Miracle-dz/UniFAR-FTUs huggingface dataset:
[Engineering Paper] Enabling the Continuous Analysis of Security Vulnerabilities with VulData7

Studies on security vulnerabilities require the analysis, investigation and comprehension of real vulnerable code instances. However, collecting and experimenting with a sufficient number of such instances is challenging. To cope with this issue, we developed VulData7, an extensible framework and dataset of real vulnerabilities, automatically collected from software archives. The current version of the dataset contains all reported vulnerabilities (in the NVD database) of 4 security critical open source systems, i.e., Linux Kernel, WireShark, OpenSSL, SystemD. For each vulnerability, VulData7 provides the vulnerability report data (description, CVE number, CWE number, CVSS severity score and others), the vulnerable code instance (list of versions), and when available its corresponding patches (list of fixing commits) and the files (before and after fix). VulData7 is automated, flexible and easily extensible. Once configured, it extracts and links information from the related software archives (through Git and NVD reports) to create a dataset that is continuously updated with the latest information available. Currently, VulData7 retrieves fixes for 1,600 out of the 2,800 reported vulnerabilities of the 4 systems. The framework also supports the collection of additional software defects and aims at easing empirical studies and analyses. We believe that our framework is a valuable resource for both developers and researchers interested in secure software development. Vul-Data7 can also serve educational purposes and trigger research on source code analysis. VulData7 is publicly available at: https://github.com/electricalwind/data7
Impulsive Noise Models Used in Power Line Communications

In a communication system, noise has always been considered as a serious cause of error. Although most common noises can be modeled by well-known Gaussian distributions, some communication systems experience other types of noise. Power Line Communication (PLC) is one of these systems which run on a difficult medium since it uses the preexisting power line network for transmission. PLC experiences several different types of human-made and natural noise which are mostly impulsive and cannot be modeled simply by Gaussian. Different statistical models are used for characterizing the impulsive noise of PLC systems in the literature. The purpose of this study is to go over the impulsive noise models previously presented in order to have a contribution to this hot topic. We point out the similarities and differences of the models, namely Middleton Class A, Bernoulli Gaussian and Alpha Stable statistical models. It is presented that all have heavy tails which make them appropriate for impulsive noise. Although Middleton Class A is more generic, Bernoulli Gaussian model is wide enough and can be preferable because of its compact simple form especially when analytical results are needed.
Screening and Identification of Cardioprotective Compounds From Wenxin Keli by Activity Index Approach and in vivo Zebrafish Model

Wenxin Keli (WXKL) is a widely used Chinese botanical drug for the treatment of arrhythmia, which is consisted of four herbs and amber. In the present study, we analyzed the chemical composition of WXKL using liquid chromatography coupled with high-resolution mass spectrometry (LC-HRMS) to tentatively identify 71 compounds. Through typical separate procession, the total extract of WXKL was divided into fractions for further bioassays. Cardiomyocytes and zebrafish larvae were applied for assessment. In vivo arrhythmia model in Cmlc2-GFP transgenic zebrafish was induced by terfenadine, which exhibited obvious reduction of heart rate and occurrence of atrioventricular block. Dynamic beating of heart was recorded by fluorescent microscope and sensitive camera to automatically recognize the rhythm of heartbeat in zebrafish larvae. By integrating the chemical information of WXKL and corresponding bioactivities of these fractions, activity index (AI) of each identified compound was calculated to screen potential active compounds. The results showed that dozens of compounds including ginsenoside Rg1, ginsenoside Re, notoginsenoside R1, lobetyolin, and lobetyolinin were contributed to cardioprotective effects of WXKL. The anti-arrhythmic activities of five compounds were further validated in larvae model and mature zebrafish by measuring electrocardiogram (ECG). Our findings provide a successful example for rapid discovery of bioactive compounds from traditional Chinese medicine (TCM) by activity index based approach coupled with in vivo zebrafish model.
Study of Triple Ar Gas Puff Z-Pinches on 0.9-MA, 200-ns COBRA

Ar gas puff z-pinch experiments were conducted on the 0.9-MA, 200-ns COBRA generator at Cornell University. In the experiments, a triple-nozzle gas valve was used to produce the z-pinch load with concentric outer and inner gas puffs and a central gas jet. We used a planer laser-induced fluorescence to measure the initial gas profile of the load, two four-frame 4-ns gated extreme ultraviolet cameras to image the imploding plasma, and filtered photoconducting diamond detectors to measure the X-ray output. We have observed the shock heating and/or photoionization of the dense center jet by the imploding plasma. With an outer-to-inner plenum pressure ratio of 1:3, deacceleration of the imploding plasma occurred when the outer plasma shell imploded on the inner shell resulting in a stable implosion. By varying the plenum pressure for the center jet, the X-ray emission was optimized. An Ar K-shell X-ray (3–4 keV) yield of 77 ± 17 J was produced at a current level of 0.87 ± 0.2 MA and an implosion time of 200 ns.
Privacy-Preserving Collaborative Model Learning: The Case of Word Vector Training

Nowadays, machine learning is becoming a new paradigm for mining hidden knowledge in big data. The collection and manipulation of big data not only create considerable values, but also raise serious privacy concerns. To protect the huge amount of potentially sensitive data, a straightforward approach is to encrypt data with specialized cryptographic tools. However, it is challenging to utilize or operate on encrypted data, especially to perform machine learning algorithms. In this paper, we investigate the problem of training high quality word vectors over large-scale encrypted data (from distributed data owners) with the privacy-preserving collaborative neural network learning algorithms. We leverage and also design a suite of arithmetic primitives (e.g., multiplication, fixed-point representation, sigmoid function computation, etc.) on encrypted data, served as components of our construction. We theoretically analyze the security and efficiency of our proposed construction, and conduct extensive experiments on representative real-world datasets to verify its practicality and effectiveness.
QUIC Protocol Performance in Wireless Networks

Google's Quick UDP Internet Connections (QUIC) transport layer protocol was developed in 2013 as a successor to its own SPDY networking protocol, which itself led to the formation of the HTTP/2 standard. QUIC's main motives were to take the advantages of TCP/IP and HTTP/2, and build them over UDP, in terms of reliability, flow control, and congestion control. The primary objective of this paper is to explore QUIC functionalities to suggest techniques to improve throughput, speedup and efficiency in wireless networks. The experimental results were established on a local test bed setup connected to a wireless access point in a campus network environment. Experimental results show that QUIC performance in the form of throughput and speedup over TCP/IP in live network environment. The fairness of QUIC in competing flow situations is also examined, and found to perform well in long life traffic. We also propose the reintroduction of FEC for minimization of retransmission latencies.
DiffViz: A Diff Algorithm Independent Visualization Tool for Edit Scripts

A number of approaches and tools exist that extract and visualize the changes between two versions of a file and thereby help developers to understand them. DiffViz is an interactive visualization tool that visualizes the changes independent from the differencing algorithm. It supports, but is not limited to, a granularity on the level of abstract syntax trees. Furthermore, it provides several new features, such as node matching and the mini-map, to navigate and analyze the changes. A demo of the installation and example usage of the tool is available here: https://youtu.be/RF93ey9GYoc
A Study on SEPIC Converter for Voltage Modification Fed by Solar Photovoltaic Systems

In modern age due to expanding demand of electricity and limited traditional sources solar photovoltaic energy turns into good option as it is free of cost, pollution free and less maintenance. This paper discusses the power conversion circuit topology of DC-DC SEPIC converter by using pulse width modulation technique. The SEPIC converter is a fourth ordered DC-DC converter which is able to operate in the manner of buck and boost operation. The input voltage to SEPIC converter is sustained by sunlight based PV array, which depends upon the radiation. The SEPIC converter is placed between PV array and load. With the help of design equation of photon current and reverse saturated current, the modeling of solar panel is obtained. The SEPIC converter allows range of dC voltages to be adjusted to maintain a constant voltage output.
A Spiral Design Photonic Crystal Fiber Based Surface Plasmon Resonance Biosensor with High Sensitivity

This paper exhibits a simulation work of spiral design photonic crystal fiber (PCF) biosensor that demonstrations high sensitivity. The recommended biosensor mechanisms with surface plasmon resonance (SPR) phenomenon. The sensor performance is investigated utilizing finite element method (FEM) based software with perfectly matched layer (PML) and MATLAB software environment. In this design, gold is treated as an active plasmonic material. With a view to mitigating the fabrication challenge, gold is coated outside the fiber structure compared to inside coating on gold. The planned sensor indications the wavelength sensitivity of 2500 nm/RID (calculated by wavelength interrogation technique), the sensor resolution of $\pmb{4\times 10}^{-5}$ RIU and high linearity with the value of coefficient of determination $(R^{2})$ is equal to 0.9677. Considering its high sensing performance, this proposed sensor can be used as an excellent candidate to detect unknown analytes.
No-Reference Image Quality Assessment and Application Based on Spatial Domain Coding

Aiming at the problem that a no-reference (NR) image quality assessment algorithm is not accurate enough to predict the image quality at present, we proposed a method of NR image quality assessment based on spatial domain coding (SDC). We extracted the spatial structure features of the image on different bit planes. The extracted features quantify the structural information between pixels, which can more accurately reflect the distortion degree of the image. We used a neural network to establish an image quality assessment model. The experimental results show that the proposed image assessment algorithm is more accurate than the existing mainstream image quality assessment algorithms and highly consistent with subjective perception of human eyes. Finally, the proposed algorithm is applied to the auto focus of the camera verifying the practicability and accuracy of the algorithm.
Improving Passive 3D Model Reconstruction using Image Enhancement

Recently, 3D model reconstruction becomes a reality. That is because of using many algorithms that are implemented as automated tools and the ability to process dozens of images which might be calibrated or uncalibrated. When dealing with uncalibrated images, more processing time is usually required to perform "self-calibration". When using uncalibrated images, the images are usually not in perfect quality due to the uncalibrated nature of the capturing process. For this reason, this paper aims to study the impact of using image enhancement techniques on the quality of the building process of 3D models. This work mainly focus on the contrast of images. After applying the preprocessing procedures, the image sets where used to build the 3D models using three software kits (COLMAP, VSFM and Agisoft Photoscan). The results show that applying histogram equalization enhances the quality of the generated 3D models.
Determination of Local Goal for a Mobile Robot with Sporadic Human Commands of Tele-Operation

In this study, we propose a probabilistic approach to overcome time delay for a mobile robot. The robot receives sporadic user input when using brain-computer interface, and related studies have been considered about various strategies to control the robot as intended. However, the most of experiments heavily depended on the driving environment because of the low information transfer rate. To solve this problem, we propose a probabilistic local goal determination method. Our approach accumulates user input sequence, and specifies it as a feasible path. Simulations in the room and on the track are provided. As a result, we show that the proposed method is robust to long time delay, and achieves the mission with fewer inputs.
An Evaluation of E-Readiness Cloud Computing Service Model Adoption on Indonesian Higher Education

The adoption rate of cloud computing technology in Indonesia is still low. In fact, Indonesia is still considered in the early stages of adopting (early adopter). One of the requirements for a successful digital transformation program can be seen in the cloud adoption rate. Already many industries, businesses and government institutions that adopt cloud technology, but higher education are not optimal in utilizing it. Higher Education known the potential benefits of using cloud computing. This allows higher education to avoid providing excessive Information Technology (IT) infrastructure and reduce maintenance costs. With the development of technology and services in cloud computing, higher education must be ready and courageous in utilizing this technology. The previous research mentions that the factors that determine the adoption of IT in higher education are the Impact and Benefits of IT Products for Higher Education, Infrastructure Availability, Intercultural Competition, IT Knowledge Academic Civitas, Civitas Academic Participation, IT Leadership, Technology Trend, and the existence of Blue Print IT. Another study related to e-readiness of Cloud Computing Service states that the factors that greatly affect the process of readiness of cloud computing adoption in higher education are the availability of IT Infrastructure and good IT Planning. The problem arises when the concept of e-readiness is combined with the adoption of information technology in this case cloud computing. This paper will present the results of the evaluation of the e-readiness cloud computing adoption service model at higher education. The research method used is Design Science Research Method (DSRM) from Peffers with statistical data processing using SPSS software tools. The result of data processing shows that the measurement of e-readiness adoption of cloud computing service model in higher education is influenced by the availability and competence of IT staff, Infrastructure Availability, and the existence of good IT planning.
Principles of Mashup System Semistructured Data Processing

The principles of processing of input data in Mashup system of the semistructured data dynamic integration have been described. The main sources of semistructured data have been highlighted. The factors are affecting on the process of information resources integrating have been analyzed. The procedure of ontological classification of input data has been offered.
Second-Order Semi-Global Stereo Matching Algorithm Based on Slanted Plane Iterative Optimization

Stereo matching is a difficult and challenging task due to many uncontrollable factors that affect the results. These factors include the non-ideal radiometric conditions and the presence of weak-textured regions. A stereo matching algorithm including cost computation, cost aggregation, disparity computation, and disparity refinement is proposed to overcome these limitations. First, census, gradient, and HSL measures are combined as the pixel-wise matching cost to reduce the radiometric distortions. Second, a second-order smoothness constraint based on angle direction priors is utilized to improve the matching accuracy in the weak-textured regions. Then, the matching cost and smoothness constraint are applied to semi-global matching. Third, a winner-takes-all strategy is adopted to calculate the initial disparity maps. Finally, a coarse-to-fine simple linear iterative clustering superpixel segmentation is proposed to split the image into some regions with similar colors, adjacent pixels, and similar disparity planes. Through the segmentation result, boundary lines are classified into occluded, hinge, and coplanar lines. Slanted plane iterative optimization is performed to obtain the optimized disparity maps according to the segmentation result and boundary lines classification. The algorithm is evaluated in the Middlebury and KITTI datasets. The experimental results show that the method has good performance under non-ideal radiometric conditions and in the weak-textured regions. The disparity maps obtained by the method have a lower mismatching rate compared with other algorithms.
Alcoholism Detection by Wavelet Entropy and Support Vector Machine Trained by Genetic Algorithm

Nowadays, alcoholism becomes a more serious social problem, and we proposed a method to help doctors to detect the alcoholism patients. In our method, wavelet entropy (WE) was used to extract the features, support vector machine (SVM) was used classify the samples, and genetic algorithm (GA) was used to optimize our classifier. In our method, the average sensitivity is 88.42±1.74%, the average specificity is 88.93±1.62%, and the average accuracy is 88.68±0.30%, which is better than the three state-of-the-art approaches: FA-PNN, HMI, and FRFT. Our method is effective in alcoholism detection and can help doctors to reduce the massive detection work.
The Dynamics of Borrower Reputation Following Financial Misreporting

We study the dynamics of borrower reputation in bank loan markets following revelations of financial misreporting by the borrower. Misreporting firms pay greater loan spreads than matched firms for at least six years following revelation of the misreporting, and there is no evidence of a downward trend in the misreporting premium. Following revelation, misreporting firms are more likely to engage in various actions to potentially rebuild their reputations, but even firms that engage in multiple actions continue to pay greater loan spreads for at least six years. Our results suggest that misreporting causes long-lasting and costly reputation losses that firms find very difficult or prohibitively costly to restore. Data and the online appendix are available at https://doi.org/10.1287/mnsc.2017. 2739 . This paper was accepted by Wei Jiang, finance.
Path Planning of an Autonomous Quadcopter Based Delivery System

This paper presents a novel offline path planning algorithm for the quadcopter based delivery system. For any quadcopter based system, time of execution needs to be minimized as it affects the system performance. This proposed algorithm is a combination of sampling based Bidirectional RRT algorithm and improved Artificial potential field (APF) algorithm, so that the former is made more goal oriented. Algorithm rely on the knowledge of the environment from sensors, to plan a path efficiently with reduced execution time. Improved APF limits the search space to a smaller section of the environment in turn reducing the execution time. Validation of the algorithm is done using ROS TUM simulator AR Drone in presence of obstacles. A proportional position and altitude controller are used to track the path which is planned using proposed algorithm.
A Single-Phase Symmetrical Embedded Modified-Quasi-Z-Source Hybrid Three-Level Inverter

This paper proposes the novel topologies implemented by embedding a single dc voltage source or two separate dc voltage sources in the impedance network of an existing modified quasi-Z-source (MqZS) hybrid three-level inverter topology, which combines the modified quasi-Z-source impedance and the hybrid three-level inverter. The proposed topology can provide the continuous dc source currents without any additional external filter, and can increase the boost factor by a factor of two by embedding two separate dc sources in the impedance network. The operating principle of the proposed topologies is analyzed, and the performances of the proposed topology are demonstrated by the simulation and experimental results.
Estimating Demand Function for Gold Using the Vector Auto Regression Model

Gold has been a commodity of interest among economists and there are quite a few studies analyzing the supply, demand and price of gold at a global context. Unlike for other commodities where supply and demand together contribute to the movement of price of the commodity, in the case of gold, the supply being more or less constant, the price swings are largely driven by the demand for gold. With the investment demand for gold increasing in India and given the dynamic relation between price and demand, it is interesting to estimate the demand function for gold using econometric models. When the drivers of demand for gold are different in developed and developing countries, there are only a few studies which have examined the factors influencing demand for gold in India. Unlike a few earlier studies which estimate the demand function for gold using the multiple regression model, the current study estimates the demand function for gold using the Vector Auto Regression model which helps in identifying the influence of the lagged values of the given variables and their interaction effects.
Primary Pitfalls in Detection and Analysis of Diseased Chunk of Leaf Images

In Agriculture, the plant leafs may be affected by various diseases and they can be detected by image processing techniques based on color, shape and its texture. The increase in differences from a healthy to diseased leaf shows the increase in severity of the disease. But, the image which is undergoing the analysis for the detection of the diseased chunk may have busy or complex background or presence of shadow in the leaf image, inclusion of sunlight reflection which turns out to be a difficult task in applying segmentation/ clustering techniques. The diseased Plant leaf can undergo various techniques such as Image Preprocessing, Histogram, Thresholding, Segmentation, Feature extraction and Classification to identify the diseased fragment. The challenging aspect in establishing these techniques is the image condition. Certain affected leaf images may also contain more than one symptom of diseased spot which is also a pitfall in identifying the disease. This study reveals the complications of the diseased images and how can be they dealt with.
CoEP: A secure & lightweight application protocol for the Internet of Things

The growth of wireless sensor networks and Internet of Things (IoT) applications happened in many areas, such as electronics, hardware, sensors, operational systems and protocols. However, it is noticed that, while there are many efforts to reduce energy consumption and develop leaner applications, the application protocol of the IoT communications has remained the same for all hardware. This paper presents CoEP, a simple, secure and lean protocol for the IoT which relies on very low-power and low footprint features.