nils-herrmann/clean_scirepeval huggingface dataset:
A Methodology for Sign Language Video Analysis and Translation into Text in Hindi Language

Sign language based communication is basically essential by consistently changing distinctive hand shapes and development by a signer. The proposed framework going to recognize the sign of gesture based communication from a video stream of the signer and deciphers into relating words and sentences in Hindi. The proposed framework utilizes the image preparing strategies and synthetic intelligence procedures to get the target. To do the proposed task execution it utilizes picture handling strategies, for example, frame differencing based tracking, edge recognition, wavelet change, picture combination systems to area shapes in our videos. It moreover utilizes descriptors of elliptical Fourier for feature extraction of shape and also uses analysis for the purpose of feature set reduction as well as optimization. Extracted features are stored into the database and compared with given input video of the signer by a trained unclear inference system
Video Object Description of Short Videos in Hindi Text Language

Video object recognition has become a creative area of analysis in recent years. This strategy combines the outcome of state of the art object and activity indicators with "real world" information to pick the in all subjectverb object triplet for depicting a video. A general data driven approach is denoted in this paper that produces descriptions of video content into text description in the Hindi language. The proposed work provides preliminary and basic text description in the Hindi language that is producing simple words and sentence formation. But the main challenging effort in this work is to extract grammatically correct and expressive text information in Hindi text regarding video content. Using this triplet choice technique, a video is tagged by the trainer, in particular, Subject, Verb, and Object (SVO) and then this data is mined to improve the result of testing video explanation by means of activity as well as object identification.
Cardiac arrhythmia classification using statistical and mixture modeling features of ECG signals
Venue: Pattern Recognit. Lett.

Gaussian mixture modeling is used to fit the probability density function of heartbeats.Expectation maximization algorithm estimates the parameters of statistical model.Skewness, kurtosis and 5th moment of ECG signals express the shape parameters.RR interval information represents the time domain characteristics of ECG signals.An ensemble of decision trees are used to perform the final classification. In this paper we propose a novel method for accurate classification of cardiac arrhythmias. Morphological and statistical features of individual heartbeats are used to train a classifier. Two RR interval features as the exemplars of time domain information are utilized in this study. Gaussian mixture modeling (GMM) with an enhanced expectation maximization (EM) solution is used to fit the probability density function of heartbeats. Parameters of GMM together with shape parameters such as skewness, kurtosis and 5th moment are also included in feature vector. These features are then used to train an ensemble of decision trees. MIT BIH arrhythmia database containing various types of common arrhythmias is employed to test the algorithm. The overall accuracy of 99.70% in "class oriented" scheme and 96.15% in "subject oriented" scheme is achieved. Both cases express a significant improvement of accuracy compared to other methods.
Using Group Based Trajectory and Growth Mixture Modeling to Identify Classes of Change Trajectories

Many issues of interest to counseling psychologists involve questions regarding how individuals change over time. Although counseling psychologists often examine average levels of change, statistical methods can also identify patterns of change over time by empirically grouping together individuals with similar patterns of change (e.g. group based trajectory modeling and latent growth mixture modeling) The purpose of this article is to provide an overview of these methods for counseling psychologists. We discuss the conceptual frameworks and assumptions of average level and person centered techniques such as group based trajectory modeling and latent growth mixture modeling. We provide a nontechnical guide for conducting these analyses using data from a study of psychotherapy outcomes in a sample of mental health center clients (N 1,050) We discuss caveats associated with these methods, including the potential for overinterpreting nongeneralizable results. Last, we suggest best practices for reporting and interpreting results.
Risk Factors for Malnutrition in Seniors Aged 75+ Living in Home Environment in Selected Regions of the Czech Republic.
Venue: Central European journal of public health

Nutrition is an important social determinant of health that influences the ageing process. The aim of this study was to evaluate the nutritional condition of a group of seniors and identify the bio psycho social factors that increase the risk of malnutrition.  The research was conducted using a quantitative method. The standardised scales Mini Nutritional Assessment Short Form (MNA SF) and the Geriatric depression scale (GDS 5) were used to evaluate the nutritional condition and tendency towards depression of the tested group. This group consisted of seniors aged 75 and above living in home environment in the Ceske Budejovice region. The group was comprised of 320 seniors, 115 men (35.9% and 205 women (64.1% which corresponds to the composition of the population in the chosen region of the Czech Republic. Statistical data analysis was conducted using SASD 1.4.10 and SPSS 15.0 programs. Pearson's chi squared test (Kh2) and Cramer's V were chosen for statistical testing. The significance level was set at 5% RESULTS The average BMI value of the seniors was 26.2 kg/m2 (overweight) This value decreased with age. More than one third of the respondents were evaluated as being at risk of malnutrition (36.3% Unintended weight loss was determined as the strongest risk factor of malnutrition. Seniors who had lowered their food intake stated unintended weight loss 10 times more often than respondents with no noticeable reduction in food intake. Seniors who showed signs of depression indicated weight loss three and a half times more often than respondents without depression. Meanwhile acute illness increased the risk by three times. Depression was found to be the cause and also the consequence of malnutrition.  Despite the high prevalence of overweight and obesity, a large proportion of the respondents were running the risk of malnutrition. It was concluded that the strongest risk factors for malnutrition in the respondents were unintended weight loss, depression and lowered food intake. Compared to biological factors, social and economic factors were less significant in causing malnutrition in seniors.
Unmanned Aircraft System Traffic Management (UTM) Concept of Operations

Many applications of small Unmanned Aircraft System (UAS) have been envisioned. These include surveillance of key assets such as pipelines, rail, or electric wires, deliveries, search and rescue, traffic monitoring, videography, and precision agriculture. These operations are likely to occur in the same airspace in presence of many static and dynamic constraints such as airports, and high wind areas. Therefore, small UAS, typically 55 lb and below, operations need to be managed to ensure safety and efficiency of operations is maintained. This paper will describe the Concept of Operations (ConOps) for NASA's UAS Traffic Management (UTM) research initiative. The UTM ConOps is focused on safely enabling large scale small UAS (sUAS) operations in low altitude airspace. The UTM construct supports large scale visual line of sight and beyond visual line of sight operations. It is based on two primary mantras: (1) flexibility where possible and structure where necessary (2) a risk based approach where geographical needs and use case indicate the airspace performance requirements. Preliminary stakeholder feedback and initial UTM tests conducted by NASA show promise of UTM to enable large scale low altitude UAS operations safely.
Discriminative multiple sound source localization based on deep neural networks using independent location model
Venue: 2016 IEEE Spoken Language Technology Workshop (SLT)

We propose a training method for multiple sound source localization (SSL) based on deep neural networks (DNNs) Such networks function as posterior probability estimator of sound location in terms of position labels and achieve high localization correctness. Since the previous DNNs' configuration for SSL handles one sound source cases, it should be extended to multiple sound source cases to apply it to real environments. However, a naive design causes 1) an increase in the number of labels and training data patterns and 2) a lack of label consistency across different numbers of sound sources, such as one and two or more sound cases. These two problems were solved using our proposed method, which involves an independent location model for the former and an block wise consistent labeling with ordering for the latter. Our experiments indicated that the SSL based on DNNs trained by our proposed training method out performed a conventional SSL method by a maximum of 18 points in terms of block level correctness.
Gaussian Process Bandit Optimisation with Multi fidelity Evaluations
Venue: NIPS

In many scientific and engineering applications, we are tasked with the optimisation of an expensive to evaluate black box function \func$ Traditional methods for this problem assume just the availability of this single function. However, in many cases, cheap approximations to \func$ may be obtainable. For example, the expensive real world behaviour of a robot can be approximated by a cheap computer simulation. We can use these approximations to eliminate low function value regions cheaply and use the expensive evaluations of \func$ in a small but promising region and speedily identify the optimum. We formalise this task as a \emph{multi fidelity} bandit problem where the target function and its approximations are sampled from a Gaussian process. We develop \mfgpucb, a novel method based on upper confidence bound techniques. In our theoretical analysis we demonstrate that it exhibits precisely the above behaviour, and achieves better regret than strategies which ignore multi fidelity information. \mfgpucbs outperforms such naive strategies and other multi fidelity methods on several synthetic and real experiments.
Giant Panda Maternal Care: A Test of the Experience Constraint Hypothesis
Venue: Scientific reports

The body condition constraint and the experience condition constraint hypotheses have both been proposed to account for differences in reproductive success between multiparous (experienced) and primiparous (first time) mothers. However, because primiparous mothers are typically characterized by both inferior body condition and lack of experience when compared to multiparous mothers, interpreting experience related differences in maternal care as support for either the body condition constraint hypothesis or the experience constraint hypothesis is extremely difficult. Here, we examined maternal behaviour in captive giant pandas, allowing us to simultaneously control for body condition and provide a rigorous test of the experience constraint hypothesis in this endangered animal. We found that multiparous mothers spent more time engaged in key maternal behaviours (nursing, grooming, and holding cubs) and had significantly less vocal cubs than primiparous mothers. This study provides the first evidence supporting the experience constraint hypothesis in the order Carnivora, and may have utility for captive breeding programs in which it is important to monitor the welfare of this species' highly altricial cubs, whose survival is almost entirely dependent on receiving adequate maternal care during the first few weeks of life.
High throughput analysis of the satellitome illuminates satellite DNA evolution
Venue: Scientific reports

Satellite DNA (satDNA) is a major component yet the great unknown of eukaryote genomes and clearly underrepresented in genome sequencing projects. Here we show the high throughput analysis of satellite DNA content in the migratory locust by means of the bioinformatic analysis of Illumina reads with the RepeatExplorer and RepeatMasker programs. This unveiled 62 satDNA families and we propose the term "satellitome" for the whole collection of different satDNA families in a genome. The finding that satDNAs were present in many contigs of the migratory locust draft genome indicates that they show many genomic locations invisible by fluorescent in situ hybridization (FISH) The cytological pattern of five satellites showing common descent (belonging to the SF3 superfamily) suggests that non clustered satDNAs can become into clustered through local amplification at any of the many genomic loci resulting from previous dissemination of short satDNA arrays. The fact that all kinds of satDNA (micro mini and satellites) can show the non clustered and clustered states suggests that all these elements are mostly similar, except for repeat length. Finally, the presence of VNTRs in bacteria, showing similar properties to non clustered satDNAs in eukaryotes, suggests that this kind of tandem repeats show common properties in all living beings.
deconstructSigs: delineating mutational processes in single tumors distinguishes DNA repair deficiencies and patterns of carcinoma evolution
Venue: Genome Biology

BackgroundAnalysis of somatic mutations provides insight into the mutational processes that have shaped the cancer genome, but such analysis currently requires large cohorts. We develop deconstructSigs, which allows the identification of mutational signatures within a single tumor sample.ResultsApplication of deconstructSigs identifies samples with DNA repair deficiencies and reveals distinct and dynamic mutational processes molding the cancer genome in esophageal adenocarcinoma compared to squamous cell carcinomas.ConclusionsdeconstructSigs confers the ability to define mutational processes driven by environmental exposures, DNA repair abnormalities, and mutagenic processes in individual tumors with implications for precision cancer medicine.
Learning Analytics and Digital Badges: Potential Impact on Student Retention in Higher Education
Venue: Technol. Knowl. Learn.

Learning analytics and digital badges are emerging research fields in educational science. They both show promise for enhancing student retention in higher education, where withdrawals prior to degree completion remain at about 30 in Organisation for Economic Cooperation and Development member countries. This integrative review provides an overview of the theoretical literature as well as current practices and experience with learning analytics and digital badges in higher education with regard to their potential impact on student retention to enhance students' first year experience. Learning analytics involves measuring and analyzing dynamic student data in order to gain insight into students' learning processes and optimize learning and teaching. One purpose of learning analytics is to construct predictive models to identify students who risk failing a course and thus are more likely to drop out of higher education. Personalized feedback provides students with information about academic support services, helping them to improve their skills and therefore be successful in higher education. Digital badges are symbols for certifying knowledge, skills, and competencies on web based platforms. The intention is to encourage student persistence by motivating them, recognizing their generic skills, signaling their achievements, and capturing their learning paths. This article proposes a model that synthesizes learning analytics, digital badges, and generic skills such as academic competencies. The main idea is that generic skills can be represented as digital badges, which can be used for learning analytics algorithms to predict student success and to provide students with personalized feedback for improvement. Moreover, this model may serve as a platform for discussion and further research on learning analytics and digital badges to increase student retention in higher education.
Questions as data: illuminating the potential of learning analytics through questioning an emergent field
Venue: Res. Pract. Technol. Enhanc. Learn.

In providing a meta analysis of a series of workshop papers and questions arising on the emergent field of learning analytics, this paper contributes to the ongoing formation of a shared research agenda. The first ICCE Learning Analytics workshop in 2014 demonstrated the effectiveness of a focused questioning session for collecting relevant data beyond the content of the papers themselves. In December 2014, approximately 40 participants attended the workshop held in Nara, Japan, and contributed to the collection of open research questions. Six papers were presented covering topics including scope; interoperability standards; privacy and control of individual data, extracting data from learning content and processes; and the development of conceptual frameworks. These papers established a base from which the group generated a set of questions that invite further investigation. Utilising the first stage of the Question Formulation Technique, a pedagogical approach designed to stimulate student inquiry, a prominent finding from the workshop that questions emerging from focused inquiry provide a useful set of data in their own right. With an explicit workshop focus on learning analytics interoperability, this paper reports on the emergent issues identified in the workshop and the kinds of questions associated with each issue in the context of current research in the field of learning analytics. The study considers the complexity arising from the fact that data associated with learning is itself becoming a digital learning resource while also enabling analysis of learner behaviours and systems usage.
Understanding and addressing mathematics anxiety using perspectives from education, psychology and neuroscience

Mathematics anxiety is a significant barrier to mathematical learning. In this article, we propose that state or on task mathematics anxiety impacts on performance, while trait mathematics anxiety leads to the avoidance of courses and careers involving mathematics. We also demonstrate that integrating perspectives from education, psychology and neuroscience contributes to a greater understanding of mathematics anxiety in its state and trait forms. Research from cognitive psychology and neuroscience illustrates the effect of state mathematics anxiety on performance and research from cognitive, social and clinical psychology, and education can be used to conceptualise the origins of trait mathematics anxiety and its impact on avoidant behaviour. We also show that using this transdisciplinary framework to consider state and trait mathematics anxiety separately makes it possible to identify strategies to reduce the negative effects of mathematics anxiety. Implementation of these strategies among particularly vulnerable groups, such as pre service teachers, could be beneficial.
Asynchronous H filtering for linear switched systems with average dwell time
Venue: Int. J. Syst. Sci.

This paper is concerned with the H filtering problem for a class of continuous time linear switched systems with the asynchronous behaviours, where 'asynchronous' means that the switching of the filters to be designed has a lag to the switching of the system modes. By using the Lyapunov like functions and the average dwell time technique, a sufficient condition is obtained to guarantee the asymptotic stability with a weighted H performance index for the filtering error system. Moreover, the results are formulated in the form of linear matrix inequalities that are numerical feasible. As a result, the filter design problem is solved. Finally, an illustrative numerical example is presented to show the effectiveness of the results.
Review of selected research in applied linguistics published in Australia (2008 2014)
Venue: Language Teaching

This article reviews the significant and diverse range of research in applied linguistics published in Australia in the period 2008 2014. Whilst acknowledging that a great deal of research by Australian scholars has been published internationally during these seven years, this review is based on books, journal articles, and conference proceedings published in Australia. Many of these sources will be unfamiliar to an international audience, and the purpose of this article is to highlight this body of research and the themes emerging from it. The journals selected in this review include Australian Journal of Language and Literacy, Australian Review of Applied Linguistics (ARAL) BABEL, English in Australia, English Australia, Papers in Language Testing and Assessment, Prospect: An Australian Journal of TESOL, TESOL in Context, and University of Sydney Papers in TESOL. Selected refereed proceedings are from key national conferences including: ALAA (Applied Linguistics Association of Australia) ACTA (Australian Council of TESOL Association) ASFLA (Australian Systemic Functional Linguistics Association) and ALS (Australian Linguistics Society) Our review of selected applied linguistics work revolves around the following themes: the responses to the needs of government planning and policy; the complexity of Australia's multicultural, multilingual society; the concern for recognizing context and culture as key factors in language and language learning; social activism in supporting language pedagogy and literacy programmes at all levels of education; and acknowledgement of the unique place held by Indigenous languages and Aboriginal English in the national linguistic landscape.
Clinical outcomes in elderly patients with human papillomavirus positive squamous cell carcinoma of the oropharynx treated with definitive chemoradiation therapy
Venue: Head neck

The benefit of combined chemoradiation in elderly patients with human papillomavirus (HPV) positive locally advanced oropharyngeal squamous cell carcinoma (SCC) must be balanced with the potential for higher toxicity rates. We performed a retrospective review of our institutional experience.
Mucositis in head and neck cancer patients treated with radiotherapy and systemic therapies: Literature review and consensus statements.
Venue: Critical reviews in oncology/hematology

Oral mucositis (OM) due to radiotherapy and systemic therapies in head and neck cancer treatment represents a major problem causing a wide spectrum of clinical signs and symptoms. This adverse event may reduce quality of life, resulting from debilitating oral pain, bleeding, dysphagia, infections, impairment of food intake, high rate of hospitalization and may interfere with the delivery of programmed treatment plans, ultimately jeopardizing patient outcome. Globally, there is a lack of evidence on effective measures for the prevention and treatment of OM, and only scant uniform conclusions and recommendations can be derived from the existing literature and guidelines. A multidisciplinary team of Italian head and neck cancer experts met in Milan 17 18 February 2013 with the aim of reaching consensus on prophylaxis and management of mucositis. The results of the literature review and the statements that achieved consensus are reported and discussed in this paper.  The Delphi Appropriateness Method was used as a structured communication method for achieving consensus. Subsequently, external expert reviewers evaluated the conclusions carefully according to their area of expertise.  This paper presents 13 clusters of statements on prophylaxis and treatment of mucositis that achieved consensus.  OM represents a very stressful situation for head and neck cancer patients submitted to chemo radiation or exclusive radiation treatment. A multidisciplinary approach is mandatory, but there is still no gold standard protocol that is prominently better than others.