Reviewerly/exHarmony huggingface dataset:
Review of Tools for Semantics Extraction: Application in Tsunami Research Domain

Immense numbers of textual documents are available in a digital form. Research activities are focused on methods of how to speed up their processing to avoid information overloading or to provide formal structures for the problem solving or decision making of intelligent agents. Ontology learning is one of the directions which contributes to all of these activities. The main aim of the ontology learning is to semi-automatically, or fully automatically, extract ontologies—formal structures able to express information or knowledge. The primary motivation behind this paper is to facilitate the processing of a large collection of papers focused on disaster management, especially on tsunami research, using the ontology learning. Various tools of ontology learning are mentioned in the literature at present. The main aim of the paper is to uncover these tools, i.e., to find out which of these tools can be practically used for ontology learning in the tsunami application domain. Specific criteria are predefined for their evaluation, with respect to the “Ontology learning layer cake”, which introduces the fundamental phases of ontology learning. ScienceDirect and Web of Science scientific databases are explored, and various solutions for semantics extraction are manually “mined” from the journal articles. ProgrammableWeb site is used for exploration of the tools, frameworks, or APIs applied for the same purpose. Statistics answer the question of which tools are mostly mentioned in these journal articles and on the website. These tools are then investigated more thoroughly, and conclusions about their usage are made with respect to the tsunami domain, for which the tools are tested. Results are not satisfactory because only a limited number of tools can be practically used for ontology learning at present.
Assessing the Cost of Quantum Security for Automotive Over -The-Air Updates

Over- The-Air (OTA) update is an innovative paradigm that is rapidly spreading through the automotive industry. Software updates can be capillary distributed thanks to the many Vehicle-to-Everything (V2X) communication infrastructures that are part of a Smart City. Unfortunately, the majority of the existing OTA frameworks and schemes are not quantum resistant, meaning that when quantum computing will become reality, they will not be secure anymore. The U.S. National Institute of Standards and Technology (NIST) has announced a contest to determine the post-quantum standards for digital signatures schemes. In this paper, we evaluate the performance of the digital signature verification algorithms of two out of the three finalists for the NIST contest, namely FALCON and CRYSTALS-DILITHIUM. These algorithms are tested on automotive-oriented evaluation board, namely the Xilinx Zynq Ultrascale+ ZCU102. The results show that FALCON is a more promising algorithm compared to DILITHIUM both regarding signature verification execution time and signature size.
Simulating macroscopic quantum correlations in linear networks

Many developing quantum technologies make use of quantum networks of different types. Even linear quantum networks are nontrivial, as the output photon distributions can be exponentially complex. Despite this, they can still be computationally simulated. The methods used are transformations into equivalent phase-space representations, which can then be treated probabilistically. This provides an exceptionally useful tool for the prediction and validation of experimental results, including decoherence. As well as experiments in Gaussian boson sampling, which are intended to demonstrate quantum computational advantage, these methods are applicable to other types of entangled linear quantum networks as well. This paper provides a tutorial and review of work in this area, to explain quantum phase-space techniques using the positive-P and Wigner distributions.
Diversification of Time-Varying Tangency Portfolio under Nonlinear Constraints through Semi-Integer Beetle Antennae Search Algorithm

In finance, the most efficient portfolio is the tangency portfolio, which is formed by the intersection point of the efficient frontier and the capital market line. This paper defines and explores a time-varying tangency portfolio under nonlinear constraints (TV-TPNC) problem as a nonlinear programming (NLP) problem. Because meta-heuristics are commonly used to solve NLP problems, a semi-integer beetle antennae search (SIBAS) algorithm is proposed for solving cardinality constrained NLP problems and, hence, to solve the TV-TPNC problem. The main results of numerical applications in real-world datasets demonstrate that our method is a splendid substitute for other evolutionary methods.
Energy-Efficient Task Scheduling Using Quantum-Inspired Genetic Algorithm for Cloud Data Center

Misra, Santanu Kumar Kuila, PratyayCloud computing is a collection of heterogeneous autonomous systems to support the emerging computational paradigm with the help of flexible computational architecture. In order to support enormous computational load, it exhibits huge energy consumption (EC) which causes the degradation of system performance. In this paper, we have proposed an optimized energy-efficient scheduling model using quantum-inspired genetic algorithm (QIGA) to minimize energy consumption in the cloud data center. Our model establishes a trade-off between execution time and EC without compromising system performance. A suitable comparison between our proposed model with some existing model has been performed to validate our task.
Discrete-time distributed Kalman filter design for networks of interconnected systems with linear time-varying dynamics

This paper addresses the problem of designing distributed state estimation solutions for a network of interconnected systems modelled by linear time-varying (LTV) dynamics in a discrete-time framework. The problem is formulated as a classical optimal estimation problem, for the global system, subject to a given sparsity constraint on the filter gain, which reflects the distributed nature of the network. Two methods are presented, both of them able to compute a sequence of well-performing stabilising gains. Moreover, both methods are validated by resorting to simulations of: (i) a randomly generated synthetic LTV system; and (ii) a large-scale nonlinear network of interconnected tanks. One of the proposed methods relies on a computationally efficient solution, thus it is computed very rapidly. The other achieves better performance, but it is computationally more expensive and requires that a window of the future dynamics of the system is known. When implemented to a nonlinear network, approximated by an LTV system, the proposed methods are able to compute well-performing gains that stabilise the estimation error dynamics. Both algorithms are scalable, being adequate for implementation in large-scale networks.
Prediction of Task Execution Time in Cloud Computing

Now-a-days all cloud service providers are providing all resources to end users at very cheap rate and at the same time by designing scheduling algorithms cloud service providers are ensuring that all users can get response data in quick time. Proper utilization of resources can be enhanced by better resource allocation which will decrease the total makespan and the response time of the task. In this proposed work, a novel scheduling algorithm called Prediction of Execution Time of Tasks algorithm (PETT) to estimate minimum task execution time for cloud computing environment is proposed. In PETT algorithm, an array with all tasks and processors is built as a Directed Acyclic Graph (DAG). The matrix contains estimated execution task time on each processor and all rows of the matrix are filled with all processors execution time for all tasks. On generated matrix, PCA algorithm has been applied to choose that processor, which consumes less execution time for selected task. This process continues till all tasks assigned to all processors. By applying PETT algorithm, further computation and communication cost can be reduced at the cloud end.
Efficient Gradient-Based Neural Architecture Search For End-to-End ASR

Neural architecture search (NAS) has been successfully applied to tasks like image classification and language modeling for finding efficient high-performance network architectures. In ASR field especially end-to-end ASR, the related research is still in its infancy. In this work, we focus on applying NAS on the most popular manually designed model: Conformer, and propose an efficient ASR model searching method that benefits from the natural advantage of differentiable architecture search (Darts) in reducing computational overheads. We fuse Darts mutator and Conformer blocks to form a complete search space, within which a modified architecture called Darts-Conformer cell is found automatically. The entire searching process on AISHELL-1 dataset costs only 0.7 GPU days. Replacing the Conformer encoder by stacking searched architecture, we get an end-to-end ASR model (named as Darts-Conformner) that outperforms the Conformer baseline by 4.7% relatively on the open-source AISHELL-1 dataset. Besides, we verify the transferability of the architecture searched on a small dataset to a larger 2k-hour dataset.
The complementarities of digitalisation and productivity: redefining boundaries for financial sector

Digitalisation is portrayed as a transformative force, remodelling the way we live and businesses operate. In today's unprecedented business environment, the survival of organisations is in technological advancement and online presence. When masses rely on digital financial payments, there is a pressing need for the financial sector to offer innovative products and services to meet customers' needs and achieve sustainable performance. This paper aims to investigate the impact of data analytics on the productivity of banks in Pakistan and employed two-step system generalised methods of moments for estimation. The findings suggest that 5.9% productivity is increased for banks that invested in data analytics on average. It was also found that productivity increase is associated with an investment in data analytics compared to a mere investment in any software. However, the moderating role of dynamic capabilities on the relationship between data analytics and banks' productivity is insignificant, which raises a question on the relevance of research and development expense with human capital development. It is recommended that banks should invest in those analytics that have predictive, visualising and analytical capabilities. The use of these innovative technologies should be combined with training and human capital development to ensure sustainable firm performance.
Coupling Relationship Analysis of Gold Content Using Gaofen-5 (GF-5) Satellite Hyperspectral Remote Sensing Data: A Potential Method in Chahuazhai Gold Mining Area, Qiubei County, SW China

The gold (Au) geochemical anomaly is an important indicator of gold mineralization. While the traditional field geochemical exploration method is time-consuming and expensive, the hyperspectral remote sensing technique serves as a robust technique for the delineation and mapping of hydrothermally altered and weathered mineral deposits. Nonetheless, mineralization element anomaly detection was still seldomly used in previous hyperspectral remote sensing applications in mineralization. This study explored the coupling relationship between Gaofen-5 (GF-5) hyperspectral data and Au geochemical anomalies through several models. The Au geochemical anomalies in the Chahuazhai mining area, Qiubei County, Yunnan Province, SW China, was studied in detail. First, several noise reduction methods including radiometric calibration, Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes (FLAASH), Savitzky–Golay filter, and endmember choosing methods including Minimum Noise Fraction (MNF) transformation, matched filtering, and Fast Fourier Transform (FFT) transformation were applied to the Gaofen-5 (GF-5) hyperspectral data processing. The Spectrum-Area (S-A) method was introduced to build an FFT filter to highlight the spectral abnormal characteristics associated with Au geochemical anomaly information. Specifically, the Matched Filtering (MF) technique was applied to the dataset to find the Au geochemical anomaly abundances of endmembers with innovative large-sample learning. Then, Multiple Linear Regression (MLR), Partial Least Squares (PLS) regression, a Back Propagation (BP) network, and Geographically Weighted Regression (GWR) were used to reveal the coupling relationship between the spectra of the processed hyperspectral data and the Au geochemical anomalies. The results show that the GWR analysis has a much higher coefficient of determination, which implies that the Au geochemical anomalies and the spectral information are highly related to spatial locations. GWR works especially well for showing the regional Au geochemical anomaly trend and simulating the Au concentrated areas. The GWR model with application of the S-A method is applicable to the detection of Au geochemical anomalies, which could provide a potential method for Au deposit exploration using GF-5 hyperspectral data.
Real-Time Monitoring of COVID-19 Vaccination Compliance: A Ubiquitous IT Convergence Approach

As most countries relax restrictions on lockdown and social activities returns due to massive response to COVID-19 vaccination, there is need to put in place a universally acceptable technological innovation that can checkmate and enforce compliance to avoid resurgence of another deadly wave as witnessed previously. Combining vaccination effort with disruptive technology for compliance enforcement is an unarguable panacea. This paper presents an IT-convergence solution that fuses disruptive technologies to distinguish between vaccinated and non-vaccinated individuals in real-time and initiate strict and appropriate compliance directives and consequent denial of access to certain places. The proposed design is a fusion of facial recognition, mask wearing detection technology using Yolov5 deep learning model, network-based vaccination record management application, biometric feature-based vaccination status validation, and compliance enforcement in real-time. The system achieved 99.5% accurate detection and 100% real-time authentication with less computational complexities. This innovation guarantees intuitive monitoring of vaccination progress and curtailment of COVID-19 spread through compliance enforcement.
Costmap Generation Based on Dynamic Obstacle Detection and Velocity Obstacle Estimation for Autonomous Mobile Robot

The environmental conditions corresponding to dangerous or collided areas are generally represented by Costmap when the Autonomous Mobile Robot (AMR) is navigated. Here, this paper provides a Costmap 2D layer plug-in, Velocity Obstacle layer, it can accurately detect obstacle&#x0027;s coordination and radius and then estimate the obstacle&#x0027;s velocity to create Velocity Obstacle which can represent the potential collision vector in the future. In the simulation, we assume the robot&#x0027;s max velocity is 0.2m/s and an obstacle move forward to the robot with 0.3m/s. The results show the AMR can avoid the obstacle well. In experiment, the AMR also can avoid the people moving toward it in the real world.
Mineralogical and elemental data for soil discriminating and geolocation tracing

One of the key tasks of soil analysis in forensic sciences is to provide information about its diversities and geolocation. In fact, soil analysis is relevant for forensic geologists. In this study, a total of 80 soil samples were collected from eight Chinese cities (10 samples per city). Different minerals and their relative percentages were analyzed by the X-ray diffraction (XRD) method. In addition, the relative amounts of montmorillonite, kaolinite, amphibole, feldspar, calcite, and dolomite provided information about the origin of a soil, either if it came from a northern or southern city of China. The oxide weight percentages of 10 elements of Al2O3, SiO2, Fe2O3, K2O, Na2O, MgO, CaO, P2O5, MnO, and TiO2 were also obtained by using X-ray fluorescence (XRF) from the 80 soil samples. Moreover, principal component analysis (PCA) and hierarchical clustering analysis (HCA) methods were performed for dimensionality reduction, elemental marker identification and soils classification to the city they came from purposes. The eighty soils analyzed in this study could be tracked correctly to their city of origin. The K-Nearest Neighbors (KNN) model was done to evaluate the prediction ability based on the soil elemental composition, and it was confirmed by cross validation methods. The results demonstrated that mineralogical and elemental composition can provide powerful information for soil discrimination and source tracing.
An Analysis of English Classroom Multimedia Teaching Quality Based on Elite Teaching Optimization Algorithm

As China is increasingly developing towards internationalization, English has become one of the essential skills for foreign communication. In view of the evaluation of music teaching quality, this paper tries to introduce elite teaching optimization algorithm, use multimedia to assist, sort out the factors affecting English teaching quality to build the corresponding indexes, select and optimize the English teaching quality evaluation model, and finally judge the results of English teaching quality. The simulation results show that the elite teaching optimization algorithm is effective and can support the quality analysis of English classroom multimedia teaching.
Estimating Contemporary Relevance of Past News

Our society generates massive amounts of digital data, significant portion of which is being archived and made accessible to the public for the current and future use. In addition, historical born-analog documents are being increasingly digitized and included in document archives which are available online. Professionals who use document archives tend to know what they wish to search for. Yet, if the results are to be useful and attractive for ordinary users they need to contain content which is interesting and familiar. However, the state-of-the-art retrieval methods for document archives basically apply same techniques as search engines for synchronic document collections. In this paper, we introduce a novel concept of estimating the relation of archival documents to the present times, called contemporary relevance. Contemporary relevance can be used for improving access to archival document collections so that users have higher probability of finding interesting or useful content. We then propose an effective method for computing contemporary relevance degrees of news articles using Learning to Rank with a range of diverse features, and we successfully test it on the New York Times Annotated document collection. Our proposal offers a novel paradigm of information access to archival document collections by incorporating the context of contemporary time.
OpenAPI Thing Descriptions for the Web of Things

The Web of Things (WoT) recommendation of W3C suggests a model for integrating Things (e.g. devices) in the Web. In addition to W3C, OpenAPI specification provides a method for documenting RESTful services so that a user or another service can comprehend their purpose and reuse them in applications. This work applies the OpenAPI service description framework to Web objects (i.e. Things). As a result, OpenAPI descriptions of Web Things provide a complete documentation of the services exposed by Things and of their capabilities. The resulting descriptions can be converted to an ontology in order to allow a machine to better understand the inherent meaning of Thing descriptions and interact with them. Then, Thing descriptions exposed in the Web can be easily discovered, queried by Semantic Web query languages (e.g. SPARQL) and checked by reasoners (e.g. Pellet) for consistency or, for inferencing hidden properties. The approach is compared to the Web of Things Description (TD) model of W3C in terms of completeness of the representation.
Examining Open Innovation in Science (OIS): what Open Innovation can and cannot offer the science of science

Scholars across disciplines increasingly hear calls for more open and collaborative approaches to scientific research.The concept of Open Innovation in Science (OIS) provides a framework that integrates dispersed research efforts aiming to understand the antecedents, contingencies, and consequences of applying open and collaborative research practices.While the OIS framework has already been taken up by science of science scholars, its conceptual underpinnings require further specification.In this essay, we
Assessment of the impact of blockchain technology in the banking industry

The study aims to assess the impact of blockchain technology in the banking sector. Blockchain technology is considered as one of the revolutionary technology that provides several benefits to the banking sector. Infrastructure, channel, platforms, and scenarios are the four vintages considered a great success of internet finance. Blockchains are known as evolving technology that has brought attention from energy suppliers, start-ups, and developers of technologies. The technology has brought several benefits to innovate new procedures by reducing financial constraints. Blockchain technology ensures transparency and a secure system for business solutions. In addition, it works for the energy sector as well as the state of the by reviewing present business perspectives along with the cases and literature. However, the issues involved in blockchain scalability, the systems are essential in adapting and improving the process. Apart from these, cryptocurrencies like Bitcoin have become a major idea in the current business world. The issues include time consumed to put a transaction in the block and time consumed for reaching a consensus.
An Improved Coordinate Registration for Over-the-Horizon Radar Using Reference Sources

In the target localization of skywave over-the-horizon radar (OTHR), the error of the ionospheric parameters is one main error source. To reduce the error of ionospheric parameters, a method using both the information of reference sources (e.g., terrain features, ADS-B) in ground coordinates and the corresponding OTHR measurements is proposed to estimate the ionospheric parameters. Describing the ionospheric electron density profile by the quasi-parabolic model, the estimation of the ionospheric parameters is formulated as an inverse problem, and is solved by a Markov chain Monte Carlo method due to the complicated ray path equations. Simulation results show that, comparing with using the a prior value of the ionospheric parameters, using the estimated ionospheric parameters based on four airliners in OTHR coordinate registration process, the ground range RMSE of interested targets is reduced from 2.86 to 1.13 km and the corresponding improvement ratio is up to 60.39%. This illustrates that the proposed method using reference sources is able to significantly improve the accuracy of target localization.