OpenRaiser/Intern-Atlas huggingface dataset:
FAIL: Analyzing Software Failures from the News Using LLMs
Venue: arXiv cs.SE 2024
Year: 2024

Software failures inform engineering work, standards, regulations. For example, the Log4J vulnerability brought government and industry attention to evaluating and securing software supply chains. Accessing private engineering records is difficult, so failure analyses tend to use information reported by the news media. However, prior works in this direction have relied on manual analysis. That has limited the scale of their analyses. The community lacks automated support to enable such analyses to consider a wide range of news sources and incidents. In this paper, we propose the Failure Analysis Investigation with LLMs (FAIL) system to fill this gap. FAIL collects, analyzes, and summarizes software failures as reported in the news. FAIL groups articles that describe the same incidents. It then analyzes incidents using existing taxonomies for postmortems, faults, and system characteristics. To tune and evaluate FAIL, we followed the methods of prior works by manually analyzing 31 software failures. FAIL achieved an F1 score of 90% for collecting news about software failures, a V-measure of 0.98 for merging articles reporting on the same incident, and extracted 90% of the facts about failures. We then applied FAIL to a total of 137,427 news articles from 11 providers published between 2010 and 2022. FAIL identified and analyzed 2457 distinct failures reported across 4,184 articles. Our findings include: (1) current generation of large language models are capable of identifying news articles that describe failures, and analyzing them according to structured taxonomies; (2) high recurrences of similar failures within organizations and across organizations; and (3) severity of the consequences of software failures have increased over the past decade. The full FAIL database is available so that researchers, engineers, and policymakers can learn from a diversity of software failures.
OpenCOLE: Towards Reproducible Automatic Graphic Design Generation
Venue: arXiv cs.CV 2024
Year: 2024

Automatic generation of graphic designs has recently received considerable attention. However, the state-of-the-art approaches are complex and rely on proprietary datasets, which creates reproducibility barriers. In this paper, we propose an open framework for automatic graphic design called OpenCOLE, where we build a modified version of the pioneering COLE and train our model exclusively on publicly available datasets. Based on GPT4V evaluations, our model shows promising performance comparable to the original COLE. We release the pipeline and training results to encourage open development.
PTHelper: An open source tool to support the Penetration Testing process
Venue: arXiv cs.CR 2024
Year: 2024

Offensive security is one of the state of the art measures to protect enterprises and organizations. Penetration testing, broadly called pentesting, is a branch of offensive security designed to find, rate and exploit these vulnerabilities, in order to assess the security posture of an organization. This process is often time-consuming and the quantity of information that pentesters need to manage might also be difficult to handle. This project takes a practical approach to solve the automation of pentesting and proposes a usable tool, called PTHelper. This open-source tool has been designed in a modular way to be easily upgradable by the pentesting community, and uses state of the art tools and artificial intelligence to achieve its objective.
Leveraging Large Language Models for Web Scraping
Venue: arXiv cs.CL 2024
Year: 2024

Large Language Models (LLMs) demonstrate remarkable capabilities in replicating human tasks and boosting productivity. However, their direct application for data extraction presents limitations due to a prioritisation of fluency over factual accuracy and a restricted ability to manipulate specific information. Therefore to overcome these limitations, this research leverages the knowledge representation power of pre-trained LLMs and the targeted information access enabled by RAG models, this research investigates a general-purpose accurate data scraping recipe for RAG models designed for language generation. To capture knowledge in a more modular and interpretable way, we use pre trained language models with a latent knowledge retriever, which allows the model to retrieve and attend over documents from a large corpus. We utilised RAG model architecture and did an in-depth analysis of their capabilities under three tasks: (i) Semantic Classification of HTML elements, (ii) Chunking HTML text for effective understanding, and (iii) comparing results from different LLMs and ranking algorithms. While previous work has developed dedicated architectures and training procedures for HTML understanding and extraction, we show that LLMs pre-trained on standard natural language with an addition of effective chunking, searching and ranking algorithms, can prove to be efficient data scraping tool to extract complex data from unstructured text. Future research directions include addressing the challenges of provenance tracking and dynamic knowledge updates within the proposed RAG-based data extraction framework. By overcoming these limitations, this approach holds the potential to revolutionise data extraction from vast repositories of textual information.
It is Never Too Late to Mend: Separate Learning for Multimedia Recommendation
Venue: arXiv cs.IR 2024
Year: 2024

Multimedia recommendation, which incorporates various modalities (e.g., images, texts, etc.) into user or item representation to improve recommendation quality, and self-supervised learning carries multimedia recommendation to a plateau of performance, because of its superior performance in aligning different modalities. However, more and more research finds that aligning all modal representations is suboptimal because it damages the unique attributes of each modal. These studies use subtraction and orthogonal constraints in geometric space to learn unique parts. However, our rigorous analysis reveals the flaws in this approach, such as that subtraction does not necessarily yield the desired modal-unique and that orthogonal constraints are ineffective in user and item high-dimensional representation spaces. To make up for the previous weaknesses, we propose Separate Learning (SEA) for multimedia recommendation, which mainly includes mutual information view of modal-unique and -generic learning. Specifically, we first use GNN to learn the representations of users and items in different modalities and split each modal representation into generic and unique parts. We employ contrastive log-ratio upper bound to minimize the mutual information between the general and unique parts within the same modality, to distance their representations, thus learning modal-unique features. Then, we design Solosimloss to maximize the lower bound of mutual information, to align the general parts of different modalities, thus learning more high-quality modal-generic features. Finally, extensive experiments on three datasets demonstrate the effectiveness and generalization of our proposed framework. The code is available at SEA and the full training record of the main experiment.
SonicID: User Identification on Smart Glasses with Acoustic Sensing
Venue: arXiv cs.HC 2024
Year: 2024

Smart glasses have become more prevalent as they provide an increasing number of applications for users. They store various types of private information or can access it via connections established with other devices. Therefore, there is a growing need for user identification on smart glasses. In this paper, we introduce a low-power and minimally-obtrusive system called SonicID, designed to authenticate users on glasses. SonicID extracts unique biometric information from users by scanning their faces with ultrasonic waves and utilizes this information to distinguish between different users, powered by a customized binary classifier with the ResNet-18 architecture. SonicID can authenticate users by scanning their face for 0.06 seconds. A user study involving 40 participants confirms that SonicID achieves a true positive rate of 97.4%, a false positive rate of 4.3%, and a balanced accuracy of 96.6% using just 1 minute of training data collected for each new user. This performance is relatively consistent across different remounting sessions and days. Given this promising performance, we further discuss the potential applications of SonicID and methods to improve its performance in the future.
Pre-Training Identification of Graph Winning Tickets in Adaptive Spatial-Temporal Graph Neural Networks
Venue: arXiv cs.LG 2024
Year: 2024

In this paper, we present a novel method to significantly enhance the computational efficiency of Adaptive Spatial-Temporal Graph Neural Networks (ASTGNNs) by introducing the concept of the Graph Winning Ticket (GWT), derived from the Lottery Ticket Hypothesis (LTH). By adopting a pre-determined star topology as a GWT prior to training, we balance edge reduction with efficient information propagation, reducing computational demands while maintaining high model performance. Both the time and memory computational complexity of generating adaptive spatial-temporal graphs is significantly reduced from $\mathcal{O}(N^2)$ to $\mathcal{O}(N)$. Our approach streamlines the ASTGNN deployment by eliminating the need for exhaustive training, pruning, and retraining cycles, and demonstrates empirically across various datasets that it is possible to achieve comparable performance to full models with substantially lower computational costs. Specifically, our approach enables training ASTGNNs on the largest scale spatial-temporal dataset using a single A6000 equipped with 48 GB of memory, overcoming the out-of-memory issue encountered during original training and even achieving state-of-the-art performance. Furthermore, we delve into the effectiveness of the GWT from the perspective of spectral graph theory, providing substantial theoretical support. This advancement not only proves the existence of efficient sub-networks within ASTGNNs but also broadens the applicability of the LTH in resource-constrained settings, marking a significant step forward in the field of graph neural networks. Code is available at https://anonymous.4open.science/r/paper-1430.
Decoupling the Class Label and the Target Concept in Machine Unlearning
Venue: arXiv cs.LG 2024
Year: 2024

Machine unlearning as an emerging research topic for data regulations, aims to adjust a trained model to approximate a retrained one that excludes a portion of training data. Previous studies showed that class-wise unlearning is successful in forgetting the knowledge of a target class, through gradient ascent on the forgetting data or fine-tuning with the remaining data. However, while these methods are useful, they are insufficient as the class label and the target concept are often considered to coincide. In this work, we decouple them by considering the label domain mismatch and investigate three problems beyond the conventional all matched forgetting, e.g., target mismatch, model mismatch, and data mismatch forgetting. We systematically analyze the new challenges in restrictively forgetting the target concept and also reveal crucial forgetting dynamics in the representation level to realize these tasks. Based on that, we propose a general framework, namely, TARget-aware Forgetting (TARF). It enables the additional tasks to actively forget the target concept while maintaining the rest part, by simultaneously conducting annealed gradient ascent on the forgetting data and selected gradient descent on the hard-to-affect remaining data. Empirically, various experiments under the newly introduced settings are conducted to demonstrate the effectiveness of our TARF.
Dynamical evolution of social network polarization and its impact on the propagation of a virus
Venue: arXiv cs.SI 2024
Year: 2024

The COVID-19 pandemic that emerged in 2020 has highlighted the complex interplay between vaccine hesitancy and societal polarization. In this study, we analyse the dynamical polarization within a social network as well as the network properties before and after a vaccine was made available. Our results show that as the network evolves from a less structured state to one with more clustered communities. Then using an agent-based modeling approach, we simulate the propagation of a virus in a polarized society by assigning vaccines to pro-vaccine individuals and none to the anti-vaccine individuals. We compare this propagation to the case where the same number of vaccines is distributed homogeneously across the population. In polarized networks, we observe a significantly more widespread diffusion of the virus, highlighting the importance of considering polarization for epidemic forecasting.
Review of Autonomous Mobile Robots for the Warehouse Environment
Venue: arXiv cs.RO 2024
Year: 2024

Autonomous mobile robots (AMRs) have been a rapidly expanding research topic for the past decade. Unlike their counterpart, the automated guided vehicle (AGV), AMRs can make decisions and do not need any previously installed infrastructure to navigate. Recent technological developments in hardware and software have made them more feasible, especially in warehouse environments. Traditionally, most wasted warehouse expenses come from the logistics of moving material from one point to another, and is exhaustive for humans to continuously walk those distances while carrying a load. Here, AMRs can help by working with humans to cut down the time and effort of these repetitive tasks, improving performance and reducing the fatigue of their human collaborators. This literature review covers the recent developments in AMR technology including hardware, robotic control, and system control. This paper also discusses examples of current AMR producers, their robots, and the software that is used to control them. We conclude with future research topics and where we see AMRs developing in the warehouse environment.
Diff-A-Riff: Musical Accompaniment Co-creation via Latent Diffusion Models
Venue: arXiv cs.SD 2024
Year: 2024

Recent advancements in deep generative models present new opportunities for music production but also pose challenges, such as high computational demands and limited audio quality. Moreover, current systems frequently rely solely on text input and typically focus on producing complete musical pieces, which is incompatible with existing workflows in music production. To address these issues, we introduce "Diff-A-Riff," a Latent Diffusion Model designed to generate high-quality instrumental accompaniments adaptable to any musical context. This model offers control through either audio references, text prompts, or both, and produces 48kHz pseudo-stereo audio while significantly reducing inference time and memory usage. We demonstrate the model's capabilities through objective metrics and subjective listening tests, with extensive examples available on the accompanying website: sonycslparis.github.io/diffariff-companion/
RRLS : Robust Reinforcement Learning Suite
Venue: arXiv cs.LG 2024
Year: 2024

Robust reinforcement learning is the problem of learning control policies that provide optimal worst-case performance against a span of adversarial environments. It is a crucial ingredient for deploying algorithms in real-world scenarios with prevalent environmental uncertainties and has been a long-standing object of attention in the community, without a standardized set of benchmarks. This contribution endeavors to fill this gap. We introduce the Robust Reinforcement Learning Suite (RRLS), a benchmark suite based on Mujoco environments. RRLS provides six continuous control tasks with two types of uncertainty sets for training and evaluation. Our benchmark aims to standardize robust reinforcement learning tasks, facilitating reproducible and comparable experiments, in particular those from recent state-of-the-art contributions, for which we demonstrate the use of RRLS. It is also designed to be easily expandable to new environments. The source code is available at \href{https://github.com/SuReLI/RRLS}{https://github.com/SuReLI/RRLS}.
Memory Is All You Need: An Overview of Compute-in-Memory Architectures for Accelerating Large Language Model Inference
Venue: arXiv cs.AR 2024
Year: 2024

Large language models (LLMs) have recently transformed natural language processing, enabling machines to generate human-like text and engage in meaningful conversations. This development necessitates speed, efficiency, and accessibility in LLM inference as the computational and memory requirements of these systems grow exponentially. Meanwhile, advancements in computing and memory capabilities are lagging behind, exacerbated by the discontinuation of Moore's law. With LLMs exceeding the capacity of single GPUs, they require complex, expert-level configurations for parallel processing. Memory accesses become significantly more expensive than computation, posing a challenge for efficient scaling, known as the memory wall. Here, compute-in-memory (CIM) technologies offer a promising solution for accelerating AI inference by directly performing analog computations in memory, potentially reducing latency and power consumption. By closely integrating memory and compute elements, CIM eliminates the von Neumann bottleneck, reducing data movement and improving energy efficiency. This survey paper provides an overview and analysis of transformer-based models, reviewing various CIM architectures and exploring how they can address the imminent challenges of modern AI computing systems. We discuss transformer-related operators and their hardware acceleration schemes and highlight challenges, trends, and insights in corresponding CIM designs.
OmniCorpus: A Unified Multimodal Corpus of 10 Billion-Level Images Interleaved with Text
Venue: arXiv cs.CV 2024
Year: 2024

Image-text interleaved data, consisting of multiple images and texts arranged in a natural document format, aligns with the presentation paradigm of internet data and closely resembles human reading habits. Recent studies have shown that such data aids multimodal in-context learning and maintains the capabilities of large language models during multimodal fine-tuning. However, the limited scale and diversity of current image-text interleaved data restrict the development of multimodal large language models. In this paper, we introduce OmniCorpus, a 10 billion-scale image-text interleaved dataset. Using an efficient data engine, we filter and extract large-scale high-quality documents, which contain 8.6 billion images and 1,696 billion text tokens. Compared to counterparts (e.g., MMC4, OBELICS), our dataset 1) has 15 times larger scales while maintaining good data quality; 2) features more diverse sources, including both English and non-English websites as well as video-centric websites; 3) is more flexible, easily degradable from an image-text interleaved format to pure text corpus and image-text pairs. Through comprehensive analysis and experiments, we validate the quality, usability, and effectiveness of the proposed dataset. We hope this could provide a solid data foundation for future multimodal model research. Code and data are released at https://github.com/OpenGVLab/OmniCorpus.
Improving Noise Robustness through Abstractions and its Impact on Machine Learning
Venue: arXiv cs.LG 2024
Year: 2024

Noise is a fundamental problem in learning theory with huge effects in the application of Machine Learning (ML) methods, due to real world data tendency to be noisy. Additionally, introduction of malicious noise can make ML methods fail critically, as is the case with adversarial attacks. Thus, finding and developing alternatives to improve robustness to noise is a fundamental problem in ML. In this paper, we propose a method to deal with noise: mitigating its effect through the use of data abstractions. The goal is to reduce the effect of noise over the model's performance through the loss of information produced by the abstraction. However, this information loss comes with a cost: it can result in an accuracy reduction due to the missing information. First, we explored multiple methodologies to create abstractions, using the training dataset, for the specific case of numerical data and binary classification tasks. We also tested how these abstractions can affect robustness to noise with several experiments that explore the robustness of an Artificial Neural Network to noise when trained using raw data \emph{vs} when trained using abstracted data. The results clearly show that using abstractions is a viable approach for developing noise robust ML methods.
Wiki Entity Summarization Benchmark
Venue: arXiv cs.IR 2024
Year: 2024

Entity summarization aims to compute concise summaries for entities in knowledge graphs. Existing datasets and benchmarks are often limited to a few hundred entities and discard graph structure in source knowledge graphs. This limitation is particularly pronounced when it comes to ground-truth summaries, where there exist only a few labeled summaries for evaluation and training. We propose WikES, a comprehensive benchmark comprising of entities, their summaries, and their connections. Additionally, WikES features a dataset generator to test entity summarization algorithms in different areas of the knowledge graph. Importantly, our approach combines graph algorithms and NLP models as well as different data sources such that WikES does not require human annotation, rendering the approach cost-effective and generalizable to multiple domains. Finally, WikES is scalable and capable of capturing the complexities of knowledge graphs in terms of topology and semantics. WikES features existing datasets for comparison. Empirical studies of entity summarization methods confirm the usefulness of our benchmark. Data, code, and models are available at: https://github.com/msorkhpar/wiki-entity-summarization.
Equivalence of Countable and Computable
Venue: arXiv cs.CC 2024
Year: 2024

The concept of ``countable set'' is attributed to Georg Cantor, who set the boundary between countable and uncountable sets in 1874. The concept of ``computable set'' arose in the study of computing models in the 1930s by the founders of computer science, including \Godel, Church, and Turing. However, the connection between countability and computability has not been thoroughly studied in the past eight decades. A {\em counting bijection} of a set $S$ is a bijection from the set of natural numbers to $S$. We say $S$ is {\em enumerable} if either $S$ is finite or $S$ has a computable counting bijection. We prove that $S$ is enumerable if and only if $S$ is computable. This equivalence provides insights to the study of set theory and computability theory and reveals that the concept of countability is related to space and time complexity. We show that the set of total computable functions (or deciders, algorithms) is unenumerable. We also provide a sufficient and necessary condition for a set to be enumerable and use the concept of {\em counting order} to explain why some sets are unenumerable. We show that some popular statements about countable sets, such as ``every subset of a countable set is countable'' or ``every formal language is countable,'' lack a valid proof. These unproven statements are widely taught in college courses on discrete mathematics, set theory, and computability theory.