Miracle-dz/UniFAR-FTUs huggingface dataset:
Rumor Detection of Sina Weibo Based on SDSMOTE and Feature Selection

Internet provides great convenience for our life and becomes an important channel to get information. However, a large amount of false information, called rumors, come with it. In terms of automatically detecting rumors, two main contributions of this paper are as follows: (1) To reduce the impact of unbalanced data on classification, we proposed an improvement SMOTE algorithm to resample data. (2) We proposed six new features based on Sina microblogs, including Words with Guidance (WG), Words with Menace (WM), Suspected Topic (ST), Recognition of Information (RI), Degree of Attention to Users (DAU) and Credit Rating (CR), which are related to user-based features, content-based features, propagation-based features and microblog-based features. By building subsets with new features and using machine learning algorithms including Xgboost etc. We tested the effect of rumor detection on a real data set. Experiments showed that our rumor detection method was significantly improved compared with the most advanced method of the same type, with precision, recall and F1 at 0.827, 0.837 and 0.825 respectively, and AUC at 0.895.
VM Selection using DVFS Technique to Minimize Energy Consumption in Cloud System

Energy consumption becoming a key issue for the execution of operation and maintenance of cloud system. The virtual machine selection plays an important role in the execution of the task without violating SLA. In this paper, a VM selection technique is proposed using Dynamic Voltage Frequency Scaling (DVFS) for optimizing the energy consumption and makespan in the cloud system.We have proposed a heuristic for the selection of VM for each task to optimize the energy utilization by applying the DVFS technique. The proposal extends to incorporate an energy model supporting the evaluation of energy consumption in cloud data centers. Each task has an energy-based SLA to execute in the cloud system. The DVFS Mechanism is applied to the virtual machines level to reduce the energy of the cloud system. Moreover, the performance of the diverse algorithms (Random allocation, and FCFS) are compared with the proposed DVFS-based VM selection strategy with the help of CloudSim.
Application of Improved Convolution Neural Network in Financial Forecasting

The world economy is in a stage of rapid development, and financial development is also continuing. Financial activities are increasing, and the uncertainty of its changing trend is also increasing. An effective financial forecast can provide a basis for financial planning and decision-making while maintaining the healthy development of financial markets. Convolution neural network is a multilayer neural network structure that simulates the operation mechanism of biological vision system. It is a neural network composed of multiple layers of convolution layers and down sampling layers. It can obtain useful feature descriptions from raw data and is an effective method for extracting features from data. Therefore, this paper introduces the convolution neural network structure to predict the financial time series data, establishes a convolution neural network model, and studies the influence of model parameters on the stock prediction results. Through simulation and comparison, the feasibility and effectiveness of the prediction model given in this paper are verified.
Different Model for Hand Gesture Recognition with a Novel Line Feature Extraction

Hand gestures are commonly used for communication between both impaired community and normal people. Sign languages stand for the human languages of deaf people. They form the most growing domain of research worldwide. A number of techniques was developed in this area lately. It is recognized by means of deducing the features involved in the use of hand gesture. As a matter of fact, various approaches, namely the vision-based, the data-glove-based, the colored-marker and the Electromyogram (EMG) approaches have been utilized by researchers to recognize the different hand gestures implemented in many different fields such as the whole approaches which can be divided into four main categories, viz. Data Collection, Image Processing, Feature Extraction and Gesture Recognition. Only few of those categories have been discussed in this paper to be compared between the accuracy rates by applying Artificial Neural Network (ANN) classification. This classification is based on different models and a novel method for Real-Time Hand Gesture Recognition System (RTHGRS). The latter has used one line (fifty features) extracted from black and white processed images to recognize the numbers from (1–10) in Kurdish Sign Language (KurdSL) using one hand only with accuracy 98%.
Finite-Time Stochastic $H_\infty$ Control for Singular Markovian Jump Systems With $(x,v)$ -Dependent Noise and Generally Uncertain Transition Rates

This paper is concerned with the problems of finite-time stochastic <inline-formula> <tex-math notation="LaTeX">$H_\infty $ </tex-math></inline-formula> control for singular Itô Markovian jump systems with <inline-formula> <tex-math notation="LaTeX">$(x,v)$ </tex-math></inline-formula>-dependent noise and generally uncertain transition rates (GUTRs). Based on two equivalent sets, a new criterion, ensuring the considered systems with completely known TRs to be finite-time stochastically bounded with <inline-formula> <tex-math notation="LaTeX">$H_\infty $ </tex-math></inline-formula> performance, is first established, which is less conservative than the existing one. Then, the obtained results are extended to the case of GUTRs. To overcome the nonlinear difficulty resulting in the Young inequality for designing a convex controller, an approach called the association of free variables and slack variables are used. The state feedback controller and observer-based controller are respectively designed such that the corresponding closed-loop systems with GUTRs are finite-time stochastically bounded while achieving <inline-formula> <tex-math notation="LaTeX">$H_\infty $ </tex-math></inline-formula> performance. Finally, the numerical examples are addressed to illustrate the effectiveness and efficiency of our obtained results.
Leveraging word embeddings and medical entity extraction for biomedical dataset retrieval using unstructured texts

Abstract The recent movement towards open data in the biomedical domain has generated a large number of datasets that are publicly accessible. The Big Data to Knowledge data indexing project, biomedical and healthCAre Data Discovery Index Ecosystem (bioCADDIE), has gathered these datasets in a one-stop portal aiming at facilitating their reuse for accelerating scientific advances. However, as the number of biomedical datasets stored and indexed increases, it becomes more and more challenging to retrieve the relevant datasets according to researchers’ queries. In this article, we propose an information retrieval (IR) system to tackle this problem and implement it for the bioCADDIE Dataset Retrieval Challenge. The system leverages the unstructured texts of each dataset including the title and description for the dataset, and utilizes a state-of-the-art IR model, medical named entity extraction techniques, query expansion with deep learning-based word embeddings and a re-ranking strategy to enhance the retrieval performance. In empirical experiments, we compared the proposed system with 11 baseline systems using the bioCADDIE Dataset Retrieval Challenge datasets. The experimental results show that the proposed system outperforms other systems in terms of inference Average Precision and inference normalized Discounted Cumulative Gain, implying that the proposed system is a viable option for biomedical dataset retrieval. Database URL: https://github.com/yanshanwang/biocaddie2016mayodata
Effect of material properties on PCB frequencies in electronic control unit

With advancement in Artificial intelligence, Internet of things and next generation of mobility going towards autonomous driving, the electronic control units used for power steering need to be redundant, highly reliable and intelligent. The above challenge forces design engineers to smartly enhance the electronic components with use of multiple PCB stacked one above another. PCB is one of the main parts of the control unit on whichSMD components are populated. Therefore, comprehensive knowledge of PCB dynamic behavior is important for fatigue evaluation of SMD and other electronic components.This paper discusses the mechanical dynamic responses of PCBs using different material properties, e.g. isotropic, orthotropic and PCB homogenizing properties. Simulations are carried out in Ansys to predict the PCB frequencies and mode shapes. In order to validate the simulation results, measurements are also performed to predict PCB frequencies, mode shapes and frequency response funsctions. Measurements are carried out with PSV-400 scanning laser vibrometer from Polytech and post processing of measurement data is done with help of Polytech for visualization of mode shapes.
Wireless Network Intrusion Detection Based on Improved Convolutional Neural Network

The diversification of wireless network traffic attack characteristics has led to the problems what traditional intrusion detection technology with high false positive rate, low detection efficiency, and poor generalization ability. In order to enhance the security and improve the detection ability of malicious intrusion behavior in a wireless network, this paper proposes a wireless network intrusion detection method based on improved convolutional neural network (ICNN). First, the network traffic data is characterized and preprocessed, then modeled the network intrusion traffic data by ICNN. The low-level intrusion traffic data is abstractly represented as advanced features by CNN, which extracted autonomously the sample features, and optimizing network parameters by stochastic gradient descent algorithm to converge the model. Finally, we conducted a sample test to detect the intrusion behavior of the network. The simulation results show that the method proposed in our paper has higher detection accuracy and true positive rate together with a lower false positive rate. The test results on the test set KDDTest + in our paper show that compared with the traditional models, the detection accuracy is 8.82% and 0.51% higher than that of LeNet-5 and DBN, respectively, and the recall rate is 4.24% and 1.16% higher than that of LeNet-5 and RNN, respectively, while the false positive rate is lower than the other three types of models. It also has a big advantage compared to the IDABCNN and NIDMBCNN methods.
An Automated Tomato Quality Grading using Clustering based Support Vector Machine

This paper focus on representing the technique of fruit grade classification, automated machine vision based technology has become more potential and important to many areas like agricultural sector and food processing industry. The proposed system which calculates the grade of fruit based on its external features. Grading of fruits is one of the most important processes in post harvesting, but this procedure is mostly carried out manually which is not efficient as it requires enormous number of employment, and tends to human error. The grading process is carried out by capturing the fruit image using digital camera and this image is interpreted using image various processing techniques. Color is very prominent feature for recognizing defect and ripeness of the fruit. The major objective is to check the fruit quality with high speed for analyzing maximum number of fruits in least amount of time. The conventional process of fruit quality assessment needs new tools deciding the quality of fruit. This system performs color features and size of fruit and captures the fruit side view image. The Otsu thresholding and K-Means clustering algorithms are used to extract the features of fruit. This system achieve the fruit quality sorting using Support Vector Machine and gives a few advantages over conventional techniques. This system will help in the advancement of an automated non destructive grading system with high speed, high accuracy and low cost. Implementation of this system will have applications in fruit quality grading in field like food science and trades where standardization is essential.
Energy Conservation Mode Based on Dijkstra’s Algorthim with Link Cost Function for WBANS

Wireless Body Area Network (WBAN) is a network in which it employs wireless sensor technology and it forms a network to continuously observe the surrounding area. For human body communication WBAN is a best reasonable system yet observing of physiological and biochemical parameters in human body are quiet challenging. Maintenance of sensor nodes is a real problem and prohibits their widespread used in health monitoring application. This work deals with the implementation of Ad Hoc On-Demand Distance Vector routing (AODV) and Dynamic Source Routing (DSR) protocols, comparative analysis of routing proto-cols has been performed based on the end to end delay, packet delivery ratio, and throughput.
What Can You Do with Educational Technology that is Getting More Human?

Technology is advancing at an ever-increasing speed. The backend capabilities and the frontend means of interaction are revolutionizing all kinds of applications. In this paper, we analyze how the technological breakthroughs seem to make educational interactions look smarter and more human. After defining Education 4.0 following the Industry 4.0 idea, we identify the key breakthroughs of the last decade in educational technology, basically revolving around the concept cloud computing, and imagine a new wave of educational technologies supported by machine learning that allows defining educational scenarios where computers interact and react more and more like humans.
Investigation of laminar flow and convective heat transfer in a constricted channel based on double MRT-LBM

The overall aim intended of the present work is to simulate an incompressible laminar flow and thermal transfer in a constricted channel. A double multiple relaxation time (DMRT) thermal lattice Boltzmann method is carried out on this problem. The application of this method will be performed using a multiple relaxation time D2Q9 and D2Q5 model to simulate numerically the flow field and the thermal field, respectively. This paper deals with various interesting features of the flow and temperature fields in the constricted channel to describe the fluid behavior. Furthermore, the effects of several parameters (Reynolds number, height of constricted part…) were studied and discussed in detail in order to find some improvements about the heat transfer mechanism in the presence of the constricted part.
A New Criterion for Optimal Dielectric Design of High Voltage Bushing Internal Shields in Large Power Transformer

The high voltage (HV) bushings are the most sensitive components of power transformers, they provide electrical insulation between the HV line and grounded tank, and also provide necessary mechanical support. The majority of transformer breakdowns are attributed to the HV bushings failures which are graded with conductive layers to improve their dielectric strength. The design of bushings is quite challenging with tradeoff between the axial and radial electric stresses. The bushing design is also governed by its mounting angle and transformer tank configuration to meet the internal and external flashover clearances and creepage requirements. At higher voltage levels, the configuration of HV bushing shield and grounded structural components have significant impact on dielectric withstand strength other than the clearances. Hence, it is very important to study the electrode configuration inside transformer tank for a particular bushing mounting arrangement before finalizing the bushing internal shield design. The 3D electrostatic field analysis of high voltage generator transformer was carried out for different bushing shield configurations to evaluate the electric stress distribution and the dielectric breakdown strength of the equipment was verified experimentally. The authors recommend to study the dielectric design of transformer in tandem with transformer configuration and a new evaluation criterion for optimal design of bushing internal shield is proposed.
A Fully Integrated MIMO Multi-Band Direct-Conversion CMOS Transceiver for WLAN Applications (802.11n)

A single-chip multi-band direct-conversion CMOS MIMO transceiver (2 times 2) targeted for WLAN applications is presented. This transceiver is capable of satisfying the requirements of the Enhanced Wireless Consortium and achieves PHY rates of >270Mb/s. The receivers and transmitters achieve an EVM of better than -41 dB (0.9%) and -40dB (1.0%) operating in legacy g and a modes, respectively. From a 1.8V supply and with both cores operating, the chip draws 275mA in RX mode and 280mA in TX mode.
Hemihepatic Ischemia for Laparoscopic Liver Resection

Laparoscopic hepatectomy has been recently proposed for the treatment of liver tumors. However, because of technical difficulties such as control of hemorrhage from the transection plane and large intrahepatic veins, laparoscopic hepatectomy has not been widely developed. The technique of hemihepatic ischemia has been used by the authors in conventional liver resection over the past 10 years with reduced splanchnic congestion and excellent hemostatic control. To minimize both intraoperative bleeding and circulatory and biochemical disturbances due to the interruption of blood flow to the liver, the authors describe a new technique combining hemihepatic ischemia and laparoscopic liver resection.
A new Security Mechanism for Vehicular Cloud Computing Using Fog Computing System

Recently Vehicular Cloud Computing (VCC) has become an attractive solution that support vehicle's computing and storing service requests. This computing paradigm insures a reduced energy consumption and low traffic congestion. Additionally, VCC has emerged as a promising technology that provides a virtual platform for processing data using vehicles as infrastructures or centralized data servers. However, vehicles are deployed in open environments where they are vulnerable to various types of attacks. Furthermore, traditional cryptographic algorithms failed in insuring security once their keys compromised. In order to insure a secure vehicular platform, we introduce in this paper a new decoy technology DT and user behavior profiling (UBP) as an alternative solution to overcome data security, privacy and trust in vehicular cloud servers using a fog computing architecture. In the case of a malicious behavior, our mechanism shows a high efficiency by delivering decoy files in such a way making the intruder unable to differentiate between the original and decoy file.
A Gradient Pursuit Algorithm Based on Multi-Step Quasi-Newton Method

In order to solve the high computational complexity problem of gradient pursuit in compressed sensing, we applied multi-step quasi-Newton method to gradient pursuit and proposed a gradient pursuit algorithm based on multi-step quasi-Newton method (MSQN-GP). The MSQN-GP algorithm uses the pre-multiple gradient information to approximate the inverse matrix of the Hessian matrix of the objective function, and this scheme is applied to the gradient pursuit algorithm to efficiently solve the update direction problem, while avoiding complex matrix inversion operations. Firstly, we give a detailed theoretical derivation, which proved that the algorithm has both the decent property of the steepest descent method and the second-order convergence of the Newton method. Then, we present extensive simulation results to show that our proposed MSQN-GP algorithm can achieve significant reduction in complexity on the basis of ensuring reconstruction accuracy compared with the existing gradient pursuit algorithms.
Identifying Defective IR Cameras through a Machine Learning Approach to Image Artifact Detection

Title: Identifying Defective IR Cameras through a Machine Learning Approach to Image Artifact Detection This report is about methods for automatic detection of subtle image artifacts. Such methods could be a part of the image quality control procedure in the production process of infrared cameras. Currently image quality control requires manual inspection. The main goal of the investigation described in this report was to suggest and evaluate different measures that can be used to detect image artifacts and asses image quality. In many cases these measures are attempts to find structure in what should be random white noise and will assume that test images are acquired against a uniform radiation. Often single-valued measures are sufficient for achieving good detection accuracy, but in some cases more complex, vector valued measures (termed features) in combination with a machine learning classifier are needed. This report conclusively shows that automatic detection of subtle image artifacts with high accuracy is possible and in many cases achievable using single-valued measures. Sammanfattning Titel: Identifiering av defekta IR-kameror med ett lärande system för detektion av bildartefakter Denna rapport handlar om metoder för automatisk detektion av diffusa bildartefakter. Sådana metoder kan bli en del av den bildkvalitetskontroll som utförs vid produktion av värmekameror. I nuläget krävs manuell granskning av varje kamera. Den utredning som beskrivs har fokuserats på att föreslå och utvärdera olika mått som kan användas för att detektera artefakter i bilder och för att mäta bildkvalité. I många fall är dessa mått försök att finna struktur i bilder som borde bestå av enbart vitt brus och kräver att testbilden tas mot en likformig strålare. Ofta räcker envärda mått för att detektera artefakter med god precision, men i vissa fall krävs mer komplexa vektorvärda mått (som kommer att kallas features) i kombination med en lärande klassificerare. Denna rapport visar slutgiltigt att automatisk detektion av artefakter med hög precision är möjlig och ofta kan uppnås med envärda mått. Preface This thesis project was suggested and sponsored by FLIR Systems AB in Danderyd, Sweden. I would like to thank my supervisors at FLIR, Malin Ingerhed and Lars-Åke Tunell, for the great support and encouragement that I have received during my work on this thesis. I also thank my other colleagues at FLIR for their good spirit and enthusiasm and for their interest in this project. The academic supervision of the thesis was provided by Örjan Ekeberg and the Computational Biology and Neurocomputing (CBN) group at the School of Computer Science and Communication, KTH. I thank him for his support. Oscar Danielsson, December 2006, Danderyd, Sweden
The Effect of Computer Self-Efficacy on Entrepreneurial Aspirations of Students

The evolution of Information Technology has undoubtedly made a significant contribution to many entrepreneurial smash hits of the last decades. Computer Self-Efficacy is an IT specific measure of the general ability to use computers and software and its relation to entrepreneurial intention, a valid precursor of entrepreneurial behavior, consists an interesting topic which recently came into focus of entrepreneurship research. In this empirical research conducted among secondary education students, the efficacy of autonomous use of computer software is highlighted as an influential factor for the entrepreneurial intentions of students. Autonomous use of computer software incorporates increased learning motivation, risk taking and openness to new experiences, which are essential for entrepreneurship. The findings have useful implications both for instructors and software designers.