Reviewerly/exHarmony huggingface dataset:
Self-Paced Active Learning: Query the Right Thing at the Right Time

Active learning queries labels from the oracle for the most valuable instances to reduce the labeling cost. In many active learning studies, informative and representative instances are preferred because they are expected to have higher potential value for improving the model. Recently, the results in self-paced learning show that training the model with easy examples first and then gradually with harder examples can improve the performance. While informative and representative instances could be easy or hard, querying valuable but hard examples at early stage may lead to waste of labeling cost. In this paper, we propose a self-paced active learning approach to simultaneously consider the potential value and easiness of an instance, and try to train the model with least cost by querying the right thing at the right time. Experimental results show that the proposed approach is superior to state-of-the-art batch mode active learning methods.
Partial Label Learning via Label Enhancement

Partial label learning aims to learn from training examples each associated with a set of candidate labels, among which only one label is valid for the training example. The common strategy to induce predictive model is trying to disambiguate the candidate label set, such as disambiguation by identifying the ground-truth label iteratively or disambiguation by treating each candidate label equally. Nonetheless, these strategies ignore considering the generalized label distribution corresponding to each instance since the generalized label distribution is not explicitly available in the training set. In this paper, a new partial label learning strategy named PL-LE is proposed to learn from partial label examples via label enhancement. Specifically, the generalized label distributions are recovered by leveraging the topological information of the feature space. After that, a multi-class predictive model is learned by fitting a regularized multi-output regressor with the generalized label distributions. Extensive experiments show that PL-LE performs favorably against state-ofthe-art partial label learning approaches.
Multi-Robot Path Planning Based on Multi-Objective Particle Swarm Optimization

In this paper, a new method is proposed for the path planning of multi-robots in unknown environments. The method is inspired by multi-objective particle swarm optimization (MOPSO) and is named multi-robot MOPSO. It considers shortness, safety, and smoothness. Due to the obscurity of the environment, the robots should decide the moving direction based on the information gathered by sensors only such that the optimal path between the start and goal positions can be found at the end of the algorithm. Sharing knowledge among the navigating robots is necessary to achieve this aim. So, a new concept, named the probabilistic window, is introduced in this paper. It combines the current information obtained through the robot sensors and experiences of the previous robots to select the paths that seem more likely to achieve higher fitness in the mentioned objectives. The proposed method has an outstanding performance on different complex benchmarks, and the results have shown that it is more effective and efficient compared with the classic and the state-of-the-art methods.
Non-interactive privacy-preserving neural network prediction

Neural network is a particular machine learning framework which has gained widespread popularity due to its superior performance in many applications, such as complex board games, face recognition and disease diagnosis. A new service paradigm which offers online neural-network-based prediction to clients is increasingly popular. Although the prediction service has clear benefits, serious privacy issues have also emerged from the clients' sensitive data and the neural network model itself. In this paper, we present a new outsourcing model for privacy-preserving neural network prediction under two non-colluding servers framework. In this model, the original neural network owner can securely outsource an existing neural network model to the two servers who will thereafter provide prediction service to the public users utilize encrypted input data. We propose the first fully non-interactive privacy-preserving neural network prediction scheme. Extensive security and efficiency analysis demonstrate that the proposed scheme satisfies the security requirement of model privacy and data privacy, and highly efficient with respect to computation and communication overhead.
The Impact of Correlated Metrics on the Interpretation of Defect Models

Defect models are analytical models for building empirical theories related to software quality. Prior studies often derive knowledge from such models using interpretation techniques, e.g., ANOVA Type-I. Recent work raises concerns that correlated metrics may impact the interpretation of defect models. Yet, the impact of correlated metrics in such models has not been investigated. In this paper, we investigate the impact of correlated metrics on the interpretation of defect models and the improvement of the interpretation of defect models when removing correlated metrics. Through a case study of 14 publicly- available defect datasets, we find that (1) correlated metrics have the largest impact on the consistency, the level of discrepancy, and the direction of the ranking of metrics, especially for ANOVA techniques. On the other hand, we find that removing all correlated metrics (2) improves the consistency of the produced rankings regardless of the ordering of metrics (except for ANOVA Type-I); (3) improves the consistency of ranking of metrics among the studied interpretation techniques; (4) impacts the model performance by less than 5 percentage points. Thus, when one wishes to derive sound interpretation from defect models, one must (1) mitigate correlated metrics especially for ANOVA analyses; and (2) avoid using ANOVA Type-I even if all correlated metrics are removed.
Template Matching-Based Method for Intelligent Invoice Information Identification

The increasing use of invoicing has created an unnecessary burden on labor and material resources in the financial sector. This paper proposes a method to intelligently identify invoice information based on template matching, which retrieves the required information by image preprocessing, template matching, an optical character recognizing, and information exporting. The origin invoice image is preprocessed first to remove the useless background information by secondary rotation and edge cutting. Then, the region of the required information in the obtained regular image is extracted by template matching, which is the core of the intelligent invoice information identification. The optical character recognizing is utilized to convert the image information into text so that the extracted information can be directly used. The text information is exported for backup and subsequent use in the last step. The experimental results indicate that the method using normalized correlation coefficient matching is the best choice, demonstrating a high accuracy of 95%, and the average running time of 14 milliseconds.
MultiCAM: Multiple Class Activation Mapping for Aircraft Recognition in Remote Sensing Images

Aircraft recognition in remote sensing images has long been a meaningful topic. Most related methods treat entire images as a whole and do not concentrate on the features of parts. In fact, a variety of aircraft types have small interclass variance, and the main evidence for classifying subcategories is related to some discriminative object parts. In this paper, we introduce the idea of fine-grained visual classification (FGVC) and attempt to make full use of the features from discriminative object parts. First, multiple class activation mapping (MultiCAM) is proposed to extract the discriminative parts of aircrafts of different categories. Second, we present a mask filter (MF) strategy to enhance the discriminative object parts and filter the interference of the background from original images. Third, a selective connected feature fusion method is proposed to fuse the features extracted from both networks, focusing on the original images and the results of MF, respectively. Compared with the single prediction category in class activation mapping (CAM), MultiCAM makes full use of the predictions of all categories to overcome the wrong discriminative parts produced by a wrong single prediction category. Additionally, the designed MF preserves the object scale information and helps the network to concentrate on the object itself rather than the interfering background. Experiments on a challenging dataset prove that our method can achieve state-of-the-art performance.
The Fishyscapes Benchmark: Measuring Blind Spots in Semantic Segmentation

Abstract Deep learning has enabled impressive progress in the accuracy of semantic segmentation. Yet, the ability to estimate uncertainty and detect failure is key for safety-critical applications like autonomous driving. Existing uncertainty estimates have mostly been evaluated on simple tasks, and it is unclear whether these methods generalize to more complex scenarios. We present Fishyscapes, the first public benchmark for anomaly detection in a real-world task of semantic segmentation for urban driving. It evaluates pixel-wise uncertainty estimates towards the detection of anomalous objects. We adapt state-of-the-art methods to recent semantic segmentation models and compare uncertainty estimation approaches based on softmax confidence, Bayesian learning, density estimation, image resynthesis, as well as supervised anomaly detection methods. Our results show that anomaly detection is far from solved even for ordinary situations, while our benchmark allows measuring advancements beyond the state-of-the-art. Results, data and submission information can be found at https://fishyscapes.com/ .
Parameter-Free Weighted Multi-View Projected Clustering with Structured Graph Learning

In many real-world applications, we are often confronted with high dimensional data which are represented by various heterogeneous views. How to cluster this kind of data is still a challenging problem due to the curse of dimensionality and effectively integration of different views. To address this problem, we propose two parameter-free weighted multi-view projected clustering methods which perform structured graph learning and dimensionality reduction simultaneously. We can use the obtained structured graph directly to extract the clustering indicators, without performing other discretization procedures as previous graph-based clustering methods have to do. Moreover, two parameter-free strategies are adopted to learn an optimal weight for each view automatically, without introducing a regularization parameter as previous methods do. Extensive experiments on several public datasets demonstrate that the proposed methods outperform other state-of-the-art approaches and can be used more practically.
A novel joint histogram equalization based image contrast enhancement

The limitation to the most commonly used histogram equalization (HE) technique is the inconsideration of the neighborhood info near each pixel for contrast enhancement. This gives rise to noise in the output image. To overcome this effect, a novel joint histogram equalization (JHE) based technique is suggested. The focus is to utilize the information among each pixel and its neighbors, which improves the contrast of an image. The suggested method is developed in a truly two-dimensional domain. The joint histogram is constructed using the original image and its average image. Further, it does not require a target uniform distribution for generating the output. The two-dimensional cumulative distribution function (CDF) is utilized as a mapping function to get the output pixel intensity. Extensive experiments are performed using 300 test images from BSD database. The experimental analysis indicates that the procedure produces better results than the state-of-the-art HE based contrast enhancement algorithms. More significantly, it produces the best results even for images having a narrow dynamic range. The implementation simplicity of the proposed algorithm may attract researchers to explore the idea for new applications in image processing.
An ensemble feature selection method for high-dimensional data based on sort aggregation

With the rapid development of the Internet, big data has been applied in a large amount of application. However, there are often redundant or irrelevant features in high dimensional data, so feature selection is particularly important. Because the feature subset obtained by a single feature selection method may be biased, an ensemble feature selection method named SA-EFS based on sort aggregation is proposed in this paper, and this method is oriented to classification tasks. For high-dimensional data sets, the results of three feature selection methods, chi-square test, maximum information coefficient and XGBoost, are aggregated by specific strategy. The integration effects of arithmetic mean and geometric mean aggregation strategy on this model are analyzed. In order to evaluate the classification and prediction performance of feature subset, three classifiers with excellent performance, KNN, Random Forest and XGBoost, are tested respectively, and the influence of threshold on classification performance is analyzed. The experimental results show that compared with the single feature selection method, the arithmetic mean aggregation ensemble feature selection can effectively improve the classification accuracy, and the threshold interval setting of 0.1 is a better choice.
Analysis and Detection of Information Types of Open Source Software Issue Discussions

Most modern Issue Tracking Systems (ITSs) for open source software (OSS) projects allow users to add comments to issues. Over time, these comments accumulate into discussion threads embedded with rich information about the software project, which can potentially satisfy the diverse needs of OSS stakeholders. However, discovering and retrieving relevant information from the discussion threads is a challenging task, especially when the discussions are lengthy and the number of issues in ITSs are vast. In this paper, we address this challenge by identifying the information types presented in OSS issue discussions. Through qualitative content analysis of 15 complex issue threads across three projects hosted on GitHub, we uncovered 16 information types and created a labeled corpus containing 4656 sentences. Our investigation of supervised, automated classification techniques indicated that, when prior knowledge about the issue is available, Random Forest can effectively detect most sentence types using conversational features such as the sentence length and its position. When classifying sentences from new issues, Logistic Regression can yield satisfactory performance using textual features for certain information types, while falling short on others. Our work represents a nontrivial first step towards tools and techniques for identifying and obtaining the rich information recorded in the ITSs to support various software engineering activities and to satisfy the diverse needs of OSS stakeholders.
DNS-IdM: A Blockchain Identity Management System to Secure Personal Data Sharing in a Network

Identity management (IdM) is a method used to determine user identities. The centralized aspect of IdM introduces a serious concern with the growing value of personal information, as well as with the General Data Protection Regulation (GDPR). The problem with currently-deployed systems and their dominating approach, with identity providers (IdP) and single-point services, is that a third party is in charge of maintaining and controlling the personal data. The main challenge to manage data securely lies in trusting humans and institutes who are responsible for controlling the entire activity. Identities are not owned by the rightful owners or the user him/herself, but by the mentioned providers. With the rise of blockchain technology, self-sovereign identities are in place utilizing decentralization; unfortunately, the flaws still exist. In this research, we propose DNS-IdM, a smart contract-based identity management system that enables users to maintain their identities associated with certain attributes, accomplishing the self-sovereign concept. DNS-IdM has promising outcomes in terms of security and privacy. Due to the decentralized nature, DNS-IdM is able to avoid not only the conventional security threats, but also the limitations of the current decentralized identity management systems.
One-Pass Incomplete Multi-View Clustering

Real data are often with multiple modalities or from multiple heterogeneous sources, thus forming so-called multi-view data, which receives more and more attentions in machine learning. Multi-view clustering (MVC) becomes its important paradigm. In real-world applications, some views often suffer from instances missing. Clustering on such multi-view datasets is called incomplete multi-view clustering (IMC) and quite challenging. To date, though many approaches have been developed, most of them are offline and have high computational and memory costs especially for large scale datasets. To address this problem, in this paper, we propose an One-Pass Incomplete Multi-view Clustering framework (OPIMC). With the help of regularized matrix factorization and weighted matrix factorization, OPIMC can relatively easily deal with such problem. Different from the existing and sole online IMC method, OPIMC can directly get clustering results and effectively determine the termination of iteration process by introducing two global statistics. Finally, extensive experiments conducted on four real datasets demonstrate the efficiency and effectiveness of the proposed OPIMC method.
Twin-Systems to Explain Artificial Neural Networks using Case-Based Reasoning: Comparative Tests of Feature-Weighting Methods in ANN-CBR Twins for XAI

In this paper, twin-systems are described to address the eXplainable artificial intelligence (XAI) problem, where a black box model is mapped to a white box “twin” that is more interpretable, with both systems using the same dataset. The framework is instantiated by twinning an artificial neural network (ANN; black box) with a case-based reasoning system (CBR; white box), and mapping the feature weights from the former to the latter to find cases that explain the ANN’s outputs. Using a novel evaluation method, the effectiveness of this twin-system approach is demonstrated by showing that nearest neighbor cases can be found to match the ANN predictions for benchmark datasets. Several feature-weighting methods are competitively tested in two experiments, including our novel, contributions-based method (called COLE) that is found to perform best. The tests consider the ”twinning” of traditional multilayer perceptron (MLP) networks and convolutional neural networks (CNN) with CBR systems. For the CNNs trained on image data, qualitative evidence shows that cases provide plausible explanations for the CNN’s classifications.
A Knowledge Regularized Hierarchical Approach for Emotion Cause Analysis

Emotion cause analysis, which aims to identify the reasons behind emotions, is a key topic in sentiment analysis.A variety of neural network models have been proposed recently, however, these previous models mostly focus on the learning architecture with local textual information, ignoring the discourse and prior knowledge, which play crucial roles in human text comprehension.In this paper, we propose a new method to extract emotion cause with a hierarchical neural model and knowledge-based regularizations, which aims to incorporate discourse context information and restrain the parameters by sentiment lexicon and common knowledge.The experimental results demonstrate that our proposed method achieves the state-of-the-art performance on two public datasets in different languages (Chinese and English), outperforming a number of competitive baselines by at least 2.08% in F-measure.
Adversarial Learning with Contextual Embeddings for Zero-resource Cross-lingual Classification and NER

Contextual word embeddings (e.g.GPT, BERT, ELMo, etc.) have demonstrated stateof-the-art performance on various NLP tasks.Recent work with the multilingual version of BERT has shown that the model performs very well in cross-lingual settings, even when only labeled English data is used to finetune the model.We improve upon multilingual BERT's zero-resource cross-lingual performance via adversarial learning.We report the magnitude of the improvement on the multilingual ML-Doc text classification and CoNLL 2002/2003 named entity recognition tasks.Furthermore, we show that language-adversarial training encourages BERT to align the embeddings of English documents and their translations, which may be the cause of the observed performance gains.
Cross-lingual Structure Transfer for Relation and Event Extraction

Ananya Subburathinam, Di Lu, Heng Ji, Jonathan May, Shih-Fu Chang, Avirup Sil, Clare Voss. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). 2019.
From Whole Slide Imaging to Microscopy: Deep Microscopy Adaptation Network for Histopathology Cancer Image Classification

Deep learning (DL) has achieved remarkable performance on digital pathology image classification with whole slide images (WSIs). Unfortunately, high acquisition costs of WSIs hinder the applications in practical scenarios, and most pathologists still use microscopy images (MSIs) in their workflows. However, it is especially challenging to train DL models on MSIs, given limited image qualities and high annotation costs. Alternatively, directly applying a WSI-trained DL model on MSIs usually performs poorly due to huge gaps between WSIs and MSIs. To address these issues, we propose to exploit deep unsupervised domain adaptation to adapt DL models trained on the labeled WSI domain to the unlabeled MSI domain. Specifically, we propose a novel Deep Microscopy Adaptation Network (DMAN). By reducing domain discrepancies via adversarial learning and entropy minimization, and alleviating class imbalance with sample reweighting, DMAN can classify MSIs effectively even without MSI annotations. Extensive experiments on colon cancer diagnosis demonstrate the effectiveness of DMAN and its potential in customizing models for each pathologist’s microscope.
Heterogeneous Domain Adaptation via Soft Transfer Network

Heterogeneous domain adaptation (HDA) aims to facilitate the learning task in a target domain by borrowing knowledge from a heterogeneous source domain. In this paper, we propose a Soft Transfer Network (STN), which jointly learns a domain-shared classifier and a domain-invariant subspace in an end-to-end manner, for addressing the HDA problem. The proposed STN not only aligns the discriminative directions of domains but also matches both the marginal and conditional distributions across domains. To circumvent negative transfer, STN aligns the conditional distributions by using the soft-label strategy of unlabeled target data, which prevents the hard assignment of each unlabeled target data to only one category that may be incorrect. Further, STN introduces an adaptive coefficient to gradually increase the importance of the soft-labels since they will become more and more accurate as the number of iterations increases. We perform experiments on the transfer tasks of image-to-image, text-to-image, and text-to-text. Experimental results testify that the STN significantly outperforms several state-of-the-art approaches.