Miracle-dz/UniFAR-FTUs huggingface dataset:
Variable Convolution and Pooling Convolutional Neural Network for Text Sentiment Classification

With the popularity of the internet, the expression of emotions and methods of communication are becoming increasingly abundant, and most of these emotions are transmitted in text form. Text sentiment classification research mainly includes three methods based on sentiment dictionaries, machine learning and deep learning. In recent years, many deep learning-based works have used TextCNN (text convolution neural network) to extract text semantic information for text sentiment analysis. However, TextCNN only considers the length of the sentence when extracting semantic information. It ignores the semantic features between word vectors and only considers the maximum feature value of the feature image in the pooling layer without considering other information. Therefore, in this paper, we propose a convolutional neural network based on multiple convolutions and pooling for text sentiment classification (variable convolution and pooling convolution neural network, VCPCNN). There are three contributions in this paper. First, a multiconvolution and pooling neural network is proposed for the TextCNN network structure. Second, four convolution operations are introduced in the word embedding dimension or direction, which are helpful for mining the local features on the semantic dimensions of word vectors. Finally, average pooling is introduced in the pooling layer, which is beneficial for saving the important feature information of the extracted features. The verification test was carried out on four emotional datasets, including English emotional polarity, Chinese emotional polarity, Chinese subjective and objective emotion and Chinese multicategory. Our apporach is effective in that its result was up to 1.97% higher than that of the TextCNN network.
Design of Non-Adaptive Querying Policies Based on Error Control Coding

This paper designs non-adaptive querying policies (NQPs) for the noisy 20 questions game based on error-correction codes. The querying accuracy of a specific NQP is upper bounded by a function of the minimum distance among its codewords. As a result, the row-column constraint is put on codewords of NQPs for scenarios with limited detection to enlarge their minimum distance for improving the querying accuracy, where the limited detection means that only a small number of intervals can be detected at each querying round. Then, it is used to protect the least significant bits in unequal error protection NQPs with linear codes for unlimited detection scenarios. In particular, these structures allow us to deterministically optimize parameters for better querying accuracy. Simulation results show that our methods achieve quantized mean squared error up to several magnitudes compared with the NQPs based on random block coding.
Whole-Body Motion Planning for Walking Excavators

This article presents a trajectory planning framework for all-terrain vehicles with legs and wheels such as walking excavators. Our formulation takes into account the whole body of the robot while computing the plans for locomotion. Hence, we can produce motion plans over the rough terrain that would be hard to plan without considering all Degrees of Freedom (DoF) simultaneously. Our planner can also optimize over the contact schedule for all limbs, thereby finding the feasible motions even for the infeasible initial contact schedule. Furthermore, we introduce a novel formulation of the support area constraint. We generate plans for a Menzi Muck M545, a 31 DoF walking excavator with five limbs: four wheeled legs and an arm. We show motion plans for traversing a variety of terrains that require whole-body planning. To the best of our knowledge, this is the first work that addresses motion planning in rough terrain for vehicles with legs and wheels.
Generating a Key Pose Sequence Based on Kinematics and Statics Optimization for Manipulating a Heavy Object by a Humanoid Robot

When a humanoid robot manipulates a heavy object, balance and heavy loads become problems. To solve these problems, we provide a method to generate a key pose sequence of the robot and the object which balance constraints and joint torque limits are kept. As we consider the configurations of both the robot and the object, the key poses of them are optimized in a view of kinematics and statics. Moreover, we generate a smooth key pose sequence by adding an objective function which makes adjacent poses closer. By using the proposed method, we make a humanoid robot RHP4B place a heavy suitcase on a step.
GSP Analysis of Brain Imaging Data from Athletes with History of Multiple Concussions

Study of neurological disorders affecting the structure-function relationships in the brain has been an ongoing challenge in neuroscience. Joint analysis of structure and function of the brain may disentangle a number of mechanisms and operations that can help interpret the interdependence between white matter degeneration and degradation of cognitive abilities. In this scenario, graph signal processing analysis of different signals generated within the physical structure of the brain may provide new insights and corroborate existing clinical findings. This paper illustrates the utility of graph signal processing tools in the joint analysis of diffusion and functional magnetic resonance imaging (i.e. dMRI and fMRI) data collected from a population of former athletes with a history of multiple concussions, and healthy subjects. Specifically, the distributions of the energy of low-graph-frequency components of the functional networks (derived from fMRI) are observed to be significantly different for fronto-temporal regions of the brain in athletes and healthy subjects. Furthermore, for the two groups of subjects, we observe significantly different associations between the ages of subjects and the energies of high graph frequency components in lingual region. While the effect on fronto-temporal regions for former athletes is in line with the existing clinical studies on concussion, significantly different associations between age and features extracted using GSP for the two groups of subjects could inform future clinical applications and medical diagnosis.
Low-Frequency Variable Magnetic Field Propagation in Seawater

The problems of underwater communication is discussed in the paper. The alternating magnetic field was chosen for communication in the seawater due to its good penetrating ability in conductive medium. As the transmitting and the receiving elements the loop magnetic antennas (coils of inductance) were used. The comparison of magnetic field propagation in the seawater and in the air was made. It was proved the difference in alternative magnetic field attenuation in mentioned mediums at the frequency in 1 kHz and less was negligible and it was raised with the frequency increasing. At the frequency of alternating magnetic field in 10 kHz the difference in alternating magnetic field attenuation was raised up to 5 dB. Some restrictions of the use of loop magnetic antennas were disc used. Some recommendations to loop magnetic antennas design were made.
DLD: A Deep Learning Based Line Descriptor for Line Feature Matching

In this paper, we present an appearance based line descriptor which was developed with the help of machine learning. Our descriptor uses a ResNet which we modified in its size to improve the performance. We utilized the Unreal Engine and multiple scenes provided for it to create training data. The training was performed using a triplet loss, where the loss of the network is calculated with triplets each consisting of three lines including a matching pair and another non-matching line. During learning, the goal of the minimization function is to calculate descriptors with minimal descriptor distance to matching lines’ descriptors and maximal descriptor distance to other lines’ descriptors. We evaluate the performance of our descriptor on our synthetic datasets, on real-world stereo images from the Middlebury Stereo Dataset and on a benchmark for line segment matching. The results show that in comparison to state-of-the-art line descriptors our method achieves a greatly improved line matching accuracy.
Evaluation of Metabolic Syndrome in Type-2 Diabetes Mellitus and the Role of Gut Microbiota: the microDIAB Study

The increasing societal and economic burden of the metabolic and cardiovascular diseases associated with a high incidence of morbidity and mortality warrants an increased effort to better understand and prevent these conditions. The metabolic syndrome (MetS) is defined by the occurrence of several risk factors including obesity, high blood pressure, dyslipidemia and insulin resistance. The MetS molecular imbalance is based on underlying processes of inflammation, oxidative stress, and adipocyte dysfunction. Several studies have shown that an imbalance in the gut microflora (i.e., dysbiosis) plays a role in insulin resistance, a hallmark of type 2 diabetes mellitus (T2DM). Clinical randomized cohort studies assessing the contribution of gut microbiota in MetS on large number of patients are still scarce and the results inconclusive. Our microDIAB project aims to study the direct effects of specific bacterial strains on improving the severity of MetS in a double blind randomized clinical trial involving T2DM patients from NE part of Romania. The paper depicts the overall approach of our project, presents several preliminary results and future directions.
Energy- and Spectral-Efficiency Tradeoff in Beam Domain Massive MIMO Downlink with Statistical CSIT

We investigate energy- and spectral-efficiency trade-off by optimizing resource efficiency (RE) for single-cell massive multiple-input multiple-output (MIMO) downlink transmission with only statistical channel state information available at the transmitter (CSIT). We first show that beam domain transmission is favorable for RE optimization in massive MIMO downlink, by deriving a closed-form solution for the eigenvectors of the optimal transmit covariance matrices. With this conclusion, the RE optimization precoding design is reduced to a real-valued power allocation problem. Exploiting sequential optimization and random matrix theory, we further propose a low-complexity two-layer water-filling-structured power allocation algorithm. Numerical results demonstrate the effectiveness of our proposed statistical CSI aided RE optimization approach.
Robust Minimum Variance Distortionless Response Beamformer based on Target Activity Detection in Binaural Hearing Aid Applications

In this work, a new beamforming algorithm for binaural hearing aids is introduced, with a good robustness to errors in the estimated target direction of arrival. The proposed design is based on a selection between a monaural MVDR beamformer and a binaural MVDR beamformer at each time-frequency bin. The decision of using either the monaural MVDR or the binaural MVDR at each time-frequency bin depends on a proposed target activity detection system, over an a priori determined target zone. The proposed target activity detection system uses a binaural beamforming method in target-cancelling mode, in order to get an estimate of the dominant speaker direction at each time-frequency bin. Simulations are performed using signals and propagation models obtained from real-life multichannel binaural hearing aids, to evaluate the resulting noise reduction and target distortion. Simulation results show the robustness of the proposed design to errors in the estimated target direction of arrival, in comparison with both the monaural MVDR and binaural MVDR algorithms.
Can a Social Robot Encourage Children’s Self-Study?

This paper presents a robot behavioral model designed to support children during self-study. In particular, we want to investigate how a robot could increase the time children keep concentration. The behavioral model was developed by observing children during self-study and by collecting information from experienced tutors through interviews. After observing the children, we decided to consider three states corresponding to different levels of concentration. The child can be smoothly performing the task (“learning” state), encountering some difficulties (“stuck” state) or distracted (“distracted” state). The behavioral model was designed to increase the time spent concentrating on the task by implementing adequate behaviors for each of these three states. These behaviors were designed using the advices collected during the interview survey of the experienced tutors. A self-study system based on the proposed behavior model was implemented. In this system, a small robot sits on the table and encourages the child during self-study. An operator is in charge of determining the state of the child (Wizard of Oz) and the behavioral model triggers the appropriate behaviors for the different states. To demonstrate the effectiveness of the proposed behavioral model, a user study was conducted: 22 children were asked to solve problems alone and to solve problems with the robot. The children spent significantly (p = 0.024) more time in the “learning” state when studying with the robot.
Design of Dynamic Parameter Testing System for Satellite Power

When the satellite changes its working state, its power output will produce instantaneous jump. Accurate measurement of dynamic parameters of satellite power is the basis to ensure the normal operation of other modules. Aiming at the shortcomings of low automation level, low precision, poor data recording and analysis ability in dynamic parameter testing of satellite power system, a dynamic parameter testing system with high accuracy and automation level is designed, which integrates data acquisition, analysis and calculation, display and storage functions. Solar cell simulator and electronic load are used to simulate the output of solar panels and the power consumption change of loads when satellites are in orbit. With high-speed acquisition card, dynamic characteristic measurement unit is developed. Satellite power system and dynamic parameter measurement system are established and carried out. The test results show the reliability of the system.
Inference of user-intention in remote robot wheelchair assistance using multimodal interfaces

Shared control methodologies have the potential of enabling wheelchair-bound users with limited motor abilities to perform tasks that would usually be beyond their capabilities. Deriving such methodologies in advance is challenging, since they are frequently heavily dependent on unique characteristics of users. Learning Assistance by Demonstration paradigms allow derivation of customized policies by recording how remote human assistants assist particular users. However, for accurate determination of the optimal policies for each user and context, the remote assistant needs to infer the intention of the driver, which is frequently obscured by noisy signals dependent on the user’s motor impairment. In this paper, we propose a multimodal teleoperation interface, incorporating map information, haptic feedback and user eye-gaze data, and examine which of these factors are most important for allowing accurate determination of user intention in a simulated tremor experiment. Our study indicates that, for expert assistants, presence of additional haptic and gaze information increases their ability to accurately infer the user’s intention, providing supporting evidence for the utility of multimodal interfaces in remote assistance scenarios for Learning Assistance by Demonstration. Our study also reveals strong individual preferences on the different modalities, with large variations of performance occurring depending on whether supplemental eye-gaze or haptic information was given.
Edge-oriented Collaborative Caching in Information-Centric Networking

In-network caching is a key feature of information-centric networking (ICN), in which routers take charge of caching passing contents. Such cache design enables efficient content distribution, but the benefit comes at a non-trivial cost, given adding workload to routers. With the emergence of edge computing, more edge devices with large storages are available, which is a good opportunity for new caching design. To this end, we propose a novel Edge-oriented Collaborative Caching (ECC) in ICN. In ECC, edge devices (such as edge server, micro datacenter, etc.) cache contents while routers only maintain cache indexes which are used to redirect subsequent requests towards the cached content. This enables ECC to work in a lightweight and collaborative fashion. We propose an optimization method to properly create cache indexes, considering both content popularity and cache benefit. Moreover, we also present a failure recovery mechanism to ensure system robustness. Simulation results show when deploying the same total cache capacity, ECC outperforms typical ICN caching schemes in terms of response latency, server load and bandwidth consumption of links.
Visual-Inertial On-Board Throw-and-Go Initialization for Micro Air Vehicles

We propose an approach to the throw-and-go (TnG) problem for micro air vehicles (MAVs) using visual and inertial sensors. The key challenge is the fast on-board initialization of the visual odometry (VO) system, which usually requires user input to recover the visual scale. Our approach is based on the identification of the gravity vector from the acceleration data computed with images of the ground during in free fall. This enables scaling of the poses reconstructed with visual information. The proposed framework use inertial data to control the MAV attitude so the ground is visible after the throw. Using image to image homography a metric scale is estimated with which the MAV’s height is propagated. Unlike existing literature, this approach requires no additional sensor nor user input or pre-throw assumptions and can recover from any initial attitude. We show results on both simulation and real data.
Design of a Cable-Driven Auto-Charging Robot for Electric Vehicles

With the increasing popularity of electric vehicles, low level of automation for charging process has become one of the main factors restricting the development of electric vehicles. Recently, auto-charging robots which have the ability to transform manual process plugging charging plugs into charging ports to automatic plugging-unplugging operation have arisen. This paper presents a 4-DOF cable-driven auto-charging robot (CDACR) consisting of a 3-DOF cable-driven serial manipulator (CDSM) and a moving platform. In this design, the 3-DOF CDSM is actuated by six cables being routed through five disks fixed to the CDSM’s rigid links. The end-effector of CDACR is a flexible plug that has the ability to withstand small elastic deformation. The control algorithm and the plugging-unplugging strategy were designed to respond to various parking situations with or without yaw error. This paper takes the lead in introducing the cable-driven robot into the field of automatic charging. Besides, through simulated charging experiments, the feasibility and effectiveness of using CDACR to realize auto-charging for electric vehicles has been demonstrated.
A Method for Guiding a Person Combining Robot Movement and Projection

This paper proposes a new method to guide a person along with a route when a mobile robot explains an exhibition. In previous work, there were problems that people overtake the robot and go out of the guide route, and that people move to positions that interferes with the movement of the robot. In response to these problems, a conventional solution is to frequently instruct people to move by using voice, displays, etc., and it was not a comfortable for people who are guided. In this paper, we focus on a mobile robot with a projection function and propose a method of controlling human positions without explicit instructions by combining movement and projection of a robot. We introduce three basic guiding behaviors combining projection and movement, each controlling a person and a robot into different positional relationship according to the situation of guiding. Experiments of basic guiding behaviors showed that both the existence of the robot and the position of the projected image are combined to affect the movement of the guided person. The result indicates that by selecting appropriate guide behavior according to the situation of guiding, the robot can guide a person effectively while controlling the position of the person.
Enabling Human-Like Task Identification From Natural Conversation

A robot as a coworker or a cohabitant is becoming mainstream day-by-day with the development of low-cost sophisticated hardware. However, an accompanying software stack that can aid the usability of the robotic hardware remains the bottleneck of the process, especially if the robot is not dedicated to a single job. Programming a multi-purpose robot requires an on the fly mission scheduling capability that involves task identification and plan generation. The problem dimension increases if the robot accepts tasks from a human in natural language. Though recent advances in NLP and planner development can solve a variety of complex problems, their amalgamation for a dynamic robotic task handler is used in a limited scope. Specifically, the problem of formulating a planning problem from natural language instructions is not studied in details. In this work, we provide a non-trivial method to combine an NLP engine and a planner such that a robot can successfully identify tasks and all the relevant parameters and generate an accurate plan for the task. Additionally, some mechanism is required to resolve the ambiguity or missing pieces of information in natural language instruction. Thus, we also develop a dialogue strategy that aims to gather additional information with minimal question-answer iterations and only when it is necessary. This work makes a significant stride towards enabling a human-like task understanding capability in a robot.
Assistive Rehabilitation Using a 7-DoF Robotic Arm with Self-Collision and Obstacle Avoidance System

With the help of Robot Operating System (ROS) running on ubuntu 16 environment we have managed to control a seven-degrees-of-freedom (7-DOF) robotic arm developed with dynamixel servos on a Pioneer P3-DX robotic platform. The developed software is able to control any dynamixel-based arm out of the box and can be adapted to other actuators as well in a few easy steps. Thanks to the abstraction power of ROS-based systems, the arm can be easily adapted to do new tasks. The main features of the developed software are: direct kinematics, inverse kinematics, and collision and obstacle avoidance planner. Is this versatility in controlling the arm that makes it for a good candidate regarding prosthetic or exoskeleton arm. These applications can serve to help people in regaining their mobility and in rehabilitation process. For example, a myoelectric control can be achieved easily by using direct kinematics after the message of the muscles was decrypted, a brain-to-machine interface can be used together with the planner responsible with inverse kinematics and collision/obstacle avoidance to control the end-point of the effector while keeping the user and the arm safe.