Reviewerly/exHarmony huggingface dataset:
Automatic Neural Network Search Method for Open Set Recognition

Real-world recognition or classification tasks in computer vision are not apparent in controlled environments and often get involved in open set. Previous research work on real-world recognition problem is knowledge- and labor-intensive to pursue good performance for there are numbers of task domains. Auto Machine Learning (AutoML) approaches supply an easier way to apply advanced machine learning technologies, reduce the demand for experienced human experts and improve classification performance on close set. This paper proposes an automatic neural network search method for designing effective convolution neural network (CNN) models for open set recognition (OSR). Feature distribution information is explicitly incorporated into the main objective. So during the search process, the sampled models will enlarge interclass differences and reduce intra-class variations. We design a flexible search space based on classic CNN models to diversify neural architectures and also add some search principles to limit the size of the search space. Experimental results on CIFAR-10 and Dunhuang historical Chinese datasets show that our approach improves performances on both close and open set. Comparing with the other two OSR algorithms, our method also achieves the best performance.
Impact of Meteorological Parameters on Short-Term Forecasting: Application to the Dakar Site

This paper aims at evaluating impact of the temperature and humidity on the short-term of solar potential forecast. Therefore, discrete Kalman filter model based upon AR process and EM algorithm is applied in Dakar for a lead-time of 20 minutes. The model input parameters at time noticed by t, of the model at a time t, are air temperature, relative humidity and global solar radiation. Expectation at (t +T), is the global solar radiation. Input data are measured at the Polytechnic School of Dakar and cover one year. Results are verified using performance criteria such criteria such the nRMSE, nMAE, nMBE. Criteria values are of about 4.9% for the nRMSE, of 0.272% for the nMAE and about of -0.7% for the nMBE. Without considering the impact of temperature and relative humidity criteria calculation lead to following values : nRMSE of 4.8%, nMAE of 0.271% and nMBE of 0.4%. The analysis of the results showed that studied meteorological parameters have very soft influence (difference of nRMSE = 1% and nMAE = 0.001%) on the performances of the model and that the model without impact is perceived as the most efficient on the site.
Multi-Scale Defense of Adversarial Images

Deep learning has achieved great success in image classification. However, recent researches show that existing deep learning-based classifiers remain weak for recognizing adversarial images. In this paper, an effective multi-scale defense method is proposed to solve the problem. In our method, an input image is first evolved by Gaussian kernels of different intensities to generate a multi-scale representation of the image. These evolved images are then fed into a classifier trained with a multi-scale strategy to yield multi-scale confidences. Finally, an average confidence is exploited to generate classification result. Furthermore, by monitoring the change of confidence values during the image evolution process, our method is able to achieve an indication of the attacking risk. Experimental results show that our method performs favorably against a number of state-of-the-art methods.
A preliminary apparatus and teaching structure for passive tactile training of stenography

Passive tactile learning (PTL) is a method of training motor skills using repeated tactile cues applied to the body even while the user is focused on unrelated tasks.Prior inquiry into PTL has determined that it may be used to teach a variety of input methods, including those that require simultaneous actions (chords) and spatial tasks that give individual fingers multiple responsibilities. No research has combined these techniques to construct a training system that teaches a spatial, chorded input method. Computer stenography is one such input method, and the need for improved training techniques for computer stenography is apparent. Many aspiring stenographers quickly lose motivation due to the difficulty of the input method and the scarcity of effective learning resources. Passive tactile training may be a useful training mechanism to combat the learning crisis in the stenography field. In a study with 12 participants, we demonstrate that PTL may be a useful aide to learning computer stenography. Despite the complexity of the input method, results suggest that PTL may be effective in teaching users fundamental building blocks of stenography that can be applied broadly to the task of mastering the input method.
Estimating The Spatial Resolution of Overhead Imagery Using Convolutional Neural Networks

We focus on the novel problem of estimating the spatial resolution of overhead imagery. More and more overhead imagery is becoming available without such meta-data either because it was not collected in the first place or was not preserved with the imagery. We propose a bottom-up, data-driven approach using convolutional neural networks. We show that an extended model which incorporates dilated convolution to expand the receptive field of the network outperforms a baseline model on an evaluation dataset with a range of simulated spatial resolutions. We make a number of interesting observations to motivate future work on this novel problem.
Augmented Reality Technology: Research Situation and Key Technologies

Augmented reality (AR) technique can integrate the real world with virtual scene perfectly, nowadays it has been applied to industrial fields. This paper reviews the development and the research status of AR at home and abroad, the relation and difference between augmented reality and virtual reality are also analyzed. After that, three key techniques in augmented reality are discussed, i.e. display technique, tracking registration technique and human-computer interaction technique. The implement methods for AR are also introduced briefly. Lastly, we provide an outlook for the future development of AR technique.
Soils of Technogenic Landscapes from Tungsten Mine: Micromorphological Structure, Mineral and Chemical Compositions

The main idea of the presented data was to reveal the micromorphological structureStructure, mineral and chemical compositions of the unique soilsSoils, which have been covered with waste mining and processing production of sulfide-tungsten depositsTungsten deposits of Transbaikalia for a long timeTime. In comparison with natural alluvial swampSwamp soilsSoils, these buried soilsSoils are characterized by the increase in content of (i) ferrous neoplasma and (ii) weathered grains of mica and feldspar, and (iii) the density of microaggregates along with the proportions of potentially dangerous chemical elements represented by heavy metalsHeavy metals. The technogenic nature of pollution in these soilsSoils is confirmed by the accumulation of the following potentially hazardous chemical elements—Cd, Zn, F, Mo, Cu, Ni, As, Co, Pb namely in the upper organogenic horizons. It was revealed that the intensity of soil contamination is affected by the duration of storage of ore dressing waste, composition and level content of toxic elements in it.
The Verb Collective

The Verb Collective is a toolkit designed to aid instructors in teaching virtual and augmented reality to artists and creatives who are exploring the medium of immersive technologies but who might have little or no background in coding. It is designed to fit seamlessly into the interface of the popular Unity development platform and, though it leverages some of the same structures of visual coding environments, it is intentionally designed without specialized interfaces. Though it has been found that visual scripting environments offer useful tools for rapid prototyping they also constrain students to the structures they employ while also teaching them a set of tools subject to change or becoming unusable if the proprietary codebase is not regularly updated. The Verb Collective is designed to be modular and lightweight and for student expertise in its functions to be directly transferable to the Unity platform as well as other similar platforms. While other popular toolkits are focused either on rapid production or on teaching code itself, the Verb Collective is focused on emergent dynamics and exploratory play with code. This model is suited to people whose interest in immersive environments and interactivity is spurred by creative pursuits. The modularity of the system is such that while it is limited in scope it is highly customizable. Designed with three tiers of users in mind, it functions as well for beginners as it does for more skilled developers. Focusing on the expressive qualities of the medium and on the the dynamic and emergent interplay of simple elements is a strategy designed to make the platform of VR and AR accessible to a broader range of people for whom novel experiences are privileged over interaction with existing industries or models.
Question-Answering Dialog System for Large Audiovisual Archives

In this paper, we present our spoken dialog system that serves as a search interface of the MALACH archive. The voice interface and natural language input allow the users to retrieve information contained in large audiovisual archives more comfortably. Especially, finding answers to a more structured question should be easier in comparison with typical search input options. The dialog is build on top of a system that automatically annotates and indexes the archive using automatic speech recognition. These indexes were searchable so far only in a full-text search for any arbitrary text query. Our proposed approach improves this system and leverages named entity recognition to create a knowledge base of semantic information contained in the recognized utterances. We describe the design of the dialog system, as well as the automatic knowledge base generation and the approach to creating queries using a spoken natural language as an input.
Efficient Motion Deblurring with Feature Transformation and Spatial Attention

Convolutional Neural Networks (CNN) have recently advanced the state-of-the-art in generalized motion deblurring. Literature suggests that restoration of high-resolution blurred images requires a design with a large receptive field, which existing networks achieve by increasing the number of generic convolution layers, kernel-size, or the scales at which the image is processed. However, increasing the network capacity in this form comes with the burden of increased model size and lower speed. To resolve this, we propose a novel architecture composed of dynamic convolutional modules, namely feature transformation (FT) and spatial attention (SA). An FT module addresses the camera shifts responsible for the global blur in the input image, while a SA module addresses spatially varying blur due to dynamic objects and depth changes. Qualitative and quantitative comparisons on deblurring benchmarks demonstrate that our network outperforms prior art across factors of accuracy, compactness, and speed, enabling real-time deblurring.
VQAR: Review on Information Retrieval Techniques based on Computer Vision and Natural Language Processing

Recently Computer vision and Natural language processing paradigm contains enormous research progress in their respective areas. Despite the progress in both areas, still it remains as a challenging task for machines to extract image semantics and then communicate this extracted information with the desired users. These problems will be solved by Visual Question Answering (VQA) system by connecting both computer vision and natural language processing paradigms. In VQA, system is presented with an image and textual question related to that image. The system will generate the answer by processing on both image and textual features. Answer generated by VQA is in one word, phrase or in sentence. Various datasets are available for training and evaluating VQA system which contains real or abstract images and question-answer pairs related to the semantics available in the image. VQA is being used in many areas such as for blind and visually impaired users, robotics, art gallery and many more areas. This paper discusses VQA techniques, VQA datasets and highlights the parametric evaluation of these techniques along with generic issues in VQA system.
UXmood - A Tool to Investigate the User Experience (UX) Based on Multimodal Sentiment Analysis and Information Visualization (InfoVis)

Evaluating User Experience (UX) is not a trivial task, and UX specialists have used a variety of tools to ana- lyze data collected from user tests, which causes difficulty in synchronizing the data. This paper presents UXmood, a tool that condenses multiple distinct data types (audio, video, text, and eye-tracking) in a dashboard of coordinated visualizations to ease the analysis process and allow to manage several projects where each project has several logs of user interaction. The tool replays sessions of tests and uses a combination of different sentiment analysis techniques to present a suggestion of user sentiment at any given time during the tasks. The visualizations support brushing and details-on-demand interactions and are synchronized with a temporal slider, allowing analysts to see specific moments of the tests freely. Also, the uses of the sentiment analysis in the collected data may improved the qualitative analysis of UX.
Deep Structured Semantic Model for Recommendations in E-commerce

This paper presents an approach for building a recommender system that makes use of heterogeneous side information based on a modification of Deep Structured Semantic Model (DSSM). The core idea is to unite all side-information features into two subnetworks of user-related and item-related features and learn the similarity between their latent representations using neural matrix factorization. We tested the proposed model in the task of products recommendation on the dataset provided by a Russian online-marketing company. To deal with the sparsity of the data, we suggest recommending categories of items first and then use any other algorithm to rank items inside categories. We compared the performance of the proposed model to several traditional methods and demonstrated that DSSM with heterogeneous input significantly increases overall recommendation quality which makes it suitable for recommendations with rich side information about items and users.
Temporal Feature Enhancing Network for Human Pose Estimation in Videos

Although state-of-the-art methods for human pose estimation have achieved superior results on the single image, their performance on videos usually deteriorates dramatically due to motion blur and occlusion. Since there is close temporal correlation among video frames, exploiting the contextual information properly can be helpful to tackle the problem. In this paper, we present a Temporal Feature Enhancing Network (TFEN) for video human pose estimation. It boosts the per-frame features by utilizing motion information in terms of optical flow and conducting temporal feature encoding by the convolution gated recurrent units (convGRU). It is an end-to-end learning framework and can extend any image based algorithm to video pose estimation. The experimental results validate the effectiveness of the proposed approach on two large-scale video pose estimation benchmarks.
A Novel No-Reference Quality Assessment Model of Tone-Mapped HDR Image

Research on tone mapping operators (TMOs) attracts more attention recently, which can transform high dynamic range (HDR) images to low dynamic range (LDR) images for visualizing them on the common displays. In this paper, we propose a novel no-reference image quality assessment (IQA) model to evaluate the perceptual quality of tone-mapped images (TMIs). Specifically, local phase congruency (LPC) is first computed to evaluate the image sharpness and some statistical characteristics are extracted on the edge maps to measure the halo effect. Meanwhile, TMIs are transformed to opponent color (OC) space to gain the global image chromaticity and local image contract in the chromatic field. Finally, a regression module is learnt using support vector regression (SVR) to train the mapping function that maps all the features to subjective quality scores. The model shows admirable performance when tested on ESPL-LIVE HDR image database.
Stress Characterization of Ceramic Substrates by Laser Speckle Photometry

Direct copper bonding (DCB) substrate consisting of an Al2O3-ceramic layer in between two thick copper layers is one of the most powerful ceramic substrates for electronic applications. During the manufacturing process or under the service loading, high thermal or mechanical stress would be occurred. Extreme stress concentration might increase and in consequence copper structure rip off the ceramic substrates by cracking and conchoidal fractures. To avoid the crack initiation and guarantee a good quality state of ceramic substrates, the residual stress condition should be monitored. In this paper, an optical non-destructive testing method - Laser Speckle Photometry (LSP) will be demonstrated to determine mechanical stresses in ceramic substrates. The static speckle pattern is generated on the sample surface by illumination of a laser source, and sample is loaded by a 3-point-bending device to introduce surface strain, which activates a time resolved speckle signal. By recording and analyzing the quasi static speckle pattern, the speckle signal can be related to applied external mechanical signal. The resulting measurement signal was calibrated by stresses, calculated via a finite-element-model FEM. The potential of LSP for non-destructive characterization and monitoring of stress condition will be shown in details.
Input-Perturbation-Sensitivity for Performance Analysis of CNNS on Image Recognition

Performance assessment is critical to learning systems, but it is tough to explain the relationship between data, model, and performance. In this paper, the Input-Perturbation-Sensitivity (IPS) is proposed to investigate this problem in a class of Convolutional Neural Networks (CNNs) for image recognition and try to explain their relationships. First, IPS is defined and the CNNs parameters are divided into groups according to the layers of the model. Second, the output perturbations of the CNNs caused by input perturbations are analyzed with a group of local sensitivities (LS). Third, global sensitivity (GS) is obtained over all local IPS. Finally, experiments are carried out on a few CNNs with different hyper-parameters on the image recognition datasets. The analytic and experimental results show that the proposed method correlates well with data, model, and performance. Moreover, the IPS can provide a reasonable explanation of the different networks for image classification, showing the potential to evaluate other learning systems.
Automatic Extrinsic Calibration of Camera Networks Based on Pedestrians

Extrinsic camera calibration is essential for any computer vision tasks in a camera network. Usually, researchers place calibration objects in the scene to calibrate the cameras. However, when installing cameras in the field, this approach can be costly and impractical, especially when recalibration is needed. This paper proposes a novel accurate and fully automatic extrinsic calibration framework for camera networks with partially overlapping views. It is based on the analysis of pedestrian tracks without other calibration objects. Compared to the state of the art, the new method is fully automatic and robust. Our method detects human poses in the camera images and then models walking persons as vertical sticks. We propose a brute-force method to determine the pedestrian correspondences in multiple camera images. This information along with 3D estimated locations of the head and feet of the pedestrians are then used to compute the camera extrinsic matrices. We verified the robustness of the method in different camera setups and for both single pedestrian and multiple walking people. The results show that the proposed method can obtain the triangulation error of a few centimeters. Typically, it requires 40 seconds of collecting data from walking people to reach this accuracy in controlled environments and a few minutes for uncontrolled environments. As well as compute relative extrinsic parameters connecting the coordinate systems of cameras in a pairwise fashion automatically. Our proposed method could perform well in various situations such as multi-person, occlusions, or even at real intersections on the street.
A Comparative Study of Extraction Cylinder Features in Industrial Point Clouds

With the technological advancement in the field of Computer Aided Design such as the rapid development of scanning technologies, the reconstruction of complete and incomplete cylinders given noisy point clouds with form defects becomes an important issue. In fact, cylindrical surfaces are found in domestic to industrial contexts. In this paper, a comparative study of cylinder fitting algorithms manufactured in the LIPPS laboratory is proposed. The aim of the proposed approach is to determine the diameter of cylindrical feature for minimizing roundness error from experimental data-points. The roundness error is evaluated using two internationally defined methods: Minimum Circumscribed Cylinder (MCC) and Maximum Inscribed Cylinder (MIC). All algorithms give similar results in the case where the scanned cylinder is complete and without form defects, but in the case of missing data some algorithms give unacceptable results. The two reference cylinders have been independently analyzed, respecting six criteria (calculation complexity, damping parameter, initial guess, time, circularity error and complexity cylinder). The results of algorithms are also compared to help manufacturers and inspectors facilitate and improve the application of these methods and to select the appropriate algorithm for size and form evaluation.
Let’s Listen Through WhatsApp: An energizing listening exercise in EFL Class

Abstract This research is focused on analyzing the impact on the use of ‘WhatsApp’ as a media to develop listening skill for a group of 26 ten-grade senior high school students. Since the phenomenal booming about the tremendous use of smartphone in real-life among teenagers which might bring negative effects, it is urgent to empower the phenomenal behavior of teenagers toward their mobile phone in the context of language learning. The 26 ten grade senior high students were put in ‘WhatsApp’ group messenger. The learning activities were performed in ‘virtual’ ‘WhatsApp’ group class called ‘Fun In English’. The one-group pretest-posttest design was implemented in this study. The instruments used in this study were 1) learning activities 2)English comprehension tests (pretest and posttest) and 3) a questionnaire and interview of the students ‘response. Regarding the data analysis, mean, percentage and t-test scores for the dependent sample were employed. The result indicated that 1) the students’ English listening comprehension ability increased significantly after learning with MP3 which uploaded in ‘WhatsApp’ group called ‘Fun In English’. 2) the questionnaires told that the students reflected a good response. The mean score of pre-test was 6,9 then mean score of posttest was 8,7 which means that there is a significant increasing score. The students’ score were increased after the treatment which is the students was given learning activities through ‘WhatsApp’ group exercises. Thus, the learning model is confirmed to be effective and powerful. The students gave their response through questionnaires that reflected their enthusiasm, intimate interaction, and enjoyable learning activities.