Reviewerly/exHarmony huggingface dataset:
Ranking-Based Survival Prediction on Histopathological Whole-Slide Images

Survival prediction for patients based on gigapixel histopathological whole-slide images (WSIs) has attracted increasing attention in recent years. Previous studies mainly focus on the framework of predicting the survival hazard scores based on one individual WSI for each patient directly. These prediction methods ignore the relative survival differences among patients, i.e., the ranking information, which is important for a regression task. Under such circumstances, we propose a ranking-based survival prediction method on WSIs – RankSurv, which takes the ranking information into consideration during the learning process. First, a hypergraph representation is introduced to conduct hazard prediction on each WSI respectively, which is able to learn the high-order correlation among different patches in the WSI. Then, a ranking-based prediction process is conducted using pairwise survival data. Experiments are conducted on three public carcinoma datasets (i.e., LUSC, GBM, and NLST). Quantitative results show that the proposed method significantly outperforms state-of-the-art methods on all three datasets, which demonstrates the effectiveness of the proposed ranking-based survival prediction framework.
DuEE: A Large-Scale Dataset for Chinese Event Extraction in Real-World Scenarios

This paper introduces DuEE, a new dataset for Chinese event extraction (EE) in real-world scenarios. DuEE has several advantages over previous EE datasets. (1) Scale: DuEE consists of 19,640 events categorized into 65 event types, along with 41,520 event arguments mapped to 121 argument roles, which, to our knowledge, is the largest Chinese EE dataset so far. (2) Quality: All the data is human annotated with crowdsourced review, ensuring that the annotation accuracy is higher than 95%. (3) Reality: The schema covers trending topics from Baidu Search and the data is collected from news on Baijiahao. The task is also close to real-world scenarios, e.g., a single instance is allowed to contain multiple events, different event arguments are allowed to share the same argument role, and an argument is allowed to play different roles. To advance the research on Chinese EE, we release DuEE as well as a baseline system to the community. We also organize a shared competition on the basis of DuEE, which has attracted 1,206 participants. We analyze the results of top performing systems and hope to shed light on further improvements.
Robust feature matching via advanced neighborhood topology consensus

Feature matching is one of the key techniques in many vision-based tasks, which aims to establish reliable correspondences between two sets of features. In this paper, we present a new feature matching method, which formulates the matching of two feature sets as a mathematical model based on two common consistency constraints. We first propose an advanced consensus of neighborhood topology, which can better exploit the consensus of topological structures to identify inliers. In order to have reliable neighborhood information for the feature points, a subset with high percentage inliers obtained by a guided matching strategy from the putative matches for the neighborhood construction is used. We demonstrate the advantages of our proposed method on various real image pairs. The results demonstrate that the proposed method is superior to the state-of-the-art feature matching methods.
Few-Shot Anomaly Detection for Polyp Frames from Colonoscopy

Anomaly detection methods generally target the learning of a normal image distribution (i.e., inliers showing healthy cases) and during testing, samples relatively far from the learned distribution are classified as anomalies (i.e., outliers showing disease cases). These approaches tend to be sensitive to outliers that lie relatively close to inliers (e.g., a colonoscopy image with a small polyp). In this paper, we address the inappropriate sensitivity to outliers by also learning from inliers. We propose a new few-shot anomaly detection method based on an encoder trained to maximise the mutual information between feature embeddings and normal images, followed by a few-shot score inference network, trained with a large set of inliers and a substantially smaller set of outliers. We evaluate our proposed method on the clinical problem of detecting frames containing polyps from colonoscopy video sequences, where the training set has 13350 normal images (i.e., without polyps) and less than 100 abnormal images (i.e., with polyps). The results of our proposed model on this data set reveal a state-of-the-art detection result, while the performance based on different number of anomaly samples is relatively stable after approximately 40 abnormal training images. Code is available at https://github.com/tianyu0207/FSAD-Net .
VisCode: Embedding Information in Visualization Images using Encoder-Decoder Network

We present an approach called VisCode for embedding information into visualization images. This technology can implicitly embed data information specified by the user into a visualization while ensuring that the encoded visualization image is not distorted. The VisCode framework is based on a deep neural network. We propose to use visualization images and QR codes data as training data and design a robust deep encoder-decoder network. The designed model considers the salient features of visualization images to reduce the explicit visual loss caused by encoding. To further support large-scale encoding and decoding, we consider the characteristics of information visualization and propose a saliency-based QR code layout algorithm. We present a variety of practical applications of VisCode in the context of information visualization and conduct a comprehensive evaluation of the perceptual quality of encoding, decoding success rate, anti-attack capability, time performance, etc. The evaluation results demonstrate the effectiveness of VisCode.
Multi-organ Segmentation via Co-training Weight-Averaged Models from Few-Organ Datasets

Multi-organ segmentation requires to segment multiple organs of interest from each image. However, it is generally quite difficult to collect full annotations of all the organs on the same images, as some medical centers might only annotate a portion of the organs due to their own clinical practice. In most scenarios, one might obtain annotations of a single or a few organs from one training set, and obtain annotations of the other organs from another set of training images. Existing approaches mostly train and deploy a single model for each subset of organs, which are memory intensive and also time inefficient. In this paper, we propose to co-train weight-averaged models for learning a unified multi-organ segmentation network from few-organ datasets. Specifically, we collaboratively train two networks and let the coupled networks teach each other on un-annotated organs. To alleviate the noisy teaching supervisions between the networks, the weighted-averaged models are adopted to produce more reliable soft labels. In addition, a novel region mask is utilized to selectively apply the consistent constraint on the un-annotated organ regions that require collaborative teaching, which further boosts the performance. Extensive experiments on three publicly available single-organ datasets LiTS [], KiTS [], Pancreas [] and manually-constructed single-organ datasets from MOBA [] show that our method can better utilize the few-organ datasets and achieves superior performance with less inference computational cost.
Sequential Deformation for Accurate Scene Text Detection

Scene text detection has been significantly advanced over recent years, especially after the emergence of deep neural network. However, due to high diversity of scene texts in scale, orientation, shape and aspect ratio, as well as the inherent limitation of convolutional neural network for geometric transformations, to achieve accurate scene text detection is still an open problem. In this paper, we propose a novel sequential deformation method to effectively model the line-shape of scene text. An auxiliary character counting supervision is further introduced to guide the sequential offset prediction. The whole network can be easily optimized through an end-to-end multi-task manner. Extensive experiments are conducted on public scene text detection datasets including ICDAR 2017 MLT, ICDAR 2015, Total-text and SCUT-CTW1500. The experimental results demonstrate that the proposed method has outperformed previous state-of-the-art methods.
Rainfall prediction by using ANFIS times series technique in South Tangerang, Indonesia

Excessive rainfall is one of the triggers for the flooding phenomenon, especially in the tropics with flat or concave areas. Some critical points in the South Tangerang region, which are currently one of the most rapidly developing cities, cannot be ignored from the flooding problem. Floods cause disturbing human activities, loss of life and property, and in turn affect the economic stretch in an area. This paper aimed to predict rainfall by exploring the application of artificial intelligence techniques such as ANFIS (Adaptive NeuroFuzzy Inference System). The proposed technique combines neural network learning abilities with transparent linguistic representations of fuzzy systems. The ANFIS model with various input structures and membership functions was built, trained, and tested to evaluate the capability of a model. Analyses of six-year rainfall data on a monthly basis in South Tangerang City, Banten found that rainfall prediction based on ANFIS time series is promising where 80% of data testing is well predicted.
Context-aware Sampling of Large Networks via Graph Representation Learning

Numerous sampling strategies have been proposed to simplify large-scale networks for highly readable visualizations. It is of great challenge to preserve contextual structures formed by nodes and edges with tight relationships in a sampled graph, because they are easily overlooked during the process of sampling due to their irregular distribution and immunity to scale. In this paper, a new graph sampling method is proposed oriented to the preservation of contextual structures. We first utilize a graph representation learning (GRL) model to transform nodes into vectors so that the contextual structures in a network can be effectively extracted and organized. Then, we propose a multi-objective blue noise sampling model to select a subset of nodes in the vectorized space to preserve contextual structures with the retention of relative data and cluster densities in addition to those features of significance, such as bridging nodes and graph connections. We also design a set of visual interfaces enabling users to interactively conduct context-aware sampling, visually compare results with various sampling strategies, and deeply explore large networks. Case studies and quantitative comparisons based on real-world datasets have demonstrated the effectiveness of our method in the abstraction and exploration of large networks.
Site-specific adjustment of a NWP-based photovoltaic production forecast

Despite the increased share of photovoltaic (PV) systems, several integration challenges are still present. For small-scale PV, the incentives for monitoring and forecasting the output are generated from the gains of optimizing of local electricity usage. PV output can often be the only measured parameter available from a PV site. Thereby, its efficient utilization in adjusting the PV conversion model is essential. The aim of this study is to develop a straightforward and explicit approach for utilizing PV output data in adjusting a site-specific PV output forecast. A parametric PV output conversion model is utilized as the baseline model for 23 PV sites in Finland, implemented on top of five summer months of day-ahead Numerical Weather Prediction (NWP) forecasts, provided by the MetCoOp system. The proposed approach is validated by comparing the performance of the baseline and adjusted forecasts against actual PV output. The method is shown to provide a solution for adjusting a generic PV output forecast to any specific site, reducing the hourly Mean Absolute Error (MAE) of the aggregated forecast from 0.08 to 0.06 W/Wp, while reducing bias from 0.05 to 0.01 W/Wp, respectively. The adjustment increases modeling accuracy by capturing potential systematic biases of the NWP forecast and site-specific conditions, e.g., variable shadowing and conversion losses, affecting the output of the PV system. Due to the fact that the approach can be implemented both for observations and forecast input data, the method is considered to be a helpful approach for several different PV modeling applications.
Automated X-ray computer tomography segmentation method for finite element analysis of non-crimp fabric reinforced composites

In this study a complete procedure is presented of how to generate finite element models based on X-ray computer tomography data on the fibre bundle scale for non-crimp fabric reinforced composites. Non-crimp fabric reinforced composites are nowadays extensively used in the load carrying parts of wind turbine blades. Finite element analysis based on X-ray computer tomographic data will allow faster and cheaper developments of key material parameters. However, automated procedures for computer tomography data transfer into finite elements models are lacking. In the current study, an X-ray computer tomography aided engineering (XAE) process including a fully automated segmentation method and an element-wise material orientation mapping of X-ray computer tomographic data is presented for the first time. The proposed methodology combines recent research progress and improvements in image analysis, and provides a fast, accurate and repeatable data transfer and analysis process with a high degree of automation.
Reinforcement Learning for Weakly Supervised Temporal Grounding of Natural Language in Untrimmed Videos

Temporal grounding of natural language in untrimmed videos is a fundamental yet challenging multimedia task facilitating cross-media visual content retrieval. We focus on the weakly supervised setting of this task that merely accesses to coarse video-level language description annotation without temporal boundary, which is more consistent with reality as such weak labels are more readily available in practice. In this paper, we propose a Boundary Adaptive Refinement (BAR) framework that resorts to reinforcement learning (RL) to guide the process of progressively refining the temporal boundary. To the best of our knowledge, we offer the first attempt to extend RL to temporal localization task with weak supervision. As it is non-trivial to obtain a straightforward reward function in the absence of pairwise granular boundary-query annotations, a cross-modal alignment evaluator is crafted to measure the alignment degree of segment-query pair to provide tailor-designed rewards. This refinement scheme completely abandons traditional sliding window based solution pattern and contributes to acquiring more efficient, boundary-flexible and content-aware grounding results. Extensive experiments on two public benchmarks Charades-STA and ActivityNet demonstrate that BAR outperforms the state-of-the-art weakly-supervised method and even beats some competitive fully-supervised ones.
A deep community based approach for large scale content based X-ray image retrieval

A computer assisted system for automatic retrieval of medical images with similar image contents can serve as an efficient management tool for handling and mining large scale data, and can also be used as a tool in clinical decision support systems. In this paper, we propose a deep community based automated medical image retrieval framework for extracting similar images from a large scale X-ray database. The framework integrates a deep learning-based image feature generation approach and a network community detection technique to extract similar images. When compared with the state-of-the-art medical image retrieval techniques, the proposed approach demonstrated improved performance. We evaluated the performance of the proposed method on two large scale chest X-ray datasets, where given a query image, the proposed approach was able to extract images with similar disease labels with a precision of 85%. To the best of our knowledge, this is the first deep community based image retrieval application on large scale chest X-ray database.
Supervised Hierarchical Deep Hashing for Cross-Modal Retrieval

Cross-modal hashing has attracted much attention in the large-scale multimedia search area. In many real applications, labels of samples have hierarchical structure which also contains much useful information for learning. However, most existing methods are originally designed for non-hierarchical labeled data and thus fail to exploit the rich information of the label hierarchy. In this paper, we propose an effective cross-modal hashing method, named Supervised Hierarchical Deep Cross-modal Hashing, SHDCH for short, to learn hash codes by explicitly delving into the hierarchical labels. Specifically, both the similarity at each layer of the label hierarchy and the relatedness across different layers are implanted into the hash-code learning. Besides, an iterative optimization algorithm is proposed to directly learn the discrete hash codes instead of relaxing the binary constraints. We conducted extensive experiments on two real-world datasets and the experimental results show the superior performance of SHDCH over several state-of-the-art methods.
Artificial neural network models for global solar energy and photovoltaic power forecasting over India

For design and development of solar-based photovoltaic (PV) system, accuracy in obtaining data of solar energy has been a prime concern. Forecasting of global solar energy in clear or sunny weather conditions are easily calculated; however, the challenge lies in estimating under the impact of foggy and cloudy conditions. Therefore, this research investigates and presents the Artificial Neural Network (ANN) models for global solar energy forecasting for distinct sky-conditions such as sunny, hazy, partly, and fully cloudy conditions. This research further applied for distinct climatic conditions namely composite, warm & humid, moderate, hot & dry, and cold & cloudy climate zone using meteorological parameters. Further, PV power forecasting has been essential for planning the operating capability of energy systems and its reliability. The present work explores the implementation of proposed model for short-term photovoltaic power forecast in solar energy systems. Lastly, comparative analysis of the developed model with other neural network models are carried out using statistical evaluation indices. It has been observed from the overall analysis that by using the Radial-Basis-Function-Neural-Network (RBFNN) model, a significant reduction in error has been noticed as compared to other methods. Further, it is noted that the sunny model provides improved performance for hot & dry climate zone; hazy model gives favorable output for composite climate zone; partly cloudy model is favorable for warm & humid climate zone; and lastly, fully cloudy model provides favorable results for cold & cloudy climate zone. The model has been implemented for broader application series and it has been observed that for short-term photovoltaic power forecasting under composite climatic zone, hazy model emerges out to provide better performance with Mean Absolute Percentage Error (MAPE) of 0.0019% followed by sunny, partly, and fully cloudy model with MAPE 0.024%, 0.054%, and 0.109% respectively. The models presented in this research will be useful for scholars to design and engineer the solar energy systems.
Secure smart contracts for cloud‐based manufacturing using Ethereum blockchain

Abstract Past international trade practices have been associated with opaque information flows that have hindered traceability and created hurdles in hassle‐free trade. Blockchain and allied technologies have been investigated as a panacea for the problems faced by the Cloud‐Based Manufacturing industry. However, previous literature has focused on limited aspects of a typical Cloud‐Based Manufacturing chain, such as monitoring assets and securing traceability, which is widely neglecting data integrity and data access. To overcome such drawbacks, the current paper proposes secure smart contracts based on the ERC20 interface in a permissioned blockchain with relevant processes and functions to obtain a holistic framework for securing Cloud‐Based Manufacturing operations. The efficacy of the proposed framework was demonstrated on the case study. It was found that critical loopholes in a current supply chain can be overcome using the proposed framework. Additionally, several outlines for future research are outlined.
MetroSets: Visualizing Sets as Metro Maps

We propose MetroSets, a new, flexible online tool for visualizing set systems using the metro map metaphor. We model a given set system as a hypergraph $\mathcal{H} = (V, \mathcal{S})$, consisting of a set $V$ of vertices and a set $\mathcal{S}$, which contains subsets of $V$ called hyperedges. Our system then computes a metro map representation of $\mathcal{H}$, where each hyperedge $E$ in $\mathcal{S}$ corresponds to a metro line and each vertex corresponds to a metro station. Vertices that appear in two or more hyperedges are drawn as interchanges in the metro map, connecting the different sets. MetroSets is based on a modular 4-step pipeline which constructs and optimizes a path-based hypergraph support, which is then drawn and schematized using metro map layout algorithms. We propose and implement multiple algorithms for each step of the MetroSet pipeline and provide a functional prototype with easy-to-use preset configurations. Furthermore, using several real-world datasets, we perform an extensive quantitative evaluation of the impact of different pipeline stages on desirable properties of the generated maps, such as octolinearity, monotonicity, and edge uniformity.
Ontology-based solutions for interoperability among product lifecycle management systems: A systematic literature review

During recent years, globalization has had an impact on the competitive capacity of industries, forcing them to integrate their productive processes with other, geographically distributed, facilities. This requires the information systems that support such processes to interoperate. Significant attention has been paid to the development of ontology-based solutions, which are meant to tackle issues from inconsistency to semantic interoperability and knowledge reusability. This paper looks into how the available technology, models and ontology-based solutions might interact within the manufacturing industry environment to achieve semantic interoperability among industrial information systems. Through a systematic literature review, this paper has aimed to identify the most relevant elements to consider in the development of an ontology-based solution and how these solutions are being deployed in industry. The research analyzed 54 studies in alignment with the specific requirements of our research questions. The most relevant results show that ontology-based solutions can be set up using OWL as the ontology language, Protégé as the ontology modeling tool, Jena as the application programming interface to interact with the built ontology, and different standards from the International Organization for Standardization Technical Committee 184, Subcommittee 4 or 5, to get the foundational concepts, axioms, and relationships to develop the knowledge base. We believe that the findings of this study make an important contribution to practitioners and researchers as they provide useful information about different projects and choices involved in undertaking projects in the field of industrial ontology application.