Reviewerly/exHarmony huggingface dataset:
Smooth Path Planning of 6-DOF Robot Based on Reinforcement Learning

The current path planning algorithms such as A-star(all stars) algorithm and RRT (Rapidly-exploring Random Trees) algorithm can meet the obstacle avoidance planning of the 6-DOF robot, but the smoothness of the path is not considered. Working in an unreasonable path for a long time will produce a great load on the joints of the 6-DOF robot and seriously affect its life. In this paper, we use reinforcement learning reconcile A-star algorithm and RRT algorithm for smooth path planning of the robot. Experimental results show that compared with A-star algorithm and RRT algorithm, the fusion algorithm has smoother path and more reasonable time.
Multiple Waypoint Navigation in Unknown Indoor Environments

Indoor motion planning focuses on solving the problem of navigating an agent through a cluttered environment. To date, quite a lot of work has been done in this field, but these methods often fail to find the optimal balance between computationally inexpensive online path planning, and optimality of the path. Along with this, these works often prove optimality for single-start single-goal worlds. To address these challenges, we present a multiple waypoint path planner and controller stack for navigation in unknown indoor environments where waypoints include the goal along with the intermediary points that the robot must traverse before reaching the goal. Our approach makes use of a global planner (to find the next best waypoint at any instant), a local planner (to plan the path to a specific waypoint) and an adaptive Model Predictive Control strategy (for robust system control and faster maneuvers). We evaluate our algorithm on a set of randomly generated obstacle maps, intermediate waypoints and start-goal pairs, with results indicating significant reduction in computational costs, with high accuracies and robust control.
The Editor/<i>Bluetooth and Mondegreens</i>

"The Editor/Bluetooth and Mondegreens." Journal of the California Dental Association, 48(4), pp. 177–178
Espousing AI to Enhance Cost-Efficient and Immersive Experience for Human Computer Interaction

Because of recent technological and interface advancements in the field, the virtual reality (VR) movement has entered a new era.Mobility is one of the most crucial behaviours in virtual reality.In this research, popular virtual reality mobility systems are compared, and it is shown that gesture control is a key technology for allowing distinctive virtual world communication paradigms.Gesture based movements are very beneficial when there are a lot of spatial restrictions.With a focus on cost-effectiveness, the current study introduces a gesture-based virtual movement (GVM) system that eradicates the obligation for expensive hardware/controllers for virtual world mobility (i.e., walk/ jump/ hold for this research) using artificial intelligence (AI).Additionally, the GVM aims to prevent users from becoming dizzy by allowing them to change the trajectory by simply turning their head in the intended direction.The GVM was assessed on its interpreted realism, presence, and spatial drift in the actual environment in comparison to the state-of-the-art techniques.The results demonstrated how the GVM outperformed the prevailing methodologies in a number of common interaction components.Additionally, the empirical analysis showed that GVM offers customers a real-time experience with a latency of ~65 milliseconds.
Effect of Indiscipline on Academic Performance of Secondary School Students’ in Aba South Local Government Area of Abia State

The study investigated the effects of indiscipline on the academic performance of secondary school students in Aba South Local Government Area of Abia State. The study adopted descriptive survey research design. 1, 576 senior secondary school students in 8511 SS in all the secondary schools in Aba South Local Government comprising 676 boys and 920 girls made up the sample population. Simple random technique was used to select the total of 350 senior secondary students from nine senior secondary schools in Aba South Local Government for the study. The instrument for data collection was the researcher made questionnaire. The mean was used to analyze the research questions while the hypotheses were tested using z-test statistics at 0.05 levels of significance. The study revealed that there is no significant influence of truancy and alcohol and substance abuse. It was concluded from the results of the data and test of the hypotheses that there is significant influence of truancy, cheating in examination and alcohol and substance abuse on students’ academic performance. It is recommended that the management of adolescents (schools) should be in joint responsibility with parents and the society to check indiscipline in the secondary schools.
Deep Learning in the Service of Customer Service: The Value of AI-Enabled Timely Performance Feedback

Customer service agents serve a critical role towards post-purchase customer satisfaction, retention, and loyalty. However, properly evaluating their performance involves several challenges: commonly used customer survey measures suffer from scarce, biased, and delayed responses. This study addresses these challenges by presenting a novel approach, which we term Conversational Hierarchical Attention Network (C-HAN), a deep learning algorithm that predicts agents’ performance for each customer service transaction in real-time. The algorithm leverages the hierarchical structure of document classification and incorporates various conversational features present in a customer-agent interaction—e.g., who the speaker is; when and how long each person spoke; and the total duration of the conversation—to better predict transaction quality. To examine the proposed algorithm’s effectiveness and understand the implications of providing timely feedback in practice, we conduct a controlled field experiment that presents algorithm-derived performance metrics to customer service agents. The results demonstrate how the ability to provide timely feedback improves overall agent performance. Yet, when relative feedback, i.e., the average team measures, is additionally presented, the performance gain diminishes. Furthermore, the effects of AI-derived performance feedback on agents of different initial abilities, call-related behavior, as well as the short- and long-term implications are also studied.
Sign Language Detection System Using Adaptive Neuro Fuzzy Inference System (ANFIS) Method

Sign language is a language that prioritizes communication with hands, body language, and lip movements to communicate. The deaf are the main group who use this language, often combining hand shape, hand, arm and body orientation and movement, and facial expressions to express their thoughts. The sign language detection system is designed using the Adaptive Neuro Fuzzy Inference System (ANFIS). This study uses data from the kaggle.com dataset, which is a site that provides research data on artificial intelligence. This study was conducted to recognize empty hand signals. Where it will help users naturally without any additional help. The test is carried out using a data set as evidenced by 1 display. In this process, The characteristics of the hand were carried out using the Histogram Oriented Gradient (HOG) method. Meanwhile, to separate it from the background image, it is used with color segmentation. The results of the process are then taken for classification. The classification process uses the Adaptive Neuro Fuzzy Inference System method. The results of the tests carried out for accuracy are as much as
Deep Learning Based Network News Text Classification System

Today, text information includes all the information in the form of natural language text, among which text information occupies an important position in life and becomes an important part of people's use of social information resources. The main purpose of this paper is to study a deep learning-based online message classification system. Based on deep learning and related text segmentation theory, this paper proposes modules such as segmentation result evaluation for the first time. Then, the first implementation and experimental results of network message segmentation are introduced and analyzed in depth. The research shows that text classification is a multidisciplinary field. Among all the literatures in the statistics, computer science has the largest number of related books in the field of text classification, with a total of 1827 books published, accounting for 87.88% of the total. In addition, it can be seen from the topic distribution map that the vocabulary has many applications and development prospects in education, news broadcasting, business management, information dissemination, mathematics and other fields.
Multi-modal Personalized Goods Recommendation based on Graph Enhanced Attention GNN

Most traditional goods recommendation algorithm have difficulty in capturing higher-order information about interaction, while heterogeneous graph neural networks can capture complex topological information and preserve the heterogeneous information of attributes in heterogeneous network, thus extracting richer structural and semantic representation information and improving goods recommendation. Therefore, we proposed a multi-modal personalized goods recommendation based on Graph Attention-Enhanced Graph Neural Network (GAGN) by combining graph neural network related method and using the interaction behavioral data of user and goods. GAGN mines user and goods node representation on user-goods interaction graph through long-term user interest model and goods model; Text information extractor to learn user and goods reviews, goods descriptions and information representations; Extracting users’ short-term preference representations by model their short-term interests and interlinking multi-modal information such as node representation, review representation, descriptions of goods, and users’ short-term preferences through a graph attention-enhanced aggregator to achieve better information fusion. Finally, the proposed algorithm is fully experimented on three real data sets, thus demonstrating the superiority of the proposed algorithm over others benchmark model.
Genetic Algorithm-based Transformer Architecture Design for Neural Machine Translation

A Great progress of Neural Machine Translation (NMT) tasks has been achieved by the transformer models in recent years, which is largely owing to the careful design of multi-head attention and feed-forward neural network layers in its encoder-decoder architecture. However, these layers are often manually designed by expertise, makes it time-consuming and hard to explore potentially promising ordering patterns. In this paper, an automatic transformer architecture design algorithm based on genetic algorithm is proposed to evolve the optimal transformer architecture for NMT tasks. Particularly, a novel gene encoding strategy is developed in the proposed algorithm to effectively enable transformer architectures to have various layer ordering patterns and hyper-parameters, and then the effective genetic operators are designed to perform the efficient evolutionary search for finding optimal architecture. To validate the effectiveness of the proposed algorithm, the experiments are conducted on a widely used machine translation benchmark, and the result shows that the model automatically searched by the proposed algorithm outperforms the vanilla transformer under different model sizes.
Multi Strategy Machineenglish Translation System based on Machine Learning Algorithm

The advent of the big data era has brought unprecedented data to machine translation, which is particularly important for data-driven models and algorithms. The degree of public quotation of source text is becoming more and more difficult to understand, but the overall translation performance is improving. On this basis, the multi Strategy machineenglish translation (MSMT) system based on MLA is designed in this paper. Firstly, the MSMT system and machineenglish translation process are analyzed, and the Interactive MSMT system is discussed. Then the neural network translation model of machine learning is proposed. In order to improve the performance of the MSMT system based on MLA proposed in this paper, The results show that in the case of full-text retrieval, the time of English translation is greatly increased, and these time are basically spent in finding and matching these sentences. The sentences that cannot be translated due to the failure of feature index can be translated by the Multi Strategy machine English translation system based on MLA proposed in this paper, which verifies the rationality of the translation system proposed in this paper.
Lexicon-matched Word Injection for Chinese NER

Recently, Chinese named entity recognition has attracted a lot of attention. Most of the work utilizes words matching with lexicon which integrates potential word information with lattice structure or graph structure. Although existing approaches have been proved to be effective for exploiting abundant word boundary information, it is difficult to model global semantic interactions due to the inherent one-way sequential nature of the DAG structure. Meanwhile, more interfering lexicon words have been introduced leading to word boundary conflicts.
Transformer Recommendation Machine for Rate Prediction

Rate prediction has always been the focus of research in Recommender Systems (RSs), and deep learning based models have achieved remarkable results in this field. Previous works use techniques like FNN to explore new cross features and output the final prediction. Compared with this, there are still relatively few researches on attention-based models. At present, most models only process only one side of users or items through deep networks, and can not use the information of both parties to make a prediction. In this paper, we propose a TRM model that uses both users' and items' information based on attention mechanism. On this basis, it can effectively deal with the cold start problem of users or items.
WikAnalytics: A Web-based Application for Identifying Linguistic Features of a Text Group Supporting Filipino, English, and Taglish Languages

Reading is one of the first things humans learn to improve their comprehension, vocabulary, and imagination. Determining what to read for your level will be difficult, but forcing you to understand a high text level is more complex. It can result in not learning at all. The Philippines has two official languages, which are English and Filipino. Filipinos tend to engage with text written in both languages. Although the Philippines has two official languages, not all individuals are bilingual and only focus on one language, especially those who do not have the privilege to study in school. Thus, having bilingual text to read might be challenging to understand its context. A system that can analyze and recognize either English, Filipino, or Taglish languages while giving the readability index of the given text could help readers determine the readability level they want to read. Wikanalytics, a web application text analysis tool, was developed in this paper. The application aims to provide analysis of text files while calculating its readability index. The Agile Software Development Method was utilized for developing the web application. The system handles text documents written in English, Filipino, or Taglish Languages. The extracted features of an analyzed text are Readability Index, Traditional Features, Lexical Features, Syllable Patterns, Sentences and Paragraphs, and Token Ratios. The accuracy of the web application tool in identifying the language is 97.5% in English, 96% for Filipino, and 96.89% for Taglish. The runtime is tested for English, Filipino, and Taglish files, and the longest runtime out of the three languages mentioned is the Taglish file. Having two different languages in the file affected the runtime of the application's Lexical and Token ratio feature.
Solving Imbalanced Data in Credit Risk Prediction: A Comparison of Resampling Strategies for Different Machine Learning Classification Algorithms, Taking Threshold Tuning into Account

Effective credit risk prediction is critical for commercial banks to actively manage their lending book and reduce negative impact from potential credit losses. In a benign credit markets where default rates are low, the datasets for customers’ credit default status prediction are often imbalanced. While many studies have been conducted to address the class imbalance problem in the machine learning field under various contexts, our research focuses on the credit risk sector. We experiment five resampling strategies (SMOTE, Borderline-SMOTE, Random Undersampling, NearMiss, SMOTE + Tomek Links) and a thresholding technique (Optimal G-Mean) on eight different classifiers separately. We also explore whether the combination of resampling and thresholding can enhance the overall classifier performance. The performance of models is evaluated on both threshold metrics (G-Mean, F1-score) and ranking metrics (area under the receiver operating characteristic (ROC) curve). Our findings suggest that pure threshold tuning can often outperforms resampling methods whereas the effect of thresholding on the basis of resampled dataset is minor.
An Empirical Analysis of Vision Transformer and CNN in Resource-Constrained Federated Learning

Federated learning (FL) is an emerging distributed machine learning method that collaboratively trains a universal model among clients while maintaining their data privacy. Recently, several efforts attempt to introduce vision transformer (ViT) models into FL training. However, deploying and training such ViT models from scratch in practice is not trivial, existing works overlook the existence of the clients with low resources (e.g., mobile phones), which is a common and practical FL setting. In this paper, we use low-resolution images as model input to satisfy the resource constraints and investigate several ViT models to explore whether ViT models still outperform CNN models in this setting. Our experiment was performed on CIFAR10 and Fashion MNIST with their IID and non-IID versions, and the results demonstrate that ViT models can achieve a better global test accuracy than CNN models while using a comparable training cost, suggesting that they are ideally suitable for FL training with resource-constrained devices.
Fast Residual Network for Person Re-identification

This paper proposes a method for fast residual network learning features to enhance the model effect, which can effectively improve the effect of person re-identification. Unlike most other person re-identification methods, the proposed model is more general and can be used in many applications as it does not need to be optimized for consistency with the characteristics of a specific dataset. In addition, the model is modular and easy to be included in any other network for improving network performance. The results tested on Market1501, DukeMTMC-reID, CUHK03 datasets show that the model is better to obtain effective features.
Determining Student's Engagement in Synchronous Online Classes Using Deep Learning (Compute Vision) and Machine Learning

In 2020 Scaled-YOLOv4 was introduced. It is one of the best object detection models outclassing its peers in MS COCO test-dev. In this study, the proponents used Scaled-YOLOv4 as their object detection model. The model will be used in the environment of Pasig River, the Philippines in detecting plastic and paper. The model's performance will be tested using a dilapidated trash dataset. Object detection models usually face difficulties in detecting the object because of deformation, occlusion, illumination conditions, and cluttered background. The proponents’ Scaled-YOLOv4 model produced 63% average precision, 67% precision for plastic, and 59% precision for paper. The model can be used in detecting trash materials found on the surface of the Pasig River.
RoI Uncertainty for RGB-Thermal Image Segmentation

Estimating uncertainty for deep networks is a good way to assess the reliability of prediction results and helps self-driving cars avoid traffic accidents. Compared to single RGB or thermal image segmentation, fusing RGB and thermal adds more sources of uncertainty to object boundaries including blurred boundaries of thermal images and the discrepancy of object boundary between RGB and thermal images because the objects can not be perfectly aligned. Besides, it is important to model the relationship between uncertainty and segmentation quality of RGB-T segmentation. To address these challenges, we propose Region of Interest uncertainty (RoI uncertainty) to focus on the uncertainty inside an object with indicative meaning and ignore the boundary uncertainty due to the imaging mechanism and alignment mechanism. We improve the correlation between accuracy and RoI uncertainty compared to the correlation between accuracy and conventional uncertainty. The RoI uncertainty can be obtained not only by morphological operations based on estimated uncertainty but by deep learning-based RoI uncertainty networks using uncertainty as input. We design two types of RoI uncertainty estimation networks, one using RoI as labels called RoI-Net and the other using region uncertainty as labels called RoI-Unc-Net. Furthermore, we compare three uncertainty estimation methods including Monte Carlo Dropout (MC Dropout), Deep Ensemble, and MC Dropout Ensemble, and three measures including predictive entropy, mutual information, and variance that are introduced to quantify uncertainty. From extensive experiments on the RGB-T dataset, MC Dropout with entropy measure shows better uncertainty estimation and model calibration. The RoI uncertainty based on MC Dropout with entropy can be used to show the segmentation quality. Besides, it is an unsupervised metric to indicate the degree of risk of the segmentation result in real road scenes. We compare the growth rate of RoI uncertainty on several state-of-the-art RGB-T fusion methods, and the results show that fusing RGB and thermal features in multiple stages is more effective than a single-stage fusion strategy.
Application of C5.0 Algorithm in Prediction of Learning Outcomes in Calculus Subject

Calculus is one of the basic subject that must be studied at the computer science faculty of the informatics engineering study program. For some students, especially in the Faculty of Informatics Engineering, calculus is a subject that is considered quite difficult, even though this subject is important for them. And the resulted for some students having to repeat this subject. For this reason, predictions of calculus learning outcomes are carried out by applying the data mining process and using the C5.0 method for the prediction process based on the classification concept that will be carried out. This study applies the Cross Industry Standard Process for Data Mining (CRISP – DM) methodology with the C5.0 algorithm. The results are in the form of a decision tree (Decision tree) and the rules in it using the attributes of guardian, number of family members, status of residence, internet, activity, desire to continue study, the last education of parents (father and mother), parents' occupations, grades on assignments, UAS, and UTS. The C5.0 algorithm is able to predict the results of learning calculus. The evaluation results show that the applied C5.0 algorithm has an accuracy of 95%.