Reviewerly/exHarmony huggingface dataset:
L2 teachers’ mobile-related beliefs and practices: contributions of a professional development initiative

Mobile phones are turning into a growingly widespread educational tool across various contexts. However, the scope of research on teachers' role in mobile-oriented instruction is limited, much less examining how professional development influences their associated beliefs and practices. Grounded in sociocultural theory, this study aimed to fill some of the void by exploring the contributions of a professional development initiative framed around mobile phone usage to L2 teachers' beliefs and practices. Data were collected from semi-structured interviews, classroom observations, and online reflections. Data analyses indicated that the teachers gradually developed further awareness of educational exploitability of mobile phones and experienced restructuring in their associated beliefs. Additionally, the analysis of observational data showed direct and indirect patterns of utilizing mobiles by the teachers, undergoing changes during the research process. Results of the study offer implications for mobile-related teacher education, particularly professional development courses geared toward enhancing in teachers greater awareness of utilizing mobile devices.
Hierarchical U-Shape Attention Network for Salient Object Detection

Salient object detection aims at locating the most conspicuous objects in natural images, which usually acts as a very important pre-processing procedure in many computer vision tasks. In this paper, we propose a simple yet effective Hierarchical U-shape Attention Network (HUAN) to learn a robust mapping function for salient object detection. Firstly, a novel attention mechanism is formulated to improve the well-known U-shape network [1], in which the memory consumption can be extensively reduced and the mask quality can be significantly improved by the resulting U-shape Attention Network (UAN). Secondly, a novel hierarchical structure is constructed to well bridge the low-level and high-level feature representations between different UANs, in which both the intra-network and inter-network connections are considered to explore the salient patterns from a local to global view. Thirdly, a novel Mask Fusion Network (MFN) is designed to fuse the intermediate prediction results, so as to generate a salient mask which is in higher-quality than any of those inputs. Our HUAN can be trained together with any backbone network in an end-to-end manner, and high-quality masks can be finally learned to represent the salient objects. Extensive experimental results on several benchmark datasets show that our method significantly outperforms most of the state-of-the-art approaches.
Improved Harris Hawks Optimization Based on Adaptive Cooperative Foraging and Dispersed Foraging Strategies

Harris hawks optimization (HHO) is a new swarm intelligence optimization technique.Because of its simple structure and easy to implement, HHO has attracted research interest from scholars in different fields.However, the low population diversity and the single search method in the exploration phase weakened the global search capability of the HHO algorithm.In response to these defects, this paper proposes an improved HHO algorithm based on adaptive cooperative foraging and dispersed foraging strategies.First, the adaptive cooperative foraging strategy uses three random individuals to guide the position update, which achieves cooperation between individuals.Then the cooperation behavior is embedded in the onedimensional update operation framework, and the one-dimensional or total-dimensional update operation is adaptively selected.This way allows the algorithm to perform position update operations for a specific dimension of individual vectors with a certain probability, which improves the population diversity.Second, the dispersed foraging strategy is introduced into the HHO, forcing a part of Harris hawks to leave their current position to find more prey to obtain a better candidate solution.This way effectively avoids the algorithm falling into local optimum.Finally, a randomly shrinking exponential function is used to simulate the energy change of the prey, so that the algorithm maintains the exploration ability in the later exploitation process, effectively balancing the exploration and exploitation ability of the algorithm.The performance of the proposed ADHHO algorithm is evaluated using Wilcoxon's test on unimodal, multimodal and CEC 2014 benchmark functions.Numerical results and statistical experiments show that ADHHO provides better solution quality, convergence accuracy and stability compared with other state-of-the-art algorithms.
Mobile Robot Wall-Following Control Using Fuzzy Logic Controller with Improved Differential Search and Reinforcement Learning

In this study, a fuzzy logic controller with the reinforcement improved differential search algorithm (FLC_R-IDS) is proposed for solving a mobile robot wall-following control problem. This study uses the reward and punishment mechanisms of reinforcement learning to train the mobile robot wall-following control. The proposed improved differential search algorithm uses parameter adaptation to adjust the control parameters. To improve the exploration of the algorithm, a change in the number of superorganisms is required as it involves a stopover site. This study uses reinforcement learning to guide the behavior of the robot. When the mobile robot satisfies three reward conditions, it gets reward +1. The accumulated reward value is used to evaluate the controller and to replace the next controller training. Experimental results show that, compared with the traditional differential search algorithm and the chaos differential search algorithm, the average error value of the proposed FLC_R-IDS in the three experimental environments is reduced by 12.44%, 22.54% and 25.98%, respectively. Final, the experimental results also show that the real mobile robot using the proposed method can effectively implement the wall-following control.
Unsupervised Feature Selection Via Data Reconstruction and Side Information

Data reconstruction, which aims at preserving statistical properties of the data during the reconstruction has become a new criterion for feature selection. Although feature selection could benefit from the perspective of data reconstruction, it is unable to exploit other crucial information, namely, graph structure and pairwise constraints. To address previously mentioned deficiency, we propose a novel feature selection approach in this paper, known as unsupervised feature selection via data reconstruction and side information. More specifically, the proposed method takes advantage of the prior knowledge regarding pairwise constraints (side information), the minimization of data reconstruction error, and the graph embedding simultaneously, such that pivotal features are selected with preserving data manifold structure. To obtain the robust solution, a robust loss function is applied to the feature selection problem, which interpolates between ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> -norm and ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> -norm. Eventually, extensive experiments are conducted to demonstrate the effectiveness of the proposed method.
Digital contact-tracing and pandemics: Institutional and technological preparedness in Africa

Several countries in Africa have either deployed or considering using digital contact-tracing (DCT) as part of their Covid-19 containment strategy, amidst calls for the use of technology to improve the efficiency of traditional contact-tracing. We discuss some of the complexities entailed in using DCT in Africa. Adopting a socio-technical perspective, we argue that if DCT design and deployment are not well thought out, it can lead to unintended consequences, particularly in a continent like Africa with disproportionate levels of digital divides and other structural inequalities. We suggest that any adoption of DCT by African countries must take account of their compatibility with local resources, values, social structure, and domestic political factors. Accordingly, we propose a process of translation whereby DCT adaptation is made to accommodate the unique institutional and technological characteristics of African countries by leveraging local practices learned from previous pandemics like Ebola to develop a blended epidemiological approach to (digital) contact-tracing.
Fifty years of digital literacy studies: A meta-research for interdisciplinary and conceptual convergence

The following research has as its starting point the previous existence of different approaches to the study of digital literacy, which reflect a specialisation by area of study as well as connections and complementarity between them. The paper analyses research from the last 50 years through 11 key terms associated with the study of this subject. The article seeks to understand the contribution of each term for an integrated conceptualisation of digital literacy. From the data science approach, the methodology used is based on a systematized review of the literature and a network analysis using Gephi. The study analyses 16,753 articles from WoS and 5,809 from Scopus, between the period of 1968 to 2017. The results present the input to each key term studied as a map of keywords and a conceptual framework in different levels of analysis; in these, we show digital literacy as a central term that connects and integrates the others, and we define it as a process that integrates all the perspectives. The conclusions emphasise the comprehensive sense of digital literacy and its social condition, as well as the transversality to human life. This research aims to understand the relationships that exist between the different areas and contribute to the debate from a meta-theoretical level, validating meta-research for this interdisciplinary purpose.
Mediatisation and datafication in the global COVID-19 pandemic: on the urgency of data literacy

In the COVID-19 pandemic, societal discourses and social interaction are subject to rapid mediatisation and digitalisation, which accelerate datafication. This indicates urgency for increasing data literacy: individual abilities in understanding and critically assessing datafication and its social implications. Immediate challenges concern misconceptions about the crisis, data misuses, widening (social) divides and (new) data biases. Citizens need to be on guard in respect to the crisis’ impact on the next stages of the digital transformation.
Iterative local re-ranking with attribute guided synthesis for face sketch recognition

Because of the large texture and spatial structure discrepancies between face sketches and photos, face sketch recognition becomes a challenging problem in face recognition community. For example, in law enforcement and security, the specific face sketch generation process could introduce some inevitable biases which results in poor face sketch recognition performance. In order to mimic the modality gap introduced by the biases during face sketch creation process, the novel iterative local re-ranking with attribute guided synthesis method is proposed for face sketch recognition, which does not require any extra manually annotation or human interaction. The clues of face attributes are utilized to generate images with varying local characteristic from probe sketches, which could help eliminate the unavoidable biases. Considering the special property of face sketches, the iterative local re-ranking algorithm is designed to encode the contextual information integrated with local invariant discriminative information for matching sketches with photos. Experimental results on multiple face sketch databases demonstrate that the proposed method achieves superior performances compared with state-of-the-art methods.
High throughput unified architecture of LEA algorithm for image encryption

Among instances when the data and image security in resource constrained environment (smart devices and the like) are to be taken into consideration, lightweight encryption algorithms accede in popularity and are deemed of to be of great merit. In devices as those of the sort mentioned, reducing both power consumption and gate count is always a prime concern. LEA, therefore, triumphs on merit, owing to its low consumption levels of hardware resources, which itself is a result of the adaption of simple modular addition, bitwise rotation and bitwise XOR (ARX) operation instead of the relatively more complex S-box technique. The algorithm in question supports keys of varying sizes three, to be precise: 128, 192 and 256-bit for the intent of providing three levels of image and data security. The hardware implementation of the LEA algorithm for the varying key sizes as mentioned above share significant kinship and hardware resources. This paper puts forward the proposition regarding a high speed low area unified architecture for LEA algorithm for three different sizes of key. The individual hardware implementations of the three key sizes tend to consume more hardware resources than those involved in the proposed unified architecture. The pipelined implementation of the proposed design improves operating frequency to a significant degree, with a little increase in the hardware costs. The design proposed in this paper notably supports three varying tile sizes, i.e. 256×256,128×128,64×64, of an input image. As resolution of an input image may not necessarily be an integer multiple of the size of tile, smaller tile sizes at the image boundary can also be handled by the same architecture. The design hereby proposed has been implemented on both UMC ASIC 0.09-μm and XC5VLX330T FPGA platforms and achieved maximum operating frequencies of 740 MHz and 292 MHz respectively. As its increasingly evident on account of the FPGA implementation outcomes, the proposed unified architecture results in vastly improved levels of operating frequency (34%, 33% and 131%) as opposed to the individually available LEA architectures (corresponding to three different key sizes: 128, 192 and 256-bit, respectively). Proposed architecture shows 28%, 35% and 45% increment in the hardware resources with respect to available LEA architectures with single key. The incorporation of the unified key generation technique and pipeline implementation of the algorithm are the main reasons for the more hardware utilization.
An efficient and accurate recommendation strategy using degree classification criteria for item-based collaborative filtering

An efficient and accurate recommender system provides online users with a variety of personalized recommendation services, thus effectively improving the satisfaction and experience of users. However, there is a trade-off between the accuracy and efficiency in recommender systems. Accordingly, this study introduces a recommendation strategy to address this limitation. The extended degree classification criteria are first proposed to assign items to more fine-grained classes. Later, item similarity measure is deployed to quickly evaluate similarities between items within the same class, which greatly reduces the runtime of similarity calculation. To obtain a better recommendation result, a Hellinger distance (HD) based item similarity is presented to calculate item similarity from the perspective of rating probability distribution. Additionally, a sigmoid function is considered in the HD similarity to emphasize the importance of the co-rated items and effectively distinguish differences between a pair of items. The experimental results on two benchmark datasets show that the proposed similarity method using the classification criteria has better performance in both accuracy and efficiency compared to other methods. Also, the results verify the effectiveness of the proposed classification criteria, especially the runtime of item-based CF method is reduced by at least 61% while maintaining a relatively stable or higher accuracy.
Dynamic Federated Learning

Federated learning has emerged as an umbrella term for centralized coordination strategies in multi-agent environments. While many federated learning architectures process data in an online manner, and are hence adaptive by nature, most performance analyses assume static optimization problems and offer no guarantees in the presence of drifts in the problem solution or data characteristics. We consider a federated learning model where at every iteration, a random subset of available agents perform local updates based on their data. Under a nonstationary random walk model on the true minimizer for the aggregate optimization problem, we establish that the performance of the architecture is determined by three factors, namely, the data variability at each agent, the model variability across all agents, and a tracking term that is inversely proportional to the learning rate of the algorithm. The results clarify the trade-off between convergence and tracking performance.
Convolutional neural networks to automate the screening of malaria in low-resource countries

Malaria is an infectious disease caused by Plasmodium parasites, transmitted through mosquito bites. Symptoms include fever, headache, and vomiting, and in severe cases, seizures and coma. The World Health Organization reports that there were 228 million cases and 405,000 deaths in 2018, with Africa representing 93% of total cases and 94% of total deaths. Rapid diagnosis and subsequent treatment are the most effective means to mitigate the progression into serious symptoms. However, many fatal cases have been attributed to poor access to healthcare resources for malaria screenings. In these low-resource settings, the use of light microscopy on a thin blood smear with Giemsa stain is used to examine the severity of infection, requiring tedious and manual counting by a trained technician. To address the malaria endemic in Africa and its coexisting socioeconomic constraints, we propose an automated, mobile phone-based screening process that takes advantage of already existing resources. Through the use of convolutional neural networks (CNNs), we utilize a SSD multibox object detection architecture that rapidly processes thin blood smears acquired via light microscopy to isolate images of individual red blood cells with 90.4% average precision. Then we implement a FSRCNN model that upscales 32 × 32 low-resolution images to 128 × 128 high-resolution images with a PSNR of 30.2, compared to a baseline PSNR of 24.2 through traditional bicubic interpolation. Lastly, we utilize a modified VGG16 CNN that classifies red blood cells as either infected or uninfected with an accuracy of 96.5% in a balanced class dataset. These sequential models create a streamlined screening platform, giving the healthcare provider the number of malaria-infected red blood cells in a given sample. Our deep learning platform is efficient enough to operate exclusively on low-tier smartphone hardware, eliminating the need for high-speed internet connection.
Open science for responsible innovation in Australia: understanding the expectations and priorities of scientists and researchers

ABSTRACT Recent arguments for responsible innovation to progress beyond the narrow focus on open access and toward open science present the opportunity for a deliberate global transition to a culture of transparent and open scientific conduct that will deliver greater societal benefit. This paper presents results from a survey of 171 Australian scientists, researchers and other professionals on their expectations and perspectives of transparency and openness in current scientific research practice. The results suggest that for this cultural transition to occur, the responsibility for strengthening transparency and openness must be undertaken not only by scientists and researchers, but also research funding and delivery agencies, and even those beyond the research and innovation sector. These findings are a first step towards defining and understanding what open science means in an Australian context, and what shifts are needed from researchers, research institutions and policy makers to move toward open science for responsible innovation.
Swarm Intelligence and its applications towards Various Computing: A Systematic Review

Swarm intelligence is the discipline deals with artificial and natural systems that consists various individuals coordinated using self-organization and decentralized control. It consists of simple autonomous agents that come as emergent collective intelligence. The commands from global plan or leader are not followed by autonomous agent. This type of systems has been seen in various domains that makes swarm intelligence as a multidisciplinary character. Due to its popularity, various researchers have started working on it even in computing tasks but still they are unable to give a good survey on various swarm intelligence algorithms and use of it in computing work. Most of the people are unaware about the newly most effective invented swarm intelligence algorithms. In this paper we have given a comprehensive review on various swarm intelligence algorithms that prove to be efficient in multiple fields. The main focus is given to Bat algorithm, Firefly algorithm, Lion optimization algorithm, Chicken swarm optimization algorithm, Social Spider Algorithm and Spider Moneky optimization algorithm. Another thing covered in this paper is the comparative research on use of Swarm intelligence algorithm in Computing work. Cloud computing is the application that delivered as services through Internet and data centers software and hardware. We have covered the research work done by various researchers in cloud computing, Fog computing and Edge computing using swarm intelligence. The motive of this is to show the improvement in work comes after the introduction of Swarm intelligence in computing work.
Simplifying Neural Networks Using Formal Verification

Deep neural network (DNN) verification is an emerging field, with diverse verification engines quickly becoming available. Demonstrating the effectiveness of these engines on real-world DNNs is an important step towards their wider adoption. We present a tool that can leverage existing verification engines in performing a novel application: neural network simplification, through the reduction of the size of a DNN without harming its accuracy. We report on the work-flow of the simplification process, and demonstrate its potential significance and applicability on a family of real-world DNNs for aircraft collision avoidance, whose sizes we were able to reduce by as much as 10%.
Disturbance observer based adaptive model predictive control for uncalibrated visual servoing in constrained environments

This paper presents an adaptive model predictive control (MPC) method based on disturbance observer (DOB) to improve the disturbance rejection performance of the image-based visual servoing (IBVS) system. The proposed control method is developed based on the depth-independent interaction matrix, which can simultaneously handle unknown camera intrinsic and extrinsic parameters, unknown depth parameters, system constraints, as well as external disturbances. The proposed control scheme includes two parts which are the feedback regulation part based on the adaptive MPC and the feedforward compensation part based on the modified DOB. Unlike the traditional DOB that is based on the fixed nominal plant model, the modified DOB here is based on the estimated plant model. The adaptive MPC controller consists of an iterative identification algorithm, which not only can provide the model parameters for both the controller and the modified DOB, but also can be used to control plant dynamics and to minimize the effects of DOB. Simulations for both the eye-in-hand and eye-to-hand camera configurations are conducted to illustrate the effectiveness of the proposed method.