nils-herrmann/clean_scirepeval huggingface dataset:
Performance assessment of a Multi fuel Hybrid Engine for Future Aircraft

This paper presents performance assessment of the proposed hybrid engine concept using Liquid Natural Gas (LNG) and kerosene. The multi fuel hybrid engine is a new engine concept integrated with contra rotating fans, sequential dual combustion chambers to facilitate "Energy Mix" in aviation and a Cryogenic Bleed Air Cooling System (CBACS) The current analysis focuses on three aspects: 1) effects of the CBACS on the HPT cooling air requirement and the associated effects on the cycle efficiency; 2) performance optimization of the hybrid engine; 3) assessment of the emission reduction by the hybrid engine. An integrated model framework consisting of an engine performance model, a turbine cooling model, and a Cryogenic Heat Exchanger (CHEX) model is used to perform the analyses. The parametric analysis shows that using the CHEX, the bleed air temperature can be reduced significantly (up to 600 K) which reduces the turbine cooling air requirement by more than 50% while increasing the LNG temperature by 300K. Consequently, the cycle efficiency improves even further. Depending on the fuel flow distribution between two combustors. The CO2 emission from the hybrid engine is lower by 15% to 30% The mission analysis along with the Multi Fuel Blended Wing Body aircraft shows a reduction in NOx emissions by 80% and CO2 emission by 50% when compared to B 777 200ER.
Research on a pneumatic hybrid engine with regenerative braking and compressed air assisted cranking

With the aim of improving the energy efficiency of conventional vehicles, this paper proposes a pneumatic hybrid engine concept, which features regenerative braking and compressed air assisted cranking functions. In the regenerative braking mode, the engine works as a two stroke compressor and converts the kinematic energy of the vehicle into compressed air. In the two stroke air motor mode, a throttle valve in the intake manifold is used to block the ambient air, and an electropneumatic solenoid valve is employed to control the compressed air injection. The compressor mode is modelled on the basis of thermodynamics and gas dynamics theories and is improved according to the bench test results of a single cylinder engine converted from a two cylinder diesel engine. Transient engine cranking tests are also conducted and are discussed. Urban driving cycle simulation results show that the fuel consumption of a light duty vehicle with a pneumatic hybrid system can be reduced by 8%
Machine Learned Resume Job Matching Solution
Venue: ArXiv

Job search through online matching engines nowadays are very prominent and beneficial to both job seekers and employers. But the solutions of traditional engines without understanding the semantic meanings of different resumes have not kept pace with the incredible changes in machine learning techniques and computing capability. These solutions are usually driven by manual rules and predefined weights of keywords which lead to an inefficient and frustrating search experience. To this end, we present a machine learned solution with rich features and deep learning methods. Our solution includes three configurable modules that can be plugged with little restrictions. Namely, unsupervised feature extraction, base classifiers training and ensemble method learning. In our solution, rather than using manual rules, machine learned methods to automatically detect the semantic similarity of positions are proposed. Then four competitive "shallow" estimators and "deep" estimators are selected. Finally, ensemble methods to bag these estimators and aggregate their individual predictions to form a final prediction are verified. Experimental results of over 47 thousand resumes show that our solution can significantly improve the predication precision current position, salary, educational background and company scale.
Hirability in the Wild: Analysis of Online Conversational Video Resumes
Venue: IEEE Transactions on Multimedia

Online social media is changing the personnel recruitment process. Until now, resumes were among the most widely used tools for the screening of job applicants. The advent of inexpensive sensors combined with the success of online video platforms has enabled the introduction of a new type of resume. Video resumes are short video messages where job applicants present themselves to potential employers. Online video resumes represent an opportunity to study the formation of first impressions in an employment context at a scale never attempted before, and to our knowledge they have not been studied from a behavioral standpoint. We collected a dataset of 939 conversational English speaking video resumes from YouTube. Annotations of demographics, skills, and first impressions were collected using the Amazon Mechanical Turk crowdsourcing platform. Basic demographics were then analyzed to understand the population who uses video resumes to find a job, and results showed that applicants mainly consisted of young people looking for internship and junior positions. We developed a computational framework for the prediction of organizational first impressions, where the inference and nonverbal cue extraction steps were fully automated. Results demonstrate automatic prediction of first impressions of up to 27% of the variance explained for extraversion, and up to 20% for social and communication skills.
Visualization and knowledge discovery in metadata enriched aggregated data repositories harvesting from Scopus and Web of Science

Our objective was to visualize scientific published production in order to discover how Slovenian researchers registered in six principal disciplines (Biotechnical sciences (agriculture and related) Engineering sciences technologies, Humanities, Medical sciences, Natural sciences mathematics, Social sciences) relate to each other in co authorship maps and which patterns are identifiable on different levels of subject categorization. 65,000 unified (duplicates excluded) records (1996 2014) were harvested from Scopus and Web of Science, and linked to COBISS (Co operative Online Bibliographic System Services) enriched by SICRIS classification data (Slovenian CRIS) COBISS/SciMet (COBISS Scientometrics) extracts large metadata sets for the purposes of metrics. VOSviewer was employed for visualization as a method of Big data analytics. Some disciplines cooperate within more uniform clusters: medicine, and especially humanities and social sciences. Natural sciences show the strongest interdisciplinary characteristics occurring in several different clusters together with other disciplines/fields/subfields. Biotechnical research cooperates most strongly with natural sciences and to some extent medicine.
Explorative hyperbolic tree based clustering tool for unsupervised knowledge discovery
Venue: 2016 14th International Workshop on Content Based Multimedia Indexing (CBMI)

Exploring and annotating collections of images without meta data is a laborious task. Visual analytics and information visualization can help users by providing interfaces for exploration and annotation. In this paper, we show a prototype application that allows users from the medical domain to use feature based clustering to perform explorative browsing and annotation in an unsupervised manner. For this, we utilize global image feature extraction, different unsupervised clustering algorithms and hyperbolic tree representation. First, the prototype application extracts features from images or video frames, and then, one or multiple features at the same time can be used to perform clustering. The clusters are presented to the users as a hyperbolic tree for visual analysis and annotation.
First Report of Outbreaks of the Fall Armyworm Spodoptera frugiperda (J E Smith) (Lepidoptera, Noctuidae) a New Alien Invasive Pest in West and Central Africa
Venue: PloS one

The fall armyworm Spodoptera frugiperda is a prime noctuid pest of maize on the American continents where it has remained confined despite occasional interceptions by European quarantine services in recent years. The pest has currently become a new invasive species in West and Central Africa where outbreaks were recorded for the first time in early 2016. The presence of at least two distinct haplotypes within samples collected on maize in Nigeria and Sao Tome suggests multiple introductions into the African continent. Implications of this new threat to the maize crop in tropical Africa are briefly discussed.
Poverty in a Rising Africa

This report is the first of a two part volume on poverty in Africa. This study documents the data challenges and revisits the core broad facts about poverty in Africa; the second report will explore ways to accelerate its reduction. The report takes a broad, multidimensional view of poverty, assessing progress over the past two decades along both monetary and nonmonetary dimensions. The dearth of comparable, good quality household consumption surveys makes assessing monetary poverty especially challenging. The report scrutinizes the data used to assess monetary poverty in the region and explores how adjustments for data issues affect poverty trends. At the same time, the remarkable expansion of standardized household surveys on nonmonetary dimensions of well being, including opinions and perceptions, opens up new opportunities. The report examines progress in education and health, the extent to which people are free from violence and able to shape their lives, and the joint occurrence of various types of deprivation. It also reviews the distributional aspects of poverty,by studying various dimensions of inequality. To shed light on Africa's diversity, the report examines differences in performance across countries, by location, and by gender. Countries are characterized along four dimensions that have been shown to affect growth and poverty: resource richness, fragility, landlockedness and income status. To conclude, a portion of inequality in Africa can be attributed to inequality of opportunity, circumstances at birth that are major determinants of one's poverty status as an adult.
Expanding rural access to mental health care through online postgraduate nurse practitioner education
Venue: Journal of the American Association of Nurse Practitioners

Workforce shortages in mental health care are especially relevant to rural communities. People often turn to their primary care providers for mental healthcare services, yet primary care providers indicate that more education is needed to fill this role. Rural primary care nurse practitioners (NPs) are ideal candidates for educational enhancement. Online programs allow NPs to continue living and working in their communities while developing the competencies to provide comprehensive and integrated mental healthcare services. This article presents a review of current online postgraduate psychiatric mental health NP (PMHNP) options.  Website descriptions of online PMHNP programs were located using keywords: PMHNP or psychiatric nurse practitioner, postgraduate or post master's, and distance or online.  PRACTICE For rural primary care NPs who are ready, willing, and able, a postgraduate PMHNP specialty certificate can be obtained online in as few as three to four semesters. The expected outcome is a cadre of dually credentialed NPs capable of functioning in an integrated role and of increasing rural access to comprehensive mental healthcare services.
Promoting Access Through Integrated Mental Health Care Education
Venue: The open nursing journal

Mental disorders are the leading cause of non communicable disability worldwide. Insufficient numbers of psychiatrically trained providers and geographic inequities impair access. To close this treatment gap, the World Health Organization (WHO) has called for the integration of mental health services with primary care. A new innovative online program is presented that increases access to mental health education for primary care nurse practitioners in designated mental health professional shortage areas. To create successful and sustainable change, an overlapping three phase strategy is being implemented. Phase I is recruiting and educating primary care nurse practitioners to become competent and certified psychiatric mental health nurse practitioners. Phase II is developing partnerships with state and local agencies to identify and support the psychiatric mental health nurse practitioner education and clinical training. Phase III is sustaining integrated mental health care services through the development of nurse leaders who will participate in interdisciplinary coalitions and educate future students.
Applications of VoiceThread(c) Technology in Graduate Nursing Education.
Venue: The Journal of nursing education

Online graduate courses provide opportunities for faculty to use technology and digital applications to enhance student learning and learning environments. In nursing education, as we become increasingly dependent on technology, it is important to ensure that both faculty and students add digital literacy to their repertoire of knowledge and skills. VoiceThread(c) one type of Web based digital application tool, allows students and faculty to verbally communicate and collaborate asynchronously.  This article discusses the use of VoiceThread technology in graduate nursing education and offers four examples of VoiceThread teaching methods: personal introductions, issues discussions, case presentations, and the elevator speech.  Student participation in VoiceThread assignments is evaluated using leveled rubrics. A poll of the students in one of the graduate courses showed high overall satisfaction with VoiceThread in the online classroom.  Strategies for effective use of VoiceThread technology to enhance student engagement and learning are recommended.
Altered Kinematics of Facial Emotion Expression and Emotion Recognition Deficits Are Unrelated in Parkinson's Disease
Venue: Frontiers in neurology

Altered emotional processing, including reduced emotion facial expression and defective emotion recognition, has been reported in patients with Parkinson's disease (PD) However, few studies have objectively investigated facial expression abnormalities in PD using neurophysiological techniques. It is not known whether altered facial expression and recognition in PD are related.  To investigate possible deficits in facial emotion expression and emotion recognition and their relationship, if any, in patients with PD.  Eighteen patients with PD and 16 healthy controls were enrolled in this study. Facial expressions of emotion were recorded using a 3D optoelectronic system and analyzed using the facial action coding system. Possible deficits in emotion recognition were assessed using the Ekman test. Participants were assessed in one experimental session. Possible relationship between the kinematic variables of facial emotion expression, the Ekman test scores, and clinical and demographic data in patients were evaluated using the Spearman's test and multiple regression analysis.  The facial expression of all six basic emotions had slower velocity and lower amplitude in patients in comparison to healthy controls (all Ps 0.05) Patients also yielded worse Ekman global score and disgust, sadness, and fear sub scores than healthy controls (all Ps 0.001) Altered facial expression kinematics and emotion recognition deficits were unrelated in patients (all Ps 0.05) Finally, no relationship emerged between kinematic variables of facial emotion expression, the Ekman test scores, and clinical and demographic data in patients (all Ps 0.05)  The results in this study provide further evidence of altered emotional processing in PD. The lack of any correlation between altered facial emotion expression kinematics and emotion recognition deficits in patients suggests that these abnormalities are mediated by separate pathophysiological mechanisms.
Real Time Hybrid In Station Bus Dispatching Strategy Based on Mixed Integer Programming
Venue: Inf.

The actual bus headway often deviates from the planned departure frequency because of external factors, such as traffic conditions and public transport demand, leading to transit resource waste and reducing the quality of service. In view of the existing shortcomings of the current dispatching strategy, a mixed integer programming model, integrating a bus holding and stop skipping strategy, is constructed to improve transit service with a minimum cost. The real time optimal holding and stop skipping strategies can be obtained by solving the proposed model using the Lagrangian relaxation algorithm. A numerical example is conducted using real transit GPS (Global Position System) and IC (Intelligent Card) data in Harbin. The results show that compared to a single control strategy, the proposed hybrid model is a better trade off between the quality of the transit service and the operation cost. Notably, such a strategy would produce a minimal passengers' average travel time coefficient. It is a great help for promoting the transit service level and increasing competitiveness.
A New Attack on Three Variants of the RSA Cryptosystem
Venue: ACISP

In 1995, Kuwakado, Koyama and Tsuruoka presented a new RSA type scheme based on singular cubic curves $y^2\equiv x^3+bx^2\pmod N$ where $N=pq$ is an RSA modulus. Then, in 2002, Elkamchouchi, Elshenawy and Shaban introduced an extension of the RSA scheme to the field of Gaussian integers using a modulus $N=PQ$ where P and Q are Gaussian primes such that $p=|P| and $q=|Q| are ordinary primes. Later, in 2007, Castagnos proposed a scheme over quadratic field quotients with an RSA modulus $N=pq$ In the three schemes, the public exponent e is an integer satisfying the key equation $ed k\left p^2 1\right \left q^2 1\right =1$ In this paper, we apply the continued fraction method to launch an attack on the three schemes when the private exponent d is sufficiently small. Our attack can be considered as an extension of the famous Wiener attack on the RSA.
An All Elastomeric Transparent and Stretchable Temperature Sensor for Body Attachable Wearable Electronics.
Venue: Advanced materials

A transparent stretchable (TS) gated sensor array with high optical transparency, conformality, and high stretchability of up to 70% is demonstrated. The TS gated sensor array has high responsivity to temperature changes in objects and human skin. This unprecedented TS gated sensor array, as well as the integrated platform of the TS gated sensor with a transparent and stretchable strain sensor, show great potential for application to wearable skin electronics for recognition of human activity.
Fast CU Size Decisions for HEVC Intra Frame Coding Based on Support Vector Machines
Venue: 2016 IEEE 14th Intl Conf on Dependable, Autonomic and Secure Computing, 14th Intl Conf on Pervasive Intelligence and Computing, 2nd Intl Conf on Big Data Intelligence and Computing and Cyber Science and Technology Congress(DASC/PiCom/DataCom/CyberSciTech)

High Efficiency Video Coding (HEVC) achieves themost significant coding efficiency compared with all the existingvideo coding standards. However, the Intra encoding complexityis increased dramatically since the complex recursive searchalgorithm for the coding unit (CU) size decisions. In this paper, afast CU size decision algorithm based on Support Vector Machines(SVM) is proposed to further alleviate the encoder computationload. Firstly, some effective image features are extracted to judgethe CU complexity. Then, a three classifier structure of CU sizedecisions is established based on SVM. The experimental resultsshow that the proposed algorithm can reduce 53% intra codingtime on average with 1.2% BDBR loss on average
Identifying users of traditional and Internet based resources for meal ideas: An association rule learning approach
Venue: Appetite

Increasing home cooking while decreasing the consumption of food prepared away from home is a commonly recommended weight management strategy, however research on where individuals obtain ideas about meals to cook at home is limited. This study examined the characteristics of individuals who reported using traditional and Internet based resources for meal ideas. 583 participants who were =50% responsible for household meal planning were recruited to approximate the 2014 United States Census distribution on sex, age, race/ethnicity, and household income. Participants reported demographic characteristics, home cooking frequency, and their use of 4 traditional resources for meal ideas (e.g. cookbooks) and 7 Internet based resources for meal ideas (e.g. Pinterest) in an online survey. Independent samples t tests compared home cooking frequency by resource use. Association rule learning identified those demographic characteristics that were significantly associated with resource use. Family and friends (71% food community websites (45% and cookbooks (41% were the most common resources reported. Cookbook users reported preparing more meals at home per week (M 9.65, SD 5.28) compared to non cookbook users (M 8.11, SD 4.93; t 3.55, p 0.001) Resource use was generally higher among parents and varied systematically with demographic characteristics. Findings suggest that home cooking interventions may benefit by modifying resources used by their target population.
Deep Metric Learning with Improved Triplet Loss for Face Clustering in Videos
Venue: PCM

Face clustering in videos is to partition a large amount of faces into a given number of clusters, such that some measure of distance is minimized within clusters and maximized between clusters. In real world videos, head pose, facial expression, scale, illumination, occlusion and some uncontrolled factors may dramatically change the appearance variations of faces. In this paper, we tackle this problem by learning non linear metric function with a deep convolutional neural network from the input image to a low dimensional feature embedding with the visual constraints among face tracks. Our network directly optimizes the embedding space so that the Euclidean distances correspond to a measure of semantic face similarity. This is technically realized by minimizing an improved triplet loss function, which pushes the negative face away from the positive pairs, and requires the distance of the positive pair to be less than a margin. We extensively evaluate the proposed algorithm on a set of challenging videos and demonstrate significant performance improvement over existing techniques.
Sparse Manifold Discriminant Embedding for Face Recognition

The sparse manifold clustering and embedding (SMCE) algorithm can be used to find a low dimensional face manifold subspace. The neighborhoods are defined automatically and a sparse weight matrix is calculated with non zero values only for the members of the neighborhood. To obtain well separated classes in the low dimensional feature space, we propose to use discriminant functions during the embedding process. Existing manifold techniques do not give accurate results when images of different individuals with varying pose angles, lighting and facial expressions are considered. For these methods a proper choice of the neighborhood used to build the neighborhood graph is important. In SMCE this neighborhood is defined using a sparse optimization technique. K nearest neighbor (KNN) classifier is used to classify the face images. The proposed method is compared with benchmarks using standard face datasets for varying facial expressions, lighting and poses. Leave one out cross validation testing strategy is used to validate the results.