Reviewerly/exHarmony huggingface dataset:
Research on the Artificial Intelligence Teaching System Model for Online Teaching of Classical Music under the Support of Wireless Networks

For the past year, everyone has been facing difficulties due to the fast spreading of the Corona Virus. As an extension, students, parents, and teachers are handling the challenges in the education sector. Since the COVID days, the schools and colleges were closed, and hence, the students were lagging in their subjects. As an alternative to this scenario, offline classes are converted to online courses, otherwise called virtual classes with virtual classrooms. Due to this conversion, the teaching has become a little more advanced by incorporating various computer-based technologies. The technologies like artificial intelligence, cloud computing, and machine learning paved the way for exploring concepts in data transmission in terms of timely delivery of content, less error rate, and nontechnical terms like making the classes interactive and understanding the subject concepts. In this research work, the online teaching class on music is considered. To be specific, traditional Chinese music is taken for the study. An artificial intelligence model is designed with the aid of wireless sensor networks for the online class on the musical subject. Q-learning algorithm, which is an artificial intelligence-based reinforcement learning algorithm, is implemented. The aim of the Q-learning algorithm in this online teaching of classical music is to check the frequency level of the music that aids in the automatic transfer of another wavelength inside the dataset.
Lane-Changing Decision Model at Urban Tunnel Convergence Section Based on Multi-Source Information Fusion

The traffic lane-changing behavior at the convergence section of adjacent tunnel entrance and exit of urban expressway is a kind of micro driving environment with great randomness. The influencing factors include multi-source information like traffic flow conditions, road conditions, driving behavior characteristics and so on. In this paper, the mechanism of the influence of the information sources on the tunnel entrance is analyzed. Seven influencing factors of lane-changing decision, such as expected lane, driver’s temperament, traffic sign recognition, gap between vehicles of back side, fixation probability of interest area, speed difference between vehicles of front side and distance between vehicles of front side are ranked by analysis method of grey correlation entropy. Then, considering coupling of different drivers in the decision-making process of lane change, the driver’s lane-changing decision model is built based on multi-source information fusion.
Design of Secure Microcontroller-Based Systems: Application to Mobile Robots for Perimeter Monitoring

This paper describes an original methodology for the design of microcontroller-based physical security systems and its application for the system of mobile robots. The novelty of the proposed methodology lies in combining various design algorithms on the basis of abstract and detailed system representations. The suggested design approach, which is based on the methodology, is modular and extensible, takes into account the security of the physical layer of the system, works with the abstract system representation and is looking for a trade-off between the security of the final solution and the resources expended on it. Moreover, unlike existing solutions, the methodology has a strong focus on security. It is aimed at ensuring the protection of the system against attacks at the design stage, considers security components as an integral part of the system and checks if the system can be designed in accordance with given requirements and limitations. An experimental evaluation of the methodology was conducted with help of its software implementation that consists of Python script, PostgreSQL database, Tkinter interface and available for download on our GitHub. As a use case, the system of mobile robots for perimeter monitoring was chosen. During the experimental evaluation, the design time was measured depending on the parameters of the attacker against which system security must be ensured. Moreover, the software implementation of the methodology was analyzed in compliance with requirements and compared with analogues. The advantages and disadvantages of the methodology as well as future work directions are indicated.
Impact of Land Use and Climate Changes on Flood Inundation Areas in the Lower Cimanuk Watershed, West Java Province

Land use and climatic changes potentially affect the surface runoff and inundation in watershed zones. Every year, the outflow of the Cimanuk River causes floods across the majority of the upper area of the Cimanuk Watershed, as well as the lower area. This study aimed to assess the impact of climatic and land use changes on future flood inundation in the Lower Cimanuk Watershed using a RRI model. Land-use change has been prepared by modeling using a multi-layer perceptron neural network and Markov Chain approach, while climate change using HadGEM2-ES global climate model data under scenarios RCP4.5. In particular, the forest area was projected to decline in this watershed zone, from 19.54% of the total area in 2019 to 17.73% in 2050. Similarly, the area of paddy fields was predicted to decline from approximately 34.36% in 2019 to 29.65% in 2050. In contrast, other types of land use such as dryland agriculture, mixed dryland agriculture, and settlements were projected to increase in the future. The coverage of the simulated flood inundation area using the Rainfall-Runoff Inundation model estimated to reach 179.4 km2 in 2019. The simulation results showed an increase in flood inundation areas in 2030 and 2050, alongside changes in land use and climate. The areas affected by flood inundation were estimated to reach 253.3 km2in 2030. This coverage was expected to increase by 311.9 km2 in 2050, with severely affected land uses including settlements, dry land agriculture, mixed dry land agriculture, paddy fields, and ponds.
The Impact of Knowledge Sharing Behavior on Project Team Performance: Mediating Role of Project Team Effectiveness

The purpose of the study was to explore the influence of Knowledge Sharing Behavior (KSB) on the Protect Team Performance (PTP). The study also determines the mediating role of Project Team Effectiveness (PTE). The existing study shows that population includes the employees of different Project based organization in the twin cities of Peshawar and Mardan. The study was carried out with a cross-sectional survey in the target cities. The study contains of 193 and the data was gathered through an adopted questionnaire. In the present study the covariance sampling technique of the non-probability sampling was used. The gathered data was coded and run via various statistical techniques such as reliability analysis, CFA, descriptive statistics, correlation analysis,and the regression analysis was done by using the Preacher and Hayes (2004) process macro for mediating analysis. Consequently thereafter, the results revealed that Knowledge Sharing Behavior of the protect team and managers in these organizations is positively and significantly associated with Project Team Performance. The study further displays that Project Team Effectiveness is partially mediate the relation between Knowledge Sharing Behavior and Project Team Performance. The study significantly contributes in literature that the effects of Knowledge Sharing Behavior in project based organizations eventually increases the sense in the team members and it triggers proactive working behavior within ethical standards that eventually leads toward improved project team performance. The findings of the study established that Knowledge Sharing Behavior and Project Team Effectiveness have significant impact toward providing Project Team Performance. In further research the impact of Knowledge Sharing Behavior on Project Team Performance can also be identified by using other variables.
Voice Pathology Detection Using the Adaptive Orthogonal Transform Method, SVM and MLP.

In this paper, an automatic voice pathology recognition system is realized. The special features are extracted by the Adaptive Orthogonal Transform method, and to provide their statistical properties we calculated the average, variance, skewness and kurtosis values. The classification process uses two models that are widely used as a classification method in the field of signal processing: Support Vector Machine (SVM) and Multilayer Perceptron (MLP). The proposed system is tested by using a German voice database: the Saarbruecken Voice Database (SVD). The experimental results show that the Adaptive Orthogonal Transform method works perfectly with the Multilayer Perceptron Neural Network, which achieved 98.87% accuracy. On the other hand, the combination of the Adaptive Orthogonal Transform method and Support Vector Machine reached 85.79% accuracy.
An analysis of blockchain in Supply Chain Management: system perspective current and future research

Purpose: This study aims to reviews the current academic research on block chain, especially in the subject area of business and economics. Based on a systematic review of the literature retrieved from the Web of Science service, we explore the top-cited articles, most productive countries, and most common keywords. Design/ methodology/ approach: this research conducts a clustering analysis and identify the following five research themes: "economic benefit," "blockchain technology," "initial coin offerings," "fintech revolution," and "sharing economy." Findings: The results succeeded to support Recommendations o and future research directions and practical applications are also provided
An Aerial Cinematographer AI for Settlements in Minecraft–Toward Their Crowd Assessment

This paper proposes an aerial cinematographer AI for shooting videos of settlements in Minecraft worlds. A supervised learning approach is adopted to train the AI using a decision tree with movement behavior data of a target player. Results on unseen data show the effectiveness of the proposed AI when its position is close to the settlement center.
Android Application for Tourism Planning in Bangladesh

Tourism is currently considered one of the fastest-growing socio-economic phenomenon of the world. Like all the other countries, Bangladesh is experiencing a steady growth in the tourism sector, producing economic benefits as well as employment opportunities. This paper describes the entire process comprising the theory and software implementation of a tour planner Android application. Our proposed tour planner application has been designed to overcome the primary challenges of travel and tourism by reducing the stress of the traveler and making a simple tour planning concerning individual user demand. This Android application is basically a personalized travel guide, where users can arrange a tour based on their budget and duration of the estimated travel. In this work, the front-end part of the application has been designed by the React Native framework. Apollo Client library has been used to connect the front-end app with the back-end express server. Several functions, e.g., Bcrypt, JSON Web Token, and cookie-parser have been utilized to provide security of the application. Users will get detailed information about a tourist spot with a simple tap or swipe, including the weather, transport, hotels, local guides, etc. There is a review option in the application that will help people to find and choose the best destination according to a few criteria like safety, service, cleanness, connectivity, transportation, and other available facilities.
Multi-parameter identification of permanent magnet synchronous motor based on improved grey wolf optimization algorithm

Considering the deficiency of the parameter identification algorithm of permanent magnet synchronous motor(PMSM), such as low identification accuracy and difficulty in identifying multiple parameters at the same time, an improved hybrid grey wolf optimization algorithm (IHGWO) is proposed. Firstly, Cat mapping and reverse learning strategy are used to generate the initial population, which can ensure the diversity of population and improve the convergence. Secondly, an improved sine cosine algorithm (SCA) is used to update the position of the leading wolf, so as to balance the global search and local development ability of IHGWO. Finally, Levy flight strategy is applied to update the optimal predation position of grey wolf group. It can improve the convergence accuracy and the ability to avoid trapping in local optimum. The full rank discrete model of PMSM is established on the d-q frame and the fitness function for parameter identification is given. The corresponding fitness value is obtained by comparing the output value of actual model with that of identification model. The IHGWO algorithm is compared with GWO and other two variants. The simulation and experimental results show that the IHGWO algorithm has the best accuracy, convergence speed and stability for parameter identification of PMSM.
Computational Perspectives on Phonological Constituency and Recursion

Whether or not phonology has recursion is often conflated with whether or not phonology has strings or trees as data structures. Taking a computational perspective from formal language theory and focusing on how phonological strings and trees are built, we disentangle these issues. We show that even considering the boundedness of words and utterances in physical realization and the lack of observable examples of potential recursive embedding of phonological constituents beyond a few layers, recursion is a natural consequence of expressing generalization in phonological grammars for strings and trees. While prosodically-conditioned phonological patterns can be represented using grammars for strings, e.g., with bracketed string representations, we show how grammars for trees provide a natural way to express these patterns and provide insight into the kinds of analyses that phonologists have proposed for them.
Infrared Image Super-Resolution via Progressive Compact Distillation Network

Deep convolutional neural networks are capable of achieving remarkable performance in single-image super-resolution (SISR). However, due to the weak availability of infrared images, heavy network architectures for insufficient infrared images are confronted by excessive parameters and computational complexity. To address these issues, we propose a lightweight progressive compact distillation network (PCDN) with a transfer learning strategy to achieve infrared image super-resolution reconstruction with a few samples. We design a progressive feature residual distillation (PFDB) block to efficiently refine hierarchical features, and parallel dilation convolutions are utilized to expand PFDB’s receptive field, thereby maximizing the characterization power of marginal features and minimizing the network parameters. Moreover, the bil-global connection mechanism and the difference calculation algorithm between two adjacent PFDBs are proposed to accelerate the network convergence and extract the high-frequency information, respectively. Furthermore, we introduce transfer learning to fine-tune network weights with few-shot infrared images to obtain infrared image mapping information. Experimental results suggest the effectiveness and superiority of the proposed framework with low computational load in infrared image super-resolution. Notably, our PCDN outperforms existing methods on two public datasets for both ×2 and ×4 with parameters less than 240 k, proving its efficient and excellent reconstruction performance.
Analysis of an Online English Teaching Model Application Based on Improved Multiorganizational Particle Population Optimization Algorithm

This paper uses an improved multiorganizational particle population optimization algorithm to conduct an in-depth analysis and study of an online English teaching model and uses the altered model for practical applications. The model building elements are extracted from it for the initial construction of a blended learning model of English-speaking teaching in junior high school. The main purpose of the first round of action research is to test the rationality of each element of the model, the main purpose of the second round of action research is to refine the model links and improve the operability of the model, and the main purpose in the third round of action research is to test the perfected model and explore the model. The main purpose of the third round of action research is to test the refined model and explore the application suggestions of the model. After the three rounds of action research, we finally obtained a more mature blended learning model for teaching English as a foreign language in junior high school. Mainly through the comparison of the pre- and posttest scores of English speaking of the experimental subjects and the comparison of the pre- and posttest data of the relevant questionnaires, the following experimental conclusions were drawn: adopting the blended learning-based speaking teaching model can effectively improve students’ interest in learning English, their attitudes and their English speaking skills including pronunciation, phonetic intonation, conversational communication, and oral expression and can enhance students’ group cooperation and communication ability, independent learning ability, evaluation awareness, and ability. This single-guided learning mechanism can effectively avoid the shocks that are easily caused by the dual-guided role of traditional PSO. The dimensional learning strategy constructs a learning paradigm for each particle by learning from each dimension of the individual optimal position of the particle to the corresponding dimension of the group optimal position, respectively. Dimensional learning is formally integrated into the learning paradigm only if it can improve the fitness of the paradigm so that the dimensional learning strategy can avoid the phenomenon of degradation of the learning paradigm and the phenomenon of “two steps forward, one step back.” In the dimensional learning strategy, since each particle learns from best, although it has a strong exploitation capability, it may cause all particles to converge to best quickly, making the algorithm converge prematurely.
Adaptive Signal Detection Method Based on Model-Driven for Massive MIMO Systems

In this paper, the model-driven deep learning (DL) technology is used to solve the problem of either high complexity or poor performance in traditional massive multiple input multiple output (MIMO) signal detection and the model-driven detection network is proposed: JC-Net. The JC-Net structure is designed by unfolding the damped Jacobi detector and adding three trainable parameters to each layer, which are used to control the residual vector, adjust the relationship between the current layer and the previous layer, and for soft projection. Furthermore, the performance of JC-Net can be further improved by increasing the dimension of the residual vector and the JC-Net-Improved is proposed later. Simulation results show that the proposed model-driven massive MIMO detection networks can significantly improve the performance of the corresponding damped Jacobi detector and achieve superior detection performance with low complexity.
Families of Generalized Quasisymmetry Models: A ϕ-Divergence Approach

The quasisymmetry (QS) model for square contingency tables is revisited, highlighting properties and features on the basis of its alternative definitions. More parsimonious QS-type models, such as the ordinal QS model for ordinal classification variables and models based on association models (AMs) with homogeneous row and column scores, are discussed. All these models are linked to the local odds ratios (LOR). QS-type models and AMs were extended in the literature for generalized odds ratios other than LOR. Furthermore, in an information-theoretic context, they are expressed as distance models from a parsimonious reference model (the complete symmetry for QS and the independence for AMs), while they satisfy closeness properties with respect to Kullback–Leibler (KL) divergence. Replacing the KL by ϕ divergence, flexible classes of QS-type models for LOR, AMs for LOR, and AMs for generalized odds ratios were generated. However, special QS-type models that are based on homogeneous AMs for LOR have not been extended to ϕ-divergence-based classes so far, or the QS-type models for generalized odds ratios. In this work, we develop these missing extensions, and discuss QS-type models and their generalizations in depth. These flexible families enrich the modeling options, leading to models of better fit and sound interpretation, as illustrated by representative examples.
Post-Processing and Dimensionality Reduction for Extreme Learning Machine in Text Classification

Text classification is one of the core technologies of textual analysis, with interesting applications varying from sentiment classification, language identification to online abuse detection and many more.Many approaches have been taken to the machine learning classifiers employed in text categorization, with a focus on boosting model performance and efficiency.This paper proposes a new effective framework for the input processing of the Extreme Learning Machine (EML) algorithm, illustrated on the Reuters-21578 test collection of documents.We employ Glove word embeddings to provide a compact and semantic meaningful representation for each word of the input documents.Given the bi-dimensionality constraint of ELM inputs, we reduce the dimensionality of word embeddings using Latent Semantic Analysis and Principal Components Analysis (PCA).Our results reveal that PCA together with the postprocessing operation led to more accurate results with lower computational costs.
Anti-Counterfeiting and Traceability Mechanism Based on Blockchain

The anti-counterfeiting traceability system based on blockchain technology can ensure the accuracy and consistency of the data stored by each participating node, protect the legitimacy of the data, ensure the product quality, improve the credibility of enterprises, and enhance consumers’ confidence in products. However, due to the low system throughput, high energy consumption and poor data availability, the combination of blockchain and traditional anti-counterfeiting traceability mode has many challenges, such as low efficiency. This paper aims to find an improved consensus mechanism based on contribution proof to improve the mining efficiency of honest miners. And plan to introduce a credit system, give priority to the high credit value of the mining block to package, improve the overall packaging efficiency of the system, to solve the problem of low feedback efficiency of the blockchain anti-counterfeiting system.