Reviewerly/exHarmony huggingface dataset:
Research and Optimization of D-Start Lite Algorithm in Track Planning

With the widespread application of the aircraft, how to achieve the safety and efficiency of the aircraft during flight has attracted widespread attention.The track planned by the traditional D-Start Lite algorithm is very close to the obstacle.Due to the large size of the aircraft itself, it is very likely that the aircraft will collide with the obstacle when operating in a real environment.However, the existing D-Start Lite algorithm is only a single solution to the safety problem of trajectory planning or to improve the efficiency of the algorithm's trajectory search.Therefore, this article optimizes the existing D-Start Lite algorithm.First, this article adopts the strategy of selecting priority level by child nodes.The child nodes in the vertical direction are selected as the first-level nodes.The state of the first-level node determines whether the secondlevel node on the diagonal needs to be expanded, so that a safe distance is generated between the planned path and the obstacle, thus improving the safety of the path.However, a certain space is reserved between the track and the obstacle, which results in the increase of the number of nodes in the path and the low efficiency of search.This article improves and optimizes the valuation function and heuristic function in the algorithm.Therefore, the number of expansion nodes and the number of inflection points are reduced, and the search efficiency of the algorithm is improved.Therefore, the improved algorithm improves the efficiency of trajectory search while ensuring the safety of trajectory planning.And the effectiveness and feasibility of the algorithm are proved by the MATLAB platform.
Combined Text Mining: Fuzzy Clustering for Opinion Mining on the Traditional Culture Arts Work

The Indonesian government is currently intensifying work programs in the field of traditional arts and culture.In order to realize the promotion of the country's culture, the government has enacted a law on cultural promotion.One indicator of the achievement of the promotion of culture, among others, with the collection of data on traditional culture, the data mapping and data inventory can be processed into information and knowledge.In this research, indicators of performance indicators were compiled from connoisseurs of traditional works of art using data in the city of Malang, East Java, Indonesia.The results of the audience's opinion on cultural offerings can be used as a benchmark for the success of the promotion of traditional culture.When the culture is explored and tried to be displayed again, it is important to know the audience's satisfaction and understanding of the display that has just been witnessed.The results of the description of respondents in the form of opinions on the artwork will be collected as data processed using Text Mining with the Clustering of Fuzzy C-Means method to determine the audience's opinion about Feeling , which is related to feelings when viewing the beauty of the artwork, Value is related to the assessment to an art work that can be in the form of art weights contained in the work of art, and Emphasizing , which is related to empathy or respect for the art world, including professions related to the world such as dancers, musicians and others.The results achieved from this study show that has good performance on the proposed method.This can be known from the results of data testing using cluster variance V = 0.00000217901.Based on these values it can be concluded that the value of all cluster variants is good.
Improved Weighted Random Forest for Classification Problems

Several studies have shown that combining machine learning models in an appropriate way will introduce improvements in the individual predictions made by the base models. The key in making well-performing ensemble model is in the diversity of the base models. Of the most common solutions for introducing diversity into the decision trees are bagging and random forest. Bagging enhances the diversity by sampling with replacement and generating many training data sets, while random forest adds selecting random number of features as well. This has made random forest a winning candidate for many machine learning applications. However, assuming equal weights for all base decision trees does not seem reasonable as the randomization of sampling and input feature selection may lead to different levels of decision-making abilities across base decision trees. Therefore, we propose several algorithms that intend to modify the weighting strategy of regular random forest and consequently make better predictions. The designed weighting frameworks include optimal weighted random forest based on accuracy, optimal weighted random forest based on area under the curve (AUC), performance-based weighted random forest, and several stacking-based weighted random forest models. The numerical results show that the proposed models are able to introduce significant improvements compared to regular random forest.
A Hybrid Deep Learning Model for Arabic Text Recognition

Arabic text recognition is a challenging task because of the cursive nature of Arabic writing system, its joint writing scheme, the large number of ligatures and many other challenges.Deep Learning (DL) models achieved significant progress in numerous domains including computer vision and sequence modelling.This paper presents a model that can recognize Arabic text that was printed using multiple font types including fonts that mimic Arabic handwritten scripts.The proposed model employs a hybrid DL network that can recognize Arabic printed text without the need for character segmentation.The model was tested on a custom dataset comprised of over two million word samples that were generated using (18) different Arabic font types.The objective of the testing process was to assess the model's capability in recognizing a diverse set of Arabic fonts representing a varied cursive styles.The model achieved good results in recognizing characters and words and it also achieved promising results in recognizing characters when it was tested on unseen data.The prepared model, the custom datasets and the toolkit for generating similar datasets are made publically available, these tools can be used to prepare models for recognizing other font types as well as to further extend and enhance the performance of the proposed model.
How can Distance Learning be Used in the Physical Education of Students?

Russian universities switched to distance students learning for before the global pandemic threat. Physical education departments are also required to provide students with distance learning opportunities. The organization of high-quality distance learning in physical education of students is not an easy task for most Russian universities. The purpose of the research - analysis of the level of effectiveness of using distance learning methods in the physical education of students in self-isolation during the COVID-19. Effectiveness criteria of distance learning: the quantity, quality and speed of completion of distance tasks. The research participants - 347 students. The research period - 30 days. It was revealed that medical students have a significant (Р&lt;0.05) advantage in the speed and number of completed distance tasks. Humanities and technical students have lower scores. The completed tasks quality is evaluated moderate scores. Can be explained moderate and low scores by the lack of students’ motivation to perform distance learning tasks. Dissatisfaction of the majority of students with a large academic workload and lack of motor training sessions was revealed. Improving the quality of distance learning of physical education of students is necessary. Students want to reduce the number of theoretical tasks and more motor training sessions use.
Properties of the stochastic approximation EM algorithm with mini-batch sampling

To deal with very large datasets a mini-batch version of the Monte Carlo Markov Chain Stochastic Approximation Expectation-Maximization algorithm for general latent variable models is proposed. For exponential models the algorithm is shown to be convergent under classicalconditions as the number of iterations increases. Numerical experiments illustrate the performance of the mini-batch algorithm in various models.In particular, we highlight that mini-batch sampling results in an important speed-up of the convergence of the sequence of estimators generated by the algorithm. Moreover, insights on the effect of the mini-batch size on the limit distribution are presented. Finally, we illustrate how to use mini-batch sampling in practice to improve results when a constraint on the computing time is given.
Understanding Objects in Video: Object-Oriented Video Captioning via Structured Trajectory and Adversarial Learning

Traditional video captioning requests a holistic description of the video, yet the detailed descriptions of the specific objects may not be available. Besides, most methods adopt frame-level inter-tangled features among objects and ambiguous descriptions during training, which is difficult for learning the vision-language relationships. Without identifying the classes and locations of separate objects, neither associating the transition trajectories among the objects, these data-driven image-based video captioning methods cannot reason the activities with visual features only, not to mention performing well under small-samples. We propose a novel task, namely the object-oriented video captioning, which focuses on understanding the videos in object-level. We re-annotate the object-oriented video captioning dataset (Object-Oriented Captions) with object-sentence pairs to facilitate more effective cross-modal learning. Thereafter, we design the video-based structured trajectory network via adversarial learning (STraNet) to effectively analyze the activities along the time domain and proactively capture the vision-language connections under small datasets. The proposed STraNet consists of four components: the structured trajectory representation, the attribute explorer, the attribute-enhanced caption generator, and the adversarial discriminator. The high-level structured trajectory representation provides useful supplement over previous image-based approaches, allowing to reason the activities from the temporal evolution of visual features and the dynamic movement of spatial locations. The attribute explorer helps to capture discriminative features among different objects, with which the subsequent caption generator can generate more informative and accurate descriptions. Finally, by adding an adversarial discriminator on the caption generation task, we can improve learning the inter-relationships between the visual contents and the corresponding visual words. To demonstrate the effectiveness, we evaluate the proposed method on the new dataset and compare it with the state-of-the-arts for video captioning. From the experimental results, the STraNet exhibits the ability of precisely describing the concurrent objects and their activities in details.
Detection of Potentially Compromised Computer Nodes and Clusters Connected on a Smart Grid, Using Power Consumption Data

Monitoring what application or type of applications running on a computer or a cluster without violating the privacy of the users can be challenging, especially when we may not have operator access to these devices, or specialized software. Smart grids and Internet of things (IoT) devices can provide power consumption data of connected individual devices or groups. This research will attempt to provide insides on what applications are running based on the power consumption of the machines and clusters. It is therefore assumed that there is a correlation between electric power and what software application is running. Additionally, it is believed that it is possible to create power consumption profiles for various software applications and even normal and abnormal behavior (e.g., a virus). In order to achieve this, an experiment was organized for the purpose of collecting 48 h of continuous real power consumption data from two PCs that were part of a university computer lab. That included collecting data with a one-second sample period, during class as well as idle time from each machine and their cluster. During the second half of the recording period, one of the machines was infected with a custom-made virus, allowing comparison between power consumption data before and after infection. The data were analyzed using different approaches: descriptive analysis, F-Test of two samples of variance, two-way analysis of variance (ANOVA) and autoregressive integrated moving average (ARIMA). The results show that it is possible to detect what type of application is running and if an individual machine or its cluster are infected. Additionally, we can conclude if the lab is used or not, making this research an ideal management tool for administrators.
A Web Shell Detection Method Based on Multiview Feature Fusion

Web shell is a malicious script file that can harm web servers. Web shell is often used by intruders to perform a series of malicious operations on website servers, such as privilege escalation and sensitive information leakage. Existing web shell detection methods have some shortcomings, such as viewing a single network traffic behavior, using simple signature comparisons, and adopting easily bypassed regex matches. In view of the above deficiencies, a web shell detection method based on multiview feature fusion is proposed based on the PHP language web shell. Firstly, lexical features, syntactic features, and abstract features that can effectively represent the internal meaning of web shells from multiple levels are integrated and extracted. Secondly, the Fisher score is utilized to rank and filter the most representative features, according to the importance of each feature. Finally, an optimized support vector machine (SVM) is used to establish a model that can effectively distinguish between web shell and normal script. In large-scale experiments, the final classification accuracy of the model on 1056 web shells and 1056 benign web scripts reached 92.18%. The results also surpassed well-known web shell detection tools such as VirusTotal, ClamAV, LOKI, and CloudWalker, as well as the state-of-the-art web shell detectionmethods.
Cooperative Pathfinding Based on Memory-Efficient Multi-Agent RRT*

In cooperative pathfinding problems, non-conflict paths that bring several agents from their start location to their destination need to be planned. This problem can be efficiently solved by Multi-agent RRT*(MA-RRT*) algorithm, which is still state-of-the-art in the field of coupled methods. However, the implementation of this algorithm is hindered in systems with limited memory because the number of nodes in the tree of RRT* grows indefinitely as the paths get optimized. This paper proposes an improved version of MA-RRT*, called Multi-agent RRT* Fixed Node(MA-RRT*FN), which limits the number of nodes stored in the tree of RRT* by removing the weak nodes on the path which are not likely to reach the goal. The results show that MA-RRT*FN performs close to MA-RRT* in terms of scalability and solution quality while the memory required is much lower and fixed.
Human Segmentation Based on Compressed Deep Convolutional Neural Network

Most semantic segmentation models based on deep convolutional neural network (CNN) typically require a large number of weight parameters, high hardware resources for storage and computation. Moreover, redesigning a compact network suffers from some training problems, such as under-fitting. A human segmentation algorithm is proposed based on compressed deep CNN to optimize the convolution layers and filters. PSPNet-50 is fine-tuned on the human segmentation dataset to obtain the human segmentation model with higher accuracy. Then the convolutional-layer level pruning and corresponding structure optimization are performed so that the parameters of the model are substantially reduced. Finally, the two-stage global filter-level pruning strategy is used. Compared with the method of layer by layer pruning and retraining, our strategy not only reduces parameters of the model and saves the time of retraining, but also keeps the high IoU (Intersection over Union) accuracy. In addition, by adding auxiliary losses in the network during training CNN, the supervised training of the network is improved, and IoU is further increased. Compared to the model before compression, the sufficient experiments show that the parameter number, computation cost, memory consumption, and parameter storage are decreased by 1/7.5, 5.6/6.6, 0.7/1, 6.5/7.5, respectively, while the segmentation speed is accelerated by 2.4 times, and IoU on test set reaches 93.2%.
Fuzzy Protoform for Hyperactive Behaviour Detection Based on Commercial Devices

Hyperactive behaviour refers to a person making more movement than expected for his or her age and development, acting impulsively, and being easily distracted. There is a need to encourage early and reliable detection through the proposal of new methodologies and systems in the context of hyperactive behaviour to prevent or lessen related problems and disorders. This paper presents a methodology to compute a fuzzy protoform (a linguistic description) as an estimator for hyperactive behaviour. The proposed methodology is developed in a system called Smart HyBeDe, which integrate non-invasive and commercial wearable devices, such as activity bracelets, in order to capture data streams from inertial measurement units and optical heart rate sensors. The generated data by the wearable device are synchronized with a mobile device to process the fuzzy protoform to inform family members and professionals. Three datasets generated by the wearable device in real contexts are presented. These datasets are used to evaluate the impact of wrist choice for the wearable device, multiple fuzzy temporal windows, different aggregation operators, and relevant linguistic terms to define the fuzzy protoform as an estimator for the hyperactive behaviour. The results, analysed by a hyperactive behaviour expert, show that the proposed protoform is a suitable hyperactive behaviour estimator.
Lung Cancer Detection System Using Image Processing and Machine Learning Techniques

In recent time the Lungs Cancer diseases are increases widely, the human body made up of with diverse fundamental organs, one of those is Lung, The Lungs are of two section right lung and left lung and function of lungs are to trade of gas which we called as breathing or respiration.The present modern lifestyle, ecological contamination is colossally expanding the Human Lungs issue.What's more, correspondingly there are various Image processing techniques are concocting the tremendous answer for the Medical field to identify and analyze the Lungs illnesses.This article is contemplating and exploring few of those Image Processing techniques, The Computer Aided Diagnosis (CAD) frameworks requires the preprocessing and highlight extraction from X-rays, Computer Tomography (CT) scan to analyze the Lungs disorders in human body, this paper is bringing the emerging image processing and machine learning techniques for implementing purpose, and by comparing the diverse classification techniques will assist to improve the accuracy in lung cancer detection system using robust segmentation and classification techniques.
Predicting Crypto Currency Prices Using Machine Learning and Deep Learning Techniques

In the past eight years of Bitcoin's history, the economy has seen the price of Bitcoin rapidly grow due to its promising outlook on the future for crypto currencies.Investors have taken note of several advantages Bitcoin provides over the traditional banking system.One such trait is that Bitcoin allows for decentralized banking, meaning that Bitcoin cannot be regulated by powerful banks.There is also a market cap of 21 million Bitcoins that can be in circulation, therefore a surplus of Bitcoins cannot be "printed" which would result in inflation.Bitcoin resolves the issue with transaction security by using a block chain, or a ledger, which records the history of every transaction ever made into one long hexadecimal "chain" of anonymous transactions, which keeps transaction history transparent, but also confidential.Bitcoin as a result has become a very bullish investing opportunity, and due to the huge volatility of the Bitcoin market price, this paper attempts to aid in investment decision making by providing Bitcoin market price prediction.Our team explored several Machine Learning algorithms by using Supervised Learning to train a prediction model and provide informative analysis of future market prices.We start with Linear Regression models, and train on several important features, then We proceed with the implementation of Recurrent Neural Networks (RNN) with Long Short Term Memory (LSTM) cells.All code is written in Python using Google's Tensor Flow software library.We show that the price of Bitcoin can be predicted with Machine Learning with high degree of accuracy.
Converting Text to Numerical RepresentationusingModified Bayesian Vectorization Technique for Multi-Class Classification

The first step towards making the text documents machine-readable is vectorization.Vectorisation allows the machines to understand textual content by transforming it into meaningful numerical representations.This study proposes a modified Bayesian vectorization and employing the Laplace smoothing method to reduce the dimensionality of features and improve the classification accuracy.Dataset of news articles was used in building the model and was evaluated across the metrics of precision, recall, F1-score, and accuracy.To validate the effectiveness of the enhancement, the model was compared to the Term Frequency and Inverse Document Frequency (TF-IDF) method.The results revealed that the proposed enhancement has significantly better results having 98% classification accuracy compared to 81% classification accuracy of the TF-IDF vectorization technique.
Automatic Recognition of Palaeobios Images Under Microscope Based on Machine Learning

The research of paleontology is an essential part of contemporary earth science. However, the time-consuming manual identification process has always been cumbrous in the field of paleontology. Since conventional algorithms have limited efficiency in processing images of complicated paleontological fossils. In this work, a combinational machine learning method, which comprises appropriate image preprocessing, Scale-invariant feature transform (SIFT), K-means clustering (K-means), and Support Vector Machine (SVM) are applied to realize automatic recognition of paleontological images under microscope. It is demonstrated that this combined algorithm has superior performance in morphological feature extraction in the case of complex mineral textures. With this technique, the overall average accuracy of image recognition is 84.5%, which significantly improved the efficiency of sample analysis in the field of paleontology.