Reviewerly/exHarmony huggingface dataset:
Formal and informal professional development during different Covid-19 periods: the role of teachers’ career stages

This mixed-method study is based on interviews with 60 elementary-school teachers who participated in TPD (Teacher professional development) during two periods of the Covid-19 pandemic. Teachers were in early, middle and senior career stages. Teacher statements revealed three categories: (1) Informal and formal TPD – without appropriate formal TPD, teachers, especially those in the middle and senior career stages, turned to colleagues and to social media to cope with online instruction. (2) Self-regulated learning, in which teachers listed their motivating factors and expressed their TPD difficulties during the Covid-19 periods. (3) Perceived learning – the cognitive aspect was found to be higher than the emotional and social aspects, especially during the first Covid-19 period. The practical recommendations are intended for policy makers, to adapt the TPD system to teachers’ particular needs according to their ‘career cycle’, in order to cope with technology-integrated learning-teaching challenges, even in routine teaching.
Factors Influencing Technology Adoption in Online Learning among Private University Students in Bangladesh Post COVID-19 Pandemic

Technology acceptance in higher education, especially during and after the crisis of COVID-19, is very important in the current environment, especially in online learning adoption. This study aimed to determine the impact of performance expectancy, effort expectancy, social influence, and facilitating conditions on the adoption of the online line among university students in Dhaka in the post-pandemic period. In addition, the moderating role of voluntariness was also ascertained. In this quantitative study, primary data were collected using a survey method. The target population was students of private universities located in Dhaka, Bangladesh. There was a total of 130 respondents, and non-probability sampling was found to be more appropriate. Data were analyzed using the Smart-PLS system. The results revealed that effort expectancy was the most important predictor of intention to adopt online learning. The next significant predictor was facilitating conditions. However, it was found that performance expectancy and social influence were not significant predictors of intention to adopt online learning. Additionally, it was found that voluntariness was not a mediator. In terms of practical implications, educators and designers should focus on effort expectancy and facilitating conditions to increase online learning adoption.
Generalized Image Captioning for Multilingual Support

Image captioning is a problem of viewing images and describing images in language. This is an important problem that can be solved by understanding the image, and combining two fields of image processing and natural language processing into one. The purpose of image captioning research so far has been to create general explanatory captions in the learning data. However, various environments in reality must be considered for practical use, as well as image descriptions that suit the purpose of use. Image caption research requires processing new learning data to generate descriptive captions for specific purposes, but it takes a lot of time and effort to create learnable data. In this study, we propose a method to solve this problem. Popular image captioning can help visually impaired people understand their surroundings by automatically recognizing and describing images into text and then into voice and is an important issue that can be applied to many places such as image search, art therapy, sports commentary, and real-time traffic information commentary. Through the domain object dictionary method proposed in this study, we propose a method to generate image captions without the need to process new learning data by adjusting the object dictionary for each domain application. The method proposed in the study is to change the dictionary of the object to focus on the domain object dictionary rather than processing the learning data, leading to the creation of various image captions by intensively explaining the objects required for each domain. In this work, we propose a filter captioning model that induces generation of image captions from various domains while maintaining the performance of existing models.
Reinforcement Learning in Game Industry—Review, Prospects and Challenges

This article focuses on the recent advances in the field of reinforcement learning (RL) as well as the present state–of–the–art applications in games. First, we give a general panorama of RL while at the same time we underline the way that it has progressed to the current degree of application. Moreover, we conduct a keyword analysis of the literature on deep learning (DL) and reinforcement learning in order to analyze to what extent the scientific study is based on games such as ATARI, Chess, and Go. Finally, we explored a range of public data to create a unified framework and trends for the present and future of this sector (RL in games). Our work led us to conclude that deep RL accounted for roughly 25.1% of the DL literature, and a sizable amount of this literature focuses on RL applications in the game domain, indicating the road for newer and more sophisticated algorithms capable of outperforming human performance.
Effects of the Uncertainty of Interpersonal Communications on Behavioral Responses of the Participants in an Immersive Virtual Reality Experience: A Usability Study

Two common difficulties which people face in their daily lives are managing effective communication with others and dealing with what makes them feel uncertain. Past research highlights that the result of not being able to handle these difficulties influences people's performance in the task at hand substantially, especially in the context of a social environment such as a workplace. Perceived uncertainty of information is a key influential factor in this regard, with effects on the quality of the information transfer between sender and receiver. Uncertainty of information can be induced into the communication system in three ways: when there is any kind of information deficit that makes the target message unclear for the receiver, when there are some requested changes that could not be predicted by the receiver, and when the content of the message is so interconnected and complex that it limits understanding. Since uncertainty is an inseparable feature of our lives, studying the effects that different levels of it have on individuals and how individuals nevertheless accomplish the tasks of daily living is of high importance. Modern technologies such as immersive virtual reality (VR) have been successful in providing effective platforms to support human behavioral and social well-being studies. In this paper, we suggest the design, development, and evaluation of an immersive VR serious game platform to study behavioral responses to the uncertain features of interpersonal communications. In addition, we report the result of a within-subject user study with 17 participants aged between 20 and 35 and their behavioral responses to two levels of uncertainty with subjective and objective measures. The results convey that the application successfully and meaningfully measured some behavioral responses related to exposure to different levels of uncertainty and overall, the participants were satisfied with the experience.
Software integration model: An assessment tool for global software development vendors

Abstract The trend toward global software development (GSD) has grown tremendously in recent years because of the rapid acceleration in information and communication technologies (ICTs). The reason for the changing trend toward GSD is to develop high‐quality software with minimum cost and time with round the clock. Despite the benefits gained from GSD, there are certain challenges associated with it. Literature reveals that software integration is an integral challenge in the GSD domain and vendors face numerous difficulties in integrating the software components to build the final product. The main aim of this research study is to develop a software integration model (SIM) that will assist GSD vendors to address the factors linked with various stages of software integration. This model is basically designed for GSD vendor organizations to assess and improve their software integration‐related activities. However, it is also beneficial for GSD client organizations in getting knowledge about the status of the GSD vendor organization's software integration capabilities. The research design is composed of well‐known research strategies including systematic literature review (SLR), questionnaire survey, and case study approach. This research study yielded the SIM. We identified nine critical success factors (CSFs) and 10 critical barriers (CBs) in software integration through SLR‐1 by extracting the data from a sample of 105 papers. Similarly, we also identified a total of 132 practices/solutions, for the identified CSFs and CBs in software integration, through SLR‐2 from a sample of 40 papers. These identified factors and practices were validated from 96 global software industry experts/practitioners through online survey. The identified CSFs, CBs, and their associated practices have been arranged adequately across the six software integration levels of SIM. We have adapted SIM structure from capability maturity model integration (CMMI), implementation maturity model (IMM), and software outsourcing vendors readiness model (SOVRM). We also conducted six case studies in the software industry for SIM applicability and validity. The participants' feedback from the six case studies in GSD vendor's organizations portray that the SIM is beneficial for GSD vendors to assess their software integration capability.
BUS‐Set: A benchmark for quantitative evaluation of breast ultrasound segmentation networks with public datasets

BUS-Set is a reproducible benchmark for breast ultrasound (BUS) lesion segmentation, comprising of publicly available images with the aim of improving future comparisons between machine learning models within the field of BUS.Four publicly available datasets were compiled creating an overall set of 1154 BUS images, from five different scanner types. Full dataset details have been provided, which include clinical labels and detailed annotations. Furthermore, nine state-of-the-art deep learning architectures were selected to form the initial benchmark segmentation result, tested using five-fold cross-validation and MANOVA/ANOVA with Tukey statistical significance test with a threshold of 0.01. Additional evaluation of these architectures was conducted, exploring possible training bias, and lesion size and type effects.Of the nine state-of-the-art benchmarked architectures, Mask R-CNN obtained the highest overall results, with the following mean metric scores: Dice score of 0.851, intersection over union of 0.786 and pixel accuracy of 0.975. MANOVA/ANOVA and Tukey test results showed Mask R-CNN to be statistically significant better compared to all other benchmarked models with a p-value >0.01. Moreover, Mask R-CNN achieved the highest mean Dice score of 0.839 on an additional 16 image dataset, that contained multiple lesions per image. Further analysis on regions of interest was conducted, assessing Hamming distance, depth-to-width ratio (DWR), circularity, and elongation, which showed that the Mask R-CNN's segmentations maintained the most morphological features with correlation coefficients of 0.888, 0.532, 0.876 for DWR, circularity, and elongation, respectively. Based on the correlation coefficients, statistical test indicated that Mask R-CNN was only significantly different to Sk-U-Net.BUS-Set is a fully reproducible benchmark for BUS lesion segmentation obtained through the use of public datasets and GitHub. Of the state-of-the-art convolution neural network (CNN)-based architectures, Mask R-CNN achieved the highest performance overall, further analysis indicated that a training bias may have occurred due to the lesion size variation in the dataset. All dataset and architecture details are available at GitHub: https://github.com/corcor27/BUS-Set, which allows for a fully reproducible benchmark.
EmoKbGAN: Emotion controlled response generation using Generative Adversarial Network for knowledge grounded conversation

Neural open-domain dialogue systems often fail to engage humans in long-term interactions on popular topics such as sports, politics, fashion, and entertainment. However, to have more socially engaging conversations, we need to formulate strategies that consider emotion, relevant-facts, and user behaviour in multi-turn conversations. Establishing such engaging conversations using maximum likelihood estimation (MLE) based approaches often suffer from the problem of exposure bias. Since MLE loss evaluates the sentences at the word level, we focus on sentence-level judgment for our training purposes. In this paper, we present a method named EmoKbGAN for automatic response generation that makes use of the Generative Adversarial Network (GAN) in multiple-discriminator settings involving joint minimization of the losses provided by each attribute specific discriminator model (knowledge and emotion discriminator). Experimental results on two bechmark datasets i.e the Topical Chat and Document Grounded Conversation dataset yield that our proposed method significantly improves the overall performance over the baseline models in terms of both automated and human evaluation metrics, asserting that the model can generate fluent sentences with better control over emotion and content quality.
A deep Gaussian process model for seismicity background rates

The spatio-temporal properties of seismicity give us incisive insight into the stress state evolution and fault structures of the crust. Empirical models based on self-exciting point-processes continue to provide an important tool for analyzing seismicity, given the epistemic uncertainty associated with physical models. In particular, the epidemic-type aftershock sequence (ETAS) model acts as a reference model for studying seismicity catalogs. The traditional ETAS model uses simple parametric definitions for the background rate of triggering-independent seismicity. This reduces the effectiveness of the basic ETAS model in modelling the temporally complex seismicity patterns seen in seismic swarms that are dominated by aseismic tectonic processes such as fluid injection rather than aftershock triggering. In order to robustly capture time-varying seismicity rates, we introduce a deep Gaussian process formulation for the background rate as an extension to ETAS. Gaussian processes (GPs) are a robust non-parametric model for function spaces with covariance structure. By conditioning the lengthscale structure of a GP with another GP, we have a deep-GP: a probabilistic, hierarchical model that automatically tunes its structure to match data constraints. We show how the deep-GP-ETAS model can be efficiently sampled by making use of a Metropolis-within-Gibbs scheme, taking advantage of the branching process formulation of ETAS and a stochastic partial differential equation (SPDE) approximation for Mat\'ern GPs. We illustrate our method using synthetic examples, and show that the deep-GP-ETAS model successfully captures multiscale temporal behavior in the background forcing rate of seismicity. We then apply the results to two real-data catalogues: the Ridgecrest, CA July 5 2019 Mw 7.1 event catalogue and the 2016--2019 Cahuilla, CA earthquake swarm.
Blockchain Framework for Insurance Industry

The traditional financial industry has changed drastically with innovations in digital technology in the sector of finance. The new technology has not only altered the operations of financial services, but also changes the value chain of financial sector. Blockchain is one such technology which has proved to be a game changer in the financial industry. There are various studies on application of blockchain technology in the financial sector. This paper recommends a blockchain-based framework for the insurance industry. The need for this study is that there is an increasing requirement to improve the efficiency and customer experience, reduce the chances of fraud in insurance industry. Blockchain technology can prove to be a solution to the above-mentioned challenges. The methodology used to conduct this study is secondary data analysis and vast literature review. This study finds that there are various studies conducted in identifying the scope and application of blockchain in insurance industry but none of them suggests any framework to be implemented. This study suggests a framework to implement blockchain technology in insurance industry based on industry and academic literature.
Self-adaptive logit balancing for deep neural network robustness: Defence and detection of adversarial attacks

With the widespread applications of Deep Neural Networks (DNNs), the safety of DNNs has become a significant issue. The vulnerability of the neural networks against adversarial examples deepens concerns about the safety of DNNs applications. This paper proposed a novel defence method to improve the adversarial robustness of DNN classifiers without using adversarial training. This method introduces two new loss functions. First, a zero-cross-entropy loss is used to punish overconfidence and find the appropriate confidence for different instances. Second, a logit balancing loss is proposed to protect DNNs from non-targeted attacks by regularising incorrect classes’ logits distribution. This method achieved competitive adversarial robustness compared to advanced adversarial training methods. Meanwhile, a novel robustness diagram is proposed to analyse, interpret and visualise the robustness of DNN classifiers against adversarial attacks. Furthermore, a Log-Softmax-pattern-based adversarial attack detection method is proposed. This detection method can distinguish clean inputs and multiple adversarial attacks via one multi-classification MLP. In particular, it is state-of-the-art in identifying white-box gradient-based attacks; it achieved at least 95.5% accuracy for classifying four white-box gradient-based attacks with maximum 0.1% false positive ratio.
Exploring attribute localization and correlation for pedestrian attribute recognition

Pedestrian Attribute Recognition (PAR) is currently an emerging research topic in the field of video surveillance. For PAR, it usually needs to analyze dozens of attributes simultaneously, e.g., age, gender and Clothing type. However, different attributes may focus on different image regions, which makes it difficult to concurrently extract exhaustive features over all attributes. Moreover, some of these attributes are highly correlated, which is the other challenge for pedestrian attribute recognition. To remedy the aforementioned two issues, we propose two novel modules, namely Attribute Localization Module (ALM) and Attribute Correlation Module (ACM). For ALM, it is constructed based on a multi-stream architecture with each stream processing a specific attribute individually. More specifically, an attention mechanism is employed to discover and enhance the attribute-related features while suppressing less important regions. For ACM, the Transformer structure is employed to effectively explore the correlations among different attributes. In particular, we place the Transformer blocks behind the ALM module, with regarding each attribute-specific feature as an input token. The ALM and ACM modules focus on different aspects, which exploits the interrelated and complementary information. We combine the proposed modules to form a unified network with Exploring Attribute Localization and Correlation (abbreviated as EALC). Our approach is validated on five large-scale pedestrian attribute datasets, including PETA, RAP, PA-100 K, Market-1501 and Duke attribute datasets. Experiments demonstrate the effectiveness and advancement of the proposed EALC.
Part-aware Prototypical Graph Network for One-shot Skeleton-based Action Recognition

In this paper, we study the problem of one-shot skeleton-based action recognition, which poses unique challenges in learning transferable representation from base classes to novel classes, particularly for fine-grained actions. Existing meta-learning frameworks typically rely on the body-level representations in spatial dimension, which limits the generalisation to capture subtle visual differences in the fine-grained label space. To overcome the above limitation, we propose a part-aware prototypical representation for one-shot skeleton-based action recognition. Our method captures skeleton motion patterns at two distinctive spatial levels, one for global contexts among all body joints, referred to as body level, and the other attends to local spatial regions of body parts, referred to as the part level. We also devise a class-agnostic attention mechanism to highlight important parts for each action class. Specifically, we develop a part-aware prototypical graph network consisting of three modules: a cascaded embedding module for our dual-level modelling, an attention-based part fusion module to fuse parts and generate part-aware prototypes, and a matching module to perform classification with the part-aware representations. We demonstrate the effectiveness of our method on two public skeleton-based action recognition datasets: NTU RGB+D 120 and NW-UCLA.
T2V-DDPM: Thermal to Visible Face Translation using Denoising Diffusion Probabilistic Models

Modern-day surveillance systems perform person recognition using deep learning-based face verification networks. Most state-of-the-art facial verification systems are trained using visible spectrum images. But, acquiring images in the visible spectrum is impractical in scenarios of low-light and nighttime conditions, and often images are captured in an alternate domain such as the thermal infrared domain. Facial verification in thermal images is often performed after retrieving the corresponding visible domain images. This is a well-established problem often known as the Thermal-to-Visible (T2V) image translation. In this paper, we propose a Denoising Diffusion Probabilistic Model (DDPM) based solution for T2V translation specifically for facial images. During training, the model learns the conditional distribution of visible facial images given their corresponding thermal image through the diffusion process. During inference, the visible domain image is obtained by starting from Gaussian noise and performing denoising repeatedly. The existing inference process for DDPMs is stochastic and time-consuming. Hence, we propose a novel inference strategy for speeding up the inference time of DDPMs, specifically for the problem of T2V image translation. We achieve the state-of-the-art results on multiple datasets. The code and pretrained models are publically available at http://github.com/Nithin-GK/T2V-DDPM