Reviewerly/exHarmony huggingface dataset:
A Review: Blockchain Technology Applications in the Field of Higher Education

Education is undergoing a continuous digital change. The administrative departments must be able to handle grading, admissions, enrollments, and certificate recognition efficiently and securely. Course administrators should have confidence in both e-learning and e-assessment. Lastly, students should be able to access course materials from anywhere and take tests outside their institutions. With the advancement of education governing systems and governance abilities, technology has broader applicability in the education sector, as it is the ideal technology for securing data. This article aims to determine the state of blockchain applications in education, their benefits, and concerns, as well as to show that the blockchain is becoming a vital technology in future education. In conjunction with digital signatures for identification and verification, blockchain may be the best approach to digital transformation in education. This article cites papers evaluating and detailing the applications and challenges of blockchain technology in the higher education sector. An extensive literature review was conducted, and 63 papers that contributed significantly to the discussion of blockchain's potential in education were chosen for the review. The analysis of these papers provided important information and answered the research questions about how blockchains are currently being used in education, which features can help this field, and what problems need to be solved. Analysis shows that blockchain could become important in the field of education. However, some important technological, regulatory, and academic issues must be resolved before adoption.
The transfer of entanglement negativity at the onset of interactions

Abstract Quantum information, in the form of entanglement with an ancilla, can be transmitted to a third system through interaction. Here, we investigate this process of entanglement transmission perturbatively in time. Using the entanglement monotone negativity, we determine how the proclivity of an interaction to either generate, transfer or lose entanglement depends on the choice of Hamiltonians and initial states. These three proclivities are captured by Hamiltonian- and state-dependent quantities that we call negativity susceptibility, negativity transmissibility and negativity vulnerability respectively. These notions could serve, for example, as cost functions in quantum technologies such as machine-learned quantum error correction.
A Hybrid Image Denoising Method Based on Discrete Wavelet Transformation with Pre-Gaussian Filtering

Background/Objectives: At the time of acquisition and transmission noise is embedded with the images.It introduces new but unwanted information (noise) in images.The elimination of noise to analyze such data is an essential step in preprocessing.The purpose of this study is to propose a novel image denoising approach to recover original images at high noise densities without introducing unwanted artifacts.Methods: A new hybrid method based on approximation subband thresholding with pre-Gaussian filtering is presented in this study.Google Colab as a platform and python as a programming language is used for the implementation of the proposed technique.To evaluate the performance Peak Signal to Noise Ratio (PSNR) is chosen.The standard jpeg images (Cameraman, Lena, Astronaut, Cat) have been taken as an input and random noise with different noise ratios (σ =0.05,0.20,0.30,0.50) is applied to get the noisy images for the experiment.In random noise scenarios, the proposed method experimented on different grayscale standard images, and performance is compared with different existing methods.Findings: The standard images with different noise ratios are denoised by the proposed method, and the quality of images is calculated in terms of PSNR.The results obtained from the proposed method on different standard images improve PSNR (PSNR= 25.80dB, σ =0.50) at high noise levels significantly.Novelty: Gaussian filter improve the quality of images.However, when wavelet decomposition is blended with filtered image and thresholding is applied on approximation band improved the quality of images.Hence, the proposed method has a wide area of application to improve image quality in the field of character recognition, agriculture, medical science, and remote sensing.
A Survey on Crowd Anomaly Detection

Automated crowd anomaly detection and crowd scene analysis is a novel and emerging field of computer science and engineering domain.The analysis of crowd behavior based on density, trajectory, and motion helps prevent abnormal and unwanted incidents.The analysis of crowd behavior is complex and challenging due to visual occlusions, clutter, ambiguities, dense crowd, and scene semantics.These days, researchers are focusing on developing machine learning-based approaches for "crowd behavior, activity analysis, motion pattering, and anomaly detection in real-time applications".Firstly this study presents insight on crowd anomaly detection, ways to achieve it, and its applications and importance today.Secondly, it presents a detailed analysis of conventional machine learning as well as deep learning approaches for serving the purpose based on features, methods, datasets, and shortcomings.Thirdly it presents a thorough analysis of datasets and performance parameters.Finally, it presents the current challenges and future work in this field.
Successive Prompting for Decomposing Complex Questions

Answering complex questions that require making latent decisions is a challenging task, especially when limited supervision is available. Recent works leverage the capabilities of large language models (LMs) to perform complex question answering in a few-shot setting by demonstrating how to output intermediate rationalizations while solving the complex question in a single pass. We introduce ``Successive Prompting'', where we iteratively break down a complex task into a simple task, solve it, and then repeat the process until we get the final solution. Successive prompting decouples the supervision for decomposing complex questions from the supervision for answering simple questions, allowing us to (1) have multiple opportunities to query in-context examples at each reasoning step (2) learn question decomposition separately from question answering, including using synthetic data, and (3) use bespoke (fine-tuned) components for reasoning steps where a large LM does not perform well. The intermediate supervision is typically manually written, which can be expensive to collect. We introduce a way to generate a synthetic dataset which can be used to bootstrap a model's ability to decompose and answer intermediate questions. Our best model (with successive prompting) achieves an improvement of ~5% absolute F1 on a few-shot version of the DROP dataset when compared with a state-of-the-art model with the same supervision.
Use of Blockchain Technology to Manage the Supply Chains: Comparison of Perspectives between Technology Providers and Early Industry Adopters

Following the interest in blockchain technology (BCT) business solutions and the nascent stage of technology in supply chain (SC) practices, this research compares views from business practitioners who are experienced in real cases of BCT adoption with the views of technology consultants proficient in the complexities of BCT to analyze the benefits and challenges BCT holds for SCs. Based on the comparison of the two sides, the joint views that both adopters and technology consultants share is the ability that BCT holds to speed up processes across SCs through decentralized data access, thus decreasing human errors and reducing paperwork. However, technology consultants perceive the need to increase BCT awareness levels of businesses, to prevent BCT implementation just for reasons such as ‘recordkeeping’ and to reduce misconceptions in areas such as cryptocurrency applications. The findings also revealed that technology consultants insist on the careful evaluation and definition of records to be kept on BCT platforms prior to the adoption process, in order to avoid unnecessary data input. Currently, according to early industry adopters’ cases, most business attempts of BCT adoption use private networks, so technology consultants promote business entities on developing plans towards open-access public networks.
Enhancing cancer differentiation with synthetic MRI examinations via generative models: a systematic review

Abstract Contemporary deep learning-based decision systems are well-known for requiring high-volume datasets in order to produce generalized, reliable, and high-performing models. However, the collection of such datasets is challenging, requiring time-consuming processes involving also expert clinicians with limited time. In addition, data collection often raises ethical and legal issues and depends on costly and invasive procedures. Deep generative models such as generative adversarial networks and variational autoencoders can capture the underlying distribution of the examined data, allowing them to create new and unique instances of samples. This study aims to shed light on generative data augmentation techniques and corresponding best practices. Through in-depth investigation, we underline the limitations and potential methodology pitfalls from critical standpoint and aim to promote open science research by identifying publicly available open-source repositories and datasets.
Digital Skills and Online Collaborative Learning: The Study Report

This paper discusses digital skills and their role in collaborative distance learning. Collaboration is considered to be one of the most effective, yet challenging, methods of teaching and learning. Many learners avoid collaborative tasks, for different reasons. We believe that the level of digital skills may be a key to understanding their attitude. A study was conducted at Nicolaus Copernicus University in Toruń, Poland. The respondents assessed, amongst other items, the level of their own digital skills. Based on this assessment, three groups of students with different skill levels were distinguished. Further analyses showed that students with different level of digital skills vary according to their own experiences in collaborative online learning, but not in terms of views on the phenomenon, technologies used, or computer hardware. Most differences are between the low and high skill groups. However, they are also present between the low and average, and the average and high skill groups. These results contribute to the discussion on the need to individualise the teaching and learning process and highlight the roles of digital competence development for all participants in the learning process.
The insufficiency of the Malaysian contact tracing app from the perspective of Chinese tourists: preparing for international tourism in the post-COVID-19 world

Understanding tourists' feedback on using Mysejahtera is critical for tourism recovery in these destinations, and even more so for countries like Malaysia and China, where national Contact Tracing Applications (CTA) are mandatory. However, Previous surveys on CTA use have mainly focused on voluntary CTA users, using qualitative research methods. In this research, Chinese overseas students in Malaysia are included as the reference group, and Chinese tourists with experiences traveling overseas are put into the experimental group. A total of 890 questionnaires were collected and taken as the original data to carry out the Chi-squared and Mann-Whitney U tests. Meanwhile, the experiment implemented a multiple linear regression mechanism to explore the variables that may improve the app Mysejahtera, with further analysis being conducted. According to the results, language issues are the most significant barrier to Chinese visitors using MySejahtera; the inability to register with a Chinese mobile phone number and the need to register a permanent address in Malaysia have a significant negative impact on the use of MySejahtera; and visitors' trust in science positively related to MySejahtera use.
Testing cognitive theories with multivariate pattern analysis of neuroimaging data

Multivariate pattern analysis (MVPA) has emerged as a powerful method for the analysis of functional magnetic resonance imaging, electroencephalography and magnetoencephalography data. The new approaches to experimental design and hypothesis testing afforded by MVPA have made it possible to address theories that describe cognition at the functional level. Here we review a selection of studies that have used MVPA to test cognitive theories from a range of domains, including perception, attention, memory, navigation, emotion, social cognition and motor control. This broad view reveals properties of MVPA that make it suitable for understanding the ‘how’ of human cognition, such as the ability to test predictions expressed at the item or event level. It also reveals limitations and points to future directions.
Eye gaze as a resource in handling trouble around mobile devices in EFL classroom interactions

This article offers an insight into how interaction is multimodally built during task-accomplishment around mobile devices in English as a Foreign Language (EFL) classrooms. More specifically, it investigates eye gaze as a resource in recruiting help and pursuing response during interactional or task-related trouble sequences. The data come from videorecorded EFL lessons at Finnish comprehensive schools where mobile devices are used for learning tasks. Through multimodal conversation analysis, the article demonstrates that gaze is employed as one of the first resources to address trouble. Although tasks often require gaze to be directed at devices, it can be flexibly reoriented to peers when needed. The findings therefore highlight the need to study not only the use of technology but also the interaction around it. An emic perspective to applied linguistics is especially called for to understand how participants manage interaction in today’s technology-rich educational contexts.
Modeling Algorithms for Task Scheduling in Cloud Computing Using CloudSim

As the number of cloud users are spontaneously growing globally, there is an urgent need to constantly provide quality services to consumers. Consequently, task scheduling plays an essential role in improving the performance of the cloud computing environment. Most of the published research in this field share common goals, which can be summarized in maximizing resource utilization, reducing cost, and increasing performance. This research provides the foundation knowledge on the latest works done to enhance and optimize the existing task scheduling algorithm in cloud computing by considering various parameters. Furthermore, in this study, we have applied comparative study to analyze the performance of three task scheduling algorithms namely Max-Min, First Come First Serve (FCFS), and Round Robin (RR) in cloud computing environments based on the performance metric of the Virtual Machines (VM) resources' cost, average time and makespan to find the best performing algorithm in the cloud environment. The experimental evaluations were conducted using CloudSim simulation tool. The results show that Max-Min achieved better performance based on makespan and average waiting time than other algorithms in Space and Time-shared policies.
ODNet: A Convolutional Neural Network for Asteroid Occultation Detection

We propose to design and build an algorithm that will use a Convolutional Neural Network (CNN) and observations from the Unistellar network to reliably detect asteroid occultations. The Unistellar Network, made of more than 10,000 digital telescopes owned by citizen scientists, and is regularly used to record asteroid occultations. In order to process the increasing amount of observational produced by this network, we need a quick and reliable way to analyze occultations. In an effort to solve this problem, we trained a CNN with artificial images of stars with twenty different types of photometric signals. Inputs to the network consists of two stacks of snippet images of stars, one around the star that is supposed to be occulted and a reference star used for comparison. We need the reference star to distinguish between a true occultation and artefacts introduced by poor atmospheric condition. Our Occultation Detection Neural Network (ODNet), can analyze three sequence of stars per second with 91\% of precision and 87\% of recall. The algorithm is sufficiently fast and robust so we can envision incorporating onboard the eVscopes to deliver real-time results. We conclude that citizen science represents an important opportunity for the future studies and discoveries in the occultations, and that application of artificial intelligence will permit us to to take better advantage of the ever-growing quantity of data to categorize asteroids.
Partial Domain Adaptation without Domain Alignment

Unsupervised domain adaptation (UDA) aims to transfer knowledge from a well-labeled source domain to a related and unlabeled target domain with identical label space. The main workhorse in UDA is domain alignment and has proven successful. However, it is practically difficult to find an appropriate source domain with identical label space. A more practical scenario is partial domain adaptation (PDA) where the source label space subsumes the target one. Unfortunately, due to the non-identity between label spaces, it is extremely hard to obtain an ideal alignment, conversely, easier resulting in mode collapse and negative transfer. These motivate us to find a relatively simpler alternative to solve PDA. To achieve this, we first explore a theoretical analysis, which says that the target risk is bounded by both model smoothness and between-domain discrepancy. Then, we instantiate the model smoothness as an intra-domain structure preserving (IDSP) while giving up possibly riskier domain alignment. To our best knowledge, this is the first naive attempt for PDA without alignment. Finally, our empirical results on benchmarks demonstrate that IDSP is not only superior to the PDA SOTAs (e.g., ∼ +10% on Cl → Rw and ∼ +8% on Ar → Rw), but also complementary to domain alignment in the standard UDA.
( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:mrow><mml:mn>3</mml:mn><mml:mo>+</mml:mo><mml:mn>1</mml:mn></mml:mrow></mml:math> )-dimensional Schwinger pair production with quantum computers

Real-time quantum simulation of quantum field theory in $(3+1)\mathrm{D}$ requires large quantum computing resources. With a few-qubit quantum computer, we develop a novel algorithm and experimentally study the Schwinger effect, the electron-positron pair production in a strong electric field, in $(3+1)\mathrm{D}$. The resource reduction is achieved by treating the electric field as a background field, working in Fourier space transverse to the electric field direction, and considering parity symmetry, such that we successfully map the three spatial dimension problems into one spatial dimension problems. We observe that the rate of pair production of electrons and positrons is consistent with the theoretical predication of the Schwinger effect. Our work paves the way towards exploring quantum simulation of quantum field theory beyond one spatial dimension.
Digital Quantum Simulation of the Spin-Boson Model under Markovian Open-System Dynamics

Digital quantum computers have the potential to simulate complex quantum systems. The spin-boson model is one of such systems, used in disparate physical domains. Importantly, in a number of setups, the spin-boson model is open, i.e., the system is in contact with an external environment which can, for instance, cause the decay of the spin state. Here, we study how to simulate such open quantum dynamics in a digital quantum computer, for which we use an IBM hardware. We consider in particular how accurate different implementations of the evolution result as a function of the level of noise in the hardware and of the parameters of the open dynamics. For the regimes studied, we show that the key aspect is to simulate the unitary portion of the dynamics, while the dissipative part can lead to a more noise-resistant simulation. We consider both a single spin coupled to a harmonic oscillator, and also two spins coupled to the oscillator. In the latter case, we show that it is possible to simulate the emergence of correlations between the spins via the oscillator.
ІНФОРМАЦІЙНО-КОМУНІКАТИВНІ ТЕХНОЛОГІЇ В ОСВІТНІХ КЕЙСАХ: ШАНСИ ТА РИЗИКИ ЇХ ЗАПРОВАДЖЕННЯ В ПЕРІОД ГЛОБАЛЬНИХ ВИКЛИКІВ

&lt;p&gt;&lt;strong&gt;&lt;em&gt;The purpose &lt;/em&gt;&lt;/strong&gt;&lt;em&gt;of the study is to substantiate the chances and risks of introducing information and communication technologies (ICT) in educational cases in the context of global challenges for students of higher education, including pedagogical specialties.&lt;/em&gt;&lt;/p&gt;&lt;p&gt;&lt;strong&gt;&lt;em&gt;Research methodology.&lt;/em&gt;&lt;/strong&gt;&lt;em&gt; general and special; theoretical and empirical methods. Among the theoretical methods used critical, analytical, synthetic, inductive, deductive, comparative, concrete-historical. The main special methods were systemic and structural-functional, which were used to build the design of an educational case using ICT. The main empirical method was to monitor the educational process with the use of ICT in the context of the Covid-19 pandemic and the Russian war against Ukraine.&lt;/em&gt;&lt;/p&gt;&lt;p&gt;&lt;strong&gt;&lt;em&gt;Results.&lt;/em&gt;&lt;/strong&gt;&lt;em&gt; The scientific work substantiates the chances and risks of introducing information and communication technologies (ICT) in educational cases in the context of global challenges, such as the Covid-19 pandemic and the full-scale Russian-Ukrainian war (from February 24, 2022). The content and features of educational cases are considered. Basic ICTs (Internet platforms, services, mobile applications, Internet tools, etc.) are presented, with the help of which educational cases are implemented.&lt;/em&gt;&lt;/p&gt;&lt;p&gt;&lt;strong&gt;&lt;em&gt;N&lt;/em&gt;&lt;/strong&gt;&lt;strong&gt;&lt;em&gt;ovelty&lt;/em&gt;&lt;/strong&gt;&lt;em&gt; of the work is to substantiate the methodology of developing the structure of educational cases with the use of ICT to train specialists in higher education, describe their structural elements, justify the chances and risks of using educational cases in globalization challenges.&lt;/em&gt;&lt;/p&gt;&lt;p&gt;&lt;strong&gt;&lt;em&gt;The practical significance&lt;/em&gt;&lt;/strong&gt;&lt;em&gt; of the work is that the use of ICT educational cases in higher education institutions significantly increases the chances of maintaining and ensuring the proper level of educational process, ensuring its continuity in pandemics and martial law, promotes mental activity and information literacy of teachers and applicants for higher education. Focusing on the potential risks that must be considered when developing ICT-based educational cases will help prevent destructive influence on the world process.&lt;/em&gt;&lt;/p&gt;&lt;p&gt;&lt;strong&gt;&lt;em&gt;Key&lt;/em&gt;&lt;/strong&gt;&lt;strong&gt;&lt;em&gt; &lt;/em&gt;&lt;/strong&gt;&lt;strong&gt;&lt;em&gt;words: &lt;/em&gt;&lt;/strong&gt;&lt;em&gt;an Internet-resource,&lt;/em&gt;&lt;em&gt; &lt;/em&gt;&lt;em&gt;an educational case, educational process, information and communication technologies, information content, chances, risks.&lt;/em&gt;&lt;/p&gt;