OpenRaiser/Intern-Atlas huggingface dataset:
Self-supervised Anomaly Detection Pretraining Enhances Long-tail ECG Diagnosis
Venue: arXiv cs.CV 2024
Year: 2024

Current computer-aided ECG diagnostic systems struggle with the underdetection of rare but critical cardiac anomalies due to the imbalanced nature of ECG datasets. This study introduces a novel approach using self-supervised anomaly detection pretraining to address this limitation. The anomaly detection model is specifically designed to detect and localize subtle deviations from normal cardiac patterns, capturing the nuanced details essential for accurate ECG interpretation. Validated on an extensive dataset of over one million ECG records from clinical practice, characterized by a long-tail distribution across 116 distinct categories, the anomaly detection-pretrained ECG diagnostic model has demonstrated a significant improvement in overall accuracy. Notably, our approach yielded a 94.7% AUROC, 92.2% sensitivity, and 92.5\% specificity for rare ECG types, significantly outperforming traditional methods and narrowing the performance gap with common ECG types. The integration of anomaly detection pretraining into ECG analysis represents a substantial contribution to the field, addressing the long-standing challenge of long-tail data distributions in clinical diagnostics. Furthermore, prospective validation in real-world clinical settings revealed that our AI-driven approach enhances diagnostic efficiency, precision, and completeness by 32%, 6.7%, and 11.8% respectively, when compared to standard practices. This advancement marks a pivotal step forward in the integration of AI within clinical cardiology, with particularly profound implications for emergency care, where rapid and accurate ECG interpretation is crucial. The contributions of this study not only push the boundaries of current ECG diagnostic capabilities but also lay the groundwork for more reliable and accessible cardiovascular care.
Optimizing Traversal Queries of Sensor Data Using a Rule-Based Reachability Approach
Venue: arXiv cs.DB 2024
Year: 2024

Link Traversal queries face challenges in completeness and long execution time due to the size of the web. Reachability criteria define completeness by restricting the links followed by engines. However, the number of links to dereference remains the bottleneck of the approach. Web environments often have structures exploitable by query engines to prune irrelevant sources. Current criteria rely on using information from the query definition and predefined predicate. However, it is difficult to use them to traverse environments where logical expressions indicate the location of resources. We propose to use a rule-based reachability criterion that captures logical statements expressed in hypermedia descriptions within linked data documents to prune irrelevant sources. In this poster paper, we show how the Comunica link traversal engine is modified to take hints from a hypermedia control vocabulary, to prune irrelevant sources. Our preliminary findings show that by using this strategy, the query engine can significantly reduce the number of HTTP requests and the query execution time without sacrificing the completeness of results. Our work shows that the investigation of hypermedia controls in link pruning of traversal queries is a worthy effort for optimizing web queries of unindexed decentralized databases.
Secure Transmission in Cell-Free Massive MIMO under Active Eavesdropping
Venue: arXiv cs.IT 2024
Year: 2024

We study secure communications in cell-free massive multiple-input multiple-output (CF-mMIMO) systems with multi-antenna access points (APs) and protective partial zero-forcing (PPZF) precoding. In particular, we consider an active eavesdropping attack, where an eavesdropper contaminates the uplink channel estimation phase by sending an identical pilot sequence with a legitimate user of interest. We formulate an optimization problem for maximizing the received signal-to-noise ratio (SINR) at the legitimate user, subject to a maximum allowable SINR at the eavesdropper and maximum transmit power at each AP, while guaranteeing specific SINR requirements on other legitimate users. The optimization problem is solved using a path-following algorithm. We also propose a large-scale-based greedy AP selection scheme to improve the secrecy spectral efficiency (SSE). Finally, we propose a simple method for identifying the presence of an eavesdropper within the system. Our findings show that PPZF can substantially outperform the conventional maximum-ratio transmission (MRT) scheme by providing around 2-fold improvement in the SSE compared to the MRT scheme. More importantly, for PPZF precoding scheme, our proposed AP selection can achieve a remarkable SSE gain of up to 220%, while our power optimization approach can provide an additional gain of up to 55% compared with a CF-mMIMO system with equal power allocation.
Privacy-Preserving Set-Based Estimation Using Differential Privacy and Zonotopes
Venue: arXiv cs.CR 2024
Year: 2024

For large-scale cyber-physical systems, the collaboration of spatially distributed sensors is often needed to perform the state estimation process. Privacy concerns arise from disclosing sensitive measurements to a cloud estimator. To solve this issue, we propose a differentially private set-based estimation protocol that guarantees true state containment in the estimated set and differential privacy for the sensitive measurements throughout the set-based state estimation process within the central and local differential privacy models. Zonotopes are employed in the proposed differentially private set-based estimator, offering computational advantages in set operations. We consider a plant of a non-linear discrete-time dynamical system with bounded modeling uncertainties, sensors that provide sensitive measurements with bounded measurement uncertainties, and a cloud estimator that predicts the system's state. The privacy-preserving noise perturbs the centers of measurement zonotopes, thereby concealing the precise position of these zonotopes, i.e., ensuring privacy preservation for the sets containing sensitive measurements. Compared to existing research, our approach achieves less privacy loss and utility loss through the central and local differential privacy models by leveraging a numerically optimized truncated noise distribution. The proposed estimator is perturbed by weaker noise than the analytical approaches in the literature to guarantee the same level of privacy, therefore improving the estimation utility. Numerical and comparison experiments with truncated Laplace noise are presented to support our approach.
Further Investigation on Differential Properties of the Generalized Ness-Helleseth Function
Venue: arXiv cs.CR 2024
Year: 2024

Let $n$ be an odd positive integer, $p$ be a prime with $p\equiv3\pmod4$, $d_{1} = {{p^{n}-1}\over {2}} -1 $ and $d_{2} =p^{n}-2$. The function defined by $f_u(x)=ux^{d_{1}}+x^{d_{2}}$ is called the generalized Ness-Helleseth function over $\mathbb{F}_{p^n}$, where $u\in\mathbb{F}_{p^n}$. It was initially studied by Ness and Helleseth in the ternary case. In this paper, for $p^n \equiv 3 \pmod 4$ and $p^n \ge7$, we provide the necessary and sufficient condition for $f_u(x)$ to be an APN function. In addition, for each $u$ satisfying $χ(u+1) = χ(u-1)$, the differential spectrum of $f_u(x)$ is investigated, and it is expressed in terms of some quadratic character sums of cubic polynomials, where $χ(\cdot)$ denotes the quadratic character of $\mathbb{F}_{p^n}$.
Non-Promise Version of Unique Sink Orientations
Venue: arXiv cs.DM 2024
Year: 2024

A unique sink orientation (USO) is an orientation of the edges of a hypercube such that each face has a unique sink. Many optimization problems like linear programs reduce to USOs, in the sense that each vertex corresponds to a possible solution, and the global sink corresponds to the optimal solution. People have been studying intensively the problem of find the sink of a USO using vertex evaluations, i.e., queries which return the orientation of the edges around a vertex. This problem is a so called promise problem, as it assumes that the orientation it receives is a USO. In this paper, we analyze a non-promise version of the USO problem, in which we try to either find a sink or an efficiently verifiable violation of the USO property. This problem is worth investigating, because some problems which reduce to USO are also promise problems (and so we can also define a non-promise version for them), and it would be interesting to discover where USO lies in the hierarchy of subclasses of $\texttt{TFNP}^\texttt{dt}$, and for this a total search problem is required (which is the case for the non-promise version). We adapt many known properties and algorithms from the promise version to the non-promise one, including known algorithms for small dimensions and lower and upper bounds, like the Fibonacci Seesaw Algorithm. Furthermore, we present an efficient resolution proof of the problem, which shows it is in the search complexity class $\texttt{PLS}^\texttt{dt}$ (although this fact was already known via reductions). Finally, although initially the only allowed violations consist of $2$ vertices, we generalize them to more vertices, and provide a full categorization of violations with $4$ vertices, showing that they are also efficiently verifiable.
Human Rights for the Digital Age
Venue: arXiv cs.CY 2024
Year: 2024

The emergence of digital technology has fundamentally transformed all facets of human existence, posing important queries about the safeguarding and implementation of human rights in the digital domain. The research focuses on important topics including privacy, freedom of speech, and information access. The methodology involves an extensive review of existing literature, legal frameworks, and relevant case studies to provide a comprehensive understanding of the intersection between technology and human rights. The paper highlights the challenges posed by surveillance, data breaches, and the digital divide while also exploring the role of international law and policy in safeguarding digital rights. The review highlights the significance of modifying human rights frameworks for the digital era, pointing out gaps in existing research and offering recommendations for future investigations.
XULIA -- Comprehensive control system for Windows$^{tm}$ devices designed for people with tetraplegia
Venue: arXiv cs.HC 2024
Year: 2024

XULIA is a comprehensive control system for Windows computers designed specifically to be used by quadriplegic people or people who do not have the ability to move their upper limbs accurately. XULIA allows you to manage all the functions necessary to control all Windows functions using only your voice. As a voice-to-text transcription system, it uses completely free modules combining the Windows SAPI voice recognition libraries for command recognition with Google's cloud-based voice recognition systems indirectly through a Google Chrome browser, which allows you to use Google's paid voice-to-text transcription services completely free of charge. XULIA manages multiple grammars simultaneously with automatic activation to ensure that the set of commands to be recognized is reduced to a minimum at all times, which allows false positives in command recognition to be reduced to a minimum.
LASSO-MOGAT: A Multi-Omics Graph Attention Framework for Cancer Classification
Venue: arXiv cs.LG 2024
Year: 2024

The application of machine learning methods to analyze changes in gene expression patterns has recently emerged as a powerful approach in cancer research, enhancing our understanding of the molecular mechanisms underpinning cancer development and progression. Combining gene expression data with other types of omics data has been reported by numerous works to improve cancer classification outcomes. Despite these advances, effectively integrating high-dimensional multi-omics data and capturing the complex relationships across different biological layers remains challenging. This paper introduces LASSO-MOGAT (LASSO-Multi-Omics Gated ATtention), a novel graph-based deep learning framework that integrates messenger RNA, microRNA, and DNA methylation data to classify 31 cancer types. Utilizing differential expression analysis with LIMMA and LASSO regression for feature selection, and leveraging Graph Attention Networks (GATs) to incorporate protein-protein interaction (PPI) networks, LASSO-MOGAT effectively captures intricate relationships within multi-omics data. Experimental validation using five-fold cross-validation demonstrates the method's precision, reliability, and capacity for providing comprehensive insights into cancer molecular mechanisms. The computation of attention coefficients for the edges in the graph by the proposed graph-attention architecture based on protein-protein interactions proved beneficial for identifying synergies in multi-omics data for cancer classification.
How Knowledge Distillation Mitigates the Synthetic Gap in Fair Face Recognition
Venue: arXiv cs.CV 2024
Year: 2024

Leveraging the capabilities of Knowledge Distillation (KD) strategies, we devise a strategy to fight the recent retraction of face recognition datasets. Given a pretrained Teacher model trained on a real dataset, we show that carefully utilising synthetic datasets, or a mix between real and synthetic datasets to distil knowledge from this teacher to smaller students can yield surprising results. In this sense, we trained 33 different models with and without KD, on different datasets, with different architectures and losses. And our findings are consistent, using KD leads to performance gains across all ethnicities and decreased bias. In addition, it helps to mitigate the performance gap between real and synthetic datasets. This approach addresses the limitations of synthetic data training, improving both the accuracy and fairness of face recognition models.
Getting Inspiration for Feature Elicitation: App Store- vs. LLM-based Approach
Venue: arXiv cs.SE 2024
Year: 2024

Over the past decade, app store (AppStore)-inspired requirements elicitation has proven to be highly beneficial. Developers often explore competitors' apps to gather inspiration for new features. With the advance of Generative AI, recent studies have demonstrated the potential of large language model (LLM)-inspired requirements elicitation. LLMs can assist in this process by providing inspiration for new feature ideas. While both approaches are gaining popularity in practice, there is a lack of insight into their differences. We report on a comparative study between AppStore- and LLM-based approaches for refining features into sub-features. By manually analyzing 1,200 sub-features recommended from both approaches, we identified their benefits, challenges, and key differences. While both approaches recommend highly relevant sub-features with clear descriptions, LLMs seem more powerful particularly concerning novel unseen app scopes. Moreover, some recommended features are imaginary with unclear feasibility, which suggests the importance of a human-analyst in the elicitation loop.
CinePreGen: Camera Controllable Video Previsualization via Engine-powered Diffusion
Venue: arXiv cs.CV 2024
Year: 2024

With advancements in video generative AI models (e.g., SORA), creators are increasingly using these techniques to enhance video previsualization. However, they face challenges with incomplete and mismatched AI workflows. Existing methods mainly rely on text descriptions and struggle with camera placement, a key component of previsualization. To address these issues, we introduce CinePreGen, a visual previsualization system enhanced with engine-powered diffusion. It features a novel camera and storyboard interface that offers dynamic control, from global to local camera adjustments. This is combined with a user-friendly AI rendering workflow, which aims to achieve consistent results through multi-masked IP-Adapter and engine simulation guidelines. In our comprehensive evaluation study, we demonstrate that our system reduces development viscosity (i.e., the complexity and challenges in the development process), meets users' needs for extensive control and iteration in the design process, and outperforms other AI video production workflows in cinematic camera movement, as shown by our experiments and a within-subjects user study. With its intuitive camera controls and realistic rendering of camera motion, CinePreGen shows great potential for improving video production for both individual creators and industry professionals.
Information-Based Trajectory Planning for Autonomous Absolute Tracking in Cislunar Space
Venue: arXiv cs.RO 2024
Year: 2024

The resurgence of lunar operations requires advancements in cislunar navigation and Space Situational Awareness (SSA). Challenges associated to these tasks have created an interest in autonomous planning, navigation, and tracking technologies that operate with little ground-based intervention. This research introduces a trajectory planning tool for a low-thrust mobile observer, aimed at maximizing navigation and tracking performance with satellite-to-satellite relative measurements. We formulate an expression for the information gathered over an observation period based on the mutual information between augmented observer/target states and the associated measurement set collected. We then develop an optimal trajectory design problem for a mobile observer, balancing information gain and control effort, and solve this problem with a Sequential Convex Programming (SCP) approach. The developed methods are demonstrated in scenarios involving spacecraft in the cislunar regime, demonstrating the potential for improved autonomous navigation and tracking.
Channel Knowledge Map for Cellular-Connected UAV via Binary Bayesian Filtering
Venue: arXiv cs.IT 2024
Year: 2024

Channel knowledge map (CKM) is a promising technology to enable environment-aware wireless communications and sensing. Link state map (LSM) is one particular type of CKM that aims to learn the location-specific line-of-sight (LoS) link probability between the transmitter and the receiver at all possible locations, which provides the prior information to enhance the communication quality of dynamic networks. This paper investigates the LSM construction for cellularconnected unmanned aerial vehicles (UAVs) by utilizing both the expert empirical mathematical model and the measurement data. Specifically, we first model the LSM as a binary spatial random field and its initial distribution is obtained by the empirical model. Then we propose an effective binary Bayesian filter to sequentially update the LSM by using the channel measurement. To efficiently update the LSM, we establish the spatial correlation models of LoS probability on the location pairs in both the distance and angular domains, which are adopted in the Bayesian filter for updating the probabilities at locations without measurements. Simulation results demonstrate the effectiveness of the proposed algorithm for LSM construction, which significantly outperforms the benchmark scheme, especially when the measurements are sparse.
Detecting Misinformation in Multimedia Content through Cross-Modal Entity Consistency: A Dual Learning Approach
Venue: arXiv cs.MM 2024
Year: 2024

The landscape of social media content has evolved significantly, extending from text to multimodal formats. This evolution presents a significant challenge in combating misinformation. Previous research has primarily focused on single modalities or text-image combinations, leaving a gap in detecting multimodal misinformation. While the concept of entity consistency holds promise in detecting multimodal misinformation, simplifying the representation to a scalar value overlooks the inherent complexities of high-dimensional representations across different modalities. To address these limitations, we propose a Multimedia Misinformation Detection (MultiMD) framework for detecting misinformation from video content by leveraging cross-modal entity consistency. The proposed dual learning approach allows for not only enhancing misinformation detection performance but also improving representation learning of entity consistency across different modalities. Our results demonstrate that MultiMD outperforms state-of-the-art baseline models and underscore the importance of each modality in misinformation detection. Our research provides novel methodological and technical insights into multimodal misinformation detection.
Pupil-Adaptive 3D Holography Beyond Coherent Depth-of-Field
Venue: arXiv cs.CV 2024
Year: 2024

Recent holographic display approaches propelled by deep learning have shown remarkable success in enabling high-fidelity holographic projections. However, these displays have still not been able to demonstrate realistic focus cues, and a major gap still remains between the defocus effects possible with a coherent light-based holographic display and those exhibited by incoherent light in the real world. Moreover, existing methods have not considered the effects of the observer's eye pupil size variations on the perceived quality of 3D projections, especially on the defocus blur due to varying depth-of-field of the eye. In this work, we propose a framework that bridges the gap between the coherent depth-of-field of holographic displays and what is seen in the real world due to incoherent light. To this end, we investigate the effect of varying shape and motion of the eye pupil on the quality of holographic projections, and devise a method that changes the depth-of-the-field of holographic projections dynamically in a pupil-adaptive manner. Specifically, we introduce a learning framework that adjusts the receptive fields on-the-go based on the current state of the observer's eye pupil to produce image effects that otherwise are not possible in current computer-generated holography approaches. We validate the proposed method both in simulations and on an experimental prototype holographic display, and demonstrate significant improvements in the depiction of depth-of-field effects, outperforming existing approaches both qualitatively and quantitatively by at least 5 dB in peak signal-to-noise ratio.
Evaluating ChatGPT on Nuclear Domain-Specific Data
Venue: arXiv cs.CL 2024
Year: 2024

This paper examines the application of ChatGPT, a large language model (LLM), for question-and-answer (Q&A) tasks in the highly specialized field of nuclear data. The primary focus is on evaluating ChatGPT's performance on a curated test dataset, comparing the outcomes of a standalone LLM with those generated through a Retrieval Augmented Generation (RAG) approach. LLMs, despite their recent advancements, are prone to generating incorrect or 'hallucinated' information, which is a significant limitation in applications requiring high accuracy and reliability. This study explores the potential of utilizing RAG in LLMs, a method that integrates external knowledge bases and sophisticated retrieval techniques to enhance the accuracy and relevance of generated outputs. In this context, the paper evaluates ChatGPT's ability to answer domain-specific questions, employing two methodologies: A) direct response from the LLM, and B) response from the LLM within a RAG framework. The effectiveness of these methods is assessed through a dual mechanism of human and LLM evaluation, scoring the responses for correctness and other metrics. The findings underscore the improvement in performance when incorporating a RAG pipeline in an LLM, particularly in generating more accurate and contextually appropriate responses for nuclear domain-specific queries. Additionally, the paper highlights alternative approaches to further refine and improve the quality of answers in such specialized domains.