Reviewerly/exHarmony huggingface dataset:
Design of network information visualization security cognition system based on QSOFM network and FR algorithm

ABSTRACTNetwork information is complex, there is no lack of hidden risks of information to the user caused by bad effects. But analyzing and evaluating them is complex and requires more diverse and powerful techniques. This research will combine Fruchterman-Reingold (FR) information visualization algorithm and information security risk assessment method based on Quantum self-organizing feature map (QSOFM) network to build information visualization security cognitive system. By introducing quantum neurons into self-organizing feature mapping network (SOFM), a network information security evaluation model is constructed. The system can evaluate the security level of network information and visualize the evaluation results. The experimental results show that the system has fast response speed and strong interactivity. The accuracy of QSOFM algorithm is 86%, 12% higher than the traditional algorithm, which can provide more powerful technical support for the visual security cognition of network information.KEYWORDS: Network informationvisualisation techniquessecurity awarenessQSOFM algorithmFR algorithm Disclosure statementNo potential conflict of interest was reported by the author(s).
Developing Multilingual Education Policies: Theory, Research, Practice

Click to increase image sizeClick to decrease image size AcknowledgmentsThe authors would like to express their heartfelt appreciation and gratitude to the Lembaga Pengelola Dana Pendidikan- LPDP (Indonesia Endowment Fund for Education) under the Ministry of Finance of the Republic of Indonesia for sponsoring the author master’s degree as well as for supporting this publication and collaboration.Disclosure statementNo potential conflict of interest was reported by the author(s).
Research and Exploration on the Bilingual Course Construction of Java Web Application Development Based on AI

The bilingual course "Java Web Application Development" is the core course of computer application technology major and is a vocational ability course that combines theory and practice.By studying this course, students can understand the overall approach to JSP development and be able to independently complete JSP website development work.The project teaching mode aims to cultivate quasi professionals with comprehensive professional abilities, gradually meeting the needs of OBE talent cultivation mode.This article analyzes the main problems in the teaching of Java Web Development, actively explores the ideas for curriculum teaching reform, and proposes a blended teaching mode of "online offline experimental" that conforms to the characteristics of the profession.
Monitoring of Trace Molecular Impurities in Clean-Room Air

A new measurement system has been introduced to perform real-time measurements of molecular air contaminants at different location in a clean room down to a concentration of approximately 0.1 ppb. The system consists of an air sampling unit coupled to a proton transfer mass spectrometer and a Cavity Ring-Down Spectrometer. The measurements show that the time average concentrations of more than 10 different volatile impurity molecules exceed 1 ppb.
Governing Blockchains and Blockchaining Government

This chapter aims at taking stock of Blockchain from a public administration perspective. Blockchain is a technology allowing the tamper-resistant recording of transactions and the resilient distribution of data at a global level without the need for trust in any central actor. This chapter presents the technology to a non- specialist audience. It emphasises that the competitive advantage of this technology over centralised databases is decentralisation and security; centralised databases will always remain more efficient and easier to update. Its main application to date has been in the private financial sector; building further on the Bitcoin network and later evolutions, a cryptocurrency industry has emerged. This chapter presents this emerging sector to a public administration audience with a view on establishing trust through upcoming regulatory initiatives. Its key properties (capacity to achieve coordination with no need for a central actor to be trusted, and public availability of coordination outputs) make it especially relevant in the public sector, with numerous potential applications in the field of ownership documents, financial management, and public participation (Beryhill et al., 2018). A case study related to diplomas in Europe and lessons from a first generation of public sector Blockchain projects are presented. It is suggested that the technology can indeed allow running some government workflows. Reasonable expectations should be adopted, beyond the hype surrounding the technology.
The reform model of China’s university English language teaching in the context of educational informatization;

In this article, the integration path of IT and China's University English language teaching (ELT) is deeply studied, with a view to promoting the growth of university ELT reform and improving the instructional level, students' English proficiency and autonomous learning ability.
QoE-Analysis of 5G Network Resource Allocation Schemes for Competitive Multi-User Video Streaming Applications

Competitive demand for network resources has only increased during the emergence of 5G next generation cellular technology. As video streaming accounts for an overwhelming percentage of this demand, the importance of considering the often-neglected Quality of Experience (QoE) metric is essential to ensure network resources are allocated in the most effective manner. Generalized network throughput metrics are insufficient in capturing the full human experience as increased data rates do not necessarily translate to improvements in user utility. Our study compares the efficacy of existing network allocation algorithms and proposes new approaches to 5G network resource allocation schemes using a more inclusive snapshot of user demand. We provide recommendations on which approach provides the highest QoE performance and suggestions for future network-side improvements. We further propose a QoE-driven network resource allocation (QENA) algorithm that shows a 20% improvement in overall average QoE across a large set of heterogeneous users.
Mask Wearing Detection in Dim Lighting Conditions based on Improved YOLOv5

The COVID-19 epidemic is still rampant around the world. Wearing masks can effectively block the spread of novel coronavirus, while mask wearing detection can timely remind people in public places to wear masks. In order to solve the problem of low accuracy of mask wearing detection under under dim lighting conditions, an improved YOLOv5 algorithm is proposed. Firstly, the Low Light Intensity Image Enhancement module (LLIIE) was embedded into the original YOLOv5 algorithm to improve the algorithm's night vision ability; Secondly, we added the Convolutional Block Attention Module (CBAM) to enhance feature extraction ability; The CIoU loss function is used to replace the original loss function, which makes the detected target position more accurate. The improved YOLOv5 algorithm is trained and tested on the self-built dataset, and its mean Average Precision, Accuracy and Recall reach 87.1%, 89.3% and 82.7% respectively. The experimental results show that the improved YOLOv5 algorithm has the best detection performance and higher practical value compared with other excellent algorithms in dim light condition.
Interval Type-2 Fuzzy Control for Multi-Agent Systems: Application to Formation and Containment Problem of Unmanned Surface Vessels

In this paper, an interval type-2 fuzzy (IT2F) formation and containment control problems of Multiple Unmanned Surface Vessels (MUSVs) nonlinear system are discussed. In many practical industrial systems, it has been proved that various tasks can be completely more efficiently with the cooperative control of multiple systems instead of single system. The MUSVs system is one of the most popular control issues of the multi-agent system because of the widely application in real-life especially military. However, the dynamic system of each vessel will be affected by the uncertain problem all the times such as wear and tear of the equipment. To obtain the more precise control of ship dynamics nowadays, the uncertain problems are inevitable. Thus, the IT2F model is applied in this paper to represent the vessel dynamics with nonlinearities and uncertainties more completely. Based on the model, the corresponding IT2F controller design method is proposed to better achieve the formation and containment objective with uncertain factors. Different from the existing formation-containment control method, the information communication between leaders is not necessary in the formation control such that the problem of communication fault can be avoided efficiently. Moreover, the containment control problem is also solved without the assumption that is required in the existing paper. Finally, simulation results are presented for the formation and containment control problems of Nonlinear MUSVs system to verify the feasibility and applicability of the proposed IT2F controller design method.
Access to Finance and Human Development in Sub-Saharan Africa

The study examines the relationship between access to finance and human development in Sub-Saharan Africa. The study used a panel data covering the period 2004–2021and employed the two-step system Generalized Method of Moments for the analysis of the data. The indicators of financial access used in the study are number of bank branches, commercial bank private sector credits and number of automatic teller machines (ATM) per. The study found that commercial bank private sector credits and number of number of automatic teller machines per 1000 adults have a positive and significant relationship with human development index in Sub-Saharan Africa. Base on the findings, the study recommends that the government of various Sub-Saharan African countries should intensify efforts to increase the access to financial services for the poor and low income group. This include reducing the costs and charges on credits for the low income earners and building more bank agents and automatic teller machines that will provide financial services to the populace especially the unbanked. The findings of the study lead to the conclusion that most Sub-Saharan African countries need to do more to boost access to finance for the unbanked and low income earners.
Managing Technical Debt

In the realm of software engineering, there exists a silent yet formidable enemy known as technical debt. It lurks within the codebase, quietly accumulating interest over time, until one day it demands its due. This chapter delves into the crucial subject of managing technical debt and provides insights and strategies for software engineers to build robust, flexible systems that stand the test of time.
Image super-resolution reconstruction based on attention and wide-activated dense residual network

Abstract： To address the problem of the blurring of the texture details of reconstructed images due to the insufficient utilization of global and local high-and low-frequency spatial information， this paper proposes an image super-resolution reconstruction model based on attention and a wide-activated dense residual net• work.First， four parallel convolution kernels with different scales are used to fully extract the low-frequen• cy features of the image as the prior information for spatial feature transformation.Second， a wide-activat• ed residual block fused with attention is constructed in the deep feature mapping module， and the low-fre• 文章编号 1004-924X (2023)15-2273-14 收稿日期： 2022-11-01； 修订日期： 2022-11-28.基金项目： 中央高校基本科研业务费专项资金资助项目(No. 2020QN49) 第 31 卷 光学 精密工程 quency prior information is used to guide the extraction of the high-frequency features.In addition， the wide-activated residual block extracts deeper feature maps by expanding the number of feature channels be• fore the activation function.As a result， the constructed global and local residual connections not only strengthen the forward propagation of the residual blocks and network features， but also enrich the diversi• ty of the extracted features without increasing the number of parameters.Finally， the feature map is upsampled and reconstructed to obtain a clear high-resolution image.the experimental results show that compared with the LatticeNet model， the peak signal-to-noise ratio of the proposed algorithm is improved by 0. 14 dB， and the structural similarity is improved by 0. 001 at 4× super resolution on the BSD100 da• taset.In addition， the local texture details of the reconstructed image are also clearer in subjective visual• ization.Key words： residual network；
Identifying system archetypes in Nigeria’s rice agri-food system using fuzzy cognitive mapping

Nigeria is a major rice-producing and rice-importing country in Africa, challenged with ensuring rice-food security for its growing population.Successive governments have implemented several strategies to increase local rice production such as rice import restriction policies and agricultural investments.These strategies have yielded results but achieving long-term sustainable growth in Nigeria's rice agri-food system has remained elusive.Addressing food security and sustainability in agri-food systems requires a systemsthinking approach.In this study, we applied two systems thinking techniques, fuzzy cognitive mapping (for describing the system structure and behavior) and archetype analysis (to reveal generic system archetypes and effective strategies to improve the system).Our analysis revealed three system archetypes: limits to success, fixes that fail, and drifting goals.Rice production is limited by low agricultural productivity indicating the "limits to success" archetype.Farmers tend to increase rice area as a "quick fix" to productivity issues but this quick fix leads to unintended consequences such as soil degradation (fixes that fail archetype).Additionally, because of the importrestriction policies generating an unmet demand for rice, the government may face pressure to lower the goal of self-sufficiency falling into the "drifting goals" archetype.However, our analysis shows that suspending import-restriction policies would result in undesirable system states, with reduced demand for local rice and lower rice production.Our results underscore the importance of government policies in increasing rice production sustainably and ensuring food security.
GrapHAR: A Lightweight Human Activity Recognition Model by Exploring the Sub-carrier Correlations

Human activity recognition (HAR) is an important task due to its far-reaching applications, such as surveillance, healthcare systems, and human-computer interaction. Recently, Channel State Information (CSI)-based HAR has attracted increasing attention in the research community due to its ubiquitous availability, good user privacy, and fewer constraints on working conditions. Most of the existing methods for CSI-based HAR use various deep learning models, such as Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM), and Transformers, to distinguish activities based on their temporal patterns. Despite their remarkable effectiveness, these methods solely focus on temporal patterns while ignoring the correlations among sub-carriers. This limitation prevents them from achieving further performance improvement. Moreover, recent works often involve advanced yet massive and inefficient neural architectures, like Transformers, to obtain satisfactory recognition accuracy. The performance gain is traded off with a steep increase in model complexity, which leads to low efficacy and high training/inference costs outsides the small time window. To address these issues, we propose a lightweight CSI-based HAR model. Our model makes the first effort to explore the graphical correlations of CSI sub-carriers, working in conjunction with a temporal causal convolution module. The high efficacy design enables our model to be highly effective without requiring excessive model complexity. Extensive experiments conducted on four real-world datasets demonstrate that our model outperforms state-of-the-art methods, including a strong Transformer-based baseline. It achieves an average improvement of 8 percentage points in recognition accuracy, with only 10% of the parameters compared to the Transformer-based method (4.95M vs. 49.24M). Additionally, our model is significantly faster, with empirical training and execution times at least 2.07 times faster than the baseline.
Evo-Panel: Dynamic Visualization Tool for Optimization Process

Evolutionary algorithms (EAs) are efficient computational intelligence (CI) techniques for solving complex optimization problems in different areas. Explainable artificial intelligence (XAI) has recently emerged as a popular research topic as people are increasingly attempting to know the manner in which AI algorithms attain results. In this context, understanding and analyzing the optimization processes of EAs to effectively enhance their performance are crucial and challenging tasks. Most traditional visualization methods focus on dimensionality reduction, visualization of the fitness landscape and the optimization process for a particular algorithm. This study represents the first attempt at establishing a comprehensive dynamic visualization tool, Evo-Panel, that can directly and flexibly illustrate the detailed procedures of different optimization algorithms in solving numerical benchmark functions. The objective is to help users identify the influence of the design formulas, operations, and parameters on the capabilities and performances of the algorithms. Using Evo-Panel, users can trace each movement, observe the process in an intuitive manner, and interpret the processes related to EAs, which are important to XAI. Moreover, Evo-Panel can be used to perform a comparative analysis to illustrate the differences between algorithms. Results of case studies demonstrate that the use of Evo-Panel can allow professors to explain optimization algorithms in a visually interesting manner and facilitate the students' understanding of EAs. The tool can promote CI-related education from teaching and learning perspectives. Furthermore, researchers can use it to obtain information for analysis, facilitate debugging, verify design ideas, gain insights into the process, and enhance the design of EAs.
Constructions and bounds for codes with restricted overlaps

Non-overlapping codes have been studied for almost 60 years. In such a code, no proper, non-empty prefix of any codeword is a suffix of any codeword. In this paper, we study codes in which over-laps of certain specified sizes are forbidden. We prove some general bounds and we give several constructions in the case of binary codes. Our techniques also allow us to provide an alternative, elementary proof of a lower bound on non-overlapping codes due to Levenshtein [9] in 1964.
Policing Morality: The Inconsistent Application of the Moral Fitness and Fitness Factor

Policing Morality - The Inconsistent Application of the Moral Fitness and Fitness Factor depicts the importance of reforming the best interest of the child factors through the elimination or replacement of the moral fitness and fitness factors. This article provides an examination into the states who have a moral fitness or fitness factor and how each state applies the factor inconsistently, both within its own jurisdiction and amongst the other states with the factor. Moreover, many facts applied to the moral fitness and fitness factors can arguably be applied to other best interest of the child factors rather than the moral fitness and fitness factors, further highlighting the ambiguous application of this factor between the states. This article is beneficial to family law practitioners as it provides a succinct recommendation for the elimination or replacement of the moral fitness factor though the introduction of a criminal activity factor. This factor would look to the parties’ criminal activity and provides clear guidelines for a judge when applying the facts to the best interest of the child factors, rather than allowing for broad interpretation within the moral fitness and fitness factors. Further, the application of a clearly defined factor puts parties on notice that their criminal activity will be considered and may reduce contentious litigation when parties are made aware of the potential results of litigation.
Combinatorial Aspect of Code Restructuring for Virtual Memory Computer Systems Under WS Swapping Strategy

This paper presents innovative findings on the restructuring of code for virtual memory systems operating under a working set swapping strategy. Despite extensive research spanning five decades and numerous studies dedicated to restructuring, the persisting absence of definitive solutions has motivated this inquiry. The NP-hard problem of code block relocation across virtual memory pages to minimize cost function reflects a core challenge inherent to the problem. For ill-defined programs, many practical cluster-based solutions lack a quantifiable approximation error to the unknown optimal or ε-optimal solution. This paper elucidates the computational process by offering a geometric interpretation, enabling the construction of a combinatorial mathematical model of the restructuring process. This model incorporates both functional elements and constraints to define acceptable solutions. The unique aspects of the model provide a foundation for subsequent research aimed at designing an algorithm that delivers an optimal or ε-optimal solution to the original problem, with some algorithmic details discussed herein. The model also paves the way for the development of a swift, cost-effective working set-like swapping algorithm, amplifying the applicability of the results obtained.
Can Pre-trained Language Models Help in Understanding Handwritten Symbols?

The emergence of transformer models like BERT, GPT-2, GPT-3, RoBERTa, T5 for natural language understanding tasks has opened the floodgates towards solving a wide array of machine learning tasks in other modalities like images, audio, music, sketches and so on. These language models are domain-agnostic and as a result could be applied to 1-D sequences of any kind. However, the key challenge lies in bridging the modality gap so that they could generate strong features beneficial for out-of-domain tasks. This work focuses on leveraging the power of such pre-trained language models and discusses the challenges in predicting challenging handwritten symbols and alphabets.