nils-herrmann/clean_scirepeval huggingface dataset:
A 2D Human Body Model Dressed in Eigen Clothing

Detection, tracking, segmentation and pose estimation of people in monocular images are widely studied. Two-dimensional models of the human body are extensively used, however, they are typically fairly crude, representing the body either as a rough outline or in terms of articulated geometric primitives. We describe a new 2D model of the human body contour that combines an underlying naked body with a lowdimensional clothing model. The naked body is represented as a Contour Person that can take on a wide variety of poses and body shapes. Clothing is represented as a deformation from the underlying body contour. This deformation is learned from training examples using principal component analysis to produce eigen clothing. We find that the statistics of clothing deformations are skewed and we model the a priori probability of these deformations using a Beta distribution. The resulting generative model captures realistic human forms in monocular images and is used to infer 2D body shape and pose under clothing. We also use the coefficients of the eigen clothing to recognize different categories of clothing on dressed people. The method is evaluated quantitatively on synthetic and real images and achieves better accuracy than previous methods for estimating body shape under clothing.
Ultra-wideband sampling down converter with sampling phase detector

Ultra-wideband radar is an excellent tool for nondestructive examination of walls and highway structures. Therefore often steep edged narrow pulses with rise-, fall-times in the range of 100 ps are used. For digitizing of the reflected pulses a down conversion has to be accomplished. A new low cost sampling down converter with a sampling phase detector for use in ultra-wideband radar applications is presented.
Application discoverability and user satisfaction in mobile application stores: An environmental psychology perspective

a Information Systems and Quantitative Sciences Area, Rawls College of Business Administration, Texas Tech University, Lubbock, TX 79409, United States b Department of Management Information Systems, School of Business and Management, American University of Sharjah, Sharjah, United Arab Emirates c Department of Decision and Information Sciences, C.T. Bauer College of Business, University of Houston, Houston, TX 77204, United States
One pixel attack for fooling deep neural networks

Recent research has revealed that the output of Deep Neural Networks (DNN) can be easily altered by adding relatively small perturbations to the input vector. In this paper, we analyze an attack in an extremely limited scenario where only one pixel can be modified. For that we propose a novel method for generating one-pixel adversarial perturbations based on differential evolution. It requires less adversarial information and can fool more types of networks. The results show that 68.36% of the natural images in CIFAR10 test dataset and 41.22% of the ImageNet (ILSVRC 2012) validation images can be perturbed to at least one target class by modifying just one pixel with 73.22% and 5.52% confidence on average. Thus, the proposed attack explores a different take on adversarial machine learning in an extreme limited scenario, showing that current DNNs are also vulnerable to such low dimension attacks.
Perception-based 3D Triangle Mesh Segmentation Using Fast Marching Watersheds

In this paper, we describe an algorithm called Fast Marching Watersheds that segments a triangle mesh into visual parts. This computer vision algorithm leverages a human vision theory known as the minima rule. Our implementation computes the principal curvatures and principal directions at each vertex of a mesh, and then our hillclimbing watershed algorithm identifies regions bounded by contours of negative curvature minima. These regions fit the definition of visual parts according to the minima rule. We present evaluation analysis and experimental results for the
Inductive wireless power transfer for electric vehicle dynamic charging

The aim of the paper is to present an overview of inductive wireless power transfer (WPT) technologies for the application of electric vehicles (EVs) wireless charging. The basic principles of this technology are introduced followed by a classification of power electronic architectures and the magnetic coupler design. The advantages and limitations of each technology for EV dynamic charging are discussed. The recent progress, technical issues, challenges and state-of-art researches are introduced. An efficient design of WPT charging system is proposed and studied in VEDECOM institute. The innovative solution is an optimized magnetic coupler associated with the use of new semiconductors components the most challenging EV charging application's requirements.
An Experimental Comparison of Stereo Algorithms

While many algorithms for computing stereo correspondence have been proposed, there has been very little work on experimentally evaluating algorithm performance, especially using real (rather than synthetic) imagery. In this paper we propose an experimental comparison of several different stereo algorithms. We use real imagery, and explore two different methodologies, with different strengths and weaknesses. Our first methodology is based upon manual computation of dense ground truth. Here we make use of a two stereo pairs: one of these, from the University of Tsukuba, contains mostly fronto-parallel surfaces; while the other, which we built, is a simple scene with a slanted surface. Our second methodology uses the notion of prediction error, which is the ability of a disparity map to predict an (unseen) third image, taken from a known camera position with respect to the input pair. We present results for both correlation-style stereo algorithms and techniques based on global methods such as energy minimization. Our experiments suggest that the two methodologies give qualitatively consistent results. Source images and additional materials, such as the implementations of various algorithms, are available on the web from http://www.research.microsoft.com/ ̃szeliski/stereo.
Reliable method for driving events recognition

Motor vehicles greatly influence human life but are also a major cause of death and road congestion, which is an obstacle to future economic development. We believe that by learning driving patterns, useful navigation support can be provided for drivers. In this paper, we present a simple and reliable method for the recognition of driving events using hidden Markov models (HMMs), popular stochastic tools for studying time series data. A data acquisition system was used to collect longitudinal and lateral acceleration and speed data from a real vehicle in a normal driving environment. Data were filtered, normalized, segmented, and quantified to obtain the symbolic representation necessary for use with discrete HMMs. Observation sequences for training and evaluation were manually selected and classified as events of a particular type. An appropriate model size was selected, and the model was trained for each type of driving events. Observation sequences from the training set were evaluated by multiple models, and the highest probability decides what kind of driving event this sequence represents. The recognition results showed that HMMs could recognize driving events very accurately and reliably.
Intelligent IGBT drivers with exceptional driving and protection features

The family of intelligent IPS-drivers was invented to drive high power IGBT modules with blocking voltages up to 6,500V. They may be used use in industrial drives, power supplies, transportation, renewable energies as well as induction heating applications. The IPS- drivers decrease the switching losses and offer a reliable protection for high power IGBT modules. Varying the software enables an easy adaptation to specific applications. The main features of the IPS-drivers are: variable gate ON and OFF resistors, advanced desaturation and di/dt protections, active feedback clamping, high peak output current and output power, short signal transition times, and multiple soft shut down.
Underwater optical communication performance for laser beam propagation through weak oceanic turbulence.

In clean ocean water, the performance of a underwater optical communication system is limited mainly by oceanic turbulence, which is defined as the fluctuations in the index of refraction resulting from temperature and salinity fluctuations. In this paper, using the refractive index spectrum of oceanic turbulence under weak turbulence conditions, we carry out, for a horizontally propagating plane wave and spherical wave, analysis of the aperture-averaged scintillation index, the associated probability of fade, mean signal-to-noise ratio, and mean bit error rate. Our theoretical results show that for various values of the rate of dissipation of mean squared temperature and the temperature-salinity balance parameter, the large-aperture receiver leads to a remarkable decrease of scintillation and consequently a significant improvement on the system performance. Such an effect is more noticeable in the plane wave case than in the spherical wave case.
Concept and application of LPM - a novel 3-D local position measurement system

Precise measurement of the local position of moveable targets in three dimensions is still considered to be a challenge. With the presented local position measurement technology, a novel system, consisting of small and lightweight measurement transponders and a number of fixed base stations, is introduced. The system is operating in the 5.8-GHz industrial-scientific-medical band and can handle up to 1000 measurements per second with accuracies down to a few centimeters. Mathematical evaluation is based on a mechanical equivalent circuit. Measurement results obtained with prototype boards demonstrate the feasibility of the proposed technology in a practical application at a race track.
An adjustable scotch yoke mechanism for robotic dolphin

This paper describes the design, construction, and performance analysis of an adjustable Scotch yoke mechanism mimicking the dorsoventral movement for dolphin-like robots. Since dolphins propel themselves by vertical oscillations following a sinusoidal path with alterable amplitudes, a two- motor-driven Scotch yoke mechanism is adopted as the main propulsor to generate sinusoidal oscillations, where leading screw mechanism and rack and pinion mechanism actuated by the minor motor are incorporated to independently change the length of the crank actuated by the major motor. Meanwhile, the output of the Scotch yoke, i.e., reciprocating motion, is converted into the up-and-down oscillation via rack and gear transmission. A motion control scheme based on the novel Scotch yoke is then formed and applied to achieve desired propulsion. Preliminary tests in a robotics context finally confirm the feasibility of the developed mechanism in mechanics and propulsion.
Adoption and use of social media among public health departments

Effective communication is a critical function within any public health system. Social media has enhanced communication between individuals and organizations and has the potential to augment public health communication. However, there is a lack of reported data on social media adoption within public health settings. The purposes of this study were to assess: 1) the extent to which state public health departments (SHDs) are using social media; 2) which social media applications are used most often; and 3) how often social media is used interactively to engage audiences.



This was a non-experimental, cross sectional study of SHD social media sites. Screen capture software Snag-It® was used to obtain screenshots of SHD social media sites across five applications. These sites were coded for social media presence, interactivity, reach, and topic.



Sixty percent of SHDs reported using at least one social media application. Of these, 86.7% had a Twitter account, 56% a Facebook account, and 43% a YouTube channel. There was a statistically significant difference between average population density and use of social media (p = .01). On average, SHDs made one post per day on social media sites, and this was primarily to distribute information; there was very little interaction with audiences. SHDs have few followers or friends on their social media sites. The most common topics for posts and tweets related to staying healthy and diseases and conditions. Limitations include the absence of a standard by which social media metrics measure presence, reach, or interactivity; SHDs were only included if they had an institutionally maintained account; and the study was cross sectional.



Social media use by public health agencies is in the early adoption stage. However, the reach of social media is limited. SHDs are using social media as a channel to distribute information rather than capitalizing on the interactivity available to create conversations and engage with the audience. If public health agencies are to effectively use social media then they must develop a strategic communication plan that incorporates best practices for expanding reach and fostering interactivity and engagement.
A small-baseline approach for investigating deformations on full-resolution differential SAR interferograms

This paper presents a differential synthetic aperture radar (SAR) interferometry (DIFSAR) approach for investigating deformation phenomena on full-resolution DIFSAR interferograms. In particular, our algorithm extends the capability of the small-baseline subset (SBAS) technique that relies on small-baseline DIFSAR interferograms only and is mainly focused on investigating large-scale deformations with spatial resolutions of about 100/spl times/100 m. The proposed technique is implemented by using two different sets of data generated at low (multilook data) and full (single-look data) spatial resolution, respectively. The former is used to identify and estimate, via the conventional SBAS technique, large spatial scale deformation patterns, topographic errors in the available digital elevation model, and possible atmospheric phase artifacts; the latter allows us to detect, on the full-resolution residual phase components, structures highly coherent over time (buildings, rocks, lava, structures, etc.), as well as their height and displacements. In particular, the estimation of the temporal evolution of these local deformations is easily implemented by applying the singular value decomposition technique. The proposed algorithm has been tested with data acquired by the European Remote Sensing satellites relative to the Campania area (Italy) and validated by using geodetic measurements.
An Ensemble Model with Ranking for Social Dialogue

Open-domain social dialogue is one of the long-standing goals of Artificial Intelligence. This year, the Amazon Alexa Prize challenge was announced for the first time, where real customers get to rate systems developed by leading universities worldwide. The aim of the challenge is to converse “coherently and engagingly with humans on popular topics for 20 minutes”. We describe our Alexa Prize system (called ‘Alana’) consisting of an ensemble of bots, combining rule-based and machine learning systems, and using a contextual ranking mechanism to choose a system response. The ranker was trained on real user feedback received during the competition, where we address the problem of how to train on the noisy and sparse feedback obtained during the competition.
Automated Vehicle Parking System using RFID

Radio Frequency Identification (RFID) technology is very useful technology in automation of vehicle parking system in mall/building. One of the challenging problems for many vehicle owners in big cities is where to park their vehicles. If the parking slot is known in advance one can save precious time and fuel wastage. In our proposed system the user is informed about the parking slot availability at a particular parking location .The slot availability details are collected using an RFID system and are updated periodically into the database . Entry-point and exit-point of the parking-lots will be under control with RFID readers, labels and barriers. Personnel costs will be reduced considerably using this technology. Entry-point and exit-point will be handled in a fast manner without having to stop the cars so that traffic jam problem will be avoided during these processes. Drivers will not have to stop at the circulation points and parking tickets will be out of usage during Entry-point and exit-point. Because we have added recharge module therefore user has to register into the system and he will get message of balance on his mobile. It will be avoided ticket-jamming problems for the ticket processing machines as well. Vehicle owners will not have to make any payments at each Entry-point thus a faster traffic flow will be possible. Since there won't be any waiting during Entry-point and exit-points the pollution problem will be avoided. Automated parking system certainly reduce the total cost of RFID parking system infrastructure without re-modifying the existed hardware. It also gives functioning for video surveillance which will captures objects which are in front of camera .
Hedges: A study in meaning criteria and the logic of fuzzy concepts

Logicians have, by and large, engaged in the convenient fiction that sentences of natural languages (at least declarative sentences) are either true or false or, at worst, lack a truth value, or have a third value often interpreted as 'nonsense'. And most contemporary linguists who have thought seriously about semantics, especially formal semantics, have largely shared this fiction, primarily for lack of a sensible alternative. Yet students o f language, especially psychologists and linguistic philosophers, have long been attuned to the fact that natural language concepts have vague boundaries and fuzzy edges and that, consequently, natural language sentences will very often be neither true, nor false, nor nonsensical, but rather true to a certain extent and false to a certain extent, true in certain respects and false in other respects. It is common for logicians to give truth conditions for predicates in terms of classical set theory. 'John is tall' (or 'TALL(j) ' ) is defined to be true just in case the individual denoted by 'John' (or ' j ') is in the set of tall men. Putting aside the problem that tallness is really a relative concept (tallness for a pygmy and tallness for a basketball player are obviously different) 1, suppose we fix a population relative to which we want to define tallness. In contemporary America, how tall do you have to be to be tall? 5'8"? 5'9"? 5'10"? 5'11"? 6'? 6'2"? Obviously there is no single fixed answer. How old do you have to be to be middle-aged? 35? 37? 39? 40? 42? 45? 50? Again the concept is fuzzy. Clearly any attempt to limit truth conditions for natural language sentences to true, false and "nonsense' will distort the natural language concepts by portraying them as having sharply defined rather than fuzzily defined boundaries. Work dealing with such questions has been done in psychology. To take a recent example, Eleanor Rosch Heider (1971) took up the question of whether people perceive category membership as a clearcut issue or a matter of degree. For example, do people think of members of a given
Biomolecular Event Extraction using a Stacked Generalization based Classifier

In this paper we propose a stacked generalization (or stacking) model for event extraction in bio-medical text. Event extraction deals with the process of extracting detailed biological phenomenon, which is more challenging compared to the traditional binary relation extraction such as protein-protein interaction. The overall process consists of mainly three steps: event trigger detection, argument extraction by edge detection and finding correct combination of arguments. In stacking, we use Linear Support Vector Classification (Linear SVC), Logistic Regression (LR) and Stochastic Gradient Descent (SGD) as base-level learning algorithms. As meta-level learner we use Linear SVC. In edge detection step, we find out the arguments of triggers detected in trigger detection step using a SVM classifier. To find correct combination of arguments, we use rules generated by studying the properties of bio-molecular event expressions, and form an event expression consisting of event trigger, its class and arguments. The output of trigger detection is fed to edge detection for argument extraction. Experiments on benchmark datasets of BioNLP2011 show the recall, precision and Fscore of 48.96%, 66.46% and 56.38%, respectively. Comparisons with the existing systems show that our proposed model attains state-of-the-art performance.
Discriminative learning with latent variables for cluttered indoor scene understanding

We address the problem of understanding an indoor scene from a single image in terms of recovering the room geometry (floor, ceiling, and walls) and furniture layout. A major challenge of this task arises from the fact that most indoor scenes are cluttered by furniture and decorations, whose appearances vary drastically across scenes, thus can hardly be modeled (or even hand-labeled) consistently. In this paper we tackle this problem by introducing latent variables to account for clutter, so that the observed image is jointly explained by the room and clutter layout. Model parameters are learned from a training set of images that are only labeled with the layout of the room geometry. Our approach enables taking into account and inferring indoor clutter without hand-labeling of the clutter in the training set, which is often inaccurate. Yet it outperforms the state-of-the-art method of Hedau et al. that requires clutter labels. As a latent variable based method, our approach has an interesting feature that latent variables are used in direct correspondence with a concrete visual concept (clutter in the room) and thus interpretable.
Redesign of rescue mobile robot Quince

On March 11, 2011, a massive earthquake and tsunami hit eastern Japan, particularly affecting the Tohoku area. Since then, the Fukushima Daiichi Nuclear Power Station has been facing a crisis. To respond to this crisis, we considered using our rescue robots for surveillance missions. Before delivering a robot to TEPCO (Tokyo Electric Power Company), we needed to solve some technical issues and add some functions to respond to this crisis. Therefore, we began a redesign project to equip the robot for disaster response missions. TEPCO gave us two specific missions. One was to explore the inside and outside of the reactor buildings to perform dose measurements. The other one was to sample contaminated water and install a water gauge in the basement of the reactor buildings. To succeed in the above two missions, we redesigned our mobile robot, Quince, and performed repeated operational test to improve it. Finally, one of the robots was delivered to the Fukushima Daiichi Nuclear Power Station on June 20, 2011. In this paper, we will introduce the requirements for the above two missions and report how we fulfilled them.