Assessing Sensor Integrity for Nuclear Waste Monitoring Using Graph Neural Networks

Abstract: A deep geological repository for radioactive waste, such as Andra's Cigéo project, requires long-term (persistent) monitoring. To achieve this goal, data from a network of sensors are acquired. This network is subject to deterioration over time due to environmental effects (radioactivity, mechanical deterioration of the cell, etc.), and it is paramount to assess each sensor's integrity and ensure data consistency to enable the precise monitoring of the facilities. Graph neural networks (GNNs) are suitable for detecting faulty sensors in complex networks because they accurately depict physical phenomena that occur in a system and take the sensor network's local structure into consideration in the predictions. In this work, we leveraged the availability of the experimental data acquired in Andra's Underground Research Laboratory (URL) to train a graph neural network for the assessment of data integrity. The experiment considered in this work emulated the thermal loading of a high-level waste (HLW) demonstrator cell (i.e., the heating of the containment cell by nuclear waste). Using real experiment data acquired in Andra's URL in a deep geological layer was one of the novelties of this work. The used model was a GNN that inputted the temperature field from the sensors (at the current and past steps) and returned the state of each individual sensor, i.e., faulty or not. The other novelty of this work lay in the application of the GraphSAGE model which was modified with elements of the Graph Net framework to detect faulty sensors, with up to half of the sensors in the network being faulty at once. This proportion of faulty sensors was explained by the use of distributed sensors (optic fiber) and the environmental effects on the cell. The GNNs trained on the experimental data were ultimately compared against other standard classification methods (thresholding, artificial neural networks, etc.), which demonstrated their effectiveness in the assessment of data integrity.


Toward Verification of DAG-Based Distributed Ledger Technologies through Discrete-Event Simulation

Abstract: As the potential of directed acyclic graph (DAG)-based distributed ledgers in IoT systems unfolds, a need arises to understand their intricate dynamics in real-world scenarios. It is well known that discrete event simulations can provide high-fidelity evaluations of protocols. However, there is a lack of public discrete event simulators capable of assessing DAG-based distributed ledgers. In this paper, a discrete-event-based distributed ledger simulator is introduced, with which we investigate a custom Python-based implementation of IOTA's Tangle DAG protocol. The study reveals the dynamics of Tangle (particularly Poisson processes in transaction dynamics), the efficiency and intricacies of the random walk in Tangle, and the quantitative assessment of node convergence. Furthermore, the research underscores the significance of weight updates without depth limitations and provides insights into the role, challenges, and implications of the coordinator/validator in DAG architectures. The results are striking, and although the findings are reported only for Tangle, they demonstrate the need for adaptable and versatile discrete event simulators for DAG architectures and tip selection methodologies in general.


MTS-PRO2SAT: Hybrid Mutation Tabu Search Algorithm in Optimizing Probabilistic 2 Satisfiability in Discrete Hopfield Neural Network

Abstract: The primary objective of introducing metaheuristic algorithms into traditional systematic logic is to minimize the cost function. However, there is a lack of research on the impact of introducing metaheuristic algorithms on the cost function under different proportions of positive literals. In order to fill in this gap and improve the efficiency of the metaheuristic algorithm in systematic logic, we proposed a metaheuristic algorithm based on mutation tabu search and embedded it in probabilistic satisfiability logic in discrete Hopfield neural networks. Based on the traditional tabu search algorithm, the mutation operators of the genetic algorithm were combined to improve its global search ability during the learning phase and ensure that the cost function of the systematic logic converged to zero at different proportions of positive literals. Additionally, further optimization was carried out in the retrieval phase to enhance the diversity of solutions. Compared with nine other metaheuristic algorithms and exhaustive search algorithms, the proposed algorithm was superior to other algorithms in terms of time complexity and global convergence, and showed higher efficiency in the search solutions at the binary search space, consolidated the efficiency of systematic logic in the learning phase, and significantly improved the diversity of the global solution in the retrieval phase of systematic logic.


Searchable Blockchain-Based Healthcare Information Exchange System to Enhance Privacy Preserving and Data Usability

Abstract: Ensuring the security and usability of electronic health records (EHRs) is important in health information exchange (HIE) systems that handle healthcare records. This study addressed the need to balance privacy preserving and data usability in blockchain-based HIE systems. We propose a searchable blockchain-based HIE system that enhances privacy preserving while improving data usability. The proposed methodology includes users collecting healthcare information (HI) from various Internet of Medical Things (IoMT) devices and compiling this information into EHR blocks for sharing on a blockchain network. This approach allows participants to search and utilize specific health data within the blockchain effectively. The results demonstrate that the proposed system mitigates the issues of traditional HIE systems by providing secure and user-friendly access to EHRs. The proposed searchable blockchain-based HIE system resolves the trade-off dilemma in HIE by achieving a balance between security and the data usability of EHRs.


A Lightweight Remote Sensing Aircraft Object Detection Network Based on Improved YOLOv5n

Abstract: Due to the issues of remote sensing object detection algorithms based on deep learning, such as a high number of network parameters, large model size, and high computational requirements, it is challenging to deploy them on small mobile devices. This paper proposes an extremely lightweight remote sensing aircraft object detection network based on the improved YOLOv5n. This network combines Shufflenet v2 and YOLOv5n, significantly reducing the network size while ensuring high detection accuracy. It substitutes the original CIoU and convolution with EIoU and deformable convolution, optimizing for the small-scale characteristics of aircraft objects and further accelerating convergence and improving regression accuracy. Additionally, a coordinate attention (CA) mechanism is introduced at the end of the backbone to focus on orientation perception and positional information. We conducted a series of experiments, comparing our method with networks like GhostNet, PP-LCNet, MobileNetV3, and MobileNetV3s, and performed detailed ablation studies. The experimental results on the Mar20 public dataset indicate that, compared to the original YOLOv5n network, our lightweight network has only about one-fifth of its parameter count, with only a slight decrease of 2.7% in mAP@0.5. At the same time, compared with other lightweight networks of the same magnitude, our network achieves an effective balance between detection accuracy and resource consumption such as memory and computing power, providing a novel solution for the implementation and hardware deployment of lightweight remote sensing object detection networks.


Visualizing road connectivity under traffic influence: a CiteSpace-based method

Abstract: The connectivity of roads under the influence of traffic is determined by the topological connections established by the road network and the transport connections formed by moving vehicles between two roads simultaneously. A thorough examination of this connectivity is crucial for comprehending traffic patterns and providing guidance to address transportation issues such as traffic congestion. However, existing methods ignore the intuitive representation and explanation of this connectivity, limiting the understanding of actual traffic patterns. This study aimed to intuitively visualize the connectivity of roads under the influence of traffic using CiteSpace, a bibliometric tool for analyzing the relationship between literature keywords. Here, the literature was replaced by trajectory, and keywords were replaced by roads. We then presented the connectivity of roads under the influence of traffic in a network, where nodes represented roads and edges represented the topological and transport connections between roads. A case study was conducted in Beijing, demonstrating the dynamic connectivity of roads under the influence of traffic in the visualized network. By comparing the visualized network with the topological road network, we identified three types of road connectivity under the influence of traffic: roads with topological connections but lacking transport connections, roads with transport connections but lacking topological connections, and roads with both topological and transport connections. This finding further revealed the traffic relationship between roads. Additionally, we simultaneously presented the multidimensional traffic characteristics in the visualized network, aiding in predicting traffic states.


PEDAGOGY OF THE FUTURE: ECONOMIC TRENDS IN THE EDUCATION OF THE NEW GENERATION

Abstract: The paper examines the influence of economic factors on the development of education in the context of global changes and the requirements of modern society. The author analyzes current economic trends and their impact on the formation of educational strategies, methods and approaches. Special attention is paid to the transition to the digital economy, the growing importance of information skills and technological literacy. The issues of financing education, accessibility of quality education for all segments of the population, as well as the impact of global competition on educational programs and innovations are also considered.


TOPSIS-ACO based feature selection for multi-label classification

Abstract: This paper introduces an approach for optimal feature selection (FS) in multi-label (ML) data, where each sample can be associated with multiple class labels. The complexity of the feature space is significantly increased in comparison to single-label data, making decision-making challenging. Therefore, FS plays a crucial role in ML classification. The proposed method, called ML-TOPSIS-ACO, leverages the ‘Technique of Order Preference by Similarity to Ideal Solution’ (TOPSIS) as a Multi-Criteria Decision Making (MCDM) technique and ‘Ant Colony Optimization’ (ACO) as a meta-heuristic technique. The ML-TOPSIS-ACO method operates in two phases. In the first phase, Modified TOPSIS (MTOPSIS) is used to perform feature ranking and select the most important features. These selected features are then passed to Modified-ACO for feature re-ranking, aiming to identify the best subset of features for ML classification. To evaluate the effectiveness of ML-TOPSIS-ACO, experiments were conducted on nine benchmark datasets. The experimental results demonstrate that ML-TOPSIS-ACO outperforms other existing methods, achieving a winning percentage of 83%.


Text Mining Approaches to Language Use in Social Media: The Case of Portuguese Bué

Abstract: This study describes processes of language change in Angolan Portuguese focusing on the use of intensifiers. Previous studies have shown that intensifiers are a relevant category for the study of language change due to their rapid change and variable meaning. It has been noted that intensifiers are particularly prone to renewal, suggesting speakers’ desire to innovate. Informed by a Digital Humanities approach, we collect and analyze data from Twitter (now X), focusing on the multi-functional intensifier bué, ‘very’, in Angolan Portuguese (AP). In this paper, we (1) provide an overview of the word’s distribution in AP, (2) consider the processes of change involved in bué’s variation, and (3) discuss the role of linguistic borrowing in language change and grammaticalization, shedding light on some of the cultural aspects that play a role in this word’s development, such as the influence of the media and the contact situation between Angolan and European Portuguese.


Effects of caring climate on career commitment for preschool teachers: the mediating roles of interpersonal trust and cooperative learning

Abstract: Preschool teachers in Taiwan face pedagogical and emotionalchallenges that shape their job satisfaction, commitment, and socialidentity. Understanding their views on professional developmentand organizational commitment in early childhood education is critical. This study aims to examine the relationship between caring climate and career commitment and test the multiple mediating effects of interpersonal trust and cooperative learning in this relationship. A survey was conducted in Taiwan to test preschool teachers’ attitudes toward career commitment and other related factors. The questionnaire consisted of four latent factors: caring climate, career commitment, interpersonal trust and cooperative learning. We used partial least squares to analyze the sample data and examine the reliability and validity in the multiple mediation model. The results of the multiple mediating analysis indicated that interpersonal trust and cooperative learning fully positively mediated the association between caring climate and career commitment. Preschool teachers’ career commitment is influenced by their perceptions of caring climate in workplaces. Their positive attitudes toward kind interactions and teamwork with colleagues in preschools actively support sustainable development of their career paths.


Analog spin simulators: How to keep the amplitude homogeneous

Abstract: A setup that simulates ground states of spin graphs would allow one to solve computationally hard optimization problems efficiently. Current optical setups to this goal have difficulties decoupling the amplitude and phase degrees of freedom of each effective spin, which risks that the resultant mapping will be invalid, a problem known as amplitude heterogeneity. Here, we propose a setup with coupled active optical cavity modes, where this problem is eliminated through their particular geometric arrangement. Acting as an effective Monte Carlo solver, the ground state can be found exactly. By tuning a parameter, the setup solves $XY$ or Ising problems.


Decomposing large unitaries into multimode devices of arbitrary size

Abstract: In the quest for the realization of larger and larger quantum networks, the established decomposition in 2 \ifmmode\times\else\texttimes\fi{} 2 subunitaries, easily implemented by beam splitters and phase shifters, comes up against borders if the network becomes too big. A new mathematical decomposition algorithm to decompose large network unitaries into subunitaries of arbitrary size takes heed of technological advances to build larger physical components, realizing larger subunitary sizes in order to increase the achievable size of high-quality networks.


MANAGEMENT OF DISASTER THROUGH KNOWLEDGE MANAGEMENT SYSTEM: A CASE STUDY ON SAMBALPUR DISTRICT, ODISHA

Abstract: Odisha is one of the hazard-prone state and suffers both natural and man-made hazards extensively threatening millions of lives and causing large-scale losses. Disaster management in India also saw a paradigm shift after 2002 from-the humanitarian relief and rehabilitation of victims to the holistic management of disasters viewpoint. The present research addressed this absence of information related to hazards and disasters affecting the district-level Sambalpur. This research aimed to find out some aid in the management of disasters at the district level by collecting and synthesizing all related information about disasters that occur in Sambalpur using an integrated approach. The research used publicly available tools as well as data from credible sources (e.g., government reports). To full-fill the aim, the research compiled information about disasters in the Sambalpur district and prepares a spatial database. Based on socio-economic factors-population density and working population, exposure of the blocks was calculated. Risk and exposure to it assisted in making a vulnerability map of the district at the block level. Based on vulnerability, blocks were prioritized for the policy-makers to ease their management in pre-during and post-disaster situations. The result of this paper would assist policy makers to take proper and quick decisions for the management of the blocks making their management efficient and effective and it could be replicated for other districts of Odisha and India.


Identifying families of multipartite states with non-trivial local entanglement transformations

Abstract: The study of state transformations by spatially separated parties with local operations assisted by classical communication (LOCC) plays a crucial role in entanglement theory and its applications in quantum information processing. Transformations of this type among pure bipartite states were characterized long ago and have a revealing theoretical structure. However, it turns out that generic fully entangled pure multipartite states cannot be obtained from nor transformed to any inequivalent fully entangled state under LOCC. States with this property are referred to as isolated. Nevertheless, multipartite states are classified into families, the so-called SLOCC classes, which possess very different properties. Thus, the above result does not forbid the existence of particular SLOCC classes that are free of isolation, and therefore, display a rich structure regarding LOCC convertibility. In fact, it is known that the celebrated <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>n</mml:mi></mml:math>-qubit GHZ and W states give particular examples of such classes and in this work, we investigate this question in general. One of our main results is to show that the SLOCC class of the 3-qutrit totally antisymmetric state is isolation-free as well. Actually, all states in this class can be converted to inequivalent states by LOCC protocols with just one round of classical communication (as in the GHZ and W cases). Thus, we consider next whether there are other classes with this property and we find a large set of negative answers. Indeed, we prove weak isolation (i.e., states that cannot be obtained with finite-round LOCC nor transformed by one-round LOCC) for very general classes, including all SLOCC families with compact stabilizers and many with non-compact stabilizers, such as the classes corresponding to the <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>n</mml:mi></mml:math>-qunit totally antisymmetric states for <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>n</mml:mi><mml:mo>&#x2265;</mml:mo><mml:mn>4</mml:mn></mml:math>. Finally, given the pleasant feature found in the family corresponding to the 3-qutrit totally antisymmetric state, we explore in more detail the structure induced by LOCC and the entanglement properties within this class.


Visual analysis method for unmanned pumping stations on dynamic platforms based on data fusion technology

Abstract: Abstract As the scale of water conservancy projects continues to expand, the amount and complexity of analytical data have also correspondingly increased. At present, it is difficult to realize project management decision support based on a single data source, and most manual analysis methods not only have high labor costs, but also are prone to the risk of misjudgment, resulting in huge property losses. Based on this problem, this paper proposes visual analysis method for unmanned pumping stations on dynamic platforms based on data fusion technology. First, the method uses the transfer learning method to enable ResNet18 obtain generalization ability. Secondly, the method uses ResNet18 to extract image features, and outputs fixed length sequence data as the input of long short-term memory (LSTM). Finally, the method uses LSTM outputs the classification results. The experimental results demonstrate that the algorithm model can achieve an impressive accuracy of 99.032%, outperforming the combination of traditional feature extraction and machine learning methods. This model effectively recognizes and classifies images of pumping stations, significantly reducing the risk of accidents in these facilities.


Real-time and accurate deep learning-based multi-organ nucleus segmentation in histology images

Abstract: Abstract Automated nucleus segmentation is considered the gold standard for diagnosing some severe diseases. Accurate instance segmentation of nuclei is still very challenging because of the large number of clustered nuclei, and the different appearance of nuclei for different tissue types. In this paper, a neural network is proposed for fast and accurate instance segmentation of nuclei in histopathology images. The network is inspired by the Unet and residual nets. The main contribution of the proposed model is enhancing the classification accuracy of nuclear boundaries by moderately preserving the spatial features by relatively d the size of feature maps. Then, a proposed 2D convolution layer is used instead of the conventional 3D convolution layer, the core of CNN-based architectures, where the feature maps are first compacted before being convolved by 2D kernel filters. This significantly reduces the processing time and avoids the out of memory problem of the GPU. Also, more features are extracted when getting deeper into the network without degrading the spatial features dramatically. Hence, the number of layers, required to compensate the loss of spatial features, is reduced that also reduces the processing time. The proposed approach is applied to two multi-organ datasets and evaluated by the Aggregated Jaccard Index (AJI), F1-score and the number of frames per second. Also, the formula of AJI is modified to reflect the object- and pixel-level errors more accurately. The proposed model is compared to some state-of-the-art architectures, and it shows better performance in terms of the segmentation speed and accuracy.


High-frequency channel attention and contrastive learning for image super-resolution

Abstract: Abstract Over the last decade, convolutional neural networks (CNNs) have allowed remarkable advances in single image super-resolution (SISR). In general, recovering high-frequency features is crucial for high-performance models. High-frequency features suffer more serious damages than low-frequency features during downscaling, making it hard to recover edges and textures. In this paper, we attempt to guide the network to focus more on high-frequency features in restoration from both channel and spatial perspectives. Specifically, we propose a high-frequency channel attention (HFCA) module and a frequency contrastive learning (FCL) loss to aid the process. For the channel-wise perspective, the HFCA module rescales channels by predicting statistical similarity metrics of the feature maps and their high-frequency components. For the spatial perspective, the FCL loss introduces contrastive learning to train a spatial mask that adaptively assigns high-frequency areas with large scaling factors. We incorporate the proposed HFCA module and FCL loss into an EDSR baseline model to construct the proposed lightweight high-frequency channel contrastive network (HFCCN). Extensive experimental results show that it can yield markedly improved or competitive performances compared to the state-of-the-art networks of similar model parameters.


NLU-V: A Family of Instruction Set Extensions for Efficient Symmetric Cryptography on RISC-V

Abstract: Cryptographic primitives nowadays are not only implemented in high-performance systems but also in small-scale systems, which are increasingly powered by open-source processors, such as RISC-V. In this work, we leverage RISC-V’s modular base instruction set and architecture to propose a generic instruction set extension (ISE) for symmetric cryptography. We adapt the work from Engels et al. in ARITH’13, the non-linear/linear instruction set extension (NLU), which presents a generic hardware/software co-design solution for efficient symmetric crypto implementations through a hardware unit extending the 8-bit AVR instruction set. These new instructions realize non-linear and linear layers, which are widely used to implement the block ciphers in symmetric cryptography. Our proposal modifies and extends the NLU instructions to a 32-bit RISC-V architecture; hence, we call the proposed ISE ‘NLU-V’. The proposed architecture is integrated into the open-source RISC-V implementation ‘Icicle’ and synthesized on a Xilinx Kintex-7 XC7K160T FPGA. The area overhead for the proposed NLU-V ISE is 1088 slice registers and 4520 LUTs. As case studies, the PRESENT and AES block ciphers are implemented using the new ISE on RISC-V in assembly. Our evaluation metric to showcase the performance gain, Z ‘time-area-product (TAP)’ (the execution time in clock cycles times code memory consumption), reflects the impact of the proposed family of instructions on the performance of the cipher implementations. The simulations show that the NLU-V achieves 89% gain for PRESENT and 68% gain for AES. Further, the NLU-V requires 44% less lines of code for the PRESENT and 23% less for the AES implementation.


Precision-Driven Product Recommendation Software: Unsupervised Models, Evaluated by GPT-4 LLM for Enhanced Recommender Systems

Abstract: This paper presents a pioneering methodology for refining product recommender systems, introducing a synergistic integration of unsupervised models—K-means clustering, content-based filtering (CBF), and hierarchical clustering—with the cutting-edge GPT-4 large language model (LLM). Its innovation lies in utilizing GPT-4 for model evaluation, harnessing its advanced natural language understanding capabilities to enhance the precision and relevance of product recommendations. A flask-based API simplifies its implementation for e-commerce owners, allowing for the seamless training and evaluation of the models using CSV-formatted product data. The unique aspect of this approach lies in its ability to empower e-commerce with sophisticated unsupervised recommender system algorithms, while the GPT model significantly contributes to refining the semantic context of product features, resulting in a more personalized and effective product recommendation system. The experimental results underscore the superiority of this integrated framework, marking a significant advancement in the field of recommender systems and providing businesses with an efficient and scalable solution to optimize their product recommendations.


Mono‐MVS: textureless‐aware multi‐view stereo assisted by monocular prediction

Abstract: Abstract The learning‐based multi‐view stereo (MVS) methods have made remarkable progress in recent years. However, these methods exhibit limited robustness when faced with occlusion, weak or repetitive texture regions in the image. These factors often lead to holes in the final point cloud model due to excessive pixel‐matching errors. To address these challenges, we propose a novel MVS network assisted by monocular prediction for 3D reconstruction. Our approach combines the strengths of both monocular and multi‐view branches, leveraging the internal semantic information extracted from a single image through monocular prediction, along with the strict geometric relationships between multiple images. Moreover, we adopt a coarse‐to‐fine strategy to gradually reduce the number of assumed depth planes and minimise the interval between them as the resolution of the input images increases during the network iteration. This strategy can achieve a balance between the computational resource consumption and the effectiveness of the model. Experiments on the DTU, Tanks and Temples, and BlendedMVS datasets demonstrate that our method achieves outstanding results, particularly in textureless regions.


Increasing Students' Digital Literacy Through the Use of Google Cardboard Media on the Theme of My Dreams In Class IV of Elementary School

Abstract:
Purpose of the study: This research aims to determine and improve students' digital literacy using Google Cardboard media for class IV students at Elementary School and assessing the extent to which students' participation and involvement in learning activities using Google Cardboard can motivate them to actively learn.
 Methodology: This research is classroom action research. The subjects of this research were 25 grade IV students at Elementary School. The object of the research is to increase students' digital literacy through Google Cardboard media. Data techniques use tests and observations. The research instrument uses observation sheets and test questions or evaluation of learning outcomes.
 Main Findings: The results of the study showed that before the implementation of Google Cardboard media on the theme of my dreams, 5 students (20%) had completed it and 20 students (80%) had not completed it and the class average score was 60%. However, after learning using Google Cardboard media in learning the theme of my dreams in cycles 1 and II, data was obtained that student learning outcomes increased. With the increase that occurred in students who had reached 88%.
 Novelty/Originality of this study: The novelty of this research is that through the use of Google Cardboard with a focus on the theme "My Dreams", class IV students elementary school can develop their digital literacy skills while gaining a deeper understanding of technology and information, thereby increasing students' digital literacy. through more contextual learning experiences.


Faithful quantum teleportation through common and independent qubit-noise configurations and multi-parameter estimation in the output of teleported state

Abstract: Quantum teleportation allows the transmission of unknown quantum states over arbitrary distances. This paper studies quantum teleportation via two non-interacting qubits coupled to local fields and Ornstein Uhlenbeck noise. We consider two different qubit-noise configurations, i.e., common qubit-noise interactions and independent qubit-noise interactions. We introduce a Gaussian Ornstein Uhlenbeck process to take into account the noisy effects of the local external fields. Furthermore, we address the intrinsic behavior of classical fields toward single- and two-qubit quantum teleportation as a function of various parameters. Additionally, using a quantum estimation theory, we study single- and multi-parameter estimation of the teleported state output for single and two-qubit quantum teleportation scenarios. One important application of this work is obtaining more valuable information in quantum remote sensing.


Advances in AI and machine learning for predictive medicine

Abstract: The field of omics, driven by advances in high-throughput sequencing, faces a data explosion. This abundance of data offers unprecedented opportunities for predictive modeling in precision medicine, but also presents formidable challenges in data analysis and interpretation. Traditional machine learning (ML) techniques have been partly successful in generating predictive models for omics analysis but exhibit limitations in handling potential relationships within the data for more accurate prediction. This review explores a revolutionary shift in predictive modeling through the application of deep learning (DL), specifically convolutional neural networks (CNNs). Using transformation methods such as DeepInsight, omics data with independent variables in tabular (table-like, including vector) form can be turned into image-like representations, enabling CNNs to capture latent features effectively. This approach not only enhances predictive power but also leverages transfer learning, reducing computational time, and improving performance. However, integrating CNNs in predictive omics data analysis is not without challenges, including issues related to model interpretability, data heterogeneity, and data size. Addressing these challenges requires a multidisciplinary approach, involving collaborations between ML experts, bioinformatics researchers, biologists, and medical doctors. This review illuminates these complexities and charts a course for future research to unlock the full predictive potential of CNNs in omics data analysis and related fields.


Distracted by Previous Experience: Integrating Selection History, Current Task Demands and Saliency in an Algorithmic Model

Abstract: Abstract Attention can be biased by previous learning and experience. We present an algorithmic-level model of this selection history bias in visual attention that predicts quantitatively how stimulus-driven processes, goal-driven control and selection history compete to control attention. In the model, the output of saliency maps as stimulus-driven guidance interacts with a history map that encodes learning effects and a goal-driven task control to prioritize visual features. The model works on coded features rather than image pixels which is common in many traditional saliency models. We test the model on a reaction time (RT) data from a psychophysical experiment. The model accurately predicts parameters of reaction time distributions from an integrated priority map that is comprised of an optimal, weighted combination of separate maps. Analysis of the weights confirms selection history effects on attention guidance. The model is able to capture individual differences between participants’ RTs and response probabilities per group. Moreover, we demonstrate that a model with a reduced set of maps performs worse, indicating that integrating history, saliency and task information are required for a quantitative description of human attention. Besides, we show that adding intertrial effect to the model (as another lingering bias) improves the model’s predictive performance.


Analyzing variational quantum landscapes with information content

Abstract: Abstract The parameters of the quantum circuit in a variational quantum algorithm induce a landscape that contains the relevant information regarding its optimization hardness. In this work, we investigate such landscapes through the lens of information content, a measure of the variability between points in parameter space. Our major contribution connects the information content to the average norm of the gradient, for which we provide robust analytical bounds on its estimators. This result holds for any (classical or quantum) variational landscape. We validate the analytical understating by numerically studying the scaling of the gradient in an instance of the barren plateau problem. In such instance, we are able to estimate the scaling pre-factors in the gradient. Our work provides a way to analyze variational quantum algorithms in a data-driven fashion well-suited for near-term quantum computers.


SC-Unext: A Lightweight Image Segmentation Model with Cellular Mechanism for Breast Ultrasound Tumor Diagnosis

Abstract: Automatic breast ultrasound image segmentation plays an important role in medical image processing. However, current methods for breast ultrasound segmentation suffer from high computational complexity and large model parameters, particularly when dealing with complex images. In this paper, we take the Unext network as a basis and utilize its encoder-decoder features. And taking inspiration from the mechanisms of cellular apoptosis and division, we design apoptosis and division algorithms to improve model performance. We propose a novel segmentation model which integrates the division and apoptosis algorithms and introduces spatial and channel convolution blocks into the model. Our proposed model not only improves the segmentation performance of breast ultrasound tumors, but also reduces the model parameters and computational resource consumption time. The model was evaluated on the breast ultrasound image dataset and our collected dataset. The experiments show that the SC-Unext model achieved Dice scores of 75.29% and accuracy of 97.09% on the BUSI dataset, and on the collected dataset, it reached Dice scores of 90.62% and accuracy of 98.37%. Meanwhile, we conducted a comparison of the model’s inference speed on CPUs to verify its efficiency in resource-constrained environments. The results indicated that the SC-Unext model achieved an inference speed of 92.72 ms per instance on devices equipped only with CPUs. The model’s number of parameters and computational resource consumption are 1.46M and 2.13 GFlops, respectively, which are lower compared to other network models. Due to its lightweight nature, the model holds significant value for various practical applications in the medical field.


Developer Ecosystems for Software Safety

Abstract: How to design and implement information systems so that they are safe and secure is a complex topic. Both high-level design principles and implementation guidance for software safety and security are well established and broadly accepted. For example, Jerome Saltzer and Michael Schroeder's seminal overview of principles of secure design was published almost 50 years ago, and various community and governmental bodies have published comprehensive best practices about how to avoid common software weaknesses. This article argues, based on experience at Google, that focusing on developer ecosystems is both practical and effective, and can achieve a drastic reduction in the rate of common classes of defects across hundreds of applications being developed by thousands of developers.


The Nexus of Smart Contracts and Digital Twins Transforming Green Finance With Automated Transactions in Investment Agreements

Abstract: In recent years, the integration of digital twins and smart contracts has become a revolutionary force in the field of green finance. The purpose of this chapter is to examine how smart contracts and digital twins are interwoven, particularly in relation to how they have significant influence in the field of green finance. It looks into the way these innovations work together to transform agreements regarding investments by making automated transactions possible and maximising effectiveness and transparency. This investigation demonstrates how working together can simplify complicated financing processes and increase responsibility and dependability for sustainable investment projects. The chapter demonstrates the real-world use of digital twins and smart contracts via case studies and theoretical insights, highlighting their revolutionary influence on changing the face of green finance.


Blockchain-Based Financial Systems Deployment and Challenges

Abstract: Web3 encompasses decentralized finance (DeFi), which leverages blockchain and smart contracts to create an open and accessible financial system. They promote decentralization, trust, and innovation across industries. However, challenges include scalability, energy consumption, price volatility, regulatory uncertainty, and security vulnerabilities. Governments are working on regulatory frameworks, and adoption is growing. Education and awareness are crucial. In particular, there is a need to identify and address the technical, economic, social, and legal issues that may arise from the adoption and implementation of Web 3.0, including concerns around governance, scalability, interoperability, security, and regulation. Therefore, the purpose of the book chapter is to cover the key issues and challenges associated with blockchain technology and to develop a comprehensive understanding of the opportunities and limitations of this technology in order to inform the development and adoption of Web 3.0 applications and services.


Blockchain and Digital Twins

Abstract: Digital twins and blockchain technology have revolutionized data management and accessibility in a variety of areas and opened a plethora of new and profitable business opportunities. The “foundations of blockchain and digital twins” book chapter delves into the advantageous relationship between these two cutting-edge technologies. The basic ideas, unique characteristics, and real-world use cases of blockchain technology have been covered in the first section of this chapter. Blockchain technology's decentralized, transparent, and unchangeable features have the potential to completely transform a number of industries, including finance, healthcare, and supply chain management. The chapter also examines the consensus, processes, smart contracts, and cryptographic techniques that support the blockchain's dependability and security. The final section investigates the interesting relationship between blockchain and digital twins, assessing their individual benefits. Blockchain technology has the potential to improve the reliability, authenticity, and safety of digital twins.


Blockchain in Human Resource Management

Abstract: This chapter aims to provide fresh ideas from philosophy to help human resources managers adopt blockchain technology in their work. In order to categorize the 25 significant items, the authors examined the 126 publications that were made available for acceptance between 2016 and 2021 in the Scopus and Web of Science databases. Using bibliometric data, the headings, titles, and keyword phrases of each of those 25 papers have all been meticulously analysed. Depending on the bibliometric data gathered, word relationship network maps were produced using VOS viewer. There are four topic groupings that were made using the linked visualisations. It has been decided that the ideas of employee-systems interface (ESI) and blockchain framework (BCF) for HR are going to encourage the adoption and application of distributed computing technologies in the area of the administration of human resources. After reading all 25 articles, the authors thoroughly and in-depth examined the key terms that supported the two philosophical subjects and determined the validity of the results.


Advanced Data Analytics in Green Finance

Abstract: This chapter explores how cutting-edge technologies, such as big data analytics, digital twins, and blockchain, are revolutionizing the green finance industry. It examines how these technologies' convergence improves sustainable financial practices efficiency, transparency, and decision-making while providing a fresh viewpoint on their complementary effects. The chapter describes the distinct functions of digital twins in asset optimization and risk management, blockchain in guaranteeing security and trust, and sophisticated data analytics in facilitating well-informed financial decisions.


Blockchain-Driven NFTs

Abstract: Blockchain technology and non-fungible tokens (NFTs) are transforming industries like finance, art, and gaming. This chapter explores their intersection and profound implications for sustainable investments. Blockchain's transparency and immutability enhance trust in sustainability projects, while NFTs tokenizing real-world assets democratize access and promote eco-friendly initiatives. Real-world cases demonstrate the practical impact of blockchain-driven NFTs in sectors like renewable energy, sustainable agriculture, and carbon credits. Challenges include regulatory frameworks and environmental concerns, but the future promises a more robust ecosystem, increased accessibility, and greater transparency in sustainable finance.


Foundations of Blockchain and Digital Twins

Abstract: The rapid evolution of transformative technologies, including blockchain and digital twins, is reshaping numerous industries. Blockchain, recognized for its distributed ledger capabilities, is gaining widespread acceptance owing to its secure and transparent data management features. Simultaneously, digital twins, virtual replicas of physical assets, are proving instrumental in offering real-time insights and enabling remote monitoring and control. This chapter explores the convergence of these two technologies, emphasizing their potential in the real world. Commencing with an overview of blockchain and digital twins, the chapter proceeds to scrutinize their architecture and consensus mechanisms. Subsequently, it delves into the concept of digital twins and their versatile applications across various sectors. Finally, it articulates the advantages derived from the integration of blockchain and digital twins.


Unlocking Efficiency

Abstract: Blockchain technology has gained considerable attention in recent years due to its potential to revolutionize various industries. Human resources (HR) management in large enterprises is an area that can greatly benefit from the transparency, security, and efficiency offered by blockchain technology. This study explores the concept of blockchain as a service (BaaS) in enterprise HR management and its potential to unlock efficiency, streamline processes, and enhance data security elaborating a comprehensive exploration of the theoretical underpinnings, practical application, and potential ramifications of BaaS within HR operations, highlighting the profound transformative prospects inherent in this emerging technology. The integration of blockchain as a service (BaaS) in enterprise human resources management is the focus of this study. The findings suggest that BaaS has the potential to revolutionize HR management by offering a secure, transparent, and efficient platform for various HR processes, from recruitment and onboarding to payroll and talent management.


Blockchain Technology for Sustainable Investment

Abstract: The purpose of this review is to provide a comprehensive analysis of how blockchain technology can enhance sustainable investment practices. The authors aim to assess the current applications, benefits, and challenges of blockchain in sustainable investment and identify opportunities for future development. They conducted a thorough review of existing literature and case studies to analyze the current landscape of blockchain technology in sustainable investment. This involved examining various blockchain platforms and applications, as well as assessing their impact on transparency, security, and efficiency in sustainable investment. This analysis reveals that blockchain technology offers significant advantages for sustainable investment, including improved transparency, reduced fraud, and increased accessibility for smaller investors. However, challenges such as scalability and regulatory hurdles persist. The review also highlights successful cases and real-world examples of blockchain implementation in sustainable investment.


A two-step passenger flow anomaly detection scheme based on machine learning methods

Abstract: Abstract Subway is an important transportation means for residents due to its large volume, punctuality and environmental friendliness. However, weather factors, sports events, concerts and some unexpected events can lead to a surge or abnormality in passenger flow, which brings enormous pressure to the management of stations and passenger flow guidance. Inspired by this, this paper formulates the abnormal passenger flows into different categories in terms of the characteristics and periodical trends, and proposes a two-step abnormal detection scheme to identify the anomalies and their type, and locate abnormal positions. First, two abnormal passenger flows recognition methods based on Jensen–Shannon divergence, dynamic time warping, and density-based spatial clustering of applications with noise are established to identify the station-level abnormal passenger flow. Then, a triple standard deviation algorithm based on sliding window is further proposed to identify the abnormal type and position. Real-world smart card data of the Beijing subway in China, and the manual mutation data of the real data are employed to evaluate effectiveness of our framework. The results show that our two-step scheme is superior to the state-of-the-art algorithms, which can detect out and locate abnormal passenger flows with various characteristics. On more mutation data, this paper discusses the performances on various anomalies of different types of stations in depth, which further indicates our framework is robust and effective in practice.


Statistical Fault Analysis of TinyJambu

Abstract: Abstract The resource-constrained IoT devices have limited resources such as processing power, memory, and battery capacity. Therefore it is challenging to adopt traditional cryptographic algorithms on them. In order to find a solution, the National Institute of Standards and Technology (NIST) initiated the Lightweight Cryptography (LWC) competition to standardize cryptographic algorithms for resource-constrained devices. The primary aim of our work is to implement and analyse selected finalist algorithms from the NIST competition using modern cryptanalysis techniques, with a focus on statistical fault attacks. Traditional analysis methods, such as linear and differential analysis, were not prioritized as most finalist algorithms have established defences against these methods. We implemented six of the selected finalist algorithms from the competition: Ascon, Elephant, GIFT-COFB, ISAP, TinyJambu, and Xoodyak. We chose TinyJumbu for statistical fault analysis because of its attractiveness, compact block size, and provision of a more lightweight keyed permutation.


Global Classroom

Abstract: In this paper Brownlie discusses a module entitled 'Global Classroom'. There are three participating universities: Le Mans Université, France where the module is part of the Masters in International Cultural Studies; the University of Pretoria, South Africa; and Fundação Armando Alvares Penteado, São Paulo, Brazil; participants from the latter two universities are fourth year students in political science. In order to facilitate communication, the module operates via the video-conferencing platform Zoom. Each week focuses on a particular topic, for example, green action, media and misinformation, democracy in action, national elections and youth attitudes, with guest speakers, student presentations and discussion. The paper defines and is structured by four concepts/practices: "small cultures", "postdigitality", "virtual exchange", and "postdigital critical cosmopolitanism." The Global Classroom module is analyzed through the prism of these concepts/practices. With respect to virtual exchange and postdigital critical cosmopolitanism, it is found that the module partially equates with these concepts/practices. It is suggested that in future iterations of the module, particular choices with respect to topic and task design could better allow students to develop intercultural skills and self-reflection.


Virtual Exchange as a Mechanism for Digital Education

Abstract: Virtual exchange (VE), a pedagogical approach towards intercultural dialogue among young people supported by technology, has been prominent over the past two to three decades. In this chapter, I offer a general introduction to the different types of Virtual Exchange and focus on a particular approach provided by the Sharing Perspectives Foundation (SPF). The logic and theoretical foundation on which the SPF programmes and courses are built are presented and the efficacy of using Virtual Exchange as a tool to prepare the ground for the seeds of social inclusion and civic engagement are debated, while various virtual exchange programmes offered by the SPF and its partners are also discussed. This chapter combines scientific research with years of practice to offer a holistic view of the application of Virtual Exchange in peace education. I dwell on my own ten years of experience in the field of VE, wearing the various hats of participant, facilitator, coach and mentor, trainer, programme officer and partnership coordinator at the Sharing Perspectives Foundation (SPF), as well as a lecturer at Hebron University in Palestine.


Exploring the Interplay of Lifewide Learning, Migration, and Social Network Sites in the Postdigital Field of Action

Abstract: Lifewide learning encompasses all forms of learning and personal development in formal, non-formal and informal modalities. This article discusses the relationship between Lifewide Learning, Social Network Sites (SNSs) and Migration by reflecting on the role of SNSs as a resource for informal learning in the context of migration and its impact on the postdigital field of action. This article argues that SNSs are fields of action that are ubiquitously used and are deeply interwoven in everyday life, especially for people who wish to/or have already relocated. In these fields of action, interaction, emotional support and constant learning take place, impacting the lives and experiences of people undergoing migration. Therefore, this article considers that SNSs are highly relevant resources for learning 'informally', where sharing personal experiences not only provides concrete information regarding a situation but perhaps, more importantly, people who share or seek information find companionship in the realization that they are not alone with their doubts or situations in their new location.


Digital Competences in the Educational Sphere

Abstract: Digital skills are one of the most contested key competences in the educational sphere. On the one hand, they have become essential to guarantee standards of educational quality and progress, on the other hand they tend to further hamper educational inequalities among the contemporary highly-diversified student population. This paper ties into these premises with a case study located in Turin, Italy based on 19 teachers and educators from all school levels (primary, lower-secondary and upper-secondary education) to highlight the transformation of needs and challenges related to the different phases of the life cycle of students. Three main issues were investigated from a comparative perspective among the study participants: (1) the coherence of their knowledge regarding opportunities and the challenges of digitalization; (2) their skills to implement digital instruments in their working context; and (3) their attitude towards the transfer of potential benefits that enhance learning outcomes through digital instruments. Findings suggest that high-quality and effective staff formation represents one of the most critical issues when talking about digitality in the educational sphere. A chronic lack of time for training, the ongoing work in emergency conditions, the heterogenous institutional endowment with technical devices (PCs, digital infrastructure, etc.), and the suspicion towards the benefits of digitality in the classroom are some of the major barriers to the forwarding of digital competences as a set of skills, knowledge and attitudes within the educational context.


Embrace sustainable AI: Dynamic data subset selection for image classification

Abstract: Data selection is commonly used to reduce costs and energy usage by training on a subset of available data. However, determining the appropriate subset size requires extensive dataset knowledge and experimentation, limiting transferability. Varying the validation set also produces unstable results and wastes computational resources. In this paper, we propose a data selection method for dynamically determining subset ratios based on model performance using only a training set. The data search space is narrowed through weighted sampling, leveraging statistical selection patterns. Parallel analysis of class distributions identifies the most representative samples with high selection potential. Extensive experiments validate our approach and demonstrate improved training efficiency. Our method speeds up various subset ratios by up to 2.2x on CIFAR-10, 1.9x on CIFAR-100, 2.0x on TinyImageNet, and 2.1x on ImageNet with negligible accuracy drops.


Discriminating quartz host rock based on its trace element chemistry using machine learning-a new tool for sedimentary provenance studies

Abstract: Quartz is one of the most abundant mineral in the continental crust, occurring in a wide variety of igneous, metamorphic, and sedimentary rocks. It incorporates several trace elements such as Ti, Al, Li, Ge, B, and P in its structure during crystallization/precipitation from magma/fluid, which can potentially be used as petrogenetic indicators. Being physically and chemically resilient during sedimentary recycling, detrital quartz can retain the signatures of the physicochemical environment of their formation. Earlier studies have mostly attempted to discriminate quartz from magmatic and magmatic-hydrothermal systems using bivariate or ternary discrimination diagrams. In this study, we developed three robust machine learning (ML) methods (XGBoost, LightGBM, and CatBoost models) to discriminate quartz from a wide variety of magmatic/magmatic-hydrothermal host rocks based on their trace element chemistry. For this purpose, we compiled published 11,267 trace element analyses of quartz grains considering 50 trace elements from 14 different host rocks, namely A-, S-, I- types granitoids and pegmatites, A-, I- types volcanic rocks, A-, S- types granite greisen, hydrothermal veins associated with A-, S-, I- type intrusions, and metamorphic rocks. The three ML classifiers can successfully discriminate quartz from the aforementioned groups with an average accuracy of ~95%. Quartz hosted in each system can be correctly predicted with >75% classification accuracy, except for I-type pegmatite. The Shapley Additive exPlanations (SHAP) algorithm, which measures the contribution of each element to the output predicted by the ML models, indicates that Ti, Li, Ge, Al, B, and P exert the highest control on the ML classifiers. We propose our ML models as a new tool for constraining sedimentary provenance and demonstrate the usefulness of our approach using the trace element data of detrital quartz from sediments in the catchment area of the Bega River in the Lachlan Fold Belt, Australia.


Evaluation of 3D optical techniques for modal analysis during climatic test inside environmental chamber

Abstract: The increasing demand for ensuring the reliability of industrial components under diverse temperature and dynamic solicitations has led to develop standards and testing methods that incorporate both thermal and vibration loads to analyse the dependency of mechanical properties with temperature. This research presents an assessment of a novel experimental approach that combines 3D full-field optical techniques and an environmental chamber to perform modal identification under different temperatures. The study performs a thorough comparative of scanning Laser Doppler Vibrometry (SLDV) and Digital Image Correlation (DIC) techniques. By integrating the environmental chamber with these optical methodologies, valuable insights into the behaviour of components were obtained. Both SLDV and DIC successfully identify variations in modal parameters due to temperature changes. Nevertheless, the results obtained employing SLDV were proved less effective for a reasonable test duration.


A new McEliece-type cryptosystem using Gabidulin-Kronecker product codes

Abstract: This paper presents a new McEliece-type cryptosystem using Gabidulin-Kronecker product codes in the rank metric. The contributions of this paper are as follows. Firstly, we propose a new Gabidulin-Kronecker product code which is a kind of block circulant code, and give an efficient decoding algorithm. Secondly, we design a one-way secure public key encryption scheme based on the Gabidulin-Kronecker product codes. Thirdly, we obtain an IND-CCA2 secure public key encryption scheme by converting our one-way secure public key encryption scheme under the hardness assumption of the RSD Dual Problem. In terms of efficiency, our scheme has a smaller public key size by taking advantage of the block circulant structure. For 128-bit security, the public key size of our proposal is 13% of Lau-Tan's cryptosystem (in the rank metric), and 19% of BIKE (in the Hamming metric). In terms of security, our scheme can resist Overbeck attack, Coggia-Couvreur attack and Sendrier attack.


Sparsifying Count Sketch

Abstract: The seminal work of Charikar et al. [1] called Count-Sketch suggests a sketching algorithm for real-valued vectors that has been used in frequency estimation for data streams and pairwise inner product estimation for real-valued vectors etc. One of the major advantages of Count-Sketch over other similar sketching algorithms, such as random projection, is that its running time, as well as the sparsity of sketch, depends on the sparsity of the input. Therefore, sparse datasets enjoy space-efficient (sparse sketches) and faster running time. However, on dense datasets, these advantages of Count-Sketch might be negligible over other baselines. In this work, we address this challenge by suggesting a simple and effective approach that outputs (asymptotically) a sparser sketch than that obtained via Count-Sketch, and as a by-product, we also achieve a faster running time. Simultaneously, the quality of our estimate is closely approximate to that of Count-Sketch. For frequency estimation and pairwise inner product estimation problems, our proposal Sparse-Count-Sketch provides unbiased estimates. These estimations, however, have slightly higher variances than their respective estimates obtained via Count-Sketch. To address this issue, we present improved estimators for these problems based on maximum likelihood estimation (MLE) that offer smaller variances even w.r.t. Count-Sketch. We suggest a rigorous theoretical analysis of our proposal for frequency estimation for data streams and pairwise inner product estimation for real-valued vectors.


Distributed representations of entities in open-world knowledge graphs

Abstract: Graph neural network (GNN)-based methods have demonstrated remarkable performance in various knowledge graph (KG) tasks. However, most existing approaches rely on observing all entities during training, posing a challenge in real-world knowledge graphs where new entities emerge frequently. To address this limitation, we introduce Decentralized Attention Network (DAN). DAN leverages neighbor context as the query vector to score the neighbors of an entity, thereby distributing the entity semantics only among its neighbor embeddings. To effectively train a DAN, we introduce self-distillation, a technique that guides the network in generating desired representations. Theoretical analysis validates the effectiveness of our approach. We implement an end-to-end framework and conduct extensive experiments to evaluate our method, showcasing competitive performance on conventional entity alignment and entity prediction tasks. Furthermore, our method significantly outperforms existing methods in open-world settings.


Dataset for Bibliometric Data-Driven Research Team Formation: Case of Politehnica University of Timisoara scholars for the interval 2010-2022

Abstract: Data-driven expert team formation is a complicated and multifaceted process that requires access to accurate information to identify researchers’ areas and level of expertise and their collaborative prospects. In this respect, bibliometric data represents a valuable and reliable source of information that can be effectively employed in revealing key insights regarding candidates. Due to its complex and complete structure of publication metadata records, IEEE Xplore database may offer the possibility to compute an extensive set of indicators about researchers’ publication production and how they have interacted during time. Considering the case of Politehnica University of Timisoara scholars for the interval 2010-2022, current dataset encapsulates relevant and rich information for assembling multidisciplinary research teams, being also a testing ground for experimenting and calibrating the expert team formation methods and mechanisms.


Improved likelihood ratios for face recognition in surveillance video by multimodal feature pairing

Abstract: In forensic and security scenarios, accurate facial recognition in surveillance videos, often challenged by variations in pose, illumination, and expression, is essential. Traditional manual comparison methods lack standardization, revealing a critical gap in evidence reliability. We propose an enhanced images-to-video recognition approach, pairing facial images with attributes like pose and quality. Utilizing datasets such as ENFSI 2015, SCFace, XQLFW, ChokePoint, and ForenFace, we assess evidence strength using calibration methods for likelihood ratio estimation. Three models-ArcFace, FaceNet, and QMagFace-undergo validation, with the log-likelihood ratio cost (Cllr) as a key metric. Results indicate that prioritizing high-quality frames and aligning attributes with reference images optimizes recognition, yielding similar Cllr values to the top 25% best frames approach. A combined embedding weighted by frame quality emerges as the second-best method. Upon preprocessing facial images with the super resolution CodeFormer, it unexpectedly increased Cllr, undermining evidence reliability, advising against its use in such forensic applications.


A semi-automatic approach for joint orientation recognition using 3D trace network analysis

Abstract: Identifying rock mass discontinuities and their plane orientation are crucial factors when determining rock mass characteristics. Rock mass discontinuity mapping is fundamentally dependent on joint trace surveying since traces of them are most often the only visible features in rock outcrops. Traditional methods for joint trace surveying using tape and a geological compass are challenging, time-consuming, and hazardous. Fortunately, non-contact measuring techniques offer the advantage of generating accurate objective records of rock masses and enabling the measurement of discontinuities from digital surface models and 3D point clouds of outcrops without requiring direct access to the rock mass and associated constraints. Herein, an innovative approach for identifying discontinuity planes in rock formations using 3D trace data is presented. We introduce the concept of curved and straight traces, with a curvature index indicating the trace's accuracy by representing its discontinuity plane. In addition, we identified co-planar traces by analyzing intersecting straight traces, thereby further contributing to discontinuity plane determination. The methodology's effectiveness was established through validation using a predefined 3D trace network of discontinuity planes with known orientations on a 3D digital rock outcrop model. The methodology was then applied to trace data collected from an actual rock outcrop. The algorithm successfully matched >66% of traces with their corresponding discontinuity planes. After clustering, 80% of the identified planes aligned with principal joint sets within the rock mass. These identified planes accurately aligned with their expected jointing pattern, thereby validating the robustness of the method. Despite challenges presented by discontinuous and complex trace segments obtained from semi-automatic trace detection techniques, the algorithm effectively processed such traces, thereby enabling a comprehensive understanding of the rock mass's jointing system. This enables swift identification of the main joint orientations. We leveraged stereonet analysis to identify principal joint sets using kernel-based and fuzzy C-means clustering techniques. Our approach represents an important advancement in the characterization of rock mass structural properties.


Security issues in visual cryptography scheme with known random number sequence

Abstract: The visual cryptography scheme (VCS) is usually considered unconditionally secure, meaning that participants in the forbidden subset cannot access any secret information. However, this assumes that the random number sequence (RNS) used in the secret sharing process is completely inaccessible to attackers. In reality, the random number generator could have vulnerabilities or backdoors, allowing attackers to obtain RNS. Thus, we study an extreme scenario where attackers successfully acquire RNS. Our work explores the relationship between VCS security and RNS, revealing intriguing findings when there is complete or partial leakage of secret information. To enhance VCS security, we propose three innovative algorithms designed to prevent attackers from accessing all original secret image information, even when they possess RNS and shares within the forbidden subset. Theoretical and experimental results demonstrate the effectiveness of these proposed schemes in enhancing the security of existing VCSs.


A multi-objectives framework for secure blockchain in fog-cloud network of vehicle-to-infrastructure applications

Abstract: The Intelligent Transport System (ITS) is an emerging paradigm that offers numerous services at the infrastructure level for vehicle applications. Vehicle-to-infrastructure (V2I) is an advanced form of ITS where diverse vehicle services are deployed on the roadside unit. V2I consists of distributed computing nodes where transport applications are parallel processed. Many research challenges exist in the presented V2I paradigms regarding security, cyber-attacks, and application processing among heterogeneous nodes. These cyber-attacks, Sybil attacks, and their attempts cause a lack of security and degrade the V2I performance in the presented paradigms. This paper presents a new secure blockchain framework that handles cyber-attacks, as mentioned earlier. This paper formulates this complex problem as a combinatorial problem, encompassing concave and convex problems. The convex function minimizes the given constraints, such as time and security risk, and the concave function improves performance and accuracy. Therefore, numerous constraints, such as time, energy, malware detection accuracy, and application deadlines, require optimization for the considered problem. Combining the jointly non-dominated sorting genetic algorithm (NSGA-II) and long short-term memory (LSTM) schemes is the best way to meet the problem’s limitations. In this study, the paper designed a malware dataset with known and unknown malware. The different kinds of malware lists (e.g., cyber-attacks) are considered in the form of known and unknown malware lists with the characteristics, size of code, where malware comes from, attack on which data, and current status of the workload after being attacked by the malware. Our main idea is to present blockchain, NSGA-II, and LSTM schemes that handle phishing, routing, Sybil, and 51% of cyber-attacks without compromising application performance. Simulation results show that the study reduces delay and energy, improves accuracy, and minimizes security risks for vehicular applications.


Dual-blockchain based multi-layer grouping federated learning scheme for heterogeneous data in industrial IoT

Abstract: Federated Learning (FL) allows data owners to train neural networks together without sharing local data, allowing the Industrial Internet of Things (IIoT) to share a variety of data. However, traditional federated learning frameworks suffer from data heterogeneity and outdated models. To address these issues, this paper proposed a dual-blockchain based multi-layer grouping federated learning architecture (BMFL). BMFL divides the participant groups based on the training tasks, then realizes the model training combining synchronous and asynchronous through the multi-layer grouping structure, and uses the model blockchain to record the characteristic tags of the global model, allowing group-manners to extract the model based on the feature requirements and solving the problem of data heterogeneity. In addition, to protect the privacy of the model gradient parameters and manage the key, the global model is stored in ciphertext, and the chameleon hash algorithm is used to perform the modification and management of the encrypted key on the key blockchain while keeping the block header hash unchanged. Finally, we evaluate the performance of BMFL on different public datasets and verify the practicality of the scheme with real fault dataset. The experimental results show that the proposed BMFL exhibits more stable and accurate convergence behavior than the classic FL algorithm, and the key revocation overhead time is reasonable.


GEP optimization for load balancing of virtual machines (LBVM) in cloud computing

Abstract: Cloud computing relies heavily on load balancing to distribute workloads evenly among servers, network connections, and drives. The cloud system has been assigned some load which can be underloaded, overloaded, or balanced depending on the cloud architecture and user requests. An important component of task scheduling in clouds is the load balancing of workloads that may be dependent or independent of virtual machines (VMs). To overcome these drawbacks, a novel Load Balancing of Virtual Machine (LBVM) in Cloud Computing has been proposed in this paper. The input tasks from multiple users were collected in a single task collector and sent towards the load balancer, which contains the deep learning network called the Bi-LSTM technique. When the load is unbalanced, the VM migration will begin by sending the task details to the load balancer. The Bi-LSTM is optimized by a Genetic Expression Programming (GEP) optimizer and finally, it balances the input loads in VMs. The efficiency of the proposed LBVM has been determined using the existing techniques such as MVM, PLBVM, and VMIS in terms of evaluation metrics such as configuration latency, detection rate, accuracy etc. Experimental results shows that the proposed method reduces the Migration Time of 49%, 41.7%, and 17.8% than MVM, PLBVM, VMIS existing techniques respectively.


Feedback control of automatic navigation for cyborg cockroach without external motion capture system

Abstract: Due to their size and locomotion ability, cockroaches are favorable as hybrid robot platforms in search and rescue (SAR) missions. However, cockroaches most likely approach the corner area and stay for an uncertain time. This natural behavior will hinder the utilization of cyborg cockroaches in SAR missions under rubble, unstructured, and unknown areas. Therefore, we proposed onboard automatic obstacle avoidance and human detection that can run on the wireless backpack stimulator without an external motion capture system. A low-power and small-size Time of Flight (ToF) sensor was selected as a distance measurement sensor, while a low-resolution thermopile array sensor was applied for human presence detection. The implemented feedback control based on IMU and ToF sensors has successfully navigated the cyborg cockroach to avoid obstacles and escape from the sharp corners in the laboratory unstructured area without stopping or being trapped. It could also recognize the human presence when the human was in front of it in real-time. Due to its performance, the random forest classifier was implemented as an embedded human detection system. It could achieve the highest accuracy at a distance of around 25 cm (92.5%) and the lowest accuracy at about 100 cm (70%).


Solving differential‐algebraic equations in power system dynamic analysis with quantum computing

Abstract: Abstract Power system dynamics are generally modeled by high dimensional nonlinear differential‐algebraic equations (DAEs) given a large number of components forming the network. These DAEs' complexity can grow exponentially due to the increasing penetration of distributed energy resources, whereas their computation time becomes sensitive due to the increasing interconnection of the power grid with other energy systems. This paper demonstrates the use of quantum computing algorithms to solve DAEs for power system dynamic analysis. We leverage a symbolic programming framework to equivalently convert the power system's DAEs into ordinary differential equations (ODEs) using index reduction methods and then encode their data into qubits using amplitude encoding. The system nonlinearity is captured by Hamiltonian simulation with truncated Taylor expansion so that state variables can be updated by a quantum linear equation solver. Our results show that quantum computing can solve the power system's DAEs accurately with a computational complexity polynomial in the logarithm of the system dimension. We also illustrate the use of recent advanced tools in scientific machine learning for implementing complex computing concepts, that is, Taylor expansion, DAEs/ODEs transformation, and quantum computing solver with abstract representation for power engineering applications.


Intra-and inter-sector contextual information fusion with joint self-attention for file fragment classification

Abstract: File fragment classification (FFC) aims to identify the file type of file fragments in memory sectors, which is of great importance in memory forensics and information security. Existing works focused on processing the bytes within sectors separately and ignoring contextual information between adjacent sectors. In this paper, we introduce a joint self-attention network (JSANet) for FFC to learn intra-sector local features and inter-sector contextual features. Specifically, we propose an end-to-end network with the byte, channel, and sector self-attention modules. Byte self-attention adaptively recognizes the intra-sector significant bytes, and channel self-attention re-calibrates the features between channels. Based on the insight that adjacent memory sectors are most likely to store a file fragment, sector self-attention leverages contextual information in neighboring sectors to enhance inter-sector feature representation. Extensive experiments on seven FFC benchmarks show the superiority of our method compared with state-of-the-art methods. Moreover, we construct VFF-16, a variable-length file fragment dataset to reflect file fragmentation. Integrated with sector self-attention, our method improves accuracy by more than 16.3% against the baseline on VFF-16, and the runtime achieves 5.1 s/GB with GPU acceleration. In addition, we extend our model to malware detection and show its applicability.


Quantum computing library for quantum chemistry applications

Abstract: Abstract Quantum computing is aimed to solve tasks, which are believed to be exponentially hard to existing computational devices and tools. A prominent example of such classically hard problems is simulating complex quantum many-body systems, in particular, for quantum chemistry. However, solving realistic quantum chemistry problems with quantum computers encounters various difficulties, which are related, first, to limited computational capabilities of existing quantum devices and, second, to the efficiency of algorithmic approaches. In the present work, we address the algorithmic side of quantum chemistry applications by introducing a Python 3 code library, whose primary objective is to speed up the development of variational quantum algorithms for electronic structure problems. We describe the various features and capabilities of this library, including its ease in constructing customized versions of variational quantum algorithms. We elucidate how the developed library allows one to design quantum circuits and enable for the efficient execution of quantum algorithms. Furthermore, the library facilitates the integration of classical and quantum algorithms for hybrid computations and helps to realize the cross-verification of data with traditional computational methods, thereby enhancing the overall reliability of quantum chemistry simulations.


ICT-based agricultural extension literacy and the needs for agriculture development and sustainability: a case study of rice farmer in Aceh

Abstract: Abstract In the current era of technological development, agricultural extension considers as one of the determinant factors for the smoothness and success of agriculture development and sustainability. This study aims to determine the literacy and the needs of lowland rice farmers towards agricultural extension using social media and its contributing factors. The study employs a questionnaire survey using simple random sampling of 48 farmers located in Kecamatan Delima, Pidie –one of the central paddy fields in the region. Data analysis used a descriptive approach and binary logistic regression analysis. The study finds that almost half of respondents had a high literacy, while 29.5 percent were moderate, and only 12.5 percent were low. Related to needs, about two-thirds of respondents stated the urgency of interest and need of using ICT-based media as a means for outreach as a medium of information, training, and satisfaction. Statistically, access to information and farmer age affect the need for information and satisfaction, while educational attributes influence the need for training. The employment of the current and latest technological media as a learning tool and diffusion of innovation is necessary for estimable and admirable alternative changing behavior in agricultural development.


Evaluating urban mini plant factories: engineering and software cost perspectives for agriculture sustainability

Abstract: Abstract Precision agriculture serves as a crucial solution to address the challenges of food security and agriculture sustainability. It provides significant benefits and enables effective monitoring of agriculture throughout the entire supply chain using a technology-driven approach that prioritizes internet connectivity and leverages big data. A noteworthy application of precision agriculture is the Mini Plant Factory, where plants are cultivated in closed systems with artificial lighting. The development of the Mini Plant Factory requires an economic feasibility analysis as a determinant of technology viability before widespread implementation, aiming to promote agriculture sustainability. The Software Cost Estimation Model (SCEM) assesses software development’s cost and time requirements. Engineering economics evaluates economic feasibility using metrics like NPV , BCR , IRR , PBP , and BEP methods. Function Point Analysis for UMPF software development estimates a cost of Rp 33,052,414, equivalent to 1,311 man-hours. Investment feasibility analysis considers the utilization of UMPF as viable, with an NPV of IDR 46,938,615, BCR of 1.08, IRR of 28%, and projected profitability within four years. Precision agriculture strategically addresses complex agricultural challenges, fostering sustainability with innovations such as the UMPF . The SCEM and engineering economics confirm the feasibility and rationale for implementing UMPF technology, providing essential insights for sustainable agricultural practices.


Optimizing Echo State Networks for Continuous Gesture Recognition in Mobile Devices: A Comparative Study

Abstract: Continuous gesture recognition can be used to enhance human-computer interaction. This can be accomplished by capturing human movement with the use of the Inertial Measurement Units in smartphones and using machine learning algorithms to predict the intended gestures. Echo State Networks (ESNs) consist of a fixed internal reservoir that is able to generate rich and diverse nonlinear dynamics in response to input signals that capture temporal dependencies within the signal. This makes ESNs well-suited for time series prediction tasks, such as continuous gesture recognition. However, their application has not been rigorously explored, with regard to gesture recognition. In this study, we sought to enhance the efficacy of ESN models in continuous gesture recognition by exploring diverse model structures, fine-tuning hyperparameters, and experimenting with various training approaches. We used three different training schemes that used the Leave-one-out Cross-validation (LOOCV) protocol to investigate the performance in real-world scenarios with different levels of data availability: Leaving out data from one user to use for testing (F1-score: 0.89), leaving out a fraction of data from all users to use in testing (F1-score: 0.96), and training and testing using LOOCV on a single user (F1-score: 0.99). The obtained results outperformed the Long Short-Term Memory (LSTM) performance from past research (F1-score: 0.87) while maintaining a low training time of approximately 13 seconds compared to 63 seconds for the LSTM model. Additionally, we further explored the performance of the ESN models through behaviour space analysis using memory capacity, Kernel Rank, and Generalization Rank. Our results demonstrate that ESNs can be optimized to achieve high performance on gesture recognition in mobile devices on multiple levels of data availability. These findings highlight the practical ability of ESNs to enhance human-computer interaction.


Dynamic Selection of Machine Learning Models for Time-Series Data

Abstract: Adapting to changes in time-series data is a major challenge in machine learning. This problem is particularly acute in the case of limited computing power (e.g., edge devices) that does not enable the independent training of new models. While multiple studies attempt to adequately train a machine learning model on rapidly shifting data (i.e., concept drift), the challenge of dynamically selecting the most effective machine learning model for future time steps has been largely overlooked. In this study, we propose Adaptive machine learning for Dynamic ENvironments (ADEN ), a method for analyzing future trends in time-series data and selecting the most suitable ML model. Our approach models multiple aspects of the analyzed data and analyzes the behavior of multiple machine learning models on earlier time steps. By training ADEN on multiple datasets, we can deploy a zero-shot model that does not require additional training when applied to new datasets. Our evaluation, conducted on 46 time-series classification datasets, shows that ADEN not only outperforms all baselines in terms of average performance but is also capable of avoiding sudden drops in performance that characterize all other evaluated algorithms.


BPFL: Blockchain-Based Privacy-Preserving Federated Learning against Poisoning Attack

Abstract: In federated learning (FL), multiple clients use local datasets to train models and submit local gradients to the server for aggregation. However, malicious clients may compromise the performance of the model by submitting poisonous gradients. Moreover, the clients do not want to reveal their training models in most application scenarios since their private data may be inferred from them. In addition, most of FL protocols lack an incentive mechanism to supervise participants and cannot punish malicious participants, which is unfair to honest participants. To tackle these problems, we propose a blockchain-based privacy-preserving federated learning against poisoning attack (BPFL). In BPFL, a blockchain-based incentive mechanism is constructed to supervise participants and promptly track malicious behaviors. BPFL can also protect the privacy of aggregated and local model if some participants are malicious, and detect poisonous data by computing cosine similarity between the aggregated gradient and local gradient of the client by using Paillier cryptosystem with threshold decryption. The experiments show that BPFL improves the accuracy of the model from 10% to 75% on CIFAR-10 against poisoning attacks, and therefore BPFL can effectively resist poisoning attacks based on the privacy of local and aggregated models.


Nonlinear circumference-based robust ellipse detection in low-SNR images

Abstract: This study aims to present an effective method to detect ellipses on images with low signal-to-noise ratios (SNR). Firstly, we analyze four major interferences caused by low SNR images. A nonlinear circumference-based ellipse detection method is proposed, which uses a nonlinear circumference to vote on the parameter space constructed from input images and a spatial hierarchical search strategy to find the optimum ellipse parameters. The nonlinear circumference is designed to describe the possibility that selected edge points will generate a given ellipse. Experiments on six low SNR datasets show that our method outperforms five existing methods in terms of recall, precision and F-measure. Furthermore, a definition based on the nonlinear circumference is proposed to quantify SNR of synthetic images with ellipses. Experimental results demonstrate that our method can detect ellipses on images close to 0.2 dB while state-of-the-art methods can almost only reach 1.2 dB.


Model-agnostic Adversarial Example Detection via High-Frequency Amplification

Abstract: Image classification based on Deep Neural Networks (DNNs) is vulnerable to adversarial examples, which make the classifier output incorrect predictions. One approach to defending against this attack is to detect whether the input is an adversarial example. Unfortunately, existing adversarial example detection methods heavily rely on the underlying classifier and may fail when the classifier is upgraded. In this paper, we propose a model-agnostic detection method that leverages high-frequency signals from adversarial noises in adversarial examples and does not need interactions with the underlying classifier. We amplify redundant high-frequency signals brought by adversarial noises and represent object boundaries with these signals in an image. Our key insight is that the boundaries extracted by redundant high-frequency signals have a strong correlation with the boundaries of images in adversarial examples, while this correlation does not exist in clean images. Furthermore, adversarial examples of large images have more high-frequency signals and make adversarial detection easier on large image datasets. Experimental results show that our method has good transferability and can accurately detect various adversarial examples on different datasets.


Complete multi-view subspace clustering via auto-weighted combination of visible and latent views

Abstract: Multi-view subspace clustering aims to leverage the consensus and complementary information embedded within the data and effectively fuse the multiple views. However, existing methods have limitations in fully exploring the complementary information. Tensor-based methods tend to overlook the underlying structures, while latent representation-based methods struggle to capture high-order correlations, both of which are important components of complementary information. In this paper, we introduce a novel method called complete multi-view subspace clustering (CMVSC) using visible and latent views. The latent view with the underlying structures is obtained by the latent representation, along with visible views (i.e., original features) constitute the complete multi-view data. Then, the low-rank tensor constraint is imposed on the complete views to comprehensively explore the consensus and complementary information. Besides, to effectively combine the advantages of visible and latent views, we devise an auto-weighted strategy that can automatically assign the ideal weights to each view. Finally, an efficient alternating iterative algorithm using the augmented Lagrange multiplier method is designed to solve the CMVSC model. Experimental results show that our model outperforms the state-of-the-art counterparts while achieving almost perfect clustering performance on several benchmark datasets.


Multi-object tracking algorithm based on interactive attention network and adaptive trajectory reconnection

Abstract: Multi-object tracking (MOT) detects multiple targets in an image and assigns a unique identifier to each target. However, challenges such as rapid motion, occlusion, and camera motion in the tracking scene may lead to identity switches (IDs) and missing trajectory problems, which degrade the performance of the tracker. To address these issues, this paper presents an MOT algorithm based on an interactive attention network and adaptive trajectory reconnection. First, an interactive attention network was created to learn the features for two different tasks of detection and tracking to alleviate feature conflicts in order to extract sufficient feature information. A new cost matrix was then designed to fuse the motion and feature information, thereby reducing the number of IDs. Meanwhile, the extreme gradient boosting reconnection module was used to achieve adaptive trajectory reconnection and reduce missing trajectories. The proposed algorithm achieved 61.5 % and 55.4 % HOTA using the standard MOT17 and MOT20 datasets, respectively. In comparison to FairMOT, our algorithm showcased notable enhancements of 3% and 1.6% on these datasets. Furthermore, when compared to state-of-the-art algorithms, the proposed algorithm demonstrated superior tracking performance.


Does bitcoin still enhance an investment portfolio in a post Covid-19 world?

Abstract: We investigate the ability of bitcoin to enhance investment portfolios of traditional assets. We find evidence of a structural break in the correlation and volatility processes around the onset of the Covid-19 pandemic. The correlations between bitcoin and traditional assets have increased, thereby reducing bitcoin’s diversification benefits. As a result prior studies using data from pre-2020 may have inadvertently overestimated the value of bitcoin in improving Sharpe ratios and certainty equivalent returns in the post-pandemic era.


MuLAN: Multi-level attention-enhanced matching network for few-shot knowledge graph completion

Abstract: Recent years have witnessed increasing interest in the few-shot knowledge graph completion due to its potential to augment the coverage of few-shot relations in knowledge graphs. Existing methods often use the one-hop neighbors of the entity to enhance its embedding and match the query instance and support set at the instance level. However, such methods cannot handle inter-neighbor interaction, local entity matching and the varying significance of feature dimensions. To bridge this gap, we propose the Multi-Level Attention-enhanced matching Network (MuLAN) for few-shot knowledge graph completion. In MuLAN, a multi-head self-attention neighbor encoder is designed to capture the inter-neighbor interaction and learn the entity embeddings. Then, entity-level attention and instance-level attention are responsible for matching the query instance and support set from the local and global perspectives, respectively, while feature-level attention is utilized to calculate the weights of the feature dimensions. Furthermore, we design a consistency constraint to ensure the support instance embeddings are close to each other. Extensive experiments based on two well-known datasets (i.e., NELL-One and Wiki-One) demonstrate significant advantages of MuLAN over 11 state-of-the-art competitors. Compared to the best-performing baseline, MuLAN achieves 14.5% higher MRR and 13.3% higher Hits@K on average.


Hardness of (M)LWE with Semi-Uniform Seeds

Abstract: Let (D,S,χ,m)-LWEn,q be the LWE problem in matrix form (A,y=As+emodq), where A,s,e are randomly chosen respectively from the seed distribution D over Zqm×n, secret distribution S over Zqn and noise distribution χm over Zm (or Rm), i.e., A←D,s←S,e←χm. For various secret-noise distributions (S,χ), the (D,S,χ,m)-LWEn,q problem is shown to be as hard as some standard worst-case lattice problems, but most of the known results require D to be the uniform distribution over Zqm×n. In this paper, we show that under the standard LWE assumption, the problem (D,S,χ,m)-LWEn,q can still be hard for some distribution D that is not (even computationally indistinguishable from) the uniform distribution over Zqm×n. Specifically, we show that if D is a semi-uniform distribution over Zqm×n (namely, D can be publicly derived from and has a “small difference” to the uniform distribution over Zqm×n), then for appropriate choices of (S,χ), the problem (D,S,χ,m)-LWEn,q is hard under the standard LWE assumption. Moreover, we also show that the semi-uniform MLWE problem is hard under the standard MLWE assumption. As a direct application, our results pave the way to prove the security of public-key encryptions with rounded public keys under the standard (M)LWE assumption.


A robust script independent handwriting system for gender identification

Abstract: Gender identification at the word level in a multi-script environment is challenging due to variations posed by free-style handwriting of individuals and geographical differences in writing styles. This paper presents a new approach, Multi-Orientation-Scale Gabor Response Fusion (MOSGF), for gender identification at the word level using handwritten text. Our method has two steps: (i) word segmentation from unconstrained lines and (ii) gender identification at the word level. In the first step, the method explores the number of zero crossing points and gradient information for word segmentation from handwritten text lines. In the second step, employs Gabor responses at different orientations and scales to detect fine details in female and male handwriting. For each Gabor response, the proposed model estimates the correlation between average templates of all Gabor responses and the individual Gabor response to extract global consistency in writing. To strengthen correlation features, the proposed method uses the Mahalanobis distance measure, which extracts local similarity. Further, the proposed approach fuses correlation coefficient and distance-based features in a novel way. The fused features are then fed to a Neural Network (NN) for gender identification. Experiments on our dataset, which comprises Roman (English), Chinese, Farsi (Persian), Arabic, and Indian scripts, and a benchmark dataset, namely, IAM which includes English text, KHATT which includes Arabic, and QUWI which includes both English and Arabic, show that the proposed system outperforms the existing methods in terms of word segmentation and gender identification.