Reviewerly/exHarmony huggingface dataset:
“Discipline Informatization” in the Information Age Viewed from the Growth of Natural Science

This paper discusses the “what”, “why”, and “how” of the issue of “discipline informatization”. Since the Renaissance, the humanities and social sciences established under the guidance of physical-mathematical methods, such as Comte’s “social physics”, have focused mainly on the material characteristics and information carriers of cultural phenomena. Although they have gained much, they have basically failed to grasp the central axis, baseline, and link of “social information”. Therefore, the authors of this paper take the growth of natural sciences as a reference and focus on the discipline informatization of the humanities and social sciences.
Robust Controller Design for a Generic Helicopter Model: An AI-Aided Application for Terrain Avoidance

This paper focuses on robust controller design for a generic helicopter model and terrain avoidance problem via artificial intelligence (AI). The helicopter model is presented as a hybrid system that covers hover and forward dynamics. By defining a set of easily accessible parameters, it can be used to simulate the motion of different helicopter types. A robust control structure based on reinforcement learning is proposed to ensure the system is robust against model parameter uncertainties. The developed generic model can be utilized in many helicopter applications that have been attempted to be solved with sampling-based algorithms or reinforcement learning approaches that take the dynamical constraints into consideration. This study also focuses on the helicopter terrain avoidance problem to illustrate how the model can be useful in these types of applications and provide an artificial intelligence-aided solution to terrain avoidance.
A Cross-Modal Dynamic Attention Neural Architecture to Detect Anomalies in Data Streams from Smart Communication Environments

Detecting anomalies in data streams from smart communication environments is a challenging problem that can benefit from novel learning techniques. The Attention Mechanism is a very promising architecture for addressing this problem. It allows the model to focus on specific parts of the input data when processing it, improving its ability to understand the meaning of specific parts in context and make more accurate predictions. This paper presents a Cross-Modal Dynamic Attention Neural Architecture (CM-DANA) by expanding on state-of-the-art techniques. It is a novel dynamic attention mechanism that can be trained end-to-end along with the rest of the model using multimodal data streams. The attention mechanism calculates attention weights for each position in the input data based on the model’s current state by a hybrid method called Cross-Modal Attention. Specifically, the proposed model uses multimodal learning tasks where the input data comes from different cyber modalities. It combines the relevant input data using these weights to produce an attention vector in order to detect suspicious abnormal behavior. We demonstrate the effectiveness of our approach on a cyber security anomalies detection task using multiple data streams from smart communication environments.
Interactive Pedagogical Approach to Teaching in Nigerian Universities: Using Metaverse as Enabling Technology

This paper explored metaverse literature and scholarly works to dissect the concept and potential s of metaverse, being a combination of three-dimensional virtual and real world inhabited by blockchain, artificial intelligence, digital twins, and other cutting -edge intelligent technologies. The study revealed that Metaverse offers a fully immersive educational experience like real-world classroom layouts in 3D form or constructed as partially or fully virtual scenes according to the learning content and the resources can be visualized, especially for the invisible or abstract concepts, items, or events in the physical world. The study further revealed the various challenges confronting the utilization of metaverse technologies in Nigerian Universities which range from inadequate infrastructural facilities, poor research activities, lack of stable electricity, lack of bandwidth and connectivity, inadequate ICT facilities and lack of technically experienced lecturers . The researchers recommended that government should allocate 26 percent of her total budget as recommended by UNESCO to educational sector to enhance the provision of necessary infrastructural facilities needed for the utilization of metaverse technologies in Nigerian Universities. The researchers further recommended that Federal, State government and Universities should undertake in-service training of lecturers through workshops, conferences, seminars and symposia on usage of metaverse, connect the Universities to stable electric grid , employ only competent and qualified lecturers as well as motivate them in terms of salary and other allowances so as to enable them discharged their duties effectively.
What Is It Like to Make a Prototype? Practitioner Reflections on the Intersection of User Experience and Digital Humanities/Social Sciences during the Design and Delivery of the “Getting to Mount Resilience” Prototype

The digital humanities and social sciences are critical for addressing societal challenges such as climate change and disaster risk reduction. One way in which the digital humanities and social sciences add value, particularly in an increasingly digitised society, is by engaging different communities through digital services and products. Alongside this observation, the field of user experience (UX) has also become popular in industrial settings. UX specifically concerns designing and developing digital products and solutions, and, while it is popular in business and other academic domains, there is disquiet in the digital humanities/social sciences towards UX and a general lack of engagement. This paper shares the reflections and insights of a digital humanities/social science practitioner working on a UX project to build a prototype demonstrator for disaster risk reduction. Insights come from formal developmental and participatory evaluation activities, as well as qualitative self-reflection. The paper identifies lessons learnt, noting challenges experienced—including feelings of uncertainty and platform dependency—and reflects on the hesitancy practitioners may have and potential barriers in participation between UX and the digital humanities/social science. It concludes that digital humanities/social science practitioners have few skill barriers and offer a valued perspective, but unclear opportunities for critical engagement may present a barrier.
Low-Light Image Enhancement Method for Electric Power Operation Sites Considering Strong Light Suppression

Insufficient light, uneven light, backlighting, and other problems lead to poor visibility of the image of an electric power operation site. Most of the current methods directly enhance the low-light image while ignoring local strong light that may appear in the electric power operation site, resulting in overexposure and a poor enhancement effect. Aiming at the above problems, we propose a low-light image enhancement method for electric power operation sites by considering strong light suppression. Firstly, a sliding-window-based strong light judgment method was designed, which used a sliding window to segment the image, and a brightness judgment was performed based on the average value of the deviation and the average deviation of the subimages of the grayscale image from the strong light threshold. Then, a light effect decomposition method based on a layer decomposition network was used to decompose the light effect of RGB images with the presence of strong light and eliminate the light effect layer. Finally, a Zero-DCE (Zero-Reference Deep Curve Estimation) low-light enhancement network based on a kernel selection module was constructed to enhance the low-light images with reduced or no strong light interference. Comparison experiments using the electric power operation private dataset and the SICE (Single Image Contrast Enhancement) Part 2 public dataset showed that our proposed method outperformed the current state-of-the-art low-light enhancement methods in terms of both subjective visual effects and objective evaluation metrics, which effectively improves the image quality of electric power operation sites in low-light environments and provides excellent image bases for other computer vision tasks, such as the estimation of operators’ posture.
DNA SECRET WRITING WITH LAPLACE TRANSFORM OF MITTAG-LEFFLER FUNCTION

In this study, we present a new cryptosystem named Deoxyribose Nucleic Acid (DNA) secret writing with the Laplace transform of the Mittag-Leffler function. The method is proper for encrypting large files. In this technique, we consider the original message as binary sequence. These binary streams corresponding to the plain text is transformed to DNA bases by utilizing DNA encoding, then the DNA codes are transformed to positive integers. We apply the Laplace transform to these numbers which are coefficients of the expansion of the Mittag-Leffler function. To provide multi-stage protection, the outcome coefficients are transformed to binary sequences and other level of encryption with cumulative XOR is applied and equivalent MSBs obtained at every iteration are utilized for building cipher text. Decryption is implemented in the opposite way. We employ monobit test, correlation analysis for measuring the reliability of encryption, and Python programming language to obtain secret message, the plain text, and computations of statistical tests.
Education continuity for safety

The Lifelong Learning Initiative is based on creating education opportunities for all ages. In view of the enormous importance of lifelong learning in the life and development of modern man, it seems indisputable the need to address this subject in relation to the sphere of security. Hence, the aim of this study is to draw attention to the role of lifelong learning in education for security. The first part of the discussion concerns the essence and main assumptions of lifelong education. The main content is the analysis of education for safety in terms of the whole life - starting from the family as the first environment of education for safety, through preschool education, school education and higher education, and ending with adult education and mass media. Therefore, all forms (including self-education) of education for safety in various environments, institutions (not only educational), addressed to all age groups were included.
Security Education as a Missing Element in Public Policies

Both public policies and security education focus on social problems and threats which are brought with civilization development we all experience. The aim of the discussion in that paper is analyzing the main targets of public policies and security education which prepare society to perform tasks effectively and respond to threats in various spheres of life. The research problem is: Would it be worthy to complement public policies with knowledge and tasks which are in the realm of security education? The methods used for conducting the research are critical quantitative and qualitative analysis of collected source materials, literature and legal acts.The finding formulated on the basis of the conducted research is that it is worth considering completion of public policies with knowledge and practical tasks of security education.
Cybernetics approach to social norms, motivations and information flow in the society — selected issues

This paper is the second one (after “Informing, pseudo-informing, disinforming — problems and methods”) in the series of papers on information policy. Effectiveness of actions taken within the framework of information policy depends largely on understanding of the essence of information and information process. Theories of information may be helpful in achieving this understanding. Most important theories of information are here: Marian Mazur’s qualitative theory of information (which was discussed in the previous article in the series) and Claude Elwood Shannon’s mathematical theory of communication (which will be discussed in this paper). Theories of information are part of cybernetics — i.e. science of control (control means exerting a deliberate influence on specific phenomena). Understanding of the properties of objects that one wants to control is of fundamental importance to the effectiveness of any control action — this is the reason, why Józef Kossecki’s cybernetic theory of social norms and motivations is discussed in this article.
Generation of Achievable Three-Dimensional Trajectories for Autonomous Wheeled Vehicles via Tracking Differentiators

Planning an achievable trajectory for a mobile robot usually consists of two steps: (i) finding a path in the form of a sequence of discrete waypoints and (ii) transforming this sequence into a continuous and smooth curve. To solve the second problem, this paper proposes algorithms for automatic dynamic smoothing of the primary path using a tracking differentiator with sigmoid corrective actions. Algorithms for setting the gains of the differentiator are developed, considering a set of design constraints on velocity, acceleration, and jerk for various mobile robots. When tracking a non-smooth primary path, the output variables of the differentiator generate smooth trajectories implemented by a mechanical plant. It is shown that the tracking differentiator with a different number of blocks also generates derivatives of the smoothed trajectory of any required order, taking into account the given constraints. Unlike standard analytical methods of polynomial smoothing, the proposed algorithm has a low computational load. It is easily implemented in real time on the on-board computer. In addition, simple methods for modeling a safety corridor are proposed, taking into account the dimensions of the vehicle when planning a polygon with stationary obstacles. Confirming results of numerical simulation of the developed algorithms are presented.
DAG: Dual Attention Graph Representation Learning for Node Classification

Transformer-based graph neural networks have accomplished notable achievements by utilizing the self-attention mechanism for message passing in various domains. However, traditional methods overlook the diverse significance of intra-node representations, focusing solely on internode interactions. To overcome this limitation, we propose a DAG (Dual Attention Graph), a novel approach that integrates both intra-node and internode dynamics for node classification tasks. By considering the information exchange process between nodes from dual branches, DAG provides a holistic understanding of information propagation within graphs, enhancing the interpretability of graph-based machine learning applications. The experimental evaluations demonstrate that DAG excels in node classification tasks, outperforming current benchmark models across ten datasets.
Throughput/Area-Efficient Accelerator of Elliptic Curve Point Multiplication over GF(2233) on FPGA

This paper presents a throughput/area-efficient hardware accelerator architecture for elliptic curve point multiplication (ECPM) computation over GF(2233). The throughput of the proposed accelerator design is optimized by reducing the total clock cycles using a bit-parallel Karatsuba modular multiplier. We employ two techniques to minimize the hardware resources: (i) a consolidated arithmetic unit where we combine a single modular adder, multiplier, and square block instead of having multiple modular operators, and (ii) an Itoh–Tsujii inversion algorithm by leveraging the existing hardware resources of the multiplier and square units for multiplicative inverse computation. An efficient finite-state-machine (FSM) controller is implemented to facilitate control functionalities. To evaluate and compare the results of the proposed accelerator architecture against state-of-the-art solutions, a figure-of-merit (FoM) metric in terms of throughput/area is defined. The implementation results after post-place-and-route simulation are reported for reconfigurable field-programmable gate array (FPGA) devices. Particular to Virtex-7 FPGA, the accelerator utilizes 3584 slices, needs 7208 clock cycles, operates on a maximum frequency of 350 MHz, computes one ECPM operation in 20.59 μs, and the calculated value of FoM is 13.54. Consequently, the results and comparisons reveal that our accelerator suits applications that demand throughput and area-optimized ECPM implementations.
SoftVein-WELM: A Weighted Extreme Learning Machine Model for Soft Biometrics on Palm Vein Images

Contactless biometric technologies such as palm vein recognition have gained more relevance in the present and immediate future due to the COVID-19 pandemic. Since certain soft biometrics like gender and age can generate variations in the visualization of palm vein patterns, these soft traits can reduce the penetration rate on large-scale databases for mass individual recognition. Due to the limited availability of public databases, few works report on the existing approaches to gender and age classification through vein pattern images. Moreover, soft biometric classification commonly faces the problem of imbalanced data class distributions, representing a limitation of the reported approaches. This paper introduces weighted extreme learning machine (W-ELM) models for gender and age classification based on palm vein images to address imbalanced data problems, improving the classification performance. The highlights of our proposal are that it avoids using a feature extraction process and can incorporate a weight matrix in optimizing the ELM model by exploiting the imbalanced nature of the data, which guarantees its application in realistic scenarios. In addition, we evaluate a new class distribution for soft biometrics on the VERA dataset and a new multi-label scheme identifying gender and age simultaneously. The experimental results demonstrate that both evaluated W-ELM models outperform previous existing approaches and a novel CNN-based method in terms of the accuracy and G-mean metrics, achieving accuracies of 98.91% and 99.53% for gender classification on VERA and PolyU, respectively. In more challenging scenarios for age and gender–age classifications on the VERA dataset, the proposed method reaches accuracies of 97.05% and 96.91%, respectively. The multi-label classification results suggest that further studies can be conducted on multi-task ELM for palm vein recognition.
Remarks on Speeding up the Digital Camera Identification using Convolutional Neural Networks

In this paper, we consider the issue of digital camera identification based on images. This topic matches the area of digital forensics. The problem is well known in the literature and many algorithms based on camera’s fingerprint have been proposed. In this paper, we discuss the digital camera identification based on convolutional neural networks (CNN). CNNs are state-of-the-art method in computer vision and are widely utilized in many applications. Our goal is to find out whether it is possible to speed up the process of learning the networks by the images. We conduct a set of representative experiments which show that replacing the ReLU with SELU activation function and adjusting the network’s hyperparamethers (e.g. learning rate) may have a significant impact on reduction time of learning. We also consider using the dropout layer. The experiments are held on representative image dataset, consisting of many images coming from modern cameras and show effectiveness of our propositions.
PP-JPEG: A Privacy-Preserving JPEG Image-Tampering Localization

The widespread availability of digital image-processing software has given rise to various forms of image manipulation and forgery, which can pose a significant challenge in different fields, such as law enforcement, journalism, etc. It can also lead to privacy concerns. We are proposing that a privacy-preserving framework to encrypt images before processing them is vital to maintain the privacy and confidentiality of sensitive images, especially those used for the purpose of investigation. To address these challenges, we propose a novel solution that detects image forgeries while preserving the privacy of the images. Our method proposes a privacy-preserving framework that encrypts the images before processing them, making it difficult for unauthorized individuals to access them. The proposed method utilizes a compression quality analysis in the encrypted domain to detect the presence of forgeries in images by determining if the forged portion (dummy image) has a compression quality different from that of the original image (featured image) in the encrypted domain. This approach effectively localizes the tampered portions of the image, even for small pixel blocks of size 10×10 in the encrypted domain. Furthermore, the method identifies the featured image's JPEG quality using the first minima in the energy graph.
Episodes and Topics in Multivariate Temporal Data

Abstract The term ‘episode’ refers to a time interval in the development of a dynamic process or behaviour of an entity. Episode‐based data consist of a set of episodes that are described using time series of multiple attribute values. Our research problem involves analysing episode‐based data in order to understand the distribution of multi‐attribute dynamic characteristics across a set of episodes. To solve this problem, we applied an existing theoretical model and developed a general approach that involves incrementally increasing data abstraction. We instantiated this general approach in an analysis procedure in which the value variation of each attribute within an episode is represented by a combination of symbols treated as a ‘word’. The variation of multiple attributes is thus represented by a combination of ‘words’ treated as a ‘text’. In this way, the the set of episodes is transformed to a collection of text documents. Topic modelling techniques applied to this collection find groups of related (i.e. repeatedly co‐occurring) ‘words’, which are called ‘topics’. Given that the ‘words’ encode variation patterns of individual attributes, the ‘topics’ represent patterns of joint variation of multiple attributes. In the following steps, analysts interpret the topics and examine their distribution across all episodes using interactive visualizations. We test the effectiveness of the procedure by applying it to two types of episode‐based data with distinct properties and introduce a range of generic and data type‐specific visualization techniques that can support the interpretation and exploration of topic distribution.
Spot Market Cloud Orchestration Using Task-Based Redundancy and Dynamic Costing

Cloud computing has become ubiquitous in the enterprise environment as its on-demand model realizes technical and economic benefits for users. Cloud users demand a level of reliability, availability, and quality of service. Improvements to reliability generally come at the cost of additional replication. Existing approaches have focused on the replication of virtual environments as a method of improving the reliability of cloud services. As cloud systems move towards microservices-based architectures, a more granular approach to replication is now possible. In this paper, we propose a cloud orchestration approach that balances the potential cost of failure with the spot market running cost, optimizing the resource usage of the cloud system. We present the results of empirical testing we carried out using a simulator to compare the outcome of our proposed approach to a control algorithm based on a static reliability requirement. Our empirical testing showed an improvement of between 37% and 72% in total cost over the control, depending on the specific characteristics of the cloud models tested. We thus propose that in clouds where the cost of failure can be reasonably approximated, our approach may be used to optimize the cloud redundancy configuration to achieve a lower total cost.