nils-herrmann/clean_scirepeval huggingface dataset:
Powering a Ventricular Assist Device (VAD) With the Free-Range Resonant Electrical Energy Delivery (FREE-D) System

Wireless data communication technology has eliminated wired connections for data transfer to portable devices. Wireless power technology offers the possibility of eliminating the remaining wired connection: the power cord. For ventricular assist devices (VADs), wireless power technology will eliminate the complications and infections caused by the percutaneous wired power connection. Integrating wireless power technology into VADs will enable VAD implants to become a more viable option for heart failure patients (of which there are 80 000 in the United States each year) than heart transplants. Previous transcutaneous energy transfer systems (TETS) have attempted to wirelessly power VADs ; however, TETS-based technologies are limited in range to a few millimeters, do not tolerate angular misalignment, and suffer from poor efficiency. The free-range resonant electrical delivery (FREE-D) wireless power system aims to use magnetically coupled resonators to efficiently transfer power across a distance to a VAD implanted in the human body, and to provide robustness to geometric changes. Multiple resonator configurations are implemented to improve the range and efficiency of wireless power transmission to both a commercially available axial pump and a VentrAssist centrifugal pump . An adaptive frequency tuning method allows for maximum power transfer efficiency for nearly any angular orientation over a range of separation distances. Additionally, laboratory results show the continuous operation of both pumps using the FREE-D system with a wireless power transfer efficiency upwards of 90%.
What are good parts for hair shape modeling?

Hair plays an important role in human appearance. However, hair segmentation is still a challenging problem partially due to the lack of an effective model to handle its arbitrary shape variations. In this paper, we present a part-based model robust to hair shape and environment variations. The key idea of our method is to identify local parts by promoting the effectiveness of the part-based model. To this end, we propose a measurable statistic, called Subspace Clustering Dependency (SC-Dependency), to estimate the co-occurrence probabilities between local shapes. SC-Dependency guarantees output reasonability and allows us to evaluate the effectiveness of part-wise constraints in an information-theoretic way. Then we formulate the part identification problem as an MRF that aims to optimize the effectiveness of the potential functions. Experiments are performed on a set of consumer images and show our algorithm's capability and robustness to handle hair shape variations and extreme environment conditions.
BioRuby: bioinformatics software for the Ruby programming language

The BioRuby software toolkit contains a comprehensive set of free development tools and libraries for bioinformatics and molecular biology, written in the Ruby programming language. BioRuby has components for sequence analysis, pathway analysis, protein modelling and phylogenetic analysis; it supports many widely used data formats and provides easy access to databases, external programs and public web services, including BLAST, KEGG, GenBank, MEDLINE and GO. BioRuby comes with a tutorial, documentation and an interactive environment, which can be used in the shell, and in the web browser.


AVAILABILITY
BioRuby is free and open source software, made available under the Ruby license. BioRuby runs on all platforms that support Ruby, including Linux, Mac OS X and Windows. And, with JRuby, BioRuby runs on the Java Virtual Machine. The source code is available from http://www.bioruby.org/.
A survey of parallel distributed genetic algorithms

In this work we review the most important existing developments and future trends in the class of Parallel Genetic Algorithms (PGAs). PGAs are mainly subdivided into coarse and fine grain PGAs, the coarse grain models being the most popular ones. An exceptional characteristic of PGAs is that they are not just the parallel version of a sequential algorithm intended to provide speed gains. Instead, they represent a new kind of meta-heuristics of higher efficiency and efficacy thanks to their structured population and parallel execution. The good robustness of these algorithms on problems of high complexity has led to an increasing number of applications in the fields of artificial intelligence, numeric and combinatorial optimization, business, engineering, etc. We make a formalization of these algorithms, and present a timely and topic survey of their most important traditional and recent technical issues. Besides that, useful summaries on their main applications plus Internet pointers to important web sites are included in order to help new researchers to access this growing area.
The Ambivalent Sexism Inventory : Differentiating Hostile and Benevolent Sexism

The authors present a theory of sexism formulated as ambivalence toward women and validate a corresponding measure, the Ambivalent Sexism Inventory (ASI). The ASI taps 2 positively correlated components of sexism that nevertheless represent opposite evaluative orientations toward women: sexist antipathy or Hostile Sexism (HS) and a subjectively positive ( for sexist men ) orientation toward women, Benevolent Sexism (BS). HS and BS are hypothesized to encompass 3 sources of male ambivalence: Paternalism, Gender Differentiation, and Heterosexuality. Six ASI studies on 2,250 respondents established convergent, discriminant, and predictive validity. Overall ASI scores predict ambivalent attitudes toward women, the HS scale correlates with negative attitudes toward and stereotypes about women, and the BS scale (for nonstudent men only) correlates with positive attitudes toward and stereotypes about women. A copy of the ASI is provided, with scoring instructions, as a tool for further explorations of sexist ambivalence.
Trust Between Humans and Machines, and the Design of Decision Aids

A problem in the design of decision aids is how to design them so that decision makers will trust them and therefore use them appropriately. This problem is approached in this paper by taking models of trust between humans as a starting point, and extending these to the human-machine relationship. A definition and model of human-machine trust are proposed, and the dynamics of trust between humans and machines are examined. Based upon this analysis, recommendations are made for calibrating users' trust in decision aids.
Adaptation , adaptive capacity and vulnerability

This paper reviews the concept of adaptation of human communities to global changes, especially climate change, in the context of adaptive capacity and vulnerability. It focuses on scholarship that contributes to practical implementation of adaptations at the community scale. In numerous social science fields, adaptations are considered as responses to risks associated with the interaction of environmental hazards and human vulnerability or adaptive capacity. In the climate change field, adaptation analyses have been undertaken for several distinct purposes. Impact assessments assume adaptations to estimate damages to longer term climate scenarios with and without adjustments. Evaluations of specified adaptation options aim to identify preferred measures. Vulnerability indices seek to provide relative vulnerability scores for countries, regions or communities. The main purpose of participatory vulnerability assessments is to identify adaptation strategies that are feasible and practical in communities. The distinctive features of adaptation analyses with this purpose are outlined, and common elements of this approach are described. Practical adaptation initiatives tend to focus on risks that are already problematic, climate is considered together with other environmental and social stresses, and adaptations are mostly integrated or mainstreamed into other resource management, disaster preparedness and sustainable development programs.
A comparative study of heterogeneous item recommendations in social systems

While recommendation approaches exploiting different input sources have started to proliferate in the literature, an explicit study of the effect of the combination of heterogeneous inputs is still missing. On the other hand, in this context there are sides to recommendation quality requiring further characterisation and methodological research –a gap that is acknowledged in the field. We present a comparative study on the influence that different types of information available in social systems have on item recommendation. Aiming to identify which sources of user interest evidence –tags, social contacts, and user-item interaction data– are more effective to achieve useful recommendations, and in what aspect, we evaluate a number of content-based, collaborative filtering, and social recommenders on three datasets obtained from Delicious, Last.fm, and MovieLens. Aiming to determine whether and how combining such information sources may enhance over individual recommendation approaches, we extend the common accuracy-oriented evaluation practice with various metrics to measure further recommendation quality dimensions, namely coverage, diversity, novelty, overlap, and relative diversity between ranked item recommendations. We report empiric observations showing that exploiting tagging information by content-based recommenders provides high coverage and novelty, and combining social networking and collaborative filtering information by hybrid recommenders results in high accuracy and diversity. This, along with the fact that recommendation lists from the evaluated approaches had low overlap and relative diversity values between them, gives insights that meta-hybrid recommenders combining the above strategies may provide valuable, balanced item suggestions in terms of performance and non-performance metrics.
Efficient descriptor of histogram of salient edge orientation map for finger vein recognition.

Finger vein images are rich in orientation and edge features. Inspired by the edge histogram descriptor proposed in MPEG-7, this paper presents an efficient orientation-based local descriptor, named histogram of salient edge orientation map (HSEOM). HSEOM is based on the fact that human vision is sensitive to edge features for image perception. For a given image, HSEOM first finds oriented edge maps according to predefined orientations using a well-known edge operator and obtains a salient edge orientation map by choosing an orientation with the maximum edge magnitude for each pixel. Then, subhistograms of the salient edge orientation map are generated from the nonoverlapping submaps and concatenated to build the final HSEOM. In the experiment of this paper, eight oriented edge maps were used to generate a salient edge orientation map for HSEOM construction. Experimental results on our available finger vein image database, MMCBNU_6000, show that the performance of HSEOM outperforms that of state-of-the-art orientation-based methods (e.g., Gabor filter, histogram of oriented gradients, and local directional code). Furthermore, the proposed HSEOM has advantages of low feature dimensionality and fast implementation for a real-time finger vein recognition system.
The ecological impacts of marine debris: unraveling the demonstrated evidence from what is perceived.

Anthropogenic debris contaminates marine habitats globally, leading to several perceived ecological impacts. Here, we critically and systematically review the literature regarding impacts of debris from several scientific fields to understand the weight of evidence regarding the ecological impacts of marine debris. We quantified perceived and demonstrated impacts across several levels of biological organization that make up the ecosystem and found 366 perceived threats of debris across all levels. Two hundred and ninety-six of these perceived threats were tested, 83% of which were demonstrated. The majority (82%) of demonstrated impacts were due to plastic, relative to other materials (e.g., metals, glass) and largely (89%) at suborganismal levels (e.g., molecular, cellular, tissue). The remaining impacts, demonstrated at higher levels of organization (i.e., death to individual organisms, changes in assemblages), were largely due to plastic marine debris (> 1 mm; e.g., rope, straws, and fragments). Thus, we show evidence of ecological impacts from marine debris, but conclude that the quantity and quality of research requires improvement to allow the risk of ecological impacts of marine debris to be determined with precision. Still, our systematic review suggests that sufficient evidence exists for decision makers to begin to mitigate problematic plastic debris now, to avoid risk of irreversible harm.
Deconstructing Resilience : Myriad Conceptualizations and Interpretations

The construct of resilience has captured the imagination of researchers across various disciplines over the last five decades (Ungar, 2008a). Despite a growing body of research in the area of resilience, there is little consensus among researchers about the definition and meaning of this concept. Resilience has been used to describe eight kinds of phenomena across different disciplines. These eight phenomena can be divided into two clusters based on the disciplinary origin. The first cluster mainly involves definitions of resilience derived from the discipline of psychology and covers six themes including (i) personality traits, (ii) positive outcomes/forms of adaptation despite high-risk, (iii) factors associated with positive adaptation, (iv) processes, (v) sustained competent functioning/stress resistance, and (vi) recovery from trauma or adversity. The second cluster of definitions is rooted in the discipline of sociology and encompasses two themes including (i) human agency and resistance, and (ii) survival. This paper discusses the inconsistencies in the varied definitions used within the published literature and describes the differing conceptualizations of resilience as well as their limitations. The paper concludes by offering a unifying conceptualization of resilience and by discussing implications for future research on resilience.
Flying Ad-Hoc Networks: Technological and Social Implications

With the world turning into a global village due to technological advancements, automation in all aspects of life is gaining momentum. Wireless technologies address the everincreasing demands of portable and flexible communications. Wireless ad-hoc networks, which allow communication between devices without the need for any central infrastructure, are gaining significance, particularly for monitoring and surveillance applications. A relatively new research area of ad-hoc networks is flying ad-hoc networks (FANETs), governing the autonomous movement of unmanned aerial vehicles (UAVs) . In such networks multiple UAVs are allowed to communicate so that an ad-hoc network is established between them. All UAVs in the network carry UAV-to-UAV communication and only groups of UAVs interact with the ground station. This feature eliminates the need for deployment of complex hardware in each UAV. Moreover, even if one of the UAV communication links breaks down; there is no link breakage with the base station due to the ad-hoc network between UAVs.
Deep Reinforcement Learning for Active Breast Lesion Detection from DCE-MRI

We present a novel methodology for the automated detection of breast lesions from dynamic contrast-enhanced magnetic resonance volumes (DCE-MRI). Our method, based on deep reinforcement learning, significantly reduces the inference time for lesion detection compared to an exhaustive search, while retaining state-of-art accuracy. This speed-up is achieved via an attention mechanism that progressively focuses the search for a lesion (or lesions) on the appropriate region(s) of the input volume. The attention mechanism is implemented by training an artificial agent to learn a search policy, which is then exploited during inference. Specifically, we extend the deep Q-network approach, previously demonstrated on simpler problems such as anatomical landmark detection, in order to detect lesions that have a significant variation in shape, appearance, location and size. We demonstrate our results on a dataset containing 117 DCE-MRI volumes, validating run-time and accuracy of lesion detection.
Stud krill herd algorithm

Recently, Gandomi and Alavi proposed a meta-heuristic optimization algorithm, called Krill Herd (KH), for global optimization [Gandomi AH, Alavi AH.  A New Bio-Inspired Optimization Algorithm. Communications in Nonlinear Science and Numerical Simulation, 17(12), 4831–4845, 2012.]. This paper represents an optimization method to global optimization using a novel variant of KH. This method is called the Stud Krill Herd (SKH). Similar to genetic reproduction mechanisms added to KH method, an updated genetic reproduction schemes, called stud selection and crossover (SSC) operator, is introduced into the KH during the krill updating process dealing with numerical optimization problems. The introduced SSC operator is originated from original Stud genetic algorithm. In SSC operator, the best krill, the Stud, provides its optimal information for all the other individuals in the population using general genetic operators instead of stochastic selection. This approach appears to be well capable of solving various functions. Several problems are used to test the SKH method. In addition, the influence of the different crossover types on convergence and performance is carefully studied. Experimental results indicate an instructive addition to the portfolio of swarm intelligence techniques. & 2013 Elsevier B.V. All rights reserved.
Segmentation and Modeling of Full Human Body Shape From 3-D Scan Data: A Survey

The recent advances in full human body (HB) imaging technology illustrated by the 3D human body scanner (HBS), a device delivering full HB shape data, opened up large perspectives for the deployment of this technology in various fields such as the clothing industry, anthropology, and entertainment. However, these advances also brought challenges on how to process and interpret the data delivered by the HBS in order to bridge the gap between this technology and potential applications. This paper presents a literature survey of research work on HBS data segmentation and modeling aiming at overcoming these challenges, and discusses and evaluates different approaches with respect to several requirements.
Integrating predictive analytics and social media

A key analytical task across many domains is model building and exploration for predictive analysis. Data is collected, parsed and analyzed for relationships, and features are selected and mapped to estimate the response of a system under exploration. As social media data has grown more abundant, data can be captured that may potentially represent behavioral patterns in society. In turn, this unstructured social media data can be parsed and integrated as a key factor for predictive intelligence. In this paper, we present a framework for the development of predictive models utilizing social media data. We combine feature selection mechanisms, similarity comparisons and model cross-validation through a variety of interactive visualizations to support analysts in model building and prediction. In order to explore how predictions might be performed in such a framework, we present results from a user study focusing on social media data as a predictor for movie box-office success.
Grandiose and vulnerable narcissism and the DSM-5 pathological personality trait model.

The Diagnostic and Statistical Manual of Personality Disorders (4th ed., American Psychiatric Association, 2000) personality disorders (PDs) that will be included in the DSM-5 will be diagnosed in an entirely different manner; the explicit criterion sets will be replaced with impairments in self and interpersonal functioning and personality traits from a 25-trait dimensional model of personality pathology. From a trait perspective, narcissistic personality disorder (NPD), the focus of this study, is assessed using 2 specific traits: grandiosity and attention seeking. Using a sample collected online from Amazon's Mechanical Turk (MTurk; N=306), we examined the relations among traits from a new measure of DSM-5's trait model--the Personality Inventory for DSM-5 (PID5; Krueger, Derringer, Markon, Watson, & Skodol, in press)--and grandiose and vulnerable narcissism. The 25 traits from PID5 captured a significant portion of the variance in grandiose and vulnerable factors, although the 2 specific facets designated for the assessment of NPD fared substantially better in the assessment of grandiose rather than vulnerable narcissism. These results are discussed in the context of improving the DSM-5's ability to capture both narcissism dimensions.
Power-steering control architecture for automatic driving

The unmanned control of the steering wheel is, at present, one of the most important challenges facing researchers in autonomous vehicles within the field of intelligent transportation systems (ITSs). In this paper, we present a two-layer control architecture for automatically moving the steering wheel of a mass-produced vehicle. The first layer is designed to calculate the target position of the steering wheel at any time and is based on fuzzy logic. The second is a classic control layer that moves the steering bar by means of an actuator to achieve the position targeted by the first layer. Real-time kinematic differential global positioning system (RTK-DGPS) equipment is the main sensor input for positioning. It is accurate to about 1 cm and can finely locate the vehicle trajectory. The developed systems are installed on a Citroe/spl uml/n Berlingo van, which is used as a testbed vehicle. Once this control architecture has been implemented, installed, and tuned, the resulting steering maneuvering is very similar to human driving, and the trajectory errors from the reference route are reduced to a minimum. The experimental results show that the combination of GPS and artificial-intelligence-based techniques behaves outstandingly. We can also draw other important conclusions regarding the design of a control system derived from human driving experience, providing an alternative mathematical formalism for computation, human reasoning, and integration of qualitative and quantitative information.