Miracle-dz/UniFAR-FTUs huggingface dataset:
On the Use of API Calls for Detecting Repackaged Malware Apps: Challenges and Ideas

Traces of API calls from mobile applications are a very valuable source of information for multiples security analyses including the detection of malware and repackaged apps. Although API call traces are widely, extracting and using API call traces for the detection of repackaged apps remains a challenging task. In this paper, we briefly review (a) the challenges associated with using API calls and (b) the limitations of malware detection approaches that rely on API calls.
Spatial Registration Model of TOF Camera Depth Data based on BP Algorithm

For the errors in the original depth data obtained by TOF depth camera, which lead to image distortion, the method of establishing BP neural network space registration model was proposed to calibrate the depth data deviation caused by non-systematic random error in the process of TOF camera depth data measurement and systematic error of TOF camera measurement principle. The results are compared and analyzed with the calibration results based on the principle of keyhole imaging, BP algorithm is adopted to establish the space registration of BP neural network model, and its precision is higher than the calibration results based on the principle of keyhole imaging, stronger generalization ability, better able to recover the real scene depth data. It provides a new idea and method for the calibration of depth data measured by TOF camera.
Lattice Boltzmann Method for Fluid-Thermal Systems: Status, Hotspots, Trends and Outlook

Great advances have been made with the lattice Boltzmann (LB) method for complicated fluid phenomena and fundamental thermal processes over the past three decades. This paper presents a systematic overview of the LB method from 1990 to 2018, based on bibliometric analysis and the Science Citation Index Expanded (SCI-E) database. The results show that China took the leading position in this field, followed by the USA and UK. The Chinese Academy of Sciences had the most publications, while the Los Alamos National Laboratory was first as far as highest average citation per paper and h-index are concerned. Physical Review E was the most productive journal and “Mechanics” was the most frequently used subject category. Keyword analysis indicated that recent research has focused on the natural convection and heat transfer of nanofluid or multiphase flow in complex porous media. Hydrothermal treatment of nanofluid with shape factor on the conditions, such as variable magnetic fields, thermal radiation and slipping boundary, were the research hotspots. Further research perspectives mainly explore the multiscale models for coupling multiple transport phenomena, morphology optimization of porous parameters, new nanoparticles with shape factor, multicomponent LB method considering Knudsen diffusion effect, LB-based hybrid methods, radiation performance or boiling-heat transfer of nanofluid, and the active control of droplets, may continue to attract more attention. Moreover, some new applications, such as phase change of metal foam, erosion induced by nanaofluid, anode circulating, 3D modeling in thermal systems with vibration, and magnetohydrodynamics microfluid devices, could be of interest going forward.
Energy Management Strategy of CAES-BS-SC Hybrid Energy Storage System Based on EEMD

The inherent multi-time-scale fluctuation of renewable energy is the main reason that restricts its large-scale application. In order to improve the overall economic benefit and energy efficiency of the energy storage system. This paper proposes a hybrid energy storage system with compressed air energy storage, battery and supercapacitor. Firstly, analyzes the characteristics of supercapacitors and batteries, and the cooling and heating effects of compressed air system are also analyzed. Secondly, the energy management strategy of hybrid energy storage system using active energy storage strategy is studied. Using extreme leaning machine (ELM) to predict the distribute energy power generation in long time scale and determine the target power of HESS. EEMD method is used to distribute the target power of compressed air, battery and supercapacitor, then according to the constraints of energy storage frequency range, power range, state of energy and other conditions to select the optimal demarcation frequency. Finally, simulation, comparison and analysis are carried out to verify the effectiveness of the proposed method.
A Review of the Blockly Programming on M5Stack Board and MQTT Based for Programming Education

The Blockly Programming is one way to represent Programming code or script languages on High-Level Programming like Python, C / C ++ Language to easily understandable image forms, these images have different forms that represent if, for, while, input, output values, etc. in this research, an analysis using M5STACK Board can be programmed using the Arduino IDE or flow.m5stack based on Blockly Programming. Basically, Blockly programming is used for Programming Education. In this research, the Blockly programming application is used flow.m5stack to run the Motion Sensor, basically, there are many sensors that can be used on the M5Stack board as well as the Arduino Board with various shapes and variants. In this research, the M5Stack Microcontroller application is easily explained using Blockly Programming, so programming feels easier and more enjoyable. Microcontroller based M5STACK board can be developed in the Internet of Things (IoT) application using MQTT Protocol. furthermore, Message from the Queuing Telemetry Transport (MQTT) The protocol that is used to represent commands and processes and produces the appropriate output in real time via Internet protocol (TCP / IP). M5Stack is using the MQTT API Cloud server, and its application to Smart home, furthermore, the M5Stack client board sends data to the Cloud MQTT or MQTT Broker using the establishment of IoT on Smart home using MQTT Server and M2M Technology using multi M5Stack boards.
Extract EEG Features by Combining Power Spectral Density and Correntropy Spectral Density

The electroencephalogram(EEG)-based brain computer interface (BCI) has been applied to many fields, such as medication, old-age help, transportation and entertainment. How to extract efficient features from low signal to noise ratio (SNR) EEG signals is one of the challenges in EEG signal analysis. In EEG-based BCI systems, power spectral density (PSD) is an efficient and widely used frequency feature, but the performance may degrade seriously when applied to data with low SNR. To improve the performance of EEG feature extraction, in this paper, we attempt to use the correntropy spectral density (CSD) as an EEG feature, and further combine the CSD and PSD together to construct a combinatorial EEG feature, called CSD & PSD. Firstly, an experiment on artificial EEG data is performed to validate that the CSD is more suitable for non-Gaussian and low SNR signal analysis. Then, the PSD, CSD and CSD & PSD are used to extract features of real motor imagery (MI) EEG signals. The results show that CSD & PSD often outperforms both PSD and CSD in different noise scenarios, which validate that the weighted combination feature CSD & PSD inherits the characteristics of CSD and PSD, and it is more suitable for EEG signal analysis in various SNRs.
Detection of Adventitious Respiratory Sounds based on Convolutional Neural Network

Nowadays, the respiratory disease has become one of the most dangerous diseases that threaten human health, especially in the developing countries. The early diagnosis of respiratory disease gives patients the opportunity to receive proper treatment in time, and hence artificial intelligent (AI) auscultation using electronic stethoscope may play a promising role here. The core idea of AI auscultation of respiratory disease is to detect or recognize two kinds of adventitious respiratory sounds related to respiratory diseases: wheeze and crackle. Constrained by the number of available data, in the traditional methods, subjectively defined features were extracted and used to detect these adventitious respiratory sounds. However, to make the detection results robust, the features had better to be learned automatically from the data, which can be realized by applying deep learning in a big data. In this paper, the convolutional neural network (CNN) is exploited to detect adventitious sounds. The data used in this study consists of two parts: the public database provided by the International Conference on Biomedical and Health Informatics (ICBHI) involving 126 subjects and our recorded pediatric auscultation data including 222 subjects. The detection performance of employed CNN is evaluated using ICBHI database, our pediatric auscultation database as well as the combination of them.
Pursuit and evasion game between UVAs based on multi-agent reinforcement learning

Pursuit and evasion game between UVAs is a typical differential game. Differential games are usually difficult to obtain the optimal solutions because of the complex bilateral extremum problems. Reinforcement learning has superiorities in solving differential games with the advantages such as it does not need accurate controlled models and a lot of training data. In this paper, a multi-agent reinforcement learning model is established for UAV pursuit and evasion game. The relative motion state equation is used to describe the state to simplify the state set, and the pursuit and evasion game is transformed into a zero-sum game which is solved by Minimax-Q learning. The reinforcement learning model established in this paper reduces the complexity of solving problem and guarantees the convergence speed. Finally, the simulation results verify the rationality of the obtained control policy which makes both the pursuer and the evader tend to be advantageous to their own direction in the course of the countermeasures.
Metal Artifact Recognition using Deep Convolutional Neural Network in Abdomen and Pelvis CT Image

In medicine, radiotherapy image obtained from CT or MRI is an important part in planning to find dosage for radiation treatment for cancer patients. Those images that obtained from CT may have artifacts caused by many factors such as motion artifact, metal artifact, scatter, ring artifact, pseudo enhancement and cone beam effect which is a component that makes image analysis worse. For improvement the quality of image that have artifact, image processing technique is used to manage and reduce it before bring to apply in radiotherapy of medicine. However, for finding the artifacts of 3D image is so difficultly and take more time because 3D image has to split into 2D image before. The aim of this research is to identify the artifact noise in 2D slice by using Deep Convolutional Neural Network (DCNN) model of metal artifact for reduce time. In this experiment have divided dataset into two types of image include 100 image of artifacts and 100 image Non-artifacts for training and 20 images for testing. The result has been shown that accuracy of artifacts and Non-artifacts are 76% and 62.28% respectively. In addition, this studied has found that a small amount of data from Thailand individual results in less accuracy.
Review of 28GHz CMOS Phased-Array Transceivers for 5G New Radio

The development of 28-GHz CMOS phased-array transceivers for 5G new radio applications has been grown rapidly in recent years. This paper discusses the challenges and requirements in the design of the 28-GHz CMOS phased-array transceiver RFICs. The key architectures of analog phase shifting are examined in detail. Several RFICs of the 28-GHz CMOS phased-array transceiver are presented to give a brief overview on the state-of-the-art work.
Machine Tools Fingerprinting for Distributed Numerical Control Systems

As machine tools are connected to Industrial Ethernet and external interfaces in the wave of the fourth industrial revolution, new attacks and vulnerabilities are emerging. However, there is little security analysis on Distributed Numerical Control (DNC) system and Computerized Numerical Control (CNC) system. Researchers have demonstrated how to combine the characteristics of Industrial Control System (ICS) to augment existing Intrusion Detection System (IDS) solutions. To the best of our knowledge, there is no such work on DNC network. In response to this situation, a fingerprinting method is proposed as an enhancement technology to existing IDS for DNC systems. The first step is to extract the number of data collection points of each machine tool and the length of TCP payload of each packet. And the second step is to use data response processing times of machine tools to construct unique fingerprint for each machine. Finally, the optimum period slice k is selected and classification accuracy is evaluated using a real-world dataset from a small-scale smart factory. It is demonstrated that our fingerprinting method can be a valuable tool to enhance IDS for DNC network.
Computing and Relaying: Utilizing Mobile Edge Computing for P2P Communications

Besides increasing the computing capacity of edge devices, mobile edge computing (MEC) can also be utilized to help communication. This paper proposes an MEC-assisted computing and relaying scheme to enhance the throughput of uncompressed data for mobile peer-to-peer (P2P) communications. We assume that the target data has a dynamic compression rate during the transmission from one mobile device to another through a relay node with MEC. In order to obtain the optimal transmission and compression strategy for the mobile devices and the relay node, a cost function that defines the tradeoff between energy consumption and latency time is investigated first. Then a closed-form solution is derived by minimizing the cost function with respect to practical constraints. Compared with conventional P2P communications without MEC, the proposed model breaks the bottleneck of P2P communications by decoupling the data compression rates at the two sides of MEC server. Numerical results verify the effectiveness of the proposed scheme.
Factors Influencing Ferroelectric Switching Behavior in Perovskite Nanofibers

Given the complexity in probing ferroelectricity on the nanoscale, piezo force microscopy observations have often become defacto proof of ferroelectric behavior, without considering the factors influencing the signal origination. In this work, limitations in using existing ferroelectric characterization for nanofibers are discussed. Decreasing piezo-response and shrinking hysteresis loops with increasing relative humidity were observed which could be attributed to tip-surface mechanics influenced by the testing environment.
Blockchain-Based Anti-Key-Leakage Key Aggregation Searchable Encryption for IoT

The Internet of Things (IoT) makes our life more intelligent. Its combination with the cloud server can solve big data processing problems to meet users’ needs and bring us great convenience. However, there are two challenges we need to face: data sharing and key leakage. To solve the above challenges, attribute-based encryption (ABE) is used to achieve data sharing combined with searchable encryption (SE). Most of the existing attribute-based searchable encryption (SE) schemes are inefficient and not suitable for IoT devices because of the large amount of attributes and keys. The key-leakage problem is serious in practice which very little literature focused on it. In order to address both problems, in this article, we propose a key aggregation searchable encryption (KASE) scheme based on the blockchain with auxiliary input (AI), which is capable of achieving secure data sharing on the encrypted data. Our scheme is presented through a novel chosen plaintext attack (CPA) secure scheme. We prove our scheme is chosen ciphertext attack (CCA) secure against key leakage under the decisional Diffie–Hellman assumption and Goldreich–Leivin theorem. Moreover, we adopt the proposed scheme to establish a data-sharing system based on blockchain, which improves search efficiency and connects the global ecology. In addition, extensive performance evaluations are conducted, and the results indicate our scheme is really efficient in cloud-computing-enhanced IoT.
Multi-label Emotion Classification in Music Videos Using Ensembles of Audio and Video Features

Video as well as music are potent means to convey emotions. However, despite their importance in several applications, few works deal with the issue of emotion classification in videos. The main reason is possibly the lack of available databases. In this work we extend the CAL500 database by including music videos, since the CAL500 was originally proposed as an audio-only database. The main rationale here is that the music videos must be official as they were developed to convey the same emotion as the song. After adapting the database, we have extracted audio and video features to perform our computational experiments. Our main result is that there is a complementarity between the audio and video features as the best result was achieved using their combination.
Exploration of usage behavioral model construction for university library electronic resources from Deep Learning Multilayer perceptron

The majority of this study is to use the MLP, Multilayer perceptron, operation method of deep learning to predict the reader's behavior intention and the quality of use behavior generated from the electronic library service quality of the university library. A questionnaire was built based on the quality of network electronic resource services and the theoretical structure of UTAUT. Combining the public and private university students who are four-year universities and two-year masters in Taiwan, there were 1,206 questionnaires issued and 1,071 valid questionnaires were collected. The predicted results of the MLP were based on the Pearson coefficient with the calculated results of the SPSS statistical method. It was found that the calculation method of AI can accurately predict the usage behavior of 90.75% or more. According to the practice, MLP regression models can be applied to the construction of library electronic resources, such as the addition of electronic multimedia resources, or the choice of books in the future. You can use this type of deep learning model to quickly predict and make decisions.
Voltage Vector Directional Control for IPMSM Based on MTPA Strategy

Space vector pulse width modulation (SVPWM) is used in motor control because of its high dynamic response and flexible control. Maximum torque per ampere (MTPA) strategy has become the most commonly used choice for internal permanent magnet synchronous motor (IPMSM). However, complicated coordinate transformations are required in the usual SVPWM algorithm and the performance of the MTPA algorithm can be affected by the parameters variation. This paper proposes a voltage vector directional control (VVDC) method based on the MTPA strategy. The inverse Park transformation is avoided in the proposed control scheme. And the proposed algorithm not only is briefer and clearer in the sector judgment process but also has lesser calculation during the duty cycle generation. In order to realize the MTPA strategy, two voltage feedback closed loops are designed to compensate for the <inline-formula> <tex-math notation="LaTeX">$d$ </tex-math></inline-formula>-axis and <inline-formula> <tex-math notation="LaTeX">$q$ </tex-math></inline-formula>-axis currents. A simple and effective multi-parameter identification method is also presented to solve the parameters variation problem. The effectiveness of the proposed method is verified by simulation and experiments.
Precise Iterative Closest Point Algrithm Based on Correntropy for 3-D Oral Data Registration

This paper proposes a new iterative closest point approach based on correntropy with feature guided. Iterative Closest Point (ICP) algorithm can deal with most rigid registration problems, but for point sets with lots of noise and outliers, ICP cannot achieve high precision. We introduce correntropy into ICP to handle this problem by suppressing the influence of the noise and outliers. In terms of point sets contain a large proportion of planes or a curved surface, and have single structure, such as a three-dimensional model of upper jaw, we propose a feature-guided model to solve the oral data registration problem, which uses both the feature and the original data to participate in the registration, but with different weights. Our method mainly deals with the point set registration which has single structure and contains outliers. Experimental results demonstrate that the proposed algorithm is precise and robust.
Efficient Data Aggregation and Routing Algorithm for IoT Wireless Sensor Networks

The Internet of Things (IoT) Wireless Sensor Network (WSN) mobile node has the following essential resources; Average Energy, Bandwidth, and Memory. These resources have to be used aptly for effectiveness in the application, and hence, it becomes challenging. The data aggregation and routing algorithm make use of energy, bandwidth, and memory in their operation. The existing data aggregation and routing algorithms reduce energy consumption by avoiding redundancy in data, hence effectively reducing bandwidth, and memory. There has been a tradeoff between the level of security (in data aggregation and routing), and energy consumption, as a high level of security will have more intensive computational operations and vice versa. The proposed research is presenting the improved Modified Power-Efficient Gathering (Aggregation) in Sensor Information System (Modified-PEGASIS) algorithm for efficient data aggregation and routing in IoT WSN. The simulation results of the proposed work show superior data safety and protection, leading to better throughput, packet delivery ratio, end to end delay, routing overhead, energy consumption, and security.
Disturbance Observer Based Formation Control Of Multi Agent System

In this paper, a novel method of disturbance observer based formation control using modified gradient control law for single integrator agents is proposed. Gradient control law involves defining a potential function which is the sum of squares of distance errors and the control law is derived from the negative gradient of this function. Modified gradient control law augments a number of virtual coordinates to the actual variables such that the formation problem is transferred into a higher dimensional plane. The potential function defined and gradient control law derived are with respect to the new variables. In real life situations, agents will be continuously subjected to disturbances and may fail to achieve formation. Hence, the objective of this work is to design an observer to estimate the disturbances and to manipulate the control effort appropriately so as to maintain the formation even in the presence of disturbances. The effectiveness of the theoretical results are validated using simulation.
Wideband ± 45° Dual-polarized Magnetoelectric Dipole Antenna with Stable Wide Beamwidth

A wideband ± 45° dual-polarized magnetoelectric dipole antenna is proposed in this paper. Placed on aluminum plate and fed by two L-shaped feeding lines, the proposed antenna can provide a wide bandwidth with satisfactory radiation properties. By bending the antenna arm, the current distribution of the vertical parasitic structure is increased, and then the radiation of the vertical parasitic structure is increased. The antenna achieved a wide impedance bandwidth of about 45.4% for $\text{VSWR} < 1.75$ at both ports, covering the frequency range from 1.7 to 2.7GHz. Over the operating frequency, a gain>5.3dBi is achieved for both ports. The isolation between the two ports is larger than 28dB. The results show a stable radiation pattern with the half-power beamwidth $95^{\circ}\pm 5.5^{\circ}$ at H-plane and V-plane.