OpenRaiser/Intern-Atlas huggingface dataset:
Aquarium: A Comprehensive Framework for Exploring Predator-Prey Dynamics through Multi-Agent Reinforcement Learning Algorithms
Venue: arXiv cs.AI 2024
Year: 2024

Recent advances in Multi-Agent Reinforcement Learning have prompted the modeling of intricate interactions between agents in simulated environments. In particular, the predator-prey dynamics have captured substantial interest and various simulations been tailored to unique requirements. To prevent further time-intensive developments, we introduce Aquarium, a comprehensive Multi-Agent Reinforcement Learning environment for predator-prey interaction, enabling the study of emergent behavior. Aquarium is open source and offers a seamless integration of the PettingZoo framework, allowing a quick start with proven algorithm implementations. It features physics-based agent movement on a two-dimensional, edge-wrapping plane. The agent-environment interaction (observations, actions, rewards) and the environment settings (agent speed, prey reproduction, predator starvation, and others) are fully customizable. Besides a resource-efficient visualization, Aquarium supports to record video files, providing a visual comprehension of agent behavior. To demonstrate the environment's capabilities, we conduct preliminary studies which use PPO to train multiple prey agents to evade a predator. In accordance to the literature, we find Individual Learning to result in worse performance than Parameter Sharing, which significantly improves coordination and sample-efficiency.
Does More Advice Help? The Effects of Second Opinions in AI-Assisted Decision Making
Venue: arXiv cs.HC 2024
Year: 2024

AI assistance in decision-making has become popular, yet people's inappropriate reliance on AI often leads to unsatisfactory human-AI collaboration performance. In this paper, through three pre-registered, randomized human subject experiments, we explore whether and how the provision of {second opinions} may affect decision-makers' behavior and performance in AI-assisted decision-making. We find that if both the AI model's decision recommendation and a second opinion are always presented together, decision-makers reduce their over-reliance on AI while increase their under-reliance on AI, regardless whether the second opinion is generated by a peer or another AI model. However, if decision-makers have the control to decide when to solicit a peer's second opinion, we find that their active solicitations of second opinions have the potential to mitigate over-reliance on AI without inducing increased under-reliance in some cases. We conclude by discussing the implications of our findings for promoting effective human-AI collaborations in decision-making.
Classifying Proposals of Decentralized Autonomous Organizations Using Large Language Models
Venue: arXiv cs.CY 2024
Year: 2024

Our study demonstrates the effective use of Large Language Models (LLMs) for automating the classification of complex datasets. We specifically target proposals of Decentralized Autonomous Organizations (DAOs), as the clas-sification of this data requires the understanding of context and, therefore, depends on human expertise, leading to high costs associated with the task. The study applies an iterative approach to specify categories and further re-fine them and the prompt in each iteration, which led to an accuracy rate of 95% in classifying a set of 100 proposals. With this, we demonstrate the po-tential of LLMs to automate data labeling tasks that depend on textual con-text effectively.
Dual-View Data Hallucination with Semantic Relation Guidance for Few-Shot Image Recognition
Venue: arXiv cs.CV 2024
Year: 2024

Learning to recognize novel concepts from just a few image samples is very challenging as the learned model is easily overfitted on the few data and results in poor generalizability. One promising but underexplored solution is to compensate the novel classes by generating plausible samples. However, most existing works of this line exploit visual information only, rendering the generated data easy to be distracted by some challenging factors contained in the few available samples. Being aware of the semantic information in the textual modality that reflects human concepts, this work proposes a novel framework that exploits semantic relations to guide dual-view data hallucination for few-shot image recognition. The proposed framework enables generating more diverse and reasonable data samples for novel classes through effective information transfer from base classes. Specifically, an instance-view data hallucination module hallucinates each sample of a novel class to generate new data by employing local semantic correlated attention and global semantic feature fusion derived from base classes. Meanwhile, a prototype-view data hallucination module exploits semantic-aware measure to estimate the prototype of a novel class and the associated distribution from the few samples, which thereby harvests the prototype as a more stable sample and enables resampling a large number of samples. We conduct extensive experiments and comparisons with state-of-the-art methods on several popular few-shot benchmarks to verify the effectiveness of the proposed framework.
6Rover: Leveraging Reinforcement Learning-based Address Pattern Mining Approach for Discovering Active Targets in IPv6 Unseeded Space
Venue: arXiv cs.NI 2024
Year: 2024

The discovery of active IPv6 addresses represents a pivotal challenge in IPv6 network survey, as it is a prerequisite for downstream tasks such as network topology measurements and security analysis. With the rapid spread of IPv6 networks in recent years, many researchers have focused on improving the hit rate, efficiency, and coverage of IPv6 scanning methods, resulting in considerable advancements. However, existing approaches remain heavily dependent on seed addresses, thereby limiting their effectiveness in unseeded prefixes. Consequently, this paper proposes 6Rover, a reinforcement learning-based model for active address discovery in unseeded environments. To overcome the reliance on seeded addresses, 6Rover constructs patterns with higher generality that reflects the actual address allocation strategies of network administrators, thereby avoiding biased transfers of patterns from seeded to unseeded prefixes. After that, 6Rover employs a multi-armed bandit model to optimize the probing resource allocation when applying patterns to unseeded spaces. It models the challenge of discovering optimal patterns in unseeded spaces as an exploration-exploitation dilemma, and progressively uncover the potential patterns applied in unseeded spaces, leading to the efficient discovery of active addresses without seed address as the prior knowledge. Experiments on large-scale unseeded datasets show that 6Rover has a higher hit rate than existing methods in the absence of any seed addresses as prior knowledge. In real network environments, 6Rover achieved a 5% - 8% hit rate in seedless spaces with 100 million budget scale, representing an approximate 200\% improvement over the existing state-of-the-art methods.
ScripTONES: Sentiment-Conditioned Music Generation for Movie Scripts
Venue: arXiv cs.MM 2024
Year: 2024

Film scores are considered an essential part of the film cinematic experience, but the process of film score generation is often expensive and infeasible for small-scale creators. Automating the process of film score composition would provide useful starting points for music in small projects. In this paper, we propose a two-stage pipeline for generating music from a movie script. The first phase is the Sentiment Analysis phase where the sentiment of a scene from the film script is encoded into the valence-arousal continuous space. The second phase is the Conditional Music Generation phase which takes as input the valence-arousal vector and conditionally generates piano MIDI music to match the sentiment. We study the efficacy of various music generation architectures by performing a qualitative user survey and propose methods to improve sentiment-conditioning in VAE architectures.
Graph Language Models
Venue: arXiv cs.CL 2024
Year: 2024

While Language Models (LMs) are the workhorses of NLP, their interplay with structured knowledge graphs (KGs) is still actively researched. Current methods for encoding such graphs typically either (i) linearize them for embedding with LMs -- which underutilize structural information, or (ii) use Graph Neural Networks (GNNs) to preserve the graph structure -- but GNNs cannot represent text features as well as pretrained LMs. In our work we introduce a novel LM type, the Graph Language Model (GLM), that integrates the strengths of both approaches and mitigates their weaknesses. The GLM parameters are initialized from a pretrained LM to enhance understanding of individual graph concepts and triplets. Simultaneously, we design the GLM's architecture to incorporate graph biases, thereby promoting effective knowledge distribution within the graph. This enables GLMs to process graphs, texts, and interleaved inputs of both. Empirical evaluations on relation classification tasks show that GLM embeddings surpass both LM- and GNN-based baselines in supervised and zero-shot setting, demonstrating their versatility.
One Agent Too Many: User Perspectives on Approaches to Multi-agent Conversational AI
Venue: arXiv cs.HC 2024
Year: 2024

Conversational agents have been gaining increasing popularity in recent years. Influenced by the widespread adoption of task-oriented agents such as Apple Siri and Amazon Alexa, these agents are being deployed into various applications to enhance user experience. Although these agents promote "ask me anything" functionality, they are typically built to focus on a single or finite set of expertise. Given that complex tasks often require more than one expertise, this results in the users needing to learn and adopt multiple agents. One approach to alleviate this is to abstract the orchestration of agents in the background. However, this removes the option of choice and flexibility, potentially harming the ability to complete tasks. In this paper, we explore these different interaction experiences (one agent for all) vs (user choice of agents) for conversational AI. We design prototypes for each, systematically evaluating their ability to facilitate task completion. Through a series of conducted user studies, we show that users have a significant preference for abstracting agent orchestration in both system usability and system performance. Additionally, we demonstrate that this mode of interaction is able to provide quality responses that are rated within 1% of human-selected answers.
Concrete Surface Crack Detection with Convolutional-based Deep Learning Models
Venue: arXiv cs.CV 2024
Year: 2024

Effective crack detection is pivotal for the structural health monitoring and inspection of buildings. This task presents a formidable challenge to computer vision techniques due to the inherently subtle nature of cracks, which often exhibit low-level features that can be easily confounded with background textures, foreign objects, or irregularities in construction. Furthermore, the presence of issues like non-uniform lighting and construction irregularities poses significant hurdles for autonomous crack detection during building inspection and monitoring. Convolutional neural networks (CNNs) have emerged as a promising framework for crack detection, offering high levels of accuracy and precision. Additionally, the ability to adapt pre-trained networks through transfer learning provides a valuable tool for users, eliminating the need for an in-depth understanding of algorithm intricacies. Nevertheless, it is imperative to acknowledge the limitations and considerations when deploying CNNs, particularly in contexts where the outcomes carry immense significance, such as crack detection in buildings. In this paper, our approach to surface crack detection involves the utilization of various deep-learning models. Specifically, we employ fine-tuning techniques on pre-trained deep learning architectures: VGG19, ResNet50, Inception V3, and EfficientNetV2. These models are chosen for their established performance and versatility in image analysis tasks. We compare deep learning models using precision, recall, and F1 scores.
Choiceless Computation and Symmetry: Limitations of Definability
Venue: arXiv cs.LO 2024
Year: 2024

The search for a logic capturing PTIME is a long standing open problem in finite model theory. One of the most promising candidate logics for this is Choiceless Polynomial Time with counting (CPT). Abstractly speaking, CPT is an isomorphism-invariant computation model working with hereditarily finite sets as data structures. While it is easy to check that the evaluation of CPT-sentences is possible in polynomial time, the converse has been open for more than 20 years: Can every PTIME-decidable property of finite structures be expressed in CPT? We attempt to make progress towards a negative answer and show that Choiceless Polynomial Time cannot compute a preorder with colour classes of logarithmic size in every hypercube. The reason is that such preorders have super-polynomially many automorphic images, which makes it impossible for CPT to define them. While the computation of such a preorder is not a decision problem that would immediately separate P and CPT, it is significant for the following reason: The so-called Cai-Fürer-Immerman (CFI) problem is one of the standard benchmarks for logics and maybe best known for separating fixed-point logic with counting (FPC) from P. Hence, it is natural to consider this also a potential candidate for the separation of CPT and P. The strongest known positive result in this regard says that CPT is able to solve CFI if a preorder with logarithmically sized colour classes is present in the input structure. Our result implies that this approach cannot be generalised to unordered inputs. In other words, CFI on unordered hypercubes is a PTIME-problem which provably cannot be tackled with the state-of-the-art choiceless algorithmic techniques.
Discovering Command and Control Channels Using Reinforcement Learning
Venue: arXiv cs.CR 2024
Year: 2024

Command and control (C2) paths for issuing commands to malware are sometimes the only indicators of its existence within networks. Identifying potential C2 channels is often a manually driven process that involves a deep understanding of cyber tradecraft. Efforts to improve discovery of these channels through using a reinforcement learning (RL) based approach that learns to automatically carry out C2 attack campaigns on large networks, where multiple defense layers are in place serves to drive efficiency for network operators. In this paper, we model C2 traffic flow as a three-stage process and formulate it as a Markov decision process (MDP) with the objective to maximize the number of valuable hosts whose data is exfiltrated. The approach also specifically models payload and defense mechanisms such as firewalls which is a novel contribution. The attack paths learned by the RL agent can in turn help the blue team identify high-priority vulnerabilities and develop improved defense strategies. The method is evaluated on a large network with more than a thousand hosts and the results demonstrate that the agent can effectively learn attack paths while avoiding firewalls.
Exploring Compressed Image Representation as a Perceptual Proxy: A Study
Venue: arXiv cs.CV 2024
Year: 2024

We propose an end-to-end learned image compression codec wherein the analysis transform is jointly trained with an object classification task. This study affirms that the compressed latent representation can predict human perceptual distance judgments with an accuracy comparable to a custom-tailored DNN-based quality metric. We further investigate various neural encoders and demonstrate the effectiveness of employing the analysis transform as a perceptual loss network for image tasks beyond quality judgments. Our experiments show that the off-the-shelf neural encoder proves proficient in perceptual modeling without needing an additional VGG network. We expect this research to serve as a valuable reference developing of a semantic-aware and coding-efficient neural encoder.
Understanding Emotional Disclosure via Diary-keeping in Quarantine on Social Media
Venue: arXiv cs.HC 2024
Year: 2024

Quarantine is a widely-adopted measure during health crises caused by highly-contagious diseases like COVID-19, yet it poses critical challenges to public mental health. Given this context, emotional disclosure on social media in the form of keeping a diary emerges as a popular way for individuals to express emotions and record their mental health status. However, the exploration of emotional disclosure via diary-keeping on social media during quarantine is underexplored, understanding which could be beneficial to facilitate emotional connections and enlighten health intervention measures. Focusing on this particular form of self-disclosure, this work proposes a quantitative approach to figure out the prevalence and changing patterns of emotional disclosure during quarantine, and the possible factors contributing to the negative emotions. We collected 58, 796 posts with the "Quarantine Diary" keyword on Weibo, a popular social media website in China. Through text classification, we capture diverse emotion categories that characterize public emotion disclosure during quarantine, such as annoyed, anxious, boring, happy, hopeful and appreciative. Based on temporal analysis, we uncover the changing patterns of emotional disclosure from long-term perspectives and period-based perspectives (e.g., the gradual decline of all negative emotions and the upsurge of the annoyed emotion near the end of quarantine). Leveraging topic modeling, we also encapsulate the possible influencing factors of negative emotions, such as freedom restriction and solitude, and uncertainty of infection and supply. We reflect on how our findings could deepen the understanding of mental health on social media and further provide practical and design implications to mitigate mental health issues during quarantine.
Trust from Ethical Point of View: Exploring Dynamics Through Multiagent-Driven Cognitive Modeling
Venue: arXiv cs.SI 2024
Year: 2024

The paper begins by exploring the rationality of ethical trust as a foundational concept. This involves distinguishing between trust and trustworthiness and delving into scenarios where trust is both rational and moral. It lays the groundwork for understanding the complexities of trust dynamics in decision-making scenarios. Following this theoretical groundwork, we introduce an agent-based simulation framework that investigates these dynamics of ethical trust, specifically in the context of a disaster response scenario. These agents, utilizing emotional models like Plutchik's Wheel of Emotions and memory learning mechanisms, are tasked with allocating limited resources in disaster-affected areas. The model, which embodies the principles discussed in the first section, integrates cognitive load management, Big Five personality traits, and structured interactions within networked or hierarchical settings. It also includes feedback loops and simulates external events to evaluate their impact on the formation and evolution of trust among agents. Through our simulations, we demonstrate the intricate interplay of cognitive, emotional, and social factors in ethical decision-making. These insights shed light on the behaviors and resilience of trust networks in crisis situations, emphasizing the role of rational and moral considerations in the development of trust among autonomous agents. This study contributes to the field by offering an understanding of trust dynamics in socio-technical systems and by providing a robust, adaptable framework capable of addressing ethical dilemmas in disaster response and beyond. The implementation of the algorithms presented in this paper is available at this GitHub repository: \url{https://github.com/abbas-tari/ethical-trust-cognitive-modeling}.
SpineCLUE: Automatic Vertebrae Identification Using Contrastive Learning and Uncertainty Estimation
Venue: arXiv cs.CV 2024
Year: 2024

Vertebrae identification in arbitrary fields-of-view plays a crucial role in diagnosing spine disease. Most spine CT contain only local regions, such as the neck, chest, and abdomen. Therefore, identification should not depend on specific vertebrae or a particular number of vertebrae being visible. Existing methods at the spine-level are unable to meet this challenge. In this paper, we propose a three-stage method to address the challenges in 3D CT vertebrae identification at vertebrae-level. By sequentially performing the tasks of vertebrae localization, segmentation, and identification, the anatomical prior information of the vertebrae is effectively utilized throughout the process. Specifically, we introduce a dual-factor density clustering algorithm to acquire localization information for individual vertebra, thereby facilitating subsequent segmentation and identification processes. In addition, to tackle the issue of interclass similarity and intra-class variability, we pre-train our identification network by using a supervised contrastive learning method. To further optimize the identification results, we estimated the uncertainty of the classification network and utilized the message fusion module to combine the uncertainty scores, while aggregating global information about the spine. Our method achieves state-of-the-art results on the VerSe19 and VerSe20 challenge benchmarks. Additionally, our approach demonstrates outstanding generalization performance on an collected dataset containing a wide range of abnormal cases.
ELLA-V: Stable Neural Codec Language Modeling with Alignment-guided Sequence Reordering
Venue: arXiv cs.CL 2024
Year: 2024

The language model (LM) approach based on acoustic and linguistic prompts, such as VALL-E, has achieved remarkable progress in the field of zero-shot audio generation. However, existing methods still have some limitations: 1) repetitions, transpositions, and omissions in the output synthesized speech due to limited alignment constraints between audio and phoneme tokens; 2) challenges of fine-grained control over the synthesized speech with autoregressive (AR) language model; 3) infinite silence generation due to the nature of AR-based decoding, especially under the greedy strategy. To alleviate these issues, we propose ELLA-V, a simple but efficient LM-based zero-shot text-to-speech (TTS) framework, which enables fine-grained control over synthesized audio at the phoneme level. The key to ELLA-V is interleaving sequences of acoustic and phoneme tokens, where phoneme tokens appear ahead of the corresponding acoustic tokens. The experimental findings reveal that our model outperforms VALL-E in terms of accuracy and delivers more stable results using both greedy and sampling-based decoding strategies. The code of ELLA-V will be open-sourced after cleanups. Audio samples are available at https://ereboas.github.io/ELLAV/.