Reviewerly/exHarmony huggingface dataset:
Smooth Trajectory Planning at the Handling Limits for Oval Racing

In motion planning for autonomous racing, the challenge arises in planning smooth trajectories close to the handling limits of the vehicle with a sufficient planning horizon. Graph-based trajectory planning methods can find the global discrete-optimal solution, but they suffer from the curse of dimensionality. Therefore, to achieve low computation times despite a long planning horizon, coarse discretization and simple edges that are efficient to generate must be used. However, the resulting rough trajectories cannot reach the handling limits of the vehicle and are also difficult to track by the controller, which can lead to unstable driving behavior. In this paper, we show that the initial edges connecting the vehicle’s estimated state with the actual graph are crucial for vehicle stability and race performance. We therefore propose a sampling-based approach that relies on jerk-optimal curves to generate these initial edges. The concept is introduced using a layer-based graph, but it can be applied to other graph structures as well. We describe the integration of the curves within the graph and the required adaptation to racing scenarios. Our approach enables stable driving at the handling limits and fully autonomous operation on the race track. While simulations show the comparison of our concept with an alternative approach based on uniform acceleration, we also present experimental results of a dynamic overtake with speeds up to 74 m/s on a full-size vehicle.
Detection of Shoplifting on Video Using a Hybrid Network

Shoplifting is a major problem for shop owners and many other parties, including the police. Video surveillance generates huge amounts of information that staff cannot process in real time. In this article, the problem of detecting shoplifting in video records was solved using a classifier, which was a hybrid neural network. The hybrid neural network included convolutional and recurrent ones. The convolutional network was used to extract features from the video frames. The recurrent network processed the time sequence of the video frames features and classified the video fragments. In this work, gated recurrent units were selected as the recurrent network. The well-known UCF-Crime dataset was used to form the training and test datasets. The classification results showed a high accuracy of 93%, which was higher than the accuracy of the classifiers considered in the review. Further research will focus on the practical implementation of the proposed hybrid neural network.
Small Object Detection in Infrared Images: Learning from Imbalanced Cross-Domain Data via Domain Adaptation

Deep learning-based object detection is one of the most popular research topics. However, in cases where large-scale datasets are unavailable, the training of detection models remains challenging due to the data-driven characteristics of deep learning. Small object detection in infrared images is such a case. To solve this problem, we propose a YOLOv5-based framework with a novel training strategy based on the domain adaptation method. First, an auxiliary domain classifier is combined with the YOLOv5 architecture to compose a detection framework that is trainable using datasets from multiple domains while maintaining calculation costs in the inference stage. Secondly, a new loss function based on Wasserstein distance is proposed to deal with small-sized objects by overcoming the problem of the intersection over union sensitivity problem in small-scale cases. Then, a model training strategy inspired from domain adaptation and knowledge distillation is presented. Using the domain confidence output of the domain classifier as a soft label, domain confusion loss is backpropagated to force the model to extract domain-invariant features while training the model with datasets with imbalanced distributions. Additionally, we generate a synthetic dataset in both the visible light and infrared spectrum to overcome the data shortage. The proposed framework is trained on the MS COCO, VEDAI, DOTA, ADAS Thermal datasets along with a constructed synthetic dataset for human detection and vehicle detection tasks. The experimental results show that the proposed framework achieved the best mean average precision (mAP) of 64.7 and 57.5 in human and vehicle detection tasks. Additionally, the ablation experiment shows that the proposed training strategy can improve the performance by training the model to extract domain-invariant features.
STEM Distance Teaching: Investigating STEM Teachers’ Attitudes, Barriers, and Training Needs

The aim of this study was to investigate STEM teachers’ attitude towards STEM Distance Teaching (DT), as well as their perceived barriers and training needs. A mixed survey was conducted on 158 STEM teachers in secondary education who taught their courses fully online due to COVID-19. The results revealed that STEM teachers perceive STEM DT quite positively, but their attitude can be affected by several factors, such as the efficiency of the schools’ digital infrastructure, as well as their gender, age, and STEM teaching subject. The qualitative thematic analysis identified several barriers to efficiently applying STEM DT, including the (i) lack of students’ interaction and engagement, (ii) inefficiency of digital infrastructure, (iii) lack of students’ and teachers’ digital skills, (iv) lack of space/equipment, and (v) increased teaching workload. The generated themes of training needs highlighted the need for targeted and adjusted training to every STEM discipline, as well as training on DT tools and pedagogies. Finally, the results indicated the STEM teachers’ need for psychological support and consulting.
Analysing mobile learning designs: A framework for transforming learning post-COVID

Mobile learning is well established in literature and practice, but under-evolved from a rigorous learning design perspective. Activity theory presents a sophisticated way of mapping and understanding learning design, but for mobile learning this does not always translate into change in practice. The reported research addresses this by coupling a mobile learning specific approach to activity theory with a practice-based framework: the design for transformative mobile learning framework mapped to the pedagogy-andragogy-heutagogy continuum matrix (the DTML-PAH Matrix). Seven case studies are analysed using this approach and presented narratively along with framework informed analysis. Findings include that the DTML-PAH Matrix can be used to provide clearer implications and guidance for mobile learning practice, and that the DTML-PAH Matrix can also be guided by the practice over time. Implications for further research and practice are discussed. Implications for practice or policy: Provide technological and pedagogical scaffolds to students. Learning designs should focus upon enabling elements of learner agency and creativity. To develop learning solutions to real world problems utilise a design-based research approach. Create authentic collaborative learning activities and tasks. Integrate mobile learning affordances in the design of the course and curriculum.
New Real-Time Impulse Noise Removal Method Applied to Chest X-ray Images

In this paper, we propose a new Modified Laplacian Vector Median Filter (MLVMF) for real-time denoising complex images corrupted by "salt and pepper" impulsive noise. The method consists of two rounds with three steps each: the first round starts with the identification of pixels that may be contaminated by noise using a Modified Laplacian Filter. Then, corrupted pixels pass a neighborhood-based validation test. Finally, the Vector Median Filter is used to replace noisy pixels. The MLVMF uses a 5 × 5 window to observe the intensity variations around each pixel of the image with a rotation step of π/8 while the classic Laplacian filters often use rotation steps of π/2 or π/4. We see better identification of noise-corrupted pixels thanks to this rotation step refinement. Despite this advantage, a high percentage of the impulsive noise may cause two or more corrupted pixels (with the same intensity) to collide, preventing the identification of noise-corrupted pixels. A second round is then necessary using a second set of filters, still based on the Laplacian operator, but allowing focusing only on the collision phenomenon. To validate our method, MLVMF is firstly tested on standard images, with a noise percentage varying from 3% to 30%. Obtained performances in terms of processing time, as well as image restoration quality through the PSNR (Peak Signal to Noise Ratio) and the NCD (Normalized Color Difference) metrics, are compared to the performances of VMF (Vector Median Filter), VMRHF (Vector Median-Rational Hybrid Filter), and MSMF (Modified Switching Median Filter). A second test is performed on several noisy chest x-ray images used in cardiovascular disease diagnosis as well as COVID-19 diagnosis. The proposed method shows a very good quality of restoration on this type of image, particularly when the percentage of noise is high. The MLVMF provides a high PSNR value of 5.5% and a low NCD value of 18.2%. Finally, an optimized Field-Programmable Gate Array (FPGA) design is proposed to implement the proposed method for real-time processing. The proposed hardware implementation allows an execution time equal to 9 ms per 256 × 256 color image.
An integrated framework of robust local mean decomposition and bidirectional long short-term memory to forecast solar irradiance

An accurate forecasting of solar global horizontal irradiance (GHI) provides the estimation of future output from photovoltaic (PV) cells, resulting in guaranteed sustainable functioning of solar integrated smart grids. Traditional forecasting algorithms find it relatively hard to obtain the intrinsic non-linearity of solar GHI, leading to poor forecasting accuracy. This work offers a deep learning (DL)-based hybrid model called RLMD BILSTM that combines robust local mean decomposition (RLMD) algorithm with bidirectional long short-term memory (BILSTM) predictor. The proposed RLMD BILSTM model has been developed and evaluated for two separate Indian locations: Hisar and Jaipur, by comparing with benchmark models: persistence, long short-term memory (LSTM), gated recurrent unit (GRU), BILSTM, and their RLMD-based hybrid variations (RLMD LSTM and RLMD GRU). The performance of the proposed model has been verified by the statistical and visual results of short-term annual and seasonal forecast of solar GHI. From the results, it has been observed that the proposed model achieved lower root mean square error (RMSE) (16.34 W/m2-35.07 W/m2) compared to contrast mforecastsodels. The higher annual R2 yielding a value of 0.977–0.995 for the proposed model compared to the contrast model proved its better fitness. Moreover, the study showed that the RLMD with BILSTM improved the RMSE (59.16%–88.88%) and mean absolute error (MAE) (66.45%–92.22%) over various contrast models. The study also conducted the sensitivity analysis using the forecasting efficacy and forecast skill to prove the significance of the proposed model.
Structure learning enhances concept formation in synthetic Active Inference agents

Humans display astonishing skill in learning about the environment in which they operate. They assimilate a rich set of affordances and interrelations among different elements in particular contexts, and form flexible abstractions (i.e., concepts) that can be generalised and leveraged with ease. To capture these abilities, we present a deep hierarchical Active Inference model of goal-directed behaviour, and the accompanying belief update schemes implied by maximising model evidence. Using simulations, we elucidate the potential mechanisms that underlie and influence concept learning in a spatial foraging task. We show that the representations formed–as a result of foraging–reflect environmental structure in a way that is enhanced and nuanced by Bayesian model reduction, a special case of structure learning that typifies learning in the absence of new evidence. Synthetic agents learn associations and form concepts about environmental context and configuration as a result of inferential, parametric learning, and structure learning processes–three processes that can produce a diversity of beliefs and belief structures. Furthermore, the ensuing representations reflect symmetries for environments with identical configurations.
Collectives and Their Artifact Ecologies

Computing today happens across multiple devices, applications, users, organizational units, and in the rest of the world outside. Groups and communities come together for different reasons and operate within contexts that may differ from dominant modes of production and consumption. With a foundation in activity theoretical HCI, we develop the concept of collective artifact ecologies. This concept enables us to identify struggles of collective use of computational devices today, delimiting collective artifact ecologies in order to study and explain how they develop and overlap. Through an analysis of three empirical cases, we illustrate the notion of collectives and how they face challenges in establishing, maintaining and negotiating their artifact ecologies. This paper, therefore, contributes a theoretical foundation for analyzing groups and communities as collectives, with a particular emphasis on the multiple tools and artifacts they use. To serve as a starting point for further engagement with these concepts, we provide guiding questions to support the understanding of collective artifact ecologies.
Faithful and efficient hyperentanglement purification for spatial-polarization-time-bin photon system

We present a faithful and efficient hyperentanglement purification protocol (hyper-EPP) for nonlocal two-photon systems in spatial-polarization-time-bin hyperentangled Bell states. As the single-photon detectors can detect and herald the undesirable properties caused by side leakage and finite coupling strength, the parity-check gates and swap gates of our hyper-EPP in the spatial, polarization and time-bin mode degrees of freedom (DoFs) work faithfully. The qubit-flip errors in photon systems in three DoFs can be corrected effectively with the faithful parity-check gates and the photon pairs can be reused to distill high-fidelity ones by introducing the faithful swap gates, which greatly increases the efficiency of our hyper-EPP. Further, the maximal hyperentanglement can be obtained in principle by operating multiple rounds of the hyper-EPP.
A Model for Scripting and Designing a Digital Textbook

The digital transformation of institutions renovates universities to strengthen the integration of educational technologies with a focus on the quality of learning. Our objective is to analyze the needs of students to determine the essential indicators to be used in the design of a digital textbook of soft skills, to measure the approval of the textbook by specialists and to measure the concordance of the criteria of the textbook with the expectations of students. The sample consisted of 60 university students and 8 educational and technical specialists. A digital textbook scripting and design model is designed to assist in the design of the textbook. The results showed that the identification of students' needs led to the conclusion that they are interested in learning through active teaching and learning methods, through the integration of ICT tools and using a textbook. In addition, students and pedagogical and technical specialists were satisfied with the digital textbook designed and to be used.
Flipped Learning and E-Learning as Training Models Focused on the Metaverse

Virtual Learning Environments (EVA) have acquired special importance in the educational field in recent years. The metaverse has been constructed as a learning space with enormous potential. As such, the immersion possibilities of the metaverse increase when compared to other methodologies that already implement technology, such as flipped learning and e-learning. In these learning environments, students require a set of specific abilities and skills. Therefore, this study aims to understand which training approach (flipped learning or e-learning) helps students acquire better skills through a teaching and learning process in the metaverse. This thesis used a pre-post quasi-experimental design of a group containing 173 Spanish high school students to achieve its aim. The data collection has been carried out by the Teaching and Learning Experiences Questionnaire (ETLQ). Among the obtained results, it is discovered that in all the dimensions analyzed, a significant relationship is observed. The greatest difference in means occurs in the LO dimension, meaning that these educational experiences directly impact the student’s academic results. It is concluded that both training approaches are adequate in preparing students for training processes carried out in the metaverse since they complement each other. Therefore, as preliminary instruction, the sequential use of these approaches is necessary when familiarizing students with a new learning reality such as the metaverse. Doi: 10.28991/ESJ-2022-SIED-013 Full Text: PDF
Automatic story and item generation for reading comprehension assessments with transformers

Reading comprehension is one of the essential skills for students as they make a transition from learning to read to reading to learn. Over the last decade, the increased use of digital learning materials for promoting literacy skills (e.g., oral fluency and reading comprehension) in K-12 classrooms has been a boon for teachers. However, instant access to reading materials, as well as relevant assessment tools for evaluating students’ comprehension skills, remains to be a problem. Teachers must spend many hours looking for suitable materials for their students because high-quality reading materials and assessments are primarily available through commercial literacy programs and websites. This study proposes a promising solution to this problem by employing an artificial intelligence (AI) approach. We demonstrate how to use advanced language models (e.g., OpenAI’s GPT-2 and Google’s T5) to automatically generate reading passages and items. Our preliminary findings suggest that with additional training and fine-tuning, open-source language models could be used to support the instruction and assessment of reading comprehension skills in the classroom. For both automatic story and item generation, the language models performed reasonably; however, the outcomes of these language models still require a human evaluation and further adjustments before sharing them with students. Practical implications of the findings and future research directions are discussed.
Arabic text watermarking tuned for medical e-record semi-authentication

This study focuses on Arabic text watermarking semi-verification utilizing recent counting-based secret-sharing to partially validate the ownership and correctness for all sensitive medical e-records. The benefit of this approach is its semi-trust of e-records, as needed services provided, even if complete text medical report is a bit delayed for full verification. The Proposed work is presents two approaches of text-watermarking which are Seed Shares approach and WM Shares approach. The capacity results of our approaches is achieved without effect of secret bits hidden. The security is achieved via using XOR operation and CBSS technique. The comparisons result shows that the Accuracy percentage of the seed shares approach is much better than the WM in terms of tampering attack. The exploration tested its proposed strategy on standard Arabic benchmark of 42 Nawawi Hadiths which provided promising remarks, which can also be useful for Urdu and Farsi texting.
Research on a Visual Servoing Control Method Based on Perspective Transformation under Spatial Constraint

Visual servoing has been widely employed in robotic control to increase the flexibility and precision of a robotic arm. When the end-effector of the robotic arm needs to be moved to a spatial point without a coordinate, the conventional visual servoing control method has difficulty performing the task. The present work describes space constraint challenges in a visual servoing system by introducing an assembly node and then presents a two-stage visual servoing control approach based on perspective transformation. A virtual image plane is constructed using a calibration-derived homography matrix. The assembly node, as well as other objects, are projected into the plane after that. Second, the controller drives the robotic arm by tracking the projections in the virtual image plane and adjusting the position and attitude of the workpiece accordingly. Three simple image features are combined into a composite image feature, and an active disturbance rejection controller (ADRC) is established to improve the robotic arm’s motion sensitivity. Real-time simulations and experiments employing a robotic vision system with an eye-to-hand configuration are used to validate the effectiveness of the presented method. The results show that the robotic arm can move the workpiece to the desired position without using coordinates.
The Alienation of Fact

An investigation of the role of educational privatization and technology in the crises of truth and agency. Today, conspiracy theories run rampant, attacks on facts have become commonplace, and systemic inequities are on the rise as individual and collective agency unravels. The Alienation of Fact explains the educational, technological, and ideological preconditions for these contemporary crises of truth and agency and explores the contradictions and competing visions for the future of education that lie at the center of the problem. Schools are increasingly reimagined as businesses, and high-stakes standardized testing and curricula, for-profit charter schools, and the rise of educational AI put capital and technology at the center of education. Yet even as our society demands measure, data, and facts, politicians and news outlets regularly make unfounded assertions. How should we make sense of the contradictions between the demand for radical data-driven empiricism and the flight from evidence, argument, or theoretical justification? In this critical investigation of the new digital directions of educational privatization—AI education, adaptive learning technology, biometrics, the quantification of play and social emotional learning—and the politics of the body, Saltman shows how the false certainty of bodies and numbers replaces deliberative and thoughtful agency in a time of increasing precarity. A distinctive contribution to scholarship on public school privatization and educational technology, politics, policy, pedagogy, and theory, The Alienation of Fact is a spirited call for democratic education that values creating a society of “thinking people” over capitalistic gains.