OpenRaiser/Intern-Atlas huggingface dataset:
CVAM-Pose: Conditional Variational Autoencoder for Multi-Object Monocular Pose Estimation
Venue: arXiv cs.CV 2024
Year: 2024

Estimating rigid objects' poses is one of the fundamental problems in computer vision, with a range of applications across automation and augmented reality. Most existing approaches adopt one network per object class strategy, depend heavily on objects' 3D models, depth data, and employ a time-consuming iterative refinement, which could be impractical for some applications. This paper presents a novel approach, CVAM-Pose, for multi-object monocular pose estimation that addresses these limitations. The CVAM-Pose method employs a label-embedded conditional variational autoencoder network, to implicitly abstract regularised representations of multiple objects in a single low-dimensional latent space. This autoencoding process uses only images captured by a projective camera and is robust to objects' occlusion and scene clutter. The classes of objects are one-hot encoded and embedded throughout the network. The proposed label-embedded pose regression strategy interprets the learnt latent space representations utilising continuous pose representations. Ablation tests and systematic evaluations demonstrate the scalability and efficiency of the CVAM-Pose method for multi-object scenarios. The proposed CVAM-Pose outperforms competing latent space approaches. For example, it is respectively 25% and 20% better than AAE and Multi-Path methods, when evaluated using the $\mathrm{AR_{VSD}}$ metric on the Linemod-Occluded dataset. It also achieves results somewhat comparable to methods reliant on 3D models reported in BOP challenges. Code available: https://github.com/JZhao12/CVAM-Pose
SimpleStrat: Diversifying Language Model Generation with Stratification
Venue: arXiv cs.CL 2024
Year: 2024

Generating diverse responses from large language models (LLMs) is crucial for applications such as planning/search and synthetic data generation, where diversity provides distinct answers across generations. Prior approaches rely on increasing temperature to increase diversity. However, contrary to popular belief, we show not only does this approach produce lower quality individual generations as temperature increases, but it depends on model's next-token probabilities being similar to the true distribution of answers. We propose SimpleStrat, an alternative approach that uses the language model itself to partition the space into strata. At inference, a random stratum is selected and a sample drawn from within the strata. To measure diversity, we introduce CoverageQA, a dataset of underspecified questions with multiple equally plausible answers, and assess diversity by measuring KL Divergence between the output distribution and uniform distribution over valid ground truth answers. As computing probability per response/solution for proprietary models is infeasible, we measure recall on ground truth solutions. Our evaluation show using SimpleStrat achieves higher recall by 0.05 compared to GPT-4o and 0.36 average reduction in KL Divergence compared to Llama 3.
AttnGCG: Enhancing Jailbreaking Attacks on LLMs with Attention Manipulation
Venue: arXiv cs.CL 2024
Year: 2024

This paper studies the vulnerabilities of transformer-based Large Language Models (LLMs) to jailbreaking attacks, focusing specifically on the optimization-based Greedy Coordinate Gradient (GCG) strategy. We first observe a positive correlation between the effectiveness of attacks and the internal behaviors of the models. For instance, attacks tend to be less effective when models pay more attention to system prompts designed to ensure LLM safety alignment. Building on this discovery, we introduce an enhanced method that manipulates models' attention scores to facilitate LLM jailbreaking, which we term AttnGCG. Empirically, AttnGCG shows consistent improvements in attack efficacy across diverse LLMs, achieving an average increase of ~7% in the Llama-2 series and ~10% in the Gemma series. Our strategy also demonstrates robust attack transferability against both unseen harmful goals and black-box LLMs like GPT-3.5 and GPT-4. Moreover, we note our attention-score visualization is more interpretable, allowing us to gain better insights into how our targeted attention manipulation facilitates more effective jailbreaking. We release the code at https://github.com/UCSC-VLAA/AttnGCG-attack.
MiRAGeNews: Multimodal Realistic AI-Generated News Detection
Venue: arXiv cs.CV 2024
Year: 2024

The proliferation of inflammatory or misleading "fake" news content has become increasingly common in recent years. Simultaneously, it has become easier than ever to use AI tools to generate photorealistic images depicting any scene imaginable. Combining these two -- AI-generated fake news content -- is particularly potent and dangerous. To combat the spread of AI-generated fake news, we propose the MiRAGeNews Dataset, a dataset of 12,500 high-quality real and AI-generated image-caption pairs from state-of-the-art generators. We find that our dataset poses a significant challenge to humans (60% F-1) and state-of-the-art multi-modal LLMs (< 24% F-1). Using our dataset we train a multi-modal detector (MiRAGe) that improves by +5.1% F-1 over state-of-the-art baselines on image-caption pairs from out-of-domain image generators and news publishers. We release our code and data to aid future work on detecting AI-generated content.
Unraveling and Mitigating Safety Alignment Degradation of Vision-Language Models
Venue: arXiv cs.CL 2024
Year: 2024

The safety alignment ability of Vision-Language Models (VLMs) is prone to be degraded by the integration of the vision module compared to its LLM backbone. We investigate this phenomenon, dubbed as ''safety alignment degradation'' in this paper, and show that the challenge arises from the representation gap that emerges when introducing vision modality to VLMs. In particular, we show that the representations of multi-modal inputs shift away from that of text-only inputs which represent the distribution that the LLM backbone is optimized for. At the same time, the safety alignment capabilities, initially developed within the textual embedding space, do not successfully transfer to this new multi-modal representation space. To reduce safety alignment degradation, we introduce Cross-Modality Representation Manipulation (CMRM), an inference time representation intervention method for recovering the safety alignment ability that is inherent in the LLM backbone of VLMs, while simultaneously preserving the functional capabilities of VLMs. The empirical results show that our framework significantly recovers the alignment ability that is inherited from the LLM backbone with minimal impact on the fluency and linguistic capabilities of pre-trained VLMs even without additional training. Specifically, the unsafe rate of LLaVA-7B on multi-modal input can be reduced from 61.53% to as low as 3.15% with only inference-time intervention. WARNING: This paper contains examples of toxic or harmful language.
Towards Trustworthy LLMs for Code: A Data-Centric Synergistic Auditing Framework
Venue: arXiv cs.SE 2024
Year: 2024

LLM-powered coding and development assistants have become prevalent to programmers' workflows. However, concerns about the trustworthiness of LLMs for code persist despite their widespread use. Much of the existing research focused on either training or evaluation, raising questions about whether stakeholders in training and evaluation align in their understanding of model trustworthiness and whether they can move toward a unified direction. In this paper, we propose a vision for a unified trustworthiness auditing framework, DataTrust, which adopts a data-centric approach that synergistically emphasizes both training and evaluation data and their correlations. DataTrust aims to connect model trustworthiness indicators in evaluation with data quality indicators in training. It autonomously inspects training data and evaluates model trustworthiness using synthesized data, attributing potential causes from specific evaluation data to corresponding training data and refining indicator connections. Additionally, a trustworthiness arena powered by DataTrust will engage crowdsourced input and deliver quantitative outcomes. We outline the benefits that various stakeholders can gain from DataTrust and discuss the challenges and opportunities it presents.
Circuits and Systems for Embodied AI: Exploring uJ Multi-Modal Perception for Nano-UAVs on the Kraken Shield
Venue: arXiv cs.AR 2024
Year: 2024

Embodied artificial intelligence (AI) requires pushing complex multi-modal models to the extreme edge for time-constrained tasks such as autonomous navigation of robots and vehicles. On small form-factor devices, e.g., nano-sized unmanned aerial vehicles (UAVs), such challenges are exacerbated by stringent constraints on energy efficiency and weight. In this paper, we explore embodied multi-modal AI-based perception for Nano-UAVs with the Kraken shield, a 7g multi-sensor (frame-based and event-based imagers) board based on Kraken, a 22 nm SoC featuring multiple acceleration engines for multi-modal event and frame-based inference based on spiking (SNN) and ternary (TNN) neural networks, respectively. Kraken can execute SNN real-time inference for depth estimation at 1.02k inf/s, 18 μJ/inf, TNN real-time inference for object classification at 10k inf/s, 6 μJ/inf, and real-time inference for obstacle avoidance at 221 frame/s, 750 μJ/inf.
The Impact of Network Structure on Ant Colony Optimization
Venue: arXiv cs.NE 2024
Year: 2024

Ant Colony Optimization (ACO) is a swarm intelligence methodology utilized for solving optimization problems through information transmission mediated by pheromones. As ants sequentially secrete pheromones that subsequently evaporate, the information conveyed predominantly comprises pheromones secreted by recent ants. This paper introduces a network structure into the information transmission process and examines its impact on optimization performance. The network structure is characterized by an asymmetric BA model with parameters for in-degree $r$ and asymmetry $ω$. At $ω=1$, the model describes a scale-free network; at $ω=0$, a random network; and at $ω=-1$, an extended lattice. We aim to solve the ground state search of the mean-field Ising model, employing a linear decision function for the ants with their response to pheromones quantified by the parameter $α$. For $ω>-1$, the pheromone rates for options converge to stable fixed points of the stochastic system. Below the critical threshold $α_c$, there is one stable fixed point, while above $α_c$, there are two. Notably, as $ω\to -1$, both the driving force toward stable fixed points and the strength of the noise reach their maximum, significantly enhancing the probability of finding the ground state of the Ising model.
Knowledge-Augmented Reasoning for EUAIA Compliance and Adversarial Robustness of LLMs
Venue: arXiv cs.CL 2024
Year: 2024

The EU AI Act (EUAIA) introduces requirements for AI systems which intersect with the processes required to establish adversarial robustness. However, given the ambiguous language of regulation and the dynamicity of adversarial attacks, developers of systems with highly complex models such as LLMs may find their effort to be duplicated without the assurance of having achieved either compliance or robustness. This paper presents a functional architecture that focuses on bridging the two properties, by introducing components with clear reference to their source. Taking the detection layer recommended by the literature, and the reporting layer required by the law, we aim to support developers and auditors with a reasoning layer based on knowledge augmentation (rules, assurance cases, contextual mappings). Our findings demonstrate a novel direction for ensuring LLMs deployed in the EU are both compliant and adversarially robust, which underpin trustworthiness.
Diagnosing Robotics Systems Issues with Large Language Models
Venue: arXiv cs.CL 2024
Year: 2024

Quickly resolving issues reported in industrial applications is crucial to minimize economic impact. However, the required data analysis makes diagnosing the underlying root causes a challenging and time-consuming task, even for experts. In contrast, large language models (LLMs) excel at analyzing large amounts of data. Indeed, prior work in AI-Ops demonstrates their effectiveness in analyzing IT systems. Here, we extend this work to the challenging and largely unexplored domain of robotics systems. To this end, we create SYSDIAGBENCH, a proprietary system diagnostics benchmark for robotics, containing over 2500 reported issues. We leverage SYSDIAGBENCH to investigate the performance of LLMs for root cause analysis, considering a range of model sizes and adaptation techniques. Our results show that QLoRA finetuning can be sufficient to let a 7B-parameter model outperform GPT-4 in terms of diagnostic accuracy while being significantly more cost-effective. We validate our LLM-as-a-judge results with a human expert study and find that our best model achieves similar approval ratings as our reference labels.
Securing UAV Communication: Authentication and Integrity
Venue: arXiv cs.CR 2024
Year: 2024

Recent technological advancements have seen the integration of unmanned aerial networks (UAVs) into various sectors, from civilian missions to military operations. In this context, ensuring security, precisely authentication, is essential to prevent data theft and manipulation. A Man-in-the-Middle attack not only compromises network integrity but also threatens the original data, potentially leading to theft or alteration. In this work, we proposed an authentication method to secure UAV data exchange over an insecure communication channel. Our solution combines Diffie-Hellman (DH) key exchange and Hash-based Message Authentication Code (HMAC) within ROS communication channels to authenticate exchanged UAV data. We evaluated our method by measuring transmission time and simulating key tampering, finding acceptable performance for DH key sizes below 4096 bits but longer times for larger sizes due to increased complexity. Both drones successfully detected tampered keys, affirming our method's efficacy in protecting UAV communication. However, scalability challenges in resource-constrained environments warrant further research.
Different Cybercrimes and their Solution for Common People
Venue: arXiv cs.CR 2024
Year: 2024

In today's digital age, cyberspace has become integral to daily life, however it has also led to an increase in cybercriminal activities. This paper explores cybercrime trends and highlights the need for cybercrime awareness (cyberawareness) to mitigate vulnerabilities. The study also examines Indian statistics on cybercrime. We review the existing literature on cybercrime and cybersecurity, focusing on various types of cybercrimes and their impacts. We present a list of 31 technical as well as non-technical solutions considering that a "common man" may not be technologically aware. Common man solutions, considering that they are not technologically updated. Expanding the list of solutions and validating their effectiveness in cyber threats can be the future scope of the research.
M$^2$-ViT: Accelerating Hybrid Vision Transformers with Two-Level Mixed Quantization
Venue: arXiv cs.AR 2024
Year: 2024

Although Vision Transformers (ViTs) have achieved significant success, their intensive computations and substantial memory overheads challenge their deployment on edge devices. To address this, efficient ViTs have emerged, typically featuring Convolution-Transformer hybrid architectures to enhance both accuracy and hardware efficiency. While prior work has explored quantization for efficient ViTs to marry the best of efficient hybrid ViT architectures and quantization, it focuses on uniform quantization and overlooks the potential advantages of mixed quantization. Meanwhile, although several works have studied mixed quantization for standard ViTs, they are not directly applicable to hybrid ViTs due to their distinct algorithmic and hardware characteristics. To bridge this gap, we present M$^2$-ViT to accelerate Convolution-Transformer hybrid efficient ViTs with two-level mixed quantization. Specifically, we introduce a hardware-friendly two-level mixed quantization (M$^2$Q) strategy, characterized by both mixed quantization precision and mixed quantization schemes (i.e., uniform and power-of-two), to exploit the architectural properties of efficient ViTs. We further build a dedicated accelerator with heterogeneous computing engines to transform our algorithmic benefits into real hardware improvements. Experimental results validate our effectiveness, showcasing an average of $80\%$ energy-delay product (EDP) saving with comparable quantization accuracy compared to the prior work.
SoK: Verifiable Cross-Silo FL
Venue: arXiv cs.LG 2024
Year: 2024

Federated Learning (FL) is a widespread approach that allows training machine learning (ML) models with data distributed across multiple devices. In cross-silo FL, which often appears in domains like healthcare or finance, the number of participants is moderate, and each party typically represents a well-known organization. For instance, in medicine data owners are often hospitals or data hubs which are well-established entities. However, malicious parties may still attempt to disturb the training procedure in order to obtain certain benefits, for example, a biased result or a reduction in computational load. While one can easily detect a malicious agent when data used for training is public, the problem becomes much more acute when it is necessary to maintain the privacy of the training dataset. To address this issue, there is recently growing interest in developing verifiable protocols, where one can check that parties do not deviate from the training procedure and perform computations correctly. In this paper, we present a systematization of knowledge on verifiable cross-silo FL. We analyze various protocols, fit them in a taxonomy, and compare their efficiency and threat models. We also analyze Zero-Knowledge Proof (ZKP) schemes and discuss how their overall cost in a FL context can be minimized. Lastly, we identify research gaps and discuss potential directions for future scientific work.
Multi-Agent Actor-Critics in Autonomous Cyber Defense
Venue: arXiv cs.CR 2024
Year: 2024

The need for autonomous and adaptive defense mechanisms has become paramount in the rapidly evolving landscape of cyber threats. Multi-Agent Deep Reinforcement Learning (MADRL) presents a promising approach to enhancing the efficacy and resilience of autonomous cyber operations. This paper explores the application of Multi-Agent Actor-Critic algorithms which provides a general form in Multi-Agent learning to cyber defense, leveraging the collaborative interactions among multiple agents to detect, mitigate, and respond to cyber threats. We demonstrate each agent is able to learn quickly and counter act on the threats autonomously using MADRL in simulated cyber-attack scenarios. The results indicate that MADRL can significantly enhance the capability of autonomous cyber defense systems, paving the way for more intelligent cybersecurity strategies. This study contributes to the growing body of knowledge on leveraging artificial intelligence for cybersecurity and sheds light for future research and development in autonomous cyber operations.
Context-Aware SQL Error Correction Using Few-Shot Learning -- A Novel Approach Based on NLQ, Error, and SQL Similarity
Venue: arXiv cs.CL 2024
Year: 2024

In recent years, the demand for automated SQL generation has increased significantly, driven by the need for efficient data querying in various applications. However, generating accurate SQL queries remains a challenge due to the complexity and variability of natural language inputs. This paper introduces a novel few-shot learning-based approach for error correction in SQL generation, enhancing the accuracy of generated queries by selecting the most suitable few-shot error correction examples for a given natural language question (NLQ). In our experiments with the open-source Gretel dataset, the proposed model offers a 39.2% increase in fixing errors from the baseline approach with no error correction and a 10% increase from a simple error correction method. The proposed technique leverages embedding-based similarity measures to identify the closest matches from a repository of few-shot examples. Each example comprises an incorrect SQL query, the resulting error, the correct SQL query, and detailed steps to transform the incorrect query into the correct one. By employing this method, the system can effectively guide the correction of errors in newly generated SQL queries. Our approach demonstrates significant improvements in SQL generation accuracy by providing contextually relevant examples that facilitate error identification and correction. The experimental results highlight the effectiveness of embedding-based selection in enhancing the few-shot learning process, leading to more precise and reliable SQL query generation. This research contributes to the field of automated SQL generation by offering a robust framework for error correction, paving the way for more advanced and user-friendly database interaction tools.
L3Cube-MahaSum: A Comprehensive Dataset and BART Models for Abstractive Text Summarization in Marathi
Venue: arXiv cs.CL 2024
Year: 2024

We present the MahaSUM dataset, a large-scale collection of diverse news articles in Marathi, designed to facilitate the training and evaluation of models for abstractive summarization tasks in Indic languages. The dataset, containing 25k samples, was created by scraping articles from a wide range of online news sources and manually verifying the abstract summaries. Additionally, we train an IndicBART model, a variant of the BART model tailored for Indic languages, using the MahaSUM dataset. We evaluate the performance of our trained models on the task of abstractive summarization and demonstrate their effectiveness in producing high-quality summaries in Marathi. Our work contributes to the advancement of natural language processing research in Indic languages and provides a valuable resource for future research in this area using state-of-the-art models. The dataset and models are shared publicly at https://github.com/l3cube-pune/MarathiNLP
Long Range Named Entity Recognition for Marathi Documents
Venue: arXiv cs.CL 2024
Year: 2024

The demand for sophisticated natural language processing (NLP) methods, particularly Named Entity Recognition (NER), has increased due to the exponential growth of Marathi-language digital content. In particular, NER is essential for recognizing distant entities and for arranging and understanding unstructured Marathi text data. With an emphasis on managing long-range entities, this paper offers a comprehensive analysis of current NER techniques designed for Marathi documents. It dives into current practices and investigates the BERT transformer model's potential for long-range Marathi NER. Along with analyzing the effectiveness of earlier methods, the report draws comparisons between NER in English literature and suggests adaptation strategies for Marathi literature. The paper discusses the difficulties caused by Marathi's particular linguistic traits and contextual subtleties while acknowledging NER's critical role in NLP. To conclude, this project is a major step forward in improving Marathi NER techniques, with potential wider applications across a range of NLP tasks and domains.