Miracle-dz/UniFAR-FTUs huggingface dataset:
How do the personality traits affect observational behaviors when judging whether smiles are genuine or not?

Humans' ability to discriminate a Duchenne (genuine) smile from non-Duchenne (“presented”) smile attracted scientific interests among various types of smiles. To better understand humans' capability and its mechanisms to discriminate Duchenne and non-Duchenne smiles, we examined people's observational behaviors during Duchenne/non-Duchenne discrimination tasks using an eye-tracker. In so doing, we also collected data on participants' personality traits as some previous studies showed that observational behaviors are strongly influenced by the personality traits of observers. With two Experiment, we found differences in observational behaviors between correct and incorrect discrimination, and that different personality traits were associated with different observational behaviors. However, we did not found any significant relationship between observers' personality traits and judgment accuracies. Thus, although we found differential observational behaviors for correct and incorrect respondents, it seems that these differences were nothing to do with participants' personality traits.
Imaging sonar simulator for assessment of image registration techniques

This work focuses on building a Forward-Looking sonar simulator capable of generating large volumes of ground truth data to test algorithms such as: novel image registration techniques for trajectory estimation, three-dimensional reconstruction or to be used as training data for machine learning algorithms. The simulator is capable of generating realistic data sets of images and providing ground truth data with the exact position and attitude of the sonar related to objects in a test scenario. The sonar simulator is developed using the Unity software platform. This work also shows an example application. Simulated image data sets for different sonar trajectories and seabed textures were created. These data sets are used by an attitude-trajectory estimation method and a quantitative analysis of the method is presented.
Depth Map Inpainting Using a Fully Convolutional Network*

Emerging consumer depth cameras, such as Microsoft Kinect and ASUS Xtion Pro, are low-cost depth sensors which can capture the depth maps of the scene, and obtaining color images of the corresponding scenes at the same time. Depth information is very important for computer vision applications, and to acquire the accurate depth information is challengeable. However, due to the limitations of imaging principle, depth maps acquired by such devices are often noisy and will lose depth values for certain areas. In this paper, we present a novel method for depth inpainting problem using a Fully Convolutional Network (FCN). The network has two input branches. One branch is a color image that has been processed by a convoluted hidden layer, and the other one is the missing depth map. Besides, we add a shortcut in order to improve the feature extraction capability of the network. We have validated our method in the Middlebury Stereo Dataset and real-world scenes captured by Kinect 2.0. Experiments show that our method has better applicability and reliability than traditional methods in depth inpainting.
Model Learning for Two-Wheeled Robot Self-Balance Control

Two-wheeled robots have many advantages over other mobile robots, but they are difficult to self-balance compared with other wheeled robots. Reinforcement learning (RL) is a general framework for sequential decision-making problems. So far, there are many applications of reinforcement learning to solve robot control problems, but most of them are used in simulators because of the large amount of data required. In addition, due to the reality gap, the policy learned in a simulation environment cannot be transferred directly to a real robot. Real robot data often expensive due to the potential damage to the robot. Model-based methods require far fewer robot data than model-free methods, but these methods have the problem of model bias. In this paper, we use a model-based reinforcement learning method to achieve self-balance of a two-wheeled robot. We present a model learning method that can reduce the problem of model bias. Our method combines the simulator and a few real robot data to learn a probabilistic dynamics model of the robot through an iterative way, which requires no expertise and can learn from scratch. Then the control policy is optimized based on the learned model.
A Compact Antenna Based on Metasurface for WLAN Band

This paper presents a design of a compact antenna based on metasurface. The working frequency of the proposed antenna covers the industrial, scientific, and medical (ISM) 2.45 GHz band. The metasurface of this design, consists of a 3×3 array of the unit cells. A printed coplanar waveguide (CPW) monopole antenna is used to feed the metasurface. Unlike the traditional antennas based metasurface that the metasurface as a reflector, the metasurface of this design acts as a radiator and works in the operating frequency band, which can significantly decrease the dimension of the antenna. The size of the antenna is 0. 094 $\lambda _0^2$, and the wideband of 11.02 % is achieved with S11 below -10 dB. The total efficiency is over 88% and the maximum gain is 3.3 dBi in the operating frequency band. This design is a promising candidate in wireless communication.
Wide-band Wide-beam Circular Polarization Cone-plane Composite Helical Millimeter-wave Antenna

In millimeter wave band, the width of the antenna arm of the planar helical antenna is difficult to be sufficiently small because of the processing accuracy. It results in a decrease in the circular polarization performance of the antenna at high frequencies. In this paper, the 3D structure of a conical and planar composite spiral is used. This configuration does not require changing the width of the antenna arm. At the same time, the spiral density of the antenna arm is increased. This makes the Axial Ratio of circular polarization better at high-frequency. Also it can reduce the difficulty in processing the antenna arm.
Satellite-derived bathymetry using a radiative transfer-based method: A comparison of different atmospheric correction methods

Accurate bathymetry information, in form of maps of water depth, is essential for a wide variety of purposes including offshore activities (e.g., pipeline laying), resource management (e.g., fishery), and defense (e.g., navigation). In this study, we compared three atmospheric correction algorithms and used a radiative transfer model (RTM) to derive water depth from a multispectral image of an area from Florida, USA. Our results showed that with accurate atmospheric correction, water depth can be reasonably estimated up to 15 m, where optical conditions are favorable for the use of multispectral images.
SPECIAL SECTION ON DATA MINING FOR INTERNET OF THINGS

Recently, in IoT, wireless sensor network has become a critical technology with which many applications in industries and human life can achieve smart IoT control. However, for those daily applications usingWSN technology, malicious users can capture the sensor nodes much easily since these wireless sensor nodes are usually deployed in easily touched places. Once this node captured attack occurs, wireless sensor network soon faces various security risks. In this paper, in order to resist node captured attack, we propose a novel authentication information exchange scheme in WSN, which is very different from the previous authentication researches. Our idea is to add an authentication information exchange scheme in previous authentication scheme but not propose new one. We develop this scheme based on the idea of the association scheme of Home GWN and local sensor nodes. In this study, HGWN should contact all local sensor nodes and meanwhile is responsible for performing an authentication information exchange scheme for resisting security risk. In order to prevent the attacker from guessing communication period between HGWN and the sensor, we also design a dynamic contacting mechanism. We give a detail discussion of this scheme and validate it by three ways, security evaluation, BAN logic and performance evaluation, which proves that our authentication information exchange scheme can achieve security features and goals. INDEX TERMS Authentication protocol, node captured attack, IoT, wireless sensor networks.
A Triangle Feature Based Map-to-map Matching and Loop Closure for 2D Graph SLAM

The loop closure problem in 2D LiDAR simultaneous localization and mapping (SLAM) is interesting yet to be solved efficiently, as it suffers from a lack of information in 2D laser rangefinder readings. For this reason, we compare two grid submaps, where features and constraints are enriched, to find loops. We propose a geometric environment descriptor called a Triangle Feature (TF). It exploits the Euclidean distance constraint any three feature points in a submap can form. A 2D graph SLAM system using TF to close loops is also developed. The global maps we build outperform some popular open-source SLAM solutions, and the system can run up to 2.4 times of real-time in our experiments.
Design of A Prosthetic Hand for Multiple hand Motions*

In this paper, a prosthetic hand which has been considered both of multiple hand motions and lightweight was presented. By integrating different digits functions of stable linkage four fingers and adaptive thumb, the proposed hand is capable to realize 10 types of hand motions based on the frequently-used hand motions. Furthermore, the proposed hand is modeled based on an adult hand size, with three motors embedded in the palm. Finally, the experiment had been conducted to verify the motion function and grasp performance.
Attention Grasping Network: A Real-time Approach to Generating Grasp Synthesis

This paper presents a real-time, pixelwise method to generate grasp synthesis based on fully convolutional netural networks (FCN). Our proposed Attention Grasping Network (AGN) applies a novel attention mechanism to robotic grasp detection, which automatically learns to focus on salient features of the input image. The model with attention mechnisms can compensate for the loss of detail information in standard FCN, which increases the sensitivity of the model and accuracy of prediction. In addition,in order to ensure a real-time grasp and save computing resources, the light-weight AGN model predicts the position and angle of grasping point. Our method only takes 22ms to execute the grasp detection pipeline on a GPU-equipped computer, and achieves 97.8% accuracy on Cornell Grasping Dataset.
Scene Adaptivity Analysis for the Image-Guided Surgical Robots

During the surgical procedure, repeated puncture would bring great damage to the patient and decrease the success rate of surgery. Good scene adaptivity of surgical robots could reduce the possibility of the occurrence of the problem. This paper introduces a method for increasing scene adaptivity preoperatively for image-guided surgical robots. The differential transformation method for getting the Jacobian Matrix is used to decrease the time spent preparing the surgery. The singularity classification method is used to analyzing different types of singular points to spend less time to make the robot move smooth. With the help of the tumor model, the experiment would test the final results. It indicated that, with the method proposed, the robot could smoothly move and accurately arrives at the destination.
An Improvement of The Motion Controllability of Paramecium by The Media Exchange Type Long Electrode Distance Pool

We herein propose a new type of motion control pool for Paramecium, in which an electrical field is applied. In order to prevent a pH change, the culture medium in the electrode area is exchangeable. The experimental area and the electrode area are separated by a membrane filter in order to maintain electrical conductivity and to prevent mixing of the media. Two types of pools were constructed: one in which eight electrodes were fixed (as in our previous studies) and one incorporating four rotatable electrodes in order to improve the angular resolution. The controllability of the Paramecium was much improved, and an average control deviation of 0.149 mm was achieved.
A pipelined Single-Phase Paxos Extension without lease

Variety of single-phase consensus protocols based on Paxos have made significant progress in low latency and high performance. However, these improvements are either limited to specific favorable conditions, such as most commands rarely dependencies on each other, or weaken system availability with bigger fast writes quorums requirement or designated leader. In this paper, Decree Promise Paxos (DP Paxos) is proposed and implemented. DP Paxos provides single-phase fast writes without commands dependency restriction by leveraging a no lease and dynamic Decree Promised leader. The single-phase fast writes only require quorums composed of a majority of legislator nodes, these quorums are the minimum requirement to solve consensus. In most leader and lease system, followers redirect all requests to the leader, leader failure causes the system stuck till the lease expired and new leader is elected. DP Paxos solved the stuck problem by allowing followers proposal commands via two-phase Paxos instances when it receives client requests. Followers and leader may compete to proposal different commands. Normally, too many collisions of competing proposals will result in expensive coordinate cost and poor performance. In our scenario, 3-node cluster provides remarkable available improvement and acceptable cost for large-scale deployment. In DP Paxos, we design a ’reserve’ and ’odd-even gear’ mechanism to reduce collisions, it is very efficient for coordinating two nodes competing proposals in 3-nodes cluster. Generally, each proposer node propose 50 percent of client requests will cause massive collision, but after the DP Paxos optimization, these collisions are decreased to unbelievable rarely. Moreover, a busiest follower proposer substitutes the previous leader in very simple and cheap way, just attaching few bytes of election info within a normal command. The busiest node play the leader role, ensure more than half of client requests are reach consensus in single-phase. Decree Promise Paxos is not only a theoretical algorithm, but also implemented and applied in some online games data cache service of Tencent, where it processes 3.6 billion writes per day.
Automatic Brain Tumor Segmentation Using 3D Architecture Based on ROI Extraction

MRI (magnetic resonance image) analysis is crucial for diagnosis, monitoring, and treatment of brain tumors. Manual MRI segmentation of brain tumors requires professional knowledge and costs huge amount of time. Automatic segmentation for multimodal 3D MR images is thus very desirable for clinical applications. In this paper, we present an automatic brain tumor segmentation method based on the U-Net architecture, which is composed of ROI (region of interest) extraction and 3D segmentation networks with long and short skip connections. The introduced method is able to capture detailed features and improves the problem of the imbalanced classification of tumors’ sub-regions. The method was evaluated on the BRATS 2017 dataset and the results were promising.
An Improved iMemComp OR Gate and its Applications in Logic Circuits

The iMemComp (Intelligent memristive computing) gates are a family of logic gates based on the RRAM (Resistive Random Access Memory) devices. It has potential advantage for the design of high-performance logic circuits, because the iMemComp NAND, AND, NOT, and transmission gates only consume single cycle, respectively. However, the original two-input iMemComp OR gate, which requires three cycles, is a relatively slow gate. It decreases the performance of some logic circuits. This work proposes an improved iMemComp OR gate with only one cycle and three RRAM cells. Both the circuit performance and area consumption of the full-adder and LFSR circuits are improved by the application of the proposed OR gate. Furthermore, we propose a general synthesis method of logic circuits based on the improved logic gate. The synthesis results show that the circuits generated from the proposed synthesis method outperform the previous RRAM based counterparts for most cases of the MCNC benchmark circuits.
A 2.5GHz LTE Doherty Power Amplifier in SOI-CMOS Technology

The need for RF Front-Ends Module (FEM) with reduced size and cost is driving research towards highly integrated Power Amplifiers (PA) with challenging linearity and efficiency requirements. To overcome efficiency challenges, the Doherty PA (DPA) architecture represents an attractive solution because of its high back-off efficiency and relatively simple structure. This paper presents a state-of-the art SOI CMOS DP A design implemented in a 0.13um SOI CMOS industrial process. At 2.5GHz, the 2-stage SOI CMOS DPA achieves a measured peak PAE of 57% at 32.7dBm output power under 3.4V voltage supply. At IdB compression point, output power and PAE are 32.4dBm and 56% respectively. With a 10MHz LTE uplink signal, a linear output power of 29.4dBm is measured with a PAE of 48.8% at an ACLR level of −33dBc without using DPD.
Decoupling of Two Dual-Band Antennas With a Reactively Loaded Dummy Element

A decoupling technique for two closely spaced dual-band antennas using reactively loaded dummy element is presented. A double-T monopole is inserted between the two active radiators as a coupling dummy element. With the corresponding dual-band reactive load being precisely determined, the simulated port isolation can be efficiently improved to more than 30dB at 2.5/5.2GHz. The return losses of the MIMO antenna are better than 25 dB at both frequencies by cascading a three-section dual-band transformer.
Circularly Polarized Leaky-wave Antenna Based on SubstrateIntegrated Waveguide Space Harmonic

A circularly polarized leaky-wave antenna (LWA) is implemented on the substrate integrated waveguide (SIW) based on space harmonic radiation. The LWA cuts the current in two different directions by opening a cross-shaped slot on the substrate integrated waveguide. It realizes circular polarization and frequency sweeping characteristics by adjusting parameters of the slot. The antenna achieves a sweep range of 36°~13° in the θ direction backward radiation by -1 space harmonic from 15GHz to 17GHz. The gain is basically above 13.5dB. Parameters of the cross-shaped slot on the antenna performance are studied. The antenna is fabricated and the measurement results are agree well with the simulation ones.
The Design of Dual-Sense Circularly Polarized Series-Fed Microstrip Patch Array

A dual circularly polarized traveling wave series-fed microstrip patch array working at 24 GHz is proposed. The antenna array consists of eight slotted patches each of which can produce circularly polarized waves. With two ports at both ends of the series-fed array, a dual-sense circularly polarized narrow-beam high-gain antenna is obtained when the dual ports are excited respectively. The simulated results show that the array achieves a good overlap bandwidth of impedance bandwidth (IBW) and axial ratio bandwidth (ARBW) from 23.63 GHz to 25.25 GHz (6.63%). Excellent radiation patterns with low back lobes and cross polarization are obtained over the entire band of operation.
Wideband Multi-layer Frequency Selective Surface Based Transmitarray Unit Cell for Satellite Communication Applications

In this paper, a double square ring with center patch based wideband multilayer frequency selective surface unit cell is designed for transmitarray antenna at 12GHz. The proposed FSS unit cell has high transmittance magnitude and covers the full 360° phase range required for transmitarray design. Effect of increasing the number of FSS layers on transmittance is illustrated using parametric simulations. Combined analysis of multilayer designs over wideband frequency range is carried out. The effect of relative permittivity of substrates is illustrated using FR4 and RT/Duroid 5880. For RT/Duroid based 4-layer FSS unit cell, high transmittance magnitude value above −1dB for 4.7GHz wideband and phase range of 395.6° is achieved. The proposed unit cell will find applications in high gain, wideband transmitarrays for satellite communication applications.
Bolt Detection of Key Component for High-speed Trains Based on Deep Learning

Safety inspection is a crucial task for high-speed trains. Conventional methods based on computer vision are insufficient in achieving this task by using template matching, the main reason is that it is difficult to extract image features, especially for the images of high-speed trains whose image edges are difficult to detect. In this paper, deep learning is introduced to key component detection. Deep learning method is not suitable when samples are rare, and as for our task, there are few abnormal samples because key component rarely become abnormal. However, abnormal detection can be transformed to the problem of bolt detection; it will be abnormal if bolts are lost. From this paper, Faster R-CNN and YOLO are adopted to detect bolts. Considering the purpose of the abnormal detection of bolts is to confirm whether the bolts are lost, and the location information of bolts contained in the extracted information of target detection is redundant. Therefore, a bolt number detection network based on ResNet is proposed to examine how many bolts on the key component. The number of bolts determine whether the bolts are lost. It shows that, this bolt number detection network achieves the task well.
The Stacked Seq2seq-attention Model for Protocol Fuzzing

Fuzzing is an effective approach to discover vulnerabilities in software by generating the amount of unexcepted data as inputs to a program. It is difficult to fuzz the protocol automatically because it is necessary to manually construct a template that satisfies the protocol specification to generate test cases. In this paper, we establish stacked seq2seq-attention models to generate protocol test cases automatically. Seq2seq-attention is a machine learning technique which has an encoder-decoder structure to output text sequences based on context. We evaluate the training effect of seq2seq-attention models with different layers of LSTM and point out that the highest correctness of test cases is achieved by 3 layers LSTM. Besides, we implement a fuzzer based on stacked seq2seq attention model and compare with grammar-based fuzzer, which result indicates the test cases generated by our fuzzer discover more unique basic blocks.