Reviewerly/exHarmony huggingface dataset:
NNBench-X: Benchmarking and Understanding Neural Network Workloads for Accelerator Designs

The tremendous impact of deep learning algorithms over a wide range of application domains has encouraged a surge of neural network (NN) accelerator research. An evolving benchmark suite and its associated benchmark method are needed to incorporate emerging NN models and characterize NN workloads. In this paper, we propose a novel approach to understand the performance characteristic of NN workloads for accelerator designs. Our approach takes as input an application candidate pool and conducts an operator-level analysis and application-level analysis to understand the performance characteristics of both basic tensor primitives and whole applications. We conduct a case study on the TensorFlow model zoo by using this proposed characterization method. We find that tensor operators with the same functionality can have very different performance characteristics under different input sizes, while operators with different functionality can have similar characteristics. Additionally, we observe that without operator-level analysis, the application bottleneck is mischaracterized for 15 out of 57 models from the TensorFlow model zoo. Overall, our characterization method helps users select representative applications out of the large pool of possible applications, while providing insightful guidelines for the design of NN accelerators.
Formation of remote education as a means of restoration of Russian recessive regions’ economy

Purpose The purpose of this paper is to substantiate the perspectives of using remote education as a means of restoring Russian recessing regions’ economy and develop framework recommendations for this. Design/methodology/approach The set goal is achieved by finding the dependence of the values of indicators of socio-economic development on the level of development of education in Russian regions. The methods of regression and correlation analysis are used for that. Findings The findings showed a moderate direct connection between the development of education and socio-economic position of the region if found. Qualitative analysis allowed substantiating large perspectives of using remote education as a means of restoring Russian recessing regions’ economy, which have two manifestations. The first one is related to the increase of accessibility of educational services in Russian regions. Due to this, the potential of development of region’s human potential is better realized. Being one of the key resources in the conditions of knowledge economy, human resources stimulate the activation of entrepreneurial activities, growth of labor efficiency and innovational activity of region’s companies. The second manifestation envisages the formation of entrepreneurship in the sphere of remote education as a growth vector and source of competitive advantages of recessing region’s economy. This opens possibilities for the formation of knowledge economy in region and supporting its high competitiveness. Low capital intensity of entrepreneurship in the sphere of remote education makes it accessible for development even in recessing regions, stimulating the growth of the volume of tax revenues into the regional state budget and formation of its positive balance. Originality/value For the practical implementation of the determined large perspectives of using remote education as a means of restoring Russian recessing regions’ economy, authors’ framework recommendations are offered, which are aimed at the improvement of normative and legal conditions for the conduct of entrepreneurial activities in the sphere of remote education, information support for this entrepreneurship and its marketing support and promotion as a basis of region’s economy’s competitiveness.
Component-Graph Construction

Component-trees are classical tree structures for grey-level image modelling. Component-graphs are defined as a generalization of component-trees to images taking their values in any (totally or partially) ordered sets. Similar to component-trees, component-graphs are a lossless image model; then, they can allow for the development of various image processing approaches. However, component-graphs are not trees, but directed acyclic graphs. This makes their construction non-trivial, leading to nonlinear time cost and resulting in nonlinear space data structures. In this theoretical article, we discuss the notion(s) of component-graph, and we propose a strategy for their efficient building and representation, which are necessary conditions for further involving them in image processing approaches.
Status quo and aspects to consider with ultraviolet optical versus high voltage energy relation investigations

Ultraviolet solar-blind cameras are used for inspection and research within the High Voltage domain. The current research in the field is compared with each other, considering image processing and radiometry (energy). From this comparison and research experience, the aspects for the relation between optical and electrical energy are identified. These aspects include Ultraviolet camera selection, characterization, calibration, saturation and processing. Furthermore, the electrical aspects also highlighted are test configurations, partial discharge, atmospheric conditions and test standards. Practical examples are presented.
A new nonconvex approach for image restoration with Gamma noise

In this paper, a new total generalized variational(TGV) model for restoring images with multiplicative noise is proposed, which contains a nonconvex fidelity term and a TGV term. We use a difference of convex functions algorithm (DCA) to deal with the proposed model. For multiplicative noise removal, there exist many models and algorithms, most of which focus on convex approximation so that numerical algorithms with guaranteed convergence can be designed. Unlike these algorithms, we use the DCA algorithm to remove multiplicative noise. By numerical experiments, it is shown that the proposed approach leads to a better solution compared with the gradient projection algorithm for solving the classic multiplicative noise removal models. We prove that the sequence generated by the DCA algorithm converges to a stationary point, which satisfies the first order optimality condition. Finally, we demonstrate the performance of our whole scheme by numerical examples. A comparison with other methods is provided as well. Numerical results demonstrate that the proposed algorithm significantly outperforms some previous methods for multiplicative Gamma noise removal.
Cognitive Security for Incident Management Process

This work presents the literature review about the process of handling security incidents to identify standards or guidelines published by international organizations. Based on this research we identified the phases of the incident management processes with the goal of analyze automation proposals for improve efficiency and response times. Finally, we analyzed the contribution of cognitive security to enhanced the cognitive skills of security specialists in the execution of tasks that are associated with the detection phase in the incident management process.
Deep Dive into Authorship Verification of Email Messages with Convolutional Neural Network

Authorship verification is the task of determining whether a specific individual did or did not write a text, which very naturally can be reduced to the binary-classification problem. This paper deals with the authorship verification of short email messages. Hereafter, we use “message” to identify the content of the information that is transmitted by email. The proposed method implements the binary classification with a sequence-to-sequence (seq2seq) model and trains a convolutional neural network (CNN) on positive (written by the “target” user) and negative (written by “someone else”) examples. The proposed method differs from previously published works, which represent text by numerous stylometric features, by requiring neither advanced text preprocessing nor explicit feature extraction. All messages are submitted to the CNN “as is,” after padding to the maximal length and replacing all words by their ID numbers. CNN learns the most appropriate features with backpropagation and then performs classification. The experiments performed on the Enron dataset using the TensorFlow framework show that the CNN classifier verifies message authorship very accurately.
Advances in Low-Memory Subgradient Optimization

This chapter is devoted to the blackbox subgradient algorithms with the minimal requirements for the storage of auxiliary results, which are necessary to execute these algorithms. To provide historical perspective this survey starts with the original result of Shor which opened this field with the application to the classical transportation problem. The theoretical complexity bounds for smooth and nonsmooth convex and quasiconvex optimization problems are briefly exposed in what follows to introduce the relevant fundamentals of nonsmooth optimization. Special attention in this section is given to the adaptive step size policy which aims to attain lowest complexity bounds. Nondifferentiability of objective function in convex optimization significantly slows down the rate of convergence in subgradient optimization compared to the smooth case, but there are different modern techniques that allow to solve nonsmooth convex optimization problems faster than dictate theoretical lower complexity bounds. In this work the particular attention is given to Nesterov smoothing technique, Nesterov universal approach, and Legendre (saddle point) representation approach. The new results on universal mirror prox algorithms represent the original parts of the survey. To demonstrate application of nonsmooth convex optimization algorithms to solution of huge-scale extremal problems we consider convex optimization problems with nonsmooth functional constraints and propose two adaptive mirror descent methods. The first method is of primal-dual variety and proved to be optimal in terms of lower oracle bounds for the class of Lipschitz continuous convex objectives and constraints. The advantages of application of this method to the sparse truss topology design problem are discussed in essential details. The second method can be used for solution of convex and quasiconvex optimization problems and it is optimal in terms of complexity bounds. The conclusion part of the survey contains the important references that characterize recent developments of nonsmooth convex optimization.
Political institutions, human capital and innovation: evidence from sub-Saharan Africa

Using data for a sample of 35 sub-Saharan African economies for 1995–2015, this study examines the extent to which political institutions identified as belonging to democratic or autocratic regimes explains the existing differences in innovation across sub-Saharan Africa. While the very few existing studies focus only on the direct effect of institutions, this article examines the impact of the interaction between different regime types and human capital development on innovation in developing countries. The evidence provides very strong support for the direct effect of democratic development on innovation as well as for its indirect effect via its impact on human capital development. However, the results do not support theories that argue in favour of interaction between democracy and human capital, thereby pointing to the need for better calibration of the numerous existing theories and related empirical measures.
An Effective Method of Vehicle Speed Evaluation in Systems Using Anisotropic Magneto-Resistive Sensors

Cross-correlation is an accurate method which can be used to find time shift between two signals. However, this method necessitates a high usage of microcontroller (MCU) resources. This situation occurs in a microprocessor system dedicated to evaluate speed values of cars driving on roads. Cross-correlation and the other computational methods aiming to use a reduced amount of operations on sampled data are discussed. The accuracy of the evaluated speed as well as the execution time of the program code are presented for each method. The usage of the centers of mass in two discrete signals as a speed detection method resulted in the root mean square error up to 3 times larger in comparison with the cross-correlation method. Finally, the combination of two methods was used: center of mass?for designating result quickly and then cross-correlation with a limited lag range. This sped up the MCU program code execution 15-70 times and it provided good speed evaluation accuracy.
BIR: A method for selecting the best interpretable multidimensional scaling rotation using external variables

Interpreting nonlinear dimensionality reduction models using external features (or external variables) is crucial in many fields, such as psychology and ecology. Multidimensional scaling (MDS) is one of the most frequently used dimensionality reduction techniques in these fields. However, the rotation invariance of the MDS objective function may make interpretation of the resulting embedding difficult. This paper analyzes how the rotation of MDS embeddings affects sparse regression models used to interpret them and proposes a method, called the Best Interpretable Rotation (BIR) method, which selects the best MDS rotation for interpreting embeddings using external information.
Low‐resolution face recognition and the importance of proper alignment

Face recognition methods for low resolution are often developed and tested on down-sampled images instead of on real low-resolution images. Although there is a growing awareness that down-sampled and real low-resolution images are different, few efforts have been made to analyse the differences in recognition performance. Here, the authors explore the differences and demonstrate that alignment is a major cause, especially in the absence of pose and illumination variations. The authors found that the recognition performances on down-sampled images are flattered mostly due to the fact that the images are perfectly aligned before down-sampling using high-resolution landmarks, while the real low-resolution images have much poorer alignment. To obtain better alignment for real low-resolution images, the authors apply matching score-based registration which does not rely on accurate landmarks. The authors propose to divide low resolution into three ranges to harmonise the terminology: upper low resolution (ULR), moderately low resolution (MLR), and very low resolution (VLR). Most face recognition methods perform well on ULR. MLR is a challenge for commercial systems, but a low-resolution deep-learning method can handle it very well. The performance of most methods degrades significantly for VLR, except for simple holistic methods which perform the best.
Sketching by Cross-Surface Collaboration

Human-Computer Interaction and agile practices in software engineering are not two separate domains, but rather agile is a work principle that is applied in software development, of which user interface design represents a significant part. Agile projects could require such an approach, which typically iterates on the user interface using low-fidelity prototypes. This paper motivates, presents, and assess capabilities of a software for collaborative sketching of user interfaces on multiple surfaces of interaction, ranging from mobile phones to wall screens. We proposes a Collaborative User Centered Design (CUCD) method for user interface prototyping to supporting cross-surface collaboration by sketching, enabling fast, flexible, intuitive and reusable prototype.
A mission management system for complex aerial logistics by multiple unmanned aerial vehicles in MBZIRC 2017

Abstract In this study, we present a system that manages multiple unmanned aerial vehicles (UAVs) for a search, pickup, and drop mission in the 2017 Mohamed Bin Zayed International Robotics Challenge (MBZIRC). Three UAVs picked up and dropped 23 circular and rectangular targets into a designated drop box. To control the operation of three UAVs flying over an arena of 90 × 60 m, we designed and integrated a set of technologies into our system: airspace allocation, communication framework among UAVs, anticollision based on geofencing, and a token‐based prioritization for coordination. The proposed UAV system uses a single GPS and its error of a few meters is solved by means of the following component technologies: (a) flight path generator based on one reference point, (b) vision‐based redefinition of a reference point for GPS correction, and (c) calibration of flight path to update the reference point. The pickup‐and‐drop mission is conducted via color‐ and shape‐based vision processing and a magnetic gripper to pickup and drop‐off the targets. Our proposed system is able to successfully manage three UAVs, recognize targets on the ground, and drop the targets into a drop box in the drop zone. Finally, we achieved fourth place among 18 teams in Challenge 3.
High Level 3D Structure Extraction from a Single Image Using a CNN-Based Approach

High-Level Structure (HLS) extraction in a set of images consists of recognizing 3D elements with useful information to the user or application. There are several approaches to HLS extraction. However, most of these approaches are based on processing two or more images captured from different camera views or on processing 3D data in the form of point clouds extracted from the camera images. In contrast and motivated by the extensive work developed for the problem of depth estimation in a single image, where parallax constraints are not required, in this work, we propose a novel methodology towards HLS extraction from a single image with promising results. For that, our method has four steps. First, we use a CNN to predict the depth for a single image. Second, we propose a region-wise analysis to refine depth estimates. Third, we introduce a graph analysis to segment the depth in semantic orientations aiming at identifying potential HLS. Finally, the depth sections are provided to a new CNN architecture that predicts HLS in the shape of cubes and rectangular parallelepipeds.
AspeRa: Aspect-Based Rating Prediction Model

We propose a novel end-to-end Aspect-based Rating Prediction model (AspeRa) that estimates user rating based on review texts for the items and at the same time discovers coherent aspects of reviews that can be used to explain predictions or profile users. The AspeRa model uses max-margin losses for joint item and user embedding learning and a dual-headed architecture; it significantly outperforms recently proposed state-of-the-art models such as DeepCoNN, HFT, NARRE, and TransRev on two real world data sets of user reviews. With qualitative examination of the aspects and quantitative evaluation of rating prediction models based on these aspects, we show how aspect embeddings can be used in a recommender system.
LCrowdV: Generating labeled videos for pedestrian detectors training and crowd behavior learning

We present a novel procedural framework to generate an arbitrary number of labeled crowd videos (LCrowdV). The resulting crowd video datasets are used to design accurate algorithms or training models for crowded scene understanding. Our overall approach is composed of two components: a procedural simulation framework to generate crowd movements and behaviors, and a procedural rendering framework to generate different videos or images. Each video or image is automatically labeled based on the environment, number of pedestrians, density, behavior (personality), flow, lighting conditions, viewpoint, type of noise, etc. Furthermore, we can increase the realism by combining synthetically-generated behaviors with real-world background videos. We demonstrate the benefits of LCrowdV over prior lableled crowd datasets by augmenting a real dataset with it and improving the accuracy in pedestrian detection and crowd classification. Furthermore, we evaluate the impact of removing the variety in different LCrowdV parameters to show the importance of the diversity of data generated from our framework. LCrowdV has been made available as an online resource.
Algebraic Bayesian Networks: The Use of Parallel Computing While Maintaining Various Degrees of Consistency

This paper presents approaches to parallelization of algorithms for maintaining external and internal consistency in algebraic Bayesian networks as one of the representatives of probabilistic graphical models. The algorithms modified based on these approaches are described and presented in the form of schemes.
Fitness-Aware Containerization Service Leveraging Machine Learning

Containerized deployment of microservices has gained immense traction across industries. To meet demand, traditional cloud providers offer container-as-a-service, where selection of the container and containerization of workloads remain developer’s responsibility. This task is arduous for a developer since the choice of containers across different cloud providers is many. Furthermore, there does not exist any mechanism using which one can compare and contrast the capabilities of containers across different providers. In this scenario, we envisage the need for a smart cloud broker that can automatically deploy a chosen IT service into the best-fit container environment mapped to performance requirements, from among the set of available underpinning brokered container hosting systems spread across multiple cloud providers. We propose a novel fitness-aware containerization-as-a-service to achieve this. We show why a best-fit container selection process is operationally complex and time consuming, and how we heuristically prune the associated decision tree in two phases so that it becomes viable to implement this as an on-demand service. We propose a new metric called fitness quotient ( <inline-formula><tex-math notation="LaTeX">$FQ$</tex-math></inline-formula> ) to evaluate containers obtained from heterogeneous providers. We leverage machine learning techniques to inject automation into these two phases: unsupervised K-Means clustering in the first-level build-time phase to accurately classify IaaS cost and performance data, and polynomial regression during the second-level provisioning-time phase to discover relationships between SaaS performance and container strength. We also show that the utility of the framework that we propose is not limited to the container fitness use case that we analyze in this paper; rather it can be generalized to address a class of problems where overall time and cost complexity for provisioning-time decision making needs to be controlled under a given set of constraints.
Learning Entropy as a Learning-Based Information Concept

Recently, a novel concept of a non-probabilistic novelty detection measure, based on a multi-scale quantification of unusually large learning efforts of machine learning systems, was introduced as learning entropy (LE). The key finding with LE is that the learning effort of learning systems is quantifiable as a novelty measure for each individually observed data point of otherwise complex dynamic systems, while the model accuracy is not a necessary requirement for novelty detection. This brief paper extends the explanation of LE from the point of an informatics approach towards a cognitive (learning-based) information measure emphasizing the distinction from Shannon's concept of probabilistic information. Fundamental derivations of learning entropy and of its practical estimations are recalled and further extended. The potentials, limitations, and, thus, the current challenges of LE are discussed.