Reviewerly/exHarmony huggingface dataset:
On the Optimization Landscape of Dynamic Output Feedback Linear Quadratic Control

Authors:
raw_author_name: Jingliang Duan
raw_affiliation_string: Department of Electrical and Computer Engineering, National University of Singapore, Singapore.; Department of Electrical and Computer Engineering, University of California San Diego, USA.; School of Mechanical Engineering, University of Science and Technology Beijing, Beijing, China; School of Vehicle and Mobility, Tsinghua University, Beijing, 100084, China.
raw_author_name: Wenhan Cao
raw_affiliation_string: Department of Electrical and Computer Engineering, National University of Singapore, Singapore.; Department of Electrical and Computer Engineering, University of California San Diego, USA.; School of Mechanical Engineering, University of Science and Technology Beijing, China,; School of Vehicle and Mobility, Tsinghua University, Beijing, 100084, China.
raw_author_name: Yang Zheng
raw_affiliation_string: Department of Electrical and Computer Engineering, National University of Singapore, Singapore.; Department of Electrical and Computer Engineering, University of California San Diego, La Jolla, CA, USA; School of Mechanical Engineering, University of Science and Technology Beijing, China,; School of Vehicle and Mobility, Tsinghua University, Beijing, 100084, China.
raw_author_name: Lin Zhao
raw_affiliation_string: Department of Electrical and Computer Engineering, National University of Singapore, Singapore.; Department of Electrical and Computer Engineering, University of California San Diego, USA.; School of Mechanical Engineering, University of Science and Technology Beijing, China,; School of Vehicle and Mobility, Tsinghua University, Beijing, 100084, China.

Abstract: The convergence of policy gradient algorithms in reinforcement learning hinges on the optimization landscape of the underlying optimal control problem. Theoretical insights into these algorithms can often be acquired from analyzing those of linear quadratic control. However, most of the existing literature only considers the optimization landscape for static full-state or output feedback policies (controllers). We investigate the more challenging case of dynamic output-feedback policies for linear quadratic regulation (abbreviated as dLQR), which is prevalent in practice but has a rather complicated optimization landscape. We first show how the dLQR cost varies with the coordinate transformation of the dynamic controller and then derive the optimal transformation for a given observable stabilizing controller. At the core of our results is the uniqueness of the stationary point of dLQR when it is observable, which is in a concise form of an observer-based controller with the optimal similarity transformation. These results shed light on designing efficient algorithms for general decision-making problems with partially observed information.
Self-Calibrating Gaze Estimation With Optical Axes Projection for Head-Mounted Eye Tracking

Authors:
raw_author_name: Hanyuan Zhang
raw_affiliation_string: Key Laboratory of Metallurgical Equipment and Control Technology, Ministry of Education, Wuhan University of Science and Technology, Wuhan, Hubei, China
raw_author_name: Shiqian Wu
raw_affiliation_string: Institute of Robotics and Intelligent Systems, School of Information Science and Engineering, Wuhan University of Science and Technology, Wuhan, Hubei, China
raw_author_name: Zheng Gao
raw_affiliation_string: Institute of Robotics and Intelligent Systems, School of Information Science and Engineering, Wuhan University of Science and Technology, Wuhan, Hubei, China
raw_author_name: Zhonghua Wan
raw_affiliation_string: Institute of Robotics and Intelligent Systems, School of Information Science and Engineering, Wuhan University of Science and Technology, Wuhan, Hubei, China

Abstract: Gaze estimation suffers from burdensome personal calibration or complex all-device calibration. Self-calibrating methods can meet this challenge but depend on scenes and sacrifice accuracy. We propose a flexible and accurate gaze estimation approach calibrated implicitly with potential gaze patterns. By constructing an optical axis projection (OAP) plane and a visual axis projection (VAP) plane simultaneously, the optical axis and the visual axis can be represented as 2-D points, i.e., the OAP and VAP, which have a similarity transformation, indicating the linear consistency of OAP patterns with gaze patterns. Hence, a 3-D gaze estimation model using the OAP as an eye feature to predict the VAP is built. The unknown parameters are calculated separately by linearly aligning OAP patterns to natural and easily detectable gaze patterns. Experimental results show that the proposed gaze estimation approach is more accurate than state-of-the-art head-mounted gaze estimation methods, which require explicit calibration or multi-scene saliency.
Energy-Motion Features Aggregation Network for Players’ Fine-Grained Action Analysis in Soccer Videos

Authors:
raw_author_name: Runze Li
raw_affiliation_string: Department of Computer Science and Engineering, The University of California, Riverside, CA, USA
raw_author_name: Bir Bhanu
raw_affiliation_string: Department of Electrical and Computer Engineering, The University of California, Riverside, CA, USA

Abstract: Rich and complex events in sports have led to the development of a wide-variety of techniques for interpreting content of sports videos in terms of players’ actions, poses, gait, performance, etc. This is due to the requirements from coaches, trainers and players who expect to analyze actions in top sports events, as well as sports fans who practice to imitate professional playing skills, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e.g</i> ., dribbling, shooting, etc. However, this poses two key challenges for automated sports analysis community. Firstly, there are extremely limited public sports datasets. Secondly, recent advances in interpretations of sports activities, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e.g</i> ., soccer, are predominantly made through analyzing coarse-grained contents. Players’ fine-grained skills analysis still remains under-explored. To alleviate these problems, this paper (a) collects the dataset of highlight videos of soccer players, including two coarse-grained action types of soccer players and six fine-grained actions of players. Detailed annotations are provided for the collected dataset, in terms of action classes, bounding boxes, segmentation maps, and body keypoints of soccer players, and positions of a soccer ball in a game. (b) leverages the understanding of complex highlight videos by proposing an energy-motion features aggregation network- <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">EMA-Net</i> to fully exploit energy-based representation of soccer players movements in video sequences and explicit motion dynamics of soccer players in videos for soccer players’ fine-grained action analysis. Experimental results and ablation studies validate the proposed approach in recognizing soccer players actions using the collected soccer highlight video datasets.
A Label-Efficient Semi Self-Supervised Learning Framework for IoT Devices in Industrial Process

Authors:
raw_author_name: Vandana Bharti
raw_affiliation_string: Department of Computer Science and Engineering, Indian Institute of Technology BHU, Varanasi, India
raw_author_name: Abhinav Kumar
raw_affiliation_string: Department of Computer Science and Engineering, Indian Institute of Technology BHU, Varanasi, India
raw_author_name: Vishal Purohit
raw_affiliation_string: VCA Lab, Indian Institute of Technology BHU, Varanasi, India
raw_author_name: Rishav Singh
raw_affiliation_string: Department of Computer Science and Engineering, National Institute of Technology Delhi, Delhi, India
raw_author_name: Amit Kumar Singh
raw_affiliation_string: Department of Computer Science and Engineering, National Institute of Technology Patna, Patna, India
raw_author_name: Sanjay Kumar Singh
raw_affiliation_string: Department of Computer Science and Engineering, Indian Institute of Technology BHU, Varanasi, India

Abstract: The industrial sector has experienced a tremendous advancement in deep supervised learning due to its representation ability, but it comes with high computing and labeled data demands. Recently, the demand for intelligent IoT devices on assembly and disassembly lines has surged. This necessitates algorithms that can use data to make intelligent decisions and a framework that can enable multiple IoT devices to learn collaboratively. Further, huge image generation using IoTs also needs an efficient data annotation scheme for classification problems. WeCollab is one such framework in federated learning that significantly reduces human efforts in data annotation with breakthroughs in self-supervised learning. The proposed framework is generic and can be adapted to any specific image data generated by industrial robots involved in assembly and disassembly lines. Our method outperforms supervised learning by 25% and 20% on the CIFAR-10 and CINIC-10 datasets, respectively, for the labeling task. We generate pseudo-labels for the unlabeled part of the data and train a model to achieve 30% better test accuracy on CIFAR-10 and 20% better test accuracy on the CINIC-10 dataset as compared to supervised learning. Extensive experiments unveil the effectiveness of the method and proposed combination of loss functions used by WeCollab.
Vertical Federated Learning Over Cloud-RAN: Convergence Analysis and System Optimization

Authors:
raw_author_name: Yuanming Shi
raw_affiliation_string: School of Information Science and Technology, ShanghaiTech University, Shanghai, China
raw_author_name: Shuhao Xia
raw_affiliation_string: School of Information Science and Technology, ShanghaiTech University, Shanghai, China
raw_author_name: Yong Zhou
raw_affiliation_string: School of Information Science and Technology, ShanghaiTech University, Shanghai, China
raw_author_name: Yijie Mao
raw_affiliation_string: School of Information Science and Technology, ShanghaiTech University, Shanghai, China
raw_author_name: Chunxiao Jiang
raw_affiliation_string: Tsinghua Space Center and the Beijing National Research Center for Information Science and Technology, Tsinghua University, Beijing, China
raw_author_name: Meixia Tao
raw_affiliation_string: Department of Electronic Engineering, Shanghai Jiao Tong University, Shanghai, China

Abstract: Vertical federated learning (FL) is a collaborative machine learning framework that enables devices to learn a global model from the feature-partition datasets without sharing local raw data. However, as the number of the local intermediate outputs is proportional to the training samples, it is critical to develop communication-efficient techniques for wireless vertical FL to support high-dimensional model aggregation with full device participation. In this paper, we propose a novel cloud radio access network (Cloud-RAN) based vertical FL system to enable fast and accurate model aggregation by leveraging over-the-air computation (AirComp) and alleviating communication straggler issue with cooperative model aggregation among geographically distributed edge servers. However, the model aggregation error caused by AirComp and quantization errors caused by the limited fronthaul capacity degrade the learning performance for vertical FL. To address these issues, we characterize the convergence behavior of the vertical FL algorithm considering both uplink and downlink transmissions. To improve the learning performance, we establish a system optimization framework by joint transceiver and fronthaul quantization design, for which successive convex approximation and alternate convex search based system optimization algorithms are developed. We conduct extensive simulations to demonstrate the effectiveness of the proposed system architecture and optimization framework for vertical FL.
Green, Quantized Federated Learning Over Wireless Networks: An Energy-Efficient Design

Authors:
raw_author_name: Minsu Kim
raw_affiliation_string: Bradley Department of Electrical and Computer Engineering, Wireless&#x0040;VT Group, Virginia Tech, Arlington, VA, USA
raw_author_name: Walid Saad
raw_affiliation_string: Bradley Department of Electrical and Computer Engineering, Wireless&#x0040;VT Group, Virginia Tech, Arlington, VA, USA
raw_author_name: Merouane Debbah
raw_affiliation_string: Electrical Engineering and Computer Science, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates

Abstract: The practical deployment of federated learning (FL) over wireless networks requires balancing energy efficiency, convergence rate, and a target accuracy due to the limited available resources of devices. Prior art on FL often trains deep neural networks (DNNs) to achieve high accuracy and fast convergence using 32 bits of precision level. However, such scenarios will be impractical for resource-constrained devices since DNNs typically have high computational complexity and memory requirements. Thus, there is a need to reduce the precision level in DNNs to reduce the energy expenditure. In this paper, a green-quantized FL framework, which represents data with a finite precision level in both local training and uplink transmission, is proposed. Here, the finite precision level is captured through the use of quantized neural networks (QNNs) that quantize weights and activations in fixed-precision format. In the considered FL model, each device trains its QNN and transmits a quantized training result to the base station. Energy models for the local training and the transmission with quantization are rigorously derived. To minimize the energy consumption and the number of communication rounds simultaneously, a multi-objective optimization problem is formulated with respect to the number of local iterations, the number of selected devices, and the precision levels for both local training and transmission while ensuring convergence under a target accuracy constraint. To solve this problem, the convergence rate of the proposed FL system is analytically derived with respect to the system control variables. Then, the Pareto boundary of the problem is characterized to provide efficient solutions using the normal boundary inspection method. Design insights on balancing the tradeoff between the two objectives while achieving a target accuracy are drawn from using the Nash bargaining solution and analyzing the derived convergence rate. Simulation results show that the proposed FL framework can reduce energy consumption until convergence by up to 70% compared to a baseline FL algorithm that represents data with full precision without damaging the convergence rate.
A Unified Object Counting Network With Object Occupation Prior

Authors:
raw_author_name: Shengqin Jiang
raw_affiliation_string: School of Computer Science, Nanjing University of Information Science and Technology, Nanjing, China
raw_author_name: Qing Wang
raw_affiliation_string: School of Computer Science, Nanjing University of Information Science and Technology, Nanjing, China
raw_author_name: Yuankai Qi
raw_affiliation_string: Australian Institute for Machine Learning, The University of Adelaide, Adelaide, SA, Australia
raw_author_name: Qingshan Liu
raw_affiliation_string: School of Computer Science, Nanjing University of Posts and Telecommunications, Nanjing, China

Abstract: The counting task, which plays a fundamental role in numerous applications (e.g., crowd counting, traffic statistics), aims to predict the number of objects with various densities. Existing object counting tasks are designed for a single object class. However, it is inevitable to encounter newly coming data with new classes in our real world. We name this scenario as <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">evolving object counting</i> . In this paper, we build the first evolving object counting dataset and propose a unified object counting network as the first attempt to address this task. The proposed network consists of two key components: a class-agnostic mask module and a class-incremental module. The class-agnostic mask module learns generic object occupation prior by predicting a class-agnostic binary mask (e.g., 1 denotes there exists an object at the considering position in an image and 0 otherwise). The class-incremental module is used to handle new classes and provides discriminative class guidance for density map prediction. The combined outputs of the class-agnostic mask module and image feature extractor are used to predict the final density map. When new classes arrive, we first add new neural nodes to the last regression and classification layers of the class-incremental module. Then, instead of retraining the model from scratch, we utilize knowledge distillation to help the model retain and consolidate what it has previously learned. We also employ a support sample bank to store a small number of typical training samples for each class, which are used to prevent the model from forgetting key information from old data. With this design, our model can efficiently and effectively adapt to new classes while maintaining good performance on already-seen data without large-scale retraining. Extensive experiments on the collected dataset demonstrate favorable performance. The dataset and code will be available at: https://github.com/Tanyjiang/EOCO.
Complete coverage path planning scheme for autonomous navigation ROS-based robots

Authors:
raw_author_name: Shengmin Zhao
raw_affiliation_string: Division of Electronics and Electrical Engineering, Dongguk university, Seoul, Republic of Korea
raw_author_name: Seung-Hoon Hwang
raw_affiliation_string: Division of Electronics and Electrical Engineering, Dongguk university, Seoul, Republic of Korea

Abstract: In this paper, a new Complete Coverage Path Planning (CCPP) scheme is proposed which combines path planning and dynamic tracking for robot operating system-based robots. For the path planning, firstly a sub-area division algorithm is considered to decompose the occupancy map according to the wall or obstacle position after simultaneous localization and mapping process. For each sub-area, an “S” shape path planning is employed, and then a Bidirectional A-star connects them. Additionally, the dynamic tracking ensures that the robot moves continuously. Simulation results show that the coverage ratio of the planning path is improved as 98% by the proposes scheme.
Time-Aware LSTM Neural Networks for Dynamic Personalized Recommendation on Business Intelligence

Authors:
raw_author_name: Xuan Yang
raw_affiliation_string: Shandong Provincial University Laboratory for Protected Horticulture, Weifang University of Science and Technology, Weifang 262700, China.; is with Graduate School, Angeles University Foundation, Angeles City 2009, Philippines,
raw_author_name: James A. Esquivel
raw_affiliation_string: is with Graduate School, Angeles University Foundation, Angeles City 2009, Philippines,

Abstract: Personalized recommendation plays a critical role in providing decision-making support for product and service analysis in the field of business intelligence.Recently, deep neural network-based sequential recommendation models gained considerable attention.However, existing approaches pay little attention to users' dynamically evolving interests, which are influenced by product attributes, especially product category.To overcome these challenges, we propose a dynamic personalized recommendation model: DynaPR.Specifically, we first embed product information and attribute information into a unified data space.Then, we exploit long short-term memory (LSTM) networks to characterize sequential behavior over multiple time periods and seize evolving interests by hierarchical LSTM networks.Finally, similarity values between users are measured through pairwise interest features, and personalized recommendation lists are generated.A series of experiments reveal the superiority of the proposed method compared with other advanced methods.
A fast density peak clustering based particle swarm optimizer for dynamic optimization

Authors:
raw_author_name: Qiang Yue
raw_affiliation_string: Anhui Province Engineering Laboratory of Intelligent Demolition Equipment, Anhui University of Technology, Maanshan, 243032, China; School of Electrical and Information Engineering, Anhui University of Technology, Maanshan, 243002, China
raw_author_name: Yuanchao Liu
raw_affiliation_string: State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University, Shenyang, 110819, China
raw_author_name: Haibin Ouyang
raw_affiliation_string: School of Mechanical and Electric Engineering, Guangzhou University, Guangzhou, 510006, China
raw_author_name: Fangqing Gu
raw_affiliation_string: Guangdong University of Technology, Guangzhou, Guangdong Province, 510520, China

Abstract: Dynamic optimization problems (DOPs) are optimization problems with time evolution characteristics. In this type of problem, the decision variables and the state variables change over time and produce results that will impact the future. To effectively address DOPs, this paper proposes a fast density peak clustering based particle swarm optimizer for dynamic optimization (DPCPSO). The main innovations of DPCPSO contain three critical components. First, a fast density peak clustering is applied to create multiple sub-populations, which can help the algorithm locate peaks. Second, stagnation detection is used to tackle the loss of diversity. Third, an optimal particle calibration strategy which can find the optimal solution quickly in a changing environment is proposed in response to environmental changes. Moreover, the hill climbing method is applied to help the memory quickly locate new peaks if the environment changes. The performance of our proposed algorithm has been tested on Mobile Peak Benchmark (MPB), Generalized Dynamic Benchmark Generator (GDBG) and Generalized Moving Peaks Benchmark (GMPB) problems and compared with seven state-of-the-art dynamic optimization algorithms. The experimental results validate the proposed algorithm performed competitively while solving DOPs.