Reviewerly/exHarmony huggingface dataset:
Preserving Missing Data Distribution in Synthetic Data

Data from Web artifacts and from the Web is often sensitive and cannot be directly shared for data analysis. Therefore, synthetic data generated from the real data is increasingly used as a privacy-preserving substitute. In many cases, real data from the web has missing values where the missingness itself possesses important informational content, which domain experts leverage to improve their analysis. However, this information content is lost if either imputation or deletion is used before synthetic data generation. In this paper, we propose several methods to generate synthetic data that preserve both the observable and the missing data distributions. An extensive empirical evaluation over a range of carefully fabricated and real world datasets demonstrates the effectiveness of our approach.
Automated Content Moderation Increases Adherence to Community Guidelines

Online social media platforms use automated moderation systems to remove or reduce the visibility of rule-breaking content. While previous work has documented the importance of manual content moderation, the effects of automated content moderation remain largely unknown. Here, in a large study of Facebook comments (n = 412M), we used a fuzzy regression discontinuity design to measure the impact of automated content moderation on subsequent rule-breaking behavior (number of comments hidden/deleted) and engagement (number of additional comments posted). We found that comment deletion decreased subsequent rule-breaking behavior in shorter threads (20 or fewer comments), even among other participants, suggesting that the intervention prevented conversations from derailing. Further, the effect of deletion on the affected user’s subsequent rule-breaking behavior was longer-lived than its effect on reducing commenting in general, suggesting that users were deterred from rule-breaking but not from commenting. In contrast, hiding (rather than deleting) content had small and statistically insignificant effects. Our results suggest that automated content moderation increases adherence to community guidelines.
Learning Disentangled Representation via Domain Adaptation for Dialogue Summarization

Dialogue summarization, which aims to generate a summary for an input dialogue, plays a vital role in intelligent dialogue systems. The end-to-end models have achieved satisfactory performance in summarization, but the success is built upon enough annotated data, which is costly to obtain, especially in the dialogue summarization. To leverage the rich external data, previous works first pre-train the model on the other domain data (e.g., the news domain), and then fine-tune it directly on the dialogue domain. The data from different domains are equally treated during the training process, while the vast differences between dialogues (usually informal, repetitive, and with multiple speakers) and conventional articles (usually formal and concise) are neglected. In this work, we propose to use a disentangled representation method to reduce the deviation between data in different domains, where the input data is disentangled into domain-invariant and domain-specific representations. The domain-invariant representation carries context information that is supposed to be the same across domains (e.g., news, dialogue) and the domain-specific representation indicates the input data belongs to a particular domain. We use adversarial learning and contrastive learning to constrain the disentangled representations to the target space. Furthermore, we propose two novel reconstruction strategies, namely backtracked and cross-track reconstructions, which aim to reduce the domain characteristics of out-of-domain data and mitigate the domain bias of the model. Experimental results on three public datasets show that our model significantly outperforms the strong baselines.
Web Photo Source Identification based on Neural Enhanced Camera Fingerprint

With the growing popularity of smartphone photography in recent years, web photos play an increasingly important role in all walks of life. Source camera identification of web photos aims to establish a reliable linkage from the captured images to their source cameras, and has a broad range of applications, such as image copyright protection, user authentication, investigated evidence verification, etc. This paper presents an innovative and practical source identification framework that employs neural-network enhanced sensor pattern noise to trace back web photos efficiently while ensuring security. Our proposed framework consists of three main stages: initial device fingerprint registration, fingerprint extraction and cryptographic connection establishment while taking photos, and connection verification between photos and source devices. By incorporating metric learning and frequency consistency into the deep network design, our proposed fingerprint extraction algorithm achieves state-of-the-art performance on modern smartphone photos for reliable source identification. Meanwhile, we also propose several optimization sub-modules to prevent fingerprint leakage and improve accuracy and efficiency. Finally for practical system design, two cryptographic schemes are introduced to reliably identify the correlation between registered fingerprint and verified photo fingerprint, i.e. fuzzy extractor and zero-knowledge proof (ZKP). The codes for fingerprint extraction network and benchmark dataset with modern smartphone cameras photos are all publicly available at https://github.com/PhotoNecf/PhotoNecf 1.
KGTrust: Evaluating Trustworthiness of SIoT via Knowledge Enhanced Graph Neural Networks

Social Internet of Things (SIoT), a promising and emerging paradigm that injects the notion of social networking into smart objects (i.e., things), paving the way for the next generation of Internet of Things. However, due to the risks and uncertainty, a crucial and urgent problem to be settled is establishing reliable relationships within SIoT, that is, trust evaluation. Graph neural networks for trust evaluation typically adopt a straightforward way such as one-hot or node2vec to comprehend node characteristics, which ignores the valuable semantic knowledge attached to nodes. Moreover, the underlying structure of SIoT is usually complex, including both the heterogeneous graph structure and pairwise trust relationships, which renders hard to preserve the properties of SIoT trust during information propagation. To address these aforementioned problems, we propose a novel knowledge-enhanced graph neural network (KGTrust) for better trust evaluation in SIoT. Specifically, we first extract useful knowledge from users’ comment behaviors and external structured triples related to object descriptions, in order to gain a deeper insight into the semantics of users and objects. Furthermore, we introduce a discriminative convolutional layer that utilizes heterogeneous graph structure, node semantics, and augmented trust relationships to learn node embeddings from the perspective of a user as a trustor or a trustee, effectively capturing multi-aspect properties of SIoT trust during information propagation. Finally, a trust prediction layer is developed to estimate the trust relationships between pairwise nodes. Extensive experiments on three public datasets illustrate the superior performance of KGTrust over state-of-the-art methods.
An Attentional Multi-scale Co-evolving Model for Dynamic Link Prediction

Dynamic link prediction is essential for a wide range of domains, including social networks, bioinformatics, knowledge bases, and recommender systems. Existing works have demonstrated that structural information and temporal information are two of the most important information for this problem. However, existing works either focus on modeling them independently or modeling the temporal dynamics of a single structural scale, neglecting the complex correlations among them. This paper proposes to model the inherent correlations among the evolving dynamics of different structural scales for dynamic link prediction. Following this idea, we propose an Attentional Multi-scale Co-evolving Network (AMCNet). Specifically, We model multi-scale structural information by a motif-based graph neural network with multi-scale pooling. Then, we design a hierarchical attention-based sequence-to-sequence model for learning the complex correlations among the evolution dynamics of different structural scales. Extensive experiments on four real-world datasets with different characteristics demonstrate that AMCNet significantly outperforms the state-of-the-art in both single-step and multi-step dynamic link prediction tasks.
xGCN: An Extreme Graph Convolutional Network for Large-scale Social Link Prediction

Graph neural networks (GNNs) have seen widespread usage across multiple real-world applications, yet in transductive learning, they still face challenges in accuracy, efficiency, and scalability, due to the extensive number of trainable parameters in the embedding table and the paradigm of stacking neighborhood aggregations. This paper presents a novel model called xGCN for large-scale network embedding, which is a practical solution for link predictions. xGCN addresses these issues by encoding graph-structure data in an extreme convolutional manner, and has the potential to push the performance of network embedding-based link predictions to a new record. Specifically, instead of assigning each node with a directly learnable embedding vector, xGCN regards node embeddings as static features. It uses a propagation operation to smooth node embeddings and relies on a Refinement neural Network (RefNet) to transform the coarse embeddings derived from the unsupervised propagation into new ones that optimize a training objective. The output of RefNet, which are well-refined embeddings, will replace the original node embeddings. This process is repeated iteratively until the model converges to a satisfying status. Experiments on three social network datasets with link prediction tasks show that xGCN not only achieves the best accuracy compared with a series of competitive baselines but also is highly efficient and scalable.
Federated Node Classification over Graphs with Latent Link-type Heterogeneity

Federated learning (FL) aims to train powerful and generalized global models without putting distributed data together, which has been shown effective in various domains of machine learning. The non-IIDness of data across local clients has been a major challenge for FL. In graphs, one specifically important perspective of non-IIDness is manifested in the link-type heterogeneity underlying homogeneous graphs– the seemingly uniform links captured in most real-world networks can carry different levels of homophily or semantics of relations, while the exact sets and distributions of such latent link-types can further differ across local clients. Through our preliminary data analysis, we are motivated to design a new graph FL framework that can simultaneously discover latent link-types and model message-passing w.r.t. the discovered link-types through the collaboration of distributed local clients. Specifically, we propose a framework FedLit that can dynamically detect the latent link-types during FL via an EM-based clustering algorithm and differentiate the message-passing through different types of links via multiple convolution channels. For experiments, we synthesize multiple realistic datasets of graphs with latent heterogeneous link-types from real-world data, and partition them with different levels of link-type heterogeneity. Comprehensive experimental results and in-depth analysis have demonstrated both superior performance and rational behaviors of our proposed techniques.
Demystifying Mobile Extended Reality in Web Browsers: How Far Can We Go?

Mobile extended reality (XR) has developed rapidly in recent years. Compared with the app-based XR, XR in web browsers has the advantages of being lightweight and cross-platform, providing users with a pervasive experience. Therefore, many frameworks are emerging to support the development of XR in web browsers. However, little has been known about how well these frameworks perform and how complex XR apps modern web browsers can support on mobile devices. To fill the knowledge gap, in this paper, we conduct an empirical study of mobile XR in web browsers. We select seven most popular web-based XR frameworks and investigate their runtime performance, including 3D rendering, camera capturing, and real-world understanding. We find that current frameworks have the potential to further enhance their performance by increasing GPU utilization or improving computing parallelism. Besides, for 3D scenes with good rendering performance, developers can feel free to add camera capturing with little influence on performance to support augmented reality (AR) and mixed reality (MR) applications. Based on our findings, we draw several practical implications to provide better XR support in web browsers.
Differentiable Optimized Product Quantization and Beyond

Vector quantization techniques, such as Product Quantization (PQ), play a vital role in approximate nearest neighbor search (ANNs) and maximum inner product search (MIPS) owing to their remarkable search and storage efficiency. However, the indexes in vector quantization cannot be trained together with the inference models since data indexing is not differentiable. To this end, differentiable vector quantization approaches, such as DiffPQ and DeepPQ, have been recently proposed, but existing methods have two drawbacks. First, they do not impose any constraints on codebooks, such that the resultant codebooks lack diversity, leading to limited retrieval performance. Second, since data indexing resorts to operator, differentiability is usually achieved by either relaxation or Straight-Through Estimation (STE), which leads to biased gradient and slow convergence. To address these problems, we propose a Differentiable Optimized Product Quantization method (DOPQ) and beyond in this paper. Particularly, each data is projected into multiple orthogonal spaces, to generate multiple views of data. Thus, each codebook is learned with one view of data, guaranteeing the diversity of codebooks. Moreover, instead of simple differentiable relaxation, DOPQ optimizes the loss based on direct loss minimization, significantly reducing the gradient bias problem. Finally, DOPQ is evaluated with seven datasets of both recommendation and image search tasks. Extensive experimental results show that DOPQ outperforms state-of-the-art baselines by a large margin.
Disentangling Degree-related Biases and Interest for Out-of-Distribution Generalized Directed Network Embedding

The goal of directed network embedding is to represent the nodes in a given directed network as embeddings that preserve the asymmetric relationships between nodes. While a number of directed network embedding methods have been proposed, we empirically show that the existing methods lack out-of-distribution generalization abilities against degree-related distributional shifts. To mitigate this problem, we propose ODIN (Out-of-Distribution Generalized Directed Network Embedding), a new directed NE method where we model multiple factors in the formation of directed edges. Then, for each node, ODIN learns multiple embeddings, each of which preserves its corresponding factor, by disentangling interest factors and biases related to in- and out-degrees of nodes. Our experiments on four real-world directed networks demonstrate that disentangling multiple factors enables ODIN to yield out-of-distribution generalized embeddings that are consistently effective under various degrees of shifts in degree distributions. Specifically, ODIN universally outperforms 9 state-of-the-art competitors in 2 LP tasks on 4 real-world datasets under both identical distribution (ID) and non-ID settings. The code is available at https://github.com/hsyoo32/odin.
Dělen: Enabling Flexible and Adaptive Model-serving for Multi-tenant Edge AI

Model-serving systems expose machine learning (ML) models to applications programmatically via a high-level API. Cloud platforms use these systems to mask the complexities of optimally managing resources and servicing inference requests across multiple applications. Model serving at the edge is now also becoming increasingly important to support inference workloads with tight latency requirements. However, edge model serving differs substantially from cloud model serving in its latency, energy, and accuracy constraints: these systems must support multiple applications with widely different latency and accuracy requirements on embedded edge accelerators with limited computational and energy resources.
Fairguard: Harness Logic-based Fairness Rules in Smart Cities

Smart cities operate on computational predictive frameworks that collect, aggregate, and utilize data from large-scale sensor networks. However, these frameworks are prone to multiple sources of data and algorithmic bias, which often lead to unfair prediction results. In this work, we first demonstrate that bias persists at a micro-level both temporally and spatially by studying real city data from Chattanooga, TN. To alleviate the issue of such bias, we introduce Fairguard, a micro-level temporal logic-based approach for fair smart city policy adjustment and generation in complex temporal-spatial domains. The Fairguard framework consists of two phases: first, we develop a static generator that is able to reduce data bias based on temporal logic conditions by minimizing correlations between selected attributes. Then, to ensure fairness in predictive algorithms, we design a dynamic component to regulate prediction results and generate future fair predictions by harnessing logic rules. Evaluations show that logic-enabled static Fairguard can effectively reduce biased correlations while dynamic Fairguard can guarantee fairness on protected groups at run-time with minimal impact on overall performance.
Efficient and Low Overhead Website Fingerprinting Attacks and Defenses based on TCP/IP Traffic

Website fingerprinting attack is an extensively studied technique used in a web browser to analyze traffic patterns and thus infer confidential information about users. Several website fingerprinting attacks based on machine learning and deep learning tend to use the most typical features to achieve a satisfactory performance of attacking rate. However, these attacks suffer from several practical implementation factors, such as a skillfully pre-processing step or a clean dataset. To defend against such attacks, random packet defense (RPD) with a high cost of excessive network overhead is usually applied. In this work, we first propose a practical filter-assisted attack against RPD, which can filter out the injected noises using the statistical characteristics of TCP/IP traffic. Then, we propose a list-assisted defensive mechanism to defend the proposed attack method. To achieve a configurable trade-off between the defense and the network overhead, we further improve the list-based defense by a traffic splitting mechanism, which can combat the mentioned attacks as well as save a considerable amount of network overhead. In the experiments, we collect real-life traffic patterns using three mainstream browsers, i.e., Microsoft Edge, Google Chrome, and Mozilla Firefox, and extensive results conducted on the closed and open-world datasets show the effectiveness of the proposed algorithms in terms of defense accuracy and network efficiency.
Automatic Feature Selection By One-Shot Neural Architecture Search In Recommendation Systems

Feature selection is crucial in large-scale recommendation system, which can not only reduce the computational cost, but also improve the recommendation efficiency. Most existing works rank the features and then select the top-k ones as the final feature subset. However, they assess feature importance individually and ignore the interrelationship between features. Consequently, multiple features with high relevance may be selected simultaneously, resulting in sub-optimal result. In this work, we solve this problem by proposing an AutoML-based feature selection framework that can automatically search the optimal feature subset. Specifically, we first embed the search space into a weight-sharing Supernet. Then, a two-stage neural architecture search method is employed to evaluate the feature quality. In the first stage, a well-designed sampling method considering feature convergence fairness is applied to train the Supernet. In the second stage, a reinforcement learning method is used to search for the optimal feature subset efficiently. The Experimental results on two real datasets demonstrate the superior performance of new framework over other solutions. Our proposed method obtain significant improvement with a 20% reduction in the amount of features on the Criteo. More validation experiments demonstrate the ability and robustness of the framework.
Everything Evolves in Personalized PageRank

Personalized PageRank, as a graphical model, has been proven as an effective solution in many applications such as web page search, recommendation, etc. However, in the real world, the setting of personalized PageRank is usually dynamic like the evolving World Wide Web. On the one hand, the outdated PageRank solution can be sub-optimal for ignoring the evolution pattern. On the other hand, solving the solution from the scratch at each timestamp causes costly computation complexity. Hence, in this paper, we aim to solve the Personalized PageRank effectively and efficiently in a fully dynamic setting, i.e., every component in the Personalized PageRank formula is dependent on time. To this end, we propose the EvePPR method that can track the exact personalized PageRank solution at each timestamp in the fully dynamic setting, and we theoretically and empirically prove the accuracy and time complexity of EvePPR. Moreover, we apply EvePPR to solve the dynamic knowledge graph alignment task, where a fully dynamic setting is necessary but complex. The experiments show that EvePPR outperforms the state-of-the-art baselines for similar nodes retrieval across graphs.
CollabEquality: A Crowd-AI Collaborative Learning Framework to Address Class-wise Inequality in Web-based Disaster Response

Web-based disaster response (WebDR) is emerging as a pervasive approach to acquire real-time situation awareness of disaster events by collecting timely observations from the Web (e.g., social media). This paper studies a class-wise inequality problem in WebDR applications where the objective is to address the limitation of current WebDR solutions that often have imbalanced classification performance across different classes. To address such a limitation, this paper explores the collaborative strengths of the diversified yet complementary biases of AI and crowdsourced human intelligence to ensure a more balanced and accurate performance for WebDR applications. However, two critical challenges exist: 1) it is difficult to identify the imbalanced AI results without knowing the ground-truth WebDR labels a priori; ii) it is non-trivial to address the class-wise inequality problem using potentially imperfect crowd labels. To address the above challenges, we develop CollabEquality, an inequality-aware crowd-AI collaborative learning framework that carefully models the inequality bias of both AI and human intelligence from crowdsourcing systems into a principled learning framework. Extensive experiments on two real-world WebDR applications demonstrate that CollabEquality consistently outperforms the state-of-the-art baselines by significantly reducing class-wise inequality while improving the WebDR classification accuracy.