Jailbreak attacks serve as a pivotal technique for evaluating the safety alignment of Large language models.Current token-level attacks have shown remarkable efficacy on open-source models by leveraging gradient-based optimization.However, these attacks suffer from poor cross-model transferability, severely limiting their utility on proprietary ones.To address this limitation, we propose Reparameterization Invariance Gradient-based Jailbreak (RIGJ), a natural gradient based framework designed to improve cross-model transferability.Unlike prior token-level methods whose optimization paths are constrained by model-specific Euclidean geometry, RIGJ defines update directions according to differences in output distributions rather than parameter-space distances.Since language models are trained to capture similar dependency structures of natural language, their output distributions share common geometry across architectures, yielding intrinsically model-agnostic optimization trajectories and substantially stronger jailbreak transferability.Extensive experiments demonstrate superior performance, increasing the cross-model Attack Success Rate and Average Harmfulness Score by 14.9% and 1.23, respectively.Our code is provided in https://github.com/nohuma/AISafety_ transfer_jailbreak_RIGJ_2026.

We study the security resilience of large language models to prompt-chain attacks that exploit context retention and sequencing. Existing evaluations emphasize single-turn or fixed-domain settings and thus under-measure vulnerabilities to sequence-aware adversaries. We introduce SecReEvalBench, a scenario-based benchmark with six attack sequences (one-off, successive, successive-reverse, alternative, sequential ascending, sequential descending) and four metrics tailored to multi-turn security: Prompt Attack Resilience Score, Prompt Attack Refusal-Logic Score, Chain Attack Resilience Score, and Chain Attack Refusal Timing Score. The accompanying dataset spans seven security domains, sixteen attack techniques, and four maliciousness levels with dual-LLM adjudication for intent labels. Our evaluation protocol combines an unsafe-content detector (Llama-Guard 3) with a refusal classifier to disentangle eventual unsafe cooperation from refusal timing, yielding bounded, auditable, and sequence-sensitive measurements. Experiments on five representative open-weight models (Llama 3.1, Gemma 2, Mistral v0.3, DeepSeek-R1, Qwen 3) and two proprietary models (gpt-3.5-turbo and gpt-4o-mini) show that sequence-aware metrics surface vulnerabilities that single-turn tests miss, including delayed or absent refusals under escalation and leakage through visible intermediate reasoning despite surface-level refusals. SecReEvalBench standardizes multi-turn safety assessment and provides a reproducible basis for developing domain- and sequence-aware defenses. Our dataset is made publicly available at https://kaggle.com/datasets/5a7ee22cf9dab6c93b55a73f630f6c9b42e936351b0ae98fbae6ddaca7fe248d.

Deep learning underpins most of the currently advanced natural language processing (NLP) tasks such as textual classification, neural machine translation (NMT), abstractive summarization and question-answering (QA). However, the robustness of the models, particularly QA models, against adversarial attacks is a critical concern that remains insufficiently explored. This paper introduces QA-Attack (Question Answering Attack), a novel word-level adversarial strategy that fools QA models. Our attention-based attack exploits the customized attention mechanism and deletion ranking strategy to identify and target specific words within contextual passages. It creates deceptive inputs by carefully choosing and substituting synonyms, preserving grammatical integrity while misleading the model to produce incorrect responses. Our approach demonstrates versatility across various question types, particularly when dealing with extensive long textual inputs. Extensive experiments on multiple benchmark datasets demonstrate that QA-Attack successfully deceives baseline QA models and surpasses existing adversarial techniques regarding success rate, semantics changes, BLEU score, fluency and grammar error rate.

The integration of machine learning (ML) into industrial automation systems requires a comprehensive understanding of system data, including physical assets, their attributes, and the relationships between them. However, most legacy systems are designed primarily for monitoring and control, focusing on asset data points, commonly referred to as Tags, without capturing their interdependencies. This limitation results in fragmented subsystems and data silos, making it challenging to establish meaningful connections between assets and production processes, especially in large-scale systems with thousands of assets. In this paper, we introduce Tag2Graph, a novel, application-oriented framework that uses a cooperative multi-agent system to extract knowledge from tag definitions and historical values across siloed subsystems. The framework integrates time series models to uncover hidden relationships between assets, enabling the construction of a context-aware Knowledge Graph (KG) tailored to individual systems. This KG facilitates system maintenance and enhances feature selection for ML models. Tag2Graph provides a practical and adaptable end-to-end solution for industrial automation, empowering organizations to derive actionable insights from system data and enhance decision-making in complex, large-scale, and evolving industrial environments.

The field of information retrieval has significantly transformed with the integration of AI technologies. AI agents, especially those leveraging LLMs and vast computational power, have revolutionized in- formation retrieval, processing, and presentation. LLM agents, with advanced memory, reasoning, and planning capabilities, can perform complex tasks, engage in coherent conversations, and provide personalized responses. Despite these advancements, challenges such as ensuring relevance and accuracy, mitigating biases, providing real-time responses, and maintaining data security remain. This workshop aims to explore these challenges, share innovative solutions, and discuss future directions. It will provide a platform to bring together researchers and practitioners to discuss the latest theoretical advancements and practical implementations of AI agents in information retrieval. Topics include AI in search, recommendation, and personalization systems. By gathering a diverse group of experts, the workshop seeks to deepen the understanding of AI agents in information retrieval, advance the field, and enhance its societal impact. Participants will gain insights into cutting-edge research and emerging trends, and foster knowledge exchange and collaboration within the community.

Audio-driven simultaneous gesture generation is vital for human-computer communication, AI games, and film pro-duction. While previous research has shown promise, there are still limitations. Methods based on VAEs are accompa-nied by issues of local jitter and global instability, whereas methods based on diffusion models are hampered by low generation efficiency. This is because the denoising process of DDPM in the latter relies on the assumption that the noise added at each step is sampled from a unimodal distribution, and the noise values are small. DDIM bor-rows the idea from the Euler method for solving differential equations, disrupts the Markov chain process, and increases the noise step size to reduce the number of denoising steps, thereby accelerating generation. However, simply increasing the step size during the step-by-step denoising process causes the results to gradually deviate from the original data distribution, leading to a significant drop in the quality of the generated actions and the emergence of unnatural artifacts. In this paper, we break the assumptions of DDPM and achieves breakthrough progress in denoising speed and fidelity. Specifically, we introduce a conditional GAN to capture audio control signals and implicitly match the multimodal denoising distribution between the diffusion and denoising steps within the same sampling step, aiming to sample larger noise values and apply fewer denoising steps for high-speed generation. In addition, to enable the model to generate high-fidelity global gestures and avoid artifacts, we introduce an explicit motion geometric loss to enhance the quality and global stability of the generated gestures. Numerous qualitative and quantitative experiments show that compared to contemporary diffusion-based methods, our method offers faster generation speed and higher fidelity, and compared to non-diffusion methods, it provides a more stable global effect and a more natural user experience.

Generating human motion from textual descriptions is a challenging task. Existing methods either struggle with physical credibility or are limited by the complexities of physics simulations. In this paper, we present ReinDiffuse that combines reinforcement learning with motion diffusion model to generate physically credible human motions that align with textual descriptions. Our method adapts Motion Diffusion Model to output a parameterized distribution of actions, making them compatible with reinforcement learning paradigms. We employ reinforcement learning with the objective of maximizing physically plausible rewards to optimize motion generation for physical fidelity. Our approach outperforms existing state-of-the-art models on two major datasets, HumanML3D and KIT-ML, achieving significant improvements in physical plausibility and motion quality. Project: https://reindiffuse.github.io/

This supplementary material offers additional experimental results and in-depth explanations to augment the content of the main paper. In Section 1, we elaborate extensively on the preliminary work. Subsequently, in Section 2, we delve into the implementation details of the Rope module. Following this, in Section 3, we provide a wealth of experimental data and model parameters pertaining to the effectiveness of the Rope module, facilitating a more intuitive comparison. Furthermore, we supply a comprehensive visualization of the attention results and corresponding analysis. Lastly, we present a plethora of challenging data to compare and validate the efficacy of our method in handling real-world occlusion issues, and discuss its limitations and instances of failure. Beyond this, we further elucidate the details of TrajDenoiser and provide prospects for applications in 3D space.

Traffic prediction is an essential task in intelligent transportation systems dealing with complex and dynamic spatio-temporal correlations. To date, most work is focused on point estimation models, which only output a single value w.r.t an attribute of traffic data at a time, falling short of depicting diverse situations and uncertainty in future. Besides, most methods are not flexible enough to handle real complex traffic scenarios, involving missing values and non-uniformly sampled data. The interactions among different attributes of traffic data are also rarely explored explicitly. In this paper, we focus on probabilistic estimation in traffic prediction tasks, proposing a spatio-temporal multivariate probabilistic predictive model to estimate the distributions of traffic data. Specifically, we devise a multivariate spatio-temporal fusion graph block to extract spatio-temporal correlations of multiple traffic attributes at different locations. A multi-graph fusion module is designed to capture time-varying spatial relationships. We estimate the joint distributions of missing traffic data using copulas. The proposed model can simultaneously perform traffic forecasting and interpolation tasks with non-uniformly sampled data. Our experiments on two real-world traffic datasets demonstrate the advantages of our model over the state-of-the-art1.

Sequential recommendation models have achieved state-of-the-art performance using self-attention mechanism. It has since been found that moving beyond only using item ID and positional embeddings leads to a significant accuracy boost when predicting the next item. In recent literature, it was reported that a multi-dimensional kernel embedding with temporal contextual kernels to capture users’ diverse behavioural patterns results in a substantial performance improvement. In this study, we further improve the sequential recommender model’s robustness and generalization by introducing a mix-attention mechanism with a layer-wise noise injection (LNI) regularization. We refer to our proposed model as adaptive robust sequential recommendation framework (ADRRec), and demonstrate through extensive experiments that our model outperforms existing self-attention architectures.

With the development of the Intelligent Transportation Systems, a great deal of work has been proposed to tackle traffic prediction tasks. Despite their good performance, most traffic prediction models are point estimation models, lacking the capability to estimate the uncertainties of future traffic data, which is crucial in practical traffic decision-making. Aiming at this problem, we combine the probabilistic estimation capabilities of conditional normalizing flows with the spatio-temporal relationship learning of spatio-temporal graphs, leading to a Spatio-Temporal Graph Normalizing Flow (STGNF) model to estimate the distribution of future traffic data. We are the first to employ the conditional normalizing flows as the backbone for probabilistic traffic prediction. Then we design a spatio-temporal graph conditional fusion network to learn the spatio-temporal relationships between future and historical traffic data, which are provided to the conditional normalizing flows as conditional information. Extensive experiments on two real-world traffic datasets demonstrate that our proposed model significantly outperforms the state-of-the-art baselines.

The field of information retrieval has significantly transformed with the integration of AI technologies. AI agents, especially those leveraging LLMs and vast computational power, have revolutionized information retrieval, processing, and presentation. LLM agents, with advanced memory, reasoning, and planning capabilities, can perform complex tasks, engage in coherent conversations, and provide personalized responses. Despite these advancements, challenges such as ensuring relevance and accuracy, mitigating biases, providing real-time responses, and maintaining data security remain. This workshop aims to explore these challenges, share innovative solutions, and discuss future directions. It will provide a platform to bring together researchers, practitioners to discuss the latest theoretical advancements and practical implementations of AI agents in information retrieval. Topics include AI in search, recommendation, and personalization systems. By gathering a diverse group of experts, the workshop seeks to deepen the understanding of AI agents in information retrieval, advance the field, and enhance its societal impact. Participants will gain insights into cutting-edge research, emerging trends, and foster knowledge exchange and collaboration within the community.

Existing gesture generation methods primarily focus on upper body gestures based on audio features, neglecting speech content, emotion, and locomotion. These limitations result in stiff, mechanical gestures that fail to convey the true meaning of audio content. We introduce ExpGest, a novel framework leveraging synchronized text and audio information to generate expressive full-body gestures. Unlike AdaIN or one-hot encoding methods, we design a noise emotion classifier for optimizing adversarial direction noise, avoiding melody distortion and guiding results towards specified emotions. Moreover, aligning semantic and gestures in the latent space provides better generalization capabilities. ExpGest, a diffusion model-based gesture generation framework, is the first attempt to offer mixed generation modes, including audio-driven gestures and text-shaped motion. Experiments show that our framework effectively learns from combined text-driven motion and audio-induced gesture datasets, and preliminary results demonstrate that ExpGest achieves more expressive, natural, and controllable global motion in speakers compared to state-of-the-art models.

Abstract Recent studies on adversarial examples expose vulnerabilities of natural language processing models. Existing techniques for generating adversarial examples are typically driven by deterministic hierarchical rules that are agnostic to the optimal adversarial examples, a strategy that often results in adversarial samples with a suboptimal balance between magnitudes of changes and attack successes. To this end, in this research we propose two algorithms, Reversible Jump Attack (RJA) and Metropolis–Hasting Modification Reduction (MMR), to generate highly effective adversarial examples and to improve the imperceptibility of the examples, respectively. RJA utilizes a novel randomization mechanism to enlarge the search space and efficiently adapts to a number of perturbed words for adversarial examples. With these generated adversarial examples, MMR applies the Metropolis–Hasting sampler to enhance the imperceptibility of adversarial examples. Extensive experiments demonstrate that RJA-MMR outperforms current state-of-the-art methods in attack performance, imperceptibility, fluency and grammar correctness.

Recent research has revealed that natural language processing (NLP) models are vulnerable to adversarial examples. However, the current techniques for generating such examples rely on deterministic heuristic rules, which fail to produce optimal adversarial examples. In response, this study proposes a new method called the Fraud's Bargain Attack (FBA), which uses a randomization mechanism to expand the search space and produce high-quality adversarial examples with a higher probability of success. FBA uses the Metropolis-Hasting sampler, a type of Markov Chain Monte Carlo sampler, to improve the selection of adversarial examples from all candidates generated by a customized stochastic process called the Word Manipulation Process (WMP). The WMP method modifies individual words in a contextually-aware manner through insertion, removal, or substitution. Through extensive experiments, this study demonstrates that FBA outperforms other methods in terms of attack success rate, imperceptibility and sentence quality.

Recent studies on adversarial examples expose vulnerabilities of natural language processing (NLP) models. Existing techniques for generating adversarial examples are typically driven by deterministic heuristic rules that are agnostic to the optimal adversarial examples, a strategy that often results in attack failures. To this end, this research proposes Fraud's Bargain Attack (FBA), which utilizes a novel randomization mechanism to enlarge the searching space and enables high-quality adversarial examples to be generated with high probabilities. FBA applies the Metropolis-Hasting algorithm to enhance the selection of adversarial examples from all candidates proposed by a customized Word Manipulation Process (WMP). WMP perturbs one word at a time via insertion, removal, or substitution in a contextual-aware manner. Extensive experiments demonstrate that FBA outperforms the baselines in terms of attack success rate and imperceptibility.

Remote sensing (RS) images are frequently observed from multiviews. In this paper, we propose the tensor canonical correlation analysis network (TCCANet) to tackle the multiview RS recognition problem. Particularly, TCCANet learns filter banks by simultaneously maximizing arbitrary number of views with high-order-correlation and solves the optimization problem by decomposing a covariance tensor. After the convolutional stage, we utilize binarization and block-wise histogram strategies to generate the final feature. Furthermore, we also develop a Multiple Scale version of TCCANet, i.e., MS-TCCANet, to extract enriched representation of the RS data by incorporating all previous convolutional layers. Numerical experiment results on RSSCN7 and SAT-6 datasets demonstrate the advantages of TCCANet and MS-TCCANet for RS scene recognition.

Fine-grained urban flow inference focuses on inferring fine-grained urban flows based solely on coarse-grained observations, which is essential for the city management and transportation services. However, most of the existing methods assume that partial urban flows in coarse-grained regions cannot be observable. In this study, we propose a multi-task framework known as UrbanSTA with space-time attraction learning to estimate missing values in coarse-grained urban flow map and forecast fine-grained urban flows simultaneously. Specifically, UrbanSTA comprises two parts: the flow completion network STA and the fine-grained flow inference network FIN. STA captures space-time features with a separable space-time attention encoder and recovers the missing flow features with a decoder. FIN directly uses complete coarse-grained flow features for further decoding, and reconstructs fine-grained flow features based on the complex associations between coarse- and fine-grained urban flows, relying on upsampling constraints. Extensive experiments conducted on two real-world datasets demonstrate that our proposed model yields the best results compared to other state-of-the-art methods. The source code has been provided at https://github.com/Wangzheaos/UrbanSTA.

Deep learning techniques have advanced rapidly in recent years, leading to significant progress in pre-trained and fine-tuned large-scale AI models. For example, in the natural language processing domain, the traditional "pre-train, fine-tune" paradigm is shifting towards the "pre-train, prompt, and predict" paradigm, which has achieved great success on many tasks across different application domains such as ChatGPT/BARD for Conversational AI and P5 for a unified recommendation system. Moreover, there has been a growing interest in models that combine vision and language modalities (vision-language models) which are applied to tasks like Visual Captioning/Generation. Considering the recent technological revolution, it is essential to emphasize these paradigm shifts and highlight the paradigms with the potential to solve different tasks. We thus provide a platform for academic and industrial researchers to showcase their latest work, share research ideas, discuss various challenges, and identify areas where further research is needed in pre-training, fine-tuning, and prompt-learning methods for large-scale AI models. We foster the development of a strong research community focused on solving challenges related to large-scale AI models, providing superior and impactful strategies that can change people's lives in the future.

Many real-world problems deal with collections of data with missing values, e.g., RNA sequential analytics, image completion, video processing, etc. Usually, such missing data is a serious impediment to a good learning achievement. Existing methods tend to use a universal model for all incomplete data, resulting in a suboptimal model for each missingness pattern. In this paper, we present a general model for learning with incomplete data. The proposed model can be appropriately adjusted with different missingness patterns, alleviating competitions between data. Our model is based on observable features only, so it does not incur errors from data imputation. We further introduce a low-rank constraint to promote the generalization ability of our model. Analysis of the generalization error justifies our idea theoretically. In additional, a subgradient method is proposed to optimize our model with a proven convergence rate. Experiments on different types of data show that our method compares favorably with typical imputation strategies and other state-of-the-art models for incomplete data. More importantly, our method can be seamlessly incorporated into the neural networks with the best results achieved. The source code is released at https://github.com/YS-GONG/missingness-patterns.

As a developing trend of urbanization, massive amounts of urban statistical data with multiple views (e.g., views of Population and Economy) are increasingly collected and benefited to diverse domains, including transportation service, regional analysis, etc. Unfortunately, these statistical data that are divided into fine-grained regions usually suffer from missing value problem during the acquisition and storage processes. It is mianly caused by some inevitable circumstances, e.g., the document defacement, statistical difficulty in remote districts, and inaccurate information cleaning, etc. Those missing entries which make valuable information invisible may distort the further urban analysis. To improve the quality of missing data imputation, we propose an improved spatial multi-kernel learning method to guide the imputation process incorporating with the adaptive-weight non-negative matrix factorization strategy. Our model takes into account the regional latent similarities and the real geographical positions as well as the correlations among various views that are able to complete missing values precisely. We conduct intensive experiments to evaluate our method and compare with other state-of-the-art approaches on real-world datasets. All the empirical results show that the proposed model outperforms all the other state-of-the-art methods. Additionally, our model represents a strong generalization ability across multiple cities.

Deep learning models are known immensely brittle to adversarial text examples. Existing text adversarial attack strategies can be roughly divided into character-level, word-level, and sentence-level attacks. Despite the success brought by recent text attack methods, how to induce misclassification with minimal text modifications while keeping the lexical correctness, syntactic soundness, and semantic consistency is still a challenge. In this paper, we devise a Bigram and Unigram-based adaptive Semantic Preservation Optimization (BU-SPO) approach which attacks text documents not only at a unigram word level but also at a bigram level to avoid generating meaningless sentences. We also present a hybrid attack strategy that collects substitution words from both synonyms and sememe candidates, to enrich the potential candidate set. Besides, a Semantic Preservation Optimization (SPO) method is devised to determine the word substitution priority and reduce the perturbation cost. Furthermore, we constrain the SPO with a semantic Filter (dubbed SPOF) to improve the semantic similarity. To estimate the effectiveness of our proposed methods, BU-SPO and BU-SPOF, we attack four victim deep learning models trained on three text datasets. Experimental results demonstrate that our approaches accomplish the highest semantics consistency and attack success rates by making minimal word modifications compared with competitive methods.

In the current digital world, web search engines and recommendation systems are continuously evolving, opening up new potential challenges every day which require more sophisticated and efficient data mining and machine learning solutions to satisfy the needs of sellers and consumers as well as marketers. The quality of search and recommendation systems impacts customer retention, time on site, and sales volume. For instance, with often sparse conversion rates, highly personalized contents, heterogeneous digital sources, more rigorous and effective models are required to be developed by research engineers and data scientists. At the same time, deep learning has started to show great impact in many industrial applications which are capable of processing complicated, large-scale and real-time data. Deep learning not only provides more opportunities to increase conversion rates and improve revenue through a positive customer experience, but also provides customers with personalized contents along with their personal shopping journey. Due to this rapid growth of the digital world, there is a need to bring professionals together from both academic research and the industry to solve real-world problems. This workshop fosters the development of a strong research community focused on solving deep learning based large-scale web search, personalized search, recommendation and ranking relevance problems that provide superior digital experience to all users.

Time series data is ubiquitous, and accurate time series forecasting is vital for many real-world application domains, including retail, healthcare, supply chain, climate science, e-commerce and economics. Forecasting, in general, has led to broad impact and a diverse range of applications. However, with large-scale, high-dimensional time-series data available, more advanced techniques must be invented or improved for highly accurate predictions. Latest data mining and machine learning techniques play a crucial role in the next generation of forecasting models. In this Applied Machine Learning Methods for Time Series Forecasting (AMLTS) workshop, we focus on effective and accurate latest machine learning approaches to solve various real-world problems. With this workshop's ability to attract audiences across various domains, we invite experienced industrial practitioners and researchers to help uncover new approaches and break new ground in time-series modelings' challenging and vital settings.

Video and audio streams are essential and mutually complementary in multimedia immersive application scenarios. Recent studies have explored the field of deep neural net-work application on multimedia production, e.g., visually guided generation of binaural audio, where Difference Mask (DM) is the predominant strategy in the state-of-the-art (SOTA) work. However, this strategy is not interpretable and requires adding the ground truth output as the input, limiting applicability. Besides, the generated audio has a relatively low spatial sensation. This paper aims to develop an interpretable and robust approach to visually guided binaural audio generation. Specifically, we generalize a concept and new strategy from Difference Mask, named Binaural Ratio, to interpret its binaural property relevant to the Inter-aural Time Difference (ITD) and Inter-aural Level Difference (ILD). In the new strategy, the model input can be natural and arbitrary mono audio instead of the direct sum of left and right audio, i.e., ground truth output. Moreover, we identify that one reason for the low spatial sensation is the bias toward mono. Thus, we tackle it by designing new network variants to learn the Binaural Ratio robustly. Experiments show that our proposed approach significantly outperforms the SOTA methods in both objective and subjective evaluation metrics.

An average consumer spends 8+ hours a day across all devices interacting with online content almost entirely sponsored by advertisements. At over $450B global market size in 2022 and expected to pass $1T by 2027, online advertising has already surpassed traditional ads in global spend. Moreover, computational advertising in particular is perhaps the most visible and ubiquitous application of machine learning and one that interacts directly with consumers. When done right, ads help us enrich our lives and creep us out when done badly. Looking at the published literature over the last few years, many researchers might consider computational advertising as a mature field. Yet, the opposite is true. The field is evolving, however, from ads controlled by monolithic publishers and randomly rotating banner ads to highly personalized content experiences in news feeds on mobile devices and even on TV-all utilizing data amassed from petabytes of stored user data. Ads are far from done.

Machine learning techniques for developing industry-scale search engines have long been a prominent part of most domains and their online products. Search relevance algorithms are key components of products across different fields, including e-commerce, streaming services, and social networks. In this tutorial, we give an introduction to such large-scale search ranking systems, specifically focusing on deep learning techniques in this area. The topics we cover are the following: (1) Overview of search ranking systems in practice, including classical and machine learning techniques; (2) Introduction to sequential and language models in the context of search ranking; and (3) Knowledge distillation approaches for this area. For each of the aforementioned sessions, we first give an introductory talk and then go over an hands-on tutorial to really hone in on the concepts. We cover fundamental concepts using demos, case studies, and hands-on examples, including the latest Deep Learning methods that have achieved state-of-the-art results in generating the most relevant search results. Moreover, we show example implementations of these methods in python, leveraging a variety of open-source machine-learning/deep-learning libraries as well as real industrial data or open-source data.

Abstract Deep-learning based natural language processing (NLP) models are proven vulnerable to adversarial attacks. However, there is currently insufficient research that studies attacks to neural machine translations (NMTs) and examines the robustness of deep-learning based NMTs. In this paper, we aim to fill this critical research gap. When generating word-level adversarial examples in NLP attacks, there is a conventional trade-off in existing methods between the attacking performance and the amount of perturbations. Although some literature has studied such a trade-off and successfully generated adversarial examples with a reasonable amount of perturbations, it is still challenging to generate highly successful translation attacks while concealing the changes to the texts. To this end, we propose a novel Hybrid Attentive Attack method to locate language-specific and sequence-focused words, and make semantic-aware substitutions to attack NMTs. We evaluate the effectiveness of our attack strategy by attacking three high-performing translation models. The experimental results show that our method achieves the highest attacking performance compared with other existing attacking strategies.

Abstract In recent years, it has been revealed that machine learning models can produce discriminatory predictions. Hence, fairness protection has come to play a pivotal role in machine learning. In the past, most studies on fairness protection have used traditional machine learning methods to enforce fairness. However, these studies focus on low dimensional inputs, such as numerical inputs, whereas more recent deep learning technologies have encouraged fairness protection with image inputs through deep model methods. These approaches involve various object functions and structural designs that break the spurious correlations between targets and sensitive features. With these connections broken, we are left with fairer predictions. To better understand the proposed methods and encourage further development in the field, this paper summarizes fairness protection methods in terms of three aspects: the problem settings, the models, and the challenges. Through this survey, we hope to reveal research trends in the field, discover the fundamentals of enforcing fairness, and summarize the main challenges to producing fairer models.

The principal component analysis network (PCANet) is an unsupervised deep network, utilizing principal components as convolution filters in its layers. Albeit powerful, the PCANet suffers from two fundamental problems responsible for its performance degradation. First, the principal components transform the data as column vectors (which we call the amalgamated view) and incur a loss of spatial information present in the data. Second, the generalized pooling in the PCANet is unable to incorporate spatial statistics of the natural images, and it also induces redundancy among the features. In this research, we first propose a tensor-factorization-based deep network called the tensor factorization network (TFNet). The TFNet extracts features by preserving the spatial view of the data (which we call the minutiae view). We then proposed HybridNet, which simultaneously extracts information with the two views of the data since their integration can improve the performance of classification systems. Finally, to alleviate the feature redundancy among hybrid features, we propose Attn-HybridNet to perform attention-based feature selection and fusion to improve their discriminability. Classification results on multiple real-world datasets using features extracted by our proposed Attn-HybridNet achieves significantly better performance over other popular baseline methods, demonstrating the effectiveness of the proposed techniques.

As a key mission of the modern traffic management, crowd flow prediction (CFP) benefits in many tasks of intelligent transportation services. However, most existing techniques focus solely on forecasting entrance and exit flows of metro stations that do not provide enough useful knowledge for traffic management. In practical applications, managers desperately want to solve the problem of getting the potential passenger distributions to help authorities improve transport services, termed as crowd flow distribution (CFD) forecasts. Therefore, to improve the quality of transportation services, we proposed three spatiotemporal models to effectively address the network-wide CFD prediction problem based on the online latent space (OLS) strategy. Our models take into account the various trending patterns and climate influences, as well as the inherent similarities among different stations that are able to predict both CFD and entrance and exit flows precisely. In our online systems, a sequence of CFD snapshots is used as the training data. The latent attribute evolutions of different metro stations can be learned from the previous trend and do the next prediction based on the transition patterns. All the empirical results demonstrate that the three developed models outperform all the other state-of-the-art approaches on three large-scale real-world datasets.

Convolutional neural network (CNN) has been proven an effective way to extract high-level features from remote sensing (RS) images automatically. Many variants of the CNN model have been proposed, including principal component analysis network (PCANet), canonical correlation analysis network (CCANet), multiple scale CCANet (MS-CCANet) and multiview CCANet (MCCANet). The PCANet is specialized for single view feature abstraction, while in many real-world practices, the RS data are frequently observed from many more views. Although CCANet, MS-CCANet and MCCANet can be applied to two or more view data, they consider only the pair-wise correlation by calculating a series oftwo-ordercovariance matrices. However, the high-order consistence, which can only be explored by collectively and simultaneously examining all views, remains undiscovered. In this paper, we propose the tensor canonical correlation analysis network (TCCANet) to tackle this problem. Particularly, TCCANet learns filter banks by simultaneously maximizing arbitrary number of views with high-order-correlation and solves the optimization problem by decomposing a covariance tensor. After the convolutional stage, we utilize binarization and block-wise histogram strategies to generate the final feature. Furthermore, we also develop a Multiple Scale version of TCCANet, i.e., MS-TCCANet, to extract enriched representation of the RS data by incorporating all previous convolutional layers. Numerical experiment results on RSSCN7 and SAT-6 datasets demonstrate the advantages of TCCANet and MS-TCCANet for RS scene recognition.

Differentiating subcategories of a common visual category is challenging because of the similar appearance shared among different classes in fine-grained recognition. Existing mixture-of-expert based methods divide the fine-grained space into some specific regions and solve the integrated problem by conquering subspace ones. However, it is not feasible to learn diverse experts directly through data partition strategy because of limited data available for fine-grained recognition problems. To address the issue, we leverage visual attention to learn an enhanced experts' mixture. Specifically, we introduce a gradually-enhanced learning strategy from model attention. The strategy promotes diversity among experts by feeding each expert with full-size data distinct in granularity. We further promote expert's learning by providing it with a larger data space, which is achieved by swapping attentive regions within positive pairs. Our method learns new experts on the dataset with the prior knowledge from former experts sequentially and enforces the experts to learn more diverse but discriminative representation. These enhanced experts are finally combined to make stronger predictions. We conduct extensive experiments on fine-grained benchmarks. The results show that our method consistently outperforms the state-of-the-art method in both weakly supervised localization and fine-grained image classification. Our code is publicly available at https://github.com/lbzhang/Enhanced-Expert-FGVC-Pytorch.git.

Deep neural networks (DNNs) are known to be vulnerable to adversarial images, while their robustness in text classification are rarely studied. Several lines of text attack methods have been proposed in the literature, such as character-level, word-level, and sentence-level attacks. However, it is still a challenge to minimize the number of word distortions necessary to induce misclassification, while simultaneously ensuring the lexical correctness, syntactic correctness, and semantic similarity. In this paper, we propose the Bigram and Unigram based Monotonic Heuristic Search (BU-MHS) method to examine the vulnerability of deep models. Our method has three major merits. Firstly, we propose to attack text documents not only at the unigram word level but also at the bigram level to avoid producing meaningless outputs. Secondly, we propose a hybrid method to replace the input words with both their synonyms and sememe candidates, which greatly enriches potential substitutions compared to only using synonyms. Lastly, we design a search algorithm, i.e., Monotonic Heuristic Search (MHS), to determine the priority of word replacements, aiming to reduce the modification cost in an adversarial attack. We evaluate the effectiveness of BU-MHS on IMDB, AG's News, and Yahoo! Answers text datasets by attacking four state-of-the-art DNNs models. Experimental results show that our BU-MHS achieves the highest attack success rate by changing the smallest number of words compared with other existing models.

Modern Natural Language Processing (NLP) models are known immensely brittle towards text adversarial examples. Recent attack algorithms usually adopt word-level substitution strategies following a pre-computed word replacement mechanism. However, their resultant adversarial examples are still imperfect in achieving grammar correctness and semantic similarities, which is largely because of their unsuitable candidate word selections and static optimization methods. In this research, we propose BESA, a BERT-based Simulated Annealing algorithm, to address these two problems. Firstly, we leverage the BERT Masked Language Model (MLM) to generate contextual-aware candidate words to produce fluent adversarial text and avoid grammar errors. Secondly, we employ Simulated Annealing (SA) to adaptively determine the word substitution order. The SA provides sufficient word replacement options via internal simulations, with an objective to obtain both a high attack success rate and a low word substitution rate. Besides, our algorithm is able to jump out of local optima with a controlled probability, making it closer to achieve the best possible attack (i.e., the global optima). Experiments on five popular datasets manifest the superiority of BESA compared with existing methods, including TextFooler, BAE, BERT-Attack, PWWS, and PSO.

There has been an increasing emphasis on building large-scale datasets as the driver of deep learning-based trackers' success. However, accurately annotating tracking data is highly labor-intensive and expensive, making it infeasible in real-world applications. In this study, we investigate the necessity of large-scale training data to ensure tracking algorithms' performance. To this end, we introduce a FAT (Few-Annotation Tracking) benchmark constructed by sampling one or a few frames per video from some existing tracking datasets. The proposed dataset can be used to evaluate the effectiveness of tracking algorithms considering data efficiency and new data augmentation approaches for object tracking. We further present AMMC (Augmentation by Mimicking Motion Change), a data augmentation strategy that enables learning high-performing trackers using small-scale datasets. AMMC first cuts out the tracked targets and performs a sequence of transformations to simulate the possible change by object motion. Then the transformed targets are pasted on the inpainted background images and further conjointly augmented to mimic variability caused by camera motion. Compared with standard augmentation methods, AMMC explicitly considers tracking data characteristics, which synthesizes more valid data for object tracking. We extensively evaluate our approach with two popular trackers on the FAT datasets. Experiments show that our method allows these trackers to even trained on a dataset requiring much less annotation to achieve comparable or even better performance to those on the full-annotation dataset. The results imply complete video annotation might not be necessary for object tracking if leveraging motion-driven data augmentations during training.

Recent works such as Mixup and CutMix have demonstrated the effectiveness of augmenting training data for deep models. These methods generate new data by generally blending random image contents and mixing their labels proportionally. However, this strategy tends to produce unreasonable training samples for fine-grained recognition, leading to limited improvement. This is because mixing random image contents may potentially produce images containing destructed object structures. Further, as the category differences mainly reside in small part regions, mixing labels proportionally to the number of mixed pixels might result in label noisy problem. To augment more reasonable training data, we propose Intra-class Part Swapping (InPS) that produces new data by performing attention-guided content swapping on input pairs from the same class. Compared with previous approaches, InPS avoids introducing noisy labels and ensures a likely holistic structure of objects in generated images. We demonstrate InPS outperforms the most recent augmentation approaches in both fine-grained recognition and weakly object localization. Further, by simply incorporating the mid-level feature learning, our proposed method achieves state-of-the-art performance in the literature while maintaining the simplicity and inference efficiency. Our code is publicly available.

Real-world traffic sign recognition is an important step toward building autonomous vehicles, most of which highly dependent on deep neural networks (DNNs). Recent studies demonstrated that DNNs are surprisingly susceptible to adversarial examples. Many attack methods have been proposed to understand and generate adversarial examples, such as gradient-based attack, score-based attack, decision-based attack, and transfer-based attacks. However, most of these algorithms are ineffective in real-world road sign attack, because 1) iteratively learning perturbations for each frame is not realistic for a fast moving car and 2) most optimization algorithms traverse all pixels equally without considering their diverse contribution. To alleviate these problems, this article proposes the targeted attention attack (TAA) method for real-world road sign attack. Specifically, we have made the following contributions: 1) we leverage the soft attention map to highlight those important pixels and skip those zero-contributed areas-this also helps to generate natural perturbations; 2) we design an efficient universal attack that optimizes a single perturbation/noise based on a set of training images under the guidance of the pretrained attention map; 3) we design a simple objective function that can be easily optimized; and 4) we evaluate the effectiveness of TAA on real-world data sets. Experimental results validate that the TAA method improves the attack successful rate (nearly 10%) and reduces the perturbation loss (about a quarter) compared with the popular RP2 method. Additionally, our TAA also provides good properties, e.g., transferability and generalization capability. We provide code and data to ensure the reproducibility: https://github.com/AdvAttack/RoadSignAttack.

A critical challenge in machine learning is the vulnerability of learning models in defending attacks from malicious adversaries. In this research, we propose game theoretical learning between a variational adversary and a Convolutional Neural Network (CNN), participating in a variable-sum two-player sequential Stackelberg game. Our adversary manipulates the input data distribution to make the CNN misclassify the manipulated data. Our ideal adversarial manipulation is a minimum change to the data which yet is large enough to mislead the CNNs. We propose an optimization procedure to find optimal adversarial manipulations by solving for the Nash equilibrium of the Stackelberg game. Specifically, the adversary's payoff function depends on the data manipulation which is determined by a Variational Autoencoder, while the CNN classifier's payoff functions are evaluated by misclassification errors. The optimization of our adversarial manipulations is defined by Alternating Least Squares and Simulated Annealing. Experimental results demonstrate that our game-theoretic manipulations are able to mislead CNNs that are well trained on the original data as well as on data generated by other models. We then let the CNNs to incorporate our manipulated data which leads to secure classifiers that are empirically the most robust in defending various types of adversarial attacks.

E-commerce businesses are increasingly dependent on recommendation systems to introduce personalized services and products to targeted customers. Providing effective recommendations requires sufficient knowledge about user preferences and product (item) characteristics. Given the current abundance of available data across domains, achieving a thorough understanding of the relationship between users and items can bring in more collaborative filtering power and lead to a higher recommendation accuracy. However, how to effectively utilize different types of knowledge obtained across domains is still a challenging problem. In this paper, we propose to discover both explicit and implicit similarities from latent factors across domains based on matrix tri-factorization. In our research, common factors in a shared dimension (users or items) of two coupled matrices are discovered, while at the same time, domain-specific factors of the shared dimension are also preserved. We will show that such preservation of both common and domain-specific factors are significantly beneficial to cross-domain recommendations. Moreover, on the non-shared dimension, we propose to use the middle matrix of the tri-factorization to match the unique factors, and align the matched unique factors to transfer cross-domain implicit similarities and thus further improve the recommendation. This research is the first that proposes the transfer of knowledge across the non-shared (non-coupled) dimensions. Validated on real-world datasets, our approach outperforms existing algorithms by more than two times in terms of recommendation accuracy. These empirical results illustrate the potential of utilizing both explicit and implicit similarities for making across-domain recommendations.

In recent years, the advances in data collection and statistical analysis promotes canonical correlation analysis (CCA) available for more advanced research. CCA is the main technique for two-set data dimensionality reduction such that the correlation between the pairwise variables in the common subspace is mutually maximized. Over 80-years of developments, a number of CCA models have been proposed according to different machine learning mechanisms. However, the field lacks an insightful review for the state-of-art developments. This survey targets to provide a well-organized overview for CCA and its extensions. Specifically, we first review the CCA theory from the perspective of both model formation and model optimization. The association between two popular solution methods, i.e., eigen value decomposition (EVD) and singular value decomposition (SVD), are discussed. Following that, we present a taxonomy of current progresses and classify them into seven groups: 1) multi-view CCA, 2) probabilistic CCA, 3) deep CCA, 4) kernel CCA, 5) discriminative CCA, 6) sparse CCA and 7) locality preserving CCA. For each group, we demonstrate two or three representative mathematical models, identifying their strengths and limitations. We summarize the representative applications and numerical results of these seven groups in real-world practices, collecting the data sets and open-sources for implementation. In the end, we provide several promising future research directions that can improve the current state of the art.

Recently, practical applications for passenger flow prediction have brought many benefits to urban transportation development. With the development of urbanization, a real-world demand from transportation managers is to construct a new metro station in one city area that never planned before. Authorities are interested in the picture of the future volume of commuters before constructing a new station, and estimate how would it affect other areas. In this paper, this specific problem is termed as potential passenger flow (PPF) prediction, which is a novel and important study connected with urban computing and intelligent transportation systems. For example, an accurate PPF predictor can provide invaluable knowledge to designers, such as the advice of station scales and influences on other areas, etc. To address this problem, we propose a multi-view localized correlation learning method. The core idea of our strategy is to learn the passenger flow correlations between the target areas and their localized areas with adaptive-weight. To improve the prediction accuracy, other domain knowledge is involved via a multi-view learning process. We conduct intensive experiments to evaluate the effectiveness of our method with real-world official transportation datasets. The results demonstrate that our method can achieve excellent performance compared with other available baselines. Besides, our method can provide an effective solution to the cold-start problem in the recommender system as well, which proved by its outperformed experimental results.

Multimodal sentiment analysis utilizes multiple heterogeneous modalities for sentiment classification. The recent multimodal fusion schemes customize LSTMs to discover intra-modal dynamics and design sophisticated attention mechanisms to discover the inter-modal dynamics from multimodal sequences. Although powerful, these schemes completely rely on attention mechanisms which is problematic due to two major drawbacks 1) deceptive attention masks, and 2) training dynamics. Nevertheless, strenuous efforts are required to optimize hyperparameters of these consolidate architectures, in particular their custom-designed LSTMs constrained by attention schemes. In this research, we first propose a common network to discover both intra-modal and inter-modal dynamics by utilizing basic LSTMs and tensor based convolution networks. We then propose unique networks to encapsulate temporal-granularity among the modalities which is essential while extracting information within asynchronous sequences. We then integrate these two kinds of information via a fusion layer and call our novel multimodal fusion scheme as Deep-HOSeq (Deep network with higher order Common and Unique Sequence information). The proposed Deep-HOSeq efficiently discovers all-important information from multimodal sequences and the effectiveness of utilizing both types of information is empirically demonstrated on CMU-MOSEI and CMU-MOSI benchmark datasets. The source code of proposed Deep-HOSeq is available at https://github.com/sverma88/Deep-HOSeq-ICDM-2020.

Large volumes of urban statistical data with multiple views imply rich knowledge about the development degree of cities. These data present crucial statistics which play an irreplaceable role in the regional analysis and urban computing. In reality, however, the statistical data divided into fine-grained regions usually suffer from missing data problems. Those missing values hide the useful information that may result in a distorted data analysis. Thus, in this paper, we propose a spatial missing data imputation method for multi-view urban statistical data. To address this problem, we exploit an improved spatial multi-kernel clustering method to guide the imputation process cooperating with an adaptive-weight non-negative matrix factorization strategy. Intensive experiments are conducted with other state-of-the-art approaches on six real-world urban statistical datasets. The results not only show the superiority of our method against other comparative methods on different datasets, but also represent a strong generalizability of our model.

Estimations on people movement behaviour within a country can provide valuable information to government strategic resource plannings. In this paper, we propose to utilize multi-domain statistical data to estimate people movements under the assumption that most population tend to move to areas with similar or better living conditions. We design a Multi-domain Matrix Factorization (MdMF) model to discover the underlying consistency patterns from these cross-domain data and estimate the movement trends using the proposed model. This research can provide important theoretical support to government and agencies in strategic resource planning and investments.

Data across many business domains can be represented by two or more coupled data sets. Correlations among these coupled datasets have been studied in the literature for making more accurate cross-domain recommender systems. However, in existing methods, cross-domain recommendations mostly assume the coupled mode of data sets share identical latent factors, which limits the discovery of potentially useful domain-specific properties of the original data. In this paper, we proposed a novel cross-domain recommendation method called Coupled Factorization Machine (CoFM) that addresses this limitation. Compared to existing models, our research is the first model that uses factorization machines to capture both common characteristics of coupled domains while simultaneously preserving the differences among them. Our experiments with real-world datasets confirm the advantages of our method in making across-domain recommendations.

We aim to divide the problem space of fine-grained recognition into some specific regions. To achieve this, we develop a unified framework based on a mixture of experts. Due to limited data available for the fine-grained recognition problem, it is not feasible to learn diverse experts by using a data division strategy. To tackle the problem, we promote diversity among experts by combing an expert gradually-enhanced learning strategy and a Kullback-Leibler divergence based constraint. The strategy learns new experts on the dataset with the prior knowledge from former experts and adds them to the model sequentially, while the introduced constraint forces the experts to produce diverse prediction distribution. These drive the experts to learn the task from different aspects, making them specialized in different subspace problems. Experiments show that the resulting model improves the classification performance and achieves the state-of-the-art performance on several fine-grained benchmark datasets.

Multimodal sentiment analysis combines information available from visual, textual, and acoustic representations for sentiment prediction. The recent multimodal fusion schemes combine multiple modalities as a tensor and obtain either; the common information by utilizing neural networks, or the unique information by modeling low-rank representation of the tensor. However, both of these information are essential as they render inter-modal and intra-modal relationships of the data. In this research, we first propose a novel deep architecture to extract the common information from the multi-mode representations. Furthermore, we propose unique networks to obtain the modality-specific information that enhances the generalization performance of our multimodal system. Finally, we integrate these two aspects of information via a fusion layer and propose a novel multimodal data fusion architecture, which we call DeepCU (Deep network with both Common and Unique latent information). The proposed DeepCU consolidates the two networks for joint utilization and discovery of all-important latent information. Comprehensive experiments are conducted to demonstrate the effectiveness of utilizing both common and unique information discovered by DeepCU on multiple real-world datasets. The source code of proposed DeepCU is available at https://github.com/sverma88/DeepCU-IJCAI19.

Growing awareness towards ethical use of machine learning (ML) models has created a surge for the development of fair models. Existing work in this regard assumes the presence of sensitive attributes in the data and hence can build classifiers whose decisions remain agnostic to such attributes. However, in the real world settings, the end-user of the ML model is unaware of the training data; besides, building custom models is not always feasible. Moreover, utilizing a pre-trained model with high accuracy on certain dataset can not be assumed to be fair. Unknown biases in the training data are the true culprit for unfair models (i.e., disparate performance for groups in the dataset). In this preliminary research, we propose a different lens for building fair models by enabling the user with tools to discover blind spots and biases in a pre-trained model and augment them with corrective measures.

Class labels are required for supervised learning but may be corrupted or missing in various applications. In binary classification, for example, when only a subset of positive instances is labeled whereas the remaining are unlabeled, positive-unlabeled (PU) learning is required to model from both positive and unlabeled data. Similarly, when class labels are corrupted by mislabeled instances, methods are needed for learning in the presence of class label noise (LN). Here we propose adaptive sampling (AdaSampling), a framework for both PU learning and learning with class LN. By iteratively estimating the class mislabeling probability with an adaptive sampling procedure, the proposed method progressively reduces the risk of selecting mislabeled instances for model training and subsequently constructs highly generalizable models even when a large proportion of mislabeled instances is present in the data. We demonstrate the utilities of proposed methods using simulation and benchmark data, and compare them to alternative approaches that are commonly used for PU learning and/or learning with LN. We then introduce two novel bioinformatics applications where AdaSampling is used to: 1) identify kinase-substrates from mass spectrometry-based phosphoproteomics data and 2) predict transcription factor target genes by integrating various next-generation sequencing data.

Multi-label classification is defined as the problem of identifying the multiple labels or categories of new observations based on labeled training data. Multi-labeled data has several challenges, including class imbalance, label correlation, incomplete multi-label matrices, and noisy and irrelevant features. In this article, we propose an integrated multi-label classification approach with incomplete label space and class imbalance (ML-CIB) for simultaneously training the multi-label classification model and addressing the aforementioned challenges. The model learns a new label matrix and captures new label correlations, because it is difficult to find a complete label vector for each instance in real-world data. We also propose a label regularization to handle the imbalanced multi-labeled issue in the new label, and l 1 regularization norm is incorporated in the objective function to select the relevant sparse features. A multi-label feature selection (ML-CIB-FS) method is presented as a variant of the proposed ML-CIB to show the efficacy of the proposed method in selecting the relevant features. ML-CIB is formulated as a constrained objective function. We use the accelerated proximal gradient method to solve the proposed optimisation problem. Last, extensive experiments are conducted on 19 regular-scale and large-scale imbalanced multi-labeled datasets. The promising results show that our method significantly outperforms the state-of-the-art.

We develop an adversarial learning algorithm for supervised classification in general and Convolutional Neural Networks (CNN) in particular. The algorithm's objective is to produce small changes to the data distribution defined over positive and negative class labels so that the resulting data distribution is misclassified by the CNN. The theoretical goal is to determine a manipulating change on the input data that finds learner decision boundaries where many positive labels become negative labels. Then we propose a CNN which is secure against such unforeseen changes in data. The algorithm generates adversarial manipulations by formulating a multiplayer stochastic game targeting the classification performance of the CNN. The multiplayer stochastic game is expressed in terms of multiple two-player sequential games. Each game consists of interactions between two players-an intelligent adversary and the learner CNN-such that a player's payoff function increases with interactions. Following the convergence of a sequential noncooperative Stackelberg game, each two-player game is solved for the Nash equilibrium. The Nash equilibrium finds a pair of strategies (learner weights and evolutionary operations) from which there is no incentive for either learner or adversary to deviate. We then retrain the learner over all the adversarial manipulations generated by multiple players to propose a secure CNN which is robust to subsequent adversarial data manipulations. The adversarial data and corresponding CNN performance is evaluated on MNIST handwritten digits data. The results suggest that game theory and evolutionary algorithms are very effective in securing deep learning models against performance vulnerabilities simulated as attack scenarios from multiple adversaries.

To recommend the next item to a user in a transactional context is practical yet challenging in applications such as marketing campaigns. Transactional context refers to the items that are observable in a transaction. Most existing transaction based recommender systems (TBRSs) make recommendations by mainly considering recently occurring items instead of all the ones observed in the current context. Moreover, they often assume a rigid order between items within a transaction, which is not always practical. More importantly, a long transaction often contains many items irreverent to the next choice, which tends to overwhelm the influence of a few truly relevant ones. Therefore, we posit that a good TBRS should not only consider all the observed items in the current transaction but also weight them with different relevance to build an attentive context that outputs the proper next item with a high probability. To this end, we design an effective attention based transaction embedding model (ATEM) for context embedding to weight each observed item in a transaction without assuming order. The empirical study on real-world transaction datasets proves that ATEM significantly outperforms the state-of-the-art methods in terms of both accuracy and novelty.

Crowd Flow Prediction (CFP) is one major challenge in the intelligent transportation systems of the Sydney Trains Network. However, most advanced CFP methods only focus on entrance and exit flows at the major stations or a few subway lines, neglecting Crowd Flow Distribution (CFD) forecasting problem across the entire city network. CFD prediction plays an irreplaceable role in metro management as a tool that can help authorities plan route schedules and avoid congestion. In this paper, we propose three online non-negative matrix factorization (ONMF) models. ONMF-AO incorporates an Average Optimization strategy that adapts to stable passenger flows. ONMF-MR captures the Most Recent trends to achieve better performance when sudden changes in crowd flow occur. The Hybrid model, ONMF-H, integrates both ONMF-AO and ONMF-MR to exploit the strengths of each model in different scenarios and enhance the models' applicability to real-world situations. Given a series of CFD snapshots, both models learn the latent attributes of the train stations and, therefore, are able to capture transition patterns from one timestamp to the next by combining historic guidance. Intensive experiments on a large-scale, real-world dataset containing transactional data demonstrate the superiority of our ONMF models.

Adversarial learning is the study of machine learning techniques deployed in non-benign environments. Example applications include classification for detecting spam, network intrusion detection, and credit card scoring. In fact, as the use of machine learning grows in diverse application domains, the possibility for adversarial behavior is likely to increase. When adversarial learning is modelled in a game-theoretic setup, the standard assumption about the adversary (player) behavior is the ability to change all features of the classifiers (the opponent player) at will. The adversary pays a cost proportional to the size of the “attack”. We refer to this form of adversarial behavior as a dense feature attack. However, the aim of an adversary is not just to subvert a classifier but carry out data transformation in a way such that spam continues to remain effective. We demonstrate that an adversary could potentially achieve this objective by carrying out a sparse feature attack. We design an algorithm to show how a classifier should be designed to be robust against sparse adversarial attacks. Our main insight is that sparse feature attacks are best defended by designing classifiers which use ℓ1regularizers.

Real world data is usually generated over multiple time periods associated with multiple labels, which can be represented as multiple labeled tensor sequences. These sequences are linked together, sharing some common features while exhibiting their own unique features. Conventional tensor factorization techniques are limited to extract either common or unique features, but not both simultaneously. However, both types of these features are important in many machine learning systems as they inherently affect the systems' performance. In this paper, we propose a novel supervised tensor factorization technique which simultaneously extracts ordered common and unique features. Classification results using features extracted by our method on CIFAR-10 database achieves significantly better performance over other factorization methods, illustrating the effectiveness of the proposed technique.

High-dimensional multi-labeled data contain instances, where each instance is associated with a set of class labels and has a large number of noisy and irrelevant features. Feature selection has been shown to have great benefits in improving the classification performance in machine learning. In multi-label learning, to select the discriminative features among multiple labels, several challenges should be considered: interdependent labels, different instances may share different label correlations, correlated features, and missing and flawed labels. This work is part of a project at The Children's Hospital at Westmead (TB-CHW), Australia to explore the genomics of childhood leukaemia. In this paper, we propose a CMFS (Correlated- and Multi-label Feature Selection method), based on non-negative matrix factorization (NMF) for simultaneously performing feature selection and addressing the aforementioned challenges. Significantly, a major advantage of our research is to exploit the correlation information contained in features, labels and instances to select the relevant features among multiple labels. Furthermore, l2,1 -norm regularization is incorporated in the objective function to undertake feature selection by imposing sparsity on the feature matrix rows. We employ CMFS to decompose the data and multi-label matrices into a low-dimensional space. To solve the objective function, an efficient iterative optimization algorithm is proposed with guaranteed convergence. Finally, extensive experiments are conducted on high-dimensional multi-labeled datasets. The experimental results demonstrate that our method significantly outperforms state-of-the-art multi-label feature selection methods.

Learning from positive and unlabeled data frequently occurs in applications where only a subset of positive instances is available while the rest of the data are unlabeled. In such scenarios, often the goal is to create a discriminant model that can accurately classify both positive and negative data by modelling from labeled and unlabeled instances. In this study, we propose an adaptive sampling (AdaSampling) approach that utilises prediction probabilities from a model to iteratively update the training data. Starting with equal prior probabilities for all unlabeled data, our method "wraps" around a predictive model to iteratively update these probabilities to distinguish positive and negative instances in unlabeled data. Subsequently, one or more robust negative set(s) can be drawn from unlabeled data, according to the likelihood of each instance being negative, to train a single classification model or ensemble of models.

Traditional feature selection techniques are used to identify a subset of the most useful features, and consider the rest as unimportant, redundant or noisy. In the presence of highly correlated features, many variable selection methods consider correlated features as redundant and need to be removed. In this paper, a novel supervised feature selection algorithm SCANMF is proposed by jointly integrating correlation analysis and structural analysis of the balanced supervised non-negative matrix factorization (NMF). Furthermore, ℓ2,1-norm minimization constraint is incorporated into the objective function to guarantee sparsity in the feature matrix rows and reduce noisy features. Our algorithm exploits the discriminative information, feature combinations, and the original features in the context of a supervised NMF method which can be beneficial for both classification and interpretation. An efficient iterative algorithm is designed to solve the constrained optimization problem with guaranteed convergence. Finally, a series of extensive experiments are conducted on 8 complex datasets. Promising results using multiple classifiers demonstrate the effectiveness and efficiency of our algorithm over state-of-the-art methods.

Deep learning has been found to be vulnerable to changes in the data distribution. This means that inputs that have an imperceptibly and immeasurably small difference from training data correspond to a completely different class label in deep learning. Thus an existing deep learning network like a Convolutional Neural Network (CNN) is vulnerable to adversarial examples. We design an adversarial learning algorithm for supervised learning in general and CNNs in particular. Adversarial examples are generated by a game theoretic formulation on the performance of deep learning. In the game, the interaction between an intelligent adversary and deep learning model is a two-person sequential noncooperative Stackelberg game with stochastic payoff functions. The Stackelberg game is solved by the Nash equilibrium which is a pair of strategies (learner weights and genetic operations) from which there is no incentive for either learner or adversary to deviate. The algorithm performance is evaluated under different strategy spaces on MNIST handwritten digits data. We show that the Nash equilibrium leads to solutions robust to subsequent adversarial data manipulations. Results suggest that game theory and stochastic optimization algorithms can be used to study performance vulnerabilities in deep learning models.

Traffic congestion is a condition of a segment in the road network where the traffic demand is greater than the available road capacity. The detection of unusual traffic patterns including congestions is a significant research problem in the data mining and knowledge discovery community. However, to the best of our knowledge, the discovery of propagations, or causal interactions among detected traffic congestions has not been appropriately investigated before. In this research, we introduce algorithms which construct causality trees from congestions and estimate their propagation probabilities based on temporal and spatial information of the congestions. Frequent sub-structures of these causality trees reveal not only recurring interactions among spatio-temporal congestions, but potential bottlenecks or flaws in the designs of existing traffic networks. Our algorithms have been validated by experiments on a travel time data set recorded from an urban road network.

Supervised feature extraction methods have received considerable attention in the data mining community due to their capability to improve the classification performance of the unsupervised dimensionality reduction methods. With increasing dimensionality, several methods based on supervised feature extraction are proposed to achieve a feature ranking especially on microarray gene expression data. This paper proposes a method with twofold objectives: it implements a balanced supervised non-negative matrix factorization (BSNMF) to handle the class imbalance problem in supervised non-negative matrix factorization techniques. Furthermore, it proposes an accurate gene ranking method based on our proposed BSNMF for microarray gene expression datasets. To the best of our knowledge, this is the first work to handle the class imbalance problem in supervised feature extraction methods. This work is part of a Human Genome project at The Children's Hospital at Westmead (TB-CHW), Australia. Our experiments indicate that the factorized components using supervised feature extraction approach have more classification capability than the unsupervised one, but it drastically fails at the presence of class imbalance problem. Our proposed method outperforms the state-of-the-art methods and shows promise in overcoming this concern.

Structural health monitoring is a condition-based technology to monitor infrastructure using sensing systems. Since we usually only have data associated with the healthy state of a structure, one-class approaches are more practical. However, tuning the parameters for one-class techniques (like one-class Support Vector Machines) still remains a relatively open and difficult problem. Moreover, in structural health monitoring, data are usually multi-way, highly redundant and correlated, which a matrix-based two-way approach cannot capture all these relationships and correlations together. Tensor analysis allows us to analyse the multi-way vibration data at the same time. In our approach, we propose the use of tensor learning and support vector machines with artificial negative data generated by density estimation techniques for damage detection, localization and estimation in a one-class manner. The artificial negative data can help tuning SVM parameters and calibrating probabilistic outputs, which is not possible to do with one-class SVM. The proposed method shows promising results using data from laboratory-based structures and also with data collected from the Sydney Harbour Bridge, one of the most iconic structures in Australia. The method works better than the one-class approach and the approach without using tensor analysis.

Coupled Tensor Factorization (CTF) has become one of the most popular methods for joint analysis of high dimensional data generated from multiple sources. The goal of CTF is to factorize correlated datasets into latent factors efficiently. This research was taken with a particular goal of improving the accuracy of CTF. It is important to optimize the factorization of each single tensor of the coupled tensors. To achieve this, we introduce ASTEN, an Accurate and Scalable Tensor factorization method, where the objective function is optimized with respect to every single tensor and matrix. Differing from algorithms with a traditional objective function which forces shared modes among tensors to have identical factors, ASTEN enables each tensor to have its own discriminative factor on the shared mode and thus is capable of finding the accurate approximation of every tensor. Furthermore, to make it highly scalable in handling big data, we design it to be fully distributed and scalable with respect to the number of tensors, their dimensions, their sizes and the number of data partitions. In addition, we provide our theoretical proof and experimental evidence that our algorithm converges to an optimum. Experiments on both real and synthetic datasets demonstrate that our proposed ASTEN outperforms alternative existing algorithms.

Deep learning has become increasingly popular in both academic and industrial areas in the past years. Various domains including pattern recognition, computer vision, and natural language processing have witnessed the great power of deep networks. However, current studies on deep learning mainly focus on data sets with balanced class labels, while its performance on imbalanced data is not well examined. Imbalanced data sets exist widely in real world and they have been providing great challenges for classification tasks. In this paper, we focus on the problem of classification using deep network on imbalanced data sets. Specifically, a novel loss function called mean false error together with its improved version mean squared false error are proposed for the training of deep networks on imbalanced data sets. The proposed method can effectively capture classification errors from both majority class and minority class equally. Experiments and comparisons demonstrate the superiority of the proposed approach compared with conventional methods in classifying imbalanced data sets on deep neural networks.

Context-aware features have been widely recognized as important factors in recommender systems. However, as a major technique in recommender systems, traditional Collaborative Filtering (CF) does not provide a straight-forward way of integrating the context-aware information into personal recommendation. We propose a Coupled Collaborative Filtering (CCF) model to measure the contextual information and use it to improve recommendations. In the proposed approach, coupled similarity computation is designed to be calculated by interitem, intra-context and inter-context interactions among item, user and context-ware factors. Experiments based on different types of CF models demonstrate the effectiveness of our design.

The itemsets discovered by traditional High Utility Itemsets Mining (HUIM) methods are more useful than frequent itemset mining outcomes; however, they are usually disordered and not actionable, and sometime accidental, because the utility is the only judgement and no relations among itemsets are considered. In this paper, we introduce the concept of combined mining to select combined itemsets that are not only high utility and high frequency, but also involving relations between itemsets. An effective method for mining such actionable combined high utility itemsets is proposed. The experimental results are promising, compared to those from traditional HUIM algorithm (UP-Growth).

Document similarity analysis is increasingly critical since roughly 80% of big data is unstructured. Accordingly, semantic couplings (relatedness) have been recognized valuable for capturing the relationships between terms (words or phrases). Existing work focuses more on explicit relatedness, with respective models built. In this paper, we propose a comprehensive semantic similarity measure: Semantic Coupling Similarity (SCS), which (1) captures intra-term pair couplings within term pairs represented by patterns of explicit term co-occurrences in a document set, (2) extracts inter-term pair couplings between term pairs indicated by implicit couplings between term pairs through indirectly linked terms and paths between terms after term connections are converted to a graph presentation; and (3) semantic coupling similarity, integrating intra- and inter-term pair couplings towards a comprehensive capturing of explicit and implicit couplings between terms across documents. SCS caters for both synonymy and polysemy, and outperforms baseline methods consistently on all real data sets.

In recent years, the importance of identifying actionable patterns has become increasingly recognized so that decision-support actions can be inspired by the resultant patterns. A typical shift is on identifying high utility rather than highly frequent patterns. Accordingly, High Utility Itemset (HUI) Mining methods have become quite popular as well as faster and more reliable than before. However, the current research focus has been on improving the efficiency while the coupling relationships between items are ignored. It is important to study item and itemset couplings inbuilt in the data. For example, the utility of one itemset might be lower than user-specified threshold until one additional itemset takes part in; and vice versa, an item's utility might be high until another one joins in. In this way, even though some absolutely high utility itemsets can be discovered, sometimes it is easily to find out that quite a lot of redundant itemsets sharing the same item are mined (e.g., if the utility of a diamond is high enough, all its supersets are proved to be HUIs). Such itemsets are not actionable, and sellers cannot make higher profit if marketing strategies are created on top of such findings. To this end, here we introduce a new framework for mining actionable high utility association rules, called Combined Utility-Association Rules (CUAR), which aims to find high utility and strong association of itemset combinations incorporating item/itemset relations. The algorithm is proved to be efficient per experimental outcomes on both real and synthetic datasets.

In this research we propose to derive new features based on data samples' local information with the aim of improving the performance of general supervised learning algorithms. The creation of new features is inspired by the measure of average precision which is known to be a robust measure that is insensitive to the number of retrieved items in information retrieval. We use the idea of average precision to weight the neighbours of an instance and show that this weighting strategy is insensitive to the number of neighbours in the locality. Information captured in the new features allows a general classifier to learn additional useful peripheral knowledge that are helpful in building effective classification models. We comprehensively evaluate our method on real datasets and the results show substantial improvements in the performance of classifiers including SVM, Bayesian networks, random forest, and C4.5.

Adversarial learning is the study of machine learning techniques deployed in non-benign environments. Example applications include classifications for detecting spam email, network intrusion detection and credit card scoring. In fact as the gamut of application domains of machine learning grows, the possibility and opportunity for adversarial behavior will only increase. Till now, the standard assumption about modeling adversarial behavior has been to empower an adversary to change all features of the classifiers at will. The adversary pays a cost proportional to the size of "attack". We refer to this form of adversarial behavior as a dense feature attack. However, the aim of an adversary is not just to subvert a classifier but carry out data transformation in a way such that spam continues to appear like spam to the user as much as possible. We demonstrate that an adversary achieves this objective by carrying out a sparse feature attack. We design an algorithm to show how a classifier should be designed to be robust against sparse adversarial attacks. Our main insight is that sparse feature attacks are best defended by designing classifiers which use ℓ1regularizers.

Blockmodelling is an important technique in social network analysis for discovering the latent structure in graphs. A blockmodel partitions the set of vertices in a graph into groups, where there are either many edges or few edges between any two groups. For example, in the reply graph of a question and answer forum, blockmodelling can identify the group of experts by their many replies to questioners, and the group of questioners by their lack of replies among themselves but many replies from experts.

The problem of learning a discrimination aware model has recently received attention in the data mining community. Various methods and improved models have been proposed, with the main approach being the detection of a discrimination sensitive attribute. Once the discrimination sensitive attribute is identified, the methods aim to develop a strategy that will include the useful information from that attribute without causing any additional discrimination. Our work focuses on an aspect often overlooked in the discrimination aware classification - the scenario of an imbalanced dataset, where the number of samples from one class is disproportionate to the other. We also investigate a strategy that is directly minimizing discrimination and is independent of the class balance. Our empirical results indicate additional concerns that need to be considered when developing discrimination aware classifiers, and our proposed strategy shows promise in overcoming these concerns.

We discuss a new formulation of a fuzzy validity index that generalizes the Newman-Girvan (NG) modularity function. The NG function serves as a cluster validity functional in community detection studies. The input data is an undirected graph G = (V, E) that represents a social network. Clusters in V correspond to socially similar substructures in the network. We compare our fuzzy modularity to an existing modularity function using the well-studied Karate Club data set.

It is well known that supervised learning problems with ℓ1 (Lasso) and ℓ2 (Tikhonov or Ridge) regularizers will result in very different solutions. Forexample, the ℓ1 solution vector will be sparser and can potentially beused both for prediction and feature selection. However, given a data set it isoften hard to determine which form of regularizationis more applicable in a given context. In this paper we use mathematical propertiesof the two regularization methods followed by detailed experimentation to understand their impact basedon four characteristics: non-stationarity of the data generating process, level of noise in the data sensingmechanism, degree of correlation between dependent and independent variables and the shape of the data set. The practical outcome of our research is that it can serve as a guide forpractitioners of large scale data mining and machine learning tools in their day-to-day practice.

Support Vector Machines (SVMs) are a leading tool in machine learning and have been used with considerable success for the task of time series forecasting. However, a key challenge when using SVMs for time series is the question of how to deeply integrate time elements into the learning process. To address this challenge, we investigated the distribution of errors in the forecasts delivered by standard SVMs. Once we identified the samples that produced the largest errors, we observed their correlation with distribution shifts that occur in the time series. This motivated us to propose a time-dependent loss function which allows the inclusion of the information about the distribution shifts in the series directly into the SVM learning process. We present experimental results which indicate that using a time-dependent loss function is highly promising, reducing the overall variance of the errors, as well as delivering more accurate predictions.

Conventional non-negative tensor factorization (NTF) methods assume there is only one tensor that needs to be decomposed to low-rank factors. However, in practice data are usually generated from different time periods or by different class labels, which are represented by a sequence of multiple tensors associated with different labels. This raises the problem that when one needs to analyze and compare multiple tensors, existing NTF is unsuitable for discovering all potentially useful patterns: 1) if one factorizes each tensor separately, the common information shared by the tensors is lost in the factors, and 2) if one concatenates these tensors together and forms a larger tensor to factorize, the intrinsic discriminative subspaces that are unique to each tensor are not captured. The cause of such an issue is from the fact that conventional factorization methods handle data observations in an unsupervised way, which only considers features and not labels of the data. To tackle this problem, in this paper we design a novel factorization algorithm called CDNTF (common and discriminative subspace non-negative tensor factorization), which takes both features and class labels into account in the factorization process. CDNTF uses a set of labelled tensors as input and computes both their common and discriminative subspaces simultaneously as output. We design an iterative algorithm that solves the common and discriminative subspace factorization problem with a proof of convergence. Experiment results on solving graph classification problems demonstrate the power and the effectiveness of the subspaces discovered by our method.

In large and complex graphs of social, chemical/biological, or other relations, frequent substructures are commonly shared by different graphs or by graphs evolving through different time periods. Tensors are natural representations of these complex time-evolving graph data. A factorization of a tensor provides a high-quality low-rank compact basis for each dimension of the tensor, which facilitates the interpretation of frequent substructures of the original graphs. However, the high computational cost of tensor factorization makes it infeasible for conventional tensor factorization methods to handle large graphs that evolve frequently with time. To address this problem, in this paper we propose a novel iterative tensor factorization (ITF) method whose time complexity is linear in the cardinalities of all dimensions of a tensor. This low time complexity means that when using tensors to represent dynamic graphs, the computational cost of ITF is linear in the size (number of edges/vertices) of graphs and is also linear in the number of time periods over which the graph evolves. More importantly, an error estimation of ITF suggests that its factorization correctness is comparable to that of the standard factorization method. We empirically evaluate our method on publication networks and chemical compound graphs, and demonstrate that ITF is an order of magnitude faster than the conventional method and at the same time preserves factorization quality. To the best of our knowledge, this research is the first work that uses important frequent substructures to speed up tensor factorizations for mining dynamic graphs.

Existing graph compression techniquesmostly focus on static graphs. However for many practical graphs such as social networks the edge weights frequently change over time. This phenomenon raises the question of how to compress dynamic graphs while maintaining most of their intrinsic structural patterns at each time snapshot. In this paper we show that the encoding cost of a dynamic graph is proportional to the heterogeneity of a three dimensional tensor that represents the dynamic graph. We propose an effective algorithm that compresses a dynamic graph by reducing the heterogeneity of its tensor representation, and at the same time also maintains a maximum lossy compression error at any time stamp of the dynamic graph. The bounded compression error benefits compressed graphs in that they retain good approximations of the original edge weights, and hence properties of the original graph (such as shortest paths) are well preserved. To the best of our knowledge, this is the first work that compresses weighted dynamic graphs with bounded lossy compression error at any time snapshot of the graph.

Blockmodelling is an important technique for decomposing graphs into sets of roles. Vertices playing the same role have similar patterns of interactions with vertices in other roles. These roles, along with the role to role interactions, can succinctly summarise the underlying structure of the studied graphs. As the underlying graphs evolve with time, it is important to study how their blockmodels evolve too. This will enable us to detect role changes across time, detect different patterns of interactions, for example, weekday and weekend behaviour, and allow us to study how the structure in the underlying dynamic graph evolves. To date, there has been limited research on studying dynamic blockmodels. They focus on smoothing role changes between adjacent time instances. However, this approach can overfit during stationary periods where the underling structure does not change but there is random noise in the graph. Therefore, an approach to a) find blockmodels across spans of time and b) to find the stationary periods is needed. In this paper, we propose an information theoretic framework, SeqiBloc, combined with a change point detection approach to achieve a) and b). In addition, we propose new vertex equivalence definitions that include time, and show how they relate back to our information theoretic approach. We demonstrate their usefulness and superior accuracy over existing work on synthetic and real datasets.

The detection of outliers in spatio-temporal traffic data is an important research problem in the data mining and knowledge discovery community. However to the best of our knowledge, the discovery of relationships, especially causal interactions, among detected traffic outliers has not been investigated before. In this paper we propose algorithms which construct outlier causality trees based on temporal and spatial properties of detected outliers. Frequent substructures of these causality trees reveal not only recurring interactions among spatio-temporal outliers, but potential flaws in the design of existing traffic networks. The effectiveness and strength of our algorithms are validated by experiments on a very large volume of real taxi trajectories in an urban road network.

In this paper, we study the problem of data skewness. A data set is skewed/imbalanced if its dependent variable is asymmetrically distributed. Dealing with skewed data sets has been identified as one of the ten most challenging problems in data mining research. We address the problem of class skewness for supervised learning models which are based on optimizing a regularized empirical risk function. These include both classification and regression models for discrete and continuous dependent variables. Classical empirical risk minimization is akin to minimizing the arithmetic mean of prediction errors, in which approach the induction process is biased towards the majority class for skewed data. To overcome this drawback, we propose a quadratic mean based learning framework (QMLearn) that is robust and insensitive to class skewness. We will note that minimizing the quadratic mean is a convex optimization problem and hence can be efficiently solved for large and high dimensional data. Comprehensive experiments demonstrate that the QMLearn model significantly outperforms existing statistical learners including logistic regression, support vector machines, linear regression, support vector regression and quantile regression etc.

We propose a new decision tree algorithm, Class Confidence Proportion Decision Tree (CCPDT), which is robust and insensitive to size of classes and generates rules which are statistically significant. In order to make decision trees robust, we begin by expressing Information Gain, the metric used in C4.5, in terms of confidence of a rule. This allows us to immediately explain why Information Gain, like confidence, results in rules which are biased towards the majority class. To overcome this bias, we introduce a new measure, Class Confidence Proportion (CCP), which forms the basis of CCPDT. To generate rules which are statistically significant we design a novel and efficient top-down and bottom-up approach which uses Fisher's exact test to prune branches of the tree which are not statistically significant. Together these two changes yield a classifier that performs statistically better than not only traditional decision trees but also trees learned from data that has been balanced by well known sampling techniques. Our claims are confirmed through extensive experiments and comparisons against C4.5, CART, HDDT and SPARCCC.