POS-GIFT: A geometric and intensity-invariant feature transformation for multimodal images

Abstract: Multimodal image matching suffers from severe geometric and nonlinear intensity distortion (NID). Towards this problem, we propose a multimodal image matching algorithm based on multi-orientation filtering results, called position-orientation-scale guided geometric and intensity-invariant feature transformation (POS-GIFT). First, we design a multi-layer circular point sampling pattern to effectively capture the local image structure. Then, we propose a novel feature descriptor that can work robustly across rotational differences in [0°, 360°) in the presence of NID. Specifically, we (1) integrate the multi-orientation filtering response in the local neighborhood with a Gaussian weight to form the feature of each sampled point (GFP), (2) build feature vectors for each orientation by concatenating the features of points grouped by orientation, (3) estimate the primary orientation by finding the feature vector with the largest norm which is constructed in the previous step, (4) modify the order of elements of GFP, and (5) finally concatenate the features of all sampled points in a certain order to form the complete feature descriptor. At last, we propose a position-orientation-scale guided inlier recovery strategy (POS) by integrating the global position, orientation, and scale information to further improve the matching performance, especially the number and distribution of correct matches in texture-less and complex areas. Experimental results on various multimodal datasets from remote sensing, medical, and computer vision imaging domains show that POS-GIFT outperforms eight state-of-the-art multimodal image feature matching algorithms which are five handcrafted-based methods, OS-SIFT, PSO-SIFT, LGHD, RIFT, and LNIFT, and three learning-based methods RedFeat, MatchFormer, and SemLA by several times in terms of correct matches while improving the root-mean-square error to around 1 pixel. Our implementation is available at https://github.com/Zhuolu-Hou/POS-GIFT.


Large Language Models are Edge-Case Generators: Crafting Unusual Programs for Fuzzing Deep Learning Libraries

Abstract: Bugs in Deep Learning (DL) libraries may affect almost all downstream DL applications, and it is crucial to ensure the quality of such systems. It is challenging to generate valid input programs for fuzzing DL libraries, since the input programs need to satisfy both the syntax/semantics of the supported languages (e.g., Python) and the tensor/operator constraints for constructing valid computational graphs. Recently, the TitanFuzz work demonstrates that modern Large Language Models (LLMs) can be directly leveraged to implicitly learn all the language and DL computation constraints to generate valid programs for fuzzing DL libraries (and beyond). However, LLMs tend to generate ordinary programs following similar patterns/tokens with typical programs seen in their massive pre-training corpora (e.g., GitHub), while fuzzing favors unusual inputs that cover edge cases or are unlikely to be manually produced.


Bi-LSTM, GRU and 1D-CNN models for short-term photovoltaic panel efficiency forecasting case amorphous silicon grid-connected PV system

Abstract: Photovoltaic (PV) panels stand as a prominent solution to meet the world's growing energy demands, due to their resistance to hard climate conditions, low-cost maintenance, and long lifetime. Nonetheless, the integration of PV power into electrical grids poses a significant challenge due to its inherent intermittency. This study aims to forecast future PV power based on historical records using Bidirectional Long Short-Term Memory (Bi-LSTM), One-Dimensional Convolutional Neural Network (1D-CNN), and Gated Recurrent Unit (GRU). Various performance metrics have been used to evaluate and compare the accuracy of the three models, including mean squared error, root mean squared error, mean absolute error, max error and R-squared for evaluation. The prediction of power photovoltaic based on the values registered in the last hour was carried out. Two scenarios have been investigated, with and without nighttime values to fit the models. Results reveal that the three forecasting models provide exceptional accuracy, achieving a correlation coefficient range of 96.9–97.2% for daytime PV power prediction in both scenarios, indicating a promising potential for these DNNs forecasters for optimizing energy production and improving overall PV system efficiency. The GRU, and BiLSTM-based forecasters showed identical results in terms of RMSE, MSE and MAE in both scenarios, while the 1D-CNN based forecaster showed accurate results in the second scenario, However, despite this improvement, it still falls behind forecasters based on Bi-LSTM or GRU in both scenarios.


Governance in the exploration of global and regional determinants of ICT development

Abstract: The present study assesses how governance affects information and communication technology (ICT) at the global level contingent on macroeconomic policy factors such as trade, foreign investment (FDI), manufacturing value added, and agricultural value added. The study focuses on 183 countries from 2003 to 2021, and the empirical evidence is based on the generalized method of moments (GMM). The following main findings are established. For the full sample, governance unconditionally promotes ICT development, while trade openness (industrial added value) moderates governance to promote (dampen) ICT development. In sub-Saharan Africa, only trade openness effectively moderates governance to induce an overall positive effect on ICT, while in the Middle East and North Africa (MENA) region, all policy variables moderate governance for an overall positive incidence on ICT sector development. The findings for the MENA region are confirmed in the Europe and Central Asia (ECA) region, with the exception of the moderating role of industrial added value, which engenders an overall negative effect. In the East and South Asia and the Pacific (ESAP) countries, one overall positive incidence is apparent in the role of trade openness, while net negative effects are established from the moderating roles of industrial added value and agricultural added value. In the American sub-sample, a positive (negative) net effect is apparent from the role of industrial added value (trade) in moderating the incidence of governance on ICT sector development. Finally, policy implications are discussed.


Collaborative threat intelligence: Enhancing IoT security through blockchain and machine learning integration

Abstract: Ensuring robust security in the Internet of Things (IoT) landscape is of paramount importance. This research article presents a novel approach to enhance IoT security by leveraging collaborative threat intelligence and integrating blockchain technology with machine learning (ML) models. The iOS application acts as a central control centre, facilitating the reporting and sharing of detected threats. The shared threat data is securely stored on a blockchain network, enabling ML models to access and learn from a diverse range of threat scenarios. The research focuses on implementing Random Forest, Decision Tree classifier, Ensemble, LSTM, and CNN models on the IoT23 dataset within the context of a Collaborative Threat Intelligence Framework for IoT Security. Through an iterative process, the models’ accuracy is improved by reducing false negatives through the collaborative threat intelligence system. The article investigates the implementation details, privacy considerations, and the seamless integration of ML-based techniques for continuous model improvement. Experimental evaluations on the IoT23 dataset demonstrate the effectiveness of the proposed system in enhancing IoT security and mitigating potential threats. The research contributes to the advancement of collaborative threat intelligence and blockchain technology in the context of IoT security, paving the way for more secure and reliable IoT deployments.


Estimating quantum mutual information through a quantum neural network

Abstract: Abstract We propose a method of quantum machine learning called quantum mutual information neural estimation (QMINE) for estimating von Neumann entropy and quantum mutual information, which are fundamental properties in quantum information theory. The QMINE proposed here basically utilizes a technique of quantum neural networks (QNNs), to minimize a loss function that determines the von Neumann entropy, and thus quantum mutual information, which is believed more powerful to process quantum datasets than conventional neural networks due to quantum superposition and entanglement. To create a precise loss function, we propose a quantum Donsker-Varadhan representation (QDVR), which is a quantum analog of the classical Donsker-Varadhan representation. By exploiting a parameter shift rule on parameterized quantum circuits, we can efficiently implement and optimize the QNN and estimate the quantum entropies using the QMINE technique. Furthermore, numerical observations support our predictions of QDVR and demonstrate the good performance of QMINE.


Media Forensic Considerations of the Usage of Artificial Intelligence Using the Example of DeepFake Detection

Abstract: In recent discussions in the European Parliament, the need for regulations for so-called high-risk artificial intelligence (AI) systems was identified, which are currently codified in the upcoming EU Artificial Intelligence Act (AIA) and approved by the European Parliament. The AIA is the first document to be turned into European Law. This initiative focuses on turning AI systems in decision support systems (human-in-the-loop and human-in-command), where the human operator remains in control of the system. While this supposedly solves accountability issues, it includes, on one hand, the necessary human–computer interaction as a potential new source of errors; on the other hand, it is potentially a very effective approach for decision interpretation and verification. This paper discusses the necessary requirements for high-risk AI systems once the AIA comes into force. Particular attention is paid to the opportunities and limitations that result from the decision support system and increasing the explainability of the system. This is illustrated using the example of the media forensic task of DeepFake detection.


Regression-based Deep-Learning predicts molecular biomarkers from pathology slides

Abstract: Deep Learning (DL) can predict biomarkers from cancer histopathology. Several clinically approved applications use this technology. Most approaches, however, predict categorical labels, whereas biomarkers are often continuous measurements. We hypothesize that regression-based DL outperforms classification-based DL. Therefore, we develop and evaluate a self-supervised attention-based weakly supervised regression method that predicts continuous biomarkers directly from 11,671 images of patients across nine cancer types. We test our method for multiple clinically and biologically relevant biomarkers: homologous recombination deficiency score, a clinically used pan-cancer biomarker, as well as markers of key biological processes in the tumor microenvironment. Using regression significantly enhances the accuracy of biomarker prediction, while also improving the predictions' correspondence to regions of known clinical relevance over classification. In a large cohort of colorectal cancer patients, regression-based prediction scores provide a higher prognostic value than classification-based scores. Our open-source regression approach offers a promising alternative for continuous biomarker analysis in computational pathology.


Hebbian dreaming for small datasets

Abstract: The dreaming Hopfield model constitutes a generalization of the Hebbian paradigm for neural networks, that is able to perform on-line learning when "awake" and also to account for off-line "sleeping" mechanisms. The latter have been shown to enhance storing in such a way that, in the long sleep-time limit, this model can reach the maximal storage capacity achievable by networks equipped with symmetric pairwise interactions. In this paper, we inspect the minimal amount of information that must be supplied to such a network to guarantee a successful generalization, and we test it both on random synthetic and on standard structured datasets (i.e., MNIST, Fashion-MNIST and Olivetti). By comparing these minimal thresholds of information with those required by the standard (i.e., always "awake") Hopfield model, we prove that the present network can save up to ∼90% of the dataset size, yet preserving the same performance of the standard counterpart. This suggests that sleep may play a pivotal role in explaining the gap between the large volumes of data required to train artificial neural networks and the relatively small volumes needed by their biological counterparts. Further, we prove that the model Cost function (typically used in statistical mechanics) admits a representation in terms of a standard Loss function (typically used in machine learning) and this allows us to analyze its emergent computational skills both theoretically and computationally: a quantitative picture of its capabilities as a function of its control parameters is achieved and consistency between the two approaches is highlighted. The resulting network is an associative memory for pattern recognition tasks that learns from examples on-line, generalizes correctly (in suitable regions of its control parameters) and optimizes its storage capacity by off-line sleeping: such a reduction of the training cost can be inspiring toward sustainable AI and in situations where data are relatively sparse.


Quantum field theory for coherent photons: isomorphism between stokes parameters and spin expectation values

Abstract: Stokes parameters ( S ) on the Poincaré sphere are very useful values to describe the polarisation state of photons. However, the fundamental principle on the nature of polarisation is not completely understood, yet, because we have no concrete consensus on how to describe spin of photons, quantum-mechanically. Here, we have considered a monochromatic coherent ray of photons, described by a many-body coherent state, and established a fundamental basis to describe the spin state of photons, in connection with a classical description based on Stokes parameters. We show that a spinor description of the coherent state is equivalent to Jones vector for polarisation states, and obtain the spin operators <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="m1"><mml:mrow><mml:mo stretchy="false">(</mml:mo><mml:mrow><mml:mover accent="true"><mml:mrow><mml:mi mathvariant="bold">S</mml:mi></mml:mrow><mml:mo stretchy="false">̂</mml:mo></mml:mover></mml:mrow><mml:mo stretchy="false">)</mml:mo></mml:mrow></mml:math> of all components based on rotators in an SU(2) group theory. Polarisation controllers such as phase-shifters and rotators are also obtained as quantum-mechanical field operators to change the phase of the wavefunction for polarisation states. We show that the Stokes parameters are quantum-mechanical average of the spin operators, <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="m2"><mml:mi mathvariant="bold">S</mml:mi><mml:mo>=</mml:mo><mml:mrow><mml:mo stretchy="false">⟨</mml:mo><mml:mrow><mml:mover accent="true"><mml:mrow><mml:mi mathvariant="bold">S</mml:mi></mml:mrow><mml:mo stretchy="false">̂</mml:mo></mml:mover></mml:mrow><mml:mo stretchy="false">⟩</mml:mo></mml:mrow></mml:math> .


Individual differences in emoji comprehension: Gender, age, and culture

Abstract: Emoji are an important substitute for non-verbal cues (such as facial expressions) in online written communication. So far, however, little is known about individual differences regarding how they are perceived. In the current study, we examined the influence of gender, age, and culture on emoji comprehension. Specifically, a sample of 523 participants across the UK and China completed an emoji classification task. In this task, they were presented with a series of emoji, each representing one of six facial emotional expressions, across four commonly used platforms (Apple, Android, WeChat, and Windows). Their task was to choose from one of six labels (happy, sad, angry, surprised, fearful, disgusted) which emotion was represented by each emoji. Results showed that all factors (age, gender, and culture) had a significant impact on how emojis were classified by participants. This has important implications when considering emoji use, for example, conversation with partners from different cultures.


Mathematics, word problems, common sense, and artificial intelligence

Abstract: The paper discusses the capacities and limitations of current artificial intelligence (AI) technology to solve word problems that combine elementary mathematics with commonsense reasoning. No existing AI systems can solve these reliably. We review three approaches that have been developed, using AI natural language technology: outputting the answer directly, outputting a computer program that solves the problem, and outputting a formalized representation that can be input to an automated theorem verifier. We review some benchmarks that have been developed to evaluate these systems and some experimental studies. We discuss the limitations of the existing technology at solving these kinds of problems. We argue that it is not clear whether these kinds of limitations will be important in developing AI technology for pure mathematical research, but that they will be important in applications of mathematics, and may well be important in developing programs capable of reading and understanding mathematical content written by humans.


The Role of Financial Sanctions and Financial Development Factors on Central Bank Digital Currency Implementation

Abstract: This study investigates the influence of a country’s financial access and stability and the adoption of retail central bank digital currencies (CBDCs) across 71 countries. Using an ordinal logit model, we examine how individual financial access, the ownership of credit cards, financing accessibility by firms, offshore loans, financial sanctions, and the ownership structure of financial institutions influence the probability of CBDC adoption in nations. These findings reveal that nations facing financial sanctions and those with substantial offshore bank loans are more inclined to adopt CBDCs. Furthermore, a significant relationship is observed in countries where many people have restricted financial access, indicating heightened interest in CBDC adoption. Interestingly, no statistically significant relationship was found between the adoption of CBDCs and the percentage of foreign-owned banks in each country. The results show that countries with low financial stability and financial access adopt CBDCs faster. This study expands our knowledge of how a nation’s financial situation influences its adoption of CBDCs. The results provide important and relevant insights into the current discussion of the direction of global finance.


A Blockchain-Based Scalability Solution with Microgrids Peer-to-Peer Trade

Abstract: In recent years, advancements in rooftop solar panel technology have sparked a revolution in the electricity markets. This has given rise to a new concept of energy exchange—the ability for consumers and producers to trade localized energy. This concept has been made possible by the emergence of blockchain technology, which has gained significant traction in the energy markets. Its unique ability to facilitate peer-to-peer (P2P) energy transactions has made it a promising solution for the trilemma of scalability, security, and decentralization. However, while blockchain technology has shown great potential, it is still in its early stages of development and has yet to reach its full potential. To fully understand the potential of blockchain technology in P2P energy trading, it is important to explore the trilemma in depth. This study proposes a blockchain-based scalability solution with a focus on P2P energy trading. This strategy is supported by empirical modeling, utilizing data gathered from a trial case study. The results of this study demonstrate that the suggested technique outperforms base-layer models in terms of scalability while still maintaining the essential elements of security and decentralization. This proposed solution not only has the potential to revolutionize the energy markets but also has broader implications. By providing a more secure and decentralized platform for P2P energy trading, it has the potential to address issues of energy distribution inequality and promote the adoption of renewable energy. With blockchain technology, individuals and communities have the opportunity to take control of their energy usage and production, reducing their reliance on traditional centralized energy systems. This not only has the potential to lower energy costs for consumers but also contributes to the overall goal of reducing carbon emissions and mitigating the effects of climate change. The combination of rooftop solar panel technology and blockchain applications has the potential to create a significant shift toward decentralized and sustainable energy systems. This will not only benefit individuals and communities but also have a positive impact on the environment and the global energy market. However, for this transition to occur, it is crucial that governments and energy companies continue to support and invest in these advancements.


Blockchain in global supply chains: A comparative review of USA and African practices

Abstract: In the contemporary landscape of international trade, the adoption of blockchain technology in global supply chains has garnered significant attention. This comparative review delves into the practices of the United States and African countries, examining the implementation, challenges, and potential benefits of blockchain in enhancing supply chain efficiency. The United States, as a global economic powerhouse, has been at the forefront of integrating blockchain into its supply chain processes. The American approach emphasizes transparency, traceability, and accountability. Blockchain technology provides a decentralized and immutable ledger, enabling seamless tracking of goods from their origin to the end consumer. The use of smart contracts automates and streamlines contractual agreements, reducing paperwork and minimizing delays. While the adoption in the USA is relatively widespread, challenges include the need for standardized protocols, regulatory frameworks, and overcoming resistance to change within established systems. In contrast, the African continent presents a diverse and evolving landscape in the adoption of blockchain within supply chains. Several African nations have recognized the potential of blockchain to address longstanding issues such as counterfeit goods, fraud, and inefficiencies in logistics. Blockchain offers a decentralized solution that can empower local producers and facilitate international trade. However, challenges in Africa include limited technological infrastructure, varying levels of regulatory frameworks, and the need for capacity building. Collaborative efforts between governments, private sectors, and international organizations are essential to overcoming these obstacles. Both the USA and African countries stand to benefit from blockchain adoption in global supply chains. Increased transparency reduces the risk of fraud and enhances trust between stakeholders. The immutable nature of blockchain records ensures data integrity, contributing to the overall resilience of the supply chain. However, successful implementation requires a nuanced understanding of local contexts, regulatory frameworks, and technological capacities. This comparative review serves as a foundation for future research and policy development, shedding light on the dynamic landscape of blockchain adoption in global supply chains across diverse socio-economic environments.


Comparative analysis of the hybrid gazelle‐Nelder–Mead algorithm for parameter extraction and optimization of solar photovoltaic systems

Abstract: Abstract The pressing need for sustainable energy solutions has driven significant research in optimizing solar photovoltaic (PV) systems which is crucial for maximizing energy conversion efficiency. Here, a novel hybrid gazelle‐Nelder–Mead (GOANM) algorithm is proposed and evaluated. The GOANM algorithm synergistically integrates the gazelle optimization algorithm (GOA) with the Nelder–Mead (NM) algorithm, offering an efficient and powerful approach for parameter extraction in solar PV models. This investigation involves a thorough assessment of the algorithm's performance across diverse benchmark functions, including unimodal, multimodal, fixed‐dimensional multimodal, and CEC2020 benchmark functions. Notably, the GOANM consistently outperforms other optimization approaches, demonstrating enhanced convergence speed, accuracy, and reliability. Furthermore, the application of the GOANM is extended to the parameter extraction of the single diode and double diode models of RTC France solar cell and PV model of Photowatt‐PWP201 PV module. The experimental results consistently demonstrate that the GOANM outperforms other optimization approaches in terms of accurate parameter estimation, low root mean square values, fast convergence, and alignment with experimental data. These results emphasize its role in achieving superior performance and efficiency in renewable energy systems.


Semi-supervised imbalanced multi-label classification with label propagation

Abstract: Multi-label learning tasks usually encounter the problem of the class-imbalance, where samples and their corresponding labels are non-uniformly distributed over multi-label data space. It has attracted increasing attention during the past decade, however, there is a lack of methods capable of handling the imbalanced problem in a semi-supervised setting. This study proposes a label propagation technique to settle the semi-supervised imbalanced multi-label issue. Specially, we first utilize a collaborative manner to exploit the correlations from labels and instances, and learn a label regularization matrix to overcome the imbalanced problem in the labeled instance. After that, we extend to semi-supervised learning and explore to represent the similarity of instances with weighted graphs on labeled and unlabeled data. Then, the data distribution information and label correlations are fully utilized to design the loss function under the consistency assumption manner. At last, we present an iterative scheme to settle the optimization issue, thereby achieving label propagation to address the imbalanced challenge. Experiments on a variety of multi-label data sets show the favorable performance of the proposed method against related comparing approaches. Notably, the proposed method is also validated to be robust with a limited number of training instances.


Difficulties during virtual education with students during the confinement by the Covid - 19 pandemic in Cañar, Ecuador

Abstract: The purpose of this research was to determine the difficulties caused by the confinement due to the pandemic caused by COVID-19 in "initial 2" (preschool) students of the Cañar parish, Ecuador. The study involved students residing in this community, who by means of questionnaires to their representatives allowed to know first-hand the main difficulties during the confinement. For this purpose, a quantitative research approach with a descriptive scope was developed; the data were collected using a randomized method, which consisted of using a Google form to administer a survey in which it was possible to evidence, among other things, the inequality of conditions that affect educational effectiveness, especially the lack of adequate spaces for learning. In addition, variations in the academic routines of students, with a significant percentage lacking an established routine, suggesting deficiencies in the management of activities by teachers. Lack of interaction with peers and teachers affected motivation and enjoyment of learning. In addition, confinement had an impact on fine motor skills and the learning of letters and numbers, and the problems of access to virtual classes showed the need for improvements in the technological infrastructure.


Learning efficient decoders for quasichaotic quantum scramblers

Abstract: Scrambling of quantum information is an important feature at the root of randomization and benchmarking protocols, the onset of quantum chaos, and black-hole physics. Unscrambling this information is possible given perfect knowledge of the scrambler [arXiv:1710.03363]. We show that one can retrieve the scrambled information even without any previous knowledge of the scrambler, by a learning algorithm that allows the building of an efficient decoder. Remarkably, the decoder is classical in the sense that it can be efficiently represented on a classical computer as a Clifford operator. It is striking that a classical decoder can retrieve with fidelity one all the information scrambled by a random unitary that cannot be efficiently simulated on a classical computer, as long as there is no full-fledged quantum chaos. This result shows that one can learn the salient properties of quantum unitaries in a classical form and sheds a new light on the meaning of quantum chaos. Furthermore, we obtain results concerning the algebraic structure of $t$-doped Clifford circuits, i.e., Clifford circuits containing $t$ non-Clifford gates, their gate complexity, and learnability that are of independent interest. In particular, we show that a $t$-doped Clifford circuit ${U}_{t}$ can be decomposed into two Clifford circuits ${U}_{0},{U}_{0}^{\ensuremath{'}}$ that sandwich a local unitary operator ${u}_{t}$, i.e., ${U}_{t}={U}_{0}{u}_{t}{U}_{0}^{\ensuremath{'}}$. The local unitary operator ${u}_{t}$ contains $t$ non-Clifford gates and acts nontrivially on at most $t$ qubits. As simple corollaries, the gate complexity of the $t$-doped Clifford circuit ${U}_{t}$ is $O({n}^{2}+{t}^{3})$, and it admits a efficient process tomography using $poly(n,{2}^{t})$ resources.


Autonomous error correction of a single logical qubit using two transmons

Abstract: Large-scale quantum computers will inevitably need quantum error correction to protect information against decoherence. Traditional error correction typically requires many qubits, along with high-efficiency error syndrome measurement and real-time feedback. Autonomous quantum error correction instead uses steady-state bath engineering to perform the correction in a hardware-efficient manner. In this work, we develop a new autonomous quantum error correction scheme that actively corrects single-photon loss and passively suppresses low-frequency dephasing, and we demonstrate an important experimental step towards its full implementation with transmons. Compared to uncorrected encoding, improvements are experimentally witnessed for the logical zero, one, and superposition states. Our results show the potential of implementing hardware-efficient autonomous quantum error correction to enhance the reliability of a transmon-based quantum information processor.


S2S-Sim: A Benchmark Dataset for Ship Cooperative 3D Object Detection

Abstract: The rapid development of vehicle cooperative 3D object-detection technology has significantly improved the perception capabilities of autonomous driving systems. However, ship cooperative perception technology has received limited research attention compared to autonomous driving, primarily due to the lack of appropriate ship cooperative perception datasets. To address this gap, this paper proposes S2S-sim, a novel ship cooperative perception dataset. Ship navigation scenarios were constructed using Unity3D, and accurate ship models were incorporated while simulating sensor parameters of real LiDAR sensors to collect data. The dataset comprises three typical ship navigation scenarios, including ports, islands, and open waters, featuring common ship classes such as container ships, bulk carriers, and cruise ships. It consists of 7000 frames with 96,881 annotated ship bounding boxes. Leveraging this dataset, we assess the performance of mainstream vehicle cooperative perception models when transferred to ship cooperative perception scenes. Furthermore, considering the characteristics of ship navigation data, we propose a regional clustering fusion-based ship cooperative 3D object-detection method. Experimental results demonstrate that our approach achieves state-of-the-art performance in 3D ship object detection, indicating its suitability for ship cooperative perception.


Shortcuts to Adiabaticity in Krylov Space

Abstract: Shortcuts to adiabaticity provide fast protocols for quantum state preparation. A new way to construct the auxiliary controls for guiding the system's dynamics boosts their application to many-body systems.


A framework for demonstrating practical quantum advantage: comparing quantum against classical generative models

Abstract: Abstract Generative modeling has seen a rising interest in both classical and quantum machine learning, and it represents a promising candidate to obtain a practical quantum advantage in the near term. In this study, we build over an existing framework for evaluating the generalization performance of generative models, and we establish the first quantitative comparative race towards practical quantum advantage (PQA) between classical and quantum generative models, namely Quantum Circuit Born Machines (QCBMs), Transformers (TFs), Recurrent Neural Networks (RNNs), Variational Autoencoders (VAEs), and Wasserstein Generative Adversarial Networks (WGANs). After defining four types of PQAs scenarios, we focus on what we refer to as potential PQA, aiming to compare quantum models with the best-known classical algorithms for the task at hand. We let the models race on a well-defined and application-relevant competition setting, where we illustrate and demonstrate our framework on 20 variables (qubits) generative modeling task. Our results suggest that QCBMs are more efficient in the data-limited regime than the other state-of-the-art classical generative models. Such a feature is highly desirable in a wide range of real-world applications where the available data is scarce.


Medical image segmentation based on dynamic positioning and region-aware attention

Abstract: Transformer has already proven its ability to model long-distance dependencies. However, medical images have strong local structures. Directly using Transformer to extract features would not only contain redundant information increasing the computational effort, but also be detrimental to extracting local details. Given these issues, we propose a network based on dynamic positioning and region-aware attention, which adopts a two-stage feature extraction strategy. In the shallow layer, we design Dynamic Positioning Attention (DPA). It will localize to the key feature information and construct a variable window for it, then perform attention calculation. DPA improves the learning ability of local details, reduces the amount of computation. At the deep level, Bi-Level Routing Attention (BRA) is used to discard irrelevant key–value pairs, achieve content-aware sparse attention for the deep dispersed semantic information, and improve computational efficiency. After several experiments, the results show that our method achieves advanced performance on different types of datasets.


Limitations of Linear Cross-Entropy as a Measure for Quantum Advantage

Abstract: Demonstrating quantum advantage requires experimental implementation of a computational task that is hard to achieve using state-of-the-art classical systems. One approach is to perform sampling from a probability distribution associated with a certain class of highly entangled many-body wave functions. It has been suggested that such a quantum advantage can be certified with the linear cross-entropy benchmark (XEB). We critically examine this notion. First, we consider a “benign” setting, where an honest implementation of a noisy quantum circuit is assumed, and characterize the conditions under which the XEB approximates the fidelity of quantum dynamics. Second, we assume an “adversarial” setting, where all possible classical algorithms are considered for comparisons, and show that achieving relatively high XEB values does not imply faithful simulation of quantum dynamics. Specifically, we present an efficient classical algorithm that achieves high XEB values, namely 5–12% of those obtained in the state-of-the-art experiments, within just a few seconds using a single GPU machine. This is made possible by identifying and exploiting several vulnerabilities of the XEB, which allows us to achieve high XEB values without simulating a full quantum circuit. Remarkably, our algorithm features better scaling with the system size than a noisy quantum device for commonly studied random circuit ensembles in various architecture. We quantitatively explain the success of our algorithm and the limitations of the XEB by using a theoretical framework, in which the dynamics of the average XEB and fidelity are mapped to classical statistical mechanics models. Using this framework, we illustrate the relation between the XEB and the fidelity for quantum circuits in various architectures, with different choices of gate sets, and in the presence of noise. Taken together, our results demonstrate that XEB’s utility as a proxy for fidelity hinges on several conditions, which should be independently checked in the benign setting, but cannot be assumed in the general adversarial setting. Therefore, the XEB on its own has a limited utility as a benchmark for quantum advantage. We briefly discuss potential ways to overcome these limitations.6 MoreReceived 28 January 2022Revised 24 July 2023Accepted 20 November 2023DOI:https://doi.org/10.1103/PRXQuantum.5.010334Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasQuantum benchmarkingQuantum chaosQuantum computationQuantum protocolsQuantum verificationQuantum Information, Science & Technology


Anomaly detection in the presence of irrelevant features

Abstract: A bstract Experiments at particle colliders are the primary source of insight into physics at microscopic scales. Searches at these facilities often rely on optimization of analyses targeting specific models of new physics. Increasingly, however, data-driven model-agnostic approaches based on machine learning are also being explored. A major challenge is that such methods can be highly sensitive to the presence of many irrelevant features in the data. This paper presents Boosted Decision Tree (BDT)-based techniques to improve anomaly detection in the presence of many irrelevant features. First, a BDT classifier is shown to be more robust than neural networks for the Classification Without Labels approach to finding resonant excesses assuming independence of resonant and non-resonant observables. Next, a tree-based probability density estimator using copula transformations demonstrates significant stability and improved performance over normalizing flows as irrelevant features are added. The results make a compelling case for further development of tree-based algorithms for more robust resonant anomaly detection in high energy physics.


Particle Swarm Intelligence

Abstract: This comprehensive exploration delves into the integration of particle swarm intelligence (PSO) within the domain of supply chain management, emphasizing the optimization of supply chain network design. The importance of efficient supply chain network design has been underscored, with a focus on resource allocation, decision-making, and adaptability as key drivers of long-term competitiveness and sustainability. The study navigates through the fundamental components and working mechanisms of the PSO algorithm, highlighting its capacity to optimize complex solution spaces, and compares its advantages with other optimization techniques. Challenges within supply chain network design, encompassing the complexities of modern networks and factors influencing their design, have been examined.


Embracing the future of Artificial Intelligence in the classroom: the relevance of AI literacy, prompt engineering, and critical thinking in modern education

Abstract: Abstract The present discussion examines the transformative impact of Artificial Intelligence (AI) in educational settings, focusing on the necessity for AI literacy, prompt engineering proficiency, and enhanced critical thinking skills. The introduction of AI into education marks a significant departure from conventional teaching methods, offering personalized learning and support for diverse educational requirements, including students with special needs. However, this integration presents challenges, including the need for comprehensive educator training and curriculum adaptation to align with societal structures. AI literacy is identified as crucial, encompassing an understanding of AI technologies and their broader societal impacts. Prompt engineering is highlighted as a key skill for eliciting specific responses from AI systems, thereby enriching educational experiences and promoting critical thinking. There is detailed analysis of strategies for embedding these skills within educational curricula and pedagogical practices. This is discussed through a case-study based on a Swiss university and a narrative literature review, followed by practical suggestions of how to implement AI in the classroom.


Exploring the current status and future opportunities of blockchain technology adoption and application in supply chain management

Abstract: This study comprises a systematic literature review that offers a thorough analysis of the present state-of-the-art knowledge regarding the adoption and application of blockchain technology within the context of supply chain management. Based on an extensive review of 133 articles from highly-regarded journals, we developed a framework where we highlight the integration of the transition from blockchain technology adoption to its application. This transition process is explained by two theories: the Technology Acceptance Model and the Diffusion of Innovation Theory. In the adoption phase, we identified nine internal drivers and seven external drivers, together with comprehensive barriers. Our framework also integrates and examines the blockchain technology application stage, focusing not only the current development of benefits of blockchain technology implementation but also the challenges that impede the successful implementation of blockchain technology in supply chain management. The future development prospects are also highlighted which include standardization, platform development, regulation system development, cost reduction, and talent cultivation. These future developments were critical yet were missing in the current academic literature. By providing an in-depth analysis of these challenges, our study can assist supply chain practitioners in making informed decisions about blockchain-enabled supply chain management initiatives.


Revolutionizing Health Data Management With Blockchain Technology

Abstract: This chapter thoroughly investigates the revolutionary use of blockchain technology in the administration of medical data. Blockchain integration has become a disruptive force in the era of digital health records, with the potential to completely revolutionize how healthcare organizations manage, trade, and safeguard data. The main objective is to analyze blockchain's complex role in transforming healthcare data management. The foundations of blockchain in healthcare data management are among the important subjects addressed; this section introduces the technology's basic ideas, composition, and operation. Examining how blockchain improves data security and privacy in healthcare takes up much of this chapter. The analysis focuses on how blockchain breaks down the conventional boundaries of data silos to enable smooth data interchange across heterogeneous healthcare systems. Examining the difficulties and real-world applications of implementing blockchain technology in healthcare rounds off the chapter.


Relationships between Pedagogical Practices and Affective States for Effective Teaching during the COVID-19 Pandemic: Insights from University Professors

Abstract: Objectives: This study aimed to analyze how pedagogical practices and affective states during emergency remote teaching influence professors’ perspectives on their capabilities and the professor-student relationship. Method: We used a nonexperimental quantitative design, collecting data through an online survey during the first year of the pandemic. Participants were recruited using a non-probability sampling method. A total of 636 university professors from Chilean universities participated. We performed descriptive and correlation analyses between variables. Also, to gain a deeper understanding of the factors influencing perceptions of the professors’ competence and the professor–student relationship, we conducted linear regression models. Results: A higher perception of pedagogical competence was related to male professors reporting a high evaluation of teaching quality and a high diversification of assessment strategies. A better professor–student relationship was related to female professors reporting a high level of tutoring activities and activities in which students work collaboratively. Additionally, unpleasant affective states were experienced at a higher rate by younger and female professors. Conclusions: Variables that make up the perception of professor competence and the professor–student relationship were identified, relating to gender and pedagogical practices, such as diversification of assessment strategies, tutoring, and collaborative activities. Implications: The results can guide future institutional actions to improve conditions and establish guidelines for quality virtual education in the post-pandemic scenario.


SExpSMA-based T5: Serial exponential-slime mould algorithm based T5 model for question answer and distractor generation

Abstract: Generally, multiple choice questions are an effective and extensive form used in standard tests in order to evaluate the learner’s skills and knowledge. Nonetheless, the multiple-choice question composition particularly the distractor construction is quite difficult. The distracters are needed to be both plausible and inappropriate and adequate to mystify the learners who did not master the information. Thus, the distractor generation emergence is important that can help several standard tests in an extensive range of domain. In this research, question-answer generation system is developed with a distractor model by developing an optimized T5 model. At first, BERT tokenization is used to pre-process the passage/context and question, which are given as the input to train the approach. Then, the question and answer generation is performed by utilizing the T5 approach that is trained by proposed Serial Exponential-Slime Mould approach (SExpSMA). Exponential weighted moving average is extended to Serial Exponential weighted moving average and incorporated in Slime Mould Algorithm (SMA) to propose SExpSMA. In addition, the proposed SExpSMA-based T5 model is employed to generate distractors for the questions. Eventually, experimentation analysis exhibits that proposed SExpSMA-based T5 model achieves better outcomes regarding the metrics, like ROUGE, BLEU, and METEOR with the values of 0.919, 0.918, 0.488, respectively.


Grammar and Technology: A Bibliometric Exploration of Research Trends and Influential Works

Abstract: The study delves into the pivotal role of Grammar in scholarly communication, emphasising the need for a nuanced understanding of evolving grammatical research. Conducting a bibliometric analysis of 381 papers from the Scopus database (2015-2023), it explores grammar teaching and learning via technology. Employing advanced methods, the research identifies influential authors, institutions, and countries, tracks evolving themes, and assesses funding sources. The study utilises tools like Microsoft Excel, VOS Viewer, and Harzing to unveil partnerships, current research areas, and impact assessment. By categorising five clusters, it sheds light on underexplored research areas, offering insights for future investigations into grammar research.


Assessing Gender Differences in Technological Pedagogical Content Knowledge (TPACK) among EFL High School Teachers in Inner Mongolia

Abstract: This study examines gender differences in Technological Pedagogical Content Knowledge (TPACK) among 437 English as a Foreign Language (EFL) high school teachers in Inner Mongolia, China. Quantitative data revealed significant TPACK disparities between male and female teachers, with males exhibiting higher levels. The findings suggest that male teachers demonstrate greater confidence in integrating technology into their English classrooms. To address the TPACK gender gap, it is recommended that female EFL teachers receive specialized training through mentorship programs, online communities, and workshops. Policy changes are urged to create a collaborative, supportive, and inclusive digital environment for effective technology integration.


Research on error detection in English translation texts using machine learning algorithms

Abstract: This paper provides a concise overview of a grammatical error correction algorithm based on the encoder-decoder structure. The traditional unidirectional long short-term memory (LSTM) in the encoder was transformed into a bidirectional LSTM. Subsequently, the grammatical error correction algorithm was simulated and experimented with. In the experiments, it was compared with two other error correction algorithms: one based on the LSTM classification model and the other on the traditional unidirectional LSTM translation model. The results indicated that the three error correction algorithms exhibited little difference in detection performance when faced with distinct corpus databases. Furthermore, when dealing with the same corpus database, the bidirectional LSTM algorithm demonstrated the most robust detection performance, followed by the one based on the traditional unidirectional LSTM translation model, and lastly, the one based on the LSTM classification model performed the least effectively.


A study on human perception of document paragraph layout using surveys1

Abstract: We identify a list of characteristics of a text document that we use as the basis for a study on human perception of what the structure of a document should look like. Our study reveals that the native language dimension has a significant impact on the human perception of some such characteristics, including the average number of paragraphs starting with a verb, as well as other characteristics describing average paragraph proportions. The study results also show what the mean values are, for the different document characteristics, and their distribution across the native language dimension, therefore providing some idea of what a normalised structure of a document should look like. The results of the study have a direct application in a new method for embedding secret messages in text documents that has been recently proposed by the authors and which uses manipulations in the paragraph layout of a document.


B-HPD: Bagging-based hybrid approach for the early diagnosis of Parkinson’s disease1

Abstract: Parkinson’s disease (PD) is a neurodegenerative condition that affects the neurological, behavioral, and physiological systems of the brain. According to the most recent WHO data, 0.51 percent of all fatalities in India are caused by PD. It is a widely recognized fact that about one million people in the United States suffer from PD, relative to nearly five million people worldwide. Approximately 90% of Parkinson’s patients have speech difficulties. As a result, it is crucial to identify PD early on so that appropriate treatment may be determined. For the early diagnosis of PD, we propose a Bagging-based hybrid (B-HPD) approach in this study. Seven classifiers such as Random Forest (RF), Decision Tree (DT), Logistic Regression (LR), Naïve Bayes (NB), K nearest neighbor (KNN), Random Under-sampling Boost (RUSBoost) and Support Vector Machine (SVM) are considered as base estimators for Bagging ensemble method and three oversampling techniques such as Synthetic Minority Oversampling Technique (SMOTE), Adaptive Synthetic (ADASYN) and SVMSmote are implemented under this research work. Feature Selection (FS) is also used for data preprocessing and further performance enhancement. We obtain the Parkinson’s Disease classification dataset (imbalanced) from the Kaggle repository. Finally, using two performance measures: Accuracy and Area under the curve (AUC), we compare the performance of the model with ALL features and with selected features. Our study suggests bagging with a base classifier: RF is showing the best performance in all the cases (with ALL features: 754, with FS: 500, with three Oversampling techniques) and may be used for PD diagnosis in the healthcare industry.


Student media committee in the information policy system of the Higher School of Folk Arts: concept of activity

Abstract: The article examines the role of media resources, incl. social networks as the main channels for informing students and positioning higher educational institutions in the modern world. Discusses how universities can use these tools to share knowledge, demonstrate achievements, communicate activities and build their own brand. A concept for the work of the media committee of the Student Council of the Higher School of Folk Arts (Academy) is proposed, which includes improving the quality of content, developing new platforms, high-quality visual design and differentiated content to expand audience coverage. An important aspect is also the formation of an effective team for successful and productive work.


Forensic Analysis of Drones Attacker Detection Using Deep Learning

Abstract: Purpose: This research proposes deep learning techniques to assist forensic analysis in drone accident cases. This process is focused on detecting attacking drones. In this research, we also compare several deep learning and make some comparisons of the best methods for detecting drone attackers.Methods: The methods applied in this research are YOLO, SSD, and Fast R-CNN. Additionally, to validate the effectiveness of the results, extensive experiments were conducted on the dataset. The dataset we use contains videos taken from drones, especially drone collisions. Evaluation metrics such as Precision, Recall, F1-Score, and mAP are used to assess the system's performance in detecting and classifying drone attackers.Results: This research show performance results in detecting and attributing drone-based threats accurately. In this experiment, it was found that YOLOV5 had superior results compared to YOLOV3 YOLOV4, SSD300, and Fast R-CNN. In this experiment we also detected ten types of objects with an average accuracy value of more than 0.5.Novelty: The proposed system contributes to improving security measures against drone-related incidents, serving as a valuable tool for law enforcement agencies, critical infrastructure protection and public safety. Furthermore, this underscores the growing importance of deep learning in addressing security challenges arising from the widespread use of drones in both civil and commercial contexts.


Patriotic Education in Secondary Vocational Education: Creation and Implementation of the General Educational Subject "Russia in the World"

Abstract: In the article, the author presents methodological approaches to the development of the general education discipline “Russia in the World”. It is indicated that the “Russia in the World” program is a patriotic program that systematizes the entire historical process and implements strategic guidelines for education. As part of the program, patriotic education is effectively carried out at NMPC. The author in the article speaks in some detail about the design and implementation of the academic discipline “Russia in the World”. As a result of the study, a conclusion is formulated that this program is quite effective, since with its help it was possible to establish the main models, problems and ways of patriotic education of students. It is noted that the high effectiveness of patriotic education within the framework of the development of the OD “Russia in the World” is due to such factors as the systematic nature of the program, reliance on the patriotic potential of historical science, inclusion in the professional training system and taking into account the psychological and other characteristics of students. In this regard, the conclusion is formulated that at the present stage of development, quite effective models have been developed for the patriotic education of the younger generation that are applicable in practice.


Self-Efficacy in the Context of Professional Activity

Abstract: This article examines the concept of self-efficacy, highlights the main approaches to its study, components, properties, factors and functions. The methods of diagnosis and development of this concept are also revealed. It is shown that the development of self-efficacy affects success in professional activity and is one of the important success factors. This concept refers to a person's ability to effectively use their resources, skills and knowledge to achieve their goals. Self-effective people have high self-confidence, faith in their own abilities and confidence in achieving results. They are able to motivate themselves, make decisions and act decisively in difficult situations. Self-efficacy contributes to increased productivity and quality of work, the development of leadership and leadership skills, as well as the ability to self-development and self-regulation. It helps to cope with difficulties and strive for continuous improvement in the professional field. Understanding and developing self-efficacy are important aspects of career success, so it becomes an integral factor in professional activity. In conclusion, the problems of self-efficacy are considered, which are a common phenomenon in modern society and affect the results of people's activities in various spheres of life, from professional activities to personal life.


Digital Intellectual Property and its Protection

Abstract: Purpose — to determine the meaning and content of new terms of the theory of law, which will later be used in other branches of law. To actualize the problem of digital intellectual property objects. Identify the gray zones of legal regulation that have arisen in practice and analyze changes in judicial practice in 2022-2023. Methods. Classical methods are used. It showed high efficiency: a comparative legal, dialectical-materialistic method, formal-legal. Results. The list of intellectual property objects is expanding. New entrepreneurial activities are emerging, based on the collection of information and the creation of 3D models. Gaps in legislation lead to many years of litigation, unforeseen expenses, the inability to determine the outcome of a legal conflict. As a legislative proposal: to eliminate the identified problems, it is advisable to revise Article 1225 of the Civil Code of the Russian Federation "Protected Results of Intellectual Activity and Means of Individualization" taking into account those objects that have formed in the construction sector. Discussion. Actualization of problems in the terminology of the theory of law, which create difficulties in the application of legal norms in various types of legal activity. Increasing the importance and scientific value of interdisciplinary research. It is necessary to unify terminology affecting digital intellectual property objects in various branches of law. The protection of intellectual rights can be strengthened.


Policy Framework for Women’s Economic Empowerment: What is Amiss?

Abstract: Abstract The paper is an assessment of the policies of the government of India addressing the issue of women’s economic empowerment and gender equality. The paper highlights the systemic issues related to women’s inclusion in the policy framework, focusing on women and the labour market. In the process it briefly explores the barriers faced by women in accessing labour markets and critically examines the current government programmes aiming to lower these barriers. The arguments locate the fault-lines of the policies that rarely take into account the social and patriarchal constructs and hence impact the overall designs and financing strategies. The paper extensively uses government documents, secondary resources, and databases provided by the government in support of the arguments made.


Probability Matching

Abstract: Abstract Chapter 5 describes an experiment in which human subjects are asked to play a computer game with real monetary payoffs for correctly guessing the outcome of a binary lottery. Most subjects’ guesses conform to an irrational behaviour known as probability matching, in which the frequency of their guesses matches the underlying probability of the binary lottery. In addition to probability matching, we find different tendencies towards randomization under different payoff schemes, as predicted by Chapter 2’s evolutionary model of behaviour, and even after controlling for a wide range of demographic and socio-economic variables. We also find several individual differences in the tendency to maximize or randomize, correlated with wealth and other socio-economic factors. Surprisingly, subjects who have taken probability and statistics classes, and those who self-reported finding a pattern in the game, are more likely to act irrationally, underscoring the importance of environment as a driver of behavioural anomalies.


Bigdata clustering and classification with improved fuzzy based deep architecture under MapReduce framework

Abstract: The current state of economic, social ideas, and the advancement of cutting-edge technology are determined by the primary subjects of the contemporary information era, big data. People are immersed in a world of information, guided by the abundance of data that penetrates every element of their surroundings. Smart gadgets, the IoT, and other technologies are responsible for the data’s explosive expansion. Organisations have struggled to store data effectively throughout the past few decades. This disadvantage is related to outdated, expensive, and inadequately large storage technology. In the meanwhile, large data demands innovative storage techniques supported by strong technology. This paper proposes the bigdata clustering and classification model with improved fuzzy-based Deep Architecture under the Map Reduce framework. At first, the pre-processing phase involves data partitioning from the big dataset utilizing an improved C-Means clustering procedure. The pre-processed big data is then handled by the Map Reduce framework, which involves the mapper and reducer phases. In the mapper phase. Data normalization takes place, followed by the feature fusion approach that combines the extracted features like entropy-based features and correlation-based features. In the reduction phase, all the mappers are combined to produce an acceptable feature. Finally, a deep hybrid model, which is the combination of a DCNN and Bi-GRU is used for the classification process. The Improved score level fusion procedure is used in this case to obtain the final classification result. Moreover, the analysis of the proposed work has proved to be efficient in terms of classification accuracy, precision, recall, FNR, FPR, and other performance metrics.


THE ROLE OF ARTIFICIAL INTELLIGENCE IN DEVELOPING THE SKILLS OF THE CENTURY THE TWENTY-FIRST AMONG EDUCATIONAL LEADERS IN LIGHT OF SMART DIGITAL TRANSFORMATIONS

Abstract: The study aimed to identify proposals to activate the role of artificial intelligence in developing the skills of the century The twenty-first among educational leaders in light of smart digital transformations, and the study adopted the curriculum Qualitative, the researcher conducted interviews with (20) faculty members from Jordanian universities Governmental and private. The results of these interviews showed that faculty members presented a range Proposals to activate the role of artificial intelligence in developing twenty-first century skills among leaders Educational education in light of smart digital transformations included: activating modern technology in all its forms The educational process in Jordanian public and private universities continues on an ongoing basis within their centers and structures Administrative and organizational, universities innovate modern mechanisms that contribute to enhancing the use of intelligence applications Artificial among educational leaders, educational leaders in Jordanian universities seek to develop Their skills by accepting change and not resisting it, and the universities’ keenness to activate electronic platforms Based on modern technologies that are compatible with current and future requirements and goals, we direct Jordanian universities, in their policies and future plans, seek to raise awareness among educational leaders of the importance of activating Applications of artificial intelligence and spreading the culture of digital transformations in educational circles. The study recommended Conducting further studies related to artificial intelligence and modern technologies and linking them to other variables


THE DEGREE OF APPLICATION OF KNOWLEDGE MANAGEMENT AMONG DEPARTMENT HEADS AT THE UNIVERSITY OF BAGHDAD FROM THE POINT OF VIEW OF THE FACULTY

Abstract: A field study aimed to determine the degree of application of knowledge management among department heads at the University of Baghdad from the point of view of the faculty. The research adopted the descriptive analytical method. The research community consisted of (1103) faculty members in the colleges of education at the University of Baghdad. The research sample was chosen by a simple random method by (25%) of the research community, as the number of sample members reached (280) of the teaching staff. A questionnaire was designed and the questionnaire consisted of four areas (the concept of knowledge management, knowledge generation, knowledge organization, and knowledge application). The researcher confirmed its validity and reliability. The results of the research data were analyzed using the (SPSS) program, and I reached the most important results: Department heads have awareness of the importance of spreading and supporting the culture of applying knowledge management in various fields in the departments of the colleges of education / University of Baghdad and activating knowledge management activities among the teaching staff, which drives and motivates them to accomplish work and achieve goals.. In addition to arriving at a number of recommendations and proposals related to the research


Do the People Matter in Policymaking in Ghana? A Reflection on the E-Levy and Debt Exchange Programs

Abstract: The extent to which the masses have a say in matters concerning their lives is crucial in governance. It makes a significant amount of knowledge to say that people vote for elected policymakers to make policies that will make their lives better off and not the opposite. However, in the making of policies, the views of the people who either benefit or suffer the ramifications of policies are not taken into consideration. Therefore, the content analysis methodology has been employed in this study to systematically analyze secondary sources about the recent adoption of the E-Levy policy and Debt Exchange Program to ascertain whether the people mattered in adopting and implementing these policies. The adoption of these policies has raised a lot of controversies, with the public agitating and calling for its termination. The government of Ghana, being keen on continuing with the implementation of these policies as the only way out of the country's economic hardship, raises a lot of questions. After a systematic analysis of the literature, the paper argues that both policies were passed without the involvement of the people. The implication of the government’s failure to adopt a participatory policymaking approach accounts for the citizenry's loss of trust in the government.


Development of Radar System for Laser Tracking System

Abstract: Korea Astronomy and Space Science Institute (KASI) developed an satellite laser ranging (SLR) system for tracking space objects using ultra-pulsed lasers. For the safe operation of SLR system, aircraft surveillance radar system (ASRS) was developed to prevent human damage from high power laser transmitted from the SLR system. The ASRS consists of the radar hardware subsystem (RHS) and main control subsystem (MCS), in order to detect flying objects in the direction of laser propagation and then stop immediately the laser transmission. The RHS transmits the radio frequency (RF) pulse signals and receives the returned signals, while the MCS analyzes the characteristics of received signals and distinguishes the existence of flying objects. If the flying objects are determined to be existed, the MCS sends the command signal to the laser controller in SLR system to pause the laser firing. In this study, we address the interface and operational scenarios of ASRS, including the design of RHS and MCS. It was demonstrated in the aircraft experiments that the ASRS could detect an aircraft and then stop transmitting high power laser successfully.


Study on the Visual Tactility of 3D Virtual Material Images According to Lighting Environments

Abstract: When shopping online, customers rely on three-dimensional images of digital products to make their purchase decisions. These decisions are often influenced by the emotional response the digital images elicit. Lighting can play a crucial role in enhancing the three-dimensional effect of virtual reality visualization technology, thereby creating a spatial atmosphere that can stimulate consumer emotions while providing valuable product information. The significance of this study lies in its ability to analyze how lighting conditions affect a customer


Importance of Using Interactive Methods in Russian Language Lessons

Abstract: This article is devoted to the relevance of using interactive methods of teaching Russian as a foreign language in medical universities. This allows to activate the learning process, as a result of which students develop skills, knowledge and competences necessary for communication in various fields.


Elder Biaoqing: investigating the indexicalities of memes on Chinese social media

Abstract: Abstract This paper adopts a digital ethnographic approach to analyze concrete communicative practices with Elder Biaoqing, a type of graphic semiotic resources comparable to emojis and memes, typically designed for and used by older people on Chinese social media. Following Silverstein’s theorizing, it reveals the emergence of multiple indexicalities of Elder Biaoqing that are a result of several social factors: the growth of an older population online, people’s reflections on their communicative needs engendered by specific social facts, and people’s ethno-metapragmatics. The study of Elder Biaoqing reveals users’ agency in creating semiotic resources, the inequality between digital natives and digital migrants, and age anxiety in Chinese society. The findings invite a re-imagination of social facts – the existence of an online–offline nexus and a re-thinking of theories for sociocultural research in a digital era – ontological perspectives on multimodal resources and digital infrastructures, developments of the theoretical perspective of indexicality, and a total-semiotic-fact approach to digitally mediated social interaction.


Optimizing Collaboration Skills in the 21st Century: Recent Innovations in Project Model Differentiated Learning

Abstract: Collaboration skills are important. Therefore, group learning needs to be held for hands-on experience that results in understanding and effective learning methods. This study aims to analyze the effect of project model differentiated learning on students' collaboration skills in grade V elementary school in science subjects. This type of research is a quasi-experiment with a one-group pretest-posttest design. The sampling technique used was the saturated sampling technique with 26 students. The research instruments used in the form of learning style questionnaires, observation sheets of the learning process in the classroom, and observation sheets to measure student collaboration skills consist of initial and final observation sheets. Data analysis used in this research is descriptive analysis technique and inferential analysis technique with Paired Sample t Test. Before the hypothesis test, the prerequisite test includes the normality test and homogeneity test. The average collaboration skills of grade V students before being given treatment using the differentiated learning project model were none in the Very High category, 9.99% in the High category, 70.76% in the Medium category, and 19.76% in the Low category. After being given treatment using a differentiated learning project model, there were 9.23% in the Very High category, 59.23% in the High category, 31.54% in the Moderate category, and none in the Low category. Furthermore, data processing is carried out with t-value ≥ critical t-value (39.034 > 2.05954), which indicates that there is a positive effect of the differentiated learning project model on student collaboration skills in grade V elementary school.


Hate Speech in Tiktok Comments in the First Debate of the 2024 Vice Presidential Candidates

Abstract: Hate speech is common on social media today. The advancement of technology is the reason why people pursue the freedom of opinion and the delivery of information that is not necessarily true. This study aims to describe some of the hate speeches that occurred during the 2024 presidential democratic party. More precisely, the researchers took data from netizen comments after the first debate of the 2024 vice president candidate. The method in this research uses qualitative descriptive with a pragmatic approach. The data used is a netizen's comment that contains hate speech on social media tiktok. The data-gathering technique uses logging and documentation. The technique analyzes the data by classifying what kind of hate speech it belongs to after explaining what it means. The results of this study revealed forms of hate speech that were found, among other things, insulting, provoking, and polluting the good name of a 2024 Vice President candidate. Based on the analysis of data on hate speeches on netizen comments on tiktok, researchers found data that was intended to harm a person's good name and honour and could lead to social conflict.


Analysis of Syntactic Material in the Textbook of SDN 03 Cemaka Putih Class 1-3

Abstract: This research is entitled "Analysis of Syntactic Material in Textbook Material at SDN 03 Cempaka Putih Grades 1-3." Many people do not know that syntactic material has also been studied since elementary school, therefore the researcher decided to conduct this research. The purpose of this research is to find out what material is related to syntax in class 1-3 textbooks at SDN 03 Cempaka Putih. This type of research is descriptive qualitative. The data collection technique was carried out through observation by borrowing textbooks from grade 1-3 teachers at SDN 03 Cempaka Putih and carrying out reading, listening and note-taking techniques. The subject of this study is the syntactic material contained in the textbooks used at SDN 03 Cempaka Putih grades 1-3. The results showed that of the 17 textbooks used at SDN 03 Cempaka Putih grades 1-3, there were 9 materials related to syntax. The material found in the textbooks used at SDN 03 Cempaka Putih grades 1-3, namely words and sentences.


Controlling the Mobile Robot with the Pure Pursuit Algorithm to Tracking the Reference Path Sent from the Android Device

Abstract: Many of the equipment and machines we use in our everyday lives have changed due to major advancements in today's technology. Smartphones, which have made great progress especially in the last decade, perform many tasks in addition to interpersonal communication. Controlling robots, which are increasingly used in daily life and widely included in the literature, is one of these tasks. In this study, the pure pursuit algorithm was used to control the position of a non-holonomic differential drive mobile robot, and the path information to be tracked was received from an Android mobile device as a reference. An application design has been carried out for Android devices. The information for the path drawn here was transferred via the internet to a Google Spreadsheet. Coordinate information obtained from Google tables in MATLAB was separated as x and y axis information and entered into MATLAB/Simulink as waypoints of the pure pursuit algorithm and the position control of the robot was carried out. Error analysis was made by taking the differences between the reference path and the actual movement and the control performance was examined. Additionally, the effect of the approach distance value of the pure pursuit algorithm on the error is presented.


University Culture: A quali-quantitative study on the emotional representations of online learning by psychology university students

Abstract: The pandemic of covid-19 has led to the conversion from face-to-face to online learning in almost every university in the world. Online learning was perceived by stu-dents as an opportunity and an impediment to the learning process and an obstacle for social contact. The main aim of this research was to explore the representations of dis-tance learning by university students. We collected 127 interviews from university stu-dents and used the paradigm of Emotional Text Mining (EMT) for their analysis. Three factors (Learning Process, University Life, Blended learning) and four clusters (Being in a Relationship, Online learning, Missed Rituality, Process of Adapting) were identified. The factors highlight an unconscious defence mechanism which "separates" the reality of online learning (without relationships) from the reality of the face-to-face learning (with relationships). The clusters show how university students represent online learning as useful at a practical level, but as an obstacle to social contact and a sense of belonging to the university culture. In addition, the interpretation of the clusters reveals an imma-ture process of adaptation of students to the post-pandemic reality. All these findings highlight face-to-face learning as a place for interaction and social sharing and necessary to feel integrated in university culture.


Problems in Conducting Multivariate Logistic Regression Analysis

Abstract: We read with great interest the original article “The Relationship Between Procalcitonin Level and Short Term Mortality in Emergency Department” by Cehreli et al. (1). A high initial level of procalcitonin (PCT) is a sensitive marker of bacterial infection, and the degree of PCT elevation can determine which patients are at greater risk of adverse outcomes. In this study, the authors wanted to analyse whether there was an association between PCT levels and seven-day mortality in patients whom PCT levels were measured in the emergency department. However, we think that there are some serious methodological mistakes.


Databases in the investigation of household armed robberies: Challenges and ways of improvement

Abstract: Amidst the full-scale war in Ukraine, an increase in the number of crimes against property involving weapons, characterised by suddenness, brutality, and aggressiveness, has occurred. Such crimes violate not only the inviolability of housing and property rights but also harm the health and lives of victims. In this regard, the study aims to examine the possibilities of using automated information retrieval systems of the Ministry of Internal Affairs and the National Police of Ukraine in the investigation of robberies involving the use of weapons. The following methods were used in the course of the study: comparative legal, analytical, systemic, and structural, generalisation, induction, deduction, and synthesis. These methods were used to analyse the world experience of databases of automated information retrieval systems to improve the investigation of robberies involving the use of weapons against citizensʼ homes. Based on this, the most progressive and effective methods of investigating this category of criminal offences are identified, proposals for improving national practice are formulated, and an indicative set of parameters for typical actions during such robberies is developed. The author proposes to improve information and analytical systems by filling in new databases of serious crimes committed with the use of weapons, which will facilitate ease of use and efficiency of their investigation. The development of such a database provides for the possibility of updating and collecting information based on specified requests. The content parameters are determined by the ability to collect information on criminals, the forensic traces that identify them, behavioural traces (use of a type or types of weapons, home invasion tools, nature of bodily harm, features of disguise, etc. The practical value of the study lies in the possibility of considering theoretical recommendations when creating databases that will ensure the completeness, efficiency and convenience of obtaining the necessary information for the effective investigation of robberies involving the use of weapons


Cross-lingual entity alignment method based on attribute weight updating network

Abstract: Due to the large number of attributes and the low repetition rate in a cross-lingual knowledge graph, it is difficult for an alignment task to embed attribute information efficiently. To solve the problem, an entity alignment model based on attribute weight updating network was proposed. Firstly, in order to embed attribute information efficiently, attribute embedding is approximately constructed with entity embedding through a constructor, thus avoiding their separate training. Secondly, based on the fact that different attributes make different contributions to entity alignment, an attribute weight updating module based on graph attention network was proposed to update the weight of each attribute through using attention scores in the process of training. Finally, attribute embedding and attribute weight information were aggregated into entity embedding with an attribute aggregation module to strengthen the representation of entity embedding and improve the entity alignment performance. The experimental results show that the proposed model achieves 0.751, 0.805 and 0.915 scores respectively from the Hits@1 score in three cross-lingual datasets. Its alignment performance is better than that of the current mainstream entity alignment method.


Unsupervised 3D reconstruction method based on multi-view propagation

Abstract: In this paper, an end-to-end deep learning framework for reconstructing 3D models by computing depth maps from multiple views is proposed. An unsupervised 3D reconstruction method based on multi-view propagation is introduced, which addresses the issues of large GPU memory consumption caused by most current research methods using 3D convolution for 3D cost volume regularization and regression to obtain the initial depth map, as well as the difficulty in obtaining true depth values in supervised methods due to device limitations. The method is inspired by the Patchmatch algorithm, and the depth is divided into n layers within the depth range to obtain depth hypotheses through multi-view propagation. What's more, a multi-metric loss function is constructed based on luminosity consistency, structural similarity, and depth smoothness between multiple views to serve as a supervisory signal for learning depth predictions in the network. The experimental results show our proposed method has a very competitive performance and generalization on the DTU, Tanks & Temples and our self-made dataset; Specifically, it is at least 1.7 times faster and requires more than 75% less memory than the method that utilizes 3D cost volume regularization.


Pedagogical support of educational work with the modern family as a means of forming spiritual and moral values of the children

Abstract: The article is devoted to the consideration of issues of educational work with parents and its importance in the formation of value orientations and morality in modern children. In the context of studying the scientific literature of Russian scientists, the author concludes that this problem is relevant and requires further research. The highlighted methodological approaches – systemic, axiological, and familistic - contribute to the disclosure of the scientific nature of the research. The purpose of the study is to identify the importance of pedagogical support for educational work with the family in the context of the formation of moral values of children at the present stage of development of Russian society and the state. The author's understanding of the essence of the definition of "pedagogical enlightenment" is revealed, its essence and significance in the upbringing of spiritual and moral values of modern children are determined. The article highlights the pedagogical features of pedagogical education, manifested at different stages of family development in matters of education: educational (propaedeutic) work with future parents to educate children the moral values, educational pedagogical work with parents raising children of preschool, junior, middle and senior school age. Traditional and innovative forms of educational work in different periods of child development in the family are proposed. The difficulties of educational work with the family are highlighted, consisting in the slow process of developing parental literacy and competence, in the absence of uniform rules for raising children in the family, in the absence of clear mechanisms for interaction between parents and social institutions, in the search for effective forms of cooperation, in the absence of a single model of educational work with the family, in insufficient pedagogical education of parents in the mass media. The material under study allows us to conclude that educational and educational work will be successful if all social institutions are engaged in this activity, which will increase the pedagogical activity of parents, raise the status of family and parenthood, as well as the competence of teachers in organizing educational activities with the family and the development of spiritual and moral values of children.


Recent Developments of Semantic Web

Abstract: Sharing data and facts rather than the content of a website is what the semantic web is all about. Sir Tim Berners-Lee proposed the semantic web concept in 2001. The semantic web assists in the development of a technological stack that supports a “web of data” rather of a “web of documents.” The ultimate goal of the web of data is to provide computers the ability to do more meaningful jobs and to create systems that can enable trustworthy network connections. Different data interchange formats (e.g. Turtle, RDF/XML, N3, NTriples), query languages (SPARQL, DL query), ontologies, and notations (e.g. RDF Schema and Web Ontology Language (OWL)) are all used in semantic web technologies (SWTs) to provide a formal description of entities and correspondences within a given knowledge domain. These technologies are useful in accomplishing the semantic web's ultimate goal. Linked data is at the core of the semantic web since it allows for large-scale data integration and reasoning. SPARQL, RDF, OWL, and SKOS are among the technologies that have made linked data more powerful; however, there are many difficulties that have been detailed in different publications.


SISA En-Decryption Algorithm for Multilingual Data Privacy and Security in IoT

Abstract: Because of worldwide integration, multilingual databases and document transfers are quite widespread, which raises the requirement for data security against hackers. Without regard to Unicode, the suggested SISA En-De-Cryption (SEDC) method is capable of encrypting any language. The multilingual data and documents in the devices are secured using the proposed SEDC method in this work. Because the proposed SEDC technique has no size or language restrictions on the key, it is substantially more secure than current encryption algorithms in the literature. The brute force attack is also decreased by this SEDC trait. The SEDC algorithm's design is so straightforward that it may be created in any computer language. The suggested SEDC algorithm may also be fused with several other technologies like cloud-based web applications, IoT, block chain etc. and can be utilized for data security in multiple areas like healthcare, smart agriculture and mobile communication etc. When compared to other existing algorithms, the suggested SEDC method performs better.


Privacy-Preserving Machine Learning Techniques for IoT Data in Cloud Environments

Abstract: The proliferation of internet of things (IoT) devices has resulted in an unprecedented influx of data, leading to heightened concerns regarding the privacy and security of sensitive information in cloud environments. Privacy-preserving machine learning techniques have emerged as essential tools for ensuring the confidentiality of IoT data while facilitating meaningful analysis. This chapter provides an overview of the key principles and methodologies employed in privacy-preserving machine learning for IoT data in cloud environments. Key considerations encompass data anonymization, secure transmission, and adherence to stringent data protection regulations such as the General Data Protection Regulation (GDPR). Robust encryption and access control mechanisms are implemented to safeguard data integrity while allowing for effective analysis. Techniques like homomorphic encryption and secure multi-party computation enable secure computations on encrypted data, ensuring privacy while maintaining the utility of the data.


Landscape of Serverless Computing Technology and IoT Tools in the IT Sectors

Abstract: This chapter delves into the challenges of securing sensitive data and systems in the digital landscape, focusing on serverless computing, IoT, and authentication protocols. It discusses best practices for implementing security measures in serverless architectures, including encryption, access control, and monitoring. The rise of IoT devices has revolutionized industries by enabling real-time data collection and analysis, but also presents a significant cyber threat surface. To secure IoT ecosystems, strategies include device authentication, encryption, and intrusion detection systems. Authentication protocols like multi-factor authentication, biometrics, and blockchain-based solutions are crucial. Understanding serverless computing, IoT security, and authentication protocols is essential for businesses to proactively address security gaps and safeguard assets in an evolving threat landscape.


Transfer learning of pre-trained CNNs on digital transaction fraud detection

Abstract: This article proposes an artificial intelligence-empowered and efficient detection approach for customers with Severe Failure in Digital Transactions (SFDT) through a deep transfer network learning approach from discretized fraud data. Presently, the Real-time global payment system is suffered primarily by fraudsters based on customer behavior. For the identification of fraud, scientists used many techniques. However, identifying and tracking the customers infected by the fraud takes a significant amount of time. The proposed study employs pre-trained convolution neural network-based (CNN) architectures to find SFDT. CNN is pre-trained on the various network architectures using fraud data. This article contributed to pre-trained networks with newly developed versions ResNet152, DenseNet201, InceptionNetV4, and EfficientNetB7 by integrating the loss function to minimize the error. We run numerous experiments on large data set of credit payment transactions which are public in nature, to determine the high rate of SFDT with our model by comparing accuracy with other fraud detection methods and also proved best in evaluating minimum loss cost.


Analisis Kualitas Layanan Website SMA Terhadap Kepuasan Siswa Menggunakan Metode Webqual 4.0

Abstract: The problem with the Xaverius 1 City High School website in Jambi is that there are several shortcomings, namely information that is not updated, the web often experiences bugs or errors when accessing available features, loading takes too long, the web appearance is too old-fashioned and does not have clear instructions. The purpose of this study was to analyze the service quality of the Xaverius 1 High School website in Jambi City by applying the webqual 4.0 method and to determine the effect of research variables on website user satisfaction. research that has been carried out using the webqual 4.0 method where there are 3 independent variables (quality of use, quality of information, quality of interaction) and one dependent variable (user satisfaction) and in this study adopted one additional variable from the EUCS method, namely content. This study is a multiple linear regression which produces two hypotheses accepted while 3 hypotheses are rejected. The rejected hypotheses include H1 (quality of use), H2 (quality of information) and H3 (quality of interaction) on user satisfaction, while the accepted hypothesis is H4 (content) there is a relationship with user satisfaction, and H5 regarding all variables influencing user satisfaction. In this study, the researchers suggested to SMA Xaverius 1 Jambi City to improve the website in terms of usability quality, information quality and interaction quality on the website. Which can be seen in the research results of the three qualities have no effect on user satisfaction.


MEDIATION AS A CONFLICT RESOLUTION TECHNOLOGY

Abstract: Abstract. Todayʼs society is characterized by conflict as a permanent feature of a person, determined by both natural inclinations and social experience, and represents a threat to society. The article analyzes the conflict as a result of the struggle for status, attention, leadership, when the causes are intolerance, enmity, mistrust, inadequate expression of emotions, etc. The article also highlightssubstantive foundations of mediation technologies in conflict resolution, clarifiesthe role and functions of the mediator for the formation of skills in conflict resolution, and outlinesthe structure of the mediation procedure to ensure the partiesʼ communicative readiness for objective reasoning and making the right decisions for conflict resolution.


The Study of Cardiology Through an Augmented Reality-Based System

Abstract: This chapter presents an augmented reality project applied to the study of cardiology, which is crystallized through a system known as service-oriented architecture (SOA). This system serves as an innovative and efficient learning platform for students interested in cardiology because it helps them to understand abstract concepts in cardiology, which require visual and manipulable objects that are difficult to obtain, due to the large space they occupy in magnetic media or because of the difficulty of obtaining their models in physical form. This system strengthened the process of anatomical identification of the human heart and allowed a better interaction with the student, i.e., the system enhanced the use of sight, hearing, and kinesthetic, which, together, allowed a better assimilation of knowledge. The effectiveness of this system was validated using a survey of 389 students from four public universities where the following aspects were verified: 1) Significant learning; 2) Motivation; 3) Ease of use, and; 4) Performance.


Integration of Biomedical Engineering in Augmented Reality and Virtual Reality Applications

Abstract: This book chapter delves into the profound synergy between Biomedical Engineering and the burgeoning realm of augmented reality (AR) and virtual reality (VR) applications. Through a meticulous examination of state-of-the-art advancements and a forward-looking analysis of future prospects, this chapter endeavors to offer a comprehensive resource for a diverse audience, including dedicated researchers, seasoned healthcare practitioners, discerning educators, and aspiring students. The integration of these technologies has the potential to revolutionize medical training, patient education, surgical planning, and therapy delivery, ushering in an era of more personalized and effective healthcare solutions. This chapter serves as a guiding beacon in navigating this transformative landscape, ensuring that the adoption of AR/VR technologies is not only innovative but also ethically grounded and attuned to the highest standards of patient care.