OpenRaiser/Intern-Atlas huggingface dataset:
RAT: Retrieval-Augmented Transformer for Click-Through Rate Prediction
Venue: arXiv cs.IR 2024
Year: 2024

Predicting click-through rates (CTR) is a fundamental task for Web applications, where a key issue is to devise effective models for feature interactions. Current methodologies predominantly concentrate on modeling feature interactions within an individual sample, while overlooking the potential cross-sample relationships that can serve as a reference context to enhance the prediction. To make up for such deficiency, this paper develops a Retrieval-Augmented Transformer (RAT), aiming to acquire fine-grained feature interactions within and across samples. By retrieving similar samples, we construct augmented input for each target sample. We then build Transformer layers with cascaded attention to capture both intra- and cross-sample feature interactions, facilitating comprehensive reasoning for improved CTR prediction while retaining efficiency. Extensive experiments on real-world datasets substantiate the effectiveness of RAT and suggest its advantage in long-tail scenarios. The code has been open-sourced at \url{https://github.com/YushenLi807/WWW24-RAT}.
Energy Allocation for Multi-User Cooperative Molecular Communication Systems in the Internet of Bio-Nano Things
Venue: arXiv cs.IT 2024
Year: 2024

Cooperative molecular communication (MC) is a promising technology for facilitating communication between nanomachines in the Internet of Bio-Nano Things (IoBNT) field. However, the performance of IoBNT is limited by the availability of energy for cooperative MC. This paper presents a novel transmitter design scheme that utilizes molecule movement between reservoirs, creating concentration differences through the consumption of free energy, and encoding information on molecule types. The performance of the transmitter is primarily influenced by energy costs, which directly impact the overall IoBNT system performance. To address this, the paper focuses on optimizing energy allocation in cooperative MC for enhanced transmitter performance. Theoretical analysis is conducted for two transmitters. For scenarios with more than two users, a genetic algorithm is employed in the energy allocation to minimize the total bit error rate (BER). Finally, numerical results show the effectiveness of the proposed energy allocation strategies in the considered cooperative MC system.
Towards a New Configurable and Practical Remote Automotive Security Testing Platform
Venue: arXiv cs.CR 2024
Year: 2024

In the automotive security sector, the absence of a testing platform that is configurable, practical, and user-friendly presents considerable challenges. These difficulties are compounded by the intricate design of vehicle systems, the rapid evolution of attack vectors, and the absence of standardized testing methodologies. We propose a next-generation testing platform that addresses several challenges in vehicle cybersecurity testing and research domains. In this paper, we detail how the Vehicle Security Engineering Cloud (VSEC) Test platform enables easier access to test beds for efficient vehicle cybersecurity testing and advanced (e.g., penetration, fuzz) testing and how we extend such test beds to benefit automotive security research. We highlight methodology on how to use this platform for a variety of users and use cases with real implemented examples.
A Survey of Web Content Control for Generative AI
Venue: arXiv cs.IR 2024
Year: 2024

The groundbreaking advancements around generative AI have recently caused a wave of concern culminating in a row of lawsuits, including high-profile actions against Stability AI and OpenAI. This situation of legal uncertainty has sparked a broad discussion on the rights of content creators and publishers to protect their intellectual property on the web. European as well as US law already provides rough guidelines, setting a direction for technical solutions to regulate web data use. In this course, researchers and practitioners have worked on numerous web standards and opt-out formats that empower publishers to keep their data out of the development of generative AI models. The emerging AI/ML opt-out protocols are valuable in regards to data sovereignty, but again, it creates an adverse situation for a site owners who are overwhelmed by the multitude of recent ad hoc standards to consider. In our work, we want to survey the different proposals, ideas and initiatives, and provide a comprehensive legal and technical background in the context of the current discussion on web publishers control.
From Delays to Densities: Exploring Data Uncertainty through Speech, Text, and Visualization
Venue: arXiv cs.HC 2024
Year: 2024

Understanding and communicating data uncertainty is crucial for making informed decisions in sectors like finance and healthcare. Previous work has explored how to express uncertainty in various modes. For example, uncertainty can be expressed visually with quantile dot plots or linguistically with hedge words and prosody. Our research aims to systematically explore how variations within each mode contribute to communicating uncertainty to the user; this allows us to better understand each mode's affordances and limitations. We completed an exploration of the uncertainty design space based on pilot studies and ran two crowdsourced experiments examining how speech, text, and visualization modes and variants within them impact decision-making with uncertain data. Visualization and text were most effective for rational decision-making, though text resulted in lower confidence. Speech garnered the highest trust despite sometimes leading to risky decisions. Results from these studies indicate meaningful trade-offs among modes of information and encourage exploration of multimodal data representations.
Heat Death of Generative Models in Closed-Loop Learning
Venue: arXiv cs.LG 2024
Year: 2024

Improvement and adoption of generative machine learning models is rapidly accelerating, as exemplified by the popularity of LLMs (Large Language Models) for text, and diffusion models for image generation. As generative models become widespread, data they generate is incorporated into shared content through the public web. This opens the question of what happens when data generated by a model is fed back to the model in subsequent training campaigns. This is a question about the stability of the training process, whether the distribution of publicly accessible content, which we refer to as "knowledge", remains stable or collapses. Small scale empirical experiments reported in the literature show that this closed-loop training process is prone to degenerating. Models may start producing gibberish data, or sample from only a small subset of the desired data distribution (a phenomenon referred to as mode collapse). So far there has been only limited theoretical understanding of this process, in part due to the complexity of the deep networks underlying these generative models. The aim of this paper is to provide insights into this process (that we refer to as "generative closed-loop learning") by studying the learning dynamics of generative models that are fed back their own produced content in addition to their original training dataset. The sampling of many of these models can be controlled via a "temperature" parameter. Using dynamical systems tools, we show that, unless a sufficient amount of external data is introduced at each iteration, any non-trivial temperature leads the model to asymptotically degenerate. In fact, either the generative distribution collapses to a small set of outputs or becomes uniform over a large set of outputs.
Task-priority Intermediated Hierarchical Distributed Policies: Reinforcement Learning of Adaptive Multi-robot Cooperative Transport
Venue: arXiv cs.RO 2024
Year: 2024

Multi-robot cooperative transport is crucial in logistics, housekeeping, and disaster response. However, it poses significant challenges in environments where objects of various weights are mixed and the number of robots and objects varies. This paper presents Task-priority Intermediated Hierarchical Distributed Policies (TIHDP), a multi-agent Reinforcement Learning (RL) framework that addresses these challenges through a hierarchical policy structure. TIHDP consists of three layers: task allocation policy (higher layer), dynamic task priority (intermediate layer), and robot control policy (lower layer). Whereas the dynamic task priority layer can manipulate the priority of any object to be transported by receiving global object information and communicating with other robots, the task allocation and robot control policies are restricted by local observations/actions so that they are not affected by changes in the number of objects and robots. Through simulations and real-robot demonstrations, TIHDP shows promising adaptability and performance of the learned multi-robot cooperative transport, even in environments with varying numbers of robots and objects. Video is available at https://youtu.be/Rmhv5ovj0xM
A Unified Editing Method for Co-Speech Gesture Generation via Diffusion Inversion
Venue: arXiv cs.HC 2024
Year: 2024

Diffusion models have shown great success in generating high-quality co-speech gestures for interactive humanoid robots or digital avatars from noisy input with the speech audio or text as conditions. However, they rarely focus on providing rich editing capabilities for content creators other than high-level specialized measures like style conditioning. To resolve this, we propose a unified framework utilizing diffusion inversion that enables multi-level editing capabilities for co-speech gesture generation without re-training. The method takes advantage of two key capabilities of invertible diffusion models. The first is that through inversion, we can reconstruct the intermediate noise from gestures and regenerate new gestures from the noise. This can be used to obtain gestures with high-level similarities to the original gestures for different speech conditions. The second is that this reconstruction reduces activation caching requirements during gradient calculation, making the direct optimization on input noises possible on current hardware with limited memory. With different loss functions designed for, e.g., joint rotation or velocity, we can control various low-level details by automatically tweaking the input noises through optimization. Extensive experiments on multiple use cases show that this framework succeeds in unifying high-level and low-level co-speech gesture editing.
A neuroergonomics model to evaluating nuclear power plants operators' performance under heat stress driven by ECG time-frequency spectrums and fNIRS prefrontal cortex network: a CNN-GAT fusion model
Venue: arXiv cs.HC 2024
Year: 2024

Operators experience complicated physiological and psychological states when exposed to extreme heat stress, which can impair cognitive function and decrease performance significantly, ultimately leading to severe secondary disasters. Therefore, there is an urgent need for a feasible technique to identify their abnormal states to enhance the reliability of human-cybernetics systems. With the advancement of deep learning in physiological modeling, a model for evaluating operators' performance driven by electrocardiogram (ECG) and functional near-infrared spectroscopy (fNIRS) was proposed, demonstrating high ecological validity. The model fused a convolutional neural network (CNN) backbone and a graph attention network (GAT) backbone to extract discriminative features from ECG time-frequency spectrums and fNIRS prefrontal cortex (PFC) network respectively with deeper neuroscience domain knowledge, and eventually achieved 0.90 AUC. Results supported that handcrafted features extracted by specialized neuroscience methods can alleviate overfitting. Inspired by the small-world nature of the brain network, the fNIRS PFC network was organized as an undirected graph and embedded by GAT. It is proven to perform better in information aggregation and delivery compared to a simple non-linear transformation. The model provides a potential neuroergonomics application for evaluating the human state in vital human-cybernetics systems under industry 5.0 scenarios.
Text-driven Affordance Learning from Egocentric Vision
Venue: arXiv cs.CV 2024
Year: 2024

Visual affordance learning is a key component for robots to understand how to interact with objects. Conventional approaches in this field rely on pre-defined objects and actions, falling short of capturing diverse interactions in realworld scenarios. The key idea of our approach is employing textual instruction, targeting various affordances for a wide range of objects. This approach covers both hand-object and tool-object interactions. We introduce text-driven affordance learning, aiming to learn contact points and manipulation trajectories from an egocentric view following textual instruction. In our task, contact points are represented as heatmaps, and the manipulation trajectory as sequences of coordinates that incorporate both linear and rotational movements for various manipulations. However, when we gather data for this task, manual annotations of these diverse interactions are costly. To this end, we propose a pseudo dataset creation pipeline and build a large pseudo-training dataset: TextAFF80K, consisting of over 80K instances of the contact points, trajectories, images, and text tuples. We extend existing referring expression comprehension models for our task, and experimental results show that our approach robustly handles multiple affordances, serving as a new standard for affordance learning in real-world scenarios.
Manipulating and Mitigating Generative Model Biases without Retraining
Venue: arXiv cs.CV 2024
Year: 2024

Text-to-image (T2I) generative models have gained increased popularity in the public domain. While boasting impressive user-guided generative abilities, their black-box nature exposes users to intentionally- and intrinsically-biased outputs. Bias manipulation (and mitigation) techniques typically rely on careful tuning of learning parameters and training data to adjust decision boundaries to influence model bias characteristics, which is often computationally demanding. We propose a dynamic and computationally efficient manipulation of T2I model biases by exploiting their rich language embedding spaces without model retraining. We show that leveraging foundational vector algebra allows for a convenient control over language model embeddings to shift T2I model outputs and control the distribution of generated classes. As a by-product, this control serves as a form of precise prompt engineering to generate images which are generally implausible using regular text prompts. We demonstrate a constructive application of our technique by balancing the frequency of social classes in generated images, effectively balancing class distributions across three social bias dimensions. We also highlight a negative implication of bias manipulation by framing our method as a backdoor attack with severity control using semantically-null input triggers, reporting up to 100% attack success rate. Key-words: Text-to-Image Models, Generative Models, Bias, Prompt Engineering, Backdoor Attacks
scenario.center: Methods from Real-world Data to a Scenario Database
Venue: arXiv cs.SE 2024
Year: 2024

Scenario-based testing is a promising method to develop, verify and validate automated driving systems (ADS) since pure on-road testing seems inefficient for complex traffic environments. A major challenge for this approach is the provision and management of a sufficient number of scenarios to test a system. The provision, generation, and management of scenario at scale is investigated in current research. This paper presents the scenario database scenario.center ( https://scenario.center ) to process and manage scenario data covering the needs of scenario-based testing approaches comprehensively and automatically. Thereby, requirements for such databases are described. Based on those, a four-step approach is proposed. Firstly, a common input format with defined quality requirements is defined. This is utilized for detecting events and base scenarios automatically. Furthermore, methods for searchability, evaluation of data quality and different scenario generation methods are proposed to allow a broad applicability serving different needs. For evaluation, the methodology is compared to state-of-the-art scenario databases. Finally, the application and capabilities of the database are shown by applying the methodology to the inD dataset. A public demonstration of the database interface is provided at https://scenario.center .
Incremental Learning with Concept Drift Detection and Prototype-based Embeddings for Graph Stream Classification
Venue: arXiv cs.LG 2024
Year: 2024

Data stream mining aims at extracting meaningful knowledge from continually evolving data streams, addressing the challenges posed by nonstationary environments, particularly, concept drift which refers to a change in the underlying data distribution over time. Graph structures offer a powerful modelling tool to represent complex systems, such as, critical infrastructure systems and social networks. Learning from graph streams becomes a necessity to understand the dynamics of graph structures and to facilitate informed decision-making. This work introduces a novel method for graph stream classification which operates under the general setting where a data generating process produces graphs with varying nodes and edges over time. The method uses incremental learning for continual model adaptation, selecting representative graphs (prototypes) for each class, and creating graph embeddings. Additionally, it incorporates a loss-based concept drift detection mechanism to recalculate graph prototypes when drift is detected.
Determining the Tactical Challenge of Scenarios to Efficiently Test Automated Driving Systems
Venue: arXiv cs.SE 2024
Year: 2024

The selection of relevant test scenarios for the scenario-based testing and safety validation of automated driving systems (ADSs) remains challenging. An important aspect of the relevance of a scenario is the challenge it poses for an ADS. Existing methods for calculating the challenge of a scenario aim to express the challenge in terms of a metric value. Metric values are useful to select the least or most challenging scenario. However, they fail to provide human-interpretable information on the cause of the challenge which is critical information for the efficient selection of relevant test scenarios. Therefore, this paper presents the Challenge Description Method that mitigates this issue by analyzing scenarios and providing a description of their challenge in terms of the minimum required lane changes and their difficulty. Applying the method to different highway scenarios showed that it is capable of analyzing complex scenarios and providing easy-to-understand descriptions that can be used to select relevant test scenarios.
Multi-Scale Spatial-Temporal Self-Attention Graph Convolutional Networks for Skeleton-based Action Recognition
Venue: arXiv cs.CV 2024
Year: 2024

Skeleton-based gesture recognition methods have achieved high success using Graph Convolutional Network (GCN). In addition, context-dependent adaptive topology as a neighborhood vertex information and attention mechanism leverages a model to better represent actions. In this paper, we propose self-attention GCN hybrid model, Multi-Scale Spatial-Temporal self-attention (MSST)-GCN to effectively improve modeling ability to achieve state-of-the-art results on several datasets. We utilize spatial self-attention module with adaptive topology to understand intra-frame interactions within a frame among different body parts, and temporal self-attention module to examine correlations between frames of a node. These two are followed by multi-scale convolution network with dilations, which not only captures the long-range temporal dependencies of joints but also the long-range spatial dependencies (i.e., long-distance dependencies) of node temporal behaviors. They are combined into high-level spatial-temporal representations and output the predicted action with the softmax classifier.
One Stack to Rule them All: To Drive Automated Vehicles, and Reach for the 4th level
Venue: arXiv cs.RO 2024
Year: 2024

Most automated driving functions are designed for a specific task or vehicle. Most often, the underlying architecture is fixed to specific algorithms to increase performance. Therefore, it is not possible to deploy new modules and algorithms easily. In this paper, we present our automated driving stack which combines both scalability and adaptability. Due to the modular design, our stack allows for a fast integration and testing of novel and state-of-the-art research approaches. Furthermore, it is flexible to be used for our different testing vehicles, including modified EasyMile EZ10 shuttles and different passenger cars. These vehicles differ in multiple ways, e.g. sensor setups, control systems, maximum speed, or steering angle limitations. Finally, our stack is deployed in real world environments, including passenger transport in urban areas. Our stack includes all components needed for operating an autonomous vehicle, including localization, perception, planning, controller, and additional safety modules. Our stack is developed, tested, and evaluated in real world traffic in multiple test sites, including the Test Area Autonomous Driving Baden-Württemberg.
A Satellite Band Selection Framework for Amazon Forest Deforestation Detection Task
Venue: arXiv cs.CV 2024
Year: 2024

The conservation of tropical forests is a topic of significant social and ecological relevance due to their crucial role in the global ecosystem. Unfortunately, deforestation and degradation impact millions of hectares annually, necessitating government or private initiatives for effective forest monitoring. This study introduces a novel framework that employs the Univariate Marginal Distribution Algorithm (UMDA) to select spectral bands from Landsat-8 satellite, optimizing the representation of deforested areas. This selection guides a semantic segmentation architecture, DeepLabv3+, enhancing its performance. Experimental results revealed several band compositions that achieved superior balanced accuracy compared to commonly adopted combinations for deforestation detection, utilizing segment classification via a Support Vector Machine (SVM). Moreover, the optimal band compositions identified by the UMDA-based approach improved the performance of the DeepLabv3+ architecture, surpassing state-of-the-art approaches compared in this study. The observation that a few selected bands outperform the total contradicts the data-driven paradigm prevalent in the deep learning field. Therefore, this suggests an exception to the conventional wisdom that 'more is always better'.
History Trees and Their Applications
Venue: arXiv cs.DC 2024
Year: 2024

In the theoretical study of distributed communication networks, "history trees" are a discrete structure that naturally models the concept that anonymous agents become distinguishable upon receiving different sets of messages from neighboring agents. By conveniently organizing temporal information in a systematic manner, history trees have been instrumental in the development of optimal deterministic algorithms for networks that are both anonymous and dynamically evolving. This note provides an accessible introduction to history trees, drawing comparisons with more traditional structures found in existing literature and reviewing the latest advancements in the applications of history trees, especially within dynamic networks. Furthermore, it expands the theoretical framework of history trees in new directions, also highlighting several open problems for further investigation.