Reviewerly/exHarmony huggingface dataset:
Smart City: Exploring the Role of Augmented Reality in Placemaking

Continued technological advancements in Big-Data-Driven Communication, Virtual Reality (VR), Augmented Reality (AR), Social Media, and accelerated organizations' needs for engaging or managing various stakeholders highlight continuous research into building mutual and interactive cooperation models and solution within smart cities. So, as an initial step to building a smart city, municipalities can use advanced and interactive technologies such as augmented reality to improve services provisions and citizens' welfare. The purpose of this research is to explore how cities can use AR for installing public art projects through diverse placemaking approaches. Three internationally AR-placemaking projects as case studies reviewed briefly. Different types of solutions and layouts for each placemaking approach are illustrated to define how it has significant impacts on cities all over the world. Besides, we found that municipalities could implement strategies like cutting-edge Augmented Reality technologies and tools as a step forward toward developing smart cities. Moreover, the AR technology can easily mix with the Internet of Things' (IoT) components and create a unique sight for the fusion of science, technology, and art in the 21st century smart cities.
Discrete time symmetry breaking in quantum circuits: exact solutions and tunneling

Abstract We discuss general properties of discrete time quantum symmetry breaking in degenerate parametric oscillators. Recent experiments in superconducting quantum circuit with Josephson junction nonlinearities give rise to new properties of strong parametric coupling and nonlinearities. Exact analytic solutions are obtained for the steady-state of this single-mode case of subharmonic generation. We also obtain analytic solutions for the tunneling time over which the time symmetry-breaking is lost above threshold. We find that additional anharmonic terms found in the superconducting case increase the tunneling rate, and can also lead to new regimes of tristability as well as bistability. Our analytic results are confirmed by number state calculations.
Ground motion prediction at gravitational wave observatories using archival seismic data

Gravitational wave observatories have always been affected by tele-seismic earthquakes leading to a decrease in duty cycle and coincident observation time. In this analysis, we leverage the power of machine learning algorithms and archival seismic data to predict the ground motion and the state of the gravitational wave interferometer during the event of an earthquake. We demonstrate improvement from a factor of 5 to a factor of 2.5 in scatter of the error in the predicted ground velocity over a previous model fitting based approach. The level of accuracy achieved with this scheme makes it possible to switch control configuration during periods of excessive ground motion thus preventing the interferometer from losing lock. To further assess the accuracy and utility of our approach, we use IRIS seismic network data and obtain similar levels of agreement between the estimates and the measured amplitudes. The performance indicates that such an archival or prediction scheme can be extended beyond the realm of gravitational wave detector sites for hazard-based early warning alerts.
An extended intelligent water drop approach for efficient VM allocation in secure cloud computing framework

Cloud computing provides many advantages services to its users like online resources accessibility, better cost management, dynamic resource pooling, and efficient Virtual Machine (VM) allocation. With so many useful services, cloud computing also introduces several security risks of cloud user information privacy as well as select the minimum number of VM to execute task load and improve resource utilization are some challenging issues. The objective of this work is to achieve higher resource utilization rate while considering the security of the cloud data. We extended the natural phenomena based Intelligent Water Drop (IWD) algorithm and proposed a VM allocation algorithm which optimizes the task execution in a secure cloud environment. The proposed work is implemented on the CloudSim simulation toolkit, and to test the validity of the algorithm, a comparison is performed with other well-known VM allocation policies in the cloud computing. The experimental simulation results showed that the proposed VM allocation policy performed better than exiting VM allocation approaches.
Learning Bayesian networks from demographic and health survey data

Child mortality from preventable diseases such as pneumonia and diarrhoea in low and middle-income countries remains a serious global challenge. We combine knowledge with available Demographic and Health Survey (DHS) data from India, to construct Causal Bayesian Networks (CBNs) and investigate the factors associated with childhood diarrhoea. We make use of freeware tools to learn the graphical structure of the DHS data with score-based, constraint-based, and hybrid structure learning algorithms. We investigate the effect of missing values, sample size, and knowledge-based constraints on each of the structure learning algorithms and assess their accuracy with multiple scoring functions. Weaknesses in the survey methodology and data available, as well as the variability in the CBNs generated by the different algorithms, mean that it is not possible to learn a definitive CBN from data. However, knowledge-based constraints are found to be useful in reducing the variation in the graphs produced by the different algorithms, and produce graphs which are more reflective of the likely influential relationships in the data. Furthermore, valuable insights are gained into the performance and characteristics of the structure learning algorithms. Two score-based algorithms in particular, TABU and FGES, demonstrate many desirable qualities; (a) with sufficient data, they produce a graph which is similar to the reference graph, (b) they are relatively insensitive to missing values, and (c) behave well with knowledge-based constraints. The results provide a basis for further investigation of the DHS data and for a deeper understanding of the behaviour of the structure learning algorithms when applied to real-world settings.
Electric Circuit Induced by Quantum Walk

We consider the Szegedy walk on graphs adding infinite length tails to a finite internal graph. We assume that on these tails, the dynamics is given by the free quantum walk. We set the $\ell^\infty$-category initial state so that the internal graph receives time independent input from the tails, say $\boldsymbol{\alpha}_{in}$, at every time step. We show that the response of the Szegedy walk to the input, which is the output, say $\boldsymbol{\beta}_{out}$, from the internal graph to the tails in the long time limit, is drastically changed depending on the reversibility of the underlying random walk. If the underlying random walk is reversible, we have $\boldsymbol{\beta}_{out}=\mathrm{Sz}(\boldsymbol{m}_{\delta E})\boldsymbol{\alpha}_{in}$, where the unitary matrix $\mathrm{Sz}(\boldsymbol{m}_{\delta E})$ is the reflection matrix to the unit vector $\boldsymbol{m}_{\delta E}$ which is determined by the boundary of the internal graph $\delta E$. Then the global dynamics so that the internal graph is regarded as one vertex recovers the local dynamics of the Szegedy walk in the long time limit. Moreover if the underlying random walk of the Szegedy walk is reversible, then we obtain that the stationary state is expressed by a linear combination of the reversible measure and the electric current on the electric circuit determined by the internal graph and the random walk's reversible measure. On the other hand, if the underlying random walk is not reversible, then the unitary matrix is just a phase flip; that is, $\boldsymbol{\beta}_{out}=-\boldsymbol{\alpha}_{in}$, and the stationary state is similar to the current flow but satisfies a different type of the Kirchhoff laws.
Improving Deep Forest by Screening

Most studies about deep learning are based on neural network models, where many layers of parameterized nonlinear differentiable modules are trained by backpropagation. Recently, it has been shown that deep learning can also be realized by non-differentiable modules without backpropagation training called deep forest. We identify that deep forest has high time costs and memory requirements—this has inhibited its use on large-scale datasets. In this paper, we propose a simple and effective approach with three main strategies for efficient learning of deep forest. First, it substantially reduces the number of instances that needs to be processed through redirecting instances having high predictive confidence straight to the final level for prediction, by-passing all the intermediate levels. Second, many non-informative features are screened out, and only the informative ones are used for learning at each level. Third, an unsupervised feature transformation procedure is proposed to replace the supervised multi-grained scanning procedure. Our theoretical analysis supports the proposed approach in varying the model complexity from low to high as the number of levels increases in deep forest. Experiments show that our approach achieves highly competitive predictive performance with reduced time cost and memory requirement by one to two orders of magnitude.
Construction of genuine multipartite entangled states

Genuine multipartite entanglement is of great importance in quantum information, especially from the experimental point of view.Nevertheless, it is difficult to construct genuine multipartite entangled states systematically, because the genuine multipartite entanglement is unruly.We propose another product based on the Kronecker product in this paper.The Kronecker product is a common product in quantum information with good physical interpretation.We mainly investigate whether the proposed product of two genuine multipartite entangled states is still a genuine entangled one.We understand the entanglement of the proposed product better by characterizing the entanglement of the Kronecker product.Then we show the proposed product is a genuine multipartite entangled state in two cases.The results provide a systematical method to construct genuine multipartite entangled states of more parties.Contents I. Introduction 1 II.Preliminaries 3 III.Characterization of the entanglement of the Kronecker product of two states 4 A. Pure states 4 B. Mixed states 5IV. Construct an (n + 2)-partite genuine entangled state from two (n + 1)-partite states 6V. conclusion
Stratified manifold of quantum states, actions of the complex special linear group

We review the geometry of the space of quantum states $\mathscr{S}(\mathcal{H})$ of a finite-level quantum system with Hilbert space $\mathcal{H}$ from a group-theoretical point of view. This space carries two stratifications generated by the action of two different Lie groups: the special unitary group $\mathcal{SU}(\mathcal{H})$ and its complexification $\mathcal{SL}(\mathcal{H})$, the complex special linear group. A stratum of the stratification generated by $\mathcal{SU}(\mathcal{H})$ is composed of isospectral states, that is, density operators with the same spectrum, A stratum of the stratification generated by $\mathcal{SL}(\mathcal{H})$ is composed of quantum states with the same rank. We prove that on every submanifold of isospectral quantum states there is also a canonical left action of $\mathcal{SL}(\mathcal{H})$ which is related with the canonical K\"{a}hler structure on isospectral quantum states. The fundamental vector fields of this $\mathcal{SL}(\mathcal{H})$-action are divided into Hamiltonian and gradient vector fields. The former give rise to invertible maps on $\mathscr{S}(\mathcal{H})$ that preserve the von Neumann entropy and the convex structure of $\mathscr{S}(\mathcal{H})$, while the latter give rise to invertible maps on $\mathscr{S}(\mathcal{H})$ that preserve the von Neumann entropy but not the convex structure of $\mathscr{S}(\mathcal{H})$. A similar decomposition is given for the $\mathcal{SL}(\mathcal{H})$-action generating the stratification of $\mathscr{S}(\mathcal{H})$ into manifolds of quantum states with the same rank, where gradient vector fields preserve the rank but do not preserve entropy. Some comments on multipartite quantum systems are made. It is proved that the sets of product states of a multipartite quantum system are homogeneous manifolds for the action of the complex special linear group associated with the partition.
Self-supervised Multi-task Procedure Learning from Instructional Videos

We address the problem of unsupervised procedure learning from instructional videos of multiple tasks using Deep Neural Networks (DNNs). Unlike existing works, we assume that training videos come from multiple tasks without key-step annotations or grammars, and the goals are to classify a test video to the underlying task and to localize its key-steps. Our DNN learns task-dependent attention features from informative regions of each frame without ground-truth bounding boxes and learns to discover and localize key-steps without key-step annotations by using an unsupervised subset selection module as a teacher. It also learns to classify an input video using the discovered key-steps using a learnable key-step feature pooling mechanism that extracts and learns to combine key-step based features for task recognition. By experiments on two instructional video datasets, we show the effectiveness of our method for unsupervised localization of procedure steps and video classification.
Transfer Learning based Data-Efficient Machine Learning Enabled Classification

Recently, waste sorting has become more and more important in our daily life. It plays an essential role in the big picture of waste recycling, reducing environmental pollution significantly. Deep learning (DL) methods have been dominating the field of image classification and have been successfully applied to waste sorting tasks to achieve state-of-art performance. However, most traditional DL methods require a massive amount of annotated data for the training phase. Unfortunately, there is only one small data set for waste sorting, TrashNet created by Standford. In addition, manually collecting and labeling a massive data-set can be too costly. To address this issue, we decided to implement transfer learning (TL) techniques to construct a robust model based on a fairly small set of training data by transferring knowledge from existing deep networks, such as AlexNet, Resnet, and DensNet. As an innovation, we propose a novel domain loss function, Dual Dynamic Domain Distance (4D), to produce a more accurate domain distance measurement. There are three contributions to this paper. First, our model has achieved the best performance on the TrashNet data. Secondly, it is the first time that TL has been used for waste sorting. Finally, the proposed novel 4D domain loss has improved the performance of TL for this task. In this paper, we implemented two types of transfer learning methods, DDC, DeepCoral, to TrashNet data-set. Moreover, the DeepCoral-Resnet50 model yields the best performance of 96% test accuracy. More importantly, this work can be easily generalized to other image classification tasks.
A Blockchain Based Land Registration System Proposal for Turkey

Although the popularity of Blockchain is thought to increase with the appeal of Bitcoin, this technology promises much more. The eye-catching advantages of Blockchain technology are the high level of security it provides, transparency, and allowing stakeholders to trade without the need of any intermediaries. Land registration systems are also one of the areas with high potential where Blockchain technology can be used. When we look at the examples from all over the world, we see that countries have started to update their current land registration processes to be Blockchain-based. Considering the local land registration process in Turkey; some serious problems are observed like the number of physical transactions is quite high and the fact that property sale prices declared to land registry office lower than it actually is to avoid the increment value tax. Therefore, a Blockchain-based solution has been prepared suitable for use in Tukey. In order to this; the land registration process, which took place in eight steps, was analyzed, system participants were identified, and an application was developed using the selected smart contract infrastructure. In this paper, these steps of developing a Blockchain-based land registration system for Turkey will be explained.
Automatic clustering-based surrogate-assisted genetic algorithm for groundwater remediation system design

Simulation-optimization techniques in support of groundwater management are computationally expensive. To tackle such computational burden, a variety of surrogate modeling-frameworks have been proposed, where a cheaper-to-run model referred to as a surrogate is used in lieu of a computationally intensive model. These frameworks are generally based on what referred herein to as ‘global surrogate modelling’ where a single surrogate approximates the underlying response surface of a model. Such classic frameworks, however, are sub-optimal when the response surface is complex and/or high-dimensional. This paper proposes a novel ‘local surrogate modelling’ framework that simultaneously builds and evolves multiple local surrogates, guided by an automatic clustering method. Unlike traditional clustering methods that select the number of clusters a priori, the proposed automatic clustering method concurrently determines the optimum number of clusters and the clustering scheme itself. To serve as the surrogate, Artificial Neural Networks (ANNs) are used. The proposed framework is applied to solve a computationally intensive groundwater remediation optimization problem. This study shows that the proposed automatic clustering-based local surrogate modeling is effective and reliable while reducing at least 60 percent of the computational burden.
Beyond accuracy: assessing software documentation quality

Good software documentation encourages good software engineering, but the meaning of "good" documentation is vaguely defined in the software engineering literature. To clarify this ambiguity, we draw on work from the data and information quality community to propose a framework that decomposes documentation quality into ten dimensions of structure, content, and style. To demonstrate its application, we recruited technical editors to apply the framework when evaluating examples from several genres of software documentation. We summarise their assessments -- for example, reference documentation and README files excel in quality whereas blog articles have more problems -- and we describe our vision for reasoning about software documentation quality and for the expansion and potential of a unified quality framework.
Wave-particle duality employing quantum coherence in superposition with non-orthogonal pointers

We propose the notion of quantum coherence for superpositions over states which are not necessarily mutually orthogonal. This anticipatedly leads to a resource theory of non-orthogonal coherence. We characterize free states and free operations in this theory, and connect the latter with free operations in the resource theory of quantum coherence for orthogonal bases. We show that the concept of non-orthogonal coherence naturally furnishes us with a wave-particle duality in quantum double-slit experiments where the channels beyond the slits are leaky between them. Furthermore, we demonstrate existence of a unique maximally coherent qubit state corresponding to any given purity. In addition, and in contradistinction with the case of orthogonal bases, there appears a non-trivial minimally coherent qubit state for a given purity. We also study the behavior of quantum coherence for some typical configurations of non-orthogonal bases which have no analogs for orthogonal bases. We further investigate the problem of determining the energy cost of creating non-orthogonal coherence, and find that it scales linearly with the non-orthogonal coherence created.
Minimizing Energy Consumption Scheduling Algorithm of Workflows With Cost Budget Constraint on Heterogeneous Cloud Computing Systems

Cloud computing is a promising platform to conduct large-scale workflow applications according to the pay-per-use model. Minimizing the energy consumption of precedence constrained workflows with cost budget constraints has become one of the popular topics in cloud data centers. Most existing scheduling algorithms mainly consider execution time or cost of a given workflow application under a budget constraint; however, these algorithms do not adequately consider energy saving. A reducing energy consumption strategy using a critical task remapping (RMREC) algorithm is proposed in this study. This algorithm is decomposed into two phases: energy consumption reduction and critical task remapping. In the first phase, the adjustable cost budget and spare cost are determined on the basis of cost budget, critical task path, and adjustable budget factor. All workflow tasks are further allocated to virtual machines (VMs) with the lowest energy consumption to achieve preliminary mapping between tasks and VMs while satisfying the adjustable cost budget constraint. In the second phase, critical tasks are remapped to VMs according to spare cost to decrease energy consumption caused by task migration. Experiments on two types of workflow applications with different scales demonstrate that the presented RMREC algorithm effectively reduces energy consumption without violating cost budget constraints compared with existing algorithms.
Sentiment Analysis by using Recurrent Neural Network

Sentiment analysis is the process of emotion extraction and opinion mining from given text. This research paper gives the detailed overview of different feature selection methods, sentiment classification techniques and deep learning approaches for sentiment analysis. The feature selection methods include n-grams, stop words and negation handling. This paper also discusses about various sentiment classification techniques named as machine learning based approach and lexicon based approach. There is various classification algorithms such as SVM, Maximum Entropy and Naïve Bayes used for sentiment classification. In this paper we also discuss about deep learning models such as RNN, CNN and LSTM which is used for sentiment analysis. There are various application of sentiment analysis in decision making, prediction and business application.
Adapting blackboard-collaborate ultra as an interactive online learning tool during the COVID-19 pandemic

The 2019-2020 coronavirus pandemic has affected educational systems worldwide, leading to widespread closures of schools and universities and social distancing. Hence, the shift to an online tool was required.The main problem postulated is the lack of student-teacher interaction that occurs with online learning.The Blackboard Collaborate Ultra platform was used to deliver lectures on clinical biochemistry and the reproductive module to our students. Our main goal was to achieve students' engagement and interaction.There were 189 male students enrolled in the reproductive module. The attendance rate was 93%-95%. The download of the recording was 100%. The active participation rate was up to 87%. Hence, Blackboard Collaborate Ultra's virtual classrooms are very useful tools for online interactive lecturing.Based on this experience gained, we could conclude that interactive virtual classroom lecturing can be used in addition to or instead of traditional lectures during ordinary situations as a successful online learning community tool.