Reviewerly/exHarmony huggingface dataset:
Exploring Digital Literacy Strategies for Students with Special Educational Needs in the Digital Age.

21st century learning requires students to be equipped with learning skills, knowledge, media literacy and also life skills. In order to achieve these skills, the school curriculum embeds the use of technological tools and strategies to provide teaching and learning strategies for learners, including students with special needs or Special Needs Students (SNS). Studies found that students struggle to cope with their studies in the digital learning environment due to their limited digital literacy skills. Digital Literacy skills are essential to develop as independent learners in the digital age. Previous studies have shown that digital literacy skills have demonstrated a positive influence on student performance. However, limited studies have been conducted on the issue. This paper aims to discuss the concept of digital literacy skills to support teaching and learning strategies for SNS in Malaysia from teachers’ perspectives. The digital literacy skills model consists of cognitive skills, technology and ethics as basic guidelines to explore digital literacy teaching and learning strategies for SNS. This concept has been tested as a preliminary study in a course by interview with five teachers who taught a Desktop Publishing among SNS. From the findings, it was shown that the digital literacy skills model is able to improve the teaching and learning strategies needed for SNS in the digital environment.
Rethinking Stealthiness of Backdoor Attack against NLP Models

Recent researches have shown that large natural language processing (NLP) models are vulnerable to a kind of security threat called the Backdoor Attack.Backdoor attacked models can achieve good performance on clean test sets but perform badly on those input sentences injected with designed trigger words.In this work, we point out a potential problem of current backdoor attacking research: its evaluation ignores the stealthiness of backdoor attacks, and most of existing backdoor attacking methods are not stealthy either to system deployers or to system users.To address this issue, we first propose two additional stealthiness-based metrics to make the backdoor attacking evaluation more credible.We further propose a novel word-based backdoor attacking method based on negative data augmentation and modifying word embeddings, making an important step towards achieving stealthy backdoor attacking.Experiments on sentiment analysis and toxic detection tasks show that our method is much stealthier while maintaining pretty good attacking performance.Our code is available at
The Value of Immersive Visualization

In recent years, research on immersive environments has experienced a new wave of interest, and immersive analytics has been established as a new research field. Every year, a vast amount of different techniques, applications, and user studies are published that focus on employing immersive environments for visualizing and analyzing data. Nevertheless, immersive analytics is still a relatively unexplored field that needs more basic research in many aspects and is still viewed with skepticism. Rightly so, because in our opinion, many researchers do not fully exploit the possibilities offered by immersive environments and, on the contrary, sometimes even overestimate the power of immersive visualizations. Although a growing body of papers has demonstrated individual advantages of immersive analytics for specific tasks and problems, the general benefit of using immersive environments for effective analytic tasks remains controversial. In this article, we reflect on when and how immersion may be appropriate for the analysis and present four guiding scenarios. We report on our experiences, discuss the landscape of assessment strategies, and point out the directions where we believe immersive visualizations have the greatest potential.
Systematic review and meta-analyses of studies analysing instructions to authors from 1987 to 2017

Abstract To gain insight into changes of scholarly journals’ recommendations, we conducted a systematic review of studies that analysed journals’ Instructions to Authors (ItAs). We summarised results of 153 studies, and meta-analysed how often ItAs addressed: 1) authorship, 2) conflicts of interest, 3) data sharing, 4) ethics approval, 5) funding disclosure, and 6) International Committee of Medical Journal Editors’ Uniform Requirements for Manuscripts. For each topic we found large between-study heterogeneity. Here, we show six factors that explained most of that heterogeneity: 1) time (addressing of topics generally increased over time), 2) country (large differences found between countries), 3) database indexation (large differences found between databases), 4) impact factor (topics were more often addressed in highest than in lowest impact factor journals), 5) discipline (topics were more often addressed in Health Sciences than in other disciplines), and 6) sub-discipline (topics were more often addressed in general than in sub-disciplinary journals).
A Human-Centered Systematic Literature Review of Cyberbullying Detection Algorithms

Cyberbullying is a growing problem across social media platforms, inflicting short and long-lasting effects on victims. To mitigate this problem, research has looked into building automated systems, powered by machine learning, to detect cyberbullying incidents, or the involved actors like victims and perpetrators. In the past, systematic reviews have examined the approaches within this growing body of work, but with a focus on the computational aspects of the technical innovation, feature engineering, or performance optimization, without centering around the roles, beliefs, desires, or expectations of humans. In this paper, we present a human-centered systematic literature review of the past 10 years of research on automated cyberbullying detection. We analyzed 56 papers based on a three-prong human-centeredness algorithm design framework - spanning theoretical, participatory, and speculative design. We found that the past literature fell short of incorporating human-centeredness across multiple aspects, ranging from defining cyberbullying, establishing the ground truth in data annotation, evaluating the performance of the detection models, to speculating the usage and users of the models, including potential harms and negative consequences. Given the sensitivities of the cyberbullying experience and the deep ramifications cyberbullying incidents bear on the involved actors, we discuss takeaways on how incorporating human-centeredness in future research can aid with developing detection systems that are more practical, useful, and tuned to the diverse needs and contexts of the stakeholders.
A New 4-D Multi-Stable Hyperchaotic System With No Balance Point: Bifurcation Analysis, Circuit Simulation, FPGA Realization and Image Cryptosystem

In this work, we describe the model of a new 4-D hyperchaotic system with no balance point and deduce that the new hyperchaotic system has a hidden attractor. We present a detailed bifurcation analysis for the new hyperchaotic dynamo system with respect to the system parameters and also exhibit that the new hyperchaotic system displays multistability with coexisting attractors. Using Multisim, we design an electronic circuit for the implementation of the new 4-D hyperchaotic system and present the circuit simulation results. We also show the implementation of the new 4-D hyperchaotic system by using a field programmable gate array (FPGA). The hardware resources are reduced by designing single-constant multipliers, adders, subtractors and multipliers. The FPGA design is done for three numerical methods, namely: Forward-Euler, Backward-Euler and fourth-order Runge-Kutta. We demonstrate that experimental chaotic attractors are in good agreement with theoretical simulations. To verify the ability of the presented hyperchaotic system for designing robust cryptosystems, we suggest a novel image cryptosystem using the proposed hyperchaotic system. Simulation outcomes confirm the effectiveness of the proposed image cryptosystem, and consequently, the effectiveness of the proposed 4-D hyperchaotic system in designing diverse cryptographic purposes.
The Dawn of Quantum Natural Language Processing

In this paper, we discuss the initial attempts at boosting understanding human language based on deep-learning models with quantum computing. We successfully train a quantum-enhanced Long Short-Term Memory network to perform the parts-of-speech tagging task via numerical simulations. Moreover, a quantum-enhanced Transformer is proposed to perform the sentiment analysis based on the existing dataset.
Generative Adversarial Networks in Cardiology

Generative adversarial networks (GANs) are state-of-the-art neural network models used to synthesise images and other data. GANs brought a considerable improvement to the quality of synthetic data, quickly becoming the standard for data-generation tasks. In this work, we summarise the applications of GANs in the field of cardiology, including generation of realistic cardiac images, electrocardiography signals, and synthetic electronic health records. The utility of GAN-generated data is discussed with respect to research, clinical care, and academia. And we present illustrative examples of our GAN-generated cardiac magnetic resonance and echocardiography images, showing the evolution in image quality across 6 different models, which have become almost indistinguishable from real images. Finally, we discuss future applications, such as modality translation or patient trajectory modelling. Moreover, we discuss the pending challenges that GANs need to overcome, namely, their training dynamics, the medical fidelity or the data regulations and ethics questions, to become integrated in cardiology workflows.
Collapse and Revival of an Artificial Atom Coupled to a Structured Photonic Reservoir

A superconducting quantum circuit coupled to a tailored waveguide reservoir provides a platform for exploring non-Markovian quantum-optical dynamics, in which the reservoir maintains a memory of past events.
Recognition of Gurmukhi Handwritten City Names Using Deep Learning and Cloud Computing

The challenges involved in the traditional cloud computing paradigms have prompted the development of architectures for the next generation cloud computing. The new cloud computing architectures can generate and handle huge amount of data, which was not possible to handle with the help of traditional architectures. Deep learning algorithms have the ability to process this huge amount of data and, thus, can now solve the problem of the next generation computing algorithms. Therefore, these days, deep learning has become the state-of-the-art approach for solving various tasks and most importantly in the field of recognition. In this work, recognition of city names is proposed. Recognition of handwritten city names is one of the potential research application areas in the field of postal automation For recognition using a segmentation-free approach (Holistic approach). This proposed work demystifies the role of convolutional neural network (CNN), which is one of the methods of deep learning technique. Proposed CNN model is trained, validated, and analyzed using Adam and stochastic gradient descent (SGD) optimizer with a batch size of 2, 4, and 8 and learning rate (LR) of 0.001, 0.01, and 0.1. The model is trained and validated on 10 different classes of the handwritten city names written in Gurmukhi script, where each class has 400 samples. Our analysis shows that the CNN model, using an Adam optimizer, batch size of 4, and a LR of 0.001, has achieved the best average validation accuracy of 99.13.
A novel plaintext-related chaotic image encryption scheme with no additional plaintext information

In the latest image chaotic encryption research, more and more encryption schemes associate the key generation mechanism with the plaintext in order to resist the chosen plaintext attack. However, large amounts of the additional data related to the plaintext need to be sent to the receiver when the sender encrypts a large number of images, which leads to the problems of large transmission cost, high key storage space requirement and complicated key management. In order to solve such problems, this paper proposes a novel plaintext-related chaotic image encryption scheme with no additional plaintext information. The relationship between the plaintext and the secret keys is established by selecting some key pixels in the original image randomly and securely by Henon map. The encryption process is divided into two parts. The first part is to encrypt the selected key pixel while keeping their secret positions unchanged. The second part is to encrypt the remainder pixels based on hyperchaotic Lorenz system and DNA encoding using the generated keys. The proposed encryption mechanism can not only achieve the equivalent effect of one-time pad to resist the chosen plaintext attack, but also avoid transmitting additional information associated with the plaintext in the communication channel, which greatly saves the transmission cost and the required key storage space, and simplify the key management. The encryption scheme in this paper has also passed various security analyses to ensure the security of the ciphertext image.
FoV-NeRF: Foveated Neural Radiance Fields for Virtual Reality

Virtual Reality (VR) is becoming ubiquitous with the rise of consumer displays and commercial VR platforms. Such displays require low latency and high quality rendering of synthetic imagery with reduced compute overheads. Recent advances in neural rendering showed promise of unlocking new possibilities in 3D computer graphics via image-based representations of virtual or physical environments. Specifically, the neural radiance fields (NeRF) demonstrated that photo-realistic quality and continuous view changes of 3D scenes can be achieved without loss of view-dependent effects. While NeRF can significantly benefit rendering for VR applications, it faces unique challenges posed by high field-of-view, high resolution, and stereoscopic/egocentric viewing, typically causing low quality and high latency of the rendered images. In VR, this not only harms the interaction experience but may also cause sickness. To tackle these problems toward six-degrees-of-freedom, egocentric, and stereo NeRF in VR, we present the first gaze-contingent 3D neural representation and view synthesis method. We incorporate the human psychophysics of visual- and stereo-acuity into an egocentric neural representation of 3D scenery. We then jointly optimize the latency/performance and visual quality while mutually bridging human perception and neural scene synthesis to achieve perceptually high-quality immersive interaction. We conducted both objective analysis and subjective studies to evaluate the effectiveness of our approach. We find that our method significantly reduces latency (up to 99% time reduction compared with NeRF) without loss of high-fidelity rendering (perceptually identical to full-resolution ground truth). The presented approach may serve as the first step toward future VR/AR systems that capture, teleport, and visualize remote environments in real-time.
LMSD-YOLO: A Lightweight YOLO Algorithm for Multi-Scale SAR Ship Detection

At present, deep learning has been widely used in SAR ship target detection, but the accurate and real-time detection of multi-scale targets still faces tough challenges. CNN-based SAR ship detectors are challenged to meet real-time requirements because of a large number of parameters. In this paper, we propose a lightweight, single-stage SAR ship target detection model called YOLO-based lightweight multi-scale ship detector (LMSD-YOLO), with better multi-scale adaptation capabilities. The proposed LMSD-YOLO consists of depthwise separable convolution, batch normalization and activate or not (ACON) activation function (DBA) module, Mobilenet with stem block (S-Mobilenet) backbone module, depthwise adaptively spatial feature fusion (DSASFF) neck module and SCYLLA-IoU (SIoU) loss function. Firstly, the DBA module is proposed as a general lightweight convolution unit to construct the whole lightweight model. Secondly, the improved S-Mobilenet module is designed as the backbone feature extraction network to enhance feature extraction ability without adding additional calculations. Then, the DSASFF module is proposed to achieve adaptive fusion of multi-scale features with fewer parameters. Finally, the SIoU is used as the loss function to accelerate model convergence and improve detection accuracy. The effectiveness of the LMSD-YOLO is validated on the SSDD, HRSID and GFSDD datasets, respectively, and the experimental results show that our proposed model has a smaller model volume and higher detection accuracy, and can accurately detect multi-scale targets in more complex scenes. The model volume of LMSD-YOLO is only 7.6MB (52.77% of model size of YOLOv5s), the detection speed on the NVIDIA AGX Xavier development board reached 68.3 FPS (32.7 FPS higher than YOLOv5s detector), indicating that the LMSD-YOLO can be easily deployed to the mobile platform for real-time application.
Privacy Loss Classes: The Central Limit Theorem in Differential Privacy

Abstract Quantifying the privacy loss of a privacy-preserving mechanism on potentially sensitive data is a complex and well-researched topic; the de-facto standard for privacy measures are ε -differential privacy (DP) and its versatile relaxation ( ε, δ )-approximate differential privacy (ADP). Recently, novel variants of (A)DP focused on giving tighter privacy bounds under continual observation. In this paper we unify many previous works via the privacy loss distribution (PLD) of a mechanism. We show that for non-adaptive mechanisms, the privacy loss under sequential composition undergoes a convolution and will converge to a Gauss distribution (the central limit theorem for DP). We derive several relevant insights: we can now characterize mechanisms by their privacy loss class , i.e., by the Gauss distribution to which their PLD converges, which allows us to give novel ADP bounds for mechanisms based on their privacy loss class; we derive exact analytical guarantees for the approximate randomized response mechanism and an exact analytical and closed formula for the Gauss mechanism, that, given ε , calculates δ , s.t., the mechanism is ( ε, δ )-ADP (not an over-approximating bound).
Securing quantum networking tasks with multipartite Einstein-Podolsky-Rosen steering

Einstein-Podolsky-Rosen (EPR) steering is the explicit demonstration of the fact that the measurements of one party can influence the quantum state held by another distant party and do so even if the measurements themselves are untrusted. This has been shown to allow one-sided device-independent quantum information tasks between two remote parties. However, in general, advanced multiparty protocols for generic quantum technologies, such as quantum secret sharing and blind quantum computing for quantum networks, demand multipartite quantum correlations of graph states shared between more than two parties. Here, we show that when one part of a quantum multidimensional system composed of a two-colorable graph state (e.g., cluster and Greenberger-Horne-Zeilinger states) is attacked by an eavesdropper using a universal cloning machine, only one of the copy subsystems can exhibit multipartite EPR steering but not both. Such a no-sharing restriction secures both state sources and channels against cloning-based attacks for generic quantum networking tasks, such as distributed quantum information processing, in the presence of uncharacterized measurement apparatuses.
Bordered constructions of self-dual codes from group rings and new extremal binary self-dual codes

We introduce a bordered construction over group rings for self-dual codes. We apply the constructions over the binary field and the ring F2+uF2, using groups of orders 9, 15, 21, 25, 27, 33 and 35 to find extremal binary self-dual codes of lengths 20, 32, 40, 44, 52, 56, 64, 68, 88 and best known binary self-dual codes of length 72. In particular we obtain 41 new binary extremal self-dual codes of length 68 from groups of orders 15 and 33 using neighboring and extensions. All the numerical results are tabulated throughout the paper.
Image Inpainting Based on Patch-GANs

In this paper, we propose a novel image inpainting framework that takes advantage of holistic and structure information of the broken input image. Different from the existing models that complete the broken pictures using the holistic features of the input, our method adopts Patch-generative adversarial networks (GANs) equipped with multi-scale discriminators and edge process function to extract holistic, structured features, and restore the damaged images. After pre-training our Patch-GANs, the proposed network encourages our generator to find the best encoding of the broken input images in the latent space using a combination of a reconstruction loss, an edge loss, and global and local guidance losses. Besides, the reconstruction and the global guidance losses ensure the pixel reliability of the generated images, and the remaining losses guarantee the contents consistency between the local and global parts. The qualitative and quantitative experiments on multiple public datasets show that our approach has the ability to produce more realistic images compared with some existing methods, demonstrating the effectiveness and superiority of our method.
Reinforcement Learning in Stationary Mean-field Games

Multi-agent reinforcement learning has made significant progress in recent years, but it remains a hard problem. Hence, one often resorts to developing learning algorithms for specific classes of multi-agent systems. In this paper we study reinforcement learning in a specific class of multi-agent systems systems called mean-field games. In particular, we consider learning in stationary mean-field games. We identify two different solution concepts---stationary mean-field equilibrium and stationary mean-field social-welfare optimal policy---for such games based on whether the agents are non-cooperative or cooperative, respectively. We then generalize these solution concepts to their local variants using bounded rationality based arguments. For these two local solution concepts, we present two reinforcement learning algorithms. We show that the algorithms converge to the right solution under mild technical conditions and demonstrate this using two numerical examples.
An adaptive ball-head positioning visual servoing method for aircraft digital assembly

Purpose To gain accurate support for large aircraft structures by ball joints in aircraft digital assembly, this paper aims to propose a novel approach based on visual servoing such that the positioner’s ball-socket can automatically and adaptively approach the ball-head fixed on the aircraft structures. Design/methodology/approach Image moments of circular marker labeled on the ball-head are selected as visual features to control the three translational degrees of freedom (DOFs) of the positioner, where the composite Jacobian matrix is full rank. Kalman–Bucy filter is adopted for its online estimation, which makes the control scheme more flexible without system calibration. A combination of proportional control with sliding mode control is proposed to improve the system stability and compensate uncertainties of the system. Findings The ball-socket can accurately and smoothly reach its desired position in a finite time (50 s ). Positional deviations between the spherical centers of ball-head and ball-socket in the X - Y plane can be controlled within 0.05 mm which meets the design requirement. Practical implications The proposed approach has been integrated into the pose alignment system. It has shown great potential to be widely applied in the leading support for large aircraft structures in aircraft digital assembly. Originality/value An adaptive approach for accurate support of large aircraft structures is proposed, which possesses characteristics of high precision, high efficiency and excellent stability.