Reviewerly/exHarmony huggingface dataset:
New energy utilization in environmental design and realization

Energy is an important resource for human society and plays an important role in the development of society. However, due to the growing demand for energy in many countries around the world, the consumption of traditional fossil fuels such as coal is accelerating lead to problems such as resource scarcity, environmental pollution, and energy crisis are becoming increasingly prominent, which greatly restricts the sustainable development of the economy and human society, especially in areas where the utilization rate of new and renewable energy is not high. In order to realize environmental design and reduce pollution and solve problems such as resource shortages, this paper studies the utilization of new energy based on environmental design and realization. The experimental results show that the research on new energy based on environmental design and implementation can improve the utilization efficiency of new energy by 5.31%. This eases the future energy crisis and enables new energy to gradually replace solid resources such as fossil fuels as the main energy consumption. It can also solve the problem of energy shortage, reduce the waste and pollution of old energy in the environment, and improve the protection of the environment.
Computational and Numerical Methods for Combinatorial Geometric Series and its Applications

The growing complexity of mathematical and computational modelling demands the simplicity of mathematical and computational equations for solving today’s scientific problems and challenges. This paper presents combinatorial geometric series, innovative binomial coefficients, combinatorial equations, binomial expansions, calculus with combinatorial geometric series, and innovative binomial theorems. Combinatorics involves integers, factorials, binomial coefficients, discrete mathematics, and theoretical computer science for finding solutions to the problems in computing and engineering science. The combinatorial geometric series with binomial expansions and its theorems refer to the methodological advances which are useful for researchers who are working in computational science. Computational science is a rapidly growing multi-and inter-disciplinary area where science, engineering, computation, mathematics, and collaboration use advance computing capabilities to understand and solve the most complex real life problems.
Constrained quantum optimization for extractive summarization on a trapped-ion quantum computer

Abstract Realizing the potential of near-term quantum computers to solve industry-relevant constrained-optimization problems is a promising path to quantum advantage. In this work, we consider the extractive summarization constrained-optimization problem and demonstrate the largest-to-date execution of a quantum optimization algorithm that natively preserves constraints on quantum hardware. We report results with the Quantum Alternating Operator Ansatz algorithm with a Hamming-weight-preserving XY mixer (XY-QAOA) on trapped-ion quantum computer. We successfully execute XY-QAOA circuits that restrict the quantum evolution to the in-constraint subspace, using up to 20 qubits and a two-qubit gate depth of up to 159. We demonstrate the necessity of directly encoding the constraints into the quantum circuit by showing the trade-off between the in-constraint probability and the quality of the solution that is implicit if unconstrained quantum optimization methods are used. We show that this trade-off makes choosing good parameters difficult in general. We compare XY-QAOA to the Layer Variational Quantum Eigensolver algorithm, which has a highly expressive constant-depth circuit, and the Quantum Approximate Optimization Algorithm. We discuss the respective trade-offs of the algorithms and implications for their execution on near-term quantum hardware.
Creating an Immersive XR Learning Experience: A Roadmap for Educators

The use of extended reality (XR) technologies, namely Augmented Reality (AR), Virtual Reality (VR) and Mixed Reality (MR) in education, has attracted much attention in recent years. Many educators have described how XR benefits learners and how useful AR and VR technologies are in the classroom. However, creating AR and VR educational tools, apps or learning environments is a complex process, hence providing an immersive learning experience using these technologies is not a straightforward journey. As a result, the adoption of these emerging technologies in education might be delayed or halted despite their reported benefits to today’s learners. In this paper, websites, technical articles, academic journals, reports and mobile app stores, relating to the use of XR technologies in education, have been examined. A number of themes have emerged and been reported in this paper, which provides a roadmap for those who would like to create XR experiences for learning and training purposes. The paper also describes the factors that should be considered when selecting an option to follow to introduce such immersive learning experiences.
Chip-Based High-Dimensional Optical Neural Network

Parallel multi-thread processing in advanced intelligent processors is the core to realize high-speed and high-capacity signal processing systems. Optical neural network (ONN) has the native advantages of high parallelization, large bandwidth, and low power consumption to meet the demand of big data. Here, we demonstrate the dual-layer ONN with Mach-Zehnder interferometer (MZI) network and nonlinear layer, while the nonlinear activation function is achieved by optical-electronic signal conversion. Two frequency components from the microcomb source carrying digit datasets are simultaneously imposed and intelligently recognized through the ONN. We successfully achieve the digit classification of different frequency components by demultiplexing the output signal and testing power distribution. Efficient parallelization feasibility with wavelength division multiplexing is demonstrated in our high-dimensional ONN. This work provides a high-performance architecture for future parallel high-capacity optical analog computing.
Blockchain-Based Privacy Preservation for IoT-Enabled Healthcare System

Blockchain technology provides a secure and reliable platform for managing data in various application areas, such as supply chain management, multimedia, financial sector, food sector, Internet of Things (IoT) , healthcare, and many more. The recent emergence of blockchain with IoT provides significant growth in the healthcare industry to improve security, privacy, efficiency, and transparency with more business opportunities. Nevertheless, conventional healthcare schemes suffer from various security attacks like collusion, phishing, masquerade, etc. Therefore, a privacy-preserving Distributed Application (DA) is proposed in this paper using blockchain technology to create and maintain healthcare certificates. Here, the distributed application provides an interface between the blockchain network and system objects like healthcare centers, verifiers, and regular authorities to generate and issue medical documents. In addition, it also ensures security by specifying rules using various smart contracts. To evaluate the performance of the proposed scheme, various experimental tests are conducted using the Etherscan tool for measuring operation cost, latency, and processing time. Here, the efficiency of the proposed system is also compared to the existing systems in terms of latency, throughput, and response time. The experimental results and comparative analysis show that the proposed work is more efficient than the existing techniques.
A Learning-Based Approach for Vehicle-to-Vehicle Computation Offloading

Vehicle-to-vehicle (V2V) computation offloading has emerged as a promising solution to facilitate computing-intensive vehicular task processing, where task vehicles (i.e., TaVs) will be requested to offload computing-intensive tasks to server vehicles (i.e., SeVs) in order to keep task delay low. However, it is challenging for TaVs to obtain the optimal V2V computation offloading decisions (i.e., realizing the minimal task delay) due to the constraints, including: 1) incomplete offloading information; 2) degraded Quality-of-Service (QoS) of SeVs; and 3) privacy leakage risks. In this article, we develop a learning-based V2V computation offloading algorithm enhanced by SeV’s ability & trustfulness awareness to solve these problems. We emphasize that the proposed algorithm learns the offloading performance of candidate SeVs based on history offloading selections, without requiring the complete offloading information in advance. Additionally, both the QoS of SeVs and safe V2V computation offloading are enhanced in the proposed learning-based algorithm. Furthermore, we conduct extensive simulation experiments to validate the proposed algorithm. The results demonstrate that the proposed algorithm reduces the average task delay by 35% and 40%, and at the same time decreases the learning regret by 39% and 41%, compared to the algorithms without SeV’s ability and trustfulness awareness.
Communication-Efficient Stochastic Gradient Descent Ascent with Momentum Algorithms

Numerous machine learning models can be formulated as a stochastic minimax optimization problem, such as imbalanced data classification with AUC maximization. Developing efficient algorithms to optimize such kinds of problems is of importance and necessity. However, most existing algorithms restrict their focus on the single-machine setting so that they are incapable of dealing with the large communication overhead in a distributed training system. Moreover, most existing communication-efficient optimization algorithms only focus on the traditional minimization problem, failing to handle the minimax optimization problem. To address these challenging issues, in this paper, we develop two novel communication-efficient stochastic gradient descent ascent with momentum algorithms for the distributed minimax optimization problem, which can significantly reduce the communication cost via the two-way compression scheme. However, the compressed momentum makes it considerably challenging to investigate the convergence rate of our algorithms, especially in the presence of the interaction between the minimization and maximization subproblems. In this paper, we successfully addressed these challenges and established the convergence rate of our algorithms for nonconvex-strongly-concave problems. To the best of our knowledge, our algorithms are the first communication-efficient algorithm with theoretical guarantees for the minimax optimization problem. Finally, we apply our algorithm to the distributed AUC maximization problem for the imbalanced data classification task. Extensive experimental results confirm the efficacy of our algorithm in saving communication costs.
<i>In Situ</i> Characterization of Qubit Control Lines: A Qubit as a Vector Network Analyzer

We propose and experimentally realize a technique to measure the transfer function of a control line in the frequency domain using a qubit as a vector network analyzer. Our method requires coupling the line under test to the longitudinal component of the Hamiltonian of the qubit and the ability to induce Rabi oscillations through simultaneous driving of the transverse component. The method can be used to increase the fidelity of entangling gates in a quantum processor. We have demonstrated that by characterizing the ``flux'' control line of a superconducting transmon qubit in the range from 1 to 450 MHz and using this characterization to improve the fidelity of an entangling cphase gate between two transmon qubits.
DEVELOPMENT OF TRANSVERSAL COMPETENCES IN SCHOOL EDUCATION (A DIDACTIC INTERPRETATION)

The contemporary socio-cultural and educational situation points the attention to the analysis and construction of opportunities for the formation and development of transversal competences in school education. It is expedient therefore that within the process of education in all school subjects and throughout all ages and cognitive activities the decisions for the design of a suitable educational environment should be systematized and specified in such a way as to guarantee that knowledge and skills can be continuously regrouped in accordance with context. In other words, these skills and the knowledge they are based on, should form the foundation of transversal competences that can be applied regardless of age and activities. The design of invariant technologies for the development of transversal competences is also related to the coordination of a variety of approaches, principles and conditions of education in such a manner as to provide effectiveness when these technologies are specifically applied as variants. This creates a necessity for a didactic interpretation of the means of development of transversal competences. Additionally, it poses a necessity to improve teachers’ special preparation and qualification to design educational environment that can guarantee the achievement of transversal competences as an educational outcome. The present research offers opportunities to realize this goal in an integrative and integrating process rather than as an isolated endevour on part of the teachers or as an unorganized effort on part of the students. This in turn poses some new requirements to the system of school education as a whole.
How Difficult is it to Prepare a Quantum State?

Consider a quantum system prepared in an input state. One wants to drive it into a target state. Assuming classical states and operations as free resources, I identify a geometric cost function which quantifies the difficulty of the protocol in terms of how different it is from a classical process. The quantity determines a lower bound to the number of commuting unitary transformations required to complete the task. I then discuss the link between the quantum character of a state preparation and the amount of coherence and quantum correlations that are created in the target state.
Lower‐ and Higher‐Order Nonclassical Properties of Photon Added and Subtracted Displaced Fock States

Abstract Nonclassical properties of photon added and subtracted displaced Fock states are studied using various witnesses of lower‐ and higher‐order nonclassicality. Compact analytic expressions are obtained for the nonclassicality witnesses. Using those expressions, it is established that these states and the states that can be obtained as their limiting cases (except coherent states) are highly nonclassical as they show the existence of lower‐ and higher‐order antibunching and sub‐Poissonian photon statistics, in addition to the nonclassical features revealed through the Mandel parameter, zeros of Q function, Klyshko's criterion, and Agarwal–Tara criterion. Further, some comparison between the nonclassicality of photon added and subtracted displaced Fock states have been performed using witnesses of nonclassicality. This has established that between the two types of non‐Gaussianity inducing operations (i.e., photon addition and subtraction) used here, photon addition influences the nonclassical properties more strongly. Further, optical designs for the generation of photon added and subtracted displaced Fock states from squeezed vacuum state have also been proposed.
Avoiding Irreversibility: Engineering Resonant Conversions of Quantum Resources

We identify and explore the intriguing property of resource resonance arising within resource theories of entanglement, coherence and thermodynamics. While the theories considered are reversible asymptotically, the same is generally not true in realistic scenarios where the available resources are bounded. The finite-size effects responsible for this irreversibility could potentially prohibit small quantum information processors or thermal machines from achieving their full potential. Nevertheless, we show here that by carefully engineering the resource interconversion process any such losses can be greatly suppressed. Our results are predicted by higher order expansions of the trade-off between the rate of resource interconversion and the achieved fidelity, and are verified by exact numerical optimizations of appropriate approximate majorization conditions.
Exploratory Mediation Analysis with Many Potential Mediators

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Resource-efficient analyzer of Bell and Greenberger-Horne-Zeilinger states of multiphoton systems

We propose a resource-efficient error-rejecting entangled-state analyzer for polarization-encoded multiphoton systems. Our analyzer is based on two single-photon quantum-nondemolition detectors, where each of them is implemented with a four-level emitter (e.g., a quantum dot) coupled to a one-dimensional system (such as a micropillar cavity or a photonic nanocrystal waveguide). The analyzer works in a passive way and can completely distinguish $2^n$ Greenberger-Horne-Zeilinger~(GHZ) states of $n$ photons without using any active operation or fast switching. The efficiency and fidelity of the GHZ-state analysis can, in principle, be close to unity, when an ideal single-photon scattering condition is fulfilled. For a nonideal scattering, which typically reduces the fidelity of a GHZ-state analysis, we introduce a passively error-rejecting circuit to enable a near-perfect fidelity at the expense of a slight decrease of its efficiency. Furthermore, the protocol can be directly used to perform a two-photon Bell-state analysis. This passive, resource-efficient, and error-rejecting protocol can, therefore, be useful for practical quantum networks.
LWeb: information flow security for multi-tier web applications

This paper presents LWeb, a framework for enforcing label-based, information flow policies in database-using web applications. In a nutshell, LWeb marries the LIO Haskell IFC enforcement library with the Yesod web programming framework. The implementation has two parts. First, we extract the core of LIO into a monad transformer (LMonad) and then apply it to Yesod’s core monad. Second, we extend Yesod’s table definition DSL and query functionality to permit defining and enforcing label-based policies on tables and enforcing them during query processing. LWeb’s policy language is expressive, permitting dynamic per-table and per-row policies. We formalize the essence of LWeb in the λ LWeb calculus and mechanize the proof of noninterference in Liquid Haskell. This mechanization constitutes the first metatheoretic proof carried out in Liquid Haskell. We also used LWeb to build a substantial web site hosting the Build it, Break it, Fix it security-oriented programming contest. The site involves 40 data tables and sophisticated policies. Compared to manually checking security policies, LWeb imposes a modest runtime overhead of between 2% to 21%. It reduces the trusted code base from the whole application to just 1% of the application code, and 21% of the code overall (when counting LWeb too).
Robust Reflection Removal Based on Light Field Imaging

In daily photography, it is common to capture images in the reflection of an unwanted scene. This circumstance arises frequently when imaging through a semi-reflecting material such as glass. The unwanted reflection will affect the visibility of the background image and introduce ambiguity that perturbs the subsequent analysis on the image. It is a very challenging task to remove the reflection of an image since the problem is severely ill-posed. In this paper, we propose a novel algorithm to solve the reflection removal problem based on light field (LF) imaging. For the proposed algorithm, we first show that the strong gradient points of an LF epipolar plane image (EPI) are preserved after adding to the EPI of another LF image. We can then make use of these strong gradient points to give a rough estimation of the background and reflection. Rather than assuming that the background and reflection have absolutely different disparity ranges, we propose a sandwich layer model to allow them to have common disparities, which is more realistic in practical situations. Then, the background image is refined by recovering the components in the shared disparity range using an iterative enhancement process. Our experimental results show that the proposed algorithm achieves superior performance over traditional approaches both qualitatively and quantitatively. These results verify the robustness of the proposed algorithm when working with images captured from real-life scenes.
Digital politics: internet and democracy in Africa

Purpose The purpose of this paper is to investigate the relationship between internet use and democracy in Africa. It examines the non-linearities and causality between the two variables in the short and long run for 38 countries in Africa. Design/methodology/approach The study is empirical. It uses pooled mean group and causality tests for the sample of 38 African countries. Findings The panel long-run and short-run estimates show evidence of significant non-linear relationship between internet usage and democracy. While internet usage is significantly and negatively related to democracy, squared internet usage is significantly but positively related. This suggests that internet usage increases with the decrease of democracy, but after a certain level of internet usage which is the turning point, democracy starts to increase. Additionally, there is uni-directional causality from internet usage to democracy. However, a bi-directional causality exists between squared internet usage and democracy. Research limitations/implications The empirical evidence from this study suggests that internet usage and democracy are highly interrelated to each other in Africa. The findings support that at the macro level, Africa is moving toward a new stage, where internet will lead to improved levels of democracy and digital politics. Practical implications Remarkably, the paper shows that democracy displays a quadratic relationship with internet usage. As a whole, the findings indicate a U-shaped pattern: democracy decreases with internet usage, stabilizes, and then increases. In other words, internet usage increases with the decrease of democracy, but after a certain level of internet usage which is the turning point, democracy starts to increase. Social implications Many African Governments that have frequently imposed restrictions on internet and social media need to stop. The decline in democracy as internet usage increases may be explained by more severity of these restrictions. However, the findings support that at the macro level, Africa is moving toward a new stage, where internet will lead to improved levels of democracy and digital politics. Originality/value Contrary to previous conceptual papers, the current study empirically investigates the causality between internet and democracy in 38 African countries. The findings indicate a U-shaped pattern: democracy decreases with internet usage, stabilizes, and then increases. In other words, internet usage increases with the decrease of democracy but after a certain level of internet usage which is the turning point, democracy starts to increase.
A Survey on Semantic Parsing

A significant amount of information in today's world is stored in structured and semi-structured knowledge bases. Efficient and simple methods to query them are essential and must not be restricted to only those who have expertise in formal query languages. The field of semantic parsing deals with converting natural language utterances to logical forms that can be easily executed on a knowledge base. In this survey, we examine the various components of a semantic parsing system and discuss prominent work ranging from the initial rule based methods to the current neural approaches to program synthesis. We also discuss methods that operate using varying levels of supervision and highlight the key challenges involved in the learning of such systems.