Miracle-dz/UniFAR-FTUs huggingface dataset:
Android Malware Family Classification and Characterization Using CFG and DFG

Android malware has become a serious threat for our daily life, and thus there is a pressing need to effectively mitigate or defend against them. Recently, many approaches and tools to analyze Android malware have been proposed to protect legitimate users from the threat. However, most approaches focus on malware detection, while only a few of them consider malware classification or malware characterization. In this paper, we propose an extension of CDGDroid to classifying and characterizing Android malware families automatically. We first perform static analysis used in CDGDroid to extract control-flow graphs and data-flow graphs on the instruction level. Then we encode the graphs into matrices, and use them to build the family classification models via deep learning. For family characterization, we extract the n-gram sequences from the graphs, which are filtered according to the weights of the classification model built for the target family. And then we construct a vector space model and select the top-k sequences as a characterization of the target family. We have conducted some experiments to evaluate our approach and have identified that the family classification model taking the horizontal combination of CFG and DFG as features offers the best performance in terms of accuracy among all the models. Compared with CDGDroid, Drebin and many antivirus tools gathered in VirusTotal, our family classification model gives a better performance. Finally, We have also conducted experiments on family characterization, and the experimental results have shown that our characterization can capture the malicious behaviors of the testing families.
Multi-dimensional Electric- and Magnetic-field Sensor for Low-frequency Applications

A growing number of electric-field sensors and magnetic-field sensors are becoming commercially and federally available. However, there is a lack of compact sensor systems that integrate both modalities for more advanced sensing scenarios. The CCDC Army Research Laboratory has developed a “4-D Cube” sensor which precisely measures the local 3-D magnetic field and 1-D electric field at frequencies between 30 Hz and 32 kHz. By using a non-metallic transducer for the electric-field sensor, and three loop sensors without a ferrite core for the magnetic-field sensors, we minimize cross-axis and cross-modality field distortion, even when these sensors are placed in close proximity to each other in an array. This paper will cover the quasi-static field theory behind the design of the complete sensor system, and characterization results of the electric- and magnetic-field sensors for frequency response and noise spectral density. The 4-channel analog sensor is approximately $\mathbf{200\times 200} \times \mathbf{130\ mn}$, weighs under 0.5 kg, and consumes less than 50 mW during full operation. Cross-axis effects have been measured to be $\leq \mathbf{5\%}$. The noise spectra is $< \mathbf{10\ mA/m}$ and $< \mathbf{1\ mV/m}$ over most of the 32-kHz frequency range, and all sensors have $> \mathbf{80-dB}$ dynamic range regarding single-tone signals.
Traffic Monitoring and Anomaly Detection based on Simulation of Luxembourg Road Network

Traffic incidents which commonly result from traffic accidents, anomalous construction events and inclement weather can cause a wide range of negative impacts on urban road networks. Developing a high efficiency and transferable traffic incident detection system plays an important role in solving the imbalance caused by traffic incidents between traffic demand and capacity. However, the existing literature on transferability of traffic incident detection is rather limited. The objective of this paper is to provide an accurate and transferable incident detection approach based on the relationship between traffic variables and observed traffic incidents, in particular at a network level. We propose a deep learning based method which has been calibrated using part of the collected traffic variables and the pre-assigned traffic incidents and then tested against the rest of the dataset. The proposed method is compared to other benchmarks commonly used in traffic incident detection, in terms of detection rate, false positive rate, f-measurement and detection time. The results indicate that the proposed method is significantly promising for traffic incident detection with high accuracy and transferability compared to the more widely used techniques in the literature.
Design and Implementation of Digital Active Gate Control with Variable 63-level Drivability Controlled by Serial 4-bit Signals

Active gate driving techniques using fully digital circuits and ICs have been actively investigated in the past half-decade. It has been reported that they contribute to improve loss and overshoot voltage and current, simultaneously during switching transients. However, a major concern of the digital active gate driver is an increase in the number of input signals for a practical implementation. This paper presents a verification of a digital gate driver IC with variable 63-level drivability controlled by serial four-bit input signals to decrease the implementation size and cost. Experimental results in a half-bridge converter using Si-IGBT module prove that the proposed gate driver IC realizes the active gate control effectively as the same with the conventional IC with parallel inputs despite the reduced number of the input signals. It is demonstrated that the developed digital gate driver circuit contributes to reduce the number of the input signals and digital isolators by one-thirds, and footprint of the PCB by 20% compared with the conventional gate driver IC with parallel 12-bit signal inputs.
Failure Mode Analysis of the 3-Phase 5-Level E-Type Converter

The power electronic converters have a heavily influence in our life-style, since they are today used everywhere in-home applications, office, medicine, industry, and energy transmission machines and processes. The power electronic systems are based on fast-switching power semiconductors, such as diodes, mosfets, igbts, etc. These power devices represent the fragile part in a power electronic converter. Thus, to ensure safety and reliability of the power conversion system, a fault detection method must be provided. When a failure occurs, the fault detection and protection system become the most important function of the converter control unit. If the fault management is not adequate, the entire converter can be totally destroyed, causing catastrophic effects on the entire power conversion system. Accordingly, fault detection, classification, and diagnoses for all power electronic converters, including three phase rectifiers, three phase inverters, DC-DC converters and AC-AC converters, have to be investigated. In this paper, the fault analysis of the 3-phase 5-level E-Type Converter (3Φ5L E-Type Converter) is addressed. The 3Φ5L E-Type Converter is proposed for grid connected applications. All possible converter semiconductor switches and diodes faults are theoretically analyzed. The fault effects, the detectability and the safety shutdown actions are identified and summarized. The analysis is covered by simulation results performed in MATLAB/Simulink. The control algorithm has been implemented in FPGA using LabVIEW environmental. The experimental tests have been performed on the 20kVA prototype multilevel converter in order to validate the proposed analysis.
Enhancement of Thermo-mechanical Behavior of IGBT Modules through Engineered Threshold Voltages

In this paper, a purposely-imbalanced current density distribution in insulated-gate bipolar transistor (IGBT) chips is introduced to reduce the surface temperature inhomogeneity of standard chips technology. The idea is implemented by modifying the gate threshold voltage across the active chip area, to counteract the uneven temperature distribution of a standard IGBT chip. Coupled thermomechanical analysis realized by finite element method (FEM) is used for validating the engineered IGBT chip via comparing different layouts.
Optimal Dispatch of Multi-stakeholder Based on Pareto-Nash Equilibrium Game for Active Distribution Network

Distributed generation operators and users involved in demand response(DR) have gradually become new stakeholders in the active distribution network (ADN), while different stakeholders have different optimization dispatch objectives. Aiming at the problem optimal dispatch of distribution grid with multi-stakeholder participation, this paper first introduce the concept of virtual-microgrid (V-MG), and the multi-objective optimal dispatch model of independent stakeholder is established by constructing a joint optimal dispatch platform of ADN. Secondly, this paper introduces information entropy to improve the traditional NSGA-II algorithm and proposes a new NSGAEN algorithm. The NSGAEN algorithm can improve convergence speed and avoid falling into local optimum. Finally, a method for selecting the compromise solution in the Pareto optimal solution set by Nash equilibrium game is proposed, which can effectively solve the problem of excessive human preference factors in choosing the compromise solution. The effectiveness of the proposed model and algorithm is verified by an example of 33-node distribution network.
A New Method for Measuring Parasitics of Super Junction Power MOSFETs

This document proposes a new methodology to measure the voltage-dependent behavior of parasitic capacitances of super junction power MOSFETs. The measurement technique allows to extract all parasitic elements (capacitances and inductances) with only one measurement while a variable DC voltage is applied between the drain and source pin of the super junction MOSFET. The results can be used to create simulation models of MOSFETs and possibly complete power modules, that accurately represent their high frequency behavior to solve electromagnetic compatibility (EMC) problems in transient simulators, such as LTspice.
Synthesis of Airgap Magnetic Field Modulation Phenomena in Electric Machines

The brushless doubly-fed machine (BDFM) is a family of ac machines with two distributed stator windings of different pole pairs and a single rotor. The rotor may take the form of short-circuited coils, simple salient poles or flux barriers/guides. Most existing research on the design and analysis of BDFMs are machine-specific though they have shown many similarities in magnetic field distributions and steady-state performance. This paper presents a unified analysis of BDFMs based on the general airgap field modulation theory. It is found that the signal flow in any electric machine is a "sampling-modulation-filtering" process. The electromotive forces induced in windings and torque can be obtained by calculating the response of a simple signal system comprising a series of chained blocks. The generalized theory greatly simplifies the analysis and comparison of different BDFM topologies and thus proves to be an effective approach to analyzing electric machines with multiple spatial harmonics.
Simulating Photo-realistic Snow and Fog on Existing Images for Enhanced CNN Training and Evaluation

Verification and robustness testing of machine learning algorithms for autonomous driving is crucial. Due to the increasing complexity and quantity of those systems in a single vehicle, just driving the required distance with a newly developed vehicle is not feasible anymore: billions of hours on the street without failure are necessary to qualify for industry standards like ISO 26262. That is where simulation comes into play: machine learning algorithms are trained and evaluated on well known image data sets like KITTI or Cityscapes. But today’s data sets mostly contain images taken under perfect weather conditions and therefore do not harden optical object detection algorithms against various weather conditions. This paper focuses on reusing these established and labeled data sets by augmenting them with adverse weather effects like snow and fog. Those effects are rendered physically correct and life like while being added to existing real world images. Thanks to easy parametrization the weather influences may be varied as necessary and allow for finely tuned learning and optimization processes. The weather effects are evaluated with regard to realism and impact on an established object detection algorithm. These newly created weather-influenced images may be used to validate or train new object detection algorithms.
A robust two-feedback loops position control algorithm for compliant low-cost series elastic actuators*

Elastic joints are considered to outperform rigid joints in terms of peak dynamics, collision tolerance, robustness, and energy efficiency. Therefore, intrinsically elastic joints have become progressively prominent over the last years for a variety of robotic applications. In this article, a two-feedback loops position control algorithm is proposed for an elastic actuator to deal with the influence from external disturbances. The considered elastic actuator was recently designed by our research group for Serpens, a low-cost, open-source and highly-compliant multi-purpose modular snake robot. In particular, the inner controller loop is implemented as a model reference adaptive controller (MRAC) to cope with uncertainties in the system parameters, while the outer control loop adopts a fuzzy proportional-integral controller (FPIC) to reduce the effect of external disturbances on the load. The advantage of combining the FPIC and the MRAC controllers is the possibility of achieving independence with respect to imprecise system parameters. A mathematical model of the considered elastic actuator is also presented to validate the proposed controller through simulations. The operability of the presented control scheme is demonstrated. In closed-loop the load swing is rapidly confined and eliminated thereafter.
Online ECG-based Features for Cognitive Load Assessment

This study was concerned with the development and testing of online cognitive-load monitoring methods by means of a working-memory experiment using electrocardiogram (ECG) analyses for future applications in mixed-initiative human-machine interaction (HMI). To this end, we first identified potentially reliable cognitive-workload-related cardiac metrics and algorithms for online processing. We then compared our online results to those conventionally obtained with state-of-the-art offline methods. Finally, we evaluated the possibility of classifying low versus high working-memory load using different classification algorithms. Our results show that both offline and online methods reliably estimate the workload associated with a multi-level working-memory task at the group level, whether it is with the heart rhythm or the heart rate variation (standard deviation of the RR interval). Moreover, we found significant working-memory load classification accuracy using both two-dimensional linear discriminant analyses (LDA) or a support vector machine (SVM). We hence argue that our online algorithm is reliable enough to provide online electrocardiographic metrics as a tool for real-life workload evaluation and can be a valuable feature for mixed-initiative systems.
A high power density three phase inverter for microcars based on 100V/600A Six-pack MOSFET module

In this paper, a high power density inverter for microcar with a MOSFET module is designed to overcome disadvantages of discrete MOSFET based inverter. The high current MOSFET module enables high power density with 6.8 kW/L. In order to verify the performance of the inverter and the MOSFET module, voltage stress, efficiency and short-circuit test is performed over the normal and overload operating conditions. A 15 kW laboratory prototype inverter is implemented, and the test results show 96.04 % peak efficiency at 5kHz switching frequency
Enhancing Crash Injury Severity Prediction on Imbalanced Crash Data by Sampling Technique with Variable Selection

The analysis of road crash data has long been used as a premise for influencing the road and automobile designs and guiding the implementation of various policies with the view to enhance the road safety. However, the crash data is associated with class imbalance and high dimensionality which may severely impact the predictions of the analysis model.This study suggests a framework for the combined use of variable selection and the synthetic minority over sampling (SMOTE) data balance techniques. We explored three variable selection (VS) techniques including two filter-based i.e., Chi-square (CS) and correlation feature selection (CFS) and an embedded method i.e., random forest (RF). To study the imbalance data problem and the implications for VS, two training data scenarios were considered: (1) VS based on original data and modelling based on original data (2) VS based on sampled data and modelling based on original data. The impact of varying the data class distribution was also examined. The Naïve Bayes classifiers were trained on the various selection subsets and their predictions captured in two metrics types. Overall, eight models were developed and analysed.The empirical results demonstrate that using balanced data can be helpful to identify the most prolific predictors of the crash injury severity. The filter-based ranking methods are more robust against the data imbalance than the wrapper. The NB classifier produced better predictions on the optimal subsets identified by the filter-based method than the one chosen by the wrapper
A Deep Time-delay Embedded Algorithm for Unsupervised Stress Speech Clustering

In this paper, we introduce a new unsupervised clustering algorithm to categorize the stress speech data, called deep time-delay embedded clustering (DTEC). DTEC consisted of a time-delay neural network (TDNN) based autoencoder and the joint supervision of discriminative loss, reconstruction loss, and clustering loss. The TDNN-based autoencoder was designed to transform a high-dimensional input into its low-dimensional output by supervision of reconstruction loss. The inter-cluster push-force and the intracluster pull-force were optimized by supervision of discriminative loss. The distance between the softmax prediction probability and its auxiliary target distribution was minimized by supervision of clustering loss. Based on our experiments, DTEC outperforms the popular deep clustering algorithm and able to increase the clustering performance in terms of accuracy(ACC) and normalized mutual information (NMI).
An algorithm of cooperative V2V trajectory planning on a winding road considering the drivers’ characteristics

A cooperative trajectory planning algorithm for two connected vehicles driven on a winding road is presented in this paper, considering drivers’ characteristics of preview action, time delay, and steering gain. The algorithm of cooperative game is introduced to plan trajectories without collision with each other for the involved encountering vehicles, with satisfaction of requirements including vehicle stability and road-departure avoidance. The proposed trajectory planning algorithm is then converted to a Model Predictive Control (MPC) problem and solved with the concept of Pareto Optimality. The proposed algorithm is verified with simulations of lane exchanging on an arc shaped road. Results show that the algorithm can accomplish the task of trajectory planning on a winding road successfully, with considering the driver’s characteristics.
Advanced Self-Improving Ramp Metering Algorithm based on Multi-Agent Deep Reinforcement Learning

We proposed a novel ramp metering algorithm embedding multi-agent deep reinforcement learning (DRL) techniques, based on the data of loop detectors. A multi-agent DRL framework is adopted to generate proper ramp metering scheme for each ramp meter in real time to improve the operation efficiency of urban freeway with less investment. A simulation platform is developed to simplify the implementation and training of the algorithm. A set of simulation experiments – encompassing both single and multi-ramp scenarios with various traffic demand profiles – are conducted. Comparing with the state-of-the-practice ramp metering methods, the simulation results demonstrate that the proposed DRL-based algorithm outperforms in a comprehensive evaluation index considering mainstream speed at bottleneck and queue size on ramp. The method presents robustness, scalability, and the capability of further improvement by online learning during implementation.
A Multi-Criteria Reverse Auction to Support Public Purchasing in Brazil*

In this paper, we propose a Multi-Criteria Reverse Auction (MCRA) approach based on the Composition of Probabilistic Preferences (CPP) multi-criteria method. In CPP method, the evaluations of alternatives in each criterion are converted into possible values by using a triangular distribution, allowing imprecision to be considered. The evaluations of the alternatives in each criterion are converted into a vector of probabilities of each alternative being the best (or the worst) in the criterion. Then, the measure of preference for any alternative, considering all the criteria, is given by the composition of its respective probabilities, not being necessary to provide intercriteria information, which is an advantage of this method over the others multi-criteria methods regarding its applicability to real life-problems. The MCRA approach was structured to support Brazilian public reverse auctions. An example of application was performed to demonstrate its applicability, demonstrating that the proposed approach can be easily incorporated into the Brazilian public reverse auctions. The MCRA approach improves the quality of public purchasing decision by assuring the consideration of multiple criteria in the evaluation phase and providing better results for both parties (buyer and sellers).
Modelling TSO-DSO coordination: The value of distributed flexible resources to the power system

The interaction between the transmission and distribution system operators is mainly based on a unidirectional flow of information (transmission-to-distribution system operators). The resources in the distribution systems are, hence, not utilized fully in overall power system operations, regardless that they may serve as sources of flexibility to manage renewable energy sources fluctuations. In the existing literature, is not very clear how the coordination of the transmission and distribution system operators can and should be modelled. Accordingly, there is limited insights to the potential value coordination may bring to the overall power system operations. As a result, this paper presents a modelling approach for coordination of the transmission and distribution system operators, where flexibility is provided by distributed energy resources located in the distribution systems. Key findings suggest that the total costs of power system operations are reduced when distributed flexible resources are incorporated under a joint coordination framework.
Using Pre-trained Models As Feature Extractor To Classify Video Styles Used In MOOC Videos

Massive Open Online Courses (MOOCs) have emerged as new learning phenomenon in the field of e-learning. Over recent years, it has attracted a significant number of learners as well as researchers. A wide range of researches is being carried out across multiple aspects of MOOCs. Video lectures are the most fundamental component in a MOOC. There are standard video styles that are normally used across several MOOC platforms, such as, talking head, demonstration, slides, animation etc. This paper presents an Image-Based classification approach of the video styles where a single video is split into multiple image frames, and then each frame is classified into one of the video style-category. Different classifier models built on top of each state-of-the-art deep neural architectures, including VGG16, InceptionV3, and ResNet50 are evaluated and the comparison of results is shown. Furthermore, the paper also discusses a numeric method to calculate the composition level of a single video style in multi-style filed videos based on the classification results.