Reviewerly/exHarmony huggingface dataset:
Bifurcations and chaos in nonlinear Lindblad equations

Abstract The Lindblad equation describes the dissipative time evolution of a density matrix that characterizes an open quantum system in contact with its environment. The widespread ensemble interpretation of a density matrix requires its time evolution to be linear. However, when the dynamics of the density matrix is of a quantum system results not only from the interaction with an external environment, but also with other quantum systems of the same type, the ensemble interpretation is inappropriate and nonlinear dynamics arise naturally. We therefore study the dynamical behavior of nonlinear Lindblad equations using the example of a two-level system. By using techniques developed for classical dynamical systems we show that various types of bifurcations and even chaotic dynamics can occur. As specific examples that display the various types of dynamical behavior, we suggest explicit models based on systems of interacting spins at finite temperature and exposed to a magnetic field that can change in dependence of the magnetization. Due to the interaction between spins, which is treated at mean-field level, the Hamiltonian as well as the transition rates of the Lindblad equation become dependent on the density matrix.
A Hybrid Algorithm Based on PSO and GA for Feature Selection

One of the main problems of machine learning and data mining is to develop a basic model with a few features, to reduce the algorithms involved in classification’s computational complexity. In this paper, the collecti... | Find, read and cite all the research you need on Tech Science Press
A Review and Analysis of Big Data and MapReduce

The recent years consume the exemplary growth of data generation.This enormous amount of data has brought new kind of problem.The existing RDBMS systems are unable to process the Big Data, or they are not efficient in handling it.The significant problems appeared with the Big Data are storage and processing.Hadoop is brought in the solutions for storage and processing in the form of HDFS (Hadoop Distributed File System) and MapReduce respectively.The traditional systems not construct for keeping the Big Data, and also they can only process structured data.One of the industries, first to face the Big Data challenges is financial sector.In this work, an unstructured stocks data is processed using Hadoop MapReduce.Efficient processing of unstructured data is analyzed, and all the phases involved in implementation explicated.
Solar Power Ramp Event Forewarning With Limited Historical Observations

Timely and accurate forewarning of solar power ramp events (SPREs) is crucial for a power system operation. In this article, a novel forewarning method for SPREs is proposed based on credal network (CN) and imprecise Dirichlet model (IDM). A new expression of SPRE is proposed, which focuses on the power change caused by meteorological fluctuation. Considering that the single-valued probability may not provide convincing results in case of insufficient ramp event records, probability interval is adopted to reflect the ambiguous correlation between SPREs and meteorological conditions. The meteorological evidences are mapped to ramp events by a CN to enhance the sensitivity of SPRE identification. The structure of the CN is established by the learning algorithm with respect to the relations between SPRE and meteorological conditions, as well as different meteorological conditions. Furthermore, an extended IDM is developed to estimate the interval-valued parameters in the CN. Then, a credal classifier is established to output the ramp forewarning result. The effectiveness of the proposed method is verified through case studies, and obvious improvement on the accuracy of ramp forewarning can be seen.
KnowBe4: The Rise of Security Culture

Culture is a difficult concept to pin down. Everyone instinctively feels they know what it is – or will know it when they see it. But when the very existence of your organisation could depend on creating and nurturing a ‘security culture’, and getting it right, then it would be useful to have a more concrete understanding of what the term means.
Impact of perceived organisational support and workplace incivility on work engagement and creative work involvement: a moderating role of creative self-efficacy

This paper analyses the impact of perceived organisational support and workplace incivility on work engagement and in turn, on creative work involvement. It also investigates whether creative self-efficacy moderates the positive relationship between work engagement and creative work involvement. Using a survey questionnaire in a non-contrived field study setting, a cross-sectional sample of 212 responses is drawn from a leading courier and logistic service company of Pakistan. Hypotheses are tested using a covariance-based structural equation modelling method in AMOS. The results show that perceived organisational support has significant impact on work engagement; however, workplace incivility does not predict work engagement. Moreover, work engagement is found to be a very strong predictor of creative work involvement. The positive relationship between work engagement and creative work involvement is stronger among participants who reported higher level of creative self-efficacy. The theoretical contribution and directions for future studies are discussed.
Blockchain and COVID-19 Pandemic: Applications and Challenges

&lt;p&gt;The year 2020 has witnessed the emergence of coronavirus (COVID-19) that has rapidly spread and adversely affected the global economy, health, and human lives. The COVID-19 pandemic has exposed the limitations of existing healthcare systems regarding their inadequacy to timely and efficiently handle public health emergencies. A large portion of today's healthcare systems are centralized and fall short in providing necessary information security and privacy, data immutability, transparency, and traceability features to detect fraud related to COVID-19 vaccination certification, and anti-body testing. Blockchain technology can assist in combating the COVID-19 pandemic by ensuring safe and reliable medical supplies, accurate identification of virus hot spots, and establishing data provenance to verify the genuineness of personal protective equipment. This paper discusses the potential blockchain applications for the COVID-19 pandemic. It presents the high-level design of three blockchain-based systems to enable governments and medical professionals to efficiently handle health emergencies caused by COVID-19. It discusses the important ongoing blockchain-based research projects, use cases, and case studies to demonstrate the adoption of blockchain technology for COVID-19. Finally, it identifies and discusses future research challenges, along with their key causes and guidelines.&lt;/p&gt;
11 Tera-FLOP/s photonic convolutional accelerator for optical neural networks

Convolutional neural networks (CNNs), inspired by biological visual cortex systems, are a powerful category of artificial neural networks that can extract the hierarchical features of raw data to greatly reduce the network parametric complexity and enhance the predicting accuracy. They are of significant interest for machine learning tasks such as computer vision, speech recognition, playing board games and medical diagnosis [1-7]. Optical neural networks offer the promise of dramatically accelerating computing speed to overcome the inherent bandwidth bottleneck of electronics. Here, we demonstrate a universal optical vector convolutional accelerator operating beyond 10 Tera-FLOPS (floating point operations per second), generating convolutions of images of 250,000 pixels with 8-bit resolution for 10 kernels simultaneously — enough for facial image recognition. We then use the same hardware to sequentially form a deep optical CNN with ten output neurons, achieving successful recognition of full 10 digits with 900 pixel handwritten digit images with 88% accuracy. Our results are based on simultaneously interleaving temporal, wavelength and spatial dimensions enabled by an integrated microcomb source. This approach is scalable and trainable to much more complex networks for demanding applications such as unmanned vehicle and real-time video recognition.
Photogrammetric evaluation of shear modulus of glulam timber using torsion test method and dual stereo vision system

Abstract The shear modulus of timber and timber-based composite materials is a fundamental mechanical property, which is used in the design of timber and engineered wood products. The problem of experimentally determining appropriate values of shear modulus for timber-based composite is not as simple and straightforward as in isotropic materials. Although the torsion test is a recommended standard approach to determine the shear modulus of structural-size timber and glulam beams, it is difficult to measure the rotational deformations of the timber beams. Therefore, in this paper, a stereo camera system combined with a photogrammetric approach is proposed to evaluate the values and variations of the shear modulus of glulam beams under the torsion test. The photogrammetric approach is a non-contact method, which provides an efficient and alternative approach for measuring the deformations of the torsion specimens in three dimensions. A series of experiments was conducted on glulam timber beams under the torsion test to investigate the applicability of the optical approach to evaluate the values and variations of shear modulus as well as to investigate the effect of applying torques in a clockwise or anticlockwise direction on the shear modulus of the beams. This optical system not only allows the performance and reliability of the traditional sensors to be assessed, but also allows the rotational deformation of the torsion samples to be monitored at various locations. This enables the values of shear modulus at different cross-sections of the torsion specimens to be evaluated without the need to use more devices. The test results showed that applying torques to the glulam timber specimens during loading and unloading in either a clockwise or anticlockwise direction does not influence or cause a significant change in the shear modulus of the beams. By comparing shear modulus values of glulam beams measured based on different shear spans, it was found that the larger the shear span the smaller the shear modulus value. This might indicate that the variations of shear modulus values at these different gauge lengths need to be considered.
Blockchain for SME Clusters: An Ideation using the Framework of Ostrom Commons Governance

Small and medium-sized enterprises (SMEs) organize themselves into clusters by sharing a set of limited resources to achieve the holistic success of the cluster. However, these SMEs often face conflicts and deadlock situations that hinder the fundamental operational dynamics of the cluster due to varied reasons, including lack of trust and transparency in interactions, lack of common consensus, and lack of accountability and non-repudiation. Blockchain technology brings trust, transparency, and traceability to systems, as demonstrated by previous research and practice. In this paper, we explore the role of blockchain technology in building a trustworthy yet collaborative environment in SME clusters through the principles of community self-governance based on the work of Nobel Laureate Elinor Ostrom. We develop and present a blockchain commons governance framework for the three main dimensions i.e., interaction, autonomy, and control, based on the theoretical premise of equivalence mapping and qualitative analysis. This paper examines the role of blockchain technology to act as a guiding mechanism and support the smooth functioning of SMEs for their holistic good. The study focuses on sustainability and improving productivity of SMEs operating in clusters under public and private partnership. This is the first study to address the operational challenges faced by SEMs in clusters by highlighting the dimensions of blockchain commons governance dimensions.
An efficient ANFIS-EEBAT approach to estimate effort of Scrum projects

Abstract Software effort estimation is a significant part of software development and project management. The accuracy of effort estimation and scheduling results determines whether a project succeeds or fails. Many studies have focused on improving the accuracy of predicted results, yet accurate estimation of effort has proven to be a challenging task for researchers and practitioners, particularly when it comes to projects that use agile approaches. This work investigates the application of the adaptive neuro-fuzzy inference system (ANFIS) along with the novel Energy-Efficient BAT (EEBAT) technique for effort prediction in the Scrum environment. The proposed ANFIS-EEBAT approach is evaluated using real agile datasets. It provides the best results in all the evaluation criteria used. The proposed approach is also statistically validated using nonparametric tests, and it is found that ANFIS-EEBAT worked best as compared to various state-of-the-art meta-heuristic and machine learning (ML) algorithms such as fireworks, ant lion optimizer (ALO), bat, particle swarm optimization (PSO), and genetic algorithm (GA).
A Hybrid Feature Selection Framework Using Improved Sine Cosine Algorithm with Metaheuristic Techniques

Feature selection is the procedure of extracting the optimal subset of features from an elementary feature set, to reduce the dimensionality of the data. It is an important part of improving the classification accuracy of classification algorithms for big data. Hybrid metaheuristics is one of the most popular methods for dealing with optimization issues. This article proposes a novel feature selection technique called MetaSCA, derived from the standard sine cosine algorithm (SCA). Founded on the SCA, the golden sine section coefficient is added, to diminish the search area for feature selection. In addition, a multi-level adjustment factor strategy is adopted to obtain an equilibrium between exploration and exploitation. The performance of MetaSCA was assessed using the following evaluation indicators: average fitness, worst fitness, optimal fitness, classification accuracy, average proportion of optimal feature subsets, feature selection time, and standard deviation. The performance was measured on the UCI data set and then compared with three algorithms: the sine cosine algorithm (SCA), particle swarm optimization (PSO), and whale optimization algorithm (WOA). It was demonstrated by the simulation data results that the MetaSCA technique had the best accuracy and optimal feature subset in feature selection on the UCI data sets, in most of the cases.
Analysis of sentiments on the onset of Covid-19 using Machine Learning Techniques

The novel coronavirus (Covid-19) pandemic has struck the whole world and is one of the most striking topics on social media platforms. Sentiment outbreak on social media enduring various thoughts, opinions, and emotions about the Covid-19 disease, expressing views they are feeling presently. Analyzing sentiments helps to yield better results. Gathering data from different blogging sites like Facebook, Twitter, Weibo, YouTube, Instagram, etc., and Twitter is the largest repository. Videos, text, and audio were also collected from repositories. Sentiment analysis uses opinion mining to acquire the sentiments of its users and categorizes them accordingly as positive, negative, and neutral. Analytical and machine learning classification is implemented to 3586 tweets collected in different time frames. In this paper, sentiment analysis was performed on tweets accumulated during the Covid-19 pandemic, Coronavirus disease. Tweets are collected from the Twitter database using Hydrator a web-based application. Data-preprocessing removes all the noise, outliers from the raw data. With Natural Language Toolkit (NLTK), text classification for sentiment analysis and calculate the score subjective polarity, counts, and sentiment distribution. N-gram is used in textual mining -and Natural Language Processing for a continuous sequence of words in a text or document applying uni-gram, bi-gram, and tri-gram for statistical computation. Term frequency and Inverse document frequency (TF-IDF) is a feature extraction technique that converts textual data into numeric form. Vectorize data feed to our model to obtain insights from linguistic data. Linear SVC, MultinomialNB, GBM, and Random Forest classifier with Tfidf classification model applied to our proposed model. Linear Support Vector classification performs better than the other two classifiers. Results depict that RF performs better.
Factors Affecting Adaptability of Cryptocurrency: An Application of Technology Acceptance Model

Cryptocurrency has revolutionized the economic system of the world. It provides a new and innovative means of exchange that has speedily invaded the financial market trends and changed the traditional cash world. However, consumers have low acceptability for blockchain-based cryptocurrency due to increasing online scams and the absence of a regulatory framework. There is also a misconception about its usage on many platforms, which has created a clear gap in the literature to address this issue. Therefore, the current study intends to investigate the effect of technology awareness on the behavioral intention of crypto users through perceived factors (usefulness, ease of use, risk). It also empirically examines the moderating role of government support on these indirect paths. The underlying framework is investigated by surveying 333 respondents from the Z generation. Results revealed that perceived factors (usefulness, ease of use, risk) mediate the relationship between technology awareness and behavioral intention. Furthermore, government support strengthens the indirect relationship of technology awareness on behavioral intention through technology acceptance determinants, such that the effect of technology awareness on behavioral intention through perceived factors (usefulness, ease of use, risk) is more assertive when government support is high. The findings will provide a new dimension to different financial bodies implementing monetary policy and highlight the need to adopt innovative digital technologies in Pakistan.
Influences of Distributive Injustice and Job Insecurity Amid COVID-19 on Unethical Pro-Organisational Behaviour: Mediating Role of Employee Turnover Intention

Drawn on Social Exchange Theory and Conservation of Resources Theory, this study developed a research model to examine the direct influence of job insecurity and distributive injustice, which were common in many hotels amid COVID-19, on unethical pro-organisation behaviour (UPoB) among hotel employees. The study also examines the mediating role of turnover intention in the relationship between job insecurity, and distributive injustice, which was result of the COVID-19 pandemic on UPoB. For this purpose, a questionnaire survey was self-dropped and collected through personal network to hotel employees in Eastern Province of Saudi Arabia. The results of structural equation modelling using AMOS (version 23) supported all the study hypotheses. The results showed a significant positive influence of distributive injustice and job insecurity on UPoB among hotel employees. Moreover, turnover intention was found to have a partial mediation role in the relationship between job insecurity, distributive injustice and UPoB. The results extend our understanding of Social Exchange Theory and Conservation of Resources Theory that employees in hotels are more likely to protect themselves and their job by engaging in UPoB if they perceived their job at threat due to a crisis, i.e., the COVID-19 pandemic. The major conclusion of current research is that when hotels employees perceived job insecurity and distributive injustice because of the pandemic, they responded with high turnover intention and as a last choice engaging in UPoB to save their resources, in this case their jobs, since they have no other alternatives outside the organisations. However, this inappropriate antisocial behaviour could have a negative influence on both employees and organisation at the long term. The results of current research have several theoretical implications for tourism scholars and managerial implication for hoteliers.
Point RCNN: An Angle-Free Framework for Rotated Object Detection

Rotated object detection in aerial images is still challenging due to arbitrary orientations, large scale and aspect ratio variations, and extreme density of objects. Existing state-of-the-art rotated object detection methods mainly rely on angle-based detectors. However, angle-based detectors can easily suffer from a long-standing boundary problem. To tackle this problem, we propose a purely angle-free framework for rotated object detection, called Point RCNN. Point RCNN is a two-stage detector including both PointRPN and PointReg which are angle-free. Given an input aerial image, first, the backbone-FPN extracts hierarchical features, then, the PointRPN module generates an accurate rotated region of interests (RRoIs) by converting the learned representative points of each rotated object using the MinAreaRect function of OpenCV. Motivated by RepPoints, we designed a coarse-to-fine process to regress and refine the representative points for more accurate RRoIs. Next, based on the learned RRoIs of PointRPN, the PointReg module learns to regress and refine the corner points of each RRoI to perform more accurate rotated object detection. Finally, the final rotated bounding box of each rotated object can be attained based on the learned four corner points. In addition, aerial images are often severely unbalanced in categories, and existing rotated object detection methods almost ignore this problem. To tackle the severely unbalanced dataset problem, we propose a balanced dataset strategy. We experimentally verified that re-sampling the images of the rare categories can stabilize the training procedure and further improve the detection performance. Specifically, the performance was improved from 80.37 mAP to 80.71 mAP in DOTA-v1.0. Without unnecessary elaboration, our Point RCNN method achieved new state-of-the-art detection performance on multiple large-scale aerial image datasets, including DOTA-v1.0, DOTA-v1.5, HRSC2016, and UCAS-AOD. Specifically, in DOTA-v1.0, our Point RCNN achieved better detection performance of 80.71 mAP. In DOTA-v1.5, Point RCNN achieved 79.31 mAP, which significantly improved the performance by 2.86 mAP (from ReDet’s 76.45 to our 79.31). In HRSC2016 and UCAS-AOD, our Point RCNN achieved higher performance of 90.53 mAP and 90.04 mAP, respectively.