Miracle-dz/UniFAR-FTUs huggingface dataset:
Community Search in a Multi-Attributed Graph using Collaborative Similarity Measure and Node Filtering

Community search is a well studied problem in literature to find similar and strongly connected vertices in a graph for a give set of query vertices. It can assist in finding similar structures in a large graph and has many potential applications in different domains, such as molecular biology, data science, and sociology. However, it is non-trivial to identify analogous communities in a large multi-attributed graph for a given query graph due to complex structure of underlying network. Majority of the existing approaches either focus on structural or attributed aspect of the network for community search in a multi-attributed graph, while other are computation intensive. Therefore, we introduce a simple and efficient approach to find communities for a given query graph using collaborative similarity measure (CSM) and representation selection strategy. We apply an incremental clustering approach to determine k sets of nodes from the original graph based on structural and attribute similarity. Afterwards, we find representative and most relevant vertices in each cluster using PageRank approach. In order to get optimal communities, we perform clustering coefficient based pruning on the resultant communities. The experimental analysis on various real-world graphs shows the effectiveness and efficiency of our approach in terms of execution time and results accuracy.
Performance Analysis of Grid Connected Solar Photovoltaic System Under Network Faults

This paper presents an analysis of a 100 kWp Grid Connected Two-Stage Solar PV System (GC-TS-SPS) in MATLAB/Simulink environment. Solar photovoltaic system (SPS) based distributed energy sources are increasing at large in the grid. Power system dynamics play a very crucial role in enabling the effective integration of such intermittent sources. The performance of the GC-TS-SPS is analysed under different Grid operational scenarios caused due to the occurrence of short circuit faults in the network. Both symmetrical and unsymmetrical faults are considered, and their impact on the component of the solar power system is analysed with varying fault locations. This analysis may be used to design the control and protection of SPS ensuring reliable Grid connectivity and PV module design.
Behavioral Issues of Children in Terms of Internet Usages Time in Bangladesh

The Internet is a blessing to our modern era. We cannot think of our life without it. It is used for various purposes. Children are most likely spending their leisure time watching YouTube videos or playing different online games. Sometimes they become addicted. Internet addiction is one of the most cynical, dangerous consequences of this. This study aims to determine any typical behavioral pattern in the average level and excessive level of Internet users based on watching YouTube videos and playing online games. This study is conducted for eight to seventeen years old children. It is a qualitative study, and the method used for this study is a questionnaire survey and case study. Results indicate that some behavioral issues are present in both average and excessive levels of internet users. For the case study, a difference is found between the actual and assumed Internet usage time of participants. This paper will help parents and many researchers because it will give them a new viewpoint on this serious issue.
Effectiveness of Discrete Particle Swarm Optimization and Binary Differential Evolution for Process Formation

The advantage of flexible structure makes it possible for reconfigurable manufacturing systems (RMS) to respond to business opportunities. One important issue is to form production process in RMS to meet the demand based on available resources. To form a process, it is required to assign manufacturing resources to carry out the operations in it efficiently. The decision problem is to find a configuration using the available resources. Recently, Particle Swarm Optimization (PSO) and Differential Evolution (DE) algorithms have been developed to optimize processes. In this paper, another two mutation strategies are developed for binary DE. Performance of these algorithms to form production processes will be compared.
In-Situ Bonding of Multi-Layer Microfluidic Devices Assisted by an Automated Alignment System

Three-dimensional multi-layer microfluidic device (MMD) fabricated by polydimethylsiloxane (PDMS) is a solution for on-chip high-complexity serial or parallel processes. In this letter, we propose and set up an automated 8-DOF alignment system assisted by computer vision, which is capable of automatic leveling, aligning, and in-situ bonding of multiple PDMS layers. A microscope is motorized by a Z stage with a grating ruler to optically focus the marks and record the Z positions. A 3-PRS mechanism with flexible hinges is proposed to achieve the leveling of the top layer. An XYZR platform is utilized for in-plane alignment of the top layer and moving the top layer down to bond with the bottom layer. The Z positions of marks, translational and rotational offsets obtained by image processing are used for the automated leveling and aligning of the PDMS layers. Experimental results showed that translational and rotational alignment errors are less than 5 μm and 0.1°, respectively. The whole bonding procedure, including the plasma treatment, took the time shorter than 3 mins. Finally, a fabricated 3-layer microfluidic device with a deformable microchannel is applied to the cell squeezing and proved that the proposed alignment system has great potential in developing the functional MMDs.
Application of the Spectra of Graphs in Network Forensics

Network forensics is a discipline of growing importance. The ability to mathematically evaluate network intrusion incidents can substantially improve investigations. Graph theory is a robust mathematical tool that is readily applied to network traffic and has had been used in a limited fashion for network forensics. However, the full scope of graph theory has not previously been applied to network forensics. In particular, spectral graph theory has not been previously utilized for analyzing network forensics. This paper describes the application of spectral graph theory to specific network intrusion issues. This provides a mathematical tool to be utilized in network forensics. A case study is also utilized to demonstrate precisely how the methodology described in this paper should but utilized in an actual case.
Multi-View Object Pose Refinement With Differentiable Renderer

This letter introduces a novel multi-view 6 DoF object pose refinement approach focusing on improving methods trained on synthetic data. It is based on the DPOD detector, which produces dense 2D-3D correspondences between the model vertices and the image pixels in each frame. We have opted for the use of multiple frames with known relative camera transformations, as it allows introduction of geometrical constraints via an interpretable ICP-like loss function. The loss function is implemented with a differentiable renderer and is optimized iteratively. We also demonstrate that a full detection and refinement pipeline, which is trained solely on synthetic data, can be used for auto-labeling real data. We perform quantitative evaluation on LineMOD, Occlusion, Homebrewed and YCB-V datasets and report excellent performance in comparison to the state-of-the-art methods trained on the synthetic and real data. We demonstrate empirically that our approach requires only a few frames and is robust to close camera locations and noise in extrinsic camera calibration, making its practical usage easier and more ubiquitous.
A Functional Model for Unifying Brain Computer Interface Terminology

Brain Computer Interface (BCI) technology is a critical area both for researchers and clinical practitioners. The IEEE P2731 working group is developing a comprehensive BCI lexicography and a functional model of BCI. The glossary and the functional model are inextricably intertwined. The functional model guides the development of the glossary. Terminology is developed from the basis of a BCI functional model. This paper provides the current status of the P2731 working group's progress towards developing a BCI terminology standard and functional model for the IEEE.
Credibility-Based Countermeasure Against Slow HTTP DoS Attacks by Using SDN

In slow HTTP DoS (SHD) attacks, the attacker sends HTTP requests in pieces slowly, one at a time to a web server to exhaust its resource and achieve denial of service. Such attacks are easy to launch but hard to defend by conventional solutions like firewall. By exploiting the software-defined networking (SDN) technique, the paper proposes a credibility-based countermeasure against SHD attacks (CCSA), which appraises each client by its connections and the frequency that it sends fragmented requests. The connections of low-credibility clients will be blocked to avoid them depleting resource. When the server is short of resource, suspicious connections are then suspended to ensure the server's availability. Simulation results verify that CCSA can efficiently stop SHD attacks and keep low memory usage for the controller.
Paramagnetic Meissner Effect and Current Flow in YBCO Nanofiber Mats

Microstructural and magnetic characterizations of YBa2Cu3O7−δ nanowire fabric-like samples were performed. Fabric-like ceramic superconductors are nanoporous materials and can be applied, e.g., in magnetic shielding, since the porous morphology makes them extremely light-weight and effectively to be cooled. The samples were produced by the solution blow spinning (SBS) technique, starting from a sol-gel solution of the precursor materials in polyvinyl-alcohol. The morphology of the samples was characterized by scanning electron microscopy, transmission electron microscopy, and x-ray diffraction with the aim to analyze the possible current paths. The magnetic hysteresis loops revealed typical polycrystalline behavior, and field-cooling in various small applied fields shows the presence of the paramagnetic Meissner effect (PME), but in a distinctly different way as compared to melt-texured Bi2Sr2CaCu2O8+δ or Nb.
CentroidReg: A Global-to-Local Framework for Partial Point Cloud Registration

Point cloud registration is a key problem for robotics, computer vision, and other applications. Previous global registration algorithms are sensitive to noises or partial occlusion, while local registration algorithms are highly dependent on initial angles. To solve these problems, we propose CentroidReg, a global-to-local framework for partial point cloud registration, desensitizing noises and initial angles. The partial point clouds have consistent global information (the centroid of the complete 3D shape) and local surface structures, which motivates us to combine rotation-invariant global and local information for correspondence generating. To estimate the centroid of the complete 3D shape for partial point clouds, we propose the CentroidNet with centroid voting and maximum likelihood estimation (MLE) aggregation. Then, we use the proposed RegistrationNet to extract hybrid rotation-invariant features (HRIF) combining global information of the entire 3D shape and local structures in the local area to describe each point. Furthermore, we propose the Reliable Correspondence Generating (RCG) module to generate correspondences in the overlapping area. Finally, a weighted differentiable singular value decomposition (SVD) step is performed to estimate the final rigid transformation. The proposed framework presents state-of-the-art performance on partial point cloud registration. Experiments show that our method is robust to initial angles and noises.
CoTrRank: Trust Ranking on Twitter

Trust evaluation of people and information on social media is critical for maintaining a healthy online social environment. How to evaluate the trustworthiness of users and tweets is challenging due to the complex and complicated relationships between/among users and their posts. As existing approaches use a single network to represent users, posts, and their relationships, they have the limitation to reflect the different statistical features of users and tweets, which has reduced the ability to determine the trustworthiness of users and tweets. To address this issue, we develop a trust evaluation method that models users and tweets separately in two networks that are coupled with each other via interactions. We provide mapping functions to map the statistical numbers of actions of users/tweets to trust values that indicate their relevant trust degrees. The proposed method provides a configurable solution that has the capability to consider the effects of users and tweets differently in different trust ranking situations. A set of experiments are conducted against real-data collected from Twitter. The experimental results show that the proposed approach is more effective in trust evaluation compared with several baseline methods.
Mixed-Mode Active Load-Pull Using one Single-Ended Device-Under-Test

A method to emulate differential transmitter architectures is presented. The technique, which is based on mixed-mode active load-pull measurements, predicts power amplifier (PA) performance while avoiding the need to manufacture the complete PA. The method is based on an iterative procedure using transistor/branch PA active load-pull measurements together with the S-parameters of the load network. Advantageously, real world performance of the complete differential PA can be evaluated in the design stage. Thereby, many different output combiners and configurations, e.g., biases, transistors, and branch phases, can be fully evaluated without fabrication. Compared to prior art, the method requires only a single representative device-under-test, while providing flexibility in the input signal and the target load network. Thus, a novel powerful measurement tool for PA designers is presented. The technique is demonstrated by performing mixed-mode load-pull and emulating a differential amplifier at 2.14 GHz using continuous wave signals.
Optimization of AlN and AlScN Film ICP Etching

We present the inductively coupled plasma (ICP) etching characteristics of (0002) oriented Aluminum Nitride (AlN) and Aluminum Scandium Nitride (Al<inf>0.94</inf>Sc<inf>0.06</inf>N) piezoelectric thin films and implementation in AlN piezoelectric lamb wave resonators. A profile of 84° with an etching rate of about 230nm/min using Cl<inf>2</inf>/ BCl<inf>3</inf>/N<inf>2</inf> mixed gas is achieved at AlN film, and a profile of 77° using same mixed gas is obtained at Al<inf>0.94</inf>Sc<inf>0.06</inf>N film. Finally, we demonstrate the fabrication and characteristics of AlN contour mode resonators (CMRs), and report a CMR operating at approximately 400MHz with the quality factor exceeding 1600.
Comparison Analysis of Bipolar and Double D-shape Configurations in Inductive Power Transfer Electric Vehicle Chargers

Inductive electric vehicle charging systems is a revolutionary step toward transportation electrification. One of the major challenges in Inductive Power Transfer(IPT) system which is comprised of transmitting and receiving coils is the appropriate selection of couplers' configuration for dynamic charging application while EV is moving. The performance of Bipolar Power Pads (BPP) as the proposed coupler in this paper is compared with the existing Double D-Shape (DD) power pad for IPT Electric Vehicle (EV) chargers by analyzing the misalignment tolerance, power transfer capability, and safety index. 3D-Finite Element Analysis (FEA) models of these two types of coupler is built in ANSYS MAXWELL. A parametric FEA study is carried out to simulate the dynamic IPT system with either DD or BPP via defining the secondary position as the variable, and results of them are compared. To examine the public safety index, the flux density distribution in distance of 400mm from either the BPP setup or DD coupler are shown to show which configuration provides safer charging environment for public exposure considering the maximum allowable electromagnetic field exposure $(EMF\leq EMF_{Max})$.
Permittivity Characterization of Dispersive Materials Using Power Measurements

This article presents an inexpensive transmission line (TL) approach to reconstruct the dielectric properties of dispersive and non-dispersive materials, with low or high loss. This method uses amplitude-only transmission measurements, namely power measurements, thereby removing the need for phase measurements. The proposed method only requires a signal generator and a power sensor for complex permittivity characterization without the need for either a scalar or a vector network analyzer. In the proposed method, a Debye model for the frequency dispersion of the material under test (MUT) is used; however, the applicability extends to other models. A coaxial line is considered as an ideal TL, and by using the inverse solution of the forward scattering equation in different frequencies, which requires numerically solving a system of nonlinear equations, the Debye parameters are extracted. For experimental validation, a suspended coaxial line was designed and fabricated. The permittivities of several liquid chemicals were measured within a frequency band of 0.3–3 GHz to demonstrate the validity of the technique.
Holoscopic 3D Microgesture Recognition by Deep Neural Network Model Based on Viewpoint Images and Decision Fusion

Finger microgestures have been widely used in human computer interaction (HCI), particularly for interactive applications, such as virtual reality (VR) and augmented reality (AR) technologies, to provide immersive experience. However, traditional 2D image-based microgesture recognition suffers from low accuracy due to the limitations of 2D imaging sensors, which have no depth information. In this article, we proposed an innovative 3D microgesture recognition system based on a holoscopic 3D imaging sensor. Due to the lack of holoscopic 3D datasets, a comprehensive holoscopic 3D microgesture (HoMG) database is created and used to develop a robust 3D microgesture recognition method. Then, a fast algorithm is proposed to extract multiviewpoint images from one holoscopic image. Furthermore, we applied a CNN model with an attention-based residual block to each viewpoint image to improve the algorithm performance. Finally, bagging classification tree decision-level fusion is applied to combine the predictions. The experimental results demonstrate that the proposed method outperforms state-of-the-art methods and delivers a better accuracy than existing methods.
Thermal Strain-Induced Self-Rolling Mesh Cuff Electrodes for Non-Linear Peripheral Nerve

Cuff electrodes have been widely utilized on peripheral nerve for neural stimulation and recording applications. In this paper, we have proposed a self-rolling meshed cuff electrode induced by the thermal strain. The final cuff diameter is about 1.4 mm, which can match well with the peripheral nerve of rat (diameter of 1.54 mm at ischial tuberosity of sciatic nerve). The mechanical simulation of the mesh cuff illustrates the oblivious change from the plane cuff when bending with the nerve model. The mesh cuff not only facilities the flow of interstitial fluid in and out, but also shows the potential to combine with microfluidics or optogenetics technology.
A Frequency-Domain Tuning Method for a Class of Reset Control Systems

Constant in gain Lead in phase (CgLp) compensators, which are a type of reset elements, have shown high potential to overcome limitations of linear control systems. There are few works which investigate the tuning of these compensators. However, there are some significant drawbacks which make those methods unreliable. First, their analyses are performed in the open-loop configuration which do not guarantee the existence of steady-state response of the closed-loop. If it is guaranteed, unlike linear control systems, open-loop analyses cannot precisely predict the closed-loop steady-state performance. In addition, the stability condition could not be assessed during the tuning process. These significant challenges have been separately solved in our recent works by proposing frequency-domain frameworks for analyzing the closed-loop performance and stability of reset control systems. However, they are not formulated and implemented for tuning CgLp compensators. In this paper, based on the loop-shaping approach, the recent frequency-domain framework and the frequency-domain stability method are utilized to provide a reliable frequency-domain tuning method for CgLp compensators. Finally, different performance metrics of a CgLp compensator, tuned by the proposed method, are compared with those of a PID controller on a precision positioning stage. The results show that this method is effective, and the tuned CgLp can achieve more favorable dynamic performance than the PID controller.
Comparison of Fiber-Based Gas Pressure Sensors Using Hollow-Core Photonic Crystal Fibers

A systematic comparison of fiber-based gas pressure sensors using different types of hollow-core photonic crystal fibers (PCFs) is conducted. The sensor was fabricated by splicing a segment of hollow-core silica capillary between a single mode fiber (SMF) and a hollow-core PCF. A Fabry-Perot (FP) cavity configuration is thus formed with the capillary tube as the sensing cavity, while the PCF acts as gas passage to the external environment. External pressure change would lead to the variation in the refractive index of air in the sensing cavity, resulting in a wavelength shift of the interference dips. The pressure sensitivity is measured to be around 4 nm/MPa, with a high linearity of 99.7% or above, regardless of the type of the PCFs we used as gas inlet. Among the four different PCFs, the large-mode-area (LMA) PCF shows the highest fringe contrast in the reflection spectrum. Modal analysis reveals that this is due to the high reflectivity caused by the solid core of LMA-PCF. Our experimental results also indicate that the length of the sensing cavity, as well as the offset fusion splice will influence the fringe contrast. The sensor has potential application in gas pressure sensing for advantages of high sensitivity, compact size and ease of fabrication.
State of Art and Comprehensive Study on Charging Systems for Electric Vehicles

This article presents a comprehensive study on electric vehicle charging systems. The charging systems of electric vehicle are very important as they provide energy to electric vehicle for their operation by charging their batteries. In the area of battery charging operation, various topologies have been proposed in the literature. A discussion on various techniques has been presented in this article. Topologies in this article include both charging circuits for on-board and off-board electric vehicles. Many researchers have integrated solar power to the charging grid so that the same can be used for charging the batteries, feeding the domestic loads and also feeding power into the grid during sunshine hours in that case grid provide backup for loads at the time when solar power is not available. Many techniques reported in literature have enabled the power flow in both the directions that made the charger a multifunctional unit.
Study of Machine Learning Techniques for Plant Disease Recognition in Agriculture

Usage of machine learning in agriculture is showing exponential improvement and gateway for interdisciplinary research. The implementation of autonomous techniques in agriculture can help farmers to perceive the disorder in crop yields, including identification of patterns. In the horticulture field, detection of disease for plant protection and crop management perform the significant role. In this paper, the objective is to study and analyze the various disease classification methodologies, advance techniques of machine learning used by different researchers in agriculture application.