Miracle-dz/UniFAR-FTUs huggingface dataset:
A Novel Control Operation Switching Strategy in Virtual Synchronous Generator

In micro grid, with purpose of ensuring the safe and stable operation of the inverter, it is necessary to switch the operation mode of the inverter according to the actual operation. In this paper, the virtual synchronous generator (VSG) is modeled and simulated. Taking VISMA model as the research object, the active control module and the reactive power control module are improved. Simulation model is established on MATLAB / Simulink to verify the effectiveness and feasibility of the proposed control strategy.
Low Cost Smartphone Controlled Wireless UAV

This paper discusses the development of Smartphone Controlled UAV for video surveillance and maneuvering. This user friendly device equipped with many sensors and processing unit, which make this a perfect device for various purpose. And controlling of UAV is one of them. We present the design of UAV and implementation of and integration with smartphone. In this document we demonstrate the use of mathematical model in the development and design of controllers for stability of UAV. And to control the Quadcopters with smartphone application.
Remote Laboratory to Study Mobile Robot Odometry

Remote laboratories allow interacting with real equipment through remote access. The described laboratory stand is the wheeled mobile robot equipped with IMU sensors, odometers on wheels, and one stereo camera. The principle to estimate the position of an observed object with sufficient accuracy is described. Remote users of our laboratory can evaluate how correct and applicable already known and their own algorithms are for visual, wheel, and inertial odometry.
An Improved Index Based on MapReduce for Path Queries in Public Transportation Networks

In this paper, we consider the path queries in public transportation networks which are widely used in path planning and location-based services. In general, path index is often adopted to speed up the path queries in public transportation networks. However, the pre-processing for building path index for the existing best-known query algorithms is often not efficient. Aimed at this shortcoming, we propose an improved index, which is based on MapReduce for path queries. Specifically, the proposed algorithm includes the map and reduce phases. In map phase, the public transportation network is divided into different subgraphs and generate key-value pairs of index for subgraphs. In reduce phase, the result of map function are aggregated by subgraphs' key-value pairs to obtain the index. The experimental results based on the real datasets show the effectiveness of the proposed algorithms.
Switch Unit (SU): A Novel Type of Unit for the Activation Function

The paper presents a novel unit - switch unit (SU). This unit represents a conditional include function, which is able to map the input from one dimensional space to two dimensional spaces depending on whether the input is positive or not. In the traditional neural networks, there always exist one of derivatives of two entries from output that refers to input as one and the other as zero. The proposed unit is able to cope with the vanishing gradient issues induced by the factor of activation function such as ReLUs (rectified linear units). In the experiments, different neural networks are trained based on the proposed units and tested on CIFAR-10 and CIFAR-100 datasets, which yielded the comparable results to ReLUs based neural networks using the same parameters.
Agricultural Exposures and Breast Cancer among Latina in the San Joaquin Valley of California.

OBJECTIVES To assess the role of agricultural work, pesticide exposure and age at first farm labor exposure in breast cancer risk among Hispanic women in Central California. METHODS A breast cancer (BC) case control study was conducted. Latina BC cases were identified through the California Cancer Registry and controls were recruited. Both cases and controls completed a detailed questionnaire. Pesticide exposure data was obtained by linking the crops, work locations and dates worked in specific farm jobs with the California Department of Pesticide Regulation (DPR) Pesticide Use Reports (PUR). RESULTS Chemicals associated with breast cancer risk included organophosphates, organochlorines and a phthalimide, captan. Age at first work in farm labor was younger in cases than controls (p = 0.03). CONCLUSION Agricultural work, may be associated with increased BC risk in female Hispanic farm workers.
A Practical Interactive Chess Board with Automatic Movement Control

The interactive chess board game is unlike games in its ordinary way. This board game together with tangible movements of all pieces is considered to be users attraction. Therefore, the new chessboard with an automatic moving mechanism for every piece is chosen. Initially, we have designed and developed an aluminum core structure for positioning X and Y-axis. Furthermore, a controllable magnet is deliberated for holding and moving an individual chess piece according to player manipulations. Purpose of this interactive chess board is applying technology to board game for excitement, interest, amazement, and attraction. Arduino microcontroller is used for controlling every step of piece movement. The microcontroller receives control information through the user interface and then moves the chess piece to the destination on the board. The position calculation is brought to identify the chess piece and drive accurately the stepper motors in X and Y-axis.
The Methodology of Implementation and Simulation of Quantum Algorithms and Processes

This paper describes a detailed analysis of the fundamental components involved in the quantum algorithms design in accordance with the narrowly defined software and hardware for the implementation of quantum algorithms and the stages of their design. A modular development and subsequent simulation of the process of super dense quantum coding and quantum teleportation was realized. In accordance with the use of vector and matrix algebra, within the framework of quantum algorithms, the “quantum” software is defined. This software consists of quantum intermediate representation of information, quantum language of physical operations and quantum assembler. The software environment for quantum algorithms design and further implementation according to the defined stages is proposed and considered.
Ham and Spam E-Mails Classification Using Machine Learning Techniques

ABSTRACT Spam e-mail has become a very serious problem. Sending inappropriate messages to a large number of recipients indiscriminately has resulted in anger by users but large profits for spammers. This article looks at classifying spam e-mails from inboxes. Ten alternative classifiers are applied on one benchmark dataset to evaluate which classifier gives better result. A 10-fold cross validation is used to provide the accuracy. Results of the classification algorithms are compared with the spambase UCI dataset. The experimental results approve that the spam mails can be classified correctly, with accuracy reaching up to 95.45% for the Random Forest technique, compared to other classifiers used.
Nanobiotechnology medical applications: Overcoming challenges through innovation

Abstract Biomedical Nanotechnology (BNT) has rapidly become a revolutionary force that is driving innovation in the medical field. BNT is a subclass of nanotechnology (NT), and often operates in cohort with other subclasses, such as mechanical or electrical NT for the development of diagnostic assays, therapeutic implants, nano-scale imaging systems, and medical machinery. BNT is generating solutions to many conventional challenges through the development of enhanced therapeutic delivery systems, diagnostic techniques, and theranostic therapies. Therapeutically, BNT has generated many novel nanocarriers (NCs) that each express specifically designed physiochemical properties that optimize their desired pharmacokinetic profile. NCs are also being integrated into nanoscale platforms that further enhance their delivery by controlling and prolonging their release profile. Nano-platforms are also proving to be highly efficient in tissue regeneration when combined with the appropriate growth factors. Regarding diagnostics, NCs are being designed to perform targeted delivery of luminescent tags and contrast agents that enhance the NC -aided imaging capabilities and resulting diagnostic accuracy of the presence of diseased cells. This technology has also been advancing the ability for surgeons to practice true precision surgical techniques. Incorporating therapeutic and diagnostic NC-components within a single NC can facilitate both functions, referred to as theranostics, which facilitates real-time in vivo tracking and observation of drug release events via enhanced imaging. Additionally, stimuli-responsive theranostic NCs are quickly developing as vectors for tumor ablation therapies by providing a model that facilitates the location of cancer cells for the application of an external stimulus. Overall, BNT is an interdisciplinary approach towards health care, and has the potential to significantly improve the quality of life for humanity by significantly decreasing the treatment burden for patients, and by providing non-invasive therapeutics that confer enhanced therapeutic efficiency and safety
Effective Link Prediction Based on Community Relationship Strength

Link prediction is one of the research hotspots in complex network analysis and has a wide range of applications in both theory and reality. To improve the prediction accuracy, this paper proposes a new link prediction framework by considering both node similarity and community information, which overcomes the weaknesses of existing community-based prediction methods. In the proposed framework, a reasonable measure, called community relationship strength (CRS), is defined to estimate the closeness between communities. In this paper, we hold the view that the connection likelihood between two target nodes rests upon not only their similarity but also the closeness of communities that they belong to. Therefore, to measure the connection likelihood, the proposed framework combines CRS with traditional similarity indexes. Three CRS-based methods are derived from the framework. The performance of the CRS-based methods is comprehensively studied on 12 real-world networks compared with several groups of baselines. The experimental results indicate that the CRS-based methods are more effective and robust than others.
Outage Probability Bounds of EGC Over Correlated Lognormal Fading Channels

Lognormal fading channel is an important channel model for wireless communications and free-space optical communications, but it is much more mathematically intractable than Rayleigh, Rician, and Nakagami fading channels. The cumulative distribution function of a sum of lognormal random variables does not yield a closed-form expression, thus it is challenging to approximate the outage probability of a diversity system over lognormal fading channels. In this work, we obtain the closed-form expressions of outage probability bounds for dual-branch equal-gain combining (EGC) system over correlated lognormal fading channels. A detailed proof and numerical results verify the tightness of the bounds from low to high signal-to-noise ratio (SNR) regimes. The new analytical tool provides an efficient way to compare two EGC systems over correlated lognormal fading channels, and shows that correlation can cause an infinite SNR loss to the EGC system.
Examining the Pathway to Motor Skill Competence in a Mastery Motivational Climate: An Appreciative Inquiry

ABSTRACT Purpose: This study followed a strengths-based approach to identify the pathway children follow as they develop from novice to skillful learners during a mastery-motivational physical education setting. Method: Eleven 4-year-old children (nine boys) participated in a motor activity program delivered twice weekly across 26 weeks. The teacher participated in monthly 30–45-min interviews that sought to identify the critical moments of the program as it had progressed to that point in time. Photographic images of the children’s experiences in the climate acted as prompts during interviews. Interview transcripts were subject to a deductive analysis in which the ideas of “what was working well” and “what the future might look like” were the initial categories. Results: Interview and photo analyses revealed that the children moved through three phases on their pathway towards mastery. In phase 1, “captivation” and “exploration,” the children presented the teacher with challenges in developing the managerial system. In phase 2, “cooperation” and “consolidation,” there was significantly less task modification and more time in deliberate practice. In phase 3, “dedication” and “collaboration,” the children began to actively seek out the teacher as a resource to help them learn. Conclusions: The identification of critical teaching behaviors during the program adds support for our contention that the teacher is a critical component in helping children advance along the pathway of mastery when placed in an autonomy-supportive climate. These are allowing time for exploration and experiencing freedom, adding structure, and helping students learn to manage themselves.
Cascade-Based Control for Distributed Consensus Tracking of Multi-USV Systems Under Sampled-Data Interaction

We proposed a cascade-based control for the consensus tracking problem of multiple underactuated surface vessels (USVs) with uncertainties and the environment disturbance under sampled-data interaction. The proposed control contains two parts. The frist part is a second-order distributed estimator which is designed to obtain the estimate position of the leader. Thus the leader-follower problem become as an estimate trajectory tracking problem. The second part is the local control law of the USV which is designed based on the estimate position by using sliding mode and backstepping methods to achieve the consensus tracking. In terms of Lyapunov and graph theory, several sufficient conditions are given to ensure the consensus tracking of the multi-USV system. Besides, the tracking error can be arbitrary small if we choose proper parameters in the control input and the distributed estimator.
Human Action Recognition Based on Sparse LSTM Auto-encoder and Improved 3D CNN

In computer vision area, extracting effective information from video data to realize automatic human action recognition has become one of the most active research points. In order to deal with the high dimensional challenge from massive amount of video data and extract effective features by feature self-learning model based on deep learning, a method based on a sparse long-short term memory (LSTM)auto-encoder and improved 3D convolution neural network (3D CNN)is proposed in this paper. We first construct a sparse LSTM auto-encoder to extract the key frames. And then, to extract lower dimensional features for approximating the upper limit of these features classification ability, the 3D CNN model is improved by combing with the feature engineering method, that is recursive feature elimination (RFE)algorithm. Finally, we extract high-level features by the improved 3D CNN for human action recognition. The experimental results on typical datasets UCF101 demonstrate that our algorithm has significant effect on data dimensionality reduction and outperforms some competing hand-craft feature and single feature methods.
Mitigation of Multi-vector Network Attacks via Orchestration of Distributed Rule Placement

In this paper we propose a framework for mitigating detected multi-vector anomalies in typical enterprise networks via the distribution of Access Control Rules. Our distributed, non-proprietary approach takes advantage of the capabilities offered by all devices along an attack path enhancing their mitigation potential. These devices are organized into distinct defense stages and network operators express their defense preferences for specific attack types. Our mechanism automatically assigns generic mitigation rules to each stage. Subsequently, device-specific access control rules are generated and seamlessly distributed to the corresponding defense stages of the network substrate via commonly used protocols. The proposed mitigation schema models the rule assignment to defense stages as a Generalized Assignment Problem. Items, i.e. generic mitigation rules, are assigned to bins, i.e. defense stages, based on capacity constraints and reward values guided by operator policies. Our approach considers reducing the GAP input size to enable reasonable execution of the resulting integer programming formulation. This is accomplished by aggregating malicious IP sources into prefixes and organizing rules into groups. The proposed mechanism is validated in a proof of concept prototype, used to mitigate realistic multi-vector attack scenarios.
3D Facial Landmark Detection Using Deep Convolutional Neural Networks

Various applications have been developed in facial research to aid the recognition of personal attributes, analysis of race, and personal authentication for the security industry and other research fields. Thus, it is possible to identify the differences in facial shape based on place of birth or country. The present study analyzes facial shape using scanned 3D facial images and investigates ways to extract facial landmarks from the 3D facial images. The detection of the facial landmark requires the normalization of the facial scale and position among in the 3D image data to analyze the facial shape. Therefore, it is difficult to obtain accurate facial landmarks from 3D facial images. Our method decomposes the task into the following three parts: (a) conversion of data from the 3D facial image to a 2D image, (b) extraction of facial landmarks from the 3D image using Convolutional Neural Network (CNN), (c) inversion of the identified facial landmarks from 2D to 3D images. In experiments, we compared the accuracy of this model for facial landmark detection.
Genome analyses of the new model protist Euplotes vannus focusing on genome rearrangement and resistance to environmental stressors

As a model organism for studies of cell and environmental biology, the free‐living and cosmopolitan ciliate Euplotes vannus shows intriguing features like dual genome architecture (i.e., separate germline and somatic nuclei in each cell/organism), “gene‐sized” chromosomes, stop codon reassignment, programmed ribosomal frameshifting (PRF) and strong resistance to environmental stressors. However, the molecular mechanisms that account for these remarkable traits remain largely unknown. Here we report a combined analysis of de novo assembled high‐quality macronuclear (MAC; i.e., somatic) and partial micronuclear (MIC; i.e., germline) genome sequences for E. vannus, and transcriptome profiling data under varying conditions. The results demonstrate that: (a) the MAC genome contains more than 25,000 complete “gene‐sized” nanochromosomes (~85 Mb haploid genome size) with the N50 ~2.7 kb; (b) although there is a high frequency of frameshifting at stop codons UAA and UAG, we did not observe impaired transcript abundance as a result of PRF in this species as has been reported for other euplotids; (c) the sequence motif 5′‐TA‐3′ is conserved at nearly all internally‐eliminated sequence (IES) boundaries in the MIC genome, and chromosome breakage sites (CBSs) are duplicated and retained in the MAC genome; (d) by profiling the weighted correlation network of genes in the MAC under different environmental stressors, including nutrient scarcity, extreme temperature, salinity and the presence of ammonia, we identified gene clusters that respond to these external physical or chemical stimulations, and (e) we observed a dramatic increase in HSP70 gene transcription under salinity and chemical stresses but surprisingly, not under temperature changes; we link this temperature‐resistance to the evolved loss of temperature stress‐sensitive elements in regulatory regions. Together with the genome resources generated in this study, which are available online at Euplotes vannus Genome Database (http://evan.ciliate.org), these data provide molecular evidence for understanding the unique biology of highly adaptable microorganisms.
Impact of Thickness of Liquid Crystal Cell on Holographic Properties with the Assistant of Photoconductive ZnSe Film

As short as response time (5.4 ms) and as large as exponential gain coefficient (1795.0 cm<sup>−1</sup>) were obtained in C<inf>60</inf> doped 5CB liquid crystal cells sealed between two ITO glass plates coated with ZnSe film.
Harmonic Quantitative Analysis for Dead-Time Effects in SPWM Inverters

Dead-time is commonly designed to avoid shoot-through phenomenon within each arm in voltage source converter. The presence of dead-time tends to cause a power quality issue. Quantitative analysis for dead-time effects is important to mitigate power quality issue. This paper presents two harmonic quantitative analysis methods for dead-time effects in SPWM inverters. First, the harmonic analysis method based on double Fourier series is modified to adapt any initial phase angle and calculate additional harmonics from dead-time effects. Then, a calculation method for switching angles in sinusoidal pulse width modulation (SPWM) is presented. Based on the obtained switching angles and single Fourier series, a more concise calculation method is proposed to perform quantitative analysis for the impact of dead-time on harmonics. The harmonic model is able to reveal the mechanism of harmonics generation from dead-time. Finally, the inner relationship and essential difference between double- and single-Fourier series are summarized. The simulation and experimental results show that both of the proposed quantitative analysis methods can accurately evaluate the effect of dead-time on harmonics.
Garbled Neural Networks are Practical

We show that garbled circuits offer a practical choice for secure evaluation of neural network classifiers, comparable with complex, specialized protocols using less robust assumptions, many rounds of interaction, and/or tailor-made neural networks. In particular, we develop a scheme for garbling “off the shelf” pre-trained neural networks, where the only model preprocessing required is a mild discretization step as opposed to requiring a specialized SFE-friendly model to be independently trained. Moreover, as our solution is a garbling scheme, it inherits a much more diverse range of applications than non-garbling-based solutions, perhaps most notably, efficient compilers for the malicious setting. At the protocol level, we start with the garbling scheme of Ball, Malkin & Rosulek (ACM CCS 2016) for arithmetic circuits and introduce new optimizations for modern neural network activation functions. We develop fancy-garbling, the first implementation of the BMR16 garbling scheme along with our new optimizations, as part of heavily optimized garbled-circuits tool that is driven by a TensorFlow classifier description. We evaluate our constructions on a wide range of neural networks. We find that our approach is up to 100× more efficient than straight-forward boolean garbling. It is also roughly 40% more efficient than DeepSecure (Rouhani et al., DAC 2018), a recent garbled-circuit-based approach for secure neural network evaluation, which incorporates significant optimization techniques for boolean circuits. Furthermore, our approach provides competitive performance tradeoffs (efficiency and latency vs. communication) also when compared with non-garbled-circuit approaches.