Reviewerly/exHarmony huggingface dataset:
Human Bond Communication with Head-Mounted Displays: Scope, Challenges, Solutions, and Applications

In this article, we introduce a human bond communication (HBC) method, based on head-mounted displays (HMDs), which enables continuous bidirectional communication among multiple users with or without Internet connections. The disadvantages of the existing technologies limit the scope of this study to provide a communication platform for HMD-based HBC. However, the features of the optical camera communication (OCC) technology will enrich the possibilities of HBC. This leads us to introduce a visual communication approach for virtual applications. To deploy OCC in HMDs for HBC, the camera of an HMD can be used as a receiver, while a near-infrared light source must be added to the HMD as a transmitter. We also discuss some potential future applications, suggest effective architectures, and briefly describe the scope, challenges, and solutions of OCC-based HMDs for HBC.
Influence of Krawtchouk and Charlier moment’s parameters on image reconstruction and classification

The discrete orthogonal moments such as Krawtchouk and Charlier moments have been used successfully in the field of image analysis. These moments are based on discrete orthogonal polynomials that depend on the locality parameters (p for Krawtchouk and a1 for Charlier). However, the choice of the value of these parameters during the image analysis remains a problem without solutions. In the absence of an analytical study to make these choices, we will study experimentally, in this article, the influence of the variation of the parameters of discrete orthogonal polynomials on the analysis and in particular on the reconstruction and classification of the image. The results of the simulation show the influence of the variation of discrete orthogonal moments parameters of Krawtchouk and Charlier on the quality of reconstruction of the binary, grayscale and colors images in order to make the choice of the numerical value of each parameter. This allows us to obtain a range of parameter values that will be exploited in the classification task of image forms using the invariant moments of Krawtchouk and Charlier.
WignerMSER: Pseudo-Wigner Distribution Enriched MSER Feature Detector for Object Recognition in Thermal Infrared Images

In this paper, we introduce WignerMSER, a robust local feature detector for thermal infrared images, a new variant of renowned maximally stable extremal regions (MSER) feature detector. Thermal infrared images pose challenges of low contrast and lack of sharp edges or boundaries typically required for identifying keypoints using traditional feature detectors. This paper addresses this challenge by designing a local feature detector suitable for infrared images. The proposed WignerMSER features are obtained by transforming the image from the spatial domain to joint space-spatial-frequency domain using pseudo Wigner-Ville Distribution, and thereafter detecting MSERs in the Wigner transformed space. Different variants of WignerMSER are proposed and their performance is compared over traditional MSER feature detector in a bag of words framework for the purpose of object recognition in thermal infrared images. The performance of WignerMSER feature detector is evaluated on two infrared datasets. The first dataset is CSIR-CSIO thermal infrared (TIR) vehicle dataset comprising of four vehicle categories commonly seen on urban roads in India and the second dataset is a subset of FLIR Thermal dataset comprising of two object categories, vehicles, and pedestrians. All the variants of the proposed WignerMSER feature detector have demonstrated superior classification performance against traditional MSER features for object categorization on both the TIR datasets. Fused WignerMSER feature detector has shown the best performance with more than 5% increase in overall classification accuracy over traditional MSER for both the datasets.
Research on application of multimedia image processing technology based on wavelet transform

Abstract With the development of information technology, multimedia has become a common information storage technology. The original information query technology has been difficult to adapt to the development of this new technology, so in order to be able to retrieve useful information in a large amount of multimedia information which has become a hot topic in the development of search technology, this paper takes the image in the multimedia information storage technology as the research object, uses the wavelet transform to divide the picture into the advantages of the low-frequency and high-frequency characteristics, and establishes the multimedia processing technology model based on the wavelet transform. The simulation results of face, vehicle, building, and landscape images show that different wavelet basis functions and different layers of images are decomposed, and the retrieval results and retrieval speed of images are different, When taking four layers of wavelet decomposition, the cubic b-spline wavelet as the wavelet basis function makes the classification result optimal, and the accuracy rate is 89.08%.
Consumer Smart Homes

Currently a variety of smart home systems are available from different vendors, who made different designs choices in how these systems operate, which in turn affect end users. A key question that we consider in this work is how these commercially available smart home systems differ in practice, what are the implications of those differences, and whether other design alternatives might be better. To answer these questions, we systematically evaluate seven popular smart homes and identify their underlying design choices around access control, privacy, and automation, and highlight the implications of those design choices for end users. We surface challenges and tensions for design choices around topics like security, privacy, usability, automation, and reliability, and we make design recommendations where possible. Our findings lay the groundwork for future research in this area.
FF-SMOTE: A Metaheuristic Approach to Combat Class Imbalance in Binary Classification

Nature inspired intelligent computation-based algorithms have been grown remarkably over the previous years. These algorithms are applied for optimizing the values to a number of problem areas ranging from scientific research to industry or commerce. Class imbalance is a challenging problem of classification to identify smaller class when dealing with skewed distributions. This paper proposed a firefly-based oversampling technique to combat class imbalance in binary classification. The proposed technique is applied on 10 UCI data sets with the imbalance ratio ranging from high to low and is compared with the other state-of-the art oversampling techniques. The performance of the proposed method is assessed through performance metrics area under the curve and geometric mean. The techniques are also analyzed statistically using Friedman and Wilcoxon matched signed rank Test. Through experimental and statistical analysis, it is reported that the proposed technique outperformed other oversampling techniques.
Calibration of 6-DOF industrial robots based on line structured light

To improve the accuracy of an industrial 6-DOF robot, this paper proposes a new calibration method based on a line structured light measurement system. The line structured light measurement system mounted on the end-effector is used as a tool to acquire the three-dimensional coordinate information of the centre of a standard sphere. Hand-eye calibration is used to establish the transformation between the robot and the measurement system. A combined kinematic model that includes both the kinematic parameters of the robot and the initial hand-eye relationship is established by applying the modified Denavit and Hartenberg (MDH) model. An error model is derived based on the principle that the coordinate of the sphere centre is invariant in the base frame of the robot. Consequently, the errors in the kinematic parameters of the robot and the hand-eye relationship can be identified by substituting the measurement results and the original parameters of the robot into the error model. The results of the simulations and experiments show that the accuracy of the robot is significantly improved by implementing the proposed calibration method. The proposed method is effective, simple, time-saving and low-cost and is therefore a suitable candidate for on-site calibration of industrial robots.
Noise-robust line detection using normalized and adaptive second-order anisotropic Gaussian kernels

Line detection is an essential procedure for many tasks in computer vision. Although literature is rich in proposals for this topic, most of existing methods are vulnerable to noise, especially when lines are fairly narrow. In order to design a line detector that minimizes the impact of noise, regardless of the scale or direction of the lines, in this paper, we present a framework for multiscale line detection based on second-order anisotropic Gaussian kernels. Firstly, we model a line segment using a directional Gaussian function. Secondly, with the help of a newly proposed normalization method, we enable the second-order anisotropic Gaussian kernels to quantitatively measure the line prominence as well as the line scale. Subsequently, based on a noise-robustness analysis in terms of the signal-to-noise ratio, an adaptive anisotropy factor is proposed. By incorporating postprocessing techniques, an automated line detector using the normalized and adaptive second-order anisotropic Gaussian kernels is developed. The performance of the proposed method is quantitatively evaluated by comparing it with five competing methods on a publicly available dataset.
The Impact of Asymmetric Left and Asymmetric Right Face Images on Accurate Age Estimation

Aging affects left and right half face differently owing to numerous factors such as sleeping habits, exposure to sun light, and weaker face muscles of one side of face. In computer vision, age of a given face image is estimated using features that are correlated with age, such as moles, scars, and wrinkles. In this study we report the asymmetric aging of the left and right sides of face images and its impact on accurate age estimation. Left symmetric faces were perceived as younger while right symmetric faces were perceived as older when presented to the state-of-the-art age estimator. These findings show that facial aging is an asymmetric process which plays role in accurate facial age estimation. Experimental results on two large datasets verify the significance of using asymmetric right face image to estimate the age of a query face image more accurately compared to the corresponding original or left asymmetric face image.
Affective certainty and congruency of touch modulate the experience of the rubber hand illusion

Our sense of body ownership relies on integrating different sensations according to their temporal and spatial congruency. Nevertheless, there is ongoing controversy about the role of affective congruency during multisensory integration, i.e. whether the stimuli to be perceived by the different sensory channels are congruent or incongruent in terms of their affective quality. In the present study, we applied a widely used multisensory integration paradigm, the Rubber Hand Illusion, to investigate the role of affective, top-down aspects of sensory congruency between visual and tactile modalities in the sense of body ownership. In Experiment 1 (N = 36), we touched participants with either soft or rough fabrics in their unseen hand, while they watched a rubber hand been touched synchronously with the same fabric or with a 'hidden' fabric of 'uncertain roughness'. In Experiment 2 (N = 50), we used the same paradigm as in Experiment 1, but replaced the 'uncertainty' condition with an 'incongruent' one, in which participants saw the rubber hand being touched with a fabric of incongruent roughness and hence opposite valence. We found that certainty (Experiment 1) and congruency (Experiment 2) between the felt and vicariously perceived tactile affectivity led to higher subjective embodiment compared to uncertainty and incongruency, respectively, irrespective of any valence effect. Our results suggest that congruency in the affective top-down aspects of sensory stimulation is important to the multisensory integration process leading to embodiment, over and above temporal and spatial properties.
Non-Interactive ID-Based Proxy Re-Signature Scheme for IoT Based on Mobile Edge Computing

The Internet of Things (IoT) is the internetworking of terminal physical devices depends on application programming interfaces and sensors to be connected to the Internet for collecting and exchanging data. According to the characteristics of IoT, it can provide rich services for the terminal user. However, the centralized cloud computing-based storage architecture in IoT faces many challenges, such as data security, identity privacy, and high latency. To overcome these challenges, this paper introduces an IoT network storage architecture based on mobile edge computing, which not only achieves low-latency message response and computation offloading of the terminal user but also preserves identity-privacy of the terminal user. Afterward, we presented a novel non-interactive pairing-free ID-based proxy re-signature scheme (ID-PRS), which is a kernel technique to construct the aforementioned storage architecture. Compared with most of proxy re-signature schemes constructed based on traditional public-key-infrastructure, the proposed scheme avoids expensive pairing operation and intricate certificate-maintenance. And it is demonstrated to be provably secure under the classic security assumptions: integer factoring problem and the RSA assumption. Finally, in contrast to the other two ID-PRS schemes, the proposed ID-PRS scheme has more advantages in terms of security, functionability, and computation costs. Thus, it is very suitable to be applied to the resource-constrained mobile users.
Embedding Inequality Constraints for Quantum Annealing Optimization

Quantum annealing is a model for quantum computing that is aimed at solving hard optimization problems by representing them as quadratic unconstrained binary optimization (QUBO) problems. Although many NP-hard problems can easily be formulated as binary-variable problems with a quadratic objective function, such formulations typically also include constraints, which are not allowed in a QUBO. Hence, such constraints are usually incorporated in the objective function as additive penalty terms. While there is substantial previous work on implementing linear equality constraints, the case of inequality constraints has not much been studied. In this paper, we propose a new approach for formulating and embedding inequality constraints as penalties and describe early implementation results.
A robust anisotropic diffusion filter with low arithmetic complexity for images

Image smoothing with edge preservation in the presence of outliers is a challenge in image processing. Anisotropic diffusion smoothing is a well-established paradigm in digital image smoothing with edge preservation. Anisotropic diffusion smoothing filters are not robust to impulse noise. They employ two or more stages for robustness in the presence of outliers, and, therefore, they demand significantly increased arithmetic operations for robustness; consequently, they are not power efficient. This paper introduces a low arithmetic complexity image smoothing model and proposes an intrinsically robust and power efficient algorithm for anisotropic diffusion smoothing of images. The algorithm outperforms the foundational robust smoothing algorithms in terms of the standard performance metrics and visual quality.
A Class of Quadratic Polynomial Chaotic Maps and its Application in Cryptography

At present, the probability density of most chaotic systems is unknown, and the statistical characteristics of chaotic sequences cannot be described by the probability density of chaotic maps. This paper constructs a class of quadratic polynomial chaotic maps with three system parameters, which are topologically conjugated with Tent maps. The probability density functions of this kind of chaotic maps are given. Then, an arcsine function is designed to transform the chaotic sequence generated by the quadratic polynomial chaotic map into a new random sequence, which obeys the uniform distribution on the interval (-0.5, 0.5). In order to show the application of the new uniform random numbers, the applications of it in generating random arrangement, Gaussian measurement matrix of compressed sensing, and pseudo random number generator are discussed.
Integrating Artificial Bee Colony Algorithm and BP Neural Network for Software Aging Prediction in IoT Environment

Software aging is a common phenomenon that exists in systems that require long periods of operation, especially in Internet-of-Things environments. The back propagation (BP) neural network has been adopted widely to predict the trend of software aging. However, the weight and threshold of the BP neural network are randomly initialized, so it is easy to get the unsatisfactory local optimal solutions and the convergence speed of computing is slow. In this paper, we propose a novel software aging prediction method using the artificial bee colony algorithm to optimize the BP neural network model for achieving better software aging prediction accuracy. The experiment results show that our method fits the prediction trend of software aging more accurately than the traditional BP neural network, and our method also has faster convergence speed and more stable prediction results.
Identity-Based Broadcast Encryption with Efficient Revocation

Identity-based broadcast encryption (IBBE) is an effective method to protect the data security and privacy in multi-receiver scenarios, which can make broadcast encryption more practical. This paper further expands the study of scalable revocation methodology in the setting of IBBE, where a key authority releases a key update material periodically in such a way that only non-revoked users can update their decryption keys. Following the binary tree data structure approach, a concrete instantiation of revocable IBBE scheme is proposed using asymmetric pairings of prime order bilinear groups. Moreover, this scheme can withstand decryption key exposure, which is proven to be semi-adaptively secure under chosen plaintext attacks in the standard model by reduction to static complexity assumptions. In particular, the proposed scheme is very efficient both in terms of computation costs and communication bandwidth, as the ciphertext size is constant, regardless of the number of recipients. To demonstrate the practicality, it is further implemented in Charm, a framework for rapid prototyping of cryptographic primitives.
Identity-Based Signature Schemes for Multivariate Public Key Cryptosystems

Abstract In this paper, we proposed an idea to construct a general multivariate public key cryptographic (MPKC) scheme based on a user’s identity. In our construction, each user is distributed a unique identity by the key distribution center (KDC) and we use this key to generate user’s private keys. Thereafter, we use these private keys to produce the corresponding public key. This method can make key generating process easier so that the public key will reduce from dozens of Kilobyte to several bits. We then use our general scheme to construct practical identity-based signature schemes named ID-UOV and ID-Rainbow based on two well-known and promising MPKC signature schemes, respectively. Finally, we present the security analysis and give experiments for all of our proposed schemes and the baseline schemes. Comparison shows that our schemes are both efficient and practical.
3-D Modeling of the Lisheen and Silvermines Deposits, County Tipperary, Ireland:Insights into Structural Controls on the Formation of Irish Zn-Pb Deposits

Faults are important structures in the formation of many mineral deposits, often acting as conduits for ore-forming fluids and sometimes providing, or generating, the bounding structures to associated mineralizing sites. Using 3-D analysis and modeling of the Lisheen and Silvermines deposits within the Irish ore field, we investigate the geometry of normal fault systems and their implications on the origin and nature of associated deposits. These Irish-type deposits are carbonate hosted and developed within the hanging walls of normal faults arising from an Early Carboniferous episode of north-south rifting, with relatively limited amounts of later deformation. Structural analysis of high-quality mine datasets indicates that fault segmentation is ubiquitous with left-stepping segments arising from north-south stretching developed above generally ENE-NE-trending fault arrays, which are subparallel to older Caledonian penetrative fabrics and structure within underlying Silurian and Ordovician rocks. Fault segments occur on different scales and have a profound impact on structural evolution, with larger scale segments and intervening relay ramps defining distinct orebodies within deposits and smaller scale segments and relays potentially providing paths for upfault fluid flow. The difference in behavior is attributed to the integrity of associated relay ramps where intact ramps represent orebody-bounding structures, and smaller breached ramps provide enhanced associated hydraulic properties and act as vertical conduits. Hanging-wall deformation along the rheological boundary between host-rock limestones and underlying shales has an important control on the localization of earlier dolomitization and/or brecciation and later mineralization adjacent to this contact, and on the migration pathways for basinal brines and mineralizing fluids.
Incremental Deep Computation Model for Wireless Big Data Feature Learning

Big data feature learning is a crucial issue for the service management for Internet of Things. However, big data collected from Internet of Things is of dynamic nature at a high speed, which poses an important challenge on wireless big data learning models, especially the deep computation model. In this paper, an incremental deep computation model is proposed for wireless big data feature learning in Internet of Things. First, two incremental tensor auto-encoders (ITAE) are developed by devising two incremental learning algorithms, namely parameter-based incremental learning algorithm (PI-TAE) and structure-based incremental learning algorithm (SI-TAE), when new wireless samples are available. PI-TAE only updates the network parameters while SI-TAE simultaneously adjusts the structure and updates the parameters to adapt to the new arriving wireless big data. Furthermore, an incremental deep computation model is constructed by stacking several ITAEs. Experiments are conducted to evaluate the performance of the proposed model by comparing with the conventional deep computation model and other two representative incremental learning algorithms, i.e., OANN and PIE. Results demonstrate that the presented model can modify the network in an incremental manner for new arriving data learning efficiently with preserving the prior knowledge for the previous data learning, proving its potential for dynamic wireless big data learning in Internet of Things.