Reviewerly/exHarmony huggingface dataset:
Simultaneous Clustering and Dynamic Feature Selection Methodology Based on Dimensional Isotropy Preservation

There are some challenging issues in clustering problems and clustering methodologies, which frequently attract researchers&#x2019; interests. Ability of handling high dimensional feature space is among them, since robustness of all machine learning algorithms is reduced by the concept of &#x201C;curse of dimensionality&#x201D;. In unsupervised machine learning literature, a general consensus can be seen on the clustering process: If the data is high dimensional, as a preprocessing step, first reduce the number of dimension using dimension reduction techniques, then cluster data. However, such a kind of cascaded way may cause information loss and redundant repeating operations, consequently reduce the validity of the clustering output and may yield low quality clustering schema and bring more computational burden. In the literature there is no methodology, which is based on classical statistical concept and capable of handling high dimensional data, except graph theory based ones. In that study; a hybrid approach is proposed for high dimensional data clustering which is mainly based on subspace clustering and projected clustering using a statistical concept: isotropic position. More precisely, proposed algorithm deals with clustering by simultaneously feature selection upon dimensional isotropic preservation. As a result, preprocessing step for dimensional reduction will not be required, when proposed methodology is preferred for the clustering process. Moreover, since the principal features are selected dynamically in the proposed methodology by not a holistic concern of the dataset; high quality clustering schema is expected, while low computational burden will be incurred. Experimental analysis of the proposed algorithm points out promising results for simultaneous clustering and dimension reduction.
A New Classifier by Remixing Features and Adjusting Logits for Long-Tailed Visual Data

The natural visual recognition for long-tailed distribution data is a difficult problem because the classifier is more biased towards the head classes, which often results in incorrect prediction. To solve this problem, we propose a simple one-stage network by remixing features and adjusting logits (RFAL) to handle long-tailed recognition. Our method enhances the expression ability of the tail class through the effective re-organization of deep features, and reassigns the label in favor of the tail class by providing a higher weight to the tail class. Extensive experiments on three long-tailed visual recognition datasets are conducted and the comparison of the proposed network with the existing state-of-the-art networks is made. The results show the better perfromance of the proposed network in long-tailed data recognition.
A Short Overview of Horizon Detection Methods Applied to Maritime Video Stabilization

The horizon feature in maritime scenes is generally defined as the boundary separating the sea region and non-sea region. Such a feature is important and has more than one application. Thus, our objective in this paper is to provide a brief review of eight techniques detecting the horizon with a particular interest in their relevance to the application of video stabilization in the maritime environment. We conclude some factors that are important in the development of a horizon detection algorithm for video stabilization.
Application of BERT Based on Entity-Aware in HICH Emergency Clinical Diagnosis

Emergency clinical diagnosis is an important part of medical diagnosis. The number of emergency doctors is small, and the time cost is high. While emergency clinical diagnosis is particularly important in HICH. Therefore, this paper proposes an application of BERT based on Entity-Aware in HICH emergency clinical diagnosis for improving the performance of automating HICH medical diagnosis. In our model, we establish an interpretability framework with designed multiple structures of electronic medical records (EMRs). Furthermore, in order to implement EMRs reasoning and ensure the accuracy of diagnostic results, weighted structure and entity-aware network are explored for entity-relationship feature re-processing. Finally, experimental results indicate that our proposed model outperforms other compared methods.
Dynamic path planning of UAV for three-dimensional moving target search in complex environment

In view of the fact that most of the current studies on path planning are limited to the fixed target or the static environment, a path planning method of unmanned aerial vehicle (UAV) for three-dimensional moving target search in complex environments is proposed. In this method, the interactive maneuvering model is used to predict the motion state of the target, and then using the idea of hierarchical planning for reference, the guidance point is generated by the weighted sparse A star algorithm, which is combined with the model predictive control algorithm. The motion state of the target can be estimated more accurately, and the UAV can respond in real time accordingly. By introducing the large step strategy in the stage of weighted sparse A star algorithm, not only the time cost of path planning can be reduced, but also the infeasible areas in complex environment can be avoided in real time. The simulation results show that this method has strong predictability for environmental change and target motion, so as to guide the UAV to respond in advance and get a better path while meeting the real-time requirements.
Revisiting audio visual scene-aware dialog

Audio Visual Scene-Aware Dialog (AVSD) has drawn intense interests, in which models are required to understand dynamic scenes in videos and dialog contexts in order to converse with human users by generating responses to given questions. Existing works have laid a solid foundation towards solving the AVSD problem. In contrast to previous studies, this paper empirically revisits the AVSD task and argues that this task exhibits a variety of biases in terms of models, dataset, and evaluation metrics: (1) as for the models, we believe that the state-of-the-art frameworks do not utilize multimodal features to their full extent; (2) as for the dataset, we conduct a deep analysis into dataset statistics from different types of questions and find that the dataset is slightly biased in several specific aspects; by simply implementing a caption-only baseline that has never seen the video, we achieve state-of-the-art performance on the AVSD task; (3) as for the evaluation metrics, we argue that the current metrics for AVSD primarily focus on the naturalness of generated responses while ignoring the truthfulness, which makes them fall short of disclosing the consistency of model predictions and the actual visual content. Overall, our analysis aims to provide a detailed inspection of the AVSD task and we hope that our empirical observations can inspire further improvement to the task.
The Study of Methods to Improve the Accuracy of Glycemic Control Based on Statistical Methods

Erroneous determination of blood glucose concentration in patients with diabetes can lead to errors in treatment. To prescribe the correct and non-traumatic treatment to the patient, you need to increase the accuracy of determining the concentration of glucose in the patient&#x0027;s blood. The authors of the article suggest the use of adaptive statistical methods to improve accuracy, which work well with a small number of repeated measurements under conditions of uncertainty of external factors. Such methods have proven themselves well in various fields of science and technology, where precise control of a large group of measurements is required. Such statistical methods have been well studied in terms of mathematics and applied in high-precision manufacturing. They allow you to adjust the required dose of the injected substance on the basis of several previous measurements in order to exclude insufficient or excessive administration. Both of these cases - an insufficient and excessive amount of the drug - lead to an incorrect glucose content in the patient&#x0027;s blood (hypo- and hyperglycemia), and have a bad effect on further treatment. To exclude such cases and improve the quality of treatment and the future life of patients with diabetes mellitus, a solution method using statistics is proposed.
Chaotic Lévy Whale Optimization Algorithm with Simulated Annealing and Differential Evolution

Typical Whale Optimization Algorithm (WOA) suffers from slow convergence, early trapping into local optima, and weak exploration ability. This work aims to solve these problems by combining Differential Evolution (DE), chaos theory, L&#x00E9;vy flight, and Simulated Annealing (SA). This work designs a Chaotic Differential Whale optimization based on Simulated annealing and Levy flight (CDWSL). CDWSL improves randomness of solutions, and reduces the possibility of falling into local optima. CDWSL is evaluated with ten typical functions, two composite ones and a real-world one of computation offloading in vehicular edge computing. In addition, experiments on higher- dimension problems are also conducted to evaluate the performance of CDWSL. The comparison of experimental results shows that CDWSL achieves superior performance over its typical state-of-the-art peers in solving both low-dimension and high- dimension simple benchmark problems. Furthermore, CDWSL yields better results for both composite benchmark functions and the real-world computation offloading problem than some typical algorithms.
An Editor Calculus With Undo/Redo

Structure editors provide many potential usability benefits to an end-user by allowing them to edit the AST representation of a program rather than a textual representation of it. In addition, they all but remove syntax errors by only allowing the constructing of programs that are syntactically valid. However, structure editors only rarely include undo/redo functionality into the editor itself, and to the best of our knowledge, an underlying, formal specification for undo/redo has yet to be developed. This paper continues previous work on an editor calculus; we extend the calculus with undo and redo and present a history-based operational semantics of the extension. The history used an underlying graph-based structure, containing a history of user actions in the particular structure editor. We study the expressive power of the calculus, give a simple proof of its Turing-power and use the expressiveness result to show how our history-based extension with undo and redo can be expressed in the original editor calculus.
Analysis of the Influence of Bound Constraint Handling Strategies on the Search Direction in Differential Evolution Algorithms

This paper is aiming to analyze the influence of the boundary constraints handling methods (BCHM) on the search direction in the case of Differential Evolution (DE) optimization algorithms. An experimental analysis for 8 correction methods was conducted, its main aim being the comparison between cosine similarities corresponding to the search direction computed before the application of BCHM and the search direction computed after the application of BCHM. The results of the analysis provide information about how much of the initial search direction is conserved by applying a correction method. Some preliminary theoretical results are also presented.
Power Equipment Defect Text Mining Based on New Word Discovery and Feature Fusion

With the intelligent development of power grid equipment operation and maintenance, how to effectively use a large number of defect text records has become an important issue. Since the text is complex unstructured data, it is difficult to effectively mine defect information. To solve this problem, the new word discovery method of solidification degree-degree of freedom is used in text preprocessing to extract the word features in the defective text; further, the word2vec word vector model is used to map the word features to a multi-dimensional vector space; finally based on feature fusion Constructed an attention mechanism to optimize the convolutional neural network defect text classification model. The analysis of the calculation example makes a comprehensive comparison and analysis of the attention mechanism optimized convolutional neural network based on new word discovery and feature fusion and the traditional neural network model. The proposed method has better semantic learning ability than the traditional deep learning method and can improve the classification accuracy, which is conducive to fully mining the defect text information.
High Available and Secured Data Center Network Architecture for One-Stop E-Agency Web Portal

One-stop e-agency refers to the integration of agencies' services and the interoperability of government agencies. Currently, the online services offered by Nigerian government agencies have two major setbacks which are: data duplication and lack of interoperability within government agencies that offer similar services. The aim of this research is to come up with the design of a high available and secured data center network architecture for a one-stop e-agency data center to solve the above-mentioned challenges.
Shape Feature Based Multi-class Classification Approach towards Odia Characters employing Extreme Learning Machine

Character recognition of Odia alphabets using computer-aided techniques has become a challenging research issue due to its complexity. Odia is recognised as one of the classical languages. Though various image processing methods have been used for classification or Odia character recognition but still there is scope for improvement. The multi-class classification demands the implementation of an elevated constructive learning algorithm. In this paper, we have proposed a conjunctive approach of shape-based feature extraction and Extreme Learning Machine (ELM) to classify the Odia alphabets. The proposed method is implemented over 1500 Odia alphabet images comprising of 52 classes. ELM brings an integrated learning domain with extensive feature transformation which will act as a catalyst for effective fulfillment of classification purposes in the multi class domain. ELM based technique is tested for different activation functions and the output result shows the effectiveness of ELM classifier over traditional Naive Bayes and support vector machine (SVM) classifier. The ELM based technique gives more promising results in comparison with the above two classifiers for the multi class handwritten Odia alphabet classification.
Automatic Recognition of Indian Sign Language using Image Processing and Computer Vision

The main objective of this work is to develop an intelligent system which will be able to take hand gestures (sign language), recognize and interpret it automatically using image processing and computer vision techniques. Several techniques are available in literature, however, to improve the accuracy of the existing recognition system, an unique method has been adopted here that uses Histogram of Gradient based algorithm for feature extraction &#x0026; SVM based feature classifier. It is seen that the average recognition rate is as high as 96.3 &#x0025; for 10 alphabets (A-J) and 95.05&#x0025; for 10 numerals (0&#x2013;9) using this technique. The proposed system has been developed in Python-3.6 and OpenCV 3.0 platform.
A Novel Approach for Solving Light Reflections on the Blackboard by Machine Vision Algorithm

As the light reflection problem keeps bothering students around the world, we thought of a novel approach to solve the problem through a blackboard that can rotate itself. In this project, mechanical design and embedded technology are used. And used 3D printing to process some parts. Verified the performance of the equipment through experiments. This can prevent the light reflections from interrupting the students.
Toward a Comprehensive Framework of Social Media Analytics

Social media is rapidly becoming an essential platform for social connectivity and information diffusion leading to more efficient service operations. Despite the emergent interest in studying the online behavior of social media users from different viewpoints, such as Sentiment Analysis, Topic Modeling, Emotion Mining, and Social Network Analysis, a gap remains in utilizing all such methods concurrently toward building a comprehensive analytical model. In this chapter, the mentioned aspects are combined to propose a practical analytics framework for gaining more actionable insights from social media content. In doing so, the framework is developed based on a series of machine learning and data analysis algorithms along with the required ETL modules. To evaluate the performance, our framework is exposed to a real social media dataset comprising around 600,000 records. The high level of compatibility between the actionable insights generated by designated machine learning algorithms and actual events that occurred in the same period reveals the merit of the proposed approach in processing the social media datasets. Having the ability to get embedded in big data clusters, the proposed analytics engine can be utilized in analyzing the large social media datasets through big data analytics solutions.
Understanding Evolutionary Based Genetic Algorithm and Implementation using R

Genetic Algorithm is a search-based optimization algorithm which is developed in the late 70s and the concept is purely based on Darwin's theory of natural evolution. The core principle is that the fittest would survive, and they would be selected for reproducing the next generation. It works on random-based selection processes and can solve complex and non-linear problems [1]. Recently, we have seen many use cases of GA in AI, VLSI design, Computer vision, and medical science. In this paper, we study the Genetic Algorithm (GA) and implement it in R with an optimization problem.
Sorted Uniform Local Binary Patterns

Sorted Uniform LBP (SULBP) is a rotation invariant Local Binary Patterns (LBP) variant that is proposed for leaf image identification. This method is simple and effective, and is shown to outperform other LBP variants in leaf recognition. However, comparative analysis of this method in terms of texture classification is limited. In this paper, we present our extensive comparative analysis of SULBP. Additionally, we have explored different parameters of this system and their effect on the performance. Finally, we have used multiple datasets and classifiers to show the validity of these experiments.
Research on Named Entity Recognition Based on ELECTRA and Intelligent Face Image Processing

Aiming at the problem that the corpus of drug-related fields is not rich and the relevant information of drug-related personnel is insufficient, this paper constructs a 600,000-word-scale drug-related text data set, and proposes a named entity recognition method for drug-related personnel based on ELECTRA-BiLSTM-CRF. First input the labeled text into the ELECTRA pre-training language model to obtain a word vector with better semantic representation; then input the trained word vector into the bidirectional long short-term memory (BiLSTM) network to extract the context feature; finally, the best predicted label sequence is obtained through the conditional random field(CRF). The performance of this model was evaluated on the drug-related text data set. The experimental results showed that the F1 value of the ELECTRA-BiLSTM-CRF model reached 94&#x0025;, which was better than the BERT-BiLSTM-CRF, BERT-CRF, and BiLSTM-CRF models, which proved this model has a good effect on the named entity recognition of drug-related personnel.
Examining Embodied Sensation and Perception in Singing

This paper introduces my PhD research on the relationship which vocalists have with their voice. The voice, both instrument and body, provides a unique perspective to examine embodied practice. The interaction with the voice is largely without a physical interface and it is difficult to describe the sensation of singing; however, voice pedagogy has been successful at using metaphor to communicate sensory experience between student and teacher. I examine the voice through several different perspectives, including experiential, physiological, and communicative interactions, and explore how we convey sensations in voice pedagogy and how perception of the body is shaped through experience living in it. Further, through externalising internal movement using sonified surface electromyography, I aim to give presence to aspects of vocal movement which have become subconscious or automatic. The findings of this PhD will provide understanding of how we perceive the experience of living within the body and perform through using the body as an instrument.
Face Recognition Using Convolutional Neural Network Architectures on Mask-Occluded Face Images

In epidemic situations such as the novel coronavirus disease (COVID-19) pandemic that spreads through physical contact, security and presence systems that previously used fingerprints-based or were contact-based are no longer safe for users. Compared to other popular biometrics such as fingerprints, irises, palms, and veins, the face has much better potential to recognize identity in a nonintrusive manner. Therefore, this study will employ two convolutional neural network (CNN) architectures, LeNet-5 and MobileNetV2, for face recognition on mask-occluded face images. Data were taken from 12 subjects face-to-face were preprocessed by cropping, artificial mask augmentation, resizing, and image augmentation. The model was trained with the configured hyperparameter for 50 epochs with a 60:40 data split. Model testing was performed using image data without augmentation wearing a mask. The test results are measured with classification accuracy for 12 classes. The highest testing accuracy on LeNet-5 models is 98.15%, with <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$64\times 64$</tex> input size and 64 batch size. Meanwhile, the highest testing accuracy for MobileNetV2 is 97.22% with input size <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$96\times 96$</tex> , batch size 16, and the weight of the MobileNetV2 model initialized with ImageNet <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$96\times 96$</tex> .
Multi-scale residual network-based image restoration

Abstract All Image restoration technology has undergone extensive research. For image degradation, inverse filtering was proposed in the last century. Then Helstrom proposed a Wiener filtering algorithm. In recent years, the convolutional neural network has promoted the further development of image restoration technology. The coarse-to-fine multi-scale residual network Multiscale Deblur Net (MDN) used in this paper is stable in operation, simple in structure, and easy to train. It has a good deblurring ability for motion-blurred images by testing in the GO_PRO dataset.