nils-herrmann/clean_scirepeval huggingface dataset:
Further Optimal Regret Bounds for Thompson Sampling

Thompson Sampling is one of the oldest heuristics for multi-armed bandit problems. It is a randomized algorithm based on Bayesian ideas, and has recently generated significant interest after several studies demonstrated it to have comparable or better empirical performance compared to the state of the art methods. In this paper, we provide a novel regret analysis for Thompson Sampling that proves the first near-optimal problem-independent bound of O( √ NT lnT ) on the expected regret of this algorithm. Our novel martingalebased analysis techniques are conceptually simple, and easily extend to distributions other than the Beta distribution. For the version of Thompson Sampling that uses Gaussian priors, we prove a problemindependent bound of O( √ NT lnN) on the expected regret, and demonstrate the optimality of this bound by providing a matching lower bound. This lower bound of Ω( √ NT lnN) is the first lower bound on the performance of a natural version of Thompson Sampling that is away from the optimal bound (O( √ NT )) achievable for the multi-armed bandit problem by another algorithm . Our near-optimal problem-independent bounds for Thompson Sampling solve a COLT 2012 open problem of Chapelle and Li. Additionally, our techniques simultaneously provide the optimal problem-dependent bound of (1 + ) ∑ i lnT d(μi,μ1) +O( 2 ) on the expected regret. The optimal problem-dependent regret bound for this problem was first Preliminary work. Under review by AISTATS 2013. Do not distribute. proven recently by Kaufmann et al. .
Video-Based Human Walking Estimation Using Joint Gait and Pose Manifolds

We study two fundamental issues about video-based human walking estimation, where the goal is to estimate 3D gait kinematics (i.e., joint positions) from 2D gait appearances (i.e., silhouettes). One is how to model the gait kinematics from different walking styles, and the other is how to represent the gait appearances captured under different views and from individuals of distinct walking styles and body shapes. Our research is conducted in three steps. First, we propose the idea of joint gait-pose manifold (JGPM), which represents gait kinematics by coupling two nonlinear variables, pose (a specific walking stage) and gait (a particular walking style) in a unified latent space. We extend the Gaussian process latent variable model (GPLVM) for JGPM learning, where two heuristic topological priors, a torus and a cylinder, are considered and several JGPMs of different degrees of freedom (DoFs) are introduced for comparative analysis. Second, we develop a validation technique and a series of benchmark tests to evaluate multiple JGPMs and recent GPLVMs in terms of their performance for gait motion modeling. It is shown that the toroidal prior is slightly better than the cylindrical one, and the JGPM of 4 DoFs that balances the toroidal prior with the intrinsic data structure achieves the best performance. Third, a JGPM-based visual gait generative model (JGPM-VGGM) is developed, where JGPM plays a central role to bridge the gap between the gait appearances and the gait kinematics. Our proposed JGPM-VGGM is learned from Carnegie Mellon University MoCap data and tested on the HumanEva-I and HumanEva-II data sets. Our experimental results demonstrate the effectiveness and competitiveness of our algorithms compared with existing algorithms.
Mass detection on mammogram images : A first assessment of deep learning techniques

Deep Learning approaches have gathered a lot of attention lately. In this work, we study their application to the breast cancer field, in particular for mass detection in mammograms. Several experiments were made on a real mammogram benchmark dataset. Deep Learning approaches were compared to other classification methodologies. It was concluded that, although useful, the implementation used does not outperform SVMs. Further study and adjustment of the method for this application is needed.
Overview of the TREC 2017 Real-Time Summarization Track

Jimmy Lin, Adam Roegiest, Luchen Tan, Richard McCreadie, Ellen Voorhees, and Fernando Diaz 1 David R. Cheriton School of Computer Science, University of Waterloo, Ontario, Canada 2 School of Computing Science, University of Glasgow, Scotland, the United Kingdom 3 National Institute for Standards and Technology, Maryland, USA 4 Microsoft Research, New York, USA {jimmylin, aroegies, luchen.tan}@uwaterloo.ca richard.mccreadie@glasgow.ac.uk, ellen.voorhees@nist.gov, fdiaz@microsoft.com
Observer based robust tension control for a segmented stator coil winding machine

A control system for automatic regulation of the tension of the copper wire in a winding machine for segmented tooth stator coils of permanent magnet synchronous motors is presented. The use of a transducer to directly measure the tension force is avoided. Instead, an observer is used to estimate the tension force using a model of the permanent magnet synchronous tensioning motor, the only measurements being the rotor speed together with the direct axis and quadrature axis stator current components. This tension force estimate is then used as if it was a measurement to feed a second observer based on a complete model of the controlled plant, providing a state estimate. The control variables are the direct and quadrature axis stator voltage components. Forced dynamic control is applied to control the direct axis stator current component to nearly zero, as required for the vector control while observer based robust control is applied to control the tension force. The simulations predict that the system is very insensitive to winding feed speed variations and also to variations in the length of wire in tension feeding the stator segment coil being wound.
How to Learn Effectively in Medical School: Test Yourself, Learn Actively, and Repeat in Intervals

Students in medical school often feel overwhelmed by the excessive amount of factual knowledge they are obliged to learn. Although a large body of research on effective learning methods is published, scientifically based learning strategies are not a standard part of the curriculum in medical school. Students are largely unaware of how to learn successfully and improve memory. This review outlines three fundamental methods that benefit learning: the testing effect, active recall, and spaced repetition. The review summarizes practical learning strategies to learn effectively and optimize long-term retention of factual knowledge.
Aromatherapy positively affects mood, EEG patterns of alertness and math computations.

EEG activity, alertness, and mood were assessed in 40 adults given 3 minutes of aromatherapy using two aromas, lavender (considered a relaxing odor) or rosemary (considered a stimulating odor). Participants were also given simple math computations before and after the therapy. The lavender group showed increased beta power, suggesting increased drowsiness, they had less depressed mood (POMS) and reported feeling more relaxed and performed the math computations faster and more accurately following aromatherapy. The rosemary group, on the other hand, showed decreased frontal alpha and beta power, suggesting increased alertness. They also had lower state anxiety scores, reported feeling more relaxed and alert and they were only faster, not more accurate, at completing the math computations after the aromatherapy session.
Low permittivity cladding to improve the performance of dielectric rod waveguides and dielectric end-fire antennas

A low loss dielectric rod waveguide design is proposed for use at Ku band. Measured waveguide performance from 10 to 18 GHz is presented and validated using full wave numerical simulations. The proposed design uses a low permittivity cladding to improve performance at the low end of the operating frequency band. Moreover, an improvement of the waveguide isolation due to the cladding is analyzed as well. The proposed ABS cladding is also implemented on a dielectric end-fire antenna obtaining an improvement in return loss, gain and half power beamwidth at the low end of the Ku band; the measured peak gain at 12.2 GHz is 14.6 dBi and 9.8 dBi with and without the cladding, respectively, and the 3 dB beamwidth changes from 22 to 61 degrees.
OpenStreetMap: User-Generated Street Maps

The  project is a knowledge collective that provides user-generated street maps. OSM follows the peer production model that created Wikipedia; its aim is to create a set of map data that's free to use, editable, and licensed under new copyright schemes. A considerable number of contributors edit the world map collaboratively using the OSM technical infrastructure, and a core group, estimated at approximately 40 volunteers, dedicate their time to creating and improving OSM's infrastructure, including maintaining the server, writing the core software that handles the transactions with the server, and creating cartographical outputs. There's also a growing community of software developers who develop software tools to make OSM data available for further use across different application domains, software platforms, and hardware devices. The OSM project's hub is the main OSM Web site.
Differential Cryptanalysis of Feal and N-Hash

In 1,2] we introduced the notion of diierential cryptanalysis and described its application to DESS11] and several of its variants. In this paper we show the applicability of diierential cryptanalysis to the Feal family of encryption algorithms and to the N-Hash hash function. In addition, we show how to transform diierential cryptanalytic chosen plaintext attacks into known plaintext attacks.
A Unified Framework for Concurrent Pedestrian and Cyclist Detection

Extensive research interest has been focused on protecting vulnerable road users in recent years, particularly pedestrians and cyclists, due to their attributes of vulnerability. However, comparatively little effort has been spent on detecting pedestrian and cyclist together, particularly when it concerns quantitative performance analysis on large datasets. In this paper, we present a unified framework for concurrent pedestrian and cyclist detection, which includes a novel detection proposal method (termed UB-MPR) to output a set of object candidates, a discriminative deep model based on Fast R-CNN for classification and localization, and a specific postprocessing step to further improve detection performance. Experiments are performed on a new pedestrian and cyclist dataset containing 30 490 annotated pedestrian and 26 771 cyclist instances in over 50 000 images, recorded from a moving vehicle in the urban traffic of Beijing. Experimental results indicate that the proposed method outperforms other state-of-the-art methods significantly.
Hiding Privacy Leaks in Android Applications Using Low-Attention Raising Covert Channels

Covert channels enable a policy-breaking communication not foreseen by a system's design. Recently, covert channels in Android were presented and it was shown that these channels can be used by malware to leak confidential information (e.g., contacts) between applications and to the Internet. Performance aspects as well as means to counter these covert channels were evaluated. In this paper, we present novel covert channel techniques linked to a minimized footprint to achieve a high covertness. Therefore, we developed a malware that slowly leaks collected private information and sends it synchronously based on four covert channel techniques. We show that some of our covert channels do not require any extra permission and escape well know detection techniques like TaintDroid. Experimental results confirm that the obtained throughput is correlated to the user interaction and show that these new covert channels have a low energy consumption - both aspects contribute to the stealthiness of the channels. Finally, we discuss concepts for novel means capable to counter our covert channels and we also discuss the adaption of network covert channel features to Android-based covert channels.
A SiC-Based High-Efficiency Isolated Onboard PEV Charger With Ultrawide DC-Link Voltage Range

In LLC-based onboard battery charging architectures used in plug-in electric vehicles (PEV), the dc link voltage can be actively regulated to follow the battery pack voltage so that the LLC converter can operate in proximity of resonant frequency and achieve high efficiencies over the wide range of battery pack voltage. However, conventional boost-type power factor correction (PFC) converters are unable to provide ultrawide dc link voltages since their output voltages should always be larger than their input voltages. This paper proposes a Silicon Carbide (SiC)-based onboard PEV charger using single-ended primary-inductor converter (SEPIC) PFC converter followed by an isolated LLC resonant converter. With the proposed charger architecture, the SEPIC PFC converter is able to provide an ultrawide range for dc link voltage, and consequently enhance the efficiency of the LLC stage by ensuring operation in proximity of resonant frequency. A 1-kW SiC-based prototype is designed to validate the proposed idea. The experimental result shows that the SEPIC PFC converter achieves unity power factor, 2.72% total harmonic distortion, and 95.3% peak conversion efficiency. The LLC  converter achieves 97.1% peak efficiency and always demonstrates a very high efficiency across the ultrawide dc-link voltage range. The overall efficiency of the charger is 88.5% to 93.5% from 20% of the rated load to full load.
Plant Classification from Leaf Textures

This work describes a methodology for plant classification based on the analysis of leaf textures by combining a multi-resolution technique, such as the two-dimensional (2D) Discrete Wavelet Transform (2D-DWT), statistical models and Gray-Level Co-occurrence Matrices (GLCM) in which some invariance (e.g. rotation and scale) are achieved. As a second step, an Artificial Neural Network (ANN) model is trained for automatic classifying plant species. The proposed approach was tested on the Flavia database. An overall classification accuracy of 91.85% was achieved which demonstrates that plants can be reliably classified using texture samples extracted from leaf tissues.
Deep Neural Networks for Object Detection

Deep Neural Networks (DNNs) have recently shown outstanding performance on image classification tasks . In this paper we go one step further and address the problem of object detection using DNNs, that is not only classifying but also precisely localizing objects of various classes. We present a simple and yet powerful formulation of object detection as a regression problem to object bounding box masks. We define a multi-scale inference procedure which is able to produce high-resolution object detections at a low cost by a few network applications. State-of-the-art performance of the approach is shown on Pascal VOC.
Predicting Knowledge Sharing Intention Based on Theory of Reasoned Action Framework : An Empirical Study on Higher Education Institution

Based on theory of reasoned action (TRA) theoretical framework, this study try to predict individual knowledge sharing intention on higher education institution context. This study also integrate some potential factors (channel richness, psychological forces and organizational climate factors) which supporting individuals' knowledge-sharing intentions Through a field survey of 242 lecturer on public and privateuniversity at Purwokerto and Yogyakarta city, this study confirm our hypothesis that attitudes toward knowledge sharing and subjective norms with regard to knowledge sharing as well as organizational climate affect individuals' intentions to share knowledge. This study showed thatPerceived Reciprocal Benefits, Perceived Enjoyment in Helping Others, Perceived Reputation Enhancement and channel richness affect individuals'attitudes toward knowledge sharing while organizational climate affect subjective norms. Overall, the results of the study advance prior research in the area of knowledge sharing by shedding light on the determinants of knowledge sharing behaviors of knowledge workers. These insights could be used by university in developing work environments that are conducive for knowledge worker to share their knowledge.  Knowledge sharing intention, TRA, Knowledge worker, attitude, university.
A Semantic Recommender System for Adaptive Learning

The ever-more complex labor world and the current economic crisis ask learners and workers to continuously update their qualification levels to stay relevant on the job. Hence, education and training providers need to adjust their offerings to cope with such evolving requirements. However, the huge number of variables to consider means that finding the right learning content that lets an individual fill his or her competency gap might be difficult. The authors' semantic-based recommender system crosses heterogeneous information about learners' and workers' backgrounds as well as advertised job positions with a catalog of online courses to identify the most appropriate learning resources. Experimental observations showed a good agreement between human and automatic recommendations, confirming the applicability of the emerging semantic technology to the generation of user-centered services that can adapt to individuals' learning needs. The authors discuss the details of their system in a video Web extra at http://youtu.be/A8c5MwnlGvA. A second Web extra shows the training booklet for evaluating the authors' system and the results of the automatic recommendation; it's available at https://s3.amazonaws.com/ieeecs.cdn.csdl.public/mags/it/2015/05/mit2015050050s.pdf.
Deep content-based music recommendation

Automatic music recommendation has become an increasingly relevant problem in recent years, since a lot of music is now sold and consumed digitally. Most recommender systems rely on collaborative filtering. However, this approach suffers from the cold start problem: it fails when no usage data is available, so it is not effective for recommending new and unpopular songs. In this paper, we propose to use a latent factor model for recommendation, and predict the latent factors from music audio when they cannot be obtained from usage data. We compare a traditional approach using a bag-of-words representation of the audio signals with deep convolutional neural networks, and evaluate the predictions quantitatively and qualitatively on the Million Song Dataset. We show that using predicted latent factors produces sensible recommendations, despite the fact that there is a large semantic gap between the characteristics of a song that affect user preference and the corresponding audio signal. We also show that recent advances in deep learning translate very well to the music recommendation setting, with deep convolutional neural networks significantly outperforming the traditional approach.
Development of the lightweight hydraulic quadruped robot — MiniHyQ

This paper presents the development of the lightweight hydraulic quadruped robot MiniHyQ. To the authors' best knowledge, MiniHyQ is the lightest and smallest hydraulic quadruped robot that has been built so far. MiniHyQ is a fully torque controlled robot. It has reconfigurable leg configurations. It has wide joint range of motion and an onboard compact power pack. The robot has almost the same leg length as the previous robot (HyQ , built by our group), but its link segment lengths are 15% less in flex configuration, due to the special isogram knee joint mechanism. Its weight is only 35kg (24kg with an offboard pump unit), which makes it portable by one person. To achieve this lightweight, miniature hydraulic actuators were carefully selected, allowing us to reduce the required pump size inside the torso. By using a hydraulic rotary actuator for the hip and linear actuators with isogram mechanism for the knee joint, a wider range of motion is achieved, allowing a self-righting motion. For the design validation and hardware testing, series of experiments are conducted on MiniHyQ single leg.
A New Method for Image Segmentation

Image processing is a form of signal processing. One of the typical operations on image processing is image segmentation. In this paper, we use new image segmentation algorithms based on information bottleneck method. Here we are going to use three algorithms; first we introduce the split-and-merge algorithm, where an image is segmented into set of regions (input) and the intensity histogram bins (output) is obtained. The second algorithm is the histogram clustering algorithm, where the input variable represents the histogram bins and the output is given by the set of regions. Finally, the registration based segmentation for two registered multimodal images. Image registration is the process of overlaying images (two or more) of the same scene taken at different times, from different viewpoints, and/or by different sensors. The registration geometrically aligns two images (the reference and sensed images). Index Terms -Image Segmentation, Image Registration, Quad tree, Clustering, Information Bottleneck Method
A New Algorithm for Polygon Intersection Operation

Polygon Intersection is among the most fundamental operations in Computer Graphics, which is also broadly applied in GIS. The existing algorithms for polygon intersection may fail because of being specified ambiguously, or can not handling the concave polygons or those with holes. In this paper, introducing the concept of Monotone chain from Geometry, a rapid polygon intersection algorithm has been addressed. This algorithm could be adapted to any general polygons, with no need to process abnormal conditions; moreover, it could tolerate arithmetic errors. The algorithm has already been applied in GIS platform software, and proved of great stability and reliability.
NE-Rank: A Novel Graph-Based Keyphrase Extraction in Twitter

The massive growth of the micro-blogging service Twitter has shed the light on the challenging problem of summarizing a collection of large number of tweets. This paper attempts to extract topical key phrases that would represent topics in tweets. Due to the informality, noise, and short length of tweets, such research is nontrivial. We tackle such challenges with extensive preprocessing approach. Followed by, introduction of new features that improve topical key phrase extraction in Twitter. We start by proposing a novel unsupervised graph-based keyword ranking method, called NE-Rank, that considers word weights in addition to edge weights when calculating the ranking. Then we introduce a new approach of leveraging hash tags when extracting key phrases. We have conducted a set of experiments showing the potential of both approaches with 16% to 39% improvement for NE-Rank and 20% improvement for hash tag enhanced extraction.
A Typology of Abusive Language Detection Subtasks

As the body of research on abusive language detection and analysis grows, there is a need for critical consideration of the relationships between different subtasks that have been grouped under this label. Based on work on hate speech, cyberbullying, and online abuse we propose a typology that captures central similarities and differences between subtasks and we discuss its implications for data annotation and feature construction. We emphasize the practical actions that can be taken by researchers to best approach their abusive language detection subtask of interest.
A Printed Monopole Antenna With Two Steps and a Circular Slot for UWB Applications

This letter presents a printed monopole antenna with two steps and a circular slot for ultrawide band (UWB) applications. The proposed antenna is fabricated and tested. The proposed antenna has a wide frequency bandwidth of 8.4 GHz starting from 3 GHz up to 11.4 GHz for a return loss (S_11) of less than - 10dB and gain flatness over the frequency range. Measured results show also that the proposed antenna features satisfactory radiation characteristics within the achieved impedance bandwidth. By introducing a simple and proper narrow slot in the radiating element, frequency-notched characteristics can be obtained and a good band-notched performance in the 56 GHz band can be achieved.