nils-herrmann/clean_scirepeval huggingface dataset:
Gender Incongruence of Adolescence and Adulthood: Acceptability and Clinical Utility of the World Health Organization’s Proposed ICD-11 Criteria

The World Health Organization (WHO) is currently updating the tenth version of their diagnostic tool, the International Classification of Diseases (ICD,  1992). Changes have been proposed for the diagnosis of Transsexualism (ICD-10) with regard to terminology, placement and content. The aim of this study was to gather the opinions of transgender individuals (and their relatives/partners) and clinicians in the Netherlands, Flanders (Belgium) and the United Kingdom regarding the proposed changes and the clinical applicability and utility of the ICD-11 criteria of 'Gender Incongruence of Adolescence and Adulthood' (GIAA). A total of 628 participants were included in the study: 284 from the Netherlands (45.2%), 8 from Flanders (Belgium) (1.3%), and 336 (53.5%) from the UK. Most participants were transgender people (or their partners/relatives) (n = 522), 89 participants were healthcare providers (HCPs) and 17 were both healthcare providers and (partners/relatives of) transgender people. Participants completed an online survey developed for this study. Most participants were in favor of the proposed diagnostic term of 'Gender Incongruence' and thought that this was an improvement on the ICD-10 diagnostic term of 'Transsexualism'. Placement in a separate chapter dealing with Sexual- and Gender-related Health or as a Z-code was preferred by many and only a small number of participants stated that this diagnosis should be excluded from the ICD-11. In the UK, most transgender participants thought there should be a diagnosis related to being trans. However, if it were to be removed from the chapter on "psychiatric disorders", many transgender respondents indicated that they would prefer it to be removed from the ICD in its entirety. There were no large differences between the responses of the transgender participants (or their partners and relatives) and HCPs. HCPs were generally positive about the GIAA diagnosis; most thought the diagnosis was clearly defined and easy to use in their practice or work. The duration of gender incongruence (several months) was seen by many as too short and required a clearer definition. If the new diagnostic term of GIAA is retained, it should not be stigmatizing to individuals. Moving this diagnosis away from the mental and behavioral chapter was generally supported. Access to healthcare was one area where retaining a diagnosis seemed to be of benefit.
Robust Face Representation Using Hybrid Spatial Feature Interdependence Matrix

A key issue in face recognition is to seek an effective descriptor for representing face appearance. In the context of considering the face image as a set of small facial regions, this paper presents a new face representation approach coined spatial feature interdependence matrix (SFIM). Unlike classical face descriptors which usually use a hierarchically organized or a sequentially concatenated structure to describe the spatial layout features extracted from local regions, SFIM is attributed to the exploitation of the underlying feature interdependences regarding local region pairs inside a class specific face. According to SFIM, the face image is projected onto an undirected connected graph in a manner that explicitly encodes feature interdependence-based relationships between local regions. We calculate the pair-wise interdependence strength as the weighted discrepancy between two feature sets extracted in a hybrid feature space fusing histograms of intensity, local binary pattern and oriented gradients. To achieve the goal of face recognition, our SFIM-based face descriptor is embedded in three different recognition frameworks, namely nearest neighbor search, subspace-based classification, and linear optimization-based classification. Extensive experimental results on four well-known face databases and comprehensive comparisons with the state-of-the-art results are provided to demonstrate the efficacy of the proposed SFIM-based descriptor.
Ranking Wily People Who Rank Each Other

We study rank aggregation algorithms that take as input the opinions of players over their peers, represented as rankings, and output a social ordering of the players (which reflects, e.g., relative contribution to a project or fit for a job). To prevent strategic behavior, these algorithms must be impartial, i.e., players should not be able to influence their own position in the output ranking. We design several randomized algorithms that are impartial and closely emulate given (nonimpartial) rank aggregation rules in a rigorous sense. Experimental results further support the efficacy and practicability of our algorithms.
Nine year old boy with chromosome 1q23.3-q25.1 deletion.

Interstitial deletions of the long arm of chromosome 1 are rare, and recent reports of individuals with molecularly characterized deletions have resulted in advances in genotype-phenotype correlation. The recognizable phenotype associated with 1q23.3-q25.1 includes pre- and post-natal growth retardation, microcephaly, intellectual disability, delayed language acquisition, small hands and feet with brachydactyly and single palmar crease, and distinctive facial features including short bulbous nose, micrognathia, and ear malformations. We report a patient with an 11.35 Mb deletion from 1q23.3-q25.1 who has these features in addition to a rarely reported complication-profound sensorineural hearing loss. He has both pre- and post-natal growth deficiency and growth hormone deficiency that was diagnosed at age 2 years. However, unlike other individuals with this deletion and growth hormone deficiency, this boy has responded to treatment with human growth hormone.
Brake Analysis and NVH Optimization Using MSC .

Brake Analysis and NVH (Noise, Vibration and Harshness) Optimization have become critically important areas of application in the Automotive Industry. Brake Noise and Vibration costs approximately $1Billion/year in warranty work in Detroit alone. NVH optimization is now increasingly being used to predict the vehicle tactile and acoustic responses in relation to the established targets for design considerations. Structural optimization coupled with frequency response analysis is instrumental in driving the design process so that the design targets are met in a timely fashion. Usual design targets include minimization of vehicle weight, the adjustment of fundamental eigenmodes and the minimization of acoustic pressure or vibration at selected vehicle locations. Both, Brake Analysis and NVH Optimization are computationally expensive analyses involving eigenvalue calculations. From a computational sense and the viewpoint of MSC.Nastran, brake analysis exercises the CEAD (Complex Eigenvalue Analysis Dmap) module, while NVH optimization invokes the DSADJ (Design Sensitivity using ADJoint method DMAP) module. In this paper, two automotive applications are presented to demonstrate the performance improvements of the CEAD and DSADJ modules on NEC vector-parallel supercomputers. Dramatic improvements in the DSADJ module resulting in approx. 8-9 fold performance improvement as compared to MSC.Nastran V70 were observed for NVH optimization. Also, brake simulations and experiences at General Motors will be presented. This analysis method has been successfully applied to 4 different programs at GM and the simulation results were consistent with laboratory experiments on test vehicles.
Framework for Evaluation of Sound Event Detection in Web Videos

The largest source of sound events is web videos. Most videos lack sound event labels at segment level, however, a significant number of them do respond to text queries, from a match found using metadata by search engines. In this paper we explore the extent to which a search query can be used as the true label for detection of sound events in videos. We present a framework for large-scale sound event recognition on web videos. The framework crawls videos using search queries corresponding to 78 sound event labels drawn from three datasets. The datasets are used to train three classifiers, and we obtain a prediction on 3.7 million web video segments. We evaluated performance using the search query as true label and compare it with human labeling. Both types of ground truth exhibited close performance, to within 10%, and similar performance trend with increasing number of evaluated segments. Hence, our experiments show potential for using search query as a preliminary true label for sound event recognition in web videos.
Design for NOMA: Combat Eavesdropping and Improve Spectral Efficiency in the Two-User Relay Network

Non-orthogonal multiple access (NOMA) is important in 5G, and the users served in NOMA are often paired to avoid the excessive interference. However, for a two-user network, if the channel condition of one user is serious, this network may require the other user to relay this user's signals. In this case, the demands of these users are possibly different. Specifically, the relay user may want the node to increase the spectral efficiency for compensating the cost of relay. On the other hand, because privacy information may be contained in signals, the indirect communication user may primarily focus on his or her information security. Therefore, we propose a novel physical layer scheme to satisfy these demands. Different from the existing relay schemes in NOMA, our scheme has the following characteristics: (i) through power allocation, the relay user can extract his or her signals with spectral efficiency improvement; (ii) through a signal-level method, the relay user can forward the indirect communication user's signals, but he or she is difficult to learn the privacy information in these signals; (iii) through a mechanism, the indirect communication user can attain his or her privacy information. Our scheme is able to support the relay user's demand, and this eavesdropping suppression in our scheme doe not depend on the complicated encryption techniques and positive secrecy rate.
Universal Artificial Intelligence: Sequential Decisions Based On Algorithmic Probability

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Design and kinematic analysis of an elephant-trunk-like robot with shape memory alloy actuators

In this paper, a new elephant-trunk-like robot with shape memory alloy (SMA) actuators was designed using a modular method. It consisted of a series of 2-degree of freedom (DOF) joint modules and showed the merits of light weight, compact structure, and flexibility. For each joint module, a sensor module based on MPU6050 chip was used to measure the pose of the end-effector in the form of Yaw-Pitch-Row Euler angles. Based on a piecewise-constant-curvature assumption, the kinematic analyses for the one-module robot and n-module robot were performed and developed. Experimental results obtained using the one-module elephant-trunk-like robot demonstrates the efficiency of the kinematic model.
Applying business intelligence innovations to emergency management.

The use of business intelligence (BI) is common among corporations in the private sector to improve business decision making and create insights for competitive advantage. Increasingly, emergency management agencies are using tools and processes similar to BI systems. With a more thorough understanding of the principles of BI and its supporting technologies, and a careful comparison to the business model of emergency management, this paper seeks to provide insights into how lessons from the private sector can contribute to the development of effective and efficient emergency management BI utilisation.
ICPR2018 Contest on Object Detection in Aerial Images (ODAI-18)

Object detection in aerial images plays a significant role in intelligent interpretation of aerial images. Hence many effective methods, especially the new-generation data-driven methods, have been developed for this task. Here, we hold the ODAI, a new contest that focused on object detection in aerial images, based on a new large-scale aerial image dataset called DOTA . This contest contains over 3000 large-size images ($4k\times 4k$ pixels), which cover 211,581 instances divided into 15 categories. Each instance is labeled by an arbitrary (8 d.o.f.) quadrilateral. Besides, we propose two tasks for this contest, named object detection with the horizontal bounding box (OD-HBB) and object detection with the oriented bounding box (OD-OBB). The contest was opened on February 7, 2018, and ended on April 30, 2018. A website is open to the public, which provides links to download data and evaluation server. We have totally received 60 registrations. There are 8 teams that have successfully submitted results on the OD-HBB task with the top mAP as 0.719, and 9 teams that have successfully submitted results on the OD-OBB task with the top mAP as 0.705. Through the contest, we hope to draw extensive attention from a wide range of communities and call for more future research and efforts for the task of object detection in aerial images.
Computer Vision-based Bangladeshi Sign Language Recognition System

Sign language is a specific area of human gesture communication and a full-edged complex language that is used by various deaf communities. In Bangladesh, there are many deaf and dumb people. It becomes very difficult to communicate with them for the people who are unable to understand the Sign Language. In this case, an interpreter can help a lot. So it is desirable to make computer to understand the Bangladeshi sign language that can serve as an interpreter. In this paper, a Computer Vision-based Bangladeshi Sign Language Recognition System (BdSL) has been proposed. In this system, separate PCA (Principal Component Analysis) is used for Bengali Vowels and Bengali Numbers recognition. The system is tested for 6 Bengali Vowels and 10 Bengali Numbers.
The rate of growth in scientific publication and the decline in coverage provided by Science Citation Index

The growth rate of scientific publication has been studied from 1907 to 2007 using available data from a number of literature databases, including Science Citation Index (SCI) and Social Sciences Citation Index (SSCI). Traditional scientific publishing, that is publication in peer-reviewed journals, is still increasing although there are big differences between fields. There are no indications that the growth rate has decreased in the last 50 years. At the same time publication using new channels, for example conference proceedings, open archives and home pages, is growing fast. The growth rate for SCI up to 2007 is smaller than for comparable databases. This means that SCI was covering a decreasing part of the traditional scientific literature. There are also clear indications that the coverage by SCI is especially low in some of the scientific areas with the highest growth rate, including computer science and engineering sciences. The role of conference proceedings, open access archives and publications published on the net is increasing, especially in scientific fields with high growth rates, but this has only partially been reflected in the databases. The new publication channels challenge the use of the big databases in measurements of scientific productivity or output and of the growth rate of science. Because of the declining coverage and this challenge it is problematic that SCI has been used and is used as the dominant source for science indicators based on publication and citation numbers. The limited data available for social sciences show that the growth rate in SSCI was remarkably low and indicate that the coverage by SSCI was declining over time. National Science Indicators from Thomson Reuters is based solely on SCI, SSCI and Arts and Humanities Citation Index (AHCI). Therefore the declining coverage of the citation databases problematizes the use of this source.
Three Myths about Dynamic Time Warping Data Mining

The Dynamic Time Warping (DTW) distance measure is a technique that has long been known in speech recognition community. It allows a non-linear mapping of one signal to another by minimizing the distance between the two. A decade ago, DTW was introduced into Data Mining community as a utility for various tasks for time series problems including classification, clustering, and anomaly detection. The technique has flourished, particularly in the last three years, and has been applied to a variety of problems in various disciplines. In spite of DTW’s great success, there are still several persistent “myths” about it. These myths have caused confusion and led to much wasted research effort. In this work, we will dispel these myths with the most comprehensive set of time series experiments ever conducted.
Evaluating interactions with non-existing automated vehicles: three Wizard of Oz approaches

Highly automated test vehicles are rare today, and (independent) researchers have often limited access to them. Also, developing fully functioning system prototypes is time and effort consuming. In this paper, we present three adaptions of the Wizard of Oz technique as a means of gathering data about interactions with highly automated vehicles in early development phases. Two of them address interactions between drivers and highly automated vehicles, while the third one is adapted to address interactions between pedestrians and highly automated vehicles. The focus is on the experimental methodology adaptations and our lessons learned.
Design, implementation and performance analysis of a dual-axis autonomous solar tracker

This paper proposed a design, implementation & performance of an energy efficient solar tracking system based on closed loop technique. This solar tracking system is autonomous dual axis hybrid type. Solar efficiency depends on PV cell and its tracking system. In our works we concentrate on the panel tracking system. Our tracking system is a sensor based tracking system and it can track the sun continuously. A sensor placed on the surface of the panel and sensor compare light intensity continuously. Then our control unit sends signal to actuator unit to reposition the panel. This system is energy efficient because the actuator unit shut down in cloudy weather and turns on in sunny weather. Powerful microcontroller used to calculate and evaluate the light intensity form sensor unit, then send instruction to actuator unit. For graceful and accurate angular motion we used the servo actuator system. It's no need additional real time clock to track annual motion and daily motion because it's based on active closed loop system. Our implemented design is more efficient and convenient than other tracker
Automatic liver tumor segmentation in follow-up CT studies using Convolutional Neural Networks

We present a new, fully automatic algorithm for liver tumors segmentation in follow-up CT studies. The inputs are a baseline CT scan and a delineation of the tumors in it and a follow-up scan; the outputs are the tumors delineations in the follow-up CT scan. The algorithm consists of four steps: 1) deformable registration of the baseline scan and tumors delineations to the followup CT scan; 2) automatic segmentation of the liver; 3) training a Convolutional Neural Network (CNN) as a voxel classifier on all baseline; 4) segmentation of the tumor in the follow-up study with the learned classifier. The main novelty of our method is the combination of follow-up based detection with CNN-based segmentation. Our experimental results on 67 tumors from 21 patients with ground-truth segmentations approved by a radiologist yield an average overlap error of 16.26% (std=10.33).
UiTM campus bus tracking system using Arduino based and smartphone application

A prototype campus bus tacking system is designed and implemented for helping UiTM Student to pinpoint the location and estimate arrival time of their respective desired bus via their smartphone application. This project comprises integration between hardware and software. An Arduino UNO is used to control the GPS module to get the geographic coordinates. An android smartphone application using App Inventor is also developed for the user not only to determine the time for the campus bus to arrive and also will be able to get the bus information. This friendly user system is named as "UiTM Bus Checker" application. The user also will be able to view position of the bus on a digital mapping from Google Maps using their smartphone application and webpage. In order to show the effectiveness of this UiTM campus bus tracking system, the practical implementations have been presented and recorded.
A Normalization scheme for Terrestrial LiDAR Intensity Data by Range and Incidence Angle

Automatic registration, classification and segmentation of Terrestrial Laser Scanner (TLS) data are of great interest in Geoinformatics & Autonomous vehicle research. Along with dense and accurate 3D geometric data, laser scanners also collect return intensity information. Inclusion of this spectral information has potential to improve the working of the above mentioned processes. However, these intensity values need to be normalized, prior to their use, as they are subject to a large number of errors. This paper presents a technique to carry out normalization of intensity values using the range and incidence angle corrections. The developed approach has been tested on a large number of data and results are found satisfactory.  Terrestrial Laser Scanner, LiDAR, Intensity, Normalization, Incidence angle correction