Reviewerly/exHarmony huggingface dataset:
Cybersecurity Awareness Among Students During the COVID-19 Digital Transformation of Education: A Case Study at the Muscat (Oman) Schools

Abstract The COVID-19 pandemic has developed numerous complicated challenges, and it has equally opened the door to another deadly force, known as cybercrime; many reports have indicated that cyberattacks have increased by 500% since the pandemic. While many awareness campaigns are conducted, these campaigns go through many challenges to develop effectiveness, cost coverage, and sustainability. Increasing public awareness of cybersecurity can be a critical strategy in preventing cybercrime or mitigating its impact at some level. Unfortunately, young people are an essential part of the community, and they are immature to recognize the dangers of these kinds of crimes to avoid being a target by cybercriminals. Therefore, the paper's viewpoint is to reform and update the education curricula through the following two objectives: discovering the consequences of COVID-19 on children's digital lives and generating information about current cybersecurity awareness levels among post-Basic education students in Muscat. The survey was conducted in early 2021 on 265 participants from six private schools. The results show that 83% of the students were exposed to cybersecurity incidents during the pandemic, and two-thirds think cybersecurity should be integrated into school curricula. However, awareness campaigns seem limited since 77% of students did not attend or hear about them.
Teks Prosedur dalam Peningkatan Kemampuan Menulis Bahasa Inggris Siswa Sekolah Dasar

Having the ability to write in English has started to become a necessity as a form of active reaction to the development of global communication where English is the official means of international communicaation. It is a huge challenge for students, especially in Indonesia. Of the four language skills, writing has its own challenges and is still considered the most difficult skill to master by most Indonesian students. The quality of a writing is assessed by four indicators, development of ideas, vocabulary choices, and the proper use of grammar and punctuation. These four variables must then be presented in a writing structure appropriate to the genre. The present research is part of a community service activity where the research target is students at an elementary school in Merauke City. There are several stages in the activity, observation, teaching, and post-test. Preliminary observations showed students' weak writing skills which later became the main consideration in preparing teaching materials. The writing learning method using procedural text is selected on the basis of the suitability of students' interests and abilities. The results of the post-test found that more than half of the total number of students showed an increase in writing ability with procedure text and most errors are found in the use of grammar
Generating Adversarial Examples through Latent Space Exploration of Generative Adversarial Networks

Artificial Neural Networks are vulnerable to adversarial examples, malicious inputs that aim to subvert neural networks' outputs. Generative Adversarial Networks (GANs) are generative models capable of generating data that follows the training data distribution. We explore the hypothesis of using the latent space of the trained GAN to find adversarial examples. We test the adversarial examples on external classifiers trained on the same training data. Thus, we propose a framework for Generating adversariaL exAmpleS through latent Space Exploration (GLASSE). A Genetic Algorithm evolves latent vectors as individuals and uses a trained GAN to generate examples to maximise a target activation value of the discriminator network. After the evolutionary process, an external classifier trained on the same dataset evaluates whether it is adversarial. The results indicate that we can optimise the objective and find adversarial examples. We tested the generated examples with models from the adversarial learning literature, showing that 82% on average of the generated examples resulted in successful attacks. We show a t-SNE analysis of the examples, showcasing that generated adversarial examples are blended in the cluster of each belonging class and visually similar to the training dataset examples, showcasing the viability of the proposed approach.
A Deep Learning based Scalable and Adaptive Feature Extraction Framework for Medical Images

Abstract Features extraction has a fundamental value in enhancing the scalability and adaptability n of medical image processing framework. The outcome of this stage has a tremendous effect on the reliability of the medical application being developed, particularly disease classification and prediction. The challenging side of features extraction frameworks, in relation to medical images, is influenced by the anatomical and morphological structure of the image which requires a powerful extraction system that highlights high- and low- level features. The complementary of both feature types reinforces the medical image content-based retrieval and allows to access visible structures as well as an in-depth understanding of related deep hidden components. Several existing techniques have been used towards extracting high- and low-level features separately, including Deep Learning based approaches. However, the fusion of these features remains a challenging task. Towards tackling the drawback caused by the lack of features combination and enhancing the reliability of features extraction methods, this paper proposes a new hybrid features extraction framework that focuses on the fusion and optimal selection of high- and low-level features. The scalability and reliability of the proposed method is achieved by the automated adjustment of the final optimal features based on real-time scenarios resulting an accurate and efficient medical images disease classification. The proposed framework has been tested on two different datasets to include BraTS and Retinal sets achieving an accuracy rate of 97% and 98.9%, respectively.
Spatial-Temporal Variables for Deepfake Video Identification

Deepfake detection at the video level has not been as widely studied as at the image level, as it involves analyzing sequences of frames in a temporal context to detect manipulation, which is more complex than analyzing individual images. In this research, we seek to demonstrate how the current image and sequence classifiers-based approaches for deepfake detection struggle to generalize to new manipulation techniques. In order to improve the generalisation ability to identify new types of deepfake images or videos, we suggest spatio-temporal variables that are described by 3D CNNs. We demonstrate that spatial features acquire unique deep fake method-specific qualities, but spatio-temporal features capture common deep fake method-specific attributes. Using the FaceForensics++ dataset, we present a detailed examination of how the sequential and 3D CNN video encoders are making use of temporal information. We discover that, in contrast to previous sequence encoders, our method incorporates local spatio-temporal relationships as well as irregularities in the deepfake videos. We demonstrate that our technique surpasses conventional approaches in terms of generalisation abilities via substantial investigations on the FF++.
Maintain a Better Balance between Performance and Cost for Image Captioning by a Size-Adjustable Convolutional Module

Image captioning is a challenging AI problem that connects computer vision and natural language processing. Many deep learning (DL) models have been proposed in the literature for solving this problem. So far, the primary concern of image captioning has been focused on increasing the accuracy of generating human-style sentences for describing given images. As a result, state-of-the-art (SOTA) models are often too expensive to be implemented in computationally weak devices. In contrast, the primary concern of this paper is to maintain a balance between performance and cost. For this purpose, we propose using a DL model pre-trained for object detection to encode the given image so that features of various objects can be extracted simultaneously. We also propose adding a size-adjustable convolutional module (SACM) before decoding the features into sentences. The experimental results show that the model with the properly adjusted SACM could reach a BLEU-1 score of 82.3 and a BLEU-4 score of 43.9 on the Flickr 8K dataset, and a BLEU-1 score of 83.1 and a BLEU-4 score of 44.3 on the MS COCO dataset. With the SACM, the number of parameters is decreased to 108M, which is about 1/4 of the original YOLOv3-LSTM model with 430M parameters. Specifically, compared with mPLUG with 510M parameters, which is one of the SOTA methods, the proposed method can achieve almost the same BLEU-4 scores, but the number of parameters is 78% less than the mPLUG.
A License Management and Fine-Grained Verifiable Data Access Control System for Online Catering

To address the law enforcement challenges arising from the rapid expansion of the online catering industry, China’s market regulatory authorities are pursuing countermeasures through legislative efforts and innovative regulatory models. At present, administrative law enforcement for online catering faces difficulties in license management and ensuring the authenticity and security of data. Specifically, there is a prevalence of fraudulent licenses in the industry, and the market supervision department struggles to verify the authenticity of the data gathered during investigations and evidence collection. The tamper-proof and transparent features of blockchain technology cannot be directly applied to the online catering domain. Consequently, to attain trusted license management and secure data-sharing measures, further system design is necessary. To achieve trusted license management, we employ blockchain technology for managing qualification certificates and delineate the format for valid certificates and the application process. The secure data-sharing protocol is divided into two stages. In the first stage, we ensure data authenticity before uploading it to the blockchain through a consensus process involving platforms, users, and merchants. In the second stage, we implement fine-grained access control based on the on-chain data from the previous stage, utilizing conditional proxy reencryption and ultimately employing the data digest on the blockchain for verification. The experiment thoroughly validates the system’s functionality, which is traceable, transparent, and scalable. Moreover, the minimal delay introduced by ensuring data authenticity is virtually negligible. The system’s additional storage overhead does not surpass 10%, remaining within acceptable limits.
Enhanced Neural Speech Recognizer for Quranic Recitations

In this paper, we build on our recent efforts to build a Neural Speech Recognizer (NSR) for Quranic recitations that can be utilized by individuals regardless of their gender, age, or experience. In our previous papers, we presented the Quran recitations by females and males (QRFAM) dataset, which is a large benchmark dataset of audio recordings made by male and female reciters from different age groups and proficiency levels. Along with this dataset, we created several baseline NSR systems based on Mozilla's DeepSpeech model using different subsets of the QRFAM dataset for training, validation and testing purposes. In this work, we present our efforts to optimize and improve these baseline models. We show how to improve the WER by up to <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$27.34{\%}$</tex> compared to prior work.
Single-frame remote sensing image defogging network based on attention residual blocks and contrast learning

High-resolution remote sensing image defogging can bring a lot of convenience to downstream image tasks, however, existing remote sensing image defogging algorithms have many drawbacks. In this paper, an O-type network structure for remote sensing image defogging is proposed. On the one hand, a flipped inverted attention residual block is designed to solve the shortcomings of information loss and gradient explosion of the basic residual block, so that the network can fuse features in multiple scales and directions to provide more detailed edge information and texture information for the decoding stage. On the other hand, a multi-loss function constraint network is designed to improve the shortcomings of traditional overall learning by making full use of positive and negative samples to help the network learn a kind of ‘mapping’ to achieve the transformation from fog to fog-free and adding structural similarity loss to make the output image with more detailed edges. The colour distortion is mitigated. In addition, a data set RSI_Haze is proposed for remote sensing image defogging, and fogs with different transmittance are synthesized to obtain a data set closer to the real one with higher richness to train the defogging network, which makes the parameters of the network more robust and can remove fogs of different thicknesses adaptively. Experiments show that the network has a better recovery effect in high-resolution remote sensing images than other SOTA networks, enabling remote sensing images to play more value for human activities.
A secure and trusted context prediction for next generation autonomous vehicles

To ensure better facilitation of vehicular services and improve driving safety in the Internet of Vehicles (IoV), context prediction among vehicles plays a very crucial role. However, as more malicious IoV devices get involved in the network, the context prediction accuracy shared among various servers may degrade severely. Existing schemes have used cryptographic mechanisms to securely and accurately identify malicious devices. However, time and the subsequent delay in identifying and rating the legitimate communicating IoV devices emerge as a crucial issue. Hence, to solve this critical problem, we put forth an efficient and reliable trust framework where trust and context prediction is achieved by Tidal Trust Mechanism (TTM) and Contract Theory (CT). TTM can successfully rate the degree of trust between the devices with a high level of accuracy, whereas CT can verify the context prediction reliably. The proposed mechanism based on TTM and CT ensures that trusted IoV devices are identified with high accuracy and verified reliably. The proposed framework is simulated over real-world data set in MATLAB for various performance metrics, such as altered records, accuracy prediction, response time, and utilities of IoV devices. Simulation results show that the proposed framework provides a significant improvement of approximately 87% in comparison to existing (baseline) approaches while analyzing the accuracy, record alteration, and resource utility among the devices in the network.
A novel method of ATM Anti-theft Design using System on Chip

The idea of designing the Automated teller Machine security/Anti-theft system originated with the observation of real-life incidents which are prevalent around us. As the number of ATMs has been increasing abruptly, there is an obvious shortcoming in the securities of the ATMs. This ATM anti-theft system deals with robbery prevention which occurs at ATMs and is reported very lately thus helping the security agency to catch the culprits. This system is made using Embedded System (SOC) Therefore to overcome this issue of late reporting and delayed investigation, this project came to light. Thus if something fishy happened with the money tray in the ATM, the system gets activated and the ESP-32 initiates the process by sensing that something is not okay thereby sending the message to H12E to get an alert and send thereby alerting the camera to click the photograph of the culprits and at the same time alarming the nearest police station and other concerned authorities by sending the message through GSM module and alarming the buzzer. This is how our project is intended to work. In our project, an RF module, GSM module, ESP-32, H12E encoder, microcontroller, and camera are used. GPS module is used to track the location of the robbed ATM.
AI-Based advanced Talk-chatbot for Implementation

Traditional technologies are transforming how businesses interact with their customers, creating demands for prompt responses & continuous accessibility. Students frequently have questions about college & university policies & procedures, academic process, extracurricular, as well as other aspects of educational career. There is a lack of service satisfaction because the educational advisers as well as the student affairs staff are overloaded with inquiries & unable to respond to them right away. In our work, we develop a multilingual talkbot that converses among learners in both English & Arabic using Artificial Intelligence (AI) & Natural Language Processing (NLP) technologies. The dialog bot was created in Python & is intended for use by students to answer questions about advice. We are utilizing a intent built domain-specific compilation as the knowledge source for the talkbots, which is made up of the typical queries students ask advisers & their answers. By analysing the inputs, the talktbot engine ascertains the client's needs and, with an efficiency of 80.00 % in English and 75.00 % in Arabic, finds the most reasonable answer that meets the intention. We also tested the software solution as well as the talkbot's precision using field tests with pupils to see how well it responded to live input.
Face Recognition in Images with Missing Content using SVM for Vehicle Security

Face Recognition has been an excellent advancement to the present-day technology and is used in many aspects. This paper presents a model for face recognition using Support Vector Machines algorithm that recognizes the face of a person with the missing content from images. The main aim of the proposed model is to recognize a face of the driver and compares it with the images available from the dataset and used for unlocking the vehicle and give access to drive it. Then, it unlocks the steering if the correct face is matched. In this way, one can improve the vehicles’ protection from being theft. This recognition technique is included in the real time project and provided the outcomes with higher accuracies. The extension of this proposed model has also been included which uses fingerprints as one more recognition technique. This model describes the methodology and components that have been used and the experiments show that the proposed model is working with high accuracy.
Local Stochastic Differentiable Architecture Search for Memetic Neuroevolution Algorithms

Even the most efficient approaches to neural architecture search can be very computationally expensive, which leaves little room for inefficiencies. Unfortunately, evolutionary approaches to neural architecture search (NAS) - neuroevolution - often suffer from these inefficiencies due in part to their massive, intractable search spaces. In the past several years, differentiable approaches to neural architecture search have garnered significant attention for their efficiency and performance. In this work, we suggest an approach to create a hybrid algorithm: a neuroevolutionary metaheuristic which employs a local differentiable stochastic neural architecture search during the fitness evaluation step. Our preliminary results demonstrate that this approach is successful in selecting recurrent neural network memory cells.
Learning to Write Poetry Text using Project Based Learning Method with the Help of Powtoon Media

ABSTRACTThe number of students who experience scores below the KKM in writing learning is the background for carrying out this research. Problem based learning (PBL) methods and powtoon media are used in poetry text learning to provide solutions to these problems. The purpose of this study was to find out the results and difficulties experienced by students while participating in poetry text learning using the problem based learning (PBL) method with the help of powtoon media. The research method used is descriptive qualitative. Data collection techniques used are observation sheets and test sheets. The results showed that as many as 81.82% of students were able to get a score above the KKM and there were 18.18% of students still having difficulties. The difficulty of students is in the use of diction aspects when writing poetry texts, it is known from getting the lowest score in each aspect, which is only getting 43 points. But overall the problem based learning (PBL) method with powtoon media in poetry text learning has a positive impact on student achievement.ABSTRAKBanyaknya siswa yang mengalami nilai dibawah KKM pada pembelajran menulis menjadi latar belakang dilaksanakan penelitian ini. Metode problem based learning (PBL) dan media powtoon digunakan pada pembelajaran teks puisi untuk memberikan solusi dari permasalah tersebut. Tujuan penelitian ini untuk mencari tahu hasil dan kesulitan-kesulitan yang dialami siswa selama mengikuti pembelajaran teks puisi menggunaan metode problem based learning (PBL) dengan bantuan media powtoon . Metode penelitian yang digunakan yaitu deksriptif kualitatif. Teknik pengambilan data yang digunakan yaitu lembar observasi dan lembar tes. Hasil penelitian menunjukan bahwa sebanyak 81,82% siswa mampu memperoleh nilai diatas KKM dan terdapat 18,18% siswa masih mengalami kesulitan. Kesulitan siswa terdapat pada penggunaan aspek diksi saat menulis teks puisi hal tersebut diketahui dari peroleh nilai terendah pada setiap aspeknya yaitu hanya mendapatkan 43 point saja.Â Tetapi secara keseluruhan metode problem based learning (PBL) dengan media powtoon pada pembelajaran teks puisi memberi dampak positif dalam pencapaian prestasi belajar siswa.
Learning Behavior Trees by Evolution-Inspired Approaches

As a reactive and modular policy control architecture, Behavior Tree (BT) has been used in computer games and robotics for autonomous agents' task switching. However, constructing BTs manually for complex tasks requires expert domain-knowledge and is error-prone. As a solution, researchers have proposed to auto-construct BTs using evolutionary algorithms such as Genetic Programming (GP) and Grammatical Evolution (GE). Nevertheless, their effectiveness in practical situations is in doubt and there are different drawbacks in the application.
RANCANGAN SISTEM INFORMASI PERSEDIAAN BARANG ALAT TULIS KANTOR (ATK) BERBASIS WEB PADA PT SUMBER NIAGA PRATAMA

One of the pieces of information needed by the company is inventory information. The warehouse department must record every time a transaction occurs, both incoming and outgoing goods and stock of goods. This requires accuracy from the warehouse department so that there are no errors in the stock request report which will be a problem for the company. PT. Sumber Niaga Pratama is a company engaged in trading office stationery. Currently often experience error information in its inventory system. These information errors resulted in delays in preparing stock reports, usage, and ordering of goods. The purpose of this research is to design a web-based inventory information system (ATK). The research method used is field research, with data collection techniques through observation and interviews. The results of the research are in the form of a prototype of a web-based inventory information system that can be applied at PT Sumber Niaga Pratama.