nils-herrmann/clean_scirepeval huggingface dataset:
The Overview of Research on Microgrid Protection Development

In general, the microgrid can operate in both grid-connection mode and islanded mode that challenges the traditional over-current protection scheme in the distribution network. The novel research and development of protection, including both the microgrid and distribution network are comprehensively analyzed in this paper, which mainly focuses on two aspects. The first one analyses the impact on the network protection from the fault current feature while microgrid-connected. Secondly, several innovative protection schemes for network and microgrid are discussed. It is essential to improve traditional protection schemes by using communication techniques. In order to protect the microgrid in islanded mode, two innovative voltage protection schemes are discussed. However, the differential current protection scheme is possibly still the most effective solution for both network and microgrid.
Grid-connected hybrid PV/wind power generation system with improved DC bus voltage regulation strategy

The objective of this paper is to propose an improved dc bus voltage regulation strategy for the grid-connected PV/Wind power generation system. The proposed dc bus voltage regulation strategy can reduce the variation of the dc bus voltage and the size of the dc bus capacitor bank, significantly. Also, the change of the injected ac current amplitude will be moderate and the power quality of the utility can be improved. A 1.2 kW prototype system is built and tested to verify the feasibility of the proposed dc bus voltage regulation strategy. Hardware experimental results are shown to confirm the performance of the proposed dc bus voltage regulation strategy.
Reading Text in the Wild with Convolutional Neural Networks

In this work we present an end-to-end system for text spotting—localising and recognising text in natural scene images—and text based image retrieval. This system is based on a region proposal mechanism for detection and deep convolutional neural networks for recognition. Our pipeline uses a novel combination of complementary proposal generation techniques to ensure high recall, and a fast subsequent filtering stage for improving precision. For the recognition and ranking of proposals, we train very large convolutional neural networks to perform word recognition on the whole proposal region at the same time, departing from the character classifier based systems of the past. These networks are trained solely on data produced by a synthetic text generation engine, requiring no human labelled data. Analysing the stages of our pipeline, we show state-of-the-art performance throughout. We perform rigorous experiments across a number of standard end-to-end text spotting benchmarks and text-based image retrieval datasets, showing a large improvement over all previous methods. Finally, we demonstrate a real-world application of our text spotting system to allow thousands of hours of news footage to be instantly searchable via a text query.
Magnetic Position and Orientation Tracking System

Three-axis generation and sensing of quasi-static magneticdipole fields provide information sufficient to determine both the position and orientation of the sensor relative to the source. Linear rotation transformations based upon the previous measurements are applied to both the source excitation and sensor output vectors, yielding quantities that are linearly propotional to small changes in the position and orientation. Changes are separated using linear combinations of sensor output vectors, transformed to the desired coordinate frame, and used to update the previous measurements. Practical considerations for a head-tracking application are discussed.
Biometric-oriented Iris Identification Based on Mathematical Morphology

A new method for biometric identification of human irises is proposed in this paper. The method is based on morphological image processing for the identification of unique skeletons of iris structures, which are then used for feature extraction. In this approach, local iris features are represented by the most stable nodes, branches and endpoints extracted from the identified skeletons. Assessment of the proposed method was done using subsets of images from the University of Bath Iris Image Database (1000 images) and the CASIA Iris Image Database (500 images). Compelling experimental results demonstrate the viability of using the proposed morphological approach for iris recognition when compared to a state-of-the-art algorithm that uses a global feature extraction approach.
On the dimensionality of organizational justice: a construct validation of a measure.

This study explores the dimensionality of organizational justice and provides evidence of construct validity for a new justice measure. Items for this measure were generated by strictly following the seminal works in the justice literature. The measure was then validated in 2 separate studies. Study 1 occurred in a university setting, and Study 2 occurred in a field setting using employees in an automobile parts manufacturing company. Confirmatory factor analyses supported a 4-factor structure to the measure, with distributive, procedural, interpersonal, and informational justice as distinct dimensions. This solution fit the data significantly better than a 2- or 3-factor solution using larger interactional or procedural dimensions. Structural equation modeling also demonstrated predictive validity for the justice dimensions on important outcomes, including leader evaluation, rule compliance, commitment, and helping behavior.
Comparing ability and self-report trait emotional intelligence , fluid intelligence , and personality traits in career decision

This study investigated the role of ability and trait emotional intelligence (EI), fluid intelligence, and personality traits in career decision-making self-efficacy, career indecision and indecisiveness. The Advanced Progressive Matrices, Big Five Questionnaire, Mayer-Salovey-Caruso Emotional Intelligence Test, Bar-On Emotional Intelligence Inventory, Trait Emotional Intelligence Questionnaire, Career Decision SelfEfficacy Scale: Short Form, Career Decision-Making Difficulties Questionnaire, and Indecisiveness Scale were administered to 194 Italian high school students. These results highlighted the potentially important role of self-reported EI in career decisions.
Real-Time Standard Scan Plane Detection and Localisation in Fetal Ultrasound Using Fully Convolutional Neural Networks

Fetal mid-pregnancy scans are typically carried out according to fixed protocols. Accurate detection of abnormalities and correct biometric measurements hinge on the correct acquisition of clearly defined standard scan planes. Locating these standard planes requires a high level of expertise. However, there is a worldwide shortage of expert sonographers. In this paper, we consider a fully automated system based on convolutional neural networks which can detect twelve standard scan planes as defined by the UK fetal abnormality screening programme. The network design allows real-time inference and can be naturally extended to provide an approximate localisation of the fetal anatomy in the image. Such a framework can be used to automate or assist with scan plane selection, or for the retrospective retrieval of scan planes from recorded videos. The method is evaluated on a large database of 1003 volunteer mid-pregnancy scans. We show that standard planes acquired in a clinical scenario are robustly detected with a precision and recall of 69% and 80%, which is superior to the current state-of-the-art. Furthermore, we show that it can retrospectively retrieve correct scan planes with an accuracy of 71% for cardiac views and 81% for non-cardiac views.
Automobile seat comfort: occupant preferences vs. anthropometric accommodation.

Automobile seat design specifications cannot be established without considering the comfort expectations of the target population. This contention is supported by published literature, which suggests that ergonomics criteria, particularly those related to physiology, do not satisfy consumer comfort. The objective of this paper is to challenge ergonomics criteria related to anthropometry in the same way. In this context, 12 subjects, representing a broad range of body sizes, evaluated five different compact car seats during a short-term seating session. Portions of a reliable and valid survey were used for this purpose. The contour and geometry characteristics of the five seats were quantified and compared to the survey information. Discrepancies were discovered between published anthropometric accommodation criteria and subject-preferred lumbar height, seatback width, cushion length, and cushion width. Based on this finding, it was concluded that automobile seat comfort is a unique science. Ergonomics criteria, while serving as the basis for this science, cannot be applied blindly for they do not ensure comfortable automobile seats.
Countering kernel rootkits with lightweight hook protection

Kernel rootkits have posed serious security threats due to their stealthy manner. To hide their presence and activities, many rootkits hijack control flows by modifying control data or hooks in the kernel space. A critical step towards eliminating rootkits is to protect such hooks from being hijacked. However, it remains a challenge because there exist a large number of widely-scattered kernel hooks and many of them could be dynamically allocated from kernel heap and co-located together with other kernel data. In addition, there is a lack of flexible commodity hardware support, leading to the socalled protection granularity gap -- kernel hook protection requires byte-level granularity but commodity hardware only provides page level protection.
 To address the above challenges, in this paper, we present HookSafe, a hypervisor-based lightweight system that can protect thousands of kernel hooks in a guest OS from being hijacked. One key observation behind our approach is that a kernel hook, once initialized, may be frequently "read"-accessed, but rarely write"-accessed. As such, we can relocate those kernel hooks to a dedicated page-aligned memory space and then regulate accesses to them with hardware-based page-level protection. We have developed a prototype of HookSafe and used it to protect more than 5,900 kernel hooks in a Linux guest. Our experiments with nine real-world rootkits show that HookSafe can effectively defeat their attempts to hijack kernel hooks. We also show that HookSafe achieves such a large-scale protection with a small overhead (e.g., around 6% slowdown in performance benchmarks).
Comparison of CoAP and MQTT Performance Over Capillary Radios

The IoT protocols used in the application layer, namely the Constraint Application Protocol (CoAP) and Message Queue Telemetry Transport (MQTT) have dependencies to the transport layer. The choice of transport, Transmission Control Protocol (TCP) or the User Datagram Protocol(UDP), on the other hand, has an impact on the Internet of Things (IoT) application level performance, especially over a wireless medium. The motivation of this work is to look at the impact of the protocol stack on performance over two different wireless medium realizations, namely Bluetooth Low Energy and Wi-Fi. The use case studied is infrequent small reports sent from the sensor device to a central cloud storage over a last mile radio access link. We find that while CoAP/UDP based transport performs consistently better both in terms of latency and power consumption over both links, MQTT/TCP may also work when the use requirements allow for longerlatency providing better reliability. All in all, the full connectivity stack needs to be considered when designing an IoT deployment.
A Trigraph Based Centrality Approach Towards Text Summarization

As the electronic documents are increasing due to the revolution of information there is an urgent need for summarizing the text documents. From the previous works we observed that there is no generalized graph model for text summarization and low order ngrams could not preserve the contextual meaning. This paper focuses on an extractive based graphical approach for text summarization, based on trigrams and graph based centrality measure. Trigraph is generated and the centrality of the connected trigraph is taken to extract the important trigrams. A mapping is done between the original words and the trigrams to regain the link between the words. And after comparing the centrality from the graph, the summary is extracted. The ROUGE-SU4 F-measure obtained for the proposed approach is 0.036 which is significantly better than the previous approaches.
Design and Governance of Platform Ecosystems - Key Concepts and Issues for Future Research

The purpose of this paper is to give an overview of current research in IS on the design and governance of platform ecosystems. To this end, we conduct a literature review of relevant journals and conferences. We show that platform ecosystems have been analysed from two different perspectives: technologyand market-oriented. Thereby, most studies take on the viewpoint of the platform owner. Furthermore, we summarize key concepts on the design and governance of platform ecosystems that have been discussed in literature. As most relevant concepts we identify the definition of roles, pricing, boundary resources and openness. Based on this analysis, we derive issues for future research: the integration of marketand technology-oriented perspectives, an individual level of analysis to include complementors and end-users and the role of data as boundary resource in platform ecosystems. This paper contributes to the understanding of platform ecosystems in IS literature by structuring existing research with regard to different perspectives and concepts and by providing starting points for future work. In addition, it lays out which concepts practitioners need to consider when designing and governing platform ecosystems.
A selectional auto-encoder approach for document image binarization

Binarization plays a key role in the automatic information retrieval from document images. This process is usually performed in the first stages of documents analysis systems, and serves as a basis for subsequent steps. Hence it has to be robust in order to allow the full analysis workflow to be successful. Several methods for document image binarization have been proposed so far, most of which are based on hand-crafted image processing strategies. Recently, Convolutional Neural Networks have shown an amazing performance in many disparate duties related to computer vision. In this paper we discuss the use of convolutional auto-encoders devoted to learning an end-to-end map from an input image to its selectional output, in which activations indicate the likelihood of pixels to be either foreground or background. Once trained, documents can therefore be binarized by parsing them through the model and applying a global threshold. This approach has proven to outperform existing binarization strategies in a number of document types.
Leadership and Decision Making

Several scholarly disciplines share an interest in the decision-making process. On one hand, there are related fields of operations research and management science, both concerned with how to improve the decisions which are made. Their models of decision making, aimed at providing a rational basis for selecting among alternative courses of action, are termed normative or prescriptive models. On the other hand, there have been attempts by psychologists, sociologists, and political scientists to understand the decisions and choices that people do make. March and Simon (1958) were among the first to suggest that an understanding of the decision-making process could be central to an understanding of the behavior of organizations—a point of view that was later amplified by Cyert and March (1963) in their behavioral theory of the firm. In this tradition, the goal is understanding rather than improvement, and the models are descriptive rather than normative. Whether the models are normative or descriptive, the common ingredient is a conception of decision making as an information-processing activity, frequently one which takes place within a single manager. Both sets of models focus on the set of alternative decisions or problem solutions from which the choice is, or should be, made. The normative models are based on the consequences of choices among these alternatives, the descriptive models on the determinants of these choices. In this article, the authors take a somewhat different, although complementary, view of managerial decision making. They view decision making as
An LTV model and customer segmentation based on customer value: a case study on the wireless telecommunication industry

Since the early 1980s, the concept of relationship management in marketing area has gained its importance. Acquiring and retaining the most profitable customers are serious concerns of a company to perform more targeted marketing campaigns. For effective customer relationship management, it is important to gather information on customer value. Many researches have been performed to calculate customer value based on Customer lifetime value (LTV). It, however, has some limitations. It is difficult to consider the defection of customers. Prediction models have focused mainly on expected future cash flow derived from customers’ past profit contribution. In this paper we suggest an LTV model considering past profit contribution, potential benefit, and defection probability of a customer. We also cover a framework for analyzing customer value and segmenting customers based on their value. Customer value is classified into three categories: current value, potential value, and customer loyalty. Customers are segmented according to three types of customer value. A case study on calculating customer value and segmenting customers of a wireless communication company will be illustrated. q 2003
A Privacy-Preserving Pay-by-Phone Parking System

Most cities around the world require drivers to pay for the time they occupy a parking spot. In this way, drivers are encouraged to shorten the parking time so that other drivers are given a reasonable chance of finding parking. The traditional way, based on moving to a pay station and placing the issued parking ticket on the dashboard of the car, presents several drawbacks, such as having to predict in advance the duration of parking or the need to move to the car in case the parking time has to be extended. Over the past few years, several applications permitting payment through mobile phones have appeared. Such applications manage detailed information about parking operations so that accurate profiles of parking habits of car owners can be created. In this paper, we propose a system to pay for parking by phone that preserves the privacy of drivers. The information managed by the system is proven not to provide any help to the creation of profiles of parking habits. An attacker with full access to all this information can not do better than he or she would do by patrolling the city for collecting information about parked cars.
Terabit free-space data transmission employing orbital angular momentum multiplexing

The recognition in the 1990s that light beams with a helical phase front have orbital angular momentum has benefited applications ranging from optical manipulation to quantum information processing. Recently, attention has been directed towards the opportunities for harnessing such beams in communications. Here, we demonstrate that four light beams with different values of orbital angular momentum and encoded with 42.8 3 4 Gbit s quadrature amplitude modulation (16-QAM) signals can be multiplexed and demultiplexed, allowing a 1.37 Tbit s aggregated rate and 25.6 bit s Hz spectral efficiency when combined with polarization multiplexing. Moreover, we show scalability in the spatial domain using two groups of concentric rings of eight polarization-multiplexed 20 3 4 Gbit s 16-QAM-carrying orbital angular momentum beams, achieving a capacity of 2.56 Tbit s and spectral efficiency of 95.7 bit s Hz. We also report data exchange between orbital angular momentum beams encoded with 100 Gbit s differential quadrature phase-shift keying signals. These demonstrations suggest that orbital angular momentum could be a useful degree of freedom for increasing the capacity of free-space communications.
WebSelF: A Web Scraping Framework

We present WebSelF, a framework for web scraping which models the process of web scraping and decomposes it into four conceptually independent, reusable, and composable constituents. We have validated our framework through a full parameterized implementation that is flexible enough to capture previous work on web scraping. We conducted an experiment that evaluated several qualitatively different web scraping constituents (including previous work and combinations hereof) on about 11,000 HTML pages on daily versions of 17 web sites over a period of more than one year. Our framework solves three concrete problems with current web scraping and our experimental results indicate that composition of previous and our new techniques achieve a higher degree of accuracy, precision and specificity than existing tech-
Gamification of Information Systems and Security Training : Issues and Case Studies

This paper discusses gamification, the application of gaming elements in non-game contexts, with regard to information systems and information security training. The authors have developed gamification tools for use in the classroom as well as several educational games in order to explore the viability of gamified curricula in both high school and college environments. Early results indicate positive student attitudes toward gamified approaches, as well as improved attendance and success rate. Issues encountered in curriculum gamification and game development are described along with best practices for both.
A Wearable Sensor for Measuring Sweat Rate

Wearable sensors present a new frontier in the development of monitoring techniques. They are of great importance in sectors such as sport and healthcare as they enable physiological signals and biological fluids, such as human sweat, to be continuously monitored. Until recently this could only be carried out in specialized laboratories using cumbersome and often expensive devices. Sweat monitoring sensors integrated onto textile substrates are not only innovative but they also represent the first attempt to use such an idea in a system that will be worn directly on the body. This study outlines the development of a wearable sweat-rate sensor integrated onto a textile.
Facebook groups as LMS: A case study

This paper describes a pilot study in using Facebook as an alternative to a learning management system (LMS). The paper reviews the current research on the use of Facebook in academia and analyzes the differences between a Facebook group and a regular LMS. The paper reports on a precedent-setting attempt to use a Facebook group as a course Web site, serving as a platform for delivering content and maintaining interactions among the students and between the students and the instructor. The paper presents findings from the students’ self-assessments and reflections on their experience. The students expressed satisfaction with learning in Facebook and willingness to continue using these groups in future courses.