Illicit activities such as financial fraud and fake promotions are increasingly prevalent on social applications. The diverse behavioral structures of different types of illicit accounts pose difficulties in designing unified detection strategies. Unlike existing studies that focus on binary detection and often rely on user static profiles, we introduce a novel task: illicit account detection based solely on user behavior sequences. This task presents two key challenges: 1) illicit accounts often mimic benign users by performing normal-looking behavior subsequences, and 2) behaviors with the same action (e.g., add-friend, initiate-transaction) can serve different purposes, such as illicit or benign. To address these challenges, we propose a Pattern-aware Illicit Accounts Detection (PIAD) framework that consists of three components: 1) a dual-perspective pattern mining module that extracts category-specific self- and interaction-behavior patterns from behavior sequences to capture distinct behavioral regularities across different user types; 2) a contextualized action semantic encoding algorithm that aligns action codings with contextual dependencies among behaviors within user sequences to capture variations in purposes when behaviors with the same actions occur under different contexts; and 3) a pattern-aware fusion model that integrates the mined patterns with the context and interaction in behavior sequences to learn discriminative representations for detection. Extensive experiments on real-world datasets demonstrate that PIAD consistently outperforms state-of-the-art baselines with an average 7.51% improvement on F1 score.

Data transformation is a critical challenge in modern data management systems, particularly when handling complex operations over multiple data sources. However, existing approaches rely on supervised learning, which requires tremendous data labeling and training overhead. To alleviate such overhead while improving accuracy, we demonstrate a novel system DataMorpher that leverages Large Language Models (LLMs) to generate code that transforms source datasets into a user-specified target format. To generate a high-quality and token-efficient prompt, we leverage data profiling to extract features from the source datasets and historical examples of the target data. We also select a subset of features to reduce noise and costs using a ranking algorithm. These selected features are finally translated into a declarative language, which is inspired by SQL's data definition language (DDL), before being added to the prompt. We will demonstrate the workflow and effectiveness of DATAMORPHER using real-world data transformation workflows from Microsoft's GitHub benchmark, smart building, and medical data integration. (A5-min video of our demo is available at https://youtu.be/CuDm46K-_eA.)

The surge in detecting fake news on social networks leads to increased research attention, particularly in the realm of deep learning models based on graph neural networks (GNNs). However, as research progresses, concerns emerge about the vulnerability of these detection models. In this study, we introduce an attack problem that perturbs user-news engagements by injecting bots to shield the targeted fake news from being detected by GNN-based fake news detection models. We propose a black-box attack method named Query-enhanced Surrogate-based Attack under Assortativity Constraint (QSA-AC) to work for this attack problem. QSA-AC combines surrogate-based and query-based approaches to improve attack effectiveness. At the same time, QSA-AC maintains a balance between attack effectiveness and imperceptibility by adjusting the local fluctuations of the assortativity with respect to the news on the social network. In addition, we introduce an evaluation metric, local strength assortativity perturbation rate (LSAPR), to assess the imperceptibility of the attack from the local perspective. Extensive experiments on two fake news datasets demonstrate that the proposed QSA-AC can achieve the optimal attack effectiveness, and control the trade-off between the attack effectiveness and imperceptibility.

Deep visual graph matching (GM) is a challenging combinatorial task that involves finding a permutation matrix that indicates the correspondence between keypoints from a pair of images. Like many learning systems, empirical studies have shown that visual GM is susceptible to adversarial attacks, with reliability issues in downstream applications. To the best of our knowledge, certifying robustness for deep visual GM remains an open challenge with two main difficulties: how to handle the paired inputs together with the heavily non-linear permutation output space (especially at large scale), and how to balance the trade-off between certified robustness and matching performance.

Existing approaches to automatic data transformation are insufficient to meet the requirements in many real-world scenarios, such as the building sector. First, there is no convenient interface for domain experts to provide domain knowledge easily. Second, they require significant training data collection overheads. Third, the accuracy suffers from complicated schema changes. To address these shortcomings, we present a novel approach that leverages the unique capabilities of large language models (LLMs) in coding, complex reasoning, and zero-shot learning to generate SQL code that transforms the source datasets into the target datasets. We demonstrate the viability of this approach by designing an LLM-based framework, termed SQLMorpher, which comprises a prompt generator that integrates the initial prompt with optional domain knowledge and historical patterns in external databases. It also implements an iterative prompt optimization mechanism that automatically improves the prompt based on flaw detection. The key contributions of this work include (1) pioneering an end-to-end LLM-based solution for data transformation, (2) developing a benchmark dataset of 105 real-world building energy data transformation problems, and (3) conducting an extensive empirical evaluation where our approach achieved 96% accuracy in all 105 problems. SQLMorpher demonstrates the effectiveness of utilizing LLMs in complex, domain-specific challenges, highlighting the potential of their potential to drive sustainable solutions.

Many real-world machine learning problems involve structured prediction beyond categorical labels. However, most existing robustness certification works are devoted to the classification case. It remains open for robustness certification for more general outputs. In this paper, we propose a novel framework of robustness certification for structured prediction problems, where the output space is modeled as a semimetric space with a distance function that satisfies non-negativity and symmetry but not necessarily the triangle inequality. We further develop our tailored certification methods for binary, numerical, and hybrid inputs in structured prediction. Experiment results show that our method achieves tighter robustness guarantees than the SOTA structured certification baseline for numerical inputs (for which it only supports) with ℓ2 norm perturbation when outputs are measured by intersection over union (IoU) similarity, total variation distance, and perceptual distance. Moreover, we achieve good robustness certification for binary inputs with ℓ0 norm perturbation and hybrid inputs with corresponding perturbation when outputs are measured by Manhattan distance.

Edge-cloud collaborative tasks with real-world services emerge in recent years and attract worldwide attention. Unfortunately, state-of-the-art edge-cloud collaborative machine-learning services are still not that reliable due to the data heterogeneity on the edge, where we usually have access to a mixed-up training set, which is intrinsically collected from various distributions of underlying tasks. Finding such hidden tasks that need to be revealed from given datasets is called the Task Partition problem. Manual task partition is usually expensive, unscalable, and biased. Accordingly, we propose Quality-aware Task Partition (QTP) problem, in which final tasks are partitioned by the performance of task models. To the best of our knowledge, this work is the first one to study the QTP problem with an emphasis on task quality. We also implement a public service, HiLens on Huawei Cloud, to support the whole process. We develop a polynomial-time algorithm namely the Task-Forest algorithm (TForest). TForest shows its superiority based on a case study with 57 real-world cameras. Compared with STOA baselines, TForest has on average 9.2% higher F1-scores and requires 43.1% fewer samples when deploying new cameras. Partial code of the framework has been adopted and released to KubeEdge-Sedna.

A host of different KG embedding techniques have emerged recently and have been empirically shown to be very effective in accurately predicting missing facts in a KG, thus improving its coverage and quality. Unfortunately, embedding techniques can fall prey to adversarial data poisoning attack. In this form of attack, facts may be added to or deleted from a KG, called performing perturbations, that results in the manipulation of the plausibility of target facts in a KG. While recent works confirm this intuition, the attacks considered there ignore the risk of exposure. Intuitively, an attack is of limited value if it is highly likely to be caught, i.e., exposed. To address this, we introduce a notion of the exposure risk and propose a novel problem of attacking a KG by means of perturbations where the goal is to maximize the manipulation of the target fact's plausibility while keeping the risk of exposure under a given budget. We design a deep reinforcement learning-based framework, called RATA, that learns to use low-risk perturbations without compromising on the performance, i.e., manipulation of target fact plausibility. We test the performance of RATA against recently proposed strategies for KG attacks, on two different benchmark datasets and on different kinds of target facts. Our experiments show that RATA achieves state-of-the-art performance even while using a fraction of the risk.

In many industry scale applications, large and resource consuming machine learning models reside in powerful cloud servers. At the same time, large amounts of input data are collected at the edge of cloud. The inference results are also communicated to users or passed to downstream tasks at the edge. The edge often consists of a large number of low-power devices. It is a big challenge to design industry products to support sophisticated deep model deployment and conduct model inference in an efficient manner so that the model accuracy remains high and the end-to-end latency is kept low. This paper describes the techniques and engineering practice behind Auto-Split, an edge-cloud collaborative prototype of Huawei Cloud. This patented technology is already validated on selected applications, is on its way for broader systematic edge-cloud application integration, and is being made available for public use as an automated pipeline service for end-to-end cloud-edge collaborative intelligence deployment. To the best of our knowledge, there is no existing industry product that provides the capability of Deep Neural Network (DNN) splitting.

Federated learning has become increasingly popular as it facilitates collaborative training of machine learning models among multiple clients while preserving their data privacy. In practice, one major challenge for federated learning is to achieve fairness in collaboration among the participating clients, because different clients' contributions to a model are usually far from equal due to various reasons. Besides, as machine learning models are deployed in more and more important applications, how to achieve model fairness, that is, to ensure that a trained model has no discrimination against sensitive attributes, has become another critical desiderata for federated learning. In this tutorial, we discuss formulations and methods such that collaborative fairness, model fairness, and privacy can be fully respected in federated learning. We review the existing efforts and the latest progress, and discuss a series of potential directions.

More and more AI services are provided through APIs on cloud where predictive models are hidden behind APIs. To build trust with users and reduce potential application risk, it is important to interpret how such predictive models hidden behind APIs make their decisions. The biggest challenge of interpreting such predictions is that no access to model parameters or training data is available. Existing works interpret the predictions of a model hidden behind an API by heuristically probing the response of the API with perturbed input instances. However, these methods do not provide any guarantee on the exactness and consistency of their interpretations. In this paper, we propose an elegant closed form solution named OpenAPI to compute exact and consistent interpretations for the family of Piecewise Linear Models (PLM), which includes many popular classification models. The major idea is to first construct a set of overdetermined linear equation systems with a small set of perturbed instances and the predictions made by the model on those instances. Then, we solve the equation systems to identify the decision features that are responsible for the prediction on an input instance. Our extensive experiments clearly demonstrate the exactness and consistency of our method.

Given a temporal weighted graph that consists of a potentially endless stream of updates, we are interested in finding density bursting subgraphs (DBS for short), where a DBS is a subgraph that accumulates its density at the fastest speed. Online DBS detection enjoys many novel applications. At the same time, it is challenging since the time duration of a DBS can be arbitrarily long but a limited size storage can buffer only up to a certain number of updates. To tackle this problem, we observe the critical decomposability of DBSs and show that a DBS with a long time duration can be decomposed into a set of indecomposable DBSs with equal or larger burstiness. We further prove that the time duration of an indecomposable DBS is upper bounded and propose an efficient method TopkDBSOL to detect indecomposable DBSs in an online manner. Extensive experiments demonstrate the effectiveness, efficiency and scalability of TopkDBSOL in detecting significant DBSs from temporal graphs in real applications.

Both text detection and structured data extraction are imperative in an optical character recognition (OCR) processing pipeline. Text detection, especially for indistinct, diverse, multi-language text regions, is one of the most challenging tasks in computer vision and has attracted increasing attention recently. Moreover, although there are some studies in data mining related to structured data extraction, it has not received its deserved attention as one of important steps in OCR. The previous methods for structural data extraction, including layout template-based, rule-based, and natural language processing (NLP)-based methods, usually leads to either inaccurate results or complex modules. In this paper, we integrate text detection and structured data extraction into a unified deep learning-based Image Text Extraction (ITE) scheme. Our ITE is an end-to-end trainable model and able to handle multi-scale and multi-lingual text in a single process. Experiments on large-scale real-world passport and medical receipt datasets have demonstrated the superiority of the proposed method in terms of both effectiveness and efficiency.

Strong intelligent machines powered by deep neural networks are increasingly deployed as black boxes to make decisions in risk-sensitive domains, such as finance and medical. To reduce potential risk and build trust with users, it is critical to interpret how such machines make their decisions. Existing works interpret a pre-trained neural network by analyzing hidden neurons, mimicking pre-trained models or approximating local predictions. However, these methods do not provide a guarantee on the exactness and consistency of their interpretations. In this paper, we propose an elegant closed form solution named $OpenBox$ to compute exact and consistent interpretations for the family of Piecewise Linear Neural Networks (PLNN). The major idea is to first transform a PLNN into a mathematically equivalent set of linear classifiers, then interpret each linear classifier by the features that dominate its prediction. We further apply $OpenBox$ to demonstrate the effectiveness of non-negative and sparse constraints on improving the interpretability of PLNNs. The extensive experiments on both synthetic and real world data sets clearly demonstrate the exactness and consistency of our interpretation.

Document stores that provide the efficiency of a schema-less interface are widely used by developers in mobile and cloud applications. However, the simplicity developers achieved controversially leads to complexity for data management due to lack of a schema. In this paper, we present a schema management framework for document stores. This framework discovers and persists schemas of JSON records in a repository, and also supports queries and schema summarization. The major technical challenge comes from varied structures of records caused by the schema-less data model and schema evolution. In the discovery phase, we apply a canonical form based method and propose an algorithm based on equivalent sub-trees to group equivalent schemas efficiently. Together with the algorithm, we propose a new data structure, eSiBu-Tree, to store schemas and support queries. In order to present a single summarized representation for heterogenous schemas in records, we introduce the concept of "skeleton", and propose to use it as a relaxed form of the schema, which captures a small set of core attributes. Finally, extensive experiments based on real data sets demonstrate the efficiency of our proposed schema discovery algorithms, and practical use cases in real-world data exploration and integration scenarios are presented to illustrate the effectiveness of using skeletons in these applications.