LLukas22/scidocs huggingface dataset:
Causal Tempo Tracking of Audio

We introduce a causal approach to tempo tracking for musical audio signals. Our system is designed towards an eventual real-time implementation; requiring minimal highlevel knowledge of the musical audio. The tempo tracking system is divided into two sections: an onset analysis stage, used to derive a rhythmically meaningful representation from the input audio, followed by a beat matching algorithm using autoand cross-correlative methods to generate short term predictions of future beats in the audio. The algorithm is evaluated over a range of musical styles by comparing the predicted output to beats tapped by a musician. An investigation is also presented into three rhythmically complex beat tracking problems, where the tempo is not constant. Preliminary results demonstrate good accuracy for this type of system.
Social Exclusion of Children: Developmental Origins of Prejudice

Over the past decade, developmental and social psychological research has explicitly adopted a developmental intergroup framework, integrating social and developmental psychology fields to understand the origins of social exclusion and prejudice. This paper argues that a social developmental analysis of how groups and individuals experience, evaluate, and understand exclusion is essential for a complete picture of the human experience, interpretation, and consequences of exclusion. What has been missing in much of the social psychological research on exclusion is an incorporation of developmental perspectives; likewise, what has been missing in development psychological research is a focus on group identity and group dynamics for understanding the basis for exclusionary behaviour in childhood. Yet, the roots of adult forms of exclusion can be documented in childhood, and children who experience exclusion are particularly at risk for negative outcomes, and particularly when exclusion is based on group membership. Moreover, interventions designed to ameliorate social problems associated with exclusion need to be based on an understanding of how, why, and under what conditions, children and groups make decisions to exclude others, how they experience this exclusion, and how exclusion originates and changes over the course of the lifespan. Thus, a growing body of psychological work, exemplified in this issue of the Journal of Social Issues (2014), highlights implications for theory in psychology and related social sciences, and for interventions and policies to tackle social exclusion.
E-learning Indicators Approach to Developing E-learning Software Solutions

The research is proposing a new way of tackling the process of creation of e-learning solutions as interactive environments by integrating and undertaking the software engineering approach based on e-learning indicators. The study proposes and recommends as starting point in any e-learning initiative assessment, measurement and evaluation of concepts and factors that influence e-learning further represented as e-learning indicators. The main research was focused towards software engineering 3 (three) different e-learning interactive environments based on e-learning indicators approach taking into consideration theories of learning and pedagogical approach in their development. The main focus was set on assessing and evaluating the e-learning indicators and based on them designing and building the e-learning software solutions. In order to further analyze issues and propose solutions several e-learning software solutions are designed and build and further analyzed as case study experiments. Also a methodology for evaluation of the e-learning effectiveness is also proposed and assessed. This research makes several contributions: First it proposes a new development methodology called e-learning indicators methodology approach to creation of e-learning software solutions. Secondly it proposes an assessment and evaluation novel methodology called ELUAT (E-learning Usability Attributes Testing) and as measuring instrument the PET (predefined evaluation tasks) inspection technique and gives insights and evaluation of their usage. Thirdly it investigates different learning approaches: 1) the content-oriented, 2) the tool-oriented, and the 3) task-oriented approach and analyses of these models in supporting: project, problem and task based learning.
Social media analytics for competitive advantage

Big Data Analytics is getting a great deal of attention in the business and government communities. If it lives up to its name, visual analytics will be a prime path by which visualization competes successfully in this arena. This paper discusses some fundamental work we have done in this area through integration of interactive visualization and automated analysis methods and the applications that have resulted.
Intravitreal ranibizumab (Lucentis) for branch retinal vein occlusion-induced macular edema: nine-month results of a prospective study.

PURPOSE
The purpose of this study was to evaluate the effect of individualized repeated intravitreal injections of ranibizumab (Lucentis, Genentech, South San Francisco, CA) on visual acuity and central foveal thickness (CFT) for branch retinal vein occlusion-induced macular edema.


METHODS
This study was a prospective interventional case series. Twenty-eight eyes of 28 consecutive patients diagnosed with branch retinal vein occlusion-related macular edema treated with repeated intravitreal injections of ranibizumab (when CFT was >225 microm) were evaluated. Optical coherence tomography and fluorescein angiography were performed monthly.


RESULTS
The mean best-corrected distance visual acuity improved from 62.67 Early Treatment of Diabetic Retinopathy Study letters (logarithm of the minimum angle of resolution = 0.74 +/- 0.28 [mean +/- standard deviation]) at baseline to 76.8 Early Treatment of Diabetic Retinopathy Study letters (logarithm of the minimum angle of resolution = 0.49 +/- 0.3; statistically significant, P < 0.001) at the end of the follow-up (9 months). The mean letter gain (including the patients with stable and worse visual acuities) was 14.3 letters (2.9 lines). During the same period, 22 of the 28 eyes (78.6%) showed improved visual acuity, 4 (14.2%) had stable visual acuity, and 2 (7.14%) had worse visual acuity compared with baseline. The mean CFT improved from 349 +/- 112 microm at baseline to 229 +/- 44 microm (significant, P < 0.001) at the end of follow-up. A mean of six injections was performed during the follow-up period. Our subgroup analysis indicated that patients with worse visual acuity at presentation (<or=50 letters in our series) showed greater visual benefit from treatment. "Rebound" macular edema was observed in 5 patients (17.85%) at the 3-month follow-up visit and in none at the 6- and 9-month follow-ups. In 18 of the 28 patients (53.6%), the CFT was <225 microm at the last follow-up visit, and therefore, further treatment was not instituted. No ocular or systemic side effects were noted.


CONCLUSION
Individualized repeated intravitreal injections of ranibizumab showed promising short-term results in visual acuity improvement and decrease in CFT in patients with macular edema associated with branch retinal vein occlusion. Further studies are needed to prove the long-term effect of ranibizumab treatment on patients with branch retinal vein occlusion.
The effect of dynamic mathematics software geogebra on student achievement in teaching of trigonometry

The purpose of this study is to determine the effects of dynamic mathematics software GeoGebra on student achievement in teaching of trigonometry. The sample of the study consists of 51 students. The experimental group was subjected to the lessons arranged with the GeoGebra software in computer assisted teaching method, while the control group was subjected to the lessons shaped with constructivist instruction. The data collected after 5 weeks of application show that there is a meaningful difference between experimental and control groups’ achievement in trigonometry. This difference is in favor of the experimental group which had lessons with GeoGebra. © 2011 Published by Elsevier Ltd.
Design and development of a context-aware decision support system for real-time accident handling in logistics

Available online 23 November 2011
A cluster-based energy efficient MAC protocol for multi-hop cognitive radio sensor networks

In this paper,we concentrate on the design, implementation and performance evaluation of the medium access control (MAC) for the multi-hop sensor networks. Being operated in unlicensed bands, the battery-operated sensor nodes need to sense available spectrum and consume precious energy. Taking account of the cognitive radio as well as the traditional characters of the wireless sensor networks, we design a MAC protocol for the cognitive radio sensor network (CRSN) that supplies schemes for solution. We propose a cluster-based protocol (KoN-MAC) for the battery-operated sensor nodes in multi-hop networks, which allows sensor nodes to dynamically select an interference free channel for data communication. We evaluate the performance of the proposed protocol by NS-2 and the simulation results show that our protocol can achieve better energy gains as well as higher throughput, less delay and lower packet loss radio.
Adrenocortical carcinoma: clinical outcomes and prognosis of 330 patients at a tertiary care center.

OBJECTIVE
Adrenocortical carcinoma (ACC) is a rare malignancy with a poor prognosis. Herein, we describe the clinical features and outcomes for a large series of ACC patients.


DESIGN AND METHODS
Retrospective review of ACC patients seen at The University of Texas MD Anderson Cancer Center from 1998 through 2011.


RESULTS
A total of 330 patients with median age at diagnosis of 48.5 years; 12 (3.6%) patients were under 18 years. Hormonally functioning tumors represented 41.8% (n=138) of all cases. Surgical resection for the primary tumor was done in 275 (83.3%) patients (45 at MD Anderson (16.4%)). For those who had surgical resection, the median local-recurrence-free time was 1.04 years. Factors associated with local recurrence included positive surgical margins (P=0.007) and advanced disease stage (P=0.026). Median overall survival time for all patients was 3.21 years. Median survival times were 24.1, 6.08, 3.47, and 0.89 years for stages I, II, III, and IV respectively. In multivariable analysis, older age, functioning tumors, and higher disease stage remained significant prognostic factors associated with poor survival.


CONCLUSION
ACC prognosis remains poor with the use of currently available treatments. Older age, functioning tumors, and incomplete resections are clinical factors associated with poor survival. Surgical expertise is important to achieve complete resections and to improve outcome.
Adaptive neuro-fuzzy inference system for diagnosis of the heart valve diseases using wavelet transform with entropy

Listening via stethoscope is a preferential method, being used by physicians for distinguishing normal and abnormal cardiac systems. On the other hand, listening with stethoscope has a number of constraints. The interpretation of various heart sounds depends on physician’s ability of hearing, experience, and skill. Such limitations may be reduced by developing biomedical-based decision support systems. In this study, a biomedical-based decision support system was developed for the classification of heart sound signals, obtained from 120 subjects with normal, pulmonary, and mitral stenosis heart valve diseases via stethoscope. Developed system comprises of three stages. In the first stage, for feature extraction, obtained heart sound signals were separated to its sub-bands using discrete wavelet transform (DWT). In the second stage, entropy of each sub-band was calculated using Shannon entropy algorithm to reduce the dimensionality of the feature vectors via DWT. In the third stage, the reduced features of three types of heart sound signals were used as input patterns of the adaptive neuro-fuzzy inference system (ANFIS) classifiers. Developed method reached 98.33% classification accuracy, and it was showed that purposed method is effective for detection of heart valve diseases.
Effect of Prayer and “ OM ” Meditation in EnhancingGalvanic Skin Response

Abstract The research was conducted with the purpose to study the effect of prayer and meditation on galvanic skin response (GSR). It was hypothesized that there was a significant positive effect of prayer and meditation (Om chanting) on galvanic skin response (GSR). The sample consisted of 20 normal, healthy female participants through purposive sampling. The age group of the sample was 18 to 24 years (Mean= 18.7, SD= 1.55). Gender was female and minimum education was graduation. The daily practice time of prayer and meditation session was 30 minutes for one month. PrePost data were recorded before and after intervention of prayer and meditation session by using single group pre-post research design. Recordings of galvanic skin response (GSR) were made on a computerized polygraph (Model Physiopac, PP 4, Medicaid Systems, Chandigarh, India) test. The results revealed a significant increase in GSR values as an effect of prayer and meditation which suggested the psychophysiological relaxation. Practicing prayer and meditation increases the galvanic skin response and hence decreases the stress level of the individual.
Anytime incremental planning with E-Graphs

Robots operating in real world environments need to find motion plans quickly. Robot motion should also be efficient and, when operating among people, predictable. Minimizing a cost function, e.g. path length, can produce short, reasonable paths. Anytime planners are ideal for this since they find an initial solution quickly and then improve solution quality as time permits. In previous work, we introduced the concept of Experience Graphs, which allow search-based planners to find paths with bounded sub-optimality quickly by reusing parts of previous paths where relevant. Here we extend planning with Experience Graphs to work in an anytime fashion so a first solution is found quickly using prior experience. As time allows, the dependence on this experience is reduced in order to produce closer to optimal solutions. We also demonstrate how Experience Graphs provide a new way of approaching incremental planning as they naturally reuse information when the environment, the starting configuration of the robot or the goal configuration change. Experimentally, we demonstrate the anytime and incremental properties of our algorithm on mobile manipulation tasks in both simulation and on a real PR2 robot.
Ethical Attitudes of Accounting Practitioners: Are Rank and Ethical Attitudes Related?

We address a previous finding in the business ethics literature in which accounting professionals in higher rank levels, i.e., ‘‘manager’’ or ‘‘partner’’ of auditing firms, appear to have lower moral reasoning ability than their junior counterparts. Prior investigations have relied upon a similar methodology for estimating ethical beliefs, namely testing ‘‘moral reasoning ability’’ using either the Moral Judgment Interview or Defining Issues Test. In the present study, we use a multiple vignettes approach to test for the existence of the inverse rank-ethical beliefs effect. With only 2 of the 30 vignettes resulting in both managers and partners being more accepting of the ethically charged behaviors, the results presented here using this alternative methodology are generally not supportive of the inverse rank-ethical beliefs phenomenon. We also use a multivariate analysis in order to control for demographic characteristics. Our results suggest that the most robust predictor of ethical attitudes among accounting practitioners is age, not rank within a firm.
Protein Thermal Stability Enhancement by Designing Salt Bridges: A Combined Computational and Experimental Study

Protein thermal stability is an important factor considered in medical and industrial applications. Many structural characteristics related to protein thermal stability have been elucidated, and increasing salt bridges is considered as one of the most efficient strategies to increase protein thermal stability. However, the accurate simulation of salt bridges remains difficult. In this study, a novel method for salt-bridge design was proposed based on the statistical analysis of 10,556 surface salt bridges on 6,493 X-ray protein structures. These salt bridges were first categorized based on pairing residues, secondary structure locations, and Cα-Cα distances. Pairing preferences generalized from statistical analysis were used to construct a salt-bridge pair index and utilized in a weighted electrostatic attraction model to find the effective pairings for designing salt bridges. The model was also coupled with B-factor, weighted contact number, relative solvent accessibility, and conservation prescreening to determine the residues appropriate for the thermal adaptive design of salt bridges. According to our method, eight putative salt-bridges were designed on a mesophilic β-glucosidase and 24 variants were constructed to verify the predictions. Six putative salt-bridges leaded to the increase of the enzyme thermal stability. A significant increase in melting temperature of 8.8, 4.8, 3.7, 1.3, 1.2, and 0.7°C of the putative salt-bridges N437K-D49, E96R-D28, E96K-D28, S440K-E70, T231K-D388, and Q277E-D282 was detected, respectively. Reversing the polarity of T231K-D388 to T231D-D388K resulted in a further increase in melting temperatures by 3.6°C, which may be caused by the transformation of an intra-subunit electrostatic interaction into an inter-subunit one depending on the local environment. The combination of the thermostable variants (N437K, E96R, T231D and D388K) generated a melting temperature increase of 15.7°C. Thus, this study demonstrated a novel method for the thermal adaptive design of salt bridges through inference of suitable positions and substitutions.
Unsupervised and Semi-supervised Clustering : a Brief Survey ∗

Clustering (or cluster analysis) aims to organize a collection of data items into clusters, such that items within a cluster are more “similar” to each other than they are to items in the other clusters. This notion of similarity can be expressed in very different ways, according to the purpose of the study, to domain-specific assumptions and to prior knowledge of the problem. Clustering is usually performed when no information is available concerning the membership of data items to predefined classes. For this reason, clustering is traditionally seen as part of unsupervised learning. We nevertheless speak here of unsupervised clustering to distinguish it from a more recent and less common approach that makes use of a small amount of supervision to “guide” or “adjust” clustering (see section 2). To support the extensive use of clustering in computer vision, pattern recognition, information retrieval, data mining, etc., very many different methods were developed in several communities. Detailed surveys of this domain can be found in [25], [27] or [26]. In the following, we attempt to briefly review a few core concepts of cluster analysis and describe categories of clustering methods that are best represented in the literature. We also take this opportunity to provide some pointers to more recent work on clustering.
An automatic mobile-health based approach for EEG epileptic seizures detection

In this article, we develop a comprehensive mobile-based approach, which is able to perform the essential processes needed to automatically analyze and detect epileptic seizures using the information contained in electroencephalography (EEG) signals. We first develop and implement an appropriate combination of different algorithms that resample, smooth, remove artifacts, and constantly and adaptively segment signals to prepare them for further processing. We then improve and fully implement a large variety of features introduced in the literature of epileptic seizures detection. We also select the relevant features to reduce a feature vector space and improve the classification process by developing two automated filter and wrapper selection algorithms. We thoroughly compare between these selection algorithms in terms of redundant features, execution time and classification accuracy through three experiments. We subsequently exploit the selected features as input to a machine learning classifier to detect epileptic seizure states in a reasonable time. We experimentally and theoretically evaluate the scalability of the whole algorithm respectively on patients’ data available in standard clinical database and on 500 EEG recordings including 500 seizures. Having efficient and scabble algorithm, we develop two extra algorithms to dynamically acquire and transmit EEG signals from wireless sensors attached to patients and to visualize on mobile devices the obtained processing and analysis results. We finally integrate all our algorithms together along with an android mobile application to implement an effective mobile-based EEG monitoring system where its accuracy is tested on live EEG data. 2015 Published by Elsevier Ltd.
Long-term reductions in tinnitus severity

BACKGROUND: This study was undertaken to assess long-term changes in tinnitus severity exhibited by patients who completed a comprehensive tinnitus management program; to identify factors that contributed to changes in tinnitus severity within this population; to contribute to the development and refinement of effective assessment and management procedures for tinnitus. METHODS: Detailed questionnaires were mailed to 300 consecutive patients prior to their initial appointment at the Oregon Health & Science University Tinnitus Clinic. All patients were then evaluated and treated within a comprehensive tinnitus management program. Follow-up questionnaires were mailed to the same 300 patients 6 to 36 months after their initial tinnitus clinic appointment. RESULTS: One hundred ninety patients (133 males, 57 females; mean age 57 years) returned follow-up questionnaires 6 to 36 months (mean = 22 months) after their initial tinnitus clinic appointment. This group of patients exhibited significant long-term reductions in self-rated tinnitus loudness, Tinnitus Severity Index scores, tinnitus-related anxiety and prevalence of current depression. Patients who improved their sleep patterns or Beck Depression Inventory scores exhibited greater reductions of tinnitus severity scores than patients who continued to experience insomnia and depression at follow-up. CONCLUSIONS: Individualized tinnitus management programs that were designed for each patient contributed to overall reductions in tinnitus severity exhibited on follow-up questionnaires. Identification and treatment of patients experiencing anxiety, insomnia or depression are vital components of an effective tinnitus management program. Utilization of acoustic therapy also contributed to improvements exhibited by these patients.