Reviewerly/exHarmony huggingface dataset:
Effects of Visual Communication Design Accessibility (VCDA) Guidelines for Low Vision on Public and Open Government Health Data

Since 2019, the Korean government's investments in making data more accessible to the public have grown by 337%. However, open government data, which should be accessible to everyone, are not entirely accessible to people with low vision, who represent an information-vulnerable class. Emergencies, such as the COVID-19 pandemic, decrease face-to-face encounters and inevitably increase untact encounters. Thus, the information gap experienced by low-vision people, who are underprivileged in terms of information, will be further widened, and they may consequently face various disadvantages. This study proposed visual communication design accessibility (VCDA) guidelines for people with low vision. Introduced screens enhanced by accessibility guidelines were presented to 16 people with low vision and 16 people with normal vision and the speed of visual information recognition was analyzed. No statistically significant difference (p > 0.05) was found due to the small sample size; however, this study's results approached significance with improved visual recognition speed for people with low vision after adopting VCDA. As a result of the intervention, the visual information recognition speed of both normal and low-vision people improved. Thus, our results can help improve information recognition speed among people with normal and low vision.
Application of graph auto-encoders based on regularization in recommendation algorithms

Social networking has become a hot topic, in which recommendation algorithms are the most important. Recently, the combination of deep learning and recommendation algorithms has attracted considerable attention. The integration of autoencoders and graph convolutional neural networks, while providing an effective solution to the shortcomings of traditional algorithms, fails to take into account user preferences and risks over-smoothing as the number of encoder layers increases. Therefore, we introduce L1 and L2 regularization techniques and fuse them linearly to address user preferences and over-smoothing. In addition, the presence of a large amount of noisy data in the graph data has an impact on feature extraction. To our best knowledge, most existing models do not account for noise and address the problem of noisy data in graph data. Thus, we introduce the idea of denoising autoencoders into graph autoencoders, which can effectively address the noise problem. We demonstrate the capability of the proposed model on four widely used datasets and experimentally demonstrate that our model is more competitive by improving up to 1.3, 1.4, and 1.2, respectively, on the edge prediction task.
Coherent Quantum Network of Superconducting Qubits as a Highly Sensitive Detector of Microwave Photons for Searching of Galactic Axions

We propose a novel approach to detect a low power microwave signal with a frequency of the order of several GHz based on a coherent collective response of quantum states occurring in a superconducting qubits network (SQN). An SQN composes of a large number of superconducting qubits embedded in a low-dissipative superconducting resonator. Our theory predicts that an SQN interacting with the off-resonance microwave radiation, demonstrates the collective alternating current Stark effect that can be measured even in the limit of single photon counting. A design of the layout of three terminals SQN detectors containing 10 flux qubits weakly coupled to a low-dissipative R-resonator and T-transmission line was developed. The samples were fabricated by Al-based technology with Nb resonator. The SQN detector was tested in terms of microwave measurements of scattering parameters and two-tone spectroscopy. A substantial shift of the frequency position of the transmission coefficient drop induced by a second tone pump signal was observed, and this effect clearly manifests a nonlinear multiphoton interaction between the second-tone microwave pump signal and an array of qubits.
Higher Education Institutions’ Security Capability the Leads to the Creation of Standardized Campus Security System

This study aimed to assess the current state of campus security in selected HEIs in Nueva Ecija.It utilizes the descriptive-quantitative method of research.This study was conducted at the selected HEIs in Nueva Ecija.The administrators, faculty and students, and security personnel are the respondents of the study, and a questionnaire was used to gather pertinent data.Weighted mean was used to determine the current state of campus security, the satisfaction of respondents with the security services and the function of security personnel.Findings revealed that the five selected HEIs adhere to security practices and procedures in terms of physical security, personnel security, and document security as rated by respondents to be very much implemented.Among the fifteen problems encountered, the top three are: "There are limited CCTV cameras installed in strategic locations"."Sprinkler systems and smoke detectors are not installed in offices and laboratories" and "There are inconsistent in the implementation of the NO-ID NO Entry Policy, Visitor's ID and logbook, and Gate Pass policy".The findings show that the five selected HEIs have no difference in their current state of campus security.The very much satisfaction rating in the area of the chief security officer and much satisfaction along security guards by the group of respondents signify that they are pleased with the function and services rendered by security personnel.It is recommended that selected HEIs shall continue and maintain the security practices and procedures that were already put into operation.Installation of Security cameras in strategic locations should be considered including sprinkler systems.Security guards should continue and consistently implement the security policies, rules, and regulations of the school.Future studies should be conducted focusing on safety measures against a natural or manmade disaster that would happen inside the campus and creating a Safety manual for the college and Adoption of the proposed security manual for the improvement of the implementation of campus security.
Improved RetinaNet Target Detection Model

RetinaNet can perform the detection task well compared with other one-stage target detection models, but it still has problems such as insufficient extraction and fusion of features from different stages, inaccurate bounding box regression and slow convergence of the network. For the above problems, an improved RetinaNet target detection model is proposed. The specific implementation is: Firstly, introduce Mosaic data enhancement to enrich the image background and enhance the training speed, in addition to this, add a Spatial Transformer Network (STN) to the feature extraction network; Secondly, change the original feature pyramid network to a Multi-Scale Feature Fusion network(MSF), and add Atrous Spatial Pyramid Pooling (ASPP) structures to adequately fusion of multi-scale semantic and location information, which increase the perceptual field; Finally, the original bounding box regression loss is changed to F-EIOU loss. This improved RetinaNet target detection model is adequately done on MS COCO and PASCAL VOC datasets with extensive experiments. Compared with RetinaNet, proposed target detection model improves the detection accuracy by 1.2% on MS COCO dataset and the average accuracy by 3.1% on PASCAL VOC dataset in the experiments.
Access Control Method of SDN Network Based on Zero Trust

In order to use relevant methods to control SDN network access based on zero trust, this paper will carry out relevant research. This paper first introduces the basic concepts of zero trust and SDN, then constructs the access control framework of SDN, and puts forward the relevant control principles. Through this study, SDN network access can be effectively controlled on the basis of zero trust, which can greatly guarantee access security.
The Carbon Emissions of Writing and Illustrating Are Lower for AI than for Humans

As AI systems proliferate, their greenhouse gas emissions are an increasingly important concern for human societies. In this article, we present a comparative analysis of the carbon emissions associated with AI systems (ChatGPT, BLOOM, DALL-E2, Midjourney) and human individuals performing equivalent writing and illustrating tasks. Our findings reveal that AI systems emit between 130 and 1500 times less CO2e per page of text generated compared to human writers, while AI illustration systems emit between 310 to 2900 times less CO2e per image than their human counterparts. Emissions analyses do not account for social impacts such as professional displacement, legality, and rebound effects. In addition, AI is not a substitute for all human tasks. Nevertheless, at present, the use of AI holds the potential to carry out several major activities at much lower emission levels than can humans.
The educational crisis in today's information and digital society

The aim of the study is to analyze the traditional education crisis and highlight the benefits and opportunities of digitalizing educational activities. The main methodology applied in this research is a review of current literature, as well as the experiences of teachers and students. The results show that the development of modern information and digital technologies, and their widespread use worldwide, significantly affect the production of goods and services, scientific work, educational processes, cultural traditions, and social relations. The obtained results underscore the fundamental basis of the educational crisis issue in the information-digital society and distance education research in particular. The need to transform the educational industry by incorporating Internet technologies into the educational process represents a significant challenge for educators, especially in terms of overcoming the educational crisis caused by the urgent need to transition to distance learning. Under the influence of the latest digital technologies, significant configuration processes are observed in public development of a global nature.
The influence of event segmentation by context on stimulus–response binding.

A core characteristic of auditory stimuli is that they develop over time. Referring to the event segmentation theory, we assume that the on- and offset of a contextual sound indicates the start and end of an event. As a consequence, stimuli and responses appearing within a common auditory context may be integrated more likely/strongly, forming so-called event files, than those appearing in different auditory contexts. In two experiments, this hypothesis was tested using the negative priming paradigm and the distractor-response binding paradigm. In prime-probe presentations, participants identified target sounds via keypresses while ignoring distractor sounds. Additional sine tones acted as the context in the prime, whereas the probe context was silence. In the common context condition, the context started with the prime sounds and ended with the prime response. In the changing context condition, the context started with the prime sounds but changed to another tone after the offset of the prime sounds. Results from both experiments revealed a larger stimulus-response binding effect in the common than in the changing context condition. We conducted a control experiment to test the alternative account of contextual similarity between the prime and the probe. Together, our results suggest that common context can temporally segment stimuli and responses into event files, providing evidence of common context as a binding principle. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
Ultrasound breast tumor detection based on vision graph neural network

Breast cancer is the most commonly diagnosed cancer in women in the United States. Early detection of breast tumors enables prompt determination of cancer status, significantly boosting patient survival rate. Non-invasive and non-ionizing ultrasound imaging is a widely used diagnosing modality in clinic. To assist clinicians in breast cancer diagnosis, we implemented a vision graph neural networks (ViG)-based pipeline that can achieve accurate binary classification (normal vs. breast tumor) and multiclass classification (normal, benign, and malignant) from breast ultrasound images. Our results demonstrated that the average accuracy of ViG is 100.00% for binary and 87.18% for multiclass classification tasks. To the best of our knowledge, this is the first end-to-end, graph-feature-based deep learning pipeline to achieve accurate breast tumor detection from ultrasound images. The proposed ViG-based classifier is accessible for clinical implementation and has the potential to enhance lesion detection from ultrasound images.
Cloud Container Service Orchestrated with Kubernetes: a State-of-the-Art Technology Review and Application Proposal

This paper discusses specific technology related issues with the latest release of the container orchestrator Kubernetes and combining it with certain container runtimes. We provide a technology-focused state-of-the-art review, addressing identified research gaps in this area. Following the depreciation of Dockershim in the latest K8s release 1.26, we’ve tested the migration to another container management tool, that enables using the latest functions or the Containerd runtime (Nerdctl). At the same time this migration also allows running containers in a secure and resource effective way and managing them together with container images. The application we study is orchestrating a container cluster-based cloud NIDS service by using the latest Snort 3 system. We’ve also outlined directions for future research.
Blockchain Technology

To encourage industry discussion of cutting-edge blockchain applications, this chapter was written. This chapter provides an introduction to blockchain technology as well as details on how to secure transactions using technology and how the secure element works. It examines common implementation worries and provides use cases for current commercial or trial projects. The following paragraphs go over the concept of a blockchain, how bitcoin transactions operate, and how to finish a transaction. The network of Marco Polo will be highlighted. In this chapter, the authors show how to use blockchain and cloud technology with Marco Polo to remove corporate pain problems. The chapter goes on to give a full overview of blockchain, including its function, traits, and applications.
Research on Global BloomFilter-Based Data Routing Strategy of Deduplication in Cloud Environment

The application of data deduplication technology reduces the demand for data storage and improves resource utilization. Compared with limited storage capacity and computing capacity of a single node, cluster data deduplication technology has great advantages. However, the cluster data duplication technology also brings new issues on deduplication rate reduction and load balancing of storage nodes. The application of data routing strategy can well balance the problem of deduplication rate and load balancing. The paper introducesa global BloomFilter routing strategy. In order to avoid the communication overhead caused by sending the fingerprints to the data storage node and inquiring about the BloomFilter maintained in the memory, a BloomFilter array is maintained in the memory of the client-server. Each row of the array corresponds to a storage node; before sending the Superchunk to the storage node, it will inquire the BloomFilter array and storage capacity information to get the optimal node. The theoretical analysis and experimental results prove the feasibility of the strategies proposed by this paper.
The Effect of Artificial Intelligence and Gamification on Online Purchase Intention Mediated by Customer Experience: Study on Indonesian Marketplace Users

Objectives: This study aims to analyze the effect of Artificial intelligence and gamification on online purchase intention, as well as the role of customer experience as a variable that mediates the effect of artificial intelligence and gamification on online purchase intention. The marketing approach in today's era tends to lean towards digital personalization using Artificial intelligence and gamification to improve customer engagement and experience. This is ongoing with the fast rate at which technology has been expanding. The development of artificial intelligence has increased consumer happiness even more, making it even more crucial in the contemporary environment. Also, Gamification has clearly been identified as a method of influencing consumer behavior. The use of game design elements in non-game contexts is known as gamification. Gamification and persuasive technologies have been used for business purposes and to influence customer behavior.Methodology: The method used in this study is a quantitative method using survey research with marketplace users in Indonesia as the object of the research. Data collection which was used is the cross-sectional method by distributing online questionnaires from Google Forms. The research sample was selected using the purposive sampling method in order to obtain 272 respondents from various users (Tokopedia, Shopee, Lazada, Blibli, and Bukalapak users). The data analysis that is used is the path analysis Structural Equation Model (SEM) by SmartPLS 3.0 software. The first stage in data analysis is to recapitulate data on the characteristics of the respondents. The next step is to measure the validity and reliability using discriminant validity, convergent validity, composite reliability, and Cronbach's alpha. Then, to test the inner model, a Goodness of Fit test was carried out through R-Square and significance testing by interpreting path analysis values, T-values, and P-Values.Finding: In this research conducted on marketplace users in Indonesia, it was found that Artificial Intelligence and Gamification are determinants of Customer Experience but these two variables cannot directly support Online Purchase Intention. Further findings indicate that Customer Experience can be an important determining factor for Online Purchase Intention. Customer experience can strengthen the online purchase intention among marketplace users.Conclusion: The result of this study shows that Artificial intelligence and gamification cannot affect online purchase intention directly but artificial intelligence and gamification can affect online purchase intention indirectly through customer experience
Transition from breccia pipe to stratabound mineralization in Mississippi Valley-type hydrothermal systems: The Nayongzhi Zn-Pb deposit, world-class Sichuan−Yunnan−Guizhou triangle, South China

Unraveling the time-space evolution of Mississippi Valley-type (MVT) hydrothermal systems is critically important for understanding ore genesis and exploration. We studied a complete mineral system from conduit-filling to tail-end facies in the world-class Sichuan−Yunnan−Guizhou triangle, South China, via field geology, mineralogy, fluid inclusions, and in situ sulfide S-Pb isotopes to propose an integrated model for the evolution of MVT hydrothermal systems. Geological mapping shows that the mineralization transitions from breccia pipe to stratabound style: high-grade ores occur mainly as open-space infill associated with F12 fault (fluid conduit), while low-grade evaporite-related replacement ores developed distal from the fault. The δ34S value in hydrothermal sulfides shows a wide range (+3.6‰ to +27.9‰), with significant intragrain variation (up to +12.8‰), which suggests a mixture of 34S-rich sulfur produced by thermochemical sulfate reduction of evaporite within the ore host and 32S-rich sedimentary/diagenetic pyrite. In situ sulfide Pb ratios decrease away from the F12 fault, which indicates that metals were sourced from different degrees of mixing between the Proterozoic metamorphic basement and wall rocks. Fluid-inclusion microthermometric data identify two distinct fluids: a hotter (&amp;gt;155 °C), more saline (&amp;gt;18 wt% NaCl equivalent), and metal-rich fluid with a cooler (&amp;lt;90 °C), low-salinity (&amp;lt;4 wt% NaCl equivalent), and reduced sulfur-rich fluid. The mixing of two fluids was responsible for precipitating the high-grade ores near the F12 fault, and the acid-producing (H+) process created new space for stratabound ore distal to the fault. This study highlights that fluid mixing is critical for efficient sulfide accumulation, and the metal zoning pattern provides guidelines for exploration.
Lagrange Programming Neural Networks for Sparse Portfolio Design

Although there were some works on analog neural networks for sparse portfolio design, the existing works do not allow us to control the number of the selected assets and to adjust the weighting between the risk and return. This paper proposes a Lagrange programming neural network (LPNN) model for sparse portfolio design, in which we can control the number of selected assets. Since the objective function of the sparse portfolio design contains a non-differentiable $$\ell _1$$ -norm term, we cannot directly use the LPNN approach. Hence, we propose a new formulation based on an approximation of the $$\ell _1$$ -norm. In the theoretical side, we prove that state of the proposed LPNN network globally converges to the nearly optimal solution of the sparse portfolio design. The effectiveness of the proposed LPNN approach is verified by the numerical experiments. Simulation results show that the proposed analog approach is superior to the comparison analog neural network models.
DAGKT: Difficulty and Attempts Boosted Graph-Based Knowledge Tracing

In the field of intelligent education, knowledge tracing (KT) has attracted increasing attention, which estimates and traces students’ mastery of knowledge concepts to provide high-quality education. In KT, there are natural graph structures among questions and knowledge concepts so some studies explored the application of graph neural networks (GNNs) to improve the performance of the KT models which have not used graph structure. However, most of them ignored both the questions’ difficulties and students’ attempts at questions. Actually, questions with the same knowledge concepts have different difficulties, and students’ different attempts also represent different knowledge mastery. In this paper, we propose a difficulty and attempts boosted graph-based KT (DAGKT) ( https://github.com/DMiC-Lab-HFUT/DAGKT ), using rich information from students’ records. Moreover, a novel method is designed to establish the question similarity relationship inspired by the F1 score. Extensive experiments on three real-world datasets demonstrate the effectiveness of the proposed DAGKT.