Reviewerly/exHarmony huggingface dataset:
The role of collectivism and moderating effect of IT proficiency on intention to disclose protected health information

This paper aims to identify and understand factors affecting insiders' intention to disclose patients' medical information and to investigate how these factors affect the intention to disclose. Based on the literature review on deterrence theory and health information security awareness (HISA), we identify relevant factors and develop a research model explaining insiders' intention to disclose patients' health information. We collect data (N = 105) through scenario-based experiments. Results show that two personal factors, collectivism, and IT proficiency, play a significant role in the model. While collectivism affects two components (health information security regulation awareness and punishment severity awareness) of HISA which influences intention to disclose, IT proficiency moderates the relationship between HISA and intention to disclose. In addition, HISA negatively affects reporting assessment and intention to disclose. This paper aims to fill a research gap in understanding factors affecting insiders' intentions to disclose protected health information. We identify and investigate factors (e.g., collectivism, HISA, reporting assessment, and IT proficiency) that may affect insiders' disclosing intentions. We find that collectivism affects two components of HISA which influence reporting assessment and disclosing intention. We also discover that IT proficiency moderates the relationship between HISA and intention to disclose. Our findings suggest that we need to carefully consider personal factors such as collectivistic nature and IT proficiency in managing insiders' security breaches.
Beyond sonic realism: a cinematic sound approach in documentary 360° film

Sound is often recognised as critical to the success of 360° film, but in a new medium fraught with technological challenges and time constraints, there is little research to guide sound designers in their creative practice. As practitioners engage with this new 360° format, the wisdom and techniques developed from decades of documentary sound practice promise more compelling viewing experiences; however, there are many differences between cinematic documentary and non-fiction 360° film. This article contributes towards a new language of sound for this medium by exploring the sound design approaches of four non-fiction 360° films that experiment with cinematic sound practices. The findings discussed were gained from interviews conducted with leading sound designers Tom Myers from Skywalker Sound (Collisions, 2016); Joel Douek (Under the Canopy, 2017); Roland Heap (My Africa, 2018); and Mike Lange, Michael Thomas and Heath Plumb (Inside Manus, 2017). The findings detail the benefits of including sound designers from the beginning of pre-production, the implications for sound recording, and the post-production considerations in the sound studio. The practice-centred guidelines presented in this paper can be used by sound designers, directors and screen educators in the creative design and development of 360° film soundscapes.
A novel feature selection technique for enhancing performance of unbalanced text classification problem

Since the last few decades, Text Classification (TC) is being witnessed as an important research direction due to the availability of a huge amount of digital text documents on the web. It would be tedious to manually organize and label them by human experts. Again digging a large number of highly sparse terms and skewed categories present in the documents put a lot of challenges in the correct labeling of the unlabeled documents. Hence feature selection is an essential aspect in text classification, which aims to select more concise and relevant features for further mining of the documents. Additionally, if the text in the document set is associated with multiple categories and the distribution of classes in the dataset is unbalanced, it imposes more challenges on the suitable selection of features for text classification. In this paper, a Modified Chi-Square (ModCHI) based feature selection technique is proposed for enhancing the performance of classification of multi-labeled text documents with unbalanced class distributions. It is an improved version of the Chi-square (Chi) method, which emphasizes selecting maximum features from the classes with a large number of training and testing documents. Unlike Chi, in which the top features are selected with top Chi value, in this proposed technique a score is calculated by considering the total number of relevant documents corresponding to each class with respect to the total number of documents in the original dataset. According to the score the features related to the highly relevant classes as well as high Chi-square value are selected for further processing. The proposed technique is verified with four different classifiers such as Linear SVM (LSVM), Decision tree (DT), Multilevel KNN (MLKNN), Random Forest (RF) over Reuters benchmark multi-labeled, multi-class, unbalanced dataset. The effectiveness of the model is also tested by comparing it with four other traditional feature selection techniques such as term frequency-inverse document frequency (TF-IDF), Chi-square, and Mutual Information (MI). From the experimental outcomes, it is clearly inferred that LSVM with ModCHI produces the highest precision value of 0.94, recall value of 0.80, f-measure of 0.86 and the least hamming loss value of 0.003 with a feature size 1000. The proposed feature selection technique with LSVM produces an improvement of 3.33%, 2.19%, 16.25% in the average precision value, 3.03%, 33.33%, 21.42% in the average recall value, 4%, 34.48%, 14.70% in average F-measure value and 14%, 37.68%, 31.74% in average hamming loss value compared to TF-IDF, Chi and MI techniques respectively. These findings clearly interpret the better performance of the proposed feature selection technique compared to TF_IDF, Chi and MI techniques on the unbalanced Reuters Dataset.
Large‐scale semi‐automated migration of legacy C/C++ test code

Abstract This is an industrial experience report on a large semi‐automated migration of legacy test code in C and C++. The particular migration was enabled by automating most of the maintenance steps. Without automation this particular large‐scale migration would not have been conducted, due to the risks involved in manual maintenance (risk of introducing errors, risk of unexpected rework, and loss of productivity). We describe and evaluate the method of automation we used on this real‐world case. The benefits were that by automating analysis, we could make sure that we understand all the relevant details for the envisioned maintenance, without having to manually read and check our theories. Furthermore, by automating transformations we could reiterate and improve over complex and large scale source code updates, until they were “just right.” The drawbacks were that, first, we have had to learn new metaprogramming skills. Second, our automation scripts are not readily reusable for other contexts; they were necessarily developed for this ad‐hoc maintenance task. Our analysis shows that automated software maintenance as compared to the (hypothetical) manual alternative method seems to be better both in terms of avoiding mistakes and avoiding rework because of such mistakes. It seems that necessary and beneficial source code maintenance need not to be avoided, if software engineers are enabled to create bespoke (and ad‐hoc) analysis and transformation tools to support it.
Conversational Virtual Humans on 360-Degree Virtual Sites: Guiding Students on a Site Visit to a Mechanical Room

Effective communication is essential for students in construction management and relevant fields. Nevertheless, very little emphasis on communication practices has been placed in the construction management curriculum, and limited communication skills are still widely found among students. Ideal construction site visits not only supplement traditional learning in the classrooms but also provide opportunities to communicate with professionals onsite. However, challenges exist that limit the application of site visits and ultimately reduce such opportunities to practice communication skills with experts on the jobsites. This research aims to help overcome the barriers by proposing a novel approach that leverages 360-degree digital sites with virtual human as conversational partners on site (iVisit-Communicate). In this paper, the design and development processes of iVisit-Communicate were described in detail, followed by a case study of its implementation on a digital site visit to a mechanical room. It was found that most students agreed that iVisit-Communicate provided them an opportunity to practice communication skills.
Reflections on Visualization Research Projects in the Manufacturing Industry

The rise of Industry 4.0 and cyber-physical systems has led to an abundance of large amounts of data, particularly in the manufacturing industry. Visualization and visual analytics play essential roles in harnessing this data. They have already been acknowledged as being among the key enabling technologies in the fourth industrial revolution. However, there are many challenges attached to applying visualization successfully, both from the manufacturing industry and visualization research perspectives. As members of research institutions involved in several applied research projects dealing with visualization in manufacturing, we characterized and analyzed our experiences for a detailed qualitative view, to distill important lessons learned, and to identify research gaps. With this article, we aim to provide added value and guidance for both manufacturing engineers and visualization researchers to avoid pitfalls and make such interdisciplinary endeavors more successful.
A comparative study on malaria cell detection using computer vision

The detection of malaria causing organism is done in labs under a microscope manually by people. In this case, this job being done manually there is a maximum risk of error and false detection can cause a life at stake. so, a fare detection of this disease can help control and cure the disease in time. The traditional way of performing this task includes a lot of manual work to be done by a human which takes a lot of time and efforts to complete it. In order to solve this problem a lot of researchers have proposed different algorithms and model in which they used algorithms and concepts of transfer learning, Deep learning and Computer Vision algorithms like Visual Geometry Group Network (VGG net), Convolution Neural Network, ResNet50, YIQ color space Faster-RCNN and many more to classify and check if the cell image belongs to Uninfected class or Parasitized Class. These approaches were found to be very efficient in terms of accurately classifying an image and fast in terms of time taken to provide results. In order to identify malaria cell though Computer Vision and Deep Neural Network in this paper we have conducted a comparative study among four most efficient and widely used algorithms. These four algorithms will be tested on several performance evaluation parameters like Confusion Matrix, Accuracy, True Positivity Rate and Precision. This will help us to check the different aspects of these algorithms. Also, we will be performing hyperparameter tuning in every algorithm which we use. This will help us to make sure that these algorithms work with their maximum potential. The algorithms used in this paper are Convolution Neural Network, Yolo version 4, Yolo version 5 and Single Shot Detector. In this we will be retraining the whole Single Shot Detector algorithm with our dataset. The reason to choose these algorithms is that they are some of the most popular and widely used algorithms also they are highly efficient, and the main reason is that the core principle on which these algorithms works is different from each other so it will be very useful t compare these algorithms and see how they performs.
Image Generation by Residual Block Based Generative Adversarial Networks

Generative adversarial network is a popular deep learning technique for solving artificial intelligence tasks, and it has been widely studied and applied for processing images, voices, texts and so on. Especially, generative adversarial network is adopted in the field of image processing, such as image style transfer, image restoration, image super-resolution and so on. Although generative adversarial networks show remarkable success in image generation, training process is usually unstable and trained models collapse where many of the generated images may contain the same color or texture pattern. In this paper, the network of generator and discriminator are modified, and the residual block is added to the generative adversarial network architecture to learn better image features. To reduce the loss of image feature during training and get more features to stabilize image generation, we use feature matching to minimize feature loss between the real and generated images for stable training. In the experiment, performance improvement can be obtained by adopting our proposed method, which is also better than some state-of-the-art methods.
Inclusive online community arts: COVID and beyond COVID

Inclusivity is an underlying principle of community arts, particularly for learning disabled and autistic people for whom the arts can create spaces of equity and inclusive participation. The Covid-19 pandemic required practitioners to find ways of replicating this sense of inclusivity through online delivery. This “digital turn” raised two recurring concerns. First, the accessibility and inclusivity of online activities; second, the quality of alternative digital provision. This paper examines these themes in the specific context of the Creative Doodle Book, which modelled inclusive online practice with learning disabled participants. Drawing on over 20 interviews with learning-disability focused community arts groups, the paper explores barriers to access, but also issues surrounding support and expectations. However, the focus is equally on the benefits once within an online “space”, including new skills, widening networks, the development of inclusive capital and the opportunity to enable greater agency and self-advocacy both during Covid and beyond.
Robust unsupervised image categorization based on variational autoencoder with disentangled latent representations

Recently, deep generative models have been successfully applied to unsupervised clustering analyses, due to the model capabilities for learning good representations of the input data from a lower-dimensional latent space. In this work, we propose a robust deep generative clustering method based on a variational autoencoder (VAE) for unsupervised image categorization. The merits of our method can be summarized as follows. First, each latent representation generated by the encoder is disentangled into the cluster representation and generation representation, where the cluster representation is responsible for preserving the clustering information, while the generation representation is responsible for conserving the generation information. Thus, by only utilizing the cluster representation, we can improve the performance and efficiency of clustering tasks without interference from generating tasks. Second, a Student’s-t mixture model is adopted as the prior over the cluster representation to enhance the robustness of our method against clustering outliers. Third, we propose a biaugmentation module to promote the training stability for our model. In contrast with most of the existing deep generative clustering methods that require a pretraining step to stabilize the training process, our model is able to provide a stable training process through feature disentanglement and data augmentation. We validate the proposed robust deep generative clustering method through extensive experiments by comparing it with state-of-the-art methods on unsupervised image categorization.
Evaluating and reducing cloud waste and cost—A data-driven case study from Azure workloads

Cloud computing has been growing at an unprecedented speed in recent years. The coronavirus pandemic is further accelerating the transition of many businesses from traditional self-hosted systems to the cloud. However, an excessive number of users are struggling with over-spent budget on the cloud. Meanwhile, cloud waste is common when users provision resources beyond what they actually need. There is a clear gap in the literature to study the user behaviors in the cloud and provide viable solutions to reduce cloud cost and waste. This paper addresses these concerns by conducting a comprehensive analysis of the Microsoft Azure 2019 traces and makes the following contributions. First, we analyze the cost of 6,687 Azure cloud users, who created nearly 2.7 million virtual machines (VMs), and find that a large portion of VMs are under-utilized or over-provisioned for resources. Second, we propose Cloud Waste Points (CWP) to quantitatively evaluate the waste of each VM. We further categorize VMs that utilize cloud resource efficiently as green VMs and those that waste cloud resources as red VMs. Third, we propose Cloud Waste Indicator (CWI) as a metric to classify Azure users as red, green, and normal users, depending on their efficiency in utilizing cloud resources. In addition, we introduce Cloud Utilization Score (CUS) to rank the relative performance of Azure users in term of cloud waste. Lastly, we propose an algorithm to identify red VMs and recommend lower priced VMs that can help users reduce cost without compromising quality of service (QoS). Our experiments show that over $22 million cost savings (i.e. approximately 36% in total cost reduction) can be achieved if our recommendations are adopted by users.
Efficient Coefficient Bit-Width Reduction Method for Ising Machines

Ising machines such as quantum annealing machines and semiconductor-based annealing machines can solve various combinatorial optimization problems very efficiently by transforming it into a data structure called an Ising model. At that time, the bit-widths of the coefficients of the Ising model have to be kept within the range that an Ising machine can deal with. However, by reducing the Ising-model bit-widths, its minimum energy state, or ground state, may become different from that of the original one and hence the targeted combinatorial optimization problem cannot be well solved. This paper proposes an effective method for reducing Ising-model bit-widths. The proposed method is composed of the two processes: First, given an Ising model with large coefficient bit-widths, the shift method is applied to reduce its bit-widths roughly. Second, the spin-adding method is applied to further reduce its bit-widths to those that Ising machines can deal with. Without adding too many extra spins, we efficiently reduce the coefficient bit-widths of the original Ising model. Furthermore, the ground state before and after reducing the coefficient bit-widths is not much changed in most of the practical cases. Experimental evaluations demonstrate the effectiveness of the proposed method, compared to existing methods.
Digital education: risks or benefits with business collaboration

Digital education can supply the framework to support new learning approaches that engage students, bolster new revenue streams, develop with business collaboration, cut operational costs and preserve highly valued school and university brands and reputations. Effective digital transformation isn’t just about technology, though. It requires a willingness to adopt technology in new ways, beyond administrative process. The article actualizes the need to solve the problem mismatch between potential employer expectations and how universities prepare students for the future workforce. The factors that change digital technologies are determined. Approaches to creation next-generation learning environments that effectively prepare students for the future by offering access to the tools they need to prepare for the workplace while also providing a fulfilling learning experience are described.
VideoSticker: A Tool for Active Viewing and Visual Note-taking from Videos

Video is an effective medium for knowledge communication and learning. Yet active viewing and note-taking from videos remain a challenge. Specifically, during note-taking, viewers find it difficult to extract essential information such as representation, composition, motion, and interactions of graphical objects and narration. Current approaches rely on creating static screenshots, manual clipping, manual annotation and transcription. This is often done by repeatedly pausing and rewinding the video, thus disrupting the viewing experience. We propose VideoSticker, a tool designed to support visual note-taking by extracting expressive content and narratives from videos as 'object stickers.' VideoSticker implements automated object detection and tracking, linking objects to the transcript, and supporting rapid extraction of stickers across space, time, and events of interest. VideoSticker's two-pass approach allows viewers to capture high-level information uninterrupted and later extract specific details. We demonstrate the usability of VideoSticker for a variety of videos and note-taking needs.
Implementation of Covid Chatbot

The days when one used to engage with a service through keyboards are gone. Users nowadays interact with systems through Chatbots or voice assistants. They are most likely to benefit humans by providing intelligent and suitable answers. Chatbot&#x0027;s aim is to make a productive conversation between human and machine. Every time the chatbot receives user input, it saves the input and response, which aids Chatbots with some initial knowledge. The utilization of Chatbots for health has certainly been fruitful from aiding doctors with their schedules to helping the public with answering their tiny queries. We know how the global pandemic caused by corona virus has adversely affected everyone&#x0027;s lives. People need to be informed about various aspects. The goal of this particular paper is to discuss the implementation and model of a telegram based Chatbot system with respect to helping people with the facts, precautions, preventive measures, and various other aspects related to the corona virus. It also throws light on protecting one&#x0027;s mental health.
Adversarial Attacks on Deepfake Detectors: A Practical Analysis

In this day and age, fake images can be easily generated using some of the state-of-the-art Generative Adversarial Networks (GANs), including Deepfake. These fake images, along with unfactual content, can pose a substantial threat to our society since they are indistinguishable from real ones by the human eye. Therefore, Deepfake detection has gained immense interest in academia and industry. In practice, most detection methods use simple deep neural networks (DNNs) as the backbone. However, they are vulnerable to adversarial examples. This work presents practical pipelines in both white-box and black-box attack scenarios that can fool DNN-based Deepfake detectors into classifying fake images as real. We show that adversarial attacks can be a real threat to Deepfake detectors, even in a black-box setting. We also analyze the transferability of the white-box attacks from one model to another. Then, defensive perspectives are considered based on the practical context.