Reviewerly/exHarmony huggingface dataset:
PlotQA: Reasoning over Scientific Plots

Existing synthetic datasets (FigureQA, DVQA) for reasoning over plots do not contain variability in data labels, real-valued data, or complex reasoning questions. Consequently, proposed models for these datasets do not fully address the challenge of reasoning over plots. In particular, they assume that the answer comes either from a small fixed size vocabulary or from a bounding box within the image. However, in practice, this is an unrealistic assumption because many questions require reasoning and thus have real-valued answers which appear neither in a small fixed size vocabulary nor in the image. In this work, we aim to bridge this gap between existing datasets and real-world plots. Specifically, we propose PlotQA with 28.9 million question-answer pairs over 224,377 plots on data from real-world sources and questions based on crowd-sourced question templates. Further, 80.76% of the out-of-vocabulary (OOV) questions in PlotQA have answers that are not in a fixed vocabulary. Analysis of existing models on PlotQA reveals that they cannot deal with OOV questions: their overall accuracy on our dataset is in single digits. This is not surprising given that these models were not designed for such questions. As a step towards a more holistic model which can address fixed vocabulary as well as OOV questions, we propose a hybrid approach: Specific questions are answered by choosing the answer from a fixed vocabulary or by extracting it from a predicted bounding box in the plot, while other questions are answered with a table question-answering engine which is fed with a structured table generated by detecting visual elements from the image. On the existing DVQA dataset, our model has an accuracy of 58%, significantly improving on the highest reported accuracy of 46%. On PlotQA, our model has an accuracy of 22.52%, which is significantly better than state of the art models.
MonoLayout: Amodal scene layout from a single image

In this paper, we address the novel, highly challenging problem of estimating the layout of a complex urban driving scenario. Given a single color image captured from a driving platform, we aim to predict the bird's-eye view layout of the road and other traffic participants. The estimated layout should reason beyond what is visible in the image, and compensate for the loss of 3D information due to projection. We dub this problem amodal scene layout estimation, which involves "hallucinating" scene layout for even parts of the world that are occluded in the image. To this end, we present MonoLayout, a deep neural network for real-time amodal scene layout estimation from a single image. We represent scene layout as a multi-channel semantic occupancy grid, and leverage adversarial feature learning to hallucinate plausible completions for occluded image parts. Due to the lack of fair baseline methods, we extend several state-of-the-art approaches for road-layout estimation and vehicle occupancy estimation in bird's-eye view to the amodal setup for rigorous evaluation. By leveraging temporal sensor fusion to generate training labels, we significantly outperform current art over a number of datasets. On the KITTI and Argoverse datasets, we outperform all baselines by a significant margin. We also make all our annotations, and code publicly available. A video abstract of this paper is available https://www.youtube.com/watch?v=HcroGyo6yRQ .
Street Scene: A new dataset and evaluation protocol for video anomaly detection

Progress in video anomaly detection research is currently slowed by small datasets that lack a wide variety of activities as well as flawed evaluation criteria. This paper aims to help move this research effort forward by introducing a large and varied new dataset called Street Scene, as well as two new evaluation criteria that provide a better estimate of how an algorithm will perform in practice. In addition to the new dataset and evaluation criteria, we present two variations of a novel baseline video anomaly detection algorithm and show they are much more accurate on Street Scene than two state-of-the-art algorithms from the literature.
Modifikasi Interaksi Fisik dalam Pameran Virtual

The shift from physical media to digital media has become increasingly unavoidable since the Covid-19 pandemic began in early 2020. However, with or without this pandemic, changes in this medium are unavoidable. Virtual art exhibitions are starting to be commonly applied on various occasions for the sake of the sustainability of the art ecosystem in Indonesia. The existence of virtual exhibitions is not to replace conventional exhibitions but as an alternative approach that can be taken by art activists. Anxiety about virtual exhibitions is due to the limitations of virtual exhibitions in creating emotional connections with the various parties involved. Based on this, this study will discuss the efforts to implement gamification design that replicates the physical interactions that commonly occur in an exhibition. The purpose of implementing gamification design is to maximize user experience (UX) when visiting exhibitions virtually so that the experience value of virtual exhibitons can increase. The output of this research is the concept of gamification in a virtual exhibition.
Methods of implementation and operation of hardware DRM protection on the example of printer cartridges

The subject of this paper is to analyze the way of implementation and operation of hardware DRM protection systems on the example of selected printer cartridges. Thanks to the analysis, it is possible to understand the way of operation of typical security chips used in printer cartridges. Using the knowledge gained, it becomes possible to modify the data on the security chips and thus reset the protection, so that the printer sees the chip with the modified data as a new one. However, the process itself was difficult due to the lack of publicly available materials on this topic. In this paper, different approaches to DRM security analysis have been used, so that the reader can try to perform the described steps to analyze other hardware DRM systems by himself. In addition, devices called resetters have been designed and fabricated to quickly reset the analyzed security chips. Their design and documentation is available on the author's Github.
Interactive Teaching Methods as a Method of Implementing a Competency-Based Approach in the Preparation of a Doctor

Orientation towards the development of professional and socio-psychological competence makes it expedient not only the use of traditional technologies in the pedagogical process, but also the development of innovative technologies. Innovations in the university can be the use of well-forgotten active learning methods. One of such methods, which gives impetus to the development of the necessary professional competence among students, is a role-playing game.
Simulation Trainings Iin Improving Professional Competencies as a Part of Professional Retraining

The publication reflects the results of a study of self-assessment of the level of professional competencies in the field of emergency medical care among persons who have passed the primary specialized accreditation on the basis of Kursk State Medical University. Self-assessment was carried out, among other things, among specialists who completed a simulation course aimed at improving professional competencies in providing medical care in emergency and emergency forms as part of the implementation of additional professional retraining programs.
Prospects for the Implementation of Clinical Scenarios for Assessing the Practical Competencies of Students

Simulation training is a necessary condition for the safe and confident development of practical skills. The development of clinical scenarios and the objectification of the assessment make it possible to determine the level of formation of professional competencies at all levels of education. The authors present the experience of implementing adapted checklists at the stages of preparing and conducting the final certification of graduates.
Interval Valued Intuitionistic Fuzzy β-Filters on β-Algebras

This study establishes the concept of interval valued intuitionistic fuzzy (InVInF) β-filters on β-algebras and a few of its related properties are investigated. Some compelling results of interval valued fuzzy β-filters have been examined. Further, the notions of products and strong β-filters are also introduced. In addition that, the level set and homomorphism of interval valued intuitionistic fuzzy β-filters are too discussed. Furthermore, we enacted that the intersection between two interval valued intuitionistic fuzzy β−filters is again an interval valued intuitionistic fuzzy β-filter.
Higher Education During and After the Pandemic: Philosophy of Digital Change

The authors discuss the problems associated with the accelerated digitalization of higher education during and after the pandemic. Various approaches proposed by specialists dealing with problems of digitalized higher education are analyzed, and the benefits and risks associated with digital change are identified. The theoretical approach includes some famous theories in the philosophy of education constructed first by John Dewey and developed further by contemporary scholars. The empirical basis contains several studies of students, teachers and experts conducted in Armenia and Belarus. The authors present a comparative analysis of the situation in higher education in both countries during the pandemic. The research results show that the online form of learning poses several difficulties and offers advantages over the traditional form of learning. At the same time, the rejection of traditional forms of learning is also related to the problems and unpredictable consequences of the quality of learning. The authors conclude that currently, the most promising form of learning is hybrid. Its implementation demands a special educational environment to embrace the advantages of online and offline forms of education and account for factors influencing the effectiveness of these forms.
Factors Influencing the Digital Transformation of Universities in Thailand

Rapid advances in digital technology have brought about changes in the administration and operations of all kinds of organizations. Those that are able to efficiently transform themselves into digital organizations in a short time and have a high capacity for self-adjustment to the new socio-economic situation achieve competitive advantages. Many universities in Thailand have committed to a strategy of transformation into digital organizations. This research was conducted to study the factors behind the success of the digital transformation of Thai universities, using a quantitative research method. The data were collected using a questionnaire administered to 495 administrators from 6 Thai universities, of whom 303 were high-level administrators, 174 middle-level administrators, and 18 low-level administrators. Statistical analyses and confirmatory factor analysis were performed on the data obtained. The results revealed 6 factors that influence the success of digital transformation in the context of Thai universities: 1) digital culture, 2) digital strategies, 3) management process, 4) organization leaders, 5) digital technologies, and 6) staff. The results are useful for academics and organizations’ administrators or authorities to set clearer directions to propel their operations towards digital success.
Evaluasi Penggunaan Software Lathe Simulator Dengan Menggunakan User Experience Questionaire

&lt;p&gt;&lt;strong&gt;Manufacturing industry is one of industrial sector in Indonesia that interesting to research. Based on Indonesian GDP in 2015, this sector has a contribution up to 21.13 percent. Most of production process in manufacturing industry used to machining process. One of them is lathe. Moreover, HR preparation is needed, due to have knowledge about this machine. One of them is using machine simulator called “Lathe Simulator 1k62”. The purpose of this research is to find out user experienced of “Lathe Simulator 1k62”. The research method is giving several tasked to respondent, then to fill the User Experienced Questionnaire (UEQ) to find out the Attractiveness, Perspicuity, Efficiency, Dependability, Stimulation, and Novelty. The result found that all of the respondent had complete the assigned task. The UEQ, found that “Lathe Simulator 1k62” in generally had positive value, based on Attractiveness, Perspicuity, Efficiency, Dependability, Stimulation, and Novelty all above 0.8. Furthermore, user experienced of “Lathe Simulator 1k62” had “good” category compared to other software.&lt;/strong&gt;&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Keywords&lt;/strong&gt;&lt;em&gt;: User Experienced, UEQ, Usability, Simulator&lt;/em&gt;&lt;/p&gt;
Machine Learning-Based Psychology Evaluation of College Students for Building Innovative Health Service System

Leadership psychology among college students is a multidimensional concept that primarily encompasses practical ability, teamwork ability, political literacy, emotional intelligence, etc. At present, a common problem among the surveyed college students is their leadership ability is not strong. Reasons include the lack of leadership-related knowledge and leadership awareness among college students as well as the fact that the public, particularly college educators, do not give leadership training a sufficient attention. Leadership psychology is a primary factor influencing the development of college students. It is necessary to strengthen college students' leadership education. This work extracts the key factors that influence the leadership of college students by analyzing their big behavior data. The extracted factors include the theoretical knowledge of leadership, practical ability, leadership psychology, home education, and exercise. To evaluate the degrees of these factors influencing the students' leadership, we develop an advanced multicriteria decision-making framework based on the ENtropy theory and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), called EN-TOPSIS. In EN-TOPSIS, an entropy method is used to determine the criteria weights rather than using the subjective weighting method. Three groups of college students are surveyed and evaluated based on the five factors. Each group contains ten students. The evaluation results show that the leadership of the students is mainly influenced by their practical ability and leadership psychology. The students with the highest evaluation score are recognized as having high levels of leadership.
APPROXIMATION BY HAAR WAVELET AND THEIR ORDER OF APPROXIMATION IN DIFFERENT SPACES

Wavelet analysis uses as a new class of orthogonal expansion in with regularity-approximation properties. In this study, we approximate any general function in by Haar wavelet in different smoothness spaces such as Lebesgue space, Lipschitz continuous space, Sobolev and Besov spaces.
Using Evolving Algorithm with Distance Indicator for Solving Different Non-linear Optimization Problems

In this paper, we have relied on the dominant control system as an important tool in building the group of leaders because it allows leaders to contain less dense areas, avoid local areas and produce a more compact and diverse Pareto front. Nine standard nonlinear functions yielded this result. MaBAT/R2 appears to be more efficient than MOEAD, NSGAII, MPSOD, and SPEA2. MATLAB was used to generate all the results of the proposed method and other methods in the same field of work.
A Malicious Webpage Detection Method Based on Graph Convolutional Network

In recent years, with the rapid development of the Internet and information technology, video websites, shopping websites, and other portals have grown rapidly. However, malicious webpages can disguise themselves as benign websites and steal users’ private information, which seriously threatens network security. Current detection methods for malicious webpages do not fully utilize the syntactic and semantic information in the web source code. In this paper, we propose a GCN-based malicious webpage detection method (GMWD), which constructs a text graph to describe and then a GCN model to learn the syntactic and semantic correlations within and between webpage source codes. We replace word nodes in the text graph with phrase nodes to better maintain the syntactic and semantic integrity of the webpage source code. In addition, we use the URL links appearing in the source code as auxiliary detection information to further improve the detection accuracy. The experiments showed that the proposed method can achieve 99.86% accuracy and a 0.137% false negative rate, achieving a better performance than other related malicious webpage detection methods.
Supplementary material to "Key drivers of the annual carbon budget of biocrusts from various climatic zones determined with a mechanistic data-driven model"

Supporting figuresFigure S1: Calibration of abiotic parameters for the data-driven model via comparing the seasonal (left panel) and diurnal (right panel) surface temperature of lichen-dominated species at sites D1 (A, B), D2 (C, D), T1 (E, F), T3 (G, H) and A1 (I, J); at site T3 the simulated patterns of moss-dominated species was compared to measured temperature data by four sensors at different locations.In the right panel describing the diurnal patterns, the (a), (b), (c) and (d) indicate the comparison of patterns of hourly average data in January,
Content based Image Retrieval Using Clustering Method

Content-based image retrieval (CBIR) is the deployment of computer vision methods to the information retrieval challenge, that is, the subject of seeking out digital images in vast databases. Techniques based on automated feature extraction methods for obtaining similar images from image databases are under the purview of CBIR. Traditional content based image retrieval (CBIR) systems extract a single feature at a time and use it to categorize and group images in response to a query. To bridge the gap between high-level concepts and low-level features, our innovative method integrates many feature extraction algorithms. In color-based retrieval, we use quadratic distance formulas to calculate the HSV affinity matrix for photos in the query and the database. Wavelet decomposition at six stages is used in texture-based retrieval. Finding the similarity measures between the query image and the images in the database is done with the help of the Euclidean distance classifier. The integrated method used to decrease the file sizes of the retrieved photographs keeps the user from having to pay as much attention to the process.
A Novel Histological Dataset and Machine Learning Applications

Histology has significant importance in the medical field and healthcare services in terms of microbiological studies. Automatic analysis of tissues and organs based on histological images is an open problem due to the shortcomings of necessary tools. Moreover, the accurate identification and analysis of tissues that is a combination of cells are essential to understanding the mechanisms of diseases and to making a diagnosis. The effective performance of machine learning (ML) and deep learning (DL) methods has provided the solution to several state-of-the-art medical problems. In this study, a novel histological dataset was created using the preparations prepared both for students in laboratory courses and obtained by ourselves in the Department of Histology and Embryology. The created dataset consists of blood, connective, epithelial, muscle, and nervous tissue. Blood, connective, epithelial, muscle, and nervous tissue preparations were obtained from human tissues or tissues from various human-like mammals at different times. Various ML techniques have been tested to provide a comprehensive analysis of performance in classification. In experimental studies, AdaBoost (AB), Artificial Neural Networks (ANN), Decision Tree (DT), Logistic Regression (LR), Naive Bayes (NB), Random Forest (RF), and Support Vector Machines (SVM) have been analyzed. The proposed artificial intelligence (AI) framework is useful as educational material for undergraduate and graduate students in medical faculties and health sciences, especially during pandemic and distance education periods. In addition, it can also be utilized as a computer-aided medical decision support system for medical experts to minimize spent-time and job performance losses.
WIRELESS SENSING AND COMMUNICATION DESIGN FOR THE INTERNET OF THINGS: AN ANALYSIS

The Internet is evolving from a network that is centred on people to one that is centred on objects.By 2020, it is predicted that 50 billion things will be connected to the Internet.However, maintaining compatibility across diverse Internet things, such as handheld mobile devices, wireless connectivity, and Radio Frequency Authentication, becomes incredibly challenging as a result of this shift.Numerous little devices are now being used in various facets of daily life thanks to advancements in wireless information and electronic equipment.These devices are capable of sensing, computing, and communicating.Common parts include integrated CPUs, several intelligent systems, and low-power antennas.A WSN, which is necessary for sensing and monitoring environmental factors, is built using these sensors.In addition to WSNs, the idea of the Internet of Things (IoT) is gaining popularity.According to this definition, the IoT is the connection of identifiable objects to the internet for the purpose of detection and monitoring.
Encrypting security for virtualized environments in big data storage with generalized anonymization strategies

Big data security has grown in importance as a result of its strong correlation to users. Because big data contains individually identifying information, it poses a considerable safety issue. To protect personal information, data confidentiality is commonly employed in non-interactive information sharing and transmission settings. This entails hiding identifiable and confidential material from the owners of acquired data.Anonymization is a computational technology that eliminates or alters personal identifying details, resulting in anonymous data which cannot be linked to every single citizen. The anonymization method enables the release of knowledge, allowing for study and assurance. Privacy protection protects sensitive data from a range of threats. Utilizing data encryption and data anonymization is indeed the method of making modifications to the information to be utilised or used in a method which will avoid pertinent data from being identified. Several organisations make data available anonymously to the general public. Employing data anonymization, important bits of private information are masked in order to protect private information.Anonymous data may be stored and accessed in the cloud without fear of being intercepted by others. Anonymization protects against data abuse and insider exploit hazards that lead to regulatory compliance loss.
eShadow+: Mixed Reality Storytelling Inspired by Traditional Shadow Theatre

eShadow is a digital storytelling platform inspired by traditional Shadow Theatre. It enables the creation of digital stories within a project-based approach that may start from scenario development and include the creation of digital puppets and sceneries, the set-up and recording of story scenes and the final assembly of a digital story. This paper presents details about eShadow architecture, features and how its use has been enhanced, employing the PerFECt framework, to combine it with immersive technologies such as projection mapping, to create mixed reality installations that offer rich learning experiences to participants in informal learning settings. Two installations are described and compared. The evaluation results demonstrate the effectiveness of the approach towards engaging learning experiences that combine mixed reality approaches with arts such as black light theatre.