nils-herrmann/clean_scirepeval huggingface dataset:
Regulation of synchronous generators by means of hydrostatic transmissions

 In many applications within the engineering world, an isolated generator is needed (e.g. in ships). Diesel units (diesel engine and synchronous generator) are the most common solution. However, the diesel engine can be eliminated if the energy from another source (e.g. the prime mover in a ship) is used to move the generator. This is the case for the Shaft Coupled Generator, where the coupling between the mover and the generator is made via a hydrostatic transmission. So that the mover can have different speeds and the generator is able to keep a constant frequency. The main problem of this system is the design of a speed governor that make possible the desired behaviour. In this paper a simulation model is presented in order to analyse the behaviour of this kind of systems and to help in the speed governor design. The model is achieved with an parameter identification process also depicted in the paper. A comparison between simulation results and measurements is made to shown the model validity.  Identification, Hydrostatic Transmission.
Generative Adversarial Networks-Based Semi-Supervised Learning for Hyperspectral Image Classification

Classification of hyperspectral image (HSI) is an important research topic in the remote sensing community. Significant efforts (e.g., deep learning) have been concentrated on this task. However, it is still an open issue to classify the high-dimensional HSI with a limited number of training samples. In this paper, we propose a semi-supervised HSI classification method inspired by the generative adversarial networks (GANs). Unlike the supervised methods, the proposed HSI classification method is semi-supervised, which can make full use of the limited labeled samples as well as the sufficient unlabeled samples. Core ideas of the proposed method are twofold. First, the three-dimensional bilateral filter (3DBF) is adopted to extract the spectral-spatial features by naturally treating the HSI as a volumetric dataset. The spatial information is integrated into the extracted features by 3DBF, which is propitious to the subsequent classification step. Second, GANs are trained on the spectral-spatial features for semi-supervised learning. A GAN contains two neural networks (i.e., generator and discriminator) trained in opposition to one another. The semi-supervised learning is achieved by adding samples from the generator to the features and increasing the dimension of the classifier output. Experimental results obtained on three benchmark HSI datasets have confirmed the effectiveness of the proposed method, especially with a limited number of labeled samples.
Copyright© 2013 by Turbomachinery Laboratory, Texas A&M Engineering Experiment Station HISTORY OF EVOLUTION, PROGRESS AND APPLICATION OF SAFE DIAGRAM FOR TUNED AND MISTUNED SYSTEMS

Concept of Safe diagram was introduced 30 years ago (Singh and Schiffer, 1982) for the analysis of the vibration characteristics of packeted bladed disc for steam turbines. A detailed description of Safe diagram for steam turbine blades was presented 25 years ago in the 17 th Turbo Symposium (Singh et. el, 1988). Since that time it has found application in the design and failure analysis of many turbo machineries e.g. steam turbines, centrifugal compressor, axial compressor, expanders etc. The theory was justified using the argument of natural modes of vibration containing single harmonics and alternating forcing represented by pure sine wave around 360 degrees applied to bladed disk. This case is referred as tuned system. It was also explained that packeted bladed disc is a mistuned system where geometrical symmetry is broken deliberately by breaking the shroud in many places. This is a normal practice which provides blade packets design. This is known as deliberate geometrical mistuning. This mistuning gave rise to frequency of certain modes being split in two different modes which otherwise existed in duplicate. Natural modes of this type construction exhibited impurity i.e. it contained many harmonics in place of just one as it occurs in a tuned case. As a result, this phenomenon gives rise to different system response for each split mode. Throughout the years that have passed, Safe diagram has been used for any mistuned systemrandom, known or deliberate. Many co-workers and friends have asked me to write the history of the evolution and of the first application of this concept and its application in more general case. This paper describes application of Safe diagram for general case of tuned system and mistuned system.
A Development of the Blended Learning Model Using Edmodo for Maximizing Students' Oral Proficiency and Motivation

The purposes of this study were to develop a blended learning model using Edmodo as a learning tool in language learning and examine the effectiveness of the model implemented in an English course regarding oral proficiency, motivation, and attitude. To achieve these purposes, a quasiexperimental design was employed to collect data using the tests and questionnaires. The samples in this study were the second-year students enrolled in an intermediate English course. The samples were from two classes, each of which contained 42 students. One class was used for the control group; another one for the experimental group. In the control group, students were only taught in a face-to-face learning environment using the PPP model whereas the experimental group studied through the PPP model in class supported by online learning using Edmodo. The findings indicate that blended learning was more effective than traditional learning. That is, students in the experimental group not only outperformed those in the control group in oral proficiency, but they also exhibited higher learning motivation. Although students in both groups did not differ in their attitude toward the PPP model, those in the experimental group expressed a positive attitude toward Edmodo. The findings of this study have implications for English language instructors, encouraging them to see how technology can facilitate learning and help learners produce better academic performance. blended learning, Edmodo, oral proficiency, motivation
TextTiling: Segmenting Text into Multi-paragraph Subtopic Passages

TextTiling is a technique for subdividing texts into multi-paragraph units that represent passages, or subtopics. The discourse cues for identifying major subtopic shifts are patterns of lexical co-occurrence and distribution. The algorithm is fully implemented and is shown to produce segmentation that corresponds well to human judgments of the subtopic boundaries of 12 texts. Multi-paragraph subtopic segmentation should be useful for many text analysis tasks, including information retrieval and summarization.
94 GHz Substrate Integrated Monopulse Antenna Array

A planar W-band monopulse antenna array is designed based on the substrate integrated waveguide (SIW) technology. The sum-difference comparator, 16-way divider and 32 × 32 slot array antenna are all integrated on a single dielectric substrate in the compact layout through the low-cost PCB process. Such a substrate integrated monopulse array is able to operate over 93 ~ 96 GHz with narrow-beam and high-gain. The maximal gain is measured to be 25.8 dBi, while the maximal null-depth is measured to be - 43.7 dB. This SIW monopulse antenna not only has advantages of low-cost, light, easy-fabrication, etc., but also has good performance validated by measurements. It presents an excellent candidate for W-band directional-finding systems.
Learning Deep Convolutional Neural Networks for X-Ray Protein Crystallization Image Analysis

Obtaining a protein’s 3D structure is crucial to the understanding of its functions and interactions with other proteins. It is critical to accelerate the protein crystallization process with improved accuracy for understanding cancer and designing drugs. Systematic high-throughput approaches in protein crystallization have been widely applied, generating a large number of protein crystallization-trial images. Therefore, an efficient and effective automatic analysis for these images is a top priority. In this paper, we present a novel system, CrystalNet, for automatically labeling outcomes of protein crystallization-trial images. CrystalNet is a deep convolutional neural network that automatically extracts features from X-ray protein crystallization images for classification. We show that (1) CrystalNet can provide real-time labels for crystallization images effectively, requiring approximately 2 seconds to provide labels for all 1536 images of crystallization microassay on each plate; (2) compared with the stateof-the-art classification systems in crystallization image analysis, our technique demonstrates an improvement of 8% in accuracy, and achieve 90.8% accuracy in classification. As a part of the high-throughput pipeline which generates millions of images a year, CrystalNet can lead to a substantial reduction of labor-intensive screening.
A bibliometric analysis of the scientific literature on Internet, video games, and cell phone addiction.

The aim of this study was to locate the scientific literature dealing with addiction to the Internet, video games, and cell phones and to characterize the pattern of publications in these areas.



One hundred seventy-nine valid articles were retrieved from PubMed and PsycINFO between 1996 and 2005 related to pathological Internet, cell phone, or video game use.



The years with the highest numbers of articles published were 2004 (n = 42) and 2005 (n = 40). The most productive countries, in terms of number of articles published, were the United States (n = 52), China (n = 23), the United Kingdom (n = 17), Taiwan (n = 13), and South Korea (n = 9). The most commonly used language was English (65.4%), followed by Chinese (12.8%) and Spanish (4.5%). Articles were published in 96 different journals, of which 22 published 2 or more articles. The journal that published the most articles was Cyberpsychology & Behavior (n = 41). Addiction to the Internet was the most intensely studied (85.3%), followed by addiction to video games (13.6%) and cell phones (2.1%).



The number of publications in this area is growing, but it is difficult to conduct precise searches due to a lack of clear terminology. To facilitate retrieval, bibliographic databases should include descriptor terms referring specifically to Internet, video games, and cell phone addiction as well as to more general addictions involving communications and information technologies and other behavioral addictions.
Target value design : using collaboration and a lean approach to reduce construction cost

Target Costing is an effective management technique that has been used in manufacturing for decades to achieve cost predictability during new products development. Adoption of this technique promises benefits for the construction industry as it struggles to raise the number of successful outcomes and certainty of project delivery in terms of cost, quality and time. Target Value Design is a management approach that takes the best features of Target Costing and adapts them to the peculiarities of construction. In this paper the concept of Target Value Design is introduced based on the results of action research carried out on 12 construction projects in the USA. It has been shown that systemic application of Target Value Design leads to significant improvement of project performance – the final cost of projects was on average 15% less than market cost. The construction industry already has approaches that have similarities with elements of the Target Value Design process or uses the same terminology, e.g. Partnering and Target Cost Contracts, Cost planning, etc. Following an exploration of the similarities and differences Target Value Design is positioned as a form of Target Costing for construction that offers a more reliable route to successful projects outcomes.
JointDNN: An Efficient Training and Inference Engine for Intelligent Mobile Cloud Computing Services

Deep neural networks are among the most influential architectures of deep learning algorithms, being deployed in many mobile intelligent applications. End-side services, such as intelligent personal assistants (IPAs), autonomous cars, and smart home services often employ either simple local models or complex remote models on the cloud. Mobile-only and cloud-only computations are currently the status-quo approaches. In this paper, we propose an efficient, adaptive, and practical engine, JointDNN, for collaborative computation between a mobile device and cloud for DNNs in both inference and training phase. JointDNN not only provides an energy and performance efficient method of querying DNNs for the mobile side, but also benefits the cloud server by reducing the amount of its workload and communications compared to the cloud-only approach. Given the DNN architecture, we investigate the efficiency of processing some layers on the mobile device and some layers on the cloud server. We provide optimization formulations at layer granularity for forward and backward propagation in DNNs, which can adapt to mobile battery limitations and cloud server load constraints and quality of service. JointDNN achieves up to 18× and 32× reductions on the latency and mobile energy consumption of querying DNNs compared to the status-quo approaches, respectively.
A deep learning approach for pose estimation from volumetric OCT data

Tracking the pose of instruments is a central problem in image-guided surgery. For microscopic scenarios, optical coherence tomography (OCT) is increasingly used as an imaging modality. OCT is suitable for accurate pose estimation due to its micrometer range resolution and volumetric field of view. However, OCT image processing is challenging due to speckle noise and reflection artifacts in addition to the images' 3D nature. We address pose estimation from OCT volume data with a new deep learning-based tracking framework. For this purpose, we design a new 3D convolutional neural network (CNN) architecture to directly predict the 6D pose of a small marker geometry from OCT volumes. We use a hexapod robot to automatically acquire labeled data points which we use to train 3D CNN architectures for multi-output regression. We use this setup to provide an in-depth analysis on deep learning-based pose estimation from volumes. Specifically, we demonstrate that exploiting volume information for pose estimation yields higher accuracy than relying on 2D representations with depth information. Supporting this observation, we provide quantitative and qualitative results that 3D CNNs effectively exploit the depth structure of marker objects. Regarding the deep learning aspect, we present efficient design principles for 3D CNNs, making use of insights from the 2D deep learning community. In particular, we present Inception3D as a new architecture which performs best for our application. We show that our deep learning approach reaches errors at our ground-truth label's resolution. We achieve a mean average error of 14.89 ± 9.3 µm and 0.096 ± 0.072° for position and orientation learning, respectively.
Security Challenges of Small Cell as a Service in Virtualized Mobile Edge Computing Environments

Research on next-generation 5G wireless networks is currently attracting a lot of attention in both academia and industry. While 5G development and standardization activities are still at their early stage, it is widely acknowledged that 5G systems are going to extensively rely on dense small cell deployments, which would exploit infrastructure and network functions virtualization (NFV), and push the network intelligence towards network edges by embracing the concept of mobile edge computing (MEC). As security will be a fundamental enabling factor of small cell as a service (SCaaS) in 5G networks, we present the most prominent threats and vulnerabilities against a broad range of targets. As far as the related work is concerned, to the best of our knowledge, this paper is the rst to investigate security challenges at the intersection of SCaaS, NFV, and MEC. It is also the rst paper that proposes a set of criteria to facilitate a clear and e ective taxonomy of security challenges of main elements of 5G networks. Our analysis can serve as a staring point towards the development of appropriate 5G security solutions. These will have crucial e ect on legal and regulatory frameworks as well as on decisions of businesses, governments, and end-users.
Chapter 1 Non-negative Matrix Factorizations for Clustering : A

Recently there has been significant development in the use of non-negative matrix factorization (NMF) methods for various clustering tasks. NMF factorizes an input nonnegative matrix into two nonnegative matrices of lower rank. Although NMF can be used for conventional data analysis, the recent overwhelming interest in NMF is due to the newly discovered ability of NMF to solve challenging data mining and machine learning problems. In particular, NMF with the sum of squared error cost function is equivalent to a relaxed K-means clustering, the most widely used unsupervised learning algorithm. In addition, NMF with the I-divergence cost function is equivalent to probabilistic latent semantic indexing, another unsupervised learning method popularly used in text analysis. Many other data mining and machine learning problems can be reformulated as an NMF problem. This chapter aims to provide a comprehensive review of non-negative matrix factorization methods for clustering. In particular, we outline the theoretical foundations on NMF for clustering, provide an overview of different variants on NMF formulations, and examine several practical issues in NMF algorithms. We also summarize recent advances on using NMF-based methods for solving many other clustering problems including coclustering, semi-supervised clustering, and consensus clustering and discuss some future research directions.
Introducing Robustness in Multi-Objective Optimization

In optimization studies including multi-objective optimization, the main focus is placed on finding the global optimum or global Pareto-optimal solutions, representing the best possible objective values. However, in practice, users may not always be interested in finding the so-called global best solutions, particularly when these solutions are quite sensitive to the variable perturbations which cannot be avoided in practice. In such cases, practitioners are interested in finding the robust solutions which are less sensitive to small perturbations in variables. Although robust optimization is dealt with in detail in single-objective evolutionary optimization studies, in this paper, we present two different robust multi-objective optimization procedures, where the emphasis is to find a robust frontier, instead of the global Pareto-optimal frontier in a problem. The first procedure is a straightforward extension of a technique used for single-objective optimization and the second procedure is a more practical approach enabling a user to set the extent of robustness desired in a problem. To demonstrate the differences between global and robust multi-objective optimization principles and the differences between the two robust optimization procedures suggested here, we develop a number of constrained and unconstrained test problems having two and three objectives and show simulation results using an evolutionary multi-objective optimization (EMO) algorithm. Finally, we also apply both robust optimization methodologies to an engineering design problem.
An Architectural Smells Detection Tool for C and C++ Projects

Architectural smells gained great attention in the past few years since they directly affect software quality and increase architectural technical debt. However, while it is straightforward to understand why they are important, it is more difficult to find techniques and tools to detect and remove architectural smells. The purpose of this paper is to introduce an open-source tool for automatic architectural smells detection for C/C++ projects, by creating an abstraction of the project and defining the concept of dependency between elements belonging to the project in order to identify architectural smells. The tool has been validated on some open-source projects with promising results.
Varying Linguistic Purposes of Emoji in (Twitter) Context

Early research into emoji in textual communication has focused largely on highfrequency usages and ambiguity of interpretations. Investigation of a wide range of emoji usage shows these glyphs serving at least two very different purposes: as content and function words, or as multimodal affective markers. Identifying where an emoji is replacing textual content allows NLP tools the possibility of parsing them as any other word or phrase. Recognizing the import of non-content emoji can be a a significant part of understanding a message as well. We report on an annotation task on English Twitter data with the goal of classifying emoji uses by these categories, and on the effectiveness of a classifier trained on these annotations. We find that it is possible to train a classifier to tell the difference between those emoji used as linguistic content words and those used as paralinguistic or affective multimodal markers even with a small amount of training data, but that accurate sub-classification of these multimodal emoji into specific classes like attitude, topic, or gesture will require more data and more feature engineering.
Pedestrian detection procedure integrated into an 24 GHz automotive radar

Driving a car is a dangerous task! There are about 5000 fatalities on German streets every year, which is absolutely too much. Human beings have strong limitations in the ability to measure precisely the distance and the speed difference between cars which is the reason for several accidents. Therefore some individual assistance is needed. A radar sensor measures the target range and radial velocity very accurate and with high resolution even in multiple target situations and there is tremendous progress in 24 GHz radar sensor development. The objective of this paper is to protect vulnerable road users by a target recognition scheme. Therefore, a pedestrian detection procedure is additionally integrated into the 24 GHz automotive radar sensor.
FX trading via recurrent reinforcement learning

This study investigates high frequency currency trading with neural networks trained via Recurrent Reinforcement Learning (RRL). We compare the performance of single layer networks with networks having a hidden layer, and examine the impact of the fixed system parameters on performance. In general, we conclude that the trading systems may be effective, but the performance varies widely for different currency markets and this variability cannot be explained by simple statistics of the markets. Also we find that the single layer network outperforms the two layer network in this application.
Habits of programming in scratch

Visual programming environments are widely used to introduce young people to computer science and programming; in particular, they encourage learning by exploration. During our research on teaching and learning computer science concepts with Scratch, we discovered that Scratch engenders certain habits of programming: (a) a totally bottom-up development process that starts with the individual Scratch blocks, and (b) a tendency to extremely fine-grained programming. Both these behaviors are at odds with accepted practice in computer science that encourages one: (a) to start by designing an algorithm to solve a problem, and (b) to use programming constructs to cleanly structure programs. Our results raise the question of whether exploratory learning with a visual programming environment might actually be detrimental to more advanced study.