Miracle-dz/UniFAR-FTUs huggingface dataset:
Vision-based tactile sensing using multiple contact images generated by re-propagated frustrated total internal reflections

Current vision-based tactile sensors have several limitations, such as their size and measurable surface. Therefore, we propose a novel vision-based tactile sensor based on the re-propagated frustrated total internal reflection (FTIR). The part of the FTIR generated by the contact is re-propagated through the medium, and the FTIR are observed from the side of the medium. We validate the physical principle of observation, including multiple contact images by simulations. In addition, a prototype system is developed to estimate the contact position through observations and regression algorithms. Finally, several experiments were performed to confirm the feasibility of the proposed contact estimation based on the repropagated FTIR.
Adjustable Robust Scheduling of Commercial HVAC under Weather Uncertainty

Heating, Ventilation and Air conditioning (HVAC) systems consume a large amount of the building energy consumption. Therefore, their optimal scheduling plays an important role in achieving a sustainable future. However, given the underlying uncertainties involved in the scheduling process, user comfort might be compromised. This paper proposes an adjustable robust scheduling formulation for the HVAC problem. The formulation aims at minimizing the HVAC consumption and ensuring the temperature comfort while considering the uncertainty associated with the weather forecast. The problem formulation allows the operator to control both the robustness level as well as the robustness time during the operational day. The proposed formulation is compared to its deterministic counterpart as well as the conservative robust optimization case. The results proved that the proposed approach can ensure a good comfort without significantly increasing the associated power consumption. The Monte Carlo simulation results showed that the proposed approach can reduce the discomfort probability to as low as 4% compared to 20% for the deterministic case.
Dynamics of an Agent-Based Opinion Model with Project-Based Interorganizational Networks

The opinions of multi-agent in the project-based interorganizational networks (PIONs) are identified as major roles in the success of interorganizational cooperation. Existing models of opinion dynamics consider only the influence of the heterogeneous bounded confidence of neighbors on agents, ignoring the combined effect of the global reputation of the entire network, neighbors’ similar opinions, and their degree of stubbornness. In this paper, a perspective exchange framework for project-based interorganizational networks is used to identify agents who can proactively raise the level of their opinions. A modified agent-based opinion model of PIONs based Hegselmann-Krause (HK) model is then proposed. Extensive case experiments are conducted on an artificially generated network dataset, and the theory of Shannon-like entropy is applied to verify the stability and convergence of the proposed model. The simulation results show that the increase in the proportion of global reputation leads the group to evolve toward consensus and converge slowly, while the increase in the proportion of neighbor’s opinions makes the group evolve toward polarization and converge quickly.
Optical and Near-infrared Observations of the Afterglow of GRB 980329 from 15 Hours to 10 Days

We report I-band observations of the GRB 980329 field made on 1998 March 29 with the 1.34 m Tautenberg Schmidt telescope, R-, J- and K-band observations made on 1998 April 1 with the APO 3.5 m telescope, R- and I-band observations made on 1998 April 3 with the Mayall 4 m telescope at KPNO, and J- and K-band observations made 1998 April 6-8 with the Keck-I 10 m telescope. We show that these and other reported measurements are consistent with a power-law fading of the optical/near-infrared source that is coincident with the variable radio source VLA J0702+3850. This firmly establishes that this source is the afterglow of GRB 980329.
A Novel Performance Evaluation Strategy of Automatic Machine Learning on Electricity Services

In electricity services, the customers have many different requests, and different solutions need to be designed for filling these demands. For example, the customer sentiment classification task and meter image classification task requires experts to design different models to solve them respectively. However, it is time comsuming to design a specific model for machine learning experts, which largely limits the application of AI in electricity operation. In recent years, with the development of neural structure search algorithms, people have started to apply this technique to various scenarios. In this paper, based on the latest academic research on the neural structure search algorithm, we innovatively improve the neural structure search by a novel performance evaluation strategy in terms of model size and time consumption to effectively balance model accuracy, size, and latency for the large task volume and short response time of electricity services. The experimental results show that the algorithm can search for models with almost no loss of model accuracy while effectively constraining the memory size and reducing the computational latency of the models, thus better adapting to the tasks related to electricity services.
PEM Fuel Cell Thermal Management Strategy Based on Multi-model Predictive Control

Temperature is a key factor affecting the efficiency and safety of proton exchange membrane fuel cells. It is especially important to keep the fuel cell stack temperature stable under the operating conditions where the load current changes frequently. Firstly, a nonlinear model of the stack and cooling system is developed for a 50kW commercial fuel cell system. Then, a series of local models are established at different operating points to accurately describe the nonlinear characteristics of the proton exchange membrane fuel cell (PEMFC) system. Finally, a multi-model predictive control method based on an adaptive switching strategy is proposed. The simulation results show that the temperature overshoot can be controlled within 1K in the operating range of 50-200A. Compared with the PID and model predictive control(MPC) methods, the multi-model predictive control(MMPC) method has better robustness.
AI Assurance for the Public – Trust but Verify, Continuously

Artificial intelligence (AI) systems are increasingly seen in many public facing applications such as self-driving land vehicles, autonomous aircraft, medical systems and financial systems. AI systems should equal or surpass human performance, but given the consequences of failure or erroneous or unfair decisions in these systems, how do we assure the public that these systems work as intended and will not cause harm? For example, that an autonomous vehicle does not crash or that intelligent credit scoring system is not biased, even after passing substantial acceptance testing prior to release. In this paper we discuss AI trust and assurance and related concepts, that is, assured autonomy, particularly for critical systems. Then we discuss how to establish trust through AI assurance activities throughout the system development lifecycle. Finally, we introduce a “trust but verify continuously” approach to AI assurance, which describes assured autonomy activities in a model based systems development context and includes postdelivery activities for continuous assurance.
Feature Extraction from a Static and a Moving One-Dimensional Voltage Sensor Line for the Electric Field-Based Determination of Object Size and Position in Aqueous Media

Biological beings perceive different signals from their environment through their sensory organs to estimate object size and position. In this work, the aspect of object localization and size is investigated in a detailed simulation study based on a simplified, analytical model of a weakly electric fish. These fishes generate a three-dimensional dipole-like electric field for object localization and communication. With the abstracted model of this bio-template, different features of a voltage profile that is obtained from an assumed sensor line (representing the voltage-sensitive skin of fish) were identified for the determination of object size and position. These features were categorized according to their suitability for measurement scenarios with a stationary or moving sensor line. On the one hand, some features are considered individually to obtain information about the object size and position, and on the other hand, a combination of these features create a distance measure which is independent of the object size.
A Distributed Cooperative Co-Evolutionary Algorithm Based on Ring Network for Distributed Large-Scale Optimization

With the rapid development of distributed computing paradigms like edge computing and Internet of Things (IoT), many distributed edge nodes involve in data collection and decision making, resulting in many distributed optimization problems (DOPs). In this paper, we consider the DOPs with the following features. First, decision variables of a problem are naturally distributed in several spatial-distributed edges. Each computing node is responsible for one subproblem, and it can only access its corresponding local data and perform local objective evaluation. Second, some decision variables appear in different groups simultaneously, which are called overlapping variables. Third, the computing nodes can only communicate following a certain network topology. They need to work together to solve the overall problem. Because of its divide-and-conquer nature, cooperative coevolution (CC) has good potential for handling such distributed problems. Therefore, we develop a new distributed CC framework to solve them. First, a new CC architecture based on the ring network without any central node is designed. Second, an asynchronously communication strategy with low communication frequency and volume is proposed. Third, a competitive selection strategy is adopted to achieve consistency in asynchronous evolution. We define a set of distributed benchmark problems and the experimental results validate the effectiveness of the proposed approach.
A Node Backup Strategy for Routing Protocol in Software-Defined Vehicular Networks

Vehicle Ad-hoc Networks have laid an essential technical foundation for realizing intelligent transportation. Unexpected mobility change of a specific node often causes a communication link failure. Thus, this work proposes a node backup strategy for routing protocol in software-defined vehicular networks. The core of the strategy is to promote communication link stability through backup nodes. A node backup routing algorithm is designed to search for alternative nodes for each node in a communication link. A node can flexibly select the next-hop node during packet transmission based on actual conditions. When an unexpected mobility change of a specific node causes a communication link failure, we can restore the link by enabling the alternative nodes. The influence of various factors on packet reception rate and communication delay is studied through simulation experiments. By comparing with two existing routing protocols, the effectiveness of the proposed approach is verified.
MMF-Net: A Novel Multimodal Multiscale Fusion Network for Artery/Vein Segmentation in Retinal Fundus

Automatic artery/vein (Arkers for the early diagnosis of many systemic diseases. Unfortunately, current methods have some limitations in AN segmentation, especially the lack of annotated data and the serious data imbalance. Thus, A novel multimodal multiscale fusion network (MMF-Net) is proposed to alleviate the above problems, which utilizes the internal semantic information of vessels adequately to enhance the AN segmentation. Particularly, the MMF-Net introduces a multimodal (MM) module that could highlight the vessel structure from the original fundus image to constrain the AN image features, which reduces the influence of background noise. In addition, the MMF-Net exploits a multiscale transformation (MT) module to extract the vessel information efficiently from the multimodal feature representations. Finally, A multi-feature fusion (MF) module is applied in MMF-Net to split and reorganize the pixel feature from different scales to improve the robustness of AN segmentation. Experiments on two public benchmark datasets show that our method has achieved superior performance and surpassed other existing state-of-the-art methods in the accuracy of AN segmentation.
A Switched-Capacitor based Amplifier using Organic Thin-Film Transistor

This paper proposed a switched-capacitor based amplifier architecture for the organic thin-film transistor (OTFT) technology. By utilizing the switching characteristics of the device for signal amplification, the design can reduce the influence of OTFT performance fluctuation on the circuit behaviour. An amplifier with gain of 36 is built and simulated to verify the proposed circuit architecture and method, demonstrating the advantages of the switched-capacitor OTFT amplifier.
Towards a POMDP-based Control in Hybrid Brain-Computer Interfaces

Brain-Computer Interfaces (BCI) provide a unique communication channel between the brain and computer systems. After extensive research and implementation on ample fields of application, numerous challenges to assure reliable and quick data processing have resulted in the hybrid BCI (hBCI) paradigm, consisting on the combination of two BCI systems. However, not all challenges have been properly addressed (e.g. re-calibration, idle-state modelling, adaptive thresholds, etc) to allow hBCI implementation outside of the lab. In this paper, we review electroencephalography based hBCI studies and state potential limitations. We propose a sequential decision-making framework based on Partially Observable Markov Decision Process (POMDP) to design and to control hBCI systems. The POMDP framework is an excellent candidate to deal with the limitations raised above. To illustrate our opinion, an example of architecture using a POMDP-based hBCI control system is provided, and future directions are discussed. We believe this framework will encourage research efforts to provide relevant means to combine information from BCI systems and push BCI out of the laboratory.
Advances in Preference-based Reinforcement Learning: A Review

Reinforcement Learning (RL) algorithms suffer from the dependency on accurately engineered reward functions to properly guide the learning agents to do the required tasks. Preference-based reinforcement learning (PbRL) addresses that by utilizing human preferences as feedback from the experts instead of numeric rewards. Due to its promising advantage over traditional RL, PbRL has gained more focus in recent years with many significant advances. In this survey, we present a unified PbRL framework to include the newly emerging approaches that improve the scalability and efficiency of PbRL. In addition, we give a detailed overview of the theoretical guarantees and benchmarking work done in the field, while presenting its recent applications in complex real-world tasks. Lastly, we go over the limitations of the current approaches and the proposed future research directions.
Enhanced Plastic Recycling using RGB+Depth Fusion with MassFaster and MassMask R-CNN

The rapid increase in waste generation from electrical and electronic equipment (WEEE) has created the need for more advanced sensor-based systems to sort this complex type of waste. Therefore, this study proposes a method for object detection, instance segmentation, and mass estimation of plastics and contaminants using the fusion of RGB and depth (D) images. The methodology is based on the Faster and Mask R-CNN with an extra head for the mass estimation. In addition, a pre-processing method to enhance the depth image (ED) is proposed. To evaluate the data fusion and pre-processing method, two data sets of plastics and impurities were created containing images with and without overlapping samples. The first data set contains 174 RGB images and depth (D) maps of 3146 samples, excluding their mass value, while the second data set contains 42 RGB and D images of 766 pieces together with their mass. The first and second data sets were used to evaluate the performance of Mask and Faster R-CNN. Further, the second data set was used to evaluate the network’s performance with the additional head for mass estimation.The proposed method achieved 0.75 R2, 1.39 RMSE, and 0.81 MAE with an IoU greater than 50% using the network Resnet50_FPN_RGBED. Hence, it can be concluded that the presented method can distinguish plastics from other materials with reasonable accuracy. Furthermore, the mass of each detected particle can be estimated individually, which is of great relevance for the recycling sector. Knowing the mass distribution and the percentage of contaminants in a waste stream of mixed plastics can be valuable for adjusting the parameters of upstream and downstream sorting processes.
UAV swarm regular geometric formation transformation and interactive control algorithm

Compared with a single UAV, a UAV swarm has higher task execution efficiency, more robust system scalability, and better environmental adaptability when performing tasks. Different mission scenarios have additional requirements for the formation of a UAV swarm. In this context, the UAV swarm regular geometric formation transformation algorithm is proposed and implemented. The remote interactive control algorithm of the intelligent terminal based on Huawei SCM-W09 Pad and NOLO VR equipment is proposed and implemented. The deployment of the algorithm is mainly based on the Crazyflie miniature quadrotor, the Optitrack motion capture system, and the Crazyswarm software framework. Under limited communication resources, Crazyradio PA is used to expand the controllable UAV swarm scale from 17 to 20 under single-channel conditions. Through the actual flight experiment of the UAV swarm algorithm, the feasibility and practicability of the proposed algorithm are fully verified. Finally, we provide technical ideas and feasibility demonstrations to improve the proposed UAV swarm algorithm further.
SCSE-E2VID: Improved event-based video reconstruction with an event camera

The recently emerging event camera has grown into a new type of sensor in the realm of vision, with benefits such as low power consumption, high dynamic range (HDR), microsecond time resolution, and no motion blur. While event cameras offer numerous advantages over conventional cameras, they only capture changes in intensity and give up lots of environmental details. This paper proposes an end-to-end UNet network called SCSE-E2VID to synthesize gray images from asynchronous events. We design an event fusion block to feed more related events to the encoder, allowing the network to extract more valuable features. The famous attention module called Spatial and Channel ‘Squeeze & Excitation’ Block (SCSE) is utilized to remove artifacts and better extract spatiotemporal features for the decoder. Besides, we add parallel convolutions in the upsampling block and refine the output features, which supplement content in reduced channels. In order to evaluate the performance of our proposed SCSE-E2VID, we implement quantitative and qualitative comparisons based on the public IJRR and HQF datasets. The results show that our method achieves better performance in terms of perceptual similarity and structural similarity when compared with state-of-art methods and demonstrates comparable performance in terms of squared error.
Bio-Inspired Locomotion Control of Gecko-Mimic Robot: From Biological Observation to Robot Control

By studying animal and human locomotion strategies, mechanisms, patterns, kinematics, and dynamics, bio-inspired locomotion aims to establish a new theory and method of bionic mechanism, motion planning, and stable control, as well as a new concept for developing bio-inspired robots and unmanned systems. Throughout the long history of evolution, animals have shaped robust, complex, and adaptive movements in order to live in difficult environments. Nature provides an endless supply of robot design concepts for many environmental conditions, such as flight [1], swimming [2], and terrestrial movement [3].
Nondeterministic-Mobility-Based Incentive Mechanism for Efficient Data Collection in Crowdsensing

Mobile crowdsensing (MCS) booms the implementation of the Internet of Things (IoT) in different areas due to flexibility and low deployment cost. However, collecting sufficient high quality sensing data is crucial for the success of various applications. Incentive mechanism design plays a critical role in the successful implementation of mobile MCS systems. Most of existing work consider that the platform exactly knows the trajectory of mobile users. However, in most cases, it is difficult to obtain the accurate information of the location of mobile users due to either privacy issue or the lack of information. In this article, we consider nondeterministic mobility of mobile users, where only the probability distribution of users’ mobility is available. We design an effective mechanism to achieve the quality data collection with the objective of maximizing the expected social welfare. Simulation results show that the proposed mechanism achieves her expected social welfare compared with four existing schemes, while satisfying truthfulness, individual rationality, and computational efficiency.
Programming Course Student Performance Prediction based on Feature Construction

Programming course has become an important basic course for the cultivation of college students' computing thinking ability. Student performance prediction is very useful for assisting teachers' teaching, promoting students' learning and providing management decision-making suggestions. In the blended teaching environment, the students' homework and unit exam data collected by the online system are used to predict the students' performance at the end of the semester. This paper proposes to use the genetic programming method to construct the features of the collected data, generate more features that can be used for performance prediction, and finally predict the final exam. The experimental results show that the method used in this paper improves the accuracy of course performance prediction. (The datasets, source code and experimental result can be downloaded from https://gitee.com/wb0817002/spr)
Mining Multi-source Campus Data: An Empirical Analysis of Student Portrait using Clustering Method

The findings of educational data mining (EMD) can help researchers better understand the educational law and the evaluation of learning effectiveness. This paper collected multi-source education data of students in a university located in Beijing and constructed 19 features from students' data set, including categorical and numerical types. The K-Prototype method based on the mixed measurement of Hamming distance and Euclidean distance is used to study the student portrait. Moreover, we prove the superiority of the proposed method in educational data mining through the silhouette coefficient. In addition, according to the clustering results, we constructed three kinds of educational portraits of student groups and further analyzed the differences among these groups based on the Kruskal-Wallis test. Corresponding management suggestions about educational management of college students are put forward based on the analysis results.