Reviewerly/exHarmony huggingface dataset:
Putting the Role of Personalization into Context

Personalization is omnipresent in our life, with applications ranging from entertainment and commercial uses to smart devices and medical treatments. The integration of personalization in various products turned rapidly from an unnecessary luxury to a commodity that is expected by customers. While different machine learning fields present state-of-the-art advances and super-human performance, personalization applications are often late-adopters of novel solutions due to their complex framing and multiple stakeholders' with different business goals. The role of personalisation applications is also ambiguous: it is unclear, for instance, whether models just predict a user's next action or proactively affect the user's selections. This talk focuses on examining the role of recommenders and their ability to adapt to customer feedback. Key topics such as causality and active exploration are depicted with real examples and demonstrated alongside business considerations and implementation challenges. It relies on recent advances in the field and on work conducted at Booking.com, where we implement personalization models on one of the world's leading online travel platform.
An In-Network Parameter Aggregation using DPDK for Multi-GPU Deep Learning

In distributed deep neural network using remote GPU nodes, communication occurs iteratively between remote nodes for gradient aggregation. This communication latency limits the benefit of distributed training with faster GPUs. In this paper, we therefore propose to offload the gradient aggregation to a DPDK (Data Plane Development Kit) based network switch between a host machine and remote GPUs. In this approach, the aggregation process is completed in the network using extra computation resources in the network switch and efficiently overlapped without increasing workload on remote nodes. The proposed DPDK-based switch supports reliable communication protocols for exchanging gradients data and can handle a part of MPI over TCP-based communication. We evaluate the proposed switch when GPUs and the host communicate with a standard IP communication over 40GbE, a PCI Express (PCIe) over 40Gbit Ethernet (40GbE) product and MPI communication over 10GbE, respectively. The evaluation results using a standard IP communication show that the aggregation is accelerated by 2.2-2.5x compared to the aggregation executed by a host machine. The results using the PCIe over 40GbE product show that the proposed switch outperforms the aggregation done by the host machine by 1.16x. The evaluations using MPI communication using Jetson Xaviers cluster show that the proposed switch provides up to 5.5-5.8x faster reduction operations than the conventional method.
Role of academician A. A. Dorodnitsyn in the development of computer technologies

Examines the scientific achievements and scientific-organizational activity of one of the brightest Scientists of the 20th century, a deep analyst, a specialist in the field of exact research of Hero of Socialist Labor, holder of three orders of Lenin, order of Friendship of Peoples and many other awards, laureate of Lenin, three State, many other awards, one of the organizers and President (1968-71) of IFIP (International Federation for information processing), Director (1955-1989), scientific Director (1989-94) of Computing Centre of the USSR Academy of Sciences, one of the initiators of the Moscow Institute of physics and technology, founder and editor-in-chief (1960-94) of the Computing Mathematics and Mathematical Physics magazine, academician Anatoly A. Dorodnitsyn. The article focuses on the influence of A. A. Dorodnitsyn's activity on the development of computer technologies.
BOS-Based Three-Dimensional Reconstruction of Rotor Blade Tip Vortex Positions

The present work discusses a methodology to reconstruct three-dimensional positions of rotor blade tip vortices based on stereoscopic background-oriented schlieren data. The reconstruction procedure is first applied on a rotor test stand where a comparison with previous PIV measurements is performed to validate the reconstruction scheme. Subsequently, it is adapted for the reconstruction of the vortex system surrounding a model helicopter in free flight during an unsteady takeoff maneuver.
Measurement and Characterization of Hearing Protection Devices in the Presence of Impulse Sound

Impulsive sounds can cause severe hearing damage and even hearing loss. Sound protection devices are widely used to attenuate impulsive sounds and reduce their impact on hearing. Properly measuring and characterizing the sound attenuation is essential when choosing a specific hearing protection device. Currently, hearing protection devices are often characterized using the Impulse Peak Insertion Loss (IPIL) that measures the total attenuation across all frequency bands. IPIL does not provide any information about the spectral attenuation of the device. Human hearing is spectrally sensitive, and the risk of noise-induced hearing damage is frequency-dependent. Therefore, characterizing hearing protection devices has to be done for both the peak and the full audible frequency spectrum from 20 Hz to 20k Hz. In this paper, we propose a novel energy preserving method for estimating the 1/3 octave band insertion loss using the continuous wavelet transform. To do so, we collected gunshot audio sounds from firing a sniper rifle and evaluated the sound attenuation effect of adding a sound protection device (or sound suppressor) to the rifle. The method that we called Wavelets Octave Band Insertion Loss (WOBIL) is compared with existing methods such as the IPIL, the Impulsive Spectral Insertion Loss (ISIL) and the recently published Octave Band Impulse Peak Insertion Loss (OBIPIL).
Dual Optimization for Kolmogorov Model Learning Using Enhanced Gradient Descent

Data representation techniques have made a substantial contribution to advancing data processing and machine learning (ML). Improving predictive power was the focus of previous representation techniques, which unfortunately perform rather poorly on the interpretability in terms of extracting underlying insights of the data. Recently, the Kolmogorov model (KM) was studied, which is an interpretable and predictable representation approach to learning the underlying probabilistic structure of a set of random variables. The existing KM learning algorithms using semi-definite relaxation with randomization (SDRwR) or discrete monotonic optimization (DMO) have, however, limited utility to big data applications because they do not scale well computationally.In this paper, we propose a computationally scalable KM learning algorithm, based on the regularized dual optimization combined with enhanced gradient descent (GD) method. To make our method more scalable to large-dimensional problems, we propose two acceleration schemes, namely, the eigenvalue decomposition (EVD) elimination strategy and an approximate EVD algorithm. Furthermore, a thresholding technique by exploiting the error bound analysis and leveraging the normalized Minkowski <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\ell _1$</tex-math></inline-formula> -norm, is provided for the selection of the number of iterations of the approximate EVD algorithm. When applied to big data applications, it is demonstrated that the proposed method can achieve compatible training/prediction performance with significantly reduced computational complexity; roughly two orders of magnitude improvement in terms of the time overhead, compared to the existing KM learning algorithms. Furthermore, it is shown that the accuracy of logical relation mining for interpretability by using the proposed KM learning algorithm exceeds 80%.
Smart Digital Terminal Devices with Speech Recognition and Speech Control

This paper discusses, proposes and describes the possibilities and solution of using speech technologies for improving the provision of services at self-service digital tellers or counters. It is interesting in the situation initiated by the pandemic caused by the COVID 19 virus. It enables to the users of services at self-service digital tellers/counters that do not touch device surfaces, that were previously touched by a large number of people and that are rarely disinfected. One of ways of realization such services is described on a multifunctional smart digital terminal devices with speech recognition and speech control, with a specific software solution, implemented for a specific purpose. It can be used for the realization of a wide range of services, that are provided through self-service digital devices. Places where such digital devices could be located are: banks, post offices, municipalities, companies that provide services as electricity and water distribution, hotels, schools, universities, parking lots, theatres, cinemas, various types of transportation, etc.
Research on Efficient Image Inpainting Algorithm Based on Deep Learning

The rapid development of deep learning has brought a new development direction for image inpainting, changing the traditional image inpaiting algorithm, which can only repair the problem of small area damage based on the structure and texture of the damaged image. In recent years, image inpainting algorithm based on deep learning has received widespread attention from industry and academia so that it has made great progress. However, the current image inpainting algorithm based on deep learning still has the problem of consuming so much time. In order to solve the above problem, an end-to-end image inpainting algorithm suitable for real-time scene was proposed. The mask of the damaged image generated by D-linkNet network, the edge information of the damaged image, and damaged image were used to control the network input, which avoided the damage to the existing semantics of the image and retained the intact image outside the damaged. On this basis, in order to improve the performance of image inpainting, Convolutional Block Attention Module (CBAM) was used in the residual network. Experimental results show that, compared with edge information-based deep learning algorithm Edge Connect, the repair speed is twice as fast as Edge Connect while the repair results are similar.
Robust GAN Based on Attention Mechanism

Deep neural networks (DNNs) have been found to be easily mislead by adversarial examples that add small perturbations to inputs to produce false results. Different attack and defense strategies have been proposed to better study the security of deep neural networks. But these works only focus on an aspect such as attack or defense. In this work, we propose a robust GAN based on the attention mechanism, which uses the deep latent features of the original image as prior knowledge to generate adversarial examples, and it can jointly optimize the generator and discriminator in the case of adversarial attacks. The generator generates fake images based on the attention mechanism to deceive discriminator, the adversarial attacker perturbs the real images to deceive discriminator, and the discriminator wants to minimize the loss between fake images and adversarial images. Through this training, we can not only improve the quality of adversarial images generated by GAN, but also enhance the robustness of the discriminator under strong adversarial attacks. Experimental results show that our classifier is more robust than Rob-GAN [14], and the generator outperforms Rob-GAN on CIFAR-10.
Opinion Mining-Based Conjoint Analysis of Consumer Brands

Nowadays, online customer reviews in the form of feedback and ratings are available for a large number of product or service-based categories. These product reviews signify individually perceived strengths and shortcomings of a given product category, which can essentially provide design engineers and market researchers with invaluable insights into product design. In order to analyze the available individual customer opinions and turn them into aggregate consumer preferences that can be used in the product development and improvement process, we proposed a hybrid of sentiment analysis and conjoint analysis to determine the best brand among a list of brands for a specified product category. The outcome of this hybrid model is compared with the hybrid of sentiment analysis and multi-criteria decision making. The proposed framework estimates the relative effect of product attributes and brand names on the overall preference of consumers. We demonstrated the effectiveness of our approach using product review data taken from the literature. Our proposed approach turned out to be useful in determining the best of market availability.
Stochastic local search for efficient hybrid feature selection

There is a need to study not only accuracy but also computational cost in machine learning. Focusing on both accuracy and computational cost of feature selection, we develop and test stochastic local search (SLS) heuristics for hybrid feature selection.
Quality Assessment of Digital Elevation Models in a Treeless High-Mountainous Landscape A Case Study from Mount Aragats, Armenia

Global Digital Elevation Models (DEMs) are widely employed in geoarchaeology. Usually, their adequacy for particular landscapes is not tested. We assessed 30m-resolution-DEMs (ASTER, SRTM, ALOS, EU-DEM, NASADEM, NEXTMap) with local precision datasets. Our results reveal considerable differences (ASTER unsuitable for the region, NEXTMap and EU-DEM fit most closely to our reference model). This outcome does not necessarily apply to all similar regions. It rather stresses the need for a check of DEMs’ quality in any given study area, and it encourages the use of detailed topographic visualisations of DEMs in absence of suitable reference data.
Taking togetherness apart: From digital footprints to geno-digital spores

Increasingly, the lines of our lives are prescribed, mediated, drawn and knotted together by digital technologies. It has been argued that ‘digital footprints’, as a trail of user data points collected from online communities and networks, can assist in better understanding human behaviour and social interaction, initially focused on mainly real-time and retrospective analysis. In our attempts at sense-making of togetherness in a COVID-19/post-COVID-19 world, we believe it may be an oversimplification to conceptualise our daily data trails as ‘digital footprints’. The nature of our interaction with these technologies as well as their interaction with us have changed deeply ever since the rapid growth of digital connectivity. The character of these symbiotic relationships has been accentuated even more by our global experience of ‘connected disconnection’ during the pandemic’s lockdowns. Against this background, we expand the concept of ‘geno-digital spores’ as a more appropriate metaphor for the manner within which data and technology combine in new ways to create (or fracture) lines of togetherness.
Utilization of the Cube as a Medium for the Introduction of the English Alphabet for Preschoolers

Abstract Awareness of the importance of mastering English now raises efforts to introduce and teach children about English as early as possible, such as preschool education or children aged 3-6 years. Constraints that are often faced are usually in the form of lack of enthusiasm of children to learn this. This is evidenced by the difficulty of children in memorizing and pronouncing letters in English. The choice of instructional media also greatly influences the level of students’ understanding of the material. Considering that preschool age is a time when children will be very interested in interactive multimedia content, this problem can be applied augmented reality technology. The learning concept used is based on student centers. So that the affective and psychomotor scope of students can be achieved. This instructional media in the form of a cube which contains a letter marker forming pieces. Students will scan from the cube containing letter markers. The scan results are a 3D view of the object marker. 3D objects will display the basic words beginning with the selected alphabet. Besides that, the application will also display the way of spelling the word accompanied by sound. The aim is to make it easier for children to remember words and letters in English. There is also a quiz feature as a material for evaluating students’ understanding of the material. From the results of the trial regarding the appropriateness of the application conducted at TK Dharma Wanita Unity the data obtained were obtained from the questionnaire given to the accompanying teacher, as many as 5.71% said the application was quite helpful, then as much as 42.86% said they agreed, and as much as 51.43% said strongly agreed that the application can help students’ level of understanding of the alphabet in English.
Synchronization of High-Speed Quantum Random Number Generators via Digital Television Channels

The problem of synchronizing the process of generating secret keys by quantum random number generators via different communication channels is relevant for promising cryptographic complexes for various purposes. Reference timestamps broadcast by Global Navigation Satellite Systems of various countries have found wide application in synchronization systems for the generation and transmission of cryptographic keys. Signals from these systems are susceptible to spoofing and jamming attacks, which have been implemented in practice by various researchers. Therefore, it is proposed to use digital television channels as an alternative channel for transmitting timestamps. The process of synchronization using signals from digital television channels is understudied.This paper presents an algorithm for extracting reference timestamps from the wavelet spectrum of an image broadcast on digital television channels. The algorithm is based on the selection of the vector of wavelet coefficients of the video signal fragment corresponding to the timestamp. The procedure for extracting the wavelet spectrum of the timestamp is carried out using a multilayer neural network. Timestamps are used to temporally synchronize the process of generating secret keys by a quantum random number generator.
Improving Self-Attention Based News Recommendation with Document Classification

Online news services have become the first choice to read news for many internet users. However, thousands of news articles are released and updated on a daily basis, which makes it impossible for users to select relevant and intriguing stories by themselves. The news recommendation models are developed to tackle information overload. News stories on various topics are recommended to users from diversified backgrounds by an automated system. In this paper, we propose a neural news recommendation model with self-attention jointly trained by document classification, SARC. The self-attention mechanism captures the long-term relationships among words. The joint training of recommendation and classification improves representation and generalization capability. We demonstrate our model’s superior performances over other state-of-the-art baselines on a large-scale news recommendation dataset.
Visually-Interactive Systems: Structural Optimization of Software Development

Visually-interactive systems development is a complex process that has the software architecture at the core. Software architecture research is trying to determine the best ways to split the system into parts, define these parts' interaction with each other, transfer information between them, decide how these parts evolve individually, etc. The construction of the system architecture is done by defining the goals of the system, its input and output data, decomposition of the system into subsystems, components or modules, and development of its overall structure. The work aims to consider existing architectural approaches to creating visually-interactive systems, taking into account their specifications, including ways to represent game objects, work with graphical resources, optimize memory usage, rendering of textures, and 3D-models, work with artificial intelligence (AI) characters and others. Each approach will identify conditions of use, advantages and disadvantages, techniques that are organically combined with them. The work presents the results of optimizing structures of software systems based on classical approaches, analyzing the feasibility of their use in cases where the features of the visually-interactive component of these applications should be considered. The discussed approaches can be applied to most modern mobile games.
Sparse random feature maps for the item-multiset kernel

Random feature maps are a promising tool for large-scale kernel methods. Since most random feature maps generate dense random features causing memory explosion, it is hard to apply them to very-large-scale sparse datasets. The factorization machines and related models, which use feature combinations efficiently, scale well for large-scale sparse datasets and have been used in many applications. However, their optimization problems are typically non-convex. Therefore, although they are optimized by using gradient-based iterative methods, such methods cannot find global optimum solutions in general and require a large number of iterations for convergence. In this paper, we define the item-multiset kernel, which is a generalization of the itemset kernel and dot product kernels. Unfortunately, random feature maps for the itemset kernel and dot product kernels cannot approximate the item-multiset kernel. We thus develop a method that converts an item-multiset kernel into an itemset kernel, enabling the item-multiset kernel to be approximated by using a random feature map for the itemset kernel. We propose two random feature maps for the itemset kernel, which run faster and are more memory efficient than the existing feature map for the itemset kernel. They also generate sparse random features when the original (input) feature vector is sparse and thus linear models using proposed methods . Experiments using real-world datasets demonstrated the effectiveness of the proposed methodology: linear models using the proposed random feature maps ran from 10 to 100 times faster than ones based on existing methods.
Adversarial Attacks Technology in Deep Learning Models

Abstract Deep learning related to computer vision, speech recognition, and language processing has been developing rapidly over recent years. The applications of these models, however, have underlying risks. Recent studies have shown that small perturbation from adversarial examples could result in false interpretation of the neural network examples and false judgment. Therefore, understanding adversarial example technologies is essential for promoting the safety and robustness of neural network models. This paper summarizes current adversarial example technologies in different applications, discusses the current prospects and challenges, and envision potential future developments in related fields.