Reviewerly/exHarmony huggingface dataset:
<i>S</i>tudents’ perceptions of the use of the flipped classroom during the educational process of linear functions (<i>Percepciones de los estudiantes sobre el uso del aula invertida durante el proceso educativo de las funciones lineales</i>)

(2021). Students' perceptions of the use of the flipped classroom during the educational process of linear functions (Percepciones de los estudiantes sobre el uso del aula invertida durante el proceso educativo de las funciones lineales) Culture and Education: Vol. 33, No. 3, pp. 431-454.
Privacy Amplification for Federated Learning via User Sampling and Wireless Aggregation

In this paper, we study the problem of federated learning over a wireless channel with user sampling, modeled by a Gaussian multiple access channel, subject to central and local differential privacy (DP/LDP) constraints. It has been shown that the superposition nature of the wireless channel provides a dual benefit of bandwidth efficient gradient aggregation, in conjunction with strong DP guarantees for the users. Specifically, the central DP privacy leakage has been shown to scale as <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathcal{O}(1/\sqrt{K})$</tex> , where <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$K$</tex> is the number of users. It has also been shown that user sampling coupled with orthogonal transmission can enhance the central DP privacy leakage with the same scaling behavior. In this work, we show that, by jointly incorporating both wireless aggregation and user sampling, one can obtain even stronger central DP guarantees. We propose a private wireless gradient aggregation scheme, which relies on independently randomized participation decisions by each user. The central DP leakage of our proposed scheme scales as <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathcal{O}(1/K^{3/4})$</tex> . In addition, we show that LDP is also boosted by user sampling.
A Residual Chaotic System for Image Security and Digital Video Watermarking

Multimedia data security has become a noticeable problem in the continuous development of multimedia technology. Traditional chaotic systems usually require larger bit-width iterative calculations to affect the generation rate of chaotic systems when generating. To solve this problem, we proposed a general Residual Chaotic System (RCS) for image security in heterogeneous multimedia signal processing environments. Based on the Residue Number System (RNS) commonly used in VLSI, we generate the general chaotic sequences by iterating unary polynomial function through it in Galois Field (GF). Moreover, we make use of the Box-Muller algorithm to generate RCS, which improved the speed of operation by a large margin when doing R/B backward transformation. Experiments show that the key sensitivity and plaintext sensitivity of the proposed encryption system are good, and the system can encrypt pictures efficiently and safely. Then, we proposed a watermark embedding and extraction algorithm with block selection. Based on the proposed new judgment conditions, the 4×4 brightness blocks are divided into five categories, then the appropriate types of blocks are selected accordingly. After testing the performance indexes of cypher-text images, including statistical and sensitivity-related tests, all results indicate that our algorithm has good randomness and security. The invisibility is good, the watermark capacity has been significantly improved, and the video bit-rate growth rate is in a good range. The watermark can resist attacks such as noise and the bit error rate under re-encoding and Gaussian low-pass filtering attacks. Compared with other algorithms, the algorithm that we proposed is faster, much more efficient, and more secure. Therefore, it is a practical alternative scheme for heterogeneous communication environments.
Using black feminist epistemologies and activist frameworks to counter structural racism in design

Community + Culture features practitioner perspectives on designing technologies for and with communities. We highlight compelling projects and provocative points of view that speak to both community technology practice and the interaction design field as a whole. --- Sheena Erete, Editor
Efficient sequential covering strategy for classification rules mining using a discrete equilibrium optimization algorithm

Rule-based classification is one of the important tasks in data mining due to its wide applications, particularly in the domains that need to interpret the classification decision such as medical diagnosis. The rule-based classification is a combination of the classification and association rule mining fields which aims at building interpretable classifiers by means of classification rules. This paper presents a novel and efficient sequential covering strategy for Classification Rule Mining to improve the interpretability of classifiers using a Discrete Equilibrium Optimization Algorithm called DEOA-CRM. Our approach benefits from the advantages of associative classification and population-based intelligence. It is inspired by the recent meta-heuristic equilibrium optimization algorithm. New discrete operators defined enable our approach to avoid local solutions and find global ones, improving the exploration and exploitation power in the search space. The proposed DEOA-CRM is tested on a total of 12 test data sets of various sizes and benchmarked with four recent and well-known rule-based classification mining algorithms. The obtained results confirm the efficiency of our algorithm in three chosen measures. Our approach fully deserves its use for classification rules generation to help decision-makers generate accurate and interpretable models.
AI-Driven Salient Soccer Events Recognition Framework for Next-Generation IoT-Enabled Environments

The salient event recognition of soccer matches in the next-generation Internet of Things (Nx-IoT) environment aims to analyze the performance of players/teams by the sports analytics and managerial staff. The embedded Nx-IoT devices carried by the soccer players during the match capture and transmit data to an artificial intelligence (AI)-assisted computing platform. The interconnectivity of data acquisition devices with an AI-assisted computing platform in the Nx-IoT environment will not only allow the spectators to track the formation of their favorite players during a soccer match but will also enable the managerial staff to evaluate the players’ performance in the soccer match as well as in practice sessions. This Nx-IoT-enabled salient event detection feature can be provided to spectators and sports’ managerial staff as a financial technology (FinTech) service. In this article, we propose an efficient deep-learning-based framework for multiperson salient soccer event recognition in IoT-enabled FinTech. The proposed framework performs event recognition in three steps: 1) frames preprocessing; 2) frame-level discriminative features extraction; and 3) high-level events recognition in soccer videos. Moreover, we introduce a new soccer video events (SVE) data set containing videos of six salient events of soccer games. To provide a strong baseline, we evaluate our newly created SVE data set using different traditional machine learning and deep learning algorithms. We also perform event recognition on untrimmed soccer videos using our proposed framework and compare the results with state-of-the-art methods. The obtained results validate the suitability of our proposed framework for salient event recognition in Nx-IoT environments.
Wavelength-tuning multiple-surface interferometric analysis with compression of Zernike piston phase error

In this study, we propose a 6N – 3 algorithm for suppressing the Zernike piston phase error by expanding 6N – 5 algorithm using algorithmic polynomial theory. We demonstrate that the proposed algorithm can be used for simultaneous measurement of the surface profile and thickness homogeneity by rotating the characteristic diagram on the complex plane. We applied Fourier analysis to confirm the properties of 6N – 3 algorithm in the frequency domain. We then estimate the error-compensating capability of 6N – 3 algorithm, after which we compare this capability to that of classical algorithms using numerical error analysis. To verify the effectiveness, we conducted an experiment using 6N – 3 algorithm and Fizeau interferometer to measure the thickness homogeneity and surface profile of a glass plate. The standard deviation of the measured thickness homogeneity was 1.965 nm, which was significantly smaller than that of the other classical algorithms.
Global trends in downward surface solar radiation from spatial interpolated ground observations during 1961-2019

Abstract Downward surface solar radiation (SSR) is a crucial component of the Global Energy Balance, affecting temperature and the hydrological cycle profoundly, and it provides crucial information about climate change. Many studies have examined SSR trends, however they are often concentrated on specific regions due to limited spatial coverage of ground based observation stations. To overcome this spatial limitation, this study performs a spatial interpolation based on a machine learning method, Random Forest, to interpolate monthly SSR anomalies using a number of climatic variables (various temperature indices, cloud coverage, etc.), time point indicators (years and months of SSR observations), and geographical characteristics of locations (latitudes, longitudes, etc). The predictors that provide the largest explanatory power for interannual variability are diurnal temperature range and cloud coverage . The output of the spatial interpolation is a 0:5° ×0:5° monthly gridded dataset of SSR anomalies with complete land coverage over the period 1961-2019, which is used afterwards in a comprehensive trend analysis for i) each continent separately, and ii) the entire globe. The continental level analysis reveals the major contributors to the global dimming and brightening. In particular, the global dimming before the 1980s is primarily dominated by negative trends in Asia and North America, while Europe and Oceania have been the two largest contributors to the brightening after 1982 and up until 2019.
Accountable Federated Machine Learning in Government: Engineering and Management Insights

Machine learning offers promising capabilities to improve administrative procedures. At the same time, adequate training of models using traditional learning techniques requires the collection and storage of enough training data in a central place. Unfortunately, due to legislative and jurisdictional constraints, data in a central place is scarce and training a model becomes unfeasible. Against this backdrop, federated machine learning, a technique to collaboratively train models without transferring data to a centralized location, has been recently proposed. With each government entity keeping their data private, new applications that previously were impossible now can be a reality. In this paper, we demonstrate that accountability for the federated machine learning process becomes paramount to fully overcoming legislative and jurisdictional constraints. In particular, it ensures that all government entities' data are adequately included in the model and that evidence on fairness and reproducibility is curated towards trustworthiness. We also present an analysis framework suitable for governmental scenarios and illustrate its exemplary application for online citizen participation scenarios. We discuss our findings in terms of engineering and management implications: feasibility evaluation, general architecture, involved actors as well as verifiable claims for trustworthy machine learning.
Online Analysis of Children Handwritten Words in Dictation Context

This paper presents a method for fine analysis of children handwriting on pen-based tablets. This work is in the context of the P2IA project, funded by the French government, which aims at designing a virtual notebook in order to foster handwriting learning for primary school pupils. In this work, we consider the task of analysing handwritten words in the context of a dictation exercise. This task is complex due to different factors: the children do not master yet the morphological aspects of handwriting, nor do they master orthography or translating phonetic sounds to actual graphemes (parts of word). In order to tackle this problem, we extend to the context of dictation exercises an analysis engine that was developed previously to deal with copying exercises. Two strategies were developed, the first one is a baseline approach and relies on double child input: the pupil types the word on a virtual keyboard after writing it with the stylus, thus the prior knowledge of the written word will drive the engine analysis. The second one relies on a single input: the child handwritten strokes. To drive the analysis, the strategy consists in generating hypotheses that are phonetically similar to the dictated instruction, which will act as probable approximations of the written word (sequence of letters), to cover potential orthographic mistakes by the pupil. To assist the learning process of the pupils, the engine returns different types of real-time feedbacks, that depend on the confidence of the analysis process (confident assessment on errors, warning, or reject).
Nanoparticles in various media on surfaces of ore deposits: Study of the more than 1000 m deep concealed Shaling gold deposit

Surface nanoparticles in nature have become important research objects because they contain information about the deep Earth. In this study, nanoparticles were analyzed via Transmission Electron Microscopy (TEM) in gradient media (groundwater, ascending gas, and soil) on the surface overlying the concealed Shaling gold deposit in China, which was buried more than 1000 m below the surface. The morphologies, ultramicroscopic structure, and chemical composition of the nanoparticles were analyzed via High-Resolution TEM (HRTEM), Selected Area Electron Diffraction (SAED), and Energy Dispersive Spectroscopy (EDS). These nanoparticles were characterized as (i) Amorphous Nano-Particles (ANPs), (ii) Amorphous-and-Crystalline-mixed Nano-Particles (A&C mixed NPs), and (iii) Crystallized Nano-Particles (CNPs). Furthermore, the newly proposed A&C mixed NPs were further classified into wrapped-, adsorbed- and coexisting-type nanoparticles. The ANPs exhibited a disorderly arrangement, A&C mixed NPs comprised amorphous materials as well as crystalline grains, and CNPs predominantly consisted of crystallized bodies. Oxidized particles derived from concealed ore bodies contain ore-related information and migrate to the surface via the flow of groundwater and ascending gas along faults. Among these particles, amorphous-bearing nanoparticles can contain elements with similar geochemical behaviors, such as Ca and Zn. Overall, this study provides a new basis for the exploration of concealed ore bodies.
HistoClean: Open-source software for histological image pre-processing and augmentation to improve development of robust convolutional neural networks

The growth of digital pathology over the past decade has opened new research pathways and insights in cancer prediction and prognosis. In particular, there has been a surge in deep learning and computer vision techniques to analyse digital images. Common practice in this area is to use image pre-processing and augmentation to prevent bias and overfitting, creating a more robust deep learning model. This generally requires consultation of documentation for multiple coding libraries, as well as trial and error to ensure that the techniques used on the images are appropriate. Herein we introduce HistoClean; a user-friendly, graphical user interface that brings together multiple image processing modules into one easy to use toolkit. HistoClean is an application that aims to help bridge the knowledge gap between pathologists, biomedical scientists and computer scientists by providing transparent image augmentation and pre-processing techniques which can be applied without prior coding knowledge. In this study, we utilise HistoClean to pre-process images for a simple convolutional neural network used to detect stromal maturity, improving the accuracy of the model at a tile, region of interest, and patient level. This study demonstrates how HistoClean can be used to improve a standard deep learning workflow via classical image augmentation and pre-processing techniques, even with a relatively simple convolutional neural network architecture. HistoClean is free and open-source and can be downloaded from the Github repository here: https://github.com/HistoCleanQUB/HistoClean.
Secure and Privacy-Preserving Stored Surveillance Video Sharing atop Permissioned Blockchain

At present, more than a billion closed-circuit television (CCTV) cameras are watching the world. These cameras garner a lot of visual information that is often processed and stored in remote and centralized cloud servers. Multiple occasions have revealed that this traditional approach is plagued with security and privacy breaches. The breaches could be the interception of raw videos while in transit to distant surveillance analytics centers (SAC), infiltration to cameras and network video records (NVR), or abuse of cameras and stored videos. Hence, the traditional video surveillance system (VSS) cannot guarantee the protection of the privacy of individuals caught on CCTV cameras. Therefore, this paper proposes a Secure and Privacy-preserving Stored surveillance video sharing (SePriS) mechanism for authorized users/nodes based on blockchain (BC), smart contracts, and the enciphering of video frames using DAB, a mechanism developed based on discrete cosine transform (DCT), advanced encryption standard (AES), and a block shuffling (BS) algorithm. The BC-based solution creates an environment auspicious for creating decentralized, reliable SACs and storage sites with secure and privacy-aware sharing of stored surveillance videos across SAC nodes and by law enforcers, police departments, and courts securely connected to the SAC nodes. The experiments and analyses validate that the proposed BC-based SePriS solution achieves the design purpose.
SCAF-Net: Scene Context Attention-Based Fusion Network for Vehicle Detection in Aerial Imagery

In recent years, deep learning methods have achieved great success for vehicle detection tasks in aerial imagery. However, most existing methods focus only on extracting latent vehicle target features, and rarely consider the scene context as vital prior knowledge. In this letter, we propose a scene context attention-based fusion network (SCAF-Net), to fuse the scene context of vehicles into an end-to-end vehicle detection network. First, we propose a novel strategy, patch cover, to keep the original target and scene context information in raw aerial images of a large scale as much as possible. Next, we use an improved YOLO-v3 network as one branch of SCAF-Net, to generate vehicle candidates on each patch. Here, a novel branch for the scene context is utilized to extract the latent scene context of vehicles on each patch without any extra annotations. Then, these two branches above are concatenated together as a fusion network, and we apply an attention-based model to further extract vehicle candidates of each local scene. Finally, all vehicle candidates of different patches, are merged by global nonmax suppress (g-NMS) to output the detection result of the whole original image. Experimental results demonstrate that our proposed method outperforms the comparison methods with both high detection accuracy and speed. Our code is released at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/minghuicode/SCAF-Net</uri> .
Remote Sensing Image Super-Resolution Based on Dense Channel Attention Network

In the recent years, convolutional neural networks (CNN)-based super resolution (SR) methods are widely used in the field of remote sensing. However, complicated remote sensing images contain abundant high-frequency details, which are difficult to capture and reconstruct effectively. To address this problem, we propose a dense channel attention network (DCAN) to reconstruct high-resolution (HR) remote sensing images. The proposed method learns multi-level feature information and pays more attention to the important and useful regions in order to better reconstruct the final image. Specifically, we construct a dense channel attention mechanism (DCAM), which densely uses the feature maps from the channel attention block via skip connection. This mechanism makes better use of multi-level feature maps which contain abundant high-frequency information. Further, we add a spatial attention block, which makes the network have more flexible discriminative ability. Experimental results demonstrate that the proposed DCAN method outperforms several state-of-the-art methods in both quantitative evaluation and visual quality.
Rethinking preventing class-collapsing in metric learning with margin-based losses

Metric learning seeks perceptual embeddings where visually similar instances are close and dissimilar instances are apart, but learned representations can be sub-optimal when the distribution of intra-class samples is diverse and distinct sub-clusters are present. Although theoretically with optimal assumptions, margin-based losses such as the triplet loss and margin loss have a diverse family of solutions. We theoretically prove and empirically show that under reasonable noise assumptions, margin-based losses tend to project all samples of a class with various modes onto a single point in the embedding space, resulting in class collapse that usually renders the space ill-sorted for classification or retrieval. To address this problem, we propose a simple modification to the embedding losses such that each sample selects its nearest same-class counterpart in a batch as the positive element in the tuple. This allows for the presence of multiple sub-clusters within each class. The adaptation can be integrated into a wide range of metric learning losses. Our method demonstrates clear benefits on various fine-grained image retrieval datasets over a variety of existing losses; qualitative retrieval results show that samples with similar visual patterns are indeed closer in the embedding space.
The THREAT-ARREST Cyber Range Platform

Emerging technologies are facilitating our daily activities and drive the digital transformation. The Internet of Things (IoT) and 5G communications will provide a wide range of new applications and business opportunities, but with a wide and quite complex attack surface. Several users are not aware of the underlying threats and most of them do not possess the knowledge to set and operate the various digital assets securely. Therefore, cyber security training is becoming mandatory both for simple users and security experts. Cyber ranges constitute an advance training technique where trainees gain hands-on experiences on a safe virtual environment, which can be a realistic digital twin of an actual system. This paper presents the cyber ranges platform THREAT-ARREST. Its design is fully model-driven and offers all modern training features (i.e. emulation, simulation, serious games, and fabricated data). The platform has been evaluated under the smart energy, intelligent transportation, and healthcare domains.