nils-herrmann/clean_scirepeval huggingface dataset:
Theoretical and Experimental Reevaluation of Synchronous Reluctance Machine

This paper theoretically introduces and develops a new operation diagram (OPD) and parameter estimator for the synchronous reluctance machine (SynRM). The OPD demonstrates the behavior of the machine's main performance parameters, such as torque, current, voltage, frequency, flux, power factor (PF), and current angle, all in one graph. This diagram can easily be used to describe different control strategies, possible operating conditions, both below- and above-rated speeds, etc. The saturation effect is also discussed with this diagram by finite-element-method calculations. A prototype high-performance SynRM is designed for experimental studies, and then, both machines' [corresponding induction machine (IM)] performances at similar loading and operation conditions are tested, measured, and compared to demonstrate the potential of SynRM. The laboratory measurements (on a standard 15-kW Eff1 IM and its counterpart SynRM) show that SynRM has higher efficiency, torque density, and inverter rating and lower rotor temperature and PF in comparison to IM at the same winding-temperature-rise condition. The measurements show that the torque capability of SynRM closely follows that of IM.
Localized image watermarking based on feature points of scale-space representation

This paper proposes a novel method for content-based watermarking based on feature points of an image. At each feature point, the watermark is embedded after scale normalization according to the local characteristic scale. Characteristic scale is the maximum scale of the scale-space representation of an image at the feature point. By binding watermarking with the local characteristics of an image, resilience against a5ne transformations can be obtained easily. Experimental results show that the proposed method is robust against various image processing steps including a5ne transformations, cropping, 7ltering and JPEG compression. ? 2004 Pattern Recognition Society.
A process for systematically reviewing the literature: providing the research evidence for public health nursing interventions.

Several groups have outlined methodologies for systematic literature reviews of the effectiveness of interventions. The Effective Public Health Practice Project (EPHPP) began in 1998. Its mandate is to provide research evidence to guide and support the Ontario Ministry of Health in outlining minimum requirements for public health services in the province. Also, the project is expected to disseminate the results provincially, nationally, and internationally. Most of the reviews are relevant to public health nursing practice.



This article describes four issues related to the systematic literature reviews of the effectiveness of public health nursing interventions: (1) the process of systematically reviewing the literature, (2) the development of a quality assessment instrument, (3) the results of the EPHPP to date, and (4) some results of the dissemination strategies used.



The eight steps of the systematic review process including question formulation, searching and retrieving the literature, establishing relevance criteria, assessing studies for relevance, assessing relevant studies for methodological quality, data extraction and synthesis, writing the report, and dissemination are outlined. Also, the development and assessment of content and construct validity and intrarater reliability of the quality assessment questionnaire used in the process are described.



More than 20 systematic reviews have been completed. Content validity was ascertained by the use of a number of experts to review the questionnaire during its development. Construct validity was demonstrated through comparisons with another highly rated instrument. Intrarater reliability was established using Cohen's Kappa. Dissemination strategies used appear to be effective in that professionals report being aware of the reviews and using them in program planning/policymaking decisions.



The EPHPP has demonstrated the ability to adapt the most current methods of systematic literature reviews of effectiveness to questions related to public health nursing. Other positive outcomes from the process include the development of a critical mass of public health researchers and practitioners who can actively participate in the process, and the work on dissemination has been successful in attracting external funds. A program of research in this area is being developed.
A compact 433 MHz antenna with enhanced performance by using multi-resonant meander line structure

In this paper, a miniaturized planar antenna with enhanced bandwidth is designed for the ISM 433 MHz applications. The antenna is realized by cascading two resonant structures with meander lines, thus introducing two different radiating branches to realize two neighboring resonant frequencies. The techniques of shorting pin and novel ground plane are adopted for bandwidth enhancement. Combined with these structures, a novel antenna with a total size of 23 mm × 49.5 mm for the ISM band application is developed and fabricated. Measured results show that the proposed antenna has good performance with the -10 dB impedance bandwidth is about 12.5 MHz and the maximum gain is about -2.8 dBi.
77 GHz SiGe based bipolar transceivers for automotive radar applications — An industrial perspective

Radar sensors operating in the 76–81 GHz range are considered key for Advanced Driver Assistance Systems (ADAS) like adaptive cruise control (ACC), collision mitigation and avoidance systems (CMS) or lane change assist (LCA). These applications are the next wave in automotive safety systems and have thus generated increased interest in lower-cost solutions especially for the mm-wave front-end (FE) section. Today, most of the radar sensors in this frequency range use GaAs based FEs. These multi-chip GaAs FEs are a main cost driver in current radar sensors due to their low integration level. The step towards monolithic microwave integrated circuits (MMIC) based on a 200 GHz ft silicon-germanium (SiGe) technology integrating all needed RF building blocks (mixers, VCOs, dividers, buffers, PAs) on an single die does not only lead to cost reductions but also benefits the testability of these MMICs. This is especially important in the light of upcoming functional safety standards like ASIL-D and ISO26262.
A comprehensive review of cuckoo search: variants and hybrids

Cuckoo search (CS) is an efficient swarm-intelligence-based algorithm and significant developments have been made since its introduction in 2009. CS has many advantages due to its simplicity and efficiency in solving highly non-linear optimisation problems with real-world engineering applications. This paper provides a timely review of all the state-of-the-art developments in the last five years, including the discussions of theoretical background and research directions for future development of this powerful algorithm.
1 Prejudice , Stereotyping and Discrimination : Theoretical and Empirical Overview

This chapter has two main objectives: to review influential ideas and findings in the literature and to outline the organization and content of the volume. The first part of the chapter lays a conceptual and empirical foundation for other chapters in the volume. Specifically, the chapter defines and distinguishes the key concepts of prejudice, stereotypes, and discrimination, highlighting how bias can occur at individual, institutional, and cultural levels. We also review different theoretical perspectives on these phenomena, including individual differences, social cognition, functional relations between groups, and identity concerns. We offer a broad overview of the field, charting how this area has developed over previous decades and identify emerging trends and future directions. The second part of the chapter focuses specifically on the coverage of the area in the present volume. It explains the organization of the book and presents a brief synopsis of the chapters in the volume. Throughout psychology’s history, researchers have evinced strong interest in understanding prejudice, stereotyping, and discrimination (Brewer & Brown, 1998; Dovidio, 2001; Duckitt, 1992;  1998), as well as the phenomenon of intergroup bias more generally (Hewstone, Rubin, & Willis, 2002). Intergroup bias generally refers to the systematic tendency to evaluate one’s own membership group (the ingroup) or its members more favorably than a non-membership group (the outgroup) or its members. These topics have a long history in the disciplines of anthropology and sociology (e.g., Sumner, 1906). However, social psychologists, building on the solid foundations of Gordon Allport’s (1954) masterly volume, The Nature of Prejudice, have developed a systematic and more nuanced analysis of bias and its associated phenomena. Interest in prejudice, stereotyping, and discrimination is currently shared by allied disciplines such as sociology and political science, and emerging disciplines such as neuroscience. The practical implications of this 4 OVERVIEW OF THE TOPIC large body of research are widely recognized in the law (Baldus, Woodworth, & Pulaski, 1990; Vidmar, 2003), medicine (Institute of Medicine, 2003), business (e.g., Brief, Dietz, Cohen, et al., 2000), the media, and education (e.g., Ben-Ari & Rich, 1997; Hagendoorn &
Compact Global Descriptors for Visual Search

The first step in an image retrieval pipeline consists of comparing global descriptors from a large database to find a short list of candidate matching images. The more compact the global descriptor, the faster the descriptors can be compared for matching. State-of-the-art global descriptors based on Fisher Vectors are represented with tens of thousands of floating point numbers. While there is significant work on compression of local descriptors, there is relatively little work on compression of high dimensional Fisher Vectors. We study the problem of global descriptor compression in the context of image retrieval, focusing on extremely compact binary representations: 64-1024 bits. Motivated by the remarkable success of deep neural networks in recent literature, we propose a compression scheme based on deeply stacked Restricted Boltzmann Machines (SRBM), which learn lower dimensional non-linear subspaces on which the data lie. We provide a thorough evaluation of several state-of-the-art compression schemes based on PCA, Locality Sensitive Hashing, Product Quantization and greedy bit selection, and show that the proposed compression scheme outperforms all existing schemes.
Aerial 3D reconstruction with line-constrained dynamic programming

Aerial imagery of an urban environment is often characterized by significant occlusions, sharp edges, and textureless regions, leading to poor 3D reconstruction using conventional multi-view stereo methods. In this paper, we propose a novel approach to 3D reconstruction of urban areas from a set of uncalibrated aerial images. A very general structural prior is assumed that urban scenes consist mostly of planar surfaces oriented either in a horizontal or an arbitrary vertical orientation. In addition, most structural edges associated with such surfaces are also horizontal or vertical. These two assumptions provide powerful constraints on the underlying 3D geometry. The main contribution of this paper is to translate the two constraints on 3D structure into intra-image-column and inter-image-column constraints, respectively, and to formulate the dense reconstruction as a 2-pass Dynamic Programming problem, which is solved in complete parallel on a GPU. The result is an accurate cloud of 3D dense points of the underlying urban scene. Our algorithm completes the reconstruction of 1M points with 160 available discrete height levels in under a hundred seconds. Results on multiple datasets show that we are capable of preserving a high level of structural detail and visual quality.
Voice Activity Detection Based on an Unsupervised Learning Framework

How to construct models for speech/nonspeech discrimination is a crucial point for voice activity detectors (VADs). Semi-supervised learning is the most popular way for model construction in conventional VADs. In this correspondence, we propose an unsupervised learning framework to construct statistical models for VAD. This framework is realized by a sequential Gaussian mixture model. It comprises an initialization process and an updating process. At each subband, the GMM is firstly initialized using EM algorithm, and then sequentially updated frame by frame. From the GMM, a self-regulatory threshold for discrimination is derived at each subband. Some constraints are introduced to this GMM for the sake of reliability. For the reason of unsupervised learning, the proposed VAD does not rely on an assumption that the first several frames of an utterance are nonspeech, which is widely used in most VADs. Moreover, the speech presence probability in the time-frequency domain is a byproduct of this VAD. We tested it on speech from TIMIT database and noise from NOISEX-92 database. The evaluations effectively showed its promising performance in comparison with VADs such as ITU G.729B, GSM AMR, and a typical semi-supervised VAD.
Head pose-free eye gaze prediction for driver attention study

Driver's gaze direction is an indicator of driver state and plays a significantly role in driving safety. Traditional gaze zone estimation methods based on eye model have disadvantages due to the vulnerability under large head movement. Different from these methods, an appearance-based head pose-free eye gaze prediction method is proposed in this paper, for driver gaze zone estimation under free head movement. To achieve this goal, a gaze zone classifier is trained with head vectors and eye image features by random forest. The head vector is calculated by Pose from Orthography and Scaling with ITerations (POSIT) where a 3D face model is combined with facial landmark detection. And the eye image features are derived from eye images which extracted through eye region localization. These features are presented as the combination of sparse coefficients by sparse encoding with eye image dictionary, having good potential to carry information of the eye images. Experimental results show that the proposed method is applicable in real driving environment.
Warning traffic sign recognition using a HOG-based K-d tree

Traffic signs are an essential part of a Driver Assistance System (DAS) and provide drivers with safety information. They are designed to be easily seen and understood. The triangular signs warn the drivers of imminent dangers such as wild animals or a sharp curve. In this paper, an efficient algorithm for the detection and recognition of warning signs is presented. A Histogram of Oriented Gradients (HOG) is used to detect 95% of the triangular warning signs. A blackhat filter eliminates a large part of the false alarms. An approximate nearest neighbors search using a KD-tree refines the result. It eliminates 100% of the remaining false detections and distinguishes amongst the different types of signs. The advantage of using HOG features is that all the warning signs, including static (red frame) and dynamic warning signs (illuminated) can be detected with a single detector and therefore, only one image scan.
Music recommendation via heterogeneous information graph embedding

Traditional music recommendation techniques suffer from limited performance due to the sparsity of user-music interaction data, which is addressed by incorporating auxiliary information. In this paper, we study the problem of personalized music recommendation that takes different kinds of auxiliary information into consideration. To achieve this goal, a Heterogeneous Information Graph (HIG) is first constructed to encode different kinds of heterogeneous information, including the interactions between users and music pieces, music playing sequences, and the metadata of music pieces. Based on HIG, a Heterogeneous Information Graph Embedding method (HIGE) is proposed to learn the latent low-dimensional representations of music pieces. Then, we further develop a context-aware music recommendation method. Extensive experiments have been conducted on real-world datasets to compare the proposed method with other state-of-the-art recommendation methods. The results demonstrate that the proposed method significantly outperforms those baselines, especially on sparse datasets.
Optimising 5G infrastructure markets: The business of network slicing

In addition to providing substantial performance enhancements, future 5G networks will also change the mobile network ecosystem. Building on the network slicing concept, 5G allows to “slice” the network infrastructure into separate logical networks that may be operated independently and targeted at specific services. This opens the market to new players: the infrastructure provider, which is the owner of the infrastructure, and the tenants, which may acquire a network slice from the infrastructure provider to deliver a specific service to their customers. In this new context, we need new algorithms for the allocation of network resources that consider these new players. In this paper, we address this issue by designing an algorithm for the admission and allocation of network slices requests that (i) maximises the infrastructure provider's revenue and (ii) ensures that the service guarantees provided to tenants are satisfied. Our key contributions include: (i) an analytical model for the admissibility region of a network slicing-capable 5G Network, (ii) the analysis of the system (modelled as a Semi-Markov Decision Process) and the optimisation of the infrastructure provider's revenue, and (iii) the design of an adaptive algorithm (based on Q-learning) that achieves close to optimal performance.
An exact algorithm for the elementary shortest path problem with resource constraints: Application to some vehicle routing problems

In this paper, we propose a solution procedure for the Elementary Shortest Path Problem with Resource Constraints (ESPPRC). A relaxed version of this problem in which the path does not have to be elementary has been the backbone of a number of solution procedures based on column generation for several important problems, such as vehicle routing and crew-pairing. In many cases relaxing the restriction of an elementary path resulted in optimal solutions in a reasonable computation time. However, for a number of other problems, the elementary path restriction has too much impact on the solution to be relaxed or might even be necessary. We propose an exact solution procedure for the ESPPRC which extends the classical label correcting algorithm originally developed for the relaxed (non-elementary) path version of this problem. We present computational experiments of this algorithm for our specific problem and embedded in a column generation scheme for the classical Vehicle Routing Problem with Time Windows.
Time-Dependent Models in Collaborative Filtering Based Recommender System

In recent years, time information is more and more important in collaborative filtering (CF) based recommender system because many systems have collected rating data for a long time, and time effects in user preference is stronger. In this paper, we focus on modeling time effects in CF and analyze how temporal features influence CF. There are four main types of time effects in CF: (1) time bias, the interest of whole society changes with time; (2) user bias shifting, a user may change his/her rating habit over time; (3) item bias shifting, the popularity of items changes with time; (4) user preference shifting, a user may change his/her attitude to some types of items. In this work, these four time effects are used by factorized model, which is called TimeSVD. Moreover, many other time effects are used by simple methods. Our time-dependent models are tested on Netflix data from Nov. 1999 to Dec. 2005. Experimental results show that prediction accuracy in CF can be improved significantly by using time information.
A Faster Algorithm Computing String Edit Distances

The edit distance between two character strings can be defined as the minimum cost of a sequence of editing operations which transforms one string into the other. The operations we admit are deleting, inserting and replacing one symbol at a time, with possibly different costs for each of these operations. The problem of finding the longest common subsequence of two strings is a special case of the problem of computing edit distances. We describe an algorithm for computing the edit distance between two strings of length n and m, n > m, which requires O(n * max( 1, m/log n)) steps whenever the costs of edit operations are integral multiples of a single positive real number and the alphabet for the strings is finite. These conditions are necessary for the algorithm to achieve the time bound.
Programming & storytelling: opportunities for learning about coding & composition

The focus of this paper is to investigate how writing computer programs can help children develop their storytelling and creative writing abilities. The process of writing a program---coding---has long been considered only in terms of computer science, but such coding is also reflective of the imaginative and narrative elements of fiction writing workshops. Writing to program can also serve as programming to write, in which a child learns the importance of sequence, structure, and clarity of expression---three aspects characteristic of effective coding and good storytelling alike. While there have been efforts examining how learning to write code can be facilitated by storytelling, there has been little exploration as to how such creative coding can also be directed to teach students about the narrative and storytelling process. Using the introductory programming language Scratch, this paper explores the potential of having children create their own digital stories with the software and how the narrative structure of these stories offers kids the opportunity to better understand the process of expanding an idea into the arc of a story.
Convolutional neural networks for acoustic modeling of raw time signal in LVCSR

In this paper we continue to investigate how the deep neural network (DNN) based acoustic models for automatic speech recognition can be trained without hand-crafted feature extraction. Previously, we have shown that a simple fully connected feedforward DNN performs surprisingly well when trained directly on the raw time signal. The analysis of the weights revealed that the DNN has learned a kind of short-time time-frequency decomposition of the speech signal. In conventional feature extraction pipelines this is done manually by means of a filter bank that is shared between the neighboring analysis windows. Following this idea, we show that the performance gap between DNNs trained on spliced hand-crafted features and DNNs trained on raw time signal can be strongly reduced by introducing 1D-convolutional layers. Thus, the DNN is forced to learn a short-time filter bank shared over a longer time span. This also allows us to interpret the weights of the second convolutional layer in the same way as 2D patches learned on critical band energies by typical convolutional neural networks. The evaluation is performed on an English LVCSR task. Trained on the raw time signal, the convolutional layers allow to reduce the WER on the test set from 25.5% to 23.4%, compared to an MFCC based result of 22.1% using fully connected layers.