Reviewerly/exHarmony huggingface dataset:
Securing the Sensitive Data in Cloud Like Environment: An Approach of DNA Encryption

Due to the rapid development of technology in distributed computing, most of the developers are attracted to the cloud-like computing environment for developing their applications, as it is available in every smart device. The term 'cloud computing’ defines the storing and accessing the data over the internet. The data that we store can be an image, file, application or program. Nowadays cloud computing is widely accepted because of its unique properties like scalability, reliability, and remote access. In traditional approach consumers store data in a cloud environment with the assumption of their data is in secure hands, but many case studies are proof that the security of the cloud-like environment can be compromised and the users have faced the data leakage. Algorithms like sign encryption and identity- based integrity auditing is designed to increase the security of the environment and protect data from being compromised. However, the amount of memory utilization and cost is high in these techniques. In this paper, we have proposed a novel approach for encrypting data keeping constrain of memory utilization and the time consumed. The proposed model will only encrypt the part of the data, which is very much sensitive using DNA encryption algorithm. Results have been compared with existing methods and it has been observed that our proposed model performed well.
Privacy-Protected Denoising for Signals on Graphs from Distributed Systems

The fast-growing networked computing devices create many distributed systems and generate new signals on a large scale. Typical applications include peer-to-peer streaming of multimedia data, crowd- sourcing, and measurement by sensor networks. Therefore, the massive amount of networked data is a form of big data, calling for new data structures and algorithms different from classical ones suitable for small data sizes. We consider a vital data format for recording information from networked distributed systems: signals on graphs. A significant concern is to protect the privacy of large scales of signals when processed at third parties, such as cloud data centers. A de-facto solution is to outsource encrypted data before they arrive at the third-parties. We propose a novel and efficient privacy-protected outsourced denoising algorithm based on the information-theoretic secure multi-party computation (secure MPC). Among the operations of signals on graphs, denoising is useful before further meaningful processing can occur. We experiment with our algorithms in a popular platform of secure MPC and compare it with Paillier's homomorphic encryption approach. The results demonstrate a better efficiency of our approach.
A Comparative Approach on Classification of Images with Convolutional Neural Networks

Image degradation, such as blurring, or various sources of noise are common reasons for distortion happening during image procurement. In this paper, we will study in a systematical manner the efficiency of various Convolutional Neural Networks (CNN) approaches, in respects to the type of architecture and optimization strategies, with two main objectives in mind. Firstly, we examine the CNN performance in classifying clean images, with a dataset containing 8 classes and more than 18,000 images, observing comparatively the obtained results from training on a standard architecture with those obtained from training on a hyper parameters fine-tuned network and lastly, from training on a wider pre fine-tuned network. Secondly, training our model after a degradation function is applied, and after analyzing the results, we propose an approach which will gently balance the efforts stemming from difficult architecture de-sign or adopting the best optimization decisions with obtaining a satisfactory efficiency in a simple manner. We have offered a standard convolution architecture as a solution for classifying images which are distorted, and our results suggest that, departing from a simple design, with possible alterations of hyper parameters and other optimizing routes, the efficiency could massively increase.
New pedagogical reality: challenges and answers

The relevance of the research is expressed in the study of the impact of changes in society on pedagogical reality and in determining the directions of its transformation. The purpose of the paper is to identify the professional challenges of the teaching profession and to substantiate the need to revise the basic functions of teaching activities. The research methods are: theoretical analysis of factors influencing the teaching profession; synthesis and interpretation of data on the characteristics of the activities of the subjects of the educational process, difficulties, and opportunities of pedagogical interaction. The result of the research is revealed in the substantiation of the provisions on advanced professional training of future teachers at the university, the formation of their pedagogical picture of the world. The conclusion about the need for future teachers to master the norms of the pedagogical ethos has been made, and the stages of this process have been provided. The scientific novelty of the research lies in the enrichment of the theory of pedagogy with knowledge about the value foundations and the content of pedagogical activity in a society of high modernity; new functions of pedagogical activity; features of the development of teaching practice. The practical significance is seen in the possibility of using the paper materials for further research; studying the processes of teaching, upbringing and development in new conditions; development of training materials for university students and students of advanced training courses.
The real time modeling and design of multiple vector vehicles combination

This paper introduces a new type of deformable aerial robot, which is based on our redesigned vector vehicle, using the new vector vehicle as a single unit for free combination in the air, different combinations will produce different characteristics to meet various requirements. For example, the horizontal combination of vehicle can make the combined robot one more degree of freedom, and through some special combination methods, the combined robot can speed up and increase the degree of freedom. In the further algorithm research on problem for the free combination of multiple vector vehicles in the air, the first issue is to establish the mathematical model of the vector vehicle group after the free splicing in the air, which is obtained through the communication between the magnet contacts on the vehicles. Second, in order to obtain the geometric center of the newly constructed robot, the coordinate system is constructed with the mother machine as the center. Third, we use Newton-Euler equation to model the new combination of robot. The real-time mathematical model of any number and combination of vehicles can be obtained by using this method, which has important applications in the splicing and separation of vehicle.
When to stop testing software: economic approach

When should we stop testing software and release it? Many software engineering papers attempt to answer this question—from the computer science viewpoint. However, the ultimate objective of every company—and software companies are not an exception—is to bring profit. In view of this fact, in this paper, we analyze the problem of when to stop testing from the economic viewpoint. For the simplified first-approximation model, we provide an explicit answer, and we describe how this answer can be made more accurate by using more adequate models.
Filtration mapping as complete Bell state analyzer for bosonic particles

Abstract In this paper, we present the approach to complete Bell state analysis based on filtering mapping. The key distinctive feature of this appoach is that it avoids complications related to using either hyperentanglement or representation of the Bell states as concatenated Greenber–Horne–Zeilinger (C-GHZ) state to perform discrimination procedure. We describe two techniques developed within the suggested approach and based on two-step algorithms with two different types of filtration mapping which can be called the non-demolition and semi-demolition filtrations. In the method involving non-demolition filtration measurement the filtration process employs cross-Kerr nonlinearity and the probe mode to distinguish between the two pairs of the Bell states. In the case of semi-demolition measurement, the two states are unambiguously discriminated and hence destroyed, whereas filtraton keeps the other two states intact. We show that the measurement that destroys the single photon subspace in every mode and preserves the superposition of zero and two photons can be realized with discrete photodetection based on microresonator with atoms.
On Bridging the Gap Between Far Eastern Cultures and the User Interface

Chinese characters cement Far Eastern cultures: they can be found for example in the Chinese, Japanese, Korean and Vietnamese writing systems. Digital computer systems originate from the Occident, during or shortly after the second world war. Their usage has thus been centred on horizontal, left-to-right layouts, typesetting and I/O, which has created a gap between these systems and, for instance, Far Eastern cultures whose writing systems are traditionally vertical right-to-left (RTL). Although some improvements have been made in this field over time, there are still several major issues. In this paper, we conduct an experiment, which is a proof of concept, to investigate the feasibility and usability of a user interface that abides by the vertical RTL typesetting rules. The results obtained show that while some of these gaps could be filled, there remain several technological challenges.
SLFB-CNN: An interpretable neural network privacy protection framework

The feedforward-designed convolutional neural network (FF-CNN) method was recently proposed by Kuo et al. It has strong interpretability and low training complexity. In this paper, we have proposed two improvements (1) We merge two algorithms Layer-wise Relevance Propagation (LRP) and FF-CNN to build an interpretable neural network framework called LFB-CNN. The back-propagation (BP) algorithm is used to train the fully connected layer of FF-CNN. Meanwhile, the LRP algorithm is used to decompose and calculate the correlation between the input and output of the fully connected layer, and further improve the model performance without reducing the interpretability. (2) We conducted a privacy analysis on the LFB-CNN framework. Once the parameters of the framework are disclosed, the privacy of the data provider will be leaked. Therefore, we use differential privacy to propose a secure LFB-CNN (SLFB-CNN) algorithm. At last, we verified the effectiveness of our proposed method on the MNIST, Fashion-MNIST and CIFAR-10 datasets.
A Case Study of User Privacy based on WiFi Data in a Campus Setting

In many walks of life, data recording/capture and its subsequent use can be of benefit to society and businesses alike. In many cases, users are fully willing to share data about themselves as part of getting access to some perceived benefit through this sharing, e.g. using the geo-location of their mobile device to obtain local information for route planning. However in other cases, information is captured on individuals where they have little choice. This might be CCTV images of pedestrians walking down the streets, or as presented in this paper: WiFi data from individuals at a given educational organisation. Individuals at the University of Melbourne often depend on having access to University wireless to undertake their courses/degrees or their jobs respectively. The terms and conditions that they agree to when they sign up to WiFi access include a description of how the data can be used by the University, e.g. to assist in understanding the use of the WiFi network and/or the physical University campus for space/infrastructure management. The University also has stringent privacy policies that prevent the tracking of individuals. In this context, the need to provide services that can use WiFi data for business purposes, but protect the information so that individuals cannot be re-identified is paramount. There are also many researchers that wish to access safe versions of this data for research purposes, e.g. way-finding. This paper explores case studies exploring this data based on WiFi data analytics. We show how (consenting) individuals can be re-identified with minimal external and seemingly innocuous (i.e. non-identity-revealing) data.
Sentiment-Driven Breakdown Detection Model Using Contextual Embedding ElMo

The recent rapid developments in neural networks have stimulated significant performance progress in detecting breakdown in dialogue. The existing research focuses on detecting breakdown from the data label using different features such as word similarity and topic transition; however, these features are insufficient to capture contextual features. Therefore, we used ELMo language model to extract the contextual feature to help to detect the breakdown which faces a challenge due to the basis of human opinion. Another crucial challenge facing breakdown detection is the lack annotated dataset. To take this challenge we used sentiment as the data label with contextualized ELMo embedding model to detect the breakdown in dialogue. The model was evaluated with state-of-the-art models and the results shows the proposed model outperforms other models.
Re-verification of the Improved Software Project Monitoring Task Model of the Agile Kanban (i-KAM)

The improved software project monitoring task model of the Agile Kanban (i-KAM) is a model that is developed to enhance the progress monitoring task of the software development projects. Although the i-KAM components and criteria have been initially verified through experts’ review; however, a re-verification process for the updated version of i-KAM is needed after applying the suggestions provided by the experts. Therefore, this paper presents the re-verification results of i-KAM effectiveness, which was assessed based on five factors, which are understandability, relevance, feasibility, organization, and comprehensiveness. In this study, seven software practitioners from different software industries have participated in a focus group to re-verify the revised model. Outcomes show that the revised i-KAM is an effective model characterized by various advantages and numerous features, which can assist software practitioners to effectively monitor their projects’ progress during the software development process. Future directions should recognize the practical implementation of i-KAM within real environments, focus on the educational and industrial software houses, and validate the model applicability through conducting case studies in developing actual software projects.
Visual Saliency Oriented Vehicle Scale Estimation

Vehicle scale estimation with a single camera is a typical application for intelligent transportation and it faces the challenges from visual computing while intensity-based method and descriptor-based method should be balanced. This paper proposed a vehicle scale estimation method based on salient object detection to resolve this problem. The regularized intensity matching method is proposed in Lie Algebra to achieve robust and accurate scale estimation, and descriptor matching and intensity matching are combined to minimize the proposed loss function. The visual attention mechanism is designed to select image patches with texture and remove the occluded image patches. Then the weights are assigned to pixels from the selected image patches which alleviates the influence of noise-corrupted pixels. The experiments show that the proposed method significantly outperforms state-of-the-art methods with regard to the robustness and accuracy of vehicle scale estimation.
Constraints on the nature of metamorphism in the Kalgoorlie gold camp (Yilgarn Craton, Western Australia) and implications for genetic models of gold mineralisation

The timing and nature of metamorphism in the Kalgoorlie gold camp (Yilgarn Craton, Western Australia) are key aspects to genetic models for gold mineralisation. Despite this, existing constraints on metamorphism are equivocal as to whether the Fimiston and Oroya Au–Te lodes formed pre- or post-peak regional metamorphism. Evaluation of an actinolite (S1)–chlorite (S2) crenulation cleavage in the Hannan’s Lake Serpentinite on the western margin of the Golden Mile Super Pit supports a syn-D1 timing for peak metamorphism and a post-peak metamorphic timing for the Fimiston and Oroya Au–Te lodes. These results support a key aspect of metamorphic models for gold mineralisation. If intrusion-related models are correct, they should not be premised on the argument that the Fimiston and Oroya Au–Te lodes are equivalent to other examples of metamorphosed, epithermal-type mineralisation in orogenic belts.KEY POINTSThe definition of an S1 cleavage in the Hannan’s Lake Serpentinite by actinolite supports a syn-D1 timing (ca 2685–2680 Ma) for peak regional metamorphism (sub- to lower-greenschist facies) in the Kalgoorlie gold camp.A syn-D1 timing for peak regional metamorphism suggests that the D2 (ca 2675–2665 Ma) Fimiston and Oroya Au–Te lodes and D3 (ca 2650–2640 Ma) Mt Charlotte stockwork Au orebodies developed in post-peak metamorphic settings.A post-peak metamorphic timing for the Fimiston and Oroya Au–Te lodes suggests that these mineralisation types do not represent metamorphosed equivalents of epithermal Au–Te mineralisation.
Website Creation as a Means of Fulfilling Muslim Funeral Information Needs Amid Covid-19 Pandemic

A website can serve as a means of fulfilling information needs on the internet. The need for websites as a source of information increased during the Covid-19 pandemic along with many people doing their activities at home. The Memorial Advisor sees this situation as an opportunity. They created a website to convey information about Muslim funeral products to the public. The purpose of this research is to examine website creation as a means of fulfilling the information needs of the public regarding Muslim funerals amid the Covid-19 Pandemic. This research uses the descriptive qualitative method. This research concludes that Memorial Advisors is free to inform and promote Muslim funerals through any media, including the website. To increase the website's visibility, the MA chose a perfect domain name according to the Search Engine Optimization (SEO). They also pay attention to the content, design, page navigation, and most importantly, the placement of contact numbers. © 2021. All Rights Reserved.
Effects of VR on Users' Speaking Performance and Cognitive Load: Design of VR-assisted English-Speaking Application on Mobile Phones

Virtual reality (VR) is now widely introduced into English education, while mobile VR applications for English as a foreign language (EFL) learners to practice their speaking are still scant. In order to design a prototype for the VR-assisted English-speaking application, a mixed-method research is conducted in the current study. It investigates 22 learners' oral performance, cognitive load and perspectives in a mobile-based VR learning condition. The results indicate learners' speaking performance has significant improvement, while their cognitive load is increased at the same time. Therefore, the application needs to provide functions helpful for users' better understanding on tasks and content. Besides, varied interaction is also necessary to enhance users' immersive learning experience. The prototype design based on the assessment findings is presented in the end of the study, which can be a reference for the application development in the future.
Create EC2 Instance, Install Splunk and Configure Using Ansible

In today’s world, there is burgeoning of the projects in IT firms which unfortunately leads to tedious work, and human errors rise to a great extent. To take the above situation under control, computer automation is the best way. The behoof of this study is to lower the human task by the help of automation scripts. Considering this, there is an idea to automate the industrial task. Whenever there are large number virtual systems and if Splunk is required to install on each of them, it would be tedious. For this Ansible, the tool provided by the Red Hat for automation, will be used. The script will be written in the YAML file which will be executed and a number of infrastructures will be created and Splunk will be installed on it. After the installation and configuring the whole infrastructure by the automation script, it will be further used for data monitoring and analysis. Basically, the whole automation script is divided into several tasks which will execute step by step to become a great infrastructure.
Innovative Nurse call System For Patients in Healthcare Centres

Nurses response time to patients call has not been the best as a result of the stress and manual nursing call systems available in many hospitals. This situation has caused a lot of dissatisfaction among patients admitted. This study seeks to provide an improvement on already existing nurse call systems. Despite the existence of nurse call systems that make use of audible signal, nurses still need to manually search for patients who called for their assistance. Hence, this study designed and implemented an automatic nurse call system that uses an audiovisual signal to assist nurses find the location of the patient and reason for admission. An experimental approach was adopted while an electronic device based on Arduino board was designed and constructed. The system operates on 5VDC and can be powered with a DC source operating within the range of 9-32V. To test the communication efficiency of the design, systems data were recorded on system's response time. The average response time of 0.24 seconds was obtained with 15 sampled inputs.
An Information Theoretical Model to Analyze the Security of Blockchains

We propose an information theoretical model to analyze blockchains. Recently, blockchain has become a trend in the technology domain. We proposed a model that will help to reduce challenges brought on/posed by this technology. This paper will cover three main sections. First is an introduction about Blockchain. Then, we introduce our method, in which we explain how the least action principle, maximum independent set, and N-Queen solutions are applied to analyze the security of blockchains. Finally, we present our case study implementation, and results.
TReM: A Task Revocation Mechanism for GPUs

GPUs in datacenters and cloud environments are mainly offered in a dedicated manner to applications, which leads to GPU under-utilization. Previous work has focused on increasing utilization by sharing GPUs across batch and user-facing tasks. With the presence of long-running tasks, scheduling approaches without GPU preemption fail to meet the SLA of user-facing tasks. Existing GPU preemption mechanisms introduce variable delays up to several seconds, which is intolerable, or require kernel source code, which is not always available.In this paper, we design TReM, a GPU revocation mechanism that stops a task at any point in its execution. TReM has a constant latency, of about 5ms to stop the currently executing kernel and about 17ms to start a new task. TReM does not store the state of the revoked kernel to obviate transfer latencies. We design and implement two scheduling policies, Priority and Elastic, that prioritize user-facing over batch tasks and utilize TReM to improve SLAs for user-facing tasks. To evaluate TReM, we use a workload generator that creates workloads with different characteristics, based on real traces. TReM reduces SLA violations by up to 10% compared to baseline policies that do not use a revocation mechanism. TReM incurs negligible overhead for non-revoked tasks and wastes only 3% of computation due to revocations for the workloads we examine.