Reviewerly/exHarmony huggingface dataset:
NEW APPROACH IN QUANTIFYING USER EXPERIENCE IN WEB-ORIENTED APPLICATIONS

The primary goal of every developer is to develop the highest quality web application. The quality of the application is not only a subjective assessment of the developer, but objective and representative criteria for measuring performance must be defined. Google provides a model called Web Vitals with a subset of core Web Vitals that are important for quantifying user experience on the web. Some of the metrics are LCP (Largest Contentful Paint, refers to loading), FID (First Input Delay, refers to interactivity) and CLS (Cumulative Layout Shift, refers to visual stability). This paper will present modern technologies and tools for measuring the performance of websites and analyze them on a real example of a web application. The analysis will include the use and measurement of the most important parameters: Lighthouse, PageSpeed Insights, Chrome DevTools, Search Console, web.dev's measure tool, the Web Vitals Chrome extension and Chrome UX Report API.
Model Agnostic Answer Reranking System for Adversarial Question Answering

While numerous methods have been proposed as defenses against adversarial examples in question answering (QA), these techniques are often model specific, require retraining of the model, and give only marginal improvements in performance over vanilla models.In this work, we present a simple model-agnostic approach to this problem that can be applied directly to any QA model without any retraining.Our method employs an explicit answer candidate reranking mechanism that scores candidate answers on the basis of their content overlap with the question before making the final prediction.Combined with a strong base QA model, our method outperforms state-of-theart defense techniques, calling into question how well these techniques are actually doing and strong these adversarial testbeds are.
Ambi Detect: An Ambiguous Software Requirements Specification Detection Tool

Software Requirements Specification (SRS) is considered a highly critical artifact in the software development. All phases in software development are influenced by this artifact. Defects in software requirements may higher the risk of project overschedule that contributes to cost overrun of the project.Researchers have shown that finding defects in the initial software development phase is important becausethe cost of the bug is cheaper if it is fixed early. Hence, our main goal is to provide a platform for requirement engineers to produce better requirement specifications. We propose AmbiDetect, a (prototype) tool toautomatically classify ambiguous software requirements. AmbiDetect combines text mining and machine learning for ambiguous requirement specification detection. The text mining technique is used to extract classification features as well as generating the training set.AmbiDetect usesa machine learning technique to perform the ambiguous requirement specification detection. From an initial user study to validate the tool, the result indicates that the accuracy of detection is reasonably acceptable.Although AmbiDetect is an early experimental tool, we optimist that this tool can be a platform to improve SRS quality.
Online decision making and path planning framework for safe operation of unmanned aerial vehicles in urban scenarios

As the potential for deploying low-flying unmanned aerial vehicles (UAVs) in urban spaces increases, ensuring their safe operations is becoming a major concern. Given the uncertainties in their operational environments caused by wind gusts, degraded state of health, and probability of collision with static and dynamic objects, it becomes imperative to develop online decision-making schemes to ensure safe flights. In this paper, we propose an online decision-making framework that takes into account the state of health of the UAV, the environmental conditions, and the obstacle map to assess the probability of mission failure and re-plan accordingly. The online re-planning strategy considers two situations: (1) updating the current trajectory to reduce the probability of collision; and (2) defining a new trajectory to find a new safe landing spot, if continued flight would result in risk values above a pre-specified threshold. The re-planning routine uses the differential evolution optimization method and takes into account the dynamics of the UAV and its components as well as the environmental wind conditions. The new trajectory generation routine combines probabilistic road-maps with B-spline smoothing to ensure a dynamically feasible trajectory. We demonstrate the effectiveness of our approach by running UAV flight simulation experiments in urban scenarios.
Semi-Automatic Concept Map Generation Approach of Web-Based Kit-Build Concept Map Authoring Tool

Apart from contributing to students’ learning outcomes, learning activities with digital concept maps were useful, fun, and engaging. Kit-Build concept map is a learning framework that incorporated concept map recomposition as its essential activity. It has been used to learn English as a Foreign Language (EFL) reading comprehension. Students learn through recomposing digital concept maps from a set of teacher’s concept map components; hence, the teacher’s concept map is essential in Kit-Build. A teacher’s concept map should reflect the learning context and strategy, the teacher’s purpose and intention, students’ understanding level, and focus questions. However, automatic-generated concept maps with Concept Map Mining (CMM) can only produce general concept maps that are not fit and difficult to correspond to said reasons. As teacher’s concept maps are essential in learning with Kit-Build, improving teachers’ productivity in composing concept maps of a particular learning material becomes necessary. This study proposed a semi-automatic concept map generation approach of EFL reading comprehension texts with CMM to assist teachers composed their concept maps. The proposed concept map generation approach was integrated into the current Kit-Build concept map authoring tool as an authoring support feature. The accuracy of the support feature in generating concept map components is presented in this research. The result suggested that the proposed authoring support tool is better used to refine a concept map.
Leveraging Grammatical Roles for Measuring Semantic Similarity Between Texts

Semantic similarity between texts can be defined based on their meaning. Assessing the textual similarity is a prerequisite in almost all applications in the field of language processing and information retrieval. However, the diversity in the sentence structure makes it formidable to estimate the similarity. Some sentences pairs are lexicographically similar but semantically dissimilar. That is why the trivial lexical overlapping is not enough for measuring the similarity. To attain the semanticity of sentences, the context of the words and the structure of the sentence should be considered. In this paper, we propose a new method for capturing the semantic similarity between sentences based on their grammatical roles through word semantics. First, the sentences are divided grammatically into different parts where each part is considered as a grammatical role. Then multiple new measures are introduced to estimate the role-based similarity exploiting word semantics considering the sentence structure. The proposed similarity measures focus on inter-role and intra-role similarity between the sentence-pair. The word-level semantic information is extracted from a pre-trained word-embedding model. The performance of the proposed method was verified by conducting a wide range of experiments on the SemEval STS dataset. The experimental results indicated the effectiveness of the proposed method in terms of different standard evaluation metrics and outperformed some known related works.
Digital literacy practices in tertiary education: A case of EFL postgraduate students

Over the past few decades, technologies grew rapidly, transforming traditional instructions to be more digitalized and stimulating many researchers to study digital literacy practices in a school-educational context. However, studies exploring digital literacy practices in the context of tertiary education are still scarce. Within the qualitative approach, this study investigated digital literacy practices by EFL (English as a Foreign Language) postgraduate students. Moreover, it intensively discusses the kinds of digital tools used by the participants and the purposes of using them. Additionally, the way the participants conceptualized digital literacy was also elaborated. Thirty-four EFL Master students of a public university in Bandung, Indonesia, participated in this study. An online open-ended questionnaire and semi-structured interviews were applied in data collection. Meanwhile, eight dimensions of digital literacy in the Hague and Payton’s frameworks were used as the basis of the thematical analysis of the interview data. Results showed that the participants conceptualized digital literacy as soft skills for managing digital information covering the acts of searching, comprehending, evaluating, creating, and sharing. The prominent result of this study relates to how the participants used digital tools for academic/research and general purposes. Subsequently, this study recommends that tertiary education institutions provide more training on maximizing digital tools for academic writing and broader access to prepaid journal articles. Further exploration of lecturers’ digital literacy practices is highly recommended.
3D Hand Pose Estimation in Point Cloud Using 3D Convolutional Neural Network on Egocentric Datasets

3D hand pose estimation from egocentric vision is an important study in the construction of assistance systems and modeling of robot hand in robotics. In this paper, we propose a complete method for estimating 3D hand posefrom the complex scene data obtained from the egocentric sensor. In which we propose a simple yet highly efficient pre-processing step for hand segmentation. In the estimation process, we used the Hand PointNet (HPN), V2V-PoseNet(V2V), Point-to-Point Regression PointNet (PtoP) for finetuning to estimate the 3D hand pose from the collected data obtained from the egocentric sensor, such as CVRA, FPHA (First-Person Hand Action) datasets. HPN, V2V, PtoP are thedeep networks/Convolutional Neural Networks (CNNs) for estimating 3D hand pose that uses the point cloud data of the hand. We evaluate the estimation results using the preprocessing step and do not use the pre-processing step to see the effectiveness of the proposed method. The results show that 3D distance error is increased many times compared to estimates on the hand datasets are not obstructed (the hand data obtained from surveillance cameras, are viewed from top view, front view, sides view) such as MSRA, NYU, ICVL datasets. The results are quantified, analyzed, shown on the point cloud data of CVAR dataset and projected on the color image of FPHA dataset.
Knowledge Representations for Constructing Chains of Contexts in Geographic Information Systems

Solving complex informal problems using spatial data is often used in industry and business.In the absence of a solution algorithm, analyst resorts to a heuristic search for a solution, which is based on an interactive dialogue with a geographic information system (GIS).The analyst builds a cartographic workspace by sending GIS queries.The workspace is explored visually using software tools for spatial, statistical, and other special types of analysis.During the analysis, the user raises his situational awareness.It is important to organize the dialogue wisely as there is a danger of cognitive overload.In the latter case, there is a possibility of making the wrong decision.This paper proposes a method for representing knowledge about the process of analyzing spatial situations.The essence of the method is that knowledge is represented by chains of contexts and patterns of appropriate user behavior in visual analysis.A model of an image of a chain of contexts is proposed, which consists of the center of the image and its admissible transformations.A method of organizing a dialogue is described, which allows a GIS to develop recommendations for choosing a context in a chain of contexts.Patterns of level, tendency, and rhythm are proposed to represent knowledge about appropriate behavior.Application of the proposed method of knowledge representation in recommender GIS improves the quality of spatial data analysis.
Can the Earth–Moon Distance Influence the Accuracy of Lunar Irradiance with the Plane-Parallel Assumption in Atmospheric Radiative Transfer at Night?

Abstract The plane-parallel atmosphere as an underlying assumption in physics is appropriately used in the rigorous numerical simulation of the atmospheric radiative transfer model (RTM) with incident solar light. The solar irradiance is a constant with the plane-parallel assumption, which is attributed to the small difference in the distance between any point on Earth’s surface to the sun. However, at night, atmospheric RTMs use the moon as a unique incident light source in the sky. The Earth–moon distance is approximately 1/400 of the Earth–sun distance. Thus, the varying Earth–moon distance on Earth’s surface can influence the top of atmosphere (TOA) lunar irradiance for the plane-parallel atmosphere assumption. In this investigation, we observe that the maximum biases in Earth–moon distance and day/night band lunar irradiance at the TOA are ±1.7% and ±3.3%, respectively, with the plane-parallel assumption. According to our calculations, this bias effect on the Earth–moon distance and lunar irradiance shows a noticeable spatiotemporal variation on a global scale that can impact the computational accuracy of an RTM at night. In addition, we also developed a fast and portable correction algorithm for the Earth–moon distance within a maximum bias of 18 km or ±0.05%, because of the relatively low computational efficiency and the large storage space necessary for the standard ephemeris computational software. This novel correction algorithm can be easily used or integrated into the atmospheric RTM at night.
Multi‐angle head pose classification with masks based on color texture analysis and stack generalization

Head pose classification is an important part of the preprocessing process of face recognition, which can independently solve application problems related to multi-angle. But, due to the impact of the COVID-19 coronavirus pandemic, more and more people wear masks to protect themselves, which covering most areas of the face. This greatly affects the performance of head pose classification. Therefore, this article proposes a method to classify the head pose with wearing a mask. This method focuses on the information that is helpful for head pose classification. First, the H-channel image of the HSV color space is extracted through the conversion of the color space. Then use the line portrait to extract the contour lines of the face, and train the convolutional neural networks to extract features in combination with the grayscale image. Finally, stacked generalization technology is used to fuse the output of the three classifiers to obtain the final classification result. The results on the MAFA dataset show that compared with the current advanced algorithm, the accuracy of our method is 94.14% on the front, 86.58% on the more side, and 90.93% on the side, which has better performance.
SLAP: Improving Physical Adversarial Examples with Short−Lived Adversarial Perturbations

Whilst significant research effort into adversarial examples (AE) has emerged in recent years, the main vector to realize these attacks in the real-world currently relies on static adversarial patches, which are limited in their conspicuousness and can not be modified once deployed. In this paper, we propose Short-Lived Adversarial Perturbations (SLAP), a novel technique that allows adversaries to realize robust, dynamic real-world AE from a distance. As we show in this paper, such attacks can be achieved using a light projector to shine a specifically crafted adversarial image in order to perturb real-world objects and transform them into AE. This allows the adversary greater control over the attack compared to adversarial patches: (i) projections can be dynamically turned on and off or modified at will, (ii) projections do not suffer from the locality constraint imposed by patches, making them harder to detect. We study the feasibility of SLAP in the self-driving scenario, targeting both object detector and traffic sign recognition tasks. We demonstrate that the proposed method generates AE that are robust to different environmental conditions for several networks and lighting conditions: we successfully cause misclassifications of state-of-the-art networks such as Yolov3 and Mask-RCNN with up to 98% success rate for a variety of angles and distances. Additionally, we demonstrate that AE generated with SLAP can bypass SentiNet, a recent AE detection method which relies on the fact that adversarial patches generate highly salient and localized areas in the input images.
Author Correction: Towards real-time photorealistic 3D holography with deep neural networks

A Correction to this paper has been published: https://doi.org/10.1038/s41586-021-03476-5.
Extreme Gradient Boosting for Recommendation System by Transforming Product Classification into Regression Based on Multi-Dimensional Word2Vec

Now that untact services are widespread and worldwide, the number of users visiting online shopping malls has increased. For example, the recommendation systems in Netflix, Amazon, etc., have gained a lot of attention by attracting many users and have made large profit by recommending suitable products to their users. In the paper, we conduct a study to enhance recommendation accuracy using Word2Vec, widely used in natural language processing. We collect user shopping history with personal click preference information of product items as data, representing a document for natural language processing. The sequence of product item clicks is fed into the Word2Vec technology algorithm to obtain the vectors symmetrically representing all of the product items clicked by users. Training and test data have a series of vectors representing a sequence of the clicked product items as inputs and a purchased product as a target. Machine learning models recommend a product as a symmetric vector for each input and calculate the similarity among the recommended vectors and all other registered products they sell in the system to recommend multiple products as final recommendation results. We use XGBoost regressor and classifier models to recommend some products that users would like and evaluate the recommendation accuracy. A finally recommended product by the models is a vector, and the system recommends some more products by calculating the similarity as mentioned above. We evaluated the classifier model’s recommendation accuracy without Word2Vec encoding first and then with the Word2Vec technique. Meanwhile, we can represent the products with single or multiple dimensional vectors. We noted that the recommendation accuracy increases when we use multiple dimensions of Word2Vec vectors from the experiments. We also evaluated the performances when the system recommends one or multiple products. For the recommendation of multiple products (five here), a regression model has higher accuracy than a classification model in all dimensions of vectors.
A Straightness Error Compensation System for Topography Measurement Based on Thin Film Interferometry

Straightness error compensation is a critical process for high-accuracy topography measurement. In this paper, a straightness measurement system was presented based on the principle of fringe interferometry. This system consisted of a moving optical flat and a stationary prism placed close to each other. With a properly aligned incident light beam, the air wedge between the optical flat and the prism would generate the interferogram, which was captured by a digital camera. When the optical flat was moving with the motion stage, the variation in air wedge thickness due to the imperfect straightness of the guideway would lead to a phase shift of the interferogram. The phase shift could be calculated, and the air wedge thickness could be measured accordingly using the image processing algorithm developed in-house. This air wedge thickness was directly correlated with the straightness of the motion stage. A commercial confocal sensor was employed as the reference system. Experimental results showed that the repeatability of the proposed film interferometer represented by σ was within 25 nm. The measurement deviation between the film interferometer and the reference confocal sensor was within ±0.1 µm. Compared with other interferometric straightness measurement technologies, the presented methodology was featured by a simplified design and good environment robustness. The presented system could potentially be able to measure straightness in both linear and angular values, and the main focus was to analyze its linear value measurement capability.
EFFECTIVENESS OF DIGITAL RESOURCES IN THE LEARNING MANAGEMENT SYSTEM WITHIN ONLINE EDUCATION OF FUTURE ENTREPRENEURS

The paper analyses the application of advanced digital resources when teaching English for future entrepreneurs. Relevance of the academic process digitalization under conditions of distance learning is proved. Modern digital resources have been identified and approaches to their classification have been determined. Opportunities of using Learning Management System within distance learning have been clarified. It has been established that digital resources can be effectively applied in the learning management system within online training at higher educational institutions. Our practical conclusions and results are based on purposeful survey and analyses of students’ priorities in the researched field. It is concluded that proper use of digital resources results in the enhancement of foreign language proficiency of students, increases the involvement of students into interactive cooperation during online learning, and provides the students with the resources required for the formation of foreign language skills. It is found that active interaction during the classes let the students feel themselves a part of the community and motivates to perform the tasks.