With the rapid development of digital technologies, a large range of real-world systems, spanning from cloud servers, IoT devices, to industrial control systems, continuously generate vast amounts of time series data. Time series anomaly detection (AD) plays a crucial role in maintaining system stability by identifying unusual patterns from normal distributions, with the primary challenge lies in learning effective anomaly-discriminative representations. Recently, diffusion models have been applied to time series AD due to their strong representational capabilities. However, existing diffusion-based methods typically rely on reconstruction errors, which not only fail to fully exploit the representational potential of diffusion models but also be computationally intensive. To address these limitations, through experimental observation and theoretical analysis, we show thatspecific regions of the diffusion noises exhibit stronger representation capabilitiesfor normal patterns, which can be leveraged to enhance AD performance and reduce computational costs. Building on these insights, we propose NoiseAD, a diffusion noise-guided anomaly detection method incorporating an optimal noise steps selection approach to identify diffusion steps with higher resolution. Extensive experiments on diverse benchmarks demonstrate the superiority of NoiseAD over state-of-the-art methods, further substantiated by insightful visualizations. Code could be available athttps://github.com/shiwang-Xing/NoiseAD.

Deep learning models often suffer performance degradation when test data diverges from training data. Test-Time Adaptation (TTA) aims to adapt a trained model to the test data distribution using unlabeled test data streams. In many real-world applications, it is quite common for the trained model to be deployed across multiple devices simultaneously. Although each device can execute TTA independently, it fails to leverage information from the test data of other devices. To address this problem, we introduce Federated Learning (FL) to TTA to facilitate on-the-fly collaboration among devices during test time. The workflow involves clients (i.e., the devices) executing TTA locally, uploading their updated models to a central server for aggregation, and downloading the aggregated model for inference. However, implementing FL in TTA presents many challenges, especially in establishing inter-client collaboration in dynamic environment, where the test data distribution on different clients changes over time in different manners. To tackle these challenges, we propose a server-side Temporal-Spatial Aggregation (TSA) method. TSA utilizes a temporal-spatial attention module to capture intra-client temporal correlations and inter-client spatial correlations. To further improve robustness against temporal-spatial heterogeneity, we propose a heterogeneity-aware augmentation method and optimize the module using a self-supervised approach. More importantly, TSA can be implemented as a plug-in to TTA methods in distributed environments. Experiments on multiple datasets demonstrate that TSA outperforms existing methods and exhibits robustness across various levels of heterogeneity. The code is available at https://github.com/ZhangJiayuan-BUAA/FedTSA.

Multivariate time series anomaly detection, crucial for ensuring the safety of real-world systems, primarily focuses on extracting characteristics from time series under normal condition, and identifying potential anomalies throughout the evaluation process. Recent studies have achieved fruitful progress through mining the spatio-temporal relationships from multivariate time series, however, these approaches mostly neglect the latency among series which could lead to higher false alarm. Granger causality presents a promising solution to extract these inherent time-lagged relationships. Nonetheless, the intricate and dynamic relationships among numerous time series in real-world systems surpass the ability of linear Granger causality. To address this, we extend the linear Granger causality and propose the Granger Causal Former (GCFormer), a novel approach that leverages attention mechanisms to learn the inherent causal spatio-temporal relationships between historical and current timestamps across multiple time series. Specifically, GCFormer develops a Spatio-Mask (SM) to select the top-k most relevant series and a Temporal-Mask (TM) to concentrate attention on more recent historical timestamps. Moreover, to mitigate overfitting and ensure a smooth training process, GCFormer introduces an adjust top-k method and a TM penalty term. We evaluated GCFormer on four real-world benchmark datasets, demonstrating its superior performance over state-of-the-art approaches. Further analysis and a case study highlight the model’s novelty and interpretability.

The low-rank matrix completion problem is fundamental in both machine learning and computer vision fields with many important applications, such as recommendation system, motion capture, face recognition, and image inpainting. In order to avoid solving the rank minimization problem which is NP-hard, several surrogate functions of the rank have been proposed in the literature. However, the matrix restored from the optimization problem based on the existing surrogate functions seriously deviates from the original one. In this paper, we first design a new non-convex Schatten capped$p$norm which generalizes several existing non-convex matrix norms and balances between the rank and the nuclear norm of the matrix. Then, a matrix completion method based on the Schatten capped$p$norm is proposed by exploiting the framework of the alternating direction method of multipliers. Meanwhile, the Schatten capped$p$norm regularized least squares subproblem is analyzed in detail and is solved explicitly. Finally, we evaluate the performance of the proposed matrix completion method based on extensive experiments in the field of image inpainting. All the experimental results demonstrate that the proposed method can indeed improve the accuracy of matrix completion compared with the existing methods.

As sharing of accounts is quite common among family members or roommates, the design of efficient recommender schemes for shared accounts has raised much attention recently. Generally speaking, after each login, it is essential for a recommender system to identify the current user behind and leverage this information to make recommendations. One naive approach is first to identify the identity of the current user and then make recommendations. However, this two-stage based approach may not achieve satisfactory performance. The key is that the recommended items favoring identifying users in the first stage may not be interesting to the users, which can deplete the user's patience quickly and cause early termination of users. To address the problem, we propose a novel recommendation scheme that makes a tradeoff between recommending discriminating items (helpful for identifying the user) and recommending interesting ones to the user (helpful for increasing the number of clicks). Under this scheme, we develop a patience model to capture the user's dynamic patience level during the recommendation process. Moreover, considering the increasing popularity of mobile devices, we also incorporate mobile sensor data (i.e., angle, accelerometer, gyroscope, etc.) into our approach to further improve the performance of the system. We implemented the above system in an App on mobile devices and carried out extensive experiments. The results demonstrate that our proposed scheme significantly outperforms the existing state-of-the-art approaches.

Twitter sentiment analysis has become a hot research topic in recent years. Most of existing solutions to Twitter sentiment analysis basically only consider textual information of Twitter messages, and struggle to perform well when facing short and ambiguous Twitter messages. Recent studies show that sentiment diffusion patterns on Twitter have close relationships with sentiment polarities of Twitter messages. Therefore, in this paper, we focus on how to fuse textual information of Twitter messages and sentiment diffusion patterns to obtain better performance of sentiment analysis on Twitter data. To this end, we first analyze sentiment diffusion by investigating a phenomenon called sentiment reversal, and find some interesting properties of sentiment reversals. Then, we consider the inter-relationships between textual information of Twitter messages and sentiment diffusion patterns, and propose an iterative algorithm called SentiDiff to predict sentiment polarities expressed in Twitter messages. To the best of our knowledge, this work is the first to utilize sentiment diffusion patterns to help improve Twitter sentiment analysis. Extensive experiments on real-world dataset demonstrate that compared with state-of-the-art textual information based sentiment analysis algorithms, our proposed algorithm yields PR-AUC improvements between 5.09 and 8.38 percent on Twitter sentiment classification tasks.

With the blossoming of social networking platforms like Twitter and Facebook, how to infer the opinions of online social network users on specific topics they had not directly given yet, has received much attention. Existing solutions mainly rely on one's previous posted messages. However, recent studies show that over 40% of users opt to be silent all or most of the time and post very few messages. Consequently, the performance of existing solutions will drop dramatically when they are applied to infer silent users' opinions, and how to infer the opinions of these silent users becomes a meaningful while challenging task. Inspired by the collaborative filtering techniques in cold-start recommendations, we infer the opinions of silent users by leveraging the text content posted by active users and their relationships between silent users. Specifically, we first consider both observed and pseudo relationships among users, and cluster users into communities in order to extract various kinds of features for opinion inference. We then design a coupled sparse matrix factorization (CSMF) model to capture the complex relations among these features. Extensive experiments on real-world data from Twitter show that our CSMF model achieves over 80% accuracy for the inference of silent users' opinions.

User-generated reviews on the e-commerce site reflect consumers' sentiment about products, which can further direct consumers' purchasing behaviors and sellers' marketing strategies. In this paper, we propose a semi-supervised approach to mine the aspects of product discussed in Chinese online reviews and also the sentiments expressed in different aspects. We first apply the Latent Dirichlet Allocation model to discover multiaspect global topics of the product reviews, then extract the opinion short sentences based on sliding windows and pattern matching from context over the review text. The polarity of the associated sentiment is classified by the domain lexicon-based method. Finally the results are collected as features for the feedback for the machine learning method and applied in semantic orientation classification. The experiment results show that the novel method we proposed could help to discover multi-aspect fine-grained topics and associated sentiment, which helps to improve semantic orientation classification simultaneously.

An infrastructure-as-a-service cloud system provides computational capacities to remote users. Parallel processing in the cloud system can shorten the execution of jobs. Parallel processing requires a mechanism to scheduling the executions order as well as resource allocation. Furthermore, a preemptable scheduling mechanism can improve the utilization of resources in clouds. In this paper, we present a preemptable job scheduling mechanism in cloud system. We propose two feedback dynamic scheduling algorithms for this scheduling mechanism. We compare these two scheduling algorithms in simulations. The results show that the feedback procedure in our algorithms works well in the situation where resource contentions are fierce.