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Revisiting the efficacy of weak consistencies: a study of forward checking

science China(Information sciences) 2021年第7期64卷 233-235页

作者： Zhe LI Zhezhou YU Hongbo LI Jinsong GUO Zhanshan LI Key Laboratory for Symbol Computation and Knowledge Engineering of National Education Ministry College of Computer Science and Technology Jilin University College of Information Science and Technology Northeast Normal University Department of Computer Science University of Oxford

Dear editor,Arc consistency (AC)[1] is currently the most popular local consistency for solving constraint satisfaction problems(CSP). To solve CSPs more efficiently, extensive work [2–4]has been done to find local c... 详细信息

关键词： Information Systems and Communication Service

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Blockchain and Machine Learning for Optimized Resource Allocation in Cloud Computing Services

Blockchain and Machine Learning for Optimized Resource Alloc...

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International Conference on Contemporary Computing and Informatics (IC3I)

作者： Yagateela Pandu Rangaiah Hanamant Yaragudri K.Pushpa Rani Navdeep Singh Ali Albawi Susmita Kalyani Department of Electronics and Communication Enfineering Institute of Aeronautical Engineering Dundigal Hyderabad India Department of Mechanical Engineering New Horizon College of Engineering Bangalore India Department of Computer Science and Engineering MLR Institute of Technology Hyderabad Telangana India Lovely Professional University Phagwara India Radiology Techniques Department College of Medical Technology The Islamic University Najaf Iraq Lloyd Law College Plot No. 11 Knowledge Park II Greater Noida Uttar Pradesh

ISBN: (数字)9798350350067

ISBN: (纸本)9798350350074

Resource efficiency becomes increasingly critical as cloud computing services alter. This article uses bitcoin and machine learning to optimize resource utilization. We study PRDA, BSCA, and DRAA algorithms and propose “Blockchain and Machine Learning for Optimized Resource Allocation in Cloud Computing Services.” We employ blockchain, machine learning, and smart contracts to make resource sharing safer, clearer, and adaptable to cloud contexts. A comparative test demonstrates our resource allocation strategy works. PRDA reliably predicts resource usage using real-time data and prior patterns. This greatly reduces expenses and waste. The BSCA ensures safe and transparent delivery via guidelines. In multi-tenant cloud systems, DRAA optimizes resource consumption via reinforcement learning. This fixes underuse, exorbitant pricing, and speed loss. With this strategy, cloud service providers can remain open, keep rates low, and serve customers promptly.

关键词： Cloud computing Smart contracts Reinforcement learning Transforms Blockchains Resource management Security Reliability Standards Testing

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Interference-Aware Opportunistic Job Placement for Shared Distributed Deep Learning Clusters

SSRN

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SSRN 2023年

作者： Li, Hongliang Zhao, Hairui Sun, Ting Li, Xiang Xu, Haixiao Li, Keqin College of Computer Science and Technology Jilin University Changchun China Key Laboratory of Symbolic Computation and Knowledge Engineering The Ministry of Education Changchun China High Performance Computing Center Jilin University Changchun China Department of Computer Science State University of New York New PaltzNY12561 United States

Distributed deep learning frameworks facilitate large deep learning workloads. These frameworks support sharing one GPU device among multiple jobs to improve resource utilization. Modern deep learning training jobs consume a large amount of GPU memory. Despite that, sharing GPU memory among jobs is still possible because a training job has iterative steps that its memory usage fluctuates over time. However, resource sharing also introduces the risk of job performance degradation. Colocated jobs sharing a GPU device may suffer from different levels of interference, mainly caused by memory oversharing. How to improve resource utilization while maintaining good job performance is a novel challenge for job placement strategies. This paper studies the job placement problem. We propose an opportunistic memory sharing model to describe the time-varying job memory requirements. Based on this model, we introduce an Opportunistic Job Placement Problem (OJPP) for shared GPU clusters that seek job placement configurations using a minimum number of GPU devices and guarantee user-defined performance requirements at the same time. We propose a greedy algorithm and a heuristic algorithm with computational complexities of O(n log n) and O(n2 log n), respectively, to solve the problem. We also propose an online adjustment algorithm with the computational complexity of O(n log n) to perform updates to job placement configurations in runtime. A machine-learning-based interference prediction method is used to prepare accurate interference estimations. Extensive experiments are conducted on a GPU cluster to verify the correctness and effectiveness of our algorithms. Compared with standalone training jobs on dedicated clusters, the proposed approach reduces resource consumption by 46% in a shared cluster, while guaranteeing over 92.97% of the job performance, in terms of average job completion time. © 2023, The Authors. All rights reserved.

关键词： Computational complexity

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Method for Preoperative Prediction of Microvascular Invasion of Hepatocellular Carcinoma

Method for Preoperative Prediction of Microvascular Invasion...

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2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Zhao, Chen Bai, Tian Chu, Tongjia Wei, Feng Zhang, Fa Jilin University College of Computer Science and Technology Changchun China Ministry of Education Jilin University Key Laboratory of Symbolic Computation and Knowledge Engineering Changchun China First Hospital of Jilin University General Surgery Center Hepatobiliary and Pancreas Department Changchun China School of Medical Technology Beijing Institute of Technology Beijing China

ISBN: (纸本)9781665468190

Microvascular invasion (MVI) in hepatocellular carcinoma (HCC) is of great guiding significance for the formulating treatment strategies and accessing the prognosis before the surgery. However, in traditional medicine, the gold standard for the diagnosis of MVI is obtained by examining pathological images which can only be obtained by sampling and sectioning tumors after surgery. At this time, MVI results have lost the timeliness of guiding tumor resection surgery. In order to solve this problem, existing studies began to use deep learning-based methods for preoperative prediction of MVI using non-invasive imaging. Most of these methods adopt the fusion methods of multi-sequence images to predict MVI, but fail to make full use of the characteristics of multiply sequences as prior knowledge to combine into the model, resulting in no further improvement of prediction performance. So we propose a multi-sequence image difference and correlation deep learning model. The model can extract the difference and correlation information between sequences from different scales and combine them into the model. To validate proposed model, we collected a data set consists of 120 HCC patients, including 50 MVI-positive patients. Compared with existing studies, our method has greatly improved in all evaluation metrics. © 2022 IEEE.

关键词： Forecasting

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Edge-Minimum Saturated k-Planar Drawings 29th

Edge-Minimum Saturated k-Planar Drawings

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29th International Symposium on Graph Drawing and Network Visualization, GD 2021

作者： Chaplick, Steven Klute, Fabian Parada, Irene Rollin, Jonathan Ueckerdt, Torsten Department of Data Science and Knowledge Engineering Maastricht University Maastricht Netherlands Utrecht University Utrecht Netherlands TU Eindhoven Eindhoven Netherlands Department of Mathematics and Computer Science FernUniversität in Hagen Hagen Germany Institute of Theoretical Informatics Karlsruhe Institute of Technology Karlsruhe Germany

ISBN: (纸本)9783030929305

For a class D of drawings of loopless (multi-)graphs in the plane, a drawing D∈ D is saturated when the addition of any edge to D results in D′∉ D —this is analogous to saturated graphs in a graph class as introduced by Turán (1941) and Erdős, Hajnal, and Moon (1964). We focus on k-planar drawings, that is, graphs drawn in the plane where each edge is crossed at most k times, and the classes D of all k-planar drawings obeying a number of restrictions, such as having no crossing incident edges, no pair of edges crossing more than once, or no edge crossing itself. While saturated k-planar drawings are the focus of several prior works, tight bounds on how sparse these can be are not well understood. We establish a generic framework to determine the minimum number of edges among all n-vertex saturated k-planar drawings in many natural classes. For example, when incident crossings, multicrossings and selfcrossings are all allowed, the sparsest n-vertex saturated k-planar drawings have 2k-(kmod2)(n-1) edges for any k≥ 4, while if all that is forbidden, the sparsest such drawings have 2(k+1)k(k-1)(n-1) edges for any k≥ 6. © 2021, Springer Nature Switzerland AG.

关键词： Drawing (graphics)

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Recent developments in the thermal radiative flow of dusty Ellis trihybrid nanofluid with activation energy using Hamilton-Crosser thermal conductivity model

International Journal of Thermofluids

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International Journal of Thermofluids 2025年 27卷

作者： Okasha, Mostafa Mohamed Abbas, Munawar Akgül, Ali Formanova, Shoira Ibrahim, Talib K. Hassani, Murad Khan Department of Mechanical Engineering College of Engineering Northern Border University Arar Saudi Arabia Department of Mathematics Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Tamil Nadu Chennai602105 India Department of Electronics and Communication Engineering Saveetha School of Engineering SIMATS Tamil Nadu Chennai India Siirt University Art and Science Faculty Department of Mathematics Siirt56100 Turkey Department of Computer Engineering Biruni University Topkapı Istanbul34010 Turkey Department of Chemistry and Its Teaching Methods Tashkent State Pedagogical University Tashkent Uzbekistan Department of Petroleum Engineering College of Engineering Knowledge University Erbil 44001 Iraq Department of Petroleum Engineering Al-Kitab University Altun Kupri Iraq Ghazni University Department of Mathematics Afghanistan Near East University Mathematics Research Center Department of Mathematics Near East Boulevard PC: 99138 Nicosia /Mersin 10 Turkey

This study scrutinizes the characteristics of activation energy on Darcy Forchheimer radiative flow of dusty Ellis trihybrid nanofluid over a Riga plate when dust and nanoparticles are present. The goal of the present work is to use the Hamilton-Crosser thermal conductivity model to scrutinize the heat transmission for the Darcy Forchheimer flow of dusty Ellis trihybrid nanofluid. The flow is impacted by heat source with the properties of Marangoni convection. The base fluid, propylene glycol (C3H8O2), is mixed with Ag, TiO2 and Al2O3 nanoparticles. The model is applicable to sophisticated heat transfer systems, including solar energy harvesting and electronic device cooling technologies. Additionally, it finds application in thermal management of industrial processes using nanofluids and aerospace engineering. Using the shooting technique, the numerical results of the governing equations are obtained (RKF-45th). The impacts on dimensionless physical quantities of interest of geometrical and physical properties relevant to this study are analysed using the required tables and figures. The results demonstrated that the Ellis fluid parameter raised the heat transmission, mass transmission rate, and velocity profiles. As the chemical reaction parameter upsurges, the concentration distributions decrease. © 2025 The Author(s)

关键词： Activation energy

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Heterojunction p-n type α-Fe₂O₃/CeO₂ composite for photocatalytic dye degradation

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Journal of the Indian Chemical Society 2025年第7期102卷

作者： Shinde, Trupti B. Mharsale, Nilesh N. Raut, Pradnya S. Patil, Ganesh E. Kapadnis, Kailas H. Department of Chemistry MGVs Loknete Vyankatrao Hiray Arts Science and Commerce College Panchavati Nashik 422003 India Department of Chemistry K. K. Wagh Arts Commerce Science and Computer Science College Nashik 422 003 India Department of Physics MVPs Arts Commerce and Science College Tryambakeshwar 422212 India Department of Engineering Sciences METS Institute of Engineering Bhujbal Knowledge City Adgaon Nashik 422003 India Department of Physics SNJBs KKHA Arts SMGL Commerce and SPHJ Science College Chandwad 423101 India

Composite p-n type heterojunction photocatalysis offers an environmentally friendly and energy-efficient solution for wastewater treatment, leveraging visible light to degrade pollutants effectively. Herein, firstly CeO₂ and Fe₂O₃ were synthesized using chemical reaction methods, followed by the preparation of a p-n type α-Fe₂O₃/CeO₂ composite catalyst via ball milling and calcination at 500 °C. The synthesized composite was characterized using XRD, FTIR, SEM, Raman spectroscopy, PL and UV–visible spectroscopy to investigate their structural and optical properties. The successful integration of CeO₂ and Fe₂O₃ into the composite was confirmed by the combined features in XRD and Raman analyses, as well as characteristic FTIR peaks at 1143 cm⁻¹ and 1084 cm⁻¹, corresponding to Ce–O–Ce and Fe–O–Fe bridging vibrations. SEM analysis revealed roughly spherical nanoparticles with an average size of 357 nm. UV–visible spectroscopy measurements indicated a reduced band gap of 1.15 eV for the composite, enhancing its visible light absorption. The formation of a stable p-n heterojunction between CeO₂ and Fe₂O₃ improved charge separation and photocatalytic efficiency. The photocatalytic activity of the α-Fe₂O₃/CeO₂ composite was evaluated for the degradation of methylene green dye under visible light irradiation, achieving a maximum degradation efficiency of 95.45 % at an optimal pH of 6 and a catalyst mass of 0.15 g. The relatively low PL intensity observed at 636 nm in the α-Fe₂O₃/CeO₂ nanocomposite suggests that the formation of the heterojunction between α-Fe₂O₃ and CeO₂ facilitates efficient charge carrier separation. This study demonstrates that the α-Fe₂O₃/CeO₂ composite is an effective and s

关键词： Methylene green dye p-n type composite Photocatalyst α-Fe2O3/CeO2

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Foundation models in bioinformatics

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National science Review 2025年第04期 400-418页

作者： Fei Guo Renchu Guan Yaohang Li Qi Liu Xiaowo Wang Can Yang Jianxin Wang Hunan Provincial Key Lab on Bioinformatics School of Computer Science and Engineering Central South University Xiangjiang Laboratory Key Laboratory for Symbol Computation and Knowledge Engineering of the Ministry of Education College of Computer Science and Technology Jilin University Department of Computer Science Old Dominion University School of Life Sciences and Technology Tongji University Department of Automation Tsinghua University Department of Mathematics State Key Laboratory of Molecular Neuroscience and Big Data Bio-Intelligence Lab The Hong Kong University of Science and Technology

With the adoption of foundation models(FMs),artificial intelligence(AI) has become increasingly significant in bioinformatics and has successfully addressed many historical challenges,such as pre-training frameworks,model evaluation and *** demonstrate notable proficiency in managing large-scale,unlabeled datasets,because experimental procedures are costly and labor *** various downstream tasks,FMs have consistently achieved noteworthy results,demonstrating high levels of accuracy in representing biological entities.A new era in computational biology has been ushered in by the application of FMs,focusing on both general and specific biological *** this review,we introduce recent advancements in bioinformatics FMs employed in a variety of downstream tasks,including genomics,transcriptomics,proteomics,drug discovery and single-cell *** aim is to assist scientists in selecting appropriate FMs in bioinformatics,according to four model types:language FMs,vision FMs,graph FMs and multimodal *** addition to understanding molecular landscapes,AI technology can establish the theoretical and practical foundation for continued innovation in molecular biology.

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Data Cleaning of Binary Sensor Events in Activity Recognition by Cluster-Based Methods 7th

Data Cleaning of Binary Sensor Events in Activity Recognitio...

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7th International Conference on Human Aspects of IT for the Aged Population, ITAP 2021, held as part of the 23rd International Conference, HCI International 2021

作者： Zhao, Chunyang Que, Xia Yin, Yue Xing, Xiaoman Yang, Jiaoyun An, Ning Key Laboratory of Knowledge Engineering with Big Data of Ministry of Education Hefei University of Technology Hefei China School of Computer Science and Information Engineering Hefei University of Technology Hefei China Medical Imaging Department Chinese Academy of Sciences Suzhou China

ISBN: (纸本)9783030781101

The Ambient Assisted Living (AAL) systems use sensors to detect the daily behavior of older adults and provide necessary assistance based on changes in their cognitive status and physical functions, thus enabling older adults to maintain their independence at home. However, the effectiveness of the AAL systems depends on the accuracy of the data provided by sensors. Namely, when a human error or a hardware failure occurs, the activity recognition model can become inaccurate. This inaccuracy hinders the identification of critical and potentially life-threatening activities. Although there are many methods for cleaning sensor data, there is no method for binary sensors deployed in smart homes. By considering noisy sensor events and unintentional forgetting of turning off the device, this paper proposes two clustering-based methods for denoising and splitting binary sensor events to address possible inaccuracy due to the two mentioned problems. The effectiveness of the proposed methods is verified by the experiments using four machine learning models and three real-world smart home datasets and adopting different sensor configurations. The experimental results demonstrate that compared to the original unprocessed datasets, by combining the two proposed methods, the average accuracy and F-measure are improved by 15.00% and 17.25%, respectively. © 2021, Springer Nature Switzerland AG.

关键词： Automation

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Misspelling Semantics in Thai

arXiv

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arXiv 2022年

作者： Nakwijit, Pakawat Purver, Matthew Cognitive Science Research Group School of Electronic Engineering and Computer Science Queen Mary University of London United Kingdom Department of Knowledge Technologies Jožef Stefan Institute Ljubljana Slovenia

User-generated content is full of misspellings. Rather than being just random noise, we hypothesise that many misspellings contain hidden semantics that can be leveraged for language understanding tasks. This paper presents a fine-grained annotated corpus of misspelling in Thai, together with an analysis of misspelling intention and its possible semantics to get a better understanding of the misspelling patterns observed in the corpus. In addition, we introduce two approaches to incorporate the semantics of misspelling: Misspelling Average Embedding (MAE) and Misspelling Semantic Tokens (MST). Experiments on a sentiment analysis task confirm our overall hypothesis: additional semantics from misspelling can boost the micro F1 score up to 0.4-2%, while blindly normalising misspelling is harmful and suboptimal. © 2022, CC BY.

关键词： Semantics

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