OpenRaiser/Intern-Atlas huggingface dataset:
Leveraging Ontologies to Document Bias in Data
Venue: arXiv cs.AI 2024
Year: 2024

Machine Learning (ML) systems are capable of reproducing and often amplifying undesired biases. This puts emphasis on the importance of operating under practices that enable the study and understanding of the intrinsic characteristics of ML pipelines, prompting the emergence of documentation frameworks with the idea that ``any remedy for bias starts with awareness of its existence''. However, a resource that can formally describe these pipelines in terms of biases detected is still amiss. To fill this gap, we present the Doc-BiasO ontology, a resource that aims to create an integrated vocabulary of biases defined in the \textit{fair-ML} literature and their measures, as well as to incorporate relevant terminology and the relationships between them. Overseeing ontology engineering best practices, we re-use existing vocabulary on machine learning and AI, to foster knowledge sharing and interoperability between the actors concerned with its research, development, regulation, among others. Overall, our main objective is to contribute towards clarifying existing terminology on bias research as it rapidly expands to all areas of AI and to improve the interpretation of bias in data and downstream impact.
Wooly Graphs : A Mathematical Framework For Knitting
Venue: arXiv cs.DS 2024
Year: 2024

This paper aims to develop a mathematical foundation to model knitting with graphs. We provide a precise definition for knit objects with a knot theoretic component and propose a simple undirected graph, a simple directed graph, and a directed multigraph model for any arbitrary knit object. Using these models, we propose natural categories related to the complexity of knitting structures. We use these categories to explore the hardness of determining whether a knit object of each class exists for a given graph. We show that while this problem is NP-hard in general, under specific cases, there are linear and polynomial time algorithms which take advantage of unique properties of common knitting techniques. This work aims to bridge the gap between textile arts and graph theory, offering a useful and rigorous framework for analyzing knitting objects using their corresponding graphs and for generating knitting objects from graphs.
Real-Time Energy Measurement for Non-Intrusive Well-Being Monitoring of Elderly People -- a Case Study
Venue: arXiv cs.CY 2024
Year: 2024

This article presents a case study demonstrating a non-intrusive method for the well-being monitoring of elderly people. It is based on our real-time energy measurement system, which uses tiny beacons attached to electricity meters. Four participants aged 67-82 years took part in our study. We observed their electric power consumption for approx. a month, and then we analyzed them, taking into account the participants' notes on their activities. We created typical daily usage profiles for each participant and used anomaly detection to find unusual energy consumption. We found out that real-time energy measurement can give significant insight into someone's daily activities and, consequently, bring invaluable information to caregivers about the well-being of an elderly person, while being discreet and entirely non-intrusive.
Blockchain based Decentralized Petition System
Venue: arXiv cs.CR 2024
Year: 2024

A decentralized online petition system enables individuals or groups to create, sign, and share petitions without a central authority. Using blockchain technology, these systems ensure the integrity and transparency of the petition process by recording every signature or action on the blockchain, making alterations or deletions impossible. This provides a permanent, tamper-proof record of the petition's progress. Such systems allow users to bypass traditional intermediaries like government or social media platforms, fostering more democratic and transparent decision-making. This paper reviews research on petition systems, highlighting the shortcomings of existing systems such as lack of accountability, vulnerability to hacking, and security issues. The proposed blockchain-based implementation aims to overcome these challenges. Decentralized voting systems have garnered interest recently due to their potential to provide secure and transparent voting platforms without intermediaries, addressing issues like voter fraud, manipulation, and trust in the electoral process. We propose a decentralized voting system web application using blockchain technology to ensure the integrity and security of the voting process. This system aims to provide a transparent, decentralized decision-making process that counts every vote while eliminating the need for centralized authorities. The paper presents an overview of the system architecture, design considerations, and implementation details, along with the potential benefits and limitations. Finally, we discuss future research directions, examining the technical aspects of the application, including underlying algorithms and protocols. Our research aims to enhance the integrity and accessibility of democratic processes, improve security, and ensure fairness, transparency, and tamper-proofness.
AI-powered multimodal modeling of personalized hemodynamics in aortic stenosis
Venue: arXiv cs.CE 2024
Year: 2024

Aortic stenosis (AS) is the most common valvular heart disease in developed countries. High-fidelity preclinical models can improve AS management by enabling therapeutic innovation, early diagnosis, and tailored treatment planning. However, their use is currently limited by complex workflows necessitating lengthy expert-driven manual operations. Here, we propose an AI-powered computational framework for accelerated and democratized patient-specific modeling of AS hemodynamics from computed tomography. First, we demonstrate that our automated meshing algorithms can generate task-ready geometries for both computational and benchtop simulations with higher accuracy and 100 times faster than existing approaches. Then, we show that our approach can be integrated with fluid-structure interaction and soft robotics models to accurately recapitulate a broad spectrum of clinical hemodynamic measurements of diverse AS patients. The efficiency and reliability of these algorithms make them an ideal complementary tool for personalized high-fidelity modeling of AS biomechanics, hemodynamics, and treatment planning.
Explaining Chest X-ray Pathology Models using Textual Concepts
Venue: arXiv cs.CV 2024
Year: 2024

Deep learning models have revolutionized medical imaging and diagnostics, yet their opaque nature poses challenges for clinical adoption and trust. Amongst approaches to improve model interpretability, concept-based explanations aim to provide concise and human-understandable explanations of any arbitrary classifier. However, such methods usually require a large amount of manually collected data with concept annotation, which is often scarce in the medical domain. In this paper, we propose Conceptual Counterfactual Explanations for Chest X-ray (CoCoX), which leverages the joint embedding space of an existing vision-language model (VLM) to explain black-box classifier outcomes without the need for annotated datasets. Specifically, we utilize textual concepts derived from chest radiography reports and a pre-trained chest radiography-based VLM to explain three common cardiothoracic pathologies. We demonstrate that the explanations generated by our method are semantically meaningful and faithful to underlying pathologies.
Automated Robot Recovery from Assumption Violations of High-Level Specifications
Venue: arXiv cs.RO 2024
Year: 2024

This paper presents a framework that enables robots to automatically recover from assumption violations of high-level specifications during task execution. In contrast to previous methods relying on user intervention to impose additional assumptions for failure recovery, our approach leverages synthesis-based repair to suggest new robot skills that, when implemented, repair the task. Our approach detects violations of environment safety assumptions during the task execution, relaxes the assumptions to admit observed environment behaviors, and acquires new robot skills for task completion. We demonstrate our approach with a Hello Robot Stretch in a factory-like scenario.
A Simple Representation of Tree Covering Utilizing Balanced Parentheses and Efficient Implementation of Average-Case Optimal RMQs
Venue: arXiv cs.DS 2024
Year: 2024

Tree covering is a technique for decomposing a tree into smaller-sized trees with desirable properties, and has been employed in various succinct data structures. However, significant hurdles stand in the way of a practical implementation of tree covering: a lot of pointers are used to maintain the tree-covering hierarchy and many indices for tree navigational queries consume theoretically negligible yet practically vast space. To tackle these problems, we propose a simple representation of tree covering using a balanced parenthesis representation. The key to the proposal is the observation that every micro tree splits into at most two intervals on the BP representation. Utilizing the representation, we propose several data structures that represent a tree and its tree cover, which consequently allow micro tree compression with arbitrary coding and efficient tree navigational queries. We also applied our data structure to average-case optimal RMQ by Munro et al.~[ESA 2021] and implemented the RMQ data structure. Our RMQ data structures spend less than $2n$ bits and process queries in a practical time on several settings of the performance evaluation, reducing the gap between theoretical space complexity and actual space consumption. We also implement tree navigational operations while using the same amount of space as the RMQ data structures. We believe the representation can be widely utilized for designing practically memory-efficient data structures based on tree covering.
DDRM: Distributed Drone Reputation Management for Trust and Reliability in Crowdsourced Drone Services
Venue: arXiv cs.DC 2024
Year: 2024

This study introduces the Distributed Drone Reputation Management (DDRM) framework, designed to fortify trust and authenticity within the Internet of Drone Things (IoDT) ecosystem. As drones increasingly play a pivotal role across diverse sectors, integrating crowdsourced drone services within the IoDT has emerged as a vital avenue for democratizing access to these services. A critical challenge, however, lies in ensuring the authenticity and reliability of drone service reviews. Leveraging the Ethereum blockchain, DDRM addresses this challenge by instituting a verifiable and transparent review mechanism. The framework innovates with a dual-token system, comprising the Service Review Authorization Token (SRAT) for facilitating review authorization and the Drone Reputation Enhancement Token (DRET) for rewarding and recognizing drones demonstrating consistent reliability. Comprehensive analysis within this paper showcases DDRM's resilience against various reputation frauds and underscores its operational effectiveness, particularly in enhancing the efficiency and reliability of drone services.
Hierarchical Memory for Long Video QA
Venue: arXiv cs.CV 2024
Year: 2024

This paper describes our champion solution to the LOVEU Challenge @ CVPR'24, Track 1 (Long Video VQA). Processing long sequences of visual tokens is computationally expensive and memory-intensive, making long video question-answering a challenging task. The key is to compress visual tokens effectively, reducing memory footprint and decoding latency, while preserving the essential information for accurate question-answering. We adopt a hierarchical memory mechanism named STAR Memory, proposed in Flash-VStream, that is capable of processing long videos with limited GPU memory (VRAM). We further utilize the video and audio data of MovieChat-1K training set to fine-tune the pretrained weight released by Flash-VStream, achieving 1st place in the challenge. Code is available at project homepage https://invinciblewyq.github.io/vstream-page .
Fast-OverlaPIM: A Fast Overlap-driven Mapping Framework for Processing In-Memory Neural Network Acceleration
Venue: arXiv cs.AR 2024
Year: 2024

Processing in-memory (PIM) is promising to accelerate neural networks (NNs) because it minimizes data movement and provides large computational parallelism. Similar to machine learning accelerators, application mapping, which determines the operation scheduling and data layout, plays a critical role in the NN acceleration on PIM. The mapping optimization of previous NN accelerators focused on optimizing the latency of sequential execution. However, PIM accelerators feature a distinct design space of application mapping from conventional NN accelerators, due to the spatial execution of NN layers across different memory locations. This enables opportunities for overlapping execution of consecutive NN layers to improve the latency, where the succeeding layer can start execution before the preceding layer fully completes the computation. In this paper, we propose Fast-OverlaPIM framework that incorporates the computational overlapping optimization into the DNN mapping exploration process on PIM architectures. Fast-OverlaPIM includes analytical algorithms for fast and accurate overlap analysis. Furthermore, it proposes a novel mapping search strategy and a transformation mechanism to enable efficient design space exploration on the overlap-based mapping for the whole network. Our framework demonstrates a significant improvement in runtime performance from 3.4x to 323.1x compared to the previous state-of-the-art overlap-based framework. Our experiments show that Fast-OverlaPIM can efficiently produce mappings that are 4.6x to 18.1x faster than the state-of-the-art mapping optimization framework under the same architecture constraints.
ESGNN: Towards Equivariant Scene Graph Neural Network for 3D Scene Understanding
Venue: arXiv cs.CV 2024
Year: 2024

Scene graphs have been proven to be useful for various scene understanding tasks due to their compact and explicit nature. However, existing approaches often neglect the importance of maintaining the symmetry-preserving property when generating scene graphs from 3D point clouds. This oversight can diminish the accuracy and robustness of the resulting scene graphs, especially when handling noisy, multi-view 3D data. This work, to the best of our knowledge, is the first to implement an Equivariant Graph Neural Network in semantic scene graph generation from 3D point clouds for scene understanding. Our proposed method, ESGNN, outperforms existing state-of-the-art approaches, demonstrating a significant improvement in scene estimation with faster convergence. ESGNN demands low computational resources and is easy to implement from available frameworks, paving the way for real-time applications such as robotics and computer vision.
Dense Retrieval with Continuous Explicit Feedback for Systematic Review Screening Prioritisation
Venue: arXiv cs.IR 2024
Year: 2024

The goal of screening prioritisation in systematic reviews is to identify relevant documents with high recall and rank them in early positions for review. This saves reviewing effort if paired with a stopping criterion, and speeds up review completion if performed alongside downstream tasks. Recent studies have shown that neural models have good potential on this task, but their time-consuming fine-tuning and inference discourage their widespread use for screening prioritisation. In this paper, we propose an alternative approach that still relies on neural models, but leverages dense representations and relevance feedback to enhance screening prioritisation, without the need for costly model fine-tuning and inference. This method exploits continuous relevance feedback from reviewers during document screening to efficiently update the dense query representation, which is then applied to rank the remaining documents to be screened. We evaluate this approach across the CLEF TAR datasets for this task. Results suggest that the investigated dense query-driven approach is more efficient than directly using neural models and shows promising effectiveness compared to previous methods developed on the considered datasets. Our code is available at https://github.com/ielab/dense-screening-feedback.
A Collocation-based Method for Addressing Challenges in Word-level Metric Differential Privacy
Venue: arXiv cs.CL 2024
Year: 2024

Applications of Differential Privacy (DP) in NLP must distinguish between the syntactic level on which a proposed mechanism operates, often taking the form of $\textit{word-level}$ or $\textit{document-level}$ privatization. Recently, several word-level $\textit{Metric}$ Differential Privacy approaches have been proposed, which rely on this generalized DP notion for operating in word embedding spaces. These approaches, however, often fail to produce semantically coherent textual outputs, and their application at the sentence- or document-level is only possible by a basic composition of word perturbations. In this work, we strive to address these challenges by operating $\textit{between}$ the word and sentence levels, namely with $\textit{collocations}$. By perturbing n-grams rather than single words, we devise a method where composed privatized outputs have higher semantic coherence and variable length. This is accomplished by constructing an embedding model based on frequently occurring word groups, in which unigram words co-exist with bi- and trigram collocations. We evaluate our method in utility and privacy tests, which make a clear case for tokenization strategies beyond the word level.
BAPO: Base-Anchored Preference Optimization for Overcoming Forgetting in Large Language Models Personalization
Venue: arXiv cs.AI 2024
Year: 2024

While learning to align Large Language Models (LLMs) with human preferences has shown remarkable success, aligning these models to meet the diverse user preferences presents further challenges in preserving previous knowledge. This paper examines the impact of personalized preference optimization on LLMs, revealing that the extent of knowledge loss varies significantly with preference heterogeneity. Although previous approaches have utilized the KL constraint between the reference model and the policy model, we observe that they fail to maintain general knowledge and alignment when facing personalized preferences. To this end, we introduce Base-Anchored Preference Optimization (BAPO), a simple yet effective approach that utilizes the initial responses of reference model to mitigate forgetting while accommodating personalized alignment. BAPO effectively adapts to diverse user preferences while minimally affecting global knowledge or general alignment. Our experiments demonstrate the efficacy of BAPO in various setups.
Detection of Dark Web Threats Using Machine Learning and Image Processing
Venue: arXiv cs.CR 2024
Year: 2024

This paper aimed to discover the risks associated with the dark web and to detect the threats related to human trafficking using image processing with OpenCV and Python. Apart from that, a development environment was set up by installing TensorFlow, OpenCV and Python. Through exploratory data analysis (EDA), significant insights into the distribution and interactions of dataset features were obtained, which are crucial for evaluating various cyberthreats. The construction and evaluation of logistic regression and support vector machine (SVM) models revealed that the SVM model outperforms logistic regression in accuracy. The paper delves into the intricacies of data preprocessing, EDA, and model development, offering valuable insights into network protection and cyberthreat response.
Beyond the Yield Barrier: Variational Importance Sampling Yield Analysis
Venue: arXiv cs.CE 2024
Year: 2024

Optimal mean shift vector (OMSV)-based importance sampling methods have long been prevalent in yield estimation and optimization as an industry standard. However, most OMSV-based methods are designed heuristically without a rigorous understanding of their limitations. To this end, we propose VIS, the first variational analysis framework for yield problems, enabling a systematic refinement for OMSV. For instance, VIS reveals that the classic OMSV is suboptimal, and the optimal/true OMSV should always stay beyond the failure boundary, which enables a free improvement for all OMSV-based methods immediately. Using VIS, we show a progressive refinement for the classic OMSV including incorporation of full covariance in closed form, adjusting for asymmetric failure distributions, and capturing multiple failure regions, each of which contributes to a progressive improvement of more than 2x. Inheriting the simplicity of OMSV, the proposed method retains simplicity and robustness yet achieves up to 29.03x speedup over the state-of-the-art (SOTA) methods. We also demonstrate how the SOTA yield optimization, ASAIS, can immediately benefit from our True OMSV, delivering a 1.20x and 1.27x improvement in performance and efficiency, respectively, without additional computational overhead.
Large Language Models Struggle in Token-Level Clinical Named Entity Recognition
Venue: arXiv cs.CL 2024
Year: 2024

Large Language Models (LLMs) have revolutionized various sectors, including healthcare where they are employed in diverse applications. Their utility is particularly significant in the context of rare diseases, where data scarcity, complexity, and specificity pose considerable challenges. In the clinical domain, Named Entity Recognition (NER) stands out as an essential task and it plays a crucial role in extracting relevant information from clinical texts. Despite the promise of LLMs, current research mostly concentrates on document-level NER, identifying entities in a more general context across entire documents, without extracting their precise location. Additionally, efforts have been directed towards adapting ChatGPT for token-level NER. However, there is a significant research gap when it comes to employing token-level NER for clinical texts, especially with the use of local open-source LLMs. This study aims to bridge this gap by investigating the effectiveness of both proprietary and local LLMs in token-level clinical NER. Essentially, we delve into the capabilities of these models through a series of experiments involving zero-shot prompting, few-shot prompting, retrieval-augmented generation (RAG), and instruction-fine-tuning. Our exploration reveals the inherent challenges LLMs face in token-level NER, particularly in the context of rare diseases, and suggests possible improvements for their application in healthcare. This research contributes to narrowing a significant gap in healthcare informatics and offers insights that could lead to a more refined application of LLMs in the healthcare sector.
Chest-Diffusion: A Light-Weight Text-to-Image Model for Report-to-CXR Generation
Venue: arXiv cs.CV 2024
Year: 2024

Text-to-image generation has important implications for generation of diverse and controllable images. Several attempts have been made to adapt Stable Diffusion (SD) to the medical domain. However, the large distribution difference between medical reports and natural texts, as well as high computational complexity in common stable diffusion limit the authenticity and feasibility of the generated medical images. To solve above problems, we propose a novel light-weight transformer-based diffusion model learning framework, Chest-Diffusion, for report-to-CXR generation. Chest-Diffusion employs a domain-specific text encoder to obtain accurate and expressive text features to guide image generation, improving the authenticity of the generated images. Meanwhile, we introduce a light-weight transformer architecture as the denoising model, reducing the computational complexity of the diffusion model. Experiments demonstrate that our Chest-Diffusion achieves the lowest FID score 24.456, under the computation budget of 118.918 GFLOPs, which is nearly one-third of the computational complexity of SD.