Reviewerly/exHarmony huggingface dataset:
Technology-Enhanced and Student-Centered Learning as a Method to Foster Students’ ICT Competence and Problem Coping Skills

The aim of this research-to-practice full paper is to present the results of most recent analyses of the long-term school pilot project named "learning office approach". The learning office represents an alternative branch of a technology-oriented vocational secondary school in Europe encompassing the grades K9–K13 and has been implemented at the IT department in 2016. Students of a learning office cohort have flexible course schedules allowing them to choose between several subjects on a daily basis, which in turn enables differentiation and individualization: Students seeking help or striving to deepen their knowledge and skills may visit chosen subjects more often. Since learning office subjects do not rely on lectures and direct instruction, teachers become facilitative coaches helping students become self-responsible learners and study in a self-driven, self-directed way, which in turn fosters their 21 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">st</sup> century skills.The COVID-19 pandemic and resulting emergency remote teaching strategies demonstrated the supreme importance of digital competences and self-efficacy: Students were abruptly required to work autonomously with digital systems and study at home in a self-driven way. Since the learning office heavily relies on students working with digital educational material and tools in a self-directed and student-centered way, the question arises if learners show indications of increased ICT competences and self-efficacy in the learning office.This paper evaluates the interplay of students’ self-reported ICT skills, school-specific self-efficacy, and academic performance in the learning office approach and traditional classrooms. Based on an extensive literature review, a questionnaire was developed and distributed among all 552 students of the grades 9–13 and students’ final grades of the winter term 2021/22 were analyzed. The results show a significant increase in self-perceived problem coping skills and ICT competences in the learning office approach, including the sub-scales "general ICT self-concept", "communication", "process and store", and "generate content". We conclude that technology-enhanced student-centered approaches, such as the learning office, are a viable method to foster students’ 21 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">st</sup> century digital skills.
A Study of Effectiveness and Problem Solving on Security Concepts with Model-Eliciting Activities

Security is a critical aspect in the process of designing, developing, and testing software systems. Due to the increasing need for security-related skills within software systems, there is a growing demand for these skills to be taught in computer science. A series of security modules was developed not only to meet the demand but also to assess the impact of these modules on teaching critical cyber security topics in computer science courses. This full paper in the innovative practice category presents the outcomes of six security modules in a freshman-level course at two institutions. The study adopts a Model-Eliciting Activity (MEA) as a project for students to demonstrate an understanding of the security concepts. Two experimental studies were conducted: 1) Teaching effectiveness of implementing cyber security modules and MEA project, 2) Students’ experiences in conceptual modeling tasks in problem-solving. In measuring the effectiveness of teaching security concepts with the MEA project, students’ performance, attitudes, and interests as well as the instructor’s effectiveness were assessed. For the conceptual modeling tasks in problem-solving, the results of student outcomes were analyzed. After implementing the security modules with the MEA project, students showed a great understanding of cyber security concepts and an increased interest in broader computer science concepts. The instructor’s beliefs about teaching, learning, and assessment shifted from teacher-centered to student-centered during their experience with the security modules and MEA project. Although 64.29% of students’ solutions do not seem suitable for real-world implementation, 76.9% of the developed solutions showed a sufficient degree of creativity.
Educating Reflective Systems Developers at Scale: Towards “productive feedback” in a semi-capstone large-scale software engineering course

Feedback is critical in education. This Innovative Practice Full Paper reports lessons learned from improving the quality of feedback in a semi-capstone software engineering course, with particular focus on how to deliver productive feedback in large scale during project work. The bachelor-level introduction to software engineering course is taken by about 500 students from eight study programs, organised into 72 project teams. The course aims to educate reflective systems developers. The teaching staff includes 29 teaching assistants as supervisor and product owners for teams. Project teams get feedback on seven deliverables as part of formative portfolio assessment. Students expressed frustration on feedback not being aligned, that they got critique on topics not stated in assignments and that teaching assistants were reluctant to discuss the feedback. This article provides a description of the course design, an assessment of the quality of feedback and lessons learned from three main changes: Revising assignments and rubrics, reorganising the teaching staff and increasing training of teaching assistants. In discussing the changes, we draw on a survey to students with 142 respondents, a survey to teaching assistants with 18 respondents, meeting minutes from a student reference group and experience reports from teaching assistants as well as literature and own experience. The article concludes with three actionable lessons learned for large-scale semi-capstone courses.
Drawing on Augmented Reality to Develop STEM Competencies in Primary Education

This article proposes PluggedAR, an application aimed at children aged 6 to 9 and based on Augmented Reality (AR) for mobile devices. PluggedAR allows working on basic concepts of programming as binary numbers, image representation, programming languages, graphics, and minimum spanning trees. In order to test the effectiveness of PluggedAR, a study has been conducted questioning whether the use of AR increases the enthusiasm of students to learn and encourages them to solve the proposed activities. In fact, all the participating students (100%) agreed that learning new concepts with PluggedAR is more entertaining and fun, and their desire to use it in the classrooms was expressed by all of them (100%). In addition, the wide variety of activities has been met with mixed preferences by students. A group of teachers has also participated, and a high percentage of them (50%) believe that PluggedAR is highly recommended for working with children, and 33% of them highlight the wide range of concepts offered by PluggedAR. It is expected that the use of PluggedAR supports the development of STEM (Science, Technology, Engineering, and Mathematics) skills such as problem-solving, logical thinking, or decision making.
Evaluation of Recurrent Neural Network Model Training for Health Care Suggestions

Neural networks have revolutionized the field of data analytics and decision support, where recurrent neural networks are designed for solving time-series data. Total care is a synonym for complete patient care recently to respond patient’s physical, emotional, social, economic, and spiritual needs, and as such an efficient prediction system for total care suggestions could help physicians and other healthcare providers in making clinical judgement. Therefore, this study aims to compare neural network models on predicting patients in need of the total care. The experimental results show that the LSTM (long short-term memory) prediction model outperforms other neural network models on forecasting total care needs.
Near duplicate detection of images with area and proposed pixel‐based feature extraction

Summary Efficient and accurate near‐duplicate recognition is the trendy research area. Identification of invalid near‐duplicate images offers a wide range of applications, including digital picture forensics, web‐scale retrieval, and, social media analysis. This article intends to introduce a novel near duplicate detection model of images that consists of two stages such as (i) feature extraction and (ii) similarity computation. Originally, the image database is subjected to extracting the features, in which the area‐based features and pixel‐based features are extracted. Here, the area‐based feature extraction includes the contrast context histogram (CCH‐descriptors) and improved weighted bag of visual word (w‐BovW) features; the pixel‐based feature extraction includes the texture features like the proposed local vector pattern. Once the query image is given as the input, it is subjected to the feature extraction stage. Then, the feature vector database and the extracted features of query images are evaluated under similarity computation via improved Jaccard similarity evaluation. Thus, the near duplicate detection of the image is obtained in an effective manner.
Simultaneous Pose and Posture Estimation with a Two-stage Particle Filter for Visuo-inertial Fusion

We address the problem of human pose and posture estimation without any high precision marker-based motion capture systems, by merging inertial data from wearable sensors and a single RGB camera. Our proposition is based on a biomechanical model of the human body and two coupled filters: the first filter takes advantage of the accurate posture observations provided by wearable sensors and a factorization of joints to estimate the human posture with a reduced number of particles while the second filter uses RGB camera observations to estimate the drift of the wearable sensor so as to estimate the global state (pose and posture). In order to combine those filters, the estimated human posture distribution of the first filter is used as a proposal distribution for the second fusion filter so as to focus on particles with an already high-likelihood posture and to improve the efficiency of the pose estimation. Results showed this approach can perform online estimation of the human posture and the human pose (through the drift of the wearable sensor) and performed better than techniques relying only on inertial sensor or on direct pose estimation.
Neural machine translation for Hungarian

Abstract In the scope of this research, we aim to give an overview of the currently existing solutions for machine translation and we assess their performance on the English-Hungarian language pair. Hungarian is considered to be a challenging language for machine translation because it has a highly different grammatical structure and word ordering compared to English. We probed various machine translation systems from both academic and industrial applications. One key highlight of our work is that our models (Marian NMT, BART) performed significantly better than the solutions offered by most of the market-leader multinational companies. Finally, we fine-tuned different pre-finetuned models (mT5, mBART, M2M100) for English-Hungarian translation, which achieved state-of-the-art results in our test corpora.
Anomaly-PTG: A Time Series Data-Anomaly-Detection Transformer Framework in Multiple Scenarios

In actual scenarios, industrial and cloud computing platforms usually need to monitor equipment and traffic anomalies through multivariable time series data. However, the existing anomaly detection methods can not capture the long-distance temporal correlations of data and the potential relationships between features simultaneously, and only have high detection accuracy for specific time sequence anomaly detection scenarios without good generalization ability. This paper proposes a time-series anomaly-detection framework for multiple scenarios, Anomaly-PTG (anomaly parallel transformer GRU), given the above limitations. The model uses the parallel transformer GRU as the information extraction module of the model to learn the long-distance correlation between timestamps and the global feature relationship of multivariate time series, which enhances the ability to extract hidden information from time series data. After extracting the information, the model learns the sequential representation of the data, conducts the sequential modeling, and transmits the data to the full connection layer for prediction. At the same time, it also uses the autoencoder to learn the potential representation of the data and reconstruct the data. The two are optimally combined to form an anomaly detection module of the model. The module combines timestamp prediction with time series data reconstruction, improving the detection rate of rare anomalies and detection accuracy. By using three public datasets of physical devices and one dataset of network traffic intrusion detection, the model’s effectiveness was verified, and the model’s generalization ability and strong robustness were demonstrated. Compared with the most advanced method, the average F1 value of the Anomaly-PTG model on four datasets was increased by 2.2%, and the F1 value on each dataset was over 94%.
Polarimetric Quantum-Strong Correlations with Independent Photons on the Poincaré Sphere

Controllable, quantum-strong correlations of polarization states can be implemented with multi-photon independent states. Polarization-based photonic quantum correlations can be traced back to the overlap of the polarization Stokes vectors on the Poincaré sphere between two polarization filters. The quantum Rayleigh scattering prevents a single photon from propagating in a straight line inside a dielectric medium, and it also provides a mechanism for the projective measurement of polarization. Complexities associated with single-photon sources and detectors can be eliminated because the quantum Rayleigh scattering in a dielectric medium destroys entangled photons. Entanglement-free, identical sources and processing devices give rise to correlations rather than these being caused by “quantum nonlocality”. These analytic developments were prompted by the vanishing expectation values of the Pauli spin vector for a single photon of maximally entangled photonic Bell states.
User Authentication Recognition Process Using Long Short-Term Memory Model

User authentication (UA) is the process by which biometric techniques are used by a person to gain access to a physical or virtual site. UA has been implemented in various applications such as financial transactions, data privacy, and access control. Various techniques, such as facial and fingerprint recognition, have been proposed for healthcare monitoring to address biometric recognition problems. Photoplethysmography (PPG) technology is an optical sensing technique which collects volumetric blood change data from the subject’s skin near the fingertips, earlobes, or forehead. PPG signals can be readily acquired from devices such as smartphones, smartwatches, or web cameras. Classical machine learning techniques, such as decision trees, support vector machine (SVM), and k-nearest neighbor (kNN), have been proposed for PPG identification. We developed a UA classification method for smart devices using long short-term memory (LSTM). Specifically, our UA classifier algorithm uses raw signals so as not to lose the specific characteristics of the PPG signal coming from each user’s specific behavior. In the UA context, false positive and false negative rates are crucial. We recruited thirty healthy subjects and used a smartphone to take PPG data. Experimental results show that our Bi-LSTM-based UA algorithm based on the feature-based machine learning and raw data-based deep learning approaches provides 95.0% and 96.7% accuracy, respectively.
Background-lead self-attention for image harmonization

Image composition constructs a new image by cutting out a part of one image and then pasting it on another image. However, the quality of the new image is generally low due to inconsistency between the two parts. To overcome this, image harmonization aims to obtain realistic composite images by adjusting the color, illumination, and visual style of the foreground, to make it compatible with the background. Nevertheless, previous image harmonization techniques mostly concentrate on learning a mapping network from composite image to real image, and they ignore the significant role of background visual styles as a mapping guide. In this task, we consider image harmonization as a style transfer problem. Specifically, we take foreground as the content image and background as the style image, to transform the foreground's style through background's style features. To do so, we propose a unique self-attention-based module to learn the mapping between foreground features and background features using a modified self-attention mechanism. The proposed module can calculate the degree of correlation between the foreground and background according to the semantic distribution and also aligns the channel-wise mean and variance of the foreground features with that of the background features in the meantime. To comprehensively investigate the effectiveness of proposed module, we perform multiple experiments and ablation studies on the existing benchmark dataset iHarmony4. The experimental results prove that our module is more effective compared to the baseline in our task, and our harmonized image looks much closer to the real image.
A Data Protection-Oriented System Model Enforcing Purpose Limitation for Connected Mobility

Cars are getting rapidly connected with their environment allowing all kind of mobility services based on the data from various sensors in the car. Data privacy is in many cases only ensured by legislation, i. e., the European General Data Protection Regulation (GDPR), but not technically enforced. Therefore, we present a system model for enforcing purpose limitation based on data tagging and attribute-based encryption. By encrypting sensitive data in a way only services for a certain purpose can decrypt the data, we ensure access control based on the purpose of a service. In this paper, we present and discuss our system model with the aim to improve technical enforcement of GDPR principles.
A Novel Dynamic Bit Rate Analysis Technique for Adaptive Video Streaming over HTTP Support

Recently, there has been an increase in research interest in the seamless streaming of video on top of Hypertext Transfer Protocol (HTTP) in cellular networks (3G/4G). The main challenges involved are the variation in available bit rates on the Internet caused by resource sharing and the dynamic nature of wireless communication channels. State-of-the-art techniques, such as Dynamic Adaptive Streaming over HTTP (DASH), support the streaming of stored video, but they suffer from the challenge of live video content due to fluctuating bit rate in the network. In this work, a novel dynamic bit rate analysis technique is proposed to model client-server architecture using attention-based long short-term memory (A-LSTM) networks for solving the problem of smooth video streaming over HTTP networks. The proposed client system analyzes the bit rate dynamically, and a status report is sent to the server to adjust the ongoing session parameter. The server assesses the dynamics of the bit rate on the fly and calculates the status for each video sequence. The bit rate and buffer length are given as sequential inputs to LSTM to produce feature vectors. These feature vectors are given different weights to produce updated feature vectors. These updated feature vectors are given to multi-layer feed forward neural networks to predict six output class labels (144p, 240p, 360p, 480p, 720p, and 1080p). Finally, the proposed A-LSTM work is evaluated in real-time using a code division multiple access evolution-data optimized network (CDMA20001xEVDO Rev-A) with the help of an Internet dongle. Furthermore, the performance is analyzed with the full reference quality metric of streaming video to validate our proposed work. Experimental results also show an average improvement of 37.53% in peak signal-to-noise ratio (PSNR) and 5.7% in structural similarity (SSIM) index over the commonly used buffer-filling technique during the live streaming of video.
Analysis of quantum scattering characteristics for a cone illuminated with multiphoton in the remote sensing scene

The quantum radar cross sections (QRCS) for the targets are the critical basis for the optimization designs of stealth structures and quantum detection strategy. According to the three-dimensional structure of the conical surface, the one-dimensional Fourier expression of conical multiphoton QRCS in a gradually illuminated area is established using the two-dimensional Fourier expression of QRCS. The advantages of the derived expression in computational complexity are analyzed and verified by simulations. The influence of radius, height, and incident photon numbers on the QRCS for a cone is analyzed. The results show that there are scattering peaks in the center and the direction orthogonal to the side. The scattering peak in the center is proportional to the bottom radius and inversely proportional to the height. In addition, the height mainly affects the side scattering peak, and with the increase in height, the side scattering peak becomes sharp. With the radius increase, the scattering peaks at the center and both sides become sharp. With the increase of incident photon number, the width of scattering main lobe will be narrowed, and the peak difference between main lobe and side lobe will be increased, which indicates that fewer photons are beneficial to the detection of sidelobe, and more photons are beneficial to the detection of the main lobe. This work will help optimize the quantum detection strategy and stealth structure design of the missile and aircraft head, which will provide prior information for research in many fields, such as photonic technology, radar technology, and precision metrology.
Game Design Prototyping Workshop

This short paper provides a short theoretical exposition of game prototyping, a discussion of available game prototyping software and typical challenges in designing games for educational purposes, and practical examples of game design workshops. In these workshops, the participants developed their own open-ended game ideas and related immersive environments in groups of three or four over half a day or longer.
Coal not diamonds

The popularity of video streaming on smartphones has led to rising demands for high-quality mobile video streaming. Consequently, we are observing growing support for higher resolution videos (e.g., HD, FHD, QHD) and higher video frame rates (e.g., 48 FPS, 60 FPS). However, supporting high-quality video streaming on smartphones introduces new challenges---besides the available network capacity, the smartphone itself can become a bottleneck due to resource constraints, such as low available memory. In this paper, we conduct an in-depth investigation of memory usage on smartphones and its impacts on mobile video streaming. Our investigation - driven by a combination of a user study, user survey, and experiments on real smartphones - reveals that (i) most smartphones observe memory pressure (i.e., low available memory scenarios), (ii) memory pressure can have a significant impact on mobile video QoE when streaming high-quality videos, e.g., resulting in the mean frame drop rate of 9--100% across smartphones and significantly lower user ratings, and (iii) the drop in mobile video QoE happens primarily due to the way in which video processes interact with kernel-level memory management mechanism, with opportunities for improving mobile video QoE through better adaptation by video clients.