Data Structures - Science topic

Subgraph query can be applied in various scenarios, such as fraud detection and cyberattack pattern analysis. However, computing subgraph queries usually traverses a huge search space. Many efforts have been made to reduce this search space. The size of the answer set can be exponential, providing a substantial lower bound for the search space. Add...

Fibonacci numbers have applications in mobile computing, particularly in algorithms and data structures. For example, Fibonacci heaps are data structures that provide better amortized running time compared to binomial heaps, which can be advantageous in mobile computing environments where efficiency is crucial. In addition, the Fibonacci search tec...

This study presents a modified Atbash cipher that consists of special characters and utilizes a stack data structure to significantly enhance its security. The traditional Atbash cipher, which substitutes letters with their alphabetical inverses, is limited by its simplicity and vulnerability to frequency analysis attacks. To address these weakness...

A big room of opportunities exists regarding Warehouse Layout Design and space utilization. Although, in order to place and register the storage location at the minimum distance from the entrance, in relationship of the warehouse space and dimensions of Storage locations such as shelves, to improve the efficiency of material handling, we might ask:...

Analyzing and comparing sequences of symbols is among the most fundamental problems in computer science, possibly even more so in bioinformatics. Maximal Common Subsequences (MCSs), i.e., inclusion-maximal sequences of non-contiguous symbols common to two or more strings, have only recently received attention in this area, despite being a basic not...

Real analysis is a branch of mathematics that deals with real numbers and related functions, where supremum (smallest upper bound) and infimum (largest lower bound) are fundamental concepts. Although essential in understanding limits, integrals, and series, these concepts are often considered abstract and difficult for students to understand. This...

Compared to classic ray marching‐based approaches, Monte Carlo ray tracing for volume visualization can provide faster frame times through progressive rendering, improved image quality, and allows for advanced illumination models more easily. Techniques such as the view‐dependent optimization of visibility and illumination of important regions, how...

In the paper we suggest an algorithm of fuzzy clustering with uninorm‐based distance measure. The algorithm follows a general scheme of fuzzy c‐means (FCM) clustering, but in contrast to the existing algorithm it implements logical distance between data instances. The centers of the clusters calculated by the algorithm are less deviated and are con...

We propose a unified framework based on persistent homology to characterize both local and global structures in disordered systems. It can simultaneously generate local and global descriptors using the same algorithm and data structure, and has been shown to be highly effective and interpretable in predicting particle rearrangements and classifying...

This article presents a technical analysis of SMS and iMessage services on the macOS Sequoia operating system from a digital forensics perspective. Apple's messaging platforms can contain critical information for both personal and corporate investigations. Therefore, accurate forensic examination of these services is essential in digital evidence c...

In recent years, scholars and practitioners have been more interested in big data analytics capabilities. Though typically understudied, research in this new sector is growing. An enhanced green supply chain can be achieved, according to this article, provided businesses adopt and restructure some of the big data resources and capabilities inside t...

Simulation-based inference provides a powerful framework for cryo-electron microscopy, employing neural networks in methods like CryoSBI to infer biomolecular conformations via learned latent representations. This latent space represents a rich opportunity, encoding valuable information about the physical system and the inference process. Harnessin...

The problem of finding a path between two points while avoiding obstacles is critical in robotic path planning. We focus on the feasibility problem: determining whether such a path exists. We model the robot as a query-specific rectangular object capable of moving parallel to its sides. The obstacles are axis-aligned, rectangular, and may overlap....

Priority queues are used in a wide range of applications, including prioritized online scheduling, discrete event simulation, and greedy algorithms. In parallel settings, classical priority queues often become a severe bottleneck, resulting in low throughput. Consequently, there has been significant interest in concurrent priority queues with relax...

This paper introduces the Cartesian Merkle Tree, a deterministic data structure that combines the properties of a Binary Search Tree, a Heap, and a Merkle tree. The Cartesian Merkle Tree supports insertions, updates, and removals of elements in $O(\log n)$ time, requires $n$ space, and enables membership and non-membership proofs via Merkle-based a...

The rapid advancements in Artificial Intelligence (AI) have significantly enhanced the optimization process across various domains, from algorithm design to real-world problem-solving. This paper explores how AI-driven optimization techniques contribute to the development of more efficient algorithms, focusing on their ability to refine existing ap...

The rapid growth of data in various fields, such as healthcare, finance, and social media, has created significant challenges in processing and analyzing large datasets. Traditional data processing techniques often struggle with the scale and complexity of modern datasets, which require more scalable and efficient algorithms. This paper explores th...

In statistical applications it has become increasingly common to encounter data structures that live on non-linear spaces such as manifolds. Classical linear regression, one of the most fundamental methodologies of statistical learning, captures the relationship between an independent variable and a response variable which both are assumed to live...

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Generative retrieval seeks to replace traditional search index data structures with a single large-scale neural network, offering the potential for improved efficiency and seamless integration with generative large language models. As an end-to-end paradigm, generative retrieval adopts a learned differentiable search index to conduct retrieval by d...

Using Isabelle/HOL, we verify a union-find data structure with an explain operation due to Nieuwenhuis and Oliveras. We devise a simpler, more naive version of the explain operation whose soundness and completeness is easy to verify. Then, we prove the original formulation of the explain operation to be equal to our version. Finally, we refine this...

In recent years, dense retrieval has been the focus of information retrieval (IR) research. While effective, dense retrieval produces uninterpretable dense vectors, and suffers from the drawback of large index size. Learned sparse retrieval (LSR) has emerged as promising alternative, achieving competitive retrieval performance while also being able...

We propose novel techniques that exploit data and computation sharing to improve the performance of complex stateful parallel computations, like agent-based simulations. Parallel computations are translated into behavioral equations, a novel formalism layered on top of the foundational process calculus $\pi$-calculus. Behavioral equations blend cod...

Diabetes Mellitus Tipe 2 (DMT2) adalah penyakit metabolik kronis yang ditandai oleh resistensi insulin serta gangguan sekresi insulin. Salah satu pendekatan terapi yang potensial adalah penghambatan enzim-enzim kunci yang berperan dalam metabolisme glukosa. Kulit buah manggis (Garcinia mangostana L.) diketahui mengandung senyawa bioaktif, seperti α...

In German public administration, there are 45 different offices to which incoming messages need to be distributed. Since these messages are often unstructured, the system has to be based at least partly on message content. For public service no data are given so far and no pretrained model is available. The data we used are conducted by Governikus...

Suffix trees are a cornerstone data structure in string processing, commonly used in areas such as bioinformatics, plagiarism detection, and full-text indexing. However, their application to large-scale text corpora is often hindered by excessive memory consumption. This paper presents a Java-based approach to constructing memory-efficient suffix t...

Source-free unsupervised domain adaptation (SFDA), which needs only pre-trained source model and unlabeled target data (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$...

Analyzing high-dimensional data presents challenges due to the "curse of dimensionality'', making computations intensive. Dimension reduction techniques, categorized as linear or non-linear, simplify such data. Non-linear methods are particularly essential for efficiently visualizing and processing complex data structures in interactive and graphic...

Large language models have become a popular tool in software development, providing coding assistance. The proper measurement of the accuracy and reliability of the code produced by such tools is a challenge due to natural language prompts. We propose a simple pipeline that uses state-of-the-art implementation of classic and universal genres of alg...

The advent of big data has revolutionized various sectors, with healthcare being one of the most significantly impacted. Predictive analytics, empowered by big data, is transforming how healthcare providers, researchers, and policymakers anticipate health outcomes, optimize patient care, and improve operational efficiencies. This paper explores the...

CairoZero is a programming language for running decentralized applications (dApps) at scale. Programs written in the CairoZero language are compiled to machine code for the Cairo CPU architecture and cryptographic protocols are used to verify the results of execution efficiently on blockchain. We explain how we have extended the CairoZero compiler...

An anonymous dynamic network is a network of indistinguishable processes whose communication links may appear or disappear unpredictably over time. Previous research has shown that deterministically computing an arbitrary function of a multiset of input values given to these processes takes only a linear number of communication rounds (Di Luna–Vigl...

Ranking nodes in networks according to a defined measure of importance is an extensively studied task, with applications in ecology, economic trade networks, and social networks. This paper introduces a method based on a non-linear iterative map to evaluate node relevance in bipartite networks. By tuning a single parameter γ, the method captures di...

Outlier detection in high-dimensional tabular data is challenging since data is often distributed across multiple lower-dimensional subspaces -- a phenomenon known as the Multiple Views effect (MV). This effect led to a large body of research focused on mining such subspaces, known as subspace selection. However, as the precise nature of the MV eff...

Sorting of data according to a certain order according to the increase or decrease in its essence belongs to the class of combinatorial tasks. According to specialists, approximately 25% of computer time is spent on systematic sorting tasks. Therefore, the mentioned algorithms deserve special attention. As usual, every organization sorts this or th...

In this paper we show that two-dimensional nearest neighbor queries can be answered in optimal $O(\log n)$ time while supporting insertions in $O(\log^{1+\varepsilon}n)$ time. No previous data structure was known that supports $O(\log n)$-time queries and polylog-time insertions. In order to achieve logarithmic queries our data structure uses a new...

With the rapid development of information technology, the construction industry has gradually realized that the traditional two-dimensional design and construction methods can no longer meet the needs of complex projects. BIM, an integrated approach to design and management, is revolutionizing the construction industry through digital modeling, col...

The study of beam–plasma interactions is important in many research fields, such as astrophysics, inertial confinement fusion, and high-energy density physics. Considering the significant impact of electromagnetic fields on the evolution of beam–plasma systems, comprehensive three-dimensional modeling of the system is needed. In this work, a relati...

The rapid evolution of cyber threats has underscored the necessity for advanced cybersecurity mechanisms to safeguard sensitive data and digital infrastructures. Intrusion Detection Systems (IDS) have been critical in identifying malicious activities in real-time, but their effectiveness is often hindered by scalability and reliability issues. The...

Dynamic race detection based on the happens before (HB) partial order has now become the de facto approach to quickly identify data races in multi-threaded software. Most practical implementations for detecting these races use timestamps to infer causality between events and detect races based on these timestamps. Such an algorithm updates timestam...

Comprehensive collections approaching millions of sequenced genomes have become central information sources in the life sciences. However, the rapid growth of these collections has made it effectively impossible to search these data using tools such as the Basic Local Alignment Search Tool (BLAST) and its successors. Here, we present a technique ca...

In this article, we introduce a neuro-symbolic approach that combines a low-level perception task performed by a neural network with a high-level reasoning task performed by a possibilistic rule-based system. The goal is to be able to derive for each input instance the degree of possibility that it belongs to a target (meta-)concept. This (meta-)co...

For any given metric space, obtaining an offline optimal solution to the classical $k$-server problem can be reduced to solving a minimum-cost partial bipartite matching between two point sets $A$ and $B$ within that metric space. For $d$-dimensional $\ell_p$ metric space, we present an $\tilde{O}(\min\{nk, n^{2-\frac{1}{2d+1}}\log \Delta\}\cdot \P...

Ensuring fairness in machine learning is a critical and challenging task, as biased data representations often lead to unfair predictions. To address this, we propose Deep Fair Learning, a framework that integrates nonlinear sufficient dimension reduction with deep learning to construct fair and informative representations. By introducing a novel p...

As leading examples of large language models, ChatGPT and Gemini claim to provide accurate and unbiased information, emphasizing their commitment to political neutrality and avoidance of personal bias. This research investigates the political tendency of large language models and the existence of differentiation according to the query language. For...

A Bloom filter is a probabilistic data structure designed to provide a compact representation of a set S of elements from a large universe U. The trade-off for this succinctness is allowing some errors. The Bloom filter efficiently answers membership queries: given any query x, if x is in S, it must answer ’Yes’; if x is not in S, it should answer...

Applications in domains ranging from bioinformatics to advertising feature strings that come with numerical scores (utilities). The utilities quantify the importance, interest, profit, or risk of the letters occurring at every position of a string. Motivated by the ever-increasing rate of generating such data, as well as by their importance in seve...

The Structured Encryption (StE) framework can be used to capture the encryption and querying of complex data structures on an honest-but-curious server. In this work, we introduce a new type of StE called indirectly addressed multimap encryption (IA-MME). We propose two IA-MME schemes: the layered multimaps approach" which extends and generalizes t...

This study introduces a novel trigonometric-based family of distributions for modeling continuous data through a newly proposed framework known as the ASP family, where ‘ASP’ represents the initials of the authors Aadil, Shamshad, and Parvaiz. A specific subclass of this family, termed the “ASP Rayleigh distribution” (ASPRD), is introduced that fea...

There is a complex and still poorly understood relationship between pancreatic ductal adenocarcinoma (PDAC) and type 2 diabetes. On the other hand, it's important to highlight that single-cell and spatial transcriptomics allow us to study cells individually and within their spatial context, but they also introduce a hierarchical data structure tha...

Recently, diffusion-based recommendation methods have achieved impressive results. However, existing approaches predominantly treat each user's historical interactions as independent training samples, overlooking the potential of higher-order collaborative signals between users and items. Such signals, which encapsulate richer and more nuanced rela...

Project development is commonly used to assess disciplines in higher education courses linked to Computer Science. Data Structure is a subject of great importance for courses in this area, however, its contents are often considered complex and tedious by students. Looking for an attractive alternative to partially assess the students of a Data Stru...

An essential step in radionuclide activity measurement comparisons is the counting signal processing performed by laboratory-specific software. This has led to initiatives comparing software performance using a unique dataset from a list-mode data structure, as defined in IEC 63047:2021. A virtual Radionuclide Metrology Algorithm Comparison Platfor...

The integration of heterogeneous databases into a unified querying framework remains a critical challenge, particularly in resource-constrained environments. This paper presents a novel Small Language Model(SLM)-driven system that synergizes advancements in lightweight Retrieval-Augmented Generation (RAG) and semantic-aware data structuring to enab...

Processing graphs with temporal information (the temporal graphs) has become increasingly important in the real world. In this paper, we study efficient solutions to temporal graph applications using new algorithms for Incremental Minimum Spanning Trees (MST). The first contribution of this work is to formally discuss how a broad set of setting-pro...

Phasor measurement units (PMUs) are increasingly being deployed in power systems due to their high sampling rates and diverse data sampling types. However, this undoubtedly poses significant challenges to data centers in terms of data storage and transmission. This article proposes an adaptive rank-based tensor ring (TR) method for PMU data compres...

NoSQL and relational databases have been integrated in response to the increasing demand for scalable and efficient data management in hybrid cloud environments. The differences in data structures and query processing methods between these databases present both challenges and opportunities when designing an optimized hybrid system. This study expl...

Robotic weed removal in precision agriculture introduces a repetitive heterogeneous task planning (RHTP) challenge for a mobile manipulator. RHTP has two unique characteristics: 1) an observe-first-and-manipulate-later (OFML) temporal constraint that forces a unique ordering of two different tasks for each target and 2) energy savings from efficien...

Despite numerous initiatives and research efforts dedicated to increasing female representation in computer science, the overall percentage of women in this field continues to remain low. Over time, research has shown the existence of negative stereotypes and "myths" regarding the cognitive abilities and academic skills of women in computer science...

We develop a novel linear-complexity bottom-up sketching-based algorithm for constructing a H 2 matrix, and present its high performance GPU implementation. The construction algorithm requires both a black-box sketching operator and an entry evaluation function. The novelty of our GPU approach centers around the design and implementation of the abo...

The Fourier transform (FT) and convolution are fundamental tools for signal analysis and training convolutional neural networks. However, their extension and computation on arbitrary data structures (e.g., graphs, discrete 3D surfaces, or nD point sets) remain an active research area. As an alternative to discrete convolution and FTs, we introduce...

Recently, research on neural light field (NLF), which applies implicit neural representation (INR) to light field (LF), has been actively conducted. NLF can reconstruct dense and realistic LF from relatively sparse and unstructured images, which alleviates the high acquisition difficulty of existing LFs. On the other hand, NLF has a slow rendering...

Data sharing between concurrent tasks can lead to significant correctness and performance challenges if not managed properly. TBB provides well-tested, open source solutions that have established themselves as reliable tools over the years. TBB containers can be used independently or alongside other TBB components.

Rust is a non-Garbage Collected (GCed) language, but the lack of GC makes expressing data-structures that require shared ownership awkward, inefficient, or both. In this paper we explore a new design for, and implementation of, GC in Rust, called Alloy. Unlike previous approaches to GC in Rust, Alloy maps existing Rust destructors to finalizers: th...

Recently, there has been a growth in the research interest on applied machine learning (ML) in safety analysis in the construction industry. The increased interest is part of a search for improved prevention of occupational accidents with a focus on text analysis and natural language processing (NLP). However, ML-based approaches have been less ada...

This paper presents a multi-contract blockchain framework for inter-provider agreements in 6G networks, emphasizing performance analysis under a realistic Proof-of-Stake (PoS) setting on Ethereum's Sepolia testnet. We begin by quantifying Ethereum Virtual Machine (EVM)-based gas usage for critical operations such as provider registration, service a...

The article addresses the problem of routing autonomous devices in three-dimensional space, which is a relevant task for intelligent control. The three-dimensional space is characterized by a high degree of freedom, complex topology, and dynamic environmental changes, which significantly complicate the task of effective trajectory planning. The dev...

Generative networks have shown remarkable success in learning complex data distributions, particularly in generating high-dimensional data from lower-dimensional inputs. While this capability is well-documented empirically, its theoretical underpinning remains unclear. One common theoretical explanation appeals to the widely accepted manifold hypot...

Low Autocorrelation Binary Sequences (LABS) is a particularly challenging binary optimization problem which quickly becomes intractable in finding the global optimum for problem sizes beyond 66. This aspect makes LABS appealing to use as a test-bed for meta-heuristic optimization solvers to target large problem sizes. In this work, we introduce a m...

The Nancy Grace Roman Space Telescope will implement a devoted weak gravitational lensing program with its High Latitude Wide Area Survey. For cosmological purposes, a critical step in Roman image processing is to combine dithered undersampled images into unified oversampled images and thus enable high-precision shape measurements. I mcom is an ima...

As data volumes continue to grow rapidly, traditional search algorithms, like the red-black tree and B+ Tree, face increasing challenges in performance, especially in big data scenarios with intensive storage access. This paper presents the Linked Array Tree (LAT), a novel data structure designed to achieve constant-time complexity for search, inse...

There has been an increased demand for structured data mining. Graphs are among the most extensively researched data structures in discrete mathematics and computer science. Thus, it should come as no surprise that graph-based data mining has gained popularity in recent years. Graph-based methods for a transaction database are necessary to transfor...

Micro-scale mechanisms, such as inter-particle and particle-fluid interactions, govern the behaviour of granular systems. While particle-scale simulations provide detailed insights into these interactions, their computational cost is often prohibitive. Attended by researchers from both the granular materials (GM) and machine learning (ML) communiti...

Countertransference (CT) as a specific aspect of the therapeutic relationship plays a crucial role in psychotherapy. It serves as a source of clinical information for initial diagnostics and is relevant over the course of the therapeutic process. Theory and research have found that some personality features in patients are significantly related to...

The self-similar structure-based analysis of digital images offers many new practical possibilities. The fractal dimension is one of the most frequently measured parameters if we want to use image data in measurable analyses in metric spaces. In practice, the fractal dimension can be measured well in simple files containing only image data. In the...

This paper introduces a novel and efficient algorithm named Dominance-Based Boundary Nodes Finding Algorithm (DBBNFA). The DBBNFA applies the Pareto dominance principle to identify boundary nodes within a set of points or nodes in an N-dimensional space, such as a cloud or graph. Its core concept combines four Pareto frontiers, each detecting one-q...

Topological Data Analysis (TDA) has emerged as a powerful tool for extracting meaningful features from complex data structures, driving significant advancements in fields such as neuroscience, biology, machine learning, and financial modeling. Despite its success, the integration of TDA with time-series prediction remains underexplored due to three...

Background Pathology departments generate large volumes of unstructured data as free-text diagnostic reports. Converting these reports into structured formats for analytics or artificial intelligence projects requires substantial manual effort by specialized personnel. While recent studies show promise in using advanced language models for structur...

Since the disruption in LLM technology brought about by the release of GPT-3 and ChatGPT, LLMs have shown remarkable promise in programming-related tasks. While code generation remains a popular field of research, code evaluation using LLMs remains a problem with no conclusive solution. In this paper, we focus on LLM-based code evaluation and attem...

Between 2011 and 2016, global oil prices experienced a significant decline, leading to two major currency devaluations in Azerbaijan in 2015, which in turn had a profound impact on the country's economy. This study aims to evaluate the efficiency of the ten largest Azerbaijani banks by assets in 2016, specifically in the aftermath of these devaluat...

A consistent finding in experiments involving telepathic and clairvoyance phenomena is the difficulty of a receiver of visual psi information to name objects and symbols in what they receive. This deficit has been called “the naming problem” by Russell Targ. To gain insight into this issue, the perceptual and cognitive process used by receivers of...

Generative AI models have gained considerable attention across various fields, demonstrating remarkable success in generating text and image data. This paper presents the first phase of a comprehensive three-step project focusing on the development of a data creation pipeline for robotic fabrication. In this phase, we propose a computational framew...

This study introduces a novel model that integrates the Chen distribution with entropy transformation techniques to enhance cross-disciplinary data analysis. The Chen distribution, known for its applicability in medical and engineering longevity studies, is extended through entropy-based transformations to capture diverse data patterns. The propose...

The main purpose of the article is to consider the traditional data structure of a geographic information system and highlight the interconnected cartographic and attribute components in it. It consists of analyzing the results obtained as a result of color digital aerial photography of an area of 2,350 km2 in the cities of Baku and Sumgait.

Unlike code generation, which involves creating code from scratch, code completion focuses on integrating new lines or blocks of code into an existing codebase. This process requires a deep understanding of the surrounding context, such as variable scope, object models, API calls, and database relations, to produce accurate results. These complex c...

In dynamic concurrent data structures, memory management poses a significant challenge due to the diverse types of memory access and operations. Timestamps are widely used in concurrent algorithms, but existing safe memory reclamation algorithms that utilize timestamps often fail to achieve a balance among performance, applicability, and robustness...

In this study, the participation status of individuals in formal education and the factors affecting this status are analysed using the micro data set of the 2022 Adult Education Survey of the Turkish Statistical Institute (TurkStat). The analysed dataset consists of 14 variables and 24,462 observations. After the data preprocessing steps, the data...

We present a deterministic fully-dynamic data structure for maintaining information about the cut-vertices in a graph; i.e. the vertices whose removal would disconnect the graph. Our data structure supports insertion and deletion of edges, as well as queries to whether a pair of connected vertices are either biconnected, or can be separated by a cu...

The increasing use of Electronic Health Records (EHR) has emphasized the need for standardization and interoperability in healthcare data management. The Ministry of Health and Family Welfare, Government of India, has introduced the Electronic Health Record Minimum Data Set (EHRMDS) to facilitate uniformity in clinical documentation. However, the c...

Effective data discovery is a cornerstone of modern data-driven decision-making. Yet, identifying datasets with specific distributional characteristics, such as percentiles or preferences, remains challenging. While recent proposals have enabled users to search based on percentile predicates, much of the research in data discovery relies on heurist...

The implementation of neural network components for solving specialized tasks under conditions of input data uncertainty is becoming a promising direction in the development of digital technologies. One of the key challenges is the limitation of data available for training, necessitating data manipulation, particularly the use of different sequence...

The article focuses on the review and analysis of data processing information technologies in the course of modern computer science. The article also presents theoretical aspects of such concepts as algorithms, data structures, computer architecture, logic, software, programming languages and concepts of their development, which have a significant...

We report on a recent breakthrough in rule-based graph programming, which allows us to reach the time complexity of imperative linear-time algorithms. In general, achieving the complexity of graph algorithms in conventional languages using graph transformation rules is challenging due to the cost of graph matching. Previous work demonstrated that w...

The purpose of this research is to develop a banking application that will employ block chain technology to help banks combat the problem of fraudulent transactions caused by stolen credit cards. Accidental losses, theft and fraud, loss of privacy and confidentiality, loss of data integrity, and insecure backups are among risks that traditional dat...

Graph Neural Networks (GNNs) are playing an increasingly important role in the efficient operation and security of computing systems, with applications in workload scheduling, anomaly detection, and resource management. However, their vulnerability to network perturbations poses a significant challenge. We propose $\beta$-GNN, a model enhancing GNN...