nils-herrmann/clean_scirepeval huggingface dataset:
A Depth Space Approach for Evaluating Distance to Objects - with Application to Human-Robot Collision Avoidance

We present a novel approach to estimate the distance between a generic point in the Cartesian space and objects detected with a depth sensor. This information is crucial in many robotic applications, e.g., for collision avoidance, contact point identification, and augmented reality. The key idea is to perform all distance evaluations directly in the depth space. This allows distance estimation by considering also the frustum generated by the pixel on the depth image, which takes into account both the pixel size and the occluded points. Different techniques to aggregate distance data coming from multiple object points are proposed. We compare the Depth space approach with the commonly used Cartesian space or Configuration space approaches, showing that the presented method provides better results and faster execution times. An application to human-robot collision avoidance using a KUKA LWR IV robot and a Microsoft Kinect sensor illustrates the effectiveness of the approach.
VRCodes: Unobtrusive and active visual codes for interaction by exploiting rolling shutter

We show a new visible tagging solution for active displays which allows a rolling-shutter camera to detect active tags from a relatively large distance in a robust manner. Current planar markers are visually obtrusive for the human viewer. In order for them to be read from afar and embed more information, they must be shown larger thus occupying valuable physical space on the design. We present a new active visual tag which utilizes all dimensions of color, time and space while remaining unobtrusive to the human eye and decodable using a 15fps rolling-shutter camera. The design exploits the flicker fusion-frequency threshold of the human visual system, which due to the effect of metamerism, can not resolve metamer pairs alternating beyond 120Hz. Yet, concurrently, it is decodable using a 15fps rolling-shutter camera due to the effective line-scan speed of 15×400 lines per second. We show an off-the-shelf rolling-shutter camera can resolve the metamers flickering on a television from a distance over 4 meters. We use intelligent binary coding to encode digital positioning and show potential applications such as large screen interaction. We analyze the use of codes for locking and tracking encoded targets. We also analyze the constraints and performance of the sampling system, and discuss several plausible application scenarios.
Learning long-term dependencies in NARX recurrent neural networks

It has previously been shown that gradient-descent learning algorithms for recurrent neural networks can perform poorly on tasks that involve long-term dependencies, i.e. those problems for which the desired output depends on inputs presented at times far in the past. We show that the long-term dependencies problem is lessened for a class of architectures called nonlinear autoregressive models with exogenous (NARX) recurrent neural networks, which have powerful representational capabilities. We have previously reported that gradient descent learning can be more effective in NARX networks than in recurrent neural network architectures that have "hidden states" on problems including grammatical inference and nonlinear system identification. Typically, the network converges much faster and generalizes better than other networks. The results in this paper are consistent with this phenomenon. We present some experimental results which show that NARX networks can often retain information for two to three times as long as conventional recurrent neural networks. We show that although NARX networks do not circumvent the problem of long-term dependencies, they can greatly improve performance on long-term dependency problems. We also describe in detail some of the assumptions regarding what it means to latch information robustly and suggest possible ways to loosen these assumptions.
Mahalanobis Distance Learning for Person Re-identification

Recently, Mahalanobis metric learning has gained a considerable interest for single-shot person re-identification. The main idea is to build on an existing image representation and to learn a metric that reflects the visual camera-to-camera transitions, allowing for a more powerful classification. The goal of this chapter is twofold. We first review the main ideas of Mahalanobis metric learning in general and then give a detailed study on different approaches for the task of single-shot person re-identification, also comparing to the state-of-the-art. In particular, for our experiments we used Linear Discriminant Metric Learning (LDML), Information Theoretic Metric Learning (ITML), Large Margin Nearest Neighbor (LMNN), Large Margin Nearest Neighbor with Rejection (LMNN-R), Efficient Impostor-based Metric Learning (EIML), and KISSME. For our evaluations we used four different publicly available datasets (i.e., VIPeR, ETHZ, PRID 2011, and CAVIAR4REID). Additionally, we generated the new, more realistic PRID 450S dataset, where we also provide detailed segmentations. For the latter one, we also evaluated the influence of using well segmented foreground and background regions. Finally, the corresponding results are presented and discussed.
Survey of Spatio-Temporal Databases

Spatio-temporal databases aim to support extensions to existing models of Spatial Information Systems (SIS) to include time in order to better describe our dynamic environment. Although interest into this area has increased in the past decade, a number of important issues remain to be investigated. With the advances made in temporal database research, we can expect a more uni®ed approach towards aspatial temporal data in SIS and a wider discussion on spatio-temporal data models. This paper provides an overview of previous achievements within the ®eld and highlights areas currently receiving or requiring further investigation.
Inverse Graphics with Probabilistic CAD Models

Recently, multiple formulations of vision problems as probabilistic inversions of generative models based on computer graphics have been proposed. However, applications to 3D perception from natural images have focused on low-dimensional latent scenes, due to challenges in both modeling and inference. Accounting for the enormous variability in 3D object shape and 2D appearance via realistic generative models seems intractable, as does inverting even simple versions of the many-tomany computations that link 3D scenes to 2D images. This paper proposes and evaluates an approach that addresses key aspects of both these challenges. We show that it is possible to solve challenging, real-world 3D vision problems by approximate inference in generative models for images based on rendering the outputs of probabilistic CAD (PCAD) programs. Our PCAD object geometry priors generate deformable 3D meshes corresponding to plausible objects and apply affine transformations to place them in a scene. Image likelihoods are based on similarity in a feature space based on standard mid-level image representations from the vision literature. Our inference algorithm integrates single-site and locally blocked Metropolis-Hastings proposals, Hamiltonian Monte Carlo and discriminative datadriven proposals learned from training data generated from our models. We apply this approach to 3D human pose estimation and object shape reconstruction from single images, achieving quantitative and qualitative performance improvements over state-of-the-art baselines.
Burnout and satisfaction with work-life balance among US physicians relative to the general US population.

Despite extensive data about physician burnout, to our knowledge, no national study has evaluated rates of burnout among US physicians, explored differences by specialty, or compared physicians with US workers in other fields.



We conducted a national study of burnout in a large sample of US physicians from all specialty disciplines using the American Medical Association Physician Masterfile and surveyed a probability-based sample of the general US population for comparison. Burnout was measured using validated instruments. Satisfaction with work-life balance was explored.



Of 27 276 physicians who received an invitation to participate, 7288 (26.7%) completed surveys. When assessed using the Maslach Burnout Inventory, 45.8% of physicians reported at least 1 symptom of burnout. Substantial differences in burnout were observed by specialty, with the highest rates among physicians at the front line of care access (family medicine, general internal medicine, and emergency medicine). Compared with a probability-based sample of 3442 working US adults, physicians were more likely to have symptoms of burnout (37.9% vs 27.8%) and to be dissatisfied with work-life balance (40.2% vs 23.2%) (P < .001 for both). Highest level of education completed also related to burnout in a pooled multivariate analysis adjusted for age, sex, relationship status, and hours worked per week. Compared with high school graduates, individuals with an MD or DO degree were at increased risk for burnout (odds ratio [OR], 1.36; P < .001), whereas individuals with a bachelor's degree (OR, 0.80; P = .048), master's degree (OR, 0.71; P = .01), or professional or doctoral degree other than an MD or DO degree (OR, 0.64; P = .04) were at lower risk for burnout.



Burnout is more common among physicians than among other US workers. Physicians in specialties at the front line of care access seem to be at greatest risk.
Image-Based Rendering and Synthesis

One of the most important applications in multiview imaging (MVI) is the development of advanced immersive viewing or visualization systems using, for instance, 3DTV. With the introduction of multiview TVs, it is expected that a new age of 3DTV systems will arrive in the near future. Image-based rendering (IBR) refers to a collection of techniques and representations that allow 3-D scenes and objects to be visualized in a realistic way without full 3-D model reconstruction. IBR uses images as the primary substrate. The potential for photorealistic visualization has tremendous appeal, and it has been receiving increasing attention over the years. Applications such as video games, virtual travel, and E-commerce stand to benefit from this technology. This article serves as a tutorial introduction and brief review of this important technology. First the classification, principles, and key research issues of IBR are discussed. Then, an object-based IBR system to illustrate the techniques involved and its potential application in view synthesis and processing are explained. Stereo matching, which is an important technique for depth estimation and view synthesis, is briefly explained and some of the top-ranked methods are highlighted. Finally, the challenging problem of interactive IBR is explained. Possible solutions and some state-of-the-art systems are also reviewed.
Speaker-independent isolated word recognition using dynamic features of speech spectrum

This paper proposes a new isolated word recognition technique based on a combination of instantaneous and dynamic features of the speech spectrum. This technique is shown to be highly effective in speaker-independent speech recognition. Spoken utterances are represented by time sequences of cepstrum coefficients and energy. Regression coefficients for these time functions are extracted for every frame over an approximately 50 ms period. Time functions of regression coefficients extracted for cepstrum and energy are combined with time functions of the original cepstrum coefficients, and used with a staggered array DP matching algorithm to compare multiple templates and input speech. Speaker-independent isolated word recognition experiments using a vocabulary of 100 Japanese city names indicate that a recognition error rate of 2.4 percent can be obtained with this method. Using only the original cepstrum coefficients the error rate is 6.2 percent.
A Random Walk around the City: New Venue Recommendation in Location-Based Social Networks

The popularity of location-based social networks available on mobile devices means that large, rich datasets that contain a mixture of behavioral (users visiting venues), social (links between users), and spatial (distances between venues) information are available for mobile location recommendation systems. However, these datasets greatly differ from those used in other online recommender systems, where users explicitly rate items: it remains unclear as to how they capture user preferences as well as how they can be leveraged for accurate recommendation. This paper seeks to bridge this gap with a three-fold contribution. First, we examine how venue discovery behavior characterizes the large check-in datasets from two different location-based social services, Foursquare and Go Walla: by using large-scale datasets containing both user check-ins and social ties, our analysis reveals that, across 11 cities, between 60% and 80% of users' visits are in venues that were not visited in the previous 30 days. We then show that, by making constraining assumptions about user mobility, state-of-the-art filtering algorithms, including latent space models, do not produce high quality recommendations. Finally, we propose a new model based on personalized random walks over a user-place graph that, by seamlessly combining social network and venue visit frequency data, obtains between 5 and 18% improvement over other models. Our results pave the way to a new approach for place recommendation in location-based social systems.
Image Segmentation Algorithms Overview

The technology of image segmentation is widely used in medical image processing, face recognition pedestrian detection, etc. The current image segmentation techniques include region-based segmentation, edge detection segmentation, segmentation based on clustering, segmentation based on weakly-supervised learning in CNN, etc. This paper analyzes and summarizes these algorithms of image segmentation, and compares the advantages and disadvantages of different algorithms. Finally, we make a prediction of the development trend of image segmentation with the combination of these algorithms.
Sentiment lexicon construction using SentiWordNet 3.0

Opinion mining and sentiment analysis have become popular in linguistic resource rich languages. Opinions for such analysis are drawn from many forms of freely available online/electronic sources, such as websites, blogs, news re-ports and product reviews. But attention received by less resourced languages is significantly less. This is because the success of any opinion mining algorithm depends on the availability of resources, such as special lexicon and WordNet type tools. In this research, we implemented a less complicated but an effective approach that could be used to classify comments in less resourced languages. We experimented the approach for use with Sinhala Language where no such opinion mining or sentiment analysis has been carried out until this day. Our algorithm gives significantly promising results for analyzing sentiments in Sinhala for the first time.
An efficient and reliable DDoS attack detection using a fast entropy computation method

The threat of Distributed Denial of Service (DDoS) has become a major issue in network security and is difficult to detect because all DDoS traffics have normal packet characteristics. Various detection and defense algorithms have been studied. One of them is an entropy-based intrusion detection approach that is a powerful and simple way to identify abnormal conditions from network channels. However, the burden of computing information entropy values from heavy flow still exists. To reduce the computing time, we have developed a DDoS detection scheme using a compression entropy method. It allows us to significantly reduce the computation time for calculating information entropy. However, our experiment suggests that the compression entropy approach tends to be too sensitive to verify real network attacks and produces many false negatives. In this paper, we propose a fast entropy scheme that can overcome the issue of false negatives and will not increase the computational time. Our simulation shows that the fast entropy computing method not only reduced computational time by more than 90% compared to conventional entropy, but also increased the detection accuracy compared to conventional and compression entropy approaches.
Competency-based medical education: implications for undergraduate programs.

Changes in educational thinking and in medical program accreditation provide an opportunity to reconsider approaches to undergraduate medical education. Current developments in competency-based medical education (CBME), in particular, present both possibilities and challenges for undergraduate programs. CBME does not specify particular learning strategies or formats, but rather provides a clear description of intended outcomes. This approach has the potential to yield authentic curricula for medical practice and to provide a seamless linkage between all stages of lifelong learning. At the same time, the implementation of CBME in undergraduate education poses challenges for curriculum design, student assessment practices, teacher preparation, and systemic institutional change, all of which have implications for student learning. Some of the challenges of CBME are similar to those that can arise in the implementation of any integrated program, while others are specific to the adoption of outcome frameworks as an organizing principle for curriculum design. This article reviews a number of issues raised by CBME in the context of undergraduate programs and provides examples of best practices that might help to address these issues.
Direct Torque Control for Induction Motor Using Fuzzy Logic

In this paper we propose an approach to improve the direct torque control (DTC) of an induction motor (IM). The proposed DTC is based on fuzzy logic technique switching table, is described compared with conventional direct torque control (DTC). To test the fuzzy control strategy a simulation platform using MATLAB/SIMULINK was built which includes induction motor d-q model, inverter model, fuzzy logic switching table and the stator flux and torque estimator. The simulation results verified the new control strategy.
Causal filter selection in microarray data

The importance of bringing causality into play when designing feature selection methods is more and more acknowledged in the machine learning community. This paper proposes a filter approach based on information theory which aims to prioritise direct causal relationships in feature selection problems where the ratio between the number of features and the number of samples is high. This approach is based on the notion of interaction which is shown to be informative about the relevance of an input subset as well as its causal relationship with the target. The resulting filter, called mIMR (min-Interaction Max-Relevance), is compared with state-of-the-art approaches. Classification results on 25 real microarray datasets show that the incorporation of causal aspects in the feature assessment is beneficial both for the resulting accuracy and stability. A toy example of causal discovery shows the effectiveness of the filter for identifying direct causal relationships.
Big data in mobile social networks: a QoE-oriented framework

Due to the rapid development of mobile social networks, mobile big data play an important role in providing mobile social users with various mobile services. However, as mobile big data have inherent properties, current MSNs face a challenge to provide mobile social user with a satisfactory quality of experience. Therefore, in this article, we propose a novel framework to deliver mobile big data over content- centric mobile social networks. At first, the characteristics and challenges of mobile big data are studied. Then the content-centric network architecture to deliver mobile big data in MSNs is presented, where each datum consists of interest packets and data packets, respectively. Next, how to select the agent node to forward interest packets and the relay node to transmit data packets are given by defining priorities of interest packets and data packets. Finally, simulation results show the performance of our framework with varied parameters.
WalkRanker: A Unified Pairwise Ranking Model With Multiple Relations for Item Recommendation

Top-N item recommendation techniques, e.g., pairwise models, learn the rank of users’ preferred items through separating items into positive samples if user-item interactions exist, and negative samples otherwise. This separation results in an important issue: the extreme imbalance between positive and negative samples, because the number of items with user actions is much less than those without actions. The problem is even worse for “cold-start” users. In addition, existing learning models only consider the observed user-item proximity, while neglecting other useful relations, such as the unobserved but potentially helpful user-item relations, and highorder proximity in user-user, item-item relations. In this paper, we aim at incorporating multiple types of user-item relations into a unified pairwise ranking model towards approximately optimizing ranking metrics mean average precision (MAP), and mean reciprocal rank (MRR). Instead of taking statical separation of positive and negative sets, we employ a random walk approach to dynamically draw positive samples from short random walk sequences, and a rank-aware negative sampling method to draw negative samples for efficiently learning the proposed pairwise ranking model. The proposed method is compared with several state-of-the-art baselines on two large and sparse datasets. Experimental results show that our proposed model outperforms the other baselines with average 4% at different top-N metrics, in particular for coldstart users with 6% on average.
The Fundamental System of Spatial Schemas in Language

Linguistic research to date has determined many of the principles that govern the structure of the spatial schemas represented by closed-class forms across the world’s languages. contributing to this cumulative understanding have, for example, been Gruber 1965, Fillmore 1968, Leech 1969, Clark 1973, Bennett 1975, Herskovits 1982, Jackendoff 1983, Zubin and Svorou 1984, as well as myself, Talmy 1983, 2000a, 2000b). It is now feasible to integrate these principles and to determine the comprehensive system they belong to for spatial structuring in spoken language. The finding here is that this system has three main parts: the componential, the compositional, and the augmentive.
Power loss comparison with different PWM methods for 3L-NPC inverter and 3L-T type inverter

As the increase of large-scale distributed generation system, the interest in the efficiency of the inverter has been higher. In this paper, the power losses are classified and calculated according to the switching states of the three level NPC inverter and three level T-type inverter. And they are compared with the results from the PSiM thermal module simulation. These power loss comparisons are carried out at the different switching frequencies and modulation index values with the different types of PWM methods. From the results, it is verified that the power loss from the T-type inverter is lower than that from the NPC inverter and it can be reduced by using the appropriate PWM method as like the DPWM method.
Optimisation and integration of an active clamping circuit for IGBT series association

Among the various solutions for the series association of high power IGBTs, the active clamping circuit insures both protection and voltage balancing, within good reliability and compactness. Therefore, this structure has been chosen to be integrated closed to the IGBTs. The design of this circuit leads to the resolution of a compromise between a good balancing and limited additional losses. The aim of this paper is to optimise this circuit, in order to reduce the losses, in the IGBTs as well as in the active clamping circuit. This design has been validated in a 3 kV 400 A test bench, using three 1.7 kV components in series.