nils-herrmann/clean_scirepeval huggingface dataset:
Unsupervised Deep Learning for Optical Flow Estimation

Recent work has shown that optical flow estimation can be formulated as a supervised learning problem. Moreover, convolutional networks have been successfully applied to this task. However, supervised flow learning is obfuscated by the shortage of labeled training data. As a consequence, existing methods have to turn to large synthetic datasets for easily computer generated ground truth. In this work, we explore if a deep network for flow estimation can be trained without supervision. Using image warping by the estimated flow, we devise a simple yet effective unsupervised method for learning optical flow, by directly minimizing photometric consistency. We demonstrate that a flow network can be trained from endto-end using our unsupervised scheme. In some cases, our results come tantalizingly close to the performance of methods trained with full supervision.
Serious Games for education and training

Serious Games (SGs) are gaining an ever increasing interest for education and training. Exploiting the latest simulation and visualization technologies, SGs are able to contextualize the player’s experience in challenging, realistic environments, supporting situated cognition. However, we still miss methods and tools for effectively and deeply infusing pedagogy and instruction inside digital games. After presenting an overview of the state of the art of the SG taxonomies, the paper introduces the pedagogical theories and models most relevant to SGs and their implications on SG design. We also present a schema for a proper integration of games in education, supporting different goals in different steps of a formal education process. By analyzing a set of well-established SGs and formats, the paper presents the main mechanics and models that are being used in SG designs, with a particular focus on assessment, feedback and learning analytics. An overview of tools and models for SG design is also presented. Finally, based on the performed analysis, indications for future research in the field are provided.
Terahertz Imaging Systems With Aperture Synthesis Techniques

This paper presents the research and development of two terahertz imaging systems based on photonic and electronic principles, respectively. As part of this study, a survey of ongoing research in the field of terahertz imaging is provided focusing on security applications. Existing terahertz imaging systems are reviewed in terms of the employed architecture and data processing strategies. Active multichannel measurement method is found to be promising for real-time applications among the various terahertz imaging techniques and is chosen as a basis for the imaging instruments presented in this paper. An active system operation allows for a wide dynamic range, which is important for image quality. The described instruments employ a multichannel high-sensitivity heterodyne architecture and aperture filling techniques, with close to real-time image acquisition time. In the case of the photonic imaging system, mechanical scanning is completely obsolete. We show 2-D images of simulated 3-D image data for both systems. The reconstruction algorithms are suitable for 3-D real-time operation, only limited by mechanical scanning.
Verbal fluency and repetition skills in healthy older Spanish-English bilinguals.

The influence of bilingualism on cognitive test performance in older adults has received limited attention in the neuropsychology literature. The aim of this study was to examine the impact of bilingualism on verbal fluency and repetition tests in older Hispanic bilinguals. Eighty-two right-handed participants (28 men and 54 women) with a mean age of 61.76 years (SD = 9.30; range = 50-84) and a mean educational level of 14.8 years (SD = 3.6; range 2-23) were selected. Forty-five of the participants were English monolinguals, 18 were Spanish monolinguals, and 19 were Spanish-English bilinguals. Verbal fluency was tested by electing a verbal description of a picture and by asking participants to generate words within phonemic and semantic categories. Repetition was tested using a sentence-repetition test. The bilinguals' test scores were compared to English monolinguals' and Spanish monolinguals' test scores. Results demonstrated equal performance of bilingual and monolingual participants in all tests except that of semantic verbal fluency. Bilinguals who learned English before age 12 performed significantly better on the English repetition test and produced a higher number of words in the description of a picture than the bilinguals who learned English after age 12. Variables such as task demands, language interference, linguistic mode, and level of bilingualism are addressed in the Discussion section.
Road marking detection using LIDAR reflective intensity data and its application to vehicle localization

A correct perception of road signalizations is required for autonomous cars to follow the traffic codes. Road marking is a signalization present on road surfaces and commonly used to inform the correct lane cars must keep. Cameras have been widely used for road marking detection, however they are sensible to environment illumination. Some LIDAR sensors return infrared reflective intensity information which is insensible to illumination condition. Existing road marking detectors that analyzes reflective intensity data focus only on lane markings and ignores other types of signalization. We propose a road marking detector based on Otsu thresholding method that make possible segment LIDAR point clouds into asphalt and road marking. The results show the possibility of detecting any road marking (crosswalks, continuous lines, dashed lines). The road marking detector has also been integrated with Monte Carlo localization method so that its performance could be validated. According to the results, adding road markings onto curb maps lead to a lateral localization error of 0.3119 m.
Cross-language web page classification via dual knowledge transfer using nonnegative matrix tri-factorization

The lack of sufficient labeled Web pages in many languages, especially for those uncommonly used ones, presents a great challenge to traditional supervised classification methods to achieve satisfactory Web page classification performance. To address this, we propose a novel Nonnegative Matrix Tri-factorization (NMTF) based Dual Knowledge Transfer (DKT) approach for cross-language Web page classification, which is based on the following two important observations. First, we observe that Web pages for a same topic from different languages usually share some common semantic patterns, though in different representation forms. Second, we also observe that the associations between word clusters and Web page classes are a more reliable carrier than raw words to transfer knowledge across languages. With these recognitions, we attempt to transfer knowledge from the auxiliary language, in which abundant labeled Web pages are available, to target languages, in which we want classify Web pages, through two different paths: word cluster approximations and the associations between word clusters and Web page classes. Due to the reinforcement between these two different knowledge transfer paths, our approach can achieve better classification accuracy. We evaluate the proposed approach in extensive experiments using a real world cross-language Web page data set. Promising results demonstrate the effectiveness of our approach that is consistent with our theoretical analyses.
Lightweight Cybersecurity Schemes Using Elliptic Curve Cryptography in Publish-Subscribe fog Computing

The rapid growth of connected things across the globe has been brought about by the deployment of the Internet of things (IoTs) at home, in organizations and industries. The innovation of smart things is envisioned through various protocols, but the most prevalent protocols are pub-sub protocols such as Message Queue Telemetry Transport (MQTT) and Advanced Message Queuing Protocol (AMQP). An emerging paradigm of communication architecture for IoTs support is Fog computing in which events are processed near to the place they occur for efficient and fast response time. One of the major concerns in the adoption of Fog computing based publishsubscribe protocols for the Internet of things is the lack of security mechanisms because the existing security protocols such as SSL/TSL have a large overhead of computations, storage and communications. To address these issues, we propose a secure, Fog computing based publish-subscribe lightweight protocol using Elliptic Curve Cryptography (ECC) for the Internet of Things. We present analytical proofs and results for resource efficient security, comparing to the existing protocols of traditional Internet.
Tactile acuity is enhanced in blindness.

Functional imaging studies in blind subjects have shown tactile activation of cortical areas that normally subserve vision, but whether blind people have enhanced tactile acuity has long been controversial. We compared the passive tactile acuity of blind and sighted subjects on a fully automated grating orientation task and used multivariate Bayesian data analysis to determine predictors of acuity. Acuity was significantly superior in blind subjects, independently of the degree of childhood vision, light perception level, or Braille reading. Acuity was strongly dependent on the force of contact between the stimulus surface and the skin, declined with subject age, and was better in women than in men. Despite large intragroup variability, the difference between blind and sighted subjects was highly significant: the average blind subject had the acuity of an average sighted subject of the same gender but 23 years younger. The results suggest that crossmodal plasticity may underlie tactile acuity enhancement in blindness.
A 500 nA quiescent, 100 mA maximum load CMOS low-dropout regulator

Ultra low quiescent, wide output current range low-dropout regulators (LDO) are in high demand in portable applications to extend battery lives. This paper presents a 500 nA quiescent, 0 to 100 mA load, 3.5–7 V input to 3 V output LDO in a digital 0.35 μm 2P3M CMOS technology. The challenges in designing with nano-ampere of quiescent current are discussed, namely the leakage, the parasitics, and the excessive DC gain. CMOS super source follower voltage buffer and input excessive gain reduction are then proposed. The LDO is internally compensated using Ahuja method with a minimum phase margin of 55° across all load conditions. The maximum transient voltage variation is less than 150 and 75 mV when used with 1 and 10 μF external capacitor. Compared with existing work, this LDO achieves the best transient flgure-of-merit with close to best dynamic current efficiency (maximum-to-quiescent current ratio).
NIFTY: a system for large scale information flow tracking and clustering

The real-time information on news sites, blogs and social networking sites changes dynamically and spreads rapidly through the Web. Developing methods for handling such information at a massive scale requires that we think about how information content varies over time, how it is transmitted, and how it mutates as it spreads.
 We describe the News Information Flow Tracking, Yay! (NIFTY) system for large scale real-time tracking of "memes" - short textual phrases that travel and mutate through the Web. NIFTY is based on a novel highly-scalable incremental meme-clustering algorithm that efficiently extracts and identifies mutational variants of a single meme. NIFTY runs orders of magnitude faster than our previous Memetracker system, while also maintaining better consistency and quality of extracted memes.
 We demonstrate the effectiveness of our approach by processing a 20 terabyte dataset of 6.1 billion blog posts and news articles that we have been continuously collecting for the last four years. NIFTY extracted 2.9 billion unique textual phrases and identified more than 9 million memes. Our meme-tracking algorithm was able to process the entire dataset in less than five days using a single machine. Furthermore, we also provide a live deployment of the NIFTY system that allows users to explore the dynamics of online news in near real-time.
Optimizing two-pass connected-component labeling algorithms

We present two optimization strategies to improve connected-component labeling algorithms. Taking together, they form an efficient two-pass labeling algorithm that is fast and theoretically optimal. The first optimization strategy reduces the number of neighboring pixels accessed through the use of a decision tree, and the second one streamlines the union-find algorithms used to track equivalent labels. We show that the first strategy reduces the average number of neighbors accessed by a factor of about 2. We prove our streamlined union-find algorithms have the same theoretical optimality as the more sophisticated ones in literature. This result generalizes an earlier one on using union-find in labeling algorithms by Fiorio and Gustedt (). In tests, the new union-find algorithms improve a labeling algorithm by a factor of 4 or more. Through analyses and experiments, we demonstrate that our new two-pass labeling algorithm scales linearly with the number of pixels in the image, which is optimal in computational complexity theory. Furthermore, the new labeling algorithm outperforms the published labeling algorithms irrespective of test platforms. In comparing with the fastest known labeling algorithm for two-dimensional (2D) binary images called contour tracing algorithm, our new labeling algorithm is up to ten times faster than the contour tracing program distributed by the original authors.
Multi-target multi-object tracking, sensor fusion of radar and infrared

This paper presents algorithms and techniques for single-sensor tracking and multi-sensor fusion of infrared and radar data. The results show that fusing radar data with infrared data considerably increases detection range, reliability and accuracy of the object tracking. This is mandatory for further development of driver assistance systems. Using multiple model filtering for sensor fusion applications helps to capture the dynamics of maneuvering objects while still achieving smooth object tracking for not maneuvering objects. This is important when safety and comfort systems have to make use of the same sensor information. Comfort systems generally require smoothly filtered data whereas for safety systems it is crucial to capture maneuvers of other road users as fast as possible. Multiple model filtering and probabilistic data association techniques are presented and all presented algorithms are tested in real-time on standard PC systems.
Design and implementation of an active multibeam antenna system with 64 RF channels and 256 antenna elements for massive MIMO application in 5G wireless communications

This paper focuses on the design and implementation of an active multibeam antenna system for massive MIMO applications in 5G wireless communications. The highly integrated active multibeam antenna system is designed and implemented at 5.8 GHz with 64 RF Channels and 256 antenna elements. The 64-channel highly integrated active multibeam antenna system provides a verification platform for digital beamforming algorithm and massive MIMO channel estimation for next generation wireless communications.
Interventive interviewing: Part III. Intending to ask lineal, circular, strategic, or reflexive questions?

Every question asked by a therapist may be seen to embody some intent and to arise from certain assumptions. Many questions are intended to orient the therapist to the client's situation and experiences; others are asked primarily to provoke therapeutic change. Some questions are based on lineal assumptions about the phenomena being addressed; others are based on circular assumptions. The differences among these questions are not trivial. They tend to have dissimilar effects. This article explores these issues and offers a framework for distinguishing four major groups of questions. The framework may be used by therapists to guide their decision making about what kinds of questions to ask, and by researchers to study different interviewing styles.
Single Image 3D without a Single 3D Image

Do we really need 3D labels in order to learn how to predict 3D? In this paper, we show that one can learn a mapping from appearance to 3D properties without ever seeing a single explicit 3D label. Rather than use explicit supervision, we use the regularity of indoor scenes to learn the mapping in a completely unsupervised manner. We demonstrate this on both a standard 3D scene understanding dataset as well as Internet images for which 3D is unavailable, precluding supervised learning. Despite never seeing a 3D label, our method produces competitive results.
Downlink Coordinated Multi-Point with Overhead Modeling in Heterogeneous Cellular Networks

Coordinated multi-point (CoMP) communication is attractive for heterogeneous cellular networks (HCNs) for interference reduction. However, previous approaches to CoMP face two major hurdles in HCNs. First, they usually ignore the inter-cell overhead messaging delay, although it results in an irreducible performance bound. Second, they consider the grid or Wyner model for base station locations, which is not appropriate for HCN BS locations which are numerous and haphazard. Even for conventional macrocell networks without overlaid small cells, SINR results are not tractable in the grid model nor accurate in the Wyner model. To overcome these hurdles, we develop a novel analytical framework which includes the impact of overhead delay for CoMP evaluation in HCNs. This framework can be used for a class of CoMP schemes without user data sharing. As an example, we apply it to downlink CoMP zero-forcing beamforming (ZFBF), and see significant divergence from previous work. For example, we show that CoMP ZFBF does not increase throughput when the overhead channel delay is larger than 60% of the channel coherence time. We also find that, in most cases, coordinating with only one other cell is nearly optimum for downlink CoMP ZFBF.
A discriminative CNN video representation for event detection

In this paper, we propose a discriminative video representation for event detection over a large scale video dataset when only limited hardware resources are available. The focus of this paper is to effectively leverage deep Convolutional Neural Networks (CNNs) to advance event detection, where only frame level static descriptors can be extracted by the existing CNN toolkits. This paper makes two contributions to the inference of CNN video representation. First, while average pooling and max pooling have long been the standard approaches to aggregating frame level static features, we show that performance can be significantly improved by taking advantage of an appropriate encoding method. Second, we propose using a set of latent concept descriptors as the frame descriptor, which enriches visual information while keeping it computationally affordable. The integration of the two contributions results in a new state-of-the-art performance in event detection over the largest video datasets. Compared to improved Dense Trajectories, which has been recognized as the best video representation for event detection, our new representation improves the Mean Average Precision (mAP) from 27.6% to 36.8% for the TRECVID MEDTest 14 dataset and from 34.0% to 44.6% for the TRECVID MEDTest 13 dataset.
SVM Based Models for Predicting Foreign Currency Exchange Rates

Support vector machine (SVM) has appeared as a powerful tool for forecasting forex market and demonstrated better performance over other methods, e.g., neural network or ARIMA based model. SVM-based forecasting model necessitates the selection of appropriate kernel function and values of free parameters: regularization parameter and ε– insensitive loss function. In this paper, we investigate the effect of different kernel functions, namely, linear, polynomial, radial basis and spline on prediction error measured by several widely used performance metrics. The effect of regularization parameter is also studied. The prediction of six different foreign currency exchange rates against Australian dollar has been performed and analyzed. Some interesting results are presented.
Attributed Grammars for Joint Estimation of Human Attributes, Part and Pose

In this paper, we are interested in developing compositional models to explicit representing pose, parts and attributes and tackling the tasks of attribute recognition, pose estimation and part localization jointly. This is different from the recent trend of using CNN-based approaches for training and testing on these tasks separately with a large amount of data. Conventional attribute models typically use a large number of region-based attribute classifiers on parts of pre-trained pose estimator without explicitly detecting the object or its parts, or considering the correlations between attributes. In contrast, our approach jointly represents both the object parts and their semantic attributes within a unified compositional hierarchy. We apply our attributed grammar model to the task of human parsing by simultaneously performing part localization and attribute recognition. We show our modeling helps performance improvements on pose-estimation task and also outperforms on other existing methods on attribute prediction task.
Stressful life events, personality, and health: an inquiry into hardiness.

Personality was studied as a conditioner of the effects of stressful life events on illness onset. Two groups of middle and upper level executives had comparably high degrees of stressful life events in the previous 3 years, as measured by the Holmes and Rahe Schedule of Recent Life Events. One group (n = 86) suffered high stress without falling ill, whereas the other (n = 75) reported becoming sick after their encounter with stressful life events. Illness was measured by the Wyler, Masuda, and Holmes Seriousness of Illness Survey. Discriminant function analysis, run on half of the subjects in each group and cross-validated on the remaining cases, supported the prediction that high stress/low illness executives show, by comparison with high stress/high illness executives, more hardiness, that is, have a stronger commitment to self, an attitude of vigorousness toward the environment, a sense of meaningfulness, and an internal locus of control.
Decaf: Moving Device Drivers to a Modern Language

Writing code to interact with external devices is inherently difficult, and the added demands of writing device drivers in C for kernel mode compounds the problem. This environment is complex and brittle, leading to increased development costs and, in many cases, unreliable code. Previous solutions to this problem ignore the cost of migrating drivers to a better programming environment and require writing new drivers from scratch or even adopting a new operating system. We present Decaf Drivers, a system for incrementally converting existing Linux kernel drivers to Java programs in user mode. With support from programanalysis tools, Decaf separates out performance-sensitive code and generates a customized kernel interface that allows the remaining code to be moved to Java. With this interface, a programmer can incrementally convert driver code in C to a Java decaf driver. The Decaf Drivers system achieves performance close to native kernel drivers and requires almost no changes to the Linux kernel. Thus, Decaf Drivers enables driver programming to advance into the era of modern programming languages without requiring a complete rewrite of operating systems or drivers. With five drivers converted to Java, we show that Decaf Drivers can (1) move the majority of a driver’s code out of the kernel, (2) reduce the amount of driver code, (3) detect broken error handling at compile time with exceptions, (4) gracefully evolve as driver and kernel code and data structures change, and (5) perform within one percent of native kernel-only drivers.