Reviewerly/exHarmony huggingface dataset:
PERANCANGAN APLIKASI PENGADUAN KERUSAKAN JALAN BERBASIS GEOGRAFIC INFORMATION SYSTEM (GIS)

The purpose of this study is to design a running information system related to road complaint GIS. In conducting this research, researchers used an experimental method where the process begins with analyzing data requirements and developing existing systems. The initial data that you want to search for is by sharing information about this application in social media groups, both Facebook, WhatsApp and others who are domiciled in Pinrang. In addition, there is a system implementation and system maintenance, which in this case uses the PHP programming language as the backend language for this project.The results achieved from making the system with the data entered in the application using the Black Box method, it is found that the functional application runs well according to the design made.
Dart Games Optimizer with Deep Learning-Based Computational Linguistics Named Entity Recognition

Computational linguistics is an engineering-based scientific discipline. It deals with understanding written and spoken language from a computational viewpoint. Further, the domain also helps construct the artefacts that are useful in processing and producing a language either in bulk or in a dialogue setting. Named Entity Recognition (NER) is a fundamental task in the data extraction process. It concentrates on identifying and labelling the atomic components from several texts grouped under different entities, such as organizations, people, places, and times. Further, the NER mechanism identifies and removes more types of entities as per the requirements. The significance of the NER mechanism has been well-established in Natural Language Processing (NLP) tasks, and various research investigations have been conducted to develop novel NER methods. The conventional ways of managing the tasks range from rule-related and hand-crafted feature-related Machine Learning (ML) techniques to Deep Learning (DL) techniques. In this aspect, the current study introduces a novel Dart Games Optimizer with Hybrid Deep Learning-Driven Computational Linguistics (DGOHDL-CL) model for NER. The presented DGOHDL-CL technique aims to determine and label the atomic components from several texts as a collection of the named entities. In the presented DGOHDL-CL technique, the word embedding process is executed at the initial stage with the help of the word2vec model. For the NER mechanism, the Convolutional Gated Recurrent Unit (CGRU) model is employed in this work. At last, the DGO technique is used as a hyperparameter tuning strategy for the CGRU algorithm to boost the NER’s outcomes. No earlier studies integrated the DGO mechanism with the CGRU model for NER. To exhibit the superiority of the proposed DGOHDL-CL technique, a widespread simulation analysis was executed on two datasets, CoNLL-2003 and OntoNotes 5.0. The experimental outcomes establish the promising performance of the DGOHDL-CL technique over other models.
Reform and innovation of saxophone teaching mode in colleges and universities based on Bayesian model

Abstract To improve the teaching level of saxophone performance in colleges and universities, this paper constructs a saxophone performance education optimization platform based on Bayesian theory. Bayesian clustering is used to informally cluster the saxophone performance education situation in colleges and universities, and the maximum likelihood algorithm is used to calculate the student behavior parameters. The Bayesian network tracking technique analyzes all learning behavior indicators to serve as a reference to obtain labels of learner motivation. To ensure the validity of the feedback metrics, the Bayesian stochastic calculation method was invoked to calculate the AUC (metric) mean ratio of the three feedback metrics. A practical teaching analysis was conducted to verify the platform’s timeliness. The results show that: The response time of login and resource search of the platform constructed in this paper is less than 2.5s and 1.2s, respectively, and the AUC mean ratio of three feedback metrics, namely, download rate, registration rate, and click rate, has improved by about 0.75%. Moreover, the positive attitude of university students towards learning has reached 88%. This shows the reliability of the educational platform constructed in this paper in optimizing higher education saxophone performance education, which can effectively improve the teaching level of saxophone performance education.
Pancreas Segmentation Optimization Based on Coarse-to-Fine Scheme

As the pancreas only occupies a small region in the whole abdominal computed tomography (CT) scans and has high variability in shape, location and size, deep neural networks in automatic pancreas segmentation task can be easily confused by the complex and variable background. To alleviate these issues, this paper proposes a novel pancreas segmentation optimization based on the coarse-to-fine structure, in which the coarse stage is responsible for increasing the proportion of the target region in the input image through the minimum bounding box, and the fine is for improving the accuracy of pancreas segmentation by enhancing the data diversity and by introducing a new segmentation model, and reducing the running time by adding a total weights constraint. This optimization is evaluated on the public pancreas segmentation dataset and achieves 87.87% average Dice-Sørensen coefficient (DSC) accuracy, which is 0.94% higher than 86.93%, result of the state-of-the-art pancreas segmentation methods. Moreover, this method has strong generalization that it can be easily applied to other coarse-to-fine or one step organ segmentation tasks.
Enhanced Metaheuristics with Machine Learning Enabled Cyberattack Detection Model

The Internet of Things (IoT) is considered the next-gen connection network and is ubiquitous since it is based on the Internet. Intrusion Detection System (IDS) determines the intrusion performance of terminal equipment and IoT communication procedures from IoT environments after taking equivalent defence measures based on the identified behaviour. In this background, the current study develops an Enhanced Metaheuristics with Machine Learning enabled Cyberattack Detection and Classification (EMML-CADC) model in an IoT environment. The aim of the presented EMML-CADC model is to detect cyberattacks in IoT environments with enhanced efficiency. To attain this, the EMML-CADC model primarily employs a data preprocessing stage to normalize the data into a uniform format. In addition, Enhanced Cat Swarm Optimization based Feature Selection (ECSO-FS) approach is followed to choose the optimal feature subsets. Besides, Mayfly Optimization (MFO) with Twin Support Vector Machine (TSVM), called the MFO-TSVM model, is utilized for the detection and classification of cyberattacks. Here, the MFO model has been exploited to fine-tune the TSVM variables for enhanced results. The performance of the proposed EMML-CADC model was validated using a benchmark dataset, and the results were inspected under several measures. The comparative study concluded that the EMML-CADC model is superior to other models under different measures.
Multidomain Object Detection Framework Using Feature Domain Knowledge Distillation

Object detection techniques have been widely studied, utilized in various works, and have exhibited robust performance on images with sufficient luminance. However, these approaches typically struggle to extract valuable features from low-luminance images, which often exhibit blurriness and dim appearence, leading to detection failures. To overcome this issue, we introduce an innovative unsupervised feature domain knowledge distillation (KD) framework. The proposed framework enhances the generalization capability of neural networks across both low-and high-luminance domains without incurring additional computational costs during testing. This improvement is made possible through the integration of generative adversarial networks and our proposed unsupervised KD process. Furthermore, we introduce a region-based multiscale discriminator designed to discern feature domain discrepancies at the object level rather than from the global context. This bolsters the joint learning process of object detection and feature domain distillation tasks. Both qualitative and quantitative assessments shown that the proposed method, empowered by the region-based multiscale discriminator and the unsupervised feature domain distillation process, can effectively extract beneficial features from low-luminance images, outperforming other state-of-the-art approaches in both low-and sufficient-luminance domains.
Design of a text evaluation and interaction system for university English textbooks based on a perceptual machine model in the context of globalization

Abstract In the globalization of big data, how to use big data technology to evaluate the text of college English teaching materials and design the interactive system is a hot issue of current concern. In this paper, firstly, we design the interactive system of university English teaching based on the middle dense vector, knowledge embedding vector, learning feature vector, and other letter cores of fused interactive information, implement the interactive interaction system and debug it. Then the index system is constructed based on the evaluation data of English majors and students in a Normal University on university English teaching materials. Finally, the university English teaching materials evaluation grade data analysis uses a multilayer perceptron neural network. The results showed that the accuracy rate of nine indicators was 73.56%~80.36% for excellent evaluation grade, 86.23%~88.53% for good evaluation grade, and 63.96%~69.43% for average evaluation grade. The accuracy rate of good evaluation grades is higher than the other two categories. This study comprehensively and accurately analyzes the text evaluation of college English textbooks by English majors and students in central China; this university to improve the applicability and group of college English textbooks in a targeted way and promote the improvement of the teaching quality of college English courses in universities.
Theoretical Innovation of English Teaching Methodology Based on Continuous Discretization Method

Abstract In the new era, English teaching methods urgently need to be innovated to meet modernization needs. In this paper, we study English teaching based on the continuous discretization method, build a framework of discretization process based on the theoretical foundation of discretization, implement continuous attribute discretization using the rough set method according to the characteristics of English teaching data, and further reduce the classification error by using variable precision rough set to address the limitations of the general rough set. Then, the continuous discretization method based on variable-precision rough sets is used to study the key influencing factors of English teaching outcomes and propose targeted innovative teaching methods. In teaching practice, students’ listening ability improved by 15.77%, and speaking ability improved by 15.13% under the innovative teaching method. Reading ability increased by 11.34%, and writing ability increased by 11.86%. This research method can optimize the English teaching evaluation method, build a higher quality English classroom for students, ensure the overall development of students’ English literacy and comprehensive quality, and effectively improve students’ overall English ability.
Blockchain-based Edge Collaboration with Incentive Mechanism for MEC-Enabled VR Systems

This work investigates the secure resource collaboration among selfish edge servers for multi-access edge computing (MEC)-enabled VR systems in a dynamic scenario. Due to the time-varying and stochastic nature of VR user requests, the edge servers usually have significant differences in workload. To this end, we first propose a type judgment method to perceive their service capability and divide them into two types, i.e., the requesting node (RN) with a poor service capability and the cooperative node (CN) with a powerful service capability. To promote collaboration among self-interest nodes, we then model the competitive interactions among RNs and CNs as a multi-leader and multi-follower Stackelberg game. For the RN (as the leader), we design a novel pricing strategy based on deep reinforcement learning (DRL) to motivate CNs to provide resource assistance. Meanwhile, an optimal selling strategy for the CN (as the follower) is presented to maximize its payoffs from the network. To overcome the security problem during the resource collaboration, we finally introduce the blockchain as a secure and trusted platform for resource publishing and trading, where an efficient consensus mechanism called Proof-of-Trust (PoT) is developed to improve the performance of blockchain. The simulation results show that the proposed approach achieves superior performance.
Efficient Numerical Analysis for Few-Atom Cavity Quantum Electrodynamics Using the Coupling Matrix Approach

Numerical analysis of few-atom cavity quantum electrodynamics (QED) is challenging due to the exponentially scaling size of the Hilbert space when multiple quantized electromagnetic modes are considered. To overcome this, we propose an approach based on coupling matrix transformations that allows adjustment of the coupling structures of QED Hamiltonians so that they are more compatible with tensor network algorithms such as matrix product states. We present the relevant Hamiltonians along with the coupling matrix transformation scheme and present few-atom cavity QED simulations in the time domain that is able to characterize the interactions between atoms efficiently.
Bearing fault diagnosis optimization of probabilistic neural network based on artificial jellyfish search algorithm

The reliability and accuracy are low when the traditional method is used for fault diagnosis of shearer bearing. It was proposed that bearing fault diagnosis of shearer based on artificial jellyfish search (JS) optimized probabilistic neural network (PNN). The vibration signals of different states of bearings are identified and classified. The artificial jellyfish search algorithm is used to optimize the smoothing factor of the probabilistic neural network model. Finally, the optimized model is used for fault diagnosis. The experimental results show that the diagnostic accuracy of the probabilistic neural network model optimized by the artificial jellyfish search algorithm is higher than that of the traditional probabilistic neural network model and particle swarm optimization probabilistic neural network model, with an accuracy of 95.4%.
Evaluation mechanism of local universities’ faculty construction based on SOAP technology

Abstract Accelerating the construction of a high-quality faculty that can adapt to the development of schools is one of the important issues facing universities today. In this paper, we build a comprehensive evaluation system for university teachers based on SOAP technology and hierarchical analysis, adding new security feature elements to the extended namespace of SOAP messages to achieve the system’s ability to handle access control, data confidentiality, and integrity. The system uses hierarchical analysis to hierarchize complex problems, decomposing them into different constituent factors according to the problem and the goals to be achieved and aggregating and combining the factors at different levels according to their interconnectedness and affiliation to form a multi-level analysis structure model. Using this system to analyze the teaching evaluation of college teachers and the six factors of teaching, we get a combined 91.35 and 84.35 for teacher 1 and teacher 2, respectively. From the comparison results, we can see that the teaching effect of Teacher 1 is generally higher than the other five factors, and the teaching attitude of Teacher 1 is lower than the other five factors, which shows that the teaching of Teacher 1 can achieve a good teaching effect. Therefore, the comprehensive evaluation system of college teachers established in this paper based on SOAP technology and hierarchical analysis method has good practicality.
Enhancing Efficiency of Arabic Spam Filtering Based on Gradient Boosting Algorithm and Manual Hyperparameters Tuning

Nowadays, the task of spam filtering in the Arabic language poses a unique challenge due to the complexity of its grammar and linguistic features, which makes it more challenging than in other languages. In this paper, we present a novel approach to enhance the efficiency of Arabic spam filtering using the Gradient Boosting algorithm and optimal hyperparameter selection. We examine the performance of our method using a greater range of experimental metrics than those used in the existing works. The experimental results demonstrate that our proposed model outperforms 6 Machine Learning algorithms for spam filtering in the Arabic language namely K-Nearest Neighbors, Decision Tree, AdaBoost, Naive Bayes, Logistic Regression and Support Vector Machines with an accuracy of 92.63%.
Feature extraction from unstructured texts as a combination of the morphological and the syntactic analysis and its usage in fake news classification tasks

Abstract In this paper, a new technique of feature extraction is proposed, which is considered an essential part of natural language processing. Feature extraction is the process of transformation of the unstructured text to a format which is recognizable by computers. This means a transformation to a vector of numbers. The study evaluates and compares the performance of three methods: M1, which is the baseline method TfIdf; M2, which combines TfIdf with POS tags; and M3, a novel technique called MDgwPosF that incorporates weighted TfIdf values based on word depths and the relative frequency of POS tags. The primary focus of the study is to assess and compare the performance of these methods, with particular emphasis on evaluating how M3 performs in comparison with M1 and M2. Two different datasets and feed-forward, LSTM and GRU neural networks were used in this study. The results showed that the feed-forward model with the proposed method MDgwPosF in moderate topology achieved the best performance across various measures. The dataset created automatically performed better than the manual dataset. The differences between methods and topologies were not statistically significant. Statistically significant differences between the classification models were proven. The MDgwPosF method achieved higher accuracy compared to the baseline TfIdf, indicating that incorporating additional information into the vector can enhance the performance of TfIdf.
Development of Data Acquisition System of Yalong YL-163A Innovative Motor Application Training Device

According to the limitations of Yalong YL-163A innovative motor application training device, this paper uses automatic control technology to design a motor test acquisition system, which completes the test of various characteristics of the motor, and collects, manages, displays and saves the experimental parameters of the tested motor in the test process. It is helpful to improve the teaching effect and the theoretical research of motor technology, and to improve the experimental teaching effect and efficiency.
A Flexible, Extendable and Adaptable Model to Support AI Coaching

We present a model based on coaching definitions, concepts, and theories to support AI coaching. The model represents the evidence-based coaching practice in different coaching domains by identifying the common elements in the coaching process. We then map the elements of the coaching model with Conversational AI design and development strategies to highlight how an AI coach can be instantiated from the model. We showcase the instantiation through an example use case of an HIV coaching chatbot.
Pengembangan Literasi di Taman Bacaan Masyarakat (TBM) Sangkuriang, Kelurahan Rancaekek, Kabupaten Bandung

Introduction to literacy for children will impact the improvement of creativity, quality of life, competitiveness, and good thinking skills. Sangkuriang Reading Centre is a reading park located in Rancaekek Village, Bandung Regency. The existing book collection is not attractive enough to encourage children to engage in literacy activities. This community service activity aims to help provide information, knowledge, and facilities that can support the improvement of children's literacy interest in Sangkuriang Reading Centre. This community service activity is carried out offline. The team directly visited the location and conducted several stages of activities there. Some of the program activities carried out in this activity are book donation, book inventory, numbering, installation of bookshelves, banners, posters, and literacy and games to support the improvement of children's reading interest. After all community service activities were carried out at Sangkuriang Reading Centre, several changes were observed, including changes in knowledge, attitudes, and skills
Text-Independent Speaker Verification Based on Mutual Information Disentanglement

In text-independent speaker verification, the speaker features extracted by the speaker model contain features of semantic content. Entanglement of speaker features and semantic features can lead to poor performance of speaker verification systems. To solve this problem, we propose the MI-TISV method to disentangle the semantic content features from the speaker features. The minimization of mutual information between speaker embedding and semantic content embedding can help us retain more useful information for speaker features. We conducted experiments in CN-Celeb and VoxCeleb, and found that MI-TISV method improves the generalization performance of speaker models while maintaining the accuracy of the existing models.
GENE: Global Enhanced Graph Neural Network Embedding for Session-Based Recommendation

The session-based recommendation aims to generate personalized item suggestions by using short-term anonymous sessions to model user behavior and preferences. Early studies cast the session-based recommendation as a personalized ranking task, and adopt graph neural networks to aggregate information about users and items. Although these methods are effective to some extent, they have primarily focused on adjacent items tightly connected in the session graphs in general and overlooked the global preference representation. In addition, it is difficult to overcome the special properties of popularity bias in the real-world scenario. To address these issues, we propose a new method named GENE, short for Global Enhanced Graph Neural Network Embedding, to learn the session graph representations for the downstream session-based recommendation. Our model consists of three components. First, we propose to construct the session graph based on the order in which the items interact in the session with normalization. Second, we employ a graph neural network to obtain the latent vectors of items, then we represent the session graph by attention mechanisms. Third, we explore the session representation fusion for prediction incorporating linear transformation. The three components are integrated in a principled way for deriving a more accurate item list. Both quantitative results and qualitative analysis verify the effectiveness and rationality of our GENE method.