OpenRaiser/Intern-Atlas huggingface dataset:
Enhancement of Approximation Spaces by the Use of Primals and Neighborhood
Venue: arXiv cs.AI 2024
Year: 2024

Rough set theory is one of the most widely used and significant approaches for handling incomplete information. It divides the universe in the beginning and uses equivalency relations to produce blocks. Numerous generalized rough set models have been put out and investigated in an effort to increase flexibility and extend the range of possible uses. We introduce four new generalized rough set models that draw inspiration from "neighborhoods and primals" in order to make a contribution to this topic. By minimizing the uncertainty regions, these models are intended to assist decision makers in more effectively analyzing and evaluating the provided data. We verify this goal by demonstrating that the existing models outperform certain current method approaches in terms of improving the approximation operators (upper and lower) and accuracy measurements. We claim that the current models can preserve nearly all significant aspects associated with the rough set model. Preserving the monotonic property, which enables us to assess data uncertainty and boost confidence in outcomes, is one of the intriguing characterizations derived from the existing models. With the aid of specific instances, we also compare the areas of the current approach. Finally, we demonstrate that the new strategy we define for our everyday health-related problem yields more accurate findings.
Cooperation and Personalization on a Seesaw: Choice-based FL for Safe Cooperation in Wireless Networks
Venue: arXiv cs.NI 2024
Year: 2024

Federated learning (FL) is an innovative distributed artificial intelligence (AI) technique. It has been used for interdisciplinary studies in different fields such as healthcare, marketing and finance. However the application of FL in wireless networks is still in its infancy. In this work, we first overview benefits and concerns when applying FL to wireless networks. Next, we provide a new perspective on existing personalized FL frameworks by analyzing the relationship between cooperation and personalization in these frameworks. Additionally, we discuss the possibility of tuning the cooperation level with a choice-based approach. Our choice-based FL approach is a flexible and safe FL framework that allows participants to lower the level of cooperation when they feel unsafe or unable to benefit from the cooperation. In this way, the choice-based FL framework aims to address the safety and fairness concerns in FL and protect participants from malicious attacks.
Topology Bench: Systematic Graph Based Benchmarking for Core Optical Networks
Venue: arXiv cs.NI 2024
Year: 2024

Topology Bench is a comprehensive topology dataset designed to accelerate benchmarking studies in optical networks. The dataset, focusing on core optical networks, comprises publicly accessible and ready-to-use topologies, including (a) 105 georeferenced real-world optical networks and (b) 270,900 validated synthetic topologies. Prior research on real-world core optical networks has been characterised by fragmented open data sources and disparate individual studies. Moreover, previous efforts have notably failed to provide synthetic data at a scale comparable to our present study. Topology Bench addresses this limitation, offering a unified resource and represents a 61.5% increase in spatially-referenced real world optical networks. To benchmark and identify the fundamental nature of optical network topologies through the lens of graph-theoretical analysis, we analyse both real and synthetic networks using structural, spatial and spectral metrics. Our comparative analysis identifies constraints in real optical network diversity and illustrates how synthetic networks can complement and expand the range of topologies available for use. Currently, topologies are selected based on subjective criteria, such as preference, data availability, or perceived suitability, leading to potential biases and limited representativeness. Our framework enhances the generalisability of optical network research by providing a more objective and systematic approach to topology selection. A statistical and correlation analysis reveals the quantitative range of all of these graph metrics and the relationships between them. Finally, we apply unsupervised machine learning to cluster real-world topologies into distinctive groups using nine optimal graph metrics using K-means. We conclude the analysis by providing guidance on how to use such clusters to select a diverse set of topologies for future studies.
Quantum Diffusion Models for Few-Shot Learning
Venue: arXiv cs.LG 2024
Year: 2024

Modern quantum machine learning (QML) methods involve the variational optimization of parameterized quantum circuits on training datasets, followed by predictions on testing datasets. Most state-of-the-art QML algorithms currently lack practical advantages due to their limited learning capabilities, especially in few-shot learning tasks. In this work, we propose three new frameworks employing quantum diffusion model (QDM) as a solution for the few-shot learning: label-guided generation inference (LGGI); label-guided denoising inference (LGDI); and label-guided noise addition inference (LGNAI). Experimental results demonstrate that our proposed algorithms significantly outperform existing methods.
Labels in Extremes: How Well Calibrated are Extreme Multi-label Classifiers?
Venue: arXiv cs.LG 2024
Year: 2024

Extreme multilabel classification (XMLC) problems occur in settings such as related product recommendation, large-scale document tagging, or ad prediction, and are characterized by a label space that can span millions of possible labels. There are two implicit tasks that the classifier performs: \emph{Evaluating} each potential label for its expected worth, and then \emph{selecting} the best candidates. For the latter task, only the relative order of scores matters, and this is what is captured by the standard evaluation procedure in the XMLC literature. However, in many practical applications, it is important to have a good estimate of the actual probability of a label being relevant, e.g., to decide whether to pay the fee to be allowed to display the corresponding ad. To judge whether an extreme classifier is indeed suited to this task, one can look, for example, to whether it returns \emph{calibrated} probabilities, which has hitherto not been done in this field. Therefore, this paper aims to establish the current status quo of calibration in XMLC by providing a systematic evaluation, comprising nine models from four different model families across seven benchmark datasets. As naive application of Expected Calibration Error (ECE) leads to meaningless results in long-tailed XMC datasets, we instead introduce the notion of \emph{calibration@k} (e.g., ECE@k), which focusses on the top-$k$ probability mass, offering a more appropriate measure for evaluating probability calibration in XMLC scenarios. While we find that different models can exhibit widely varying reliability plots, we also show that post-training calibration via a computationally efficient isotonic regression method enhances model calibration without sacrificing prediction accuracy. Thus, the practitioner can choose the model family based on accuracy considerations, and leave calibration to isotonic regression.
Novel Non-Prehensile Rolling Problem: Modelling and Balance Control of Pendulum-Driven Reconfigurable Disks Motion with Magnetic Coupling in Simulation
Venue: arXiv cs.RO 2024
Year: 2024

This paper presents a novel type of mobile rolling robot designed as a modular platform for non-prehensile manipulation, highlighting the associated control challenges in achieving balancing control of the robotic system. The developed rolling disk modules incorporate an innovative internally actuated magnetic-pendulum coupling mechanism, which introduces a compelling control problem due to the frictional and sliding interactions, as well as the magnetic effects between each module. In this paper, we derive the nonlinear dynamics of the robot using the Euler-Lagrange formulation. Then, through simulation, the motion behavior of the system is studied and analyzed, providing critical insights for future investigations into control methods for complex non-prehensile motion between robotic modules. Also, we study the balancing of this new platform and introduce a new motion pattern of lifting. This research aims to enhance the understanding and implementation of modular self-reconfigurable robots in various scenarios for future applications.
Enhancing Security Control Production With Generative AI
Venue: arXiv cs.CR 2024
Year: 2024

Security controls are mechanisms or policies designed for cloud based services to reduce risk, protect information, and ensure compliance with security regulations. The development of security controls is traditionally a labor-intensive and time-consuming process. This paper explores the use of Generative AI to accelerate the generation of security controls. We specifically focus on generating Gherkin codes which are the domain-specific language used to define the behavior of security controls in a structured and understandable format. By leveraging large language models and in-context learning, we propose a structured framework that reduces the time required for developing security controls from 2-3 days to less than one minute. Our approach integrates detailed task descriptions, step-by-step instructions, and retrieval-augmented generation to enhance the accuracy and efficiency of the generated Gherkin code. Initial evaluations on AWS cloud services demonstrate promising results, indicating that GenAI can effectively streamline the security control development process, thus providing a robust and dynamic safeguard for cloud-based infrastructures.
Demo: Paving the Way for Smart Manufacturing with 5G/TSN Convergence and Augmented Reality
Venue: arXiv cs.NI 2024
Year: 2024

The fifth-generation (5G) mobile/cellular and time-sensitive networking (TSN) technologies are widely recognized as the key to shaping smart manufacturing for Industry 4.0 and beyond. Converged operation of the two offers end-to-end real-time and deterministic connectivity over hybrid wired and wireless segments. On the other hand, the augmented reality (AR) technology provides various benefits for the manufacturing sector. To this end, this demonstration showcases AR-aided remote assistance use-case over a hybrid TSN and 5G system. The demonstration setup comprises off-the-shelf 5G and TSN devices, a near product-grade 5G system, and an AR solution based on smart glasses. The demonstration shows the viability of over-the air transmission of scheduled TSN traffic and real-time assistance for a local user from a remote environments. Performance results from the demonstration setup are also shown.
Enhancing classroom teaching with LLMs and RAG
Venue: arXiv cs.LG 2024
Year: 2024

Large Language Models have become a valuable source of information for our daily inquiries. However, after training, its data source quickly becomes out-of-date, making RAG a useful tool for providing even more recent or pertinent data. In this work, we investigate how RAG pipelines, with the course materials serving as a data source, might help students in K-12 education. The initial research utilizes Reddit as a data source for up-to-date cybersecurity information. Chunk size is evaluated to determine the optimal amount of context needed to generate accurate answers. After running the experiment for different chunk sizes, answer correctness was evaluated using RAGAs with average answer correctness not exceeding 50 percent for any chunk size. This suggests that Reddit is not a good source to mine for data for questions about cybersecurity threats. The methodology was successful in evaluating the data source, which has implications for its use to evaluate educational resources for effectiveness.
Impact of white noise in artificial neural networks trained for classification: performance and noise mitigation strategies
Venue: arXiv cs.LG 2024
Year: 2024

In recent years, the hardware implementation of neural networks, leveraging physical coupling and analog neurons has substantially increased in relevance. Such nonlinear and complex physical networks provide significant advantages in speed and energy efficiency, but are potentially susceptible to internal noise when compared to digital emulations of such networks. In this work, we consider how additive and multiplicative Gaussian white noise on the neuronal level can affect the accuracy of the network when applied for specific tasks and including a softmax function in the readout layer. We adapt several noise reduction techniques to the essential setting of classification tasks, which represent a large fraction of neural network computing. We find that these adjusted concepts are highly effective in mitigating the detrimental impact of noise.
Automated Update of Android Deprecated API Usages with Large Language Models
Venue: arXiv cs.SE 2024
Year: 2024

Android apps rely on application programming interfaces (APIs) to access various functionalities of Android devices. These APIs however are regularly updated to incorporate new features while the old APIs get deprecated. Even though the importance of updating deprecated API usages with the recommended replacement APIs has been widely recognized, it is non-trivial to update the deprecated API usages. Therefore, the usages of deprecated APIs linger in Android apps and cause compatibility issues in practice. This paper introduces GUPPY, an automated approach that utilizes large language models (LLMs) to update Android deprecated API usages. By employing carefully crafted prompts, GUPPY leverages GPT-4, one of the most powerful LLMs, to update deprecated-API usages, ensuring compatibility in both the old and new API levels. Additionally, GUPPY uses GPT-4 to generate tests, identify incorrect updates, and refine the API usage through an iterative process until the tests pass or a specified limit is reached. Our evaluation, conducted on 360 benchmark API usages from 20 deprecated APIs and an additional 156 deprecated API usages from the latest API levels 33 and 34, demonstrates GUPPY's advantages over the state-of-the-art techniques.
A Bayesian Mixture Model of Temporal Point Processes with Determinantal Point Process Prior
Venue: arXiv cs.LG 2024
Year: 2024

Asynchronous event sequence clustering aims to group similar event sequences in an unsupervised manner. Mixture models of temporal point processes have been proposed to solve this problem, but they often suffer from overfitting, leading to excessive cluster generation with a lack of diversity. To overcome these limitations, we propose a Bayesian mixture model of Temporal Point Processes with Determinantal Point Process prior (TP$^2$DP$^2$) and accordingly an efficient posterior inference algorithm based on conditional Gibbs sampling. Our work provides a flexible learning framework for event sequence clustering, enabling automatic identification of the potential number of clusters and accurate grouping of sequences with similar features. It is applicable to a wide range of parametric temporal point processes, including neural network-based models. Experimental results on both synthetic and real-world data suggest that our framework could produce moderately fewer yet more diverse mixture components, and achieve outstanding results across multiple evaluation metrics.
Large Sets of Quasi-Complementary Sequences From Polynomials over Finite Fields and Gaussian Sums
Venue: arXiv cs.IT 2024
Year: 2024

Perfect complementary sequence sets (PCSSs) are widely used in multi-carrier code-division multiple-access (MC-CDMA) communication systems. However, the set size of a PCSS is upper bounded by the number of row sequences of each two-dimensional matrix in the PCSS. Then quasi-complementary sequence sets (QCSSs) were proposed to support more users in MC-CDMA communications. For practical applications, it is desirable to construct an $(M,K,N,\vartheta_{\max})$-QCSS with $M$ as large as possible and $\vartheta_{max}$ as small as possible, where $M$ is the number of matrices with $K$ rows and $N$ columns in the set and $\vartheta_{\max}$ denotes its periodic tolerance. There exists a tradeoff among these parameters. Constructing QCSSs achieving or nearly achieving the known correlation lower bound has been an interesting research topic. Up to now, only a few constructions of asymptotically optimal or near-optimal periodic QCSSs have been reported in the literature. In this paper, based on polynomials over finite fields and Gaussian sums, we construct five new families of asymptotically optimal or near-optimal periodic QCSSs with large set sizes and low periodic tolerances. These families of QCSSs have set size $Θ(K^2)$ or $Θ(K^3)$ and flock size $K$. To the best of our knowledge, only a small amount of known families of periodic QCSSs with set size $Θ(K^2)$ have been constructed and most of other known periodic QCSSs have set sizes much smaller than $Θ(K^2)$. Our new constructed periodic QCSSs with set size $Θ(K^2)$ and flock size $K$ have the best parameters among all known ones. They have larger set sizes or lower periodic tolerances. The periodic QCSSs with set size $Θ(K^3)$ and flock size $K$ constructed in this paper have the largest set size among all known families of asymptotically optimal or near-optimal periodic QCSSs.
Synergy-Guided Regional Supervision of Pseudo Labels for Semi-Supervised Medical Image Segmentation
Venue: arXiv cs.CV 2024
Year: 2024

Semi-supervised learning has received considerable attention for its potential to leverage abundant unlabeled data to enhance model robustness. Pseudo labeling is a widely used strategy in semi supervised learning. However, existing methods often suffer from noise contamination, which can undermine model performance. To tackle this challenge, we introduce a novel Synergy-Guided Regional Supervision of Pseudo Labels (SGRS-Net) framework. Built upon the mean teacher network, we employ a Mix Augmentation module to enhance the unlabeled data. By evaluating the synergy before and after augmentation, we strategically partition the pseudo labels into distinct regions. Additionally, we introduce a Region Loss Evaluation module to assess the loss across each delineated area. Extensive experiments conducted on the LA dataset have demonstrated superior performance over state-of-the-art techniques, underscoring the efficiency and practicality of our framework.
Continuous Sign Language Recognition System using Deep Learning with MediaPipe Holistic
Venue: arXiv cs.LG 2024
Year: 2024

Sign languages are the language of hearing-impaired people who use visuals like the hand, facial, and body movements for communication. There are different signs and gestures representing alphabets, words, and phrases. Nowadays approximately 300 sign languages are being practiced worldwide such as American Sign Language (ASL), Chinese Sign Language (CSL), Indian Sign Language (ISL), and many more. Sign languages are dependent on the vocal language of a place. Unlike vocal or spoken languages, there are no helping words in sign language like is, am, are, was, were, will, be, etc. As only a limited population is well-versed in sign language, this lack of familiarity of sign language hinders hearing-impaired people from communicating freely and easily with everyone. This issue can be addressed by a sign language recognition (SLR) system which has the capability to translate the sign language into vocal language. In this paper, a continuous SLR system is proposed using a deep learning model employing Long Short-Term Memory (LSTM), trained and tested on an ISL primary dataset. This dataset is created using MediaPipe Holistic pipeline for tracking face, hand, and body movements and collecting landmarks. The system recognizes the signs and gestures in real-time with 88.23% accuracy.
Analysis of Blockchain Assisted Energy Sharing Algorithms with Realistic Data Across Microgrids
Venue: arXiv cs.DC 2024
Year: 2024

With escalating energy demands, innovative solutions have emerged to supply energy affordably and sustainably. Energy sharing has also been proposed as a solution, addressing affordability issues while reducing consumers' greed. In this paper, we analyse the feasibility of two energy sharing algorithms, centralized and peer-to-peer, within two scenarios, between microgrids within a county, and between microgrids across counties. In addition, we propose a new sharing algorithm named Selfish Sharing, where prosumers take advantage of consumers' batteries in return for letting them consume part of the shared energy. The results for sharing between microgrids across counties show that the dependency on the grid could be reduced by approximately 5.72%, 6.12%, and 5.93% using the centralized, peer-to-peer and selfish sharing algorithms respectively, compared to trading only. The scenario of sharing between microgrids within a county has an average decrease in dependency on the grid by 5.66%, 6.0%, and 5.80% using the centralized, peer-to-peer and selfish algorithms respectively, compared to trading without sharing. We found that trading with batteries and the proposed sharing algorithms prove to be beneficial in the sharing between microgrids case. More specifically, the case of trading and sharing energy between microgrids across counties outperforms sharing within a county, with P2P sharing appearing to be superior.
Automatic Identification of Political Hate Articles from Social Media using Recurrent Neural Networks
Venue: arXiv cs.HC 2024
Year: 2024

The increasing growth of social media provides us with an instant opportunity to be informed of the opinions of a large number of politically active individuals in real-time. We can get an overall idea of the ideologies of these individuals on governmental issues by analyzing the social media texts. Nowadays, different kinds of news websites and popular social media such as Facebook, YouTube, Instagram, etc. are the most popular means of communication for the mass population. So the political perception of the users toward different parties in the country is reflected in the data collected from these social sites. In this work, we have extracted three types of features, such as the stylometric feature, the word-embedding feature, and the TF-IDF feature. Traditional machine learning classifiers and deep learning models are employed to identify political ideology from the text. We have compared our methodology with the research work in different languages. Among them, the word embedding feature with LSTM outperforms all other models with 88.28% accuracy.
An Axiomatic Study of the Evaluation of Enthymeme Decoding in Weighted Structured Argumentation
Venue: arXiv cs.AI 2024
Year: 2024

An argument can be seen as a pair consisting of a set of premises and a claim supported by them. Arguments used by humans are often enthymemes, i.e., some premises are implicit. To better understand, evaluate, and compare enthymemes, it is essential to decode them, i.e., to find the missing premisses. Many enthymeme decodings are possible. We need to distinguish between reasonable decodings and unreasonable ones. However, there is currently no research in the literature on "How to evaluate decodings?". To pave the way and achieve this goal, we introduce seven criteria related to decoding, based on different research areas. Then, we introduce the notion of criterion measure, the objective of which is to evaluate a decoding with regard to a certain criterion. Since such measures need to be validated, we introduce several desirable properties for them, called axioms. Another main contribution of the paper is the construction of certain criterion measures that are validated by our axioms. Such measures can be used to identify the best enthymemes decodings.