Reviewerly/exHarmony huggingface dataset:
Research on the reform and practice of thinking and teaching of computer network technology course based on ant colony algorithm

Abstract Integrating Civic Education into computer network technology courses can make the professional content more profound. This paper proposes an optimization scheme to improve the teaching effect of the course with an ant colony algorithm, tests the optimization effect of the ant colony scheduling algorithm on the computer network technology course through the index of assignment completion time, and compares the completion time of the same assignment content using four schedulers, ACOScheduler, FIFOScheduler, CapacityScheduler, and FairScheduler, respectively, in the same The results show that the completion time of the same job content in the same environment using ACOScheduler, FIFOScheduler, CapacityScheduler, and FairScheduler. The results show that the task completion time of ACOSchduler is 9.8% lower than that of FairScheduler and 11.4% lower than that of CapacityShcduler at a task size of 500MB, thus optimizing the effectiveness of teaching computer network technology courses. The performance optimization effect of the ACOS scheduling algorithm on the course teaching reform, the achievement of all the target functions to improve the efficiency of task scheduling, more efficient data analysis tasks, and the achievement of the requirements of the expected teaching reform of the subject.
The Smart Performance Prediction and Optimization Analysis for Deep Semantic Networks Using Sentimental Mining

Deep semantic networks using sentimental mining is a powerful tool for analyzing user sentiment in a wide range of contexts. Sentimental mining extracts and analyzes user comments and reviews from natural language text and then interprets this data to produce insights. By leveraging a deep learning model, sentimental mining allows for accurate sentiment identification and analysis. However, this approach can be computationally expensive, limiting its practical applications. To address this challenge, various optimization methods have been proposed to reduce the computational complexity of the sentiment analysis process. These optimization methods analyze the sentiment data to determine which parameters can be adjusted for the network structure to reduce the computational complexity. Additionally, advanced optimization algorithms have been developed to allow for a more efficient training process and better results. Research has also been conducted on the use of evolving deep neural networks as a way to optimize sentiment analysis performance. Through the combination of these optimization techniques, sentiment mining can be significantly improved in terms of accuracy, speed, and efficiency.
Optical Fractional Fourier Transform - Experimental Implementation

Light processing plays a central role in quantum communication, since optical quantum networks that will allow for secure quantum communication [1] and linking quantum computers [2] are of significant scientific and engineering interest [3], [4]. In this work, we present the experimental implementation of optical Fractional Fourier Transform. The protocol is realised in gradient echo memory. Hermite-Gaussian modes of light are encoded in rubidium atoms with the coupling impulse linearly chirped in frequency - this corresponds to the temporal lens [5]. Then the ac-Stark beam is applied to the atoms which leads to them acquiring the spectral phase corresponding to the propagation through a spectral lens. This operation results in the rotation of the Wigner function of the input state [6] and is exactly the Fractional Fourier Transform (FrFT) of the input function. The Hermite-Gaussian modes of light are the eigenfunctions of FrFT.
mTreeIllustrator: A Mixed-Initiative Framework for Visual Exploratory Analysis of Multidimensional Hierarchical Data

Multidimensional hierarchical (mTree) data are very common in daily life and scientific research. However, mTree data exploration is a laborious and time-consuming process due to its structural complexity and large dimension combination space. To address this problem, we present mTreeIllustrator, a mixed-initiative framework for exploratory analysis of multidimensional hierarchical data with faceted visualizations. First, we propose a recommendation pipeline for the automatic selection and visual representation of important subspaces of mTree data. Furthermore, we design a visual framework and an interaction schema to couple automatic recommendations with human specifications to facilitate progressive exploratory analysis. Comparative experiments and user studies demonstrate the usability and effectiveness of our framework.
ANALYSIS OF THE AVAILABILITY AND UTILIZATION OF LEARNING MEDIA IN AN INCLUSIVE CLASSROOM SETTING

The purpose of this study was to investigate the degree to which different types of educational media can be accessed, how useful they are, and how challenging they can be. This inquiry used qualitative research methodologies in the form of a case study for data collection and analysis. Conducting interviews, making observations, and maintaining meticulous records are all methods that fall under the category of data gathering methods and techniques for studying information in real time. The findings of this investigation lend credence to this idea. Access to various educational resources, including traditional and alternative forms of media. Teachers play a crucial part in ensuring that their students genuinely take in and use the information presented in their classes because of their ability to communicate the course goals to the students. The necessary work and the assignments that the students are currently working on have a significant degree of familiarity with one another. Teachers may also deliver presentations; therefore, they must be familiar with a range of formats for different types of material. As an example of what may be learned in the form of follow-up, some options include providing final comments and homework papers. Keywords: Learning Media, Inclusive Classroom, Educational Media DOI： https://doi.org/10.35741/issn.0258-2724.58.4.71
Cloth Interactive Transformer for Virtual Try-On

The 2D image-based virtual try-on has aroused increased interest from the multimedia and computer vision fields due to its enormous commercial value. Nevertheless, most existing image-based virtual try-on approaches directly combine the person-identity representation and the in-shop clothing items without taking their mutual correlations into consideration. Moreover, these methods are commonly established on pure convolutional neural networks (CNNs) architectures which are not simple to capture the long-range correlations among the input pixels. As a result, it generally results in inconsistent results. To alleviate these issues, in this article, we propose a novel two-stage cloth interactive transformer (CIT) method for the virtual try-on task. During the first stage, we design a CIT matching block, aiming at precisely capturing the long-range correlations between the cloth-agnostic person information and the in-shop cloth information. Consequently, it makes the warped in-shop clothing items look more natural in appearance. In the second stage, we put forth a CIT reasoning block for establishing global mutual interactive dependencies among person representation, the warped clothing item, and the corresponding warped cloth mask. The empirical results, based on mutual dependencies, demonstrate that the final try-on results are more realistic. Substantial empirical results on a public fashion dataset illustrate that the suggested CIT attains competitive virtual try-on performance.
Exploring the Intersection of Knowledge Management and Spirituality in Higher Education Institutions

Higher Education Institutions (HEIs) have the fundamental role of teaching, research, and interaction with society, which are the three main vertices in the creation and dissemination of knowledge. HEIs can enhance their sustainability and competitiveness by effectively managing the knowledge of each collaborator and creating more value. In this way, HEIs lay the foundation for sustainable growth and extend their competitive advantages. However, it requires motivated and committed collaborators aligned with the mission, values, and objectives of the organization. Therefore, we suggest that the incorporation of spirituality can promote innovation, organizational sustainability, and increase knowledge management practices. To address the research gap on the integration of KM and spirituality in HEIs, we conducted a literature review using three databases, Web of Science, Scopus, and EBSCO. The review revealed that the integration of KM and organizational spirituality has not been studied in an integrated way. As such, we present suggestions for future research and practice, emphasizing the need for HEIs to focus on managing knowledge and incorporating spirituality into their practices to enhance their competitiveness and sustainability. Accordingly, this study contributes to the literature by awarding and discussing KM and spirituality as two key components of HEIs. Once, incorporating spirituality can promote innovation, organizational sustainability, and increase knowledge management practices. Moreover, this study provides valuable insights for HEIs to improve their management practices, promote sustainability, and enhance their competitive advantages.
Use of Knowledge Management Strategies to Prevent Knowledge Loss in a Government Agency

There is increasing evidence that tacit knowledge is “the important strategic resource that assists in accomplishing a task (Woo, 2004).” According to Haughton (2021), Knowledge is a valuable asset, and managing that knowledge is now recognised as a significant contributor to organizations in the current business climate’. When people with expertise leave a job, the organization often loses critical tacit knowledge because the person did not pass it on to others. This knowledge gap can be costly and time-consuming or impossible to replace (Leonard, 2014). However, government institutions and agencies are known to be not good when it comes to knowledge management (KM) implementation and thus often suffer knowledge loss when employees/people with expertise leave a job, retire, or pass away. A qualitative study was conducted in one of the government agencies which has recently embarked on KM initiatives to find out what their strategies and challenges are in preventing knowledge loss. The study used the Knowledge Management Capability Assessment Tool (KM CAT) theory to assess KM initiatives in the agency. The data was collected by purposefully interviewing senior officials/managers in the agency’s KM directorate. Despite the results of the study revealing that there are several challenges experienced by the agency in its endeavour to implement an effective and efficient KM process and strategy to help stem the tight against loss of knowledge, there are signs of good improvement. However, the agency needs to be resolute in its adoption of a systematic approach to KM to ensure the full utilisation of the organisation’s knowledge base, coupled with the potential of individual skills, competencies, thoughts, innovations, and ideas to create a more efficient and effective organisation.
Knowledge Management in Multimedia Communication Using Software Agents

This paper presents the problem of knowledge management related to software agents and agent systems (i.e. systems composed of communicating and cooperating software agents). The author presents basic theoretical issues, but the main focus is on the areas of application of software agents in multimedia communication - both existing and prospective applications. Particular attention has been paid to the applications of software agents for: supporting customer service processes through the Contact Centre system; supporting the distance learning system; creating virtual assistants in the form of conversation bots; searching for information in the Semantic Web, and assisting in setting up a video conference call. Software agents are often referred to as 'intelligent' - due to their intelligent behaviour. At their core, they involve: perception, reasoning, learning, communication and operation in complex environments, using all multimedia techniques and procedures. Software agents and agent systems, as presented in this article, represent a new approach to the analysis, design and implementation of complex (usually decentralised and distributed) computer systems, offering a whole range of concepts, techniques and tools to significantly improve the software development process. The ability of software agents to make autonomous decisions have been a subject of considerations. Among other things, they are based on the example of cognitive sciences, which deal with modelling real processes of information processing in humans and building programmes that imitate these processes. Research on the common understanding of shared knowledge and knowledge management in agent-based systems focuses on ontologies, understood as a description of the relationship between the way knowledge is represented and the concept represented by that knowledge. In contrast, research on knowledge sharing has focused on communication languages and their associated - interaction protocol and message transport protocol.
An Iterative Cleaning Method for Redundant Data in Multi-Source Heterogeneous Data Based on Decision Tree Algorithm

The current conventional iterative cleaning method for multi-source heterogeneous data mainly calculates the data repetition to achieve the redundant data rejection, which leads to poor data cleaning effect due to the lack of parsimonious processing for data. In this regard, an iterative cleaning method based on decision tree algorithm is proposed for redundant data in multi-source heterogeneous data. By calculating the information gain value of redundant data in multi-source heterogeneous data, and using the highest value as the classification criterion, a decision tree is constructed to realize data classification. And the deep belief network model is constructed to extract the data mixture features, and finally the data is iteratively cleaned by data table repetition judgment rule pairs for data parsimony. In the experiments, the proposed method is verified for the cleaning effect. The experimental results show that when the proposed method is used to clean multi-source heterogeneous data, the data checking accuracy is high and has a more desirable data cleaning performance.
Quantum Key Distribution and Security Studies

The current development of quantum computing threatens the security of conventional encryption algorithms such as RSA. In recent years, Quantum Key Distribution (QKD) has introduced to the world of information security a viable method that is anticipated to provide security protection against the threat of quantum computing. In this paper, we investigate the protocols of the QKD system. Next, we focus on an experimental study of quantum key distribution. A physical QKD system is leveraged to assist us in further investigating quantum key distribution processes. In our study, an eavesdropper was evaluated to analyze the impact on the QKD processes. In the experimental study results, the difference in key generation, exchange, and error rates between normal and attack scenarios can be observed.
An Integrated Multi-Factor Machine Learning Regression Prediction Model Based on CBCA

Based on the bat algorithm, this study combines the principles of metacellular automata to improve the way of individual bat movement behavior, and optimizes the iteration speed of the kernel function parameters and the way of finding the loss function parameters in support vector machines using the improved bat algorithm. This paper selects and counts the data of the recent hot Wordle game as the data set for model training and detection. Among them, the alphabetic code of the word, the frequency of using the word, and the average frequency of using the alphabet of the word are used as independent variables, and the percentage of people who guessed the correct answer in different attempts is used as the dependent variable for model training. Compared with traditional prediction models, the proposed prediction model has better accuracy on both training and test sets with the same number of iterations, and does not overfit within a moderate number of iterations. In addition, due to the presence of regression chains, the model can consider more input factors and has good generalization effect, so it can be widely applied to realistic prediction scenarios with several complex factors, such as: future trend of stock, expected change of population, etc.
English Teachers' Perceptions of a Language Assessment Literacy Course

Language assessment literacy has gained recent attention in the field of language testing, particularly on teachers’ profile. However, the literature on LAL is limited regarding teachers’ perceptions of language assessment courses. In this paper, we used a case study method to characterize the perceptions of eighteen English language teachers into three components of an online assessment course: contents, activities, and impact on their professional development. For data collection, we used a questionnaire and a focus group interview. Findings indicate that the teachers perceived course contents as organized, relevant, and useful; they also considered test analysis and collaborative tasks as valuable. Regarding professional development, the teachers explained that test-analysis tasks made them aware of their mistakes in assessment. Finally, the teachers suggested that the course raised their awareness of what language assessment is and does. Based on these results, we provide recommendations for LAL courses elsewhere.
Exploring knowledge management from a software engineering perspective

Context: The knowledge domain of software engineering (SE) gradually expands due to fast emerging technologies and complex organisational processes. The software industry represents a special focus of interest from a Knowledge Management (KM) perspective, as it is characterized by high employee turnover, a high level of outsourced projects, and an accelerated pace of technology innovations. Knowledge management can substantially influence SE knowledge's life cycle, contributing to better structuring of organisational processes of knowledge creation, knowledge transfer, and knowledge sharing. Reflecting on the trends for SE automation with artificial intelligence tools, KM approaches can enable companies and professionals to reconsider the future of SE work. Purpose: The present research aims to identify and investigate how implementing knowledge management principles and processes within the software engineering domain can facilitate the future transformation of software-related jobs. Based on a conceptual model, built upon the literature review, the paper analyses the quantitative data, collected from 91 software experts. Evaluating the impact of both organizational and domain types of SE knowledge, the discussion evaluates the unique value-adding SE processes along with models of how to explore AI applications to automate the SE domain. Results: The outcomes of the analysis identify the models of structuring knowledge management processes in SE both within the technological domain and organisational layers. The discussion section establishes suitable KM methods and approaches for supporting SE job transformation. It identifies the main knowledge management integration practices, demonstrating how combining KM with AI-based innovations may change the SE knowledge flow, improving substantially SE activities like onboarding new team members, monitoring software documentation, version control, and error tracking.
EarAE: An Autoencoder based User Authentication using Earphones

Nowadays, earphone acts an increasingly vital role in human computer interaction. Throughout this trend, earphone-based user authentication, a new modality of biometric authentication, is winning more attention. Previous research leverage ear canal related features to authenticate users. These methods establish a binary classification model, based on the assumption that the identity information of all invalid users is given. However, they may not be effective when confronting new attackers.In this paper, we propose EarAE, a lightweight auto-encoder to address this problem. The basic idea is to model the authentication as a one-class classification problem, with features of the valid user as the single training class. Specifically, during training, the ear canal features of the valid user are fed into an autoencoder and reconstructed. EarAE learns to minimize the loss function between the input and the reconstructed counterparts. In this way, valid users will have a much lower loss function score than invalid users. Finally, a threshold is set to distinguish between valid/invalid users. Experimental results show that EarAE can achieve an averaged balanced accuracy of over 96% across 30 subjects. This demonstrates a promising prospect of EarAE to serve as a reliable means of user authentication.
An Edge Detection Method Based on Improved Sobel Operator for Infrared Target Extraction

Target extraction of infrared images is being used in an increasing number of applications. Processing using the traditional Sobel operator suffers from the problem that edge localization is not accurate enough. To improve this problem, in this paper, we propose an edge detection method based on an improved Sobel operator, where the edge points and non-edge points of an infrared image are separated by the Sobel operator in four directions. By comparing the experimental results, we find that this method has more accurate edge localization and better target extraction.
Towards Experimental Implementation of Adaptive Measurements for Efficient Qubit Phase Estimation

We work towards the experimental implementation of an adaptive quantum measurement for single-qubit phase estimation that can theoretically achieve the quantum bound over the entire range of phases [0,2 π ].
Ensemble Dynamic Machine Learning Algorithm (EDMLA) for E-commerce sentiment product recommendation system with the integration of AACSD-an empirical study

In this paper, an attempt has been made to investigate the benefits of the Amalgamate Architecture Centric Software Development (AACSD) method through an experimental setup using Machine Learning techniques on an E-Commerce product recommender system. The system recommends products based on authorized user reviews. As part of this research, an Ensemble Dynamic Machine Learning Algorithm (EDMLA) was designed and developed with the integration of AACSD to improve performance quality. Performance was evaluated based on parameters such as sensitivity, specificity, and accuracy.
Software Engineering: New Methodologies, Tools, and Best Practices in Software Development

Software engineering is a dynamic field that is always evolving to meet the demands of difficult software development projects. This journal article investigates contemporary software engineering methodologies, tools, and best practices in order to enhance the efficiency, quality, and dependability of software development processes. We discuss the most recent advancements in quality assurance, DevOps, agile software development, and automated testing. We also look at the role that cooperation, communication, and project management have in efficient software engineering methods. By highlighting these advances, this article aims to promote the delivery of high-quality software products and improve software development processes.