Reviewerly/exHarmony huggingface dataset:
Multivariable Decoupling and Nonlinear Correction Method for Image-Based Closed-Loop Tracking of the Risley Prisms System

Image-based closed-loop tracking (IBCLT) is an important part of the process of target tracking. The Risley prism system has a unique advantage in improving the target tracking ability because of its compact and lightweight structure. Compared with traditional target tracking equipment, the Risley prism system has two difficulties in the process of IBCLT. First, the Risley prism is a complex coupling system of double input and double output. Second, the Risley prism itself is a nonlinear system. These problems lead to decrease in dynamic response and inconsistent target tracking capabilities. Thus, this paper proposes a method to implement multivariable decoupling and reduce the nonlinear effect. First, the boresight error of IBCLT is decoupled to the azimuth and elevation directions by the rotation matrix error-decoupling (RMED) method. Second, the gains of IBCLT in azimuth and elevation directions are independent variables that comes from two functions of the target elevation angle. The experimental results show that the IBCLT error deviation of different static targets in the field of view is within 0.025 arcsec, which is 70% lower compared with the fixed gain method. Furthermore, the steady-state error deviation of moving targets is controlled within 2.5 arcsec. These experimental results prove the feasibility and effectiveness of the proposed method.
Variational satisfiability solving: efficiently solving lots of related SAT problems

Abstract Incremental satisfiability (SAT) solving is an extension of classic SAT solving that enables solving a set of related SAT problems by identifying and exploiting shared terms. However, using incremental solvers effectively is hard since performance is sensitive to the input order of subterms and results must be tracked manually. For analyses that generate sets of related SAT problems, such as those in software product lines, incremental solvers are either not used or their use is not clearly described in the literature. This paper translates the ordering problem to an encoding problem and automates the use of incremental solving. We introduce variational SAT solving, which differs from incremental solving by accepting all related problems as a single variational input and returning all results as a single variational output. Variational solving syntactically encodes differences in related SAT problems as local points of variation. With this syntax, our approach automates the interaction with the incremental solver and enables a method to automatically optimize sharing in the input. To evaluate these ideas, we formalize a variational SAT algorithm, construct a prototype variational solver, and perform an empirical analysis on two real-world datasets that applied incremental solvers to software evolution scenarios. We show, assuming a variational input, that the prototype solver scales better for these problems than four off-the-shelf incremental solvers while also automatically tracking individual results.
Enhanced sentiment analysis regarding COVID-19 news from global channels

For a healthy society to exist, it is crucial for the media to focus on disease-related issues so that more people are widely aware of them and reduce health risks. Recently, deep neural networks have become a popular tool for textual sentiment analysis, which can provide valuable insights and real-time monitoring and analysis regarding health issues. In this paper, as part of an effort to develop an effective model that can elicit public sentiment on COVID-19 news, we propose a novel approach Cov-Att-BiLSTM for sentiment analysis of COVID-19 news headlines using deep neural networks. We integrate attention mechanisms, embedding techniques, and semantic level data labeling into the prediction process to enhance the accuracy. To evaluate the proposed approach, we compared it to several deep and machine learning classifiers using various metrics of categorization efficiency and prediction quality, and the experimental results demonstrate its superiority with 0.931 testing accuracy. Furthermore, 73,138 pandemic-related tweets posted on six global channels were analyzed by the proposed approach, which accurately reflects global coverage of COVID-19 news and vaccination.
Higher Entrepreneurship Education: Guidelines for the Formation and Mitigation of Restrictions

The publication focuses on the guidelines for the formation of entrepreneurship education as an important direction in the space of Russian higher education. Based on the functional map of entrepreneurship as a special type of professional activity and the allocation of groups of professional entrepreneurial functions, the article substantiates the appropriateness of including professional entrepreneurship competencies in educational programs on entrepreneurship. The formation of entrepreneurship competencies in graduates is regarded as achieving key learning outcomes based on the application of competency-based and practice-oriented approaches. In this conceptual context, the prospects for the implementation of the «Startup as a Diploma» program are considered. It is noted that in the absence of agreement on the recognition of entrepreneurship as an independent direction of higher education in the Russian Federation, universities are forced to search for non-standard ways to build entrepreneurship education programs aimed at the formation of necessary competencies by graduates. The authors draw attention to the possibility for graduates to obtain professional qualifications simultaneously in two types of professional activity as a result of mastering educational programs on entrepreneurship in universities, which traditionally have an industry orientation. The current results of the activities of the Federal Innovative Educational Platform created for the development of models of continuous entrepreneurship education have shown inexpediency of compulsory inclusion of entrepreneurship programs in bachelor’s and master’s degrees in management. The article supports the idea of separating entrepreneurship education as an independent direction of higher education in Russia and provides the corresponding arguments.
Uniformly complete consistency of frequency polygon estimation for dependent samples and an application

The frequency polygon estimation, which is based on histogram technique, has a similar convergence rate as those of modern non-negative kernel estimators and the advantage of computational simplicity. This paper will study the uniformly complete consistency and the rates of uniformly complete consistency of frequency polygon estimation for widely orthant dependent samples under some general conditions, which improve and extend the corresponding ones in the literature. As an application, the uniformly complete consistency and the corresponding rates of hazard rate function estimator are also obtained.
A novel non-linear neuron model based on multiplicative aggregation in quaternionic domain

Abstract The learning algorithm for a three-layered neural structure with novel non-linear quaternionic-valued multiplicative (QVM) neurons is proposed in this paper. The computing capability of non-linear aggregation in the cell body of biological neurons inspired the development of a non-linear neuron model. However, unlike linear neuron models, most non-linear neuron models are built on higher order aggregation, which is more mathematically complex and difficult to train. As a result, building non-linear neuron models with a simple structure is a difficult and time-consuming endeavor in the neurocomputing field. The concept of a QVM neuron model was influenced by the non-linear neuron model, which has a simple structure and the great computational ability. The suggested neuron’s linearity is determined by the weight and bias associated with each quaternionic-valued input. Non-commutative multiplication of all linearly connected quaternionic input-weight terms accommodates the non-linearity. To train three-layered networks with QVM neurons, the standard quaternionic-gradient-based backpropagation (QBP) algorithm is utilized. The computational and generalization capabilities of the QVM neuron are assessed through training and testing in the quaternionic domain utilizing benchmark problems, such as 3D and 4D chaotic time-series predictions, 3D geometrical transformations, and 3D face recognition. The training and testing outcomes are compared to conventional and root-power mean (RPM) neurons in quaternionic domain using training–testing MSEs, network topology (parameters), variance, and AIC as statistical measures. According to these findings, networks with QVM neurons have greater computational and generalization capabilities than networks with conventional and RPM neurons in quaternionic domain.
Neural operator search

Existing neural architecture search (NAS) methods usually explore a limited feature-transformation-only search space, ignoring other advanced feature operations such as feature self-calibration by attention and dynamic convolutions. This disables the NAS algorithms to discover more advanced network architectures. We address this limitation by additionally exploiting feature self-calibration operations, resulting in a heterogeneous search space. To solve the challenges of operation heterogeneity and significantly larger search space, we formulate a neural operator search (NOS) method. NOS presents a novel heterogeneous residual block for integrating the heterogeneous operations in a unified structure, and an attention guided search strategy for facilitating the search process over a vast space. Extensive experiments show that NOS can search novel cell architectures with highly competitive performance on the CIFAR and ImageNet benchmarks.
Automatic phytoplankton image smoothing through integrated dual image histogram specification and enhanced background removal method

Diatom is a dominant phytoplankton and commonly found in oceans or waterways.The captured phytoplankton microscopic images suffer from low contrast and surrounding debris.These images are not appropriated for identification.Integrated dual image contrast adaptive histogram specification with enhanced background removal (DIHS-BR) is proposed to address these issues by automatically removes the background of the phytoplankton image and improves the image quality while cropping phytoplankton cell.DIHS-BR will automatically remove the background and noises.DIHS-BR consists of two major steps, namely, contrast adaptive histogram specification and background removal by means of edge mask cropping.Results demonstrated that DIHS-BR filtered out the image background and left only the required phytoplankton cell image.Noises are minimized, while the contrast and colour of phytoplankton cells are improved.The average edge-based contrast measure (EBCM) of 83.065 demonstrates the best contrast improvement of the proposed methods compared with the other state-of-the-art methods.
On the economic nature of behavioural control in smart real estate

The implementation of smart technologies in the built environment presents unprecedented opportunities and challenges for the real estate sector. Among the challenges is building occupants’ behavioural control due to smart buildings’ technological apparatus underpinned by pervasive computing. Since the early days of cybernetics, control stemming from information technology has generated many arguments about freedom, privacy and surveillance. Arguments only focused on technology or ethics tend to foster a Manichean view which obscures our ability to rationally assess calm and transparent technology’s role in controlling space users’ behaviours in smart buildings. The paper applies two classic economic frameworks to decipher the economic nature of behavioural control in smart real estate. In the process, it sheds some light on the complex utilitarian relationship between behavioural control and smart space’s user centricity. It concludes by assessing whether regulators should step in, for instance, through de jure property rights allocation among all parties.
A Modification of Shortest Path Algorithm According to Adjustable Weights Based on Dijkstra Algorithm

Dijkstra algorithm was used in this study.• The shortest path was found using the Dijkstra algorithm for static weights for each of the single-direction and twodirection maps • The dynamic shortest path was found using the Dijkstra algorithm according to changing the following weights for each of single direction and two-direction maps It is possible to represent the road map on the paper and study it using Dijkstra`s algorithm to find the shortest path on the real earth.Dijkstra`s Algorithms are used for calculating the shortest path from source to sink to enable query operations that follow.Dynamic shortest-route techniques are needed to accommodate the modifications within the underlying community topology.Every solution includes figuring out the nodes whose shortest routes could be impacted through the updates and producing a list of affected vertices and their updated shortest pathways.In this work, the advent of the retroactive priority queue information structure makes the Dijkstra algorithm dynamic.In this research, the stepping is changed forward in the shortest direction for 2 networks and two directions.That changed by locating the shortest static path, then circulating to the first node after the beginning node.At this node, the weights inside the segments directed from this node will exchange and cancel the vintage shortest direction and locate the new direction.Then flow to the next node in the new find shortest path, and repeat the operation until the end.The idea is that the best path can be constantly changed based on the latest data.These continuous changes are addressed in this paper, where the proposed system can find the best methods and update them automatically according to the variables.
Edge-based 3D vibration measurement of rotating cylinder-shaped structure through epipolar line-based corresponding point extraction between two camera images

Three-dimensional (3D) vibration measurements are important for monitoring rotating structures in order to reproduce the directional displacements of vibration signals such that they are similar to the actual movement. Photogrammetric techniques, which can be used to determine the relationship between 3D objects and their two-dimensional images, allow 3D vibrations in a structure to be measured without direct contact. Therefore, we herein present a multipoint 3D vibration measurement approach for rotating cylinder-shaped structures, which performs the imaging of a structure from two directions using two cameras. First, an epipolar line-based corresponding point extraction technique is applied to extract the corresponding regions of interest (ROIs) in videos acquired by the two cameras placed at 90° to the structure. Subsequently, an edge-based vibration measurement approach is used to detect vibrations in the corresponding ROIs. Noise reduction is then applied to reduce the noise, induced by the cameras or edge-based vibration measurement technique, by extracting and employing the frequencies at which the vibrations were occurring to the phase-based motion magnification technique. Subsequently, vibrations in the corresponding ROIs of the magnified videos are detected. For 3D plotting, the two vibration signals are combined. Simulation and real datasets are prepared for the qualitative and quantitative assessments of the proposed method. The simulation data results are compared with the results obtained before noise reduction, those obtained after noise reduction via bandpass filtering, and the reference data. A 3D vibration analysis is performed on the real data in multiple ROIs. The results yielded by the proposed method can be used to identify the weak points in a rotating cylinder-shaped structure that cause the structure to function abnormally.
The OneGraph vision: Challenges of breaking the graph model lock-in1

Amazon Neptune is a graph database service that supports two graph models: W3C’s Resource Description Framework (RDF) and Labeled Property Graphs (LPG). Customers choose one or the other model. This choice determines which data modeling features can be used and – perhaps more importantly – which query languages are available. The choice between the two technology stacks is difficult and time consuming. It requires consideration of data modeling aspects, query language features, their adequacy for current and future use cases, as well as developer knowledge. Even in cases where customers evaluate the pros and cons and make a conscious choice that fits their use case, over time we often see requirements from new use cases emerge that could be addressed more easily with a different data model or query language. It is therefore highly desirable that the choice of the query language can be made without consideration of what graph model is chosen and can be easily revised or complemented at a later point. To this end, we advocate and explore the idea of OneGraph (“1G” for short), a single, unified graph data model that embraces both RDF and LPGs. The goal of 1G is to achieve interoperability at both data level, by supporting the co-existence of RDF and LPG in the same database, as well as query level, by enabling queries and updates over the unified data model with a query language of choice. In this paper, we sketch our vision and investigate technical challenges towards a unification of the two graph data models.
A collaborative scheduling method for cloud computing heterogeneous workflows based on deep reinforcement learning

Aiming at the problem of low overall service quality caused by the disordered collaboration of heterogeneous workflows and discontinuous task execution in cloud computing scenarios, this paper proposes a collaborative scheduling method for heterogeneous workflows in cloud computing based on deep reinforcement learning. The method optimizes workflow makespan, cost, fairness and continuity in cloud computing under the constraints of task execution continuity. First, the structure and time sequence features are extracted for the dynamic scheduling process, and a reasonable scheduling decision support feature set is constructed. Second, a time-step adaptive scheduling mechanism is designed to simplify redundant information in the scheduling process and enables the agent to achieve efficient learning. In addition, using equilibrium, priority and preference scheduling strategies, an immediate-lag compound reward mechanism and a scheduling-switching hybrid action are designed to achieve a unification of the agent’s learning objectives and actual scheduling requirements. Finally, by constructing a simulation platform and conducting comparative experiments with four other algorithms, the results show that the proposed method has advantages in collaborative optimization of high-dimensional objectives under task continuity constraints. Including the task loading strategy can optimize the makespan performance by 16.6% and improve the fairness index by 5.3%.
3D hand pose estimation from a single RGB image by weighting the occlusion and classification

In this paper, a new framework for 3D hand pose estimation using a single RGB image is proposed. The framework is composed of two blocks. The first block formulates the hand pose estimation as a classification problem. Since the human hand can perform numerous poses, the classification network needs a huge number of parameters. So, we propose to classify hand poses based on three different aspects, including hand gesture, hand direction, and palm direction. In this way, the number of parameters will be significantly reduced. The motivation behind the classification block is that the model deals with the image as a whole and extracts global features. Furthermore, the output of the classification model is a valid pose that does not include any unexpected angle at joints. The second block estimates the 3D coordinates of the hand joints and focuses more on the details of the image pattern. RGB-based 3D hand pose estimation is an inherently ill-posed problem due to the lack of depth information in the 2D image. We propose to use the occlusion status of the hand joints to solve this problem. The occlusion status of the joints has been labeled manually. Some joints are partially occluded, and we propose to compute the extent of the occlusion by semantic segmentation. The existing methods in this field mostly used synthetic datasets. But all the models proposed in this paper are trained on more than 50 K real images. Extensive experiments on our new dataset and two other benchmark datasets show that the proposed method can achieve good performance. We also analyze the validity of the predicted poses, and the results show that the classification block increases the validity of the poses.
Learning-based underwater image enhancement: An efficient two-stream approach

Underwater image enhancement (UIE) attracts extensive attention due to its crucial importance in marine missions. Built upon the powerful representation capacity of deep neural networks (DNNs), recent years have witnessed the exponential growth of learning-based UIE methods. Thus, this work first thoroughly reviews numerous learning-based UIE methods including the algorithm description, dataset generation, and evaluation methodology. Although these learned UIE methods demonstrate remarkable efficiency for improving the reconstruction quality, they still present noticeable limitations, e.g., model generalization and unexpected quality impairments. To tackle these issues, we develop an embarrassingly-efficient two-stream UIE approach to improve both the enhancement quality of underwater images. One stream is the Significant Region Refinement (SRR) that stacks channel attention optimized residual groups to improve the image illumination and resolve the color casting problem, and the other is the Global Appearance Adjustment (GAA) that relies on several dense blocks to enhance the global image sharpness. The final output is derived by intelligently weighing contributions from the SRR and GAA branches. We verify our method using a variety of UIE datasets. Simulations show that our model outperforms state-of-the-art works in both quantitative and qualitative measurements with affordable complexity consumption.