Reviewerly/exHarmony huggingface dataset:
Group Creativities

This project demonstrates how mobile learning can be embedded into an undergraduate course in popular music and society at the Education University of Hong Kong. Students learn how to play drum sets, electric bass, and electric guitar on the application GarageBand on i-pad as a mobile music creation in class. This project explores the process of group creativity in popular music education in mobile music through (1) creation, (2) co-creation, and (3) re-creation to engage the students in learning popular music in a classroom setting. This kind of group learning activities can pave the way in learning different tracks and layers of popular music in context by using application GarageBand as a virtual instrument. The creative process of group creativity was video-recorded and analyzed by the researcher to have a deeper understanding of the educational values, learning outcomes, and communication skills from students during the process of learning and re-creating. Group interview data were collected and used to formulate how the workflow of mobile learning engaged the creative process in group creativity.
Dynamical modelling of player engagement and skill in the Quantum Moves 2 citizen science game

Citizen science games and gamified tasks have shown promise in engaging citizens in scientific work through often digitalized version of real-life scientific tasks while producing useful data.This work has spurred an increasing interest in investigating how to design such interactions to entice players to produce as high quantities of scientifically useful data as possible.Player modelling, the computational analysis of player interactions with and within games, has been applied to better understand which UI features or particular player interactions can best predict player behavior.However, much of this work has relied on modelling approaches that exhibit low degrees of explainability, and that rely on large quantities of player data.In this paper we present Saturable Dynamic Bayesian Network Models (SDBN), a novel player modelling approach that avoids these two issues using a feature activation network that quantifies the user's gradually extending familiarity with the interface.This makes our approach usable for more fine-grained design-decisions when building and improving citizen science games, and it makes our approach applicable to games with smaller numbers of players.We apply our novel method to data collected from approximately 2,000 players of the citizen science game Quantum Moves 2. Our study shows that the SDBN approach can reliably model player drop out down to 10 participants and allows for both bulk and individual level insights of how player interactions with and within our game predicts player engagement and the quality of consequent data.
Multitask Learning Based Intra-Mode Decision Framework for Versatile Video Coding

In mid-2020, the new international video coding standard, namely versatile video coding (VVC), was officially released by the Joint Video Expert Team (JVET). As its name indicates, the VVC enables a higher level of versatility with better compression performance compared to its predecessor, high-efficiency video coding (HEVC). VVC introduces several new coding tools like multiple reference lines (MRL) and matrix-weighted intra-prediction (MIP), along with several improvements on the block-based hybrid video coding scheme such as quatree with nested multi-type tree (QTMT) and finer-granularity intra-prediction modes (IPMs). Because finding the best encoding decisions is usually preceded by optimizing the rate distortion (RD) cost, introducing new coding tools or enhancing existing ones requires additional computations. In fact, the VVC is 31 times more complex than the HEVC. Therefore, this paper aims to reduce the computational complexity of the VVC. It establishes a large database for intra-prediction and proposes a multitask learning (MTL)-based intra-mode decision framework. Experimental results show that our proposal enables up to 30% of complexity reduction while slightly increasing the Bjontegaard bit rate (BD-BR).
Image Inpainting with Bilateral Convolution

Due to sensor malfunctions and poor atmospheric conditions, remote sensing images often miss important information/pixels, which affects downstream tasks, therefore requiring reconstruction. Current image reconstruction methods use deep convolutional neural networks to improve inpainting performances as they have a powerful modeling capability. However, deep convolutional networks learn different features with the same group of convolutional kernels, which restricts their ability to handle diverse image corruptions and often results in color discrepancy and blurriness in the recovered images. To mitigate this problem, in this paper, we propose an operator called Bilateral Convolution (BC) to adaptively preserve and propagate information from known regions to missing data regions. On the basis of vanilla convolution, the BC dynamically propagates more confident features, which weights the input features of a patch according to their spatial location and feature value. Furthermore, to capture different range dependencies, we designed a Multi-range Window Attention (MWA) module, in which the input feature is divided into multiple sizes of non-overlapped patches for several heads, and then these feature patches are processed by the window self-attention. With BC and MWA, we designed a bilateral convolution network for image inpainting. We conducted experiments on remote sensing datasets and several typical image inpainting datasets to verify the effectiveness and generalization of our network. The results show that our network adaptively captures features between known and unknown regions, generates appropriate content for various corrupted images, and has a competitive performance compared with state-of-the-art methods.
Assessing effectiveness of many-objective evolutionary algorithms for selection of tag SNPs

Studies on genome-wide associations help to determine the cause of many genetic diseases. Genome-wide associations typically focus on associations between single-nucleotide polymorphisms (SNPs). Genotyping every SNP in a chromosomal region for identifying genetic variation is computationally very expensive. A representative subset of SNPs, called tag SNPs, can be used to identify genetic variation. Small tag SNPs save the computation time of genotyping platform, however, there could be missing data or genotyping errors in small tag SNPs. This study aims to solve Tag SNPs selection problem using many-objective evolutionary algorithms.Tag SNPs selection can be viewed as an optimization problem with some trade-offs between objectives, e.g. minimizing the number of tag SNPs and maximizing tolerance for missing data. In this study, the tag SNPs selection problem is formulated as a many-objective problem. Nondominated Sorting based Genetic Algorithm (NSGA-III), and Multi-Objective Evolutionary Algorithm based on Decomposition (MOEA/D), which are Many-Objective evolutionary algorithms, have been applied and investigated for optimal tag SNPs selection. This study also investigates different initialization methods like greedy and random initialization. optimization.The evaluation measures used for comparing results for different algorithms are Hypervolume, Range, SumMin, MinSum, Tolerance rate, and Average Hamming distance. Overall MOEA/D algorithm gives superior results as compared to other algorithms in most cases. NSGA-III outperforms NSGA-II and other compared algorithms on maximum tolerance rate, and SPEA2 outperforms all algorithms on average hamming distance.Experimental results show that the performance of our proposed many-objective algorithms is much superior as compared to the results of existing methods. The outcomes show the advantages of greedy initialization over random initialization using NSGA-III, SPEA2, and MOEA/D to solve the tag SNPs selection as many-objective optimization problem.
Eavesdropping Against Bidirectional Physical Layer Secret Key Generation in Fiber Communications

Physical layer secret key exploits the random but reciprocal channel features between legitimate users to encrypt their data against fiber-tapping.We propose a novel tapping-based eavesdropper scheme, leveraging its tapped signals from legitimate users to reconstruct their common features and the secret key.
A Novel Hybrid Multi-Modal Deep Learning for Detecting Hashtag Incongruity on Social Media

Hashtags have been an integral element of social media platforms over the years and are widely used by users to promote, organize and connect users. Despite the intensive use of hashtags, there is no basis for using congruous tags, which causes the creation of many unrelated contents in hashtag searches. The presence of mismatched content in the hashtag creates many problems for individuals and brands. Although several methods have been presented to solve the problem by recommending hashtags based on the users' interest, the detection and analysis of the characteristics of these repetitive contents with irrelevant hashtags have rarely been addressed. To this end, we propose a novel hybrid deep learning hashtag incongruity detection by fusing visual and textual modality. We fine-tune BERT and ResNet50 pre-trained models to encode textual and visual information to encode textual and visual data simultaneously. We further attempt to show the capability of logo detection and face recognition in discriminating images. To extract faces, we introduce a pipeline that ranks faces based on the number of times they appear on Instagram accounts using face clustering. Moreover, we conduct our analysis and experiments on a dataset of Instagram posts that we collect from hashtags related to brands and celebrities. Unlike the existing works, we analyze these contents from both content and user perspectives and show a significant difference between data. In light of our results, we show that our multimodal model outperforms other models and the effectiveness of object detection in detecting mismatched information.
Utilization of Alternative Buffer Solutions for Staining Thin Blood smears by the Giemsa, Wright stain and Romanowsky method

To make a reliable diagnosis on haematological examination, it is necessary to examine further the morphology of the blood cells previously stained in the procedure for staining thin blood smears using a buffer solution with a standard pH of 6.4 to 6.8. One of the problems that may occur in the laboratory is that the buffer reagents are damaged, past the expiration date or running out, so alternative buffers are needed that are cheap, fast and easy to obtain. This study aims to evaluate the staining results from alternative buffers. Study used a quasi-experimental method, and using alternative buffers from bottled mineral water following SNI-01-0553 2006. The colour produced by some of these alternative buffers is almost equivalent to blood cells stained with phosphate buffer. The percentage of assessment results in the alternative buffer codes B, C, D, E and F compared with control (A) were 62.67%, 92.00%, 82.67%, 80.00% 88, 00%, and 68.00%. The use of alternative buffers for staining thin blood smears using the Giemsa, Wright stain, and Romanowsky method can be done with mineral water as an alternative buffer for sample codes C, D, E and F, while B and G cannot be used. This alternative buffer can be applied by laboratory personnel in urgent situations in limited equipment and material facilities.
TipScreener: A Framework for Mining Tips for Online Review Readers

User-generated content explodes in popularity daily on e-commerce platforms. It is crucial for platform manipulators to sort out online reviews with repeatedly expressed opinions and a large number of irrelevant topics in order to reduce the information processing burden on review readers. This study proposes a framework named TipScreener that generates a set of useful sentences that cover all of the information of features of a business. Called tips in this work, the sentences are selected from the reviews in their original, unaltered form. Firstly, we identify information tokens of the business. Second, we filter review sentences that contain no tokens and remove duplicates. We then use a convolutional neural network to filter uninformative sentences. Next, we find the tip set with the smallest cardinality that contains all off the tokens, taking opinion words into account. The sentences of the tip set contain a full range of information and have a very low repetition rate. Our work contributes to the work of online review organizing. Review operators of e-commerce platforms can adopt tips generated by TipScreener to facilitate decision makings of review readers. The convolutional neural network that classifies sentences into two classes also enriches deep learning studies on text classification.
Local Probabilistic Decoding of a Quantum Code

flip is an extremely simple and maximally local classical decoder which has been used to great effect in certain classes of classical codes. When applied to quantum codes there exist constant-weight errors (such as half of a stabiliser) which are uncorrectable for this decoder, so previous studies have considered modified versions of flip, sometimes in conjunction with other decoders. We argue that this may not always be necessary, and present numerical evidence for the existence of a threshold for flip when applied to the looplike syndromes of a three-dimensional toric code on a cubic lattice. This result can be attributed to the fact that the lowest-weight uncorrectable errors for this decoder are closer (in terms of Hamming distance) to correctable errors than to other uncorrectable errors, and so they are likely to become correctable in future code cycles after transformation by additional noise. Introducing randomness into the decoder can allow it to correct these "uncorrectable" errors with finite probability, and for a decoding strategy that uses a combination of belief propagation and probabilistic flip we observe a threshold of $\sim5.5\%$ under phenomenological noise. This is comparable to the best known threshold for this code ($\sim7.1\%$) which was achieved using belief propagation and ordered statistics decoding [Higgott and Breuckmann, 2022], a strategy with a runtime of $O(n^3)$ as opposed to the $O(n)$ ($O(1)$ when parallelised) runtime of our local decoder. We expect that this strategy could be generalised to work well in other low-density parity check codes, and hope that these results will prompt investigation of other previously overlooked decoders.
Power Users’ Branded Virtual Reality Experience: A Preliminary Study

Virtual reality advertising campaigns allow consumers to interact with companies in a novel way. This study examined how individual differences in power usage (confidence using technology in innovative and functional ways) and the trust-schema (trustworthiness perception of a company) interact to affect consumers’ experience of control, attitudinal outcomes, and behavioral intentions (sharing their VR experience). The findings revealed that power users are more likely to recommend VR experiences, have a positive attitude toward VR experiences, and share their experiences, than non-power users. However, these effects were moderated by consumers’ trust-schema levels. Power users were less sensitive to the effect of trust-schema than non-power users. The implications of the findings and future studies on the emerging metaverse were further discussed.
RefinePose: Towards More Refined Human Pose Estimation

Human pose estimation is a very important research topic in computer vision and attracts more and more researchers. Recently, ViTPose based on heatmap representation refreshed the state of the art for pose estimation methods. However, we find that ViTPose still has room for improvement in our experiments. On the one hand, the PatchEmbedding module of ViTPose uses a convolutional layer with a stride of 14 × 14 to downsample the input image, resulting in the loss of a significant amount of feature information. On the other hand, the two decoding methods (Classical Decoder and Simple Decoder) used by ViTPose are not refined enough: transpose convolution in the Classical Decoder produces the inherent chessboard effect; the upsampling factor in the Simple Decoder is too large, resulting in the blurry heatmap. To this end, we propose a novel pose estimation method based on ViTPose, termed RefinePose. In RefinePose, we design the GradualEmbedding module and Fusion Decoder, respectively, to solve the above problems. More specifically, the GradualEmbedding module only downsamples the image to 1/2 of the original size in each downsampling stage, and it reduces the input image to a fixed size (16 × 112 in ViTPose) through multiple downsampling stages. At the same time, we fuse the outputs of max pooling layers and convolutional layers in each downsampling stage, which retains more meaningful feature information. In the decoding stage, the Fusion Decoder designed by us combines bilinear interpolation with max unpooling layers, and gradually upsamples the feature maps to restore the predicted heatmap. In addition, we also design the FeatureAggregation module to aggregate features after sampling (upsampling and downsampling). We validate the RefinePose on the COCO dataset. The experiments show that RefinePose has achieved better performance than ViTPose.
Internet of things in Saudi public healthcare organizations: The moderating role of facilitating conditions

The Internet of Things (IoT) is an innovative technology that has the potential to help public hospitals better meet the demands of hospitalization. However, only a small portion of the research looked at patients' behavioural intentions (BI) to utilise IoT healthcare devices (IoTHD). This study intends to investigate the variables that influence the BI's use of IoTHD. The research suggests that the BI may be explained by factors of UTATU. The patients of public hospitals make up the population. A questionnaire was used to obtain the data using convenience sampling. Participants in this research totalled 161. Smart Partial Least Square results demonstrated that social influence (SI) has an impact on performance expectancy (PE). Technological complexity (TC) and playfulness (PP) had an impact on effort expectancy (EE). Additionally, the BI to adopt IoTHD was impacted by PE, EE, perceived security (PS), and perceived privacy (PV). The impact of PE and EE on BI to use IoTHD was not moderated by the facilitating conditions (FC). In order to improve patients' perceptions of IoTHD usage in public health organisations, simple process and more positive word of mouth is required.
Application for Recognizing Sign Language Gestures Based on an Artificial Neural Network

This paper presents the development and implementation of an application that recognizes American Sign Language signs with the use of deep learning algorithms based on convolutional neural network architectures. The project implementation includes the development of a training set, the preparation of a module that converts photos to a form readable by the artificial neural network, the selection of the appropriate neural network architecture and the development of the model. The neural network undergoes a learning process, and its results are verified accordingly. An internet application that allows recognition of sign language based on a sign from any photo taken by the user is implemented, and its results are analyzed. The network effectiveness ratio reaches 99% for the training set. Nevertheless, conclusions and recommendations are formulated to improve the operation of the application.
Higher Education Challenges in the Era of Covid-19, from the Perspective of Educators and Students (Ghana, Georgia and Pakistan Cases) – A literature Review

For the last three years, the entire world has faced a colossal phenomenon - the covid-19 pandemic. All sectors and areas of life have been affected, forcing rapid and radical changes towards adaptation in its wake. Inevitably, the unexpected pandemic’s mark and impact on education is more severe and longer lasting than imagined. It disrupted education provision at an unprecedented scale. This article is intended as a review of literature on the experience of different countries and education systems during the Covid-19 pandemic. Based on the analysis of the existing literature and research on this issue, from the perspective of educators and students, including the experience of different countries around the world, the pandemic has had a great impact on higher education and pushed it to digital transformation, implicitly overcoming important challenges. The review uses particular examples of higher education in the era of Covid-19 in Georgia, Ghana and Pakistan, exposing measures taken to continue educating in spite of the pandemic. However challenging this phenomenon proved to be, it equally gave way to enormous opportunities for creativity within progress. Discussed are barriers that students and academics faced during online teaching-learning, the pros and cons of online teaching-learning, as well as the quality of teaching-learning and the state of preparedness for future education.
Expanding the Frontiers of Industrial Robots beyond Factories: Design and in the Wild Validation

Robots able to coexist and interact with humans are key elements for Society 5.0. To produce the right expectations towards robots, it will be necessary to expose the true current capabilities of robots to the general public. In this context, Human–Robot Interaction (HRI) in the wild emerges as a relevant paradigm. In this article, we affront the challenge of bringing an industrial robot (NEXTAGE Open) outside factories and laboratories to be used in a public setting. We designed a multi-modal interactive scenario that integrates state-of-the-art sensory devices, deep learning methods for perception, and a human–machine graphical interface that monitors the system and provides useful information to participants. The main objective of the presented work is to build a robust and fully autonomous robotic system able to: (1) share the same space as humans, (2) work in a public and crowded space, and (3) provide an intuitive and engaging experience for a robotic exposition. In addition, we measured the attitudes, perceptions, expectations, and emotional reactions of volunteers. Results suggest that participants considered our proposed scenario as enjoyable, safe, interesting, and clear. Those points are also the main concerns of participants about sharing workspaces of daily environments with robots. However, we can point out some limitations with a biased population mainly composed of Japanese and males. In future work, we will improve our scenario with non-functional features or emotional expressions from the robot.