Reviewerly/exHarmony huggingface dataset:
Construction, test and application of a geochemical gold metallogene: Case studies in China

In order to trace the geological materials basing on the geochemical survey data, a gold metallogene is constructed according to the idea of the geochemical gene. Eleven elements are sequenced in the gold metallogene as Ti, Sb, Th, Bi, Nb, Au, Zr, Ag, La, Pb, and Y. The reference values to normalize the contents of the 11 elements in the spectral line of the gene are also proposed. The gold metallogene of the ideal ore or the ideal gold mineralized sample is 12020202020 while the gene of the ideal background sample is 10202020202. The properties of heredity, inheritance, variability, and similarity of the metallogene are tested on 8 weathering profiles developed over granite, granodiorite, basalt, andesite, schist outside the gold deposit areas and 3 weathering profiles formed on dolomite, schist, and andesite in the gold deposit areas in China. According to the criterion of the 80% value of the similarity between two genes viewed as the similar gene, the gold metallogene exhibits stable heredity, inheritance, and similarity during weathering of many kinds of rocks but may also display variability in some cases. The similarity value of 40% of the gold metallogene relative to the ideal ore can be viewed as the criterion to discriminate samples with or without gold mineralization. Two weathering profiles developed over granite with K-feldspar alteration and phyllite respectively near gold deposit areas and a cross section profile of a gold ore vein in gold deposit area are selected as case studies to apply the gold metallogene. The results indicate that the gold metallogene may be viewed as an integrated index of recognizing the gold mineralization in geochemical exploration.
A linguistic steganography framework using Arabic calligraphy

In linguistic steganography, languages’ features are employed to hide information. The Arabic language has a rich set of features which have not been utilized until now in this area. In particular, Arabic calligraphy contains multiple fonts and multiple shapes of Arabic alphabet letters. In this paper, a framework that uses Arabic calligraphy to hide information is proposed. The phases of the framework are preparation phase, embedding phase and extraction phase. The embedding phase uses string matching to generate stego text and accompanying letter shapes according to a secret message. The framework also includes corpus creation and a modification of the Aho-Corasick string-matching algorithm. The Arabic font Naskh was used as a case study. A set of Arabic poetry and proverbs were used as a dataset. The framework was evaluated on capacity and security. Because the visual difference between the cover and the stego-cover must be unnoticeable to the human in any stego-system, the security in this framework is satisfying due there is no cover used. The cover represents the secret message itself and it provides high capacity to hide data also. The evaluation showed the potential of using the multiple shapes of Arabic letters to satisfy steganography requirements.
Design and analysis of a public key infrastructure for SBAS data authentication

Integral to any authentication system is the design of its public key infrastructure (PKI), allowing the system to distribute, maintain, and revoke cryptographic keys while dividing and allocating the responsibility of security between different entities. This paper develops a PKI for two SBAS candidate authentication schemes: one each for an L5 I-channel and Q-channel implementation. A simulator, introduced as MCOS, is developed to enable large scale Monte Carlo tests of these PKI designs. This work concludes that once a receiver has been deployed with a set of preinstalled encrypted public keys, a strictly over-the-air method of delivering PKI information is feasible for SBAS data authentication in nominal conditions. This work also provides a strong candidate PKI that is designed to be standardized internationally for current and future SBAS service providers.
FlexPass

This paper presents a new user authentication paradigm which is based on a flexible user authentication method, namely FlexPass. FlexPass relies on a single, user-selected secret that can be reflected in both textual and graphical authentication secrets. Such an approach facilitates adaptability in nowadays ubiquitous user interaction contexts within the Internet of Things (IoT), in which end-users authenticate multiple times per day through a variety of interaction device types. We present an initial evaluation of the new authentication method based on an in-lab experiment with 32 participants. Analysis of results reveal that the FlexPass paradigm is memorable and that users like the adaptable perspective of the new approach. Findings are expected to scaffold the design of more user-centric knowledge-based authentication mechanisms within nowadays ubiquitous computation realms.
Bibliometric Study of Indian Institutes of Technology in Computer Science

The Indian Institutes of Technology (IITs) are considered as top most engineering colleges in India in terms of research and development. The Institutes of Technology Act, 1961 govern these IITs and has declared them as of national importance and provides more funds when compared to other universities. Hence it is necessary to measure the performance of IITs based on their journal, conference, review and other publications. It involves precise measurement of bibliometric indicators like average citation, h-index, g-index, etc. To achieve this goal, the paper analyses the research work of IITs (established before 2015) and other Indian institutes in Computer Science discipline based on citation data available in Scopus database (till November 2018).
Solar Energy Potential Forecasting and Optimization Using Artificial Neural Network: South Africa Case Study

Explored in this work is the comparison of different artificial neural network (ANN) training functions for modeling the daily average solar irradiance (DASI) forecaster. The aim of this work is to establish the comparison studies with reference to the performances of four training functions namely, the Resilient Backpropagation (TRAIN-RP), Levenberg-Marquardt (TRAIN-LM), Scaled Conjugate Gradient (TRAIN-SCG), and Quasi-Newton (TRAIN-BFGS) implemented in the training of the ANN for forecasting the DASI. Using the Levenberg-Marquardt's learning rule the feed-forward network with back propagation architecture is implemented in the approach of the neural network design. The comparison is done by looking at the Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and the coefficient of correlation (R) of the models. The TRAIN-LM performed better than the other models and achieved a 99.742% coefficient of correlation with a mean absolute percentage error (MAPE) of 2.394%. The training, testing, and validation of the neural network is done by utilizing the actual data from six different locations in the North West cities, and these data are mined from the NASA database. The findings in this paper will be useful in the optimizing the micro-grid and its performance.
<i>k</i>-uniform maximally mixed states from multi-qudit phase states

This paper concerns the construction of k-uniform maximally mixed multipartite states by using the formalism of phase states for finite dimensional systems (qudits). The k-uniform states are a special kind of entangled (n)-qudits states, such that after tracing out arbitrary (n[Formula: see text]k) subsystems, the remaining (k) subsystems are maximally mixed. We recall some basic elements about unitary phase operators of a multi-qudit system and we give the corresponding separable density matrices. Evolved density matrices arise when qudits of the multipartite system are allowed to interact via an Hamiltonian of Heisenberg type. The expressions of maximally mixed states are explicitly derived from multipartite evolved phase states and their properties are discussed.
User behavior pattern detection in unstructured processes – a learning management system case study

Process mining methodologies are designed to uncover underlying business processes, deviations from them, and in general, usage patterns. One of the key limitations of these methodologies is that they struggle in cases in which there is no structured process, or when a process can be performed in many ways. Learning Management Systems are a classic case of unstructured processes since each learner follows a different learning process. In this paper, we address this limitation by proposing and validating the user behavior pattern detection (UBPD) methodology which is based on detecting very short user activities and clustering them based on shared variance to construct a more meaningful behavior. We develop and validate this methodology by using two datasets of unstructured processes from different implementations of a learning management system. The first dataset uses a gamified course where users have the freedom to choose how to use the system, and the second dataset uses data from a massive online open course, where again, system usage is based on personal learning preferences. The key contribution of the methodology is its ability to discover user-specific usage patterns and cluster users based on them, even in noisy systems with no clear process. It provides great value to course designers and teachers trying to understand how learner interact with their system and sets the foundation for additional research in this class of systems.
Classifying User Reviews at Sentence and Review Levels Utilizing Naïve Bayes

Many products are sold on electronic commerce websites. Online customer reviews are available to help in selecting products to purchase. The products should be recommended by a special system that is capable to analyse and classify reviews because it is very hard for users to read many reviews and result of the recommendation should be personalized to suit user’s requirements. The aim of this research is to classify the online customer reviews accurately to obtain opinion mining techniques of the recommendation system. The research focuses on classifying the Japanese reviews into positive or non-positive. In this study, we classify the reviews at the sentence and the review level. The data set for the sentence-level classification contains the reviews of Electronic Devices category. The data set for the review-level classification contains the reviews of Mobile Phone Accessories category. This research also compares the results of our experiments and another research to evaluate the experimental results. This research is successful to obtain opinion mining techniques and the better results at the review-level classifications instead of the sentence-level classifications. The experimental results will contribute to the opinion mining phase of the recommendation system.
Troubling Innovation

Craft practices such as needlework, ceramics, and woodworking have long informed and broadened the scope of HCI research. Whether through sewable microcontrollers or programs of small-scale production, they have helped widen the range of people and work recognised as technological and innovative. However, despite this promise, few organisational resources have successfully drawn together the disparate threads of scholarship and practice attending to HCI craft. In this workshop, we propose to gather a globally distributed group of craft contributors whose work reflects crucial but under-valued HCI positions, practices, and pedagogies, Through historically and politically engaged work, we seek to build community across boundaries and meaningfully broaden what constitutes innovation in HCI to date.
Visual Analytics for Comparing the Impact of Outliers in k-Means and k-Medoids Algorithm

Clustering is an unsupervised machine learning approach which plays a great role in assigning the data sets into specific clusters based upon some similarity or dissimilarity criterions. K-Means and K-Medoids are the well-known clustering algorithms that are widely used in different application areas of machine learning. K-Means algorithm is sensitive to the outliers due to influence on mean values by outliers in comparison to K-Medoids algorithm which uses medoids, the most centrally located values in a cluster. In this paper, the comparison of both algorithms have been done to evaluate the impact of outliers on their performances by using iris dataset and an interactive web application has been developed with visual analytics to display the impact of outliers on both these clustering algorithms for better insight. The application is accessible through the internet browser.
Distributed Emergent Software: Assembling, Perceiving and Learning Systems at Scale

Emergent software systems take a reward signal, an environment signal, and a collection of possible behavioural compositions implementing the system logic in a variety of ways, to learn in real-time how best to assemble a system.This reduces the burden of complexity in systems building by making human programmers responsible only for developing potential building blocks while the system determines how best to use them in its deployment conditions -with no architectural models or training regimes.In this paper we generalise the approach to distributed systems, to demonstrate for the first time how a single reward signal can form the basis of complex decision making about how to compose the software running on each host machine, where to place each sub-unit of software, and how many instances of each sub-unit should be created.We provide an overview of the necessary system mechanics to support this concept, and discuss the key challenges in machine learning needed to realise it.We present our current implementation in both datacentre and pervasive computing environments, with experimental results for a baseline learning approach.
The Fourth Industrial Revolution: Towards Energy 4.0 in Kazakhstan

Large-scale changes in technological advancements will lead to large scale innovations and stimulation of economic growth across the globe. Nowadays, as the leading industrialized economies move to intelligent manufacturing, called industry 4.0, there is a growing impact on the energy sector in any country. The latter may be referred to as Energy 4.0, which incorporates the period of mechanization, the gas and oil era, and the new digital revolution with renewable energy era. Generation, distribution, consumption and smart production of energy - all of these are going to experience an enormous revolutionary changes due to technologies as big data, IoT and cloud computing. This work describes the current state of art of Energy 4.0 in Kazakhstan, its future potential, benefits, challenges and the vision of government to introduce further discussions in the formation of a new technological revolution of Industry 4.0 that will have fundamental and structural nature. The article analyzes the global industrial revolutions and the relevant global energy steps implemented over the past 10 years by illustrating it through Networked Readiness Index (NRI) charts, that show the positions of Kazakhstan by different criteria established of reliable international organizations. Moreover, paper presents the experience of the Energy 4.0 strategy development in technologically advanced countries. In this work, we also offer the approaches and proposals associated with the Industry 4.0 strategy adaptation to the Kazakhstani energy sector, which can be taken as a basis for the new industry development strategy during the upcoming global technological changes.
Optimal Bayesian Classification With Vector Autoregressive Data Dependency

In classification theory, it is generally assumed that the data are independent and identically distributed. However, in many practical applications, we face a set of observations that are collected sequentially with a dependence structure among samples. The primary focus of this investigation is to construct the optimal Bayesian classifier (OBC) when the training observations are serially dependent. To model the effect of dependency, we assume the training observations are generated from VAR (p), which is a multidimensional vector autoregressive process of order p. At the same time, we assume there exists uncertainty about parameters governing the VAR(p) model. To model this uncertainty, we assume that model parameters (coefficient matrices) are random variables with a prior distribution, and find the resulting OBC under the assumption of known covariance matrices of white-noise processes. We employ simulations using both synthetic and real data to demonstrate the efficacy of the constructed OBC.
Improving the Input Accuracy of Touchscreens using Deep Learning

Touchscreens combine input and output in a single interface. While this enables an intuitive interaction and dynamic user interfaces, the fat-finger problem and the resulting occlusions still impact the input accuracy. Previous work presented approaches to improve the touch accuracy by involving visual features on the top side of fingers, as well as static compensation functions. While the former is not applicable on recent mobile devices as the top side of a finger cannot be tracked, compensation functions do not take properties such as finger angle into account. In this work, we present a data-driven approach to estimate the 2D touch position on commodity mutual capacitive touchscreens which increases the touch accuracy by 23.0% over recently implemented approaches.
RoboPlanner

Autonomous robots are being increasingly integrated into Industry 4.0 manufacturing and retail industries due to the twin advantages of improved throughput and adaptivity. In order to handle complex tasks, the autonomous robots require robust action plans, that can self-adapt to runtime changes. A further requirement is efficient implementation of knowledge bases, that may be queried during planning and execution. In this paper, we propose RoboPlanner, a framework to generate action plans in autonomous robots. In RoboPlanner, we model the knowledge of world models, robotic capabilities and task templates using knowledge property graphs and graph databases. Design time queries and robotic perception are used to enable intelligent action planning, that can adapt at runtime. We demonstrate these solutions on autonomous picker robots deployed in Industry 4.0 warehouses.
Using a Game to Explore Notions of Responsibility for Cyber Security in Organisations

Improving the cyber literacy of employees reduces a company's risk of cyber security breach. Game-based methods are found to be more effective in teaching users how to avoid fraudulent phishing links than traditional learning material such as videos and text. This paper reports on the development of a mobile app designed to improve cyber literacy and provoke users' perceptions of who is responsible for cyber security in organisations. Based on a preliminary trial with 17 participants, we investigated users perceptions of a tongue-in-cheek, provocative cyber security awareness game where users' jobs depend on their aptitude for protecting their organisations' cyber security. Findings suggest that users accepted the high responsibility levelled upon them in the game and that ludic elements hold promise for engagement and increasing users' cyber awareness.
Card-Based Cryptographic Protocols with the Minimum Number of Cards Using Private Operations

This paper proposes new card-based cryptographic protocols with the minimum number of cards using private operations under the semi-honest model. Though various card-based cryptographic protocols were shown, the minimum number of cards used in the protocol has not been achieved yet for many problems. Operations executed by a player where the other players cannot see are called private operations. Private operations have been introduced in some protocols to solve a particular problem or to input private values. However, the effectiveness of introducing private operations to the calculation of general logic functions has not been considered. This paper introduces three new private operations: private random bisection cuts, private reverse cuts, and private reveals. With these three new operations, we show that all of logical and, logical xor, and copy protocols are achieved with the minimum number of cards by simple three round protocols. This paper, then shows a protocol to calculate any logical functions using these private operations.
Resolutional Analysis of Multiplicative High-Frequency Speckle Noise Based on SAR Spatial De-Speckling Filter Implementation and Selection

Due to the inherent characteristics of the electromagnetic wave scattering phenomenon, synthetic aperture radar (SAR) images are directly degraded by high-frequency multiplicative speckle (HMS) noise, which makes image de-speckling filter application and selection a challenge. In this regard, an adverse effects analysis of the HMS under implementation of seven different spatial de-speckling filters on a reference SAR image is considered in this paper. The investigation includes the formulation of the backscattered data and the HMS based on the pixel statistics and their distribution as an image noise behavioral analysis method. The resulting complex behavioral model is used for HMS power spectral density (PSD) function modeling. This paper also includes HMS system resolution effects analysis on the raw data generation (RDG) and the received frequency profile (RFP). An objective quality assessment procedure was also carried out to investigate both the de-speckled image resolution and the spatial filter evaluation in the presence of the HMS. As a result, the simulations verify that speckles are embedded within the high-frequency parts of the raw data, directly affecting the spatial resolution and the image resolution with non-specific patterns. The results also show that no spatial de-speckling filter consistently outperforms others, and their implementation is completely dependent on the texture, the system parameters, and their evaluation index. As a novel approach, HMS spectral behavioral modeling within the filtered images, as well as the proposed spatial de-speckling filter evaluation methods, are the proper techniques for optimum filter selection and specific purpose applications. The results are very helpful for remote sensing image restoration and data preservation when dealing with SAR images with a less fine resolution, such as ice-covered areas, coastal change detection, vegetation texture detection, geological structures mapping, and so forth. The SAR system resolution analysis is completed based on inversed problem solution (IPS) and with the help of a hybrid-domain image formation algorithm (IFA).