Miracle-dz/UniFAR-FTUs huggingface dataset:
DynaMalDroid: Dynamic Analysis-Based Detection Framework for Android Malware Using Machine Learning Techniques

Android malware is continuously evolving at an alarming rate due to the growing vulnerabilities. This demands more effective malware detection methods. This paper presents DynaMalDroid, a dynamic analysis-based framework to detect malicious applications in the Android platform. The proposed framework contains three modules: dynamic analysis, feature engineering, and detection. We utilized the well-known CICMalDroid2020 dataset, and the system calls of apps are extracted through dynamic analysis. We trained our proposed model to recognize malware by selecting features obtained through the feature engineering module. Further, with these selected features, the detection module applies different Machine Learning classifiers like Random Forest, Decision Tree, Logistic Regression, Support Vector Machine, Naïve-Bayes, K-Nearest Neighbour, and AdaBoost, to recognize whether an application is malicious or not. The experiments have shown that several classifiers have demonstrated excellent performance and have an accuracy of up to 99%. The models with Support Vector Machine and AdaBoost classifiers have provided better detection accuracy of 99.3% and 99.5%, respectively.
Hyper-bent functions of the Mesnager family and hyperelliptic curves

The connection between exponential sums and algebraic varieties has been known for at least six decades. Recently, Lison¥k exploited it to reformulate the Charpin‐Gong characterization of a large class of hyper-bent functions in terms of numbers of points on hyperelliptic curves. As a consequence, he obtained a polynomial time and space algorithm for certain subclasses of functions in the Charpin‐Gong family. In this paper, we settle a more general framework, together with detailed proofs, for such an approach and show that it applies naturally to a distinct family of functions proposed by Mesnager. Doing so, a polynomial time and space test for the hyper-bentness of functions in this family is obtained as well. Nonetheless, a straightforward application of such results does not provide a satisfactory criterion for explicit generation of functions in the Mesnager family. To address this issue, we show how to obtain a more ecient test leading to a substantial practical gain. We finally elaborate on an open problem about hyperelliptic curves related to a family of Boolean functions studied by Charpin and Gong.
Texting and Driving Recognition leveraging the Front Camera of Smartphones

The recognition of the activity of texting while driving is an open problem in literature and it is crucial for the security within the scope of automotive. This can bring to life new insurance policies and increase the overall safety on the roads. Many works in literature leverage smartphone sensors for this purpose, however it is shown that these methods take a considerable amount of time to perform a recognition with sufficient confidence. In this paper we propose to leverage the smartphone front camera to perform an image classification and recognize whether the subject is seated in the driver position or in the passenger position. We first applied standalone Convolutional Neural Networks with poor results, then we focused on object detection-based algorithms to detect the presence and the position of discriminant objects (i.e. the security belts and the car win-dow). We then applied the model over short videos by classifying frame by frame until reaching a satisfactory confidence. Results show that we are able to reach around 90 % accuracy in only few seconds of the video, demonstrating the applicability of our method in the real world.
Analysis of UWB Wilkinson Power Divider Design Using 4-Stepped Patch and Ring Structure

A Wilkinson ultra-wideband (UWB) power splitter is proposed and analyzed in this paper. The power divider line is added with a resistive load to keep the two output ports matched. The power divider is designed with a 4-stepped patch and ring capable of producing UWB frequencies. The proposed power divider design approach is derived from the analysis of even/odd mode circuits and stripline transmission lines. The power splitter design process was optimized and simulated using CST Studio Suite with a 1.524mm thickness RO4350B substrate and a dielectric constant of 3.66. The overall dimensions of the proposed power divider are 85mm x 37mm. The proposed wilkinson power divider has an operating frequency of 3GHz - 13GHz. The insertion loss obtained has the lowest value of about -9dB, and the isolation value is below -10dB in the operating frequency range.
Research on terminal security of 5G network under different attack scenes

With the continuous development of technology in 5G network, different forms of terminal devices are designed to serve the demand of a large consumers and commercial industries. In 5G network, the 5G terminal devices have to store more identity information, certificates and secret keys, and the 5G terminal devices should support more application scenarios, like Internet of Things, smart homes, smart transportation, etc. Besides, abundant terminal devices along with data produces make 5G network more complex, and also bring out many new threats to 5G network. In this paper, we research the terminal security of 5G network under different attack scenes, and study the robustness and connectivity of 5G network.
Smart Sharing Bicycle for a Sustainable City: An Application of IoT

This study mainly focused on smart sharing bicycles and their application. A survey was conducted in three metropolitan cities that include Chandigarh, Panchkula, and Mohali. The design for the smart IoT-based sharing bicycle has many advantages like green transportation, real-time health monitoring of the user that is riding sharing bicycle, and energy conservation. The results obtained show that using a smart sharing bicycle is reducing pollution and also helping people to live a stress-free life with the conservation of energy. The applications of IoT are vast with advancements in technology and communication in the upcoming future.
Edge system with multi-LIDAR sensor network for tracking micro-mobility vehicles

This paper proposes an edge system with a sensor network formed by multiple light-detection-and-ranging (LI-DAR) sensors to identify the location of micro-mobility vehicles. The main contribution of this work is that we train machine learning (ML) models for detecting micro-mobility vehicles from point-cloud data acquired by multiple LIDAR sensors. An experiment verifies that the ML models work well for detecting a micro-mobility vehicle accurately.
Hook Point Estimation for System Call Detection by Virtual Machine Monitor

Virtual machine monitors (VMMs) are used to monitor the behavior of virtual machine (VM) programs. System call detection is frequently used to monitor behavior. To monitor the system calls, the VMM inserts a breakpoint, called a hook point, into the memory of the monitored VM. Researchers have determined hook points based on experimental knowledge or reading the source code of operating systems (OSes). However, reading the source codes of OS requires specialized knowledge. In addition, the appropriate hook point differs among OS versions. Therefore, analyzing the source code in each OS update is impractical. We propose a method for estimating the hook point using a memory analysis technique. The proposed method acquires the memory of the monitored VM and then searches for an appropriate instruction appropriate to hook. The search instructions depend on the processor architecture. Therefore, the proposed method does not require knowledge of OS versions or reading the source code of OSes. The experimental results showed that the proposed method precisely estimates the hook point. In addition, the overhead of the proposed method is small, considering the boot time of the monitored VM.
A Method for Correction and Enhancement of Non-Uniformly Illuminated Images

Non-contact imaging detection of explosives and other hazardous materials is currently a hot spot for research in the field of explosives detection technology, and near-infrared hyperspectral detection technology based on multivariate optical calculations is one of the feasible means. However, in practical applications, the application of this technology is limited due to uneven illumination, which is prone to false alarms and missed alarms. In this paper, we first briefly introduce the explosive imaging detection system based on multivariate optical computation and analyze the characteristics of the scored image; then we adopt the multi-scale top-hat transformation to calculate the background illumination and correct the light inhomogeneity of the scored image; finally, we convert the scored image into a signal-to-noise ratio image through the steps of image segmentation and calculation of signal-to-noise ratio, which further solves the problem of threshold drift caused by light inhomogeneity and thus greatly improves the accuracy of recognition results is greatly improved. The experimental results show that the recognition accuracy and stability of the method in this paper are higher compared with the previous recognition results, which provides technical support for practical applications.
Performance Analysis of Five Level Boost Inverter for Grid Connected PV Systems

The performance of a five level single phase trans-formerless boost inverter for grid tied operation is discussed in this work. This seven switch scheme with a gain of two is a modified version of HERIC topology with switched diodes and floating capacitors to achieve boosting feature. Three different PWM schemes are found to be suitable for this system to provide constant common mode voltage to reduce leakage current which is an added advantage. To empower the use of renewable resources, solar PV with MPPT is also employed. Simulation based analysis is done to assess the performance of the Phase locked loop(PLL) scheme with proportional integral(PI) control for the grid tied operation. THD is found to be satisfactory. The scheme is found to be efficient than conventional inverters because there is no internal energy losses and extra component costs.
Basic design for the implementation of automatic surveillance system on helmet detection

Deep learning has lately received acclaim for its success in certain fields, such as digital image pattern recognition and feature extraction. These methods have been used by researchers to solve a variety of issues, such as the detection of traffic violations specifically for motorcycle riders who are not wearing helmets in video surveillance. In this paper, we proposed a basic implementation and design steps of detecting two-wheeler bike rider wearing helmet utilizing a compatible and faster deep learning approach known as Single Shot Detector (SSD) in Linux operating system. We used and created a customized dataset of images by taking screenshots of a surveillance video of CCTV from a legal source. The traffic police can use this system to monitor the vehicles passing through specific surveillance nodes. After further implementation, number plates of vehicles could automatically be logged in a database which may be helpful in narrowing down options during a crime investigation.
The Maximum Rectilinear Crossing Number of the n Dimensional Cube Graph

In this research we find and prove the maximum rectilinear crossing number of the three-dimensional cube graph (Q 3). We demonstrate a method of drawing the n-cube graph, Q n , with many crossings, and thus find a lower bound for the maximum rectilinear crossing number of Q n. We conjecture that this bound is sharp. We also prove an upper bound for the maximum rectilinear crossing number of Q n .
Practical Aspects of Radar Rainfall Estimation Using Specific Differential Propagation Phase

Abstract Rainfall estimation using specific differential phase (KDP) involves estimation of KDP over a propagation path. The choice of pathlength plays an important role in the performance of rainfall algorithms using KDP. The path-integrated nature of KDP-based rainfall estimates may involve inhomogeneous paths, thereby having potential implications on the pathlengths used in algorithms for rainfall estimation and measurement errors. The effect of inhomogeneous rainfall paths (over which KDP is estimated) on the choice of rainfall rate algorithms is studied as a function of rain pathlength. Rainfall estimation is biased when algorithms relating rainfall rate and KDP are used over nonuniform paths. This bias is evaluated and compared for different rainfall algorithms. Radar and rain gauge data collected during the Convection and Precipitation/Electrification Experiment (CaPE) are used to evaluate the performance of KDP-based rainfall algorithms, for different pathlengths.
Steady-State Analysis of DFIGs and BDFRGs

This paper explores the steady-state analysis of a Brushless Doubly Fed Reluctance Generator (BDFRG) as an alternative to a doubly-fed induction generator (DFIG) for variable-speed wind power applications. The steady-state equations describing the model of the DFIG and the BDFRG are presented. Furthermore, the effect of the rotor/secondary voltage on the both machines control in the motor and generator mode of operation is studied. The results of steady-state analysis based on equivalent circuits of the two machines having the same rated power and the number of rotor poles show a great deal of similarity in the behavior. But, when the generators are running sub-synchronously in the case of wind speed drop, the BDFRG produces higher active power than the DFIG. However, the copper losses of the DFIG are less.
Localization Verifiability Under Outlier Measurements With Linear Programming

The problem of localizing a set of nodes from relative pairwise measurements appears in different fields, such as computer vision, sensor networks, and robotics. In practice, the measurements might be contaminated by noise and outliers that lead to erroneous localization. Previous work has empirically shown that robust algorithms can, in some situations, almost completely cancel the effect of outliers. However, there is a theoretical gap in answering the following question: Under what conditions on the number, magnitude, and arrangement of the outlier measurements can we guarantee that a robust algorithm will recover the ground truth locations from the relative measurements alone? We denote this concept as verifiability, and answer the question for the case of an $\ell _{1}$-norm robust optimization formulation, with translation measurements that are affected only by large-magnitude outliers and no small-magnitude noise. We prove that verifiability depends only on the topology of the graph, the location of the edges affected by the outliers, and the sign of the outliers, while it is independent of the (a priori unknown) true location of the nodes, and the magnitude of the outliers. We present an algorithm based on the dual simplex algorithm that checks the verifiability of a problem, and, if not verifiable, completely characterizes the space of equivalent solutions that are consistent with the given pairwise measurements. Our theory and algorithms can be used to compute the a priori probability of recovering a solution congruent or equivalent to the ground truth, without having access to the true locations.
Prediction of Parkinson Disease using Autoencoder Convolutional Neural Networks

Parkinson's Disease (PD) diagnosis is a challenging task for doctors because of the non-availability of separate testing and prediction methodology. PD is identified through various clinical tests, symptoms, and repeated clinical trials. Diagnosis of PD at an early stage is essential to improving patients' quality of life. An autoencoder feature extraction methodology is proposed for prediction of PD using Convolutional Neural Networks (CNNs). The feature extraction and de-noising of input data are performed using an autoencoder. CNN is used for classification and prediction. This algorithm has three layers: a convolutional layer, a pooling layer, and a dense layer. Feature extraction and image segmentation are automatically performed in the convolutional layer. A pooling layer performs downsizing. Finally, the dense layer is used for classification. The PPMI dataset is used for experimentation. The performance assessment is based on accuracy, precision, recall, and F1 measures.
A Data Transfer Method Combining Erasure-coding with Cumulative Acknowledgment for Lossy Optical Packet Switching Networks

In lossy networks, including optical packet switching (OPS) networks and wireless networks, data recovery from frequent packet loss is essential. Erasure-coding, a type of forward error correction (FEC), is one of effective solutions, and furthermore, it could be combined with a technique that feeds back decoding progress information from the receiver to the sender for efficient data transmission. However, conventional feedback methods cannot inform the sender of the decoding progress information at the receiver when acknowledgment (ACK) packets are lost. Therefore, we propose a new data transmission method that is robust to high packet loss by feeding back accumulated information of decoded symbols at the receiver, and furthermore that smartly reduces the calculation overhead of encoding/decoding process. The proposed method can reduce the redundancy by up to 5.84% compared to conventional methods in environments where random packet loss occurs.
Role of CBIR In a Different fields-An Empirical Review

According to its many applications in remote sensing, agriculture, healthcare, e-commerce, artificial intelligence (AI), and machine learning (ML), as well as other fields, Content Based Image Retrieval (CBIR) continues to be a popular research area. It is frequently used to search through a sizable image library and obtain images that are comparable to the query image in a significant way (QI). Indeed, a crucial part of the CBIR model is the principal dimensionality reduction technique, which seeks to collect both high- and low-level characteristics. Caused of the growing necessity of searching clinic images for diagnostic applications, and image archiving, in addition to networks of communication, the medical sector is expanding CBMIR in addition to standard computer vision (PACS). Recent developments in deep learning (DL) models allow for the efficient building of CBIR models across all industries. The medical profession is expanding retrieval of medical images depending on their content (CBMIR) in addition to generic computer vision to successfully search hospital PACS. In the past few decades, productivity in the agriculture sector has decreased. An increase in plant diseases was discovered to be the biggest factor. This research describes the Content-Based Image Retrieval (CBIR) methodology, which is used for the identification and categorization of agricultural, medical, artificial intelligence, and machine-learning objects. Here, how to use CBIR in all industries will be demonstrated.
Virtual Neuron: A Neuromorphic Approach for Encoding Numbers

Neuromorphic computers perform computations by emulating the human brain and are expected to be indispensable for energy-efficient computing in the future. They are primarily used in spiking neural network-based machine learning applications. However, neuromorphic computers are unable to preprocess data for these applications. Currently, data is preprocessed on a CPU or a GPU-this incurs a significant cost of transferring data from the CPU/GPU to the neuromorphic processor and vice versa. This cost can be avoided if preprocessing is done on the neuromorphic processor. To efficiently preprocess data on a neuromorphic processor, we first need an efficient mechanism for encoding data that can lend itself to all general-purpose preprocessing operations. Current encoding approaches have limited applicability and may not be suitable for all preprocessing operations. In this paper, we present the virtual neuron as a mechanism for encoding integers and rational numbers on neuromorphic processors. We evaluate the performance of the virtual neuron on physical and simulated neuromorphic hardware and show that it can perform an addition operation using 23 nJ of energy on average using a mixed-signal, memristor-based neuromorphic processor. The virtual neuron encoding approach is the first step in preprocessing data on a neuromorphic processor.
Satellite image super-resolution for forest localization

With the ever-increasing amount of satellite missions in orbit, Earth observation and remote sensing have advanced rapidly and are applied in a multitude of fields, such as ecosystem monitoring and natural disaster prevention. At the same time, more and more machine-learning architectures are developed, which attempt to map and detect pixel-scale changes on the Earth’s surface. Such an application is forest monitoring, which is deemed nowadays highly critical, considering the threat of deforestation on a global scale. However, the spatial resolution of available open-source satellite imagery is limited. One way to address this issue is to augment the remote sensing imagery resolution using Image Super Resolution Networks. The goal of this work is to perform the Semantic Segmentation task with Sentine1-2 satellite images in order to detect forest areas. Three Deep Neural Network architectures are trained and tested. Moreover, we use a Super Resolution Deep Network to augment the resolution of the images. We create two datasets, one with original Sentine1-2 images and one with super-resolved images. We train the three architectures on the two datasets independently and we test them on a test set which combines original and super-resolved images. We show that Swin Transformer produces the best results, while training on the super-resolved images enhances the modeling power of all architectures.
LinUCB-Based Handover Algorithm for Throughput Maximization in Heterogeneous Cellular Networks

Recently, the topic for the development of efficient handover algorithms to select the target (serving) base station (BS) such that the average throughput of user equipments (UEs) is maximized in heterogeneous cellular networks has received much attention. In the literature, the latest handover algorithms employ the upper confidence bound (UCB) policy, one efficient method for the multi-armed bandit (MAB) problem, since the UCB policy can identify the critical actions quickly by taking the uncertainty of the action reward into account. However, the UCB policy does not take advantage of the observed features, which could lead to poor performance in the UE throughput since the throughput of a UE served by a BS is highly correlated to the observed features including the bandwidth of the BS, the number of UEs served by the BS, and the spectral efficiency of the UE achieved by the BS. By contrast, besides the consideration of the uncertainty of the action reward, the linear upper confidence bound (LinUCB) policy also makes use of the observed features to estimate the action reward. In this paper, we make the first attempt to propose a LinUCB-based handover algorithm. The challenge is to define the reward and observed features highly correlated to the UE throughput such that the reward is a linear combination of the observed features as demanded by the LinUCB policy. We conduct simulations on the network simulator ns-3 to show that the proposed algorithm outperforms the state-of-the-art handover algorithms in terms of the average UE throughput.