Reviewerly/exHarmony huggingface dataset:
Far-field far-subwavelength spatial resolution using relative motion with structured illumination

Authors:
raw_author_name: Justin A. Patel
raw_affiliation_string: School of Electrical and Computer Engineering, Purdue University, West Lafayette, Indiana 47907-2035, USA

Abstract: Relative motion of structured optical illumination with respect to an object and far-field measurement of intensity are presented as a means to obtain far-subwavelength spatial resolution with a direct imaging arrangement. The principle behind this approach is that the variable interaction of an object with a background field generates information about nanometer-scale features that is encoded in the propagating plane-wave spectrum, allowing far-field data that are modulated with motion according to the nanostructure. Information theory supports this superresolution mechanism and illustrates sensitivity with respect to the illumination and detection arrangements. Simulations indicate that available lasers and detectors would enable a resolution of $\ensuremath{\lambda}/1000$ with modest signal-to-noise requirements and single-pixel detection. Relative motion in structured fields is shown to enhance spatial resolution achievable using data inversion with constraints. Importantly, far-subwavelength sensitivity is shown to be achievable even when the illuminating field is unknown. These results suggest applications that include material defect detection and unlabeled protein sensing, and direct extensions to estimating geometrical features at unprecedented spatial resolution become possible.
Union-find quantum decoding without union-find

Authors:
raw_author_name: Sam J. Griffiths
raw_affiliation_string: Department of Physics and Astronomy, University College London, London WC1E 6BT, United Kingdom
raw_author_name: Dan E. Browne
raw_affiliation_string: Department of Physics and Astronomy, University College London, London WC1E 6BT, United Kingdom

Abstract: The union-find decoder is a leading algorithmic approach to the correction of quantum errors on the surface code, achieving code thresholds comparable to minimum-weight perfect matching (MWPM) with amortized computational time scaling near-linearly in the number of physical qubits. This complexity is achieved via optimizations provided by the disjoint-set data structure. We demonstrate, however, that the behavior of the decoder at scale underutilizes this data structure for twofold analytic and algorithmic reasons, and that improvements and simplifications can be made to architectural designs to reduce resource overhead in practice. To reinforce this, we model the behavior of erasure clusters formed by the decoder and show that there does not exist a percolation threshold within the data structure for any mode of operation. This yields a linear-time worst-case complexity for the decoder at scale, even with a naive implementation omitting popular optimizations.
Two methods for breaking down a quantum algorithm

Authors:
raw_author_name: Miguel Murça
raw_affiliation_string: Centro de Física e Engenharia de Materiais Avançados (CeFEMA), Physics of Information and Quantum Technologies Group, Lisboa 1049-001, Portugal; Instituto Superior Técnico, Universidade de Lisboa, Lisboa 1049-001, Portugal; PQI – Portuguese Quantum Institute, Lisboa 1600-531, Portugal
raw_author_name: Duarte Magano
raw_affiliation_string: Instituto Superior Técnico, Universidade de Lisboa, Lisboa 1049-001, Portugal; Instituto de Telecomunicações, Lisboa 1049-001, Portugal
raw_author_name: Yasser Omar
raw_affiliation_string: Centro de Física e Engenharia de Materiais Avançados (CeFEMA), Physics of Information and Quantum Technologies Group, Lisboa 1049-001, Portugal; Instituto Superior Técnico, Universidade de Lisboa, Lisboa 1049-001, Portugal; PQI – Portuguese Quantum Institute, Lisboa 1600-531, Portugal

Abstract: Despite the promise that fault-tolerant quantum computers can efficiently solve classically intractable problems, it remains a major challenge to find quantum algorithms that may reach computational advantage in the present era of noisy, small-scale quantum hardware. Thus, there is a substantial ongoing effort to create new quantum algorithms (or adapt existing ones) to accommodate depth and space restrictions. By adopting a hybrid query perspective, we identify and characterize two methods of breaking down quantum algorithms into rounds of lower (query) depth, designating these approaches as ``parallelization'' and ``interpolation.'' To the best of our knowledge, these had not been explicitly identified and compared side by side, although one can find instances of them in the literature. We apply them to two problems with known quantum speedup: calculating the $k$-threshold function and computing a NAND tree. We show that for the first problem parallelization offers the best performance, while for the second interpolation is the better choice. This illustrates that no approach is strictly better than the other, and that there is more than one good way to break down a quantum algorithm into a hybrid quantum-classical algorithm.
EAO: Enhanced aquila optimizer for solving optimization problem

Authors:
raw_author_name: Hairu Guo
raw_affiliation_string: Department of Computer Science and Technology, Henan Polytechnic University, Jiaozuo, China
raw_author_name: Jin’ge Wang
raw_affiliation_string: Department of Computer Science and Technology, Henan Polytechnic University, Jiaozuo, China
raw_author_name: Yongli Liu
raw_affiliation_string: Department of Computer Science and Technology, Henan Polytechnic University, Jiaozuo, China
raw_author_name: Yudong Zhang
raw_affiliation_string: Department of Computer Science and Technology, Henan Polytechnic University, Jiaozuo, China; Department of Information Systems, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia

Abstract: The Aquila optimization (AO) algorithm has the drawbacks of local optimization and poor optimization accuracy when confronted with complex optimization problems. To remedy these drawbacks, this paper proposes an Enhanced aquila optimization (EAO) algorithm. To avoid elite individual from entering the local optima, the elite opposition-based learning strategy is added. To enhance the ability of balancing global exploration and local exploitation, a dynamic boundary strategy is introduced. To elevate the algorithm’s convergence rapidity and precision, an elite retention mechanism is introduced. The effectiveness of EAO is evaluated using CEC2005 benchmark functions and four benchmark images. The experimental results confirm EAO’s viability and efficacy. The statistical results of Freidman test and the Wilcoxon rank sum test are confirmed EAO’s robustness. The proposed EAO algorithm outperforms previous algorithms and can useful for threshold optimization and pressure vessel design.
Sparse Random Hamiltonians Are Quantumly Easy

Authors:
raw_author_name: Chi-Fang Chen
raw_affiliation_string: AWS Center for Quantum Computing, Pasadena, California, USA; Institute for Quantum Information and Matter, California Institute of Technology, Pasadena, California, USA
raw_author_name: Alexander M. Dalzell
raw_affiliation_string: AWS Center for Quantum Computing, Pasadena, California, USA; California Institute of Technology, Pasadena, California, USA
raw_author_name: Mario Berta
raw_affiliation_string: Department of Computing, Imperial College London, London, United Kingdom; Institute for Quantum Information, RWTH Aachen University, Aachen, Germany
raw_author_name: Fernando G. S. L. Brandão
raw_affiliation_string: AWS Center for Quantum Computing, Pasadena, California, USA; Institute for Quantum Information and Matter, California Institute of Technology, Pasadena, California, USA
raw_author_name: Joel A. Tropp
raw_affiliation_string: Department of Computing and Mathematical Sciences, Caltech, Pasadena, California, USA

Abstract: Identification of a large class of Hamiltonians that are easy to solve on quantum computers but difficult on classical ones provides a possible path to practical quantum advantage in the simulation of quantum systems.
Shapley value: from cooperative game to explainable artificial intelligence

Authors:
raw_author_name: Meng Li
raw_affiliation_string: College of Electronics and Information Engineering, Tongji University, Shanghai, 201804, China
raw_author_name: Hengyang Sun
raw_affiliation_string: College of Automotive Studies, Tongji University, Shanghai, 201804, China
raw_author_name: Yanjun Huang
raw_affiliation_string: College of Automotive Studies, Tongji University, Shanghai, 201804, China
raw_author_name: Hong Chen
raw_affiliation_string: Shanghai Research Institute for Intelligent Autonomous Systems, Shanghai, 201203, China

Abstract: Abstract With the tremendous success of machine learning (ML), concerns about their black-box nature have grown. The issue of interpretability affects trust in ML systems and raises ethical concerns such as algorithmic bias. In recent years, the feature attribution explanation method based on Shapley value has become the mainstream explainable artificial intelligence approach for explaining ML models. This paper provides a comprehensive overview of Shapley value-based attribution methods. We begin by outlining the foundational theory of Shapley value rooted in cooperative game theory and discussing its desirable properties. To enhance comprehension and aid in identifying relevant algorithms, we propose a comprehensive classification framework for existing Shapley value-based feature attribution methods from three dimensions: Shapley value type, feature replacement method, and approximation method. Furthermore, we emphasize the practical application of the Shapley value at different stages of ML model development, encompassing pre-modeling, modeling, and post-modeling phases. Finally, this work summarizes the limitations associated with the Shapley value and discusses potential directions for future research.
Classification Of Adaptive Back Propagation Neural Network Along with Fuzzy Logic in Chronic Kidney Disease

Authors:
raw_author_name: Dr. KR Vineetha
raw_affiliation_string: Assistant Professor, Computer Science, Christ Deemed to be University, Bangalore, Karnataka-560029
raw_author_name: Mrs Bhagyashree K
raw_affiliation_string: ASSISTANT PROFESSOR, ELECTRONICS AND COMMUNICATION ENGINEERING, ALVA'S INSTITUTE OF ENGINEERING AND TECHNOLOGY, MIJAR,MOODBIDRI, KARNATAKA 74225
raw_author_name: Dr. N SIVAKUMAR
raw_affiliation_string: Associate Professor, Artificial Intelligence & Data Science, Panimalar Engineering college, Chennai, Tamilnadu 600123, India

Abstract: A steady deterioration in kidney function over months or years is known as chronic kidney disease (CKD). Through a range of techniques, such as pharmacological intervention in moderate cases and hemodialysis and renal transport in severe cases, early identification of CKD is crucial and has a substantial influence on reducing the patient's health development. The outcomes show the patient's kidneys' present state. It is suggested to develop a system for detecting chronic renal disease using machine learning. Finding the best feature sets typically involves using metaheuristic algorithms since feature selection is an NP-hard issue with amorphous polynomials. Semi-crystalline tabu search (TS) is frequently used for both local and global searches. In this study, we employ a brand-new hybrid TS with stochastic diffusion search (SDX)-based feature selection. The adaptive backpropagation neural network (ABPNN-ANFIS) is then classified using fuzzy logic. Fuzzy logic may be used to combine the ABPNN findings. Consequently, these techniques can aid experts in determining the stage of chronic renal disease. The Adaptive Neuron Clearing Inference System (ABPNN-ANFIS) was utilised to develop adaptive inverse neural networks using the MATLAB programme. The outcomes demonstrate that the suggested ABPNN-ANFIS is 98% accurate in terms of efficiency.
An efficient deepfake video detection using robust deep learning

Authors:
raw_author_name: Abdul Qadir
raw_affiliation_string: Computer Science Department, UET, Taxila, Pakistan
raw_author_name: Rabbia Mahum
raw_affiliation_string: Computer Science Department, UET, Taxila, Pakistan
raw_author_name: Mohammed A. El-Meligy
raw_affiliation_string: Industrial Engineering Department, College of Engineering, King Saud University, P.O. Box 800, Riyadh, 11421, Saudi Arabia
raw_author_name: Abdulmalik AlSalman
raw_affiliation_string: Computer Science Department, King Saud University, Riyadh, Saudi Arabia
raw_author_name: Haseeb Hassan
raw_affiliation_string: College of Big Data and Internet, Shenzhen Technology University (SZTU), China

Abstract: <h2>Abstract</h2> The creation and manipulation of synthetic images have evolved rapidly, creating serious concerns about their effects on society. Although there have been various attempts to identify deep fake videos, these approaches are not universal. Identifying these misleading deepfakes is the first step in preventing them from following on social media sites. We introduce a unique deep-learning technique to identify fraudulent clips. Most deepfake identifiers currently focus on identifying face exchange, lip synchronous, expression modification, puppeteers, and other factors. However, exploring a consistent basis for all forms of fake video and images in real-time forensics is challenging. We propose a hybrid technique that takes input from videos of successive targeted frames, then feeds these to the ResNet-Swish-BiLSTM, an optimized convolutional BiLSTM-based residual network for training and classification. This proposed method helps identify artifacts in deepfake images that do not seem real. To assess the robustness of our proposed model, we used the open deepfake detection challenge dataset (DFDC) and Face Forensics deepfake collections (FF++.) We achieved 96.23% accuracy when using the FF++ digital record. In contrast, we attained 78.33% accuracy using the aggregated records from FF++ and DFDC. We performed extensive experiments and believe that our proposed method provides more significant results than existing techniques.
Online teaching intention among distance education course tutors: Modelling the effects of human resource factors and moderating role of gender

Authors:
raw_author_name: Moses Segbenya
raw_affiliation_string: Department of Business Studies, College of Distance Education, University of Cape Coast, Ghana
raw_author_name: Vincent Mensah Minadzi
raw_affiliation_string: Department of Education, College of Distance Education, University of Cape Coast, Ghana
raw_author_name: Brandford Bervell
raw_affiliation_string: Centre for Instructional Technology and Multimedia, Universiti Sains Malaysia, Penang, Malaysia; Department of Maths, Science and ICT, College of Distance Education, University of Cape Coast, Ghana
raw_author_name: Beatrice Asante Somuah
raw_affiliation_string: Department of Education, College of Distance Education, University of Cape Coast, Ghana

Abstract: This study examined the effect of human resource factors and the moderating role of gender on online teaching intention among course tutors in distance education. A quantitative approach and survey design were adopted for the study. Data were gathered with a self-administered questionnaire from 539-course tutors (teachers on the distance mode) in a university of distance education programmes. Data were analysed with the Partial Least Squares-Structural Equation Modelling (PLS-SEM) technique. The study found that remuneration and work relations were the two most important human resource factors that have significant predictive relationships with online use intention. Regarding the moderating role of gender, the results revealed that remuneration was relevant to both male and female facilitators. In addition, while work relations were found to be very important to male facilitators, retrenchment was considered to be important among female facilitators. It was therefore recommended that managers of distance education institutions pay attention to gender-biased human resource factors such as remuneration, work relations, and retrenchment as they embark on the use of online learning technologies for distance education delivery.
Breast Cancer Classification from Histopathological Images using Future Search Optimization Algorithm and Deep Learning

Authors:
raw_author_name: Ramalingam Gurumoorthy
raw_affiliation_string: Department of Computer and Information Science, Annamalai University, India
raw_author_name: Mari Kamarasan
raw_affiliation_string: Department of Computer and Information Science, Annamalai University, India

Abstract: In medical imaging, precise recognition of Breast Cancer (BC) is a challenge due to the complications of breast tissues. Histopathological detection is still considered the standard in BC detection. Still, the dramatic increase in workload and the complexity of histopathological image (HPI) make this task labor-intensive and dependent on the pathologist, making the advance of automated and precise HPI analysis techniques needed. Due to the automated feature extraction capability, Deep Learning (DL) methods have been effectively used in different sectors, particularly in the medical imaging sector. This study develops the future search algorithm with a DL-based breast cancer detection and classification (FSADL-BCDC) method. The FSADL-BCDC technique examines HPIs to detect and classify BC. To achieve this, the FSADL-BCDC technique implements Wiener Filtering (WF)-based preprocessing to eliminate the noise in the images. Additionally, the FSADL-BCDC uses the ResNeXt method for feature extraction with a Future Search Algorithm (FSA)-based tuning procedure. For BCDC, the FSADL-BCDC technique employs a Hybrid Convolutional Neural Network along with the Long Short-Term Memory (HCNN-LSTM) approach. Finally, the Sunflower Optimization (SFO) approach adjusts the hyperparameter values of the HCNN-LSTM. The outcomes of the FSADL-BCDC are inspected on a standard medical image dataset. Extensive relational studies highlighted the improved performance of the FSADL-BCDC approach in comparison with known methods by exhibiting an output of 96.94% and 98.69% under diverse datasets.
Governance, debt service, information technology and access to electricity in Africa

Authors:
raw_author_name: Simplice A. Asongu
raw_affiliation_string: Oxford Brookes Business School, Oxford Brookes University, Oxford, UK
raw_author_name: Sara le Roux
raw_affiliation_string: Oxford Brookes Business School, Oxford Brookes University, Oxford, UK

Abstract: Abstract The study investigates the role of governance (i.e., ‘voice and accountability,’ political stability/no violence, regulatory quality, government effectiveness, corruption‐control and the rule of law) in the incidence of short‐term debt services on infrastructure development in the perspective of telecommunication infrastructure and access to electricity. The focus of the study is on 52 African countries for the period 2002–2021. The generalised method of moments is employed as estimation strategy and the following findings are established. Debt service has a negative unconditional effect on access to electricity and telecommunication infrastructure. Governance dynamics moderate the negative effect of debt service on infrastructure dynamics. Effective moderation is from regulatory quality and corruption‐control for access to electricity and from government effectiveness, regulatory quality, corruption‐control and rule of law, for telecommunication infrastructure. Policy implications are discussed.
ADAM: a robotic companion for enhanced quality of life in aging populations

Authors:
raw_author_name: Alicia Mora
raw_affiliation_string: RoboticsLab, Systems Engineering and Automation Department, Universidad Carlos III, Madrid, Spain
raw_author_name: Adrian Prados
raw_affiliation_string: RoboticsLab, Systems Engineering and Automation Department, Universidad Carlos III, Madrid, Spain
raw_author_name: Alberto Mendez
raw_affiliation_string: RoboticsLab, Systems Engineering and Automation Department, Universidad Carlos III, Madrid, Spain
raw_author_name: Victor Muñoz
raw_affiliation_string: RoboticsLab, Systems Engineering and Automation Department, Universidad Carlos III, Madrid, Spain
raw_author_name: Luis Moreno
raw_affiliation_string: RoboticsLab, Systems Engineering and Automation Department, Universidad Carlos III, Madrid, Spain
raw_author_name: Santiago Garrido
raw_affiliation_string: RoboticsLab, Systems Engineering and Automation Department, Universidad Carlos III, Madrid, Spain
raw_author_name: Ramon Barber
raw_affiliation_string: RoboticsLab, Systems Engineering and Automation Department, Universidad Carlos III, Madrid, Spain

Abstract: One of the major problems of today's society is the rapid aging of its population. Life expectancy is increasing, but the quality of life is not. Faced with the growing number of people who require cognitive or physical assistance, new technological tools are emerging to help them. In this article, we present the ADAM robot, a new robot designed for domestic physical assistance. It mainly consists of a mobile base, two arms with grippers and vision systems. All this allows the performance of physical tasks that require navigation and manipulation of the environment. Among ADAM's features are its modularity, its adaptability to indoor environments and its versatility to function as an experimental platform and for service applications. In addition, it is designed to work respecting the user's personal space and is collaborative, so it can learn from experiences taught by them. We present the design of the robot as well as examples of use in domestic environments both alone and in collaboration with other domestic platforms, demonstrating its potential.
A stochastic variance reduced gradient method with adaptive step for stochastic optimization

Authors:
raw_author_name: Dan Xue
raw_affiliation_string: School of Mathematics and Statistics Qingdao University Qingdao China
raw_author_name: Lei Liu
raw_affiliation_string: School of Mathematics and Statistics Qingdao University Qingdao China

Abstract: Abstract In this paper, we propose a stochastic variance reduction gradient method with adaptive step size, referred to as the SVRG‐New BB method, to solve the convex stochastic optimization problem. The method could be roughly viewed as a hybrid of the SVRG algorithm and a new BB step mechanism. Under the condition that the objective function is strongly convex, we provide the linear convergence proof of this algorithm. Numerical experiment results show that the performance of the SVRG‐New BB algorithm can surpass other existing algorithms if parameters in the algorithm are properly chosen.