nils-herrmann/clean_scirepeval huggingface dataset:
Natural Language Processing for Question Answering ( NLP 4

The recent developments in Question Answering have kept with open-domain questions and collections, sometimes argued as being more di fficult than narrow domain-focused questions and corpora. The biomedical field is indeed a specialized domain; however, its scope is fairly broad, so that considering a biomedical QA task is not necessarily such a simplification over open-domain QA as represented in the recent TREC evaluations. We shall try to characterize salient aspects of biomedical QA as well as to give a short review of useful resources to address this task.
McKibben artificial muscle can be in accordance with the Hill skeletal muscle model

McKibben pneumatic artificial muscle shows a great functional analogy with skeletal muscle. To better characterize this dynamic analogous behavior, we analyze in what conditions McKibben muscle can be applied in accordance with the fundamental Hill muscle model. We show that the choice of an adequate textile sheath can lead to a velocity-tension curve in conformity with the Hill model equation. We propose to relate this phenomenon to specific textile yarn hydrodynamic friction. In terms of control we emphasize the interest of this agreement with the Hill model for giving the artificial muscle a dynamic behaviour more naturally adapted to load variations
TURKSENT: A Sentiment Annotation Tool for Social Media

In this paper, we present an annotation tool developed specifically for manual sentiment analysis of social media posts. The tool provides facilities for general and target based opinion marking on different type of posts (i.e. comparative, ironic, conditional) with a web based UI which supports synchronous annotation. It is also designed as a SaaS (Software as a Service). The tool’s outstanding features are easy and fast annotation interface, detailed sentiment levels, multi-client support, easy to manage administrative modules and linguistic annotation capabilities.
Human Interaction With Robot Swarms: A Survey

Recent advances in technology are delivering robots of reduced size and cost. A natural outgrowth of these advances are systems comprised of large numbers of robots that collaborate autonomously in diverse applications. Research on effective autonomous control of such systems, commonly called swarms, has increased dramatically in recent years and received attention from many domains, such as bioinspired robotics and control theory. These kinds of distributed systems present novel challenges for the effective integration of human supervisors, operators, and teammates that are only beginning to be addressed. This paper is the first survey of human-swarm interaction (HSI) and identifies the core concepts needed to design a human-swarm system. We first present the basics of swarm robotics. Then, we introduce HSI from the perspective of a human operator by discussing the cognitive complexity of solving tasks with swarm systems. Next, we introduce the interface between swarm and operator and identify challenges and solutions relating to human-swarm communication, state estimation and visualization, and human control of swarms. For the latter, we develop a taxonomy of control methods that enable operators to control swarms effectively. Finally, we synthesize the results to highlight remaining challenges, unanswered questions, and open problems for HSI, as well as how to address them in future works.
Classification of Text Documents

The exponential growth of the internet has led to a great deal of interest in developing useful and efficient tools and software to assist users in searching the Web. Document retrieval, categorization, routing and filtering can all be formulated as classification problems. However, the complexity of natural languages and the extremely high dimensionality of the feature space of documents have made this classification problem very difficult. We investigate four different methods for document classification: the naive Bayes classifier, the nearest neighbour classifier, decision trees and a subspace method. These were applied to seven-class Yahoo news groups (business, entertainment, health, international, politics, sports and technology) individually and in combination. We studied three classifier combination approaches: simple voting, dynamic classifier selection and adaptive classifier combination. Our experimental results indicate that the naive Bayes classifier and the subspace method outperform the other two classifiers on our data sets. Combinations of multiple classifiers did not always improve the classification accuracy compared to the best individual classifier. Among the three different combination approaches, our adaptive classifier combination method introduced here performed the best. The best classification accuracy that we are able to achieve on this seven-class problem is approximately 83 %, which is comparable to the performance of other similar studies. However, the classification problem considered here is more difficult because the pattern classes used in our experiments have a large overlap of words in their corresponding documents.
A Survey of Collaborative Filtering Techniques

As one of the most successful approaches to building recommender systems, collaborative filtering (CF) uses the known preferences of a group of users to make recommendations or predictions of the unknown preferences for other users. In this paper, we first introduce CF tasks and their main challenges, such as data sparsity, scalability, synonymy, gray sheep, shilling attacks, privacy protection, etc., and their possible solutions. We then present three main categories of CF techniques: memory-based, modelbased, and hybrid CF algorithms (that combine CF with other recommendation techniques), with examples for representative algorithms of each category, and analysis of their predictive performance and their ability to address the challenges. From basic techniques to the state-of-the-art, we attempt to present a comprehensive survey for CF techniques, which can be served as a roadmap for research and practice in this area.
Efficient Linear Programming for Dense CRFs

The fully connected conditional random field (CRF) with Gaussian pairwise potentials has proven popular and effective for multi-class semantic segmentation. While the energy of a dense CRF can be minimized accurately using a linear programming (LP) relaxation, the state-of-the-art algorithm is too slow to be useful in practice. To alleviate this deficiency, we introduce an efficient LP minimization algorithm for dense CRFs. To this end, we develop a proximal minimization framework, where the dual of each proximal problem is optimized via block coordinate descent. We show that each block of variables can be efficiently optimized. Specifically, for one block, the problem decomposes into significantly smaller subproblems, each of which is defined over a single pixel. For the other block, the problem is optimized via conditional gradient descent. This has two advantages: 1) the conditional gradient can be computed in a time linear in the number of pixels and labels, and 2) the optimal step size can be computed analytically. Our experiments on standard datasets provide compelling evidence that our approach outperforms all existing baselines including the previous LP based approach for dense CRFs.
FML-based feature similarity assessment agent for Japanese/Taiwanese language learning

In this paper, we propose a fuzzy markup language (FML)-based feature similarity assessment agent with machine-learning ability to evaluate easy-to-learn degree of the Japanese and Taiwanese words. The involved domain experts define knowledge base (KB) and rule base (RB) of the proposed agent. The KB and RB are stored in the constructed ontology, including features of pronunciation similarity, writing similarity, and culture similarity. Next, we calculate feature similarity in pronunciation, writing, and culture for each word pair between Japanese and Taiwanese. Finally, we infer the easy-to-learn degree for one Japanese word and its corresponding Taiwanese one. Additionally, a genetic learning is also adopted to tune the KB and RB of the intelligent agent. The experimental results show that after-learning results perform better than before-learning ones.
Question Classification using Head Words and their Hypernyms

Question classification plays an important role in question answering. Features are the key to obtain an accurate question classifier. In contrast to Li and Roth (2002)’s approach which makes use of very rich feature space, we propose a compact yet effective feature set. In particular, we propose head word feature and present two approaches to augment semantic features of such head words using WordNet. In addition, Lesk’s word sense disambiguation (WSD) algorithm is adapted and the depth of hypernym feature is optimized. With further augment of other standard features such as unigrams, our linear SVM and Maximum Entropy (ME) models reach the accuracy of 89.2% and89.0% respectively over a standard benchmark dataset, which outperform the best previously reported accuracy of 86.2%.
Performance on Raven ’ s Advanced Progressive Matrices by African , East Indian , and White engineering students in South Africa

The hypothesis is tested that the Raven’s Advanced Progressive Matrices (APM) has the same construct validity in African university students as it does in non-African university students. Analyses were made of scores from 294 highly select 17–23-year-olds in the Faculties of Engineering and the Built Environment at the University of the Witwatersrand (187 Africans, 40 East Indians, 67 Whites; 70 women, 224 men). Out of a total of 36 problems, the African students solved an average of 22, the East Indian students, 25, and the White students, 29 (P < .001), placing them at the 57th, 64th, and 86th percentiles, respectively, and yielding IQ equivalents of 103, 106, and 117 on the 1993 US norms. Four months earlier, they had completed the Standard Progressive Matrices. The two tests correlated .60 or higher for both the Africans and the non-Africans, and both tests predicted final end-of-year grades with mean r’s = .30 (P’s < .05). Items found difficult by one group were difficult for the others; items found easy by one group were easy for the others (mean r’s = .90, P < .001). The African–East Indian–White differences were ‘‘Jensen Effects,’’ being most pronounced on the general factor of intelligence (measured in this instance by items with the highest item-total correlations). Indeed, the g loadings showed cross-cultural generality: For example, item-total correlations calculated on the East Indian students predicted the magnitude of the African–White differences. When each of the 0160-2896/03/$ – see front matter D 2003  S0160 -2896 (02 )00140 -X * Corresponding author.  E-mail addresses: Rushton@uwo.ca (J.P. Rushton), 135skuy@mentor.edcm.wits.ac.za (M. Skuy). 1 Tel.: +27-11-716-5286; fax: +27-11-339-3844.  (2003) 123–137 36 g loadings and race effects were aggregated into nine four-item ‘‘subtests,’’ the magnitude of the Jensen Effect was Spearman’s r = .52. There were no sex differences. D 2003
Human motion tracking for rehabilitation - A survey

Human motion tracking for rehabilitation has been an active research topic since the 1980s. It has been motivated by the increased number of patients who have suffered a stroke, or some other motor function disability. Rehabilitation is a dynamic process which allows patients to restore their functional capability to normal. To reach this target, a patients’ activities need to be continuously monitored, and subsequently corrected. This paper reviews recent progress in human movement detection/tracking systems in general, and existing or potential application for stroke rehabilitation in particular. Major achievements in these systems are summarised, and their merits and limitations individually presented. In addition, bottleneck problems in these tracking systems that remain open are highlighted, along with possible solutions.
Circular Polarized Patch Antenna Generating Orbital Angular Momentum

The recent extension of the orbital angular momentum (OAM) concept from optical to microwave frequencies has led some researchers to explore how well established antenna techniques can be used to radiate a non-zero OAM electromagnetic field. In this frame, the aim of the present paper is to propose a new approach to generate a non-zero OAM field through a single patch antenna. Using the cavity model, we first analyze the radiated field by a standard circular patch and show that a circular polarized (CP) TMnm mode excited by using two coaxial cables generates an electromagnetic field with an OAM of order ±(n−1). Then, in order to obtain a simpler structure with a single feed, we design an elliptical patch antenna working on the right-handed (RH) CP TM21 mode. Using full-wave simulations and experiments on a fabricated prototype, we show that the proposed antenna effectively radiates an electromagnetic field with a first order OAM. Such results prove that properly designed patch antennas can be used as compact and low-cost generators of electromagnetic fields carrying OAM.
FinFET-based SRAM design

Intrinsic variations and challenging leakage control in today's bulk-Si MOSFETs limit the scaling of SRAM. Design tradeoffs in six-transistor (6-T) and four-transistor (4-T) SRAM cells are presented in this work. It is found that 6-T and 4-T FinFET-based SRAM cells designed with built-in feedback achieve significant improvements in the cell static noise margin (SNM) without area penalty. Up to 2x improvement in SNM can be achieved in 6-T FinFET-based SRAM cells. A 4-T FinFET-based SRAM cell with built-in feedback can achieve sub-100pA per-cell standby current and offer the similar improvements in SNM as the 6-T cell with feedback, making them attractive for low-power, low-voltage applications
THE MEANINGS OF TRUST

What does the word ‘trust’ mean? Scholars continue to express concern regarding their collective lack of consensus about trust’s meaning. Conceptual confusion on trust makes comparing one trust study to another problematic. To facilitate cumulative trust research, the authors propose two kinds of trust typologies: (a) a classification system for types of trust, and (b) definitions of six related trust types that form a model. Some of the model’s implications for management are also outlined.
Maximum Power Tracking for Photovoltaic Power Systems

The electric power supplied by a photovoltaic power generation system depends on the solar radiation and temperature. Designing efficient PV systems heavily emphasizes to track the maximum power operating point. This work develops a novel three-point weight comparison method that avoids the oscillation problem of the perturbation and observation algorithm which is often employed to track the maximum power point. Furthermore, a low cost control unit is developed, based on a single chip to adjust the output voltage of the solar cell array. Finally, experimental results confirm the superior performance of the proposed method.
Makeup like a superstar: Deep Localized Makeup Transfer Network

In this paper, we propose a novel Deep Localized Makeup Transfer Network to automatically recommend the most suitable makeup for a female and synthesis the makeup on her face. Given a before-makeup face, her most suitable makeup is determined automatically. Then, both the beforemakeup and the reference faces are fed into the proposed Deep Transfer Network to generate the after-makeup face. Our end-to-end makeup transfer network have several nice properties including: (1) with complete functions: including foundation, lip gloss, and eye shadow transfer; (2) cosmetic specific: different cosmetics are transferred in different manners; (3) localized: different cosmetics are applied on different facial regions; (4) producing naturally looking results without obvious artifacts; (5) controllable makeup lightness: various results from light makeup to heavy makeup can be generated. Qualitative and quantitative experiments show that our network performs much better than the methods of [Guo and Sim, 2009] and two variants of NerualStyle [Gatys et al., 2015a].
An OFDMA based multiple access protocol with QoS guarantee for next generation WLAN

To provide better QoS guarantee for the next generation WLAN, IEEE 802.11ax task group is founded in March 2014. As a promising technology to accommodate multiple nodes concurrent transmissions in dense deployment scenario, orthogonal frequency division multiple access (OFDMA) will be adopted in IEEE 802.11ax with great possibility. In this paper, an OFDMA based multiple access protocol with QoS guarantee is proposed for the next generation WLAN. Firstly, a redundant access mechanism is given to increase the access success probability of the video traffic where the video stations can concurrently send multiple RTS packets in multiple subchannels. Secondly, a priority based resource allocation scheme is presented to let AP allocate more resources to the video stations. Simulation results show that our protocol outperforms the existing OFDMA based multiple access for IEEE 802.11ax (OMAX) protocol in terms of delay and delay jitter of video traffic in dense deployment scenario.
Three-Fingered Eight-DOF Hand That Exerts 100-N Grasping Force With Force-Magnification Drive

This paper presents a three-fingered, eight-DOF hand, 100 N Hand II, which can exert a grasping force of 100 N. A force-magnification drive, which can maintain a large grasping force without energy consumption, allows the hand to exert such large grasping force, and a high-speed driving mechanism enables all joints to perform high-speed motions of over 400°/s. In our previous prototype (100 N Hand I), a screw and nut mechanism was used to implement force magnification. The linear motion of this mechanism was translated to rotational joint motion via a linkage mechanism, producing a relatively small motion range for each joint and with large palm dimensions. 100 N Hand II employs a worm gear instead of a screw and nut mechanism, so it can magnify the torque at an arbitrary joint angle. This improved force-magnification drive increases the motion range of each joint and reduces the palm size. The force and speed are confirmed experimentally.
Preserving Privacy in Social Networks Against Neighborhood Attacks

Recently, as more and more social network data has been published in one way or another, preserving privacy in publishing social network data becomes an important concern. With some local knowledge about individuals in a social network, an adversary may attack the privacy of some victims easily. Unfortunately, most of the previous studies on privacy preservation can deal with relational data only, and cannot be applied to social network data. In this paper, we take an initiative towards preserving privacy in social network data. We identify an essential type of privacy attacks: neighborhood attacks. If an adversary has some knowledge about the neighbors of a target victim and the relationship among the neighbors, the victim may be re-identified from a social network even if the victim's identity is preserved using the conventional anonymization techniques. We show that the problem is challenging, and present a practical solution to battle neighborhood attacks. The empirical study indicates that anonymized social networks generated by our method can still be used to answer aggregate network queries with high accuracy.
A Cascade Multilevel Converter Topology With Reduced Number of Switches

This paper introduces a new multilevel converter topology that has many steps with fewer power electronic switches. The proposed circuit consists of series-connected submultilevel converters blocks. The optimal structures of this topology are investigated for various objectives, such as minimum number of switches and capacitors, and minimum standing voltage on switches for producing maximum output voltage steps. A new algorithm for determination of dc voltage sourcespsila magnitudes has also been presented. The proposed topology results in reduction of the number of switches, losses, installation area, and converter cost. The operation and performance of the proposed multilevel converter has been verified by the simulation and experimental results of a single-phase 53-level multilevel converter.
Physics-based hand interaction with virtual objects

We propose an intuitive user interaction system with virtual objects by directly using user's hands through a physics simulation. This system aims to provide a more direct way to manipulate and interact with virtual objects and to induce more realistic reactions of the virtual objects in the interaction than the existing methods. The key contribution of this paper is to model the deformations of the user's hands in two ways: global deformation by the hand poses and the local deformation by contacts with objects. By the two deformation models, objects can be manipulated with better stability and robustness without any predefinition of interaction methods depending on the object shapes, by solely using a physics simulation. The experiment shows that the proposed method enables a user to intuitively manipulate virtual object accurately.
Volitional personality trait change: Can people choose to change their personality traits?

Previous research has found that most people want to change their personality traits. But can people actually change their personalities just because they want to? To answer this question, we conducted 2, 16-week intensive longitudinal randomized experiments. Across both studies, people who expressed goals to increase with respect to any Big Five personality trait at Time 1 tended to experience actual increases in their self-reports of that trait-as well as trait-relevant daily behavior-over the subsequent 16 weeks. Furthermore, we tested 2 randomized interventions designed to help participants attain desired trait changes. Although 1 of the interventions was inefficacious, a second intervention that trained participants to generate implementation intentions catalyzed their ability to attain trait changes. We also tested several theoretical processes through which volitional changes might occur. These studies suggest that people may be able to change their self-reported personality traits through volitional means, and represent a first step toward understanding the processes that enable people to do so.
Nonsuicidal self-injury as a gateway to suicide in young adults.

To investigate the extent to which nonsuicidal self-injury (NSSI) contributes to later suicide thoughts and behaviors (STB) independent of shared risk factors.



One thousand four hundred and sixty-six students at five U.S. colleges participated in a longitudinal study of the relationship between NSSI and suicide. NSSI, suicide history, and common risk/protective factors were assessed annually for three years. Analyses tested the hypotheses that the practice of NSSI prior to STB and suicide behavior (excluding ideation) reduced inhibition to later STB independent of shared risk factors. Analyses also examined factors that predicted subsequent STB among individuals with NSSI history.



History of NSSI did significantly predict concurrent or later STB (AOR 2.8, 95%, CI 1.9-4.1) independent of covariates common to both. Among those with prior or concurrent NSSI, risk of STB is predicted by > 20 lifetime NSSI incidents (AOR 3.8, 95% CI, 1.4-10.3) and history of mental health treatment (AOR 2.2, 95% CI, 1.9-4.6). Risk of moving from NSSI to STB is decreased by presence of meaning in life (AOR .6, 95% CI, .5-.7) and reporting parents as confidants (AOR, .3, 95% CI, .1-.9).



NSSI prior to suicide behavior serves as a "gateway" behavior for suicide and may reduce inhibition through habituation to self-injury. Treatments focusing on enhancing perceived meaning in life and building positive relationships with others, particularly parents, may be particularly effective in reducing suicide risk among youth with a history of NSSI.