nils-herrmann/clean_scirepeval huggingface dataset:
Color Transfer between Images

ne of the most common tasks in image processing is to alter an image's color. Often this means removing a dominant and undesirable color cast, such as the yellow in photos taken under incandescent illumination. This article describes a method for a more general form of color correction that borrows one image's color characteristics from another. Figure 1 shows an example of this process, where we applied the colors of a sunset photograph to a daytime computer graphics rendering. We can imagine many methods for applying the colors of one image to another. Our goal is to do so with a simple algorithm, and our core strategy is to choose a suitable color space and then to apply simple operations there. When a typical three channel image is represented in any of the most well-known color spaces, there will be correlations between the different channels' values. For example, in RGB space, most pixels will have large values for the red and green channel if the blue channel is large. This implies that if we want to change the appearance of a pixel's color in a coherent way, we must modify all color channels in tandem. This complicates any color modification process. What we want is an orthogonal color space without correlations between the axes. Recently, Ruderman et al. developed a color space, called lαβ, which minimizes correlation between channels for many natural scenes. 2 This space is based on data-driven human perception research that assumes the human visual system is ideally suited for processing natural scenes. The authors discovered lαβ color space in the context of understanding the human visual system , and to our knowledge, lαβ space has never been applied otherwise or compared to other color spaces. There's little correlation between the axes in lαβ space, which lets us apply different operations in different color channels with some confidence that undesirable cross-channel artifacts won't occur. Additionally, this color space is logarithmic, which means to a first approximation that uniform changes in channel intensity tend to be equally detectable. Because our goal is to manipulate RGB images, which are often of unknown phosphor chromaticity, we first show a reasonable method of converting RGB signals to Ruderman et al.'s perception-based color space lαβ. Because lαβ is a transform of LMS cone space, we first convert the image to LMS space in two steps. The first is a conversion from RGB …
Cardiovascular and autonomic response to environmental noise during sleep in night shift workers.

Polysomnograph, beat-by-beat heart rate and blood pressure were monitored in night shift workers exposed to environmental and laboratory noise events during day sleep. The study was carried out in a sleep laboratory. Subjects were nine young, healthy female night shift workers. Recorded noises from trucks, civilian aircraft, low altitude military aircraft and tones were presented at 55, 65, and 75 L(Amax). Sleep stage, heart rate, and systolic and diastolic blood pressures before and immediately after onset of noise events were compared. Spectral analyses of heart rate and blood pressure variabilities were used to compare sympathetic and parasympathetic nervous tone in 10-min. intervals containing noise and quiet. Heart rate was responsive to noise level but not noise type. Blood pressure increased primarily to sounds of sudden onset. Noise-induced awakening and alpha EEG responses were related to BP increase. Increase in HR was greatest when subjects were awakened by noise or already awake. Spectral analysis of BP variabilities indicated increased sympathetic vascular tone due to noise. Similar analyses of HR data indicated no noise effect. No habituation to noise was apparent over three consecutive sleep sessions. It was concluded that over the range of noise levels used, heart rate responds to noise level during sleep; blood pressure to sounds of sudden onset. Spectral analysis of blood pressure variabilities is a sensitive measure of autonomic nervous response to environmental noise and should also be studied in subjects sleeping at home.
Need Satisfaction , Motivation , and Well-Being in the Work Organizations of a Former Eastern Bloc Country : A Cross-Cultural Study of Self-Determination

Past studies in U.S. work organizations have supported a model derived from self-determination theory in which autonomy-supportive work climates predict satisfaction of the intrinsic needs for competence, autonomy, and relatedness, which in turn predict task motivation and psychological adjustment on the job. To test this model cross-culturally, the authors studied employees of state-owned companies in Bulgaria, a country that has traditionally had a central-planning economy, a totalitarian political system, and collectivist values. A sample from a privately owned American corporation was used for comparison purposes. Results using structural equation modeling suggested that the model fit the data from each country, that the constructs were equivalent across countries, and that some paths of the structural model fit equivalently for the two countries but that county moderated the other paths.
Backstepping control with speed estimation of PMSM based on MRAS

This paper presents a backstepping control method with speed estimation of permanent magnet synchronous motor (PMSM) based on model reference adaptive system (MRAS). First, a comprehensive dynamical model of PMSM in d–q axis and its space state equations are established. Next, using Lyapunov stability theorem, based on the backstepping control theory, the PMSM rotor speed and current backstepping controllers are designed. Furthermore, using Popov stability theory, based on MRAS, the PMSM rotor speed observer is designed. Finally, Matlab/Simulink simulation results show that the backstepping control and speed observer are effective and feasible.
Document Distance Estimation via Code Graph Embedding

Accurately representing the distance between two documents (i.e. pieces of textual information extracted from various software artifacts) has far-reaching applications in many automated software engineering approaches, such as concept location, bug location and traceability link recovery. This is a challenging task, since documents containing different words may have similar semantic meanings. In this paper, we propose a novel document distance estimation approach. This approach captures latent semantic associations between documents through analyzing structural information in software source code: first, we embed code elements as points in a shared representation space according to structural dependencies between them; then, we represent documents as weighted point clouds of code elements in the representation space and reduce the distance between two documents to an earth mover's distance transportation problem. We define a document classification task in StackOverflow dataset to evaluate the effectiveness of our approach. The empirical evaluation results show that our approach outperforms several state-of-the-art approaches.
Crowd Scene Understanding with Coherent Recurrent Neural Networks

Exploring crowd dynamics is essential in understanding crowd scenes, which still remains as a challenging task due to the nonlinear characteristics and coherent spatio-temporal motion patterns in crowd behaviors. To address these issues, we present a Coherent Long Short Term Memory (cLSTM) network to capture the nonlinear crowd dynamics by learning an informative representation of crowd motions, which facilitates the critical tasks in crowd scene analysis. By describing the crowd motion patterns with a cloud of keypoint tracklets, we explore the nonlinear crowd dynamics embedded in the tracklets with a stacked LSTM model, which is further improved to capture the collective properties by introducing a coherent regularization term; and finally, we adopt an unsupervised encoder-decoder framework to learn a hidden feature for each input tracklet that embeds its inherent dynamics. With the learnt features properly harnessed, crowd scene understanding is conducted effectively in predicting the future paths of agents, estimating group states, and classifying crowd events. Extensive experiments on hundreds of public crowd videos demonstrate that our method is state-of-theart performance by exploring the coherent spatiotemporal structures in crowd behaviors.
Persistence Diagrams with Linear Machine Learning Models

Persistence diagrams have been widely recognized as a compact descriptor for characterizing multiscale topological features in data. When many datasets are available, statistical features embedded in those persistence diagrams can be extracted by applying machine learnings. In particular, the ability for explicitly analyzing the inverse in the original data space from those statistical features of persistence diagrams is significantly important for practical applications. In this paper, we propose a unified method for the inverse analysis by combining linear machine learning models with persistence images. The method is applied to point clouds and cubical sets, showing the ability of the statistical inverse analysis and its advantages. This work is partially supported by JSPS KAKENHI Grant Number JP 16K17638, JST CREST Mathematics15656429, JST “Materials research by Information Integration” Initiative (MI2I) project of the Support Program for Starting Up Innovation Hub, Structural Materials for Innovation Strategic Innovation Promotion Program D72, and New Energy and Industrial Technology Development Organization (NEDO). Ippei Obayashi Advanced Institute for Materials Research (WPI-AIMR), Tohoku University. 2-1-1 Katahira, Aoba-ku, Sendai, 980-8577 Japan Tel.: +81-22-217-6320 Fax: +81-22-217-5129 E-mail: ippei.obayashi.d8@tohoku.ac.jp Y. Hiraoka Advanced Institute for Materials Research (WPI-AIMR), Tohoku University. Center for Materials research by Information Integration (CMI2), Research and Services .
Genetics-Based Machine Learning for Rule Induction: State of the Art, Taxonomy, and Comparative Study

The classification problem can be addressed by numerous techniques and algorithms which belong to different paradigms of machine learning. In this paper, we are interested in evolutionary algorithms, the so-called genetics-based machine learning algorithms. In particular, we will focus on evolutionary approaches that evolve a set of rules, i.e., evolutionary rule-based systems, applied to classification tasks, in order to provide a state of the art in this field. This paper has a double aim: to present a taxonomy of the genetics-based machine learning approaches for rule induction, and to develop an empirical analysis both for standard classification and for classification with imbalanced data sets. We also include a comparative study of the genetics-based machine learning (GBML) methods with some classical non-evolutionary algorithms, in order to observe the suitability and high potential of the search performed by evolutionary algorithms and the behavior of the GBML algorithms in contrast to the classical approaches, in terms of classification accuracy.
Security frameworks for mobile cloud computing: A survey

Cloud Computing is the long envisaged vision of computing as a utility. Innovative advances in hardware, networking, middleware, and virtual machine technologies have led to an emergence of new, globally distributed computing platforms, namely cloud computing. Cloud Computing provides computation facilities and storage as services accessible from anywhere via the Internet without investing in new infrastructure, training, or software licensing. In other words, Cloud Computing is a way to increase the capacity or add capabilities dynamically. The main advantage of Cloud Computing is that users only utilize what they require and only pay for what they really use. With an exponential growth of the mobile applications and evolution of cloud computing concept, Mobile Cloud Computing (MCC) has been presented as a potential technology for mobile services. MCC incorporates the cloud computing into the mobile environment. Mobile Cloud Computing refers to an infrastructure where data processing and storage can happen away from mobile device. Mobile Cloud Computing (MCC) has transformed the way in which mobile users across the globe leverage services on the go. The obstacles related to performance (e.g. battery life, storage, and bandwidth), environment (e.g. heterogeneity, scalability, availability) and security (e.g. reliability and privacy) are overcome by integrating cloud computing into the mobile environment using MCC. Mobile cloud is a service model, where a mobile device can use the cloud for information storage, searching, data mining and multimedia processing. Cloud Computing technology also brings forth many new challenges for data security and access control when users store sensitive data on cloud servers. As the users no longer have physical possession of the outsourced data, makes the data integrity, privacy and authenticity protection in Cloud Computing a very challenging and potentially difficult task. Though the Cloud Computing benefits are clear, surrendering physical possession of user data, inevitably poses new security risks. In this paper, we discuss Mobile Cloud Computing Security frameworks found in the literature related to Cloud Computing and its environment.
Adverse Drug Extraction in Twitter Data Using Convolutional Neural Network

The study of health-related topics on social media has become a useful tool for the early detection of the different adverse medical conditions. In particular, it concerns cases related to the treatment of mental diseases, as the effects of medications here often prove to be unpredictable. In our research, we use convolutional neural networks (CNN) with word2vec embedding to classify user comments on Twitter. The aim of the classification is to reveal adverse drug reactions of users. The results obtained are highly promising, showing the overall usefulness of neural network algorithms in this kind of tasks.
Trade-Offs in PMU Deployment for State Estimation in Active Distribution Grids

Monitoring systems are expected to play a major role in active distribution grids, and the design of the measurement infrastructure is a critical element for an effective operation. The use of any available and newly installed, though heterogeneous, metering device providing more accurate and real-time measurement data offers a new paradigm for the distribution grid monitoring system. In this paper the authors study the meter placement problem for the measurement infrastructure of an active distribution network, where heterogeneous measurements provided by Phasor Measurement Units (PMUs) and other advanced measurement systems such as Smart Metering systems are used in addition to measurements that are typical of distribution networks, in particular substation measurements and a-priori knowledge. This work aims at defining a design approach for finding the optimal measurement infrastructure for an active distribution grid. The design problem is posed in terms of a stochastic optimization with the goal of bounding the overall uncertainty of the state estimation using heterogeneous measurements while minimizing the investment cost. The proposed method is also designed for computational efficiency so to cover a wide set of scenarios.
Schema Management for Document Stores

Document stores that provide the efficiency of a schema-less interface are widely used by developers in mobile and cloud applications. However, the simplicity developers achieved controversially leads to complexity for data management due to lack of a schema. In this paper, we present a schema management framework for document stores. This framework discovers and persists schemas of JSON records in a repository, and also supports queries and schema summarization. The major technical challenge comes from varied structures of records caused by the schema-less data model and schema evolution. In the discovery phase, we apply a canonical form based method and propose an algorithm based on equivalent sub-trees to group equivalent schemas efficiently. Together with the algorithm, we propose a new data structure, eSiBuTree, to store schemas and support queries. In order to present a single summarized representation for heterogenous schemas in records, we introduce the concept of “skeleton”, and propose to use it as a relaxed form of the schema, which captures a small set of core attributes. Finally, extensive experiments based on real data sets demonstrate the efficiency of our proposed schema discovery algorithms, and practical use cases in real-world data exploration and integration scenarios are presented to illustrate the effectiveness of using skeletons in these applications.
Shunt active filter for harmonic and reactive power compensation using p-q theory

Now-a-days, decadence of power quality is foremost issue of electrical society. In practice, utility of switching device is increased in industrial as well as in domestic applications. Non-linearity causes adverse effects on system efficiency, utility of the power supply, power factor, etc. As reduction in power factor increases reactive power which does not have any contribution in energy transfer so its compensation is needed. So efforts are made to upgrade power quality, concept of Filter is in demand. In contrast to passive filter, active filters are popular due to its smaller size and weight. In this paper, work is done on shunt active power filter (SAPF) using P-Q theory for current harmonic mitigation and reactive power compensation. Here the simulation as well as its parameters, with or without active filter is well presented. We commence the paper with power quality issues then better understanding of PQ theory, simulation circuits, results, comparison and finally ended with conclusion. An effort is made to achieve THD value of source current below 5% to meet the required IEEE standards.
Implementing an Effective Test Automation Framework

Testing automation tools enable developers and/or testers to easily automate the entire process of testing in software development. Nevertheless, adopting automated testing is not easy and unsuccessful due to a lack of key information and skills. In order to help solve such problems, we have designed a new framework to support a clear overview of the test design and/or plan for automating tests in distributed environments. Those that are new to testing do not need to delve into complex automation tools or test scripts. This framework allows a programmer or tester to graphically specify the granularity of execution control and aids communication between various stakeholders and software developers. It also enables them to perform the automated Continuous Integration environment. In this paper, we describe details on experiences and usage of the proposed framework.
On the Use of Logic Trees for Ground-Motion Prediction Equations in Seismic-Hazard Analysis

Logic trees are widely used in probabilistic seismic hazard analysis as a tool to capture the epistemic uncertainty associated with the seismogenic sources and the ground-motion prediction models used in estimating the hazard. Combining two or more ground-motion relations within a logic tree will generally require several conversions to be made, because there are several definitions available for both the predicted ground-motion parameters and the explanatory parameters within the predictive ground-motion relations. Procedures for making conversions for each of these factors are presented, using a suite of predictive equations in current use for illustration. The sensitivity of the resulting ground-motion models to these conversions is shown to be pronounced for some of the parameters, especially the measure of source-to-site distance, highlighting the need to take into account any incompatibilities among the selected equations. Procedures are also presented for assigning weights to the branches in the ground-motion section of the logic tree in a transparent fashion, considering both intrinsic merits of the individual equations and their degree of applicability to the particular application.
1 TOURISM ONTOLOGY AND SEMANTIC MANAGEMENT SYSTEM : STATE-OFTHE-ARTS ANALYSIS

The global importance of tourism is steadily rising,creating new job opportunities in many countries. Today’s information management solutions for the complex tasks of tourism intermediaries are still at an early stage from a semantic point of view. This paper presents some preliminary results of OnTourism Project. OnTourism is aimed at (1) applying, concretizing and evaluating Semantic Web technologies such as ontologies, semantic annotation of content, and semantic search to the information-rich and economically important tourism domain, (2) identifying, developing and integrating reference ontologies for the tourism industry, and (3) showing the proof-of-concept in a real-world scenario of the Austrian tourism industry. First results presented in this paper identify publicly available tourism ontologies and existing freely available ontology management tools for the tourism domain. We identify seven tourism ontologies which are suitable as a basis for creating problem-specific ontologies. Furthermore we review and evaluate five freely available ontology management tools that are suited for application in the tourism domain.
A series resonant converter with constant on-time control for capacitor charging applications

A capacitor charging power supply (CCPS) has been assembled. A full-bridge series resonant topology using MOSFETs as switching elements makes up the bulk of the high-power section of the CCPS. A high-voltage transformer couples the output stage to the resonant inverter, and the secondary current of this transformer is rectified to provide the charging current. The CCPS uses constant on-time control with zero current switching. The control scheme is implemented with Unitrode's UC3860 resonant mode power supply controller. The control circuitry also protects the CCPS against both overvoltage and overcurrent conditions. The CCPS has been tested in the laboratory, where it successfully charged capacitors ranging from 90 nF to 1 mu F to 2000 V.<<ETX>>
Design of fractal based microstrip rectangular patch antenna for multiband applications

In this paper a multiband fractal based rectangular microstrip patch antenna is designed. FR4 substrate having thickness of 1.58 mm is used as substrate material for the design of proposed antenna and microstrip feed line provides the excitation to the antenna. The antenna operating frequency range is from 1 to 10 GHz. The proposed antenna resonate at twelve different frequencies as 1.86, 2.33, 3.67, 4.57, 5.08, 6.06, 7.03, 7.75, 8.08, 8.84, 9.56 and 10 GHz and the return losses are -15.39, -16.48, -10.02, -17.29, -13.15, -23.41, -10.22, -11.28, -17.02, -10.94, -15.15 and -15.48 dB respectively. The proposed antenna is designed and simulated by using the Ansoft HFSS V13 (high frequency structure simulator) software.