Statistically Significant Duration-Independent-based Noise-Robust Speaker Verification

Abstract: A speaker verification system models individual speakers using different speech features to improve their robustness. However, redundant features degrade the system's performance. This paper presents Statistically Significant Duration-Independent Mel frequency Cepstral Coefficients (SSDI-MFCC) features with the Extreme Gradient Boost classifier for improving the noise-robustness of speaker models. Eight statistical descriptors are used to generate signal duration-independent features, and a statistically significant feature subset is obtained using a t-test. A redeveloped Librispeech database by adding noises from the AURORA database to simulate real-world test conditions for speaker verification is used for evaluation. The SSDI-MFCC is compared with Principal Component Analysis (PCA) and Genetic Algorithm (GA). The comparative results showed average equal error rate improvements by 4.93 % and 3.48 % with the SSDI-MFCC than GA-MFCC and PCA-MFCC in clean and noisy conditions, respectively. A significant reduction in verification time is observed using SSDI-MFCC than the complete feature set.


Towards Feature-based ML-enabled Behaviour Location

Abstract: Mapping behaviours to the features they relate to is a prerequisite for variability-intensive systems (VIS) reverse engineering. Manually providing this whole mapping is labour-intensive. In black-box scenarios, only execution traces are available (e.g., process mining). In our previous work, we successfully experimented with variant-based mapping using supervised machine learning (ML) to identify the variants responsible of the production of a given execution trace, and demonstrated that recurrent neural networks (RNNs) work well (≥ 80% accuracy) when trained on datasets in which we label execution traces with variants. However, this mapping (i) may not scale to large VIS because of combinatorial explosion and (ii) makes the internal ML representation hard to understand. In this short paper, we discuss the design of a novel approach: feature-based mapping learning.


<i>q</i>-analog qudit Dicke states

Abstract: Abstract Dicke states are completely symmetric states of multiple qubits (2-level systems), and qudit Dicke states are their d -level generalization. We define here q -deformed qudit Dicke states using the quantum algebra <?CDATA $su_q(d)$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mi>s</mml:mi> <mml:msub> <mml:mi>u</mml:mi> <mml:mi>q</mml:mi> </mml:msub> <mml:mo stretchy="false">(</mml:mo> <mml:mi>d</mml:mi> <mml:mo stretchy="false">)</mml:mo> </mml:math> . We show that these states can be compactly expressed as a weighted sum over permutations with q -factors involving the so-called inversion number, an important permutation statistic in Combinatorics. We use this result to compute the bipartite entanglement entropy of these states. We also discuss the preparation of these states on a quantum computer, and show that introducing a q -dependence does not change the circuit gate count.


<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si1.svg"><mml:mi mathvariant="double-struck">F</mml:mi></mml:math>-valued trace of a finite-dimensional commutative <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si1.svg"><mml:mi mathvariant="double-struck">F</mml:mi></mml:math>-algebra

Abstract: A non-zero F-valued F-linear map on a finite-dimensional commutative F-algebra is called an F-valued trace if its kernel contains no non-zero ideals. However, given an F-algebra, such a map may not always exist. We find an infinite class of finite-dimensional commutative F-algebras which admit an F-valued trace. In fact, in these cases, we explicitly construct a trace map. An F-valued trace on a finite-dimensional commutative F-algebra induces a non-degenerate bilinear form on the F-algebra which may be theoretically and computationally helpful. In this article, we suggest a couple of applications of an F-valued trace map of an F-algebra to algebraic coding theory.


An optimized Q-Learning algorithm for mobile robot local path planning

Abstract: The Q-Learning algorithm is a reinforcement learning technique widely used in various fields such as path planning, intelligent transportation, penetration testing, among others. It primarily involves the interaction between an agent and its environment, enabling the agent to learn an optimal strategy that maximizes cumulative rewards. Most non-agent-based path planning algorithms face challenges in exploring completely unknown environments effectively, lacking efficient perception in unfamiliar settings. Additionally, many Q-Learning-based path planning algorithms suffer from slow convergence and susceptibility to getting stuck in local optimal solutions. To address these issues, an optimized version of the Q-Learning algorithm (Optimized Q-Learning, O-QL) is proposed and applied to local path planning of mobile robots. O-QL introduces novel Q-table initialization methods, incorporates a new action-selection policy, and a new reward function, and adapts the Root Mean Square Propagation (RMSprop) method in the learning rate adjustment. This adjustment dynamically tunes the learning rate based on gradient changes to accelerate learning and enhance path planning efficiency. Simulation experiments are carried out in three maze environments with different complexity levels, and the performance of the algorithm in local path planning is evaluated using steps, exploration reward, learning rate change and running time. The experimental results demonstrate that O-QL exhibits improvements across all four metrics compared to existing algorithms.


Safe path planning and adjustable zonotope-tube model predictive tracking control for autonomous vehicle

Abstract: The path planning and tracking control problem of autonomous vehicle with model matched uncertainty and external disturbance is studied in this paper. A safe lane change zone is designed by considering lane change time, distance, and driving speed. Meanwhile, the candidate paths are generated using cubic quasi-uniform B-spline curve, and the optimal path is selected based on feasibility, comfort, and efficiency criteria. The proposed planning method ensures an efficient and comfortable lane change path without collision with surrounding vehicles. Model uncertainty and external disturbance may affect lane change tracking control of autonomous vehicle. Hence, a fault-tolerant path tracking controller is implemented by using deep neural networks and adjustable zonotope-tube model predictive control. A deep neural network-based controller is proposed to compensate for model matched uncertainty, thereby enhancing the fault tolerance of the control system. Then, an adjustable zonotope-tube model predictive controller is designed to enclose the actual trajectory within a zonotope-tube with the nominal state as the center and the disturbance set as the radius by using the feedback control to achieve the asymptotic stabilization of the closed-loop system. The effectiveness of this path planning and tracking control method is finally verified by numerical simulation.


Unlabeled scene adaptive crowd counting via meta-ensemble learning

Abstract: The objective of unlabeled scene adaptive crowd counting (USACC) is to adapt the crowd counting model to a particular scene by utilizing only a handful of unlabeled images from that scene, rather than considering all the diverse scenarios that may occur in the unknown environment at once. The resolution of this problem facilitates the fast widespread deployment of crowd counting models, mitigating the issue of performance deterioration caused by domain shift. To tackle the USACC problem, we propose a novel method called meta-ensemble learning that incorporates ensemble learning into the meta-learning paradigm. Specifically, we pass the input data through the network with stochasticity multiple times, implicitly creating an ensemble of multiple models, to produce multiple distinct outputs which can be averaged as pseudo labels to adapt the model. In an iteration of offline training, the scene-specific parameters are learned by minimizing the consistency loss between the actual predictions and the pseudo labels generated from a few unlabeled images belonging to that scene. Then, we optimize the model using the remaining labeled images from the same scene to alleviate error accumulation caused by pseudo labels, and thus improve the accuracy of the pseudo labels in subsequent iterations. The training process explicitly simulates the process of adapting to a particular scene during the test. Therefore, the model is able to adapt to the target scene using a handful of unlabeled images by minimizing the consistency loss during the test. Extensive experiments on several benchmarks demonstrate our method surpasses both baselines and state-of-the-art methods.


Low-complexity adaptive reconciliation protocol for continuous-variable quantum key distribution

Abstract: Abstract In continuous-variable quantum key distribution systems, reconciliation is a crucial step that significantly affects the secret key rate (SKR). The rateless protocol based on Raptor codes can achieve high reconciliation efficiency at low signal-to-noise ratios (SNRs). However, the high complexity of low-density parity-check (LDPC) codes used for the precoding in Raptor codes limits the speed of reconciliation. In this paper, we propose an adaptive reconciliation protocol by modifying Raptor codes. The length of random binary sequences is increased because we remove the LDPC precoding that adds redundancy. The modified Raptor codes reduce the complexity of encoding with better performance. The proposed protocol gives a reconciliation efficiency higher than 98.1% in the SNR below −20 dB and maintains a certain SKR in long-distance transmission.


Scaled local gate controller for optically addressed qubits

Abstract: Scalable classical controllers are a key component of future fault-tolerant quantum computers. Neutral atom quantum computers leverage commercially available optoelectronic devices for generating large-scale tweezer arrays and performing parallel readout, but implementing massively parallel, locally addressed gate operations is an open challenge. In this work, we demonstrate an optical modulator system based on off-the-shelf components, which can generate a two-dimensional array of over 10,000 focused spots with uniform frequency and amplitude, and switching them on and off individually in arbitrary configurations at rates of up to 43 kHz. Through careful control of aberrations, the modulator achieves an extinction ratio of 46 dB, and nearest-neighbor crosstalk of −44dB with a beam spacing of 4.6 waists. The underlying components can operate at wavelengths from the UV to the NIR, and sustain high laser intensities. This approach is suitable for local addressing of gates with low cross-talk error rates in any optically addressed qubit platform, including neutral atoms, trapped ions, or solid-state atomic defects.


Intelligent path planning by an improved RRT algorithm with dual grid map

Abstract: This research addresses the limitations of existing autonomous vehicle path planning algorithms, notably their slow processing speeds and suboptimal route efficiency. We introduce an innovative path planning algorithm that synergizes the A* algorithm with the Rapidly-exploring Random Tree (RRT) approach. This hybrid model significantly enhances route timeliness and reliability, particularly in obstacle avoidance scenarios for driverless vehicles. Our methodology employs a 'two-level map' approach, where a lower-resolution grid map is derived from a high-resolution map. Utilizing the A* algorithm on this framework, we ascertain a preliminary 'coarse path' for the navigation target. The RRT algorithm, modified to reduce the traditional redundancy associated with random uniform sampling, is then applied for probabilistic sampling within this defined area. ovel aspect of our approach is the simultaneous generation of two trees, originating from both the start and end points, guided by a target-biased strategy and dual-direction theory. This method probabilistically expands towards the node of the opposite tree, thereby enhancing both the generation speed and trajectory viability. Further refinements are made through a pruning process, optimizing the path, and employing Bezier curves for smoothing, ensuring compliance with the dynamic constraints of Ackerman chassis vehicles. Comparative analysis in complex environments demonstrates the superiority of our proposed algorithm. It outperforms traditional methods with a 400 % increase in planning speed relative to the RRT-Connect algorithm, and a 30 % reduction in average path length. Additionally, the mean curvature of routes generated by our algorithm is 19 % lower than traditional routes, underscoring significant advancements in both the timeliness and viability of the planned routes.


CTBANet: Convolution transformers and bidirectional attention for medical image segmentation

Abstract: In the last few years, Transformer has revolutionized the area of medical image segmentation. Several similar studies have used the UNet architecture to combine convolutional neural networks with transformers. However, these approaches fail to account for the speed at which segmentation occurs and the ability to extract features within the Transformer. They fail to consider the fact that changing the shape of the feature maps in a subtle way can be used for rapid extraction of local and global information. To solve the above problems, CTBANet (Convolutional Transformer and Bidirectional Attention Based for Medical Image Segmentation) is proposed, which has two prominent components, CTblock (Convolutional Combined Transformer module) and BAblock (Bidirectional Attentionblock). CTblock integrates the strengths of CNNs and Transformers, enabling it to extract spatial details and global data. In order to improve the speed and accuracy of the model, multi-scale pyramid pooling is embedded into PAM, named APAM (Asymmetric PAM), and strip convolution is embedded into CAM, named ACAM (Asymmetric CAM). Medical image segmentation is a critical issue in the medical field, and the experimental results of the benchmarks show that our model is obviously more accurate and faster than the other methods in segmenting medical images.


Performance comparison of multiple scoring algorithms: A study of automatic scoring of English text

Abstract: Objective:In order to save teachers’ correcting time, improve the accuracy and efficiency of English composition grading.Methods: This paper briefly introduces the algorithm of deep sentence smoothness and text semantic matching based on graph neural network, and then designs an automatic scoring algorithm for English text. Result: The experimental data was collected from 12,000 essays written by international students in the United States in the Pratt & Whitney Foundation’s Automated Student Value Assessment Project (ASAP), and these data were graded through a comparative experiment,Through comparative tests, the automatic scoring algorithm designed in this paper can achieve better scoring results and better handle automatic essay scoring problems. Among all the experimental mean values of evaluation methods, the experimental mean value of the algorithm designed in this paper is 0.790, the smoothness algorithm is 0.768, and the text matching vector is 0.759. The experimental mean values of the other two traditional automatic scoring algorithms are 0.710 and 0.712 respectively, and the results are lower than the algorithm designed in this paper. Conclusion: According to the experimental results, it can be concluded that good feature selection can give good scoring performance to the algorithm and cope with the problem of automatic scoring. At the same time, it also confirms the feasibility of the algorithm designed in this paper, which can be effectively applied in practical English composition scoring.


EGLR: Two-staged Explanation Generation and Language Reasoning framework for commonsense question answering

Abstract: In the field of natural language processing, strong commonsense reasoning abilities are often required for intelligent systems to excel in the task of commonsense question answering (QA). To enhance the interpretability of QA systems, a natural approach is to generate textual explanations in addition to the predicted answers, ensuring the understandability of the results. Recent efforts address this by using a prompted language model (LM) with frozen parameters to generate explanations in a few-shot manner. These explanations are then used as additional context to guide a finetuned LM in making the final decision. However, these methods still underutilize the semantic information embedded in the explanatory text. Consequently, the reasoning models tend to rely on word co-occurrence and the knowledge stored in the model itself rather than fully exploiting the explanations. Therefore, we propose a two-staged Explanation Generation and Language Reasoning Framework (EGLR), Our framework takes advantage of the in-context learning capability of LMs to generate explanations and reformulates the reasoning task based on explanations as a semantic matching problem. Through joint prompting and training, our model can select the most appropriate explanation by comparing multiple explanations. Experimental results on three public datasets demonstrate that our framework achieves superior performance on the full dataset while maintaining performance on out-of-domain scenarios.


Learning across diverse biomedical data modalities and cohorts: Challenges and opportunities for innovation

Abstract: In healthcare, machine learning (ML) shows significant potential to augment patient care, improve population health, and streamline healthcare workflows. Realizing its full potential is, however, often hampered by concerns about data privacy, diversity in data sources, and suboptimal utilization of different data modalities. This review studies the utility of cross-cohort cross-category (C4) integration in such contexts: the process of combining information from diverse datasets distributed across distinct, secure sites. We argue that C4 approaches could pave the way for ML models that are both holistic and widely applicable. This paper provides a comprehensive overview of C4 in health care, including its present stage, potential opportunities, and associated challenges.


Branch-cut algorithm with second-order residues in phase unwrapping

Abstract: This Letter presents a novel, to the best of our knowledge, branch-cut algorithm for unwrapping phase maps acquired through fringe projection systems. The algorithm exposes second-order residues with vortex phase structures, computed from the original wrapped phase distribution using a second-order derivative that considers the wrap cycle. Remarkably, it reveals a consistent vorticity direction associated with these vortex structures, significantly facilitating branch-cut connections between residues, regardless of whether they carry the same or opposite charges. This innovative approach challenges a longstanding misconception in conventional branch-cut methods, which traditionally connected matched residues only when they had opposite charges. As a result, the algorithm provides a highly accurate methodology for explaining how phase behavior relates to surface characteristics. Its effectiveness is demonstrated through both computer simulations and practical experiments.


Utilisation of Service Robots in Accommodation Businesses From the Perspective of the Technology Acceptance Model

Abstract: The advancement of technology and increased sales of service robots have led to discussions about their usage. Recently, the use of service robots in accommodation businesses has become a topic of examination. This study aims to explore the behavioural intentions of accommodation business managers towards using service robots from the perspective of the technology acceptance model. As part of this project-supported study, the 'Sanbot Elf' service robot was rented for 14 days and demonstrated in six accommodation businesses in Kastamonu. Semi-structured interviews were conducted with managers and unit supervisors after the demonstration, and the process was also monitored through participant observation. The data were analysed using content analysis and participant observation methods. It was determined that participants' opinions on service robots were generally positive. Participants stated that personalised service presentations could be provided with service robots and that these robots could be used in different departments and marketing processes. The study concluded that service robots could positively impact service quality, are an exciting technology for accommodation businesses and the use of service robots could increase in the future. Participants' attitudes towards using service robots in accommodation businesses were positive in this context.


Blockchain aware decentralized identity management and access control system

Abstract: Identity Management Systems (IMS) manage individual profiles, user authorization, authentication, and access privileges. Paper-based profiles and defacement are vulnerable to spoofing, loss, and theft. Digital platforms with efficient IMS boost administrative processes and bring transparency and interoperability among establishments. The traditional paper-based identity systems are slow and rigid. Conventional centralized IMS lack transparency and are vulnerable to single-point failure. This paper presents a blockchain-aware decentralized identity management and access control (BADIMAC) model that mitigates these barriers. BADIMAC allows users to keep their digital identity within their control (i.e., self-sovereign identity). On the other hand, with the help of BADIMAC, service providers across enterprise boundaries can verify their identity without depending on any central authority. BADIMAC offers transparency and better security. The decentralized consensus-based design of BADIMAC enables the verifier to validate claims and establish proof of digital identity with process effectiveness.


Learning a Holistic-Specific color transformer with Couple Contrastive constraints for underwater image enhancement and beyond

Abstract: Underwater images suffer from different types of degradation due to medium characteristics and interfere with underwater tasks. While deep learning methods based on the Convolutional Neural Network (CNN) excel at detection tasks, they have inherent limitations when it comes to handling long-range dependencies. The enhanced images generated by these methods often have problems such as color cast, artificial traces and insufficient contrast. To address these limitations, we present a novel Holistic-Specific attention (HSA) mechanism based on the Vision Transformer (ViT). This mechanism allows us to capture global information in finer detail and perform initial enhancements on underwater images. Notably, even when combined with ViT, CNNs do not always approach the ideal state of image enhancement, as reference images themselves may involve human intervention. To tackle this, we design a loss function that incorporates contrastive learning, using the source image as a negative example. This approach guides the enhancement results to be closer to the ideal enhancement state while keeping away from the degraded state, not just closer to the reference. Additionally, we introduce patch-based contrastive learning to address the shortcomings of image-based methods in fine-detail correction. Our extensive qualitative and quantitative experiments demonstrate that the proposed method outperforms state-of-the-art techniques.


Continuous-variable quantum key distribution robust against environmental disturbances

Abstract: Continuous variable quantum key distribution (CV-QKD) can guarantee that two parties share secure keys even in the presence of an eavesdropper. However, the polarization direction of the coherent state transmitted in CV-QKD is susceptible to environmental disturbances during channel transmission, making it difficult to share keys consistently over long periods of time. Therefore, a CV-QKD system that can resist environmental disturbance is very urgent. In this paper, we propose a new optical architecture for CV-QKD based on the Faraday-Michelson interference (FMI) structure, and finally form an all-single-mode (SM) fiber-based stable CV-QKD system which employs transmitted local oscillator (TLO) scheme and discrete modulation coherent state (DMCS) protocol. Specifically, since the Faraday mirror rotates the polarization direction of light by 90 o , the birefringence effect of light can be effectively dealt with, thus ensuring the same polarization state of light before and after reflection. The final simulation results show that the theoretical secret key rate of this scheme can reach 139 kbps at 70 km, which can further improve the stability and robustness of CV-QKD in the real environment, and provide technical support for the next-generation high-stability QKD system.


Exploring a context-gated network for effective image deraining

Abstract: The existing deraining methods have obtained noteworthy improvements, but it is a challenging problem to extend the methods for complicated rain conditions where rain streaks exhibit different distribution densities, sizes, shapes, etc. The main challenges are the ability to fully explore and utilize the multi-scale context information of rain streaks that maintain both global structure completeness and local detail accurateness. To this end, this paper proposes an Exploring Context-Gated Network, known as ECGNet. To adequately explore the richer context information, the proposed method consists of two key elements: context-enhanced feature block (CEFB) and multi-scale-gated aggregation block (MGAB). Specifically, the various scale features can be captured by CEFB with the multi-scale operation, to better remove the rain streaks and effectively restore the local detail textures. Subsequently, the captured features from different spaces are sent to MGAB, to aggregate and transmit these different scale features from the encoder to the decoder and reduce the transmission loss of information. Massive experiments on the commonly used benchmarks have demonstrated that the proposed method obtains more appealing performances against other competitive methods.


Reality Technologies (AR, VR, MR, XR) in Tourism

Abstract: The change in the technological environment within the macro-environment factors in recent years affects states, businesses, societies and individuals and concerns not only technology-based sectors but also many fields. In particular, trends such as artificial intelligence, metaverse, robotics, advanced connectivity, the Internet of Things, big data, small data, blockchain, cloud technologies and reality technologies, which are called new technology, are developing very quickly compared to the past and expanding their global usage areas. Creating strategies and policies without considering these factors creates problems in many areas. These problems are marketing, competition, cost, efficiency and productivity.Reality technologies, which are the research area in this chapter and enable users to interact with the digital world, have a wide application area in the tourism industry. With technological tools such as smartphones and virtual reality (VR) glasses; personalisation, interactive experience, information gathering and decision-making; many different solutions are produced in areas such as education, service and security. Along with its many advantages, the disadvantages of reality technologies and the negative outputs of this transformation are significant for the understanding and future of the subject.


Service Robots in the Gastronomy Industry

Abstract: The gastronomy industry is one of the industries in which consumers use technology most frequently, such as robotics, artificial intelligence and automation systems. Service robots are generally defined as online programs that can act independently or under the control of a human that can carry human characteristics such as speaking, understanding and acting. This study aims to examine and evaluate the studies on service robots in the gastronomy industry regarding quantity or quality, to understand the phenomenon more comprehensively, and to determine a holistic perspective. To this end, a qualitative research approach was adopted. Document technique was used to collect data within a systematic literature review framework, and the data were analysed by descriptive analysis. According to the findings, the papers on service robots in the gastronomy industry are still in their infancy, and these studies have increasingly gained speed in the last five years.


Voice Control and Voice Searching

Abstract: Voice command and voice search are becoming increasingly popular in the hospitality and tourism industry, with many hotels and travel companies investing in voice-enabled technology to improve guest experiences and streamline operations. This technology allows travellers to manage their travel plans, request services and get information through natural voice commands on any voice-enabled device. Voice assistants are also multilingual, allowing hotels to customise responses to guests who do not speak the local language. Angie, a multilingual, in-room voice assistant, is an example of this technology. It can fulfil guest requests, answer common questions about the property and create streamlined access to a wide range of hotel amenities, such as ordering room service or requesting extra towels. Hotels can control questions and responses to assist stretched staff and provide upsell and advertising revenue through digital promotions or recommended onsite amenities or discounts. In addition, voice command technology can be used to book travel and find things to do at a destination. Google Assistant can help with travel plans like booking a hotel, checking flight status and finding things to do at a destination. In conclusion, voice command and voice search technology are transforming the hospitality and tourism industry by improving guest experiences, reducing operational costs and increasing revenue.


Low-Order Automatic Domain Splitting Approach for Nonlinear Uncertainty Mapping

Abstract: This paper introduces a novel method for the automatic detection and handling of nonlinearities in a generic transformation. A nonlinearity index that exploits second-order Taylor expansions and polynomial bounding techniques is first introduced to estimate the Jacobian variation of a nonlinear transformation. This index is then embedded into a low-order automatic domain splitting algorithm that accurately describes the mapping of an initial uncertainty set through a generic nonlinear transformation by splitting the domain whenever nonlinearities grow above a predefined threshold. The algorithm is illustrated in the critical case of orbital uncertainty propagation, and it is coupled with a tailored merging process that limits the growth of the domains in time by recombining them when nonlinearities decrease. The low-order automatic domain splitting algorithm is then combined with Gaussian mixture models to accurately describe the propagation of a probability density function.


Visual Observer-Based State-Feedback Autonomous Motion Control on SE(3)

Abstract: This paper introduces a visual observer-based state-feedback control tailored for autonomous motion on SE(3) to address challenges posed by modeling uncertainties and measurement noise. The proposed approach unfolds in two fundamental phases. In the initial stage, the visual observer, based on modeling information, visual sensors, and pre-deployed landmarks, along with the state-feedback controller, are developed independently, underpinning their individual semi-global practical asymptotic (SPA) stability. This modular approach ensures a robust foundation for the subsequent synthesis. The latter stage applies the well-established Small Gain Principle to regulate observer and controller parameters to guarantee the SPA stability of the closed-loop system with the visual-observer based state feedback control. The effectiveness of the proposed method is validated through simulation and experiments.


FRPNet: An improved Faster-ResNet with PASPP for real-time semantic segmentation in the unstructured field scene

Abstract: The agricultural environment has numerous unstructured scenes, like back roads, alameda, and farmland. Existing semantic segmentation approaches of structured roads cannot meet the real-time and accuracy requirements when encountering unstructured scenes, thus hindering the autonomous operation of the intelligent agents in these scenes. To address the gordian problems, FRPNet is proposed to conduct real-time unstructured semantic segmentation in the field scene. Specifically, the semantic contexts are accurately extracted by introducing customized residual connections into the lightweight FasterNet-based encoder. Afterward, a modified partial Atrous spatial pyramid pooling (PASPP) is proposed to extract multi-scale features from the high-level semantic embedding, which improves the recognition of irregular boundaries and confused classes. Finally, a decoder whose structure is symmetric with the encoder is proposed to segment unstructured scenes by decoding the multi-scale semantic embedding. Additionally, a niche-targeting loss function called Ohd-Loss is proposed to optimize FRPNet. It enhances the model's focus on small-sample classes and addresses the issues of imbalanced class distribution and loss of scene details. Quantitative evaluations show that MIoU of FRPNet reaches 55.10% and 53.17% in the RUGD and RELLIS test sets, respectively. Meanwhile, FLOPs and Params are reduced to 5.27G and 9.74 M, which indicates that FRPNet effectively improves the segmentation accuracy in unstructured field scenes while satisfying real-time requirements. Qualitative evaluations of the self-developed unmanned vehicle running on the back roads verify the generalization performance of FRPNet. In a nutshell, FRPNet endows autonomous agents to perceive the surrounding field scenes with low-cost RGB cameras in real-time, facilitating the subsequent decision-making process. The code will be released at https://github.com/beautifulgirl11/FRPNet.


Temporal relation extraction with contrastive prototypical sampling

Abstract: Temporal relation extraction aims to infer the temporal order of either two events in the document. Because of the nature of events in real life, severe imbalanced temporal relation classes exist in the temporal relation extraction task. Even though various methods have been proposed to improve the overall performance, the accuracy of these methods on minority temporal classes is limited. In this work, we present a contrastive prototypical learning architecture to address this problem, which explicitly models the spatial similarity between instances in the embedding space so that instances from minority classes can be distinguished from the large classes. To make it compatible with current temporal relation extraction settings, we propose a novel sampling memory queue-based method so that the architecture can be applied to a limited batch size scenario. We further design a context encoding layer that incorporates both contextualized information and linguistic features such as tense information and dependency. Our extensive experiments on TimeBank-Dense, TDDiscourse, and MATRES datasets demonstrate that our model can significantly improve the performance of minority relation classes and, therefore increase the overall learning ability.


In situ three-dimensional reconstruction of laser powder bed fusion parts by light field camera

Abstract: The assessment of output quality in the laser powder bed fusion process entails the analysis of the three-dimensional morphology of the manufactured parts, which plays a crucial role in quality control to prevent metallurgical defects. This study proposes a novel approach of three-dimensional reconstruction for laser powder bed fusion parts using a light field camera. Initially, a consistency method for high-precision camera calibration was explored to determine the virtual camera pixel size and coefficient of effective focal length of light field camera. Subsequently, the model based light field epipolar-plane image Unet is designed to obtain disparity information without any data augmentation techniques. By leveraging both binocular and light field optical paths, a robust mapping relationship of the disparity, depth, and visually coherent three-dimensional contour was established. Finally, the three-dimensional contours of laser powder bed fusion parts were reconstructed by the established mapping with a single exposure of the light field camera. The proposed method enables three-dimensional contour visualization of laser powder bed fusion parts from multi-view perspectives, which facilitates in situ monitoring of the spattering distance, three-dimensional spatial points and lines of the printed layers and contours of printed parts. With a calibration error of 50 μm and resolution of 180 μm for the light field camera (Lytro illum camera), the maximum relative and absolute errors between reconstructed three-dimensional spatial points were 4.33 % and 260 μm respectively when compared with theoretical dimension, and 0.5 % and 210 μm respectively when compared with manufactured dimension. The maximum root-mean-square error of reconstructed three-dimensional contour surfaces was found to be 1.55 mm. This study represents the first application of a light field camera for in situ three-dimensional reconstruction of laser powder bed fusion parts, which notably can be obtained from only a single round of calibration and exposure, enabling seamless monitoring of their surface morphology and facilitating online quality control. These advancements have vast potential for enhancing high-end additive manufacturing.


The world in a grain of sand: Condensing the string vacuum degeneracy

Abstract: The proliferation of vacuum solutions to string theory highly deter the search for the Standard Model. We propose a novel approach to this problem by finding an efficient measure of similarity of vacua. Using one million concrete examples, the paradigm of few-shot machine-learning represents them as points in Euclidean three-space with similar points clustered together. We thereby compress the search space for desired physics to within one percent of the original. Our analysis provides an explicit method for machine-learning the landscape and finding 'typicality', even with minuscule data.


Lightweight Whole-Body Human Pose Estimation With Two-Stage Refinement Training Strategy

Abstract: Human whole-body pose estimation is a challenging task since the model needs to learn more keypoints than the body-only case. To meet the needs of real-time performance while maintaining accuracy is also a hard issue in whole-body pose estimation due to the learning capability of lightweight networks. In order to solve the above problems to a large extent, we propose a light whole-body pose estimation method with an optimized training strategy. The model is designed based on bottom-up architecture as a base network followed by a refinement network. We propose a two-stage training process, which learns rough features in the first stage and then improves estimation precision in the second stage. An online data augmentation procedure is proposed in the second stage to improve refinement performance. We also introduce a separate learning refinement structure that fine-tunes for body, foot, and hand part independently. Experimental results show that our method improves over 8%–10% average precision compared with other lightweight state-of-the-art approaches in the whole-body pose estimation task, with nearly a quarter (25%) size of model parameters saved.


A combined deep CNN with a chimp optimization approach for lung cancer diagnosis

Abstract: The most common type of disease that is normal among women is lung cancer. It is one of the main reasons among women, despite great efforts to prevent it through trackers. An automatic disease detection system helps doctors identify and provide accurate results, thus minimizing the mortality rate. Computer Aided Diagnosis (CAD) has minimal human intervention and produces more accurate results than humans. It will be a difficult and lengthy task that depends on the experience of the pathologists. Deep learning methods have been shown to give better results when correlated with ML and extract the best highlights from images. The main objective of this article is to propose a deep learning technique in combination with a convolution neural network (CNN) with a chimpanzee optimization algorithm to diagnose lung cancer. Here, CNN is used for feature extraction and used for extracted feature detection. Experimental results show that the proposed system achieves 100% accuracy, 99% sensitivity, 99% recall, and 98% F1 score compared to other traditional models. As the system achieved correct results, it can help doctors easily investigate lung cancer.


Blending of a novel all sky imager model with persistence and a satellite based model for high-resolution irradiance nowcasting

Abstract: High shares of variable energy sources such as photovoltaics (PV) make balancing network load and generation increasingly challenging. Electricity grids with high PV-penetration benefit from the consideration of intra-minute and intra-hour variabilities via nowcasts (shortest-term forecasts). In this study we present a novel all-sky imager (ASI) nowcasting system which is benchmarked against an established ASI method, a satellite nowcasting system and persistence. Nowcasts of our novel ASI method, satellite method and persistence are subsequently combined to a hybrid model with improved accuracy. ASI systems exploit sky images from fisheye cameras to analyze sky conditions, predict upcoming cloud situations and derive irradiance nowcasts from these. Our ASI method uses a novel machine-learning (ML) based approach that makes direct use of pixel values and other image features. Measurements from a spatially distributed irradiance measuring network of eight stations within a radius of 10km around the camera position are used to train our ASI and our hybrid model and validate our nowcasting results. In the evaluation we put a special focus on irradiance variability conditions. The high share of stable cloud situations within our dataset (∼68 %) results in a relatively good performance of persistence nowcasts. In higher variability situations persistence is quickly outperformed by all other nowcasting methods. Our ASI method clearly outperforms the reference ASI method at all lead times (LTs). It moreover exhibits a significantly lower root mean square error (RMSE) than the satellite-based method up to LT≤11min ahead and a lower mean absolute error (MAE) throughout the entire interval. The hybrid model brings about an RMSE improvement score of 5-13% over the respective optimal individual method.


DVF:Multi-agent Q-learning with difference value factorization

Abstract: In cooperative multi-agent games, agents are required to learn effective cooperative behaviors within complex action spaces. An effective approach is to utilize the Individual-Global-Max (IGM) principle to decompose the global reward signal into individual contributions from each agent. However, existing methods confront two significant challenges: ensuring the accuracy of the value function decomposition process and guaranteeing monotonic improvement in joint policies. These challenges become exacerbated in scenarios involving non-monotonic matrices. To address these challenges, we introduce a novel and flexible value factorization method called Difference Value Factorization (DVF). The key idea of our method is to transform the IGM principle into a new form DVF-IGM, which addresses non-monotonic constraints by ensuring the consistency of the IGM process between the joint difference value and the complex non-linear sum of independent difference values. A centralized evaluator is employed to estimate global Q-values, which not only enhances expressiveness but also constructs difference values for updating individual value functions. We demonstrate that DVF-IGM is an equivalent transformation of IGM and that DVF has the monotonic improvement property. Empirically, our method has been shown to maintain and recover the optimal policy in non-monotonic matrix games and achieve state-of-the-art performance in cooperative tasks within the StarCraft Multi-Agent Challenge (SMAC).


Quartzite complexities: Non-destructive analysis of bifacial points from Västerbotten, Sweden

Abstract: Northern Fennoscandia is a geologically complex region affected by both glacial and postglacial processes. Quartzite was a key material type utilized by hunter-gatherers in Northern Sweden around the period 4 000 – 2 000 BP, and is thus critical to the understanding of raw material procurement and material flow within the region. However, there is a severe lack of methodological development in the characterization of these materials, and provenance of locally available geological material is complex and fraught with uncertainty. 126 quartz/quartzite points and preforms were sampled from 47 archaeological sites along the upper Ångerman river valley in Västerbotten, Sweden. The material has been analysed non-destructively using three separate portable spectroscopic instrumentations (Near-infrared, Raman, X-Ray Fluorescence). Evaluation of the spectra and exploratory data analysis using Principal Component Analysis demonstrates detectable differences in the material that likely stem from diagenetic/paragenetic origin. The presence of graphite, muscovite and biotite could likewise provide information on the material's metamorphic grade. In addition to reaffirming the potential of field-based screening instrumentation, these results will benefit future surveys of geological sources in the region. They also indicate potential for the construction of a predictive model that could classify the quartzite based on its chemical characteristic. Such a model would prove useful in future spatial analysis and testing of models of raw material management.


Context-based modeling for accurate logo detection in complex environments

Abstract: Logo detection involves the tasks of locating and classifying logo objects in images and videos, and has been widely applied in the real world. However, most existing approaches rely on general object detection strategies that do not fully utilize the unique characteristics of logos. This can lead to sub-optimal performance in complex environments, especially when logos are small or have varying sizes and shapes. We observe that logos belonging to the same category often share similar context information, such as background dependency of the logo. This motivates us to incorporate contextual information to improve logo detection. Our proposed method, Context-based Modeling Enhancement Network (CME-Net), aims to enhance the distinctive region feature of logos using contextual information. We achieve this by modeling both the logo and its background region to extract their contextual information. This contextual information serves as a guide for enhancing the saliency of the distinctive regions within the logo image. To further improve the accuracy of detection, we have implemented a scale feature balance strategy. This strategy solves the problem of losing scale information caused by enhancement, ensuring that all scales are appropriately considered. Additionally, noise generated during the enhancement process is also effectively suppressed. By effectively leveraging contextual information, our method successfully tackles the challenge of accurately locating logo objects. Our extensive experiments on four public benchmark datasets demonstrate that CME-Net improves the accuracy of logo detection in complex environments.


CKTN: Commonsense knowledge transfer network for human activity understanding

Abstract: In most human activity understanding research, various deep learning networks are well designed to fit the character distribution of different human activities in images. After being fed with enough training data, the networks can perceive related patterns passively. Such abilities originate from the stimulation of training data, but these networks lack the ability to perform subjective judgment. When lacking sufficient training data, networks have difficulty perceiving valuable information, which leads to poor performance. However, human beings can infer human activities in complex situations through subjective judgment with their knowledge. On this point, we propose the commonsense knowledge transfer network (CKTN), which is empowered with human cognitive mechanisms and is able to understand human activities by using prior commonsense knowledge. More specifically, we study the structural combination rules of human bodies and explore the co-occurrence relationship between different body parts and objects. Then, we construct the part action cooccurrence probability matrix and human skeleton knowledge matrix, which work as prior knowledge, guiding our network to absorb and reason what the human activities in images are. Compared with traditional deep learning methods, our network realizes the unification of both subjective cognition and passive perception abilities. Experiments show the effectiveness of this method.


Images of Gaussian and other stochastic processes under closed, densely-defined, unbounded linear operators

Abstract: Gaussian Processes (GPs) are widely-used tools in spatial statistics and machine learning and the formulae for the mean function and covariance kernel of a GP [Formula: see text] that is the image of another GP [Formula: see text] under a linear transformation [Formula: see text] acting on the sample paths of [Formula: see text] are well known, almost to the point of being folklore. However, these formulae are often used without rigorous attention to technical details, particularly when [Formula: see text] is an unbounded operator such as a differential operator, which is common in many modern applications. This note provides a self-contained proof of the claimed formulae for the case of a closed, densely-defined operator [Formula: see text] acting on the sample paths of a square-integrable (not necessarily Gaussian) stochastic process. Our proof technique relies upon Hille’s theorem for the Bochner integral of a Banach-valued random variable.


Consolidating Trees of Robotic Plans Generated Using Large Language Models to Improve Reliability

Abstract: The inherent probabilistic nature of Large Language Models (LLMs) introduces an element of unpredictability, raising concerns about potential discrepancies in their output. This paper presents a novel approach designed to generate correct and optimal robotic task plans for diverse real-world demands and scenarios. LLMs have been used to generate task plans, but they are unreliable and may contain wrong, questionable, or high-cost steps. The proposed approach uses LLM to generate a number of task plans as trees and amalgamates them into a graph by removing questionable paths. Then an optimal task tree can be retrieved to circumvent questionable and high-cost nodes, thereby improving planning accuracy and execution efficiency. The approach is further improved by incorporating a large knowledge network. Leveraging GPT-4 further, the high-level task plan is converted into a low-level Planning Domain Definition Language (PDDL) plan executable by a robot. Evaluation results highlight the superior accuracy and efficiency of our approach compared to previous methodologies in the field of task planning.


Hidden Markov model for spatial analysis of three-dimensional mineralization distribution: Insights into magma flow and mineral exploration targets in the Jinchuan Ni-Cu-(PGE) sulfide deposit, China

Abstract: The spatial distribution of mineralization includes significant clues about the magma/fluid flow process for ore formation. However, given the complex nature of the ore-forming process and its weak signature in the mineralization data, revealing the magma/fluid flow process in their mineralization footprint presents a considerable challenge. In this paper, we present a hidden Markov model (HMM) for the analysis of three-dimensional (3D) spatial distribution of mineralization. The HMM assumes the magma flow is a Markov process that is "hidden" in the "observable" mineralization features. Under this setting, the HMM quantifies the states of magma flow and links them as a whole in a probabilistic manner. This allows for the comprehensive combination of mineralization information, facilitating holistic inference of magma flow hidden in the mineralization signal. Based on the maximum likelihood principle, the magma trajectories and the associated injection points are inferred from the HMM. The proposed model is applied to spatial analysis of the mineralization in the world-class Jinchuan Ni-Cu-(PGE) magmatic sulfide deposit in NW China. Based on the 3D mineralization data interpolated from drill assays, the HMM infers magma trajectories and the associated injection points at Jinchuan, which indicate the Jinchuan deposit was formed by a multiple magma conduit system that comprises four independent injection points of magma ingress. According to the identified magma trajectories and injection points, four sites beneath the injection points and along the extension of magma trajectories are considered promising for future mineral exploration. With the insights gained from this case study, the proposed HMM can serve as an effective tool in the exploration information system to unveil magma/fluid flow history and guide exploration efforts toward concealed, deep-seated mineralization.


Machine learning approach to optimal task scheduling in cloud communication

Abstract: Cloud communication is a combination of distributed computing and parallel computing. One of the biggest challenges in cloud communications is task scheduling, which is difficult due to the nondeterministic polynomial completeness (NP) of cloud systems. To solve this problem, various approximation techniques based on swarm intelligence have been developed. This study proposes a dual machine learning strategy using kmeans to optimize performance and aid in selecting cloud scheduling technologies. The first technique is called Efficient Kmeans (Ekmeans) and the second technique is called Kmeans HEFT (KmeanH), where HEFT stands for Heterogeneous Earliest End Time. Our main contribution is to reduce processing time and increase speed and efficiency for a given set of tasks. We evaluate the impact of both algorithms on different virtual machines (ranging from 2 to 32) and task sizes (ranging from 50 to 1000).


The multifractal nature of worldclass orogenic gold systems in greenstone belts: A multiscale approach and case study at the Córrego do Sítio deposit, Rio das Velhas greenstone belt, Brazil

Abstract: Understanding the controls on the spatial distribution of mineral deposits is crucial for prospecting new occurrences. Various geological processes operate together at microscopic to macroscopic scales, generating mineral systems responsible for significant accumulations of economic minerals. This characteristic of functioning at various scales, accompanied by repetition of features over space during mineralization, indicates that these processes are self-similar, meaning they exhibit fractal geometry. Previous research has demonstrated that the combined action of processes involved in mineralization in such critical, nonlinear systems, produces objects with multifractal characteristics. Therefore, the spatial distribution of mineral deposits should be studied using appropriate methods for better understanding of their controls. In this context, this study investigated the multiscale geometric characteristics of worldclass orogenic gold systems in Archean greenstone belts using fractal and multifractal analyses. First, at the macroscopic-scale, regional-scale features were considered in the major greenstone belt provinces with orogenic gold deposits worldwide, namely: Abitibi (Canada), Norseman–Wiluna (Australia), Rio das Velhas (Brazil), and Central Lapland (Finland). Next, at an intermediate scale, local-scale features were examined in detail within the context of the Córrego do Sítio Lineament. Finally, at the microscopic scale, the focus was on the Córrego do Sítio orogenic gold deposit—a key deposit in the Rio das Velhas greenstone belt. Fractal dimensions of the spatial distributions of mineral deposits, magnetic lineaments, and their intersections were investigated at the macroscopic to local scales, as well as the spatial distribution of ore minerals at the microscopic scale, using the box-counting method. Maps of fractal dimensions of magnetic lineaments were generated and compared with the spatial distribution and tonnage of orogenic gold deposits at the regional scale. The same features were analyzed using the method of multifractal moments. The results at the three scales were analyzed and synthesized to identify meaningful patterns for understanding these orogenic gold systems. With regard to fractality, the orogenic gold deposits in all the investigated provinces exhibited unique spatial distributions at different scales, namely: less than 2–4 km, from 2 to 4 km to 10–30 km, and more than 10–30 km. The analysis of magnetic lineaments yielded a correlation between gradients of fractal dimensions and gold tonnages in the orogenic gold deposits. The multifractal analyses indicate multifractal geometries extending from the microscopic to the macroscopic scale. The calculated multifractal indices of Δα (singularity variation), τ“(1) (multifractality index), and Δf(α) (multifractal spectrum variation) showed direct relationships with endowment of orogenic gold in the studied provinces, as well as with differences in mineralization styles in thin sections. The findings demonstrate the effectiveness of the analyses used for improved characterization of the studied mineral systems; they also allow comparing similar systems at different scales and locations and they can be used to study other mineral systems with similar geological controls.


Transformation of a class of pure multipartite entangled states

Abstract: Entanglement transformation can give insight into quantum entanglement and provide desired resources for quantum information processing. However, the complexity for analyzing entanglement transformation exponentially grows with the increased particle numbers. We find that a sort of multipartite entangled states can be described by the Valence-bond state (VBS) picture and can be decomposed into a series of cells. Thus the study of multipartite entanglement can be greatly simplified. We propose an inverse entanglement resource searching method by which one can find states that can be transformed into desired entangled resource states via local operations and classical communication (LOCC) and stochastic LOCC (SLOCC). In the case of bipartite entanglement, our method includes Nielsen’s majorization theorem as a special case. We apply the method to measurement based quantum computation (MBQC) and find some resource states for MBQC that can be transformed into Cluster states via LOCC or SLOCC. We also construct the Hamiltonians of two-body nearest neighbor interactions with the found resource states as their eigenstates.


Scale perception data compression method with blockchain technology for UAV communication

Abstract: The demand for high data throughput and security in Unmanned Aerial Vehicle (UAV) communication is rising. Efficient data transmission remains challenging due to limited wireless resources. This paper introduces a Scale Perception Compression Method that leverages compression and blockchain technology to optimize UAV communication. The method involves normalizing data into matrices, generating compression and decompression matrix codebook pairs, and selecting suitable pairs for small or large-scale matrices. Simulation results show that the error rate is associated with the distributions of the data and compression matrix. It remains relatively low when the data and compression matrix subjected to Uniform distribution compared to Gaussian distribution, regardless of the values of the number of rows m and columns r in compression matrix. Bubble method is more suitable to find out the proper matrix pair to compress and decompress transmitted data for small-scale codebook because it is stable, fast and results in lower difference between original and recovered data matrices. The same applies to Merge method for the large-scale codebook. The ER is always lower based on a small-scale codebook searching by Bubble method than that of on a large-scale codebook searching by Merge method, which indicates the former is sufficient for compression.


High-precision and high-speed surface topography measurement method of microstructures based on laser scanning transverse differential confocal

Abstract: The manufacturing of microstructured devices requires higher performance regarding the increasing development of finer structures and more complex structural shapes, which poses a challenge for existing measurement techniques in terms of anti-scattering, high-precision and high-speed. Accordingly, this study proposes a new technology for surface topography measurement of microstructures based on laser scanning transverse differential confocal (LSTDCM), which inserts a D-shaped aperture into the existing confocal system, and performs split-focal spot detection on the focal plane. This method exhibits improved axial resolution by performing differential phase subtraction of the front- and back-focal signals to obtain a highly sensitive axial response signal. Further normalization of the transverse differential confocal signal eliminates the multiplicative noise of the system, avoids the impact of changes in roughness on the measurement results, and achieves an anti-scattering measurement. The linear region of the axial response signal around the zero-crossing point were used to achieve a "non-axial scanning" height measurement, which was combined with high-speed beam lateral scanning to achieve a high-speed areal topography measurement. Theoretical analysis and experimental results indicate that, for samples with height changes within the linear region of axial response, the method can complete areal topography measurement in 1.7 s with the axial resolution of 1 nm and the measurement range of 128 × 128 μm. A microstructured device processed using a nanoform single-point diamond machine tool and a semiconductor wafer were employed in the application validation experiment. And achieve a wide range stitching measurement of 1 mm × 1 mm under a 100 × objective along with the transverse scanning stage. The measurement efficiency is about three times higher than that of Olympus OLS4100 confocal microscope. The method provides a new and effective technical approach that is not limited by the surface roughness and complex structure of the tested devices, and can achieve high-precision as well as high-speed optical nondestructive measurement without moving the tested device. Thus, this work is expected to has important practical applications in the field of ultra-precision measurement.


The completeness of reasoning algorithms for clause sets in description logic <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si4.svg" display="inline" id="d1e76"><mml:mi mathvariant="script">ALC</mml:mi></mml:math>

Abstract: Metadata and ontologies are used to enable computers to read data on the Semantic Web. Description logics provide formal languages and reasoning algorithms for such metadata and ontologies. Tableau-based algorithms have been developed for deciding concept satisfiability in description logics but they lead to inefficient reasoning for complex logical expressions. The resolution principle and the Davis–Putnam–Logemann–Loveland (DPLL) solve the weakness by treating simplified clausal forms in description logics. However, resolution-based reasoning for description logics causes to compute various combinations of clause pairs to branch on. On the other hand, DPLL-based approaches have not presented a complete and decidable reasoning algorithm for directly deciding the satisfiability of clauses in description logics. In this paper, we propose a decidable and complete reasoning algorithm for clauses in description logic ALC. Using DPLL techniques, this algorithm can reduce reasoning steps for conjunctive normal form (CNF) concepts in ALC. We prove the termination, soundness, and completeness of the reasoning algorithm for the satisfiability of CNF concepts by introducing a restricted tableau for CNF concepts. We show some examples of reasoning steps for applying clauses in ALC to the algorithm.


Attack graph-based stochastic modeling approach for enabling cybersecure semiconductor wafer fabrication

Abstract: The increasing interconnectivity between information technology (IT) and operational technology (OT) has brought numerous benefits to advanced manufacturing, including the semiconductor industry, by enhancing the quality and by reducing the costs of wafer and integrated circuits (IC) fabrication. However, the transition of the semiconductor industry to secure digital manufacturing has also increased the importance of safeguarding networked assets from cybercriminals. To this end, a flexible scheme is developed in this paper to model wafer fabrication as a directed attack graph. This graph representation is then used as the basis of a two-stage stochastic programming model that considers uncertainty in attack probabilities while minimizing expected cyber risk by employing optimal, budget-constrained countermeasures (i.e., arc interdiction strategies). A sample average approximation coupled with a progressive hedging algorithm or Bender's decomposition are proposed. Numerical experiments are performed to assess the viability of the proposed solution algorithms, the optimal arc interdiction strategies under various budget levels, and the effect of attack probability mappings. The results indicate that computing terminals nearest the manufacturing process have the highest defense priority and that the sample average approximation coupled with a progressive hedging algorithm is a suitable hybrid solution procedure for large instances when compared with sample average approximation coupled with Benders.


Image paragraph captioning with topic clustering and topic shift prediction

Abstract: Image paragraph captioning involves generating a semantically coherent paragraph describing an image's visual content. The selection and shifting of sentence topics are critical when a human describes an image. However, previous hierarchical image paragraph captioning methods have not fully explored or utilized sentence topics. In particular, the continuous and implicit modeling of topics in these methods makes it difficult to supervise the topic prediction process explicitly. We propose a new method called topic clustering and topic shift prediction (TCTSP) to solve this problem. Topic clustering (TC) in the sentence embedding space generates semantically explicit and discrete topic labels that can be directly used to supervise topic prediction. By introducing a topic shift probability matrix that characterizes human topic shift patterns, topic shift prediction (TSP) predicts subsequent topics that are both logical and consistent with human habits based on visual features and language context. TCTSP can be combined with various image paragraph captioning model structures to improve performance. Extensive experiments were conducted on the Stanford image paragraph dataset, and superior results were reported compared with previous state-of-the-art approaches. In particular, TCTSP improved the consensus-based image description evaluation (CIDEr) performance of image paragraph captioning to 41.67%. The codes are available at https://github.com/tt0059/TCTSP.


Securing IIoT applications in 6G and beyond using adaptive ensemble learning and zero-touch multi-resource provisioning

Abstract: The advent of Industry 5.0, driven by cutting-edge 6G technologies such as immersive cloud eXtended Reality (XR), Autonomous vehicles, holographic communication, and Digital Twin (DT), is set to trigger a substantial upswing in the deployment of the Industrial Internet of Things (IIoT) devices interconnected within networks. This expansion of IIoT devices will create a wider attack surface and increase the risk of data breaches, privacy violations, and system disruptions. Therefore, it is essential to design innovative mechanisms to ensure the reliability and security of these advanced 6G applications, as well as the IIoT devices that support them. In this context, we propose a novel Software-defined Networking (SDN)-based Ensemble Learning (EL) Framework for Secure IIoT applications in 6G and beyond, called AdaptSDN. The proposed framework leverages SDN technology to dynamically allocate network resources and deploy security measures on demand. It also leverages EL techniques to improve the intrusion detection system's accuracy. By isolating IIoT devices into network slices, the framework limits the impact of attacks and reduces the potential for cascading failures. Digital twins are used for creating a virtual replica of the IIoT network, allowing for a real-time security threat detection. In particular, AdaptSDN includes three main modules: (1) A novel digital Twin (DT)-enabled data gathering and selection of informative features module to achieve two main objectives: reducing computational complexity and improving detection performance; (2) An SDN-based lightweight adaptive boosting module that uses an advanced boosting EL techniques to dynamically adjust weights and to effectively identify and respond to IIoT attacks in real-time; and (3) A zero-touch resources provisioning module that employs a non-cooperative game theory approach. This approach allows for automatically provisioning various resources in a network to efficiently mitigate network attacks; it enables SDN nodes to obtain the required virtual resources, i.e., storage, computing, and bandwidth, from the main orchestrator with respect to IIoT attack type. We carried out comprehensive experiments to assess the effectiveness of our proposed framework in detecting real-world IIoT attacks. The numerical results confirm that AdaptSDN has the potential to enable secure and reliable IIoT applications in 6G and beyond, meeting the stringent service requirements of the new emerging applications.


Using sky-classification to improve the short-term prediction of irradiance with sky images and convolutional neural networks

Abstract: Clouds moving in front or away from the sun are the leading cause of irradiance variability. These variations have a repercussion on the electricity production of photovoltaic systems. Predicting such changes is essential for proper control of these systems and for maintaining grid stability. Images from the sky have proven to help with short-term solar irradiance forecasting, especially when combined with artificial intelligence. Nevertheless, these models tend to smooth the irradiance fluctuations. We propose a forecasting model to predict the clear-sky index in a forecast horizon of 20 min with a 1-minute resolution. Our model, based on a classifier to determine the sky conditions and, on an optical flow, applies an artificial intelligence model explicitly trained on each class of sky conditions. This strategy has an equivalent performance to an unclassified model and a forecast skill between 5 and 20% with respect to the smart persistence model for most classes of sky conditions while requiring considerably less training data. Although our model reduces the overall predicting error, it still has difficulties predicting irradiance changes and mainly overcast days. Our classifying strategy can be applied to other models targeting different objectives to predict sudden changes in either irradiance or power related to photovoltaic systems.


Indium concentrations as a potential indicator of orebody-intrusion distance in a skarn Pb-Zn system, elucidated by the Fozichong Orefield (South China)

Abstract: Critical metal indium tends to be enriched in skarn Pb-Zn systems, but the spatiotemporal distribution pattern of indium and its controls in a specific skarn Pb-Zn deposit remains unclear. To address this problem, we conducted a combination of geological, mineralogical, and trace element geochemical investigations on the Fozichong skarn Pb-Zn Orefield (South China) that contains two individual Pb-Zn deposits with different distances from ore-causative intrusion (the proximal Longwan and the distal Guyi deposits). Three types of sphalerites with different mineralogical characteristics have been identified that is Sp1 (in the early sulfide stage with chalcopyrite solid solution), Sp2 (with pyrrhotite inclusions), and Sp3 (in the late sulfide stage without solid solution). Further analyses by EPMA (Electron Probe Micro-Analyzer) and LA-ICP-MS (Laser Ablation Inductively Coupled Plasma Mass Spectrometer) reveal that the sphalerite from the two Pb-Zn deposits have different indium concentrations. That is, indium is significantly enriched in the proximal Longwan sphalerite, varying from 1.20 to 287.69 ppm (mean = 78.67 ppm). By contrast, indium is relatively barren in the distal Guyi sphalerite, ranging from 0.04 to 4.79 ppm (mean = 2.11 ppm). The calculated GGIMFis sphalerite geothermometer confirms that the temperatures of proximal Longwan (283 – 392 °C) are higher than those of the distal Guyi (295 – 336 °C). Collectively, the sphalerite proximal to the magmatic intrusion with higher temperatures is concentrated into more indium, but indium in the distal sphalerite significantly decreases by one order of magnitude. Furthermore, the indium contents in sphalerite can be used as a robust indicator of the distance between Pb-Zn orebodies and magmatic intrusion in the skarn Pb-Zn system.


EESSO: Exploiting Extreme and Smooth Signals via Omni-frequency learning for Text-based Person Retrieval

Abstract: Most of the existing methods manage to tackle the problem of text-based person retrieval from the spatial-wise perspective. In this paper, we manage to address the problem of this task from a novel perspective, namely, the frequency-wise perspective. To this end, we propose to Exploit Extreme and Smooth Signals via Omni-frequency learning (EESSO) through a jointly optimized multi-stream architecture. It consists of a Spatial Information Stream (SIS), an Extreme Signal Stream (ESS) and a Smooth Signal Stream (SSS). EESSO aims to excavate the complementary effect between spatial-wise and frequency-wise features, so as to achieve a superior performance. A novel Uncertainty-Guided Mutual Learning Mechanism (UG-MLM) is utilized during training, which not only enables the three streams to communicate with and learn from each other, but also models the data-related heteroscedastic uncertainty as a weight for knowledge transference, and hence enables each stream to adaptively allocate knowledge from the other two streams. A large number of experiments are carried out on the widely-used CUHK-PEDES, RSTPReid and ICFG-PEDES datasets to verify the effectiveness of EESSO. Through the experimental results, it may not be hard to find that EESSO has achieved the state-of-the-art performance in supervised, weakly supervised and cross-domain text-based person retrieval settings.


Variation-aware semantic image synthesis

Abstract: Semantic image synthesis (SIS) aims to produce photorealistic images aligning to given conditional semantic layout and has witnessed a significant improvement in recent years. Although the diversity in image-level has been discussed heavily, class-level mode collapse widely exists in current algorithms. Therefore, we declare a new requirement for SIS to achieve more photorealistic images, variation-aware, which consists of inter- and intra-class variation. The inter-class variation is the diversity between different semantic classes while the intra-class variation stresses the diversity inside one class. Through analysis, we find that current algorithms elusively embrace the inter-class variation but the intra-class variation is still not enough. Further, we introduce two simple methods to achieve variation-aware semantic image synthesis (VASIS) with a higher intra-class variation, semantic noise and position code. We combine our method with several state-of-the-art algorithms and the experimental result shows that our models generate more natural images and achieve slightly better FIDs and/or mIoUs than the counterparts. Our codes and models will be publicly available.


Anomaly detection for synthetic diamond grain using monocular depth estimation

Abstract: This paper studies the problem of estimating the maximum depth from a single two-dimensional monocular image of a grain of synthetic diamond to detect anomalies that would breach the quality criteria. To target this problem, we propose two models that employ different approaches to the depth map predicted by the zero-shot monocular depth estimation model, MiDaS, in combination with EfficientNet. The first model utilizes a normal map generated by differentiating the MiDaS-predicted depth map, taking an RGB image as input. The inclusion of the normal map provides valuable information about the pseudo-roughness of the diamond grain, aiding in the identification of anomalies. The second model incorporates a weighting scheme that enhances the MiDaS-predicted depth map through pointwise convolution with the RGB image. By assigning appropriate weights, the model highlights crucial features of the image, thus improving the overall performance. The main objective of both approaches is to improve the accuracy of the models by incorporating the per-pixel relative depth information extracted by MiDaS. Both of these proposed models outperformed existing state-of-the-art models in detecting anomalous synthetic diamond particles. Through this research, we aim to contribute to the advancement of anomaly detection in the field of synthetic diamond quality assessment, leveraging the potential of monocular depth estimation and innovative model architectures.


Soil organic matter content prediction based on two-branch convolutional neural network combining image and spectral features

Abstract: Soil organic matter (SOM) is the main source of soil nutrients. Rapid determination of SOM content is of great significance for guiding field management. The change of SOM content will change the physical properties of soil, such as color and porosity, and then lead to obvious differences in soil reflectance in the visible and near infrared range. In this study, 417 topsoil (5–15 cm) samples were collected from five experimental fields in North China, and spectral data in the range of 350–1700nm and image data of sampling points were obtained. Multi-CNN network was established to realize the feature fusion of image and spectrum based on two-branch CNN to predict SOM content. Firstly, the spectral feature extraction module based on the improved CNN was constructed, and the spectral information from 768 to 140 bands was compressed by using Auto Encoder, and the feature was extracted by CNN. The feature extraction of the soil image is realized by the ResNet50 network, and finally the feature information of the image and the spectrum is fused by the bilinear pooling fusion module in the fully connected layer to complete the SOM content prediction and output the results. After experimental verification, BLMulti CNN had the highest accuracy in predicting organic matter models, in which the prediction set R2 is 0.910, 0.017 higher than that of the Multi-CNN based on concat, 0.075 higher than that of the prediction method of AE-CNN and 0.10 higher than that of the image prediction model based on ResNet50. The BLMulti-CNN effectively improves the prediction accuracy of the model and provides technical guidance for the rapid detection of SOM content in the field.


The Concordance Index decomposition: A measure for a deeper understanding of survival prediction models

Abstract: The Concordance Index (C-index) is a commonly used metric in Survival Analysis for evaluating the performance of a prediction model. In this paper, we propose a decomposition of the C-index into a weighted harmonic mean of two quantities: one for ranking observed events versus other observed events, and the other for ranking observed events versus censored cases. This decomposition enables a finer-grained analysis of the relative strengths and weaknesses between different survival prediction methods. The usefulness of this decomposition is demonstrated through benchmark comparisons against classical models and state-of-the-art methods, together with the new variational generative neural-network-based method (SurVED) proposed in this paper. The performance of the models is assessed using four publicly available datasets with varying levels of censoring. Using the C-index decomposition and synthetic censoring, the analysis shows that deep learning models utilize the observed events more effectively than other models. This allows them to keep a stable C-index in different censoring levels. In contrast to such deep learning methods, classical machine learning models deteriorate when the censoring level decreases due to their inability to improve on ranking the events versus other events.


Adaptive learning on hierarchical data streams using window-weighted Gaussian probabilities

Abstract: The hierarchical data stream classification task addresses challenges in both hierarchical and data stream classification primary areas. In these scenarios, machine learning models must simultaneously deal with class hierarchies and adapt to respond to nonstationary data. Given such a challenging set of traits, existing techniques are deficient, as they perform incremental learning and are slow to adapt to newer data, thus not capturing their dynamics in a timely fashion. In this study, we propose two novel adaptive Gaussian Naive Bayes classifiers tailored to classify hierarchical data streams. The models use window-weighted Gaussian probabilities to consider current and historical data and improve the adaptability of the classifiers, especially for nonstationary data streams. As a result of our research, we introduce a unified protocol for evaluating and comparing hierarchical data stream classifiers and establish a benchmark for the hierarchical data stream classification task encompassing the proposed methods and state-of-the-art classifiers. The results demonstrate that our proposed algorithms achieve better prediction correctness than their state-of-the-art counterparts while responding more swiftly to changes in data distribution.


CBReT: A Cluster-Based Resampling Technique for dealing with imbalanced data in code smell prediction

Abstract: Code smell refers to substandard design patterns in software's source code that may lead to faults-prone implementation. Machine learning-based code smell prediction models suffer from data imbalance problems, i.e., one class contains significantly more instances than another. The existing oversampling approaches, such as SMOTE (Synthetic Minority Over-sampling Technique), have been used for balancing the code smell dataset by generating synthetic samples for the minority class. However, the distribution of classes of code smell datasets is overlapped; hence, randomly generated instances can damage the decision boundary between both classes. This paper addresses this issue and proposes a novel Cluster-Based Resampling Technique, CBReT, that generates synthetic instances by considering the distribution of the code smell data. The CBReT first formulates clusters (containing minority and majority instances) based on the data distribution using Gaussian Mixture Model (GMM). Next, each cluster is balanced separately by synthesizing minority instances. While balancing the clusters, the CBReT also checks the validity of the synthetic instances so that each synthetic instance holds similar properties as the other minority instances. To assess the performance of CBReT, extensive experiments have been conducted on the four publicly available benchmark code smell datasets. We have used various performance metrics to evaluate our model's performance. The experimental results show that the CBReT technique significantly increased the performance of the code smell prediction model by 0.18% (min) and 9.08% (max) compared to the state-of-the-art imbalance learning approaches.


Mineral prediction based on prototype learning

Abstract: In the field of mineral resource prediction, acquiring labeled data and bearing high annotation costs pose significant challenges. Moreover, distinct characteristics are present in different types of data, including geophysical, geochemical, and geological data. However, conventional deep learning methods often treat all data uniformly, neglecting the specificities inherent in various domains of knowledge. To address these issues, we propose Geo-Meta, an optimized prototype learning model that combines prototype learning, metric learning, and meta-learning strategies to classify multi-source geological samples. Geo-Meta utilizes tailored network architectures for different data types to fuse features and compute prototypes in the metric space. Additionally, it employs a generative model to identify subtle anomaly features and incorporates a semi-supervised algorithm based on label propagation to refine the initial prototypes, enhancing their robustness and category-awareness. Furthermore, Geo-Meta incorporates a dynamic distance metric module to quantify the similarity between sample features and class prototypes, facilitating effective category partitioning. Extensive experimentation demonstrates that our proposed model achieves remarkable performance. It attains an impressive Area Under the Receiver Operator Curve (AUC) of 98% without data augmentation, using a significantly limited number of samples. Moreover, it accurately identifies approximately 93% of ore deposits within only 7% of the study area, surpassing similar models in both accuracy and efficiency. The mineral resource prediction map generated by Geo-Meta aligns with the spatial distribution of mineral resources and adheres to geological knowledge, providing valuable technical support for decision-making in target area exploration. Our proposed model enables the adaptation of deep learning algorithms to geological big data, offering substantial potential for cost and time reduction in mineral prediction. Thus, it holds great relevance and practicality for the mining industry.


Parrots and Paragrams: AI Language Models and Erasure Poetry

Abstract: This essay on poetry and computation borrows a provocative trope from an essay written by AI specialists who describe large language models as "stochastic parrots," copyists relying on probability statistics to generate answers. The history of poetry affords us many instances of textual borrowing that might provocatively be called parroting, repetition embedded in procedural selection from a source text, most recently in the minor genre of erasure poetry. This essay pursues analogies and differences between the digital systems and erasure poetics with the assistance of a poetic device known as the paragram, associated with eccentric searchers for hidden or occult messages in literary classics, as well as linguistic theorists such as Ferdinand de Saussure, Roman Jakobson, and Julia Kristeva. The essay centers on a prominent erasure poem, Voyager (2010) by Srikanth Reddy, and also discusses more briefly texts by Tom Phillips, Ronald Johnson, Janet Holmes, and Susan Howe.


Non-degenerate-pump four-wave mixing kinetic inductance travelling-wave parametric amplifiers

Abstract: Abstract Kinetic inductance travelling-wave parametric amplifiers (KITWPAs) have been demonstrated to achieve high gain over broad bandwidths whilst achieving near quantum-limited noise performance, properties which are extremely important for many ultra-sensitive experiments. In early KITWPA designs, the requirement for phase-matching lead to the creation of a large zero-gain gap in the centre of the gain profile where the peak gain is, which also slightly narrows down the operational bandwidth of the device. This has been mitigated in more recent designs by introducing a DC bias to the KITWPA device, which allows the gap to be tuned away from the amplification band. However, the added DC biasing requires a more complicated experimental setup and potentially leads to unwanted heat leak in the cryogenic environment. Additionally, operation with a DC bias also become challenging at higher frequencies beyond the microwave regime. In this paper, we present the concept of a KITWPA operating in a non-degenerate-pump four-wave mixing (NP-4WM) regime, whereby the injection of two pump tones along with a weak signal results in a broad, flat gain profile that removes the zero-gain gap as well as eliminates the need for a DC bias and the complexities associated with it. We demonstrate how a NP-4WM KITWPA is feasible to achieve broadband amplification at a range of frequencies, first in the microwave range where most KITWPAs reported to-date have been successfully experimentally characterised. We then extend the designs to several millimetre (mm) bands to illustrate how we can use this technique to design a broadband front-end pre-amplifier that covers several Atacama Large Millimetre/sub-millimetre Array (ALMA) Bands.


Performance improvement in reservoir computing by using HfZrO2 FeFETs through operating voltage optimization

Abstract: Abstract We have investigated how the parameters of an input gate voltage ( V g ) waveform and a drain voltage ( V d ) impact the performance of reservoir computing (RC) using a Hf 0.5 Zr 0.5 O 2 ferroelectric FET (FeFET). The RC performance is maximized by the high swing amplitude of the V g and the most symmetrical polarization switching condition in the triangular-shaped input waveform, obtained by the center V g of 0.5 V, because of the enhanced polarization switching of the FeFETs. Regarding the V d dependence, the amount of the drain current and polarization switching have a trade-off relationship. As a result, a moderate V d of 1.0 V becomes optimum in terms of the RC performance because a difference in drain current responses between different gate input patterns is maximized with this V d . Furthermore, high computing capacities are achieved by combining the above optimal bias condition with drain current responses to both original and inverted gate input patterns.


Object aroused emotion analysis network for image sentiment analysis

Abstract: From cognitive psychology, objects are closely related to emotions, and inherently possess the ability to arouse human emotions. Hence, fully utilizing the relationships between objects and emotions can help achieve more accurate visual emotion recognition. In this paper, we propose a novel object aroused emotion analysis network to realize image sentiment classification by investigating the interactions between objects and emotions. To quantify the various emotion potencies of each object, a novel object emotion distribution module is proposed to explore the mapping among objects and emotions, and quantitatively demonstrate how various objects arouse different emotions. An object emotion-modeling mapping module is proposed to analyze the effect of objects and object combinations with emotion information on image sentiment; this module maps common objects to emotion dimensions and improves image sentiment classification with abundant object combination information. Then, we fuse the object-emotion mapping relation and multi-model features using BiGRU, thus realizing more accurate emotion recognition. Extensive experiments on widely used emotion datasets prove that our proposed method achieves excellent performance and outperforms most state-of-the-art image sentiment classification methods.


Artificial Intelligence (AI) in Tourism

Abstract: Artificial Intelligence (AI) has revolutionised the tourism industry, offering personalised experiences and streamlining operations. AI provides customised recommendations for travellers through data analysis and machine learning, making their journeys more meaningful. It has also improved efficiency through automated processes, chatbots and enhanced security measures. AI's ability to analyse large volumes of data enables tourism organisations to make data-driven decisions and target their marketing strategies effectively. One of the most notable contributions of AI in tourism is its ability to offer personalised recommendations. By analysing vast travel history, preferences and online behaviour, AI systems can provide tailored suggestions for destinations, accommodations, activities and dining options. This level of customisation enhances the overall travel experience, making it more relevant and satisfying for individual travellers. AI has also greatly improved operational efficiency within the tourism sector. Chatbots, powered by natural language processing, are increasingly being deployed by hotels, airlines and travel agencies to provide instant customer support and assistance. These chatbots can answer queries, offer recommendations and handle booking processes, reducing waiting times and enhancing customer satisfaction. In addition, facial recognition technology allows for quick and accurate identity verification at airports, hotels and other travel-related facilities. This improves security and provides travellers with a seamless and efficient experience. As technology advances, we expect AI to play a more prominent role in augmented reality, voice recognition and virtual assistants, further enhancing the travel experience and facilitating seamless interactions. In conclusion, AI has transformed the tourism industry by providing personalised recommendations, improving operational efficiency, enhancing security measures and enabling data-driven destination management.


Estimating the structural diversity introduced by decision forest algorithms : A probabilistic approach

Abstract: Structurally diverse decision trees are important for knowledge discovery and classification/prediction accuracy. Over the years, researchers have devoted much effort to the development of algorithms to increase diversity among the trees within an ensemble. While Kappa is commonly used to measure diversity among the decision trees, it does not measure the ability of the tree building algorithms to introduce diversity. Further, Kappa does not consider the structural diversity amongst the trees. Instead, Kappa measures the diversity of the predictions made from the trees produced, and are dependent on the datasets used. This paper presents a novel data-independent metric, called R index, for measuring the diversity that can be introduced by a decision forest algorithm without building the entire decision forest. The proposed measure is applied to five well-known algorithms that involve bagging and random subspacing. An efficient practical approach for calculating the R index empirically - R finder - is also proposed, and is implemented. Both R finder and Kappa were applied to thirty-two publicly available benchmark datasets under various algorithms to estimate the resulting diversity. The results indicate a generally strong negative correlation between R finder and Kappa, implying that R finder is effective at estimating the diversity of trees without the added computational costs associated with calculating Kappa.


Temporal ECDSA: A timestamp and signature mask enabled ECDSA algorithm for IoT client node authentication

Abstract: Internet of Things (IoT) networks are vulnerable to various security threats, especially in client authentication. The current authentication methods require significant resources and are vulnerable to specific attacks. The Non-Interactive Zero Knowledge Proof (NIZKP) concept suits IoT device authentication. Elliptic Curve Digital Signature (ECDSA) is a popular algorithm for IoT device authentication based on elliptic curve cryptography (ECC) and NIZKP. However, an intelligent attacker who captures the ECDSA signatures generated by the same random number can recover the private key. This paper proposes a timestamp based approach to prevent signature reuse and fake signature generation in the ECDSA algorithm. The signature proof is generated differently at each occurrence to prevent the generation of valid signatures from leaked private keys. The proposed approach eliminates the expensive modular inversion operations in signature generation and verification phases, enhancing execution efficiency. The analysis results indicate that the proposed Temporal ECDSA algorithm effectively prevents most attacks on client authentication in IoT networks. Various metrics, including computational complexity and security measures, were used to evaluate the effectiveness of the proposed approach, demonstrating that it outperforms most ECDSA variants.


The Impact of VR-Based Learning on Student Engagement and Learning Outcomes in Higher Education

Abstract: This research investigates virtual reality (VR) in higher education, covering types of VR, implementation in academic settings, effects on student engagement and learning outcomes, and exploring potential benefits for enhancing learning and developing graduate capabilities. A comprehensive review covered VR types like simulations, virtual labs, 360-degree experiences, and virtual classrooms, enabling experiential learning. The implementation process involves goal setting, content creation, hardware/software selection, infrastructure setup, curriculum integration, assessment, and evolution. Effective VR content and prepared instructors positively impact engagement, influencing comprehension, retention, and academic performance. AR/VR tech refines cognitive skills, practical knowledge, and personalised learning, fostering teamwork and critical thinking for graduates' career readiness.