sequences. Our model is applied to assess semantic similarity between sentences, where we exceed state of the art, outperforming carefully handcrafted features and recently proposed neural network systems of greater complexity. For these applications, we provide wordembedding vectors supplemented with synonymic information to the LSTMs, which use a fixed size vector to encode the underlying meaning expressed in a sentence (irrespective of the particular wording/syntax). By restricting subsequent operations to rely on a simple Manhattan metric, we compel the sentence representations learned by our model to form a highly structured space whose geometry reflects complex semantic relationships. Our results are the latest in a line of findings that showcase LSTMs as powerful language models capable of tasks requiring intricate understanding.

Effective Feature Integration for Automated Short Answer Scoring <=SEP=> A major opportunity for NLP to have a realworld impact is in helping educators score student writing, particularly content-based writing (i.e., the task of automated short answer scoring). A major challenge in this enterprise is that scored responses to a particular question (i.e., labeled data) are valuable for modeling but limited in quantity. Additional information from the scoring guidelines for humans, such as exemplars for each score level and descriptions of key concepts, can also be used. Here, we explore methods for integrating scoring guidelines and labeled responses, and we find that stacked generalization (Wolpert, 1992) improves performance, especially for small training sets.

A tutorial on support vector regression <=SEP=> In this tutorial we give an overview of the basic ideas underlying Support Vector (SV) machines for function estimation. Furthermore, we include a summary of currently used algorithms for training SV machines, covering both the quadratic (or convex) programming part and advanced methods for dealing with large datasets. Finally, we mention somemodifications and extensions that have been applied to the standard SV algorithm, and discuss the aspect of regularization from a SV perspective.

Automatic Segmentation of Hair in Images <=SEP=> Based on a multi-step process, an automatic hair segmentation method is created and tested on a database of 115 manually segmented hair images. By extracting various information components from an image, including background color, face position, hair color, skin color, and skin mask, a heuristic-based method is created for the detection and segmentation of hair that can detect hair with an accuracy of approximately 75% and with a false hair overestimation error of 34%. Furthermore, it is shown that downsampling the image down to a face width of 25px results in a 73% reduction in computation time with insignificant change in detection accuracy.

Constrained Deep Metric Learning for Person Re-identification <=SEP=> Person re-identification aims to re-identify the probe image from a given set of images under different camera views. It is challenging due to large variations of pose, illumination, occlusion and camera view. Since the convolutional neural networks (CNN) have excellent capability of feature extraction, certain deep learning methods have been recently applied in person re-identification. However, in person re-identification, the deep networks often suffer from the over-fitting problem. In this paper, we propose a novel CNN-based method to learn a discriminative metric with good robustness to the over-fitting problem in person re-identification. Firstly, a novel deep architecture is built where the Mahalanobis metric is learned with a weight constraint. This weight constraint is used to regularize the learning, so that the learned metric has a better generalization ability. Secondly, we find that the selection of intraclass sample pairs is crucial for learning but has received little attention. To cope with the large intra-class variations in pedestrian images, we propose a novel training strategy named moderate positive mining to prevent the training process from over-fitting to the extreme samples in intraclass pairs. Experiments show that our approach significantly outperforms state-of-the-art methods on several benchmarks of person re-identification.

Deep Convolutional Neural Networks on Multichannel Time Series for Human Activity Recognition <=SEP=> This paper focuses on human activity recognition (HAR) problem, in which inputs are multichannel time series signals acquired from a set of bodyworn inertial sensors and outputs are predefined human activities. In this problem, extracting effective features for identifying activities is a critical but challenging task. Most existing work relies on heuristic hand-crafted feature design and shallow feature learning architectures, which cannot find those distinguishing features to accurately classify different activities. In this paper, we propose a systematic feature learning method for HAR problem. This method adopts a deep convolutional neural networks (CNN) to automate feature learning from the raw inputs in a systematic way. Through the deep architecture, the learned features are deemed as the higher level abstract representation of low level raw time series signals. By leveraging the labelled information via supervised learning, the learned features are endowed with more discriminative power. Unified in one model, feature learning and classification are mutually enhanced. All these unique advantages of the CNN make it outperform other HAR algorithms, as verified in the experiments on the Opportunity Activity Recognition Challenge and other benchmark datasets.

Is it really about me?: message content in social awareness streams <=SEP=> In this work we examine the characteristics of social activity and patterns of communication on Twitter, a prominent example of the emerging class of communication systems we call "social awareness streams." We use system data and message content from over 350 Twitter users, applying human coding and quantitative analysis to provide a deeper understanding of the activity of individuals on the Twitter network. In particular, we develop a content-based categorization of the type of messages posted by Twitter users, based on which we examine users' activity. Our analysis shows two common types of user behavior in terms of the content of the posted messages, and exposes differences between users in respect to these activities.

A Unified Model for Cross-Domain and Semi-Supervised Named Entity Recognition in Chinese Social Media <=SEP=> Named entity recognition (NER) in Chinese social media is important but difficult because of its informality and strong noise. Previous methods only focus on in-domain supervised learning which is limited by the rare annotated data. However, there are enough corpora in formal domains and massive in-domain unannotated texts which can be used to improve the task. We propose a unified model which can learn from out-of-domain corpora and in-domain unannotated texts. The unified model contains two major functions. One is for cross-domain learning and another for semi-supervised learning. Cross-domain learning function can learn out-of-domain information based on domain similarity. Semi-Supervised learning function can learn in-domain unannotated information by self-training. Both learning functions outperform existing methods for NER in Chinese social media. Finally, our unified model yields nearly 11% absolute improvement over previously published results.

A clutched parallel elastic actuator concept: Towards energy efficient powered legs in prosthetics and robotics <=SEP=> Parallel passive-elastic elements can reduce the energy consumption and torque requirements for motors in powered legged systems. However, the hardware design for such combined actuators is challenged by the need to engage and disengage the parallel elasticity depending on the gait phase. Although clutches in the drive train are often proposed, compact and low cost solutions of clutched parallel elastic actuators have so far not been established. Here we present the design and control of an initial prototype for a parallel elastic actuator. The actuator combines a DC motor with a parallel spring that is engaged and disengaged by a commercially available, compact and low-cost electric clutch. In experiments that mimic the torque and motion patterns of knee extensor muscles in human rebounding tasks we find that the parallel spring in the prototype reduces the energy consumption of the actuator by about 80% and the peak torque requirement for the DC motor by about 66%. In addition, we find that a simple trigger-based control can reliably engage and disengage the electric clutch during the motion, allowing the spring to support the motor in rebound, to remove stored energy from the system as necessary for stopping, and to virtually disappear at the actuator output level. On the other hand, the hardware experiments also reveal that our initial design limits the precision in the torque control, and we propose specific improvements to overcome these limitations.

Socializing by Gaming: Revealing Social Relationships in Multiplayer Online Games <=SEP=> Multiplayer Online Games (MOGs) like Defense of the Ancients and StarCraft II have attracted hundreds of millions of users who communicate, interact, and socialize with each other through gaming. In MOGs, rich social relationships emerge and can be used to improve gaming services such as match recommendation and game population retention, which are important for the user experience and the commercial value of the companies who run these MOGs. In this work, we focus on understanding social relationships in MOGs. We propose a graph model that is able to capture social relationships of a variety of types and strengths. We apply our model to real-world data collected from three MOGs that contain in total over ten years of behavioral history for millions of players and matches. We compare social relationships in MOGs across different game genres and with regular online social networks like Facebook. Taking match recommendation as an example application of our model, we propose SAMRA, a Socially Aware Match Recommendation Algorithm that takes social relationships into account. We show that our model not only improves the precision of traditional link prediction approaches, but also potentially helps players enjoy games to a higher extent.

Document Distance Estimation via Code Graph Embedding <=SEP=> Accurately representing the distance between two documents (i.e. pieces of textual information extracted from various software artifacts) has far-reaching applications in many automated software engineering approaches, such as concept location, bug location and traceability link recovery. This is a challenging task, since documents containing different words may have similar semantic meanings. In this paper, we propose a novel document distance estimation approach. This approach captures latent semantic associations between documents through analyzing structural information in software source code: first, we embed code elements as points in a shared representation space according to structural dependencies between them; then, we represent documents as weighted point clouds of code elements in the representation space and reduce the distance between two documents to an earth mover's distance transportation problem. We define a document classification task in StackOverflow dataset to evaluate the effectiveness of our approach. The empirical evaluation results show that our approach outperforms several state-of-the-art approaches.

Traffic road line detection based on the vanishing point and contour information <=SEP=> This paper describes an algorithm to detect the road lane boundary for a driver who changes lane as ‘access road’, ‘passing lane’, ‘approach ramp’, etc. The proposed algorithm starts with binarization and labeling for the image. The binary image is generated by adaptive threshold. The binary image is labeled recursively to blobs. From each blob, the algorithm analyses principal axes calculated by Eigen value and vector. Those are based on the covariance matrix of pixel distribution for the blobs. Then it shows intersection points by the combination of each long principal axis. Among intersection points, the highest density cluster is selected to a dominant vanishing point as the parallel road lines. In experiments of road line detection from the urban road image, the proposed method shows the effectiveness of lane boundary detection in the rate of more than 94.3%.

Disentangled Representation Learning for Text Style Transfer <=SEP=> This paper tackles the problem of disentangling the latent variables of style and content in language models. We propose a simple, yet effective approach, which incorporates auxiliary objectives: a multi-task classification objective, and dual adversarial objectives for label prediction and bag-ofwords prediction, respectively. We show, both qualitatively and quantitatively, that the style and content are indeed disentangled in the latent space, using this approach. This disentangled latent representation learning method is applied to attribute (e.g. style) transfer on non-parallel corpora. We achieve similar content preservation scores compared to previous state-of-the-art approaches, and significantly better style-transfer strength scores. Our code is made publicly available for replicability and extension purposes .

Combining and benchmarking methods of foetal ECG extraction without maternal or scalp electrode data. <=SEP=> Despite significant advances in adult clinical electrocardiography (ECG) signal processing techniques and the power of digital processors, the analysis of non-invasive foetal ECG (NI-FECG) is still in its infancy. The Physionet/Computing in Cardiology Challenge 2013 addresses some of these limitations by making a set of FECG data publicly available to the scientific community for evaluation of signal processing techniques.The abdominal ECG signals were first preprocessed with a band-pass filter in order to remove higher frequencies and baseline wander. A notch filter to remove power interferences at 50 Hz or 60 Hz was applied if required. The signals were then normalized before applying various source separation techniques to cancel the maternal ECG. These techniques included: template subtraction, principal/independent component analysis, extended Kalman filter and a combination of a subset of these methods (FUSE method). Foetal QRS detection was performed on all residuals using a Pan and Tompkins QRS detector and the residual channel with the smoothest foetal heart rate time series was selected.The FUSE algorithm performed better than all the individual methods on the training data set. On the validation and test sets, the best Challenge scores obtained were E1 = 179.44, E2 = 20.79, E3 = 153.07, E4 = 29.62 and E5 = 4.67 for events 1-5 respectively using the FUSE method. These were the best Challenge scores for E1 and E2 and third and second best Challenge scores for E3, E4 and E5 out of the 53 international teams that entered the Challenge. The results demonstrated that existing standard approaches for foetal heart rate estimation can be improved by fusing estimators together. We provide open source code to enable benchmarking for each of the standard approaches described.

Absolute Orientation for Word Embedding Alignment <=SEP=> We develop a family of techniques to align word embeddings which are derived from different source datasets or created using different mechanisms (e.g., GloVe or word2vec). Our methods are simple and have a closed form to optimally rotate, translate, and scale to minimize root mean squared errors or maximize the average cosine similarity between two embeddings of the same vocabulary into the same dimensional space. Our methods extend approaches known as Absolute Orientation, which are popular for aligning objects in three-dimensions, and generalize an approach by Smith etal (ICLR 2017). We prove new results for optimal scaling and for maximizing cosine similarity. Then we demonstrate how to evaluate the similarity of embeddings from different sources or mechanisms, and that certain properties like synonyms and analogies are preserved across the embeddings and can be enhanced by simply aligning and averaging ensembles of embeddings.

Trust, But Verify: On the Importance of Chemical Structure Curation in Cheminformatics and QSAR Modeling Research <=SEP=> With the recent advent of high-throughput technologies for both compound synthesis and biological screening, there is no shortage of publicly or commercially available data sets and databases that can be used for computational drug discovery applications (reviewed recently in Williams et al.). Rapid growth of large, publicly available databases (such as PubChem or ChemSpider containing more than 20 million molecular records each) enabled by experimental projects such as NIH’s Molecular Libraries and Imaging Initiative provides new opportunities for the development of cheminformatics methodologies and their application to knowledge discovery in molecular databases. A fundamental assumption of any cheminformatics study is the correctness of the input data generated by experimental scientists and available in various data sets. Nevertheless, a recent study showed that on average there are two errors per each medicinal chemistry publication with an overall error rate for compounds indexed in the WOMBAT database as high as 8%. In another recent study, the authors investigated several public and commercial databases to calculate their error rates: the latter were ranging from 0.1 to 3.4% depending on the database. How significant is the problem of accurate structure representation (given that the error rates in current databases may appear relatively low) since it concerns exploratory cheminformatics and molecular modeling research? Recent investigations by a large group of collaborators from six laboratories have clearly demonstrated that the type of chemical descriptors has much greater influence on the prediction performance of QSAR models than the nature of model optimization techniques. These findings suggest that having erroneous structures represented by erroneous descriptors should have a detrimental effect on model performance. Indeed, a recent seminal publication clearly pointed out the importance of chemical data curation in the context of QSAR modeling. The authors have discussed the error rates in several known databases and evaluated the consequences of both random and systematic errors with respect to the prediction performances of the derivative QSAR models. They also presented several illustrative examples of incorrect structures generated from either correct or incorrect SMILES. The main conclusions of the study were that small structural errors within a data set could lead to significant losses of predictive ability of QSAR models. The authors further demonstrated that manual curation of structural data leads to substantial increase in the model predictivity. This conclusion becomes especially important in light of the aforementioned study of Oprea et al. that cited a significant error rate in medicinal chemistry literature. Alarmed by these conclusions, we have examined several popular public databases of bioactive molecules to assess possible error rates in structure representation. For instance, the NCI AIDS Antiviral Screen (the paper describing this screen has been cited 57 times in PubMed Central only) comprises 42687 chemical records with their associated activity. Even quick analysis of this data sets revealed that 4202 (i.e., ca. 10%) compounds should be either treated with care or removed before any cheminformatics investigation: 3350 compounds were mixtures and salts, and we detected 741 pairs of exact duplicates and stereoisomers, possessing different or opposite reported activities. Similar observations can be made for several well-known public databases such as NCI Human Tumor Cell Line and PubChem, as well as for smaller data sets studied and reported in published literature. For instance, in a study already mentioned above, six research teams each specializing in QSAR modeling (including the authors of this paper!) collaborated on the analysis of the Tetrahymena pyriformis aquatic toxicity data set comprising 1093 compounds. Later this exact data set was used by the organizers of CADASTER toxicity challenge. However, our re-examination of this data set showed the presence of six pairs of duplicates among 1093 compounds because of the presence of different metal cations in salts (with different aquatic toxicities measured by pIGC50 ranging from ∼0.1 to 1 logarithmic unit. Furthermore, in the new external set compiled by the organizers of the CADASTER challenge to evaluate the comparative performance of competing models, eight out of 120 compounds were found to be structurally identical to modeling set compounds but with different toxicity values (∼0.3 pIGC50). Such disappointing observations may, at least in part, explain why QSAR models may sometimes fail, which is * To whom correspondence should be addressed. E-mail: alex_tropsha@ unc.edu. † University of North Carolina at Chapel Hill. ‡ A.V. Bogatsky Physical-Chemical Institute NAS of Ukraine. J. Chem. Inf. Model. XXXX, xxx, 000 A

Cyberbullying and Primary-School Aged Children : The Psychological Literature and the Challenge for Sociology <=SEP=> Cyberbullying is an international issue for schools, young people and their families. Whilst many research domains have explored this phenomenon, and bullying more generally, the majority of reported studies appear in the psychological and educational literatures, where bullying, and more recently, cyberbullying has been examined primarily at the individual level: amongst adolescents and young people, with a focus on the definition, its prevalence, behaviours, and impact. There also is growing evidence that younger children are increasingly accessing technology and engaging with social media, yet there is limited research dedicated to this younger age group. The purpose of this paper is to report on a systematic literature review from the psychological and educational research domains related to this younger age group, to inform future research across the disciplines. Younger children require different methods of engagement. This review highlights the methodological challenges associated with this age group present in the psychological literature, and argues for a greater use of sociological, child-centred approaches to data collection. This review examined studies published in English, between 2009 and 2014, and conducted with children aged 5–12 years, about their experiences with cyberbullying. Searches were conducted on seven key databases using keywords associated with cyberbullying and age of children. A Google Scholar search also examined published and unpublished reports. A total of 966 articles and reports were retrieved. A random peer review process was employed to establish inter-rater reliability and veracity of the review. Findings revealed 38 studies reported specifically on children aged 5–12 years. The dominant focus of these articles was on prevalence of cyberbullying, established through survey methodology. Few studies noted OPEN ACCESS Societies 2015, 5 493 impacts, understanding and behaviours or engaged children’s independent voice. This review highlights current gaps in our knowledge about younger children’s experiences with this form of bullying, and the importance of employing cross-disciplinary and developmentally appropriate methodologies to inform future research.

Smooth contact-aware facial blendshapes transfer <=SEP=> In this paper, we propose a method to improve the quality of the facial blendshapes synthesized using deformation transfer by introducing the idea of contact and smoothness awareness. Simply applying deformation transfer for face models easily results in penetrations and separations which do not exist in the original data. As there is no concept of smoothness, crumpling artifacts appear where the mesh is extremely concentrated, e.g. eyelids and mouth corners. We solve these issues by first introducing the concept of virtual triangles and then adding a Laplacian energy term into the linear problem to be solved for the deformation transfer. Experimental results show that our system reduces the amount of artifacts caused by the original deformation transfer, improving the efficiency of the animation pipeline.

Educational Software Applied in Teaching Electrocardiogram: A Systematic Review <=SEP=> Background
The electrocardiogram (ECG) is the most used diagnostic tool in medicine; in this sense, it is essential that medical undergraduates learn how to interpret it correctly while they are still on training. Naturally, they go through classic learning (e.g., lectures and speeches). However, they are not often efficiently trained in analyzing ECG results. In this regard, methodologies such as other educational support tools in medical practice, such as educational software, should be considered a valuable approach for medical training purposes.


Methods
We performed a literature review in six electronic databases, considering studies published before April 2017. The resulting set comprises 2,467 studies. From this collection, 12 studies have been selected, initially, whereby we carried out a snowballing process to identify other relevant studies through the reference lists of these studies, resulting in five relevant studies, making up a total of 17 articles that passed all stages and criteria.


Results
The results show that 52.9% of software types were tutorial and 58.8% were designed to be run locally on a computer. The subjects were discussed together with a greater focus on the teaching of electrophysiology and/or cardiac physiology, identifying patterns of ECG and/or arrhythmias.


Conclusions
We found positive results with the introduction of educational software for ECG teaching. However, there is a clear need for using higher quality research methodologies and the inclusion of appropriate controls, in order to obtain more precise conclusions about how beneficial the inclusion of such tools can be for the practices of ECG interpretation.

Self-disclosure on Facebook among female users and its relationship to feelings of loneliness <=SEP=> URLs: Self-disclosure on Facebook among female users and its relationship to feelings of loneliness Author(s): Al-Saggaf, Y.M. ; Nielsen, S.G. Computers in Human Behavior

Optimal dimensioning of active cell balancing architectures <=SEP=> This paper presents an approach to optimal dimensioning of active cell balancing architectures, which are of increasing relevance in Electrical Energy Storages (EESs) for Electric Vehicles (EVs) or stationary applications such as smart grids. Active cell balancing equalizes the state of charge of cells within a battery pack via charge transfers, increasing the effective capacity and lifetime. While optimization approaches have been introduced into the design process of several aspects of EESs, active cell balancing architectures have, until now, not been systematically optimized in terms of their components. Therefore, this paper analyzes existing architectures to develop design metrics for energy dissipation, installation volume, and balancing current. Based on these design metrics, a methodology to efficiently obtain Pareto-optimal configurations for a wide range of inductors and transistors at different balancing currents is developed. Our methodology is then applied to a case study, optimizing two state-of-the-art architectures using realistic balancing algorithms. The results give evidence of the applicability of systematic optimization in the domain of cell balancing, leading to higher energy efficiencies with minimized installation space.

Algorithms for Unevenly Spaced Time Series : Moving Averages and Other Rolling Operators <=SEP=> This paper describes algorithms for efficiently calculating certain rolling time series operators for unevenly spaced data. In particular, we show how to calculate simple moving averages (SMAs), exponential moving averages (EMAs), and related operators in linear time with respect to the number of observations in a time series. A web appendix provides an implementation of these algorithms in the programming language C and a package (forthcoming) for the statistical software R.

Effects of strength training with blood flow restriction on torque, muscle activation and local muscular endurance in healthy subjects <=SEP=> The present study aimed to analyse the effects of six weeks of strength training (ST), with and without blood flow restriction (BFR), on torque, muscle activation, and local muscular endurance (LME) of the knee extensors. Thirty-seven healthy young individuals were divided into four groups: high intensity (HI), low intensity with BFR (LI+BFR), high intensity and low intensity + BFR (COMB), and low intensity (LI). Torque, muscle activation and LME were evaluated before the test and at the 2nd, 4th and 6th weeks after exercise. All groups had increased torque, muscle activation and LME (p<0.05) after the intervention, but the effect size and magnitude were greater in the HI, LI+BFR and COMB groups. In conclusion, the groups with BFR (LI+BFR and COMB) produced magnitudes of muscle activation, torque and LME similar to those of the HI group.

Discovery-driven graph summarization <=SEP=> Large graph datasets are ubiquitous in many domains, including social networking and biology. Graph summarization techniques are crucial in such domains as they can assist in uncovering useful insights about the patterns hidden in the underlying data. One important type of graph summarization is to produce small and informative summaries based on user-selected node attributes and relationships, and allowing users to interactively drill-down or roll-up to navigate through summaries with different resolutions. However, two key components are missing from the previous work in this area that limit the use of this method in practice. First, the previous work only deals with categorical node attributes. Consequently, users have to manually bucketize numerical attributes based on domain knowledge, which is not always possible. Moreover, users often have to manually iterate through many resolutions of summaries to identify the most interesting ones. This paper addresses both these key issues to make the interactive graph summarization approach more useful in practice. We first present a method to automatically categorize numerical attributes values by exploiting the domain knowledge hidden inside the node attributes values and graph link structures. Furthermore, we propose an interestingness measure for graph summaries to point users to the potentially most insightful summaries. Using two real datasets, we demonstrate the effectiveness and efficiency of our techniques.

Wound microbiology and associated approaches to wound management. <=SEP=> The majority of dermal wounds are colonized with aerobic and anaerobic microorganisms that originate predominantly from mucosal surfaces such as those of the oral cavity and gut. The role and significance of microorganisms in wound healing has been debated for many years. While some experts consider the microbial density to be critical in predicting wound healing and infection, others consider the types of microorganisms to be of greater importance. However, these and other factors such as microbial synergy, the host immune response, and the quality of tissue must be considered collectively in assessing the probability of infection. Debate also exists regarding the value of wound sampling, the types of wounds that should be sampled, and the sampling technique required to generate the most meaningful data. In the laboratory, consideration must be given to the relevance of culturing polymicrobial specimens, the value in identifying one or more microorganisms, and the microorganisms that should be assayed for antibiotic susceptibility. Although appropriate systemic antibiotics are essential for the treatment of deteriorating, clinically infected wounds, debate exists regarding the relevance and use of antibiotics (systemic or topical) and antiseptics (topical) in the treatment of nonhealing wounds that have no clinical signs of infection. In providing a detailed analysis of wound microbiology, together with current opinion and controversies regarding wound assessment and treatment, this review has attempted to capture and address microbiological aspects that are critical to the successful management of microorganisms in wounds.

The Psychometric Properties of the Internet Addiction Test <=SEP=> There is growing concern about excessive Internet use and whether this can amount to an addiction. In researching this topic, a valid and reliable assessment instrument is essential. In her survey of Internet addiction, Young designed the Internet Addiction Test (IAT), which provides a basis for developments. The IAT has high face validity, but it has not been subjected to systematic psychometric testing. This study sought to replicate and expand Young's survey, and to examine the IAT more systematically. A questionnaire that existed as a Web page was devised, consisting of the IAT and 15 other questions regarding the respondents' demographic information and Internet usage. Participants were recruited through the Internet, yielding 86 valid responses (29 males and 57 females). Factor analysis of the IAT revealed six factors--salience, excessive use, neglecting work, anticipation, lack of control, and neglecting social life. These factors showed good internal consistency and concurrent validity, with salience being the most reliable. Younger and more recent users reported more problems, mainly concerning the neglect of work and social life. We expected interactive Internet functions to be more addictive; however, this was not found to be so. Overall, the IAT is a valid and reliable instrument that may be used in further research on Internet addiction.

Futures of elderly care in Iran: A protocol with scenario approach <=SEP=> Background: The number of people aged 60 and older is increasing faster than other age groups worldwide. Iran will experience a sharp aging population increase in the next decades, and this will pose new challenges to the healthcare system. Since providing high quality aged-care services would be the major concern of the policymakers, this question arises that what types of aged care services should be organized in the coming 10 years? This protocol has been designed to develop a set of scenarios for the future of elderly care in Iran. Methods: In this study, intuitive logics approach and Global Business Network (GBN) model were used to develop scenarios for elderly care in Iran. In terms of perspective, the scenarios in this approach are normative, qualitative with respect to methodology and deductive in constructing the process of scenarios. The three phases of GBN model are as follows: 1) Orientation: Identifying strategic levels, stakeholders, participants and time horizon; 2) Exploration: Identifying the driving forces and key uncertainties; 3) Synthesis: Defining the scenario logics and constructing scenario storyline. Results: Presently, two phases are completed and the results will be published in mid-2016. Conclusion: This study delivers a comprehensive framework for taking appropriate actions in providing care for the elderly in the future. Moreover, policy makers should specify and provide the full range of services for the elderly, and in doing so, the scenarios and key findings of this study could be of valuable help.

Adolescent self-compassion : Associations with narcissism , self-esteem , aggression , and internalizing symptoms in at-risk males <=SEP=> Self-compassion is an attitude toward oneself that involves perceiving one’s experiences as an opportunity for self-awareness and improvement, as well as limited self-judgment after failure. Self-compassion has not been extensively studied in adolescence, a time when self-perception and self-appraisals regarding success and failure take on notable importance. This study considered the connection between self-compassion, narcissism, self-esteem, aggression, and internalizing problems in a sample of 251 male adolescents, ages 16–18, attending a residential program. Self-compassion was negatively correlated with aggression and vulnerable narcissism and positively correlated with self-esteem. In general, selfcompassion did not exhibit the hypothesized protective effect on the relation between narcissism and aggression. Findings indicate that, as expected, self-compassion is indicative of a relatively secure, positive sense of self in adolescents. 2014 Elsevier Ltd. All rights reserved.