Reviewerly/exHarmony huggingface dataset:
The effect of linguistic intelligence and memory on air traffic control performance of field aviation polytechnic

Some research has been conducted to understand how general knowledge and short-term memory affect academic achievement. However, research that links these two predictors with air traffic control performance in a laboratory has not been found. Twenty-four ATC cadets participated in this research. This is associative research with multiple correlation analysis that aims to find the correlation between linguistic intelligence and short-term memory toward cadets’ air traffic control performance in Aerodrome Control Tower (TWR) Laboratory. The research instruments used are oral proficiency interview, Intelligence Structure Test (IST), and air traffic control simulation. Data obtained, then, are analyzed by using the product-moment method of correlation. The results show that there is a positive correlation among the three variables with the level of closeness that falls in the high category that can be seen from the correlation coefficient value. In fact, linguistic intelligence and short-term memory both contribute to air traffic control performance during simulations. These findings offer several implications for the training program to promote success in learning air traffic control, especially in the laboratory.
SF2SE3: Clustering Scene Flow into SE(3)-Motions via Proposal and Selection

We propose SF2SE3, a novel approach to estimate scene dynamics in form of a segmentation into independently moving rigid objects and their SE(3)-motions. SF2SE3 operates on two consecutive stereo or RGB-D images. First, noisy scene flow is obtained by application of existing optical flow and depth estimation algorithms. SF2SE3 then iteratively (1) samples pixel sets to compute SE(3)-motion proposals, and (2) selects the best SE(3)-motion proposal with respect to a maximum coverage formulation. Finally, objects are formed by assigning pixels uniquely to the selected SE(3)-motions based on consistency with the input scene flow and spatial proximity. The main novelties are a more informed strategy for the sampling of motion proposals and a maximum coverage formulation for the proposal selection. We conduct evaluations on multiple datasets regarding application of SF2SE3 for scene flow estimation, object segmentation and visual odometry. SF2SE3 performs on par with the state of the art for scene flow estimation and is more accurate for segmentation and odometry.
Cybersecurity ontology and defense solutions: the POC platform

The paper presents the Platform Ontology of Cybersecurity (POC) as developed by the Pragmema team for big data analytics and early detection of cybersecurity incidents as needed in operational defense systems. The modeling of knowledge representation for threat intelligence, info sharing, and incidents reports is based on the theoretical assumptions provided by general linguistics (specifically text linguistics and pragmalinguistics), semantics and cognitive psychology in an AI perspective and turned into neural networks DDBB.
Unstable Depth Perception of Aerial Images in Crossed Mirror Array Can Be Controlled by Changing Fixation Distance

The unstable depth perception of aerial images in a crossed-mirror array (CMA) can be improved by changing the distance of fixation points. The perceived depths of aerial images in CMA suffer from large instability even when an aerial image is a geometrical optical real image. The perceived depth instability of aerial images is mainly influenced by their geometrical position from CMA. The perceived depths of aerial images shift toward the fixation points, and the stable depth perception of aerial images can be obtained if the fixation points are located near the aerial image distance. To stabilize the depth perception of aerial images, it is sufficient to appropriately control the observer's vergence even if the fixation point is not a real object. In addition, the luminance of aerial images affects the stability of their depth perception.
Distance Olympiads in the organization of joint vocational guidance work of the school and the university

The article shows how using network technologies, in particular the electronic distance learning system (LMS) Moodle, you can build career guidance among schoolchildren, involving them in a conscious choice of a profession in the field of IT technologies (information security specialist, data protection specialist, cryptographer). Some Russian educational Internet platforms are considered, designed to help school graduates decide on their future professional activities. The ways of vocational guidance work at school are shown, which can be implemented both during school hours and after school hours. It proposes its own approach to this type of activity through the involvement of schoolchildren in distance Olympiads. A specific example reveals the technology for the development and implementation of distance Olympiads. The three-year experience of preparing distance Olympiads on the basis of the university is described.
Communication Patterns of Deaf Students with Hearing Lecturer in Classroom

Deaf people communicate differently, such as using sign language or writing. This communication style difference becomes a problem when a deaf person is in higher education, where most of the communication is verbal orally. Most deaf students in the university in Indonesia do not have a standardized facility to help them study in class; therefore, each student could have a different communication style during class lectures. This article identifies the classroom communication patterns between deaf students and hearing lecturers. The research's informants are deaf students from three universities in Central Java. The characteristics of the informant are; 1) deaf student, 2) use more than one communication medium, and 3) the communication with the teacher happened in the classroom (offline class). This study uses a qualitative approach with a phenomenological method to understand the phenomenon of deaf students in the classroom—data obtained through in-depth interviews and literature study. The results of this study indicate that deaf students have a variety of communication styles in classrooms, such as a mobile application called Live Transcribe, a note taker, and an interpreter. However, there are still many obstacles, such as the transcripts from the application that are not per the speech, note takers that do not give a piece of comprehensive information, and the limited number of interpreters. Therefore, the students must change the media communication regularly depending on the situation to overcome those problems. To cite this article (7th APA style): Aprilia, R. & Nisa, F. K. (2022). Communication patterns of deaf students with hearing lecturer in the classroom. Journal Communication Spectrum: Capturing New Perspectives in Communication 12(2), 135-145. https://doi.org/10.36782/jcs.v12i2.2277
No-Label User-Level Membership Inference for ASR Model Auditing

With the advancement of speech recognition techniques, AI-powered voice assistants become ubiquitous. However, it also increases privacy concerns regarding users’ voice recordings. User-level membership inference detects whether a service provider misused users’ audio to build its Automatic Speech Recognition (ASR) model without users’ consent. Previous research assumes the model’s outputs, including its label (i.e., transcription) and confidence score, are available for security auditing. However, the model’s outputs are unavailable in many real-world cases, i.e., no-label black-box scenarios, which is a big challenge. We propose a substitute model analysis to transfer the knowledge of the service system to that of its built-in ASR model’s behavior with semantic analysis techniques. Based on this analysis, our auditor can determine the user-level membership with high accuracy ( $$\sim $$ 80%) by utilizing a shadow system technique and a gap inference method. The gap inference-based auditor is generic and independent of ASR models.
K-Means Clustering in Image Compression

Two techniques of machine learning are supervised learning and unsupervised learning. In Supervised learning, data is labelled whereas in a non-supervised learning algorithm input data is unlabelled. Supervised learning is further divided into classification and regression and unsupervised into clustering. This paper discusses the idea of clustering and its classification into hierarchical and partitional clustering, further discussing the types of partitional clustering, mainly K-means, and its difference over partition around medoids (PAM) and clustering large applications (CLARA) is also explained. This paper talks about the application of K-means clustering in image compression, and a practical case of compressing an image is also discussed.
SeAct: Semantic Adaptive Segmentation of Sensor Data Streams for Human Activity Recognition

Pervasive computing delivers services based on user needs through smart environments that incorporate and integrate everyday objects discreet and non-intrusive. Personal applications provide the data collected by sensors for Human Activity Recognition. The main limitation is that these activities need to be continuously segmented for HAR. Furthermore, a growing problem is related to the disambiguation of activities since some actions generated by the same sensors belong to different activities. This paper proposes a hybrid method, SeAct, which dynamically adjusts segment size, combining machine learning and semantic inference. Experiments with CAD-120 data sets and a state-of-the-art hybrid method improve recognition accuracy and precision.
Path Planning Method for Multi-robot Formation System Based on Hierarchical Reinforcement Learning

For the path planning in complex environments, a multi-robot formation path planning algorithm based on hierarchical reinforcement learning is proposed. The hierarchical reinforcement learning algorithm reasonably divides the task subspace, and designs a goal-oriented method for the robot’s stage sub-tasks, and the robot sequentially completes the sub-tasks and receives level-by-level rewards. The improved learning algorithm not only reduces unnecessary path exploration for the multi-robot system, but also improves the learning efficiency of the system.
INDEPENDENT LEARNING AND INDEPENDENT WORK IN THE MODERN EDUCATIONAL SPACE

Defining the interrelationships between self-learning and self-employment are particularly important in a global pandemic and online learning environment. The developed skills for independent learning strongly influence the independent activity of the students. Positive attitude towards learning, motivation, self-confidence, curiosity and perseverance are just some of the most important competencies of independent learning. The acquired skills, qualities and abilities allow each student to find his place in educational and research aspects.
Sentiment Analysis in Airlines Industry Using Machine Learning Techniques

With the increasing power of Internet, businesses get a huge number of customer feedbacks through: their business website, social media page, business listings, etc. Majority of business do not know how to use this information to improve themselves. However, unstructured feedback on Facebook/Instagram/Twitter is where the volume lies. But the problem is these feedbacks are unstructured and there is no aggregated sentiment that we may conclude from them. To analyze these unstructured customer feedbacks at scale, machine learning is used. In this work we present a survey on various machine learning techniques that have been used in past eight years for analysis of tweets/comments related to airline industry.
TEACHING DURING PANDEMIC; IMPACT ON STUDENTS’ ENGLISH SKILLS OF JUNIOR HIGH SCHOOL IN KOTO TANGAH PADANG

This paper investigates the impact of online teaching and learning undertaken during pandemic of Covid-19 on students’ English skills. The skills include speaking, listening, reading, and writing skills. This study used a qualitative method with a descriptive approach exploring what skills got impacted of 2 years online learning situation. Students' English skill, in particular, speaking and listening skills were negatively affected by the application of online learning that they had been done in prior. While, reading and writing skills had a positive impact on students' English skills as it used Geschool application therefore reading skills were more taught. This learning also made students do writing activities such as writing material from learning videos. The observation activities showed students were better able to participate in reading and writing lessons. The resdult of this studi was also indicated that in general English skills were badly affected which was showed by the average value of students was 50. Mpreover, all of those skills were impacted due to inadequate of facilities, motivation, and affectiveness of online teaching itself.
Performance analysis of inter-satellite round-robin differential-phase-shift quantum key distribution

Abstract As the vision of global-scale unconditional information security becomes gradually realized, the importance of inter-satellite quantum communications has been rapidly increasing. The recently proposed round-robin differential-phase-shift (RRDPS) quantum key distribution (QKD) protocol has attracted much attention not only due to its potential high error tolerance, but also due to its distinct feature that the information leakage can be bounded without monitoring signal disturbances. Despite many existing implementations over fiber-optic channels, the feasibility of RRDPS QKD over an inter-satellite channel is still unknown. Moreover, despite the current advances in orbital angular momentum (OAM) encoding and temporal mode (TM) encoding, most of the existing studies on RRDPS QKD are restricted to time-bin encoding. In this work, we remedy this situation by exploring the feasibility of performing RRDPS QKD using OAM encoding and TM encoding over an inter-satellite channel. Our results indicate that OAM encoding is preferable to time-bin encoding only under the circumstances where a low dimension and a large receiver aperture are used. However, we find that TM encoding is the best encoding scheme in RRDPS QKD over an inter-satellite channel. In particular, we show that TM encoding not only leads to the best performance and the largest feasible parameter range, but also, for the first time, enables all the theoretically available advantages of an increased dimension to be realized in the context of RRDPS QKD.
Education Digitalization as a Factor in Human Capital Asset’s Development: Modern Challenges and Development Prospects

The study aims to scientifically search the optimal socio-economic and technical solutions in the field of educational process digitalization in the context of human capital asset’s development. The scientific novelty is predetermined by the goals of minimizing the options for choosing between a wide variety of software products presented at this technical development stage, the use of which involves the transfer of some educational process elements into the virtual space. On the example of natural scientific disciplines, the possibility of transferring the educational process to virtual space is considered. The working hypothesis is due to an intensive search for the methods to reduce financial costs by the educational institution while ensuring the goal of quality education achievement with the intensive use of information and communication technologies. The research methods include a comprehensive analysis of the digital education elements, the selection of effective tools for implementing the transition to a digital model, an assessment of the potential prospects for the development of a mixed traditional-digital approach in education, a scientific study of the distance learning concept as one of the promising trajectories for the further educational development and one of the main components of the digital learning. The current state of education in the context of a market economic model is assessed in research methodology development. The result of the study is the substantiation of the strategic directions for increasing the education system functioning efficiency using digital technologies in the context of human capital development.
Breaking the Fourth Wall of Data Stories Through Interaction

Interaction is increasingly integrating into data stories to support data exploration and explanation. Interaction can also be combined with the narrative device, breaking the fourth wall (BTFW), to build a deeper connection between readers and data stories. BTFW interaction directly addresses readers by requiring their input. Such user input is then integrated into the narrative or visuals of data stories to encourage readers to inspect the stories more closely. In this work, we explore the design patterns of BTFW interaction commonly used in data stories. Six design patterns were identified through the analysis of 58 high-quality data stories collected from a range of online sources. Specifically, the data stories were categorized using a coding framework, including the input of BTFW interaction provided by readers and the output of BTFW interaction generated by data stories to respond to the input. To explore the benefits as well as concerns of using BTFW interaction, we conducted a three-session user study including the reading, interview, and recall sessions. The results of our user study suggested that BTFW interaction has a positive impact on self-story connection, user engagement, and information recall. We also discussed design implications to address the possible negative effects on the interactivity-comprehensibility balance, information privacy, and the learning curve of interaction brought by BTFW interaction.
The Influence of Visual Provenance Representations on Strategies in a Collaborative Hand-off Data Analysis Scenario

Conducting data analysis tasks rarely occur in isolation. Especially in intelligence analysis scenarios where different experts contribute knowledge to a shared understanding, members must communicate how insights develop to establish common ground among collaborators. The use of provenance to communicate analytic sensemaking carries promise by describing the interactions and summarizing the steps taken to reach insights. Yet, no universal guidelines exist for communicating provenance in different settings. Our work focuses on the presentation of provenance information and the resulting conclusions reached and strategies used by new analysts. In an open-ended, 30-minute, textual exploration scenario, we qualitatively compare how adding different types of provenance information (specifically data coverage and interaction history) affects analysts' confidence in conclusions developed, propensity to repeat work, filtering of data, identification of relevant information, and typical investigation strategies. We see that data coverage (i.e., what was interacted with) provides provenance information without limiting individual investigation freedom. On the other hand, while interaction history (i.e., when something was interacted with) does not significantly encourage more mimicry, it does take more time to comfortably understand, as represented by less confident conclusions and less relevant information-gathering behaviors. Our results contribute empirical data towards understanding how provenance summarizations can influence analysis behaviors.
Trinity: End-to-End In-Database Near-Data Machine Learning Acceleration Platform for Advanced Data Analytics

Three Important yet Independent Technology Trends
Designing an Edutainment Game for Teaching Computer Concepts for Children's

E-learning today is very widely common technique by using mobile phones or computers and other electronic devices, where new educational tools and programs have been developed that include text, media, images, and graphics, as in video games. Knowing that they are available free of charge on mobile application. However, there are a few that use it for the purpose of learning. Therefore, this report discusses a special mobile application for children to learn how to control the computer in general. Where it focuses on the primary category of children to show their creativity and build their own world through e-learning. Where educational videos will be published, through which the child can understand how the mobile application works.
Image denoising algorithm for millimeter wave imaging

In this paper, a denoising method for active millimeter wave (mmWave) synthetic aperture radar (SAR) imaging system is proposed based on quadratic template matching and stable variance variation. In order to eliminate the background noise while preserving the details of the main part, we use the result of distance regularized level set evolution (DRLSE) as a primary template for background filtering, and then use the result of background filtering as a secondary template. In order to eliminate the mixed Poisson-Gaussian noise in the image, we transform the noise data into an approximate uniform Gaussian distribution by variance-stabilizing transform (VST), and then use the improved wavelet threshold denoising (WTD) to denoise the data. The experimental results reveal that the algorithm is effective.
Framework and Path: A Media Convergence Platform Based on Blockchain Technology

Media convergence has become the trend of media development nowadays, but it still faces problems such as lack of vitality of practitioners, serious homogenization of content, lack of accurate media positioning and user portrait, lack of profit model and difficulties in information traceability. Blockchain technology has a catalytic effect on the construction of convergent media platforms because of its decentralization, openness, information immutability, traceability and other characteristics. In this paper, the framework of Converged media platform based on blockchain technology is studied, and its technical framework and logical framework are built to provide reference for the construction of media convergence platform.
A systematic review of government casebooks and empirical studies on elementary English education in the era of COVID-19

The main purpose of this paper is to draw implications for online English classes by systematically reviewing government casebooks and research studies dealing with online elementary school English classes in Korea during the COVID-19 epidemic.&#x0D; In-depth analyses of Korean government casebooks and 16 primary studies published during the years 2020 and 2021 showed that the Ministry of Education tried to support elementary schools by providing examples of restructuring standards for achievement, examples of instructional plans, and examples of evaluation and feedback. Some other findings are as follows. The ratio of content utilization-centered classes using the ‘E-learning platform’ for elementary English was high. Of the entire studies, 74% was conducted in the metropolitan area. Learners valued interaction with teachers the most. Teachers were concerned about copyright infringement and the learning gap between learners. It is necessary to further explore various types of online English classes, taking into account the characteristics of elementary learners.