Reviewerly/exHarmony huggingface dataset:
The Role of Data Visualization in Science Communication

Scientists and researchers use different channels to communicate their findings to peers, students, or the general audience. With the information overload we are currently experiencing, especially on online platforms, and with such a diverse audience that perceives and understands science in different ways, it is necessary to understand the usefulness of data visualization in science communication as a process of exploration, discovery, and understanding. Science communication is an essential process for the sustainability of the scientific system that contributes to the recognition of the value of science and the increase of scientific literacy. This study aims to identify design patterns that help communicators disseminate science through data visualization, allowing complex information to be reorganized and contributing to the understanding and interpretation of the content being communicated that would otherwise not be accessible to the reader. Based on the case studies presented, we conclude that it is essential for the scientific community to be able to identify and use the appropriate data visualization. The main goal of this work is to introduce a guide to design patterns that help bridge the gap between scientists and the general audience and contribute to the effectiveness of science communication by making science a public asset accessible to all.
Mobile Learning in Education: Inevitable Substitute during COVID-19 Era

The incorporation of mobile phones is persistent in the development of language learning, particularly in the EFL context. The present investigation aims to determine the impact of mobile-assisted language learning (MALL) applications on developing “learners” grammar proficiency. The current study employed a quantitative research method to explore MALL-assisted feedback in a WhatsApp group during the group chat. 60 Saudi male EFL learners, randomly assigned to the control and experimental group. Participants in the Experimental group practiced grammar in WhatsApp chat, whereas the same material was sent to the control group on Blackboard. The data was gathered by administering pre and post-test of grammar proficiency. Descriptive statistics and ANCOVA were used to analyze the data. The study’s findings exhibited that WhatsApp group practice and feedback positively impacted learners’ performance.
Software Defect Prediction: An ML Approach-Based Comprehensive Study

Software testing is a vital phase in the software development life cycle. Testing validates a developed software against the input test cases by identifying the defects present in the system. The phenomenon of testing is not only time-consuming but also a costly affair. Although there are automated tools available that reduce the effort of testing up to some extent, the high maintenance cost of these tools only increases the cost. Earlier defect prediction in software significantly reduces the effort and cost without affecting the constraints. It identifies the defect-prone modules that require more rigorous testing. A practical and effective defect prediction mechanism is the need of the hour due to the challenges, namely dimensionality reduction and class imbalance, present in software defect prediction. Lately, machine learning (ML) has emerged as a powerful decision-making approach in this regard. This research work aims to do an extensive study on the implementation of ML techniques in software defect prediction. This comprehensive report is based on two different aspects named feature selection/reduction techniques and ensemble learning methods that have been used in software defect prediction. This study has also discussed the widely used software and performance measure metrics used in software defect prediction. This concise work would guide future researchers in this emerging research area. Further, this paper also emphasizes the need to identify a suitable feature selection approach that could enhance the model's predictive performance when applied with ensemble learning.
Particle Swarm Optimization-Based Energy-Aware Task Scheduling Algorithm in Heterogeneous Cloud

Task scheduling in a cloud computing environment is one of the important aspects in the field of information technology. An efficient schedule is required to enhance the performance of the whole system which results a good quality of services (QoS). It is an NP-complete problem and attracts many researchers to use various meta-heuristics algorithms to develop task scheduling methods in the cloud environment. In most of the evolutionary methods, search space is large and initialized randomly which is one of the key components. In this paper, using the working mechanism of particle swarm optimization (PSO) algorithm, a set of solutions or schedules is created. Solution with efficient QoS parameters like makespan, cloud utilization, and energy consumption is chosen for allocation of the task into the heterogeneous multi-cloud environment. The algorithm undergoes a simulation process and is tested upon benchmark datasets which shows a better result in comparison to some existing cloud scheduling algorithms like min-min, max–min, cloud min-min scheduling (CMMS), cloud max–min scheduling (CMAXMS), and cloud normalized min-min max–min (CNXM) algorithms, genetic algorithm, etc.
SumStatsRehab: an efficient algorithm for GWAS summary statistics assessment and restoration

Generating polygenic risk scores for diseases and complex traits requires high quality GWAS summary statistic files. Often, these files can be difficult to acquire either as a result of unshared or incomplete data. To date, bioinformatics tools which focus on restoring missing columns containing identification and association data are limited, which has the potential to increase the number of usable GWAS summary statistics files.SumStatsRehab was able to restore rsID, effect/other alleles, chromosome, base pair position, effect allele frequencies, beta, standard error, and p-values to a better extent than any other currently available tool, with minimal loss.SumStatsRehab offers a unique tool utilizing both functional programming and pipeline-like architecture, allowing users to generate accurate data restorations for incomplete summary statistics files. This in turn, increases the number of usable GWAS summary statistics files, which may be invaluable for less researched health traits.
High-performance and Configurable SW/HW Co-design of Post-quantum Signature CRYSTALS-Dilithium

CRYSTALS-Dilithium is a lattice-based post-quantum digital signature scheme that is resistant to attacks by quantum computers and has been selected to be standardized in the NIST post-quantum cryptography (PQC) standardization process. However, the speed performance and design flexibility of the Dilithium still need to be evaluated. This article presents a high-performance software/hardware co-design of CRYSTALS-Dilithium based on the NIST PQC round-3 parameters. High-speed pipelined hardware modules for NTT/INTT, point-wise multiplication/addition, and for SHAKE are included in the design to accelerate the time-consuming operations in Dilithium. All hardware modules are parameterized, thus allowing full support of runtime configuration to increase versatility. Moreover, the proposed software/hardware architecture and tight operating workflows reduce the data transmission overhead between the processor and other hardware modules. The hardware accelerator is implemented with a reconfigurable logic on FPGA and is integrated with the high-performance ARM Cortex-A9 processor in the Xilinx Zynq Architecture. We measure the performance of the software/hardware system for Dilithium in NIST security levels 2, 3, and 5. Compared to pure software implementations, we achieve 8.7–12.5 times speedup in Key generation, 6.3–7.3 times speedup in Sign, and 9.1–12.2 times speedup in Verify operations.
ONION: Joint Unsupervised Feature Selection and Robust Subspace Extraction for Graph-based Multi-View Clustering

Graph-based Multi-View Clustering (GMVC) has received extensive attention due to its ability to capture the neighborhood relationship among data points from diverse views. However, most existing approaches construct similarity graphs from the original multi-view data, the accuracy of which heavily and implicitly relies on the quality of the original multiple features. Moreover, previous methods either focus on mining the multi-view commonality or emphasize on exploring the multi-view individuality, making the rich information contained in multiple features cannot be effectively exploited. In this work, we design a novel GMVC framework via c O mmo N ality and I ndividuality disc O vering in late N t subspace ( ONION ), seeking for a robust and discriminative subspace representation compatible across multiple features for GMVC. To be specific, our method simultaneously formulates the unsupervised sparse feature selection and the robust subspace extraction, as well as the target graph learning in a unified optimization model, which can help the learning of the discriminative subspace representation and the target graph in a mutual reinforcement manner. Meanwhile, we manipulate the target graph by an explicit structural penalty, rendering the connected components in the graph directly reveal clusters. Experimental results on seven benchmark datasets demonstrate the effectiveness of our proposed method.
Towards a dignity manifesto of design - for people living with dementia

This editorial is intended to introduce The Dignity Manifesto of Design and request meaningful commentary on this guide for environments for people living with dementia, so that design will support values of dignity, autonomy, independence, equality of opportunity, and non-discrimination. The two-part manifesto – a short list of values followed by ten design principles - follows Recommendation 1 of the Alzheimer’s Disease International World Alzheimer’s Report 2020.
A fast piecewise image encryption scheme combining NC1DNSM and P-Box

By adding control parameters to the one-dimensional compound nested sinusoidal map, it is found that it has larger chaos region, better Lyapunov exponent and better chaos characteristics. The suggested chaotic map has superior chaotic properties than the original, and several simulation studies demonstrate that the chaotic system is suited for image encryption. We design a fast segmentation image encryption scheme. According to P-box, the key creation step generates the beginning conditions for NC1DNSM by changing the initial key, and the chaotic key matrix for encryption is formed iteratively. The plaintext picture and chaotic key matrix are doubly segmented throughout the encryption stage, and the key is refreshed while the pixel position is disrupted. The encryption system described in this article incorporates the encryption function of the FEAL algorithm with the diffusion algorithm, resulting in an image encryption system. The encryption scheme proposed in this paper expands the space of the secret key, speeds up the running time, and passes the tests of various attack types. Theoretical research and practical evidence indicate that the encrypted picture is very secure.
GC-NLDP: A graph clustering algorithm with local differential privacy

Clustering distributed star social graphs, which can offer useful insights into the structure of the data, has attracted considerable attention. Each star social graph often contains sensitive personal information. Unrestricted collection and analysis of such information by curators may reveal privacy. Local differential privacy (LDP), as a privacy model un-requiring any trusted third party, has been widely utilized in distributed privacy-preserving clustering. Most existing solutions adopt the edge-LDP model, compromising privacy strength for high utility. To enhance privacy, we turn to node-LDP and design a two-stage privacy-preserving graph clustering framework, achieving more robust privacy strength while keeping a higher clustering quality. Concretely, in the first stage, we design a node aggregation approach based on the silhouette coefficient measurement model and combine some connection information to form initial clusters. Besides, a novel cluster-based perturbation mechanism is developed to improve data statistics accuracy by leveraging an adaptive noise injecting method. The second stage develops a feedback loop strategy between clients and the curator. The curator iteratively optimizes the perturbation mechanism and node aggregation method based on the feedback information to improve the clustering quality further. Theoretical analysis and experimentation on real-world datasets demonstrate that our proposed method can obtain desirable clustering results while satisfying node-LDP.
Systematic analysis and comparison of security advice as datasets

A long list of documents have been offered as security advice, codes of practice, and security guidelines for building and using security products, including Internet of Things (IoT) devices. To date, little or no systematic analysis has been carried out on the advice datasets themselves. Towards addressing this, with IoT as a case study, we begin with an informal analysis of two documents offering advice related to IoT security—the ETSI Provisions and the UK DCMS Guidelines—and then carry out what we believe is the first systematic analysis of these advice datasets. Our analysis explains in what ways the ETSI Provisions are a positive evolution of the UK DCMS Guidelines. We also suggest aspects of security advice warranting special attention by those offering security advice. Such parties may find the systematic analysis method, which categorizes advice into predefined categories, to be of general interest beyond IoT itself.
The use of the Video Platform FlipGrid for Practicing Science Oral Communication

Oral communication and presentation skills are considered key competencies for many academic fields and professions, including science-related ones. Therefore, it is vital that students are given sufficient class time in the curriculum for practicing public speaking. It is well documented in the literature that video technology can be of valuable aid in enhancing oral skills, and FlipGrid is a relatively new video discussion platform that has become popular for this purpose. The aim of this study is two-fold. First, the literature regarding FlipGrid is explored to understand how educators around the globe use the platform. Second, a case study is presented where FlipGrid is used and evaluated as a tool for practicing and assessing oral science communication. A mixed-method approach was used to evaluate FlipGrid with the participation of 112 students. Findings indicate that although the use FlipGrid in science fields is limited, it can be a useful platform for practicing oral science communication.
Disentangling the Nonlinearity Effect in Cryptocurrency Markets During the Covid-19 Pandemic: Evidence from a Regime-Switching Approach

In this paper, we attempt to understand and identify the cyclical fluctuations in cryptocurrency markets. To this end, we apply the Markov-Switching approach on daily prices of 17 selected digital currencies. This model allows us to capture the nonlinear structure in cryptocurrencies’ prices. The empirical results clearly show potential difference(s) among digital currencies when they react to the varying levels of the pandemic's severity. The existence of two distinguishable states and each state seems to be characterized by different features of market cycle’s phase for each cryptocurrency. So, the Covid19 pandemic affects asymmetrically the different market phases of digital currencies. Such findings can have insightful portfolios implications.
Neural Architecture Search via Proxy Validation

This paper searches for the optimal neural architecture by minimizing a proxy of validation loss. Existing neural architecture search (NAS) methods used to discover the optimal neural architecture that best fits the validation examples given the up-to-date network weights. These intermediate validation results are invaluable but have not been fully explored. We propose to approximate the validation loss landscape by learning a mapping from neural architectures to their corresponding validate losses. The optimal neural architecture thus can be easily identified as the minimum of this proxy validation loss landscape. To improve the efficiency, a novel architecture sampling strategy is developed for the approximation of the proxy validation loss landscape. We also propose an operation importance weight (OIW) to balance the randomness and certainty of architecture sampling. The representation of neural architecture is learned through a graph autoencoder (GAE) over both architectures sampled during search and randomly generated architectures. We provide theoretical analyses on the validation loss estimator learned with our sampling strategy. Experimental results demonstrate that the proposed proxy validation loss landscape can be effective in both the differentiable NAS and the evolutionary-algorithm-based (EA-based) NAS.
FDMLNet: A Frequency-Division and Multiscale Learning Network for Enhancing Low-Light Image

Low-illumination images exhibit low brightness, blurry details, and color casts, which present us an unnatural visual experience and further have a negative effect on other visual applications. Data-driven approaches show tremendous potential for lighting up the image brightness while preserving its visual naturalness. However, these methods introduce hand-crafted holes and noise enlargement or over/under enhancement and color deviation. For mitigating these challenging issues, this paper presents a frequency division and multiscale learning network named FDMLNet, including two subnets, DetNet and StruNet. This design first applies the guided filter to separate the high and low frequencies of authentic images, then DetNet and StruNet are, respectively, developed to process them, to fully explore their information at different frequencies. In StruNet, a feasible feature extraction module (FFEM), grouped by multiscale learning block (MSL) and a dual-branch channel attention mechanism (DCAM), is injected to promote its multiscale representation ability. In addition, three FFEMs are connected in a new dense connectivity meant to utilize multilevel features. Extensive quantitative and qualitative experiments on public benchmarks demonstrate that our FDMLNet outperforms state-of-the-art approaches benefiting from its stronger multiscale feature expression and extraction ability.
FPGA-based Learning Acceleration for LSTM Neural Network

This paper presents and discusses the implementation of a learning accelerator for an LSTM neural network that utilizes an FPGA. The accelerator consists of a backpropagation through time algorithm for an LSTM. The presented net performs a binary classification task and consists of an LSTM and a dense layer. The performance is then compared to both a hard-coded Python implementation and an implementation using Keras library and the GPU. The implementation is executed using the DSP blocks, available via the Vivado Design Suite, which is in compliance with the IEEE754 standard. The results of the simulation show that the FPGA implementation remains accurate and achieves higher speed than the other solutions.
IBVS based on adaptive sliding mode control for a quadrotor target tracking under perturbations

This paper presents the design of a visual control formulated on an adaptive sliding mode controller for a quadrotor executing a target tracking task subject to disturbances. An image projection of the target from a virtual camera approach, and an image-based visual servoing technique are considered to obtain a singularity-free set of image features to control the position and yaw of the rotorcraft. While, an adaptive sliding mode strategy improves the robustness against bounded external perturbations and uncertainties and provides adaptivity to the visual servoing scheme. Furthermore, an analysis based on Lyapunov theory provides sufficient conditions that guarantee the stability of the closed-loop system. A comparison of the proposed adaptive visual servoing against two recent visual servoing strategies is provided, showing superiority in simulation results. Finally, experimental tests of a Parrot AR.Drone 2.0 tracking a static and moving target further demonstrates the advantages and performance.
Learning multimodal entity representations and their ensembles, with applications in a data-driven advisory framework for video game players

We investigate the impact of combining multiple representations derived from heterogeneous data sources on the performance of machine learning (ML) models. In particular, we experimentally compare the approach in which independent models are trained on data representations from different sources with the one in which a single model is trained on joined data representations. As a case study, we discuss various entity representation learning methods and their applications in our data-driven advisory framework for video game players, called SENSEI. We show how to use the discussed methods to learn representations of cards and decks for two popular collectible card games (CCGs), namely Clash Royale (CR) and Hearthstone: Heroes of Warcraft (HS). Then, we follow our approach to create ML models which constitute the back-end for several out of SENSEI's end-user functionalities. When learning representations, we consider techniques inspired by the NLP domain, as they allow us to create embeddings which capture various aspects of similarity between entities. We put them together with representations composed of manually engineered features and standard bags-of-cards. On top of that, we propose a new end2end deep learning architecture with an attention mechanism aimed at reflecting meaningful inter-entity interactions.
Designing for Social Interactions in a Virtual Art Gallery

The dawn of a new digital world has emerged with new ways to communicate and collaborate with other people across the globe. Metaverses and Mirror Worlds have broadened our perspectives on the ways we can utilize 3D virtual environments. A Mirror World is a 3D virtual space that depicts a real-life place or environment that people may want to see physically or would like to manipulate to create something new. A perfect example of this would be an art gallery which provides people an outlet to express themselves through various art forms and be able to socialize and have that human interaction that is needed during times when physical presence may be difficult.
Seeing our Blind Spots: Smart Glasses-based Simulation to Increase Design Students’ Awareness of Visual Impairment

As the population ages, many will acquire visual impairments. To improve design for these users, it is essential to build awareness of their perspective during everyday routines, especially for design students.
DEEP: 3D Gaze Pointing in Virtual Reality Leveraging Eyelid Movement

Gaze-based target suffers from low input precision and target occlusion. In this paper, we explored to leverage the continuous eyelid movement to support high-efficient and occlusion-robust dwell-based gaze pointing in virtual reality. We first conducted two user studies to examine the users’ eyelid movement pattern both in unintentional and intentional conditions. The results proved the feasibility of leveraging intentional eyelid movement that was distinguishable with natural movements for input. We also tested the participants’ dwelling pattern for targets with different sizes and locations. Based on these results, we propose DEEP, a novel technique that enables the users to see through occlusions by controlling the aperture angle of their eyelids and dwell to select the targets with the help of a probabilistic input prediction model. Evaluation results showed that DEEP with dynamic depth and location selection incorporation significantly outperformed its static variants, as well as a naive dwelling baseline technique. Even for 100% occluded targets, it could achieve an average selection speed of 2.5s with an error rate of 2.3%.