OpenRaiser/Intern-Atlas huggingface dataset:
A comprehensive review and new taxonomy on superpixel segmentation
Venue: arXiv cs.CV 2024
Year: 2024

Superpixel segmentation consists of partitioning images into regions composed of similar and connected pixels. Its methods have been widely used in many computer vision applications since it allows for reducing the workload, removing redundant information, and preserving regions with meaningful features. Due to the rapid progress in this area, the literature fails to catch up on more recent works among the compared ones and to categorize the methods according to all existing strategies. This work fills this gap by presenting a comprehensive review with new taxonomy for superpixel segmentation, in which methods are classified according to their processing steps and processing levels of image features. We revisit the recent and popular literature according to our taxonomy and evaluate 20 strategies based on nine criteria: connectivity, compactness, delineation, control over the number of superpixels, color homogeneity, robustness, running time, stability, and visual quality. Our experiments show the trends of each approach in pixel clustering and discuss individual trade-offs. Finally, we provide a new benchmark for superpixel assessment, available at https://github.com/IMScience-PPGINF-PucMinas/superpixel-benchmark.
Artificial-Intelligence Generated Code Considered Harmful: A Road Map for Secure and High-Quality Code Generation
Venue: arXiv cs.CR 2024
Year: 2024

Generating code via a LLM (rather than writing code from scratch), has exploded in popularity. However, the security implications of LLM-generated code are still unknown. We performed a study that compared the security and quality of human-written code with that of LLM-generated code, for a wide range of programming tasks, including data structures, algorithms, cryptographic routines, and LeetCode questions. To assess code security we used unit testing, fuzzing, and static analysis. For code quality, we focused on complexity and size. We found that LLM can generate incorrect code that fails to implement the required functionality, especially for more complicated tasks; such errors can be subtle. For example, for the cryptographic algorithm SHA1, LLM generated an incorrect implementation that nevertheless compiles. In cases where its functionality was correct, we found that LLM-generated code is less secure, primarily due to the lack of defensive programming constructs, which invites a host of security issues such as buffer overflows or integer overflows. Fuzzing has revealed that LLM-generated code is more prone to hangs and crashes than human-written code. Quality-wise, we found that LLM generates bare-bones code that lacks defensive programming constructs, and is typically more complex (per line of code) compared to human-written code. Next, we constructed a feedback loop that asked the LLM to re-generate the code and eliminate the found issues (e.g., malloc overflow, array index out of bounds, null dereferences). We found that the LLM fails to eliminate such issues consistently: while succeeding in some cases, we found instances where the re-generated, supposedly more secure code, contains new issues; we also found that upon prompting, LLM can introduce issues in files that were issues-free before prompting.
Feature-Prescribed Iterative Learning Control of Waggle Dance Movement for Social Motor Coordination in Joint Actions
Venue: arXiv cs.HC 2024
Year: 2024

Extensive experiments suggest that motor coordination among human participants may contribute to social affinity and emotional attachment, which has great potential in the clinical treatment of social disorders or schizophrenia. Mirror game provides an effective experimental paradigm for studying social motor coordination. Nevertheless, the lack of movement richness prevents the emergence of high-level coordination in the existing one-dimensional experiments. To tackle this problem, this work develops a two-dimensional experimental paradigm of mirror game by playing waggle dance between two participants. In particular, an online control architecture of customized virtual player is created to coordinate with human player. Therein, an iterative learning control algorithm is proposed by integrating position tracking and behavior imitation with prescribed kinematic feature. Moreover, convergence analysis of control algorithm is conducted to guarantee the online performance of virtual player. Finally, the proposed control strategy is validated by matching experimental data and compared with other control methods using a set of performance indexes.
Sharing-Based Channel Access Procedure For Next Generation of Wireless LAN
Venue: arXiv cs.NI 2024
Year: 2024

This paper proposes a new channel access procedure to mitigate the channel access contention in next generation of Wireless Local-Area Networks (WLANs) by allowing cooperation among devices belonging to same network, while maintaining high flexibility in terms of how each device may contend the medium. After introducing the details of the proposed procedure, which is here referred to as sharing-based protocol, an analytical analysis is provided to compare it with the two state-of-art protocols currently adopted in IEEE 802.11 standard, i.e, Enhanced Distributed Channel Access (EDCA)-based and trigger-based protocol. In this regards, closed form expressions are derived to evaluate the success probability of channel access for each protocol. In order to show the merit of the proposed procedure, a comprehensive system level analysis is also provided, which highlights that the proposed procedure outperforms the two state-of-art protocols in terms of mitigating the End-to-End (E2E) delay and allowing a better spectrum utilization by reducing the overall congestion in the system.
Zorro: A Flexible and Differentiable Parametric Family of Activation Functions That Extends ReLU and GELU
Venue: arXiv cs.LG 2024
Year: 2024

Even in recent neural network architectures such as Transformers and Extended LSTM (xLSTM), and traditional ones like Convolutional Neural Networks, Activation Functions are an integral part of nearly all neural networks. They enable more effective training and capture nonlinear data patterns. More than 400 functions have been proposed over the last 30 years, including fixed or trainable parameters, but only a few are widely used. ReLU is one of the most frequently used, with GELU and Swish variants increasingly appearing. However, ReLU presents non-differentiable points and exploding gradient issues, while testing different parameters of GELU and Swish variants produces varying results, needing more parameters to adapt to datasets and architectures. This article introduces a novel set of activation functions called Zorro, a continuously differentiable and flexible family comprising five main functions fusing ReLU and Sigmoid. Zorro functions are smooth and adaptable, and serve as information gates, aligning with ReLU in the 0-1 range, offering an alternative to ReLU without the need for normalization, neuron death, or gradient explosions. Zorro also approximates functions like Swish, GELU, and DGELU, providing parameters to adjust to different datasets and architectures. We tested it on fully connected, convolutional, and transformer architectures to demonstrate its effectiveness.
Fast Approximate Counting of Cycles
Venue: arXiv cs.DS 2024
Year: 2024

We consider the problem of approximate counting of triangles and longer fixed length cycles in directed graphs. For triangles, Tětek [ICALP'22] gave an algorithm that returns a $(1 \pm \eps)$-approximation in $\tilde{O}(n^ω/t^{ω-2})$ time, where $t$ is the unknown number of triangles in the given $n$ node graph and $ω<2.372$ is the matrix multiplication exponent. We obtain an improved algorithm whose running time is, within polylogarithmic factors the same as that for multiplying an $n\times n/t$ matrix by an $n/t \times n$ matrix. We then extend our framework to obtain the first nontrivial $(1 \pm \eps)$-approximation algorithms for the number of $h$-cycles in a graph, for any constant $h\geq 3$. Our running time is \[\tilde{O}(\mathsf{MM}(n,n/t^{1/(h-2)},n)), \textrm{the time to multiply } n\times \frac{n}{t^{1/(h-2)}} \textrm{ by } \frac{n}{t^{1/(h-2)}}\times n \textrm{ matrices}.\] Finally, we show that under popular fine-grained hypotheses, this running time is optimal.
Sustaining model performance for covid-19 detection from dynamic audio data: Development and evaluation of a comprehensive drift-adaptive framework
Venue: arXiv cs.SD 2024
Year: 2024

Background: The COVID-19 pandemic has highlighted the need for robust diagnostic tools capable of detecting the disease from diverse and evolving data sources. Machine learning models, especially convolutional neural networks (CNNs), have shown promise. However, the dynamic nature of real-world data can lead to model drift, where performance degrades over time as the underlying data distribution changes. Addressing this challenge is crucial to maintaining accuracy and reliability in diagnostic applications. Objective: This study aims to develop a framework that monitors model drift and employs adaptation mechanisms to mitigate performance fluctuations in COVID-19 detection models trained on dynamic audio data. Methods: Two crowd-sourced COVID-19 audio datasets, COVID-19 Sounds and COSWARA, were used. Each was divided into development and post-development periods. A baseline CNN model was trained and evaluated using cough recordings from the development period. Maximum mean discrepancy (MMD) was used to detect changes in data distributions and model performance between periods. Upon detecting drift, retraining was triggered to update the baseline model. Two adaptation approaches were compared: unsupervised domain adaptation (UDA) and active learning (AL). Results: UDA improved balanced accuracy by up to 22% and 24% for the COVID-19 Sounds and COSWARA datasets, respectively. AL yielded even greater improvements, with increases of up to 30% and 60%, respectively. Conclusions: The proposed framework addresses model drift in COVID-19 detection, enabling continuous adaptation to evolving data. This approach ensures sustained model performance, contributing to robust diagnostic tools for COVID-19 and potentially other infectious diseases.
CausalVE: Face Video Privacy Encryption via Causal Video Prediction
Venue: arXiv cs.CV 2024
Year: 2024

Advanced facial recognition technologies and recommender systems with inadequate privacy technologies and policies for facial interactions increase concerns about bioprivacy violations. With the proliferation of video and live-streaming websites, public-face video distribution and interactions pose greater privacy risks. Existing techniques typically address the risk of sensitive biometric information leakage through various privacy enhancement methods but pose a higher security risk by corrupting the information to be conveyed by the interaction data, or by leaving certain biometric features intact that allow an attacker to infer sensitive biometric information from them. To address these shortcomings, in this paper, we propose a neural network framework, CausalVE. We obtain cover images by adopting a diffusion model to achieve face swapping with face guidance and use the speech sequence features and spatiotemporal sequence features of the secret video for dynamic video inference and prediction to obtain a cover video with the same number of frames as the secret video. In addition, we hide the secret video by using reversible neural networks for video hiding so that the video can also disseminate secret data. Numerous experiments prove that our CausalVE has good security in public video dissemination and outperforms state-of-the-art methods from a qualitative, quantitative, and visual point of view.
Model X-Ray: Detection of Hidden Malware in AI Model Weights using Few Shot Learning
Venue: arXiv cs.CR 2024
Year: 2024

The potential for exploitation of AI models has increased due to the rapid advancement of Artificial Intelligence (AI) and the widespread use of platforms like Model Zoo for sharing AI models. Attackers can embed malware within AI models through steganographic techniques, taking advantage of the substantial size of these models to conceal malicious data and use it for nefarious purposes, e.g. Remote Code Execution. Ensuring the security of AI models is a burgeoning area of research essential for safeguarding the multitude of organizations and users relying on AI technologies. This study leverages well-studied image few-shot learning techniques by transferring the AI models to the image field using a novel image representation. Applying few-shot learning in this field enables us to create practical models, a feat that previous works lack. Our method addresses critical limitations in state-of-the-art detection techniques that hinder their practicality. This approach reduces the required training dataset size from 40000 models to just 6. Furthermore, our methods consistently detect delicate attacks of up to 25% embedding rate and even up to 6% in some cases, while previous works were only shown to be effective for a 100%-50% embedding rate. We employ a strict evaluation strategy to ensure the trained models are generic concerning various factors. In addition, we show that our trained models successfully detect novel spread-spectrum steganography attacks, demonstrating the models' impressive robustness just by learning one type of attack. We open-source our code to support reproducibility and enhance the research in this new field.
1st Place Solution of Multiview Egocentric Hand Tracking Challenge ECCV2024
Venue: arXiv cs.CV 2024
Year: 2024

Multi-view egocentric hand tracking is a challenging task and plays a critical role in VR interaction. In this report, we present a method that uses multi-view input images and camera extrinsic parameters to estimate both hand shape and pose. To reduce overfitting to the camera layout, we apply crop jittering and extrinsic parameter noise augmentation. Additionally, we propose an offline neural smoothing post-processing method to further improve the accuracy of hand position and pose. Our method achieves 13.92mm MPJPE on the Umetrack dataset and 21.66mm MPJPE on the HOT3D dataset.
Membership Privacy Evaluation in Deep Spiking Neural Networks
Venue: arXiv cs.CR 2024
Year: 2024

Artificial Neural Networks (ANNs), commonly mimicking neurons with non-linear functions to output floating-point numbers, consistently receive the same signals of a data point during its forward time. Unlike ANNs, Spiking Neural Networks (SNNs) get various input signals in the forward time of a data point and simulate neurons in a biologically plausible way, i.e., producing a spike (a binary value) if the accumulated membrane potential of a neuron is larger than a threshold. Even though ANNs have achieved remarkable success in multiple tasks, e.g., face recognition and object detection, SNNs have recently obtained attention due to their low power consumption, fast inference, and event-driven properties. While privacy threats against ANNs are widely explored, much less work has been done on SNNs. For instance, it is well-known that ANNs are vulnerable to the Membership Inference Attack (MIA), but whether the same applies to SNNs is not explored. In this paper, we evaluate the membership privacy of SNNs by considering eight MIAs, seven of which are inspired by MIAs against ANNs. Our evaluation results show that SNNs are more vulnerable (maximum 10% higher in terms of balanced attack accuracy) than ANNs when both are trained with neuromorphic datasets (with time dimension). On the other hand, when training ANNs or SNNs with static datasets (without time dimension), the vulnerability depends on the dataset used. If we convert ANNs trained with static datasets to SNNs, the accuracy of MIAs drops (maximum 11.5% with a reduction of 7.6% on the test accuracy of the target model). Next, we explore the impact factors of MIAs on SNNs by conducting a hyperparameter study. Finally, we show that the basic data augmentation method for static data and two recent data augmentation methods for neuromorphic data can considerably (maximum reduction of 25.7%) decrease MIAs' performance on SNNs.
Machine Learning Operations: A Mapping Study
Venue: arXiv cs.SE 2024
Year: 2024

Machine learning and AI have been recently embraced by many companies. Machine Learning Operations, (MLOps), refers to the use of continuous software engineering processes, such as DevOps, in the deployment of machine learning models to production. Nevertheless, not all machine learning initiatives successfully transition to the production stage owing to the multitude of intricate factors involved. This article discusses the issues that exist in several components of the MLOps pipeline, namely the data manipulation pipeline, model building pipeline, and deployment pipeline. A systematic mapping study is performed to identify the challenges that arise in the MLOps system categorized by different focus areas. Using this data, realistic and applicable recommendations are offered for tools or solutions that can be used for their implementation. The main value of this work is it maps distinctive challenges in MLOps along with the recommended solutions outlined in our study. These guidelines are not specific to any particular tool and are applicable to both research and industrial settings.
The Importance of Adaptive Decision-Making for Autonomous Long-Range Planetary Surface Mobility
Venue: arXiv cs.RO 2024
Year: 2024

Long-distance driving is an important component of planetary surface exploration. Unforeseen events often require human operators to adjust mobility plans, but this approach does not scale and will be insufficient for future missions. Interest in self-reliant rovers is increasing, however the research community has not yet given significant attention to autonomous, adaptive decision-making. In this paper, we look back at specific planetary mobility operations where human-guided adaptive planning played an important role in mission safety and productivity. Inspired by the abilities of human experts, we identify shortcomings of existing autonomous mobility algorithms for robots operating in off-road environments like planetary surfaces. We advocate for adaptive decision-making capabilities such as unassisted learning from past experiences and more reliance on stochastic world models. The aim of this work is to highlight promising research avenues to enhance ground planning tools and, ultimately, long-range autonomy algorithms on board planetary rovers.
A House United Within Itself: SLO-Awareness for On-Premises Containerized ML Inference Clusters via Faro
Venue: arXiv cs.DC 2024
Year: 2024

This paper tackles the challenge of running multiple ML inference jobs (models) under time-varying workloads, on a constrained on-premises production cluster. Our system Faro takes in latency Service Level Objectives (SLOs) for each job, auto-distills them into utility functions, "sloppifies" these utility functions to make them amenable to mathematical optimization, automatically predicts workload via probabilistic prediction, and dynamically makes implicit cross-job resource allocations, in order to satisfy cluster-wide objectives, e.g., total utility, fairness, and other hybrid variants. A major challenge Faro tackles is that using precise utilities and high-fidelity predictors, can be too slow (and in a sense too precise!) for the fast adaptation we require. Faro's solution is to "sloppify" (relax) its multiple design components to achieve fast adaptation without overly degrading solution quality. Faro is implemented in a stack consisting of Ray Serve running atop a Kubernetes cluster. Trace-driven cluster deployments show that Faro achieves 2.3$\times$-23$\times$ lower SLO violations compared to state-of-the-art systems.
IWN: Image Watermarking Based on Idempotency
Venue: arXiv cs.MM 2024
Year: 2024

In the expanding field of digital media, maintaining the strength and integrity of watermarking technology is becoming increasingly challenging. This paper, inspired by the Idempotent Generative Network (IGN), explores the prospects of introducing idempotency into image watermark processing and proposes an innovative neural network model - the Idempotent Watermarking Network (IWN). The proposed model, which focuses on enhancing the recovery quality of color image watermarks, leverages idempotency to ensure superior image reversibility. This feature ensures that, even if color image watermarks are attacked or damaged, they can be effectively projected and mapped back to their original state. Therefore, the extracted watermarks have unquestionably increased quality. The IWN model achieves a balance between embedding capacity and robustness, alleviating to some extent the inherent contradiction between these two factors in traditional watermarking techniques and steganography methods.
GenTel-Safe: A Unified Benchmark and Shielding Framework for Defending Against Prompt Injection Attacks
Venue: arXiv cs.CR 2024
Year: 2024

Large Language Models (LLMs) like GPT-4, LLaMA, and Qwen have demonstrated remarkable success across a wide range of applications. However, these models remain inherently vulnerable to prompt injection attacks, which can bypass existing safety mechanisms, highlighting the urgent need for more robust attack detection methods and comprehensive evaluation benchmarks. To address these challenges, we introduce GenTel-Safe, a unified framework that includes a novel prompt injection attack detection method, GenTel-Shield, along with a comprehensive evaluation benchmark, GenTel-Bench, which compromises 84812 prompt injection attacks, spanning 3 major categories and 28 security scenarios. To prove the effectiveness of GenTel-Shield, we evaluate it together with vanilla safety guardrails against the GenTel-Bench dataset. Empirically, GenTel-Shield can achieve state-of-the-art attack detection success rates, which reveals the critical weakness of existing safeguarding techniques against harmful prompts. For reproducibility, we have made the code and benchmarking dataset available on the project page at https://gentellab.github.io/gentel-safe.github.io/.
Video DataFlywheel: Resolving the Impossible Data Trinity in Video-Language Understanding
Venue: arXiv cs.CV 2024
Year: 2024

Recently, video-language understanding has achieved great success through large-scale pre-training. However, data scarcity remains a prevailing challenge. This study quantitatively reveals an "impossible trinity" among data quantity, diversity, and quality in pre-training datasets. Recent efforts seek to refine large-scale, diverse ASR datasets compromised by low quality through synthetic annotations. These methods successfully leverage useful information in multimodal video content (frames, tags, ASR transcripts, etc.) to refine the original annotations. Nevertheless, they struggle to mitigate noise within synthetic annotations and lack scalability as the dataset size expands. To address these issues, we introduce the Video DataFlywheel framework, which iteratively refines video annotations with improved noise control methods. For iterative refinement, we first leverage a video-language model to generate synthetic annotations, resulting in a refined dataset. Then, we pre-train on it and fine-tune on human refinement examples for a stronger model. These processes are repeated for continuous improvement. For noise control, we present AdaTaiLr, a novel noise control method that requires weaker assumptions on noise distribution, thereby proving more effective in large datasets with theoretical guarantees. The combination of iterative refinement and AdaTaiLr can achieve better scalability in video-language understanding. Extensive experiments show that our framework outperforms existing data refinement baselines, delivering a 3% performance boost and improving dataset quality with minimal diversity loss. Furthermore, our refined dataset facilitates significant improvements in various video-language understanding tasks, including video question answering and text-video retrieval.