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1st International Conference on computer, science, Engineering and Technology, ICComSET 2018

作者： Djufri, Muhamad A Tambi, Abdul Malek Bin Mamat, Mustafa Sukono Budiono Hadi, Ismanto Bandung Indonesia Faculty of Economics and Management Science Universiti Sultan Zainal Abidin Malaysia Faculty of Informatice and Computer Science Universiti Sultan Zainal Abidin Malaysia Department of Mathematics Faculty of Mathematics and Natural Sciences Universitas Padjadjaran Malaysia Master Programme in Management Post Graduate Programme Universitas Narotama Surabaya Indonesia

Career planning at the organizational level must be translated into career planning at the individual employee level. Performance appraisal can be used more broadly so that it can be the main tool for companies in employee career planning. The purpose of this study is to measure how much influence the Performance Evaluation of Career Planning from employees who have occupied managerial levels goes to the next career level. In this paper, analysis of individual employee performance is carried out using the Spider Plot model. The results of analyzing individual performance from managers in different units can explain quickly and easily the appearance of the Spider Plot diagram. So, it is expected that individual career planning can be easily mapped through performance appraisal so that it can be used in career planning. © 2019 IOP Publishing Ltd. All rights reserved.

关键词： Managers

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Structured Multi-Track Accompaniment Arrangement via Style Prior Modelling 38

Structured Multi-Track Accompaniment Arrangement via Style P...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Zhao, Jingwei Xia, Gus Wang, Ziyu Wang, Ye Institute of Data Science NUS Singapore School of Computing NUS Singapore Integrative Sciences and Engineering Programme NUS Graduate School Singapore Machine Learning Department MBZUAI United Arab Emirates Computer Science Department NYU Shanghai China

In the realm of music AI, arranging rich and structured multi-track accompaniments from a simple lead sheet presents significant challenges. Such challenges include maintaining track cohesion, ensuring long-term coherence, and optimizing computational efficiency. In this paper, we introduce a novel system that leverages prior modelling over disentangled style factors to address these challenges. Our method presents a two-stage process: initially, a piano arrangement is derived from the lead sheet by retrieving piano texture styles;subsequently, a multi-track orchestration is generated by infusing orchestral function styles into the piano arrangement. Our key design is the use of vector quantization and a unique multi-stream Transformer to model the long-term flow of the orchestration style, which enables flexible, controllable, and structured music generation. Experiments show that by factorizing the arrangement task into interpretable sub-stages, our approach enhances generative capacity while improving efficiency. Additionally, our system supports a variety of music genres and provides style control at different composition hierarchies. We further show that our system achieves superior coherence, structure, and overall arrangement quality compared to existing baselines. © 2024 Neural information processing systems foundation. All rights reserved.

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MoRe-Fi: Motion-robust and Fine-grained Respiration Monitoring via Deep-Learning UWB Radar 21

MoRe-Fi: Motion-robust and Fine-grained Respiration Monitori...

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19th ACM Conference on Embedded Networked Sensor Systems, SenSys 2021

作者： Zheng, Tianyue Chen, Zhe Zhang, Shujie Cai, Chao Luo, Jun School of Computer Science and Engineering Nanyang Technological University Singapore Singapore Energy Research Institute Interdisciplinary Graduate Programme Nanyang Technological University Singapore Singapore China-Singapore International Joint Research Institute Guangzhou China

ISBN: (纸本)9781450390972

Crucial for healthcare and biomedical applications, respiration monitoring often employs wearable sensors in practice, causing inconvenience due to their direct contact with human bodies. Therefore, researchers have been constantly searching for contact-free alternatives. Nonetheless, existing contact-free designs mostly require human subjects to remain static, largely confining their adoptions in everyday environments where body movements are inevitable. Fortunately, radio-frequency (RF) enabled contact-free sensing, though suffering motion interference inseparable by conventional filtering, may offer a potential to distill respiratory waveform with the help of deep learning. To realize this potential, we introduce MoRe-Fi to conduct fine-grained respiration monitoring under body movements. MoRe-Fi leverages an IR-UWB radar to achieve contact-free sensing, and it fully exploits the complex radar signal for data augmentation. The core of MoRe-Fi is a novel variational encoder-decoder network;it aims to single out the respiratory waveforms that are modulated by body movements in a non-linear manner. Our experiments with 12 subjects and 66-hour data demonstrate that MoRe-Fi accurately recovers respiratory waveform despite the interference caused by body movements. We also discuss potential applications of MoRe-Fi for pulmonary disease diagnoses. © 2021 ACM.

关键词： Decoding

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Heterogeneous 360 Degree Videos in Metaverse: Differentiated Reinforcement Learning Approaches

Heterogeneous 360 Degree Videos in Metaverse: Differentiated...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Wenhan Yu Jun Zhao Interdisciplinary Graduate Programme Nanyang Technological University School of Computer Science & Engineering Nanyang Technological University

Advanced video technologies are driving the development of the futuristic Metaverse, which aims to connect users from anywhere and anytime. As such, the use cases for users will be much more diverse, leading to a mix of 360-degree videos with two types: non-VR and VR 360° videos. This paper presents a novel Quality of Service model for heterogeneous 360° videos with different requirements for frame rates and cybersickness. We propose a frame-slotted structure and conduct frame-wise optimization using self-designed differentiated deep reinforcement learning algorithms. Specifically, we design two structures, Separate Input Differentiated Output (SIDO) and Merged Input Differentiated Output (MIDO), for this heterogeneous scenario. We also conduct comprehensive experiments to demonstrate their effectiveness.

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Virtual Reality in Metaverse Over Wireless Networks with User-Centered Deep Reinforcement Learning

Virtual Reality in Metaverse Over Wireless Networks with Use...

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IEEE International Conference on Communications (ICC)

作者： Wenhan Yu Terence Jie Chua Jun Zhao Interdisciplinary Graduate Programme Nanyang Technological University School of Computer Science & Engineering Nanyang Technological University

The Metaverse and its promises are fast becoming reality as maturing technologies are empowering the different facets. One of the highlights of the Metaverse is that it offers the possibility for highly immersive and interactive socialization. Virtual reality (VR) technologies are the backbone for the virtual universe within the Metaverse as they enable a hyper-realistic and immersive experience, and especially so in the context of socialization. As the virtual world 3D scenes to be rendered are of high resolution and frame rate, these scenes will be offloaded to an edge server for computation. Besides, the metaverse is user-center by design, and human users are always the core. In this work, we introduce a multi-user VR computation offloading over wireless communication scenario. In addition, we devised a novel user-centered deep reinforcement learning approach to find a near-optimal solution. Extensive experiments demonstrate that our approach can lead to remarkable results under various requirements and constraints.

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Computation-Efficient Model Predictive Control of a Multilevel Current Source Converter 49

Computation-Efficient Model Predictive Control of a Multilev...

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49th Annual Conference of the IEEE Industrial Electronics Society, IECON 2023

作者： Chen, Muyu Yuan, Zhige Ghias, Amer M. Y. M. Nicholas, Vun Chan Hua School of Electrical and Electronic Engineering Collab. Initiative Interdisciplinary Graduate Programme Nanyang Technological University Singapore School of Electrical and Electronic Engineering Nanyang Technological University Singapore School of Computer Science and Engineering Nanyang Technological University Singapore

ISBN: (纸本)9798350331820

This paper presents a modified finite control set model predictive control (FCS-MPC) that can significantly reduce computation time and maintain good quality of output voltage. The switching states of a three-phase five-level current source converter (5L-CSC) are sorted according to the corresponding output converter current level. The influences of every switching state on the converter inductor current are analyzed. Then the optimal switching state can be anchored based on the converter current level that minimizes the cost function and converter inductor current conditions. The proposed method eliminates the need for the discrete-time model establishment for the converter. It avoids the ergodic search through all switching states, easing the controller's computation burden. Besides, this algorithm achieves multiple control goals without assigning different weighting factors to various control objectives. The simulated output voltage total harmonics distortion (THD), the inductor current ripple, and the execution time of the modified model predictive control and traditional model predictive control are compared to validate the effectiveness of the proposed algorithm. © 2023 IEEE.

关键词： Model predictive control

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Towards Robust Temporal Activity Localization Learning with Noisy Labels 30

Towards Robust Temporal Activity Localization Learning with ...

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Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Liu, Daizong Qu, Xiaoye Fang, Xiang Dong, Jianfeng Zhou, Pan Nan, Guoshun Tang, Keke Fang, Wanlong Cheng, Yu Wangxuan Institute of Computer Technology Peking University China School of Cyber Science and Engineering Huazhong University of Science of Technology China Interdisciplinary Graduate Programme Nanyang Technological University Singapore College of Computer Science and Technology Zhejiang Gongshang University China School of Cyberspace Security Beijing University of Posts and Telecommunications China Cyberspace Institute of Advanced Technology Guangzhou University China Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong

ISBN: (纸本)9782493814104

This paper addresses the task of temporal activity localization (TAL). Although recent works have made significant progress in TAL research, almost all of them implicitly assume that the dense frame-level correspondences in each video-query pair are correctly annotated. However, in reality, such an assumption is extremely expensive and even impossible to satisfy due to subjective labeling. To alleviate this issue, in this paper, we explore a new TAL setting termed Noisy Temporal activity localization (NTAL), where a TAL model should be robust to the mixed training data with noisy moment boundaries. Inspired by the memorization effect of neural networks, we propose a novel method called Co-Teaching Regularizer (CTR) for NTAL. Specifically, we first learn a Gaussian Mixture Model to divide the mixed training data into preliminary clean and noisy subsets. Subsequently, we refine the labels of the two subsets by an adaptive prediction function so that their true positive and false positive samples could be identified. To avoid single model being prone to its mistakes learned by the mixed data, we adopt a co-teaching paradigm, which utilizes two models sharing the same framework to teach each other for robust learning. A curriculum strategy is further introduced to gradually learn the moment confidence from easy to hard. Experiments on three datasets demonstrate that our CTR is significantly more robust to the noisy training data compared to the existing methods. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Gaussian distribution

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Purify Unlearnable Examples via Rate-Constrained Variational Autoencoders 41

Purify Unlearnable Examples via Rate-Constrained Variational...

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41st International Conference on Machine Learning, ICML 2024

作者： Yu, Yi Wang, Yufei Xia, Song Yang, Wenhan Lu, Shijian Tan, Yap-Peng Kot, Alex C. Rapid-Rich Object Search Lab Interdisciplinary Graduate Programme Nanyang Technological University Singapore School of Electrical and Electronic Engineering Nanyang Technological University Singapore PengCheng Laboratory Shenzhen China School of Computer Science and Engineering Nanyang Technological University Singapore

Unlearnable examples (UEs) seek to maximize testing error by making subtle modifications to training examples that are correctly labeled. Defenses against these poisoning attacks can be categorized based on whether specific interventions are adopted during training. The first approach is training-time defense, such as adversarial training, which can mitigate poisoning effects but is computationally intensive. The other approach is pre-training purification, e.g., image short squeezing, which consists of several simple compressions but often encounters challenges in dealing with various UEs. Our work provides a novel disentanglement mechanism to build an efficient pre-training purification method. Firstly, we uncover rate-constrained variational autoencoders (VAEs), demonstrating a clear tendency to suppress the perturbations in UEs. We subsequently conduct a theoretical analysis for this phenomenon. Building upon these insights, we introduce a disentangle variational autoencoder (D-VAE), capable of disentangling the perturbations with learnable class-wise embeddings. Based on this network, a two-stage purification approach is naturally developed. The first stage focuses on roughly eliminating perturbations, while the second stage produces refined, poison-free results, ensuring effectiveness and robustness across various scenarios. Extensive experiments demonstrate the remarkable performance of our method across CIFAR-10, CIFAR-100, and a 100-class ImageNet-subset. Code is available at https://***/yuyi-sd/D-VAE. Copyright 2024 by the author(s)

关键词： Variational techniques

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Virtual Reality in Metaverse over Wireless Networks with User-centered Deep Reinforcement Learning

arXiv

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arXiv 2023年

作者： Yu, Wenhan Chua, Terence Jie Zhao, Jun Interdisciplinary Graduate Programme Nanyang Technological University Singapore School of Computer Science & Engineering Nanyang Technological University Singapore

关键词： Reinforcement learning

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Deadline-constrained Multi-resource Task Mapping and Allocation for Edge-Cloud Systems

Deadline-constrained Multi-resource Task Mapping and Allocat...

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GLOBECOM 2022 - 2022 IEEE Global Communications Conference

作者： Chuanchao Gao Aryaman Shaan Arvind Easwaran School of Computer Science and Engineering Energy Research Institute @ NTU Interdisciplinary Graduate Programme Nanyang Technological University Singapore

ISBN: (纸本)9781665435413

In an edge-cloud system, mobile devices can offload their computation intensive tasks to an edge or cloud server to guarantee the quality of service or satisfy task deadline requirements. However, it is challenging to determine where tasks should be offloaded and processed, and how much network and computation resources should be allocated to them, such that a system with limited resources can obtain a maximum profit while meeting the deadlines. A key challenge in this problem is that the network and computation resources could be allocated on different servers, since the server to which a task is offloaded (e.g., a server with an access point) may be different from the server on which the task is eventually processed. To address this challenge, we first formulate the task mapping and resource allocation problem as a non-convex Mixed-Integer Nonlinear Programming (MINLP) problem, known as NP-hard. We then propose a zero-slack based greedy algorithm (ZSG) and a linear discretization method (LDM) to solve this MINLP problem. Experiment results with various synthetic tasksets show that ZSG has an average of 2.98% worse performance than LDM with a minimum unit of 5 but has an average of 6.88% better performance than LDM with a minimum unit of 15.

关键词： Wireless communication Processor scheduling Quality of service Programming Mobile handsets Servers Resource management

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