Reviewerly/exHarmony huggingface dataset:
Modularization in Belief-Desire-Intention agent programming and artifact-based environments

This article proposes an extension for the Agents and Artifacts meta-model to enable modularization. We adopt the Belief-Desire-Intention (BDI) model of agency to represent independent and reusable units of code by means of modules. The key idea behind our proposal is to take advantage of the syntactic notion of namespace, i.e., a unique symbol identifier to organize a set of programming elements. On this basis, agents can decide in BDI terms which beliefs, goals, events, percepts and actions will be independently handled by a particular module. The practical feasibility of this approach is demonstrated by developing an auction scenario, where source code enhances scores of coupling, cohesion and complexity metrics, when compared against a non-modular version of the scenario. Our solution allows to address the name-collision issue, provides a use interface for modules that follows the information hiding principle, and promotes software engineering principles related to modularization such as reusability, extensibility and maintainability. Differently from others, our solution allows to encapsulate environment components into modules as it remains independent from a particular BDI agent-oriented programming language.
Multi-Branch Gated Fusion Network: A Method That Provides Higher-Quality Images for the USV Perception System in Maritime Hazy Condition

Image data acquired by unmanned surface vehicle (USV) perception systems in hazy situations is characterized by low resolution and low contrast, which can seriously affect subsequent high-level vision tasks. To obtain high-definition images under maritime hazy conditions, an end-to-end multi-branch gated fusion network (MGFNet) is proposed. Firstly, residual channel attention, residual pixel attention, and residual spatial attention modules are applied in different branch networks. These attention modules are used to focus on high-frequency image details, thick haze area information, and contrast enhancement, respectively. In addition, the gated fusion subnetworks are proposed to output the importance weight map corresponding to each branch, and the feature maps of three different branches are linearly fused with the importance weight map to help obtain the haze-free image. Then, the network structure is evaluated based on the comparison with pertinent state-of-the-art methods using artificial and actual datasets. The experimental results demonstrate that the proposed network is superior to other previous state-of-the-art methods in the PSNR and SSIM and has a better visual effect in qualitative image comparison. Finally, the network is further applied to the hazy sea–skyline detection task, and advanced results are still achieved.
APPLYING DEEP LEARNING FOR HEALTHCARE IN SMART CITY VIA INTERNET OF THINGS

In the traditional city for healthcare in IoT, it has been proposed to replace traditional yield models with mathematical models that do not require the assumption of defect density functions. The selection of input parameters in these models is very important, and all the variation factors on the wafer must be included as far as possible. The factors of clustering are usually described by clustering indicators, but some specific clustering patterns will cause the clustering indicators to misjudge the clustering degree, resulting in the yield estimation error becoming larger. In view of this, the proposed study has classified the defect patterns on the wafer into four types: random distribution, regional concentrated distribution, linear distribution and circular distribution, by means of three pattern characteristics analysis. A comparison is made only using cluster indicators to describe cluster phenomena and a model that uses cluster indicators and cluster graphs to describe cluster phenomena. The research results show that when constructing the yield model, the clustering pattern and the clustering index are used to describe the clustering phenomenon in smart city via Internet of things, which is preferred to solely considering the clustering index, as the consequent degree of accuracy far exceeds the improvement of changing the “number of effective grains” in relation to the clustering index. Therefore, the yield rate can be estimated more accurately by using clustering graphs with clustering indicators; the estimated yield in the yield model, with the clustering pattern parameter, is indeed closer to the actual yield than the yield model without the clustering pattern parameter.
Obstacle Avoidance Algorithm for Autonomous Mobile Robots in the Indoor Environment

Autonomous mobile robots (AMR) are commonly used in intelligent manufacturing systems. For these robots to operate autonomously in the working environment, in addition to being equipped with a sensor system, algorithms are required to process information collected from the working environment and make control decisions to control the robot's movement in different situations. In this paper, the authors present an algorithm to avoid static, dynamic obstacles and generate local guidance trajectories for the robot to overcome the barriers on its way based on information from the sensor network equipped with the robot. In this, dynamic obstacles are understood as objects moving in the same direction opposite to the direction of the robot or crossing the motion of the robot. The proposed algorithm has been experimentally verified on a differential drive mobile robot combined with a mecanum wheel mobile robot. The results of this research have practical implications for motion control of AMR in industries.
Twin prototype networks with noisy label self-correction for fault diagnosis of wind turbine gearboxes

Abstract Deep strong-supervised learning-based methods have been widely used and have made significant progress in intelligent fault diagnosis for wind turbine (WT) gearboxes. The superior performance of such methods relies on high-quality labels. However, correctly labeling the data is challenging because of the complexity of fault vibration signals and fault modes in real industrial scenarios, resulting in noisy labels in datasets, which significantly restricts the application of strong-supervised fault diagnosis models. In this study, a method based on twin prototype networks with noisy label self-correction was proposed to address fault diagnosis for WT gearboxes with noisy labels. This method introduced a collaborative learning architecture to improve the confirmation bias in the self-training of individual networks and to slow the speed of learning noisy-labeled samples. Simultaneously, the loss distribution of the samples from each network was modeled using the Gaussian mixture model to dynamically identify mislabeled samples in the training dataset. Finally, a collaborative relabeling prototype refinement module was designed to optimize the prototype learning process and enable self-correction of noisy labels. The experiments demonstrated the effectiveness and superiority of the proposed method.
Classification of Located Acoustic Emission Events Using Neural Network

Abstract Location of acoustic emission (AE) events is one of the main evaluation tools in AE analysis. Reliable location of AE sources enables accurate investigation of the mechanisms that led to a crack in the material. It is known that the location errors are influenced by several factors, including the accuracy of the elastic wave arrival time reading, the geometric distribution of the AE sensors, and most importantly, by the physical properties of the propagation medium. The aim of this study is the application of a neural network to classify clustered AE events, which were detected during six hydraulic fracturing tests in massive salt rock. A fully connected feed forward network was used for pattern recognition and classification of the input events according to target classes. For input data the signal arrival time profiles of the longitudinal (L) and transversal (T) elastic waves were used to train, to test, and to validate the neural network. In total 765 AE events were classified in various target classes. Receiver operating characteristic analysis (ROC) was applied for analyzing the result of the neural network approach. The neural network classified clustered events correctly, while few spatially scattered events outside the region of a cluster could not be matched to any cluster. Bootstrap analysis showed that the results are robust and demonstrates the high potential of Deep Learning (DL) methods in the location of AE events.
Video-based Student Classroom Classroom Behavior State Analysis

Looking back on the development of education, education has evolved along with the development of human society, and the development of science and technology has been the fundamental force driving the change of education. And with the increasing demand for personalized education for students, the focus of education and teaching has gradually shifted to the direction of personalization and specialization of students. In the teaching process, students' classroom behavior is an important reference indicator for evaluating students' classroom learning, but teachers cannot pay attention to each student's classroom behavior in time, and can only derive students' classroom learning status from their homework or exams. Many subjective factors can easily affect teachers' judgment of students' learning status, and the evaluation obtained is not always comprehensive and objective enough. In traditional teaching activities, teachers mainly identify and control students' classroom videos manually, but this method is not only time-consuming and labor-intensive, but also affects teachers' classroom quality. Therefore, it is urgent to study an intelligent and effective system for analyzing students' classroom behavior status. It is of great significance to improve the quality of students' classroom learning and to assist teachers in obtaining teaching feedback information. At present, the rapid development of artificial intelligence is reshaping the world's industrial ecology and pushing human society into the era of intelligence. Artificial intelligence is deeply integrating with education and promoting innovation in educational philosophy, teaching methods and management modes, and is expected to lead systematic changes in education. In fact, the development of artificial intelligence technology cannot be separated from the continuous research of computer vision technology. How to make the computer obtain external information through video acquisition device to simulate human for analysis and recognition is the main research content of computer vision technology, and its research direction includes target tracking, object recognition, human behavior recognition, etc. In this paper, through the combination of artificial intelligence technology and computer vision technology, we analyze the video images of students' classes in classroom scenes, identify students' classroom behavior status, build a judgment system on students' classroom learning status, and assist teachers to obtain teaching feedback information.
EMES: Efficient Multi-Encoding Schemes for HEVC-based Adaptive Bitrate Streaming

In HTTP Adaptive Streaming (HAS), videos are encoded at multiple bitrates and spatial resolutions ( i.e. , representations ) to adapt to the heterogeneity of network conditions, device attributes, and end-user preferences. Encoding the same video segment at multiple representations increases costs for content providers. State-of-the-art multi-encoding schemes improve the encoding process by utilizing encoder analysis information from already encoded representation(s) to reduce the encoding time of the remaining representations. These schemes typically use the highest bitrate representation as the reference to accelerate the encoding of the remaining representations. Nowadays, most streaming services utilize cloud-based encoding techniques, enabling a fully parallel encoding process to reduce the overall encoding time. The highest bitrate representation has the highest encoding time than the other representations. Thus, utilizing it as the reference encoding is unfavorable in a parallel encoding setup as the overall encoding time is bound by its encoding time. This paper provides a comprehensive study of various multi-rate and multi-encoding schemes in both serial and parallel encoding scenarios. Furthermore, it introduces novel heuristics to limit the Rate Distortion Optimization (RDO) process across various representations. Based on these heuristics, three multi-encoding schemes are proposed, which rely on encoder analysis sharing across different representations: (i) optimized for the highest compression efficiency , (ii) optimized for the best compression efficiency-encoding time savings trade-off , and (iii) optimized for the best encoding time savings . Experimental results demonstrate that the proposed multi-encoding schemes (i) , (ii) , and (iii) reduce the overall serial encoding time by 34.71%, 45.27%, and 68.76% with a 2.3%, 3.1%, and 4.5% bitrate increase to maintain the same VMAF, respectively compared to stand-alone encodings. The overall parallel encoding time is reduced by 22.03%, 20.72%, and 76.82% compared to stand-alone encodings for schemes (i) , (ii) , and (iii) , respectively.
Shadow Pattern-Enhanced Building Height Extraction Using Very-High-Resolution Image

Building height is valuable for a variety of foci in urban studies. The traditional field investigations are not practical for the updates of massive building height in a large-scale urban area. Given the relationship between building structures and their shadow sizes, the building shadow becomes practical for estimating its corresponding building height when its geometrical shape is visible in newly emerging very-high-resolution (VHR) images. However, the shadow shape of different buildings might vary significantly, posing a great challenge to determining the edge of shadow useful for predicting building height. This study proposes a shadow pattern classification system ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ShadowClass</i> ) to summarize the varied shadow shapes into a number of pattern categories and employ a cutting-edge CNN model to classify the extracted shadows into a pattern for automatically determining the edge of a building shadow being useful for building height estimation. We integrated the proposed approach into two branches of the state-of-the-art approaches: shadow-based building height estimation with open cyberinfrastructure and shadow-based building height estimation with VHR image. The experimental results proved that the proposed method could be a practical solution for single and isolated buildings that have their complete shadow shape.
To the question of professional and moral criteria in the organization of the educational process

The publication is devoted to aspects of the development and understanding of professional and moral criteria in the organization of the educational process in the system of higher professional education in the Russian Federation. The crisis of the ethical component in the professional training of Russian students is argued , the importance of the development of professional ethics in the development of human potential in the interests of the Russian state and society is shown . A distinction is made between moral and professional criteria for the organization of the educational process . A system of professional and moral criteria in the educational process in the system of higher professional education , aspects of their assessment , as well as recommendations for implementation in practice are presented .
A broad approach to expert detection using syntactic and semantic social networks analysis in the context of Global Software Development

Social network analysis has been widely used in different application contexts. For example, in Global Software Development, where multiple developers with diverse skills and knowledge are involved, the use of social networking models helps to understand how these developers collaborate. Finding experts who can help address critical elements or issues in a project is a challenging and critical task. It is especially true in the context of Global Software Development projects, where developers with specific skills and knowledge often need to be identified. In this sense, searching for essential members is a valuable task, as they are fundamental to the evolution of the network. This article proposes a broad solution for syntactic and semantic analysis in social networks in the Global Software Development context. In this solution, we define a model for the social network capable of capturing collaboration between developers, incorporate strategies for temporal analysis of the network, explore the network using machine learning algorithms, and propose an ontology to enrich the data semantically. We conducted three case studies using data extracted from GitHub to evaluate the proposed approach. The case studies provide evidence that our proposed method can identify specialists, highlighting their expertise and importance to the evolution of the social network.
An adaptive gradient-descent-based neural networks for the on-line solution of linear time variant equations and its applications

It is well-known that, the classical gradient-descent-based neural network (CGNN) model is used widely for the time-invariant problem solving. However, it is an extremely common problem for the time varying cases in the practical engineering applications, while the CGNN effective neural solver for the time-variant problems. For this reason, in this article, an adaptive GNN (AGNN) is presented for the linear time variant matrix equation (LTVME) on-line solving based on Lyapunov theory. Theoretical analysis already verified that the presented AGNN model could achieve the correct state solution effectively. The simulated experiment results further validate that the state solution of the AGNN model could be convergent to the correct solution of the solved time variant problems in theory.
An Improved Synthesis Phase Unwrapping Method Based on Three-Frequency Heterodyne

An improved three-frequency heterodyne synthesis phase unwrapping method is proposed to improve the measurement accuracy through phase difference and phase sum operations. This method can reduce the effect of noise and increase the equivalent phase frequency. According to the distribution found in the phase difference calculation process, the Otsu segmentation is introduced to judge the phase threshold. The equivalent frequency obtained from the phase sum is more than those of all projected fringe patterns. In addition, the appropriate period combinations are also studied. The simulations and related experiments demonstrate the feasibility of the proposed method and the ability to improve the accuracy of the measurement results further.
Few-Shot Radar Emitter Signal Recognition Based on Attention-Balanced Prototypical Network

In recent years, radar emitter signal identification has been greatly developed via the utilization of deep learning and has achieved significant improvements in identification accuracy. However, with the continuous emergence of complex regime radars and the increasing complexity of the electromagnetic environment, some new kinds of radar emitter signals collected are not in sufficient quantities to satisfy the demand of deep learning. As a result, in this paper, we adopted the prototypical network (PN) belonging to metric-based meta-learning to realize few-shot radar emitter signal recognition with the aim of meeting the needs of modern electronic warfare. Additionally, considering the problems that may arise in the field of few-shot radar emitter signal recognition, such as discriminative location bias caused by a small number of base classes or the large difference between base classes and novel classes, we proposed an attention-balanced strategy to improve meta-learning. Specifically, each channel in the feature map is forced to make the same contribution in the distinguishment of different classes. In addition, for PN, taking into account that the feature vectors of each support sample in the class are different, we set a network to exploit the relation between each support sample in the same classes, and weighted each feature vector of the support samples according to the relation. Large quantities of experiments indicate that our algorithm possesses more advantages than other algorithms.
Truss Structure Optimization Design Based on FE-PSO-SQP Algorithm

Compared with other structural optimization design algorithms, particle swarm optimization (PSO) gains many superiorities, like being easy to understand the principle and fewer parameters in the calculation model. When we use the PSO to deal with truss structure optimization problems, this algorithm usually has low computational accuracy, slow rates of convergence, and poor population varieties in the further model calculation. To overcome these shortcomings and better solve the truss structure optimization problem, FE-PSO-SQP algorithm, a new structure optimization method, is proposed herein by combining the PSO algorithm with the sequential quadratic programming (SQP) algorithm and finite element method (FE). In addition, a set of calculation program is developed by ANSYS software. When the self-made program is used to conduct simulation calculation on the truss structure optimization problem, the calculation results show that FE-PSO-SQP algorithm has faster convergence speed and higher calculation accuracy than FE-PSO algorithm, and can be used for structure optimization design.
Path Generator with Unpaired Samples Employing Generative Adversarial Networks

Interactive technologies such as augmented reality have grown in popularity, but specialized sensors and high computer power must be used to perceive and analyze the environment in order to obtain an immersive experience in real time. However, these kinds of implementations have high costs. On the other hand, machine learning has helped create alternative solutions for reducing costs, but it is limited to particular solutions because the creation of datasets is complicated. Due to this problem, this work suggests an alternate strategy for dealing with limited information: unpaired samples from known and unknown surroundings are used to generate a path on embedded devices, such as smartphones, in real time. This strategy creates a path that avoids virtual elements through physical objects. The authors suggest an architecture for creating a path using imperfect knowledge. Additionally, an augmented reality experience is used to describe the generated path, and some users tested the proposal to evaluate the performance. Finally, the primary contribution is the approximation of a path produced from a known environment by using an unpaired dataset.