Reviewerly/exHarmony huggingface dataset:
Hybrid multi-criteria decision-making technique for the selection of best cryptographic multivalued Boolean function

Abstract Robustness of modern information confidentiality algorithm depends on its individual components. Modern block ciphers highly depend on two components namely confusion and diffusion. These two main characteristics in any modern cipher are achieved by substitution and permutation boxes. In this article, a decision-making algorithm is utilized for the selection of optimum substitution box (S-box), which plays a significant role in the field of information confidentiality. For this purpose, an interval-based Pythagorean fuzzy technique for order of preference by similarity to the ideal solution (IVPF–TOPSIS) method is introduced to get the desired nonlinear confusion component of block cipher namely S-box. In this manner, we performed cryptographic analyses of standard S-boxes namely nonlinearity (NL), strict avalanche criterion (SAC), bit-independent criterion (BIC), absolute indicator (ABI), the sum of square and absolute indicator (SSAI), algebraic degree (AD), algebraic immunity (AI), transparency order (TO), composite algebraic immunity (CAI), robustness (RB), signal to noise ratio (SNR), confusion coefficient of variance (CCV). With these cryptographic characteristics, we have used interval-valued based Pythagorean fuzzy TOPSIS multi-criteria decision-making technique to classify standard S-boxes suitable for construction of modern block ciphers.
Towards a Framework for the Personalization of Cybersecurity Awareness

Significant evidence indicates that insecure employee behavior can be a major threat, undermining cybersecurity in organizations. Although cybersecurity awareness programs aim to enhance behavior and mitigate security risk, much of the current provision is essentially designed to offer a one-size-fits-all and does not pay attention to the differences in security behavior and other important traits that distinguish users. Similarly, while many guidelines exist to promote good practice, this in itself does not account for how people internalize security-related knowledge and make security-related decision. This research explores the impact of human-centric variables, organization culture and security awareness communication approaches on cybersecurity, leading towards the proposal of an initial concept for a Personalized Security Awareness Program (PSAP) framework, the intention of which is to recognize the relevant differences in the profile of the users that require awareness-related support, and then take account of this in how security messaging is delivered and how the resulting performance is evaluated. This work-in-progress paper presents the background justification for the approach and outlines the key elements to be considered in its further realization.
The Development of an Image Searching Method by a Content Based Image Retrieval System

Image searching enables the users to meet up their thirst of information with images. It is a method by which the users can check out, browse or retrieve images from any database. The use of image search methodology is being used in many search engines alongside text-based searching techniques but this technique is still under development as it is quite different than a text-based searching process. Unlike text-based searching method, this method need not necessitate manual searching and gives more close output in a shorter interval of time. Thus, the development of image searching method is the demand of time. In this paper, an image searching method is proposed through a content-based image retrieval (CBIR) system which uses an image for searching and sorting the output image. The result of the output image is compared with the input image by using histogram analysis and an accuracy of 89.7% is obtained.
Influence of light intensity and magnification on the precision of nanoscale positioning in micro-vision-based dark-field imaging

Dark-field imaging is widely used in shape analysis and defect detection in micro-vision-based systems because it reflects topography clearly. However, the unclear relationship between dark-field imaging parameters and image quality, along with the lack of dark-field-based image positioning algorithms, has resulted in difficulty combining dark-field imaging with positioning measurements. This paper introduces dark-field imaging into positioning on etched surface patterns. We establish an electromagnetic field model for dark-field imaging and derive an incoherent light point spread function model. Subsequently, the Gaussian fitting grayscale positioning (GFGP) method is proposed for the achievement of pixel-level positioning. Through simulation and physical experiments, we explore the influence of light intensity and magnification on positioning measurement precision and obtain the optimal parameter interval. In the optimal parameter configuration, the GFGP method achieves a precision of 8.2 nm at a speed of 50 Hz. Additionally, the GFGP method assists in restoring image super-resolution, as indicated by experimental results showing a similarity of 0.8714 between the topography under 4 × interpolation and the real topography.
Factors Influencing Internet of Medical Things (IoMT) Cybersecurity Protective Behaviours Among Healthcare Workers

The healthcare sector is increasingly adopting technologies like the Internet of Things (IoT) to improve healthcare service delivery and outcomes. As the adoption rates increase, so do the attack rates. The Internet of Medical Things (IoMT) has increasingly become a target of cyberattacks. There is an awareness that human factors significantly contribute to IoMT vulnerabilities but minimal research on the phenomenon. This research explores the factors influencing the behaviours of South African healthcare workers as they relate to IoMT cybersecurity. The theoretical model for the research was developed based on literature and the Information-Motivation-Behavioural (IMB) Skills model to study how these factors influence the behaviour of South African healthcare workers. Partial Least Squares Structural and Equation Modeling (PLS-SEM) were used to assess the model. The results suggest that Information and Behavioural Skills positively influence behaviour, indicating that knowledge and perceived ability are required to enact cybersecurity protective behaviour.
Game-Theoretic Optimization Towards Diffeomorphism-Based Robust Control of Fuzzy Dynamical Systems with State and Input Constraints

This work investigates a game-theoretic optimization approach towards robust control of uncertain dynamical systems with state and input constraints. The uncertainty involved is possibly rapidly time-varying but bounded within a prescribed fuzzy set. For this, the associated fuzzy dynamical system is appropriately established and constructed based on fuzzy set theory. To cope with the bounded state and input constraints, a novel state-and-input diffeomorphism technique is proposed, where a transformed system is formulated such that the prescribed inequality constraints are innovatively merged into the stabilization and trajectory tracking problems. Furthermore, a diffeomorphism-based robust control (DBRC) strategy is developed to ensure the uniform boundedness (UB) and uniform ultimate boundedness (UUB) of the transformed system. Under this proposed control architecture, the constraint satisfaction of the original system is thus always analytically ensured based on the rigorous properties of the diffeomorphism technique. The resulting control parameter optimization problem then has to take into account the multiple considerations (and compromise) amongst the factors of the steady-state performance; the finite convergence time; and the control effort. For this, a two-player Nash game is formulated and solved in an effective manner. The Nash equilibrium is obtained and the existence of the solution is also proved theoretically. With this methodology, and with the resulting attainment of the desired Nash equilibrium, the attendant outcome of superior system performance is achieved. Finally, numerical simulations on a steer-by-wire (SBW) system demonstrate the effectiveness of the proposed approach.
Towards Roles and Responsibilities in a Cyber Security Awareness Framework for South African Small, Medium, and Micro Enterprises (SMMEs)

Internet users including Small, Medium and Micro Enterprises (SMMEs) can access a variety of global opportunities through cyberspace however SMMEs are also targets of cyber-attacks, although they do not have adequate information technology (IT) skills and financial support to prevent prevalent cyber threats. The main aim of this research study is to identify and propose roles and responsibilities for the cyber security awareness framework for SMMEs. A scoping literature review was conducted to collect and analyse existing studies within the research domain of cyber security awareness towards SMMEs in developing countries. These studies were analysed to identify roles and responsibilities which contributes towards improving the culture of cyber security awareness in South Africa, specifically within SMMEs. These roles and responsibilities are important for the successful implementation of the cyber security awareness framework within SMMEs. The proposed roles also enable collaboration towards the development and rollout strategy of the cyber security awareness within SMMEs.
Transdisciplinary Approach to Virtual Narratives - Towards Reliable Measurement Methods

Abstract We have recently observed intense growth in the film industry’s interest in VR creations. Cinematic VR artists encounter challenges that result from discrepancies between established techniques of storytelling, stylistic conventions, and organizational culture indicative of traditional modes of film practice and the requirements of the new medium and new audience. We propose a transdisciplinary approach to cinematic VR research. Thanks to the cooperation of art &amp; science - a collaboration between psychologists, information technology specialists, film scholars, and filmmakers will contribute to the emergence of a new VR narrative paradigm. We use a number of quantitative and qualitative methods to study the perception of cinematic VR works, an illusion of spatial presence and copresence, attention, emotions, and arousal of its users, narrative understanding, and character engagement. We measure participants’ reactions in many independent ways: in addition to subjective assessments and declarative methods, we use more objective data: eye tracking, multi-point position skeleton tracking, and psychophysiological responses. We show the effectiveness of the adopted approach by studying three artistic cinematic VR works: narrative and non-narrative, live-action, and animated. We compare the user experience and present the possibilities of interpretation and feedback benefits for art.
A Deep Learning Framework for the Detection of Malay Hate Speech

Although social media can efficiently disseminate information, they also facilitate the dissemination of online abuse, harassment, and hate speech.In 2019, United Nations Secretary-General introduced the United Nations Strategy and Plan of Action on Hate Speech in response to the alarming global trend of rising hate speech.It is crucial to prevent hate speech because it can have severe negative effects on both individuals and society.While much research has been conducted on detecting online hate speech in English, little research has been conducted in other languages, such as Malay.In this paper, we present the first benchmark dataset HateM for detecting hate speech in Malay, comprised of over 4,892 annotated tweets.We created a two-channel deep learning model, XLCaps, to effectively manage noisy Malay language posts.One channel's input is the XLNet language model followed by the capsule network, while the other channel's input is the FastText embedding with Bi-GRU.Our proposed model surpasses the baseline models in terms of overall accuracy and F1 measurement, which are 80.69% and 80.41%, respectively.This work contributes to the prevention of hate speech in Malay and can serve as a basis for future study in this area.The approach to effectively managing noisy Malay posts can be also applied to other languages.The code and dataset are available at https://github.com/MaityKrishanu/Hate_Malay.
#SuperLibrarian – the evolving role of librarians in technology spaces

Purpose The purpose of this paper is to discuss changes in the roles of librarians against the backdrop of emerging technologies. Design/methodology/approach Through reflection on practice drawn from the author’s 30 years in librarianship, this paper explores the evolving role of librarians in the constantly developing technological environment and demonstrates how librarians can combine information resources, technology and research assistance in these constantly changing spaces. It also explores how technology enhances the role of the librarian, with specific reference to ChatGPT. Findings The author stressed the need for library courses for credit to allow for feedback, assessment and critical thinking. The need for continuing professional development for librarians was highlighted. Additionally, areas such as Scholarly Publishing, Metrics and Analytics, Academic Integrity and Intellectual Property were identified as areas of focus for the 21st century Librarian. Research limitations/implications This study is limited to changes in roles as a result of emerging technologies which impact librarians. It is set in the Caribbean. Practical implications This paper is relevant for librarians, regardless of their geographical location, who are also required to be fluid, keep on the cutting edge, adapt and adopt to deliver service in the face of constantly advancing technology. Originality/value This paper is a reflection on an original experience from a Caribbean territory. it also explores recent developments in technology, more specifically ChatGPT, and its impact on the roles of librarians. It provides contributions to the ongoing discourse on the evolving role of the librarian from a different perspective.
Robust Perception and Visual Understanding of Traffic Signs in the Wild

As autonomous vehicles (AVs) become increasingly prevalent on the roads, their ability to accurately interpret and understand traffic signs is crucial for ensuring reliable navigation. While most previous research has focused on addressing specific aspects of the problem, such as sign detection and text extraction, the development of a comprehensive visual processing method for traffic sign understanding remains largely unexplored. In this work, we propose a robust and scalable traffic sign perception system that seamlessly integrates the essential sensor signal processing components, including sign detection, text extraction, and text recognition. Furthermore, we propose a novel method to estimate the sign relevance with respect to the ego vehicle, by computing the 3D orientation of the sign from the 2D image. This critical step enables AVs to prioritize the detected signs based on their relevance. We evaluate the effectiveness of our perception solution through extensive validation across various real and simulated datasets. This includes a novel dataset we created for sign relevance that features sign orientation. Our findings highlight the robustness of our approach and its potential to enhance the performance and reliability of AVs navigating complex road environments.
SWF-GAN: A Text-to-Image model based on sentence–word fusion perception

Synthesizing images from descriptive text is an exciting and challenging task in multimodal deep learning, which has broad prospects of application in the fields of visual reasoning, image editing, style migrating, and so on. This paper proposes SWF-GAN to solve such problems: the limited constraint of coarse-grained information leads to difficulties in building semantic mappings of text-to-image accurately and ordinary mask predictors do not have enough representational capacity to accurately perceive the global information of images. SWF-GAN designs a sentence–word fusion perceptual module which divides the semantic perception of the generative model into two major layers, sentence and word, building affine transformations to constrain image synthesis using the coarse-grained feature on the sentence level and specific image synthesis using the fine-grained feature on the word level. Additionally, a weakly supervised coordinate mask predictor is employed in the sentence layer, extracting long-range dependencies with precise positional information vertically and horizontally to assign more information to the subject in the complex image background thus accurately generating the structure of the target object. The experiments show that the sentence–word fusion perceptual generative adversarial network model proposed in this paper can generate clearer and more lively images without a heavy computational burden. Compared with the baseline model, the proposed model improves the IS and FID scores by 0.97% and 22.95% respectively, and the experimental results on different datasets and the ablation study results show the effectiveness of our model.
A novel image registration-based dynamic photometric stereo method for online defect detection in aluminum alloy castings

The adoption of three-dimensional (3D) measurement technology for parts surface defect detection can improve inspection reliability. For online inspection purposes, 3D measurement technologies must possess the characteristics of high speed and high efficiency. The photometric stereo method is a potential 3D measurement method with high speed and low cost. However, the traditional photometric stereo method is unsuitable for dynamic scenes due to its initial design for static scenes. In this paper, we propose a novel dynamic photometric stereo method based on an image registration method. To achieve fast speed and high efficiency, we reduce the computational cost by automatically generating regions of interest (ROI). Additionally, we innovatively map the depth information (the surface normal vectors) to a mean curvature map of the surface and use it to detect defects, which combines the robustness of 3D methods and the fast speed of 2D methods. We designed experiments and the results showed that our method can detect defects on the surfaces of aluminum alloy castings accurately and robustly in an online manner. This paper also aims to reveal the importance of utilizing multidimensional information in high-speed online inspections.
Improvement and Optimization of Vulnerability Detection Methods for Ethernet Smart Contracts

Smart contracts based on blockchain are widely used in finance, management, Internet of Things, healthcare, and other fields. However, with the rapid development of smart contracts, the corresponding security vulnerability attack cases occur frequently. Existing Ethereum smart contract vulnerability detection tools based on static analysis techniques rely too much on expert rules, for this reason, this paper proposes an Ethereum smart contract vulnerability detection method SCSVM based on support vector machine technology. A representation of smart contracts is constructed based on the word-to-vector technique, the features of Ethereum smart contracts are extracted based on the support vector machine technique, and these features are combined to identify vulnerabilities. Experiments on Smartbugs and Smartbugs-wild show that SCSVM is significantly effective. It achieves a detection accuracy of 87.51%, outperforming five typical static analysis vulnerability detection tools in terms of F1-score. To alleviate the problems of deep learning methods over-relying on large-scale data to train models and collecting a large number of smart contract attack samples in a short period, this paper proposes a basic learner-meta-learner framework, SCLMF. solc-based acquisition of the bytecode of Ethereum smart contract Solidity, on which smart contract representations are constructed via Python and the use of SCLMF for vulnerability detection. The experiments on WScrawlD show that SCLMF has a certain detection effect. Also, to further verify the effectiveness of SCLMF, experiments were conducted on Omniglot, and the detection accuracy was 96.7% and 98.5% under 5-way 1-shot and 5-way 5-shot conditions, respectively, which exceeded Memory-Augmented Neural Networks and CONVOLUTIONAL SIAMESE NETS. In summary, the experiments proved the effectiveness of SCSVM and SCLMF in Ethereum smart contract vulnerability detection.
Astromos: An Astronomy Application for Augmented Reality-Based Learning

Today’s educational system has progressed from the conventional technique of learning through textbooks to technology-based learning, in which students have access to a vast array of learning tools, without which learners are more likely to form misconceptions about the world around them. Today’s augmented reality (AR) technologies provide a more hands-on experience for their audience, which aids in the understanding of spatial ideas, academic motivation, and degree of focus. Compared to traditional learning methods, they can comprehend information rapidly and retain it for longer periods. Smartphones make learning portable because a user may readily investigate concepts by simply utilizing the camera. This paper focuses on the development of the mobile application ‘Astromos’ based on multi-viewpoint smartphone AR-based learning (M-VSARL) that further allows users to grasp astronomy ideas in an illustrated, easy-to-use, and descriptive manner. A study has been conducted for the developed application to see how AR can benefit secondary and high school learners. The findings of the study revealed that the system had a strong learning effect, assisting students in completing the observation job. The content, design, and navigation of the application were appreciated by more than 50% of the users. The SPSS software proved the questionnaire was reliable, and the conclusions drawn from it point toward positive impacts of the application. Furthermore, notably via the use of this technique, students’ interest in astronomy was piqued, and 76% of users would recommend the application to their friends.
Towards improving decision tree induction by combining split evaluation measures

Explainability is essential for users to effectively understand, trust, and manage powerful artificial intelligence solutions. Decision trees are one of the pioneer explanaible artificial intelligence algorithms widely used by experts in several contexts. C4.5 is a prominent decision tree that continues to serve as the base for subsequent improvements. One of main pillars on the decision tree induction is the split evaluation measure used for assesing the candidate splits. Usually, one single split evaluation measure is used during the induction procedure. However, combining multiple split evaluation measures into the induction procedure could improve the classification results. Our proposal aims to obtain better classification results by selecting the best candidate split taking advantage of the power of voting methods to aggregate the ranking issued by multiple split evaluation measures. Consequently, we performed a set of experiments to appraise the performance of our proposal on 114 databases. From these experiments, two novel split evaluation measures, proposed in this paper, were assessed against 11 individual and prominent state-of-the-art split evaluation measures. Our classification results show that our proposal outperforms the other state-of-the-art split evaluation measures.