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Co-linear Chaining with Overlaps and Gap Costs 26th

Co-linear Chaining with Overlaps and Gap Costs

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26th International Conference on Research in computational Molecular Biology, RECOMB 2022

作者： Jain, Chirag Gibney, Daniel Thankachan, Sharma V. Department of Computational and Data Sciences Indian Institute of Science Bangalore India School of Computer Science and Engineering Georgia Institute of Technology Atlanta United States Department of Computer Science University of Central Florida Orlando United States

ISBN: (纸本)9783031047480

Co-linear chaining has proven to be a powerful heuristic for finding near-optimal alignments of long DNA sequences (e.g., long reads or a genome assembly) to a reference. It is used as an intermediate step in several alignment tools that employ a seed-chain-extend strategy. Despite this popularity, efficient subquadratic-time algorithms for the general case where chains support anchor overlaps and gap costs are not currently known. We present algorithms to solve the co-linear chaining problem with anchor overlaps and gap costs in O~ (n) time, where n denotes the count of anchors. We also establish the first theoretical connection between co-linear chaining cost and edit distance. Specifically, we prove that for a fixed set of anchors under a carefully designed chaining cost function, the optimal ‘anchored’ edit distance equals the optimal co-linear chaining cost. Finally, we demonstrate experimentally that optimal co-linear chaining cost under the proposed cost function can be computed orders of magnitude faster than edit distance, and achieves correlation coefficient above 0.9 with edit distance for closely as well as distantly related sequences. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Anchors

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EDSep: An Effective Diffusion-Based Method for Speech Source Separation

EDSep: An Effective Diffusion-Based Method for Speech Source...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Dong, Jinwei Wang, Xinsheng Mao, Qirong School of Computer Science and Communication Engineering Jiangsu University China Jiangsu Engineering Research Center of Big Data Ubiquitous Perception and Intelligent Agriculture Applications China Provincial Key Laboratory of Computational Intelligence and New Technologies in Low-Altitude Digital Agriculture Zhenjiang China Audio Speech and Language Processing Group School of Computer Science Northwestern Polytechnical University Xi'an China

ISBN: (纸本)9798350368741

Generative models have attracted considerable attention for speech separation tasks, and among these, diffusion-based methods are being explored. Despite the notable success of diffusion techniques in generation tasks, their adaptation to speech separation has encountered challenges, notably slow convergence and suboptimal separation outcomes. To address these issues and enhance the efficacy of diffusion-based speech separation, we introduce EDSep, a novel single-channel method grounded in score matching via stochastic differential equation (SDE). This method enhances generative modeling for speech source separation by optimizing training and sampling efficiency. Specifically, a novel denoiser function is proposed to approximate data distributions, which obtains ideal denoiser outputs. Additionally, a stochastic sampler is carefully designed to resolve the reverse SDE during the sampling process, gradually separating speech from mixtures. Extensive experiments on databases such as WSJ0-2mix, LRS2-2mix, and VoxCeleb2-2mix demonstrate our proposed method's superior performance over existing diffusion and discriminative models, validating its efficacy. © 2025 IEEE.

关键词： diffusion score matching speech separation stochastic differential equation

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Detecting Ethereum Phishing Scams via Multi-transaction-view Graph Attention Network 29

Detecting Ethereum Phishing Scams via Multi-transaction-view...

引用

29th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2023

作者： Zhuo, Shaoxuan Li, Guang Wu, Weigang Bian, Jing Sun Yat-Sen University School of Computer Science and Engineering Guangzhou China Guangdong Province Key Laboratory of Big Data Analysis and Processing China Sun Yat-Sen University Guangdong Province Key Laboratory of Computational Science Guangzhou China

ISBN: (纸本)9798350330717

Cryptocurrency phishing scams is a significant treat to Ethereum, one of the most popular blockchain platforms. Most of existing Ethereum phishing detection methods are based on traditional machine learning or graph representation learning, which mostly rely on only statistical and structural features in local scope. In this paper, we propose Multi-transaction-view Graph Attention Network (MTvGAT), a novel phishing scam detection model that can make use of transaction patterns of different scopes. To obtain global-view information, we apply graph clustering and construct the global-view graph with multiple clusters, including all the nodes of the original transaction network. To obtain local view information, we apply neighborhood sampling, and construct local-view graphs with target nodes and their neighborhood nodes. Then, node features, edge features, and attention coefficients are aggregated to merge multi-view information into representation of nodes. We further combine global-view and local-view representations to finally identify phishing addresses from target nodes. Extensive experiments demonstrate that the proposed method can outperform existing ones with significant improvement. © 2023 IEEE.

关键词： Ethereum Graph neural network Phishing scams

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Optimization Models for Teaching Load Formation of University Teachers

Optimization Models for Teaching Load Formation of Universit...

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AITU: Digital Generation, IEEE

作者： Diana Chigambayeva Makar Goryakin Polina Batova Department of Computational and Data Science Astana IT University Astana Kazakhstan Depatment of Computer Engineering Astana IT University Astana Kazakhstan

ISBN: (数字)9798350364378

ISBN: (纸本)9798350364385

Functional and mathematical models for the distribution of academic workload at the stage of preparing the educational process at a university are considered, which make it possible to largely determine the uniformity of teachers' workload with various types of academic work when fulfilling the criteria and restrictions adopted for the distribution.

关键词： Training Optimization models Human-machine systems Education Load modeling Information systems

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Improving Autoencoder-Based Recommendation Systems 7th

Improving Autoencoder-Based Recommendation Systems

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7th International Conference on data Management, Analytics and Innovation, ICDMAI 2023

作者： Sinhababu, Nilanjan Sarma, Monalisa Samanta, Debasis Center for Computational and Data Sciences Indian Institute of Technology Kharagpur India Subir Chowdhury School of Quality and Reliability Indian Institute of Technology Kharagpur India Computer Science and Engineering Indian Institute of Technology Kharagpur India

ISBN: (纸本)9789819914135

Recommendation systems help in providing suggestions regarding the products/services that the users might be interested in by analyzing ratings, reviews, and other feedback obtained from the users on their previous purchases. These types of systems can be seen in domains like e-commerce, entertainment, news, etc. The ratings and reviews in e-commerce are obtained from user feedback which are far less in number when compared with the users and items count. This leads to data sparsity issues where the recommendation systems are prone to provide in-accurate recommendations. To counter this issue, in our study, we used both autoencoder and sentiment analysis on the reviews using the BERT language model to generate a more accurate version of the rating matrix. The empirical studies done on the Amazon baby product reviews dataset have shown that the approach has significantly increased the accuracy of predicted ratings. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Sentiment analysis

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Predicting the Cerebral Blood Flow Change Condition during Brain Strokes using Feature Fusion of FMRI Images and Clinical Features 14

Predicting the Cerebral Blood Flow Change Condition during B...

引用

14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Sharma, Vandana Sinha, Anurag Wiryaseputra, Michael Kumar, Biresh Kumar, Tarun Raj Alkhayyat, Ahmed Christ University Institute of Information Technology Delhi Campus India Ignou Department of Computer Science and Information Technology New Delhi India Soegijapranata Catholic University Semarang Indonesia Amity Univeristy Department of Computer Science Jharkhand India Iit Madras Computational Mathematics & Data Science India The Islamic University College of Technical Engineering Najaf Iraq

ISBN: (纸本)9798350335095

By fusing clinical information with functional magnetic resonance imaging (fFMRI) pictures, this study describes a novel method for predicting changes in cerebral blood flow during brain strokes. The FMRI data and patient-specific variables, such as age, gender, and medical history, are combined via feature fusion in the proposed technique. As a result, the model developed can accurately forecast changes in cerebral blood flow that occur during brain strokes. The efficiency of the suggested strategy is shown by experimental findings. The performance of the model is greatly enhanced when FMRI data and clinical characteristics are combined as opposed to just one data source. The findings of this study have important ramifications for increasing the accuracy of stroke diagnosis and treatment and, eventually, for bettering patient outcomes. The experimental results showed that the proposed method a high level of accuracy in predicting changes in cerebral blood flow after brain strokes. The performance of the model was much enhanced by combining clinical characteristics with FMRI data as opposed to using only one of these data sources. This emphasizes the value of including pertinent clinical information in the diagnosis and management of stroke. © 2023 IEEE.

关键词： Forecasting

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Graph Neural Networks for Optimizing Reliability in Vehicle-to-Infrastructure Communication in Autonomous Systems

Graph Neural Networks for Optimizing Reliability in Vehicle-...

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International Conference on Communication and Electronics Systems (ICCES)

作者： G Neelamegam J. Revathy M. Kannukkiniyal M. Devendran J. Akshya Mani Deepak Choudhry Department of Computer Science and Engineering VSB College of Engineering and Technology Coimbatore Department of Artificial Intelligence and Data Science Christ the King Engineering College Coimbatore Department of Computer Science and Engineering Kongu Engineering College Erode Department of Computer Science and Engineering Hindustan institute of technology Coimbatore Department of Computational Intelligence SRM Institute of Science and Technology Chennai Department of Computing Technologies SRM Institute of Science and Technology Chennai

ISBN: (数字)9798350377972

ISBN: (纸本)9798350377989

High reliance on autonomous systems necessitates efficient and reliable data exchange through Vehicle-to-Infrastructure (V2I) communication to have appropriate and stable network performance in dynamic vehicular environments. With heuristic approaches, Dijkstra's algorithm, and Deep Q-Networks (DQN), several methods are proposed for optimizing latency reduction, packet delivery rate improvement, and power efficiency in the direction of V2I communications. However, these approaches suffer from important challenges: low adaptability of the network to real-time dynamics, suboptimal resource usage, and difficulties in scalability towards complex large-scale networks. In this scenario, we propose a framework that incorporates a GNN inside autonomous systems for V2I communication to optimize performance. The GNN will process real-time graph features, including signal strength, packet loss, and mobility speed, as it dynamically changes in adaptation to the evolution of network behaviour and other functions. This work shows the superiority of the proposed GNN model for the efficiency of experimentally conducted simulations compared to the current models; it achieved latency reduced by up to 28%, 5-10% PDR, and 20% energy efficiency across the various scenarios with urban, rural, highway, and suburban networks. The results validate robustness and scalability for practical implementations by the proposed approach in real-world autonomous systems. Further development is possible with this framework: for example, the use of multi-agent reinforcement learning techniques within cooperative decision-making and the enhancement through blockchain-based security mechanisms to ensure the integrity of the data.

关键词： Road transportation Adaptation models Vehicle-to-infrastructure Scalability Heuristic algorithms Reinforcement learning Graph neural networks Real-time systems Energy efficiency Vehicle dynamics

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Weather Prediction from Imbalanced data Stream using 1D-Convolutional Neural Network 10

Weather Prediction from Imbalanced Data Stream using 1D-Conv...

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10th International Conference on Emerging Trends in engineering and Technology - Signal and Information Processing, ICETET-SIP-22 2022

作者： Alex, Suja A. Ghosh, Uttam Mohammad, Nazeeruddin St. Xavier's Catholic College of Engineering Department of Information Technology India Meharry School of Applied Computational Sciences Department of Computer Science and Data Science NashvilleTN United States College of Computer Science Engineering Prince Mohammad Bin Fahd University Saudi Arabia

ISBN: (纸本)9781665467414

data stream classification is a complex task in the real world due to its varying characteristics. The most common challenges are concept drift and class imbalance. Concept drift shifts in the underlying function generating the data. The biggest obstacle in achieving an effective classifier is due to class imbalance. In general, batch and ensemble solutions are mainly used to train the classifier with chunks of imbalanced data streams. In the paper, we proposed a framework called Self Organizing Auto-Encoder based 1D-CNN for Weather data forecasting. The objective of this framework is to classify imbalanced non-stationary data streams. A proposed model's concept drift handling strength is assessed using Population Stability Index (PSI). The Weather dataset is used for the evaluation of the proposed model. The results shows improvement in prediction accuracy and the classifier is stable with good PSI. © 2022 IEEE.

关键词： Neural networks Stability criteria Sociology Weather forecasting Information processing Market research Indexes

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Optimizing Forwarding Strategies in Sparsely Delay-Tolerant Networks with Short Contact Durations 15

Optimizing Forwarding Strategies in Sparsely Delay-Tolerant ...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Nemera, Yadesa Bedasa Jule, Leta Tesfaye Sivakumar, S. Pavithra, J. Sampathkumar, A. Rastogi, Ravi Dambi Dollo Univeristy Department of Information Technology Ethiopia Dambi Dollo University College of Natural and Computational Science Department of Physics Dambi Dollo Ethiopia Nehru Institute of Engineering and Technology Department of Cse Coimbatore India Dhanalakshmi Srinivasan College of Engineering Department of Artificial intelligence and data science Coimbatore India Dambi Dollo Univeristy Department of Computer Science Ethiopia Koneru Lakshmaiah Education Foundation Department of Cse AP Guntur India

ISBN: (纸本)9798350370249

Delay-tolerant networks (DTNs) are specific settings that experience either instability or the absence of a complete path between the source and destination nodes. The contact lifetime between pairs of network nodes is constrained by the mobility of nodes and the restricted bandwidth of peer- to-peer wireless transmission. In sparsely delay-tolerant networks, the re-establishment of contact between pairs of nodes is significantly delayed beyond the duration of the initial contact. Hence, the need for careful thought in designing a buffer control forwarding policy arises due to the limited contact period. Although forwarding algorithms have been designed extensively, much research has not been done on forwarding when there is a short contact time. When the message exchange between the pairs of network nodes is not completed during the contract period, they must wait until they are within communication range once more. To tackle the issue, we have suggested an innovative forwarding policy in buffer management that considers the duration of interaction. To assess the effectiveness of the proposed effort, we utilized the Opportunistic Network Environment (ONE) networking simulator. The suggested approach is combined with the MaxProp routing protocol buffer control forwarding policy and evaluated using two distinct metrics: bundle delivery rate and average delivery delay. In addition, we evaluated the performance of the suggested work by considering the number of nodes, buffer size, and simulation time. Across all the trials conducted in this thesis, the simulation results consistently demonstrate that our suggested method beats the existing forwarding strategy. © 2024 IEEE.

关键词： Buffer Management Forwarding Policy Contact Duration DTN Sparse DTN

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Problems and Solutions of Point Cloud Mapping for VR and CAVE Environments for data Visualization and Physics Simulation

Problems and Solutions of Point Cloud Mapping for VR and CAV...

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2023 IEEE Applied Imagery Pattern Recognition Workshop, AIPR 2023

作者： Dewberry, Nicholas Alhmoud, Issa W. Chowdhury, Shuva Gokaraju, Balakrishna North Carolina a&t State University Computer Science GreensboroNC United States North Carolina a&t State University Computational Data Science & Engineering GreensboroNC United States North Carolina a&t State University Civil Architectural & Environmental Engineering GreensboroNC United States

In this paper, we explore the utilization of point clouds to enhance the visual fidelity and realism of VR environments. A pipeline was successfully developed to take point cloud data, process it through optimization algorithms such as spatial subsampling, and then virtualize it in a VR environment with custom scripted interactivity. Also, a multitude of problems such as frames-per-second (FPS) optimization, nausea reduction, point cloud sparsity minimization and controller interactivity all need to be addressed before progress can be made. © 2023 IEEE.

关键词： Virtual reality

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