Reviewerly/exHarmony huggingface dataset:
“How technical do you get? I’m an English teacher”: Teaching and Learning Cybersecurity and AI Ethics in High School

Today’s cybersecurity and AI technologies are often fraught with ethical challenges. One promising direction is to teach cybersecurity and AI ethics to today’s youth. However, we know little about how these subjects are taught before college. Drawing from interviews of US high school teachers (n=16) and students (n=11), we find that cybersecurity and AI ethics are often taught in non-technical classes such as social studies and language arts. We also identify relevant topics, of which epistemic norms, privacy, and digital citizenship appeared most often. While teachers leverage traditional and novel teaching strategies including discussions (treating current events as case studies), gamified activities, and content creation, many challenges remain. For example, teachers hesitate to discuss current events out of concern for appearing partisan and angering parents; cyber hygiene instruction appears very ineffective at educating youth and promoting safer online behavior; and generational differences make it difficult for teachers to connect with students. Based on the study results, we offer practical suggestions for educators, school administrators, and cybersecurity practitioners to improve youth education on cybersecurity and AI ethics.
FLUTE: Fast and Secure Lookup Table Evaluations

The concept of using Lookup Tables (LUTs) instead of Boolean circuits is well-known and been widely applied in a variety of applications, including FPGAs, image processing, and database management systems. In cryptography, using such LUTs instead of conventional gates like AND and XOR results in more compact circuits and has been shown to substantially improve online performance when evaluated with secure multi-party computation. Several recent works on secure floating-point computations and privacy-preserving machine learning inference rely heavily on existing LUT techniques. However, they suffer from either large overhead in the setup phase or subpar online performance.We propose FLUTE, a novel protocol for secure LUT evaluation with good setup and online performance. In a two-party setting, we show that FLUTE matches or even outperforms the online performance of all prior approaches, while being competitive in terms of overall performance with the best prior LUT protocols. In addition, we provide an open-source implementation of FLUTE written in the Rust programming language, and implementations of the Boolean secure two-party computation protocols of ABY2.0 and silent OT. We find that FLUTE outperforms the state of the art by two orders of magnitude in the online phase while retaining similar overall communication.
Method for detecting surface defects of underwater buildings: Binocular vision based on sinusoidal grating fringe assistance

In underwater building-surface defect detection, high pressure, cold and other underwater factors will cause harm to frogmen, sonar and other detection techniques contain limited information and accuracy is difficult to meet the demand. In view of the above disadvantages, a sine stripe-assisted visual detection method of underwater building surface-defects is proposed. By generating a fixed sine stripe by program and projected it on the surface of the underwater building, and then control a binocular CCD to acquire six frames of sinusoidal stripes with different phases in one sampling period. Experiment results show that the error of our method does not exceed 0.5 mm and the reconstruction efficiency is not less than 5 m2/s in the 3D dimensional measurement of the defects on the surface of the submerged building body.
Enhanced Attendance Monitoring: Utilizing QR Code for Online Attendance with Laravel Framework and SMS Notification

This paper presents an attendance monitoring system that utilizes QR codes for online tracking. Developed on the Laravel framework, the system offers an efficient and user-friendly solution for attendance management. By scanning QR codes, students can easily check-in, eliminating manual roll calls. The system also includes SMS notifications for prompt attendance status updates. Thorough testing ensures system functionality and security. Overall, this system represents a significant advancement in attendance tracking, enhancing efficiency and communication for students, instructors, and administrators
Audio features based ADS-CNN method for flight attitude recognition of quadrotor UAV

With the rapid development of unmanned flight technology, it has been widely used in all walks of life. Quad-rotor unmanned aerial vehicle (QrUAV) is the most widely used, due to its advantages of simple structure, high stability, and easy control. Currently, efficient, fast and accurate identification of QrUAV is still a hot issue. To this end, this paper proposes an audio features based flight attitude recognition method for QrUAV. Using the newly recorded UAV Attitude Audio data set (UAVAA), a lightweight convolutional neural network (ADS-CNN) integrated with attention mechanism is established for the flight attitude recognition of QrUAV. The feature of this method is that a lightweight network structure is designed through depthwise separable convolution and residual connection, this structure effectively reduces the network parameter consumption, and increases the network depth as much as possible when the number of UAVAA samples is small, which effectively suppresses overfitting and improves the recognition accuracy. In addition, by introducing of the region of interest focusing module (IFA) into the network, the weight of the attitude features is divided to realize the importance of the features, so as to achieve efficient and high-accuracy recognition. Finally, by fusing the MFCC features and STFT features of the QrUAV audio, the feature dimension is increased and the recognition accuracy is improved. By experimental comparison and analysis, the accuracy rate of ADS-CNN reached 98.81%, which was 2.7% higher than that of VGG16, the model parameters of ADS-CNN were only 27.6% of VGG16, and the operation time was only 32% of VGG16, which shows that ADS –CNN can identify the flight attitude of the QrUAV more accurately and quickly by using attitude audio.
Approximation error for neural network operators by an averaged modulus of smoothness

In the present paper we establish estimates for the error of approximation (in the Lp-norm) achieved by neural network (NN) operators. The above estimates have been given by means of an averaged modulus of smoothness introduced by Sendov and Popov, also known with the name of τ-modulus, in case of bounded and measurable functions on the interval [−1,1]. As a consequence of the above estimates, we can deduce an Lp convergence theorem for the above family of NN operators in case of functions which are bounded, measurable, and Riemann integrable on the above interval. In order to reach the above aims, we preliminarily establish a number of results; among them we can mention an estimate for the p-norm of the operators, and an asymptotic type theorem for the NN operators in case of functions belonging to Sobolev spaces.
Real-time self-supervised tone curve estimation for HDR image

High dynamic range (HDR) image acquisition technology can record real-scene information. However, most display devices only support standard dynamic range images; therefore, dynamic range compression via tone mapping operators is a key technology for HDR image visualization. Recently, deep learning has achieved significantly better results than traditional methods in tone mapping; however, some problems remain. On the one hand, the labels of datasets are usually selected from the results of existing traditional methods, which limit the quality of newly generated results. On the other hand, these algorithms require huge computational resources and cannot be practically applied. As the dynamic range can be compressed using an intuitive and simple sigmoid curve mapping, a lightweight network is designed in this study to estimate the patch-wise tone curve parameters for HDR images. Simultaneously, the differentiable approximation of tone mapped image quality assessment is introduced as a self-supervised loss term so that the method only needs HDR data as training data. Experimental results demonstrate that the proposed method achieves better results than existing methods in both objective and subjective metrics at a low computational cost.
StegaEdge: learning edge-guidance steganography

Steganography is critical in traceability, authentication, and secret delivery for multimedia. In this paper, we propose a novel image steganography framework, named StegaEdge, via learning edge-guidance network to simultaneously address three challenges, capacity, multi-task, and invisibility. First, we use an upsampling strategy to expand the embedding space and thus increase the capacity of the embedded message. Second, our algorithm improves the embedding way of messages so that it can handle different messages embedded in the same image and achieve split-task recovery completely. Different information can be embedded in one cover image without affecting each other. Third, we innovatively propose an edge-guidance strategy to solve the problem of poor invisibility in smooth regions. The human eye is significantly less perceptive of intensity changes in edges than in smooth areas. Unlike traditional steganography methods, our edge-guidance steganography can appropriately embed part of the information into non-edge regions when the amount of embedded information is too large. Experimental results on three datasets show that the newly proposed StegaEdge algorithm achieves satisfactory results in terms of capacity, multi-task, imperceptibility, and security compared to the state-of-the-art algorithms.
A Socio-Physics-Based Hybrid Metaheuristic for Solving Complex Non-convex Constrained Optimization Problems

Several artificial intelligence-based heuristic and metaheuristic algorithms have been developed so far. These algorithms have shown their superiority toward solving complex problems from different domains. However, it is necessary to critically validate these algorithms for solving real-world constrained optimization problems. The search behavior in those problems is different as it involves large number of linear, nonlinear, and non-convex type equality and inequality constraints. In this work a 57 real-world constrained optimization problems test suite is solved using 2 constrained metaheuristic algorithms originated from a socio-based Cohort Intelligence (CI) algorithm. The first CI-based algorithm incorporates a self-adaptive penalty function approach, i.e., CI-SAPF. The second algorithm combines CI-SAPF with the intrinsic properties of the physics-based colliding bodies optimization (CBO) referred to as CI-SAPF-CBO. The performance of the proposed CI-SAPF and CI-SAPF-CBO algorithms is compared with other constrained optimization algorithms such as IUDE, ϵMAg-ES, and iLSHADEϵ. The superiority of the proposed algorithms is discussed in details followed by future directions to evolve the constrained handling techniques.
Deep Learning Based Occluded Person Re-Identification: A Survey

Occluded person re-identification (Re-ID) focuses on addressing the occlusion problem when retrieving the person of interest across non-overlapping cameras. With the increasing demand for intelligent video surveillance and the application of person Re-ID technology, the real-world occlusion problem draws considerable interest from researchers. Although a large number of occluded person Re-ID methods have been proposed, there are few surveys that focus on occlusion. To fill this gap and help boost future research, this article provides a systematic survey of occluded person Re-ID. In this work, we review recent deep learning based occluded person Re-ID research. First, we summarize the main issues caused by occlusion as four groups: position misalignment, scale misalignment, noisy information, and missing information. Second, we categorize existing methods into six solution groups: matching, image transformation, multi-scale features, attention mechanism, auxiliary information, and contextual recovery. We also discuss the characteristics of each approach, as well as the issues they address. Furthermore, we present the performance comparison of recent occluded person Re-ID methods on four public datasets: Partial-ReID, Partial-iLIDS, Occluded-ReID, and Occluded-DukeMTMC. We conclude the study with thoughts on promising future research directions.
Flexible non-parametric regression models for compositional response data with zeros

Abstract Compositional data arise in many real-life applications and versatile methods for properly analyzing this type of data in the regression context are needed. When parametric assumptions do not hold or are difficult to verify, non-parametric regression models can provide a convenient alternative method for prediction. To this end, we consider an extension to the classical k - Nearest Neighbours ( k - NN ) regression, that yields a highly flexible non-parametric regression model for compositional data. A similar extension of kernel regression is proposed by adopting the Nadaraya–Watson estimator. Both extensions involve a power transformation termed the $$\alpha $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>α</mml:mi> </mml:math> -transformation. Unlike many of the recommended regression models for compositional data, zeros values (which commonly occur in practice) are not problematic and they can be incorporated into the proposed models without modification. Extensive simulation studies and real-life data analyses highlight the advantage of using these non-parametric regressions for complex relationships between compositional response data and Euclidean predictor variables. Both the extended K - NN and kernel regressions can lead to more accurate predictions compared to current regression models which assume a, sometimes restrictive, parametric relationship with the predictor variables. In addition, the extended k - NN regression, in contrast to current regression techniques, enjoys a high computational efficiency rendering it highly attractive for use with large sample data sets.
YOLO Based Segmentation and CNN Based Classification Framework for Epithelial and Pus Cell Detection

Identifying the cells, such as pus and epithelial cells, from microscopic images is one of the important steps in medical diagnostics. Microscopic examination by hand is labor-intensive and unreliable. Therefore, it is helpful to have an automated approach for classifying these cells to enable quick and accurate diagnosis. Creating a model for automated cell identification is challenging because of the numerous variable parameters such as various stains and magnifications and cell overlapping. This paper offers a robust object detection model that detects the pus and epithelial cells images obtained from the microscopic analysis of direct samples of Gram-stained patient samples such as pus and sputum. This paper also presents a novel classifier that addresses the overlapping issues present in the cells. The proposed methodology offers an mAP of 0.87 and a classification accuracy of 94.5%.
Automatic Sentiment Labelling of Multimodal Data

This study investigates the challenging problem of automatically providing sentiment labels for training and testing multimodal data containing both image and textual information for supervised machine learning. Because both the image and text components, individually and collectively, convey sentiment, assessing the sentiment of multimodal data typically requires both image and text information. Consequently, the majority of studies classify sentiment by combining image and text features ('Image+Text-features'). In this study, we propose 'Combined-Text-Features' that incorporate the object names and attributes identified in an image, as well as any accompanying superimposed or captioned text of that image, and utilize these text features to classify the sentiment of multimodal data. Inspired by our prior research, we employ the Afinn labelling method to automatically provide sentiment labels to the 'Combined-Text-Features'. We test whether classifier models, using these 'Combined-Text-Features' with the Afinn labelling, can provide comparable results as when using other multimodal features and other labelling (human labelling). CNN, BiLSTM, and BERT models are used for the experiments on two multimodal datasets. The experimental results demonstrate the usefulness of the 'Combined-Text-Features' as a representation for multimodal data for the sentiment classification task. The results also suggest that the Afinn labelling approach can be a feasible alternative to human labelling for providing sentiment labels.
Graph Attention in Attention Network for Image Denoising

Image denoising aims to remove the noise from noisy images. With the increasing complexity of the noise within the noisy images, current denoising methods cannot satisfactorily address this issue. This article proposes a graph attention in attention network (GAiA-Net) for image denoising. First, we introduce a novel approach to graph construction for the GAiA-Net. In the process of such graph construction, the noisy images are divided into patches to formulate the nodes in a graph. The edges are initialized using <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k $ </tex-math></inline-formula> -nearest neighbors. Hence, through iterative transformation and learning, both the pixel-level and structure-level features can be captured by different information exchanges and aggregation within (pixel-level) and outside (structure-level) of the nodes, respectively. Second, we propose the graph attention in attention (GAiA) in the GAiA-Net. The proposed GAiA produces the pixel-level attention within nodes to be further induced to the nodes with various distances to generate the final attention. Therefore, our GAiA-Net can capture the long dependencies on both the pixel-level and structure-level features, which can effectively reduce the complex noise in the denoising process. Comprehensive experiments demonstrate that the proposed GAiA-Net produces state-of-the-art performances on both synthetic noise image and real noise image datasets. Especially, when experimenting on complex noisy Nam datasets, our GAiA-Net achieves a PSNR of 40.40 dB and SSIM of 0.989. These results prove the satisfactory potential and effectiveness of our GAiA-Net.
Hierarchically Composing Level Generators for the Creation of Complex Structures

Procedural content generation (PCG) is a growing field, with numerous applications in the video game industry and great potential to help create better games at a fraction of the cost of manual creation. However, much of the work in PCG is focused on generating relatively straightforward levels in simple games, as it is challenging to design an optimisable objective function for complex settings. This limits the applicability of PCG to more complex and modern titles, hindering its adoption in industry. Our work aims to address this limitation by introducing a compositional level generation method that recursively composes simple low-level generators to construct large and complex creations. This approach allows for easily-optimisable objectives and the ability to design a complex structure in an interpretable way by referencing lower-level components. We empirically demonstrate that our method outperforms a non-compositional baseline by more accurately satisfying a designer's functional requirements in several tasks. Finally, we provide a qualitative showcase (in Minecraft) illustrating the large and complex, but still coherent, structures that were generated using simple base generators.
Marrying Global–Local Spatial Context for Image Patches in Computer-Aided Assessment

Computer-aided assessment using whole slide images (WSIs) is one of the critical steps in clinical procedures. How do doctors recognize cancer in a WSI? A quick answer is that they consider the spatial structure of a WSI rather than only considering single patches. We argue that two clues are essential for computer-aided deep learning: 1) global spatial context and 2) local semantic information. This is because local, semi-local, and global tissue observing are the principal assessment means of pathologists, perfectly corresponding with both clues. However, most existing methods only consider local spatial information learning within each patch rather than developing an effective local-to-global reaction, leading to an incapable of capturing robust and enriched representation. Toward a new area for computer-aided assessment, we propose novel neural networks to learn the global–local spatial context in WSIs, called GLSCL. The GLSCL is among the first trials that understand both clues for WSI understanding. Furthermore, the proposed novel operators enable the GLSCL to learn spatial semantic representation sufficiently. We evaluate the GLSCL using renal cell carcinoma (RCC) samples with synthetic ambiguity collected from the public benchmark and clinical procedures. Enhanced by global and local spatial information, the GLSCL achieves state-of-the-art performance, including classification accuracy, survival prediction index, and cancer tissue attention rate.
Exploring Deep Learning Models Aimed at Favorable Optimization and Enhancement of Fiber Optic Sensor’s Performance

We present a deep learning (DL)-assisted extrapolation approach for modeling a function that maximizes the performance of a fiber optic sensor (in terms of the figure of merit, i.e., FOM) under the variation of design parameters. We exploit a presumption that FOM should be treated as a relative entity. The recurrent neural network (RNN) framework is used to build the models that treat the data sequentially and relatively detect the hidden patterns. The objective of the adopted methodology is to forecast the best probable value of FOM for a particular combination of design parameters considered under finer resolutions. Our proposed model displays promising results on the test set and in the extrapolation corresponding to a subsequence length ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${l}$ </tex-math></inline-formula> ) value of 35. Specifically, the sequential–sequential (Seq2Seq)-attention model’s performance on the test metrics is root-mean-square error (RMSE) = 2.12 ± 0.62, mean absolute error (MAE) = 0.54 ± 0.16, and symmetric mean absolute percentage error (SMAPE) = 0.92 ± 0.18. To verify the generality of our model for finer resolution data, we conducted tests for different values of wavelength. The results show that the model can capture the pattern and momentum of the FOM but marginally struggles to keep track of its magnitude for finer resolution data. However, the predictions for the training resolution are adequate. The processing time to predict FOM is nearly 1 s (68 ms/step), at least 100 times lesser than traditional simulation techniques. Our proposed network is relatively robust, converges quickly, has fewer parameters, and performs well on the test dataset. The analysis enables highly efficient and cost-effective optimization and enhancement of the fiber optic sensor’s performance.