nils-herrmann/clean_scirepeval huggingface dataset:
Low carbohydrate, high fat diet impairs exercise economy and negates the performance benefit from intensified training in elite race walkers

Three weeks of intensified training and mild energy deficit in elite race walkers increases peak aerobic capacity independent of dietary support. Adaptation to a ketogenic low carbohydrate, high fat (LCHF) diet markedly increases rates of whole-body fat oxidation during exercise in race walkers over a range of exercise intensities. The increased rates of fat oxidation result in reduced economy (increased oxygen demand for a given speed) at velocities that translate to real-life race performance in elite race walkers. In contrast to training with diets providing chronic or periodised high carbohydrate availability, adaptation to an LCHF diet impairs performance in elite endurance athletes despite a significant improvement in peak aerobic capacity.



We investigated the effects of adaptation to a ketogenic low carbohydrate (CHO), high fat diet (LCHF) during 3 weeks of intensified training on metabolism and performance of world-class endurance athletes. We controlled three isoenergetic diets in elite race walkers: high CHO availability (g kg-1  day-1 : 8.6 CHO, 2.1 protein, 1.2 fat) consumed before, during and after training (HCHO, n = 9); identical macronutrient intake, periodised within or between days to alternate between low and high CHO availability (PCHO, n = 10); LCHF (< 50 g day-1 CHO; 78% energy as fat; 2.1 g kg-1  day-1 protein; LCHF, n = 10). Post-intervention, V̇O2 peak during race walking increased in all groups (P < 0.001, 90% CI: 2.55, 5.20%). LCHF was associated with markedly increased rates of whole-body fat oxidation, attaining peak rates of 1.57 ± 0.32 g min-1 during 2 h of walking at ∼80% V̇O2 peak . However, LCHF also increased the oxygen (O2 ) cost of race walking at velocities relevant to real-life race performance: O2 uptake (expressed as a percentage of new V̇O2 peak ) at a speed approximating 20 km race pace was reduced in HCHO and PCHO (90% CI: -7.047, -2.55 and -5.18, -0.86, respectively), but was maintained at pre-intervention levels in LCHF. HCHO and PCHO groups improved times for 10 km race walk: 6.6% (90% CI: 4.1, 9.1%) and 5.3% (3.4, 7.2%), with no improvement (-1.6% (-8.5, 5.3%)) for the LCHF group. In contrast to training with diets providing chronic or periodised high-CHO availability, and despite a significant improvement in V̇O2 peak , adaptation to the topical LCHF diet negated performance benefits in elite endurance athletes, in part due to reduced exercise economy.
Interacting with the SOA-Based Internet of Things: Discovery, Query, Selection, and On-Demand Provisioning of Web Services

The increasing usage of smart embedded devices in business blurs the line between the virtual and real worlds. This creates new opportunities to build applications that better integrate real-time state of the physical world, and hence, provides enterprise services that are highly dynamic, more diverse, and efficient. Service-Oriented Architecture (SOA) approaches traditionally used to couple functionality of heavyweight corporate IT systems, are becoming applicable to embedded real-world devices, i.e., objects of the physical world that feature embedded processing and communication. In such infrastructures, composed of large numbers of networked, resource-limited devices, the discovery of services and on-demand provisioning of missing functionality is a significant challenge. We propose a process and a suitable system architecture that enables developers and business process designers to dynamically query, select, and use running instances of real-world services (i.e., services running on physical devices) or even deploy new ones on-demand, all in the context of composite, real-world business applications.
Ecological resilience in urban ecosystems: Linking urban patterns to human and ecological functions

Urban ecosystems evolve over time and space as the outcome of dynamic interactions between socio-economic and biophysical processes operating over multiple scales. The ecological resilience of urban ecosystems—the degree to which they tolerate alteration before reorganizing around a new set of structures and processes—is influenced by these interactions. In cities and urbanizing areas fragmentation of natural habitats, simplification and homogenization of species composition, disruption of hydrological systems, and alteration of energy flow and nutrient cycling reduce cross-scale resilience, leaving systems increasingly vulnerable to shifts in system control and structure. Because varied urban development patterns affect the amount and interspersion of built and natural land cover, as well as the human demands on ecosystems differently, we argue that alternative urban patterns (i.e., urban form, land use distribution, and connectivity) generate varied effects on ecosystem dynamics and their ecological resilience. We build on urban economics, landscape ecology, population dynamics, and complex system science to propose a conceptual model and a set of hypotheses that explicitly link urban pattern to human and ecosystem functions in urban ecosystems. Drawing on preliminary results from an empirical study of the relationships between urban pattern and bird and aquatic macroinvertebrate diversity in the Puget Sound region, we propose that resilience in urban ecosystems is a function of the patterns of human activities and natural habitats that control and are controlled by both socio-economic and biophysical processes operating at various scales. We discuss the implications of this conceptual model for urban planning and design.
An empirical study of customers' perceptions of security and trust in e-payment systems

It is commonly believed that good security improves trust, and that the perceptions of good security and trust will ultimately increase the use of electronic commerce. In fact, customers’ perceptions of the security of e-payment systems have become a major factor in the evolution of electronic commerce in markets. In this paper, we examine issues related to e-payment security from the viewpoint of customers. This study proposes a conceptual model that delineates the determinants of consumers’ perceived security and perceived trust, as well as the effects of perceived security and perceived trust on the use of epayment systems. To test the model, structural equation modeling is employed to analyze data collected from 219 respondents in Korea. This research provides a theoretical foundation for academics and also practical guidelines for service providers in dealing with the security aspects of e-payment systems.
Venture: a higher-order probabilistic programming platform with programmable inference

We describe Venture, an interactive virtual machine for probabilistic programming that aims to be sufficiently expressive, extensible, and efficient for general-purpose use. Like Church, probabilistic models and inference problems in Venture are specified via a Turing-complete, higher-order probabilistic language descended from Lisp. Unlike Church, Venture also provides a compositional language for custom inference strategies, assembled from scalable implementations of several exact and approximate techniques. Venture is thus applicable to problems involving widely varying model families, dataset sizes and runtime/accuracy constraints. We also describe four key aspects of Venture’s implementation that build on ideas from probabilistic graphical models. First, we describe the stochastic procedure interface (SPI) that specifies and encapsulates primitive random variables, analogously to conditional probability tables in a Bayesian network. The SPI supports custom control flow, higher-order probabilistic procedures, partially exchangeable sequences and “likelihood-free” stochastic simulators, all with custom proposals. It also supports the integration of external models that dynamically create, destroy and perform inference over latent variables hidden from Venture. Second, we describe probabilistic execution traces (PETs), which represent execution histories of Venture programs. Like Bayesian networks, PETs capture conditional dependencies, but PETs also represent existential dependencies and exchangeable coupling. Third, we describe partitions of execution histories called scaffolds that can be efficiently constructed from PETs and that factor global inference problems into coherent sub-problems. Finally, we describe a family of stochastic regeneration algorithms for efficiently modifying PET fragments contained within scaffolds without visiting conditionally independent random choices. Stochastic regeneration insulates inference algorithms from the complexities introduced by changes in execution structure, with runtime that scales linearly in cases where previous approaches often scaled quadratically and were therefore impractical. We show how to use stochastic regeneration and the SPI to implement general-purpose inference strategies such as Metropolis-Hastings, Gibbs sampling, and blocked proposals based on hybrids with both particle Markov chain Monte Carlo and mean-field variational inference techniques.
A process model for analyzing and managing flexibility in information systems

23 May 2011  20 February 2012 2nd  7 November 2012  Competitive pressures are forcing organizations to be flexible. Being responsive to changing environmental conditions is an important factor in determining corporate performance. Earlier research, focusing primarily on IT infrastructure, has shown that organizational flexibility is closely related to IT infrastructure flexibility. Using real-world cases, this paper explores flexibility in the broader context of the IS function. An empirically derived framework for better understanding and managing IS flexibility is developed using grounded theory and content analysis. A process model for managing flexibility is presented; it includes steps for understanding contextual factors, recognizing reasons why flexibility is important, evaluating what needs to be flexible, identifying flexibility categories and stakeholders, diagnosing types of flexibility needed, understanding synergies and tradeoffs between them, and prescribing strategies for proactively managing IS flexibility. Three major flexibility categories, flexibility in IS operations, flexibility in IS systems & services development and deployment, and flexibility in IS management, containing 10 IS flexibility types are identified and described.  (2014) 23, 151–184. doi:10.1057/ejis.2012.53; published online 8 January 2013
A comparison of the pressure exerted on soft tissue by 2 myofascial rollers.

Self-myofascial release (SMR) is a technique used to treat myofascial restrictions and restore soft-tissue extensibility.



To determine whether the pressure and contact area on the lateral thigh differ between a Multilevel rigid roller (MRR) and a Bio-Foam roller (BFR) for participants performing SMR.



Ten healthy young men and women.



Participants performed an SMR technique on the lateral thigh using both myofascial rollers. Thin-film pressure sensels recorded pressure and contact area during each SMR trial.



Mean sensel pressure exerted on the soft tissue of the lateral thigh by the MRR (51.8 +/- 10.7 kPa) was significantly (P < .001) greater than that of the conventional BFR (33.4 +/- 6.4 kPa). Mean contact area of the MRR (47.0 +/- 16.1 cm2) was significantly (P < .005) less than that of the BFR (68.4 +/- 25.3 cm2).



The significantly higher pressure and isolated contact area with the MRR suggest a potential benefit in SMR.
Unsupervised Learning of Evolving Relationships Between Literary Characters

Understanding inter-character relationships is fundamental for understanding character intentions and goals in a narrative. This paper addresses unsupervised modeling of relationships between characters. We model relationships as dynamic phenomenon, represented as evolving sequences of latent states empirically learned from data. Unlike most previous work our approach is completely unsupervised. This enables data-driven inference of inter-character relationship types beyond simple sentiment polarities, by incorporating lexical and semantic representations, and leveraging large quantities of raw text. We present three models based on rich sets of linguistic features that capture various cues about relationships. We compare these models with existing techniques and also demonstrate that relationship categories learned by our model are semantically coherent.
Extraction of Level 2 and Level 3 Features for Fragmentary Fingerprint Comparison

Most current commercial automated fingerprint-authentication systems on the market are based on the extraction of the fingerprint minutiae, and on medium resolution (500 dpi) scanners. Sensor manufacturers tend to reduce the sensing area in order to adapt it to low-power mobile hand-held communication systems and to lower the cost of their devices. An interesting alternative is designing a novel fingerprintauthentication system capable of dealing with an image from a small, high resolution (1000 dpi) sensor area based on combined level 2 (minutiae) and level 3 (sweat pores) feature extraction. In this paper, we propose a new strategy and implementation of a series of techniques for automatic level 2 and level 3 feature extraction in fragmentary fingerprint comparison. The main challenge in achieving high reliability while using a small portion of a fingerprint for matching is that there may not be a sufficient number of minutiae but the uniqueness of the pore configurations provides a powerful means to compensate for this insufficiency. A pilot study performed to test the presented approach confirms the efficacy of using pores in addition to the traditionally used minutiae in fragmentary fingerprint comparison.
Corda : An Introduction

A distributed ledger made up of mutually distrusting nodes would allow for a single global database that records the state of deals and obligations between institutions and people. This would eliminate much of the manual, time consuming effort currently required to keep disparate ledgers synchronised with each other. It would also allow for greater levels of code sharing than presently used in the financial industry, reducing the cost of financial services for everyone. We present Corda, a platform which is designed to achieve these goals. This paper provides a high level introduction intended for the general reader. A forthcoming technical white paper elaborates on the design and fundamental architectural decisions.
Explaining Explanations to Society

There is a disconnect between explanatory artificial intelligence (XAI) methods and the types of explanations that are useful for and demanded by society (policy makers, government officials, etc.) Questions that experts in artificial intelligence (AI) ask opaque systems provide inside explanations, focused on debugging, reliability, and validation. These are different from those that society will ask of these systems to build trust and confidence in their decisions. Although explanatory AI systems can answer many questions that experts desire, they often don’t explain why they made decisions in a way that is precise (true to the model) and understandable to humans. These outside explanations can be used to build trust, comply with regulatory and policy changes, and act as external validation. In this paper, we focus on XAI methods for deep neural networks (DNNs) because of DNNs’ use in decision-making and inherent opacity. We explore the types of questions that explanatory DNN systems can answer and discuss challenges in building explanatory systems that provide outside explanations for societal requirements and benefit.
Gender differences in ADHD: a meta-analysis and critical review.

To quantitatively review and critically evaluate literature examining gender differences in attention-deficit hyperactivity disorder (ADHD).



A meta-analysis of relevant research based on 18 studies meeting inclusion criteria was performed. Domains evaluated included primary symptomatology, intellectual and academic functioning, comorbid behavior problems, social behavior, and family variables.



Gender differences were not found in impulsivity, academic performance, social functioning fine motor skills, parental education, or parental depression. However, compared with ADHD boys, ADHD girls displayed greater intellectual impairment, lower levels of hyperactivity, and lower rates of other externalizing behaviors; it was not possible to evaluate the extent to which referral bias affected these findings. Some gender differences were clearly mediated by the effects of referral source; among children with ADHD identified from nonreferred populations, girls with ADHD displayed lower levels of inattention, internalizing behavior, and peer aggression than boys with ADHD, while girls and boys with ADHD identified from clinic-referred samples displayed similar levels of impairment on these variables.



The need for future research examining gender differences in ADHD is strongly indicated, with attention to methodological limitations of the current literature, including the potential confounding effects of referral bias, comorbidity, developmental patterns, diagnostic procedures, and rater source.
A Study on Optical Character Recognition Techniques

Optical Character Recognition (OCR) is the process which enables a system to without human intervention identifies the scripts or alphabets written into the users’ verbal communication. Optical Character identification has grown to be individual of the mainly flourishing applications of knowledge in the field of pattern detection and artificial intelligence. In our survey we study on the various OCR techniques. In this paper we resolve and examine the hypothetical and numerical models of Optical Character Identification. The Optical character identification or classification (OCR) and Magnetic Character Recognition (MCR) techniques are generally utilized for the recognition of patterns or alphabets. In general the alphabets are in the variety of pixel pictures and it could be either handwritten or stamped, of any series, shape or direction etc. Alternatively in MCR the alphabets are stamped with magnetic ink and the studying machine categorize the alphabet on the basis of the exclusive magnetic field that is shaped by every alphabet. Both MCR and OCR discover utilization in banking and different trade appliances. Earlier exploration going on Optical Character detection or recognition has shown that the In Handwritten text there is no limitation lying on the script technique. Hand written correspondence is complicated to be familiar through due to diverse human handwriting style, disparity in angle, size and shape of calligraphy. An assortment of approaches of Optical Character Identification is discussed here all along through their achievement.
Security Improvement in Authentication Protocol for Gen-2 Based RFID System

The EPC Class-1 Generation-2 specification(Gen-2 in brief) is widely accepted as the standard for RFID tags under grant number ISO18000-6c. However, there are two problems in view of its security authentication protocols. First of all, there is no unified standard for the security requirements, such as which threats should be protected against. Secondly, there is no widely acceptable means to guarantee the security, for the provable security methods are not applicable without using encryption function or hash function. In this paper, we study the security requirements presented in the current Gen-2 based RFID authentication protocols, and summarize an Enhancing Security Standard that contains all 10 security characteristics discussed in literatures [10-17]. We point out the security drawbacks of Chien’s mutual authentication protocol[10] , and improve the protocol based on the 10 security requirements. Our improved protocol merely uses CRC and PRNG operations supported by Gen-2 that require very low communication and computation loads. We also develop two methods based on BAN logic and AVISTA to prove the security of RFID protocol. BAN logic is used to give the proof of protocol correctness, and AVISTA is used to affirm the authentication and secrecy properties.
Hybrid Particle Swarm Optimization-Firefly algorithm (HPSOFF) for combinatorial optimization of non-slicing VLSI floorplanning

Floorplanning is the initial step in the process of designing layout of the chip. It is employed to plan the positions and shapes of modules during the process of VLSI Design cycle to optimize the cost metrics like layout area and wirelength. In this paper, a Hybrid Particle Swarm Optimization-Firefly (HPSOFF) algorithm is proposed which integrates Particle Swarm Optimization (PSO), Firefly (FF) and Modified Corner List (MCL) algorithms. Initially, PSO algorithm utilizes MCL algorithm for non-slicing floorplan representations and fitness value evaluation. The solutions obtained from PSO are provided as initial solutions to FF algorithm. Fitness function evaluation and floorplan representations for FF algorithm are again carried out using MCL algorithm. The proposed algorithm is illustrated using Microelectronics Centre of North Carolina (MCNC) and Gigascale Systems Research Centre (GSRC) benchmark circuits. The results obtained are compared with the solutions derived from other stochastic algorithms and the proposed algorithm provides better solutions for both the benchmark circuits. 9
Virtual, Augmented and Mixed Reality: Interaction, Navigation, Visualization, Embodiment, and Simulation

Touch gestures can be a very important aspect when developing mobile applications with enhanced reality. The main purpose of this research was to determine which touch gestures were most frequently used by engineering students when using a simulation of a projectile motion in a mobile AR applica‐ tion. A randomized experimental design was given to students, and the results showed the most commonly used gestures to visualize are: zoom in “pinch open”, zoom out “pinch closed”, move “drag” and spin “rotate”.
Fast dropout training

Preventing feature co-adaptation by encouraging independent contributions from different features often improves classification and regression performance. Dropout training (Hinton et al., 2012) does this by randomly dropping out (zeroing) hidden units and input features during training of neural networks. However, repeatedly sampling a random subset of input features makes training much slower. Based on an examination of the implied objective function of dropout training, we show how to do fast dropout training by sampling from or integrating a Gaussian approximation, instead of doing Monte Carlo optimization of this objective. This approximation, justified by the central limit theorem and empirical evidence, gives an order of magnitude speedup and more stability. We show how to do fast dropout training for classification, regression, and multilayer neural networks. Beyond dropout, our technique is extended to integrate out other types of noise and small image transformations.