Reviewerly/exHarmony huggingface dataset:
RPF: Reference-Based Progressive Face Super-Resolution Without Losing Details and Identity

Face super-resolution involves generating a high-resolution facial image from a low-resolution one. It is, however, quite a difficult task when the resolution difference between input and output images is too large. In order to tackle this challenge, many approaches use generative adversarial networks that are pre-trained on a large facial image dataset, but they often generate fake details and distort the person’s original face, leading to a loss of identity. Hence, in this paper, we propose a progressive face super-resolution network, called RPF, to super-resolve a facial image without losing details and personal identity by progressively exploiting the same person’s high-resolution image as a reference image. First, we remove unnecessary detail information, such as hair and background, from the reference image, which may be different from the low-resolution input. Next, we align the high-resolution reference image to the low-resolution input image and blend them to generate a synthesized image. Finally, we refine the synthesized image to generate a faithful super-resolved image containing both details and identity information. Experimental results demonstrate that the proposed RPF algorithm outperforms recent state-of-the-art methods in terms of detail restoration and identity preservation, with improvements of 0.0098 and 0.0478 in LPIPS and ISC, respectively, on the CelebA-HQ dataset.
Design and Experimentation of a Residual-Input Tube-End Cutting System for Plasma Bags Based on Machine Vision

In response to the lack of plasma bag-breaking equipment and time-consuming manual recovery of plasma in China, this study focuses on the distal end cutting of a plasma bag residual-input tube and designs a machine-vision-based plasma bag residual-input tube cutting system. The system comprises a plasma bag rotation device, a bottom disc, an image acquisition device, a cutting device, and a device control system. The image acquisition device captures images of the rotating plasma bag and calculates the Euclidean distance from the end of the plasma bag input-tube distal end to the outer edge of the bottom support plate and the distance from the input-tube distal end to the centerline of the plasma bag. Two distinct value acquisition algorithms are designed for the two types of input tubes. The screw slide moves accordingly. When the plasma bag is above the cutting part, the cutting part cuts the end of the residual-input tube of the plasma bag. The prototype performance tests reveal that the cutting error is within 10% of the end of the plasma bag input tube distal end, the cutting success rate is 100%, and the processing time for each bag is 2.5 s, which meets the time requirements of the plasma bag breaker.
Cultural background in digital literacy of elementary and middle school students: Self‐appraisal versus actual performance

Abstract Background Integration of digital technologies in schools raises the need of students to master technological, cognitive, and social digital literacy (DL) competencies. Objectives Based on Hofstede's dimensional paradigm for defining culture, we address the cultural context and examine perceived and actual DL of Arabic‐speaking minority students in Israel. Methods First, 402 Arabic‐speaking minority students from elementary Arabic‐speaking schools and 232 students from middle schools reported their DL based on the DL framework. Among them, 347 elementary and 205 middle school students performed a battery of DL tasks. Findings and Conclusions The findings revealed a very low level of DL performance by minority students in both elementary and middle schools. The highest performance score was in information literacy: M = 1.49 and M = 1.55 out of 5.00 for elementary and middle school students respectively. Despite ubiquitous use of social networks, the lowest score was obtained in social–emotional literacy—0.23 for both elementary and middle school students. In contrast to performance, both elementary and middle school minority students estimated their digital literacy competencies as high or very high. Among the elementary students, a weak correlation was found between their DL perceptions and performance of the photo‐visual, branching, and social–emotional literacies. Among the middle school students, the only significant correlation was found between perceptions and performance of reproduction literacy. The findings raise the need to improve DL competencies of minority students and develop their metacognitive abilities. This helps to assess their DL more accurately, in order to ensure their successful functioning in digital environments. Theoretical and educational implications of the findings are discussed.
Defending Privacy Inference Attacks to Federated Learning for Intelligent IoT with Parameter Compression

Federated learning has been popularly studied with people’s increasing awareness of privacy protection. It solves the problem of privacy leakage by its ability that allows many clients to train a collaborative model without uploading local data collected by Internet of Things (IoT) devices. However, there are still threats of privacy leakage in federated learning. The privacy inference attacks can reconstruct the privacy data of other clients based on GAN from the parameters in the process of iterations for global models. In this work, we are motivated to prevent GAN-based privacy inference attacks in federated learning. Inspired by the idea of gradient compression, we propose a defense method called Federated Learning Parameter Compression (FLPC) which can reduce the sharing of information for privacy protection. It prevents attackers from recovering the private information of victims while maintaining the accuracy of the global model. Extensive experimental results demonstrated that our method is effective in the prevention of GAN-based privacy inferring attacks. In addition, based on the experimental results, we propose a norm-based metric to assess the performance of privacy-preserving.
Reliability validation enabling framework (RVEF) for digital forensics in criminal investigations

This paper proposes a formal reliability validation enabling framework (RVEF) for evaluation of digital forensics in criminal investigations. The RVEF is informed by examined theoretical and conceptual gaps between law and digital forensics related to reliability and validation. Identified are validation criteria and validation testing techniques for digital forensics as well as their limitations and challenges. The proposed RVEF aims to satisfy the objective for documenting the chain of evidence and custody as standard process. It is a generic and extensible approach to create a formal procedure for documentation of reliability information at three levels: technology, method, and application. For each level reliability criteria are compared against international digital forensic standards, guidelines, and best practices in order to elaborate concrete minimum documentation requirements necessary to enable reliability validation by law enforcement. The framework aims to increase accountability, reliability testing, and machine-human error mitigation in digital forensics. It can also serve judges and defense lawyers to cross-examine the forensic report in a formalized process, access the proportionality of the investigation measures, and potential risks from the inappropriate use of technology.
Deep Generative Models for Synthetic Data: A Survey

A growing interest in synthetic data has stimulated the development and advancement of a large variety of deep generative models for a wide range of applications.However, as this research has progressed, its streams have become more specialized and disconnected from one another.This is why models for synthesizing text data for natural language processing cannot readily be compared to models for synthesizing health records anymore.To mitigate this isolation, we propose a data-driven evaluation framework for generative models for synthetic sequential data, an important and challenging sub-category of synthetic data, based on five high-level criteria: representativeness, novelty, realism, diversity and coherence of a synthetic data-set relative to the original data-set regardless of the models' internal structures.The criteria reflect requirements different domains impose on synthetic data and allow model users to assess the quality of synthetic data across models.In a critical review of generative models for sequential data, we examine and compare the importance of each performance criterion in numerous domains.We find that realism and coherence are more important for synthetic data natural language, speech and audio processing tasks.At the same time, novelty and representativeness are more important for healthcare and mobility data.We also find that measurement of representativeness is often accomplished using statistical metrics, realism by using human judgement, and novelty using privacy tests.
Short-Term Memory Capacity across Time and Language Estimated from Ancient and Modern Literary Texts. Study-Case: New Testament Translations

We study the short-term memory capacity of ancient readers of the original New Testament written in Greek, of its translations to Latin and to modern languages.To model it, we consider the number of words between any two contiguous interpunctions I p , because this parameter can model how the human mind memorizes "chunks" of information.Since I P can be calculated for any alphabetical text, we can perform experiments-otherwise impossiblewith ancient readers by studying the literary works they used to read.The "experiments" compare the I P of texts of a language/translation to those of another language/translation by measuring the minimum average probability of finding joint readers (those who can read both texts because of similar short-term memory capacity) and by defining an "overlap index".We also define the population of universal readers, people who can read any New Testament text in any language.Future work is vast, with many research tracks, because alphabetical literatures are very large and allow many experiments, such as comparing authors, translations or even texts written by artificial intelligence tools.
An Evidence Theoretic Approach for Traffic Signal Intrusion Detection

The increasing attacks on traffic signals worldwide indicate the importance of intrusion detection. The existing traffic signal Intrusion Detection Systems (IDSs) that rely on inputs from connected vehicles and image analysis techniques can only detect intrusions created by spoofed vehicles. However, these approaches fail to detect intrusion from attacks on in-road sensors, traffic controllers, and signals. In this paper, we proposed an IDS based on detecting anomalies associated with flow rate, phase time, and vehicle speed, which is a significant extension of our previous work using additional traffic parameters and statistical tools. We theoretically modelled our system using the Dempster-Shafer decision theory, considering the instantaneous observations of traffic parameters and their relevant historical normal traffic data. We also used Shannon's entropy to determine the uncertainty associated with the observations. To validate our work, we developed a simulation model based on the traffic simulator called SUMO using many real scenarios and the data recorded by the Victorian Transportation Authority, Australia. The scenarios for abnormal traffic conditions were generated considering attacks such as jamming, Sybil, and false data injection attacks. The results show that the overall detection accuracy of our proposed system is 79.3% with fewer false alarms.
MARU-Net: Multiscale Attention Gated Residual U-Net With Contrastive Loss for SAR-Optical Image Matching

Accurate synthetic aperture radar-optical matching is essential for combining the complementary information from the two sensors. However, the main challenge is overcoming the different heterogeneous characteristics of the two imaging sensors. In this article, we propose an end-to-end machine learning pipeline inspired by recent advances in image segmentation. We develop a siamese multiscale attention-gated residual U-Net for feature extraction from satellite images. The siamese architecture shares weights and transforms the heterogeneous images into a homogeneous feature space. Fast Fourier transform is used to compute the cross-correlation between the feature maps and produce a similarity map. A contrastive loss is introduced to aid the training procedure of the model and maximize the discriminability of the model. The experimental results on a benchmark dataset show that the proposed method has superior matching accuracy and precision compared to other state-of-the-art methods.
Spatial Scene Memories Are Biased Towards a Fixed Amount of Semantic Information

Scene memory has known spatial biases. Boundary extension is a well-known bias whereby observers remember visual information beyond an image's boundaries. While recent studies demonstrate that boundary contraction also reliably occurs based on intrinsic image properties, the specific properties that drive the effect are unknown. This study assesses the extent to which scene memory might have a fixed capacity for information. We assessed both visual and semantic information in a scene database using techniques from image processing and natural language processing, respectively. We then assessed how both types of information predicted memory errors for scene boundaries using a standard rapid serial visual presentation (RSVP) forced error paradigm. A linear regression model indicated that memories for scene boundaries were significantly predicted by semantic, but not visual, information and that this effect persisted when scene depth was considered. Boundary extension was observed for images with low semantic information, and contraction was observed for images with high semantic information. This suggests a cognitive process that normalizes the amount of semantic information held in memory.
Visual working memory in immersive visualization: a change detection experiment and an image-computable model

Abstract Visual working memory (VWM) is a cognitive mechanism essential for interacting with the environment and accomplishing ongoing tasks, as it allows fast processing of visual inputs at the expense of the amount of information that can be stored. A better understanding of its functioning would be beneficial to research fields such as simulation and training in immersive Virtual Reality or information visualization and computer graphics. The current work focuses on the design and implementation of a paradigm for evaluating VWM in immersive visualization and of a novel image-based computational model for mimicking the human behavioral data of VWM. We evaluated the VWM at the variation of four conditions: set size, spatial layout, visual angle (VA) subtending stimuli presentation space, and observation time. We adopted a full factorial design and analysed participants’ performances in the change detection experiment. The analysis of hit rates and false alarm rates confirms the existence of a limit of VWM capacity of around 7 ± 2 items, as found in the literature based on the use of 2D videos and images. Only VA and observation time influence performances ( p &lt;0.0001). Indeed, with VA enlargement, participants need more time to have a complete overview of the presented stimuli. Moreover, we show that our model has a high level of agreement with the human data, r &gt;0.88 ( p &lt;0.05).
Research on Efficient Multi-Behavior Recommendation Method Fused with Graph Neural Network

Currently, most recommendation algorithms only use a single type of user behavior information to predict the target behavior. However, when browsing and selecting items, users generate other types of behavior information, which is important, but often not analyzed or modeled by traditional recommendation algorithms. This study aims to design a multi-behavior recommendation algorithm based on graph neural networks by analyzing multiple types of behavior information in users’ product purchasing process, to fully utilize multiple types of user behavior information. The algorithm models users, items, and user behavior in multiple dimensions by incorporating attention mechanisms and multi-behavior learning into graph neural networks, and solves the problem of imbalanced user behavior weights from the perspective of multi-task loss optimization. After experimental verification, we proposed that the multi-behavior graph attention network (MGAT) algorithm has better performance compared to four other classical recommendation algorithms on the Beibei and Taobao datasets. The results demonstrate that the multi-behavior recommendation algorithm based on graph neural networks has practicality in fully utilizing multiple types of user information, and can solve the problem of imbalanced user behavior weights to some extent.
The Impact of an Online Professional Development Course on Teachers’ Comprehension and Self-Efficacy in Developing Students’ Self-Regulated Learning Skills

The ongoing process of digitalisation requires teachers not only to use technologies in their lessons to provide high quality opportunities for learning, but also to learn through this format themselves. In recent years, a new competence-based curriculum has been implemented in Latvia, introducing new content and paying attention to self-regulated learning (SRL) as one of the transversal skills. This requires appropriate teacher professional development (TPD) and the implementation of various digital solutions in teaching and learning, especially since the COVID-19 pandemic. Considering the benefits of online learning and the importance of SRL skills for ensuring sustainable lifelong learning, an online TPD course was conducted to enhance teachers’ knowledge of SRL and self-efficacy in developing students’ SRL skills. The course was attended by 126 teachers of grades 7–12. The research analyses teachers’ self-evaluation of the impact of the online TPD course on their knowledge and skills in developing students’ SRL skills, and data prove that both teachers’ theoretical understanding and competence in developing students’ SRL skills can be significantly improved through this format of TPD.
Gesture-ProxylessNAS: A Lightweight Network for Mid-Air Gesture Recognition Based on UWB Radar

Hand gesture recognition with radar sensors is essential because they can detect gestures despite environmental factors like lighting, dust, and complex backgrounds. Considering the complexity of a system, it is challenging to design CNNs on CPU devices and realize the carry-on mid-air gesture recognition. We propose a mid-air gesture recognition method based on a novel discriminant feature, and it be used as part of a measurement system of hand movements using an ultrawideband (UWB) radar. The Gesture-ProxylessNAS (GPNAS) is presented to enhance the adaptability of model search and overcome the challenge of the network's computational complexity. In order to fully extract local spatial discriminant features and prevent information loss, Local Binary Pattern (LBP) encoders are utilized to extract local spatial information. In the meantime, multi-layer ShuffleNet with depthwise separable convolution is used to gradually leverage high-level spatial features. The Gesture-ProxylessNAS module revisits the multi-layer ShuffleNet's design spaces using an optimization problem, greatly reducing the network's parameters and computational complexity. According to experimental verification on real UWB hand gestures, the proposed framework provides more satisfactory recognition performance and efficiency with a deeper network structure and fewer parameters. The proposed hand gesture recognition system can recognize gestures with a promising accuracy of 96.52% on the UWB-gestures public dataset [1].
Usability Assessments for Augmented Reality Head-Mounted Displays in Open Surgery and Interventional Procedures: A Systematic Review

Augmented reality (AR) head-mounted displays (HMDs) are an increasingly popular technology. For surgical applications, the use of AR HMDs to display medical images or models may reduce invasiveness and improve task performance by enhancing understanding of the underlying anatomy. This technology may be particularly beneficial in open surgeries and interventional procedures for which the use of endoscopes, microscopes, or other visualization tools is insufficient or infeasible. While the capabilities of AR HMDs are promising, their usability for surgery is not well-defined. This review identifies current trends in the literature, including device types, surgical specialties, and reporting of user demographics, and provides a description of usability assessments of AR HMDs for open surgeries and interventional procedures. Assessments applied to other extended reality technologies are included to identify additional usability assessments for consideration when assessing AR HMDs. The PubMed, Web of Science, and EMBASE databases were searched through September 2022 for relevant articles that described user studies. User assessments most often addressed task performance. However, objective measurements of cognitive, visual, and physical loads, known to affect task performance and the occurrence of adverse events, were limited. There was also incomplete reporting of user demographics. This review reveals knowledge and methodology gaps for usability of AR HMDs and demonstrates the potential impact of future usability research.
An Efficient Evolution-Based Technique for Moving Target Search with Unmanned Aircraft Vehicle: Analysis and Validation

Recent advances in technology have led to a surge in interest in unmanned aerial vehicles (UAVs), which are remote-controlled aircraft that rely on cameras or sensors to gather information about their surroundings during flight. A UAV requires a path-planning technique that can swiftly recalculate a viable and quasi-optimal path in flight if a new obstacle or hazard is recognized or if the target is moved during the mission. In brief, the planning of UAV routes might optimize a specific problem determined by the application, such as the moving target problem (MTP), flight time and threats, or multiobjective navigation. The complexity of MTP ranges from NP-hard to NEXP-complete because there are so many probabilistic variables involved. Therefore, it is hard to detect a high-quality solution for this problem using traditional techniques such as differential calculus. Therefore, this paper hybridizes differential evolution (DE) with two newly proposed updating schemes to present a new evolution-based technique named hybrid differential evolution (HDE) for accurately tackling the MTP in a reasonable amount of time. Using Bayesian theory, the MTP can be transformed into an optimization problem by employing the target detection probability as the fitness function. The proposed HDE encodes the search trajectory as a sequence of UAV motion pathways that evolve with increasing the current iteration for finding the near-optimal solution, which could maximize this fitness function. The HDE is extensively compared to the classical DE and several rival optimizers in terms of several performance metrics across four different scenarios with varying degrees of difficulty. This comparison demonstrates the proposal’s superiority in terms of the majority of used performance metrics.