Reviewerly/exHarmony huggingface dataset:
Read and Attend: Temporal Localisation in Sign Language Videos

The objective of this work is to annotate sign instances across a broad vocabulary in continuous sign language. We train a Transformer model to ingest a continuous signing stream and output a sequence of written tokens on a large-scale collection of signing footage with weakly-aligned subtitles. We show that through this training it acquires the ability to attend to a large vocabulary of sign instances in the input sequence, enabling their localisation. Our contributions are as follows: (1) we demonstrate the ability to leverage large quantities of continuous signing videos with weakly-aligned subtitles to localise signs in continuous sign language; (2) we employ the learned attention to automatically generate hundreds of thousands of annotations for a large sign vocabulary; (3) we collect a set of 37K manually verified sign instances across a vocabulary of 950 sign classes to support our study of sign language recognition; (4) by training on the newly annotated data from our method, we outperform the prior state of the art on the BSL-1K sign language recognition benchmark.
Do digital literacies matter in employee engagement in digitalised workplace?

Purpose This study aims to understand the role of digital literacies as a moderator between employee engagement and its antecedents, namely, workplace digitalisation and innovative culture. Design/methodology/approach A total of 256 valid samples were used in the analysis. The respondents were individuals used as management-level executives in companies located in Selangor/Kuala Lumpur. The model was tested using structural equation modelling. Findings The findings reveal that there exists a significant association between employee engagement and its antecedents, namely, workplace digitalisation and innovative culture. Digital literacies are found to moderate the relationships between workplace digitalisation-employee engagement and innovative culture-employee engagement. Practical implications This paper provides new insight to the practitioners about the role of digital literacies in raising employee engagement in the digital workplace. Originality/value These findings enrich the literature on employee engagement, whereby, improving employee digital literacies strengthens employee acceptance to workplace digitalisation and benefit from the innovative culture to stay engaged.
A Crowdsourced Open-Source Kazakh Speech Corpus and Initial Speech Recognition Baseline

We present an open-source speech corpus for the Kazakh language.The Kazakh speech corpus (KSC) contains around 332 hours of transcribed audio comprising over 153,000 utterances spoken by participants from different regions and age groups, as well as both genders.It was carefully inspected by native Kazakh speakers to ensure high quality.The KSC is the largest publicly available database developed to advance various Kazakh speech and language processing applications.In this paper, we first describe the data collection and preprocessing procedures followed by a description of the database specifications.We also share our experience and challenges faced during the database construction, which might benefit other researchers planning to build a speech corpus for a low-resource language.To demonstrate the reliability of the database, we performed preliminary speech recognition experiments.The experimental results imply that the quality of audio and transcripts is promising (2.8% character error rate and 8.7% word error rate on the test set).To enable experiment reproducibility and ease the corpus usage, we also released an ESPnet recipe for our speech recognition models.
Assessment of the development level of regional industrialized building based on cloud model: A case study in Guangzhou, China

The purpose of this paper is to develop an evaluation cloud model which could assess the development level of the regional industrialized building. The objectives are to understand which factors hinder the development of industrialized buildings and how to promote the development of industrialized buildings. The research was conducted in three stages. The first stage consisted of an extensive literature review and policy textual analysis to document 33 different indicators affecting the industrialized buildings. In Stage two, returned questionnaires were analyzed to sift the final 22 indicators which contribute to the valid index system. In the third stage, take Guangzhou as a case study to do an assessment based on the cloud model, and then strategies were given based on results. The findings emphasize the construction of an index system to evaluate industrialized buildings based on the cloud model and takes Guangzhou as an example to verify the validity. The results of this study show that there is an "acceptable" foundation for the development of industrialized buildings in Guangzhou, which is in line with the actual development situation.
Resource-effective quantum key distribution: a field trial in Padua city center

Field trials are of key importance for novel technologies seeking commercialization and widespread adoption. This is also the case for quantum key distribution (QKD), which allows distant parties to distill a secret key with unconditional security. Typically, QKD demonstrations over urban infrastructures require complex stabilization and synchronization systems to maintain a low quantum bit error and high secret key rates over time. Here we present a field trial that exploits low-complexity self-stabilized hardware and a novel synchronization technique, to perform QKD over optical fibers deployed in the city center of Padua, Italy. Two techniques recently introduced by our research group are evaluated in a real-world environment: the iPOGNAC polarization encoder was used for preparation of the quantum states, while temporal synchronization was performed with the Qubit4Sync algorithm. The results here presented demonstrate the validity and robustness of our resource-effective QKD system, which can be easily and rapidly installed in an existing telecommunication infrastructure, thus representing an important step towards mature, efficient, and low-cost QKD systems.
Bath‐Induced Collective Phenomena on Superconducting Qubits: Synchronization, Subradiance, and Entanglement Generation

Abstract A common environment acting on a pair of qubits gives rise to a plethora of different phenomena, such as the generation of qubit–qubit entanglement, quantum synchronization, and subradiance. Here, time‐independent figures of merit for entanglement generation, quantum synchronization, and subradiance are defined, and an extensive analytical and numerical study of their dependence on model parameters is performed. A recently proposed measure of the collectiveness of the dynamics driven by the bath is also addressed, and it is found that it almost perfectly witnesses the behavior of entanglement generation. The results show that synchronization and subradiance can be employed as reliable local signatures of an entangling common‐bath in a general scenario. Finally, an experimental implementation of the model based on two transmon qubits capacitively coupled to a common resistor is proposed, which provides a versatile quantum simulation platform of the open system in any regime.
Multi-Target Multi-Camera Tracking of Vehicles Using Metadata-Aided Re-ID and Trajectory-Based Camera Link Model

In this paper, we propose a novel framework for multi-target multi-camera tracking (MTMCT) of vehicles based on metadata-aided re-identification (MA-ReID) and the trajectory-based camera link model (TCLM). Given a video sequence and the corresponding frame-by-frame vehicle detections, we first address the isolated tracklets issue from single camera tracking (SCT) by the proposed traffic-aware single-camera tracking (TSCT). Then, after automatically constructing the TCLM, we solve MTMCT by the MA-ReID. The TCLM is generated from camera topological configuration to obtain the spatial and temporal information to improve the performance of MTMCT by reducing the candidate search of ReID. We also use the temporal attention model to create more discriminative embeddings of trajectories from each camera to achieve robust distance measures for vehicle ReID. Moreover, we train a metadata classifier for MTMCT to obtain the metadata feature, which is concatenated with the temporal attention based embeddings. Finally, the TCLM and hierarchical clustering are jointly applied for global ID assignment. The proposed method is evaluated on the CityFlow dataset, achieving IDF1 76.77%, which outperforms the state-of-the-art MTMCT methods.
Prompting in-depth learning in immersive virtual reality: Impact of an elaboration prompt on developing a mental model

Immersive virtual reality learning environments (VRLEs) have great potential but they do not guarantee effective or in-depth learning. Activating learners by including prompts, has been reported to increase learning in VR. Based on their promising effects in conventional settings, elaboration prompts should increase learning outcomes by activating learners to explain the learning content in their own words or to contrast it to prior knowledge. In our study, we investigated the effect of an elaboration prompt before entering the VRLE on learning robotics. Our between-subject study (prompt vs. no prompt) included N = 64 participants. We applied a multi-method approach (functional and structural) to measure mental models. We used a differentiated learning outcome measurement (knowledge, comprehension, and application) as a functional measurement. By using the structural assessment of knowledge technique, we deduce the structural accuracy of the developed mental model. We found an increase by the elaboration prompt on knowledge level but not on comprehension or application level. The interplay of pre-accuracy and prompt had a significant impact on structural accuracy after learning. Application level and structural accuracy after learning were positively correlated. Our multi-method approach delivered insights on different aspects of the developed mental model when prompting before entering the VRLE. For future research, further approaches to explore in-depth learning in VRLEs are recommended.
statsExpressions: R Package for Tidy Dataframes and Expressions with Statistical Details

The statsExpressions package has two key aims: to provide a consistent syntax to do statistical analysis with tidy data, and to provide statistical expressions (i.e., pre-formatted in-text statistical results) for plotting functions.Currently, it supports common types of statistical approaches and tests: parametric, nonparametric, robust, and Bayesian t-test, one-way ANOVA, correlation analyses, contingency table analyses, and meta-analyses.The functions are pipe-friendly and compatible with tidy data.
Security of quantum key distribution with intensity correlations

The decoy-state method in quantum key distribution (QKD) is a popular technique to approximately achieve the performance of ideal single-photon sources by means of simpler and practical laser sources. In high-speed decoy-state QKD systems, however, intensity correlations between succeeding pulses leak information about the users' intensity settings, thus invalidating a key assumption of this approach. Here, we solve this pressing problem by developing a general technique to incorporate arbitrary intensity correlations to the security analysis of decoy-state QKD. This technique only requires to experimentally quantify two main parameters: the correlation range and the maximum relative deviation between the selected and the actually emitted intensities. As a side contribution, we provide a non-standard derivation of the asymptotic secret key rate formula from the non-asymptotic one, in so revealing a necessary condition for the significance of the former.
Riemannian geometry and automatic differentiation for optimization problems of quantum physics and quantum technologies

Optimization with constraints is a typical problem in quantum physics and quantum information science that becomes especially challenging for high-dimensional systems and complex architectures like tensor networks. Here we use ideas of Riemannian geometry to perform optimization on manifolds of unitary and isometric matrices as well as the cone of positive-definite matrices. Combining this approach with the up-to-date computational methods of automatic differentiation, we demonstrate the efficacy of the Riemannian optimization in the study of the low-energy spectrum and eigenstates of multipartite Hamiltonians, variational search of a tensor network in the form of the multiscale entanglement-renormalization ansatz, preparation of arbitrary states (including highly entangled ones) in the circuit implementation of quantum computation, decomposition of quantum gates, and tomography of quantum states. Universality of the developed approach together with the provided open source software enable one to apply the Riemannian optimization to complex quantum architectures well beyond the listed problems, for instance, to the optimal control of noisy quantum systems.
Groundwater Level Analyse In Urgench City With Using Modflow Modeling And Forecasting System

The study was conducted as part of a project to develop an automation system for monitoring groundwater in the city of Urgench. The possibilities of modeling and forecasting modeling MODFLOW in groundwater were studied. According to the results, regularities of the level and behavior of groundwater in the city of Urgench were revealed. According to the survey, the direction of movement of groundwater in the city of Urgench shifts in a northwesterly direction to a slope of 0.0008. According to the geophilized model, its high-traffic zone is located at a depth of 10-15 m, from 4-5 m per second.
Emerging Digital Technologies to Combat Future Crises: Learnings From COVID-19 to be Prepared for the Future

In 2020, the world has witnessed an unprecedented global pandemic with COVID-19. It has led nations to take measures that have an enormous impact on individuals, society, and the economy. Researchers and practitioners responded rapidly, evaluating the opportunities to capitalize on technology for tackling the associated challenges. We investigate the innovative potentials of three emerging digital technologies — namely, the Internet of Things, artificial intelligence, and distributed ledgers — to tackle pandemic-related challenges. We present our findings on the most effective means of leveraging each technology’s potential, the implications for use in crises, and the convergence of the three technologies.
Emotion Classification in a Resource Constrained Language Using Transformer-based Approach

Although research on emotion classification has significantly progressed in highresource languages, it is still infancy for resource-constrained languages like Bengali.However, unavailability of necessary language processing tools and deficiency of benchmark corpora makes the emotion classification task in Bengali more challenging and complicated.This work proposes a transformer-based technique to classify the Bengali text into one of the six basic emotions: anger, fear, disgust, sadness, joy, and surprise.A Bengali emotion corpus consists of 6243 texts is developed for the classification task.Experimentation carried out using various machine learning (LR, RF, MNB, SVM), deep neural networks (CNN, BiLSTM, CNN+BiLSTM) and transformer (Bangla-BERT, m-BERT, XLM-R) based approaches.Experimental outcomes indicate that XLM-R outdoes all other techniques by achieving the highest weighted f 1 -score of 69.73% on the test data.
CoAtNet: Marrying Convolution and Attention for All Data Sizes

Transformers have attracted increasing interests in computer vision, but they still fall behind state-of-the-art convolutional networks. In this work, we show that while Transformers tend to have larger model capacity, their generalization can be worse than convolutional networks due to the lack of the right inductive bias. To effectively combine the strengths from both architectures, we present CoAtNets(pronounced "coat" nets), a family of hybrid models built from two key insights: (1) depthwise Convolution and self-Attention can be naturally unified via simple relative attention; (2) vertically stacking convolution layers and attention layers in a principled way is surprisingly effective in improving generalization, capacity and efficiency. Experiments show that our CoAtNets achieve state-of-the-art performance under different resource constraints across various datasets: Without extra data, CoAtNet achieves 86.0% ImageNet top-1 accuracy; When pre-trained with 13M images from ImageNet-21K, our CoAtNet achieves 88.56% top-1 accuracy, matching ViT-huge pre-trained with 300M images from JFT-300M while using 23x less data; Notably, when we further scale up CoAtNet with JFT-3B, it achieves 90.88% top-1 accuracy on ImageNet, establishing a new state-of-the-art result.
On the Design of Federated Learning in the Mobile Edge Computing Systems

The combination of artificial intelligence and mobile edge computing (MEC) is considered as a promising evolution path of the future wireless networks. As a model-level coordination learning paradigm, federated learning can make full use of the distributed computation resource in the MEC systems, which allows the users to keep their private data locally. However, due to the unreliable wireless transmission circumstances and resource constraints in the MEC systems, both the performance and training efficiency of federated learning cannot be guaranteed. To solve this problem, the optimization design of federated learning in the MEC systems is studied in this paper. First, an optimization problem is formulated to manage the tradeoff between model accuracy and training cost. Second, a joint optimization algorithm is designed to optimize the model compression, sample selection, and user selection strategies, which can approach a stationary optimal solution in a computationally efficient way. Finally, the performance of our proposed optimization scheme is evaluated by numerical simulation and experiment results, which show that both the accuracy loss and the cost of federated learning in the MEC systems can be reduced significantly by employing our proposed algorithm.
A new PC-PSO algorithm for Bayesian network structure learning with structure priors

Bayesian network structure learning is the basis of parameter learning and Bayesian inference. However, it is a NP-hard problem to find the optimal structure of Bayesian networks because the computational complexity increases exponentially with the increasing number of nodes. Hence, numerous algorithms have been proposed to obtain feasible solutions, while almost all of them are of certain limits. In this paper, we adopt a heuristic algorithm to learn the structure of Bayesian networks, and this algorithm can provide a reasonable solution to combine the PC and Particle Swarm Optimization (PSO) algorithms. Moreover, we consider structure priors to improve the performance of our PC-PSO algorithm. Meanwhile, we utilize a new mutation operator called Uniform Mutation by Addition and Deletion (UMAD) and a crossover operator called Uniform Crossover. Experiments on different networks show that the approach proposed in this paper has achieved better Bayesian Information Criterion (BIC) scores than other algorithms.
Multi-Marginal Optimal Transport and Probabilistic Graphical Models

We study multi-marginal optimal transport problems from a probabilistic graphical model perspective. We point out an elegant connection between the two when the underlying cost for optimal transport allows a graph structure. In particular, an entropy regularized multi-marginal optimal transport is equivalent to a Bayesian marginal inference problem for probabilistic graphical models with the additional requirement that some of the marginal distributions are specified. This relation on the one hand extends the optimal transport as well as the probabilistic graphical model theories, and on the other hand leads to fast algorithms for multi-marginal optimal transport by leveraging the well-developed algorithms in Bayesian inference. Several numerical examples are provided to highlight the results.
Smart Pedagogy as a Driving Wheel for Technology-Enhanced Learning

People have long talked about the use of technology in education and are looking for ways to incorporate different scientific advances into the learning environment, both to help students learn and make learning more interesting for them as well as to ensure that future generations can innovate based on previously accumulated knowledge. Today, when we talk about technology-enhanced learning, we mean the possibilities created by digital technology, which has become widely available to everyone thanks to both the creation of Intel’s digital microprocessor in 1971 (Chan et al., in Research and Practice in Technology Enhanced Learning 1:3–29, 2006) and the creation of the world wide web in 1990 (Berners-Lee, T., & Cailliau, R. (1990, November 12). WorldWideWeb: Proposal for a HyperText Project. https://www.w3.org/Proposal.html ). The current crisis caused by the Covid-19 pandemic has given a further boost to technological developments to ensure access to education, which is one of the most important areas of society. Everyone has come across education in its various forms, either by learning at any level or form of education or by being a parent who has become a provider of home-schooling during remote learning (Daniela et al., in Sustainability 13:3640, 2021), whether as an educator or a creator of learning materials and technologies. The Covid crisis has shown that the use of technology is sometimes the only way to provide access to education, but despite the potential of technology to organize synchronous and asynchronous learning, there are still many problems in using technology, both in terms of just sending students materials to learn by e-mail and also with the initial inflexibility of the learning process. For instance, synchronous online classes were organized for students according to the traditional agenda of classes without thinking about the students’ ability to focus on on-screen activities for many hours and, after that, to do independent work.
Big Data and Internet of Things (IoT) Technologies' Influence on Higher Education

To respond to the needs of digital transformation, universities must continue to play their role as proving ground for educating the future generation and innovation. The article is devoted to overview, discussion, and investigation of application in higher education of two modern information technologies: big data and internet of things. The article identifies the role of analytics, based on big data, in improvement of education process and outlines the challenges, related with big data mining, storage, and security. Proposed statements are based on practical experience of the authors; architecture of program and methodological solution are the focus of the article. The article contributes to theory by the new approach to combination of big data and internet of things technologies in educational resources and, at the same time, includes implications for practice, presenting examples of the approach's realization and sharing the authors' experiences of such realization.