nils-herrmann/clean_scirepeval huggingface dataset:
Doping control in sport: An investigation of how elite athletes perceive and trust the functioning of the doping testing system in their sport

Doping testing is a key component enforced by anti doping authorities to detect and deter doping in sport. Policy is developed to protect athletes' right to participate in doping free sport; and testing is a key tool to secure this right. Accordingly, athletes' responses to anti doping efforts are important. This article explores how the International Standards for Testing, which face different interpretations and challenges when policy is implemented, are perceived by elite athletes. Particularly, this article aims to investigate how elite athletes perceive the functioning of the testing system (i.e. the efforts of stakeholders involved in testing) in their own sport both nationally and worldwide. Moreover, it seeks to identify whether specific factors such as previous experience of testing and perceived proximity of doping have an impact on athletes' perceptions of the testing system. The study comprises a web based questionnaire (N=645; response rate 43% and uses qualitative findings to elaborate on and explain quantitative results. Results showed that two thirds of the athletes reported the national testing programme in their sport to be appropriate. A majority of the athletes who had an opinion on the subject regarded testing programmes in some countries as not extensive enough or believed that in certain countries doping control was downgraded to win medals. Past experience of testing seemed to have a positive influence on trust in the concrete measures; however, if athletes experienced flaws during the control procedures, this could increase distrust and cause worry. The proximity of doping in an athlete's sport influenced the athlete's perception of the testing system. Particularly, athletes who need the testing system to be effective and to function well across the world show greater distrust of or dissatisfaction with the current testing system. The athletes' diverging views indicate that contemporary anti doping policy is simultaneously met with support, (dis)trust and frustration. By integrating the views and experiences of Danish elite athletes, this study confirms that the current testing system is confronted with obstacles, and it contributes knowledge about some of the challenges WADA faces when policy is implemented. Implications of results and recommendations for anti doping authorities are outlined in the paper.
Aspect Based Sentiment Analysis Survey

Sentiment analysis or Opinion mining is becoming an important task both from academics and commercial standpoint. In recent years text mining has become most promising area for research. There is an exponential growth with respect to World Wide Web, Mobile Technologies, Internet usage and business on electronic commerce applications. Because of which web opinion sources like online shopping portals, discussion forums, peer to peer networks, groups, blogs, micro blogs and social networking applications are extensively used to share the information, experience and opinions. In sentiment analysis, the opinion is evaluated to its positivity, negativity and neutrality with respect to the complete document or object. But this level of analysis does not provide the necessary detailed information for many applications. To obtain more fine grained analysis we need to go to Aspect Based Sentiment Analysis (ABSA) Aspect Based Sentiment analysis introduces a suite of problems which require deeper NLP capabilities and also produces a rich set of results.
Corporate social responsibility reporting and organizational stigma: the case of "sin" industries.

We examine corporate social responsibility (CSR) reporting strategies by focusing on stigmatized firms belonging to the alcohol, tobacco, gambling, nuclear energy and firearm sectors. These are often described as "sins" due to their perceived deviation from broadly endorsed standards. We employ a sample of 109 U.S. listed "sin" firms for a seven year period (2003 2009) and control with another set of 109 similar sized, non "sin" firms for the same period. We find that "sin" firms are more prone to issuing standalone CSR reports. We also demonstrate that a greater risk of litigation by third parties increases the likelihood of a "sin" firm instigating CSR reports, while variations in ownership structure do not. By drawing upon literature on organizational stigma, we argue that CSR disclosures constitute an integral part of "sin" firms' strategic goal to distract attention from their controversial activities, lessen the negative consequences of stigmatization and neutralize the impact of litigation proceedings.
Novel methods to select environmental variables in MaxEnt: A case study using invasive crayfish

The popularity of MaxEnt in species distribution modeling has been driven by several factors including its high degree of accuracy, and flexibility to tailor efforts to species specific situations. Although many recent studies have identified the importance of adjusting mathematical transformation (feature class) and regularization of coefficient values, collectively known as tuning, few studies have addressed the need to customize the variables used in species distribution modeling, and use unselected variable sets. This study presents two novel methods to select for environmental variables in MaxEnt. The first involves selecting from a priori determined environmental variable sets (pre selected based on ecological or biological knowledge) and the second utilizes a reiterative process of model formation and stepwise removal of least contributing variables. Both methods were tested on eight known species of invasive crayfish, with results reinforcing the need for species specific environmental variable sets. While the reiterative process generally performs better than the a priori selected variables, selection of method can be based on information availability. These techniques appear to outperform the current practice of utilizing unselected variable sets and is especially important considering the increasing application of species distribution modeling (across spatial and temporal barriers) in conservation and management efforts whereby inaccurate predictions might have adverse effects.
Effects of performance level on lower limb kinematics during table tennis forehand loop.
Venue: Acta of bioengineering and biomechanics

Understanding of biomechanics is important in performance development since each skill has a fundamental mechanical structure. The purpose of this study was to investigate differences in lower limb kinematics during table tennis forehand loop between superior players (SP) and intermediate players (IP) Thirteen male players as superior and thirteen as intermediate participated in this test. A VICON motion analysis system and a Novel Pedar insole plantar pressure measurement system were used to record kinematic and contact area data, respectively. Participants were asked to execute single forehand loop against topspin ball with maximal power. Key findings were that SP showed significantly larger hip flexion and knee external rotation at backward end and larger hip internal rotation and extension at forward end compared with IP. Contact areas at both events were larger for SP. In addition, SP showed significantly larger joints angular changing rate during forward swing at the ankle and hip. Results indicated that SP possessed better ability of using lower limb drive in forehand loop.
FPGA Based Accelerator for Deep Convolutional Neural Networks for the SPARK Environment
Venue: 2016 IEEE International Conference on Smart Cloud (SmartCloud)

Deep Convolutional Neural Networks are known for their high performance. However, their complexity is one of their most challenging aspects. In this paper, we propose a design of an FPGA based accelerator for the distributed training of convolutional neural networks. The accelerator is intended for use in the SPARK run time environment to accelerate the training of deep convolutional neural networks in the data center. Our accelerator is very energy efficient and achieves 40 to 250 times speedup in the computation of the multi layer convolution operation, a key operation in the training/inference of deep networks.
Deep Gaussian Processes for Regression using Approximate Expectation Propagation
Venue: ICML

Deep Gaussian processes (DGPs) are multilayer hierarchical generalisations of Gaussian processes (GPs) and are formally equivalent to neural networks with multiple, infinitely wide hidden layers. DGPs are nonparametric probabilistic models and as such are arguably more flexible, have a greater capacity to generalise, and provide better calibrated uncertainty estimates than alternative deep models. This paper develops a new approximate Bayesian learning scheme that enables DGPs to be applied to a range of medium to large scale regression problems for the first time. The new method uses an approximate Expectation Propagation procedure and a novel and efficient extension of the probabilistic backpropagation algorithm for learning. We evaluate the new method for non linear regression on eleven real world datasets, showing that it always outperforms GP regression and is almost always better than state of the art deterministic and sampling based approximate inference methods for Bayesian neural networks. As a by product, this work provides a comprehensive analysis of six approximate Bayesian methods for training neural networks.
New records of the Japanese seahorse Hippocampus mohnikei in Southeast Asia lead to updates in range, habitat and threats.
Venue: Journal of fish biology

New records of the Japanese seahorse Hippocampus mohnikei from Cambodia, Malaysia, Thailand and Vietnam, along with recently published studies from India and Singapore, have greatly expanded the known range of H. mohnikei within Southeast Asia. These new records reveal novel habitat preferences and threats to H. mohnikei in the region. Although the global conservation status of H. mohnikei is classified as Data Deficient according to the IUCN Red List of Threatened Species, new sightings indicate that this species is found in similar habitats and faces similar threats as other Hippocampus species that are considered Vulnerable.
Population biology of an endangered species: the common guitarfish Rhinobatos rhinobatos in Lebanese marine waters of the eastern Mediterranean Sea.
Venue: Journal of fish biology

This study focuses on the population biology of the common guitarfish Rhinobatos rhinobatos, a cartilaginous fish listed as Endangered in the International Union for the Conservation of Nature (IUCN) Red List. Between December 2012 and January 2014, 67 individuals were collected by bottom longlining in coastal Lebanese marine waters at different ports at depths ranging from 10 to 110 m. The total length (L(T) of the specimens ranged from 50 to 143 cm, and the mean s.d. was 76.2 19.7 cm. The most common L(T) classes were between 60 and 70 cm. The total mass of the specimens ranged from 410 to 10,000 g, and the mean s.d. was 1841 1987 g. A total of 34 males and 33 females were collected, and the sex ratio was not significantly different from 1:1. The mass and L(T) relationship showed positive allometric growth (b 3.096 and r(2) 0.99) and the mean s.d. L(T) at which 50% of the individuals were sexually mature was 84.73 5.81 cm for females and 78.57 4.88 cm for males. The gonado somatic and hepato somatic indices were determined along with a condition factor, and parturition appeared to occur in winter. The primary prey items found in the fish stomachs during the autumn and winter seasons were Penaeidae. The results of this study will help to parameterize models of the population dynamics for this exploited fish stock to ensure the long term sustainability of its fishery.
Systems genetics reveals key genetic elements of drought induced gene regulation in diploid potato.
Venue: Plant, cell environment

In plants, tolerance to drought stress is a result of numerous minor effect loci in which transcriptional regulation contributes significantly to the observed phenotypes. Under severe drought conditions, a major expression quantitative trait loci hotspot was identified on chromosome five in potato. A putative Nuclear factor y subunit C4 was identified as key candidate in the regulatory cascade in response to drought. Further investigation of the eQTL hotspots suggests a role for a putative Homeobox leucine zipper protein 12 in relation to drought in potato. Genes strongly co expressed with Homeobox leucine zipper protein 12 were plant growth regulators responsive to water deficit stress in Arabidopsis thaliana, implying a possible conserved mechanism. Integrative analysis of genetic, genomic, phenotypic and transcriptomic data provided insights in the downstream functional components of the drought response. The abscisic acid and environmental stress inducible protein TAS14 was highly induced by severe drought in potato and acts as a reliable biomarker for the level of stress perceived by the plant. The systems genetics approach supported a role for multiple genes responsive to severe drought stress of Solanum tuberosum. The combination of gene regulatory networks, expression quantitative trait loci mapping and phenotypic analysis proved useful for candidate gene selection.
PUBLIC KEY ENCRYPTION WITH CONJUNCTIVE FIELD FREE KEYWORD SEARCH SCHEME
Venue: BIOINFORMATICS 2016

Searchable encryption allows a remote server to search over encrypted documents without knowing the sensitive data contents. Prior searchable symmetric encryption schemes focus on single keyword search. Conjunctive Keyword Searches (CKS) schemes improve system usability by retrieving the matched documents. In this type of search, the user has to repeatedly perform the search protocol for many times. Most of existent (CKS) schemes use conjunctive keyword searches with fixed position keyword fields, this type of search is not useful for many applications, such as unstructured text. In our paper, we propose a new public key encryption scheme based on bilinear pairings, the scheme supports conjunctive keyword search queries on encrypted data without needing to specify the positions of the keywords where the keywords can be in any arbitrary order. Instead of giving the server one trapdoor for each keyword in the conjunction set, we use a bilinear map per a set of combined keywords to make them regarded as one keyword. In another meaning, the proposed method will retrieve the data in one round of communication between the user and server. Furthermore, the search process could not reveal any information about the number of keywords in the query expression. Through analysis section we determine how such scheme could be used to guarantee fast and secure access to the database.
Asymmetric Searchable Encryption from Inner Product Encryption
Venue: 3PGCIC

Asymmetric searchable encryption(ASE) enables one to retrieve encrypted data stored on an untrusted server without revealing the contents. Now, beyond single keyword search, more and more attention have already been paid to the problem of multi keyword search. However, existing schemes are mainly based on composite order bilinear groups. In this paper, we propose a public key encryption with conjunctive and disjunctive keyword search(PECDK) scheme which can simultaneously support conjunction and disjunction within each keyword field for cloud storage. It is based on prime order bilinear groups, and can be proved fully secure under the standard model.
The Role of Motivation and Cognitive Engagement in Science Achievement.

The purpose of this study was to investigate the contribution of the motivational beliefs (self efficacy and task value) and cognitive engagement to seventh grade students' science achievement. For the specified purpose, crosssectional correlational research design was used. The data were gathered from the seventh grade students of public middle schools by means of three data collection instruments namely,  Survey (BCS) Motivation and Cognitive Engagement Scale (MCES) and Science Achievement Test for 7th Grade (SAT) The MCES is a self report instrument including the selected items from the Science Learning Inventory (SLIPart A) and from Turkish Version of Motivated Strategies for Learning Questionnaire (MSLQ) in order to measure students' motivational beliefs (self efficacy and task value) and the level of their cognitive engagement. A total of 861 seventh grade students (398 girls and 456 boys) participated in the study. Multiple Linear Regression Analysis was used to analyze the data. Results revealed that motivational beliefs (i.e. self efficacy and task value) positively and significantly contributed to the prediction of students' science achievement and the self efficacy appeared as the best predictor of the science achievement. Cognitive engagement failed to significantly predict students' science achievement. Finally, bivariate relations among independent variables (self efficacy, task value and cognitive engagement) were examined through simple correlation analyses. The result indicated positive and significant correlations among self efficacy, task value and cognitive engagement variables.
Timing Analysis for Inferring the Topology of the Bitcoin Peer to Peer Network
Venue: 2016 Intl IEEE Conferences on Ubiquitous Intelligence Computing, Advanced and Trusted Computing, Scalable Computing and Communications, Cloud and Big Data Computing, Internet of People, and Smart World Congress (UIC/ATC/ScalCom/CBDCom/IoP/SmartWorld)

Flooding Peer to Peer (P2P) networks form the basis of services such as the electronic currency system Bitcoin. The decentralized architecture enables robustness against failure. However, knowledge of the network's topology can allow adversaries to attack specific peers in order to, e.g. isolate certain peers or even partition the network. Knowledge of the topology might be gained by observing the flooding process, which is inherently possible in such networks, performing a timing analysis on the observations. In this paper we present a timing analysis method that targets flooding P2P networks, show its theoretical, practical feasibility. A validation in the real world Bitcoin network proves the possibility of inferring network links of actively participating peers with substantial precision, recall (both 40% potentially enabling attacks on the network. Additionally, we analyze the countermeasure of trickling, quantify the tradeoff between the effectiveness of the countermeasure, the expected performance penalty. The analysis shows that inappropriate parametrization can actually facilitate inference attacks.
Pseudomonas aeruginosa Evolutionary Adaptation and Diversification in Cystic Fibrosis Chronic Lung Infections
Venue: Trends in microbiology

Pseudomonas aeruginosa populations undergo a characteristic evolutionary adaptation during chronic infection of the cystic fibrosis (CF) lung, including reduced production of virulence factors, transition to a biofilm associated lifestyle, and evolution of high level antibiotic resistance. Populations of P. aeruginosa in chronic CF lung infections typically exhibit high phenotypic diversity, including for clinically important traits such as antibiotic resistance and toxin production, and this diversity is dynamic over time, making accurate diagnosis and treatment challenging. Population genomics studies reveal extensive genetic diversity within patients, including for transmissible strains the coexistence of highly divergent lineages acquired by patient to patient transmission. The inherent spatial structure and spatial heterogeneity of selection in the CF lung appears to play a key role in driving P. aeruginosa diversification.
Solving the Sophistication Population Paradox of Game Refinement Theory
Venue: ICEC

A mathematical model of game refinement was proposed based on uncertainty of game outcome. This model has been shown to be useful in measuring the entertainment element in the domains such as boardgames and sport games. However, game refinement theory has not been able to explain the correlation between the popularity of a game and the game refinement value. This paper introduces another aspect in the study of game entertainment, the concept of "attractiveness" to reasonably explain the sophistication population paradox of game refinement theory.
Measuring force of game information in the brain: Linking game refinement theory and neuroscience
Venue: 2016 8th International Conference on Knowledge and Smart Technology (KST)

In this research the concept of game refinement is linked to a new approach of neuroscience in order to model the force of the game information acting in the brain. The amount of force acting in the brain is related to the emotional impacts in the mind as an excitement or an addiction to the game. Measures of the acceleration of the game information have been proposed in earlier studies. A conceptual idea for a possible measure of the mass component of the force of the game information in the brain is proposed. This measure of the mass is based on the neuronal activity in the brain. The idea of the force model is supported with examples of well known sports and video games.
Model Selection for Gaussian Process Regression by Approximation Set Coding

Gaussian processes are powerful, yet analytically tractable models for supervised learning. A Gaussian process is characterized by a mean function and a covariance function (kernel) which are determined by a model selection criterion. The functions to be compared do not just differ in their parametrization but in their fundamental structure. It is often not clear which function structure to choose, for instance to decide between a squared exponential and a rational quadratic kernel. Based on the principle of approximation set coding, we develop a framework for model selection to rank kernels for Gaussian process regression. In our experiments approximation set coding shows promise to become a model selection criterion competitive with maximum evidence (also called marginal likelihood) and leave one out cross validation.