OpenRaiser/Intern-Atlas huggingface dataset:
Where is Dmitry going? Framing 'migratory' decisions in the criminal underground
Venue: arXiv cs.CR 2024
Year: 2024

The cybercriminal underground consists of hundreds of forum communities that function as marketplaces and information-exchange platforms for both established and wannabe cybercriminals. The ecosystem is continuously evolving, with users migrating between forums and platforms. The emergence of cybercrime communities in Telegram and Discord only highlights the rising fragmentation and adaptability of the ecosystem. In this position paper, we explore the economic incentives and trust-building mechanisms that may drive a participant (hereafter, Dmitry) of the cybercriminal underground ecosystem to migrate from one forum or platform to another. What are the market signals that matter to Dmitry's decision of joining a specific community, and what roles and purposes do these communities or platforms play within the broader ecosystem? Ultimately, we build towards our thesis that by studying these mechanisms we could explain, and therefore act upon, Dmitry's choice of joining a criminal community rather than another. To build this argument, we first discuss previous work evaluating differences in trust signals depicted in criminal forums. We then present preliminary results evaluating criminal channels on Telegram using those same lenses. Further, we analyze the different roles these channels play in the criminal ecosystem. We then discuss implications for future research.
Exploring Privacy and Security as Drivers for Environmental Sustainability in Cloud-Based Office Solutions (Extended Abstract)
Venue: arXiv cs.SE 2024
Year: 2024

This paper explores the intersection of privacy, cybersecurity, and environmental impacts, specifically energy consumption and carbon emissions, in cloud-based office solutions. We hypothesise that solutions that emphasise privacy and security are typically "greener" than solutions that are financed through data collection and advertising. To test our hypothesis, we first investigate how the underlying architectures and business models of these services, e.g., monetisation through (personalised) advertising, contribute to the services' environmental impact. We then explore commonly used methodologies and identify tools that facilitate environmental assessments of software systems. By combining these tools, we develop an approach to systematically assess the environmental footprint of the user-side of online services, which we apply to investigate and compare the influence of service design and ad-blocking technology on the emissions of common web-mail services. Our measurements of a limited selection of such services does not yet conclusively support or falsify our hypothesis regarding primary impacts. However, we are already able to identify the greener web-mail services on the user-side and continue the investigation towards conclusive assessment strategies for online office solutions.
Towards Foundation Models for Critical Care Time Series
Venue: arXiv cs.LG 2024
Year: 2024

Notable progress has been made in generalist medical large language models across various healthcare areas. However, large-scale modeling of in-hospital time series data - such as vital signs, lab results, and treatments in critical care - remains underexplored. Existing datasets are relatively small, but combining them can enhance patient diversity and improve model robustness. To effectively utilize these combined datasets for large-scale modeling, it is essential to address the distribution shifts caused by varying treatment policies, necessitating the harmonization of treatment variables across the different datasets. This work aims to establish a foundation for training large-scale multi-variate time series models on critical care data and to provide a benchmark for machine learning models in transfer learning across hospitals to study and address distribution shift challenges. We introduce a harmonized dataset for sequence modeling and transfer learning research, representing the first large-scale collection to include core treatment variables. Future plans involve expanding this dataset to support further advancements in transfer learning and the development of scalable, generalizable models for critical healthcare applications.
Synthesising Handwritten Music with GANs: A Comprehensive Evaluation of CycleWGAN, ProGAN, and DCGAN
Venue: arXiv cs.CV 2024
Year: 2024

The generation of handwritten music sheets is a crucial step toward enhancing Optical Music Recognition (OMR) systems, which rely on large and diverse datasets for optimal performance. However, handwritten music sheets, often found in archives, present challenges for digitisation due to their fragility, varied handwriting styles, and image quality. This paper addresses the data scarcity problem by applying Generative Adversarial Networks (GANs) to synthesise realistic handwritten music sheets. We provide a comprehensive evaluation of three GAN models - DCGAN, ProGAN, and CycleWGAN - comparing their ability to generate diverse and high-quality handwritten music images. The proposed CycleWGAN model, which enhances style transfer and training stability, significantly outperforms DCGAN and ProGAN in both qualitative and quantitative evaluations. CycleWGAN achieves superior performance, with an FID score of 41.87, an IS of 2.29, and a KID of 0.05, making it a promising solution for improving OMR systems.
Low-Data Classification of Historical Music Manuscripts: A Few-Shot Learning Approach
Venue: arXiv cs.IR 2024
Year: 2024

In this paper, we explore the intersection of technology and cultural preservation by developing a self-supervised learning framework for the classification of musical symbols in historical manuscripts. Optical Music Recognition (OMR) plays a vital role in digitising and preserving musical heritage, but historical documents often lack the labelled data required by traditional methods. We overcome this challenge by training a neural-based feature extractor on unlabelled data, enabling effective classification with minimal samples. Key contributions include optimising crop preprocessing for a self-supervised Convolutional Neural Network and evaluating classification methods, including SVM, multilayer perceptrons, and prototypical networks. Our experiments yield an accuracy of 87.66\%, showcasing the potential of AI-driven methods to ensure the survival of historical music for future generations through advanced digital archiving techniques.
Shortest Path Lengths in Poisson Line Cox Processes: Approximations and Applications
Venue: arXiv cs.IT 2024
Year: 2024

We derive exact expressions for the shortest path length to a point of a Poisson line Cox process (PLCP) from the typical point of the PLCP and from the typical intersection of the underlying Poisson line process (PLP), restricted to a single turn. For the two turns case, we derive a bound on the shortest path length from the typical point and demonstrate conditions under which the bound is tight. We also highlight the line process and point process densities for which the shortest path from the typical intersection under the one turn restriction may be shorter than the shortest path from the typical point under the two turns restriction. Finally, we discuss two applications where our results can be employed for a statistical characterization of system performance: in a re-configurable intelligent surface (RIS) enabled vehicle-to-vehicle (V2V) communication system and in electric vehicle charging point deployment planning in urban streets.
Distributed, communication-efficient, and differentially private estimation of KL divergence
Venue: arXiv cs.LG 2024
Year: 2024

A key task in managing distributed, sensitive data is to measure the extent to which a distribution changes. Understanding this drift can effectively support a variety of federated learning and analytics tasks. However, in many practical settings sharing such information can be undesirable (e.g., for privacy concerns) or infeasible (e.g., for high communication costs). In this work, we describe novel algorithmic approaches for estimating the KL divergence of data across federated models of computation, under differential privacy. We analyze their theoretical properties and present an empirical study of their performance. We explore parameter settings that optimize the accuracy of the algorithm catering to each of the settings; these provide sub-variations that are applicable to real-world tasks, addressing different context- and application-specific trust level requirements. Our experimental results confirm that our private estimators achieve accuracy comparable to a baseline algorithm without differential privacy guarantees.
Jaya R Package -- A Parameter-Free Solution for Advanced Single and Multi-Objective Optimization
Venue: arXiv cs.MS 2024
Year: 2024

The Jaya R package offers a robust and versatile implementation of the parameter-free Jaya optimization algorithm, suitable for solving both single-objective and multi-objective optimization problems. By integrating advanced features such as constraint handling, adaptive population management, Pareto front tracking for multi-objective trade-offs, and parallel processing for computational efficiency, the package caters to a wide range of optimization challenges. Its intuitive design and flexibility allow users to solve complex, real-world problems across various domains. To demonstrate its practical utility, a case study on energy modeling explores the optimization of renewable energy shares, showcasing the package's ability to minimize carbon emissions and costs while enhancing system reliability. The Jaya R package is an invaluable tool for researchers and practitioners seeking efficient and adaptive optimization solutions.
Enhancing Few-Shot Learning with Integrated Data and GAN Model Approaches
Venue: arXiv cs.LG 2024
Year: 2024

This paper presents an innovative approach to enhancing few-shot learning by integrating data augmentation with model fine-tuning in a framework designed to tackle the challenges posed by small-sample data. Recognizing the critical limitations of traditional machine learning models that require large datasets-especially in fields such as drug discovery, target recognition, and malicious traffic detection-this study proposes a novel strategy that leverages Generative Adversarial Networks (GANs) and advanced optimization techniques to improve model performance with limited data. Specifically, the paper addresses the noise and bias issues introduced by data augmentation methods, contrasting them with model-based approaches, such as fine-tuning and metric learning, which rely heavily on related datasets. By combining Markov Chain Monte Carlo (MCMC) sampling and discriminative model ensemble strategies within a GAN framework, the proposed model adjusts generative and discriminative distributions to simulate a broader range of relevant data. Furthermore, it employs MHLoss and a reparameterized GAN ensemble to enhance stability and accelerate convergence, ultimately leading to improved classification performance on small-sample images and structured datasets. Results confirm that the MhERGAN algorithm developed in this research is highly effective for few-shot learning, offering a practical solution that bridges data scarcity with high-performing model adaptability and generalization.
Recent Trends in Linear Text Segmentation: a Survey
Venue: arXiv cs.CL 2024
Year: 2024

Linear Text Segmentation is the task of automatically tagging text documents with topic shifts, i.e. the places in the text where the topics change. A well-established area of research in Natural Language Processing, drawing from well-understood concepts in linguistic and computational linguistic research, the field has recently seen a lot of interest as a result of the surge of text, video, and audio available on the web, which in turn require ways of summarising and categorizing the mole of content for which linear text segmentation is a fundamental step. In this survey, we provide an extensive overview of current advances in linear text segmentation, describing the state of the art in terms of resources and approaches for the task. Finally, we highlight the limitations of available resources and of the task itself, while indicating ways forward based on the most recent literature and under-explored research directions.
Imperceptible Adversarial Examples in the Physical World
Venue: arXiv cs.CV 2024
Year: 2024

Adversarial examples in the digital domain against deep learning-based computer vision models allow for perturbations that are imperceptible to human eyes. However, producing similar adversarial examples in the physical world has been difficult due to the non-differentiable image distortion functions in visual sensing systems. The existing algorithms for generating physically realizable adversarial examples often loosen their definition of adversarial examples by allowing unbounded perturbations, resulting in obvious or even strange visual patterns. In this work, we make adversarial examples imperceptible in the physical world using a straight-through estimator (STE, a.k.a. BPDA). We employ STE to overcome the non-differentiability -- applying exact, non-differentiable distortions in the forward pass of the backpropagation step, and using the identity function in the backward pass. Our differentiable rendering extension to STE also enables imperceptible adversarial patches in the physical world. Using printout photos, and experiments in the CARLA simulator, we show that STE enables fast generation of $\ell_\infty$ bounded adversarial examples despite the non-differentiable distortions. To the best of our knowledge, this is the first work demonstrating imperceptible adversarial examples bounded by small $\ell_\infty$ norms in the physical world that force zero classification accuracy in the global perturbation threat model and cause near-zero ($4.22\%$) AP50 in object detection in the patch perturbation threat model. We urge the community to re-evaluate the threat of adversarial examples in the physical world.
Leakage-Robust Bayesian Persuasion
Venue: arXiv cs.GT 2024
Year: 2024

We introduce the concept of leakage-robust Bayesian persuasion. Situated between public persuasion [KG11, CCG23, Xu20] and private persuasion [AB19], leakage-robust persuasion considers a setting where one or more signals privately sent by a sender to the receivers may be leaked. We study the design of leakage-robust persuasion schemes and quantify the price of robustness using two formalisms: - The first notion, $k$-worst-case persuasiveness, requires a scheme to remain persuasive as long as each receiver observes at most $k$ leaked signals. We quantify the Price of Worst-case Robustness (PoWR$_k$) -- i.e., the gap in sender's utility as compared to the optimal private scheme -- as $Θ(\min\{2^k,n\})$ for supermodular sender utilities and $Θ(k)$ for submodular or XOS utilities, where $n$ is the number of receivers. This result also establishes that in some instances, $Θ(\log k)$ leakages are sufficient for the utility of the optimal leakage-robust persuasion to degenerate to that of public persuasion. - The second notion, expected downstream utility robustness, relaxes the persuasiveness and considers the impact on sender's utility when receivers best respond to their observations. By quantifying the Price of Downstream Robustness (PoDR) as the gap between the sender's expected utility over random leakage patterns as compared to private persuasion, we show that over several natural and structured distributions of leakage patterns, PoDR improves PoWR to $Θ(k)$ or even $Θ(1)$, where $k$ is the maximum number of leaked signals observable to each receiver across leakage patterns in the distribution. En route to these results, we show that subsampling and masking are general-purpose algorithmic paradigms for transforming private persuasion signaling schemes to leakage-robust ones, with minmax optimal loss in the sender's utility.
Preventing Jailbreak Prompts as Malicious Tools for Cybercriminals: A Cyber Defense Perspective
Venue: arXiv cs.CR 2024
Year: 2024

Jailbreak prompts pose a significant threat in AI and cybersecurity, as they are crafted to bypass ethical safeguards in large language models, potentially enabling misuse by cybercriminals. This paper analyzes jailbreak prompts from a cyber defense perspective, exploring techniques like prompt injection and context manipulation that allow harmful content generation, content filter evasion, and sensitive information extraction. We assess the impact of successful jailbreaks, from misinformation and automated social engineering to hazardous content creation, including bioweapons and explosives. To address these threats, we propose strategies involving advanced prompt analysis, dynamic safety protocols, and continuous model fine-tuning to strengthen AI resilience. Additionally, we highlight the need for collaboration among AI researchers, cybersecurity experts, and policymakers to set standards for protecting AI systems. Through case studies, we illustrate these cyber defense approaches, promoting responsible AI practices to maintain system integrity and public trust. \textbf{\color{red}Warning: This paper contains content which the reader may find offensive.}
Footstep recognition as people identification: A Systematic literature review
Venue: arXiv cs.HC 2024
Year: 2024

Footstep recognition is a relatively new biometric which aims to discriminate people using walking characteristics. There are several feature and technology have been adopted in various research. This study will attempt to show a comparative technology and feature which is offered each previous related works. We performed a broad manually search to find SLRs published in the time period 1st January 2006 to 30th November 2018. Our broad search found 12 SLRs articles refer to 3 similar technology and 5 cluster feature. In over time, the number of published footstep recognition has increased, especially in conference publications. The differences in footsteps can be known from the power spectral density of sounds and vibrations generated by footsteps. Every footstep of the human has a certain density of frequency, either from density of sounds or vibrations generated. To improve accurately of the result, this paper suggests furthermore research to combining several measuring sensor and data processing method
Recurrent Joint Embedding Predictive Architecture with Recurrent Forward Propagation Learning
Venue: arXiv cs.NE 2024
Year: 2024

Conventional computer vision models rely on very deep, feedforward networks processing whole images and trained offline with extensive labeled data. In contrast, biological vision relies on comparatively shallow, recurrent networks that analyze sequences of fixated image patches, learning continuously in real-time without explicit supervision. This work introduces a vision network inspired by these biological principles. Specifically, it leverages a joint embedding predictive architecture incorporating recurrent gated circuits. The network learns by predicting the representation of the next image patch (fixation) based on the sequence of past fixations, a form of self-supervised learning. We show mathematical and empirically that the training algorithm avoids the problem of representational collapse. We also introduce \emph{Recurrent-Forward Propagation}, a learning algorithm that avoids biologically unrealistic backpropagation through time or memory-inefficient real-time recurrent learning. We show mathematically that the algorithm implements exact gradient descent for a large class of recurrent architectures, and confirm empirically that it learns efficiently. This paper focuses on these theoretical innovations and leaves empirical evaluation of performance in downstream tasks, and analysis of representational similarity with biological vision for future work.
A New 0(klog n) Algorithm for Josephus Problem
Venue: arXiv cs.DS 2024
Year: 2024

We present a new O(k log n) algorithm of the Josephus problem. The time complexity of our algorithm is O(k log n), and this time complexity is on a par with the existing O(k log n) algorithm. We do not have any recursion overhead or stack overflow because we do not use any recursion. Therefore, the space complexity of our algorithm is O(1), and ours is better than the existing O(k log n) algorithm in this respect. When k is small and n is large, our algorithm is better than the existing O(k log n) algorithm. This new algorithm is based on a relation between the Josephus problem and a maximum Nim of combinatorial game theory.
Dynamic Resource Manager for Automating Deployments in the Computing Continuum
Venue: arXiv cs.DC 2024
Year: 2024

With the growth of real-time applications and IoT devices, computation is moving from cloud-based services to the low latency edge, creating a computing continuum. This continuum includes diverse cloud, edge, and endpoint devices, posing challenges for software design due to varied hardware options. To tackle this, a unified resource manager is needed to automate and facilitate the use of the computing continuum with different types of resources for flexible software deployments while maintaining consistent performance. Therefore, we propose a seamless resource manager framework for automated infrastructure deployment that leverages resources from different providers across heterogeneous and dynamic Edge-Cloud resources, ensuring certain Service Level Objectives (SLOs). Our proposed resource manager continuously monitors SLOs and reallocates resources promptly in case of violations to prevent disruptions and ensure steady performance. The experimental results across serverless and serverful platforms demonstrate that our resource manager effectively automates application deployment across various layers and platforms while detecting SLO violations with minimal overhead.
Exploring the determinants on massive open online courses continuance learning intention in business toward accounting context
Venue: arXiv cs.CY 2024
Year: 2024

Massive open online courses (MOOC) have become important in the learning journey of college students and have been extensively implemented in higher education. However, there are few studies that investigated the willingness to continue using Massive open online courses (MOOC) in the field of business in higher education. Therefore, this paper proposes a comprehensive theoretical research framework based on the Theory of Planned Behavior (TPB). In the field of business, a representative accounting course is taken as an example. We adopt the questionnaire survey method and use the partial least squares structural equation model to analyze the collected feedback data from college students and test the hypotheses. This paper focuses on the potential influencing factors and mechanisms of the willingness to continuously use Massive open online courses (MOOC) in accounting. The results show that interface convenience (IC) and interface design aesthetics (IDA) have positive effects on user attitude (ATT). User attitude (ATT), perceived behavioral control (PBC), and subjective norms (SN) have positive effects on the continuance learning intention. In addition, academic self-efficacy (EF) not only significantly affects continuance learning intention (CI) but also moderates the relationship between the Theory of Planned Behavior (user attitude, perceived behavior control, subjective norms) and the continuance learning intention of accounting MOOC. Therefore, the Theory of Planned Behavior(TPB) is extended in social science accounting Massive open online courses environment. Based on these findings, this paper provides several theoretical and practical implications for researchers and practitioners of MOOC, accounting, and the design of learning systems in higher education contexts.