Reviewerly/exHarmony huggingface dataset:
A bifunctional system for measuring geometrical parameters and stress distribution in fiber optic preforms

A bifunctional system for nondestructive characterization of fiber optic preforms in transverse illumination is presented. It allows for measurements of geometrical parameters of the preform core as well as the distribution of stress-induced birefringence. The system measures the core thicknesses versus its angular orientation and by numerical fitting of the experimental data, the core orientation, its ellipticity and dimensions of ellipse axis are determined. After inserting the polarizer into the illuminating beam, the system is capable of measuring the preform birefringence induced by thermal stress. Maps of retardation distributions are obtained simultaneously at RGB channels of the CMOS camera with the use of the rotating polarizer method. This allows to determine the exact value of the stress-related retardation without interference order unambiguity and finally to calculate the stress-induced birefringence and axial stress distribution using inverse Abel transformation. Sample measurements for step index preform with nominally circular core are presented.
Accelerating Video Analytic Processing on Edge Intelligence

The most demanding Artificial Video analytic applications require in-edge inference of the AI model to ensure low-latencies to obtain the result. In general, the training process of the model can be executed on the cloud taking benefit from the high-performance computing capabilities available on those premises. This work presents an AI Video analytic application implemented on an Edge-computing device. This device is capable of accelerating the inference of AI models and Video compression by dedicated hardware. This paper presents the architecture designed to implement Image, Networking and Deep Learning Processing functionalities on a reconfigurable System-on-Chip. Additionally, the design tools and design flow followed to generate all software and hardware configuration is detailed. This Edge Intelligence platform is currently in-service, providing the preliminary results for the targeted applications. The proposed solution can process 33 times more video data volume in real-time than the software GPU accelerated implementation for the testing conditions described in the paper.
High-Resolution Range Profile Target Recognition based on Deep Kernel Extreme Learning Machine

High-resolution range profiles (HRRP) of radar targets contain rich distinguishing information. Radar target recognition based on HRRP has always been the focus and difficulty in the military field. In this paper, a radar target recognition algorithm of HRRP based on Deep Kernel Extreme Learning Machine (DKELM) is proposed. Firstly, the geometric structure features and Merlin features of the radar target are extracted from its HRRP, respectively. Then, the multidimensional features are fused via the deep belief network (DBN) to retain the deep representative features of the radar target and reduce the feature dimension at the same time. Finally, these deep fusion features are used to train the kernel extreme learning machine (KELM) classifier to identify target class. Numerical experiments show that the DBN can improve target feature representation and reduce data redundancy. Compared with the traditional SVM classifier, KELM has faster operation speed and better prediction performance. The classification recognition rate of the algorithm in this paper is greater than 95%, which has good application value.
From the Art of Software Testing to Test-as-a-Service in Cloud Computing

Researchers consider that the first edition of the book "The Art of Software Testing" by Myers (1979) initiated research in Software Testing. Since then, software testing has gone through evolutions that have driven standards and tools. This evolution has accompanied the complexity and variety of software deployment platforms. The migration to the cloud allowed benefits such as scalability, agility, and better return on investment. Cloud computing requires more significant involvement in software testing to ensure that services work as expected. In addition to testing cloud applications, cloud computing has paved the way for testing in the Test-as-a-Service model. This review aims to understand software testing in the context of cloud computing. Based on the knowledge explained here, we sought to linearize the evolution of software testing, characterizing fundamental points and allowing us to compose a synthesis of the body of knowledge in software testing, expanded by the cloud computing paradigm.
Data extractivism and public access to algorithms

Algorithms play an ever-increasing role in today’s information society as filters, gatekeepers, and organisers of information. As such, they represent a key component of the power and influence of big tech companies engaged in ever-increasing data extractivism on a global scale. While legal provisions characteristically exclude procedures and mathematical methods from copyright protection (TRIPs Agreement, art. 9.2), political pressure is increasing to ensure legally binding secrecy for algorithms. This pressure has resulted in the inclusion of provisions which impose strict limits on disclosure of a software’s source code and algorithms in a number of free trade agreements signed by the USA and Japan. These provisions, which are on the verge of becoming standard norms in international digital trade, enhance the market power of tech companies in control of algorithms, while at the same time thwarting the quest for increased transparency and accountability of these new informational tools. The chapter examines these emerging norms, discusses their scope and potential effect on the global informational ecosystem, and questions their legitimacy in light of fundamental principles of intellectual property law.
Image Classification Using Convolutional Neural Networks

Abstract: Convolutional neural networks (CNNs), which are composed of multiple processing layers to learn the representations of data with multiple abstract levels, are the most successful machine learning models in recent years. Within this paper, we present the usage of a trained deep convolutional neural network model to extract the features of the images, and then classify the images. We will study image processing and understand image classification. It has good application prospects.
Reducing the Risk of Completeness Loss by Subgraph Reasoning in Engineering Applications

The ontology knowledge base is divided into TBox and ABox, the former is the schema-level information of the knowledge base, which is used to describe the relationship between the recognized concepts and attributes, and the latter is a collection of instance assertions or fact statements in the domain. Using TBox, the process of reasoning more implicit assertions in ABox is called ABox materialization, which plays an important role in knowledge base applications. Some ontology reasoners for OWL DL and DL-safe SWRL, the reasoning results are considered to be correct and complete. However, in practical applications, this paper finds that in some cases, the reasoning results are not complete when reasoning about the ontology knowledge base constructed by OWL DL and DL-sale SWRL. In those application scenarios that require completeness, blindly trusting these reasoners will bring risks. In response to this problem, this paper proposes a method based on subgraph reasoning, which can identify whether the result of ontology reasoning is complete in engineering, and at the same time, when it is incomplete, new assertions that have not been found before can be inferred. The comparative experimental results on two open-source ontologies verify the method in this paper.
Atendimento de pacientes com COVID-19 no Hospital Universitário Onofre Lopes: contribuições da enfermagem

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Ship Target Detection in SAR Images Based on FPGA

Since Synthetic Aperture Radar (SAR) has the characteristics of all-day and all-weather operating, it is significant to carry out maritime ship target detection based on SAR images. Although deep learning algorithms have been widely used for target detection, there are problems of high model complexity and slow detection speed, which cannot meet the requirements of embedded deployment and real-time detection in certain complex environments. To address the above problems, this paper investigates the SAR image ship target detection method based on deep learning technology and FPGA devices. Firstly, a lightweight target detection network based on You Only Look Once (YOLO) is proposed, which combines EfficientNet with the YOLO backbone network to reduce the parameters and size of the network and increase the speed of detection. Secondly, the model is compressed and lightweighted by operations such as quantization to facilitate deployment on resource-constrained devices. The results demonstrate that the improved algorithm proposed in this paper can improve the detection speed while guaranteeing certain accuracy, and realize the deployment on FPGAs, demonstrating the feasibility of the deployment scheme, which can better meet the needs of terminal deployment and real-time detection.
Atuação da enfermagem no enfrentamento da COVID-19 em um hospital universitário no Amazonas

3) .
Characterizing the Efficiency of Graph Neural Network Frameworks with a Magnifying Glass

Graph neural networks (GNNs) have received great attention due to their success in various graph-related learning tasks. Several GNN frameworks have then been developed for fast and easy implementation of GNN models. Despite their popularity, they are not well documented, and their implementations and system performance have not been well understood. In particular, unlike the traditional GNNs that are trained based on the entire graph in a full-batch manner, recent GNNs have been developed with different graph sampling techniques for mini-batch training of GNNs on large graphs. While they improve the scalability, their training times still depend on the implementations in the frameworks as sampling and its associated operations can introduce non-negligible overhead and computational cost. In addition, it is unknown how much the frameworks are ‘eco-friendly’ from a green computing perspective. In this paper, we provide an in-depth study of two mainstream GNN frameworks along with three state-of-the-art GNNs to analyze their performance in terms of runtime and power/energy consumption. We conduct extensive bench mark experiments at several different levels and present detailed analysis results and observations, which could be helpful for further improvement and optimization.
Comparing the characteristics of blockchains via building a NFT marketplace – A Survey

In this attempt we try to focus on building an NFT market place on the Internet Computer using and comparing the application with the existing popular systems like Opensea etc., whose major transaction volume is on the Ethereum blockchain network. We intend to compare various aspects of functioning like transaction fee[ also known as gas fee ] and also check the system’s ability to scale. We in the end intend to compare it with some of ethereum’s modern alternatives like solana , cardano , etc., We would be using motoko as the language for interacting with the Internet computer and reactjs to build the front end of the application
Design of a small material handling robot gripping and storage system

Abstract This paper completed the development status and working principle of the handling robot, analyzed the performance index parameters and system functional requirements of the robot, and proposed an overall design scheme of the handling robot control system. The mechanical structure design of the handling robot was developed, and the selection of the core components of the system was studied and designed in detail based on theoretical analysis and calculation. Secondly, this paper selected the STM32 microcontroller as the core of the handling robot. It completed the circuit schematic diagram of the systems, and investigated the selection of core sensors for the navigation and positioning module, obstacle avoidance module, and wireless communication module. Finally, this paper studied the automatic control part of the handling robot, which mainly included the modeling of the robot’s manipulator, PID control, and MATLAB simulation verification. In addition, the application prospect of the handling robot was explored. It had the advantages of high efficiency, strong practicability, and perfect function, and had certain practical significance in the application of the logistics industry.
Research on Kinematics Analysis and Trajectory Planning of UR5 Robot

Abstract In order to solve the problems in the practical application of the UR5 robot, improve the work efficiency and ensure the stability of the movement, the related kinematics analysis and trajectory planning of the UR5 robot is carried out, and the D-H parameter kinematics model is established to carry out forward and reverse motion. The transformation relationship of each link coordinate system in the forward kinematics and the rotation angle of each joint in the inverse kinematics are solved, and then MATLAB is used to perform related calculations to verify the feasibility of the algorithm. The robot arm performs interpolation to complete the trajectory planning between the beginning and end points passing through multiple intermediate nodes.
AIRRT*: An improved fast path planning algorithm for manipulators

Abstract Path planning algorithms based on sampling have been widely studied and applied in recent years, among which the Informed-RRT* algorithm is a typically progressive optimization algorithm. To overcome the weakness of low optimization efficiency of the Informed-RRT* algorithm, an improved algorithm AIRRT* (Adaptive Informed-RRT*) is proposed, which can greatly improve optimization efficiency and improve the quality of optimized paths. According to the node distribution of the initial path, the appropriate node construction areas are dynamically selected first in the process of direct local sampling. Then collision detection and reconstruction are performed. Unlike the traditional algorithm, the AIRRT* algorithm determines the area to be reconstructed firstly, then performs direct local sampling in the reconstructed area, and finally reconstructs the path to improve the sampling efficiency. In addition, to reduce the searching difficulty of global path nodes, the invalid nodes are removed in reconstruction, which limits the growth of nodes in optimization. AIRRT* algorithm is compared with the common algorithms through simulation experiments, which proves that AIRRT* algorithm has obvious advantages in algorithm efficiency and path quality and has good applicability in varied scenarios.
High-Level Data Flow

Given our self-imposed constraint of using GraphQL, in this chapter, we’ll consider the high-level data flow of a system that utilizes it to back the API contract with user interfaces and other collaborating systems. I’ll start with an explanation of Event Sourcing (ES) and Command Query Responsibility Segregation (CQRS), followed by a primer on how to think in terms of GraphQL’s schema language, and finally bring all three concepts together.
Hate Speech and the Gender Perspective: A Problem from the Teaching of Social Sciences in School

&lt;p style="text-align: justify;"&gt;This study explores a socially relevant problem related to teachers' conceptions of hate speech from a gender perspective present in the discourse of teaching history and social science educational practices. The methodology was used within the paradigm with a case study design, as a generalization was not intended. The sample consisted of 80 professors of history and social sciences. A quantitative questionnaire was used to determine the conceptions of the participants. The main findings include the constraints placed by programs and curricula on the presence of hate speech, student interventions in classes, and strategies implemented by participating teachers to position counter-narratives to hate in their practice. The conclusions include the importance teachers place on analyzing the prevalence of hate speech in their practice, both by students and the media, and its impact on teaching. In addition, the results offer some educational insights and perspectives for positioning hate speech as a transformative social justice perspective.&lt;/p&gt;
Peningkatan Kompetensi Literasi Digital Guru Melalui Bimtek Pembuatan Media dan Evaluasi Pembelajaran

Online learning (on the network) is a demand in the 4.0 era (current industrial revolution. This is because of the development of technology and information so that learning must be carried out using technology. In addition, the Covid-19 outbreak gave birth to implementation policies learning carried out at home using online methods. For this reason, teachers are required tohave today's digital literacy competencies in order to be able to do online learning. However, the current problem is that there are still many teachers who do not understand technology digital, namely 97.5% of teachers. This is as known from the results of interviews in Malalak District Agam Regency, West Sumatra that there are still many teachers who have not been able to implement learning using digital technology. And also there are still many teachers who are not able to carry out online learning. There are still many teachers who have not been able to evaluate online learning. The solution to overcome these problems is to transfer knowledge through online learning guidance for teachers. The first step is to do testing digital literacy competence of teachers through a questionnaire. After that give understanding to the teacher about online learning. Then the next stage is conducting technical guidance on the manufacture of innovative and interactive learning media for learning. Next, technical guidance is carried out about the implementation of online learning evaluation for teachers. The last stage is to evaluate about the digital literacy competence of teachers regarding the implementation and evaluation of online learning using a questionnaire. The output of the results of this community service activity are scientific publications, namely: articles on Journals with ISSN / Proceedings of National journals, namely colloquium journals, video service activities, and publication in print/online mass media, namely the Haluan newspaper. And also activity report.
Problems and Solutions for the Flood Control Program in Medan City and its Surroundings

Medan City is currently experiencing flooding and this indicates all existing flood control projects implemented over the years have been unsuccessful. Therefore, the main objective of this research is to determine the best solution to overcome floods in the city through a literature study supplemented by a perception survey. The steps implemented include discovering the causes of the flood, determining the failure of the past flood control projects, disclosing the perception of the people affected by floods toward the city government’s commitment, and formulating the best solution to overcome the flood. It was discovered that there are two kinds of floods in Medan city which include the flash/river and the local/residence floods. The flash type was caused by the deforestation in the upstream area of the rivers flowing through the city while the local type is associated with high rainfall, lack of pervious area, and poor urban stormwater management. Moreover, the existing flood mitigation projects were observed to be unsuccessful because they applied the old paradigm with a normalization approach without compensating for the increase in the runoff discharge. This means there is a need to establish naturalization in the upper region of all rivers flowing through the city to reduce river flooding. The maintenance of the existing urban drainage system also needs to be combined with sustainable programs such as storing, absorbing, and reusing strategies to mitigate residence flooding. Furthermore, ten recommendations were proposed to stakeholders to overcome or significantly reduce flooding in Medan City and its surroundings. The first is the alignment of paradigms among stakeholders toward flood management while the last is completing related regulations and law enforcement.
Terrain Specific Real Time Pixelated Camouflage Texture Generation &amp; its Impact Assessment

“Camouflage” is a natural or nature identical phenomenon where the sensory route of vision is delayed toavoid visual detection. Reducing detection capability and hiding in the background environment is critical for Army vehicles, equipment, and soldiers. This research aims to implement a process that will generate digital camouflage patterns specific to the terrain. The adapted digital pattern helps an object blend symmetrically into the background environment. Pixelated textures combine macro and micro designs that blend with ambient shrubs, trees, branches, and shadows. The technique presented in this paper consists of the following main modules: terrain classification model, pixelated camouflage texture generation, and texture evaluation. Experiments have been conducted to detect camouflage objects in the scene to evaluate the performance of the resultant camouflage texture generated for a natural environment. Photo simulation and saliency maps for hidden object detection have been used to evaluate the effectiveness of generated textures for a given terrain
Analysis and methodology for enabling DNN inference in an IoT edge environment in depth completion tasks

IoT and artificial intelligence (AI) have attracted a huge amount of interest in recent years, creating the so-called edge artificial intelligence field. However, due to the tendency in AI to create larger and more complex models, these algorithms are often not suitable for resource-restricted devices, and efforts have to be made in the direction of optimizing AI for IoT. This paper presents an analysis of three different tools that allow the use of Deep Neural Network (DNN) models in resource-limited devices: (i) a deep neural network compressor, (ii) the use of different bit-precision operations in the model, and (iii) the use of a specific accelerator engine in the constrained device. For that purpose, an IoT architecture is followed in which the edge device is a constrained device which takes the model pre-trained from the cloud and performs real-time inference. In particular, four common CNN building blocks have been compared in <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">an</sup> encoder-decoder architecture, in order to improve the depth maps generated by LiDAR-like cameras in situ. The resulting architectures have been analyzed in terms of quality, energy consumption and inference time, measuring the effects of the compressor, the bit precision and the accelerator engine. The results obtained show that the use of a methodology based on these three tools combined is beneficial to reduce the size of the model more than ×8 times, energy consumption by almost ×3 times, and an increase in speed by ×3 times from the base model, without compromising the quality of the model.