Miracle-dz/UniFAR-FTUs huggingface dataset:
Privacy risk estimation of online social networks

With the growing risk of privacy breaches in online social networks, privacy protection has become a key issue. To increase users' privacy awareness and protect their data, there is a need for a simple and effective method of quantifying privacy risk. A user with a higher privacy risk score is more likely to face a serious privacy breach. In this paper, we propose an effective and reasonable privacy risk scoring method. Our method takes into account the granularity of the shared profile items, combines sensitivity and visibility, and generates a privacy risk score for each user. With the privacy risk score, users can acquire a more intuitive awareness of their privacy status and then defend it by altering privacy settings or lowering the granularity of shared data. In addition, our experiments analyzing real-world and synthetic datasets demonstrate that our method is capable of effectively assessing user privacy risks in online social networks.
Thermal Conductivity Measurement of a Sb2Te3 Phase Change Nanowire

In this work we present the measurements of thermal conductivity of nanowire Sb2Te3 phase change. These measurements are made using a thermal scanning probe microscopy (SThM) operating in regime modulated type 3ω. The spatial resolution of the probe is of the order of 100 nm. The measurement of amplitude and phase are used to identify unknown radius of contact between the nanowire and the sensor parameters, the contact resistance at the interface probe and nanowire and the thermal conductivity of the nanowire. An identification method is used which minimizes the difference between the measured values and those from a simulated model of heat transfer in the materials. This model uses a matched model heat transfer in the probe
Exact solutions to the combined KdV and mKdV equation

This paper presents two different methods for the construction of exact solutions to the combined KdV and mKdV equation. The first method is a direct one based on a general form of solution to both the KdV and the modified KdV (mKdV) equations. The second method is a leading order analysis method. The method was devised by Jeffrey and Xu. Each of these methods is capable of solving the combined KdV and mKdV equation exactly.
Ellerman Bombs and Jets Associated with Resistive Flux Emergence

Using two-dimensional (2D) magnetohydrodynamic simulations we study the effects of resistive processes in the dynamics of magnetic flux emergence and its relation to Ellerman bombs and other dynamic phenomena in the Sun. The widely accepted scenario of flux emergence is the formation and expansion of Ω-shaped loops due to the Parker instability. Since the Parker instability has the largest growth rate at finite wavelength λp ~ 10H-20H, where H is the scale height (≈200 km in the solar photosphere), a number of magnetic loops may rise from the initial flux sheet if it is sufficiently long. This process is shown in our numerical simulations. The multiple emerging loops expand in the atmosphere and interact with each other, leading to magnetic reconnection. At first reconnection occurs in the lower atmosphere, which allows the sinking part of the flux sheet to emerge above the photosphere. This reconnection also causes local heating that may account for Ellerman bombs. In the later stage, reconnection between the expanding loops occurs at higher levels of the atmosphere and creates high-temperature reconnection jets, and eventually a large (≫λp) coronal loop is formed. Cool and dense plasma structures, which are similar to Hα surges, are also formed. This is not because of magnetic reconnection but due to the compression of the plasma in between the expanding loops.
Tversky-RideNN based Feature Fusion and Optimized Deep RNN for Stock Market Prediction

The stock market prediction has been considered a significant time-series forecasting research domain. The accurate prediction of stock prices helps the investors to gain more profits and to make better decisions while selling and buying the stock products. However. , due to stock data's inherent. , dynamic. , and non-linear characteristics. , the prediction process is more complex and remains a challenging task. Hence. , this research proposes an effective strategy for accurate stock market prediction using the newly proposed Adaptive Competitive Feedback Particle Swarm optimization (CFPSO) method. The proposed approach includes four steps: extraction of technical indicators. , feature fusion. , data augmentation. , and stock market forecasting. The stock market prediction mechanism is done using Deep Recurrent Neural Network (Deep RNN). , where the network is trained using Adaptive CFPSO. Moreover. , the proposed approach has achieved a minimum MAE of 0.113. , minimum MSE of 0.095. , and minimum RMSE of 0.213.
Corrected Background-weighted Histogram Mean Shift Tracking Algorithm Based on Adaptive Bandwidth

The CBWH (corrected background-weighted histogram) scheme can effectively reduce backgrounds interference in target localization. But it still has the problem of scale and spatial localization inaccuracy. To solve the above issues, we proposed a method which generates a color probability distribution by taking advantage of the targets salient features. In the binary image, we calculate the invariant moment and thus to resize the tracking window of the next frame. A simple background-weighted model updating method is adopted to adapt to the complex background in tracking. Experimental results show that the proposed algorithm improves the robustness of object tracking by self-adaptive kernel-bandwidth updating.
Automatic Control of Hybrid Solar Cell and Diesel Power Distribution System

this study aims to present an introduction to the Optimized design and performance of a hybrid ‘Solar cell and Diesel power’ distribution system and a detailed approach to its controller. The major goal of such a system is to create a power system that uses maximum renewable energy, provides low greenhouse gas emissions, and reduces energy costs by decreasing the use of diesel power. Through researches on this system, people are creating a developed and effective alternative to fossil fuel power systems through renewable energy and cleaner power system in diesel-powered vehicles. We are creating a developed controller for such a system that aims to improve the performance criteria by reducing the steady-state error as well as time response of the system. The cost of the energy produced by the system is heavily influenced by the optimal dispatch method for these systems. Briefly, in this work, the recent technologies related to this subject are also included. A special focus has been put on the system's shortcomings. Few of these include estimation of accurate gain demand and respective data estimation, predictions on system performance, system component models, optimal system sizing, and operation set-point optimization. Many factors must be considered while optimizing the operating setpoints. First of all, the system should be highly reliable; minimum error should be presented in the system; the cost of the system has to be lesser than previous models. What we have achieved is control of a system that reduces the use of diesel in the system and instead uses renewable energy in the form of solar energy. Costs are reduced and greenhouse gases emissions are reduced as well. This hybrid solar-diesel system could be installed in a car. For further studies, car's power demand could be estimated, and modeling and simulation studies would be used on daily and yearly performance to evaluate the power system, including the efficiency and capacity factors of the energy conversion systems. The total electrical power produced by the solar Photovoltaic, PEM fuel cell, and diesel generator would be estimated, as well as the electricity cost and greenhouse gas and particulate matter (PM) emissions. The proposed control system has a high achieved minimum error, as well as a fast response time. Integration of hybrid systems with car power generation is an appealing alternative. They are cost-effective, cleaner than combustion engines, and less reliant on fossil fuels. This brings a significant enhancement and value to our project.
LoRa-Based Asset Tracking System with Data Encryption Using AES-256 Algorithm

In general, organizations have a set of secured physical assets- However, the possibility of theft is still there, so an Asset Tracking System is needed to help asset owners detect and track the position of stolen assets. We made an asset tracking system which consists of a transmitter (placed on the asset) and a receiver (placed in the monitoring room). The communication between the transmitter and the receiver is based on LoRa with a frequency of 920-923 MHz by implementing the AES-256 cryptographic algorithm to guarantee its safety from any possible eavesdropper or attacks, which can ultimately threaten the confidentiality of the communication. The system we built can detect theft and track assets with 100% accuracy from a distance of 28.38 meters until the communication is lost, i.e., more than 10.22 kilometers in LOS conditions, 400 meters in NLOS with tree obstructions, and 350 meters in NLOS conditions with building obstacles. We have also demonstrated that the communications are secure from Eavesdropper attacks.
Sensorless Unmanned Bicycle Robot

In this work, we present a new approach for designing an autonomous bicycle robot. It is well known that bicycles can be laterally stable at certain velocities. This means that if the steering bar remains free to rotate, a bicycle can be designed so that it naturally steers in a way that corrects for deviations, keeping the bicycle upright. However, to the best of our knowledge this desirable property is not taken advantage of in previous works, since connecting a standard actuator to the steering bar, which is required in order to guide the bicycle to a desired direction, does not allow the steerer to be free to rotate. In this work we propose a unique actuation mechanism that allows guidance while keeping the steerer free to rotate, thus preserving the natural stability of the bicycle. Since the bicycle is mechanically stable there is no need for a low level controller, and thus, no need for sensors and sensor fusion algorithms. We demonstrate the concept with experiments performed on a 3D printed bicycle powered by a small electric motor and guided remotely. We provide all the CAD files and software in hope that it will be used by additional researchers in the field, and advance the frontiers of autonomous systems.
Content-Aware Adaptive Point Cloud Delivery

Point clouds are an important enabler for a wide range of applications in various domains, including autonomous vehicles and virtual reality applications. Hence, the practical applicability of point clouds is gaining increasing importance and presenting new challenges for communication systems where large amounts of data need to be shared with low latency. Point cloud content can be very large, especially when multiple objects are involved in the scene. Major challenges of point clouds delivery are related to streaming in bandwidth-constrained networks and to resource-constrained devices. In this work, we are exploiting object-related knowledge, i.e., content-driven metrics, to improve the adaptability and efficiency of point clouds transmission. This study proposes applying a 3D point cloud semantic segmentation deep neural network and using object-related knowledge to assess the importance of each object in the scene. Using this information, we can semantically adapt the bit rate and utilize the available bandwidth more efficiently. The experimental results conducted on a real-world dataset showed that we can significantly reduce the requirement for multiple object point cloud transmission with limited quality degradation compared to the baseline without modifications.
Measurement of dwarf galaxies in the rich clusters Abell 665 and 963 at z=0.2

We show that the luminosity functions of the distant rich clusters Abell 665 (z=0.182) and Abell 963 (z=0.206) are flat or gradually rising down to MR=−14, with α≈−1.2? ? 0.4 [here α is the logarithmic slope of the luminosity function: φ(L)∝Lα at the faint end]. We do not confirm the steep luminosity functions (α≤−1.8) that have been recently proposed for these two clusters. Several technical points are discussed in detail. In particular, we compute the corrections to the background contamination caused by gravitational lensing from the cluster dark matter, and show that the corrections are small unless we wish to determine variations in the luminosity function on small scales. Recent observations have also shown that the field galaxy luminosity function at z≈0.2 is also shallow between MB=−19 and MB=−13. Abell 665 and 963 are two of the richest clusters known at that redshift. We therefore propose that the galaxy luminosity function might be universal in this magnitude range at z=0.2. The dwarf galaxies that we see in Abell 665 have a colour distribution that is strongly peaked at B−R=1.9. We compute K-corrections based on the spectral energy distributions of local galaxies, and show that these are probably dwarf spheroidal galaxies. This might suggest that the dwarf spheroidal population observed in Virgo already existed at z=0.2.
Modeling and Control of JetQuad Aerial Robot

The payload of the electric aerial robot is always not very large which leads to limited usage scenarios, so the research of jet aerial robots has become popular recently. This paper proposes a dynamic modeling and linearization method for a class of JetQuad aerial robots with rotatable vectoring nozzles. Meanwhile, a thrust vector control method is proposed. By transforming the control allocation problem into a nonlinear multi-objective optimization problem, the design control law can adapt to the dynamic response characteristics of the jet thrust and the vector mechanism. Taking JetQuad designed by US company FusionFlight as an example, simulation verification is performed in two scenarios of square trajectory tracking and circular trajectory tracking, which proves the effectiveness of the proposed thrust vector control method.
Study of Latency for a Teleoperation System with Video Feedback using Wi-Fi and 4G-LTE Networks

In this study was developed a teleoperable robotic arm with the aim of testing and analysing the teleoperation latency for three different communication channels. A closed-loop control (direct teleoperation) was adopted, with bidirectional communication, the operator receiving the necessary information for the teleoperation process in real-time from a video camera mounted on the robotic arm. In order to connect the control device with the teleoperable robotic arm were used three different communication channels and the latency of the teleoperation process was measured and analyzed for each of these three communication channels. This paper contains information about the mechanical, hardware and software parts of the developed robotic system. It also presents the communication channels that were tested and the results obtained. The aim is to identify the advantages and disadvantages of each of these three communication channels, to detect the limitations and to ascertain to what extent the teleoperation of such a system can be done efficiently.
Using metal-organic templates to steer the growth of Fe and Co nanoclusters

Regularly sized Fe and Co nanostructures are created using two-dimensional metal-organic honeycomb lattices as templates. They consist of dicarbonitrile-polyphenyl molecules coordinated to Co centers on Ag(111). Subsequently deposited Fe or Co atoms nucleate clusters at specific sites on top of the metal-organic lattices while leaving their hexagonal pores empty. Choosing the deposition temperature, one can selectively decorate the molecules (120K) or the Co coordination nodes (190K) with Fe. The distance between the Fe and Co clusters and their shape can be adjusted through the lattice constant of the template by varying the number of phenyl rings in the molecules.
Trust-Aware Security system for Dynamic Southbound Communication in Software Defined Network

The vast proliferation of the connected devices makes the operation of the traditional networks so complex and drops the network performance, particularly, failure cases. In fact, a novel solution is proposed to enable the management of the network resources and services named software defined network (SDN). SDN splits the data plane and the control plane by centralizing all the control plane on one common platform. Further, SDN makes the control plane programmable by offering high flexibility for the network management and monitoring mostly in failure cases. However, the main challenge in SDN is security that is presented as the first barrier for its development. Security in SDN is presented at various levels and forms, particularly, the communication between the data plane and control plane that presents a weak point in SDN framework. In this article, we suggest a new security framework focused on the combination between the trust and awareness concepts (TAS-SDN) for a dynamic southbound communication SDN. Further, TAS-SDN uses trust levels to establish a secure communication between the control plane and data plane. As a result, we discuss the implementation and the performance of TAS-SDN which presents a promote security solution in terms of time execution, complexity and scalability for SDN.
Estimating the Derivative of a Convex Density

We obtain a relation between the time between two bird‐catchings and the total resting period of a bird, leading to the problem of estimating the derivative of a convex density. We state a fundamental result on the nonparametric maximum likelihood estimator of a convex density. Further, we derive the optimal rate in the minimax risk sense for estimating the derivative of a convex density.
Non-Invasive Monitoring: Panthera Onca Recognition Based on Image Processing

Currently, Panama has projects to monitor endangered species such as the jaguar; for which they have been mainly using camera traps. These projects generate a large number of images that exceed, more often than not, the manpower capacity to label them. A faster way to do the tagging labor is to use new technologies associated with computer vision, which can reduce the time it takes to check whether or not a jaguar exists among the captured images. In this article, an algorithm has been developed using Matlab to perform image processing and recognition of jaguars based on background subtraction and the characteristic shape of jaguar spots. The algorithm achieves a high degree of precision of 80% and an accuracy rate of 79%, demonstrating that positive results can be obtained using computer vision for camera trap image analysis.
Leveled Homomorphic Encryption Based on Finite Field Isomorphism Problem Over Matrix Algebra

Finite fields of order pm are isomorphic. The elements of these fields can be denoted by a polynomial with coefficients modulo p or by matrices with entries chosen modulo p. The length of an element of field can be associated with the norm of that element. In general, a non-trivial isomorphic map between any two fields does not preserve the length of the elements. The entries of the images of short elements distribute randomly and uniformly over modulo p. This key feature yields new kind of problems, called finite field isomorphism problems. In this paper, we studied the finite field isomorphism problem and introduced a leveled homomorphic encryption scheme based on the finite field isomorphism problem over the matrix field.
Generic Application Layer Features For IoT Devices Identification

The wide adoption of Internet of Things (IoT) in traditional networks and critical infrastructures has brought many advantages. At the same time, insecure IoT devices provide a loophole in existing infrastructure that miscreants can exploit. Mirai incident is a well-known example, where attackers exploited the internet using IoT devices. Hence, a quick, accurate and energy-efficient IoT device identification mechanism is required to cope with these emerging challenges. In this research, we have proposed a generic set of application layer features for IoT device identification. To show the effectiveness of proposed generic application layer features we have compared them with network layer features using machine learning models and IoT devices traces. Previously most of the work was done on network layer features. However, for IoT devices network layer features show constancy in their pattern as compared to application layer features which are distinct and unique in nature. As a consequence, large number of network layer features are required for identification of devices causing use of higher prediction time, computational and feature extraction cost. We have also developed the first available open-source application layer feature extractor tool. Researchers can utilize this tool for acquiring application layer datasets and utilize them in different research domains.
Transient Performance of Voltage Source Converter in V2G and G2V Electric Vehicles Application

Electric vehicles (EVs) always integrate into the utility grid via power electronics interfaces like voltage source converters (VSCs). The VSCs manage the power flow between the utility grid and the EVs in bidirectional mode depending upon various conditions. However, the smooth transition of the bidirectional mode depends upon the controller used to drive the references of the VSC. In this paper, the transient performance of the VSC in vehicle- to-grid (V2G) and grid- to-vehicle( G 2V) EVs application is tested under voltage sag/swell using the fractional order sliding mode control (FOSMC). The primary focus is to manage the power flow between the utility grid and the EVs based on the state of charge (SOC) of the EV battery and the ampacity of the utility grid under grid transients. A simulation model with a utility grid, VSC, and an EV battery has been modeled at 180kVAR, 400V, and 50 Hz distribution feeder. The stability analysis of FOSMC has been ensured with the Lyapunov candidate function. The results of the proposed control have been compared with the classical PI control. It has been noticed that the proposed control is robust in terms of speedy tracking, fast convergence, and finest damping.
Detection of Brain Tumor using CNN

Brain, which is mainly composed of billions of neurons, hosts the most complicated processes that occur in the body. A brain tumor is a tissue growth in the brain that grows in an area of the brain, which should not be significantly increased. To analyze brain tumors, an open source dataset available in Kaggle is used in the paper. There are photos in the dataset and all images are in different colors and dimensions. All noisy images are converted into gray scale using GaussianBlur filter with dimensions 256*256. Then the dataset is divided into 80% for training and 20% for testing. A CNN model is proposed and evaluated against ROC Curve, Confusion Matrix, and Accuracy Curve. It is observed that the proposed model achieved better accuracy when compared with state-of-the-art models.
Metasurface-Based Reflective Type V- and W- Band Polarization Converter for Various Microwave Applications

In this study, we present an effective wideband, thin metasurface-based polarization converter. This structure's purpose is to control the polarization of incident electromagnetic waves. The proposed polarization converter application performs polarization conversion from linear polarization to linear polarization (LP-LP) and linear polarization to circular polarization (LP-CP) in reflection mode. The proposed polarization converter performs LP-LP conversation in V- and entire W- radar bands between 55.53 - 110.5 GHz range with %92 PCR rate. Also, It supports right-handed circular polarization conversion (RHCP) in the band range of 50.1 - 51 GHz.