Reviewerly/exHarmony huggingface dataset:
Anomaly Motion Detection and Tracking for Real-Time Security System

Almost every corner of the world is under surveillance. However, crimes (anomalies) can occur anywhere and anytime. Duration of anomalous activities is for a very short time as compared to normal activities. Advancements in computer vision have proved Convolutional Neural Networks (CNN) to be a compelling solution for anomaly detection in videos. This paper proposes to bring together different methods for effective anomaly detection. The dependency of Machine Learning on massive sets of the hand-labeled training dataset is time-consuming. To avoid this, a predictive model is constructed that learns with weak supervision using Multiple Instance Learning (MIL) approaches. The combination of motion information (optical flow) along with spatial information (RGB) obtained from 3-dimensional Convolutional Networks (C3D), using 2-stream architecture, offers a stronger visual representation of videos, thus enhancing anomaly detection performance. TVL1 is used to obtain the optical flow representation of videos. After detection, suspicious behaviors in videos are tracked on the UCF Crime dataset videos.
Library Publishing Projects in the Top List of Non/fictio№ 22

The article considers the content of publishing activities of the Russian State Library (RSL) in the context of the event-communication process of the International Fair of Intellectual Literature Non/fictio№ 22, held on March 24—28, 2021 in Moscow in the Gostiny Dvor complex. The author presents new products of the “Pashkov Dom” publishing house of the RSL, included in the top list of the Fair, as well as the best-selling books, which were successfully sold at the RSL stand during the forum. Special attention is paid to the presentation of the two-volume book “The Great Librarian. To the 120th Birth Anniversary of Margarita Ivanovna Rudomino” as the central event of the RSL participation program in the Fair. This publishing project has been prepared for publication in cooperation with the Margarita Rudomino All-Russia State Library for Foreign Literature and is intended to open a book series about outstanding librarians.
A Robust Approach for Students Detection via Multi Cameras with Mask-RCNN

With the development of science and technology and continuous improvement of social and economic level, the use of video surveillance equipment has got dramatically increase. It is an effective and robust method to execute people detecting and counting work that use continuous multi-videos streams directly and accomplish automatic people counting in matching analysis system. We use the improved Mask-RCNN via multi cameras for students detecting and counting. Students of overlap regions in multi-cameras also need re-calibration to compensate in statistic and analysis system. Excellent algorithms were proposed for students detecting in a multi cameras using environment. The developed multi camera approach achieves high object detection performance in our handcrafted dataset. Our multi-cameras students detecting and counting system, which train with smaller handcrafted dataset and align overlap region for counting accuracy improvement, gets a good accuracy in higher frame rate. This methodology is fill of challenging particularly testing in a higher real time video frame rate. The proposed method is expected to get excellent performance while it was extended to more complicated students recognition situation or relevant tasks.
Increasable doubly substochastic matrices with application to infinite linear equations

It has been proved that every n×n doubly substochastic matrix A=[aij] can be “increased” into a doubly stochastic matrix in the sense that there is a doubly stochastic matrix D=[dij] with aij≤dij([Marshall AW, Olkin I, Arnold BC. Inequalities; theory of majorization and its applications. 2nd ed. New York (NY): Springer; 2011], Theorem C.1). In this paper, we show this assertion does not necessarily hold for all infinite doubly substochastic matrices. Then we characterize all such matrices which are called increasable doubly substochastic matrices. We also provide an application of this result to the existence of a non-negative solution for some infinite linear equations.
Efficiency Analysis of Deeplearning4J Neural Network Classifiers in Development of Transition Based Dependency Parsers

Abstract Dependency parsing is a complex process in natural language text processing, text to semantic transformation. The efficiency improvement of dependency parsing is a current and an active research area in the NLP community. The paper presents four transition-based dependency parser models with implementation using DL4J classifiers. The efficiency of the proposed models were tested with Hungarian language corpora. The parsing model uses a data representation form based on lightweight embedding and a novel morphological-description-vector format is proposed for the input layer. Based on the test experiments on parsing Hungarian text documents, the proposed list-based transitions parsers outperform the widespread stack-based variants.
VAULT-Style Risk-Limiting Audits and the Inyo County Pilot

In 2020, Inyo County, California partnered with nonprofit VotingWorks to pilot the use of the Verifiable Audits Using Limited Transparency technique (called VAULT) to conduct an efficient, privacy-preserving, publicly verifiable risk-limiting audit of seven contests in the November general election. We describe VAULT, the pilot, and the software implementation that made this pilot possible.
An Enriched Category Theory of Language: From Syntax to Semantics

State of the art language models return a natural language text continuation from any piece of input text. This ability to generate coherent text extensions implies significant sophistication, including a knowledge of grammar and semantics. In this paper, we propose a mathematical framework for passing from probability distributions on extensions of given texts, such as the ones learned by today’s large language models, to an enriched category containing semantic information. Roughly speaking, we model probability distributions on texts as a category enriched over the unit interval. Objects of this category are expressions in language, and hom objects are conditional probabilities that one expression is an extension of another. This category is syntactical—it describes what goes with what. Then, via the Yoneda embedding, we pass to the enriched category of unit interval-valued copresheaves on this syntactical category. This category of enriched copresheaves is semantic—it is where we find meaning, logical operations such as entailment, and the building blocks for more elaborate semantic concepts.
Rank algorithm of web English educational resources based on fuzzy sets and RSS

In order to improve the acquisition and recommendation effect of English education resources, this paper proposes a Rank algorithm of web English educational resources based on fuzzy sets and RSS, and deeply studies the basic principles of the algorithm and introduces several keyword extraction techniques. The user’s browsing behavior and user interest acquisition methods are classified. Researchers can plan to further explore the page ranking algorithm to improve the performance of the scheme based on the damping factor. In addition, this paper uses Web technology to acquire English education resources and build a recommendation model, and uses crawler technology to build an overall system model. Finally, this paper designs experiments to verify the performance of the algorithm model constructed in this paper, and analyses the experimental results by mathematical statistics. The research results show that the algorithm model proposed in this paper has significant effects and is of great significance to the acquisition and recommendation of English education resources.
CFD Simulation of Air Flow Behaviour at Different Flow Rates in a Turkish Woodwind Instrument (Turkish Treble Recorder)

In musical instruments, the geometric design and material features of the instrument are the most important factors that determine the sound characteristics of the instrument.Traditional replication and experiment-based handcrafting methods are predominant in the production of Turkish Folk Music wind instruments.The instrument manufacturing and standardization approaches, which include the relevant rules of physics and engineering practices, are limited purely to prototype studies for scientific research purposes.It is almost impossible to find studies on Turkish Folk Music wind instrument design and production involving computer aided design and engineering applications.In this study, an example Turkish woodwind instrument, the Turkish Treble Recorder (dilli kaval) is considered, and the air flow behaviour and acoustic (sound) power magnitudes that occur at different air flow rates are simulated in a computer environment using a Computational Fluid Dynamics (CFD) simulation technique.In the study, numerical and visual outputs related to air behaviour at different air flow rates that may be used in the instrument manufacturing phases were obtained.Acoustic power level was also measured experimentally.Simulation outputs (the acoustic power level) were compared to experimental results in order to validate the simulation results.The comparison revealed that the highest relative difference was calculated as 13.32(%).This value indicated that the simulation results were reasonably consistent with the results of the experimental measurement.Additionally, this study was constructed as a case study that may provide reference for future research studies in this field.
Should We Trust a Black Box to Safeguard Human Rights? a Comparative Analysis of Ai Governance

The race to take advantage of the numerous economic, security, and social opportunities made possible by artificial intelligence (AI) is on with nations, intergovernmental organizations, cities, and firms publishing an array of AI strategies. Simultaneously, there are various efforts to identify and distill an array of AI norms. Thus far, there has been limited effort to mine existing AI strategies to see whether common AI norms such as transparency, human-centered design, accountability, awareness, and public benefit are entering into these strategies. Such data is vital to identify areas of convergence and divergence that could highlight opportunities for further norm development in this space by crystallizing State practice. This Article analyzes more than forty existing national AI strategies paying particular attention to the U.S. context, and then comparing those strategies with private-sector efforts and addressing common criticisms of this process within a polycentric framework. Our findings support the contention that State practices are converging around certain AI principles, focusing primarily upon public benefit. AI is a critical component of international peace, security, and sustainable development in the twenty-first century, and as such reaching consensus on AI governance will become vital to help build bridges and trust.
An Estimation Algorithm of Power System Frequency in Impulse Noise Environment

With the widespread use of distributed power generation devices and nonlinear power electronic devices in smart grid, the noise in the actual environment of the smart grid shows obvious impulse characteristics. To solve the above problems, this paper proposed a generalized weighted linear prediction algorithm based on the least p norm (lp-GWLP). That is, combining the linear prediction property with the weighted least p norm. Firstly, the whitening transform is used to preprocess the data to remove the correlation in the error. Then the least p norm (lp) is used to calculate the independent error data. Finally, the iterative reweighted least square method is used to achieve the frequency estimation. Simulation results show that the proposed scheme can achieve reliable frequency estimation in the case of harmonic, amplitude oscillation, and voltage interruption in the impulse noise environment.
Guarded Ontology-Mediated Queries

We concentrate on ontology-mediated queries (OMQs) expressed using guarded Datalog\(^\exists \) and conjunctive queries. Guarded Datalog\(^\exists \) is a rule-based knowledge representation formalism inspired by the guarded fragment of first-order logic, while conjunctive queries represent a prominent database query language that lies at the core of relational calculus (i.e., first-order queries). For such guarded OMQs we discuss three main algorithmic tasks: query evaluation, query containment, and first-order rewritability. The first one is the task of computing the answer to an OMQ over an input database. The second one is the task of checking whether the answer to an OMQ is contained in the answer of some other OMQ on every input database. The third one asks whether an OMQ can be equivalently rewritten as a first-order query. For query evaluation, we explain how classical results on the satisfiability problem for the guarded fragment of first-order logic can be applied. For query containment, we discuss how tree automata techniques can be used. Finally, for first-order rewritability, we explain how techniques based on a more sophisticated automata model, known as cost automata, can be exploited.
A Two Phase Denoising Approach to Remove Uneven illumination From Ancient Note Book Images

Aged handwritten document binarization is a tedious process of removing a type of noise like uneven illumination. Most of the aged documents have a challenge like uneven illumination. The proposed work mainly focus on removal of uneven illumination from aged handwritten Malayalam documents. The dataset which is used for experimentation is notebook image named `Panchathanthram' written in 1857 AD to 1866 AD. The proposed work deals with a two phase model which is followed by degraded notebook image as dataset, first level obtaining the threshold image, second level removing the border noise. In first level gray scale image is passed to Butterworth filter as well as Otsu thresholding to obtain the threshold image. At the second level threshold image is passed to a flood fill algorithm for creating a masked image and applies bitwise-AND followed by slicing operation obtained output image is padded to make border as white.
Cognitive Covert Traffic Synthesis Method Based on Generative Adversarial Network

In the intelligent era of human-computer symbiosis, the use of machine learning method for covert communication confrontation has become a hot topic of network security. The existing covert communication technology focuses on the statistical abnormality of traffic behavior and does not consider the sensory abnormality of security censors, so it faces the core problem of lack of cognitive ability. In order to further improve the concealment of communication, a game method of “cognitive deception” is proposed, which is aimed at eliminating the anomaly of traffic in both behavioral and cognitive dimensions. Accordingly, a Wasserstein Generative Adversarial Network of Covert Channel (WCCGAN) model is established. The model uses the constraint sampling of cognitive priors to construct the constraint mechanism of “functional equivalence” and “cognitive equivalence” and is trained by a dynamic strategy updating learning algorithm. Among them, the generative module adopts joint expression learning which integrates network protocol knowledge to improve the expressiveness and discriminability of traffic cognitive features. The equivalent module guides the discriminant module to learn the pragmatic relevance features through the activity loss function of traffic and the application loss function of protocol for end-to-end training. The experimental results show that WCCGAN can directly synthesize traffic with comprehensive concealment ability, and its behavior concealment and cognitive deception are as high as 86.2% and 96.7%, respectively. Moreover, the model has good convergence and generalization ability and does not depend on specific assumptions and specific covert algorithms, which realizes a new paradigm of cognitive game in covert communication.
EKF-SLAM with Autonomous Exploration using a Low Cost Robot

Ahsiriri-Autonomous exploration and SLAM (Simultaneous Localization and Mapping) are two of the main problems in robotics due to their relevance for many military and commercial real world applications. This article proposes a solution, tested using a low cost robot (less than US$500), based on EK -SLAM (Extended Kalman Filter) to autonomously explore an static and unknown 21) environment. This environment Ls assumed to be composed of walls and cylinders which are modeled respectively as line segments and circles. The proposed solution applies border detection techniques to occupancy grid maps and performs path planning using the A* search algorithm to generate safe and feasible paths for the robot autonomous exploration. A differential drive robot with a Raspberry Pi 3 Model B+ single board computer, a laser scanner, a electronic compass and wheel encoders was designed and built to test the proposed solution. This robot, via a Wi-Fl network, communicates with a remote personal computer that runs the autonomous exploration and EKF-SLAM algorithms. Simulation and real world experiments are shown for an indoor closed environment with approximately 41 square meters, 6 thin cylinders and 10 walls. In the real world experiment the proposed solution achieved an average error of 8.11 cm for the walls' characteristic points and 4.33 cm for the cylinders' centroids.
Evaluation on Lung Cancer Detection Using Computer Assisted Diagnosis (CAD) System

Lung cancer considered to be the worst threat to humankind, and it is also painstaking to be the main cause of death among other death casualties of cancer. This paper will review the detection techniques of lung cancer by using data science technology. Computer-assisted diagnosis CAD system is very effective in the detection of lung cancer and it is of high importance as it can increase the survival of the patient. Mainly there are four main stages in the CAD. First is the image preprocessing, Segmentation and ROI selection of lung portions, next is nodule detection inside the lung fields, and then comes the classification of that nodule and then evaluation of the nodules. Early detection of this disease can increase the survival chances of the patient because the time factor plays a succor role in this regard. Image Processing technique helps in getting the exact core image of the defected area which can be treated in time. The image processing system must be accurate and clear with a good pixel percentage and labeling of the mask. Pulmonary diseases can cause serious impact on human body if it is misdiagnosed and improper diagnosis done thru the medical analysis. Medical professionals assess and checks numerous of radiology images (CT Images) and inspects the lung cancer disease from it. However now a days Image processing technique applies with Computer aided system in this regard and becoming paramount aid in the field of medical science. This paper will follow segmentation processes, based on morphology operation there is technique that can diminish misleading results that is known as region of interest (ROI) extraction. The better results possible to achieved with the use of a convolution neural network. Computer aided detection has more advantages in terms of speed and accuracy. Today researchers are trying to improve the working of computer aided systems for better performance, they are also striving hard to make computer aided systems as a best approach for lung cancer detection.
Transformation of the States’ Academic Attractiveness under the Pandemic

This conceptual article aims at analyzing the impact of political and economic actions taken by states during the first wave of COVID-19 pandemic on their academic attractiveness for international students. The resulting crisis conditions demanded to make a lot of decisions in a very short time, thus speeding up the dynamic of the situation development and allowing to more accurately trace necessary interconnections over a short observation period.The authors focus their attention on English-speaking countries, which traditionally attract large numbers of international students: Australia, Canada, the United Kingdom and the USA. The research is based on the analysis of secondary empirical data obtained from foreign sources, as well as on official statistics. The short-term impact of political and economic decisions, made by heads of states and responsible institutions, on countries’ academic attractiveness and their perception by international students is assessed via critical and reflective analysis of surveys and researches, as well as via available data on international students’ enrollment in 2020/2021 academic year.The authors found a correlation between the decisions taken by the countries during Spring – Summer 2020 and the subsequent transformation of their academic attractiveness under the increasing competition between countries and the students’ choice of the best opportunities for their future career and life. The authors assessed the following factors affecting the attractiveness of a state as a studying destination: economic support measures taken by the governments of the considered countries during the first wave of the COVID-19 pandemic, new adjustments in visa rules for foreign students and related regulatory changes, as well as the most significant public statements by officials. The research topic can be further expanded and supplemented through data actualization, including additional factors into the analysis and expanding the time period of the study. The findings and recommendations given in the conclusion of the article can be practically applied when developing state education export strategies or universities’ and educational agencies’ recruiting approaches.
Improved Scheduling Algorithm for Load Balancing in Cloud Computing

Distributed computing becomes a substitution to the global pattern of registering. It's a cutting-edge kind of exploitation of the intensity of web and wide space organize (WAN) to give assets remotely. It's a substitution goal and technique to see high accessibility, adaptability, cost diminished, and on request calculable. In any case, distributed computing has a few difficulties like poor asset use that includes profound effects inside the exhibition of distributed computing. These issues emerged in light of the enormous measures of information. Along these lines, the need for efficient and ground-breaking distributed computing load-leveling calculations is one of the premier fundamental issues during this space to fortify the presence of distributed computing. A few specialists arranged shifted load levels and employment arranging calculations in distributed computing, anyway, there’s still some powerlessness inside the framework execution freight lopsidedness. Along these lines, during this examination, in general, propose a heap leveling equation to fortify the exhibition and intensity in heterogeneous distributed computing setting. Also, propose a crossbreed equation bolstered association and ravenous recipe; it takes endowments of each arbitrary and avaricious calculations. The equation thinks about this asset information and the centralized server capacity issue to comprehend the targets.