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Video-based activity recognition has a wide array of applications across various industries. A persistent challenge in practice is the scarcity of labeled datasets from target domains needed for training machine learning models capable of accurate activity recognition. Cross-Domain Few-Shot Learning (CDFSL) offers a promising solution by facilitating knowledge transfer from a label-rich source domain to a data-scarce target domain. However, existing CDFSL approaches often overlook the similarities between the source and target domains on the achievable performance of activity recognition in the target domain. Existing metrics for measuring domain similarity, such as Maximum Mean Discrepancy, focus primarily on data distribution and fail to provide actionable guidance for selecting source domains. To address this gap, we explore domain similarities and their effects from various angles, including video attributes like camera angle, background scene, and label granularity. For our case study, we focus on activity recognition within industrial environments as the target application and apply state-of-the-art CDFSL methods across diverse source-target combinations, using open datasets such as Kinetics-100, HMDB51, HA-VID, and Meccano. Our results offer valuable insights into the influence of domain similarities, which can aid in the selection of source domains.

Cameras are widely deployed for visual inspection in manufacturing and construction industries, generating massive volumes of image and video data that demand automated solutions for defect detection. Knowledge distillation has shown strong potential for unsupervised industrial visual anomaly detection, a task of increasing importance in such large-scale, data-intensive environments. However, the conventional single-student-single-teacher framework often yields suboptimal learning of normal patterns, primarily due to the absence of specific constraints and potential feature loss. To address these challenges, we propose S2TKD, a novel dual-student-single-teacher architecture. It incorporates a pre-trained teacher network, an Anomalous Feature Denoising Student (AFDS) network, and a Normal Feature Student (NFS) network. The AFDS network focuses on filtering out anomalies by imposing stronger constraints on anomalous data, while the NFS network extracts normal features to recover subtle patterns suppressed by the AFDS network. To further improve the richness of feature representations within each student network, we integrate a multi-scale feature fusion module Dual Pyramid Network (DPN) between the encoder and the decoder. Furthermore, we propose a Dual Fusion Network (DFN) that accurately identifies anomalous regions by fusing the similarity of the outputs of each student and teacher network to obtain multi-scale similarity maps, which are then adaptively aggregated to generate the final anomaly map. Experimental results on four benchmark datasets demonstrate S2TKD's outperforms compared to the state-of-the-art. On the representative MVTec AD dataset, the I-AUROC, P-AUROC, and PRO reached 99.5%, 99.1%, and 96.9%, respectively, with the PRO showing a 1.9% increase over the current best result. The results highlight S2TKD’s effectiveness and scalability for large-scale industrial visual anomaly detection, making it a promising solution for real-world big data inspection systems.

Detecting social bots is crucial for mitigating the spread of misinformation and preserving online conversation authenticity. State-of-the-art solutions typically leverage graph neural networks (GNNs) to model user representations from social relationships and metadata. However, these approaches overlook two key factors: the similarity of a user and her neighbors, as well as the coordinated behaviors of social bots, resulting in a suboptimal detection performance. To address these issues, we propose HyperScan, a novel representation learning method for social bot detection. Specifically, we introduce three effective learners to capture pair-wise, hop-wise, and group-wise relations. HyperScan learns pair-wise user representations based on social relations and user features. It then enhances user representations by building hop-wise interactions across the learned pair-wise user representations for capturing the structure-level proximity information. Subsequently, it models user representations by constructing higher-order (group-wise) relations derived from user profiles, tweets, and social relations to capture the feature-level proximity knowledge. By leveraging hop-wise interactions and higher-order relations, HyperScan significantly improves bot detection performance. Our extensive experiments demonstrate that HyperScan outperforms state-of-the-art methods on three benchmark datasets. Additional studies validate the robustness and effectiveness of each component of HyperScan.

The rapid proliferation of learning systems in an arbitrarily changing environment mandates the need to manage tensions between exploration and exploitation. This work proposes a quantum-inspired bandit learning approach for the learning-and-adapting-based offloading problem where a client observes and learns the costs of each task offloaded to the candidate resource providers, e.g., fog nodes. In this approach, a new action update strategy and novel probabilistic action selection are adopted, provoked by the amplitude amplification and collapse postulate in quantum computation theory. We devise a locally linear mapping between a quantum-mechanical phase in a quantum domain, e.g., Grover-type search algorithm, and a distilled probability-magnitude in a value-based decision-making domain, e.g., adversarial multi-armed bandit algorithm. The proposed algorithm is generalized, via the devised mapping, for better learning weight adjustments on favorable/unfavorable actions, and its effectiveness is verified via simulation.

Up-to-date and accurate road maps with traffic rules are crucial for traffic safety and efficiency. However, detecting changes in traffic rules, particularly in road signs in an efficient manner still remains an open challenge. This paper proposes a method to detect and classify seven types of changes, relying on observable traffic flow changes like average vehicle speed. Our method employs a deep learning-based binary relevance approach, treating each type of change as a separate binary classification task. Input data comprise information from citywide induction loops, detailing congestion levels and average speed for each road, which vary with time and location. Our model outputs change type probabilities for 2.5 km2city regions for every 10 minutes. Unlike GPS traces of buses and taxis, which provide merely a partial view of the traffic, induction loop data offers a comprehensive, cost-effective view of traffic. However, there are three key challenges of utilizing induction loop data for change detection and classification, including sparse deployment of induction loops, high dimensionality of input data, and severe class imbalance. To address the first two challenges, we introduce novel strategies comprising dimensionality reduction, multi-dimensional sliding windows, neural network architectural choices such as residual connections and stateful recurrency, and data augmentation. Regarding the class imbalance, we apply sample weighing in the loss function. These novel design choices result in effective solutions with F1-scores exceeding 80% in experiments with simulated and real-world traffic data.

Online developer communities like GitHub allow a massive number of developers to collaborate. However, the openness of the communities makes them vulnerable to different types of malicious attacks, since attackers can easily join these communities and interact with legitimate users. In this work, we propose GitSec, a deep learning-based solution for detecting malicious accounts in online developer communities. GitSec distinguishes malicious accounts from legitimate ones based on the account profiles, dynamic activity characteristics, as well as social interactions. First, GitSec introduces two user activity sequences and applies a parallel neural network design with an attention mechanism to process the sequences. Second, GitSec constructs two graphs to represent the interactions between users according to their repository operations. Especially, graph neural networks and structural hole theory are employed to deal with the two constructed graphs. Third, GitSec makes use of the descriptive features to enhance the detection performance. The final judgement is made by a decision maker implemented by a supervised machine learning-based classifier. Based on the real-world data of GitHub users, our comprehensive evaluations show that GitSec achieves a better performance than state-of-the-art solutions, with an AUC value of 0.916.

Online social networks (OSNs) have become a commodity in our daily life. As an important concept in sociology and viral marketing, the study of social influence has received a lot of attentions in academia. Most of the existing proposals work well on dominant OSNs, such as Twitter, since these sites are mature and many users have generated a large amount of data for the calculation of social influence. Unfortunately, cold-start users on emerging OSNs generate much less activity data, which makes it challenging to identify potential influential users among them. In this work, we propose a practical solution to predict whether a cold-start user will become an influential user on an emerging OSN, by opportunistically leveraging the user’s information on dominant OSNs. A supervised machine learning-based approach is adopted, transferring the knowledge of both the descriptive information and dynamic activities on dominant OSNs. Descriptive features are extracted from the public data on a user’s homepage. In particular, to extract useful information from the fine-grained dynamic activities that cannot be represented by the statistical indices, we use deep learning technologies to deal with the sequential activity data. Using the real data of millions of users collected from Twitter (a dominant OSN) and Medium (an emerging OSN), we evaluate the performance of our proposed framework to predict prospective influential users. Our system achieves a high prediction performance based on different social influence definitions.

Accurate traffic speed prediction is an important and challenging topic for transportation planning. Previous studies on traffic speed prediction predominately used spatio-temporal and context features for prediction. However, they have not made good use of the impact of traffic incidents. In this work, we aim to make use of the information of incidents to achieve a better prediction of traffic speed. Our incident-driven prediction framework consists of three processes. First, we propose a critical incident discovery method to discover traffic incidents with high impact on traffic speed. Second, we design a binary classifier, which uses deep learning methods to extract the latent incident impact features. Combining above methods, we propose a Deep Incident-Aware Graph Convolutional Network (DIGC-Net) to effectively incorporate traffic incident, spatio-temporal, periodic and context features for traffic speed prediction. We conduct experiments using two real-world traffic datasets of San Francisco and New York City. The results demonstrate the superior performance of our model compared with the competing benchmarks.

In this work, we investigate activity recognition using multimodal inputs from heterogeneous sensors. Activity recognition is commonly tackled from a single-modal perspective using videos. In case multiple signals are used, they come from the same homogeneous modality, e.g. in the case of color and optical flow. Here, we propose an activity network that fuses multimodal inputs coming from completely different and heterogeneous sensors. We frame such a heterogeneous fusion as a non-local operation. The observation is that in a non-local operation, only the channel dimensions need to match. In the network, heterogeneous inputs are fused, while maintaining the shapes and dimensionalities that fit each input. We outline both asymmetric fusion, where one modality serves to enforce the other, and symmetric fusion variants. To further promote research into multimodal activity recognition, we introduce GloVid, a first-person activity dataset captured with video recordings and smart glove sensor readings. Experiments on GloVid show the potential of heterogeneous non-local fusion for activity recognition, outperforming individual modalities and standard fusion techniques.

Online developer communities like GitHub provide services such as distributed version control and task management, which allow a massive number of developers to collaborate online. However, the openness of the communities makes themselves vulnerable to different types of malicious attacks, since the attackers can easily join and interact with legitimate users. In this work, we formulate the malicious account detection problem in online developer communities, and propose GitSec, a deep learning-based solution to detect malicious accounts. GitSec distinguishes malicious accounts from legitimate ones based on the account profiles as well as dynamic activity characteristics. On one hand, GitSec makes use of users' descriptive features from the profiles. On the other hand, GitSec processes users' dynamic behavioral data by constructing two user activity sequences and applying a parallel neural network design to deal with each of them, respectively. An attention mechanism is used to integrate the information generated by the parallel neural networks. The final judgement is made by a decision maker implemented by a supervised machine learning-based classifier. Based on the real-world data of GitHub users, our extensive evaluations show that GitSec is an accurate detection system, with an F1-score of 0.922 and an AUC value of 0.940.

Accurate prediction of traffic congestion at the granularity of road segment is important for planning travel routes and optimizing traffic control in urban areas. Previous works often calculated only the average congestion levels of a large region covering many road segments and did not take into account spatial correlation between road segments, resulting in inaccurate and coarse-grained prediction. To overcome these issues, we propose in this paper CPM-ConvLSTM, a spatiotemporal model for short-term prediction of congestion level in each road segment. Our model is built on a spatial matrix which incorporates both the congestion propagation pattern and the spatial correlation between road segments. The preliminary experiments on the traffic data set collected from Helsinki, Finland prove that CPM-ConvLSTM greatly outperforms 6 counterparts in terms of prediction accuracy.