Miracle-dz/UniFAR-FTUs huggingface dataset:
An Analysis of Peer CSR Tone Based on Python—from the Perspective of Green Innovations

Text sentiment analysis is a crucial part of natural language processing, and Chinese Jieba Segmentation based on Python Technology is an important preprocessing operation of natural language processing. We use python web crawler technology to perform samples pretreating, obtain the number of positive and negative words and measure corporate social responsibility tone of peer companies (Peer CSR tone hereafter). On this basis, we empirically tests the impact of peer CSR tone and target companies’ green innovations. We find that peer CSR tone has a significantly positive relationship with green innovations of target companies. The analysis of the effects channel suggests that their relationship can be realized through social learning mechanisms and social competition mechanisms. Further, compared with non-state-owned enterprises, the peer CSR tone has a more significant impact on the green innovations of state-owned enterprises. The research is of great significance to expand the application of python web crawler technology and enhance corporate green innovations. It is also a further application of Python in the field of text sentiment analysis.
Killing Planet Candidates with EVEREST

We exploit high-quality photometry from the EVEREST pipeline to evaluate false-positive exoplanet candidates from the K2 mission. We compare the practical capabilities of EVEREST’s pixel-level decorrelation scheme to the data analysis pipelines widely used at the time of these planet candidates’ discovery. Removing stellar variability from the EVEREST-corrected light curves, we search for potential secondary eclipses. For each object exhibiting a secondary eclipse, we compare the implied brightness temperature of the planet candidate to its calculated equilibrium temperature. We thereby identify objects whose brightness temperature is too high to be consistent with a planet. We identify seven systems previously flagged as planetary candidates in preliminary vetting pipelines, and use EVEREST to instead identify six of them as eclipsing binaries. We also project the importance of optimal photometric vetting for TESS data. We find that the majority of blended eclipsing binaries could be identified using TESS photometry, and a systematic study of that kind could in principle also yield valuable information on the mass ratio distribution in stellar eclipsing binaries.
Detection of Perceived Discomfort in SAE L2 Automated Vehicles through Driver Takeovers and Physiological Spikes

As more vehicles on the road are equipped with driver assistance technologies, research on perceived driver discomfort in highly automated vehicles emerged. Driver discomfort estimation is important to automated feature acceptance, user satisfaction and economic factors. Existing literature mainly focused on driver takeover detections, because drivers are more likely to takeover with high level of discomfort. This paper investigated detections of driver discomfort through both takeovers and physiological spikes, to include more subtle and latent discomfort. We present a multimodal dataset from an automated vehicle simulator study, with eye gaze and physiological measurements. The dataset included 32 participants, and each experiment lasted 120 minutes. We recorded their takeover intentions and extracted the physiological spikes that were related to driver discomfort. Machine learning models were then built to detect driver takeovers, physiological spikes and them together through mutli-task learning. Machine learning results showed good performance on takeover and physiological spike detections. The multi-task learning-based detection had improved performance, indicating correlations between takeovers and physiological spikes. Our results demonstrate the potential of driver discomfort prediction through driver's physiological and behavioral data. The result indicates a potential that AD system can learn user preference from driver without explicit takeovers.
A Spatial-Temporal Deep Reinforcement Learning Model for Large-Scale Centralized Traffic Signal Control

Deep Reinforcement Learning (DRL) has achieved great success in traffic signal control. Most DRL-based methods regard intersections as agents, which cooperate in a decentralized way. There are two main issues with the decentralized way: cooperation and stability. To overcome these issues, we propose a novel centralized control method with a global agent to control the whole network. To mitigate the curse of dimensionality problem, we use three techniques: first, a decomposition mechanism is proposed to decompose the high dimensional state-action space; second, an action-feedback technique is introduced to learn the temporal pattern from the historical decisions so as to improve the decision-making; third, a GAT model is applied to learn the spatial feature of surrounding intersection to effectively estimate the future rewards. By using the three techniques, our model can easily tackle the large-scale traffic network. We conduct extensive experiments on both synthetic and real-world data. The experiment results demonstrate that our model outperforms the traditional and state-of-the-art DRL-based control methods.
Development of a Bespoke Chatbot Design Tool to Facilitate a Crowd-based Co-creation Process

Developing an effective educational chatbot is a challenging and time-consuming process. One of the main challenges in a co-creation process, is ensuring multiple stakeholders, as professionals in a variety of roles such as academics, learners, researchers, and subject experts, are all able to contribute successfully. More common online learning resources are designed using traditional methods such as group storyboarding workshops. With chatbot design, it is especially difficult to maintain a group focus on pedagogical requirements while minimizing technological interaction. Furthermore, more time is required to gather sufficient and accurate data used to train a chatbot to the point where it can be deployed for education. This paper describes the development of a web-based chatbot design tool formed to break down these barriers and support the overall design process.
Automated Lane Changing Control in Mixed Traffic: An Adaptive Dynamic Programming Approach

In this work, we have introduced an optimal data-driven control algorithm to solve the lane changing problem of Autonomous Vehicles (AVs), where the input-state data of the AV is utilized to generate a near-optimal lane-changing controller by approximate/adaptive dynamic programming (ADP) technique. The dynamics of the AV is assumed to be a parameter varying linear system, which induces two challenges for the lane-changing controller design: parameter varying and unknown dynamics. With the help of both gain scheduling and ADP techniques, a data-driven control algorithm is proposed which can generate a near-optimal lane-changing controller in the absence of the accurate model of the AV. Then, a data-driven lane-changing decision-making algorithm is used that can make the AV perform a lane abortion if safety conditions are violated during a lane change. Finally, the data-driven gain scheduling controller and lane-change decision-making algorithm are validated by SUMO and MATLAB simulations.
Coupling dense point cloud correspondence and template model fitting for 3D human pose and shape reconstruction from a single depth image

In this paper, we address the problem of capturing both the shape and the pose of a character using a single depth sensor. Some previous works proposed to fit a parametric generic human template in the depth image, while others developed deep learning (DL) approaches to find the correspondence between depth pixels and vertices of the template. In this paper, we explore the possibility of combining these two approaches to benefit from their respective advantages. The hypothesis is that DL dense correspondence should provide more accurate information to template model fitting, compared to previous approaches which only use estimated joint position only. Thus, we stacked a state-of-the-art DL dense correspondence method (namely double U-Net) and parametric model fitting (namely Simplify-X). The experiments on the SURREAL [1], DFAUST datasets [2] and a subset of AMASS [3], show that this hybrid approach enables us to enhance pose and shape estimation compared to using DL or model fitting separately. This result opens new perspectives in pose and shape estimation in many applications where complex or invasive motion capture set-ups are impossible, such as sports, dance, ergonomic assessment, etc.
Deposition-temperature-dependent stress of capping oxide and its effect on Pt/Pb(Zr1−xTix)O3/Pt ferroelectric capacitor

Two different interlayer dielectric (ILD) materials, electron cyclotron resonance chemical vapor deposition oxide (ECR-OXIDE) and plasma enhanced chemical vapor deposition TEOS oxide (PE-TEOS), were prepared at 400 and 200 °C respectively, on silicon substrates and Pt/Pb(Zr1−xTix)O3 (PZT)/Pt capacitors. It was found that the ILD deposition temperature is a most important parameter for minimizing the degradation of remnant polarization (Pr) during the ILD deposition. Since the stress of PZT capacitor strongly depends on the ILD deposition temperature, the PZT capacitor with PE-TEOS showed more compressive stress than that with ECR-OXIDE, which results in severe Pr degradation of PZT capacitor with PE-TEOS. This large stress effect of PE-TEOS was confirmed by x-ray diffraction (XRD) patterns in which the d spacing of (111) PZT films with PE-TEOS was much larger than that of PZT films with ECR-OXIDE. Therefore, the low ILD deposition temperature is a key parameter for achieving an ILD integration without any.
Research on Garbage Image Classification Based on Neural Network

With the advent of the global information era, the traditional waste image sorting algorithm can no longer meet the needs of modern urban waste sorting, and modern society has an increasingly strong desire for intelligent automation of waste image sorting technology. Therefore, the research and innovation of waste image sorting technology are essential for the development of the computer field and the modernization process of society. For garbage image classification, to solve the problem of garbage classification, a pre-trained ResNet34 model with migration parameters based on the ImageNet dataset is proposed in this paper to solve this problem. The model uses batch normalization to improve network stability and accelerate network convergence. At the same time, a lightweight CBAM attention mechanism module is added to enhance the focus on the main features of images and ignore irrelevant features to improve network performance further. The experimental results show that the performance of the improved model is considerably improved, and the performance results are better on the relevant garbage classification dataset, which has some practical value.
State-of-the-art of battery state-of-charge determination

From the early days of its discovery, humanity has depended on electricity, a phenomenon without which our technological advancements would not have been possible. With the increased need for mobility, people moved to portable power storage—first for wheeled applications, then for portable and finally nowadays wearable use. Several types of rechargeable battery systems, including those of lead–acid, nickel–cadmium, nickel–metal hydride, lithium ion and lithium-ion polymer exist in the market. The most important of them will be discussed in this review. Almost as long as rechargeable batteries have existed, systems able to give an indication about the state-of-charge (SoC) of a battery have been around. Several methods, including those of direct measurements, book-keeping and adaptive systems (Bergveld et al 2002 Battery Management Systems, Design by Modelling (Philips Research Book Series) vol 1 (Boston: Kluwer)) are known in the art for determining the SoC of a cell or battery of cells. An accurate SoC determination method and an understandable and reliable SoC display to the user will improve the performance and reliability, and will ultimately lengthen the lifetime of the battery. However, many examples of poor accuracy and reliability can be found in practice (Bergveld et al 2002, cited above). This review presents an overview on battery technology and the state-of-the-art of SoC methods. The goal of all the presented SoC indication methods is to design an SoC indication system capable of providing an accurate SoC indication under all realistic user conditions, including those of spread—in both battery and user behaviour, a large temperature and current range and ageing of the battery.
Work-in-Progress: NoRF: A Case Against Register File Operands in Tightly-Coupled Accelerators

Accelerators are often used to increase performance and/or energy efficiency of general-purpose CPUs. However, Tightly-Coupled Accelerators (TCAs) often perform computations on data structures that may not be a natural fit for general-purpose registers. The designer can either use the existing register file (RF), a RF tailored for the accelerator, or eschew a RF entirely (NoRF), accessing operands directly from the memory hierarchy. Designers for embedded and edge devices are particularly conscientious towards energy-efficient compute and data transfer. We explore the possibility of mini-DGEMM accelerators (example TCAs) within the context of CPUs and edge devices, which also have increasing applications for DGEMM compute. At a high level, register files help reduce memory accesses (steps 1, 2, 5, and 6 in Figure 1 ) when the compiler finds reuse of operands in the program dataflow. On the other hand, direct memory access simplifies the data movement by completely eliminating the intermediate reads and writes to a register file but issues more memory requests. This paper evaluates the difference between these options of operand delivery. Figure 2 shows that all recent vector extensions use a register file implementation. By this trend, it may seem natural to incorporate mini-matrices into the RF. However, we present quantitative and qualitative evidence to advocate for direct cache access for operands.
Self-Supervised Pretraining for Point Cloud Object Detection in Autonomous Driving

Autonomous driving promises a safer and more efficient means of transportation. However, one of many challenges it faces is understanding the complex driving environment. Modern perception systems often utilize neural networks and tackle complicated driving scenarios in a data-driven manner. Unfortunately, such systems require a large amount of labeled data, which can be prohibitively expensive to collect. In this paper, we investigate self-supervised learning as a method to reduce the reliance on labeled data in the context of autonomous driving. We specifically focus on point cloud recognition and apply contrastive and geometric pretext tasks to pretrain neural networks using unlabeled point cloud data. We conduct experiments in the nuScenes autonomous driving dataset with various amounts of labeled data. Our experiments reveal three insights: (1) pretraining with contrastive loss alone improves the average precision (AP) but negatively impacts the object heading accuracy, (2) combining contrastive and geometric pretext tasks benefits both the average precision and heading accuracy, and (3) the improvement by self-supervised pretraining remains even with an increased amount of labeled data and training steps.
Proposal and evaluation of reference-free model predictive control incorporating human-like driving features

Autonomous driving is achieved through environment recognition, path planning, and path tracking control. To achieve safe driving, it is necessary to increase the performance of these functions; however, this can lead to increased computational complexity. These functions share computational resources on the vehicle and must be realized in real time to guarantee safety. Therefore, reducing the computation time is a key issue in autonomous driving. We propose reference-free model predictive control (MPC) that incorporates features of human-like driving; this method can reduce the computation time. The proposed method does not follow a unique optimal path; it travels within a certain safety region. Thus, switching the control objective from trajectory tracking to constraint satisfaction reduces computational complexity. Besides, an event-triggered MPC (ETMPC) framework has been adopted to further reduce the computation time. We implemented the proposed method and compared the results with those of the ETMPC. The results confirm that computation time can be minimized to 1/10 further compared with that of the other methods. ETMPC is sensitive to observation errors and has the drawback of increased computation time. To solve this aspect, the proposed method incorporated the reference-free approach. The performance of the proposed method was evaluated using the observation error because it was robust against observation noise by human-like features. As a result, it was possible to reduce the computation time to 1/30.
Flexible Hardware Accelerator Design Generation with Spiral

Hardware specialization has become a widely employed technique for approaching higher performance and en-ergy efficiency in computer systems. Yet obtaining efficient cus-tom hardware designs remains a challenging and tedious task, calling for the automated approaches. In the past, Spiral has been used for generating high-throughput streaming hardware designs for linear transform kernels. This paper is motivated by an observation that a memory-based iterative computing model may allow us to trade off throughput for algorithmic flexibility. In this paper, we present a hardware generation approach that generates and optimizes algorithms using Spiral's multi-level domain-specific languages (DSLs), targeting a scalar load-store architecture. We have incorporated this approach as a hardware backend into the Spiral system. Our evaluation of this approach on several fundamental kernels shows flexibility with reasonable performance and resource utilization.
Simulating Common Indoor Crowd Behaviours in 3D Environments Using ABMU

Being able to model common crowd behaviours is vital in architecture and similar built environment fields. This is becoming even more pressing, especially in recent years with COVID-19 having a significant impact in how we interact in indoor spaces. As such, common crowd simulation approaches such as Agent-Based Modelling (ABM) need to evolve rapidly to be able to accommodate new requirements in crowd interaction; for indoor crowds, this usually requires a model to accommodate three-dimensional spaces, as the existing 2D modelling approaches are often inadequate in capturing the nuances of crowd interaction in indoor spaces, which are inherently three-dimensional. In this paper we present an application of such a 3D ABM modelling framework through a model of queuing behaviour, a common behaviour in indoor crowds. The model is developed in three-dimensional space on the Unity3D development platform, using the Agent-Based Modelling Framework for Unity3D (ABMU).
Non-planar Phase-Gradient Metasurfaces for Spatially-Dispersive Beam-Steering Devices

In this contribution, we investigate the angular response of planar deflecting metasurfaces and show that the angular stability of their meta-atoms does not allow to engineering the lens behavior vs. the illumination angle. Then, we show how the metasurface geometry can be exploited as an additional degree of freedom to enhance its overall spatial dispersion and obtain different response with the impinging angle. A relevant example, involving the use of the deflecting lens for phased antenna arrays is also discussed.
SixthSense: Smart Integrated Extreme Environment Health Monitor with Sensory Feedback for Enhanced Situation Awareness

Natural disasters occurring in inaccessible rural areas are on the rise, leading to the multiplication of first responders’ missions. However, engagement in fighting wildfires or participating in rescue missions includes risks for the well-being of the engaged first responders. Consequently, a system that monitors their actions and provides real-time and actionable information without obstructing their operational capacity is needed. The EU-funded SIXTHSENSE project aims to improve the efficiency and safety of first responders’ engagement in difficult environments by optimizing on-site team coordination and mission implementation. The project proposes an innovative wearable health monitoring system based on multimodal biosensor data that enables first responders to detect risk factors early on and allows real-time monitoring of all deployed responders. This paper is an introduction to the overall concept of the project, to the methodology and the system architecture, moreover details on Alpha version of SixthSense prototype are presented.
Electric Vehicle Autonomy: Realtime Dynamic Route Planning and Range Estimation Software

As electric vehicles (EV) become more dominant for general traditional and autonomous transportation, more accurate range prediction and efficient route planning is necessary to improve adoption and better support EV owners. The EV Path Range Estimator (EVPRE) presented proposes to fill gaps in current route and path planners by providing energy optimal paths that consider real-time external factors and environmental conditions. EVPRE has effective range estimation which considers specific vehicle models and locations. This paper demonstrates development of vehicle models (validated in hardware), energy efficient route generation, and range prediction. Information is visualized through simple isochrones to quickly convey range information for specific situations and vehicle types.
PAT: Point Cloud Analysis with Local Filter Embedding in Transformer

3D point clouds can offer rich geometric content of information for the scenes. How to learn geometric features locally and globally has become a growing concern recently. This paper proposes a novel approach for Point cloud Analysis based on Transformer network, called PAT, which utilizes interactive-attention to capture multi-angle features in irregular point clouds for classification and segmentation. Specifically, the double-coordinate encoder is designed as local filter operations by mining neighborhood information from point coordinates and direction simultaneously in the bi-coordinate system. The proposed PAT for point cloud analysis can interactively perform local and global attention across different feature spaces. Validation was conducted on two public datasets and results show that the proposed PAT achieves good performance.
An Approach to Global and Behavioral Planning for Automated Forklifts in Structured Environments

Nowadays, automation solutions for material handling in confined areas are flourishing due to the repetitive nature of the tasks, helping to improve efficiency, lower costs, and smooth operations. Although current automated forklift technologies are available, most solutions focus on path planning and tracking at the local level, leaving the higher levels of planning to a secondary role. In this work, a novel approach for global and behavioral planning for automated forklifts is presented. First, the global planner explores a graph to find and refine a path to the desired destination. Second, the behavioral planner uses a finite state machine that handles the transitions among driving maneuvers. The approach is verified in a virtual framework considering UDP communication protocols for future real implementation. Results demonstrate the applicability, using two origin-to-destination driving scenarios.