Reviewerly/exHarmony huggingface dataset:
Cyberpragmatics: Complaints and the Collective Perspective

Abstract The aim of this article is to lay some groundwork for expanding the study of speech act pragmatics within the cyber-world. For this purpose, we have investigated complaints in Hebrew online, in an attempt to highlight features that distinguish online communication from face-to-face communication. We have analysed complaints in three major spheres: a) complaints towards public institutions, b) complaints against commercial firms and c) sharing complaints for the common benefit of a social community. In all instances, complainers online co-construct the context and the shared knowledge of the offence (complainable). Like many other studies of online communication, we too found a tendency towards uncivil or aggressive expressions (Vladimirou et al., 2021) especially when there was no hope for “repair” of the situation (ex: these damned people lie to us (in Hebrew ‘arurim’)). Furthermore, there were individual complainers who used the online platform to gain social and political power presenting themselves as defenders of justice and of the well-being of the community.
Intellectual Property Rights and Data Ownership

This chapter exposits the concerns surrounding intellectual property and data ownership in AI systems. In light of what is pinpointed here, it then examines the options available for protecting AI-based algorithms—copyright, patents, databases’ and trade secrets’ protection, among others—in aims of safeguarding innovation.
Asymptotic distributions of a new type of design‐based incomplete U‐statistics

The U‐statistic has been an important part of the arsenal of statistical tools. Meanwhile, the computation of it could easily become expensive. As a remedy, the idea of incomplete U‐statistics has been adopted in practice, where only a small fraction of combinations of units are evaluated. Recently, researchers proposed a new type of incomplete U‐statistics called ICUDO, which needs substantially less time of computing than all existing methods. This paper aims to study the asymptotic distributions of ICUDO to facilitate the corresponding statistical inference. This is a non‐trivial task due to the restricted randomization in the sampling scheme of ICUDO. The bootstrap approach for the finite sample distribution of ICUDO is also discussed. Lastly, we observe some intrinsic connections between U‐statistics and computer experiments in the context of integration approximation. This allows us to generalize some existing theoretical results in the latter topic.
CNN based Image Steganography Techniques: A Cutting Edge/State of Art Review

Data security is essential for information distribution in the world of information and communication tools today. Data concealing has grown more and more important with the rise of intense multimedia sharing and secret discussions. Steganography is a method of obscuring data in a way that makes it nearly impossible to find. According to a recent study, when the networks between the layers closest to the input and those closest to the output are thinner, convolutional neural networks can become noticeably deeper, more precise, and easier to train. The fundamental drawback of R-CNN, which was previously utilized in place of CNN, is that it adds the characteristics while CNN is used to concatenate them.
Introduction to Cyber Tensions Preventative Analysis and Honeypotting Strategy

A cyberforce tasked to counteract cyberthreats must be constantly adapting to the threat level and technologies. This requires constant research, updating of knowledge and training of the workforce. This paper presents an understanding of the cyber workforce as well, cyber, seen as two different areas of operation: attack versus secure. Even though honeypots are seen as a securing mechanism implementation, two scenarios have been presented to justify how it is also seen as an attacking mechanism. The advantages and disadvantages of employing a honeypot have been discussed. The conclusion of the research is that honeypots need to be used in conjunction with an Intrusion Detection System (IDS) and Firewalls, as it has both advantages, however, disadvantages if used solely.
PEVGraphRec: a PEV method-based graph neural networks for social recommendations

Most existing recommendation systems assume that items are independent and identically distributed. These methods only leverage user-item interactions as model input, while neglecting the relationship between items, leading to limited performance. To address this issue, this paper proposes the PEVGraphRec, which provides recommendations by utilizing Proximity-Effect-Value (PEV) similarity measurement to generate an item-item implicit network and employing graph neural network with attention mechanisms to intelligently aggregate the user-user connections, the user-item interactions, and the item-item relations. Specially, compared with traditional similarity measurement, the PEV method can reduce the negative effects of cold start. Experiments on three real-world datasets verify the interpretability of our model by examining the contribution of each component. The results show that the PEVGraphRec is robust and superior to other baseline methods.
Public Sentiment Analysis of Indonesian Tweets About COVID-19 Vaccination Using Different Machine Learning Approaches

More than two years after the start of the coronavirus disease (COVID-19) pandemic, the whole world continues to be impacted by this global crisis. Indonesians use the social media platform Twitter to share information and opinions about coronavirus disease (COVID-19) vaccination. This study was conducted to determine the views of Indonesians toward the government's COVID-19 vaccination program and to test the capability of several machine learning techniques to classify sentiments expressed on Twitter. The performance of four machine learning algorithms was tested: the Naïve Bayes, k-Nearest Neighbors (kNN), Decision Tree, and Support Vector Machine (SVM) algorithms. The findings show that the SVM algorithm exhibited the best performance in terms of accuracy (92%) compared to the Naïve Bayes, kNN, and Decision Tree algorithms. A grid search technique was also used to optimize performance based on parameter settings in the algorithm used.
Optimised Hardware Implementation of AES for Improving Energy Efficiency of Low-Power Devices

Most commercial ultra-low power microcontrollers available in the market have inbuilt circuitry for crypto operations, radio for transmission and multiple sleep modes. Sleep modes put the processor into sleep mode while using integrated peripherals, thereby saving the power and battery life of the constrained devices. The add-on circuitry in the microcontrollers has promised higher security at extremely low power by optimising the sleep modes using low-level programming. Dedicated hardware blocks carry out most encryption techniques within the processor for energy, time, and memory efficiency. This necessitates the power optimisation in embedded applications to be an integral part of the development process for overall efficiency, as it is essential to understand how crypto accelerators, software algorithms, and power-saving modes work together. In this work, different optimisation techniques have been adopted to enhance the power consumption of EFM32PG12, an IoT hardware platform developed by Silicon Labs, using sleep modes and the hardware accelerator of the board. The sleep modes have been utilised in the AES encryption algorithm to reduce the overall power consumption on a hardware mote. The combination of the hardware accelerator and the sleep modes significantly improved the performance and reduced the power consumption of AES encryption and decryption on the hardware platform.
Data Balance Optimization of Fraud Classification for E-Commerce Transaction

The purpose of this study is to solve the problem of unbalanced data for prediction and classification of fraudulent E-Commerce transactions. Data from Digital Commerce 360 in 2015 showed that fraud occurred as much as 35% of total e-commerce transactions. Quoted from Bisnis.com, based on the 2017 Fraud Management Insight report, this percentage of fraud can reduce consumer confidence. One method for predicting fraud is machine learning. Fraud data does not have a balance between data that is not fraudulent, causing the classification to be biased. So it is necessary to balance the data using the SMOTE algorithm. The results of the data balancing will be classified as fraudulent transactions using the Support vector machine, K-Nearst Neighbor, Naïve Bayes and C45 algorithms.
Analysis of Discussion Tendency on Twitter with Text Classification

Discussion on social media sometimes results in the emergence of several groups. The groups that often appear are Pro, Neutral, and Contra. However, there are several others that have emerged, namely Uncorrelated, and Sarcasm. This research focuses to develop a text classification model that can analyze the relationship between tweets and retweets in a discussion to detect groups. Two architectural approaches were used, namely shallow learning and deep learning. The classification algorithms used in the shallow learning architecture are SVM, XGBoost, and CatBoost. BERT and XLMRoBERTa were used as embedding layer algorithms for deep learning architectures. Based on the experiments conducted, the best model was obtained by using XLMRoBERTa as the embedding layer, BiLSTM as the RNN layer, fine-tuning technique by freezing the embedding layer parameters in the first five epochs, where the training data used containing emojis and did not have Uncorrelated and Sarcasm labels. The model with this configuration has an f1 score of 0.597633, and based on qualitative analysis, the model tends to predict the Neutral label and is quite good at detecting Pro and Contra.
An experimental study on binary optimization using quantum annealing in D-Wave

Quantum annealing is a field of quantum computing to solve complex optimization problems. NP problems are hard to obtain the optimal result in polynomial time, and quantum annealing is able to fasten the process of computation. This article proceeds through a brief introduction to quantum annealing and then performs an experimental study on unconstrained binary optimization. Two mathematical models, namely, QUBO and Ising, are used for the design of the optimization to experiment with a quantum computer architecture named D-Wave. Ising model maps the problem statement for realization into a time-dependent Hamiltonian. The obtained results are finally analyzed and explained.
Ensemble-Based Machine Learning Models for the Preemptive Diagnosis of Cervical Cancer using Clinical Data

Early detection and classification of a cancer type can aid the patient's subsequent clinical therapy. Cervical cancer is a prevalent type of cancer that affects a woman's cervix because of abnormal cell growth. It is the fourth most common disease in women globally, and early detection has the potential to save lives. In spite of the fact that previous studies achieved outstanding results, their models were highly computationally complex due to the significant number of features incorporated. Therefore, this study is intended to use clinical data to diagnose cervical cancer in its early stages and investigate the effect of Recursive Feature Elimination (RFE) in reducing the number of attributes. The models were trained and tested using a dataset obtained from the Machine Learning Repository at the University of California at Irvine (UCI). The models included Extreme Gradient Boosting (XGBoost), Adaptive Boosting (AdaBoost), and Extra Trees (ET). The findings showed that XGBoost and AdaBoost performed the best, with an accuracy rate of 99.79%, recall rate of 100%, and precision rate of 99.6%, using only 8 features.
An HAR-11 Net Model for Human Action Recognition using Deep Learning Techniques

One of the biggest new trends in artificial intelligence is the ability to recognise people's movements and take their actions into account. It can be used in a variety of ways, including for surveillance, security, human-computer interaction, and content-based video retrieval. Vision-based approaches for recognising human action have been introduced by numerous researchers. Several challenges need to be addressed in the creation of a vision-based human activity recognition system, including illumination variations in human activity recognition, interclass similarity between scenes, the environment and recording setting, and temporal variation. Many works have been reported on CNN that use only a few layersto fix HAR issues. Feature extraction from high-resolution images using standard shallow CNNs with simple learning schemes. In thiswork, we have developed HAR-11 Net model for identification and recognition of human activities. The experimental findings demonstrate that the proposed system is capable of identifying human actions, and that it performs significantly better than existing systems.
A Study of XXE Attacks Prevention Using XML Parser Configuration

Online attacks are outcomes of cyber vulnerabilities. XML (eXtensible Markup Language) is a self-descriptive markup language, and XML eXternal Entity injection (XXE) is a well-recognised web security vulnerability. XXE injection can attack web applications that several popular XML parsers are unable to withstand. Most of the available literature on XXE is based on vulnerability testing, but not much work has been done on ways for prevention of such attacks. The aim of this research is investigation of XXE attacks, and ways of prevention of such attacks. A sample vulnerable web application utilizing the standard XML parser that accompanies the Java Development Kit (JDK) has been developed in this work for executing different attacks and detection of attacks. Additionally, ways for prevention of XXE attacks are suggested as well. A virtual machine image of the developed framework is provided to perform trial and error for experimentation and representation of vulnerabilities. Different techniques and exploitation approaches for evaluating the performance of sample web application in terms of vulner-abilities are utilized to develop a platform for executing such attacks. Since the vulnerabilities of misconfigured XML parsers are depicted in this work in detail, effective methods for securing websites from XXE attacks are provided. Furthermore, this work also includes discourse on tools for developing platform for XXE attacks, and ways to eliminate vulnerabilities.
Web Application Security Threats and Mitigation Strategies when Using Cloud Computing as Backend

Cloud computing plays an important role in businesses' digital transformation as they offer easy-to-use services that save time and effort. Despite incredible features that are provided by cloud computing platforms, these platforms become the desirable target of attackers. This study aims to survey the literature for security threats related to web applications that have been developed using cloud computing services and then provide a set of recommendations to mitigate these threats. In this study, we first surveyed the literature for documented cases of threats faced while relying on cloud computing, then an online survey was sent to Computer Science students and web developers. The survey's questions were related to web threats whether they are aware of these threats or not and whether they have already applied any prevention measures for these threats. Then, a set of recommendations were provided that can help them to mitigate these threats. Finally, a tool was designed for generating security policies for the Broken Access Control threat for Firebase. Eighty-five responses were considered for this study. The average participants' awareness of all threats is 61 % despite 92% of participants having taken at least one security course. The main causes for neglecting to implement mitigation techniques was the lack of training and that developers are relying on the web services to provide security measures, then comes the process being time-consuming. The designed tool for mitigating Broken Access control showed promising results to ease the implementation of mitigation techniques. We conclude that due to the lack of awareness and negligence in implementing mitigation techniques, many present web apps may be compromised. Developing security tools for novice users, whenever possible, provides a solution for the main causes of the neglect to implement such measures and should be investigated further.
Fine Tuning an AraT5 Transformer for Arabic Abstractive Summarization

Creating an abstractive summary of a document by rephrasing its most crucial sentences is a challenging but crucial task in natural language processing. The field witnessed a remarkable development with deep learning techniques, especially with the emergence of pre-trained models that achieved the best results by training them on very large data and trained later on specific tasks. In this paper, we used the T5 model, which achieved results that are considered the best in different tasks of natural language processing. AraT5 is the newly launched Arabic language version, as we have worked on fine-tuning it on a dataset of 267,000 Arabic articles. The model was evaluated through ROUGE-1, ROUGE-2, ROUGE-L, and BLEU and the results were 0.494 0.339 0.469 0.4224, respectively. In addition, the AraT5 model is superior to other state-of-the-art research studies using the sequence-to-sequence model.
Sentiment analysis for Algerian Dialect tweets

Twitter Arabic Sentiment Analysis refers to identify and classify the sentiments expressed in the tweet. The Algerian dialect is one of the Arabic dialects used on Twitter and has some peculiarities and few resources. Our study aims to prepare and annotate a gold standard dataset for the Algerian dialect and then make a classification model with robust predictions using deep learning techniques such as pre-trained transformers which are now the de facto models in Natural Language Processing. Due to their state-of-the-art results in many tasks such as Arabic Sentiment Analysis. In this paper, we used our dataset of 20400 tweets to train three traditional machine learning classifiers (Support Vector Machine SVM, Bernoulli Naive Bayes BNB, Multinomial Naive Bayes MNB) and two deep learning architectures (Long Short-Term Memory (LSTM) and Pre-trained language model like BERT. We find that our pre-trained model performs best with 82,36% accuracy.
Low-light Color Image Enhancement based on Dark Channel Prior with Retinex Model

Low light image enhancement plays the crucial role in night vision applications, and road monitoring systems of artificial intelligence assisted vehicles. But the conventional methods are unable to remove the darkness from source images and resulted in poor visibility performance. Thus, this article proposed an advanced low light image enhancement approach using dark channel prior (DCP). Initially, light reflection (retinex) angles are identified and red channel estimation was used to restore light direction attention. Further, DCP is used to identify the background darkness region with light illumination properties. Then, new anthropic light properties were generated by using transmission map estimation and refinement. Further, image light radiance is recovered by using this updated transmission map values, which generates darkness removed image. Finally, denoising operation is performed to get the best visual quality output image. The simulations conducted on ExDark dataset shows that the proposed method resulted in superior subjective and objective performance as compared to state of art approaches.
Design and Implementation of Intelligent Acquisition Terminal Based on Modbus

With the continuous development of social economy, intelligent acquisition terminal plays an important role in some remote and abusive environments. In this paper, an intelligent integrated acquisition terminal is proposed, which can be effectively applied to irrigation area information collection, earthquake disaster monitoring and other scenarios. The acquisition terminal (RTU) proposed in this paper adopts a design scheme combining GD32F407 embedded system and embedded industrial computer to complete data collection, processing, transmission, display, storage and intelligent control of equipment. The RTU acquisition base plate is designed by GD32F407 chip, including RS485, 4-20MA, SSI Gray code, relay control, Ethernet and other interfaces [1]. The RS485 interface uses Modbus Rtu protocol to request data from sensors connected to the outside. SSI Gray code interface can be connected to absolute value encoder to collect data, gray code interface carries on data transmission through 422 interface on hardware, and carries on data latching at corresponding edge through analog clock signal on software level. Modbus TCP protocol is used to exchange information between the acquisition baseboard and the industrial computer through Ethernet. The industrial computer runs the fast control configuration software, which can receive the transmitted data and realize intelligent control of external devices.
K-mean Clustering: a case study in Yvelines, Île-de-France

Cluster analysis is widely used in various fields to classify data that have similarities into the same group, but are different from the objects in the other group, to gain insights into various applications such as marketing, urban planning, fraud detection, biology, and many more. The ability to cluster the number of fire operations in the Île-de-France, especially in Yvelines, will definitely help the fire and rescue service to make better decisions in emergency response and increase the efficiency of material and human resources, which, if they can be reduced, can lower financial costs. In this paper, a collection of fire operations in different departments of the Île-de-France was made, but only the information on the 20 largest departments of Yvelines was selected. After choosing the optimal k-value, the K-means clustering method was applied, and further research was carried out to summarize the criteria by which the Insee were classified into clusters. Breakpoints were detected, statistics were obtained for each insee, linear regression was implemented, and finally time series decomposition was performed. The results show that the clusters are well separated and have a dense grouping of insee with approximately the same trend, meaning the same number of interventions.
Enhancing Monocular Depth Estimation via Image Pre-processing Techniques

Robots and drones are getting popular in many applications nowadays. Autonomous operations of drones and robots are highly desirable to minimize human interventions and enhance operation efficiency. However, there are several challenges that need to be overcome before robots and drones can be automated with minimum hardware requirements. Currently, robotics industry employs costly sensors such as Lidar to estimate distance between a vehicle and objects. Recent advancement in Artificial Intelligence (AI) encouraged researcher to investigate techniques to estimate the distance between vehicle and objects using monocular camera and AI. However, distance (depth) estimation using monocular camera still suffers from low accuracy rate in depth estimation. This paper aims to improve the depth estimation values through applying several image pre-processing techniques such as Nonuniform Illumination Removal, Local Adaptive Thresholding, Histogram Equalization, Adaptive Histogram Equalization, White Balance, and Homo- morphic filtering techniques.