Bluetooth Device Identification Using RF Fingerprinting and Jensen-Shannon Divergence

Abstract: The proliferation of radio frequency (RF) devices in contemporary society, especially in the fields of smart homes, Internet of Things (IoT) gadgets, and smartphones, underscores the urgent need for robust identification methods to strengthen cybersecurity. This paper delves into the realms of RF fingerprint (RFF) based on applying the Jensen-Shannon divergence (JSD) to the statistical distribution of noise in RF signals to identify Bluetooth devices. Thus, through a detailed case study, Bluetooth RF noise taken at 5 Gsps from different devices is explored. A noise model is considered to extract a unique, universal, permanent, permanent, collectable, and robust statistical RFF that identifies each Bluetooth device. Then, the different JSD noise signals provided by Bluetooth devices are contrasted with the statistical RFF of all devices and a membership resolution is declared. The study shows that this way of identifying Bluetooth devices based on RFF allows one to discern between devices of the same make and model, achieving 99.5% identification effectiveness. By leveraging statistical RFFs extracted from noise in RF signals emitted by devices, this research not only contributes to the advancement of the field of implicit device authentication systems based on wireless communication but also provides valuable insights into the practical implementation of RF identification techniques, which could be useful in forensic processes.


Synthetic Corpus Generation for Deep Learning-Based Translation of Spanish Sign Language

Abstract: Sign language serves as the primary mode of communication for the deaf community. With technological advancements, it is crucial to develop systems capable of enhancing communication between deaf and hearing individuals. This paper reviews recent state-of-the-art methods in sign language recognition, translation, and production. Additionally, we introduce a rule-based system, called ruLSE, for generating synthetic datasets in Spanish Sign Language. To check the usefulness of these datasets, we conduct experiments with two state-of-the-art models based on Transformers, MarianMT and Transformer-STMC. In general, we observe that the former achieves better results (+3.7 points in the BLEU-4 metric) although the latter is up to four times faster. Furthermore, the use of pre-trained word embeddings in Spanish enhances results. The rule-based system demonstrates superior performance and efficiency compared to Transformer models in Sign Language Production tasks. Lastly, we contribute to the state of the art by releasing the generated synthetic dataset in Spanish named synLSE.


Beyond hard workout: A multimodal framework for personalised running training with immersive technologies

Abstract: Abstract Training to run is not straightforward since without proper personalised supervision and planning, people will not improve their performance and will increase the risk of injuries. This study aims to identify the different factors that influence running training programmes, examining the benefits, challenges or limitations of personalised plans. Moreover, this study explores how multimodal, immersive and artificial intelligence technologies can support personalised training. We conducted an exploratory sequential mixed research consisting of interviews with 11 running coaches from different countries and a survey of 12 running coaches. Based on the interviews and survey analysis, we identified and extracted relevant factors of the training process. We identified four relevant aspects for running training: physical, technical, mental and body awareness. Using these aspects as a reference, we derived a framework using a bottom‐up approach. This framework proposes multimodal, immersive and artificial intelligence technologies to facilitate personalised running training. It also allows coaches to personally guide their athletes on each aspect. Practitioner notes What is already known about this topic Running is a popular sport that provides health benefits and is practised by many people around the world. Training is a process that enables athletes to improve their development in various aspects of their sport; in the case of running, it helps them to increase their speed and endurance. Personalised training supports the needs and abilities of athletes, by helping them to achieve their potential through individualised activities or programmes. Sports science research indicates that personalised training can be improved by applying technology to tackle its challenges and limitations. What this paper adds We show that personalising the training requires not only focusing on the runners' physical condition but also on their mental, technical and body awareness aspects, where each of them has a different adaptation to training. We show that multimodal and immersive technologies offer suitable and portable ways to measure and target the mental and body awareness aspects during running training. Implications for practice and/or policy This paper presents a list of factors, measures and devices that coaches can use to plan and design their training sessions in a more personalised manner. This study can serve as a foundation for future research that aims to identify and target the various factors that influence the learning and training of sports.


Concept of «Mystery» and its Realization in the Works of J. Lippard and N. Buntline

Abstract: Introduction. The article is devoted to the analysis of the linguistic means expressing the concept of «mystery» in the fictional works of J. Lippard and N. Buntline, the allocation of its socio-cultural component and the field model construction of lexical units representing this concept. The relevance of the research is due to the interest of modern linguistics in the study of the conceptual sphere of artistic works of the «urban mysteries» genre by American authors. Methodology and sources. The study was conducted within the framework of a linguistic and cultural approach using methods of description, comparison, interpretative analysis, conceptual analysis, the method of dictionary definitions analyzing, structural and semantic analysis, and the method of quantitative calculations. The illustrative material of the study consisted of more than 74 lexical units representing the «mystery» concept in the language, 45 of which were found in the analyzed works. The material was obtained by the method of directed sampling from the fictional works of American authors of the 19th century. Results and discussion. This article discusses and analyzes various interpretations of the concept of «mystery» by domestic and foreign scientists; defines the role of the concept in the work of fiction as a whole and in the analyzed novels of two authors J. lippard and N. Buntline; identifies the features of the linguistic implementation of the «mystery» concept; classifies the linguistic units of the concept according to their parts of speech, semantics and stylistic coloring; analyzes the writers’ linguistic pictures of the world and the conceptual sphere of their works, the illustrative zone of which reveals the reasons for the similarity not only of the storylines of the works, but also of their lexical content. Conclusion. The study made it possible to describe and illustrate the individual style of American authors of the 19th century through a system of expressive means that correlates the inner world of the writer with the artistic reality, the artistic world of the text created by the «poetic» language. The mental world can be interpreted in cognitive terminology as an individual poetic conceptual sphere, or the conceptual system of the author, consisting of a conceptual, objective, associative, figurative and symbolic layer of the concept. The study showed that the learning of the writer's linguistic picture of the world and the conceptual sphere of the work can be carried out using a field model, analyzing the LSF forming the «mystery» concept. The analyzed LSF is distinguished by a more specific vocabulary composition, the complication of the seme structure of words, the seme expansion and the restructuring of their hierarchy, which together form the artistic worldview of the authors.


Dehazing, enhancing the boundaries and corners in hazed images using optimal adaptive technique

Abstract: Objects or images captured from the remote area will not have good quality and will not be visible because of mist, smoke, moisture and fog. Here, we rub/remove the unwanted hazes from the image using the optimal adaptive technique. An examination of the built-in boundary constraint of the transmission function is extremely valuable to our method. This constraint is handled as an optimisation problem to predict the unknown scene transmission. In addition, transmission refining techniques are used to remedy the issue based on variable splitting methodology. With only a few broad assumptions, the proposed method may recover haze-free, high-quality images having original colours and better image information. Experiment findings are done with plenty of foggy images indicating the efficiency and effectiveness of the proposed technique by using image quality parameters.


Finite impulse response design based on two‐level transpose Vedic multiplier for medical image noise reduction

Abstract: Abstract Medical signal processing requires noise and interference‐free inputs for precise segregation and classification operations. However, sensing and transmitting wireless media/devices generate noise that results in signal tampering in feature extractions. To address these issues, this article introduces a finite impulse response design based on a two‐level transpose Vedic multiplier. The proposed architecture identifies the zero‐noise impulse across the varying sensing intervals. In this process, the first level is the process of transpose array operations with equalization implemented to achieve zero noise at any sensed interval. This transpose occurs between successive array representations of the input with continuity. If the continuity is unavailable, then the noise interruption is considerable and results in signal tampering. The second level of the Vedic multiplier is to optimize the transpose speed for zero‐noise segregation. This is performed independently for the zero‐ and nonzero‐noise intervals. Finally, the finite impulse response is estimated as the sum of zero‐ and nonzero‐noise inputs at any finite classification.


Optimized systems of multi-layer perceptron predictive model for estimating pile-bearing capacity

Abstract: Abstract The primary goal of this research is to leverage the advancements in machine learning techniques to forecast the bearing capacity of piles effectively. Accurately predicting load-bearing capability is an indispensable aspect in the field of substructure engineering. It is worth noting that determining load-bearing capability via in-place burden tests is a resource-intensive and labor-intensive process. This study presents a pragmatic soft computing methodology to tackle the aforementioned challenge, employing a multi-layer perceptron (MLP) for the estimation of load-bearing capacity. The dataset employed in this research encompasses a multitude of field-based pile load tests, with a meticulous selection of the most impactful factors influencing pile-bearing capacity as input variables. For a comprehensive comparative analysis, genetic algorithm-based optimizers (Crystal Structure Algorithm (CSA) and Fox Optimization (FOX)) were incorporated with MLP, leading to the development of hybrid models referred to as MLFO and MLSC, both structured with three layers. The performance of these models was rigorously evaluated using five key performance indices. The findings indicated a consistent superiority of MLFO over MLSC across all three layers. Remarkably, MLFO exhibited exceptional performance in the second layer (MLFO (2)), boasting an impressive R 2 value of 0.992, an RMSE of 33.470, and a minimal SI value of 0.031. On the other hand, MLCS (1) registered the lowest accuracy in predicting the process with the least R 2 value related to the validation phase of the model with 0.953. Taken together, these results affirm that the optimized MLP model stands as a valuable and practical tool for accurately estimating pile-bearing capacity in civil engineering applications.


Prediction and explainability in AI: Striking a new balance?

Abstract: The debate regarding prediction and explainability in artificial intelligence (AI) centers around the trade-off between achieving high-performance accurate models and the ability to understand and interpret the decisionmaking process of those models. In recent years, this debate has gained significant attention due to the increasing adoption of AI systems in various domains, including healthcare, finance, and criminal justice. While prediction and explainability are desirable goals in principle, the recent spread of high accuracy yet opaque machine learning (ML) algorithms has highlighted the trade-off between the two, marking this debate as an inter-disciplinary, inter-professional arena for negotiating expertise. There is no longer an agreement about what should be the “default” balance of prediction and explainability, with various positions reflecting claims for professional jurisdiction. Overall, there appears to be a growing schism between the regulatory and ethics-based call for explainability as a condition for trustworthy AI, and how it is being designed, assimilated, and negotiated. The impetus for writing this commentary comes from recent suggestions that explainability is overrated, including the argument that explainability is not guaranteed in human healthcare experts either. To shed light on this debate, its premises, and its recent twists, we provide an overview of key arguments representing different frames, focusing on AI in healthcare.


Knowledge-based recommender systems: overview and research directions

Abstract: Recommender systems are decision support systems that help users to identify items of relevance from a potentially large set of alternatives. In contrast to the mainstream recommendation approaches of collaborative filtering and content-based filtering, knowledge-based recommenders exploit semantic user preference knowledge, item knowledge, and recommendation knowledge, to identify user-relevant items which is of specific relevance when dealing with complex and high-involvement items. Such recommenders are primarily applied in scenarios where users specify (and revise) their preferences, and related recommendations are determined on the basis of constraints or attribute-level similarity metrics. In this article, we provide an overview of the existing state-of-the-art in knowledge-based recommender systems. Different related recommendation techniques are explained on the basis of a working example from the domain of survey software services. On the basis of our analysis, we outline different directions for future research.


Time-dependant Bayesian knowledge tracing—Robots that model user skills over time

Abstract: Creating an accurate model of a user's skills is an essential task for Intelligent Tutoring Systems (ITS) and robotic tutoring systems. This allows the system to provide personalized help based on the user's knowledge state. Most user skill modeling systems have focused on simpler tasks such as arithmetic or multiple-choice questions, where the user's model is only updated upon task completion. These tasks have a single correct answer and they generate an unambiguous observation of the user's answer. This is not the case for more complex tasks such as programming or engineering tasks, where the user completing the task creates a succession of noisy user observations as they work on different parts of the task. We create an algorithm called Time-Dependant Bayesian Knowledge Tracing (TD-BKT) that tracks users' skills throughout these more complex tasks. We show in simulation that it has a more accurate model of the user's skills and, therefore, can select better teaching actions than previous algorithms. Lastly, we show that a robot can use TD-BKT to model a user and teach electronic circuit tasks to participants during a user study. Our results show that participants significantly improved their skills when modeled using TD-BKT.


LiDAR point clouds analysis computer tools for teaching autonomous vehicles perception algorithms

Abstract: Abstract The technological developments behind autonomous vehicles cover several areas and engineers training in this field represents a challenge. The main layers include perception, decision making, and acting. In the first one, different technologies can be used. The processing of the information provided by the sensors must allow successive modules to understand the environment and Laser imaging Detection and Ranging (LiDAR) technology is one of the most promising ones nowadays for this task. It offers great robustness in detection, but the extraction of information from the point cloud involves the development of complex algorithms that could be very time‐consuming if an experimental teaching is intended. This article presents two educational solutions for deepening in perception algorithms using LiDAR for autonomous driving: a closed ad‐hoc computer application for two‐dimensional (2D) LiDAR point cloud processing and an oriented set of commands for three‐dimensional (3D) LiDARs in Matlab. Their use allows main concept exploration in practical sessions with little time consumption and provides students a general overview of the tasks that must be performed by the perception layer in the autonomous vehicles. Furthermore, these tools provide the possibility of organizing different activities in the classroom related to theoretical and experimental issues, and understanding of results because the most tedious tasks are eased.


Benchmarking the influence of pre-training on explanation performance in MR image classification

Abstract: Convolutional Neural Networks (CNNs) are frequently and successfully used in medical prediction tasks. They are often used in combination with transfer learning, leading to improved performance when training data for the task are scarce. The resulting models are highly complex and typically do not provide any insight into their predictive mechanisms, motivating the field of "explainable" artificial intelligence (XAI). However, previous studies have rarely quantitatively evaluated the "explanation performance" of XAI methods against ground-truth data, and transfer learning and its influence on objective measures of explanation performance has not been investigated. Here, we propose a benchmark dataset that allows for quantifying explanation performance in a realistic magnetic resonance imaging (MRI) classification task. We employ this benchmark to understand the influence of transfer learning on the quality of explanations. Experimental results show that popular XAI methods applied to the same underlying model differ vastly in performance, even when considering only correctly classified examples. We further observe that explanation performance strongly depends on the task used for pre-training and the number of CNN layers pre-trained. These results hold after correcting for a substantial correlation between explanation and classification performance.


Interpreting, translating or investigating? Framing the competences of a hybrid translational field: intercept interpreting

Abstract: When monitoring suspected criminals’ communications by bugging telephones, apartments or cars, the police collect information for ongoing investigations and the later proceedings in court. If foreign-language-speaking suspects are involved, intercept interpreters are indispensable for police work. Intercept interpreting is a hybrid translational activity that encompasses elements of interpreting and translation. This article explores the competences and skills required for this activity. It is based on 24 semi-structured interviews with intercept interpreters and police officers and on an observational study conducted in a Swiss police department. Core elements of translation and interpreting competence models are combined to reflect the hybrid nature of the activity. In a qualitative data analysis, we deductively identified typical translation and interpreting sub-competences, while a concurrent inductive analysis allowed us to extend the competence framework by new, specific elements of intercept interpreting. The results show that intercept interpreters not only exhibit special investigative and forensic skills, but that they also have many mental and emotional skills that enable them to carry out their demanding activity.


Efficient Information Retrieval and Response Generation with Retrieval-Augmented Generation (RAG)

Abstract: <strong> How to efficiently retrieve information for different applications </strong> <strong> Author: Wenyi Pi </strong> <strong> https://orcid.org/0009-0002-2884-2771 </strong> This article aims to explore various ways in which Retrieval-Augmented Generation (RAG) can be utilised to retrieve information and generate responses effectively within the dialogue system.


Real-Time Pose Recognition for Billiard Players Using Deep Learning

Abstract: In this book chapter, the authors propose a method for player pose recognition in billiards matches by combining keypoint extraction and an optimized transformer. Given that those human pose analysis methods usually require high labour costs, the authors explore deep learning methods to achieve real-time, high-precision pose recognition. Firstly, they utilize human key point detection technology to extract the key points of players from real-time videos and generate key points. Then, the key point data is input into the transformer model for pose analysis and recognition. In addition, the authors design a human skeletal alignment method for comparison with standard poses. The experimental results show that the method performs well in recognizing players' poses in billiards matches and provides real-time and timely feedback on players' pose information. This research project provides a new and efficient tool for training billiard players and opens up new possibilities for applying deep learning in sports analytics. In addition, one of these contributions is the creation of a dataset for pose recognition.


Risky Dynamic Typing Related Practices in Python: An Empirical Study

Abstract: Python’s dynamic typing nature provides developers with powerful programming abstractions. However, many type related bugs are accumulated in code bases of Python due to the misuse of dynamic typing. The goal of this paper is to aid in the understanding of developers’ high-risk practices towards dynamic typing and the early detection of type related bugs. We first formulate the rules of six types of risky dynamic typing related practices (type smells for short) in Python. We then develop a rule-based tool named RUPOR which builds an accurate type base to detect type smells. Our evaluation shows that RUPOR outperforms the existing type smell detection techniques (including the LLM-based approaches, Mypy, and PYDYPE) on a benchmark of 900 Python methods. Based on RUPOR, we conduct an empirical study on 25 real-world projects. We find that type smells are significantly related to the occurrence of post-release faults. The fault-proneness prediction model built with type smell features slightly outperforms the model built without them. We also summarize the common patterns including inserting type check to fix type smell bugs. These findings provide valuable insights for preventing and fixing type related bugs in the programs written in dynamic-typed languages.


Smart Attendance System using Face Recognition

Abstract: Face recognition offers a wide range of valuable applications in social media, security, and surveillance contexts. The software used for building facial recognition algorithms is Python and OpenCV. "Attendance using Face Recognition" is a method for tracking and managing attendance that makes use of facial recognition technology. By seamlessly integrating the 'Face Recognition' module, a native Python feature, and the OpenCV library, our system excels in accuracy and dependability. The system then stores attendance records in a database and provides real-time reports. In this article, we demonstrate how to create a face recognition system in Python utilizing the built-in "Face Recognition" module and the OpenCV library. Our results show that our system achieves high accuracy and robustness while being efficient and scalable, catering to a wide spectrum of educational institutions, organizations, and enterprises.


Virtual reality and perceived learning effectiveness in accounting studies: the mediating role of task-technology fit

Abstract: The use of virtual reality (VR) for the improvement of learning outcomes for accounting studies is an area in which the research is still limited. Technology-based accounting learning is very important because, currently, the accounting profession is very much affected by rapid technological change which means that it must adapt to remain relevant in the business world. This study aims to examine the role of the mediating effect of task-technology fit (TTF) on the relationship between the use of VR and learning outcomes for accounting studies. This research model states that the use of VR will be able to increase TTF, technology quality and accessibility, and then increase reflective thinking and reduce cognitive overload thereby increasing perceived learning effectiveness (PLE). The hypotheses derived from this model were tested empirically using survey data from 199 users of VR engaged in accounting studies. The data analysis uses partial least squares-structural equation modeling (PLS-SEM), and the results support the hypothesis that TTF mediates the relationship between VR use, learning behavior, and PLE.


Improved Oversampling Algorithm for Imbalanced Data Based on K-Nearest Neighbor and Interpolation Process Optimization

Abstract: The problems of imbalanced datasets are generally considered asymmetric issues. In asymmetric problems, artificial intelligence models may exhibit different biases or preferences when dealing with different classes. In the process of addressing class imbalance learning problems, the classification model will pay too much attention to the majority class samples and cannot guarantee the classification performance of the minority class samples, which might be more valuable. By synthesizing the minority class samples and changing the data distribution, unbalanced datasets can be optimized. Traditional oversampling algorithms have problems of blindness and boundary ambiguity when synthesizing new samples. A modified reclassification algorithm based on Gaussian distribution is put forward. First, the minority class samples are reclassified by the KNN algorithm. Then, different synthesis strategies are selected according to the combination of the minority class samples, and the Gaussian distribution is used to replace the uniform random distribution for interpolation operation under certain classification conditions to reduce the possibility of generating noise samples. The experimental results indicate that the proposed oversampling algorithm can achieve a performance improvement of 2∼8% in evaluation metrics, including G-mean, F-measure, and AUC, compared to traditional oversampling algorithms.


Requirement Engineering Methods for Virtual Reality Software Product Development - A Mapping Study

Abstract: Software practitioners use various methods in Requirements Engineering (RE) to elicit, analyze and specify the requirements of a enterprise products. The methods impact the final product characteristics and influence product delivery. Ad-hoc usage of the methods by software practitioners can lead to inconsistency and ambiguity in the product. With the notable rise in enterprise products, games, etc. across various domains, Virtual Reality (VR) has become an essential technology for the future. The methods adopted for requirement engineering for developing VR products requires a detailed study. This paper presents a mapping study on requirement engineering methods prescribed and used for developing VR applications including requirements elicitation, requirements analysis, and requirements specification. Our study provides insights into the use of such methods in the VR community and suggests using specific requirement engineering methods in various fields of interest. We also discuss future directions in requirement engineering for VR products.


Minimal qubit representations of Hamiltonians via conserved charges

Abstract: In the last years, we have been witnessing a tremendous push to demonstrate that quantum computers can solve classically intractable problems. This effort, initially focused on the hardware, progressively included the simplification of the models to be simulated. We consider Hamiltonians written in terms of Pauli operators and systematically cut all qubits that are not essential to simulate the system. Our approach is universally applicable and lowers the complexity by first ensuring that the largest possible portion of the Hilbert space becomes irrelevant, and then by finding and exploiting all conserved charges of the system, i.e., symmetries that can be expressed as Pauli operators. Remarkably, both processes are classically efficient and optimal. To showcase our algorithm, we simplify chemical molecules, lattice gauge theories, and the Hubbard and Kitaev models.


A novel framework for multiple disease prediction in telemedicine systems using deep learning

Abstract: Telemedicine systems are gaining popularity due to their ability to provide remote medical services. These systems produce a lot of data, which may be used for a variety of purposes, including quality improvement, decision-making, and predictive analytics. Deep learning is an effective data mining method that may be applied to this data to bring out significant findings. Telemedicine systems, which allow patients to receive medical consultation and treatment remotely, generate vast amounts of data. Analyzing this data can provide valuable insights for improving patient care and optimizing the telemedicine system. Data mining techniques can be incredibly valuable for telemedicine systems, as they can help to identify patterns and insights in large amounts of patient data. Data mining techniques can assist telemedicine systems in making better decisions and offer better care to patients. In this paper, a novel framework for multiple disease prediction in telemedicine system using an effective deep learning algorithm was developed. The proposed multiple disease prediction system is composed of Long Short Term Memory (LSTM) unit.The experimental results revealed that the suggested disease prediction model exceeded the present models with an accuracy of 98.51%.


Accelerated variational quantum eigensolver with joint Bell measurement

Abstract: The variational quantum eigensolver (VQE) stands as a prominent quantum-classical hybrid algorithm for near-term quantum computers to obtain the ground states of molecular Hamiltonians in quantum chemistry. However, due to the noncommutativity of the Pauli operators in the Hamiltonian, the number of measurements required on quantum computers increases significantly as the system size grows, which may hinder practical applications of VQE. In this work, we present a protocol termed joint Bell measurement VQE (JBM-VQE) to reduce the number of measurements and speed up the VQE algorithm. Our method employs joint Bell measurements, enabling the simultaneous measurement of the absolute values of all expectation values of Pauli operators present in the Hamiltonian. In the course of the optimization, JBM-VQE estimates the absolute values of the expectation values of the Pauli operators for each iteration by the joint Bell measurement, while the signs of them are measured less frequently by the conventional method to measure the expectation values. Our approach is based on the empirical observation that the signs do not often change during optimization. We illustrate the speed-up of JBM-VQE compared to conventional VQE by numerical simulations for finding the ground states of molecular Hamiltonians of small molecules, and the speed-up of JBM-VQE at the early stage of the optimization becomes increasingly pronounced in larger systems. Our approach based on the joint Bell measurement is not limited to VQE and can be utilized in various quantum algorithms whose cost functions are expectation values of many Pauli operators.


Attention-based speech feature transfer between speakers

Abstract: In this study, we propose a simple yet effective method for incorporating the source speaker's characteristics in the target speaker's speech. This allows our model to generate the speech of the target speaker with the style of the source speaker. To achieve this, we focus on the attention model within the speech synthesis model, which learns various speaker features such as spectrogram, pitch, intensity, formant, pulse, and voice breaks. The model is trained separately using datasets specific to the source and target speakers. Subsequently, we replace the attention weights learned from the source speaker's dataset with the attention weights from the target speaker's model. Finally, by providing new input texts to the target model, we generate the speech of the target speaker with the styles of the source speaker. We validate the effectiveness of our model through similarity analysis utilizing five evaluation metrics and showcase real-world examples.


Cyber Threat Intelligence on Blockchain: A Systematic Literature Review

Abstract: Cyber Threat Intelligence (CTI) has become increasingly important in safeguarding organizations against cyber threats. However, managing, storing, analyzing, and sharing vast and sensitive threat intelligence data is a challenge. Blockchain technology, with its robust and tamper-resistant properties, offers a promising solution to address these challenges. This systematic literature review explores the recent advancements and emerging trends at the intersection of CTI and blockchain technology. We reviewed research papers published during the last 5 years to investigate the various proposals, methodologies, models, and implementations related to the distributed ledger technology and how this technology can be used to collect, store, analyze, and share CTI in a secured and controlled manner, as well as how this combination can further support additional dimensions such as quality assurance, reputation, and trust. Our findings highlight the focus of the CTI and blockchain convergence on the dissemination phase in the CTI lifecycle, reflecting a substantial emphasis on optimizing the efficacy of communication and sharing mechanisms, based on an equitable emphasis on both permissioned, private blockchains and permissionless, public blockchains, addressing the diverse requirements and preferences within the CTI community. The analysis reveals a focus towards the tactical and technical dimensions of CTI, compared to the operational and strategic CTI levels, indicating an emphasis on more technical-oriented utilization within the domain of blockchain technology. The technological landscape supporting CTI and blockchain integration emerges as multifaceted, featuring pivotal roles played by smart contracts, machine learning, federated learning, consensus algorithms, IPFS, deep learning, and encryption. This integration of diverse technologies contributes to the robustness and adaptability of the proposed frameworks. Moreover, our exploration unveils the overarching significance of trust and privacy as predominant themes, underscoring their pivotal roles in shaping the landscape within our research realm. Additionally, our study addresses the maturity assessment of these integrated systems. The approach taken in evaluating maturity levels, distributed across the Technology Readiness Level (TRL) scale, reveals an average balance, indicating that research efforts span from early to mid-stages of maturity in implementation. This study signifies the ongoing evolution and maturation of research endeavors within the dynamic intersection of CTI and blockchain technology, identifies trends, and also highlights research gaps that can potentially be addressed by future research on the field.


COPE Discussion Document: Artificial intelligence (AI) in decision making


<scp>Tech‐infused</scp> classrooms: A comprehensive study on the interplay of mobile learning, <scp>ChatGPT</scp> and social media in academic attainment

Abstract: Abstract Mobile learning (M‐learning), ChatGPT and social media are integral to university education, improving accessibility, personalization and interactive engagement in the learning process. This paper aimed to investigate the role of M‐learning, ChatGPT and social media in university students' academic performance. It was a cross‐sectional quantitative investigation, and data were collected from 600 university students through a self‐developed questionnaire having four subscales (M‐learning, ChatGPT, social media and academic performance). Simple linear regression and process macro were deployed to test study hypotheses. The analysis revealed that M‐learning is a significant contributor to students' academic performance at the university level ( β = .460, t (598) = 12.663, p < .001). Social media were found to be significantly mediating the relationship between M‐learning and academic performance with coefficient B = .267, SE = .038, t (597) = 7.08, p < .001, 95% CI [0.193, 0.341]. ChatGPT was significantly contributing to academic performance ( B = .288, t (596) = 2.50, p = .013), but the interaction term had an insignificant effect on the relationship between M‐learning and academic performance ( β = .292, t (596) = −0.43, p = .668). The study suggests that M‐learning can improve students' performance. The influence of social media and the non‐moderating role of ChatGPT indicate the need for strategic integration of these tools to enhance university students' outcomes. It is recommended that university students should judiciously use social media to enhance M‐learning. In contrast, universities should foster this integration to achieve educational aims and explore how AI tools like ChatGPT can be optimally deployed to improve learning.


A Time-Series-Based Sample Amplification Model for Data Stream with Sparse Samples

Abstract: Abstract The data stream is a dynamic collection of data that changes over time, and predicting the data class can be challenging due to sparse samples, complex interdependent characteristics between data, and random fluctuations. Accurately predicting the data stream in sparse data can create complex challenges. Due to its incremental learning nature, the neural networks suitable approach for streaming visualization. However, the high computational cost limits their applicability to high-speed streams, which has not yet been fully explored in the existing approaches. To solve these problems, this paper proposes an end-to-end dynamic separation neural network (DSN) approach based on the characteristics of data stream fluctuations, which expands the static sample at a given moment into a sequence of sample streams in the time dimension, thereby increasing the sparse samples. The Temporal Augmentation Module (TAM) can overcome these challenges by modifying the sparse data stream and reducing time complexity. Moreover, a neural network that uses a Variance Detection Module (VDM) can effectively detect the variance of the input data stream through the network and dynamically adjust the degree of differentiation between samples to enhance the accuracy of forecasts. The proposed method adds significant information regarding the data sparse samples and enhances low dimensional samples to high data samples to overcome the sparse data stream problem. In VDM the preprocessed data achieve data augmentation and the samples are transmitted to VDM. The proposed method is evaluated using different types of data streaming datasets to predict the sparse data stream. Experimental results demonstrate that the proposed method achieves a high prediction accuracy and that the data stream has significant effects and strong robustness compared to other existing approaches.


Unsupervised Conditional Diffusion Models in Video Anomaly Detection for Monitoring Dust Pollution

Abstract: Video surveillance is widely used in monitoring environmental pollution, particularly harmful dust. Currently, manual video monitoring remains the predominant method for analyzing potential pollution, which is inefficient and prone to errors. In this paper, we introduce a new unsupervised method based on latent diffusion models. Specifically, we propose a spatio-temporal network structure, which better integrates the spatial and temporal features of videos. Our conditional guidance mechanism samples frames of input videos to guide high-quality generation and obtains frame-level anomaly scores, comparing generated videos with original ones. We also propose an efficient compression strategy to reduce computational costs, allowing the model to perform in a latent space. The superiority of our method was demonstrated by numerical experiments in three public benchmarks and practical application analysis in coal mining over previous SOTA methods with better AUC, of at most over 3%. Our method accurately detects abnormal patterns in multiple challenging environmental monitoring scenarios, illustrating the potential application possibilities in the environmental protection domain and beyond.


PLATINUM GROUP MINERALS OF THE BAIMKA GOLD PLACER CLUSTER, WESTERN CHUKOTKA: THE NEW DATA

Abstract: The results of the study of the platinum group minerals of the Baimka gold placer cluster, Western Chukotka, Russia, are presented. Platinum group minerals belong to the iridium-platinum and platinum miner- alogical-geochemical types with the Late Jurassic cumulative pyroxenite-gabbro complexes as a probable source. Platinum group minerals came to alluvial gold placers primarily from intermediate reservoirs, which is the Volgian volcanic-sedimentary sequence. Rounded silicate glass inclusions are a specific feature of platinum minerals from the Baimka placer cluster.


Integrative Approach for High-Speed Road Surface Monitoring: A Convergence of Robotics, Edge Computing, and Advanced Object Detection

Abstract: To ensure precise and real-time perception of high-speed roadway conditions and minimize the potential threats to traffic safety posed by road debris and defects, this study designed a real-time monitoring and early warning system for high-speed road surface anomalies. Initially, an autonomous mobile intelligent road inspection robot, mountable on highway guardrails, along with a corresponding cloud-based warning platform, was developed. Subsequently, an enhanced target detection algorithm, YOLOv5s-L-OTA, was proposed. Incorporating GSConv for lightweight improvements to standard convolutions and employing the optimal transport assignment for object detection (OTA) strategy, the algorithm’s robustness in multi-object label assignment was enhanced, significantly improving both model accuracy and processing speed. Ultimately, this refined algorithm was deployed on the intelligent inspection robot and validated in real-road environments. The experimental results demonstrated the algorithm’s effectiveness, significantly boosting the capability for real-time, precise detection of high-speed road surface anomalies, thereby ensuring highway safety and substantially reducing the risk of liability disputes and personal injuries.


The Optimization of a Natural Language Processing Approach for the Automatic Detection of Alzheimer’s Disease Using GPT Embeddings

Abstract: The development of noninvasive and cost-effective methods of detecting Alzheimer's disease (AD) is essential for its early prevention and mitigation. We optimize the detection of AD using natural language processing (NLP) of spontaneous speech through the use of audio enhancement techniques and novel transcription methodologies. Specifically, we utilized Boll Spectral Subtraction to improve audio fidelity and created transcriptions using state-of-the-art AI services-locally-based Wav2Vec and Whisper, alongside cloud-based IBM Cloud and Rev AI-evaluating their performance against traditional manual transcription methods. Support Vector Machine (SVM) classifiers were then trained and tested using GPT-based embeddings of transcriptions. Our findings revealed that AI-based transcriptions largely outperformed traditional manual ones, with Wav2Vec (enhanced audio) achieving the best accuracy and F-1 score (0.99 for both metrics) for locally-based systems and Rev AI (standard audio) performing the best for cloud-based systems (0.96 for both metrics). Furthermore, this study revealed the detrimental effects of interviewer speech on model performance in addition to the minimal effect of audio enhancement. Based on our findings, current AI transcription and NLP technologies are highly effective at accurately detecting AD with available data but struggle to classify probable AD and mild cognitive impairment (MCI), a prodromal stage of AD, due to a lack of training data, laying the groundwork for the future implementation of an automatic AD detection system.


Privacy-Preserving Classification on Deep Learning with Exponential Mechanism

Abstract: Abstract How to protect the privacy of training data in deep learning has been the subject of increasing amounts of related research in recent years. Private Aggregation of Teacher Ensembles (PATE) uses transfer learning and differential privacy methods to provide a broadly applicable data privacy framework in deep learning. PATE combines the Laplacian mechanism and the voting method to achieve deep learning privacy classification. However, the Laplacian mechanism may greatly distort the histogram vote counts of each class. This paper proposes a novel exponential mechanism with PATE to ensure the privacy protection. This proposed method improves the protection effect and accuracy through the screening algorithm and uses the differential privacy combination theorems to reduce the total privacy budget. The data-dependent analysis demonstrates that the exponential mechanism outperforms the original Laplace mechanism. Experimental results show that the proposed method can train models with improved accuracy while requiring a smaller privacy budget when compared to the original Pate framework.


Real-Time Billiard Shot Stability Detection Based on YOLOv8

Abstract: This book chapter presents a real-time system for detecting the stability of a player's billiard shot, based on the YOLOv8 neural network. The system comprises a real-time object detection model and a real-time slope monitoring system. The model focuses on detecting four classes: The cue ball, hand, cue stick tip, and the bridge hand (hand support point). The project involved iterative model training on a custom dataset, eventually achieving a YOLOv8 model with 95% accuracy. The stability of a player's shot is detected by simulating slope change of cue stick during aiming, using the cue stick tip and bridge hand. Overall, the project highlights the immense potential of YOLOv8 in sports applications.


Robust Adversarial Deep Reinforcement Learning

Abstract: Deep reinforcement learning has shown remarkable results across various tasks. However, recent studies highlight the susceptibility of DRL to targeted adversarial disruptions. Furthermore, discrepancies between simulated settings and real-world applications often make it challenging to transfer these DRL policies, particularly in situations where safety is essential. Several solutions have been proposed to address these issues to enhance DRL's robustness. This chapter delves into the significance of adversarial attack and defense strategies in machine learning, emphasizing the unique challenges in adversarial DRL settings. It also presents an overview of recent advancements, DRL foundations, adversarial Markov decision process models, and comparisons among different attacks and defenses. The chapter further evaluates the effectiveness of various attacks and the efficacy of multiple defense mechanisms using simulation data, specifically focusing on policy success rates and average rewards. Potential limitations and prospects for future research are also explored.


Inclusion of blockchain in university accounting curricula: an overview of practices and strategies

Abstract: Blockchain is characterized as a disruptive technology in many sectors, including accounting and auditing. Despite blockchain’s great potential, several studies point out that tangible applications in accounting and auditing are slow to develop and to become widespread. This article looks at the practices and strategies of blockchain education in university accounting courses. We study the curricula of the world’s top 50 universities (Shanghai Ranking) and identify five types of course approaches: 1) The professional practice; 2) The dual or triple competency; 3) The entrepreneurship and business; 4) The sector-specific; 5) The critical thinking and holistic. We discuss these results in terms of isomorphism and distinctions between universities and propose an agenda for research on blockchain education in accounting.


Systematic Review of Web-Based Decision Support Systems for Clinical Applications: Enhancing Ontology with Unified Modeling Language and Ontology Web Language

Abstract: This article demonstrates an ontology-based technique that aids in envisioning the problem domain before software development, presenting an approach to assist decision-making for reliable and optimal healthcare administration. The proposed approach is designed for utilization by a Web-Based Decision Support System (WebDSS). The purpose of the study is to aid readers in comprehending how ontologies in healthcare management platforms can be modeled using UML and OWL. For the conceptualization and the design, service modelling and the semantics of the web language are crucial. We demonstrate the use of the Unified Modeling Language (UML) and the Ontology Web Language (OWL) in the administration of healthcare operations and key elements of the model-base of healthcare administration systems. Highly educated systems with automated assistance will allow decision-makers to more effectively incorporate aspects of sustainability into their decisions. In order to generate semantic ontologies for medical management, the present study proposes an ontology creation technique and a Semantic Web framework called Protégé. Web-based technologies, decision trees, data mining, and computational algorithms are implemented by WebDSS to assess medical resources administration information in real-time. These methods are employed to develop a decision making framework for this study. Time series graphs, email reports, and webpages are used to display the test findings. When healthcare thresholds are surpassed, the system uses text messages as email reports to automatically deliver warnings. Our study's primary contribution is the creation of a useful tool that makes it easier for academics studying medical concerns to locate online sites with information.


Core network traffic prediction based on vertical federated learning and split learning

Abstract: Wireless traffic prediction is vital for intelligent cellular network operations, such as load-aware resource management and predictive control. Traditional centralized training addresses this but poses issues like excessive data transmission, disregarding delays, and user privacy. Traditional federated learning methods can meet the requirement of jointly training models while protecting the privacy of all parties' data. However, challenges arise when the local data features among participating parties exhibit inconsistency, making the training process difficult to sustain. Our study introduces an innovative framework for wireless traffic prediction based on split learning (SL) and vertical federated learning. Multiple edge clients collaboratively train high-quality prediction models by utilizing diverse traffic data while maintaining the confidentiality of raw data locally. Each participant individually trains dimension-specific prediction models with their respective data, and the outcomes are aggregated through collaboration. A partially global model is formed and shared among clients to address statistical heterogeneity in distributed machine learning. Extensive experiments on real-world datasets demonstrate our method's superiority over current approaches, showcasing its potential for network traffic prediction and accurate forecasting.


Automated visual quality assessment for virtual and augmented reality based digital twins

Abstract: Abstract Virtual and augmented reality digital twins are becoming increasingly prevalent in a number of industries, though the production of digital-twin systems applications is still prohibitively expensive for many smaller organisations. A key step towards reducing the cost of digital twins lies in automating the production of 3D assets, however efforts are complicated by the lack of suitable automated methods for determining the visual quality of these assets. While visual quality assessment has been an active area of research for a number of years, few publications consider this process in the context of asset creation in digital twins. In this work, we introduce an automated decimation procedure using machine learning to assess the visual impact of decimation, a process commonly used in the production of 3D assets which has thus far been underrepresented in the visual assessment literature. Our model combines 108 geometric and perceptual metrics to determine if a 3D object has been unacceptably distorted during decimation. Our model is trained on almost 4, 000 distorted meshes, giving a significantly wider range of applicability than many models in the literature. Our results show a precision of over 97% against a set of test models, and performance tests show our model is capable of performing assessments within 2 minutes on models of up to 25, 000 polygons. Based on these results we believe our model presents both a significant advance in the field of visual quality assessment and an important step towards reducing the cost of virtual and augmented reality-based digital-twins.


Embedding memory-efficient stochastic simulators as quantum trajectories

Abstract: By exploiting the complexity intrinsic to quantum dynamics, quantum technologies promise a host of computational advantages. One such advantage lies in the field of stochastic modeling, where it has been shown that quantum stochastic simulators can operate with a lower memory overhead than their best classical counterparts. This advantage is particularly pronounced for continuous-time stochastic processes; however, the corresponding quantum stochastic simulators heretofore prescribed operate only on a quasi-continuous-time basis and suffer an ever-increasing circuit complexity with increasing temporal resolution. Here, by establishing a correspondence with quantum trajectories (a method for modeling open quantum systems), we show how truly continuous-time quantum stochastic simulators can be embedded in such open quantum systems, bridging this gap and obviating previous constraints. We further show how such an embedding can be made for discrete-time stochastic processes, which manifest as jump-only trajectories, and discuss how viewing the correspondence in the reverse direction provides a means of studying structural complexity in quantum systems themselves.


Characterization and optimized engineering of bosonic quantum interfaces under single-mode operational constraints

Abstract: Controlling the quantum interface between two bosonic modes is essential in countless implementations of quantum information processing. However, full controllability is rarely achieved in most platforms due to specific physical limitations. In this work, we completely characterize the linear two-mode interfaces under the most pessimistic restriction that only single-mode operation is available. When arbitrary Gaussian single-mode operations can be applied to both modes, we find that every interface can be characterized by an invariant transmission strength. Moreover, in the practical situation that squeezing is restricted in one of the modes, we discover two additional quantities, irreducible squeezing and irreducible shearing, that are invariant under the allowable controls. By using this characterization, we develop systematic strategies to engineer an arbitrary linear interface through cascading multiple fixed component interfaces. Without squeezing restriction, our protocol is optimal and requires at most three component interfaces. Under the squeezing constraint, our protocol can be extended to engineer also the additional invariants by using no more than two more rounds of cascade. We also propose the remote squeezing scheme to tackle the squeezing restriction through interfacing with an active auxiliary mode.


AFRNet: Anchor-Free Object Detection Using Roadside LiDAR in Urban Scenes

Abstract: In urban settings, roadside infrastructure LiDAR is a ground-based remote sensing system that collects 3D sparse point clouds for the traffic object detection of vehicles, pedestrians, and cyclists. Current anchor-free algorithms for 3D point cloud object detection based on roadside infrastructure face challenges related to inadequate feature extraction, disregard for spatial information in large 3D scenes, and inaccurate object detection. In this study, we propose AFRNet, a two-stage anchor-free detection network, to address the aforementioned challenges. We propose a 3D feature extraction backbone based on the large sparse kernel convolution (LSKC) feature set abstraction module, and incorporate the CBAM attention mechanism to enhance the large scene feature extraction capability and the representation of the point cloud features, enabling the network to prioritize the object of interest. After completing the first stage of center-based prediction, we propose a refinement method based on attentional feature fusion, where fused features incorporating raw point cloud features, voxel features, BEV features, and key point features are used for the second stage of refinement to complete the detection of 3D objects. To evaluate the performance of our detection algorithms, we conducted experiments using roadside LiDAR data from the urban traffic dataset DAIR-V2X, based on the Beijing High-Level Automated Driving Demonstration Area. The experimental results show that AFRNet has an average of 5.27 percent higher detection accuracy than CenterPoint for traffic objects. Comparative tests further confirm that our method achieves high accuracy in roadside LiDAR object detection.


Transforming Nursing Education: Developing Augmented Reality Procedural Training

Abstract: The shortage of nursing faculty and the scarcity of clinical placements have compelled researchers to investigate innovative solutions for procedural development to bridge the gap between didactic teaching and clinical experiences. This feasibility study uses augmented reality (AR) with Microsoft HoloLens2 and Dynamics 365 Guides to train graduate nursing students on advanced nursing procedures, focusing on lumbar puncture. A convenience sample of 24 nurse practitioner students participated in the study. The System Usability Scale, Acceptability Scale, and Engagement Scale were used to assess participant's experiences and perceptions. The results are positive for the feasibility and acceptance of AR technology for procedural training. Participants found the HoloLens2 device easy to use and showed confidence in its functionality. The step-by-step instructions provided by Microsoft 365 Guides were understandable, useful, and satisfactory. The students reported high levels of engagement and found the AR experience to be helpful and motivating for learning. Faculty time was significantly reduced using the HoloLens2 for procedural training compared to traditional methods. This study demonstrates the potential for AR as an effective and efficient modality for nursing education. The findings support the integration of AR technology to enhance procedural development, address the challenges of limited clinical sites, and provide students with an immersive and self-paced learning experience. Additional studies will need to explore the impact of AR on clinical competency, patient outcomes, and cost-effectiveness. Overall, the use of AR technology may be useful and effective for nursing pedagogy.


An Approach Based on Intuitionistic Fuzzy Sets for Considering Stakeholders’ Satisfaction, Dissatisfaction, and Hesitation in Software Features Prioritization

Abstract: This paper introduces a semi-automated approach for the prioritization of software features in medium- to large-sized software projects, considering stakeholders’ satisfaction and dissatisfaction as key criteria for the incorporation of candidate features. Our research acknowledges an inherent asymmetry in stakeholders’ evaluations, between the satisfaction from offering certain features and the dissatisfaction from not offering the same features. Even with systematic, ordinal scale-based prioritization techniques, involved stakeholders may exhibit hesitation and uncertainty in their assessments. Our approach aims to address these challenges by employing the Binary Search Tree prioritization method and leveraging the mathematical framework of Intuitionistic Fuzzy Sets to quantify the uncertainty of stakeholders when expressing assessments on the value of software features. Stakeholders’ rankings, considering satisfaction and dissatisfaction as features prioritization criteria, are mapped into Intuitionistic Fuzzy Numbers, and objective weights are automatically computed. Rankings associated with less hesitation are considered more valuable to determine the final features’ priorities than those rankings with more hesitation, reflecting lower indeterminacy or lack of knowledge from stakeholders. We validate our proposed approach with a case study, illustrating its application, and conduct a comparative analysis with existing software requirements prioritization methods.


Domain Adaptive Channel Pruning

Abstract: Domain adaptation is an effective approach to improve the generalization ability of deep learning methods, which makes a deep model more stable and robust. However, these methods often suffer from a deployment problem when deep models are deployed on different types of edge devices. In this work, we propose a new channel pruning method called Domain Adaptive Channel Pruning (DACP), which is specifically designed for the unsupervised domain adaptation task, where there is considerable data distribution mismatch between the source and the target domains. We prune the channels and adjust the weights in a layer-by-layer fashion. In contrast to the existing layer-by-layer channel pruning approaches that only consider how to reconstruct the features from the next layer, our approach aims to minimize both classification error and domain distribution mismatch. Furthermore, we propose a simple but effective approach to utilize the unlabeled data in the target domain. Our comprehensive experiments on two benchmark datasets demonstrate that our newly proposed DACP method outperforms the existing channel pruning approaches under the unsupervised domain adaptation setting.


HBMD-Net: Feature Fusion Based Breast Cancer Classification with Class Imbalance Resolution

Abstract: Breast cancer, a widespread global disease, represents a significant threat to women's health and lives, ranking as one of the most vulnerable malignant tumors they face. Many researchers have proposed their computer-aided diagnosis systems for classifying breast cancer. The majority of these approaches primarily utilize deep learning (DL) methods, which are not entirely reliable. These approaches overlook the crucial necessity of incorporating both local and global information for precise tumor detection, despite the fact that the subtle nuances are crucial for precise breast cancer classification. In addition, there are a limited number of publicly available breast cancer datasets, and the ones that are available tend to be imbalanced in nature. Therefore, this paper presents the hybrid breast mass detection-network (HBMD-Net) to address two critical challenges: class imbalance and the need to recognize that relying solely on either global or local features falls short in achieving precise tumor classification. To overcome the problem of class imbalance, HBMD-Net incorporates the borderline synthetic minority over-sampling technique (BSMOTE). Simultaneously, it employs a feature fusion approach, combining features by utilizing ResNet50 to extract deep features that provide global information, while handcrafted features are derived using histogram orientation gradient (HOG), that provide local information. In addition, an ROI segmentation has been implemented to avoid misclassifications. This integrated strategy substantially enhances breast cancer classification performance. Moreover, the proposed method integrates the block matching and 3D (BM3D) denoising filter to effectively eliminate multiplicative noise that has enhanced the performance of the system. The evaluation of the proposed HBMD-Net encompasses two breast ultrasound (BUS) datasets, namely BUSI and UDIAT. The proposed model has demonstrated a satisfactory performance, achieving accuracies of 99.14% and 94.49% respectively.


Modified Archimedes optimization algorithm for global optimization problems: a comparative study

Abstract: Abstract Archimedes Optimization Algorithm (AOA) is a recent optimization algorithm inspired by Archimedes’ Principle. In this study, a Modified Archimedes Optimization Algorithm (MDAOA) is proposed. The goal of the modification is to avoid early convergence and improve balance between exploration and exploitation. Modification is implemented by a two phase mechanism: optimizing the candidate positions of objects using the dimension learning-based (DL) strategy and recalculating predetermined five parameters used in the original AOA. DL strategy along with problem specific parameters lead to improvements in the balance between exploration and exploitation. The performance of the proposed MDAOA algorithm is tested on 13 standard benchmark functions, 29 CEC 2017 benchmark functions, optimal placement of electric vehicle charging stations (EVCSs) on the IEEE-33 distribution system, and five real-life engineering problems. In addition, results of the proposed modified algorithm are compared with modern and competitive algorithms such as Honey Badger Algorithm, Sine Cosine Algorithm, Butterfly Optimization Algorithm, Particle Swarm Optimization Butterfly Optimization Algorithm, Golden Jackal Optimization, Whale Optimization Algorithm, Ant Lion Optimizer, Salp Swarm Algorithm, and Atomic Orbital Search. Experimental results suggest that MDAOA outperforms other algorithms in the majority of the cases with consistently low standard deviation values. MDAOA returned best results in all of 13 standard benchmarks, 26 of 29 CEC 2017 benchmarks (89.65%), optimal placement of EVCSs problem and all of five real-life engineering problems. Overall success rate is 45 out of 48 problems (93.75%). Results are statistically analyzed by Friedman test with Wilcoxon rank-sum as post hoc test for pairwise comparisons.


Variation of chalcophile elements in base metal sulfide minerals from the Jinchuan magmatic Ni Cu sulfide deposit, NW China: Implications for mineral exploration

Abstract: Magmatic sulfide ores from the western and eastern intrusions of the world's third-largest Jinchuan NiCu deposit are separated into disseminated, net-textured, and massive sulfides, or Fe-rich, transitional, Cu-rich ores. However, the composition of base metal sulfide (BMS) minerals from each intrusion and ore type still lacks investigation. By comparing the variations of chalcophile element composition of BMS minerals from different intrusions, sulfide textures, and ore types, new discriminant diagrams are constructed for subsequent exploration. BMS minerals from the eastern intrusion host more slightly chalcophile elements (Zn As, Sn Cd) because of the higher degree of crustal contamination, and less strongly chalcophile elements (Co, Bi, Se, Re, Ag, Te) due to the lower amount of prior sulfide segregation. R-factor modeling indicates that disseminated sulfides are formed at the highest R-factors, while massive sulfides are formed at the lowest. Correspondingly, R-factor upgrading results in higher concentrations of Se and Ag in BMS minerals from disseminated sulfides compared to net-textured sulfides. Most chalcophile elements in BMS minerals show positive or negative correlations with whole-rock Cu/Nic ratios (Nic represents corrected whole-rock Ni content after deducting Ni in olivine), suggesting extensive chalcophile element fractionation occurred during progressive sulfide fractionation. Furthermore, the elements that are slightly to moderately incompatible with monosulfide solid solution (MSS) while predominately hosted by BMS minerals (e.g., Cd, Se, Ag, and Te in chalcopyrite) show better correlations, making them preferable for tracing the sulfide fractionation. Utilizing the partial least squares-discriminant analysis models, we propose plots of Cd/(Co + As) vs. Bi/(Co + As), Ag vs. Bi/(Ag + Cd), and Co/(Ag + Cd) vs. Bi/(Ag + Cd) to discriminate chalcopyrite from different intrusions, sulfide textures, and ore types, respectively. Additionally, the occurrence of chalcopyrite with increased Bi/(Ag + Cd) and Co/(Ag + Cd) ratios and decreased Ag content presents promising clues for exploring Fe-rich massive sulfide ores.


Global forecasts in reservoir computers

Abstract: A reservoir computer is a machine learning model that can be used to predict the future state(s) of time-dependent processes, e.g., dynamical systems. In practice, data in the form of an input-signal are fed into the reservoir. The trained reservoir is then used to predict the future state of this signal. We develop a new method for not only predicting the future dynamics of the input-signal but also the future dynamics starting at an arbitrary initial condition of a system. The systems we consider are the Lorenz, Rossler, and Thomas systems restricted to their attractors. This method, which creates a global forecast, still uses only a single input-signal to train the reservoir but breaks the signal into many smaller windowed signals. We examine how well this windowed method is able to forecast the dynamics of a system starting at an arbitrary point on a system's attractor and compare this to the standard method without windows. We find that the standard method has almost no ability to forecast anything but the original input-signal while the windowed method can capture the dynamics starting at most points on an attractor with significant accuracy.


Random Fourier Surrogate for simulation calibration

Abstract: Abstract In this paper we propose Random Fourier Surrogate (RFS) as a method for simulation calibration. Computer simulations are actively used in various fields to model and analyze complex real-world phenomena, enabling practitioners to explore situations inaccessible by other means. To create a simulation that accurately reflects the desired real-world scenario requires calibrating the simulation parameters, a non-trivial process that requires domain expertise and iterative trial-and-error. The model-bridge paradigm has been proposed to automatize this process, where a more computationally efficient surrogate model is used in lieu of the real simulation, and a bridge model is used to map the noninterpretable surrogate parameters to calibrated simulation parameters, which are physically interpretable. Recently an efficient solution called tangent slope-intercept (TSI) descriptors has been proposed, where tangent lines of a principal curve are used to represent the simulation in low dimensionality. However, we find that it has two key problems: it is prone to overfitting and is sensitive to initialization. We address these issues using Random Fourier Features to construct a surrogate model, thereby reducing calibration time by avoiding complex computations, while retaining high accuracy. We evaluate the effectiveness of our method with different experiments on synthetic signal simulations and physical simulations of turbulent flow dynamics.


Effect on a QKD Chain Going Through a Generic Pauli Channel Together with a Correction Based on the 3-qubit Code

Abstract: Abstract Quantum cryptography is an emergent area in quantum technologies to set secure communication. BB84 protocol is a procedure to exchange a secure key to encode communication in the form of a chain of quantum bits. In this work, we model the non-ideal transmission of such a key through a parametric family of noisy channels: Pauli channels (including Bit-flipping and Dephasing noises). Assuming that Quantum Key Distribution is performed under such protocol, we analyse the fidelity of the transmission and the probability distribution for the imperfect outcomes. The analysis also includes the application of the quantum error correction algorithm: the 3-qubit code. Finally, we add the presence of an eavesdropper getting his conditional success rate to become unnoticed. The agreement in the basis selection between the transmitter and receiver is assumed as under a tentative reconciliation procedure. Outcomes show just positive effects on the transmission between sender and receiver, but a mild improvement for the success probability for the eavesdropper.


Suitability evaluation of selected sand geological formations for synthesis high purity crystalline silica, Iraq

Abstract: Abstract This study aims determination if the sand collected from Injana and Dibdibba Formations are suitable for use as low-cost raw materials in the manufacture of high-purity crystalline silica. Sand is distributed in vast quantities within the two Formations, which occupy large portions of Iraq’s southern desert. Quartz is the predominant mineral phase, followed by calcite, feldspar, and dolomite respectively, based on mineral analysis data. SiO 2 is the main oxide in sand with an average of (82.76%), (90.24%) and the rest of oxides are CaO (6.28%), (1.64%), Al 2 O 3 (4.02%),(3.19%), Fe 2 O 3 (2.73%), (0.86%), K 2 O+ Na 2 O (2.05%), (2.51%), SO 3 (0.05%), (0.47%), MnO (0.01%), (0.03%) P 2 O 2 (0.23%),(0.03%), and MgO (1.75%), (1.01%) in Injana and Dibdibba Formations respectively, which are considered as impurities impact the manufacturing properties of sand. The coprecipitation method, which produces high purity crystalline silica at a low cost, involves three steps: the first involves soaking raw sand powder in 2M HCl for 24 hours; The second involves mix purified sand with NaOH while stirring and heating it to 95C° to produce Na 2 SiO 3 ; and the third involves adding 3M HCl to sodium silicate solution to produce Si(OH) 4 and drying it to produce high purity of silica. The prepared silica got mineral analysis and chemical analysis. The synthesized silica was in the crystalline phase, according to the results of the mineral characterization. In synthetic silica of Injana and Dibdibba sand, The amount of SiO 2 increased according to chemical analysis, reaching 96.62%, and 97.69% respectively, while all other oxides (impurities) were lowered to their lowest levels. FE-SEM revealed the existence of spherical, well-agglomerated synthetic silica particles within the size 28.6 nm and 24.76 nm in synthetic silica particles of Injana sand and Dibdibba sand respectively.


Unsupervised texture classification of 3D X-ray Micro-computed Tomography images

Abstract: Abstract Characterizing rock proprieties is crucial in the oilfield to evaluate hydrocarbon reserves. Several studies showed a high correlation between rock properties and textures. Therefore, we propose integrating texture information in the images to identify precisely the most representative textures in highly heterogeneous rocks to estimate their properties. First, we implemented a steerable pyramid decomposition to extract the texture features. Then, those parameters were used as input for the Self-organizing map to classify the textures. Finally, by applying several models and comparing their results, we suggested the best approach to implement for texture classification.


Gaze analysis: A survey on its applications

Abstract: The examination of ocular movements has a wide range of applications due to the current developments in sensors that are now able to collect this biometric. This type of investigation is known as “gaze analysis”. The gaze has successfully examined a subject's physical and mental status in the past. As a result, over the last few decades, a large and diverse amount of literature on this subject has been generated and presented. The aim of this study is to collect and debate current gaze analysis methods based on their application field. Due to the context-specific needs for performance and efficiency, the eye movements under research are frequently evaluated from completely distinct perspectives. As a result, a collection of data, methods, and discussions ranging from the medical community to virtual and augmented reality, as well as human computer interface and remote learning, has been produced. In addition to providing a peek of novel observation on the issue of gaze analysis, the gaps between and within areas are also discussed to provide points for researchers to pursue.


Assessment the satisfaction level of faculty members from virtual teaching during corona crisis in Rafsanjan School of Medicine

Abstract: Evaluating the experiences and the satisfaction level of the academic members is an important parameter in planning for virtual education during COVID-19 pandemic. The present study was designed to evaluate the satisfaction level of faculty members of Rafsanjan School of Medicine regarding virtual education in COVID-19 crisis in two stages between 2019 and 2022.This descriptive study was conducted on the faculty members of Rafsanjan Medical School. The satisfaction level with virtual education during the COVID-19 pandemic was determined using a researcher-made questionnaire with appropriate validity and reliability. For analysing of quantitative variables, the Kolmogorov-Smirnov test, independent t tests and one-way analysis of variance and multiple linear regression were used.Data showed that only 15.2% of the faculty members had a previous experience of virtual teaching prior to the pandemic and 30.3% had a history of passing the empowerment course on virtual education before the COVID-19 crisis. Moreover, 68.2% passed the empowerment course on virtual education at the same time as the COVID-19 epidemic spread. The overall satisfaction with virtual education in the first and second stages of the study was 49.05 and 49.22 out of 100, respectively. The satisfaction of NAVID learning management system was 66.66 percent among faculty members. The level of satisfaction in non-clinical members was significantly more than clinical members.The overall satisfaction of faculty members with virtual education was at an average or medium level. From the point of view of the faculty members, some aspects of virtual education need to be improved. Therefore, it seems necessary to improve the infrastructure and empower the faculty members to enhance the quality of virtual education.


The influence of job security on job performance among Jordanian nurses

Abstract: Job security is one of the basic needs of nurses and it has a great influence on their willingness to work. The effectiveness of job performance of nurses will reflect on the whole organization’s performance by providing high quality health services. The purpose of this study is to investigate the relationship between job security and employee performance among Jordanian nurses. The study utilized a descriptive, comparative cross-sectional design. It was conducted in two governmental and two private hospitals. A sample of 156 nurses was recruited conveniently to participate in the study. The Job security questionnaire and The Six-Dimension Scale of Nursing Performance were used to collect data. The mean value of the job security score was (3.26) which reflect a moderate level of job security. The employee performance score average was (2.68) reflecting a high level of job performance. There is a significant moderate positive relationship between job security and employee performance (r = 0.404, p < 0.01). Years of experience as a registered nurse (r = −0.18, p < 0.05), years of experience in the current hospital (r = −0.20, p < 0.05), and years of experience in the current unit (r = −0.19, p < 0.05) had a weak negative but significant relationship with employee performance. Nurses with moderate levels of job security obtained high scores on job performance. Future research can look into the characteristics of Jordanian nursing work environments that might have contributed to the observed differences between job security and employee performance.


Reprint of: Some thoughts about transfer learning. What role for the source domain?

Abstract: Transfer learning is called for when the training and test data do not share the same input distributions (PXS≠PXT) or/and not the same conditional ones (PY|XS≠PY|XT). In the most general case, the input spaces and/or output spaces can be different: XS≠XT and/or YS≠YT. However, most work assume that XS=XT. Furthermore, a common held assumption is that it is necessary that the source hypothesis be good on the source training data and that the “distance” between the source and the target domains be as small as possible in order to get a good (transferred) target hypothesis. This paper revisits the reasons for these beliefs and discusses the relevance of these conditions. An algorithm is presented which can deal with transfer learning problems where XS≠XT, and that furthermore brings a fresh perspective on the role of the source hypothesis (it does not have to be good) and on what is important in the distance between the source and the target domains (translations between them should belong to a limited set). Experiments illustrate the properties of the method and confirm the theoretical analysis. Determining beforehand a relevant source hypothesis remains an open problem, but the vista provided here helps understanding its role.


Image Segmentation and Classification Fields Are Generated Using Binary Random Fields Based on Planar Graph and Neighborhood Spanning Tree

Abstract: Image analysis plays a pivotal role in computer vision, with image segmentation and classification being fundamental tasks in this domain. This abstract presents a novel approach to image processing that leverages Binary Random Fields (BRF) with a foundation in planar graphs and neighborhood spanning trees. This innovative methodology seeks to enhance the accuracy and efficiency of image segmentation and classification, addressing key challenges in computer vision applications. Binary Random Fields (BRF) is probabilistic graphical models that have proven effective in capturing spatial dependencies and contextual information within images. Our proposed method extends the utility of BRF by incorporating planar graph theory and neighborhood spanning trees to refine the segmentation and classification processes. Planar graphs offer a structured representation of image data, preserving topological relationships among pixels, while neighborhood spanning trees provide a hierarchical framework for modeling image regions


Detailed Report on the Measurement of the Positive Muon Anomalous Magnetic Moment to 0.20 ppm

Abstract: We present details on a new measurement of the muon magnetic anomaly, $a_\mu = (g_\mu -2)/2$. The result is based on positive muon data taken at Fermilab's Muon Campus during the 2019 and 2020 accelerator runs. The measurement uses $3.1$ GeV$/c$ polarized muons stored in a $7.1$-m-radius storage ring with a $1.45$ T uniform magnetic field. The value of $ a_{\mu}$ is determined from the measured difference between the muon spin precession frequency and its cyclotron frequency. This difference is normalized to the strength of the magnetic field, measured using Nuclear Magnetic Resonance (NMR). The ratio is then corrected for small contributions from beam motion, beam dispersion, and transient magnetic fields. We measure $a_\mu = 116 592 057 (25) \times 10^{-11}$ (0.21 ppm). This is the world's most precise measurement of this quantity and represents a factor of $2.2$ improvement over our previous result based on the 2018 dataset. In combination, the two datasets yield $a_\mu(\text{FNAL}) = 116 592 055 (24) \times 10^{-11}$ (0.20 ppm). Combining this with the measurements from Brookhaven National Laboratory for both positive and negative muons, the new world average is $a_\mu$(exp) $ = 116 592 059 (22) \times 10^{-11}$ (0.19 ppm).


A Framework for the Interoperability of Cloud Platforms: Towards FAIR Data in SAFE Environments

Abstract: As the number of cloud platforms supporting scientific research grows, there is an increasing need to support interoperability between two or more cloud platforms. A well accepted core concept is to make data in cloud platforms Findable, Accessible, Interoperable and Reusable (FAIR). We introduce a companion concept that applies to cloud-based computing environments that we call a Secure and Authorized FAIR Environment (SAFE). SAFE environments require data and platform governance structures and are designed to support the interoperability of sensitive or controlled access data, such as biomedical data. A SAFE environment is a cloud platform that has been approved through a defined data and platform governance process as authorized to hold data from another cloud platform and exposes appropriate APIs for the two platforms to interoperate.


Joint wireless resource allocation and bitrate adaptation for QoE improvement in IRS-aided RSMA-enabled IoMT streaming systems

Abstract: In the context of the increasing demand for Internet of Multimedia Things (IoMT) services, rate splitting multiple access (RSMA) and intelligent reflecting surface (IRS) technologies have been considered potential networking enablers to provide ultra-throughput wireless access. However, challenges arise due to the heterogeneity of IoMT devices and arbitrary network quality changes, resulting in unwanted service quality fluctuations and downgradation. This study addresses this problem by jointly optimizing wireless resource allocation and bitrate adaptation with Deep Reinforcement Learning (DRL)-based QoE management for IRS-aided RSMA-enabled IoMT streaming systems. We formulated the problem as a Markov decision process (MDP) and apply Proximal Policy Optimization (PPO) method to flexibly adjust IoMT bitrate, transmission beamforming, IRS phase shift, and RSMA parameters. As a result, our algorithm mitigates overestimation of client-side bandwidth, leading to smoother playback and reduced quality fluctuations. Simulations show that our approach outperforms baseline methods in terms of video resolution (up to 2.5 times) and achievable sum-rate (up to 50%), contributing to a superior streaming experience in IoMT systems.


Expectation maximization pseudo labels

Abstract: In this paper, we study pseudo-labelling. Pseudo-labelling employs raw inferences on unlabelled data as pseudo-labels for self-training. We elucidate the empirical successes of pseudo-labelling by establishing a link between this technique and the Expectation Maximisation algorithm. Through this, we realise that the original pseudo-labelling serves as an empirical estimation of its more comprehensive underlying formulation. Following this insight, we present a full generalisation of pseudo-labels under Bayes’ theorem, termed Bayesian Pseudo Labels. Subsequently, we introduce a variational approach to generate these Bayesian Pseudo Labels, involving the learning of a threshold to automatically select high-quality pseudo labels. In the remainder of the paper, we showcase the applications of pseudo-labelling and its generalised form, Bayesian Pseudo-Labelling, in the semi-supervised segmentation of medical images. Specifically, we focus on: (1) 3D binary segmentation of lung vessels from CT volumes; (2) 2D multi-class segmentation of brain tumours from MRI volumes; (3) 3D binary segmentation of whole brain tumours from MRI volumes; and (4) 3D binary segmentation of prostate from MRI volumes. We further demonstrate that pseudo-labels can enhance the robustness of the learned representations. The code is released in the following GitHub repository: https://github.com/moucheng2017/EMSSL.


Graph neural networks for clinical risk prediction based on electronic health records: A survey

Abstract: This study aims to comprehensively review the use of graph neural networks (GNNs) for clinical risk prediction based on electronic health records (EHRs). The primary goal is to provide an overview of the state-of-the-art of this subject, highlighting ongoing research efforts and identifying existing challenges in developing effective GNNs for improved prediction of clinical risks. A search was conducted in the Scopus, PubMed, ACM Digital Library, and Embase databases to identify relevant English-language papers that used GNNs for clinical risk prediction based on EHR data. The study includes original research papers published between January 2009 and May 2023. Following the initial screening process, 50 articles were included in the data collection. A significant increase in publications from 2020 was observed, with most selected papers focusing on diagnosis prediction (n = 36). The study revealed that graph attention networks (GAT) (n = 19) was the most prevalent architecture, and MIMIC-III (n = 23) was the most common data resource. GNNs are relevant tools for predicting clinical risk by accounting for the relational aspects among medical events and entities and managing large volumes of EHR data. Future studies in this area may address challenges such as EHR data heterogeneity, multimodality, and model interpretability, aiming to develop more holistic GNN models that can produce more accurate predictions, be effectively implemented in clinical settings, and ultimately improve patient care.


Deep learning-Aided Dull Grading Scheme for Drill Bits

Abstract: Drill bit design plays a key role in drilling operations. A suitable choice of the drill bit parameters (such as the profile shape, the orientation, and the distribution of cutters, etc.) will significantly improve the durability and stability of the drilling process, increase the rate of penetration (ROP) and enhance the drilling efficiency. Traditional strategies of the drill bit design usually involve a trial-and-error process to adjust the parameters, which is usually time-consuming and contains an element of subjectivity. Alternatively, machine learning techniques provide powerful tools to make full use of the collected data, and analyze the relation between the design parameters and the performance of the drill bits on specified rock mechanics data. In the present work, we aim at building a machine learning-aided scheme to help design the parameters of the drill bits. A machine learning-aided scheme is proposed to perform the drill bit design. First, a dull grading system is established to evaluate the wear extent of the drill bits from images. The grading system includes a cutter localization model to separate the cutters from drill bit images, a reconstruction model to regenerate the 3D geometries of the cutters from several 2D images, a classifier to evaluate the wear extent of the cutters, and an aggregative algorithm to grade the worn drill bits. The grading system is then applied to build the data set of the worn drill bits. Finally, an individual deep learning model is adopted to learn the relation between the design parameters and the wear extent, and a sensitivity analysis is performed to optimize the design parameters. The research results showed that: (1) the proposed machine learning-aided scheme could produce instructive guides on the drill bit design, reduce the analysis cost and provide accurate prediction on the impact of the design parameters. (2) The 3D reconstruction algorithm could efficiently extract the geometry feature of the drill bits from 2D data, and provide detailed geometry parameters of each cutter. The performance of the 3D reconstruction model is mainly related to the number of corresponding 2D images of a given drill bit and the coincidence degree among them. (3) The classifier based on convolutional neural networks (CNN) could provide accurate wear extent assessments of drill bits. In this paper a novel machine learning-aided drill bit design scheme is proposed to effectively enhance the efficiency of the evaluation and optimization of the design parameters. A 3D reconstruction method is established to extract the 3D geometry parameters of the drill bits from several 2D images. The deep learning-based dull grading model is proposed to accurately evaluate the wear extent of the drill bits.


Research on the Construction of Petroleum Software Cloud Platform Based on Distributed Architecture

Abstract: Abstract Researchers in exploration and development (E&D) field need several professional software resources for data processing and analysis. Enterprises can achieve cloud-based usage and shared management of petroleum exploration and development software through the deployment of cloud platforms. With the increasing demand for remote and overseas collaborative research, the single-node cloud platform deployment mode no longer meets the needs of collaborative research. This paper proposes a construction scheme of cloud platform for petroleum software based on distributed architecture, which realizes the unified management of software research environment, software licenses, and data. The proposed architecture has been implemented in a division of CNPC, realizing the cloudification of more than 20 mainstream professional E&D software applications such as Petrel and LandMark. By establishing central sites in Beijing and branch sites in Dubai and Hangzhou, the researchers are provided with a cross-regional collaborative research environment on the cloud, thereby enhancing the research efficiency, improving the efficiency of cross-regional collaborative research, and facilitating the unified data management and the digital transformation of petroleum enterprises in the cloud environment.


The Effect of Research and Development on Economic Growth in Ethiopia: The Untapped Potential for Prosperity

Abstract: This research measured the links between R&D and economic growth in Ethiopia employing multiple regression models. Citable journal articles, patents, technology exports, and research expenditures are the four predictor variables that are the focus of the study. GDP per capita growth is the outcome variable of the study, which serves as a proxy indicator for economic growth. The study used a panel dataset with data from a sample of 21 years between 2001 and 2021. The results show a significant link between growth of GDP per capita and citable journal articles, patents, and technology exports, demonstrating that these factors are essential for fostering economic growth. However, government expenditure on research and development has no discernible impact on economic growth, which accordingly, raises the possibility that raising research spending by itself may not be sufficient to boost the economy. These findings have tremendous policy implications because they emphasize the importance of concentrating on particular R&D initiatives that have a direct impact on economic growth. The study adds to the existing frontiers of knowledge inline with the connection between R&D and economic growth by demonstrating the value of citation-worthy journals, patents, and technology exports in fostering economic growth.


VCSEL-based photonic spiking neural networks for ultrafast detection and tracking

Abstract: Abstract Inspired by efficient biological spike-based neural networks, we demonstrate for the first time the detection and tracking of target patterns in image and video inputs at high-speed rates with networks of multiple artificial spiking optical neurons. Using photonic systems of in-parallel spiking Vertical Cavity Surface Emitting Lasers (VCSELs), we demonstrate the implementation of multiple convolutional kernel operators which, in combination with optical spike signalling, enable the detection and tracking of target features in images/video feeds at an ultrafast photonic operation speed of 1 ns per pixel. Alongside a single layer optical Spiking Neural Network (SNN) demonstration, a multi-layer network of photonic (GHz-rate) spike-firing neurons is reported where the photonic system successfully tracks a large complex feature (Handwritten Digit 3). The consecutive photonic layers perform spike-enabled image reduction and convolution operations, and interact with a software-implemented SNN, that learns the feature patterns that best identify the target to provide a high detection efficiency even in the presence of a distractor feature. This work therefore highlights the effectiveness of combining neuromorphic photonic hardware and software SNNs, for efficient learning and ultrafast operation, thanks to the use of spiking light signals, towards tackling complex AI and computer vision problems.