Reviewerly/exHarmony huggingface dataset:
Video Quality Assessment with Texture Information Fusion for Streaming Applications

The rise in video streaming applications has increased the demand for video quality assessment (VQA). In 2016, Netflix introduced Video Multi-Method Assessment Fusion (VMAF), a full reference VQA metric that strongly correlates with perceptual quality, but its computation is time-intensive. We propose a Discrete Cosine Transform (DCT)-energy-based VQA with texture information fusion (VQ-TIF) model for video streaming applications that determines the visual quality of the reconstructed video compared to the original video. VQ-TIF extracts Structural Similarity (SSIM) and spatiotemporal features of the frames from the original and reconstructed videos and fuses them using a long short-term memory (LSTM)-based model to estimate the visual quality. Experimental results show that VQ-TIF estimates the visual quality with a Pearson Correlation Coefficient (PCC) of 0.96 and a Mean Absolute Error (MAE) of 2.71, on average, compared to the ground truth VMAF scores. Additionally, VQ-TIF estimates the visual quality at a rate of 9.14 times faster than the state-of-the-art VMAF implementation, along with an 89.44 % reduction in energy consumption, assuming an Ultra HD (2160p) display resolution.
Unsupervised clustering-based domain adaptation for estimating occupancy and recognizing activities in smart buildings

Smart buildings rely on estimating the number of occupants and recognizing their behaviors to provide several benefits such as energy efficiency, HVAC (Heating, Ventilation, and Air Conditioning) systems optimization, and health safety. Examining the behavior and the counts of residents in different building areas can provide efficient management of resources (heating, cooling, lighting), and energy distribution across the building. Artificial intelligence (AI) and machine learning (ML) can exploit Internet of Things (IoT) data, such as power consumption data, to provide occupancy estimation (OE) and activity recognition (AR). Obtaining labeled smart building data can be very challenging because it is costly, sensitive, and time-consuming. Data scarcity is a serious problem for research development since few labeled data samples may lead to weak classifiers that do not satisfy real-world applications. Researchers have examined domain adaptation (DA) approaches to share knowledge from source domains where labeled data is abundant to target domains where labeled is scarce. Furthermore, researchers have considered a particular case of DA which is the unsupervised method that solves the case of unavailability of labeled data in target domains. Most previous research in domain adaptation has focused on minimizing the discrepancy between source and target feature distributions. In this work, we adapt and evaluate deep unsupervised domain adaptation (UDA) methods based on clustering (UCDA) to estimate the number of occupants and recognize their activities. We have adapted three methods called Structurally Regularized Deep Clustering (SRDC), Hybrid Structurally Regularized Deep Clustering (H-SRDC), and Distilled Discriminative Clustering for Domain Adaptation (DisClusterDA). We have used OE and AR datasets to evaluate the efficiency of the adapted techniques. The findings, with accuracy up to 99%, make these methods open for potential real-world applications. To further enhance the current research, we have provided the code in the following repository: https://github.com/JawDri/Unsupervised-Clustering-based-DA-for-OE-and-AR.git
Augmented reality in medical education: Exploring ethical considerations

• AR and VR can improve medical practice and education, they also present ethical questions. • Perception of AR devices and patient consent are essential for utilizing AR in healthcare environments. • Learners' autonomy and privacy are at risk when using and collecting VR data. • Users and developers of AR and VR technologies should abide by moral standards and laws.
Fusing consensus knowledge: A federated learning method for fault diagnosis via privacy-preserving reference under domain shift

Recently, federated fault diagnosis has garnered growing attention due to its promising capabilities in information fusion with privacy preservation. However, most of the existing approaches are based on the assumptions of no domain shift between multiple factories and no unseen domains for online applications. In actual industry, these assumptions are generally unsatisfied due to prominent environmental noises, mechanical wear, and changes in working conditions. Federated models that ignore domain shifts would face the negative aggregation problem and are not robust to unseen domains. To solve the domain shift problem, a federated domain generalization method is proposed for privacy-preserving fault diagnosis in this article. The key idea is to construct a sharable reference domain in cloud, which can convert the privacy-risky centralized alignment problem into a privacy-preserving pairwise alignment problem. Based on the recognition that any fault category in a discriminative feature space can be characterized by a particular position and volatility, we design a shareable domain generator to provide a reference for pairwise alignment. Then, the non-deterministic sampling and non-parametric alignment criterion are introduced to realize local domain alignment, which facilitates the domain-invariant feature extraction. Finally, by the alternation of local domain alignment and global reference synchronization, the alignment of multi-source domains is achieved implicitly. We give convergence guarantees for the proposed method and derive the generalization error bound of federated DG, which illustrates the positive effect of the proposed method on improving generalization. Experiments on two cases demonstrate the consistent superior generalization performance of our method without the risk of data leakage.
Multi-task few-shot text steganalysis based on context-attentive prototypes

The current mainstream text steganalysis methods are based on supervised training, requiring a sufficient number of training samples, and necessitating that the training and testing distributions meet the prerequisite of being independently and identically distributed (i.i.d.). However, in practical applications, the distribution of stego text may vary with factors such as text corpora, steganographic algorithms, and embedding rates. This issue, known as domain mismatch, will significantly impact the models’ detection performance. To address this issue, we propose a multi-task few-shot text steganalysis model based on Context-sensitive Prototypes, namely CP-Stega. CP-Stega first extracts generic and task-specific text features from the input sentences and constructs a prototype for each task by averaging the sentence representations of the support set. Next, CP-Stega measures the similarity between the query sample and each task by calculating the distance between the sentence representations and each prototype for any given query sample. To improve the model's adaptability to diverse tasks, we design a loss function consisting of multi-margin loss and KL divergence loss, which expands inter-task prototype distance and shortens intra-task distance, enabling the model to update its meta-parameters accordingly. Experiments reveal that our model can effectively adapt to diverse tasks under different meta-learning scenarios based on different embedding algorithms and embedding payloads, significantly improving text steganalysis performance compared to current foremost steganalysis models and other meta-learning algorithms. By calculating the distance distribution between the sentence representations and each prototype for any given query sample, CP-Stega effectively measures the similarity between the query sample and each task. To enhance the model's adaptability to diverse tasks, we design a loss function comprising multi-margin loss and KL divergence loss. The experiments demonstrate that CP-Stega can effectively perform various steganalysis detection tasks on three self-built meta datasets with different embedding algorithms and embedding capacities.
Digital education: new paradigms for teaching

From the advent of the internet to the latest innovations in artificial intelligence and virtual reality, it is essential to identify and understand the technological changes shaping this educational landscape. Thus, the general objective of the research is to analyze how the adoption of Digital Education as a new paradigm in teaching impacts the effectiveness of the teaching-learning process, considering the specific challenges faced by educators in the integration of these innovative practices. Regarding the methods, a literature review with a qualitative approach was used. It is concluded, therefore, that Digital Education emerges as a catalyzing tool for a more effective education, preparing students for the challenges of the 21st century.
Multi-view representation learning for tabular data integration using inter-feature relationships

An applied problem facing all areas of data science is harmonizing data sources. Joining data from multiple origins with unmapped and only partially overlapping features is a prerequisite to developing and testing robust, generalizable algorithms, especially in healthcare. This integrating is usually resolved using meta-data such as feature names, which may be unavailable or ambiguous. Our goal is to design methods that create a mapping between structured tabular datasets derived from electronic health records independent of meta-data.We evaluate methods in the challenging case of numeric features without reliable and distinctive univariate summaries, such as nearly Gaussian and binary features. We assume that a small set of features are a priori mapped between two datasets, which share unknown identical features and possibly many unrelated features. Inter-feature relationships are the main source of identification which we expect. We compare the performance of contrastive learning methods for feature representations, novel partial auto-encoders, mutual-information graph optimizers, and simple statistical baselines on simulated data, public datasets, the MIMIC-III medical-record changeover, and perioperative records from before and after a medical-record system change. Performance was evaluated using both mapping of identical features and reconstruction accuracy of examples in the format of the other dataset.Contrastive learning-based methods overall performed the best, often substantially beating the literature baseline in matching and reconstruction, especially in the more challenging real data experiments. Partial auto-encoder methods showed on-par matching with contrastive methods in all synthetic and some real datasets, along with good reconstruction. However, the statistical method we created performed reasonably well in many cases, with much less dependence on hyperparameter tuning. When validating feature match output in the EHR dataset we found that some mistakes were actually a surrogate or related feature as reviewed by two subject matter experts.In simulation studies and real-world examples, we find that inter-feature relationships are effective at identifying matching or closely related features across tabular datasets when meta-data is not available. Decoder architectures are also reasonably effective at imputing features without an exact match.
Measuring large tensile deformation of polymers using fluorescent 3D-digital image correlation with adaptive incremental calculation strategy

Digital image correlation (DIC) techniques have been widely used for experimentally characterizing the mechanical behavior of polymer materials. However, practical applications of DIC for tensile testing of polymers often face serious image decorrelation caused by crazing and excessive deformation. In this work, a fluorescent three-dimensional digital image correlation (3D-DIC) with an adaptive incremental calculation strategy is proposed to realize reliable large deformation measurement of polymers by eliminating or mitigating the image decorrelation caused by these issues. The proposed technique first utilizes fluorescent 3D-DIC to eliminate image decorrelation caused by the crazing effect. Then, an adaptive incremental calculation strategy that can automatically update reference images is adopted to mitigate image decorrelation due to excessive deformation. The effectiveness and practicality of the proposed technique were demonstrated by measuring the full-field deformation in chloroprene rubber samples subjected to large tensile deformation. Also, the proposed method was applied to investigate the mechanical behavior of a specific waterproof coating material. Experimental results indicate that the combination of fluorescent 3D-DIC and adaptive incremental calculation strategy can effectively address image decorrelation problems in large deformations of polymers, yielding high-accuracy displacement and strain measurements. This technique holds potential for broader applications in studying the mechanical behavior of other materials or structures undergoing large or super-large deformations.
Monotonicity conjecture for multi-party entanglement. Part I

A bstract In this paper, we conjecture a monotonicity property that we call monotonicity under coarse-graining for a class of multi-partite entanglement measures. We check these properties by computing the measures for various types of states using different methods.
Management of cyber risks in the library: analysis of information security awareness of Estonian library employees

Purpose This article surveys why libraries are vulnerable to social engineering attacks and how to manage risks of human-caused cyber threats on organizational level; investigates Estonian library staff awareness of information security and shares recommendations concerning focus areas that should be given more attention in the future. Design/methodology/approach The data used in this paper is based on an overview of relevant literature highlighting the theoretical points and giving the reasons why human factor is considered the weakest link in information security and cyber security and studying how to mitigate the related risks in the organisation. To perform the survey, a web questionnaire was designed which included 63 sentences and was developed based on the knowledge-attitude-behaviour (KAB) model supported by Kruger and Kearney and Human Aspects of Information Security Questionnaire (HAIS-Q) designed by Parsons et al . Findings The research results show that the information security awareness of library employees is at a good level; however, awareness in two focus areas needs special attention and should be improved. The output of this study is the mapping of seven focus areas of information security policy in libraries based on the HAIS-Q framework and the KAB model. Originality/value The cyber awareness of library employees has not been studied in the world using HAIS-Q and KAB model, and to the best of the authors’ knowledge, no research has been previously carried out in the Estonian library context into cyber security awareness.
IGXSS: XSS payload detection model based on inductive GCN

Abstract To facilitate the management, Internet of Things (IoT) vendors usually apply remote ways such as HTTP services to uniformly manage IoT devices, leading to traditional web application vulnerabilities that also endanger the cloud interfaces of IoT, such as cross‐site scripting (XSS), code injection, and Remote Command/Code Execute (RCE). XSS is one of the most common web application attacks, which allows the attacker to obtain private user information or attack IoT devices and IoT cloud platforms. Most of the existing XSS payload detection models are based on machine learning or deep learning, which usually require a lot of external resources, such as pretrained word vectors, to achieve a better performance on unknown samples. But in the field of XSS payload detection, high‐quality vector representations of samples are often difficult to obtain. In addition, existing models all perform substantially worse when the distribution of XSS payloads and benign samples in the test dataset is extremely unbalanced (e.g., XSS payloads: benign samples = 1: 20). While in the real XSS attack scenario against IoT, an XSS payload is often hidden in a massive amount of normal user requests, indicating that these models are not practical. In response to the above issues, we propose an XSS payload detection model based on inductive graph neural networks, IGXSS (XSS payload detection model based on inductive GCN), to detect XSS payloads targeting IoT. Firstly, we treat the samples and words obtained from segmenting the samples as nodes and attach lines between them in order to form a graph. Then, we obtain the feature matrix of nodes and edges utilizing information between nodes only (instead of external resources such as pretrained word vectors). Finally, we feed the obtained feature matrix into a two‐layer GCN for training and validate the performance of models in several datasets with different sample distributions. Extensive experiments on the real datasets show that IGXSS performs better compared to other models under various sample distributions. In particular, when the sample distribution is extremely unbalanced, the recall and F1 score of IGXSS still reach 1.000 and 0.846, demonstrating that IGXSS is more robust and more suitable for practical scenarios.
Young children’s conceptions of computing in an African setting

Background and Context Recognizing that digital technologies can enable economic transformation in Africa, computing education has been considered a subject relevant for all within the compulsory level of education. The implementation of the subject in many schools is, however, characterized by a myriad of challenges, including pedagogical affordances, especially within early stages of basic education. There is a need, therefore, to understand how children within primary education can be supported to learn computing.
A Deep Convolutional Encoder–Decoder–Restorer Architecture for Image Deblurring

Abstract The accuracy of many computer vision tasks is reduced by blurred images, so deblur is important. More details of the image can be captured by a common multi-stage network, but the computational complexity of this method is higher compared with a single-stage network. However, a single-stage network cannot capture multi-scale information well. To tackle the problem, a novel convolutional encoder–decoder–restorer architecture is proposed. In this architecture, a multi-scale input structure is used in the encoder. Improved supervised attention module is inserted into the encoder for enhanced feature acquisition. In decoder, information supplement block is proposed to fuse multi-scale features. Finally, the fused features are used for image recovery in the restorer. In order to optimise the model in multiple domains, the loss function is calculated separately in the spatial and frequency domains. Our method is compared with existing methods on the GOPRO dataset. In addition, to verify the applications of our proposed method, we conduct experiments on the Real image dataset, the VOC2007 dataset and the LFW dataset. Experimental results show that our proposed method outperforms state-of-the-art deblurring methods and improves the accuracy of different vision tasks.
Exploring Transformer Model in Longitudinal Pharmacokinetic/Pharmacodynamic Analyses and Comparing with Alternative Natural Language Processing Models

There remains a substantial need for a comprehensive assessment of various natural language processing (NLP) algorithms in longitudinal pharmacokinetic/pharmacodynamic (PK/PD) modeling despite recent advances in machine learning in the space of quantitative pharmacology. We herein investigated the application of the transformer model and further compared the performance among several different NLP models, including long short-term memory (LSTM) and neural-ODE (Ordinary Differential Equation) in analyzing longitudinal PK/PD data using virtual data containing three different regimens. Results suggested that LSTM and neural-ODE, along with their respective variants provide a strong performance when predicting from training-included (seen) regimens, albeit with slight information loss for training-excluded (unseen) regimens. Similarly, as with neural-ODE, the transformer exhibited superior performance in describing time-series PK/PD data. Nonetheless, when extrapolating to unseen regimens, while outlining the general data trends, it encountered difficulties in precisely capturing data fluctuations. Remarkably, a small integration of unseen data into the training dataset significantly bolsters predictive performance for both seen and unseen regimens. Our study marks a pioneering effort in deploying the transformer model for time-series PK/PD analysis and provides a systematic exploration of the currently available NLP models in this field.
Child-adult speech diarization in naturalistic conditions of preschool classrooms using room-independent ResNet model and automatic speech recognition-based re-segmentation

Speech and language development are early indicators of overall analytical and learning ability in children. The preschool classroom is a rich language environment for monitoring and ensuring growth in young children by measuring their vocal interactions with teachers and classmates. Early childhood researchers are naturally interested in analyzing naturalistic vs controlled lab recordings to measure both quality and quantity of such interactions. Unfortunately, present-day speech technologies are not capable of addressing the wide dynamic scenario of early childhood classroom settings. Due to the diversity of acoustic events/conditions in such daylong audio streams, automated speaker diarization technology would need to be advanced to address this challenging domain for segmenting audio as well as information extraction. This study investigates alternate deep learning-based lightweight, knowledge-distilled, diarization solutions for segmenting classroom interactions of 3–5 years old children with teachers. In this context, the focus on speech-type diarization which classifies speech segments as being either from adults or children partitioned across multiple classrooms. Our lightest CNN model achieves a best F1-score of ∼76.0% on data from two classrooms, based on dev and test sets of each classroom. It is utilized with automatic speech recognition-based re-segmentation modules to perform child-adult diarization. Additionally, F1-scores are obtained for individual segments with corresponding speaker tags (e.g., adult vs child), which provide knowledge for educators on child engagement through naturalistic communications. The study demonstrates the prospects of addressing educational assessment needs through communication audio stream analysis, while maintaining both security and privacy of all children and adults. The resulting child communication metrics have been used for broad-based feedback for teachers with the help of visualizations.