Reviewerly/exHarmony huggingface dataset:
Machine Learning Approach for Facial Expression Recognition

This paper outlines the effectiveness of several popular machine learning algorithms for facial expression recognition. The dataset used for this paper consists of 35887 images of size 48×48 pixels which are all depicting faces posed in one of seven expressions (anger, disgust, fear, happy, sad, surprise, neutral). This is a popularly used dataset for practice and exploration and there are many different approaches suggested in the literature. In this paper, the following algorithms are applied and tested: AdaBoost, Logistic Regression, Dense Neural Network (DNN), and Convolutional Neural Network (CNN). CNN is shown to provide the highest accuracy compared to other algorithms.
Fully Optimized Convolutional Neural Network Based on Small-Scale Crowd

Crowd counting is of considerable significance to society in terms of public safety and urban development. Manual counting of people in a video or photo is often time-consuming and labour-intensive. People will need an efficient and economy way instead of counting manually. Nowadays, the convolutional neural network was popularly utilized as the baseline for crowd counting. However, the more complex the CNN-based algorithm, the more computing resources will be consumed. This article aims to present a simpler and faster fully optimized convolutional neural network for crowd counting with desired performance. To minimize the computational cost on training networks, we proposed a fully optimized method to build our network. Extensive experiments on our fully optimized convolutional neural network indicate the superiority of our network that has very high accuracy and speed on small scale crowd.
Local Propagation in Constraint-based Neural Networks

In this paper we study a constraint-based representation of neural network architectures. We cast the learning problem in the Lagrangian framework and we investigate a simple optimization procedure that is well suited to fulfil the so-called architectural constraints, learning from the available supervisions. The computational structure of the proposed Local Propagation (LP) algorithm is based on the search for saddle points in the adjoint space composed of weights, neural outputs, and Lagrange multipliers. All the updates of the model variables are locally performed, so that LP is fully parallelizable over the neural units, circumventing the classic problem of gradient vanishing in deep networks. The implementation of popular neural models is described in the context of LP, together with those conditions that trace a natural connection with Backpropagation. We also investigate the setting in which we tolerate bounded violations of the architectural constraints, and we provide experimental evidence that LP is a feasible approach to train shallow and deep networks, opening the road to further investigations on more complex architectures, easily describable by constraints.
An Off-line Handwriting Recognition Employing Tensorflow

Handwriting has been a conventional means of communication and recording in daily life since early time. Given its ubiquity in human transactions, machine recognition of handwriting has practical significance, such as, in reading handwritten notes in a PDA, in postal addresses on envelopes, in amounts in bank checks, or in handwritten fields in forms [1]. Handwriting recognition is a vital application in daily activities and the researches of especially handwritten digits recognition is vital. This paper focuses on using simpler neural network instead of complicated ones that require high quality of computer configuration to recognize handwriting digits with relatively promising accuracy. To do this research, a neural network to recognize handwriting in MNIST dataset using Softmax Regression algorithm with a high accuracy is built.
Learning user interest with improved triplet deep ranking and web-image priors for topic-related video summarization

Video summarization facilitates rapid browsing and efficient video indexing in many video browsing website applications, such as sport video highlights, dynamic video cover. In these applications, it is most important to generate user video summaries that capture interesting video content that users prefer. While many existing methods generate video summaries based on low-level features, this paper first proposes to mine large-scale Flickr images and find “interest” and “non-interest” images from Flickr for the same query to learn what is of interest to users. Unlike existing pairwise ranking-based methods for video summarization, we then propose an improved triplet deep ranking model that is easier to converge to learn the relationship between “interest” and “non-interest” Flickr images, and exploit what visual content of the original video is indeed preferred by users. In the training process, triplets (interest image p+, interest image p′+, non-interest image p-) are selected as input to train a model with three parallel deep convolutional networks. In the video summarization process, an efficient entropy-based video segmentation method is proposed for dividing the original video into segments and the visual interest scores of the segments are estimated using the trained ranking network for summarization (SumNet). Then, an optimal subset of the segments is selected to create a summary capturing interesting visual content. We evaluate and compare our method with several state-of-the-art methods, experimental results show that our method achieves an improvement over the best baseline method by 9.6% in terms of mean Average Precision (mAP) accuracy.
Wasserstein Exponential Kernels

In the context of kernel methods, the similarity between data points is encoded by the kernel function which is often defined thanks to the Euclidean distance; the squared exponential kernel is a common example. Recently, other distances relying on optimal transport theory - such as the Wasserstein distance between probability distributions - have shown their practical relevance for different machine learning techniques. In this paper, we study the use of exponential kernels defined thanks to the regularized Wasserstein distance and discuss their positive definiteness. More specifically, we define Wasserstein feature maps and illustrate their interest for supervised learning problems involving shapes and images. Empirically, Wasserstein squared exponential kernels are shown to yield smaller classification errors on small training sets of shapes, compared to analogous classifiers using Euclidean distances.
A Quad Joint Relational Feature for 3D Skeletal Action Recognition with Circular CNNs

To deal with the limitations of human action recognition systems that apply deep neural networks (DNNs) to 3D skeletal feature maps, we propose an improved set of features that enable better pattern discrimination when using a spectrally enriched circular convolutional neural network (CCNN). These new features exploit the local relationships between joint movements based on 3D quadrilaterals constructed for all possible sets of four joints. Next, we compute the volumes of these time-varying quadrilaterals, by generating color-coded images, named spatio-temporal quad-joint relative volume feature maps (QjRVMs). To preserve the pixel frequency distribution while training a DNN, which is otherwise lost due to vanishing gradients and random dropouts, we propose a new architecture CCNNs. CCNNs use cyclic multi-resolution filters in a four-stream architecture, requiring only batch normalization and ReLU operations to identify multiple pixel pattern variations simultaneously. Applying the proposed CCNN to QjRVM images illustrates that combining multi-resolution features enhances the overall classification accuracy. Finally, we evaluate our proposed human action framework using our own 102-class, 5-subject action dataset, created using 3D motion capture technology, named KLHA3D-102. We also evaluate our framework using 3 publicly available datasets: CMU, HDM05, and NTU RGB-D.
Estimation Of Visual Contents Based On Question Answering From Human Brain Activity

We propose an estimation method for free-form Visual Question Answering (VQA) from human brain activity, brain decoding VQA. The task of VQA in the field of computer vision is generating an answer given an image and a question about its contents. The proposed method can realize answering arbitrary visual questions about images from brain activity measured by functional Magnetic Resonance Imaging (fMRI) while viewing the same images. We enable estimating various information from brain activity via a unique VQA model, which can realize a more detailed understanding of images and complex reasoning. In addition, we newly make use of un-labeled images not used in the training phase to improve the performance of the transformation, since fMRI datasets are generally small. The proposed method can answer a visual question from a little amount of fMRI data measured while subjects are viewing images.
Paper2Wire

This paper reflects on a case study of a user-centred concept development process for a Machine Learning (ML) based design tool, conducted at an industry partner. The resulting concept uses ML to match graphical user interface elements in sketches on paper to their digital counterparts to create consistent wireframes. A user study (N=20) with a working prototype shows that this concept is preferred by designers, compared to the previous manual procedure. Reflecting on our process and findings we discuss lessons learned for developing ML tools that respect practitioners' needs and practices.
What you see isn't necessarily what you get

A key characteristic for the effectiveness of Virtual Reality Environments is a high sense of presence - the feeling of being in that virtual place, even though being physically in another location. For a more differentiated perspective on presence, the term can be defined by subtypes, such as physical, social and self-presence. The Multimodal Presence Scale (MPS) by Makransky and colleagues deals with these dimensions, was translated into German, and evaluated regarding the sensitivity and specificity of the social presence subscale. The results raise the question how well differences in physical and self-presence can be detected by the scale. We conducted an experiment by constructing two virtual worlds, manipulating the polygon count of objects in each world, and measuring presence. Additionally, we assessed the correlation of the MPS with the Igroup Presence Questionnaire (IPQ). No significant differences in physical or self-presence were found. However, when examining an item that closely matches the manipulation of the self-presence world (the user's virtual hands), a statistically significant difference was found. We provide three possible explanations for these results: 1) an insufficient impact of the abstraction levels, e.g., due to insufficient time and attention to the manipulation, or too little difference between the abstraction levels, 2) a lack of sensitivity of the used MPS and IPQ, or 3) the polygon count not being important for physical or self-presence. We conclude that high polygon count might not be that crucial for presence and provide suggestions for future research.
Time Series Prediction by Quaternionic Qubit Neural Network

We propose a neural network model based on quantum information processing with quaternionic representation and operations, called Quaternionic Qubit Neural Network. The state of a neuron is represented by a point on the Bloch sphere with incorporating quaternionic representation. The operations for this neuron also follow the operations in quaternions. The proposed neural networks are evaluated through numerical experiments for predicting chaotic time series produced by a Lorentz system. They have better performances in predicting long-term series, as compared to conventional (real-valued) neural networks.
Keypoint-Based Feature Matching For Partial Person Re-Identification

As a derivative of person re-identification (re-ID), partial re- ID aims to retrieve a partial pedestrian across holistic person images captured by non-overlapping cameras. This task is more challenging and closer to real-world applications. Since we cannot locate the part of the partial image, the misaligned region compromises the performance greatly when directly (a) compare a partial pedestrian with a holistic one. To alleviate this issue, we propose a Keypoint-Based Feature Matching (KBFM) network, which constructs a simple and effective framework for partial re-ID. Specifically, our architecture explicitly leverages the keypoints generated by pose estimation. Based on the visible keypoints, coordinates of the corresponding visible region can be computed. And the keypoint-based feature embeddings can be generated by bilinear sampling. When matching two images, we extract their features on the basis of shared visible keypoints, avoiding the misalignment and disturbance. Moreover, considering the triplet loss cannot be flexibly built in the partial re-ID pipeline, we improve the original sampling method and achieve significant performance. Extensive experimental results on two widely used benchmarks demonstrate significant performance improvements of our method over most state-of-the-art methods.
THE WEBPORTAL OF V.O. SUKHOMLYNSKY STATE SCIENTIFIC AND PEDAGOGICAL LIBRARY OF UKRAINE AS AN INFORMATION RESOURCE FOR IMPLEMENTATION OF RESEARCH IN EDUCATION, PEDAGOGY AND PSYCHOLOGY

In the context of the digital transformation of society and the integration of Ukrainian science and education into the European scientific and educational space, it becomes necessary to carry out high-quality, competitive scientific research which is demanded by theory and practice. The key factor in this process is the creation and successful functioning of a system of information and analytical support for research in education, pedagogy and psychology.&#x0D; Today libraries accumulate materials for digital education and they can effectively serve as centres for learning throughout life. A library should be a full-fledged part of the open scientific and educational space. The paper presents to the scientific and educational community the educational information resources of the V. O. Sukhomlynsky State Scientific and Pedagogical Library of Ukraine (SSPL) of the National Academy of Educational Sciences of Ukraine (NAES of Ukraine). The authors feature the major characteristics of the SSPL web portal, its contents, functional specificity and purpose.&#x0D; It has been substantiated that the library site meets the requirements of relevance, substantial completeness, usability, accessibility, topicality, it has its own unique style, harmoniously combines the traditions of the scientific and pedagogical library and new technological solutions. It has been substantiated that the SSPL web portal fully fulfils informational, search, educational, advertising, navigation functions and serves as an integrated resource, a powerful tool for promoting information and library products and services that meet modern scientific and educational needs of society.&#x0D; The authors, among other things, have provided an overview of the SSPL electronic catalogue as the basis for scientific research. The relevance of the institution’s web portal has been statistically confirmed. It has been substantiated that the web portal is a powerful tool for conducting research in the issues of education, pedagogy and psychology. Also, its functioning is a significant factor that affects the image of both the SSPL and the NAES of Ukraine. The priority tasks for improving the web portal have been identified.
Enhanced Teleoperation Using Autocomplete

Controlling and manning robots from a remote location is difficult because of the limitations one faces in perception and available degrees of actuation. Although humans can become skilled teleoperators, the amount of training time required to acquire such skills is typically very high. In this paper, we propose a novel solution (named Autocomplete) to aid novice teleoperators in manning robots adroitly. At the input side, Autocomplete relies on machine learning to detect and categorize human inputs as one from a group of motion primitives. Once a desired motion is recognized, at the actuation side an automated command replaces the human input in performing the desired action. So far, Autocomplete can recognize and synthesize lines, arcs, full circles, 3-D helices, and sine trajectories. Autocomplete was tested in simulation on the teleoperation of an unmanned aerial vehicle, and results demonstrate the advantages of the proposed solution versus manual steering.
OMAF4Cloud: Standards-Enabled 360° Video Creation as a Service

A work-in-progress standard-powered media processing system is presented to demonstrate a full end-to-end interactive media service for an immersive user experience. The system converts traditional 360° videos to the Omnidirectional MediA Format (OMAF) standard-compliant format. Media processing, in general, demands complex and tailored workflows for advanced media processing and distribution such as virtual reality content stitching, packaging, and adaptive streaming. To achieve the desired result, these complex workflows require many advanced functionalities to work together on the media content. To address the needs of advanced services, the Motion Picture Experts Group (MPEG) working group is developing a new standard called Network-Based Media Processing (NBMP), a standard that aims at increased media processing efficiency, faster and lower deployment cost of interoperable media processing functions, and the ability to provide large-scale deployment by leveraging public, private, or hybrid cloud services. This paper partially covers both OMAF and NBMP standards. Additionally, an end-to-end design and proof of concept is provided to enable an immersive virtual reality experience to the end user.
Learning to Transfer Dynamic Models of Underactuated Soft Robotic Hands

Transfer learning is a popular approach to bypassing data limitations in one domain by leveraging data from another domain. This is especially useful in robotics, as it allows practitioners to reduce data collection with physical robots, which can be time-consuming and cause wear and tear. The most common way of doing this with neural networks is to take an existing neural network, and simply train it more with new data. However, we show that in some situations this can lead to significantly worse performance than simply using the transferred model without adaptation. We find that a major cause of these problems is that models trained on small amounts of data can have chaotic or divergent behavior in some regions. We derive an upper bound on the Lyapunov exponent of a trained transition model, and demonstrate two approaches that make use of this insight. Both show significant improvement over traditional fine-tuning. Experiments performed on real underactuated soft robotic hands clearly demonstrate the capability to transfer a dynamic model from one hand to another.
High-Performance Image Filters via Sparse Approximations

We present a numerical optimization method to find highly efficient (sparse) approximations for convolutional image filters. Using a modified parallel tempering approach, we solve a constrained optimization that maximizes approximation quality while strictly staying within a user-prescribed performance budget. The results are multi-pass filters where each pass computes a weighted sum of bilinearly interpolated sparse image samples, exploiting hardware acceleration on the GPU. We systematically decompose the target filter into a series of sparse convolutions, trying to find good trade-offs between approximation quality and performance. Since our sparse filters are linear and translation-invariant, they do not exhibit the aliasing and temporal coherence issues that often appear in filters working on image pyramids. We show several applications, ranging from simple Gaussian or box blurs to the emulation of sophisticated Bokeh effects with user-provided masks. Our filters achieve high performance as well as high quality, often providing significant speed-up at acceptable quality even for separable filters. The optimized filters can be baked into shaders and used as a drop-in replacement for filtering tasks in image processing or rendering pipelines.
Solving Equations Describing Processes in a Piecewise Homogeneous Medium on Radial Basis Functions Networks

The solution of boundary value problems describing piecewise-homogeneous media on networks of radial basis functions is considered. The proposed algorithm is based on solving individual problems for each area with different properties of the medium and using a common error functional that takes into account errors at the interface between the media. This removes the restrictions on the use of radial basis functions and allows the use of radial basis functions with both unlimited and limited definition areas. We used the fast algorithm proposed by the authors for training networks of radial basis functions by the Levenberg-Marquardt method with analytical calculation of the Jacobi matrix. The algorithm makes it possible to reduce the number of iterations by several orders of magnitude compared to the gradient descent algorithm currently used and to obtain the accuracy of the solution, which is practically unattainable by the gradient descent algorithm. The results of solving the model problem showed the effectiveness of the proposed algorithm.
Hierarchical Fourier Disparity Layer Transmission For Light Field Streaming

In this paper, we present a novel approach to efficiently transmit light fields in the Fourier Disparity Layer (FDL) representation using a binary hierarchical scheme.The FDL model consists of a set of additive layers which can be simply shifted and summed to render a view of the light field at any angular coordinate.In order to transmit the FDL model, we propose a method for building a binary tree where the root consists of a single compound layer obtained as the sum of all the original layers.Subsequent levels of the tree are obtained by splitting a parent layer into two children layers whose sum is equal to the parent layer.Hence, the FDL model is recursively refined with additional layers at each new level, resulting in a scalable representation.An efficient scheme is proposed to encode a single image in order to split a parent layer into its two children.Thanks to this approach, the total number of images to decode for receiving the complete tree is equal to the number of layers in the original FDL model, which is typically smaller than the number of views required in the traditional light field representation.