Reviewerly/exHarmony huggingface dataset:
Expected Policy Gradients for Reinforcement Learning

We propose expected policy gradients (EPG), which unify stochastic policy gradients (SPG) and deterministic policy gradients (DPG) for reinforcement learning. Inspired by expected sarsa, EPG integrates (or sums) across actions when estimating the gradient, instead of relying only on the action in the sampled trajectory. For continuous action spaces, we first derive a practical result for Gaussian policies and quadratic critics and then extend it to a universal analytical method, covering a broad class of actors and critics, including Gaussian, exponential families, and policies with bounded support. For Gaussian policies, we introduce an exploration method that uses covariance proportional to the matrix exponential of the scaled Hessian of the critic with respect to the actions. For discrete action spaces, we derive a variant of EPG based on softmax policies. We also establish a new general policy gradient theorem, of which the stochastic and deterministic policy gradient theorems are special cases. Furthermore, we prove that EPG reduces the variance of the gradient estimates without requiring deterministic policies and with little computational overhead. Finally, we provide an extensive experimental evaluation of EPG and show that it outperforms existing approaches on multiple challenging control domains.
Radar image modeling and recognition

This article is devoted to the problems of radar sensing. Herein, we have considered the tasks of modeling and recognizing radar images. The modeling technology was based on the independent creation of terrain models and objects, which were then integrated into a three-dimensional (3D) scene. This approach enabled the operative creation of a number of image variants of different classes. Recognition methods and algorithms were based on the use of the so-called conjugacy index as a measure of proximity. At the same time, support subspaces of the minimum dimension were formed by vectors, components of which were samples of the radar image. Problems of higher accuracy of recognition due to a division of classes into subclasses and a combination of the support subspace method with the neural convolutional networks were considered.
Fusion of color and depth information for human actions recognition

The solution to the problem of recognizing human actions on video sequences is one of the key areas on the path to the development and implementation of computer vision systems in various spheres of life. Such areas as video surveillance systems, monitoring, contactless control interfaces, video processing as a preliminary stage of processing, etc. Most of the approaches published in the literature can be divided into two groups: approaches based on constructing a global descriptor or a description of local points, unrelated to the human skeleton; and approaches based on the construction of the feature points (joint) of the human skeleton. In most cases, only the second group of methods use depth sensors to obtain clear information about the human skeleton. While additional sources of information (such as depth sensors, thermal sensors) allow you to get more informative features, and thus increase the reliability and stability of recognition. In the article, we present the algorithm, combining information from visible cameras and depth sensors based on the PLIP model (parameterized model of logarithmic image processing) close to the perception of the human visual system, and the development of a global descriptor characterizing the action taking place in the frame. The proposed algorithm takes advantage of the fusion of various modalities that provide the construction of a more informative descriptor. Experimental results showed the effectiveness of the proposed algorithm.
Classification of Outlier’s Detection Methods Based on Quantitative or Semantic Learning

The problem of outliers (Anomalies) detection has been generally presented as a single-minded problem, in which outliers are defined as objects that do not conform to a given definition. In this chapter, we propose a novel taxonomy that groups the methods into two categories: (1) quantitative outlier detection and (2) semantic outlier detection. For quantitative outliers, outliers are defined based on a calculated outlier score. For semantic outliers, there is a conceptual meaning behind the outlier based on the context of the dataset, shifting the focus to finding the anomalous class of data. We also discuss the use of the proposed definition of semantic learning in detecting credit card frauds. CCS CONCEPTS Computing methodologies → Anomaly detection
Sensor Network Warns of Stealth Tsunamis

A next-generation network of seismic and wave sensors in the southwestern Pacific will warn coastal residents of an approaching tsunami before they see the wave.
Criteria for Rating the Level and Quality of Digitalization of the Educational Process in Universities of the Russian Federation

Introduction. The article reflects the authors’ system for calculating the level and quality of digitalization of the educational process in Russian universities. This task is actualized by a number of circumstances of modern realities. The need to create a digital twin / double for each university has become a trend due to the importance of providing applicants, students and graduates with the most accessible educational environment. The high-quality digital environment of a university during the spread of pandemics is an important condition for maintaining the stability of the educational process. Today, the ability of universities to quickly adapt the digital educational space to the needs of distance learning is a significant sign of the viability and quality of the management staff of higher education institutions. Methods and materials. To develop the system, the authors used a mathematical tool – the method of summary indicators for calculating the level of digitalization of universities, which allows to evaluate various characteristics of the value, efficiency and quality of objects with a significant number of different parameters. Analysis. To develop indicators – basic rating indicators – the authors used the results of a preliminary study of the best practices of digitalization of the university environment of leading foreign and Russian universities. The system takes into account the needs for digitalization of the educational environment of three different categories: applicants, students and graduates. Results. The introduction of the rating into the Russian practice will allow to justify a system of recommendations to university administrations to improve the level and quality of their digitalization of the educational process.
Generalization Error of Generalized Linear Models in High Dimensions

At the heart of machine learning lies the question of generalizability of learned rules over previously unseen data. While over-parameterized models based on neural networks are now ubiquitous in machine learning applications, our understanding of their generalization capabilities is incomplete. This task is made harder by the non-convexity of the underlying learning problems. We provide a general framework to characterize the asymptotic generalization error for single-layer neural networks (i.e., generalized linear models) with arbitrary non-linearities, making it applicable to regression as well as classification problems. This framework enables analyzing the effect of (i) over-parameterization and non-linearity during modeling; and (ii) choices of loss function, initialization, and regularizer during learning. Our model also captures mismatch between training and test distributions. As examples, we analyze a few special cases, namely linear regression and logistic regression. We are also able to rigorously and analytically explain the \emph{double descent} phenomenon in generalized linear models.
Sentimental classification analysis of polarity multi-view textual data using data mining techniques

The data and information available in most community environments is complex in nature. Sentimental data resources may possibly consist of textual data collected from multiple information sources with different representations and usually handled by different analytical models. These types of data resource characteristics can form multi-view polarity textual data. However, knowledge creation from this type of sentimental textual data requires considerable analytical efforts and capabilities. In particular, data mining practices can provide exceptional results in handling textual data formats. Besides, in the case of the textual data exists as multi-view or unstructured data formats, the hybrid and integrated analysis efforts of text data mining algorithms are vital to get helpful results. The objective of this research is to enhance the knowledge discovery from sentimental multi-view textual data which can be considered as unstructured data format to classify the polarity information documents in the form of two different categories or types of useful information. A proposed framework with integrated data mining algorithms has been discussed in this paper, which is achieved through the application of X-means algorithm for clustering and HotSpot algorithm of association rules. The analysis results have shown improved accuracies of classifying the sentimental multi-view textual data into two categories through the application of the proposed framework on online polarity user-reviews dataset upon a given topics.
Training Linear Neural Networks: Non-Local Convergence and Complexity Results

Linear networks provide valuable insights into the workings of neural networks in general. This paper identifies conditions under which the gradient flow provably trains a linear network, in spite of the non-strict saddle points present in the optimization landscape. This paper also provides the computational complexity of training linear networks with gradient flow. To achieve these results, this work develops a machinery to provably identify the stable set of gradient flow, which then enables us to improve over the state of the art in the literature of linear networks (Bah et al., 2019;Arora et al., 2018a). Crucially, our results appear to be the first to break away from the lazy training regime which has dominated the literature of neural networks. This work requires the network to have a layer with one neuron, which subsumes the networks with a scalar output, but extending the results of this theoretical work to all linear networks remains a challenging open problem.
Super-Resolution and Inpainting with Degraded and Upgraded Generative Adversarial Networks

Image super-resolution (SR) and image inpainting are two topical problems in medical image processing. Existing methods for solving the problems are either tailored to recovering a high-resolution version of the low-resolution image or focus on filling missing values, thus inevitably giving rise to poor performance when the acquisitions suffer from multiple degradations. In this paper, we explore the possibility of super-resolving and inpainting images to handle multiple degradations and therefore improve their usability. We construct a unified and scalable framework to overcome the drawbacks of propagated errors caused by independent learning. We additionally provide improvements over previously proposed super-resolution approaches by modeling image degradation directly from data observations rather than bicubic downsampling. To this end, we propose HLH-GAN, which includes a high-to-low (H-L) GAN together with a low-to-high (L-H) GAN in a cyclic pipeline for solving the medical image degradation problem. Our comparative evaluation demonstrates that the effectiveness of the proposed method on different brain MRI datasets. In addition, our method outperforms many existing super-resolution and inpainting approaches.
Multi-scale contextual attention based HDR reconstruction of dynamic scenes

High dynamic range (HDR) image reconstruction of dynamic scenes from several low dynamic range (LDR) images captured with different exposure time is a challenging problem. Although several methods based on optical flow or patch-match have been proposed to address this problem, they are not robust enough and results still suffer from ghost-like artifacts for challenging scenes where large foreground motions exist. To this end, this paper proposes a multi-scale contextual attention guided alignment network called CAHDRNet and presents some evaluation results. In stark contrast to methods based on optical flow, this paper demonstrates that HDR reconstruction can be formulated as an image inpainting task and CAHDRNet conducts patch replacement on the deep feature maps unlike previous patch-based reconstruction methods. The contextual attention module proposed by an image inpainting work is extended in CAHDRNet with multi-scale attention rebalance to help model flexibly handle different scenes and reduce patch replacement error. Experiments on the public dataset indicate that the proposed CAHDRNet produces ghost-free results where detail in ill-exposed areas is well recovered. The proposed method scores 40.97 on test sequences with PSNR metric averagely while the best PSNR score of non-flow-based methods is 38.60. The flow-based method scores 40.95 with PSNR metric while it has 5 points better score than our result with HDR-VDP-2 metric. According to quantitative and qualitative evaluations, the proposed method outperforms all non-flow-based methods and has its merits and demerits compared with the flow-based method.
A Data Mining Approach to Prediction of Liver Diseases

Abstract One of the major diseases in the world is liver disease. The liver is an important part of the large organs in the human body and is also considered a gland. This is because it creates and bites bile. Liver disease is a liver problem that causes the disease. The objective of this study is to propose a rule-based classification model with basic decision making techniques to predict various types of heart disease. To get better results the experiment was done using a different data mining algorithm compared with previous liver disease predictions. All experiments have been implemented at Azure Machine Learning tool. This paper is about to study the prediction of liver disease to produce better performance accuracy by comparing various mining data classification algorithms.
Deep Residual Reinforcement Learning

We revisit residual algorithms in both model-free and model-based reinforcement learning settings. We propose the bidirectional target network technique to stabilize residual algorithms, yielding a residual version of DDPG that significantly outperforms vanilla DDPG in the DeepMind Control Suite benchmark. Moreover, we find the residual algorithm an effective approach to the distribution mismatch problem in model-based planning. Compared with the existing TD(k) method, our residual-based method makes weaker assumptions about the model and yields a greater performance boost.
Blockchain-Based Personal Health Records for Patients’ Empowerment

With the current trend of patient-centric health-care, blockchain-based Personal Health Records (PHRs) frameworks have been emerging. The adoption of these frameworks is still in its infancy stage and is dependent on a broad range of factors. In this paper we look at some of the typical concerns raised from a centralized medical records solution such as the one deployed in France. Based on the state of the art literature in terms of Electronic Health Records (EHRs) and PHRs, we discuss the main implementation bottlenecks that can be encountered when deploying a blockchain solution and how to avoid them. In particular, we explore these bottlenecks in the context of the French PHR system and suggest some recommendations for a paradigm shift towards patients’ empowerment.
Towards Positively Surprising Non-Player Characters in Video Games

Video games often populate their in-game world with numerous ambient non-playable characters. Manually crafting interesting behaviors for such characters can be prohibitively expensive. As scripted AI gets re-used across multiple characters, they can appear overly similar, shallow and generally uninteresting for the player to interact with. In this paper we propose to evolve interesting behaviors in a simulated evolutionary environment. Since only some evolution runs may give rise to such behaviors, we propose to train deep neural networks to detect such behaviors. The paper presents work in progress in this direction.
Towards Deployment of Robust Cooperative AI Agents: An Algorithmic Framework for Learning Adaptive Policies

We study the problem of designing an AI agent that can robustly cooperate with agents of unknown type (i.e., previously unobserved behavior) in multi-agent scenarios. Our work is inspired by real-world applications in which an AI agent, e.g., a virtual assistant, has to cooperate with new types of agents/users after its deployment. We model this problem via parametric Markov Decision Processes where the parameters correspond to a user's type and characterize her behavior. In the test phase, the AI agent has to interact with a user of an unknown type. We develop an algorithmic framework for learning adaptive policies: our approach relies on observing the user's actions to make inferences about the user's type and adapting the policy to facilitate efficient cooperation. We show that without being adaptive, an AI agent can end up performing arbitrarily bad in the test phase. Using our framework, we propose two concrete algorithms for computing policies that automatically adapt to the user in the test phase. We demonstrate the effectiveness of our algorithms in a cooperative gathering game environment for two agents.
Ternary Hybrid Neural-Tree Networks for Highly Constrained IoT Applications

Machine learning-based applications are increasingly prevalent in IoT devices. The power and storage constraints of these devices make it particularly challenging to run modern neural networks, limiting the number of new applications that can be deployed on an IoT system. A number of compression techniques have been proposed, each with its own trade-offs. We propose a hybrid network which combines the strengths of current neural- and tree-based learning techniques in conjunction with ternary quantization, and show a detailed analysis of the associated model design space. Using this hybrid model we obtained a 11.1% reduction in the number of computations, a 52.2% reduction in the model size, and a 30.6% reduction in the overall memory footprint over a state-of-the-art keyword-spotting neural network, with negligible loss in accuracy.
PROBLEMS OF DISTANCE EDUCATION THROUGH THE EYES OF TEACHERS: LESSONS OF CORONAVIRUS

The article presents the results of a sociological survey conducted in an online form. The survey involved 476 teachers of the Volgograd State Technical University, implementing educational programs in electronic form using a remote format in connection with the coronavirus pandemic.
Searching for Winograd-aware Quantized Networks

Lightweight architectural designs of Convolutional Neural Networks (CNNs) together with quantization have paved the way for the deployment of demanding computer vision applications on mobile devices. Parallel to this, alternative formulations to the convolution operation such as FFT, Strassen and Winograd, have been adapted for use in CNNs offering further speedups. Winograd convolutions are the fastest known algorithm for spatially small convolutions, but exploiting their full potential comes with the burden of numerical error, rendering them unusable in quantized contexts. In this work we propose a Winograd-aware formulation of convolution layers which exposes the numerical inaccuracies introduced by the Winograd transformations to the learning of the model parameters, enabling the design of competitive quantized models without impacting model size. We also address the source of the numerical error and propose a relaxation on the form of the transformation matrices, resulting in up to 10% higher classification accuracy on CIFAR-10. Finally, we propose wiNAS, a neural architecture search (NAS) framework that jointly optimizes a given macro-architecture for accuracy and latency leveraging Winograd-aware layers. A Winograd-aware ResNet-18 optimized with wiNAS for CIFAR-10 results in 2.66x speedup compared to im2row, one of the most widely used optimized convolution implementations, with no loss in accuracy.
Extraction method of a nonuniform auxiliary laser stripe feature for three-dimensional reconstruction of large components

3D measurement plays an important role in the processing and assembling of large components in the aviation and aerospace industry. However, precision control is a challenging problem due to the complex on-site illumination environment and serious background interference. For the binocular stereovision measurement system based on auxiliary laser scanning, this paper proposes an extraction method of laser stripe for 3D reconstruction. First, an evaluation method for the laser stripe is proposed by analyzing the features of the stripe image. Then, a laser stripe extraction method based on self-adaptive threshold is proposed. To further improve the efficiency of image processing, an improved Kalman filter algorithm is adopted to fast-track and locate the region of interest of laser stripes in the sequence images. Finally, measurement experiments for a large-scale aircraft panel are carried out on-site. The results show that the center extraction error is less than 0.1 pixel and 3D reconstruction error is less than 0.06 mm. The proposed methods improve the efficiency and accuracy of 3D reconstruction of large components, and the feasibility of on-site application is also verified.
Socio-Technical Aspirations for Children with Special Needs

Society’s expectations and assistance for children with special needs is rooted in its cultural, societal, and political backdrop. Previous work on the role of culture on assistive or adaptive technology design for children with special needs identified a three-part framework: lifestyle, socio-technical infrastructure, and monetary and informational resources. Through our work in India, we proposed a fourth dimension to this framework: socio-technical aspirations. We defined socio-technical aspirations as the individual- or community-driven ambition and desire to own or use a specific technology for personal benefit or societal acceptance or both. In Finland, we interviewed four parents of children enrolled in a rehabilitation program, with the aim to understand their expectations from and current usage of technology. Findings from Finland reveal a desire for technology for children with special needs to be more engaging than what is currently available. We also identified several attributes that can contribute to socio-technical aspirations in a given context, including but not limited to: the level of inclusiveness supported in the school, which directly affects how technology is viewed with respect to the social acceptance it provides; the socio-technical aspirations of the child and how they are perceived and met by the parents and teachers; and previous technology experience of the various stakeholders involved in raising a child with special needs, which determines their attitude toward technology for not only for themselves but also for the child. In this article, we validate the dimension of socio-technical aspirations to strengthen our case for incorporating stakeholder’s socio-technical aspirations for technology designed or adapted for children with special needs.