Reviewerly/exHarmony huggingface dataset:
Improving the power of hypothesis tests in sparse contingency tables

Abstract When analyzing data in contingency tables it is frequent to deal with sparse data, particularly when the sample size is small relative to the number of cells. Most analyses of this kind are interpreted in an exploratory manner and even if tests are performed, little attention is paid to statistical power. This paper proposes a method we call redundant procedure, which is based on the union–intersection principle and increases test power by focusing on specific components of the hypothesis. This method is particularly helpful when the hypothesis to be tested can be expressed as the intersections of simpler models, such that at least some of them pertain to smaller table marginals. This situation leads to working on tables that are naturally denser. One advantage of this method is its direct application to (chain) graphical models. We illustrate the proposal through simulations and suggest strategies to increase the power of tests in sparse tables. Finally, we demonstrate an application to the EU-SILC dataset.
A systematic scoping review of 360-degree videos in teacher education

Purpose 360-degree video is recorded with omnidirectional or multi-camera systems that capture all directions at the same time in a spherical view. With immersive technologies gaining momentum and reducing educational cost, it has attracted the interest of the academic community. However, little is known about using 360-degree video in teacher education. The purpose of this study is to conduct a systematic scoping review through a systematic process based on 15 included studies to determine the characteristics, impacts, strengths and weaknesses of the 360-degree video applied to teacher education. Design/methodology/approach This study combines scoping and systematic review based on the PRISMA paradigm. Findings This paper explores that 360-degree videos are applicable to teacher education, specifically with their positive effects on pre-service teachers’ immersion, noticing, reflection and interpersonal competence. However, as for learners’ reactions, physical discomfort is reported, like motion sickness. Research limitations/implications First, some recently published studies on the subjects were partially accessible, which precluded the authors from adding their findings to this study. Second, the sample of articles is constrained to the search and selection strategies described in the methods section, which increases the possibility that pertinent research may be omitted. Furthermore, this study’s summary of the selected research may be inadequate. Third, only English-language publications were included in this study. Future researchers can expand on this topic by gathering additional relevant empirical data from publications in other languages. Practical implications Practically, findings in this study reveal the positive effects of 360-degree video in teacher education. The results may help researchers and preservice teachers better understand 360-degree video and use it more frequently in teaching. Instructional video technologies have been found to have a nearly medium effect on learning effectiveness in educational practice from a broader perspective. Originality/value The findings in this study can shed light on future educational technology research on instructional video technologies and technology-enhanced teacher education.
Control issues, artificial neural network (ANN) for acrobot system

Acrobot is a robotic system with several levels of operational states investigated by the author. Due to the limited nature of the investigation under certain ideal conditions, designers have to create some algorithms that control the system most appropriately in a given working environment. In this paper, the author proposed the problem of designing, modeling and controlling an acrobot system, including ANN. Mathematical models, Simulink are also presented in a specific way. Simulation parameters have been adjusted to be the most suitable and intuitive. Based on the simulation data, the performance analysis of the system becomes more accurate. Above suggestions are intended to serve vocational education and scientific research. ANN is the most intelligent control method currently added in this paper to firmly confirm its effectiveness in all problems. Proposing control strategies for different models is also applied by the author.
Order Independence for Rationalizability

This paper provides a sufficient monotonicity condition for the solution of a rationalizability procedure to be independent from the order of elimination. The analysis unfolds in an abstract environment that applies to any form of finite game and elimination procedure. The result applies to rationalizability procedures with monotone belief operators, such as "directed" rationalizability, interim correlated rationalizability and, in sequential games, initial and backwards rationalizability.
Super‐resolution reconstruction of vertebrate microfossil computed tomography images based on deep learning

Abstract Micropaleontologists use the fine structures of microfossils to extract evolutionary information. These structures could not be directly observed with the naked eye. Recently, paleontologists resort to computed tomography (CT) images to mine the information, and pursue higher resolution CT images with in‐depth research. Therefore, we propose a new model, weighted super‐resolution generative adversarial network (WSRGAN), for the super‐resolution reconstruction of CT images. The model proposed herein (WSRGAN) obtained higher LPIPS (0.0757) on the experimental dataset, compared with Bilinear (0.4289), Bicubic (0.4166), EDSR (0.2281), WDSR (0.2640), and SRGAN (0.0815). WSRGAN meets the requirements of paleontologists for reconstructing fish microfossils. We hope that more super‐resolution reconstruction methods based on deep learning could be applied to paleontology and achieve better performance.
DFGR: Diversity and Fairness Awareness of Group Recommendation in an Event-based Social Network

Abstract An event-based social network is a new type of social network that combines online and offline networks, and one of its important problems is recommending suitable activities to users. However, the current research seldom considers balancing the accuracy, diversity and fairness of group activity recommendations. To solve this problem, we propose a group activity recommendation approach that considers fairness and diversity perception. Firstly, we calculate activity similarity based on the context and construct an activity similarity graph. We define the weighted coverage on the similarity graph as a submodular function and transform the problem of fair and diverse group activity recommendation into maximizing the weighted coverage on the similarity graph while considering accuracy, fairness, and diversity. Secondly, we employ a greedy algorithm to find an approximate solution that maximizes the weighted coverage with an approximation ratio. Finally, we conducted experiments on two real datasets and demonstrate the superiority of our method compared to existing approaches. Specifically, in the domain of diversity-based recommendation algorithms, our method achieves a remarkable 0.02% increase in recall rate. Furthermore, in the domain of fairness-based recommendation algorithms, our proposed method outperforms the latest approach by 0.05% in terms of overall metrics. These results highlight the effectiveness of our method in achieving a better balance among accuracy, fairness, and diversity.
Employment of Graduates of Secondary Vocational and Higher Education Institutions of the Kursk Region in Conditions of Disproportion between the Educational Segment and the Labor Market

Relevance. In modern conditions, when significant social and social changes are taking place, the problem of employment of graduates of institutions of secondary vocational and higher education is becoming important and needs fundamental changes in the field of employment. Due to the imbalance in the process of training specialists at all levels of education with the real needs of the labor market, there is periodically an oversaturation or shortage of certain professions. Consequently, there is a disproportion between supply and demand in the field of educational services, on the one hand, and the employment market, on the other. The purpose is to identify the causes and consequences of the imbalance of the educational sphere and the labor market and employment. Objectives: to characterize theoretical approaches to the analysis of the socio-economic nature of the labor market; to determine the value attitudes of young people in the professional and labor sphere; to identify the problems of employment of young professionals in the labor market of the Kursk region. Methodology. The study is based on concepts and theories that reveal the socio-cultural and socio-economic nature of the Russian labor market and employment. Methods of synthesis, comparison and analogy were applied, which made it possible, on the basis of specific empirical material, to determine the main problems of employment of graduates of institutions of secondary vocational and higher education in the Kursk region. Results. Educational institutions have a low orientation towards the training of qualified specialists. Communication employer (economic entity) - graduate (university) is in a state of imbalance and ultimately does not solve the problem of youth entering the labor market. Conclusions. The main difficulties in finding a decent job are the rather high requirements of the employer to the applicant and the lack of work experience in the chosen profession. The most significant factors in employment are wages, proximity of work to home and flexible employment schedule. It is not always good (excellent) academic performance at a university (SSU) that becomes the determining factor on the part of the employer for choosing a potential candidate for a particular vacancy.
Newsgist: video generation from news stories

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Small Object Detection in Remote Sensing Images Based on Window Self-Attention Mechanism

For remorte sensing image object detection tasks in the small object feature, extraction ability is insufficient and difficult to locate, and other problems. This paper proposes an improved algorithm for small object detection in remote sensing images based on a window self-attention mechanism. On the basis of You Only Look Once (YOLO)v5s, a shallow feature extraction layer with four times downsampling is added to the feature fusion pyramid and the window self-attention mechanism is added to the Path Aggregation Network. Experiments show that the improved model obtained the Mean Average Precision (mAP) of 78.3% and 91.8% on the DIOR and Remote Sensing Object Detection public data sets with frames per second of 65 and 51, respectively. Compared with the basal YOLOv5s network, the mAP has improved by 5.8% and 3.3%, respectively. Compared with other object detection methods, the detection accuracy and real-time performance have been improved.
Harmonized Portrait‐Background Image Composition

Abstract Portrait‐background image composition is a widely used operation in selfie editing, video meeting, and other portrait applications. To guarantee the realism of the composited images, the appearance of the foreground portraits needs to be adjusted to fit the new background images. Existing image harmonization approaches are proposed to handle general foreground objects, thus lack the special ability to adjust portrait foregrounds. In this paper, we present a novel end‐to‐end network architecture to learn both the content features and style features for portrait‐background composition. The method adjusts the appearance of portraits to make them compatible with backgrounds, while the generation of the composited images satisfies the prior of a style‐based generator. We also propose a pipeline to generate high‐quality and high‐variety synthesized image datasets for training and evaluation. The proposed method outperforms other state‐of‐the‐art methods both on the synthesized dataset and the real composited images and shows robust performance in video applications.
Improvement Airport Security System with Face Recognition Using Neural Network Based on the Arduino Uno Microcontroller

The aim of this study is to implement an algorithm for face recognition, based on the Arduino uno microcontroller. This paper presents it as an airport security system to detect passenger’s face and compare the result with the database of unwanted people. The system combines laser trigger circuit for image capturing and artificial neural network for image recognition. The laser trigger circuit has many benefits such as avoiding camera shakes or taking a picture without a timer. The captured image will be analyzed and processed in MATLAB using an artificial neural network to recognize the passenger’s face from the real captured images after the training phase. Many experiments have been conducted on our face databases with various numbers of iterations. The recognitions’ accuracy and efficiency of the proposed model are 93.33% and 2.67 respectively with 0.696530 seconds as execution time. The result shows the robustness of the developed model in terms of mean squared error, execution time, recognitions’ efficiency and accuracy. The smallest obtained mean squared error is 9.9991e-04 for the training data set and 0.1764 for the testing data set when they are recorded for a modified neural network which makes the developed system more reliable. Finally, the artificial neural network demonstrates the ability to detect the unseen relationship between features belong to the same face.
Passive identification of subjective preferences toward individual items using eye-tracking in a virtual reality environment

Abstract The usage of virtual reality (VR) has been growing in many fields of research and therapy thanks to its immersive and gamified nature. Detection of the users’ subjective experience is thus essential for the effective personalization of content. Eye-tracking (ET) data and specifically gaze, in two-dimensional tasks, has been linked to value-based choices and emotional states. Therefore, here we aimed to develop a method for passive identification of subjective preferences based on ET data collected during a VR experience. For this purpose, we developed a naturalistic dynamic VR task where participants searched and looked at complex objects of pets and their control shapes that appeared in pre-defined locations in random order. At the end of the task, participants ranked their preference, valence, and arousal of the items they saw during the task. ET data was recorded using a built-in binocular eye-tracker within the VR headset. We found that the gaze behavior features of the median distance of gaze from the center of objects and the median gaze scan speed showed a significant interaction with object type (pets/shapes), as well as a significant positive relation to preference and valence rankings of pets. Our results suggest that these gaze behavior features could be used as passive biomarkers for detecting individual preferences and pleasantness, and in the future may enable successful personalization of VR content in real-time for various applications such as optimization of psychiatric diagnosis and treatment sessions.
Metode Design Thinking Pada Sistem Informasi Presensi Pegawai Kejaksaan Negeri Kota Bogor

Employee presence is an important factor for an agency or company to achieve goals, this is related to discipline and has an impact on the performance of each employee. But in reality, there are still several agencies that still use manual absences that have not been computerized, one of which is the Kejaksaan Negeri Kota Bogor's Office. The system used by the Kejaksaan Negeri Kota Bogor in the attendance process is still manual, namely using a daily attendance book which affects the efficiency and effectiveness of data collection, data search, and calculations and requires a relatively long time. Therefore, it is necessary to have special data collection to record attendance and absence so that work activities can be recorded in real-time and properly, one of which is by using a computerized system with Information Systems. The information system built on a website basis uses the CodeIgniter 3 framework and MySQL. The method used is the Design Thinking Method which has 5 stages. The output of this study is known to be tested with usability testing by user on the Learnability aspect of 75%, then on the efficiency aspect of 100% and the memorable aspect of 66.77%. The average result of the test is quite good at 80.56%. So by making this information system, it can facilitate the process of being on time, searching for data, calculating and summarizing timeliness and minimizing errors when recording presence data.
Immersive Virtual Reality, Tool for Accessible Design: Perceived Usability in an Interior Design Studio Setting

Despite the benefits of virtual reality (VR), there has been limited exploration of how immersive VR can be implemented in interior design course content and how students perceive its use. For this study, we investigated how an immersive VR tool can be incorporated into the interior design studio to support second-year students’ ( n = 18) design process and outcomes and examined its perceived usability (i.e., measured as usefulness, ease of use, enjoyment, and intention to use) for accessible space design. We also explored VR’s impact on advancing the interaction with space, understanding spatial information and problem-solving (PS), and how students accept the use of this technology. The students used immersive VR throughout the design process for one semester and then participated in a questionnaire. We found that immersive VR is helpful by enhancing students’ interaction with the designed space, developing better spatial understanding, and advancing PS. VR was also perceived to be highly enjoyable and moderately improved job performance but was slightly less easy to use. Participants showed a high intention to utilize VR, and spatial interaction and perceived enjoyment were significantly associated with that intention. Our findings suggest that immersive VR has potential benefits for teaching and learning accessible design and can be used more generally in diverse contexts of interior design pedagogy. While these results are promising, continued research to more effectively adopt the use of this technology in the interior design studio is needed.
Trilattice-Based Access Control Models: How to Secure Current Computer Network Mikhail

Designing security, from the hardware level, is essential to ensure the integrity of the intelligent cyberphysical infrastructure that is the Industrial Internet of Things (IIoT). If intelligent cyber-physical infrastructure fails to do the right things because it is insecure and vulnerable, then there will be negative social consequences [1]. Security is, in a sense, the access control to IIoT systems, which increasingly relies on the ability to compose different policies. Therefore, the advantage in any framework for compiling policies is that it is intuitive, formal, expressive, application-independent, as well as expandable to create domain-specific instances. Recently, such a scheme was proposed based on Belnap logic FOUR2 [2]. Four values of the Belnap bilattice have been interpreted as grant, deny, conflict, or unspecified with respect to access-control policy. Belnap's four-valued logic has found a variety of applications in various fields, such as deductive database theory, distributed logic programming, and other areas. However, it turns out that the truth order in FOUR2 is a truth-and-falsity order at the same time [3]. The smallest lattice, where the orders of truth and falsity are independent of each other, which is especially important for security policy, is that of Shramko-Wansing’s SIXTEEN3. This generalization is well-motivated and leads from the bilattice FOUR2 with an information and a truth-and-falsity ordering to another algebraic structure, namely the trilattice SIXTEEN3 with an information ordering together with a truth ordering and a (distinct) falsity ordering. Based on SIXTEEN3 and new Boolean predicates to control access [4], we define an expressive access-control policy language, having composition statements based on the statements of Schramko-Wansing’s logic. Natural orderings on politics are obtained by independent lifting the orders of truth and falsity of trilattice, which results in a query language in which conflict freedom analysis can be developed. The reduction of formal verification of queries to that on predicates over access requests enables to carry out policy analysis. We evaluate our approach through examples of control access model policy.
View recommendation for multi-camera demonstration-based training

Abstract While humans can effortlessly pick a view from multiple streams, automatically choosing the best view is a challenge. Choosing the best view from multi-camera streams poses a problem regarding which objective metrics should be considered. Existing works on view selection lack consensus about which metrics should be considered to select the best view. The literature on view selection describes diverse possible metrics. And strategies such as information-theoretic, instructional design, or aesthetics-motivated fail to incorporate all approaches. In this work, we postulate a strategy incorporating information-theoretic and instructional design-based objective metrics to select the best view from a set of views. Traditionally, information-theoretic measures have been used to find the goodness of a view, such as in 3D rendering. We adapted a similar measure known as the viewpoint entropy for real-world 2D images. Additionally, we incorporated similarity penalization to get a more accurate measure of the entropy of a view, which is one of the metrics for the best view selection. Since the choice of the best view is domain-dependent, we chose demonstration-based training scenarios as our use case. The limitation of our chosen scenarios is that they do not include collaborative training and solely feature a single trainer. To incorporate instructional design considerations, we included the trainer’s body pose, face, face when instructing, and hands visibility as metrics. To incorporate domain knowledge we included predetermined regions’ visibility as another metric. All of those metrics are taken into account to produce a parameterized view recommendation approach for demonstration-based training. An online study using recorded multi-camera video streams from a simulation environment was used to validate those metrics. Furthermore, the responses from the online study were used to optimize the view recommendation performance with a normalized discounted cumulative gain (NDCG) value of 0.912, which shows good performance with respect to matching user choices.