A critical review of multi-output support vector regression

Abstract: Single-output regression is a widely used statistical modeling method to predict an output based on one or more features of a datapoint. If a dataset has multiple outputs, they can be predicted independently from each other although this disregards potential correlations and thus may negatively affect the predictive performance. Therefore, multi-output regression methods predict multiple outputs simultaneously. One way to approach single-output regression is by using methods based on support vectors such as support vector regression (SVR) or least-squares SVR (LS-SVR). Based on these two, previous works have devised multi-output support vector regression methods. In this review, we introduce a unified notation to summarize the single-output support vector regression methods SVR and LS-SVR as well as state-of-the-art multi-output support vector regression methods. Furthermore, we implemented a workflow for subject- and record-wise bootstrapping and nested cross-validation experiments, which we used for an exhaustive evaluation of all single- and multi-output support vector regression methods on synthetic and non-synthetic datasets. Although the reviewed papers claim that their multi-output methods improve regression performance, we find that none of them outperform both single-output methods SVR and LS-SVR for various reasons. Due to these results, we reflected about the general concept of support vector regression and then concluded that support vector regression methods do not appear to be suitable for the task of multi-output regression.


ELTS-Net: An enhanced liver tumor segmentation network with augmented receptive field and global contextual information

Abstract: The liver is one of the organs with the highest incidence rate in the human body, and late-stage liver cancer is basically incurable. Therefore, early diagnosis and lesion location of liver cancer are of important clinical value. This study proposes an enhanced network architecture ELTS-Net based on the 3D U-Net model, to address the limitations of conventional image segmentation methods and the underutilization of image spatial features by the 2D U-Net network structure. ELTS-Net expands upon the original network by incorporating dilated convolutions to increase the receptive field of the convolutional kernel. Additionally, an attention residual module, comprising an attention mechanism and residual connections, replaces the original convolutional module, serving as the primary components of the encoder and decoder. This design enables the network to capture contextual information globally in both channel and spatial dimensions. Furthermore, deep supervision modules are integrated between different levels of the decoder network, providing additional feedback from deeper intermediate layers. This constrains the network weights to the target regions and optimizing segmentation results. Evaluation on the LiTS2017 dataset shows improvements in evaluation metrics for liver and tumor segmentation tasks compared to the baseline 3D U-Net model, achieving 95.2% liver segmentation accuracy and 71.9% tumor segmentation accuracy, with accuracy improvements of 0.9% and 3.1% respectively. The experimental results validate the superior segmentation performance of ELTS-Net compared to other comparison models, offering valuable guidance for clinical diagnosis and treatment.


Explainable deep learning for automatic rock classification

Abstract: As deep learning (DL) gains popularity for its ability to make accurate predictions in various fields, its applications in geosciences are also on the rise. Many studies focus on achieving high accuracy in DL models by selecting models, developing more complex architectures, and tuning hyperparameters. However, the interpretability of these models, or the ability to understand how they make their predictions, is less frequently discussed. To address the challenge of high accuracy but low interpretability of DL models in geosciences, we study rock classification from thin-section photomicrographs of six types of sedimentary rocks, including quartz arenite, feldspathic arenite, lithic arenite, siltstone, oolitic packstone, and dolomite. These rocks' characteristic framework grains and grain textures are their distinguishing features, such as the rounded or oval ooids in oolitic packstone. We first train regular DL models, such as ResNet-50, on these photomicrographs and achieve an accuracy of over 0.94. However, these models make classifications based on features like cracks, cements, and scale bars, which are irrelevant for distinguishing sedimentary rocks in real-world applications. We then propose an attention-based dual network incorporating both global (overall photomicrograph) and local (distinguishing framework grains) features to address this issue. Our proposed model achieves not only high accuracy (0.99) but also provides interpretable feature extractions. Our study highlights the need to consider interpretability and geological knowledge in developing DL models, in addition to aiming for high accuracy.


CalD3r and MenD3s: Spontaneous 3D facial expression databases

Abstract: In the last couple of decades, the research on 3D facial expression recognition has been fostered by the creation of tailored databases containing prototypical expressions of different individuals and by the advances in cost effective acquisition technologies. Though, most of the currently available databases consist of exaggerated facial expressions, due to the imitation principle which they rely on. This makes these databases only partially employable for real world applications such as human-computer interaction for smart products and environments, health, and industry 4.0, as algorithms learn on these ‘inflated’ data which do not respond to ecological validity requirements. In this work, we present two novel 2D + 3D spontaneous facial expression databases of young adults with different geographical origin, in which emotions have been evoked thanks to affective images of the acknowledged IAPS and GAPED databases, and verified with participants’ self-reports. To the best of our knowledge, these are the first three-dimensional facial databases with emotions elicited by validated affective stimuli.


Certificate-based authenticated encryption with keyword search: Enhanced security model and a concrete construction for Internet of Things

Abstract: The Internet of Things (IoT) devices produce a humongous amount of data frequently stored on cloud servers, and as a result, cryptographic techniques to guarantee the privacy of outsourced data while preserving search ability on servers are becoming indispensable research topics. A prominent example of such topics is the concept of public key authenticated encryption with keyword search (PAEKS). In spite of the large number of PAEKS schemes in the literature, most existing schemes exhibit issues in certificate management, key escrow, or even key distribution. To address these issues, recently, PAEKS schemes in the certificate-based setting have gained attention. To the best of our knowledge, there exist only two Certificate-Based Authenticated Encryption with Keyword Search (CBAEKS) schemes, both presented with rather weak security models based on single keyword challenges. In this paper, we propose an enhanced security model for CBAEKS which captures notions of multi-ciphertext and multi-trapdoor indistinguishability, then proceed to devise a concrete instantiation for a CBAEKS scheme and formally prove its security under our enhanced model. Furthermore, we prove that the existing CBAEKS schemes are insecure under the enhanced security model. Comparisons with related schemes in the literature are also provided to demonstrate that the enhanced security is achieved at some affordable costs.


Artificial intelligence and machine learning applications in the Spanish nuclear field

Abstract: Machine Learning and Artificial Intelligence techniques are increasingly applied in the nuclear field. In this way, Spanish companies and research institutions have been developing different projects in the field of nuclear energy applying such techniques. A brief overview of some of these projects is presented, which cover areas as diverse as nuclear data, nuclear fuel manufacturing, simulation codes, fuel performance monitoring, nuclear inspection, and operation training. Among the techniques used to develop them are deep neural networks for building surrogate models, convolutional neural networks for computer vision, Bayesian networks and Gaussian processes for predictive models, and speech recognition with large language models.


DUIANet: A double layer U-Net image hiding method based on improved Inception module and attention mechanism

Abstract: Image hiding is the process of hiding secret image into cover image, and revealing secret image from steganographic image. However, the quality of generated images and the ability to resist steganalysis detection of steganographic image can be further improved. We propose a novel double-layer U-Net image hiding method based on improved Inception and attention mechanism. The secret image undergoes Haar wavelet transform, hiding network hides the wavelet sub-band of secret image into cover image. We use improved Inception while cascading convolution and attention mechanisms to better extract and fuse information, resulting in high-quality steganographic image. In the revealing stage, Attention mechanism can help networks extract more features about secret image, then we using inverse Haar wavelet transform to revealing high-quality secret image. The experimental results show that the method can obtain high quality images and have good security. When hiding multiple images, the method still has good performance.


Batch image encryption using cross image permutation and diffusion

Abstract: Chaotic encryption mostly has been proposed to protect a single image or batch images of identical size. Typically, it splices these images into a huge one, and later performs batch image encryption. Yet, this solution may not be feasible given arbitrary-size images, which could not be directly connected one by one. To address it, we propose an arbitrary-size encryption scheme via chaos, for efficiently protecting batch/multiple images at one go. Our scheme supports to automatically read size of images, and then perform cross image permutation and diffusion. The cross image permutation is generic, which could output each permutation with equal expectation. We newly propose a bi-direction cross image diffusion, which has superior performance with only 1 round operation to spread the encryption influence to all images. At last, we conduct extensive experiments and security analyses. The experimental results show that our algorithm can mask the statistical information and resist differential attacks, with metrics such as information entropy, correlation coefficients, NPCR, and UACI closely approximating their theoretical values, namely 8, 0, 99.609%, and 33.464%, respectively. Our code is available at [https://github.com/TcSong/CrossImgEncryption].


DCCNMF: Deep Complementary and Consensus Non-negative Matrix Factorization for multi-view clustering

Abstract: Deep non-negative matrix factorization-based methods have recently been explored in multi-view clustering due to their ability to deal with complex non-linear data. Although a few methods exist that learn both complementary and consensus information simultaneously by adding new regularizations or adjusting hyperparameters without providing a solid architecture, they do not fully exploit this information. This paper proposes a novel method called Deep Complementary and Consensus Non-negative Matrix Factorization (DCCNMF) that combines the strengths of Non-negative Matrix Factorization and Coupled Non-negative Matrix Factorization using a novel architecture to simultaneously learn both complementary and consensus information present in multi-view data. Two manifold regularization terms, namely complementary manifold and consensus manifold are introduced to preserve the view-specific and view-shared geometric structures during the dimensionality reduction from higher to lower order. Further, smoothness regularization is employed to achieve distinct low-order representations by maximizing the cosine similarity among data points with similar orientations and minimizing it among data points with dissimilar orientations. DCCNMF and benchmark methods are evaluated on several real-world datasets and the results demonstrate that DCCNMF significantly outperforms state-of-the-art multi-view learning approaches.


En-DeepONet: An enrichment approach for enhancing the expressivity of neural operators with applications to seismology

Abstract: The Eikonal equation plays a central role in seismic wave propagation and hypocenter localization, a crucial aspect of efficient earthquake early warning systems. Despite recent progress, real-time earthquake localization remains challenging due to the need to learn a generalizable Eikonal operator. We introduce a novel deep learning architecture, Enriched-DeepONet (En-DeepONet), addressing the limitations of current operator learning models in dealing with moving-solution operators. Leveraging addition and subtraction operations and a novel ‘root’ network, En-DeepONet is particularly suitable for learning such operators and achieves up to four orders of magnitude improved accuracy without increased training cost. We demonstrate the effectiveness of En-DeepONet in earthquake localization under variable velocity and arrival time conditions. Our results indicate that En-DeepONet paves the way for real-time hypocenter localization for velocity models of practical interest. The proposed method represents a significant advancement in operator learning that is applicable to a gamut of scientific problems, including those in seismology, fracture mechanics, and phase-field problems.


An electronic medical record access control model based on intuitionistic fuzzy trust

Abstract: Electronic Medical Records (EMR), as the core data of medical big data, has improved the efficiency of medical services in the process of sharing and use, but a series of data privacy leakage problems have emerged. Access control technology can ensure users' legitimate and appropriate access to data resources, but traditional access control has many limitations such as static and coarse-grained, which is difficult to meet in the dynamic and complex healthcare environment. We create a model to solve the privacy leakage problem of healthcare big data. The model utilizes the Latent Delicacy Allocation (LDA) topic model to quickly search documents and extract the topic probability distributions between the target patient and other patients' electronic medical records, which improves the efficiency of medical record document analysis; Calculating physician access similarity, we quantify physician access behavior to more accurately describe physician access behavior in the form of data. We combined the intuitionistic fuzzy theory with the trust evaluation method to create a novel trust evaluation method-- Intuitionistic Fuzzy Trust Evaluation Method, which can reflect the uncertainty characteristics that exist in the physician's diagnostic and treatment process when quantifying the trust of the access by using this method, and it is more flexible and practical, which enhances the accuracy of the quantification of the trust. Improving the traditional trust aggregation method by adding a time window and time decay function, the optimized method improves the accuracy of trust aggregation, and the experimental results are more in line with the actual law. We created a new intuitionistic fuzzy trust access control model using a relative entropy-based intuitionistic fuzzy clustering algorithm and a reward and punishment mechanism to achieve adaptive and dynamic access control for physician access behavior. Experiments show that the access control model based on intuitionistic fuzzy trust proposed in this article achieves 95% correctness in identifying user access behaviors and 94.29% correctness in identifying excessive user access behaviors. Experiments have proved that this model has obvious advantages over other models in the process of privacy protection of electronic medical record systems and has certain control effects.


Rationalizing Graph Neural Networks with Data Augmentation

Abstract: Graph rationales are representative subgraph structures that best explain and support the graph neural network (GNN) predictions. Graph rationalization involves the joint identification of these subgraphs during GNN training, resulting in improved interpretability and generalization. GNN is widely used for node-level tasks such as paper classification and graph-level tasks such as molecular property prediction. However, on both levels, little attention has been given to GNN rationalization and the lack of training examples makes it difficult to identify the optimal graph rationales. In this work, we address the problem by proposing a unified data augmentation framework with two novel operations on environment subgraphs to rationalize GNN prediction. We define the environment subgraph as the remaining subgraph after rationale identification and separation. The framework efficiently performs rationale–environment separation in the representation space for a node’s neighborhood graph or a graph’s complete structure to avoid the high complexity of explicit graph decoding and encoding. We conduct experiments on 17 datasets spanning node classification, graph classification, and graph regression. Results demonstrate that our framework is effective and efficient in rationalizing and enhancing GNNs for different levels of tasks on graphs.


An Improved Heterogeneous Comprehensive Learning Symbiotic Organism Search for Optimization Problems

Abstract: The Symbiotic Organism Search (SOS) algorithm, and most of its variants, exhibit the same search behaviour. That is, each organism or individual in the ecosystem applies the same update rules according to 3 phases: mutualism, commensalism, and parasitism. By applying this strategy, an organism is capable of crippling the search effort of another organism. To overcome this problem, this paper proposes a Heterogeneous Comprehensive Learning SOS (HCLSOS) which divides the population into two distinct subpopulations namely: the exploration and exploitation subpopulations. HCLSOS allows each organism to follow one of two search behaviour according to into its subpopulation: whether to explore or exploit. This addresses the problem of balancing exploration and exploitation in SOS. The proposed algorithm employs a comprehensive learning strategy for the exploration group to generate a new type of mutual vector called the multispecies mutual vector capable of preserving organisms' diversity and discouraging premature convergence. Information exchange between the two groups is unidirectional and managed through a random elite learning strategy. Through this collaboration, HCLSOS can effectively evolve organisms to explore the search space and properly exploit the discovered optimal regions. The study tested HCLSOS on 23 benchmark functions, CEC2014, CEC2017, and the recent CEC 2022 test suites. The outcome of HCLSOS is compared with 15 state-of-the-art algorithms, and the results obtained showed that HCLSOS can attain competitive or even better results. Furthermore, we applied HCLSOS to solve 3 constrained engineering problems and to design an efficient frequency control of a two-area islanded microgrid system.


Dual-resolution transformer combined with multi-layer separable convolution fusion network for real-time semantic segmentation

Abstract: Environmental perception is crucial for unmanned mobile platforms such as autonomous vehicles and robots. Precise and fast semantic segmentation of the surrounding scene is a key task to enhance this capability. Existing real-time semantic segmentation networks are typically based on convolutional neural networks (CNNs), which have achieved good results, but they still lack control over global context features. In recent years, the Transformer architecture has achieved significant success in capturing global context, which is beneficial for improving segmentation accuracy. However, Transformers tend to ignore local connections, and their computational complexity makes real-time segmentation challenging. We propose a lightweight real-time semantic segmentation network called DTMC-Net, which combines the advantages of CNNs and Transformers. We design a special residual convolution module called the Lightweight Multi-layer Separable Convolution Attention module (LMSCA) to reduce the parameter count and perform multi-scale feature fusion to capture local features effectively. We introduce the Simple Dual-Resolution Transformer (SDR Transformer) that utilizes lightweight attention mechanisms and residual feed forward networks to capture and maintain features, with multiple bilateral fusions between two branches to exchange information. The proposed Anti-artifact Aggregation Pyramid Pooling Module (AAPPM) optimizes the upsampling process, refines features, and performs multi-scale feature fusion again. DTMC-Net only contains 4.2M parameters and achieves good performance on multiple public datasets with different scenarios.


Frequency-aware network for low-light image enhancement

Abstract: Low-light images often suffer from severe visual degradation, affecting both human perception and high-level computer vision tasks. Most existing methods process images in the spatial domain, making it challenging to simultaneously improve brightness while suppressing noise. In this paper, we present a novel perspective to enhance images based on frequency domain characteristics. Specifically, we reveal that the low-frequency components are closely related to luminance and color, whereas the high-frequency components are not. Based on this observation, we propose the Frequency-aware Network (FaNet) for low-light image enhancement. By selectively adjusting low-frequency components, FaNet preserves more high-frequency details while achieving low-light image enhancement. Additionally, we employ a multi-scale framework and selective fusion for effective feature learning and image reconstruction. Experimental results demonstrate the superiority of the proposed method.


Accurate road user localization in aerial images captured by unmanned aerial vehicles

Abstract: Unmanned aerial vehicles (UAVs) have become increasingly popular for traffic data collection. However, the depth relief of road users and the perspective distortion of the onboard camera induce nonnegligible measurement errors while exploiting UAVs to localize road users. To address this issue, this paper presents a method for accurate road user location estimation in aerial images. First, a deep-learning-based method was employed to detect road users in aerial images using oriented bounding boxes. Then, the localization error induced by the depth relief and perspective distortion was examined and modeled, based on which an error compensation scheme was developed to offset the localization error for each road user so that higher localization accuracy is attainable. Field experiments were conducted to evaluate the proposed method's performance. The results demonstrated a promising accuracy in estimating the location of road users, signifying the method's potential to improve the credibility of UAVs in traffic applications.


Artificial Intelligence and the Future of Chinese Language Pedagogy

Abstract: This chapter explores the function of cutting-edge technologies including artificial intelligence, augmented reality, and the metaverse in teaching Chinese as a foreign language. It's an example of how AI is being put to use in tailor-made programs for mastering Chinese and all its subtleties. The chapter covers how to use augmented reality and how it can help you have more meaningful conversations with others. The potential of ChatGPT and the metaverse as interactive learning environments is also discussed in this chapter. The effects on educational policy and teacher preparation, as well as an examination of students' independence in technologically advanced classrooms, are discussed in the last section.


Measuring near-field wave elevation with monocular vision using self-supervised learning techniques

Abstract: Ocean waves are among the most influential factors affecting marine mechanical systems. The existing methods for measuring wide-area wave-field data have limitations and require high maintenance costs. Therefore, it is crucial to develop a reliable and efficient field measurement method for wide-area wave fields. This study proposes a monocular vision-based approach for wave field measurement, where the depth of each pixel is equalised to summation of the depth of the mean water surface and the depth variation induced by wave elevation. A deep network was trained using a self-supervised algorithm to approximate the depth variation induced by wave elevation. A loss of representative features was proposed to improve the performance of deep networks. The images of an open-source dataset were used to train and validate the proposed model. By using the proposed method, the images were utilised to reconstruct the three-dimensional wave field of the test set. The time history and wave spectrum of the wave fields were compared with those calculated by existing method and measured by wave gauge. The influence of the training set on the measurement results was also investigated. The results indicate that the proposed method is capable of performing effective near-field wave measurements by monocular images, effectively reducing the device requirements, and increasing update frequency for ocean wave measurement.


Multiple-instance Learning from Triplet Comparison Bags

Abstract: Multiple-instance learning (MIL) solves the problem where training instances are grouped in bags, and a binary (positive or negative) label is provided for each bag. Most of the existing MIL studies need fully labeled bags for training an effective classifier, while it could be quite hard to collect such data in many real-world scenarios, due to the high cost of data labeling process. Fortunately, unlike fully labeled data, triplet comparison data can be collected in a more accurate and human-friendly way. Therefore, in this article, we for the first time investigate MIL from only triplet comparison bags , where a triplet (X a , X b , X c ) contains the weak supervision information that bag X a is more similar to X b than to X c . To solve this problem, we propose to train a bag-level classifier by the empirical risk minimization framework and theoretically provide a generalization error bound. We also show that a convex formulation can be obtained only when specific convex binary losses such as the square loss and the double hinge loss are used. Extensive experiments validate that our proposed method significantly outperforms other baselines.


High-low level task combination for object detection in foggy weather conditions

Abstract: For the object detection task in foggy weather conditions, image dehazing network is often used as preprocessing method to get a clear input. However, there is not strictly a strong positive correlation between image dehazing task and object detection task. Moreover, the preprocessing module can increase the inference time of the whole model to a certain extent. To alleviate these problems, we propose a novel High-Low level task combination network (HLNet) based on multitask learning, which can learn both high-level and low-level tasks. Specially, instead of restoring the features to clear pixel-wise feature space like common image dehazing method, we opt to perform a restoration in feature level to mitigate the influence of the Batch Normalization (BN) layer of encoder on dehazing task. HLNet jointly learn dehazing task and detection task in an end-to-end fashion, which ensures that the weather-specific information in latent feature space is suppressed. Moreover, we applied the HLNet framework on three different object detection networks, including RetinaNnet, YOLOv3 and YOLOv5s network, and achieved improvements of 1.7 percent, 2.3 percent, and 1.2 percent in mAP respectively. The experimental results demonstrate the effectiveness and generalization ability of our proposed HLNet framework in real foggy scenarios.


SPGAN: Siamese projection Generative Adversarial Networks

Abstract: Noise-to-image synthesis continues to be challenging, despite the application of the advanced loss functions in Generative Adversarial Networks (GANs). The main issue lies in the fact that discriminators employ hard margin classification, which is susceptible to misclassification. Moreover, the features of real image distribution learned by the generator are limited during adversarial training, thus the generated images are visually inferior to the real images. To tackle these challenges, a GAN method based on Siamese projection network (abbreviated as SPGAN) is proposed to learn a similarity measurement of features for image synthesis. Then, the similarity measurement is incorporated into the loss functions of the generator and discriminator, forming a similar feature adversarial learning. Through similar feature adversarial learning, SPGAN encourages the discriminator to maximize the dissimilarity between the features of real and generated images during recognition process. Simultaneously, it encourages the generator to synthesize images that contain more features resembling those of real images. Furthermore, we extend the SPGAN method by rewriting five representative loss functions, showcasing its compatibility with different loss functions. Experimental results demonstrate that the performance of SPGAN outperforms the advanced loss functions.


Three-dimensional mineral prospectivity mapping Considering structural restoration in the Dayingezhuang gold Deposit, eastern china

Abstract: In the era of big data and the rapid advancements in machine learning, particularly deep learning, three-dimensional mineral prospectivity mapping (3D MPM) has emerged as a powerful tool for accurately deciphering the complex nonlinear relationships between ore-controlling factors hidden within geoscientific datasets and known mineralization. However, post-mineralization structural deformations can significantly distort the structures and mineralization distribution. This distortion introduces a substantial amount of error into 3D MPM, thereby diminishing its effectiveness in mineral resource exploration. In response to this challenge, our study introduces an innovative 3D MPM approach incorporating structural restoration. This approach has been applied to the Dayingezhuang gold deposit, where the Zhaoping fault, experienced disruption due to the post-mineralization NW-trending Dayingezhuang fault. The existing 3D model of the Zhaoping fault was first constructed using the observation data. A 3D geometric transformation technique was then employed to restore the original structures and mineralization distribution during the mineralization period. Subsequently, morphological analysis and prospectivity modeling were performed using deep learning algorithms including the Bayesian decomposition model for regression and multilayer perceptron model for classification. The results indicated several crucial findings: (1) structural restoration effectively eliminated the spatial distortions (250 m to 500 m) within the Zhaoping fault and rectified the discontinuities in mineralization zones; (2) the post-mineralization ore-controlling features exhibited smaller dips and undulations, with the main mineralization concentrated in areas transitioning from steep to gentle dips (-10°/100 m to 2°/100 m); (3) the post-restoration regression and classification models showed higher accuracy and reliability compared to the pre-restoration models, with an improvement of approximately 27 % and 10 %, respectively. As per our research findings, structural restoration effectively eliminates distortions in both fault and mineralization induced by post-ore structural deformations. Consequently, this method significantly enhances the reliability of 3D MPM. In the context of structural deformations, it underscores the necessity and effectiveness of structural restoration in conducting 3D MPM on a deposit scale.


Enhancing Heterogeneous Knowledge Graph Completion with a Novel GAT-based Approach

Abstract: Knowledge graphs (KGs) play a vital role in enhancing search results and recommendation systems. With the rapid increase in the size of KGs, they are becoming inaccurate and incomplete. This problem can be solved by the KG completion methods, of which graph attention network (GAT)-based methods stand out because of their superior performance. However, existing GAT-based KG completion methods often suffer from overfitting issues when dealing with heterogeneous KGs, primarily due to the unbalanced number of samples. Additionally, these methods demonstrate poor performance in predicting the tail (head) entity that shares the same relation and head (tail) entity with others. To solve these problems, we propose GATH, a novel GAT -based method designed for H eterogeneous KGs. GATH incorporates two separate attention network modules that work synergistically to predict the missing entities. We also introduce novel encoding and feature transformation approaches, enabling the robust performance of GATH in scenarios with imbalanced samples. Comprehensive experiments are conducted to evaluate GATH’s performance. Compared with the existing state-of-the-art GAT-based model on Hits@10 and MRR metrics, our model improves performance by 5.2% and 5.2% on the FB15K-237 dataset and by 4.5% and 14.6% on the WN18RR dataset, respectively.


REQA: Coarse-to-fine assessment of image quality to alleviate the range effect

Abstract: Blind image quality assessment (BIQA) of User Generated Content (UGC) suffers from the range effect, which indicates that on the overall quality range, mean opinion score (MOS) and predicted MOS (pMOS) are well correlated, but when focusing on a particular narrow range, the correlation is lower. To tackle this problem, a novel method is proposed from coarse-grained metric to fine-grained prediction. Concretely, we utilize global context features and local detailed features for the multi-scale distortion perception. Then, to further boost the ability of fine-grained assessment, we introduce the feedback mechanism, which is in accord with Human Vision System (HVS), to perceive detailed distortions gradually. Also, two coarse-to-fine loss functions are proposed to facilitate the feedback perception progress: a rank-and-gradient loss for coarse-grained metric keeps the assessment rank and gradient consistency between pMOS and MOS; a multi-level tolerance loss following the curriculum learning strategy is proposed to make a fine-grained prediction. Both coarse-grained and fine-grained experiments demonstrate that the proposed method outperforms the state-of-the-art ones, which validates that our method effectively alleviates the range effect. The codes are available at https://github.com/huofushuo/REQA.


DAHGN: Degree-Aware Heterogeneous Graph Neural Network

Abstract: In recent years, Graph Neural Networks (GNNs), an emerging technology for learning from graph-structured data, have attracted much attention. Despite the widespread applications of GNNs across diverse domains, recent studies have shown that insufficient information aggregation for low-degree nodes (i.e., tail nodes) leads to inferior performance of GNNs. While numerous recent studies have aimed to address the issue of degree bias in homogeneous graphs, there are limited studies on analyzing such unfairness for heterogeneous information networks. In this article, we propose a novel semi-supervised contrastive learning framework named Degree-Aware Heterogeneous Graph Neural Network (DAHGN), which achieves promising performance by alleviating the degree bias. In brief, we first construct two contrastive graph views, one from a heterogeneous graph that focuses on capturing complementary information to enhance the node representations and one from an extracted homogeneous subgraph that focuses on applying different strategies to low- and high- degree nodes to eliminate the degree bias. We then leverage the label learned from the heterogeneous view to reconstruct the positive node pairs in the contrastive learning framework. Finally, we combine the task loss (semi-supervised) and the contrastive loss to optimize the model performance. We conduct extensive experiments on benchmark datasets to validate the effectiveness of our model, and the results demonstrate that the DAHGN outperforms a wide range of state-of-the-art methods in the semi-supervised node classification problem.


Leveraging zero knowledge proofs for blockchain-based identity sharing: A survey of advancements, challenges and opportunities

Abstract: Identity sharing systems, regardless of their architectural models, share common vulnerabilities. These systems compel users to divulge personal information and furnish proof of identity for accessing services, leaving them susceptible to data breaches that can culminate in identity theft and jeopardize online data security. While blockchain technology offers a potential remedy, delivering enhanced security, immutability, and traceability, it simultaneously raises pertinent concerns surrounding privacy and transparency. The integration of zero-knowledge proof (ZKP) technology has emerged as a promising solution, particularly in enhancing privacy within the transparent blockchain ecosystem. Our paper conducts an exhaustive survey of the existing literature, with a particular focus on the assimilation of ZKP technology into blockchain for the secure sharing of user identities. We undertake a critical evaluation of the advancements achieved in this domain, pinpoint the formidable challenges that must be confronted, and uncover nascent opportunities for further exploration. Our contribution transcends the realms of mere summarization and analysis; we go a step further by offering recommendations drawn from real-world case studies and delineating future research directions.


Integrative measurement method for tensile test based on DIC using modified second-order shape function

Abstract: Conventional tensile tests still require significant manual intervention for assessment. Therefore, the digital image correlation (DIC) technique is introduced into the field of conventional tensile test. Flexible chain, chain subset-based DIC (C-DIC) technique and modified second-order shape function (MSSF) are proposed for methodology and implementation. Tensile tests of several standard metal specimens were carried out to validate estimation accuracy and effectiveness of the proposed method. Various material properties (e.g. necking effect, elastic modulus, ultimate elongation and necking stress–strain curve) have been automatically measured by the proposed method. The relative errors of different material properties measured by the proposed method are generally less than 3% compared to that of conventional methods. The efficiency and accuracy of the proposed method, validated by experiments, demonstrate its capability to achieve automatic measurements for tensile tests. These results suggest that the method has the potential for widespread industrial adoption in the field of tensile testing.


A Novel Cross-Domain Recommendation with Evolution Learning

Abstract: In this “info-plosion” era, recommendation systems (or recommenders) play a significant role in finding interesting items in the surge of online digital activities and e-commerce. Several techniques have been widely applied for recommendation systems, but the cold-start and sparsity problems remain a major challenge. The cold-start problem occurs when generating recommendations for new users and items without sufficient information. Sparsity refers to the problem of having a large amount of users and items but with few transactions or interactions. In this article, a novel cross-domain recommendation model, Cross-Domain Evolution Learning Recommendation (abbreviated as CD-ELR), is developed to communicate the information from different domains in order to tackle the cold-start and sparsity issues by integrating matrix factorization and recurrent neural network. We introduce an evolutionary concept to describe the preference variation of users over time. Furthermore, several optimization methods are developed for combining the domain features for precision recommendation. Experimental results show that CD-ELR outperforms existing state-of-the-art recommendation baselines. Finally, we conduct experiments on several real-world datasets to demonstrate the practicability of the proposed CD-ELR.


Cybersecurity Risk and Audit Pricing—A Machine Learning-Based Analysis

Abstract: ABSTRACT Cybersecurity risk represents a growing business threat. However, little attention has been paid to its assessment. This study proposes a machine learning algorithm that considers firm cybersecurity risk disclosure, information technology governance, external monitoring by financial analysts and auditors, and general firm characteristics to estimate cybersecurity risk (i.e., the likelihood of a firm experiencing data breaches during a year). This measure outperforms the measure produced by logistic regression models, is higher in industries more prone to cyberattacks, and effectively predicts future data breaches and firm use of cybersecurity insurance policies. I also examine whether auditors consider firm cybersecurity risk in the engagement planning process, finding that, on average, a one-percentage-point increase in cybersecurity risk is associated with a 1.15 percent increase in audit fees. In addition, auditors charge a fee premium after a data breach only if the client has heightened cybersecurity risk. Data Availability: Data are available from the public sources cited in the text.


Grid neighbourhood based three way clustering (3WC)

Abstract: The key idea behind three-way clustering (3WC) is to group items into three regions to reveal patterns that might not be apparent using two-way clustering. Three-way clustering, contrasting with two-way, considers data across three regions instead of two regions. The three regions are referred to as inside, outside, and partial regions, contrary to the two-way clustering that uses inside and outside regions. The objects in the inside region are members of a cluster, whereas those in the outside region are not, and partial objects may be members of a cluster. Three-way clustering encounters two challenges: 1) Investigation of sophisticated evaluation functions. 2) Finding an optimized pair of thresholds used to identify three regions for a cluster. In the context of Three-Way Clustering (3WC), applying conditional probability can be beneficial for assessing the likelihood of patterns or relationships within each region. In this study, the first challenge is addressed by employing a grid-based approach for a sophisticated evaluation function based on conditional probability. Whereas the second challenge is addressed by employing the elbow-based method to determine a pair of thresholds within a grid-based histogram. Further, two approaches namely the OGN3 and RGN3 are introduced to discover clear and crisp clusters. Experimental results on UCI and Kaggle datasets indicate improvements for commonly used F1 measures for OGN3 and RGN3 up to 4.97% and 12.88%, respectively


A knowledge-guided graph attention network for emotion-cause pair extraction

Abstract: Emotion-Cause Pair Extraction (ECPE) is a research objective focused on identifying and extracting all emotion-clause and cause-clause pairs from unannotated emotional text. Traditional methodologies have predominantly employed attention mechanisms or joint learning techniques for feature information interaction. However, these approaches often overlook the aggregation of features under the guidance of external knowledge. To enhance performance in addressing this ECPE challenge, we propose a novel knowledge-guided graph attention network, i.e., GAT-ECPE. This model chiefly relies on an interclause dependency graph as a guiding principle. By employing this knowledge-guided graph attention network, we can proficiently combine semantic and structural information between clauses. In addition, an interpair possibility graph, derived from the outcomes of subtasks, is integrated as an additional guiding principle. As such, we are able to aggregate features between clause pairs, thereby facilitating interaction between multiple tasks. Extensive experiments were conducted to validate our proposed model, and the obtained results demonstrate its superiority when compared to 12 considered baselines. In terms of performance metrics, our model achieves an F1 score F1 of 74.92% and a recall R of 77.52%. These values significantly outperform those achieved by state-of-the-art approaches, indicating the effectiveness and superiority of our GAT-ECPE.


Blockchain-based access control architecture for multi-domain environments

Abstract: Numerous users from diverse domains access information and perform various operations in multi-domain environments. These users have complex permissions that increase the risk of identity falsification, unauthorized access, and privacy breaches during cross-domain interactions. Consequently, implementing an access control architecture to prevent users from engaging in illicit activities is imperative. This paper proposes a novel blockchain-based access control architecture for multi-domain environments. By integrating the multi-domain environment within a federated chain, the architecture utilizes Decentralized Identifiers (DIDs) for user identification and relies on public/secret key pairs for operational execution. Verifiable credentials are used to authorize permissions and release resources, thereby ensuring authentication and preventing tampering and forgery. In addition, the architecture automates the authorization and access control processes through smart contracts, thereby eliminating human intervention. Finally, we performed a simulation evaluation of the architecture. The most time-consuming process had a runtime of 1074 ms, primarily attributed to interactions with the blockchain. Concurrent testing revealed that with a concurrency level of 2000 demonstrated that the response times for read and write operations were maintained within 1000 ms and 4600 ms, respectively. In terms of storage efficiency, except for user registration, which incurred two gas charges, all the other processes required only one charge.


Memory-enhanced appearance-motion consistency framework for video anomaly detection

Abstract: Modern network communication systems extensively utilize video data for various applications, creating a pressing need for efficient Video Anomaly Detection (VAD) mechanisms. The essence of VAD lies in the detection of discrepancies in the appearance-motion dynamics of normal and abnormal events. However, existing methods primarily focus on the isolated analysis of appearance or motion anomalies, thereby overlooking the crucial aspect of appearance-motion consistency semantics. To this end, we introduce a Memory-enhanced Appearance-Motion Consistency (MAMC) framework, which is focused on understanding complex appearance-motion consistency patterns in video data for the discernment of anomalies. The first stage of our model involves the design of an Appearance-Motion Fusion (AMF) module, engineered to generate a robust representation of scene dynamics that captures appearance-motion consistency. This consistency data is then processed through the memory module, augmenting the distinction between normal and anomalous events. Experimental results on three benchmark datasets validate the effectiveness of our approach, which achieves AUCs of 96.7%, 87.6%, and 71.5% on three benchmark datasets (UCSD Ped2, CUHK Avenue, and ShanghaiTech datasets). Additional experiments and visualization analysis confirm the effectiveness of the proposed MAMC framework in anomaly detection scenarios.


Information gain ratio-based subfeature grouping empowers particle swarm optimization for feature selection

Abstract: Feature selection is a critical preprocessing step in machine learning with significant real-world applications. Despite the widespread use of particle swarm optimization (PSO) for feature selection, owing to its robust global search capabilities, developing an effective PSO method for this task is still a substantial challenge. This study introduces a novel PSO variant, ISPSO, which integrates the information gain ratio for assessing feature importance. ISPSO's feature selection process involves partitioning features into distinct groups to establish the initial population. Recognizing that feature selection tasks are inherently binary, ISPSO replaces the traditional PSO velocity concept with a probabilistic approach. In addition, introducing a penalty term enhances the algorithm's ability to achieve superior results. Experimental evaluations on 16 datasets consistently show that ISPSO surpasses compared algorithms, highlighting its efficiency in eliminating redundant and irrelevant features.


Functional Mixed Membership Models

Abstract: Mixed membership models, or partial membership models, are a flexible unsupervised learning method that allows each observation to belong to multiple clusters. In this article, we propose a Bayesian mixed membership model for functional data. By using the multivariate Karhunen-Loève theorem, we are able to derive a scalable representation of Gaussian processes that maintains data-driven learning of the covariance structure. Within this framework, we establish conditional posterior consistency given a known feature allocation matrix. Compared to previous work on mixed membership models, our proposal allows for increased modeling flexibility, with the benefit of a directly interpretable mean and covariance structure. Our work is motivated by studies in functional brain imaging through electroencephalography (EEG) of children with autism spectrum disorder (ASD). In this context, our work formalizes the clinical notion of "spectrum" in terms of feature membership proportions. Supplementary materials, including proofs, are available online. The R package BayesFMMM is available to fit functional mixed membership models.


Recovering a clean background: A parallel deep network architecture for single-image deraining

Abstract: Deep convolutional neural networks have been popularly applied in single-image deraining recently. Nevertheless, as the network becomes deeper, it is easy to cause training over-fitting and performance saturation, particularly in the case of insufficient training data. In this paper, we report the design of a new network, namely parallel deraining convolutional neural network (PARDNet), for single-image deraining. Specifically, PARDNet adopts two parallel residual sub-networks based on different receptive fields to extract more comprehensive characteristics of the rain streaks, as well as decrease the depth of the network. The hybrid dilated convolution is employed to enlarge the sub-network's receptive field to capture more context information. The efficient channel attention module is integrated into the proposed PARDNet to capture rain streaks more effectively and preserve more background details. Furthermore, to facilitate the network training, the residual learning is also fused into PARDNet in a holistic way. Extensive experiments on synthetic and real-world rainy image datasets demonstrate the superiority of PARDNet for single-image deraining.


Energy-latency trade-off analysis for scientific workflow in cloud environments: The role of processor utilization ratio and mean grey wolf optimizer

Abstract: Cloud computing has demonstrated its effectiveness in handling complex data that requires substantial computational power, immediate responsiveness, and ample storage capacity. However, there are significant challenges when it comes to distributing intricate tasks, such as workflows structured in a Directed Acyclic Graph (DAG), across cloud resources. These challenges include increased energy consumption and delays experienced by end-users due to geographical disparities between users and service providers. To address these issues, this research work introduces an innovative solution that utilizes a multi-objective Grey Wolf Optimizer (GWO) algorithm for optimizing the allocation of complex workflows. The primary objective of this workflow optimization is to achieve the highest possible processor utilization ratio while adhering to latency constraints. This approach consists of a two-step process. In the initial step, it assesses processor utilization by calculating the number of completed transactions within a specified period divided by the average power consumption. This evaluation ensures that processors operate at the most power-efficient utilization levels, resulting in the highest service profit. In the second step, an optimal mapping algorithm assigns workflow tasks to the most efficient destinations. This is accomplished by applying the Mean Grey Wolf Optimization (GWO) algorithm, which is a metaheuristic technique. The goal is to find an optimal pairing of tasks and virtual machines (VMs) that balances multiple objectives. The simulation of this approach was conducted using the Java-based open-source CloudSim simulator version 5.0. The experimental results from a series of trials indicate that the ELSCiW approach leads to various improvements, including a reduction in latency ranging from approximately 5.35% to 12.92%, a decrease in end-to-end delay costs of about 5.03% to 13.80%, a reduction in energy consumption within the range of 4.71% to 11.19%, an enhancement in load balancing of around 14.84% to 37.30%, an improvement in performance degradation of about 12.52% to 35.14%, a decrease in overhead by approximately 17.27% to 26.92%, and an increase in the quality of service of roughly 56.21% to 64.85%. Furthermore, the ELSCiW approach outperforms recent state-of-the-art metaheuristic methods, as confirmed by statistical findings that emphasize the robustness of the proposed algorithm.


Knowledge-based dynamic prompt learning for multi-label disease diagnosis

Abstract: Pretrained language models (PLMs) have been developed rapidly which establish impressive performance on many open-domain downstream tasks. However, conducting these pretrained models directly without additional network architectures on special domain tasks like multi-label disease diagnosis cannot perform well. Recently, prompt learning has been a new paradigm in PLM field which is more convenient and well-performed than the traditional fine-tuning approach for different domain tasks. However, prompt engineering is challenging because it takes time and experience. In this paper, we propose a new prompt learning method named Knowledge-based Dynamic PrompT (KBDPT) to deal with these problems. Firstly, we import medical knowledge into PLMs by prompt templates which make results of the disease diagnosis more reasonable and qualified. Compared with the fine-tuning approach, this method needs fewer trainable parameters and less training data but achieve better performance. Secondly, unlike most existing pre-defined prompt methods, KBDPT dynamically generates prompts based on personal medical information and a large-scale medical knowledge graph, which can provide more valuable guidance information for disease diagnosis. Lastly, the proposed model also ensembles multiple prompts from all possible diseases to introduce more knowledge and obtain differential diagnosis results. Experiments of multi-label disease diagnosis are conduct on three real-world EMR datasets. Results demonstrate that our model can be used in various pretrained models and outperform both classical deep learning methods and fine-tuning PLMs. The source code of our proposed model has been released at: https://github.com/loxs123/KBDPT.


Multiscale cascaded domain-based approach for Arabic fake reviews detection in e-commerce platforms

Abstract: Fake reviews in e-commerce can lead to customer deception and financial losses. Despite the importance of fake reviews detection, studies for Arabic language are scarce due to the lack of comprehensive datasets. This study addresses this gap by introducing a full-gold standard dataset, the Arabic Fake Reviews Detection (AFRD), across hotels, restaurants, and product domains. To identify the most effective model for each domain in the context of fake review detection, this research employed Bi-LSTM, Bi-GRU, CNN+Bi-LSTM, and CNN+Bi-GRU models. These models were then used in a cascading approach called Multiscale Cascaded domain-based (MCDB), which transfers knowledge from one domain to enhance results in other domains. Experimental results demonstrated that the MCDB approach improved the results of the models by 2.09% to 7.8% in terms of accuracy. The introduced dataset can be used to build effective models for Arabic e-commerce platforms, in addition to further Natural Language Processing applications. This study demonstrates that leveraging domain-specific datasets in a cascading manner can significantly improve performance, holding substantial implications for future research in problems with limited-size datasets.


Treatment of non-Markovian effects to investigate non-locality, dense coding and non-local information

Abstract: In this contribution, we explore the time-evolution of Bell non-locality, dense coding capacity, local and non-local information with an exact solution in a two-qubit system, for the case in which the second qubit is coupled to a Markovian reservoir. Our results reveal a significant relationship between dense coding capacity and the behavior of Bell non-locality. In particular, we observe that optimal dense coding occurs when the violation of Bell non-locality is at its maximum. Furthermore, we study the dynamics of both local and non-local information, considering different initial states, including maximally/partially entangled states. However, our outcomes indicate that all quantities are sensitive to temperature variations, where a temperature threshold is obtained. Beyond this threshold, all proposed quantities exhibit unstable behavior. Specifically, Bell non-locality and dense coding capacity stabilize eventually to steady values below their classical limits. Moreover, the non-Markovian behavior is more prominent when using partially entangled states compared to those used maximally.


A novel approach for breast cancer detection using optimized ensemble learning framework and XAI

Abstract: Breast cancer (BC) is a common and highly lethal ailment. It stands as the second leading contributor to cancer-related deaths in women worldwide. The timely identification of this condition is of utmost importance in mitigating mortality rates. This research paper presents a novel framework for the precise identification of BC, utilising a combination of image and numerical data features with explainable Artificial Intelligence (XAI). The utilisation of the U-NET transfer learning model is employed for image-based prediction. Additionally, an ensemble model is constructed by integrating characteristics from a customised convolutional neural network (CNN) model with an ensemble comprising random forest (RF) and support vector machine (SVM). The experiments aim to evaluate the influence of original features compared to convoluted features. A comparative analysis is carried out to assess the efficacy of various classifiers in accurately detecting BC, utilising the Wisconsin dataset. The model under consideration exhibits promising capabilities in enhancing BC diagnosis, with a remarkable accuracy rate of 99.99%. The present study contributes to the advancement of BC diagnosis by introducing a novel strategy based on machine learning and discussing the interpretation of the variables using XAI. The primary objective of this approach is to get a notable level of precision, hence facilitating the early and reliable identification of BC. Ultimately, the implementation of this approach is expected to enhance patient outcomes.


An iterated greedy heuristic for collaborative Human-UAV search of missing tourists

Abstract: This paper investigates a problem of collaborative human-unmanned aerial vehicle (UAV) search for missing tourists. A mathematical model including detection stage and rescue stage is constructed to describe practical operation scenarios. The objective function is formulated using conditional probability theory by introducing the weighted probability that tourists being rescued at an allowable time limit. To solve this combinatorial optimization problem, an iterated greedy heuristic (IGH) is designed to search solution space. IGH starts with an initialization procedure based on problem-specific knowledge, then a neighborhood perturbation-based local search method is implemented on perturbed solution to intensify exploitation capability. Meanwhile, an acceptance criterion is presented to avoid premature convergence. Ultimately, Taguchi method is utilized to calibrate parameters of IGH for best performance. Comparison with other algorithms on objective function value and search performance validates the effectiveness of our proposed algorithm.


A federated learning system with data fusion for healthcare using multi-party computation and additive secret sharing

Abstract: In the Internet of medical things, data from a single source can be easily analyzed. Besides, it is paramount to collect data from multiple sources to provide consistent, accurate, and vital information as compared to that collected from a single source. Data fusion enables the combination of data from multiple sensors. However, privacy concerns need to be addressed during data aggregation. Furthermore, since medical data is becoming increasingly sophisticated via multimodal means, there is a high likelihood that a complex relationship among biological processes exists. This challenge motivates the study. This study is proposing a secure federated learning system based on data fusion using multi-party computation and additive secret-sharing method. The gradient parameters of the federated training model are protected rather than the actual data. The purpose of protecting the gradient parameters is to address the tradeoff between prediction accuracy and data privacy. In the proposed model, a convolutional neural network, which has an efficient weight-sharing approach, is employed for the prediction. The weight of the model is encrypted using a multi-computation and additive secret sharing. Extensive simulations are carried out to ascertain the efficiency of the proposed model. Besides, the proposed system model is compared with existing works in the literature in terms of accuracy, precision, recall and f1-score. Simulation results show that the proposed model has 97% accuracy as compared to 93% for random forest, 91% for Naive Bayes, 92% for logistic regression and 95% for decision tree. Furthermore, F1-score of the proposed model is 89% as compared to 85% for random forest, 80% for Naive Bayes, 82% for logistic regression and 79% for decision tree. Security analysis of the proposed system model is performed, which shows that the system is robust against weighted majority vote attack, honest-but-curious party and other security related attacks.


Post-training quantization for re-parameterization via coarse & fine weight splitting

Abstract: Although neural networks have made remarkable advancements in various applications, they require substantial computational and memory resources. Network quantization is a powerful technique to compress neural networks, allowing for more efficient and scalable AI deployments. Recently, Re-parameterization has emerged as a promising technique to enhance model performance while simultaneously alleviating the computational burden in various computer vision tasks. However, the accuracy drops significantly when applying quantization on the re-parameterized networks. We identify that the primary challenge arises from the large variation in weight distribution across the original branches. To address this issue, we propose a coarse & fine weight splitting (CFWS) method to reduce quantization error of weight, and develop an improved KL metric to determine optimal quantization scales for activation. To the best of our knowledge, our approach is the first work that enables post-training quantization applicable on re-parameterized networks. For example, the quantized RepVGG-A1 model exhibits a mere 0.3% accuracy loss. The code is in https://github.com/NeonHo/Coarse-Fine-Weight-Split.git


Three dimensional tracking of rigid objects in motion using 2D optical flows

Abstract: Detecting and tracking objects in image sequences is paramount for any video analytic. While object detectors have become increasingly robust, motion estimates based on the straightforward approach of running a detector and linking detections are prone to bounding box noise. An algorithm for the monocular estimation of the 3D motion of rigid objects is presented, combining an object detector, minimal camera calibration, and 2D optical flows observed on the image plane. The algorithm utilizes the 2D Delaunay triangulation over geometrically consistent optical flow tracks to identify regions common to the same object. The algorithm is evaluated on the special case of image sequences of vehicles on roads. Experiments on BrnoCompSpeed dataset show that speed estimates from both detector-based tracking (mean error of 1.62 km/h) and optical flow based tracking (mean error of 2.19 km/h) perform competitively on straight roads. An extensive empirical evaluation is also conducted on a new dataset containing synthetic and real world scenes that also include vehicle trajectories involving rotation. A naive baseline bounding box track based method obtained a mean error of 5.29 km/h for vehicle speed, but optical flow tracks performed significantly better with mean error of 1.65 km/h on the new dataset including rotation. With the new method, video analytics such as vehicle speed estimation and lane change detection can obtain precise information about the 3D trajectory of rigid objects in motion.


Speech driven video editing via an audio-conditioned diffusion model

Abstract: Taking inspiration from recent developments in visual generative tasks using diffusion models, we propose a method for end-to-end speech-driven video editing using a denoising diffusion model. Given a video of a talking person, and a separate auditory speech recording, the lip and jaw motions are re-synchronised without relying on intermediate structural representations such as facial landmarks or a 3D face model. We show this is possible by conditioning a denoising diffusion model on audio mel spectral features to generate synchronised facial motion. Proof of concept results are demonstrated on both single-speaker and multi-speaker video editing, providing a baseline model on the CREMA-D audiovisual data set. To the best of our knowledge, this is the first work to demonstrate and validate the feasibility of applying end-to-end denoising diffusion models to the task of audio-driven video editing. All code, datasets, and models used as part of this work are made publicly available here: https://danbigioi.github.io/DiffusionVideoEditing/.


Methods you can count on: A simulation experiment showing the advantages generalized linear modeling (GLM) over the linear regression of log-transformed count data

Abstract: Count data are the oldest and most basic form of data in the field of archaeology. Count data, however, have long been known to defy the assumptions of many commonly used parametric statistical methods, such as linear regression, in generally failing to follow a normal distribution. Most archaeological analyses have dealt with this problem through the logarithmic transformation of count data for the purposes of normalization. Recently, other scientific fields have recognized major shortcomings associated with log transformation and they have adopted nonparametric alternatives, such as generalized linear modeling (GLM). With a few exceptions, the field of archaeology has not followed suit. This paper presents the results of a simulation experiment designed to compare the performance of linear regression of log-transformed data and GLM under some commonly occurring conditions for count data in the field of archaeology. The results of this simulation experiment confirm the striking superiority of GLM compared with the linear regression of log-transformed data, showing that the latter is particularly prone to false-negative results (i.e. Type II error). We argue that archaeologists, therefore, should join other scientific fields in more widely adopting GLM and its nonparametric counterparts in analyzing count data.


A multi-in and multi-out dendritic neuron model and its optimization

Abstract: Artificial neural networks (ANNs), inspired by the interconnection of real neurons, have achieved unprecedented success in various fields such as computer vision and natural language processing. Recently, a novel mathematical ANN model, known as the dendritic neuron model (DNM), has been proposed to address nonlinear problems by more accurately reflecting the structure of real neurons. However, the single-output design limits its capability to handle multi-output tasks, significantly lowering its applications. In this paper, we propose a novel multi-in and multi-out dendritic neuron model (MODN) to tackle multi-output tasks. Our core idea is to introduce a filtering matrix to the soma layer to adaptively select the desired dendrites to regress each output. Because such a matrix is designed to be learnable, MODN can explore the relationship between each dendrite and output to provide a better solution to downstream tasks. We also model a telodendron layer into MODN to simulate better the real neuron behavior. Importantly, MODN is a more general and unified framework that can be naturally specialized as the DNM by customizing the filtering matrix. To explore the optimization of MODN, we investigate both heuristic and gradient-based optimizers and introduce a two-step training method for MODN. Extensive experimental results performed on 11 datasets on both binary and multi-class classification tasks demonstrate the effectiveness of MODN, with respect to accuracy, convergence, and generality.


Variations on a Hexagon: Iterative Design of Interactive Cyberphysical Tokens and Constraints

Abstract: We describe iterations on the design of a hexagonal token and constraint tangible and cyberphysical interface. Our system has been co-designed both for use in tangible, embedded, embodied interaction and extended reality classroom contexts, and within broader applied generalizations. We survey some of the related literature in hexagonal, rectangular, and triangular tangible interfaces. We express a design space characterizing several aspects of these prior systems; and apply these to a number of additional virtual and physical iterations. We accompany our text with 3D printable, circuit board, and virtual models for a number of these interactors, usable in both tangible and purely virtual forms, including versions engaging four different embedded computing platforms (Raspberry Pi Zero, Raspberry Pi Pico, Adafruit Circuit Playground, and Blinks). We discuss experiences from use of these in classroom settings, and steps toward broader applications.


MorphMatrix: A Toolkit Facilitating Shape-Changing Interface Design

Abstract: Shape-changing interfaces offer transformative potential for user interaction across numerous sectors, facilitating more intuitive and immersive experiences. In parallel, their significant design complexity and cost constrain the widespread adoption of these interfaces. To tackle these challenges, MorphMatrix is a toolkit that lowers the entry barrier for designing and implementing shape-changing interfaces. MorphMatrix includes a software system with customized GUI and simulation capabilities; a flexible physical framework adaptable for various application scenarios; and a kinetic system that governs interface transformations. We illustrate the potential of MorphMatrix through five diverse application scenarios, complemented by a user study validating its effectiveness and usability. MorphMatrix streamlines the design and construction of shape-changing interfaces, thus facilitating broader applicability and potential impacts across interactive computational systems.


UFPS: A unified framework for partially annotated federated segmentation in heterogeneous data distribution

Abstract: Partially supervised segmentation is a label-saving method based on datasets with fractional classes labeled and intersectant. Its practical application in real-world medical scenarios is, however, hindered by privacy concerns and data heterogeneity. To address these issues without compromising privacy, federated partially supervised segmentation (FPSS) is formulated in this work. The primary challenges for FPSS are class heterogeneity and client drift. We propose a unified federated partially labeled segmentation (UFPS) framework to segment pixels within all classes for partially annotated datasets by training a comprehensive global model that avoids class collision. Our framework includes unified label learning (ULL) and sparse unified sharpness aware minimization (sUSAM) for class and feature space unification, respectively. Through empirical studies, we find that traditional methods in partially supervised segmentation and federated learning often struggle with class collision when combined. Our extensive experiments on real medical datasets demonstrate better deconflicting and generalization capabilities of UFPS.


Systematic literature review: Quantum machine learning and its applications

Abstract: Quantum physics has changed the way we understand our environment, and one of its branches, quantum mechanics, has demonstrated accurate and consistent theoretical results. Quantum computing is the process of performing calculations using quantum mechanics. This field studies the quantum behavior of certain subatomic particles (photons, electrons, etc.) for subsequent use in performing calculations, as well as for large-scale information processing. These advantages are achieved through the use of quantum features, such as entanglement or superposition. These capabilities can give quantum computers an advantage in terms of computational time and cost over classical computers. Nowadays, scientific challenges are impossible to perform by classical computation due to computational complexity (more bytes than atoms in the observable universe) or the time it would take (thousands of years), and quantum computation is the only known answer. However, current quantum devices do not have yet the necessary qubits and are not fault-tolerant enough to achieve these goals. Nonetheless, there are other fields like machine learning, finance, or chemistry where quantum computation could be useful with current quantum devices. This manuscript aims to present a review of the literature published between 2017 and 2023 to identify, analyze, and classify the different types of algorithms used in quantum machine learning and their applications. The methodology follows the guidelines related to Systematic Literature Review methods, such as the one proposed by Kitchenham and other authors in the software engineering field. Consequently, this study identified 94 articles that used quantum machine learning techniques and algorithms and shows their implementation using computational quantum circuits or ansatzs. The main types of found algorithms are quantum implementations of classical machine learning algorithms, such as support vector machines or the k-nearest neighbor model, and classical deep learning algorithms, like quantum neural networks. One of the most relevant applications in the machine learning field is image classification. Many articles, especially within the classification, try to solve problems currently answered by classical machine learning but using quantum devices and algorithms. Even though results are promising, quantum machine learning is far from achieving its full potential. An improvement in quantum hardware is required for this potential to be achieved since the existing quantum computers lack enough quality, speed, and scale to allow quantum computing to achieve its full potential.


Parallel Light Fields: A Perspective and A Framework

Abstract: Light fields give relatively complete description of scenes from perspective of angles and positions of rays. At present time, most of the computer vision algorithms take 2D images as input which are simplified expression of light fields with depth information discarded. In theory, computer vision tasks may achieve better performance as long as complete light fields are acquired. Light field data enjoy a natural advantage over images or videos in 3D reconstruction, and are in great demand in applications such as virtual reality (VR) and augmented reality (AR). However, the high cost hardware and complicated synchronization issues in their array deployment severely hindered development of light field cameras. As a consequence, available light field data are much smaller than traditional images and videos in volume, and a lot of deep learning based computer vision algorithms that take light field images as input are difficult to be deployed.


Abstract: Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Twitter Facebook Reddit LinkedIn Tools Icon Tools Reprints and Permissions Cite Icon Cite Search Site Citation Ah Reum Yang, Yu Chen, Hee Ra Kim, Yu Jin Ahn, Jung Bog Kim; The Relative Motion When Accelerating the Container of an Object Submerged in a Fluid. Phys. Teach. 1 February 2024; 62 (2): 110–112. https://doi.org/10.1119/5.0109132 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAmerican Association of Physics TeachersThe Physics Teacher Search Advanced Search |Citation Search


Revolutionizing Education: Navigating the New Landscape Post-COVID-19: A Scoping Review

Abstract: Education systems worldwide have been significantly disrupted due to the COVID-19 pandemic, creating an immediate need for a revamp of conventional teaching and learning techniques. To explore how this has affected the educational landscape, a scoping review was conducted. This scoping review aimed to examine the changes that occurred in the education field and to explore how it has transformed the educational landscape review. Using Arksey and O'Malley's methodology, 51 articles were selected for analysis from two leading databases: Scopus and Web of Science. All chosen articles were then subjected to thematic analysis. Three main aspects impacted by this global event were uncovered, which are technological advancements and digital transformation, changes in pedagogy and teaching methods, and mental health and well-being issues. This scoping review provides valuable insights into one of the most critical sectors affected by COVID-19, which can assist with planning future strategies for similar crises.


Extract, detect, eliminate: Enhancing reliability and performance of computational pathology through artifact processing pipelines

Abstract: Cancer diagnosis involves obtaining tissue specimens from suspicious areas to prepare a glass slide for a microscopic examination. However, histological tissue processing results in the introduction of artifacts, which are ultimately transferred to the digitized version of the glass slide, known as the whole slide image (WSI). Histological artifacts are diagnostically irrelevant areas and may result in wrong predictions by a deep learning (DL) algorithm. These WSIs are also typically large giga-pixel images, raising computational concerns for processing all WSIs simultaneously. Therefore, an artifact processing pipeline that can extract, detect, and exclude artifacts from WSI before running a diagnostic algorithm is much needed for a reliable computational pathology (CPATH) system. Artifact detection will not only ensure accurate CPATH predictions but also provide quality control. In this video, we demonstrate an artifact processing pipeline that can detect five notable artifacts: blood, folded tissue, blurry areas, damaged tissue, and air bubbles, and provide various post-processing outputs such as a segmentation mask for highlighting regions of interest, artifact report and artifact-refined WSI for further computational analysis. Equipping modern CPATH systems with the proposed artifact detection pipeline can ensure histologically relevant areas for diagnostic algorithms, stepping towards bringing automated diagnostics into clinical practice.


A High-Capacity and High-Security Image Steganography Network Based on Chaotic Mapping and Generative Adversarial Networks

Abstract: With the enhancement of information volume, people are not satisfied with transmitting only a single secret image at a time but chase to hide multiple secret images in a single picture; however, the large-capacity steganographic scale can easily lead to the degradation of the quality of the image, which attracts the attention of eavesdroppers. In this paper, we propose a Chaotic mapping-enHanced imAge Steganography nEtwork (CHASE), which pioneers to hide colour images in grey images and reduces the difference between the container image and the cover image through the image permutation method, so as to enhance the security of the steganography. The method demonstrates excellent steganalysis resistance in experiments and introduces Generative Adversarial Networks (GANs) to improve the image fidelity in large-capacity steganographic scales. The fusion of chaotic mapping and GAN optimisation enables the steganographic network to simultaneously balance security and image quality. The experimental results show that CHASE can keep the secret image with good invisibility under large-capacity steganographic scales, and at the same time, it can reveal the secret image with high fidelity, and its steganalysis-resistant capability is much better than other state-of-the-art methods.


On clustering levels of a hierarchical categorical risk factor

Abstract: Abstract Handling nominal covariates with a large number of categories is challenging for both statistical and machine learning techniques. This problem is further exacerbated when the nominal variable has a hierarchical structure. We commonly rely on methods such as the random effects approach to incorporate these covariates in a predictive model. Nonetheless, in certain situations, even the random effects approach may encounter estimation problems. We propose the data-driven Partitioning Hierarchical Risk-factors Adaptive Top-down algorithm to reduce the hierarchically structured risk factor to its essence, by grouping similar categories at each level of the hierarchy. We work top-down and engineer several features to characterize the profile of the categories at a specific level in the hierarchy. In our workers’ compensation case study, we characterize the risk profile of an industry via its observed damage rates and claim frequencies. In addition, we use embeddings to encode the textual description of the economic activity of the insured company. These features are then used as input in a clustering algorithm to group similar categories. Our method substantially reduces the number of categories and results in a grouping that is generalizable to out-of-sample data. Moreover, we obtain a better differentiation between high-risk and low-risk companies.


Factors affecting Thai EFL students’ behavioral intentions toward mobile-assisted language learning

Abstract: Introduction Recently, researchers have begun to pay more attention to topics related to the adoption of mobile devices for supporting second or foreign language learning. Mobile-assisted language learning (MALL) is now prevalent among language learners and educators because of its convenient and enjoyable features. This study combined and extended the Technology Acceptance Model (TAM) and Expectation Confirmation Theory (ECT) to investigate the factors influencing English as a Foreign Language (EFL) students’ behavioral intentions to use MALL at two universities in Bangkok, Thailand. Methods Quantitative methods were utilized in this study and the researchers obtained a total of 507 valid responses by using three-step sampling. After using confirmatory factor analysis (CFA) to determine that the study had enough construct validity, structural equation modeling (SEM) was applied to test the research’s hypotheses. Results The findings revealed that all 15 hypotheses were supported, except that social influence cannot significantly influence behavioral intention. Discussion and implication By acquiring a deeper understanding of the factors that impact the behavioral intentions of language learners to utilize MALL, developers and providers can improve their capacity to design more enjoyable and effective applications that align with customer expectations and enhance financial gains. By understanding students’ behavioral intentions towards MALL, educators can efficiently raise awareness of its benefits and provide effective training, enabling students to utilize available resources and enhance their language learning experience.


Breast Tumor Classification with Enhanced Transfer Learning Features and Selection Using Chaotic Map-Based Optimization

Abstract: Abstract Among women, breast cancer remains one of the most dominant cancer types. In the year 2022, around 2,87,800 new cases were diagnosed, and 43,200 women faced mortality due to this disease. Analysis and processing of mammogram images is vital for its earlier identification and thus helps in reducing mortality rates and facilitating effective treatment for women. Accordingly, several deep-learning techniques have emerged for mammogram classification. However, it is still challenging and requires promising solutions. This study proposed a newer automated computer-aided implementation for breast cancer classification. The work starts with enhancing the mammogram contrast using a haze-reduced adaptive technique followed by augmentation. Afterward, EfficientNet-B4 pre-trained architecture is trained for both original and enhanced sets of mammograms individually using static hyperparameters’ initialization. This provides an output of 1792 feature vectors for each set and then fused using a serial mid-value-based approach. The final feature vectors are then optimized using a chaotic-crow-search optimization algorithm. Finally, the obtained significant feature vectors were classified with the aid of machine learning algorithms. The evaluation is made using INbreast and CBIS-DDSM databases. The proposed framework attained a balanced computation time with a maximum classification performance of 98.459 and 96.175% accuracies on INbreast and CBIS-DDSM databases, respectively.


Path integral quantum algorithm for simulating non-Markovian quantum dynamics in open quantum systems

Abstract: Research in open quantum system dynamics has received growing interest in recent years, and its ongoing investigation has uncovered many intriguing physics departing from closed systems. A particularly useful application of the open quantum system theory is to simulate quantum dynamical processes in condensed phase materials. As the quantum degree of freedom is constantly under the influence of its thermal environment, the accurate description of the dynamics often requires non-Markovian time evolution at finite temperature. Such calculation is usually quite challenging on classical computers as extensive memory storage is required. In this work, we focus on a quantum system linearly coupled to its harmonic bath and present a path integral based quantum algorithm that time-evolves the reduced density matrix under finite temperature non-Markovian dynamics. To treat the nonunitary time evolution, the Sz.-Nagy dilation scheme is used for the conversion to unitary dynamics that can be implementable on gate-based quantum computers. The modified Hadamard test is then used to retrieve all the information of the reduced density matrix. Complexity analysis shows that the memory requirement has exponential reduction on the quantum machine whereas the runtime complexity stays roughly the same as for the classical computer. It points to the possibility of using this algorithm to simulate multilevel and multisite non-Markovian quantum dynamics that are beyond the reach of classical computers. The algorithm makes no ad hoc assumptions and extends naturally beyond Markovian and weak coupling regimes. We validated the algorithm on the quantum simulator with the spin-boson model and demonstrated its excellent agreement with the classical computer benchmark.


Immune subtyping of melanoma whole slide images using multiple instance learning

Abstract: Determining early-stage prognostic markers and stratifying patients for effective treatment are two key challenges for improving outcomes for melanoma patients. Previous studies have used tumour transcriptome data to stratify patients into immune subgroups, which were associated with differential melanoma specific survival and potential predictive biomarkers. However, acquiring transcriptome data is a time-consuming and costly process. Moreover, it is not routinely used in the current clinical workflow. Here, we attempt to overcome this by developing deep learning models to classify gigapixel haematoxylin and eosin (H&E) stained pathology slides, which are well established in clinical workflows, into these immune subgroups. We systematically assess six different multiple instance learning (MIL) frameworks, using five different image resolutions and three different feature extraction methods. We show that pathology-specific self-supervised models using 10x resolution patches generate superior representations for the classification of immune subtypes. In addition, in a primary melanoma dataset, we achieve a mean area under the receiver operating characteristic curve (AUC) of 0.80 for classifying histopathology images into ‘high’ or ‘low immune’ subgroups and a mean AUC of 0.82 in an independent TCGA melanoma dataset. Furthermore, we show that these models are able to stratify patients into ‘high’ and ‘low immune’ subgroups with significantly different melanoma specific survival outcomes (log rank test, P < 0.005). We anticipate that MIL methods will allow us to find new biomarkers of high importance, act as a tool for clinicians to infer the immune landscape of tumours and stratify patients, without needing to carry out additional expensive genetic tests.


Unravelling the global landscape of Bitcoin research: insights from bibliometric analysis

Abstract: Bitcoin has been gaining increasing attention in academia and industry. This article investigates Bitcoin’s research status and evolution via bibliometrics using a dataset of 3,873 publications between 2012 and 2022 from the Web of Science Core Collection. The findings reveal a significant increase in research on Bitcoin since 2017, coinciding with the cryptocurrency bull market. The article identifies publication trends, influential contributors, collaboration networks, and topics evolution in Bitcoin research. The three Bitcoin research stages are conceptualisation and fundamentals of Bitcoin (2012–2016), cryptocurrency and market efficiency (2017–2018), and technical analysis, big data, data privacy, and the connection between Bitcoin and financial markets (2019–2022). The four prominent emerging areas for future Bitcoin research are decentralised finance (DeFi), non-fungible tokens (NFTs), clean energy and mining, and monetary policy. The article offers valuable insights for researchers, policymakers, and practitioners, facilitating a better understanding of the status quo of Bitcoin research.


Regularization and optimization in model-based clustering

Abstract: Due to their conceptual simplicity, k-means algorithm variants have been extensively used for unsupervised cluster analysis. However, one main shortcoming of these algorithms is that they essentially fit a mixture of identical spherical Gaussians to data that vastly deviates from such a distribution. In comparison, general Gaussian Mixture Models (GMMs) can fit richer structures but require estimating a quadratic number of parameters per cluster to represent the covariance matrices. This poses two main issues: (i) the underlying optimization problems are challenging due to their larger number of local minima, and (ii) their solutions can overfit the data. In this work, we design search strategies that circumvent both issues. We develop more effective optimization algorithms for general GMMs, and we combine these algorithms with regularization strategies that avoid overfitting. Through extensive computational analyses, we observe that optimization or regularization in isolation does not substantially improve cluster recovery. However, combining these techniques permits a completely new level of performance previously unachieved by k-means algorithm variants, unraveling vastly different cluster structures. These results shed new light on the current status quo between GMM and k-means methods and suggest the more frequent use of general GMMs for data exploration. To facilitate such applications, we provide open-source code as well as Julia packages (UnsupervisedClustering.jl and RegularizedCovarianceMatrices.jl) implementing the proposed techniques.


Optimal Transport and Model Predictive Control-based Simultaneous Task Assignment and Trajectory Planning for Unmanned System Swarm

Abstract: Abstract This paper presents a simultaneous task assignment and trajectory planning method for unmanned system swarm by using optimal transport and model predictive control (OT-MPC). Unlike the conventional hierarchical assignment and planning, the proposed approach addresses both the task assignment and trajectory planning subproblems concurrently. To be specific, a unified cost function is designed to solve task assignment and trajectory planning problem. Moreover, the multi-tasks are assigned by using optimal transport, which establishes an optimal mapping between tasks and unmanned system vehicles based on transportation cost. The trajectory planning is achieved by using model predictive control, which generates high-quality navigation trajectories considering obstacle avoidance. Finally, the proposed method is applied to the unmanned surface vehicles swarm. Numerical simulations and experiments were conducted to validate the effectiveness of the proposed method.


Mixed reality and digital twins for astronaut training

Abstract: Astronaut training is a complex activity given its nature; the infrastructure and equipment required are very specific and expensive. Immersive technologies have the capability to simulate complete environments and allow users to interact with them. The effectiveness of immersive technologies for training and learning activities has already been addressed in various works. However, merely simulating scenarios is not enough; participants need to interact with vehicles, machines, and spacecraft systems to see how they affect the environment and surrounding objects. This study develops four scenarios across three modules, inspired by NASA and ESA astronaut training programs. The first module introduces a theoretical scenario visualized through mixed reality (MR) with topics from ESA’s training module. The second module presents two practical scenarios: an ISS emergency simulation involving Hohmann transfer and circular motion concepts, and a spacewalk for repair and maintenance tasks, in these scenarios, the digital twins of the ISS propulsion and navigation systems were developed as well as the digital twin of a space suit. The third module simulates a spaceship launch, utilizing 3D models from SpaceX’s Falcon Heavy and digital twins of the propulsion and navigation systems. During the interaction of the participants with the digital twins and the scenarios, the data generated is stored in a database so it can be analyzed and compared against the ISS dataset from the Jet Propulsion Laboratory (JPL) and the telemetry from SpaceX Falcon heavy launch. In the first phase of this work, the main goal was to check if digital twins can be implemented in conjunction with immersive technologies and therefore be suitable for training activities. We used a mean squared error metric between the digital twins and the physic object data, demonstrating that developed digital twins closely align with physical systems, validating their efficacy for astronaut training.


UoloNet: based on multi-tasking enhanced small target medical segmentation model

Abstract: Abstract In recent years, UNET (Ronneberger et al. 2015) and its derivative models have been widely used in medical image segmentation with more superficial structures and excellent segmentation results. Due to the lack of modeling for the overall characteristics of the target, the division tasks of minor marks will produce some discrete noise points, resulting in a decline in model accuracy and application effects. We propose a multi-tasking medical image analysis model UoloNet, a YOLO-based (Redmon et al. 2016; Shafiee et al. 2017) object detection branch is added based on UNET. The shared learning of the two tasks through semantic segmentation and object detection has promoted the model’s mastery of the overall characteristics of the target. In the reasoning stage, merging the two functions of object detection and semantic segmentation can effectively remove discrete noise points in the division and enhance the accuracy of semantic segmentation. In the future, the object detection task will be the problem of excessive convergence of semantic segmentation tasks. The model uses CIOU (Zheng et al. 2020) losses instead of IOU losses in YOLO, which further improves the model’s overall accuracy. The effectiveness of the proposed model is verified both in the MRI dataset SEHPI, which we posted and in the public dataset LITS (Christ 2017).