Reviewerly/exHarmony huggingface dataset:
Construction of an Intelligent Robot Path Recognition System Supported by Deep Learning Network Algorithms

In recent years, intelligent robots have been widely used in fields such as express transportation, industrial automation, and healthcare, bringing great convenience to people's lives. As one of the core technologies of intelligent robots, path planning technology has become a research highlight in the field of robotics. To achieve path planning in unknown environments, a path planning algorithm based on an improved dual depth Q-network is proposed. In both simple and complex grid environments, the planned path inflection points for the improved dual depth Q-network is 4 and 9, respectively, with path lengths of 27.21m and 28.63m, respectively. Both are less than double depth Q network and adaptive Ant colony optimization algorithms. The average reward values of the improved dual depth Q network in simple and complex environments are 1.12 and 1.02, respectively, which are higher than those of the dual depth Q network. In a random environment, the lowest probability of the improved dual depth Q network successfully reaching the destination without colliding with obstacles is 95.1%, which is higher than the other two algorithms. In the Gazebo environment, when the number of iterations reaches 2000, the average cumulative reward value is positive. The average cumulative reward value in the range of iterations from 3500 to 4000 and iterations from 4000 to 4500 exceeds 500. The average cumulative reward value of the dual depth Q network is only positive within the two intervals of iterations 2500-3000 and 3000-3500. The average cumulative reward value does not exceed 100. According to the findings, the path planning ability of the improved dual depth Q network is better than that of the dual depth Q network and the adaptive Ant colony optimization algorithms.
An Integrated, Bidirectional Pronunciation, Morphology, and Diacritics Finite-State System

A bidirectional phonetizer, morphologizer, and diacritizer pipeline (FSPMD) for modern standard Arabic (MSA) that integrated pronunciation, concatenative and templatic morphology, and diacritization were developed. Grammar and segmental phonology rules were applied in the forward direction to ensure the order of the proper rules, which were supplemented with special backward direction rules. The FSPMD comprises bidirectional finite-state transducers (FSTs) consisting of an ordered composition of FSTs, unordered parallel FSTs, unioned FSTs, and for validity, finite-state acceptors. The FSPMD has unique, innovative features and can be used as an integrated pipeline or standalone phonetizer (FSAP), morphologizer (FSAM), or diacritizer (FSAD). As the system is bidirectional, it can be used in forward (generation, synthesis) and backward (analysis, decomposition) directions and can be integrated into systems such as automatic speech recognition (ASR) and language learning tools. The FSPMD is rule-based and avoids stem listings for morphology or pronunciation dictionaries, which makes it scalable and generalizable to similar languages. The FSPMD models authentic rules, including fine granularity and nuances, such as rewrite and morphophonemic rules, subcategory identification and utilization, such as irregular verbs. FSAP performance regarding text from the Tashkeela corpus and Wikipedia demonstrated that the pronunciation system can accurately pronounce all text and words, with the only errors related to foreign words and misspellings, which were out of the system’s scope. FSAM and FSAD coverage and accuracy were evaluated using the Tashkeela corpus and a gold standard derived from its intersection with the UD_PADT treebank. The coverage of extraction of root and properties from words is 82%. Accuracy results are roots computed from a word (92%), words generated from a root (100%), non-root properties (97%), and diacritization (84%). FSAM non-root results matched and/or surpassed those from MADAMIRA; however, root result comparisons were not conducted because of the concatenative nature of publicly available morphologizers.
An Artificial Intelligence Method for Automatic Assessment of Fuzzy Semantics in English Literature

This Online writing and evaluation are becoming increasingly popular, as is automatic literature assessment. The most popular way is to obtain a good evaluation of the essay and article is by the automatic scoring model. However, assessing fuzzy semantics contained in reports and papers takes much work. An automated essay and articles assessment model using the long-short-term memory (LSTM) neural network is developed and validated to obtain an appropriate assessment. The relevant theoretical basis of the recurrent neural network is introduced first, and the quadratic weighted kappa (QWK) elevation method is cited here to develop the model. The LSTM network is then awarded for developing the general automatic assessment model. The available model is modified to get better performance by adding a convolutional layer(s). Finally, a data set of 7000 essays is segmented based on the ratio of 6:2:2 to train, validate, and test the model. The results indicate that the LSTM network can effectively capture the general properties of the essay and articles. After adding the convolutional layer(s), the LSTM+convolutional layer(s) model can get better performance. The QWK values are higher than 0.6 and have an improvement of 0.097 to 0.134 compared with the LSTM network, which proves that the results of the LSTM network combined with the convolutional layer(s) model are overall satisfactory, and the modified model has practical values.
Evaluation Method of Physical Education Students' Mental Health based on Multi-source and Heterogeneous Data

To enhance the ability to evaluate the mental health status of physical education students, a method of evaluating the mental well-being state of physical education students based on multi-source heterogeneous data mining is proposed. A fuzzy information detection model of multi-source heterogeneous data on the mental health status of physical education students is constructed, with four factors as dependent variables: compulsion, interpersonal sensitivity, hostility, and depression. Combined with the hierarchical index parameter detection and analysis method, the statistical analysis of multi-source heterogeneous info is accomplished. Based on the factor extraction outcomes of multi-source heterogeneous info, combined with the subspace heterogeneous fusion method, an estimated parameter feature clustering model is established. Combining the results of characteristic distributed clustering and linear regression analysis, the psychological well-being state evaluation of physical education students is realized. The results of empirical analysis show that this method has higher accuracy and better feature resolution in the evaluation of the mental well-being state of physical education students, which improves the reliability and confidence level of the assessment of the mental well-being status of physical education students.
Investigate the Impact of Stemming on Mauritanian Dialect Classification using Machine Learning Techniques

Despite the plethora and diversity of research on Natural Language Processing (NLP). As a technique allowing computers to understand, generate, and manipulate human language; It still remains insufficient, especially with regard to the processing of Arabic texts and their dialects which are widely used. The proposed approach focuses on the application of machine learning techniques taking into account evaluation criteria such as training to comments expressed in Mauritanian dialect, published on social media notably Facebook, and compares results generated by three algorithms which we applied such as the Random Forest (RF), Na¨ıve Bayes Multinominal (NBM), and Logistic Regression (LR) algorithm. Additionally, We then study the effect of machine learning techniques when different stemmers are combined with other features such as the tokenizers used to process the dataset. Although major challenges exist such as the morphology of Arabic is completely different from Latin letter languages, and there is no pre-existing dataset or dictionary to train the algorithms, the result we obtained after the experiments carried out on Weka shows that the RF and NBM algorithms are more efficient when applied with ArbicStemmerKhoja giving results respectively 96.37% and 71.40%; However, Logistic gets better performance results with Null Stemme is 81.65%. Results obtained by the three techniques applied with a light Arabic stemmer were more than 70%. This article presents a contribution to NLP based on Machine learning, descript also an important study that can determine the best Arabic classifier.
Automatic Configuration of Deep Learning Algorithms for an Arabic Named Entity Recognition System

Word embedding models have been widely used by many researchers to extract linguistic features for Natural Language Processing (NLP) tasks. However, the creation of an adequate Word embedding model depends on choosing the right language model method and architecture, in addition to finetuning the various parameters of the language model. Each parameter combination could result in a different model, and each model can behave differently according to the targeted NLP task. In this paper, we present an approach that combines a range of Word embedding models, multiple clustering and classification methods, and Irace for automatic algorithm configuration. The goal is to facilitate the construction of the most accurate Arabic Named Entity Recognition (NER) model for our dataset. Our approach involves the creation of different Word embedding models, the implementation of these models in different classification and clustering methods, and finetuning these implementations with different parameter combinations to create an Arabic NER System with the highest accuracy rate.
Redefining Climate Policy and Economic Resilience in the Consuming World

Faced with the impacts of climate change and its resulting consequences on both societal liveabilityLiveability standards and economic drawbacks, cities and countries are re-evaluating policies regarding urban sustainability. This is reflected in global policy documents such as the Sustainable Development GoalSustainable Development Goals (SDGs) 11 and the New Urban AgendaNew Urban Agenda by the United Nations. Coupled with national strategies, there is a noticeable interest in mitigationMitigation strategies toward protecting vital urban infrastructure while safeguarding the integrity, efficiency, and performance of the urban economy. Cities and countries with low financial capabilities, such as low-income economies and Small Island Developing States (SIDS)Small Island Developing States (SIDS), cannot afford expensive mitigationMitigation measures deemed essential to their survival. Faced with this need, they often fall prey to unsustainable, unethical, and dangerous financingFinancing mechanisms supporting debtDebt structures that are principally tabled to secure the power of vital infrastructure or their strategic locations on the global map. This chapter explores this conundrum and advances the need for revised financingFinancing mechanisms in the wake of climate change. A revised Emissions Trading SystemEmissions Trading System (ETS) model is proposed to achieve regional decarbonisationRegional Decarbonisation through the use of Smart ContractsSmart Contracts in the BlockchainBlockchain.
Incorporating Natural Language Processing into Virtual Assistants: An Intelligent Assessment Strategy for Enhancing Language Comprehension

The study introduces a comprehensive technique for enhancing the Natural Language Processing (NLP) capabilities of virtual assistant systems. The method addresses the challenges of efficient information transfer and optimizing model size while ensuring improved performance, with a primary focus on model pertaining and distillation. To tackle the issue of vocabulary size affecting model performance, the study employs the SentencePiece tokenizer with unigram settings. This approach allows for the creation of a well-balanced vocabulary, which is essential for striking the right balance between task performance and resource efficiency. a novel pre-layernorm design is introduced, drawing inspiration from models like BERT and RoBERTa. This optimization optimizes the placement of layer normalization within transformer layers during the pretraining phase. Teacher models are effectively trained using masked language modeling objectives and the Deepspeed scaling framework. Modifications to model operations are made, and mixed precision training strategies are explored to ensure stability. The two-stage distillation method efficiently transfers knowledge from teacher models to student models. It begins with an intermediate model, and the data is distilled carefully using logit and hidden layer matching techniques. This information transfer significantly enhances the final student model while maintaining an ideal model size for low-latency applications. In this approach, innovative measurements, such as the precision of filling a mask, are employed to assess the effectiveness and quality of the methods. The findings demonstrate substantial improvements over publicly available models, showcasing the effectiveness of the strategy within complete virtual assistant systems. The proposed approach confirms the potential of the technique to enhance language comprehension and efficiency within virtual assistants, specifically addressing the challenges posed by real-world user inputs. Through extensive testing and rigorous analysis, the capability of the method to meet these objectives is validated.
A Comparison of Sampling Methods for Dealing with Imbalanced Wearable Sensor Data in Human Activity Recognition using Deep Learning

Human Activity Recognition (HAR) holds significant implications across diverse domains, including healthcare, sports analytics, and human-computer interaction. Deep learning models demonstrate great potential in HAR, but performance is often hindered by imbalanced datasets. This study investigates the impact of class imbalance on deep learning models in HAR and conducts a comprehensive comparative analysis of various sampling techniques to mitigate this issue. The experimentation involves the PAMAP2 dataset, encompassing data collected from wearable sensors. The research includes four primary experiments. Initially, a performance baseline is established by training four deep-learning models on the imbalanced dataset. Subsequently, Synthetic Minority Over-sampling Technique (SMOTE), random under-sampling, and a hybrid sampling approach are employed to rebalance the dataset. In each experiment, Bayesian optimization is employed for hyperparameter tuning, optimizing model performance. The findings underscore the paramount importance of dataset balance, resulting in substantial improvements across critical performance metrics such as accuracy, F1 score, precision, and recall. Notably, the hybrid sampling technique, combining SMOTE and Random Undersampling, emerges as the most effective method, surpassing other approaches. This research contributes significantly to advancing the field of HAR, highlighting the necessity of addressing class imbalance in deep learning models. Furthermore, the results offer practical insights for the development of HAR systems, enhancing accuracy and reliability in real-world applications. Future works will explore alternative public datasets, more complex deep learning models, and diverse sampling techniques to further elevate the capabilities of HAR systems.
An Improved Hybrid A*Algorithm of Path Planning for Hotel Service Robot*

Due to the increasing demand for unmanned in the hotel industry in recent years, how to efficiently use hotel service robots to further improve the efficiency of the hotel industry has become a hot research issue. To solve the problems of lengthy path-finding time and poor route security in conventional service robots in complex environments, the current study provides an improved A* path-finding algorithm for application in the hotel environment. Firstly, the conventional A* algorithm is combined with bidirectional search and Jump Point Search (JSP) algorithm, which makes the search more effective. Secondly, the traditional A* algorithm is combined with the security weight square matrix to make the path trajectory safer. A cubic spline interpolation is chosen to smoothen the transitions at the corners planned by the improved A* algorithm. Simulation experiments were done on grid maps with 10*10, 20*20 and 50*50 sizes. Compared with the conventional A* algorithm, the search time were decreased by 67%, 77% and 95% respectively. The number of search nodes was decreased by 80%, 76% and 95%, respectively. Meanwhile the distance between the robot and the obstacles was increased. The results indicate that the improved A* algorithm suggested in the present research can ensure the path trajectory safer while keeping the path search efficiency higher.
Introducing ASP.NET Core 8

Microsoft released its first full-stack web application development framework with ASP.NET in 2002 with ASP.NET Web Forms. The years that followed were rich in developments such as ASP.NET Model-View-Controller (MVC), ASP.NET Web API, and SignalR. The framework evolved too quickly with new functionalities without changing its core, more precisely, the assembly named System.Web. Very quickly, new challenges appeared, such as performance, the possibility of running ASP.NET on servers other than IIS (which is the Windows-only web server designed by Microsoft), increasing its affinity with the cloud to facilitate its deployment significantly, and greatly improving its configuration by making it more flexible. ASP.NET Core was born!
Basics of Clean REST APIs

You're looking forward to developing APIs! Here we are. Let's look at the most basic operations with minimal APIs in ASP.NET Core 8. These are the most common features; you'll use them in every API you develop. First, you'll need to expose comprehensible URIs, validate the parameters sent by the API consumer, and do object mapping, that is, transform your objects into other objects specific to the application domain. You'll also need to manage the correct HTTP status codes for each type of operation you want to perform; you'll learn how to download and upload files, stream elements to your API's client, as well as version your API and expose your API's endpoints so that your clients understand how to invoke your endpoints. Handling Cross-Origin Resource Sharing (CORS) is also a challenge; we will see how to deal with that. In this chapter, you'll learn about each of the following points:
Learning-Based Reflection-Aware Virtual Point Removal for Large-Scale 3D Point Clouds

3D point clouds are widely used for robot perception and navigation. LiDAR sensors can provide large scale 3D point clouds (LS3DPC) with a certain level of accuracy in common environment. However, they often generate virtual points as reflection artifacts associated with reflective surfaces like glass planes, which may degrade the performance of various robot applications. In this letter, we propose a novel learning-based framework to remove such virtual points from LS3DPCs. We first project 3D point clouds onto 2D image domain to investigate the distribution of the LiDAR's echo pulses, which is then used as an input to the glass probability estimation network. Moreover, the 3D feature similarity estimation network exploits the deep features to compare the symmetry and geometric similarity between real and virtual points with respect to the estimated glass plane. We provide a LS3DPC dataset with synthetically generated reflection artifacts to train the proposed network. Experimental results show that the proposed method achieves the better performance qualitatively and quantitatively compared with the existing state-of-the-art methods of 3D reflection removal.
A Tabular Variational Auto Encoder-Based Hybrid Model for Imbalanced Data Classification With Feature Selection

Cancer is the deadliest disease in humankind. Ovarian Cancer (OC) stands important among female-specific cancers. Epithelial Ovarian Cancer (EOC) is the most commonly occurring subtype of OC. The disease is identified in later stages due to the unrevealed symptoms in the early stages. Gene Expression experiments along with machine learning (ML) methodologies can lead to preventive care of OC by early identifying the malignant gene transformations and to use precision medicine which aids in fast recovery. The proposed hybrid Tabular Variational Auto Encoder oriented dictionary based Stratified K Fold Cross Validation (TVAE_dict_SKCV) is an effective model to handle the threat. The main objective is to assess the significance of EOC screening variables for categorizing high-risk patients. It initially generated synthetic data using the TVAE model to increaseF the EOC subtype data size taken from the Cancer Cell Line Encyclopedia (CCLE). The synthesized data were balanced utilizing the Synthetic Minority Oversampling Technique (SMOTE). Significant features got selected with Boruta Feature Selection (FS) method. The HYPERPARAMETERS were fine-tuned employing Optuna optimizer and applied enhanced SKCV with Random Forest (RF) classifier. The TVAE_dict_SKCV method with Boruta acquired an accuracy of 98.5 % which outperforming the experiment with Lasso FS and with original data. The Pickle tool reserved the concealed parameter values of the model. Shapley Additive explanations (SHAP) summarize the main features which classify. Optuna efficiently reduced the computing time than the Grid search CV (GsCV) optimizer.
Infrared Small Target Tracking Based on OSTrack Model

Infrared small target tracking plays an important role in military reconnaissance, early warning, video surveillance, and civil applications. For tracking small infrared targets in this paper, a one-stream deep learning model is utilized. In order to integrate the processes of feature extraction and feature fusion, the model uses transformer as the framework’s major component and creates a bidirectional information flow between the template and the search picture pairs in the feature extraction stage. Use the head of the model to get the target position. Finally, post-processing of the target area, including tracking success, saves the coordinate information of the target frame; tracking failure, near, in the middle, and far from the target box, searches for the real target. It helps to solve the situation where the target moves fast and encounters a complex background to achieve better tracking results. It is tested on an infrared small target data set, and the results show that the method in this paper reaches 80.50% average tracking accuracy. The image sequences in the data set include sky, sea, and buildings. Tracking video and original images are shown at https://github.com/AHUT507LAB/Infrared-dim-small-target-tracking.