OpenRaiser/Intern-Atlas huggingface dataset:
Mitigating Hallucination with ZeroG: An Advanced Knowledge Management Engine
Venue: arXiv cs.IR 2024
Year: 2024

The growth of digital documents presents significant challenges in efficient management and knowledge extraction. Traditional methods often struggle with complex documents, leading to issues such as hallucinations and high latency in responses from Large Language Models (LLMs). ZeroG, an innovative approach, significantly mitigates these challenges by leveraging knowledge distillation and prompt tuning to enhance model performance. ZeroG utilizes a smaller model that replicates the behavior of a larger teacher model, ensuring contextually relevant and grounded responses, by employing a black-box distillation approach, it creates a distilled dataset without relying on intermediate features, optimizing computational efficiency. This method significantly enhances accuracy and reduces response times, providing a balanced solution for modern document management. Incorporating advanced techniques for document ingestion and metadata utilization, ZeroG improves the accuracy of question-and-answer systems. The integration of graph databases and robust metadata management further streamlines information retrieval, allowing for precise and context-aware responses. By transforming how organizations interact with complex data, ZeroG enhances productivity and user experience, offering a scalable solution for the growing demands of digital document management.
Ideal Pseudorandom Codes
Venue: arXiv cs.CR 2024
Year: 2024

Pseudorandom codes are error-correcting codes with the property that no efficient adversary can distinguish encodings from uniformly random strings. They were recently introduced by Christ and Gunn [CRYPTO 2024] for the purpose of watermarking the outputs of randomized algorithms, such as generative AI models. Several constructions of pseudorandom codes have since been proposed, but none of them are robust to error channels that depend on previously seen codewords. This stronger kind of robustness is referred to as adaptive robustness, and it is important for meaningful applications to watermarking. In this work, we show the following. - Adaptive robustness: We show that the pseudorandom codes of Christ and Gunn are adaptively robust, resolving a conjecture posed by Cohen, Hoover, and Schoenbach [S&P 2025]. - Ideal security: We define an ideal pseudorandom code as one which is indistinguishable from the ideal functionality, capturing both the pseudorandomness and robustness properties in one simple definition. We show that any adaptively robust pseudorandom code for single-bit messages can be bootstrapped to build an ideal pseudorandom code with linear information rate, under no additional assumptions. - CCA security: In the setting where the encoding key is made public, we define a CCA-secure pseudorandom code in analogy with CCA-secure encryption. We show that any adaptively robust public-key pseudorandom code for single-bit messages can be used to build a CCA-secure pseudorandom code with linear information rate, in the random oracle model. These results immediately imply stronger robustness guarantees for generative AI watermarking schemes, such as the practical quality-preserving image watermarks of Gunn, Zhao, and Song (2024).
When to Commute During the COVID-19 Pandemic and Beyond: Analysis of Traffic Crashes in Washington, D.C
Venue: arXiv cs.CY 2024
Year: 2024

Many workers in cities across the world, who have been teleworking because of the COVID-19 pandemic, are expected to be back to their commutes. As this process is believed to be gradual and telecommuting is likely to remain an option for many workers, hybrid model and flexible schedules might become the norm in the future. This variable work schedules allows employees to commute outside of traditional rush hours. Moreover, many studies showed that commuters might be skeptical of using trains, buses, and carpools and could turn to personal vehicles to get to work, which might increase congestion and crashes in the roads. This study attempts to provide information on the safest time to commute to Washington, DC area analyzing historical traffic crash data before the COVID-19 pandemic. It also aims to advance our understanding of traffic crashes and other relating factors such as weather in the Washington, DC area. We created a model to predict crashes by time of the day, using a negative binomial regression after rejecting a Poisson regression, and additionally explored the validity of a Random Forest regression. Our main consideration for an eventual application of this study is to reduce crashes in Washington DC, using this tool that provides people with better options on when to commute and when to telework, if available. The study also provides policymakers and researchers with real-world insights that decrease the number of traffic crashes to help achieve the goals of The Vision Zero Initiative adopted by the district.
Utilisation of Vision Systems and Digital Twin for Maintaining Cleanliness in Public Spaces
Venue: arXiv cs.CV 2024
Year: 2024

Nowadays, the increasing demand for maintaining high cleanliness standards in public spaces results in the search for innovative solutions. The deployment of CCTV systems equipped with modern cameras and software enables not only real-time monitoring of the cleanliness status but also automatic detection of impurities and optimisation of cleaning schedules. The Digital Twin technology allows for the creation of a virtual model of the space, facilitating the simulation, training, and testing of cleanliness management strategies before implementation in the real world. In this paper, we present the utilisation of advanced vision surveillance systems and the Digital Twin technology in cleanliness management, using a railway station as an example. The Digital Twin was created based on an actual 3D model in the Nvidia Omniverse Isaac Sim simulator. A litter detector, bin occupancy level detector, stain segmentation, and a human detector (including the cleaning crew) along with their movement analysis were implemented. A preliminary assessment was conducted, and potential modifications for further enhancement and future development of the system were identified.
Do Data Center Network Metrics Predict Application-Facing Performance?
Venue: arXiv cs.NI 2024
Year: 2024

Applications that run in large-scale data center networks (DCNs) rely on the DCN's ability to deliver application requests in a performant manner. DCNs expose a complex design and operational space, and network designers and operators care how different options along this space affect application performance. One might run controlled experiments and measure the corresponding application-facing performance, but such experiments become progressively infeasible at a large scale, and simulations risk yielding inaccurate or incomplete results. Instead, we show that we can predict application-facing performance through more easily measured network metrics. For example, network telemetry metrics (e.g., link utilization) can predict application-facing metrics (e.g., transfer latency). Through large-scale measurements of production networks, we study the correlation between the two types of metrics, and construct predictive, interpretable models that serve as a suggestive guideline to network designers and operators. We show that no single network metric is universally the best predictor (even though some prior work has focused on a single predictor). We found that simple linear models often have the lowest error, while queueing-based models are better in a few cases.
Exploring the Impact of Reflexivity Theory and Cognitive Social Structures on the Dynamics of Doctor-Patient Social System
Venue: arXiv cs.SI 2024
Year: 2024

Conventional economic and socio-behavioural models assume perfect symmetric access to information and rational behaviour among interacting agents in a social system. However, real-world events and observations appear to contradict such assumptions, leading to the possibility of other, more complex interaction rules existing between such agents. We investigate this possibility by creating two different models for a doctor-patient system. One retains the established assumptions, while the other incorporates principles of reflexivity theory and cognitive social structures. In addition, we utilize a microbial genetic algorithm to optimize the behaviour of the physician and patient agents in both models. The differences in results for the two models suggest that social systems may not always exhibit the behaviour or even accomplish the purpose for which they were designed and that modelling the social and cognitive influences in a social system may capture various ways a social agent balances complementary and competing information signals in making choices.
Provocation on Expertise in Social Impact Evaluations of Generative AI (and Beyond)
Venue: arXiv cs.HC 2024
Year: 2024

Social impact evaluations are emerging as a useful tool to understand, document, and evaluate the societal impacts of generative AI. In this provocation, we begin to think carefully about the types of experts and expertise that are needed to conduct robust social impact evaluations of generative AI. We suggest that doing so will require thoughtfully eliciting and integrating insights from a range of "domain experts" and "experiential experts," and close with five open questions.
Dynamic Textual Prompt For Rehearsal-free Lifelong Person Re-identification
Venue: arXiv cs.CV 2024
Year: 2024

Lifelong person re-identification attempts to recognize people across cameras and integrate new knowledge from continuous data streams. Key challenges involve addressing catastrophic forgetting caused by parameter updating and domain shift, and maintaining performance in seen and unseen domains. Many previous works rely on data memories to retain prior samples. However, the amount of retained data increases linearly with the number of training domains, leading to continually increasing memory consumption. Additionally, these methods may suffer significant performance degradation when data preservation is prohibited due to privacy concerns. To address these limitations, we propose using textual descriptions as guidance to encourage the ReID model to learn cross-domain invariant features without retaining samples. The key insight is that natural language can describe pedestrian instances with an invariant style, suggesting a shared textual space for any pedestrian images. By leveraging this shared textual space as an anchor, we can prompt the ReID model to embed images from various domains into a unified semantic space, thereby alleviating catastrophic forgetting caused by domain shifts. To achieve this, we introduce a task-driven dynamic textual prompt framework in this paper. This model features a dynamic prompt fusion module, which adaptively constructs and fuses two different textual prompts as anchors. This effectively guides the ReID model to embed images into a unified semantic space. Additionally, we design a text-visual feature alignment module to learn a more precise mapping between fine-grained visual and textual features. We also developed a learnable knowledge distillation module that allows our model to dynamically balance retaining existing knowledge with acquiring new knowledge. Extensive experiments demonstrate that our method outperforms SOTAs under various settings.
To What Extent Does the Perceived Obesity Level of Humanoid Robots Affect People's Trust in Them?
Venue: arXiv cs.RO 2024
Year: 2024

Despite obesity being widely discussed in the social sciences, the effect of a robot's perceived obesity level on trust is not covered by the field of HRI. While in research regarding humans, Body Mass Index (BMI) is commonly used as an indicator of obesity, this scale is completely irrelevant in the context of robots, so it is challenging to operationalize the perceived obesity level of robots; indeed, while the effect of robot's size (or height) on people's trust in it was addressed in previous HRI papers, the perceived obesity level factor has not been addressed. This work examines to what extent the perceived obesity level of humanoid robots affects people's trust in them. To test this hypothesis, we conducted a within-subjects study where, using an online pre-validated questionnaire, the subjects were asked questions while being presented with two pictures of humanoids, one with a regular obesity level and the other with a high obesity level. The results show that humanoid robots with lower perceived obesity levels are significantly more likely to be trusted.
CGLearn: Consistent Gradient-Based Learning for Out-of-Distribution Generalization
Venue: arXiv cs.LG 2024
Year: 2024

Improving generalization and achieving highly predictive, robust machine learning models necessitates learning the underlying causal structure of the variables of interest. A prominent and effective method for this is learning invariant predictors across multiple environments. In this work, we introduce a simple yet powerful approach, CGLearn, which relies on the agreement of gradients across various environments. This agreement serves as a powerful indication of reliable features, while disagreement suggests less reliability due to potential differences in underlying causal mechanisms. Our proposed method demonstrates superior performance compared to state-of-the-art methods in both linear and nonlinear settings across various regression and classification tasks. CGLearn shows robust applicability even in the absence of separate environments by exploiting invariance across different subsamples of observational data. Comprehensive experiments on both synthetic and real-world datasets highlight its effectiveness in diverse scenarios. Our findings underscore the importance of leveraging gradient agreement for learning causal invariance, providing a significant step forward in the field of robust machine learning. The source code of the linear and nonlinear implementation of CGLearn is open-source and available at: https://github.com/hasanjawad001/CGLearn.
AI-Driven Stylization of 3D Environments
Venue: arXiv cs.CV 2024
Year: 2024

In this system, we discuss methods to stylize a scene of 3D primitive objects into a higher fidelity 3D scene using novel 3D representations like NeRFs and 3D Gaussian Splatting. Our approach leverages existing image stylization systems and image-to-3D generative models to create a pipeline that iteratively stylizes and composites 3D objects into scenes. We show our results on adding generated objects into a scene and discuss limitations.
Mutual-energy inner product optimization method for constructing feature coordinates and image classification in Machine Learning
Venue: arXiv cs.LG 2024
Year: 2024

As a key task in machine learning, data classification is essentially to find a suitable coordinate system to represent data features of different classes of samples. This paper proposes the mutual-energy inner product optimization method for constructing a feature coordinate system. First, by analyzing the solution space and eigenfunctions of partial differential equations describing a non-uniform membrane, the mutual-energy inner product is defined. Second, by expressing the mutual-energy inner product as a series of eigenfunctions, it shows a significant advantage of enhancing low-frequency features and suppressing high-frequency noise, compared with the Euclidean inner product. And then, a mutual-energy inner product optimization model is built to extract data features, and convexity and concavity properties of its objective function are discussed. Next, by combining the finite element method, a stable and efficient sequential linearization algorithm is constructed to solve the optimization model. This algorithm only solves equations including positive definite symmetric matrix and linear programming with a few constraints, and its vectorized implementation is discussed. Finally, the mutual-energy inner product optimization method is used to construct feature coordinates, and multi-class Gaussian classifiers are trained on the MINST training set. Good prediction results of Gaussian classifiers are achieved on the MINST test set.
Energy-efficient Hybrid Model Predictive Trajectory Planning for Autonomous Electric Vehicles
Venue: arXiv cs.RO 2024
Year: 2024

To tackle the twin challenges of limited battery life and lengthy charging durations in electric vehicles (EVs), this paper introduces an Energy-efficient Hybrid Model Predictive Planner (EHMPP), which employs an energy-saving optimization strategy. EHMPP focuses on refining the design of the motion planner to be seamlessly integrated with the existing automatic driving algorithms, without additional hardware. It has been validated through simulation experiments on the Prescan, CarSim, and Matlab platforms, demonstrating that it can increase passive recovery energy by 11.74\% and effectively track motor speed and acceleration at optimal power. To sum up, EHMPP not only aids in trajectory planning but also significantly boosts energy efficiency in autonomous EVs.
Online Parallel Multi-Task Relationship Learning via Alternating Direction Method of Multipliers
Venue: arXiv cs.LG 2024
Year: 2024

Online multi-task learning (OMTL) enhances streaming data processing by leveraging the inherent relations among multiple tasks. It can be described as an optimization problem in which a single loss function is defined for multiple tasks. Existing gradient-descent-based methods for this problem might suffer from gradient vanishing and poor conditioning issues. Furthermore, the centralized setting hinders their application to online parallel optimization, which is vital to big data analytics. Therefore, this study proposes a novel OMTL framework based on the alternating direction multiplier method (ADMM), a recent breakthrough in optimization suitable for the distributed computing environment because of its decomposable and easy-to-implement nature. The relations among multiple tasks are modeled dynamically to fit the constant changes in an online scenario. In a classical distributed computing architecture with a central server, the proposed OMTL algorithm with the ADMM optimizer outperforms SGD-based approaches in terms of accuracy and efficiency. Because the central server might become a bottleneck when the data scale grows, we further tailor the algorithm to a decentralized setting, so that each node can work by only exchanging information with local neighbors. Experimental results on a synthetic and several real-world datasets demonstrate the efficiency of our methods.
A Sharded Blockchain-Based Secure Federated Learning Framework for LEO Satellite Networks
Venue: arXiv cs.CR 2024
Year: 2024

Low Earth Orbit (LEO) satellite networks are increasingly essential for space-based artificial intelligence (AI) applications. However, as commercial use expands, LEO satellite networks face heightened cyberattack risks, especially through satellite-to-satellite communication links, which are more vulnerable than ground-based connections. As the number of operational satellites continues to grow, addressing these security challenges becomes increasingly critical. Traditional approaches, which focus on sending models to ground stations for validation, often overlook the limited communication windows available to LEO satellites, leaving critical security risks unaddressed. To tackle these challenges, we propose a sharded blockchain-based federated learning framework for LEO networks, called SBFL-LEO. This framework improves the reliability of inter-satellite communications using blockchain technology and assigns specific roles to each satellite. Miner satellites leverage cosine similarity (CS) and Density-Based Spatial Clustering of Applications with Noise (DBSCAN) to identify malicious models and monitor each other to detect inaccurate aggregated models. Security analysis and experimental results demonstrate that our approach outperforms baseline methods in both model accuracy and energy efficiency, significantly enhancing system robustness against attacks.
AI-Compass: A Comprehensive and Effective Multi-module Testing Tool for AI Systems
Venue: arXiv cs.AI 2024
Year: 2024

AI systems, in particular with deep learning techniques, have demonstrated superior performance for various real-world applications. Given the need for tailored optimization in specific scenarios, as well as the concerns related to the exploits of subsurface vulnerabilities, a more comprehensive and in-depth testing AI system becomes a pivotal topic. We have seen the emergence of testing tools in real-world applications that aim to expand testing capabilities. However, they often concentrate on ad-hoc tasks, rendering them unsuitable for simultaneously testing multiple aspects or components. Furthermore, trustworthiness issues arising from adversarial attacks and the challenge of interpreting deep learning models pose new challenges for developing more comprehensive and in-depth AI system testing tools. In this study, we design and implement a testing tool, \tool, to comprehensively and effectively evaluate AI systems. The tool extensively assesses multiple measurements towards adversarial robustness, model interpretability, and performs neuron analysis. The feasibility of the proposed testing tool is thoroughly validated across various modalities, including image classification, object detection, and text classification. Extensive experiments demonstrate that \tool is the state-of-the-art tool for a comprehensive assessment of the robustness and trustworthiness of AI systems. Our research sheds light on a general solution for AI systems testing landscape.
Multistage non-deterministic classification using secondary concept graphs and graph convolutional networks for high-level feature extraction
Venue: arXiv cs.LG 2024
Year: 2024

Graphs, comprising nodes and edges, visually depict relationships and structures, posing challenges in extracting high-level features due to their intricate connections. Multiple connections introduce complexities in discovering patterns, where node weights may affect some features more than others. In domains with diverse topics, graph representations illustrate interrelations among features. Pattern discovery within graphs is recognized as NP-hard. Graph Convolutional Networks (GCNs) are a prominent deep learning approach for acquiring meaningful representations by leveraging node connectivity and characteristics. Despite achievements, predicting and assigning 9 deterministic classes often involves errors. To address this challenge, we present a multi-stage non-deterministic classification method based on a secondary conceptual graph and graph convolutional networks, which includes distinct steps: 1) leveraging GCN for the extraction and generation of 12 high-level features: 2) employing incomplete, non-deterministic models for feature extraction, conducted before reaching a definitive prediction: and 3) formulating definitive forecasts grounded in conceptual (logical) graphs. The empirical findings indicate that our proposed approach outperforms contemporary methods in classification tasks. Across three datasets Cora, Citeseer, and PubMed the achieved accuracies are 96%, 93%, and 95%, respectively. Code is available at https://github.com/MasoudKargar.