Artificial intelligence (AI) models are increasingly deployed directly in web browsers to enable low-latency, privacy-preserving inference. While this shift offers significant usability and scalability benefits, it also exposes model code and parameters to untrusted environments, leaving them vulnerable to theft, reverse engineering, and tampering. Our analysis demonstrates that existing JavaScript-based inference frameworks are highly susceptible to model extraction, posing serious security and intellectual property risks. To address this gap, we present WAMO, a WebAssembly-based obfuscation framework that secures browser-side AI models. WAMO introduces a comprehensive conversion pipeline that translates mainstream model formats into Wasm-native modules, applying model-specific obfuscation at the Wasm layer to target weights, operators, and computation graphs. This design shifts model execution from easily inspected JavaScript assets to hardened Wasm binaries, significantly raising the difficulty of static and dynamic analysis. Evaluation shows that WAMO increases cyclomatic complexity by 71.0% and Halstead effort by 455.57%, while incurring < 1% accuracy loss and no inference slowdown.

As GPUs are widely deployed in High Performance Computing systems, it is critical to ensure that these systems can perform reliably. To improve system reliability, researchers estimate the error resilience of GPU programs by understanding resilience characteristics or modeling error propagation. However, features indicative of resilience rely on manual extraction from simulations of numerous faults, and error propagation analysis cannot target fine-grained bit-level faults. To address those problems, this paper introduces a novel paradigm, namely InstrDGM, for efficiently predicting GPU error resilience. Specifically, InstrDGM first fine-tunes a large language model using extensive sequences of GPU assembly instructions for extracting the semantic representation of instructions automatically. Meanwhile, we consider the propagation of bit-level faults during instruction execution and data transfer processes, and leverage graph neural networks to capture their distinct error propagation patterns. Then, the fault embeddings extracted from these error propagation patterns are integrated for error resilience prediction. Additionally, this paper releases a new dataset for GPU error resilience assessment, containing 1.2 million fault samples. Finally, extensive experiments show that InstrDGM significantly outperforms existing methods.

Despite the vital role of recommendation systems (RS) in delivering personalized services tailored to users' needs, user fairness issues have increasingly emerged in recent years, especially differentiated treatments caused by user sensitive attributes. This not only undermines both user experience and platform revenues, but also leads to potential social unfairness. Although many fairness-aware methods have been developed and achieved some success, many of them filter out sensitive attribute information while ignoring the potential loss of personalized information, leading to suboptimal results. Large language models (LLMs) have demonstrated remarkable capabilities across various tasks, while their potential in fairness-aware recommendation remains further unexplored. In this paper, we propose a new exploration of fairness-aware RS by prompting LLMs with the user's personalized fairness degrees to augment fair user-item interaction for training. Specifically, to estimate the fairness degree of each user, we first design a personalized unfairness modelling module, consisting of a replaceable fairness-aware representation learning model. Moreover, to enable LLMs to perceive fairness from semantic information and adapt to various scenarios, we propose a prompt tuning mechanism to optimize user-shared prompt templates with the objective of maximizing the consistency with users' preferences and the diversity of augmented data. Finally, we utilize LLMs to augment fair interaction data with the optimal prompts and integrate it with the raw data to re-train the recommendation model. Extensive experiments on two real-world datasets demonstrate the superiority of our approach in terms of recommendation performance, fairness, and robustness.

WebAssembly (Wasm) is a universal low-level bytecode designed to build modern web systems. Recent studies have shown that technologies such as voltage scaling and RowHammer attacks are expected to increase the likelihood of bit flips, which may cause unacceptable or catastrophic system failures. This raises concerns about the impact of bit flips on Wasm programs, which run as instructions in web systems, and it is an undeveloped topic since the features of Wasm differ from traditional programs. In this paper, we propose a novel paradigm, namely IVPSEG, to understand the error propagation of bit flips within Wasm programs. Specifically, we first use Large Language Models (LLMs) to automatically extract instruction embeddings containing semantic knowledge of each instruction's context. Then, we exploit these embeddings and program structure (control execution and data transfer) to construct a semantic enhanced code property graph, which implicates the potential path of error propagation. Based on this graph, we utilize graph neural networks and attention diffusion to optimize instruction embeddings by capturing different error propagation patterns for instruction vulnerability prediction. In particular, we build a Wasm compilation and fault generation system to simulate bit flips at Wasm runtime. Our experimental results with 14 benchmark programs and test cases show IVPSEG outperforms the state-of-the-art methods in terms of accuracy (average 13.06%ͽ ), F1-score (average 14.93%↑), and model robustness.

The demand for more precise and timely urban resource allocation and management has driven the extension of urban flow prediction from short-term to long-term horizons. As the time scale expands, the issue of urban flow distribution shift becomes increasingly prominent due to various impact factors, such as weather, events, city changes, etc. Traditionally, comprehensively analyzing and addressing the causal relationships underlying the distribution shift caused by these factors has been challenging. In this paper, we propose that these impact factors can be partitioned in two major types, i.e., context factors and structural factors. We then present a decomposition-based model for long-term urban flow prediction from a causal perspective, namedDeCau, which can discriminate between the two types of factors for effectively solving the problem of urban flow distribution shift. First, we employ a decomposition module to decompose urban flow into seasonal part and trend part. The seasonal part contains high frequency irregular variations caused by context factors. We advise a shared distribution estimator to approximate the unavailable prior distributions of context factors, and then apply causal intervention to mitigate the confounding impact of context factors. The distribution shift in the trend part is induced by structural factors. We design a dual causal dependency extractor to model the causality between POIs distribution and urban flow, and then eliminate spurious correlations through causal adjustment. Finally, we design an end-to-end framework for long-term urban flow prediction by combining the embeddings from two parts, enabling the model to generalize to unseen distribution. Extensive experimental results demonstrateDeCauoutperforms state-of-the-art baselines.

Point-of-Interest (POI) recommendation, an important research hotspot in the field of urban computing, plays a crucial role in urban construction. While understanding the process of users’ travel decisions and exploring the causality of POI choosing is not easy due to the complex and diverse influencing factors in urban travel scenarios. Moreover, the spurious explanations caused by severe data sparsity, i.e., misrepresenting universal relevance as causality, may also hinder us from understanding users’ travel decisions. To this end, in this article, we propose a factor-level causal explanation generation framework based on counterfactual data augmentation for user travel decisions, named Factor-level Causal Explanation for User Travel Decisions (FCE-UTD), which can distinguish between true and false causal factors and generate true causal explanations. Specifically, we first assume that a user decision is composed of a set of several different factors. Then, by preserving the user decision structure with a joint counterfactual contrastive learning paradigm, we learn the representation of factors and detect the relevant factors. Next, we further identify true causal factors by constructing counterfactual decisions with a counterfactual representation generator, in particular, it can not only augment the dataset and mitigate the sparsity but also contribute to clarifying the causal factors from other false causal factors that may cause spurious explanations. Besides, a causal dependency learner is proposed to identify causal factors for each decision by learning causal dependency scores. Extensive experiments conducted on three real-world datasets demonstrate the superiority of our approach in terms of check-in rate, fidelity, and downstream tasks under different behavior scenarios. The extra case studies also demonstrate the ability of FCE-UTD to generate causal explanations in POI choosing.

Long-term urban crowd flow prediction involving the evolution trends of crowd flow is of great importance of traffic management, public safety and urban planning. However, learning long-term crowd flow is very challenging due to the latent effect of varied urban Point-of-Interests distribution, which is quite different from the short-term crowd flow mainly influenced by readily available external factors like weather, date, etc. The key issue for us is how to learn the interaction between POI distribution and human mobility in a dynamic way. To address this problem, we propose a POI-flow interaction based spatial-temporal framework (PFIST) for long-term crowd flow prediction. First, we model the long-term evolution representations of crowd flow and POI distribution. Then we study the dynamic interaction between POI transition patterns and crowd flow variation on different POI periods and categories. Afterwards, we decompose the flow sequence into long-term trend and daily variation parts and apply the normalized POI-flow interaction attention to the long-term trend parts. Finally, we model the spatial and multi-scale temporal dependencies to predict long-term crowd flow. Extensive experiments on Beijing map query track dataset and NYC taxi dataset demonstrate the superiority of PFIST.

As a promising learning paradigm for addressing the data scarcity and distribution mismatch issues, cross-domain prediction aims to leverage the transferable knowledge from the source domain to solve the learning problems in the target domain. Indeed, many urban computing tasks, such as cross- city/mode traffic flow prediction, have to face the severe data scarcity problem due to the heterogeneity in different data sources as well as the imbalanced development among cities. To this end, in this paper, we propose a cross-domain learning framework, namely CCMHC, which exploits Hierarchical Correlation between domains for Cross-City cross-Mode traffic flow prediction. Specifically, we first measure the correlation among inter-city traffic flows by exploring the similarity of region functions and road-networks. In this step, we filter out the regions with lower transfer ability from the source city to the target city. Then, we calculate the temporal correlations of traffic flows across different modes to select a source region that is highly related to the target region in a dynamic way. Moreover, a cross-domain urban flow prediction method is devised by transferring shared knowledge from the source city to the target city. Finally, experimental results on real-world data demonstrate the superiority of CCMHC over the state-of-the-art transfer learning methods. In addition, the generalization ability of the CCMHC framework on different neural network-based models is also validated.

Unlike the traditional dock-based systems, dockless bike-sharing systems are more convenient for users in terms of flexibility. However, the flexibility of these dockless systems comes at the cost of management and operation complexity. Indeed, the imbalanced and dynamic use of bikes leads to mandatory rebalancing operations, which impose a critical need for effective bike traffic flow prediction. While efforts have been made in developing traffic flow prediction models, existing approaches lack interpretability, and thus have limited value in practical deployment. To this end, we propose an Interpretable Bike Flow Prediction (IBFP) framework, which can provide effective bike flow prediction with interpretable traffic patterns. Specifically, by dividing the urban area into regions according to flow density, we first model the spatio-temporal bike flows between regions with graph regularized sparse representation, where graph Laplacian is used as a smooth operator to preserve the commonalities of the periodic data structure. Then, we extract traffic patterns from bike flows using subspace clustering with sparse representation to construct interpretable base matrices. Moreover, the bike flows can be predicted with the interpretable base matrices and learned parameters. Finally, experimental results on real-world data show the advantages of the IBFP method for flow prediction in dockless bike sharing systems. In addition, the interpretability of our flow pattern exploitation is further illustrated through a case study where IBFP provides valuable insights into bike flow analysis.

Urban Region-of-Interest (ROI) refers to the integrated urban areas with specific functionalities that attract people's attentions and activities, such as the recreational business districts, transportation hubs, and city landmarks. Indeed, at the macro level, ROI is one of the representatives for agglomeration economies, and plays an important role in urban business planning. At the micro level, ROI provides a useful venue for understanding the urban lives, demands and mobilities of people. However, due to the vague and diversified nature of ROI, it still lacks of quantitative ways to investigate ROIs in a holistic manner. To this end, in this paper we propose a systematic study on ROI analysis through mining the large-scale online map query logs, which provides a new data-driven research paradigm for ROI detection and profiling. Specifically, we first divide the urban area into small region grids, and calculate their PageRank value as visiting popularity based on the transition information extracted from map queries. Then, we propose a density-based clustering method for merging neighboring region grids with high popularity into integrated ROIs. After that, to further explore the profiles of different ROIs, we develop a spatial-temporal latent factor model URPTM (Urban Roi Profiling Topic Model) to identify the latent travel patterns and Point-of-Interest (POI) demands of ROI visitors. Finally, we implement extensive experiments to empirically evaluate our approaches based on the large-scale real-world data collected from Beijing. Indeed, by visualizing the results obtained from URPTM, we can successfully obtain many meaningful travel patterns and interesting discoveries on urban lives.