Reviewerly/exHarmony huggingface dataset:
Uncertainty quantification in full-field displacement and strain responses of materials using Kalman filter

Digital image correlation (DIC) is a method used to measure full-field material response in several engineering applications. Sensing is often associated with uncertainties, which influence the quantities derived from these measurements. Hence, this study proposes a novel image analysis algorithm for estimating displacements and strains in materials using DIC by considering various sources of uncertainties that may be attributed to measurements available in the form of images. In this setting, a probabilistic approach based on Bayesian estimation framework is developed, and details of the derivation and steps in the implementation are provided. The proposed algorithm is validated using open source experimental data sets on materials exhibiting homogeneous and heterogeneous deformations. The results of the method are compared with the deformations predicted using open-source software, Ncorr. In these problems, the proposed method was successful in accurately capturing the displacements and strains. It is anticipated that the formulation presented in this paper will form the basis for developing computational strategies for uncertainty quantification in full-field measurements involving DIC applications.
Mobile application for two-stage linear and nonlinear image processing

The article discusses the technology of correcting blurred type distortions on images recorded by mobile devices. The aim of the development is to provide high-quality distortion correction with minimal computational costs. We use two-stage technology. At the first stage, blind identification of linear filter parameters is performed. It is assumed that the frequency response of the filter has central symmetry and consists of segments of quadratic and exponential functions. At the second stage, parameters of nonlinear filter are determined. Both automatic and manual visual settings are provided. The program code for Android OS and the results of experiments showing the effectiveness of the developed application to eliminate distortions in images are presented.
Breaking the Cyber Kill Chain by Modelling Resource Costs

To combat cybercrime, a clearer understanding of the attacks and the offenders is necessary. When there is little available data about attack incidents, which is usually the case for new technology, one can make estimations about the necessary investments an offender would need to compromise the system. The next step would be to implement measures that increase these costs to a level that makes the attack unattractive. Our research method follows the principles of design science, where cycles of research activities are used to create artefacts intended to solve real-world problems. Our artefacts are an approach for creating a resource costs model (RCM) and an accompanying modelling tool implemented as a web application. These are used to find the required attacker resources at each stage of the cyber kill chain. End user feedback show that structured visualisation of the required resources raises the awareness of the cyberthreat. This approach has its strength and provides best accuracy with specific attacks, but is more limited when there are many possible attack vectors of different types.
Privacy Preserving Features Selection for Data Mining using Machine Learning Algorithms

Features selection known as process of lessening the number of inputs while designing a predictive model using machine learning algorithms. Metadata is an asset because useful information is concealing in these large quantities of data. Data analytics needs more in-depth insight and the identification of fine-grain patterns to make precise predictions that allow better decision-making. To make identification towards the data, the privacy of the data must be preserving. It will ensure there is no leakage information to other parties. In this paper, we review features selection for data mining and machine learning algorithms to preserve data privacy.
Some efficient algorithms for morphological operations on hexagonal lattices and regular hexagonal domains

In this paper, morphological operations on regular hexagonal structures are considered, where a regular hexagonal structure is a subset of a hexagonal lattice and consists of the sampled points for the discretization of a regular hexagonal region. First, a sequence of reasonable structure elements (SE) for hexagonal lattices are provided, and the decompositions of the SEs are shown. Second, based on the decompositions of the SEs, some efficient algorithms for morphological operations on the regular hexagonal structures are developed. Finally, the algorithms are tested using a computerized tomography (CT) image, and promising applications of such algorithms on CT image reconstruction and segmentation are pointed out.
Ethical and Other Highly Desirable Reliability Requirements for Cyber-Physical Systems Engineering

In this chapter, firstReliability requirements, Cyber-Physical Systems Engineering approach to reliability issueReliability issue of CPS is cited. Accordingly, following dimensions of reliability of CPS are outlined and relevant references are given: Categories of reliability issuesReliability issue; Reliability and security aspects of computation; Reliability and failure avoidance in simulation studiesFailure avoidance in simulation studies, including: Validity and verification issues of modeling and simulation, A frame of reference for the assessment of the acceptability of simulation studiesAcceptability of simulation studies, Failure avoidance in artificial intelligenceArtificial intelligence, and Failure avoidance in or due to simulation studies; Aspects of sources of errors. The last one covers the following aspects: EthicsEthics and value systemsValue systems, including: Ethical requirements from humansEthical requirements from humans and Ethical requirements from AI in simulation and system studies; Decision-making biasesDecision-making biases, including: Human decision-making biasesDecision-making biases and AI biases; Improper use of informationImproper use of information, such as misinformation, disinformation, and mal-information; Attacks by humans and by autonomous AI systems; Flaws; Accidents; and Natural disasters.
Genre-based Emoji Usage Analysis and Prediction in Video Comments

Emojis can be seen as a visual language inserted in texts to express emotions, attitudes, and situations. It is widely used in computer-mediated communication, e.g., video comments. The emojis can express more detailed information beyond text information, and their usage can improve interlocutors' communication efficiency and emotions. The latest advances in natural language processing and deep learning have made it possible for chatbots to automatically add emojis in their dialogue. Precisely predicting emojis to be added is very challenging, especially in the video comments, where the use of emojis is complex, subtle, and associated with the cultural characteristics of video genres, e.g., anime and dancing. In this article, we first construct a benchmark dataset Bilibili comments dataset with more than 3.9 million comments that contain emojis in the video-sharing website Bilibili and then statistically analyze features of emoji's usage of video genre, comment content, and where an emoji appears in a sentence. According to the analyzed results and the gated recurrent unit (GRU) neural network, we propose a novel model of genre-based multitask GRU (GM-GRU) and its attention-added edition (GM- GRU+) to predict an emoji's category and position in a video comment. Our experiment and evaluation show that the proposed method can significantly increase the accuracy of predicted emojis for video comments.
Weighted Scheduling of Mixed Gang Jobs on Distributed Resources

Over the recent years, due to the rapid growth of the Internet of Things (IoT) and its related applications, the efficient utilization of distributed computing resources is crucial. The selection of appropriate resource scheduling techniques for workloads with specific characteristics in order to achieve low response times, is still an open research challenge. In this paper, we focus on scheduling gang-type applications with the aim of achieving low response times, while at the same time considering fairness in job service. We examine complex gang jobs and we study the impact of their characteristics on the performance of various gang scheduling techniques. The simulation results show that the performance of the scheduling approaches is dependent on the characteristics of the workload.
Detecting and Tracing Multi-Strategic Agents with Opponent Modelling and Bayesian Policy Reuse

In competitive multi-agent scenarios, the agents try to defeat their opponents by choosing the best response policies. However, non-stationary opponents make it difficult because they can also adapt to the evolved policies and behaviors of the agents. In this paper, we propose a novel Bayesian policy reuse approach for non-stationary opponents. It combines the learning of the best policy, the detection and prediction of the opponent policy, as well as the selection of the optimal response policy. We introduce an eXtended learning classifier system (XCS) for multi-agent reinforcement learning algorithm in Markov games. Besides, we incorporate the opponent models for opponent policy identification and prediction. Furthermore, we propose a novel online policy reuse technique which can accurately and quickly trace the opponents' policies in tasks with different rewards. We demonstrate the performance of the proposed approach by comparing it with state-of-art existing algorithms in competitive Markov games.
Motion Classification in Bharatanatyam Dance

The paper presents a method to classify the unique motions in Bharatanatyam dance videos. Unlike the motions in our daily activities, the motions involved in the dance is rather complex in nature. Looking at the state of art, there is a new scope of the motion classification in the domain of dance. During dance performance, the number of frames in each motion may vary, which leads to the variable feature lengths. This variability, makes comparisons of motions difficult for classification and adds to challenges of the current work. We use the velocities of the skeleton joints as a feature. The joint coordinates are captured by Kinect 1.0. Dynamic Time Warping (DTW) and kNN algorithm are used for classification. The DTW is used to measure the similarity between two motions using skeleton joint velocities and the extracted similarity measure is supplied to the kNN algorithm to identify similar motions. The paper adopts two techniques while measuring the similarity of the joint velocities; i) Non-Weighted Joints ii) Weighted Joints. To optimize the joint weights, Particle Swarm Optimization (PSO) algorithm is used. We also compare the result of the two techniques and highlight the pros and cons of each. The proposed approach is simple and very effective and eventually achieves an accuracy of more than 85%. Finally motion classification in Indian Classical Dance (ICD) can help in digital heritage, design of dance tutoring system, dance synthesis application, and the like.
Using Optimum Index Factor and Determinant Covariance Methods and Compare to PCA on Satellite Images to Determine The Earth’s Landmarks ( Part of Tar- An Najaf and Its Neighbours)

Abstract The research focuses on mixing of Landsat8 ETM+ images for sector of Tar An- Najaf Phenomenon for the sake of gaining the optimum description showing the ground guidelines of the studied area. The optimum Index Factor (OIF) method has ensured that RGB567 represents the preferred mixed bands, where vegetated areas, water and rocks and deposits more obviously distributed. The determinant covariance matrix method stated that RGB457 considered the optimum for spectral resolution. For comparison with Principal Component Analysis, the latter involves PC1,2,3 and shows clear variations that serve the interpreter as a detailed study. Through the study, the best method is the second one.
Kernel Methods for Nonlinear Connectivity Detection

In this paper, we show that the presence of nonlinear coupling between time series may be detected employing kernel feature space representations alone dispensing with the need to go back to solve the pre-image problem to gauge model adequacy. As a consequence, the canonical methodology for model construction, diagnostics, and Granger connectivity inference applies with no change other than computation using kernels in lieu of second-order moments.
Robust Cache-Aware Quantum Processor Layout

Quantum computation has taken over as one of the largest current research areas in computer architecture and information theory. With the potential to make a large number of factorization-based encryption methods obsolete, companies and governments around the globe are racing to build the first large-scale quantum computer. Currently, most quantum computers are noisy intermediate-scale quantum (NISQ), using a relatively small collection of unreliable qubits. While error correction methods exist, they require a large number of ancilla qubits to protect the data qubits which is not practical for use on current NISQ machines. However, following the Dowling-Neven Law, available qubits on a superconducting chip are growing at an exponential rate similar to Moore's Law. Looking toward larger scale quantum machines, we examine a method to increase usable qubit density of quantum machines implementing error correction by using quantum caches that utilize simpler error correction codes. Alternatively, this also allows for the design of reliable systems while meeting the performance and qubit requirements for quantum algorithms. We modify the Qiskit quantum simulation library to work with caches and investigate the effects of region size and topology on the swap characteristics of algorithm execution. We also present our results and discuss recommended topologies for each algorithm. Lastly, we present mix scale-out simulations to examine the impact of cache on future large-scale machines. The default central cache topology gains a maximum performance increase of 2.15 times compared to the worst topology, which creates a robust cache-aware quantum processor layout.
Implementation of Acceleration Measurement on FPGA platform for Real-time Monitoring Application

This paper describes the proposed implementation of accelerometer measurement on the FPGA platform. The FPGA platform is used to utilise the parallelism features offer by the FPGA instead of other processing devices. The acceleration measurement is designed using G-code in LabVIEW FPGA and then implemented on the NI sbRIO-9632 FPGA board using VHDL. The implementation results are evaluated in terms of FPGA speed and usage of FPGA resources. It shows that the acceleration measurement on the FPGA board utilises approximately 15% of FPGA resources with approximately 44MHz FPGA speed. The execution time of the acceleration measurement module shows the FPGA-based implementation outperforms the microcontroller-based implementation. The correctness of the computed output by the FPGA board is also verified. These findings provide insight into the implementation of real-time monitoring applications, particularly for the human motion measurement system on the FPGA platform.
Hybrid Discriminator With Correlative Autoencoder for Anomaly Detection

Advances in deep neural networks (DNNs) have led to impressive results and in recent years many works have exploited DNNs for anomaly detection. Among others, generative/reconstruction model-based methods have been frequently used for anomaly detection because they do not require any labels for training. The anomaly detection performance of these methods, however, varies a lot, due to the change of the intra-class variance and the difference in complexity of input samples. In addition, most previous state-of-the-art works on anomaly detection have empirically adjusted several hyperparameters to heighten their performance of anomaly detection. These sorts of procedures are known to be impractical and create obstacles in real world anomaly detection. To solve these problems, we propose a hybrid discriminator with a correlative autoencoder for anomaly detection. In the proposed framework, the discriminator implicitly estimates the conditional probability density function and the autoencoder has improved ability to control the reconstruction error. We provide theoretical foundation of our method and verify it through various experiments. We also confirm practical benefits of our interpretation of the conditional expectation and the proposed framework by comparing our results with other state-of-the-art methods.
Generation of Human-Aware Navigation Maps Using Graph Neural Networks

Minimising the discomfort caused by robots when navigating in social situations is crucial for them to be accepted. Graph Neural Networks can process representations including arbitrarily complex relationships between entities such as human interactions. This is particularly interesting in the context of social navigation, where relational information should be considered. This paper presents a model combining Graph Neural Network (GNN) and Convolutional Neural Network (CNN) layers to produce cost maps for human-aware navigation in real-time. The model leverages the relational inductive bias of GNNs to generate scenario representations that can be efficiently exploited using CNNs. In addition, a framework to bootstrap existing zero-dimensional models to generate cost map datasets is proposed. The model is evaluated against the original zero-dimensional dataset and in simulated navigation tasks. The results outperform similar state-of-the-art-methods considering the accuracy for the dataset and the navigation metrics used. The applications of the proposed framework are not limited to human-aware navigation, it could be applied to other fields where cost map generation is needed.
Fast Stochastic Ordinal Embedding With Variance Reduction and Adaptive Step Size

Learning representation from relative similarity comparisons, often called ordinal embedding, gains rising attention in recent years. Most of the existing methods are based on semi-definite programming (SDP), which is generally time-consuming and degrades the scalability, especially confronting large-scale data. To overcome this challenge, we propose a stochastic algorithm called SVRG-SBB, which has the following features: i) achieving good scalability via dropping positive semi-definite (PSD) constraints as serving a fast algorithm, i.e., stochastic variance reduced gradient (SVRG) method, and ii) adaptive learning via introducing a new, adaptive step size called the stabilized Barzilai-Borwein (SBB) step size. Theoretically, under some natural assumptions, we show the <b xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">O</b> (1/T) O(1T) rate of convergence to a stationary point of the proposed algorithm, where T T is the number of total iterations. Under the further Polyak-Łojasiewicz assumption, we can show the global linear convergence (i.e., exponentially fast converging to a global optimum) of the proposed algorithm. Numerous simulations and real-world data experiments are conducted to show the effectiveness of the proposed algorithm by comparing with the state-of-the-art methods, notably, much lower computational cost with good prediction performance.
Design and Evaluation of an Ultra Low-power Human-quality Speech Recognition System

Automatic Speech Recognition (ASR) has experienced a dramatic evolution since pioneer development of Bell Lab’s single-digit recognizer more than 50 years ago. Current ASR systems have taken advantage of the tremendous improvements in AI during the past decade by incorporating Deep Neural Networks into the system and pushing their accuracy to levels comparable to that of humans. This article describes and characterizes a representative ASR system with state-of-the-art accuracy and proposes a hardware platform capable of decoding speech in real-time with a power dissipation close to 1 Watt. The software is based on the so-called hybrid approach with a vocabulary of 200K words and RNN-based language model re-scoring, whereas the hardware consists of a commercially available low-power processor along with two accelerators used for the most compute-intensive tasks. The article shows that high performance can be obtained with very low power, enabling the deployment of these systems in extremely power-constrained environments such as mobile and IoT devices.
Feature Explainable Deep Classification for Signal Modulation Recognition

Signal modulation recognition plays a critical role in many fields to identify the modulation type of wireless signals. Since the deep learning based models have achieved great success in classification tasks, more deep neural networks are proposed for signal modulation recognition. In this paper, we explore the use of different deep neural networks in both macro network architecture level and micro cell size and layer level to compare and understand their effect on classification performance. We also bring up a feature explainable deep neural network by visualizing the critical features in the deep neural models. We visually show and compare the commonality and differences of hidden layer characteristics extracted by different network structure to explain and analyze the reason why some models can achieve better classification results than the others. We believe it is an effective way to explain how deep neural model based signal classification work. Thus the explanation will help users establish appropriate understand and trust in predictions from deep modulation recognition networks.
A methodology for structured literature network meta-analysis

Purpose Little attention has been paid to restructuring existing massive amounts of literature data such that evidence-based meaningful inferences and networks be drawn therefrom. This paper aims to structure extant literature data into a network and demonstrate by graph visualization and manipulation tool “Gephi” how to obtain an evidence-based literature review. Design/methodology/approach The main objective of this paper is to propose a methodology to structure existing literature data into a network. This network is examined through certain graph theory metrics to uncover evidence-based research insights arising from existing huge amounts of literature data. From the list metrics, this study considers degree centrality, closeness centrality and betweenness centrality to comprehend the information available in the literature pool. Findings There is a significant amount of literature on any given research problem. Approaching this massive volume of literature data to find an appropriate research problem is a complicated process. The proposed methodology and metrics enable the extraction of appropriate and relevant information from huge quantities of literature data. The methodology is validated by three different scenarios of review questions, and results are reported. Research limitations/implications The proposed methodology comprises of more manual hours to structure literature data. Practical implications This paper enables researchers in any domain to systematically extract and visualize meaningful and evidence-based insights from existing literature. Originality/value The procedure for converting literature data into a network representation is not documented in the existing literature. The paper lays down the procedure to structure literature data into a network.