Reviewerly/exHarmony huggingface dataset:
Research on SBMPC Algorithm for Path Planning of Rescue and Detection Robot

This research aims to improve autonomous navigation of coal mine rescue and detection robot, eliminate the danger for rescuers, and enhance the security of rescue work. The concept of model predictive control is introduced into path planning of rescue and detection robot in this paper. Sampling-Based Model Predictive Control (SBMPC) algorithm is proposed basing on the construction of cost function and predictive kinematics model. Firstly, input sampling is conducted in control variable space of robot motion in order to generate candidate path planning solutions. Then, robot attitude and position in future time, which are regarded as output variables of robot motion, can be calculated through predictive kinematics model and input sampling data. The optimum solution of path planning is obtained from candidate solutions through continuous moving optimization of the defined cost function. The effects of the three sampling methods (viz., uniform sampling, Halton’s sampling, and CVT sampling) on path planning performance are compared in simulations. Statistical analysis demonstrates that CVT sampling has the most uniform coverage in two-dimensional plane when sample amount is the same for three methods. Simulation results show that SBMPC algorithm is effective and feasible to plan a secure route for rescue and detection robot under complex environment.
Effects of Mobile Augmented Reality and Self-Regulated Learning on Students’ Concept Understanding

This study examines the effect of mobile augmented reality and self-regulated learning on students' concept understanding. This is a quasi-research design comprising of a pre and posttest non-equivalent control group. Data were collected from 91 students divided into two groups. The self-regulated learning instrument uses a four-point Likert scale and 20 multiple-choice test items to understand the concepts. The results showed that mobile augmented reality and high self-regulated learning abilities had a better understanding concept when compared to the use of textbooks
Effects of STEAMification Model in Flipped Classroom Learning Environment on Creative Thinking and Creative Innovation

This paper is a report on the findings from the research and development of STEAMification model in flipped classroom learning environment to enhance creative thinking and creative innovation. The purposes of this study are: 1) to develop the model; 2) to study the developed model. Results exhibited that: 1) the model consists of 6 components, 2) the students who had studied through STEAMification had higher creativity (Mean = 14.53, S.D. = 0.94) than the students who studied through normal model (Mean = 12.90, S.D. = 1.91), 3) the students who had studied through model had higher quality of creative innovation (Mean = 14.86, S.D. = 1.62) than the students who studied through normal model (Mean = 12.90, S.D. = 1.91).
Using Interval Manager Mobile Application in Saving Time and Cost

The Interval Manager (INTMAN -Mobile Workforce) is a mobile-assisted Utility that replaces the traditional paper-and-pencil strategies to gather fixed c language time-sampled observational information.An initial application that compares INTMAN -Mobile Workforce with the traditional method of paper-and-pencil was presented in this article.Smart Devices were used to run INTMAN -Mobile Workforce data collection application while a Microsoft Windows run using desktop was used to analyse the data.Changed frequencies, durations percent, conditional probabilities, and kappa settlement matrices and values were the analyses carried out.the contrast among INTMAN -Mobile Workforce and the traditional paper-and-pencil technique was primarily based on 5 dimensions: setup time, statistics entry duration, length of inter-observer agreement calculations, accuracy, and price.the outcomes show the efficiency and accurateness of the computer-assisted data collection system over the conventional technique for Time-sampled records.
Lexicon‐pointed hybrid N‐gram Features Extraction Model (<scp>LeNFEM</scp>) for sentence level sentiment analysis

Abstract Sentiment analysis of social media textual posts can provide information and knowledge that is applicable in social settings, business intelligence, evaluation of citizens' opinions in governance, and in mood triggered devices in the Internet of Things. Feature extraction and selection is a key determinant of accuracy and computational cost of machine learning models for such analysis. Most feature extraction and selection techniques utilize bag of words, N‐grams, and frequency‐based algorithms especially Term Frequency‐Inverse Document Frequency. However, these approaches do not consider relationships between words, they ignore words' characteristics and they suffer high feature dimensionality. In this paper we propose and evaluate a feature extraction and selection approach that utilizes a fixed hybrid N‐gram window for feature extraction and minimum redundancy maximum relevance feature selection algorithm for sentence level sentiment analysis. The approach improves the existing features extraction techniques, specifically the N‐gram by generating a hybrid vector from words, Part of Speech (POS) tags, and word semantic orientation. The vector is extracted by using a static trigram window identified by a lexicon where a sentiment word appears in a sentence. A blend of the words, POS tags, and the sentiment orientations of the static trigram are used to build the feature vector. The optimal features from the vector are then selected using minimum redundancy maximum relevance (MRMR) algorithm. Experiments were carried out using the public Yelp dataset to compare the performance of the proposed model and existing feature extraction models (BOW, normal N‐grams and lexicon‐based bag of words semantic orientations). Using supervised machine learning classifiers the experimental results showed that the proposed model had the highest F‐measure (88.64%) compared to the highest (83.55%) from baseline approaches. Wilcoxon test carried out ascertained that the proposed approach performed significantly better than the baseline approaches. Comparative performance analysis with other datasets further affirmed that the proposed approach is generalizable.
An Efficient and Effective Model to Handle Missing Data in Classification

Missing data is one of the most important causes in reduction of classification accuracy. Many real datasets suffer from missing values, especially in medical sciences. Imputation is a common way to deal with incomplete datasets. There are various imputation methods that can be applied, and the choice of the best method depends on the dataset conditions such as sample size, missing percent, and missing mechanism. Therefore, the better solution is to classify incomplete datasets without imputation and without any loss of information. The structure of the “Bayesian additive regression trees” (BART) model is improved with the “Missingness Incorporated in Attributes” approach to solve its inefficiency in handling the missingness problem. Implementation of MIA-within-BART is named “BART.m”. As the abilities of BART.m are not investigated in classification of incomplete datasets, this simulation-based study aimed to provide such resource. The results indicate that BART.m can be used even for datasets with 90 missing present and more importantly, it diagnoses the irrelevant variables and removes them by its own. BART.m outperforms common models for classification with incomplete data, according to accuracy and computational time. Based on the revealed properties, it can be said that BART.m is a high accuracy model in classification of incomplete datasets which avoids any assumptions and preprocess steps.
Exploring Question-Specific Rewards for Generating Deep Questions

Recent question generation (QG) approaches often utilize the sequence-to-sequence framework (Seq2Seq) to optimize the log-likelihood of ground-truth questions using teacher forcing.However, this training objective is inconsistent with actual question quality, which is often reflected by certain global properties such as whether the question can be answered by the document.As such, we directly optimize for QG-specific objectives via reinforcement learning to improve question quality.We design three different rewards that target to improve the fluency, relevance, and answerability of generated questions.We conduct both automatic and human evaluations in addition to a thorough analysis to explore the effect of each QG-specific reward.We find that optimizing question-specific rewards generally leads to better performance in automatic evaluation metrics.However, only the rewards that correlate well with human judgement (e.g., relevance) lead to real improvement in question quality.Optimizing for the others, especially answerability, introduces incorrect bias to the model, resulting in poor question quality.
Scaling up Differentially Private Deep Learning with Fast Per-Example Gradient Clipping

Abstract Recent work on Renyi Differential Privacy has shown the feasibility of applying differential privacy to deep learning tasks. Despite their promise, however, differentially private deep networks often lag far behind their non-private counterparts in accuracy, showing the need for more research in model architectures, optimizers, etc. One of the barriers to this expanded research is the training time — often orders of magnitude larger than training non-private networks. The reason for this slowdown is a crucial privacy-related step called “per-example gradient clipping” whose naive implementation undoes the benefits of batch training with GPUs. By analyzing the back-propagation equations we derive new methods for per-example gradient clipping that are compatible with auto-differeniation (e.g., in Py-Torch and TensorFlow) and provide better GPU utilization. Our implementation in PyTorch showed significant training speed-ups (by factors of 54x - 94x for training various models with batch sizes of 128). These techniques work for a variety of architectural choices including convolutional layers, recurrent networks, attention, residual blocks, etc.
The Sustainable Scale of Earthquake Awareness, Development, Validity and Reliability Study

The study group of this research, which is planned to develop the Sustainable Scale of Earthquake Awareness, consists of the students studying at Duzce University in the academic year 2018-2019. 276 volunteer individuals (213 females, 63 males) participated to the research group determined by random sampling method. For confirmatory factor analysis, 434 female and 129 male volunteer students participated. 839 students, 647 of them were females (77,12%) and 192 of them were males (22,88%), supported the study. As a result of the exploratory factor analysis which is completed after testing the suitability of the data for analysis, the scale was constituted of three sub-factors consisting of 22 items. The factors that make up the scale explain 46,393% of the variance. The sub-factors of the scale were named based on the knowledge about the earthquake. The suitability of the obtained model was 0.072 for RMSEA; 0.90 for NFI, 0,89 for GFI, 0.063 for RMR and 0.86 for AGFI. As a result of the reliability analysis, the internal consistency coefficient (Cronbach alpha) of the scale was determined as 0.884. It is thought that this developed scale will provide support as a tool for educational purposes. It is recommended to carry out different researches that can help to investigate the awareness level about the earthquake with different variables.
Dynamic Siamese Network With Adaptive Kalman Filter for Object Tracking in Complex Scenes

Due to the deficiency of prior information for online updating process, the tracking accuracy of fully-convolutional Siamese network (SiamFC) in complex scenes such as similar object interference, fast moving, and appearance change is not good.To solve the problem, a new object tracker based on a dynamic template updating strategy and the re-location mechanism based on the adaptive Kalman is proposed.To suppress the object interference and overcome the instability of fast-moving object tracking, an adaptive Kalman filter method is designed to change the selection method of search region and select the bounding box of the object closest to the predicted position.For the adaptation of appearance change, the highconfidence tracking results are fused with the initial template to dynamic update the template.Compared with the traditional Kalman filter, the expectation of residual error for the adaptive Kalman filter method can be controlled in a low range by the adjustment of the gain online.The introduction of the adaptive Kalman based re-location mechanism improves the discriminative ability of SiamFC in interference scene.With the dynamic template updating strategy, the tracker obtains strong generalization capability to adapt to the appearance change of the tracking target.It is demonstrated that the proposed method achieves competitive performance on OTB, VOT and TC128 datasets compared with other state-of-the-art trackers and can be run in real time (43fps).
Warm Starting CMA-ES for Hyperparameter Optimization

Hyperparameter optimization (HPO), formulated as black-box optimization (BBO), is recognized as essential for automation and high performance of machine learning approaches. The CMA-ES is a promising BBO approach with a high degree of parallelism, and has been applied to HPO tasks, often under parallel implementation, and shown superior performance to other approaches including Bayesian optimization (BO). However, if the budget of hyperparameter evaluations is severely limited, which is often the case for end users who do not deserve parallel computing, the CMA-ES exhausts the budget without improving the performance due to its long adaptation phase, resulting in being outperformed by BO approaches. To address this issue, we propose to transfer prior knowledge on similar HPO tasks through the initialization of the CMA-ES, leading to significantly shortening the adaptation time. The knowledge transfer is designed based on the novel definition of task similarity, with which the correlation of the performance of the proposed approach is confirmed on synthetic problems. The proposed warm starting CMA-ES, called WS-CMA-ES, is applied to different HPO tasks where some prior knowledge is available, showing its superior performance over the original CMA-ES as well as BO approaches with or without using the prior knowledge.
Algorithm for constructing logical operations to identify patterns in data

Neural networks have proven themselves in solving problems when the input and output data are known, but the cause and effect relationship between them is not obvious. A well-trained neural network will find the right answer to a given request, but will not give any idea about the rules that form this data. The paper proposes an algorithm for constructing logical operations, in terms of multi-valued logic, to identify hidden patterns in poorly formalized areas of knowledge. As the basic elements are considered many functions of the multi-valued logic of generalized addition and multiplication. The combination of these functions makes it possible to detect relationships in the data under study, as well as the ability to correct the results of neural networks. The proposed approach was considered for classification problems, in the case of multidimensional discrete features, where each feature can take k-different values and is equivalent in importance to class identification.
The Effect of Learning Experience on the Information Literacy of Students in the Ri-Png Border During Covid-19 Period

The spread of the COVID-19 pandemic presents challenges for higher education institutions, especially in Indonesia's border areas. The transformation of face-to-face learning models to distance learning models impacts the learning experience of students, especially those who are completing writing research proposals and theses that require adequate information literacy skills. This study aims to explore the effect of student learning experiences on information literacy skills. The research data were obtained from respondents from four universities in Merauke. Purposive sampling technique is used with the criteria of students who have taken research methodology courses and are taking or have taken proposal seminar courses. One hundred respondents have filled out a questionnaire distributed online. Based on the data analysis, learning experiences contributed 17.60% to students' information literacy skills. Furthermore, each increase in student learning experiences will contribute to 0.40% of information literacy skills. These research findings can be valuable information to be developed to the next stage in making modules and learning media that can provide a good quality learning experience to improve student literacy skills, especially in writing research proposals/theses. With this module or learning media, it is hoped that it can prepare students to write research proposals and theses.
Dataset Splitting Techniques Comparison For Face Classification on CCTV Images

The performance of classification models in machine learning algorithms is influenced by many factors, one of which is dataset splitting method. To avoid overfitting, it is important to apply a suitable dataset splitting strategy. This study presents comparison of four dataset splitting techniques, namely Random Sub-sampling Validation (RSV), k-Fold Cross Validation (k-FCV), Bootstrap Validation (BV) and Moralis Lima Martin Validation (MLMV). This comparison is done in face classification on CCTV images using Convolutional Neural Network (CNN) algorithm and Support Vector Machine (SVM) algorithm. This study is also applied in two image datasets. The results of the comparison are reviewed by using model accuracy in training set, validation set and test set, also bias and variance of the model. The experiment shows that k-FCV technique has more stable performance and provide high accuracy on training set as well as good generalizations on validation set and test set. Meanwhile, data splitting using MLMV technique has lower performance than the other three techniques since it yields lower accuracy. This technique also shows higher bias and variance values and it builds overfitting models, especially when it is applied on validation set.
Open BMSTU Platform and Teachers Training in Online Courses Development and Implementation Areas

The article is devoted to the strategy for the development of digital education at the Bauman Moscow State Technical University. The theoretical and practical aspects of the development of online courses are considered, in accordance with the requirements of the National Open Education Platform (NOEP) using the Open BMSTU platform. The paper includes the program of the advanced training course for teachers in in online courses development and implementation areas, tested at the Bauman Moscow State Technical University.
Person Re-Identification across Data Distributions Based on General Purpose DNN Object Detector

Solving the person re-identification problem involves making associations between the same person’s appearances across disjoint camera views. Further, those associations have to be made on multiple surveillance cameras in order to obtain a more efficient and powerful re-identification system. The re-identification problem becomes particularly challenging in very crowded areas. This mainly happens for two reasons. First, the visibility is reduced and occlusions of people can occur. Further, due to congestion, as the number of possible matches increases, the re-identification is becoming challenging to achieve. Additional challenges consist of variations of lightning, poses, or viewpoints, and the existence of noise and blurring effects. In this paper, we aim to generalize person re-identification by implementing a first attempt of a general system, which is robust in terms of distribution variations. Our method is based on the YOLO (You Only Look Once) model, which represents a general object detection system. The novelty of the proposed re-identification method consists of using a simple detection model, with minimal additional costs, but with results that are comparable with those of the other existing dedicated methods.
Higher Education and Digitalization of the Economy: The Case of Russian Regions

Building on the resource-based theory, we examine the association between the digitalization of the economy and the quality of higher education in Russian regions.We use the regression model to analyze data collected in Russian regions from 2011 to 2018.The quality of higher education is measured by the education potential in the regions.Regional factors of economic digitalization are categorized by mobile and Internet activities, technological background, and innovation activities.The information database contains official dates drawn from federal and regional statistical sources.The results showed that regions that had higher levels of innovation activities had a higher quality of higher education.On the contrary, mobile and Internet activities had a weakly negative influence on the quality of higher education.Our results are controversial with respect to the technological background of the region and its influence on the quality of higher education.Nonetheless, the study provides insights into the resource-based theory, demonstrating that the factors of economic digitalization increase the competitive advantage of the region regarding education, and they play an important role in regional sustainable development.
An Underwater Pathfinding Algorithm for Optimised Planning of Survey Dives

In scientific and technical diving, the survey of unknown or partially unexplored areas is a common task that requires an accurate planning for ensuring the optimal use of resources and the divers’ safety. In particular, in any kind of diving activity, it is essential to foresee the “dive profile” that represents the diver’s exposure to pressure over time, ensuring that the dive plan complies with the specific safety rules that have to be applied in accordance with the diver’s qualification and the environmental conditions. This paper presents a novel approach to dive planning based on an original underwater pathfinding algorithm that computes the best 3D path to follow during the dive in order to be able to maximise the number of points of interest (POIs) visited, while taking into account the safety limitations. The proposed approach, for the first time, considers the morphology of the 3D space in which the dive takes place to compute the best path, taking into account the decompression limits and avoiding the obstacles through the analysis of a 3D map of the site. Moreover, three different cost functions are proposed and evaluated to identify the one that could suit the divers’ needs better.
Detecting Web Spam Based on Novel Features from Web Page Source Code

Search engine is critical in people’s daily life because it determines the information quality people obtain through searching. Fierce competition for the ranking in search engines is not conducive to both users and search engines. Existing research mainly studies the content and links of websites. However, none of these techniques focused on semantic analysis of link and anchor text for detection. In this paper, we propose a web spam detection method by extracting novel feature sets from the homepage source code and choosing the random forest (RF) as the classifier. The novel feature sets are extracted from the homepage’s links, hypertext markup language (HTML) structure, and semantic similarity of content. We conduct experiments on the WEBSPAM-UK2007 and UK-2011 dataset using a five-fold cross-validation method. Besides, we design three sets of experiments to evaluate the performance of the proposed method. The proposed method with novel feature sets is compared with different indicators and has better performance than other methods with a precision of 0.929 and a recall of 0.930. Experiment results show that the proposed model could effectively detect web spam.