Reviewerly/exHarmony huggingface dataset:
Nonperturbative Zou-Wang-Mandel effect

Authors:
raw_author_name: T. J. Volkoff
raw_affiliation_string: Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, USA

Abstract: The Zou-Wang-Mandel (ZWM) effect is a remarkable consequence of photon indistinguishability and continuous-variable entanglement in which an optical phase shift is imprinted on photonic modes associated with optical paths that do not pass through the phase-shift source. By bringing the canonical formalism of continuous-variable Gaussian states to bear on the mode structure of the ZWM experiment, we show that the physical consequence of implementing optical path identity is a renormalization of quadrature squeezing which governs the entanglement of four effective optical modes. Nonperturbative expressions for the ZWM interference patterns and normalized first-order coherence function are derived. Generalizations to $\mathcal{H}$-graph states with more than four modes directly follow from the general method used to analyze the minimal example. We show that a ZWM interferometer with a laser-seeded signal mode, which estimates an idler phase shift by detecting photons that did not propagate through the phase shift, exhibits an optimal sensitivity comparable to that of a laser-seeded SU(1,1) interferometer if the path identity is implemented with high fidelity.
Balancing Innovation and Regulation: A Comprehensive Analysis and Neural Network Approach to AI Copyright Challenges

Authors:
raw_author_name: Hongyi Ling
raw_affiliation_string: Nanjing Forestry University
raw_author_name: Lei Cheng
raw_affiliation_string: Jilin University
raw_author_name: Zifeng Geng
raw_affiliation_string: Shanghai University
raw_author_name: Haotian Shi
raw_affiliation_string: Jilin University
raw_author_name: Weichen Wang
raw_affiliation_string: Huazhong University of Science and Technology

Abstract: This article explores emerging issues surrounding artificial intelligence (AI) and copyright through a two-pronged approach. First, it provides an extensive literature review analyzing government and industry strategies for addressing AI copyright concerns and evaluates their rationality. Second, it details experiments conducted using neural networks to examine relevant information and investigate image copyright challenges, assessing mainstream large language models’ efficacy in handling copyright matters.&#x0D; The literature review explores AI copyright perspectives of the United Kingdom, China, the European Union, and the United States. It finds that countries emphasize balanced regulation and innovation (UK), ethical content creation (China), regulating high-risk applications (EU), or principles like non-discrimination and privacy (US). However, comprehensive governance frameworks are needed to navigate AI’s ethical, social, and legal intricacies.&#x0D; The experimental portion trains a convolutional neural network on a dataset of 41 infringing and non-infringing image sets to identify copyright infringement. While achieving over 80% accuracy, enhancements through expanded training data, segmentation, and multi-domain detection could improve generalization. The paper concludes with an analysis advocating copyright adaptation for AI creations, measured protections for standalone AI works, and constructive policies from interdisciplinary dialogue.
Multidisciplinary Authoring – A Critical Foundation for Augmented Reality Systems in Training and Education

Authors:
raw_author_name: Emi Aoki
raw_affiliation_string: University of Massachusetts, Lowell
raw_author_name: Bach Tran
raw_affiliation_string: University of Akron
raw_author_name: Susan Tripathy
raw_affiliation_string: University of Massachusetts, Lowell
raw_author_name: Charles Thompson,
raw_affiliation_string: University of Massachusetts, Lowell
raw_author_name: Shivakumar Sastry
raw_affiliation_string: University of Akron
raw_author_name: Kavitha Chandra
raw_affiliation_string: University of Massachusetts, Lowell

Abstract: including AI, AR, and IoT Systems.His most recent work involves designing an AR framework that provides just-in-time informatics for training, education and maintenance activities.
Questioning the policy of improving the quality of students through the research program and publication of scientific work at the postgraduate UIN Prof. Kh Saifuddin Zuhri Purwokerto

Authors:
raw_author_name: None Umi Khomsiyatun
raw_affiliation_string: State Islamic University Prof. K.H. Saifuddin Zuhri Purwokerto

Abstract: Universities have an important role in training students in orientation and assimilation of knowledge through research and publication of scientific papers, especially in the era of society 5.0. Understanding and knowledge seem to be the only things that guarantee the social and economic progress of society that is not eroded over time. The Policy for Improving the Quality of Students through the Research Program and Publication of Postgraduate Student Scientific Work is an important initiative in advancing higher education and strengthening the role of students in their contribution to knowledge and scientific development. This research aims to describe how this program has contributed to improving the quality of postgraduate students, as well as its impact on the development of research and publication of students' scientific work.This research method uses a descriptive qualitative approach with literature study research as direct data. Data collection techniques by collecting published scientific papers from postgraduate students. To optimize the results of data collection, an article search was carried out from Google Scholar using the search terms names of postgraduate students who were used as research objects. Qualitative data analysis consists of three series: data reduction, data presentation and conclusion drawing. The research results show that: First, the condition of the quality of research activities carried out by Postgraduate students at UIN Prof. K.H. Saifudin Zuhri Purwokerto is doing well. Second, Policy and Implementation of Increasing the Quality of Students Based on Research and Scientific Writing at the UIN Postgraduate Program, Prof. K.H. Saifuddin Zuhri Purwokerto is stated in Circular Letter Number: 354/Un.19/D.PPs/PP.009/4/2022 concerning Publication of Scientific Work for Postgraduate Students at the State Islamic University of Professor Kiai Haji Saifuddin Zuhri Purwokerto in 2022. In its implementation, postgraduates participated supports this policy by organizing various supporting activities.
Quantifying non-stabilizerness via information scrambling

Authors:
raw_author_name: Arash Ahmadi
raw_affiliation_string: Delft University of Technology
raw_author_name: Eliska Greplova
raw_affiliation_string: Delft University of Technology

Abstract: The advent of quantum technologies brought forward much attention to the theoretical characterization of the computational resources they provide. A method to quantify quantum resources is to use a class of functions called magic monotones and stabilizer entropies, which are, however, notoriously hard and impractical to evaluate for large system sizes. In recent studies, a fundamental connection between information scrambling, the magic monotone mana and 2-Renyi stabilizer entropy was established. This connection simplified magic monotone calculation, but this class of methods still suffers from exponential scaling with respect to the number of qubits. In this work, we establish a way to sample an out-of-time-order correlator that approximates magic monotones and 2-Renyi stabilizer entropy. We numerically show the relation of these sampled correlators to different non-stabilizerness measures for both qubit and qutrit systems and provide an analytical relation to 2-Renyi stabilizer entropy. Furthermore, we put forward and simulate a protocol to measure the monotonic behaviour of magic for the time evolution of local Hamiltonians.
Time is -ending: Sublexical information activates the horizontal mental time line in word processing.

Authors:
raw_author_name: Simona Amenta
raw_affiliation_string: Department of Psychology, University of Milano-Bicocca.
raw_author_name: Marco Marelli
raw_affiliation_string: Department of Psychology, University of Milano-Bicocca.

Abstract: The mental time line (MTL) is a spatial continuum on which earlier events are generally associated with the left space and later events with the right space. Accordingly, past- and future-related words receive faster responses with, respectively, the left and the right hand. Yet, it is currently unclear whether the MTL is activated by the whole word or whether it can be triggered by more subtle sublexical cues, such as verb-endings, and whether the activation of this spatial continuum is an automatic phenomenon. The aim of this study is to test whether verb-endings do bring conceptual information that is in turn capable to activate the MTL and whether this activation holds also when the temporal information is not explicitly processed. We designed three experiments. In Experiment 1, consisting of a temporal categorization task, and in Experiment 2, consisting of a lexical decision task, we tested Italian tensed verbs (trov-avo "I found," trov-erò "I will find") and pseudo-verbs (trop-avo, trop-erò). Results of Experiment 1 showed that both tensed verbs and pseudo-verbs were spatially coded on the MTL. Results from Experiment 2 showed that the MTL is activated by the verb-endings also when temporal information was task-irrelevant (i.e., lexical decision task). Experiment 3 further clarified that the spatial-temporal congruency effect does not emerge during the evaluation of an inhomogeneous set of stimuli (i.e., when adding to the stimuli time-unrelated fillers). Overall, the present findings indicate that sublexical strings carry specific semantic information that comes into play in the generation of spatial-temporal associations. (PsycInfo Database Record (c) 2024 APA, all rights reserved).
Knowledge Collaboration Mechanism and Validity Assessment Based on Design Awards: A Case Study of Design Intelligence Award

Authors:
raw_author_name: Yixin Zhang
raw_affiliation_string: Zhejiang University of Technology, Hangzhou, China

Abstract: The essence of design award lies in the curation of outstanding design cases. Through the organizational mechanisms established by award organizers, it can serve as an exceptional platform for knowledge exchange, dissemination, and innovation. This paper introduces the concept of Design Award Knowledge Network (DAKN), taking Design Intelligence Award (DIA) for example, elucidates the collaborative mechanisms of knowledge dissemination and innovation within the DAKN, notably facilitated through the D·will Lecture and DI Project Workshops. Additionally, surveys were conducted among faculty and students participating in the series of events initiated by DIA. The findings underscore the prominent advantage of a ‘stimulative policy environment’ within this network, while revealing the need for improvements in the ‘accessibility of knowledge resources.’ This paper proposes that enhancing the vitality of DAKN rely on the diversification of knowledge, interdisciplinary approaches, and organizational collaboration. This entails the aggregation of talent from various universities, institutions, and enterprises to establish collaborative innovation teams. Through collaborative governance, the integration of diverse foundational capabilities, data information, and tool skills from various stakeholders should be orchestrated to achieve effectiveness in knowledge exchange.
Requirement Analysis of Smart Kitchen Dietary and Social Service System Based on Older Adults Mental Models

Authors:
raw_author_name: Fangyu Li
raw_affiliation_string: School of Design, Southwest Jiaotong University
raw_author_name: Lin Li
raw_affiliation_string: School of Design, Southwest Jiaotong University

Abstract: Using kitchen life as an entry point and the theory of mental modelling, we carried out a study on the needs analysis of a smart kitchen diet and social service system, aiming to meet the new needs of the elderly in terms of health and social interaction. Through questionnaires and user interviews, we collected and analyzed the mental data of the elderly and created a mental model of “diet and social interaction” as well as user requirements. The Analytic Hierarchy Process was used to identify the importance of dietary and social needs, covering the four demand directions of diet and nutrition, health management, social interaction, and health awareness. This study highlights the potential of user-centred research methodology in the health and social care of older people and provides useful guidance and insights for the design of future smart kitchens and related products.
“Ask Aloud Protocol”: Toward a Participant-Driven Question-Asking Design Research Method

Authors:
raw_author_name: Lintao Tang
raw_affiliation_string: Academy of Arts & Design, Tsinghua University, Beijing, China
raw_author_name: Sihang Wang
raw_affiliation_string: Academy of Arts & Design, Tsinghua University, Beijing, China

Abstract: In the field of problem formulation for design research, the conventional Think Aloud Protocol (TAP) has faced challenges in addressing complex problems such as Wicked Problems (WPs) over its two-decade history. Instead, the utilization of questions as a verbal tool has emerged as a more effective approach, producing insightful outcomes for intricate design challenges. Moreover, the largely untapped potential of participant-generated questions in design research holds promise for reducing subjective biases in designer-driven problem formulations and strengthening the role of participants. This study presents the Participant-Driven Ask Aloud Protocol (AAP) and employs it in spatial planning experiments at both QILI South Urban Village and the China World Mall’s Transit-Oriented Development (TOD) in Beijing, China. The analysis highlights AAP’s effectiveness in broadening the scope of generative design possibilities and facilitating a more precise and efficient identification of WPs in the design process.
Fast grid search: A grid search-inspired algorithm for optimizing hyperparameters of support vector regression

Authors:
raw_author_name: Mustafa AÇIKKAR

Abstract: This study presents a fast hyperparameter optimization algorithm based on the benefits and shortcomings of the standard grid search (GS) algorithm for support vector regression (SVR). This presented GS-inspired algorithm, called fast grid search (FGS), was tested on benchmark datasets, and the impact of FGS on prediction accuracy was primarily compared with the GS algorithm on which it is based. To validate the efficacy of the proposed algorithm and conduct a comprehensive comparison, two additional hyperparameter optimization techniques, namely particle swarm optimization and Bayesian optimization, were also employed in the development of models on the given datasets. The evaluation of the models' predictive performance was conducted by assessing root mean square error, mean absolute error, and mean absolute percentage error. In addition to these metrics, the number of evaluated submodels and the time required for optimization were used as determinative performance measures of the presented models. Experimental results proved that the FGS-optimized SVR models yield precise performance, supporting the reliability, validity, and applicability of the FGS algorithm. As a result, the FGS algorithm can be offered as a faster alternative in optimizing the hyperparameters of SVR in terms of execution time.
Bitcoin_ML: An Efficient Framework for Bitcoin Price Prediction Using Machine Learning

Authors:
raw_author_name: Maged Farouk
raw_affiliation_string: Department of Business Information Systems, Faculty of Business, Alamein International University, Alamein, Egypt
raw_author_name: Nashwa Shaker
raw_affiliation_string: Department of Business Information Systems, Faculty of Business, Alamein International University, Alamein, Egypt
raw_author_name: Diaa AbdElminaam
raw_affiliation_string: Department of Data Science , Faculty of Computer Science , Misr International University , Cairo , Egypt
raw_author_name: Malak Mesbah
raw_affiliation_string: Department of Business Information Systems, Faculty of Business, Alamein International University, Alamein, Egypt
raw_author_name: Mariam Ahmed
raw_affiliation_string: Department of Business Information Systems, Faculty of Business, Alamein International University, Alamein, Egypt
raw_author_name: Nouran Ahmed
raw_affiliation_string: Department of Business Information Systems, Faculty of Business, Alamein International University, Alamein, Egypt

Abstract: Econometrics can be used to understand and forecast price movements, assess market efficiency, and explore the factors influencing Bitcoin's value and adaptation. Econometrics is related to bitcoin in seven categories: price analysis and prediction, market efficiency, determination of Bitcoin prices, risk analysis, adaptation and network effects, causality tests, and simulation and stress. Testing these analyses can be invaluable for policymakers, investors, and financial institutions interested in the economics of digital currencies. Bitcoin price prediction in machine learning has many challenges that have deep roots in 2 main properties: cryptocurrencies and complexities in the Machine Learning models. Many problems are associated with machine learning for bitcoin price prediction, such as overfitting, data quality and availability, latent variables, model interpretability, computational complexity, dynamic adaptation, market manipulation, anomalies, data snooping bias risk, and time horizon mismatch. In the paper, we proposed an efficient framework for the prediction of bitcoin using nine different machine learning algorithms (linear Regression, random forest, adaboost, tree, KNN, gradient boosting, constant, neural network, SVM) on five different datasets. The results revealed that linear Regression emerged as the optimal model for the first data set. In the second data set, the random forest model demonstrated superior performance. The third data set exhibited the highest efficacy when the Adaboost model was employed. The fourth data set yielded the best outcomes with the random forest model, while linear Regression was the most effective choice for the final data set.
Octonion-based transform moments for innovative stereo image classification with deep learning

Authors:
raw_author_name: Mohamed Amine Tahiri
raw_affiliation_string: Laboratory of Electronic Signals and Systems of Information, Sidi Mohamed Ben Abdellah University, Fez, Morocco
raw_author_name: Hicham Karmouni
raw_affiliation_string: National School of Applied Sciences, Cadi Ayyad University, Marrakech, Morocco
raw_author_name: Hicham Oufettoul
raw_affiliation_string: Green Energy Park Research Platform (GEP, IRESEN/UM6P), Ben Guerir, Morocco
raw_author_name: Hicham Amakdouf
raw_affiliation_string: Institute of Sports Sciences, Sidi Mohamed Ben Abdellah University, Fez, Morocco
raw_author_name: Hassan Qjidaa
raw_affiliation_string: Laboratory of Electronic Signals and Systems of Information, Sidi Mohamed Ben Abdellah University, Fez, Morocco
raw_author_name: Mohamed Abouhawwash
raw_affiliation_string: Department of Computational Mathematics, Science and Engineering, College of Engineering, Michigan State University, East Lansing, MI, 48824, USA
raw_author_name: S. S. Askar
raw_affiliation_string: Department of Statistics and Operations Research, College of Science, King Saud University, P.O. Box 2455, 11451, Riyadh, Saudi Arabia
raw_author_name: Mhamed Sayyouri
raw_affiliation_string: Engineering, Systems and Applications Laboratory, National School of Applied Sciences, Sidi Mohamed Ben Abdellah University, Fez, Morocco

Abstract: Abstract Recent advances in imaging technologies have led to a significant increase in the adoption of stereoscopic images. However, despite this proliferation, in-depth research into the complex analysis of the visual content of these stereoscopic images is still relatively rare. The advent of stereoscopic imaging has brought a new dimension to visual content. These images offer a higher level of visual detail, making them increasingly common in a variety of fields, including medicine and industrial applications. However, exploiting the full potential of stereoscopic images requires a deeper understanding. By exploiting the capabilities of octonion moments and the power of artificial intelligence, we aim to break new ground by introducing a novel method for classifying stereoscopic images. The proposed method is divided into two key stages: The first stage involves data preprocessing, during which we strive to construct a balanced database divided into three distinct categories. In addition, we extract the stable Octonion Krawtchouk moments (SOKM) for each image, leading to a database of moment images with dimensions of 128 × 128 × 1. In the second step, we train a convolutional neural network (CNN) model using this database, with the aim of discriminating between different categories. Standard measures such as precision, accuracy, recall, F1 score, and ROC curves are used to assess the effectiveness of our method. These measures provide a quantitative assessment of the performance of our object classification approach for stereoscopic images.
Associated Factors with Premenstrual Dysphoric Disorder among Undergraduate Medical Students of a Teaching Hospital

Authors:
raw_author_name: Heera Tuladhar
raw_affiliation_string: KIST Medical College and Teaching Hospital, Imadol , Lalitpur, Nepal

Abstract: Introduction: Premenstrual Dysphoric Disorder (PMDD) is a severe form of premenstrual syndrome in women which adversely affects social, academic, or work performance. The mechanisms underlying PMDD are still insufficiently known, the findings indicate a multifactorial genesis. Prevalence data of PMDD and its potential associated risk factors is sparse in Nepal. This study aimed to determine the associated factors with PMDD among undergraduate medical students of a teaching hospital in Nepal. Methods: This was a cross sectional study conducted among unmarried female medical students at KIST medical college and teaching hospital from 1st June to 30th July, 2023. Students were assigned from each of the four MBBS years (First, Second, Third, and Final) through random sampling till the desired sample size was achieved. Questionnaires about socio-demographic, menstrual, lifestyle variables and questionnaire for diagnosis of PMDD were administered. PMDD was diagnosed from the Diagnostic and Statistical Manual – fifth edition (DSM-5) of the American Psychiatric Association (APA). Data Analysis was performed using SPSS version 16. Data calculated as frequencies, mean, standard deviation and chi square test, independent t test with p-value of &lt; 0.05 was considered as statistically significant. Results: The prevalence of PMDD according to DSM-V criteria was 41 (36.28%). PMDD was significantly associated with dysmenorrhea (P=0.030) and with intake of caffeinated beverages (P=0.034). Conclusion: PMDD is prevalent among medical students in Nepal. The potential associated risk factors are dysmenorrhea and intake of caffeinated beverages.