Miracle-dz/UniFAR-FTUs huggingface dataset:
Identification of Query-Oriented Influential Users in Online Social Platform

In recent time, online social influencers are affecting perceptions and behaviors of their connections by becoming a medium of swaying opinions. As a result, influential user identification has become inevitable to analyze the dynamic evolution of Online Social Networks (OSNs) for any new strategy either for viral marketing applications or controlling the propagation of provoking information. Our objective is to detect the influential users on Twitter, an online social networking with a flow of remarkable interconnections. Our proposed method is to demonstrate influential users using a collection of tweets matching a specified query on multiple topics in different time period by applying temporal TwitterRank. We conduct this experiments on real dataset to denote the effectiveness and performance of the proposed method. The findings provide that an user can achieve great influence based on number of tweets in various time period and the number of followers. This result also suggests that one topological measure alone may reflect less about the influence of users and provide new insights for sociology and other concepts. According to the proposed method, the experimental result shows the dynamic rank of influential users across multiple topics and time.
Data Management Mechanisms for IoT: Architecture, Challenges and Solutions

The current traditional database management mechanisms and analytics architectures are not generally suitable for addressing the intrinsic characteristics of diversity, heterogeneity, large-scale, dynamic and large amount of data generated by the Internet of Things (IoT) networks and various IoT applications needs. Then, it is challenging to provide efficient IoT data storage and query processing mechanisms for meeting the requirements of applications. This paper identifies the most relevant characteristics and mechanisms of IoT data management and categorizes them. Moreover, a new four layers fog-based architecture using the distributed approach is proposed for providing a secure storage infrastructure and efficient real-time IoT data discovering with better latency. Finally, this work shows advances on IoT data management mechanisms, highlights their advantages and limits, and discusses the challenging open research issues that need to be focused for providing guidelines for new contributions.
A Mixed Convolution Neural Network for Identifying RNA Pseudouridine sites

Pseudouridine is widely popular among various RNA modifications which has been confirmed to occur in rRNA, mRNA, tRNA, and nuclear/nucleolar RNA. Hence, identifying them has vital significance in academic research, drug development and gene therapies. Several laboratory techniques for Ψ identification have been introduced over the years. Although these techniques produce satisfactory results, they are costly, time consuming and requires skilled experience. As the lengths of RNA sequences are getting longer day by day, an efficient method for identifying pseudouridine sites using computational approach is very important. In this paper, we proposed a mixed convolution neural network using “one-hot” encoding. We employed k-fold cross- validation and grid search to tune the hyperparameters. Our model took care of the feature selection and extraction process automatically. We evaluated its performance in the independent datasets and found promising results. The results proved that our method can be used to identify pseudouridine sites for associated purposes. Our work also projects the increased performance of applying CNN for biological sequences.
Interpolation based Anime Face Style Transfer

This paper presents a new post-processing method for transferring human face to anime style with geometry factors. This is a branch of image-to-image problem, which is attracting the attention of researchers recently. Existing methods just deal with either texture transfer or geometry transfer. They still do not keep the signatures of human face. Moreover, previous methods do not allow users to control output image with some specific factors. This thing makes these approaches not user-friendly. To address these issues, we propose a system which leverages the advantages of Generative Adversarial Networks model and Thin Plate Spline algorithm for face morphing. The generative model is trained on selfie2anime dataset including anime and human face images. Another advantage of this approach is that it does not require paired images. We evaluate our system on 3400 images resized to 256x256 for each domain. Experimental results illustrate that our method is able to generate anime faces, which have geometry factor associating with human faces.
An Extension of Pantelides Algorithm for Consistent Initialization of Differential-Algebraic Equations Using Minimally Singularity

In this paper, an algorithm for index reduction of differential algebraic equations (DAE) is proposed. Pantelides algorithm has already been proposed as an algorithm for this purpose. This conventional algorithm has succeeded in reducing the calculation time required for index reduction. However, there exist some DAE systems whose index cannot be reduced correctly with the conventional algorithm. In this paper, we propose an extension of Pantelides algorithm to deal with wider class of DAE systems.
SS3: Security-Aware Vendor-Constrained Task Scheduling for Heterogeneous Multiprocessor System-on-Chips

Design for trust approaches can protect an MPSoC system from hardware Trojan attack due to the high penetration of third-party intellectual property. However, this incurs significant design cost by purchasing IP cores from various IP vendors, and the IP vendors providing particular IP are always limited, making these approaches unable to be performed in practice. This paper treats IP vendor as constraint, and tasks are scheduled with a minimized security constraint violations, furthermore, the area of MPSoC is also optimized during scheduling. Experimental results demonstrate the effectiveness of our proposed algorithm, by reducing 0.37% security constraint violations.
Optimization of Next Generation Cellular Networks using Reinforcement Learning

Due to the enormous increase of users, Internet websites and online services, the next generation networks are becoming complex. Therefore, optimization of these complex networks is a major challenge these days. In this paper, we consider a network model which uses reinforcement learning to develop some significant features like user cell association, enhanced QoS, capacity and coverage leading to ultra-high data transfer rates. We present three possible Q-learning algorithms based solutions that determine the best factor intrinsic to the learning algorithms which result in utmost throughput of the network. Simulation results show that our developed algorithms assign the channel appropriately and work for spatial reuse, allocate the resources by extending the range of small cells and finally ensure user's satisfaction by maintaining the QoS.
Wireless Torque Pulsations Measurement System for PMSMs

Torque pulsations are one of the major concerns in permanent magnet synchronous machines (PMSMs). Torque pulsations can be mitigated by proper machine design and/or by control. Independently of the means being used, precise measurement of torque pulsation is highly desirable for the validation and improvement of machine designs and control strategies. This article proposes a non-invasive, wireless vibration measurement system aimed to provide precise estimates of torque pulsations. The system is mounted on the rotor shaft of the PMSM, i.e., there is no mechanical coupling, and allows on-line measurement without interfering with the normal operation of the machine.
Analysis of temperature-sensitive on short-term electricity load forecasting

Short-termelectricityloadforecastingisessentialfor the operating energy of a nation. Accuracy of forecasting is important for power operation however it is dependent on weather. This paper focuses on model sensitive analysis temperature on short-term electricity load forecasting. The new models have proposed to analyze the effect of temperature on the electricity load forecasting. In detail, three methods (linear regression, local method and Monte Carlo) are analysis for temperature-sensitive on short-term electricity load forecasting. Then, the proposed models are applied to Vietnam electricity load forecasting. Index Terms-Sensitive Analysis, Electricity load forecasting, Short-term forecasting, Temperature effect, Regression
A Compact Superconducting Heterodyne Focal Plane Array Implemented With HPI (Hybrid Planar Integration) Scheme

The article is concerned with the experimental evidence that proves technical feasibility of the hybrid planar integration (HPI) scheme that has been proposed to construct superconductor–insulator–superconductor (SIS) mixer focal plane arrays of unprecedented compactness. The scheme is characterized by the adoption of silicon membrane-based waveguide probes, which allows superconducting monolithic microwave integrated circuits to couple signal, and local oscillator (LO) from (computer numerical control)-machined waveguides through multiple paths. A <inline-formula><tex-math notation="LaTeX">$2\times 2$</tex-math></inline-formula> dual-polarization balanced SIS mixer array has been implemented with this scheme, and assessed in <inline-formula><tex-math notation="LaTeX">$125 - \text{163}\,\text{GHz}$</tex-math></inline-formula> RF band. This compact array has demonstrated uniform LO distribution, and low crosstalk between pixels. The RF performance of component pixels has been confirmed to be little affected by the high-degree integration. The potential implementation of the HPI scheme at THz frequencies is also discussed.
FAMD: A Fast Multifeature Android Malware Detection Framework, Design, and Implementation

With Android’s dominant position within the current smartphone OS, increasing number of malware applications pose a great threat to user privacy and security. Classification algorithms that use a single feature usually have weak detection performance. Although the use of multiple features can improve the detection effect, increasing the number of features increases the requirements of the operating environment and consumes more time. We propose a fast Android malware detection framework based on the combination of multiple features: FAMD (Fast Android Malware Detector). First, we extracted permissions and Dalvik opcode sequences from samples to construct the original feature set. Second, the Dalvik opcodes are preprocessed with the N-Gram technique, and the FCBF (Fast Correlation-Based Filter) algorithm based on symmetrical uncertainty is employed to reduce feature dimensionality. Finally, the dimensionality-reduced features are input into the CatBoost classifier for malware detection and family classification. The dataset DS-1, which we collected, and the baseline dataset Drebin were used in the experiment. The results show that the combined features can effectively improve the detection accuracy of malware that can reach 97.40% on Drebin dataset, and the malware family classification accuracy can achieve 97.38%. Compared with other state-of-the-art works, our framework achieves higher accuracy and lower time consumption.
Effect of Combination Fractional Slot Number and Slotting Technique on the Cogging Torque in Permanent Magnet Machines

The purpose of this study is to find out the effect of the integration of both fractional slot number technique and the slotting by one step in the magnet edge on the cogging torque mitigation in a permanent magnet generator. The powerfulness of the method was approved using numerical analysis and analytically. To compare the performance result of the permanent magnet generator, three distinct structures of permanent magnet generators have been investigated and analyzed. In our study, the original structure of the permanent magnet machines has assumed as the base of permanent magnet generator performance. The magnet rotor was fully cover without any slotting, while the stator core is also without any slotting. Since the magnet was a full cover, the height of the magnet in all parts was the same. The magnet height in the edge is the same as the center of the magnet. At the beginning of the analysis, the structure of fractional slot numbers with 24 slots and 14 poles was selected. The result of both two cogging torque decrement techniques was investigated and the cogging torque subtraction result was contrasted with the initial cogging torque peak value. For the detailed examination of this study, it has been applied FEMM 4.2. It is obtained the cogging torque value of the proposed machine structure of 97.27 % contrasted with the original machine model.
The Method of How to Predict Weibo Users’ Recovery Experience on the Weekend Based on Weibo Big Data

The prevailing “996” overtime phenomenon in China has raised extensive consideration and discussion towards the topic of work-life balance. Following this trend, this study focused on the topic of work recovery experience. Based on Lens Model, we aimed to construct prediction models of weekend recovery experience with individuals’ social media footprints, which include their social media posts, behavioral information, and demographic information. We acquired Weibo data and Recovery Experience Questionnaire results from 493 participants and extracted Weibo data features for model training through two methods. As a result, two types of model were constructed: regression models which applied Ridge Regression, LASSO Regression, and Elastic Net; classification models which applied Gradient Boosting Decision Tree, Logistic Regression and Support Vector Machine. For the results of regression models, Pearson correlation coefficients between predicted values and self-reported scores ranged from 0.40 to 0.84; for classification models, F1-score ranged from 0.49 to 0.78. The results showed that individuals’ recovery experience on weekends could be predicted by their social media footprints. What is more, the methodology proposed in this study could help organizations to evaluate large groups of employees’ work recovery in real-time, which will have further implications for both theoretical and practical purposes.
Image Superiority Calculation Meant for Biometric Fake Detection Approach of Fingerprint

In this work study about the biometric security systems and how fake image differentiate with real image. In this review paper represent a method of fake detection that is a novel software-based method which is used for numerous biometric systems to identify various categories of fraud access attempts and also explained about quality assessment and attacks and method to overcome. Image Quality Analysis for Liveness Detection is based on the premise that: it is obvious that a fake image considered for an attack attempt would have a different quality than a true image having a standard scenario of options for which the sensor has been designed. In order to ensure the true presence of genuine legitimate biometric features in the picture as opposed to a fake or self-made synthetic or replicated sample, there is a significant problem in the field of biometric authentication, which involves the development of novel and efficient security considerations. The purpose of this review paper is to research liveness evaluation in an individual-friendly manner, easily with the use of image quality calculations.
A Two-stage P2P Botnet Detection Method Based on Statistical Features

P2P botnet has become one of the most serious threats to today's network security. It can be used to launch kinds of malicious activities, ranging from spamming to distributed denial of service attack. However, the detection of P2P botnet is always challenging because of its decentralized architecture. In this paper, we propose a two-stage P2P botnet detection method which only relies on several traffic statistical features. This method first detects P2P hosts based on three statistical features, and then distinguishes P2P bots from benign P2P hosts by means of another two statistical features. Experimental evaluations on real-world traffic datasets shows that our method is able to detect hidden P2P bots with a detection accuracy of 99.7% and a false positive rate of only 0.3% within 5 minutes.
Research on Remote Sensing Image Terrain Classification Algorithm Based on Improved KNN

In recent years, with the development of machine learning technology, neural networks have gradually become a convenient method for classification of remote sensing image features. This article briefly describes the structure and principle of the process of remote sensing image feature recognition, using three remote sensing image data sets AID, NWPU-RESISC45, UC Merced Land Use dataset for algorithm testing. First, the AlexNet neural network is used to extract the remote sensing image features, and the KNN is used to achieve image classification. The effects of extracting different alexnet feature layers on the average classification accuracy on the three data sets are compared. This paper compares the advantages of KNN in terms of time through PCA dimensionality reduction and k-means clustering optimization before classification, at the end of the article, it summarizes and briefly describes the development trend of neural network in the application of remote sensing image features classification technology.
Extremum Seeking Based on Approximated Sign of Gradient of Unknown Plant Maps

This paper presents a novel perturbation-based extremum seeking (ES) based on the smooth approximation of the sign of the gradient of unknown plant maps, which will improve the stability and the control performance of ES. The stability and the convergence speed of conventional sinusoidally perturbed gradient-based ES are very sensitive to the curvature of an unknown plant map, which has motivated the development of improved ES schemes that are less sensitive to the curvature such as Newton-like ES schemes. In this paper, an alternative ES scheme is proposed by estimating the sign of the gradient instead of the gradient itself. The ES scheme not only frees us from taking care of the curvature of the unknown map but also is able to minimize the increase of implementation complexity by just incorporating the signum function for the conventional ES scheme. Moreover, the signum function is replaced by its smooth approximation such as the softsign or a sigmoid function in order not only to suppress chattering phenomena but also to be able to adjust the behavior in the vicinity of an extremum point. It is also shown that the local-stability of the proposed ES scheme can be proved under some mild assumptions for the approximation function. The effectiveness of the proposed ES is demonstrated by some simulation examples for a simple process model and for a complex nonlinear biological wastewater treatment process model.
An Ultralow-Power Crystal-Free Batteryless TDD Radio for Medical Implantable Applications

This article presents an event-driven ultralow-power radio intended for miniaturized biomedical implants. In its powerlink, the system employs an RF-to-dc converter to scavenge energy from the electromagnetic waves in the 2.4-GHz unlicensed industrial, scientific, and medical (ISM) band. The bidirectional time-division datalink is realized by exploiting the 915-MHz ISM band, and its operation is coordinated by a smart control module, where the cyclic redundancy check is conducted to switch the system between monitoring and interrogation modes. A proof-of-concept radio is fabricated in a 0.13-<inline-formula> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula> CMOS process with the chip area of 1.4 mm<sup>2</sup>. Without employing any external components, the radio is robust to process, supply voltage, and body temperature variations, leading to a low-cost and highly integrated wireless solution. During the monitoring mode, the downlink data reception and demodulation are realized by a receiver that consumes <inline-formula> <tex-math notation="LaTeX">$27.5~\mu \text{W}$ </tex-math></inline-formula> while achieving a sensitivity of −69 and −59 dBm at the data rates of 1 and 100 kb/s, respectively. Dissipating peak power of <inline-formula> <tex-math notation="LaTeX">$49.5~\mu \text{W}$ </tex-math></inline-formula> in the interrogation mode, the data transmitter delivers an output power of −30.6 dBm with a data rate of 2.5 Mb/s.
Power Decoupling Control Strategy of 35 kV Cascaded H-Bridge Energy Storage System

New energy with increasing permeability has increased the unstable factors of power system. Large-scale energy storage system compensating for the fluctuating power of new energy power generation has a high practical significance. To make full use of the regulating ability of the energy storage system, a power decoupling control model of 35kV cascaded H-bridge energy storage system is proposed with the capacity of the energy storage system. Firstly, the topology of 35kV energy storage system is given. Secondly, according to the related technical standards and requirements, the model of cascaded unit equipment of the system is determined. Based on 35kV cascaded H-bridge energy storage system, power regulation model of energy storage power conversion system (PCS) is built and the active power and reactive power decoupling control strategy for energy storage system is obtained. Finally, the control strategy is simulated and validated by using MATLAB/Simulink simulation system. The stability of the control strategy can be verified by the results.