Reviewerly/exHarmony huggingface dataset:
CopyCAT: Masking Strategy Conscious Augmented Text for Machine Generated Text Detection

Recent developments in natural language generation have made it possible to generate fluent articles automatically. If it is maliciously used to mislead the public, it may cause potential social risks. To avoid these risks, building automatic discriminators for detecting machine-generated text is required. However, in real-world situations, it is hard for humans to identify the machine-generated text, which causes the collection of machine-generated text to be difficult, and discriminators can only be trained on insufficient data. Also, it’s hard to generate synthetic machine data ourselves because we are unable to know the masking strategy of collected machine-generated text in real-world situations. In this paper, we found that even if there is a small amount of training data, the saliency score computed by the trained discriminator can reveal the masking strategy of the machine-generated text in the training set. Based on this observation, we propose a data augmentation method, CopyCAT. CopyCAT can mimic the masking strategy of the collected machine data by the information revealed by the saliency score. Our experiments show that the discriminator trained with our augmented data can have up to 10% accuracy gain.
Tornado: An Autonomous Chaotic Algorithm for High Dimensional Global Optimization Problems

In this paper we propose an autonomous chaotic optimization algorithm, called Tornado, for high dimensional global optimization problems. The algorithm introduces advanced symmetrization, levelling and fine search strategies for an efficient and effective exploration of the search space and exploitation of the best found solutions. To our knowledge, this is the first accurate and fast autonomous chaotic algorithm solving large scale optimization problems. A panel of various benchmark problems with different properties was used to assess the performance of the proposed chaotic algorithm. The obtained results have shown the scalability of the algorithm in contrast to chaotic optimization algorithms encountered in the literature. Moreover, in comparison with some state-of-the-art metaheuristics (e.g. evolutionary algorithms, swarm intelligence), the computational results revealed that the proposed Tornado algorithm is an effective and efficient optimization algorithm. A panel of various benchmark problems with different properties was used to assess the performance of the proposed chaotic algorithm. The obtained results have shown the scalability of the algorithm in contrast to chaotic optimization algorithms encountered in the literature. Moreover, in comparison with some state-of-the-art metaheuristics (e.g. evolutionary algorithms, swarm intelligence), the computational results revealed that the proposed Tornado algorithm is an effective and efficient optimization algorithm.
Data-dependent and Scale-Invariant Kernel for Support Vector Machine Classification

Kernel similarity function allows a Support Vector Machine (SVM) classifier to learn the maximum margin hyperplane in a higher dimensional space where two classes are linearly separable without explicitly mapping the data. Most existing kernel functions (e.g., RBF) use spatial positions of two data instances in the input space to compute their similarity. These kernels are data distribution independent and sensitive to data representation (i.e., units/scales used to measure/express data). Since this can be unknown in many real-world applications, a careful selection of a suitable kernel is required for a given problem. In this paper, we present a new kernel function based on probability data mass that is both data-dependent and scale-invariant. Our empirical results show that the proposed SVM kernel outperforms popular existing kernels.
Enhancing Robustness of Prototype with Attentive Information Guided Alignment in Few-Shot Classification

In this paper, we carefully revisit the issues of conventional few-shot learning: i) gaps in highlighted features between objects in support and query samples, and ii) losing the explicit local properties due to global pooled features. Motivated by them, we propose a novel method to enhance robustness in few-shot learning by aligning prototypes with abundantly informed ones. As a way of providing more information, we smoothly augment the support image by carefully manipulating the discriminative part corresponding to the highest attention score to consistently represent the object without distorting the original information. In addition, we leverage word embeddings of each class label to provide abundant feature information, serving as the basis for closing gaps between prototypes of different branches. The two parallel branches of explicit attention modules independently refine support prototypes and information-rich prototypes. Then, the support prototypes are aligned with superior prototypes to mimic rich knowledge of attention-based smooth augmentation and word embeddings. We transfer the imitated knowledge to queries in a task-adaptive manner and cross-adapt the queries and prototypes to generate crucial features for metric-based few-shot learning. Extensive experiments demonstrate that our method consistently outperforms existing methods on four benchmark datasets.
Adaptive View-Aligned and Feature Augmentation Network for Partially View-Aligned Clustering

As an important task in multi-view clustering, partially view-aligned clustering has attracted increasing attention in recent years. However, previous algorithms have two limitations: (1) they manually calculate the fixed alignment matrix based on Euclidean distance and use the fixed matrix for common feature expression. The manual fixed alignment matrix fails to adequately reflect the similarity of the training data; (2) the process of learning features is isolated from the downstream clustering task, thus learned features are unsuitable for the clustering scenario. In this paper, we propose an adaptive view-aligned and feature augmentation network (AFAN) to tackle these two issues. First, we propose an adaptive view-aligned module to calculate the alignment matrix with the self-attention mechanism. The calculated alignment matrix can capture data similarity by jointly learning data features and view alignment. Second, we introduce a self-augmentation strategy to encourage the learned features of the same cluster to be crowded together. Extensive experimental results show that AFAN outperforms state-of-the-art approaches on four benchmark datasets.
Alignment-Aware Word Distance

Recent approaches [6, 20] formulated the task of unsupervised Semantic Textual Similarity (STS) as Earth Mover’s Distance problem, demonstrating superior interpretability and competitive performance. The main idea behind is using various word distances (or word dissimilarity) as word transportation cost, and then measure text dissimilarity by optimizing the accumulative cost of transporting all words of texts. However, these approaches use static word distance without considering the context of text pairs. Intuitively, the distance of two words tends to contribute more to text dissimilarity if they are well-aligned between texts. Inspired by this observation, we propose Alignment-aware Word Distance (AWD), which leverages prior word alignment information of sentence pairs to refine word transportation cost. Specifically, we design two simple and effective mechanisms to capture prior alignment knowledge via exploiting word position and syntactic dependency, respectively. By incorporating AWD, our method remarkably outperforms current state-of-the-art models on STS tasks.
A Fast Methodology to Find Decisively Strong Association Rules (DSR) by Mining Datasets of Security Records

Cybersecurity bulletins officially recognize and publicly share the vulnerabilities of Information Systems. The attacks exploit various aspects of those vulnerabilities, compromising confidentiality, integrity or availability of the data collected. We analyze a public dataset of security records so to obtain some common features and to be able to forecast future attacks. We propose an intervention based on history of attacks through data mining methods and so a more dynamic risk analysis, by concentrating on some specific classes of cyberattacks in a period of two years. We devise a fast algorithm to find strong rules which provide an estimate of the probability that these attacks will occur so to identify adequate controls and countermeasures.
Media Player Controller Using Hand Gestures

Many apps, including Windows Media Player, games, robots, etc., are controlled by hand gestures. Using gestures can simplify the tasks and it does not require any other device for usage. But the efficiency of audio commands may decrease in noisy environments. We discussed a system that uses dynamic hand motion recognition to control the VLC media player. This package includes several libraries and modules, including Open CV and NumPy. A new, natural way to engage with computers is introduced by this hand motion recognition system.
Community Opinion Network Maximization for Mining Top K Seed Social Network Users

The Active Community Opinion Network Mining and Maximization (ACOMax) system being proposed improves on the capabilities of an existing system, Opinion Based Influence Network (OBIN) system by integrating active opinion mining (OM) as in the system, aCtive OpinioN Estimator (CONE). This work also adds opinion maximization to enhance OBIN’s accuracy while using joint aCtive OpinioN Estimator (CONE’s) training time to solve OBIN’s cold-start problem. It does this using joint sentence-level and feature-level opinion mining of mined community opinions for filtering out non-positive opinions. ACOMax first mines multiple posts related to a product using Twitter API, before performing joint opinion mining on selected posts from these user reviews. From the selected posts, ACOMax obtains frequent features with favorable opinions of the product, which it uses to construct a community opinion network graph of users sharing positive opinions. The graph is used by the seller to actively find the top k seed users to produce maximum opinion spread when Multiple Linear Threshold (MLT) is used for opinion maximization. The proposed system improves total opinion spread in a social network over the compared systems. When a small top k = 10 seed users is used, the experimental results show a total opinion spread of 263 users for ACOMax system in comparison to the 112 users produced by the system CONE for a $$134\%$$ improvement in opinion spread. ACOMax also improves on OBIN system’s accuracy as while it achieves respective higher scores of $$98.19\%$$ , $$98.50\%$$ , and $$97.89\%$$ for $$F_1$$ , precision and recall; OBIN system achieves $$95.3\%$$ , $$98.24\%$$ , and $$93.71\%$$ .
Fine-Tuning of RoBERTa for Document Classification of ArXiv Dataset

In this paper, a short-length document classification of the arXiv dataset using RoBERTa (Robustly Optimized BERT Pre-training Approach) was performed. Here, the document classification was performed using the abstract and the title of the papers combined as it summarizes the whole paper. The maximum sequence length that can be processed by RoBERTa is 512. The length of words in the abstract varies from 150 to 250 words. The experiments performed showed that RoBERTa outperformed BERT in two datasets viz. AAPD (ArXiv Academic Paper Dataset) and Reuters dataset as compared to those stated by Adhikari et al. The work extensively explored the AAPD dataset for abstract-based document classification. The model was fine-tuned for the AAPD dataset. The hyperparameters tuned were maximum sequence length, batch size, Adam optimizer, and learning rate. The model was trained and tested for different paper frequencies, which resulted in different paper categories. The accuracy and F1-score obtained for the 68 paper categories were 0.68 and 0.69. The accuracy and F1-score of the model were 0.68 and 0.69 for the 51 paper categories. The accuracy and F1-score of the model were 0.79 for the 32 paper categories. Using the larger number of papers in each category, the accuracy and F1-score of the model was increased with the increased training time ( https://www.kaggle.com/datasets/Cornell-University/arxiv , https://git.uwaterloo.ca/jimmylin/hedwig-data/-/tree/master/datasets/AAPD ).
Short Review on Blockchain Technology for Smart City Security

Over the past 22 years, our world has increasingly become interconnected, revolutionizing how information is shared and how people interact with one another. But even with this kind of progress, there are challenges that need to be dealt with, to secure a better future for everyone. Things like climate change pose a serious danger to the world. And as part of the solution, technology is being leveraged to build smart cities that can facilitate smart living in futuristic urban environments. This paper primarily focuses on how blockchain technology could be used to secure smart cities that are evolving. The paper puts emphasis on the feasibility and advantages of deploying decentralizing security frameworks using blockchain systems to combat cyber threats in smart cities.
Baldness Recognition Using Transfer Learning Method Based on Android Application

The intention of this study is to classify one of the attributes of facial feature detection using the transfer learning approach. CelebA picture data was utilized, which contains various attributes for classifying faces. The MobileNetV2 pre-trained model is used to train and assess the model based on data loss and accuracy. The Waterfall process is used to design Android applications without iteration. As a result, it is shown that the level of accuracy and response to facial feature recognition on Android devices is very high in MobileNetV2 architecture. According to the findings of this study, the degree of accuracy and reaction to facial feature recognition on Android smartphones is quite high.
Toward Secure and Efficient CNN Recognition with Different Activation and Optimization Functions

Precise data input classification and identification are essential for developing high-quality intelligent systems, including defending systems against cyberattacks and guaranteeing data protection. The Internet of Things (IoT), in particular, offers specific problems for cybersecurity not because of the volumes of data generated in a short time but also for the diversity of data sources. Thus, creating an accurate identification system is crucial to preserve data integrity and security. It has been demonstrated that convolutional neural networks (CNNs) perform well in developing data-driven applications, including cybersecurity. However, hyperparameters play a significant role in their prediction quality which may increase/decrease the false alarm rates. CNNs’ performance may be improved by the activation and optimization functions utilized using nonlinear expression and reducing loss functions, enabling them to be more precisely fit data. This study evaluates the classification accuracy of CNNs while classifying inputs using various combinations of activation functions and optimization strategies. The performance of the CNN model with ReLU and PReLU activation functions that utilize RMSprop and Adam optimization strategies is examined. However, even the most minor enhancement may significantly impact cybersecurity. A handwriting text is one of the authentication mechanisms to identify persons by their signature and writing style. In this experiment, a CNN model with Adam optimizer and PReLU activation function achieved the highest accuracy of 98.60% when trained on the Kaggle handwritten digit dataset, making a significant difference compared to classical learning algorithms and a slight improvement to other CNN models.
Examination of Different Network Security Monitoring Tools

This article examines how black hat hackers use different hacking tools to hack into a system and gain access. Data is collected from OWASP top 10 and other websites which show the cyberattacks and the increase in the number of cyberattacks in the years 2021 and 2022. Using this data, some of the most occurred attacks like SQL injection, authentication bypass, eaves dropping, website hacking, DDOS attack, man-in-the-middle attack, malware, spyware, keyloggers, etc. were obtained. So, this paper discusses some of the important cybersecurity tools, and from these tools, it is easy to prevent such cyberattacks in the future. The first section illustrates the methodology of hacking, i.e., how black hat hacker hacks a system in five different steps, and in the following sections, some information gathering tools are explained. Using HTTrack, it is easy to clone the whole website in a system, can see each and every file in the local desktop and can easily find vulnerabilities, and it is easy to do fishing attack using this tool. The second tool is Maltego, which is one of the best tools used by black hat hackers to gather information. Next is Nmap also known as Network mapper, from which it is easy to gather all the information such as which operating system is the victim using open ports, closed ports, filtered ports, and services the victim machine is running on; using this, it is easy to hack into the victim machine. How Wireshark is used for sniffing, and how it is easy to see all the data traveling from source to destination are then summarized. Thereafter, how website hacking is done using Burp Suite, how Bettercap is used as man-in-the-middle attack, how black hat hackers create back door using msfvenom, and how the target machine is exploited using Metasploit are elaborated.
Comparative Analysis: Recommendation Techniques in E-Commerce

Recommendation systems have become more vital in addressing the current state of information overload in e-commerce. It assists in filtering data according to customer’s personal interests. This research did comparative analysis on 30 papers that developed recommendation systems, and the techniques they utilized to generate customised and personalised data according to the customer needs. Then it proposed a new model considering the shortcoming of the analysed systems. It incorporates the nature of the data whether implicit and explicit, Recommendations techniques, and view of the data to provide recommendations that can assist e-commerce businesses to provide the products and services that better suits the customers’ customized and personalised preferences from enormous amount of collected data.
A Sophisticated Review on Open Verifiable Health Care System in Cloud

A patient-centric strategy has quickly replaced the old hospital/specialist-centric model in healthcare during the past few decades, particularly in the healthcare system. The development of new technologies has accelerated this shift. With respect to the effectiveness of electronic equipment as well as dependability and reliability, the Internet of Medical Things (IoMT) plays a crucial role in the healthcare system's development. The privacy and security of IoMT are of the greatest priority and are essential in protecting the patient's life because IoMT is primarily used to collect highly sensitive personal health information. If not protected, this information could have a negative impact on the patient's health and, in the worst-case situation, even causes death. Inspired by this important necessity, numerous researchers have consistently achieved notable advancements to address the privacy and security challenges in IoMT alongside the development of IoMT technology. However, there are a lot of potential possibilities for further research. It requires a thorough review of all current privacy and security approaches in the IoMT space. In order to secure IoMT in smarter healthcare systems, this article will examine the existing research on the most prospective cutting-edge technologies, paying particular attention to safety, privacy preservation, and authenticating with public verification. Lastly, emphasize the study's findings by outlining the advantages and drawbacks of current privacy and security solutions and the possibilities and future possible directions that may inspire researchers in the coming years to advance and reshape their work on the secure incorporation of IoMT in Smart Healthcare Systems.
Comparative Analysis of Using Event Sourcing Approach in Web Application Based on the LAMP Stack

The article deals with the different solution implementations for the problems that require the usage of event sourcing design pattern. The problem of event sourcing in web applications (particularly, those that run using LAMP (Linux, Apache, MySQL, PHP) stack) is discussed and the related works are reviewed. The experiments with performance testing of each event sourcing approach for web app are described and conducted: creation with and without snapshots, using separate queries and transactions; reading the data from the snapshot and data array, as well as calculation of the balance using a database management system, back-end, and front-end. The experiment results are presented and discussed, and the conclusions are drawn based on the results. Results can be useful for web developers who design LAMP stack applications.
A Light-Weight Real-Time Anomaly Detection Framework for Edge Computing

In the Internet of Things IoT environment, the quality of delivered services essentially relies on several factors, such as the quality of data collected by the numerous numbers of embedded sensors, and the quality of the underlying network. Hence, a fault or anomaly arising from the hardware, software, or network can have devastating consequences regarding the overall quality of the corresponding services. Since that anomalous behaviour can be existed independently due to either an attack or sensor malfunction, developing methodologies that can detect anomalies and identify their sources seamlessly in real-time is a crucial demand to provide robust and reliable IoT services. This research investigates the potential data quality degradation caused by anomalies through analyzing sensory-related data. The ultimate aim is to utilize unsupervised deep-learning techniques, namely: AE-LSTM, and VAE-LSTM and adopt the edge computing concept that employs edge devices to detect anomalous data, describe, and analyze the effect of such anomalies from the quality provided perspective. This is achieved by introducing a Light-Weight Real-Time Anomaly Detection Framework that comprises two distinct layers: a back layer which includes a deep-learning-based anomaly detection trainer, and a front layer which is an edge device that acts as a Real-time anomaly detector. The evaluated models showed outperformed results compared to the state-of-the-art One-Class Support Vector Machine OCSVM unsupervised learning technique with up to 95% F1 score in detecting anomalies imposed into sensors’ readings.
Examining the Distribution of Keystroke Dynamics Features on Computer, Tablet and Mobile Phone Platforms

In Keystroke Dynamics (KD) literature, the normal distribution is the first-choice probability distributions (PD) used to model KD data. A recent study has shown that the log-logistic distribution is the best distribution to model KD data that was obtained on computer keyboards. Since the type of keyboard used can affect the characteristics of the KD features, the aim of this paper is to evaluate the impact of the keyboard used for KD data acquisition on the fits of some PDs. A public KD dataset collected using a desktop, phone and tablet keyboard was used for this study. The fits of eight 2-parameters PDs were evaluated on the dataset using the Kolmogorov–Smirnov goodness-of-fit statistic. The log-logistic distribution was ranked the best in fitting the KD data and this performance was independent of the devices considered. This is important towards generation of synthetic datasets for KD research.