bluuebunny/crossref_metadata_2025_split huggingface dataset:
Mobile Object Detection Using 2D and 3D Basic Geometric Figures in Colour and Grayscale
Authors: Castellanos Ari Ernesto Ortiz 
Published: May 2019

Abstract
In this paper, we use TensorFlow Mobile Lite for Object Detection with datasets of basic geometric figures on iOS mobile devices. Additionally, we trained 4 datasets in 2D and 3D and we compare the accuracy of detection between using colour and grayscale image data. Also, we evaluate the detection rate using 2D and 3D for some kind of normal objects in precision and label output. We used Convolutional Neural Networks (CNN) for build the datasets and OPENCV for convert into grayscale. We value the result relation between flat and volume datasets in way of label and numeric detection, also the affectation of TensorFlow Mobile with this kind of datasets. We make comparisons based on the results of the different experiments object the detection and, in this work, TensorFlow Mobile Lite implementation does not have pseudo boxes and the reason is explained for detection purposes and accuracy adjusted to this kind of experiments.
Weight Loss for Point Clouds Classification
Authors: Huang FangYuan , Xu Cheng , Tu XiaoHan , Li SiQi 
Published: May 2019

Abstract
With the widely applying of 3D sensors such as LiDAR and RGBD camera in robots and driverless technology, 3D point clouds classification has also achieved some development. PointNet[1] is a competitive method in point clouds classification due to it’s fast speed and well performance in real applications. However, when dealing with easy-classified samples overwhelming in 3D object classification, the PointNet shows incompatibility due to its gradients are mainly determined by these easy-classified samples. In order to optimize the gradients in PointNet, we designed the SFL(Sigmoid Focal Loss) function for 3D object classification to instead of the standard softmax cross-entropy loss function. The proposed SFL function will automated refined the weights of the hard-classified samples in training, so that the new neural network can focus on these hard-classified samples to avoid the unbalance of the classifier. Our results show that the new loss function can achieve the best result in classification tasks that directly processes point clouds.
The Frontier of SGD and Its Variants in Machine Learning
Authors: Du Juan 
Published: May 2019

Abstract
A Numerical optimization is a classical field in operation research and computer science, which has been widely used in areas such as physics and economics. Although optimization algorithms have achieved great success for plenty of applications, handling the big data in the best fashion possible is a very inspiring and demanding challenge in the artificial intelligence era. Stochastic gradient descent (SGD) is pretty simple but surprisingly, highly effective in machine learning models, such as support vector machine (SVM) and deep neural network (DNN). Theoretically, the performance of SGD for convex optimization is well understood. But, for the non-convex setting, which is very common for the machine learning problems, to obtain the theoretical guarantee for SGD and its variants is still a standing problem. In the paper, we do a survey about the SGD and its variants such as Momentum, ADAM and SVRG, differentiate their algorithms and applications and present some recent breakthrough and open problems.
Trust-Based Neural Collaborative Filtering
Authors: Zeng Yejia , Qu Zehui 
Published: May 2019

Abstract
In order to maintain the strong representational learning of the deep neural network model to learn the interaction function between any users and items, combining the trust relationship between users as a local relationship to enhance the ability to supplement interactive data, to achieve a better recommendation effect. This paper proposes a Trust-based Neural Collaborative Filtering model (TNCF). Firstly, trust information and scoring information are merged through the Generalized Matrix Factorization model (GMF) to obtain recommendations based on trust friend preferences. Then, using the Multi-Layer Perceptron model (MLP), the nonlinear kernel is utilized to learn the interaction function from the data to obtain the recommendation based on the user’s personal taste. Finally, all the interaction results are aggregated for implicit prediction. Compared with the three different baselines on the FilmTrust and Epinions datasets, the experimental consequences reveal that the proposed model improves the recommendation and also performs well on sparse statistics.
An Efficient Character-Level and Word-Level Feature Fusion Method for Chinese Text Classification
Authors: Wenzhen Jin , Hong Zhu , Guocai Yang 
Published: May 2019

Abstract
In order to extract semantic feature information between texts more efficiently and reduce the effect of text representation on classification results, we propose a features fusion model C_BiGRU_ATT based on deep learning. The core task of our model is to extract the context information and local information of the text using Convolutional Neural Network(CNN) and Attention-based Bidirectional Gated Recurrent Unit(BiGRU) at character-level and word-level. Our experimental results show that the classification accuracies of C_BiGRU_ATT reach 95.55% and 95.60% on two Chinese datasets THUCNews and WangYi respectively. Meanwhile, compared with the single model based on character-level and word-level for CNN, the classification accuracies of C_BiGRU_ATT is increased by 1.6%, 2.7% on the THUCNews, and is increased by 0.6%, 5.2% on the WangYi. The results show that the proposed model C_BiGRU_ATT can extract text features more effectively.
Delay-sensitive Task Scheduling with Deep Reinforcement Learning in Mobile-edge Computing Systems
Authors: Meng Hao , Chao Daichong , Guo Qianying , Li Xiaowei 
Published: May 2019

Abstract
Mobile-edge computing(MEC) is considered to be a new network architecture concept that provides cloud-computing capabilities and IT service environment for applications and services at the edge of the network, and it has the characteristics of low latency, high bandwidth and real-time access to wireless network information. In this paper, we mainly consider task scheduling and offloading problem in mobile devices, in which the computation data of tasks that are offloaded to MEC server have been determined. In order to minimize the average slowdown and average timeout period of tasks in buffer queue, we propose a deep reinforcement learning (DRL) based algorithm, which transform the optimization problem into a learning problem. We also design a new reward function to guide the algorithm to learn the offloading policy directly from the environment. Simulation results show that the proposed algorithm outperforms traditional heuristic algorithms after a period of training.
ETA: A method of Dynamic Network Device Security Assessment
Authors: Mu CHEN , Zaojian DAI , Zhipeng SHAO , Liming WANG 
Published: May 2019

Abstract
The normal operation of network devices is an important cornerstone of network security. The security evaluation of network device is very important to prevent network security problem. In order to dynamically evaluate the security level of network device and quantify the state, a method of dynamic network device security assessment is proposed. Firstly, this method makes full use of the alert log which includes state information of network device and combines with the idea of TF-IDF algorithm to analyze the frequency and the distribution of alert. Then, it puts forward a new algorithm ETA to calculate the value of event threat. Finally, these values are used for calculating the security index of network device. The experiment shows that the proposed method in this paper can find the network device in low-level security and provide effective decision support for network security administrator.
A Key Sentences Based Convolution Neural Network for Text Sentiment Classification
Authors: Mohan Zhang , Yang Xiang 
Published: May 2019

Abstract
Existing research treated all sentences in the text on an equal basis during the training process and did not consider that key sentences tend to have a stronger influence. We propose a Convolutional Neural Network text sentiment classification model based on the key sentences enhancement. The proposed model can identify key sentences in the text and generate text representation based on these key sentences to reduce noise and improve the accuracy of the model sentiment classification. The experiment results show that the proposed model improves the accuracy of the text sentiment classification compared with other classic text sentiment classification models.
Development of a Multi-component Infrared Gas Sensor Detection System
Authors: Qing WANG , Yong-ping LIU , Wei-long LI 
Published: May 2019

Abstract
A multi-component infrared gas sensor detection system is designed based on the principle that the gas under test absorbs infrared radiation of a specific wavelength,periodic infrared radiation signals pass through the chamber to reach multiple sets of filters. The infrared sensitive component extracts an electrical signal to filter and zoom which having the same period as the light source. And in a period of signal acquisition repeatedly, which achieved the effect of improving the reliability of signal acquisition,Enhanceing the capability of anti-jamming,Reduce the response time of the sensor, etc.
FunPianoAR: A Novel AR Application for Piano Learning Considering Paired Play Based on Multi-Marker Tracking
Authors: Zeng Hong , He Xingxi , Pan Honghu 
Published: May 2019

Abstract
This paper describes the design and implementation of a novel augmented reality application, referred as FunPianoAR, that aiming to activate the interests of user and improve the experience of piano learning. The FunPianoAR with a user-friendly interface considers the paired play to further reduce the difficulty of playing the piano for adult novices. For the paired play, one user plays the melody and the other plays the harmony part. The app is developed by using Android Studio 3.0 and artoolkitX 1.0, an open source augmented reality SDK, and installed on the AR smart glasses, Epson Moverio BT300. Due to the textureless features of real piano and registration precision, the application is implemented by using the fiducial marker tracking instead of the markerless recognition. Besides, we divided the piano keyboard into four zones and each zone used a separate marker for tracking to solve the limited field of view (FOV) to some extent. The virtual keys can be accurately superimposed on the piano keys by using multi-marker tracking. An evaluation was also conducted to compare the effects of two types of augmented reality information superimposed, i.e., the instant way without the hints for the next note to be played and the FunPianoAR with the hints for the coming notes to be played. The correctness rate was calculated and the time differences between the notes that two players should play at the same time were collected. The FunPianoAR shows more advantages over the instant way based on the evaluation results.
Atrial Fibrillation Detection Based on EEMD and XGBoost
Authors: Yue Zhang , Jinjing Zhu 
Published: May 2019

Abstract
The electrocardiogram (ECG) is non-invasive, inexpensive and widely used in several applications, implemented to detect the physical condition and disease of the human body. Atrial fibrillation (AF) is the most common of many different forms of sustained arrhythmia. Therefore, early diagnosis of AF may help to improve doctor’s diagnostic efficiency and is essential to prevent further progression of Atrial fibrillation to other heart disease and stroke complications. With the popularity of the machine learning and deep learning, more and more researchers apply them in image recognition, speech recognition and so on. Naturally, there are also many studies which achieve the purpose of diagnosing diseases, such as detection of arrhythmia, biometric identification based on ECG signals and machine learning or deep learning. A novel approach to detect AF from ECG signals was developed on this study, we used great filter EEMD (Ensemble Empirical Mode Decomposition) and classifier XGBoost (eXtreme Gradient Boosting) to detect normal rhythm, AF and other rhythm. Finally, the great performance was achieved with an average F1 score of 0.84 and accuracy of 0.86.
Defining and assessing urban forests to inform management and policy
Authors: Pregitzer Clara C , Ashton Mark S , Charlop-Powers Sarah , D’Amato Anthony W , Frey Brent R , Gunther Bram , Hallett Richard A , Pregitzer Kurt S , Woodall Christopher W , Bradford Mark A 
Published: May 2019

AbstractThe context in which trees and forests grow in cities is highly variable and influences the provision of ecological, social, and economic benefits. Understanding the spatial extent, structure, and composition of forests is necessary to guide urban forest policy and management, yet current forest assessment methodologies vary widely in scale, sampling intensity, and focus. Current definitions of the urban forest include all trees growing in the urban environment, and have been translated to the design of urban forest assessments. However, such broad assessments may aggregate types of urban forest that differ significantly in usage and management needs. For example, street trees occur in highly developed environments, and are planted and cared for on an individual basis, whereas forested natural areas often occur in parkland, are managed at the stand level, and are primarily sustained by natural processes such as regeneration. We use multiple datasets for New York City to compare the outcomes from assessments of the entire urban forest, street trees, and forested natural areas. We find that non-stratified assessments of the entire urban forest are biased towards abundant canopy types in cities (e.g. street trees) and underestimate the condition of forested natural areas due to their uneven spatial arrangement. These natural areas account for one quarter of the city’s tree canopy, but represent the majority of trees both numerically and in terms of biomass. Non-stratified assessments of urban forest canopy should be modified to accurately represent the true composition of different urban forest types to inform effective policy and management.
Stamping of a Cross-shaped Part with 5052, 5754 and 6016 Aluminum Alloy Sheets – Experimental and Finite Element Analysis Comparison
Authors: Esmaeilpour R , Tiji S A Nazari , Kim H , Park T , Kim H , Pourboghrat F , Mohammed B 
Published: May 2019

Abstract
The application of aluminum alloys is continuing to increase in automotive and aerospace industries. In this study, the stamping of a cross-shaped part, representing typical deep drawn automotive parts, was attempted with three different types of aluminum alloy sheets. To characterize the anisotropic properties of the material, uniaxial tensile tests were performed with ASTM standard dog-bone specimens cut from AA5052, AA5754 and AA6016 T4 alloy sheets. The stamping of the cross-shaped parts was conducted using a high capacity servo press with a servo-cushion system. Deformation strains developing in stamped parts were measured with the ARGUS system for comparison with finite element simulation results. Formed parts were also cut in two different directions using a water-jet machine, and thickness variations along the length of the cut section were measured. The measured surface strains and thickness distributions were compared with finite element simulations results obtained with LS-Dyna explicit code using the Yld2000-2D and Hill's 48 yield functions. Overall, experimental and numerical results correlated well, especially with the Yld2000-2D yield function, implying that this yield function is well suited for the modeling of the complex cross-shaped parts with aluminum alloys.
Thermomechanical Study and Fracture Properties of Silicon Wafer under Effect of Crystal Orientation
Authors: Romdhane Mohamed Cherif Ben , Mrad Hatem , Erchiqui Fouad , Mrad Ridha Ben 
Published: May 2019

Abstract
Designing and optimizing the performance of a piezoresistive pressure microsensor involves not only studying the piezoresistive properties of polysilicon but also analyse the thermomechanical behaviour of its most sensitive internal part. This work focuses on an analytical, numerical and experimental study of a silicon wafer used in a pressure sensor. A thermomechanical study was conducted to assess the effects of temperature and material orientation on the deflexion and failure mode of Silicon membrane. The chosen analytical approach is based on Kirchhoff theory in which a square Silicon membrane is subjected to a distributed pressure over all its surface. Thermomechanical simulations and fracture analysis are carried out using Abaqus software. An experimental thermomechanical technique to determine maximum wafer deflexion and its effect on crack initiation and fracture mode was implemented. Deflexions tests were carried out on a bending machine using specimens of p-Si doped wafers. Deflexion values are measured for each applied temperature. Some preliminary tests were performed to determine the impact of temperature and Silicon wafers orientation on maximum deflexion when fracture occurs. The test’s results confirmed that the failure mode is highly brittle and follow each time crystal orientations 0° and 45°. Furthermore, at room temperature, fracture occur at 389 μm of displacement, and grows by 5.2 % when temperature is increased over 40°C. Consequently, Silicon sensitivity will be increased by 4%.
Application of Technological System Based on Processes Modelling and Analysis: A Case Study in Italian Aquaculture Company
Authors: Corallo Angelo , Latino Maria Elena , Menegoli Marta , Cataldo Marco , Mancarella Luisa 
Published: May 2019

Abstract
Aquaculture is a growing sector and could satisfy the future worldwide fish demand. New sustainable consumption patterns arise from food market, based on the consumer’ need to know more information about food product. Technological and methodological innovations are needed to create more efficiency in aquaculture industry, but also to create product able to respond to the emerging market needs. The paper shows a case study in Aquaculture Company useful to demonstrate how collect product and production processes information and use it for traceability purpose. Starting from the as-is production process analysis and modelling, several strategically information for consumer are identified. The possibility of using the devices developed in S.E.A. research project has been exploited to collect some of this information and a new traceability layer was create in order to collect manually the missing data. At last the new production process was modelled. The benefits coming from the system adoption are multiple, for the final consumer that could see demonstrated the food quality and safety, for the company that could increase own environmental, economical and ethical sustainability.
Path Planning for Spray Painting Robot of Horns Surfaces in Ship Manufacturing
Authors: Zhou Yunzhong , Ma Shumei , Li Aiping , Yang Liansheng 
Published: May 2019

Abstract
Generating paint gun path for complex free-form surfaces in shipbuilding industry to ensure uniform paint deposition is highly challenging due to their complex geometry, especially for horns surfaces. In this paper, a tool trajectory optimization method was proposed based on dip angle spray in order to solve the problems of poor coating uniformity and material waste for spraying horns surface with curved patches. A dip angle spray model was developed based on the Beta distribution model on the planar surface by principle of differential geometry. The spray path optimization models for dip angle spraying on cylindrical surfaces of three cases were proposed and compared first to define the best case which yields the best painting quality. Then we used the best strategy on a horns surface in a shipyard to evaluate the effectiveness and robustness of designed algorithm. This algorithm can also be extended to other applications.
Soil water characteristic curves for laterite soil at different water contents and methods as lining system
Authors: Bello Yamusa Yamusa , Azril Hezmi Muhammad , Ahmad Kamarudin , Anuar Kassim Khairul , Sa’ari Radzuan , Alias Noraliani , Mustaffar Mushairry , Kassim Azman , Safuan A. Rashid Ahmad 
Published: May 2019

AbstractPreventing groundwater contamination from the most common method of solid waste disposal technique i.e. landfilling is by proving hydraulic barriers. Accordingly, water movement in unsaturated soil is important in the analysis of leachate migration in soil material used as hydraulic barrier in waste containment systems. A full-scale experiment would be prohibitively costly and time consuming. The only feasible recourse therefore is to construct a model, which reasonably portray the behaviour of the full-scale system and simulate the relevant physical parameters and describes the overall significant characteristics of the transport phenomena. This paper presents the trends and patterns of soil water characteristic curves (SWCC) in terms of volumetric water content versus soil suction developed for compacted laterite soil specimens using data from pressure plate tests. Specimens were prepared at three different water contents corresponding to dry of optimum, optimum, and wet of optimum conditions. Models suggested by Brooks and Corey (BC), van Genuchten (VG), and Fredlund and Xing (FX) were used to obtain curve fitting parameters using the program “SWRC Fit” which performed nonlinear fitting of soil water retention curves. The SWRC Fit can simultaneously calculate the fitting parameters of these models with Root Mean Square Error values and draw the fitting curves. By comparing the results of the models using SWRC Fit, the model that best fits the laterite soil investigated will be chosen to be used for further analysis. The results show that the BC model represents the soil water retention curves better than the VG and FX models when the soil has distinct air entry suction. On the other hand, the VG and FX models can fit most soil water retention curves very well when discussion on the pore-size distribution is desired.
Bottom ash utilization: A review on engineering applications and environmental aspects
Authors: Abdullah M H , Rashid A S A , Anuar U H M , Marto A , Abuelgasim R 
Published: May 2019

Abstract
Bottom ash produced from coal-fired power plants has been utilized in engineering applications for the purpose of recycling and solving disposal issue of bottom ash. This study reviews various applications of bottom ash in engineering fields. The engineering properties exhibited by bottom ash in terms of free draining, granular, lightweight and interlocking nanostructures allow them to be used for different functions. The bottom ash used is not limited to its raw state, but can be mixed and modified for advanced applications without neglecting the impacts on the environmental. The presence of metals in bottom ash are well known and being discussed whether they are harmful to the environment. Many countries have their own threshold limit of maximum contaminants, hence TCLP result will determine the hazardous status of bottom ash. This review helps in promoting the solution and potential usage of bottom ash, since coal is one of the dominant source of energy in power generation sector.