Reviewerly/exHarmony huggingface dataset:
Colour image encryption algorithm combining Arnold map, DNA sequence operation, and a Mandelbrot set

The third party misuse and manipulation of digital images is a threat to security and privacy of human subjects. The requirements to fulfil the security needs of digital images have led to the development of good encryption techniques. The proposed method of encryption scheme combines the techniques of chaotic image encryption and DNA (Deoxyribonucleic Acid) sequence operations. The encryption mechanism is applied separately on three channels (R, G and B) of the colour image with a suitable chaotic map. The map selection algorithm selects a good chaotic map based on image properties and other parameters. A method of DNA encoding is also applied after chaotic encryption. A new Mandelbrot Set based conditional shift algorithm is introduced to apply confusion effectively on R, G and B channels. Simulation and security analysis confirm that the proposed image encryption scheme demonstrates extraordinary performance in terms of security.
Immersive virtual reality to enforce teaching in engineering education

Abstract Prior studies on the use of digital prototyping and virtual reality (VR) in designing as well as evaluating new products have shown that VR reduces both development time and costs whilst augmenting student motivation and creativity. The current study demonstrates that VR and 3D prototyping in the context of project-based learning (PBL) promote effective communication, increase problem solving skills, and enhance learning outcomes. VR and digital prototyping have been extensively used in industries for the purpose of product design and usability evaluation. In the context of engineering education, many research studies have attempted to explore the effect of VR on teamwork, engagement, retention, and motivation. In this paper, VR is used in conjunction with PBL in self-directed approach to design and implement a product using 3D software whilst also using virtual reality immersive CAVE display to evaluate their design. The hypothesis is that the use of VR with a project-based-learning approach to facilitate the attainment of desirable goals in the engineering design project, improved achievement of course learning outcomes and promoted effective communication. According to the research findings, VR approach significantly affected the distribution of cumulative project grades. Students’ project grades improved, particularly the implementation component. In addition, the course outcomes related to project design were better achieved in VR approach. The communication and problem-solving skills were improved in the VR approach as compared to traditional approach.
SoK: A Comprehensive Reexamination of Phishing Research From the Security Perspective

Phishing and spear phishing are typical examples of masquerade attacks since trust is built up through impersonation for the attack to succeed. Given the prevalence of these attacks, considerable research has been conducted on these problems along multiple dimensions. We reexamine the existing research on phishing and spear phishing from the perspective of the unique needs of the security domain, which we call security challenges: real-time detection, active attacker, dataset quality and base-rate fallacy. We explain these challenges and then survey the existing phishing/spear phishing solutions in their light. This viewpoint consolidates the literature and illuminates several opportunities for improving existing solutions. We organize the existing literature based on detection techniques for different attack vectors (e.g., URLs, websites, emails) along with studies on user awareness. For detection techniques we examine properties of the dataset, feature extraction, detection algorithms used, and performance evaluation metrics. This work can help guide the development of more effective defenses for phishing, spear phishing and email masquerade attacks of the future, as well as provide a framework for a thorough evaluation and comparison.
Is neuron coverage a meaningful measure for testing deep neural networks?

Recent effort to test deep learning systems has produced an intuitive and compelling test criterion called neuron coverage (NC), which resembles the notion of traditional code coverage. NC measures the proportion of neurons activated in a neural network and it is implicitly assumed that increasing NC improves the quality of a test suite. In an attempt to automatically generate a test suite that increases NC, we design a novel diversity promoting regularizer that can be plugged into existing adversarial attack algorithms. We then assess whether such attempts to increase NC could generate a test suite that (1) detects adversarial attacks successfully, (2) produces natural inputs, and (3) is unbiased to particular class predictions. Contrary to expectation, our extensive evaluation finds that increasing NC actually makes it harder to generate an effective test suite: higher neuron coverage leads to fewer defects detected, less natural inputs, and more biased prediction preferences. Our results invoke skepticism that increasing neuron coverage may not be a meaningful objective for generating tests for deep neural networks and call for a new test generation technique that considers defect detection, naturalness, and output impartiality in tandem.
A Systematic Review of a Virtual Reality System from the Perspective of User Experience

Virtual reality (VR) is receiving attention enough to be considered as its revival age in both industrial and academic field. Since VR systems have various types of interaction with users and new types of interaction are constantly being developed, various studies investigating user experience (UX) of VR systems are continuously needed. However, there is still a lack of research on the taxonomy that can recognize the main characteristics of VR system at a glance by reflecting the influencing factors of UX. Therefore, we collected and reviewed the research related to the UX evaluation of the VR system in order to identify the current research status and to suggest future research direction. To achieve this, a systematic review was conducted on UX studies for VR, and taxonomies of VR system including influencing factors of UX were proposed. A total of 393 unique articles were collected, and 65 articles were selected to be reviewed via Systematic Reviews and Meta-Analyses methodology. The selected articles were analyzed according to predefined taxonomies. As a result, current status of research can be identified base on the proposed taxonomies. Besides, issues related to VR devices and technology, and research method for future research directions can be suggested.
A novel approach to workload prediction using attention-based LSTM encoder-decoder network in cloud environment

Abstract Server workload in the form of cloud-end clusters is a key factor in server maintenance and task scheduling. How to balance and optimize hardware resources and computation resources should thus receive more attention. However, we have observed that the disordered execution of running application and batching seriously cuts down the efficiency of the server. To improve the workload prediction accuracy, this paper proposes an approach using the long short-term memory (LSTM) encoder-decoder network with attention mechanism. First, the approach extracts the sequential and contextual features of the historical workload data through the encoder network. Second, the model integrates the attention mechanism into the decoder network, through which the prediction for batch workloads can be carried out. Third, experiments carried out on Alibaba and Dinda workload traces dataset demonstrate that our method achieves state-of-the-art performance in mixed workload prediction in cloud computing environment. Furthermore, we also propose a scroll prediction method, which splits a long prediction sequence into several small sequences to monitor and control prediction accuracy. This work helps to dynamically guide the configuration for workload balancing.
An Adversarial Learning Approach to Medical Image Synthesis for Lesion Detection

The identification of lesion within medical image data is necessary for diagnosis, treatment and prognosis. Segmentation and classification approaches are mainly based on supervised learning with well-paired image-level or voxel-level labels. However, labeling the lesion in medical images is laborious requiring highly specialized knowledge. We propose a medical image synthesis model named abnormal-to-normal translation generative adversarial network (ANT-GAN) to generate a normal-looking medical image based on its abnormal-looking counterpart without the need for paired training data. Unlike typical GANs, whose aim is to generate realistic samples with variations, our more restrictive model aims at producing a normal-looking image corresponding to one containing lesions, and thus requires a special design. Being able to provide a “normal” counterpart to a medical image can provide useful side information for medical imaging tasks like lesion segmentation or classification validated by our experiments. In the other aspect, the ANT-GAN model is also capable of producing highly realistic lesion-containing image corresponding to the healthy one, which shows the potential in data augmentation verified in our experiments.
A deep learning based medical image segmentation technique in Internet-of-Medical-Things domain

Medical Image Segmentation is the process of automatic or semi-automatic detection of boundaries within a 2D or 3D image in Internet-of-Medical-Things (IoMT) domain. The main difficulty of medical image segmentation is the high variability in medical images. For example, CT images contain a large amount of noise, and complex boundaries. In this paper, we propose an adaptive fully dense(AFD) neural network for CT image segmentation. By adding the horizontal connections in UNet structure, it can extract various features from all layers adaptively. And it use ensemble training for the output to extract more edge information in the multiple rounds training. We have validated our method on two data sets, a natural scene image data set and a liver cancer CT image data set. The experimental results demonstrate that it performs better than state-of-the-art segmentation methods. And our method yields superior segmentation results for CT images with complex boundaries.
A survey of surveys on the use of visualization for interpreting machine learning models

Research in machine learning has become very popular in recent years, with many types of models proposed to comprehend and predict patterns and trends in data originating from different domains. As these models get more and more complex, it also becomes harder for users to assess and trust their results, since their internal operations are mostly hidden in black boxes. The interpretation of machine learning models is currently a hot topic in the information visualization community, with results showing that insights from machine learning models can lead to better predictions and improve the trustworthiness of the results. Due to this, multiple (and extensive) survey articles have been published recently trying to summarize the high number of original research papers published on the topic. But there is not always a clear definition of what these surveys cover, what is the overlap between them, which types of machine learning models they deal with, or what exactly is the scenario that the readers will find in each of them. In this article, we present a meta-analysis (i.e. a “survey of surveys”) of manually collected survey papers that refer to the visual interpretation of machine learning models, including the papers discussed in the selected surveys. The aim of our article is to serve both as a detailed summary and as a guide through this survey ecosystem by acquiring, cataloging, and presenting fundamental knowledge of the state of the art and research opportunities in the area. Our results confirm the increasing trend of interpreting machine learning with visualizations in the past years, and that visualization can assist in, for example, online training processes of deep learning models and enhancing trust into machine learning. However, the question of exactly how this assistance should take place is still considered as an open challenge of the visualization community.
Towards Fast and Accurate Streaming End-To-End ASR

End-to-end (E2E) models fold the acoustic, pronunciation and language models of a conventional speech recognition model into one neural network with a much smaller number of parameters than a conventional ASR system, thus making it suitable for on-device applications. For example, recurrent neural network transducer (RNN-T) as a streaming E2E model has shown promising potential for on-device ASR [1]. For such applications, quality and latency are two critical factors. We propose to reduce E2E model's latency by extending the RNN-T endpointer (RNN-T EP) model [2] with additional early and late penalties. By further applying the minimum word error rate (MWER) training technique [3], we achieved 8.0% relative word error rate (WER) reduction and 130ms 90-percentile latency reduction over [2] on a Voice Search test set. We also experimented with a second-pass Listen, Attend and Spell (LAS) rescorer [4]. Although it did not directly improve the first pass latency, the large WER reduction provides extra room to trade WER for latency. RNN-T EP+LAS, together with MWER training brings in 18.7% relative WER reduction and 160ms 90-percentile latency reductions compared to the original proposed RNN-T EP [2] model.
A survey on consensus methods in blockchain for resource-constrained IoT networks

The success of blockchain as the underlying technology for cryptocurrencies has opened up possibilities for its use in other application domains as well. The main advantages of blockchain for its potential use in other areas are its inherent security mechanisms and immunity to data manipulation attacks. A blockchain relies on a consensus method for agreeing on any new data. Most of the consensus methods which are currently used for the blockchain of different cryptocurrencies require high computational power and thus are not suitable for resource-constrained systems. In this article, we survey the various blockchain-based consensus methods that are applicable to resource-constrained IoT devices and networks. In a typical IoT network, there exist several devices with limited computational and communication capabilities. Most often, these devices cannot perform intensive computations and are starved for bandwidth. Therefore, we discuss the possible measures that can be taken to reduce the computational power and convergence time for the underlying consensus methods. We also talk about some of the alternatives to the public blockchain, such as private blockchain and tangle, along with their potential adoption for IoT networks. Furthermore, we review the existing consensus methods that have been implemented and explore the possibility of utilizing them to realize a blockchain-based IoT network. Some of the open research challenges including AI-enabled blockchains are also put forward.
The impact of a virtual reality app on adolescent EFL learners’ vocabulary learning

VR technology allows learners to access simulated, immersive and interactive virtual environments to perform authentic learning activities. In particular, VR has emerged as a valuable tool for L2 learning. However, VR research has tended to pay more attention to desktop-based VR than to VR via mobile-rendered HMDs, leaving the potentials of VR through mobile-rendered HMDs yet to be investigated. Therefore, this study fills the gap by using a commercial VR app to examine the effect of VR via mobile-rendered HMDs on EFL learners’ vocabulary learning. Forty-nine seventh graders in Taiwan were recruited from two intact classes and assigned to either an experimental (VR players) or control (video watchers) group. The VR players interacted with Mondly VR app using mobile-rendered HMDs and took part in conversations with virtual characters. The video watchers watched the walkthrough video signal of the VR player’s app via a personal computer. Vocabulary tests, a perception questionnaire, and interviews were used to evaluate the participants’ vocabulary learning. The results showed that the VR players’ vocabulary learning and retention was significantly higher than the video watchers’. The majority of the VR players felt that VR-mediated vocabulary learning was motivating and beneficial. The VR app contextualized vocabulary learning by providing virtual environments with multimodal support and enhanced learner engagement through real-time interactivity and feedback. The video watchers’ feedback revealed mixed feelings. Some felt that the walkthrough video facilitated vocabulary learning by providing word meaning and use in context. Others reported it lacked interactivity and their attention was easily distracted.
A Machine Learning approach for automation of Resume Recommendation system

Finding suitable candidates for an open role could be a daunting task, especially when there are many applicants. It can impede team progress for getting the right person on the right time. An automated way of “Resume Classification and Matching” could really ease the tedious process of fair screening and shortlisting, it would certainly expedite the candidate selection and decisionmaking process. This system could work with a large number of resumes for first classifying the right categories using different classifier, once classification has been done then as per the job description, top candidates could be ranked using Content-based Recommendation, using cosine similarity and by using k-NN to identify the CVs that are nearest to the provided job description.
Bag of Tricks for 3D MRI Brain Tumor Segmentation

3D brain tumor segmentation is essential for the diagnosis, monitoring, and treatment planning of brain diseases. In recent studies, the Deep Convolution Neural Network (DCNN) is one of the most potent methods for medical image segmentation. In this paper, we review the different kinds of tricks applied to 3D brain tumor segmentation with DNN. We divide such tricks into three main categories: data processing methods including data sampling, random patch-size training, and semi-supervised learning, model devising methods including architecture devising and result fusing, and optimizing processes including warming-up learning and multi-task learning. Most of these approaches are not particular to brain tumor segmentation, but applicable to other medical image segmentation problems as well. Evaluated on the BraTS2019 online testing set, we obtain Dice scores of 0.810, 0.883 and 0.861, and Hausdorff Distances (95th percentile) of 2.447, 4.792, and 5.581 for enhanced tumor core, whole tumor, and tumor core, respectively. Our method won the second place of the BraTS 2019 Challenge for the tumor segmentation.
Assessment of machine learning, time series, response surface methodology and empirical models in prediction of global solar radiation

Solar radiation (SR) knowledge plays a vital role in the design, modelling, and operation of solar energy conversion systems and future energy investment policies of the governments. However, these data are not measured for all regions due to the non-availability of SR measurement equipment at the weather stations. Therefore, SR has to be accurately predicted using various prediction models. In this research, four models from different classes are being used to predict monthly average daily global SR data. The models used in this study are based on a machine-learning algorithm (feed-forward neural network), empirical models (3 Angstrom-type models), time series (Holt-Winters), and mathematical model (RSM). As the prediction locations, four provinces (Ankara, Karaman, Kilis, and Şırnak) in Turkey are selected. The dataset including pressure, relative humidity, wind speed, ambient temperature, and sunshine duration is supplied from the Turkish State Meteorological Service and it covers the years 2008–2018. In the study, monthly average daily global SR data for the year 2018 is being predicted, and the performance success of the models is discussed in terms of the following benchmarks R2, MBE, RMSE, MAPE, and t-stat. In the results, R2 value for all models is varying between 0.952 and 0.993 and MAPE and RMSE value for all models is smaller than 10% and 2 MJ/m2-day, respectively. Evaluation in terms of t-stat value, no models exceed the t-critic limit. Considering all the models together, ANN has presented the best results with an average R2, MBE, RMSE, MAPE, and t-stat of 0.9911, 0.1323 MJ/m2-day, 0.78 MJ/m2-day, 4.9263%, and 0.582, respectively. Then Holt-Winters, RSM, and empirical models closely followed it, respectively.
Digital Smartphone Tracking for COVID-19

Matthew S. McCoy, PhD; Timothy Libert, PhD; David Buckler, MUSA; David T. Grande, MD, MPA; Ari B. Friedman, MD, PhD
FuseSeg: Semantic Segmentation of Urban Scenes Based on RGB and Thermal Data Fusion

Semantic segmentation of urban scenes is an essential component in various applications of autonomous driving. It makes great progress with the rise of deep learning technologies. Most of the current semantic segmentation networks use single-modal sensory data, which are usually the RGB images produced by visible cameras. However, the segmentation performance of these networks is prone to be degraded when lighting conditions are not satisfied, such as dim light or darkness. We find that thermal images produced by thermal imaging cameras are robust to challenging lighting conditions. Therefore, in this article, we propose a novel RGB and thermal data fusion network named FuseSeg to achieve superior performance of semantic segmentation in urban scenes. The experimental results demonstrate that our network outperforms the state-of-the-art networks. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This article investigates the problem of semantic segmentation of urban scenes when lighting conditions are not satisfied. We provide a solution to this problem via information fusion with RGB and thermal data. We build an end-to-end deep neural network, which takes as input a pair of RGB and thermal images and outputs pixel-wise semantic labels. Our network could be used for urban scene understanding, which serves as a fundamental component of many autonomous driving tasks, such as environment modeling, obstacle avoidance, motion prediction, and planning. Moreover, the simple design of our network allows it to be easily implemented using various deep learning frameworks, which facilitates the applications on different hardware or software platforms.
MiLeNAS: Efficient Neural Architecture Search via Mixed-Level Reformulation

Many recently proposed methods for Neural Architecture Search (NAS) can be formulated as bilevel optimization. For efficient implementation, its solution requires approximations of second-order methods. In this paper, we demonstrate that gradient errors caused by such approximations lead to suboptimality, in the sense that the optimization procedure fails to converge to a (locally) optimal solution. To remedy this, this paper proposes MiLeNAS, a mixed-level reformulation for NAS that can be optimized efficiently and reliably. It is shown that even when using a simple first-order method on the mixed-level formulation, MiLeNAS can achieve a lower validation error for NAS problems. Consequently, architectures obtained by our method achieve consistently higher accuracies than those obtained from bilevel optimization. Moreover, MiLeNAS proposes a framework beyond DARTS. It is upgraded via model size-based search and early stopping strategies to complete the search process in around 5 hours. Extensive experiments within the convolutional architecture search space validate the effectiveness of our approach.