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Active Learning Query Strategies for Classification,Regression,and clustering:A Survey

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Journal of Computer Science & Technology 2020年第4期35卷 913-945页

作者： Punit Kumar Atul Gupta Indian Institute of Information Technology Design and ManufacturingJabalpurMadhya Pradesh 482005India

Generally,data is available abundantly in unlabeled form,and its annotation requires some cost.The labeling,as well as learning cost,can be minimized by learning with the minimum labeled data instances.Active learning(AL),learns from a few labeled data instances with the additional facility of querying the labels of instances from an expert annotator or oracle.The active learner uses an instance selection strategy for selecting those critical query instances,which reduce the generalization error as fast as possible.This process results in a refined training dataset,which helps in minimizing the overall cost.The key to the success of AL is query strategies that select the candidate query instances and help the learner in learning a valid hypothesis.This survey reviews AL query strategies for classification,regression,and clustering under the pool-based AL scenario.The query strategies under classification are further divided into:informative-based,representative-based,informative-and representative-based,and others.Also,more advanced query strategies based on reinforcement learning and deep learning,along with query strategies under the realistic environment setting,are presented.After a rigorous mathematical analysis of AL strategies,this work presents a comparative analysis of these strategies.Finally,implementation guide,applications,and challenges of AL are discussed.

关键词： active learning active learning query strategy active classification active regression active clustering deep active learning

Safest Route Detection via Danger Index Calculation and K-Means clustering

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Computers, Materials & Continua 2021年第11期69卷 2761-2777页

作者： Isha Puthige Kartikay Bansal Chahat Bindra Mahekk Kapur Dilbag Singh Vipul Kumar Mishra Apeksha Aggarwal Jinhee Lee Byeong-Gwon Kang Yunyoung Nam Reham R.Mostafa School of Engineering and Applied Sciences Bennett UniversityGreater NoidaIndia Department of ICT Convergence Soonchunhyang UniversityAsanKorea Department of Information Systems Faculty of Computers and Information SciencesMansoura UniversityMansouraEgypt

The study aims to formulate a solution for identifying the safest route between any two inputted Geographical locations.Using the New York City dataset,which provides us with location tagged crime statistics;we are implementing different clustering algorithms and analysed the results comparatively to discover the best-suited one.The results unveil the fact that the K-Means algorithm best suits for our needs and delivered the best results.Moreover,a comparative analysis has been performed among various clustering techniques to obtain best results.we compared all the achieved results and using the conclusions we have developed a user-friendly application to provide safe route to users.The successful implementation would hopefully aid us to curb the ever-increasing crime rates;as it aims to provide the user with a beforehand knowledge of the route they are about to take.A warning that the path is marked high on danger index would convey the basic hint for the user to decide which path to prefer.Thus,addressing a social problem which needs to be eradicated from our modern era.

关键词： Agglomerative clustering crime rate danger index DBSCAN

Lipid species dependent vesicles clustering caused by alpha-synuclein as revealed by single-vesicle imaging with total internal reflection fluorescence microscopy

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Biophysics Reports 2021年第6期7卷 437-448页

作者： Chinta Mani Aryal Owen Tyoe Jiajie Diao Department of Cancer Biology University of Cincinnati College of Medicine Department of Physics University of Cincinnati

Single-molecule methods have been applied to study the mechanisms of many biophysical systems that occur on the nanometer scale. To probe the dynamics of such systems including vesicle docking, tethering, fusion, trafficking, protein-membrane interactions, etc., and to obtain reproducible experimental data; proper methodology and framework are crucial. Here, we address this need by developing a protocol for immobilization of vesicles composed of synthetic lipids and measurement using total internal reflection fluorescence(TIRF) microscopy. Furthermore, we demonstrate applications including vesicle clustering mediated by proteins such as alpha-Synuclein(αSyn) and the influence of external ions by using TIRF microscopy. Moreover, we use this method to quantify the dependence of lipid composition and charge on vesicle clustering mediated by αSyn which is based on the methods previously reported.

关键词： Single molecule Alpha-Synuclein Lipids Vesicles clustering Total internal reflection fluorescence microscopy

New look at Geiger-Nuttall law and α clustering of heavy nuclei

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Chinese Physics C 2021年第2期45卷 6-11页

作者：钱以斌任中洲 Department of Applied Physics Nanjing University of Science and TechnologyNanjing 210094China School of Physics Science and Engineering Tongji UniversityShanghai 200092China

The Geiger-Nuttall(GN)law ofαdecay is commonly explained in terms of the quantum tunneling phenomenon.In this study,we show that such an explanation is actually not enough regarding theαparticle clustering.Such a conclusion is drawn after exploring the involved coefficients of the GN law based on the conventional description ofαdecay,namely the formation of anαcluster and its subsequent penetration.The specific roles of the two former processes,in the GN law,manifest themselves via the systematical analysis of the calculated and experimentalαdecay half-lives versus the decay energies across the Z=82 and N=126 shell closures.Theα-cluster preformation probability is then found to behave in a GN-like pattern.This previously ignored point is explicitly demonstrated as the product of an interplay between the mean-field and pairing effect,which in turn reveals the structural influence on the formation of theαcluster in a simple and clear manner.In addition to providing an effective method to evaluate the amount of surfaceαclustering in heavy nuclei,the present conjecture supports other theoretical treatments of theαpreformation probability.

关键词： Geiger-Nuttall law clustering cluster model αdecay

An Adjustable Variant of Round Robin Algorithm Based on clustering Technique

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Computers, Materials & Continua 2021年第3期66卷 3253-3270页

作者： Samih M.Mostafa Hirofumi Amano Faculty of Computers and Information South Valley UniversityQena83523Egypt Research Institute for Information Technology Kyushu UniversityFukuoka819-0395Japan

CPU scheduling is the basic task within any time-shared operating system.One of the main goals of the researchers interested in CPU scheduling is minimizing time cost.Comparing between CPU scheduling algorithms is subject to some scheduling criteria(e.g.,turnaround time,waiting time and number of context switches(NCS)).Scheduling policy is divided into preemptive and non-preemptive.Round Robin(RR)algorithm is the most common preemptive scheduling algorithm used in the time-shared operating systems.In this paper,the authors proposed a modified version of the RR algorithm,called dynamic time slice(DTS),to combine the advantageous of the low scheduling overhead of the RR and favor short process for the sake of minimizing time cost.Each process has a weight proportional to the weights of all processes.The process’s weight determines its time slice within the current period.The authors benefit from the clustering technique in grouping the processes that are similar in their attributes(e.g.,CPU service time,weight,allowed time slice(ATS),proportional burst time(PBT)and NCS).Each process in a cluster is assigned the average of the processes’time slices in this cluster.A comparative study of six popular scheduling algorithms and the proposed approach on nine groups of processes vary in their attributes was performed and the evaluation was measured in terms of waiting and turnaround times,and NCS.The experiments showed that the proposed algorithm gives better results.

关键词： clustering CPU scheduling round robin turnaround time waiting time

Radar group target recognition based on HRRPs and weighted mean shift clustering

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Journal of Systems Engineering and Electronics 2020年第6期31卷 1152-1159页

作者： GUO Pengcheng LIU Zheng WANG Jingjing National Laboratory of Radar Signal Processing Xidian UniversityXi’an 710071China Xi'an Electronic Engineering Research Institute Xi’an 710100China

When range high-resolution radar is applied to target recognition,it is quite possible for the high-resolution range profiles(HRRPs)of group targets in a beam to overlap,which reduces the target recognition performance of the radar.In this paper,we propose a group target recognition method based on a weighted mean shift(weighted-MS)clustering method.During the training phase,subtarget features are extracted based on the template database,which is established through simulation or data acquisition,and the features are fed to the support vector machine(SVM)classifier to obtain the classifier parameters.In the test phase,the weighted-MS algorithm is exploited to extract the HRRP of each subtarget.Then,the features of the subtarget HRRP are extracted and used as input in the SVM classifier to be recognized.Compared to the traditional group target recognition method,the proposed method has the advantages of requiring only a small amount of computation,setting parameters automatically,and having no requirement for target motion.The experimental results based on the measured data show that the method proposed in this paper has better recognition performance and is more robust against noise than other recognition methods.

关键词： clustering group target recognition high resolution range profile(HRRP) mean shift(MS)

Coronavirus Pandemic Analysis Through Tripartite Graph clustering in Online Social Networks

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Big Data Mining and Analytics 2021年第4期4卷 242-251页

作者： Xueting Liao Danyang Zheng Xiaojun Cao Department of Computer Science Georgia State UniversityAtlantaGA 30302USA Suzhou Key Laboratory of Advanced Optical Communication Network Technology School of Electronic and Information EngineeringSoochow UniversitySuzhou 215006China

The COVID-19 pandemic has hit the world hard.The reaction to the pandemic related issues has been pouring into social platforms,such as Twitter.Many public officials and governments use Twitter to make policy announcements.People keep close track of the related information and express their concerns about the policies on Twitter.It is beneficial yet challenging to derive important information or knowledge out of such Twitter data.In this paper,we propose a Tripartite Graph clustering for Pandemic Data Analysis(TGC-PDA)framework that builds on the proposed models and analysis:(1)tripartite graph representation,(2)non-negative matrix factorization with regularization,and(3)sentiment analysis.We collect the tweets containing a set of keywords related to coronavirus pandemic as the ground truth data.Our framework can detect the communities of Twitter users and analyze the topics that are discussed in the communities.The extensive experiments show that our TGC-PDA framework can effectively and efficiently identify the topics and correlations within the Twitter data for monitoring and understanding public opinions,which would provide policy makers useful information and statistics for decision making.

关键词： COVID-19 clustering online social network Twitter

Course Drop-out Prediction on MOOC Platform via clustering and Tensor Completion

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Tsinghua Science and Technology 2019年第4期24卷 412-422页

作者： Jinzhi Liao Jiuyang Tang Xiang Zhao National University of Defense Technology

As a supplement to traditional education, online courses offer people, regardless of their age, gender, or profession, the chance to access state-of-the-art knowledge. Nonetheless, despite the large number of students who choose to begin online courses, it is easy to observe that quite a few of them drop out in the middle, and information on this is vital for course organizers to improve their curriculum outlines. In this work, in order to make a precise prediction of the drop-out rate, we propose a combined method MOOP, which consists of a global tensor and local tensor to express all available feature aspects. Specifically, the global tensor structure is proposed to model the data of the online courses, while a local tensor is clustered to capture the inner connection of courses. Consequently, drop-out prediction is achieved by adopting a high-accuracy low-rank tensor completion method, equipped with a pigeon-inspired algorithm to optimize the parameters. The proposed method is empirically evaluated on real-world Massive Open Online Courses(MOOC) data, and is demonstrated to offer remarkable superiority over alternatives in terms of efficiency and accuracy.

关键词： MOOC platform drop-out prediction tensor completion clustering

UAV clustering Scheme for FANETs using Elbow-Hybrid Metaheuristic Techniques

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Computer Systems Science & Engineering 2021年第9期38卷 321-337页

作者： Kundan Kumar Rajeev Arya Department of Electronics and Communication Engineering National Institute of Technology PatnaPatna800005India

Great strides have been made to realistically deploy multiple Unmanned Aerial Vehicles(UAVs)within the commercial domain,which demands a proper coordination and reliable communication among the UAVs.UAVs suffer from limited time of flight.Conventional techniques suffer from high delay,low throughput,and early node death due to aerial topology of UAV networks.To deal with these issues,this paper proposes a UAV parameter vector which considers node energy,channel state information and mobility of UAVs.By intelligently estimating the proposed parameter,the state of UAV can be predicted closely.Accordingly,efficient clustering may be achieved by using suitable metaheuristic techniques.In the current work,Elbow method has been used to determine optimal cluster count in the deployed FANET.The proposed UAV parameter vector is then integrated into two popular hybrid metaheuristic algorithms,namely,water cycle-moth flame optimization(WCMFO)and Grey Wolf-Particle Swarm optimization(GWPSO),thereby enhancing the lifespan of the system.A methodology based on the holistic approach of parameter and signal formulation,estimation model for intelligent clustering,and statistical parameters for performance analysis is carried out by the energy consumption of the network and the alive node analysis.Rigorous simulations are run to demonstrate node density variations to validate the theoretical developments for various proportions of network system sizes.The proposed method presents significant improvement over conventional stateof-the-art methods.

关键词： clustering elbow method hybrid UAVs FANETs energy consumption