nils-herrmann/clean_scirepeval huggingface dataset:
ARINC 653 role in Integrated Modular Avionics (IMA)

The air transport industry has developed ARINC Specification 653 as a standardized Real-Time Operating System (RTOS) interface definition. The document specifies the interface boundary between avionics software applications and the core executive software. The standardization effort was sponsored by the airline user community and involved many interested parties, including airframe manufacturers, avionics suppliers, RTOS suppliers, government and academia. ARINC 653 is a key enabler in the development of Integrated Modular Avionics (IMA). In many ways it represents a paradigm shift for avionics development; in particular it recognizes the RTOS as key component of an IMA system. The commitment shown by industry to IMA could not be more evident than that shown by the Airbus A380 and the Boeing 787 avionics suites. This paper will provide top-level overview of IMA software architecture, the key elements of the ARINC 653 standard and its current development status.
Energy Management Based on Frequency Approach for Hybrid Electric Vehicle Applications: Fuel-Cell/Lithium-Battery and Ultracapacitors

This paper presents the ultracapacitors (U) and fuel-cell/lithium-battery connection with an original energy management method for hybrid electric vehicle (HEV) applications. The proposed method is focused on the frequency approach to meet the load energy requirement. The ultracapacitors are connected to the dc link through a buck-boost converter, and the fuel cell is connected to the dc link via a boost converter for the first topology. In the second topology, the lithium battery is connected to the dc link without a converter to avoid the dc-link voltage control. An asynchronous machine is used like the traction motor; it is related to the dc link through a dc/ac converter (inverter). The main contribution of this paper is focused on HEV energy management according to the dynamics (frequency) of the hybrid sources using polynomial correctors. The performances of the proposed method are evaluated through some simulations and the experimental tests, using the New European Driving Cycle (NEDC). This study is extended to an aggressive test cycle, such as the U.S. driving cycle (USDC), to understand the system response and the control performances.
Leadership: current theories, research, and future directions.

This review examines recent theoretical and empirical developments in the leadership literature, beginning with topics that are currently receiving attention in terms of research, theory, and practice. We begin by examining authentic leadership and its development, followed by work that takes a cognitive science approach. We then examine new-genre leadership theories, complexity leadership, and leadership that is shared, collective, or distributed. We examine the role of relationships through our review of leader member exchange and the emerging work on followership. Finally, we examine work that has been done on substitutes for leadership, servant leadership, spirituality and leadership, cross-cultural leadership, and e-leadership. This structure has the benefit of creating a future focus as well as providing an interesting way to examine the development of the field. Each section ends with an identification of issues to be addressed in the future, in addition to the overall integration of the literature we provide at the end of the article.
Opinion mining in Dutch Hansards

The question is addressed if opinion mining techniques can be successfully used to automatically retrieve political viewpoints in Dutch parliamentary publications. Two specific tasks are identified: automatically determining subjectivity in the publications and automatically determining the semantic orientation of the subjective parts. A collection of recent parliamentary publications has been collected and a golden standard annotation is created on both subjectivity and orientation. Following this, models based on subjectivity lexicons and machine learning algorithms are evaluated on automatic classification. Overall results tend to be dominated by machine learning algorithms, but methods based on subjectivity lexicons provide promising results. Based on the results it is concluded that opinion mining techniques can indeed be successfully used to automatically retrieve political viewpoints in Dutch parliamentary publications.
Choose your method: a comparison of phenomenology, discourse analysis, and grounded theory.

The purpose of this article is to compare three qualitative approaches that can be used in health research: phenomenology, discourse analysis, and grounded theory. The authors include a model that summarizes similarities and differences among the approaches, with attention to their historical development, goals, methods, audience, and products. They then illustrate how these approaches differ by applying them to the same data set. The goal in phenomenology is to study how people make meaning of their lived experience; discourse analysis examines how language is used to accomplish personal, social, and political projects; and grounded theory develops explanatory theories of basic social processes studied in context. The authors argue that by familiarizing themselves with the origins and details of these approaches, researchers can make better matches between their research question(s) and the goals and products of the study.
iCONCUR: informed consent for clinical data and bio-sample use for research

Implementation of patient preferences for use of electronic health records for research has been traditionally limited to identifiable data. Tiered e-consent for use of de-identified data has traditionally been deemed unnecessary or impractical for implementation in clinical settings.



We developed a web-based tiered informed consent tool called informed consent for clinical data and bio-sample use for research (iCONCUR) that honors granular patient preferences for use of electronic health record data in research. We piloted this tool in 4 outpatient clinics of an academic medical center.



Of patients offered access to iCONCUR, 394 agreed to participate in this study, among whom 126 patients accessed the website to modify their records according to data category and data recipient. The majority consented to share most of their data and specimens with researchers. Willingness to share was greater among participants from an Human Immunodeficiency Virus (HIV) clinic than those from internal medicine clinics. The number of items declined was higher for for-profit institution recipients. Overall, participants were most willing to share demographics and body measurements and least willing to share family history and financial data. Participants indicated that having granular choices for data sharing was appropriate, and that they liked being informed about who was using their data for what purposes, as well as about outcomes of the research.



This study suggests that a tiered electronic informed consent system is a workable solution that respects patient preferences, increases satisfaction, and does not significantly affect participation in research.
Fuzzy Set Theory ― and Its Applications

Spend your time even for only few minutes to read a book. Reading a book will never reduce and waste your time to be useless. Reading, for some people become a need that is to do every day such as spending time for eating. Now, what about you? Do you like to read a book? Now, we will show you a new book enPDFd fuzzy set theory and its applications that can be a new way to explore the knowledge. When reading this book, you can get one thing to always remember in every reading time, even step by step.
Modelling User Satisfaction with an Employee Portal

User satisfaction with general information system (IS) and certain types of information technology (IT) applications has been thoroughly studied in IS research. With the widespread and increasing use of portal technology, however, there is a need to conduct a user satisfaction study on portal use -in particular, the business-to-employee (b2e) portal. In this paper, we propose a conceptual model for determining b2e portal user satisfaction, which has been derived from an extensive literature review of user satisfaction scales and the b2e portal. Nine dimensions of b2e portal user satisfaction are identified and modeled: information content, ease of use, convenience of access, timeliness, efficiency, security, confidentiality, communication, and layout.
Photo tourism: exploring photo collections in 3D

We present a system for interactively browsing and exploring a large unstructured collection of photographs of a scene using a novel 3D interface. Our system consists of an image-based modeling front end, which automatically computes the viewpoint of each photograph as well as a sparse 3D model of the scene and image to model correspondences. Our photo navigation tool uses imagebased rendering techniques to smoothly transition between photographs, while also enabling full 3D navigation and exploration of the set of images and world geometry, along with auxiliary information such as overhead maps. Our system also makes it easy to construct photo tours of scenic or historic locations, as well as to annotate image details, which are automatically transferred to other relevant images in the collection. We demonstrate our system on several large personal photo collections as well as images gathered from photo sharing Web sites on the Internet.
A tensor-based deep learning framework

This paper presents an unsupervised deep learning framework that derives spatio-temporal features for human-robot interaction. The respective models extract high-level features from low-level ones through a hierarchical network, viz. the Hierarchical Temporal Memory (HTM), providing at the same time a solution to the curse of dimensionality in shallow techniques. The presented work incorporates the tensor-based framework within the operation of the nodes and, thus, enhances the feature derivation procedure. This is due to the fact that tensors allow the preservation of the initial data format and their respective correlation and, moreover, attain more compact representations. The computational nodes form spatial and temporal groups by exploiting the multilinear algebra, subsequently express the samples according to those groups in terms of proximity. This generic framework may be applied in a diverse of visual data, whilst it has been examined on sequences of color and depth images, exhibiting remarkable performance.
Trust and Satisfaction, Two Stepping Stones for Successful E-Commerce Relationships: A Longitudinal Exploration

Trust and satisfaction are essential ingredients for successful business relationships in business-toconsumer electronic commerce. Yet there is little research on trust and satisfaction in e-commerce that takes a longitudinal approach. Drawing on three primary bodies of literature, the theory of reasoned action, the extended valence framework, and expectation-confirmation theory, this study synthesizes a model of consumer trust and satisfaction in the context of electronic commerce. The model considers not only how consumers formulate their pre-purchase decisions, but also how they form their long-term relationships with the same website vendor by comparing their pre-purchase expectations to their actual purchase outcome. The results indicate that trust directly and indirectly affects a consumer’s purchase decision in combination with perceived risk and perceived benefit, and also indicate that trust has a longer term impact on consumer e-loyalty through satisfaction. Thus, this study extends our understanding of consumer Internet transaction behavior as a three-fold (pre-purchase, purchase and, post-purchase) process, and it recognizes the crucial, multiple roles that trust plays in this process. Implications for theory and practice as well as limitations and future directions are discussed.
A Multi-media Approach to Cross-lingual Entity Knowledge Transfer

When a large-scale incident or disaster occurs, there is often a great demand for rapidly developing a system to extract detailed and new information from lowresource languages (LLs). We propose a novel approach to discover comparable documents in high-resource languages (HLs), and project Entity Discovery and Linking results from HLs documents back to LLs. We leverage a wide variety of language-independent forms from multiple data modalities, including image processing (image-to-image retrieval, visual similarity and face recognition) and sound matching. We also propose novel methods to learn entity priors from a large-scale HL corpus and knowledge base. Using Hausa and Chinese as the LLs and English as the HL, experiments show that our approach achieves 36.1% higher Hausa name tagging F-score over a costly supervised model, and 9.4% higher Chineseto-English Entity Linking accuracy over state-of-the-art.
Shadow-Based Rooftop Segmentation in Visible Band Images

This paper presents a method to extract rooftops from aerial images with only visible red, green, and blue bands of data. In particular, it does not require near-infrared data, lidar, or multiple viewpoints. The proposed method uses shadows in the image in order to detect buildings and to determine a set of constraints on which parts can or cannot be rooftops. We then use the grabcut algorithm to identify complete rooftop regions and a method to make corrections that simulate a user performing interactive image segmentation in order to improve the precision of our results. The precision, recall, and F-score of the proposed approach show significant improvement over two very recently published papers. On our test dataset, we observe an average F-score of 89% compared to scores of 68% and 33%.
Optical Flow Based Moving Object Detection and Tracking for Traffic Surveillance

:هلاقم هصلاخ automated motion detection and tracking is achallenging task in traffic surveillance. In this paper, a systemis developed to gather useful information from stationarycameras for detecting moving objects in digital videos. Themoving detection and tracking system is developed based onoptical flow estimation together with application andcombination of various relevant computer vision and imageprocessing techniques to enhance the process. To removenoises, median filter is used and the unwanted objects areremoved by applying thresholding algorithms inmorphological operations. Also the object type restrictions areset using blob analysis. The results show that the proposedsystem successfully detects and tracks moving objects in urbanvideos.
Subgoal-labeled instructional material improves performance and transfer in learning to develop mobile applications

Mental models are mental representations of how an action changes a problem state. Creating a mental model early in the learning process is a strong predictor of success in computer science classes. One major problem in computer science education, however, is that novices have difficulty creating mental models perhaps because of the cognitive overload caused by traditional teaching methods. The present study employed subgoal-labeled instructional materials to promote the creation of mental models when teaching novices to program in Android App Inventor. Utilizing this and other well-established educational tools, such as scaffolding, to reduce cognitive load in computer science education improved the performance of participants on novel tasks when learning to develop mobile applications.
Fine-Grained Visual Comparisons with Local Learning

Given two images, we want to predict which exhibits a particular visual attribute more than the other-even when the two images are quite similar. Existing relative attribute methods rely on global ranking functions; yet rarely will the visual cues relevant to a comparison be constant for all data, nor will humans' perception of the attribute necessarily permit a global ordering. To address these issues, we propose a local learning approach for fine-grained visual comparisons. Given a novel pair of images, we learn a local ranking model on the fly, using only analogous training comparisons. We show how to identify these analogous pairs using learned metrics. With results on three challenging datasets-including a large newly curated dataset for fine-grained comparisons-our method outperforms stateof-the-art methods for relative attribute prediction.
An Enhanced ESIM Model for Sentence Pair Matching with Self-Attention

School of Computer Science and Engineering, Northeastern University, Shenyang, 110819, China  renfeiliang@cse.neu.edu.cn  Sentence pair matching is one of the most basic tasks in natural language processing and is receiving extensive research attention. Most of existing models can be categorized into sentence interaction-based models and sentence encoding-based models. Here we propose a new enhanced ESIM model that could take full advantage of both these two kinds of methods. Specifically, our method can obtain the common information of a sentence pair as the sentence interaction-based models do, and can also extract the key information of a sentence pair itself as the sentence encoding-based models do. Besides, our method also uses Siamese networks to learn a unique structure to naturally rank the similarity between two sentences, which reduces both the model complexity and the size of parameters. With the proposed method, we participated in the task-3 of CCKS2018, which is called as WeBank Intelligent Customer Service Question Match. Finally, our method ranks the first among the hugely competitive models.
Maintained Physical Activity Induced Changes in Delay Discounting.

Those who discount the subjective value of delayed rewards less steeply are more likely to engage in physical activity. There is limited research, however, showing whether physical activity can change rates of delay discounting. In a two-experiment series, treatment and maintenance effects of a novel, effort-paced physical activity intervention on delay discounting were evaluated with multiple baseline designs. Using a lap-based method, participants were instructed to exercise at individualized high and low effort levels and to track their own perceived effort. The results suggest that treatment-induced changes in discounting were maintained at follow-up for 13 of 16 participants. In Experiment 2, there were statistically significant group-level improvements in physical activity and delay discounting when comparing baseline with both treatment and maintenance phases. Percentage change in delay discounting was significantly correlated with session attendance and relative pace (min/mile) improvement over the course of the 7-week treatment. Implications for future research are discussed.
High-performance evolved packet core signaling and bearer processing on general-purpose processors

Telecommunications service providers are exploring the use of standard high-volume servers to reduce total cost of ownership while at the same time increasing flexibility, service velocity, and scalability of network functions. This article characterizes performance of general-purpose processors - specifically x86 architecture processors - for signaling and bearer processing representative of a wireless carrier's call model for a Long Term Evolution Evolved Packet Core. A radio access network emulator was used to stimulate an Evolved Packet Core software stack running on an x86 server. The goal was to prove that standard high-volume servers can execute EPC functions per representative market call models, and that workloads can scale across bearer and control plane at line rate without acceleration technologies. A call model was developed to quantify the performance on Intel® Xeon® Processor based servers using an LTE traffic simulator and a commercial EPC software stack. The traffic models represent bidirectional real-world network traffic during different times of the day. The results were that the test EPC processed control and user plane traffic with 50,000 subscribers with a total payload of 10 Gb/s downlink + 4.8 Gb/s uplink traffic using five cores for the data plane and eight cores for the rest of the system; and the user plane throughput scaled in a single blade environment to 20 Gb/s per socket or 40 Gb/s for a dual socket blade.
A dimmable and power-compensated AC direct LED driver with high efficiency

A dimmable and power-compensated AC direct LED driver with high efficiency for lighting applications is presented. A Source-Coupled Pair (SCP) is adopted to realize the soft self-commutating. There is no additional sensing or control circuitry, which lower the system power dissipation. A pulse-width modulation (PWM) dimming technique is applied in regulating the LED's brightness. Furthermore, the driver includes a Power Compensation module which enables the output power of LEDs approximately constant under various line voltage. The LED driver IC is designed and implemented in CSMC's 700 V 1 um BCD process. The post-layout simulation results show that under 220 V|rms AC voltage, the driver has a high efficiency of 90.28% and realizes dimming with a 90% duty ratio, 10 KHz dimming signal. The output power compensated only changes 4% under a 10% variation in input voltage.
Real-Time Detection of Hybrid and Stealthy Cyber-Attacks in Smart Grid

For a safe and reliable operation of the smart grid, timely detection of cyber-attacks is of critical importance. Moreover, considering smarter and more capable attackers, robust detection mechanisms are needed against a diverse range of cyber-attacks. With these purposes, we propose a robust online detection algorithm for (possibly combined) false data injection and jamming attacks, that also provides online estimates of the unknown and time-varying attack parameters and recovered state estimates. Further, considering smarter attackers that are capable of designing stealthy attacks to prevent the detection or to increase the detection delay of the proposed algorithm, we propose additional countermeasures. Numerical studies illustrate the quick and reliable response of the proposed detection mechanisms against hybrid and stealthy cyber-attacks.
Virtual reality job interview training in adults with autism spectrum disorder.

The feasibility and efficacy of virtual reality job interview training (VR-JIT) was assessed in a single-blinded randomized controlled trial. Adults with autism spectrum disorder were randomized to VR-JIT (n = 16) or treatment-as-usual (TAU) (n = 10) groups. VR-JIT consisted of simulated job interviews with a virtual character and didactic training. Participants attended 90 % of laboratory-based training sessions, found VR-JIT easy to use and enjoyable, and they felt prepared for future interviews. VR-JIT participants had greater improvement during live standardized job interview role-play performances than TAU participants (p = 0.046). A similar pattern was observed for self-reported self-confidence at a trend level (p = 0.060). VR-JIT simulation performance scores increased over time (R(2) = 0.83). Results indicate preliminary support for the feasibility and efficacy of VR-JIT, which can be administered using computer software or via the internet.