Reviewerly/exHarmony huggingface dataset:
Moodle as a Learning Management System: Perceived Efficacy and Actual Use

Smart learning means to create an enriched interactive environment that enables learners and educators to access their learning resources at the right time and place in an effective, efficient, and engaging manner.The effectiveness and efficacy of the Learning Management System (LMS) represents a crucial component in such an environment.In light of the Unified Theory of Acceptance and Use of Technology (UTAUT), Technology Acceptance Model (TAM), and the Diffusion of Innovation Model (DOI), this study aimed to scrutinize the perceived efficacy of Moodle as a learning management system as seen by instructors.It further aimed to reveal the degree to which instructors were actually using Moodle in the educational process in Jordan.A descriptive survey methodology was used for data collection purposes and statistical analysis revealed that performance expectancy, effort expectancy, and social influence have significant impacts on the perceived efficacy of Moodle.Moreover, the results revealed that the actual use was highly driven by perceived efficacy, whereas it was moderately influenced by facilitating conditions.The effect of instructors' gender, experience, and field of study, as moderating factors, were also investigated.
Multi-agent Reinforcement Learning-Based UAS Control for Logistics Environments

With recent technological developments, The UAS (unmanned aerial system) has been recognized for its value and usefulness in various fields. Prior researchers have utilized several drones in collaboration to navigate to achieve common goals such as target tracking, rescue operations, and target-finding with multi-UAS systems. Multi-agent reinforcement learning algorithms are a type of artificial intelligence technology in which many agents collaborate to perform tasks. When a multi-UAS cooperative navigation technique is deployed to a complicated environment such as an urban logistics system, the agents’ learning capacities become more tedious. In this study, we present what is termed the improved Multi-Actor-Attention-Critic (iMAAC) approach, a modified multi-agent reinforcement learning method for application to urban air mobility logistic services. A virtual simulation environment based on Unity is created to validate the suggested method. In the virtual environment, the real-world situation of UAS logistics development services is replicated. When the findings are compared to those of other landmark reinforcement algorithms, iMAAC shows a higher learning rate than those by the other algorithms when utilized in multi-agent systems.
Mobile-Assisted Language Learning Applications: Features and Characteristics from Users’ Perspectives

This study investigates the features of currently used mobile-based applications in language learning as perceived by 112 adult learners from different backgrounds in the Kingdom of Saudi Arabia. A quantitative approach is used, with a cross-sectional survey and open-ended questions. The results show the top useful and useless features of the applications available in the market for language learning. In addition, they show the most and the least common features as well as the missing features that learners would most like to have in language learning applications. It finds that most mobile-based applications seem to be more focused on technical and technological features at the expense of social, cultural, and individual features. Pedagogical implications for promoting self-access language learning software are also discussed. Keywords: self-access language learning, mobile learning, mobile-assisted language learning, language learning applications, self-directed learning
The Impact of Synchronous Learning of Marlins in Teaching Maritime English

Educators use online learning in the teaching process during the COVID-19 epidemic. Basic interactive online learning methods, such as synchronous learning, are available as a face-to-face learning process via online media. It is necessary to have learning media that can be used during learning Maritime English. The kind of platform that will be used in this study namely Marlins. This study aimed to investigate the impact of synchronous learning in teaching Maritime English through marlins English in cadets’ learning outcomes in the industrial revolution 4.0. This study was a quantitative study using a pre-experimental One-Group Pretest-Posttest Design. Collecting data applied Marlins English with reliability of 0.788 in Alpha Cronbach. The number of samples was 24 cadets. Multiple choice items and descriptions made up the data gathering instrument. A one-sample t-test, a percentage test, and an independent sample t-test were employed to analyze the data. The results of the study show that there is a mean score of 1,250, and the data table of Paired Samples Test Shows Sig. value of (2- tailed) (0.001) &lt; (0.05) and value of t count of (-4.440) &gt; t table (23; 0.05) is 1.713, so that Ho is rejected. From 24 cadets of the Engine Department of class 3A at Polytechnics of Makassar Merchant, before and after the treatment is significant. For further researchers, it is expected that they can carry out further research with more in-depth coverage related to online learning through synchronous learning.
Adma-GAN: Attribute-Driven Memory Augmented GANs for Text-to-Image Generation.

As a challenging task, text-to-image generation aims to generate photo-realistic and semantically consistent images according to the given text descriptions. Existing methods mainly extract the text information from only one sentence to represent an image and the text representation effects the quality of the generated image well. However, directly utilizing the limited information in one sentence misses some key attribute descriptions, which are the crucial factors to describe an image accurately. To alleviate the above problem, we propose an effective text representation method with the complements of attribute information. Firstly, we construct an attribute memory to jointly control the text-to-image generation with sentence input. Secondly, we explore two update mechanisms, sample-aware and sample-joint mechanisms, to dynamically optimize a generalized attribute memory. Furthermore, we design an attribute-sentence-joint conditional generator learning scheme to align the feature embeddings among multiple representations, which promotes the cross-modal network training. Experimental results illustrate that the proposed method obtains substantial performance improvements on both the CUB (FID from 14.81 to 8.57) and COCO (FID from 21.42 to 12.39) datasets.
CAMELYON 17 Challenge: A Comparison of Traditional Machine Learning (SVM) with the Deep Learning Method

The pathologist’s diagnosis is crucial in identifying and categorizing pathological cancer sections, as well as in the physician’s subsequent evaluation of the patient’s condition and therapy. It is recognised as the “gold standard”; however, both objective and subjective pathological diagnoses have limits, such as tissue corruption resulting from the nonstandard collection of diseased tissue, nonstandard tissue fixation or delivery, or a lack of necessary clinical data. In addition, diagnostic pathology encompasses too much information; thus, it requires time and effort to grow a trained pathologist. Consequently, computer-assisted diagnosis has become an essential tool for replacing or assisting pathologists with computer technology and graphical development. In this regard, the CAMELYON 17 competition was designed to identify the best algorithm for detecting cancer metastases in the lymph. Each participant was given 899 whole-slide photos for the development of their algorithms. More than 300 people enrolled on the competition. CAMELYON 17 is primarily focused on the categorization of lymph node metastases. The TNM classification system is the primary classification system. Participants at CAMELYON 17 mostly use categorization and learning techniques in deep learning and machine learning. In order to get a better understanding of the top-selected algorithms, we examine the advantages and limitations of traditional machine learning and deep learning for classifying breast cancer metastases.
Dynamics of two central spins immersed in spin baths

In this article we derive the exact dynamics of a two-qubit (spin $1/2$) system interacting centrally with separate spin baths composed of qubits in a thermal state. Furthermore, each spin of the bath is coupled to every other spin of the same bath. The corresponding dynamical map is constructed. It is used to analyze the non-Markovian nature of the two qubit central spin dynamics. We further observe the evolution of quantum correlations like entanglement and discord under the influence of the environmental interaction. Moreover, we demonstrate the comparison between this exact two-qubit dynamics and the locally acting central spin model in a spin bath. This work is a stepping stone towards the realization of non-Markovian heat engines and other quantum thermal devices.
A perspective on the current use of the phase distribution model for predicting milk‐to‐plasma drug concentration ratio

The phase distribution model, proposed by Atkinson and Begg in 1990, has been widely used for predicting breastmilk-to-plasma drug concentration ratio. However, misrepresentations of the equations have been noted in recent publications. In this perspective, we revisit the derivation of the equations and provide an R/Shiny interface for the model with a view to helping scientists in this field acquire in-depth understanding of the theoretical background and implementation of the model.
A Boson Sampling Chip for Graph Perfect Matching

We map the perfect matching problem in graph theory to a reconfigurable GBS model with the connection of the Hafnian of a matrix. We configure the linear optical circuit and squeeze parameter of the GBS model according to the decomposed unitary matrix and diagonal matrix of the graph’s adjacency matrix. The perfect matching numbers can be directly acquired from the 4-photon coincidence counts with a distribution similarity of 0.9304.
Robust Gaussian Process Regression With Input Uncertainty: A PAC-Bayes Perspective

The Gaussian process (GP) algorithm is considered as a powerful nonparametric-learning approach, which can provide uncertainty measurements on the predictions. The standard GP requires clearly observed data, unexpected perturbations in the input may lead to learned regression model mismatching. Besides, GP also suffers from the lack of good generalization performance guarantees. To deal with data uncertainty and provide a numerical generalization performance guarantee on the unknown data distribution, this article proposes a novel robust noisy input GP (NIGP) algorithm based on the probably approximately correct (PAC) Bayes theory. Furthermore, to reduce the computational complexity, we develop a sparse NIGP algorithm, and then develop a sparse PAC-Bayes NIGP approach. Compared with NIGP algorithms, instead of maximizing the marginal log likelihood, one can optimize the PAC-Bayes bound to pursue a tighter generalization error upper bound. Experiments verify that the NIGP algorithms can attain greater accuracy. Besides, the PAC-NIGP algorithms proposed herein can achieve both robust performance and improved generalization error upper bound in the face of both uncertain input and output data.
Toward an Intelligent Tutoring System for Tone Languages: Learning of Tone Levels in Mooré

This paper presents preliminary results of a research project that aims to build an Intelligent Tutoring System (ITS) to learn the tones of the Mooré language, the most spoken language in Burkina Faso. This interactive learning system is a solution that we propose to meet the needs for local language learning expressed by the staff of government and Non-Governmental Organizations. We use CommonKADS, a knowledge engineering methodology to specify the knowledge and processes of our system, particularly the inference models of the learning activities involved in the domain and pedagogical modules of the ITS.
BacalhauNet: A tiny CNN for lightning-fast modulation classification

Deep learning methods have been shown to be competitive solutions for modulation classification tasks, but suffer from being computationally expensive, limiting their use on embedded devices. We propose a new deep neural network architecture which employs known structures, depth-wise separable convolution and residual connections, as well as a compression methodology, which combined lead to a tiny and fast algorithm for modulation classification. Our compressed model won the first place in ITU's AI/ML in 5G Challenge 2021, achieving 61.73� compression over the challenge baseline and being over 2.6� better than the second best submission. The source code of this work is publicly available at github.com/ITU-AI- ML-in-5G-Challenge/ITU-ML5G-PS-007-BacalhauNet.
Investigation of Personal Space perception in Augmented Reality

Over the past five years, the interest in Augmented Reality (AR) technologies has significantly increased. Different industries adopted it, such as gaming, tourism, e-commerce, and entertainment, to name a few. Due to its non-fully immersive property, easy implementation, and compatibility with the most recent smartphones, AR seems to be suitable for daily usage. This poses a significant challenge in creating a good User Experience (UX), as this kind of technology needs to be designed for private scenarios and public ones. In its context, it is essential to understand how users are influenced in their social interactions while using AR. Through the use of humanoid Virtual Characters, this paper is intended to better the understanding of how people perceive personal space in AR. By varying Virtual Character Gender (male, female) and its Placement Modality (predefined, dynamic distance) in the real environment, the effects on UX, Emotions, and Social Presence are investigated. As Personal Space is a dynamic psychological construct, which could depend on individual factors, this paper further investigates some possible co-variant effects of users' preferences towards a female or a male character based on their Sexual Attraction. Results show that Placement Modality significantly affects Personal Space boundaries in terms of position, width, and hedonic quality of the experience. Moreover, an effect of Virtual Character Gender was found on users' Personal Space inner boundary, and Social Presence in its emotional contagion dimension. Finally, an effect of Sexual Attraction on UX, Social Presence in its emotional contagion dimension, and dominance were discovered.
Blockchain-Enabled Redundant Fractionated Spacecraft System

Services and applications provided by satellites are continuously improving in terms of quality and diversity, as well as, their complexity. Resilience of traditional monolithic spacecraft is achieved mainly through redundancy, which exponentially increases the complexity of their production, and therefore, their cost. One solution would be to use fragmented spacecraft systems. Redundancy is achieved by nature with local complexity. Furthermore, scalability of such a system is facilitated. However, data transfer and physical communication link between the &#x201C;fragments&#x201D; must be addressed. The main challenge of these systems is the management of the network of satellites. Traditional centralised networks, managed by a single entity, have been proven not to be secure, as they generate a common point of failure, susceptible to attacks. Traditional distributed networks, managed by multiple entities, require honest participants and trust between entities, restricting collaborative mission applications. In the present article, a design for a blockchain-enabled swarm fractionated system managed by multiple trustless entities is proposed. The system is composed of functionally different nano-satellites which perform tasks from different subsystems in order to replicate the functionalities of a monolithic spacecraft. The blockchain nodes are deployed in the satellites and allow sharing of sensor data between the network. A consensus protocol ensures the validity of the shared data. The proposed system has been implemented and evaluated on a local blockchain composed of four Ethereum nodes. We believe that the proposed application opens the way to new collaborative missions between entirely trustless parties, ensuring transparency and cooperation within the system.
Autonomic Dominant Resource Fairness (A-DRF) in Cloud Computing

In the world of information technology and the Internet, which has become a part of human life today and is constantly expanding, Attention to the users' requirements such as information security, fast processing, dynamic and instant access, and costs savings has become essential. The solution that is proposed for such problems today is a technology that is called cloud computing. Today, cloud computing is considered one of the most essential distributed tools for processing and storing data on the Internet. With the increasing using this tool, the need to schedule tasks to make the best use of resources and respond appropriately to requests has received much attention, and in this regard, many efforts have been made and are being made. To this purpose, various algorithms have been proposed to calculate resource allocation, each of which has tried to solve equitable distribution challenges while using maximum resources. One of these calculation methods is the DRF algorithm. Although it offers a better approach than previous algorithms, it faces challenges, especially with time-consuming resource allocation computing. These challenges make the use of DRF more complex than ever in the low number of requests with high resource capacity as well as the high number of simultaneous requests. This study tried to reduce the computations costs associated with the DRF algorithm for resource allocation by introducing a new approach to using this DRF algorithm to automate calculations by machine learning and artificial intelligence algorithms (Autonomic Dominant Resource Fairness or A-DRF).
The Role of Knowledge Management Capability, Financial Literacy, and Problem-Solving Skills on Organizational Performance for SMEs

OPINION article Front. Psychol., 06 October 2022Sec. Organizational Psychology Volume 13 - 2022 | https://doi.org/10.3389/fpsyg.2022.930742
Blockchain Management and Federated Learning Adaptation on Healthcare Management System

Recently, health management systems have some troubles such as insufficient sharing of medical data, security problems of shared information, tampering and leaking of private data with data modeling probes and developing technology. Local learning is performed together with federated learning and differential entropy method to prevent the leakage of medical confidential information, so blockchain-based learning is preferred to completely eliminate the possibility of leakage while in global learning. Qualitative and quantitative analysis of information can be made with information entropy technology for the effective and maximum use of medical data in the local learning process. The blockchain is used the distributed network structure and inherent security features, at the same time information is treated as a whole, not as islands of data. All the way through this work, data sharing between medical systems can be encouraged, access records tampered with, and better support medical research and definitive medical treatment. The M/M/1 queue for the memory pool and M/M/C queue to combine integrated blockchains with a unified learning structure. With the proposed model, the number of transactions per block, mining of each block, learning time, index operations per second, number of memory pools, waiting time in the memory pool, number of unconfirmed transactions in the whole system, total number of transactions were examined. Thanks to this study, the protection of the medical privacy information of the user during the service process and the autonomous management of the patient’s own medical data will benefit the protection of privacy within the scope of medical data sharing. Motivated by this, proposed a blockchain and federated learning-based data management system able to develop in next studies.
Revisiting data augmentation for subspace clustering

Subspace clustering is the classical problem of clustering a collection of data samples that approximately lie around several low-dimensional subspaces. The current state-of-the-art approaches for this problem are based on the self-expressive model which represents the samples as linear combination of other samples. However, these approaches require sufficiently well-spread samples for accurate representation which might not be necessarily accessible in many applications. In this paper, we shed light on this commonly neglected issue and argue that data distribution within each subspace plays a critical role in the success of self-expressive models. Our proposed solution to tackle this issue is motivated by the central role of data augmentation in the generalization power of deep neural networks. We propose two subspace clustering frameworks for both unsupervised and semi-supervised settings that use augmented samples as an enlarged dictionary to improve the quality of the self-expressive representation. We present an automatic augmentation strategy using a few labeled samples for the semi-supervised problem relying on the fact that the data samples lie in the union of multiple linear subspaces. Experimental results confirm the effectiveness of data augmentation, as it significantly improves the performance of general self-expressive model.
Automatic liver tumor segmentation on multiphase computed tomography volume using <scp>SegNet</scp> deep neural network and <scp>K‐means</scp> clustering

Abstract Liver and liver tumor segmentations are essential in computer‐aided systems for diagnosing liver tumors. These systems must operate on multiphase computed tomography (CT) images instead of a single phase for accurate diagnosis for clinical applications. We have proposed a framework that can perform segmentation from quadriphasic CT data. The liver was segmented using a fine‐tuned SegNet model and the liver tumor was segmented using the K‐means clustering method coupled with a power‐law transformation‐based image enhancement technique. The best values for liver segmentation achieved were: Dice Coefficient = 96.46 ± 0.48%, Jaccard Index = 93.16 ± 0.89%, volumetric overlap error = 6.84 ± 0.89% and average symmetric surface distance = 0.59 ± 0.3 mm and the results for liver tumor delineation were Dice Coefficient = 85.07 ± 4.5%, Jaccard Index = 74.29 ± 6.8%, volumetric overlap error = 25.71 ± 6.8% and average symmetric surface distance = 1.14 ± 1.3 mm. The proposed liver segmentation method based on deep learning is fully automatic, robust, and effective for all phases. The image enhancement technique has shown promising results and aided in better liver tumor segmentation. The liver tumors were segmented satisfactorily; however, improvements concerning false positive reduction can further increase the accuracy.