Reviewerly/exHarmony huggingface dataset:
Selection of ICT tools for the development of high school students' research competencies in specialized chemistry training

The article analyzed the place of information and communication technologies and learning tools in specialized chemistry training. The development of research competencies of high school students as one of the important results of specialized training in chemistry is considered. The analysis and systematization of ICT tools to ensure compliance with the principles of specialized chemistry training is made. The choice of ICT tools for the implementation of specialized chemistry training and development of high school students' research competencies in specialized chemistry training had substantiated. The results of research aimed at determining the feasibility of using certain ICT tools to develop of high school students' research competencies in specialized chemistry training are presented.
Lightweight convolutional neural network for aircraft small target real-time detection in Airport videos in complex scenes

Airport aircraft identification has essential application value in conflict early warning, anti-runway foreign body intrusion, remote command, etc. The scene video images have problems such as small aircraft targets and mutual occlusion due to the extended shooting distance. However, the detection model is generally complex in structure, and it is challenging to meet real-time detection in air traffic control. This paper proposes a real-time detection network of scene video aircraft-RPD (Realtime Planes Detection) to solve this problem. We construct the lightweight convolution backbone network RPDNet4 for feature extraction. We design a new core component CBL module(Conv (Convolution), BN (Batch Normalization), RELU (Rectified Linear Units)) to expand the range of receptive fields in the neural network. We design a lightweight channel adjustment module block by adding separable depth convolution to reduce the model's structural parameters. The loss function of GIou loss improves the convergence speed of network training. the paper designs the four-scale prediction module and the adjacent scale feature fusion technology to fuse the adjacent features of different abstract levels. Furthermore, a feature pyramid structure with low-level to high-level is constructed to improve the accuracy of airport aircraft's small target detection. The experimental results show that compared with YOLOv3, Faster-RCNN, and SSD models, the detection accuracy of the RPD model improved by 5.4%, 7.1%, and 23.6%; in terms of model parameters, the RPD model was reduced by 40.5%, 33.7%, and 80.2%; In terms of detection speed, YOLOv3 is 8.4 fps while RPD model reaches 13.6 fps which is 61.9% faster than YOLOv3.
Intelligent Hybrid Path Planning Algorithms for Autonomous Mobile Robots

The primary goal of a mobile robot is to reach the desired goal by traversing an optimized path defined according to some criteria such as time, distance, and safety of the robot from any obstacles that may be in its way.Therefore, the backbone of autonomous mobile robots (AMRs) is path planning and avoiding obstacles.Many algorithms for path planning and obstacle avoidance have been presented by many researchers and each of these algorithms has several benefits and drawbacks.This paper focuses on comparing the performance of several metaheuristic algorithms that result in a more efficient, smoother, and shorter path for the mobile robot to reach the target in a complex environment.These algorithms include particle swarm optimization (PSO), chaotic particle swarm optimization (CPSO), modified chaotic particle swarm optimization (MCPSO), and firefly algorithm (FA).On the other hand, the paper proposes a hybrid algorithm by combining the FA and the MCPSO, namely the (HFAMCPSO).To demonstrate the effectiveness of the proposed algorithm in terms of the optimum cost function and obtaining the shortest path length, the optimal solution is compared to those of other path planning algorithms.Moreover, inverse dynamic and kinematic modeling are utilized to obtain the best torque and the best velocity actions for the wheels of the autonomous mobile robot.The proposed hybrid (FAMCPSO) algorithm provides enhancement on the path length equals (43.3%) and (25.5%) compared to the radial cell decomposition (RCD) and the A* algorithm, respectively.Moreover, the enhancement on the path length equals (2.3%) and (22.7%) compared to the firefly algorithm (FA) and the genetic algorithm (GA), respectively.All methods are simulated in an environment with static obstacles using the 2018b MATLAB package.
Comprehensive identification method of bird’s nest on transmission line

The automatic identification of bird’s nest in the inspection image of transmission line is of great significance to the safe operation of transmission line. In this paper, a bird’s nest recognition method which combines visual saliency and depth learning is proposed. This method not only has the advantage of rich feature information of visible light image, but also has the advantage of significant bird’s nest target. The experimental results show that this method can accurately identify the images with different background, tower shape, shooting angle and shooting distance, and has good robustness and generalization, and the precision index values of Precision, Recall and IoU are 0.9622, 0.9465 and 0.9543 respectively. Compared with Faster R-CNN model, YOLO model and RetinaNet model, each index is greatly improved. This method is instructive to the operation and maintenance of transmission lines.
FlexHDR: Modeling Alignment and Exposure Uncertainties for Flexible HDR Imaging

High dynamic range (HDR) imaging is of fundamental importance in modern digital photography pipelines and used to produce a high-quality photograph with well exposed regions despite varying illumination across the image. This is typically achieved by merging multiple low dynamic range (LDR) images taken at different exposures. However, over-exposed regions and misalignment errors due to poorly compensated motion result in artefacts such as ghosting. In this paper, we present a new HDR imaging technique that specifically models alignment and exposure uncertainties to produce high quality HDR results. We introduce a strategy that learns to jointly align and assess the alignment and exposure reliability using an HDR-aware, uncertainty-driven attention map that robustly merges the frames into a single high quality HDR image. Further, we introduce a progressive, multi-stage image fusion approach that can flexibly merge any number of LDR images in a permutation-invariant manner. Experimental results show our method can produce better quality HDR images with up to 1.1dB PSNR improvement to the state-of-the-art, and subjective improvements in terms of better detail, colours, and fewer artefacts.
PSNet: Parallel symmetric network for RGB-T salient object detection

RGB-Thermal (RGB-T) salient object detection (SOD) aims to utilize RGB and thermal infrared data to discover the most salient object(s) in an image. Although SOD has achieved great progress, few efforts are devoted to RGB-T SOD. For this task, on the one hand, it is worth paying attention to how to aggregate critical saliency cues from both modalities to enhance salient feature representation and thus produce accurate salient object detection results. On the other hand, effective multi-level feature fusion remains a big challenge. To overcome the above mentioned issues, this paper proposes a Parallel Symmetric Network (PSNet) for RGB-T salient object detection. Specifically, we first develop a cascaded aggregation module (CAM), which fully accumulates and excavates the valuable saliency semantics from two different modalities to strengthen feature representation by cascading the designed residual-based enhancement unit. Then, we design a parallel-symmetric fusion (PSF) module to integrate crucial saliency cues from adjacent layers for saliency prediction in a parallel and symmetric manner. Besides, to make full use of multi-level features, we introduce a guidance strategy that enhances the details of the saliency map with the low-level features to boost the performance of salient object detection. Extensive experiments show that our proposed model significantly outperforms the existing fifteen state-of-the-art models on three challenging benchmark datasets. Moreover, the superior performance on RGB-D SOD also demonstrates the generalization and robustness of the proposed method. The source code will be made publicly available soon.
A split–merge clustering algorithm based on the k-nearest neighbor graph

Numerous graph-based clustering algorithms relying on k-nearest neighbor (KNN) have been proposed. However, the performance of these algorithms tends to be affected by many factors such as cluster shape, cluster density and outliers. To address these issues, we present a split–merge clustering algorithm based on the KNN graph (SMKNN), which is based on the idea that two adjacent clusters can be merged if the data points located in the connection layers of the two clusters tend to be consistent in distribution. In Stage 1, a KNN graph is constructed. In Stage 2, the subgraphs are obtained by removing the pivot points from the KNN graph, in which the pivot points are determined by the size of local distance ratio of data points. In Stage 3, the adjacent cluster pairs satisfying the maximum similarity are merged, in which the similarity measure of two clusters is designed with two concepts including external connection edges and internal connection edges. By the experiments on ten synthetic data sets and eight real data sets, we compared SMKNN with two traditional algorithms, two density-based algorithms, nine graph-based algorithms and four neural network based algorithms in accuracy. The experimental results demonstrate a good performance of the proposed clustering method.
Multi-modal Hypergraph Diffusion Network with Dual Prior for Alzheimer Classification

The automatic early diagnosis of prodromal stages of Alzheimer’s disease is of great relevance for patient treatment to improve quality of life. We address this problem as a multi-modal classification task. Multi-modal data provides richer and complementary information. However, existing techniques only consider lower order relations between the data and single/multi-modal imaging data. In this work, we introduce a novel semi-supervised hypergraph learning framework for Alzheimer’s disease diagnosis. Our framework allows for higher-order relations among multi-modal imaging and non-imaging data whilst requiring a tiny labelled set. Firstly, we introduce a dual embedding strategy for constructing a robust hypergraph that preserves the data semantics. We achieve this by enforcing perturbation invariance at the image and graph levels using a contrastive based mechanism. Secondly, we present a dynamically adjusted hypergraph diffusion model, via a semi-explicit flow, to improve the predictive uncertainty. We demonstrate, through our experiments, that our framework is able to outperform current techniques for Alzheimer’s disease diagnosis.
Participatory Learning and Co-Design for Sustainable Rural Living, Supporting the Revival of Indigenous Values and Community Resiliency in Sabrang Village, Indonesia

Industrialization and urbanization have affected Indonesia’s rural communities and farming culture, which were once integral parts of its ecological system. This paper presents a participatory co-design approach based on the local and traditional learning philosophy of niteni to support sustainable development. The participatory co-design approach encouraged collaboration between marginalized communities, government bodies, and a multidisciplinary academic team. Through this lens, interviews, forums, and an ethnographic study were undertaken in order to acquire data and information for idea generation and planning. Firstly, eight niteni themes were identified, including the environment and ecosystems, traditional values and farming culture, crafting skills, manufacturing, and the local economy. Building on an understanding of the challenges associated with each of these themes, we identified future development priorities. A key action identified was the reintroduction of traditional farming, in particular the planting of local rice varieties and the local tradition of Pranatamangsa, which promote human–nature connections such as farming activities and rituals following natural seasonal cycles. Finally, design approaches were used to revive the local rice farming tradition (Rojolele Delanggu), including product branding and packaging designs to support regional identity. The paper concludes that the inclusion of design thinking in a sustainable development strategy based on cultural specificity can increase participation and support traditional indigenous practices and community resiliency.
Domain-Incremental Cardiac Image Segmentation With Style-Oriented Replay and Domain-Sensitive Feature Whitening

Contemporary methods have shown promising results on cardiac image segmentation, but merely in static learning, i.e., optimizing the network once for all, ignoring potential needs for model updating. In real-world scenarios, new data continues to be gathered from multiple institutions over time and new demands keep growing to pursue more satisfying performance. The desired model should incrementally learn from each incoming dataset and progressively update with improved functionality as time goes by. As the datasets sequentially delivered from multiple sites are normally heterogenous with domain discrepancy, each updated model should not catastrophically forget previously learned domains while well generalizing to currently arrived domains or even unseen domains. In medical scenarios, this is particularly challenging as accessing or storing past data is commonly not allowed due to data privacy. To this end, we propose a novel domain-incremental learning framework to recover past domain inputs first and then regularly replay them during model optimization. Particularly, we first present a style-oriented replay module to enable structure-realistic and memory-efficient reproduction of past data, and then incorporate the replayed past data to jointly optimize the model with current data to alleviate catastrophic forgetting. During optimization, we additionally perform domain-sensitive feature whitening to suppress model’s dependency on features that are sensitive to domain changes (e.g., domain-distinctive style features) to assist domain-invariant feature exploration and gradually improve the generalization performance of the network. We have extensively evaluated our approach with the M&Ms Dataset in single-domain and compound-domain incremental learning settings. Our approach outperforms other comparison methods with less forgetting on past domains and better generalization on current domains and unseen domains.
An edge feature aware heterogeneous graph neural network model to support tax evasion detection

Tax evasion is an illegal activity that causes severe losses of government revenues and disturbs the economic order. To alleviate this problem, decision support systems that enable tax authorities to detect tax evasion efficiently have been proposed. Recent researches tend to use graph to model the tax scenario and leverage graph mining techniques to conduct tax evasion detection, as so to make full use of the rich interactive information between taxpayers and improve the detection performance. However, a more favorable graph mining solution, graph neural networks, has not yet been thoroughly investigated in such settings, leaving space for further improvement. Therefore, in this paper, we propose a novel graph neural network model, named Eagle, to detect tax evasion under the heterogeneous graph. Specifically, based on the guidance of our designed metapaths, Eagle can extract more comprehensive features through a hierarchical attention mechanism that fully aggregates taxpayers’ features with their relations. We evaluate Eagle on real-world tax dataset. The extensive experimental results show that our model performs 15.71% better than state-of-the-art tax evasion detection methods in the classification scenario, while improves 5.22% in the anomaly detection scenario.
Graph Convolutional Networks with POS Gate for Aspect-Based Sentiment Analysis

We make daily comments on online platforms (e.g., social networks), and such natural language texts often contain sentiment (e.g., positive and negative) for certain aspects (e.g., food and service). If we can automatically extract the aspect-based sentiment from the texts, then it will help many services or products to overcome their limitations of particular aspects. There have been studies of aspect sentiment classification (ASC) that finds sentiment towards particular aspects. Recent studies mostly adopt deep-learning models or graph neural networks as these techniques are capable of capturing linguistic patterns that contributed to performance improvement in various natural language processing tasks. In this paper, for the ASC task, we propose a new hybrid architecture of graph convolutional network (GCN) and recurrent neural network. We design a gate mechanism that jointly models word embeddings and syntactic representation of sentences. By experimental results on five datasets, we show that the proposed model outperforms other recent models and also verify that the gate mechanism contributes to the performance improvement. The overall F1 scores that we achieved is 66.64∼76.80%.
Hierarchical Few-Shot Object Detection

Few-shot object detection (FSOD) is to detect objects with a few examples. However, existing FSOD methods do not consider hierarchical fine-grained category structures of objects that exist widely in real life. For example, animals are taxonomically classified into orders, families, genera and species etc. In this paper, we propose and solve a new problem called hierarchical few-shot object detection (Hi-FSOD), which aims to detect objects with hierarchical categories in the FSOD paradigm. To this end, on the one hand, we build the first large-scale and high-quality Hi-FSOD benchmark dataset HiFSOD-Bird, which contains 176,350 wild-bird images falling to 1,432 categories. All the categories are organized into a 4-level taxonomy, consisting of 32 orders, 132 families, 572 genera and 1,432 species. On the other hand, we propose the first Hi-FSOD method HiCLPL, where a hierarchical contrastive learning approach is developed to constrain the feature space so that the feature distribution of objects is consistent with the hierarchical taxonomy and the model's generalization power is strengthened. Meanwhile, a probabilistic loss is designed to enable the child nodes to correct the classification errors of their parent nodes in the taxonomy. Extensive experiments on the benchmark dataset HiFSOD-Bird show that our method HiCLPL outperforms the existing FSOD methods.
CADV: A software visualization approach for code annotations distribution

Code annotations is a widely used feature in Java systems to configure custom metadata on programming elements. Their increasing presence creates the need for approaches to assess and comprehend their usage and distribution. In this context, software visualization has been studied and researched to improve program comprehension in different aspects. This study aimed at designing a software visualization approach that graphically displays how code annotations are distributed and organized in a software system and developing a tool, as a reference implementation of the approach, to generate views and interact with users. We conducted an empirical evaluation through questionnaires and interviews to evaluate our visualization approach considering four aspects: effectiveness for program comprehension, perceived usefulness, perceived ease of use, and suitability for the intended audience. The resulting data was used to perform a qualitative and quantitative analysis. The tool identifies package responsibilities providing visual information about their annotations at different levels. Using the developed tool, the participants achieved a high correctness rate in the program comprehension tasks and performed very well in questions about the overview of the system under analysis. Finally, participants perceived that the tool outperforms existing approaches for code inspection when searching for information related to code annotations. The results show that the visualization approach using the developed tool is effective in program comprehension tasks related to code annotations, which can also be used to identify responsibilities in the application packages. Moreover, it was evaluated as suitable for newcomers to overview the usage of annotations in the system and for architects to perform a deep analysis that can potentially detect misplaced annotations and abnormal growths on their usage.
CorDeep and the Sacrobosco Dataset: Detection of Visual Elements in Historical Documents

Recent advances in object detection facilitated by deep learning have led to numerous solutions in a myriad of fields ranging from medical diagnosis to autonomous driving. However, historical research is yet to reap the benefits of such advances. This is generally due to the low number of large, coherent, and annotated datasets of historical documents, as well as the overwhelming focus on Optical Character Recognition to support the analysis of historical documents. In this paper, we highlight the importance of visual elements, in particular illustrations in historical documents, and offer a public multi-class historical visual element dataset based on the Sphaera corpus. Additionally, we train an image extraction model based on YOLO architecture and publish it through a publicly available web-service to detect and extract multi-class images from historical documents in an effort to bridge the gap between traditional and computational approaches in historical studies.
PyCIL: a Python toolbox for class-incremental learning

We have presented PyCIL, a classincremental learning toolbox written in Python. It contains implementations of a number of founding studies of CIL, but also provides current state-of-the-art algorithms that can be used to conduct novel fundamental research. Code consistency makes it an easy tool for research purposes, teaching, and industrial applications.
A fast in-place interpreter for WebAssembly

WebAssembly (Wasm) is a compact, well-specified bytecode format that offers a portable compilation target with near-native execution speed. The bytecode format was specifically designed to be fast to parse, validate, and compile, positioning itself as a portable alternative to native code. It was pointedly not designed to be interpreted directly. Instead, design considerations at the time focused on competing with native code, utilizing optimizing compilers as the primary execution tier. Yet, in JIT scenarios, compilation time and memory consumption critically impact application startup, leading many Wasm engines to later deploy faster single-pass (baseline) compilers. Though faster, baseline compilers still take time and waste code space for infrequently executed code. A typical interpreter being infeasible, some engines resort to compiling Wasm not to machine code, but to a more compact, but easy to interpret format. This still takes time and wastes memory. Instead, we introduce in this article a fast in-place interpreter for WebAssembly, where no rewrite and no separate format is necessary. Our evaluation shows that in-place interpretation of Wasm code is space-efficient and fast, achieving performance on-par with interpreting a custom-designed internal format. This fills a hole in the execution tier space for Wasm, allowing for even faster startup and lower memory footprint than previous engine configurations.