nils-herrmann/clean_scirepeval huggingface dataset:
A novel, compact, low-cost, impulse ground-penetrating radar for nondestructive evaluation of pavements

This paper reports on the development of a novel, compact, low-cost, impulse ground-penetrating radar (GPR) and demonstrate its use for nondestructive evaluation of pavement structures. This GPR consists of an ultrashort-monocycle-pulse transmitter (330 ps), an ultrawide-band (UWB) sampling receiver (0-6 GHz), and two UWB antennas (0.2-20 GHz)-completely designed using microwave-integrated circuits with seamless electrical connections between them. An approximate analysis is used to determine the signal loss and power budget. Performance of this GPR has been verified through the measurements of relative permittivity and thicknesses of various samples, and a good agreement between the experimental and theoretical results has been achieved.
Neural Networks for Online Classification of Hand and Finger Movements Using Surface EMG signals

Myoelectric signals (MES) are the electrical manifestation of muscular contractions and they can be used to create myoelectric prosthesis which is able to function with the amputee's muscle movements. This signal recorded at the surface of the skin of the forearm has been exploited to provide recognition of the movement of the hand and finger movements of healthy subject. The objective of the paper is to describe the identification procedure, based on EMG patterns of forearm activity using various neural networks methods and to make a comparison between different intelligent computational methods of identification, which are used in this work. Then an online algorithm for movement identification and classification that utilises the trained neural networks is presented
Locality Sensitive Hashing based deepmatching for optical flow estimation

DeepMatching (DM) is one of the state-of-art matching algorithms to compute quasi-dense correspondences between images. Recent optical flow methods use DeepMatching to find initial image correspondences and achieves outstanding performance. However, the key building block of DeepMatching, the correlation map computation, is time-consuming. In this paper, we propose a new algorithm, LSHDM, which addresses the problem by employing Locality Sensitive Hashing (LSH) to DeepMatching. The computational complexity is greatly reduced for the correlation map computation step. Experiments show that image matching can be accelerated by our approach in ten times or more compared to DeepMatching, while retaining comparable accuracy for optical flow estimation.
Visualizing the Hidden Activity of Artificial Neural Networks

In machine learning, pattern classification assigns high-dimensional vectors (observations) to classes based on generalization from examples. Artificial neural networks currently achieve state-of-the-art results in this task. Although such networks are typically used as black-boxes, they are also widely believed to learn (high-dimensional) higher-level representations of the original observations. In this paper, we propose using dimensionality reduction for two tasks: visualizing the relationships between learned representations of observations, and visualizing the relationships between artificial neurons. Through experiments conducted in three traditional image classification benchmark datasets, we show how visualization can provide highly valuable feedback for network designers. For instance, our discoveries in one of these datasets (SVHN) include the presence of interpretable clusters of learned representations, and the partitioning of artificial neurons into groups with apparently related discriminative roles.
Optimization of electrostatic separation Processes using response surface modeling

The efficiency of electrostatic separation processes depends on a multitude of factors, including the characteristics of the granular mixtures to be sorted, the feed rate, the configuration of the electrode system, the applied high voltage, and the environmental conditions. The possibility of optimizing the operation of industrial electrostatic separators using rather simple computed-assisted experimental design techniques has already been demonstrated. The aim of the present work is to analyze the peculiarities of application of a more sophisticated group of response surface experimental design techniques that make use of quadratic functions for modeling the electrostatic separation process. One unique contribution to this work is to consider the economic value of the process in addition to the technical result. The 11 electrostatic separation tests, corresponding to a central composite design, were carried out on samples of chopped electric wire wastes. The CARPCO laboratory roll-type electrostatic separator employed for this study enabled a rigorous control of two factors: the applied high-voltage level and the speed of the rotating roll electrode. The objective was to maximize the benefits from the recycling of both constituents of the binary copper-polyvinyl chloride granular mixture. The optimum operating conditions computed with the quadratic model derived from the experimental results were in good agreement with the data of pilot-plant tests. Thus, the highest extraction of useful materials was obtained at high voltage and low speed, while the optimum conditions for greatest economic value were found to be high voltage and high speed. The response surface methodology can be easily applied to most of the industrial applications of electrostatic separation technologies.
Parsing as Language Modeling

We recast syntactic parsing as a language modeling problem and use recent advances in neural network language modeling to achieve a new state of the art for constituency Penn Treebank parsing — 93.8 F1 on section 23, using 2-21 as training, 24 as development, plus tri-training. When trees are converted to Stanford dependencies, UAS and LAS are 95.9% and 94.1%.
Context-Dependent Pre-Trained Deep Neural Networks for Large-Vocabulary Speech Recognition

We propose a novel context-dependent (CD) model for large-vocabulary speech recognition (LVSR) that leverages recent advances in using deep belief networks for phone recognition. We describe a pre-trained deep neural network hidden Markov model (DNN-HMM) hybrid architecture that trains the DNN to produce a distribution over senones (tied triphone states) as its output. The deep belief network pre-training algorithm is a robust and often helpful way to initialize deep neural networks generatively that can aid in optimization and reduce generalization error. We illustrate the key components of our model, describe the procedure for applying CD-DNN-HMMs to LVSR, and analyze the effects of various modeling choices on performance. Experiments on a challenging business search dataset demonstrate that CD-DNN-HMMs can significantly outperform the conventional context-dependent Gaussian mixture model (GMM)-HMMs, with an absolute sentence accuracy improvement of 5.8% and 9.2% (or relative error reduction of 16.0% and 23.2%) over the CD-GMM-HMMs trained using the minimum phone error rate (MPE) and maximum-likelihood (ML) criteria, respectively.
Hybrid Bidirectional Wireless EV Charging System Tolerant to Pad Misalignment

Electric vehicles (EVs) are becoming increasingly popular as a means of future transport for sustainable living. However, wireless charging of EVs poses a number of challenges related to interoperability, safety, pad misalignment, etc. In particular, pad misalignments invariably cause changes in system parameters which in turn lead to increase in losses as well as reduction in power throughput, making the charging process long and inefficient. Consequently, wireless charging systems that are less sensitive to pad misalignments have become preferable. This paper, therefore, presents a hybrid wireless power transfer (WPT) system that charges EVs at constant rate despite large misalignments between charging pads. The proposed charging system uses a combination of two different resonant networks to realize a constant and efficient charging process. A mathematical model is also developed, showing as to how the two resonant networks can be combined to compensate for pad misalignments. To demonstrate the validity of the proposed concept as well as the accuracy of the mathematical model, theoretical performance is compared with both simulations and experimental results of a prototype 3.3 kW hybrid bidirectional WPT system. Results clearly indicate that the proposed hybrid WPT system is efficient and offers a constant charging profile over a wide range of spatial (three-dimensional) pad misalignments.
Towards Opinion Summarization of Customer Reviews

In recent years, the number of texts has grown rapidly. For example, most reviewbased portals, like Yelp or Amazon, contain thousands of user-generated reviews. It is impossible for any human reader to process even the most relevant of these documents. The most promising tool to solve this task is a text summarization. Most existing approaches, however, work on small, homogeneous, English datasets, and do not account to multi-linguality, opinion shift, and domain effects. In this paper, we introduce our research plan to use neural networks on user-generated travel reviews to generate summaries that take into account shifting opinions over time. We outline future directions in summarization to address all of these issues. By resolving the existing problems, we will make it easier for users of review-sites to make more informed decisions.
ValenTo: Sentiment Analysis of Figurative Language Tweets with Irony and Sarcasm

This paper describes the system used by the ValenTo team in the Task 11, Sentiment Analysis of Figurative Language in Twitter, at SemEval 2015. Our system used a regression model and additional external resources to assign polarity values. A distinctive feature of our approach is that we used not only wordsentiment lexicons providing polarity annotations, but also novel resources for dealing with emotions and psycholinguistic information. These are important aspects to tackle in figurative language such as irony and sarcasm, which were represented in the dataset. The system also exploited novel and standard structural features of tweets. Considering the different kinds of figurative language in the dataset our submission obtained good results in recognizing sentiment polarity in both ironic and sarcastic tweets.
TROIKA: A General Framework for Heart Rate Monitoring Using Wrist-Type Photoplethysmographic Signals During Intensive Physical Exercise

Heart rate monitoring using wrist-type photoplethysmographic signals during subjects' intensive exercise is a difficult problem, since the signals are contaminated by extremely strong motion artifacts caused by subjects' hand movements. So far few works have studied this problem. In this study, a general framework, termed TROIKA, is proposed, which consists of signal decomposiTion for denoising, sparse signal RecOnstructIon for high-resolution spectrum estimation, and spectral peaK trAcking with verification. The TROIKA framework has high estimation accuracy and is robust to strong motion artifacts. Many variants can be straightforwardly derived from this framework. Experimental results on datasets recorded from 12 subjects during fast running at the peak speed of 15 km/h showed that the average absolute error of heart rate estimation was 2.34 beat per minute, and the Pearson correlation between the estimates and the ground truth of heart rate was 0.992. This framework is of great values to wearable devices such as smartwatches which use PPG signals to monitor heart rate for fitness.
Inband Full-duplex Wireless Communications for Dynamic Spectrum Sharing Systems

Over the last decade, there has been an exponential growth in the wireless demand, driven by an increase in the number of devices, users, and applications. Combined with an increase in the number of connections (due to Internet of things or IoT) and the spike in mobile video, this growth led to a spectrum crisis. To face this problem, various solutions have been proposed, including more cell densification and higher spectrum efficiency. Industry and academia are gearing to develop new wireless technologies such as fullduplex (FD) communications and massive multiple-input and multiple-output (MIMO) to enhance network performance and improve user experience. To solve the aforementioned spectrum crisis, self-interference-suppression (SIS) techniques have been developed in the previous few years to enable inband FD communications (i.e., simultaneous transmission and reception over the same channel). Traditionally, bidirectional communications can be achieved by separating the forward and reverse links in time, frequency, or code. The challenge in achieving simultaneous transmission and reception over the same band is that the received power of the intended signal is much smaller than the power of the transmitted signal generated at the same node. While this node is receiving, its own transmission is considered as self-interference, which could overwhelm the receiver and prevent correct decoding. Inspired by the recent developments in FD communications, in this dissertation, we incorporate SIS techniques in the design of wireless systems, specifically targeting dynamic spectrum sharing systems. First, we consider an opportunistic spectrum access (OSA) network in which secondary users (SUs) are equipped with partial/complete SIS,
Multiple Input Energy Harvesting Systems for Autonomous IoT End-Nodes

The Internet-of-Things (IoT) paradigm is under constant development and is being enabled by the latest research work from both industrial and academic communities. Among the many contributions in such diverse areas as sensor manufacturing, network protocols, and wireless communications, energy harvesting techniques stand out as a key enabling technology for the realization of batteryless IoT end-node systems. In this paper, we give an overview of the recent developments in circuit design for ultra-low power management units (PMUs), focusing mainly in the architectures and techniques required for energy harvesting from multiple heterogeneous sources. The paper starts by discussing a general structure for IoT end-nodes and the main characteristics of PMUs for energy harvesting. Then, an overview is given of different published works for multisource power harvesting, observing their main advantages and disadvantages and comparing their performance. Finally, some open areas of research in multisource harvesting are observed and relevant conclusions are given.
Emotions and heart rate while sitting on a chair

New methods for unobtrusive monitoring of computer users' emotion psychophysiology are very much needed in human-computer interaction research. The present aim was to study heart rate changes during emotionally provocative stimulation. Six-second long auditory, visual, and audiovisual emotionally negative, neutral, and positive stimuli were presented to 24 participants. Heart rate responses were measured with a regular office chair embedded with electromechanical film (the EMFi chair) and with traditional earlobe photoplethysmography (PPG). Ratings of the stimuli were also collected. The results showed that the two heart rate measurements were significantly correlated, r = 0.99. In line with other studies the results showed that, in general, heart rate decelerated in response to emotional stimulation and it decelerated the most in response to negative stimuli as compared with responses to positive and neutral stimuli. Especially, emotional stimulation caused significant changes in heart rate at the 6th second from the stimulus onset. We suggest that the EMFi chair could be used in human-computer interaction for unobtrusive measurement of the user's emotional reactions.
Graph cube: on warehousing and OLAP multidimensional networks

We consider extending decision support facilities toward large sophisticated networks, upon which multidimensional attributes are associated with network entities, thereby forming the so-called multidimensional networks. Data warehouses and OLAP (Online Analytical Processing) technology have proven to be effective tools for decision support on relational data. However, they are not well-equipped to handle the new yet important multidimensional networks. In this paper, we introduce Graph Cube, a new data warehousing model that supports OLAP queries effectively on large multidimensional networks. By taking account of both attribute aggregation and structure summarization of the networks, Graph Cube goes beyond the traditional data cube model involved solely with numeric value based group-by's, thus resulting in a more insightful and structure-enriched aggregate network within every possible multidimensional space. Besides traditional cuboid queries, a new class of OLAP queries, crossboid, is introduced that is uniquely useful in multidimensional networks and has not been studied before. We implement Graph Cube by combining special characteristics of multidimensional networks with the existing well-studied data cube techniques. We perform extensive experimental studies on a series of real world data sets and Graph Cube is shown to be a powerful and efficient tool for decision support on large multidimensional networks.
Real-time neuroevolution in the NERO video game

In most modern video games, character behavior is scripted; no matter how many times the player exploits a weakness, that weakness is never repaired. Yet, if game characters could learn through interacting with the player, behavior could improve as the game is played, keeping it interesting. This paper introduces the real-time Neuroevolution of Augmenting Topologies (rtNEAT) method for evolving increasingly complex artificial neural networks in real time, as a game is being played. The rtNEAT method allows agents to change and improve during the game. In fact, rtNEAT makes possible an entirely new genre of video games in which the player trains a team of agents through a series of customized exercises. To demonstrate this concept, the Neuroevolving Robotic Operatives (NERO) game was built based on rtNEAT. In NERO, the player trains a team of virtual robots for combat against other players' teams. This paper describes results from this novel application of machine learning, and demonstrates that rtNEAT makes possible video games like NERO where agents evolve and adapt in real time. In the future, rtNEAT may allow new kinds of educational and training applications through interactive and adapting games.
Exfiltration of information from air-gapped machines using monitor's LED indicator

In this paper we present a new approach for data exfiltration by leaking data from monitor's LED to Smartphone's camera. The new approach may be used by attackers to leak valuable information from the organization as part of an Advanced Persistent Threat (APT). The proof of concept that was developed is described in the paper followed by a description of an experiment that demonstrates that practically people are not aware of the attack. We propose ways that will facilitate the detection of such threats and some possible countermeasures.
WebMate: Generating Test Cases for Web 2.0

Web applications are everywhere—well tested web applications however are in short supply. The mixture of JavaScript, HTML and CSS in a variety of different browsers makes it virtually impossible to apply static analysis techniques. In this setting, systematic testing becomes a real challenge. We present a technique to automatically generate tests for Web 2.0 applications. Our approach systematically explores and tests all distinct functions of a web application. Our prototype implementation WEBMATE handles interfaces as complex as Facebook and is able to cover up to 7 times as much code as existing tools. The only requirements to use WEBMATE are the address of the application and, if necessary, user name and password.
Containers and Virtual Machines at Scale: A Comparative Study

Virtualization is used in data center and cloud environments to decouple applications from the hardware they run on. Hardware virtualization and operating system level virtualization are two prominent technologies that enable this. Containers, which use OS virtualization, have recently surged in interest and deployment. In this paper, we study the differences between the two virtualization technologies. We compare containers and virtual machines in large data center environments along the dimensions of performance, manageability and software development.
 We evaluate the performance differences caused by the different virtualization technologies in data center environments where multiple applications are running on the same servers (multi-tenancy). Our results show that co-located applications can cause performance interference, and the degree of interference is higher in the case of containers for certain types of workloads. We also evaluate differences in the management frameworks which control deployment and orchestration of containers and VMs. We show how the different capabilities exposed by the two virtualization technologies can affect the management and development of applications. Lastly, we evaluate novel approaches which combine hardware and OS virtualization.
Large-scale cluster management at Google with Borg

Google's Borg system is a cluster manager that runs hundreds of thousands of jobs, from many thousands of different applications, across a number of clusters each with up to tens of thousands of machines.
 It achieves high utilization by combining admission control, efficient task-packing, over-commitment, and machine sharing with process-level performance isolation. It supports high-availability applications with runtime features that minimize fault-recovery time, and scheduling policies that reduce the probability of correlated failures. Borg simplifies life for its users by offering a declarative job specification language, name service integration, real-time job monitoring, and tools to analyze and simulate system behavior.
 We present a summary of the Borg system architecture and features, important design decisions, a quantitative analysis of some of its policy decisions, and a qualitative examination of lessons learned from a decade of operational experience with it.
Product Classification in E-Commerce using Distributional Semantics

Product classification is the task of automatically predicting a taxonomy path for a product in a predefined taxonomy hierarchy given a textual product description or title. For efficient product classification we require a suitable representation for a document (the textual description of a product) feature vector and efficient and fast algorithms for prediction. To address the above challenges, we propose a new distributional semantics representation for document vector formation. We also develop a new two-level ensemble approach utilizing (with respect to the taxonomy tree) a path-wise, node-wise and depth-wise classifiers for error reduction in the final product classification. Our experiments show the effectiveness of the distributional representation and the ensemble approach on data sets from a leading e-commerce platform and achieve better results on various evaluation metrics compared to earlier approaches.