Reviewerly/exHarmony huggingface dataset:
Implementation of Gender-Based Interactive E-Module Learning Media

This research aims to determine the validity and practicality of gender-based interactive e-modules, which are innovative media developed to increase students' interest in learning, as well as introducing learning media that is not gender biased so that it can be well received by male and female students, without distinguish between the two in the learning process, specifically in the availability of teaching materials or learning media in schools. This research method uses a development model that was developed using the Research and Development (R&amp;D) method, namely research that creates, produces or develops a product in the form of planning through to evaluating the validity of the product that has been produced. This research uses the ADDIE model, where the ADDIE model consists of 5 stages which include analysis, design, development, implementation and evaluation, but this research only reaches the development stage. The data collection techniques used were observation, interviews and distributing questionnaires. The data analysis technique used is analysis of the validity and practicality of interactive e-modules with a Likert scale measurement scale. The results of the development of this interactive e-module were validated by experts including media experts with results (98.75%), material experts (98.88%), language experts (95%), and gender experts (98.46%) with The average validation result (97.77%) is "very valid" and suitable for use. The results of the interactive e-module practicality trial obtained scores from educators (94.44%), one to one (88.71%) and small group (92.71%) with average results (91.95%) which were categorized as very practical to apply during the learning process.
News Personalization: Do Journalism Audiences Prefer Algorithms Over Editors?

&lt;p&gt;This project-paper explores what motivates news organizations to employ algorithmically driven news personalization techniques and develops a methodology to determine whether journalism audiences have a preference between story lineups determined by an editor or by an algorithm. This work also examines whether news personalization systems meet the information needs of news audiences and works to determine what algorithmic approaches could better meet these needs.&lt;/p&gt; &lt;p&gt;Through the creation of a simple online news personalization system, this project has developed a method driven by analytic measurement coupled with a survey approach to determine audience opinions on news recommendation systems. A small user study was conducted that supported the feasibility of the system as a research tool and identified possible improvements to my methodological choices.&lt;/p&gt; &lt;p&gt;The research presented as part of this project-paper found that news organizations use news personalization systems for a variety of economic and editorial reasons. This paper also explores the social impacts of news personalization techniques and posits that there is nothing inherent to the design of personalization systems that precludes supporting the democratic and social ideals of journalism.&lt;/p&gt;
Cryptanalysis and improvement of quantum secure multi-party summation using single photons

Abstract A previous study [Entropy 25.4 (2023): 590] proposed a quantum secure multi-party summation protocol wherein n participants could obtain the modulo-2 summation result using single photons and single-particle operations. Owing to the absence of an entanglement state, this protocol was more practical than other quantum summation protocols that used entanglement properties. Thus, this study investigated the claim that this proposed protocol prevented the participants’ secret inputs from being disclosed. It was found that the previously proposed protocol was vulnerable to a quantum Trojan horse attack owing to the circular qubit transmission mode. Using a security loophole, this study proposed an attack strategy that allowed an outside attacker to steal any participant’s secret input without being detected by a third party and participants. In addition, a simulation experiment was conducted to demonstrate the feasibility of the proposed attack and evaluate the number of quantum Trojan horse probes that the attacker must inject into regular qubit sequences. Finally, a solution was proposed by modifying the protocol to securely prevent quantum Trojan horse attacks.
The debiased morphological transformations of galaxies since z=3 in CANDELS

Morphological quantitative measurements and visual-like classifications are susceptible to biases arising from the expansion of the Universe. One of these biases is the effect of cosmological surface brightness dimming (CSBD): the measured surface brightness of a galaxy decays with redshift as $(1+z)^ $. This effect might lead an observer to perceive an altered morphology compared to the real one. Our goal is to investigate the impact of CSBD on morphological classifications to determine the true evolution of morphological classes over redshift for field galaxies, and to interpret these results in the context of morphological transformations and star formation quenching. We employed artificial redshifting techniques on a sample of 268 galaxies in the five CANDELS fields, spanning redshifts from $z=0.2$ to $z=3.0$. We compared the visual classifications and morphological coefficients ($G$, $M_ $, and $A_s$) obtained from the original and simulated images. Subsequently, we developed two correction methods to mitigate the effects of CSBD. Our findings reveal that CSBD, low resolution, and signal-to-noise significantly bias the visual morphological classifications beyond $z&gt;1$. Specifically, we observed an overestimation of the fractions of spheroids and irregular galaxies by up to $50&lt;!PCT!&gt;$, while the fractions of early- and late-type disks were underestimated by $10&lt;!PCT!&gt;$ and $50&lt;!PCT!&gt;$, respectively. However, we found that morphological coefficients are not significantly affected by CSBD at $z&lt;2.25$. We validated the consistency of our correction methods by applying them to the observed morphological fractions in the IllustrisTNG-50 sample and comparing them to previous studies. We propose two potential sources of confusion regarding the visual classifications due to CSBD. Firstly, galaxies may be misclassified as spheroids, as the dimming effect primarily renders the bulge component visible. Secondly, galaxies may be misidentified as irregulars due to their more diffuse and asymmetric appearance at high redshifts. By analyzing the morphological fractions of star-forming and quiescent subsamples as a function of redshift and stellar mass, we propose a scenario where late-type disks transform into quiescent spheroids through mergers or to early-type disks through secular evolution or active galactic nucleus feedback.
COMPARATIVE ANALYSIS OF MERN, MEAN AND MEVN STACKS IN WEB DEVELOPMENT

Choosing a technology stack is a critical decision that affects how software projects progress in the ever- changing field of web development. Regarding web development, a technology stack is a set of tools, frameworks, and programming languages that are utilized in the construction of an application's front end and back end. In addition to making the development process easier, these stacks provide the groundwork for the finished product's performance, scalability, and maintainability. JavaScript-based stacks are becoming serious contenders as businesses and developers search for frameworks that provide efficiency, scalability, and flexibility. Starting with a thorough examination and comparison of three popular JavaScript stacks, this research paper examines MEAN (MongoDB, Express, Angular and Node.js), MERN (MongoDB, Express, React and Node.js) and MEVN (MongoDB, Express, Vue.js and Node.js). Keywords: Web Development, MERN Stack, MEAN Stack, MEVN Stack, ReactJS, AngularJS, VueJS
VERIFYING AND ACCESSING STUDENT PROJECT APPLICATION

This abstract presents a conceptual overview of an application design to verify and accessing the project the student project using which maintain the information about the project present in the system that contains project documentation, report, code and so on. It is difficult to maintain and search all this information manually in an Organization as it takes lot of time to do. The proposed app aims to overcome the manual search to avoid difficulty in searching. Owing to the advancement of technology, this project offers a holistic solution to enhance the validation and approval process for project development by students. By fostering collaborations, improving communication, and facilitating efficient workflows, the app aims to elevate the quality of software development outcomes while reducing project timeline and costs. It is potential to transform how project validation is conducted makes it a valuable tool for modern software development teams and organizations.
Advanced Drone Surveillance System for Traffic Management

The rapid urbanization and increasing vehicle traffic in metropolitan areas have given rise to a significant challenges in traffic management. Traditional methods of a traffic monitoring and control often struggle to cope with the dynamic and complex nature of urban traffic patterns. In this context, we propose an innovative Drone Surveillance System for Traffic Management to revolutionize the way traffic is monitored, analyzed, and optimized in urban environments. Our system leverages state-of-the-art drone technology equipped with advanced sensors, high-resolution cameras, and intelligent algorithms to provide real-time traffic insights. Drones are deployed strategically across key traffic arteries, allowing for comprehensive aerial coverage and data collection. The system focuses on several key objectives: real-time traffic monitoring, incident detection, traffic flow analysis, optimized traffic signal control, emergency response support, data-driven decision making, and ensuring privacy and security compliance. By the employing cutting-edge computer vision algorithms, machine learning techniques, and data analytics, the Drone Surveillance System processes vast amounts of traffic data with speed and precision. The system detects the traffic incidents and the such as accidents and road hazards promptly, enabling swift a responses from emergency services. It analyzes traffic patterns and predicts congestion, allowing for adaptive traffic signal control and dynamic route optimization, thereby minimizing congestion and reducing travel time for a commuters. One of the system’s notable features is its a ability to the support emergency response teams effectively. Drones identify incidents and relay on the critical information to emergency services, International Journal of Scientific Research in Engineering and Management (IJSREM) Volume: 08 Issue: 02 | February - 2024 SJIF Rating: 8.176 ISSN: 2582-3930 © 2024, IJSREM | www.ijsrem.com DOI: 10.55041/IJSREM28534 | Page 2 enabling rapid deployment and efficient management of emergencies. Moreover, the system ensures the privacy of individuals through strict adherence to regulations and deploys robust security measures to safeguard data integrity. An important advancement in the urban traffic control system is the Drone Surveillance System for Traffic control. The technology turns traditional traffic management into a proactive, flexible, and data-driven procedure by utilizing the power of drones, real-time data processing, and sophisticated algorithms. This innovation not only makes urban transportation more efficient, but it also helps to improve road safety by lowering accident rates, easing traffic, and creating a more sustainable urban environment. Keywords: Traffic Management, Real-time Monitoring, Incident Detection, Traffic Flow Analysis, Optimized Traffic Signal Control, Machine Learning Techniques. Drone Surveillance System
A Closed-form Algorithm to Shadow Segmentation using a Single Image

Abstract Abstract—The presence of shadow in natural scenes continue to pose a challenge to computer vision algorithms. While there have been many proposed approaches to detect and segment shadow in natural scenes, these methods fail to do so in real-time without requiring a priori information. This paper presents a model that can decompose an image into the product of an illumination component L and a reflectance component R and use the reflectance component to segment the shadow region, all in real-time. This is done by exploiting the fact that light intensity has less effect on the reflectance of an image and by assuming there is a single point light source. Our method is highly effective in detecting shadows in an image with a surface material that exhibits both specular and diffuse reflectance properties.
LifeWatch ERIC: papers collection on original datasets and new e-services for the biodiversity and ecosystems’ scientific community

Papers including articles that are produced because of the activities of LifeWatch ERIC, in the context of its second implementation period (2022 - 2026) and through the implementation of its new Strategic Working Plan, are published in this special collection. The articles include data papers, papers describing the development and functioning of analytical services and papers describing any other research outcome, produced either by LifeWatch ERIC or by any collaboration with any other ERIC, Research Infrastructure, global aggregator or other legal entity.
Artificial intelligence and quantum cryptography

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

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

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

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

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

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

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.