Reviewerly/exHarmony huggingface dataset:
EVM: An Educational Virtual Reality Modeling Tool; Evaluation Study with Freshman Engineering Students

Educational Virtual Modeling (EVM) is a novel VR-based application for sketching and modeling in an immersive environment designed to introduce freshman engineering students to modeling concepts and reinforce their understanding of the spatial connection between an object and its 2D projections. It was built on the Unity 3D game engine and Microsoft’s Mixed Reality Toolkit (MRTK). EVM was designed to support the creation of the typical parts used in exercises in basic engineering graphics courses with a special emphasis on a fast learning curve and a simple way to provide exercises and tutorials to students. To analyze the feasibility of using EVM for this purpose, a user study was conducted with 23 freshmen and sophomore engineering students that used both EVM and Trimble SketchUp to model six parts using an axonometric view as the input. Students had no previous experience in any of the two systems. Each participant went through a brief training session and was allowed to use each tool freely for 20 min. At the end of the modeling exercises with each system, the participants rated its usability by answering the System Usability Scale (SUS) questionnaire. Additionally, they filled out a questionnaire for assessment of the system functionality. The results demonstrated a very high SUS score for EVM (M = 92.93, SD = 6.15), whereas Trimble SketchUp obtained only a mean score of 76.30 (SD = 6.69). The completion time for the modeling tasks with EVM showed its suitability for regular class use, despite the fact that it usually takes longer to complete the exercises in the system than in Trimble SketchUp. There were no statistically significant differences regarding functionality assessment. At the end of the experimental session, participants were asked to express their opinion about the systems and provide suggestions for the improvement of EVM. All participants showed a preference for EVM as a potential tool to perform exercises in the engineering graphics course.
Weaving Together a Decolonial Imaginary Through Design for Effective River Management: Pluriversal Ontological Design in Practice

Abstract Looking at how we, as designers, can move beyond charges of neo-colonialism in social design, this article uses the empirical example of a design project focused on the restoration of a riverine system in New Zealand to provide an outline of ways that pluriversal ontological design can occur in practice. Exploring how the use of design tools and frameworks (e.g., boundary objects and infrastructuring) can help build out a decolonial imaginary, the article demonstrates how— through our design practice— we are able to successfully acknowledge, and work with, different “ways of being” in the world.
Monetizing Car Data: A Literature Review on Data-Driven Business Models in the Connected Car Domain

The amount of data generated by a single modern vehicle is exploding.Consequently, the entire global automotive industry is facing the question of how to monetize this valuable data.Triggered by the connectivity trend, data-driven business models disrupt the automotive ecosystem by changing mobility behavior, proliferation of technical enablers, new strategic collaborations, and shifting revenue streams.In this study, we analyze the existing body of literature on data-driven business models in the connected car domain and structure it according to four dimensions-value proposition, value architecture, value network, and value finance.Thereby, we contribute to the business model research by providing a comprehensive overview and categorization of existing works in this area and laying the foundation for future research.
Current Trends in Blockchain Implementations on the Paradigm of Public Key Infrastructure: A Survey

Since the emergence of the Bitcoin cryptocurrency, the blockchain technology has become the new Internet tool with which researchers claim to be able to solve any existing online problem. From immutable log ledger applications to authorisation systems applications, the current technological consensus implies that most of Internet problems could be effectively solved by deploying some form of blockchain environment. Regardless this ‘consensus’, there are decentralised Internet-based applications on which blockchain technology can actually solve several problems and improve the functionality of these applications. The development of these new blockchain-based solutions is grouped into a new paradigm called Blockchain 3.0 and its concepts go far beyond the well-known cryptocurrencies. In this paper, we study the current trends in the application of blockchain on the paradigm of Public Key Infrastructures (PKI). In particular, we focus on how these current trends can guide the exploration of a fully Decentralised Identity System, with blockchain as be part of the core technology.
The linkages between ICT access, e-government and government effectiveness and its effect on corruption

There is growing interest in the linkages between information and communication technologies (ICTs) and corruption generally, especially in the discourse on the role of ICTs in development. However, there is lack of empirical evidence on the linkages between ICT access, e-government, and government effectiveness and its implications on corruption. In this paper, we rely on archival data for 140 countries to explore these relationships. Using partial least squares-structural equation modelling and a hypothesised model grounded in the literature, our analysis of the results reveals a significant relationship between (1) ICT access and e-government development, (2) ICT access and government effectiveness, (3) e-government development and government effectiveness, and finally, (4) government effectiveness and corruption. Furthermore, our results support the mediating role of government effectiveness on the ICT access-corruption linkage as well as e-government-corruption linkage. The implications of findings to research, practice and policy are discussed.
Automatic Identification of Similar Pull-Requests in GitHub’s Repositories Using Machine Learning

Context: In a social coding platform such as GitHub, a pull-request mechanism is frequently used by contributors to submit their code changes to reviewers of a given repository. In general, these code changes are either to add a new feature or to fix an existing bug. However, this mechanism is distributed and allows different contributors to submit unintentionally similar pull-requests that perform similar development activities. Similar pull-requests may be submitted to review in parallel time by different reviewers. This will cause redundant reviewing time and efforts. Moreover, it will complicate the collaboration process. Objective: Therefore, it is useful to assign similar pull-requests to the same reviewer to be able to decide which pull-request to choose in effective time and effort. In this article, we propose to group similar pull-requests together into clusters so that each cluster is assigned to the same reviewer or the same reviewing team. This proposal allows saving reviewing efforts and time. Method: To do so, we first extract descriptive textual information from pull-requests content to link similar pull-requests together. Then, we employ the extracted information to find similarities among pull-requests. Finally, machine learning algorithms (K-Means clustering and agglomeration hierarchical clustering algorithms) are used to group similar pull-requests together. Results: To validate our proposal, we have applied it to twenty popular repositories from public dataset. The experimental results show that the proposed approach achieved promising results according to the well-known metrics in this subject: precision and recall. Furthermore, it helps to save the reviewer time and effort. Conclusion: According to the obtained results, the K-Means algorithm achieves 94% and 91% average precision and recall values over all considered repositories, respectively, while agglomeration hierarchical clustering performs 93% and 98% average precision and recall values over all considered repositories, respectively. Moreover, the proposed approach saves reviewing time and effort on average between (67% and 91%) by K-Means algorithm and between (67% and 83%) by agglomeration hierarchical clustering algorithm.
Construction of English Language Autonomous Learning Center System Based on Artificial Intelligence Technology

The vigorous development of multimedia technology and network technology has brought modern methods to English learning, and the development of digital language systems has created a vibrant learning environment. This article aimed to study how to build a learning center system based on artificial intelligence technology to improve the ability of English language autonomous learning. Based on artificial intelligence technology, this article proposes how to build a learning center system and how to apply artificial intelligence technology to improve the ability of autonomous learning. According to the experiments in this article, in the case of traditional textbook learning, the probability of autonomous learning is 35%. In the case of artificial intelligence technology systems, the probability of autonomous learning is 53%, which is 18% higher than traditional learning. From this article, it can be seen that the proportion of students who like to use artificial intelligence technology to learn independently is about 70% and that of students who like to study with textbooks is about 30%. The experimental results show that the learning system built by artificial intelligence technology has improved people’s autonomous learning ability. In short, it is necessary to expand the application fields of artificial intelligence as soon as possible in the autonomous learning of English. To improve the autonomous learning system, the power of technological changes brought about by artificial intelligence is also necessary.
From Sharing to Selling

During the COVID-19 pandemic, we witnessed how sharing of biological and biomedical data have facilitated the researchers, medical practitioners and policymakers to tackle the pandemic at a global scale. Despite the growing use of EHR by medical practitioners and wearable digital gadgets by individuals, 80% of the health and medical data remain unused, adding little value to the researchers and medical practitioners. Legislative constraints related to health data sharing, centralised siloed design of traditional data management systems and most importantly, lack of incentivisation models are thought to be the underpinning bottlenecks for sharing health data. With the advent of the General Data Protection Regulation (GDPR) of the EU and the development of technologies like blockchain and distributed ledger technologies (DLT), it is now possible to create a new paradigm of data sharing by changing the incentivisation model from current authoritative or altruistic form to a shared economic model where financial incentivisation will be the main driver for data sharing. This can be achieved by setting up a digital health data marketplace (DHDM). Here we reviewed papers that proposed technical models or implemented frameworks that use blockchain-like technologies for health data. We seek to understand and compare different technical challenges associated with implementing and optimising the DHDM operation outlined in these papers. We also examined the legal limitations in the context of European Union and other countries such as the USA to accommodate any compliance requirement for such a marketplace. Last but not least, we reviewed papers that investigated the short, medium and long terms socioeconomic impact of such a marketplace on a wide range of stakeholders.
Stain Style Transfer for Histological Images Using S3CGAN

This study proposes a new CycleGAN-based stain transfer model, called S3CGAN, equipped with a specialized color classifier structure. The specialized color classifier can assist the generative network to conquer the existing challenge in GANs, namely the instability of the network caused by the insufficient representativeness of the training data in the initial stage of network training. The color classifier is pretrained, hence it can provide correct color information feedback to the generator during the initial network training phase. The augmented information from color classification enables the generator to generate superior results. Owing to the CycleGAN architecture, the proposed model does not require representative paired inputs. The proposed model uses U-Net and a Markovian discriminator to enhance the structural retention ability to generate images with high fidelity.
Bayesian Inference

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Digital surveillance in a pandemic response: What bioethics ought to learn from Indigenous perspectives

Our paper interrogates the ethics of digital pandemic surveillance from Indigenous perspectives. The COVID-19 pandemic has shown that Indigenous peoples are among the communities most negatively affected by pandemic infectious disease spread. Similarly to other racialized subpopulations, Indigenous people have faced strikingly high mortality rates from COVID-19 owing to structural marginalization and related comorbidities, and these high rates have been exacerbated by past and present colonial dominance. At the same time, digital pandemic surveillance technologies, which have been promoted as effective tools for mitigating a pandemic, carry risks for Indigenous subpopulations that warrant an urgent and thorough investigation. Building on decolonial scholarship and debates about Indigenous data sovereignty, we argue that should Indigenous communities wish to implement digital pandemic surveillance, then they must have ownership over these technologies, including agency over their own health data, how data are collected and stored, and who will have access to the data. Ideally, these tools should be designed by Indigenous peoples themselves to ensure compatibility with Indigenous cultures, ethics and languages and the protection of Indigenous lives, health and wellbeing.
Plaintext-related image encryption scheme based on chaos and game of life

To protect the security of image information in the process of storage or transmission, we propose an efficient image encryption method using chaos and game of life (GoL). First, a plaintext-related image scrambling method based on chaos and GoL is proposed to reduce the adjacent pixel correlation of the plain image. In this method, double scrambling is performed on the original image: the first scrambling matrix is constructed by logistic map and the rules of GoL; the second scrambling matrix is generated based on two-dimensional (2D) logistic map and magic square. The original image is scrambled by the above-mentioned scrambling matrices, which can efficiently reduce the strong adjacent pixel correlation, thus enhancing the image scrambling effect. Next, the 4D hyperchaotic system and GoL are employed to diffuse the scrambled image and get the cipher image. Additionally, the average pixel value of the original image is used to generate the initial values of the logistic map and 2D logistic map in scrambling process. Therefore, our encryption scheme is very sensitive to the plain image and can effectively resist the chosen-plaintext attack and known-plaintext attack. Finally, the experimental results and performance analyses show that the proposed encryption algorithm has the advantages of large key space and good scrambling effect, and it can effectively resist various typical attacks, so it can effectively protect the security of the image information.
Improving Wearable-Based Activity Recognition Using Image Representations

Activity recognition based on inertial sensors is an essential task in mobile and ubiquitous computing. To date, the best performing approaches in this task are based on deep learning models. Although the performance of the approaches has been increasingly improving, a number of issues still remain. Specifically, in this paper we focus on the issue of the dependence of today's state-of-the-art approaches to complex ad hoc deep learning convolutional neural networks (CNNs), recurrent neural networks (RNNs), or a combination of both, which require specialized knowledge and considerable effort for their construction and optimal tuning. To address this issue, in this paper we propose an approach that automatically transforms the inertial sensors time-series data into images that represent in pixel form patterns found over time, allowing even a simple CNN to outperform complex ad hoc deep learning models that combine RNNs and CNNs for activity recognition. We conducted an extensive evaluation considering seven benchmark datasets that are among the most relevant in activity recognition. Our results demonstrate that our approach is able to outperform the state of the art in all cases, based on image representations that are generated through a process that is easy to implement, modify, and extend further, without the need of developing complex deep learning models.
Improving Haptic Response for Contextual Human Robot Interaction

For haptic interaction, a user in a virtual environment needs to interact with proxies attached to a robot. The device must be at the exact location defined in the virtual environment in time. However, due to device limitations, delays are always unavoidable. One of the solutions to improve the device response is to infer human intended motion and move the robot at the earliest time possible to the desired goal. This paper presents an experimental study to improve the prediction time and reduce the robot time taken to reach the desired position. We developed motion strategies based on the hand motion and eye-gaze direction to determine the point of user interaction in a virtual environment. To assess the performance of the strategies, we conducted a subject-based experiment using an exergame for reach and grab tasks designed for upper limb rehabilitation training. The experimental results in this study revealed that eye-gaze-based prediction significantly improved the detection time by 37% and the robot time taken to reach the target by 27%. Further analysis provided more insight on the effect of the eye-gaze window and the hand threshold on the device response for the experimental task.
A Survey on Threat-Modeling Techniques: Protected Objects and Classification of Threats

Information security is one of the most important attributes of distributed systems that often operate on unreliable networks. Enabling security features during the development of a distributed system requires the careful analysis of potential attacks or threats in different contexts, a process often referred to as «threat modeling». Information protection should be comprehensive, but it is also necessary to take into account the possibility of the emergence of threats specific to a certain information system. Many public and private organizations are still trying to implement system models and the threats directed at them on their own. The main reason for this is the lack of useful and high-quality methodologies that can help developers design system models. This review explores a variety of the literature on confidentiality- and integrity-aware system design methodologies, as well as threat classification methods, and identifies key issues that may be referenced by organizations to make design system processes easier. In particular, this article takes a look at the extent to which existing methodologies cover objects of protection and methods of classifying threats, as well as whether there are such models of systems in which the object itself and the threats directed at it are described. This includes whether the compiled models exhibit symmetry or asymmetry. This literature research shows that methodologies appear to be heterogeneous and versatile, since existing methodologies often only focus on one object of protection (a system). Based on the given analysis, it can be concluded that the existing methodologies only relate superficially to the description of system models and threats, and it is necessary to develop a more complete abstract model of the protected object and threats aimed at it in order to make this model suitable for any organization and protect it against most threats.
The Intervention of Job Satisfaction in Influence the Empowering Leadership and Talent Management Toward Staff Performance

This research investigates whether and how empowerment leadership and talent management influence the mediator variables (employees’ job satisfaction) in the organization context. It also examines how the mediators should influence employees’ job performance. 575 employees were recruited for a survey study, and Structural Equation Modeling was used to test the research hypotheses. The empirical results indicate that empowerment leadership and talent management have significant and positive effects on employees’ job satisfaction. Furthermore, employees’ job satisfaction has partial mediators between predictors variables to employees’ job performance.
A hybrid multidimensional Recommender System for radio programs

The rise of Recommender Systems has made their presence very common today in many domains. An example is the domain of radio or TV broadcasting content recommendations. The approach proposed here allows radio listeners to receive customized recommendations of radio channels they might listen to based on their specific preferences and/or historical data. Firstly, a Data Acquisition System is presented with its main task being to obtain and process data to pass to recommenders. Secondly, a dynamic hybrid Recommender System is developed based on four dimensions reflecting major aspects of radio programs: relative talk/music percentages, music genres, topics covered, and speech tone. Eight recommenders are constructed (two per dimension) using content-based or collaborative filtering algorithms depending on the nature of the data processed, whether historical data or user preferences. And thirdly, by assigning weights in accordance with the users’ preferences, a dynamic ensemble of these recommenders is formed which produces the final recommendations. Experiments were carried out illustrating the usefulness of the recommendations and its acceptance by radio listeners.
Evaluation of Tracking Algorithms for Contrast Enhanced Ultrasound Imaging Exploration

Deep Neural Networks (DNN) and Deep Learning (DL) are providing efficient solutions to many Computer Vision (CV) problems. As state-of-the-art solutions, DNNs have been successfully applied to automate the detection and classification of lesions in medical ultrasound images for various diseases. Classification of focal liver lesions (FLLs) in Contrast Enhanced Ultrasound Imaging (CEUS) recording is becoming a topic of interest as it could efficiently assist healthcare practitioners in the diagnosis process. Like all DNN-based solutions, model performance is highly dependent on the quality of the training set. To produce quality training datasets, specialists have to manually label thousands of frames, which is very labor intensive and prone to errors. In this paper, we investigate various tracking algorithms that can be utilized to efficiently generate training images using just a few annotated frames. Unlike the vast majority of use cases for which most of the tracking algorithms have been proposed and tested against, CEUS recordings are characterized as being very noisy and having low contrast, thus previous established rankings should be re-evaluated: based on experimental results, we conclude that the Kernelized Correlation Filter tracking algorithm emerged as one of the best candidates for tracking in CEUS imagery.