Reviewerly/exHarmony huggingface dataset:
Design and Empirical Study of an Online Education Platform Based on B2B2C, Focusing on the Perspective of Art Education

The purpose of this study is to provide instructive theoretical models for art (music) education institutions especially when unpredictable risks, such as pandemics, occur again.Based on the customer behavior theory of the business-to-business-to-customer (B2B2C) platform, and in combination with the technology acceptance model (TAM) and expectation confirmation model (ECM), this study proposes an online education model from the perspective of art education.The framework is based on the three decision-making processes of the customer, and includes the product owner, content owner, and customer area.This paper highlights the factors that influence customers in making decisions when art education institutions are product owners.Regression analysis was introduced to study the factors influencing the expectation confirmation, and the overall fitting testing and six hypotheses testing of 385 effective samples were performed using the structural equation modeling (SEM).The results show that the course-design and after-service positively influenced the expectation confirmation, and the domain image positively influenced the continuance behavior.Negative emotions skipped the mediator (expectation confirmation) and directly exerted a significant negative impact on customers' willingness to continue system usage (continuance behavior).In addition, expectation confirmation positively influenced continuance behavior.The paths of detailed items comprising course-design, after-service, and negative emotion were also analyzed and discussed.In this path analysis, ordinary art learners did not believe that AI partners can play a very good auxiliary role.The findings contribute to the scope of information systems acting as an art education platform academically, and provide effective and theoretical support for the actual operation of art education institutions.
On the computation of interprocedural weak control closure

Many program analysis techniques depend on capturing the control dependencies of the program. Most existing control dependence algorithms either compute intraprocedural control dependencies only, or they compute control dependence relations that are not precise in general including nonterminating systems. Weak control closure (WCC) subsumes all known nontermination insensitive control dependence relations, including those that are appropriate for nonterminating systems. In this paper, we provide the first formal development of an algorithm to compute the WCC for interprocedural programs capturing the weak form of interprocedural control dependencies. The method is widely applicable due to the generality of WCC. Theorems on the theoretical results of soundness, precision, and the worst-case complexity of our method are also included. We have compared our algorithm with a WCC computation algorithm based on a state-of-the-art interprocedural control dependence computation algorithm. The latter algorithm loses soundness, and we improve the precision by 15.21% on all our experimental benchmarks. This empirical evidence suggests that our algorithm is more effective for any client application of WCC requiring interprocedural program analysis.
Modelling the factors in the adoption of artificial intelligence in Indian management institutes

Purpose This study aims to examine which organisational and other factors can facilitate the adoption of artificial intelligence (AI) in Indian management institutes and their interrelationship. Design/methodology/approach To determine the factors influencing AI adoption, a synthesis-based examination of the literature was used. The interpretative structural modelling (ISM) method is used to determine the most effective factors among the identified ones and the inter-relationship among the factors, while the Decision-Making Trial and Evaluation Laboratory (DEMATEL) method is used to analyse the cause-and-effect relationships among the factors in a quantitative manner. The approaches used in the analysis aid in understanding the relationship among the factors affecting AI adoption in management institutes of India. Findings This study concludes that leadership support plays the most significant role in the adoption of AI in Indian management institutes. The results from the DEMATEL analysis also confirmed the findings from the ISM and Matrice d’ Impacts croises- multiplication applique and classment (MICMAC) analyses. Remarkably, no linkage factor (unstable one) was reported in the research. Leadership support, technological context, financial consideration, organizational context and human resource readiness are reported as independent factors. Practical implications This study provides a listing of the important factors affecting the adoption of AI in Indian management institutes with their structural relationships. The findings provide a deeper insight about AI adoption. The study's societal implications include the delivery of better outcomes by Indian management institutes. Originality/value According to the authors, this study is a one-of-a-kind effort that involves the synthesis of several validated models and frameworks and uncovers the key elements and their connections in the adoption of AI in Indian management institutes.
Publishing and Interlinking COVID-19 Data Using Linked Open Data Principles: Toward Effective Healthcare Planning and Decision-Making

The COVID-19 data is critical to support countries and healthcare organizations for effective planning and evidence-based practices to counter the pressures of cost reduction, improved coordination, and outcome and produce more with less. Several COVID-19 datasets are published on the web to support stakeholders in gaining valuable insights for better planning and decision-making in healthcare. However, the datasets are produced in heterogeneous proprietary formats, which create data silos and make data discovery and reuse difficult. Further, the data integration for analysis is difficult and is usually performed by the domain experts manually, which is time-consuming and error-prone. Therefore, an explicit, flexible, and widely acceptable methodology to represent, store, query, and visualize COVID-19 data is needed. In this paper, we have presented the design and development of the Linked Open COVID-19 Data system for structuring and transforming COVID-19 data into semantic format using explicitly developed ontology and publishing on the web using Linked Open Data (LOD) principles. The key motivation of this research is the evaluation of LOD technology as a potential option and application of the available Semantic Web tools (i.e., Protégé, Excel2RDF, Fuseki, Silk, and Sgvizler) for building LOD-based COVID-19 information systems. We have also underpinned several use-case scenarios exploiting the LOD format of the COVID-19 data, which could be used by applications and services for providing relevant information to the end-users. The effectiveness of the proposed methodology and system is evaluated using the system usability scale and descriptive statistical methods and results are found promising.
A Robot-based Arabic Sign Language Translating System

Services provided to deaf people in the Eastern province of Saudi Arabia were evaluated, which confirmed a high need to support the deaf community. This paper proposes utilizing the Pepper robot in the task of recognizing and translating Arabic sign language (ArSL), by which the robot recognizes static hand gestures of the letters in ArSL from each keyframe extracted from the input video and translate it into written text and vice versa. This project aims to conduct a two-way translation of the Arabic sign language in a way that fulfills the communication gap found in Saudi Arabia among deaf and non-deaf people. The methods proposed in this paper are computer vision to use the pepper robot's camera and sensors, Natural language processing to convert natural speech to sign language and Deep learning to build a convolutional neural network model that classifies the sign language gestures and convert them into their corresponding written and spoken form. Moreover, two datasets were used, first one is a collection of hand gestures for training the model and the other one is 39 animated signs of all the Arabic letters and special letters.
English-Chinese Machine Translation Model Based on Bidirectional Neural Network with Attention Mechanism

In recent years, with the development of deep learning, machine translation using neural network has gradually become the mainstream method in industry and academia. The existing Chinese-English machine translation models generally adopt the deep neural network architecture based on attention mechanism. However, it is still a challenging problem to model short and long sequences simultaneously. Therefore, a bidirectional LSTM model integrating attention mechanism is proposed. Firstly, by using the word vector as the input data of the translation model, the linguistic symbols used in the translation process are mathematized. Secondly, two attention mechanisms are designed: local attention mechanism and global attention mechanism. The local attention mechanism is mainly used to learn which words or phrases in the input sequence are more important for modeling, while the global attention mechanism is used to learn which layer of expression vector in the input sequence is more critical. Bidirectional LSTM can better fuse the feature information in the input sequence, while bidirectional LSTM with attention mechanism can simultaneously model short and long sequences. The experimental results show that compared with many existing translation models, the bidirectional LSTM model with attention mechanism can effectively improve the quality of machine translation.
A Fitts' law evaluation and comparison for human and manipulator on touch task

In the near future, robots are believed to be assistants and collaborators for mankind, and robots will operate the same user interfaces with humans. Therefore, a question naturally arises whether an interface originally designed for humans is also suitable for an ordinary robot. The question is explored and discussed by considering the kinematic difference between those of humans and ordinary robots in their natural operation ways on user interface. Methodologically, Fitts' law and several motor-related factors, such as touch accuracy, tapping efficiency, etc, are used as the evaluation principles in operations. The operation difference between humans and robot on a classic pointing task on 2D touch screen is symmetrically analysed and compared. It was found that whether the robot could finish the tasks largely related to the ratio of robot's max touch error and target width. It is believed the exploration in this work provides valuable cues for future design of user interface and intelligent algorithm in human–robot collaboration.
Answer selection in community question answering exploiting knowledge graph and context information

With the increasing popularity of knowledge graph (KG), many applications such as sentiment analysis, trend prediction, and question answering use KG for better performance. Despite the obvious usefulness of commonsense and factual information in the KGs, to the best of our knowledge, KGs have been rarely integrated into the task of answer selection in community question answering (CQA). In this paper, we propose a novel answer selection method in CQA by using the knowledge embedded in KGs. We also learn a latent-variable model for learning the representations of the question and answer, jointly optimizing generative and discriminative objectives. It also uses the question category for producing context-aware representations for questions and answers. Moreover, the model uses variational autoencoders (VAE) in a multi-task learning process with a classifier to produce class-specific representations for answers. The experimental results on three widely used datasets demonstrate that our proposed method is effective and outperforms the existing baselines significantly.
Combining 3D run-length encoding coding and searching techniques for medical image compression

&lt;p&gt;&lt;span&gt;The field of image compression became a mandatory tool to face the increasing and advancing production of medical images, besides the inevitable need for smaller size of medical images in telemedicine systems. In spite of its simplicity, run-length encoding (RLE) technique is a considerably effective and practical tool in the field of lossless image compression. Such that, it is widely recommended for 2D space that utilizes common searching techniques like linear and zigzag. This paper adopts a new algorithm taking advantage of the potential simplicity of the run-length algorithm to contribute a volumetric RLE approach for binary medical data in the 3D form. The proposed volumetric-RLE (VRLE) algorithm differs from the 2D RLE approach utilizing correlations of intra-slice only, which is used for compressing binary medical data utilizing voxel-correlations of inter-slice. Furthermore, several forms of scanning are used to extending proposed technique like Hilbert and Perimeter, which determines the best possible procedure of scanning suitable for data morphology considering the segmented organ. This work employs proposed algorithm on four image datasets to get as sufficient as possible evaluation. Experimental results and benchmarking illustrate that the performance of the proposed technique surpasses other state-of-the-art techniques with 1:30 enhancement on average.&lt;/span&gt;&lt;/p&gt;
Optimization of Music Teaching Management System for College Students Based on Similarity Distribution Method

This paper puts forward a method of similarity distribution and applies it to music teaching management system. We use Java language, using JavaME development platform to complete the basic information management, music homework management, music practice management, online classroom management, information notification management, and other modules of the technical implementation. The system uses mobile learning system to provide a convenient learning platform for learners. However, student users can only view the course information in the system. To allow students to know the course change information more quickly when teachers update course information, the system implements the course information update reminder function. Compared with the existing music teaching system, the music teaching system based on Android adopts wireless Internet technology, which is more suitable for the daily habits of today’s college students and makes full use of the fragmentary time, which is conducive to improving their learning efficiency and stimulating students’ interest in learning.
Risk Classification in Global Software Development Using a Machine Learning Approach

Software development through teams at different geographical locations is a trend of modern era, which is not only producing good results without costing lot of money but also productive in relation to its cost, low risk and high return. This shift of perception of working in a group rather than alone is getting stronger day by day and has become an important planning tool and part of their business strategy. In this research classification approaches like SVM and K-NN have been implemented to classify the true positive events of global software development project risk according to Time, Cost and Resource. Comparative analysis has also been performed between these two algorithms to determine the highest accuracy algorithms. Results proved that Support Vector Machine (SVM) performed very well in case of Cost Related Risk and Resource Related Risk. Whereas, KNN is found superior to SVM for Time Related Risk.
Computational Benefits of Intermediate Rewards for Goal-Reaching Policy Learning

&#x0D; &#x0D; &#x0D; Many goal-reaching reinforcement learning (RL) tasks have empirically verified that rewarding the agent on subgoals improves convergence speed and practical performance. We attempt to provide a theoretical framework to quantify the computational benefits of rewarding the completion of subgoals, in terms of the number of synchronous value iterations. In particular, we consider subgoals as one-way intermediate states, which can only be visited once per episode and propose two settings that consider these one-way intermediate states: the one-way single-path (OWSP) and the one-way multi-path (OWMP) settings. In both OWSP and OWMP settings, we demonstrate that adding intermediate rewards to subgoals is more computationally efficient than only rewarding the agent once it completes the goal of reaching a terminal state. We also reveal a trade-off between computational complexity and the pursuit of the shortest path in the OWMP setting: adding intermediate rewards significantly reduces the computational complexity of reaching the goal but the agent may not find the shortest path, whereas with sparse terminal rewards, the agent finds the shortest path at a significantly higher computational cost. We also corroborate our theoretical results with extensive experiments on the MiniGrid environments using Q-learning and some popular deep RL algorithms.&#x0D; &#x0D; &#x0D;
A COVID-19 forecasting system for hospital needs using ANFIS and LSTM models: A graphical user interface unit

Centers for Disease Control and Prevention data showed that about 40% of coronavirus disease 2019 (COVID-19) patients had been suffering from at least one underlying medical condition were hospitalized; in which nearly 33% of them needed to be admitted to the intensive care unit (ICU) to receive specialized medical services. Our study aimed to find a proper machine learning algorithm that can predict confirmed COVID-19 hospital admissions with high accuracy.We obtained data on daily COVID-19 cases in regular medical inpatient units, emergency department, and ICU in the time window between 21 July 2020 and 21 November 2021. Data for the first 183 days (training data set) were used for long short-term memory (LSTM) network, adaptive neuro-fuzzy inference system (ANFIS), support vector regression (SVR) and decision tree model training, whilst the remaining data for the last 60 days (test data set) were used for model validation. To predict the number of ICU and non-ICU patients, we used these models. Finally, a user-friendly graphical user interface unit was designed to load any time series data (here the trend of population of COVID-19 patients) and train LSTM, ANFIS, SVR or tree models for the prediction of COVID-19 cases for one week ahead.All models predicted the dynamics of COVID-19 cases in ICU and non- wards. The values of root-mean-square error and R2 as model assessment metrics showed that ANFIS model had better predictive power among all models.Artificial intelligence-based forecasting models such as ANFIS system or deep learning approach based on LSTM or regression models including SVR or tree regression play a key role in forecasting the required number of beds or other types of medical facilities during the coronavirus pandemic. Thus, the designed graphical user interface of the present study can be used for optimum management of resources by health care systems amid COVID-19 pandemic.
Application and Analysis of Taekwondo Techniques, Tactics, and Movement Trajectories Based on Multi-Intelligent Decision Making

The aim of this study is to compare the trajectory of cross-kick technical movements of excellent taekwondo players in different weight classes and to investigate the relationship between weight class differences and the characteristics of cross-kick technical movements and the extent to which they affect the speed of movements. To this end, an artificial intelligence system is proposed for taekwondo field decision making, combining computer and applied mathematics knowledge. The angles, angular velocities, and moments of the joints of the lower limbs, as well as the movement time, displacement, and speed of the lower limbs, of the athletes in the three weight classes were significantly different ( <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mi>P</mi> <mo>&lt;</mo> <mn>0.05</mn> </math> ).
Visual servoing with deep reinforcement learning for rotor unmanned helicopter

Visual servoing is a key approach to achieve visual control for the rotor unmanned helicopter. The challenges of the inaccurate matrix estimation and the target loss restrict the performance of the visual servoing control systems. This work proposes a novel visual servoing controller using the deep Q-network to achieve an efficient matrix estimation. A deep Q-network learning agent learns a policy estimating the interaction matrix for visual servoing of a rotor unmanned helicopter using continuous observation. The observation includes a combination of feature errors. The current matrix and the desired matrix constitute the action space. A well-designed reward guides the deep Q-network agent to get a policy to generate a time-varying linear combination between the current matrix and the desired matrix. Then, the interaction matrix is calculated by the linear combination. The potential mapping between the observation and the interaction matrix is learned by cascading the deep neural network layers. Experimental results show that the proposed method achieves faster convergence and lower target loss probability in tracking than the visual servoing methods with the fixed parameter.
Simulation Research on the Palm Mechanism of Volleyball Robot Based on Artificial Intelligence and Ant Colony Optimization Algorithm

The study of mobile robots, which began in the late 1960s, is the most dramatic development in human history in the twentieth century, and the invention has undergone radical changes in just over 50 years. The robot body is developing in the direction of flexibility and miniaturization. This is because the robot application is mostly oriented to the family and service industries, and it needs to adapt to a more complex environment. This manuscript aims to improve further the ant colony optimization algorithm by using rough set theory to improve the convergence speed and accuracy of the algorithm in robot path planning on the basis of an in-depth diagnosis on the shortcomings and its causes of development of the ant colony optimization algorithm. It overcomes the drawbacks of the algorithm that easily get trapped in partial optimality solution, the search time is much slower and the search effect is not good. In this paper, the CMA-ES algorithm, the modified ant colony, and the BK method are proposed, which have high theoretical value and exploration significance. In addition, simulation experiments are conducted to obtain the stage results on the basis of artificial information. The results of the present paper indicate the GWO algorithm performs more stable in the optimization results of each experiment when there are 14 robots and the communication range is 1.6, compared with the PSO algorithm and BA algorithm.