A dynamic thompson sampling hyper-heuristic framework for learning activity planning in personalized learning

Aslan, Ayşe; Bakir, Ilke; Vis, Iris F. A.

doi:10.1016/j.ejor.2020.03.038

Cited by 14 publications

(8 citation statements)

References 19 publications

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“…The outcomes include ergonomic discomfort ( McAllister, Costigan, Davies, & Diesbourg, 2022 ), emotional exhaustion ( Bhumika, 2020 ), body weight changes, indoor environment changes, psychological problems ( Ekpanyaskul & Padungtod, 2022 ), lifestyle changes such as sleep habits, exercise ( Ekpanyaskul & Padungtod, 2022 ), and increased food consumption ( Tyagi, Prasad, & Bhatia, 2021 ) . Concerning the perception of employees, work fatigue ( Palumbo, 2020 ) , family work conflicts perceptions ( Reimann, Peters, & Diewald, 2022 ), lower benefits, and higher barriers for older workers ( Tahlyan et al, 2022 ), better task performance and quality of work life ( Aslan, Bakir, & Vis, 2020 ), are outcomes of WFH.…”

Section: Results and Findingsmentioning

confidence: 99%

Review of work from home empirical research during Covid-19

Dogra

Priyashantha

2023

Asia Pacific Management Review

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Section: Results and Findingsmentioning

confidence: 99%

Review of work from home empirical research during Covid-19

Dogra

Priyashantha

2023

Asia Pacific Management Review

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“…A complete piece of music cannot be complete without melody and chords, so the arrangement of chords is a major consideration in music composition. Usually, it requires a high level of musicianship and expertise, as well as a long period of polishing and research, to arrange the perfect chords for the main melody, which is difficult and has a high threshold [ 2 ]. Using machine learning instead of humans to complete the interpretation and learning of music theory knowledge, and automatically arranging suitable accompaniment chords as a backing, saves a lot of labour costs, and may become a better music creation aid.…”

Section: Introductionmentioning

confidence: 99%

A Study of Piano-Assisted Automated Accompaniment System Based on Heuristic Dynamic Planning

Lin

Zhao

2022

Computational Intelligence and Neuroscience

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In this paper, a piano-assisted automated accompaniment system is designed and applied to a practical process using a heuristic dynamic planning approach. In this paper, we aim at the generation of piano vocal weaves in accompaniment from the perspective of assisting pop song writing, build an accompaniment piano generation tool through a set of systematic algorithm design and programming, and realize the generation of recognizable and numerous weaving styles within a controlled range under the same system. The mainstream music detection neural network approaches usually convert the problem into a similar way as image classification or sequence labelling and then use models such as convolutional neural networks or recurrent neural networks to solve the problem; however, the existing neural network approaches ignore the music relative loudness estimation subtask and ignore the inherent temporality of music data when solving the music detection task. However, the existing music generation neural network methods have not yet solved the problems of discrete integrability brought by piano roll representation music data and the still-limited control domain and variety of instruments generated in the controllable music generation task. To solve these two problems, this paper proposes a controlled music generation neural network model for multi-instrument polyphonic music. The effectiveness of the proposed model is verified by conducting several sets of experiments on the collected MIDICN data set, and the experimental results show that the model achieves better performance in the aspects of negative log-likelihood value, perplexity, musicality measure, domain similarity analysis, and manual evaluation.

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“…The size of an LLH set is controlled by specific approaches that are relay hybridization or tabu search. Because each instance has a different landscape, the components of HLHs have a great effect on the performance of hyper-heuristics [26] . Therefore, interest is growing in designing a novel heuristic selection mechanism or developing different acceptance criteria.…”

mentioning

confidence: 99%

“…A choice function based on online learning was proposed to address aircraft flight deck operations scheduling [33] . The online learning mechanism, which is based on dynamic Thompson sampling, has a considerable effect on the performance of local search [26] . Offline learning is another learning mechanism that is trained on sample problem instances [34] .…”

mentioning

confidence: 99%

A Novel Cooperative Multi-Stage Hyper-Heuristic for Combination Optimization Problems

Zhao

Cao

et al. 2021

Complex Syst. Model. Simul.

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A hyper-heuristic algorithm is a general solution framework that adaptively selects the optimizer to address complex problems. A classical hyper-heuristic framework consists of two levels, including the high-level heuristic and a set of low-level heuristics. The low-level heuristics to be used in the optimization process are chosen by the high-level tactics in the hyper-heuristic. In this study, a Cooperative Multi-Stage Hyper-Heuristic (CMS-HH) algorithm is proposed to address certain combinatorial optimization problems. In the CMS-HH, a genetic algorithm is introduced to perturb the initial solution to increase the diversity of the solution. In the search phase, an online learning mechanism based on the multi-armed bandits and relay hybridization technology are proposed to improve the quality of the solution. In addition, a multi-point search is introduced to cooperatively search with a single-point search when the state of the solution does not change in continuous time. The performance of the CMS-HH algorithm is assessed in six specific combinatorial optimization problems, including Boolean satisfiability problems, one-dimensional packing problems, permutation flow-shop scheduling problems, personnel scheduling problems, traveling salesman problems, and vehicle routing problems. The experimental results demonstrate the efficiency and significance of the proposed CMS-HH algorithm.

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A dynamic thompson sampling hyper-heuristic framework for learning activity planning in personalized learning

Cited by 14 publications

References 19 publications

Review of work from home empirical research during Covid-19

Review of work from home empirical research during Covid-19

A Study of Piano-Assisted Automated Accompaniment System Based on Heuristic Dynamic Planning

A Novel Cooperative Multi-Stage Hyper-Heuristic for Combination Optimization Problems

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