Reviewerly/exHarmony huggingface dataset:
Phishing websites detection using a novel multipurpose dataset and web technologies features

Phishing attacks are one of the most challenging social engineering cyberattacks due to the large amount of entities involved in online transactions and services. In these attacks, criminals deceive users to hijack their credentials or sensitive data through a login form which replicates the original website and submits the data to a malicious server. Many anti-phishing techniques have been developed in recent years, using different resource such as the URL and HTML code from legitimate index websites and phishing ones. These techniques have some limitations when predicting legitimate login websites, since, usually, no login forms are present in the legitimate class used for training the proposed model. Hence, in this work we present a methodology for phishing website detection in real scenarios, which uses URL, HTML, and web technology features. Since there is not any updated and multipurpose dataset for this task, we crafted the Phishing Index Login Websites Dataset (PILWD), an offline phishing dataset composed of 134,000 verified samples, that offers to researchers a wide variety of data to test and compare their approaches. Since approximately three-quarters of collected phishing samples request the introduction of credentials, we decided to crawl legitimate login websites to match the phishing standpoint. The developed approach is independent of third party services and the method relies on a new set of features used for the very first time in this problem, some of them extracted from the web technologies used by the on each specific website. Experimental results show that phishing websites can be detected with 97.95% accuracy using a LightGBM classifier and the complete set of the 54 features selected, when it was evaluated on PILWD dataset.
Structural Model Analysis of Factors Affecting Sustainable Teacher Job Satisfaction in Korea: Evidence from TALIS 2018

This study aims to explore the relationship between the preparedness, self-efficacy, career motivation, and job satisfaction of Korean teachers with the 2018 data of TALIS (Teaching and Learning International Survey). For this purpose, 1266 Korean middle school teachers were selected, and an analysis of mediating effects was executed by utilizing structural equation modeling and phantom variables. The results are as follows: teacher preparedness had a significant and positive effect on teacher self-efficacy and career motivation. However, it failed to show a significant effect on teacher job satisfaction, which was instead significantly and positively affected by teacher self-efficacy and career motivation. In addition, based on the analysis of the estimate of mediating effects, it was discovered that teacher career motivation had a greater positive effect than that teacher self-efficacy on teacher job satisfaction. Taking these results into consideration, this study accordingly makes suggestions concerning the improvement of sustainable teacher job satisfaction, self-efficacy and career motivation inside teacher education courses. Furthermore, this study will propose measures such as further valuing the Teaching Personality and Aptitude Test and strengthening in-depth interviews in student teacher selection, extending teaching experiences through simulated instruction and peer supervision as well as providing well-organized teaching professional counseling opportunities for student teachers.
DVTChain: A blockchain-based decentralized mechanism to ensure the security of digital voting system voting system

Voting is a fundamental democratic activity. Many experts believe that paper balloting is the only appropriate method to ensure everyone’s right to vote. But this method is prone to errors and abuse. Many nations utilize digital voting methods to solve the difficulties of paper balloting. A single flaw in digital voting may lead to massive vote-rigging. Election voting methods must be legal, accurate, safe, and convenient. However, issues with digital voting methods may restrict acceptance. Due to its end-to-end verification capabilities, blockchain technology was developed to address these problems. To guarantee We have used blockchain technology anonymity, privacy, verifiability, mobility, integrity, security, and fairness in voting. By using blockchain our proposed system ensures security, privacy, and integrity. This system provides voter anonymity by keeping the voter information as a hash in the blockchain. It also provides fairness by keeping the casted vote encrypted till the ending time of the election. After ending time, the voter can verify their casted vote, ensuring verifiability. To test our protocol, we put it on Ethereum 2.0, a blockchain platform that uses Solidity as a programming language to create smart contracts. The adoption of smart contracts provides a safe means for performing voter verification, ensuring the correctness of voting results, making the counting system public, and protecting against fraudulent activities. We analyzed the system’s performance based on security and gas costs. It improves in terms of security characteristics and the related cost for the necessary infrastructure.
Reward criteria impact on the performance of reinforcement learning agent for autonomous navigation

In reinforcement learning, an agent takes action at every time step (follows a policy) in an environment to maximize the expected cumulative reward. Therefore, the shaping of a reward function plays a crucial role in an agent's learning. Designing an optimal reward function is not a trivial task. In this article, we propose a reward criterion using which we develop different reward functions. The reward criterion chosen is based on the percentage of positive and negative rewards received by an agent. This reward criteria further gives rise to three different classes, 'Balanced Class,' 'Skewed Positive Class,' and 'Skewed Negative Class.' We train a Deep Q-Network agent on a point-goal based navigation task using the different reward classes. We also compare the performance of the proposed classes with a benchmark class. Based on the experiments, the skewed negative class outperforms the benchmark class by achieving very less variance. On the other hand, the benchmark class converges relatively faster than the skewed negative class.
Securing the Smart City Airspace: Drone Cyber Attack Detection through Machine Learning

Drones are increasingly adopted to serve a smart city through their ability to render quick and adaptive services. They are also known as unmanned aerial vehicles (UAVs) and are deployed to conduct area surveillance, monitor road networks for traffic, deliver goods and observe environmental phenomena. Cyber threats posed through compromised drones contribute to sabotage in a smart city’s airspace, can prove to be catastrophic to its operations, and can also cause fatalities. In this contribution, we propose a machine learning-based approach for detecting hijacking, GPS signal jamming and denial of service (DoS) attacks that can be carried out against a drone. A detailed machine learning-based classification of drone datasets for the DJI Phantom 4 model, compromising both normal and malicious signatures, is conducted, and results obtained yield advisory to foster futuristic opportunities to safeguard a drone system against such cyber threats.
Application of Deep Learning in the Deployment of an Industrial SCARA Machine for Real-Time Object Detection

In the spirit of innovation, the development of an intelligent robot system incorporating the basic principles of Industry 4.0 was one of the objectives of this study. With this aim, an experimental application of an industrial robot unit in its own isolated environment was carried out using neural networks. In this paper, we describe one possible application of deep learning in an Industry 4.0 environment for robotic units. The image datasets required for learning were generated using data synthesis. There are significant benefits to the incorporation of this technology, as old machines can be smartened and made more efficient without additional costs. As an area of application, we present the preparation of a robot unit which at the time it was originally produced and commissioned was not capable of using machine learning technology for object-detection purposes. The results for different scenarios are presented and an overview of similar research topics on neural networks is provided. A method for synthetizing datasets of any size is described in detail. Specifically, the working domain of a given robot unit, a possible solution to compatibility issues and the learning of neural networks from 3D CAD models with rendered images will be discussed.
Intelligent Deep Learning Enabled Oral Squamous Cell Carcinoma Detection and Classification Using Biomedical Images

Oral cancer is one of the lethal diseases among the available malignant tumors globally, and it has become a challenging health issue in developing and low-to-middle income countries. The prognosis of oral cancer remains poor because over 50% of patients are recognized at advanced stages. Earlier detection and screening models for oral cancer are mainly based on experts' knowledge, and it necessitates an automated tool for oral cancer detection. The recent developments of computational intelligence (CI) and computer vision-based approaches help to accomplish enhanced performance in medical-image-related tasks. This article develops an intelligent deep learning enabled oral squamous cell carcinoma detection and classification (IDL-OSCDC) technique using biomedical images. The presented IDL-OSCDC model involves the recognition and classification of oral cancer on biomedical images. The proposed IDL-OSCDC model employs Gabor filtering (GF) as a preprocessing step to eliminate noise content. In addition, the NasNet model is exploited for the generation of high-level deep features from the input images. Moreover, an enhanced grasshopper optimization algorithm (EGOA)-based deep belief network (DBN) model is employed for oral cancer detection and classification. The hyperparameter tuning of the DBN model is performed using the EGOA algorithm which in turn boosts the classification outcomes. The experimentation outcomes of the IDL-OSCDC model using a benchmark biomedical imaging dataset highlighted its promising performance over the other methods with maximum accu y , prec n , reca l , and Fscore of 95%, 96.15%, 93.75%, and 94.67% correspondingly.
Exploring user experience in digital libraries through questionnaire and eye-tracking data

In the field of digital libraries, research on user experience is sparse, especially in terms of emotions, impressions, and stimulation evoked by interaction with the digital library. Given the many different methods and tools available for assessing user experience, two questionnaires and eye-tracking were explored to see how each can contribute to understanding user experience in digital libraries. An eye-tracking study with 30 participants was carried out using two digital libraries. Analysis of the questionnaires revealed that overall, one digital library was experienced more positively, with the difference most evident in the hedonic quality. The eye-tracking data revealed different gaze patterns in the two digital libraries, with significant difference in measure values particularly on the search boxes. When eye-tracking was combined with the questionnaire data, several correlations were found, indicating that the search box position and the intuitiveness of the homepage had an impact on the user experience.
UltrARsound: in situ visualization of live ultrasound images using HoloLens 2

Augmented Reality (AR) has the potential to simplify ultrasound (US) examinations which usually require a skilled and experienced sonographer to mentally align narrow 2D cross-sectional US images in the 3D anatomy of the patient. This work describes and evaluates a novel approach to track retroreflective spheres attached to the US probe using an inside-out technique with the AR glasses HoloLens 2. Finally, live US images are displayed in situ on the imaged anatomy.The Unity application UltrARsound performs spatial tracking of the US probe and attached retroreflective markers using the depth camera integrated into the AR glasses-thus eliminating the need for an external tracking system. Additionally, a Kalman filter is implemented to improve the noisy measurements of the camera. US images are streamed wirelessly via the PLUS toolkit to HoloLens 2. The technical evaluation comprises static and dynamic tracking accuracy, frequency and latency of displayed images.Tracking is performed with a median accuracy of 1.98 mm/1.81[Formula: see text] for the static setting when using the Kalman filter. In a dynamic scenario, the median error was 2.81 mm/1.70[Formula: see text]. The tracking frequency is currently limited to 20 Hz. 83% of the displayed US images had a latency lower than 16 ms.In this work, we showed that spatial tracking of retroreflective spheres with the depth camera of HoloLens 2 is feasible, achieving a promising accuracy for in situ visualization of live US images. For tracking, no additional hardware nor modifications to HoloLens 2 are required making it a cheap and easy-to-use approach. Moreover, a minimal latency of displayed images enables a real-time perception for the sonographer.
BFV-Based Homomorphic Encryption for Privacy-Preserving CNN Models

Medical data is frequently quite sensitive in terms of data privacy and security. Federated learning has been used to increase the privacy and security of medical data, which is a sort of machine learning technique. The training data is disseminated across numerous machines in federated learning, and the learning process is collaborative. There are numerous privacy attacks on deep learning (DL) models that attackers can use to obtain sensitive information. As a result, the DL model should be safeguarded from adversarial attacks, particularly in medical data applications. Homomorphic encryption-based model security from the adversarial collaborator is one of the answers to this challenge. Using homomorphic encryption, this research presents a privacy-preserving federated learning system for medical data. The proposed technique employs a secure multi-party computation protocol to safeguard the deep learning model from adversaries. The proposed approach is tested in terms of model performance using a real-world medical dataset in this paper.
Regression-based spatial GIS analysis for an accurate assessment of renewable energy potential

This work aims to evaluate technically and economically the performance of a large photovoltaic power plant, emphasizing the potential of the eastern region of Morocco for the production of electricity and hydrogen. We used weather data from about 24 cities and based on the simulation of a 110 MW power plant by the SAM (System Advisor Module) software. The required data is collected and then implemented in a regression model to obtain a suitable energy production equation. This assessment method aims to provide high-resolution GIS maps and provide accurate data on the techno-economic potential of the plant in the region as well as the amounts of hydrogen that can be produced. The findings reveal that at the regional level, the simulation of the available power plant potential gives good results. The use of a regression model instead of the interpolation methods provided by GIS software allows us to obtain a better cartographic resolution, this last one depends certainly on the resolution of the spatial data used in the prediction, as far as data resolution and quality are good, as far as the results are better too.
An In-Depth Survey of Bypassing Buffer Overflow Mitigation Techniques

Buffer Overflow (BOF) has been a ubiquitous security vulnerability for more than three decades, potentially compromising any software application or system. This vulnerability occurs primarily when someone attempts to write more bytes of data (shellcode) than a buffer can handle. To date, this primitive attack has been used to attack many different software systems, resulting in numerous buffer overflows. The most common type of buffer overflow is the stack overflow vulnerability, through which an adversary can gain admin privileges remotely, which can then be used to execute shellcode. Numerous mitigation techniques have been developed and deployed to reduce the likelihood of BOF attacks, but attackers still manage to bypass these techniques. A variety of mitigation techniques have been proposed and implemented on the hardware, operating system, and compiler levels. These techniques include No-EXecute (NX) and Address Space Layout Randomization (ASLR). The NX bit prevents the execution of malicious code by making various portions of the address space of a process inoperable. The ASLR algorithm randomly assigns addresses to various parts of the logical address space of a process as it is loaded in memory for execution. Position Independent Executable (PIE) and ASLR provide more robust protection by randomly generating binary segments. Read-only relocation (RELRO) protects the Global Offset Table (GOT) from overwriting attacks. StackGuard protects the stack by placing the canary before the return address in order to prevent stack smashing attacks. Despite all the mitigation techniques in place, hackers continue to be successful in bypassing them, making buffer overflow a persistent vulnerability. The current work aims to describe the stack-based buffer overflow vulnerability and review in detail the mitigation techniques reported in the literature as well as how hackers attempt to bypass them.
Encoder and decoder network with ResNet-50 and global average feature pooling for local change detection

Background subtraction is a prevalent way of dealing with detecting the local changes from video scenes. Background subtraction divides an image frame into foreground and background. The proposed scheme is a unique attempt to detect the local changes in video using a combination of the feature pooling module (FPM) with a ResNet-50 encoder–decoder network. In this context, we proposed a robust encoder–decoder structured deep learning network that is trained with limited training data. The proposed scheme has several folds of novelties including as mentioned below. The use of the feature pooling module with the ResNet-50 encoder–decoder network is the first attempt to use background subtraction in complex video scenes. In the proposed scheme the weights of the ResNet-50 network are learnt by using the transfer learning mechanism. Further, due to the use of a selected number of layers in ResNet-50 architecture with a fewer number of trainable parameters, the proposed architecture becomes less complex as compared to competitive architecture like VGG-16. The proposed ResNet-50 encoder with the FPM module is capable of extracting relevant multi-scale features for local change detection from complex videos. The said encoder uses residual connections between the layers and is hence capable of extracting meaningful multi-scale features with a fewer number of parameters and a higher number of layers. We finally used an up-sampling in the decoder to learn a mapping from the feature space to the image-frame space. The model takes an RGB image frame as the input and generates a foreground segmented probability mask for the corresponding image. To evaluate our model, we have tested it on the three popular benchmark databases. The robustness of the proposed scheme is evaluated by comparing its results with twenty-eight state-of-the-art techniques. The evaluation of the results is carried out using visual and eight quantitative evaluation measures.
A case study of utilizing YOLOT based quantitative detection algorithm for marine benthos

Current object detection algorithms suffer from low accuracy and poor robustness when used to detect marine benthos due to the complex environment and low light levels on the seabed. To solve these problems, the YOLOT (You Only Look Once with Transformer) algorithm, a quantitative detection algorithm based on the improved YOLOv4, is proposed for marine benthos in this paper. To improve the feature extraction capability of the neural network, the transformer mechanism is introduced in the backbone feature extraction network and feature fusion part of YOLOv4, which enhances the adaptability of the algorithm to targets in complex undersea environments. On the one hand, the self-attention unit is embedded into CSPDarknet-53, which improves the feature extraction capability of the network. On the other hand, it is transformer-based feature fusion rules that are introduced to enhance the extraction of contextual semantic information in the feature pyramid network. In addition, probabilistic anchor assignment based on Gaussian distribution is introduced to network training. The experimental validation shows that compared with the original YOLOv4, the YOLOT algorithm improves the recognition precision from 75.35% to 84.44% on the marine benthic dataset. The improvement reflects that YOLOT is suitable for the quantitative detection of marine benthos.
Membrane Fouling Prediction Based on Tent-SSA-BP

In view of the difficulty in obtaining the membrane bioreactor (MBR) membrane flux in real time, considering the disadvantage of the back propagation (BP) network in predicting MBR membrane flux, such as the local minimum value and poor generalization ability of the model, this article introduces tent chaotic mapping in the standard sparrow search algorithm (SSA), which improves the uniformity of population distribution and the searching ability of the algorithm (used to optimize the key parameters of the BP network). The tent sparrow search algorithm back propagation network (Tent-SSA-BP) membrane fouling prediction model is established to achieve accurate prediction of membrane flux; compared to the BP, genetic algorithm back propagation network (GA-BP), particle swarm optimization back propagation network (PSO-BP), sparrow search algorithm extreme learning machine(SSA-ELM), sparrow search algorithm back propagation network (SSA-BP), and Tent particle swarm optimization back propagation network (Tent-PSO-BP) models, it has unique advantages. Compared with the BP model before improvement, the improved soft sensing model reduces MAPE by 96.76%, RMSE by 99.78% and MAE by 95.61%. The prediction accuracy of the algorithm proposed in this article reaches 97.4%, which is much higher than the 48.52% of BP. It is also higher than other prediction models, and the prediction accuracy has been greatly improved, which has some engineering reference value.
Accelerating parallel digital image correlation computation with feature mesh interpolation

Parallel digital image correlation (DIC) computation has been increasingly used in DIC community due to the growing requirement on real-time deformation monitoring. In existing parallel DIC speeded up with feature-based initialization method, high parallelism requires more valid initialized points, which means more feature points are needed. However, more feature points significantly increase the time of feature extracting and matching, thus reducing the whole parallelism. To solve this problem, we propose to construct a mesh of matched feature points and assign initial values by mesh interpolation. The proposed method depends less on the feature distribution and provides more and better valid initialized points with much less feature points. Along with the well-designed parallel process, the proposed method is parallel-friendly, scalable and can realize high parallelism. The effectiveness, parallel performance, and the ability of real-time measurement of the proposed method are demonstrated by processing real experimental images.