Reviewerly/exHarmony huggingface dataset:
Projection Algorithms for Distributed Stochastic Optimization

This chapter focuses on introducing and solving the problem of composite constrained convex optimization with a sum of smooth convex functions and non-smooth regularization terms (ℓ1 norm) subject to locally general constraints. Each of the smooth objective functions is further thought of as the average of several constituent functions, which is motivated by the modern large-scale information processing problems in machine learning (the samples of a training dataset are randomly distributed across multiple computing nodes). We present a novel computation-efficient distributed stochastic gradient algorithm that makes use of both the variance-reduction methodology and the distributed stochastic gradient projection method with constant step-size to solve the problem in a distributed manner. Theoretical study shows that the suggested algorithm can discover the precise optimal solution in expectation when each constituent function (smooth) is strongly convex if the constant step-size is less than an explicitly calculated upper constraint. Regarding the current distributed methods, the suggested technique not only has a low computation cost in terms of the overall number of local gradient evaluations but is also suited for addressing general restricted optimization problems. Finally, the numerical proof is offered to show the suggested algorithm’s attractive performance.
Detection and Classification of Robotic Manipulator Anomalies Using MLSTM-FCN Models

Abstract Errors and failures occur inevitably in modern Cyber-Physical Systems (CPS) due to their structural variability and internal heterogeneity. This can cause economic losses or even hazardous accidents. Currently, deep learning-based anomaly detection methods, e.g., Transformer or LSTM-based detectors, have shown tremendous results in terms of anomaly detection and prevention. However, focusing solely on improving detection performance without classification and interpretation of the detected anomalies is not enough for many industrial scenarios. Instead of only reporting an anomaly, the detection results should be understandable and transparent for the users. The interpretability can provide some guidance and help to identify suitable countermeasures for different types of anomalies. In this paper, we introduce a Multivariate Long Short Term Memory Fully Convolutional Network (MLSTM-FCN) for anomaly classification based on multivariate time-series data generated from industrial robotic manipulators. Specifically, we investigate several scenarios: no collision, collision with another manipulator, and manually injected sensor faults. We collect time-series data from the simulations of robotics arms using CoppeliaSim software. We feed these data into the MLSTM-FCN model and train it to be a multivariate time-series classifier. The paper presents the simulative case study results that show that the MLSTM-FCN model can efficiently classify different types of anomalies.
Assessing the Potential for Implementation of Distributed Ledger Technology in the Nuclear Power Plant Lifecycle

Abstract The Nuclear Power Plant (NPP) project lifecycle consists of multiple organizations, private and public, that work together to carry out the licensing and regulation, commissioning, construction, supply chain management, decommissioning, and non-proliferation of nuclear power plants. Each stage of the lifecycle generates a considerable amount of documentation that needs to be archived and made available on request. In this paper, we examined the current frameworks employed by the stakeholders of nuclear power plant lifecycles for data generation, classification, archival, and exchange and we evaluated the use of distributed ledger technology for data management. We further demonstrated the implementation of distributed ledger technology to capturing and immutable recording nuclear supply chain structure, production, and failure data. Distributed Ledger Technology (DLT) promises to address the current bottlenecks in the lifecycle management of nuclear power plants with faster data exchange times, lower processing cost of documents, bridging the gap between perceived and actual risk, faster decisions, customizable transparency in job tasks being executed, improved cash flow, instant validation of job completion, reduction in logistical friction, and sped up supply chain decisions for clients. We gave a comprehensive review of distributed ledger technology, its properties and an open-source permissioned distributed ledger system called Hyperledger Fabric. Each sector of the nuclear power plant lifecycle was analyzed for data management bottlenecks and building on our discussion of employing distributed ledger technology we discussed the pathway in which Hyperledger Fabric can be employed to address the data management bottlenecks in each stage of the lifecycle including, licensing and regulation, commissioning, construction, supply chain management, decommissioning, and non-proliferation.
Control of Flight Vehicles From the Perspective of Non-Holonomic Constraint Manifold Dynamics: Quadrotor Application

Abstract In this paper, we address the problem of flight path planning and control. We approach the problem from the perspective of non-holonomic generalized momenta. We first build a desired minimum Jerk trajectory that we use as a momentum manifold constraint for the vehicle. We then develop a trajectory reference model of the flight vehicle that evolves exactly on the flight path as a constraint, as if the vehicle were a bead on a wire in the generalized coordinate configuration space. This model uses non-holonomic generalized momenta and position and orientation variables as states, the model is of order 2N – M, where N is the full dimension of the vehicle model and M is the number of manifold constraints imposed. These momenta models are canonical without Lagrange Multipliers. Next, we build a full 2N order model of the flight vehicle and design an LQR controller linearized about nominal path independent flight. We then implement this control in the full flight model and use as the reference state trajectory the constrained momentum states computed on the desired flight path. We allow inflight disturbances. We demonstrate that this approach provides good performance for a Quadrotor flight vehicle.
On the Observability of Quantum Dynamical Systems

Abstract Quantum statistical mechanics offers an increasingly relevant theory for a wide variety of probabilistic systems including thermodynamics, particle dynamics, and robotics. Quantum dynamical systems can be described by linear time invariant systems and so there is a need to build out traditional control theory for quantum statistical mechanics. The probability information in a quantum dynamical system evolves according to the quantum master equation, whose state is a matrix instead of a column vector. Accordingly, the traditional notion of a full rank observability matrix does not apply. In this work, we develop a proof of observability for quantum dynamical systems including a rank test and algorithmic considerations. A qubit example is provided for situations where the dynamical system is both observable and unobservable.
Trajectory Tracking in the Image Frame for Autonomous Navigation of UAV in UAV-AGV Multi-Agent System

Abstract UAV-AGV heterogeneous multi-agent robotic system has drawn the attention of researchers to explore its capabilities in different perspectives. The UAV-AGV can concatenate their individual capabilities to overcome the drawbacks of each. UAV will benefit in payload and AGV will have the navigation guidance due to the presence of UAV. Collaborative kinematics between both agents is basic requirement of the system. Vision-based method is one of the techniques to implement collaborative motion. A high-level sliding mode controller is developed and validated for the vision-based navigation of UAV for reaching the target/AGV. Gazebo simulations are performed for trajectory tracking in the image frame to reach the target by the UAV. UAV autonomously detects the target and plans the trajectory to reach it. Apparent size-based depth controller is developed for the UAV and simulated in the Gazebo. Altitude trajectory tracking is implemented for the UAV using sliding model controller. Sliding mode based high-level controllers are performing well for the navigation of UAV and trajectory tracking in the image frame opens a different approach for the reaching of AGV by UAV. A non-linear depth controller is developed and simulated in Gazebo which can be useful for the landing task of UAV over AGV.
SMA-Based Haptic Gloves Usage in the Smart Factory Concept: XR Use Case

Abstract Conceptualization of the Smart Factory started with introducing the Industry 4.0 paradigm and its nine pillars, which it stands. The paradigm itself is automation and robot-centric focused, which means less and less involvement of the humans on the manufacturing shop floor. However, even robots and simulation aspects of the factories are the most crucial aspects; Industry 4.0 still focuses on the Augmented and Virtual Reality (AR and VR input methods for the human operators, making the smooth transition to the Industry 5.0 concept a human-centric. Although VR/AR is still being enabled and widely used in the Human-Robot Interaction (HRI) research aspect, the heavy headset is limited in the observation field of view. The input methods, such as headsets, have voice and gesture recognition; however, those are mainly limited by factory noise and cameras pointing to the human hands. These headsets constrain the use of smart wearables to a given boundary inside the factory environment. A Shape Memory Alloy (SMA) based haptic glove with discrete data outputs from the kinaesthetic analysis of the hand bending can remove the need for gesture recognition. The paper proposes a modular framework using the SMA-based Haptic Gloves in the Smart Manufacturing environment. These gloves, without additional wearables, can enable interactions with heavy machinery, screens, and all other assets of the industrial area, even with holographic. In this paper, the authors aim to prose the context, design, and framework with the chosen use-cases mainly based on the robotic system applications in the Technological University of the Shannon: Midlands Midwest (TUS: MMW), Ireland, and Tallinn University of Technology (TalTech), Estonia.
Simplified Geometries for Intracranial Acoustic Modeling

Abstract The geometric complexity of the contents of the head confounds mechanical analysis of intracranial structures. Conventional models for computational analysis are typically created through a laborious segmentation and reconstruction process highly dependent on expert labor and anatomical insight. This study explores a deterministic process for construction of a simplified, anatomically-relevant head model appropriate for acoustical modeling. Various key anatomical features with acoustical significance are reviewed. Models of increasing complexity are generated, spanning a range from coupled concentric spheres to more advanced geometries incorporating ventricles, brain structures, and other anatomical landmarks. Geometric relevance of the models is assessed by comparison to a high-fidelity computational geometry derived from medical imagery. These techniques and models are useful for a variety of studies investigating phenomena such as traumatic brain injury mechanics and industrial safety.
Investments Today Deliver the Data Needed for Tomorrow

Traffic Technology InternationalVol. 2021, No. 2 RegularsInvestments Today Deliver the Data Needed for TomorrowKirk SteudleKirk SteudleSearch for more papers by this authorKirk SteudlePublished Online:8 Feb 2023https://doi.org/10.12968/S1356-9252(23)40507-2AboutSectionsView articleView Full TextPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareShare onFacebookTwitterLinked InEmail View article FiguresReferencesRelatedDetails 1 June 2021Volume 2021Issue 2ISSN (print): 1356-9252ISSN (online): 2397-5970 Metrics History Published online 8 February 2023 Published in print 1 June 2021 Information© MA Business Ltd.PDF download
Review of Metrics to Measure the Stability, Robustness and Resilience of Reinforcement Learning

Reinforcement learning (RL) has received significant interest in recent years, primarily because of the success of deep RL in solving many challenging tasks, such as playing chess, Go, and online computer games. However, with the increasing focus on RL, applications outside gaming and simulated environments require an understanding of the robustness, stability, and resilience of RL methods. To this end, we conducted a comprehensive literature review to characterize the available literature on these three behaviors as they pertain to RL. We classified the quantitative and theoretical approaches used to indicate or measure robustness, stability, and resilience behaviors. In addition, we identified the actions or events to which the quantitative approaches attempted to be stable, robust, or resilient. Finally, we provide a decision tree that is useful for selecting metrics to quantify behavior. We believe that this is the first comprehensive review of stability, robustness, and resilience, specifically geared toward RL.
Analysis of IoT Research Topics Using LDA Modeling

The three major characteristics of the 4th industrial revolution are hyperconnectivity, superintelligence, and hyperconvergence. The purpose of this study is to analyze the research trend of ‘Internet of Things’, a core technology that represents these changes. In particular, an attempt was made to gain insight by identifying the fields of engineering and social sciences separately. Additionally, macroscopic research trends were confirmed through topic modeling. As a result, differences in research topics for each field were identified, and topics that could confirm research trends were derived.
Research on Development of Sponsorship Effect Analysis Module Using Text Mining Technique

To expand the sponsorship market, sponsorship activities based on sponsorship effect analysis data through scientific and systematic analysis must be carried out. As the development of the media industry and the COVID-19 pandemic have caused many changes in the method of broadcasting professional sports, it is necessary to upgrade the analysis of sponsorship effects. In a crisis situation where the sponsorship effect analysis market is shrinking due to the COVID-19 pandemic, the development of brand exposure analysis programs that can be used based on online platforms will expand the sponsorship market and promote changes in the domestic analysis market that relies on overseas analysis programs. In this study, more than 200,000 online sports media data were collected to analyze the sponsorship effect by minimizing the omission of sponsor brands exposed through online media and detecting sophisticated data. A commercial dictionary in the sports field was established and a sponsorship effect analysis module was developed by quantifying text data using morpheme analysis and TF-IDF. A module based on UI was implemented to analyze the results of the custom morpheme analyzer and Elasticsearch Term Vectors AP.
The Impact of Changes in Oil Prices on the Global and Saudi Arabia Economy

This study aimed to identify the impact of COVID-19 on oil sector which faced the significant challenges, globally and in Saudi Arabia. The study sheds light on the unprecedented crisis that occurred in the decline in demand for oil and the collapse of its prices, and its effects and consequences on world economies and Arab economies, and the extent to which the Saudi economy has been affected by them in light of what markets have witnessed global oil, and opportunities to benefit from this crisis and the study relied on the descriptive and analytical methodology. The study paper will use the NARDL to explain the asymmetric effect of changes in oil price on Government Budget for Saudi Arabia. The study reached that this situation provides a further opportunity for Saudi Arabia to diversify the economy away from a reliance on oil, Government policies seek to facing these challenges by diversifying the economic base and developing and diversifying revenues non-oil products to reduce financial volatility, in addition to enhancing the role of the private sector focusing on supporting non-oil GDP growth rates to enhance the resilience of the economy.
Predicting Bitcoin Prices Using ANFIS and Haar Model

This study aims to model and enhance the forecasting accuracy of cryptocurrency market data patterns using the daily bitcoin (BTC) close price data with 1535 observations from December 2017 to January 2022. The model employs a nonlinear spectral model of maximum overlapping discrete wavelet transform (MODWT) with Haar mathematical functions in conjunction with an adaptive network-based fuzzy inference system (ANFIS). We have selected the logarithm volume of bitcoin (LV) and logarithm trade count (LCT) as input values according to correlation and multiple regressions. The input and output variables have been collected from the cryptocurrency market. The performance of the proposed model (MODWT-Haar-ANFIS) is compared with traditional models that are the autoregressive integrated moving average (ARIMA) model and the ANFIS model. The obtained results show that the performance of MODWT-Haar-ANFIS is better than that of the traditional models. Therefore, the proposed forecasting model is a promising approach that capable of deploying in the cryptocurrency markets.
Ebb and Flow Theory in Tourism, Hospitality, and Event Management

This study aims to track the events that affected tourism in Jordan and presents the evolution of tourist inflows and outflows linked to these events. Accordingly, the Ebb and Flow Theory in the service industry is created and discussed. Many terminologies were used in the tourism industry to clarify the incoming and outgoing tourism movement, such as “peak period and recession period”, “high–shoulder and low-shoulder”, “high-season and low-season”, and others. This study sheds light on a new insight that accurately and clearly describes what is happening in the tourism, hospitality, and events industry. It describes the period of prosperity and development in the tourism movement as the Flow, and the period of stagnation and confinement in the global tourist movement as the Ebb. Also, the fluctuation between these two periods is called solstice time. Understanding these periods helps organizations develop their strategies, achieve the desired benefit, improve and innovate in providing optimal service.
On the Influence of Virtual Reality Technology (VR) on Animation Art Design

A virtual reality animation is a new form of animation after the perfect combination of technology and art. This animation is a fusion of artificial computer intelligence, simulation technology, display technology, sensor technology, and other technologies. With the development of science and technology and the advancement of technology, virtual reality technology has gradually become an essential form of technology in the contemporary era.
If You Believe, You Will Receive: VR Interview Training for Pre-employment

Covid-19 pandemic accelerates the growing use of augmented and virtual reality in various industries, especially in education sector. It is worthy to study whether VR training would apply to technology-accepted learners, i.e., does “If you believe, you will receive” apply to VR training. In this work, the researchers developed an immersive VR interview room system that allows pre-employment learners to try on a simulated environment. Pre-captured interviewer questions are played for the learners get a taste into a real-liked interview. The investigation is the relationship between learners’ perceived usefulness and interview self-efficacy in VR training in human resources management. The experiment results show they are positively correlated.
An XGBoost-Based Classification Method to Classify Breast Cancer

The people affected with this dreadful disease known as breast cancer are susceptible to complex health issues and the mortality rate has increased day by day. Diagnosing breast cancer is quite a laborious task and involves careful observation to screen the breast to identify irregularities in the breast because of many underlying features. If breast cancer is diagnosed at early stages, then it will assist the doctors and patients to handle the disease with ease. Therefore, it is worth classifying breast cancer disease to identify which stage of breast cancer the person is currently belonging to. This led to the emergence of using machine learning techniques to classify and diagnose breast cancer data. Currently, classifiers of machine learning are used in wide areas of research to perform classification, clustering and mining the rules accordingly. In this chapter breast cancer dataset has been collected from the UCI machine learning repository, which has 569 instances with 31 attributes. In this chapter, we propose a classification of breast cancer data into benign and malignant classes using XGBoost. We use XGBoost in an intuitive way where we extract the features of the dataset which are considered important by it i.e., XGBoost being used as a feature selection method. We use these features in different classification schemes. Testing is carried out to check the accuracy of the model built by the classifier. Result analysis reveals that important features yielded by XGBoost significantly increase the model accuracy of different classification methods.
Three-color and tripartite steering generated by intracavity cascaded processes

Einstein–Podolsky–Rosen (EPR) steering has important practical applications in secure quantum communication because of its unique characters. Multicolor and multipartite EPR steering is an important resource for connecting different physical systems in the quantum networks with several nodes. We investigate three-color and tripartite steering properties based on the intracavity cascaded nonlinear processes of down conversion and sum frequency. Based on the criteria proposed by Kogias [Phys. Rev. Lett. 114, 060403 (2015)], the steering parameters among down-conversion fields and sum-frequency field versus the normalized frequency and the pump parameter are studied. Our research shows that the direction of steering and steerability can be manipulated flexibly. We also show that quantum secret sharing can be realized in our scheme. And the results of asymmetric quantum steering and joint quantum steering among different modes provide a technical reference for the construction of secure quantum information network.
CloudSec (3FA): a multifactor with dynamic click colour-based dynamic authentication for securing cloud environment

Availability, scalability, and security are the main concerns in a cloud environment and so it undergoes various challenges in the availability and security of sensitive and critical data stored in it. Multifactor authentication is one solution that offers layered defence in order to perform verification and validation of the users' credentials that makes it difficult for an adversary to access the data stored in the cloud. This paper addresses the security issues in the cloud environment by suggesting a multifactor authentication model that utilises three significant factors such as static authentication, click colour-based dynamic authentication and behavioural biometrics specifically keystroke with cryptographic encryption and hashing technique. Based on the experimental results, it is clear that the proposed method verifies and validates the authorised users thereby eliminating the adversaries with more than 96% of accuracy. Also, the model decreases the false positive and error rate to an extent of 3%.