Miracle-dz/UniFAR-FTUs huggingface dataset:
Solution of Kinematics Problem of 5-DOF Manipulator Emitter for Non-Destructive Defectoscopy

This article describes the positioning system of defectoscopic equipment for non-destructive examination of complex shaped parts made of polymer composite materials. The purpose of the system and features of the investigated objects are described. The rationale for the development of the system and the range of problems it solves are presented. The solution of the kinematics problem for a 5-DOF manipulator is considered. The original algorithms for solving the kinematics problem are demonstrated. Methods for resolving collisions for a manipulator system are described. The results obtained in the course of experiments and studies are presented. The study was supported by the RSF grant 16-19-00155 “The robotic system of multi-angle microfocus radiography for aircraft parts and assemblies made of polymer composite materials with complex impact. ”
The LBD12-1 Transcription Factor Suppresses Apical Meristem Size by Repressing Argonaute 10 Expression1

Rice LBD12-1 functions as a repressor of shoot apical meristem size by down-regulating expression of AGO10 and HD-ZIP III. The shoot apical meristem (SAM) consists of a population of multipotent cells that generates all aerial structures and regenerates itself. SAM maintenance and lateral organ development are regulated by several complex signaling pathways, in which the Argonaute gene-mediated pathway plays a key role. One Argonaute gene, AGO10, functions as a microRNA locker that attenuates miR165/166 activity and positively regulates shoot apical meristem development, but little is known about when and how AGO10 is regulated at the transcriptional level. In this work, we showed that transgenic rice plants overexpressing LBD12-1, an LBD family transcription factor, exhibited stunted growth, twisted leaves, abnormal anthers, and reduced SAM size. Further research revealed that LBD12-1 directly binds to the promoter region and represses the expression of AGO10. Overexpression of AGO10 in an LBD12-1 overexpression background rescued the growth defect phenotype of LBD12-1-overexpressing plants. The expression of LBD12-1 and its binding ability to the AGO10 promoter is induced by stress. lbd12-1 loss-of-function mutants showed similar phenotypes and SAM size to the wild type under normal conditions, but lbd12-1 had a larger SAM under salt stress. Our findings provide novel insights into the regulatory mechanism of AGO10 by which SAM size is controlled under stress conditions.
Robust Coded Target Recognition in Adverse Light Conditions

We propose a new algorithm for the robust identification of coded targets in adverse light conditions. The new algorithm relies on the recognition of circle retroreflective targets. The algorithm finalizes the process of development of multi-functional coded targets for high-precision photogrammetric systems. The main advantages of the new algorithm are tolerance to adverse light conditions and a high speed. This paper reviews existing architectures of coded targets and their identification methods. We propose and describe the architecture of a coded target for high-precision photogrammetric measurements. We also analyze the efficiency of new algorithms and the range of their possible applications.
Object Recognition in Remote Sensing Images Using Combined Deep Features

Object recognition, which is also referred as object classification or object type recognition, aims at discriminating object types in remote sensing images. With the availability of high resolution remote sensing images, object recognition attracts more and more attention. Different from traditional methods mainly using hand-crafted features, we propose an object recognition method that combines deep features extracted from a convolutional neural network (CNN) to recognize aircrafts and ships in remote sensing images. The proposed method consists of two stages. In the training stage, images of objects with different types and corresponding labels are exploited to fine-tune a pre-trained CNN. Convolutional features are extracted from a convolutional layer of the fine-tuned CNN and pooled by Fisher Vector, and fully-connected features are extracted from a fully-connected layer of the CNN. These features are combined by concatenation and used to train a support vector machine (SVM). In the test stage, the type of each object is determined by the trained SVM using its combined features. Experiments on two data sets collected from Google Earth demonstrate the effectiveness of our method.
Deep Learning Midcourse Guidance for Interceptor Missile

A midcourse guidance method of interceptor missile based on Long Short-Term Memory deep learning networks is studied in this paper. Comparing with the guidance method using traditional neural networks, the miss distance of this method is significantly reduced. In the simulation process, the real-time states of interceptor missile are taken as the inputs of deep learning networks, and the trajectory integration is carried out with the output vector. Moreover, the guidance method is improved by changing three characters: the density of the selected sample trajectory, the size of the sample airspace and the size of the simulation airspace. Also, simulations of the trajectories pointing to the random prediction intercept points selected in a certain simulation space are carried out. Different deep learning guidance rules should be selected according to different application conditions.
Effect of NUDT15 on incidence of neutropenia in children with acute lymphoblastic leukemia

6‐Mercaptopurine (6‐MP) is considered the backbone of therapy in the maintenance phase of acute lymphoblastic leukemia (ALL). Gene polymorphisms involved in thiopurine degradation are predictors of toxicity in patients treated with 6‐MP. We investigated the effects of nucleoside diphosphate linked moiety X (nudix) type motif 15 (NUDT15) polymorphism NUDT15c.415C>T on neutropenia incidence, dose adjustment for 6‐MP, and survival rates in Thai children with ALL.
Design and Fabrication of a Miniaturized Microstrip Antenna Loaded by DNG Metamaterial

—In this paper the design, fabrication, and testing of a miniaturized rectangular microstrip patch antenna loaded with DNG metamaterials is reported. The metamaterial is composed of two nested spiral strips and a single straight strip which are etched on two sides of a 5.7 mm×5.7 mm Rogers RT/duroid 5880 with 0.5 mm thickness and dielectric constant of 2.2. Two units of this structure as a double negative (DNG) medium in combination with air as a double positive (DPS) medium are used as substrate of the microstrip patch antenna. By placing these metamaterial structures under the patch, a sub-wavelength resonance occurs which leads to a smaller size patch antenna compared to the conventional antenna at that frequency. The total size of the proposed antenna is reduced 54.6%. The dimensions of the proposed patch antenna are significantly smaller than the wavelength of the operation frequency with respect to the conventional patch antenna. Simulation result and test result for the proposed patch antenna are given and compared.
CEP-Wizard: Automatic Deployment of Distributed Complex Event Processing

Complex event processing (CEP) is defined as event processing for multiple stream sources to infer events that suggest complicated circumstances. As the size of stream data becomes larger, CEP engines have been parallelized to take advantage of distributed computing. Typically, deployment of such a distributed CEP engine involves manual configuration, which has been regarded as an obstacle to its widespread adoption. In this demonstration, we present CEP-Wizard, a framework of automatically configuring and deploying a distributed CEP engine with minimum effort. The demonstration shows that even inexperienced users can easily configure and deploy it on Apache Storm with achieving high performance and low resource usage.
A 60GHz CMOS Power Amplifier with Parametric Matching Networks

Modern wireless communication systems, especially orthogonal frequency-division multiplexing (OFDM) systems that have high peak-to-average power ratios (PAPR) signals, require high linearity and high power-added efficiency (PAE) in power amplifier (PA) designs. This work applies the parametric amplification method in PA designs to achieve these two goals simultaneously. A baseline Class-AB PA is designed as a reference. It has a PSAT of 11 dBm, small-signal gain of 14 dB, and peak PAE of 29% at 62 GHz. The parametric PA improves the IP1dB from −5 dBm to −2 dBm. The PAE at −6 dB back-off is increased from 10% to 15.5% (improved by 55%), the PAE at −8 dB back-off is increased from 7% to 11% (improved by 54%), and the peak PAE is increased from 29% to 33%.
Enhancing Mobile App User Understanding and Marketing With Heterogeneous Crowdsourced Data: A Review

The mobile app market has been surging in recent years. It has some key differentiating characteristics which make it different from traditional markets. To enhance mobile app development and marketing, it is important to study the key research challenges such as app user profiling, usage pattern understanding, popularity prediction, requirement and feedback mining, and so on. This paper reviews CrowdApp, a research field that leverages heterogeneous crowdsourced data for mobile app user understanding and marketing. We first characterize the opportunities of the CrowdApp, and then present the key research challenges and state-of-the-art techniques to deal with these challenges. We further discuss the open issues and future trends of the CrowdApp. Finally, an evolvable app ecosystem architecture based on heterogeneous crowdsourced data is presented.
Robot Vision Model Based on Multi-Neural Network Fusion

In the practical application of robot vision, neural networks are widely used to recognize working targets, but the reliability will decrease due to the influence of environmental factors such as illumination, background, camera orientation and so on. To solve this problem of robot vision, this paper establishes two back-propagation neural networks corresponding to the color and shape and a convolution neural network corresponding to the texture respectively to recognize the identical target in the robot’s field of view, and then fuses the recognition results of three neural networks with the D-S evidence theory to get a better judgment. The experimental results show that the proposed model in this paper can improve the reliability of robot vision by the fusion of multi-neural network of color, shape and texture, and it can be used in automatic control systems such as feeding, assembly, sorting and tracking of industrial robots.
Environment Dynamic Monitoring and Remote Control of Greenhouse with ESP8266 NodeMCU

Aiming to reduce the labour cost in managing greenhouses, an combined green-house management system including environmentally dynamic monitoring system and remote control system were developed and implemented in a greenhouse. The central unit of this combined system is the ESP8266 NodeMCU board. Environmental parameters including air temperature, air humidity, CO2 concentration and soil moisture were selected to represent the state of the interior environment state of the greenhouse. Based on HTTP protocol, the ESP8266 NodeMCU connected to the Wi-Fi and communicated with IoT platforms, Thingspeak and Bylnk. The data regarding environmental parameters was collected from sensors was processed and sent to cloud and IoT platforms though Wi-Fi. Environmental data was visualized in time-series form on IoT platforms allowing users to know about the internal environment conditions in real time. Moreover, the remote system has enabled users to change the operative condition of environment controllers remotely by altering between the on-off state of control buttons on Bylnk android application. This combined system has low-cost characteristics and is suitable for indoor garden, sized greenhouse. Moreover, it can be applied in modern smart agriculture.
A Novel Speech Enhancement Method Based on the Constraints of Temporal Continuity

The temporal continuity of speech is a vital feature, whose utilizing makes speech enhancement better. By adding the feature in this paper, we present a novel speech enhancement method based on temporal continuity constrained low-rank sparse matrix decomposition (TCCLSMD). This approach makes up for the deficiencies of the constrained low-rank and sparse matrix decomposition (CLSMD) by leading into the temporal continuity constraints. The proposed approach based on the sparse matrix obtained by singular value decomposition, and the discrete sparse matrix is reduced by adding temporal continuity to reduce discrete isolated points, retaining more speech information and reducing the speech distortion. Under various kinds of noise settings, compared with the CLSMD method, the experimental results show that the proposed method reduces speech distortion, makes the residual noise less, and raises speech intelligibility.
An Eigenmode Study of Tunable THz Nanoantenas

The current work focuses on the study of tunable THz open structures, establishing an eigenmode analysis based on an in-house developed finite element code. The key feature of this numerical tool is the modification of eigenanalysis formulation for the study of microwave structures, in order to meet the requirements for the study of devices operating at THz regime. The eigenmodes of tunable THz nanoantennas will be studied to reveal their characteristics and devise a design approach. For the truncation of the open-unbounded domain, the first order absorbing boundary conditions will be employed, while the occurring spurious modes will be eliminated with the incorporation of a tree-cotree technique. The tunability of the studied structures will be evaluated in a rather innovative approach as a piecewise constant distribution for the electro-optically controlled dielectric constant.
MLlib*: Fast Training of GLMs Using Spark MLlib

In Tencent Inc., more than 80% of the data are extracted and transformed using Spark. However, the commonly used machine learning systems are TensorFlow, XGBoost, and Angel, whereas Spark MLlib, an official Spark package for machine learning, is seldom used. One reason for this ignorance is that it is generally believed that Spark is slow when it comes to distributed machine learning. Users therefore have to undergo the painful procedure of moving data in and out of Spark. The question why Spark is slow, however, remains elusive. In this paper, we study the performance of MLlib with a focus on training generalized linear models using gradient descent. Based on a detailed examination, we identify two bottlenecks in MLlib, i.e., pattern of model update and pattern of communication. To address these two bottlenecks, we tweak the implementation of MLlib with two state-of-the-art and well-known techniques, model averaging and AllReduce. We show that, the new system that we call MLlib*, can significantly improve over MLlib and achieve similar or even better performance than other specialized distributed machine learning systems (such as Petuum and Angel), on both public and Tencent's workloads.
I-LSH: I/O Efficient c-Approximate Nearest Neighbor Search in High-Dimensional Space

Nearest Neighbor search has been well solved in low-dimensional space, but is challenging in high-dimensional space due to the curse of dimensionality. As a trade-off between efficiency and result accuracy, a variety of c-approximate nearest neighbor (c-ANN) algorithms have been proposed to return a c-approximate NN with confident at least δ. We observe that existing c-ANN search algorithms have some limitations on I/O efficiency when their indexes are resided on the external memory, which is critical for handling large scale high-dimensional data. In this paper, we introduce an incremental search based c-ANN search algorithm, named I-LSH. Unlike the previous LSH methods, which expand the bucket width in an exponential way, I-LSH adopts a more natural search strategy to incrementally access the hash values of the objects. We provide rigorous theoretical analysis to underpin our incremental search strategy. Our comprehensive experiment results show that, compared with state-of-the-art I/O efficient c-ANN techniques, our algorithm can achieve much better I/O efficiency under the same theoretical guarantee.
Systematic Erudition of Bitcoin Price Prediction using Machine Learning Techniques

Bitcoin (BTC) is an internet-based world’s top-ranking cryptocurrency. Among widespread cryptocurrencies available in the market, Bitcoin is most experienced by the people due to anonymity and transparency in the system. Daily trends in the Bitcoin market has gained popularity among the spectators, investors, consumers and many more. Bitcoin price data exhibit desirable properties where some classical time series prediction methods exploit the behavior, producing poor predictions and also lack a probabilistic interpretation. This paper conducts an in-depth study on evolution of Bitcoin and also a systematic review is done on various machine learning algorithms used for predicting the prices. Comparative analysis envisions to select optimal technique to forecast prices more precisely.
Doping effect of S2 Orbital Metal on Amorphous In-Zn-O and Its Application for Printable Transparent Thin-film Transistors (TFT)

To develop low cost printable transparent thin-film transistor (TFT) technologies, doped In-Zn-O based TFTs (IZO TFTs) are demonstrated with the sol-gel technique. The doping effect of $s^{2}$ orbital metal on IZO material system is investigated. IZO film slightly doped by a strong metallicity element such as Ca would significantly enhance its Ion/Ioff ratio without sacrifying its mobility.
Design and Assessment of Myoelectric Games for Prosthesis Training of Upper Limb Amputees

In this paper, we present the design and evaluation of our system, which provides an engaging game-based pre-prosthesis training environment for upper limb transradial amputees. We believe that patients who train using such a training tool will demonstrate significantly higher improvement in functional performance tests using a myoelectric prosthesis than when conventional pre-prosthesis training protocols are used. We re-designed two simple games to be playable using three muscle contractions which are appropriate to pre-prosthesis exercises and are detected by an EMG-based arm sleeve. Through user studies conducted with 16 non-amputee subjects, we show that the proposed games are enjoyable, fun to play, sustains motivation to continue playing and also helps in improving muscular control. Using an HMM trained on EMG data collected from the game play sessions of each user, we are able to detect if the subject felt fatigued, while continuously contracting the muscles to play the game, in 93% of the cases and with a latency of only ±15 seconds. We are also able to understand the subject's quality of muscle isolation during the game play.
Software Testing: According to Plan!

Automated planning and scheduling represents a branch of classical artificial intelligence (AI) research. Although initially used in robotics and intelligent agents, the use of planning for testing purposes has increased over the years. There sequences of actions representing interactions with the system under test guide the test execution towards reaching a test purpose. A planning problem is formally defined as a model that resembles the interaction with a real system under test (SUT). The obtained solutions are generated, i.e., the plans, directly correspond to test cases. The planning model offers the possibility to generate test cases with a great variety of interactions without the need for an extensive model definition. Until now, planning has proven to be efficient in detecting both functional and non-functional issues. The second play a major role in uncovering vulnerabilities in software. In fact, testing of any domain can be specified as a planning problem. The purpose of this paper is to summarize previous research in the domain of planning for testing including discussing examples from multiple domains.
Interpretable Multi-task Learning for Product Quality Prediction with Attention Mechanism

In this paper, we investigate the problem of mining multivariate time series data generated from sensors mounted on manufacturing stations for early product quality prediction. In addition to accurate quality prediction, another crucial requirement for industrial production scenarios is model interpretability, i.e., to understand the significance of an individual time series with respect to the final quality. Aiming at the goals, this paper proposes a multi-task learning model with an encoder-decoder architecture augmented by the matrix factorization technique and the attention mechanism. Our model design brings two major advantages. First, by jointly considering the input multivariate time series reconstruction task and the quality prediction in a multi-task learning model, the performance of the quality prediction task is boosted. Second, by incorporating the matrix factorization technique, we enable the proposed model to pay/learn attentions on the component of the multivariate time series rather than on the time axis. With the attention on components, the correlation between a sensor reading and a final quality measure can be quantized to improve the model interpretability. Comprehensive performance evaluation on real data sets is conducted. The experimental results validate that strengths of the proposed model on quality prediction and model interpretability.
Analysis of Test Structure Design Induced Variation in on Si On-wafer TRL Calibration in sub-THz

In this paper, we present on-wafer S-parameter measurement of test structures designed and fabricated on silicon substrate for transistor de-embedding upto 220 GHz. Using two different types of reflects (open circuit and short circuit) in on-wafer thru-reflect-line (TRL) calibration, we show that in the on-wafer TRL, some of the error terms could be sensitive to the use of the reflect type and may not contain always exactly the same value with change in reflect type. Further, impact of reflect induced variations in error terms is demonstrated on the on-wafer TRL calibrated S-parameters of de-embedding structures. On basis of the on-wafer TRL calibrated S-parameters of de-embedding structures, we conclude that one type of reflect could suit more in the on-wafer TRL calibration for a de-embedding structure than another type.