Reviewerly/exHarmony huggingface dataset:
Synchronous Multi-User Cross-Platform Virtual Reality for School Teachers

Motivated by a desire to apply Computer Science and Virtual Reality (VR) technology due to the need for improving secondary school education in Malawi, this paper presents a prototype of a synchronous multi-user cross-platform real-time 3D VR application. This tool can be used by VR application developers in assessing the feasibility of using VR technologies for secondary school teaching in Malawi. The prototype VR application illustrates the capabilities of the proposed development tools, as well as how the proposed technologies can be utilized in a design and implementation that accounts for the context of Malawi, where access to electricity and internet services are a challenge for many.
Attentive Graph Neural Networks for Few-Shot Learning

Graph Neural Networks (GNNs) have demonstrated superior performance in many challenging applications, including few-shot learning tasks. Despite its powerful capacity to learn and generalize a model from a few samples, GNN usually suffers from severe over-fitting and over-smoothing as the model becomes deep, which limits its scalability. In this work, we propose a novel Attentive GNN (AGNN) to tackle these challenges by incorporating a triple-attention mechanism, i.e., node self-attention, neighborhood attention, and layer memory attention. We explain why the proposed attentive modules can improve GNN for few-shot learning with theoretical analysis and illustrations. Extensive experiments demonstrate that the proposed AGNN model achieves promising results, compared to state-of-the-art GNN- and CNN-based methods for few-shot learning tasks, over the mini-ImageNet and tiered-ImageNet benchmarks under ConvNet-4 backbone with both inductive and transductive settings.
Developing digital competency in HPETE: a heuristic for and analysis of three programs in the Southern Hemisphere

This paper presents a heuristic tool that can be used to analyse and reflect on the inclusion of digital competences in Health and Physical Education Teacher Education (HPETE) programs. The tool is used to identify and compare how HPETE programs in three Southern Hemisphere countries address digital competency as an aspect of learning to teach Health and Physical Education (HPE). The results reveal that the tool provides an effective way of analysing the development of digital technologies through HPETE. While these programs are concerned with the need for technology education, they do not rely on suggested international frameworks and have a different focus related to future teachers’ digital competences. The lack of a systematic policy to improve digital skills in our data suggests that technology education has emerged from localised efforts. We suggest that our heuristic tool can support educators to refine the inclusion of digital competences in HPETE programs.
Construction Workforce Training Assisted with Augmented Reality

As augmented reality evolves, many use cases for the technology have emerged in the construction industry. Training new practitioners in the industry is an ever-growing challenge because a significant number of seasoned practitioners are leaving the workforce. Every year the construction industry is faced with fewer skilled trade workers that can train their replacement. While construction students obtain most of their theoretical knowledge in the classroom, there is a lack of resources to instruct and train trade workers. Using augmented reality for instruction and training has many applications in other industries and can be used to approach the issues that the construction industry faces. In this research, a comparison was analyzed between construction management students that learned from two-dimensional paper construction drawings to those that used an augmented reality assisted instructional device. Both sets of students were instructed on how to properly layout and erect a wood framed wall. Overall findings indicated that the students that used the augmented reality device made fewer mistakes during the exercise than the students using the paper plans. Further, the researchers collected NASA Task Load information from the students to gauge their perceived effort using the augmented reality device. The findings indicated an increased performance perception with less overall effort during the study. These findings support a case for the use of augmented reality as a support tool for workforce training – especially at a time in the construction industry when there are so few to call upon to do the training.
Adaptive to pixel value and pixel value difference irreversible spatial data hiding method using modified LSB for grayscale images

A new irreversible data hiding spatial domain method, A2MLSB, adaptive to pixel value (PV) and pixel value difference (PVD) using variable threshold on PV and PVD ranges, and modified least-significant bit (MLSB) substitution method for grayscale images is proposed. Correctness of A2MLSB is proved. Theoretical estimates of the mean squared error (MSE), peak signal-to-noise ratio (PSNR, dB) and embedding capacity (EC, bpp) for A2MLSB method matching experimental results are obtained. Theoretical MSE estimates are obtained for MLSB and LSB as functions of EC; they can be used for assessing performance of data hiding method based on MLSB and LSB, and serve as benchmarks. A2MLSB has, on average, better PSNR (by 1.33 dB) and EC (by 0.14 bpp) than 37 variants of nineteen known data hiding methods. It resists RS, PVD histograms, SPAM, and SRM steganalysis.
ViWatch

Wearable devices like smartwatches and smart wristbands have gained substantial popularity in recent years. While enabling a variety of computing applications, such devices are not always convenient to interact with because of the limited size of the touchscreen. A wide variety of approaches have been considered to improve user experiences, ranging from using customized RF sensors, to multiple sensors in smartwatches. These solutions have limitations related to the characteristics of their technology. We propose ViWatch (Vibration Watch), which harnesses vibrations with an IMU sensor on commodity smartwatches to enable fine-grained finger interactions. We detect subtle finger vibrations from noise and design a novel adversarial neural network to mitigate human body variations. ViWatch is able to recognize finger typing and writing induced vibrations with accuracy, even when users are in different states of motion or in noisy environments.
MAE and RMSE Analysis of K-means Predictive Algorithm for Photovoltaic Generation

Given the relevance of the microgrids census, the search for optimal control and management of these facilities grows, aiming at reducing costs and increasing efficiency. For this, it is essential to have a system capable of providing an adequate energy generation forecast, facilitating the optimal management of the system. The work presented by this paper introduces the monitoring of the predictions made by a k-means algorithm developed for microgrid optimization. The monitored installation is a 3kWp on-grid system, with 12 solar modules. The generation data was extracted and used as input by the predictor, along with local weather data. The monitoring took place from August 11, 2021 to December 13, 2021. Data were filtered and analyzed by calculating the Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) of the measured and predicted values. The obtained values were 0.0337 for MAE, and 0.0907 for RMSE, satisfactory compared to similar works in the literature.
An Exalted Three Dimensional Image Encryption Model Availing a Novel Twin Attractor Chaotic System

At present, encryption comprising chaotic system grasps superior attention compared to the existent models. Gearing up the information security in the assorted fields is a deliberate issue in the contemporary time being as digitization of data, paved way for dispersal of information vastly across the globe in varied forms. This paper proposes a novel Twin Attractor Chaotic (TAC) system to trounce the frangibility faced by the former crypto models. TAC system outturn two attractor possessing complex dynamical structures while revising the parameter values. The proposed TAC system effectively encrypts the color images using pixel-level confusion and diffusion schemes. Empirical results and security analysis validate the proposed systems merit and also turns out to be robust to overcome diverse attacks yielding large volume of secureness.
Interface and interaction design principles for Mixed Reality applications: the case of operator training in wire harness activities

Operator 4.0 has to deal with a vast amount of product variants and production data especially within the mass customization paradigm, high mental demanding tasks, and smart production systems. Technologies capable of supporting his training and his work become fundamental, such as the extended reality (XR). Its increasing use in industrial applications, however, opens up new challenges related to interface and interaction design, which can determine the success of both the use and development experience. The lack of guidelines for designing interfaces for mixed reality (MR) applications is what this paper aims to address. Design requirements for MR interfaces are presented and applied in the context of operator training in wire harness activities. Different interaction modes and user interfaces have been developed to evaluate the most suitable and user-friendly one for the operator. A pilot test was conducted to assess the applications’ usability and potentialities with satisfactory results.
SEDCN: An improved Deep &amp; Cross Network Recommendation Algorithm based on SENET

Advertising ranking is essential to many Internet companies such as Facebook and Sina Weibo. Among many real-world advertising ranking systems, click-through rate (CTR) prediction plays a central role. There are many proposed models in this field such as logistic regression, factorization machine based models,and deep learning based CTR models. However, many current works calculate the feature interactions in a simple way such as Hadamard product and inner product and they care less about the importance of features. In this paper, we propose a new feature importance network model SEDCN based on deep crossover network. SEDCN can learn the importance of features dynamically through the SENET mechanism. We compare the performance of SEDCN with other depth models, such as DeepFM and DCN.
Pupil Detection for Augmented and Virtual Reality based on Images with Reduced Bit Depths

For future augmented reality (AR) and virtual reality (VR) applications, several different kinds of sensors will be used. These sensors, to give some examples, are used for gesture recognition, head pose tracking and pupil tracking. All these sensors send data to a host platform, where the data must be processed in real-time. This requires high processing power which leads to higher energy consumption. To lower the energy consumption, optimizations of the image processing system are necessary. This paper investigates pupil detection for AR/VR applications based on images with reduced bit depths. It shows that images with reduced bit depths even down to 3 or 2 bits can be used for pupil detection, with almost the same average detection rate. Reduced bit depths of an image reduces the memory foot-print, which allows to perform in-sensor processing for future image sensors and provides the foundation for future in-sensor processing architectures.
The Alternation Hierarchy of the $$\mu $$-calculus over Weakly Transitive Frames

It is known that the $$\mu $$ -calculus collapses to its alternation-free fragment over transitive frames and to modal logic over equivalence relations. We adapt a proof by D’Agostino and Lenzi to show that the $$\mu $$ -calculus collapses to its alternation-free fragment over weakly transitive frames. As a consequence, we show that the $$\mu $$ -calculus with derivative topological semantics collapses to its alternation-free fragment. We also study the collapse over frames of $$\mathsf {S4.2}$$ , $$\mathsf {S4.3}$$ , $$\mathsf {S4.3.2}$$ , $$\mathsf {S4.4}$$ and $$\textsf{KD45}$$ , logics important for Epistemic Logic. At last, we use the $$\mu $$ -calculus to define degrees of ignorance on Epistemic Logic and study the implications of $$\mu $$ -calculus’s collapse over the logics above.
Graph-based visualization of merge requests for code review

Code review is a software development practice aimed at assessing code quality, finding defects, and sharing knowledge among developers. Despite its wide adoption, code review is a challenging task for developers, who often struggle to understand the content of a review change-set. Visualization techniques represent a promising approach to support reviewers. In this paper we present a new visualization approach that displays classes and methods in review changes as nodes in a graph. Then, we implemented our graph-based approach in a tool (ReviewVis) and performed a two-step feedback collection phase to assess the developers' perceptions on the tool's benefits through (1) an in-company study with nine professional software developers and (2) an online survey with 37 participants. Given the positive results obtained by this first evaluation, we performed a second survey with 31 participants with a specific focus on supporting developers' understanding of a review change-set. The collected feedback showed that the developers indeed perceive that ReviewVis can help them navigate and understand the changes under review. The results achieved also indicate possible future paths to use software visualization for code review. Data and Materials: https://doi.org/10.5281/zenodo.7047993
Double Attention Convolutional Neural Network for Sequential Recommendation

The explosive growth of e-commerce and online service has led to the development of recommender system. Aiming to provide a list of items to meet a user’s personalized need by analyzing his/her interaction 1 history, recommender system has been widely studied in academic and industrial communities. Different from conventional recommender systems, sequential recommender systems attempt to capture the pattern of users’ sequential behaviors and the evolution of users’ preferences. Most of the existing sequential recommendation models only focus on user interaction sequence, but neglect item interaction sequence. An item interaction sequence also contains rich contextual information for capturing the item’s dynamic characteristic, since an item’s dynamic characteristic can be reflected by the users who interact with it in a period. Furthermore, existing dual sequential models use the same method to handle the user interaction sequence and item interaction sequence, and do not consider their different characteristics. Hence, we propose a novel D ouble A ttention C onvolution N eural N etwork (DACNN) , which incorporates user interaction sequence and item interaction sequence into an integrated neural network framework. DACNN leverages the strength of attention mechanism to capture the temporary suitability and adopts CNN to extract local sequential features. Experimental evaluations on the real datasets show that DACNN outperforms the baseline approaches.
Online Multiple Object Tracking Using Spatial Pyramid Pooling Hashing and Image Retrieval for Autonomous Driving

Multiple object tracking (MOT) is a fundamental issue and has attracted considerable attention in the autonomous driving community. This paper presents a novel MOT framework for autonomous driving. The framework consists of two stages of object representation and data association. In the stage of object representation, we employ appearance, motion, and position features to characterize objects. We design a spatial pyramidal pooling hash network (SPPHNet) to generate the appearance features. Multiple-level representative features in the SPPHNet are mapped into a similarity-preserving binary space, called hash features. The hash features retain the visual discriminability of high-dimensional features and are beneficial for computational efficiency. For data association, a two-tier data association scheme is designed to address the occlusion issue, consisting of an affinity cost model and a hash-based image retrieval model. The affinity cost model accommodates the hash features, disparity, and optical flow as the first tier of data association. The hash-based image retrieval model exploits the hash features and adopts image retrieval technology to handle reappearing objects as the second tier of data association. Experiments on the KITTI public benchmark dataset and our campus scenario sequences show that our method has superior tracking performance to the state-of-the-art vision-based MOT methods.
Parallelization of Swarm Intelligence Algorithms: Literature Review

Abstract Swarm Intelligence (SI) algorithms are frequently applied to tackle complex optimization problems. SI is especially used when good solutions are requested for NP hard problems within a reasonable response time. And when such problems possess a very high dimensionality, a dynamic nature, or present intrinsic complex intertwined independent variables, computational costs for SI algorithms may still be too high. Therefore, new approaches and hardware support are needed to speed up processing. Nowadays, with the popularization of GPU and multi-core processing, parallel versions of SI algorithms can provide the required performance on those though problems. This paper aims to describe the state of the art of such approaches, to summarize the key points addressed, and also to identify the research gaps that could be addressed better. The scope of this review considers recent papers mainly focusing on parallel implementations of the most frequently used SI algorithms. The use of nested parallelism is of particular interest, since one level of parallelism is often not sufficient to exploit the computational power of contemporary parallel hardware. The sources were main scientific databases and filtered accordingly to the set requirements of this literature review.
What do Construction Workers Know about Artificial Intelligence? An Exploratory Case Study in an Austrian SME

Artificial intelligence (AI) is by now used in many different work settings, including construction industry. As new technologies change business and work processes, one important aspect is to understand how potentially affected workers perceive and understand the existing and upcoming AI in their work environment. In this work, we present the results of an exploratory case study with 20 construction workers in a small Austrian company about their knowledge of and attitudes toward AI. Our results show that construction workers' understanding of AI as a concept is rather superficial, diffuse, and vague, often linked to physical and tangible entities such as robots, and often based on inappropriate sources of information which can lead to misconceptions about AI and AI anxiety. Learning opportunities for promoting (future) construction workers' AI literacy should be accessible and understandable for learners at various educational levels and encompass aspects such as i) conveying the basics of digitalization, automation, and AI to enable a clear distinction of these concepts, ii) building on the learners' actual experience realm, i.e., taking into account their focus on physical, tangible, and visible entities, and iii) reducing AI anxiety by elaborating on the limits of AI.
FADER: An Authoring Tool for Creating Augmented Reality-Based Avatars from an End-User Perspective

Although augmented reality (AR) is becoming more common in our society, there are few specialized end-user tools for appropriate AR content creation. Most tools are focused on creating entire 3D applications or require extensive knowledge in programming and 3D modeling. With reference to End-User Development (EUD), we present a design case study for an end-user-friendly authoring tool that allows domain experts to create individual AR avatars in the field of Human-Food Interaction. After reviewing current approaches and design guidelines, we designed and implemented FADER, a web-based tool for creating AR-based food avatars. Our evaluation shows that playful design fosters immersion, and that abstract placeholders and highly simplified controls empower non-developers to create AR content. Our study contributes to a better understanding of end-user needs and practices during the AR creation process and informs the design of future AR authoring tools.
Beyond Immersion: Designing for Reflection in Virtual Reality

Access to immersive virtual reality (VR) technology is increasingly becoming available to the general population. However, the ability of VR to incite a state of high immersion and flow creates a paradox where immersion limits opportunities for self-awareness and critical reflection. In this work, we address this paradox and scaffold opportunities for reflection in VR by drawing upon practices from the theatre. We conduct a scoping review of 12 research papers designed to support reflection and discuss our findings regarding a proposed conceptual model. Based on these findings and practices from the Brechtian theatre, we propose a "RIOR" conceptual model (Readiness for reflection, Immersive estrangement, Observation and re-examination, Repatterning of knowledge) to create opportunities in VR for reflection. This model contributes a unified lens on how to design immersive technology for reflection and may create positive impacts in fields of mental health, wellbeing, self-care, education, and professional and personal development.
Sampling-based Sublinear Low-rank Matrix Arithmetic Framework for Dequantizing Quantum Machine Learning

We present an algorithmic framework for quantum-inspired classical algorithms on close-to-low-rank matrices, generalizing the series of results started by Tang’s breakthrough quantum-inspired algorithm for recommendation systems [STOC’19]. Motivated by quantum linear algebra algorithms and the quantum singular value transformation (SVT) framework of Gilyén et al. [STOC’19], we develop classical algorithms for SVT that run in time independent of input dimension, under suitable quantum-inspired sampling assumptions. Our results give compelling evidence that in the corresponding QRAM data structure input model, quantum SVT does not yield exponential quantum speedups. Since the quantum SVT framework generalizes essentially all known techniques for quantum linear algebra, our results, combined with sampling lemmas from previous work, suffice to generalize all prior results about dequantizing quantum machine learning algorithms. In particular, our classical SVT framework recovers and often improves the dequantization results on recommendation systems, principal component analysis, supervised clustering, support vector machines, low-rank regression, and semidefinite program solving. We also give additional dequantization results on low-rank Hamiltonian simulation and discriminant analysis. Our improvements come from identifying the key feature of the quantum-inspired input model that is at the core of all prior quantum-inspired results: ℓ 2 -norm sampling can approximate matrix products in time independent of their dimension. We reduce all our main results to this fact, making our exposition concise, self-contained, and intuitive.