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Vision Transformers (ViTs) have emerged as state-of-the-art models for various vision tasks recently. However, their heavy computation costs remain daunting for resource-limited devices. Consequently, researchers have dedicated themselves to compressing redundant information in ViTs for acceleration. However, they generally sparsely drop redundant image tokens by token pruning or brutally remove channels by channel pruning, leading to a sub-optimal balance between model performance and inference speed. They are also disadvantageous in transferring compressed models to downstream vision tasks that require the spatial structure of images, such as semantic segmentation. To tackle these issues, we propose a joint compression method for ViTs that offers both high accuracy and fast inference speed, while also maintaining favorable transferability to downstream tasks (CAIT). Specifically, we introduce an asymmetric token merging (ATME) strategy to effectively integrate neighboring tokens. It can successfully compress redundant token information while preserving the spatial structure of images. We further employ a consistent dynamic channel pruning (CDCP) strategy to dynamically prune unimportant channels in ViTs. Thanks to CDCP, insignificant channels in multi-head self-attention modules of ViTs can be pruned uniformly, greatly enhancing the model compression. Extensive experiments on benchmark datasets demonstrate that our proposed method can achieve state-of-the-art performance across various ViTs. For example, our pruned DeiT-Tiny and DeiT-Small achieve speedups of 1.7$\times$ and 1.9$\times$, respectively, without accuracy drops on ImageNet. On the ADE20k segmentation dataset, our method can enjoy up to 1.31$\times$ speedups with comparable mIoU. Our code will be publicly available.

Network-on-Chip (NoC) is widely used as the internal communication fabric in today's multicore System-on-Chip (SoC) designs. Security of the on-chip communication is crucial because exploiting any vulnerability in shared NoC would be a goldmine for an attacker. NoC security relies on effective countermeasures against diverse attacks. We investigate the security strength of existing anonymous routing protocols in NoC architectures. Specifically, this paper makes two important contributions. We show that the existing anonymous routing is vulnerable to machine learning (ML) based flow correlation attacks on NoCs. We propose a lightweight anonymous routing that use traffic obfuscation techniques which can defend against ML-based flow correlation attacks. Experimental studies using both real and synthetic traffic reveal that our proposed attack is successful against state-of-the-art anonymous routing in NoC architectures with a high accuracy (up to 99%) for diverse traffic patterns, while our lightweight countermeasure can defend against ML-based attacks with minor hardware and performance overhead.

Temporal Action Segmentation (TAS) from video is a kind of frame recognition task for long video with multiple action classes. As an video understanding task for long videos, current methods typically combine multi-modality action recognition models with temporal models to convert feature sequences to label sequences. This approach can only be applied to offline scenarios, which severely limits the TAS application. Therefore, this paper proposes an end-to-end Streaming Video Temporal Action Segmentation with Reinforce Learning (SVTAS-RL). The end-to-end SVTAS which regard TAS as an action segment clustering task can expand the application scenarios of TAS; and RL is used to alleviate the problem of inconsistent optimization objective and direction. Through extensive experiments, the SVTAS-RL model achieves a competitive performance to the state-of-the-art model of TAS on multiple datasets, and shows greater advantages on the ultra-long video dataset EGTEA. This indicates that our method can replace all current TAS models end-to-end and SVTAS-RL is more suitable for long video TAS. Code is availabel at //github.com/Thinksky5124/SVTAS.

Text-conditional image editing is a very useful task that has recently emerged with immeasurable potential. Most current real image editing methods first need to complete the reconstruction of the image, and then editing is carried out by various methods based on the reconstruction. Most methods use DDIM Inversion for reconstruction, however, DDIM Inversion often fails to guarantee reconstruction performance, i.e., it fails to produce results that preserve the original image content. To address the problem of reconstruction failure, we propose FEC, which consists of three sampling methods, each designed for different editing types and settings. Our three methods of FEC achieve two important goals in image editing task: 1) ensuring successful reconstruction, i.e., sampling to get a generated result that preserves the texture and features of the original real image. 2) these sampling methods can be paired with many editing methods and greatly improve the performance of these editing methods to accomplish various editing tasks. In addition, none of our sampling methods require fine-tuning of the diffusion model or time-consuming training on large-scale datasets. Hence the cost of time as well as the use of computer memory and computation can be significantly reduced.

We propose FLARE, the first fingerprinting mechanism to verify whether a suspected Deep Reinforcement Learning (DRL) policy is an illegitimate copy of another (victim) policy. We first show that it is possible to find non-transferable, universal adversarial masks, i.e., perturbations, to generate adversarial examples that can successfully transfer from a victim policy to its modified versions but not to independently trained policies. FLARE employs these masks as fingerprints to verify the true ownership of stolen DRL policies by measuring an action agreement value over states perturbed by such masks. Our empirical evaluations show that FLARE is effective (100% action agreement on stolen copies) and does not falsely accuse independent policies (no false positives). FLARE is also robust to model modification attacks and cannot be easily evaded by more informed adversaries without negatively impacting agent performance. We also show that not all universal adversarial masks are suitable candidates for fingerprints due to the inherent characteristics of DRL policies. The spatio-temporal dynamics of DRL problems and sequential decision-making process make characterizing the decision boundary of DRL policies more difficult, as well as searching for universal masks that capture the geometry of it.

Heavy Good Vehicles (HGVs) are the second largest source of greenhouse gas emissions in transportation, after cars and taxis. However, HGVs are inefficiently utilised, with more than one-third of their weight capacity not being used during travel. We, thus, in this paper address collaborative logistics, an effective pathway to enhance HGVs' utilisation and reduce carbon emissions. We investigate a multi-agent system approach to facilitate collaborative logistics, particularly carrier collaboration. We propose a simple yet effective multi-agent collaborative logistics (MACL) framework, representing key stakeholders as intelligent agents. Furthermore, we utilise the MACL framework in conjunction with a proposed system architecture to create an integrated collaborative logistics testbed. This testbed, consisting of a physical system and its digital replica, is a tailored cyber-physical system or digital twin for collaborative logistics. Through a demonstration, we show the utility of the testbed for studying collaborative logistics.

Text Classification is the most essential and fundamental problem in Natural Language Processing. While numerous recent text classification models applied the sequential deep learning technique, graph neural network-based models can directly deal with complex structured text data and exploit global information. Many real text classification applications can be naturally cast into a graph, which captures words, documents, and corpus global features. In this survey, we bring the coverage of methods up to 2023, including corpus-level and document-level graph neural networks. We discuss each of these methods in detail, dealing with the graph construction mechanisms and the graph-based learning process. As well as the technological survey, we look at issues behind and future directions addressed in text classification using graph neural networks. We also cover datasets, evaluation metrics, and experiment design and present a summary of published performance on the publicly available benchmarks. Note that we present a comprehensive comparison between different techniques and identify the pros and cons of various evaluation metrics in this survey.

Sequential recommendation (SR) is to accurately recommend a list of items for a user based on her current accessed ones. While new-coming users continuously arrive in the real world, one crucial task is to have inductive SR that can produce embeddings of users and items without re-training. Given user-item interactions can be extremely sparse, another critical task is to have transferable SR that can transfer the knowledge derived from one domain with rich data to another domain. In this work, we aim to present the holistic SR that simultaneously accommodates conventional, inductive, and transferable settings. We propose a novel deep learning-based model, Relational Temporal Attentive Graph Neural Networks (RetaGNN), for holistic SR. The main idea of RetaGNN is three-fold. First, to have inductive and transferable capabilities, we train a relational attentive GNN on the local subgraph extracted from a user-item pair, in which the learnable weight matrices are on various relations among users, items, and attributes, rather than nodes or edges. Second, long-term and short-term temporal patterns of user preferences are encoded by a proposed sequential self-attention mechanism. Third, a relation-aware regularization term is devised for better training of RetaGNN. Experiments conducted on MovieLens, Instagram, and Book-Crossing datasets exhibit that RetaGNN can outperform state-of-the-art methods under conventional, inductive, and transferable settings. The derived attention weights also bring model explainability.

Graph Neural Networks (GNNs) have been shown to be effective models for different predictive tasks on graph-structured data. Recent work on their expressive power has focused on isomorphism tasks and countable feature spaces. We extend this theoretical framework to include continuous features - which occur regularly in real-world input domains and within the hidden layers of GNNs - and we demonstrate the requirement for multiple aggregation functions in this context. Accordingly, we propose Principal Neighbourhood Aggregation (PNA), a novel architecture combining multiple aggregators with degree-scalers (which generalize the sum aggregator). Finally, we compare the capacity of different models to capture and exploit the graph structure via a novel benchmark containing multiple tasks taken from classical graph theory, alongside existing benchmarks from real-world domains, all of which demonstrate the strength of our model. With this work, we hope to steer some of the GNN research towards new aggregation methods which we believe are essential in the search for powerful and robust models.

Bidirectional Encoder Representations from Transformers (BERT) has shown marvelous improvements across various NLP tasks. Recently, an upgraded version of BERT has been released with Whole Word Masking (WWM), which mitigate the drawbacks of masking partial WordPiece tokens in pre-training BERT. In this technical report, we adapt whole word masking in Chinese text, that masking the whole word instead of masking Chinese characters, which could bring another challenge in Masked Language Model (MLM) pre-training task. The model was trained on the latest Chinese Wikipedia dump. We aim to provide easy extensibility and better performance for Chinese BERT without changing any neural architecture or even hyper-parameters. The model is verified on various NLP tasks, across sentence-level to document-level, including sentiment classification (ChnSentiCorp, Sina Weibo), named entity recognition (People Daily, MSRA-NER), natural language inference (XNLI), sentence pair matching (LCQMC, BQ Corpus), and machine reading comprehension (CMRC 2018, DRCD, CAIL RC). Experimental results on these datasets show that the whole word masking could bring another significant gain. Moreover, we also examine the effectiveness of Chinese pre-trained models: BERT, ERNIE, BERT-wwm. We release the pre-trained model (both TensorFlow and PyTorch) on GitHub: //github.com/ymcui/Chinese-BERT-wwm