OpenRaiser/Intern-Atlas huggingface dataset:
A Case Study of Web App Coding with OpenAI Reasoning Models
Venue: arXiv cs.SE 2024
Year: 2024

This paper presents a case study of coding tasks by the latest reasoning models of OpenAI, i.e. o1-preview and o1-mini, in comparison with other frontier models. The o1 models deliver SOTA results for WebApp1K, a single-task benchmark. To this end, we introduce WebApp1K-Duo, a harder benchmark doubling number of tasks and test cases. The new benchmark causes the o1 model performances to decline significantly, falling behind Claude 3.5. Moreover, they consistently fail when confronted with atypical yet correct test cases, a trap non-reasoning models occasionally avoid. We hypothesize that the performance variability is due to instruction comprehension. Specifically, the reasoning mechanism boosts performance when all expectations are captured, meanwhile exacerbates errors when key expectations are missed, potentially impacted by input lengths. As such, we argue that the coding success of reasoning models hinges on the top-notch base model and SFT to ensure meticulous adherence to instructions.
Data Distribution Shifts in (Industrial) Federated Learning as a Privacy Issue
Venue: arXiv cs.LG 2024
Year: 2024

We consider industrial federated learning, a collaboration between a small number of powerful, potentially competing industrial players, mediated by a third party aspiring to improve the service it provides to its customers. We argue that this configuration harbours covert privacy risks that do not arise in e.g. cross-device settings. Companies are very protective of their intellectual property and production processes. Information about changes to their production and the timing of which is to be kept private. We study a scenario in which one of the collaborators infers changes to their competitors' production by detecting potentially subtle temporal data distribution shifts. In this framing, a data distribution shift is always problematic, even if it has no negative effect on training convergence. Thus, our goal is to find means that allow the detection of distributional shifts better than customary evaluation metrics. Based on the assumption that even minor shifts translate into the collaboratively learned machine learning model, the attacker tracks the shared models' internal state with a selection of metrics from literature in order to pick up on relevant changes. In an empirical study on benchmark datasets, we show an honest-but-curious attacker to be capable of detecting subtle distributional shifts on other clients, in some cases long before they become obvious in evaluation.
Training Large ASR Encoders with Differential Privacy
Venue: arXiv cs.SD 2024
Year: 2024

Self-supervised learning (SSL) methods for large speech models have proven to be highly effective at ASR. With the interest in public deployment of large pre-trained models, there is a rising concern for unintended memorization and leakage of sensitive data points from the training data. In this paper, we apply differentially private (DP) pre-training to a SOTA Conformer-based encoder, and study its performance on a downstream ASR task assuming the fine-tuning data is public. This paper is the first to apply DP to SSL for ASR, investigating the DP noise tolerance of the BEST-RQ pre-training method. Notably, we introduce a novel variant of model pruning called gradient-based layer freezing that provides strong improvements in privacy-utility-compute trade-offs. Our approach yields a LibriSpeech test-clean/other WER (%) of 3.78/ 8.41 with ($10$, 1e^-9)-DP for extrapolation towards low dataset scales, and 2.81/ 5.89 with (10, 7.9e^-11)-DP for extrapolation towards high scales.
FracGM: A Fast Fractional Programming Technique for Geman-McClure Robust Estimator
Venue: arXiv cs.CV 2024
Year: 2024

Robust estimation is essential in computer vision, robotics, and navigation, aiming to minimize the impact of outlier measurements for improved accuracy. We present a fast algorithm for Geman-McClure robust estimation, FracGM, leveraging fractional programming techniques. This solver reformulates the original non-convex fractional problem to a convex dual problem and a linear equation system, iteratively solving them in an alternating optimization pattern. Compared to graduated non-convexity approaches, this strategy exhibits a faster convergence rate and better outlier rejection capability. In addition, the global optimality of the proposed solver can be guaranteed under given conditions. We demonstrate the proposed FracGM solver with Wahba's rotation problem and 3-D point-cloud registration along with relaxation pre-processing and projection post-processing. Compared to state-of-the-art algorithms, when the outlier rates increase from 20% to 80%, FracGM shows 53% and 88% lower rotation and translation increases. In real-world scenarios, FracGM achieves better results in 13 out of 18 outcomes, while having a 19.43% improvement in the computation time.
Boolean Product Graph Neural Networks
Venue: arXiv cs.LG 2024
Year: 2024

Graph Neural Networks (GNNs) have recently achieved significant success, with a key operation involving the aggregation of information from neighboring nodes. Substantial researchers have focused on defining neighbors for aggregation, predominantly based on observed adjacency matrices. However, in many scenarios, the explicitly given graphs contain noise, which can be amplified during the messages-passing process. Therefore, many researchers have turned their attention to latent graph inference, specifically learning a parametric graph. To mitigate fluctuations in latent graph structure learning, this paper proposes a novel Boolean product-based graph residual connection in GNNs to link the latent graph and the original graph. It computes the Boolean product between the latent graph and the original graph at each layer to correct the learning process. The Boolean product between two adjacency matrices is equivalent to triangle detection. Accordingly, the proposed Boolean product graph neural networks can be interpreted as discovering triangular cliques from the original and the latent graph. We validate the proposed method in benchmark datasets and demonstrate its ability to enhance the performance and robustness of GNNs.
BrainDreamer: Reasoning-Coherent and Controllable Image Generation from EEG Brain Signals via Language Guidance
Venue: arXiv cs.CV 2024
Year: 2024

Can we directly visualize what we imagine in our brain together with what we describe? The inherent nature of human perception reveals that, when we think, our body can combine language description and build a vivid picture in our brain. Intuitively, generative models should also hold such versatility. In this paper, we introduce BrainDreamer, a novel end-to-end language-guided generative framework that can mimic human reasoning and generate high-quality images from electroencephalogram (EEG) brain signals. Our method is superior in its capacity to eliminate the noise introduced by non-invasive EEG data acquisition and meanwhile achieve a more precise mapping between the EEG and image modality, thus leading to significantly better-generated images. Specifically, BrainDreamer consists of two key learning stages: 1) modality alignment and 2) image generation. In the alignment stage, we propose a novel mask-based triple contrastive learning strategy to effectively align EEG, text, and image embeddings to learn a unified representation. In the generation stage, we inject the EEG embeddings into the pre-trained Stable Diffusion model by designing a learnable EEG adapter to generate high-quality reasoning-coherent images. Moreover, BrainDreamer can accept textual descriptions (e.g., color, position, etc.) to achieve controllable image generation. Extensive experiments show that our method significantly outperforms prior arts in terms of generating quality and quantitative performance.
Towards Lightweight and Privacy-preserving Data Provision in Digital Forensics for Driverless Taxi
Venue: arXiv cs.CR 2024
Year: 2024

Data provision, referring to the data upload and data access, is one key phase in vehicular digital forensics. The unique features of Driverless Taxi (DT) bring new issues to this phase: 1) efficient verification of data integrity when diverse Data Providers (DPs) upload data; 2) DP privacy preservation during data upload; and 3) privacy preservation of both data and INvestigator (IN) under complex data ownership when accessing data. To this end, we propose a novel Lightweight and Privacy-preserving Data Provision (LPDP) approach consisting of three mechanisms: 1) the Privacy-friendly Batch Verification Mechanism (PBVm) based on elliptic curve cryptography, 2) Data Access Control Mechanism (DACm) based on ciphertext-policy attribute-based encryption, and 3) Decentralized IN Warrant Issuance Mechanism (DIWIm) based on secret sharing. Privacy preservation of data provision is achieved through: 1) ensuring the DP privacy preservation in terms of the location privacy and unlinkability of data upload requests by PBVm, 2) ensuring data privacy preservation by DACm and DIWIm, and 3) ensuring the identity privacy of IN in terms of the anonymity and unlinkability of data access requests without sacrificing the traceability. Lightweight of data provision is achieved through: 1) ensuring scalable verification of data integrity by PBVm, and 2) ensuring low-overhead warrant update with respect to DIWIm. Security analysis and performance evaluation are conducted to validate the security and performance features of LPDP.
Personalized Route Recommendation Based on User Habits for Vehicle Navigation
Venue: arXiv cs.RO 2024
Year: 2024

Navigation route recommendation is one of the important functions of intelligent transportation. However, users frequently deviate from recommended routes for various reasons, with personalization being a key problem in the field of research. This paper introduces a personalized route recommendation method based on user historical navigation data. First, we formulate route sorting as a pointwise problem based on a large set of pertinent features. Second, we construct route features and user profiles to establish a comprehensive feature dataset. Furthermore, we propose a Deep-Cross-Recurrent (DCR) learning model aimed at learning route sorting scores and offering customized route recommendations. This approach effectively captures recommended navigation routes and user preferences by integrating DCN-v2 and LSTM. In offline evaluations, our method compared with the minimum ETA (estimated time of arrival), LightGBM, and DCN-v2 indicated 8.72%, 2.19%, and 0.9% reduction in the mean inconsistency rate respectively, demonstrating significant improvements in recommendation accuracy.
SplatLoc: 3D Gaussian Splatting-based Visual Localization for Augmented Reality
Venue: arXiv cs.CV 2024
Year: 2024

Visual localization plays an important role in the applications of Augmented Reality (AR), which enable AR devices to obtain their 6-DoF pose in the pre-build map in order to render virtual content in real scenes. However, most existing approaches can not perform novel view rendering and require large storage capacities for maps. To overcome these limitations, we propose an efficient visual localization method capable of high-quality rendering with fewer parameters. Specifically, our approach leverages 3D Gaussian primitives as the scene representation. To ensure precise 2D-3D correspondences for pose estimation, we develop an unbiased 3D scene-specific descriptor decoder for Gaussian primitives, distilled from a constructed feature volume. Additionally, we introduce a salient 3D landmark selection algorithm that selects a suitable primitive subset based on the saliency score for localization. We further regularize key Gaussian primitives to prevent anisotropic effects, which also improves localization performance. Extensive experiments on two widely used datasets demonstrate that our method achieves superior or comparable rendering and localization performance to state-of-the-art implicit-based visual localization approaches. Project page: \href{https://zju3dv.github.io/splatloc}{https://zju3dv.github.io/splatloc}.
IMOST: Incremental Memory Mechanism with Online Self-Supervision for Continual Traversability Learning
Venue: arXiv cs.RO 2024
Year: 2024

Traversability estimation is the foundation of path planning for a general navigation system. However, complex and dynamic environments pose challenges for the latest methods using self-supervised learning (SSL) technique. Firstly, existing SSL-based methods generate sparse annotations lacking detailed boundary information. Secondly, their strategies focus on hard samples for rapid adaptation, leading to forgetting and biased predictions. In this work, we propose IMOST, a continual traversability learning framework composed of two key modules: incremental dynamic memory (IDM) and self-supervised annotation (SSA). By mimicking human memory mechanisms, IDM allocates novel data samples to new clusters according to information expansion criterion. It also updates clusters based on diversity rule, ensuring a representative characterization of new scene. This mechanism enhances scene-aware knowledge diversity while maintaining a compact memory capacity. The SSA module, integrating FastSAM, utilizes point prompts to generate complete annotations in real time which reduces training complexity. Furthermore, IMOST has been successfully deployed on the quadruped robot, with performance evaluated during the online learning process. Experimental results on both public and self-collected datasets demonstrate that our IMOST outperforms current state-of-the-art method, maintains robust recognition capabilities and adaptability across various scenarios. The code is available at https://github.com/SJTU-MKH/OCLTrav.
BRep Boundary and Junction Detection for CAD Reverse Engineering
Venue: arXiv cs.CV 2024
Year: 2024

In machining process, 3D reverse engineering of the mechanical system is an integral, highly important, and yet time consuming step to obtain parametric CAD models from 3D scans. Therefore, deep learning-based Scan-to-CAD modeling can offer designers enormous editability to quickly modify CAD model, being able to parse all its structural compositions and design steps. In this paper, we propose a supervised boundary representation (BRep) detection network BRepDetNet from 3D scans of CC3D and ABC dataset. We have carefully annotated the 50K and 45K scans of both the datasets with appropriate topological relations (e.g., next, mate, previous) between the geometrical primitives (i.e., boundaries, junctions, loops, faces) of their BRep data structures. The proposed solution decomposes the Scan-to-CAD problem in Scan-to-BRep ensuring the right step towards feature-based modeling, and therefore, leveraging other existing BRep-to-CAD modeling methods. Our proposed Scan-to-BRep neural network learns to detect BRep boundaries and junctions by minimizing focal-loss and non-maximal suppression (NMS) during training time. Experimental results show that our BRepDetNet with NMS-Loss achieves impressive results.
VLM-Vac: Enhancing Smart Vacuums through VLM Knowledge Distillation and Language-Guided Experience Replay
Venue: arXiv cs.RO 2024
Year: 2024

In this paper, we propose VLM-Vac, a novel framework designed to enhance the autonomy of smart robot vacuum cleaners. Our approach integrates the zero-shot object detection capabilities of a Vision-Language Model (VLM) with a Knowledge Distillation (KD) strategy. By leveraging the VLM, the robot can categorize objects into actionable classes -- either to avoid or to suck -- across diverse backgrounds. However, frequently querying the VLM is computationally expensive and impractical for real-world deployment. To address this issue, we implement a KD process that gradually transfers the essential knowledge of the VLM to a smaller, more efficient model. Our real-world experiments demonstrate that this smaller model progressively learns from the VLM and requires significantly fewer queries over time. Additionally, we tackle the challenge of continual learning in dynamic home environments by exploiting a novel experience replay method based on language-guided sampling. Our results show that this approach is not only energy-efficient but also surpasses conventional vision-based clustering methods, particularly in detecting small objects across diverse backgrounds.
Routing in Sparsely-gated Language Models responds to Context
Venue: arXiv cs.CL 2024
Year: 2024

Language Models (LMs) recently incorporate mixture-of-experts layers consisting of a router and a collection of experts to scale up their parameter count given a fixed computational budget. Building on previous efforts indicating that token-expert assignments are predominantly influenced by token identities and positions, we trace routing decisions of similarity-annotated text pairs to evaluate the context sensitivity of learned token-expert assignments. We observe that routing in encoder layers mainly depends on (semantic) associations, but contextual cues provide an additional layer of refinement. Conversely, routing in decoder layers is more variable and markedly less sensitive to context.
Obliviate: Neutralizing Task-agnostic Backdoors within the Parameter-efficient Fine-tuning Paradigm
Venue: arXiv cs.CL 2024
Year: 2024

Parameter-efficient fine-tuning (PEFT) has become a key training strategy for large language models. However, its reliance on fewer trainable parameters poses security risks, such as task-agnostic backdoors. Despite their severe impact on a wide range of tasks, there is no practical defense solution available that effectively counters task-agnostic backdoors within the context of PEFT. In this study, we introduce Obliviate, a PEFT-integrable backdoor defense. We develop two techniques aimed at amplifying benign neurons within PEFT layers and penalizing the influence of trigger tokens. Our evaluations across three major PEFT architectures show that our method can significantly reduce the attack success rate of the state-of-the-art task-agnostic backdoors (83.6%$\downarrow$). Furthermore, our method exhibits robust defense capabilities against both task-specific backdoors and adaptive attacks. Source code will be obtained at https://github.com/obliviateARR/Obliviate.
Efficient and Effective Model Extraction
Venue: arXiv cs.CR 2024
Year: 2024

Model extraction aims to create a functionally similar copy from a machine learning as a service (MLaaS) API with minimal overhead, typically for illicit profit or as a precursor to further attacks, posing a significant threat to the MLaaS ecosystem. However, recent studies have shown that model extraction is highly inefficient, particularly when the target task distribution is unavailable. In such cases, even substantially increasing the attack budget fails to produce a sufficiently similar replica, reducing the adversary's motivation to pursue extraction attacks. In this paper, we revisit the elementary design choices throughout the extraction lifecycle. We propose an embarrassingly simple yet dramatically effective algorithm, Efficient and Effective Model Extraction (E3), focusing on both query preparation and training routine. E3 achieves superior generalization compared to state-of-the-art methods while minimizing computational costs. For instance, with only 0.005 times the query budget and less than 0.2 times the runtime, E3 outperforms classical generative model based data-free model extraction by an absolute accuracy improvement of over 50% on CIFAR-10. Our findings underscore the persistent threat posed by model extraction and suggest that it could serve as a valuable benchmarking algorithm for future security evaluations.
Cross-course Process Mining of Student Clickstream Data -- Aggregation and Group Comparison
Venue: arXiv cs.CY 2024
Year: 2024

This paper introduces novel methods for preparing and analyzing student interaction data extracted from course management systems like Moodle to facilitate process mining, like the creation of graphs that show the process flow. Such graphs can get very complex as Moodle courses can contain hundreds of different activities, which makes it difficult to compare the paths of different student cohorts. Moreover, existing research often confines its focus to individual courses, overlooking potential patterns that may transcend course boundaries. Our research addresses these challenges by implementing an automated dataflow that directly queries data from the Moodle database via SQL, offering the flexibility of filtering on individual courses if needed. In addition to analyzing individual Moodle activities, we explore patterns at an aggregated course section level. Furthermore, we present a method for standardizing section labels across courses, facilitating cross-course analysis to uncover broader usage patterns. Our findings reveal, among other insights, that higher-performing students demonstrate a propensity to engage more frequently with available activities and exhibit more dynamic movement between objects. While these patterns are discernible when analyzing individual course activity-events, they become more pronounced when aggregated to the section level and analyzed across multiple courses.
Collaborative Text Editing with Eg-walker: Better, Faster, Smaller
Venue: arXiv cs.DC 2024
Year: 2024

Collaborative text editing algorithms allow several users to concurrently modify a text file, and automatically merge concurrent edits into a consistent state. Existing algorithms fall in two categories: Operational Transformation (OT) algorithms are slow to merge files that have diverged substantially due to offline editing; CRDTs are slow to load and consume a lot of memory. We introduce Eg-walker, a collaboration algorithm for text that avoids these weaknesses. Compared to existing CRDTs, it consumes an order of magnitude less memory in the steady state, and loading a document from disk is orders of magnitude faster. Compared to OT, merging long-running branches is orders of magnitude faster. In the worst case, the merging performance of Eg-walker is comparable with existing CRDT algorithms. Eg-walker can be used everywhere CRDTs are used, including peer-to-peer systems without a central server. By offering performance that is competitive with centralised algorithms, our result paves the way towards the widespread adoption of peer-to-peer collaboration software.
Can-Do! A Dataset and Neuro-Symbolic Grounded Framework for Embodied Planning with Large Multimodal Models
Venue: arXiv cs.AI 2024
Year: 2024

Large multimodal models have demonstrated impressive problem-solving abilities in vision and language tasks, and have the potential to encode extensive world knowledge. However, it remains an open challenge for these models to perceive, reason, plan, and act in realistic environments. In this work, we introduce Can-Do, a benchmark dataset designed to evaluate embodied planning abilities through more diverse and complex scenarios than previous datasets. Our dataset includes 400 multimodal samples, each consisting of natural language user instructions, visual images depicting the environment, state changes, and corresponding action plans. The data encompasses diverse aspects of commonsense knowledge, physical understanding, and safety awareness. Our fine-grained analysis reveals that state-of-the-art models, including GPT-4V, face bottlenecks in visual perception, comprehension, and reasoning abilities. To address these challenges, we propose NeuroGround, a neurosymbolic framework that first grounds the plan generation in the perceived environment states and then leverages symbolic planning engines to augment the model-generated plans. Experimental results demonstrate the effectiveness of our framework compared to strong baselines. Our code and dataset are available at https://embodied-planning.github.io.