Reviewerly/exHarmony huggingface dataset:
Memory-Adjustable Navigation Piles with Applications to Sorting and Convex Hulls

We consider space-bounded computations on a random-access machine, where the input is given on a read-only random-access medium, the output is to be produced to a write-only sequential-access medium, and the available workspace allows random reads and writes but is of limited capacity. The length of the input is N elements, the length of the output is limited by the computation, and the capacity of the workspace is O ( S ) bits for some predetermined parameter S ≥ lg N . We present a state-of-the-art priority queue—called an adjustable navigation pile —for this restricted model. This priority queue supports M inimum in O (1) time, C onstruct in O ( N ) time, and E xtract - min in O ( N / S + lg S ) time for any S ≥ lg N . The priority queue can be further augmented in O ( N ) time to deal with a batch of at most S elements in a specified range of values at a time, and allow to I nsert (activate) or E xtract (deactivate) an element among these elements, such that I nsert and E xtract take O ( N / S + lg S ) time for any S ≥ lg N . We show how to use our data structure to sort N elements and to compute the convex hull of N points in the Euclidean plane in O ( N 2 / S + N lg S ) time for any S ≥ lg N . Following a known lower bound for the space-time product of any branching program for finding unique elements, both our sorting and convex-hull algorithms are optimal. The adjustable navigation pile has turned out to be useful when designing other space-efficient algorithms, and we expect that it will find its way to yet other applications.
Better Model, Worse Predictions: The Dangers in Student Model Comparisons

The additive factor model is a widely used tool for analyzing educational data, yet it is often used as an off-the-shelf solution without considering implementation details. A common practice is to compare multiple additive factor models, choose the one with the best predictive accuracy, and interpret the parameters of the model as evidence of student learning. In this work, we use simulated data to show that in certain situations, this approach can lead to misleading results. Specifically, we show how student skill distribution affects estimates of other model parameters.
Methods of Information Processing and Presentation in Peer-to-Peer Online Marketplaces

In e-commerce, buyers do not have enough information about the products and services provided. In some industries, retail trade has been completely supplanted; offline trading floors have become essentially pick-up points. Therefore, online marketplaces have a disruptive effect on traditional trade. Features of the online environment often encourage consumers to take additional risks (e.g., to buy a product or service having only its virtual representation available). A system of trust is formed to streamline the virtual interaction of sellers with buyers and stimulate regulations in the purchase/sale, since the usual regulatory mechanisms and rights do not work here. The electronic market in general can break down. Such a problem can be eliminated by introducing mechanisms of trust and reputation of buyers and sellers to each other. The chapter provides an overview of the ways of presenting and processing information in the systems of reputation and trust that function on online trading sites on the social networks of the internet.
Notification System for Text Detection on Document Images Using Graphical User Interface (GUI)

Degraded images of documents typically contain several knowledge numbers. Therefore, a binarization technique must be used to extract it. Various reasons can lead document images to the degradation process, including document image quality of the inferior materials, environmental factors, and lack of interest in the handling of document images. Binarization is an image conversion process that consists of black and white pixels in pixels into a binary image. In this paper, starting process of an average red, green, and blue components of a pixel can be added to allow this transition to reach its grayscale value. Two parts of this proposed framework are primarily used in the MATLAB graphical user interface (GUI), respectively. The binarization technique for fuzzy C-Means and Deghost method amendments is used in the preliminary portion of the GUI for a document picture chosen by the purchaser. The binarization method involves IQA analysis (PSNR, accuracy, and F-measure for instance) to determine the feasibility of the methodology. In this section, the three IQA parameters will be explained in the GUI. The second part of the system offers a flexible mechanism for sending SMS over the telephone. Collectively, for the patron to accumulate the results of the photographic examination, there should be a warning framework on the cell phone. By using the GSM module coordinated with Arduino Uno, the word for the photographic examination will be sent to the phone.
Quadrotor Unmanned Aerial Vehicles: Visual Interface for Simulation and Control Development

Quadrotor control is an exciting research area. Despite last years developments, some aspects demand a deeper analysis: How a quadrotor operates in challenging trajectories, how to define trajectory limits, or how changing physical characteristics of the device affects the performance. A visual interface development platform is a valuable tool to support this effort, and one of these tools is briefly described in this Chapter. The quadrotor model uses Newton-Euler equations with Euler angles, and considers the effect of air drag and propellers’ speed dynamics, as well as measurement noise and limits for propeller speeds. The tool is able to test any device just by setting a few parameters. A three-dimensional optimal trajectory defined by a set of waypoints and corresponding times, is calculated with the help of a Minimum Snap Trajectory planning algorithm. Small Angle Control, Desired Thrust Vector (DTV) Control and Geometric Tracking Control are the available strategies in the tool for quadrotor attitude and trajectory following control. The control gains are calculated using Particle Swarm Optimization. Root Mean Square (RMS) error and Basin of Attraction are employed for validation. The tool allows to choose the control strategy by visual evaluation on a graphical user interface (GUI), or analyzing the numerical results. The tool is modular and open to other control strategies, and is available in GitHub.
2D-Image Super-Resolution on Heritage Site

One proposed method for image enhancement is single image super-resolution. For this task, many convolutional neural networks-based models were designed. These convolutional neural networks-based models perform better than the other approaches in quality measurements like structural similarity and peak signal-to-noise ratio (PSNR). Resulting super-resolved image quality is dependent on choice of a loss function. Ongoing work is to a great extent dependent on advancing mean squared reconstruction error. But PSNR and structural similarity values cannot give fine details in an image and provide higher values with unsatisfying quality. Hence, generative adversarial networks model was introduced for this problem in recent years. In this paper, image super-resolution (SR) is done with a generative adversarial network (GAN). It is the first method used for 4 × upscaling factors. Proposed approach calculates loss function which is combination of two loss functions like content and adversarial loss.
Generative Adversarial Network-Based Language Identification for Closely Related Same Language Family

Kar, Ashish Sunitha Hiremath, P. G. Gangisetty, ShankarThe discrimination between similar languages is one of the main challenges in automatic language identification. In this paper, we address this problem by proposing a generative adversarial network-based language identification method for identifying the sentences from closely related languages of same language family. The proposed method works on dual-reward feedback learning comprising of generator to generate nearly close language sentences, discriminator for determining how similar the generated sentences are to that of the training data and classifier for optimal prediction of the correct label. We evaluate the proposed model for pairs of languages and overall testing data comparison on Indo-Aryan languages dataset [12]. The effectiveness of our method is demonstrated in comparison to other existing state-of-the-art methods.
Boosted Personalized Page Rank Propagation for Graph Neural Prediction

Graph neural networks have recently achieved impressive success on many graph learning problems, including semi-supervised graph node classification. Previous message-passing algorithms are confronted with the dilemma of aggregating more neighbor’s information and avoiding over-smoothed node representations. The recently proposed personalized page rank propagation scheme separates the node prediction and information propagation into independent stages and achieves state-of-the-art performance on public datasets. However, the personalized page rank propagation scheme results in a computationally effective computation graph in model training. In this paper, we propose a boosted personalized page rank propagation scheme to reduce the model complexity and computational cost. The proposed algorithm is based on the principle of compressing multiple iterations in personalized page rank propagation into a one-step operation to simplifying the computation graph. Experiments on three public datasets reveal that the proposed algorithm reduces more than 50% training time, especially when the iteration number increases, without sacrificing the model’s prediction accuracy.
Analysis of Denoising Methods of Underwater Acoustic Pulse Signal Based on Wavelet and Wavelet Packet

Abstract Underwater acoustic countermeasures include active sonar countermeasures and passive sonar countermeasures. In order to achieve the best operational efficiency of jamming equipment, the generation of jamming signals must adapt to the development trend of underwater acoustic detection technology-nonlinear time-varying and broadband characteristics. Feature extraction is to map the high-dimensional original data to the low-dimensional transformation space through a certain mapping relationship, which can suppress a large amount of redundant information in the data and highlight the category information of the data. One of the applications of wavelet analysis in signal analysis and processing is to remove noise components from signals. In practical engineering applications, sampled signals are inevitably polluted by various noises and interferences. Through the analysis of noise characteristics, it can be seen that the wavelet denoising method is very effective in removing signal noise. In this paper, aiming at the signals with different spectrum distribution, we use various methods to de-noise in order to find the de-noising methods suitable for underwater acoustic pulse signals with different frequency characteristics, and thus find an effective means to detect signals.
ON THE PROSPECTS OF IMPLEMENTATION OF THE DIGITAL PLATFORM «ELECTRONIC RESIDENCE» IN THE REPUBLIC OF KAZAKHSTAN

The article provides an assessment of the implementation of the pilot project «Electronic Residency» in Kazakhstan. Certain difficulties were identified in the process of project implementation, including issues related to the assignment of electronic identification of a resident. Measures are proposed to optimize the process of identification and registration, verification, and control of obtaining e-residency.
Penggunaan Augmentasi Data pada Klasifikasi Jenis Kanker Payudara dengan Model Resnet-34

A recent study from the Global Cancer Observatory (GLOBOCAN) revealed that in 2020 about 2.2 milion women worldwide have been diagnosed with breast cancer. Diagnostic tissue biopsy with hematoxylin and eosin stained images is used to make decisions on the final diagnosis. Computer-assisted diagnostic systems contribute to increasing the efficiency of this process. In this study using the dataset “BreAst Cancer Histology Images (BACH)”. And a method was made to classify breast biopsy images stained with hematoxylin and eosin using convolutional neural networks. The images are classified into four classes, normal tissue, benign lesions, carcinoma in situ and invasive carcinoma. In this study, regularization techniques were also carried out in the convolutional neural networks model to achieve maximum accuracy. Keywords— Breast Cancer, Convolutional Neural Networks, Data Augmentation, Multiclass Classification.
Implementation of Chassis Number Recognition Model for Automatic Vehicle Identification

Document image recognition is required for information retrieving, searching, editing, and reporting of image text. There are several kinds of document images such as text, image, figure, signature, car license plate, chassis number, and so on. In this research, the SVD-based Document Image Recognition System is proposed for Automatic Vehicle Identification Number (VIN identification). Canny edge detection algorithm and morphological processing are applied for text area extraction. For all the segmented characters, singular value decomposition (SVD) features are calculated for the recognition process. The system is implemented by C# and the result shows that the recognition method is feasible and easy to use for users, and it can be put into practice. It can be extended to the real-time vehicle examination and identification system by using mobile device thought over the network.
USING THE BASIS OF PEDAGOGICAL SKILLS IN ORGANIZING THE EDUCATIONAL PROCESS

The article discusses ways to effectively use the basics of pedagogical skills and the need to organize lessons based on problem-based learning to increase the effectiveness of the educational process in higher education.
Dynamic Knowledge Capitalization in Big Data Context: Monte Carlo Tree Search Based Reinforcement Learning

Knowledge Management proves to be inexorable in generating value from disorganized data. Finding the connections between real-world entities is a cornerstone of building a rich knowledge tree. However, various forms of uncertainty, owing to the inherent incompleteness, missing information, uneven data distributions, and noise, affect the knowledge capitalization process.In this paper, the authors are involved in a new proposal of combining any word representation model with hybrid Monte Carlo tree search (MCTS) and reinforcement learning (RL). This combination uncovers meaningful insights and extends semantically related topics to the temporal knowledge tree. A straightforward adaptation of RL within tree search leads to a wealth of new optimal connectedness. In this method, the problem of action selection is reduced to one local reward control referred to as sub-structure coherence. Results show that the proposed approach outperforms the existing systems in terms of topic coherence, time, and memory consumption. Furthermore, the final knowledge graph improves the extracted knowledge and exhibits more attractive real-word performance comparing to the classical MCTS.
Defining ‘service levels’ for digital forensic science organisations

As demand for support from the digital forensic science discipline by criminal justice systems continues to remain high, there is a need to ensure that this field can sustain its valuable contribution to many investigations. With well documented backlogs and the presence of relevant digital behaviour documented on devices in many criminal cases means that there is a need to ensure the field's resources can be deployed effectively. It is suggested that law enforcement digital forensic science organisations could consider the deployment of a system of ‘Service Levels’, designed to transparently outline the different types of forensic capability that can be deployed within a given investigative scenario. This work defines and describes a set of seven ‘Service Level’ to be considered law enforcement digital forensic science organisations when outlining their capability to clients. In addition, a ‘Service Level Allocator’ decision model is offered to allow clients to determine what Service Level is appropriate for their investigation. • This work discusses the use of ‘service levels’ in DF examinations. • The benefits of a service level approach to digital forensic service usage are outlined. • 7 service levels are defined and described for use in digital forensic organisations. • A ‘service level allocator’ decision model is provided to support service level selection.
Software and algorithmic complex for evaluating the efficiency of an automated optical-electronic system

This paper presents a set of operation quality assessments of systems intended for detecting and localising objects of interest depending on the content of input video. The development of an input video registration generator has been carried out based on object and camera movement trajectories and on image distortions in a frame. A description of the main modules of the complex has been presented and experimental studies of localisation detection algorithms have been conducted.
Toxic Word Analyzer

In this generation social media has been a huge part of our lives and there is no need to say that the current generation spend a huge amount of time on their social media accounts. Apart from there being a good social media influencer there are a lot of people who spread hatred among these influencers as well as among each other. I have tried to make a speedometer which would be able to tell the toxicity of the words that are basically used in the input sentence or paragraph. The main processing that would be done on the sentence or the paragraph would be removing punctuation marks, tokenization on the words, ‘Stop’ word removal, bigram creation, matching tokens with predefined dictionary, generating toxicity percent using scaling.
An Image Acquisition and Processing Technique Based on Machine Vision

This paper takes the two-dimensional code image recognition technology as an example to introduce the image acquisition and processing technology based on machine vision. Our object is to filter noise reduction, edge extraction, and position correction on the randomly collected noise-disturbed images into binary images that can be effectively read. The template for filtering and noise reduction needs to select the appropriate size median filtering rectangular window according to the morphological characteristics of the image and the type of noise. Adaptive threshold method with Otsu algorithm is applied to achieve image binarization, and the image target area is successfully divided. Considering the complex edge details of the two-dimensional code image, this paper selects the Canny operator with the best accuracy and clarity for edge detection and extraction through comparison. Finally, a rotation correction algorithm based on Hough transform is adopted to perform effective positioning correction on the image. The image processing algorithm in this paper is simple and effective. It can be widely used on industrial assembly lines and other fields with a wide range of application prospects.
Eduard Rubenovich Sukiasyan — Erudition, Efficiency, Courage

The article is devoted to the memories of the talented work of Eduard Rubenovich Sukiasyan (1937—2021) — the greatest library scientist in Russia, one of the developers and later Editor-in-Chief of the Library Bibliographic Classification (LBC). The author traces his professional path as prominent theorist and practitioner of librarianship. The article presents information about the work of E.R. Sukiasyan in the V.I. Lenin State Library of the USSR (now the Russian State Library, RSL), as well as the compiler and member of the Editorial Board of the “Dictionary of Library Terms”. The author considers his contribution to the reorganization of the RSL system of catalogues as the Head of the Department of systematic and subject catalogues. Eduard Rubenovich enjoyed the well-deserved love of students of the Academy of Retraining of Workers of Art, Culture and Tourism. E.R. Sukiasyan was an active participant in the public library movement, the member of the Council of the Russian Library Association. He made great contribution to the activities of the International Society for Knowledge Organization, as well as to the work of the Classification and Indexing Section of the International Federation of Library Organisations and Institutions. E.R. Sukiasyan is the author of over one thousand scientific papers.
Learn from the past – sequentially one-to-one video deblurring network

With the growing availability of hand-held cameras in recent years, more and more images and videos are taken at any time and any place. However, they usually suffer from undesirable blur due to camera shake or object motion in the scene. In recent years, a few modern video deblurring methods are proposed and achieve impressive performance. However, they are still not suitable for practical applications as high computational cost or using future information as input. To address the issues, we propose a sequentially one-to-one video deblurring network (SOON) which can deblur effectively without any future information. It transfers both spatial and temporal information to the next frame by utilizing the recurrent architecture. In addition, we design a novel Spatio-Temporal Attention module to nudge the network to focus on the meaningful and essential features in the past. Extensive experiments demonstrate that the proposed method outperforms the state-of-the-art deblurring methods, both quantitatively and qualitatively, on various challenging real-world deblurring datasets. Moreover, as our method deblurs in an online manner and is potentially real-time, it is more suitable for practical applications.
Cross-Domain Evaluation for Vehicle Re-Identification

Vehicle Re-Identification (Re-ID) aims at matching and returning images of a query vehicle from a large-scale gallery set taken across different cameras. To extract and adapt more discriminative and useful vehicle feature information from the source to the target-domain data of interest is the foremost goal of cross-domain vehicle Re-ID. In order to better adapt to the real-complex-world surveillance environment of traffic vehicles and further improve the generalization and robustness of our model GLCAM-Net (i.e. a vehicle Re-ID network based on Global-Local feature fusion, Channel Attention Mechanism and Weighted Local Feature), we conducted cross-domain evaluation experiments and ablation studies on two large benchmark datasets VehicleID and VeRi-776. Our qualitative and quantitative results demonstrate that our approach outperforms the state-of-the-art by more than 3.05% (VeRi-776→VehicleID) and 3.13% (VehicleID→VeRi-776) improvements in term of mAP, respectively.
Application of Parameter Estimation: A Big Data Analytics Perspective

At the present time there are various ways to human interaction like human to machine and machine to machine interaction. Today the medium of human interaction is social media which is very popular. The social media interaction can be a sharing ideas, sending text messages, video, and audio call and machine to machine interaction such as automatic door opening, traffic control, telemedicine, weather forecast etc. the machine learning are using different devices for a particular purpose. Those devices are generating a huge amount of data in a various format like numerical, text, audio, video, images in every second such types of data is known as big data. Big data can be defined based on three parameters, volume, variety, and velocity. If the data has volume (large data sets), variety (text, images, audio, video) and velocity (speed of data). For the store and process those complex data we need a reliable and appropriate algorithm. In this paper, we have proposed a framework based on big data parameter estimation. The proposed framework will predict the useful information from the heterogeneous with huge amount of data sets through suitable sampling techniques.