Reviewerly/exHarmony huggingface dataset:
The Research Community under Technical Support Improves Primary School Teachers Case Study of Information Teaching Ability

In today's society with the rapid development of information technology, the application of information technology in the classroom is more and more extensive, however, although some schools in Inner Mongolia have information technology conditions but teacher information teaching ability is very limited, teacher teaching design ability to apply information technology integration is not high. So we have trained teachers in some regions through mixed training. This study mainly applies the improved Flanders coding system and GSEQ software to analyze and compare the changes of teachers' teaching ability before and after the training, to see whether their teaching design ability has improved and realize the teaching mode of learning evaluation integration.
SQLG+: Efficient k-hop Query Processing on RDBMS

Graph algorithms (e.g., k-hop queries) are widely used to find the deep association of data in various real-world applications such as business recommendation and fraud detection. However, most of the data are still stored in relational database (i.e., RDBMS) and the performance is rather limited when processing graph queries on RDBMS due to the inherent hardness of complicated table join. In this paper, we propose a fast interactive engine SQLG+, which can be integrated to any RDBMS and enable them to process k-hop graph queries efficiently. Different from naive table-join implementations, SQLG+ caches important nodes with their adjacency lists in memory (i.e., graph cache) and generates a hybrid query plan which combines the ability of graph cache and RDBMS. Also, SQLG+ removes duplicates in the end of each hop (using AdaptiveSet) and expands the frontiers in different ways. Furthermore, dynamic BFS/DFS switch is adopted to achieve the balance between query performance and memory occupation. Extensive experiments show that SQLG+ outperforms the state-of-the-art RDBMS-based implementations by up to several orders of magnitude and is even comparable to the fastest graph databases.
IoT Hub, Event Hub, and Streaming Data

Streaming data is an essential part of the data universe for many of today’s applications. And as is the case for many services across the Microsoft Azure data platform, there are several services available for working with streaming data that can be used in a serverless fashion; i.e., without having to care about scaling the underlying hardware. Whether dealing with internet of things (IoT) applications, modern service-based architectures, logging data, or financial transactions, working with data streams is essential to many applications.
Eodie - Earth Observation Data Information Extractor

Remote sensing satellites provide a vast amount of data for the monitoring and observation of Earth's surface. For this data to be usable in subsequent applications, easy-to-use and automated tools are needed for its processing. We present Earth Observation Data Information Extractor (EODIE), a toolkit for extracting object-level time-series information from multispectral satellite remote sensing data with output ready for subsequent applications. EODIE's modular design enables adjustability to end-user requirements with the possibility to exchange and add modules for flexible processing in different computing environments. With EODIE, raster remote sensing data can be processed to object level array, geotiff or statistics information of 17 different (vegetation) indices or plain wavelength intervals.
Developing Instrument for Investigation of Blockchain Technology

Modern telecommunication technology provides new opportunities for further development of the Smart City operation systems. Smart City systems basically include the Internet of Things (IoT) and CyberPhysical Systems (CPS), which can be implemented by artificial intelligence-based technologies. Significant amount of work about the implementation schemes for the Smart City are using blockchain technology, and the quality of the developed Smart City system strongly depends on the reliability of the blockchain. This article presents an approach to study several blockchain formation aspects. The main goal of this work is to create a research tool for blockchain technology and analyze its capabilities. Such an analysis allows us to determine the scope of possible use of a blockchain, depending on the underlying construction methods, in various fields of application, such as the IoT/CPS. To perform this analysis, several aspects that are needed to explain the basic principles of blockchain formation have been selected. Some minor tasks are specially simplified to avoid distraction from the main ones. This paper also demonstrates the main feature of a blockchain to place information in a distributed ledger. A blockchain formation system has been studied in a specific implementation. A model of a multi nodes system has been constructed, and an analysis of the functioning of a number of software solutions in the field of research modeling of blockchain construction is carried out.
A Mating Technique for Various Crossover in Genetic Algorithm for Optimum System Identification

System identification is the study involving the derivation of a mathematical model from input and output data to explain dynamical behavior of a system. Such derivation is made using a mathematical model based on certain specified assumptions. To researchers who are involved in the application of Genetic Algorithm (GA) in optimization, the process of choosing the best parents in the population for mating has become of great interest. Here, the application is on selecting a model structure for system identification. This step addresses selecting an adequate model, i.e. a model that has a good balance between parsimony and accuracy in approximating a dynamic system. This paper demonstrates the integration of a novel mating technique with various types of crossover to enhance the performance of GA application. Four discrete-time systems of linear and nonlinear types are simulated and identified. The results show that GA with single parent mating can speed up the search for optimal models and avoid premature convergence even with different types of crossover.
Adaptive Interfered Fluid Dynamic System Algorithm Based on Deep Reinforcement Learning Framework

In this paper, adaptive interfered fluid dynamic system algorithm (AIFDS) is proposed for unmanned aerial vehicle (UAV) path planning in dynamic obstacle environment, inspired by the natural flow to avoid rocks. In AIFDS, UAV is regarded as an agent. Through the interaction with the environment, it gradually learns how to adjust the flow field, so as to plan a path with high safety, short distance and short execution time in advance. AIFDS can be combined with almost all reinforcement learning algorithms with continuous action space. This paper studies the combination of AIFDS with SAC, DDPG, PPO, TD3 algorithm. Experiments are carried out in the environment with multiple dynamic obstacles, and the results show that AIFDS has a bright performance in the aspect of path safety.
Effects of Physics Education Using English for Students Who Are Not Good at Physics and English, Which Are Derived by Comparison to the Case of Remote Classes

Team activities with conscious Problem-Based Learning which challenges the problems of physics written in English and gives presentations to other students were conducted in face-to-face classes of “basic physics” whose most of students were not good at English and physics. According to surveys of questionnaire, students′ hope to make one′s own decision to select the themes and problems was growing and they felt more “intellectual challenging” to the problems and text written in English with progression of their project. To compare with the data of remote classes which were not able to conduct the team activities and challenge the problems of physics written in English alone, I found that these tendencies and other effects came from the team activities.
Emerging Technologies in Financing Startups

Startup financing is a highly underestimated and constantly growing topic that moves a large volume of money, from traditional bank credits, holdings and business angel investments to crowdlending or cryptocurrencies, and others yet to be discovered, under a constantly uncertain and evolving environment. Startups have disrupted traditional financing schemes as they have demonstrated that by embracing technology they can generate new transformative business models and innovative products and services, making them very attractive for potential investment; or achieve highly optimised internal processes to be more efficient and thus require less financing by absorbing resources more efficiently. Digitisation and digitalisation processes are the key elements triggering these changes facilitating a number of Emerging Technologies that accelerate and facilitate the deployment of digital solutions, together with a high-performance technological infrastructure supporting these new services, such as IT outsourcing. This chapter provides an overview of the most relevant Emerging Technologies that startups can deploy for the purposes of potential financing: Cloud and Cloud computing; Data Cybersecurity and Privacy; Internet of Things; 5G Networks; Drones; Physical Robot Automation; Robotic Process Automation; 3D & 4D printing; Virtual, Mixed and Augmented Realities; Blockchain; and Artificial Intelligence.
Sentiment analysis of product reviews using weighted distance-based whale optimisation assisted deep belief network

This paper proposes a new sentiment analysis of product review based on intelligent techniques. The proposed model involves six stages: pre-processing, keyword extraction and its sentiment categorisation, semantic word extraction, semantic similarity checking, feature extraction and classification. Initially, the MongoDB documented tweets are subjected to pre-processing steps like stop word removal, stemming, and blank space removal. Further, the keywords are extracted from the pre-processed tweets. With respect to extracted keywords, the existing semantic words are extracted after categorising the sentiment of keywords. To the next, the semantic similarity score with the keywords is measured. The upcoming stage is the feature extraction, which uses two holoentropy measures like joint holoentropy, and cross holoentropy. The classification of the extracted features is done using a deep learning classifier named DBN, in which the optimised activation function is done by WD-WOA. Finally, the enhancement of the proposed model over the conventional models is evaluated through an effective comparative analysis in terms of positive and negative performance measures.
Quantum Machine Learning Classifier

This Quantum Machine Learning Classifier (QMLC) uses the mathematics of quantum computing in a deep neural network to find and classify the specific flower type of the three different iris flower species: Versicolor, Setosa and Virginica, utilizing the SciKit-Learn dataset “Iris.” In that dataset, there are four characteristic features of each iris type: petal length, petal width, sepal length, and sepal width. The quantum computing machine learning classifier out-performed the classical deep learning neural network methods. Significant is that this classifier trained in fewer epochs.
Supervised Learning Methods: Part 2

We have discussed several regression and classification algorithms and found out ways on efficiently validating the performance of the models and tuning them well. We will extend our repository of tools and techniques through some more popular algorithms that have proven to be state of the art for various machine learning problems in the past. We will visualize the decision boundaries of the classification algorithms and their variations to understand the mechanism and the results more practically.
User-Oriented Development of Autonomous Vehicles using Immersive Visualization Tools

The automation of vehicles, the electrification of the powertrain, as well as vehicle connectivity, and new usage concepts, are considered to be some of the major mobility trends in the future of automotive development. [1] With an increasing degree of automation, human drivers increasingly hand over the tasks for longitudinal and lateral control of the vehicle and in the long term become pure users without driving task responsibilities. Accordingly the interaction with the vehicle will change, whereby the interior represents the direct interface between humans and cars, so that the “Experiencing of the interior” is becoming much more important than before. In response to these changes, new tools are necessary to integrate users more clearly into the solution finding process for innovative upcoming products. Immersive visualization tools (e.g. based on augmented or virtual reality technology) offer a key to user-oriented product development. Using the example of a dynamically configurable autonomous vehicle, this article demonstrates the conception and application of a visualization tool. In this study, a mixed reality mock-up is develop which offers unique possibilities in the validation of new prototypes. Finally, the added value for vehicle concept development can be recognized by means of user integration during the early development stage, which is validated via subject tests in the Virtual Reality Laboratory at the Automotive Research Centre Niedersachsen (NFF).
Problem of the Authorship of the ‘Ishq-name (“The Book of Love”) Poem Attributed to Sana’i from the Regard of Academic Oriental Studies and Modern Computer Technologies

One of the greatest Persian Sufi poets of the 12th century Sana’i (d. 535/1140 or 545/1150) is founder of the Sufi didactical mathnawi poem in Iran. His works exerted enormous influence upon many posterior authors in the Persian Sufi and secular poetry. But the problem of the authorship of Sana’i in regard to the short poems has been debated among the scientists during 20th century and right up to the nowadays. The paper covers the history of studying of the short poems attributed to Sana’i , special attention is devoted to ‘Ishq name (“The Book of Love”), one of the main Sufi writing in the Persian literature on the subject of Mystical Love. For the definition of the authorship of this poem the paper proposes the using of the latest computational methods in particular digital stylometry. The paper tries to answer the question, who is the author of ‘Ishq name poem: Sana’i or the Sufi shaykh and author of the 14th century ‘Izz al Din Mahmud Kashani (d. 735/1334–35).
Comparison of Machine Learning Algorithms for Hate and Offensive Speech Detection

Hate speech is not uncommon and is likely practiced almost on every networking platform. In recent times, due to exponential increase in Internet users and events such as the unprecedented pandemic and lockdown, it showed increased usage of social platforms for communicating thoughts, opinions, and ideas. Hate speech has a strong impact on people’s lives and is one of the reasons for suicidal events. There is certainly a strong need to make progress toward the mitigation of hate speech. Detection is the primary step to eradicate hate speech. In the following paper, the comparative analysis of different machine learning algorithms to detect hate speech was shown. Data from the Twitter social platform was considered. From the analysis, it was found that the long short-term memory method is a highly performant machine learning algorithm.
UAV Swarm Trajectory Planning Based on a Novel Particle Swarm Optimization

Aiming at the problem of UAV swarm trajectory planning, this paper constructs a mathematical model of UAV swarm trajectory planning, and uses an improved particle swarm optimization to optimize the solution. First of all, this paper constructs the environment model and the corresponding cost function, the cost function of the single aircraft trajectory, and the constraint function inside the UAV swarm. Then the objective function of UAV swarm trajectory optimization is constructed. After that, in view of the shortcoming of particle swarm optimization (PSO) that is easy to fall into local optimality, based on the multi-agent theory’s Holonic structure, the PSO is improved to optimize the objective function. Finally, the UAV swarm trajectory planning algorithm flow based on the improved PSO algorithm is constructed to realize the trajectory planning of the UAV swarm. Compared with the current mainstream improved PSO algorithm, the algorithm in this paper has better performance.
Data-driven Model Generalizability in Crosslinguistic Low-resource Morphological Segmentation

Abstract Common designs of model evaluation typically focus on monolingual settings, where different models are compared according to their performance on a single data set that is assumed to be representative of all possible data for the task at hand. While this may be reasonable for a large data set, this assumption is difficult to maintain in low-resource scenarios, where artifacts of the data collection can yield data sets that are outliers, potentially making conclusions about model performance coincidental. To address these concerns, we investigate model generalizability in crosslinguistic low-resource scenarios. Using morphological segmentation as the test case, we compare three broad classes of models with different parameterizations, taking data from 11 languages across 6 language families. In each experimental setting, we evaluate all models on a first data set, then examine their performance consistency when introducing new randomly sampled data sets with the same size and when applying the trained models to unseen test sets of varying sizes. The results demonstrate that the extent of model generalization depends on the characteristics of the data set, and does not necessarily rely heavily on the data set size. Among the characteristics that we studied, the ratio of morpheme overlap and that of the average number of morphemes per word between the training and test sets are the two most prominent factors. Our findings suggest that future work should adopt random sampling to construct data sets with different sizes in order to make more responsible claims about model evaluation.
A Hybrid PSO-GWO Algorithm for Robot Path Planning in Uncertain Environments

Path planning is one of the most critical tasks in mobile robotics. This paper proposes a hybridization of two algorithms for optimizing the trajectory planning a mobile robot and ensuring its navigation in a partially known environment containing static obstacles. The Grey Wolf Optimization (GWO) algorithm, which has some challenges such as slow convergence and low accuracy, is combined with the Particle Swarm Optimization (PSO) algorithm for its high convergence speed and accuracy. The hybrid PSO-GWO algorithm is proposed so that the optimal trajectory search procedure balances global and local search capabilities, taking advantage of the capacities of both GWO and PSO approaches. The validity of PSO-GWO performance has been tested for various scenarios using the mobile robot Khepera-iv and the simulation software of MATLAB and V-REP. Then, it has revealed that this method can offer a better search for an optimal trajectory when compared to other algorithms such as GWO, PSO, and Fuzzy-Logic. For implementing the PSO-GWO algorithm on Khepera-iv, a simple Extended Kalman Filter (EKF) is developed to locate the mobile robot, in its realistic environment, by fusing data from different sensors.
Mining History of the Arabian–Nubian Shield

In the previous chapters of this book, the different types of mineral deposits and occurrences have been reviewed in the countries covered by the Arabian–Nubian Shield (ANS): Saudi Arabia, Yemen, Eastern Desert of Egypt, Sudan, Eritrea, and Ethiopia. The ANS represents one of the Earth’s largest blocks of juvenile Neoproterozoic crust, which is also one of the largest repositories of Neoproterozoic metallic minerals.
Use Case of the Application of the Optical-See-Through Augmented Reality Technology in the Engineering Context

Augmented Reality (AR) is the technology that enables the overlaying of physical objects with computer-generated virtual perceptible data in real time to provide an interactive user experience in the real environment. The potential of this technology to digitize and to improve the different engineering processes (e.g. manufacturing, logistics, maintenance, training) are apparent. Therefore, the application of AR in the engineering area has gained in importance in the recent years. This development can also be observed in the high-tech industry, companies like Microsoft, Google and Apple have intensified their efforts to develop new AR software and hardware. One of the important methods of AR is optical-see-through technology. The optical see-through AR enable the display of the virtual content on a transparent projection surface with the advantage that the users can at the same time perceive the real environment directly with their own eyes. This paper presents two use cases for the implementation of AR applications using the optical see-through technology and the HoloLens as an AR device. The first use case addresses the application of AR for Pick-by-Light assisted assembly process. The second use case deals with the use of AR for the implementation of a control application for robot shelving system. The experiences gathered during the performance of these use cases and the evaluation of the applicability of the optical see-through technology in the engineering field will be discussed in this paper. This presented work has an experimental character and rather than a fundamental research. The developed AR applications were implemented as a prototype by the students of the University of Applied Silences Karlsruhe during the AR laboratory. The gained knowledge in this work can be considered as suggestions for further research works.
Prism-based tri-aperture laparoscopic objective for multi-view acquisition

This paper presents the design and prototype of a novel tri-aperture monocular laparoscopic objective that can acquire both stereoscopic views for depth information and a wide field of view (FOV) for situational awareness. The stereoscopic views are simultaneously captured via a shared objective with two displaced apertures and a custom prism. Overlapping crosstalk between the stereoscopic views is diminished by incorporating a strategically placed vignetting aperture. Meanwhile, the wide FOV is captured via a central third aperture of the same objective and provides a 2D view of the surgical field 2x as large as the area imaged by the stereoscopic views. We also demonstrate how the wide FOV provides a reference data set for stereo calibration, which enables absolute depth mapping in our experimental prototype.