Reviewerly/exHarmony huggingface dataset:
Enhancing Fidelity of Quantum Cryptography using Maximally Entangled Qubits

Securing information transmission is critical today. However, with rapidly developing powerful quantum technologies, conventional cryptography techniques are becoming more prone to attacks each day. New techniques in the realm of quantum cryptography to preserve security against powerful attacks are slowly emerging. What is important though now is the fidelity of the cryptography, because security with massive processing power is not worth much if it is not correct. Focusing on this issue, we propose a method to enhance the fidelity of quantum cryptography using maximally entangled qubit pairs. For doing so, we created a graph state along a path consisting of all the qubits of ibmqx4 and ibmq_16_melbourne respectively and we measure the strength of the entanglement using negativity measurement of the qubit pairs. Then, using the qubits with maximal entanglement, we send the modified encryption key to the receiver. The key is modified by permutation and superdense coding before transmission. The receiver reverts the process and gets the actual key. We carried out the complete experiment in the IBM Quantum Experience project. Our result shows a 15% to 20% higher fidelity of encryption and decryption than a random selection of qubits.
Defense against adversarial attacks using DRAGAN

In recent years, deep neural networks have been used in a wide range of applications such as machine vision, speech recognition, natural language processing, etc., and have achieved significant success, however, these networks are vulnerable to adversarial attacks. This has raised concerns about the security of these networks. In this paper, we are going to use Generative Adversarial Networks (GANs) to resist image classifiers against a range of adversarial attacks. To do this, we took the inspiration of Defense-GAN and improving it by using DRAGAN (Deep Regret Analytic Generative Adversarial Networks). We train the GAN on unperturbed images, and after that, the GAN will be used to reconstruct the input images before feeding to the image classifier, which makes it resistant to adversarial attacks.
Design of a Mobile Robot to Work in Special Environments

This paper will introduce the need for a robot to work in special environments and gives one possible solution with mobile robotics. In addition, it will present the research objectives, general and specific technical requirements, and design and development of a mobile robot to work in special environments (with explosive/dangerous gases to humans). This research attempts to use principles of user-centred design to guide the design of a special mobile robot. Designing a mobile robotic system must address the needs of its potential users, potential beneficiaries. The resulting (mechanical, electrical and control) hardware architecture of the experimental model of a mobile robot to work in special environments is presented.
Is Virtual Reality Sickness Elicited by Illusory Motion Affected by Gender and Prior Video Gaming Experience?

Gaming using VR headsets is becoming increasingly popular; however, these displays can cause VR sickness. To investigate the effects of gender and gamer type on VR sickness motion illusions are used as stimuli, being a novel method of inducing the perception of motion whilst minimising the "accommodation vergence conflict". Females and those who do not play action games experienced more severe VR sickness symptoms compared to males and experienced action gamers. The interaction of the gender and gamer type revealed that prior video gaming experience was beneficial for females, however, for males, it did not show the same positive effects.
Logistic Regression Model of Relationship between Breast Cancer Pathology Diagnosis with Metastasis

Abstract Management of breast cancer requires a diagnosis of breast cancer, including an accurate pathology diagnosis to direct the provision of appropriate treatment. Part of inseparable diagnosis of breast cancer itself is determining the presence of metastasis. This study aims to determine the relationship model of pathological diagnosis with the possibility of metastasis to other organs of the breast. Pathology diagnosis results include tumor position, topography, behavior, grade, and tumor status. This study used secondary data from the Hospital Information System (SIRS) of the hospital in Makassar, Indonesia. Data obtained from the results of pathology and clinical examinations of breast tumor as many as 204 patients were hospitalized during the year of 2018. Binary regression analysis modeling was performed to see the relationship between types of pathological diagnosis (laterality, topography, behavior, grade, tumor status) of breast cancer with the possibility of metastases to other organs. The results of the analysis found a significant relationship between the position and status of the tumor with the presence of metastasis. The results of the metastatic binary logistic regression model was L ( Y ) = 1.330 – 0.670lat1 – 0.779lat2 – 0.724 status Chi-square analysis between metastasis with surgery and chemotherapy found the significant relationships between metastasis with surgery (X 2 = 15.951; p value = 0.001) and metastasis with chemotherapy (X 2 = 20,796; p value = 0.001). The results of this analysis indicate that the position and status of the tumor have a relationship with the likelihood of metastasis. In addition, this metastasis further determines the management of treatment whether surgery or chemotherapy or both.
Practical AMC model based on SAE with various optimisation methods under different noise environments

Automatic modulation classification (AMC) has recently attracted widespread attention nowadays due to its desirable features of generalisability and requirement of little prior knowledge through artificial intelligence (AI) technology. The authors propose a stacked auto-encoder (SAE) based on various optimisation methods structure to intelligently process a feature space that includes spectral-based features and high-order cumulants. To unify the dimensionality of the features, they apply different normalisation methods to the feature space before training the SAE model to decide corresponding normalisations under different noise environments. Linear normalisation is superior when signal-to-noise ratio (SNR) is low, and standardisation is superior when SNR is between -1 and 4 dB. Regularisation works best when SNR is greater than 5 dB. To increase the recognition accuracy of the proposed model, they introduce the unconstrained optimisation theory to adjust the proposed SAE model, including Nelder-Mead method, Newton optimisation method, conjugate gradient method and quasi-Newton method. They observe that the quasi-Newton method offers desirable performance when optimising SAE model. It is the first time to compare these data normalisation methods and discuss unconstrained optimisation theory together to recognise modulation types. The recognition accuracy of this model for eight modulation types can reach 99.8% when SNR ranges from − 5 to 10 dB.
Theory and Practice of Search Engine Optimization

The extensive range of information resources and services is certainly one of the most important features of the internet while at the same time, web search is considered as a crucial application for managing the massive volumes of distributed web content. Beyond argument, search engines have made an enormous contribution to the web by making the process of finding information online a very quick and easy process. This chapter provides an overview of Search Engine Optimization, with a focus on its different characteristics and practices as well its history and how it has evolved over the years. In order to give a better understanding of the importance of SEO in the current state of the Internet and in information search, basic knowledge of how search engines operate along with their recent updates are also provided.
Intimate consumptions

Abstract In this paper, I investigate how the structure of and discursive performance on North American YouTube eating shows contribute to the creation of intimacy and informality. In a typical eating show, the performer eats copious amounts of food while talking to their non-copresent audience, making use of interactional registers to ‘break’ from the solitary setting and to build rapport with their viewers. The material for this study is based on two case studies drawn from a corpus of YouTube eating shows. At the heart of the eating show performances lies the persona of a ‘friend’ of the viewer, diverting from their highly structured and technologically-mediated communicative nature. While the language used on eating shows may thus seem spontaneous and unprompted, the videos are not only planned but also edited. The simultaneous presence of selected unedited moments (i.e., slips of the tongue and other mishaps), however, is evidence of a conscious blending of front- and backstage performance ( Goffman 1959 ; Shulman 2017 ).
The Development of Teaching Material For Popular Writing Based On Creative Industry Using M-Learning For College Students of Faculty of Language and Literature

Abstract The objective of the research is to produce proper teaching material for popular writing subject based on creative industry using M-learning for college students of faculty of language and literature in FBS UNJ. This research applies research and development method by Borg and Gall which consists of introduction, model development, and evaluation. This research reveals that (1) Popular writing subject which based on creative industry using M-4learning is applied with text based learning approach, (2) the text used are: books review, opinion, features, and commercials, (3) field test in both small and large scale shows that there is a significant differences between average pre-test score and average post-test score achieved by the students. Therefore, creative industry based teaching material using M-learning with text based learning approach is feasible and effective in improving students’ capability in popular writing in PBSI FBS UNJ.
Participatory Sensing in the Speculative Smart City

This paper explores a bottom-up way to speculate towards future smart cities by inviting residents of Copenhagen, Denmark to participate in sensing activities. It illustrates how the idea of “design things” could support bottom-up citizen participation in a smart city. It uses a research through design approach, deploying a wearable air quality sensor to three Copenhageners. By investigating citizens’ perception of the city through this prototype, we illustrate a possible path for engagement in the development of future smart city technologies that offer a greater sense of influence and relevance for residents. Further, citizen participation in sensing activities provides a route to different understandings of smart cities: as a place for people and participation instead of for data and rationalisation.
Sim-to-Real with Domain Randomization for Tumbling Robot Control

Tumbling locomotion allows for small robots to traverse comparatively rough terrain, however, their motion is complex and difficult to control. Existing tumbling robot control methods involve manual control or the assumption of at terrain. Reinforcement learning allows for the exploration and exploitation of diverse environments. By utilizing reinforcement learning with domain randomization, a robust control policy can be learned in simulation then transferred to the real world. In this paper, we demonstrate autonomous setpoint navigation with a tumbling robot prototype on at and non- at terrain. The flexibility of this system improves the viability of nontraditional robots for navigational tasks.
A Password Strength Evaluation Algorithm Based on Sensitive Personal Information

Many Internet service providers are still using traditional password strength evaluation methods, resulting in user passwords being vulnerable to social engineering attacks. We believe that the password strength evaluation method based on sensitive personal information has great research value for improving the security of password authentication system. In this paper, we use the structure segmentation algorithm and the bidirectional matching algorithm to investigate how users' personal information is used in passwords. Then, we present a sensitivity personal information coverage evaluation function that represents the correlation between users' password and their personal information. Finally, a password strength evaluation method based on sensitive personal information is proposed. This method is composed of three stages: preprocessing stage, prediction dictionary generation stage and password strength evaluation stage.
Multiregion Multiscale Image Segmentation with Anisotropic Diffusion

We present a multiregion image segmentation approach which utilizes multiscale anisotropic diffusion based scale spaces. By combining powerful edge preserving anisotropic diffusion smoothing with isolevel set linking and merging, we obtain coherent segments which are tracked across multiple scales. A hierarchical tree representation of the given input image with progressively simplified regions is used with intra-scale splitting and inter-scale merging for obtaining multiregion segmentations. Experimental results on natural and medical images indicate that multiregion, multiscale image segmentation (MMIS) approach obtains coherent segmentation results.
Partly Supervised Multi-Task Learning

Semi-supervised learning has recently been attracting attention as an alternative to fully supervised models that require large pools of labeled data. Moreover, optimizing a model for multiple tasks can provide better generalizability than single-task learning. Leveraging self-supervision and adversarial training, we propose a novel, general purpose semi-supervised, multiple-task model-namely, self-supervised, semi-supervised, multi-task learning (S <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sup> MTL)-for accomplishing two important medical image analysis tasks: segmentation and diagnostic classification. Experimental results on chest and spine X-ray datasets confirm that our S <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sup> MTL model significantly outperforms semi-supervised single-task, semi/fully-supervised multi-task, and fully-supervised single-task models, even with a 50% reduction in class and segmentation labels.
Time Optimal Trajectories for a Mobile Robot With Acceleration and Speed Limits in the Presence of an Obstacle

Abstract This paper studies the time optimal paths of a mobile robot navigating in a planar environment containing an obstacle. The paper considers a point-mass robot that moves with bounded acceleration and limited turn-rate controls in the presence of an obstacle. The optimal control problem yields 12 path primitives that form the time optimal paths of the point-mass robot. The problem is then extended to a disc-robot that moves in the presence of an obstacle with turn in-place capability. The optimality conditions yield 12 modified path primitives that form the time optimal paths of the disc-robot. All path primitives are analytically characterized and examples demonstrate how they form time optimal trajectories in the presence of several obstacle types.
Augmenting an Internet Voting System with Selene Verifiability using Permissioned Distributed Ledger

This paper discusses an approach for incremental change to an online voting system, introducing a verifiability layer based on the Selene protocol to a trusted-third-party-based system, resulting in a fully verifiable and transparent e-voting system. The paper also describes how to use Distributed Ledger Technology as a component of the implementation of Selene to manage the verifiability data in a distributed way for resilience and trust.
Answer Extraction with Graph Attention Network for Knowledge Graph Question Answering

In the knowledge graph question answering, the graph neural network can be used to encode the subgraph nodes related to the question entity to select the correct answer node. However, existing researches mainly focus on the modalities for the node encoding with graph neural network, ignoring that different types of subgraphs have different requirements for encoding information. To overcome the problem, this paper divides the subgraph into two types: the searching graph and the extending graph. Then we propose an answer extraction method with graph attention network for the searching graph, which can weight the information of neighbor nodes with different attention instead of the average. The hierarchical attention is also introduced to integrate question information into the subgraph node embedding to obtain the node presentation with question dependency. The accuracy of 48.2% is achieved on the CommonsenseQA dataset, which is much higher than the random guess (20%). In addition, the accuracy of the simplified model with no hierarchical attention decreases by 3.5%, which indicates the hierarchical attention mechanism can improve the predictive performance of the proposed model.
Classifying Users Through Keystroke Dynamics

The billions of users connected to the Internet together with the anonymity that each of them can have behind a computer that is a source of many risks, such as financial fraud and seduction of minors. Most methods that have been proposed to remove this anonymity are either intrusive, or violate privacy, or expensive. We propose the recognition of certain characteristics of an unknown user through keystroke dynamics, which is the way a person is typing. The evaluation of the method consists of three stages: the acquisition of keystroke dynamics data from 110 volunteers during the daily use of their device, the extraction and selection of keystroke dynamics features based on their information gain, and the testing of user characteristics recognition by training five well-known machine learning models. Experimental results show that it is possible to identify the age group, the handedness, and the educational level of an unknown user with an accuracy of 87.6, 97.0, and 84.3, respectively.
3D Information Visualization Interactive Technology and Its Application in Network Security

Abstract With the increase of network complexity, network security incidents are showing a trend of rapid growth. Aiming at the low efficiency of large-scale network security data visual analysis, this paper proposes a heterogeneous tree network security data organization method, which improves the real-time performance of data analysis, and designs a three-dimensional multi-layer spherical space for large-scale networks Visualization model (TMSM). The experimental results show that this method has strong analytical capabilities for large-scale, high-dispersion network security data, and can effectively identify different types of intrusions and facilitate network forensics.
PINE: Universal Deep Embedding for Graph Nodes via Partial Permutation Invariant Set Functions

Graph node embedding aims at learning a vector representation for all nodes given a graph. It is a central problem in many machine learning tasks (e.g., node classification, recommendation, community detection). The key problem in graph node embedding lies in how to define the dependence to neighbors. Existing approaches specify (either explicitly or implicitly) certain dependencies on neighbors, which may lead to loss of subtle but important structural information within the graph and other dependencies among neighbors. This intrigues us to ask the question: can we design a model to give the adaptive flexibility of dependencies to each node's neighborhood. In this paper, we propose a novel graph node embedding method (named PINE) via a novel notion of partial permutation invariant set function, to capture any possible dependence. Our method 1) can learn an arbitrary form of the representation function from the neighborhood, without losing any potential dependence structures, and 2) is applicable to both homogeneous and heterogeneous graph embedding, the latter of which is challenged by the diversity of node types. Furthermore, we provide theoretical guarantee for the representation capability of our method for general homogeneous and heterogeneous graphs. Empirical evaluation results on benchmark data sets show that our proposed PINE method outperforms the state-of-the-art approaches on producing node vectors for various learning tasks of both homogeneous and heterogeneous graphs.
Encryption File System Framework - Proof of Concept

Edge Computing, also known as the IoT ecosystem, has recently become a cornerstone of computational evolution. Most recently, challenges emerged in the act of protecting such technology, hence it became increasingly popular in recent years, and with this growth, safety concerns increased due the recent growing number of hacking events. Recent challenges rely in the IoT technologies e.g., virtual interfaces, to deliver solutions to end point terminals and securing this relation is significantly important hence these terminals are commonly in the end user pocket, where other personal information is also present. There are several tools available for developers to protect data, comprising international mandatory rules for security has there is common sense to do it. The purpose of this project was to create one specific and unique framework where the developer has no need to possess knowledge in the subject of cryptography, to be able to perform security operations on files with data encrypted.
An Improved K Nearest Neighbor Classifier for High-Dimensional and Mixture Data

Abstract Conventional K nearest neighbor classifier is challenged by data with high-dimensional and mixture of continuous and categorical variable characteristics. To address this issue, we propose an improved K nearest neighbor classifier by using principal component mixed also called PCAmix. First, we employed the PCAmix to preprocess high-dimensional and mixture data. Then, the KNN classifier is used to classify objects characterized by high-dimensional mixture of continuous and categorical variables. We have evaluated our method using five mixture datasets that are available from the UCI machine learning repository and compared our results with the base line approaches. The experimental results showed that the proposed method had better classification performance for high-dimensional mixture data.
Ontology-Based Correlation Detection Among Heterogeneous Data Sets: A Case Study of University Campus Issues

For data-driven decision making, it is essential to build a data infrastructure that accumulates various data types. In such organizations as universities, industries, and government bodies, the integration of heterogeneous data and cross-sectional analysis have been an issue as these various data are distributed and stored in different contexts. Knowledge Graphs with a graphical structure that can flexibly change the schema are suitable for such heterogeneous data integration. In this study, we focused on a university campus as an example of a small organization and propose an ontology that enables the cross-sectional analysis of various data. In particular, we semantically interlinked the dimensions in the data model to enable the extraction of data across multiple domains from various perspectives. Then, the unstructured data collected were accumulated as knowledge Graphs based on the proposed ontology to build a data infrastructure. In addition, we found several correlations that could help in solving university campus issues and improving university management using the developed ontology-based data infrastructure.