Reviewerly/exHarmony huggingface dataset:
Deep graph clustering with multi-level subspace fusion

• Graph Convolutional Network is bothered by over-smoothness problem • Over-smoothness may decrease the distinction between dissimilar nodes • Self-expressive learning makes robust representations • The multi-level self-expressive learning captures multi-scaled information • The fusing of structure information from different scales increases distinction between nodes Attributed graph clustering combines both node attributes and graph structure information of data samples and has demonstrated satisfactory performance in various applications. However, how to choose the proper neighborhood for attributed graph clustering remains to be a challenge. A larger neighborhood may cause over-smoothed representations with less discrimination for clustering while the short-range ignore distant nodes and fails to capture the global information. In this paper, we propose a novel deep attributed graph clustering network with a multi-level subspace fusion module to address this issue. The first contribution of our work is to insert multiple self-expressive modules between low-level and high-level layers to promote more favorable features for clustering. The constraint of shared self-expressive matrix facilitates to preserve intrinsic structure without pre-defined neighborhoods as the previous methods do. Moreover, we introduce a novel loss function that leverages traditional reconstruction and the proposed structure fusion loss to effectively preserve multi-level clustering structures with both global and local discriminative features. Extensive experiments on public benchmark datasets validate the effectiveness of our proposed model compared with the state-of-the-art attribute graph clustering competitors by considerable margins.
Hawk‐eye‐inspired perception algorithm of stereo vision for obtaining orchard 3D point cloud navigation map

Abstract The binocular stereo vision is the lowest cost sensor for obtaining 3D information. Considering the weakness of long‐distance measurement and stability, the improvement of accuracy and stability of stereo vision is urgently required for application of precision agriculture. To address the challenges of stereo vision long‐distance measurement and stable perception without hardware upgrade, inspired by hawk eyes, higher resolution perception and the adaptive HDR (High Dynamic Range) were introduced in this paper. Simulating the function from physiological structure of ‘deep fovea’ and ‘shallow fovea’ of hawk eye, the higher resolution reconstruction method in this paper was aimed at accuracy improving. Inspired by adjustment of pupils, the adaptive HDR method was proposed for high dynamic range optimisation and stable perception. In various light conditions, compared with default stereo vision, the accuracy of proposed algorithm was improved by 28.0% evaluated by error ratio, and the stability was improved by 26.56% by disparity accuracy. For fixed distance measurement, the maximum improvement was 78.6% by standard deviation. Based on the hawk‐eye‐inspired perception algorithm, the point cloud of orchard was improved both in quality and quantity. The hawk‐eye‐inspired perception algorithm contributed great advance in binocular 3D point cloud reconstruction in orchard navigation map.
Progressive deep video dehazing without explicit alignment estimation

To solve the issue of video dehazing, there are two main tasks to attain: how to align adjacent frames to the reference frame; how to restore the reference frame. Some papers adopt explicit approaches (e.g., the Markov random field, optical flow, deformable convolution, 3D convolution) to align neighboring frames with the reference frame in feature space or image space, then various restoration methods are used to achieve the final dehazing results. In this paper, we propose a progressive alignment and restoration method for video dehazing, which fuses the process of alignment and restoration and simplifies complexity of network models. The alignment process aligns consecutive neighboring frames stage by stage without using the optical flow estimation or deformable convolution. The restoration process is not only implemented under the alignment process but also uses a refinement network to improve the dehazing performance of the whole network. The proposed networks include four fusion networks and one refinement network. To decrease the parameters of networks, three fusion networks share the same parameters in the first fusion stage. Extensive experiments demonstrate that the proposed video dehazing method achieves outstanding performance against the-state-of-art methods.
Metaheuristics with federated learning enabled intrusion detection system in Internet of Things environment

Abstract Because of increased applications of Internet of Things (IoT) environment in real‐time environment, confidential data gathered by the IoT devices are being communicated to the cloud environment to train the machine learning (ML) models in understanding the patterns that exist in the data. At the same time, the sensitive nature of the IoT data attracts malicious users into hacking efforts. An intrusion detection system (IDS) can be applied to ensure security in the IoT environment. In order to improve security, the ML models can be executed at the data source instead of centralized cloud server. Federated learning (FL) is a recent progression of ML model which enables the ML models to move into the data source rather than moving the data to centralized cloud and thereby resolves cybersecurity problems in the IoT environment. In this view, this study introduces an FL based IDS using bird swarm algorithm based feature selection with classification (FLIDS‐BSAFSC) model in IoT environment. The presented FLIDS‐BSAFSC model undergoes training on multiple aspects of IoT dataset in a decentralized format to classify, detect, and defend against attacks. The proposed FLIDS‐BSAFSC model initially applies min–max normalization technique to pre‐process the IoT data. Besides, BSA based feature selection (BSA‐FS) technique is designed to elect feature subsets. Finally, social group optimization algorithm with kernel extreme learning machine model is employed for identifying various kinds of classes. In the view of FL where the IoT dataset is not distributed to the server carries out profile aggregation competently with the advantage of peer learning. The experimental validation of the FLIDS‐BSAFSC model is tested using benchmark datasets and the results are inspected under several aspects. The experimental values highlighted the better performance of the FLIDS‐BSAFSC model over recent approaches.
Video Object Segmentation using Point-based Memory Network

Recent years have witnessed the prevalence of memory-based methods for Semi-supervised Video Object Segmentation (SVOS) which utilise past frames efficiently for label propagation. When conducting feature matching, fine-grained multi-scale feature matching has typically been performed using all query points, which inevitably results in redundant computations and thus makes the fusion of multi-scale results ineffective. In this paper, we develop a new Point-based Memory Network, termed as PMNet, to perform fine-grained feature matching on hard samples only, assuming that easy samples can already obtain satisfactory matching results without the need for complicated multi-scale feature matching. Our approach first generates an uncertainty map from the initial decoding outputs. Next, the fine-grained features at uncertain locations are sampled to match the memory features on the same scale. Finally, the matching results are further decoded to provide a refined output. The point-based scheme works with the coarsest feature matching in a complementary and efficient manner. Furthermore, we propose an approach to adaptively perform global or regional matching based on the motion history of memory points, making our method more robust against ambiguous backgrounds. Experimental results on several benchmark datasets demonstrate the superiority of our proposed method over state-of-the-art methods.
Fusion of Heterogenous Sensor Data in Border Surveillance

Wide area surveillance has become of critical importance, particularly for border control between countries where vast forested land border areas are to be monitored. In this paper, we address the problem of the automatic detection of activity in forbidden areas, namely forested land border areas. In order to avoid false detections, often triggered in dense vegetation with single sensors such as radar, we present a multi sensor fusion and tracking system using passive infrared detectors in combination with automatic person detection from thermal and visual video camera images. The approach combines weighted maps with a rule engine that associates data from multiple weighted maps. The proposed approach is tested on real data collected by the EU FOLDOUT project in a location representative of a range of forested EU borders. The results show that the proposed approach can eliminate single sensor false detections and enhance accuracy by up to 50%.
FastStamp

Steganography and digital watermarking are the tasks of hiding recoverable data in image pixels. Deep neural network (DNN) based image steganography and watermarking techniques are quickly replacing traditional hand-engineered pipelines. DNN based watermarking techniques have drastically improved the message capacity, imperceptibility and robustness of the embedded watermarks. However, this improvement comes at the cost of increased computational overhead of the watermark encoder neural network. In this work, we design the first accelerator platform FastStamp to perform DNN based steganography and digital watermarking of images on hardware. We first propose a parameter efficient DNN model for embedding recoverable bit-strings in image pixels. Our proposed model can match the success metrics of prior state-of-the-art DNN based watermarking methods while being significantly faster and lighter in terms of memory footprint. We then design an FPGA based accelerator framework to further improve the model throughput and power consumption by leveraging data parallelism and customized computation paths. FastStamp allows embedding hardware signatures into images to establish media authenticity and ownership of digital media. Our best design achieves 68 times faster inference as compared to GPU implementations of prior DNN based watermark encoder while consuming less power.
Visual Comparison of Language Model Adaptation

Neural language models are widely used; however, their model parameters often need to be adapted to the specific domains and tasks of an application, which is time- and resource-consuming. Thus, adapters have recently been introduced as a lightweight alternative for model adaptation. They consist of a small set of task-specific parameters with a reduced training time and simple parameter composition. The simplicity of adapter training and composition comes along with new challenges, such as maintaining an overview of adapter properties and effectively comparing their produced embedding spaces. To help developers overcome these challenges, we provide a twofold contribution. First, in close collaboration with NLP researchers, we conducted a requirement analysis for an approach supporting adapter evaluation and detected, among others, the need for both intrinsic (i.e., embedding similarity-based) and extrinsic (i.e., prediction-based) explanation methods. Second, motivated by the gathered requirements, we designed a flexible visual analytics workspace that enables the comparison of adapter properties. In this paper, we discuss several design iterations and alternatives for interactive, comparative visual explanation methods. Our comparative visualizations show the differences in the adapted embedding vectors and prediction outcomes for diverse human-interpretable concepts (e.g., person names, human qualities). We evaluate our workspace through case studies and show that, for instance, an adapter trained on the language debiasing task according to context-0 (decontextualized) embeddings introduces a new type of bias where words (even gender-independent words such as countries) become more similar to female- than male pronouns. We demonstrate that these are artifacts of context-0 embeddings, and the adapter effectively eliminates the gender information from the contextualized word representations.
Implementation of ASTER data for lithologic and alteration zones mapping: Derhib area, South Eastern Desert, Egypt

Derhib area is dissected by narrow E–W-trending shear zones that delineate the alteration zones accommodating the infamous structurally-controlled talc mineralization in the South Eastern Desert of Egypt. A multidisciplinary remote sensing approach using ASTER images integrated with field data and petrographic investigations have been used for mapping the alteration zones at the Derhib area. Band ratios composites, band ratio, selective Principal Component Analysis (PCA) have been used for discriminating rocks and mineralization zones. Iron oxide/hydroxides-, phyllic and propyllitic-rich alteration zones were mapped using band ratios and selective PCA. Fuzzy logic modeling was implemented to integrate the most informative thematic layers for generating mineral prospectivity map. Based on eigenvector loadings, the map was produced by merging the selected PC thematic layers using fuzzy AND operator. The present study is consistent with the recent literature and shows the capability of fuzzy model as a valuable cost-effective tool to produce mineral potential maps, especially at the reconnaissance stages of mineral exploration.
Transfer Learning for Adenocarcinoma Classifications in the Transurethral Resection of Prostate Whole-Slide Images

The transurethral resection of the prostate (TUR-P) is an option for benign prostatic diseases, especially nodular hyperplasia patients who have moderate to severe urinary problems that have not responded to medication. Importantly, incidental prostate cancer is diagnosed at the time of TUR-P for benign prostatic disease. TUR-P specimens contain a large number of fragmented prostate tissues; this makes them time consuming to examine for pathologists as they have to check each fragment one by one. In this study, we trained deep learning models to classify TUR-P WSIs into prostate adenocarcinoma and benign (non-neoplastic) lesions using transfer and weakly supervised learning. We evaluated the models on TUR-P, needle biopsy, and The Cancer Genome Atlas (TCGA) public dataset test sets, achieving an ROC-AUC up to 0.984 in TUR-P test sets for adenocarcinoma. The results demonstrate the promising potential of deployment in a practical TUR-P histopathological diagnostic workflow system to improve the efficiency of pathologists.
Convolutional neural network-based homogenization for constructing a long-term global surface solar radiation dataset

Rapid development of photovoltaics industry emphasizes the importance of a long-term reliable dataset of surface solar radiation (Rs). However, long-term satellite-retrievals usually suffer from inhomogeneity due to the inhomogeneous input data from different satellites. Atmospheric reanalysis Rs data have stable quality but relatively lower accuracy. In this study, a convolutional neural network-based homogenization method is developed to use a reanalysis dataset (ERA5) as a bridge to homogenize a 36-year (1983–2018) satellite-based Rs dataset (so-called ISCCP-ITP) with 10-km spatial resolution and 3-hr interval, which has high accuracy after 2000 (monthly RMSE of 16.9 W/m2 and MBE of 0.2 W/m2) but relatively lower accuracy before 2000 (monthly RMSE of 18.2 W/m2 and MBE of 7.2 W/m2). The method is based on U-Net algorithm, which contains encoder, decoder and skipped connection parts. Monthly Rs of ISCCP-ITP and ERA5 after 2000 are used as label data and input data for the training, respectively, and monthly Rs of ERA5 before 2000 is fed into the trained model to generate homogenization results. After homogenization, the RMSE and MBE of the monthly ISCCP-ITP data are reduced to 15.8 W/m2 and -1.6 W/m2, respectively, before 2000, which are comparable to those after 2000, making the accuracy of the new dataset stable throughout the whole period. Moreover, the variations and trends of Rs derived from the new data are consistent with in-situ observations at global and regional scales. Therefore, the homogenization can contribute to the re-construction of a high-quality long-term Rs dataset suitable for photovoltaics industry.
End-to-End Optimized 360° Image Compression

The 360° image that offers a 360-degree scenario of the world is widely used in virtual reality and has drawn increasing attention. In 360° image compression, the spherical image is first transformed into a planar image with a projection such as equirectangular projection (ERP) and then saved with the existing codecs. The ERP images that represent different circles of latitude with the same number of pixels suffer from the unbalance sampling problem, resulting in inefficiency using planar compression methods, especially for the deep neural network (DNN) based codecs. To tackle this problem, we introduce a latitude adaptive coding scheme for DNNs by allocating variant numbers of codes for different regions according to the latitude on the sphere. Specifically, taking both the number of allocated codes for each region and their entropy into consideration, we introduce a flexible regional adaptive rate loss for region-wise rate controlling. Latitude adaptive constraints are then introduced to prevent spending too many codes on the over-sampling regions. Furthermore, we introduce viewport-based distortion loss by calculating the average distortion on a set of viewports. We optimize and test our model on a large 360° dataset containing 19,790 images collected from the Internet. The experiment results demonstrate the superiority of the proposed latitude adaptive coding scheme. On the whole, our model outperforms the existing image compression standards, including JPEG, JPEG2000, HEVC Intra Coding, and VVC Intra Coding, and helps to save around 15% bits compared to the baseline learned image compression model for planar images.
A Proposed Model Expert System for Disease Diagnosis in Children to Make Decisions in First Aid

Children have a weaker immune system than adults. They are susceptible to disease. Therefore, this study proposes an expert system model of disease diagnosis in children. We develop expert strategies to meet the needs of alternative diagnostic tools in making decisions and first aid for children suffering from illness. The development of an expert system model for diagnosing children's diseases using forwarding chaining based on If-Then as an inference engine. We chose the forward chaining method because it has a framework for thinking like a doctor's when diagnosing and concluding the disease. We made testing to model by doctors with 35 patients. The test results show that the expert systems model of disease diagnosis in children in this research has to be used as an alternative or comparison diagnostic tool with an accuracy rate of 79%.
Activity recognition via correlation coefficients based graph with nodes updated by multi-aggregator approach

This paper proposes a graph theory approach to perform the human activity recognition. However, as the most common signal employed for performing the activity recognitions is the motion signal while the motion signal is well structured, ordered and independent one another, the graph theory cannot be applied directly. To address this issue, this paper proposes the correlation coefficient based method for generating the graph using the signals in the UCI-HAR dataset. Here, the predefined thresholds are used for determining whether the nodes are connected or not. The features are updated according to the activities via the multi-aggregation fusion approach. Finally, the random forest is used to classify these activities. To demonstrate the effectiveness of our proposed method, the percentage accuracy and the macro averaged F1 score yielded by our proposed method with the graph weights are compared to those without the graph weights as well as with the multi-aggregator are compared with the mean aggregator. Also, our proposed method is compared to some common methods such as those based on the CNN and SVM. It is found that our proposed method can achieve the percentage accuracy up to 98.74%, which significantly outperforms the existing methods.
Detect deception on banking credit card payment system by machine learning classifiers

Credit cards are now being targeted by fraudsters in a variety of ways. Despite the fact that it is never pleasant, this is something that happens to someone in our everyday life. When cardholders disclose their credit card information to others, this happens. In this work, we have constructed a few machines learning (ML) models using anonymous credit card transaction data. The issue in detecting fraud is that it occurs far less frequently than legal transactions. The purpose of this research is to accurately predict fraud transactions. To detect fraud from a vast unbalanced dataset, we used nine different classifiers (Ridge Classifiers, Stochastic Gradient Descent (SGD), Linear discriminant analysis (LDA), Random Forest, Naive Bayes, Support Vector Machine (SVM), Decision Tree, Logistic Regression, and k-Nearest Neighbors (k-NN)). In addition, various classifiers were compared to ROC binary classifications. We have shown which classifiers has the best accuracy.
Evaluating students’ behavioral intention and system usability of augmented reality-aided distance design learning during the COVID-19 pandemic

Because of the impact of the COVID-19 pandemic, learning modes had to change. This study applied augmented reality (AR) technology to improve the learning experience of product styling design students in a distance learning environment. An AR-aided learning interface combined with a distance learning concept was constructed to enhance students' content knowledge and communication between teachers and students. This study analyzed students' attitudes and behavioral intentions toward the technology acceptance model for this AR-aided distance learning interface. Additionally, a system usability scale was used to examine the usability level of this interface. The results demonstrated that learning content directly affected distance interaction, and perceived ease of use was an essential mediating variable. Furthermore, three-dimensional (3D) visualization was a predictive factor that directly affected students' attitudes toward AR use. Students had positive attitudes toward the AR-aided learning interface, and their feedback indicated that 3D visualization stimulated their learning motivation and enhanced their behavioral intentions. Overall, the AR-aided styling learning interface supported the distance learning of product styling design students and provided teachers and students with a valuable learning communication channel during the COVID-19 pandemic.