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17th International Symposium on Distributed computing and Artificial Intelligence, DCAI 2020

作者： Canito, Alda Mota, Daniel Marreiros, Goreti Corchado, Juan M. Martins, Constantino GECAD - Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development Institute of Engineering Polytechnic of Porto Porto Portugal Department of Computer Science University of Salamanca Salamanca Spain

ISBN: (纸本)9783030538286

Information technologies are intrinsically connected to the manufacturing processes, with more data generated each second. To efficiently operate machines, users must sort out information that is relevant to them in specific moments and contexts. In this paper, we propose an architecture that combines context – e.g. location, type of order, available assets, previous actions – with information established through user stereotypes. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021.

关键词： Industry 4.0

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Lift-and-round to improve weighted completion time on unrelated machines

Lift-and-round to improve weighted completion time on unrela...

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作者： BANSAL, NIKHIL SRINIVASAN, ARAVIND SVENSSON, OLA Department of Mathematics and Computer Science TU Eindhoven Eindhoven5600 MB Netherlands Department of Computer Science and Institute for Advanced Computer Studies University of Maryland College ParkMD20742 United States School of Computer and Communication Sciences EPFL LausanneCH-1015 Switzerland

We consider the problem of scheduling jobs on unrelated machines so as to minimize the sum of weighted completion times. Our main result is a (3/2 c)-approximation algorithm for some fixed c > 0, improving upon the long-standing bound of 3/2. To do this, we first introduce a new lift-and-project-based SDP relaxation for the problem. This is necessary, as the previous convex programming relaxations have an integrality gap of 3/2. Second, we give a new general bipartiterounding procedure that produces an assignment with certain strong negative correlation properties. © 2021 Society for Industrial and Applied Mathematics Publications. All rights reserved.

关键词： Convex optimization

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Quantum Algorithms and Lower Bounds for Finite-Sum Optimization

arXiv

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arXiv 2024年

作者： Zhang, Yexin Zhang, Chenyi Fang, Cong Wang, Liwei Li, Tongyang School of Electronics Engineering and Computer Science Peking University China Computer Science Department Stanford University United States National Key Lab of General Artificial Intelligence School of Intelligence Science and Technology Peking University China Institute for Artificial Intelligence Peking University China Center on Frontiers of Computing Studies Peking University China School of Computer Science Peking University China

Finite-sum optimization has wide applications in machine learning, covering important problems such as support vector machines, regression, etc. In this paper, we initiate the study of solving finite-sum optimization problems by quantum computing. Specifically, let f1,...,fn: Rd → R be -smooth convex functions and ψ: Rd → R be a µ-strongly convex proximal function. The goal is to find an ǫ-optimal point for F(x) = n1 Pni=1 fi(x) + ψ(x). We give a quantum algorithm with complexity O(n + √d + p/µ(n1/3d1/3 + n−2/3d5/6))1 improving the classical tight bound Θ(n + pn/µ). We also prove a quantum lower bound Ω( n + n3/4(/µ)1/4) when d is large enough. Both our quantum upper and lower bounds can extend to the cases where ψ is not necessarily stronglyconvex, or each fi is Lipschitz but not necessarily smooth. In addition, when F is nonconvex, our quantum algorithm can find _an ǫ-critial point using O(n + (d1/3n1/3 + √d)/ǫ2) queries. Copyright © 2024, The Authors. All rights reserved.

关键词： Support vector machines

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Deep view of composite SNR CTA1 with LHAASO in γ-rays up to 300 TeV

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science China(Physics,Mechanics & Astronomy) 2025年第7期Mechanics & Astronomy) .卷 17-30页

作者： LHAASO Collaboration Key Laboratory of Particle Astrophysics & Experimental Physics Division & Computing Center institute of High Energy Physics Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China TIANFU Cosmic Ray Research Center Chengdu China Dublin institute for Advanced Studies 2 Dublin Ireland Max-Planck-Institut for Nuclear Physics Heidelberg Germany School of Physical Science and Technology & School of Information Science and Technology Southwest Jiaotong University Chengdu China School of Astronomy and Space Science Nanjing University Nanjing China Center for Astrophysics Guangzhou University Guangzhou China Tsung-Dao Lee Institute & School of Physics and Astronomy Shanghai Jiao Tong University Shanghai China Institute for Nuclear Research of Russian Academy of Sciences Moscow Russia Hebei Normal University Shijiazhuang China University of Science and Technology of China Hefei China State Key Laboratory of Particle Detection and Electronics Beijing China Key Laboratory of Dark Matter and Space Astronomy & Key Laboratory of Radio Astronomy Purple Mountain Observatory Chinese Academy of Sciences Nanjing China Research Center for Astronomical Computing Zhejiang Laboratory Hangzhou China Key Laboratory for Research in Galaxies and Cosmology Shanghai Astronomical Observatory Chinese Academy of Sciences Shanghai China School of Physics and Astronomy Yunnan University Kunming China Key Laboratory of Cosmic Rays (Tibet University) Ministry of Education Lhasa China Department of Physics The Chinese University of Hong Kong Hong Kong China Key Laboratory of Radio Astronomy and Technology National Astronomical Observatories Chinese Academy of Sciences Beijing China School of Physics and Astronomy (Zhuhai) & Sino-French Institute of Nuclear Engineering and Technology (Zhuhai) Sun Yat-sen University Zhuhai China School of Physics (Guangzhou) Sun Yat-sen University Guangzhou China Institute of Frontier and Interdisciplinary Science S

The ultra-high-energy（UHE） gamma-ray source 1LHAASO J0007+7303u is positionally associated with the composite SNR CTA1 that is located at high Galactic Latitude b≈10.5°.This provides a rare opportunity to spatially resolve the component of the pulsar wind nebula（PWN） and supernova remnant（SNR） at *** paper conducted a dedicated data analysis of 1LHAASO J0007+7303u using the data collected from December 2019 to July *** source is well detected with significances of 21σ and 17σ at 8-100 TeV and>100 TeV,*** corresponding extensions are determined to be 0.23°±0.03° and 0.17°±0.03°.The emission is proposed to originate from the relativistic electrons accelerated within the PWN of PSR J0007+*** energy spectrum is well described by a power-law with an exponential cutoff function dN/dE=（42.4±4.1）(E/20TeV)-2.31+0.11exp(-E/（110±25Tev）) TeV-1 cm-2s-1in the energy range from 8 to 300 TeV,implying a steady-state parent electron spectrum dNe/dEe∝（Ee/100TeV）-3.13±0.16exp[(-Ee/（373±70TeV）)2] at energies above≈50 *** cutoff energy of the electron spectrum is roughly equal to the expected current maximum energy of particles accelerated at the PWN terminal *** the X-ray and gamma-ray emission,the current space-averaged magnetic field can be limited to≈4.5 μ*** satisfy the multi-wavelength spectrum and the y-ray extensions,the transport of relativistic particles within the PWN is likely dominated by the advection process under the free-expansion phase assumption.

关键词： PWN ${\bm~\gamma}$-ray UHE

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Node Mobility and Encounter Rate Metrics to Enhance Stability in MANET

Node Mobility and Encounter Rate Metrics to Enhance Stabilit...

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Augmented Intelligence and Sustainable Systems (ICAISS), International Conference on

作者： Gnanajeyaraman Rajaram Archana Vishveswari R S R. Deepa A. Packialatha Department of Applied Machine Learning Saveetha School of Engineering Saveetha Institute of Medical And Technical Sciences Chennai Tamil Nadu India Department of Computer Science and Engineering Varuvan Vadivelan Institute of Technology Dharmapuri Tamil Nadu India Department of Computer Science and Engineering Vels Institute of Science Technology & Advanced Studies Chennai Tamil Nadu India

Mobile Ad hoc Networks (MANETs) have been broadly functional in a wide variety of scenarios, for example, in disaster recovery, health care, video conferencing, and battlefield transmissions. MANET is an independent and peer-to-peer network which builds a chain of nodes to enable data transmission from sender to receiver. Hop count is measured as the most significant metric in MANET. Though several metrics have been introduced as the replacement for the hop count metric, there is a need for outperformance in mobile scenarios. The node mobility will affect the link stability, and it creates congestion. To solve these issues, a Node Mobility and Encounter Rate metrics (NMER) to enhance the stability of the network. It selects the route based on the node mobility and Encounter Rate (ER) metrics. The congestion ingredient measures the congestion level in the MANET. The objective of this route has the least cost for forward data packets and minimizes congestion. The NMER approach simulation results demonstrate that the NMER approach minimizes the packet loss ratio and reduces the network delay.

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Analysis of Electroencephalographic Signals to Study the Behavior of Brain Frequencies for the Study of Academic Stress 2

Analysis of Electroencephalographic Signals to Study the Beh...

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2nd International Conference on Automation, computing and Renewable Systems, ICACRS 2023

作者： Moyeenudin, H.M. Hannah, S. Anuradha, T. Muthalagu, R. Seedha Devi, V. Jose Anand, A. Vels Institute of Science Technology and Advanced Studies Tamil Nadu Chennai600117 India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Tamil Nadu 6000262 India Department of Electrical and Electronics Engineering KCG College of Technology Tamil Nadu Chennai600097 India Department of Biomedical Engineering Karpaga Vinayaga College of Engineering and Technology Tamil Nadu 603308 India Department of IT Jaya Engineering College Tamil Nadu Chennai602024 India Department of ECE KCG College of Technology Tamil Nadu Chennai600 097 India

ISBN: (纸本)9798350340235

The current study perform an analysis of electroencephalographic signals to study the behavior of brain frequencies in subjects who are under academic stress generated by a cognitive task, while listening to music or being silent. Creation of a corpus of more than 10 subjects under different sound stimuli is created. Characterization of brain signals are characterized for the identification of academic stress. Protocol is designed and brain signals are collected to observe the relationship between music listening and academic stress. EEG signal classifiers are used to identify differences between different sessions. Analysis of brain frequencies are analysed obtained in the sessions for each participant. © 2023 IEEE.

关键词： Academic Stress Brain Frequencies Brainwave Patterns EEG (Electroencephalography) Stress Response

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The Path-Label Reconciliation (PLR) Dissimilarity Measure for Gene Trees

arXiv

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arXiv 2024年

作者： Sánchez, Alitzel López Ramírez-Rafael, José Antonio Flores-Lamas, Alejandro Hernández-Rosales, Maribel Lafond, Manuel Department of Computer Science University of Sherbrooke QuebecJ1K2R1 Canada Center for Research and Advanced Studies The National Polytechnic Institute Gto. Irapuato Mexico Department of Computer Science Interdisciplinary Center for Bioinformatics University of Leipzig Germany Max Planck Institute for Mathematics in the Sciences Saxony Leipzig Germany

Background: In this study, we investigate the problem of comparing gene trees reconciled with the same species tree using a novel semi-metric, called the Path-Label Reconciliation (PLR) dissimilarity measure. This approach not only quantifies differences in the topology of reconciled gene trees, but also considers discrepancies in predicted ancestral gene-species maps and speciation/duplication events, offering a refinement of existing metrics such as Robinson-Foulds (RF) and their labeled extensions LRF and ELRF. A tunable parameter α also allows users to adjust the balance between its species map and event labeling components. Our contributions: We show that PLR can be computed in linear time and that it is a semi-metric. We also discuss the diameters of reconciled gene tree measures, which are important in practice for normalization, and provide initial bounds on PLR, LRF, and ELRF. To validate PLR, we simulate reconciliations and perform comparisons with LRF and ELRF. The results show that PLR provides a more evenly distributed range of distances, making it less susceptible to overestimating differences in the presence of small topological changes, while at the same time being computationally efficient. Our findings suggest that the theoretical diameter is rarely reached in practice. The PLR measure advances phylogenetic reconciliation by combining theoretical rigor with practical applicability. Future research will refine its mathematical properties, explore its performance on different tree types, and integrate it with existing bioinformatics tools for large-scale evolutionary analyses. The open source code is available at: https://***/project/parle/. © 2024, CC BY.

关键词： Genes

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Bio-Inspired Optimization with Transfer Learning Based Crowd Density Detection on Sparse Environment

Bio-Inspired Optimization with Transfer Learning Based Crowd...

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Pervasive computing and Social Networking (ICPCSN), International Conference on

作者： A. Poongodi N. Kopperundevi S. Poorani Mohit Tiwari B. Srinivasa Rao N. Abilash School of Computing Sciences Vels Institute of Science Technology & Advanced Studies Chennai School of Computer Science and Engineering Vellore Institute of Technology Vellore Tamil Nadu Computer Technology-UG Kongu Engineering College Perundurai Computer science and engineering Bharati Vidyapeeth's College of Engineering Delhi Computer Science and Engineering Gokaraju Rangaraju Institute of Engineering & Technology Bachupally Hyderabad Mechanical Engineering Annai Vailankanni College of Engineering Pottalkulam Kanyakumari District

Crowd density estimation is a major importance for applications including crowd control, public space planning, autonomous driving, visual surveillance, and warning visually distrait drivers previous accident. With strong scale, reflective, and translational symmetry, techniques to estimate the density of the crowd yield a promising outcome. But dynamic scenes with constantly evolving spatial and temporal domains and perspective distortion yet have difficulties. The dynamic nature of scenes and the complexity of demonstrating and integrating the feature space of objects of different magnitudes as predictive prototypes are the primary reason for this. This manuscript presents a Red Fox Algorithm with Transfer Learning based Crowd Density Detection (RFOTL-CDD) technique in Sparse Environment. The purpose of the RFOTL-CDD system lies in the effectual identification and classification of distinct types of crowds in a sparse environment. To achieve this, the presented RFOTL-CDD method uses a ResNet prototype for feature vector generation. For the identification and classification of a crowd, the RFOTL-CDD technique applies Naive Bayes (NB) classifiers. In this work, the RFO algorithm is utilized for boosting the performance of the ResNet method. The stimulation outcomes of the RFOTL-CDD technique can be well studied on a crowd dataset and the outcomes confirmed the supremacy of the RFOTL-CDD technique on crowd detection.

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AI-based Analytics for Human Resource Data Insights

AI-based Analytics for Human Resource Data Insights

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Electronics and Renewable Systems (ICEARS), International Conference on

作者： Padma Mahadevan A. Sridharan Shreya Sudesh Sakpal Shantanu Sudhir Gujar Suchitra Labhane Akash Kharche Department of Management Studies ISBR Business School Bengaluru Karnataka India Department of Physical Education & Yoga Saveetha School of Law Saveetha Institute of Medical and Technical Sciences Chennai Tamil Nadu India Department of Computer Science Golisano College of Computing and Information SciencesRochester Institute of Technology Rochester NY United States Department of Cyber Security Golisano College of Computing and Information Sciences Rochester Institute of Technology Rochester NY United States Department of MBA Datta Meghe Institute of Management Studies Nagpur Maharashtra India

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509682

A rapid digital transformation in enterprises has resulted in an explosion of data pertaining to human resources (HR), which has necessitated the use of advanced analytics in order to discover insights that can be put into action. Using contemporary machine learning techniques, this research investigates the use of artificial intelligence (AI) for HR data analytics. The goal of the study is to solve issues in data quality, feature selection, and predictive modelling. A number of resampling strategies are utilized during preprocessing in order to correct class imbalances, and data transformation is utilized in order to extract useful features. Through the utilization of Principal Component Analysis (PCA) for dimensionality reduction, the process of feature selection is streamlined in order to determine which predictors have the most impact. In order to anticipate important HR outcomes, such as employee attrition and performance categorization, feedforward neural networks (FNNs) are utilized for classification purposes. Datasets from the real world of human resources are used to validate the suggested framework, which demonstrates enhanced accuracy and efficiency in the process of gaining insights. The findings of this study emphasize the potential for AI-driven approaches to change HR analytics.

关键词： Dimensionality reduction Analytical models Renewable energy sources Accuracy Data integrity Predictive models Feature extraction Data models Feedforward neural networks Principal component analysis

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A Cloud Based Information Retrieval Agent System Using Web Services

A Cloud Based Information Retrieval Agent System Using Web S...

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Advancement in Electronics & Communication Engineering (AECE), International Conference on

作者： Namit Gupta Ananta Ojha V. Subha Ramya College of Computing Science and Information Technology Teerthanker Mahaveer University Moradabad Uttar Pradesh India College of Computing Sciences and Information Technology Jain (deemed to be) University Bangalore Karnataka India Department of Biomedical Engineering Vels Institute of Science Technology Advanced Studies Channai India

Large, scalable computer resources are made available to consumers as a service via the cloud computing system, which runs over the Internet. Both the business and scientific communities have shown a lot of interest in this technology. Nevertheless, there are no mechanisms for information retrieval and knowledge discovery in the concept of cloud computing. In other words, clouds need autonomy and intelligence. Within cloud computing settings, Web Services are an essential part of Service Oriented computing (SOC). A single Web Service is unable to get targeted and specialized information from Web Services inside a cloud environment. Agents may be used to help in the successful composition of Web Services. The main goal of this project is to propose a system to extract information in cloud environment by employing web services along with concept of multi-agent systems.

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