OpenRaiser/Intern-Atlas huggingface dataset:
Exact schedulability test for sporadic mixed-criticality real-time systems using antichains and oracles
Venue: arXiv cs.OS 2024
Year: 2024

This work addresses the problem of exact schedulability assessment in uniprocessor mixed-criticality real-time systems with sporadic task sets. We model the problem by means of a finite automaton that has to be explored in order to check for schedulability. To mitigate the state explosion problem, we provide a generic algorithm which is parameterised by several techniques called oracles and simulation relations. These techniques leverage results from the scheduling literature as "plug-ins" that make the algorithm more efficient in practice. Our approach achieves up to a 99.998% reduction in the search space required for exact schedulability testing, making it practical for a range of task sets, up to 8 tasks or maximum periods of 350. This method enables to challenge the pessimism of an existing schedulability test and to derive a new dynamic-priority scheduler, demonstrating its good performance. This is the full version of an RTNS 2024 paper.
Dear Diary: A randomized controlled trial of Generative AI coding tools in the workplace
Venue: arXiv cs.SE 2024
Year: 2024

Generative AI coding tools are relatively new, and their impact on developers extends beyond traditional coding metrics, influencing beliefs about work and developers' roles in the workplace. This study aims to illuminate developers' preexisting beliefs about generative AI tools, their self perceptions, and how regular use of these tools may alter these beliefs. Using a mixed methods approach, including surveys, a randomized controlled trial, and a three week diary study, we explored the real world application of generative AI tools within a large multinational software company. Our findings reveal that the introduction and sustained use of generative AI coding tools significantly increases developers' perceptions of these tools as both useful and enjoyable. However, developers' views on the trustworthiness of AI generated code remained unchanged. We also discovered unexpected uses of these tools, such as replacing web searches and fostering creative ideation. Additionally, 84 percent of participants reported positive changes in their daily work practices, and 66 percent noted shifts in their feelings about their work, ranging from increased enthusiasm to heightened awareness of the need to stay current with technological advances. This research provides both qualitative and quantitative insights into the evolving role of generative AI in software development and offers practical recommendations for maximizing the benefits of this emerging technology, particularly in balancing the productivity gains from AI-generated code with the need for increased scrutiny and critical evaluation of its outputs.
Assessing the Creativity of LLMs in Proposing Novel Solutions to Mathematical Problems
Venue: arXiv cs.CL 2024
Year: 2024

The mathematical capabilities of AI systems are complex and multifaceted. Most existing research has predominantly focused on the correctness of AI-generated solutions to mathematical problems. In this work, we argue that beyond producing correct answers, AI systems should also be capable of, or assist humans in, developing novel solutions to mathematical challenges. This study explores the creative potential of Large Language Models (LLMs) in mathematical reasoning, an aspect that has received limited attention in prior research. We introduce a novel framework and benchmark, CreativeMath, which encompasses problems ranging from middle school curricula to Olympic-level competitions, designed to assess LLMs' ability to propose innovative solutions after some known solutions have been provided. Our experiments demonstrate that, while LLMs perform well on standard mathematical tasks, their capacity for creative problem-solving varies considerably. Notably, the Gemini-1.5-Pro model outperformed other LLMs in generating novel solutions. This research opens a new frontier in evaluating AI creativity, shedding light on both the strengths and limitations of LLMs in fostering mathematical innovation, and setting the stage for future developments in AI-assisted mathematical discovery.
Gibberish is All You Need for Membership Inference Detection in Contrastive Language-Audio Pretraining
Venue: arXiv cs.SD 2024
Year: 2024

Audio can disclose PII, particularly when combined with related text data. Therefore, it is essential to develop tools to detect privacy leakage in Contrastive Language-Audio Pretraining(CLAP). Existing MIAs need audio as input, risking exposure of voiceprint and requiring costly shadow models. We first propose PRMID, a membership inference detector based probability ranking given by CLAP, which does not require training shadow models but still requires both audio and text of the individual as input. To address these limitations, we then propose USMID, a textual unimodal speaker-level membership inference detector, querying the target model using only text data. We randomly generate textual gibberish that are clearly not in training dataset. Then we extract feature vectors from these texts using the CLAP model and train a set of anomaly detectors on them. During inference, the feature vector of each test text is input into the anomaly detector to determine if the speaker is in the training set (anomalous) or not (normal). If available, USMID can further enhance detection by integrating real audio of the tested speaker. Extensive experiments on various CLAP model architectures and datasets demonstrate that USMID outperforms baseline methods using only text data.
On maximal almost balanced non-overlapping codes and non-overlapping codes with restricted run-lengths
Venue: arXiv cs.IT 2024
Year: 2024

This paper concerns non-overlapping codes, block codes motivated by synchronisation and DNA-based storage applications. Most existing constructions of these codes do not account for the restrictions posed by the physical properties of communication channels. If undesired sequences are not avoided, the system using the encoding may start behaving incorrectly. Hence, we aim to characterise all non-overlapping codes satisfying two additional constraints. For the first constraint, where approximately half of the letters in each word are positive, we derive necessary and sufficient conditions for the code's non-expandability and improve known bounds on its maximum size. We also determine exact values for the maximum sizes of polarity-balanced non-overlapping codes having small block and alphabet sizes. For the other constraint, where long sequences of consecutive equal symbols lead to undesired behaviour, we derive bounds and constructions of constrained non-overlapping codes. Moreover, we provide constructions of non-overlapping codes that satisfy both constraints and analyse the sizes of the obtained codes.
LOGO -- Long cOntext aliGnment via efficient preference Optimization
Venue: arXiv cs.CL 2024
Year: 2024

Long-context models(LCMs) have shown great potential in processing long input sequences(even more than 100M tokens) conveniently and effectively. With significant progress, recent research has pointed out that LCMs can accurately locate token-level salient information within the context. Yet, the generation performance of these LCMs is far from satisfactory and might result in misaligned responses, such as hallucinations. To enhance the generation capability of LCMs, existing works have investigated the effects of data size and quality for both pre-training and instruction tuning. Though achieving meaningful improvement, previous methods fall short in either effectiveness or efficiency. In this paper, we introduce LOGO(Long cOntext aliGnment via efficient preference Optimization), a training strategy that first introduces preference optimization for long-context alignment. To overcome the GPU memory-bound issue caused by the long sequence, LOGO employs a reference-free preference optimization strategy and adopts a position synthesis method to construct the training data. By training with only 0.3B data on a single 8$\times$A800 GPU machine for 16 hours, LOGO allows the Llama-3-8B-Instruct-80K model to achieve comparable performance with GPT-4 in real-world long-context tasks while preserving the model's original capabilities on other tasks, e.g., language modeling and MMLU. Moreover, LOGO can extend the model's context window size while enhancing its generation performance.
Beyond Color and Lines: Zero-Shot Style-Specific Image Variations with Coordinated Semantics
Venue: arXiv cs.CV 2024
Year: 2024

Traditionally, style has been primarily considered in terms of artistic elements such as colors, brushstrokes, and lighting. However, identical semantic subjects, like people, boats, and houses, can vary significantly across different artistic traditions, indicating that style also encompasses the underlying semantics. Therefore, in this study, we propose a zero-shot scheme for image variation with coordinated semantics. Specifically, our scheme transforms the image-to-image problem into an image-to-text-to-image problem. The image-to-text operation employs vision-language models e.g., BLIP) to generate text describing the content of the input image, including the objects and their positions. Subsequently, the input style keyword is elaborated into a detailed description of this style and then merged with the content text using the reasoning capabilities of ChatGPT. Finally, the text-to-image operation utilizes a Diffusion model to generate images based on the text prompt. To enable the Diffusion model to accommodate more styles, we propose a fine-tuning strategy that injects text and style constraints into cross-attention. This ensures that the output image exhibits similar semantics in the desired style. To validate the performance of the proposed scheme, we constructed a benchmark comprising images of various styles and scenes and introduced two novel metrics. Despite its simplicity, our scheme yields highly plausible results in a zero-shot manner, particularly for generating stylized images with high-fidelity semantics.
Complexity Matters: Effective Dimensionality as a Measure for Adversarial Robustness
Venue: arXiv cs.LG 2024
Year: 2024

Quantifying robustness in a single measure for the purposes of model selection, development of adversarial training methods, and anticipating trends has so far been elusive. The simplest metric to consider is the number of trainable parameters in a model but this has previously been shown to be insufficient at explaining robustness properties. A variety of other metrics, such as ones based on boundary thickness and gradient flatness have been proposed but have been shown to be inadequate proxies for robustness. In this work, we investigate the relationship between a model's effective dimensionality, which can be thought of as model complexity, and its robustness properties. We run experiments on commercial-scale models that are often used in real-world environments such as YOLO and ResNet. We reveal a near-linear inverse relationship between effective dimensionality and adversarial robustness, that is models with a lower dimensionality exhibit better robustness. We investigate the effect of a variety of adversarial training methods on effective dimensionality and find the same inverse linear relationship present, suggesting that effective dimensionality can serve as a useful criterion for model selection and robustness evaluation, providing a more nuanced and effective metric than parameter count or previously-tested measures.
Prompting and Fine-Tuning of Small LLMs for Length-Controllable Telephone Call Summarization
Venue: arXiv cs.CL 2024
Year: 2024

This paper explores the rapid development of a telephone call summarization system utilizing large language models (LLMs). Our approach involves initial experiments with prompting existing LLMs to generate summaries of telephone conversations, followed by the creation of a tailored synthetic training dataset utilizing stronger frontier models. We place special focus on the diversity of the generated data and on the ability to control the length of the generated summaries to meet various use-case specific requirements. The effectiveness of our method is evaluated using two state-of-the-art LLM-as-a-judge-based evaluation techniques to ensure the quality and relevance of the summaries. Our results show that fine-tuned Llama-2-7B-based summarization model performs on-par with GPT-4 in terms of factual accuracy, completeness and conciseness. Our findings demonstrate the potential for quickly bootstrapping a practical and efficient call summarization system.
Supporting Assessment of Novelty of Design Problems Using Concept of Problem SAPPhIRE
Venue: arXiv cs.CL 2024
Year: 2024

This paper proposes a framework for assessing the novelty of design problems using the SAPPhIRE model of causality. The novelty of a problem is measured as its minimum distance from the problems in a reference problem database. The distance is calculated by comparing the current problem and each reference past problem at the various levels of abstraction in the SAPPhIRE ontology. The basis for comparison is textual similarity. To demonstrate the applicability of the proposed framework, The current set of problems associated with an artifact, as collected from its stakeholders, were compared with the past set of problems, as collected from patents and other web sources, to assess the novelty of the current set. This approach is aimed at providing a better understanding of the degree of novelty of any given set of current problems by comparing them to similar problems available from historical records. Since manual assessment, the current mode of such assessments as reported in the literature, is a tedious process, to reduce time complexity and to afford better applicability for larger sets of problem statements, an automated assessment is proposed and used in this paper.
PASTRAMI: Performance Assessment of SofTware Routers Addressing Measurement Instability
Venue: arXiv cs.NI 2024
Year: 2024

Virtualized environments offer a flexible and scalable platform for evaluating network performance, but they can introduce significant variability that complicates accurate measurement. This paper presents PASTRAMI, a methodology designed to assess the stability of software routers, which is critical to accurately evaluate performance metrics such as the Partial Drop Rate at 0.5% (PDR@0.5%). While PDR@0.5% is a key metric to assess packet processing capabilities of a software router, its reliable evaluation depends on consistent router performance with minimal measurement variability. Our research reveals that different Linux versions exhibit distinct behaviors, with some demonstrating non-negligible packet loss even at low loads and high variability in loss measurements, rendering them unsuitable for accurate performance assessments. This paper proposes a systematic approach to differentiate between stable and unstable environments, offering practical guidance on selecting suitable configurations for robust networking performance evaluations in virtualized environments.
Rigid Single-Slice-in-Volume registration via rotation-equivariant 2D/3D feature matching
Venue: arXiv cs.CV 2024
Year: 2024

2D to 3D registration is essential in tasks such as diagnosis, surgical navigation, environmental understanding, navigation in robotics, autonomous systems, or augmented reality. In medical imaging, the aim is often to place a 2D image in a 3D volumetric observation to w. Current approaches for rigid single slice in volume registration are limited by requirements such as pose initialization, stacks of adjacent slices, or reliable anatomical landmarks. Here, we propose a self-supervised 2D/3D registration approach to match a single 2D slice to the corresponding 3D volume. The method works in data without anatomical priors such as images of tumors. It addresses the dimensionality disparity and establishes correspondences between 2D in-plane and 3D out-of-plane rotation-equivariant features by using group equivariant CNNs. These rotation-equivariant features are extracted from the 2D query slice and aligned with their 3D counterparts. Results demonstrate the robustness of the proposed slice-in-volume registration on the NSCLC-Radiomics CT and KIRBY21 MRI datasets, attaining an absolute median angle error of less than 2 degrees and a mean-matching feature accuracy of 89% at a tolerance of 3 pixels.
Exploiting Interpretable Capabilities with Concept-Enhanced Diffusion and Prototype Networks
Venue: arXiv cs.LG 2024
Year: 2024

Concept-based machine learning methods have increasingly gained importance due to the growing interest in making neural networks interpretable. However, concept annotations are generally challenging to obtain, making it crucial to leverage all their prior knowledge. By creating concept-enriched models that incorporate concept information into existing architectures, we exploit their interpretable capabilities to the fullest extent. In particular, we propose Concept-Guided Conditional Diffusion, which can generate visual representations of concepts, and Concept-Guided Prototype Networks, which can create a concept prototype dataset and leverage it to perform interpretable concept prediction. These results open up new lines of research by exploiting pre-existing information in the quest for rendering machine learning more human-understandable.
LLM-based Online Prediction of Time-varying Graph Signals
Venue: arXiv cs.AI 2024
Year: 2024

In this paper, we propose a novel framework that leverages large language models (LLMs) for predicting missing values in time-varying graph signals by exploiting spatial and temporal smoothness. We leverage the power of LLM to achieve a message-passing scheme. For each missing node, its neighbors and previous estimates are fed into and processed by LLM to infer the missing observations. Tested on the task of the online prediction of wind-speed graph signals, our model outperforms online graph filtering algorithms in terms of accuracy, demonstrating the potential of LLMs in effectively addressing partially observed signals in graphs.
Cellpose+, a morphological analysis tool for feature extraction of stained cell images
Venue: arXiv cs.CV 2024
Year: 2024

Advanced image segmentation and processing tools present an opportunity to study cell processes and their dynamics. However, image analysis is often routine and time-consuming. Nowadays, alternative data-driven approaches using deep learning are potentially offering automatized, accurate, and fast image analysis. In this paper, we extend the applications of Cellpose, a state-of-the-art cell segmentation framework, with feature extraction capabilities to assess morphological characteristics. We also introduce a dataset of DAPI and FITC stained cells to which our new method is applied.
Does Differential Privacy Impact Bias in Pretrained NLP Models?
Venue: arXiv cs.CL 2024
Year: 2024

Differential privacy (DP) is applied when fine-tuning pre-trained large language models (LLMs) to limit leakage of training examples. While most DP research has focused on improving a model's privacy-utility tradeoff, some find that DP can be unfair to or biased against underrepresented groups. In this work, we show the impact of DP on bias in LLMs through empirical analysis. Differentially private training can increase the model bias against protected groups w.r.t AUC-based bias metrics. DP makes it more difficult for the model to differentiate between the positive and negative examples from the protected groups and other groups in the rest of the population. Our results also show that the impact of DP on bias is not only affected by the privacy protection level but also the underlying distribution of the dataset.
Search for shortest paths based on a projective description of unweighted graphs
Venue: arXiv cs.DC 2024
Year: 2024

The search is based on the preliminary transformation of matrices or adjacency lists traditionally used in the study of graphs into projections cleared of redundant information (refined) followed by the selection of the desired shortest paths. Each projection contains complete information about all the shortest paths from its base (angle vertex) and is based on an enumeration of reachability relations, more complex than the traditionally used binary adjacency relations. The class of graphs considered was expanded to mixed graphs containing both undirected and oriented edges (arcs). A method for representing graph projections in computer memory and finding shortest paths using them is proposed. The reduction in algorithmic complexity achieved, at the same time, will allow the proposed method to be used in information network applications, scientific and technical, transport and logistics, and economic fields.
PointPatchRL -- Masked Reconstruction Improves Reinforcement Learning on Point Clouds
Venue: arXiv cs.LG 2024
Year: 2024

Perceiving the environment via cameras is crucial for Reinforcement Learning (RL) in robotics. While images are a convenient form of representation, they often complicate extracting important geometric details, especially with varying geometries or deformable objects. In contrast, point clouds naturally represent this geometry and easily integrate color and positional data from multiple camera views. However, while deep learning on point clouds has seen many recent successes, RL on point clouds is under-researched, with only the simplest encoder architecture considered in the literature. We introduce PointPatchRL (PPRL), a method for RL on point clouds that builds on the common paradigm of dividing point clouds into overlapping patches, tokenizing them, and processing the tokens with transformers. PPRL provides significant improvements compared with other point-cloud processing architectures previously used for RL. We then complement PPRL with masked reconstruction for representation learning and show that our method outperforms strong model-free and model-based baselines on image observations in complex manipulation tasks containing deformable objects and variations in target object geometry. Videos and code are available at https://alrhub.github.io/pprl-website
Expanding AI Awareness Through Everyday Interactions with AI: A Reflective Journal Study
Venue: arXiv cs.HC 2024
Year: 2024

As the application of AI continues to expand, students in technology programs are poised to be both producers and users of the technologies. They are also positioned to engage with AI applications within and outside the classroom. While focusing on the curriculum when examining students' AI knowledge is common, extending this connection to students' everyday interactions with AI provides a more complete picture of their learning. In this paper, we explore student's awareness and engagement with AI in the context of school and their daily lives. Over six weeks, 22 undergraduate students participated in a reflective journal study and submitted a weekly journal entry about their interactions with AI. The participants were recruited from a technology and society course that focuses on the implications of technology on people, communities, and processes. In their weekly journal entries, participants reflected on interactions with AI on campus (coursework, advertises campus events, or seminars) and beyond (social media, news, or conversations with friends and family). The journal prompts were designed to help them think through what they had read, watched, or been told and reflect on the development of their own perspectives, knowledge, and literacy on the topic. Overall, students described nine categories of interactions: coursework, news and current events, using software and applications, university events, social media related to their work, personal discussions with friends and family, interacting with content, and gaming. Students reported that completing the diaries allowed them time for reflection and made them more aware of the presence of AI in their daily lives and of its potential benefits and drawbacks. This research contributes to the ongoing work on AI awareness and literacy by bringing in perspectives from beyond a formal educational context.