OpenRaiser/Intern-Atlas huggingface dataset:
Encryption-Aware Anomaly Detection in Power Grid Communication Networks
Venue: arXiv cs.CR 2024
Year: 2024

The shift to smart grids has made electrical power systems more vulnerable to sophisticated cyber threats. To protect these systems, holistic security measures that encompass preventive, detective, and reactive components are required, even with encrypted data. However, traditional intrusion detection methods struggle with encrypted traffic, our research focuses on the low-level communication layers of encrypted power grid systems to identify irregular patterns using statistics and machine learning. Our results indicate that a harmonic security concept based on encrypted traffic and anomaly detection is promising for smart grid security; however, further research is necessary to improve detection accuracy.
Manifolds of positive reach, differentiability, tangent variation, and attaining the reach
Venue: arXiv cs.CG 2024
Year: 2024

This paper contains three main results. Firstly, we give an elementary proof of the following statement: Let $M$ be a (closed, in both the geometrical and topological sense of the word) topological manifold embedded in $\mathbb{R}^d$. If $M$ has positive reach, then M can locally be written as the graph of a $C^{1,1}$ from the tangent space to the normal space. Conversely if $M$ can locally written as the graph of a $C^{1,1}$ function from the tangent space to the normal space, then $M$ has positive reach. The result was hinted at by Federer when he introduced the reach, and proved by Lytchak. Lytchak's proof relies heavily CAT(k)-theory. The proof presented here uses only basic results on homology. Secondly, we give optimal Lipschitz-constants for the derivative, in other words we give an optimal bound for the angle between tangent spaces in term of the distance between the points. This improves earlier results, that were either suboptimal or assumed that the manifold was $C^2$. Thirdly, we generalize a result by Aamari et al. which explains the how the reach is attained from the smooth setting to general sets of positive reach.
Hahn series and Mahler equations: Algorithmic aspects
Venue: arXiv cs.SC 2024
Year: 2024

Many articles have recently been devoted to Mahler equations, partly because of their links with other branches of mathematics such as automata theory. Hahn series (a generalization of the Puiseux series allowing arbitrary exponents of the indeterminate as long as the set that supports them is well-ordered) play a central role in the theory of Mahler equations. In this paper, we address the following fundamental question: is there an algorithm to calculate the Hahn series solutions of a given linear Mahler equation? What makes this question interesting is the fact that the Hahn series appearing in this context can have complicated supports with infinitely many accumulation points. Our (positive) answer to the above question involves among other things the construction of a computable well-ordered receptacle for the supports of the potential Hahn series solutions.
HOLa: HoloLens Object Labeling
Venue: arXiv cs.CV 2024
Year: 2024

In the context of medical Augmented Reality (AR) applications, object tracking is a key challenge and requires a significant amount of annotation masks. As segmentation foundation models like the Segment Anything Model (SAM) begin to emerge, zero-shot segmentation requires only minimal human participation obtaining high-quality object masks. We introduce a HoloLens-Object-Labeling (HOLa) Unity and Python application based on the SAM-Track algorithm that offers fully automatic single object annotation for HoloLens 2 while requiring minimal human participation. HOLa does not have to be adjusted to a specific image appearance and could thus alleviate AR research in any application field. We evaluate HOLa for different degrees of image complexity in open liver surgery and in medical phantom experiments. Using HOLa for image annotation can increase the labeling speed by more than 500 times while providing Dice scores between 0.875 and 0.982, which are comparable to human annotators. Our code is publicly available at: https://github.com/mschwimmbeck/HOLa
Flash Communication: Reducing Tensor Parallelization Bottleneck for Fast Large Language Model Inference
Venue: arXiv cs.AI 2024
Year: 2024

The ever-increasing sizes of large language models necessitate distributed solutions for fast inference that exploit multi-dimensional parallelism, where computational loads are split across various accelerators such as GPU clusters. However, this approach often introduces significant communication overhead, especially on devices with limited bandwidth. In this paper, we introduce Flash Communication, a novel low-bit compression technique designed to alleviate the tensor-parallelism communication bottleneck during inference. Our method substantially boosts intra-node communication speed by more than 3x and reduces the time-to-first-token by 2x, with nearly no sacrifice in model accuracy. Extensive experiments on various up-to-date LLMs demonstrate the effectiveness of our approach.
Succinct Data Structures for Segments
Venue: arXiv cs.DS 2024
Year: 2024

We consider succinct data structures for representing a set of $n$ horizontal line segments in the plane given in rank space to support \emph{segment access}, \emph{segment selection}, and \emph{segment rank} queries. A segment access query finds the segment $(x_1, x_2, y)$ given its $y$-coordinate ($y$-coordinates of the segments are distinct), a segment selection query finds the $j$th smallest segment (the segment with the $j$th smallest $y$-coordinate) among the segments crossing the vertical line for a given $x$-coordinate, and a segment rank query finds the number of segments crossing the vertical line through $x$-coordinate $i$ with $y$-coordinate at most $y$, for a given $x$ and $y$. This problem is a central component in compressed data structures for persistent strings supporting random access. Our main result is data structure using $2n\lg{n} + O(n\lg{n}/\lg{\lg{n}})$ bits of space and $O(\lg{n}/\lg{\lg{n}})$ query time for all operations. We show that this space bound is optimal up to lower-order terms. We will also show that the query time for segment rank is optimal. The query time for segment selection is also optimal by a previous bound. To obtain our results, we present a novel segment wavelet tree data structure of independent interest. This structure is inspired by and extends the classic wavelet tree for sequences. This leads to a simple, succinct solution with $O(\log n)$ query times. We then extend this solution to obtain optimal query time. Our space lower bound follows from a simple counting argument, and our lower bound for segment rank is obtained by a reduction from 2-dimensional counting.
'Debunk-It-Yourself': Health Professionals' Strategies for Responding to Misinformation on TikTok
Venue: arXiv cs.CR 2024
Year: 2024

Misinformation is "sticky" in nature, requiring a considerable effort to undo its influence. One such effort is debunking or exposing the falsity of information. As an abundance of misinformation is on social media, platforms do bear some debunking responsibility in order to preserve their trustworthiness as information providers. A subject of interpretation, platforms poorly meet this responsibility and allow dangerous health misinformation to influence many of their users. This open route to harm did not sit well with health professional users, who recently decided to take the debunking into their own hands. To study this individual debunking effort - which we call 'Debunk-It-Yourself (DIY)' - we conducted an exploratory survey n=14 health professionals who wage a misinformation counter-influence campaign through videos on TikTok. We focused on two topics, nutrition and mental health, which are the ones most often subjected to misinformation on the platform. Our thematic analysis reveals that the counterinfluence follows a common process of initiation, selection, creation, and "stitching" or duetting a debunking video with a misinformation video. The 'Debunk-It-Yourself' effort was underpinned by three unique aspects: (i) it targets trending misinformation claims perceived to be of direct harm to people's health; (ii) it offers a symmetric response to the misinformation; and (iii) it is strictly based on scientific evidence and claimed clinical experience. Contrasting the 'Debunk-It-Yourself' effort with the one TikTok and other platforms (reluctantly) put in moderation, we offer recommendations for a structured response against the misinformation's influence by the users themselves.
Improving Post-Earthquake Crack Detection using Semi-Synthetic Generated Images
Venue: arXiv cs.CV 2024
Year: 2024

Following an earthquake, it is vital to quickly evaluate the safety of the impacted areas. Damage detection systems, powered by computer vision and deep learning, can assist experts in this endeavor. However, the lack of extensive, labeled datasets poses a challenge to the development of these systems. In this study, we introduce a technique for generating semi-synthetic images to be used as data augmentation during the training of a damage detection system. We specifically aim to generate images of cracks, which are a prevalent and indicative form of damage. The central concept is to employ parametric meta-annotations to guide the process of generating cracks on 3D models of real-word structures. The governing parameters of these meta-annotations can be adjusted iteratively to yield images that are optimally suited for improving detectors' performance. Comparative evaluations demonstrated that a crack detection system trained with a combination of real and semi-synthetic images outperforms a system trained on real images alone.
OCEAN: Open-World Contrastive Authorship Identification
Venue: arXiv cs.AI 2024
Year: 2024

In an era where cyberattacks increasingly target the software supply chain, the ability to accurately attribute code authorship in binary files is critical to improving cybersecurity measures. We propose OCEAN, a contrastive learning-based system for function-level authorship attribution. OCEAN is the first framework to explore code authorship attribution on compiled binaries in an open-world and extreme scenario, where two code samples from unknown authors are compared to determine if they are developed by the same author. To evaluate OCEAN, we introduce new realistic datasets: CONAN, to improve the performance of authorship attribution systems in real-world use cases, and SNOOPY, to increase the robustness of the evaluation of such systems. We use CONAN to train our model and evaluate on SNOOPY, a fully unseen dataset, resulting in an AUROC score of 0.86 even when using high compiler optimizations. We further show that CONAN improves performance by 7% compared to the previously used Google Code Jam dataset. Additionally, OCEAN outperforms previous methods in their settings, achieving a 10% improvement over state-of-the-art SCS-Gan in scenarios analyzing source code. Furthermore, OCEAN can detect code injections from an unknown author in a software update, underscoring its value for securing software supply chains.
Spatial Bandwidth of Bilateral Near-Field Channels for Linear Large-Scale Antenna Array System
Venue: arXiv cs.IT 2024
Year: 2024

This paper analyzes the spatial bandwidth of line-of-sight (LoS) channels in massive MIMO systems. For the linear large-scale antenna arrays (LSAA) of transceivers placed in random locations in 3D space, a simple but accurate closed-form expression is derived to characterize the spatial bandwidth. Subsequent analysis of the LSAA's spatial bandwidth properties is also provided, leading to the formulation of an approximate expression for the effective degrees of freedom (EDoF) of bilateral near-field channels. Interestingly, as proved in this work, when the transmit and receive arrays are coplanar, with the receive array positioned perpendicular to the axis joining the centroids of the transmit and receive arrays, the EDoF of the LoS channel is found to be approximately maximized.
Towards the interoperability of low-code platforms
Venue: arXiv cs.SE 2024
Year: 2024

With the promise of accelerating software development, low-code platforms (LCPs) are becoming popular across various industries. Nevertheless, there are still barriers hindering their adoption. Among them, vendor lock-in is a major concern, especially considering the lack of interoperability between these platforms. Typically, after modeling an application in one LCP, migrating to another requires starting from scratch remodeling everything (the data model, the graphical user interface, workflows, etc.), in the new platform. To overcome this situation, this work proposes an approach to improve the interoperability of LCPs by (semi)automatically migrating models specified in one platform to another one. The concrete migration path depends on the capabilities of the source and target tools. We first analyze popular LCPs, characterize their import and export alternatives and define transformations between those data formats when available. This is then complemented with an LLM-based solution, where image recognition features of large language models are employed to migrate models based on a simple image export of the model at hand. The full pipelines are implemented on top of the BESSER modeling framework that acts as a pivot representation between the tools.
An Experimental Framework for Implementing Decentralized Autonomous Database Systems in Rust
Venue: arXiv cs.DB 2024
Year: 2024

This paper presents an experimental framework for implementing Decentralized Autonomous Database Systems (DADBS) using the Rust programming language. As traditional centralized databases face challenges in scalability, security, and autonomy, DADBS emerge as a promising solution, using blockchain principles to create distributed, self-governing database systems. Our framework explores the practical aspects of building a DADBS, focusing on Rust's unique features that improves system reliability and performance. We evaluated our DADBS implementation across several key performance metrics: throughput, latency(read), latency(write), scalability, CPU utilization, Memory Usage and Network I/O, The average results obtained over a 24-hour period of continuous operation were 3,000 transactions/second, 75 ms, 250 ms, 55%, 2.5 GB, 100MB/s. The security analysis depicts that even with an increase in the percentage of malicious nodes, DADBS still maintains high throughput and consistency. The paper discusses key design decisions, highlighting how Rust's ownership model and concurrency features address common challenges in distributed systems. We also examine the current limitations of our approach and potential areas for future research. By providing this comprehensive overview of a Rust-based DADBS implementation, we aim to contribute to the growing body of knowledge on decentralized database architectures and their practical realization.
Designing a Secure, Scalable, and Cost-Effective Cloud Storage Solution: A Novel Approach to Data Management using NextCloud, TrueNAS, and QEMU/KVM
Venue: arXiv cs.DB 2024
Year: 2024

This paper presents a novel approach to cloud storage challenges by integrating NextCloud, TrueNAS, and QEMU/KVM. Our research demonstrates how this combination creates a robust, flexible, and economical cloud storage system suitable for various applications. We detail the architecture, highlighting TrueNAS's ZFS-based storage, QEMU/KVM's virtualization, and NextCloud's user interface. Extensive testing showssuperior data integrity and protection compared to traditional solutions. Performance benchmarks reveal high read/write speeds(up to 1.22 GB/s for sequential reads and 620 MB/s for writes) and also efficient small file handling. We demonstrate the solution's scalability under increasing workloads. Security analysis showcases effective jail isolation techniques in TrueNAS. Cost analysis indicates potential 50% reduction in total ownership cost over five years compared to commercial alternatives. This research contributes a practical, high-performance, cost-effective alternative to proprietary solutions, paving new ways for organizations to implement secure, scalable cloud storage while maintaining data control. Future work will focus on improving automated scaling and integration with emerging technologies like containerization and serverless computing.
DNF: Unconditional 4D Generation with Dictionary-based Neural Fields
Venue: arXiv cs.CV 2024
Year: 2024

While remarkable success has been achieved through diffusion-based 3D generative models for shapes, 4D generative modeling remains challenging due to the complexity of object deformations over time. We propose DNF, a new 4D representation for unconditional generative modeling that efficiently models deformable shapes with disentangled shape and motion while capturing high-fidelity details in the deforming objects. To achieve this, we propose a dictionary learning approach to disentangle 4D motion from shape as neural fields. Both shape and motion are represented as learned latent spaces, where each deformable shape is represented by its shape and motion global latent codes, shape-specific coefficient vectors, and shared dictionary information. This captures both shape-specific detail and global shared information in the learned dictionary. Our dictionary-based representation well balances fidelity, contiguity and compression -- combined with a transformer-based diffusion model, our method is able to generate effective, high-fidelity 4D animations.
AI's assigned gender affects human-AI cooperation
Venue: arXiv cs.CY 2024
Year: 2024

Cooperation between humans and machines is increasingly vital as artificial intelligence (AI) becomes more integrated into daily life. Research indicates that people are often less willing to cooperate with AI agents than with humans, more readily exploiting AI for personal gain. While prior studies have shown that giving AI agents human-like features influences people's cooperation with them, the impact of AI's assigned gender remains underexplored. This study investigates how human cooperation varies based on gender labels assigned to AI agents with which they interact. In the Prisoner's Dilemma game, 402 participants interacted with partners labelled as AI (bot) or humans. The partners were also labelled male, female, non-binary, or gender-neutral. Results revealed that participants tended to exploit female-labelled and distrust male-labelled AI agents more than their human counterparts, reflecting gender biases similar to those in human-human interactions. These findings highlight the significance of gender biases in human-AI interactions that must be considered in future policy, design of interactive AI systems, and regulation of their use.
TeamCraft: A Benchmark for Multi-Modal Multi-Agent Systems in Minecraft
Venue: arXiv cs.AI 2024
Year: 2024

Collaboration is a cornerstone of society. In the real world, human teammates make use of multi-sensory data to tackle challenging tasks in ever-changing environments. It is essential for embodied agents collaborating in visually-rich environments replete with dynamic interactions to understand multi-modal observations and task specifications. To evaluate the performance of generalizable multi-modal collaborative agents, we present TeamCraft, a multi-modal multi-agent benchmark built on top of the open-world video game Minecraft. The benchmark features 55,000 task variants specified by multi-modal prompts, procedurally-generated expert demonstrations for imitation learning, and carefully designed protocols to evaluate model generalization capabilities. We also perform extensive analyses to better understand the limitations and strengths of existing approaches. Our results indicate that existing models continue to face significant challenges in generalizing to novel goals, scenes, and unseen numbers of agents. These findings underscore the need for further research in this area. The TeamCraft platform and dataset are publicly available at https://github.com/teamcraft-bench/teamcraft.
FodFoM: Fake Outlier Data by Foundation Models Creates Stronger Visual Out-of-Distribution Detector
Venue: arXiv cs.CV 2024
Year: 2024

Out-of-Distribution (OOD) detection is crucial when deploying machine learning models in open-world applications. The core challenge in OOD detection is mitigating the model's overconfidence on OOD data. While recent methods using auxiliary outlier datasets or synthesizing outlier features have shown promising OOD detection performance, they are limited due to costly data collection or simplified assumptions. In this paper, we propose a novel OOD detection framework FodFoM that innovatively combines multiple foundation models to generate two types of challenging fake outlier images for classifier training. The first type is based on BLIP-2's image captioning capability, CLIP's vision-language knowledge, and Stable Diffusion's image generation ability. Jointly utilizing these foundation models constructs fake outlier images which are semantically similar to but different from in-distribution (ID) images. For the second type, GroundingDINO's object detection ability is utilized to help construct pure background images by blurring foreground ID objects in ID images. The proposed framework can be flexibly combined with multiple existing OOD detection methods. Extensive empirical evaluations show that image classifiers with the help of constructed fake images can more accurately differentiate real OOD images from ID ones. New state-of-the-art OOD detection performance is achieved on multiple benchmarks. The code is available at \url{https://github.com/Cverchen/ACMMM2024-FodFoM}.
Specifications: The missing link to making the development of LLM systems an engineering discipline
Venue: arXiv cs.SE 2024
Year: 2024

Despite the significant strides made by generative AI in just a few short years, its future progress is constrained by the challenge of building modular and robust systems. This capability has been a cornerstone of past technological revolutions, which relied on combining components to create increasingly sophisticated and reliable systems. Cars, airplanes, computers, and software consist of components-such as engines, wheels, CPUs, and libraries-that can be assembled, debugged, and replaced. A key tool for building such reliable and modular systems is specification: the precise description of the expected behavior, inputs, and outputs of each component. However, the generality of LLMs and the inherent ambiguity of natural language make defining specifications for LLM-based components (e.g., agents) both a challenging and urgent problem. In this paper, we discuss the progress the field has made so far-through advances like structured outputs, process supervision, and test-time compute-and outline several future directions for research to enable the development of modular and reliable LLM-based systems through improved specifications.
AD-HOC: A C++ Expression Template package for high-order derivatives backpropagation
Venue: arXiv cs.MS 2024
Year: 2024

This document presents a new C++ Automatic Differentiation (AD) tool, AD-HOC (Automatic Differentiation for High-Order Calculations). This tool aims to have the following features: -Calculation of user specified derivatives of arbitrary order -To be able to run with similar speeds as handwritten code -All derivatives calculations are computed in a single backpropagation tree pass -No source code generation is used, relying heavily on the C++ compiler to statically build the computation tree before runtime -A simple interface -The ability to be used \textit{in conjunction} with other established, general-purpose dynamic AD tools -Header-only library, with no external dependencies -Open source, with a business-friendly license