Reviewerly/exHarmony huggingface dataset:
Artificial intelligence and quantum cryptography

Authors:
raw_author_name: Petar Radanliev
raw_affiliation_string: Department of Computer Sciences, University of Oxford, Oxford, UK

Abstract: Abstract The technological advancements made in recent times, particularly in artificial intelligence (AI) and quantum computing, have brought about significant changes in technology. These advancements have profoundly impacted quantum cryptography, a field where AI methodologies hold tremendous potential to enhance the efficiency and robustness of cryptographic systems. However, the emergence of quantum computers has created a new challenge for existing security algorithms, commonly called the ‘quantum threat’. Despite these challenges, there are promising avenues for integrating neural network-based AI in cryptography, which has significant implications for future digital security paradigms. This summary highlights the key themes in the intersection of AI and quantum cryptography, including the potential benefits of AI-driven cryptography, the challenges that need to be addressed, and the prospects of this interdisciplinary research area.
Quantum multiparameter estimation enhanced by a topological phase transition

Authors:
raw_author_name: Yu Yang
raw_affiliation_string: Ministry of Education Key Laboratory for Nonequilibrium Synthesis and Modulation of Condensed Matter, Shaanxi Province Key Laboratory of Quantum Information and Quantum Optoelectronic Devices, School of Physics, Xi'an Jiaotong University, Xi'an 710049, China
raw_author_name: Haidong Yuan
raw_affiliation_string: Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Shatin, Hong Kong SAR, China
raw_author_name: Fuli Li
raw_affiliation_string: Ministry of Education Key Laboratory for Nonequilibrium Synthesis and Modulation of Condensed Matter, Shaanxi Province Key Laboratory of Quantum Information and Quantum Optoelectronic Devices, School of Physics, Xi'an Jiaotong University, Xi'an 710049, China

Abstract: In quantum multiparameter estimation, multiple to-be-estimated parameters are encoded in a quantum dynamics system by a unitary evolution. As the parameters vary, the system may undergo a topological phase transition (TPT). In this paper, we investigate two SU(2) TPT models and propose the singular behavior of the quantum metric tensor around the TPT point as a tool for the simultaneous optimal estimation of multiple parameters. We find that the proposed TPT sensing protocol can achieve the same metrology performance as the quantum-control-enhanced one. Moreover, the probe state of the TPT sensing protocol is only the ground state of the Hamiltonian rather than the entangled state required in the control-enhanced one. In addition, an adaptive multiparameter estimation strategy is developed for updating the estimated values until the desired quantum Cram\'er-Rao bound is approached. Our work reinforces the connection between quantum multiparameter estimation and topology physics, with potential inspiration for quantum critical metrology.
Quantum-parallel vectorized data encodings and computations on trapped-ion and transmon QPUs

Authors:
raw_author_name: Jan Balewski
raw_affiliation_string: National Energy Research Scientific Computing Center, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA
raw_author_name: Mercy G. Amankwah
raw_affiliation_string: National Energy Research Scientific Computing Center, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA
raw_author_name: Roel Van Beeumen
raw_affiliation_string: Applied Mathematics and Computational Research Division, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA
raw_author_name: E. Wes Bethel
raw_affiliation_string: Computer Science Department, San Francisco State University, San Francisco, CA, 94132, USA
raw_author_name: Talita Perciano
raw_affiliation_string: Scientific Data Division, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA
raw_author_name: Daan Camps
raw_affiliation_string: National Energy Research Scientific Computing Center, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA

Abstract: Compact data representations in quantum systems are crucial for the development of quantum algorithms for data analysis. In this study, we present two innovative data encoding techniques, known as QCrank and QBArt, which exhibit significant quantum parallelism via uniformly controlled rotation gates. The QCrank method encodes a series of real-valued data as rotations on data qubits, resulting in increased storage capacity. On the other hand, QBArt directly incorporates a binary representation of the data within the computational basis, requiring fewer quantum measurements and enabling well-established arithmetic operations on binary data. We showcase various applications of the proposed encoding methods for various data types. Notably, we demonstrate quantum algorithms for tasks such as DNA pattern matching, Hamming weight computation, complex value conjugation, and the retrieval of a binary image with 384 pixels, all executed on the Quantinuum trapped-ion QPU. Furthermore, we employ several cloud-accessible QPUs, including those from IBMQ and IonQ, to conduct supplementary benchmarking experiments.
Motion magnification-inspired feature manipulation for deepfake detection

Authors:
raw_author_name: Hamdi DİBEKLİOĞLU

Abstract: Recent advances in deep learning, increased availability of large-scale datasets, and improvement of accelerated graphics processing units facilitated creation of an unprecedented amount of synthetically generated media content with impressive visual quality. Although such technology is used predominantly for entertainment, there is widespread practice of using deepfake technology for malevolent ends. This potential for malicious use necessitates the creation of detection methods capable of reliably distinguishing manipulated video content. In this work we aim to create a learning-based detection method for synthetically generated videos. To this end, we attempt to detect spatiotemporal inconsistencies by leveraging a learning-based magnification-inspired feature manipulation unit. Although there is existing literature on the use of motion magnification as a preprocessing step for deepfake detection, in our work, we aim to utilize learning-based magnification elements to develop an end-to-end deepfake detection model. In this research, we investigate different variations of feature manipulation networks, both with spatially constant and spatially varying amplification. To clarify, although the proposed model draws from existing literature on motion magnification, we do not perform motion magnification in our experiments but instead use the underlying architecture of such networks for feature enhancement. Our objective with this work is to take a step towards applying learnable motion manipulation in improving the target accuracy of a task at hand.
Antecedents of Employees Retention in Labor Market in Saudi Arabia

Authors:
raw_author_name: Abdelmohsen Nassani
raw_affiliation_string: King Saud University - College of Business

Abstract: Employee retention is a crucial factor that drives the attention of organizations. The objective of this study is to explore the factors that affect employee retention in the labor market in Saudi Arabia. It focuses specifically on person-organization fit, work-life balance, and job satisfaction as antecedents that affect employee retention. The study used an online survey to collect responses to examine the antecedents. The questionnaire was statically analyzed using SPSS and Amos. The total number of respondents that participated in the study is 250 from both genders and variations in experience and age. The key findings of the study show that person-organization fit and Work-life balance have emerged as major antecedents which impact job satisfaction and retention. It discusses the effect of job satisfaction as a mediator between Person-Organization fit and retention. In addition, it discusses the effect of job satisfaction as a mediator between work-life balance and retention.
https://stratfordjournals.org/journals/index.php/journal-of-accounting/article/view/2088

Authors:
raw_author_name: Kennedy Okiro
raw_affiliation_string: University of Nairobi

Abstract: This study aligns with Sustainable Development Goal 8, which borders on “economic growth by ensuring external debt is invested well in development projects that can spur economic growth”. It contributes to the economic growth literature by evaluating the role of external debt and corruption on economic growth. The Sub-Saharan African region has witnessed an increase in external debt. However, the economic growth has been reducing. The region witnessed a contraction in economic growth to 2.7 percent in 2022 down from 4.4 percent of 2021. The literature indicates that corruption can impede external debt from achieving the intended purposes. Many studies have revealed the role of external debt in economic growth but have not studied the moderating effect of corruption thus creating a gap for this study. To achieve the objective, the study uses the Fixed Effects Panel data approach on unbalanced panel data on 46 sub-Saharan African countries from 1998 to 2021 to interrogate the nexus. The results revealed a positive relationship between external debt and economic growth. However, corruption was found to reduce the effectiveness of external debt in realizing economic growth. The study therefore recommends that governments of the countries in the region need to address corruption so to achieve the desired economic growth. They should implement measures focusing on improving institutions quality to control financial corruption. In addition, the policy makers should relook at increasing anti-corruption initiatives aimed at reducing poor governance for instance financing anti-corruption agencies to ensure corruption is reduced. Key Words: Economic growth, External Debt, Corruption&nbsp
SegEIR-Net: A Robust Histopathology Image Analysis Framework for Accurate Breast Cancer Classification

Authors:
raw_author_name: Pritpal Singh
raw_affiliation_string: Department of Computer Science &amp; Engineering, Chandigarh University, Punjab 140413, India
raw_author_name: Rakesh Kumar
raw_affiliation_string: Department of Computer Science &amp; Engineering, Chandigarh University, Punjab 140413, India
raw_author_name: Meenu Gupta
raw_affiliation_string: Department of Computer Science &amp; Engineering, Chandigarh University, Punjab 140413, India
raw_author_name: Fadi Al-Turjman
raw_affiliation_string: Artificial Intelligence Engineering Dept., AI and Robotics Institute, Near East University, Nicosia, Mersin 10, Turkey

Abstract: Breast Cancer (BC) is a significant threat affecting women globally. An accurate and reliable disease classification method is required to get an early diagnosis. However, existing approaches lack accurate and robust classification.This study aims to design a model to classify BC Histopathology images accurately by leveraging segmentation techniques.This work proposes a combined segmentation and classification approach for classifying BC using histopathology images to address these issues. Chan-Vese algorithm is used for segmentation to accurately delineate regions of interest within the histopathology images, followed by the proposed SegEIR-Net (Segmentation using EfficientNet, InceptionNet, and ResNet) for classification. Bilateral Filtering is also employed for noise reduction. The proposed model uses three significant networks, ResNet, InceptionNet, and EfficientNet, concatenates the outputs from each block followed by Dense and Dropout layers. The model is trained on the breakHis dataset for four different magnifications and tested on BACH (BreAst Cancer Histology) and UCSB (University of California, Santa Barbara) datasets.SegEIR-Net performs better than the existing State-of-the-Art (SOTA) methods in terms of accuracy on all three datasets, proving the robustness of the proposed model. The accuracy achieved on breakHis dataset are 98.66%, 98.39%, 97.52%, 95.22% on different magnifications, and 93.33% and 96.55% on BACH and UCSB datasets.These performance results indicate the robustness of the proposed SegEIR-Net framework in accurately classifying BC from histopathology images.
Masked Generative Light Field Prompting for Pixel-Level Structure Segmentations

Authors:
raw_author_name: Mianzhao Wang
raw_author_name: Fan Shi
raw_affiliation_string: Tianjin University of Technology, CHINA
raw_author_name: Xu Cheng
raw_author_name: Shengyong Chen

Abstract: Pixel-level structure segmentations have attracted considerable attention, playing a crucial role in autonomous driving within the metaverse and enhancing comprehension in light field-based machine vision. However, current light field modeling methods fail to integrate appearance and geometric structural information into a coherent semantic space, thereby limiting the capability of light field transmission for visual knowledge. In this paper, we propose a general light field modeling method for pixel-level structure segmentation, comprising a generative light field prompting encoder (LF-GPE) and a prompt-based masked light field pretraining (LF-PMP) network. Our LF-GPE, serving as a light field backbone, can extract both appearance and geometric structural cues simultaneously. It aligns these features into a unified visual space, facilitating semantic interaction. Meanwhile, our LF-PMP, during the pretraining phase, integrates a mixed light field and a multi-view light field reconstruction. It prioritizes considering the geometric structural properties of the light field, enabling the light field backbone to accumulate a wealth of prior knowledge. We evaluate our pretrained LF-GPE on two downstream tasks: light field salient object detection and semantic segmentation. Experimental results demonstrate that LF-GPE can effectively learn high-quality light field features and achieve highly competitive performance in pixel-level segmentation tasks.
An Overview of Building a Global Data Area on the Web for Farming

Authors:
raw_author_name: R. Sapna
raw_affiliation_string: Department of Information Technology, Manipal Institute of Technology, Bangalore, Manipal
Academy for Higher Education, Manipal, India

Abstract: A collection of best practices for publishing as well as connecting structured data on the Web is referred to as “Linked Data.” In the past ten years, the practice of Linked Open Data has significantly increased the structured data available on the Web. This structured data provides machine-readable descriptions of real-world items, opening hitherto unheard-of opportunities for research around natural language processing. With the aid of technology, the agriculture sector advanced significantly, becoming more data-driven and intelligent. Studies on how these data can be used, for what kinds of tasks, and to what degree they can be helpful for agricultural tasks, however, are lacking. Our paper puts an effort into providing an overview of concepts and technologies in the Semantic Web and their applicability to farming.
Understanding Menstruation Material in Fiqh Learning for VII Grade Female Students at SMP Syubbanul Wathon 2 Bandongan Magelang

Authors:
raw_author_name: Dwi Fitria Nur Azizah
raw_affiliation_string: Sekolah Tinggi Agama Islam Syubbanul Wathon Magelang, Indonesia
raw_author_name: Siswanto Siswanto
raw_affiliation_string: Sekolah Tinggi Agama Islam Syubbanul Wathon Magelang, Indonesia

Abstract: This research was conducted to determine the factors that influence female students' understanding of menstruation material, how they learn menstrual material, and the extent to which class VII female students understand menstruation material at SMP Syubbanul Wathon 2 Bandongan. This research was conducted at Syubbanul Wathon 2 Bandongan Middle School with a focus on female students' understanding of menstruation material. This research was conducted using qualitative research methods with a descriptive approach and various data analysis, collection and validity techniques to support optimal results. In this research, learning about menstruation material for class VII female students uses several learning stages: learning planning, menstrual learning material, learning methods, learning media, learning evaluation and assessment. The level of understanding of class VII students' material on menstruation at Syubbanul Wathon 2 Middle School is quite good. This can be seen from their interviews and written test results, although some students don't understand it and some of the material is difficult for them.&#x0D; Keywords: Understanding Menstruation Material, Fiqh, Syubbanul Wathon
Evaluation Method of Higher Education Teaching Reform Based on Deep Learning Analysis Technology

Authors:
raw_author_name: Taolin Zhang
raw_affiliation_string: University of Sanya, China
raw_author_name: Shuwen Jia
raw_affiliation_string: University of Sanya, China
raw_author_name: Charoula Angeli
raw_affiliation_string: University of Cyprus, Cyprus

Abstract: Considering the shortcomings of large evaluation errors, long time, human, and material resources in the evaluation process of the current college teaching mode to improve the accuracy of the evaluation of college teaching mode and reduce the cost of the evaluation, this study proposes an evaluation method for college teaching methods based on deep learning algorithms. Firstly, the research status of the evaluation of college teaching mode is analyzed, and the reasons for the poor evaluation results of the current college teaching mode are found; then the existing deep learning algorithm is improved, and the effectiveness and speed of the method are verified by comparing with other models. Then, the evaluation model of college teaching mode is established, and machine learning is performed on the evaluation data of college teaching mode; finally, the evaluation data of college teaching mode is collected, and the application example test of college teaching mode evaluation is performed.
Multi-thread two-dimensional digital image correlation for full-field displacement and rotation angle measurement

Authors:
raw_author_name: Fuqiang Zhong
raw_affiliation_string: Hunan University, College of Electrical and Information Engineering, Changsha, China
raw_author_name: Weilai Jiang
raw_affiliation_string: Hunan University, College of Electrical and Information Engineering, Changsha, China
raw_author_name: Yaonan Wang
raw_affiliation_string: Hunan University, College of Electrical and Information Engineering, Changsha, China

Abstract: The conventional two-dimensional (2D) digital image correlation (DIC) method often fails to work when there is a large rotation between the reference and deformed images. The objective of this study is to propose a 2D DIC method to tackle this issue and calculate the full-field rotation angle. In our method, the deformed subset is chosen along the direction of gradient orientation angle error (GOAE) between the pseudo point of interest and the searching point. In addition, the initial deformation parameters for the inverse compositional Gaussian–Newton (IC-GN) method are set based on the GOAE to guarantee its convergence. Furthermore, a best fit method is presented to calculate the rotation angle of each target deformed subset using the converged deformation parameters from IC-GN method. Moreover, due to the high computation intensity of the proposed method, a multi-thread parallel computation technique is used to speed up its computation. Simulation and bending experiment results show that the proposed method can measure both the full-field in-plane displacement and rotation angle with relatively high accuracy and efficiency in the case of a large rotation, in comparison with the conventional DIC method and a ring template-based method. The proposed method is also robust to the strained deformation and image noise to some degree.
Cluster segmentation algorithm for enhancing edge information

Authors:
raw_author_name: Qunpo Liu
raw_affiliation_string: Henan International Joint Laboratory of Direct Drive and Control of Intelligent Equipment (China); Henan Polytechnic University, School of Electrical Engineering and Automation, Jiaozuo, China
raw_author_name: Jingwen Zhang
raw_affiliation_string: Henan Polytechnic University, School of Electrical Engineering and Automation, Jiaozuo, China
raw_author_name: Naohiko Hanajima
raw_affiliation_string: Henan International Joint Laboratory of Direct Drive and Control of Intelligent Equipment, Jiaozuo, China; Muroran Institute of Technology (Japan)

Abstract: To solve the blurred image edge segmentation problem and the influence of a complex background on the segmentation accuracy of the clustering algorithm, this paper proposes an edge gravity mean shift (EGMS) image segmentation algorithm. Aiming at the problem of partial edge information loss when the Canny algorithm processes images, the L0 gradient minimization method is introduced to iteratively optimize the gradient map to extract more edge information, and then the L0 Canny edge detection algorithm is proposed. Aiming at the problem that the Epanechnikov mean shift algorithm will converge to the local optimum due to the non-smooth Epanechnikov kernel function, the optimal bandwidth is obtained, and the smoothness of the kernel function shape is improved by cross-validation and spline function. Then, the sleek Epanechnikov kernel function is constructed by integrating the weight function, which can reflect the richness of clustering information at the peak so that the kernel function can more accurately reflect the data characteristics of the peak. Based on the sleek Epanechnikov kernel function, the sleek Epanechnikov mean shift (SEMS) clustering algorithm is proposed. The EGMS image segmentation algorithm is constructed by fusing the L0 Canny and SEMS. The experimental results indicate that EGMS can retain more edge contour information when segmenting images, and it can generate better clustering clusters for clustered datasets. In addition, compared to other clustering segmentation algorithms, the proposed EGMS algorithm demonstrates superior clustering accuracy and convergence on the majority of image datasets. Our code is available at: https://gitee.com/zhiweilu111/edge-gravity-mean-shift.
Chapter 6: Audiovisual sample 6.1 - 6.2

Authors:
raw_author_name: Pei-Ling Huang

Abstract: &lt;p&gt;Audiovisual Sample 6.1: Excerpt from Sur Mazūrī, performed at the shrine of Shah Abdul Latif Bhittai in Bhit Shah, Sindh, Pakistan. Recorded by the author on January 2, 2015.&lt;/p&gt; &lt;p&gt;&lt;br&gt;&lt;/p&gt; &lt;p&gt;Audiovisual Sample 6.2: Tand and jhaṛ in Sur Husainī. Recorded by the author on January 12, 2015.&lt;/p&gt;
Methodologies of Construction of Circular Models

Authors:
raw_author_name: S.V.S. Girija
raw_affiliation_string: Department of Mathematics, Hindu College, Guntur, India

Abstract: This paper is focused on various methodologies of construction of circular models contributed by researchers in directional data analysis for modeling angular/periodic data. Several methods of construction are discussed in detailed here. These methods can be adopted for modeling angular/circular/periodic data which may be occurred in many practical situations in various fields.