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A Machine Learning-Based Adaptive Feedback System to Enhance Programming Skill Using Computational Thinking

IEEE ACCESS 2024年 12卷 59431-59440页

作者： Kaleem, Muhammad Hassan, Muhammad Awais Khurshid, Syed Khaldoon Univ Engn & Technol Lahore Dept Comp Sci Lahore 39161 Pakistan

The solution of a typical programming task involves algorithmic thinking, pattern recognition, decomposition, and abstraction skills. These skills are basic pillars of Computational thinking (CT) and are essential for a computer programmer. Therefore, a programming teacher needs to take these skills into account for the evaluation of students. Existing methods for improving programming competency don't consider the Computational Thinking of students and perform grading of students without taking these skills into account. Due to this limitation, deficiencies of these skills in students remain unrevealed, posing difficulties for educators to provide need-oriented feedback. The performance of programming students is thus hindered by a lack of interventions. This study proposes a method to evaluate programming students in terms of CT skill components and group them based on their skill set. In this study, 780 students of object-oriented programming (OOP) class were given programming assignments for assessment of programming competencies. These students were then given small programming tasks requiring different computational thinking skill components for their solutions. A machine learning approach was used to perform grouping of these students based on their scores. Six groups of programming students, each having its own set of skill deficiencies, were identified as a result of clustering. One of the groups had an empty set of skill deficiencies and consisted of students proficient in programming. Each of the other five groups had a non-empty set of skill deficiencies and comprised non-proficient programming students. A group-specific skill development plan was built for the groups having skill deficiencies. It was found that such feedback was very effective and improved the CT skill deficiencies of 82% of students.

关键词： Programming Programming profession Codes Task analysis Source coding clustering algorithms Feedback Cognitive systems Object oriented programming Machine learning Education Performance evaluation Computational thinking assessment feedback K-means clustering programming

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Persistence-Based Motif Discovery in Time Series

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2024年第11期36卷 6814-6827页

作者： Germain, Thibaut Truong, Charles Oudre, Laurent Univ Paris Saclay Univ Paris Cite Ctr Borelli ENS Paris SaclayCNRSSSAINSERM F-91190 Gif Sur Yvette France

Motif Discovery consists of finding repeated patterns and locating their occurrences in a time series without prior knowledge about their shape or location. Most state-of-the-art algorithms rely on three core parameters: the number of motifs to discover, the length of the motifs, and a similarity threshold between motif occurrences. Setting these parameters is difficult in practice and often results from a trial-and-error strategy. In this paper, we propose a new algorithm that discovers motifs of variable length given a single motif length and without requiring a similarity threshold. At its core, the algorithm maps a time series onto a graph, summarizes it with persistent homology - a tool from topological data analysis - and identifies the most relevant motifs from the graph summary. We propose two versions of the algorithm, one requiring the number of motifs to discover and another, adaptive, that infers the number of motifs from the graph summary. Empirical evaluation on 9 labeled datasets, including 6 real-world datasets, shows that both algorithm versions significantly outperform state-of-the-art algorithms.

关键词： Time series analysis Electrocardiography clustering algorithms Euclidean distance Reviews Optimization Indexes Motif discovery persistent homology time series topological data analysis

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clustering of Typical Wind Power Scenarios Based on K-Means clustering Algorithm and Improved Artificial Bee Colony Algorithm

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IEEE ACCESS 2022年 10卷 98752-98760页

作者： Yao, Gang Wu, Yang Huang, Xiaoxu Ma, Qinfeng Du, Jiang Guizhou Power Grid Co Ltd Power Dispatch Control Ctr Guiyang 550002 Peoples R China

An improved artificial bee colony algorithm (IABC) was proposed to solve the problems of the k-means clustering (KMC) algorithm, such as poor global search ability, sensitive selection of initial cluster center, randomness of initialization, precocity and slow convergence of the original artificial bee colony (ABC) algorithm. To improve the efficiency of iterative optimization process, a fitness function adapted to KMC algorithm and a position updating formula based on global guidance were constructed. By comparing the improved artificial bee colony algorithm with the original artificial bee colony algorithm and particle swarm optimization algorithm, it is confirmed that IABC algorithm converges speed block and overcomes the shortcoming of the original algorithm which is easy to fall into local optimal solution. IABC algorithm is combined with KMC to get better clustering effect, and the algorithm is used to select typical wind power output scenarios, which plays an important role in actual production.

关键词： clustering algorithms Wind power generation Artificial bee colony algorithm Particle swarm optimization Statistics Social factors Optimization Artificial bee colony (ABC) algorithm k-means clustering (KMC) algorithm fitness function position update wind power forecast typical scenario

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A supervised graph-based deep learning algorithm to detect and quantify clustered particles

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NANOSCALE 2024年第32期16卷 15308-15318页

作者： Saavedra, Lucas A. Mosqueira, Alejo Barrantes, Francisco J. UCA CONICET Biomed Res Inst BIOMED Lab Mol Neurobiol Ave Alicia Moreau de Justo 1600C1107AFF Buenos Aires DF Argentina

Considerable efforts are currently being devoted to characterizing the topography of membrane-embedded proteins using combinations of biophysical and numerical analytical approaches. In this work, we present an end-to-end (i.e., human intervention-independent) algorithm consisting of two concatenated binary Graph Neural Network (GNNs) classifiers with the aim of detecting and quantifying dynamic clustering of particles. As the algorithm only needs simulated data to train the GNNs, it is parameter-independent. The GNN-based algorithm is first tested on datasets based on simulated, albeit biologically realistic data, and validated on actual fluorescence microscopy experimental data. Application of the new GNN method is shown to be faster than other currently used approaches for high-dimensional SMLM datasets, with the additional advantage that it can be implemented on standard desktop computers. Furthermore, GNN models obtained via training procedures are reusable. To the best of our knowledge, this is the first application of GNN-based approaches to the analysis of particle aggregation, with potential applications to the study of nanoscopic particles like the nanoclusters of membrane-associated proteins in live cells. Considerable efforts are currently being devoted to understanding the topography of membrane-embedded proteins through the integration of deep learning techniques with biophysical and numerical analytical approaches.

关键词： clustering algorithms

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Active Detection and Channel Estimation Schemes for Massive Random Access in User-Centric Cell-Free Massive MIMO System

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IEEE INTERNET OF THINGS JOURNAL 2024年第17期11卷 28078-28093页

作者： Hu, Yanfeng Wang, Qingtian Wang, Dongming Xia, Xinjiang You, Xiaohu Southeast Univ Natl Mobile Commun Res Lab Nanjing 210096 Peoples R China Purple Mt Labs Pervas Commun Res Ctr Nanjing 210096 Peoples R China China Telecom Res Inst Wireless AI Syst Res Team Wistar Beijing 100045 Peoples R China

The demand for higher transmission efficiency and denser user access has been put forth by the next generation of wireless communication systems. To cater to the future communication development, this article focuses on massive random access schemes under the user-centric cell-free massive multiple-input-multiple-output (MIMO) architecture. For uplink transmission, a data frame structure is designed to enable active user detection (AUD), channel estimation (CE), and data transmission. The association between access points (APs) and user equipment (UEs) is presented to facilitate an user-centric cell-free scalable architecture. In this article, a maximum likelihood (ML)-based method is proposed for AUD to obtain the set of active UEs. By setting appropriate thresholds and combining the UE-AP association, accurate active detection results can be obtained. CE can be accomplished with lower computational complexity by utilizing the detected active UE set in AUD module. Specifically, the generalized approximate message passing-based sparse Bayesian learning with Dirichlet process (GAMP-DP-SBL) is adopted as the CE algorithm, leveraging the spatial aggregation and dispersion characteristics of APs to enhance the estimation accuracy. Building upon GAMP-DP-SBL algorithm, a clustered algorithm (GAMP-CDP-SBL) is proposed to reduce the scale of the sensing matrix and improve the accuracy of CE for associated active UEs. Moreover, to enhance system scalability, decentralized AUD and CE algorithms are proposed in this article. Simulation results under various parameter settings and different scenarios exhibit the superior performance of the proposed scheme.

关键词： Signal processing algorithms clustering algorithms Channel estimation Massive MIMO Vectors Message passing Computer architecture Active user equipment (UE) detection cell-free massive multiple-input-multiple-output (MIMO) channel estimation (CE) compressed sensing (CS) massive random access

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Particle Competitive Mechanism Based Multiobjective Rough clustering Algorithm for Image Segmentation

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IEEE TRANSACTIONS ON FUZZY SYSTEMS 2022年第10期30卷 4127-4141页

作者： Zhao, Feng Cao, Lulu Liu, Hanqiang Tang, Zihan Fan, Jiulun Xian Univ Posts & Telecommun Sch Telecommun & Informat Engn Xian 710121 Peoples R China Shaanxi Normal Univ Sch Comp Sci Xian 710119 Peoples R China

Rough clustering has attracted increasing attention due to well dealing with the fuzziness and uncertainty of data. It is well known that it needs to manually set the threshold to determine the upper and lower approximations of rough clusters, which may bring a great effect on the clustering performance. When applied to image segmentation, rough clustering is always sensitive to the initialized cluster centers and image noise. Furthermore, only one clustering criterion is considered in rough clustering, which cannot satisfy diverse practical requirements. To handle these issues, a particle competitive mechanism based multiobjective rough clustering algorithm (PCM-MORCA) for image segmentation is proposed. First, a rough intraclass compactness function considering the nonlocal spatial information derived from an image is constructed to overcome the sensitivity to image noise. Next, the constructed rough intraclass compactness function and an interclass separation function are optimized simultaneously to make cluster centers meet diverse segmentation requirements. Then, an adaptive threshold determination mechanism by which the threshold adaptively varies with the clustered data is presented to well determine the upper and lower approximations of rough clusters. After that, to effectively search appropriate cluster centers, a novel pair competition-based particle weight updating strategy is designed for multiobjective particle swarm optimization by improving the elite particle selection and particle update. Finally, a rough clustering index with the nonlocal spatial information is constructed for selecting the optimal solution for PCM-MORCA. Segmentation experiments on Berkeley and magnetic resonance images reveal that PCM-MORCA behaves well on the segmentation accuracy and noise robustness.

关键词： clustering algorithms Image segmentation Rough sets Approximation algorithms Particle swarm optimization Optimization Linear programming Adaptive threshold image segmentation multi objective optimization pair competitive mechanism particle swarm optimization (PSO) rough clustering

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6Subpattern: Target Generation Based on Subpattern Analysis for Internet-Wide IPv6 Scanning

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IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT 2024年第4期21卷 3692-3710页

作者： Liu, Chong Li, Ruixiang Yuan, Fuxiang Ding, Shichang Liu, Yan Luo, Xiangyang Key Lab Cyberspace Situat Awareness Henan Prov Zhengzhou 450001 Peoples R China

IP scanning is crucial for network management and security. However, the brute-force scanning is infeasible in IPv6 networks due to the vast address space. Consequently, target generation algorithms (TGAs) have become necessary to address this issue. Nevertheless, existing algorithms often struggle with low hit rates due to coarse-grained pattern mining. To address this problem, we propose 6Subpattern, a target generation algorithm based on subpattern analysis for Internet-wide IPv6 scanning. 6Subpattern first clusters seeds into high-density regions according to the seed structure information. Subsequently, pattern mining and subpattern analysis are carried out in these regions. Different from previous works, 6Subpattern can obtain all fine-grained patterns while automatically avoiding the influence of outlier addresses and the quandary of setting heuristic thresholds through subpattern analysis. Moreover, pattern refining is conducted based on the distribution of nibbles in address regions to further narrow the scanning space. Finally, targets are effectively generated according to the density of the patterns. Experimental results on real-world networks demonstrate that the address patterns discovered by 6Subpattern provide a superior scanning space than existing algorithms. Further results of hit rates on nine candidate seed sets reveal that 6Subpattern can achieve a 53%-315% improvement over the static TGAs on all seed sets and achieve a 15%-25% improvement on all randomly sampled seed sets compared with dynamic TGAs in Internet-wide IPv6 scanning.

关键词： 6G mobile communication Entropy clustering algorithms IP networks Heuristic algorithms Internet Hamming distances IPv6 scanning active address pattern mining subpattern analysis target generation

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Path Tracking Coordination Control With TD3 Reinforcement Learning Based on Danger Level Recognition Considering Generalized Functional Safety

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2024年第12期73卷 18498-18511页

作者： Wang, Hongbo Wang, Chunyang Chen, Wuwei Zheng, Wenjie Hefei Univ Technol Sch Automot & Transportat Engn Hefei 230009 Peoples R China

In view of the generalized functional safety problems faced by intelligent vehicles, this paper focuses on vehicle driving safety and functional insufficiency of the steering system, and proposes a path tracking coordinated control method with TD3 reinforcement learning based on vehicle danger recognition. Based on the parameters of vehicle driving stability, the concept of vehicle danger level (DL) is established by integrating the parameters of path tracking accuracy. Affinity propagation (AP) clustering algorithm is used to cluster the vehicle driving status under different road adhesion coefficients, speeds and curvatures. The results of DL recognition are added to the design of TD3 reinforcement learning reward function, and the coordination control of automatic steering and differential braking under different conditions is realized, so as to achieve high-precision path tracking control under the premise of ensuring vehicle safety. The results of CarSim/Simulink co-simulation and hardware-in-loop test show that the vehicle DL can be more accurately recognized by using AP clustering algorithm and the reinforcement learning control strategy combined with DL recognition is superior to the traditional fixed parameter MPC controller and the reinforcement learning control strategy without considering the clustering method in improving the path tracking accuracy and lateral stability of the vehicle. The training convergence speed is improved in both the variable curvature road and the docking road, in which the functional insufficiency problem of the steering system is fully alleviated in the variable curvature road, and the energy loss caused by the additional yaw moment is less in the docking road.

关键词： Reinforcement learning clustering algorithms Safety Roads Stability criteria Adhesives Accuracy Affinity propagation clustering danger level recognition reinforcement learning coordinated control

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Multi-Channel Nonlinearity Mitigation Using Machine Learning algorithms

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IEEE TRANSACTIONS ON MOBILE COMPUTING 2024年第4期23卷 2535-2550页

作者： Zhao, Haotian Diaz, Julian Camilo Gomez Hoyos, Sebastian Texas A&M Univ Dept Elect & Comp Engn College Stn TX 77843 USA

This paper investigates multi-channel machine learning (ML) techniques in the presence of receiver nonlinearities and noise, and compares the results with the single-channel receiver architecture. It is known that the multi-channel architecture relaxes the sampling speed requirement of analog to digital conversion and provides significant robustness to clock jitter and front-end noise due to the bandwidth-splitting property inherent in these receivers. However, when a high-voltage swing signal is used in a wireline communication link, the received signal suffers from third-order harmonic distortions and inter-modulation products caused by the nonlinearity profile of the analog front-end (AFE). To this end, this paper proposes the channel decision passing (CDP) algorithm in combination with nonlinear feedback cancellation as a low-complexity candidate for nonlinearity mitigation and compares the performance of this solution with other well-known ML algorithms. Simulation results show significant improvement in a multi-channel receiver architecture equipped with nonlinear feedback cancellation and CDP in comparison with its single-channel counterpart under practical nonlinearity profiles and noise conditions.

关键词： Receivers clustering algorithms Classification algorithms Machine learning algorithms Bandwidth Supervised learning Unsupervised learning Multi-channel receiver nonlinearities machine learning supervised learning unsupervised learning reinforcement learning

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Distributed Online Learning for Leaderless Multicluster Games in Dynamic Environments

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IEEE TRANSACTIONS ON CONTROL OF NETWORK SYSTEMS 2024年第3期11卷 1548-1561页

作者： Yu, Rui Meng, Min Li, Li Tongji Univ Coll Elect & Informat Engn Dept Control Sci & Engn Shanghai 201804 Peoples R China Tongji Univ Shanghai Res Inst Intelligent Autonomous Syst Shanghai 201804 Peoples R China

In this article, multicluster games with coupled constraints in dynamic environments are considered. Agents are divided into disparate clusters, and each cluster can be regarded as a virtual noncooperative player competing against each other via a connected graph. The agents in a cluster are the actual players that aim to cooperatively minimize the cluster's time-varying payoff function, subject to local feasible set constraints and time-varying coupled inequality constraints. In this setting, a novel distributed online learning algorithm based on mirror descent and primal-dual approaches is devised to seek the generalized Nash equilibrium of the considered games. In addition, it can be proved that sublinearly bounded dynamic regrets and constraint violation can be guaranteed by felicitously choosing decreasing stepsizes in the proposed algorithm if the path length accumulation and gradient length accumulation increase sublinearly. Finally, a numerical simulation is presented to illustrate the theoretical results in this article.

关键词： Games Heuristic algorithms Cost function Nash equilibrium clustering algorithms Mirrors Network systems Generalized Nash equilibrium mirror descent online distributed learning time-varying multicluster game

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