nils-herrmann/clean_scirepeval huggingface dataset:
Development of the social brain during adolescence

This article describes recent research which informs our understanding of changes in social cognition during adolescence. The focus will be on mentalising, the ability to attribute and manipulate mental states in the self and others. Mentalising is supported by the medial prefrontal cortex (MPFC) and both anterior and posterior regions of the temporal lobes. In the past decade, studies have demonstrated development during adolescence of white and grey matter brain structure, with most protracted changes observed in frontal and temporal lobes, including those regions supporting mentalising. This article presents evidence that certain aspects of social cognition continue to change during adolescence, highlighting results from recent research investigating the use of theory of mind information in a communicative context. The findings highlight how adolescence, and not only childhood, is a time of continued maturation of brain and behaviour, when education and the environment can have an impact on cognitive development.
Implementation of wireless M-bus concentrator/gateway for remote reading of smart gas meters

In order to achieve better collecting consumption, diagnostic and status of water, natural gas and electricity metering, an electronic device known as smart meter is introduced. These devices are increasingly installed around the globe and together with Automatic Meter Reading (AMR) technology form the basis of future intelligent metering. Devices known as concentrators collect consumption records from smart meters and send them for further processing and analysis. This paper describes the implementation and analysis of one universal electronic device that can be used as concentrator, gateway or both. Implemented device has been tested in real conditions with a smart gas meters. Meter-Bus (M-Bus) standards were discussed and how they define the structure of modern gas metering system. Special analysis is carried out about the range of communication and the impact of the place of installation of the concentrator and smart meters.
Inclination Effects on the Frequency Tuning of Comb-Driven Resonators

The comb fingers of high aspect ratio structures fabricated by micromachining technology are usually not parallel. Effects of the inclination of the fingers and edge effect on the capacitance, driving electrostatic force, and electrostatic spring constant are studied. The complex nonlinear air damping in the 3-D resonators is also determined accurately. The governing equations are presented to describe the complex dynamic problem by taking both linear and nonlinear mechanical spring stiffness constants into account. The dynamic responses of the micro-resonator driven by electrostatic combs are investigated using the multiscale method. Stability analysis is presented using the maximum Lyapunov index map, and effects of vacuum pressure on the frequency tuning and stability are also discussed. The comparisons show that the numerical results agree well with the experimental data reported in the literature, and it verified the validity of the presented dynamic model. The results also demonstrate that the inclination of the fingers causes the resonance frequency to increase and the electrostatic spring to harden under applied dc voltage. Therefore, it can provide an effective approach to balance the traditional resonance frequency decreasing and stiffness softening from driving electrostatic force. The inclination of the fingers can be helpful for strengthening the stability of the MEMS resonators, and avoiding the occurrence of pull-in.
Improved automatic caricature by feature normalization and exaggeration

This sketch presents an improved formalization of automatic caricature that extends a standard approach to account for the population variance of facial features. Caricature is generally considered a rendering that emphasizes the distinctive features of a particular face. A formalization of this idea, which we term “Exaggerating the Difference from the Mean” (EDFM), is widely accepted among caricaturists [Redman 1984] and was first implemented in a groundbreaking computer program by [Brennan 1985]. Brennan’s “Caricature generator” program produced caricatures by manually defining a polyline drawing with topology corresponding to a frontal, mean, face-shape drawing, and then displacing the vertices by a constant factor away from the mean shape. Many psychological studies have applied the “Caricature Generator” or EDFM idea to investigate caricaturerelated issues in face perception [Rhodes 1997].
How Feasible is Officiating Technology in Football ?

Technology in football has been debated by pundits, players and fans all over the world for the past decade. FIFA has recently commissioned the use of ‘Hawk-Eye’ and ‘Goal Ref’ goal line technology systems at the 2014 World Cup in Brazil. This paper gives an in depth evaluation of the possible technologies that could be used in football and determines the potential benefits and implications these systems could have on the officiating of football matches. The use of technology in other sports is analyzed to come to a conclusion as to whether officiating technology should be used in football. Will football be damaged by the loss of controversial incidents such as Frank Lampard’s goal against Germany at the 2010 World Cup? Will cost, accuracy and speed continue to prevent the use of officiating technology in football? Time will tell, but for now, any advancement in the use of technology in football will be met by some with discontent, whilst others see it as moving the sport into the 21 century.
Treadmill training and overground gait: decision making for a toddler with spina bifida.

This case report describes a decision-making process that was used to progress a home-based intervention that coupled treadmill and walker stepping for a preambulatory toddler with spina bifida.



The toddler in this report had an L4-L5 level lesion, and began this home-based intervention at 18 months of age when she was pulling to stand.



The intervention included parameters for treadmill stepping that prepared this toddler for gait with orthotics and was progressed to overground walking with a walker using a decision-making algorithm based on data obtained from a parent log and coded video.



This toddler progressed from not stepping at the start of the study to ambulating 150 m with a walker at age 23 months, after 18 weeks of this intervention.



The intervention and decision-making process used in this study were family centered and may be applicable to gait intervention with other populations.
Distance Based Modeling of Interactions in Structured Regression

Graphical models, as applied to multi-target prediction problems, commonly utilize interaction terms to impose structure among the output variables. Often, such structure is based on the assumption that related outputs need to be similar and interaction terms that force them to be closer are adopted. Here we relax that assumption and propose a feature that is based on distance and can adapt to ensure that variables have smaller or larger difference in values. We utilized a Gaussian Conditional Random Field model, where we have extended its originally proposed interaction potential to include a distance term. The extended model is compared to the baseline in various structured regression setups. An increase in predictive accuracy was observed on both synthetic examples and real-world applications, including challenging tasks from climate and healthcare domains.
Lateral calcaneal artery perforator-based skin flaps for coverage of lower-posterior heel defects.

Perforator-based flaps have been explored across almost all of the lower leg except in the Achilles tendon area. This paper introduced a perforator flap sourced from this area with regard to its anatomic basis and clinical applications.



Twenty-four adult cadaver legs were dissected to investigate the perforators emerging along the lateral edge of the Achilles tendon in terms of number and location relative to the tip of the lateral malleolus, and distribution. Based on the anatomic findings, perforator flaps, based on the perforator(s) of the lateral calcaneal artery (LCA) alone or in concert with the perforator of the peroneal artery (PA), were used for reconstruction of lower-posterior heel defects in eight cases. Postoperatively, subjective assessment and Semmes-Weinstein filament test were performed to evaluate the sensibility of the sural nerve-innerved area.



The PA ended into the anterior perforating branch and LCA at the level of 6.0 ± 1.4 cm (range 3.3-9.4 cm) above the tip of the lateral malleolus. Both PA and LCA, especially the LCA, gave rise to perforators to contribute to the integument overlying the Achilles tendon. Of eight flaps, six were based on perforator(s) of the LCA and two were on perforators of the PA and LCA. Follow-up lasted for 6-28 months (mean 13.8 months), during which total flap loss and nerve injury were not found. Functional and esthetic outcomes were good in all patients.



The integument overlying the Achilles tendon gets its blood supply through the perforators of the LCA primarily and that of through the PA secondarily. The LCA perforator(s)-based and the LCA plus PA perforators-based stepladder flap is a reliable, sensate flap, and should be thought of as a valuable procedure of choice for coverage of lower-posterior heel defects in selected patients.
Ethos' Deeply Integrated Distributed Types

Programming languages have long incorporated type safety, increasing their level of abstraction and thus aiding programmers. Type safety eliminates whole classes of security-sensitive bugs, replacing the tedious and error-prone search for such bugs in each application with verifying the correctness of the type system. Despite their benefits, these protections often end at the process boundary, that is, type safety holds within a program but usually not to the file system or communication with other programs. Existing operating system approaches to bridge this gap require the use of a single programming language or common language runtime. We describe the deep integration of type safety in Ethos, a clean-slate operating system which requires that all program input and output satisfy a recognizer before applications are permitted to further process it. Ethos types are multilingual and runtime-agnostic, and each has an automatically generated unique type identifier. Ethos bridges the type-safety gap between programs by (1) providing a convenient mechanism for specifying the types each program may produce or consume, (2) ensuring that each type has a single, distributed-system-wide recognizer implementation, and (3) inescapably enforcing these type constraints.
Deep Learning to Hash with Multiple Representations

Hashing seeks an embedding of high-dimensional objects into a similarity-preserving low-dimensional Hamming space such that similar objects are indexed by binary codes with small Hamming distances. A variety of hashing methods have been developed, but most of them resort to a single view (representation) of data. However, objects are often described by multiple representations. For instance, images are described by a few different visual descriptors (such as SIFT, GIST, and HOG), so it is desirable to incorporate multiple representations into hashing, leading to multi-view hashing. In this paper we present a deep network for multi-view hashing, referred to as deep multi-view hashing, where each layer of hidden nodes is composed of view-specific and shared hidden nodes, in order to learn individual and shared hidden spaces from multiple views of data. Numerical experiments on image datasets demonstrate the useful behavior of our deep multi-view hashing (DMVH), compared to recently-proposed multi-modal deep network as well as existing shallow models of hashing.
An entropy-based distance measure for analyzing and detecting metamorphic malware

Metamorphic malware is a kind of malware which evades signature-based anti-viruses by changing its internal structure in each infection. This paper, firstly, introduces a new measure of distance between two computer programs called program dissimilarity measure based on entropy (PDME). Then, it suggests a measure for the degree of metamorphism, based on the suggested distance measure. The distance measure is defined based on the Entropy of the two malware programs. Moreover, the paper shows that the distance measure can be used for classifying metamorphic malware via K-Nearest Neighbors (KNN) method. The method is evaluated by four metamorphic malware families. The results demonstrate that the measure can indicate the degree of metamorphism efficiently, and the KNN classification method using PDME can classify the metamorphic malware with a high precision.
Multi-label Classification: A Comparative Study on Threshold Selection Methods

Dealing with multiple labels is a supervised learning problem of increasing importance. However, in some tasks, certain learning algorithms produce a confidence score vector for each label that needs to be classified as relevant or irrelevant. More importantly, multi-label models are learnt in training conditions called operating conditions, which most likely change in other contexts. In this work, we explore the existing thresholding methods of multi-label classification by considering that label costs are operating conditions. This paper provides an empirical comparative study of these approaches by calculating the empirical loss over range of operating conditions. It also contributes two new methods in multilabel classification that have been used in binary classification: score-driven and one optimal.
Unsupervised Learning of Depth and Ego-Motion from Monocular Video Using 3D Geometric Constraints

We present a novel approach for unsupervised learning of depth and ego-motion from monocular video. Unsupervised learning removes the need for separate supervisory signals (depth or ego-motion ground truth, or multi-view video). Prior work in unsupervised depth learning uses pixel-wise or gradient-based losses, which only consider pixels in small local neighborhoods. Our main contribution is to explicitly consider the inferred 3D geometry of the whole scene, and enforce consistency of the estimated 3D point clouds and ego-motion across consecutive frames. This is a challenging task and is solved by a novel (approximate) backpropagation algorithm for aligning 3D structures. We combine this novel 3D-based loss with 2D losses based on photometric quality of frame reconstructions using estimated depth and ego-motion from adjacent frames. We also incorporate validity masks to avoid penalizing areas in which no useful information exists. We test our algorithm on the KITTI dataset and on a video dataset captured on an uncalibrated mobile phone camera. Our proposed approach consistently improves depth estimates on both datasets, and outperforms the state-of-the-art for both depth and ego-motion. Because we only require a simple video, learning depth and ego-motion on large and varied datasets becomes possible. We demonstrate this by training on the low quality uncalibrated video dataset and evaluating on KITTI, ranking among top performing prior methods which are trained on KITTI
Designing Conversational Interfaces to Reduce Dissonance

Conversational interfaces-computer interfaces that use text or voice for human-computer interaction-are one of many interaction modalities in interactive systems. Their use has expanded with the growth and range of products that are both digital and physical in nature. But users' expectations of conversational experiences are not being met. In addition, expectations may not be met in different ways across people. This paper aims to provoke dialogue among design researchers and practitioners regarding the design of an interaction modality that is commonly found in human-to-human interaction. It focuses on the design of conversational interfaces such as virtual assistants to illustrate the quandary. Finally, the paper proposes a phenomenon for the issue-a type of dissonance-and introduces tactics to reduce dissonance. It sheds light on potential approaches to design as well as complications that may occur.
A RGB image encryption algorithm based on total plain image characteristics and chaos

Currently, color image encryption is important to ensure its confidentiality during its transmission on insecure networks or its storage. The fact that chaotic properties are related with cryptography properties in confusion, diffusion, pseudorandom, etc., researchers around the world have presented several image (gray and color) encryption algorithms based on chaos, but almost all them with serious security problems have been broken with the powerful chosen/known plain image attack. In this work, we present a color image encryption algorithm based on total plain image characteristics (to resist a chosen/known plain image attack), and 1D logistic map with optimized distribution (for fast encryption process) based on Murillo-Escobar's algorithm (Murillo-Escobar et al. (2014) ). The security analysis confirms that the RGB image encryption is fast and secure against several known attacks; therefore, it can be implemented in real-time applications where a high security is required. & 2014
An integrated map of structural variation in 2,504 human genomes

Structural variants are implicated in numerous diseases and make up the majority of varying nucleotides among human genomes. Here we describe an integrated set of eight structural variant classes comprising both balanced and unbalanced variants, which we constructed using short-read DNA sequencing data and statistically phased onto haplotype blocks in 26 human populations. Analysing this set, we identify numerous gene-intersecting structural variants exhibiting population stratification and describe naturally occurring homozygous gene knockouts that suggest the dispensability of a variety of human genes. We demonstrate that structural variants are enriched on haplotypes identified by genome-wide association studies and exhibit enrichment for expression quantitative trait loci. Additionally, we uncover appreciable levels of structural variant complexity at different scales, including genic loci subject to clusters of repeated rearrangement and complex structural variants with multiple breakpoints likely to have formed through individual mutational events. Our catalogue will enhance future studies into structural variant demography, functional impact and disease association.
A disciplined approach to adopting agile practices: the agile adoption framework

Many organizations aspire to adopt agile processes to take advantage of the numerous benefits that they offer to an organization. Those benefits include, but are not limited to, quicker return on investment, better software quality, and higher customer satisfaction. To date, however, there is no structured process (at least that is published in the public domain) that guides organizations in adopting agile practices. To address this situation, we present the agile adoption framework and the innovative approach we have used to implement it. The framework consists of two components: an agile measurement index, and a four-stage process, that together guide and assist the agile adoption efforts of organizations. More specifically, the Sidky Agile Measurement Index (SAMI) encompasses five agile levels that are used to identify the agile potential of projects and organizations. The four-stage process, on the other hand, helps determine (a) whether or not organizations are ready for agile adoption, and (b) guided by their potential, what set of agile practices can and should be introduced. To help substantiate the “goodness” of the Agile Adoption Framework, we presented it to various members of the agile community, and elicited responses through questionnaires. The results of that substantiation effort are encouraging, and are also presented in this paper.
MultiRefactor: Automated Refactoring to Improve Software Quality

In this paper, a new approach is proposed for automated software maintenance. The tool is able to perform 26 different refactorings. It also contains a large selection of metrics to measure the impact of the refactorings on the software and six different search based optimization algorithms to improve the software. This tool contains both monoobjective and multi-objective search techniques for software improvement and is fully automated. The paper describes the various capabilities of the tool, the unique aspects of it, and also presents some research results from experimentation. The individual metrics are tested across five different codebases to deduce the most effective metrics for general quality improvement. It is found that the metrics that relate to more specific elements of the code are more useful for driving change in the search. The mono-objective genetic algorithm is also tested against the multi-objective algorithm to see how comparable the results gained are with three separate objectives. When comparing the best solutions of each individual objective the multi-objective approach generates suitable improvements in quality in less time, allowing for rapid maintenance cycles.
Net generation students: agency and choice and the new technologies

Based on research investigating English first-year university students, this paper examined the case made for a new generation of young learners often described as the Net Generation or Digital Natives in terms of agency and choice. Generational arguments set out a case that links young people’s attitudes and orientations to their lifelong exposure to networked and digital technologies. This paper drew on interview data from mixed methods research to suggest that the picture is more complex than the equation of exposure to new technologies and a generational change of attitudes and capacities. Starting from the position that interaction with technology is mediated by activity and an intentional stance, we examined the choices students make with regard to the technologies they engage with. We explored the perceived constraints students face and the way they either comply or resist such constraints. We concluded that agency actively shapes student engagement with technology but that an adequate conception of agency must expand beyond the person and the self to include notions of collective agency identifying the meso level as an activity system that mediates between the students and their technological setting.
An IoT environmental data collection system for fungal detection in crop fields

There is a need for a system which provides real-time local environmental data in rural crop fields for the detection and management of fungal diseases. This paper presents the design of an Internet of Things (IoT) system consisting of a device capable of sending real-time environmental data to cloud storage and a machine learning algorithm to predict environmental conditions for fungal detection and prevention. The stored environmental data on conditions such as air temperature, relative air humidity, wind speed, and rain fall is accessed and processed by a remote computer for analysis and management purposes. A machine learning algorithm using Support Vector Machine regression (SVMr) was developed to process the raw data and predict short-term (day-to-day) air temperature, relative air humidity, and wind speed values to assist in predicting the presence and spread of harmful fungal diseases through the local crop field. Together, the environmental data and environmental predictions made easily accessible by this IoT system will ultimately assist crop field managers by facilitating better management and prevention of fungal disease spread.