OpenRaiser/Intern-Atlas huggingface dataset:
Bangladeshi Native Vehicle Detection in Wild
Venue: arXiv cs.CV 2024
Year: 2024

The success of autonomous navigation relies on robust and precise vehicle recognition, hindered by the scarcity of region-specific vehicle detection datasets, impeding the development of context-aware systems. To advance terrestrial object detection research, this paper proposes a native vehicle detection dataset for the most commonly appeared vehicle classes in Bangladesh. 17 distinct vehicle classes have been taken into account, with fully annotated 81542 instances of 17326 images. Each image width is set to at least 1280px. The dataset's average vehicle bounding box-to-image ratio is 4.7036. This Bangladesh Native Vehicle Dataset (BNVD) has accounted for several geographical, illumination, variety of vehicle sizes, and orientations to be more robust on surprised scenarios. In the context of examining the BNVD dataset, this work provides a thorough assessment with four successive You Only Look Once (YOLO) models, namely YOLO v5, v6, v7, and v8. These dataset's effectiveness is methodically evaluated and contrasted with other vehicle datasets already in use. The BNVD dataset exhibits mean average precision(mAP) at 50% intersection over union (IoU) is 0.848 corresponding precision and recall values of 0.841 and 0.774. The research findings indicate a mAP of 0.643 at an IoU range of 0.5 to 0.95. The experiments show that the BNVD dataset serves as a reliable representation of vehicle distribution and presents considerable complexities.
A Nearly Quadratic Improvement for Memory Reallocation
Venue: arXiv cs.DS 2024
Year: 2024

In the Memory Reallocation Problem a set of items of various sizes must be dynamically assigned to non-overlapping contiguous chunks of memory. It is guaranteed that the sum of the sizes of all items present at any time is at most a $(1-\varepsilon)$-fraction of the total size of memory (i.e., the load-factor is at most $1-\varepsilon$). The allocator receives insert and delete requests online, and can re-arrange existing items to handle the requests, but at a reallocation cost defined to be the sum of the sizes of items moved divided by the size of the item being inserted/deleted. The folklore algorithm for Memory Reallocation achieves a cost of $O(\varepsilon^{-1})$ per update. In recent work at FOCS'23, Kuszmaul showed that, in the special case where each item is promised to be smaller than an $\varepsilon^4$-fraction of memory, it is possible to achieve expected update cost $O(\log\varepsilon^{-1})$. Kuszmaul conjectures, however, that for larger items the folklore algorithm is optimal. In this work we disprove Kuszmaul's conjecture, giving an allocator that achieves expected update cost $O(\varepsilon^{-1/2} \operatorname*{polylog} \varepsilon^{-1})$ on any input sequence. We also give the first non-trivial lower bound for the Memory Reallocation Problem: we demonstrate an input sequence on which any resizable allocator (even offline) must incur amortized update cost at least $Ω(\log\varepsilon^{-1})$. Finally, we analyze the Memory Reallocation Problem on a stochastic sequence of inserts and deletes, with random sizes in $[δ, 2 δ]$ for some $δ$. We show that, in this simplified setting, it is possible to achieve $O(\log\varepsilon^{-1})$ expected update cost, even in the ``large item'' parameter regime ($δ> \varepsilon^4$).
Open-Source Assessments of AI Capabilities: The Proliferation of AI Analysis Tools, Replicating Competitor Models, and the Zhousidun Dataset
Venue: arXiv cs.CY 2024
Year: 2024

The integration of artificial intelligence (AI) into military capabilities has become a norm for major military power across the globe. Understanding how these AI models operate is essential for maintaining strategic advantages and ensuring security. This paper demonstrates an open-source methodology for analyzing military AI models through a detailed examination of the Zhousidun dataset, a Chinese-originated dataset that exhaustively labels critical components on American and Allied destroyers. By demonstrating the replication of a state-of-the-art computer vision model on this dataset, we illustrate how open-source tools can be leveraged to assess and understand key military AI capabilities. This methodology offers a robust framework for evaluating the performance and potential of AI-enabled military capabilities, thus enhancing the accuracy and reliability of strategic assessments.
Establishing Trust in the Beyond-5G Core Network using Trusted Execution Environments
Venue: arXiv cs.CR 2024
Year: 2024

The fifth generation (5G) of cellular networks starts a paradigm shift from the traditional monolithic system design to a Service Based Architecture, that fits modern performance requirements and scales efficiently to new services. This paradigm will be the foundation of future cellular core networks beyond 5G. The new architecture splits network functionalities into smaller logical entities that can be disaggregated logically, physically, and geographically. This affords interoperability between the mobile network operators and commercial software and hardware vendors or cloud providers. By making use of commodity services and products, this system construct inherits the vulnerabilities in those underlying technologies, thereby increasing its attack surface and requiring a rigorous security analysis. In this work, we review the security implications introduced in B5G networks, and the security mechanisms that are supported by the 5G standard. We emphasize on the support of Zero Trust Architecture in 5G and its relevance in decentralized deployments. We revisit the definition of trust in modern enterprise network operations and identify important Zero Trust properties that are weakened by the nature of cloud deployments. To that end, we propose a vertical extension of Zero Trust, namely, Zero Trust Execution, to model untrusted execution environments, and we provide an analysis on how to establish trust in Beyond-5G network architectures using Trusted Execution Environments. Our analysis shows how our model architecture handles the increased attack surface and reinforces the Zero Trust Architecture principles in the 5G Core, without any changes to the 5G standard. Finally, we provide experimental results over a 5G testbed using Open5GS and UERANSIM that demonstrate minimal performance overhead, and a monetary cost evaluation.
Focus on Low-Resolution Information: Multi-Granular Information-Lossless Model for Low-Resolution Human Pose Estimation
Venue: arXiv cs.CV 2024
Year: 2024

In real-world applications of human pose estimation, low-resolution input images are frequently encountered when the performance of the image acquisition equipment is limited or the shooting distance is too far. However, existing state-of-the-art models for human pose estimation perform poorly on low-resolution images. One key reason is the presence of downsampling layers in these models, e.g., strided convolutions and pooling layers. It further reduces the already insufficient image information. Another key reason is that the body skeleton and human kinematic information are not fully utilized. In this work, we propose a Multi-Granular Information-Lossless (MGIL) model to replace the downsampling layers to address the above issues. Specifically, MGIL employs a Fine-grained Lossless Information Extraction (FLIE) module, which can prevent the loss of local information. Furthermore, we design a Coarse-grained Information Interaction (CII) module to adequately leverage human body structural information. To efficiently fuse cross-granular information and thoroughly exploit the relationships among keypoints, we further introduce a Multi-Granular Adaptive Fusion (MGAF) mechanism. The mechanism assigns weights to features of different granularities based on the content of the image. The model is effective, flexible, and universal. We show its potential in various vision tasks with comprehensive experiments. It outperforms the SOTA methods by 7.7 mAP on COCO and performs well with different input resolutions, different backbones, and different vision tasks. The code is provided in supplementary material.
Generalization Ability of Feature-based Performance Prediction Models: A Statistical Analysis across Benchmarks
Venue: arXiv cs.LG 2024
Year: 2024

This study examines the generalization ability of algorithm performance prediction models across various benchmark suites. Comparing the statistical similarity between the problem collections with the accuracy of performance prediction models that are based on exploratory landscape analysis features, we observe that there is a positive correlation between these two measures. Specifically, when the high-dimensional feature value distributions between training and testing suites lack statistical significance, the model tends to generalize well, in the sense that the testing errors are in the same range as the training errors. Two experiments validate these findings: one involving the standard benchmark suites, the BBOB and CEC collections, and another using five collections of affine combinations of BBOB problem instances.
EXACT: Towards a platform for empirically benchmarking Machine Learning model explanation methods
Venue: arXiv cs.LG 2024
Year: 2024

The evolving landscape of explainable artificial intelligence (XAI) aims to improve the interpretability of intricate machine learning (ML) models, yet faces challenges in formalisation and empirical validation, being an inherently unsupervised process. In this paper, we bring together various benchmark datasets and novel performance metrics in an initial benchmarking platform, the Explainable AI Comparison Toolkit (EXACT), providing a standardised foundation for evaluating XAI methods. Our datasets incorporate ground truth explanations for class-conditional features, and leveraging novel quantitative metrics, this platform assesses the performance of post-hoc XAI methods in the quality of the explanations they produce. Our recent findings have highlighted the limitations of popular XAI methods, as they often struggle to surpass random baselines, attributing significance to irrelevant features. Moreover, we show the variability in explanations derived from different equally performing model architectures. This initial benchmarking platform therefore aims to allow XAI researchers to test and assure the high quality of their newly developed methods.
Efficient Model-Stealing Attacks Against Inductive Graph Neural Networks
Venue: arXiv cs.LG 2024
Year: 2024

Graph Neural Networks (GNNs) are recognized as potent tools for processing real-world data organized in graph structures. Especially inductive GNNs, which allow for the processing of graph-structured data without relying on predefined graph structures, are becoming increasingly important in a wide range of applications. As such these networks become attractive targets for model-stealing attacks where an adversary seeks to replicate the functionality of the targeted network. Significant efforts have been devoted to developing model-stealing attacks that extract models trained on images and texts. However, little attention has been given to stealing GNNs trained on graph data. This paper identifies a new method of performing unsupervised model-stealing attacks against inductive GNNs, utilizing graph contrastive learning and spectral graph augmentations to efficiently extract information from the targeted model. The new type of attack is thoroughly evaluated on six datasets and the results show that our approach outperforms the current state-of-the-art by Shen et al. (2021). In particular, our attack surpasses the baseline across all benchmarks, attaining superior fidelity and downstream accuracy of the stolen model while necessitating fewer queries directed toward the target model.
Self-HWDebug: Automation of LLM Self-Instructing for Hardware Security Verification
Venue: arXiv cs.CR 2024
Year: 2024

The rise of instruction-tuned Large Language Models (LLMs) marks a significant advancement in artificial intelligence (AI) (tailored to respond to specific prompts). Despite their popularity, applying such models to debug security vulnerabilities in hardware designs, i.e., register transfer language (RTL) modules, particularly at system-on-chip (SoC) level, presents considerable challenges. One of the main issues lies in the need for precisely designed instructions for pinpointing and mitigating the vulnerabilities, which requires substantial time and expertise from human experts. In response to this challenge, this paper proposes Self-HWDebug, an innovative framework that leverages LLMs to automatically create required debugging instructions. In Self-HWDebug, a set of already identified bugs from the most critical hardware common weakness enumeration (CWE) listings, along with mitigation resolutions, is provided to the framework, followed by prompting the LLMs to generate targeted instructions for such mitigation. The LLM-generated instructions are subsequently used as references to address vulnerabilities within the same CWE category but in totally different designs, effectively demonstrating the framework's ability to extend solutions across related security issues. Self-HWDebug significantly reduces human intervention by using the model's own output to guide debugging. Through comprehensive testing, Self-HWDebug proves not only to reduce experts' effort/time but also to even improve the quality of the debugging process.
Investigating the Impact of Choice on Deep Reinforcement Learning for Space Controls
Venue: arXiv cs.LG 2024
Year: 2024

For many space applications, traditional control methods are often used during operation. However, as the number of space assets continues to grow, autonomous operation can enable rapid development of control methods for different space related tasks. One method of developing autonomous control is Reinforcement Learning (RL), which has become increasingly popular after demonstrating promising performance and success across many complex tasks. While it is common for RL agents to learn bounded continuous control values, this may not be realistic or practical for many space tasks that traditionally prefer an on/off approach for control. This paper analyzes using discrete action spaces, where the agent must choose from a predefined list of actions. The experiments explore how the number of choices provided to the agents affects their measured performance during and after training. This analysis is conducted for an inspection task, where the agent must circumnavigate an object to inspect points on its surface, and a docking task, where the agent must move into proximity of another spacecraft and "dock" with a low relative speed. A common objective of both tasks, and most space tasks in general, is to minimize fuel usage, which motivates the agent to regularly choose an action that uses no fuel. Our results show that a limited number of discrete choices leads to optimal performance for the inspection task, while continuous control leads to optimal performance for the docking task.
Algorithms for Generating Small Random Samples
Venue: arXiv cs.DS 2024
Year: 2024

We present algorithms for generating small random samples without replacement. We consider two cases. We present an algorithm for sampling a pair of distinct integers, and an algorithm for sampling a triple of distinct integers. The worst-case runtime of both algorithms is constant, while the worst-case runtimes of common algorithms for the general case of sampling $k$ elements from a set of $n$ increase with $n$. Java implementations of both algorithms are included in the open source library $ρμ$.
Data Sharing at the Edge of the Network: A Disturbance Resilient Multi-modal ITS
Venue: arXiv cs.DC 2024
Year: 2024

Mobility-as-a-Service (MaaS) is a paradigm that encourages the shift from private cars to more sustainable alternative mobility services. MaaS provides services that enhances and enables multiple modes of transport to operate seamlessly and bringing Multimodal Intelligent Transport Systems (M-ITS) closer to reality. This requires sharing and integration of data collected from multiple sources including modes of transports, sensors, and end-users' devices to allow a seamless and integrated services especially during unprecedented disturbances. This paper discusses the interactions among transportation modes, networks, potential disturbance scenarios, and adaptation strategies to mitigate their impact on MaaS. We particularly discuss the need to share data between the modes of transport and relevant entities that are at the vicinity of each other, taking advantage of edge computing technology to avoid any latency due to communication to the cloud and privacy concerns. However, when sharing at the edge, bandwidth, storage, and computational limitations must be considered.
TrajCogn: Leveraging LLMs for Cognizing Movement Patterns and Travel Purposes from Trajectories
Venue: arXiv cs.LG 2024
Year: 2024

Spatio-temporal trajectories are crucial in various data mining tasks. It is important to develop a versatile trajectory learning method that performs different tasks with high accuracy. This involves effectively extracting two core aspects of information--movement patterns and travel purposes--from trajectories. However, this is challenging due to limitations in model capacity and the quality and scale of trajectory datasets. Meanwhile, large language models (LLMs) have shown great success in versatility by training on large-scale, high-quality datasets. Given the similarities between trajectories and sentences, there's potential to leverage LLMs to develop an effective trajectory learning method. However, standard LLMs are not designed to handle the unique spatio-temporal features of trajectories and cannot extract movement patterns and travel purposes. To address these challenges, we propose a model called TrajCogn that effectively utilizes LLMs to model trajectories. TrajCogn leverages the strengths of LLMs to create a versatile trajectory learning approach while addressing the limitations of standard LLMs. First, TrajCogn incorporates a novel trajectory semantic embedder that enables LLMs to process spatio-temporal features and extract movement patterns and travel purposes. Second, TrajCogn introduces a new trajectory prompt that integrates these patterns and purposes into LLMs, allowing the model to adapt to various tasks. Extensive experiments on two real-world datasets and two representative tasks demonstrate that TrajCogn successfully achieves its design goals. Codes are available at https://anonymous.4open.science/r/TrajCogn-5021.
A finite element-based physics-informed operator learning framework for spatiotemporal partial differential equations on arbitrary domains
Venue: arXiv cs.LG 2024
Year: 2024

We propose a novel finite element-based physics-informed operator learning framework that allows for predicting spatiotemporal dynamics governed by partial differential equations (PDEs). The proposed framework employs a loss function inspired by the finite element method (FEM) with the implicit Euler time integration scheme. A transient thermal conduction problem is considered to benchmark the performance. The proposed operator learning framework takes a temperature field at the current time step as input and predicts a temperature field at the next time step. The Galerkin discretized weak formulation of the heat equation is employed to incorporate physics into the loss function, which is coined finite operator learning (FOL). Upon training, the networks successfully predict the temperature evolution over time for any initial temperature field at high accuracy compared to the FEM solution. The framework is also confirmed to be applicable to a heterogeneous thermal conductivity and arbitrary geometry. The advantages of FOL can be summarized as follows: First, the training is performed in an unsupervised manner, avoiding the need for a large data set prepared from costly simulations or experiments. Instead, random temperature patterns generated by the Gaussian random process and the Fourier series, combined with constant temperature fields, are used as training data to cover possible temperature cases. Second, shape functions and backward difference approximation are exploited for the domain discretization, resulting in a purely algebraic equation. This enhances training efficiency, as one avoids time-consuming automatic differentiation when optimizing weights and biases while accepting possible discretization errors. Finally, thanks to the interpolation power of FEM, any arbitrary geometry can be handled with FOL, which is crucial to addressing various engineering application scenarios.
Last-Level Cache Side-Channel Attacks Are Feasible in the Modern Public Cloud (Extended Version)
Venue: arXiv cs.CR 2024
Year: 2024

Last-level cache side-channel attacks have been mostly demonstrated in highly-controlled, quiescent local environments. Hence, it is unclear whether such attacks are feasible in a production cloud environment. In the cloud, side channels are flooded with noise from activities of other tenants and, in Function-as-a-Service (FaaS) workloads, the attacker has a very limited time window to mount the attack. In this paper, we show that such attacks are feasible in practice, although they require new techniques. We present an end-to-end, cross-tenant attack on a vulnerable ECDSA implementation in the public FaaS Google Cloud Run environment. We introduce several new techniques to improve every step of the attack. First, to speed-up the generation of eviction sets, we introduce L2-driven candidate address filtering and a Binary Search-based algorithm for address pruning. Second, to monitor victim memory accesses with high time resolution, we introduce Parallel Probing. Finally, we leverage power spectral density from signal processing to easily identify the victim's target cache set in the frequency domain. Overall, using these mechanisms, we extract a median value of 81% of the secret ECDSA nonce bits from a victim container in 19 seconds on average.
Learning Partially Aligned Item Representation for Cross-Domain Sequential Recommendation
Venue: arXiv cs.IR 2024
Year: 2024

Cross-domain sequential recommendation (CDSR) aims to uncover and transfer users' sequential preferences across multiple recommendation domains. While significant endeavors have been made, they primarily concentrated on developing advanced transfer modules and aligning user representations using self-supervised learning techniques. However, the problem of aligning item representations has received limited attention, and misaligned item representations can potentially lead to sub-optimal sequential modeling and user representation alignment. To this end, we propose a model-agnostic framework called \textbf{C}ross-domain item representation \textbf{A}lignment for \textbf{C}ross-\textbf{D}omain \textbf{S}equential \textbf{R}ecommendation (\textbf{CA-CDSR}), which achieves sequence-aware generation and adaptively partial alignment for item representations. Specifically, we first develop a sequence-aware feature augmentation strategy, which captures both collaborative and sequential item correlations, thus facilitating holistic item representation generation. Next, we conduct an empirical study to investigate the partial representation alignment problem from a spectrum perspective. It motivates us to devise an adaptive spectrum filter, achieving partial alignment adaptively. Furthermore, the aligned item representations can be fed into different sequential encoders to obtain user representations. The entire framework is optimized in a multi-task learning paradigm with an annealing strategy. Extensive experiments have demonstrated that CA-CDSR can surpass state-of-the-art baselines by a significant margin and can effectively align items in representation spaces to enhance performance.