Reviewerly/exHarmony huggingface dataset:
A Quick Development Toolkit for Augmented Reality Visualization (QDARV) of a Factory

Augmented reality (AR) technology has been applied to the industrial field; however, at present, most industrial AR applications are developed for specific application scenarios, which are difficult to develop and have a long development cycle, and lack universality. To this end, this paper proposed a quick development toolkit for augmented reality visualization (QDARV) of a factory by using the script configuration and parsing approach. With QDARV, designers can quickly develop industrial AR applications, achieve AR registration based on quick response (QR) code and simultaneous localization and mapping (SLAM), and superpose information such as disassembly animations, industrial instruments, pictures, and texts on real scenes. In QDARV, an AR registration method based on SLAM map reloading is proposed. Through saving and reloading the map and the configuration and analysis of the SLAM-AR display content script, the AR scene configured by the designer is displayed. Meanwhile, the objects detected using YOLOv5 are used as a landmark to assist the SLAM system in positioning. The QDARV can be applied to realize AR visualization of factories with a large area.
QFC: A Parallel Software Tool for Feature Construction, Based on Grammatical Evolution

This paper presents and analyzes a programming tool that implements a method for classification and function regression problems. This method builds new features from existing ones with the assistance of a hybrid algorithm that makes use of artificial neural networks and grammatical evolution. The implemented software exploits modern multi-core computing units for faster execution. The method has been applied to a variety of classification and function regression problems, and an extensive comparison with other methods of computational intelligence is made.
A Coverage Sampling Path Planning Method Suitable for UAV 3D Space Atmospheric Environment Detection

Air pollution affects people’s life and health, and controlling air pollution requires the collection of polluting gas information. Unmanned aerial vehicles (UAVs) have been used for environmental detection due to their characteristics. However, the limitation of onboard energy sources of UAVs will limit the coverage of the detection area and the number of gas samples collected, which will affect the assessment of pollution levels. In addition, to truly and completely reflect the distribution of atmospheric pollutants, it is necessary to sample the entire three-dimensional space. This paper proposes a three-dimensional space path planning method suitable for UAV atmospheric environment detection, which can generate a full-coverage path with optimal coverage density under energy constraints. Simulation results show that the proposed method can effectively improve the coverage density compared with other path generation methods. Field experiments show that the proposed method is reliable and accurate in the application of UAV atmospheric environment space detection.
The City as a Tool for STEAM Education: Problem-Posing in the Context of Math Trails

This study presents an experience that combines problem-posing and Math Trails in the context of future teachers’ instruction. Pre-service teachers in the third year of their studies were faced with the design of tasks to be included in Math Trails for primary school students. The study analyzes, from a quantitative approach, 117 tasks contained in 11 Math Trails. The analysis was performed on the basis of classification variables (grade, mathematical content and object or real element involved in every task) and research variables which provide information about the nature of the tasks (procedural vs. problem-solving, level of cognitive demand, degree of contextualization, openness and creativity). Additionally, relationships between the different categories of analysis have been studied. The results reveal certain biases in the tasks in relation to the contents addressed (an abundance of tasks with a geometric component and a scarcity of tasks involving algebra or probability concepts). Most of the tasks are presented in a real context. However, a higher percentage of procedural tasks than problem-solving tasks is observed, with a predominance of low openness, creativity and cognitive demand. These results provide useful lines of work to address difficulties faced by future teachers in the STEAM field.
Do people intend to use AI Voice Assistants? An empirical study in Vietnam

Today, the gradual popularization of artificial intelligence (AI) application technology such as Voice Assistants is making our daily life more convenient and time-saving. However, why do people choose to use Voice assistants to help them with daily tasks? Not many studies have focused on such an exciting issue. Therefore, the current study aimed to investigate the factors influencing users’ intentions to use Voice Assistant. Specifically, this study explored the impact of smartphone voice assistants with anthropomorphic design on the use intention among users. This study collected a sample of 645 smartphone users and attempted to examine the relationships among the constructs. We used path analysis to test all the hypotheses. Besides, an independent sample t-test was used to determine whether or not there were significant differences among the groups (types of Voice Assistants, Frequent and infrequent use, and gender) of the users. The results showed that both voice cues and message interactivity positively influence the social presence and perceived expertise, which affect the use intention of voice assistants. This study confirmed that highly anthropomorphic voice assistants increase voice assistant use intention among users.
Items and Constructs of Blockchain Adoption in Software Development Industry: Experts Perspective

The use of Blockchain is becoming a matter of increasing importance, yet the software development industry has not given Blockchain adoption enough consideration. As a result of their inability to modify their software process within the project life cycle, many software development companies struggle to [adopt Blockchain technology. This is because Blockchain Oriented Software (BOS) and its projects have distinctive characteristics that are distinct from traditional software and projects. The constructs and their items to adopt Blockchain in Software development industry have not been identified or investigated. This study aimed to identify technological, organizational, and environmental constructs and their items to adopt Blockchain. Utilizing a quantitative validation approach, 10 interviews and 25 assessment questionnaires were analyzed. The results reveal the identified technological, organizational, and environmental constructs and their items to adopt Blockchain. Experts unanimously agreed with Content Validity Ratio (CVR) results. According to most evaluation questionnaire forms and interview transcripts, the top technological construct was “Trialability” and the top organizational construct was “Cost” while the top environmental construct was “Market Dynamics.” All other constructs and their items were applicable, as indicated by most evaluation questionnaire forms and interview transcripts. This research gives a comprehensive list of technological, organizational, and environmental constructs and their items that demand the attention of Blockchain adoption researchers and industry players in the software development sector.
Inhibiting Webshell Attacks by Random Forest Ensembles with XGBoost

Malign websites effectively endorse the evolution of web illicit events and force the progression of Web services. As an efficient outcome, there is powerful enthusiasm to create systemic resolutions in inhibiting the client from the call onto such Websites. Knowledge-centered Random Forest outfits with XGBoost tactic is recommended for categorizing Websites into 3 categories: Benign, Spam and Malicious. This practice evaluates the Uniform Resource Locator in the situation deprived of accessing the matter of Websites. Thus, it wipes out the run-time expectation and the likelihood of uncovering clients to the browser aimed susceptibilities. As a consequence of involving Random Forest Ensembles with XGBoost, it realizes superior enactment on expansive view and publicity correlated with blacklisting amenity. Preprocessing is performed in order to improve the quality of the data subsequently, analyze certain algorithms, thereby explore the best model are the facts discussed in this research. Work also continues to probe how well this chosen archetypal will operate in the future ahead.
Nonlinear dynamics of the dissipative anisotropic two-photon Dicke model

We study the semiclassical limit of the anisotropic two-photon Dicke model with a dissipative bosonic field and describe its rich nonlinear dynamics. Besides normal and 'superradiant'-like phases, the presence of localized fixed points reflects the spectral collapse of the closed-system Hamiltonian. Through Hopf bifurcations of superradiant and normal fixed points, limit cycles are formed in certain regions of parameters. We also identify a pole-flip transition induced by anisotropy and a region of chaotic dynamics, which appears from a cascade of period-doubling bifurcations. In the chaotic region, collision and fragmentation of symmetric attractors take place. Throughout the phase diagram we find several examples of phase coexistence, leading to the segmentation of phase space into distinct basins of attraction.
An MRC and adaptive positive‐unlabeled learning framework for incompletely labeled named entity recognition

Currently, named entity recognition (NER) is mainly evaluated on standard and well-annotated data sets. However, the construction of a well-annotated data set will consume a lot of manpower and time. In lots of applications of NER, data sets may contain a lot of noise, and a large part of noise comes from unlabeled entities. At present, the training process of most models treat unlabeled entities as nonentities, which causes these models to lean toward predicting most words of an input context as nonentities and greatly affects their performances. In this paper, as the first attempt, we innovatively propose an adaptive positive-unlabeled (adaPU) learning technology, and integrate the adaPU into a machine reading comprehension (MRC) framework for NER, which can still perform well on data sets with a large proportion of unlabeled entities. In our framework, to leverage the above problem that a model may predict most words of an input context as nonentities, we propose an adaPU learning technology by adjusting a loss coefficient of positive and negative samples. Moreover, instead of just constructing a fixed query for each entity type as input to MRC, we propose a new method of dynamically constructing multiple queries for each entity type, which also brings slight performance improvement for NER. Accordingly, we explore new training and entity inference strategies for our learning framework. The experimental results show that our framework is effective on data sets that contain a large number of unlabeled entities. When the proportion of unlabeled entities reaches 50%, our framework still can keep from losing effectiveness and maintain more than 80 F1-scores on several data sets. Also, the experiments show that our framework can achieve better or competitive performance on standard data sets. The ablation experiments further fully demonstrate our MRC framework with adaPU learning and dynamic query construction method can improve the performance of NER.
N-gram and Word2Vec Feature Engineering Approaches for Spam Recognition on Some Influential Twitter Topics in Saudi Arabia

Social media platforms, such as Twitter, have become powerful sources of information on people's perception of major events. Many people use Twitter to express their views on various issues and events and use it to develop their opinion on the diverse economic, political, technical, and social occurrences related to their daily lives. Spam and non-relevant tweets are a major challenge for Twitter trend detection. Saudi Arabia is a top ranked country in Twitter usage worldwide, and in recent years has experienced difficulties due to the use and rise of hashtags based on misleading tweets and spam. The goal of this paper is to apply machine learning techniques to identify spam on the Saudi tweets collected to the end of 2020. To date, spam detection on Twitter data has been mostly done in English, leaving other major languages, such as Arabic, insufficiently covered. Additionally, publicly accessible Arabic Twitter datasets are hard to find. For our research, we use eight Twitter datasets on some significant topics in politics, health, national affairs, economy, and sport, to train and evaluate different machine learning algorithms, with a focus on two feature generation techniques based on N-grams and Word2Vec embeddings. One contribution of this paper is providing these new labelled datasets with embeddings. The experimental results show improvement from using embeddings over N-grams in more balanced datasets vs. more unbalanced ones. We also find a superior performance of the Random Forest algorithm over other algorithms in most experiments.
Cross-domain collaborative learning for single image deraining

Deep Convolutional Neural Networks (DCNN) have achieved outstanding performance in image deraining tasks. However, current most methods regard rain streak removal as a one-to-one problem, and intra domain shift of different synthetic datasets is usually ignored. Therefore, the deraining models trained on one synthetic dataset cannot effectively remove the rain streak of other synthetic datasets. Also, image deraining models which are trained on the labeled synthetic datasets mostly suffer from performance degradation when tested on the unlabeled real datasets due to the inter domain gap. To address this issue, this paper proposes a Cross-Domain Collaborative Learning (CDCL) framework to minimize the intra domain shift and inter domain gap. Firstly, a dual branch deraining network with collaborative learning is proposed to eliminate the distribution shift of rain streaks of images within synthetic domains. Then, a Cross-Domain Pseudo Label Generation (CDPLG) method is designed to obtain more accurate and robust pseudo labels for real-world rainy images, and the online generated pseudo labels are utilized to train the dual branch deraining network for reducing the domain gap between synthetic domain and real domain. Extensive experiments are conducted on the public benchmark datasets including synthetic datasets and real datasets in image deraining, and experimental results demonstrate that our proposed framework performs favorably compared with the state-of-the-art models.
TransText: Improving scene text detection via transformer

Arbitrary shape scene text detection is still a challenging task in the computer vision community. Compared with anchor-based methods, segmentation-based methods have advantages in some respects, which detect text instances by generating accurate text components. In this paper, we propose a novel text components extraction network for arbitrary shape scene text detection. It can detect different text components through two parallel branches. In the first branch, the feature redistribution module (FRM) is proposed to extract text-related features and filter non-text information. Then, these text-related features are aggregated to generate text boundary maps. These text-related features are also fed to the second branch to progressively generate the text kernel maps. The second branch consists of a modified Transformer decoder with a multi-level progressive supervision strategy. It aims to capture spatial details and establish the dependencies between different text regions. In this way, our method can generate accurate text components without additional geometric calculations. Thanks to the powerful Transformer decoder and efficient differentiable binarization module, our method not only achieves advanced detection accuracy but also has a competitive inference speed. Specifically, with the ResNet-18 backbone, our method can run at 43.5 FPS and achieve an F-measure of 83.3% on the Total-Text dataset, 1.43 times faster than the latest state-of-the-art method. With the ResNet-50 backbone, our method achieves an F-measure of 85.2% and outperforms the latest state-of-the-art method by 0.2% on the Total-Text dataset. Code is available at: https://github.com/As-David/TransText.
A novel framework for deployment of CNN models using post-training quantization on microcontroller

Nowadays, microcontrollers are very common in a wide range of applications. Integrating Machine Learning with Embedded Systems is essential and should be provided at a low cost to satisfy modern-day requirements. The tools that currently exist for this purpose can be used only with the hardware provided by the same vendor, and are usually not flexible with IDEs. This paper proposes an open-source framework that helps to deploy a trained Neural Network (Both Convolution and Deep Neural Network) on any ARM Cortex M-based microcontroller, providing an easy and low-cost device. It converts the trained model with float32 parameters into int8 type parameters and automatically generates inference code using SIMD optimized CMSIS functions. The output obtained using the proposed framework is validated with the X-Cube-AI framework. Also, A voice-based digit classification system is implemented using a Cortex-M4 microcontroller to demonstrate the efficiency of this procedure.
LAD-GCN: Automatic diagnostic framework for quantitative estimation of growth patterns during clinical evaluation of lung adenocarcinoma

Quantitative estimation of growth patterns is important for diagnosis of lung adenocarcinoma and prediction of prognosis. However, the growth patterns of lung adenocarcinoma tissue are very dependent on the spatial organization of cells. Deep learning for lung tumor histopathological image analysis often uses convolutional neural networks to automatically extract features, ignoring this spatial relationship. In this paper, a novel fully automated framework is proposed for growth pattern evaluation in lung adenocarcinoma. Specifically, the proposed method uses graph convolutional networks to extract cell structural features; that is, cells are extracted and graph structures are constructed based on histopathological image data without graph structure. A deep neural network is then used to extract the global semantic features of histopathological images to complement the cell structural features obtained in the previous step. Finally, the structural features and semantic features are fused to achieve growth pattern prediction. Experimental studies on several datasets validate our design, demonstrating that methods based on the spatial organization of cells are appropriate for the analysis of growth patterns.
Navigating planar topologies in near-optimal space and time

We show that any embedding of a planar graph can be encoded succinctly while efficiently answering a number of topological queries near-optimally. More precisely, we build on a succinct representation that encodes an embedding of m edges within 4m bits, which is close to the information-theoretic lower bound of about 3.58m. With 4m+o(m) bits of space, we show how to answer a number of topological queries relating nodes, edges, and faces, most of them in any time in ω(1). Indeed, 3.58m+o(m) bits suffice if the graph has no self-loops and no nodes of degree one. Further, we show that with O(m) bits of space we can solve all those operations in O(1) time.
Improved F-RRT<math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <msup> <mrow /> <mrow> <mo>∗</mo> </mrow> </msup> </math> Algorithm for Flight-Path Optimization in Hazardous Weather

Hazardous weather has become a major cause of flight delays in recent years. With the development of satellite navigation systems, the study of flight-path optimization under hazardous weather conditions has become especially important. In this study, radar data were used as the basis for the initial flight-restricted area under hazardous weather conditions, and the Graham algorithm was used to delineate the dynamic flight-restricted area by comprehensively considering the hazardous weather boundary changes along with the speed and direction. Then, under the grid environment model, the range of influence, size, and distribution characteristics of the flight-restricted area was examined, and the path optimization model was created according to constraints related to the path distance, corner size, and number of turning points. An improved F-RRT <math xmlns="http://www.w3.org/1998/Math/MathML" id="M2"> <msup> <mrow /> <mrow> <mo>∗</mo> </mrow> </msup> </math> algorithm was developed to solve the model. The algorithm can overcome the problems of traditional path planning algorithms, such as strong randomness, poor guidance, slow convergence speed, unsmooth paths, and poor tracing smoothness. Finally, a simulation analysis was conducted on the Guiyang–Guangzhou route in China as an example. This study can address the drawbacks of existing research on route change and provide sufficient theoretical support and reference for the implementation of specific route change plans in the future.
Adoption of Blockchain Technology: Exploring the Factors Affecting Organizational Decision

Blockchain (BCT) is an emerging technology that promises many benefits for organizations, for instance, disintermediation, data security, data transparency, a single version of the truth, and trust among trading partners. Despite its multiple benefits, the adoption rate of BCT among organizations has not reached a significantly high level worldwide, thus requiring further research in this space. The present study addresses this issue in the Australian context. There is a knowledge gap in what specific factors, among the plethora of factors reported in the extant literature, affect the organizational adoption of BCT in Australia. To fill this gap, the study uses the qualitative interpretative research approach along with the technology-organization-environment (TOE) framework as a theoretical lens. The data was mainly drawn from the literature review and semi-structured interviews of the decision-makers and senior IT people from the BCT adopter and potential adopter organizations in Australia. According to the findings, perceived information transparency, perceived risks, organization innovativeness, organization learning capability, standards uncertainty, and competition intensity influence organizational adoption of BCT in Australia. These factors are exclusively identified in this study. The study also validates the influence of perceived benefits and perceived compatibility on BCT adoption that are reported in the past studies. Practically, these findings are helpful for the Australian government and public and private organizations to develop better policies and make informed decisions for the organizational adoption of BCT. The findings would guide decision-makers to think about the adoption of BCT strategically. The study also has theoretical implications explained in the discussion section.