CotepRes-Net: An efficient U-Net based deep learning method of liver segmentation from Computed Tomography images

Abstract: Automatic liver segmentation from CT images is challenging due to the indistinct boundaries between the liver and surrounding organs in the abdominal cavity CT. To address these limitations, we propose a deep learning model called CotepRes-Net, which is based on the U-Net architecture but with modified skip connection using a convolutional block attention module (CBAM). We introduce a modified inception block and a contextual transformer block (CoT) at the bottleneck to capture richer feature representations and leverage global contextual information to constrain the liver shape at the unclear boundary. To accelerate the network convergence in the training stage, we utilize an Efficient channel attention module (ECA) in the decoder layers and replace the convolution operation in each convolution layer with a residual structure. We evaluate the performance of the proposed model using the publicly available dataset LiTS (MICCAI 2017 Liver Tumor Segmentation Challenge) and show the segmentation results on the dataset Sliver07 (Segmentation of the Liver Competition 2007). The results show that our model achieves a dice similarity coefficient (DSC) of 0.9688, Jaccard index of 0.9422, volumetric overlap error (VOE) of 0.0578, relative absolute volume difference (RAVD) of 0.0039, average symmetric surface distance (ASSD) of 1.094 mm and maximum symmetric surface distance (MSSD) of 16.079 mm for liver segmentation. The experimental results show that the proposed method is comparable to or even outperform the state-of-the-art methods in terms of accuracy and computation cost, which can serve as a valuable tool for image-based liver analysis.


An efficient but effective writer: Diffusion-based semi-autoregressive transformer for automated radiology report generation

Abstract: It is firmly believed that manually diagnosing radiology images is clinically critical but labour-intensive and error-prone. Therefore, an automatic radiology report generation method is highly desired for alleviating the burden imposed on doctors. However, a typical report contains numerous template descriptions and only a few abnormal sentences. This unbalanced distribution makes the generation of template sentences more likely. Additionally, describing an entire report in a word-by-word manner can lead to significant latency during the inference step. Besides, the existing datasets are limited to conventional pneumonia, making them incomplete and one-sided. This work is concerned with forming a better trade-off between generation performance. One key design is an abnormal semantic diffusion module, which progressively absorbs the semantics of abnormal medical terminology and strengthens the linguistic coherence between local tokens. In detail, the generated report is refined by enhancing the incorporation of informative words with limited occurrence frequencies, which alleviates the monotony of template-based generation. Another design is a length-controllable self-attention decoder, which regulates the input length of the sentences used for target word generation. This framework preserves the autoregressive nature of word generation while also maintaining a controllable range, ensuring the efficiency of report generation. Moreover, a novel XRG-COVID-19 clinical dataset is tailored, which includes X-ray scans and professional diagnostic reports of 8676 patients. The experimental results demonstrate the proposed model achieves a better trade-off between performance and speed than those of carefully designed baselines on both the IU X-ray dataset and the proposed XRG-COVID-19 dataset.


Model-Driven IEP-GNN Framework for MIMO Detection With Bayesian Optimization

Abstract: In this letter, a model-driven detector called IEP-GNN is proposed for massive multiple-input and multiple-output (MIMO) system. Graph neural network (GNN) and improved moment matching (IMM) are integrated into the expectation propagation (EP) algorithm to improve the accuracy of posterior distribution approximation and leverage the self-correction ability of EP algorithm. Moreover, to acquire the training experiences and optimize initial parameters, hotbooting and Bayesian parameter optimization (BPO) are employed respectively, which can further improve the performance of the proposed IEP-GNN. Simulation results show that our proposed IEP-GNN with BPO outperforms other state-of-the-art EP-based detectors while maintaining an acceptable convergence and computational complexity.


A statistical model for computing global spectral solar irradiance under all-sky conditions at four stations situated in main regions of Thailand

Abstract: This paper presents a statistical model for computing global spectral solar irradiance under all-sky conditions at four stations situated in main regions of Thailand. The model expresses the global spectral irradiance under all-sky conditions as a multiplication of two functions, namely, a function of global spectral irradiance under clear-sky conditions and a cloud modification function. To develop the model, global spectral solar irradiance was measured at four stations in the main regions of Thailand, namely Chiang Mai (18.77˚ N, 98.97˚ E) in the northern region of Thailand, Ubon Ratchathani (15.25˚ N, 104.87˚ E) in the north-eastern region of the country, Nakhon Pathom (13.82˚ N, 100.04˚ E) in the central region of this country, and Songkhla (7.18˚ N, 100.60˚ E) in the southern region of Thailand. The spectral data from these stations were gathered and divided into two groups. The first group (January 2017 to December 2020) was used for modeling and the second group (January 2021 to December 2021) for validating the model. The first function was developed using the first group of data. Clear-sky conditions were detected using sky images obtained from a sky camera installed at each station. To develop the second function, a satellite-derived cloud index derived from Himawari-8 data at the stations was utilized. To validate the model of the global spectral solar irradiance under all-sky conditions, the model was employed to compute the global solar irradiance at the four stations for the year 2021, and the findings were compared to the global solar irradiance determined from the measurements taken at the four stations. It was discovered that the spectral irradiance calculated from the model and that acquired from the measurements was in good agreement. There was a discrepancy in the form of root mean square difference relative to the mean measured value (RMSD) of 9.48%. We concluded that the model performed well in computing the global spectral solar irradiance.


Generalized Rank Dirichlet distributions

Abstract: We study a new parametric family of distributions on the ordered simplex ∇d−1={y∈Rd:y1≥⋯≥yd≥0,∑k=1dyk=1}, which we call Generalized Rank Dirichlet (GRD) distributions. Their density is proportional to ∏k=1dykak−1 for a parameter a=(a1,…,ad)∈Rd satisfying ak+ak+1+⋯+ad>0 for k=2,…,d. The density is similar to the Dirichlet distribution, but is defined on ∇d−1, leading to different properties. In particular, certain components ak can be negative. Random variables Y=(Y1,…,Yd) with GRD distributions have previously been used to model capital distribution in financial markets and more generally can be used to model ranked order statistics of weight vectors. We obtain for any dimension d explicit expressions for moments of order M∈N for the Yk’s and moments of all orders for the log gaps Zk=logYk−1−logYk when a1+⋯+ad=−M. Additionally, we propose an algorithm to exactly simulate random variates in this case. In the general case a1+⋯+ad∈R we obtain series representations for these quantities and provide an approximate simulation algorithm.


Dual mode information fusion with pre-trained CNN models and transformer for video-based non-invasive anaemia detection

Abstract: Anaemia, caused by low haemoglobin levels, affects billions of people worldwide. India has one of the highest anaemia rates among developing nations, according to WHO data. Non-invasive, low-cost, and accurate anaemia screening technologies are in high demand because invasive methods are expensive and difficult to administer globally. The current effort aims to construct a reliable anaemia detection system by combining cutting-edge computational methods with the age-old practise of imprecise assessment of haemoglobin levels from pallor in palm. The proposed method utilises time-domain analysis to establish the connection between blood haemoglobin concentration and colour changes in the palm caused by pressure application and release. A smartphone camera sensor which records the full event of palm colour changes generated by a customised device is processed and analysed. Some key frames are selected from extracted frames and passed to the pre-trained deep learning models for feature extractions. These features are then passed through vision transformers for classification and regression tasks. This approach have the power of pre-trained models for feature extraction and utilises the flexibility of transformer encoders and MLP networks for efficient and effective representation learning, classification, and regression. Two different approaches are proposed i.e., decision level and feature level fusion. The suggested approach accurately estimates blood haemoglobin levels with RMSE and accuracy of 0.483 and 96.296%, respectively based on 531 video samples of palm evidence.


Improvement of Validated Manufacturing Processes with Fuzzy Logic

Abstract: SMEs are essential entities for the economy of countries, so they need to implement strategies to maintain and achieve economic stability. Technology is a great support tool to achieve this. Still, entrepreneurs, generally acting empirically, need to determine which technology to select, how to do it, and its scope of implementation. Fuzzy logic is a technology adapted to human linguistic thinking, which served as a methodology in this case study to measure the degree of behavior given by the implementation of software and hardware in a company. The result of the research validated the benefits that the gradual implementation of the technology presented to the company in terms of utility, time, and quantity of production, which were related in degrees of uncertainty with variables that were labeled as “high,” “medium,” and “low.” The established membership was validated through fuzzy graphs, showing the company’s status, and adjusted with the appropriateness of the technology until profits were placed within the “high” range. Thus, fuzzy logic is a tool that helps measure variables in degrees of belonging, with words used by business people to make it more understandable. Furthermore, the data coding in fuzzy terms showed the prediction of the behavior of the variables adjusted with technological processes.


Performance evaluation considering academic misconduct of China’s higher education institutions

Abstract: Performance evaluation is essential for managing and allocating resources in higher education institutions (HEIs), guiding their development. However, in the current evaluation system, the growing issue of academic misconduct (AM) as an undesirable output in research assessments is highly overlooked. To fill this gap, we propose the creative inclusion of AM as an undesirable output in the evaluation system. To validate the potentially questionable nature of the evaluation system without AM, we took China’s 32 HEIs within the period of 2016–2018 as an example, separately calculating efficiencies without and with AM through DEA approach. Then we use Wilcoxon test of paired samples and Pearson correlation coefficient confirming that there are increasingly significant differences between the two efficiencies and the two rankings in the sample period, respectively. Therefore, we believe that other performance evaluations for HEIs that do not incorporate AM are likely to be questionable, such as the influencing factors of research efficiency and returns to scale (RTS). Incorporating AM into the evaluation index system, we further explore the influencing factors of research efficiency in sample HEIs through Tobit regression model. Then we propose innovative models tailored specifically for analyzing RTS of HEIs in which undesirable outputs are under extended strong disposability and apply them to the empirical study of China’s HEIs. The results show that (1) AM is playing an increasingly prominent role in the research efficiency evaluation of HEIs; (2) local economic development, external exchanges and emphasis on AM in HEIs can improve research efficiency; however, government funding has a negative effect on it while the effect of human capital is not significant; (3) the investment scale in most China’s HEIs is optimize or excessive.


Why and how bug blocking relations are breakable: An empirical study on breakable blocking bugs

Abstract: Blocking bugs prevents other bugs from being fixed, which is difficult to repair and negatively impacts software quality. During software maintenance, developers usually try to break the blocking relationship between blocking and blocked bugs, e.g., propose a temporary fix. However, to our knowledge, no studies have investigated why and how blocking relations between bugs are breakable. In this study, we aim to construct an empirical analysis to explore breakable blocking bugs (BBBs). Specifically, we employ quantitative and qualitative analysis to study these BBBs from two aspects. One is to investigate the characteristics of these bugs, and the other is to explore why and how developers break the blocking relationship between bugs during software maintenance. We build a dataset on five large-scale open-source projects and classify bugs into three types (BBBs, normal blocking bugs, and other bugs) to compare the differences between BBBs and other types of bugs. We observe that BBBs have higher levels of involvement, take longer to fix, and involve more complex source code than other bugs. Moreover, we summarize four reasons blocking relationships between bugs are broken, i.e., partial association (41.87%), serious influence (26.40%), time pressure (19.73%), and flawed blocking (12.21%), and three measures developers adopt to break these blocking relationships, i.e., quick patch for blocking bugs (41.33%), quick patch for blocked bugs (38.67%), and ignore the blocking relation and fix blocked bugs directly (20.00%). Through these analyses, it is meaningful for software maintainers to have a deeper understanding of the characteristics and repair practices of BBBs, which will help solve these BBBs effectively in the future.


Joint task semi-supervised semantic segmentation for TRUS image

Abstract: Transrectal ultrasound (TRUS) is an important tool for prostate observation, playing a crucial role in medical processes such as multimodal image registration and surgical planning. However, TRUS images are often plagued by blurry boundary information and artifacts, making manual annotation a time-consuming and labor-intensive task, which results in insufficient pixel-labeled data for training neural networks. Therefore, this paper introduces a semi-supervised neural network based on joint tasks for TRUS image segmentation, which combines confidence information from target localization and semantic segmentation to generate pseudo-labels for collectively training unlabeled samples. Additionally, this method supports the generation of pseudo-labels using only location information for weakly supervised training of neural networks. Compared to other state-of-the-art models, our results show that this approach outperforms in segmentation performance with a smaller dataset, achieving an mean Dice similarity coefficient and mean Jaccard similarity coefficient of 92.8 % and 88.4 %, respectively. Furthermore, the experiments reveal that reducing the coupling of two decoders' parameters between the segmentation and detection tasks improves segmentation accuracy as dataset size decreases.


Microdisk modulator-assisted optical nonlinear activation functions for photonic neural networks

Abstract: On-chip implementation of optical nonlinear activation functions (NAFs) is essential for realizing large-scale photonic neural chips. To improve the reconfigurability and enrich the functions of photonic neural network (PNN), an NAF unit with a unique capability of performing multiple types of NAFs is highly preferred. In this work, we propose and experimentally demonstrate a microdisk modulator (MDM)-assisted NAF unit, in which multiple types of electro-optic NAFs with a high response speed and multiple types of all-optical NAFs with a low latency and reduced power consumption are performed in a single device. The fabricated MDM has an add-drop configuration, in which a lateral PN junction is incorporated to achieve high-speed resonance wavelength tuning. With the use of the high-speed nonlinear electro-optic effect, three different kinds of electro-optic NAFs including sigmoid function, radial basis function (RBF), and negative rectified linear unit (ReLU) function are realized by exploiting the free-carrier dispersion effect on silicon. Moreover, thanks to the strong optical confinement in the disk cavity, all-optical NAFs can be performed by exploiting the thermo-optic effect on silicon. In the experiment, four different kinds of all-optical NAFs including softplus function, RBF, clamped ReLU function, and leaky ReLU function are demonstrated. With the use of the realized clamped ReLU function, a convolutional neural network is simulated to perform a handwritten digit classification benchmark task, and an accuracy as high as 98 % is demonstrated. Thanks to its strong electro-optic and thermo-optic effects, the proposed MDM provides a unique capability of performing multiple types of electro-optic and all-optical NAFs, which is potential to be served as a flexible nonlinear unit in large-scale PNN chips.


Comparative Study of HMD-based Virtual and Augmented Realities for Immersive Museums: User Acceptance, Medium, and Learning

Abstract: Technologies like Head Mounted Display (HMD)-based Virtual Reality (VR) and Augmented Reality (AR) have made HMD-based immersive museums possible. To investigate the user acceptance, medium, and interaction experience for HMD-based immersive museums, an app entitled The Extended Journey has been designed, developed, and deployed on both VR headsets and AR headsets. Subsequently, a between-subjects design experiment with 62 participants was conducted to measure the user experience and learning outcome in HMD VR and HMD AR conditions. Quantitative results revealed that HMD VR museums had statistically significantly better immersion and empathy compared to HMD AR museums. Qualitative data indicated HMD-based immersive museums were embraced by most young participants while HMD VR had better user acceptance than HMD AR for immersive museums. The interview also demonstrated that the advantage of the HMD-based immersive museum over the traditional online museum is not only the sensory immersion from the medium itself but also the interactive narrative experience that the HMD medium facilitates, especially the natural interaction with the CG characters and the environment in the story.


Cross-modal Multiple Granularity Interactive Fusion Network for Long Document Classification

Abstract: Long Document Classification (LDC) has attracted great attention in Natural Language Processing and achieved considerable progress owing to the large-scale pre-trained language models. In spite of this, as a different problem from the traditional text classification, LDC is far from being settled. Long documents, such as news and articles, generally have more than thousands of words with complex structures. Moreover, compared with flat text, long documents usually contain multi-modal content of images, which provide rich information but not yet being utilized for classification. In this article, we propose a novel cross-modal method for long document classification, in which multiple granularity feature shifting networks are proposed to integrate the multi-scale text and visual features of long documents adaptively. Additionally, a multi-modal collaborative pooling block is proposed to eliminate redundant fine-grained text features and simultaneously reduce the computational complexity. To verify the effectiveness of the proposed model, we conduct experiments on the Food101 dataset and two constructed multi-modal long document datasets. The experimental results show that the proposed cross-modal method outperforms the single-modal text methods and defeats the state-of-the-art related multi-modal baselines.


A Hypersphere Information Granule-Based Fuzzy Classifier Embedded With Fuzzy Cognitive Maps for Classification of Imbalanced Data

Abstract: In this article, a hypersphere information granule-based fuzzy classifier integrated with Fuzzy Cognitive Maps (FCM), named FCM-IGFC, is proposed for the classification of imbalanced data. The proposed FCM-IGFC is structured by sequentially linking a Takagi-Sugeno-Kang (TSK) fuzzy system (FCM-TSK) – which is embedded with FCMs – and a rule-based fuzzy system (IGFS) based on hypersphere information granules. The FCM-TSK leverages the inference capabilities of the FCM to allow for the movement of data within the original space, while the IG-FS creates a mapping between samples and majority and minority classes using information granules. In this study, we introduced an innovative bottom-up granulation method and an overlap elimination technique for constructing hypersphere information granules. These methods facilitate the creation of information granules that accurately represent the structure of classes, even when dealing with im- balanced data. Moreover, the stacked structure of the FCM-IGFC offers data transfer capabilities. This helps reduce the complexity of distributions such as small, disjointed clusters and irregular class boundaries, with the support of the FCM, thereby making it easier to use information granules to describe the class structure. A series of experiments conducted on 12 publicly available datasets demonstrated that the performance of FCM-IGFC significantly surpasses that of existing granule-based fuzzy classifiers. Additionally, it is competitive with top-tier classifiers that incorporate sampling methods.


Semi-supervised, Neural Network based approaches to face mask and anomaly detection in surveillance networks

Abstract: In the post-pandemic world, surveillance cameras play a key aspect when it comes to detecting various kinds of security risks. These can range from burglars entering a premises to an individual wearing or not wearing a mask where convention dictates one way versus the other. We are proposing a system that would allow the autonomous detection of these security risks with minimal human intervention. We propose using Multi-task Cascaded Convolutional Neural Networks as the face detector, followed by a Gabor image feature extractor, and the Kernel-based Online Anomaly Detection algorithm for detecting and identifying potential risks in real-time. We have tested our proposed framework on five different datasets comprising two datasets from public online repositories, one publicly available dataset from the Georgia Institute of Technology, and two from real-life settings. We compared our results to the Viola Jones legacy face detector, and with two other anomaly detection algorithms based on Principal Component Analysis (PCA) and the Autoencoder scheme. Our proposed framework has yielded high detection rates with low false alarm rates, in addition to being adaptive, portable, and requiring minimal infrastructure.


Cyber offending predictors and pathways in middle adolescence: Evidence from the UK Millennium Cohort Study

Abstract: Despite the pervasiveness of cyber crime victimisation, knowledge is limited regarding the prevalence, characteristics and pathways of offenders. The present study examines predictors of self-reported engagement in cyber crime in middle adolescence in a large (N = 13,277) longitudinal dataset from the UK Millennium Cohort Study. We adopted an ecological systems approach to examine a range of multicausal, intersecting factors across individual, familial, psychosocial and environmental systems. The overall prevalence of self-reported cyber offending (account hacking or the deployment of viruses) was 5.6% at age 14 and 3.8% at age 17, although persistence over time by the same individuals was relatively low (1.1%). Significant predictors of cyber offending at age 17 were being male, domestic violence between parents, low parental monitoring, low wellbeing, self-harm, exclusion from school, spending more time online gaming, participating in offline leisure activities, and engaging in serious violence (weapon carrying or use), assault, and cyber crime at age 14. Findings indicate that young cyber offenders are often males and those who have experienced a range of risk factors that are connected to poorer wellbeing and engaging in multiple risky/offending behaviours. Implications for theory, policy and practice are discussed.


BERT based severity prediction of bug reports for the maintenance of mobile applications

Abstract: Mobile application maintenance is crucial to ensuring the accurate operation and continuous improvement of mobile applications (mobile apps). To effectively address issues and enhance the user experience, developers utilize issue-tracking systems that gather bug reports to refine mobile apps. Users can submit bugs through these systems, allowing them to determine the severity of each reported issue. The severity level plays a pivotal role in prioritizing bug resolution, enabling developers to address critical bugs promptly. Nonetheless, manually assessing the severity of each issue can be laborious and prone to errors. To overcome this challenge, this paper presents Bidirectional Encoder Representations from Transformers (BERT) based severity prediction of bug reports (called BERT-SBR) that leverages a deep neural network for automatic bug severity classification for mobile app maintenance. We collect the publicly available mobile apps bug reports dataset from the Hugging Face. BERT-SBR first computes the sentiment of reporters of bug reports and preprocesses them by leveraging BertTokenizer input formatting techniques. Next, it passes the formatted text and computed sentiment of each bug report to generate word embeddings. Then, it introduces a fine-tuned BERT classifier for bug report severity prediction. After that, it passes the generated word embeddings to the fine-tuned BERT classifier for training and testing. Finally, the proposed classifier’s performance is evaluated. The BERT-SBR assessment results confirm that the fine-tuned BERT classifies bug reports significantly more effectively than other deep learning classifiers. On average, BERT-SBR achieves a remarkable improvement of 40.43%, 67.78%, 40.71%, and 58.14% in the accuracy, precision, recall, and f-measure. This indicates its superiority in accurately predicting the severity of bug reports for mobile application maintenance.


Dynamic-group-aware networks for multi-agent trajectory prediction with relational reasoning

Abstract: Demystifying the interactions among multiple agents from their past trajectories is fundamental to precise and interpretable trajectory prediction. However, previous works mainly consider static, pairwise interactions with limited relational reasoning. To more comprehensively model interactions and reason relations, we propose DynGroupNet, a dynamic-group-aware network, which (i) models time-varying interactions in highly dynamic scenes; (ii) captures both pairwise and group-wise interactions; and (iii) reasons both interaction strength and category without direct supervision. Based on DynGroupNet, we further design a prediction system to forecast socially plausible trajectories with dynamic relational reasoning. The proposed prediction system leverages the Gaussian mixture model, multiple sampling and prediction refinement to promote prediction diversity for multiple future possibilities capturing, training stability for efficient model learning and trajectory smoothness for more realistic predictions, respectively. The proposed complex interaction modeling of DynGroupNet, future diversity capturing, efficient model training and trajectory smoothing of prediction system together to promote more accurate and plausible future predictions. Extensive experiments show that: (1) DynGroupNet can capture time-varying group behaviors, infer time-varying interaction category and interaction strength during prediction; (2) DynGroupNet significantly outperforms the state-of-the-art trajectory prediction methods by 28.0%, 34.9%, 13.0% in FDE on the NBA, NFL and SDD datasets.


A study on classifying Stack Overflow questions based on difficulty by utilizing contextual features

Abstract: Technical question-answering sites like Stack Overflow are gaining enormous attention from practitioners of specialized fields looking to exchange their programming knowledge. They ask questions on different topics with varying degrees of complexity and difficulty. All practitioners do not have the same level of expertise on those topics to respond to such questions. However, the current approach used by Stack Overflow mostly filters questions based on topics alone and does not take difficulty into account. For this reason, a large percentage of questions fail to attract the attention of appropriate users, resulting in questions having no answer or a significant delay in response time. To address these limitations, we incorporate three models, TF-IDF, LDA, and Doc2Vec, to extract semantic and context-dependent features that can measure the difficulty of questions. Each of these models is paired with different classifiers along with other features to classify the questions based on difficulty. Extensive experiments on three different datasets exhibit the effectiveness of our models, and Doc2Vec outperforms the other models. We also identified that the contextual features are correlated with question difficulty, and one subset of features outperforms others. The proposed approach can be beneficial for building an automatic tagger based on question difficulty.


Recursive Nonlinear Filtering via Gaussian Approximation With Minimized Kullback–Leibler Divergence

Abstract: In order to solve various problems in a Bayesian framework efficiently, it is critical to approximate a posterior distribution. This work provides a Gaussian approximation of a general distribution via Kullback–Leibler (KL) divergence minimization by deterministic sampling. Two algorithms, feasible direction method and linearized alternating direction method of multipliers, each having its strengths, are proposed for the Gaussian approximation. Theoretical results of complexity, convergence, convergence rate, and guidelines for parameter selection of the proposed algorithms are also provided. Based on the Gaussian approximation, two recursive filters are developed for nonlinear dynamic systems. Examples are given to demonstrate the effectiveness and efficiency of the proposed Gaussian approximation and the related filters.


Point cloud optimization of multi-view images in digital image correlation system

Abstract: Post-processing of the initial point cloud is one of the important ways to improve the measurement accuracy of digital image correlation (DIC). In this study, a multi-level point cloud optimization scheme is proposed for processing point clouds of multi-view images in DIC systems. Dense point clouds obtained after interpolation of primary point clouds from multi-view images contain noise. First, the local Random Sample Consensus (RANSAC) algorithm is proposed to retain local features while removing coarse matching points. Defects are filled in by the adaptive cavity repair technique based on information around the cavities and restores the original surface shape to the greatest extent. Finally, a multi-layer surface filtering is executed to smooth the mesh. Experiments verify the comprehensive performance of the multi-level optimization scheme, providing excellent results in point cloud fusion, cavity repair, and mesh smoothing, regardless of static 3D reconstruction or dynamic deformation measurement.


Placenta segmentation in magnetic resonance imaging: Addressing position and shape of uncertainty and blurred placenta boundary

Abstract: Automatic segmentation of the placenta in MRI facilitates quantitative measurements and further analysis. Automatic segmentation of the placenta in magnetic resonance imaging (MRI) is challenging due to the fact that (1) the position of the placenta varies greatly in different views of MRI. In addition, the shape of the placenta changes layer by layer in different MRI slices. The position and shape of the placenta are highly uncertain. (2) PAS results in a blurred placental boundary. To address the above issues, a refinement fusion based on the U-Net (RFU-Net) is proposed for the segmentation of the placenta with variable position and shape in MR images, and the boundary de-precision explores the distribution of blood vessels around the placental boundary. In RFU-Net, ResNet34 is used as a feature extractor, and a fusion multiscale feature (FMF) is proposed to extract multiscale features and their contextual information to solve the problem of variable placental shape. A refinement segmentation module (RSM) is designed to provide information about the position of the placenta to solve the problem of variable placental position, which effectively improves the segmentation of the placenta. The mean intersection over union (MIoU), hausdorff distance (HD), dice coefficient (Dice) and accuracy (Acc) are 0.8620, 4.3142, 0.9314 and 0.9987, respectively. Extensive experiments show that RFU-Net effectively improves the segmentation accuracy of the placenta. Boundary de-precision resolves the blurring of placental boundaries, effectively highlighting areas of the placental boundary that may contain blood vessels to aid in the physician's diagnosis.


Otel Yorumlarının Sınıflandırılmasında Makine Öğrenmesi Algoritmalarının Karşılaştırılması: Tripavdisor Yorumlarının Duygu Analizi

Abstract: Sentiment analysis can help extract meaningful information from these data piles from various websites and social media and measure consumers' reactions by classifying consumers' emotions as positive, negative or neutral. The success of sentiment analysis varies according to feature selection, vector space selection and machine learning method. For this reason, determining the most successful method in sentiment analysis is still controversial and important. A limited number of studies have been conducted comparing the success of various machine learning methods in sentiment analysis of hotel reviews in English. Considering this gap, the purpose of this research is to determine the most successful machine learning algorithm for sentiment analysis of hotel reviews. For this purpose, 708 reviews for 5-star hotels in Istanbul were collected manually. Obtained data were classified as positive and negative using logistic regression, k-nearest neighbor, naive Bayes and support vector machine methods. Analysis results show that the logistic regression method was the most successful classification algorithm, with an accuracy rate of 0.92. It is followed by support vector machine (0.90), naive Bayes method (0.77) and k-nearest neighbor algorithms (0.66).


Multivariate time series anomaly detection by fusion of deep convolution residual autoencoding reconstruction model and ConvLstm forecasting model

Abstract: Multivariate time-series anomaly detection is very essential to ensure the normal operation of physical equipment. Great achievements have been made in this field in recent years. However, two critical challenges limit the generalization ability of the model. Graph attention networks without prior knowledge are unable to extract the distribution features of dependencies among variables in a fine granularity. This reduces the accuracy of the dependence matrix to represent the correlations between variables. Beyond this challenge, the reconstruction model loses the fine-grained seasonal feature information in the spatial dimension during the reconstruction analysis process of the sample. In order to solve the above challenges, we propose a multivariate time-series anomaly detection model consisting of characterization network and forecasting network. In the characterization network, we first remove non-existent dependencies between variables by using prior knowledge. This approach can accurately capture the fine-grained dependency distribution features between variables and improve the accuracy of the dependence matrix representation of the correlations between variables. We then construct a deep convolutional residual autoencoder to reduce the loss of seasonal feature information in the spatial dimension. In forecasting network, we construct a temporal attention-based ConvLstm forecasting network to make fine-grained anomaly decisions on the output of the characterization network in the time dimension. We perform systematic experiments on six open-source datasets, in social network security scenario,the malicous acount behavior can be detected. Experimental results show that the proposed method outperforms the baseline anomaly detection methods with excellent detection performance and robustness. Specifically, our method achieves an average F1 score improvement of 0.23 over the baseline method at its highest level.


Spurious heritability of ability tilts: A comment on

Abstract: Ability tilts refer to within-individual differences between two abilities, e.g. math ability – verbal ability. Coyle et al. (2023) found ability tilts to be genetically heritable and concluded that ability tilts are genuine and, presumably, genetically coded individual characteristics. Moreover, Coyle et al. found a large portion of variance in ability tilts to be attributable to non-shared environmental factors (i.e. environmentability), which they interpreted to indicate that ability tilts are potentially generated by niche-picking. However, through simulations we show that heritability and environmentability of X-Y tilts are spurious consequences of heritability and environmentability of the constituent variables X and Y. Furthermore, we reanalyzed data used by Coyle et al. and show that the logic of their arguments would lead to the conclusions, for example, that the human genome codes for a difference between head circumference and verbal ability and that some individuals have picked a niche that includes a long nose at the expense of spatial ability. We do not find these conclusions tenable and propose, instead, that heritability and environmentability of tilts are spurious consequences of heritability and environmentability of the constituent variables.


On estimators of the mean of infinite dimensional data in finite populations

Abstract: The Horvitz-Thompson (HT), the Rao-Hartley-Cochran (RHC) and the generalized regression (GREG) estimators of the finite population mean are considered, when the observations are from an infinite dimensional space. We compare these estimators based on their asymptotic distributions under some commonly used sampling designs and some superpopulations satisfying linear regression models. We show that the GREG estimator is asymptotically at least as efficient as any of the other two estimators under different sampling designs considered in this paper. Further, we show that the use of some well known sampling designs utilizing auxiliary information may have an adverse effect on the performance of the GREG estimator, when the degree of heteroscedasticity present in linear regression models is not very large. On the other hand, the use of those sampling designs improves the performance of this estimator, when the degree of heteroscedasticity present in linear regression models is large. We develop methods for determining the degree of heteroscedasticity, which in turn determines the choice of appropriate sampling design to be used with the GREG estimator. We also investigate the consistency of the covariance operators of the above estimators. We carry out some numerical studies using real and synthetic data, and our theoretical results are supported by the results obtained from those numerical studies.


Small object detection in unmanned aerial vehicle images using multi-scale hybrid attention

Abstract: Small object detection in unmanned aerial vehicle images is always challenging due to the low resolution and the limited amount of information that they contain. Many feature enhancement effects have been introduced to improve the detection of small objects, but the extracted effective information is still insufficient, and redundant information interference is an issue. In this paper, we propose a new multi-scale hybrid attention based detector (MHA-YOLOv5), which integrates the similarity relationships between objects into you only look once version 5 (YOLOv5) for small object detection. Specifically, a novel multi-scale hybrid attention (MHA) structure is proposed to enhance the feature representation of small objects. This structure contains three modules: multi-scale attention (MsA), foreground enhancement module (FEM) and depthwise separable channel attention (DSCA). The MsA module is designed to build connections between large objects with abundant details and small objects with insufficient features on multiple scale features and capture the similarity relationships between objects. To reduce the interference of redundant information, the FEM is used to focus on the foreground features of multiple scale features, and the DSCA module is utilized to effectively extract multidimensional channel information. Sufficient experiments on the challenging VisDrone2019-DET, UAVDT and CARPK datasets demonstrate the effectiveness and superiority of the proposed approach. Specifically, compared with the performance of YOLOv5, MHA-YOLOv5 demonstrates a 2.82% mean average precision (mAP) improvement on the VisDrone2019-DET dataset, a 2.25% mAP improvement on the UAVDT dataset, and a 3.07% mAP improvement on the CARPK dataset.


Municipality2HTTPS: A study on HTTPS protocol's usage in Italian municipalities’ websites

Abstract: The usage of HTTPS protocol is essential for secure communication with websites, especially when it comes to the websites of local municipalities. This protocol ensures the confidentiality, integrity, and authenticity of online data transmissions, protecting users from unauthorised access and data breaches. By implementing HTTPS, municipalities can provide a secure and reliable online experience for their citizens and build trust in their digital services. This paper presents the Municipality2HTTPS research project, which aim is to provide a comprehensive analysis of the current state of HTTPS implementation in approximately 8,000 Italian municipalities’ websites. The study was conducted using the purpose-built software platform, named MunicipalityEvaluator, and the results were aggregated both geographically and demographically based on a numerical score derived from a specific scoring system: this system was obtained by expanding the requirements set forth by Italian regulations on the topic. The obtained results identified several areas for improvement in HTTPS implementation and regulatory compliance, including the need for more awareness-raising activities and support for municipalities with limited technical expertise. The results also revealed discrepancies between regions and demographic groups in terms of HTTPS implementation and regulatory compliance.


Detecting security vulnerabilities with vulnerability nets

Abstract: Detecting security vulnerabilities is a crucial part in secure software development. Many static analysis tools have proved to be effective in finding vulnerabilities, but generally there are some complex and subtle vulnerabilities that can escape from detection. Manual audits are a complementary approach to using tools. Unfortunately, most manual analyses are tedious and error prone. To benefit from both the tools and manual audits, some approaches incorporate the auditor's expertise into a static analysis tool during vulnerability discovery. Following this strategy, this paper presents a representation of source code called a vulnerability net, which is a special Petri net that integrates with data dependence graphs and control flow graphs. The combined representation can facilitate the detection of taint-style vulnerabilities such as buffer overflows and injection vulnerabilities. We test the proposed approach on Securibench Micro and demonstrate that it has the capability to identify a variety of vulnerabilities while keeping the rates of false negatives and positives low.


Mental image reconstruction from human brain activity: Neural decoding of mental imagery via deep neural network-based Bayesian estimation

Abstract: Visual images observed by humans can be reconstructed from their brain activity. However, the visualization (externalization) of mental imagery is challenging. Only a few studies have reported successful visualization of mental imagery, and their visualizable images have been limited to specific domains such as human faces or alphabetical letters. Therefore, visualizing mental imagery for arbitrary natural images stands as a significant milestone. In this study, we achieved this by enhancing a previous method. Specifically, we demonstrated that the visual image reconstruction method proposed in the seminal study by Shen et al. (2019) heavily relied on low-level visual information decoded from the brain and could not efficiently utilize the semantic information that would be recruited during mental imagery. To address this limitation, we extended the previous method to a Bayesian estimation framework and introduced the assistance of semantic information into it. Our proposed framework successfully reconstructed both seen images (i.e., those observed by the human eye) and imagined images from brain activity. Quantitative evaluation showed that our framework could identify seen and imagined images highly accurately compared to the chance accuracy (seen: 90.7%, imagery: 75.6%, chance accuracy: 50.0%). In contrast, the previous method could only identify seen images (seen: 64.3%, imagery: 50.4%). These results suggest that our framework would provide a unique tool for directly investigating the subjective contents of the brain such as illusions, hallucinations, and dreams.


Hierarchical features extraction and data reorganization for code search

Abstract: According to a natural language query, code search aims to retrieve relevant code snippets from a codebase. Recent works mainly rely on transformer-based pretraining models to measure the matching degree of queries and codes. Compared with works that rely on earlier deep learning methods, such as LSTM and Attention, they can significantly improve the performance of code search tasks. However, the different layers of the transformer-based models have different features that are intuitive and efficient for understanding the semantics of codes and queries but are rarely considered. Moreover, existing methods do not consider further increasing the amount of training data during training to improve the model’s performance. Toward this end, we propose a novel method called HFEDR, which utilizes the hierarchical features of transformer-based models and reorganizes original training data during a training phase. Specifically, we first extract high-level and low-level features of queries and codes from the higher and lower layers of GraphCodeBERT, respectively, achieving multi-view and comprehensive semantic representation. After that, we organize the original training data into hierarchical-uncorrelated feature pairs and then reorganize them into hierarchical-correlated feature pairs, achieving training the model with more data. Finally, we update the model’s parameters using a contrastive training method. We conduct extensive experiments on CodeSearchNet, demonstrating the effectiveness and rationality of our proposed approach.


The b-symbol weight hierarchy of the Kasami codes

Abstract: The symbol-pair read channel was first proposed by Cassuto and Blaum. Later, Yaakobi et al. generalized it to the b-symbol read channel. It is motivated by the limitations of the reading process in high density data storage systems. One main task in b-symbol coding theory is to determine the b-symbol weight hierarchy of codes. In this paper, we study the b-symbol weight hierarchy of the Kasami codes, which are well known for their applications to construct sequences with optimal correlation magnitudes. The complete symbol-pair weight distribution of the Kasami codes is determined.


Self-calibrated defocused speckle imaging for remote surface motion measurements

Abstract: Defocused Speckle Imaging (DSI) is a non-contact optical method that can measure multiaxial object motions at microscopic sensitivity using a simple and mechanically robust optical setup. The object surface is illuminated by a laser beam, and the scattered interference speckle pattern is tracked with a defocused camera. DSI measurement sensitivity increases with distance, and the sensitivity can be tuned by simple camera defocus adjustment. These characteristics make DSI an attractive choice for tracking remote objects in field conditions. However, the use of DSI for practical measurements is limited because the speckle signals due to linear and rotational surface motions mix together, and because the instrument calibration requires accurate object range and surface orientation parameters. To address these issues, the work described here combines two recently presented concepts: 1) a defocused camera pair, and 2) a diffraction-based self-calibration principle. The proposed approach was demonstrated by a series of self-calibrated multiaxial motion measurements performed at an extended (30.7-m) distance. The studied motions included in-plane displacements and out-of-plane rotations (tilts), whereas out-of-plane displacements and in-plane rotations were excluded. The experiments showed DSI's capability for high tilt sensitivity down to 0.0006°, standalone remote angle measurements, and suitability for retroreflective surfaces. The diffraction-based self-calibration approach could monitor sampling distances at a 6.4 % accuracy and the relative surface angles of 2.5–7.4° at a 0.2° accuracy. The defocused camera pair tracked microscopic in-plane displacements (400 µm) and very fine out-of-plane tilts (0.003°) at a high accuracy, with a maximum uncertainty of 6.0 %. The proposed DSI approach is particularly suited for monitoring large objects and for operating in hazardous environments. The findings pave a way for interesting new applications, like high-range remote angle measurements, high-sensitivity straightness measurements, and 3D-positioning utilizing retroreflective markers that already exist in the built infrastructure.


On triangular biregular degree sequences

Abstract: A simple graph is called triangular if every edge of it belongs to a triangle. We conjecture that any graphical degree sequence all terms of which are greater than or equal to 4 has a triangular realisation, and establish this conjecture for a class of biregular graphical degree sequences.


Calculating the distance between languages with deep learning

Abstract: Artificial intelligence (AI) has been implemented in various fields, including speech recognition. In this paper, a computational method is proposed for calculating the similarity between different languages or language varieties, with their similarity represented in terms of distance. In this process, we extracted mel spectrogram features from speech signals to provide the feature vectors and derived pairs of signal tokens based on vectors. Then, we trained a Siamese time-delay neural network to calculate the distance between two signal tokens. If the token pairs are from the same language group, the distance obtained using this Siamese network model is zero. In this preliminary experiment, three types of regional Mandarin Chinese (BJ, FJ, GD) were used as the dataset. The results gave the F1-score of 0.794, 0.623, and 0.715 for the classification task with respect to BJ, FJ, and GD dataset. In addition, 10 Taiwan Mandarin (TM) native speakers participated in identification and a pair-wise discrimination experiment to allow comparison with the Siamese network model. The 10 TM natives tended to misidentify GD-accented Mandarin as FJ-accented Mandarin resulting in a much greater distance between the two in the FJ-GD discrimination task compared to the Siamese network model. Overall, the results show that the performance of our model is better than the comparative experiment in completing the identification task, with the distance between the Siamese network model and the experiment having a mean absolute error (MAE) of 0.35. The familiarity might be the reason why the 10 TM natives displayed a bias towards BJ-accented Mandarin Chinese. To sum up, we provide a computational method to calculate the distance between two languages or language varieties, which can help linguists pre-classify sound files.


SHRIMPS: A framework for evaluating multi-user, multi-modal implicit authentication systems

Abstract: Smart devices are commonly used in multi-user scenarios, such as shared household devices and shared corporate devices for front-line workers. A multi-user device requires both identification and authentication to defend against unauthorized access and distinguish between legitimate users in real-time, especially when multiple users participate in the same session. Although implicit authentication (IA) has been proposed to provide continuous and transparent authentication throughout a session, most existing IA solutions are optimized for single-user scenarios. The challenges of designing multi-user IA systems include fusing multiple modalities for good accuracy, segmenting and labeling behavioral data while authenticating, and adapting IA models to new users and new incoming data. We propose SHRIMPS, an evaluation framework to support IA researchers in the design of multi-user, multi-modal IA systems. SHRIMPS allows the evaluation of multi-user IA solutions that incorporate multiple modalities and supports adding new users and automatically labeling new incoming data for model updating. SHRIMPS supports different score fusion strategies, including a novel score fusion strategy based on Dempster-Shafer (D-S) theory to improve accuracy with considering uncertainties among different IA mechanisms. SHRIMPS enables composing tasks with public datasets to evaluate and compare different IA schemes. We present and evaluate two sample use cases to showcase how SHRIMPS helps address practical design questions of multi-user, multi-modal IA systems. The evaluation results show that D-S theory based score fusion methods can effectively reject attackers and detect user switches for the multi-user scenario in real-time.


How memory anxiety can influence password security behavior

Abstract: Password reuse and modification are insecure password behaviors that are becoming increasingly prevalent as users are obliged to remember more passwords to access various digital services. Many users adopt these risky behaviors as a memory strategy in the belief that they have too many passwords for their memories to cope with. One important avenue in password research is metamemory, which encompasses the knowledge and understanding of memory capabilities and strategies. Previous research on password metamemory has examined the role that metamemory plays in memory performance (i.e., how well memory performs) and password recall. However, no previous research to date has investigated whether password reuse and modification are adopted as memory strategies due to an increase in knowledge and understanding of metamemory. To address this gap, two survey studies (Study1: N=50, Study 2: N=303) were implemented to examine the role that password metamemory plays in reusing and modifying passwords. Our findings suggest that of all metamemory constructs, users’ anxiety regarding their perceived ability to remember passwords can influence them to reuse and modify their passwords. These findings have potentially important implications because with an enhanced understanding of how users’ anxiety towards remembering passwords influences their security behavior, this could identify means of reducing password reuse and modification, thereby increasing password security and ultimately reduce some of the consequences of insecure password behaviors.


Learning transferable targeted universal adversarial perturbations by sequential meta-learning

Abstract: Recently, the transferability of adversarial perturbations in non-targeted scenarios has been extensively studied. However, changing the predictions of an unknown model to a pre-defined ‘targeted’ class still remains challenging. In this study, we aim to learn the targeted universal adversarial perturbations (UAPs) with higher transferability by the ensemble of multiple models. First, we observe the phenomenon that the logit of the target class will bias to a specific white-box model in existing ensemble-based attacks. To deal with the issue, we propose a normalized logit loss to narrow the margin of the targeted class's logits among different models. Besides, we introduce a novel sequential meta-learning optimization strategy to further increase transferability, consisting of the inner loop and the outer loop. In the inner loop, we sequentially learn task-specific targeted UAPs for each source model by jointly considering the perturbation from the previous model. In the outer loop, we optimize the task-agnostic targeted UAP by combining the targeted UAPs from the inner loop. Experimental results demonstrate the mutual benefits of the normalized logit loss and the sequential meta-learning optimization strategy for learning targeted adversarial perturbations, outperforming existing ensemble attacks in both white-box and black-box settings. The source code of this study is available at: Link.


Computational complexity of jumping block puzzles

Abstract: In the context of computational complexity of puzzles, the sliding block puzzles play an important role. Depending on the rules and set of pieces, the sliding block puzzles can be polynomial-time solvable, NP-complete, or PSPACE-complete. On the other hand, a relatively new notion of jumping block puzzles has been proposed in the puzzle community. This is a counterpart to the token jumping model of the combinatorial reconfiguration problems in the context of block puzzles. We investigate some variants of jumping block puzzles, which are based on actual puzzles, and a natural model from the viewpoint of combinatorial reconfiguration, and determine their computational complexities. More precisely, we investigate two generalizations of two actual puzzles which are called Flip Over puzzles and Flying Block puzzles and one natural model of jumping block puzzles from the viewpoint of combinatorial reconfiguration. We prove that they are PSPACE-complete in general. We also prove the NP-completeness of these puzzles in some restricted cases, and we give polynomial-time algorithms for some restricted cases.


Robust Graph Meta-Learning for Weakly Supervised Few-Shot Node Classification

Abstract: Graph machine learning (Graph ML) models typically require abundant labeled instances to provide sufficient supervision signals, which is commonly infeasible in real-world scenarios since labeled data for newly emerged concepts (e.g., new categorizations of nodes) on graphs is rather limited. To efficiently learn with a small amount of data on graphs, meta-learning has been investigated in Graph ML. By transferring the knowledge learned from previous experiences to new tasks, graph meta-learning approaches have demonstrated promising performance on few-shot graph learning problems. However, most existing efforts predominately assume that all the data from the seen classes is gold labeled, yet those methods may lose their efficacy when the seen data is weakly labeled with severe label noise. As such, we aim to investigate a novel problem of weakly supervised graph meta-learning for improving the model robustness in terms of knowledge transfer. To achieve this goal, we propose Meta-GIN (Meta Graph Interpolation Network), a new graph meta-learning framework. Based on a new robustness-enhanced episodic training paradigm, Meta-GIN is meta-learned to interpolate node representations from weakly labeled data and extracts highly transferable meta-knowledge, which enables the model to quickly adapt to unseen tasks with few labeled instances. Extensive experiments demonstrate the superiority of Meta-GIN over existing graph meta-learning studies on the task of weakly supervised few-shot node classification.


Improving domain-specific neural code generation with few-shot meta-learning

Abstract: Neural code generation aims to automatically generate code snippets guided by Natural Language Descriptions (NLDs). In recent years, various neural code generation models for mainstream Programming Languages (PLs), such as Java and Python, have been proposed and demonostrated significant success in prior studies. Nonetheless, due to the scarcity of available training examples for some domain-specific PLs, such as Solidity, Bash, and Clojure, simply adopting previous neural models may lead to overfitting and inadequate learning. To overcome this challenge, we propose MetaCoder, a novel meta-learning code generation approach that efficiently extracts general-purpose knowledge from a large-scale source language and rapidly adapts to domain-specific scenarios, even with relatively few samples. MetaCoder employs MAML, a powerful few-shot meta-learning method, to construct a transfer learning framework. This framework learns general-purpose knowledge from large-scale source languages and applies it in domain-specific target languages. To acquire more general-purpose knowledge, heterogeneous sub-tasks are constructed from the source language during the pre-training phase of MAML. As such, combining with CodeBERT and K-means, we design an unsupervised category assignment method for code generation samples, thereby exploiting the n-way k-shot rule to construct the heterogeneous sub-tasks. Consequently, MetaCoder can be applied to the code generation field. We evaluate MetaCoder with both tree-based (e.g., TreeGen) and sequence-based (e.g., CodeGPT) backbones on two domain-specific PLs, including Solidity and Bash. Extensive experiments demonstrate the superior performance of our approach compared to baselines and verified its capability of code generation visually in practice. MetaCoder effectively extracts general-purpose knowledge from large-scale source languages, thereby enhancing model performance. Therefore, we highly recommend MetaCoder as a code generation approach for domain-specific PLs.


Enhanced sparrow search algorithm based on improved game predatory mechanism and its application

Abstract: In response to the problems of strong randomness in initialization, imperfect search mechanism and tendency to fall into local optimum when dealing with complex optimization problems by sparrow search algorithm (SSA), an enhanced sparrow search algorithm (ESSA) is proposed, which combines Hammersley low difference sequence (HLDS) and centroid opposition-based learning (COBL), improved game predatory mechanism (GPM) and elastic collision strategy (EC). The ESSA can improve the initial individual quality, and avoid the attraction of local optima. In addition, the ESSA can effectively improve the search mechanism by increasing information exchange of individuals and balancing global search and local development. The effectiveness of ESSA is verified through simulation experiments of benchmark functions and variational modal decomposition (VMD) parameter optimization.


High-quality 3D shape recovery from scattering scenario via deep polarization neural networks

Abstract: High-quality three-dimension(3D) shape recovery from scattering scenario is of great importance with various potential applications. However, in turbid water scenarios, the absorption and scattering by water and suspending particles have a great disturbance on the generation of image clarity, texture details, and pixel-wise polarized properties such as degree of polarization (DoP) and angle of polarization (AoP), which significantly affect the 3D reconstructed performance. In this study, we present a data-driven polarimetric method for 3D reconstruction of underwater objects in turbid scenes, leveraging deep learning image priors. An underwater descattering network is designed to fit the underwater transport function of target signal light and generate clear underwater images, which are utilized to enhance the texture and remove the backscattering light. Subsequently, accurate pixel-wise DoP and AoP of the object could be calculated using the clear image prior and underwater imaging model. Finally, utilizing the obtained clear image and polarimetric information, polarimetric 3D reconstruction of object in turbid underwater scenarios is achieved. Experimental results demonstrate that accurate 3D shape of objects in turbid water can be well-reconstructed. The proposed technique has great potential for advancing marine exploration research.


Few-shot learning-based human behavior recognition model

Abstract: The challenges of human behavior recognition based on sensor data often require addressing the needs of various new users in real-world situations, which leads to difficulties in personalizing user data and resolving concerns related to resolution. This relates to the fact that persons with different characteristics who engage in the same behavior inevitably provide varying data discrepancies. Models that are faced with unfamiliar users encounter difficulties in accurately recognizing expected behavior, and it is not practicable to gather a large amount of training data every time the model needs to be reconfigured for new users. This study introduces a novel approach known as the FSLBR model, which combines the domains of few-shot learning and behavior recognition algorithms. Initially, a meta-learning technique focused on optimization is employed to categories datasets according to user categories. The paradigm of few-shot learning has shown significant effectiveness by leveraging a limited amount of data for new tasks. The FSLBR method and an attention-centered Memory module are then smoothly integrated into the FSLBR model. In the field of behavior recognition, it is observed that a small selection of data is sufficient for good classification when dealing with new users. The capacity of the model network to extract and extrapolate data features is improved. The empirical experiments conducted on the MEx dataset support the assertion that the proposed FSLBR model demonstrates improved performance under the few-shot learning framework compared to standard deep learning methods.


Multi-Stage Asynchronous Federated Learning With Adaptive Differential Privacy

Abstract: The fusion of federated learning and differential privacy can provide more comprehensive and rigorous privacy protection, thus attracting extensive interests from both academia and industry. However, facing the system-level challenge of device heterogeneity, most current synchronous FL paradigms exhibit low efficiency due to the straggler effect, which can be significantly reduced by Asynchronous FL (AFL). However, AFL has never been comprehensively studied, which imposes a major challenge in the utility optimization of DP-enhanced AFL. Here, theoretically motivated multi-stage adaptive private algorithms are proposed to improve the trade-off between model utility and privacy for DP-enhanced AFL. In particular, we first build two DP-enhanced AFL frameworks with consideration of universal factors for different adversary models. Then, we give a solid analysis on the model convergence of AFL, based on which, DP can be adaptively achieved with high utility. Through extensive experiments on different training models and benchmark datasets, we demonstrate that the proposed algorithms achieve the overall best performances and improve up to 24% test accuracy with the same privacy loss and have faster convergence compared with the state-of-the-art algorithms. Our frameworks provide an analytical way for private AFL and adapt to more complex FL application scenarios.


Neural Network-Based Method for Orbit Uncertainty Propagation and Estimation

Abstract: This paper proposes a fast method for orbit uncertainty propagation and estimation. The proposed method is based on an orbit deviation propagation approach, which consists of an analytical two-body deviation propagation solution and a deep neural network (DNN) to compensate for the errors between the two-body and the true solutions. First, five types of sample forms for training the DNN are investigated, and the optimal one is selected through learning feature and training performance analyses. Then, an uncertainty propagation solution for propagating the mean and covariance is formulated by combining the DNN-based deviation propagation approach with an unscented transformation process. Finally, a more efficient version of the unscented Kalman filter (UKF) for orbit estimation is developed based on the formulated uncertainty propagation solution. The advantage of the proposed DNN-based method is that it avoids the integration of the state transition matrix or dozens of sigma points. The performance of the proposed method is investigated on a low-Earth-orbit example. Numerical results show that the proposed DNN-based estimation method can be one order of magnitude faster than the UKF and is comparable to the UKF in estimation accuracy. In addition, it estimates more accurately than the extended Kalman filter (EKF) and is approximately 10% faster than the EKF.


On Helly numbers of exponential lattices

Abstract: Given a set S⊆R2, define the Helly number of S, denoted by H(S), as the smallest positive integer N, if it exists, for which the following statement is true: for any finite family F of convex sets in R2 such that the intersection of any N or fewer members of F contains at least one point of S, there is a point of S common to all members of F. We prove that the Helly numbers of exponential lattices {αn:n∈N0}2 are finite for every α>1 and we determine their exact values in some instances. In particular, we obtain H({2n:n∈N0}2)=5, solving a problem posed by Dillon (2021). For real numbers α,β>1, we also fully characterize exponential lattices L(α,β)={αn:n∈N0}×{βn:n∈N0} with finite Helly numbers by showing that H(L(α,β)) is finite if and only if logα(β) is rational.


Updatable searchable symmetric encryption: Definitions and constructions

Abstract: Searchable symmetric encryption (SSE) allows a client to search over encrypted data. To address the real threat of key compromise in practice, this work initiates the study of key rotation for SSE and introduces the notion of updatable SSE (USSE). In USSE, a client can issue a single update token that permits the server to convert existing encrypted data from the old key to the new key. In particular, we formalize the syntax of USSE and define the security model that captures the inference of key, search token, update token and encrypted data with bi-/uni-/no-directional key updates and bi-/uni-directional encrypted data updates. we present a USSE scheme that supports conjunctive queries with sub-linear complexity, and prove its security with no-directional key update and bi-directional encrypted data update. We also give extensions for concerning different key/encrypted data updates. we implement our USSE schemes and evaluate the performance with real-world dataset, which illustrates that our schemes achieve practically acceptable computational overhead and communication cost.


Energy-based structural least squares twin support vector clustering

Abstract: Clustering is an unsupervised learning algorithm and it is widely used in machine learning. Twin support vector clustering (TWSVC) is a new plane-based clustering algorithm, which exploits information from both within and between clusters to generate plane for each cluster. However, TWSVC suffered from relatively poor performance because it ignores the intrinsic structural information of data and solves a series of quadratic programming problems (QPPs). In order to address these problems, in this paper, we propose a novel energy-based structural least squares twin support vector clustering, termed as ESLSTWSVC. Firstly, based on least squares twin support vector clustering (LSTWSVC), we introduce within-class covariance matrix into the objective function of LSTWSVC to obtain more intrinsic structural information. ESLSTWSVC solves a series of system of linear equations rather than to solve QPPs in TWSVC, it leads to simple algorithm and less computation time. In addition, ESLSTWSVC converts the constraints of LSTWSVC into energy-based model by introducing an energy parameter for each cluster that makes ESLSTWSVC more robust. Experiments are performed on artificial datasets as well as UCI datasets, and the experimental results illustrate the effectiveness of the proposed algorithm.


Text classification supervised algorithms with term frequency inverse document frequency and global vectors for word representation: a comparative study

Abstract: Over the course of the previous two decades, there has been a rise in the quantity of text documents stored digitally. The ability to organize and categorize those documents in an automated mechanism, is known as text categorization which is used to classify them into a set of predefined categories so they may be preserved and sorted more efficiently. Identifying appropriate structures, architectures, and methods for text classification presents a challenge for researchers. This is due to the significant impact this concept has on content management, contextual search, opinion mining, product review analysis, spam filtering, and text sentiment mining. This study analyzes the generic categorization strategy and examines supervised machine learning approaches and their ability to comprehend complex models and nonlinear data interactions. Among these methods are k-nearest neighbors (KNN), support vector machine (SVM), and ensemble learning algorithms employing various evaluation techniques. Thereafter, an evaluation is conducted on the constraints of every technique and how they can be applied to real-life situations.


A descriptive behavior intention inference framework using spatio-temporal semantic features for human–robot real-time interaction

Abstract: Visual behavior intention inference is crucial for enabling escort robots to interact naturally with humans, which is very challenging due to the big inner-class similarity and the small intra-class distinguishability of successive actions in the assistive scenario. To attain a reliable behavior intention inference, not only the current state of behaviors is concerned, but also the semantic information in both spatial and temporal domains plays an important role. This paper presents a segmentation–detection–recognition hierarchical system to represent the spatio-temporal semantic features for formulating descriptions of body parts, trajectories and deep relationships of sub-behaviors. Specifically, a dense trajectory matching scheme based on temporal sampling and Binarized Normed Gradients (BING) algorithm is formulated to segment the 3-Dimensional (3D) behavior cubes, based on which, local trajectories are obtained by clustering dense trajectories according to the distance similarity, and the body parts are then detected by multi-kernel learning of the encoded local features. Moreover, a global three-stream context Convolutional Neural Networks (CNN) is proposed for behavior classification by designing a texture module using expansion, connection and 1D convolution implementations. Based on transfer learning, scene information is also recognized efficiently. Finally, the semantic descriptors are modeled by two cascaded And-Or Graphs (AoGs) constraining the spatial scenarios and temporal sequences. Our unified approach is demonstrated on two public benchmarks containing long-term activities and on an escort robot for real-world applications.


Self-supervised learning with randomized cross-sensor masked reconstruction for human activity recognition

Abstract: Self-supervised learning (SSL) has gained prominence in the field of accelerometer-based human activity recognition (HAR) due to its ability to learn from both labeled and unlabeled data. While labeled data acquisition is costly, it is relatively easy to accumulate unlabeled sensor data. However, few works utilize large-scale, unlabeled datasets for pre-training despite its positive impact on downstream HAR performance, shown in recent work. Cross-sensor upstream training has also received limited attention. We introduce a new auxiliary task, randomized cross-sensor masked reconstruction (RCSMR), for SSL. We pre-train a transformer encoder on the large-scale HUNT4 dataset with RCSMR. The resulting model exhibits better performance on two downstream datasets with the same sensor setup as HUNT4 (HARTH and HAR70+), achieving an average F1-score of 74.03%, surpassing two other auxiliary tasks (70.51% to 72.78%) and five supervised baselines (47.51% to 58.84%). Moreover, when applied to three datasets with sensor configurations distinct from HUNT4 (USC-HAD, PAMAP2, MobiAct), RCSMR outperforms nine state-of-the-art SSL methods, with an F1-score of 72.99% compared to F1-scores ranging from 51.46% to 69.88%. We further show that certain activities exhibit improved separability when utilizing latent representations learned through RCSMR, indicating reduced sensor position and orientation bias. Our method is applied in large-scale epidemiological studies, offering valuable insights into the impact of physical activity behavior on public health.


Multi-level attention network: Mixed time–frequency channel attention and multi-scale self-attentive standard deviation pooling for speaker recognition

Abstract: In this paper, we propose a more efficient lightweight speaker recognition network, the multi-level attention network (MANet). MANet aims to generate more robust and discriminative speaker features by emphasizing features at different levels in the speaker recognition network through multi-level attention. The multi-level attention contains mixed time–frequency channel (MTFC) attention and multi-scale self-attentive standard deviation pooling (MSSDP). MTFC attention combines channel, time, and frequency information to capture global features and model long-term contexts. MSSDP can capture changes in frame-level features and aggregate frame-level features with different scales, generating a long-term, robust, and discriminative utterance-level feature. Therefore, MANet emphasizes the features of different levels. We performed extensive experiments on two popular datasets, Voxceleb and CN-Celeb. The proposed method is compared with the current state-of-the-art speaker recognition methods. It achieved EER/minDCF of 1.82%/0.1965, 1.94%/0.2059, 3.69%/0.3626, and 11.98%/0.4814 on the test sets Voxceleb1-O, Voxceleb1-E, Voxceleb1-H, and CN-Celeb, respectively. It is a more effective lightweight speaker recognition network, superior to most large speaker recognition networks and all lightweight speaker recognition networks tested, with an improved performance of 64% compared to the baseline system ThinResNet-34. Compared to the lightest EfficientTDNN-Small, it has only 0.6 million more parameters but 63% better performance. The performance of MANet is only 4% different compared to the state-of-the-art large model LE-Conformer. In the ablation experiments, our proposed attention method and aggregation model achieved the best experimental performance in Voxceleb1-O with EER/minDCF of 2.46%/0.2708, 2.39%/0.2417, respectively, which indicates that our proposed methods are a significant improvement over previous methods.


GCCF: A lightweight and scalable network for underwater image enhancement

Abstract: Underwater images suffer from color deviation and low contrast arising from light attenuation and scatter, making them act as a drag on exploring and exploiting ocean resources. Existed underwater image enhancement algorithms are either inflexible due to scene-dependent design or redundant for ignoring prior knowledge. To address the above problems, we propose a lightweight and scalable deep learning-based network that consists of a Grouped Color Compensation encoder and channel Fusion decoder, abbreviated GCCF. Concretely, the least absorbed green channel contains abundant information compared to the red and blue channels. Taking this into account, the encoder extracts information independently from three grouped features according to the color. Simultaneously, the encoder flexibly compensates for the red and blue channels from the green channel with a proposed learnable module. Then, the decoder gradually fuses the grouped features into a whole part and produces competitive outputs from the integrated features. Extensive experiments on real-world underwater images demonstrate that the proposed GCCF achieves outstanding results in subjective visual comparisons and objective metrics.


Multi-UAV airborne UV area coverage and task assignment method

Abstract: To address the area coverage problem in large-scale reconnaissance and detection tasks, Unmanned Aerial Vehicles(UAVs) utilize Ultraviolet(UV) light for flight guidance. Additionally, a UV light power control method is implemented to assist UAVs in adapting to various terrains. Based on the flight characteristics of quadrotor UAVs, the target area is divided into a grid-like format. This transformation helps convert the area coverage task into a multi-UAV path planning problem on a raster map. To address this, constraints are established for assigning tasks to multiple UAVs. An algorithm is proposed to assign the target area uniformly based on the initial position of the UAV, ensuring full coverage. Additionally, the algorithm considers the differences in endurance among multiple UAVs. This improved algorithm is highly suitable for real-life application scenarios. The simulation results demonstrate the power control changes in wireless UV-guided UAV flight. Moreover, the assignment results of the improved algorithm align closely with the theoretical assignment results, considering different parameters like the number of UAVs, initial position, and endurance capacity.


Research on the construction scheme of smart library based on blockchain technology

Abstract: Facial recognition technology and recommendation systems are the main technologies in the construction of intelligent libraries, but both technologies face privacy breaches and credibility issues. Blockchain, as an emerging technology, is having significant impact in many fields. This article delves into the role and core application value of blockchain technology in the construction of smart libraries in universities, and proposes smart library service architecture based on blockchain technology. This architecture provides a value-added path for smart library services, including secure storage, resource sharing, and optimizing book borrowing and returning systems. It can improve the quality of resource services and meet the increasing service needs of readers. The effectiveness and practicality of this method have been verified through experiments.


Some relations between the irreducible polynomials over a finite field and its quadratic extension

Abstract: In this paper, we establish some relations between irreducible polynomials over a finite field Fq and its quadratic extension Fq2. First we consider a relation between the numbers of irreducible polynomials of a fixed degree over Fq and Fq2, and some relations between self-reciprocal irreducible polynomials over Fq and self-conjugate-reciprocal irreducible polynomials over Fq2. We also obtain formulas for the number and the product of all self-conjugate-reciprocal irreducible monic (SCRIM) polynomials over Fq2.


A differential evolution algorithm for solving mixed-integer nonlinear programming problems

Abstract: Many engineering optimization problems fall into the category of Mixed-Integer Nonlinear Programming (MINLP) problems, which combine nonlinear relations, constraint conditions, and different types of variables, including continuous, integer, and/or discrete variables. Solving MINLP problems can be a challenging exploration process since their landscape can be composed of many discontinuous feasible parts with different sizes. In this scheme, Evolutionary Algorithms (EAs) often fail to generate enough diversity to explore the discontinuous feasible parts. Consequently, EAs are vulnerable to being attracted to larger discontinuous feasible parts, even if they are not promising regions. In this paper, a new proposal based on two fundamental strategies is presented to improve the performance of the differential evolution algorithm when solving MINLP problems. The first strategy considers a set of "good fitness-infeasible solutions" that contribute to exploring promising regions from infeasible contours. It reduces the vulnerability of the solutions to be attracted to larger discontinuous feasible parts with unpromising objective function values. The second is a composite trial vector generation to improve the combinatorial exploration while ensuring a robust convergence capacity toward the final solution. Sixteen well-known MINLP problems are used in several experiments to evaluate the performance of the proposed algorithm, comparing it to state-of-the-art EAs. The results provided by the proposal show a better performance in terms of quality, robustness, and computational cost.


CoCo-trie: Data-aware compression and indexing of strings

Abstract: We address the problem of compressing and indexing a sorted dictionary of strings to support efficient lookups and more sophisticated operations, such as prefix, predecessor, and range searches. This problem occurs as a key task in a plethora of applications, and thus it has been deeply investigated in the literature since the introduction of tries in the ’60s. We introduce a new data structure, called the COmpressed COllapsed Trie (CoCo-trie), that hinges on a pool of techniques to compress subtries (of arbitrary depth) into succinctly-encoded and efficiently-searchable trie macro-nodes with a possibly large fan-out. Then, we observe that the choice of the subtries to compress depends on the trie structure and its edge labels. Hence, we develop a data-aware optimisation approach that selects the best subtries to compress via the above pool of succinct encodings, with the overall goal of minimising the total space occupancy and still achieving efficient query time. We also investigate some variants of this approach that induce interesting space–time trade-offs in the CoCo-trie design. Our experimental evaluation on six diverse and large datasets (representing URLs, XML data, DNA and protein sequences, database records, and search-engine dictionaries) shows that the space–time performance of well-established and highly-engineered data structures solving this problem is very input-sensitive. Conversely, our CoCo-trie provides a robust and uniform improvement over all competitors for half of the datasets, and it results on the Pareto space–time frontier for the others, thus offering new competitive trade-offs.


Properties and Styles of Software Technology Tutorials

Abstract: A large number of tutorials for popular software development technologies are available online, and those about the same technology vary widely in their presentation. We studied the design of tutorials in the software documentation landscape for five popular programming languages: Java, C#, Python, Javascript, and Typescript. We investigated the extent to which tutorial pages, i.e. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">resources , differ and report statistics of variations in resource properties. We developed a framework for characterizing resources based on their <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">distinguishing attributes , i.e. properties that vary widely for the resource, relative to other resources. Additionally, we propose that a resource can be represented by its <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">resource style , i.e. the combination of its distinguishing attributes. We discuss three techniques for characterizing resources based on our framework, to capture notable and relevant content and presentation properties of tutorial pages. We apply these techniques on a data set of 2551 resources to validate that our framework identifies valid and interpretable styles. We contribute this framework for reasoning about the design of resources in the online software documentation landscape.


Early analysis of requirements using NLP and Petri-nets

Abstract: Scenario-based approaches are widely used for software requirements specification. Since scenarios are usually written using natural language, specifications may have statements that are ambiguous, unnecessarily complicated, missing, duplicated, or conflicting. Requirements quality is challenging since it is hard to achieve consistency in requirements products. Unfortunately, if done manually, analysis of textual scenarios can be an arduous, time-consuming, and error-prone activity. This work rethinks the unambiguity, completeness, consistency, and correctness properties of scenario-based specifications; and how static and dynamic analysis strategies could automatically evaluate them. To do so, we introduce an automated requirements analysis approach to check both structural and behavioral aspects of scenarios, which combines natural language processing, Petri-nets, and visualization techniques for: (i) identifying certain types of defects and their indicators; (ii) highlighting scenario statements or relationships among scenarios that can lead to defects; and (iii) foreseeing scenario execution paths that can lead to inconsistencies. We show the feasibility of the proposed approach through the analysis of four projects specified as scenario-based descriptions. Overall, our approach produced reasonable results, with precision greater than 89% and recall greater than 98%. Our work allows researchers, as well as practitioners, to improve the quality of scenarios through an automated analysis approach.


Dynamically retrieving knowledge via query generation for informative dialogue generation

Abstract: Knowledge-driven dialogue systems have recently made remarkable breakthroughs. Compared with general dialogue systems, superior knowledge-driven dialogue systems can generate more informative and knowledgeable responses with pre-provided knowledge. However, in practical applications, the knowledge-driven dialogue systems cannot be supplied with relevant knowledge beforehand. Hence, to enhance the practicality of the knowledge-driven dialogue systems, it is crucial to devise a method to dynamically retrieve pertinent knowledge based on the context. In addressing this challenge, we introduce a knowledge-driven dialogue system called DRKQG (Dynamically Retrieving Knowledge via Query Generation for informative dialogue response). Specifically, this system is composed of two main modules: a query generation module and a response generation module. Initially, a time-aware mechanism is employed to capture contextual information, enabling the generation of a query for knowledge retrieval through a search engine. Subsequently, we incorporate the copy mechanism and transformers, empowering the response generation module to create responses based on both the context and retrieved knowledge. Experimental results at LIC2022, Language and Intelligence Technology Competition, show that our module outperforms the baseline model by a large margin on automatic evaluation metrics, while human evaluation by the Baidu Linguistics team shows that our system achieves impressive results in Factually Correct and Knowledgeable.


Teacher–student complementary sample contrastive distillation

Abstract: Knowledge distillation (KD) is a widely adopted model compression technique for improving the performance of compact student models, by utilizing the "dark knowledge" of a large teacher model. However, previous studies have not adequately investigated the effectiveness of supervision from the teacher model, and overconfident predictions in the student model may degrade its performance. In this work, we propose a novel framework, Teacher-Student Complementary Sample Contrastive Distillation (TSCSCD), that alleviate these challenges. TSCSCD consists of three key components: Contrastive Sample Hardness (CSH), Supervision Signal Correction (SSC), and Student Self-Learning (SSL). Specifically, CSH evaluates the teacher's supervision for each sample by comparing the predictions of two compact models, one distilled from the teacher and the other trained from scratch. SSC corrects weak supervision according to CSH, while SSL employs integrated learning among multi-classifiers to regularize overconfident predictions. Extensive experiments on four real-world datasets demonstrate that TSCSCD outperforms recent state-of-the-art knowledge distillation techniques.


Optimized hybrid deep learning pipelines for processing heterogeneous facial expression datasets

Abstract: Emotions, central to human experience, have been categorized through various theories. Biological theories, such as the James-Lange model, suggest that physiological reactions are the precursors to emotions. Cognitive theories, as highlighted by the Schachter-Singer model, argue that emotions emerge from a combination of physiological responses and cognitive appraisal. Evolutionary theories, rooted in Darwin's work, view emotions as adaptive mechanisms that have evolved to promote survival. In the realm of psychology, expressing emotions via facial expressions is termed face reading, a vital communication method. With the swift advancement of Deep Neural Networks (DNN) across various fields, they are increasingly used to derive exclusive features for automated Facial Expression Prediction and Recognition (FEPR). This research employs a Three-staged Deep Learning (TsDL) strategy for FEPR. Recognizing the pivotal role of datasets in determining system performance, we incorporate both laboratory-controlled and real-world data. Initially, the Generative Adversarial Networks (GAN) model, leveraging datasets like CK+ and Radboud Faces Database (RFD), is introduced. This model successfully identifies expressions across seven categories with an accuracy of 92.15 %. Subsequently, another model based on the Attention-Long Short Term Memory (A-LSTM) is trained using the Real-world Affective Face Database, given its capability to manage spatio-temporal features. The TsDL approach predicts and classifies six primary emotions, achieving an 89.22 % accuracy rate. In the culmination of our work, we harness Ensemble Learning Strategies to craft a refined hybrid model, ensuring systematic performance and optimal accuracy. The diverse theoretical perspectives on emotions greatly enrich our comprehension, especially when developing our FEPR model.


Introducing shape priors in Siamese networks for image classification

Abstract: The efficiency of deep neural networks is increasing, and so is the amount of annotated data required for training them. We propose a solution improving the learning process of a classification network with less labeled data. Our approach is to inform the classifier of the elements it should focus on to make its decision by supplying it with some shape priors. These shape priors are expressed as binary masks, giving a rough idea of the shape of the relevant elements for a given class. We resort to Siamese architecture and feed it with image/mask pairs. By inserting shape priors, only the relevant features are retained. This provides the network with significant generalization power without requiring a specific domain adaptation step. This solution is tested on some standard cross-domain digit classification tasks and on a real-world video surveillance application. Extensive tests show that our approach outperforms the classical classifier by generating a good latent space with less training data. Code is available at https://github.com/halqasir/MG-Siamese.


Rotation-equivariant correspondence matching based on a dual-activation mixer

Abstract: Learning-based correspondence matching methods have become the mainstream techniques in many computer vision and robotics applications due to their robustness to large illumination and viewpoint changes. However, it is difficult for conventional convolutional neural networks (CNNs) to extract rotation-equivariant local features. Recent work has shown that CNNs combined with group-equivariant architectures are surprisingly effective at matching correspondences even when the images are rotated to a dramatic extent. However, the inherent shape (square) of convolution kernels causes the performance bottleneck of such rotation-equivariant CNNs. To address this issue, we propose an adaptive dual rotation-equivariant correspondence matching algorithm, which performs stably at all angles. We mathematically analyze the effectiveness of our proposed rotation-equivariant correspondence matching approach and its performance with respect to different convolution kernels. Extensive experiments on the Rotated-HPatches, SIM2E, and MegaDepth datasets demonstrate the effectiveness of our proposed algorithm.