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International Journal of Communication Systems 2023年第9期36卷 e5481-e5481页

作者： Mohammadian, Vahid Navimipour, Nima Jafari Hosseinzadeh, Mehdi Darwesh, Aso Department of Computer Engineering Qeshm Branch Islamic Azad University Qeshm Iran Department of Computer Engineering Tabriz Branch Islamic Azad University Tabriz Iran Future Technology Research Center National Yunlin University of Science and Technology Yunlin Douliou64002 Taiwan Pattern Recognition and Machine Learning Lab Gachon University Seongnam Korea Republic of Information Technology Departments University of Human Development Sulaimaniyah Iraq

Recently, cloud computing has been recognized as an effective paradigm for offering an on-demand platform, software services, and an efficient infrastructure to cloud clients. Due to the exponential growth of cloud tasks and the rapidly increasing number of cloud users, scheduling and balancing these tasks among involved heterogeneous virtual machines becomes an Non-deterministic Polynomial hard (NP-hard) optimization problem considering significant constraints, such as high rate of resource usage, low scheduling time, and low implementation cost. Therefore, various meta-heuristic algorithms have been widely used to tackle the issue. The current paper proposes a novel load balancing mechanism using the ant colony optimization and artificial bee colony algorithms, called LBAA, which aims to balance the load division among systems in data centers. The simulation outcomes confirm that our algorithm outperforms previous works regarding response time, imbalance degree, makespan, and resource utilization up to 25%, 15%, 12%, and 10%, respectively. © 2023 John Wiley & Sons Ltd.

关键词： Ant colony optimization

ELLE: Efficient Lifelong Pre-training for Emerging Data

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arXiv 2022年

作者： Qin, Yujia Zhang, Jiajie Lin, Yankai Liu, Zhiyuan Li, Peng Sun, Maosong Zhou, Jie Department of Computer Science and Technology Tsinghua University Beijing China Beijing National Research Center for Information Science and Technology China Institute for Artificial Intelligence Tsinghua University Beijing China Pattern Recognition Center WeChat AI Tencent Inc China International Innovation Center of Tsinghua University Shanghai China Beijing Academy of Artificial Intelligence China Tsinghua University China Jiangsu Collaborative Innovation Center for Language Ability Xuzhou China

Current pre-trained language models (PLM) are typically trained with static data, ignoring that in real-world scenarios, streaming data of various sources may continuously grow. This requires PLMs to integrate the information from all the sources in a lifelong manner. Although this goal could be achieved by exhaustive pre-training on all the existing data, such a process is known to be computationally expensive. To this end, we propose ELLE, aiming at efficient lifelong pretraining for emerging data. Specifically, ELLE consists of (1) function preserved model expansion, which flexibly expands an existing PLM's width and depth to improve the efficiency of knowledge acquisition;and (2) pretrained domain prompts, which disentangle the versatile knowledge learned during pretraining and stimulate the proper knowledge for downstream tasks. We experiment ELLE with streaming data from 5 domains on BERT and GPT. The results show the superiority of ELLE over various lifelong learning baselines in both pre-training efficiency and downstream performances. The codes are publicly available at https://***/thunlp/ELLE. Copyright © 2022, The Authors. All rights reserved.

关键词： Efficiency

CUGE: A Chinese language Understanding and Generation Evaluation Benchmark

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arXiv 2021年

作者： Yao, Yuan Dong, Qingxiu Guan, Jian Cao, Boxi Zhang, Zhengyan Xiao, Chaojun Wang, Xiaozhi Qi, Fanchao Bao, Junwei Nie, Jinran Zeng, Zheni Gu, Yuxian Zhou, Kun Huang, Xuancheng Li, Wenhao Ren, Shuhuai Lu, Jinliang Xu, Chengqiang Wang, Huadong Zeng, Guoyang Zhou, Zile Zhang, Jiajun Li, Juanzi Huang, Minlie Yan, Rui He, Xiaodong Wan, Xiaojun Zhao, Xin Sun, Xu Liu, Yang Liu, Zhiyuan Han, Xianpei Yang, Erhong Sui, Zhifang Sun, Maosong Department of Computer Science and Technology Tsinghua University China MOE Key Lab of Computational Linguistics School of EECS Peking University China Institute of Software Chinese Academy of Sciences China JD AI Research Beijing China School of Information Science Beijing Language and Culture University China School of Information Renmin University of China China National Laboratory of Pattern Recognition Institute of Automation CAS China Gaoling School of Artificial Intelligence Renmin University of China China Wangxuan Institute of Computer Technology Peking University Beijing Academy of Artificial Intelligence China

Realizing general-purpose language intelligence has been a longstanding goal for natural language processing, where standard evaluation benchmarks play a fundamental and guiding role. We argue that for general-purpose language intelligence evaluation, the benchmark itself needs to be comprehensive and systematic. To this end, we propose CUGE, a Chinese language Understanding and Generation Evaluation benchmark with the following features: (1) Hierarchical benchmark framework, where datasets are principally selected and organized with a language capability-task-dataset hierarchy. (2) Multilevel scoring strategy, where different levels of model performance are provided based on the hierarchical framework. To facilitate CUGE, we provide a public leaderboard that can be customized to support flexible model judging criteria. Evaluation results on representative pre-trained language models indicate ample room for improvement towards general-purpose language intelligence. CUGE is publicly available at ***. Copyright © 2021, The Authors. All rights reserved.

关键词： Natural language processing systems

OLSR+: A new routing method based on fuzzy logic in flying ad-hoc networks (FANETs)

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Vehicular Communications 2022年 36卷

作者： Rahmani, Amir Masoud Ali, Saqib Yousefpoor, Efat Yousefpoor, Mohammad Sadegh Javaheri, Danial Lalbakhsh, Pooia Hassan Ahmed, Omed Hosseinzadeh, Mehdi Lee, Sang-Woong Future Technology Research Center National Yunlin University of Science and Technology Yunlin Taiwan Department of Information Systems College of Economics and Political Science Sultan Qaboos University Al Khoudh Muscat Oman Department of Computer Engineering Dezful Branch Islamic Azad University Dezful Iran Department of Computer Engineering Chosun University Gwangju 61452 South Korea Department of Data Science and Artificial Intelligence Faculty of Information Technology Monash University Clayton 3800 VIC Australia Department of Information Technology University of Human Development Sulaymaniyah Iraq Pattern Recognition and Machine Learning Lab Gachon University 1342 Seongnamdaero Sujeonggu Seongnam 13120 South Korea

Flying ad-hoc networks (FANETs) have many applications in military, industrial and agricultural areas. Due to specific features of FANETs, such as high-speed nodes, low density of nodes in the network, and rapid changes in the topology, most routing protocols designed for mobile ad hoc networks (MANETs) or vehicular ad hoc networks (VANETs) are not compatible with FANETs. In this paper, we propose a fuzzy logic-based routing approach called OLSR+ for FANETs. In this scheme, we seek to improve the optimized link state routing protocol (OLSR) so that it can efficiently be used in FANETs. OLSR+ includes four main phases: 1) Discovering neighboring nodes. In this phase, we propose a new and efficient technique for estimating the lifetime of the link between two unmanned ariel vehicles (UAVs) based on the link quality, distance, relative velocity, and movement direction. 2) Selecting multipoint relays (MPRs). In this phase, we present a fuzzy mechanism for selecting a set of MPR nodes. According to this mechanism, when a node has higher residual energy, higher link lifetime, and more neighborhood degree compared to others, it achieves more fitness to be selected as MPR. 3) Discovering the network topology. In this phase, we modify the format of the topology control (TC) message and add two fields, including route energy and route lifetime to this message. 4) Calculating the routing table. In OLSR+, we consider two parameters, including route energy and route lifetime, for establishing stable paths. Finally, we simulate OLSR+ using NS3 and compare its performance with two methods, namely greedy optimized link state routing (G-OLSR) and optimized link state routing (OLSR). The simulation results show that OLSR+ successfully reduces delay compared to G-OLSR and OLSR. In addition, it has higher packet delivery rate and throughput than others. Also, it improves energy consumption in the network. However, OLSR+ has more routing overhead than G-OLSR. © 2022 Elsevier Inc.

关键词： Artificial intelligence (AI) Flying ad hoc networks (FANETs) Fuzzy logic Routing Unmanned ariel vehicles (UAVs)

When accurate prediction models yield harmful self-fulfilling prophecies

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arXiv 2023年

作者： van Amsterdam, Wouter A.C. van Geloven, Nan Krijthe, Jesse H. Ranganath, Rajesh Cinà, Giovanni Department of Data Science and Biostatistics Julius Center of Health Sciences Primary Care University Medical Center Utrecht Heidelberglaan 100 Utrecht3584 CX Netherlands University of Utrecht Heidelberglaan 100 Utrecht3584 CX Netherlands Department of Biomedical Data Sciences Leiden University Medical Center Leiden Netherlands Pattern Recognition & Bioinformatics Delft University of Technology Delft Netherlands Courant Institute of Mathematical Science Department of Computer Science Center for Data Science New York University New York City United States Department of Medical Informatics Amsterdam University Medical Center Amsterdam Netherlands Institute for Logic Language and Computation University of Amsterdam Amsterdam Netherlands Pacmed Amsterdam Netherlands

Prediction models are popular in medical research and practice. By predicting an outcome of interest for specific patients, these models may help inform difficult treatment decisions, and are often hailed as the poster children for personalized, data-driven healthcare. We show however, that using prediction models for decision making can lead to harmful decisions, even when the predictions exhibit good discrimination after deployment. These models are harmful self-fulfilling prophecies: their deployment harms a group of patients but the worse outcome of these patients does not invalidate the predictive power of the model. Our main result is a formal characterization of a set of such prediction models. Next we show that models that are well calibrated before and after deployment are useless for decision making as they made no change in the data distribution. These results point to the need to revise standard practices for validation, deployment and evaluation of prediction models that are used in medical decisions. © 2023, CC BY.

关键词： Prediction models

A Q-learning-based smart clustering routing method in flying Ad Hoc networks

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Journal of King Saud University - computer and Information sciences 2024年第1期36卷

作者： Hosseinzadeh, Mehdi Tanveer, Jawad Rahmani, Amir Masoud Aurangzeb, Khursheed Yousefpoor, Efat Yousefpoor, Mohammad Sadegh Darwesh, Aso Lee, Sang-Woong Fazlali, Mahmood Institute of Research and Development Duy Tan University Da Nang Viet Nam School of Medicine and Pharmacy Duy Tan University Da Nang Viet Nam Department of Computer Science and Engineering Sejong University Seoul 05006 South Korea Future Technology Research Center National Yunlin University of Science and Technology Yunlin Taiwan Department of Computer Engineering College of Computer and Information Sciences King Saud University P.O. Box 51178 Riyadh 11543 Saudi Arabia Department of Computer Engineering Dezful Branch Islamic Azad University Dezful Iran Department of Information Technology University of Human Development Sulaymaniyah Iraq Pattern Recognition and Machine Learning Lab Gachon University 1342 Seongnamdaero Sujeonggu Seongnam 13120 South Korea Cybersecurity and Computing Systems Research Group School of Physics Engineering and Computer Science University of Hertfordshire Hertfordshire AL10 9AB United Kingdom

Flying ad hoc networks (FANETs) have particular importance in various military and civilian applications due to their specific features, including frequent topological changes, the movement of drones in a three-dimensional space, and their restricted energy. These features have created challenges for designing cluster-based routing protocols. In this paper, a Q-learning-based smart clustering routing method (QSCR) is suggested in FANETs. In QSCR, each node discovers its neighbors through the periodic exchange of hello messages. The hello time interval is different in each cluster, and cluster leaders determine this interval based on the average speed similarity. Next, an adaptive clustering process is presented for categorizing drones in the clusters. In this step, the cluster leader is selected based on a new parameter called merit value, which includes residual energy, centrality, neighbor degree, speed similarity, and link validity time. Then, a centralized Q-learning model is presented to tune weight coefficients related to merit parameters dynamically. In the last step, the routing process is done using a greedy forwarding technique. Finally, QSCR is run on NS2, and the simulation results of QSCR are compared with those of ICRA, WCA, and DCA. These results show that QSCR carries out the clustering process rapidly but has less cluster stability than ICRA. QSCR gets energy efficiency and improves network lifetime. In the routing process, QSCR has a high packet delivery rate compared to other schemes. Also, the number of isolated clusters created in QSCR is less than other clustering methods. However, the proposed scheme has a higher end-to-end delay than ICRA. Also, this scheme experiences more communication overhead than ICRA slightly. © 2024 The Author(s)

关键词： Clustering Flying ad hoc networks (FANETs) Machine learning (ML) Reinforcement learning (RL) Unmanned aerial vehicles (UAVs)

A smart filtering-based adaptive optimized link state routing protocol in flying ad hoc networks for traffic monitoring

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Journal of King Saud University - computer and Information sciences 2024年第4期36卷

作者： Hosseinzadeh, Mehdi Ali, Saqib Rahmani, Amir Masoud Lansky, Jan Nulicek, Vladimir Yousefpoor, Mohammad Sadegh Yousefpoor, Efat Darwesh, Aso Lee, Sang-Woong Institute of Research and Development Duy Tan University Da Nang Viet Nam School of Medicine and Pharmacy Duy Tan University Da Nang Viet Nam Department of Information Systems College of Economics and Political Science Sultan Qaboos University Al Khoudh Muscat Oman Future Technology Research Center National Yunlin University of Science and Technology Yunlin Taiwan Department of Computer Science and Mathematics Faculty of Economic Studies University of Finance and Administration Prague Czech Republic Center of Research and Strategic Studies Lebanese French University Kurdistan Region Iraq Department of Information Technology University of Human Development Sulaymaniyah Kurdistan region Iraq Pattern Recognition and Machine Learning Lab Gachon University 1342 Seongnamdaero Sujeonggu Seongnam 13120 South Korea

Nowadays, the use of drones as a fundamental element of smart cities has attracted the attention of many researchers to monitor and control the traffic of vehicles. Because of the high flexibility of multi-drone systems, like flying ad hoc networks (FANETs), they provide various services and improve modern life in smart cities. However, due to the unique features of FANET, especially the high speed of drones and rapid changes in network topology, communication reliability is a serious challenge in this network. Hence, traditional routing protocols, such as optimized link state routing (OLSR) scheme, cannot work well in these networks. In this paper, a smart filtering-based adaptive optimized link state routing (SFA-OLSR) scheme is proposed in FANETs. To increase adaptability to the FANET environment, SFA-OLSR provides a new solution to adjust the hello broadcast period so that each flying node specifies its broadcast period based on a new scale called cosine similarity between real and predicted positions. Furthermore, in SFA-OLSR, each flying node develops a filtering algorithm based on two parameters, namely link lifetime and remaining energy. The purpose of this algorithm is to reduce the size of the single-hop neighboring set of each flying node and minimize the search space when finding multi-point relays (MPRs). This increases the convergence speed of the algorithm. Then, SFA-OLSR exploits the sparrow search algorithm (SSA) to single out the best MPRs. This algorithm introduces a multi-objective function by focusing on three components, including energy, link lifespan, and neighbor degree. Lastly, the simulation process of SFA-OLSR is performed by the NS3 simulator. This process evaluates the performance of the proposed method and three schemes, namely Gangopadhyay et al., P-OLSR, and OLSR-ETX. These evaluations show that SFA-OLSR has a good performance in terms of three scales, namely packet delivery ratio, delay, and throughput, but its overhead is more than

关键词： Artificial intelligence (AI) Flying ad hoc networks (FANETs) Meta-heuristic algorithms Reliability Routing

An intelligent clustering scheme based on whale optimization algorithm in flying ad hoc networks

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Vehicular Communications 2024年 49卷

作者： Hosseinzadeh, Mehdi Tanveer, Jawad Alanazi, Faisal Aurangzeb, Khursheed Yousefpoor, Mohammad Sadegh Yousefpoor, Efat Darwesh, Aso Lee, Sang-Woong Rahmani, Amir Masoud Institute of Research and Development Duy Tan University Da Nang Viet Nam School of Medicine and Pharmacy Duy Tan University Da Nang Viet Nam Department of Computer Science and Engineering Sejong University Seoul 05006 South Korea Department of Electrical Engineering College of Engineering Prince Sattam bin Abdulaziz University Al-Kharj 11942 Saudi Arabia Department of Computer Engineering College of Computer and Information Sciences King Saud University P. O. Box 51178 Riyadh 11543 Saudi Arabia Center of Research and Strategic Studies Lebanese French University Kurdistan Region Iraq Department of Information Technology University of Human Development Kurdistan Region Sulaymaniyah Iraq Pattern Recognition and Machine Learning Lab Gachon University 1342 Seongnamdaero Sujeonggu Seongnam 13120 South Korea Future Technology Research Center National Yunlin University of Science and Technology Yunlin Taiwan

Due to the progress of unmanned aerial vehicles (UAVs), this new technology is widely applied in military and civilian areas. Multi-UAV networks are often known as flying ad hoc networks (FANETs). Due to these applications, FANET must ensure communication stability and have high scalability. These goals are achieved by presenting clustering techniques in FANETs. However, the characteristics of these networks, like high-mobility nodes, limited energy, and dynamic topology, have created great challenges in two important processes of clustering protocols, namely cluster construction and the selection of cluster heads. In this paper, an intelligent clustering scheme based on the whale optimization algorithm called ICW is suggested in flying ad hoc networks. Firstly, each UAV specifies its hello interval based on the lifespan of adjacent links to guarantee the adaptability of ICW to FANET. Then, a centralized clustering process is done using a whale optimization algorithm (WOA) to find the best cluster centers on the network. To determine the membership of each UAV in a cluster, ICW employs a new criterion, i.e. closeness ratio, so that each UAV joins a cluster with the best closeness ratio. In addition, the evaluation of each whale is carried out based on a fitness function, consisting of three components, namely the number of isolated clusters, the ratio of inter-cluster distance to intra-cluster distance, and cluster size. Then, a cluster head is selected for each cluster based on a score value. This score is dependent on the weighted sum of four metrics, namely remaining energy, the average link lifespan between each UAV and its neighbors, neighbor degree, and the average distance between each UAV and its neighbors. In the last step, two routing processes, namely intra-cluster routing and inter-cluster routing, are introduced in FANET. Then, the evaluation and implementation of ICW is performed through the NS2 simulator. After completing the simulation process, ICW i

关键词： Artificial Intelligence (AI) Clustering Flying ad hoc networks (FANETs) Nature-inspired algorithms Unmanned aerial vehicles (UAVs)

Risk-based decision making: estimands for sequential prediction under interventions

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arXiv 2023年

作者： Luijken, Kim Morzywolek, Pawel van Amsterdam, Wouter Cinà, Giovanni Hoogland, Jeroen Keogh, Ruth Krijthe, Jesse Magliacane, Sara van Ommen, Thijs Peek, Niels Putter, Hein van Smeden, Maarten Sperrin, Matthew Wang, Junfeng Weir, Daniala Didelez, Vanessa van Geloven, Nan Department of Epidemiology Julius Center for Health Sciences and Primary Care University Medical Center Utrecht Utrecht Netherlands Department of Applied Mathematics Computer Science and Statistics Ghent University Ghent Belgium Department of Statistics University of Washington Seattle United States Department of Medical Informatics Amsterdam University Medical Centers Amsterdam Netherlands Institute for Logic Language and Computation University of Amsterdam Amsterdam Netherlands Pacmed Amsterdam Netherlands Department of Epidemiology and Data Science Amsterdam University Medical Centers Amsterdam Netherlands Department of Medical Statistics London School of Hygiene & Tropical Medicine Keppel Street London United Kingdom Pattern Recognition and Bio-Informatics Group EEMCS Delft University of Technology Delft Netherlands Amsterdam Machine Learning Lab University of Amsterdam Amsterdam Netherlands Department of Information and Computing Sciences Utrecht University Utrecht Netherlands Division of Informatics Imaging and Data Science Faculty of Biology Medicine and Health University of Manchester Manchester Academic Health Science Centre Manchester United Kingdom Department of Biomedical Data Sciences Leiden University Medical Center Leiden Netherlands Division of Pharmacoepidemiology and Clinical Pharmacology Department of Pharmaceutical Sciences Utrecht University Utrecht Netherlands Department of Biometry and Data Management Leibniz Institute for Prevention Research Epidemiology - BIPS Bremen Germany Faculty of Mathematics/Computer Science University of Bremen Bremen Germany

Prediction models are used amongst others to inform medical decisions on interventions. Typically, individuals with high risks of adverse outcomes are advised to undergo an intervention while those at low risk are advised to refrain from it. Standard prediction models do not always provide risks that are relevant to inform such decisions: e.g., an individual may be estimated to be at low risk because similar individuals in the past received an intervention which lowered their risk. Therefore, prediction models supporting decisions should target risks belonging to defined intervention strategies. Previous works on prediction under interventions assumed that the prediction model was used only at one time point to make an intervention decision. In clinical practice, intervention decisions are rarely made only once: they might be repeated, deferred and re-evaluated. This requires estimated risks under interventions that can be reconsidered at several potential decision moments. In the current work, we highlight key considerations for formulating estimands in sequential prediction under interventions that can inform such intervention decisions. We illustrate these considerations by giving examples of estimands for a case study about choosing between vaginal delivery and cesarean section for women giving birth. Our formalization of prediction tasks in a sequential, causal, and estimand context provides guidance for future studies to ensure that the right question is answered and appropriate causal estimation approaches are chosen to develop sequential prediction models that can inform intervention decisions. © 2023, CC BY.

关键词： Forecasting

International Conference on technology and Innovation in Sports, Health and Wellbeing (TISHW) Vila Real, Portugal. 01-03 December 2016 Abstracts

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BMC SPORTS science MEDICINE AND REHABILITATION 2017年第SUPPL 1期9卷 5-5页

作者： [Anonymous] GAIA Association of Electronic and Information Technologies in the Basque Country Bilbao Spain Universidade de Trás-os-Montes e Alto Douro Vila Real Portugal INESC TEC and Universidade de Trás-os-Montes e Alto Douro Vila Real Portugal Vienna University of Technology Vienna Austria motion3D GmbH Vienna Austria Department of Psychology Masaryk University Faculty of Arts Brno Czech Republic Universidade Nova de Lisboa NOVA IMS Lisboa Portugal NOVA IMS Lisboa Portugal Department of Sport Sciences and CIDESD University of Beira Interior Covilhã Portugal Department of Sport Sciences University of Beira Interior and Research Centre in Sports Sciences Health Sciences and Human Development Covilhã Portugal Department of Sport Sciences University of Beira Interior Research Centre in Sports Sciences Health Sciences and Human Development Covilhã Portugal INESC TEC and University of Trás-os-Montes and Alto Douro Vila Real Portugal Digital Sports Group Pattern Recognition Lab Department of Computer Science Friedrich-Alexander University Erlangen-Nuremberg (FAU) Erlangen Germany Institute for Factory Automation and Production Systems Friedrich-Alexander University Erlangen-Nuremberg (FAU) Erlangen Germany Fisio Lógic Centro de Fisioterapia Lisboa Portugal Faculdade de Motricidade Humana Universidade de Lisboa Laboratório de Comportamento Motor Estrada da Costa 1499-002 Cruz Quebrada Portugal Universidade de Trás-os-Montes e Alto Douro (UTAD) Vila Real Portugal Universidade de Trás-os-Montes e Alto Douro (UTAD) and Instituto de Engenharia de Sistemas e Computadores Tecnologia e Ciência (INESC TEC) Vila Real Portugal INESC TEC and University of Trás-os-Montes e Alto Douro Vila Real Portugal