Miracle-dz/UniFAR-FTUs huggingface dataset:
Optimized Algorithm for Skyline Community Discovery in Multi-Valued Networks

The community discovery problem aims to find the close subgraphs from the traditional network. The traditional network with multiple numerical attributes of nodes is called the multi-valued network. The Skyline Community is the largest connected k-core that is not dominated in a multi-valued network, which can be used to solve multi-objective decision-making problems. In this paper, we focus on the skyline community discovery problem. Firstly, we propose a combined index that includes the degree-neighbor (D-N) index and inverted index to manage the multi-valued network. The index can filter the nodes whose degrees do not satisfy ${k}$ . Simultaneously, it can accelerate the judgment of the neighbor relationship between the nodes and improve the query efficiency. Then, we propose the SCDCI (Skyline Community Discovery Algorithm Based on Combined Index Structure) algorithm to compute the skyline community. The algorithm scans the nodes in turn from the inverted indexes, avoiding lots of redundant calculations. The nodes are scanned in descending order by their attributes, therefore, lots of nodes that cannot form a skyline community do not be processed. Finally, the effectiveness of SCDCI algorithm is verified through experiments. Compared with the existing algorithms, the efficiency of our algorithm is improved a lot.
A Highly-Integrated Reconfigurable Ka-Band Receiver Supporting Active and Passive Detections in a 90-nm CMOS Technology

A highly-integrated reconfigurable Ka-band receiver in a 90-nm CMOS technology is proposed for the applications of satellite wireless communications, remote sensing of atmospheric conditions, and detection of space debris. The proposed receiver can be reconfigured to support active-receiver and passive-receiver modes. At the active-detection mode, it works as a direct-conversion coherent receiver for high-speed and global-coverage satellite communications. Measured conversion gain of 45.2 dB, noise figure of 7.6 dB, and output 1-dB compression point of −6.8 dBm can be acquired at 35 GHz while only consuming 54.4 mW from a 1.2-V supply. As switched to the passive-detection mode, it can potentially operate as a non-coherent radiometer to sense atmospheric conditions which are employed to dynamically compensate the atmospheric attenuation to mitigate the fading effects. This enables the satellite communication systems to establish reliable links. Moreover, the proposed passive receiver can be utilized to detect the space debris. Collisions can be avoided and hence satellite lifetime can be enhanced. Experimental results show that the proposed receiver at the passive-detection mode exhibits voltage responsivity of 1.2 GV/W and noise equivalent power (NEP) of 2.1 aW/Hz0.5 at 35 GHz. The power dissipation is only 49.6 mW from a supply voltage of 1.2 V. To the best of the authors’ knowledge, the proposed receiver owns the highest responsivity and the lowest NEP at Ka-band reported thus far. It is also the first receiver capable of being reconfigured to support active-detection and passive-detection modes according to different application scenarios.
Adversarial Signal Augmentation for CNN-LSTM to Classify Impact Noise in Automobiles

The classification of impact noise on vehicle steering gear mainly addresses the issue of modeling the transient and impulsive signals. In particular, variations between the steering systems arising from the differences in manufacturing processes according to the vehicle types extremely limit the conventional deep acoustic models. Focusing on the fact that the major hurdles addressed can be mitigated by generating and modeling the virtual impact noise, we propose an adversarial signal augmentation method for the vehicle noise modeling. The impact noise is represented based on the Fourier transform and the variance between vehicle types is alleviated using a generative adversarial network with an auxiliary classifier in order to improve the generalization performance of the model. Experiments with the dataset of 134,400,000 time-series collected from a global motor corporation show that the proposed method has more than 3% of accuracy improvement against the conventional approaches.
Immunosensor based on MWNT and Au Nanoparticles for detection of 17ß- estradiol in pg/mL

Endocrine Disturbing Chemicals (EDC's) are rising class of environmental pollutants that disturb the human body functions. One of the main EDC's is the estrogen and estrogen-like chemicals especially the 178-estradiol (E2). Elevated levels of E2 may develop severe effects on humans and the wild life even in Pico molar level. Therefore, there is an increased demand to sensitively, precisely and affordably detect E2 in an easy and compatible with Point-Of-Care (POC) devices. In this work, sensitive electrochemical immunosensor was developed in pg/ml range with LOD = 1 pg/mL. The screen-printed working electrode surface of the electrochemical sensor was modified with carboxylated Multi-walled carbon nanotubes (MWCNTs) and gold nanoparticles (AuNPs) to amplify the electrical signal by simple drop casting in layer by layer manner. The anti-17ß-estradiol antibody, supplied as ascites fluid, dilution was optimized to achieve higher sensitivity using covalent immobilization with the carboxyl group of the MWCNTs. The electrochemical characterization of the sensor has revealed that the sensor is able to linearly detect 17ß-E2 in the pg/ml range from 1 – 1000 pg/mL. The prepared sensor is simply fabricated, has low cost, sensitive and compatible with point-of-care devices.
Vmin Prediction for Negative Capacitance MOSFET based SRAM

In this work we show that SRAM cells designed using negative capacitance (NC) based MOSFETs (NCFETs) can offer improved Vmin (minimum supply voltage needed for reliable operation) compared to conventional MOSFET based SRAM. Increased noise margins and reduced variability in NCFETs lead to this lowering of Vmin. For this work we use a modified version of the standard BSIM4 compact model for bulk MOSFETs by coupling it with the Landau-Khalatnikov model of ferroelectrics in a self-consistent manner, while the statistical variability information of the reference device is extracted from 3D statistical TCAD simulations.
Cognitive Load Estimation From Speech Commands to Simulated Aircraft

This paper investigates and compares methods for cognitive load (CL) estimation from speech. The majority of previous studies of CL estimation used speech collected in laboratory conditions and conventional speech classification methods. Traditionally laboratory speech contains balanced classes that are labeled by a third party after the speech has been collected. In contrast, the speech used in this research was recorded during an experiment focused on human-machine interaction - where spoken commands were used to control simulated aircraft. The speech was labeled using subjective assessments of CL during an experiment that manipulated workload. Current state-of-the-art Convolutional Neural Network (CNN) classification was used for cognitive load estimation and was compared with conventional Support Vector Machine (SVM) and k-Nearest Neighbor (k-NN) classification. Different speaker-dependence models were compared across 2 and 3 classes. In addition, class boundary selection was optimized to reflect the subjective human workload response sigmoidal curve and compared with linear class boundaries. Results for 3-class CL estimation showed that CNN classifiers trained using speech spectrograms for Partially Speaker Dependent (PSD) models using sigmoidal curve class boundaries provided up to 83.7% accuracy. CNN classifiers outperformed baseline SVM and k-NN classifiers (that used acoustic features) on the same dataset by 13.2% and 10.5% respectively. These outcomes indicate that spectrogram-trained CNN classifiers are a worthy consideration in paralinguistic classification problems.
Pattern-Wise Embedding System for Scalable Time-series Database

The neural network has proved to solve a wide range of problem. However, it is difficult to adjust the model parameter in a time-varying natural language change. Therefore, this paper proposes the pattern-wise embedding system to address this problem. The proposed approach adopts the online learning with general representation and the temporary online forecasting. To obtain proper time-invariant information, the embedding vector is extracted. Using random sampled embedding vectors, the proposed framework adapts general representations. The proposed system applies temporary weight to increase performance of the nearest task. Experiments on real-world datasets show the effectiveness of our proposed framework.
Resource Allocation for Energy-Efficient NOMA System in Coordinated Multi-Point Networks

This paper studies user scheduling and power allocation problem to maximize the energy efficiency (EE) for non-orthogonal multiple access (NOMA) in downlink Coordinated Multi-Point networks. In the proposed framework, a more practical scenario the imperfect channel state information, imperfect successive interference cancellation and data outage are investigated. To address the considered problem, the optimization problem is formulated constrained by the total power and the outage probability requirements. However, the EE objective function is with a non-convex structure. Accordingly, we first convert the optimization problem to make the objective function concave and analytically tractable. Furthermore, we split the joint optimization problem to find a suboptimal solutions to the original problem. As a result, we first propose a suboptimal user-scheduling algorithm to improve the system's EE. Due to the non-convex function of the transmit power, we invoke a sequential successive convex approach to address the non-convex problem by its lower bound concave function. Besides, the fractional objective function is converted to its equivalent subtractive form. Finally, we derive a power control scheme to address the proposed framework. Simulation results endorse the effectiveness of the proposed algorithm and their performance gains in terms of EE compared to both NOMA and OFDMA variants.
Investigation of Time-Dependent VTH Instability Under Reverse-bias Stress in Schottky Gate p-GaN HEMT

This article proposes a model for threshold voltage (VTH) instability induced by reverse-bias stress (i.e. drain-to-source voltage at off-state) in Schottky gate p-GaN high electron mobility transistors (HEMTs). It is suggested that two different mechanisms would take place during the reverse-bias stress. At the beginning of the stress, a sudden VTH shift is observed, which is recognized to be dominated by hole-deficiency in the further experiments. While the gradually-increased VTH shift during the long-term stress could be mainly induced by electron trapping in both barrier and buffer layers.
A High-precision Method of Flight Arrival Time Estimation based on XGBoost

Aircraft’s estimated time of arrival is an important issue in flight operation, many methods were used to get a higher accuracy. Deep learning methods can offer great favor to improve it. In this paper, a high-precision method of flight arrival time estimation based on XGBoost regression. First, Historical data should be processed by using correlation coefficient analysis of the data features, and features that are highly correlated with the flight arrival time are determined; then based on a large number of historical flight operation data, the selected features of historical data are input into the training based on XGBoost regression to build a flight arrival time prediction model. And finally input the target flight’s real-time information of the selected features to get the remaining flight time of the flight. At the end of this paper, an experiment was given to analyze the accuracy of the prediction model. With the analysis of experiment’s results, the new method this paper proposed shows a good performance.
Generate Individually Optimized Blendshapes

Blendshape based animation is a technique commonly used to animate the face. To generate realistic animation for different faces, creating appropriate blendshapes for each different face is essential. There have been many attempts to find the production-level blend shapes. However, many existing methods mostly require a professional artist’s intuition with manual intervention. In this paper, we present a novel approach to automatically generate individually optimized blendshapes from real-time captured facial expressions. The proposed method generates the blendshape from the captured face with two methods: linear regression and an autoencoder. Among results from two methods, we select the trained result that is more similar to the original face. The adopted blendshape could be used to animate the original face more naturally. In addition, the generated blendshape is utilized to retarget the original face animation to another face while preserving the original face’s animation characteristic. Comparison of results by animating the face on the screen show linear regression is suitable for retargeting the facial expressions without using the complicated neural networks.
Robots Asking for Favors: The Effects of Directness and Familiarity on Persuasive HRI

As robots work in increasingly collaborative settings with humans, scenarios will arise where robots need to successfully request favors to be effective. Communication directness and familiarity have been shown to be important factors to persuasion. However, these two critical factors have not yet been jointly investigated in human-robot interaction (HRI). This letter explores how they can be used by social robots to request favors from people. We present a social HRI study that uniquely investigates the effects of a robot's communication directness and its familiarity on persuasiveness, trustworthiness, and a person's willingness to help the robot. In the study, we present participants with scenarios where two different robots (one familiar, one unfamiliar) ask participants for favors using either direct or indirect requests. Our results show that a familiar robot is more persuasive, trustworthy, and people are more willing to help it. Furthermore, indirect requests are perceived to be more trustworthy and encourage greater willingness to help than direct requests, regardless of the robot's familiarity. Further discussions of these results highlight key considerations for collaborative social robots, particularly when robots request assistance or favors from people.
Method of Constructing a Visualization of Threat Model of Information Security

One of the most pressing tasks in the field of information security (IS) is the reduction of data processing time. Accurate and detailed data visualization optimizes the analysis process. The article proposes a method for visualizing the IS threat model, which reduce the amount of time to process the IS threat model. The classic way to display the IS threat model is a table. Processing the classical model of IS threats is quite laborious and takes a long time for IS analysts. In addition, there remains the risk of missing part of the information due to the large amount of data. The visualization method proposed in the article includes a two- and three-dimensional representation of the threat model. Such visualization reduces the data processing time for the analyst and can be applied in the field of information security management. The result of this work is an approach to visualization of the IS threat model that takes into account the spatio-temporal factor.
Performance Analysis of Phase-Locked Loop Based on Third-Order Generalized Integrator under Inter-harmonic Perturbation

With the increasing capacity of renewable energy, the accurate extraction of grid phase is important to ensure the stable operation for power electronic devices with Phase-locked Loop (PLL). Given inter-harmonic perturbation imposed on grid voltage at the Point of Common Coupling (PCC), deviation will be induced into the PLL. This paper applies Third-Order General-Integrator (TOGI) as the pre-filter in PLL, which could extract the fundamental positive sequence component under the perturbation. To analyse the TOGI-PLL performance, firstly, the PLL linearized model is given and the small-signal model of PLL is derived by harmonic linearization. The expression of TOGI is presented in s domain and in time domain, also the practical transfer function based on the TOGI structure is proposed. With the consideration of PLL nature, the performance of TOGI-PLL is analyzed theoretically. Experiment based on RT-LAB platform confirmed the analysis.
Robotically Surgical Vessel Localization Using Robust Hybrid Video Motion Magnification

Vessel and neurovascular bundle localization plays an essential role in endoscopic and robotic surgery. It still remains challenging to spare vessels and neurovascular bundles to avoid inadvertent injury due to limited visual and tactile perception of surgeons. This work assumes that surgeons have great difficulty in intuitively perceiving small pulsatile motion of vessels and neurovascular bundles from complex surgical field provided by endoscopic videos, and proposes a new surgical video pulsatile motion magnification method to help surgeons easily and precisely recognize vessels or neurovascular bundles by their visual systems. The new method consists of robust hybrid temporal filtering and deeply learned spatial decomposition. The proposed hybrid temporal filtering can significantly magnify pulsatile motion more consistent with reality and simultaneously keep non-pulsating regions in magnified videos almost identical to original videos, and learning-based spatial decomposition can reduce noise and ring artifacts in magnified videos. We evaluate our method on surgical videos acquired from robotic prostatectomy, with the experimental results showing that our method essentially outperforms current motion magnification approaches. In particular, visual quality and quantitative assessment of our method are certainly better than these methods.
Early defoliation (hand vs mechanical) for improved crop control and grape composition in Sangiovese (Vitis vinifera L.)

Early Defoliation (Hand vs Mechanical) for Improved Crop Control and GrapeComposition in Sangiovese (Vitis vinifera L.) By: C. Intrieri, I. Filippetti, M. Centinari, and S. Poni In: Australian Journal of Grape and Wine Research. 14(1):25-32. 2008 Early leaf removal caused a significant reduction in fruitset, yield per shoot, cluster weight,berries per cluster, and cluster compactness, and an increase in soluble solids, anthocyanins,and total phenols in this study. Leaf removal, either by hand or mechanical, can be an effectiveway to reduce yields and improve quality in heavily-cropping varieties, such as Sangiovese. - · With world-wide wine production exceeding consumption, crop control is currently apriority in many viticultural regions of the world. This is usually achieved through winter pruningin combination with cluster thinning. Whereas these practices work well in moderate-vigorsituations, they generally don’t work as well in vigorous vineyards, where imposed crop controlcan create excessive shoot growth and yield compensation due to a boost in the developmentof basal and secondary buds. - · As a result, and in the authors’ opinion, alternative ways of controlling crop should beexplored. The candidates with the most likelihood to succeed, they felt, included: growthregulators, mechanical flower or berry thinning, and early leaf removal. The goal of this studywas to determine whether pre- or post-bloom leaf removal, conducted by hand or mechanically,is able to limit yields in a high-cropping cultivar such as Sangiovese.
Analysis and design of an impedance source modular DC/DC converter for energy storage system

This paper proposes an improved modular multilevel DC/DC converter (MMDDC) suitable for DC energy storage system (ESS). The bidirectional quasi-Z source is introduced into the sub-module of MMDDC, which not only makes the input voltage improved, but also changes the energy conversion mode so that the system can store energy as buck conversion and release energy as boost conversion. Therefore, the system current can be strictly controlled, the overcurrent problem of the traditional MMDDC in the charging mode is solved, and the improved sub-module input voltage helps reduce the series of sub-modules, thereby simplifying the system design. Overall, the proposed converter has more advantages in DC energy storage system compares to traditional MMDDC.
TWINBOT: Autonomous Underwater Cooperative Transportation

Underwater Inspection, Maintenance, and Repair operations are nowadays performed using Remotely Operated Vehicles (ROV) deployed from dynamic-positioning vessels, having high daily operational costs. During the last twenty years, the research community has been making an effort to design new Intervention Autonomous Underwater Vehicles (I-AUV), which could, in the near future, replace the ROVs, significantly decreasing these costs. Until now, the experimental work using I-AUVs has been limited to a few single-vehicle interventions, including object search and recovery, valve turning, and hot stab operations. More complex scenarios usually require the cooperation of multiple agents, i.e., the transportation of large and heavy objects. Moreover, using small, autonomous vehicles requires consideration of their limited load capacity and limited manipulation force/torque capabilities. Following the idea of multi-agent systems, in this paper we propose a possible solution: using a group of cooperating I-AUVs, thus sharing the load and optimizing the stress exerted on the manipulators. Specifically, we tackle the problem of transporting a long pipe. The presented ideas are based on a decentralized Task-Priority kinematic control algorithm adapted for the highly limited communication bandwidth available underwater. The aforementioned pipe is transported following a sequence of poses. A path-following algorithm computes the desired velocities for the robots’ end-effectors, and the on-board controllers ensure tracking of these setpoints, taking into account the geometry of the pipe and the vehicles’ limitations. The utilized algorithms and their practical implementation are discussed in detail and validated through extensive simulations and experimental trials performed in a test tank using two 8 DOF I-AUVs.
Interdisciplinary Research Towards Creating a Design Framework for Adaptable Virtual Learning Environments

This work-in-progress paper describes an interdisciplinary research project which relies on innovation in computer science to enhance pedagogy for second language learning and teaching. A design framework for adaptation was applied to a virtual reality teaching environment aimed at teaching prepositions to learners of English as a foreign/second language. The design was informed by Cognitive Learning Theory [1], [2], [3], Cognitive Linguistics [4]–[6], and Enaction Theory [7]; more precisely, the virtual reality teaching environment presents physical tasks specifically designed to teach the different senses of the preposition ‘over’ based on Tyler and Evan’s theoretical framework [8]. The dynamic environment provides real-time data on the actions and progress of the students allowing for empirical analysis of the effects of changes on learning outcomes.
Two-Objective-Oriented Resonant Parameter Design for Symmetrical CLLC Dual Active Bridge Converter with Swarm Intelligence

An optimal design approach is proposed in this paper for the resonant parameters in symmetrical CLLC-type dual active bridge (DAB) converter with considerations of the efficiency of the DAB converter and stability in a cascaded system simultaneously. The main idea of priority-based method adopted in this paper is to give priority to one of the two design objectives at one time. And swarm intelligence is used in this method to find the suitable values of resonant parameters with accuracy and efficiency. Finally, the simulations and experimental results have proved the validity of this design method.