Reviewerly/exHarmony huggingface dataset:
Weakly Supervised Building Semantic Segmentation Based on Spot-Seeds and Refinement Process

Automatic building semantic segmentation is the most critical and relevant task in several geospatial applications. Methods based on convolutional neural networks (CNNs) are mainly used in current building segmentation. The requirement of huge pixel-level labels is a significant obstacle to achieve the semantic segmentation of building by CNNs. In this paper, we propose a novel weakly supervised framework for building segmentation, which generates high-quality pixel-level annotations and optimizes the segmentation network. A superpixel segmentation algorithm can predict a boundary map for training images. Then, Superpixels-CRF built on the superpixel regions is guided by spot seeds to propagate information from spot seeds to unlabeled regions, resulting in high-quality pixel-level annotations. Using these high-quality pixel-level annotations, we can train a more robust segmentation network and predict segmentation maps. To iteratively optimize the segmentation network, the predicted segmentation maps are refined, and the segmentation network are retrained. Comparative experiments demonstrate that the proposed segmentation framework achieves a marked improvement in the building's segmentation quality while reducing human labeling efforts.
A Deep Learning Approach for the Morphological Recognition of Reactive Lymphocytes in Patients with COVID-19 Infection

Laboratory medicine plays a fundamental role in the detection, diagnosis and management of COVID-19 infection. Recent observations of the morphology of cells circulating in blood found the presence of particular reactive lymphocytes (COVID-19 RL) in some of the infected patients and demonstrated that it was an indicator of a better prognosis of the disease. Visual morphological analysis is time consuming, requires smear review by expert clinical pathologists, and is prone to subjectivity. This paper presents a convolutional neural network system designed for automatic recognition of COVID-19 RL. It is based on the Xception71 structure and is trained using images of blood cells from real infected patients. An experimental study is carried out with a group of 92 individuals. The input for the system is a set of images selected by the clinical pathologist from the blood smear of a patient. The output is the prediction whether the patient belongs to the group associated with better prognosis of the disease. A threshold is obtained for the classification system to predict that the smear belongs to this group. With this threshold, the experimental test shows excellent performance metrics: 98.3% sensitivity and precision, 97.1% specificity, and 97.8% accuracy. The system does not require costly calculations and can potentially be integrated into clinical practice to assist clinical pathologists in a more objective smear review for early prognosis.
Comparison of Hyperledger Fabric and Ethereum Blockchain

Blockchain technology has received wide attention from researchers and scientists. Blockchain technology is also upgrading all the time. When the original Bitcoin was created, it laid the groundwork for the emergence of Ethereum. Now new federation chains like Hyperledger Fabric are growing fast. Ethereum and fabric are the two representatives. Permissioned and private blockchain platforms are increasingly used in today&#x2019;s industry. Different blockchain implementations use different consensus protocols. This article briefly compares Hyperledger Fabric and Ethereum from several aspects such as consensus and smart contacts.
Camouflaged object detection via Neighbor Connection and Hierarchical Information Transfer

Camouflaged Object Detection (COD) aims to detect objects with high similarity to the background. Unlike general object detection, COD is a more challenging task because the target boundaries are vague and the location is difficult to determine. In this paper, we propose a novel COD framework, which consists of two main components, namely, Neighbor Connection Mode (NCM) and Hierarchical Information Transfer (HIT). NCM aggregates the features from the neighboring layers of the encoder network to enhance the complementation of various level information. Our NCM not only reduces the burden of dense connection that consumes a lot of computing memory and redundant features but also weakens the phenomenon of the long-term transmission of context. We also propose a HIT module to transfer the features of different dilated rates inside each level hierarchically, which expands the receptive field of each branch and enhances the relationship between different features. Our method accurately detects camouflaged objects by considering full level information and a large receptive field. The experiments on three COD datasets show that our model achieves state-of-the-art performance.
Correlating Automated and Human Evaluation of Code Documentation Generation Quality

Automatic code documentation generation has been a crucial task in the field of software engineering. It not only relieves developers from writing code documentation but also helps them to understand programs better. Specifically, deep-learning-based techniques that leverage large-scale source code corpora have been widely used in code documentation generation. These works tend to use automatic metrics (such as BLEU, METEOR, ROUGE, CIDEr, and SPICE) to evaluate different models. These metrics compare generated documentation to reference texts by measuring the overlapping words. Unfortunately, there is no evidence demonstrating the correlation between these metrics and human judgment. We conduct experiments on two popular code documentation generation tasks, code comment generation and commit message generation, to investigate the presence or absence of correlations between these metrics and human judgments. For each task, we replicate three state-of-the-art approaches and the generated documentation is evaluated automatically in terms of BLEU, METEOR, ROUGE-L, CIDEr, and SPICE. We also ask 24 participants to rate the generated documentation considering three aspects (i.e., language, content, and effectiveness). Each participant is given Java methods or commit diffs along with the target documentation to be rated. The results show that the ranking of generated documentation from automatic metrics is different from that evaluated by human annotators. Thus, these automatic metrics are not reliable enough to replace human evaluation for code documentation generation tasks. In addition, METEOR shows the strongest correlation (with moderate Pearson correlation r about 0.7) to human evaluation metrics. However, it is still much lower than the correlation observed between different annotators (with a high Pearson correlation r about 0.8) and correlations that are reported in the literature for other tasks (e.g., Neural Machine Translation [ 39 ]). Our study points to the need to develop specialized automated evaluation metrics that can correlate more closely to human evaluation metrics for code generation tasks.
Multivariate Analysis of Geological Data for Regional Studies of Geodiversity

In Norway, a landscape map exists which gives the opportunity to collect data in landscape units for further analysis. This study covers parts of Norway and Sweden, so the landscape map was extended into Sweden to form a unified landscape structure. A collection of geological and terrain data within landscape units can serve as a tool to describe the geodiversity profile of the units, study their geographical distribution and analyse similarities and dissimilarities between them. We collected geological and terrain data for an area covering large parts of southern Norway and Sweden. The data were collected as attributes in landscape polygons. The data were then analysed using multivariate techniques (Principle component analyses) where the first four axes of variation were definable. The first axis is a terrain axis, the second a bedrock unit axis, the third a bedrock diversity axis and the fourth a soil/sediment axis. In total, the four gradients answer for 54.9% of the total variation in the material. Links are found between the terrain data and geological units, but these links are relatively weak. The four gradients represent a step-less model of the geodiversity profile of the landscape areas but are dependent on the quality and scale of the input data. Norwegian and Swedish data had to be harmonised in order to be analysed together; however, because of this they ended up having a coarser resolution than desired, both spatially and scientifically. The stepless model was clustered to form 16 geodiversity profile groups for easy comparison and regional overview. The procedure can serve as a baseline for more detailed and field-based studies of geodiversity profiles, and give the opportunity to make analytical maps through simple overlay techniques and to compare areas with each other with respect to their geodiversity profile. This can be carried out both alone and in a wider landscape setting.
Robot Crawler for Surveying Pipelines and Metal Structures of Complex Spatial Configuration

There is an obvious tendency towards increasing the information content of surveys of hard-to-reach objects at high altitudes through the use of remote-controlled robot crawlers. This can be explained by the reasonable desire of industrial objects owners to maintain their property: pipelines, containers, metal structures in operating technical condition, which contributes to reducing accident risks and increasing the economic efficiency of operation (optimization of repair planning, etc.) This paper presents the concept of a robotic device equipped with LIDAR and EMAT which can move over pipes from a diameter of 100 mm by using a special type of magnetic wheel. The robot uses convolutional neural networks to detect structural elements and classify their defects. The article contains information about tests held on a specially developed test rig. The results showed that the device could increase the information level of survey and reduce the labour intensity. In this work, we consider a prototype of the device which has not started mass operation at industrial facilities yet.
Complete coverage path planning and performance factor analysis for autonomous bulldozer

Abstract With the development of intelligent machinery, path planning of autonomous bulldozers plays an important role to solve the problems of efficiency in future construction sites. How to plan the path that meets the construction requirements is the key problem. This study investigates the standardized construction workmanship and moving rules of the bulldozer to add rules and path selection strategies to the algorithm. This study establishes the path planning model of bulldozer's leveling, considering the limitations of the traditional bioinspired neural network algorithm, an improved complete coverage path planning method is proposed. An integrated framework is proposed for an automatic bulldozer with a sensor system for a field experiment to verify the feasibility of the method. According to the comparative statistical simulation results, the effectiveness of the method is verified, and the effects of different factors on the path planning results are analyzed.
A Two-Step Approach for Classification in Alzheimer’s Disease

The classification of images is of high importance in medicine. In this sense, Deep learning methodologies show excellent performance with regard to accuracy. The drawback of these methodologies is the fact that they are black boxes, so no explanation is given to users on the reasons underlying their choices. In the medical domain, this lack of transparency and information, typical of black box models, brings practitioners to raise concerns, and the result is a resistance to the use of deep learning tools. In order to overcome this problem, a different Machine Learning approach to image classification is used here that is based on interpretability concepts thanks to the use of an evolutionary algorithm. It relies on the application of two steps in succession. The first receives a set of images in the inut and performs image filtering on them so that a numerical data set is generated. The second is a classifier, the kernel of which is an evolutionary algorithm. This latter, at the same time, classifies and automatically extracts explicit knowledge as a set of IF-THEN rules. This method is investigated with respect to a data set of MRI brain imagery referring to Alzheimer's disease. Namely, a two-class data set (non-demented and moderate demented) and a three-class data set (non-demented, mild demented, and moderate demented) are extracted. The methodology shows good results in terms of accuracy (100% for the best run over the two-class problem and 91.49% for the best run over the three-class one), F_score (1.0000 and 0.9149, respectively), and Matthews Correlation Coefficient (1.0000 and 0.8763, respectively). To ascertain the quality of these results, they are contrasted against those from a wide set of well-known classifiers. The outcome of this comparison is that, in both problems, the methodology achieves the best results in terms of accuracy and F_score, whereas, for the Matthews Correlation Coefficient, it has the best result over the two-class problem and the second over the three-class one.
Multi-Label Classification and Explanation Methods for Students’ Learning Style Prediction and Interpretation

The current paper attempts to describe the methodology guiding researchers on how to use a combination of machine learning methods and cognitive-behavioral approaches to realize the automatic prediction of a learner’s preferences for the various types of learning objects and learning activities that may be offered in an adaptive learning environment. Generative as well as discriminative machine learning methods may be applied to the classification of students’ learning styles, based on the student’s historical activities in the e-learning process. This paper focuses on the discriminative models that try to learn which input activities of the student(s) will correlate with a particular learning style, discriminating among the inputs. This paper also investigates several interpretability approaches that may be applicable for the multi-label models trained on non-correlated and partially correlated data. The investigated methods and approaches are combined in a consistent procedure that can be used in practical learning personalization.
Active learning of driving scenario trajectories

Annotated driving scenario trajectories are crucial for verification and validation of autonomous vehicles. However, annotation of such trajectories based only on explicit rules (i.e. knowledge-based methods) may be prone to errors, such as false positive/negative classification of scenarios that lie on the border of two scenario classes, missing unknown scenario classes, or even failing to detect anomalies. On the other hand, verification of labels by annotators is not cost-efficient. For this purpose, active learning (AL) could potentially improve the annotation procedure by including an annotator/expert in an efficient way. In this study, we develop a generic active learning framework to annotate driving trajectory time series data. We first compute an embedding of the trajectories into a latent space in order to extract the temporal nature of the data. Given such an embedding, the framework becomes task agnostic since active learning can be performed using any classification method and any query strategy, regardless of the structure of the original time series data. Furthermore, we utilize our active learning framework to discover unknown driving scenario trajectories. This will ensure that previously unknown trajectory types can be effectively detected and included in the labeled dataset. We evaluate our proposed framework in different settings on novel real-world datasets consisting of driving trajectories collected by Volvo Cars Corporation. We observe that active learning constitutes an effective tool for labelling driving trajectories as well as for detecting unknown classes. Expectedly, the quality of the embedding plays an important role in the success of the proposed framework.
Sequence to sequence learning for joint extraction of entities and relations

The relations of two entities contained in the sentence are often complicated. There exists multiple relation tuples which owns one or both the same entities among them. Extracting those overlapped multiple relation tuples faces great challenges. Most existing works employ the famous Seq2Seq model for joint extraction of entities and relations, which cannot handle the multiple relations properly due to the missing of graph structure information of the relations and entities in a sentence. In this paper, we propose Seq2Seq-RE, an end-to-end relation extraction model, which first utilizes the gate graph neural networks (GGNNs) for joint extraction of entities and relations. Unlike previous works, we take the interaction of entities and relations through a GGNNs-based sequence-to-sequence with attention mechanism for better extracting multiple relation tuples. Besides, our graph structure based on the forward-edge, backward-edge, self-edge, and dependency-edge. To our knowledge, we are the first to employ four types of edge into graph-based neural networks for joint extraction of entities and relations and conduct a comprehensive qualitative analysis to investigate what types of edges benefit our task. Experimental results show that our model surpasses the current strong baseline methods by 1.7% in NYT29 and 0.8% in NYT24 (F1 score).
QAOA of the Highest Order

The Quantum Approximate Optimization Algorithm (QAOA) has been one of the leading candidates for near-term quantum advantage in gate-model quantum computers. From its inception, this algorithm has sparked the desire for comparison between gate-model and annealing platforms. When preparing problem statements for these algorithms, the predominant inclination has been to formulate a quadratic Hamiltonian. This paper gives an example of a graph coloring problem that, depending on its variable encoding scheme, optionally admits higher order terms. This paper presents evidence that the higher order formulation is preferable to two other encoding schemes. The evidence then motivates an analysis of the scaling behavior of QAOA in this higher order formulation for an ensemble of graph coloring problems.
Verifiable visually meaningful image encryption based on compressed sensing (CS) and improved game of life (IGOL)

Image encryption converts the plain image into a noise-like one to protect information security. However, the obtained cipher images are easy to attract the attention of third parties during transmission and storage, and it is vulnerable to information damage and tampering caused by hacking or noise pollution, which in turn makes the receiver unable to obtain the correct decrypted image even spending many resources. To address these issues, this paper proposed a verifiable visually meaningful image encryption based on compressed sensing (CS) and improved game of life (IGOL). Specifically, the plain image is preprocessed and compressed to obtain compressed image via CS, achieving the purpose of reducing the image size. Subsequently, to improve the encryption effect, dynamic cyclic shift confusion method (DCSCM) and diffusion method based on improved game of life (DMIGOL) are presented and performed on the compressed image to obtain the secret image. Next, multiple shadow images are obtained using the secret image sharing based on the Chinese remainder theorem (CRT-SIS) on the secret image, which improves the robustness of this scheme and achieves multi-party data transfer of image. Finally, the shadow images are embedded into the carrier images respectively to obtain visually meaningful cipher images, reducing the attacker's attention to the cipher images during transmission. Additionally, the Hamming distance authentication method relying on plain image and carrier image is applied, and users can perform identity authentication and integrity check on cipher image. Experimental results demonstrate the security and effectiveness of the proposed scheme.
A unified probabilistic framework of robust and efficient color consistency correction for multiple images

The task of color consistency correction for multiple images mainly arises from applications like orthoimage producing, panoramic image stitching and 3D reconstruction. In these applications, images usually have been geometrically aligned. So correspondences can be easily extracted and used to solve color correction models. Almost all previous methods assume that the color residuals of correspondences follow Gaussian distribution and solve color models based on least squares. However, correspondences often contain unreliable ones due to altered areas and misalignments, which results in unusual large color residuals, namely, outliers. Imposing color consistency constaints on unreliable correspondences significantly affects the performance of color correction since Gaussian is highly sensitive to outliers. In this paper, to solve this problem theoretically, we first propose a unified probabilistic framework that formulates global color correction as a maximum posteriori probability (MAP) estimation. It is flexible enough to allow for any assumptions of residual distribution. And most color correction methods can be explained in this unified framework. Then, to robust against outliers, we use t-distribution with heavier tails than Gaussian to fit the color residuals. It is more robust because higher probabilities can be assigned to outliers. We show that the MAP formulation based on t-distribution actually leads to weighted least squares, which downweights outliers adaptively. Besides, our framework requires no user-defined robustness parameter. Because all parameters of color models and t-distribution are optimized jointly. In addition, to decrease the huge computational cost of large scale dataset, we extend the proposed framework to a parallel vesion which can achieve efficiency and global optimal at the same time. In the experiments, we compare our approach with the state-of-the-art approaches of Shen et al., Xia et al., etc. on several challenging datasets with outliers. The results demonstrate that our approach achieves the best robustness (average color consistency scores CD=5.4, DeltaE2000=5.7 and PSNR=24.0) and the best efficiency (given 100 images, non-parallel/parallel runs more than 5/50 times faster than others). The implementation is available at https://github.com/yinxuanLi/ColorConsistencyCorrectionForMultipleImages.