OpenRaiser/Intern-Atlas huggingface dataset:
Learning in Multiple Spaces: Few-Shot Network Attack Detection with Metric-Fused Prototypical Networks
Venue: arXiv cs.CR 2024
Year: 2024

Network intrusion detection systems face significant challenges in identifying emerging attack patterns, especially when limited data samples are available. To address this, we propose a novel Multi-Space Prototypical Learning (MSPL) framework tailored for few-shot attack detection. The framework operates across multiple metric spaces-Euclidean, Cosine, Chebyshev, and Wasserstein distances-integrated through a constrained weighting scheme to enhance embedding robustness and improve pattern recognition. By leveraging Polyak-averaged prototype generation, the framework stabilizes the learning process and effectively adapts to rare and zero-day attacks. Additionally, an episodic training paradigm ensures balanced representation across diverse attack classes, enabling robust generalization. Experimental results on benchmark datasets demonstrate that MSPL outperforms traditional approaches in detecting low-profile and novel attack types, establishing it as a robust solution for zero-day attack detection.
LLM-Virus: Evolutionary Jailbreak Attack on Large Language Models
Venue: arXiv cs.CR 2024
Year: 2024

While safety-aligned large language models (LLMs) are increasingly used as the cornerstone for powerful systems such as multi-agent frameworks to solve complex real-world problems, they still suffer from potential adversarial queries, such as jailbreak attacks, which attempt to induce harmful content. Researching attack methods allows us to better understand the limitations of LLM and make trade-offs between helpfulness and safety. However, existing jailbreak attacks are primarily based on opaque optimization techniques (e.g. token-level gradient descent) and heuristic search methods like LLM refinement, which fall short in terms of transparency, transferability, and computational cost. In light of these limitations, we draw inspiration from the evolution and infection processes of biological viruses and propose LLM-Virus, a jailbreak attack method based on evolutionary algorithm, termed evolutionary jailbreak. LLM-Virus treats jailbreak attacks as both an evolutionary and transfer learning problem, utilizing LLMs as heuristic evolutionary operators to ensure high attack efficiency, transferability, and low time cost. Our experimental results on multiple safety benchmarks show that LLM-Virus achieves competitive or even superior performance compared to existing attack methods.
Lungmix: A Mixup-Based Strategy for Generalization in Respiratory Sound Classification
Venue: arXiv cs.SD 2024
Year: 2024

Respiratory sound classification plays a pivotal role in diagnosing respiratory diseases. While deep learning models have shown success with various respiratory sound datasets, our experiments indicate that models trained on one dataset often fail to generalize effectively to others, mainly due to data collection and annotation \emph{inconsistencies}. To address this limitation, we introduce \emph{Lungmix}, a novel data augmentation technique inspired by Mixup. Lungmix generates augmented data by blending waveforms using loudness and random masks while interpolating labels based on their semantic meaning, helping the model learn more generalized representations. Comprehensive evaluations across three datasets, namely ICBHI, SPR, and HF, demonstrate that Lungmix significantly enhances model generalization to unseen data. In particular, Lungmix boosts the 4-class classification score by up to 3.55\%, achieving performance comparable to models trained directly on the target dataset.
Predicting Preschoolers' Externalizing Problems with Mother-Child Interaction Dynamics and Deep Learning
Venue: arXiv cs.LG 2024
Year: 2024

Objective: Predicting children's future levels of externalizing problems helps to identify children at risk and guide targeted prevention. Existing studies have shown that mothers providing support in response to children's dysregulation was associated with children's lower levels of externalizing problems. The current study aims to evaluate and improve the accuracy of predicting children's externalizing problems with mother-child interaction dynamics. Method: This study used mother-child interaction dynamics during a challenging puzzle task to predict children's externalizing problems six months later (N=101, 46 boys, Mage=57.41 months, SD=6.58). Performance of the Residual Dynamic Structural Equation Model (RDSEM) was compared with the Attention-based Sequential Behavior Interaction Modeling (ASBIM) model, developed using the deep learning techniques. Results: The RDSEM revealed that children whose mothers provided more autonomy support after increases of child defeat had lower levels of externalizing problems. Five-fold cross-validation showed that the RDSEM had good prediction accuracy. The ASBIM model further improved prediction accuracy, especially after including child inhibitory control as a personalized individual feature. Conclusions: The dynamic process of mother-child interaction provides important information for predicting children's externalizing problems, especially maternal autonomy supportive response to child defeat. The deep learning model is a useful tool to further improve prediction accuracy.
Adversarial Negotiation Dynamics in Generative Language Models
Venue: arXiv cs.CL 2024
Year: 2024

Generative language models are increasingly used for contract drafting and enhancement, creating a scenario where competing parties deploy different language models against each other. This introduces not only a game-theory challenge but also significant concerns related to AI safety and security, as the language model employed by the opposing party can be unknown. These competitive interactions can be seen as adversarial testing grounds, where models are effectively red-teamed to expose vulnerabilities such as generating biased, harmful or legally problematic text. Despite the importance of these challenges, the competitive robustness and safety of these models in adversarial settings remain poorly understood. In this small study, we approach this problem by evaluating the performance and vulnerabilities of major open-source language models in head-to-head competitions, simulating real-world contract negotiations. We further explore how these adversarial interactions can reveal potential risks, informing the development of more secure and reliable models. Our findings contribute to the growing body of research on AI safety, offering insights into model selection and optimisation in competitive legal contexts and providing actionable strategies for mitigating risks.
Dynamic Unit-Disk Range Reporting
Venue: arXiv cs.CG 2024
Year: 2024

For a set $P$ of $n$ points in the plane and a value $r > 0$, the unit-disk range reporting problem is to construct a data structure so that given any query disk of radius $r$, all points of $P$ in the disk can be reported efficiently. We consider the dynamic version of the problem where point insertions and deletions of $P$ are allowed. The previous best method provides a data structure of $O(n\log n)$ space that supports $O(\log^{3+ε}n)$ amortized insertion time, $O(\log^{5+ε}n)$ amortized deletion time, and $O(\log^2 n/\log\log n+k)$ query time, where $ε$ is an arbitrarily small positive constant and $k$ is the output size. In this paper, we improve the query time to $O(\log n+k)$ while keeping other complexities the same as before. A key ingredient of our approach is a shallow cutting algorithm for circular arcs, which may be interesting in its own right. A related problem that can also be solved by our techniques is the dynamic unit-disk range emptiness queries: Given a query unit disk, we wish to determine whether the disk contains a point of $P$. The best previous work can maintain $P$ in a data structure of $O(n)$ space that supports $O(\log^2 n)$ amortized insertion time, $O(\log^4n)$ amortized deletion time, and $O(\log^2 n)$ query time. Our new data structure also uses $O(n)$ space but can support each update in $O(\log^{1+ε} n)$ amortized time and support each query in $O(\log n)$ time.
Probabilistic Explanations for Linear Models
Venue: arXiv cs.AI 2024
Year: 2024

Formal XAI is an emerging field that focuses on providing explanations with mathematical guarantees for the decisions made by machine learning models. A significant amount of work in this area is centered on the computation of "sufficient reasons". Given a model $M$ and an input instance $\vec{x}$, a sufficient reason for the decision $M(\vec{x})$ is a subset $S$ of the features of $\vec{x}$ such that for any instance $\vec{z}$ that has the same values as $\vec{x}$ for every feature in $S$, it holds that $M(\vec{x}) = M(\vec{z})$. Intuitively, this means that the features in $S$ are sufficient to fully justify the classification of $\vec{x}$ by $M$. For sufficient reasons to be useful in practice, they should be as small as possible, and a natural way to reduce the size of sufficient reasons is to consider a probabilistic relaxation; the probability of $M(\vec{x}) = M(\vec{z})$ must be at least some value $δ\in (0,1]$, for a random instance $\vec{z}$ that coincides with $\vec{x}$ on the features in $S$. Computing small $δ$-sufficient reasons ($δ$-SRs) is known to be a theoretically hard problem; even over decision trees--traditionally deemed simple and interpretable models--strong inapproximability results make the efficient computation of small $δ$-SRs unlikely. We propose the notion of $(δ, ε)$-SR, a simple relaxation of $δ$-SRs, and show that this kind of explanation can be computed efficiently over linear models.
Dynamic Graph Communication for Decentralised Multi-Agent Reinforcement Learning
Venue: arXiv cs.MA 2024
Year: 2024

This work presents a novel communication framework for decentralized multi-agent systems operating in dynamic network environments. Integrated into a multi-agent reinforcement learning system, the framework is designed to enhance decision-making by optimizing the network's collective knowledge through efficient communication. Key contributions include adapting a static network packet-routing scenario to a dynamic setting with node failures, incorporating a graph attention network layer in a recurrent message-passing framework, and introducing a multi-round communication targeting mechanism. This approach enables an attention-based aggregation mechanism to be successfully trained within a sparse-reward, dynamic network packet-routing environment using only reinforcement learning. Experimental results show improvements in routing performance, including a 9.5 percent increase in average rewards and a 6.4 percent reduction in communication overhead compared to a baseline system. The study also examines the ethical and legal implications of deploying such systems in critical infrastructure and military contexts, identifies current limitations, and suggests potential directions for future research.
Interactive cybersecurity training system based on simulation environments
Venue: arXiv cs.CR 2024
Year: 2024

Rapid progress in the development of information technology has led to a significant increase in the number and complexity of cyber threats. Traditional methods of cybersecurity training based on theoretical knowledge do not provide a sufficient level of practical skills to effectively counter real threats. The article explores the possibilities of integrating simulation environments into the cybersecurity training process as an effective approach to improving the quality of training. The article presents the architecture of a simulation environment based on a cluster of KVM hypervisors, which allows creating scalable and flexible platforms at minimal cost. The article describes the implementation of various scenarios using open source software tools such as pfSense, OPNsense, Security Onion, Kali Linux, Parrot Security OS, Ubuntu Linux, Oracle Linux, FreeBSD, and others, which create realistic conditions for practical training.
A Pseudo-random Number Generator for Multi-Sequence Generation with Programmable Statistics
Venue: arXiv cs.CR 2024
Year: 2024

Pseudo-random number generators (PRNGs) are essential in a wide range of applications, from cryptography to statistical simulations and optimization algorithms. While uniform randomness is crucial for security-critical areas like cryptography, many domains, such as simulated annealing and CMOS-based Ising Machines, benefit from controlled or non-uniform randomness to enhance solution exploration and optimize performance. This paper presents a hardware PRNG that can simultaneously generate multiple uncorrelated sequences with programmable statistics tailored to specific application needs. Designed in 65nm process, the PRNG occupies an area of approximately 0.0013mm^2 and has an energy consumption of 0.57pJ/bit. Simulations confirm the PRNG's effectiveness in modulating the statistical distribution while demonstrating high-quality randomness properties.
Dominating Set, Independent Set, Discrete $k$-Center, Dispersion, and Related Problems for Planar Points in Convex Position
Venue: arXiv cs.CG 2024
Year: 2024

Given a set $P$ of $n$ points in the plane, its unit-disk graph $G(P)$ is a graph with $P$ as its vertex set such that two points of $P$ are connected by an edge if their (Euclidean) distance is at most $1$. We consider several classical problems on $G(P)$ in a special setting when points of $P$ are in convex position. These problems are all NP-hard in the general case. We present efficient algorithms for these problems under the convex position assumption. The considered problems include the following: finding a minimum weight dominating set in $G(P)$, the discrete $k$-center problem for $P$, finding a maximum weight independent set in $G(P)$, the dispersion problem for $P$, and several of their variations. For some of these problems, our algorithms improve the previously best results, while for others, our results provide first-known solutions.
An Empirical Evaluation of Large Language Models on Consumer Health Questions
Venue: arXiv cs.CL 2024
Year: 2024

This study evaluates the performance of several Large Language Models (LLMs) on MedRedQA, a dataset of consumer-based medical questions and answers by verified experts extracted from the AskDocs subreddit. While LLMs have shown proficiency in clinical question answering (QA) benchmarks, their effectiveness on real-world, consumer-based, medical questions remains less understood. MedRedQA presents unique challenges, such as informal language and the need for precise responses suited to non-specialist queries. To assess model performance, responses were generated using five LLMs: GPT-4o mini, Llama 3.1: 70B, Mistral-123B, Mistral-7B, and Gemini-Flash. A cross-evaluation method was used, where each model evaluated its responses as well as those of others to minimize bias. The results indicated that GPT-4o mini achieved the highest alignment with expert responses according to four out of the five models' judges, while Mistral-7B scored lowest according to three out of five models' judges. This study highlights the potential and limitations of current LLMs for consumer health medical question answering, indicating avenues for further development.
M2I2: Learning Efficient Multi-Agent Communication via Masked State Modeling and Intention Inference
Venue: arXiv cs.MA 2024
Year: 2024

Communication is essential in coordinating the behaviors of multiple agents. However, existing methods primarily emphasize content, timing, and partners for information sharing, often neglecting the critical aspect of integrating shared information. This gap can significantly impact agents' ability to understand and respond to complex, uncertain interactions, thus affecting overall communication efficiency. To address this issue, we introduce M2I2, a novel framework designed to enhance the agents' capabilities to assimilate and utilize received information effectively. M2I2 equips agents with advanced capabilities for masked state modeling and joint-action prediction, enriching their perception of environmental uncertainties and facilitating the anticipation of teammates' intentions. This approach ensures that agents are furnished with both comprehensive and relevant information, bolstering more informed and synergistic behaviors. Moreover, we propose a Dimensional Rational Network, innovatively trained via a meta-learning paradigm, to identify the importance of dimensional pieces of information, evaluating their contributions to decision-making and auxiliary tasks. Then, we implement an importance-based heuristic for selective information masking and sharing. This strategy optimizes the efficiency of masked state modeling and the rationale behind information sharing. We evaluate M2I2 across diverse multi-agent tasks, the results demonstrate its superior performance, efficiency, and generalization capabilities, over existing state-of-the-art methods in various complex scenarios.
VoxVietnam: a Large-Scale Multi-Genre Dataset for Vietnamese Speaker Recognition
Venue: arXiv cs.SD 2024
Year: 2024

Recent research in speaker recognition aims to address vulnerabilities due to variations between enrolment and test utterances, particularly in the multi-genre phenomenon where the utterances are in different speech genres. Previous resources for Vietnamese speaker recognition are either limited in size or do not focus on genre diversity, leaving studies in multi-genre effects unexplored. This paper introduces VoxVietnam, the first multi-genre dataset for Vietnamese speaker recognition with over 187,000 utterances from 1,406 speakers and an automated pipeline to construct a dataset on a large scale from public sources. Our experiments show the challenges posed by the multi-genre phenomenon to models trained on a single-genre dataset, and demonstrate a significant increase in performance upon incorporating the VoxVietnam into the training process. Our experiments are conducted to study the challenges of the multi-genre phenomenon in speaker recognition and the performance gain when the proposed dataset is used for multi-genre training.
"I Recall the Past": Exploring How People Collaborate with Generative AI to Create Cultural Heritage Narratives
Venue: arXiv cs.HC 2024
Year: 2024

Visitors to cultural heritage sites often encounter official information, while local people's unofficial stories remain invisible. To explore expression of local narratives, we conducted a workshop with 20 participants utilizing Generative AI (GenAI) to support visual narratives, asking them to use Stable Diffusion to create images of familiar cultural heritage sites, as well as images of unfamiliar ones for comparison. The results revealed three narrative strategies and highlighted GenAI's strengths in illuminating, amplifying, and reinterpreting personal narratives. However, GenAI showed limitations in meeting detailed requirements, portraying cultural features, and avoiding bias, which were particularly pronounced with unfamiliar sites due to participants' lack of local knowledge. To address these challenges, we recommend providing detailed explanations, prompt engineering, and fine-tuning AI models to reduce uncertainties, using objective references to mitigate inaccuracies from participants' inability to recognize errors or misconceptions, and curating datasets to train AI models capable of accurately portraying cultural features.
Impossibility of Self-Organized Aggregation without Computation
Venue: arXiv cs.RO 2024
Year: 2024

In their seminal work, Gauci et al. (2014) studied the fundamental task of aggregation, wherein multiple robots need to gather without an a priori agreed-upon meeting location, using minimal hardware. That paper considered differential-drive robots that are memoryless and unable to compute. Moreover, the robots cannot communicate with one another and are only equipped with a simple sensor that determines whether another robot is directly in front of them. Despite those severe limitations, Gauci et al. introduced a controller and proved mathematically that it aggregates a system of two robots for any initial state. Unfortunately, for larger systems, the same controller aggregates empirically in many cases but not all. Thus, the question of whether a controller exists that aggregates for any number of robots remains open. In this paper, we show that no such controller exists by investigating the geometric structure of controllers. In addition, we disprove the aggregation proof of the paper above for two robots and present an alternative controller alongside a simple and rigorous aggregation proof.
Efficient Relational Context Perception for Knowledge Graph Completion
Venue: arXiv cs.LG 2024
Year: 2024

Knowledge Graphs (KGs) provide a structured representation of knowledge but often suffer from challenges of incompleteness. To address this, link prediction or knowledge graph completion (KGC) aims to infer missing new facts based on existing facts in KGs. Previous knowledge graph embedding models are limited in their ability to capture expressive features, especially when compared to deeper, multi-layer models. These approaches also assign a single static embedding to each entity and relation, disregarding the fact that entities and relations can exhibit different behaviors in varying graph contexts. Due to complex context over a fact triple of a KG, existing methods have to leverage complex non-linear context encoder, like transformer, to project entity and relation into low dimensional representations, resulting in high computation cost. To overcome these limitations, we propose Triple Receptance Perception (TRP) architecture to model sequential information, enabling the learning of dynamic context of entities and relations. Then we use tensor decomposition to calculate triple scores, providing robust relational decoding capabilities. This integration allows for more expressive representations. Experiments on benchmark datasets such as YAGO3-10, UMLS, FB15k, and FB13 in link prediction and triple classification tasks demonstrate that our method performs better than several state-of-the-art models, proving the effectiveness of the integration.
Intuitive Analysis of the Quantization-based Optimization: From Stochastic and Quantum Mechanical Perspective
Venue: arXiv cs.LG 2024
Year: 2024

In this paper, we present an intuitive analysis of the optimization technique based on the quantization of an objective function. Quantization of an objective function is an effective optimization methodology that decreases the measure of a level set containing several saddle points and local minima and finds the optimal point at the limit level set. To investigate the dynamics of quantization-based optimization, we derive an overdamped Langevin dynamics model from an intuitive analysis to minimize the level set by iterative quantization. We claim that quantization-based optimization involves the quantities of thermodynamical and quantum mechanical optimization as the core methodologies of global optimization. Furthermore, on the basis of the proposed SDE, we provide thermodynamic and quantum mechanical analysis with Witten-Laplacian. The simulation results with the benchmark functions, which compare the performance of the nonlinear optimization, demonstrate the validity of the quantization-based optimization.