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A combinatorial algorithm for finding straight skeleton of a digital object

Multimedia Tools and Applications 2025年 1-21页

作者： Maity, Anukul Dutt, Mousumi Biswas, Arindam Department of Computer Science and Engineering Narula Institute of Technology 81 Nilgunj Road Agarpara West Bengal Kolkata700109 India Department of Computer Science and Engineering St. Thomas’ College of Engineering and Technology 4 Diamond Harbour Road Kidderpore West Bengal Kolkata700023 India Department of Information Technology Indian Institute of Engineering Science and Technology Botanical Garden Road Shibpur West Bengal Howrah711103 India

The paper presents a combinatorial algorithm to find the straight skeleton of the inner isothetic cover of a digital object imposed on a uniform background grid. The isothetic polygon (orthogonal polygon) tightly inscribes the given digital object. The algorithm stated here finds the straight skeleton of any isothetic polygon (simple or non-simple) in O(nglogng) time in a single traversal by applying combinatorial rules, where n is the number of pixels on the boundary of the digital object and g is the grid size on which the digital object is imposed. To find the straight skeleton of non-simple orthogonal polygon, the polygon is divided into sub-parts which are a set of simple orthogonal polygons. The straight skeleton is generated for each of the sub-parts and the corresponding results are merged. The orthogonal polygon containing holes is cut at each holes and the polygon becomes hole-free. The algorithm to obtain the straight skeleton is applied on it. The disconnected hole parts are rejoined and the result is merged. The straight skeleton is useful shape descriptor of digital object. This algorithm stated here has applications in shape analysis. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Combinatorial optimization

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A fast verifiable fully homomorphic encryption technique for secret computation on cloud data

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International Journal of Information technology (Singapore) 2025年第4期17卷 2193-2207页

作者： Mahato, Ganesh Kumar Chakraborty, Swarnendu Kumar Department of Computer Science and Engineering National Institute of Technology Arunachal Pradesh Jote 791113 India

In the domain of cloud computing, safeguarding the confidentiality and integrity of outsourced sensitive data during computational processes is of utmost importance. This paper introduces a pioneering verifiable homomorphic encryption (VFHE) scheme, combining data encryption with the ability to verify computation integrity through homomorphic operations. The proposed approach leverages a fast lattice-based reduction key generation method, drastically reducing key generation time and resulting in a substantial reduction in key size while maintaining security. By integrating this innovation into a fully homomorphic encryption scheme, cloud data computations are expedited while preserving data security. The efficiency of the proposed scheme is highlighted through analyses of key generation time, encryption, decryption, and verification processes across varying security parameters. Verification time remains minimal, showcasing the practical applicability of our approach. Furthermore, novel metrics such as privacy cost and amortized cost are introduced to evaluate the efficiency and overhead associated with incorporating privacy into the verifiable computation scheme. The proposed VFHE scheme not only ensures data security but also substantially improves computation speed, making it a promising solution for enhancing privacy and efficiency in cloud computing applications. © Bharati Vidyapeeth's institute of computer Applications and Management 2024.

关键词： Cloud security Homomorphic encryption Key generation Lattice-based encryption Secret computation Verifiable computation

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An infrastructure software perspective toward computation offloading between executable specifications and foundation models

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Science China(Information Sciences) 2025年第4期68卷 380-382页

作者： Dezhi RAN Mengzhou WU Yuan CAO Assaf MARRON David HAREL Tao XIE Key Laboratory of High Confidence Software Technologies (PKU) Ministry of Education School of Computer SciencePeking University School of Electronics Engineering and Computer Science Peking University Department of Computer Science and Applied Mathematics Weizmann Institute of Science

Foundation models(FMs) [1] have revolutionized software development and become the core components of large software systems. This paradigm shift, however, demands fundamental re-imagining of software engineering theories and methodologies [2]. Instead of replacing existing software modules implemented by symbolic logic, incorporating FMs' capabilities to build software systems requires entirely new modules that leverage the unique capabilities of ***, while FMs excel at handling uncertainty, recognizing patterns, and processing unstructured data, we need new engineering theories that support the paradigm shift from explicitly programming and maintaining user-defined symbolic logic to creating rich, expressive requirements that FMs can accurately perceive and implement.

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Video captioning – a survey

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Multimedia Tools and Applications 2025年第2期84卷 947-978页

作者： Vaishnavi, J. Narmatha, V. Department of Computer Applications SRM Institute of Science and Technology Trichy India Department of Computer and Information Science Annamalai University Annamalai Nagar Tamilnadu 608002 India

The explosion of the novel phenomenon of the combination of computer vision and Natural language processing is playing a vital role in converting the ordinary world into a more technological pool. Natural language processing and computer vision are the vanguard of Artificial intelligence with the enormous potential currently ruling more fields. The trending Accelerated research of the NLP and CV combo are cancer screening, surgical simulation, visual properties description, visual retrieval, visual description, etc. Video captioning is a task of localizing the event and various features are extracted from the particular event and then the description of the entire event is performed with Natural languages using various techniques. The task of video captioning has some evolution of various techniques from Traditional methods to Deep learning. Currently, Deep learning techniques are ruling the field. Several models are being proposed for the better enhancement of the video captioning task with the default Encoder-Decoder framework and some models with the variance of Attention mechanism and Transfer learning etc., The Attainment of every technique and the accuracy of the generated results are highly dependent on the nature of the problem, diversity, performance of chosen dataset and construction of the technique with various layers, and the amount of data split for train, validate, and test. This survey discusses the captioning types, real-time applications of video captioning, evolution of various methods in video captioning, advanced techniques, different datasets, metrics that are used to evaluate the results, and analysis of the results of the existing models for video captioning. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： computer vision

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Method to avert vehicle collision based on driver’s attention lapse predicted using BCI

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Multimedia Tools and Applications 2025年 1-24页

作者： Mishra, Unnati Chauhan, Hitesh Maru, Madhuri Parikh, Saurin Computer Science and Engineering Department Institute of Technology Nirma University Sarkhej Gandhinagar Highway Near Gota Gujarat Ahmedabad382481 India

In this paper, a BCI-based machine learning method is proposed to predict the attention lapse of the driver. An EEG headset is used to capture brain activity from different regions of the human brain. The data is further pre-processed and various machine learning classifiers such as KNN, decision tree, and naive Bayes are trained using the data to classify the attention state of the driver as in-attention, attention lapse or attention regain. The KNN model is the best performer showing 97% accuracy, wherein the true positive rate (TPR) of 85.5% is measured to predict attention lapse and 82% to predict attention regain states. Besides this, a sensor-based model is also proposed to avert the vehicle’s collision by alerting the driver about the attention lapse. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Nearest neighbor search

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A Novel 3D Paradigm for Target Expansion of Augmented Reality SSVEP

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IEEE Transactions on Neural Systems and Rehabilitation engineering 2025年 33卷 1562-1573页

作者： Cao, Beining Li-Ting Tsai, Charlie Zhou, Nan Do, Thomas Lin, Chin-Teng University of Technology Sydney Computational Intelligence and Brain-Computer Interfaces Laboratory Australian AI Institute Faculty of Engineering and Information Technology School of Computer Science UltimoNSW2007 Australia

Steady-State Visual Evoked Potentials (SSVEP) have proven to be practical in Brain-computer Interfaces (BCI), particularly when integrated with augmented reality (AR) for real-world application. However, unlike conventional computer screen-based SSVEP (CS-SSVEP), which benefits from stable experimental environments, AR-based SSVEP (AR-SSVEP) systems are susceptible to the interference of real-world environment and device instability. Particularly, the performance of AR-SSVEP significantly declines as the target frequency increases. Therefore, our study introduced a 3D paradigm that combines flicker frequency with rotation patterns as stimuli, enabling expansion of target sets without additional frequencies. In the proposed design, in addition to the conventional frequency-based SSVEP feature, bio-marker elicited by visual perception of rotation was investigated. An experimental comparison between this novel 3D paradigm and a traditional 2D approach, which increases targets by adding frequencies, reveals significant advantages. The 12-class 3D paradigm achieved an accuracy of 76.5% and an information transfer rate (ITR) of 70.42 bits/min using 1-second EEG segments. In contrast, the 2D paradigm exhibited a lower performance with 72.07% accuracy and 62.28 bits/min ITR. The result underscores the 3D paradigm’s superiority in enhancing the practical applications of SSVEP-based BCIs in AR settings, especially with shorter time windows, by effectively expanding target recognition without compromising system efficiency. © 2001-2011 IEEE.

关键词： Augmented reality

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Adaptive Task Scheduling in Fog Computing Using Federated DQN and K-Means Clustering

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IEEE Access 2025年 13卷 75462-75488页

作者： Choppara, Prashanth Mangalampalli, S. Sudheer VIT-AP University School of Computer Science and Engineering Andhra Pradesh Amaravati522237 India Manipal Academy of Higher Education Department of Computer Science and Engineering Manipal Institute of Technology Bengaluru Karnataka Manipal576104 India

Task scheduling in distributed cloud and fog computing applications must be efficient to optimize resource utilization, minimize latency, and comply with strict service level agreements. The dynamic and heterogeneous of fog computing challenges such as balancing performance with variable task loads, resource constraints, and energy efficiency. This paper introduces a new Federated Learning Deep Queue-Learning (FLDQN) framework that integrates reinforcement learning with federated learning to overcome these challenges. The FLDQN approach enables decentralized task scheduling because fog nodes can train their local DQN models on localized data, merging their experiences to form a global model without sensitive data sharing. It further improves task prioritization by classifying tasks based on their execution time and deadlines, ensuring that high-priority tasks are scheduled first, which reduces SLA violations and task rejection rates. The proposed model dynamically adapts to the changing conditions of the fog environment through iterative learning that continuously improves work allocation. Substantial experiments conducted on large and small sizes of dataset show that the proposed FLDQN outperforms others, including standalone DQN and graph-based GGCN. For all dataset sizes small, medium, and large datasets, it reduces makespan by up to 30%, improves throughput, and reduces energy by distributing tasks on the most efficient node based on current system states. The results demonstrate the ability of FLDQN to optimize task execution in real time, while addressing the scalability and privacy issues inherent in fog computing. Combining federated and reinforcement learning proposed framework provides a flexible solution for distributed task scheduling, which is well suited for latency-sensitive applications in industrial automation, healthcare monitoring, and smart city deployments. © 2013 IEEE.

关键词： Fog computing

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A Stratified Seed Selection Algorithm for K-Means Clustering on Big Data

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第5期6卷 1334-1344页

作者： Bajpai, Namita Paik, Jiaul H. Sarkar, Sudeshna Indian Institute of Technology Department of Artificial Intelligence Kharagpur721302 India Indian Institute of Technology Department of Computer Science Kharagpur721302 India

In k-means clustering, the selection of initial seeds significantly influences the quality of the resulting clusters. Moreover, clustering large-sized data introduces an additional challenge for seed selection. We propose a novel and scalable seed selection approach by jointly modeling the quality and diversity of the potential seeds through a principled probabilistic stochastic point process. To this end, we also propose a novel seed quality estimation approach on large data. Our approach quantifies the quality of a seed by measuring the divergence between the distribution of similarity between the closest neighbors and that of the randomly chosen neighbors from exhaustive stratified batches of samples. Unlike many existing scalable approaches, we do not rely on a small sample of the original data;instead, we use the entire data, thereby minimizing the chance of leaving out information about a potentially high-quality seed. The extensive evaluation on a set of benchmark data shows that it outperforms a number of strong, well-known, and recent algorithms measured by three standard metrics. © 2020 IEEE.

关键词： k-nearest neighbors

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A systematic review of machine learning and deep learning techniques for anomaly detection in data mining

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International Journal of computers and Applications 2025年第2期47卷 169-187页

作者： Tahir, Mahjabeen Abdullah, Azizol Izura Udzir, Nur Azhar Kasmiran, Khairul Serdang Malaysia Serdang Malaysia Department of Computer Engineering Sir Syed University of Engineering & Technology Karachi Pakistan

The growing use of the internet has increased the threat of cyberattacks. Anomaly detection systems are vital for protecting networks by spotting irregular activities. Various studies investigated anomaly detection techniques without a systematic approach. So far, the existing reviews mainly concerned time series and data streaming methods almost neglected the growing interest in graph-based data mining techniques which are vital in social networks, finance, and IoT domains. Following PRISMA guidelines, this systematic review examines anomaly detection methods applied to time series, data streaming, and graph-based data from 2018 to 2023. A total of 34 papers were selected from four key databases ScienceDirect, Scopus, Web of Science, and IEEE. In addition, this review addressed several issues with existing techniques including low scalability, explainability, and interpretability for real-time anomaly detection systems. In modern applications where data structures are complex, and processing requirements are high these traditional techniques are insufficient for real-time data processing. Finally, our study demanded more advanced, complex methods to address these evolving challenges. © 2025 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Data streams

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RBCA-ETS: enhancing extractive text summarization with contextual embedding and word-level attention

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International Journal of Information technology (Singapore) 2025年第2期17卷 1127-1135页

作者： Gangundi, Ravindra Sridhar, Rajeswari Department of Computer Science and Engineering National Institute of Technology Tamil Nadu Tiruchirappalli 620015 India

The existing limitations in extractive text summarization encompass challenges related to preserving contextual features, limited feature extraction capabilities, and handling hierarchical and compositional aspects. To address these issues, the RoBERTa-BiLSTM-CNN-Attention Extractive Text Summarization, i.e., the RBCA-ETS model, is proposed in this work. RoBERTa word embedding is used to generate contextual embeddings. Parallelly connected CNN and BiLSTM layers extract textual features. CNN focuses more on local features, and BiLSTM captures long-range dependencies that extend across sentences. These two feature sets are concatenated and forwarded to the attention layer, highlighting the most relevant features. In the output layer, a fully connected layer receives the attention vector and calculates sentence scores for each sentence. This leads to the generation of the final summary. The RBCA-ETS model has demonstrated superior performance on the CNN-Daily Mail (CNN/DM) dataset compared to many state-of-the-art methods, and it has also outperformed existing state-of-the-art techniques when tested on the out-of-domain DUC 2002 dataset. © Bharati Vidyapeeth's institute of computer Applications and Management 2024.

关键词： Attention BiLSTM CNN RoBERTa Text summarization

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