Reviewerly/exHarmony huggingface dataset:
JR-TFViT: A Lightweight Efficient Radar Jamming Recognition Network Based on Global Representation of the Time–Frequency Domain

Efficient jamming recognition capability is a prerequisite for radar anti-jamming and can enhance the survivability of radar in electronic warfare. Traditional recognition methods based on manually designed feature parameters have found it difficult to cope with the increasingly complex electromagnetic environment, and research combining deep learning to achieve jamming recognition is gradually increasing. However, existing research on radar jamming recognition based on deep learning can ignore the global representation in the jamming time–frequency domain data, while not paying enough attention to the problem of lightweighting the recognition network itself. Therefore, this paper proposes a lightweight jamming recognition network (JR-TFViT) that can fuse the global representation of jamming time–frequency domain data while combining the advantages of the Vision Transformer and a convolutional neural network (CNN). The global representation and local information in the jamming time–frequency data are fused with the assistance of the multi-head self-attention (MSA) mechanism in the transformer to improve the feature extraction capabilities of the recognition network. The model’s parameters are further decreased by modifying the standard convolutional operation mechanism and substituting the convolutional operation needed by the network with Ghost convolution, which has less parameters. The experimental results show that the JR-TFViT requires fewer model parameters while maintaining higher recognition performance than mainstream convolutional neural networks and lightweight CNNs. For 12 types of radar jamming, the JR-TFViT achieves 99.5% recognition accuracy at JNR = −6 dB with only 3.66 M model parameters. In addition, 98.9% recognition accuracy is maintained when the JR-TFViT parameter number is further compressed to 0.67 M.
Application of Augmented Reality for Learning Material Structures and Chemical Equilibrium in High School Chemistry

Material structures and chemical equilibrium are important learning units in high school chemistry. In this study, an augmented reality (AR) system is developed to assist high school students in learning chemistry. Students can use AR cards to conduct virtual chemistry experiments, and the submicroscopic view of a chemical reaction will be displayed according to the chemical equation specified by the reactants and coefficients on AR cards. They can change the AR cards to observe the experimental results and obtain the simplest integer ratio in a chemical equation. It is helpful for understanding that a chemical reaction changes the composition of reactants to form new products and that the process obeys the law of conservation of mass. Empirical research has been conducted in which the experimental group used the AR system and the control group used the traditional teaching method for learning chemistry. The analytical results show that the AR system is more effective than the traditional teaching method, especially for low-achievement students. The questionnaire results indicate that the learning motivation of the experimental group was slightly higher than that of the control group, and the cognitive load was slightly lower than that of the control group, both without achieving a significant difference.
Emotional intelligence and learning, and the role of ICTs

The ability to understand one's feelings (emotional understanding) is an element and an important component of emotional intelligence. Understanding emotions are considered essential for the child's social and emotional functioning. This paper is a literature review of both the importance of socio-emotional learning in the child's life and emphasizes the role of parents and the school in its achievement.
THE EFFECT OF TECHNOLOGICAL LITERACY, LEARNING FACILITY, AND FAMILY ENVIRONMENT ON STUDENTS’ LEARNING MOTIVATION

&lt;div&gt;&lt;table cellspacing="0" cellpadding="0" align="left"&gt;&lt;tbody&gt;&lt;tr&gt;&lt;td align="left" valign="top"&gt;&lt;p&gt;Student achievement is influenced by a variety of factors, both internal and external, such as school and home amenities. This study intends to assess the effect of technological literacy, amenities and learning environment on the learning motivation of SMK students during the pandemic. This is a quantitative Expos-Fact style of study. This study's population and sample consisted of 58 students majoring in Automation and Office Governance at SMK PGRI Turen; questionnaires and observations were used to collect data. Multiple linear regression test was employed for data analysis. The analysis reveals that technical literacy has a favorable influence on learning motivation, that facilities have a positive effect on learning motivation, and that the learning environment has no effect on learning motivation. The environmental impact is not attributable to the fact that a lack of suitable facilities at home is not a motivating factor for student learning.&lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;/tbody&gt;&lt;/table&gt;&lt;/div&gt;
New Low-Memory Algebraic Attacks on LowMC in the Picnic Setting

The security of the post-quantum signature scheme Picnic is highly related to the difficulty of recovering the secret key of LowMC from a single plaintext-ciphertext pair. Since Picnic is one of the alternate third-round candidates in NIST post-quantum cryptography standardization process, it has become urgent and important to evaluate the security of LowMC in the Picnic setting. The best attacks on LowMC with full S-box layers used in Picnic3 were achieved with Dinur’s algorithm. For LowMC with partial nonlinear layers, e.g. 10 S-boxes per round adopted in Picnic2, the best attacks on LowMC were published by Banik et al. with the meet-in-the-middle (MITM) method.In this paper, we improve the attacks on LowMC in a model where memory consumption is costly. First, a new attack on 3-round LowMC with full S-box layers with negligible memory complexity is found, which can outperform Bouillaguet et al.’s fast exhaustive search attack and can achieve better time-memory tradeoffs than Dinur’s algorithm. Second, we extend the 3-round attack to 4 rounds to significantly reduce the memory complexity of Dinur’s algorithm at the sacrifice of a small factor of time complexity. For LowMC instances with 1 S-box per round, our attacks are shown to be much faster than the MITM attacks. For LowMC instances with 10 S-boxes per round, we can reduce the memory complexity from 32GB (238 bits) to only 256KB (221 bits) using our new algebraic attacks rather than the MITM attacks, while the time complexity of our attacks is about 23.2 ∼ 25 times higher than that of the MITM attacks. A notable feature of our new attacks (apart from the 4-round attack) is their simplicity. Specifically, only some basic linear algebra is required to understand them and they can be easily implemented.
Controlled-phase gate by dynamic coupling of photons to a two-level emitter

Abstract We propose an architecture for achieving high-fidelity deterministic quantum logic gates on dual-rail encoded photonic qubits by letting photons interact with a two-level emitter (TLE) inside an optical cavity. The photon wave packets that define the qubit are preserved after the interaction due to a quantum control process that actively loads and unloads the photons from the cavity and dynamically alters their effective coupling to the TLE. The controls rely on nonlinear wave mixing between cavity modes enhanced by strong externally modulated electromagnetic fields or on AC Stark shifts of the TLE transition energy. We numerically investigate the effect of imperfections in terms of loss and dephasing of the TLE as well as control field miscalibration. Our results suggest that III-V quantum dots in GaAs membranes is a promising platform for photonic quantum information processing.
On the intuitionistic fuzzy modal feeble topological structures

In the paper, for the first time, ideas for intuitionistic fuzzy modal feeble topological structures (of two types) are introduced, and some of their properties are discussed. These topologies are based on the intuitionistic fuzzy operation @, intuitionistic fuzzy operator W, on the two intuitionistic fuzzy modal operators □, ◊ and of simplest intuitionistic fuzzy extended Dα.
Long-range QKD without trusted nodes is not possible with current technology

A recently published patent ( https://www.ipo.gov.uk/p-ipsum/Case/PublicationNumber/GB2590064 ) has claimed the development of a novel quantum key distribution protocol purporting to achieve long-range quantum security without trusted nodes and without use of quantum repeaters. Here we present a straightforward analysis of this claim, and reach the conclusion that it is largely unfounded.
Feature Map Analysis-Based Dynamic CNN Pruning and the Acceleration on FPGAs

Deep-learning-based applications bring impressive results to graph machine learning and are widely used in fields such as autonomous driving and language translations. Nevertheless, the tremendous capacity of convolutional neural networks makes it difficult for them to be implemented on resource-constrained devices. Channel pruning provides a promising solution to compress networks by removing a redundant calculation. Existing pruning methods measure the importance of each filter and discard the less important ones until reaching a fixed compression target. However, the static approach limits the pruning effect. Thus, we propose a dynamic channel-pruning method that dynamically identifies and removes less important filters based on a redundancy analysis of its feature maps. Experimental results show that 77.10% of floating-point operations per second (FLOPs) and 91.72% of the parameters are reduced on VGG16BN with only a 0.54% accuracy drop. Furthermore, the compressed models were implemented on the field-programmable gate array (FPGA) and a significant speed-up was observed.
Applying Deep Learning and Computer Vision Techniques for an e-Sport and Smart Coaching System Using a Multiview Dataset: Case of Shotokan Karate

Smart coaching and e-sport platforms have shown a great interest in the recent research studies. Through this study, we aim to globalize the practice of sport, especially Shotokan Karate, by connecting participants and coaches on an international scale through the integration of Artificial Intelligence techniques such as computer vision and deep learning, to give the possibility of carrying out national and international virtual training courses without logistical constraints. The proposed work aims to apply the latest action detection, action recognition, and action classification methods for different Karate movements using the LSTM and the ST-GCN algorithms and proposes these movements in 3D using Video Inference for Human Body Pose and Shape Estimation (VIBE). Our proper Multiview Dataset contains pose estimations of a set of basic movements captured by a karate Shotokan expert (6th DAN Black Belt) from three views (Front view, Left view, and Right view) using OpenPose and FastPose for human body keypoint detection. The current study sets out to detect, recognize, classify and score different participants' movements. We achieved greater than 96% recognition accuracy of this dataset using the LSTM algorithm, and 91.01% using the ST-GCN algorithm.
Blockchain Framework for Secure COVID-19 Pandemic Data Handling and Protection

COVID-19 pandemic caused global epidemic infections, which is one of the most severe infections in human medical history. In the absence of proper medications and vaccines, handling the pandemic has been challenging for governments and major health facilities. Additionally, tracing COVID-19 cases and handling data generated from the pandemic are also extremely challenging. Data privacy access and collection are also a challenge when handling COVID-19 data. Blockchain technology provides various features such as decentralization, anonymity, cryptographic security, smart contracts, and a distributed framework that allows users and entities to handle COVID-19 data better. Since the outbreak has made the moral crisis in the clinical and administrative centers worse than any other that has resulted in the decline in the supply of the exact information, however, it is vital to provide fast and accurate insight into the situation. As a result of all these concerns, this study emphasizes the need for COVID-19 data processing to acquire aspects such as data security, data integrity, real-time data handling, and data management to provide patients with all benefits from which they had been denied owing to misinformation. Hence, the management of COVID-19 data through the use of the blockchain framework is crucial. Therefore, this paper illustrates how blockchain technology can be implemented in the COVID-19 data handling process. The paper also proposes a framework with three main layers: data collection layer; data access and privacy layer; and data storage layer.
Use and Misuse of Machine Learning in Anthropology

Machine learning (ML), being now widely accessible to the research community at large, has fostered a proliferation of new and striking applications of these emergent mathematical techniques across a wide range of disciplines. In this article, we will focus on a particular case study: the field of paleoanthropology, which seeks to understand the evolution of the human species based on biological (e.g., bones, genetics) and cultural (e.g., stone tools) evidence. As we will show, the easy availability of ML algorithms and lack of expertise on their proper use among the anthropological research community has led to the foundational misapplications that have appeared throughout the literature. The resulting unreliable results not only undermine efforts to legitimately incorporate ML into anthropological research, but produce potentially faulty understandings about our human evolutionary and behavioral past. The aim of this article is to provide a brief introduction to some of the ways in which ML has been applied within paleoanthropology; we also include a survey of some basic ML algorithms for those who are not fully conversant with the field, which remains under active development. We discuss a series of missteps, errors, and violations of correct protocols of ML methods that appear disconcertingly often within the accumulating body of anthropological literature. These mistakes include the use of outdated algorithms and practices; inappropriate testing/training splits, sample composition, and textual explanations; as well as an absence of transparency due to the lack of data/code sharing, and the subsequent limitations imposed on independent replication. We assert that expanding samples, sharing data and code, re-evaluating approaches to peer review, and, most importantly, developing interdisciplinary teams that include experts in ML are all necessary for the progress in future research incorporating ML within anthropology and beyond.
Adaptive Multikernel Size-Based Maximum Correntropy Cubature Kalman Filter for the Robust State Estimation

The performance of the maximum correntropy criterion filter is affected by the kernel size, while the present kernel size adaptive methods are prone to excessive convergence. To achieve the adaptive adjustment for the kernel size without excessive convergence problems, an adaptive multikernel size-based maximum correntropy cubature Kalman filter (CKF) is proposed. The adaptive factor for each measurement element is constructed, and the kernel size is adjusted within a reasonable range based on the adaptive factor. Then the correntropy matrix with the adaptive multikernel size is established to achieve measurement-specific outliers processing in state estimation. The target tracking simulation and the cooperative positioning experiment are conducted to verify the proposed method. The results demonstrate that the proposed adaptive multikernel size-based maximum correntropy CKF (AMCCKF) can effectively optimize the kernel size for different noises and is more convenient for selecting tuning parameters, thus effectively achieving robust state estimation against outliers while ensuring filtering accuracy.
Small Private Exponent Attacks on RSA Using Continued Fractions and Multicore Systems

The RSA (Rivest–Shamir–Adleman) asymmetric-key cryptosystem is widely used for encryptions and digital signatures. Let (n,e) be the RSA public key and d be the corresponding private key (or private exponent). One of the attacks on RSA is to find the private key d using continued fractions when d is small. In this paper, we present a new technique to improve a small private exponent attack on RSA using continued fractions and multicore systems. The idea of the proposed technique is to find an interval that contains ϕ(n), and then propose a method to generate different points in the interval that can be used by continued fraction and multicore systems to recover the private key, where ϕ is Euler’s totient function. The practical results of three small private exponent attacks on RSA show that we extended the previous bound of the private key that is discovered by continued fractions. When n is 1024 bits, we used 20 cores to extend the bound of d by 0.016 for de Weger, Maitra-Sarkar, and Nassr et al. attacks in average times 7.67 h, 2.7 h, and 44 min, respectively.
A Comprehensive Survey of Depth Completion Approaches

Depth maps produced by LiDAR-based approaches are sparse. Even high-end LiDAR sensors produce highly sparse depth maps, which are also noisy around the object boundaries. Depth completion is the task of generating a dense depth map from a sparse depth map. While the earlier approaches focused on directly completing this sparsity from the sparse depth maps, modern techniques use RGB images as a guidance tool to resolve this problem. Whilst many others rely on affinity matrices for depth completion. Based on these approaches, we have divided the literature into two major categories; unguided methods and image-guided methods. The latter is further subdivided into multi-branch and spatial propagation networks. The multi-branch networks further have a sub-category named image-guided filtering. In this paper, for the first time ever we present a comprehensive survey of depth completion methods. We present a novel taxonomy of depth completion approaches, review in detail different state-of-the-art techniques within each category for depth completion of LiDAR data, and provide quantitative results for the approaches on KITTI and NYUv2 depth completion benchmark datasets.
Comparison of CSPDarkNet53, CSPResNeXt-50, and EfficientNet-B0 Backbones on YOLO V4 as Object Detector

YOLO v4 has a structure consisting of 3 parts: backbone, neck, and head. The backbone is a part of the YOLO v4 structure that serves as a feature extractor from the image; the backbone is also a convolutional neural network that can be replaced with another convolutional neural network. Many backbones are recommended by previous research, such as CSPDarkNet53, CSPResNeXt-50, and EfficientNet-B0. Therefore, research needs to be done to determine the effect of different backbones on the YOLO v4 model. One of the research objects that can be used is a microfossil. Research on the detection of microfossils is fundamental to assist paleontologists in knowing the species of microfossils as a determinant of rock age and distinguishing between similar microfossils. In this research, three backbones consisting of CSPDarkNet53, CSPResNeXt-50, and EfficientNet-B0 were used to train and detect image sets of 5 species of foraminiferal microfossils. The results were evaluated to determine the advantages of each backbone. There are a few metrics are that being used for evaluation, namely precision, recall, f1-score, average precision (AP), mean average precision (mAP), frames per second (FPS), and model size. As a result, the mean average precision (mAP) of the CSPDarkNet53 model reached 83.41%, the highest compared to CSPResNeXt-50 and EfficientNet-B0, which get a value of 81,00% and 81,76%. CSPResNeXt-50 model has a precision of 75.60%, recall of 81.10%, and f1-score of 78%. CSPDarkNet53 model also got the highest FPS value of 33.4FPS. However, the YOLO v4 model with the EfficientNet-B0 backbone is the lightest model, with only 156.8 MB.
Selection of Appropriate Symbolic Regression Models Using Statistical and Dynamic System Criteria: Example of Waste Gasification

In this paper, we analyze the interpretable models from real gasification datasets of the project “Centre for Energy and Environmental Technologies” (CEET) discovered by symbolic regression. To evaluate CEET models based on input data, two different statistical metrics to quantify their accuracy are usually used: Mean Square Error (MSE) and the Pearson Correlation Coefficient (PCC). However, if the testing points and the points used to construct the models are not chosen randomly from the continuum of the input variable, but instead from the limited number of discrete input points, the behavior of the model between such points very possibly will not fit well the physical essence of the modelled phenomenon. For example, the developed model can have unexpected oscillatory tendencies between the used points, while the usually used statistical metrics cannot detect these anomalies. However, using dynamic system criteria in addition to statistical metrics, such suspicious models that do fit well-expected behavior can be automatically detected and abandoned. This communication will show the universal method based on dynamic system criteria which can detect suitable models among all those which have good properties following statistical metrics. The dynamic system criteria measure the complexity of the candidate models using approximate and sample entropy. The examples are given for waste gasification where the output data (percentage of each particular gas in the produced mixture) is given only for six values of the input data (temperature in the chamber in which the process takes place). In such cases instead, to produce expected simple spline-like curves, artificial intelligence tools can produce inappropriate oscillatory curves with sharp picks due to the known tendency of symbolic regression to produce overfitted and relatively more complex models if the nature of the physical model is simple.