Reviewerly/exHarmony huggingface dataset:
PySkuwela: An Online Python Learning Tutorial and Mobile Application for Senior High School S.T.E.M. Students Specializing in Information Technology

Learning programming languages like Python can be difficult, especially for those who have no background in the subject. This study intends to design a mobile application and online Python learning lesson to solve the challenges experienced by senior high school S.T.E.M. students. S.T.E.M. students in senior high school who participated in a survey found that academic pressure, a lack of engagement, and a lack of practice chances were the main issues affecting their learning experience. For beginner to advanced levels, the PySkuwela provides a user-friendly interface with video tutorials, hands-on activities, and assessments. The PySkuwela forum promotes student cooperation and knowledge exchange. Students may study Python conveniently and without added academic pressure thanks to the self-paced learning strategy. The FURPS evaluation gave the system an overall acceptability rating of 4.21, which is a high level of agreement. According to the study’s findings, the created system is advantageous and appropriate for senior high school S.T.E.M. pupils, offering a thorough platform for learning Python.
Hydrothermal signatures and prospecting indicators in sediments along the Carlsberg Ridge

As the most widespread hydrothermal products in the oceans, metalliferous sediments are particularly useful in prospecting for seafloor massive sulfide (SMS) deposits. A comparative study of metalliferous sediments from two types of hydrothermal fields and the background sediments along the Carlsberg Ridge has been performed to reveal their hydrothermal signatures and to determine the prospecting indicators for SMS deposits. Cu–Fe–Zn sulfides, sulfates, Fe-oxides, and Fe-oxyhydroxides are dominant in the near-vent sediments from the basalt-hosted hydrothermal fields. The near-vent sediments from the ultramafic-hosted field are abundant in detrital minerals of ultramafic source in addition to the minerals mentioned above in the basalt-hosted fields. Sulfide minerals with large grains are mainly deposited close to the vent, and as the distance from the vent increases, the grain size and abundance of the minerals rapidly decrease. Geochemically, the near-vent sediments in the basalt-hosted hydrothermal fields are abundant in Fe, Cu, Zn, Ag, Pb, Mo, Co, U, V, and P, whereas the sediments from the ultramafic-hosted system are enriched in Fe, Mn, Cu, Zn, Ag, Pb, Mo, Co, Mg, Ni, Cr, U, V, and P. The enrichment of these elements and element ratios, such as (Fe + Mn)/Al, Fe/Ti, Al/(Al + Fe + Mn), Cu/Fe, Zn/Fe, U/Fe, and V/Fe, can be indicators of the intensity of the hydrothermal inputs and the distance to the SMS deposits. Temporal and spatial variations of hydrothermally derived elements (e.g., Fe, Cu, Zn, and Pb) in sediment cores can serve as effective tools for reconstructing the hydrothermal history, evaluating the mineralization potential, and tracing inactive/buried SMS deposits. Finally, a prospecting model and method for SMS deposits is proposed based on the prospecting indicators revealed by the sediment study, combined with other prospecting indicators generally used during marine exploration. The results can provide guidance for the prospecting of hydrothermal fields and SMS deposits.
Calculation of partition function of Ising model on quantum computer

We study the partition function of the Ising model on a graph with the help of quantum computing. The Boltzmann factor is modeled on a quantum computer as a trace of some evolution operator with effective Hamiltonian over ancilla spins (qubits) corresponding to graph links. We propose two methods for this which are based on effective Hamiltonian with three-spin interaction and on two-spin interaction. The limit of small temperatures allows us to find the ground state of the system that is related to the discrete combinatorial optimization problem. The partition function of the Ising model for two-spin clusters is calculated on IBM's quantum computer. The possibility of finding ground state is also demonstrated for two-spin clusters.
Preparation of non-Gaussian states based on three-photon quantum scissors

We investigate the properties of non-Gaussian quantum states generated by a three-photon quantum scissor (3-QS). Three different types of inputs to the 3-QS are considered: coherent state (CS), squeezed vacuum state (SVS), and optical field thermal state (TS). We discuss both the detection probability and fidelity of the output states. Our results demonstrate that when the input states have a smaller mean photon number, the output non-Gaussian state can exhibit high detection probability and high fidelity by selecting an appropriate transmissivity. Additionally, we calculate the Wigner functions for the various output states and analyze the non-classical properties by comparing the negative volume of the Wigner functions. The results indicate that the output non-Gaussian states display significant non-classical behavior, particularly when the transmissivity is high, with SVS as the input state showing the strongest effect. Moreover, we compare the performance of the 3-QS with that of the two-photon quantum scissors (2-QS). The findings demonstrate that the non-Gaussian states prepared by the 3-QS possess stronger non-classical characteristics. These results have potential implications for advancing the practical application of the 3-QS in quantum information technology.
A framework for real-time vehicle counting and velocity estimation using deep learning

To better control traffic and promote environmental sustainability, this study proposed a framework to monitor vehicle number and velocity at real time. First, You Only Look Once-v4 (Yolo-v4) algorithm based on deep learning can greatly improve the accuracy of object detection in an image, and trackers, including Sort and Deepsort, resolved the identity switch problem to track efficiently the multiple objects. To that end, this study combined Yolo-v4 with Sort and Deepsort to develop two trajectory models, which are known as YS and YDS, respectively. In addition, different regions of interest (ROI) with different pixel distances (PDs), named ROI-10 and ROI-14, were converted by road marking to calibrate the PD. Finally, a high-resolution benchmark video and two real-time low-resolution videos of highway both were employed to validate this proposed framework. Results show the YDS with ROI-10 achieved 90% accuracy of vehicle counting, when compared to the number of actual vehicles, and this outperformed the YS with ROI-10. However, the YDS with ROI-14 generated relatively good estimates of vehicle velocity. As shown in the real-time low-resolution videos, the YDS with ROI-10 achieved 89.5% and 83.7% accuracy of vehicle counting in Nantun and Daya sites of highway, respectively, and reasonable estimates of vehicle velocity were obtained. In the future, more bus and light truck images could be collected to effectively train the Yolo-v4 and improve the detection of bus and light truck. A better mechanism for precise vehicle velocity estimation and the vehicle detection in different environment conditions should be further investigated.
Improving Extraction of Chinese Open Relations Using Pre-trained Language Model and Knowledge Enhancement

ABSTRACT Open Relation Extraction (ORE) is a task of extracting semantic relations from a text document. Current ORE systems have significantly improved their efficiency in obtaining Chinese relations, when compared with conventional systems which heavily depend on feature engineering or syntactic parsing. However, the ORE systems do not use robust neural networks such as pre-trained language models to take advantage of large-scale unstructured data effectively. In respons to this issue, a new system entitled Chinese Open Relation Extraction with Knowledge Enhancement (CORE-KE) is presented in this paper. The CORE-KE system employs a pre-trained language model (with the support of a Bidirectional Long Short-Term Memory (BiLSTM) layer and a Masked Conditional Random Field (Masked CRF) layer) on unstructured data in order to improve Chinese open relation extraction. Entity descriptions in Wikidata and additional knowledge (in terms of triple facts) extracted from Chinese ORE datasets are used to fine-tune the pre-trained language model. In addition, syntactic features are further adopted in the training stage of the CORE-KE system for knowledge enhancement. Experimental results of the CORE-KE system on two large-scale datasets of open Chinese entities and relations demonstrate that the CORE-KE system is superior to other ORE systems. The F1-scores of the CORE-KE system on the two datasets have given a relative improvement of 20.1% and 1.3%, when compared with benchmark ORE systems, respectively. The source code is available at https://github.com/cjwen15/CORE-KE.
Modified query expansion through generative adversarial networks for information extraction in e-commerce

This work addresses an alternative approach for query expansion (QE) using a generative adversarial network (GAN) to enhance the effectiveness of information search in e-commerce. We propose a modified QE conditional GAN (mQE-CGAN) framework, which resolves keywords by expanding the query with a synthetically generated query that proposes semantic information from text input. we train a sequence-to-sequence transformer model as the generator to produce keywords and use a recurrent neural network model as the discriminator to classify an adversarial output with the generator. with the modified CGAN framework, Various forms of semantic insights gathered from the query-document corpus are introduced to the generation process. we leverage these insights as conditions for the generator model and discuss their effectiveness for the query expansion task. our experiments demonstrate that the utilization of condition structures within the mQE-CGAN framework can increase the semantic similarity between generated sequences and reference documents up to nearly 10% compared to baseline models.
Efficient Quantum Algorithms for Testing Symmetries of Open Quantum Systems

Symmetry is an important and unifying notion in many areas of physics. In quantum mechanics, it is possible to eliminate degrees of freedom from a system by leveraging symmetry to identify the possible physical transitions. This allows us to simplify calculations and characterize potentially complicated dynamics of the system with relative ease. Previous works have focused on devising quantum algorithms to ascertain symmetries by means of fidelity-based symmetry measures. In our present work, we develop alternative symmetry testing quantum algorithms that are efficiently implementable on quantum computers. Our approach estimates asymmetry measures based on the Hilbert–Schmidt distance, which is significantly easier, in a computational sense, than using fidelity as a metric. The method is derived to measure symmetries of states, channels, Lindbladians, and measurements. We apply this method to a number of scenarios involving open quantum systems, including the amplitude damping channel and a spin chain, and we test for symmetries within and outside the finite symmetry group of the Hamiltonian and Lindblad operators.
On the Unraveling of Open Quantum Dynamics

It is well known that the state operator of an open quantum system can be generically represented as the solution of a time-local equation — a quantum master equation. Unraveling in quantum trajectories offers a picture of open system dynamics dual to solving master equations. In the unraveling picture, physical indicators are computed as Monte Carlo averages over a stochastic process valued in the Hilbert space of the system. This approach is particularly adapted to simulate systems in large Hilbert spaces. We show that the dynamics of an open quantum system generically admits an unraveling in the Hilbert space of the system described by a Markov process generated by ordinary stochastic differential equations for which rigorous concentration estimates are available. The unraveling can be equivalently formulated in terms of norm-preserving state vectors or in terms of linear “ostensible” processes trace preserving only on average. We illustrate the results in the case of a two level system in a simple boson environment. Next, we derive the state-of-the-art form of the Diósi-Gisin-Strunz Gaussian random ostensible state equation in the context of a model problem. This equation provides an exact unraveling of open systems in Gaussian environments. We compare and contrast the two unravelings and their potential for applications to quantum error mitigation.
Wave Matrix Lindbladization II: General Lindbladians, Linear Combinations, and Polynomials

In this paper, we investigate the problem of simulating open system dynamics governed by the well-known Lindblad master equation. In our prequel paper [Patel and Wilde, Open Sys. &amp; Info. Dyn., 30:2350010 (2023)], we introduced an input model in which Lindblad operators are encoded into pure quantum states, called program states, and we also introduced a method, called wave matrix Lindbladization, for simulating Lindbladian evolution by means of interacting the system of interest with these program states. Therein, we focused on a simple case in which the Lindbladian consists of only one Lindblad operator and a Hamiltonian. Here, we extend the method to simulating general Lindbladians and other cases in which a Lindblad operator is expressed as a linear combination or a polynomial of the operators encoded into the program states. We propose quantum algorithms for all these cases and also investigate their sample complexity, i.e., the number of program states needed to simulate a given Lindbladian evolution approximately. Finally, we demonstrate that our quantum algorithms provide an efficient route for simulating Lindbladian evolution relative to full tomography of encoded operators, by proving that the sample complexity for tomography is dependent on the dimension of the system, whereas the sample complexity of wave matrix Lindbladization is dimension independent.
An Exploration of the Potential of Large Language Models to Enable Cognitive Flexibility in AI-Augmented Learning Environments

This paper is motivated by the possibility that large language model (LLM) artificial intelligence (AI) chatbots such as ChatGPT (Chat Generative Pre-trained Transformer) may support cognitive flexibility in AI-augmented learning environments and in doing so, forestall tendencies toward cognitive rigidity, as in, resistance to change, whether conscious or unconscious. A review of the research and practice literature for LLM AI chatbots and for cognitive flexibility and rigidity enables formulation of a conceptual framework for cognitive flexibility in AI-rich learning environments. The main research question explored is whether LLM AI chatbots support cognitive flexibility in contemporary learning environments? And if yes, how? A research design is advanced in this paper that is attentive to the need for a rethinking of methods for the study of people interacting with technologies in contemporary learning environments where AI is increasingly present. This paper provides the design of a research study focusing on Large Language Model (LLM) artificial intelligence (AI) chatbots placing a focus on cognitive flexibility challenges and opportunities in learning environments. This work also provides direction for the development of at least two other papers, one focusing on creativity and innovation and the other focusing on curiosity, interest, and engagement. This paper is significant in terms of the potential to broaden understandings of learning spaces featuring AI capabilities. Contributions of this work pertain to implications associated with enriching and augmenting human learning capabilities in support of cognitive flexibility; and extending the boundaries of AI-assisted human-computer interaction in learning.
Edge-Guided Interpretable Neural Network for Image Compressive Sensing Reconstruction

In recent years, interpretable Compressive Sensing (CS) technology has received more and more attention. Although these CS reconstruction methods can obtain good interpretability, they ignore the importance of edge information, which greatly limits the representation ability of their reconstruction models. Therefore, this paper reasonably embeds the boundary consistency constraint to the CS optimization model and proposes a CS reconstruction model with boundary constraint. This model can be expanded into Edge-Guided Interpretable Neural Network (EGINNet) for image CS reconstruction. In particular, in order to solve the problem of information loss in the iterative process, we introduce a multiple-memory enhancement mechanism to explore the feature dependency of EGINNet across different iterative stages. At the same time, we design a parallel-cross fusion module, which can selectively fuse boundary features and image features. Qualitative and quantitative experimental results show that compared with the state-of-the-art explainable CS reconstruction methods, the proposed EGINNet has better reconstruction performance while maintaining the network interpretability, especially on the restoration of boundary information.
Non-contact automated defect detection using a deep learning approach in diffraction phase microscopy

Precision measurement of defects from optical fringe patterns is a problem of significant practical relevance in non-destructive metrology. In this paper, we propose a robust deep learning approach based on atrous convolution neural network model for defect detection from noisy fringe patterns obtained in diffraction phase microscopy. The model utilizes the wrapped phase obtained from the fringe pattern as an input and generates a binary image depicting the defect and non-defect regions as output. The effectiveness of the proposed approach is validated through numerical simulations of various defects under different noise levels. Furthermore, the practical application of the proposed technique for identifying defects in diffraction phase microscopy experiments is also confirmed.
Unveiling the Mega-Kinetic-Disk Coordinate System: A Comprehensive Introduction

The Mega-Kinetic-Disks Coordinate System (MKD-Coordinate System) introduces a novel approach to evaluating dynamic and complex phenomena across different levels. It involves infinite variables interconnected within the same graphical space, operating at varying speeds of time through the utilization of kinetic energy in different sectors to achieve harmony. The central concept behind the MKD-Coordinate System revolves around the effectiveness of multidimensional graphs as the most powerful visual tool for comprehending natural phenomena from a multidimensional viewpoint. Consequently, the mission of the MKD-Coordinate System is to provide academics, researchers, and policymakers with an alternative multidimensional graphical modeling approach for the study and teaching-learning process of sciences from a comprehensive perspective.
Future Service Robot Scenarios in South Korea

Advances in digital technology, periodic threats from infectious diseases, and shrinking working-age populations have increased the demand for autonomous systems. South Korea is now in crisis because its society is aging and has limited resources. The implementation of service robots is one of the possible alternative plans that has been receiving attention both for sustainable economic growth and as a solution to social problems. However, many things should be considered for service robots to be widely used in society. The aim of this study was to identify key factors that will affect the future of service robots and discuss corresponding policy measures. Four scenarios were developed using general morphology analysis (GMA). The scenarios were defined according to six key factors: technological development, infrastructure development, commercial acceptance, social acceptance, policy and regulatory environments, and technological competition. In scenario A, policy measures need to ensure that South Korea will continue as a global service robot leader. In scenario B, it is necessary to narrow the gap between South Korea and competitors in terms of service robot technology development and adoption. In scenario C-1, policies should encourage the adoption of service robot technologies both domestically and abroad. In scenario C-2, it is necessary to develop service robot technologies and promote the service robot industry.
Analyzing Load Balancing Techniques for Cloud Computing: Pros, Cons, and Emerging Trends

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A Novel Approach for Day-Ahead Hourly Building-Integrated Photovoltaic Power Prediction by Using Feature Engineering and Simple Weather Forecasting Service

Although the accuracy of short-term prediction of building-integrated photovoltaics is essential to making an optimal decision on the management of the generated electricity, the weather forecasting service in many countries provides insufficient features for improving the prediction accuracy of the photovoltaics power output. This study suggests a machine learning model incorporated with feature engineering to improve the prediction performance of day-ahead hourly power outputs using a simple weather forecast service. A new synthetic feature, the modified sky condition, is derived to infer onsite sky condition and solar irradiation, which is not supported by the typical weather forecasting services. It evaluated the prediction performance with different training and hyper-parameter conditions for 60 days. By using the derived modified sky condition, the model outperformed other predictor configurations in most daily sky conditions; particularly, the accuracy improved by more than 50% on overcast days compared to when it used the original weather forecasting service data. The result demonstrates the feasibility and ability of the model to enable more efficient energy management of building-integrated photovoltaic power output in buildings without an onsite weather station, thus contributing toward the optimized dispatch of the integrated electricity energy storage system and other distributed energy resources.
A Recommendation System Supporting the Implementation of Sustainable Risk Management Measures in Airport Operations

This paper introduces a recommendation system aimed at enhancing the sustainable process of risk management within airport operations, with a special focus on Occupational Stress Risks (OSRs). The recommendation system is implemented via a flexible Python code that offers seamless integration into various operational contexts. It leverages Fuzzy Cognitive Maps (FCMs) to conduct comprehensive risk assessments, subsequently generating prioritized recommendations for predefined risk management measures aimed at preventing and/or reducing the most critical OSRs. The system’s reliability has been validated by iterating the procedure with diverse input data (i.e., matrices of varying sizes) and measures. This confirms the system’s effectiveness across a broad spectrum of engineering scenarios.