nils-herrmann/clean_scirepeval huggingface dataset:
Optimization and implementation of a solid state high voltage pulse generator that produces fast rising nanosecond pulses

In this paper athree-stage pulse generator architecture capable of generating high voltage, high current pulses is reported and system issues are presented. Design choices and system dynamics are explained both qualitatively and quantitatively with discussion sections followed by the presentation of closed-form expressions and numerical analysis that provide insight into the system's operation. Analysis targeted at optimizing performance focuses on diode opening switch pumping, energy efficiency, and compensation of parasitic reactances. A compact system based on these design guidelines has been built to output 8 kV, 5 ns pulses into 50 Ω. Output risetimes below 1 ns have been achieved using two different compression techniques. At only 1.5 kg, this light and compact system shows promise for a variety of pulsed power applications requiring the long lifetime, low jitter performance of a solid state pulse generator that can produce fast, high voltage pulses at high repetition rates.
BRISK: Binary Robust invariant scalable keypoints

Effective and efficient generation of keypoints from an image is a well-studied problem in the literature and forms the basis of numerous Computer Vision applications. Established leaders in the field are the SIFT and SURF algorithms which exhibit great performance under a variety of image transformations, with SURF in particular considered as the most computationally efficient amongst the high-performance methods to date. In this paper we propose BRISK1, a novel method for keypoint detection, description and matching. A comprehensive evaluation on benchmark datasets reveals BRISK's adaptive, high quality performance as in state-of-the-art algorithms, albeit at a dramatically lower computational cost (an order of magnitude faster than SURF in cases). The key to speed lies in the application of a novel scale-space FAST-based detector in combination with the assembly of a bit-string descriptor from intensity comparisons retrieved by dedicated sampling of each keypoint neighborhood.
Code to learn with Scratch? A systematic literature review

The use of computer programming in K-12 spread into schools worldwide in the 70s and 80s of the last century, but it disappeared from the educational landscape in the early 90s. With the development of visual programming languages such as Scratch, this movement has emerged again in recent years, as teachers at all educational levels and from different disciplines consider that the use of programming enhances learning in many subjects and allows students to develop important skills. The systematic literature review presented in this article aims to summarize the results of recent research using programming with Scratch in subjects not related to computing and communications, as well as studies analyzing the kind of skills students develop while learning to code in this environment. Although the analyzed papers provide promising results regarding the use of programming as an educational resource, this review highlights the need to conduct more empirical research in classrooms, using larger samples of students that allow to obtain clear conclusions about the types of learning that could be enhanced through programming.
Intrinsic and Extrinsic Evaluation of Spatiotemporal Text Representations in Twitter Streams

Language in social media is a dynamic system, constantly evolving and adapting, with words and concepts rapidly emerging, disappearing, and changing their meaning. These changes can be estimated using word representations in context, over time and across locations. A number of methods have been proposed to track these spatiotemporal changes but no general method exists to evaluate the quality of these representations. Previous work largely focused on qualitative evaluation, which we improve by proposing a set of visualizations that highlight changes in text representation over both space and time. We demonstrate usefulness of novel spatiotemporal representations to explore and characterize specific aspects of the corpus of tweets collected from European countries over a two-week period centered around the terrorist attacks in Brussels in March 2016. In addition, we quantitatively evaluate spatiotemporal representations by feeding them into a downstream classification task – event type prediction. Thus, our work is the first to provide both intrinsic (qualitative) and extrinsic (quantitative) evaluation of text representations for spatiotemporal trends.
Calibration of Strapdown Magnetometers in the Magnetic Field Domain

This paper presents an algorithm for calibrating strapdown magnetometers in the magnetic field domain. In contrast to the traditional method of compass swinging, which computes a series of heading correction parameters and, thus, is limited to use with two-axis systems, this algorithm estimates magnetometer output errors directly. Therefore, this new algorithm can be used to calibrate a full three-axis magnetometer triad. The calibration algorithm uses an iterated, batch least squares estimator which is initialized using a novel two-step nonlinear estimator. The algorithm is simulated to validate convergence characteristics and further validated on experimental data collected using a magnetometer triad. It is shown that the post calibration residuals are small and result in a system with heading errors on the order of 1 to 2 degrees.
Ensemble Empirical Mode Decomposition: a Noise-Assisted Data Analysis Method

A new Ensemble Empirical Mode Decomposition ( is presented. This new approach consists of sifting an ensemble of white noise-added signal (data) and treats the mean as the final true result. Finite, not infinitesimal, amplitude white noise is necessary to force the ensemble to exhaust all possible solutions in the sifting process, thus making the different scale signals to collate in the proper intrinsic mode functions (IMF) dictated by the dyadic filter banks. As EEMD is a time–space analysis method, the added white noise is averaged out with sufficient number of trials; the only persistent part that survives the averaging process is the component of the signal (original data), which is then treated as the true and more physical meaningful answer. The effect of the added white noise is to provide a uniform reference frame in the time–frequency space; therefore, the added noise collates the portion of the signal of comparable scale in one IMF. With this ensemble mean, one can separate scales naturally without any a priori subjective criterion selection as in the intermittence test for the original EMD algorithm. This new approach utilizes the full advantage of the statistical characteristics of white noise to perturb the signal in its true solution neighborhood, and to cancel itself out after serving its purpose; therefore, it represents a substantial improvement over the original EMD and is a truly noise-assisted data analysis (NADA) method.
Time delay deep neural network-based universal background models for speaker recognition

Recently, deep neural networks (DNN) have been incorporated into i-vector-based speaker recognition systems, where they have significantly improved state-of-the-art performance. In these systems, a DNN is used to collect sufficient statistics for i-vector extraction. In this study, the DNN is a recently developed time delay deep neural network (TDNN) that has achieved promising results in LVCSR tasks. We believe that the TDNN-based system achieves the best reported results on SRE10 and it obtains a 50% relative improvement over our GMM baseline in terms of equal error rate (EER). For some applications, the computational cost of a DNN is high. Therefore, we also investigate a lightweight alternative in which a supervised GMM is derived from the TDNN posteriors. This method maintains the speed of the traditional unsupervised-GMM, but achieves a 20% relative improvement in EER.
Matlab/Pspice hybrid simulation modeling of solar PV cell/module

The power generated by solar photovoltaic (PV) module depends on surrounding irradiance, temperature and shading conditions. Under partial shading conditions (PSC) the power from the PV module can be dramatically reduced and maximum power point tracking (MPPT) control will be affected. This paper presents a hybrid simulation model of PV cell/module and system using Matlab®/Simulink® and Pspice®. The hybrid simulation model includes the solar PV cells and the converter power stage and can be expanded to add MPPT control and other functions. The model is able to simulate both the I-V characteristics curves and the P-V characteristics curves of PV modules under uniform shading conditions (USC) and PSC. The model is used to study different parameters variations effects on the PV array. The developed model is suitable to simulate several homogeneous or/and heterogeneous PV cells or PV panels connected in series and/or in parallel.
Attack models and scenarios for networked control systems

Cyber-secure networked control is modeled, analyzed, and experimentally illustrated in this paper. An attack space defined by the adversary's system knowledge, disclosure, and disruption resources is introduced. Adversaries constrained by these resources are modeled for a networked control system architecture. It is shown that attack scenarios corresponding to replay, zero dynamics, and bias injection attacks can be analyzed using this framework. An experimental setup based on a quadruple-tank process controlled over a wireless network is used to illustrate the attack scenarios, their consequences, and potential counter-measures.
Don't Use Large Mini-Batches, Use Local SGD

Mini-batch stochastic gradient methods are state of the art for distributed training of deep neural networks. In recent years, a push for efficiency for large-scale applications has lead to drastically large mini-batch sizes. However, two significant roadblocks remain for such large-batch variants. On one hand, increasing the number of workers introduces communication bottlenecks, and efficient algorithms need to be able to adapt to the changing computation vs. communication tradeoffs in heterogeneous systems. On the other hand, independent of communication, large-batch variants do not generalize well. We argue that variants of recently proposed local SGD, which performs several update steps on a local model before communicating with other workers can solve both these problems. Our experiments show performance gains in training efficiency, scalability, and adaptivity to the underlying system resources. We propose a variant, postlocal SGD that significantly improves the generalization performance of large batch sizes while reducing communication. Additionally, post-local SGD converges to flatter minima as opposed to large-batch methods, which can be understood by relating of local SGD to noise injection. Thus, local SGD is an enticing alternative to large-batch SGD.
Product Comparison Networks for Competitive Analysis of Online Word-of-Mouth

Enabled by Web 2.0 technologies social media provide an unparalleled platform for consumers to share their product experiences and opinions---through word-of-mouth (WOM) or consumer reviews. It has become increasingly important to understand how WOM content and metrics thereof are related to consumer purchases and product sales. By integrating network analysis with text sentiment mining techniques, we propose product comparison networks as a novel construct, computed from consumer product reviews. To test the validity of these product ranking measures, we conduct an empirical study based on a digital camera dataset from Amazon.com. The results demonstrate significant linkage between network-based measures and product sales, which is not fully captured by existing review measures such as numerical ratings. The findings provide important insights into the business impact of social media and user-generated content, an emerging problem in business intelligence research. From a managerial perspective, our results suggest that WOM in social media also constitutes a competitive landscape for firms to understand and manipulate.
Strangers on Your Phone: Why People Use Anonymous Communication Applications

Anonymity online is important to people at times in their lives. Anonymous communication applications such as Whisper and YikYak enable people to communicate with strangers anonymously through their smartphones. We report results from semi-structured interviews with 18 users of these apps. The goal of our study was to identify why and how people use anonymous apps, their perceptions of their audience and interactions on the apps, and how these apps compare with other online social communities. We present a typology of the content people share, and their motivations for participation in anonymous apps. People share various types of content that range from deep confessions and secrets to lighthearted jokes and momentary feelings. An important driver for participation and posting is to get social validation from others, even though they are anonymous strangers. We also find that participants believe these anonymous apps allow more honesty, openness, and diversity of opinion than they can find elsewhere. Our results provide implications for how anonymity in mobile apps can encourage expressiveness and interaction among users.
Standalone Wearable Driver Drowsiness Detection System in a Smartwatch

Drowsiness while driving is one of the main causes of fatal accidents, especially on monotonous routes such as highways. The goal of this paper is to design a completely standalone, distraction-free, and wearable system for driver drowsiness detection by incorporating the system in a smartwatch. The main objective is to detect the driver's drowsiness level based on the driver behavior derived from the motion data collected from the built-in motion sensors in the smartwatch, such as the accelerometer and the gyroscope. For this purpose, the magnitudes of hand movements are extracted from the motion data and are used to calculate the time, spectral, and phase domain features. The features are selected based on the feature correlation method. Eight features serve as an input to a support vector machine (SVM) classifier. After the SVM training and testing, the highest obtained accuracy was 98.15% (Karolinska sleepiness scale). This user-predefined system can be used by both left-handed and right-handed users, because different SVM models are used for different hands. This is an effective, safe, and distraction-free system for the detection of driver drowsiness.
Wireless patient health monitoring system

This paper presents a wireless system which enables real-time health monitoring of multiple patient(s). In health care centers patient's data such asheart rate needs to be constantly monitored. The proposed system monitors the heart rate and other such data of patient's body. For example heart rate is measured through a Photoplethysmograph. A transmitting module is attached which continuously transmits the encoded serial data using Zigbee module. A receiver unit is placed in doctor's cabin, which receives and decodes the data and continuously displays it on a User interface visible on PC/Laptop. Thus doctor can observe and monitor many patients at the same time. System also continuously monitors the patient(s) data and in case of any potential irregularities, in the condition of a patient, the alarm system connected to the system gives an audio-visual warning signal that the patient of a particular room needs immediate attention. In case, the doctor is not in his chamber, the GSM modem connected to the system also sends a message to all the doctors of that unit giving the room number of the patient who needs immediate care.
Free-Form Image Inpainting with Gated Convolution

We present a novel deep learning based image inpainting system to complete images with free-form masks and inputs. e system is based on gated convolutions learned from millions of images without additional labelling efforts. e proposed gated convolution solves the issue of vanilla convolution that treats all input pixels as valid ones, generalizes partial convolution by providing a learnable dynamic feature selection mechanism for each channel at each spatial location across all layers. Moreover, as free-form masks may appear anywhere in images with any shapes, global and local GANs designed for a single rectangular mask are not suitable. To this end, we also present a novel GAN loss, named SN-PatchGAN, by applying spectral-normalized discriminators on dense image patches. It is simple in formulation, fast and stable in training. Results on automatic image inpainting and user-guided extension demonstrate that our system generates higher-quality and more exible results than previous methods. We show that our system helps users quickly remove distracting objects, modify image layouts, clear watermarks, edit faces and interactively create novel objects in images. Furthermore, visualization of learned feature representations reveals the eectiveness of gated convolution and provides an interpretation of how the proposed neural network lls in missing regions. More high-resolution results and video materials are available at hp://jiahuiyu.com/deepll2.
Cutting circles and polygons from area-minimizing rectangles

A set of circles, rectangles, and convex polygons are to be cut from rectangular design plates to be produced, or from a set of stocked rectangles of known geometric dimensions. The objective is to minimize the area of the design rectangles. The design plates are subject to lower and upper bounds of their widths and lengths. The objects are free of any orientation restrictions. If all nested objects fit into one design or stocked plate the problem is formulated and solved as a nonconvex nonlinear programming problem. If the number of objects cannot be cut from a single plate, additional integer variables are needed to represent the allocation problem leading to a nonconvex mixed integer nonlinear optimization problem. This is the first time that circles and arbitrary convex polygons are treated simultaneously in this context. We present exact mathematical programming solutions to both the design and allocation problem. For small number of objects to be cut we compute globally optimal solutions. One key idea in the developed NLP and MINLP models is to use separating hyperplanes to ensure that rectangles and polygons do not overlap with each other or with the circles. Another important idea used when dealing with several resource rectangles is to develop a model formulation which connects the binary variables only to the variables representing the center of the circles or the vertices of the polytopes but not to the nonoverlap or shape constraints. We support the solution process by symmetry breaking constraints. In addition we compute lower bounds, which are constructed by a relaxed model in which each polygon is replaced by the largest circle fitting into that polygon. We have successfully applied several solution techniques to solve this problem among them the Branch&Reduce Optimization Navigator (BARON) and the LindoGlobal solver called from GAMS, and, as described in Rebennack et al. (2008, ), a column enumeration approach in which the columns represent the assignments. Good feasible solutions are computed within seconds or minutes usually during preprocessing. In most cases they turn out to be globally optimal. For up to 10 circles, we prove global optimality up to a gap of the order of 10 in short time. Cases with a modest number of objects, for instance, 6 circles and 3 rectangles, are also solved in short time to global optimality. For test instances involving non-rectangular polygons it is difficult to obtain small gaps. In such cases we are content to obtain gaps of the order of 10 percent.
Multicell Multiuser Massive MIMO Transmission With Downlink Training and Pilot Contamination Precoding

In this paper, we analyze the spectral efficiency of multicell massive multiple-input-multiple-output (MIMO) systems with downlink training and a new pilot contamination precoding (PCP) scheme. First, we analyze the spectral efficiency of the beamforming training (BT) scheme with maximum-ratio transmission (MRT) precoding. Then, we derive an approximate closed-form expression of the spectral efficiency to find the optimal lengths of uplink and downlink pilots. Simulation results show that the achieved spectral efficiency can be improved due to channel estimation at the user side, but in comparison with a single-cell scenario, the spectral efficiency per cell in multicell scenario degrades because of pilot contamination. We focus on the practical case where the number of base station (BS) antennas is large but still finite and propose the BT and PCP (BT-PCP) transmission scheme to mitigate the pilot contamination with limited cooperation between BSs. We confirm the effectiveness of the proposed BT-PCP scheme with simulation, and we show that the proposed BT-PCP scheme achieves higher spectral efficiency than the conventional PCP method and that the performance gap from the perfect channel state information (CSI) scenario without pilot contamination is small.
Applied Statistics and Probability for Engineers

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A secure IoT architecture for Smart Cities

We present a secure Internet of Things (IoT) architecture for Smart Cities. The large-scale deployment of IoT technologies within a city promises to make city operations efficient while improving quality of life for city inhabitants. Mission-critical Smart City data, captured from and carried over IoT networks, must be secured to prevent cyber attacks that might cripple city functions, steal personal data and inflict catastrophic harm. We present an architecture containing four basic IoT architectural blocks for secure Smart Cities: Black Network, Trusted SDN Controller, Unified Registry and Key Management System. Together, these basic IoT-centric blocks enable a secure Smart City that mitigates cyber attacks beginning at the IoT nodes themselves.
Swarm intelligence based approach for real time UAV team coordination in search operations

This paper proposes swarm intelligence based approach for the real time coordination of groups of UAVs (Unmanned Aerial Vehicles) in tasks where values that are sensed from the aerial platform can be used to qualify the individuals. In particular, as an example application, here we consider environmental monitoring UAV teams. Their function is to monitor an area and when some undesired environmental condition arises, coordinate themselves to find the source as fast as possible. The swarm based algorithm has been extensively tested using a 3D simulation platform and validated with real UAVs flying over an industrial area.