Reviewerly/exHarmony huggingface dataset:
Provably secure authentication for the internet of vehicles

Rapid technological development has far-reaching implications for every aspect of life, including transportation. The emerging Intelligent Transportation System (ITS) based on the Internet of Vehicles (IoV) requires robust security measures due to the public channels used to communicate between vehicles and trusted authorities. These channels create opportunities for adversaries to intercept and exploit the transmitted data, leading to severe attacks. Moreover, cars are often parked in public areas, making them vulnerable to physical attacks such as theft of smart cards/devices, cloning, and impersonation. In this paper, we propose a new protocol for IoV that addresses these security concerns. Our protocol offers user authentication based on biometrics, using a biometric key to prevent revealing data from theft of smart cards/devices. Additionally, we use The Physical Unclonable Function (PUF) to resist cloning attacks. We have conducted an informal analysis, which confirms that our protocol fulfills security features. Furthermore, we have conducted a formal analysis using the RoR model and the Scyther tool to verify that our protocol achieves security properties and withstands well-known attacks. We also demonstrate the time complexity and show that our protocol has low computation time. Therefore, our protocol is secure and has a low computation time.
A shapelet-based neural network for binary and multi-class disruption prediction for prevention at JET

Disruptions in tokamaks remain, to this day, an unsolved issue on the path toward fusion power plants. Such events should be avoided or mitigated, requiring adequate detection of the disruption causes. However, due to the complex nature of disruption causes, this is, in general, challenging. Despite recent progress designing disruption prediction systems via data-driven methods, many questions remain open—such as disruption-type identification or the transfer of such methods between different tokamaks. We propose a shapelet-based neural network for binary and multi-class disruption identification that can be used for post-disruption analysis or real-time prediction. The performance is compared to two approaches from the literature, retrained on our data: support-vector machines similar to the advanced predictor of disruption, and a recurrent neural network. For the present study, we compiled a dataset of discharges from the Joint European Torus tokamak containing stable discharges and seven disruption types. Due to the availability of such classification, all shots predate the installation of the ITER-like wall. Using the binary and multi-class classification results, we report on the performance of the three models and discuss the advantages of our method. Our model ranks first and second on binary and multi-class tasks, respectively. The shapelets' contribution to the results is evaluated by conducting an ablation study. We show that shapelets with normalized Euclidean distance are enough for binary classification, but multi-class predictions require the absolute value of the signals. The good results obtained from locally normalized signals are promising for future cross-tokamak studies.
Incremental hybrid intrusion detection for 6LoWPAN

IPv6 over Low-powered Wireless Personal Area Networks (6LoWPAN) has grown in importance in recent years, with the Routing Protocol for Low Power and Lossy Networks (RPL) emerging as a major enabler. However, RPL can be subject to attack, with severe consequences. Most proposed IDSs have been limited to specific RPL attacks and typically assume a stationary environment. In this article, we propose the first adaptive hybrid IDS to efficiently detect and identify a wide range of RPL attacks (including DIO Suppression, Increase Rank, and Worst Parent attacks, which have been overlooked in the literature) in evolving data environments. We apply our framework to networks under various levels of node mobility and maliciousness. We experiment with several incremental machine learning (ML) approaches and various ‘concept-drift detection’ mechanisms (e.g. ADWIN, DDM, and EDDM) to determine the best underlying settings for the proposed scheme.
An unsolvable pixel reduced authentication method for color images with grayscale invariance

This paper introduces an advanced grayscale-invariant authentication method for color images. Prior work directly utilizes the most significant bits (MSBs) of the red and blue channels for authentication code generation and embeds these codes into the least significant bits (LSBs). Concurrently, the green channel is adjusted to sustain consistent grayscale values. However, this direct utilization of MSBs often leads to a decline in image quality and results in a significant number of unsolvable pixels. To address these challenges, we present a new approach specifically designed to combat the deterioration of image quality and the prevalence of unsolvable pixels. In our method, we propose the MSB alteration (MSBA) technique, which modifies the MSBs of the red and blue channels to generate authentication codes with minimal distortion. This technique effectively reduces image distortion and the number of unsolvable pixels. Furthermore, we introduce the pixel substitution strategy (PSS) and the constraint-reduced strategy (CRS) to enhance the embedding efficiency. Experimental results demonstrate that our method outperforms prior work by converting more unsolvable pixels to solvable ones and achieving better detection results. Moreover, our method enhances image quality by approximately 1.5 dB, while maintaining the same embedding capacity.
Taming Strategy Logic: Non-Recurrent Fragments

Strategy Logic ( for short) is one of the prominent languages for reasoning about the strategic abilities of agents in a multi-agent setting. This logic extends with first-order quantifiers over the agent strategies and encompasses other formalisms, such as and . The model-checking problem for and several of its fragments has been extensively studied. On the other hand, the picture is much less clear on the satisfiability front, where the problem is undecidable for the full logic. In this work, we study two fragments of One-Goal , where the nesting of sentences within temporal operators is constrained. We show that the satisfiability problem for these two logics, and for the corresponding fragments of and , is in and , respectively.
Are paid tools worth the cost? A prospective cross-over study to find the right tool for plagiarism detection

The increasing pressure to publish research has led to a rise in plagiarism incidents, creating a need for effective plagiarism detection software. The importance of this study lies in the high cost variation amongst the available options for plagiarism detection. By uncovering the advantages of these low-cost or free alternatives, researchers could access the appropriate tools for plagiarism detection. This is the first study to compare four plagiarism detection tools and assess factors impacting their effectiveness in identifying plagiarism in AI-generated articles.A prospective cross-over study was conducted with the primary objective to compare Overall Similarity Index(OSI) of four plagiarism detection software(iThenticate, Grammarly, Small SEO Tools, and DupliChecker) on AI-generated articles. ChatGPT was used to generate 100 articles, ten from each of ten general domains affecting various aspects of life. These were run through four software, recording the OSI. Flesch Reading Ease Score(FRES), Gunning Fog Index(GFI), and Flesch-Kincaid Grade Level(FKGL) were used to assess how factors, such as article length and language complexity, impact plagiarism detection.The study found significant variation in OSI(p < 0.001) among the four software, with Grammarly having the highest mean rank(3.56) and Small SEO Tools having the lowest(1.67). Pairwise analyses revealed significant differences(p < 0.001) between all pairs except for Small SEO Tools-DupliChecker. Number of words showed a significant correlation with OSI for iThenticate(p < 0.05) but not for the other three. FRES had a positive correlation, and GFI had a negative correlation with OSI by DupliChecker. FKGL negatively correlated with OSI by Small SEO Tools and DupliChecker.Grammarly is unexpectedly most effective in detecting plagiarism in AI-generated articles compared to the other tools. This could be due to different softwares using diverse data sources. This highlights the potential for lower-cost plagiarism detection tools to be utilized by researchers.
Cell-wise robust covariance estimation for compositions, with application to geochemical data

Cell-wise outliers are outliers in single entries of a compositional data matrix, and they can lead to a certain bias in the statistical analysis. Traditional row-wise robust methods downweight outlying observations for the estimation, independent of how many or which cells of an observation are contaminated. Cell-wise robustness still makes use of the information contained in non-contaminated cells. Here, cell-wise robustness is used for the estimation of the variation and the covariance matrix. For higher dimensional data also a regularized estimator is introduced. The advantages of the cell-wise robust estimators are demonstrated in simulation experiments and in a geochemistry application in the context of clustering and principal component analysis.
Neuroevolution with box mutation: An adaptive and modular framework for evolving deep neural networks

The pursuit of self-evolving neural networks has driven the emerging field of Evolutionary Deep Learning, which combines the strengths of Deep Learning and Evolutionary Computation. This work presents a novel method for evolving deep neural networks by adapting the principles of Geometric Semantic Genetic Programming, a subfield of Genetic Programming, and Semantic Learning Machine. Our approach integrates evolution seamlessly through natural selection with the optimization power of backpropagation in deep learning, enabling the incremental growth of neural networks’ neurons across generations. By evolving neural networks that achieve nearly 89% accuracy on the CIFAR-10 dataset with relatively few parameters, our method demonstrates remarkable efficiency, evolving in GPU minutes compared to the field standard of GPU days.
The Nature of the Biblical Hebrew Poetic Line

Abstract Chapter 3, the heart of this book’s cognitive approach, explores the nature of the line in biblical poetry. The term “line” is defined as the basic unit of structure and rhythm in a versification system. The problem with the term “line” for biblical poetry is that the biblical poetic line is not linear, graphically or conceptually. Biblical poetic lines must be reconceptualized not as straight and parallel but as various kinds of segments that create shapes or figures, the line-groupings of biblical poetry. Biblical poetry is compared with English metrical and free-verse poetry to show how the mental realization of lines differs in these versification systems. In biblical poetry, line structure emerges in the part-whole relationship of the line to the line-grouping, through the mental process of segregating lines and integrating line-groupings.
Transcription between human-readable synthetic descriptions and machine-executable instructions: an application of the latest pre-training technology

AI has been widely applied in scientific scenarios, such as robots performing chemical synthetic actions to free researchers from monotonous experimental procedures. However, there exists a gap between human-readable natural language descriptions and machine-executable instructions, of which the former are typically in numerous chemical articles, and the latter are currently compiled manually by experts. We apply the latest technology of pre-trained models and achieve automatic transcription between descriptions and instructions. We design a concise and comprehensive schema of instructions and construct an open-source human-annotated dataset consisting of 3950 description-instruction pairs, with 9.2 operations in each instruction on average. We further propose knowledgeable pre-trained transcription models enhanced by multi-grained chemical knowledge. The performance of recent popular models and products showing great capability in automatic writing (e.g., ChatGPT) has also been explored. Experiments prove that our system improves the instruction compilation efficiency of researchers by at least 42%, and can generate fluent academic paragraphs of synthetic descriptions when given instructions, showing the great potential of pre-trained models in improving human productivity.
ML-Based Methodology for HPC Facilities Supervision

Monitoring supercomputing facilities tends to be very time consuming and error-prone as the size of the collected data and the number of supervised devices do not cease to increase. In this paper, we propose a methodology to supervise those facilities based on measurements performed on devices at different levels of the infrastructure. Through its three phases -raw data cleaning, ML-based processing and visualisation using our developed tool- it facilitates the supervision of the computing center facilities and helps detecting irregular behaviours leading to manual correction actions. The case of the energy consumption is considered to illustrate the usefulness of this methodology and highlight its valuable results but it can be applied to any other target metric.
Evaluating Quantum Algorithms for Linear Solver Workflows

We normally think of implicit Mechanical Computer Aided Engineering (MCAE) as being a resource intensive process, dominated by multifrontal linear solvers that scale super linearly in complexity as the problem size grows. However, as the processor count increases, the reordering that reduces the storage and operation count for the sparse linear solver is emerging as the biggest computational bottleneck and is expected to grow. Reordering is NP-complete, and the nested dissection heuristic is generally preferred for MCAE problems. Nested dissection in turn rests on graph partitioning, another NP-complete problem. There are quantum computing algorithms which provide new heuristics for NP-complete problems, and the rapid growth of today’s noisy quantum computers and associated error mitigation techniques leads us to consider them as possible accelerators for reordering. This review reports on the evaluation of the relative merits of several short depth quantum algorithms used for graph partitioning, integration with the LS-DYNA MCAE application, and using the initial results generated on IBM quantum computers to bring to attention critical focus areas based on the methods used within.
Running Kubernetes Workloads on HPC

Cloud and HPC increasingly converge in hardware platform capabilities and specifications, nevertheless still largely differ in the software stack and how it manages available resources. The HPC world typically favors Slurm for job scheduling, whereas Cloud deployments rely on Kubernetes to orchestrate container instances across nodes. Running hybrid workloads is possible by using bridging mechanisms that submit jobs from one environment to the other. However, such solutions require costly data movements, while operating within the constraints set by each setup’s network and access policies. In this work, we explore a design that enables running unmodified Kubernetes workloads directly on HPC. With High-Performance Kubernetes (HPK), users deploy their own private Kubernetes “mini Clouds”, which internally convert container lifecycle management commands to use the system-level Slurm installation for scheduling and Singularity/Apptainer as the container runtime. We consider this approach to be practical for deployment in HPC centers, as it requires minimal pre-configuration and retains existing resource management and accounting policies. HPK provides users with an effective way to utilize resources by a combination of well-known tools, APIs, and more interactive and user-friendly interfaces as is common practice in the Cloud domain, as well as seamlessly combine Cloud-native tools with HPC jobs in converged, containerized workflows.
Survey of Blended Learning Approaches, Frameworks, Tools and Techniques for Science and Management Students

Experimentations with the blended learning approach have been going on for quite some time now and its effectiveness is continuously being tested by the educationists and researchers, in search of the most appropriate approach for any one kind of discipline of study at the level of higher education. Innovativeness has become a crucial factor in the process, tagged along with continuous transformations, in terms of technological advancements, content development and adoption of appropriate psychological makeup. The COVID-19 pandemic had actually given a tremendous boost to the process of blended learning approach—forcing even the reluctant recalcitrant to participate constructively in adopting this approach. Since this approach is here to stay, attempts are being made to find out how it can be further enriched to serve the purpose of making the learning process more effective, so that knowledge attained can become more implementable. The chapter covers all the possible blended learning approaches that are being used by various institutions for varied disciplines of study. The journey of this approach is not without its fair share of obstacles. The content development and the expertise of the faculty to implement a blended approach of teaching learning is also being questioned and worked upon. Since this approach of learning is the future to all educational initiatives, it is time to collate all the techniques that had been adopted till date and locate its application possibilities in the areas of science and management studies.
Artificial Intelligence and Voice Interfaces

Abstract This chapter explains artificial intelligence (AI) and machine learning (ML), and how these algorithms are used to personalize music services though playlists, to identify promising new artists, and to ensure that your smart speaker plays the correct song when requested. In addition, it covers the various ways that AI will make it simpler to modify existing songs to improve engagement and ultimately to create new, commercially successful musical works. The chapter outlines the legal issues surrounding these new applications, including the licensing of copyrighted works used to “train” these new tools and how artists are reacting to these new capabilities.
Topic Classification of Tweets in the Broadcasting Domain using Machine Learning Methods

Twitter is one of the microblogging sites with millions of daily users. Broadcast companies use Twitter to share short messages to engage or share opinions about a particular topic or product. With a large number of conversations available on Twitter, it is difficult to identify the category of topics in the broadcasting domain. This paper proposes the use of unsupervised learning to generate topics from unlabelled tweet data sets in the broadcasting domain using the latent Dirichlet allocation (LDA) method. Approximately six groups of topics were generated and each group was assigned a label or category. These labels were used to label the data by finding the dominating label in each tweet as the main category. Supervised learning was conducted to train six machine learning models which are multinomial logistic regression, XGBoost, decision trees, random forest, support vector machines, and multilayer perceptron (MLP). The models were able to learn from the data to predict the category of each tweet from the testing data. The models were evaluated using accuracy and the f1 score. Linear support vector machine and MLP obtained better classi-fication results compared to other trained models.
Cybersecurity Practices of Rural Underserved Communities in Africa: A Case Study from Northern Namibia

Globally, Information Communication Technology (ICT) device usage has seen a steep rise over the last few years. This also holds in developing countries, which have embarked on connecting the unconnected or previously disadvantaged parts of their populations. This connectivity enables people to interact with cyberspace, which brings opportunities and challenges. Opportunities such as the ability to conduct business online, attend online education, and perform online banking activities. Challenges experienced are the cost of Internet access and more worrying cyber-risks and potential for exploitation. There remain pockets of communities that experience sporadic connectivity to cyberspace, these communities tend to be more susceptible to cyber-attacks due to issues of lack/limited awareness of cyber secure practices, an existent culture that might be exploited by cybercriminals, and overall, a lackluster approach to their cyber-hygiene. We present a qualitative study conducted in rural Northern Namibia. Our findings indicate that both secure and insecure cybersecurity practices exist. However, through the Ubuntu and Uushiindaism Afrocentric lenses, practices such as sharing mobile devices without passwords among the community mirror community unity. Practices such as this in mainstream research can be considered insecure. We also propose interrogating “common” secure cybersecurity practices in their universality of applicability.
Graphing with MATLAB

The relationship between calculations and graphics in computing was nearly nonexistent in the past, leaving computational users to rely solely on numerical listings to evaluate research outcomes. However, with significant technological advancements, researchers, engineers, and professionals from various domains can now easily integrate graphs into their work and research. The importance of incorporating graphs has been recognized throughout our educational journey, from basic to advanced levels, and this significance extends to various fields of knowledge, including biology, applied mathematics, and evolutionary computation. Graphs find direct application in scientific and technological works, aiding in analyzing research findings, mathematical evaluations, and presentations. Graphs have become an essential tool in research, as they provide a different perspective and facilitate evaluating research results. MATLAB stands out among the widely used software for generating high-quality and precise graphs due to its ability to produce visually striking graphs with just a few simple commands. In Sect. 2.1, a short introduction to the chapter is given. Section 2.2 describes the essential elements of graphing with MATLAB software. Section 2.3 presents the implementation of different commands to plot the contour of two-dimensional functions. Likewise, Sect. 2.4 reviews the most important features of MATLAB to create a triangular mesh. Section 2.5 gives an overview of the combined plots, and Sect. 2.6 The Mapping Toolbox is implemented in creating maps, manipulating satellite images, and creating a 3D globe.