Reviewerly/exHarmony huggingface dataset:
A Journey Towards Providing Intelligence and Actionable Insights to Development Teams in Software Delivery

For delivering high-quality artifacts within the budget and on schedule, software delivery teams ideally should have a holistic and in-process view of the current health and future trajectory of the project. However, such insights need to be at the right level of granularity and need to be derived typically from a heterogeneous project environment, in a way that helps development team members with their tasks at hand. Due to client mandates, software delivery project environments employ many disparate tools and teams tend to be distributed, thus making the relevant information retrieval, insight generation, and developer intelligence augmentation process fairly complex. In this paper, we discuss our journey in this area spanning across facets like software project modelling and new development metrics, studying developer priorities, adoption of new metrics, and different approaches of developer intelligence augmentation. Finally, we present our exploration of new immersive technologies for human-centered software engineering.
An Optimized Resource Scheduling Strategy for Hadoop Speculative Execution Based on Non-cooperative Game Schemes

Hadoop is a well-known parallel computing system for distributed computing and large-scale data processes."Straggling" tasks, however, have a serious impact on task allocation and scheduling in a Hadoop system.Speculative Execution (SE) is an efficient method of processing "Straggling" Tasks by monitoring real-time running status of tasks and then selectively backing up "Stragglers" in another node to increase the chance to complete the entire mission early.Present speculative execution strategies meet challenges on misjudgement of "Straggling" tasks and improper selection of backup nodes, which leads to inefficient implementation of speculative executive processes.This paper has proposed an Optimized Resource Scheduling strategy for Speculative Execution (ORSE) by introducing non-cooperative game schemes.The ORSE transforms the resource scheduling of backup tasks into a multi-party non-cooperative game problem, where the tasks are regarded as game participants, whilst total task execution time of the entire cluster as the utility function.In that case, the most benefit strategy can be implemented in each computing node when the game reaches a Nash equilibrium point, i.e., the final resource scheduling scheme to be obtained.The strategy has been implemented in Hadoop-2.x.Experimental results depict that the ORSE can maintain the efficiency of speculative executive processes and improve fault-tolerant and computation performance under the circumstances of Normal Load, Busy Load and Busy Load with Skewed Data.
Workplace Health Promotion: Effects of an mHealth Application on Employee Behaviour and Wellness

Organizations are using digital tools to support workplace health promotion (WHP) initiatives.This study investigates the effects of a mobile health (mHealth) application on employee behaviour and wellness.This exploratory, empirical case study was conducted as part of an established workplace health initiative in the Health Service Executive (HSE) in Ireland, April 2018.User data on exercise including daily activity in the form of steps taken, mood, total daily exercise, and other variables were captured as part of the 'Steps to Health Challenge'.Phase 1 of the study involved the collection of user data from 70 active app users and a survey was conducted.The majority of participants found that the mHealth app motivated them to sustain, and in some cases increase, their daily steps.Phase 2 involved interviews.Following qualitative data analysis, we found that the app positively effected employee behaviour; specific features were highlighted as especially influential. mHealth
Keyframe extraction using binary robust invariant scalable keypoint features

In recent years, research in the field of keyframe extraction become more attractive due to its use in advanced applications like video surveillance. In this paper, we introduce a novel algorithm of keyframe extraction which utilizes Binary Robust Invariant Scalable Keypoint features to obtain the dissimilarity level of consecutive frames and establishes shot transition boundary, from where we extract keyframes. The frame at which dissimilarity level is high is taken as a keyframe. The proposed algorithm is tested on ten different videos of animation category. Performance of the method is assessed using the evaluation metrics- Figure of merit, Detection percentage, Accuracy and missing factor. The experimental results and analysis shows improved performance of the proposed algorithm over the other state-ofthe-art methods.
Applying Software Assurance and Cybersecurity NICE Job Tasks through Secure Software Engineering Labs

To meet growing demands in the United States market for cybersecurity professionals, the National Security Agency and Department of Homeland Security have jointly established the National Center for Academic Excellence. Until recently, cybersecurity efforts were focused on securing the network. However, numerous studies have revealed that significant vulnerabilities have been found within the software code. To teach programmers and software engineers having secure software engineering labs is critical. Experiential learning is the cornerstone of cybersecurity education. Laboratory exercises provide critical value to students. Real-world, malicious actors use varying tactics and techniques for cyber-attacks. Laboratory environments should mirror this dynamism, and students should be exposed to various tools and mitigation strategies.
Learning signer-invariant representations with adversarial training

Sign Language Recognition (SLR) has become an appealing topic in modern societies because such technology can ideally be used to bridge the gap between deaf and hearing people. Although important steps have been made towards the development of real-world SLR systems, signer-independent SLR is still one of the bottleneck problems of this research field. In this regard, we propose a deep neural network along with an adversarial training objective, specifically designed to address the signer-independent problem. Concretely speaking, the proposed model consists of an encoder, mapping from input images to latent representations, and two classifiers operating on these underlying representations: (i) the signclassifier, for predicting the class/sign labels, and (ii) the signer-classifier, for predicting their signer identities. During the learning stage, the encoder is simultaneously trained to help the sign-classifier as much as possible while trying to fool the signer-classifier. This adversarial training procedure allows learning signer-invariant latent representations that are in fact highly discriminative for sign recognition. Experimental results demonstrate the effectiveness of the proposed model and its capability of dealing with the large inter-signer variations.
QutNocturnal@HASOC'19: CNN for hate speech and offensive content identification in Hindi language

We describe our top-team solution to Task 1 for Hindi in the HASOC contest organised by FIRE 2019. The task is to identify hate speech and offensive language in Hindi. More specifically, it is a binary classification problem where a system is required to classify tweets into two classes: (a) Hate and Offensive (HOF) and (b) Not Hate or Offensive (NOT). In contrast to the popular idea of pretraining word vectors (a.k.a. word embedding) with a large corpus from a general domain such as Wikipedia, we used a relatively small collection of relevant tweets (i.e. random and sarcasm tweets in Hindi and Hinglish) for pretraining. We trained a Convolutional Neural Network (CNN) on top of the pretrained word vectors. This approach allowed us to be ranked first for this task out of all teams. Our approach could easily be adapted to other applications where the goal is to predict class of a text when the provided context is limited.
Robust estimation of mixing measures in finite mixture models

In finite mixture models, apart from underlying mixing measure, true kernel density function of each subpopulation in the data is, in many scenarios, unknown.Perhaps the most popular approach is to choose some kernel functions that we empirically believe our data are generated from and use these kernels to fit our models.Nevertheless, as long as the chosen kernel and the true kernel are different, statistical inference of mixing measure under this setting will be highly unstable.To overcome this challenge, we propose flexible and efficient robust estimators of the mixing measure in these models, which are inspired by the idea of minimum Hellinger distance estimator, model selection criteria, and superefficiency phenomenon.We demonstrate that our estimators consistently recover the true number of components and achieve the optimal convergence rates of parameter estimation under both the well-and misspecified kernel settings for any fixed bandwidth.These desirable asymptotic properties are illustrated via careful simulation studies with both synthetic and real data.
Requirements Prioritization Techniques Focusing on Agile Software Development: A Systematic Literature Review

Requirement prioritization plays a crucial role in the software development process. In Agile Software Development (ASD), requirement prioritization (RP) is challenging to maintain and requires a more formal process. It is essential to prioritize the requirements for making the correct decision for either single or multiple releases of a product. Ignorance of critical requirements during prioritization results in poor quality and unsatisfied clients. Various techniques have been proposed to prioritize requirements based on specific criteria. This SLR aims to identify the RP techniques focusing on Agile Software Development, as well as the issues or limitations of previous works. It will serve as a guideline for the practitioners to develop an effective RP process that can produce high-quality requirements that are expected to meet the needs of the intended users. Search terms with appropriate keywords were utilized in several online databases to recognize primary studies mostly related to RP in ASD. This SLR also shows the significant research gaps regarding techniques and issues for requirements prioritization in ASD. The researchers identified that a total of 25 papers report complete empirical results. Some critical issues of RP in ASD include severe limitations in terms of scalability, complexity, uncertainty, time consumption, starvation issue, dependency issues among requirements, limited research focused on the non-functional requirements and a lower automation approach. There are some significant challenges of RP techniques in ASD, such as a conflict between the stakeholders, changes in the priority list lead towards rework, and requirement selection factors during the RP process.
Phishing Sites Detection from a Web Developer’s Perspective Using Machine Learning

The Internet has enabled unprecedented communication and new technologies.Concomitantly, it has brought the bane of phishing and exacerbated vulnerabilities.In this paper, we propose a model to detect phishing webpages from a web developer's perspective.From this standpoint, we design 120 novel features based on content from a webpage, four time-based and two search-based novel features, plus we use 34 other content-based and 11 heuristic features to optimize the model.Moreover, we select Random Committee (Base learner: Random Tree) for our framework since it has the best performance after comparing with six other algorithms: Hellinger Distance Decision Tree, SVM, Logistic Regression, J48, Naive Bayes, and Random Forest.In real-time experiments, the model achieved 99.4% precision and 98.3% MCC with 0.1% false positive rate in 5-fold crossvalidation using the realistic scenario of an unbalanced dataset.
RBAC-HDE: On the Design of a Role-based Access Control with Smart Contract for Healthcare Data Exchange

Recently there have been many emerging innovated Blockchain applications in various sectors including healthcare. In this paper, we propose a role-based access control system, namely, RBAC-HDE, which harnesses the powerful features of Blockchain such as immutability and decentralization, in order to facilitate secure data exchange for healthcare. We design and implement an Ethereum-based testbed to demonstrate the basic idea. Our preliminary results have shown the feasibility and potential of this approach.
Trends and Augmentations of Cloud Computing

Cloud computing refers to the evolution of grid computing, parallel computing, non-centralized computing and pragmatic technologies which exemplify the configuration of a novel span. Cloud computing is an unfolding imitation of business computing. It is the recent endeavor in relinquishing computing assets as a service. It impersonates a transformation apart from computing as a decoction that is redeemed, to computing as a utility that is conveyed to end user past the cyberspace from substantial-scope data repositories– or clouds. Cloud computing denotes the set of IT assistance that are accommodated to an end user past web on a sublet structure and amidst the competence to extent at the top or bottom their assistance desideratum. Commonly the services of cloud computing are dispatched by an arbitrator supplier who dominate the framework. Cloud Computing clench the dormant to eradicate the stipulation for mounting at the apex of tremendous tariff computing framework needed by an IT-oriented products and assistance that the commercial enterprise adopts. It affiances to cater an extensible IT architecture, approachable through cyberspace from insubstantial handy gadgets. This decree consent to many-fold enhance in the competence and capabilities of the accessible and innovative software. This innovative monetary form for computing has originated productive view plus is tempting immense comprehensive venture. Numerous monopolies, like funding, medicine and literacy are poignant approaching the cloud owed to the competence of benefits catered by the pay-peruse outline situated on the assets such as handing out potential used, proceedings conceded out, transmission capacity absorbed, data transmitted, or memory engaged etc. Under cloud computing surroundings, the complete information presides ended a deposit of network where withal, sanctioning the information to be percolating throughout virtual technology. Regardless of the probable profits attained commencing the cloud computing, the industries are leisurely in accommodating it owed to defense contention and provocations linked with it. Safety measures are individual of the key issues which hinder the increase of cloud.
Multimodal Interface for Deaf and Dumb Communication

Language barrier is the most common reason for communication gap. In India, language changes after few miles with respect to education & culture of that territory. This is the condition with spoken language & if we talk about visual languages then condition becomes worst. Here we are talking about the visual language which is used by speech & hearing impaired community i.e. Deaf & Dumb. Basically Sign language is the standard language used for communication by this community. But, because of lack of standardization & education of Sign language, Deaf & Dumb can use multimodal interfaces for communication such as image, video, Leap reading, air writing, finger spelling & body gestures etc. Everyone is unable to learn sign language efficiently thus multimodal interface can serve the purpose of communication. In this paper we are introducing different modes of communication for Deaf & Dumb with the comparison of existing Sign language recognition systems & available sign language dictionaries.
Creating high fidelity 360° virtual reality with high dynamic range spherical panorama images

Abstract This research explores the development of a novel method and apparatus for creating spherical panoramas enhanced with high dynamic range (HDR) for high fidelity Virtual Reality 360 degree (VR360) user experiences. A VR360 interactive panorama presentation using spherical panoramas can provide virtual interactivity and wider viewing coverage; with three degrees of freedom, users can look around in multiple directions within the VR360 experiences, gaining the sense of being in control of their own engagement. This degree of freedom is facilitated by the use of mobile displays or head-mount-devices. However, in terms of image reproduction, the exposure range can be a major difficulty in reproducing a high contrast real-world scene. Imaging variables caused by difficulties and obstacles can occur during the production process of spherical panorama facilitated with HDR. This may result in inaccurate image reproduction for location-based subjects, which will in turn result in a poor VR360 user experience. In this article we describe a HDR spherical panorama reproduction approach (workflow and best practice) which can shorten the production processes, and reduce imaging variables, and technical obstacles and issues to a minimum. This leads to improved photographic image reproduction with fewer visual abnormalities for VR360 experiences, which can be adaptable into a wide range of interactive design applications. We describe the process in detail and also report on a user study that shows the proposed approach creates images which viewers prefer, on the whole, to those created using more complicated HDR methods, or to those created without the use of HDR at all.
Emotive Expressions on Social Chatbot

Building social chatbots to address usersâ€™ needs for communication and affection is of great value to society (Shum et al., 2018). One of which, Replika, attempts to become an artificial intelligent companion by demonstrating sufficient social and emotional skills through emotive expressions. Emotive expressions are imperative in human-computer interaction, since they tend to elicit social cooperation. The present article aims to survey emotive expressions developed on Replika in order to determine the chatbotâ€™s active-reactive skills. They are collected by means of participant observation and are analysed with qualitative method. The present article observes six emotive expressions which Replika can process. These expressions include apologizing, thanking, condoling, complimenting, greeting, and welcoming. The generation of each expression is dependent of the context of each interaction.Â
A Parallel Differential Confocal Method for Highly Precise Surface Height Measurements

A parallel differential confocal method (PDCM) is proposed to obtain rapid surface height measurements with nanoscale precision with a conventional optical microscope. In contrast to existing differential surface profiling methods where multiple sensors are required and either pixel-by-pixel horizontal or layer-by-layer vertical scanning is necessary, the PDCM uses only a single sensor without scanning. It uses parallel pinholes to generate confocal images Ia and Ib at an equal distance but in opposite directions away from the focal plane. Then the method generates differential intensity signals by subtracting these two images, and then employs these signals to determine the heights of the object surface based on precalibrated curves. The precision and relative standard error of the proposed method were 1 nm and ≤ 0.2%, respectively. Its efficiency was 13 fps for 2018 × 512 horizontal locations. Due to its advantages in efficiency and precision, it can be considered as one of the ideal online surface height profiling methods for industrial metrological applications.
Is Crowd-Algorithm Collaboration an Advanced Alternative to Crowd-Sourcing on Cytology Slides?

Modern, state-of-the-art deep learning approaches yield human like performance in numerous object detection and classification tasks. The foundation for their success is the availability of training datasets of substantially high quantity, which are expensive to create, especially in the field of medical imaging. Crowdsourcing has been applied to create large datasets for a broad range of disciplines. This study aims to explore the challenges and opportunities of crowd-algorithm collaboration for the object detection task of grading cytology whole slide images. We compared the classical crowdsourcing performance of twenty participants with their results from crowd-algorithm collaboration. All participants performed both modes in random order on the same twenty images. Additionally, we introduced artificial systematic flaws into the precomputed annotations to estimate a bias towards accepting precomputed annotations. We gathered 9524 annotations on 800 images from twenty participants organised into four groups in concordance to their level of expertise with cytology. The crowd-algorithm mode improved on average the participants’ classification accuracy by 7%, the mean average precision by 8% and the inter-observer Fleiss’ kappa score by 20%, and reduced the time spent by 31%. However, two thirds of the artificially modified false labels were not recognised as such by the contributors. This study shows that crowd-algorithm collaboration is a promising new approach to generate large datasets when it is ensured that a carefully designed setup eliminates potential biases.
Improving Student's Learning Motivation in Schools using Augmented Reality

The use of technology in teaching and learning processes are becoming more important in schools. This work shows the prototype of learning tools using Augmented Reality. The tool was implemented using three types of technologies: QR codes linked to video-tutorials; 3D modeler Sketchup with Aumentaty software using marks; and OCR mobile recognition. This work was tested with first-grade students of America school in Abancay-Peru in 2017. The results show that the 50% of the students who used augmented reality based on marks and 3D objects, were more motivated and more interested to learn Mathematics and Communication areas.
Preventing the Temperature Side Channel Attacks on Security Circuits

Security circuits are important parts of communication and data processing equipment. Nowadays technologies and computation power can be used in cryptanalytic attacks against these specialized pieces of equipment. Some collateral procedures have been developed to facilitate these attacks and they are called Side Channel Attacks. They force the physical limits of the logic circuits in order to provoke erroneous operations that will later help the cryptanalytic attacks. One of the most used side channel attack consists in forcing the security circuit to work at temperatures out of the operating limits or to quickly freeze it. Freezing at very low temperatures can block processors and memories with resident secret data stored in them. Later on, these data can be acquired and used in cryptanalytic attacks. This paper proposes a special conductive mesh that can act also as a temperature sensor. Besides its function to detect physical intrusions, its behavior at temperature variations makes it a very good detector of temperature side channel attacks. Considering that the conductive mesh is covering the security circuit, the active tamper detection circuit, which probes with signals and analyze the response of the conductive mesh, is capable of detecting more quickly the temperature variations than a temperature sensor located at the circuit board.