OpenRaiser/Intern-Atlas huggingface dataset:
Recruiting Teenage Participants for an Online Security Experiment: A Case Study Using Peachjar
Venue: arXiv cs.HC 2024
Year: 2024

The recruitment of teenagers for usable privacy and security research is challenging, but essential. This case study presents our experience using the online flier distribution service Peachjar to recruit minor teenagers for an online security experiment. By distributing fliers to 90 K-12 schools, we recruited a diverse sample of 55 participants at an estimated cost per participant of $43.18. We discuss the benefits and drawbacks of Peachjar, concluding that it can facilitate the recruitment of a geographically diverse sample of teens for online studies, but it requires careful design to protect against spam and may be more expensive than other online methods. We conclude by proposing ways of using Peachjar more effectively.
Strategic Coalitions in Networked Contest Games
Venue: arXiv cs.GT 2024
Year: 2024

In competitive resource allocation formulations multiple agents compete over different contests by committing their limited resources in them. For these settings, contest games offer a game-theoretic foundation to analyze how players can efficiently invest their resources. In this class of games the resulting behavior can be affected by external interactions among the players. In particular, players could be able to make coalitions that allow transferring resources among them, seeking to improve their outcomes. In this work, we study bilateral budgetary transfers in contest games played over networks. Particularly, we characterize the family of networks where there exist mutually beneficial bilateral transfer for some set of systems parameters. With this in mind, we provide sufficient conditions for the existence of mutually beneficial transfers. Moreover, we provide a constructive argument that guarantees that the benefit of making coalitions only depends on mild connectivity conditions of the graph structure. Lastly, we provide a characterization of the improvement of the utilities as a function of the transferred budget. Further, we demonstrate how gradient-based dynamics can be utilized to find desirable coalitional structures. Interestingly, our findings demonstrate that such collaborative opportunities extend well beyond the typical "enemy-of-my-enemy" alliances.
On Constrained and k Shortest Paths
Venue: arXiv cs.DS 2024
Year: 2024

Finding a shortest path in a graph is one of the most classic problems in algorithmic and graph theory. While we dispose of quite efficient algorithms for this ordinary problem (like the Dijkstra or Bellman-Ford algorithms), some slight variations in the problem statement can quickly lead to computationally hard problems. This article focuses specifically on two of these variants, namely the constrained shortest paths problem and the k shortest paths problem. Both problems are NP-hard, and thus it's not sure we can conceive a polynomial time algorithm (unless P = NP), ours aren't for instance. Moreover, across this article, we provide ILP formulations of these problems in order to give a different point of view to the interested reader. Although we did not try to implement these on modern ILP solvers, it can be an interesting path to explore. We also mention how these algorithms constitute essential ingredients in some of the most important modern applications in the field of data science, such as Isomap, whose main objective is the reduction of dimensionality of high-dimensional datasets.
Parkinson's Disease Detection from Resting State EEG using Multi-Head Graph Structure Learning with Gradient Weighted Graph Attention Explanations
Venue: arXiv cs.LG 2024
Year: 2024

Parkinson's disease (PD) is a debilitating neurodegenerative disease that has severe impacts on an individual's quality of life. Compared with structural and functional MRI-based biomarkers for the disease, electroencephalography (EEG) can provide more accessible alternatives for clinical insights. While deep learning (DL) techniques have provided excellent outcomes, many techniques fail to model spatial information and dynamic brain connectivity, and face challenges in robust feature learning, limited data sizes, and poor explainability. To address these issues, we proposed a novel graph neural network (GNN) technique for explainable PD detection using resting state EEG. Specifically, we employ structured global convolutions with contrastive learning to better model complex features with limited data, a novel multi-head graph structure learner to capture the non-Euclidean structure of EEG data, and a head-wise gradient-weighted graph attention explainer to offer neural connectivity insights. We developed and evaluated our method using the UC San Diego Parkinson's disease EEG dataset, and achieved 69.40% detection accuracy in subject-wise leave-one-out cross-validation while generating intuitive explanations for the learnt graph topology.
How much should you pay for restaking security?
Venue: arXiv cs.GT 2024
Year: 2024

Restaking protocols have aggregated billions of dollars of security by utilizing token incentives and payments. A natural question to ask is: How much security do restaked services \emph{really} need to purchase? To answer this question, we expand a model of Durvasula and Roughgarden [DR24] that includes incentives and an expanded threat model consisting of strategic attackers and users. Our model shows that an adversary with a strictly submodular profit combined with strategic node operators who respond to incentives can avoid the large-scale cascading failures of~[DR24]. We utilize our model to construct an approximation algorithm for choosing token-based incentives that achieve a given security level against adversaries who are bounded in the number of services they can simultaneously attack. Our results suggest that incentivized restaking protocols can be secure with proper incentive management.
PrivateGaze: Preserving User Privacy in Black-box Mobile Gaze Tracking Services
Venue: arXiv cs.CV 2024
Year: 2024

Eye gaze contains rich information about human attention and cognitive processes. This capability makes the underlying technology, known as gaze tracking, a critical enabler for many ubiquitous applications and has triggered the development of easy-to-use gaze estimation services. Indeed, by utilizing the ubiquitous cameras on tablets and smartphones, users can readily access many gaze estimation services. In using these services, users must provide their full-face images to the gaze estimator, which is often a black box. This poses significant privacy threats to the users, especially when a malicious service provider gathers a large collection of face images to classify sensitive user attributes. In this work, we present PrivateGaze, the first approach that can effectively preserve users' privacy in black-box gaze tracking services without compromising gaze estimation performance. Specifically, we proposed a novel framework to train a privacy preserver that converts full-face images into obfuscated counterparts, which are effective for gaze estimation while containing no privacy information. Evaluation on four datasets shows that the obfuscated image can protect users' private information, such as identity and gender, against unauthorized attribute classification. Meanwhile, when used directly by the black-box gaze estimator as inputs, the obfuscated images lead to comparable tracking performance to the conventional, unprotected full-face images.
Digital capabilities assessment for supporting the transformation of the customer experience
Venue: arXiv cs.CY 2024
Year: 2024

Most of organizations are increasingly investing huge amounts of money today in order to have the right digital capabilities required for their industry. The area where organisations feel they have made the most progress is in improving the customer experience, which encompasses aspects such as data analytics, social media, location-based marketing, mobile channels among others. This aspect became the most important for the survival of organisations since the outbreak of the Covid-19 pandemic. While much has been achieved, many organisations are still not satisfied. One of the major problems in moving forward is the lack of literature in both academia and industry on maturity models allowing organisations to understand their current state in terms of digital capabilities to engage with customers, as well as to plan the evolutionary path to improve in this area. To fulfil this lack, this paper presents the design and validation of a maturity model that enables organizations to assess their digital capabilities in order to improve customer experience and engagement throughout the customer lifecycle.
Caching Aided Multi-Tenant Serverless Computing
Venue: arXiv cs.DC 2024
Year: 2024

One key to enabling high-performance serverless computing is to mitigate cold-starts. Current solutions utilize a warm pool to keep function alive: a warm-start can be analogous to a CPU cache-hit. However, modern cache has multiple hierarchies and the last-level cache is shared among cores, whereas the warm pool is limited to a single tenant for security concerns. Also, the warm pool keep-alive policy can be further optimized using cache replacement algorithms. In this paper, we borrow practical optimizations from caching, and design FaasCamp, a caching-aided multi-tenant serverless computing framework. FaasCamp extends the single-tier warm pool into multi-tiers, with a reclaim pool introduced enabling secure function instance sharing among tenants. Also, FaasCamp leverages machine learning to approximate the optimal cache replacement policy to improve the warm rate. We have implemented a prototype and conducted extensive experiments under multiple scenarios. The results show that FaasCamp can outperform existing platforms with minimal overhead.
Automatic Extraction of Relationships among Motivations, Emotions and Actions from Natural Language Texts
Venue: arXiv cs.CL 2024
Year: 2024

We propose a new graph-based framework to reveal relationships among motivations, emotions and actions explicitly given natural language texts. A directed acyclic graph is designed to describe human's nature. Nurture beliefs are incorporated to connect outside events and the human's nature graph. No annotation resources are required due to the power of large language models. Amazon Fine Foods Reviews dataset is used as corpus and food-related motivations are focused. Totally 92,990 relationship graphs are generated, of which 63% make logical sense. We make further analysis to investigate error types for optimization direction in future research.
Reconstructing Richtmyer-Meshkov instabilities from noisy radiographs using low dimensional features and attention-based neural networks
Venue: arXiv cs.LG 2024
Year: 2024

A trained attention-based transformer network can robustly recover the complex topologies given by the Richtmyer-Meshkoff instability from a sequence of hydrodynamic features derived from radiographic images corrupted with blur, scatter, and noise. This approach is demonstrated on ICF-like double shell hydrodynamic simulations. The key component of this network is a transformer encoder that acts on a sequence of features extracted from noisy radiographs. This encoder includes numerous self-attention layers that act to learn temporal dependencies in the input sequences and increase the expressiveness of the model. This approach is demonstrated to exhibit an excellent ability to accurately recover the Richtmyer-Meshkov instability growth rates, even despite the gas-metal interface being greatly obscured by radiographic noise.
Community Cellular Networks Coverage Visualizer
Venue: arXiv cs.HC 2024
Year: 2024

The community cellular networks volunteers and researchers currently rarely have an access to information about the networks for each site. This makes it difficult for them to evaluate network performance, identify outrages and downtimes, or even to show the current site locations. In this paper, we propose the Community Cellular Networks Coverage Visualizer, a performance dashboard to help reduce the workload of technicians and gain trust from illustrating the reliability of the networks. The map displays the overall and in-depth performance for each current and future CCNs sites with privacy-focused implementation, while the multi-series line chart emphasizes on providing the capability of network overtime. Not only it will help users identify locations that have stronger and reliable signals nearby, but our applicaiton will also be an essential tool for volunteers and engineers to determine the optimal locations to install a new site and quickly identify possible network failures.
Piculet: Specialized Models-Guided Hallucination Decrease for MultiModal Large Language Models
Venue: arXiv cs.AI 2024
Year: 2024

Multimodal Large Language Models (MLLMs) have made significant progress in bridging the gap between visual and language modalities. However, hallucinations in MLLMs, where the generated text does not align with image content, continue to be a major challenge. Existing methods for addressing hallucinations often rely on instruction-tuning, which requires retraining the model with specific data, which increases the cost of utilizing MLLMs further. In this paper, we introduce a novel training-free method, named Piculet, for enhancing the input representation of MLLMs. Piculet leverages multiple specialized models to extract descriptions of visual information from the input image and combine these descriptions with the original image and query as input to the MLLM. We evaluate our method both quantitively and qualitatively, and the results demonstrate that Piculet greatly decreases hallucinations of MLLMs. Our method can be easily extended to different MLLMs while being universal.
Distribution Aggregation via Continuous Thiele's Rules
Venue: arXiv cs.GT 2024
Year: 2024

We introduce the class of \textit{Continuous Thiele's Rules} that generalize the familiar \textbf{Thiele's rules} \cite{janson2018phragmens} of multi-winner voting to distribution aggregation problems. Each rule in that class maximizes $\sum_if(π^i)$ where $π^i$ is an agent $i$'s satisfaction and $f$ could be any twice differentiable, increasing and concave real function. Based on a single quantity we call the \textit{'Inequality Aversion'} of $f$ (elsewhere known as "Relative Risk Aversion"), we derive bounds on the Egalitarian loss, welfare loss and the approximation of \textit{Average Fair Share}, leading to a quantifiable, continuous presentation of their inevitable trade-offs. In particular, we show that the Nash Product Rule satisfies\textit{ Average Fair Share} in our setting.
LLM as Runtime Error Handler: A Promising Pathway to Adaptive Self-Healing of Software Systems
Venue: arXiv cs.SE 2024
Year: 2024

Unanticipated runtime errors, lacking predefined handlers, can abruptly terminate execution and lead to severe consequences, such as data loss or system crashes. Despite extensive efforts to identify potential errors during the development phase, such unanticipated errors remain a challenge to to be entirely eliminated, making the runtime mitigation measurements still indispensable to minimize their impact. Automated self-healing techniques, such as reusing existing handlers, have been investigated to reduce the loss coming through with the execution termination. However, the usability of existing methods is retained by their predefined heuristic rules and they fail to handle diverse runtime errors adaptively. Recently, the advent of Large Language Models (LLMs) has opened new avenues for addressing this problem. Inspired by their remarkable capabilities in understanding and generating code, we propose to deal with the runtime errors in a real-time manner using LLMs. Specifically, we propose Healer, the first LLM-assisted self-healing framework for handling runtime errors. When an unhandled runtime error occurs, Healer will be activated to generate a piece of error-handling code with the help of its internal LLM and the code will be executed inside the runtime environment owned by the framework to obtain a rectified program state from which the program should continue its execution. Our exploratory study evaluates the performance of Healer using four different code benchmarks and three state-of-the-art LLMs, GPT-3.5, GPT-4, and CodeQwen-7B. Results show that, without the need for any fine-tuning, GPT-4 can successfully help programs recover from 72.8% of runtime errors, highlighting the potential of LLMs in handling runtime errors.
Amodal Segmentation for Laparoscopic Surgery Video Instruments
Venue: arXiv cs.CV 2024
Year: 2024

Segmentation of surgical instruments is crucial for enhancing surgeon performance and ensuring patient safety. Conventional techniques such as binary, semantic, and instance segmentation share a common drawback: they do not accommodate the parts of instruments obscured by tissues or other instruments. Precisely predicting the full extent of these occluded instruments can significantly improve laparoscopic surgeries by providing critical guidance during operations and assisting in the analysis of potential surgical errors, as well as serving educational purposes. In this paper, we introduce Amodal Segmentation to the realm of surgical instruments in the medical field. This technique identifies both the visible and occluded parts of an object. To achieve this, we introduce a new Amoal Instruments Segmentation (AIS) dataset, which was developed by reannotating each instrument with its complete mask, utilizing the 2017 MICCAI EndoVis Robotic Instrument Segmentation Challenge dataset. Additionally, we evaluate several leading amodal segmentation methods to establish a benchmark for this new dataset.
The EAP-AIAS: Adapting the AI Assessment Scale for English for Academic Purposes
Venue: arXiv cs.CY 2024
Year: 2024

The rapid advancement of Generative Artificial Intelligence (GenAI) presents both opportunities and challenges for English for Academic Purposes (EAP) instruction. This paper proposes an adaptation of the AI Assessment Scale (AIAS) specifically tailored for EAP contexts, termed the EAP-AIAS. This framework aims to provide a structured approach for integrating GenAI tools into EAP assessment practices while maintaining academic integrity and supporting language development. The EAP-AIAS consists of five levels, ranging from "No AI" to "Full AI", each delineating appropriate GenAI usage in EAP tasks. We discuss the rationale behind this adaptation, considering the unique needs of language learners and the dual focus of EAP on language proficiency and academic acculturation. This paper explores potential applications of the EAP-AIAS across various EAP assessment types, including writing tasks, presentations, and research projects. By offering a flexible framework, the EAP-AIAS seeks to empower EAP practitioners seeking to deal with the complexities of GenAI integration in education and prepare students for an AI-enhanced academic and professional future. This adaptation represents a step towards addressing the pressing need for ethical and pedagogically sound AI integration in language education.
Prototypical Partial Optimal Transport for Universal Domain Adaptation
Venue: arXiv cs.CV 2024
Year: 2024

Universal domain adaptation (UniDA) aims to transfer knowledge from a labeled source domain to an unlabeled target domain without requiring the same label sets of both domains. The existence of domain and category shift makes the task challenging and requires us to distinguish "known" samples (i.e., samples whose labels exist in both domains) and "unknown" samples (i.e., samples whose labels exist in only one domain) in both domains before reducing the domain gap. In this paper, we consider the problem from the point of view of distribution matching which we only need to align two distributions partially. A novel approach, dubbed mini-batch Prototypical Partial Optimal Transport (m-PPOT), is proposed to conduct partial distribution alignment for UniDA. In training phase, besides minimizing m-PPOT, we also leverage the transport plan of m-PPOT to reweight source prototypes and target samples, and design reweighted entropy loss and reweighted cross-entropy loss to distinguish "known" and "unknown" samples. Experiments on four benchmarks show that our method outperforms the previous state-of-the-art UniDA methods.
Validation of an Analysability Model in Hybrid Quantum Software
Venue: arXiv cs.SE 2024
Year: 2024

In the context of quantum-classical hybrid computing, evaluating analysability, which is the ease of understanding and modifying software, presents significant challenges due to the complexity and novelty of quantum algorithms. Although advances have been made in quantum software development, standard software quality evaluation methods do not fully address the specifics of quantum components, resulting in a gap in the ability to ensure and maintain the quality of hybrid software products. In this registered report proposal, we intend to validate a quality model focused on the analysability of hybrid software through an international collab orative approach involving academic institutions from Italy and Spain through a controlled experiment. This approach allows for a more detailed analysis and validation methodology and establishes a framework for future research and developments in software quality assessment in quantum computing.
IG-SLAM: Instant Gaussian SLAM
Venue: arXiv cs.CV 2024
Year: 2024

3D Gaussian Splatting has recently shown promising results as an alternative scene representation in SLAM systems to neural implicit representations. However, current methods either lack dense depth maps to supervise the mapping process or detailed training designs that consider the scale of the environment. To address these drawbacks, we present IG-SLAM, a dense RGB-only SLAM system that employs robust Dense-SLAM methods for tracking and combines them with Gaussian Splatting. A 3D map of the environment is constructed using accurate pose and dense depth provided by tracking. Additionally, we utilize depth uncertainty in map optimization to improve 3D reconstruction. Our decay strategy in map optimization enhances convergence and allows the system to run at 10 fps in a single process. We demonstrate competitive performance with state-of-the-art RGB-only SLAM systems while achieving faster operation speeds. We present our experiments on the Replica, TUM-RGBD, ScanNet, and EuRoC datasets. The system achieves photo-realistic 3D reconstruction in large-scale sequences, particularly in the EuRoC dataset.