nils-herrmann/clean_scirepeval huggingface dataset:
A survey on the communication architectures in smart grid

The next-generation electric power systems (smart grid) are studied intensively as a promising solution for energy crisis. One important feature of the smart grid is the integration of high-speed, reliable and secure data communication networks to manage the complex power systems effectively and intelligently. We provide in this paper a comprehensive survey on the communication architectures in the power systems, including the communication network compositions, technologies, functions, requirements, and research challenges. As these communication networks are responsible for delivering power system related messages, we discuss specifically the network implementation considerations and challenges in the power system settings. This survey attempts to summarize the current state of research efforts in the communication networks of smart grid, which may help us identify the research problems in the continued studies.
Millimeter Wave MIMO channel estimation based on adaptive compressed sensing

Multiple-input multiple-output (MIMO) systems are well suited for millimeter-wave (mmWave) wireless communications where large antenna arrays can be integrated in small form factors due to tiny wavelengths, thereby providing high array gains while supporting spatial multiplexing, beamforming, or antenna diversity. It has been shown that mmWave channels exhibit sparsity due to the limited number of dominant propagation paths, thus compressed sensing techniques can be leveraged to conduct channel estimation at mmWave frequencies. This paper presents a novel approach of constructing beamforming dictionary matrices for sparse channel estimation using the continuous basis pursuit (CBP) concept, and proposes two novel low-complexity algorithms to exploit channel sparsity for adaptively estimating multipath channel parameters in mmWave channels. We verify the performance of the proposed CBP-based beamforming dictionary and the two algorithms using a simulator built upon a three-dimensional mmWave statistical spatial channel model, NYUSIM, that is based on real-world propagation measurements. Simulation results show that the CBP-based dictionary offers substantially higher estimation accuracy and greater spectral efficiency than the grid-based counterpart introduced by previous researchers, and the algorithms proposed here render better performance but require less computational effort compared with existing algorithms.
Coverage metrics for requirements-based testing

In black-box testing, one is interested in creating a suite of tests from requirements that adequately exercise the behavior of a software system without regard to the internal structure of the implementation. In current practice, the adequacy of black box test suites is inferred by examining coverage on an executable artifact, either source code or a software model.In this paper, we define structural coverage metrics directly on high-level formal software requirements. These metrics provide objective, implementation-independent measures of how well a black-box test suite exercises a set of requirements. We focus on structural coverage criteria on requirements formalized as LTL properties and discuss how they can be adapted to measure finite test cases. These criteria can also be used to automatically generate a requirements-based test suite. Unlike model or code-derived test cases, these tests are immediately traceable to high-level requirements. To assess the practicality of our approach, we apply it on a realistic example from the avionics domain.
Understanding and Improving Convolutional Neural Networks via Concatenated Rectified Linear Units

Recently, convolutional neural networks (CNNs) have been used as a powerful tool to solve many problems of machine learning and computer vision. In this paper, we aim to provide insight on the property of convolutional neural networks, as well as a generic method to improve the performance of many CNN architectures. Specifically, we first examine existing CNN models and observe an intriguing property that the filters in the lower layers form pairs (i.e., filters with opposite phase). Inspired by our observation, we propose a novel, simple yet effective activation scheme called concatenated ReLU (CRelu) and theoretically analyze its reconstruction property in CNNs. We integrate CRelu into several state-of-the-art CNN architectures and demonstrate improvement in their recognition performance on CIFAR-10/100 and ImageNet datasets with fewer trainable parameters. Our results suggest that better understanding of the properties of CNNs can lead to significant performance improvement with a simple modification.
Improving Trajectory Modelling for DNN-Based Speech Synthesis by Using Stacked Bottleneck Features and Minimum Generation Error Training

We propose two novel techniques---stacking bottleneck features and minimum generation error (MGE) training criterion---to improve the performance of deep neural network (DNN)-based speech synthesis. The techniques address the related issues of frame-by-frame independence and ignorance of the relationship between static and dynamic features, within current typical DNN-based synthesis frameworks. Stacking bottleneck features, which are an acoustically informed linguistic representation, provides an efficient way to include more detailed linguistic context at the input. The MGE training criterion minimises overall output trajectory error across an utterance, rather than minimising the error per frame independently, and thus takes into account the interaction between static and dynamic features. The two techniques can be easily combined to further improve performance. We present both objective and subjective results that demonstrate the effectiveness of the proposed techniques. The subjective results show that combining the two techniques leads to significantly more natural synthetic speech than from conventional DNN or long short-term memory recurrent neural network systems.
Handbook of PI and PID Controller Tuning Rules

The first controllers with proportional , integral, and derivative (PID) feedback control action became commercially available during the 1930s. The 1940s saw widespread acceptance in industry of pneumatic PID controllers, and their electronic counterparts entered the market in the 1950s. Digital hardware has been routinely used since the 1980s with significant impact on process control. Even several decades after three-mode controllers were introduced, the vast majority of controllers used in the chemical process industry are based on PI/PID models . The popularity of these controllers has led to research on tuning methods, resulting in hundreds of publications on this topic [2]–[9]. Ziegler-Nichols tuning relations [10] and Cohen-Coon tuning rules  are among the earliest published methods. Tuning relations based on error criteria [7], as well as more recent model-based tuning rules such as internal model control (IMC)  and direct synthesis [13], offer improvements over earlier tuning methods. Tuning rules also exist for unstable processes [14] as well as for tuning in the presence of plant-model mismatch . Despite the numerous approaches available for controller tuning, surveys indicate that poorly tuned control loops are abundant in industry [16]. In fact, some control loops are not properly tuned when they are implemented, while other loops are not updated sufficiently often [16]. This situation is motivation for O'Dwyer's book, which provides a comprehensive summary of PI and PID controller tuning relations published over the last six decades. Special attention is given to the use of a consistent notation for these tuning methods. Handbook of PI and PID Controller Tuning Rules is structured into five chapters and two appendices. Chapter 1 provides a general introduction to the topic, while Chapter 2 presents background material needed for using the controller tuning tables presented in Chapters 3 and 4. The book concludes in Chapter 5 with a discussion of performance and robustness issues. A more detailed description of the contents of the book is given below. Chapter 2 presents PI and PID controller structures that appear in the literature as well as in process control equipment. A list of process models with time delay provides the foundation for defining the tuning rules in the remainder of the book. The controller tuning rules for PI and PID controllers are presented in Chapters 3 and 4, respectively. Before a controller can be tuned, it is necessary to specify the structure of the controller as well as the …
Defeating Image Obfuscation with Deep Learning

We demonstrate that modern image recognition methods based on artificial neural networks can recover hidden information from images protected by various forms of obfuscation. The obfuscation techniques considered in this paper are mosaicing (also known as pixelation), blurring (as used by YouTube), and P3, a recently proposed system for privacy-preserving photo sharing that encrypts the significant JPEG coefficients to make images unrecognizable by humans. We empirically show how to train artificial neural networks to successfully identify faces and recognize objects and handwritten digits even if the images are protected using any of the above obfuscation techniques.
Design and implementation a specific grid-tie inverter for an agent-based microgrid

Grid-tie inverters are used to convert DC power into AC power for connection to an existing electrical grid and are key components in a microgrid system. This paper discusses the design and implementation of a grid-tie inverter for connecting renewable resources such as solar arrays, wind turbines, and energy storage to the AC grid, in a laboratory microgrid system while also controlling real and reactive power flows. The Atmel EVK1100 with an AVR32UC3A0512 microcontroller, will be used to coordinate all of the different functions of this grid-tie inverter. The EVK1100 will communicate with Rockwell PLCs via Ethernet. The PLCs are part of the communication, control and sensing network of the microgrid system.
ET: events from tweets

Social media sites such as Twitter and Facebook have emerged as popular tools for people to express their opinions on various topics. The large amount of data provided by these media is extremely valuable for mining trending topics and events. In this paper, we build an efficient, scalable system to detect events from tweets (ET). Our approach detects events by exploring their textual and temporal components. ET does not require any target entity or domain knowledge to be specified; it automatically detects events from a set of tweets. The key components of ET are (1) an extraction scheme for event representative keywords, (2) an efficient storage mechanism to store their appearance patterns, and (3) a hierarchical clustering technique based on the common co-occurring features of keywords. The events are determined through the hierarchical clustering process. We evaluate our system on two data-sets; one is provided by VAST challenge 2011, and the other published by US based users in January 2013. Our results show that we are able to detect events of relevance efficiently.
PreSET: Improving performance of phase change memories by exploiting asymmetry in write times

Phase Change Memory (PCM) is a promising technology for building future main memory systems. A prominent characteristic of PCM is that it has write latency much higher than read latency. Servicing such slow writes causes significant contention for read requests. For our baseline PCM system, the slow writes increase the effective read latency by almost 2X, causing significant performance degradation.
 This paper alleviates the problem of slow writes by exploiting the fundamental property of PCM devices that writes are slow only in one direction (SET operation) and are almost as fast as reads in the other direction (RESET operation). Therefore, a write operation to a line in which all memory cells have been SET prior to the write, will incur much lower latency. We propose PreSET, an architectural technique that leverages this property to pro-actively SET all the bits in a given memory line well in advance of the anticipated write to that memory line. Our proposed design initiates a PreSET request for a memory line as soon as that line becomes dirty in the cache, thereby allowing a large window of time for the PreSET operation to complete. Our evaluations show that PreSET is more effective and incurs lower storage overhead than previously proposed write cancellation techniques. We also describe static and dynamic throttling schemes to limit the rate of PreSET operations. Our proposal reduces effective read latency from 982 cycles to 594 cycles and increases system performance by 34%, while improving the energy-delay-product by 25%.
The effects of myofascial release with foam rolling on performance.

In the last decade, self-myofascial release has become an increasingly common modality to supplement traditional methods of massage, so a masseuse is not necessary. However, there are limited clinical data demonstrating the efficacy or mechanism of this treatment on athletic performance. The purpose of this study was to determine whether the use of myofascial rollers before athletic tests can enhance performance. Twenty-six (13 men and 13 women) healthy college-aged individuals (21.56 ± 2.04 years, 23.97 ± 3.98 body mass index, 20.57 ± 12.21 percent body fat) were recruited. The study design was a randomized crossover design in which subject performed a series of planking exercises or foam rolling exercises and then performed a series of athletic performance tests (vertical jump height and power, isometric force, and agility). Fatigue, soreness, and exertion were also measured. A 2 × 2 (trial × gender) analysis of variance with repeated measures and appropriate post hoc was used to analyze the data. There were no significant differences between foam rolling and planking for all 4 of the athletic tests. However, there was a significant difference between genders on all the athletic tests (p ≤ 0.001). As expected, there were significant increases from pre to post exercise during both trials for fatigue, soreness, and exertion (p ≤ 0.01). Postexercise fatigue after foam rolling was significantly less than after the subjects performed planking (p ≤ 0.05). The reduced feeling of fatigue may allow participants to extend acute workout time and volume, which can lead to chronic performance enhancements. However, foam rolling had no effect on performance.
A FPGA-based Parallel Architecture for Scalable High-Speed Packet Classification

Multi-field packet classification is a critical function that enables network routers to support a variety of applications such as firewall processing, Quality of Service differentiation, traffic billing, and other value added services. Explosive growth of Internet traffic requires the future packet classifiers be implemented in hardware. However, most of the existing packet classification algorithms need large amount of memory, which inhibits efficient hardware implementations. This paper exploits the modern FPGA technology and presents a partitioning-based parallel architecture for scalable and high-speed packet classification. We propose a coarse-grained independent sets algorithm and then combine it seamlessly with the cross-producting scheme. After partitioning the original rule set into several coarse-grained independent sets and applying the cross-producting scheme for the remaining rules, the memory requirement is dramatically reduced. Our FPGA implementation results show that our architecture can store 10K real-life rules in a single state-of-the-art FPGA while consuming a small amount of on-chip resources. Post place and route results show that the design sustains 90 Gbps throughput for minimum size (40 bytes) packets, which is more than twice the current backbone network link rate.
Intercomparison of UAV, Aircraft and Satellite Remote Sensing Platforms for Precision Viticulture

Precision Viticulture is experiencing substantial growth thanks to the availability of improved and cost-effective instruments and methodologies for data acquisition and analysis, such as Unmanned Aerial Vehicles (UAV), that demonstrated to compete with traditional acquisition platforms, such as satellite and aircraft, due to low operational costs, high operational flexibility and high spatial resolution of imagery. In order to optimize the use of these technologies for precision viticulture, their technical, scientific and economic performances need to be assessed. The aim of this work is to compare NDVI surveys performed with UAV, aircraft and satellite, to assess the capability of each platform to represent the intra-vineyard vegetation spatial variability. NDVI images of two Italian vineyards were acquired simultaneously from different multi-spectral sensors onboard the OPEN ACCESS Remote Sens. 2015, 7 2972 three platforms, and a spatial statistical framework was used to assess their degree of similarity. Moreover, the pros and cons of each technique were also assessed performing a cost analysis as a function of the scale of application. Results indicate that the different platforms provide comparable results in vineyards characterized by coarse vegetation gradients and large vegetation clusters. On the contrary, in more heterogeneous vineyards, low-resolution images fail in representing part of the intra-vineyard variability. The cost analysis showed that the adoption of UAV platform is advantageous for small areas and that a break-even point exists above five hectares; above such threshold, airborne and then satellite have lower imagery cost.
Network energy saving technologies for green wireless access networks

The energy consumption problem in the mobile industry has become crucial. For the sustainable growth of the mobile industry, energy efficiency (EE) of wireless systems has to be significantly improved. Plenty of efforts have been invested in achieving green wireless communications. This article provides an overview of network energy saving studies currently conducted in the 3GPP LTE standard body. The aim is to gain a better understanding of energy consumption and identify key EE research problems in wireless access networks. Classifying network energy saving technologies into the time, frequency, and spatial domains, the main solutions in each domain are described briefly. As presently the attention is mainly focused on solutions involving a single radio base station, we believe network solutions involving multiple networks/systems will be the most promising technologies toward green wireless access networks.
Multilayered image representation: application to image compression

The main contribution of this work is a new paradigm for image representation and image compression. We describe a new multilayered representation technique for images. An image is parsed into a superposition of coherent layers: piecewise smooth regions layer, textures layer, etc. The multilayered decomposition algorithm consists in a cascade of compressions applied successively to the image itself and to the residuals that resulted from the previous compressions. During each iteration of the algorithm, we code the residual part in a lossy way: we only retain the most significant structures of the residual part, which results in a sparse representation. Each layer is encoded independently with a different transform, or basis, at a different bitrate, and the combination of the compressed layers can always be reconstructed in a meaningful way. The strength of the multilayer approach comes from the fact that different sets of basis functions complement each others: some of the basis functions will give reasonable account of the large trend of the data, while others will catch the local transients, or the oscillatory patterns. This multilayered representation has a lot of beautiful applications in image understanding, and image and video coding. We have implemented the algorithm and we have studied its capabilities.
Multi-Parametric Toolbox 3.0

The Multi-Parametric Toolbox is a collection of algorithms for modeling, control, analysis, and deployment of constrained optimal controllers developed under Matlab. It features a powerful geometric library that extends the application of the toolbox beyond optimal control to various problems arising in computational geometry. The new version 3.0 is a complete rewrite of the original toolbox with a more flexible structure that offers faster integration of new algorithms. The numerical side of the toolbox has been improved by adding interfaces to state of the art solvers and by incorporation of a new parametric solver that relies on solving linear-complementarity problems. The toolbox provides algorithms for design and implementation of real-time model predictive controllers that have been extensively tested.
Handwritten Character Recognition by Alternately Trained Relaxation Convolutional Neural Network

Deep learning methods have recently achieved impressive performance in the area of visual recognition and speech recognition. In this paper, we propose a handwriting recognition method based on relaxation convolutional neural network (R-CNN) and alternately trained relaxation convolutional neural network (ATR-CNN). Previous methods regularize CNN at full-connected layer or spatial-pooling layer, however, we focus on convolutional layer. The relaxation convolution layer adopted in our R-CNN, unlike traditional convolutional layer, does not require neurons within a feature map to share the same convolutional kernel, endowing the neural network with more expressive power. As relaxation convolution sharply increase the total number of parameters, we adopt alternate training in ATR-CNN to regularize the neural network during training procedure. Our previous CNN took the 1st place in ICDAR'13 Chinese Handwriting Character Recognition Competition, while our latest ATR-CNN outperforms our previous one and achieves the state-of-the-art accuracy with an error rate of 3.94%, further narrowing the gap between machine and human observers (3.87%).
Improving Text Segmentation Using Latent Semantic Analysis: A Reanalysis of Choi, Wiemer-Hastings, and Moore (2001)

Choi, Wiemer-Hastings, and Moore (2001) proposed to use Latent Semantic Analysis (LSA) to extract semantic knowledge from corpora in order to improve the accuracy of a text segmentation algorithm. By comparing the accuracy of the very same algorithm, depending on whether or not it takes into account complementary semantic knowledge, they were able to show the benefit derived from such knowledge. In their experiments, semantic knowledge was, however, acquired from a corpus containing the texts to be segmented in the test phase. If this hyper-specificity of the LSA corpus explains the largest part of the benefit, one may wonder if it is possible to use LSA to acquire generic semantic knowledge that can be used to segment new texts. The two experiments reported here show that the presence of the test materials in the LSA corpus has an important effect, but also that the generic semantic knowledge derived from large corpora clearly improves the segmentation accuracy.