nils-herrmann/clean_scirepeval huggingface dataset:
MBSE-assisted FMEA approach — Challenges and opportunities

This paper presents an “MBSE-Assisted FMEA” approach. The approach is evolutionary in nature and accommodates the current NASA and Air Force FMEA procedures and practices required for many of their contracts; therefore, it can be implemented immediately. The approach considers current MBSE philosophy, methods, and tools, introducing them gradually to evolve the current FMEA process to a model-based failure modes, causes, and effects generation process. To assist the implementation of this transitioning process from current FMEA to the Model-Based FMEA, the paper discusses challenges and opportunities/benefits that can help establish a feasible and viable near term and long term implementation plan. With the MBSE-Assisted FMEA as a near term approach and Model-Based FMEA as a longer term implementation goal, FMEA will become a more effective and cost efficient reliability process that is integrated with MBSE to support Design-for-Reliability and Model-Based Mission Assurance activities.
Question-Answer Driven Semantic Role Labeling: Using Natural Language to Annotate Natural Language

This paper introduces the task of questionanswer driven semantic role labeling (QA-SRL), where question-answer pairs are used to represent predicate-argument structure. For example, the verb “introduce” in the previous sentence would be labeled with the questions “What is introduced?”, and “What introduces something?”, each paired with the phrase from the sentence that gives the correct answer. Posing the problem this way allows the questions themselves to define the set of possible roles, without the need for predefined frame or thematic role ontologies. It also allows for scalable data collection by annotators with very little training and no linguistic expertise. We gather data in two domains, newswire text and Wikipedia articles, and introduce simple classifierbased models for predicting which questions to ask and what their answers should be. Our results show that non-expert annotators can produce high quality QA-SRL data, and also establish baseline performance levels for future work on this task.
The serotonergic 5-HT4 receptor: A unique modulator of hippocampal synaptic information processing and cognition

Serotonin (5-hydroxytryptamine, 5-HT) contributes in multifarious ways to the regulation of brain function, spanning key aspects such as the sleep-wake cycle, appetite, mood and mental health. The 5-HT receptors comprise seven receptor families (5-HT1-7) that are further subdivided into 14 receptor subtypes. The role of the 5-HT receptor in the modulation of neuronal excitability has been well documented. Recently, however, it has become apparent that the 5-HT4 receptor may contribute significantly to cognition and regulates less ostensible aspects of brain function: it engages in metaplastic regulation of synaptic responsiveness in key brain structures such as the hippocampus, thereby specifically promoting persistent forms of synaptic plasticity, and influences the direction of change in synaptic strength in selected hippocampal subfields. This highly specific neuromodulatory control by the 5-HT4 receptor may in turn explain the reported role for this receptor in hippocampus-dependent cognition. In this review article, we describe the role of the 5-HT4 receptor in hippocampal function, and describe how this receptor plays a unique and highly specialised role in synaptic information storage and cognition.
Color image interpolation in the DCT domain using a wavelet-based differential value

We propose a new image-interpolation technique using a combination of an adaptive directional wavelet transform (ADWT) and discrete cosine transform (DCT). In the proposed method, we use ADWT to decompose the low-resolution image into different frequency subband images. The high-frequency subband images are further transformed to DCT coefficients, and the zero-padding method is used for interpolation. Simultaneously, the low-frequency subband image is replaced by the original low-resolution image. Finally, we generate the interpolated image by combining the original low-resoultion image and the interpolated subband images by using inverse DWT. Experimental results demonstrate that the proposed algorithm yields better quality images in terms of subjective and objective quality metrics compared to the other methods considered in this paper.
Authorization Framework for Secure Cloud Assisted Connected Cars and Vehicular Internet of Things

Internet of Things has become a predominant phenomenon in every sphere of smart life. Connected Cars and Vehicular Internet of Things, which involves communication and data exchange between vehicles, traffic infrastructure or other entities are pivotal to realize the vision of smart city and intelligent transportation. Vehicular Cloud offers a promising architecture wherein storage and processing capabilities of smart objects are utilized to provide on-the-fly fog platform. Researchers have demonstrated vulnerabilities in this emerging vehicular IoT ecosystem, where data has been stolen from critical sensors and smart vehicles controlled remotely. Security and privacy is important in Internet of Vehicles (IoV) where access to electronic control units, applications and data in connected cars should only be authorized to legitimate users, sensors or vehicles. In this paper, we propose an authorization framework to secure this dynamic system where interactions among entities is not pre-defined. We provide an extended access control oriented (E-ACO) architecture relevant to IoV and discuss the need of vehicular clouds in this time and location sensitive environment. We outline approaches to different access control models which can be enforced at various layers of E-ACO architecture and in the authorization framework. Finally, we discuss use cases to illustrate access control requirements in our vision of cloud assisted connected cars and vehicular IoT, and discuss possible research directions.
Vehicle Detection with Occlusion Handling, Tracking, and OC-SVM Classification: A High Performance Vision-Based System

This paper presents a high performance vision-based system with a single static camera for traffic surveillance, for moving vehicle detection with occlusion handling, tracking, counting, and One Class Support Vector Machine (OC-SVM) classification. In this approach, moving objects are first segmented from the background using the adaptive Gaussian Mixture Model (GMM). After that, several geometric features are extracted, such as vehicle area, height, width, centroid, and bounding box. As occlusion is present, an algorithm was implemented to reduce it. The tracking is performed with adaptive Kalman filter. Finally, the selected geometric features: estimated area, height, and width are used by different classifiers in order to sort vehicles into three classes: small, midsize, and large. Extensive experimental results in eight real traffic videos with more than 4000 ground truth vehicles have shown that the improved system can run in real time under an occlusion index of 0.312 and classify vehicles with a global detection rate or recall, precision, and F-measure of up to 98.190%, and an F-measure of up to 99.051% for midsize vehicles.
Deep Learning for Electromyographic Hand Gesture Signal Classification Using Transfer Learning.

In recent years, deep learning algorithms have become increasingly more prominent for their unparalleled ability to automatically learn discriminant features from large amounts of data. However, within the field of electromyographybased gesture recognition, deep learning algorithms are seldom employed as they require an unreasonable amount of effort from a single person, to generate tens of thousands of examples. This work's hypothesis is that general, informative features can be learned from the large amounts of data generated by aggregating the signals of multiple users, thus reducing the recording burden while enhancing gesture recognition. Consequently, this paper proposes applying transfer learning on aggregated data from multiple users, while leveraging the capacity of deep learning algorithms to learn discriminant features from large datasets. Two datasets comprised of 19 and 17 able-bodied participants respectively (the first one is employed for pre-training) were recorded for this work, using the Myo Armband. A third Myo Armband dataset was taken from the NinaPro database and is comprised of 10 able-bodied participants. Three different deep learning networks employing three different modalities as input (raw EMG, Spectrograms and Continuous Wavelet Transform (CWT)) are tested on the second and third dataset. The proposed transfer learning scheme is shown to systematically and significantly enhance the performance for all three networks on the two datasets, achieving an offline accuracy of 98.31% for 7 gestures over 17 participants for the CWT-based ConvNet and 68.98% for 18 gestures over 10 participants for the raw EMG-based ConvNet. Finally, a use-case study employing eight able-bodied participants suggests that real-time feedback allows users to adapt their muscle activation strategy which reduces the degradation in accuracy normally experienced over time.
Code Completion with Neural Attention and Pointer Networks

Intelligent code completion has become an essential research task to accelerate modern software development. To facilitate effective code completion for dynamically-typed programming languages, we apply neural language models by learning from large codebases, and develop a tailored attention mechanism for code completion. However, standard neural language models even with attention mechanism cannot correctly predict the outof-vocabulary (OoV) words that restrict the code completion performance. In this paper, inspired by the prevalence of locally repeated terms in program source code, and the recently proposed pointer copy mechanism, we propose a pointer mixture network for better predicting OoV words in code completion. Based on the context, the pointer mixture network learns to either generate a withinvocabulary word through an RNN component, or regenerate an OoV word from local context through a pointer component. Experiments on two benchmarked datasets demonstrate the effectiveness of our attention mechanism and pointer mixture network on the code completion task.
Mining Trackman Golf Data

Recently, innovative technology like Trackman has made it possible to generate data describing golf swings. In this application paper, we analyze Trackman data from 275 golfers using descriptive statistics and machine learning techniques. The overall goal is to find non-trivial and general patterns in the data that can be used to identify and explain what separates skilled golfers from poor. Experimental results show that random forest models, generated from Trackman data, were able to predict the handicap of a golfer, with a performance comparable to human experts. Based on interpretable predictive models, descriptive statistics and correlation analysis, the most distinguishing property of better golfers is their consistency. In addition, the analysis shows that better players have superior control of the club head at impact and generally hit the ball straighter. A very interesting finding is that better players also tend to swing flatter. Finally, an outright comparison between data describing the club head movement and ball flight data, indicates that a majority of golfers do not hit the ball solid enough for the basic golf theory to apply.
The spindle-assembly checkpoint in space and time

In eukaryotes, the spindle-assembly checkpoint (SAC) is a ubiquitous safety device that ensures the fidelity of chromosome segregation in mitosis. The SAC prevents chromosome mis-segregation and aneuploidy, and its dysfunction is implicated in tumorigenesis. Recent molecular analyses have begun to shed light on the complex interaction of the checkpoint proteins with kinetochores — structures that mediate the binding of spindle microtubules to chromosomes in mitosis. These studies are finally starting to reveal the mechanisms of checkpoint activation and silencing during mitotic progression.
Proficiency in Information Communication Technology and its Use: A Survey among Clinical Students in a Ghanaian Medical School

Insufficient prior knowledge about the array of skills possessed by medical students in information communication technology account for failed efforts at incorporating ICT into their academic work. The aim of this study is to access information communication and technology skills and its use among clinical students undergoing medical training in northern Ghana. A longitudinal questionnaire was administered to 175 clinical year (1st, 2nd, and 3rd year) medical students aged between 22 and 29 years (mean ± standard deviation; 25. 0 ± 1. 26 years). Out of the total 175 questionnaires administered 140 (82. 0%) students returned their questionnaires. Questionnaires from 5 students were incomplete leaving 135 complete and analyzable questionnaires, resulting in a 77. 0% responses rate. Of the remaining 135 students, 55. 6% of the respondents were proficient in the use of ICT related tools, 37. 8% were using ICT resources for their academic work, and 85. 2% were using such resources for social purposes, while use of ICT for academic work by gender was: 88. 2% for males, and 11. 8% for females. By gender 49. 0% males and 52. 2% females were using ICT for social purposes. The study revealed high and low levels of proficiency in ICT depending upon the ICT task to be performed, and concluded that a good curriculum designed to encourage ICT use by students as well as develop in them a multiplicity of skills, coupled with a teaching methodology that is student centred and encourages student engagement in active cognitive activities involving the use of ICTs may help stem this skewedness in proficiency.
Attaining Customer Loyalty ! The Role of Consumer Attitude and Consumer Behavior

All over the world every business and profit earning firm want to make their consumer loyal. There are many factors responsible for this customer loyalty but two of them are prominent. This research study is focused on that how customer satisfaction and customer retention contribute towards customer loyalty. For analysis part of this study, Universities students of Peshawar Region were targeted. A sample of 120 were selected from three universities of Peshawar. These universities were Preston University, Sarhad University and City University of Science and Information technology. Analysis was conducted with the help of SPSS 19. Results of the study shows that customer loyalty is more dependent upon Customer satisfaction in comparison of customer retention. Customer perceived value and customer perceived quality are the major factors which contribute for the customer loyalty of Universities students for mobile handsets.
Social-cognitive predictors of health behavior: action self-efficacy and coping self-efficacy.

The effects of social-cognitive variables on preventive nutrition and behavioral intentions were studied in 580 adults at 2 points in time. The authors hypothesized that optimistic self-beliefs operate in 2 phases and made a distinction between action self-efficacy (preintention) and coping self-efficacy (postintention). Risk perceptions, outcome expectancies, and action self-efficacy were specified as predictors of the intention at Wave 1. Behavioral intention and coping self-efficacy served as mediators linking the 3 predictors with low-fat and high-fiber dietary intake 6 months later at Wave 2. Covariance structure analysis yielded a good model fit for the total sample and 6 subsamples created by a median split of 3 moderators: gender, age, and body weight. Parameter estimates differed between samples; the importance of perceived self-efficacy increased with age and weight.
Twitter Sentiment Analysis: How To Hedge Your Bets In The Stock Markets

Emerging interest of trading companies and hedge funds in mining social web has created new avenues for intelligent systems that make use of public opinion in driving investment decisions. It is well accepted that at high frequency trading, investors are tracking memes rising up in microblogging forums to count for the public behavior as an important feature while making short term investment decisions. We investigate the complex relationship between tweet board literature (like bullishness, volume, agreement etc) with the financial market instruments (like volatility, trading volume and stock prices). We have analyzed Twitter sentiments for more than 4 million tweets between June 2010 and July 2011 for DJIA, NASDAQ-100 and 11 other big cap technological stocks. Our results show high correlation (upto 0.88 for returns) between stock prices and twitter sentiments. Further, using Granger’s Causality Analysis, we have validated that the movement of stock prices and indices are greatly affected in the short term by Twitter discussions. Finally, we have implemented Expert Model Mining System (EMMS) to demonstrate that our forecasted returns give a high value of R-square (0.952) with low Maximum Absolute Percentage Error (MaxAPE) of 1.76% for Dow Jones Industrial Average (DJIA). We introduce a novel way to make use of market monitoring elements derived from public mood to retain a portfolio within limited risk state (highly improved hedging bets) during typical market conditions.
Answering questions with an n-gram based passage retrieval engine

In this paper, we present a Question Answering system based on redundancy and a Passage Retrieval method that is specifically oriented to Question Answering. We suppose that in a large enough document collection the answer to a given question may appear in several different forms. Therefore, it is possible to find one or more sentences that contain the answer and that also include tokens from the original question. The Passage Retrieval engine is almost language-independent since it is based on n-gram structures. Question classification and answer extraction modules are based on shallow patterns.
Analysis to the relationship of classification accuracy, segmentation scale, image resolution

-Information extraction encountered a new challenge while the spatial resolution is increasing quickly. People suppose that the higher the spatial resolution is, the better the result of classification is. To prove this guess we use two approaches: pixel-based classification and object-oriented classification. The former site test shows one class has different accuracy from various resolution images. Object-oriented approach is an advanced solution for image analysis. The accuracy of objectoriented approach is much higher than those of based-pixel approach. The site result shows that each class has its optimal image segmentation scale. Feature, Scale; Resolution; Image analysis.
Why and How to Perform Fraud Experiments

Fraud isn't new, but in the eyes of many experts, phishing and crimeware threaten to topple society's overall stability because they erode trust in its underlying computational infrastructure. Most people agree that phishing and crimeware must be fought, but to do so effectively, we must fully understand both types of threat; that starts by quantifying how and when people fall for deceit. In this article, we look closer at how to perform fraud experiments. Researchers typically use three approaches to quantify fraud: surveys, in-lab experiments, and naturalistic experiments.
VCAM-1-positive stromal cells from human bone marrow producing cytokines for B lineage progenitors and for plasma cells: SDF-1, flt3L, and BAFF.

The differentiation of B lymphocytes in the bone marrow is guided by the surrounding microenvironment determined by cytokines, adhesion molecules, and the extracellular matrix. These microenvironmental factors are mainly provided by stromal cells. In this paper, we report the identification of a VCAM-1-positive stromal cell population by flow cytometry. This population showed the expression of cell surface markers known to be present on stromal cells (CD10, CD13, CD90, CD105) and had a fibroblastoid phenotype in vitro. Single cell RT-PCR analysis of its cytokine expression pattern revealed transcripts for haematopoietic cytokines important for either the early B lymphopoiesis like flt3L or the survival of long-lived plasma cells like BAFF or both processes like SDF-1. Whereas SDF-1 transcripts were detectable in all VCAM-1-positive cells, flt3L and BAFF were only expressed by some cells suggesting the putative existence of different subpopulations with distinct functional properties. In summary, the VCAM-1-positive cell population seems to be a candidate stromal cell population supporting either developing B cells and/or long-lived plasma cells in human bone marrow.
An autonomous all terrain robotic system for field demining missions

Mines deployed in post-war countries pose severe threats to civilians and hamper the reconstruction effort in war hit societies. In the scope of the EU FP7 TIRAMISU Project, a toolbox for humanitarian demining missions is being developed by the consortium members. In this article we present the FSR Husky, an affordable, lightweight and autonomous all terrain robotic system, developed to assist human demining operation teams. Intended to be easily deployable on the field, our robotic solution has the ultimate goal of keeping humans away from the threat, safeguarding their lives. A detailed description of the modular robotic system architecture is presented, and several real world experiments are carried out to validate the robot’s functionalities and illustrate continuous work in progress on minefield coverage, mine detection, outdoor localization, navigation, and environment perception.
Radar waveform design and multi-target detection in vehicular applications

In traffic environment, conventional FMCW radar with triangular transmit waveform may bring out many false targets in multi-target situations and result in a high false alarm rate. An improved FMCW waveform and multi-target detection algorithm for vehicular applications is presented. The designed waveform in each small cycle is composed of two-segment: LFM section and constant frequency section. They have the same duration, yet in two adjacent small cycles the two LFM slopes are opposite sign and different size. Then the two adjacent LFM bandwidths are unequal. Within a determinate frequency range, the constant frequencies are modulated by a unique PN code sequence for different automotive radar in a big period. Corresponding to the improved waveform, which combines the advantages of both FSK and FMCW formats, a judgment algorithm is used in the continuous small cycle to further eliminate the false targets. The combination of unambiguous ranges and relative velocities can confirm and cancel most false targets in two adjacent small cycles.
CAMPARY: Cuda Multiple Precision Arithmetic Library and Applications

Many scientific computing applications demand massive numerical computations on parallel architectures such as Graphics Processing Units (GPUs). Usually, either floating-point single or double precision arithmetic is used. Higher precision is generally not available in hardware, and software extended precision libraries are much slower and rarely supported on GPUs. We develop CAMPARY: a multipleprecision arithmetic library, using the CUDA programming language for the NVidia GPU platform. In our approach, the precision is extended by representing real numbers as the unevaluated sum of several standard machine precision floating-point numbers. We make use of error-free transforms algorithms, which are based only on native precision operations, but keep track of all rounding errors that occur when performing a sequence of additions and multiplications. This offers the simplicity of using hardware highly optimized floating-point operations, while also allowing for rigorously proven rounding error bounds. This also allows for easy implementation of an interval arithmetic. Currently, all basic multiple-precision arithmetic operations are supported. Our target applications are in chaotic dynamical systems or automatic control.