Reviewerly/exHarmony huggingface dataset:
Pushing Constraints by Rule-Driven Pruning Techniques in Non-Uniform Minimum Support for Predicting Obstructive Sleep Apnea

Boosted Association-Ruled Pruned Decision Tree (ARP-DT), the improved version of the Boosted Decision Tree algorithm, was developed by using association-ruled pre-and post-pruning techniques with referring to the pushed minimum support and minimum confidence constraints as well as the association rules applied. The novelty of the Association-Ruled pruning techniques applied mainly embark on the pre-pruning techniques through researching on the maximum number of decision tree splitting, as well as the post-pruning techniques involving subtree replacement and subtree raising. The applied association rules (ARs) augment the mining of frequent itemset (s) or interesting itemset (s) such that appropriate pre-pruning or subtree pruning techniques can be applied before AdaBoost ensemble is implemented. The ARs applied involve the Adaptive Apriori (AA) augmented rule definitions and theorem as stated in this research focuses on the characteristics of the datasets accessed so as to streamline the rule-driven pruning techniques on the Boosting algorithms developed for predicting Obstructive Sleep Apnea (OSA). There is a significant improvement in the prediction accuracies when comparing the classical boosting algorithms and Boosted ARP-DT being applied to the OSA datasets and those online databases from University of California Irvine (UCI) data repositories.
Single shot fringe projection profilometry using tunable frequency Fresnel biprism interferometer for large range of measurement

We report a single shot fringe projection system for 3D-shape measurement using tunable frequency and region of interference with a single element Fresnel biprism interferometer. Fourier transform profilometry is a single shot technique making it exceedingly useful in the high-speed 3D reconstruction of surface profile, but it necessitates sufficient fringe density to distance out the first order spectrum from the DC term. Fresnel biprism interferometer is used to generate tunable fringe density for projection onto the object surface. The tunable fringe density generated by the proposed single element interferometer is employed for the 3D shape profilometry of a large range of sample sizes. Fourier transform fringe analysis is used to reconstruct 3D profile of the object. Fresnel biprism offers several advantages, i.e., it is low cost, offers no power loss, provides high-stability interference fringe generation, and easy to implement as it requires only a solo-element along with a broad range of tunable fringe density. Another significant advantage provided by the biprism is the tunability in the region of interference, which helps in the reconstruction of a larger area in a single shot. The variation in fringe density along with the region of interference helps in the microscopic profilometry as well as macroscopic profilometry using the same system without the need of any additional optical element. Experimental results of 3D-shape measurement of Si-IC chip, a step-like, and curved objects are presented.
A Maturity Model for IT-Related Security Incident Management

The purpose of the study is to validate the ability of a maturity model for measuring escalation capability of IT-related security incident. First, an Escalation Maturity Model (EMM) and a tool were developed to measure the maturity of an organization to escalate IT-related security incidents. An IT tool for self-assessment was used by a representative from three organizations in the Swedish health sector to measure the organization's ability to escalate IT-related security incident. Second, typical security incident scenarios were created. The incident managers from the different organizations were interviewed about their organization's capabilities to deal with these scenarios. Third, a number of independent information security experts, none of whom had seen the results of EMM, ranked how the three different organizations have handled the different scenarios using a measurable scale. Finally, the results of EMM are compared against the measurable result of the interviews to establish the predictive ability of EMM. The findings of the proof of concept study shows that the outcome of EMM and the way in which an organization would handle different incidents correspond well, at least for organizations with low and medium maturity levels.
Motion Planning for Climbing Mobility with Implementation on a Wall-Climbing Robot

Future autonomous planetary explorers will require extreme terrain mobility to reach areas of interest, such as walled lunar pits and steep Martian rock layers. Climbing mobility systems are one proposed answer, requiring efficient and kinematically feasible motion planning for autonomous operation. Similarly, climbing planning is applicable to other micro-gravity situations requiring constant end effector contact with discrete handholds. This paper proposes a planning framework that poses kinematic climbing planning as a discrete optimal planning problem. Motion primitives are used to encourage large robot body workspaces and beneficial connections between climbing stances. A wall-climbing planner simulation is presented, along with implementation on a hardware demonstration testbed that successfully recognized, navigated, and climbed an arbitrary vertical wall.
Enabling Researchers to Make Their Data Count

Over the last years, many organizations have been working on infrastructure to facilitate sharing and reuse of research data. This means that researchers now have ways of making their data available, but not necessarily incentives to do so. Several Research Data Alliance (RDA) working groups have been working on ways to start measuring activities around research data to provide input for new Data Level Metrics (DLMs). These DLMs are a critical step towards providing researchers with credit for their work. In this paper, I describe the outcomes of the work of the Scholarly Link Exchange (Scholix) working group and the Data Usage Metrics working group. The Scholix working group developed a framework that allows organizations to expose and discover links between articles and datasets, thereby providing an indication of data citations. The Data Usage Metrics group works on a standard for the measurement and display of Data Usage Metrics. Here I explain how publishers and data repositories can contribute to and benefit from these initiatives. Together, these contributions feed into several hubs that enable data repositories to start displaying DLMs. Once these DLMs are available, researchers are in a better position to make their data count and be rewarded for their work.
On‐line extreme learning algorithm based identification and non‐linear model predictive controller for way‐point tracking application of an autonomous underwater vehicle

In most of the surveillance applications of autonomous underwater vehicle (AUV), very often it is intended to follow the desired horizontal way-points, where some oceanography data need to be collected. In view of this, the motion planning algorithm using way-points is investigated in this study. The proposed work involves identification of dynamics of AUV and design of adaptive model predictive controllers which includes linear adaptive model predictive controller (LAMPC) and non-linear adaptive model predictive controller (NAMPC). Owing to the fast convergence rate and robustness property, on-line sequential extreme learning machine (OS-ELM) is employed for estimating the dynamics of AUV. To improve the OS-ELM modelling performance, Jaya optimisation algorithm is applied to optimise the hidden layer parameters. The desired surveillance region is formulated in terms of way-points using heading angle obtained from desired line-of-sight path. Simulations are performed using MATLAB by applying proposed NAMPC, LAMPC and a previously reported optimal controller, namely inverse optimal self-tuning PID (IOSPID) controller. Subsequently, real-time experimentation is performed using a prototype AUV in a swimming pool. From the simulation and experimental results, it is observed that the proposed controller exhibit efficient tracking performance in face of actuator constraints as compared to LAMPC and IOSPID controller.
Differentially Private Learning of Geometric Concepts

We present efficient differentially private algorithms for learning unions of polygons in the plane (which are not necessarily convex). Our algorithms are $(\alpha,\beta)$--probably approximately correct and $(\varepsilon,\delta)$--differentially private using a sample of size $\tilde{O}\left(\frac{1}{\alpha\varepsilon}k\log d\right)$, where the domain is $[d]\times[d]$ and $k$ is the number of edges in the union of polygons. Our algorithms are obtained by designing a private variant of the classical (nonprivate) learner for conjunctions using the greedy algorithm for set cover.
Acceleration of object tracking in high‐speed videogrammetry using a parallel OpenMP and SIMD strategy

Abstract High‐speed cameras are able to effectively and efficiently obtain the location of moving objects over time in many practical applications. In order to meet the requirement for fast computing and processing in the object‐tracking process, this paper adopts parallel computing to process multiple video‐image sequences by using open multi‐processing (Open MP ) and single‐instruction multiple data ( SIMD ) simultaneously, combined with a coarse‐to‐fine matching method. Experimental results showed that the proposed method: (1) has a high accuracy compared with other methods; (2) demonstrates a higher computational efficiency than other methods; and (3) has an efficiency running on a laptop equivalent to other methods running on a computer workstation.
Sized Types for Low-Level Quantum Metaprogramming

One of the most fundamental aspects of quantum circuit design is the concept of families of circuits parametrized by an instance size. As in classical programming, metaprogramming allows the programmer to write entire families of circuits simultaneously, an ability which is of particular importance in the context of quantum computing as algorithms frequently use arithmetic over non-standard word lengths. In this work, we introduce metaQASM, a typed extension of the openQASM language supporting the metaprogramming of circuit families. Our language and type system, built around a lightweight implementation of sized types, supports subtyping over register sizes and is moreover type-safe. In particular, we prove that our system is strongly normalizing, and as such any well-typed metaQASM program can be statically unrolled into a finite circuit.
An access authentication algorithm based on a hierarchical identity-based signature over lattice for the space-ground integrated network

Access authentication is a key technology to identify the legitimacy of mobile users when accessing the space-ground integrated networks (SGIN). A hierarchical identity-based signature over lattice (L-HIBS) based mobile access authentication mechanism is proposed to settle the insufficiencies of existing access authentication methods in SGIN such as high computational complexity, large authentication delay and no-resistance to quantum attack. Firstly, the idea of hierarchical identity-based cryptography is introduced according to hierarchical distribution of nodes in SGIN, and a hierarchical access authentication architecture is built. Secondly, a new L-HIBS scheme is constructed based on the Small Integer Solution (SIS) problem to support the hierarchical identity-based cryptography. Thirdly, a mobile access authentication protocol that supports bidirectional authentication and shared session key exchange is designed with the aforementioned L-HIBS scheme. Results of theoretical analysis and simulation experiments suggest that the L-HIBS scheme possesses strong unforgeability of selecting identity and adaptive selection messages under the standard security model, and the authentication protocol has smaller computational overhead and shorter private keys and shorter signature compared to given baseline protocols.
Chapter 9 An Overview of Artificial Intelligence Research and Development in China

In recent years, a great number of top conferences and workshops on artificial intelligence (AI) were held in China, showing Chinese AI plays an important role in the world. Meanwhile, Chinese government announced an ambitious scheme, “New Generation Artificial Intelligence Development Plan,” for the country to become a world leader in AI technologies by 2030. The AI research in China has covered various aspects, ranging from chips to algorithms. This chapter attempts to give an overview of the recent advances of AI research and development in China, as well as some perspectives on the future development of AI in China.
A Risk Analysis of E-Commerce: A Case of South African Online Shopping Space

Globally, online shopping is on the rise with cybercrimes expected to rise. This study presents a risk analysis of the online shopping's e-commerce in South Africa using data collected through a variety of platforms where incidents and intelligence are reported and collected. Data was collected through a self-administered web-based online survey. A randomized sample size of 459 was used to analyze perceived risks associated with online shopping. This paper further outlines perceived risk results and findings by categorizing impact type, attack vector and threat type. Results of this study show that risks associated with finance losses impact the online shopping in the e-commerce space. This has not yet been fully realized in South Africa. Results analyzed in this study also look at the online shopping confidence across the retail industry, hospitality industry and other industries aggregated in this study. Financial loss is highlighted as the major perceived risk recording a highest confidence level in terms of the results which are further categorized in terms of impact type, attack vector and threat type. A conclusion has been drawn which indicate that there is a correlation around recognized risks, impact type, attack vector and threat type in the online shopping space in South Africa.
Parallel implementation of the greedy heuristic clustering algorithms

Abstract Authors propose parallel greedy heuristic k-means clustering algorithms for implementation on the graphical processing units (GPU) for solving large-scale problems. The computational experiments illustrate high performance of the GPUs in comparison with running the greedy heuristic algorithms on a central processor unit which is especially significant in the case of big datasets and bug numbers of clusters. The efficiency of the greedy heuristic algorithms in comparison with the standard k-means algorithm remains.
Telemedicine

Telemedicine is the abstract term used to define medical services delivered through Information Technology and Telecommunications. With the growing advancements in the field of data mining, pattern recognition, expert systems, and image processing, the motivation of such a technology has increased. This allowed Telemedicine to be a stable solution especially because people are starting to trust the system more for its higher accuracy. This paper proposes a Telemedicine platform between the patient and the doctor. This platform belongs to the internet of medical things (IoMT) by enabling multiple medical sensors to connect to a server either using Wi-Fi, Bluetooth or GSM technologies. Moreover, wearable and implanted electronics can be integrated with the proposed system as well. The system collects data from several sensors and sends them using one of the aforementioned technologies using an Arduino Board, Matlab, and C#. Furthermore, a comparative study between the three communications technologies used to connect the medical sensors to the server is investigated. A recommendation of the best suited technology is provided with regard to the nature of the application itself.
Assembling iPads and Mobility in Two Classroom Settings

iPads have become increasingly popular as tools for teaching in early education. With their multifaceted and interactive affordances as handheld and mobile devices, they have been ascribed great potential to change and expand on classroom practice. However, the iPad often becomes just another technology hype in education, and explanations often point to teachers’ and schools’ lack of technical know-how and ability to utilise the devices’ affordances. In this qualitative case study, we explore how teachers organise and practise iPads in two different classrooms. Classroom observations and interviews with the teachers were conducted to enrich our knowledge about the complexities of iPad use in teaching and to strengthen the knowledge of how different settings produce different iPad practices. Using an actor–network theory approach, the study suggests that iPads are not simply put into use, but enacted through fluid, heterogeneous assemblages of human and non-human actors in the classroom. The iPad’s affordances, such as mobility, are performed rather than inherent qualities of the devices themselves, and the classroom becomes a mishmash of fostering and hindering mobile practices.
Robustness Analysis of a Hyperchaos-Based Digital Image Encryption Algorithm under Noise Attacks

Images are susceptible to degradation by noise from different sources as they undergo various processes. The effect of noise degradation affects the visual information fidelity, structural content and the decryption performance of an image encryption algorithm. In this paper, the effects of noise attacks on the performance of a hyperchaos-based digital image encryption algorithm is evaluated. In hyperchaos-based encryption algorithm, chaotic dynamics are used to encrypt the pixels of images. Two noise models, namely Gaussian noise and salt and pepper noise were added to test images, prior to encryption operations, in order to test for the robustness of the algorithm to noise attacks. The mean square error, peak signal – to – noise ratio, structural content and normalized correlation coefficient of the plain and decrypted images were evaluated. The results obtained indicated that noise has insignificant effect on the decryption performance of the algorithm, as the noise-degraded images and their decrypted counterparts were very identical. Thus, the image encryption algorithm is tolerant of noise and can therefore be used in noisy channels.
A knowledge based infrared camera system for invisible gas detection utilizing image processing techniques

An infrared camera system for real-time monitoring of invisible methane gas leakages is proposed, which can be used in production, transportation, and gas plants. Instead of using conventional lasers, a medium wavelength light-emitting diode (LED) with a center wavelength of approximately 3300 nm is used for the light source of the gas detector. We achieve compact cooling with low power consumption by using a small electronic part with a Peltier element instead of a larger cooling unit. We also propose an algorithm based on adaptive average method and adaptive histogram equalization process to extract the background efficiently. This knowledge based infrared camera system effectively detects the leakage of invisible gas. Preliminary experimental results confirm the potential and effectiveness of the developed equipment to be commercialized for use in real-time gas monitoring systems. We aim to eventually develop an image analysis system that combines image processing and knowledge engineering techniques.
Prototype development of responsive kinetic façade control system for the elderly based on Ambient Assisted Living

The elderly suffer from discomfort in social life owing to the decline of their physical, psychological, and social functions. The elderly who spend a lot of time indoors require an intelligent system to extend their time to live independently in a residential environment. Recently, the Ambient Assisted Living (AAL) concept was introduced to commercialize various technologies and apply them to urban and architectural environments. However, AAL’s connection to architectural elements is insufficient. This study intends to build a residential environment platform that integrates Internet of Things (IoT) technology and architectural elements to support the independence of the elderly based on the AAL concept. The proposed platform is designed to be applied to various scenarios and services. A responsive façade system was designed to verify the platform, and the usefulness of the system was evaluated through the interaction of the designed façade and an acting manager.
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For a prime power q and a positive integer n, we say a subspace U of Fqn is cyclically covering if the union of the cyclic shifts of U is equal to Fqn. We investigate the problem of determining the minimum possible dimension of a cyclically covering subspace of Fqn. (This is a natural generalisation of a problem posed in 1991 by the first author.) We prove several upper and lower bounds, and for each fixed q, we answer the question completely for infinitely many values of n (which take the form of certain geometric series). Our results imply lower bounds for a well-known conjecture of Isbell, and a generalisation thereof, supplementing lower bounds due to Spiga. We also consider the analogous problem for general representations of groups. We use arguments from combinatorics, representation theory and finite field theory.
Image Quality Assessment by Considering Multiscale and Multidirectional Visibility Differences in Shearlet Domain

Conventional objective image assessment metrics, such as mean squared error and peak signal-to-noise ratio, which only calculates pixel-based differences between the original and the degraded images, are not in agreement with the human vision. In this paper, we present an improved objective full-reference image quality assessment method, called the multiscale and multidirectional visibility differences (MMVD) predictor. The proposed MMVD metric considers multiscale and multidirectional visibility differences in the domain of the discrete nonseparable shearlet transform, which emulates the multichannel structure of information processing of the human vision system. In the process of constructing the visual just noticeable difference threshold in the shearlet domain, the contrast sensitivity function and the visual masking effect which are important properties of the human visual perception are considered simultaneously to approximate the sensitivities of human visual responses. Both contrast masking and entropy masking are considered to tackle the visual masking issue. All subbands of the shearlet transform are evaluated, and perceptual errors of subbands are pooled together to yield the objective quality index of a distorted image. The extensive validation experiments are conducted on five public image databases, namely, TID2008, TID2013, CSIQ, IVC, and LIVE. The experimental results demonstrate the proposed method is well coherent with human perception and has better performance compared to the several state-of-the-art image quality metrics.