Miracle-dz/UniFAR-FTUs huggingface dataset:
Hybrid Workflow Scheduling on Edge Cloud Computing Systems

Internet of Things applications can be represented as workflows in which stream and batch processing are combined to accomplish data analytics objectives in many application domains such as smart home, health care, bioinformatics, astronomy, and education. The main challenge of this combination is the differentiation of service quality constraints between batch and stream computations. Stream processing is highly latency-sensitive while batch processing is more likely resource-intensive. In this work, we propose an end-to-end hybrid workflow scheduling on an edge cloud system as a two-stage framework. In the first stage, we propose a resource estimation algorithm based on a linear optimization approach, gradient descent search (GDS), and in the second stage, we propose a cluster-based provisioning and scheduling technique for hybrid workflows on heterogeneous edge cloud resources. We provide a multi-objective optimization model for execution time and monetary cost under constraints of deadline and throughput. Results demonstrate the framework performance in controlling the execution of hybrid workflows by efficiently tuning several parameters including stream arrival rate, processing throughput, and workflow complexity. In comparison to a meta-heuristics technique using Particle Swarm Optimization (PSO), the proposed scheduler provides significant improvement for large-scale hybrid workflows in terms of execution time and cost with an average of 8% and 35%, respectively.
Data-driven Scheduling for High-mix and Low-volume Production in Semiconductor Assembly and Testing

The objective of this research is to improve scheduling decisions in high-mix low-volume (HMLV) production environments. Unique characteristics of HMLV semiconductor assembly and testing operations include: (1) Diversified Product Lines: To respond to global competition and different customer needs, manufacturers are providing diversified products to different consumers; (2) Unrelated Parallel Machines: Different machines are oftentimes procured at different capacity expansion stages. While different machines may have similar functions, the latest model oftentimes provides higher production efficiency and better quality. This leads to a production environment with parallel machines of different production characteristics; (3) Incomplete Product-machine Specific Production Data: When there are a wide variety of products produced by unrelated parallel machines, there will be a large number of possible product-machine combinations. Some of the combinations will not have enough data for accurate estimation of production characteristics, such as the required processing time information for scheduling decisions. In order to facilitate efficient and effective scheduling decisions in such HMLV production, a hierarchical prediction method is developed for mixed dataset analysis. The hierarchical prediction method generates missing parameters, such as the required processing time, for subsequent optimization of scheduling decisions. To cope with the inevitable errors in all forecast models, robust scheduling decisions are generated through a stochastic optimization framework. The framework is validated by an industry dataset collected from a leading semiconductor assembly and testing company.
Reinforcement Learning Based Vulnerability Analysis of Data Injection Attack for Smart Grids

Smart grids have to protect meter measurements against false data injection attacks. By modifying the meter measurements, the attacker misleads the control decisions of the control center, which results in physical damages of power systems. In this paper, we propose a reinforcement learning based vulnerability analysis scheme for data injection attack without relying on the power system topology. This scheme enables the attacker to choose the data injection attack vector based on the meter measurements, the power system status, the previous injected errors and the number of meters to compromise. By combining deep reinforcement learning with prioritized experience replay, the proposed scheme more frequently replays the successful vulnerability detection experiences while bypassing the bad data detection, which is able to accelerate the learning speed. Simulation results based on the IEEE 14 bus system show that this scheme increases the probability of successful vulnerability detection and reduce the number of meters to compromise compared with the benchmark scheme.
SABER: Modular Reconfigurable Robot for Industrial Applications

This study proposes a heterogeneous modular reconfigurable robot called SABER that is suitable for industrial applications. The SABER is a Step, Assembler, Bridge, Explorer Robot comprising a platform and a reconfigurable rail; it can work in three different configurations: monowheel, rail trolley, and manipulator. The monowheel configuration provides locomotion with a speed up to 10 km/h. The rail trolley configuration allows the robot to overcome gaps larger than its wheel diameter, climb steps as large as the wheel radius, and pass through passages smaller than the wheel diameter. The manipulator configuration enables the robot to manipulate objects using two robotic arms with changeable tools. This design allows several SABERs to work cooperatively, extending capabilities of their mechanics or enlarging the working area. As such, SABERs can address various tasks, including material handling, assembly, diagnostic, and maintenance applications, revealing a concept of universal robots for future factories. Robot performance in terms of the speed, acceleration, oscillation, and gap/step handling was numerically analyzed, showing that SABER is comparable with or better than existing autonomous mobile robots and modular reconfigurable robots.
MEC-Sim: An Extensible Simulator for Mobile Edge Computing System

As a new computing paradigm, mobile edge computing (MEC) is able to provide powerful computing resources to serve user-intensive and delay-sensitive applications by configuring mobile edge computing servers at the base stations close to users. To support the simulation and development of MEC, this paper proposes an extensible simulator for MEC system (MEC-Sim). In the MEC-Sim simulator, simulation configuration module, scheduler module and controller module are designed and the discrete event simulation policy is applied. Three different cases of MEC networks are simulated and evaluated by using three baseline offloading strategies (e.g., local execution, random offloading, and load balance). Simulation results show that the proposed MEC-Sim is feasible and applicable, which can satisfy the requirements of different MEC networks.
Incoherent and Robust Projection Matrix Design Based on Equiangular Tight Frame

Designing a projection matrix to optimally select the informative samples from high-dimensional data is a challenging task. Several approaches have been proposed for this task, however conventional methods obtain the projection matrix from the corresponding Gram matrix without considering the underlying structure of the equiangular frame. The study propose a framework to optimize the projections based on the equivalent tight frame, which is in turn constructed from the target Gram matrix. The proposed work optimizes the projection matrix by restricting the eigenvalues of the corresponding Gram matrix to ensure reduced pairwise correlation and tightness of the frame. Additionally, an $\ell _{2,1}$ -norm based regularization term and a projection matrix energy constraint are incorporated to reduce the effect of outliers and noisy data. This unified optimization problem results in an incoherent and robust projection matrix. Experiments are performed on synthetic data as well as real images. The performance evaluation is carried out in terms of mutual coherence, signal reconstruction accuracy, and peak signal-to-noise ratio (PSNR). The results show that the sensing error constraint enables the design of optimized projections especially when the signals are noisy and not exactly sparse which is the case in real-world scenarios.
Event-Triggered Control for Inter-Vehicle Communications of Cyber-Physical Transportation Systems

In Cyber-Physical Transportation Systems (CPTSs), inter-vehicle communication is a basis of traffic safety and vehicle applications. Vehicles need to transmit motion data with each other via wireless networks. Despite many strategies that make the vehicle network more reliable and efficient under the noise, the separate design of estimation and communication makes them incapable of ensuring state received from multiple vehicles around reliability. To address the gap, this paper proposes an event-triggered broadcast control strategy that fuses communication scheduling with estimators. The estimator is designed to estimate the information transmitted by the strategy and the event-triggered condition is designed based on estimation states. To verify the performance of the estimator and the tracking accuracy of the broadcast strategy, an autonomous driving simulation platform is built. The communication rates of our strategy in the traffic flow are evaluated by SUMO and NS3. The experiment result shows that the strategy can dynamically adjust the event-triggered conditions according to the accuracy of the vehicle estimator and the network transmission rate improve significantly.
Probing Spatial Clues: Canonical Spatial Templates for Object Relationship Understanding

Humans often leverage spatial clues to categorize scenes in a fraction of a second. This form of intelligence is very relevant in time-critical situations (e.g., when driving a car) and valuable to transfer to automated systems. This work investigates the predictive power of solely processing spatial clues for scene understanding in 2D images and compares such an approach with the predictive power of visual appearance. To this end, we design the laboratory task of predicting the identity of two objects (e.g., “man” and “horse”) and their relationship or predicate (e.g., “riding”) given exclusively the ground truth bounding box coordinates of both objects. We also measure the performance attainable in Human Object Interaction (HOI) detection, a real-world spatial task, which includes a setting where ground truth boxes are not available at test time. An additional goal is to identify the principles necessary to effectively represent a spatial template, that is, the visual region in which two objects involved in a relationship expressed by a predicate occur. We propose a scale-, mirror-, and translation-invariant representation that captures the spatial essence of the relationship, i.e., a canonical spatial representation. Tests in two benchmarks reveal: (1) High performance is attainable by using exclusively spatial information in all tasks. (2) In HOI detection, the canonical template outperforms the rest of spatial, visual, and several state-of-the-art baselines. (3) Simple fusion of visual and spatial features substantially improves performance. (4) Our methods fare remarkably well with a small amount of data and rare categories. Our results obtained on the Visual Genome (VG) and the Humans Interacting with Common Objects - Detection (HICO-DET) datasets indicate that great predictive power can be obtained from spatial clues alone, opening up possibilities for performing fast scene understanding at a glance.
Towards context-aware natural language understanding in human-robot-collaboration

Human-robot-collaboration still poses a challenge to contemporary robotic systems. Besides functional safety, usability and user experience are topics of current research. This paper describes an approach that combines contextual-awareness and natural language understanding (NLU) to convey human intents to a robotic system. A user study is then conducted using the system in a simplified collaborative assembly scenario and the findings are discussed.
Multimodal Information-Based Broad and Deep Learning Model for Emotion Understanding

Multimodal information-based broad and deep learning model (MIBDL) for emotion understanding is proposed, in which facial expression and body gesture are used to achieve emotional states recognition for emotion understanding. It aims to understand coexistence multimodal information in human-robot interaction by using different processing methods of deep network and broad network, which obtains the features of depth and width dimensions. Moreover, random mapping in the initial broad learning network could cause information loss and its shallow layer network is difficult to cope with complex tasks. To address this problem, we use principal component analysis to generate the nodes of the broad learning, and the stacked broad learning network is adapted to make it easier for the existing broad learning networks to cope with complex tasks by creating deep variations of the existing network. To verify the effectiveness of the proposal, experiments completed on benchmark database of spontaneous emotion expressions are developed, and experimental results show that the proposal outperforms the state-of-the-art methods. According to the simulation experiments on the FABO database, by using the proposed method, the multimodal recognition rate is 17,54%, 1.24%, and 0.23% higher than those of the temporal normalized motion and appearance features(TN), the multi-channel CNN (MCCNN), and the hierarchical classification fusion strategy (HCFS), respectively.
Semi-global leader-following output consensus of heterogeneous systems with position and rate-limited actuators via state feedback

Inspired by the importance of actuator position and rate constraint, we study in this paper the semi-global leader-following output consensus problem of multiple heterogeneous linear systems in the presence of actuators position and rate saturation via state feedback control. We design a distributed leader state observer, based on which a state feedback output consensus protocol is constructed for each follower. In addition, we propose a framework of formation control by combining consensus control and model predictive control to resolve the problem of obstacle and inter-agent collisions. The effectiveness of our approach is verified by simulation and actual flight experiments of UAVs.
Towards Explainable Semantic Segmentation for Autonomous Driving Systems by Multi-Scale Variational Attention

Explainable autonomous driving systems (EADS) are emerging recently as a combination of explainable artificial intelligence (XAI) and vehicular automation (VA). EADS explains events, ambient environments, and engine operations of an autonomous driving vehicular, and it also delivers explainable results in an orderly manner. Explainable semantic segmentation (ESS) plays an essential role in building EADS, where it offers visual attention that helps the drivers to be aware of the ambient objects irrespective if they are roads, pedestrians, animals, or other objects. In this paper, we propose the first ESS model for EADS based on the variational autoencoder (VAE), and it uses the multiscale second-order derivatives between the latent space and the encoder layers to capture the curvatures of the neurons’ responses. Our model is termed as Mgrad2 VAE and is bench-marked on the SYNTHIA and A2D2 datasets, where it outperforms the recent models in terms of image segmentation metrics.
Nonlinear Robust Trajectory Tracking Control for a Three-rotor UAV with Unknown Time-varying External Disturbances

In this paper, the accurate dynamics of three-rotor unmanned aerial vehicle (UAV) are detailedly analyzed and a nonlinear robust tracking control strategy is proposed considering unknown time-varying external disturbances. Aiming at the tracking control of the typical underactuated system, the dynamic model of the three-rotor UAV is divided into outer-loop position subsystem and inner-loop attitude subsystem. The feedback linearization algorithm is employed to design the outer-loop controller for the trajectory tracking of the UAV. For the inner-loop control of the UAV, the robust integral of the signum of the error (RISE) method is utilized to formulate the robust attitude controller to deal with the external disturbances. The stability of the closed loop system and the asymptotical tracking of the desired trajectory are proved via Lyapunov based stability analysis. Real-time experiments are implemented to validate the performance of the proposed control strategy.
Reinforcement Learning Control for Robot Arm Grasping Based on Improved DDPG

Although the traditional robot arm grasping control has high control accuracy, its price is based on high-precision hardware and lacks flexibility. In order to achieve high control accuracy and flexibility on a relatively inexpensive robot arm. This paper proposes an improved DDPG (Deep Deterministic Policy Gradient) reinforcement learning algorithm to control the gripping of a robot arm. First, build a simulation environment for a six-DOF (six-degree-of-freedom) manipulator with a gripper in ROS (Robot Operating System). Then, aiming at the shortcomings of traditional DDPG rewards, research and design a composite reward function. Aiming at the problem of low sampling efficiency in the free exploration of the robot arm, a batch of teaching data was added to the experience replay pool to improve learning efficiency. The simulation experiment results show that under the same number of episode of training. The improved DDPG grasping control algorithm has significantly improved the grasping success rate. The grasping success rate after comprehensive improvement reaches 70%, which is higher than the 36% level of unimproved DDPG.
RF-Prism: Versatile RFID-based Sensing through Phase Disentangling

The signal phase is one of the most important metrics in RFID-based sensing, which is a useful technique enabling many significant applications. However, existing approaches of RFID-based sensing are often restricted in terms of the sensing capability or accuracy, due to the phase entanglement problem: the phase of the RFID signal is jointly affected by multiple factors, and the change in the signal phase cannot be directly attributed to anyone of them. In order to tackle this problem, we propose RF-Prism, a versatile sensing approach that can simultaneously infer multiple physical factors (i.e., location, orientation, and material of targets), purely based on the phase readings. RF-Prism includes a comprehensive model to describe how different physical factors affect the phase of the received signal, and a complete design to disentangle the phase in the multi-frequency and multi-antenna scenario. We implement RF-Prism and evaluate its performance with extensive experiments. The results show that RF-Prism simultaneously achieves a mean localization error of 7.61 cm, a mean orientation error of 9.83 degrees, and 87.9% material identification accuracy, which outperforms state-of-the-art approaches.
A Video-Based Method to Classify Abnormal Gait for Remote Screening of Parkinson’s Disease

Telemedicine is of growing importance for the increasing number of patients with population aging, lack of medical resources in the countryside, and special situation such as the COVID-19 pandemic. Gait impairment is a major motor symptom for patients suffering from neurological disorders such as Parkinson’s disease (PD), and serves as an important indicator for early screening and diagnosis of the disease. Existing gait analysis methods typically require advanced equipment, trained professionals, and complex procedures. In this paper, we propose a method to classify the abnormal gait of patients suffering from Parkinson’s disease and normal gait, solely with the 2D walking videos recorded by a common camera or a smartphone. A pose estimation algorithm is employed to extract the skeleton of the subjects from the videos. Based on the analysis of motor disturbances resulting from Parkinson’s disease, specific gait features are defined and extracted, including step length, walking speed, arm swing magnitude, and velocity. Considering that the sample size of clinical data is limited in the early stage, classic classifiers are applied, including logistic regression (LR), support vector machine (SVM), and random forest (RF). The registered clinical study was conducted with 20 PD patients and 20 age-matched healthy controls. With the three classifiers, 87.5% (LR), 90.0% (SVM), and 92.5% (RF) of classification accuracy were achieved, respectively. Thus, video-based classification of abnormal gait promises a solution for remote screening and diagnosis of neurological diseases.
Partial anti-synchronization problem of a new 6D hyper-chaotic system

This paper investigates the partial anti-synchronization problem of a new 6D hyper-chaotic system. Firstly, the existence of partial anti-synchronization problem for such system is proven. Secondly, two non-singular transformations are found and used to divide this 6D hyper-chaotic system into two subsystems, respectively, i.e., two solutions for the partial anti-synchronization problem are obtained. Moreover, the corresponding controllers are proposed by the linear feedback control method. Finally, numerical simulations are performed to verify the correctness and effectiveness of the proposed results.
Transient Electromagnetic–Thermal Cosimulation for Micrometer-Level Components

In this article, an electromagnetic–thermal coupling algorithm based on the discontinuous Galerkin time-domain (DGTD) and spectral-element time-domain (SETD) methods is proposed, which can adopt two independent grids for thermal simulation and electrical simulation, respectively. It employs time-domain solvers for both the electrical and thermal fields. Compared with the conventional cosimulation scheme (e.g., time-domain solvers for the thermal field and frequency-domain solver for the electrical field), it can provide better simulation precision for problems characterized with small size or drastic field changes. By comparing the results of electromagnetic–thermal coupling simulation with the results of electromagnetic simulation, the effect of temperature change on electromagnetic field propagation is analyzed.
Echo State Network Learning for the Detection of Cyber Attacks in Additive Manufacturing

Owing to its layer-by-layer nature, additive manufacturing (AM) has been leveraged in various industries for the fabrication of parts with complex geometries. In the era of the Industrial Internet of Things (IIoT), AM processes are increasingly casted into both cyber and physical domains. As such, it poses AM under high risks of cyber attacks, leading to altered AM parts with potentially compromised mechanical properties and functionalities. It is imperative to develop new methodologies for the detection of cyber attacks for quality and reliability assurance of products in cyber-physical AM processes. Based on the echo state network, an online monitoring approach is developed in this study to extract features from side channels for the detection of cyber attacks. Two real-world case studies are conducted to evaluate the proposed approach on the fused filament fabrication (FFF) process. Experimental results have shown that the proposed approach is effective in identifying abnormities induced by different types of cyber attacks. The proposed approach has a strong potential to be extended to other AM processes with various sensing and monitoring devices.