Reviewerly/exHarmony huggingface dataset:
Improving Deep Learning Performance Using Sampling Techniques for IoT Imbalanced Data

In intrusion detection systems, imbalanced data is a problem since the large majority of data points are considered normal, while the minority of data points are considered anomalies, in which there is an unequal distribution of data among different classes, and many deep learning algorithms might not perform well in this situation because there is an imbalance between the classes, to solve this problem, several approaches come from different fields and have their own strengths and weaknesses, such as using feature engineering and Deep Convolutional Neural Networks (DCNNs). Other than that, it may be difficult to find the optimal feature for a given problem when using this approach. Additionally, depending on the size of the dataset, it may take a long time to search through all the possible combinations of features to identify the most optimal. This paper presents and implements different data balancing techniques such as Synthetic Minority Oversampling Technique (SMOTE), Adaptive Synthetic (ADASYN), and Random Under Sampling (RUS), analyzes the results of the generated models trained and evaluated and discusses the differences between each approach, on the IoT intrusion dataset 2020 (IoTID20), to improve security in the internet of things environment by using three advanced deep learning algorithms: Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN), and CNN-LSTM. The experimental results of our findings were evaluated in terms of accuracy, precision, recall, Area Under the Curve (AUC), f1-score and sensitivity, and compared to several other state-of-the-art studies, they were outstanding and competent, achieved an accuracy, precision and AUC of 99.49%, 99.49% and 99.93% respectively.
Using Discussion Thread Context in Sentiment Analysis for Improving Cyberbullying Detection

Cyberbullying is the act of harassing, demeaning, or threatening someone via technology such as social networking, texting, or instant messaging. It is defined as “willful and repeated harm inflicted through the medium of electronic text” (Patchin and Hinduja 2006:152) [1]. Cyberbullying is an underlying problem in social networking services, threatening users' mental and physical health. The aim of this study is to improve the detection of cyberbullying in discussion threads by utilizing the repeated negative context formed in the presence of cyberbullying.
Automatic Evaluation of Machine Translation Based on Linguistic Knowledge

On the study of machine translation, translation system evaluation method is one of the most important links, which plays a very important role in the study of machine translation. Translation quality can be evaluated both manually and by computer. Among them, manual evaluation is a more commonly used means, because it has many advantages. However, there are two problems in the manual evaluation: first, the evaluation takes a long time; second, the manual evaluation will be subjective. In addition, due to the characteristics of machine translation itself, manual evaluation cannot fully meet the needs of machine translators. The automatic evaluation method comes into being, which just fills the defect of manual evaluation. At present, the research on automatic evaluation of machine translation is not deep enough, and the existing automatic evaluation system of machine translation is mostly aimed at a certain type of text test, cannot fully reflect the degree of difference between different texts and the same text in various fields. This paper proposes a method for automatic evaluation of machine translation based on linguistic characteristics. Through analyzing the defects of common automatic evaluation methods such as BLEU and NIST, this method is improved by using regression model of support vector machine, and based on linguistic knowledge, A system for classifying linguistic phenomena or fragments of a sentence from a linguistic perspective. Finally, the experimental results show that the recall rate and accuracy of the automatic evaluation method of machine translation in this paper are 90% and 96%, respectively. Compared with BLEU and NIST two traditional automatic evaluation methods, the automatic evaluation method of machine translation in this paper has a better result.
Factors that Motivate Defense Against Social Engineering Attacks Across Organizations

This paper explores the factors that influence employees' security behaviors in organizations. It integrates the Protection Motivation Theory (PMT) and organizational effort to develop a comprehensive model for understanding the impact of antecedents and mediating factors on security behavior. PMT has been used extensively in health research, but its application in the field of information security is limited. The study found that threat and coping factors are reliable predictors of employees' motivational behavior towards security. In addition, employee awareness and organizational effort also positively influence threat and coping appraisal processes, leading to cybersecurity behavior. Several practical implications were identified, such as the effectiveness of government organizations in motivating security behavior compared to other businesses. This study provides insights into the complex interplay of factors that shape employees' security behavior and can inform organizations' efforts to promote secure behaviors.
Real-Time Maritime Semaphore Sign Detection System

In times of distress, the maritime signalling system using semaphore signs play a crucial role in facilitating communication and ensuring the safety and efficiency of maritime operations. Traditional methods that have been used over the years of semaphore sign communication rely on manual interpretation, which can be subjective, time-consuming, and prone to human error. The interpretation can also vary depending on the observer's skill and experience, leading to inconsistencies in message comprehension. The proposed system aims to provide an integrated solution to enable communication with the help of detection of semaphore signs. It utilizes convolutional neural networks (CNNs) for classification of semaphore signs in real-time. The dataset consisting of semaphore signs for six letters of the English language i.e, ‘A’, ‘E’, ‘F’, ‘O’, ‘S’, ‘V’ is created and used to train the model. The CNN model trained on this dataset showed an accuracy of 99.20% on the train set and 98.87% on the test set while detecting semaphore signs in real time.
Deep Learning Enabled Multi-Layer Urban Home Security System

Crime rates in urban areas are rising due to exponential population growth and rapid urbanization. To elevate this critical condition, the city administration has to use various approaches, including technology-integrated solutions, to monitor the urban areas reduce crimes, and protect the citizens. This work presents an intelligent smart monitoring system that is an amalgamation of the Internet of Things (IoT), deep learning, and user participation to offer security help to the users. We present the proposed two-tier architecture and the various system components for the intelligent smart monitoring system and how it increases the safety of the urban community. Our work also emphasizes the role of user participation for enhanced security and monitoring. Our experiment showed R-CNN performed better in object detection compared to other deep learning methods and ImageAI performed better in low light conditions.
Prediction models for Clustered Virtual Machines in Data Centers

In data center (DC) environments, machine learning algorithms play an important role in resource management to increase efficiency by means of proper predictive monitoring workload trends and adjusting jobs accordingly. In this paper, we propose a system to predict the CPU usage of virtual machines (VMs) of a DC. Our proposal performs clustering of VMs based on their historical information (i.e., time series) by evaluating several traditional ML algorithms using common statistical features of VM time series, which facilitates grouping VMs with similar behaviors and establishing clusters based on these features. Then, training of representative models is performed to finally choose the one with the lowest mean error per cluster. The simulation results show that by performing clustering and training the model with representative time series, it is indeed possible to obtain a low mean error while reducing the local training time per individual VM.
The Adaptive Threshold-Based FFT Spectrum Stripe Edge Detection Method Used for Measuring The High-Frequency Vibration Parameters

On account of its characteristics of non-contact, simplicity, portability, and high accuracy, machine vision has gradually become one of the most flexible and effective methods for measuring vibration parameters. When using machine vision for high-frequency vibration measurement, the captured images inevitably suffer from blurring. To ensure the effectiveness of traditional high-frequency vibration measurement methods based on clear sequential images, image restoration techniques are required to deblurring the blurred images. However, this method has drawbacks such as complex algorithms and low measurement efficiency. This study propose a method for high-frequency vibration parameter measurement using FFT spectrum stripe edge detection on a single blurred image. By combining an improved Canny edge detection algorithm with the Radon transform, we achieve high-precision extraction of the direction and position of stripes in the FFT spectrum of the blurred image, enabling further calculation of the vibration direction and amplitude. The proposed method is evaluated using simulated high-frequency vibration-blurred images with a frequency of 600 Hz. The simulation results demonstrate that the proposed method achieves accurate measurement of high-frequency vibration parameters. In low-amplitude scenarios, the measurement results outperform existing research methods and exhibit good stability.
Output Estimation of the Solar PV Plant Using System Identification

System identification is a method of estimating the behavior of a system by analyzing input and output data. System identification techniques can be applied to estimate the output of a solar PV plant, considering different input parameters, such as solar radiation, temperature, and humidity. The first step in using system identification to estimate the output of a solar PV plant is to collect data on the input parameters and the corresponding output of the plant. Once the data has been collected, it can be analyzed using various system identification techniques such as Nonlinear ARX, Hammerstein Wiener, and Correlation models. These techniques can be used to develop a mathematical model of the plant that relates the input parameters to the output. If the model accurately predicts the plant's production, we can use it to estimate the plant's output. System identification is a powerful tool to evaluate the production of a solar PV plant based on input parameters such as solar radiation, temperature, and humidity. By accurately predicting the plant's output, system identification can help optimize the operation of the plant and improve its overall performance.
Robot Mechanical Fault Diagnosis Method Based on Deep Learning

An image input ResNet-based (IIRB) method for wheeled robot mechanical fault diagnosis (MFD) is proposed. Firstly, the time series signal (TSS) of the vibration sensor is normalized, then converted into a two-dimensional (2D) line graph, and finally the 2D image is used as the input of the neural network. Secondly, a neural network is trained using image data. Finally, the trained neural network model is used to classify and identify the MFD of the wheeled robot. The gyroscope was used to collect vibration data of the wheeled robot during uniform motion under different fault conditions, and the data set was fabricated. The ResNet is trained and tested using TTS and image as input, and the results show that the latter performs better. Comparing the training and testing results of five commonly used neural network models, including ResNet, using the same datasets, shows that the use of ResNet in this paper is superior in terms of parameter training and testing results. It is concluded that the intermediate features extracted by IIRB are more diverse, which is verified on open-source datasets.
Automatic Query Quality Prediction Using Feed Forward Neural Network

Text retrieval activity involves retrieving the source code which software developers conduct on a daily basis. Current methods for retrieving source code rely on regular expressions and assume that the developers searching the code base are well-versed in the source code. Software developers ought to possess freedom to formulate queries on the codebase using natural language sentences as opposed to a search being a keyword or pattern-based, which are difficult to remember. When a bug is raised, developers who are new to the source code base may issue an NLQ to the code base to obtain the code where they must provide a fix. Nevertheless, the performance of a text search is highly dependent on query quality, and it succeeds when the textual query is of good quality. Predicting query quality in advance on a source code retrieval system can alert developers when a query is unlikely to succeed, thereby saving them time and effort from going through a long list of results. In this paper, the quality of the query is predicted using a back-propagation approach to train a feed-forward neural network (multi-layer perceptron). The model is trained and evaluated on a dataset that is created from the source code documentation. Natural language processing pre-retrieval metrics are exploited to study the significance of training the model. The model proves to be a good predictor of query quality with 85.75 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> accuracy.
Phishing Detection Using Machine Learning Techniques

As there are many cyber-attacks that are going on in this world phishing is one of the most important cyberattack phishing attacks starts with fake messages which contain dangerous links or URLs that can be sent through emails and chat applications that messages target the victim and make him open the malicious link in this way hacker gets the important information about the target victim like passwords and login details. So, the user needs to be conscious about which is a malicious link, and which is a legitimate link. Machine learning can be one of the ways to classify the malicious links and the legitimate links by which we can stop the 95% of phishing attacks. This paper is about training machine learning models using phishing datasets to classify the URLs whether they are legitimate URLs or phishing URLs. Machine learning models used to train are KNN, Decision tree, Random Forest, Logistic Regression, CNN, RNN, and all the model's accuracy and all other evaluation metrics are compared and Logistic regression and CNN gave the highest accuracy of 95% and 96% respectively.
A Formal Approach to Component Adaptation with a Tool Support

In a component-based software development, the only way to describe components is through interfaces since they are considered as black boxes. Indeed, their interfaces (required and provided) allow to express their visible behaviors. The connection between interfaces must be ensured in a suitable manner. The connection of two incompatible interfaces requires the use of a software adaptor. In this work, we define notions of compatibility between a required and a provided interfaces by focusing on protocol level. Then we specify adaptor in case of incompatibles intefaces. Finally, we check that compatibility by using the B formal method, based on the concept of refinement, and having powerful tool support, the B prover.
Improving Infrared Pedestrian Detection by Sharing Visible Light Domain Information with Enhanced UNet and YOLO Models

The development of effective pedestrian detection approaches is of great importance for ensuring safety in automatic driving and accurate monitoring in low illumination or occlusion conditions. However, the detection task is complicated due to the absence of clear visual cues and the presence of background noise in infrared images. To address these challenges, this paper proposes an infrared pedestrian detection approach that utilizes an improved UNet and YOLO network to share visible light information from multiple related datasets. Experimental results demonstrate the effectiveness of the proposed approach in achieving high pedestrian detection accuracies in different public infrared pedestrian datasets.
Automation Improvement in Cyber Risk Management

Consolidated risk posture of organizations is more than needed nowadays in extended detection and response cybersecurity environments. Integration and automation of the modern risk management includes using controls from platforms and standards like ISO 27001, CIS v8 that should be synchronized and well mapped. Integrated and converged security approach for obtaining integrated and automated risk management is used in this paper. Main goal in this research is to propose a framework for efficient cyber risk management applicable for every type of organization. Proposed framework for automation improvement in cyber risk management includes four main entities and workflow process that describes the relation between the entities. To show the possible practical implementation of this framework, it is tested on a real risk auditing tool [11]. Another benefit of this paper is the mapping of the generated risks in the framework with CIS v8 controls and ISO 27001 controls for compliance purposes, as well as with the configuration settings of the XDR appliance explained in [11]. Mapping with the configuration settings of the selected XDR appliance makes risk mitigation process more successful. Outcome of the proposed framework is to enable simplified, automated, and efficient cyber risk management. Presented solution for automated and integrated risk management is more than needed for the organizations that want to secure their working environment from cyber attacks.
Deep learning based highway vehicles detection and counting system using computer vision

In the field of highway management, intelligent vehicle detection and counting are becoming increasingly significant. City planners have been fighting the issue of traffic analysis for years. To analyze traffic and streamline the process, intelligent methods are being created. The type of vehicles and the number of vehicles in an area over some arbitrary time period may be taken into account. Over the years, humans have created a variety of mechanisms, because the vehicles are come in different sizes, it is still difficult to identify them, which can directly impact the accuracy of vehicles count. In the field of vision research, computer vision is one of the most often used applications for traffic scenarios. A variety of computer vision applications, including object detection and categorization, have proven that deep learning techniques like Convolutional Neural Networks (CNNs) greatly outperform conventional approaches. Hence in this work, deep learning-based highway vehicles detection and count system suing computer vision is presented. Vehicles on the highway can be efficiently detected by this technique. YOLOv3 (You Look Only Once Version 3) is a real-time object detection technique which is used to detect vehicles in real-time. Precision, recall and real-time rate are measured to evaluate the performance of this system.
YOLO-OR: a lightweight cross-stage object detection model for dish recycling robot

With the development of deep learning, object detection technology has been widely used in various areas. However, the large size of the model and the high computation cost limit the deployment on the devices of the robot. To address these problems, this study proposes a slight cross-stage object detection model YOLO-OR for the dish recycling robot. In detail, the cross-stage octave convolution combines with the residual structure to build a novel convolution block ORBlock, improving feature extraction capability through cross-stage convolution operations. Moreover, ORBlock significantly reduces the parameters via splitting the traditional convolution into two-dimension. In particular, the four outputs of the backbone’s last two ORBlocks are merged and transmitted to the YOLO neck for feature fusion. Based on the Dish-20 dataset, YOLO-OR achieves excellent performance with 99.56% mAP, 189.42 FPS, 4.64 G FLOPs and 3.29 MB parameters. Meanwhile, the performance of YOLO-OR outperforms other state-of-the-art YOLO models. These demonstrate the effectiveness of the proposal.
Prediction of Solar Power Generation Using NWP and Machine Learning

For effective use of renewable energy sources, accurate forecasting of solar power output is crucial. This study investigates how machine learning techniques, such as Support Vector Machines (SVM), Neural Networks (NN), Linear Regression (LR), and Decision Trees and Numerical Weather Prediction (NWP) are used to forecast solar power generation based on climatic variables, historical power generation data, and other relevant aspects. The models' accuracy in predicting solar power generation could be determined by analyzing them using metrics like Mean Absolute Error (MAE), Root Mean Squared Error (RMSE). The results advance our understanding of the potential of machine learning approaches for precisely forecasting solar power generation, promoting effective energy management, and encouraging the integration of renewable energy sources into grid.
Bengali Text Summarization with Attention-Based Deep Learning

This research paper is about summarizing text using natural language processing with limited resources. Different techniques have been created, including abstractive and extractive methods, but the most recent is the use of recurrent neural network algorithms, which have shown better results. While other languages have automatic text summarizers, very few exist for Bengali. Furthermore, high-quality data is quite scarce. The research suggests a new abstractive text summarizer for Bengali that uses an encoder-decoder system with an attention mechanism. The dataset size evaluated for this work is small, but the quality is great; it is mostly composed of news articles. This work used a Long Short-Term Memory (LSTM) network model based on the seq2seq technique to make summarizing Bengali text more efficient. This abstractive approach generates new sentences containing information, indicating the model's ability to generate meaningful sentences.
MODI Script Recognition System Using Zernike Moments and DCT Features

Modi Script or the old Marathi script was the script used to write Marathi language prominently before 1950's. Due to its intricate writing style and bent character properties, it has lost its meaning. In recent years, it has become crucial to examine old writings in order to glean meaningful information from them. It is difficult to recognize the documents since they are damaged and because the MODI script's overlapping characters make it tough. Different classifiers, including Decision Tree, SVM, KNN, and others, have been used in some MODI script recognition systems (MSRS) that have been proposed thus far. In order to achieve high accuracy, this research offers a Modi script recognition system that calculates Zernike moments and DCT coefficients as features. Each of the 27 classes in the custom dataset, which each represent a different character, was made. Different categorization methods, including Decision Tree, Random Forest, and KNN, were used. These were used to evaluate the system's performance. 97.77% high accuracy was achieved following feature fusion.