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Intelligent driving systems aim to achieve a zero-collision mobility experience, requiring interdisciplinary efforts to enhance safety performance. This work focuses on risk identification, the process of identifying and analyzing risks stemming from dynamic traffic participants and unexpected events. While significant advances have been made in the community, the current evaluation of different risk identification algorithms uses independent datasets, leading to difficulty in direct comparison and hindering collective progress toward safety performance enhancement. To address this limitation, we introduce \textbf{RiskBench}, a large-scale scenario-based benchmark for risk identification. We design a scenario taxonomy and augmentation pipeline to enable a systematic collection of ground truth risks under diverse scenarios. We assess the ability of ten algorithms to (1) detect and locate risks, (2) anticipate risks, and (3) facilitate decision-making. We conduct extensive experiments and summarize future research on risk identification. Our aim is to encourage collaborative endeavors in achieving a society with zero collisions. We have made our dataset and benchmark toolkit publicly on the project page: //hcis-lab.github.io/RiskBench/

The security of cloud field-programmable gate arrays (FPGAs) faces challenges from untrusted users attempting fault and side-channel attacks through malicious circuit configurations. Fault injection attacks can result in denial of service, disrupting functionality or leaking secret information. This threat is further amplified in multi-tenancy scenarios. Detecting such threats before loading onto the FPGA is crucial, but existing methods face difficulty identifying sophisticated attacks. We present MaliGNNoma, a machine learning-based solution that accurately identifies malicious FPGA configurations. Serving as a netlist scanning mechanism, it can be employed by cloud service providers as an initial security layer within a necessary multi-tiered security system. By leveraging the inherent graph representation of FPGA netlists, MaliGNNoma employs a graph neural network (GNN) to learn distinctive malicious features, surpassing current approaches. To enhance transparency, MaliGNNoma utilizes a parameterized explainer for the GNN, labeling the FPGA configuration and pinpointing the sub-circuit responsible for the malicious classification. Through extensive experimentation on the ZCU102 board with a Xilinx UltraScale+ FPGA, we validate the effectiveness of MaliGNNoma in detecting malicious configurations, including sophisticated attacks, such as those based on benign modules, like cryptography accelerators. MaliGNNoma achieves a classification accuracy and precision of 98.24% and 97.88%, respectively, surpassing state-of-the-art. We compare MaliGNNoma with five state-of-the-art scanning methods, revealing that not all attack vectors detected by MaliGNNoma are recognized by existing solutions, further emphasizing its effectiveness. Additionally, we make MaliGNNoma and its associated dataset publicly available.

Network security analysts gather data from diverse sources, from high-level summaries of network flow and traffic volumes to low-level details such as service logs from servers and the contents of individual packets. They validate and check this data against traffic patterns and historical indicators of compromise. Based on the results of this analysis, a decision is made to either automatically manage the traffic or report it to an analyst for further investigation. Unfortunately, due rapidly increasing traffic volumes, there are far more events to check than operational teams can handle for effective forensic analysis. However, just as packets are grouped into flows that share a commonality, we argue that a high-level construct for grouping network flows into a set a flows that share a hypothesis is needed to significantly improve the quality of operational network response by increasing Events Per Analysts Hour (EPAH). In this paper, we propose a formalism for describing a superflow construct, which we characterize as an aggregation of one or more flows based on an analyst-specific hypothesis about traffic behavior. We demonstrate simple superflow constructions and representations, and perform a case study to explain how the formalism can be used to reduce the volume of data for forensic analysis.

The task of Visual Relationship Recognition (VRR) aims to identify relationships between two interacting objects in an image and is particularly challenging due to the widely-spread and highly imbalanced distribution of <subject, relation, object> triplets. To overcome the resultant performance bias in existing VRR approaches, we introduce DiffAugment -- a method which first augments the tail classes in the linguistic space by making use of WordNet and then utilizes the generative prowess of Diffusion Models to expand the visual space for minority classes. We propose a novel hardness-aware component in diffusion which is based upon the hardness of each <S,R,O> triplet and demonstrate the effectiveness of hardness-aware diffusion in generating visual embeddings for the tail classes. We also propose a novel subject and object based seeding strategy for diffusion sampling which improves the discriminative capability of the generated visual embeddings. Extensive experimentation on the GQA-LT dataset shows favorable gains in the subject/object and relation average per-class accuracy using Diffusion augmented samples.

Vision-and-Language Navigation (VLN) stands as a key research problem of Embodied AI, aiming at enabling agents to navigate in unseen environments following linguistic instructions. In this field, generalization is a long-standing challenge, either to out-of-distribution scenes or from Sim to Real. In this paper, we propose NaVid, a video-based large vision language model (VLM), to mitigate such a generalization gap. NaVid makes the first endeavour to showcase the capability of VLMs to achieve state-of-the-art level navigation performance without any maps, odometer and depth inputs. Following human instruction, NaVid only requires an on-the-fly video stream from a monocular RGB camera equipped on the robot to output the next-step action. Our formulation mimics how humans navigate and naturally gets rid of the problems introduced by odometer noises, and the Sim2Real gaps from map or depth inputs. Moreover, our video-based approach can effectively encode the historical observations of robots as spatio-temporal contexts for decision-making and instruction following. We train NaVid with 550k navigation samples collected from VLN-CE trajectories, including action-planning and instruction-reasoning samples, along with 665k large-scale web data. Extensive experiments show that NaVid achieves SOTA performance in simulation environments and the real world, demonstrating superior cross-dataset and Sim2Real transfer. We thus believe our proposed VLM approach plans the next step for not only the navigation agents but also this research field.

The anchor-document data derived from web graphs offers a wealth of paired information for training dense retrieval models in an unsupervised manner. However, the presence of inherent noise invariably compromises the robustness of training dense retrieval models, consequently hurting the performance. In this paper, we introduce WebDRO, an efficient approach for clustering the web graph data and optimizing group weights to enhance the robustness of the pretraining process of dense retrieval models on web graphs. Initially, we build an embedding model for clustering anchor-document pairs. Specifically, we contrastively train the embedding model for link prediction, which guides the embedding model in capturing the inherent document features behind the web graph links. Subsequently, we employ the group distributional robust optimization to recalibrate the weights across different clusters of anchor-document pairs during training dense retrieval models, directing the model to assign higher weights to clusters with higher loss and focus more on worst-case scenarios. Our experiments conducted on MS MARCO and BEIR demonstrate that our method can effectively improve retrieval performance in unsupervised training settings. Further analysis confirms the stability and validity of group weights learned by WebDRO. All codes will be released via GitHub.

We present Umwelt, an authoring environment for interactive multimodal data representations. In contrast to prior approaches, which center the visual modality, Umwelt treats visualization, sonification, and textual description as coequal representations: they are all derived from a shared abstract data model, such that no modality is prioritized over the others. To simplify specification, Umwelt evaluates a set of heuristics to generate default multimodal representations that express a dataset's functional relationships. To support smoothly moving between representations, Umwelt maintains a shared query predicated that is reified across all modalities -- for instance, navigating the textual description also highlights the visualization and filters the sonification. In a study with 5 blind / low-vision expert users, we found that Umwelt's multimodal representations afforded complementary overview and detailed perspectives on a dataset, allowing participants to fluidly shift between task- and representation-oriented ways of thinking.

Normalization is known to help the optimization of deep neural networks. Curiously, different architectures require specialized normalization methods. In this paper, we study what normalization is effective for Graph Neural Networks (GNNs). First, we adapt and evaluate the existing methods from other domains to GNNs. Faster convergence is achieved with InstanceNorm compared to BatchNorm and LayerNorm. We provide an explanation by showing that InstanceNorm serves as a preconditioner for GNNs, but such preconditioning effect is weaker with BatchNorm due to the heavy batch noise in graph datasets. Second, we show that the shift operation in InstanceNorm results in an expressiveness degradation of GNNs for highly regular graphs. We address this issue by proposing GraphNorm with a learnable shift. Empirically, GNNs with GraphNorm converge faster compared to GNNs using other normalization. GraphNorm also improves the generalization of GNNs, achieving better performance on graph classification benchmarks.

Recommender systems play a crucial role in mitigating the problem of information overload by suggesting users' personalized items or services. The vast majority of traditional recommender systems consider the recommendation procedure as a static process and make recommendations following a fixed strategy. In this paper, we propose a novel recommender system with the capability of continuously improving its strategies during the interactions with users. We model the sequential interactions between users and a recommender system as a Markov Decision Process (MDP) and leverage Reinforcement Learning (RL) to automatically learn the optimal strategies via recommending trial-and-error items and receiving reinforcements of these items from users' feedbacks. In particular, we introduce an online user-agent interacting environment simulator, which can pre-train and evaluate model parameters offline before applying the model online. Moreover, we validate the importance of list-wise recommendations during the interactions between users and agent, and develop a novel approach to incorporate them into the proposed framework LIRD for list-wide recommendations. The experimental results based on a real-world e-commerce dataset demonstrate the effectiveness of the proposed framework.