Reviewerly/exHarmony huggingface dataset:
Comparison of WhatsApp and Google Classroom Group Online Learning Models to Student Learning Outcomes

The purpose of this study was to compare whether there were differences in the learning outcomes of students in the Mathematics Education Study Program of the University of PGRI Adi Buana University Surabaya in the even semester of the 2019/2020 academic year in Calculus I when applying the online learning model using the WhatsApp group and using Google Class and to know which online learning model is better between the two. The research methods used in analyzing this research are using a quantitative research approach. The type of research used is a quasi-experimental research design, namely a posttest only comparison group design. The sampling technique was applied. is a simple random sampling technique where the population is 90 students and the sample used is 45 students from classes 2019 A and 2019 C. The data used in this study are sourced from primary data, namely the scores of the 2019A class and class students' Middle Semester Examination. 2019C and secondary data derived from observations and archives. The data analysis technique used was descriptive data analysis and inferential statistical analysis through a t-test using the MS Excel program. Based on the results of the research and discussion, it can be concluded that the online learning model using WhatsApp Groups is slightly better than using Google Classroom by paying attention to the system that is applied during learning.&#x0D; Keywords: covid 19, WhatsApp group, Google Classroom, daring learning model, comparison.
Research on the university intelligent learning analysis system based on AI

The integration of Artificial Intelligence technology and school education had become a future trend, and became an important driving force for the development of education. With the advent of the era of big data, although the relationship between students’ learning status data was closer to nonlinear relationship, combined with the application analysis of artificial intelligence technology, it could be found that students’ living habits were closely related to their academic performance. In this paper, through the investigation and analysis of the living habits and learning conditions of more than 2000 students in the past 10 grades in Information College of Institute of Disaster Prevention, we used the hierarchical clustering algorithm to classify the nearly 180000 records collected, and used the big data visualization technology of Echarts + iView + GIS and the JavaScript development method to dynamically display the students’ life track and learning information based on the map, then apply Three Dimensional ArcGIS for JS API technology showed the network infrastructure of the campus. Finally, a training model was established based on the historical learning achievements, life trajectory, graduates’ salary, school infrastructure and other information combined with the artificial intelligence Back Propagation neural network algorithm. Through the analysis of the training resulted, it was found that the students’ academic performance was related to the reasonable laboratory study time, dormitory stay time, physical exercise time and social entertainment time. Finally, the system could intelligently predict students’ academic performance and give reasonable suggestions according to the established prediction model. The realization of this project could provide technical support for university educators.
Investigating Monolingual and Multilingual BERT Models for Vietnamese Aspect Category Detection

Aspect category detection (ACD) is one of the challenging tasks in the Aspect-based sentiment Analysis problem. The purpose of this task is to identify the aspect categories mentioned in user-generated reviews from a set of pre-defined categories. In this paper, we investigate the performance of various monolingual pre-trained language models compared with multilingual models on the Viet-namese aspect category detection problem. We conduct the experiments on two benchmark datasets for the restaurant and hotel domain. The experimental results demonstrated the effectiveness of the monolingual PhoBERT model than others on two datasets. We also evaluate the performance of the multilingual model based on the combination of whole SemEval-2016 datasets in other languages with the Vietnamese dataset. To the best of our knowledge, our research study is the first attempt at performing various available pre-trained language models on aspect category detection task and utilize the datasets from other languages based on multilingual models.
Adversarial Driving: Attacking End-to-End Autonomous Driving Systems.

As the research in deep neural networks advances, deep convolutional networks become feasible for automated driving tasks. There is an emerging trend of employing end-to-end models in the automation of driving tasks. However, previous research unveils that deep neural networks are vulnerable to adversarial attacks in classification tasks. While for regression tasks such as autonomous driving, the effect of these attacks remains rarely explored. In this research, we devise two white-box targeted attacks against end-to-end autonomous driving systems. The driving model takes an image as input and outputs the steering angle. Our attacks can manipulate the behaviour of the autonomous driving system only by perturbing the input image. Both attacks can be initiated in real-time on CPUs without employing GPUs. This demo aims to raise concerns over applications of end-to-end models in safety-critical systems.
Textual Complexity as an Indicator of Document Relevance

We study the textual complexity of documents as an aspect of the Information Retrieval process that influences retrieval effectiveness. Our experiments show that in many cases user queries allow determining which linguistic competency level best suits an underlying information need. The paper investigates promising first approaches on how to do so automatically and compares them to an idealistic baseline. By filtering out documents of unexpected textual complexity, we find improved search results mainly when using precision-oriented effectiveness measures.
LFI-Augmenter: Intelligent Light Field Image Editing With Interleaved Spatial-Angular Convolution

The emerging light field images (LFIs) support 6 degrees of freedom (6DoF) user interaction, which is the key feature for future virtual reality (VR) media experiences. Compared to regular 2-D images, LFIs are characterized by particular image structure with both spatial and angular information. In practice, it is infeasible for the user to manually edit each subaperture of the LFI, respectively, and the user cannot guarantee the parallax consistency between different subapertures. To address this problem, we propose a deep-learning-based LFI editing scheme named central view augmentation propagation (CVAP), which employs interleaved spatial-angular convolutional neural networks (4-D CNN) for effective learning of both spatial and angular features from the input LFI. Moreover, for comparison purposes, we also implemented a “direct editing” scheme based on the geometry correspondence between subviews, and another benchmark method based on light field super resolution (LFSR). The experimental results show that CVAP achieved higher PSNR and overall more pleasant visual quality than direct editing and LFSR.
Development of Augmented Reality Application for Canang Education Using Marker-Based Tracking Method

The aim of this study is to develop an Augmented Reality application with marker-based tracking that use for Canang education. This application was developed with waterfall model. Data collection technique that I use in this study is surveys, interview and literature review. This application can pop up information about types of Canang, when you use that types, and an example picture of the Canang. This application use vuforia software development kit (SDK) for combine the reality object and virtual object use for smartphone with QR Code. The design for information and example picture of Canang is built in Adobe Illustrator and made to Augmented Reality in Unity. Method that use in this study is Marker Based Tracking which identifies marker patterns. This application applies multi-target marker-based Augmented Reality so that each type of Canang information is stored and ran on different markers. This application can be medium for education about Canang in general for Hindusm teen.
Exploiting Multi-source Data for Adversarial Driving Style Representation Learning

Characterizing human driver's driving behaviors from GPS trajectories is an important yet challenging trajectory mining task. Previous works heavily rely on high-quality GPS data to learn such driving style representations through deep neural networks. However, they have overlooked the driving contexts that greatly govern drivers' driving activities and the data sparsity issue of practical GPS trajectories collected at a low-sampling rate. To address the limitations of existing works, we present an adversarial driving style representation learning approach, named $$\mathtt {Radar}$$ . In addition to summarizing statistic features from raw GPS data, $$\mathtt {Radar}$$ also extracts contextual features from three aspects of road condition, geographic semantic, and traffic condition. We further exploit the advanced semi-supervised generative adversarial networks to construct our learning model. By jointly considering statistic features and contextual features, the trained model is able to efficiently learn driving style representations even from sparse trajectories. Experiments on two benchmark applications, i.e., driver number estimation and driver identification, with a large real-world GPS trajectory dataset demonstrate that $$\mathtt {Radar}$$ can outperform the state-of-the-art approaches by learning more effective and accurate driving style representations.
LASER: Learning a Latent Action Space for Efficient Reinforcement Learning

The process of learning a manipulation task depends strongly on the action space used for exploration: posed in the incorrect action space, solving a task with reinforcement learning can be drastically inefficient. Additionally, similar tasks or instances of the same task family impose latent manifold constraints on the most effective action space: the task family can be best solved with actions in a manifold of the entire action space of the robot. Combining these insights we present LASER, a method to learn latent action spaces for efficient reinforcement learning. LASER factorizes the learning problem into two sub-problems, namely action space learning and policy learning in the new action space. It leverages data from similar manipulation task instances, either from an offline expert or online during policy learning, and learns from these trajectories a mapping from the original to a latent action space. LASER is trained as a variational encoder-decoder model to map raw actions into a disentangled latent action space while maintaining action reconstruction and latent space dynamic consistency. We evaluate LASER on two contact-rich robotic tasks in simulation, and analyze the benefit of policy learning in the generated latent action space. We show improved sample efficiency compared to the original action space from better alignment of the action space to the task space, as we observe with visualizations of the learned action space manifold. Additional details: this https URL
Data Migration from Relational Database to MongoDB using XAMPP and NoSQL

MongoDB is a document-oriented database which helps us group data more logically. This paper demonstrates the conversion of data from a native tabular form to unstructured documents. The document and collections within needs not to be well defined prior to the creation hence adding to its flexibility. MongoDB has lots of extensive built-in-features and is highly compatible with other software systems, with extensive ways of accessing data beyond JSON query, its highly compatible Business Intelligence Connector is highly compatible which makes it compatible. High scalability is making it remarkable and popular and hence made us think about writing a paper demonstrating the data conversion. This conversion has helped us in making the most of modern data. Data was stored on the cloud as cloud-based storage is an excellent and most cost-effective solution. Our solution is highly scalable as the built-in shading solution for data handling makes it one of the best big data handling tool. The data we have used is location based in MongoDB that can directly yield data without hitches which made us work on MongoDB with the focus on the latest feature of MongoDB to support Multi-Document ACID transactions to maintain data integrity.
QMetrology from QCosmology: Study with entangled two qubit open quantum system in De Sitter space

In this paper, our prime objective is to apply the techniques of parameter estimation theory and the concept of Quantum Metrology in the form of Fisher Information to investigate the role of certain physical quantities in the open quantum dynamics of a two entangled qubit system under the Markovian approximation. There exist various physical parameters which characterize such system, but can not be treated as any quantum mechanical observable. It becomes imperative to do a detailed parameter estimation analysis to determine the physically consistent parameter space of such quantities. We apply both Classical Fisher Information (CFI) and Quantum Fisher Information (QFI) to correctly estimate these parameters, which play significant role to describe the out-of-equilibrium and the long range quantum entanglement phenomena of open quantum system. Quantum Metrology, compared to classical parameter estimation theory, plays a two-fold superior role, improving the precision and accuracy of parameter estimation. Additionally, in this paper we present a new avenue in terms of Quantum Metrology, which beats the classical parameter estimation. We also present an interesting result of revival of out-of-equilibrium feature at the late time scales, arising due to the long range quantum entanglement at early time scale and provide a physical interpretation for the same in terms of Bell's Inequality Violation in early time scale giving rise to non-locality.
Dynamic characteristics analysis for vehicle parts based on parallel optimization algorithm with CUDA

Purpose An accurate and fast dynamic analysis innovative approach for vehicle parts is provided for engineering practice. Design/methodology/approach This paper presents an innovative dynamic analysis approach for vehicle parts based on parallel optimization algorithm with CUDA. Findings This project is supported by the National Science Foundation of China (No. 51805076, No. U1708255 and No. 51775093), the Fundamental Research Funds for the Central Universities (No. N170503011) and the Natural Science Foundation of Liaoning Province, China (No. 20180551058). Originality/value This paper presents an innovative approach for vehicle parts using parallel optimization algorithm based on CUDA, which can improve the computing accuracy and speed effectively.
Safe adaptive importance sampling: A mixture approach

This paper investigates adaptive importance sampling algorithms for which the policy, the sequence of distributions used to generate the particles, is a mixture distribution between a flexible kernel density estimate (based on the previous particles), and a “safe” heavy-tailed density. When the share of samples generated according to the safe density goes to zero but not too quickly, two results are established: (i) uniform convergence rates are derived for the policy toward the target density; (ii) a central limit theorem is obtained for the resulting integral estimates. The fact that the asymptotic variance is the same as the variance of an “oracle” procedure with variance-optimal policy, illustrates the benefits of the approach. In addition, a subsampling step (among the particles) can be conducted before constructing the kernel estimate in order to decrease the computational effort without altering the performance of the method. The practical behavior of the algorithms is illustrated in a simulation study.
Video captioning via a symmetric bidirectional decoder

The dominant video captioning methods employ the attentional encoder–decoder architecture, where the decoder is an autoregressive structure that generates sentences from left-to-right. However, these methods generally suffer from the exposure bias issue and neglect the guidance of future output contexts obtained from the right-to-left decoding. Here, the authors propose a new symmetric bidirectional decoder for video captioning. The authors first integrate the self-attentive multi-head attention and bidirectional gated recurrent unit for capturing the long-term semantic dependencies in videos. The authors then apply one single decoder to generate accurate descriptions from left-to-right and right-to-left simultaneously. The decoder in each decoding direction performs two cross-attentive multi-head attention modules to consider both the past hidden states from the same decoding direction and the future hidden states from the reverse decoding direction at each time step. A symmetric semantic-guided gated attention module is specially devised to adaptively suppress the irrelevant or misleading contents in the past or future output contexts and retain the useful ones for avoiding under-description. Experimental evaluations on two widely applied benchmark datasets: Microsoft research video to text and Microsoft video description corpus, demonstrate that the authors' proposed method obtains substantially state-of-the-art performance, which validates the superiority of the bidirectional decoder.
Cloud-Based Fusion of Residual Exploitation-Based Convolutional Neural Network Models for Image Tampering Detection in Bioinformatics

Cloud computing has evolved in various application areas such as medical imaging and bioinformatics. It raises the issues of privacy and tampering in the images especially related to the medical field and bioinformatics for various reasons. The digital images are quite vulnerable to be tampered by the interceptors. The credibility of individuals can transform through falsified information in the images. Image tampering detection is an approach to identifying and finding the tampered components in the image. For the efficient detection of image tampering, the sufficient number of features are required which can be achieved by a deep learning architecture-based models without manual feature extraction of functions. In this research work, we have presented and implemented a cloud-based residual exploitation-based deep learning architectures to detect whether or not an image is being tampered. The proposed approach is implemented on the publicly available benchmark MICC-F220 dataset with the <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mi>k</mi> </math> -fold cross-validation approach to avoid the overfitting problem and to evaluate the performance metrics.
An Approach for Selecting the Suitable Requirement Elicitation Technique

Requirements elicitation is an initial phase in software development. In this phase, requirements engineers gather the requirements of the software under development from users, stakeholders and customers. The techniques used for gathering requirements have a big influence on the quality of requirements and the success of project. Many requirements elicitation techniques (RET) such as: interview, prototype and observation can be used for requirements gathering process. However, one technique is not suitable for all different projects. Usually requirement engineers select the RET based on personal preferences and assumptions such as; this is the only technique which they know. However, this subjective decision can result in using inappropriate RET. Using unsuitable RET may decrease the quality of elicited requirements. Even though researchers have proposed many techniques for elicitation, one of the challenging issues is to choose the suitable RET for specific situations. The purpose of this paper is to help requirement engineers to choose suitable RET. To do that firstly, we identify factors that affect in selecting RET. Secondly, an approach to select suitable RET is proposed. Thirdly, a prototype is developed to help requirements engineers and to ease the process of selectingelicitation technique. Lastly, experts evaluate the proposed approach and the prototype.
Trust and fuzzy inference based cross domain serendipitous item recommendations (TFCDSRS)

Recommender System (RS) is an information filtering approach that helps the overburdened user with information in his decision making process and suggests items which might be interesting to him. While presenting recommendation to the user, accuracy of the presented list is always a concern for the researchers. However, in recent years, the focus has now shifted to include the unexpectedness and novel items in the list along with accuracy of the recommended items. To increase the user acceptance, it is important to provide potentially interesting items which are not so obvious and different from the items that the end user has rated. In this work, we have proposed a model that generates serendipitous item recommendation and also takes care of accuracy as well as the sparsity issues. Literature suggests that there are various components that help to achieve the objective of serendipitous recommendations. In this paper, fuzzy inference based approach is used for the serendipity computation because the definitions of the components overlap. Moreover, to improve the accuracy and sparsity issues in the recommendation process, cross domain and trust based approaches are incorporated. A prototype of the system is developed for the tourism domain and the performance is measured using mean absolute error (MAE), root mean square error (RMSE), unexpectedness, precision, recall and F-measure.
Estimation of Navigation Mark Floating Based on Fractional-Order Gradient Descent with Momentum for RBF Neural Network

To address the difficulty of estimating the drift of the navigation marks, a fractional-order gradient with the momentum RBF neural network (FOGDM-RBF) is designed. The convergence is proved, and it is used to estimate the drifting trajectory of the navigation marks with different geographical locations. First, the weight of the neural network is set. The navigation mark’s meteorological, hydrological, and initial position data are taken as the input of the neural network. The neural network is trained and used to estimate the mark’s position. The navigation mark’s position is taken at a later time as the output of the neural network. The difference between the later position and the estimated position obtained from the neural network is the error function of the neural network. The influence of sea conditions and months are analyzed. The experimental results and error analysis show that FOGDM-RBF is better than other algorithms at trajectory estimation and interpolation, has better accuracy and generalization, and does not easily fall into the local optimum. It is effective at accelerating convergence speed and improving the performance of a gradient descent method.