Reviewerly/exHarmony huggingface dataset:
Global Feature Attention Network: Addressing the Threat of Adversarial Attack for Aerial Image Semantic Segmentation

Aerial Image Semantic segmentation based on convolution neural networks (CNNs) has made significant process in recent years. Nevertheless, their vulnerability to adversarial example attacks could not be neglected. Existing studies typically focus on adversarial attacks for image classification, ignoring the negative effect of adversarial examples on semantic segmentation. In this article, we systematically assess and verify the influence of adversarial attacks on aerial image semantic segmentation. Meanwhile, based on the robust characteristics of global features, we construct a novel global feature attention network (GFANet) for aerial image semantic segmentation to solve the threat of adversarial attacks. GFANet uses the global context encoder (GCE) to obtain the context dependencies of global features, introduces the global coordinate attention mechanism (GCAM) to enhance the global feature representation to suppress adversarial noise, and the feature consistency alignment (FCA) is used for feature calibration. In addition, we construct a universal adversarial training strategy to improve the robustness of the semantic segmentation model against adversarial example attacks. Extensive experiments on three aerial image datasets demonstrate that GFANet is more robust against adversarial attacks than existing state-of-the-art semantic segmentation models.
Application of Image Processing Techniques for UAV Detection Using Deep Learning and Distance-Wise Analysis

Drones have many applications in our daily lives and can be employed for agricultural, military, commercial, disaster relief, research and development, and many other purposes. There has been a significant increase in the usage of small drones/unmanned aerial vehicles in recent years. Consequently, there is a rising potential for small drones to be misused for illegal activities, such as terrorism and drug smuggling. Hence, there is a need for accurate and reliable UAV identification that can be used in various environments. In this paper, different versions of the current state-of-the-art object detection model, i.e., YOLO models, are used, by working on the principles of computer vision and deep learning to detect small UAVs. To improve the accuracy of small UAV detection, this paper proposes the application of various image-processing techniques to the current detection model, which has resulted in a significant performance increase. In this study, a mAP score of 96.7% was obtained for an IoU threshold of 50% along with a precision value of 95% and a recall of 95.6%. Distance-wise analysis of drones (i.e., for close, mid, and far ranges) was also performed to measure distance-wise accuracies.
Contrastive Multi-View Kernel Learning

Kernel method is a proven technique in multi-view learning. It implicitly defines a Hilbert space where samples can be linearly separated. Most kernel-based multi-view learning algorithms compute a kernel function aggregating and compressing the views into a single kernel. However, existing approaches compute the kernels independently for each view. This ignores complementary information across views and thus may result in a bad kernel choice. In contrast, we propose the Contrastive Multi-view Kernel - a novel kernel function based on the emerging contrastive learning framework. The Contrastive Multi-view Kernel implicitly embeds the views into a joint semantic space where all of them resemble each other while promoting to learn diverse views. We validate the method's effectiveness in a large empirical study. It is worth noting that the proposed kernel functions share the types and parameters with traditional ones, making them fully compatible with existing kernel theory and application. On this basis, we also propose a contrastive multi-view clustering framework and instantiate it with multiple kernel k-means, achieving a promising performance. To the best of our knowledge, this is the first attempt to explore kernel generation in multi-view setting and the first approach to use contrastive learning for a multi-view kernel learning.
Integrating Conversational Agents and Knowledge Graphs Within the Scholarly Domain

In the last few years, chatbots have become mainstream solutions adopted in a variety of domains for automatizing communication at scale. In the same period, knowledge graphs have attracted significant attention from business and academia as robust and scalable representations of information. In the scientific and academic research domain, they are increasingly used to illustrate the relevant actors (e.g., researchers, institutions), documents (e.g., articles, patents), entities (e.g., concepts, innovations), and other related information. Following the same direction, this paper describes how to integrate conversational agents with knowledge graphs focused on the scholarly domain, a.k.a. Scientific Knowledge Graphs. On top of the proposed architecture, we developed AIDA-Bot, a simple chatbot that leverages a large-scale knowledge graph of scholarly data. AIDA-Bot can answer natural language questions about scientific articles, research concepts, researchers, institutions, and research venues. We have developed four prototypes of AIDA-Bot on Alexa products, web browsers, Telegram clients, and humanoid robots. We performed a user study evaluation with 15 domain experts showing a high level of interest and engagement with the proposed agent.
Exploiting multiple question factors for knowledge tracing

Knowledge Tracing (KT) aims to predict future students’ performance via their responses to a sequence of questions, which serves as a fundamental task for intelligent education. Most of the existing efforts directly predict students’ performance depending on their dynamically changing knowledge states. However, the individualization of questions is neglected and difficulty level differ from question to question, which would give some valuable clues to KT. Towards this end, in this paper, we propose a novel Multiple Question Factors for Knowledge Tracing (MQFKT) method, which fully exploits various question factors to generate better prediction. On one hand, calibrated student-concept connection space is established to obtain fine-grained response representations on questions according to the information of responses on questions. On the other hand, individualized difficulty levels with particular concept for different questions are introduced for improving the prediction performance. Extensive experiments on three datasets have shown that the MQFKT approach achieves more precise prediction of student performance and better interpretation of the model.
Collision-Free 4D Dynamic Path Planning for Multiple UAVs Based on Dynamic Priority RRT* and Artificial Potential Field

In this paper, a four-dimensional (4D) dynamic cooperative path planning algorithm for multiple unmanned aerial vehicles (UAVs) is proposed, in which the cooperative time variables of UAVs, as well as conflict and threat avoidance, are considered. The algorithm proposed in this paper uses a hierarchical framework that is divided into a 4D cooperative planning layer and a local threat avoidance planning layer. In the cooperative planning layer, the proposed algorithm, named dynamic priority rapidly exploring random trees (DPRRT*), would be used for the 4D cooperative path planning of all UAVs involved in a given task. We first designed a heuristic prioritization strategy in the DPRRT* algorithm to rank all UAVs to improve the efficiency of cooperative planning. Then, the improved RRT* algorithm with the 4D coordination cost function was used to plan the 4D coordination path for each UAV. Whenever the environment changes dynamically (i.e., sudden static or moving threats), the proposed heuristic artificial potential field algorithm (HAPF) in the local threat avoidance planning layer is used to plan the local collision avoidance path. After completing local obstacle avoidance planning, the DPRRT* of the 4D cooperative planning layer is again called upon for path replanning to finally realize 4D cooperative path planning for all UAVs. The simulation and comparison experiments prove the feasibility, efficiency, and robustness of the proposed algorithm.
An Online Mammography Database with Biopsy Confirmed Types

Breast carcinoma is the second largest cancer in the world among women. Early detection of breast cancer has been shown to increase the survival rate, thereby significantly increasing patients' lifespan. Mammography, a noninvasive imaging tool with low cost, is widely used to diagnose breast disease at an early stage due to its high sensitivity. Although some public mammography datasets are useful, there is still a lack of open access datasets that expand beyond the white population as well as missing biopsy confirmation or with unknown molecular subtypes. To fill this gap, we build a database containing two online breast mammographies. The dataset named by Chinese Mammography Database (CMMD) contains 3712 mammographies involved 1775 patients, which is divided into two branches. The first dataset CMMD1 contains 1026 cases (2214 mammographies) with biopsy confirmed type of benign or malignant tumors. The second dataset CMMD2 includes 1498 mammographies for 749 patients with known molecular subtypes. Our database is constructed to enrich the diversity of mammography data and promote the development of relevant fields.
A Framework of Customer Review Analysis Using the Aspect-Based Opinion Mining Approach

Opinion mining is the branch of computation that deals with opinions, appraisals, attitudes, and emotions of people and their different aspects. This field has attracted substantial research interest in recent years. Aspect-level (called aspect-based opinion mining) is often desired in practical applications as it provides detailed opinions or sentiments about different aspects of entities and entities themselves, which are usually required for action. Aspect extraction and entity extraction are thus two core tasks of aspect-based opinion mining. his paper has presented a framework of aspect-based opinion mining based on the concept of transfer learning. on real-world customer reviews available on the Amazon website. The model has yielded quite satisfactory results in its task of aspect-based opinion mining.
Active Sampling over Graphs for Bayesian Reconstruction with Gaussian Ensembles

Graph-guided semi-supervised learning (SSL) has gained popularity in several network science applications, including biological, social, and financial ones. SSL becomes particularly challenging when the available nodal labels are scarce, what motivates naturally the active learning (AL) paradigm. AL seeks the most informative nodes to label in order to effectively estimate the nodal values of unobserved nodes. It is also referred to as active sampling, and boils down to learning the sought function mapping, and an acquisition function (AF) to identify the next node(s) to sample. To learn the mapping, this work leverages an adaptive Bayesian model comprising an ensemble (E) of Gaussian Processes (GPs) with enhanced expressiveness of the function space. Unlike most alternatives, the EGP model relies only on the one-hop connectivity of each node. Capitalizing on this EGP model, a suite of novel and intuitive AFs are developed to guide the active sampling process. These AFs are then combined with weights that are adapted incrementally to further robustify performance. Numerical tests on real and synthetic datasets corroborate the merits of the novel methods.
Detection of malignancy in whole slide images of endometrial cancer biopsies using artificial intelligence

In this study we use artificial intelligence (AI) to categorise endometrial biopsy whole slide images (WSI) from digital pathology as either "malignant", "other or benign" or "insufficient". An endometrial biopsy is a key step in diagnosis of endometrial cancer, biopsies are viewed and diagnosed by pathologists. Pathology is increasingly digitised, with slides viewed as images on screens rather than through the lens of a microscope. The availability of these images is driving automation via the application of AI. A model that classifies slides in the manner proposed would allow prioritisation of these slides for pathologist review and hence reduce time to diagnosis for patients with cancer. Previous studies using AI on endometrial biopsies have examined slightly different tasks, for example using images alongside genomic data to differentiate between cancer subtypes. We took 2909 slides with "malignant" and "other or benign" areas annotated by pathologists. A fully supervised convolutional neural network (CNN) model was trained to calculate the probability of a patch from the slide being "malignant" or "other or benign". Heatmaps of all the patches on each slide were then produced to show malignant areas. These heatmaps were used to train a slide classification model to give the final slide categorisation as either "malignant", "other or benign" or "insufficient". The final model was able to accurately classify 90% of all slides correctly and 97% of slides in the malignant class; this accuracy is good enough to allow prioritisation of pathologists' workload.
Hulls of double cyclic codes

In this paper, we characterize hulls of double cyclic codes over Z2. Firstly, we give the generators of hulls of double cyclic codes under the separable case and the non-separable case with coprime odd block lengths. Then, using the concept of a generating function in combinatorics, the solution of dimensions of hulls of double cyclic codes over Z2 is given. Furthermore, the enumeration of double cyclic codes over Z2 with hulls of a fixed dimension is determined. As an application, some good entanglement-assisted quantum error-correcting codes (EAQECCs) are obtained by the hulls of non-separable double cyclic codes.
Multi-task twin bounded support vector machine and its safe screening rule

Direct multi-task twin support vector machine (DMTSVM) obtains great performance in dealing with correlated tasks. However, DMTSVM only considers the empirical risk minimization principle, so it readily causes over-fitting and lowers the prediction accuracy. To enhance the generalization ability of the classifier, we construct a multi-task twin bounded support vector machine (MT-TBSVM), in which a regularization term is introduced into the objective function, thus implementing the structural risk minimization principle. To improve the computational speed of MT-TBSVM, we additionally put forward a safe screening rule (SSR) for it. SSR could identify most inactive samples from the multiple tasks. Therefore, this will result in a significant reduction in the scale of dual problems, meanwhile the testing accuracy keeps unchanged since it does not sacrifice the optimal solution. Moreover, a fast DCDM algorithm is presented for further solving the reduced MT-TBSVM. On fifteen benchmark datasets, numerical experimental results clearly demonstrated the effectiveness of proposed algorithms. Finally, the proposed method is applied to a real Chinese wine dataset to further investigate its validity.
Joint Masked Face Recognition and Temperature Measurement System Using Convolutional Neural Networks

With the outbreak of COVID-19, epidemic prevention has become a way to prevent the spread of epidemics. Many public places, such as hospitals, schools, and office places, require disinfection and temperature measurement. To implement epidemic prevention systems and reduce the risk of infection, it is a recent trend to measure body temperature through non-contact sensing systems with thermal imaging cameras. Compared to fingerprints and irises, face recognition is accurate and does not require close contact, which significantly reduces the risk of infection. However, masks block most facial features, resulting in the low accuracy of face recognition systems. This work combines masked face recognition with a thermal imaging camera for use as an automated attendance system. It can record body temperature and recognize the person at the same time. Through the designed UI system, we can search the attendance information of each person. We not only provide the design method based on convolutional neural networks (CNNs), but also provide the complete embedded system as a real demonstration and achieve a 94.1% accuracy rate of masked face recognition in the real world. With the face recognition system combined with a thermal imaging camera, the purpose of screening body temperature when checking in at work can be achieved.
A novel global solar exposure forecasting model based on air temperature: Designing a new multi-processing ensemble deep learning paradigm

The total quantity of solar energy falling on a horizontal plane surface is the global solar exposure (GSE, i.e., total solar energy). Precise forecasting of GSE is important in many fields such as renewable energy, agriculture, and public health, particularly by the limited hydro-meteorological time series information. This research aims to develop an advanced multi-processing deep learning (DL) paradigm to forecast weekly GSE based on maximum (Tmax) and minimum (Tmin) air temperatures as the drivers at Brisbane and Perth airport stations in eastern and western Australia during 2000 to 2022. The proposed model was comprised of an extra tree feature selection (FS) integrated with two novel decomposition techniques, namely time-varying filtering-based empirical mode decomposition (TVF-EMD), and empirical wavelet transform (EWT), and a powerful ensemble deep random vector functional link (ED-RVFL) approach. To validate the main model, the RVFL, bidirectional long-short term memory (Bi-LSTM), and bagged regression tree (Bagging) machine learning (ML) models were examined in hybrid and standalone counterpart frameworks. First, the extra-tree FS determined the significant lags of the predictors based on an importance benchmark criterion. Then, by applying the optimal gained lags to the feeding models, all of the original predictors were decomposed using TVF-EMD and EWT univariate feature extraction. The final forecast was computed by aggregating all the individual forecasts of the intrinsic mode functions (IMFs) and residual components. In addition, eight statistical indicators (including coefficient of correlation: R, root mean square error: RMSE, Kling-Gupta efficiency: KGE, index of agreement: IA, uncertainty coefficient with 95% confidence level: U95%, mean absolute percent error: MAPE, Nash-Sutcliffe efficiency: NSE, and mean absolute error: MAE) and several graphical methods were utilized to evaluate the performances of the models. The modeling results indicated that the ED-RVFL-TVF-EMD (R = 0.9665, RMSE = 1.9193 MJ/m2, and KGE = 0.9565 for the Perth airport station) and ED-RVFL-EWT (R = 0.9218, RMSE = 1.9708 MJ/m2, and KGE = 0.8552 for the Brisbane station) outperformed all other models, followed by RVFL, Bi-LSTM, and Bagging in a hybrid format. With the high predictive robustness, both decomposition-based frameworks can be useful for solving energy forecasting problems. The new modeling approach developed in this study can provide more precise forecasts for decision-makers to better address climate change, agriculture, and energy crises.
MCEENet: Multi-Scale Context Enhancement and Edge-Assisted Network for Few-Shot Semantic Segmentation

Few-shot semantic segmentation has attracted much attention because it requires only a few labeled samples to achieve good segmentation performance. However, existing methods still suffer from insufficient contextual information and unsatisfactory edge segmentation results. To overcome these two issues, this paper proposes a multi-scale context enhancement and edge-assisted network (called MCEENet) for few-shot semantic segmentation. First, rich support and query image features were extracted, respectively, using two weight-shared feature extraction networks, each consisting of a ResNet and a Vision Transformer. Subsequently, a multi-scale context enhancement (MCE) module was proposed to fuse the features of ResNet and Vision Transformer, and further mine the contextual information of the image by using cross-scale feature fusion and multi-scale dilated convolutions. Furthermore, we designed an Edge-Assisted Segmentation (EAS) module, which fuses the shallow ResNet features of the query image and the edge features computed by the Sobel operator to assist in the final segmentation task. We experimented on the PASCAL-5i dataset to demonstrate the effectiveness of MCEENet; the results of the 1-shot setting and 5-shot setting on the PASCAL-5i dataset are 63.5% and 64.7%, which surpasses the state-of-the-art results by 1.4% and 0.6%, respectively.
Fuzzy Cognitive Maps: Their Role in Explainable Artificial Intelligence

Currently, artificial intelligence is facing several problems with its practical implementation in various application domains. The explainability of advanced artificial intelligence algorithms is a topic of paramount importance, and many discussions have been held recently. Pioneering and classical machine learning and deep learning models behave as black boxes, constraining the logical interpretations that the end users desire. Artificial intelligence applications in industry, medicine, agriculture, and social sciences require the users’ trust in the systems. Users are always entitled to know why and how each method has made a decision and which factors play a critical role. Otherwise, they will always be wary of using new techniques. This paper discusses the nature of fuzzy cognitive maps (FCMs), a soft computational method to model human knowledge and provide decisions handling uncertainty. Though FCMs are not new to the field, they are evolving and incorporate recent advancements in artificial intelligence, such as learning algorithms and convolutional neural networks. The nature of FCMs reveals their supremacy in transparency, interpretability, transferability, and other aspects of explainable artificial intelligence (XAI) methods. The present study aims to reveal and defend the explainability properties of FCMs and to highlight their successful implementation in many domains. Subsequently, the present study discusses how FCMs cope with XAI directions and presents critical examples from the literature that demonstrate their superiority. The study results demonstrate that FCMs are both in accordance with the XAI directives and have many successful applications in domains such as medical decision-support systems, precision agriculture, energy savings, environmental monitoring, and policy-making for the public sector.