A Comparative Study of Performance Between Federated Learning and Centralized Learning Using Pathological Image of Endometrial Cancer

Abstract: Federated learning, an innovative artificial intelligence training method, offers a secure solution for institutions to collaboratively develop models without sharing raw data. This approach offers immense promise and is particularly advantageous for domains dealing with sensitive information, such as patient data. However, when confronted with a distributed data environment, challenges arise due to data paucity or inherent heterogeneity, potentially impacting the performance of federated learning models. Hence, scrutinizing the efficacy of this method in such intricate settings is indispensable. To address this, we harnessed pathological image datasets of endometrial cancer from four hospitals for training and evaluating the performance of a federated learning model and compared it with a centralized learning model. With optimal processing techniques (data augmentation, color normalization, and adaptive optimizer), federated learning exhibited lower precision but higher recall and Dice similarity coefficient (DSC) than centralized learning. Hence, considering the critical importance of recall in the context of medical image processing, federated learning is demonstrated as a viable and applicable approach in this field, offering advantages in terms of both performance and data security.


High-fidelity robust qubit control by phase-modulated pulses

Abstract: We present a set of robust and high-fidelity pulses that realize paradigmatic operations such as the transfer of the ground state population into the excited state and arbitrary $X/Y$ rotations on the Bloch sphere. These pulses are based on the phase modulation of the control field. We provide an experimental proof-of-concept of these operations by using a transmon qubit, demonstrating resilience against deviations in the drive amplitude of more than $\ensuremath{\approx}20%$, and/or detuning from the qubit transition frequency in the order of $10\phantom{\rule{4pt}{0ex}}\mathrm{MHz}$. This modulation scheme is straightforward to implement in practice and can be deployed to any other qubit-based experimental platform.


Dendritic Growth Optimization: A Novel Nature-Inspired Algorithm for Real-World Optimization Problems

Abstract: In numerous scientific disciplines and practical applications, addressing optimization challenges is a common imperative. Nature-inspired optimization algorithms represent a highly valuable and pragmatic approach to tackling these complexities. This paper introduces Dendritic Growth Optimization (DGO), a novel algorithm inspired by natural branching patterns. DGO offers a novel solution for intricate optimization problems and demonstrates its efficiency in exploring diverse solution spaces. The algorithm has been extensively tested with a suite of machine learning algorithms, deep learning algorithms, and metaheuristic algorithms, and the results, both before and after optimization, unequivocally support the proposed algorithm’s feasibility, effectiveness, and generalizability. Through empirical validation using established datasets like diabetes and breast cancer, the algorithm consistently enhances model performance across various domains. Beyond its working and experimental analysis, DGO’s wide-ranging applications in machine learning, logistics, and engineering for solving real-world problems have been highlighted. The study also considers the challenges and practical implications of implementing DGO in multiple scenarios. As optimization remains crucial in research and industry, DGO emerges as a promising avenue for innovation and problem solving.


Algorithms of the Möbius function by random forests and neural networks

Abstract: Abstract The Möbius function $$\mu (n)$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>μ</mml:mi> <mml:mo>(</mml:mo> <mml:mi>n</mml:mi> <mml:mo>)</mml:mo> </mml:mrow> </mml:math> is known for containing limited information on the prime factorization of n . Its known algorithms, however, are all based on factorization and hence are exponentially slow on $$\log n$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mo>log</mml:mo> <mml:mi>n</mml:mi> </mml:mrow> </mml:math> . Consequently, a faster algorithm of $$\mu (n)$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>μ</mml:mi> <mml:mo>(</mml:mo> <mml:mi>n</mml:mi> <mml:mo>)</mml:mo> </mml:mrow> </mml:math> could potentially lead to a fast algorithm of prime factorization which in turn would throw doubt upon the security of most public-key cryptosystems. This research introduces novel approaches to compute $$\mu (n)$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>μ</mml:mi> <mml:mo>(</mml:mo> <mml:mi>n</mml:mi> <mml:mo>)</mml:mo> </mml:mrow> </mml:math> using random forests and neural networks, harnessing the additive properties of $$\mu (n)$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>μ</mml:mi> <mml:mo>(</mml:mo> <mml:mi>n</mml:mi> <mml:mo>)</mml:mo> </mml:mrow> </mml:math> . The machine learning models are trained on a substantial dataset with 317,284 observations (80%), comprising five feature variables, including values of n within the range of $$4\times 10^9$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mn>4</mml:mn> <mml:mo>×</mml:mo> <mml:msup> <mml:mn>10</mml:mn> <mml:mn>9</mml:mn> </mml:msup> </mml:mrow> </mml:math> . We implement the Random Forest with Random Inputs (RFRI) and Feedforward Neural Network (FNN) architectures. The RFRI model achieves a predictive accuracy of 0.9493, a recall of 0.5865, and a precision of 0.6626. On the other hand, the FNN model attains a predictive accuracy of 0.7871, a recall of 0.9477, and a precision of 0.2784. These results strongly support the effectiveness and validity of the proposed algorithms.


Recognition of REEs anomalies using an image Fusion fractal-wavelet model in Tarom metallogenic zone, NW Iran

Abstract: This study aims to detect REE geochemical anomalies in relationship to Iron-apatite ores utilizing an image Fusion based on Deep Learning (FDL). The geochemical data was modeled for elements related to Iron-apatite mineralization using multi b Spline B. The results were fusioned in applying the Deep learning method based on pre-trained networks. Wavelet-Number (WN) fractal model classified the best results based on the combination of a two-dimensional Discrete Wavelet Transformation (DWT) signal analysis and a Concentration-Area (C-A) fractal modeling. Sym8 carried the DWT as a selected wavelet pattern for REE based on Stream sediment samples collected from the Tarom region (NW Iran). In addition, the DWT was decomposed by wavelet coefficients at five levels. Furthermore, the DWT data were classified using a fractal-wavelet model to delineate REE anomalies from background levels in this region. Overlayed with the catchment basins model and weighted using the upstream and downstream parts. As a result, the prominent REE source anomalies are located in the southern parts of the study area. The results obtained by the proposed fractal-wavelet modeling are in connection with field check anomaly samples and the rock samples collected from the Iron-Apatite ore deposits.


Scheduling optimization of electric energy meter distribution vehicles for intelligent batch rotation

Abstract:
Abstract
 As industrial technology continues to advance through integration, society's demand for electricity is rapidly increasing. To meet the requirements of refined grid management and address the elevated challenges arising from the increased electrical load, this paper delves into the investigation of distribution vehicle scheduling for the practical scenario of batch rotation of smart meters. Initially, based on the practical distribution task requirements of a provincial metrology verification center, a multi-level optimization model is constructed for the batch rotation and distribution vehicle scheduling of smart meters. The primary objective is to maximize the enhancement of smart meter distribution efficiency while minimizing the overall distribution cost. Moreover, this paper introduces a refined Grey Wolf Optimization algorithm (OLC-GWO) based on Opposition-based Learning, Levy flight strategy, and Cauchy mutation to solve the model. By generating an opposite population to improve the quality of initial feasible solutions and further harnessing the global search capabilities of Levy flight and Cauchy mutation operators, the algorithm's effectiveness is enhanced. The algorithm is subjected to testing using multiple benchmark functions and its performance is compared with variants of GWO, as well as several cutting-edge intelligent optimization algorithms including Particle Swarm Optimization (PSO), Harris Hawks Optimization (HHO), and Honey Bee Algorithm (HBA). The results indicate that OLC-GWO exhibits excellent performance in terms of convergence speed and optimization capability. Finally, the improved algorithm is subjected to simulation experiments by incorporating order data from the practical distribution operations of a provincial metrology verification center. The outcomes verify the efficiency of the proposed algorithm, reinforcing the practical significance of the established model in addressing the real-world challenge of batch rotation and distribution vehicle scheduling for smart meters.


The 23 Factorial Design in R

Abstract: Abstract The paper describes the factorial design of the experiment with three input factors that change on two levels. For given values of the input parameters, it is shown how to obtain a variance analysis table and which factors and interactions between factors are significant. The example was done in the software intended for the design of the experiment and in the software R. It is shown how to use the software R to arrive at the final solution of the given example.


A zero-trust journey through the threat landscape

Abstract: Zero trust is being widely adopted as a fundamental concept in cyber security architectures, and its implementation has accelerated due to so many organisations introducing new working practices. But creating a zero-trust environment is not simple, and requires a shift in mindset away from thinking about security in terms of fixed lines of defence. And it's not just a change in technology – it requires you to look deeply at your processes and corporate culture, too. So what are the best ways of embarking on the zero-trust journey?


SRSegNet: Super-resolution-assisted small targets polyp segmentation network with combined high and low resolution

Abstract: Accurate and reliable segmentation of polyp targets is crucial in the treatment of colorectal cancer. However, in clinical practice, polyp structures represent only a small portion of the image, which is essential for the accurate diagnosis and treatment of colorectal cancer. The small size of lesion areas leads to a reduction in feature representation, thereby affecting the performance of traditional segmentation methods. To address this issue, To address this issue, recent advancements in segmentation methods for small medical objects meticulously extract feature information from these objects using attention mechanisms. However, these methods mainly focus on general small medical objects and perform poorly in the segmentation of small polyps.Therefore, this study fully exploits the advantages of joint learning and multi-task learning. By guiding the high-resolution features to guide the low-resolution encoder through joint learning and fusing effective features from the super-resolution task to assist the segmentation task, our method enhances the model’s ability to extract texture details. We also design a high-resolution guidance module and an information complementation module, effectively integrating learning between different resolutions and tasks.Evaluation experiments on two settings on the Kvasir-SEG, CVC-ClinicDB, CVC-ColonDB, CVC-300, and ETIS datasets demonstrate that our method achieves significant advantages in the segmentation of small polyps, outperforming various state-of-the-art methods.


Robust multi-view learning via M-estimator joint sparse representation

Abstract: Recently, multi-view learning has achieved extraordinary success in many research areas such as pattern recognition and data mining. Most existing multi-view methods mainly focus on exploring the correlation information between different views and their performance may severely degrade in the presence of heavy noises and outliers. In this paper, we put forward a robust multi-view joint sparse representation (RMJSR) method for multi-view learning. Firstly, we design a novel multi-view Cauchy estimator based loss function originating from robust statistics to address complex noises and outliers in reality. Based on this, we leverage the ℓ1,q norm to enhance our model by encouraging the learned representation of multiple views to share the same sparsity pattern. Secondly, to explore the optimal solution for the RMJSR model, we devise an effective optimization algorithm based on the half-quadratic (HQ) theory and the alternating direction method of multipliers (ADMM) framework. Thirdly, we provide the theoretical guarantee for revealing the theoretical condition for the success of the proposed method. Further, we have also provided extensive analysis of the proposed method, including the optimality condition, convergence analysis, and complexity analysis. Extensive experimental results validate the effectiveness and robustness of the proposed method in comparison with state-of-the-art competitors. The source code is available at https://github.com/Huyutao7/RMJSRC.


DAST-Net: Dense visual attention augmented spatio-temporal network for unsupervised video anomaly detection

Abstract: This paper introduces an innovative end-to-end trainable framework named Dense Attention-aware Spatio-Temporal Network (DAST-Net) for video anomaly detection. The framework adeptly leverages both spatial and temporal data in an unsupervised manner, eliminating the need for manually crafted features. To enhance spatial feature representation, DAST-Net incorporates visual attention-aware residual connections within the denser residual network (DenserResNet), deviating from traditional identity skip connections. The rationale behind this connection choice is to augment the contextual understanding of features across various scales. For capturing temporal patterns, the framework employs a Convolutional LSTM Autoencoder (ConvLSTM-AE) module, enabling effective learning and representation of temporal dependencies in video data. Consequently, discriminating features from attention modules are combined with the features extracted by the ConvLSTM-AE module, enhancing visual recognition capabilities for both spatial and temporal aspects. Our proposed architecture outperforms state-of-the-art methods on four benchmark datasets, showcasing AUC scores of 85.4% on Ped1, 97.9% on Ped2, 89.8% on Avenue, and 73.7% on the ShanghaiTech dataset. The results demonstrate the performance of our method in identifying unusual events in video data.


GAN-based adaptive cost learning for enhanced image steganography security

Abstract: A well-defined cost function is a key issue for image steganography to minimize the embedding distortion. In recent years, deep learning has been introduced into image steganography to automatically learn embedding costs and improve steganographic security. For most existing generative adversarial network (GAN) based cost learning works, the generator usually adopts an encoder–decoder architecture. However, due to repeated encoding and decoding operations, this architecture is prone to information loss, making the generator difficult to well capture fine-grained image features. In this work, we propose a novel GAN-based image steganography work that improves the cost function by learning better embedding probability maps. Specifically, we design an attention mechanism to be integrated into the U-Net architecture, which enables the generator to concentrate on texture-rich regions of input images. Moreover, an extra input stream, namely the enhanced image, is introduced into the generator, improving the generator’s ability to learn structural features from input images. Different skip connections are used for different input streams to facilitate information flow between different layers. Extensive experimental results demonstrate that the proposed approach effectively learns the embedding probability maps and achieves superior security against various steganalysis attacks.


Explicit knowledge transfer of graph-based correlation distillation and diversity data hallucination for few-shot object detection

Abstract: The performance of few-shot object detection has seen marked improvement through fine-tuning paradigms. However, existing methods often depend on shared parameters to implicitly transfer knowledge without explicit induction. This results in novel-class representations that are easily confused with similar base classes and poorly suited to diverse patterns of variation in the truth distribution. In view of this, the present paper focuses on mining transferable base-class knowledge, which is further subdivided into inter-class correlation and intra-class diversity. First, we design a graph to dynamically capture the relationship between base and novel class representations, and then introduce distillation techniques to tackle the shortage of correlation knowledge in few-shot labels. Furthermore, an efficient diversity knowledge transfer module based on the data hallucination is proposed, which can adaptively disentangle class-independent variation patterns from base-class features and generate additional trainable hallucinated instances for novel classes. Experiments on VOC and COCO datasets confirmed that our proposed method effectively reduces the reliance on novel-class samples and demonstrates superior performance compared to other state-of-the-art baseline methods.


A deep learning-based and adaptive region proposal algorithm for semantic segmentation

Abstract: This paper presents an adaptive and new region proposal algorithm for generating high-quality regions. The main aim of this algorithm is to investigate different features in the proposal generation process. This algorithm is based on bottom-up image segmentation and deep learning techniques. Each primary region is represented by features derived from a convolutional neural network (CNN). The adjacent and similar regions are merged based on a new proposed searching algorithm and a distance function in a hierarchical way. Additionally, various hand-crafted texture features are examined for representing each region. These texture features are not being utilized previously in region proposal generation. This method also applied both texture-based and deep learning-based features, contemporaneously. Furthermore, the proposed region proposal algorithm was evaluated on two significant challenging datasets, including VOC2012 and COCO2017. The resulting proposals show more high-quality regions in high overlaps in comparison to previous region proposal algorithms. More importantly, the results verified that the deep learning-based features together with handcrafted texture features are complement and this fusion can overcome the shortcoming of other approaches. Additionally, the proposed region proposal algorithm is employed in weakly supervised semantic segmentation. The new generated proposals are used to create some new labeled masks. These masks are very useful and effective in the training phase of deep learning. This approach is evaluated on VOC2012 and compared with previous region-based and free region-based methods. The results shows the efficacy of the proposed method.


SecureNet: Proactive intellectual property protection and model security defense for DNNs based on backdoor learning

Abstract: With the widespread application of deep neural networks (DNNs), the risk of privacy breaches against DNN models is constantly on the rise, resulting in an increasing need for intellectual property (IP) protection for such models. Although neural network watermarking techniques are widely used to safeguard the IP of DNNs, they can only achieve passive protection and cannot actively prevent unauthorized users from illicit use or embezzlement of the trained DNN models. Therefore, the development of proactive protection techniques to prevent IP infringement is imperative. To this end, we propose SecureNet, a key-based access license framework for DNN models. The proposed approach involves injecting license keys into the model through backdoor learning, enabling correct model functionality only when the appropriate license key is included in the input. To ensure the reusability of DNN models, we also propose a license key replacement algorithm. In addition, based on SecureNet, we designed defense mechanisms against adversarial attacks and backdoor attacks, respectively. Furthermore, we introduce a fine-grained authorization method that enables flexible granting of model permissions to different users. We have designed four license-key schemes with different privileges, tailored to various scenarios. We evaluated SecureNet on five benchmark datasets including MNIST, Cifar10, Cifar100, FaceScrub, and CelebA, and assessed its performance on six classic DNN models: LeNet-5, VGG16, ResNet18, ResNet101, NFNet-F5, and MobileNetV3. The results demonstrate that our approach outperforms the state-of-the-art model parameter encryption methods by at least 95% in terms of computational efficiency. Additionally, it provides effective defense against adversarial attacks and backdoor attacks without compromising the model’s overall performance.


Tri-stage offline Telugu character recognition system based on fusion of HOG and ULBP

Abstract: The domain of pattern recognition has experienced a notable upsurge in the prevalence of handwritten character recognition, which is a research area that has been widely recognized for decades.However, this task presents significant challenges, especially for Indian languages, due to the vast array of character shapes and curved linear structures they possess. Despite some research having been conducted on Indian languages such as Kannada, Hindi, Tamil Devanagari, and Telugu, among others, the field is still in its nascent stages of development. The nascent phase of handwritten Telugu script presents a promising opportunity for further investigation and exploration. We introduce a Tri-stage Telugu handwritten recognition system that utilizes hyper parameters of k- NN, SVM, and Random Forest based on the fusion of HOG and ULBP features. By implementing a Tri-stage classifier in a sequential manner, the system's recognition accuracy is significantly enhanced.The proposed Tri-stage classifier achieves exceptionally high accuracies on Telugu handwritten character HP datasets.


An ultra-lightweight method for individual identification of cow-back pattern images in an open image set

Abstract: Cow recognition forms the foundation of smart livestock management. Current closed-set cow recognition models can exclusively identify cows within their training set and are ill-equipped to recognize other subjects in the open world. Additionally, there is a dearth of comprehensive research on open-set cow recognition. Existing open-set recognition models not only exhibit suboptimal accuracy but also impose a substantial burden on hardware resources due to their extensive parameterization. These challenges hinder the practical deployment of cow recognition models. This study introduces an innovative open-set metric learning-based recognition framework for cow-back patterns, aiming to rectify a significant limitation of individual cow recognition models, which is their inability to identify cows not included in the training set. The proposed framework combines various loss functions, metric methods, and backbone networks. This integration enables the open-set recognition of cow back pattern images, thereby facilitating the identification of cows previously unrecognized by the model. To mitigate hardware resource consumption, we have devised a novel ultra-lightweight backbone network known as LightCowsNet. This network draws inspiration from existing lightweight backbone networks such as MobileFaceNet, MobileViT, and EfficientNetV2. Its purpose is to extract features from cow back pattern images efficiently. LightCowsNet employs attention mechanisms, inverted residual structures, and depthwise separable convolutions. LightCowsNet first uses attention mechanisms, inverted residual structures, and depthwise separable convolutions to design multiple new feature extraction modules. These modules, which have different structures, are subsequently placed to extract different levels of image information, which enables complete extraction of the textural information of the cows’ back patterns. The present study reorganized the Cows2021 dataset to render it suitable for open-set recognition of cow-back patterns, with the test set comprising image pairs. The results revealed that using A-SoftMax as the loss function and Euclidean distance as the metric method enabled LightCowsNet to achieve the highest accuracy of 94.26 %, with a model weight space occupation of only 4.06 MB. Compared to MobileFaceNet, MobileViT, and EfficientNetV2, LightCowsNet achieved 6.24 %, 8.82 %, and 2.3 % greater accuracy on the test set, respectively. Its weight also decreased by 0.71 MB, 0.24 MB, and 4.3 MB, respectively. These results demonstrate that the proposed model achieved both accuracy improvement and model lightweightness, and these findings could serve as reference solutions for intelligent farming on cow farms.


LPSRGAN: Generative adversarial networks for super-resolution of license plate image

Abstract: This paper proposes a super-resolution algorithm for reconstructing license plate images based on generative adversarial networks (GAN) for improving the recognition rate of low-resolution license plate images. To this end, this paper first designs a new image degradation model, called the n-stage random combination degradation model (n-RCD), which extends the traditional degradation model horizontally and vertically to better simulate the features of low-resolution license plate images in natural scenes. Subsequently, to address the problem that existing GAN models cannot adapt to the complex degradation space in natural scenes, this paper proposes a new network structure called LPSRGAN, which optimizes the SRGAN model from the generator and discriminator to improve its super-resolution capability. In addition, this paper also proposes a perceptual optical character recognitioin (OCR) loss for license plate images, which defines the connectionist temporal classification (CTC) loss of the output of the OCR network with character label values to better preserve the character details and features of the license plate images. Finally, the experiments reported in this paper show that the proposed algorithm improves the recognition rate of the original low-resolution license plate image by 12.48%, and the recognition rate of the reconstructed image reaches 93.90%. Based on this, this paper also tests the algorithm on actual captured data, and the results show that it performs well in natural scenes, demonstrating the algorithm has broad application prospects.


IDaPS – Improved data-locality aware data placement strategy based on Markov clustering to enhance MapReduce performance on Hadoop

Abstract: The execution of Map-Reduce applications on the Hadoop cluster poses significant challenges due to the non-consideration of data locality, i.e., assigning tasks to compute nodes where input data sets are located. Due to such non-consideration, high data transfer overheads are caused. Further, it increases latency, which may arise if input data needs to be transferred across the network, thereby significantly increasing execution time. To address this issue, an Improved DAta Placement Strategy IDaPS based on the intra-dependency among the data is proposed. IDaPS re-organizes the default data layouts in HDFS to ensure higher degree of parallelism. The efficiency of IDaPS is demonstrated in Hadoop clusters (10 and 15 nodes) by executing Hadoop Benchmark performance tests viz. WordCount, Grep on Project-Gutenberg book dataset (50GB) and Least Square Linear Regression (LSLR) on weather dataset (10.67GB). The results were compared with state-of-the-art algorithms viz. Hadoop Default Data Placement (HDDP), Load-Balancer and literary work RENDA. The results demonstrate that IDaPS significantly reduces execution time by 28.2% and 38.4% in 10-node and 15-node clusters while executing WordCount, and 35% and 38.1% in 10-node and 15-node clusters for Grep. Similarly, for LSLR, it reduces execution time by 32.7%.


Quantum Clustering with k-Means: a Hybrid Approach

Abstract: Quantum computing, based on quantum theory, holds great promise as an advanced computational paradigm for achieving fast computations. Quantum algorithms are expected to surpass their classical counterparts in terms of computational complexity for certain tasks, including machine learning. In this paper, we design, implement, and evaluate three hybrid quantum k-Means algorithms, exploiting different degrees of parallelism. Indeed, each algorithm incrementally leverages quantum parallelism to reduce the complexity of the cluster assignment step up to a constant cost. In particular, we exploit quantum phenomena to speed up the computation of distances. The core idea is that the computation of distances between records and centroids can be executed simultaneously, thus saving time, especially for big datasets. We show that our hybrid quantum k-Means algorithms are theoretically faster than the classical algorithm, while experiments suggest that it is possible to obtain comparable clustering results.


Deep Learning-Based Approaches for Abusive Content Detection and Classification for Multi-class Online User-Generated Data

Abstract: With the rapid growth of social media culture, the use of offensive or hateful language has surged, which necessitates the development of effective abusive language detection models for online platforms. This paper focuses on developing a multi-class classification model to identify different types of offensive language. The input data is taken in the form of labeled tweets and is classified into offensive language detection, offensive language categorization, and offensive language target identification. The data undergoes pre-processing, which removes NaN value and punctuation, as well as performs tokenization followed by the generation of a word cloud to assess data quality. Further, the tf-idf technique is used for the selection of features. In the case of classifiers, multiple deep learning techniques, namely, bidirectional gated recurrent unit, multi-dense long short-term memory, bidirectional long short-term memory, gated recurrent unit, and long short-term memory, are applied where it has been found that all the models, except long short-term memory, achieved a high accuracy of 99.9% for offensive language target identification. Bidirectional LSTM and multi-dense LSTM obtained the lowest loss and RMSE values of 0.01 and 0.1, respectively. This research provides valuable insights and contributes to the development of effective abusive language detection methods to promote a safe and respectful online environment. The insights gained can aid platform administrators in efficiently moderating content and taking appropriate actions against offensive language.


Strictly Frequentist Imprecise Probability

Abstract: Strict frequentism defines probability as the limiting relative frequency in an infinite sequence. What if the limit does not exist? We present a broader theory, which is applicable also to data that exhibit diverging relative frequencies. In doing so, we develop a close connection with the theory of imprecise probability: the cluster points of relative frequencies yield a coherent upper prevision. We show that a natural frequentist definition of conditional probability recovers the generalized Bayes rule. Finally, we prove constructively that, for a finite set of elementary events, there exists a sequence for which the cluster points of relative frequencies coincide with a prespecified set which demonstrates the naturalness, and arguably completeness, of our theory.


A privacy-preserving key transmission protocol to distribute QRNG keys using zk-SNARKs

Abstract: High-entropy random numbers are an essential part of cryptography, and Quantum Random Number Generators (QRNG) are an emergent technology that can provide high-quality keys for cryptographic algorithms but unfortunately are currently difficult to access. Existing Entropy-as-a-Service solutions require users to trust the central authority distributing the key material, which is not desirable in a high-privacy environment. In this paper, we present a novel key transmission protocol that allows users to obtain cryptographic material generated by a QRNG in such a way that the server is unable to identify which user is receiving each key. This is achieved with the inclusion of Zero Knowledge Succinct Non-interactive Arguments of Knowledge (zk-SNARK), a cryptographic primitive that allow users to prove knowledge of some value without needing to reveal it. The security analysis of the protocol proves that it satisfies the properties of Anonymity, Unforgeability and Confidentiality, as defined in this document. We also provide an implementation of the protocol demonstrating its functionality and performance, using NFC as the transmission channel for the QRNG key.


Human Face Reconstruction using Divine Proportions and Gestalt for Occluded Video Face Recovery in Forensic Analysis using Deep Learning

Abstract: Forensic video analysis has been used in diverse kind of high-profile cases, global discrepancies, and conflict zones. It is a three-phase process of scientific examination, comparison, and evaluation of video in legal matters. Human face reconstruction using deep learning for occluded video face recovery to aid in forensic analysis is the main objective of this paper. Forensic facial reconstruction is a combination of both scientific methods and artistic skill. In this paper, we introduce a method to reconstruct human faces occluded due to short noise innight-time video clips. A skull database is created with unique skull models with varying shapes, forms and proportions. Human body mathematical model biometric using golden ratio algorithm is proposed and used to find the occluded face proportions. Closure principle of gestalt theory of visual perception is used to fill in the missing parts of a face design and to create a whole face image using gan. The proposed model is found to have 50% lesser reduced Median error rate and 20% reduced Stdev than PrNet and 10% lower Mean error rate than 3Dddfav2.


‘What a funny looking video’: Using allegorical representations of technological change to reflect on future digital communication and design challenges

Abstract: This paper presents a reflective examination of challenges to design and communication from the current digital revolution, using the prism of a 1980s television advertisement for the Yellow Pages. Originating at the same time as the height of the desktop publishing revolution, the advert illuminates a transitional period in the evolution of digital technology and media communications, marked by changing user practices and experiences. The advert’s storyline follows a young man’s quest to convert an old cine film (of his father) to videotape for his mother’s birthday, in the process showcasing the impending shift from analogue to digital technologies and encapsulating the multifaceted implications and transitional challenges of the period. The hybrid setting of technologies, alongside the tensions, confusion and ambiguities of different stakeholders metaphorically symbolises and can be contrasted alongside, the challenges impacting the design, media and communications industry of the period and can inform reflection on contemporary challenges. Engaging with the narrative, the Yellow Pages advert is used as a creative device which functions as a cultural, historical, narrative lens with which to contrast against contemporary (and future) transitional challenges within the digital revolution.


Graph Convolutional Network with Syntactic Dependency for Aspect-Based Sentiment Analysis

Abstract: Abstract Aspect-based sentiment analysis (ABSA) aims to mine the sentiment tendencies expressed by specific aspect terms. The studies of ABSA mainly focus on the attention-based approaches and the graph neural network approaches based on dependency trees. However, the attention-based methods usually face difficulties in capturing long-distance syntactic dependencies. Additionally, existing approaches using graph neural networks have not made sufficient exploit the syntactic dependencies among aspects and opinions. In this paper, we propose a novel Syntactic Dependency Graph Convolutional Network (SD-GCN) model for ABSA. We employ the Biaffine Attention to model the sentence syntactic dependencies and build syntactic dependency graphs from aspects and emotional words. This allows our SD-GCN to learn both the semantic relationships of aspects and the overall semantic meaning. According to these graphs, the long-distance syntactic dependency relationships are captured by GCNs, which facilitates SD-GCN to capture the syntactic dependencies between aspects and viewpoints more comprehensively, and consequently yields enhanced aspect features. We conduct extensive experiments on four aspect-level sentiment datasets. The experimental results show that our SD-GCN outperforms other methodologies. Moreover, ablation experiments and visualization of attention further substantiate the effectiveness of SD-GCN.


Potential Applications of Explainable Artificial Intelligence to Actuarial Problems

Abstract: Explainable artificial intelligence (XAI) is a group of techniques and evaluations that allows users to understand artificial intelligence knowledge and increase the reliability of the results produced using artificial intelligence. XAI can assist actuaries in achieving better estimations and decisions. This study reviews the current literature to summarize XAI in common actuarial problems. We proposed a research process based on understanding the type of AI used in actuarial practice in the financial industry and insurance pricing and then researched XAI implementation. This study systematically reviews the literature on the need for implementation options and the current use of explanatory artificial intelligence (XAI) techniques for actuarial problems. The study begins with a contextual introduction outlining the use of artificial intelligence techniques and their potential limitations, followed by the definition of the search equations used in the research process, the analysis of the results, and the identification of the main potential fields for exploitation in actuarial problems, as well as pointers for potential future work in this area.


Applying Machine Learning and Statistical Forecasting Methods for Enhancing Pharmaceutical Sales Predictions

Abstract: In today’s evolving global world, the pharmaceutical sector faces an emerging challenge, which is the rapid surge of the global population and the consequent growth in drug production demands. Recognizing this, our study explores the urgent need to strengthen pharmaceutical production capacities, ensuring drugs are allocated and stored strategically to meet diverse regional and demographic needs. Summarizing our key findings, our research focuses on the promising area of drug demand forecasting using artificial intelligence (AI) and machine learning (ML) techniques to enhance predictions in the pharmaceutical field. Supplied with a rich dataset from Kaggle spanning 600,000 sales records from a singular pharmacy, our study embarks on a thorough exploration of univariate time series analysis. Here, we pair conventional analytical tools such as ARIMA with advanced methodologies like LSTM neural networks, all with a singular vision: refining the precision of our sales. Venturing deeper, our data underwent categorisation and were segmented into eight clusters premised on the ATC Anatomical Therapeutic Chemical (ATC) Classification System framework. This segmentation unravels the evident influence of seasonality on drug sales. The analysis not only highlights the effectiveness of machine learning models but also illuminates the remarkable success of XGBoost. This algorithm outperformed traditional models, achieving the lowest MAPE values: 17.89% for M01AB (anti-inflammatory and antirheumatic products, non-steroids, acetic acid derivatives, and related substances), 16.92% for M01AE (anti-inflammatory and antirheumatic products, non-steroids, and propionic acid derivatives), 17.98% for N02BA (analgesics, antipyretics, and anilides), and 16.05% for N02BE (analgesics, antipyretics, pyrazolones, and anilides). XGBoost further demonstrated exceptional precision with the lowest MSE scores: 28.8 for M01AB, 1518.56 for N02BE, and 350.84 for N05C (hypnotics and sedatives). Additionally, the Seasonal Naïve model recorded an MSE of 49.19 for M01AE, while the Single Exponential Smoothing model showed an MSE of 7.19 for N05B. These findings underscore the strengths derived from employing a diverse range of approaches within the forecasting series. In summary, our research accentuates the significance of leveraging machine learning techniques to derive valuable insights for pharmaceutical companies. By applying the power of these methods, companies can optimize their production, storage, distribution, and marketing practices.


Artificial intelligent Global Online Learning (GOL) theory by generalized n-ary fuzzy relation

Abstract: Abstract Following the devastating COVID pandemic, a new global strategy is required to switch from traditional education to blended or online learning method. Nevertheless, there is no adamant theoretical base available for such an important transition or similar situations in the future. On the other hand, educational systems encounter uncertainty as an integral part of multilayered teaching routes. To analyze the interactions among interconnected entities, soft computing methodologies can serve as an efficient tool to manage such systems with uncertain information through incorporating artificial intelligence (AI) techniques for assessing students performances. Nevertheless, the classical binary fuzzy relation and other existing theoretical models are not capable of explaining/configuring uncertain-based datum for multiplex correlations. To fill these gaps, the present study establishes a neoteric AI-base “Global Online Learning (GOL) theory” using the newly developed n -ary relation and n -ary fuzzy relation as the generalization of classical and binary fuzzy relations. Through the enhanced mathematical concepts and intelligent soft computing techniques, the convoluted multilayer relationships of entities can be punctiliously assessed for different values of n. Furthermore, a network-based perspective is proposed as a promising systematic model when systems are imperfect and prone to uncertainty. In the provided graphical context, the n -ary relation represents the hypergraph pattern, while the n -ary fuzzy relation refers to the generalized fuzzy hypergraph model. Fundamental characteristics of n -ary fuzzy relation, including reflexive, symmetric, transitive, composition, t -cut, support and Cartesian product, are systematically provided to extract mathematical interrelated expressions, as well as parametric connection between t -cut and Cartesian product. Based on the n -ary fuzzy relation, the n -ary fuzzy hyperoperation “ $$\circ _{\rho }$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mo>∘</mml:mo> <mml:mi>ρ</mml:mi> </mml:msub> </mml:math> ” is assigned to construct fuzzy hyperalgebra as the extension of classical algebra with illustrative examples. The relationships between fuzzy hyperalgebra and hyperalgebra are investigated through the notation of $$(\circ _{\rho })_{t}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mrow> <mml:mo>(</mml:mo> <mml:msub> <mml:mo>∘</mml:mo> <mml:mi>ρ</mml:mi> </mml:msub> <mml:mo>)</mml:mo> </mml:mrow> <mml:mi>t</mml:mi> </mml:msub> </mml:math> for $$t\in (0,1].$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>t</mml:mi> <mml:mo>∈</mml:mo> <mml:mo>(</mml:mo> <mml:mn>0</mml:mn> <mml:mo>,</mml:mo> <mml:mn>1</mml:mn> <mml:mo>]</mml:mo> <mml:mo>.</mml:mo> </mml:mrow> </mml:math> With the introduced t -cut methodology, the corresponding hypergraph is derived to simplify the analysis of educational information. The AI-base GOL theory provides a solid gadget for learning data management, e.g., the grading evaluation of online assessments, where the evaluation of components is accomplished on real data in terms of fuzzy n -ary relation, t -cut and support through a graphical attitude. The results indicate that the AI-base GOL theory is a robust platform to meticulously manage and control uncertain-based intercorrelated information. This platform can be converted into a coding gadget for artificial intelligent educational online mega-systems.


Unobtrusive Cognitive Assessment in Smart-Homes: Leveraging Visual Encoding and Synthetic Movement Traces Data Mining

Abstract: The ubiquity of sensors in smart-homes facilitates the support of independent living for older adults and enables cognitive assessment. Notably, there has been a growing interest in utilizing movement traces for identifying signs of cognitive impairment in recent years. In this study, we introduce an innovative approach to identify abnormal indoor movement patterns that may signal cognitive decline. This is achieved through the non-intrusive integration of smart-home sensors, including passive infrared sensors and sensors embedded in everyday objects. The methodology involves visualizing user locomotion traces and discerning interactions with objects on a floor plan representation of the smart-home, and employing different image descriptor features designed for image analysis tasks and synthetic minority oversampling techniques to enhance the methodology. This approach distinguishes itself by its flexibility in effortlessly incorporating additional features through sensor data. A comprehensive analysis, conducted with a substantial dataset obtained from a real smart-home, involving 99 seniors, including those with cognitive diseases, reveals the effectiveness of the proposed functional prototype of the system architecture. The results validate the system’s efficacy in accurately discerning the cognitive status of seniors, achieving a macro-averaged F1-score of 72.22% for the two targeted categories: cognitively healthy and people with dementia. Furthermore, through experimental comparison, our system demonstrates superior performance compared with state-of-the-art methods.


Matrix product and quasi-twisted codes in one class

Abstract: Abstract Many classical constructions, such as Plotkin’s and Turyn’s, were generalized by matrix product (MP) codes. Quasi-twisted (QT) codes, on the other hand, form an algebraically rich structure class that contains many codes with best-known parameters. We significantly extend the definition of MP codes to establish a broader class of generalized matrix product (GMP) codes that contains QT codes as well. We propose a generator matrix formula for any linear GMP code and provide a condition for determining the code size. We prove that any QT code has a GMP structure. Then we show how to build a generator polynomial matrix for a QT code from its GMP structure, and vice versa. Even though the class of QT codes contains many codes with best-known parameters, we present different examples of GMP codes with best-known parameters that are neither MP nor QT. Two different lower bounds on the minimum distance of GMP codes are presented; they generalize their counterparts in the MP codes literature. The second proposed lower bound replaces the non-singular by columns matrix with a less restrictive condition. Some examples are provided for comparing the two proposed bounds, as well as showing that these bounds are tight.


Uno straniero in Terra matrigna La traduzione italiana del romanzo Yaban di Yakup Kadri Karaosmanoğlu

Abstract: Between 1941 and 1942, the novel Yaban by Yakup Kadri (Karaosmanoğlu), published in Turkey in 1932, appeared in the ‘Medusa’ series of Mondadori editions. It was the first time that a twentieth‑century Turkish novel was translated into Italian and it was not done in a specialist field, i.e. by scholars, orientalists or even more precisely Turkish scholars, but within a series of a prestigious and innovative publishing house that had been set up to promote great international literature. The novel, which in Italian is entitled Terra matrigna , is however not a translation from the original but from German. Only a couple of years earlier, in fact, the same novel had been translated from Turkish into German, raising a certain clamour, especially in Turkey. This essay proposes a reconstruction of the editorial process that accompanied the Italian publication of Karaosmanoğlu’s novel, in an attempt to situate this first diffusion of a modern Turkish literary work in the Italian and European context, in order to understand the cultural relations and the framework of the understanding and reception of early twentieth‑century Turkish literature in Europe.


Using fuzzy cognitive map in bow tie method for dynamic risk assessment of spherical storage tanks: A case study

Abstract: The present study aimed to perform a dynamic risk assessment of liquefied petroleum gas (LPG) in spherical storage tanks using the Hazard and Operability (HAZOP) method. The "LPG release from spherical storage tank" scenario was determined to be a top event based on the risk level. The causes and consequences of this scenario were then specified using the Bow-Tie method. Additionally, fuzzy cognitive mapping (FCM) was utilized to identify the most critical threats, consequences, and effective barriers in a dynamic approach. Results showed that fracture/rupture in pipelines/a storage tank due to fire had the highest output degree and was identified as the most influential threat in the occurrence of the LPG release. Fatality/injury due to fire and explosion had the highest input degree and was more influenced by the release of LPG as the main consequence in the selected scenario. The findings of this study enabled us to make a logical decision about effective barriers for avoiding and minimizing the release of LPG and its threats and consequences based on the fuzzy cognitive map. Moreover, the results of this study showed that the FCM method could determine the most critical nodes with a higher degree and represent their relationships.


CNN-optimized text recognition with binary embeddings for Arabic expiry date recognition

Abstract: Abstract Recognizing Arabic dot-matrix digits is a challenging problem due to the unique characteristics of dot-matrix fonts, such as irregular dot spacing and varying dot sizes. This paper presents an approach for recognizing Arabic digits printed in dot matrix format. The proposed model is based on convolutional neural networks (CNN) that take the dot matrix as input and generate embeddings that are rounded to generate binary representations of the digits. The binary embeddings are then used to perform Optical Character Recognition (OCR) on the date images. To overcome the challenge of the limited availability of dotted Arabic expiration date images, we developed a True Type Font (TTF) for generating synthetic images of Arabic dot-matrix characters. The model was trained on a synthetic dataset of 3287 images and 658 synthetic images for testing, representing realistic expiration dates from 2019 to 2027 in the format of yyyy/mm/dd and yy/mm/dd . Our model achieved an accuracy of 98 . 94% on the expiry date recognition with Arabic dot matrix format using fewer parameters and less computational resources than traditional CNN-based models. By investigating and presenting our findings comprehensively, we aim to contribute substantially to the field of OCR and pave the way for advancements in Arabic dot-matrix character recognition. Our proposed approach is not limited to Arabic dot matrix digit recognition but can be also extended to text recognition tasks, such as text classification and sentiment analysis.


Quality Control Analysis Using the Failure Mode and Effect Analysis Method

Abstract:
This study aimed to investigate critical factors contributing to defective Epodion® rh-Erythropoietin Alfa syringes, specifically focusing on reducing cracks during manufacturing. A Failure Mode and Effect Analysis (FMEA) approach was employed to identify the primary causes of failure, revealing cracking as the predominant issue affecting 43% of defective units. To mitigate this concern, we calculated a Risk Priority Number (RPN) of 162, highlighting the significance of addressing material-related issues. By recommending enhanced quality control measures for raw materials prior to their utilization within the production process, our findings suggest potential improvements in material strength, thereby minimizing product defects
 Highlight :

 The study aimed to investigate factors contributing to defective Epodion® rh-Erythropoietin Alfa syringes, with a focus on reducing cracks during manufacturing.
 A Failure Mode and Effect Analysis (FMEA) approach was used to identify the primary causes of failure, revealing cracking as the predominant issue affecting 43% of defective units.
 Enhanced quality control measures for raw materials prior to their utilization within the production process were recommended to minimize product defects.

 Keywords: Epodion, rh-Erythropoietin Alfa, syringes, FMEA, quality control.


Recipe Generation from Unsegmented Cooking Videos

Abstract: This paper tackles recipe generation from unsegmented cooking videos, a task that requires agents to (1) extract key events in completing the dish and (2) generate sentences for the extracted events. Our task is similar to dense video captioning (DVC), which aims at detecting events thoroughly and generating sentences for them. However, unlike DVC, in recipe generation, recipe story awareness is crucial, and a model should extract an appropriate number of events in the correct order and generate accurate sentences based on them. We analyze the output of the DVC model and confirm that although (1) several events are adoptable as a recipe story, (2) the generated sentences for such events are not grounded in the visual content. Based on this, we set our goal to obtain correct recipes by selecting oracle events from the output events and re-generating sentences for them. To achieve this, we propose a transformer-based multimodal recurrent approach of training an event selector and sentence generator for selecting oracle events from the DVC’s events and generating sentences for them. In addition, we extend the model by including ingredients to generate more accurate recipes. The experimental results show that the proposed method outperforms state-of-the-art DVC models. We also confirm that, by modeling the recipe in a story-aware manner, the proposed model outputs the appropriate number of events in the correct order.


Backtranslate what you are saying and I will tell who you are

Abstract: Abstract With this work, we hypothesize that semantically enriching a user's text corpus using backtranslation and expansion modules can improve performance for author profiling tasks. To perform this textual enrichment, we translate an author's representative text. Translations are made from one language—the source language—into another—the target language—and then back to the original one. Finally, we expand an author's text by integrating the original version with the back‐translated one. Our framework includes these backtranslation and expansion modules followed by a SOTA classifier successfully employed for text classification. The framework is tested on three author profiling datasets from the last three years’ shared tasks on fake news, hate speech, irony and stereotypes detection hosted at the CLEF conference for the PAN Lab. This work is an extension of our previous one where we just presented our main idea. Here we improve our framework, and we also investigate more languages and more datasets. Finally, a qualitative analysis is provided. The results confirm that the backtranslation and expansion add‐on modules improve model performance on all three datasets evaluated.


From Code to Cure: The Impact of Artificial Intelligence in Biomedical Applications

Abstract: Artificial intelligence (AI), a branch of computer science, involves developing intelligent computer programs to mimic human intelligence and automate various processes [...]


Doing Good Business? Design Leaders' Perspectives on Ethics in Design

Abstract: Design is not neutral but involves ethical aspects, from addressing power dynamics to enabling broader accessibility and inclusion. In industry settings, design leaders can play an essential role in promoting ethical design by, for instance, setting visions, developing and advocating values, and providing guidance for design. Through co-design workshops with ten design leaders working in Sweden, we investigated how these professionals envision the cultivation-that is, the development, promotion, and dissemination-of ethically responsible design. Our study outlines what the participants regard as the main problems that stifle ethical design and designing, and how to practically address them. The findings highlight that design leaders consider the lack of awareness about the ethical dimensions of design to be one of the main problems. Additionally, they point to various strategies for addressing the problems, such as communicating compelling reasons for prioritizing ethics, creating inclusive and welcoming spaces for collaborative problem-solving and learning about ethics, and folding ethics into current practices. This could involve, for instance, adding the dimension of ethics to 'design critique' sessions, or re-evaluating portfolio reviews to prioritize diversity in recruitment. We conclude by discussing design leaders' potential for furthering ethically responsible design, and provide a practical tool, a design brief, to support this endeavor.


A novel generative adversarial network‐based super‐resolution approach for face recognition

Abstract: Abstract Face recognition is an essential feature required for a range of computer vision applications such as security, attendance systems, emotion detection, airport check‐in, and many others. The super‐resolution of subject images is an important and challenging element in numerous scenarios. At times the images are low resolution and need to be processed through super‐resolution techniques to gain more accurate results. For the problem of image super‐resolution, deep learning‐based face recognition systems have been explored in recent years; however, low‐resolution face recognition remains an arduous task. Generative adversarial network (GAN) based models are a promising approach to address this challenge. However, conventional GAN‐based models may generate images that differ significantly from an original high‐resolution image in the test set to the point that the identity of the target face may be changed. To address this shortcoming, we propose a novel U‐Net style generator architecture, where skip‐connections between the encoder and decoder layer can help in preserving the facial characteristics of the input image in the generated image, thus curbing the generator's ability to generate an entirely new image and training it to generate an image more similar in characteristics to the original image. In addition to statistical metrics like structural similarity index measure and Fréchet inception distance, we compute the pixel‐wise distance between the original and model‐generated images to ascertain that our model generates as close to the original images as possible. While we train the model for 4× super‐resolution (64 × 64 images to 256 × 256), our architecture can also be trained for an arbitrary resizing scale. Finally, the number of faces detected over high‐resolution images generated by our model is shown to be higher than state‐of‐the‐art high‐resolution image creation models for face recognition tasks.


The Influential People and Memories in the Formation of My Approach to Contextual Functional Interpretation

Abstract: It will be noted that in all my scholarly writings and studies I have used an approach I call “contextual functional interpretation”, a problem-solving method. Every word has a meaning specific to its cultural context and carries a text on its shoulders. If this text is not correctly understood, the accuracy of the sentence cannot reach a sufficient and necessary level of reliability. By ensuring the correct understanding of each word that makes up the sentence, a reliable accuracy of explanation is achieved. In the genesis of this idea, scientists and mentors who have had an impact on the formation of my mind, identity and embodiment have played a significant role. In this article, I would like to talk to you about the people who influenced me in the birth of the contextual-functional approach to interpretation, and share some memories of them.


Freeform Metasurface-Assisted Waveguide Coupler for Guided Wave Polarization Manipulation and Spin–Orbit Angular Momentum Conversion

Abstract: Metasurface integrated waveguide couplers provide a promising pathway toward advanced manipulation of free-space light for guided wave coupling, such as polarization routing, mode-selective coupling, and wavelength (de)multiplexing. However, it is a great challenge to manipulate the guided wave polarization state to be completely decoupled from the polarization of incident light. Here, an adjoint-based topology optimization method is employed to inversely design a freeform metasurface for unidirectionally coupling free-space light into customized waveguide mode, with the capabilities of polarization manipulation and guided mode selection. An arbitrary transverse magnetic or transverse electric eigenmode can be unidirectionally excited for any incident polarization state conditions, with the maximum polarization-rotated mode purity and unidirectional coupling efficiency approaching 90.97 and 43%, respectively. Furthermore, by using multiobjective topology optimization, a meta-waveguide coupler with dual functions of generating polarization-controllable hybrid mode and realizing reconfigurable spin–orbit angular momentum conversion is inversely designed, leading to the increase of a mode (de)multiplexer channel number. Our work provides an effective strategy for multifunctional integrated on-chip devices and may have potential applications in optical communication, integrated optics, and quantum optics.


The Geoeconomics of Traditional and Digital Government Money

Abstract: Abstract Government money is an instrument of state power. While extensive research has examined the strategic benefits states can extract from geoeconomic applications of their traditional fiat currencies, this work has largely examined the geoeconomics of fiat money from a perspective of unchanging monetary technology. As such, there currently exists no scholarship on the topic of geoeconomics in digital government money, despite the implications of this technological transition for strategic interaction among countries in traditional geoeconomic frameworks. This chapter organizes existing scholarship on the issue of geoeconomics in traditional government money and subsequently presents novel statistical results from new, large datasets of central bank digital currency (CBDC) projects to demonstrate the already salient issues around CBDCs in geoeconomics. The chapter concludes with policy implications for decision makers, not least including strong advocacy for proactive and informed design in central bank digital currencies, and also proposes lines of future research on strategic interaction in digital government money.


Coregistration of EEG and eye-tracking in infants and developing populations

Abstract: Abstract Infants cannot be instructed where to look; therefore, infant researchers rely on observation of their participant’s gaze to make inferences about their cognitive processes. They therefore started studying infant attention in the real world from early on. Developmental researchers were early adopters of methods combining observations of gaze and behaviour with electroencephalography (EEG) to study attention and other cognitive functions. However, the direct combination of eye-tracking methods and EEG to test infants is still rare, as it includes specific challenges. The current article reviews the development of co-registration research in infancy. It points out specific challenges of co-registration in infant research and suggests ways to overcome them. It ends with recommendations for implementing the co-registration of EEG and eye-tracking in infant research to maximise the benefits of the two measures and their combination and to orient on Open Science principles while doing so. In summary, this work shows that the co-registration of EEG and eye-tracking in infant research can be beneficial to studying natural and real-world behaviour despite its challenges.


Algorithmic Text Analysis

Abstract: Abstract Algorithmic analysis of “text as data” has proliferated throughout academic research over the past decade, offering the opportunity to examine ever-larger and more diverse collections of documents, address more complex issues, and incorporate underrepresented voices into social science inquiry. Drawing from a new survey of twelve top political science journals, this chapter discusses how current methods address a multitude of questions—pointing to what their structural assumptions and fundamental characteristics imply about their insight into latent constructs and meanings. Particular consideration is paid to the mismatch between the objective orientation of computational methods and subjective nature of social scientists’ contextual, situated inquiry. Finally, the chapter looks into areas of potential expansion that would allow these algorithmic methods to enhance concept and discourse analysis, further advancing an interpretive and inclusive understanding of political communication.


Hybrid collaborative filtering using matrix factorization and XGBoost for movie recommendation

Abstract: Nowadays, e-commerce platforms, such as Amazon, Flipkart, Netflix and YouTube, extensively use recommender systems (RS) techniques. Collaborative filtering (CF) is used widely among all RS techniques. A CF analyzes the user’s preference from past data, like ratings, and then suggests actual items to the intended user. The existing techniques compute the similarity between users/items and predict the ratings. However, most of them indicate the user’s preference for the items using a single technique, which may produce poor results. This paper proposes a hybrid CF technique to enhance the movie recommendation (HCFMR). The HCFMR consists of two modules. The first module finds the prediction score with the help of matrix factorization (MF) and passes the prediction score as input to the prediction algorithm, i.e., extreme gradient boosting (XGBoost). The second module generates handcrafted features, such as similar users and movies, along with the user, item and global average. Finally, these features are supplied to the XGBoost to predict the rating score of the movie and recommend the topmost movie to the user. We conduct various simulations on real-world datasets to verify the effectiveness of the proposed technique against the baseline techniques. The exploratory outcomes signify that the HCFMR technique outperforms the baselines and provides a better prediction on the benchmark datasets.


Abstract: Next article No AccessImage Identification and Land Use Management of Anomalous Minerals from Geophysical Information and Deep Convolutional NetworksYuan Han, Xu Zhang, and Yongxing ZhangYuan Han, Xu Zhang, and Yongxing ZhangPDFPDF PLUS Add to favoritesDownload CitationTrack CitationsPermissionsReprints Share onFacebookTwitterLinkedInRedditEmailPrint SectionsMoreDetailsFiguresReferencesCited by The Journal of Geology Just Accepted Article DOIhttps://doi.org/10.1086/730159 HistoryAccepted February 20, 2024 © 2024 The University of Chicago. All Rights reserved.PDF download Crossref reports no articles citing this article.


Measuring GitHub Copilot's Impact on Productivity

Abstract: Case study asks Copilot users about its impact on their productivity, and seeks to find their perceptions mirrored in user data.


Metadata application proﬁle as a mechanism for semantic interoperability in FAIR and open data publishing

Abstract: Application profiles, also known as metadata application profiles, are customised collections of vocabularies adapted from various namespaces and tailored for specific local applications. These profiles act as constrainers and explainers for the (meta)data. Semantic interoperability is the ability of computer systems to exchange data in a mutually understandable manner, facilitating data sharing across diverse platforms and applications without compromising its meaning. As a critical component of semantic interoperability, application profiles enforce semantics to (meta)data, enhancing its openness, interoperability, and reusability. This study assesses the feasibility of representing a comprehensive application profile in a format aligned with the semantic web, ensuring interoperability between profiles and datasets. Dublin Core Description Set Profiles (DSP) is adapted as the modeling framework for metadata application profiles, steering the associated datasets toward RDF compliance. The research outcomes include "Yet Another Metadata Application Profiles" (YAMA) as a preprocessor grounded in the DSP framework for developing and managing metadata application profiles. YAMA facilitates the generation of various standard formats of application profiles, ensuring they are represented in human-readable documentation, machine-actionable forms, and even data validation languages. A data mapping extension to YAMA is proposed to ensure the semantic interoperability of open data, bridging non-RDF data structures to RDF, thus enabling the publication of 5-star open data. This ensures smooth dataset integration and the creation of linkable, semantically rich open datasets. The work emphasizes the pivotal role of application profiles in fortifying the semantic interoperability of (meta)data, thereby elevating dataset openness.


TIAE-DSIN: A time interval aware deep session interest network for click-through rate prediction

Abstract: The users’ click prediction holds significant commercial value in online purchase platforms. Currently, some session-based interest extraction models overlook the impact of temporal interval factors on the distribution pattern of behaviors within a session. To address the above issue, this paper designs a module named session-based time-aware gated recurrent units (ST-GRU) to integrate the temporal interval factors into session sequence. The ST-GRU module utilizes a memory decay function, enabling the simulation process to identify the users’ memory changes within each session. Subsequently, to account for the interdependence between different sessions, this paper introduces bi-directional gated recurrent units (Bi-GRU) with an attention mechanism to learn interaction relations of user interests. Additionally, for high-dimensional potential features, this paper integrates above modules and proposes a method called time interval aware deep session interest network (TIAE-DSIN) model. Correspondingly, comparative experiments are carried out on three public datasets, and the obtained results indicate that the AUC of TIAE-DSIN model is superior to other session interest extraction models.


Scaling responses

Abstract: Abstract This chapter presents various ways of presenting the response options to the respondent. It begins by discussing why dichotomous responses (e.g. yes/no, true/false) are often inadequate. Different alternatives are discussed, including direct estimation methods (e.g. visual analogue scales, adjectival scales, Likert scales), comparative methods (e.g. paired comparisons, Guttman scaling), and econometric methods. It reviews some of the issues that need to be considered in writing the response options, such as whether one should use a unipolar or bipolar scale, how many steps there should be, and whether all the response options need to be labelled. It also covers what statistical tests can legitimately be used with scales. Finally, it compares ratings with rankings, and introduces the method of multidimensional scaling.


Transformer-based anomaly detection in P-LEO constellations: A dynamic graph approach

Abstract: Successful management of large space systems, such as the most recent Proliferated Low Earth Orbit (P-LEO) constellations, demands an increased level of autonomy and irregular behavior detection capability. Existing techniques for satellite network management rely on monitoring satellites individually, potentially failing to capture events shared across multiple units. In this work, we propose a pipeline for identifying anomalous connections in proliferated satellite networks. Our pipeline relies on representing a satellite constellation as a dynamic graph. Dynamic graphs can be conveniently exploited to represent P-LEO networks, as they allow to capture meaningful structural and temporal correlations. Such spatial and temporal information are first projected into an embedding space; next, this encoded representation is processed by a transformer-encoder network for the anomaly detection task. We conduct extensive analyses of the main problem parameters, including the temporal horizon, the structure of the graph, and the size of the constellation. To assess the general performance of the method, we perform a Monte Carlo analysis on 960 ground node pair connections, providing empirical evidence of the algorithm’s generalization capability. The obtained results encourage the extension of the method to more complex, realistic modeling and kindred applications such as the on-edge detection problem.


Translating meaning representations to behavioural interface specifications

Abstract: Higher-order logic can be used for meaning representation in natural language processing to encode the semantic relationships in text. Alternatively, using a formal specification language for meaning representation is more precise for specifying programs and widely supported by automatic theorem provers, while deductive verification based on higher order logic is less common for mainstream programming languages. This paper addresses the research question of translating higher-order logic meaning representations generated from method-level code comments into a formal specification language that extends first-order logic. Doing so requires resolving possible ambiguities in determining the appropriate semantics for predicates. This is an open challenge in the path toward using natural language processing with formal methods. To address this, the paper proposes an approach and constructs a compiler for translating meaning representations, generated from Java programs with method-level comments, into Java Modeling Language. We evaluate the compiler on a set of representative benchmarks, including programs and specifications from the Java API, by generating Java Modeling Language specifications and statically checking them with a theorem prover. Results show that in 94% of the cases Java Modeling Language is accurately generated and in 97% of those cases it can be automatically checked with a state-of-the-art theorem prover.


Fully Anonymous Identity-based Broadcast Signcryption with Public Verification

Abstract: In the multicast communication scenario, compared with broadcast encryption, broadcast signcryption or multi-receiver signcryption has additional ability to authenticate the source of the message. With the enhanced awareness of privacy preservation, ordinary users pay more attention to the identity leakage in the communication process. The primitive of anonymous broadcast signcryption has been proposed to solve this problem, which provides additional anonymity compared with the existing broadcast signcryption. However, most anonymous broadcast signcryption schemes only ensure the sender's identity concealment but ignore the anonymity of the receiver set. In this paper, we present a fully anonymous identity-based broadcast signcryption scheme, which meets insider unforgeablity, outsider confidentiality, identity concealment of sender and full anonymity of the receiver set. In addition, our scheme has two further desirable characteristics. One is public verifiability which means any third party can verify the validity of the message source without knowing the private key provided by the receiver. The other is statelessness which means the user does not need to update the private key due to the join or revocation of other users. Moreover, our scheme has constant-size public parameters and private key as well as constant decryption complexity, which makes the scheme more suitable for deployment in devices with limited storage or low computing power such as IoT devices.


Abstract: This paper studies the problem of constructing a robust nonlinear classifier when the data set involves uncertainty and only the first- and second-order moments are known a priori. A distributionally robust chance-constrained kernel-free quadratic surface support vector machine (SVM) model is proposed using the moment information of the uncertain data. The proposed model is reformulated as a semidefinite programming problem and a second-order cone programming problem for efficient computations. A geometric interpretation of the proposed model is also provided. For commonly used data without prescribed uncertainty, a cluster-based data-driven approach is introduced to retrieve the hidden moment information that enables the proposed model for robust classification. Extensive computational experiments using synthetic and public benchmark data sets with or without uncertainty involved support the superior performance of the proposed model over other state-of-the-art SVM models, particularly when the data sets are massive and/or imbalanced.


Empirical Bayes factors for common hypothesis tests

Abstract: Bayes factors for composite hypotheses have difficulty in encoding vague prior knowledge, as improper priors cannot be used and objective priors may be subjectively unreasonable. To address these issues I revisit the posterior Bayes factor, in which the posterior distribution from the data at hand is re-used in the Bayes factor for the same data. I argue that this is biased when calibrated against proper Bayes factors, but propose adjustments to allow interpretation on the same scale. In the important case of a regular normal model, the bias in log scale is half the number of parameters. The resulting empirical Bayes factor is closely related to the widely applicable information criterion. I develop test-based empirical Bayes factors for several standard tests and propose an extension to multiple testing closely related to the optimal discovery procedure. When only a P -value is available, an approximate empirical Bayes factor is 10 p . I propose interpreting the strength of Bayes factors on a logarithmic scale with base 3.73, reflecting the sharpest distinction between weaker and stronger belief. This provides an objective framework for interpreting statistical evidence, and realises a Bayesian/frequentist compromise.


Nonlinear dynamics and chaos in a vocal-ventricular fold system

Abstract: In humans, ventricular folds are located superiorly to the vocal folds. Under special circumstances such as voice pathology or singing, they vibrate together with the vocal folds to contribute to the production of voice. In the present study, experimental data measured from physical models of the vocal and ventricular folds were analyzed in the light of nonlinear dynamics. The physical models provide a useful experimental framework to study the biomechanics of human vocalizations. Of particular interest in this experiment are co-oscillations of the vocal and ventricular folds, occasionally accompanied by irregular dynamics. We show that such a system can be regarded as two coupled oscillators, which give rise to various cooperative behaviors such as synchronized oscillations with a 1:1 or 1:2 frequency ratio and desynchronized oscillations with torus or chaos. The insight gained from the view of nonlinear dynamics should be of significant use for the diagnosis of voice pathologies, such as ventricular fold dysphonia.


High-density cluster core-based <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="d1e5009" altimg="si356.svg"><mml:mi>k</mml:mi></mml:math>-means clustering with an unknown number of clusters

Abstract: The k-means algorithm, known for its simplicity and adaptability, faces challenges related to manual cluster number selection and sensitivity to initial centroid placement. This paper introduces an innovative framework aimed at overcoming these challenges. By proposing a data-driven cluster number estimation method and a robust initialization strategy based on high-density cluster cores, our approach revolutionizes k-means, unlocking its full unsupervised potential and ensuring superior performance, even in scenarios involving overlapping clusters. The method employs a novel density-based technique to accurately identify cluster cores, resulting in substantial improvements over existing methods. Rigorous experimentation on synthetic and real-world datasets demonstrates an average performance enhancement of 15% in terms of the Adjusted Rand Index for datasets with overlapping clusters, surpassing the capabilities of state-of-the-art density-based clustering methods and traditional k-means. Moreover, our method autonomously determines the optimal number of clusters, facilitating true unsupervised learning and eliminating the impact of initial centroid placement on clustering outcomes. This leads to stable and consistent results, addressing key limitations of the conventional k-means algorithm. The practical applicability of our approach is exemplified in image segmentation tasks, showcasing its versatility and reliability in real-world scenarios.


Advanced machine learning for real-time tibial bone force monitoring in runners using wearable sensors

Abstract: This study explores the innovative integration of machine learning (ML) with wearable sensor technologies for the real-time monitoring of tibial bone force in runners. Utilizing three distinct approaches—a linear regression model based on the Vertical Average Loading Rate (VALR), a physics-based method, and a sophisticated ML algorithm—this research was conducted with 10 participants equipped with wearable sensors. The results revealed the ML model's superior performance over both the physics-based and VALR techniques, achieving Mean Absolute Percentage Errors (MAPEs) of 6.7 percent and 11.3 percent respectively. This was accomplished through extensive training across various datasets. Additionally, the ML approach demonstrated remarkable reliability in 25 different running session simulations, underscoring its effectiveness in complex musculoskeletal analytics. These findings not only highlight the potential of ML in enhancing wearable technology for biomechanical data analysis but also emphasize the necessity for further comprehensive studies in this field.


Forensic analysis of AI-compression traces in spatial and frequency domain

Abstract: The classical JPEG compression is a rich source of cues for forensic image analysis. However, this compression standard will in the near future be complemented by a new, highly efficient learning-based compression standard called JPEG-AI. JPEG-AI is fundamentally different from classical JPEG. Hence, its forensic traces can also be expected to be fundamentally different. We argue that there is a pressing need for image forensics research to investigate these traces. In this work, we characterize forensic compression traces of different AI compression algorithms. Our analysis investigates AI compression artifacts in frequency domain and in spatial domain. Both domains exhibit similar artifacts that likely stem from upsampling operations of the decoders. Additionally, we report for one AI codec another artifact in homogeneous regions. We also investigate the artifact detectability in several scenarios including unseen AI compression traces and postprocessing. Here, frequency and autocorrelation features are better on additive noise and classical JPEG post-compression, while RGB features perform better on blurred and downsampled images.


TAN: a temporal-aware attention network with context-rich representation for boosting proposal generation

Abstract: Abstract Temporal action proposal generation in an untrimmed video is very challenging, and comprehensive context exploration is critically important to generate accurate candidates of action instances. This paper proposes a Temporal-aware Attention Network (TAN) that localizes context-rich proposals by enhancing the temporal representations of boundaries and proposals. Firstly, we pinpoint that obtaining precise location information of action instances needs to consider long-distance temporal contexts. To this end, we propose a Global-Aware Attention (GAA) module for boundary-level interaction. Specifically, we introduce two novel gating mechanisms into the top-down interaction structure to incorporate multi-level semantics into video features effectively. Secondly, we design an efficient task-specific Adaptive Temporal Interaction (ATI) module to learn proposal associations. TAN enhances proposal-level contextual representations in a wide range by utilizing multi-scale interaction modules. Extensive experiments on the ActivityNet-1.3 and THUMOS-14 demonstrate the effectiveness of our proposed method, e.g., TAN achieves 73.43% in AR@1000 on THUMOS-14 and 69.01% in AUC on ActivityNet-1.3. Moreover, TAN significantly improves temporal action detection performance when equipped with existing action classification frameworks.


Refining the Contours of Intimate Image Abuse Offences

Abstract: Abstract Research continues to bring to public consciousness the range of ways in which intimate images may culpably be used to cause significant harm to individual and collective interests. This is causing jurisdictions to compare notes and assess whether there is coherency and adequacy in the coverage of existing offences and whether reforms are necessary. The challenge is to develop offences that are wide enough to capture behaviours that are sufficiently culpable to be deserving of criminalization without bringing less harmful or less culpable behaviours within the reach of the criminal law. This requires a careful balancing of the physical, mental, and defence elements of intimate image abuse offences. With this in mind this chapter takes a comparative approach to evaluating how offences can optimally be designed to address intimate image abuse. Overall, it is argued that a ladder approach, with scaffolded offences, can best reflect harms and degrees of culpability so that the specific wrongdoing in intimate image abuse is accurately captured, packaged, and labelled.


Automated Extraction and Analysis of Sentences under Production: A Theoretical Framework and Its Evaluation

Abstract: Sentences are generally understood to be essential communicative units in writing that are built to express thoughts and meanings. Studying sentence production provides a valuable opportunity to shed new light on the writing process itself and on the underlying cognitive processes. Nevertheless, research on the production of sentences in writing remains scarce. We propose a theoretical framework and an open-source implementation that aim to facilitate the study of sentence production based on keystroke logs. We centre our approach around the notion of sentence history: all the versions of a given sentence during the production of a text. The implementation takes keystroke logs as input and extracts sentence versions, aggregates them into sentence histories and evaluates the sentencehood of each sentence version. We provide detailed evaluation of the implementation based on a manually annotated corpus of texts in French, German and English. The implementation yields strong results on the three processing aspects.


BDIDA-IoT: A Blockchain-Based Decentralized Identity Architecture Enhances the Efficiency of IoT Data Flow

Abstract: With the booming development of the Internet of Things (IoT) industry, millions of data are generated every day. How to use and manage these data safely and efficiently has become a hot issue of concern to people. Due to the accumulation of IoT data, the isolated data island phenomenon makes it difficult to connect and interact with each data owner, and the security and privacy of IoT data also become a challenge. Blockchain is a decentralized database technology that uses distributed accounting to ensure reliable data transmission and access, along with smart contracts that can be executed automatically to program and manipulate data. At the same time, blockchain techniques have stronger security and privacy, which can better meet the needs of users. In this paper, we analyze the current management mode and challenges of IoT data and propose an IoT data platform based on blockchain. The proposed platform takes into account important factors including IoT data traceability, IoT data trusted transactions, etc. Our IoT data platform achieves the trusted management and transaction of IoT data. We also propose follow-up optimization solutions to expand the application scope of our platform and achieve more efficient management of IoT data.


Iterative Camera Calibration Method Based on Concentric Circle Grids

Abstract: A concentric circle target is commonly used in the vision measurement system for its detection accuracy and robustness. To enhance the camera calibration accuracy, this paper proposes an improved calibration method that utilizes concentric circle grids as the calibration target. The method involves accurately locating the imaged center and optimizing camera parameters. The imaged concentric circle center obtained by cross-ratio invariance is not affected by perspective projection, which ensures the location accuracy of the feature point. Subsequently, the impact of lens distortion on camera calibration is comprehensively investigated. The sub-pixel coordinates of imaged centers are taken into the iterative calibration method, and camera parameters are updated. Through simulations and real experiments, the proposed method effectively reduces the residual error and improves the accuracy of camera parameters.


Pretrained Language–Knowledge Graph Model Benefits Both Knowledge Graph Completion and Industrial Tasks: Taking the Blast Furnace Ironmaking Process as an Example

Abstract: Industrial knowledge graphs (IKGs) have received widespread attention from researchers in recent years; they are intuitive to humans and can be understood and processed by machines. However, how to update the entity triples in the graph based on the continuous production data to cover as much knowledge as possible, while applying a KG to meet the needs of different industrial tasks, are two difficulties. This paper proposes a two-stage model construction strategy to benefit both knowledge graph completion and industrial tasks. Firstly, this paper summarizes the specific forms of multi-source data in industry and provides processing methods for each type of data. The core is to vectorize the data and align it conceptually, thereby achieving the fusion modeling of multi-source data. Secondly, this paper defines two interrelated subtasks to construct a pretrained language–knowledge graph model based on multi-task learning. At the same time, considering the dynamic characteristics of the production process, a dynamic expert network structure is adopted for different tasks combined with the pretrained model. In the knowledge completion task, the proposed model achieved an accuracy of 91.25%, while in the self-healing control task of a blast furnace, the proposed model reduced the incorrect actions rate to 0 and completed self-healing control for low stockline fault in 278 min. The proposed framework has achieved satisfactory results in experiments, which verifies the effectiveness of introducing knowledge into industry.


Micro-FL: A Fault-Tolerant Scalable Microservice-Based Platform for Federated Learning

Abstract: As the number of machine learning applications increases, growing concerns about data privacy expose the limitations of traditional cloud-based machine learning methods that rely on centralized data collection and processing. Federated learning emerges as a promising alternative, offering a novel approach to training machine learning models that safeguards data privacy. Federated learning facilitates collaborative model training across various entities. In this approach, each user trains models locally and shares only the local model parameters with a central server, which then generates a global model based on these individual updates. This approach ensures data privacy since the training data itself is never directly shared with a central entity. However, existing federated machine learning frameworks are not without challenges. In terms of server design, these frameworks exhibit limited scalability with an increasing number of clients and are highly vulnerable to system faults, particularly as the central server becomes a single point of failure. This paper introduces Micro-FL, a federated learning framework that uses a microservices architecture to implement the federated learning system. It demonstrates that the framework is fault-tolerant and scalable, showing its ability to handle an increasing number of clients. A comprehensive performance evaluation confirms that Micro-FL proficiently handles component faults, enabling a smooth and uninterrupted operation.


Dynamic Screening Strategy Based on Feature Graphs for UAV Object and Group Re-Identification

Abstract: In contemporary times, owing to the swift advancement of Unmanned Aerial Vehicles (UAVs), there is enormous potential for the use of UAVs to ensure public safety. Most research on capturing images by UAVs mainly focuses on object detection and tracking tasks, but few studies have focused on the UAV object re-identification task. In addition, in the real-world scenarios, objects frequently get together in groups. Therefore, re-identifying UAV objects and groups poses a significant challenge. In this paper, a novel dynamic screening strategy based on feature graphs framework is proposed for UAV object and group re-identification. Specifically, the graph-based feature matching module presented aims to enhance the transmission of group contextual information by using adjacent feature nodes. Additionally, a dynamic screening strategy designed attempts to prune the feature nodes that are not identified as the same group to reduce the impact of noise (other group members but not belonging to this group). Extensive experiments have been conducted on the Road Group, DukeMTMC Group and CUHK-SYSU-Group datasets to validate our framework, revealing superior performance compared to most methods. The Rank-1 on CUHK-SYSU-Group, Road Group and DukeMTMC Group datasets reaches 71.8%, 86.4% and 57.8%, respectively. Meanwhile, our method performance is explored on the UAV datasets of PRAI-1581 and Aerial Image, the infrared datasets of SYSU-MM01 and CM-Group and the NIR dataset of RBG-NIR Scene dataset; the unexpected findings demonstrate the robustness and wide applicability of our method.


Large-Scale 3D Reconstruction from Multi-View Imagery: A Comprehensive Review

Abstract: Three-dimensional reconstruction is a key technology employed to represent virtual reality in the real world, which is valuable in computer vision. Large-scale 3D models have broad application prospects in the fields of smart cities, navigation, virtual tourism, disaster warning, and search-and-rescue missions. Unfortunately, most image-based studies currently prioritize the speed and accuracy of 3D reconstruction in indoor scenes. While there are some studies that address large-scale scenes, there has been a lack of systematic comprehensive efforts to bring together the advancements made in the field of 3D reconstruction in large-scale scenes. Hence, this paper presents a comprehensive overview of a 3D reconstruction technique that utilizes multi-view imagery from large-scale scenes. In this article, a comprehensive summary and analysis of vision-based 3D reconstruction technology for large-scale scenes are presented. The 3D reconstruction algorithms are extensively categorized into traditional and learning-based methods. Furthermore, these methods can be categorized based on whether the sensor actively illuminates objects with light sources, resulting in two categories: active and passive methods. Two active methods, namely, structured light and laser scanning, are briefly introduced. The focus then shifts to structure from motion (SfM), stereo matching, and multi-view stereo (MVS), encompassing both traditional and learning-based approaches. Additionally, a novel approach of neural-radiance-field-based 3D reconstruction is introduced. The workflow and improvements in large-scale scenes are elaborated upon. Subsequently, some well-known datasets and evaluation metrics for various 3D reconstruction tasks are introduced. Lastly, a summary of the challenges encountered in the application of 3D reconstruction technology in large-scale outdoor scenes is provided, along with predictions for future trends in development.