Reviewerly/exHarmony huggingface dataset:
Quantifying and Reducing Registration Uncertainty of Spatial Vector Labels on Earth Imagery

Given raster imagery features and imperfect vector training labels with registration uncertainty, this paper studies a deep learning framework that can quantify and reduce the registration uncertainty of training labels as well as train neural network parameters simultaneously. The problem is important in broad applications such as streamline classification on Earth imagery or tissue segmentation on medical imagery, whereby annotating precise vector labels is expensive and time-consuming. However, the problem is challenging due to the gap between the vector representation of class labels and the raster representation of image features and the need for training neural networks with uncertain label locations. Existing research on uncertain training labels often focuses on uncertainty in label class semantics or characterizes label registration uncertainty at the pixel level (not contiguous vectors). To fill the gap, this paper proposes a novel learning framework that explicitly quantifies vector labels' registration uncertainty. We propose a registration-uncertainty-aware loss function and design an iterative uncertainty reduction algorithm by re-estimating the posterior of true vector label locations distribution based on a Gaussian process. Evaluations on real-world datasets in National Hydrography Dataset refinement show that the proposed approach significantly outperforms several baselines in the registration uncertainty estimations performance and classification performance.
Gradual AutoML using Lale

Lale is a sklearn-compatible library for automated machine learning (AutoML). It is open-source (https://github.com/ibm/lale) and addresses the need for gradual automation of machine learning as opposed to offering a black-box AutoML tool. Black-box AutoML tools are difficult to customize and thus restrict data scientists in leveraging their knowledge and intuition in the automation process. Lale is built on three principles: progressive disclosure, orthogonality, and least surprise. These enable a gradual approach offering a spectrum of usage patterns starting from total automation to controlling almost every aspect of AutoML. Lale provides compositional constructs that let data scientists control some aspects of their pipelines while leaving other aspects free to be searched automatically. This tutorial demonstrates the use of Lale for various machine-learning tasks, showing how to progressively exercise more customization. It also covers AutoML for advanced scenarios such as class imbalance correction, bias detection and mitigation, multi-objective optimization, and working with multi-table datasets. While Lale comes with hyperparameter specifications for 216 operators out-of-the-box, users can also add more operators of their own, and this tutorial covers how to do that. Overall, this tutorial teaches you how you can exercise fine-grained control over AutoML without having to be an AutoML expert.
Laser interference 3-D sensor with line-shaped beam based multipoint measurements using cylindrical lens

Laser Doppler distance sensors enable an absolute shape measurement of rotating objects for instance for monitoring the turning process of workpieces by a simultaneous velocity and distance measurement. However, the sensors allow only single-point measurement, which makes a 3-D measurement time-consuming and limits the sensor application. Therefore, this article presents a novel sensor setup for simultaneous multipoint measurements. Cylindrical lenses are employed to produce expanded, line-shaped laser beams and thereby increase the number of measuring points in a single measurement. A line camera based detection method is proposed for high-speed full field detection, lateral resolution increase and measurement uncertainty reduction. As a result, the proposed sensor system enables simultaneous velocity, distance and thus 3-D absolute shape measurements at a rotating object, with a simultaneous measurement of up to 480 points and a total measurement uncertainty below 1 μm. Moreover, the lateral resolution can be increased by the order of one magnitude to 4.2 μm.
Swarm intelligence for new materials

With the help of deep learning technology, constructing the neural network model between structures and properties of materials is one of important methods to speed up the new materials discovery and design. However, both the training efficiency and accuracy of model play important roles in application of machine learning on materials. At present, the commonly used traditional neural network model algorithm in materials is single, easy to fall into local optimization, and difficult to converge in the training process, and the training model has a low prediction power. In this paper, a novel neural network optimization method based on swarm algorithms is proposed to discover better parameters in developing new materials. In this neural network model, particle swarm and Bayesian optimization are used to improve the crossover, mutation and selection strategies of genetic algorithm. Using advantages of each algorithm to realize the optimal parameters selection of the neural network and network optimization, and to improve the prediction ability of the neural network model. By comparing energies predicted by traditional networks and the proposed network, more stable models with high efficiency and precision are trained. • The combination of particle swarm optimization algorithm and genetic algorithm. • Swarm intelligence algorithms for optimizing neural networks. • Constructing neural network potential energy for material structure prediction. • Constructing neural network with high training efficiency and prediction accuracy.
Submodular Feature Selection for Partial Label Learning

Partial label learning induces a multi-class classifier from training examples each associated with a candidate label set where the ground-truth label is concealed. Feature selection improves the generalization ability of learning system via selecting essential features for classification from the original feature set, while the task of partial label feature selection is challenging due to ambiguous labeling information. In this paper, the first attempt towards partial label feature selection is investigated via mutual-information-based dependency maximization. Specifically, the proposed approach SAUTE iteratively maximizes the dependency between selected features and labeling information, where the value of mutual information is estimated from confidence-based latent variable inference. In each iteration, the near-optimal features are selected greedily according to properties of submodular mutual information function, while the density of latent label variable is inferred with the help of updated labeling confidences over candidate labels by resorting to kNN aggregation in the induced lower-dimensional feature space. Extensive experiments over synthetic as well as real-world partial label data sets show that the generalization ability of well-established partial label learning algorithms can be significantly improved after coupling with the proposed feature selection approach.
SAMCNet: Towards a Spatially Explainable AI Approach for Classifying MxIF Oncology Data

The goal of spatially explainable artificial intelligence (AI) classification approach is to build a classifier to distinguish two classes (e.g., responder, non-responder) based on the their spatial arrangements (e.g., spatial interactions between different point categories) given multi-category point data from two classes. This problem is important for generating hypotheses towards discovering new immunotherapies for cancer treatment as well as for other applications in biomedical research and microbial ecology. This problem is challenging due to an exponential number of category subsets which may vary in the strength of their spatial interactions. Most prior efforts on using human selected spatial association measures may not be sufficient for capturing the relevant spatial interactions (e.g., surrounded by) which may be of biological significance. In addition, the related deep neural networks are limited to category pairs and do not explore larger subsets of point categories. To overcome these limitations, we propose a Spatial-interaction Aware Multi-Category deep neural Network (SAMCNet) architecture and contribute novel local reference frame characterization and point pair prioritization layers for spatially explainable classification. Experimental results on multiple cancer datasets (e.g., MxIF) show that the proposed architecture provides higher prediction accuracy over baseline methods. A real-world case study demonstrates that the proposed work discovers patterns that are missed by the existing methods and has the potential to inspire new scientific discoveries.
AutoShard: Automated Embedding Table Sharding for Recommender Systems

Embedding learning is an important technique in deep recommendation models to map categorical features to dense vectors. However, the embedding tables often demand an extremely large number of parameters, which become the storage and efficiency bottlenecks. Distributed training solutions have been adopted to partition the embedding tables into multiple devices. However, the embedding tables can easily lead to imbalances if not carefully partitioned. This is a significant design challenge of distributed systems named embedding table sharding, i.e., how we should partition the embedding tables to balance the costs across devices, which is a non-trivial task because 1) it is hard to efficiently and precisely measure the cost, and 2) the partition problem is known to be NP-hard. In this work, we introduce our novel practice in Meta, namely AutoShard, which uses a neural cost model to directly predict the multi-table costs and leverages deep reinforcement learning to solve the partition problem. Experimental results on an open-sourced large-scale synthetic dataset and Meta's production dataset demonstrate the superiority of AutoShard over the heuristics. Moreover, the learned policy of AutoShard can transfer to sharding tasks with various numbers of tables and different ratios of the unseen tables without any fine-tuning. Furthermore, AutoShard can efficiently shard hundreds of tables in seconds. The effectiveness, transferability, and efficiency of AutoShard make it desirable for production use. Our algorithms have been deployed in Meta production environment. A prototype is available at https://github.com/daochenzha/autoshard
Live Sentiment Analysis Using Multiple Machine Learning and Text Processing Algorithms

Due to the massive amount of data being generated on the platform, Twitter has been the subject of numerous sentiment analysis studies. Such social network services generate massive unstructured data streams which make working with them very challenging. The aim of this study is to reliably analyze the sentiment of trending tweets in the Twitter API data stream using a combination of different algorithms to achieve a consensus. The methods we implemented include Support-Vector Machine, Naive Bayes, Textblob, and Lexicon Approach. The hypothesis is that using these methods together would enable us to get more accurate results. Using a labeled dataset to test our model, the results show that the combination of these four algorithms all together performed best with an overall accuracy of 68.29%. We conclude that our combination method of analysis is suitable and fast enough for our data stream and also accurate for analyzing sentiment.
Optimal Interpretable Clustering Using Oblique Decision Trees

Recent years have seen a renewed interest in interpretable machine learning, which seeks insight into how a model achieves a prediction. Here, we focus on the relatively unexplored case of interpretable clustering. In our approach, the cluster assignments of the training instances are constrained to be the output of a decision tree. This has two advantages: 1) it makes it possible to understand globally how an instance is mapped to a cluster, in particular to see which features are used for which cluster; 2) it forces the clusters to respect a hierarchical structure while optimizing the original clustering objective function. Rather than the traditional axis-aligned trees, we use sparse oblique trees, which have far more modelling power, particularly with high-dimensional data, while remaining interpretable. Our approach applies to any clustering method which is defined by optimizing a cost function and we demonstrate it with two k-means variants.
Multilingual Taxonomic Web Page Classification for Contextual Targeting at Yahoo

As we move toward a cookie-less world, the ability to track users' online activities for behavior targeting will be drastically reduced, making contextual targeting an appealing alternative for advertising platforms. Category-based contextual targeting displays ads on web pages that are relevant to advertiser-targeted categories, according to a pre-defined taxonomy. Accurate web page classification is key to the success of this approach. In this paper, we use multilingual Transformer-based transfer learning models to classify web pages in five high-impact languages. We adopt multiple data sampling techniques to increase coverage for rare categories, and modify the loss using class-based re-weighting to smooth the influence of frequent versus rare categories. Offline evaluation shows that these are crucial for improving our classifiers. We leverage knowledge distillation to train accurate models that are lightweight in terms of (i) model size, and (ii) the input text used. Classifying web pages using only text from the URL addresses a unique challenge for contextual targeting in that bid requests come to ad systems as URLs without content, while crawling is time consuming and costly. We launched the proposed models for contextual targeting in the Yahoo DSP, significantly increasing its revenue.
LeapAttack: Hard-Label Adversarial Attack on Text via Gradient-Based Optimization

Generating text adversarial examples in the hard-label setting is a more realistic and challenging black-box adversarial attack problem, whose challenge comes from the fact that gradient cannot be directly calculated from discrete word replacements. Consequently, the effectiveness of gradient-based methods for this problem still awaits improvement. In this paper, we propose a gradient-based optimization method named LeapAttack to craft high-quality text adversarial examples in the hard-label setting. To specify, LeapAttack employs the word embedding space to characterize the semantic deviation between the two words of each perturbed substitution by their difference vector. Facilitated by this expression, LeapAttack gradually updates the perturbation direction and constructs adversarial examples in an iterative round trip: firstly, the gradient is estimated by transforming randomly sampled word candidates to continuous difference vectors after moving the current adversarial example near the decision boundary; secondly, the estimated gradient is mapped back to a new substitution word based on the cosine similarity metric. Extensive experimental results show that in the general case LeapAttack can efficiently generate high-quality text adversarial examples with the highest semantic similarity and the lowest perturbation rate in the hard-label setting.
In Defense of Core-set: A Density-aware Core-set Selection for Active Learning

Active learning enables the efficient construction of a labeled dataset by labeling informative samples from an unlabeled dataset. In a real-world active learning scenario, the use of diversity-based sampling is indispensable because there are many redundant or highly similar samples. Core-set approach is the promising diversity-based method selecting diverse samples by considering the distance between samples. However, the approach poorly performs compared to the uncertainty-based method that selects the most difficult samples where neural models reveal low confidence. In this work, we analyze the feature space through the lens of density and, interestingly, observe that locally sparse regions tend to have more informative samples than dense regions. Motivated by our analysis, we empower the core-set approach with the density-awareness and propose a density-aware core-set (DACS) which estimates the density of the unlabeled samples and selects diverse samples mainly from sparse regions which are treated as the informative regions. To reduce the computational bottlenecks in estimating the density, we introduce a new density approximation based on locality-sensitive hashing. Experimental results demonstrate the efficacy of DACS in both classification and regression tasks and specifically show that DACS can produce state-of-the-art performance in a practical scenario. Since DACS is weakly dependent on architectures, we also present a simple yet effective combination method to show that the existing methods can be beneficially combined with DACS.
Partial Label Learning with Discrimination Augmentation

Partial label learning is a weakly supervised learning framework where each training example is associated with multiple candidate labels, among which only one is valid. Existing works on partial label learning mainly focus on classification model induction by disambiguating candidate label sets in the output space. Nevertheless, the feature representations of partial label training examples may be less informative of the ground-truth labels, which may result in negative influences on the disambiguation process. To circumvent this difficulty, the first attempt towards discrimination augmentation for partial label learning is investigated in this paper. The feature space is enriched with confidence-rated class prototype features to replenish discriminative characteristics of the underlying ground-truth labels for partial label training examples. Specially, an optimization formulation is proposed to jointly optimize the class prototype and estimate the labeling confidence over partial label training examples, which enforces both global consistency in the feature space and local consistency in the label space. We show that the class prototypes and the labeling confidence can be solved via alternating optimization. Extensive experiments on synthetic as well as real-world data sets validate the effectiveness of the proposed approach for improving the generalization performance of state-of-the-art partial label learning algorithms.
Precision CityShield Against Hazardous Chemicals Threats via Location Mining and Self-Supervised Learning

With the unprecedented development of industrialization and urbanization, many hazardous chemicals have become an indispensable part of our daily life. They are produced, transported, and consumed in modern cities every day, which breeds many unknown hazardous chemicals-related locations (HCLs) that are out of the supervision of management departments and accompanying huge threats to urban safety. How to recognize these unknown HCLs and identify their risk levels is an essential task for urban hazardous chemicals management. To accomplish this task, in this work, we propose a system named as CityShield to discover hidden HCLs and classify their risk levels based on trajectories of hazardous chemicals transportation vehicles. The CityShield system consists of three components. The first component is Data Pre-processing, which filters noises in raw trajectories and probes stable transportation vehicles' stay points from massive uncertain GPS points. The second is HCL Recognition, which adopts the proposed HCL-Rec algorithm to cluster stay points into polygonal HCLs, and avoids the improper location merging problem caused by the skewed spatial distribution of HCLs. The third component is HCL Classification, which introduces the HCL relation graph as auxiliary information to overcome the label scarcity problem of HCLs. It adopts a self-supervised method consisting of four pre-training tasks to learn high-quality representations for HCLs from the graph, which are finally used to classify the categories and risk levels of HCLs.
G2NET: A General Geography-Aware Representation Network for Hotel Search Ranking

Hotel search ranking is the core function of Online Travel Platforms (OTPs), while geography information of location entities involved in it plays a critically important role in guaranteeing its ranking quality. The closest line of works to the hotel search ranking problem is thus the next POI (or location) recommendation problem, which has extensive works but fails to cope with two new challenges, i.e., consideration of two more location entities and effective utilization of geographical information, in a hotel search ranking scenario. To this end, we propose a General Geography-aware representation NETwork (G2NET for short) to better represent geography information of location entities so as to optimize the hotel search ranking. In G2NET, to address the first challenge, we first propose the concept of Geography Interaction Schema (GIS) which is a meta template for representing the arbitrary number of location entity types and their interactions. Then, a novel geography interaction encoder is devised providing general representation ability for an instance of GIS, followed by an attentive operation that aggregates representations of instances corresponding to all historically interacted hotels of a user in a weighted manner. The second challenge is handled by the combined application of three proposed geography embedding modules in G2NET, each of which focuses on computing embeddings of location entities based on a certain aspect of geographical information of location entities. Moreover, a self-attention layer is deployed in G2NET, to capture correlations among historically interacted hotels of a user which provides non-trivial functionality of understanding the user's behaviors. Both offline and online experiments show that G2NET outperforms the state-of-the-art methods. G2NET has now been successfully deployed to provide the high-quality hotel search ranking service at Fliggy, one of the most popular OTPs in China, serving tens of millions of users.
Trustworthy Graph Learning

Deep graph learning (DGL) has achieved remarkable progress in both business and scientific areas ranging from finance and e-commerce, to drug and advanced material discovery. Despite the progress, how to ensure various DGL algorithms behave in a socially responsible manner and meet regulatory compliance requirements becomes an emerging problem, especially in risk-sensitive domains. Trustworthy graph learning (TwGL) aims to solve the above problems from a technical viewpoint. In contrast to conventional graph learning which mainly cares about model performance, TwGL considers various reliability and safety aspects of DGL, including but not limited to adversarial robustness, explainability, and privacy protection. Whilst several previous tutorials have been made for the introduction of DGL in KDD, seldom is there a special focus on its safety aspects, including reliability, explainability, and privacy protection capability. This tutorial mainly covers the key achievements of trustworthy graph learning in recent years. Specifically, we will discuss three essential topics, that is, the reliability of DGL against inherent noise, distribution shift and adversarial attack, explainability methods, and privacy protection for DGL. Meanwhile, we will introduce some guidelines for applying DGL to risk-sensitive applications (e.g., AI drug discovery) to ensure GNN models behave in a trustworthy way. We hope our tutorial can offer a comprehensive review of recent advances in this area and also provide some useful suggestions to guide the developers to choose appropriate techniques for their applications.