Reviewerly/exHarmony huggingface dataset:
Bayesian analysis of mixture models with Yeo-Johnson transformation

AbstractAbstract–Mixture models are widely employed in the analysis of heterogeneous data. However, existing approaches are based on the assumption that the observations in each component are normally distributed. The main objective of this article is to propose mixture models with Yeo-Johnson transformation to handle general heterogeneous data. Bayesian methods are developed for estimation and model comparison. The empirical performance of the proposed methodology is assessed through simulation studies. A real analysis of a data set derived from the National Longitudinal Survey of Youth 1997 is presented for illustration.KEYWORDS: Bayesian analysisMarkov chain Monte Carlo methodsmixture distributionsYeo-Johnson transformation AcknowledgmentThe authors thank the Editor and three reviewers for their constructive comments that have improved the initial version of this paper.Disclosure statementThe authors report there are no competing interests to declare.Data Availability StatementThe data that support the findings of this study are openly available in figshare at https://doi.org/10.6084/m9.figshare.21341088.v1
An ultra-lightweight block cipher with string transformations

AbstractSecurity efficiency and hardware efficiency are equally important when designing a block cipher. In this paper, we propose a new block cipher that uses string transformations and requires less memory and fewer computational resources, making it suitable for highly constrained environments. We compare the performance of our design with AES-128 and quasigroup-based block cipher INRU in the cipher block chaining (CBC) mode of operation using the National Institute of Standards and Technology (NIST) test suite. We then analyze our design against the standard linear, differential, and algebraic attacks. Choosing suitable S-boxes and a quasigroup of a smaller order does not compromise the security of the cipher, but the storage space is reduced compared with AES-128 and INRU.Keywords: block cipherquasigroupS-boxstring transformation2010 Mathematics Subject Classification: 94A6020N05 Disclosure statementThe authors declare that they have no conflict of interest.Additional informationFundingThis research was supported by the University Grants Commission (UGC), Government of India, with reference no.: 20/12/2015(ii)EU-V.Notes on contributorsRashmi VermaDimpy Chauhan is a research scholar at the Department of Mathematics, University of Delhi, India. She has received her Masters in Mathematics from Panjab University, India. Her research interests are in algebra and its applications, especially cryptography and coding theory.Indivar Gupta is a senior scientist in Scientific Analysis Group, DRDO, Delhi, India. He completed his Ph.D. from the IIT Delhi, India. His areas of research include computational algebra, number theory, cryptography, information security, and high-performance computing.P. R. Mishra is a senior scientist in Scientific Analysis Group, DRDO, Delhi, India. He obtained his Ph.D. in number theory from Banaras Hindu University, Varanasi, India. His main research interests are cryptography and number theory.Rashmi Verma is an associate professor at the Department of Mathematics, Mata Sundri College for Women, University of Delhi, India. She completed her M.Phil. and Ph.D. from the Department of Mathematics, University of Delhi, India. Her research interest is algebraic coding theory.
Cybersecurity knowledge graphs construction and quality assessment

Abstract Cyber-attack activities are complex and ever-changing, posing severe challenges to cybersecurity personnel. Introducing knowledge graphs into the field of cybersecurity helps depict the intricate cybersecurity landscape and provides technical support for threat identification and situational awareness. However, during the process of constructing knowledge graphs, inevitable noise and conflicts may be introduced, leading to misleading inferences and decisions. This paper aims to address the issues of constructing and assessing the quality of cybersecurity knowledge graphs. We manually constructed a dataset of cybersecurity knowledge graphs (CS13K) and expanded the existing cybersecurity ontology, building a more versatile ontology applicable to existing data features. Building upon this, we utilized Neo4j to construct the cybersecurity knowledge graph. Furthermore, we propose an AttTucker model, based on Transformer, for assessing the quality of knowledge graphs. By employing numerous self-attention heads, this model captures latent information among entities and relations. While reducing the dimensionality of knowledge embeddings, the model achieves evaluation results comparable to high-dimensional embeddings. Finally, we incorporate the path-level information between entities in the knowledge graph into the evaluation of knowledge graph quality based on the AttTucker model. Experimental analysis on both generic datasets and cybersecurity datasets demonstrates that our model significantly improves F1 value and accuracy in knowledge graph quality assessment tasks, outperforming other models.
On the Issue of Copyright Protection on the Internet

This study is devoted to the consideration of certain aspects of the protection of copyrights on the Internet. Digital technologies make it possible to use any work without concluding an agreement on the transfer of the exclusive copyrights. The article notes that free use of works is a violation of personal non-property rights of authors’ science, literature and art works. Russian legal problems of intellectual property on the Internet related to the impossibility of identifying some person as the author of the work and the absence of legal mechanism for free use of works. The scientific novelty of this study lies in substantiation of possibility of using a digital work without the consent of author, but after a certain period of time. The scientific result of the study serves the proposal to introduce the procedure for fixing copyright materials on the Internet into current legislation using for obtaining some special access code to original work, in order to serve as the effective mechanism for protecting copyright.
An Enhanced Neural Network Algorithm with Quasi-Oppositional-Based and Chaotic Sine-Cosine Learning Strategies

Global optimization problems have been a research topic of great interest in various engineering applications among which neural network algorithm (NNA) is one of the most widely used methods. However, it is inevitable for neural network algorithms to plunge into poor local optima and convergence when tackling complex optimization problems. To overcome these problems, an improved neural network algorithm with quasi-oppositional-based and chaotic sine-cosine learning strategies is proposed, that speeds up convergence and avoids trapping in a local optimum. Firstly, quasi-oppositional-based learning facilitated the exploration and exploitation of the search space by the improved algorithm. Meanwhile, a new logistic chaotic sine-cosine learning strategy by integrating the logistic chaotic mapping and sine-cosine strategy enhances the ability that jumps out of the local optimum. Moreover, a dynamic tuning factor of piecewise linear chaotic mapping is utilized for the adjustment of the exploration space to improve the convergence performance. Finally, the validity and applicability of the proposed improved algorithm are evaluated by the challenging CEC 2017 function and three engineering optimization problems. The experimental comparative results of average, standard deviation, and Wilcoxon rank-sum tests reveal that the presented algorithm has excellent global optimality and convergence speed for most functions and engineering problems.
Covariance-Matrix-Based Criteria for Network Entanglement

Quantum networks offer a realistic and practical scheme for generating multiparticle entanglement and implementing multiparticle quantum communication protocols. However, the correlations that can be generated in networks with quantum sources and local operations are not yet well understood. Covariance matrices, which are powerful tools in entanglement theory, have been also applied to the network scenario. We present simple proofs for the decomposition of such matrices into the sum of positive semi-definite block matrices and, based on that, develop analytical and computable necessary criteria for preparing states in quantum networks. These criteria can be applied to networks where nodes share at most one source, such as all bipartite networks.
Robust PDF Watermarking against Print–Scan Attack

Portable document format (PDF) files are widely used in file transmission, exchange, and circulation because of their platform independence, small size, good browsing quality, and the ability to place hyperlinks. However, their security issues are also more thorny. It is common to distribute printed PDF files to different groups and individuals after printing. However, most PDF watermarking algorithms currently cannot resist print-scan attacks, making it difficult to apply them in leak tracing of both paper and scanned versions of PDF documents. To tackle this issue, we propose an invisible digital watermarking technology based on modifying the edge pixels of text strokes to hide information in PDFs, which achieves high robustness to print-scan attacks. Moreover, it cannot be detected by human perception systems. This method focuses on the representation of text by embedding watermarks by changing the features of the text to ensure that changes in these features can be reflected in the scanned PDF after printing. We first segment each text line into two sub-blocks, then select the row of pixels with the most black pixels, and flip the edge pixels closest to this row. This method requires the participation of original PDF documents in detection. The experimental results show that all peak signal-to-noise ratio (PSNR) values of our proposed method exceed 32 dB, which indicates satisfactory invisibility. Meanwhile, this method can extract the hidden information with 100% accuracy under the JPEG compression attack, and has high robustness against noise attacks and print-scan attacks. In the case of no attacks, the watermark can be recovered without any loss. In terms of practical applications, our method can be applied in the practical leak tracing of official paper documents after distribution.
Requiem for an Interface

research-article Open Access Share on Requiem for an Interface Authors: Steve Harrison Virginia Tech Virginia TechView Profile , Deborah Tatar Virginia Tech Virginia TechView Profile Authors Info & Claims InteractionsVolume 30Issue 5September - October 2023pp 30–33https://doi.org/10.1145/3613243Published:24 August 2023Publication History 0citation6,719DownloadsMetricsTotal Citations0Total Downloads6,719Last 12 Months6,719Last 6 weeks6,719 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteView all FormatsPDF
Cybersecurity Challenges of Blockchain and Smart Contracts Technologies in IIoT

This paper aims at identifying the main challenges associated with the adoption of blockchain and smart contract technologies as potential solutions for cybersecurity in an Industrial Internet of Things environment. Initially, an introduction to IIoT, blockchain and smart contracts is made, highlighting their core concepts as well as explaining their characteristics and properties. Subsequently, based on a literature review, the various benefits and challenges for their implementation are identified. The latter are presented through a hierarchical approach that first categorizes them in architecture and privacy related challenges, with each category being further divided into more specific challenges to emphasize the complex nature of the problem. Some typical use cases are also mentioned to showcase the feasibility of these technologies. Finally, a conceptual framework for IIoT cybersecurity is proposed in terms of future work directions.
Extreme Value Statistics

Data Science for the Geosciences provides students and instructors with the statistical and machine learning foundations to address Earth science questions using real-world case studies in natural hazards, climate change, environmental contamination and Earth resources. It focuses on techniques that address common characteristics of geoscientific data, including extremes, multivariate, compositional, geospatial and space-time methods. Step-by-step instructions are provided, enabling readers to easily follow the protocols for each method, solve their geoscientific problems and make interpretations. With an emphasis on intuitive reasoning throughout, students are encouraged to develop their understanding without the need for complex mathematics, making this the perfect text for those with limited mathematical or coding experience. Students can test their skills with homework exercises that focus on data scientific analysis, modeling, and prediction problems, and through the use of supplemental Python notebooks that can be applied to real datasets worldwide.
Quantum walk state transfer on a hypercube

Abstract We investigate state transfer on a hypercube by means of a quantum walk where the sender and the receiver vertices are marked by a weighted loops. First, we analyze search for a single marked vertex, which can be used for state transfer between arbitrary vertices by switching the weighted loop from the sender to the receiver after one run-time. Next, state transfer between antipodal vertices is considered. We show that one can tune the weight of the loop to achieve state transfer with high fidelity in shorter run-time in comparison to the state transfer with a switch. Finally, we investigate state transfer between vertices of arbitrary distance. It is shown that when the distance between the sender and the receiver is at least 2, the results derived for the antipodes are well applicable. If the sender and the receiver are direct neighbours the evolution follows a slightly different course. Nevertheless, state transfer with high fidelity is achieved in the same run-time.
Anomaly Detection with a LSTM Autoencoder Using InfluxDB

In the manufacturing industry, anomalies are an unfortunate but inevitable reality. If left unaddressed, they can lead to costly production defects and halted production lines. However, with the rise of Industry 4.0, many industrial machines are now equipped with sensors that can be used to detect anomalous behaviors, allowing for early identification and prevention of defects. Therefore, this study presents a solution using a Long Short-Term Memory (LSTM) autoencoder to detect abnormal behavior in an industrial machine temperature sensor dataset. The algorithm is compared with conventional methods, further demonstrating its capabilities in anomaly detection. Additionally, an implementation architecture is proposed using InfluxDB and Telegraf software, providing a simulated real-world application of the proposed solution.
A Model for Cyber Threat Intelligence for Organisations

As cyber attacks are increasing in South Africa, organisations need to strengthen cyber security controls. Cyber Threat Intelligence is an essential component of a Cybersecurity program but is often overlooked. It can assist to identify future and potential cyber threats. Organisations process large volumes of data containing Cyber Threat Intelligence, but this is often not collected, processed, or considered as Cyber Threat Intelligence. South African organizations will continue to feel the repercussions of cyber-attacks if actions are not taken. To bring clarity and allow South African organizations to leverage on Cyber Threat Intelligence, this work aims to categorize Cyber Threat Intelligence for organizations. Several characteristics of Cyber Threat Intelligence are discussed, and thereafter a model is presented. The applicability of this model is demonstrated by a short use-case.
Comparing the Effectiveness of Machine Learning and Deep Learning Techniques for Feature Envy Detection in Software Systems

Code smells are common in poorly designed software that can hinder code maintainability. Automatic detection of design flaws assists developers in identifying code smells in theirsoftware programs to avoid low-quality delivery. However, the interpretation of code smells is subjective, and existing machine learning-based detection tools are limited in their ability to capture complex semantic relationships in textual code. To address this limitation, the paper proposes a detection system that exploits machine learning and deep learning techniques and compares their effectiveness in identifying the feature envy code smell. The study applies six deep learning techniques based on code semantics features extracted from abstract syntax trees, and eleven machine learning techniques based on code structural features. The results show that both approaches are effective in detecting code smells, with Random Forest achieving exceptional performance among the machine learning classifiers.
Named Entity Recognition for Arabic Medical Texts Using Deep Learning Models

Named Entity Recognition (NER) plays a vital role in extracting meaningful information from textual data in the medical domain. This paper focuses on NER for Arabic medical texts, specifically targeting the recognition of disease entities. The study presents a comparative analysis of deep learning techniques, including Conditional Random Fields (CRF), Long Short-Term Memory (LSTM), LSTM-CRF, and Bidirectional LSTM (BiLSTM), applied to a dataset comprising Arabic medical texts related to diseases. The dataset is meticulously annotated, ensuring accurate labelling of disease entities for training and evaluation purposes. The models are trained and evaluated using appropriate loss functions and evaluation metrics, such as precision, recall, and F1-score. Comparative experiments are conducted to assess the performance of each model on the disease dataset. The results demonstrate the effectiveness of deep learning techniques for NER in Arabic medical texts, with the LSTM-CRF and BiLSTM-CRF models outperforming the standalone CRF and LSTM models. LSTM-CRF and BiLSTM-CRF models reported F1-score of 0.97 and 0.94. These hybrid models achieve higher precision, recall, and F1-score, showcasing their ability to accurately identify disease entities in Arabic medical texts. The findings of this study contribute to the advancement of NER techniques for Arabic medical texts, focusing on disease entities. The comparative analysis of CRF, LSTM, LSTM-CRF, and BiLSTM models provides valuable insights into their respective strengths and limitations of NER for Arabic medical texts. These insights can guide the selection and implementation of appropriate models for disease entity recognition in Arabic medical texts, facilitating accurate information extraction and analysis in the medical domain.
Compiler-based Web Services code conversion model for different languages of mobile application

Mobile applications are very popular nowadays. The two most common platforms depend on different languages to have native performance. Most trans-compiler-based converters between those languages didn’t take webservices conversion into consideration. However, in recent years, mobile applications developers have used web services for lighter and more dynamic mobile applications. Therefore, it becomes crucial to include web services mapping to the trans-compiler-based code converters for mobile applications code. In this paper, we present a model for web service trans-compiler code mapper from swift for iOS to java for Android to be integrated to previous trans-pilers. The testing for the conversion accuracy is done for six different web services functions. The average accuracy of converting webservice calling from iOS to Android is 89.3%. In addition, due to the rise of generative language models and their capability to generate code, we compare OpenAI’s chatGPT to our tool, evaluating its capabilities for code translation and exploring the potential of prompt engineering for seamless integration into our trans-compiler tool.
Ensemble Machine learning for Breast Cancer Detection

Breast cancer (BC) is a serious issue and the biggest global cause of death for women. Therefore, it is crucial to find BC early in order to save their lives. Models for the prognosis of some diseases have been developed using machine learning (ML), which has been shown to play a crucial role in this process. In this study, we present a model that can distinguish between cancerous and healthy BC cells in order to classify people into those who have BC and those who do not. In order to optimize the characteristics and select the best subset from them, this study leverages the feature selection (FS) phenomenon. We examine six machine learning models: the decision tree (DT), random forest (RF), KNN, support vector machine (SVM), and logistic regression (LR). In order to assess the performance Of this paper’s classification models, we calculated precision, accuracy, recall, and F1-score to assess their performance. On the dataset, both before and after applying the FS approach, the six ML models’ performance was evaluated. The experimental outcomes demonstrate that LR and SVM are equally accurate and produce the greatest results when compared to other ML models, with a 98.246% accuracy that increases to 98.83% with Fs. A voting ensemble ML model was created based on LR, SVM, and other ML techniques to attain the greatest performance. Compared to other classic ML methods, ensemble models perform better.
Incorporating Connectivity in k-Nearest Neighbors Regression

The standard k-nearest neighbors’ approach to regression analysis encounters a number of problems when used on datasets with various density distributions. This paper proposes a kNNR-relative ensemble regressor based on connectivity. In each cross-validation round, the pipeline starts by clustering the input data using any partitioning algorithm. Then, a random sample of the edges is selected from each partition favoring the edges with small distances. After that, the selected edges are transformed into a dataset in which feature values represent the amount of increase or decrease in each dimension compared to the source node’s values and the label of each feature vector is the difference in the label of the source and the destination. Then a regressor is built for each cluster based on the output of the transformer. In order to predict a label for an unseen object, the nearest centroid is identified, and, k-nearest neighbors from the corresponding cluster are identified as the source nodes. Then, a vector representing the difference between the unseen object and each source node is computed and fed to the regressor model of the corresponding cluster, the output label is the predicted difference so it is added to the label of the source node. The diversity between the suggested decision model and the traditional kNN regressor, termed kNNR motivates us to include the kNNR in the suggested ensemble. The k-nearest neighbors of kNNR are also selected from the nearest cluster. The weighted average of the predicted labels offered by the base models serves as the final output label. The sample size, the number of neighbors to be used, and the number of clusters can all be fine-tuned via cross validation. The ensemble is evaluated, and the results showed that the ensemble achieved a significant increase in effectiveness compared to its base regressors and several related algorithms.