Miracle-dz/UniFAR-FTUs huggingface dataset:
Miku VMD: Special Motion Data and Predictive Methods for Choreography Model

At present, most large-scale gesture data sets do not include dance gestures, and it takes a lot of effort to produce our own data sets, which makes a lot of trouble to train a choreography model. We find a large number of Miku's dance videos on many video websites. The videos are created by fans using MikuMikuDance software and the dance gestures are stored as VMD format. Converting them into CSV format, then we get the 3D coordinates of the key points of the human skeleton which can be used as a dataset for training choreography model. This paper presents a choreography model based on variational autoencoder and LSTM which use Miku's dance gestures as data set. Our choreography model can generate continuous and graceful dance gestures using the 3D coordinates of the key points of the human skeleton at some moment or the 3D coordinates of the key points of the human skeleton at several consecutive moments as input, helping people for better dance choreography and dance learning.
A Proposed Crowdsourcing Engine for Indonesian Cultural Heritage

Indonesia is rich in cultural heritage. Based on BPS data in 2015, there were 6238 intangible and 1413 tangible cultural heritage [1]. Ironically with this great wealth, the 2014 Podes survey shows that Indonesian participation in cultural visits and cultural performances is very low (5-15%) [2]. So it is not surprising that about 127 of Indonesian intangible cultural heritage are on the verge of extinction, one of which is the result of the low level of cultural socialization among the community. Cultural socialization can actually be done through internet media considering that the public's exposure to devices and the internet is increasing year by year[3]. However, the cultural data available on the internet has not been integrated so that it cannot be optimally utilized. Data on cultural heritage is currently spread to many parties: cultural institutions such as museums; cultural community; and individuals. Data on cultural heritage in individuals are prone to be lost if it is not stored and/or inherited. The problem is, how to collect data that is spread everywhere and then store it in a structure that can answer questions such as the number of cultures that exist, statistics, cultural distribution regarding geographical location, and kinship relations between one culture and another. Facing this problem, crowdsourcing is a solution opportunity that allows data collection from many parties. Crowdsourcing is a new way of utilizing human resources spread throughout the world to do certain jobs. The low-quality problem of crowdsourcing technology can be handled by combining crowdsourcing systems with automatic classification algorithms or with procedure or algorithm to select the appropriate worker due to their performance. In this study, a crowdsourcing machine will be built to collect heritage cultural data from various sources. The collected data is stored in an ontology format that can answer questions: statistics on cultural data, number of current cultures, geographical distribution of cultural objects, and intercultural kinship.
Simulation of Frequency Properties of Operational Amplifiers in Analog-Digital Signal Processing Devices

The mathematical models of frequency properties of operational amplifiers (OAs), formed by submicron combined gallium arsenide and silicon technologies, are presented. This approach considerably increases the dynamic range at voltage, frequency and temperature in analog-digital signal processing devices. The paper describes three areas of application of OAs. In the first area, a modernized differential cascade based on bipolar or Schottky field transistors is used, which ensures the symmetry of its characteristics and high speed. The second class of tasks relates to the development of active RC-filters, in which unpacked OA is the active element, formed by submicron gallium arsenide and silicon technologies, and RC-links formed by the original tantalum technology. The simulation of active RC-filters based on gyrators performed on OAs is realised. The use of a gyrator eliminates completely the use of inductive elements. The third type of task represents the simulation of comparators formed on unpacked OAs, which allows their effective use in microcontrollers of analog-to-digital and digital-to-analog converters at digital processing of signals.
Beyond Sync: Distributed Temporal Coordination and Its Implementation in a Multi-Robot System

We propose a technique for adaptive temporal coordination in multi-agent systems where tasks have to be scheduled in a decentralized way. It provides three states: synchronized, splay, and clustered. Due to discretization in time and phase, the model operates with low signaling effort and is robust in the presence of delays. We analyze the model through simulations and demonstrate its feasibility through experiments with small robots.
Identifying and Validating Components for National Cyber Security Framework

National Cyber Security (NCS) is an effort to safeguard nation's assets from loss caused by cyber threats and attacks. In order that NCS development can be performed in a directed and integrated manner, a framework is needed to optimally guide NCS implementation. The NCS framework were identified as previous works comprises twelve components. This paper tries to decompose these twelve main components into sub-component, The sub-components are extracted based on literature review, then validated by a group of experts and practitioners with Delphi technique. 54 sub-components were identified from literature review. Then, the sub-components were updated into 57 sub-components after validated by a group of experts. The findings of this research could be used on the future work for developing a robust Indonesia National Cyber Security Framework.
A Checkerboard Corner Detection Method Using Circular Samplers

The accuracy of camera calibration depends a large extent on the corner detection accuracy. Thus, the checkerboard corner detection plays a fundamental role in the checkerboard based camera calibration. In this paper, we present a checkerboard corner detection method utilizing the specific frequency amplitudes of the boundary sequence at the checkerboard corner, which involves a coarse-to-fine strategy. The sub-pixel coordinates of corners are also calculated for higher accuracy. Experiment results with real images show that the method is capable of achieving high detection accuracy, hence providing reliable preprocessed data for high resolution camera calibration.
Thermal and Signal Integrity Analysis of Novel 3D Crossbar Resistive Random Access Memories

The resistive random access memory (RRAM) device is a fundamental building block of novel nonvolatile memories. This paper addresses the design of suitable 3D crossbar structures, in view of their monolithic integration into large memory modules. In fact, a full 3D electro-thermal model is here adopted to simulate and study the thermal and signal integrity of a 1Diode-1Resistor RRAM x-point crossbar structure. These analysis are carried out by considering different RESET biasing schemes and heatsink locations. In addition, two different materials are considered for realizing the contact electrode material wires of the 3D crossbar: Nickel and/or Carbon nanotubes. In particular, we investigate the worst-case scenario in the electro-thermal analysis by comparing the performance results in terms of resistive voltage drop and of temperature distribution. The achieved simulation results demonstrate that the use of carbon interconnects not only provides excellent signal and thermal integrity performances, but also enables the simplest solutions for an effective biasing scheme.
Central Power Management System for Hybrid PV/Battery AC-Bus Microgrid Using Typhoon HIL

This paper presents a rule-based master/slave communication-based power management System (PMS) for a real-world grid-interactive AC-bus hybrid microgrid (MG) that incorporates photovoltaic and energy storage systems. The microgrid includes seven PV inverters, one battery inverter and AC load. The proposed power management system aims at minimizing the electricity costs for the customer as well as providing the load with a stable voltage, frequency and power supply in grid-connected and islanded conditions. Typhoon HIL Control Center has been employed for the implementation of the microgrid, SCADA interface and the power management system. The proposed power management system has been developed in Python, inside the SCADA environment and the microgrid has been simulated using the virtual HIL device.
Impact of Imbalance Netting Cooperation on Frequency Quality and Provision of Load-Frequency Control

This paper discusses Imbalance Netting Cooperation (INC) between multiple areas that was incorporated due to the high costs of balancing energy. The aim of INC is to net the demand for balancing energy between Control Areas (CAs) with different signs of interchange power variation. Furthermore, INC should also improve frequency quality. Therefore, the impact of INC on frequency quality and provision of Load-Frequency Control (LFC) is examined thoroughly with dynamic simulations of a three CA testing system. The obtained results confirm that INC reduces balancing energy and releases regulating reserve, whereas the unintended exchange of energy is also reduced. In addition to improving LFC performance indicators, the positive impact of INC on frequency quality is also shown.
Classification for Multiformat Object of Cultural Heritage using Deep Learning

The growth of information in the last two decades is dominated by multimedia data such as text, image, audio, and video. Multimedia data with low-level features should be represented in a high-level concept that is easily understood by a human. Classification of the multi-format object is a technique that is used to represent a multi-source object like text, image, audio, and video at once. The object t features are extracted then categorized in several specified classes or concepts. This paper adopts Deep Learning Techniques: (1) Convolutional Neural Networks (CNN) techniques for classifying an image, audio, and video, (2) Recurrent Neural Networks (RNN) technique for classifying text. The experiment uses small data of Indonesian cultural heritage domain. As supervised learning form, the output model is grouped into five classes based on Indonesian ethnic groups (Toraja, Bali, Batak, Dayak, Betawis. The result, this classification model can be implemented in the Multimedia Information Retrieval System and Recommender System for Indonesia Cultural Heritage.
CRC-aided Iterative Decoding of Turbo-hadamard Codes

Turbo-hadamard codes is an excellent low-rate turbo-type codes, but the decoding cost increases rapidly when the number of iterations increases with large parallel branches. This paper proposes a CRC-aided iterative decoding method to reduce the number of iterations while suffering no performance degradation. At each iteration, we perform Cyclic Redundancy Check (CRC) on the resulting bit sequence, and stop once it passes CRC to avoid unnecessary iterations. Otherwise, we generate multiple candidate paths based on the smallest corresponding LLRs. Then we perform CRC again until a first one passes CRC or reaches the maximum number of paths. Simulations show that this scheme can effectively reduce the number of iterations. Compared to the 2-state turbo-hadamard codes, the frame error rate increases by 1.1 dB at 10−3 and the bit error rate increases by 0.9 dB at 10−5 with 240-bit pseudo-random interleaving on the additive white Gaussian noise channel.
Multi-dimensional Speaker Information Recognition with Multi-task Neural Network

This paper proposes a novel approach to simultaneously estimate speaker identity and the other two traits of the speaker, specifically, emotion and gender. This simultaneous process is named as multi-dimensional speaker information recognition. We choose i-vector to represent the utterance with different duration as fixed length vector. In our paper, we first build individual neural networks (NN) for each single recognition task. In this single task, the input is i-vector of one speaker and the output is the corresponding one-hot vector. We then design a multi-task learning (MTL), the difference with the single task is the NN output, which contains three parallel one-hot vectors for MTL. The one-hot vectors denote the identity, emotion and gender information, respectively. The proposed approach is conducted on the KSU-Emotions corpus, which was recorded mainly for emotional recognition. Experimental results show that the best multi-task mechanism recognition accuracy is 4.6% better than the single task.
Provisioning Authentication On Demand in Wireless Sensor Networks: PAW

Impersonation and node compromise are common attacks on wireless sensor networks. In these attacks, the attacker gains access to a node or pretends to be some legitimate node of the network, injects and modifies messages, putting the network security at stake. To combat these attacks, authentication mechanism can be employed to ensure that data is not altered during transit and its sourced from a legitimate node. Any wireless sensor network application without authentication but encompassing other security measures are incomplete and insignificant. The authentication techniques proposed for traditional computer networks are based on certificates - issued and validated by a third party and another method is based on symmetric key cryptography that requires very high communications. Due to resource constraints of the wireless sensor networks, the authentication should be light weight along with other security features such as scalability and reduced consumption of resources. In this paper, a new security service to thwart unauthorized access into the network through PAW: Provisioning authentication on demand in Wireless Sensor Networks scheme is proposed. The PAW scheme is relatively more secure than the existing schemes and has reduced communication overhead.
Traffic Sign Detection Method of Improved SSD Based on Deep Learning

Aiming at the problem of the traffic signs detects with poor precision, an improved Single Shot MultiBox Detector (SSD) detection algorithm is proposed. Firstly, the high-layer feature maps used for detection on the basis of the SSD algorithm are removed. Secondly, adjust the low-layer feature maps scale and aspect ratio to distribute more default boxes at lower layers, thereby enriching the fine features in the traffic sign scene map. Finally, collection of a large number of real traffic signs scene as an experimental data set. The experimental results show that the proposed algorithm has good robustness to traffic with different environmental conditions, and the mean Average Precision (mAP) is 0.7528, and has raised about 10% compared to the classical SSD, which verifies the effectiveness of the proposed algorithm.
Beam Reconfigurable Antenna Based on Liquid Crystal Metasurface

A novel design method of electronically controlled beam reconfigurable antenna using liquid crystal metasurface is presented in this paper. The metasurface is a two-dimensional plane composed of a series of sub-wavelength liquid crystal unit cells. The beam in desired radiation direction can be achieved by varying the impedance distribution of liquid crystal metasurface according to microwave holographic technique. In the simulation, we can obtain an arbitrary radiation direction beam within the zenith angle range of 35°∼60° at 39 GHz, and the maximum gain reaches 10.65 dB. Such a beam reconfigurable antenna with low profile ($44\ \mathbf{mm}\ast 44\ \mathbf{mm}\ast 2.15\ \mathbf{mm}$) and continuously adjustable beam has great value in practical applications of mobile communications.
Speech Enhancement using Adaptive Filter with Bat Algorithm

Communication using speech signal is an important aspect of a telecommunication system and the quality of a speech signal can be severely degraded by the presence of background noise. Therefore recovery of the speech signal in such a situation should be the prime focus of the designer and the problem should be dealt with appropriately. Speech enhancement is achieved by designing an adaptive filter for removal of noise from a noisy speech signal. In this paper, we proposed an adaptive filter whose filter coefficients are obtained using optimization techniques based on bat algorithm. The effectiveness of the algorithm is compared with the standard adaptive filter such as least mean square (LMS) and recursive least square (RLS). The results showed that the proposed technique has a better output signal to noise ratio (SNR) and stability. The output SNR of swarm-based algorithms are 2 to 4 dB better than the conventional algorithms for various input SNR. The computational time is compared with particle swarm optimization (PSO) based adaptive filter. The simulation result further showed that bat algorithm has less time complexity.
Modeling of Synchronous Behaviors of Excitatory and Inhibitory Neurons in Complex Neuronal Networks

In the model of complex networks of excitatory (E) and inhibitory neurons (I), synchronous rhythmic firing patterns is investigated by using Hodgkin-Huxley (HH) type model. Various factors such as synaptic coupling strength, network topology and external input are considered as the main effects on synchronization behavior of the neuronal networks. It is found that when the network is small-world, the degree of synchronization will be improved with the adding edge probability increasing. As for the synaptic inhibition, without inhibitory interactions, neurons would not achieve synchronization. Then once the inhibitory neurons are added, the network would arrive synchrony. Furthermore, different external currents will induce different degree of the synchronization states. The crucial quantity is the expected frequencies of inputs per neuron. So choosing the input current at the appropriate frequency is the key point in enhancing the synchronization of the complex neuronal networks.
Wide & Deep Machine Learning Model for Transformer Health Analysis

Transformer health index (HI) is a powerful tool for quantifying the overall health of a power transformer, due to the fact that it appraises its condition based on different criteria that are related (often in complex ways) to the long-term degradation factors that cumulatively lead to its end-of-life. Several authors have proposed different approaches to the HI calculation, e.g., analytical expressions, binary logistic regression, fuzzy logic models, support vector machines, and artificial neural networks. This paper proposes using Bayesian "Wide & Deep" machine learning model for the HI calculation, where the wide model part is the Bayesian ordered robust "probit" regression, while the deep part is the Bayesian artificial neural network. Both model parts are trained simultaneously within the Bayesian setting, using the so-called "joint learning" process with a Markov-chain Monte Carlo algorithm. Model is demonstrated using the actual transformer data.
Software Defined Communication Framework for Smart Grid to Meet Energy Demands in Smart Cities

In smart cities, the electricity is an essential component since it preserves a certain level of residents' life quality and provisions the entire spectrum of their economic activities. Thus, a smart way is essential to develop cities without disregarding energy issues. In this scope, the smart grid paradigm offers power supply in an efficient, sustainable and economical manner with minimal impact on the environment and can meet the future energy demands. However, real-time monitoring and control of the smart grid (SG) for continuous and quality-aware power supply in smart cities (SCs) is challenging and requires an advanced quality of service (QoS)-aware communication framework. In this context, this research aims to present a novel data-gathering scheme by using the Internet of software-defined mobile sinks (SDMSs) and wireless sensor networks (WSNs) in the smart grid. The extensive simulation results conducted through the EstiNet9.0 indicate that the designed scheme outperforms existing approaches and achieves its defined goals for events-drive applications in the SG.
Clustering by Hybrid Canopy and PSO Algorithm for Environmental Issues Data

In today trendy world Environmental issues is a main critical area of study due to it's directly affects the human beings. Water scarcity is the main issue which we are facing now a days, which means lack of available water resources to meet the needs of water usage within an area. It previously affects every location around the world at least two to three months per year mainly during summer. Our concept mainly focuses on to predict water scarcity based on various district wise water available resources and amount of rainfall measures of past few years using data clustering based on Machine learning algorithms. Data clustering is the technique of extracting valuable information from the large collection of datasets. There are various types of machine learning clustering algorithms are available from local optimization to global optimization and hybrid of local and global optimization to get better optimum solution. This paper used hybrid canopy clustering algorithm and particle swarm optimization Technique to predict the water scarcity from the big environmental issue of water scarcity data about 800 data set with the analysis and implemented weka tool.
Risk Assessment for Integral Safety in Automated Driving

This paper presents a novel risk assessment approach that allows to control the behaviour of self driving cars. This novel continuous real-time risk assessment considers uncertainties as well as accident severity predictions to intervene integrally. Thus it not only allows predictive traffic interaction and collision avoidance, but also an intelligent crash interaction. These decisions are made on incomplete data, due to imperfect environment perception data and road users' unknown intentions. Advanced, situational and numerical dependencies are regarded. Furthermore, the benefit of multiple approximating accident severity estimations are discussed.
Intelligent station area recognition technology based on NB-IoT and SVM

Based on the Narrow Band Internet of Things (NB-IoT) and Machine Learning Support Vector Machine (SVM) algorithm, this paper proposes a new intelligent station recognition technology. Firstly, the voltage data is uploaded to the host computer by NB-IoT communication module. Through the analysis, it is found that the voltage data of the electric energy acquisition equipment in the same area have temporal and spatial correlation. On this basis, the voltage time correlation and spatial correlation are taken as the characteristic factors, and the SVM classification method is used to judge the attribution relationship of the acquisition equipment to be identified. The identification technology can provide real and accurate user stations, and achieve fine management of the stations.