Miracle-dz/UniFAR-FTUs huggingface dataset:
Improving the Attribute-Based Active Learning by Clustering the New Items

The issue that recommender system often meets is cold-start problem, where the system does not have any ratings of new items or new users. Thus, it can not provide relevant recommendation for the new users or new items. In previous research, when dealing with item cold-start problem, some scientists combined content information and active learning method, and used factorization machine to model the prediction task. However, a shortcoming in this method is that when using factorization machine model to select users to give ratings to new items, the active users may be selected for too many times, leading to a result that they refuse to give ratings for new items, or randomly give their ratings, which does not exactly show their preferences. In this paper, to solve this issue, we use clustering algorithm to divide new items into different groups and choose one item to represent the group, and only request users giving ratings for the representative items
Design of 3-D Magnetic Field Sensor and Calibration Platform for TMS

In order to build an accurate measurement structure for transcranial magnetic stimulation (TMS) pre-testing, a kind of three - dimensional (3-D) magnetic field sensor and calibration platform are designed in this paper. This measurement structure is made up of 883-D magnetic field sensors. The 3-D magnetic field sensors based on Faraday law of electromagnetic induction are used to accurately measure pulsed magnetic fields generated by the stimulating coil. The coefficients of the sensors are calibrated in a designed 3-D calibration platform. And a series of experiments are carried out to verify the accuracy and stability of the sensors. The results from experiments indicate that the average measurement accuracy of 88 sensors is 1% and sensor performance is stable. In conclusion, this work provides a promising way to avoid direct human experiments and improve the security of TMS.
Modeling mmWave Channels in High-Fidelity Simulations of Unmanned Aerial Systems

As the capabilities of Unmanned Aerial Systems (UASs) evolve, their novel and demanding applications emerge, which require improved capacity and reduced latency. Millimeter-wave (mmWave) connections are particularly attractive for UASs due to their predominantly line-of-sight regime and better signal locality. In this context, understanding the interactions between the environment, the flight dynamics, and the beam tracking capabilities is a challenge that has not been resolved by today's simulation environments. In this work, we develop the means to model these crucial considerations as well as provide the initial insights into the performance of mmWave-based UAS communications made available with the use of our proposed platform.
Display of Haptic Shape Using Ultrasound Pressure Distribution Forming Cross-Sectional Shape

In this paper, we propose a method for expressing the shape of a virtual object using ultrasound tactile presentation. In this method, the cross-sectional shape of a finger penetrating the virtual object is estimated from a point cloud acquired by a depth sensor. An ultrasound focus is moved around the cross-sectional shape at a high speed, which enables the generation of a pressure distribution on the finger corresponding to the position of the object surface. This method can be applied to any 3D polygon mesh model, and the local shape of the model can be recognized from the change in the pressure distribution when the finger is inserted into it. We conducted a numerical simulation and a user study to verify the effectiveness of this method for object shape recognition.
Mathematical Model of Equivalent Stress Value Dependence from Displacement of RF MEMS Membrane

In the paper, a mathematical model of equivalent stress value dependence from displacement of RF MEMS membrane is developed. For this the influence of displacement of RF MEMS membrane on equivalent stress value is simulated. According to the simulation results, a mathematical model describing this parameters dependence is developed. The assessment of model is presented.
Accurate Fiber Alignment using Silicon Photodiode on Grating Coupler for Wafer-Level Testing

A new fiber alignment method for wafer-level testing of silicon photonics devices achieves a high signal-to-noise (S/N) ratio. Using the response from a Si photodiode fabricated directly on a grating coupler and operating it in the avalanche region, we achieved over 4-dB improvement in the S/N ratio for fiber alignment.
Distributed Nash Equilibrium Searching via Fixed-Time Consensus-Based Algorithms

In this paper, distributed algorithms are designed to search the Nash equilibrium (NE) for an $N$-player game in continuous-time. The agents are not assumed to have direct access of other agents' states, and instead, they estimate other agents' states by communicating with their neighbours. Advanced consensus algorithms are implemented for such purposes, and consequently the game is decentralised into $N$ subsystems interacting over a communication network. It is proved that, for any communication network with a connected graph, a fixed-time consensus is achieved, independent of the initial conditions, based on which an NE can be obtained asymptotically by the gradient descent term for the fixed-time consensus-based algorithm. Then, the results are extended to a fixed-time NE seeking with modifications of the gradient terms, where both the consensus and the optimisation can be obtained in fixed time, and the upper bound of the settling time is established by the Lyapunov theory. A simulation example is presented to verify the effectiveness of the theoretical development, where some comparisons with other works are studied to demonstrate the advantages of the proposed algorithms.
Collision-free Formation Control for Quadrotor Networks Based on Distributed Quadratic Programs

This paper studies a distributed collision-free formation control problem for networked quadrotors. The quadrotor dynamics is first derived and strictly linearized. Then, the quadrotor network consisting of multiple quadrotors with the dynamics and interconnection topology between them is defined, and a consensus-based formation control law is introduced as a nominal controller. Input conditions for collision avoidance are next derived based on control barrier functions, and as the main contribution, the conditions are modified to be distributed while explicitly considering the quadrotor dynamics. This distribution enables each quadrotor to calculate the actual control input by distributed quadratic programs. This paper also presents an advanced collision avoidance method for severe situations. The effectiveness of the present controllers is demonstrated via simulation.
Particle Filter Algorithm for Underwater Acoustic Source DOA Tracking With Co-Prime Array

In this paper, the problem of direction of arrival (DOA) tracking based on the co-prime array is studied. Owing to low signal-to-noise ratio (SNR) in underwater environment, making estimating DOA of a moving source become a challenging problem. In this work, a framework of particle filter (PF) with sequential importance resampling (SIR) is developed to address this problem. The involved particles are generated from the uniform motion model, and then weighted by their measurement likelihood function. By vectorizing the autocorrelation matrix of the output data from the co-prime array, a virtual uniform linear array with extended array aperture can be obtained. Applying spatial smoothing technique to the au-correlation matrix of the virtual uniform linear array, the MUSIC pseudo-spectrum can be formulated and then used as the likelihood function. Due to the enlarged array aperture and the super resolution property of MUSIC, the involved particles can be updated more appropriately. Simulation studies are presented to verify the validity of the proposed method.
Experiments with Recognition of Melody Similarities Based on Human Independent Parameters

The problem of the automatic recognition of melody similarities based on human independent parameters is considered in the paper. Several parameters expressing mathematical entities relating mainly to differences between the pitches of melodies were proposed. These parameters do not reflect human feelings, they are of a mathematical character only. In the described research, a special data set with a number of different artificial modifications of master melodies was created to test crucial classification algorithms included in the Orange data mining software. The results showed that classifiers build for the automatic recognition of melody similarities can be trained on data sets including values of defined parameters independent of human factors.
An Efficient Robotic Grasping Pipeline Base on Fully Convolutional Neural Network

In this paper, we present a pipeline for grasping unknown objects based on RGB-D images from a stereo camera mounted on the wrist of the robot arm. The proposed grasping pipeline composes of a fully convolutional neural network (FCNN) and a Simplified Grasp Pose Detection (S-GPD). The FCNN is used to generate grasping candidates accurately from a four channel image synthesized by RGB and depth image, and the S-GPD method is responsible for scoring the candidates in the point cloud. To evaluate the performance of our proposed grasping pipeline, we demonstrate the proposed pipeline in Kinova Mico2 robotic arm to execute grasping tasks for a single and multiple objects. The experimental results show that our grasp pipeline reaches a grasping success rate of 85.8% for a single object and 84.3% for multi-objects. Furthermore, the proposed pipeline achieves a balance between effectiveness and efficiency compared with other advanced grasping methods.
An Automatic Semantic Code Repair Service Based on Deep Learning for Programs with Single Error

Semantic code repair refers to automatically fix bugs where the actual program code compiles and executes successfully but fail to generate the output the programmer intends. This problem has not been solved very well so far. In this paper, we present a semantic code repair service using a deep attentional sequence-to-sequence model to predict related information about bugs and generate potential fixes without running the program actually. We evaluate the real performance of the semantic code repair service, and verify the feasibility and effectiveness of the service.
A new type of a large-area multi-touch tactile device operated by electrotactile stimulation

A new type of a large-area multi-touch tactile device operated by electrotactile stimulation is proposed. Being able to stimulate as many types of mechanoreceptors as possible, the use of electrical stimulation is widespread. However, the area of a device is usually limited because the more electrodes a device is composed of, the more difficult wires are placed if we want to deliver different signals to different regions of one touch panel simultaneously. To get closer to the possibility of integrating multi-touch tactile devices operated by electrotactile stimulation into smartphones or other flat-panel displays, this paper focuses on developing a large-area electrotactile display device driven by electrotactile stimulation that can fully cover smartphones, which has the advantages of larger touched area and better resolution comparing to traditional ones. It is also capable of delivering different stimulation simultaneously on the same surface. The new type of touch panel with cathodes-surrounding anodes taking responsibility for delivering signals and cathodes playing the role of switches to divide signals.
Multiagent Autonomous Learning for Distributed Channel Allocation in Wireless Networks

In distributed networks such as ad-hoc and device-to-device (D2D) networks, no base station exists and conveying global channel state information (CSI) between users is costly or simply impractical. When the CSI is time-varying and unknown to the users, the users face the challenge of both learning the channel statistics online and converging to good channel allocation. This introduces a multi-armed bandit (MAB) scenario with multiple decision makers. If two or more users choose the same channel, a collision occurs and they all receive zero reward. We propose a distributed channel allocation algorithm in which each user converges to the optimal allocation while achieving an order optimal regret of $O$ (log T), where $T$ denotes the length of time horizon. The algorithm is based on a carrier sensing multiple access (CSMA) implementation of the distributed auction algorithm. It does not require any exchange of information between users. Users need only to observe a single channel at a time and sense if there is a transmission on that channel, without decoding the transmissions or identifying the transmitting users. We compare the performance of the proposed algorithm with the state-of-the-art scheme using simulations of realistic long term evolution (LTE) channels.
An improved Power Quality Control Method of Micro-grid Converter based on Equivalent Impedance Calculation

The Power quality of micro-grid converter is related to its output impedance characteristic. In order to improve the micro-grid converter power quality, this paper established an equivalent circuit of micro-grid converter based on droop control, and also given a kind of micro-grid converter equivalent impedance calculated method. According to the equivalent circuit of micro-grid converter, the effect of equivalent impedance of micro-grid converter on its output harmonic current is calculated, and also so do the analysis about the effect of output impedance on output harmonic voltage. Based on all the analysis result, this paper proposes a new control method to improve the power quality of micro-grid convertor. Finally, the control strategy proposed in this paper is verified by experiments. The experimental results show that the control strategy proposed in this paper is effective and feasible.
Vibrotactile Signal Compression Based on Sparse Linear Prediction and Human Tactile Sensitivity Function

In this work, we present a novel vibrotactile coding scheme that encompasses a sparse linear predictor and a perceptual compressor. The predictor introduces a sparsity constraint both on the prediction coefficients and the residual. The prediction residual is then filtered and quantized using a human tactile sensitivity function generated from the vibrotactile detection threshold-frequency characteristics. Furthermore, a novel objective quality assessment method (ST-SIM) for vibrotactile signals that embraces perceptual spectral and temporal similarity measures is developed. ST-SIM is then used to evaluate and validate the overall signal quality and proposed compression scheme performance using different vibrotactile signal contents.
Structural analysis of Cellulose Fibers and Rice Straw Ash derived from Sri Lankan Rice Straw

Being an agricultural country, Sri Lanka generates over eight million metric tons of rice straw per annum as a by-product of the paddy cultivation. Currently rice straw is burnt or ploughed into the field without being converted into a productive output. One such output can be isolating the cellulose fibers from this under-utilized waste material. In this study, cellulose fibers were extracted from rice straw via a three step chemical purification method which yielded $33.4 \pm 0.52$ percent. The structure, chemical composition, morphology and thermal properties of rice straw, cellulose and rice straw ash were investigated using laser diffracted particle size analyzer, Fourier transform infrared (FTIR) spectroscopy, X-ray diffractometer, scanning electron microscopy (SEM), and thermos gravimetric analysis (TGA). FTIR results and SEM images show the removal of lignin and hemicellulose and silica at each step of the isolation process. XRD results exhibit the increase in crystallinity of the fibers. This study reveals that thermal and structural properties have increased after the chemical isolation and purification treatment.
Relative Constrained SLAM for Robot Navigation

This paper presents a relative-constrained SLAM formulation where partial a priori landmark information is built into the SLAM problem. Incorporating a priori relative constraints is motivated by the desire to avoid drawbacks of global constraints and to reduce uncertainty in the overall map and pose estimates. First, a Relative Deterministic-Constrained SLAM (RDC-SLAM) method is presented, where a Lagrange multiplier term is added to the cost function of the standard graph-based SLAM method, realizing a new deterministic-constrained least squares solution. Next, this method is extended to incorporate probabilistic constraints and is solved using chance-constrained optimization for a more robust least square solution, leading to Relative Probabilistic-Constrained SLAM (RPC-SLAM). Both RDC-SLAM and RPC-SLAM are tested within a Monte-Carlo framework using a 2D dataset. It is shown that the RPC-SLAM framework outperforms the other methods considered when landmark initialization is poor.
The Visual Representation of Abstract Verbs: Merging Verb Classification with Iconicity in Sign Language

Theories like the picture superiority effect state that the visual modality has substantial advantage over the other human senses. This makes visual information vital in the acquisition of knowledge, such as in the learning of a language. Words can be graphically represented to illustrate the meaning of a message and facilitate its understanding. This method, however, becomes a limitation in the case of abstract words, like accept, belong, integrate and agree, which have no visual referent. The current research turns to sign languages to explore the common semantic elements that link words to each other. Such visual languages have been found to reveal enlightening patterns across signs of similar meanings, pointing towards the possibility of creating clusters of iconic meanings along with their respective graphic representation. By using sign language insight and VerbNet's organisation of verb predicates, this study presents a novel organisation of 506 English abstract verbs classified by visual shape. Graphic animation was used to visually represent the 20 classes of abstract verbs developed. To build confidence on the resulting product, which can be accessed on www.vroav. online, an online survey was created to achieve judgements on the visuals' representativeness. Considerable agreement between participants was found, suggesting a positive way forward for this work, which may be developed as a language learning aid in educational contexts or as a multimodal language comprehension tool for digital text.
An Online Multi-lidar Dynamic Occupancy Mapping Method

Occupancy maps offer a useful representation of the environment for applications in automated driving and robotics. If created from 3D lidar scanners, they can be very accurate. Traditional static occupancy mapping methods are not able to represent a changing environment. In this paper, we present a novel dynamic occupancy mapping (DOM) algorithm. Inspired by the phase congruency idea from computer vision, it has an intuitive formulation and yet is effective in practice. In addition, our framework provides solutions to several common challenges during occupancy mapping, such as multi-lidar fusion and ground estimation. Finally, we use several experiments to quantitatively evaluate the quality of DOM's output and our algorithm runs in real-time (10Hz).
A Lidar-based Dual-level Virtual Lanes Construction and Anticipation of Specific Road Infrastructure Events for Autonomous Driving

Autonomous vehicles require clear road markings and a high-level quality of infrastructure. This paper addresses road course detection problem in non cooperative environments (i.e. absence or poor quality of road-marking, working zones, etc.). To cope with visual lane detection challenges in these difficult scenarios, we propose a virtual lane generation system to provide a comfortable and safe ride. Based on Lidar sensor, the dual-level virtual lane system consists of the combination of two blocks: the first constructs virtual lanes based on independent road-borders detection, while the second level uses dynamic objects detection and their trajectories in order to estimate the lane parameters. Furthermore, the system is able to anticipate road infrastructures thanks to the independent detection of road borders. Thus we are able to manage difficult use cases such as bifurcations and exit lanes without cartography. The performance is tested through extensive experiments with Cruise4U Valeo self-driving cars on highway and beltway roads. Experimental results demonstrate the accurate and robust performance of the proposed system.
Implementation of Diabetic Retinopathy Prediction System using Data Mining

Diabetic retinopathy (DR) is the most common cause of newly diagnosed blindness every year. Annual eye checking for diabetic patients are suggested in order to find and treat DR in a timely manner, since blindness from this condition is preventable with early identification. DR detection is solely based on existing patient records. Now a day’s medical data growing tremendously and we need to process that data for detection. But it is time consuming hence data mining techniques helps to get rid from this issue. We use neural network (NN) and naïve bayes for classification. According to comparison results NN gives better accuracy than naïve bayes and time and memory required for NN is less as compared to naïve bayes.
Addressing Inherent Uncertainty: Risk-Sensitive Behavior Generation for Automated Driving using Distributional Reinforcement Learning

For highly automated driving above SAE level 3, behavior generation algorithms must reliably consider the inherent uncertainties of the traffic environment, e.g. arising from the variety of human driving styles. Such uncertainties can generate ambiguous decisions, requiring the algorithm to appropriately balance low-probability hazardous events, e.g. collisions, and high-probability beneficial events, e.g. quickly crossing the intersection. State-of-the-art behavior generation algorithms lack a distributional treatment of decision outcome. This impedes a proper risk evaluation in ambiguous situations, often encouraging either unsafe or conservative behavior. Thus, we propose a two-step approach for risk-sensitive behavior generation combining offline distribution learning with online risk assessment. Specifically, we first learn an optimal policy in an uncertain environment with Deep Distributional Reinforcement Learning. During execution, the optimal risk-sensitive action is selected by applying established risk criteria, such as the Conditional Value at Risk, to the learned state-action return distributions. In intersection crossing scenarios, we evaluate different risk criteria and demonstrate that our approach increases safety, while maintaining an active driving style. Our approach shall encourage further studies about the benefits of risk-sensitive approaches for self-driving vehicles.
High Performance PAM Transmission Aided by Polar Code

A polar coded PAM system is experimentally demonstrated over 10-km transmission in C-band. Results show that 30.9 dB and 23.7 dB optical budgets are respectively achieved for 28-Gbaud PAM-4 and PAM-8 using 1 0-GHz DML.