Reviewerly/exHarmony huggingface dataset:
Detection of 3D bounding boxes of vehicles using perspective transformation for accurate speed measurement

Detection and tracking of vehicles captured by traffic surveillance cameras is a key component of intelligent transportation systems. We present an improved version of our algorithm for detection of 3D bounding boxes of vehicles, their tracking and subsequent speed estimation. Our algorithm utilizes the known geometry of vanishing points in the surveilled scene to construct a perspective transformation. The transformation enables an intuitive simplification of the problem of detecting 3D bounding boxes to detection of 2D bounding boxes with one additional parameter using a standard 2D object detector. Main contribution of this paper is an improved construction of the perspective transformation which is more robust and fully automatic and an extended experimental evaluation of speed estimation. We test our algorithm on the speed estimation task of the BrnoCompSpeed dataset. We evaluate our approach with different configurations to gauge the relationship between accuracy and computational costs and benefits of 3D bounding box detection over 2D detection. All of the tested configurations run in real time and are fully automatic. Compared to other published state-of-the-art fully automatic results, our algorithm reduces the mean absolute speed measurement error by 32% (1.10 km/h to 0.75 km/h) and the absolute median error by 40% (0.97 km/h to 0.58 km/h).
Scrambled body differentiates body part ownership from the full body illusion

Abstract Illusory body ownership can be induced in a body part or a full body by visual-motor synchronisation. A previous study indicated that an invisible full body illusion can be induced by the synchronous movement of only the hands and feet. The difference between body part ownership and the full body illusion has not been explained in detail because there is no method for separating these two illusions. To develop a method to do so, we scrambled or randomised the positions of the hands and feet and compared it with the normal layout stimulus by manipulating visual-motor synchronisation. In Experiment 1, participants observed the stimuli from a third-person perspective, and the questionnaire results showed that the scrambled body stimulus induced only body part ownership, while the normal layout stimulus induced both body part ownership and full body ownership when the stimuli were synchronous with participants’ actions. In Experiment 2, we found similar results as with the first-person perspective stimuli in a questionnaire. We did not find significant skin conductance response difference between any conditions in either Experiment 2 or 3. These results suggest that a spatial relationship is necessary for the full body illusion, but not for body part ownership.
Performance of 2D Synthetic Mammography Versus Digital Mammography in the Detection of Microcalcifications at Screening

Performance of 2D Synthetic Mammography Versus Digital Mammography in the Detection of Microcalcifications at ScreeningKaterina Dodelzon1, Katherine Simon1, Eda Dou1, Allison D. Levy1, Aya Y. Michaels1, Gulce Askin2 and Janine T. Katzen1Audio Available | Share
A Generic Graph-based Neural Architecture Encoding Scheme for Predictor-based NAS

This work proposes a novel Graph-based neural ArchiTecture Encoding Scheme, a.k.a. GATES, to improve the predictor-based neural architecture search. Specifically, different from existing graph-based schemes, GATES models the operations as the transformation of the propagating information, which mimics the actual data processing of neural architecture. GATES is a more reasonable modeling of the neural architectures, and can encode architectures from both the "operation on node" and "operation on edge" cell search spaces consistently. Experimental results on various search spaces confirm GATES's effectiveness in improving the performance predictor. Furthermore, equipped with the improved performance predictor, the sample efficiency of the predictor-based neural architecture search (NAS) flow is boosted. Codes are available at https://github.com/walkerning/aw_nas.
Predicting the bug fixing time using word embedding and deep long short term memories

In bug fixing process, estimating the ‘Time to Fix Bug’ is one of the factors that helps the triager to allocate jobs in a better way. Due to the limitation of resources for bug fixing, the bugs with long fixing time must be identified, as soon as possible, after receiving the report. This helps the prioritisation and fixing process of the bug reports. In the process of bug fixing, a temporal sequence of activities is done. Each activity is represented by a term. Useful semantic information and long-term dependency are available between terms in the sequence, but it is usually underutilised by existing bug fixing time predictor approaches. This work presents a novel deep learning-based model (called DeepLSTMPred) that (i) converts constituent terms to a vector of real numbers by considering their semantic meaning, (ii) finds the long-term dependencies between terms by deep long short term memory (LSTM) and (iii) classifies sequences to short fixing time or long fixing time. DeepLSTMPred is evaluated on bug reports extracted from the Mozilla project. The results show that the proposed method has better performance in comparison with a state-of-the-art approach (that is the hidden Markov-based model). The experimental results show that DeepLSTMPred achieves 15–20% improvement in terms of accuracy, precision, f-score, and recall.
Line Drawings for Face Portraits From Photos Using Global and Local Structure Based GANs

Despite significant effort and notable success of neural style transfer, it remains challenging for highly abstract styles, in particular line drawings. In this paper, we propose APDrawingGAN++, a generative adversarial network (GAN) for transforming face photos to artistic portrait drawings (APDrawings), which addresses substantial challenges including highly abstract style, different drawing techniques for different facial features, and high perceptual sensitivity to artifacts. To address these, we propose a composite GAN architecture that consists of local networks (to learn effective representations for specific facial features) and a global network (to capture the overall content). We provide a theoretical explanation for the necessity of this composite GAN structure by proving that any GAN with a single generator cannot generate artistic styles like APDrawings. We further introduce a classification-and-synthesis approach for lips and hair where different drawing styles are used by artists, which applies suitable styles for a given input. To capture the highly abstract art form inherent in APDrawings, we address two challenging operations-(1) coping with lines with small misalignments while penalizing large discrepancy and (2) generating more continuous lines-by introducing two novel loss terms: one is a novel distance transform loss with nonlinear mapping and the other is a novel line continuity loss, both of which improve the line quality. We also develop dedicated data augmentation and pre-training to further improve results. Extensive experiments, including a user study, show that our method outperforms state-of-the-art methods, both qualitatively and quantitatively.
Long-Range Coherence and Multiple Steady States in a Lossy Qubit Array

We show that a simple experimental setting of a locally pumped and lossy array of two-level quantum systems can stabilize states with strong long-range coherence. Indeed, by explicit analytic construction, we show there is an extensive set of steady-state density operators, from minimally to maximally entangled, despite this being an interacting open many-body problem. Such nonequilibrium steady states arise from a hidden symmetry that stabilizes Bell pairs over arbitrarily long distances, with unique experimental signatures. We demonstrate a protocol by which one can selectively prepare these states using dissipation. Our findings are accessible in present-day experiments.
Spot evasion attacks: Adversarial examples for license plate recognition systems with convolutional neural networks

Recent studies have shown that convolutional neural networks (CNNs) for image recognition are vulnerable to evasion attacks with carefully manipulated adversarial examples. Previous work primarily focused on how to generate adversarial examples closed to source images, by introducing pixel-level perturbations into the whole or specific part of images. In this paper, we propose an evasion attack on CNN classifiers in the context of License Plate Recognition (LPR), which adds predetermined perturbations to specific regions of license plate, simulating some sort of naturally formed spots (such as sludge, etc.). Therefore, the problem is modeled as an optimization process searching for optimal perturbation positions, which is different from previous work that consider pixel values as decision variables. Notice that this is a complex nonlinear optimization problem, and we use a genetic-algorithm based approach to obtain optimal perturbation positions. In experiments, we use the proposed algorithm to generate various adversarial examples in the form of rectangle, circle, ellipse and spots cluster. Experimental results show that these adversarial examples are almost ignored by human eyes, but can fool HyperLPR with high attack success rate over 93%. Therefore, we believe that this kind of spot evasion attacks would pose a great threat to current LPR systems, and needs to be investigated further by the security community.
Farmer Mental Models of Biological Pest Control: Associations With Adoption of Conservation Practices in Blueberry and Cherry Orchards

Conservation practices in agriculture—such as biological pest control, provision of pollinator habitat and cover cropping—may provide ecosystem services that are beneficial to both farmers and wildlife. Despite these benefits, however, their use is not yet widespread and the factors that may limit adoption is not well understood. In this study we tested potential associations between farmers' beliefs about ecosystem services and their management practices using data collected from questionnaires and cognitive maps from 31 Michigan blueberry and cherry farmers describing their farming systems. We found that farmers who included key biological pest control concepts in their mental model representations reported the use of more conservation practices, and/or participation in conservation programs, than those who did not. In addition, the timing of management practices was a more central factor in the mental models of farmers who included both natural predators and beneficial insects than those omitting these factors. Finally, the farmers who included those two factors showed higher degrees of systems thinking based on mental model metric analysis. We suggest that outreach emphasizing the relationships between ecosystem services and the factors farmers view as most important may positively influence communication and potential of adoption of conservation practices and preventative pest management strategies.
A Group-Theoretic Framework for Data Augmentation

Data augmentation is a widely used trick when training deep neural networks: in addition to the original data, properly transformed data are also added to the training set. However, to the best of our knowledge, a clear mathematical framework to explain the performance benefits of data augmentation is not available. In this paper, we develop such a theoretical framework. We show data augmentation is equivalent to an averaging operation over the orbits of a certain group that keeps the data distribution approximately invariant. We prove that it leads to variance reduction. We study empirical risk minimization, and the examples of exponential families, linear regression, and certain two-layer neural networks. We also discuss how data augmentation could be used in problems with symmetry where other approaches are prevalent, such as in cryo-electron microscopy (cryo-EM).
MEPHAS: an interactive graphical user interface for medical and pharmaceutical statistical analysis with R and Shiny

Abstract Background Even though R is one of the most commonly used statistical computing environments, it lacks a graphical user interface (GUI) that appeals to students, researchers, lecturers, and practitioners in medicine and pharmacy for conducting standard data analytics. Current GUIs built on top of R, such as EZR and R-Commander, aim to facilitate R coding and visualization, but most of the functionalities are still accessed through a command-line interface (CLI). To assist practitioners of medicine and pharmacy and researchers to run most routines in fundamental statistical analysis, we developed an interactive GUI; i.e., MEPHAS, to support various web-based systems that are accessible from laptops, workstations, or tablets, under Windows, macOS (and IOS), or Linux. In addition to fundamental statistical analysis, advanced statistics such as the extended Cox regression and dimensional analyses including partial least squares regression (PLS-R) and sparse partial least squares regression (SPLS-R), are also available in MEPHAS. Results MEPHAS is a web-based GUI ( https://alain003.phs.osaka-u.ac.jp/mephas/ ) that is based on a shiny framework. We also created the corresponding R package mephas ( https://mephas.github.io/ ). Thus far, MEPHAS has supported four categories of statistics, including probability, hypothesis testing, regression models, and dimensional analyses. Instructions and help menus were accessible during the entire analytical process via the web-based GUI, particularly advanced dimensional data analysis that required much explanation. The GUI was designed to be intuitive for non-technical users to perform various statistical functions, e.g., managing data, customizing plots, setting parameters, and monitoring real-time results, without any R coding from users. All generated graphs can be saved to local machines, and tables can be downloaded as CSV files. Conclusion MEPHAS is a free and open-source web-interactive GUI that was designed to support statistical data analyses and prediction for medical and pharmaceutical practitioners and researchers. It enables various medical and pharmaceutical statistical analyses through interactive parameter settings and dynamic visualization of the results.
A standard expression of underwater binocular vision for stereo matching

A camera in an underwater stereo vision system conforms to a multi-layer refraction model, and the perspective projection camera model is invalid. Because the imaging light is refracted, the corresponding pixel does not conform to the traditional polar constraint, and the stereo matching methods must conduct a two-dimensional search. In this paper, a standard expression is proposed to reduce the search space to one dimension. Based on a light field, any specific underwater binocular stereo vision is converted to a standard setup in which the axes of two cameras are parallel and the image is expressed as the light field, the direction information image and the position image. We found and proved that the direction information image conforms to the rule of corresponding rows. With this standard expression, the stereo matching is efficiently conducted and the 3D information can be computed from the disparity map and the position image. In the experiment, an underwater binocular stereo vision system is developed and the proposed method is compared with the traditional method. The results confirm that the proposed method has several advantages of conciseness, correctness, efficiency, and generality.
Psychological and pedagogical foundations for the development of critical thinking of students

The article is determined by the social order of society and the state for a creative, purposeful person with critical thinking, able to create new and transform existing ways of organizing educational activities. Modern educational programs provide a large amount of information for children, however, applying this knowledge is very problematic. Therefore, the application of critical thinking development technology allows the teacher to build a lesson in the form of group lessons, create an atmosphere of partnership in the classroom, and jointly find a solution to the problem. Thus, the use of this technology in the educational process ensures the productive development of the child's intellectual abilities.
Data Processing Using Artificial Neural Networks

The artificial neural network (ANN) is a machine learning (ML) methodology that evolved and developed from the scheme of imitating the human brain. Artificial intelligence (AI) pyramid illustrates the evolution of ML approach to ANN and leading to deep learning (DL). Nowadays, researchers are very much attracted to DL processes due to its ability to overcome the selectivity-invariance problem. In this chapter, ANN has been explained by discussing the network topology and development parameters (number of nodes, number of hidden layers, learning rules and activated function). The basic concept of node and neutron has been explained, with the help of diagrams, leading to the ANN model and its operation. All the topics have been discussed in such a scheme to give the reader the basic concept and clarity in a sequential way from ANN perceptron model to deep learning models and underlying types.
When the Civic Turn turns Digital: Designing Safe and Secure Refugee Resettlement

Across Europe, refugees are required to engage with the "civic turn" -- a process of integrating refugees into the social and cultural aspects of the new land. Over a two-year period, we engaged 89 refugees settling in Sweden, to explore how accelerated and digitalised resettlement processes shape the civic turn. Framed within wider literature on transitioning and everyday insecurities, we show how this "digital turn" exacerbates existing barriers to resettlement experienced by refugees. By critically analysing these barriers, we reveal how the civic turn rests upon a series of everyday social and cultural practices and relations, which are largely ignored in digital service design. We show how this leads to a "vacuum" for our participants. We call on the HCI community to engage with this vacuum and understand resettlement as encompassing multiple digitally-mediated transitional phases of citizenry. We do so by focusing on the digitalisation processes shaping these transitions.
Improved infrared phase measuring deflectometry method for the measurement of discontinuous specular objects

For phase-measuring deflectometry (PMD), the selection of light source is a crucial factor for guaranteeing the measurement accuracy. Since visible light is sensitive to the ambient light, the reconstructed 3D shapes of PMD systems will be affected by the external environment in actual measurements. To tackle this issue, a method named Infrared-PMD (IR-PMD) has been developed, which exploits IR as the light source and directly measures specular objects having discontinuous surfaces from phase data by moving an IR camera and the measured specular surfaces together to realize two screens measurement. However, the movement of the measured objects during the experimental measurement procedure will cause random errors. For further optimizing this system, this paper exploits an IR digital display's movement to substitute the camera and the measured object's movement. A smaller IR projector projecting sinusoidal fringe patterns onto a ground glass is regarded as an IR digital screen. The projector and the ground glass are moved to two positions to realize two screens measurement. Fringe reflection technique is used to obtain the phase information. Then, 3D shape data can be directly calculated by the phase data on the IR digital screen located at two positions. Experimental results demonstrate that 3D shape of discontinuous specular objects can be effectively and accurately measured by the improved IR-PMD.
Differential Private Spatial Decomposition and Location Publishing Based on Unbalanced Quadtree Partition Algorithm

Centralized publishing of big location data provides great convenience for various location-based interactive queries and services. Privacy protection of users' location information is an indispensable issue in the security of big data applications. Partition publishing is an effective way to release statistical information of two-dimensional big location data. By combining with the differential privacy model, it can provide more accurate range counting query service on the premise of ensuring location privacy. In order to further improve the availability of location data subsequent to centralized publishing, this paper analyzes the primary noise sources of partition publishing and discusses the constraints among publishing errors, the spatial partition structure, and privacy budget allocation. An unbalanced quadtree partition algorithm based on regional uniformity is proposed. Accordingly, the gradient privacy budget allocation scheme and adjustment method are designed to ensure the effectiveness of the differential privacy model. Experimental comparison of the real-world datasets proves the advantages of the proposed algorithm in improving the querying accuracy of the published data.
Application Design Resume Medical By Using Microsoft Visual Basic.Net 2010 At The Health Center Appointments

In implementing health services, puskesmas must document all actions and treatments that are given to patients in a document called Medical Records. According to Minister of Health Regulation No.269 / MENKES / PER / III / 2008 article 1 (1), medical records are files containing notes and documents about patient identities. Medical records are of good quality if the medical record is accurate, complete, trustworthy, valid and timely. One form of management in Medical Records is reporting. According to Minister of Health Regulation No.269 / MENKES / PER / III / 2008 article 1 (1), Medical Record is a file that contains notes and documents about patient identity, examinations, actions, and other services that have been given to patients. In the statement, all information about a patient has been reflected which will be made the basis for determining further actions in services and other medical actions given to a patient who comes to the community health center. The Medical Record is said to be of high quality if the Medical Record is accurate, complete, trustworthy, valid and timely. The Medical Record Installation has activities such as registration, data processing, and storage. One form of processing data in medical records is the existence of assembling activities. Assembling is an assembling activity compiling empty Medical Record forms and storing them into Medical Records, ready to use neatly arranged both in terms of quality and quality.
Modeling Topical Relevance for Multi-Turn Dialogue Generation

Topic drift is a common phenomenon in multi-turn dialogue. Therefore, an ideal dialogue generation models should be able to capture the topic information of each context, detect the relevant context, and produce appropriate responses accordingly. However, existing models usually use word or sentence level similarities to detect the relevant contexts, which fail to well capture the topical level relevance. In this paper, we propose a new model, named STAR-BTM, to tackle this problem. Firstly, the Biterm Topic Model is pre-trained on the whole training dataset. Then, the topic level attention weights are computed based on the topic representation of each context. Finally, the attention weights and the topic distribution are utilized in the decoding process to generate the corresponding responses. Experimental results on both Chinese customer services data and English Ubuntu dialogue data show that STAR-BTM significantly outperforms several state-of-the-art methods, in terms of both metric-based and human evaluations.