nils-herrmann/clean_scirepeval huggingface dataset:
Development of an implicit overall well-being measure using the Implicit Association Test.

Usually, well-being has been measured by means of questionnaires or scales. Although most of these methods have a high level of reliability and validity, they present some limitations. In order to try to improve well-being assessment, in the present work, the authors propose a new complementary instrument: The Implicit Overall Well-Being Measure (IOWBM). The Implicit Association Test (IAT) was adapted to measure wellbeing by assessing associations of the self with well-being-related words. In the first study, the IOWBM showed good internal consistency and adequate temporal reliability. In the second study, it presented weak correlations with explicit well-being measures. The third study examined the validity of the measure, analyzing the effect of traumatic memories on implicit well-being. The results showed that people who remember a traumatic event presented low levels of implicit well-being compared with people in the control condition.
Text recognition from images

Text recognition in images is a research area which attempts to develop a computer system with the ability to automatically read the text from images. These days there is a huge demand in storing the information available in paper documents format in to a computer storage disk and then later reusing this information by searching process. One simple way to store information from these paper documents in to computer system is to first scan the documents and then store them as images. But to reuse this information it is very difficult to read the individual contents and searching the contents form these documents line-by-line and word-by-word. The challenges involved in this the font characteristics of the characters in paper documents and quality of images. Due to these challenges, computer is unable to recognize the characters while reading them. Thus there is a need of character recognition mechanisms to perform Document Image Analysis (DIA) which transforms documents in paper format to electronic format. In this paper we have discuss method for text recognition from images. The objective of this paper is to recognition of text from image for better understanding of the reader by using particular sequence of different processing module.
Infrapatellar fat pad pressure and volume changes of the anterior compartment during knee motion: possible clinical consequences to the anterior knee pain syndrome

This biomechanical study was performed to measure tissue pressure in the infrapatellar fat pad and the volume changes of the anterior knee compartment during knee flexion–extension motion. Knee motion from 120° of flexion to full extension was simulated on ten fresh frozen human knee specimens (six from males, four from females, average age 44 years) using a hydraulic kinematic simulator (30, 40, and 50 Nm extension moment). Infrapatellar tissue pressure was measured using a closed cell sensor. Infrapatellar volume change in the anterior knee compartment was evaluated subsequent to removal of the fat pad using a water-filled bladder. We found a significant increase of the infrapatellar tissue pressure during knee flexion, at flexion angles of <20° and >100°. The average tissue pressure ranged from 343 (±223) mbar at 0° to 60 (±64) mbar at 60° of flexion. The smallest volume in the anterior knee compartment was measured at full extension and 120° of flexion, whereas the maximum volume was observed at 50° of flexion. In conclusion, the data suggest a biomechanical function of the infrapatellar fat pad at flexion angles of <20° and >100°, which suggests a role of the infrapatellar fat pad in stabilizing the patella in the extremes of knee motion.
Circuitry of self-control and its role in reducing addiction

We discuss the idea that addictions can be treated by changing the mechanisms involved in self-control with or without regard to intention. The core clinical symptoms of addiction include an enhanced incentive for drug taking (craving), impaired self-control (impulsivity and compulsivity), negative mood, and increased stress re-activity. Symptoms related to impaired self-control involve reduced activity in control networks including anterior cingulate (ACC), adjacent prefrontal cortex (mPFC), and striatum. Behavioral training such as mindfulness meditation can increase the function of control networks and may be a promising approach for the treatment of addiction, even among those without intention to quit.
Benzodiazepine therapy in psychiatric outpatients is associated with deliberate self-poisoning events at emergency departments—a population-based nested case–control study

Deliberate self-poisoning (DSP), the most common form of deliberate self-harm, is closely associated with suicide. Identifying risk factors of DSP is necessary for implementing prevention strategies. This study aimed to evaluate the relationship between benzodiazepine (BZD) treatment in psychiatric outpatients and DSP cases at emergency departments (EDs). We performed a retrospective nested case–control study of psychiatric patients receiving BZD therapy to evaluate the relationship between BZD use and the diagnosis of DSP at EDs using data from the nationwide Taiwan National Health Insurance Research Database. Regression analysis yielded an odds ratio (OR) and 95 % confidence interval (95 % CI) indicating that the use of BZDs in psychiatric outpatients was significantly associated with DSP cases at EDs (OR = 4.46, 95 % CI = 3.59–5.53). Having a history of DSP, sleep disorders, anxiety disorders, schizophrenia, depression, or bipolar disorder was associated with a DSP diagnosis at EDs (OR = 13.27, 95 % CI = 8.28–21.29; OR = 5.04, 95 % CI = 4.25–5.98; OR = 3.95, 95 % CI = 3.32–4.70; OR = 7.80, 95 % CI = 5.28–11.52; OR = 15.20, 95 % CI = 12.22–18.91; and OR = 18.48, 95 % CI = 10.13–33.7, respectively). After adjusting for potential confounders, BZD use remained significantly associated with a subsequent DSP diagnosis (adjusted OR = 2.47, 95 % CI = 1.93–3.17). Patients taking higher average cumulative BZD doses were at greater risk of DSP. Vigilant evaluation of the psychiatric status of patients prescribed with BZD therapy is critical for the prevention of DSP events at EDs.
Cost-Aware Resource Management for Federated Clouds Using Resource Sharing Contracts

Cloud computing and its pay-as-you-go model continue to provide significant cost benefits and a seamless service delivery model for cloud consumers. The evolution of small-scale and large-scale geo-distributed datacenters operated and managed by individual cloud service providers raises new challenges in terms of effective global resource sharing and management of autonomously-controlled individual datacenter resources. Earlier solutions for geo-distributed clouds have focused primarily on achieving global efficiency in resource sharing that results in significant inefficiencies in local resource allocation for individual datacenters leading to unfairness in revenue and profit earned. In this paper, we propose a new contracts-based resource sharing model for federated geo-distributed clouds that allows cloud service providers to establish resource sharing contracts with individual datacenters apriori for defined time intervals during a 24 hour time period. Based on the established contracts, individual cloud service providers employ a cost-aware job scheduling and provisioning algorithm that enables tasks to complete and meet their response time requirements. The proposed techniques are evaluated through extensive experiments using realistic workloads and the results demonstrate the effectiveness, scalability and resource sharing efficiency of the proposed model.
Types of coping strategies are associated with increased depressive symptoms in patients with obstructive sleep apnea.

Some, but not all, researchers report that obstructive sleep apnea (OSA) patients experience increased depressive symptoms. Many psychological symptoms of OSA are explained in part by other OSA comorbidities (age, hypertension, body mass). People who use more passive and less active coping report more depressive symptoms. We examined relationships between coping and depressive symptoms in OSA.



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64 OSA (respiratory disturbance index (RDI) > or = 15) patients were studied with polysomnography and completed Ways of Coping (WC), Profile of Mood States (POMS), Center for Epidemiological Studies-Depression (CESD) scales. WC was consolidated into Approach (active) and Avoidance (passive) factors. Data were analyzed using SPSS 9.0 regression with CESD as the dependent variable and WC Approach and Avoidance as the independent variables.



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WC Approach factor (B=-1.105, beta=-.317, p=.009) was negatively correlated and WC Avoidance factor (B=1.353, beta=.376, p=.007) was positively correlated with CESD scores. These factors explained an additional 8% of CESD variance (p<.001) beyond that explained by the covariates: demographic variables, RDI, and fatigue (as measured by the POMS).



More passive and less active coping was associated with more depressive symptoms in OSA patients. The extent of depression experienced by OSA patients may not be due solely to effects of OSA itself. Choice of coping strategies may help determine who will experience more depressive symptoms.
A Novel Neural Sequence Model with Multiple Attentions for Word Sense Disambiguation

Word sense disambiguation (WSD) is a well researched problem in computational linguistics. Different research works have approached this problem in different ways. Some state of the art results that have been achieved for this problem are by supervised models in terms of accuracy, but they often fall behind flexible knowledge-based solutions which use engineered features as well as human annotators to disambiguate every target word. This work focuses on bridging this gap using neural sequence models incorporating the well-known attention mechanism. The main gist of our work is to combine multiple attentions on different linguistic features through weights and to provide a unified framework to accomplish this. This weighted attention allows the model to easily disambiguate the sense of an ambiguous word by attending to a suitable portion of a sentence. Our extensive experiments show that weighted multiple attention enables a more versatile encoder-decoder model leading to state of the art results.
LLVM: AN INFRASTRUCTURE FOR MULTI-STAGE OPTIMIZATION

Modern programming languages and software engineering principles are causing increasing problems for compiler systems. Traditional approaches, which use a simple compile-link-execute model, are unable to provide adequate application performance under the demands of the new conditions. Traditional approaches to interprocedural and profile-driven compilation can provide the application performance needed, but require infeasible amounts of compilation time to build the application. This thesis presents LLVM, a design and implementation of a compiler infrastructure which supports a unique multi-stage optimization system. This system is designed to support extensive interprocedural and profile-driven optimizations, while being efficient enough for use in commercial compiler systems. The LLVM virtual instruction set is the glue that holds the system together. It is a low-level representation, but with high-level type information. This provides the benefits of a low-level representation (compact representation, wide variety of available transformations, etc.) as well as providing high-level information to support aggressive interprocedural optimizations at link-and post-link time. In particular, this system is designed to support optimization in the field, both at run-time and during otherwise unused idle time on the machine. This thesis also describes an implementation of this compiler design, the LLVM compiler infrastructure , proving that the design is feasible. The LLVM compiler infrastructure is a maturing and efficient system, which we show is a good host for a variety of research. More information about LLVM can be found on its web site at: iii Acknowledgments This thesis would not be possible without the support of a large number of people who have helped me both in big ways and little. In particular, I would like to thank my advisor, Vikram Adve, for his support, patience, and especially his trust and respect. He has shown me how to communicate ideas more effectively and how to find important and meaningful topics for research. By being demanding, understanding, and allowing me the freedom to explore my interests, he has driven me to succeed. The inspiration for this work certainly stems from one person: Tanya. She has been a continuous source of support, ideas, encouragement, and understanding. Despite my many late nights, unimaginable amounts of stress, and a truly odd sense of humor, she has not just tolerated me, but loved me. Another person who made this possible, perhaps without truly understanding his contribution, has been Brian Ensink. Brian has been an invaluable sounding board for ideas, a welcoming ear to occasional frustrations, provider …
Transformation of BPMN to YAWL

Model transformations are frequently applied in business process modeling to bridge between languages on a different level of abstraction and formality. In this paper, we define a transformation between BPMN which is developed to enable business user to develop readily understandable graphical representations of business processes and YAWL, a formal workflow language that is able to capture all of the 20 workflow patterns reported. We illustrate the transformation challenges and present a suitable transformation algorithm. The benefit of the transformation is threefold. Firstly, it clarifies the semantics of BPMN via a mapping to YAWL. Secondly, the deployment of BPMN business process models is simplified. Thirdly, BPMN models can be analyzed with YAWL verification tools.
An Arabic Optical Braille Recognition System

Technology has shown great promise in providing access to textual information for visually impaired people. Optical Braille Recognition (OBR) allows people with visual impairments to read volumes of typewritten documents with the help of flatbed scanners and OBR software. This project looks at developing a system to recognize an image of embossed Arabic Braille and then convert it to text. It particularly aims to build fully functional Optical Arabic Braille Recognition system. It has two main tasks, first is to recognize printed Braille cells, and second is to convert them to regular text. Converting Braille to text is not simply a one to one mapping, because one cell may represent one symbol (alphabet letter, digit, or special character), two or more symbols, or part of a symbol. Moreover, multiple cells may represent a single symbol.
Example-Based facial animation for blend shape interpolation

This paper presents a system for example-based character animation using shape interpolation (blend shapes) generated from tracked offline facial feature. First, the subject was recorded expressing various facial expressions (happy, sad, fear, anger, surprise). The recordings were then processed and marked on specific places (feature points) on the face where certain expressions would have the most significant change (the eyes and mouth area). The facial movements, or control parameters, of the human subject are used as movement parameter for the freeform deformation blend shape interpolation.
Outlier Detection on Semantic Space for Sentiment Analysis With Convolutional Neural Networks

Sentiment analysis is a text categorization problem that consists in automatically assigning text documents to pre- defined classes that represent sentiments or a positive/negative opinion about a subject. To solve this task, machine learning techniques can be used. However, in order to achieve good gen- eralization, these techniques require a thorough pre-processing and an apropriate data representation. To deal with these fundamental issues, this work proposes the use of convolutional neural networks and density-based clustering algorithms. The word representations used in this work were obtained from vectors previously trained in an unsupervised way, denominated word embeddings. These representations are able to capture syntactic and semantic information of words, which leads to similar words to be projected closer together in the semantic space. In this scenario, in order to improve the performance of the convolutional neural network, the use of a clustering algorithm in the semantic space to extract additional information from the data is proposed. A density-based clustering algorithm was used to detect and remove outliers from the documents to be classified before these documents were used to train the con- volutional neural network. We conducted experiments with two different embeddings across three datasets in order to validate the effectiveness of our method. Results show that removing outliers from documents is capable of slightly improving the accuracy of the model and reducing computational cost for the non-static training approach.
The role of extracorporeal photopheresis in the treatment of cutaneous T-cell lymphomas.

Extracorporeal photochemotherapy (ECP) is an effective treatment modality for patients with erythrodermic myocosis fungoides (MF) and Sezary syndrome (SS). During ECP, a fraction of peripheral blood mononuclear cells is collected, incubated ex-vivo with methoxypsoralen, UVA irradiated, and finally reinfused to the patient. Although the mechanism of action of ECP is not well established, clinical and laboratory observations support the hypothesis of a vaccination-like effect. ECP induces apoptosis of normal and neoplastic lymphocytes, while enhancing differentiation of monocytes towards immature dendritic cells (imDCs), followed by engulfment of apoptotic bodies. After reinfusion, imDCs undergo maturation and antigenic peptides from the neoplastic cells are expressed on the surface of DCs. Mature DCs travel to lymph nodes and activate cytotoxic T-cell clones with specificity against tumor antigens. Disease control is mediated through cytotoxic T-lymphocytes with tumor specificity. The efficacy and excellent safety profile of ECP has been shown in a large number of retrospective trials. Previous studies showed that monotherapy with ECP produces an overall response rate of approximately 60%, while clinical data support that ECP is much more effective when combined with other immune modulating agents such as interferons or retinoids, or when used as consolidation treatment after total skin electron beam irradiation. However, only a proportion of patients actually respond to ECP and parameters predictive of response need to be discovered. A patient with a high probability of response to ECP must fulfill all of the following criteria: (1) SS or erythrodermic MF, (2) presence of neoplastic cells in peripheral blood, and (3) early disease onset. Despite the fact that ECP has been established as a standard treatment modality, no prospective randomized study has been conducted so far, to the authors' knowledge. Considering the high cost of the procedure, the role of ECP in the treatment of SS/MF needs to be clarified via well designed multicenter prospective randomized trials.
Sliding Line Point Regression for Shape Robust Scene Text Detection

Traditional text detection methods mostly focus on quadrangle text. In this study we propose a novel method named sliding line point regression (SLPR) in order to detect arbitrary-shape text in natural scene. SLPR regresses multiple points on the edge of text line and then utilizes these points to sketch the outlines of the text. The proposed SLPR can be adapted to many object detection architectures such as Faster R-CNN and R-FCN. Specifically, we first generate the smallest rectangular box including the text with region proposal network (RPN), then isometrically regress the points on the edge of text by using the vertically and horizontally sliding lines. To make full use of information and reduce redundancy, we calculate x-coordinate or y-coordinate of target point by the rectangular box position, and just regress the remaining y-coordinate or x-coordinate. Accordingly we can not only reduce the parameters of system, but also restrain the points which will generate more regular polygon. Our approach achieved competitive results on traditional ICDAR2015 Incidental Scene Text benchmark and curve text detection dataset CTW1500.
Boredom-Driven Curious Learning by Homeo-Heterostatic Value Gradients

This paper presents the Homeo-Heterostatic Value Gradients (HHVG) algorithm as a formal account on the constructive interplay between boredom and curiosity which gives rise to effective exploration and superior forward model learning. We offer an instrumental view of action selection, in which an action serves to disclose outcomes that have intrinsic meaningfulness to an agent itself. This motivated two central algorithmic ingredients: devaluation and devaluation progress, both underpin agent's cognition concerning intrinsically generated rewards. The two serve as an instantiation of homeostatic and heterostatic intrinsic motivation. A key insight from our algorithm is that the two seemingly opposite motivations can be reconciled-without which exploration and information-gathering cannot be effectively carried out. We supported this claim with empirical evidence, showing that boredom-enabled agents consistently outperformed other curious or explorative agent variants in model building benchmarks based on self-assisted experience accumulation.
The effect of pH and ionic strength of dissolution media on in-vitro release of two model drugs of different solubilities from HPMC matrices.

The evaluation of the effects of different media ionic strengths and pH on the release of hydrochlorothiazide, a poorly soluble drug, and diltiazem hydrochloride, a cationic and soluble drug, from a gel forming hydrophilic polymeric matrix was the objective of this study. The drug to polymer ratio of formulated tablets was 4:1. Hydrochlorothiazide or diltiazem HCl extended release (ER) matrices containing hypromellose (hydroxypropyl methylcellulose (HPMC)) were evaluated in media with a pH range of 1.2-7.5, using an automated USP type III, Bio-Dis dissolution apparatus. The ionic strength of the media was varied over a range of 0-0.4M to simulate the gastrointestinal fed and fasted states and various physiological pH conditions. Sodium chloride was used for ionic regulation due to its ability to salt out polymers in the midrange of the lyotropic series. The results showed that the ionic strength had a profound effect on the drug release from the diltiazem HCl K100LV matrices. The K4M, K15M and K100M tablets however withstood the effects of media ionic strength and showed a decrease in drug release to occur with an increase in ionic strength. For example, drug release after the 1h mark for the K100M matrices in water was 36%. Drug release in pH 1.2 after 1h was 30%. An increase of the pH 1.2 ionic strength to 0.4M saw a reduction of drug release to 26%. This was the general trend for the K4M and K15M matrices as well. The similarity factor f2 was calculated using drug release in water as a reference. Despite similarity occurring for all the diltiazem HCl matrices in the pH 1.2 media (f2=64-72), increases of ionic strength at 0.2M and 0.4M brought about dissimilarity. The hydrochlorothiazide tablet matrices showed similarity at all the ionic strength tested for all polymers (f2=56-81). The values of f2 however reduced with increasing ionic strengths. DSC hydration results explained the hydrochlorothiazide release from their HPMC matrices. There was an increase in bound water as ionic strengths increased. Texture analysis was employed to determine the gel strength and also to explain the drug release for the diltiazem hydrochloride. This methodology can be used as a valuable tool for predicting potential ionic effects related to in vivo fed and fasted states on drug release from hydrophilic ER matrices.