nils-herrmann/clean_scirepeval huggingface dataset:
On the Limits of Coexisting Coverage and Capacity in Multi-RAT Heterogeneous Networks

This paper devises a general modeling and analyzing framework for a heterogeneous wireless network (HetNet) in which several wireless subnetworks coexist and use multiple radio access technologies (multi-RATs). The coexisting coverage and network capacity in such a multi-RAT HetNet are hardly investigated in prior works. To characterize the coexisting interactions in a multi-RAT HetNet, in this paper, we consider a HetNet consisting of <inline-formula> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula> tiers of access points (APs) and two different RATs, RAT-<inline-formula> <tex-math notation="LaTeX">$ \mathsf {L}$ </tex-math></inline-formula> and RAT-<inline-formula> <tex-math notation="LaTeX">$ \mathsf {U}$ </tex-math></inline-formula>, are adopted in the HetNet. RAT-<inline-formula> <tex-math notation="LaTeX">$ \mathsf {L}$ </tex-math></inline-formula> is adopted by the APs in the first <inline-formula> <tex-math notation="LaTeX">$K-1$ </tex-math></inline-formula> tiers and APs in the <inline-formula> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula>th tier only use RAT-<inline-formula> <tex-math notation="LaTeX">$ \mathsf {U}$ </tex-math></inline-formula>. Both noncrossing-RAT and crossing-RAT user association scenarios are considered. In each scenario, the void probability and the channel access probability of the APs in each tier are first found and then the tight lower bounds and their lowest limits on the proposed coexisting coverage and network capacity are derived. We show that multi-RAT networks in general can achieve higher link coverage and capacity by using opportunistic carrier sense multiple access with collision avoidance that avoids/alleviates severe interfering between all coexisting APs. Also, crossing-RAT user association is shown to achieve much higher coexisting coverage and network capacity than noncrossing-RAT user association. Finally, numerical simulations for the LTE-U and WiFi networks coexisting in the HetNet validate our findings.
Automatic Driver Monitoring Using Electrical Impedance Measurement on Steering Wheel

This article deals with the analysis of dependence among the driver's bioelectrical impedance status and surrounding effects, which can negatively influence the driver's attention and thus lead to decrease the driver's safety while driving. Bioelectrical impedance was measured using AD5933 circuit and two conductive silicon electrodes placed on the steering wheel. The measurement was performed on several drivers of various age at a different daytime and in a different grade of traffic. Hereafter acquired data were statistically analyzed and significant values, which affect the measured driver's impedance with given probability were identified. Based on results of the analysis, we proposed an algorithm of software for detection the events that could possibly decrease the driver's safety. The driver's impedance data acquired from the steering wheel is used as a control variable for the detection algorithm.
Category Oriented Task Extraction

With increasing amounts of digital content, users can accomplish complex tasks online, thus making task extraction from query logs an active area of research. Recently, some approaches have proposed entity based extraction of tasks, where they either use entities as features or construct task dictionaries that contain multiple tasks. While text based features do not exploit entities directly, task dictionaries do not provide concise or distinct representation of tasks. We overcome these shortcomings by extracting category oriented tasks by exploiting properties of an existing, publicly available category hierarchy. We evaluate quality of these tasks with implicit, explicit and application based evaluation. Empirical evaluation shows that category based task extraction results in more accurate and useful tasks.
Metamaterial-Based Thin Planar Lens Antenna for Spatial Beamforming and Multibeam Massive MIMO

A metamaterial (MTM)-based thin planar lens antenna is proposed for spatial beamforming and multibeam massive multiple-input multiple-output systems. The antenna consists of a planar lens and a linear array of receive/transmit elements. To lower the insertion and reflection loss, the lens is formed by the two-layered ultrathin MTM-based surface separated with air and fed by substrate integrated waveguide-fed stacked-patch antennas. The effects of the focal-to-diameter ( $f/D$ ) on the power distribution of the lens are investigated to work out a design method. A planar lens antenna fed with seven elements is, for example, designed to operate at 28-GHz bands. The measured results show that the proposed antenna can achieve a scanning coverage of ±27° with a gain tolerance of 3.7 dB and a maximum gain of 24.2 dBi with an aperture efficiency of 24.5% over the operating bandwidth of 26.6–29 GHz. The lens antenna also features the advantages of compact size, low cost, lightweight, simple feeding network, and easy integration with other circuits for the next generation mobile communication and radar systems.
Competitive STDP-Based Spike Pattern Learning

Recently it has been shown that a repeating arbitrary spatiotemporal spike pattern hidden in equally dense distracter spike trains can be robustly detected and learned by a single neuron equipped with spike-timing-dependent plasticity (STDP) (Masquelier, Guyonneau, & Thorpe, 2008). To be precise, the neuron becomes selective to successive coincidences of the pattern. Here we extend this scheme to a more realistic scenario with multiple repeating patterns and multiple STDP neurons listening to the incoming spike trains. These listening neurons are in competition: as soon as one fires, it strongly inhibits the others through lateral connections (one-winner-take-all mechanism). This tends to prevent the neurons from learning the same (parts of the) repeating patterns, as shown in simulations. Instead, the population self-organizes, trying to cover the different patterns or coding one pattern by the successive firings of several neurons, and a powerful distributed coding scheme emerges. Taken together, these results illustrate how the brain could easily encode and decode information in the spike times, a theory referred to as temporal coding, and how STDP could play a key role by detecting repeating patterns and generating selective response to them.
Deep Blind Image Inpainting

Image inpainting is a challenging problem as it needs to fill the information of the corrupted regions. Most of the existing inpainting algorithms assume that the positions of the corrupted regions are known. Different from the existing methods that usually make some assumptions on the corrupted regions, we present an efficient blind image inpainting algorithm to directly restore a clear image from a corrupted input. Our algorithm is motivated by the residual learning algorithm which aims to learn the missing information in corrupted regions. However, directly using existing residual learning algorithms in image restoration does not well solve this problem as little information is available in the corrupted regions. To solve this problem, we introduce an encoder and decoder architecture to capture more useful information and develop a robust loss function to deal with outliers. Our algorithm can predict the missing information in the corrupted regions, thus facilitating the clear image restoration. Both qualitative and quantitative experimental demonstrate that our algorithm can deal with the corrupted regions of arbitrary shapes and performs favorably against state-of-the-art methods.
Hippocampal structural asymmetry in unsuccessful psychopaths

Structural and functional hippocampal abnormalities have been previously reported in institutionalized psychopathic and aggressive populations. This study assessed whether prior findings of a right greater than left (R > L) functional asymmetry in caught violent offenders generalize to the structural domain in unsuccessful, caught psychopaths.



Left and right hippocampal volumes were assessed using structural magnetic resonance imaging (MRI) in 23 control subjects, 16 unsuccessful psychopaths, and 12 successful (uncaught) community psychopaths and transformed into standardized space.



Unsuccessful psychopaths showed an exaggerated structural hippocampal asymmetry (R > L) relative both to successful psychopaths and control subjects (p <.007) that was localized to the anterior region. This effect could not be explained by environmental and diagnostic confounds and constitutes the first brain imaging analysis of successful and unsuccessful psychopaths.



Atypical anterior hippocampal asymmetries in unsuccessful psychopaths may reflect an underlying neurodevelopmental abnormality that disrupts hippocampal-prefrontal circuitry, resulting in affect dysregulation, poor contextual fear conditioning, and insensitivity to cues predicting capture.
Automated Neuropsychological Test Battery (CANTAB) in mild cognitive impairment and in Alzheimer's disease

Neuropsychological deficits, such as poor episodic memory, are consistent features of mild cognitive impairment and also that of early stage of dementia. The aim of the present study was to detect cognitive dysfunction among patients with Alzheimer's disease or with mild cognitive impairment (MCI), which refers to a transitional state between the cognition of normal aeging and mild dementia regarded as a high-risk condition for the development of clinically probable Alzheimer's disease (AD). Computerized tests of memory, attention and executive functions were studied in groups of AD subjects (n=15) and MCI subjects (n=25). On all measures, the performance of the AD group was significantly weaker compared to healthy individuals or to the MCI group. The performance of both the AD and MCI patients in the Paired Associate Learning test was significantly impaired, which may suggest that MCI patients are already in the early stages of the disease.
BERT and PALs: Projected Attention Layers for Efficient Adaptation in Multi-Task Learning

Multi-task learning allows the sharing of useful information between multiple related tasks. In natural language processing several recent approaches have successfully leveraged unsupervised pre-training on large amounts of data to perform well on various tasks, such as those in the GLUE benchmark (Wang et al., 2018a). These results are based on fine-tuning on each task separately. We explore the multi-task learning setting for the recent BERT (Devlin et al., 2018) model on the GLUE benchmark, and how to best add task-specific parameters to a pre-trained BERT network, with a high degree of parameter sharing between tasks. We introduce new adaptation modules, PALs or ‘projected attention layers’, which use a low-dimensional multi-head attention mechanism, based on the idea that it is important to include layers with inductive biases useful for the input domain. By using PALs in parallel with BERT layers, we match the performance of finetuned BERT on the GLUE benchmark with ≈7 times fewer parameters, and obtain state-of-theart results on the Recognizing Textual Entailment dataset.
A Novel Ultra-Wideband (UWB) Bandpass Filter (BPF) With Pairs of Transmission Zeroes

A novel, compact, and highly selective ultra-wideband bandpass filter was developed on a microstrip line and presented in this letter for use in wireless communication applications. The basic filter is composed of five short-circuited stubs separated by nonredundant connecting lines that contribute to the filter selectivity as well. In addition, a cross-coupling between the feed lines (input and output) was introduced which generated new pairs of attenuation poles at each side of the passband. As a result, the filter exhibited extremely sharp rejection skirts around the target passband without the need for increasing the number of transmission line sections. The filter was successfully designed, simulated, and fabricated. The theoretical, EM-simulated, and experimental performance is presented in this work where excellent agreement between them was obtained
Mesh repair with user-friendly topology control

Limitations of current 3D acquisition technology often lead to polygonal meshes exhibiting a number of geometrical and topological defects which prevent them from widespread use. In this paper we present a new method for model repair which takes as input an arbitrary polygonal mesh and outputs a valid two-manifold triangle mesh. Unlike previous work, our method allows users to quickly identify areas with potential topological errors and to choose how to fix them in a userfriendly manner. Key steps of our algorithm include the conversion of the input model into a set of voxels, the use of morphological operators to allow the user to modify the topology of the discrete model, and the conversion of the corrected voxel set back into a two-manifold triangle mesh. Our experiments demonstrate that the proposed algorithm is suitable for repairing meshes of a large class of shapes.
Shining the Floodlights on Mobile Web Tracking — A Privacy Survey

We present the first published large-scale study of mobile web tracking. We compare tracking across five physical and emulated mobile devices with one desktop device as a benchmark. Our crawler is based on FourthParty; however, our architecture avoids clearing state which has the benefit of continual observation of (and by) third-parties. We confirm many intuitive predictions and report a few surprises. The lists of top third-party domains across different categories devices are substantially similar; we found surprisingly few mobile-specific ad networks. The use of JavaScript by tracking domains increases gradually as we consider more powerful devices. We also analyze cookie longevity by device. Finally, we analyze a curious phenomenon of cookies that are used to store information about the user’s browsing history on the client. Mobile tracking appears to be an under-researched area, and this paper is only a first step. We have made our code and data available at http://webtransparency.org/ for others to build on.
Multipath exploitation in through-the-wall radar imaging using sparse reconstruction

Multipath exploitation and compressive sensing (CS) have both been applied independently to through-the-wall radar imaging (TWRI). Fast and efficient data acquisition is desired in scenarios where multipath effects cannot be neglected. Hence, we combine the two methods to achieve good image reconstruction in multipath environments from few spatial and frequency measurements. Ghost targets appear in the scene primarily due to specular reflections from interior walls and multiple reflections within the front wall. Assuming knowledge of the room geometry, we can invert the multipath model and eliminate ghosts by means of CS. We develop effective methods for the reconstruction of stationary scenes, which employ a group sparse CS approach. Additionally, we separate the target and wall contributions to the image by a sparse reconstruction approach joining wall and target models, which allows suppression of the ghosts and increased signal-to-clutter ratio (SCR) at the target locations. Effectiveness of the proposed approach is demonstrated using both simulated and real data.
Vlfeat: an open and portable library of computer vision algorithms

VLFeat is an open and portable library of computer vision algorithms. It aims at facilitating fast prototyping and reproducible research for computer vision scientists and students. It includes rigorous implementations of common building blocks such as feature detectors, feature extractors, (hierarchical) k-means clustering, randomized kd-tree matching, and super-pixelization. The source code and interfaces are fully documented. The library integrates directly with MATLAB, a popular language for computer vision research.
High efficiency Ka-band Gallium Nitride power amplifier MMICs

The design and performance of two high efficiency Ka-band power amplifier MMICs utilizing a 0.15μm GaN HEMT process technology is presented. Measured in-fixture continuous wave (CW) results for the 3-stage balanced amplifier demonstrates up to 11W of output power and 30% power added efficiency (PAE) at 30GHz. The 3-stage single-ended design produced over 6W of output power and up to 34% PAE. The die size for the balanced and single-ended MMICs are 3.24×3.60mm2 and 1.74×3.24mm2 respectively.
INTELIGENT CRACK DETECTING ALGORITHM ON THE CONCRETE CRACK IMAGE USING NEURAL NETWORK

Safety inspection of concrete structures should be strictly carried out since it is closely related with the structural health and reliability. However, it is difficult to find cracks by a visual check for the extremely large structures. So, the development of crack detecting systems has been a significant issue. Final objective of this research is to develop an automatic crack detection system that can analyze the concrete surface and visualize the cracks efficiently. The algorithm is composed of two parts; image processing and image classification. In the first step, cracks are distinguished from background image easily using the filtering, the improved subtraction method, and the morphological operation. The particular data such as the number of pixel and the ratio of the major axis to minor axis for connected pixels area are also extracted. In the second step, the existence of cracks are identified. Backpropagation neural network is used to automate the image classification. Target data values in the training process were generated by inspector’s manual classification. In order to verify the first and second step of the proposed algorithm, the algorithm was tested using real surface images of concrete bridge. Backpropagation neural network was trained using 105 images of concrete structure, and the trained network was tested for new 120 new images. The recognition rate of the crack image was 90% and non-crack image was 92%. This method is useful for non expert inspectors, enabling them to perform crack monitoring tasks effectively.
Privacy-Aware Dynamic Ride Sharing

Dynamic ride sharing is a service that enables shared vehicle rides in real time and on short notice. It can be an effective solution to counter the problem of increasing traffic jams at peak hours in cities. The growing use and popularity of smart phones and GPS-enabled devices provides us with tools required to efficiently implement ride sharing and significantly enhance carpooling. However, privacy and safety concerns are the main obstacles faced when encouraging people to use such a service. In this work, we present “Match Maker,” a negotiation-based model that hides exact location information data for system participants while implementing privacy preserving ride sharing. We use the concept of imprecision (not being precise about location of the user out of set of n locations) and follow the idea of obfuscation, which equates a higher degree of imprecision with a higher degree of privacy. We identify two attack types that could circumvent privacy preserving ride sharing. We compare the Match Maker model with the standard central trusted server model collecting precise location data, which we term eBay model. We provide the first comprehensive approach that integrates privacy, safety and trust in a single model. We present a recursive ellipse-based algorithm to compute an optimal driver path as well as three negotiation strategies for drivers and passengers. We conduct extensive experiments on real road networks and compare the strategies for privacy and effectiveness of ride sharing in terms of traffic load and vehicle km reduction. We show that ride sharing saves between 9% and 21% (on average 12%) of vehicle km if drivers are only prepared to accept slight detours of their usual trips. In the city of Melbourne, with 11.6 million trips a weekday and an average trip length of 10.2 km, this would save 14.2 million km per weekday.
Cuffless blood pressure estimation by error-correcting output coding method based on an aggregation of AdaBoost with a photoplethysmograph sensor

This paper presented a novel cuffless and non-invasive technique of Blood Pressure (BP) estimation with a pattern recognition method by using a Photoplethysmograph (PPG) sensor instead of a cuff. Error-Correcting Output Coding (ECOC) method was adopted as a multi-classifier machine based on an aggregation of general binary classifiers. AdaBoost was applied as binary classifier machine. 368 volunteers participated in the experiment. The estimated Systolic Blood Pressure (SBP) was calculated from their individual information and several features of their Pulse Wave (PW). As a result of the comparison between measured SBP, estimated SBP, MD = 3D-1.2 [mmHg] and SD = 3D11.7 [mmHg] were obtained. Hence, this technique would be helpful to the advance the development of continuous BP monitoring system, since the only one device to monitor BP is smaller than traditional measurements.
Simultaneous Feature and Dictionary Learning for Image Set Based Face Recognition

In this paper, we propose a simultaneous feature and dictionary learning (SFDL) method for image set-based face recognition, where each training and testing example contains a set of face images, which were captured from different variations of pose, illumination, expression, resolution, and motion. While a variety of feature learning and dictionary learning methods have been proposed in recent years and some of them have been successfully applied to image set-based face recognition, most of them learn features and dictionaries for facial image sets individually, which may not be powerful enough because some discriminative information for dictionary learning may be compromised in the feature learning stage if they are applied sequentially, and vice versa. To address this, we propose a SFDL method to learn discriminative features and dictionaries simultaneously from raw face pixels so that discriminative information from facial image sets can be jointly exploited by a one-stage learning procedure. To better exploit the nonlinearity of face samples from different image sets, we propose a deep SFDL (D-SFDL) method by jointly learning hierarchical non-linear transformations and class-specific dictionaries to further improve the recognition performance. Extensive experimental results on five widely used face data sets clearly shows that our SFDL and D-SFDL achieve very competitive or even better performance with the state-of-the-arts.
A Dynamic Self-Structuring Neural Network

Creating a neural network based classification model is commonly accomplished using the trial and error technique. However, the trial and error structuring method have several difficulties such as time and availability of experts. In this article, an algorithm that simplifies structuring neural network classification models has been proposed. The algorithm aims at creating a large enough structure to learn models from the training dataset that can be generalised well on the testing dataset. Our algorithm dynamically tunes the structure parameters during the training phase aiming to derive accurate non-overfitting classifiers. The proposed algorithm has been applied to phishing websites classification problem and it shows competitive results with respect to various evaluation measures such as Harmonic Mean (F1-score), precision, accuracy, etc. KeywordsClassification, Neural Network, Phishing, constructive, pruning.