nils-herrmann/clean_scirepeval huggingface dataset:
Feasibility of telemedicine intervention for childhood depression
Year: 2006

Childhood depression is associated with high short term and long term morbidity. Cognitive behavioural therapy (CBT) for depression has been supported as an effective treatment in the traditional clinic setting. But many families face barriers to depression treatment, including distance to providers, provider shortages, stigma, and other challenges. Telemedicine or interactive televideo offers an innovative way to provide therapy at a distance. The family and therapist see and hear each other in real time using videoconference.  This pilot project evaluated the same cognitive behavioural therapy treatment for childhood depression in two different settings face to face (F2F) or over interactive televideo (ITV)  Twenty eight children with depression and their caregivers completed an eight session, empirically supported CBT course for parent and child.  All parent and child CBT skills were implemented successfully over telemedicine. An 82% remission from de.
Fast and general tests of genetic interaction for genome wide association studies
Venue: PLoS Comput. Biol.
Year: 2017

A complex disease has, by definition, multiple genetic causes. In theory, these causes could be identified individually, but their identification will likely benefit from informed use of anticipated interactions between causes. In addition, characterizing and understanding interactions must be considered key to revealing the etiology of any complex disease. Large scale collaborative efforts are now paving the way for comprehensive studies of interaction. As a consequence, there is a need for methods with a computational efficiency sufficient for modern data sets as well as for improvements of statistical accuracy and power. Another issue is that, currently, the relation between different methods for interaction inference is in many cases not transparent, complicating the comparison and interpretation of results between different interaction studies. In this paper we present computationally efficient tests of interaction for the complete family of generalized linear models (GLMs) The tests can be applied for inference of single or multiple interaction parameters, but we show, by simulation, that jointly testing the full set of interaction parameters yields superior power and control of false positive rate. Based on these tests we also describe how to combine results from multiple independent studies of interaction in a meta analysis. We investigate the impact of several assumptions commonly made when modeling interactions. We also show that, across the important class of models with a full set of interaction parameters, jointly testing the interaction parameters yields identical results. Further, we apply our method to genetic data for cardiovascular disease. This allowed us to identify a putative interaction involved in Lp(a) plasma levels between two 'tag' variants in the LPA locus (p 2.42 10 09) as well as replicate the interaction (p 6.97 10 07) Finally, our meta analysis method is used in a small (N 16,181) study of interactions in myocardial infarction.
Pigeon inspired optimization and extreme learning machine via wavelet packet analysis for predicting bulk commodity futures prices
Venue: Science China Information Sciences
Year: 2018

In this paper, a hybrid approach consisting of pigeon inspired optimization (PIO) and extreme learning machine (ELM) based on wavelet packet analysis (WPA) is presented for predicting bulk commodity futures prices. Firstly, WPA is applied to decompose the original futures prices into a set of lower frequency subseries. Secondly, the PIO algorithm is used to optimize the parameters of ELM and then the optimized ELM is utilized to forecast the subseries. Finally, we adopt the hybrid method to calculate the final forecasting outcomes of futures prices. In order to further test the predictive ability of the hybrid forecasting model on bulk commodity futures prices, we use the prices of West Texas Intermediate crude oil futures and Chicago Board of Trade soybean futures to make one step, two step and four step ahead predictions. In comparison with complete ensemble empirical mode decomposition with adaptive noise, empirical mode decomposition and singular spectrum analysis, WPA is the most suitable for decomposing bulk commodity futures prices. The experimental outcomes show that the hybrid WPA PIO ELM model has better performance on horizontal precision, directional precision and robustness.
10.6% Certified Colloidal Quantum Dot Solar Cells via Solvent Polarity Engineered Halide Passivation.
Venue: Nano letters
Year: 2016

Colloidal quantum dot (CQD) solar cells are solution processed photovoltaics with broad spectral absorption tunability. Major advances in their efficiency have been made via improved CQD surface passivation and device architectures with enhanced charge carrier collection. Herein, we demonstrate a new strategy to improve further the passivation of CQDs starting from the solution phase. A cosolvent system is employed to tune the solvent polarity in order to achieve the solvation of methylammonium iodide (MAI) and the dispersion of hydrophobic PbS CQDs simultaneously in a homogeneous phase, otherwise not achieved in a single solvent. This process enables MAI to access the CQDs to confer improved passivation. This, in turn, allows for efficient charge extraction from a thicker photoactive layer device, leading to a certified solar cell power conversion efficiency of 10.6% a new certified record in CQD photovoltaics.
Existence results for a coupled system of nonlinear fractional differential equations with three point boundary conditions
Venue: Comput. Math. Appl.
Year: 2009

This paper studies a coupled system of nonlinear fractional differential equations with three point boundary conditions. Applying the Schauder fixed point theorem, an existence result is proved for the following system D^@au(t)=f(t,v(t),D^pv(t),D^@bv(t)=g(t,u(t),D^qu(t)[email protected](0,1),u(0)=0,u(1)[email protected]@h),v(0)=0,v(1)[email protected]@h) where @a,@b,p,q,@h,@c satisfy certain conditions.
Central Asia and South Asia: Potential of India's Multilateral Economic Diplomacy in Inter Regional Cooperation
Year: 2006

The unique feature of the Indian economy over the past few decades has been a combination of high growth and stability. It has proved its strength and resilience through this period. India's economic diplomacy with the Central Asian and South Asian nations also shows a continuing positive trend. High level visits are exchanged on a regular basis. Today's India is not only a reliable business partner on a bilateral basis, but given its own economic potential and rich experience it is one of the most suitable countries in Asia to promote active economic cooperation between Central and South Asia. India's political and economic diplomacy with Central Asian and South Asian nations are therefore strategically important from the perspective of inter regional cooperation.
System designed to enable scientific analysis on robot pollination algorithm for orchard robot research
Venue: 2019 International Conference on Electronics, Information, and Communication (ICEIC)
Year: 2019

For orchard robot research, in order to do scientific analysis on our pollination algorithm used by pollination robot, we developed an artificial canopy system. High speed light sensors are used as fake flowers. Laser pointers are used as fake pollinator nozzles mounted on robot, shooting the fake flowers and collect hit status by software. In this way precise fake flower hitting record can be scientifically analyzed. And the system can solve the following research difficulties in real orchard: (1) hard to count, no record for hitting time, no precise location information on pollinated flower can be found scientifically (2) flower season is too short (less than a month for kiwi fruit) (3) weather and (4) travel problem.
TU CD BRA 05: Atlas Selection for Multi Atlas Based Image Segmentation Using Surrogate Modeling
Year: 2015

The growing size and heterogeneity in training atlas necessitates sophisticated schemes to identify only the most relevant atlases for the specific multi atlas based image segmentation problem. This study aims to develop a model to infer the inaccessible oracle geometric relevance metric from surrogate image similarity metrics, and based on such model, provide guidance to atlas selection in multi atlas based image segmentation.  We relate the oracle geometric relevance metric in label space to the surrogate metric in image space, by a monotonically non decreasing function with additive random perturbations. Subsequently, a surrogate's ability to prognosticate the oracle order for atlas subset selection is quantified probabilistically. Finally, important insights and guidance are provided for the design of fusion set size, balancing the competing demands to include the most relevant atlases and to exclude the most irrelevant ones. A systematic solution is derived based on an optimization framework. Model verification and performance assessment is performed based on clinical prostate MR images.  The proposed surrogate model was exemplified by a linear map with normally distributed perturbation, and verified with several commonly used surrogates, including MSD, NCC and (N)MI. The derived behaviors of different surrogates in atlas selection and their corresponding performance in ultimate label estimate were validated. The performance of NCC and (N)MI was similarly superior to MSD, with a 10% higher atlas selection probability and a segmentation performance increase in DSC by 0.10 with the first and third quartiles of (0.83, 0.89) compared to (0.81, 0.89) The derived optimal fusion set size, valued at 7/8/8/7 for MSD/NCC/MI/NMI, agreed well with the appropriate range [4, 9] from empirical observation.  This work has developed an efficacious probabilistic model to characterize the image based surrogate metric on atlas selection. Analytical insights lead to valid guiding principles on fusion set size design.
Practices and Applications of Convolutional Neural Network Based Computer Vision Systems in Animal Farming: A Review
Venue: Sensors
Year: 2021

Convolutional neural network (CNN) based computer vision systems have been increasingly applied in animal farming to improve animal management, but current knowledge, practices, limitations, and solutions of the applications remain to be expanded and explored. The objective of this study is to systematically review applications of CNN based computer vision systems on animal farming in terms of the five deep learning computer vision tasks: image classification, object detection, semantic/instance segmentation, pose estimation, and tracking. Cattle, sheep/goats, pigs, and poultry were the major farm animal species of concern. In this research, preparations for system development, including camera settings, inclusion of variations for data recordings, choices of graphics processing units, image preprocessing, and data labeling were summarized. CNN architectures were reviewed based on the computer vision tasks in animal farming. Strategies of algorithm development included distribution of development data, data augmentation, hyperparameter tuning, and selection of evaluation metrics. Judgment of model performance and performance based on architectures were discussed. Besides practices in optimizing CNN based computer vision systems, system applications were also organized based on year, country, animal species, and purposes. Finally, recommendations on future research were provided to develop and improve CNN based computer vision systems for improved welfare, environment, engineering, genetics, and management of farm animals.
Impact of behavioural biases in portfolio investment decision making process
Year: 2015

This article presents a new approach in the analysis of portfolio investment decisions, namely behavioral finance. Behavioral finance is the study of the influence of the psychological factors on financial markets evolution. Psychology, including aspirations, cognition, emotions, and culture, is at the center of behavioral finance. Behavioral finance paradigm suggests that investment decision is influenced in a large proportion by psychological and emotional factors. It has been identified thatthe Investors do not always act rationally or consider all of the available information in their decision making process. As a result, they regularlymake errors. It turns out too that the errors they make repeat in the same way, and are, herefore, termed systematic errors. Luckily, because of this systematic character, these errors are often predictable and avoidable. The common errors that they make are also termed as behavioral biases some of them are overconfidence, anchoring, herding, regret aversion, misunderstanding, randomness, mental accounting, representativeness etc. This paper examines the role of behavioral biases on investment decision making process.
Using Virtual Exchange to Advance Media Literacy Competencies through Analysis of Contemporary Propaganda.
Year: 2018

With the rise of so called fake news as a global phenomenon, interest in propaganda analysis has advanced along with the recognition of the fundamentally social process of interpretation. In this essay, we explore the use of cross national dialogue among German and American undergraduate students who are seeking to better understand how media messages are interpreted and how they inform and guide the civic actions of citizens. We describe and analyze five lessons that used a virtual exchange comprised of a variety of digital media platforms, texts, and technologies to support the cross national study of contemporary propaganda. We observed that cross national dialogue enables students to gain sensitivity to the role of cultural context in interpreting propaganda. Rather than conceptualize propaganda education as an ideologically benign set of context free skills, pedagogies that include opportunities for cross national dialogue foreground the importance of cultural specificity as a means to unpack the complex discursive context of propaganda as digital political communication.
Semantic Relation Extraction with Semantic Patterns Experiment on Radiology Reports
Venue: LREC
Year: 2016

This work presents a practical system for indexing terms and relations from French radiology reports, called IMAIOS. In this paper, we present how semantic relations (causes, consequences, symptoms, locations, parts. between medical terms can be extracted. For this purpose, we handcrafted some linguistic patterns from on a subset of our radiology report corpora. As semantic patterns (de (of) may be too general or ambiguous, semantic constraints have been added. For instance, in the sentence neoplasie du sein (neoplasm of breast) the system knowing neoplasm as a disease and breast as an anatomical location, identify the relation as being a location: neoplasm r lieu breast. An evaluation of the effect of semantic constraints is proposed.
Global Software Development Offshore Insourcing Organizations Characteristics: Lessons Learned from a Case Study
Venue: 2006 IEEE International Conference on Global Software Engineering (ICGSE'06)
Year: 2006

There is a strong need to restructure the IT sector in order to successfully achieve alignment with the business. Because of this, companies are extending their operation to offshore development centers. These centers attend needs of the company using its own resources, located in a different country. In order to explore these organizations' current activities and to seek insights to guide future development, this paper describes the results of a case study conducted in four global development centers located in Latin America and Asia. From this study, lessons learned were compiled as an approach to be followed in projects with similar environments
A Sentiment Analysis Study on Recognition of Facial Expressions: Gauss and Canny Methods
Venue: 2020 International Conference on Decision Aid Sciences and Application (DASA)
Year: 2020

Human computer interaction has been the focus of today's current researches. Human computer interaction is accepted as a multidisciplinary field that takes place through interfaces. These interfaces can sometimes be software functions, or sometimes they can be interact provided with hardware components. Facial expressions give information about people's emotions play an important role in sentiment recognition. Today, facial expressions are used in many fields such as education, psychological studies, virtual reality, robotics, facial animation, health and law, and the need for analysis of facial expressions in many areas is increasing. In addition, the analysis of human facial expressions with computers is a remarkable research area, but it is considered a challenging problem. In this context, it is necessary to analyze facial expressions accurately and quickly by software. In this study, sentiment recognition from facial expressions (sad, happy, scared, confused) was performed using 50 different images obtained from various databases and internet sources. With digital image processing techniques, improved images can be obtained and feature extraction can be made. In this research, digital image processing functions and MATLAB programming language of MATLAB 2018 program, which provides advanced programming for scientific studies, were used. Image noise was removed with the Gauss filter, and edge detection operations were performed with the Canny method. Geometric ratios were used to eliminate errors. As a result of the study, it was determined that sentiment recognition procedures performed on images with similar facial expressions made incorrect sentiment classification. However, it has been observed that face recognition with MATLAB functions and MATLAB programming has generally produced successful results.
Recommendation with Heterogeneous Information Networks Based on Meta Graph
Year: 2019

In order to alleviate data sparsity and cold start problems of traditional collaborative filtering recommendation algorithm, a meta based fusion heterogeneous information network recommendation algorithm is adopted in this paper. The algorithm integrates the characteristics of multi relationship social network and user's preference degree and adopts a universal representation for different types of data. A meta graph based similarity measurement method makes it possible to better capture the semantic relationships between different types of data and a score matrix decomposition method based on multiple meta graphs is used. Each project and user generates a variety of potential feature matrices based on different meta graphs. Effectively integrates multiple feature matrices into a unified, final implicit feature matrix. We use each factor of each line of the implicit feature matrix as a neural network. The input node predicts user ratings by optimizing the scoring neural network. Finally, we used the data set provided by the Yelp website to do user rating prediction experiments, which proved the accuracy of this algorithm is 5% higher than the traditional collaborative filtering recommendation algorithm.
Learning Implicit Generative Models with the Method of Learned Moments
Venue: ICML
Year: 2018

We propose a method of moments (MoM) algorithm for training large scale implicit generative models. Moment estimation in this setting encounters two problems: it is often difficult to define the millions of moments needed to learn the model parameters, and it is hard to determine which properties are useful when specifying moments. To address the first issue, we introduce a moment network, and define the moments as the network's hidden units and the gradient of the network's output with the respect to its parameters. To tackle the second problem, we use asymptotic theory to highlight desiderata for moments namely they should minimize the asymptotic variance of estimated model parameters and introduce an objective to learn better moments. The sequence of objectives created by this Method of Learned Moments (MoLM) can train high quality neural image samplers. On CIFAR 10, we demonstrate that MoLM trained generators achieve significantly higher Inception Scores and lower Frechet Inception Distances than those trained with gradient penalty regularized and spectrally normalized adversarial objectives. These generators also achieve nearly perfect Multi Scale Structural Similarity Scores on CelebA, and can create high quality samples of 128x128 images.
App Shell and Angular Performance
Year: 2019

No one likes to wait for a long time to see that an application is loading. In fact, statistics show that if the initial rendering takes more than three seconds, it's very likely that a user leaves our app. One of the main fundamentals of PWAs is to be fast. In native apps, the user usually is presented a splash screen and after a reasonable amount of time will see the main content and skeleton. On the other hand, there will be white screen until bootstrapping is finished, especially a single page app.
Quality defects in market beef and dairy cows and bulls sold through livestock auction markets in the Western United States: II. Relative effects on selling price.
Venue: Journal of animal science
Year: 2011

Relative effects of Beef Quality Assurance (BQA) related defects in market beef and dairy cows and bulls on selling price at auction was evaluated during 2008. The presence and severity of 23 BQA related traits were determined during sales in Idaho, California, and Utah. Overall, 18,949 unique lots consisting of 23,479 animals were assessed during 125 dairy sales and 79 beef sales. Mean sale price SD (per 45.5 kg) for market beef cows, beef bulls, dairy cows, and dairy bulls was $45.15 9.42, $56.30 9.21, $42.23 12.26, and $55.10 9.07, respectively. When combined, all recorded traits explained 36% of the variation in selling price in beef cows, 35% in beef bulls, 61% in dairy cows, and 56% in dairy bulls. Premiums and discounts were determined in comparison with a "par" or "base" animal. Compared with a base BCS 5 beef cow (on a 9 point beef scale) BCS 1 to 4 cows were discounted (P 0.0001) whereas premiums (P 0.05) were estimated for BCS 6 to 8. Compared with a base BCS 3.0 dairy cow (on a 5 point dairy scale) more body condition resulted in a premium (P 0.001) whereas a less than desirable BCS of 2.0 or 2.5 was discounted (P 0.0001) Emaciated or near emaciated cows (beef BCS 1 or 2; dairy BCS 1.0 or 1.5) were discounted (P 0.0001) Compared with base cows weighing 545 to 635 kg, lighter BW beef cows were discounted (P 0.0001) whereas heavier beef cows received (P 0.05) a premium. Compared with a base dairy cow weighing 636 to 727 kg, lighter BW cows were discounted (P 0.0001) whereas heavier cows (727 to 909 kg) received a premium (P 0.01) Beef and dairy cows with any evidence of lameness were discounted (P 0.0001) Presence of ocular neoplasia in the precancerous stage discounted (P 0.05) beef cows and discounted (P 0.01) dairy cows, whereas at the cancerous stage, it discounted (P 0.0001) all cows. Hide color influenced (P 0.0001) selling price in beef cattle but had no effect (P 0.17) in dairy cows. Animals that were visibly sick were discounted (P 0.0001) Results suggest that improving BCS and BW, which producers can do at the farm or ranch level, positively affects sale price. Furthermore, animals that are visibly sick or have a defect associated with a possible antibiotic risk will be discounted. Ultimately, animals with minor quality defects should be sold in a timely manner before the defect advances and the discount increases.