Reviewerly/exHarmony huggingface dataset:
Artificial Neural Network Modelling Implication

An Artificial Neural Network (ANN) is an information processing model inspired by the way biological nervous systems “the brain” processing information, a brain transmit a signal from one neuron to another and process information, similar to the neurons in brain an ANN is designed with a collection of connected units or nodes called artificial neuron, this can receive a signal and process it, then signals other artificial neurons connected in the network, the key element of this model is the new structure of the information processing system, which is composed of a large number of highly interconnected processing elements (artificial neurons) working in unison to solve specific problems, The original goal of the ANN approach was to solve problems in the same way a human brain would process information,  However, over time the attention moved to performing specific tasks and application such as pattern recognition or data classification through a learning process, This paper gives an overview of Artificial Neural Network, its operation design and also briefs the application and advantages of ANN.
Scientific journals of Russia and the activities of international would-be scientific network structures

The paper deals with the problem of network dependence of Russian news magazines and the actual dictates of international network structures (Scopus, WOS, etc.) that set their own requirements for their content and quality. The latter influence not only the scientific rating of journals, but also the publishing policy of their publications. The situation is further complicated by the fact that the rules of the game imposed by them have been adopted by the country's state authorities and, first of all, by the Ministry of science and higher education of the Russian Federation, which has tightened the requirements for reports of scientific and educational institutions, as well as researchers and teachers, including mandatory publications in Scopus and other international databases. Despite the efforts made by the Presidium of the Russian Academy of Sciences and the leadership of higher education institutions, Russian science was dependent on these structures, which began to determine the directions and priorities of its development, including selecting the subject and language of journal publications. The scientific community of Russia is faced with the task of protecting the interests of journal editors and protecting the right of authors to Express their own scientific position and the ability to present publications in their native language.
Research on Escape Strategy of Local Optimal Solution for Underwater Hexapod Robot Based on Energy Consumption Optimization

Aiming at the local optimal solution problem of artificial potential field method, this paper takes energy consumption as the optimization objective to plan the escape strategy of underwater hexapod robot. The total energy consumption of underwater hexapod robot in three gait and turning gait is solved by establishing the dynamic model of the underwater hexapod robot, and the energy consumption coefficient of the whole robot is planned according to the different steps and different rotation angles of the robot. Simulate annealing method based on energy consumption difference as Metropolis criterion is used to plan the next crawling point. Finally, the correctness of the theory is verified by comparing the total energy consumption of different escape paths through simulation experiments. The simulation results show that the simulate annealing method based on energy consumption optimization can plan the local escape path of the underwater hexapod robot under the constraint of energy consumption, and has good practical application value.
Twofold of algebraic decomposition method used for a watermarking scheme with $LWT$ over medical images

Mathematics has always been of great importance in various sciences, especially computer science. The mechanism used to embed various types of information in a host medical images to safeguard the privacy of the patient including the patient's name, doctor's digital signature is called watermarking. There are a lot of improved watermark algorithms, however, this information is susceptible to attack when the data are transferred over universal internet channels. This paper proposed a robust watermark algorithm that uses a Lifting Wavelet Transform $(LWT)$ and two times of the Hessenberg Matrix Decomposition Method $(HMDM)$ to embed a watermark in a chosen channel of the host image after performing the transform. The experimental results demonstrate that the improvement appears (higher robustness against $JPEG$ compression attack) and good imperceptibility against some attacks, to evaluate the fineness of the original with watermarked images and the extracted watermark respectively.
Pose Estimation for Vehicle-mounted Cameras via Horizontal and Vertical Planes

We propose novel solvers for estimating the egomotion of a calibrated camera mounted to a moving vehicle from a single affine correspondence via recovering special homographies. For the first, second and third classes of solvers, the sought plane is expected to be perpendicular to one of the camera axes. For the fourth class, the plane is orthogonal to the ground with unknown normal, e.g., it is a building facade. All methods are solved via a linear system with a small coefficient matrix, thus, being extremely efficient. Both the minimal and over-determined cases can be solved by the proposed solvers. They are tested on synthetic data and on publicly available real-world datasets. The novel methods are more accurate or comparable to the traditional algorithms and are faster when included in state-of-the-art robust estimators. The source code is publicly available[1].
Fast-Reactive Probabilistic Motion Planning for High-Dimensional Robots

Many real-world robotic operations that involve high-dimensional humanoid robots require fast reaction to plan disturbances and probabilistic guarantees over collision risks, whereas most probabilistic motion planning approaches developed for car-like robots cannot be directly applied to high-dimensional robots. In this paper, we present probabilistic Chekov (p-Chekov), a fast-reactive motion planning system that can provide safety guarantees for high-dimensional robots suffering from process noises and observation noises. Leveraging recent advances in machine learning as well as our previous work in deterministic motion planning that integrated trajectory optimization into a sparse roadmap framework, p-Chekov demonstrates its superiority in terms of collision avoidance ability and planning speed in high-dimensional robotic motion planning tasks in complex environments without the convexification of obstacles. Comprehensive theoretical and empirical analysis provided in this paper shows that p-Chekov can effectively satisfy user-specified chance constraints over collision risk in practical robotic manipulation tasks.
Design of Camera Equipment Arrangement and Attitude Calculation for Aircraft Attitude Measurement

It is one of the important tasks for marine technology to track and observe, measure and calculate the attitude angle of aircraft landing in a certain range of sea area, measuring and calculating its attitude angle. Accurate identification and tracking of the target and reasonable optimization of the surveillance camera placement are the key steps to complete the mission. Based on a typical scene, this paper presents a technical scheme of aircraft tracking and shooting by three-spectrum cameras and constant-speed cameras. In addition, the design of the camera placement scheme is optimized, and the calculation method of the aircraft attitude is given. The simulation results confirm the effectiveness of the proposed approaches.
The problems of popularization of science in the Russian Federation in the context of amendments to the legal regulation of educational activity

The subject of this research is the relevant issues pertaining to popularization of science in the Russian Federation. The goal of this article lies in determination of the currently existing key issues of popularization of science in the Russian Federation: 1) the absence of effective response to the transformation of the traditional field of scientific promotion activity (distribution of new forms and methods of such activity); 2) imperfection of the new legal regulation of educational and scientific promotion activity, which may implicitly lead to negative consequences. The conducted research involves in theoretical conceptualization of scientific promotion activity in the digital age the novelties of legislative regulation of educational activity adopted in April 2021 (the so-called Law on Educational Activity). Unlike other works dedicated to the analysis of this law, this article assesses its basic provisions from the perspective of the effectiveness of achieving the goals set in the context of its impact upon the state of popularization of science in the Russian society. It is established that the model for preventing deterioration of the quality of educational content proposed in the Law on Educational Activity is not effective enough and may further reduce the quality of scientific promotion activity. Therefore, the purpose of imposed restrictions may fail to be achieved. The author believes that the more effective way would lie in intensification of the activity of traditional actors of popularization of science, rather than restriction of the activity of nontraditional actors of popularization of science.
Information-Bottleneck-Based Behavior Representation Learning for Multi-Agent Reinforcement Learning

In multi-agent deep reinforcement learning, extracting sufficient and compact information of other agents is critical to attain efficient convergence and scalability of an algorithm. In canonical frameworks, distilling of such information is often done in an implicit and uninterpretable manner, or explicitly with cost functions not able to reflect the relationship between information compression and utility in representation. In this paper, we present Information-Bottleneck-based Other agents’ behavior Representation learning for Multi-agent reinforcement learning (IBORM) to explicitly seek low-dimensional mapping encoder through which a compact and informative representation relevant to other agents’ behaviors is established. IBORM leverages the information bottleneck principle to compress observation information, while retaining sufficient information relevant to other agents’ behaviors used for cooperation decision. Empirical results have demonstrated that IBORM delivers the fastest convergence rate and the best performance of the learned policies, as compared with implicit behavior representation learning and explicit behavior representation learning without explicitly considering information compression and utility.
Checking and Coloring Graphs Through Quantum Circuits: An IBM Quantum Experience

Banerjee, Asmita Behera, Bikash K. Das, Kunal Panigrahi, Prasanta K.Checking the graph state and coloring it with possible least number of colors (or chromatic number) has many useful applications such as coloring of maps, solving Sudoku, making schedules to name a few. Though classical algorithms are used to solve this problem, it is believed that with quantum approach, the space and time complexity can be reduced. Thus, here we present quantum algorithms and design quantum circuits to check the graph states having two- and three-vertices with given edges and color them accordingly. We also propose a quantum algorithm for coloring of trees. For each of the cases, we discuss the quantum costs and complexities and compare it with the classical approach.
Annotation-Efficient Untrimmed Video Action Recognition

Deep learning has achieved great success in recognizing video actions, but the collection and annotation of training data are still quite laborious, which mainly lies in two aspects: (1) the amount of required annotated data is large; (2) temporally annotating the location of each action is time-consuming. Works such as few-shot learning or untrimmed video recognition have been proposed to handle either one aspect or the other. However, very few existing works can handle both issues simultaneously. In this paper, we target a new problem, Annotation-Efficient Video Recognition, to reduce the requirement of annotations for both large amount of samples and the action location. Such problem is challenging due to two aspects: (1) the untrimmed videos only have weak supervision; (2) video segments not relevant to current actions of interests (background, BG) could contain actions of interests (foreground, FG) in novel classes, which is a widely existing phenomenon but has rarely been studied in few-shot untrimmed video recognition. To achieve this goal, by analyzing the property of BG, we categorize BG into informative BG (IBG) and non-informative BG (NBG), and we propose (1) an open-set detection based method to find the NBG and FG, (2) a contrastive learning method to learn IBG and distinguish NBG in a self-supervised way, and (3) a self-weighting mechanism for the better distinguishing of IBG and FG. Extensive experiments on ActivityNet v1.2 and ActivityNet v1.3 verify the rationale and effectiveness of the proposed methods.
AN IN-DEPTH ANALYSIS OF THE IDENTIFIED ALGORITHMS AND THEIR COMPARATIVE STUDY IN THE EARLY DETECTION AND DIAGNOSIS OF BREAST CANCER

These days one of the major inevitable ailments for females is bosom malignancy. The appropriate medication and early findings are important stages to take to thwart this ailment. Although, it's not easy to recognize due to its few vulnerabilities and lack of data. Can use artificial intelligence to create devices that can help doctors and healthcare workers to early detection of this cancer. In This research, we investigate three specific machine learning algorithms widely used to detect bosom ailments in the breast region. These algorithms are Support vector machine (SVM), Bayesian Networks (BN) and Random Forest (RF). The output in this research is based on the State-of-the-art technique.
Forecast-Island and Bidding A*-Euclidean Selecting Boustrophedon Coordination Algorithm for Exploration

Exploration algorithms based on the Boustrophedon path seldom consider the impacts of a robot turning at corners on the exploration time. This paper proposes the Forecast-Island and Bidding A*-Euclidean Selecting Boustrophedon Coordination (FIBA*ESBC) algorithm to calculate the turning time at corners in the overall exploration time and introduces a method to estimate the walking time in the Boustrophedon paths in order to determine the directions for path execution. Typically, in bidding-based exploration tasks, the cost is the Euclidean distance between the current position of the robot and the target point. When there is an obstacle between two points, the cost is set to infinity. Therefore, the selected target point is sometimes not optimal. The FIBA*ESBC algorithm is based on the exploration cost of a combination of the Euclidean distance and A* algorithm walking path, which can effectively solve this problem. Because the bidding is based on a greedy algorithm, the robot has a small unexplored island in the later exploration stage; therefore, full exploration is not possible or requires a long time with several repeated paths. The FIBA*ESBC algorithm prioritizes the exploration and estimation of hidden and existing unexplored islands. It can realize complete exploration and decrease the exploration time. Through simulation experiments conducted using Gazebo and RViz, the feasibility of the FIBA*ESBC algorithm is verified. Moreover, a simulation experiment is conducted in MATLAB for comparison with other algorithms. The analysis of the experimental data shows that the proposed algorithm has a relatively short exploration time.
Unconstrained Aerial Scene Recognition with Deep Neural Networks and a New Dataset

Aerial scene recognition is a fundamental research problem in interpreting high-resolution aerial imagery. Over the past few years, most studies focus on classifying an image into one scene category, while in real-world scenarios, it is more often that a single image contains multiple scenes. Therefore, in this paper, we investigate a more practical yet underexplored task-multi-scene recognition in single images. To this end, we create a large-scale dataset, called Mul-tiScene dataset, composed of 100,000 unconstrained images each with multiple labels from 36 different scenes. Among these images, 14,000 of them are manually interpreted and assigned ground-truth labels, while the remaining images are provided with crowdsourced labels, which are generated from low-cost but noisy OpenStreetMap (OSM) data. By doing so, our dataset allows two branches of studies: 1) developing novel CNNs for multi-scene recognition and 2) learning with noisy labels. We experiment with extensive baseline models on our dataset to offer a benchmark for multi-scene recognition in single images. Aiming to expedite further researches, we will make our dataset and pre-trained models available <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> https://github.com/Hua-YS/Multi-Scene-Recognition.
Model-based Domain Randomization of Dynamics System with Deep Bayesian Locally Linear Embedding

Domain randomization (DR) is a powerful tool to make a policy robust to the uncertainty of dynamics caused by unobservable environmental parameters. Conventional DR has adopted model-free reinforcement learning as a policy optimizer. However, the model-free methods in DR demand high time-complexity due to the randomization process where the environment is extremely changed. In this paper, we introduce model-based dynamics and policy learning for efficient DR. A Bayesian model of locally linear embedding is designed to fit the stochastic dynamics in DR. By virtue of locally linear dynamics, model-based optimal control is substituted for the policy optimization. Unlike previous works, our proposed Bayesian model with a MNIW prior allows the locally linear embedding to capture the dynamics in DR as a stochastic model. We show that a training method that combines variational and adversarial approaches is adequate for Bayesian embedding. Finally, a model-based controller is designed on our Bayesian locally linear embedding, and it shows better performance in DR environments compared with the non-Bayesian model of locally linear embedding.
Mixed Precision ℓ1 Solver for Compressive Depth Reconstruction: An ADMM Case Study

Rapid reconstruction of depth images from sparsely sampled data is important for many machine learning applications, including robot or vehicle assistance or autonomy, which require low power LiDAR sensing for eye safety, and resource reduction for FPGA or solid state implementation, especially with constrained energy budgets. A new compressive depth reconstruction design approach is proposed using a compact ADMM solver for the lasso problem, which varies the precision scaling in an iterative optimization process. Implementations on an FPGA architecture show over 55% savings in hardware resources and 78% in power with only minor reduction in reconstructed depth image quality compared to single float precision.
A Legendre Polynomial Neural Network and its Application in Predicting Data Center Temperature

This paper investigates the potential of using of novel a Legendre polynomial neural network in predicting the temperature profile of a highly complex data center hosted in the University of the Aegean, Greece. The network comprises an input, a hidden and an output layer. The hidden layer consists of a number of nodes, which expand linear combinations of the input variables into truncated Legendre polynomial series. The output layer aggregates the nodes' outputs into a single sum and provides the network's output. Since Legendre polynomials are orthogonal under certain requirements, explicit mathematical analysis is carried out to provide conditions for satisfying those requirements and to take the advantage of the orthogonal polynomials' accurate performance. Moreover, it is analytically proven that the network can approximate any continuous function defined on a compact subset of the general Euclidean space to arbitrary accuracy. The modelling concerns the air temperature inside the data center server' s rack and takes place on the basis of a number of variables (i.e., input variables) that directly affect the above temperature. The network is compared to other two polynomial networks. The comparison has been conducted in terms of standard performance criterion and extensive non-parametric statistical analysis. Main results of the experimental comparison are enumerated as follows: (a) polynomial orders of 3 and 4 effectively predict the temperature profile, while higher polynomial orders deteriorate the network's performance due to possible fitting problems; (b) the statistical analysis indicates that the proposed network outperforms the other networks.
Facial features detection method based on improved centernet

Aiming at the problems of CenterNet detection model with many parameters, long convergence time and slow detection speed, this paper proposed an real time detection method. In this method, mobileNet is used as the back-end to extract the features of the scene before the image, and then combined with the lightweight CentrerNet detection head to detect. Based on CelebA and MSCOCO datasets, the results show thatOur Method model has the best balance in trainning time, detection speed and accuracy, and better application value compared with the improved reference model of CenterNet_ Shuffler and CenterNet_ ResNet18.
Consolidating Economic Exchange Ontologies for Corporate Reporting Standard Setting

In the last decade, several UFO-grounded economic exchange ontologies have been developed, notably COFRIS, OntoREA, REA2, and ATE. It is time to take the next step in the direction of corporate reporting standard setters, for which an ontological approach is of high potential value. In this paper, we first describe the foundational assumptions for exchange conceptualization and consolidate the latest developments in COFRIS - a core ontology for financial reporting information systems, within the most recent versions of the UFO theories and the OntoUML tool. We then confront COFRIS with the conceptual framework and standards for accounting and financial reporting and compare it with other UFO grounded exchange ontologies.