Miracle-dz/UniFAR-FTUs huggingface dataset:
Automatic Code Generation From UML State Chart Diagrams

The fact that event driven systems can be modeled and implemented using unified modeling language (UML) state chart diagrams has led to the development of code generation tools. These are tremendously helpful in making software system designs and can even generate skeletal source code from these designs. The implementation of such automatic code generation from state diagrams is not fully supported by the existing programming languages. The major down side is that there is no one-to-one correspondence between the elements in the state chart diagram and the programming constructs. The existing programming elements cannot effectively implement two main components of the state diagram namely, state hierarchy and concurrency. In this paper, we present a novel design pattern for the implementation of the state diagram which includes hierarchical, concurrent, and history states. The state transitions of parallel states are delegated to the composite state class. The architecture of the code generator and the step by step process of code generation from UML state machine are proposed in the paper. The proposed approach is implemented and compared with similar tools and the results are found to be promising.
Enabling of Predictive Maintenance in the Brownfield through Low-Cost Sensors, an IIoT-Architecture and Machine Learning

Predictive maintenance is one of the major drivers of Industry 4.0 as it can significantly reduce costs by improving overall equipment effectiveness and extending the remaining useful life of production machines. Most of the potential lies in the brownfield with old equipment where no sensors or connectivity are available. This paper shows how these production machines can be enabled for predictive maintenance by retrofitting with low-cost sensors, an Industrial-Internet-of-Things-architecture and machine learning. An industrial implementation on a heavy lift Electric Monorail System at the BMW Group will be shown.
Hardware Implementation of the Dual-Channel Spectral Subtraction Method for Lung Sounds Denoising

In this paper, a hardware architecture of dual-channel spectral subtraction method has been proposed for real-time lung sounds enhancement. The proposed architecture has been implemented on field programmable gate array (FPGA) chip using a high-level programming tool. Dual-channel spectral subtraction technique allows a dynamic estimate of the noise spectrum for an adaptive and robust lung sounds enhancement. The denoising performances of the proposed hardware architecture are compared to those obtained by MATLAB simulation, using subjective and objective evaluation tests. Resource utilization, maximum operating frequency, and total power consumption are presented for a low-cost Artix-7 FPGA chip.
Kernel Soft-neighborhood Network Fusion for MiRNA-Disease Interaction Prediction

Studies have shown that microRNAs are functionally related to human diseases. However, experimental methods for detecting miRNA-disease associations are both time consuming and laborious. Therefore, a large number of computational models for predicting potential miRNA-disease interaction have been proposed. However, few methods take into account the nonlinear structural similarity of miRNAs (diseases) and effectively integrate multiple similar metrics into one network. In this paper, we propose a kernel-based soft-neighborhood network propagation algorithm (LKSNF) to predict potential miRNA-disease interactions, which not only exploits the potential nonlinear relationship, but also effectively integrates different similar measures of miRNA (disease). The results of the 5-fold cross-validation show that the LKSNF model has significantly better predictive performance than other state-of-the-art methods. Case study further illustrates the effectiveness of LKSNF in predicting new miRNA-disease interactions.
Enabling Efficient Source and Path Verification via Probabilistic Packet Marking

The Internet lacks verification of source authenticity and path compliance between the planned packet delivery paths and the real delivery paths, which allows attackers to construct attacks like source spoofing and traffic hijacking attacks. Thus, it is essential to enable source and path verification in networks to detect forwarding anomalies and ensure correct packet delivery. However, most of the existing security mechanisms can only capture anomalies but are unable to locate the detected anomalies. Besides, they incur significant computation and communication overhead, which exacerbates the packet delivery performance. In this paper, we propose a high-efficient packet forwarding verification mechanism called PPV for networks, which verifies packet source and their forwarding paths in real time. PPV enables probabilistic packet marking in routers instead of verifying all packets. Thus, it can efficiently identify forwarding anomalies by verifying markings. Moreover, it localizes packet forwarding anomalies, e.g., malicious routers, by reconstructing packet forwarding paths based on the packet markings. We implement PPV prototype in Click routers and commodity servers, and conducts real experiments in a real testbed built upon the prototype. The experimental results demonstrate the efficiency and performance of PPV. In particular, PPV significantly improves the throughput and the goodput of forwarding verification, and achieves around 2 times and 3 times improvement compared with the-state-of-art OPT scheme, respectively.
Hot Spot Analysis over Big Trajectory Data

Hot spot analysis is the problem of identifying statistically significant spatial clusters from an underlying data set. In this paper, we study the problem of hot spot analysis for massive trajectory data of moving objects, which has many real-life applications in different domains, especially in the analysis of vast repositories of historical traces of spatio-temporal data (cars, vessels, aircrafts). In order to identify hot spots, we propose an approach that relies on the Getis-Ord statistic, which has been used successfully in the past for point data. Since trajectory data is more than just a collection of individual points, we formulate the problem of trajectory hot spot analysis, using the Getis-Ord statistic. We propose a parallel and scalable algorithm for this problem, called THS, which provides an exact solution and can operate on vast-sized data sets. Moreover, we introduce an approximate algorithm (aTHS) that avoids exhaustive computation and trades-off accuracy for efficiency in a controlled manner. In essence, we provide a method that quantifies the maximum induced error in the approximation, in relation with the achieved computational savings. We develop our algorithms in Apache Spark and demonstrate the scalability and efficiency of our approach using a large, historical, real-life trajectory data set of vessels sailing in the Eastern Mediterranean for a period of three years.
Single-Scatter Model for Short-Range Ultraviolet Communication in a Narrow Beam Case

The single-scattering model for short-range ultraviolet (UV) communication is often replaced by an approximate model due to its simplified calculation. However, there are certain errors associated with the existing approximate models. To reduce the errors, a spherical crown (SC) model in a narrow beam case is derived in this letter, where we assume that the differential volume is a thin layer of SC. Meanwhile, the applicable conditions and the error range of the SC model are provided. The calculation results indicate that the errors of the existing approximate models range from −1.4 to 6.0 dB while those of the SC model range from −0.2 to 0.4 dB for the considered transceiver geometries, which means that the SC model has a higher accuracy than the existing approximate models. In addition, the bit-error rate performance of the UV communication system under the SC model is also presented. This letter greatly improves the accuracy of the single-scattering approximate model and provides a new method to estimate the path loss of short-range UV communication.
Network Security for IoT Using SDN: Timely DDoS Detection

Software Defined Networking (SDN) provides a secure, efficient, and autonomous means of managing Internet of Things (IoT) networks. SDN facilitates the use of a centralized logical control over the network, however it exposes the network to potential vulnerabilities due to its centralized architecture. This paper details one such scenario wherein a Distributed Denial of Service (DDoS) is initiated by directing an overwhelming traffic load to the target, which makes services unavailable to valid users [1]. To mitigate the attack in a timely manner, a DDoS detection algorithm measuring entropy is implemented in the central controller to introduce a solution that is effective and lightweight in terms of usage of resources.
Robustness of Compressed Convolutional Neural Networks

Advancements in deep neural networks have revolutionized the way how we conduct our day-to-day activities ranging from how we unlock our phones to self-driving cars. Convolutional Neural Networks (CNN) play the principal role in learning high level feature representations from visual inputs. It is crucial to know how reliable those neural networks are as human lives can be at stake. Recent experiments on the robustness of CNNs show that they are highly susceptible to small adversarial perturbations. Due to the increasing popularity of mobile devices, there is a significant demand for CNN models which are smaller enough to run on a mobile device without sacrificing the accuracy. Although recent researches have been successful at achieving smaller models with comparable accuracy on standard image datasets, their robustness to adversarial attacks has not been studied. However, massive deployment of smaller models on millions of mobile devices stresses importance of their robustness. In this work, we study how robust such models are with respect to state-of-the-art compression techniques such as quantization. Our contributions are summarized as follows: (1) insights to achieve smaller and robust models (2) a compression framework which is adversarial-aware. Our findings reveal that compressed models are naturally more robust than compact models. This provides an incentive to perform compression rather than designing compact models. Additionally, the latter provides benefits of increased accuracy and higher compression rate, up to 90×.
Fabrication of Red Mud/Molecularly Imprinted Polypyrrole-Modified Electrode for the Piezoelectric Sensing of Bilirubin

Red mud/molecularly imprinted polypyrrole (RM/MIPPy) microparticles were successfully prepared via the surface modification and molecular imprinting technique. A novel piezoelectric sensing platform with excellent molecular recognition capability was established for the detection of bilirubin (BR) based on the immobilization of the RM/MIPPy microparticles onto quartz crystal microbalance (QCM) electrode. The performance of the fabricated biosensor was evaluated and the results indicated that the biosensor exhibited high sensitivity in BR detection, with a linear range from 0.01 to <inline-formula> <tex-math notation="LaTeX">$120~\mu \text{M}$ </tex-math></inline-formula> and a limit of detection of <inline-formula> <tex-math notation="LaTeX">$0.003~\mu \text{M}$ </tex-math></inline-formula>. Moreover, the biosensor presented outstanding selectivity toward BR in comparison to other interferents. The analytical application of the BR biosensor confirmed the feasibility of BR detection in serum sample.
virtual media (

In this work, we propose a virtual media (Vmedia) access protocol for interactive image browsing over the Internet. Vmedia is a multimedia communication protocol in a server-client environment. It enables the access and delivery of segments of media, and manages the delivered media segments with a local Vmedia cache. An interactive JPEG 2000 image browser is implemented with Vmedia. The image is compressed with JPEG 2000 into one single bitstream and put on the server. In the interactive browsing process, the user specifies a region of interest (ROI) with a certain spatial and resolution constraint. The Vmedia browser only downloads the media segments covering the current ROI, and the download is performed in a progressive fashion so that a coarse view of the ROI can be rendered very quickly and then gradually refined as more and more bits arrive. In the case of switch of ROI, e.g., zooming in/out or panning around, the Vmedia browser will use existing media segments in cache to quickly render a coarse view of the new ROI, and in the same time, request a new set of media segments corresponding to the updated ROI. Vmedia greatly improves the browsing experience of large images over slow networks.
A Deep Learning Models for Blind Guidance by Integrating CNN and ELM

Sign recognition is a challenging task but has many important applications. Blind guidance system in one of them. Besides accuracy, which is one of the main metrics as in many other applications, blind guidance system demands real-time response in addition to make the application practical in real word. In this paper, we propose to combine ELM classifier with Deep Convolutional Features, which are obtained from some popular CNNs including AlexNet, GoogLeNet and ResNet, etc., to reduce both training time and prediction time and meanwhile maintain the same level of accuracy comparing to a transfer learning scheme with a single hidden dense layer. Experiments on toilet sign images demonstrate that the proposed method may perform much faster and maintain comparable accuracy. Performance comparisons are also investigated with different CNNs and hyper-parameters.
Joint Extraction of Entities and Relations of Breast Ultrasound Reports Based on Deep Learning

The breast disease has become a common and prevalent disease. Ultrasound is a common way to diagnose breast diseases, so a large number of ultrasound textual reports have been generated which are valuable for breast disease. Recognition of entities and semantic relations through the ultrasound report is the premise to meet the demand of modern clinical system. The paper proposes a joint extraction method of the entity and relation based on text data of breast ultrasound reports, using practical ultrasound reports of patients with breast diseases from a 3A grade hospital in Shanghai, and focuses on an innovative point that is depending on deep learning to extract entities and relations from text data of ultrasound reports. This method uses deep learning to realize the structuralization of breast ultrasound reports to extract partial features of the test on the basis of collecting features from training data. Then, the relation between entities is classified. Finally, the extracted relation classification and the partial features of text are used to improve the accuracy of structuralization. The experiments show the accuracy of text structure increases by nearly 10% via the joint extraction of entity and relation method based on deep learning, comparing with the common structured approach relying on pipeline model, which reaches up to 80.3% in practical text data of ultrasound reports.
Detection of Cyberbullying in Social Networks Using Machine Learning Methods

Increasing Internet use and facilitating access to online communities such as social media have led to the emergence of cybercrime. Cyber bullying, a new form of bullying that emerged recently with the development of social networks, means sending messages that include slanderous statements, or verbally bullying other people or persons in front of the rest of the online community. The characteristics of online social networks enable cyberbullies to access places and countries that were previously unattainable. In this study; the use of natural language processing techniques and machine learning methods namely, Bayesian logistic regression, random forest algorithm, multilayer sensor, J48 algorithm and support vector machines have been used to determine cyber bullying. To the best of our knowledge, the successes of these algorithms with different metrics within different experiments have been compared for the first time to the real data.
The Application of Improved Particle Filtering in Ballistic Reentry Target Trajectory Tracking

To deal with the problem of ballistic reentry target trajectory tracking, a novel particle filter algorithm is proposed to mitigate the particle degeneracy. It can eliminate the inefficient particles by judging the degree of particle degradation at each time and generates new particles based on the estimation state values and their covariance according to the degeneracy ratio of particles. Compared with Standard Particle Filter (SPF) algorithm and Unscented Particle Filter (UPF) algorithm, simulation results show that the proposed algorithm can achieve better tracking performance with lower computational burden for the trajectory reentry target tracking model. Especially in the case of ballistic target maneuver, the performance stability of the proposed algorithm is even more significant.
Multi-scale face detection based on convolutional neural network

To achieve faster and more accurate face detection, this paper proposes an end-to-end multi-scale face detection algorithm based on a convolutional neural network (CNN). First, a layer of data pre-processing was added between the convolutional layers, and the pixel values of the original image were inverted and then XORed with the original image value. Second, an improved inception structure was added between the first and second convolutional layers, thus reducing dimensionality during the extraction of richer and deeper features. Third, a spatial pyramid pooling layer was added between the convolutional layer and the fully connected layer, to solve the multi-scale problem. Fourth, the image was divided into multiple grids, and faces were detected by multiple prediction frames on each grid. Finally, the loss function commonly used in the detection algorithm was improved by modifying the loss function in the detected algorithm, to improve the accuracy of the model. The test results of public face datasets revealed that this method could extract meaningful facial features and demonstrate a superior performance to that of several competitive face detection algorithms.
Multi-type Digital Recognition Based on TensorFlow

For the problem of inaccurate identification of numbers, this paper is based on LeNet-5 network structure and optimizes it. The objective function and optimizer are added after the network output, and the sample library is updated to make it more accurate to identify multiple types of numbers. The optimized architecture is applied to identify multiple types of numbers, trained and tested, and the optimization parameters are selected by comparison. The experimental results show that the optimization parameters of certain values have a higher recognition rate for identifying numbers. The study has reference value for multi-type digital recognition.
Resilient and Scalable Cloned App Detection Using Forced Execution and Compression Trees

Android markets have grown both in size and diversity, offering apps that are localized or curated for specific use cases. It is not uncommon for users to be unaware of the exact app version or name they should be installing. This has given rise to the threat of app cloning where adversaries copy the package of an app, minimally modify its code, and redistribute the clone on the market to gain a monetary advantage or to distribute malicious payloads. Existing clone detection methods use static signatures that can be evaded using control-and data-flow obfuscation. Moreover, many approaches do not scale with the number of apps, code size, and complexity, leading to prohibitive detection time requirements. In this paper, we introduce Dexsim, a dynamic analysis based system to accurately index apps and identify bytecode similarities. We propose a novel bytecode indexing and matching algorithm that employs concepts from forced execution and LZ78 compression trees, and scales linearly with the number and size of apps. Our experiments on 28k cloned benign and malicious apps showed that Dexsim is both scalable and resilient to obfuscation, ferreting out clones within 8 ms pair-wise on average with at least 90% accuracy.
Understand Human Walking Through a 2D Inverted Pendulum Model

This paper gives some macroscopic understandings on human walking about the limitations on walking speed and step length, the reachable region, capture region, and disturbance recovery through a 2D inverted pendulum model. Our concern is the most basic problems in human walking, such as what are the limitations on walking speed and step length, how people change speed during step-to-step transition, and how people prevent a fall. The concept of walking orbit is proposed as a tool to study these problems. It describes the walking motion in the state space under walking constraints, giving us an intuitive way to study human walking during a step and switch between steps. The model has a point mass on the hip and two massless legs. The two dominant control inputs, hip and ankle actuation are idealized into a free determined foot placement and an impulsive push off. Based on this model, some quantitative and qualitative analysis are given, leading to some macroscopic understandings on human walking. Although this paper does not talk about any details on how to realize the control for a real biped robot, it may serve as a helpful guide for biped robot design and control in the future.
An Online Multiview Learning Algorithm for PolSAR Data Real-Time Classification

Polarimetric synthetic aperture radar (PolSAR) data are sequentially acquired and usually large scale. Fast and accurate classification is particularly important for their applications. By introducing online learning, the PolSAR system can learn a classification model incrementally from a stream of instances, which is of high efficiency for newly arrived samples processing, strong adaptability for a dynamically changing environment, and excellent scalability for rapidly increasing data. In this paper, we propose an Online Multi-view Passive-Aggressive learning algorithm, named OMPA, for PolSAR data real-time classification. The polarimetric, color, and texture features are extracted to characterize PolSAR data, and each type of features corresponds to one view. In order to exploit the consistency and complementary property of these views, we give a new optimization model that ensembles the classifiers of multiple distinct views and enforces the agreement between each predictor and the combined predictor. The corresponding algorithms for both binary and multiclass classification tasks are derived, and the update steps have analytical solutions. In addition, we rigorously derive a bound on the number of prediction mistakes of the method. The proposed OMPA algorithm is evaluated on two real PolSAR datasets for built-up areas extraction and land cover classification, respectively. Experimental results demonstrate that OMPA consistently maintains a smaller mistake rate with low time cost and achieves about 1% and 2% accuracy improvements on the datasets, respectively, compared with the best results of the previously known online single-view and multiview learning methods.