Reviewerly/exHarmony huggingface dataset:
On optimization of coherent and incoherent controls for two-level quantum systems

This paper considers some control problems for closed and open two-level quantum systems. The closed system's dynamics is governed by the Schrödinger equation with coherent control. The open system dynamics is governed by the Gorini-Kossakowski-Sudarshan-Lindblad master equation whose Hamiltonian depends on coherent control and superoperator of dissipation depends on incoherent control. For the closed system, we consider the problem of generation of the phase shift gate for some values of phases and final times for which we numerically show that zero coherent control, which is a stationary point of the objective functional, is not optimal; it gives an example of subtle point for practical solving quantum control problems. The two-stage method, which was developed in [48] for generic $N$-level open quantum systems for approximate generation of a given target density matrix, is modified here for the case of two-level systems. We modify the first ("incoherent") stage by numerically optimizing piecewise constant incoherent control instead of using constant incoherent control analytically computed using eigenvalues of the target density matrix. Exact analytical formulas are derived for the system state evolution, the objective functions and their gradients for the modified first stage. These formulas are then applied in the two-step gradient projection method. The numerical simulations show that the modified first stage duration can be significantly less than the unmodified first stage duration, but at the cost of performing optimization in the class of piecewise constant controls.
Semantic Segmentation of High-Resolution Remote Sensing Images with Improved U-Net Based on Transfer Learning

Abstract Semantic segmentation of high-resolution remote sensing images has emerged as one of the foci of research in the remote sensing field, which can accurately identify objects on the ground and determine their localization. In contrast, the traditional deep learning-based semantic segmentation, on the other hand, requires a large amount of annotated data, which is unsuitable for high-resolution remote sensing tasks with limited resources. It is therefore important to build a semantic segmentation method for high-resolution remote sensing images. In this paper, it is proposed an improved U-Net model based on transfer learning to solve the semantic segmentation problem of high-resolution remote sensing images. The model is based on the symmetric encoder–decoder structure of U-Net. For the encoder, transfer learning is applied and VGG16 is used as the backbone of the feature extraction network, and in the decoder, after upsampling using bilinear interpolation, it is performed multiscale fusion with the feature maps of the corresponding layers of the encoder in turn and is finally obtained the predicted value of each pixel to achieve precise localization. To verify the efficacy of the proposed network, experiments are performed on the ISPRS Vaihingen dataset. The experiments show that the applied method has achieved high-quality semantic segmentation results on the high-resolution remote sensing dataset, and the MIoU is 1.70%, 2.20%, and 2.33% higher on the training, validation, and test sets, respectively, and the IoU is 4.26%, 6.89%, and 5.44% higher for the automotive category compared to the traditional U-Net.
Natural Language Processing

This introductory chapter begins with human language and intelligence constituting six levels of linguistics followed by a brief history of NLP with major components and applications. It serves as the cornerstone to the NLP concepts and technology in the following chapters. This chapter also serves as the conceptual basis for Workshop#1—Basics of Natural Language Toolkit (NLTK) in Chap. 10 .
An empirical study of the efficacy of AI chatbots for English as a foreign language learning in primary education

This experimental research examined the effectiveness of using chatbots in English as a Foreign Language (EFL) classrooms at a Chinese elementary school. Seventy-four students were divided into two groups: one employing traditional methods, and the other using chatbots. Before and after the 3-month teaching period, pre- and post-tests were used to measure students’ oral English proficiency, and a questionnaire was employed to assess their willingness to communicate (WTC). Data from interviews with teachers and students provided insights into chatbot usage and modification. Data analysis involved using SPSS to identify significant differences in pre- and post-test scores and NVivo 11 to code qualitative feedback from student and teacher interviews. The results show that chatbot integration significantly improved oral English proficiency and WTC in the experimental group compared with the control group. Teachers can enhance instruction by adopting tailored chatbot features to refine teaching methods, thus creating a more adaptive learning path for students. Further, chatbot improvements such as personalised error correction and user-centric features pave the way for immersive and fruitful language learning. This study provides valuable insights for integrating chatbots into language classrooms and suggests opportunities for further chatbot enhancements..
EasyScale: Elastic Training with Consistent Accuracy and Improved Utilization on GPUs

Distributed synchronized GPU training is commonly used for deep learning. The resource constraint of using a fixed number of GPUs makes large-scale training jobs suffer from long queuing time for resource allocation, and lowers the cluster utilization. Adapting to resource elasticity can alleviate this but often introduces inconsistent model accuracy, due to lacking of capability to decouple model training procedure from resource allocation. We propose EasyScale, an elastic training system that achieves consistent model accuracy under resource elasticity for both homogeneous and heterogeneous GPUs. EasyScale preserves the data-parallel training behaviors strictly, traces the consistency-relevant factors carefully, utilizes the deep learning characteristics for EasyScaleThread abstraction and fast context-switching. To utilize heterogeneous cluster, EasyScale dynamically assigns workers based on the intra-/inter-job schedulers, minimizing load imbalance and maximizing aggregated job throughput. Deployed in an online serving cluster, EasyScale powers the training jobs to utilize idle GPUs opportunistically, improving overall cluster utilization by 62.1%.
FISCO-BCOS: An Enterprise-grade Permissioned Blockchain System with High-performance

Enterprise-grade permissioned blockchain systems provide a promising infrastructure for data sharing and cooperation between different companies. However, performance bottlenecks seriously hinder the adoption of these systems in many industrial applications that process complex business logic and huge transaction volumes. Our research identifies two key factors that limit the system performance: 1) At the block level, the serial dependency of inter-block processing severely limits the system throughput. A new block must wait for the completion of all previous blocks. 2) At the transaction level, the lack of efficient intra-block transactions concurrency makes it difficult to achieve high performance, especially when dealing with multiple CPU-heavy contracts which are commonly used in industrial scenarios.
A Secure and IoT-Enabled Data Sharing System Based on IPFS and IOTA Blockchain

With the increasing amount of Internet of Things (IoT) data, there is an increasing need to manage those data efficiently and securely. In this paper, we propose a decentralized and privacy-preserving IoT data sharing and management system based on the Interplanetary File System (IPFS) and the IOTA Tangle. Our system enables data owners to securely upload and manage their data on IPFS. We maintain the privacy of the data through Fuzzy Identity-Based Encryption (FIBE). Our system also includes a tag-based search mechanism, which allows data users to easily find the data they need without knowing specific transaction IDs. Compared with existing solutions, our system offloads data owners from local storage and backup and provides a more efficient and effective data search mechanism.
Leveraging AI and Blockchain for Streamlining Healthcare Payments

This paper explores the benefits of integrating artificial intelligence (AI) and blockchain technology in healthcare payment systems. Healthcare payment systems are complex and face numerous challenges, such as inaccuracies, inefficiencies, and security breaches. By leveraging AI and Blockchain technology, healthcare payment systems can be streamlined, resulting in increased accuracy, efficiency, and security. This paper discusses the role of AI and Blockchain technology in healthcare payment systems and outlines the advantages of their integration. It also presents case studies of successful implementation of these technologies in healthcare payment systems and suggests strategies for overcoming implementation challenges. The paper concludes with potential future developments and opportunities for leveraging AI and Blockchain technology in healthcare payment systems.
Actis: A Strictly Local Union–Find Decoder

Fault-tolerant quantum computing requires classical hardware to perform the decoding necessary for error correction. The Union–Find decoder is one of the best candidates for this. It has remarkably organic characteristics, involving the growth and merger of data structures through nearest-neighbour steps; this naturally suggests the possibility of its realisation using a lattice of simple processors with nearest-neighbour links. In this way the computational load can be distributed with near-ideal parallelism. Here we show for the first time that this strict (rather than partial) locality is practical, with a worst-case runtime <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow class="MJX-TeXAtom-ORD"><mml:mi class="MJX-tex-caligraphic" mathvariant="script">O</mml:mi></mml:mrow><mml:mo stretchy="false">(</mml:mo><mml:msup><mml:mi>d</mml:mi><mml:mn>3</mml:mn></mml:msup><mml:mo stretchy="false">)</mml:mo></mml:math> and mean runtime subquadratic in the surface code distance <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>d</mml:mi></mml:math>. A novel parity-calculation scheme is employed which can simplify previously proposed architectures, and our approach is optimised for circuit-level noise. We compare our local realisation with one augmented by long-range links; while the latter is of course faster, we note that local asynchronous logic could negate the difference.
Inverse Free Zap Stochastic Approximation Extended Abstract

The Zap Zero algorithm is introduced, a stochastic approximation algorithm designed to approximate the Newton-Raphson flow. It is stable and convergent under under the same assumptions required for Zap stochastic approximation, which achieves the same approximation. The significant advantage of the new algorithm is that matrix inversion is avoided. To estimate a parameter of dimension d, the Zap Zero algorithm requires parameter updates of dimension 3 × d.Motivation and stability analysis is based on the ODE method and the theory of two time-scale stochastic approximation. The ODE itself can be interpreted as the solution to a particular linear quadratic regulator problem, even though the algorithm is designed for nonlinear root finding problems.
The Power of ANN-Random Forest Algorithm in Human Activities Recognition Using IMU Data

Human Activity Recognition (HAR) plays a crucial role in numerous applications, ranging from healthcare to sports analytics. This study presents a novel approach to HAR that combines Artificial Neural Networks (ANNs) and Random Forests to enhance the accuracy of HAR in diverse real-world conditions, especially when dealing with noisy and imperfect data. ANNs are known for extracting intricate features from raw data, making them well-suited for classification. Random Forest excels at learning from multiple decision trees and utilizing collective knowledge to make predictions, making it suitable for handling data in real-world applications. Harnessing the power of ANNs in feature extraction, coupled with the collective decision-making capability of Random Forest, the combined model demonstrates improved accuracy in classifying human activities. This study showcases the potential of combining ANN and Random Forest in classifying multi-dimensional Inertial Measurement Unit (IMU) data, a widely-used data source in HAR. By leveraging the strengths of both ANN and Random Forest, the combined model addresses the challenges associated with real, imperfect data, leading to a more robust and accurate classification model. The results highlight the effectiveness of feature extraction by ANNs and underscore the importance of incorporating Random Forest in HAR systems to obtain 98.84% accuracy. The findings of this study offer valuable insights into the synergistic effects of combining ANN and Random Forest for HAR. The outcomes can contribute to developing more reliable and effective HAR systems, with potential applications in healthcare monitoring, activity recognition in smart environments, and other domains requiring accurate human activity classification.
Evaluating the Impact of Data Transformation Techniques on the Performance and Interpretability of Software Defect Prediction Models

The performance of software defect prediction (SDP) models determines the priority of test resource allocation. Researchers also use interpretability techniques to gain empirical knowledge about software quality from SDP models. However, SDP methods designed in the past research rarely consider the impact of data transformation methods, simple but commonly used preprocessing techniques, on the performance and interpretability of SDP models. Therefore, in this paper, we investigate the impact of three data transformation methods (Log, Minmax, and Z-score) on the performance and interpretability of SDP models. Through empirical research on (i) six classification techniques (random forest, decision tree, logistic regression, Naive Bayes, K-nearest neighbors, and multilayer perceptron), (ii) six performance evaluation indicators (Accuracy, Precision, Recall, F1, MCC, and AUC), (iii) two interpretable methods (permutation and SHAP), (iv) two feature importance measures (Top-k feature rank overlap and difference), and (v) three datasets (Promise, Relink, and AEEEM), our results show that the data transformation methods can significantly improve the performance of the SDP models and greatly affect the variation of the most important features. Specifically, the impact of data transformation methods on the performance and interpretability of SDP models depends on the classification techniques and evaluation indicators. We observe that log transformation improves NB model performance by 7%–61% on the other five indicators with a 5% drop in Precision. Minmax and Z-score transformation improves NB model performance by 2%–9% across all indicators. However, all three transformation methods lead to substantial changes in the Top-5 important feature ranks, with differences exceeding 2 in 40%–80% of cases (detailed results available in the main content). Based on our findings, we recommend that (1) considering the impact of data transformation methods on model performance and interpretability when designing SDP approaches as transformations can improve model accuracy, and potentially obscure important features, which lead to challenges in interpretation, (2) conducting comparative experiments with and without the transformations to validate the effectiveness of proposed methods which are designed to improve the prediction performance, and (3) tracking changes in the most important features before and after applying data transformation methods to ensure precise and traceable interpretability conclusions to gain insights. Our study reminds researchers and practitioners of the need for comprehensive considerations even when using other similar simple data processing methods.
Demonstrating Scalable Randomized Benchmarking of Universal Gate Sets

Randomized benchmarking (RB) protocols are the most widely used methods for assessing the performance of quantum gates. However, the existing RB methods either do not scale to many qubits or cannot benchmark a universal gate set. Here, we introduce and demonstrate a technique for scalable RB of many universal and continuously parametrized gate sets, using a class of circuits called randomized mirror circuits. Our technique can be applied to a gate set containing an entangling Clifford gate and the set of arbitrary single-qubit gates, as well as gate sets containing controlled rotations about the Pauli axes. We use our technique to benchmark universal gate sets on four qubits of the Advanced Quantum Testbed, including a gate set containing a controlled-S gate and its inverse, and we investigate how the observed error rate is impacted by the inclusion of non-Clifford gates. Finally, we demonstrate that our technique scales to many qubits with experiments on a 27-qubit IBM Q processor. We use our technique to quantify the impact of crosstalk on this 27-qubit device, and we find that it contributes approximately 2/3 of the total error per gate in random many-qubit circuit layers.9 MoreReceived 21 July 2022Revised 9 June 2023Accepted 25 August 2023DOI:https://doi.org/10.1103/PhysRevX.13.041030Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasQuantum benchmarkingQuantum computationQuantum gatesQuantum information processingQuantum Information, Science & Technology
Gender and natural resources management in Nigeria: The role of corporate social responsibility in the oil host communities

Abstract This paper critically examines the multinational oil companies' (MOCs) corporate social responsibility (CSR) initiatives in Nigeria. Its special focus is to investigate the impact of the global memorandum of understanding (GMoU) on addressing inequalities and empowering women for sustainable ecosystem management in the Niger Delta region of Nigeria. The paper adopts a survey research technique, aimed at gathering information from a representative sample of the population, as it is essentially cross‐sectional, describing and interpreting the current situation. A total of 768 women respondents were sampled across the rural areas of the Niger Delta region. Results from the use of logistic regression model indicate that despite women's unique and important responsibilities in the use and management of natural resources, women are typically less involved in the formal governance processes, resulting in their interests, goals, knowledge, and capabilities being both under‐represented and under‐utilized. It also shows that the MOCs' CSR using GMoU model has recorded significant success in addressing gender inequalities and enhancing the capacity of the rural women in natural resources and ecosystem management. The finding suggests that if the MOCs' CSR targeted at addressing gender issue is increased by one unit, the odd ratio is almost 13 times as high. This implies that addressing gender–related barriers and challenges and championing equitable natural resource governance leads to better livelihoods outcomes. It concludes that business has an obligation to help in solving problems of public concern.
Perceptions and use of metaverse in higher education: A descriptive study in China and Spain

The coronavirus disease – 2019 pandemic has accelerated the proliferation of virtual technologies and had have an enormous impact on education. In this paper, we analyse teachers’ perceptions about the use of the metaverse in educational practices and how it contributes to the local development. We focused our study in China and Spain as reference countries in the metaverse, but whose collaboration, in this arena, is not significant. For that purpose, we designed a questionnaire to explore the views of 20 teachers from each of the two universities selected, one in China and the other one in Spain, which applies an innovation and entrepreneurship education. The results show that the educational use of the metaverse is in its initial experimentation phase. Its application in innovation and entrepreneurship education is not limited to teachers' attitudes towards teaching, but rather to how the use of metaverse allows students to acquire real skills, which can contribute to the development of the society and improve international cooperation, thus creating a virtuous circle. Faculty training and university facilities remain limited for its implementation. Further research is needed on the data protection and privacy aspects on the practical educational use of metaverse.
A peer-to-peer blockchain-based architecture for trusted and reliable agricultural product traceability

The lack of new and advanced technologies, such as the Internet of Things (IoT) and Fog computing, makes it challenging to trace the origin of agricultural products in supply chains. In addition, the system’s traditional centralized architecture cannot provide a trusted traceability service for farming goods in these supply chains. This paper proposes a peer-to-peer Blockchain-based architecture integrating self-sovereign identity (SSI) and a decentralized key management system (DKMS) for a trusted and reliable traceability service for agricultural food products in the supply chain networks. The public Blockchain network Fantom will provide faster and cheaper transactions. In contrast, SSI will ensure the identity of each entity by adopting DKMS technology on top of Blockchain to facilitate faster authenticity, integrity, and confidentiality service of the supply chain’s transaction and verification process. The system is deployed on the different public networks, i.e., Fantom and Ethereum, and shows that the proposed Fantom-based Blockchain network outperforms in terms of transaction and verification timing.
Automated Stability Analysis of Piecewise Affine Dynamics Using Vertices

This paper presents an automated algorithm to analyze the stability of piecewise affine (PWA) dynamical systems due to their broad applications. We parametrize the Lyapunov function as a PWA function, with polytopic regions defined by the PWA dynamics. Using this parametrization, Stability conditions can be expressed as linear constraints restricted to polytopes so that the search for a Lyapunov function involves solving a linear program. However, a valid Lyapunov function might not be found given these polytopic regions. A natural response is to increase the size of the parametrization of the Lyapunov function by dividing regions and solving the new linear program. This paper proposes two new methods to divide each polytope into smaller ones. The first approach divides a polytope based on the sign of the derivative of the candidate Lyapunov function, while the second divides it based on the change in the vector field of the PWA dynamical system. In addition, we propose using Delaunay triangulation to achieve automated division of regions and preserve the continuity of the PWA Lyapunov function. Examples involving learned models and explicit MPC controllers demonstrate that the proposed method of dividing regions leads to valid Lyapunov functions with fewer regions than existing methods, reducing the computational time taken for stability analysis.