Miracle-dz/UniFAR-FTUs huggingface dataset:
B2SFinder: Detecting Open-Source Software Reuse in COTS Software

COTS software products are developed extensively on top of OSS projects, resulting in OSS reuse vulnerabilities. To detect such vulnerabilities, finding OSS reuses in COTS software has become imperative. While scalable to tens of thousands of OSS projects, existing binary-to-source matching approaches are severely imprecise in analyzing COTS software products, since they support only a limited number of code features, compute matching scores only approximately in measuring OSS reuses, and neglect the code structures in OSS projects. We introduce a novel binary-to-source matching approach, called B2SFINDER, to address these limitations. First of all, B2SFINDER can reason about seven kinds of code featuresthat are traceable in both binary and source code. In order to compute matching scores precisely, B2SFINDER employs a weighted feature matching algorithm that combines three matching methods (for dealing with different code features) with two importance-weighting methods (for computing the weight of an instance of a code feature in a given COTS software application based on its specificity and occurrence frequency). Finally, B2SFINDER identifies different types of code reusesbased on matching scores and code structures of OSS projects. We have implemented B2SFINDER using an optimized datastructure. We have evaluated B2SFINDERusing 21991 binaries from 1000 popular COTS software products and 2189 candidateOSS projects. Our experimental results show that B2SFINDER is not only precise but also scalable. Compared with the state ofthe art, B2SFINDER has successfully found up to 2.15x as many reuse cases in 53.85 seconds per binary file on average. We also discuss how B2SFINDER can be leveraged in detecting OSS reusevulnerabilities in practice.
Evaluation of Acute Tonic Cold Pain From Microwave Transcranial Transmission Signals Using Multi-Entropy Machine Learning Approach

This study aims to improve the accuracy of detecting acute tonic cold pain (CP) perception from microwave transcranial transmission (MTT) signals. Two different types of CP and no-pain (NP) MTT signals are obtained from 15 subjects. Four features, namely, power spectral exponential entropy, improved multiscale permutation entropy, refined composite multiscale dispersion entropy, and refined composite multiscale fuzzy entropy, are extracted in the variational modal decomposition domain. The feature datasets are divided into training datasets and test datasets in a 3:1 ratio. Random forest (RF) and support vector machine (SVM) are selected as classifiers. The training datasets are imported into the classifier, and the optimal training dataset is obtained with a 10-fold cross validation strategy. The feature dimension reduction algorithm of the principal component analysis is used to reduce the complexity of the feature datasets and select the most recognizable features. The classification performance of the test datasets is evaluated by the optimal classifiers. Results showed that the RF classifier performs better than the SVM classifier. The RF classifier provides high values of specificity (91.67%), sensitivity (95.83%), positive predictive value (92.00%), accuracy (93.75%), and area under curve (0.867). The combination of the microwave detection approach and machine learning algorithm can effectively detect brain activity induced by nociceptive stimulation. This approach is important in improving the accuracy of pain detection.
Adaptive GLRT-, Rao- and Wald-Based CFAR Detectors for Distributed Targets

Distributed targets detection in Gaussian noise is studied. Since the noise power is possible to change from cell to cell due to the heterogeneity of the surveillance area, the noise in the test/training data is assumed to have the same structure of covariance matrix (CM) but various powers. Three detectors are derived via the generalized likelihood ratio test, Rao and Wald tests, respectively. The new detectors have the statistical constant false alarm rate (CFAR) properties against the noise power and structure of the noise CM. Monte Carlo simulations show that the new detectors outperform their counterparts.
A Simple Macromodel Circuit for Operational Transconductance Amplifier with Low Frequency OTA-C Filter Application

This work presents an operational transconductance amplifier (OTA) macromodel for operation in low frequency OTA-C filters. The proposed macromodel is simple in structure, modeling characteristics similar to that of transistor-level based CMOS-OTA. The macromodel is based on a differential input balanced output OTA with finite input and output impedance. The development of the macromodel is based on four design stages providing input and output characteristics, limiting the range of operation controlled by limiting circuit of diodes, addition of non-linearity of the OTA using power series terms, and providing balanced output. The proposed macromodel design is tested in simulation of a fourth-order elliptic OTA-C filter, having macromodel based results compared to those based on transistor-level OTA using 90 nm CMOS technology in LTspice. The obtained simulation results reflect close agreement between those from macromodel and those of the transistor-level with reduction in computation time. Moreover, tuning of the filter for variable gain, bandwidth, and notch frequency is also tested using both the macromodel and CMOS-OTA.
ATR: Automatic Trajectory Repairing With Movement Tendencies

GPS trajectories are always embedded with errors, due to the weather or environmental variables. Existing trajectory repairing methods have employed Kalman filters or sequential data cleaning methods. Kalman filter or its variants change all observed measurements, while generally most measurements are originally accurate. Sequential data cleaning methods are mainly applied on one-dimensional data sequences, and when encountering multi-dimensional trajectories, their performance will be compromised due to that the features of multi-dimensional trajectories are not fully utilized. To address these issues, we propose to repair GPS trajectory with movement tendencies, speed change tendency, travel distance tendency and repair distance tendency. We formalize the tendency based trajectory repairing, and propose an exact solution to find the repair which minimize movement tendency score. Then we propose high quality candidate selection and dynamic error range estimation, to improve the efficiency and effectiveness of exact solution. Experiments on three data sets demonstrate the superiority of our proposal.
Humanoid co-workers: How is it like to work with a robot?

Human-robot interaction in corporate workplaces is a research area which remains unexplored. In this paper, we present the results and analysis of a social experiment we conducted by introducing a humanoid robot (Nadine) into a collaborative social workplace. The humanoid's primary task was to function as a receptionist and provide general assistance to the customers. Moreover, the employees who interacted with Nadine were given over a month to get used to her capabilities, after which, the feedback was collected from the staff on the grounds of influence on productivity, affect experienced during interaction and their views on social robots assisting with regular tasks. Our results show that the usage of social robots for assisting with normal day-to-day tasks is taken quite positively by the co-workers and that in the near future, more capable humanoid social robots can be used in workplaces for assisting with menial tasks. Finally, we posit that surveys such as ours could result in constructive opinions based on technological awareness, rather than opinions from media-driven fears about the threats of technology.
Optimization of Fabrication Parameters on Hybrid Fibre Reinforced Polymer Composite by ANN Design

Natural fiber based composites fabricated usually posses low strength and finds less use in automotives. Hybridization with stronger synthetic fibers like carbon or glass improves the strength. Composites prepared with synthetic fibers as outer layers and natural fibers as inner core shall serve in automotives. This work explodes the potential reinforcement of fibers from palm fruit for fabricating polymer composites. The fibers were pre-treated with glass fibers to form as fiber mats and hybridized with glass fibers prior to reinforcement. These mats were reinforced in the polyester resin matrix as different layers to form a hybrid composite. Experiments were done by varying the length of fiber, fiber volume and with different treatments using response surface methodology. Tensile strength was measured as response. The tensile strength was spotted maximum in 8% potassium permanganate (KMnO4) treated palm fibers with optimum fiber length 40 mm and volume fraction of 20%. The surface study on these composites through SEM (Scanning Electron Microscope) examination was satisfactory. Hence this specimen combination was best suited and recommended to manufacture components like car bonnet, bumper etc.
Robust Mixed $\mu$ -Synthesis Frequency Regulation in AC Mobile Power Grids

This article proposes a novel controller for the secondary load frequency control (LFC) of an alternating current shipboard microgrid (MG). The suggested controller is based on the robust <inline-formula> <tex-math notation="LaTeX">$ {\mu }$ </tex-math></inline-formula>- and mixed <inline-formula> <tex-math notation="LaTeX">$ {\mu }$ </tex-math></inline-formula>-synthesis in which the structured system uncertainties and external disturbance inputs are considered in the design procedure of the controller. The offline designed controller is resilient against the power fluctuations of the load and renewable energy sources (RESs), such as solar and wave energies. The output of the controller manipulates a diesel generator, as well as energy storage systems (ESSs) to effectively regulate the frequency of the ac MG to the desired value and reduce the frequency oscillations originated by the loads and RES power changes. Several hardware-in-the-loop (HiL) real-time dSPACE emulator-based simulations have been performed to show the effectiveness of the suggested robust control scheme. In addition, the comparison results are provided to better highlight the advantages of the proposed <inline-formula> <tex-math notation="LaTeX">${\mu }$ </tex-math></inline-formula>-synthesis-based approach over the state-of-the-art techniques.
Coal Price Prediction Using Financial Indices

The price movement prediction in the futures market is difficult due to fluctuating demands and supplies. This thesis addresses the problem of coal price movement prediction. The study compares two prediction models using two different datasets. The first dataset includes daily trading data, while the second dataset contains both daily trading data and computed financial indices. The data from Indonesia and Australia between 2010 and 2019 is used for the experiment. The experimental results show that the second model achieves higher accuracy. The market simulation also indicates that the second model enjoys a larger trade gain higher than 30% of the budget within a year.
Automatic Building Extraction From High-Resolution Aerial Imagery via Fully Convolutional Encoder-Decoder Network With Non-Local Block

Extracting buildings automatically from high-resolution aerial images is a significant and fundamental task for various practical applications, such as land-use statistics and urban planning. Recently, various methods based on deep learning, especially the fully convolution networks, achieve impressive scores in this challenging semantic segmentation task. However, the lack of global contextual information and the careless upsampling method limit the further improvement of the performance for building extraction task. To simultaneously address these problems, we propose a novel network named Efficient Non-local Residual U-shape Network(ENRU-Net), which is composed of a well designed U-shape encoder-decoder structure and an improved non-local block named asymmetric pyramid non-local block (APNB). The encoder-decoder structure is adopted to extract and restore the feature maps carefully, and APNB could capture global contextual information by utilizing self-attention mechanism. We evaluate the proposed ENRU-Net and compare it with other state-of-the-art models on two widely-used public aerial building imagery datasets: the Massachusetts Buildings Dataset and the WHU Aerial Imagery Dataset. The experiments show that the accuracy of ENRU-Net on these datasets has remarkable improvement against previous state-of-the-art semantic segmentation models, including FCN-8s, U-Net, SegNet and Deeplab v3. The subsequent analysis also indicates that our ENRU-Net has advantages in efficiency for building extraction from high-resolution aerial images.
Resource Management in LTE-U Systems: Past, Present, and Future

With the tremendous growth in mobile data traffic, wireless cellular networks are facing a rigorous challenge to increase network capacity. Despite that many advanced technologies are used, the shortage of spectrum resource is still the main bottleneck for capacity enhancement. To address the challenge, the cellular networks have been motivated to seek for more fruitful radio spectra. Amongst many others, the unlicensed 5 GHz spectrum is a promising candidate due to its low channel attenuation, large available bandwidth, and easy to utilize. Therefore, the innovative technology of long-term evolution (LTE) using the unlicensed spectrum, known as LTE-Unlicensed (LTE-U), has been widely investigated as a promising means to increase the data rate of cellular networks. The unique characteristics of the unlicensed spectrum bring new challenges to resource management in the LTE-U system. During the last few years, there have been a lot of resource management designs for the newly-born but vigorous LTE-U technologies. This paper provides a comprehensive overview of the state-of-the-art resource management scenarios in LTE-U systems, including single small cell base station (SBS), multiple SBSs, device-to-device (D2D) networks, vehicular networks, and unmanned aerial vehicle (UAV) systems. The future research issues of resource management in LTE-U for 5G are also outlined.
An Automatic Accident Detection System: A Hybrid Solution

Over the past few decades, the world has undergone rapid progress in the fields of technology as well as the transportation system. Moving vehicles are not just modernized and efficient but unfortunately cause the number of causalities in road accidents. Unidentified crashes and late rescue create more edgy situation, particularly on unpopulated highways. To lessen accidental mortality rate, a permissible low-cost automatic accident detection system is proposed that automatically detects the crash and sends notification through Short Message Service (SMS) to the emergency services and concerned family member with precise location. The proposed system consists of hardware and software modules. The hardware module is based on the Arduino board with vibration sensor and accelerometer is deployed in vehicle whereas the software part is an Android application that is installed on users mobile. The experimental results indicate that the presented system functions well as intended.
Tortuosity of Retinal Main and Branching Arterioles, Venules in Patients With Type 2 Diabetes and Diabetic Retinopathy in China

The epidemic studies have demonstrated an excessively increase of diabetic patients in recent years, who are at high risk of developing microvascular complications. Those microvascular complications usually lead to chronic health problems, e.g. diabetic retinopathy (DR), thus early and regular screening is indeed necessary. Retinal vascular tortuosity, which is one of geometrical parameters related to microvascular morphological changes, has been investigated in many studies related to diabetes, diabetic risk factors and diabetic complications. However, the global tortuosity of retinal vessels in DR progression is quantified either subjectively or imprecisely in those previous studies. Furthermore, to our best knowledge, the association of tortuosity, diabetes and DR has never been investigated based on a Chinese population-based cohort, even though it accounts for more than 30% of diabetic patients worldwide. Therefore, it is of great necessary and clinical significance to extract retinal tortuosity in a more reliable way and explore its associations with diabetic risk factors and DR severity in China. In this study, high contrast retinal images from 495 Chinese patients with type 2 diabetes were acquired by confocal scanning laser ophthalmoscope and the global tortuosity was extracted from main and branching arterioles, venules in the whole retinal image based on the theory of best-fit exponential curves in the roto-translation group SE(2). The statistical analysis results show that retinal arteriolar and venular tortuosity might be remarkable indices for assessing retinopathy severity and identifying individuals with high-risk of renal disease in diabetes, which may provide additional insights on microvascular changes for diabetic retina and kidney diseases.
Deep Learning for 3D Classification Based on Point Cloud with Local Structure

Point cloud consists of many unordered and unstructured points, which makes the simple deep learning (DL) network hard to capture the local structure of point cloud. This shortcoming limits the ability of the DL network to recognize the fine-grained features of objects. Network structure is changed in some studies for this problem, but this increases the network complexity. This paper proposes an effective preprocessing method for point cloud to deal with this problem. The local region that represents the local structure of point is searched by using a cube with fixed side length. All of the points in the local region are used to construct the feature vector of the center point located at the center of the cube. These feature vectors are input into a simple convolutional neural network. The ModelNet40 shape classification benchmark is used to evaluate the proposed method. Experimental results show that the proposed method improves the classification accuracy of the simple deep learning network.
Performance Enhancement of Lightning Protection Systems for Offshore Wind Turbine Blades

When offshore wind turbines are polluted with salt, their surface characteristics change. Pollution affects the performance of the protection systems which can result in damages when it is hit by lightning. In this paper, two lightning protection systems (metallic cap and receptor methods), commonly in use, are applied on a full scale polluted offshore wind turbine to compare their performance. The lightning attachment to the offshore wind turbine blade is studied by analyzing the variations in maximum electric field strength required for the initiation of upward leader due to a vertical tri-pole cloud charge distribution. The computation is done for a polluted Vestas V100, 2 MW wind turbine. A new protection system (enhanced method) with higher lightning protection efficiency is developed. The performance of the metallic cap was then enhanced by adding a receptor at the middle of the blade. This modification has a significant impact on the electric field distribution of the blade. The new method was compared with the metallic cap and the tip receptor. The proposed method can markedly improve the lightning protection performance of offshore wind turbines. The results of the point of initiation of upward leader coincides with experimental data from lightning discharge attachments.
A Node Selecting Approach for Traffic Network Based on Artificial Slime Mold

The node selecting problem of traffic network is a significant issue and is difficult to be solved. In this paper, an artificial slime mold method is proposed to help us solve the problem. First, the chief components of an artificial slime mold are introduced to simulate the foraging behavior of a true slime mold, including external food sources, plasmodium, myxamoeba, nucleus, and nutrients. Then the learning mechanism of nutrient concentration for the artificial slime mold is illustrated, though there is no brain or neuron in its body. After that, the node selecting approach is described according to the propagation capabilities of nodes. Second, the algorithm flow is designed to show how to solve this kind of complex selecting problem. The algorithm flow to select important traffic nodes by artificial slime mold is composed of 4 main steps, including initialization, food searching, feeding, and selecting for output. Third, a comprehensive example is designed and derived from references to certificate that the proposed artificial slime mold can help us select important traffic nodes by their generated traffic topologies. The contributions of this paper are important both for traffic node selecting and artificial learning mechanism in theoretical and practical aspects.
Effect of Human Hand Dynamics on Haptic Rendering of Stiff Springs using Virtual Mass Feedback

Hard surfaces are typically simulated in a haptic interface as stiff springs. Stable interaction with these surfaces using force feedback is challenging due to the discrete nature of the controller. Previous research has shown that adding a virtual damping or virtual mass to the rendered surface helps to increase the stiffness of the surface for stable interaction. In this paper, we analyze the effect of adding virtual mass on the range of stiffness that can be stably rendered. The analysis is performed in the discrete time domain. Specifically, we study the coupled (with human hand dynamics) stability of the haptic interface. Stability, when the human interacts with the robot, is investigated by considering different human hand models. Our analysis shows that, when the human operator is coupled to an uncoupled stable system, an increase in the mass of a human hand decreases maximum renderable stiffness. Moreover, the increase in human hand damping increases the stably renderable stiffness.
Why Don ' t We

Vv research (Ekman and O'Sullivan, 1990; Frank and Ekman, forthcoming; Ekman, Erank, and O'Sullivan, forthcoming) suggests that most people cannot tell from demeanor whether someone is lying or telling the truth. Such poor performance is not because lies are told flawlessly. Most liars make mistakes which could be detected but usually are missed. Both perpetrating a lie and detecting a lie, in most people, seem to be poorly developed skills. In this article, I consider six explanations for why most of us do not catch liars from demeanor. I will first explain how I distinguish lying from other forms of deceit, and then discuss the evidence which suggests that people are such poor lie catchers. The intent of the liar is one of the two criteria I (Ekman, [1985] 1992) use to distinguish lies from other kinds of deception. The liar deliberately chooses to mislead the target. Liars may actually tell the truth, but that is not their intent. And truthful people may provide false information—bad advice from a stock broker—but that is not their intent. The liar has a choice; the liar could chose not to lie. We are all tempted to lie, but we do not always do so. Lying is not irresistible; it is, by my definition, a conscious, considered choice. I do recognize that lying can become a habit and then performed with little consideration, but, at least initially, all such habits began as considered choices about whether or not to do so. Presumably, a pathological liar is compelled to lie and by my definition, therefore, is not a liar.