Reviewerly/exHarmony huggingface dataset:
Teaching and Learning Physics with Digital Technologies—What Digitalization-Related Competencies Are Needed?

In the physics classroom, many phenomena are treated experimentally and theoretically that cannot be experienced completely, or not at all, with the human senses. Digital technologies can support teachers in the teaching of physics, and students in gaining knowledge, by bridging the gap between human perception and physical reality. However, physics teachers need appropriate digitalization-related competencies in order to use digital technologies in the classroom in a meaningful and didactically sound way. The DiKoLAN framework (Digital Competencies for Teaching in Science Education, orig. Digitale Kompetenzen für das Lehramt in den Naturwissenschaften) is a suitable tool for planning, structuring, implementing, and evaluating science-specific pre-service teacher training and is already being used at several German-speaking corresponding institutions. Appropriately trained teachers can thus improve their own teaching through the use of digital technologies and also promote the media competencies of their students. This chapter describes problem areas, technology-based approaches to solutions, demands on teacher education, the DiKoLAN framework as a structuring aid, and exemplary approaches to promoting digitalization-related competencies in physics teaching and physics teacher education.
Compact Stateful Deterministic Wallet from Isogeny-Based Signature Featuring Uniquely Rerandomizable Public Keys

Deterministic wallets are promising cryptographic primitives that are employed in cryptocurrencies to safeguard user’s fund. In CCS’19, a generic construction of deterministic wallets was proposed by Das et al. leveraging signature schemes with rerandomizable keys. This is an advanced form of signatures that enables separate but consistent rerandomization of secret and public keys. Das et al. instantiated their deterministic wallet construction from rerandomizable signatures based on BLS and ECDSA. However, these wallets are not quantum-resistant. In this work, we offer a strategy for post-quantum migration of secure deterministic wallets based on isogenies. Rerandomizable signatures being at the center of the wallet construction, we initially propose ways to design such signature schemes from isogenies. Employing the signature schemes CSI-FiSh and CSI-SharK, we present two quantum-resistant signature schemes with rerandomizable keys. We provide rigorous security proof showing our constructions are secure against existential unforgeability under chosen-message attacks with honestly rerandomized keys. Our rerandomized signature from CSI-SharK gives the most compact post-quantum secure rerandomized signature. Finally, we integrate our rerandomized signature scheme from CSI-FiSh to design the first isogeny-based deterministic wallet with compact key sizes. We present a detailed security analysis showing our wallet is secure against wallet unlinkability and wallet unforgeability.
Real‐time collision‐free landing path planning for drone deliveries in urban environments

Abstract This study presents a novel safe landing algorithm for urban drone deliveries. The rapid advancement of drone technology has given rise to various delivery services for everyday necessities and emergency relief efforts. However, the reliability of drone delivery technology is still insufficient for application in urban environments. The proposed approach uses the “landing angle control” method to allow the drone to land vertically and a rapidly exploring random tree‐based collision avoidance algorithm to generate safe and efficient vertical landing paths for drones while avoiding common urban obstacles like trees, street lights, utility poles, and wires; these methods allow for precise and reliable urban drone delivery. We verified the approach within a Gazebo simulation operated through ROS using a six‐degree‐of‐freedom drone model and sensors with similar specifications to actual models. The performance of the algorithms was tested in various scenarios by comparing it with that of state‐of‐the‐art 3D path planning algorithms.
Sentiment Analysis of a Text Story Dataset Collected Using Illustration Cards

This paper introduces a crowdsourced text story dataset using the story card images of a commercial storytelling board game. We collect 705 text stories using illustration cards, where each story is classified with its ending type - either a happy or sad ending. Then, the sentiment patterns for a series of illustration cards matching each text story are analyzed to explore possible emotional arcs present in each story. The most dominant sentiment pattern is Negative-Negative-Positive (N-N-P) in happy-ending stories; Negative-Negative-Negative (N-N-N) in sad-ending stories.
Motion Distortion Elimination for LiDAR-Inertial Odometry Under Rapid Motion Conditions

Under rapid motion conditions, such as high-speed and vigorous angular velocity, motion distortion in light detection and ranging (LiDAR) point cloud will be severe and reduce the accuracy of LiDAR-inertial odometry (LIO). To address this issue, a novel algorithm based on multioutput Gaussian process regression (MOGPR) is proposed in this work to eliminate motion distortion. First, the single sample plus previous sample (SSPS) algorithm is used to obtain an accurate vehicle state at the same rate as the raw inertial measurement unit (IMU) data. Next, MOGPR is applied to obtain interpolated states that correspond to the moment of each point in the LiDAR point cloud. The motion distortion can be eliminated by aligning all points in the same LiDAR point cloud to a common reference coordinate frame, using the interpolated states as a guide. Finally, the corrected LiDAR point cloud and IMU data are fused using a factor graph, and the IMU bias is estimated to compensate the raw IMU data, further improving the accuracy of motion distortion elimination. Experiments are conducted on simulation data, the public KITTI dataset, and our platform, demonstrating the effectiveness of the proposed method in motion distortion elimination compared to existing methods. In addition, the localization accuracy of the LIO algorithm integrating the proposed method is improved by approximately 30%.
The Use of Dynamic Characteristics in Handwriting Recognition Tasks

In the presented paper different approaches are investigated to recognize handwritten characters represented as points of the pen trajectory. In doing so, datasets in different languages are investigated. In addition, a method for extracting dynamic characteristics from a data set of handwritten characters is described. The peculiarity of the method is that the analysis of motion focuses on the oscillatory component of the writer's hand motion. After preprocessing the trajectory data, a very compact vector of features characterizing the symbol is obtained, which leads to a significant reduction in the amount of resources and time required for processing. The proposed method can significantly improve the accuracy of optical recognition of handwritten characters. Also, the extracted information can be used in other fields related to character recognition, such as analysis of glyphs or signatures, as well as analysis or transfer of writing style.
Quantum sensing using multiqubit quantum systems and the Pauli polytope

Quantum sensing has highly practical potential applications in fields ranging from fundamental physics and quantum communication to biophysics and bioengineering. However, achieving high fidelity and control of entangled qubits that enables sensing beyond the quantum limit is still a challenging endeavor. In this paper, we present an alternative approach to quantum sensing, which we call open-system quantum sensing, where we exploit a generalization of the Pauli exclusion principle to sense the openness of a multiqubit quantum system from only measurement of the qubit occupations. Qubit occupations of a pure state obey generalized Pauli exclusion constraints that define a convex set known as the Pauli polytope, and hence violation of one of these constraints---a facet of the polytope---reveals a mixed state from the interaction of a quantum system with its environment without performing full-state tomography. We examine experimental ultrafast spectroscopic data from the photosynthetic light-harvesting complex in green sulfur bacteria and show that we can sense and decode the relaxation of the complex due to environmental noise. More generally, we can apply open-system quantum sensing with any general multiqubit quantum system, where it provides a unique, visual approach that promises enhanced sensitivity and fidelity.
MR-Unet: Modified Recurrent Unet for Medical Image Segmentation

In recent years, there has been significant interest in medical image segmentation. Traditional methods are being surpassed by deep learning, which has demonstrated their superiority. However, one drawback of using standard convolutional neural networks (CNNs) in deep learning is their large model size, which leads to excessive memory consumption and longer computation time. To address this issue, a novel model called MR-Unet was introduced in this study. MR-Unet combines the advantages of Recurrent Convolutional Neural Networks (RCNN) with the multiple layers Unet (Un-Net) architecture. By incorporating RCNN and leveraging all output feature maps in the convolution units of the network nodes, the overall network size was reduced. To assess the effectiveness of our proposed model, we conducted experiments on liver segmentation using the LiTS 2017 dataset, spleen segmentation using the Medical Segmentation Decathlon Challenge 2018 datasets, and skin lesion segmentation on dataset is supplied by the ISIC-2018 Challenge. The experimental results clearly demonstrate that our model not only achieves smaller size but also improves performance compared to existing models.
Towards Ensuring Software Interoperability Between Deep Learning Frameworks

Abstract With the widespread of systems incorporating multiple deep learning models, ensuring interoperability between target models has become essential. However, due to the unreliable performance of existing model conversion solutions, it is still challenging to ensure interoperability between the models developed on different deep learning frameworks. In this paper, we propose a systematic method for verifying interoperability between pre- and post-conversion deep learning models based on the validation and verification approach. Our proposed method ensures interoperability by conducting a series of systematic verifications from multiple perspectives. The case study confirmed that our method successfully discovered the interoperability issues that have been reported in deep learning model conversions.
Assembling three one‐camera images for three‐camera intersection classification

Abstract Determining whether an autonomous self‐driving agent is in the middle of an intersection can be extremely difficult when relying on visual input taken from a single camera. In such a problem setting, a wider range of views is essential, which drives us to use three cameras positioned in the front, left, and right of an agent for better intersection recognition. However, collecting adequate training data with three cameras poses several practical difficulties; hence, we propose using data collected from one camera to train a three‐camera model, which would enable us to more easily compile a variety of training data to endow our model with improved generalizability. In this work, we provide three separate fusion methods (feature, early, and late) of combining the information from three cameras. Extensive pedestrian‐view intersection classification experiments show that our feature fusion model provides an area under the curve and F1‐score of 82.00 and 46.48, respectively, which considerably outperforms contemporary three‐ and one‐camera models.
Sports competition tactical analysis model of cross-modal transfer learning intelligent robot based on Swin Transformer and CLIP

This paper presents an innovative Intelligent Robot Sports Competition Tactical Analysis Model that leverages multimodal perception to tackle the pressing challenge of analyzing opponent tactics in sports competitions. The current landscape of sports competition analysis necessitates a comprehensive understanding of opponent strategies. However, traditional methods are often constrained to a single data source or modality, limiting their ability to capture the intricate details of opponent tactics.Our system integrates the Swin Transformer and CLIP models, harnessing cross-modal transfer learning to enable a holistic observation and analysis of opponent tactics. The Swin Transformer is employed to acquire knowledge about opponent action postures and behavioral patterns in basketball or football games, while the CLIP model enhances the system's comprehension of opponent tactical information by establishing semantic associations between images and text. To address potential imbalances and biases between these models, we introduce a cross-modal transfer learning technique that mitigates modal bias issues, thereby enhancing the model's generalization performance on multimodal data.Through cross-modal transfer learning, tactical information learned from images by the Swin Transformer is effectively transferred to the CLIP model, providing coaches and athletes with comprehensive tactical insights. Our method is rigorously tested and validated using Sport UV, Sports-1M, HMDB51, and NPU RGB+D datasets. Experimental results demonstrate the system's impressive performance in terms of prediction accuracy, stability, training time, inference time, number of parameters, and computational complexity. Notably, the system outperforms other models, with a remarkable 8.47% lower prediction error (MAE) on the Kinetics dataset, accompanied by a 72.86-second reduction in training time.The presented system proves to be highly suitable for real-time sports competition assistance and analysis, offering a novel and effective approach for an Intelligent Robot Sports Competition Tactical Analysis Model that maximizes the potential of multimodal perception technology. By harnessing the synergies between the Swin Transformer and CLIP models, we address the limitations of traditional methods and significantly advance the field of sports competition analysis. This innovative model opens up new avenues for comprehensive tactical analysis in sports, benefiting coaches, athletes, and sports enthusiasts alike.
Digital Transformation for Vietnam Education: From Policy to School Practices

The Vietnamese Government is enhancing the implementation of the digital transformation plan to make Vietnam one of the leading digital nations. In the digital transformation process, education is prioritized with the mission of applying new models and teaching methods to implement STEM education, contributing to student career orientation to meet the Fourth Industrial Revolution. This chapter presents the current situation of digital transformation in education and the practice with examples of using COACH software to implement STEM education based on the digital transformation trend, which shifts from traditional teaching to multimedia teaching, combined with software and hardware exploitation such as traditional experimental equipment and sensors for data collection and automatic control.
Playing Story Creation Games with Large Language Models: Experiments with GPT-3.5

We created a web application where human users can play a story creation game with OpenAI's GPT-3.5, based on the Tell Tale card game. Tell Tale requires players to generate a brand new and coherent story based on a set of initial story elements, making the game a useful structure for exploring how well GPT-3.5 performs in generating coherent and engaging narratives. We show that GPT-3.5 performs remarkably well in generating such a narrative based on a random set of initial story elements, and that GPT-3.5 is even able to incorporate other literary elements such as suspense and flashbacks into its stories to enhance them and make them more engaging. By having human testers play Tell Tale with GPT-3.5 through our web application, we also demonstrate GPT-3.5's strong potential to be used as an interactive storytelling system, one that can both write and evaluate different narratives. We evaluate this potential using both quantitative and qualitative data from the human testers. Results indicate that, while GPT-3.5's narrative abilities are far from perfect, large language models have great potential in many different automated narrative situations.
PV panels as a renewable energy source for residential building – An economical comparison before and during the energy crisis

The application of renewable energy sources is a current trend today due to the limited sources of fossil. A substantial part of these energy sources is solar radiation. The development of electricity prices was significantly affected by two global crises, the COVID-19 pandemic and the energy crisis associated with the Russia-Ukraine conflict. The essence of the article is to compare the economic sense and appropriateness of investing in a photovoltaic power plant system and its return over time, considering the prediction of the development of electricity prices between 2020 and 2023 and subsequently also with a 15-year price prediction. The article deals with investment, energy consumption and financial evaluation of a PV system with PV panels installed on the ventilated façade of a residential building in the Czech Republic. The conclusions of the article show that the increase in investment in the PV panels themselves between 2020 and 2023 is a crucial factor for the return of the entire system. Predictive models of the development of electricity prices and associated liquidity of the investment also play a significant role in this issue.
Leveraging linear mapping for model-agnostic adversarial defense

In the ever-evolving landscape of deep learning, novel designs of neural network architectures have been thought to drive progress by enhancing embedded representations. However, recent findings reveal that the embedded representations of various state-of-the-art models are mappable to one another via a simple linear map, thus challenging the notion that architectural variations are meaningfully distinctive. While these linear maps have been established for traditional non-adversarial datasets, e.g., ImageNet, to our knowledge no work has explored the linear relation between adversarial image representations of these datasets generated by different CNNs. Accurately mapping adversarial images signals the feasibility of generalizing an adversarial defense optimized for a specific network. In this work, we demonstrate the existence of a linear mapping of adversarial inputs between different models that can be exploited to develop such model-agnostic, generalized adversarial defense. We further propose an experimental setup designed to underscore the concept of this model-agnostic defense. We train a linear classifier using both adversarial and non-adversarial embeddings within the defended space. Subsequently, we assess its performance using adversarial embeddings from other models that are mapped to this space. Our approach achieves an AUROC of up to 0.99 for both CIFAR-10 and ImageNet datasets.
A Wrapping Encryption Based on Double Randomness Mechanism

Currently, data security mainly relies on password (<i>PW</i>) or system channel key (<i>SK</i><sub><i>CH</i></sub>) to encrypt data before they are sent, no matter whether in broadband networks, the 5th generation (5G) mobile communications, satellite communications, and so on. In these environments, a fixed password or channel key (e.g., <i>PW</i>/<i>SK</i><sub><i>CH</i></sub>) is often adopted to encrypt different data, resulting in security risks since this <i>PW</i>/<i>SK</i><sub><i>CH</i></sub> may be solved after hackers collect a huge amount of encrypted data. Actually, the most popularly used security mechanism Advanced Encryption Standard (AES) has its own problems, e.g., several rounds have been solved. On the other hand, if data protected by the same <i>PW</i>/<i>SK</i><sub><i>CH</i></sub> at different time points can derive different data encryption parameters, the system’s security level will be then greatly enhanced. Therefore, in this study, a security scheme, named Wrapping Encryption Based on Double Randomness Mechanism (WEBDR), is proposed by integrating a password key (or a system channel key) and an Initialization Vector (<i>IV</i>) to generate an Initial Encryption Key (<i>IEK</i>). Also, an Accumulated Shifting Substitution (<i>ASS</i>) function and a three-dimensional encryption method are adopted to produce a set of keys. Two randomness encryption mechanisms are developed. The first generates system sub-keys and calculates the length of the first pseudo-random numbers by employing <i>IEK</i> for providing subsequent encryption/decryption. The second produces a random encryption key and a sequence of internal feedback codes and computes the length of the second pseudo-random numbers for encrypting delivered messages. A wrapped mechanism is further utilized to pack a ciphertext file so that a wrapped ciphertext file, rather than the ciphertext, will be produced and then transmitted to its destination. The findings are as follows. Our theoretic analyses and simulations demonstrate that the security of the WEBDR in cloud communication has achieved its practical security. Also, AES requires 176 times exclusive OR (XOR) operations for both encryption and decryption, while the WEBDR consumes only 3 operations. That is why the WEBDR is 6.7~7.09 times faster than the AES, thus more suitable for replacing the AES to protect data transmitted between a cloud system and its users.
Multi-Branch Deepfake Detection Algorithm Based on Fine-Grained Features

With the rapid development of deepfake technology, the authenticity of various types of fake synthetic content is increasing rapidly, which brings potential security threats to people's daily life and social stability. Currently, most algorithms define deepfake detection as a binary classification problem, i.e., global features are first extracted using a backbone network and then fed into a binary classifier to discriminate true or false. However, the differences between real and fake samples are often subtle and local, and such global feature-based detection algorithms are not optimal in efficiency and accuracy. To this end, to enhance the extraction of forgery details in deep forgery samples, we propose a multi-branch deepfake detection algorithm based on fine-grained features from the perspective of fine-grained classification. First, to address the critical problem in locating discriminative feature regions in fine-grained classification tasks, we investigate a method for locating multiple different discriminative regions and design a lightweight feature localization module to obtain crucial feature representations by augmenting the most significant parts of the feature map. Second, using information complementation, we introduce a correlation-guided fusion module to enhance the discriminative feature information of different branches. Finally, we use the global attention module in the multi-branch model to improve the cross-dimensional interaction of spatial domain and channel domain information and increase the weights of crucial feature regions and feature channels. We conduct sufficient ablation experiments and comparative experiments. The experimental results show that the algorithm outperforms the detection accuracy and effectiveness on the FaceForensics++ and Celeb-DF-v2 datasets compared with the representative detection algorithms in recent years, which can achieve better detection results.