Reviewerly/exHarmony huggingface dataset:
Efficient Deep Vision for Aerial Visual Understanding

Unmanned Aerial Vehicles (UAVs) are becoming a growing necessity for a broad range of applications, such as emergency response, monitoring critical infrastructures, and disaster management. UAVs, due to their affordability and camera capabilities, have become a common mobile camera platform for these kinds of applications. Thus, visual perception by utilizing Convolutional Neural Networks (CNNs) and Deep Learning is a key necessity for UAV-based applications. The remarkable performance of deep neural networks (DNNs) for vision tasks comes at a cost of high computational demands where the problem is amplified in drone-based applications due to limited energy resource. To address these drawbacks, this chapter highlights some of the key techniques of making deep vision more efficient for such resource-constrained applications. The techniques include but are not limited to data selection and reduction, efficient neural network design, and hardware-oriented model optimization. Results on different use cases show that such techniques can provide improvements either when applied as standalone or in a combined manner.
A toolkit for automatically generating and modifying VR hierarchy tile menus

Abstract Current VR/AR system development studios lack a toolkit that can automatically generate hierarchical tile menu layouts and menu prototypes for VR/AR devices without the need for user programming. This paper proposes a solution in the form of a toolkit that automatically generates a hierarchy tile menu layout using a modified circular treemap algorithm. Users can interactively arrange and resize tiles to create various layouts according to their preferences or needs, utilizing a circle packer method. The toolkit further automatically generates a VR/AR menu prototype based on the resulting layout. Notably, reprogramming is not necessary each time the hierarchy is modified or the menu layout is redesigned. The user test results demonstrate that the proposed toolkit simplifies the creation of hierarchical tile menu layouts, enhances user efficiency in the design process, and enables users to flexibly create hierarchical tile menu prototypes according to their design ideas.
Reading Comprehension and the <scp>COVID</scp>‐19 Pandemic: What Happened and What Can We Do About It?

Abstract Covid‐19‐related school closures worldwide had an enormous impact on literacy instruction and learning. In this article, we share an overview of what research tells us about what happened to elementary learners' reading comprehension during these school closures and what reading comprehension instruction looked like in homes and schools during this time. We also provide recommendations for the path forward on the road to revitalizing reading comprehension achievement.
Multimodal Dialog Systems with Dual Knowledge-enhanced Generative Pretrained Language Model

Text response generation for multimodal task-oriented dialog systems, which aims to generate the proper text response given the multimodal context, is an essential yet challenging task. Although existing efforts have achieved compelling success, they still suffer from two pivotal limitations: (1) overlook the benefit of generative pretraining and (2) ignore the textual context-related knowledge . To address these limitations, we propose a novel dual knowledge-enhanced generative pretrained language mode for multimodal task-oriented dialog systems (DKMD), consisting of three key components: dual knowledge selection , dual knowledge-enhanced context learning , and knowledge-enhanced response generation . To be specific, the dual knowledge selection component aims to select the related knowledge according to both textual and visual modalities of the given context. Thereafter, the dual knowledge-enhanced context learning component targets seamlessly, integrating the selected knowledge into the multimodal context learning from both global and local perspectives, where the cross-modal semantic relation is also explored. Moreover, the knowledge-enhanced response generation component comprises a revised BART decoder, where an additional dot-product knowledge-decoder attention sub-layer is introduced for explicitly utilizing the knowledge to advance the text response generation. Extensive experiments on a public dataset verify the superiority of the proposed DKMD over state-of-the-art competitors.
ARCTIC: A knowledge distillation approach via attention-based relation matching and activation region constraint for RGB-to-Infrared videos action recognition

The recognition effect of existing infrared-based action recognition is greatly reduced when clear appearance and texture are required. To address this limitation, the amalgamation of RGB data presents an opportunity to compensate for this deficiency. However, effectively leveraging RGB data to bridge the gap between RGB and infrared modalities poses a significant challenge. In this paper, we propose a knowledge distillation method based on attention relation matching and activation region consistency constraint (ARCTIC) for RGB-to-Infrared action recognition, which guides infrared data for recognition by acquiring the information of RGB data modality. To enhance the precision of knowledge screening across different modalities at various feature levels and ensure accurate information transfer, we constructed attention-based relation matching modules between different feature layers of the teacher and student networks. To minimize the dissimilarity between the two modalities and obtain more advantageous complementary information, we consider the spatial activation consistency constraint to maintain the consistency between the most salient features of the teacher network and the student network, and employ knowledge distillation loss to favor the selection of accurate predictions while judiciously mitigating the occurrence of erroneous logical outputs. Our experimentation substantiates that the ARCTIC method surpasses the performance of state-of-the-art action recognition techniques across the NTU RGB+D and PKU-MMD datasets.
The increased interest in Bitcoin and the immediate and long-term impact of Bitcoin volatility on global stock markets

Recently retail and institutional investors have begun to include Bitcoin in their portfolios, creating a transmission channel for Bitcoin volatility and therefore increasing regulatory concerns. Using intraday data from 2013 to 2022, I propose a dynamic econometric model to examine the extent to which Bitcoin volatility is transmitted to stock markets in North America, Europe, and Asia-Pacific at different frequencies: contemporaneous and long-run. My findings challenge previous literature which has found Bitcoin volatility risk minimal and not long-lasting. I provide evidence that, since March 2020, there was a substantial increase in contemporaneous volatility transmission in all regions. However, the long-term effect of Bitcoin on North American and three European markets diminished, while it remained significant in most Asia-Pacific markets. Hence, policymakers should coordinate actions at the regional level to mitigate financial stability risks. Likewise, Bitcoin volatility is relevant for investors when deciding on the sizes and timing of their investments.
Lifted inference for statistical statements in probabilistic answer set programming

In 1990, Halpern proposed the distinction between Type 1 and Type 2 statements: the former express statistical information about a domain of interest while the latter define a degree of belief. An example of Type 1 statement is “30% of the elements of a domain share the same property” while an example of Type 2 statement is “the element x has the property y with probability p”. Recently, Type 1 statements were given an interpretation in terms of probabilistic answer set programs under the credal semantics in the PASTA framework. The algorithm proposed for inference requires the enumeration of all the answer sets of a given program, and so it is impractical for domains of not trivial size. The field of lifted inference aims to identify programs where inference can be computed without grounding the program. In this paper, we identify some classes of PASTA programs for which we apply lifted inference and develop compact formulas to compute the probability bounds of a query without the need to generate all the possible answer sets.
Unsupervised concept drift detection for time series on Riemannian manifolds

Concept drifts generally refer to the changing of statistical characteristics of non-stationary series over time, which considerably affect the analysis of time series including prediction, anomaly detection and classification, etc. However, since the external noise interference and internal uncertainty of time series, it is still an open problem to detect the occurrence of concept drifts timely and effectively in real applications. In this article, based on Riemannian manifolds and statistical process control, we propose a novel online algorithm for the concept drift detection of time series. Using the online segmentations with multiple sliding windows, phase space reconstruction of time series is implemented, based on which multi-scale features of series data are calculated. By means of information geometry theory, the obtained features are projected into Riemannian manifolds for the evading of noise interference and structural redundancy in the time series. Finally, with statistical process control, the detection of concept drifts is implemented. The experimental results reveal the promising detection performances verified by both artificial data sets and real-life data sets.
Single-Qubit Loss-Tolerant Quantum Position Verification Protocol Secure against Entangled Attackers

We give a tight characterization of the relation between loss tolerance and error rate of the most popular protocol for quantum position verification, which is based on BB84 states. Combining it with classical information, we show, using semidefinite programming, for the first time a fault-tolerant protocol that is secure against attackers who preshare a linear amount of entanglement (in the classical information), arbitrarily slow quantum information and that tolerates a certain amount of photon loss. We also extend this analysis to the case of more than two bases, showing even stronger loss tolerance for that case. Finally, we show that our techniques can be applied to improve the analysis of one-sided device-independent quantum key distribution protocols.
Confidence-Aware and Self-supervised Image Anomaly Localisation

Universal anomaly detection still remains a challenging problem in machine learning and medical image analysis. It is possible to learn an expected distribution from a single class of normative samples, e.g., through epistemic uncertainty estimates, auto-encoding models, or from synthetic anomalies in a self-supervised way. The performance of self-supervised anomaly detection approaches is still inferior compared to methods that use examples from known unknown classes to shape the decision boundary. However, outlier exposure methods often do not identify unknown unknowns. Here we discuss an improved self-supervised single-class training strategy that supports the approximation of probabilistic inference with loosen feature locality constraints. We show that up-scaling of gradients with histogram-equalised images is beneficial for recently proposed self-supervision tasks. Our method is integrated into several out-of-distribution (OOD) detection models and we show evidence that our method outperforms the state-of-the-art on various benchmark datasets.
Feature-Based Pipeline for Improving Unsupervised Anomaly Segmentation on Medical Images

Unsupervised methods for anomaly segmentation are promising for computer-aided diagnosis since they can increase the robustness of medical systems and do not require large annotated datasets. In this work, we propose a simple yet effective two-stage pipeline for improving the performance of existing anomaly segmentation methods. The first stage is used for better anomaly localization and false positive rate reduction. For this stage, we propose the PatchCore3D method, which is based on the PatchCore algorithm and a backbone, pre-trained on 3D medical images. Any existing anomaly segmentation method can be used at the second stage for the precise anomaly segmentation in the region suggested by PatchCore3D. We evaluate PatchCore3D and the proposed pipelines in combination with six top-performing anomaly segmentation methods of different types. We use brain MRI datasets, testing healthy subjects against subjects with brain tumors. Using PatchCore3D pipeline with every considered anomaly segmentation method increases segmentation AUROC almost twice by better anomaly localization.
On Multiple Encryption for Public-Key Cryptography

Using multiple, individual encryption schemes is a well-established method to increase the overall security of encrypted data. These so-called multiple encryption or hybrid schemes have regained traction in the context of public-key cryptography due to the rise of quantum computers, since it allows the combination of well-known classical encryption schemes with novel post-quantum schemes. In this paper, we conduct a survey of the state-of-the-art public-key multiple encryption (M-PKE) schemes. For the first time, we describe the most relevant M-PKE schemes in detail and discuss their security in a unified model, which allows better comparison between the schemes. Hence, we compare the security, efficiency, and complexity of the schemes and offer recommendations for usage based on common use cases. Our survey emphasizes the importance of being deliberate when combining encryption schemes, as small nuances can easily break security.
Unsupervised Vehicle Re-Identification Method Based on Source-Free Knowledge Transfer

The unsupervised domain-adaptive vehicle re-identification approach aims to transfer knowledge from a labeled source domain to an unlabeled target domain; however, there are knowledge differences between the target domain and the source domain. To mitigate domain discrepancies, existing unsupervised domain-adaptive re-identification methods typically require access to source domain data to assist in retraining the target domain model. However, for security reasons, such as data privacy, data exchange between different domains is often infeasible in many scenarios. To this end, this paper proposes an unsupervised domain-adaptive vehicle re-identification method based on source-free knowledge transfer. First, by constructing a source-free domain knowledge migration module, the target domain is consistent with the source domain model output to train a generator to generate the “source-like samples”. Then, it can effectively reduce the model knowledge difference and improve the model’s generalization performance. In the experiment, two mainstream public datasets in this field, VeRi776 and VehicleID, are tested experimentally, and the obtained rank-k (the cumulative matching features) and mAP (the mean Average Precision) indicators are both improved, which are suitable for object re-identification tasks when data between domains cannot be interoperated.
Studying Imbalanced Learning for Anomaly-Based Intelligent IDS for Mission-Critical Internet of Things

Training-anomaly-based, machine-learning-based, intrusion detection systems (AMiDS) for use in critical Internet of Things (CioT) systems and military Internet of Things (MioT) environments may involve synthetic data or publicly simulated data due to data restrictions, data scarcity, or both. However, synthetic data can be unrealistic and potentially biased, and simulated data are invariably static, unrealistic, and prone to obsolescence. Building an AMiDS logical model to predict the deviation from normal behavior in MioT and CioT devices operating at the sensing or perception layer due to adversarial attacks often requires the model to be trained using current and realistic data. Unfortunately, while real-time data are realistic and relevant, they are largely imbalanced. Imbalanced data have a skewed class distribution and low-similarity index, thus hindering the model’s ability to recognize important features in the dataset and make accurate predictions. Data-driven learning using data sampling, resampling, and generative methods can lessen the adverse impact of a data imbalance on the AMiDS model’s performance and prediction accuracy. Generative methods enable passive adversarial learning. This paper investigates several data sampling, resampling, and generative methods. It examines their impacts on the performance and prediction accuracy of AMiDS models trained using imbalanced data drawn from the UNSW_2018_IoT_Botnet dataset, a publicly available IoT dataset from the IEEEDataPort. Furthermore, it evaluates the performance and predictability of these models when trained using data transformation methods, such as normalization and one-hot encoding, to cover a skewed distribution, data sampling and resampling methods to address data imbalances, and generative methods to train the models to increase the model’s robustness to recognize new but similar attacks. In this initial study, we focus on CioT systems and train PCA-based and oSVM-based AMiDS models constructed using low-complexity PCA and one-class SVM (oSVM) ML algorithms to fit an imbalanced ground truth IoT dataset. Overall, we consider the rare event prediction case where the minority class distribution is disproportionately low compared to the majority class distribution. We plan to use transfer learning in future studies to generalize our initial findings to the MioT environment. We focus on CioT systems and MioT environments instead of traditional or non-critical IoT environments due to the stringent low energy, the minimal response time constraints, and the variety of low-power, situational-aware (or both) things operating at the sensing or perception layer in a highly complex and open environment.
Towards Real-Time On-Drone Pedestrian Tracking in 4K Inputs

Over the past several years, significant progress has been made in object tracking, but challenges persist in tracking objects in high-resolution images captured from drones. Such images usually contain very tiny objects, and the movement of the drone causes rapid changes in the scene. In addition, the computing power of mission computers on drones is often insufficient to achieve real-time processing of deep learning-based object tracking. This paper presents a real-time on-drone pedestrian tracker that takes as the input 4K aerial images. The proposed tracker effectively hides the long latency required for deep learning-based detection (e.g., YOLO) by exploiting both the CPU and GPU equipped in the mission computer. We also propose techniques to minimize detection loss in drone-captured images, including a tracker-assisted confidence boosting and an ensemble for identity association. In our experiments, using real-world inputs captured by drones at a height of 50 m, the proposed method with an NVIDIA Jetson TX2 proves its efficacy by achieving real-time detection and tracking in 4K video streams.
Using a neural network in the work of an informatics teacher

Neural networks in education are a promising direction for encreasing the level of teaching and improving the quality of learning. Neural networks can be used for various types of learning, from teaching to creating personalized learning materials for individual students. The article describes the prospects and possibilities of using neural networks in the work of an informatics teacher, provides examples of queries to the neural network and options for their execution by the neural network, and analyzes the responses issued by the neural network to queries. Methodological ideas for using a neural network will be useful to informatics teachers when preparing for informatics lessons, extracurricular activities and competitions. The use of neural networks by teachers in their activities will allow the teacher to save time when preparing for a lesson, use a creative approach when planning lessons, diversify the teacher's teaching materials and will contribute to the formation of the ability to use information technology to solve teaching problems.
Leapfrog game

The article discusses the rules and features of conducting one of the mathematical games – the game "Leapfrog" at extracurricular activities. The author presents his experience of conducting games of this type with different age categories of students, analyzes the results, offers a certain set of tasks in which there are both borrowed and own tasks. The article also discusses some nuances of the strategy for the participants of the game, provides examples of calculating the rating of participants' decisions, outlines considerations that are useful to consider when compiling a set of tasks for the game. The author organized games of this type both in mathematics and computer science. In fact, it is possible to conduct this game on any subject. At the same time, tasks should be formulated in such a way that there are many solutions, for example: "Bring as many substances as possible with the property ...", "Come up with the shortest word in which ...", etc. In the future, solutions are ranked by quality and authors of better solutions are awarded more points. The specifics of the game is that the current position of the participants is displayed on a numerical line using chips. When moving, the chips jump over each other (hence the name of the game), which allows you to overtake rivals not only due to the quality of problem solving, but also with the help of tactical considerations. This gives the game an unusually emotional coloring.
How to use the Python programming language in fulfilling crypto-arithmetic tasks in secondary school

The article provides a way to optimize an enumeration process in the Python programming language while solving crypto-arithmetic (enumerative) tasks where two multi-digit numbers add to each other in a column. It is noted that there are two directions in fulfilling enumeration problems which suggest being optimized by combinatorial (algebraic) and programming methods. Moreover, it has been found out that it is impractical to express algebraic optimization methods directly in program codes, since programming languages have their own possibilities for optimizing search procedures. Therefore, the process of learning how to optimize search while fulfilling enumerative tasks should be carried out in two stages. Combinatorial methods are the best means of forming students’ logical mindsets and algorithmic skills. That is the reason why these methods should necessarily be included in the informatics course at the level of general secondary education. Then, at the advanced stage of training, or at the level of secondary general education, it is useful for students to move to the programming direction mastering high-level programming languages. The optimizing strategy of solving crypto-arithmetic problems described in the article, and its implementation in the Python language involves omitting conventional cyclic structures, as well as widely known methods of processing character data. When compiling a computer program "слова.py", the authors applied the join method and the permutations function of the itertools module from the Python standard library. This choice has greatly simplified the recording of the program codes and reduced the time it took to search for the solution.
New Iraqi US dollar rules risk economic instability

Headline IRAQ: New US dollar rules risk economic instability