OpenRaiser/Intern-Atlas huggingface dataset:
Masked Conditional Diffusion Model for Enhancing Deepfake Detection
Venue: arXiv cs.CV 2024
Year: 2024

Recent studies on deepfake detection have achieved promising results when training and testing faces are from the same dataset. However, their results severely degrade when confronted with forged samples that the model has not yet seen during training. In this paper, deepfake data to help detect deepfakes. this paper present we put a new insight into diffusion model-based data augmentation, and propose a Masked Conditional Diffusion Model (MCDM) for enhancing deepfake detection. It generates a variety of forged faces from a masked pristine one, encouraging the deepfake detection model to learn generic and robust representations without overfitting to special artifacts. Extensive experiments demonstrate that forgery images generated with our method are of high quality and helpful to improve the performance of deepfake detection models.
Gain of Grain: A Film Grain Handling Toolchain for VVC-based Open Implementations
Venue: arXiv cs.MM 2024
Year: 2024

Film grain is a distinctive visual characteristic cherished by filmmakers and cinephiles for its ability to evoke nostalgia and artistic aesthetics. However, faithful preservation of film grain during encoding poses unique challenges. Film grain introduces random noise, complicating traditional compression techniques. Consequently, specialized algorithms and encoding strategies have emerged, aiming to strike a harmonious equilibrium. This paper delves into the nuanced realm of film grain handling in Versatile Video Coding (VVC) encoding. We explore the delicate balance between retaining the cinematic charm of film grain and achieving efficient compression. Moreover, we discuss the importance of perceptual quality assessment and adaptive encoding techniques in preserving film grain fidelity. Additionally, we delve into the impact of film grain handling on bitrate control and compression efficiency using VVenC, an open and optimized VVC encoder. Understanding the role of film grain and its nuanced treatment within encoders becomes increasingly pivotal for delivering high-quality, grain-inclusive content in the digital age.
Improving the accuracy of freight mode choice models: A case study using the 2017 CFS PUF data set and ensemble learning techniques
Venue: arXiv cs.LG 2024
Year: 2024

The US Census Bureau has collected two rounds of experimental data from the Commodity Flow Survey, providing shipment-level characteristics of nationwide commodity movements, published in 2012 (i.e., Public Use Microdata) and in 2017 (i.e., Public Use File). With this information, data-driven methods have become increasingly valuable for understanding detailed patterns in freight logistics. In this study, we used the 2017 Commodity Flow Survey Public Use File data set to explore building a high-performance freight mode choice model, considering three main improvements: (1) constructing local models for each separate commodity/industry category; (2) extracting useful geographical features, particularly the derived distance of each freight mode between origin/destination zones; and (3) applying additional ensemble learning methods such as stacking or voting to combine results from local and unified models for improved performance. The proposed method achieved over 92% accuracy without incorporating external information, an over 19% increase compared to directly fitting Random Forests models over 10,000 samples. Furthermore, SHAP (Shapely Additive Explanations) values were computed to explain the outputs and major patterns obtained from the proposed model. The model framework could enhance the performance and interpretability of existing freight mode choice models.
Revising Apetrei's bounding volume hierarchy construction algorithm to allow stackless traversal
Venue: arXiv cs.DS 2024
Year: 2024

Stackless traversal is a technique to speed up range queries by avoiding usage of a stack during the tree traversal. One way to achieve that is to transform a given binary tree to store a left child and a skip-connection (also called an escape index). In general, this operation requires an additional tree traversal during the tree construction. For some tree structures, however, it is possible achieve the same result at a reduced cost. We propose one such algorithm for a GPU hierarchy construction algorithm proposed by Karras in [Karras, 2012]. Furthermore, we show that our algorithm also works with the improved algorithm proposed by Apetrei in [Apetrei, 2014], despite a different ordering of the internal nodes. We achieve that by modifying the Apetrei's algorithm to restore the original Karras' ordering of the internal nodes. Using the modified algorithm, we show how to construct a hierarchy suitable for a stackless traversal in a single bottom-up pass.
Exploring Homogeneous and Heterogeneous Consistent Label Associations for Unsupervised Visible-Infrared Person ReID
Venue: arXiv cs.CV 2024
Year: 2024

Unsupervised visible-infrared person re-identification (USL-VI-ReID) endeavors to retrieve pedestrian images of the same identity from different modalities without annotations. While prior work focuses on establishing cross-modality pseudo-label associations to bridge the modality-gap, they ignore maintaining the instance-level homogeneous and heterogeneous consistency between the feature space and the pseudo-label space, resulting in coarse associations. In response, we introduce a Modality-Unified Label Transfer (MULT) module that simultaneously accounts for both homogeneous and heterogeneous fine-grained instance-level structures, yielding high-quality cross-modality label associations. It models both homogeneous and heterogeneous affinities, leveraging them to quantify the inconsistency between the pseudo-label space and the feature space, subsequently minimizing it. The proposed MULT ensures that the generated pseudo-labels maintain alignment across modalities while upholding structural consistency within intra-modality. Additionally, a straightforward plug-and-play Online Cross-memory Label Refinement (OCLR) module is proposed to further mitigate the side effects of noisy pseudo-labels while simultaneously aligning different modalities, coupled with an Alternative Modality-Invariant Representation Learning (AMIRL) framework. Experiments demonstrate that our proposed method outperforms existing state-of-the-art USL-VI-ReID methods, highlighting the superiority of our MULT in comparison to other cross-modality association methods. Code is available at https://github.com/FranklinLingfeng/code_for_MULT.
Neural Policy Style Transfer
Venue: arXiv cs.RO 2024
Year: 2024

Style Transfer has been proposed in a number of fields: fine arts, natural language processing, and fixed trajectories. We scale this concept up to control policies within a Deep Reinforcement Learning infrastructure. Each network is trained to maximize the expected reward, which typically encodes the goal of an action, and can be described as the content. The expressive power of deep neural networks enables encoding a secondary task, which can be described as the style. The Neural Policy Style Transfer (NPST) algorithm is proposed to transfer the style of one policy to another, while maintaining the content of the latter. Different policies are defined via Deep Q-Network architectures. These models are trained using demonstrations through Inverse Reinforcement Learning. Two different sets of user demonstrations are performed, one for content and other for style. Different styles are encoded as defined by user demonstrations. The generated policy is the result of feeding a content policy and a style policy to the NPST algorithm. Experiments are performed in a catch-ball game inspired by the Deep Reinforcement Learning classical Atari games; and a real-world painting scenario with a full-sized humanoid robot, based on previous works of the authors. The implementation of three different Q-Network architectures (Shallow, Deep and Deep Recurrent Q-Network) to encode the policies within the NPST framework is proposed and the results obtained in the experiments with each of these architectures compared.
Real Evaluations Tractability using Continuous Goal-Directed Actions in Smart City Applications
Venue: arXiv cs.RO 2024
Year: 2024

One of the most important challenges of Smart City Applications is to adapt the system to interact with non-expert users. Robot imitation frameworks aim to simplify and reduce times of robot programming by allowing users to program directly through demonstrations. In classical frameworks, actions are modeled using joint or Cartesian space trajectories. Other features, such as visual ones, are not always well represented with these pure geometrical approaches. Continuous Goal-Directed Actions (CGDA) is an alternative to these methods, as it encodes actions as changes of any feature that can be extracted from the environment. As a consequence of this, the robot joint trajectories for execution must be fully computed to comply with this feature-agnostic encoding. This is achieved using Evolutionary Algorithms (EA), which usually requires too many evaluations to perform this evolution step in the actual robot. Current strategies involve performing evaluations in a simulation, transferring the final joint trajectory to the actual robot. Smart City applications involve working in highly dynamic and complex environments, where having a precise model is not always achievable. Our goal is to study the tractability of performing these evaluations directly in a real-world scenario. Two different approaches to reduce the number of evaluations using EA, are proposed and compared. In the first approach, Particle Swarm Optimization (PSO)-based methods have been studied and compared within CGDA: naive PSO, Fitness Inheritance PSO (FI-PSO), and Adaptive Fuzzy Fitness Granulation with PSO (AFFG-PSO). The second approach studied the introduction of geometrical and velocity constraints within CGDA. The effects of both approaches were analyzed and compared in the wax and paint actions, two CGDA commonly studied use cases. Results from this paper depict an important reduction in the number of evaluations.
Profiling and Modeling of Power Characteristics of Leadership-Scale HPC System Workloads
Venue: arXiv cs.DC 2024
Year: 2024

In the exascale era in which application behavior has large power & energy footprints, per-application job-level awareness of such impression is crucial in taking steps towards achieving efficiency goals beyond performance, such as energy efficiency, and sustainability. To achieve these goals, we have developed a novel low-latency job power profiling machine learning pipeline that can group job-level power profiles based on their shapes as they complete. This pipeline leverages a comprehensive feature extraction and clustering pipeline powered by a generative adversarial network (GAN) model to handle the feature-rich time series of job-level power measurements. The output is then used to train a classification model that can predict whether an incoming job power profile is similar to a known group of profiles or is completely new. With extensive evaluations, we demonstrate the effectiveness of each component in our pipeline. Also, we provide a preliminary analysis of the resulting clusters that depict the power profile landscape of the Summit supercomputer from more than 60K jobs sampled from the year 2021.
AnimateLCM: Computation-Efficient Personalized Style Video Generation without Personalized Video Data
Venue: arXiv cs.CV 2024
Year: 2024

This paper introduces an effective method for computation-efficient personalized style video generation without requiring access to any personalized video data. It reduces the necessary generation time of similarly sized video diffusion models from 25 seconds to around 1 second while maintaining the same level of performance. The method's effectiveness lies in its dual-level decoupling learning approach: 1) separating the learning of video style from video generation acceleration, which allows for personalized style video generation without any personalized style video data, and 2) separating the acceleration of image generation from the acceleration of video motion generation, enhancing training efficiency and mitigating the negative effects of low-quality video data.
Minimum-Cost Sensor Channel Selection For Wearable Computing
Venue: arXiv cs.NI 2024
Year: 2024

Sensor systems are constrained by design and finding top sensor channel(s) for a given computational task is an important but hard problem. We define an optimization framework and mathematically formulate the minimum-cost channel selection problem. We then propose two novel algorithms of varying scope and complexity to solve the optimization problem. Branch and bound channel selection finds a globally optimal channel subset and the greedy channel selection finds the best intermediate subset based on the value of a score function. Proposed channel selection algorithms are conditioned with performance as well as the cost of the channel subset. We evaluate both algorithms on two publicly available time series datasets of human activity recognition and mental task detection. Branch and bound channel selection achieved a cost saving of up to 94.8% and the greedy search reduced the cost by 89.6% while maintaining performance thresholds.
Security and Privacy Challenges of Large Language Models: A Survey
Venue: arXiv cs.CL 2024
Year: 2024

Large Language Models (LLMs) have demonstrated extraordinary capabilities and contributed to multiple fields, such as generating and summarizing text, language translation, and question-answering. Nowadays, LLM is becoming a very popular tool in computerized language processing tasks, with the capability to analyze complicated linguistic patterns and provide relevant and appropriate responses depending on the context. While offering significant advantages, these models are also vulnerable to security and privacy attacks, such as jailbreaking attacks, data poisoning attacks, and Personally Identifiable Information (PII) leakage attacks. This survey provides a thorough review of the security and privacy challenges of LLMs for both training data and users, along with the application-based risks in various domains, such as transportation, education, and healthcare. We assess the extent of LLM vulnerabilities, investigate emerging security and privacy attacks for LLMs, and review the potential defense mechanisms. Additionally, the survey outlines existing research gaps in this domain and highlights future research directions.
Privacy and Security Implications of Cloud-Based AI Services : A Survey
Venue: arXiv cs.CR 2024
Year: 2024

This paper details the privacy and security landscape in today's cloud ecosystem and identifies that there is a gap in addressing the risks introduced by machine learning models. As machine learning algorithms continue to evolve and find applications across diverse domains, the need to categorize and quantify privacy and security risks becomes increasingly critical. With the emerging trend of AI-as-a-Service (AIaaS), machine learned AI models (or ML models) are deployed on the cloud by model providers and used by model consumers. We first survey the AIaaS landscape to document the various kinds of liabilities that ML models, especially Deep Neural Networks pose and then introduce a taxonomy to bridge this gap by holistically examining the risks that creators and consumers of ML models are exposed to and their known defences till date. Such a structured approach will be beneficial for ML model providers to create robust solutions. Likewise, ML model consumers will find it valuable to evaluate such solutions and understand the implications of their engagement with such services. The proposed taxonomies provide a foundational basis for solutions in private, secure and robust ML, paving the way for more transparent and resilient AI systems.
An Early Categorization of Prompt Injection Attacks on Large Language Models
Venue: arXiv cs.CR 2024
Year: 2024

Large language models and AI chatbots have been at the forefront of democratizing artificial intelligence. However, the releases of ChatGPT and other similar tools have been followed by growing concerns regarding the difficulty of controlling large language models and their outputs. Currently, we are witnessing a cat-and-mouse game where users attempt to misuse the models with a novel attack called prompt injections. In contrast, the developers attempt to discover the vulnerabilities and block the attacks simultaneously. In this paper, we provide an overview of these emergent threats and present a categorization of prompt injections, which can guide future research on prompt injections and act as a checklist of vulnerabilities in the development of LLM interfaces. Moreover, based on previous literature and our own empirical research, we discuss the implications of prompt injections to LLM end users, developers, and researchers.
MUSTAN: Multi-scale Temporal Context as Attention for Robust Video Foreground Segmentation
Venue: arXiv cs.CV 2024
Year: 2024

Video foreground segmentation (VFS) is an important computer vision task wherein one aims to segment the objects under motion from the background. Most of the current methods are image-based, i.e., rely only on spatial cues while ignoring motion cues. Therefore, they tend to overfit the training data and don't generalize well to out-of-domain (OOD) distribution. To solve the above problem, prior works exploited several cues such as optical flow, background subtraction mask, etc. However, having a video data with annotations like optical flow is a challenging task. In this paper, we utilize the temporal information and the spatial cues from the video data to improve OOD performance. However, the challenge lies in how we model the temporal information given the video data in an interpretable way creates a very noticeable difference. We therefore devise a strategy that integrates the temporal context of the video in the development of VFS. Our approach give rise to deep learning architectures, namely MUSTAN1 and MUSTAN2 and they are based on the idea of multi-scale temporal context as an attention, i.e., aids our models to learn better representations that are beneficial for VFS. Further, we introduce a new video dataset, namely Indoor Surveillance Dataset (ISD) for VFS. It has multiple annotations on a frame level such as foreground binary mask, depth map, and instance semantic annotations. Therefore, ISD can benefit other computer vision tasks. We validate the efficacy of our architectures and compare the performance with baselines. We demonstrate that proposed methods significantly outperform the benchmark methods on OOD. In addition, the performance of MUSTAN2 is significantly improved on certain video categories on OOD data due to ISD.
Nanomechanically Induced Transparency
Venue: arXiv cs.IT 2024
Year: 2024

In this paper, we investigate a nanomechanically induced transparency (NIT) effects that arises from the coupling of a nanoelectromechanical system and a trapped ion. By confining the ion in mesoscopic traps and capacitively coupling it with a nanoelectromechanical system suspended as electrodes, the research is intricately focussed on the implications of including the ion's degrees of freedom. The Lamb--Dicke approximation is crucial to understanding the effects of phonon exchange with electronic qubits and revealing transparency phenomena in this unique coupling. The results underline the importance of the Lamb--Dicke approximation in modelling the effects of transparency windows in nanoelectromechanical systems.
Semantic Constraint Inference for Web Form Test Generation
Venue: arXiv cs.SE 2024
Year: 2024

Automated test generation for web forms has been a longstanding challenge, exacerbated by the intrinsic human-centric design of forms and their complex, device-agnostic structures. We introduce an innovative approach, called FormNexus, for automated web form test generation, which emphasizes deriving semantic insights from individual form elements and relations among them, utilizing textual content, DOM tree structures, and visual proximity. The insights gathered are transformed into a new conceptual graph, the Form Entity Relation Graph (FERG), which offers machine-friendly semantic information extraction. Leveraging LLMs, FormNexus adopts a feedback-driven mechanism for generating and refining input constraints based on real-time form submission responses. The culmination of this approach is a robust set of test cases, each produced by methodically invalidating constraints, ensuring comprehensive testing scenarios for web forms. This work bridges the existing gap in automated web form testing by intertwining the capabilities of LLMs with advanced semantic inference methods. Our evaluation demonstrates that FormNexus combined with GPT-4 achieves 89% coverage in form submission states. This outcome significantly outstrips the performance of the best baseline model by a margin of 25%.
Exploring Spatial Schema Intuitions in Large Language and Vision Models
Venue: arXiv cs.CL 2024
Year: 2024

Despite the ubiquity of large language models (LLMs) in AI research, the question of embodiment in LLMs remains underexplored, distinguishing them from embodied systems in robotics where sensory perception directly informs physical action. Our investigation navigates the intriguing terrain of whether LLMs, despite their non-embodied nature, effectively capture implicit human intuitions about fundamental, spatial building blocks of language. We employ insights from spatial cognitive foundations developed through early sensorimotor experiences, guiding our exploration through the reproduction of three psycholinguistic experiments. Surprisingly, correlations between model outputs and human responses emerge, revealing adaptability without a tangible connection to embodied experiences. Notable distinctions include polarized language model responses and reduced correlations in vision language models. This research contributes to a nuanced understanding of the interplay between language, spatial experiences, and the computations made by large language models. More at https://cisnlp.github.io/Spatial_Schemas/
Credal Learning Theory
Venue: arXiv cs.LG 2024
Year: 2024

Statistical learning theory is the foundation of machine learning, providing theoretical bounds for the risk of models learned from a (single) training set, assumed to issue from an unknown probability distribution. In actual deployment, however, the data distribution may (and often does) vary, causing domain adaptation/generalization issues. In this paper we lay the foundations for a `credal' theory of learning, using convex sets of probabilities (credal sets) to model the variability in the data-generating distribution. Such credal sets, we argue, may be inferred from a finite sample of training sets. Bounds are derived for the case of finite hypotheses spaces (both assuming realizability or not), as well as infinite model spaces, which directly generalize classical results.