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Real-Time Challenges and Opportunities for an Effective Resource Management in Multi-cloud Environment

SN computer science 2024年第2期5卷 1-8页

作者： Basha, H. Anwar Anilkumar, B.H. Swetha, G. Reddy, Raghavendra Manoli, Sunil Department of Computer Science and Engineering Rajalakshmi Institute of Technology Tamilnadu Chennai India Department of Computer Science and Engineering Vidyavardhaka College of Engineering Mysuru India R R Institute of Technology Bengaluru India School of Computer Science and Engineering REVA University Bengaluru India

As the usage of multi-cloud setups grows, resource management will become a major concern. Because of the dynamic nature of these environments, as well as fluctuating workloads and service-level targets, an effective algorithm for real-time resource allocation and optimisation is required. The Adaptive Multi-Cloud Resource Management Algorithm (AMCRMA) is proposed in this study to handle the obstacles and capitalise on the benefits of resource management in a multi-cloud context. To ensure optimal resource utilisation, cost effectiveness, and service performance, AMCRMA incorporates real-time monitoring, workload prediction, and intelligent resource allocation. Experiment results show that AMCRMA is successful in adapting to changing situations and managing resources in real time, resulting in enhanced resource utilisation and overall performance in multi-cloud systems. © 2024, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： AMCRMA Cloud federation Cloud orchestration Data management Intelligent resource allocation Resource management

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Question-Answering Pair Matching Based on Question Classification and Ensemble Sentence Embedding

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computer Systems science & engineering 2023年第9期46卷 3471-3489页

作者： Jae-Seok Jang Hyuk-Yoon Kwon Department of Computer Science and Engineering Seoul National University of Science and TechnologySeoul01811Korea Department of Industrial Engineering/Graduate School of Data Science Seoul National University of Science and TechnologySeoul01811Korea

Question-answering(QA)models find answers to a given *** necessity of automatically finding answers is increasing because it is very important and challenging from the large-scale QA data *** this paper,we deal with the QA pair matching approach in QA models,which finds the most relevant question and its recommended answer for a given *** studies for the approach performed on the entire dataset or datasets within a category that the question writer manually *** contrast,we aim to automatically find the category to which the question belongs by employing the text classification model and to find the answer corresponding to the question within the *** to the text classification model,we can effectively reduce the search space for finding the answers to a given ***,the proposed model improves the accuracy of the QA matching model and significantly reduces the model inference ***,to improve the performance of finding similar sentences in each category,we present an ensemble embedding model for sentences,improving the performance compared to the individual embedding *** real-world QA data sets,we evaluate the performance of the proposed QA matching *** a result,the accuracy of our final ensemble embedding model based on the text classification model is 81.18%,which outperforms the existing models by 9.81%∼14.16%***,in terms of the model inference speed,our model is faster than the existing models by 2.61∼5.07 times due to the effective reduction of search spaces by the text classification model.

关键词： Question-answering text classification model data augmentation text embedding

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Multimodal Labor Exploitation Detections for Taiwan Distant Water Fishing Industry

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SN computer science 2024年第1期5卷 46页

作者： Karthikeyan, P. Hsiung, Pao-Ann School of Computer Science and Engineering RV University Bengaluru India Department of Computer Science and Information Engineering National Chung Cheng University Chiayi 62102 Taiwan

Taiwan plays a significant role in global seafood supply chains, accounting for approximately 10% of global tuna catches. The country is a substantial producer of tuna, shrimp, and squid, and its seafood industry is worth an estimated $1.3 billion annually. The Taiwan Distant Water Fishing (DWF) industry faces several challenges, including concerns about labor practices and illegal fishing. However, the government is working to address these issues and improve the industry’s sustainability. This research addresses labor exploitation, specifically within Taiwan’s DWF industry. The proposed research introduces a Mobile face verification system (MFVS) to overcome human error in work-hour recording. A multimodal labor exploitation detection system is also developed, utilizing Retrained-You Only Look Once-v7 and the Global Fishing Watch (GFW) model. The multimodal performance is evaluated using DWF data, demonstrating superior performance to the GFW model. The overall accuracy of the multimodal labor exploitation detection system for the Taiwan DWF Industry is 0.87, while the GFW model achieves an accuracy of 0.83 explicitly. The research facilitates combat labor exploitation and improves industry sustainability. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Distant water fishing Global fishing watch Labor exploitations Multimodal Retrained YOLOv7

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A Streamlined Method for Detecting Strawberry Leaf Diseases Utilizing Deep Learning Techniques 3

A Streamlined Method for Detecting Strawberry Leaf Diseases ...

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3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Singh, Jashandeep Singh, Gursharan School of Computer Science and Engineering Lovely Professional University Phagwara India Department of Computer Science and Engineering Lovely Professional University Phagwara India

ISBN: (纸本)9798331527549

Strawberry leaf scorch presents major risks to fruit yields through its harmful effects on plant health so early detection methods are vital for growers. This research analyzes how different Convolutional Neural Networks classify strawberry leaf diseases while fixing problems found in earlier studies about speed and diagnosis precision. The proposed research evaluated five CNN models namely AlexNet, DenseNet, ResNet, MobileNet, and EfficientNet through implementation on a total of 6146 images that showcased both healthy and scorch-affected strawberry leaves. The proposed research optimized feature extraction techniques and added model comparison to find the most effective configuration. EfficientNet achieved higher accuracy and precision than all test models when detecting diseases in samples. The strong disease identification abilities of DenseNet and MobileNet show promise for developing better ways to manage agricultural health problems. This study expands precision agriculture methods while helping farmers sustain their operations through modern machine learning tools. © 2025 IEEE.

关键词： Fruits

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Parameterized Algorithms and Hardness for the Maximum Edge q-Coloring Problem 44

Parameterized Algorithms and Hardness for the Maximum Edge q...

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44th IARCS Annual Conference on Foundations of Software Technology and Theoretical computer science, FSTTCS 2024

作者： Mathew, Rogers Panolan, Fahad Seshikanth Department of Computer Science and Engineering IIT Hyderabad India School of Computer Science University of Leeds United Kingdom

ISBN: (纸本)9783959773553

An edge q-coloring of a graph G is a coloring of its edges such that every vertex sees at most q colors on the edges incident on it. The size of an edge q-coloring is the total number of colors used in the coloring. Given a graph G and a positive integer t, the Maximum Edge q-Coloring problem is about whether G has an edge q-coloring of size t. Studies on this coloring problem were motivated by its application in the channel assignment problem in wireless networks. Goyal, Kamat, and Misra (MFCS 2013) studied Maximum Edge 2-Coloring from the perspective of parameterized complexity. Given a graph on n vertices, they considered the standard parameter t, the number of colors in an optimal edge 2-coloring, and the dual parameter , where n - is the number of colors in an optimal edge 2-coloring. They designed FPT algorithms for Maximum Edge 2-Coloring parameterized by t and . In this paper, we revisit and study Maximum Edge 2-Coloring from the perspective of parameterized complexity and show the following results. 1. Let γ(G) denote the maximum matching size in a given graph G. It is easy to see that a maximum edge 2-coloring of G is of size at least γ(G). Goyal, Kamat, and Misra (MFCS 2013) had asked if there exists an FPT algorithm for Maximum Edge 2-Coloring parameterized by k, where k := (size of a maximum edge 2-coloring of G) - γ(G). We show that Maximum Edge 2-Coloring parameterized by k is W[1] hard. 2. On the positive side, we show that there is an algorithm that, given a graph G on n vertices and a tree decomposition of width tw, runs in time 2O(qtw log qtw)n and outputs a maximum edge q-coloring of G. © 2024 Rogers Mathew, Fahad Panolan, and Seshikanth.

关键词： Color

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Text-Tuple-Table: Towards Information Integration in Text-to-Table Generation via Global Tuple Extraction

Text-Tuple-Table: Towards Information Integration in Text-to...

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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Deng, Zheye Chan, Chunkit Wang, Weiqi Sun, Yuxi Fan, Wei Zheng, Tianshi Yim, Yauwai Song, Yangqiu Department of Computer Science and Engineering HKUST Hong Kong School of Computer Science Fudan University Shanghai China

ISBN: (纸本)9798891761643

The task of condensing large chunks of textual information into concise and structured tables has gained attention recently due to the emergence of Large Language Models (LLMs) and their potential benefit for downstream tasks, such as text summarization and text mining. Previous approaches often generate tables that directly replicate information from the text, limiting their applicability in broader contexts, as text-to-table generation in real-life scenarios necessitates information extraction, reasoning, and integration. However, there is a lack of both datasets and methodologies towards this task. In this paper, we introduce LIVESUM, a new benchmark dataset created for generating summary tables of competitions based on real-time commentary texts. We evaluate the performances of state-of-the-art LLMs on this task in both fine-tuning and zero-shot settings, and additionally propose a novel pipeline called T3(Text-Tuple-Table) to improve their performances. Extensive experimental results demonstrate that LLMs still struggle with this task even after fine-tuning, while our approach can offer substantial performance gains without explicit training. Further analyses demonstrate that our method exhibits strong generalization abilities, surpassing previous approaches on several other text-to-table datasets. Our code and data can be found at https://***/HKUST-KnowComp/LiveSum. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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Spatial Correlation Module for Classification of Multi-Label Ocular Diseases Using Color Fundus Images

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computers, Materials & Continua 2023年第7期76卷 133-150页

作者： Ali Haider Khan Hassaan Malik Wajeeha Khalil Sayyid Kamran Hussain Tayyaba Anees Muzammil Hussain Department of Software Engineering Faculty of Computer SciencesLahore Garrison UniversityLahorePakistan Department of Computer Science School of Systems and TechnologyUniversity of Management and TechnologyLahore54000Pakistan Department of Computer Science&Information Technology University of Engineering and Technology(UET)PeshawarPakistan Department of Computer Science TIMES InstituteMultan60000Pakistan Department of Software Engineering School of Systems and TechnologyUniversity of Management and TechnologyLahore54000Pakistan

To prevent irreversible damage to one’s eyesight,ocular diseases(ODs)need to be recognized and treated *** fundus imaging(CFI)is a screening technology that is both effective and *** to CFIs,the early stages of the disease are characterized by a paucity of observable symptoms,which necessitates the prompt creation of automated and robust diagnostic *** traditional research focuses on image-level diagnostics that attend to the left and right eyes in isolation without making use of pertinent correlation data between the two sets of *** addition,they usually only target one or a few different kinds of eye diseases at the same *** this study,we design a patient-level multi-label OD(PLML_ODs)classification model that is based on a spatial correlation network(SCNet).This model takes into consideration the relevance of patient-level diagnosis combining bilateral eyes and multi-label ODs ***_ODs is made up of three parts:a backbone convolutional neural network(CNN)for feature extraction i.e.,DenseNet-169,a SCNet for feature correlation,and a classifier for the development of classification *** DenseNet-169 is responsible for retrieving two separate sets of attributes,one from each of the left and right *** then,the SCNet will record the correlations between the two feature sets on a pixel-by-pixel *** the attributes have been analyzed,they are integrated to provide a representation at the patient *** the whole process of ODs categorization,the patient-level representation will be *** efficacy of the PLML_ODs is examined using a soft margin loss on a dataset that is readily accessible to the public,and the results reveal that the classification performance is significantly improved when compared to several baseline approaches.

关键词： Ocular disease multi-label spatial correlation CNN eye disease

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A Rearrangement and Restore-Based Mixer Model for Target-Oriented Multimodal Sentiment Classification

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第6期5卷 3109-3119页

作者： Jia, Li Ma, Tinghuai Rong, Huan Sheng, Victor S. Huang, Xuejian Xie, Xintong Nanjing University of Information Science and Technology School of Computer Science Nanjing Jiangsu210044 China Nanjing University of Information Science and Technology School of Computer and Software Nanjing Jiangsu210044 China Jiangsu Ocean University School of Computer Engineering Lianyungang Jiangsu222005 China Nanjing University of Information Science and Technology School of Artificial Intelligence Nanjing Jiangsu210044 China Texas Tech University Department of Computer Science LubbockTX79409 United States

With the development of fine-grained multimodal sentiment analysis tasks, target-oriented multimodal sentiment (TMSC) analysis has received more attention, which aims to classify the sentiment of target with the help of textual and associated image features. Existing methods focus on exploring fine-grained image features and incorporate transformer-based complex fusion strategies, while ignoring the heavy computational burden. Recently, some lightweight multilayer perceptrons (MLP)-based methods have been successfully applied to multimodal sentiment classification tasks. In this article, we propose an effective rearrangement and restore mixer model (RR-Mixer) for TMSC, which dedicates the interaction of image, text, and targets along the modal-axis, sequential-axis, and feature channel-axis through rearrangement and restore operations. Specifically, we take vision transformer (ViT) and robustly optimized BERT (RoBERTa) pretrained models to extract image and textual features, respectively. Further, we adopt cosine similarity to select the most semantically relevant image features. Then, an RR-Mixer module is designed for mixed multimodal features, with the core technology consisting of rolling, grouping rearrangement and restore operations. Moreover, we introduce MLP Unit to learn the information of different modalities for intermodal interaction. The results show that our model achieves superior performance on two benchmark multimodal datasets, TWITTER-15 and TWITTER-17, with a significant improvement of 4.66% and 1.26% in terms of macro-F1. © 2020 IEEE.

关键词： Feature extraction

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PclGPT: A Large Language Model for Patronizing and Condescending Language Detection

PclGPT: A Large Language Model for Patronizing and Condescen...

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2024 Findings of the Association for Computational Linguistics, EMNLP 2024

作者： Wang, Hongbo Li, Mingda Lu, Junyu Xia, Hebin Yang, Liang Xu, Bo Liu, Ruizhu Lin, Hongfei School of Computer Science and Technology Key Laboratory of Social Computing and Cognitive Intelligence Dalian University of Technology China Computer Science and Engineering College Dalian Minzu University China

ISBN: (纸本)9798891761681

Patronizing and condescending language (PCL) is a form of speech directed at vulnerable groups. As an essential branch of toxic language, this type of language exacerbates conflicts and confrontations among Internet communities and detrimentally impacts disadvantaged groups. Traditional pre-trained language models (PLMs) perform poorly in detecting PCL due to its implicit toxicity traits like hypocrisy and false sympathy. With the rise of large language models (LLMs), we can harness their rich emotional semantics to establish a paradigm for exploring implicit toxicity. In this paper, we introduce PclGPT 1, a comprehensive LLM benchmark designed specifically for PCL. We collect, annotate, and integrate the Pcl-PT/SFT dataset, and then develop a bilingual PclGPT-EN/CN model group through a comprehensive pre-training and supervised fine-tuning staircase process to facilitate implicit toxic detection. Group detection results and fine-grained detection from PclGPT and other models reveal significant variations in the degree of bias in PCL towards different vulnerable groups, necessitating increased societal attention to protect them. © 2024 Association for Computational Linguistics.

关键词： Semantics

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Collectively Simplifying Trajectories in a Database: A Query Accuracy Driven Approach 40

Collectively Simplifying Trajectories in a Database: A Query...

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40th IEEE International Conference on Data engineering, ICDE 2024

作者： Wang, Zheng Long, Cheng Cong, Gao Jensen, Christian S. School of Computer Science and Engineering Nanyang Technological University Singapore Aalborg University Department of Computer Science Denmark

ISBN: (纸本)9798350317152

Increasing and massive volumes of trajectory data are being accumulated that may serve a variety of applications, such as mining popular routes or identifying ridesharing candidates. As storing and querying massive trajectory data is costly, trajectory simplification techniques have been introduced that intuitively aim to reduce the sizes of trajectories, thus reducing storage and speeding up querying, while preserving as much information as possible. Existing techniques rely mainly on hand-crafted error measures when deciding which point to drop when simplifying a trajectory. While the hope may be that such simplification affects the subsequent usability of the data only minimally, the usability of the simplified data remains largely unexplored. Instead of using error measures that indirectly may to some extent yield simplified trajectories with high usability, we adopt a direct approach to simplification and present the first study of query accuracy driven trajectory simplification, where the direct objective is to achieve a simplified trajectory database that preserves the query accuracy of the original database as much as possible. Specifically, we propose a multi-agent reinforcement learning based solution with two agents working cooperatively to collectively simplify trajectories in a database while optimizing query usability. Extensive experiments on four real-world trajectory datasets show that the solution is capable of consistently outperforming baseline solutions over various query types and dynamics. © 2024 IEEE.

关键词： Trajectories

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