Reviewerly/exHarmony huggingface dataset:
Modern folklore plot ‟doing homework”: a pragmatic aspect

The pragmatic aspect of the study of modern folklore texts is due to the ability of these texts to produce a communicative effect with a minimum amount of textual content, to evoke pragmatic attitudes towards the message in the receptor. The purpose of the work is to conduct a pragmatic analysis of the modern folklore text, including the determination of the author’s position, the pragmatic setting of the text and the types of pragmatic meanings of words. The research methods are linguo-sociological and comparative; semantic, structural and interpretive methods are used to conduct a pragmatic analysis of a modern folklore text. In the course of the study, the definition of school folklore and Internet lore was given, and a scheme for a pragmatic analysis of a modern folklore text was developed. The research material is classified by genre. The author comes to the conclusion that the plot ‟to do homework” have a pragmatic effect on the recipient of information.
PENGEMBANGAN E-LKPD BERBASIS MODEL PEMBELAJARAN PROBLEM SOLVING LEARNING PADA PEMBELAJARAN TEMATIK DI SEKOLAH DASAR

This study aims to develop E-LKPD learning media based on problem solving learning models in thematic learning in class V SD and to produce E-LKPD learning media based on problem solving learning models that meet the criteria of being feasible to implement in thematic learning in class V. Methods the research used is the research and development method modified by Trianto, namely define, design, develop, and disseminate. This learning media is subjected to expert validation tests conducted by media experts, material experts and language experts. After a validation test was carried out by a team of experts, a product trial was then carried out with the research subject, namely fifth grade elementary school students. The results of the media expert test showed that this learning media obtained an average score of 88% in the "very decent" category. The results of the material expert test showed that this learning media obtained an average score of 80% in the "decent" category. The results of the linguist test showed that this learning media obtained an average score of 71% in the "decent" category, then the results of student responses obtained an average score of 82% in the "very good" category. Based on the results of the study it can be concluded that the E-LKPD learning media based on the problem solving learning model in thematic learning in class V is "appropriate" for use in thematic learning in class V SD.
INTELLIGENT FRAUD DETECTION IN FINANCIAL SYSTEM USING MACHINE LEARNING TECHNIQUE

Fraudulent financial statements (FFS) are the results of manipulating financial elements by overvaluing incomes, assets,sales, and profits while underrating expenses, debts, or losses. To identify such fraudulent statements, traditional methods, including manual auditing and inspections, are costly, imprecise, and time-consuming. Intelligent methods can significantly help auditors in analyzing a large number of financial statements. In this study, we systematically review and synthesize the existing literature on intelligent fraud detection in corporate financial statements. In particular, the focus of this review is on exploring machine learning and data mining methods, as well as the various datasets that are studied for detecting financial fraud. We adopted the Kitchenham methodology as a well-defined protocol to extract, synthesize, and report the results. Accordingly, 47 articles were selected, synthesized, and analyzed. We present the key issues, gaps, and limitations in the areaof fraud detection in financial statements and suggest areas for future research. Since supervised algorithms were employed more than unsupervised approaches like clustering, the future research should focus on unsupervised, semi-supervised, as well as bio-inspired and evolutionary heuristic methods for anomaly (fraud) detection. In terms of datasets, it is envisaged that future research making use of textual and audio data. While imposing new challenges, this unstructured data deserves furtherstudy as it can show interesting results for intelligent fraud detection.
Survey on Various Security Issues Associated with Cloud Authentication Techniques

The requirement for computing in the cloud has been steadily growing over the course of the past few decades, which has led to an increase in the amount of attention that has been given to it ever since its inception. The use of cloud computing comes with a range of benefits, some of which include an improvement in productivity, a reduction in overhead costs brought on by centralization, and the flexibility to work from any location as long as there is an Internet connection. The privacy and safety concerns that come along with cloud computing, which has been a core research interest on this survey. The study identifies various existing solutions to the security issues that involves the use of authentication. From numerous authentication models used in cloud service models and cloud deployment models, this study seeks to resolve questions about data security. The study also provided the suggestions to secure the privacy of data and verifying their authentication with a third-party service provider.
Aircraft recognition using convolutional neural network

Satellite image object-recognition offers detailed location representation where ground-based sampling is limited or inaccessible, such as high sea or in the mountains. However, an accurate and effective method is required to ensure the precision of the information generated due to small, ambiguous objects against the background. Having an automatic object detection system would greatly speed up satellite image recognition and find extensive applications in defense, transportation, agriculture, and emergency relief. With the advancement of artificial neural networks, the technology has seen applications in various fields from image processing to speech synthesis. We aimed to develop a neural network-based commercial aircraft detection system on satellite images at major airports. We proposed a R-CNN-based aircraft recognition system where CNN is utilized to extract features of aircraft before passing the feature maps to the binary classifier. We trained the model with 2100 images of satellite images containing aircraft which are segmented to produce positive and negative instances and validated on 900 images. The system is equipped to detect and localize the position of the aircraft in an image while registering excellent accuracy. However, as the number of aircraft in a frame increases, slight deterioration in the model performance is observed.
Smart toolbox with missing toolstracking capability

The toolbox is important to keep all kinds of tools. It is usually used by technicians bring to the working places. Some companies provide toolboxes for their technicians to use. The tools inside the toolboxes sometime could be missing due to unknown reasons. Therefore, it is important for the company to trace the missing tools in the toolbox. This paper shows the smart toolbox invention to overcome the problems on missing tools and presents a smart way to trace the persons who used the toolbox. The propose invention uses NodeMCU microcontroller, fingerprint reader and Blynk app to trace the persons use the toolbox. The propose invention will record the user use the toolbox via the fingerprint reader. A valid user will cause the toolbox open and his fingerprint recorded. To record the user uses the toolbox, each user has to register his or her name into Blynk app together with fingerprint. Therefore, whenever user wants to open the toolbox, user has to turn on the mobile hotspot. The fingerprint reader will then read the fingerprint of the user and the user information will send to the Blynk app for record as database.
A Deep Neural Network Attack Simulation against Data Storage of Autonomous Vehicles

&lt;div&gt;In the pursuit of advancing autonomous vehicles (AVs), data-driven algorithms have become pivotal in replacing human perception and decision-making. While deep neural networks (DNNs) hold promise for perception tasks, the potential for catastrophic consequences due to algorithmic flaws is concerning. A well-known incident in 2016, involving a Tesla autopilot misidentifying a white truck as a cloud, underscores the risks and security vulnerabilities. In this article, we present a novel threat model and risk assessment (TARA) analysis on AV data storage, delving into potential threats and damage scenarios. Specifically, we focus on DNN parameter manipulation attacks, evaluating their impact on three distinct algorithms for traffic sign classification and lane assist. Our comprehensive tests and simulations reveal that even a single bit-flip of a DNN parameter can severely degrade classification accuracy to less than 10%, posing significant risks to the overall performance and safety of AVs. Additionally, we identify critical parameters based on bit position, layer position, and bit-flipping direction, offering essential insights for developing robust security measures in autonomous vehicle systems.&lt;/div&gt;
Why Johnny Can’t Use Secure Docker Images: Investigating the Usability Challenges in Using Docker Image Vulnerability Scanners through Heuristic Evaluation

This paper explores the usability of Docker Image Vulnerability Scanners (DIVSes) through heuristic evaluations. Docker simplifies the process of software development, distribution, deployment, and execution by providing a container-based execution environment. However, vulnerabilities in Docker images can pose security risks to containers. To mitigate this, DIVSes are crucial in helping developers identify and address these vulnerabilities in the software packages and libraries within Docker images. Despite their importance, research on the usability of DIVSes has been limited. To address this gap, we developed 11 customized heuristics and applied them to three widely-used DIVSes (Grype, Trivy, and Snyk). Our evaluations revealed 239 usability issues within the tools evaluated. Our findings highlight that the evaluated DIVSes do not provide sufficient information to comprehend the risks associated with identified vulnerabilities, prioritize them, or effectively fix them. Our study offers valuable insights and practical recommendations for enhancing the usability of DIVSes, making it easier for developers to identify and address vulnerabilities in Docker images.
JCDL2023 Workshop: Joint Workshop of the 4th Extraction and Evaluation of Knowledge Entities from Scientific Documents (EEKE2023) and the 3rd AI + Informetrics (AII2023)

The 4th Workshop on Extraction and Evaluation of Knowledge Entities from Scientific Documents (EEKE 2023) and the 3rd AI + Informetrics (AII2023) will be held in New Mexico, USA and online at the ACM/IEEE Joint Conference on Digital Libraries (JCDL) 2023. The goal of these two workshop series (https://eeke-workshop.github.io/ and https://ai-informetrics.github.io/) are to engage the related communities in open problems in the extraction and evaluation of knowledge entities from scientific documents and the modeling and applications of AI + Informetrics for broad interests in science of science, science, technology, & innovation (ST&I), etc. This joint workshop consists of keynotes, oral presentations, and posters, and will attract not only academic researchers and librarians but also decision makers from governments and practical sectors.
Regulating China's health code system to prepare for future pandemics

This study investigates the challenges that China's health code system presents to individuals' lives and social development, using normative analysis and a case study. It looks for effective strategies to reform and regulate this system to prepare for future pandemics. Health code apps and mini programs have been widely deployed as effective tools for COVID-19 containment in China. However, their widespread and improper use has created risks due to the lack of both a systematic design and a basic supervision mechanism. The health code system risks infringing on individual privacy during data collection and storage. During the pandemic, the right to liberty and the right to treatment of Chinese citizens who lacked an appropriate health code were severely compromised. In some instances, the health code system was used as a stability maintenance tool by the authorities through arbitrary health code conversions. This article argues that China's health code systems should be reformed and regulated in preparation for future pandemics and that a new act regulating its management and use should be launched at the national level. Data collection, retention, and processing should be limited to the minimum amount of data needed to achieve the objective of protecting public health. The health code conversion power wielded by the authorities should be defined and regulated, the rules and procedures of code conversion should be transparent, arbitrary health code conversion behaviors should be prevented and punished, and persons whose rights have been violated by wrongful code conversion should have access to legal remedies.
An opponent model for agent-based shared decision-making via a genetic algorithm

Introduction Shared decision-making (SDM) has received a great deal of attention as an effective way to achieve patient-centered medical care. SDM aims to bring doctors and patients together to develop treatment plans through negotiation. However, time pressure and subjective factors such as medical illiteracy and inadequate communication skills prevent doctors and patients from accurately expressing and obtaining their opponent's preferences. This problem leads to SDM being in an incomplete information environment, which significantly reduces the efficiency of the negotiation and even leads to failure. Methods In this study, we integrated a negotiation strategy that predicts opponent preference using a genetic algorithm with an SDM auto-negotiation model constructed based on fuzzy constraints, thereby enhancing the effectiveness of SDM by addressing the problems posed by incomplete information environments and rapidly generating treatment plans with high mutual satisfaction. Results A variety of negotiation scenarios are simulated in experiments and the proposed model is compared with other excellent negotiation models. The results indicated that the proposed model better adapts to multivariate scenarios and maintains higher mutual satisfaction. Discussion The agent negotiation framework supports SDM participants in accessing treatment plans that fit individual preferences, thereby increasing treatment satisfaction. Adding GA opponent preference prediction to the SDM negotiation framework can effectively improve negotiation performance in incomplete information environments.
Multi-scale Rain Removal Across Multiple Frequencies Sub-bands Using MS-CycleGANs

Real rain removal is a complex and challenging problem on real rain images. Although rain removal using the CycleGAN (Tang et al. in ECCV 2018 Workshop Proceedings, Part V [1]) was found to be superior as compared to other existing techniques, there are two aspects that can be improved on: (1) Preserve original scene details, and (2) Remove low-frequency rain distortion. Hence, this paper proposes a multi-scale representation technique, called the MS-CycleGANs, to address the remaining gaps of the CycleGAN in rain removal. It uses a pyramid framework that is made up of multi-scale CycleGANs. Such multi-scale representation is capable of removing rain distortions at multiple frequency sub-bands without sacrificing the scene details because the characteristics of both rain and no-rain domains can be learnt simultaneously by the proposed network at their frequency sub-bands. We compare the MS-CycleGANs to the CycleGAN on similar rain images for the networks’ training and testing. The results are compared quantitatively using the NIQE metric (Mittal et al. in IEEE Signal Process Lett 22:209–212, [2]) in this paper as we do not have any prior information on real rain distortions. The results have demonstrated the MS-CycleGANs’ superiority in removing real rain distortions, both qualitatively by visual check and also quantitatively using the NIQE metric (Mittal et al. in IEEE Signal Process Lett 22:209–212, [2]).
NLFA: A Non Local Fusion Alignment Module for Multi-Scale Feature in Object Detection

Recently, in order to pursue better detection results, more convolutional layers and deeper networks are a direction pursued by everyone. However, more and more down-sampling convolution or up-sampling operations generate feature maps of different scales, which makes it difficult to avoid the loss of detailed information of the image, and the distribution of different scales features will be misaligned. In particular, the loss and dislocation of the target boundary information will affect the features learned by the model and reduce the accuracy. This paper proposes a feature alignment method based on non-local idea, and designed two modules—Non Local Align Module (NLA) and Channel Fusion Augment Module (CFA). At the same time, the neighborhood calculation algorithm is also designed for it, which strengthens the binding force on the calculation of boundary information. These two modules can be easily embedded into the current mainstream object detection network to improve the detection effect of the model. Compared to the previous model, the network using our NLA module and CFA module achieves better results than the original model.
Fault-Tolerant SINS/Doppler Radar/Odometer Integrated Navigation Method Based on Two-Stage Fault Detection Structure

To improve the reliability of strapdown inertial navigation system (SINS)/Doppler radar/odometer integrated navigation system, the federated Kalman filter with two-stage fault detection structure is designed, and a fault-tolerant SINS/Doppler radar/odometer integrated navigation method is proposed. Firstly, the pre-fault detection module sets before the local filter, and the residual chi-square test in the carrier coordinate system is selected to detect the abrupt faults of Doppler radar and odometer. Then, the secondary-fault detection module emplaces between the local filter and the main filter, and the sequential probability ratio test (SPRT) is selected to further detect the ramp faults that are difficult to detect by the residual chi-square test. To address the limitation of the SPRT in accurately determining the end time of faults, an improved SPRT is proposed. The improved SPRT reduces the influence of historical fault on the fault statistics by introducing forgetting factors to improve its sensitivity to the fault end. The simulation experiment indicates that the proposed method can quickly detect and isolate abrupt and ramp faults, and promptly restore normal operation of the integrated navigation system after the fault ends, effectively improving the fault tolerance and reliability of the integrated navigation system.
When Broad Learning System Meets Label Noise Learning: A Reweighting Learning Framework

Broad learning system (BLS) is a novel neural network with efficient learning and expansion capacity, but it is sensitive to noise. Accordingly, the existing robust broad models try to suppress noise by assigning each sample an appropriate scalar weight to tune down the contribution of noisy samples in network training. However, they disregard the useful information of the noncorrupted elements hidden in the noisy samples, leading to unsatisfactory performance. To this end, a novel BLS with adaptive reweighting (BLS-AR) strategy is proposed in this article for the classification of data with label noise. Different from the previous works, the BLS-AR learns for each sample a weight vector rather than a scalar weight to indicate the noise degree of each element in the sample, which extends the reweighting strategy from sample level to element level. This enables the proposed network to precisely identify noisy elements and thus highlight the contribution of informative ones to train a more accurate representation model. Thanks to the separability of the model, the proposed network can be divided into several subnetworks, each of which can be trained efficiently. In addition, three corresponding incremental learning algorithms of the BLS-AR are developed for adding new samples or expanding the network. Substantial experiments are conducted to explicate the effectiveness and robustness of the proposed BLS-AR model.
Strategi Contextual Teaching And Learning (CTL) dalam Pembentukan Minat Belajar Siswa

The purpose of this journal is to examine contextual teaching and learning strategies in forming students' interest in learning. The method used in this research is library research (Library Research). The research results that can be obtained from researchers, that from the educational level, contextual teaching and learning strategies are able to restore and shape children's learning interests, this strategy can help students to be smarter, active, and able to think critically, giving rise to new enthusiasm that shapes interest. learning for every child/student at every level of education. This will be more advanced for the cognitive, affective and psychomotor development of students in learning.&#x0D; Keywords: Strategy, Formation, Learning&#x0D;
Detection of Natural Disasters Using Machine Learning and Computer Vision by Replacing the Need of Sensors

Natural disasters not only cause death and property destruction around the world but the severity of these disasters is also reflected in many other forms like the economic loss, loss of lives, and the ability of populations to rebuild. This paper focuses on using machine learning, deep learning, and computer vision to detect natural disasters. Previously, many attempts have been made to detect natural disasters using different techniques including the use of sensors, satellites, drones, crowdsourcing, and basic ML models and reducing the severity of these disasters. But natural disaster detection still faces challenges due to the limitations of the above techniques like high cost of equipment and training time, lower accuracy, etc. Therefore, in order to combat these issues, transfer learning models are being used, namely a few architectures from VGG, ResNet, and EfficientNet. Many different values for epochs and dataset sizes were experimented with, as well as different configurations for the hidden layers and optimizers to improve the classification accuracy. From the results obtained after training the models, ResNet50 gave the best results with an accuracy of 96.35%.
Analysis of Beginning Stage of Reading Skills of First Grade Students of Indriasana Palembang Primary School.

This study aims to describe the beginning stage reading ability of first grade students at SD Indriasana Palembang. This research method is descriptive qualitative, with data collection through observation, interviews, tests, and documentation. The sample used saturated sampling technique. The research sample was 61 first grade students at SD Indriasana Palembang. The results of the study, first in the aspect of reading letters as much as 98% of respondents achieved the criteria of "very good" and 2% achieved "good". Second, in the aspect of reading syllables, 98% of respondents with the criteria of "very good" and 2% "enough". Third, in the aspect of reading words 92% of respondents with the criteria of "very good" and 8% "enough". Fourth, in the aspect of reading sentences 84% ​​of respondents with "very good", 11% "good", and 5% "enough".