Miracle-dz/UniFAR-FTUs huggingface dataset:
Scenario-based modeling of ontic personae for automatic personality perception

A model describing the wide variety of human behaviours called personality, is becoming increasingly popular among researchers due to the widespread availability of personal data generated from the use of prevalent digital devices, e.g., smartphones and wearables. Such an approach can be used to model an individual and even digitally clone a person, e.g., a Cyber-I (cyber individual), which can establish a unique and comprehensive description for an individual to mesh with various personalized services and applications. An extensive research literature on or related to personality computing exists, i.e., into automatic personality perception. However, the precision and integrity of current automatic personality perception is insufficient for the elaborate modeling in Cyber-I. Due to the heterogeneity of personal data, it is critical to classify the mass of personal data to valuable knowledge for sufficient personality perception. In addition, the precise calculation of personality based on such knowledge is also important. Therefore, this research is focused on designing scenario-based ontic personae as various individuals' facets in different situations for automatic personality perception. Using personal data, special scenarios, and based on specific ontic personae, our research is designed to ensure accurate modeling of ontic personae and personality perception.
Dynamic analysis of converter based fault current limiter of an AC/DC microgrid

Over the years there has been a drastic increase in the usage of number of renewable energy resources like solar energy, wind energy etc. They exist in islanded as well as in interconnected mode with the utility system. Use of fault current limiter is an efficient way of improving the reliability of a micro grid. This paper discusses a bridge type converter based fault current limiter (FCL) having energy management capabilities. The FCL can act both as a protective device and as an energy management device. The FCL is implemented using a bridge rectifier and a boost converter. The inductor used in the boost converter is used for limiting the current during fault conditions. The energy stored in the inductor during faulted as well as normal condition is boosted and is used for powering dc loads. The dynamic analysis of the FCL is done in MATLAB Simulink. A prototype hardware model of the fault current limiter is implemented and tested to verify the effectiveness of the circuit.
Use of accelerometer sensors to measure the states of retaining steel networks and dynamic barriers

The paper deals with the use of accelerometer sensors for measuring the states of retaining steel networks and barriers. It is discussed a design of the measuring chain for monitoring the state of these protective devices and its individual components are described. For transferring data from accelerometers to a superior system, IQRF technology is used to communicate between wireless nodes and network coordinator in the MESH topology. An integral part of the article is a description of two pilot experiments and evaluation of the measured data.
BRNADS: Big data real-time node anomaly detection in social networks

Anomalous activities are increased during recent years due to large usage of Social Networks such as Twitter, Facebook, etc. The processes of identifying the abnormal or illegal or irregular activities in the Social Networks are called anomaly detection. Many researchers are proposed different mechanisms to identify unlawful activities of the Social Networks users. It is very much important and useful to detect anomalous activities to prevent huge losses. We can carry out anomaly detection in either static or dynamic way. In this paper, we are proposing a dynamic anomaly detection mechanism to investigate the outlier's in the Social Networks. The proposed work takes the real time stream data using big data ecosystem called Apache Kafka and processing are done using spark streaming. The algorithm works on all types of data, implemented and tested in Big data environment using Java and Apache Spark.
Resource Allocation With Minimum Outage Probability in Multicarrier Multicast Systems

Several innovative applications are emerging that require significant multimedia data transmissions, which unfortunately, current communication systems struggle to provide. In this paper, we examine the high outage tendencies of conventional multimedia multicasting (CVM) due to the bottleneck imposed by the user with minimum signal-to-noise ratio (SNR) and we present a technique based on the statistical approximations of the amortized weighted averaging (AWG) of users’ SNR. For completeness, we propose a suboptimal multicast resource allocation algorithm using memoization with stochastic rounding. We compare the system performance of AWG scheme with the CVM scheme in multicarrier wireless network using our newly derived system outage probability and average throughput as performance metrics. Numerical results not only show that the performance gap of our suboptimal algorithm is reasonably within 10% from the optimal solution, but also show that the AWG scheme admits more users and is more energy efficient than the CVM especially when low power is available to the system.
Mutual authentication and key distribution protocol based on the Trust-Third-Party in mobile RFID environment

With the popularity of intelligent terminals, the era that the mobile RFID be extensively used is coming. RFID tags will be present in every aspect of our lives, to help intelligent device recognize and sense the external environment, timely and intelligent execute some commands, so that our lives more convenient. However, these intelligent devices are vulnerable to various adversaries' attacks. To solve these problems, we propose a new simplified cryptographic protocol for mobile RFID environment. The protocol uses a Trust-Third-Party (TTP) to generate and distribute the secret key for authorizing those RFID tags and readers who want to access the database servers, to ensure the security and scalability of the entire system. What's more, the use of XOR algorithm, which has high computational efficiency, guarantees the computation efficiency. Nonetheless, the simplified scheme carries obvious risks from the key disclosure attack which caused by the malicious members inside this system. In order to improve the simplified protocol's deficiencies, we then proposed an enhanced one, which based on the more secure public key cryptography system, to ensure the security of the system under several common attacks. The paper also discusses the two schemes can satisfy the safety requirements when intelligent devices play different roles. Schemes described in this paper may be applicable to some practical applications for it quickly achieves authentication and access authorization as well as the superior flexibility and security.
Thermal Propagation Control Using a Thermal Diffusion Equation

Controlling heat flow is necessary in many fields, such as the communications, health care, and manufacturing industries. In the communication field, the techniques for the reproduction of thermal sensations have been researched for many years. In order to extend the area of controlling the heat, the thermal diffusion in time and space needs to be considered. However, attaining precise control based on conventional methods is difficult because it requires several sensors and devices to monitor and control the temperature distribution. In this study, the thermal system consists of a metal wire and a Peltier device attached to the end of the wire, and a one-dimensional thermal propagation system is constructed. Modeling the thermal diffusion equation, it was found that the thermal system has characteristics of damping and propagation delay. Therefore, a control method using these system characteristics is proposed. The thermal system is treated as a time delay system with damping, which is compensated by a time delay and damping compensator. The thermal diffusion through the system can be considered by the proposed method. Experimental results show the viability of the proposed control system.
FastTrust: Fast and Anonymous Spatial-Temporal Trust for Connected Cars on Expressways

Connected cars have received massive attention in Intelligent Transportation System. Many potential services, especially safety-related ones, rely on spatial-temporal messages periodically broadcast by cars. Without a secure authentication algorithm, malicious cars may send out invalid spatial-temporal messages and then deny creating them. Meanwhile, a lot of private information may be disclosed from these spatial-temporal messages. Since cars move on expressways at high speed, any authentication must be performed in real-time to prevent crashes. In this paper, we propose a Fast and Anonymous Spatial-Temporal Trust (FastTrust) mechanism to ensure these properties. In contrast to most authentication protocols which rely on fixed infrastructures, FastTrust is distributed and mostly designed on symmetric-key cryptography and an entropy-based commitment, and is able to fast authenticate spatial-temporal messages. FastTrust also ensures the anonymity and unlinkability of spatial-temporal messages by developing a pseudonym-varying scheduling scheme on cars. We provide both analytical and simulation evaluations to show that FastTrust achieves the security and privacy properties. FastTrust is low-cost in terms of communication and computational resources, authenticating 20 times faster than existing Elliptic Curve Digital Signature Algorithm.
A comparative review of privacy preservation techniques in data publishing

Most enterprises generate a huge amount of public and private dataset actively with the integration of modern technology. So, security is a big concern of these dataset. Initially the security is provided at enterprise level but now-a-days it is an inevitable task to provide security at personal level. So to achieve the security generalization, suppression, slicing and one attribute per column slicing is used till now. The aim of this paper is to draw a review of all the existing techniques which are used in privacy preservation with comparative analysis of all anonymization techniques and show the flaw of privacy preservation techniques with respect to different parameters.
A joint PDA data detection algorithm with MMSE channel estimation for uplink transmission of massive MU-MIMO system

Massive multiple-input-multiple-output (MIMO), also known as a large-scale antenna system has been adopting by many 4G and 5G wireless communication standards due to its high data rate and outstanding link reliability. This paper considers a joint data detection scheme for uplink transmission of an L-cell massive multiple-user MIMO (MU-MIMO) system. In each MIMO cell, one base station with a large number of antennas communicates with multiple single-antenna users. A mathematical model is first proposed for received signals at a base station, and then a pilot-based minimum mean square error (MMSE) scheme is derived. A zero-forcing (ZF) data detection method is used to recover the transmitted signal, and categorize the interference by using the estimated channel information. The power of signal, interference, and noise are analyzed respectively. Then a more sophisticated probabilistic data association (PDA) detection algorithm with MMSE channel estimation is applied to jointly detect data with high accuracy. Finally, simulation results are presented to demonstrate that the proposed joint MMSE-PDA data detection algorithm outperforms the MMSE-ZF scheme.
Performance comparison of modulation schemes for Downlink NOMA

Spectral efficiency in 5G wireless communication systems can be maximized by using Non Orthogonal Multiple Access. The concept of NOMA has only been studied in theory but not in the practical. This paper discusses the performance comparison of NOMA for various modulation schemes like QPSK, 16QAM and 64 QAM in Downlink system. The main focus is on the how the BER is varying with different modulation schemes transmit and receiver antenna diversity ignoring the Successive Interference Cancellation at the receiver end. From simulation results it is observed that the NOMA with 64 QAM is giving low BER when compared with QPSK and 16 QAM.
Compressive sensing based channel estimation for MIMO-OFDM systems

In this paper, an adaptive structured-generalized orthogonal matching pursuit (AS-gOMP) algorithm is proposed for time domain channel estimation by utilizing the characteristics of multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) system. This algorithm uses the properties of the PN sequence to obtain partial channel prior information firstly, then the remaining support sets are obtained by the improved generalized orthogonal matching pursuit (gOMP) algorithm in MIMO system. A good channel estimation result is achieved by exploiting the characteristics of PN sequence and the common sparsity in spatial and time domain. The simulation results show that the proposed method can reduce the bit error rate (BER) of channel estimation and improve the performance of the MIMO-OFDM system.
Predictive DTC Strategy With Fault-Tolerant Function for Six-Phase and Three-Phase PMSM Series-Connected Drive System

On the one hand, with the use of direct torque control (DTC) for a six-phase permanent magnet synchronous motor (PMSM) and a three-phase PMSM series-connected system supplied by a six-phase inverter, it is possible to realize fast torque control for two PMSMs. On the other hand, because the number of inverter output voltage vectors (VVs) is increased to 64, it is difficult to divide sectors for traditional switching table DTC (ST-DTC). Furthermore, it is difficult to choose an optimal switching vector to reduce torque and flux ripples of two PMSMs in ST-DTC. A predictive control strategy of torque and stator flux is introduced into a two-PMSM series-connection DTC system, including winding fault, in this paper. Based on the principle of minimum errors of torque and stator flux amplitude and minimum zero-sequence current, an optimal VV is predicted. In order to realize uninterrupted torque control before and after open-phase fault, open-phase winding flux is considered in stator flux, and the inverter VV considering open-phase voltage is constructed in an open-phase control strategy. The experimental results show that the steady-state torque and stator flux ripples of a series-connected drive system are greatly reduced, and the drive system can operate without interruption from failure-free to failure operation.
A novel approach to wireless power transfer

The fundamental capacity of Wireless Power Transfer (WPT) is to permit electrical gadgets to be persistently charged and conquer the requirement of a power line. Remote power can be bridled and actualized in a home to such an extent that a portable PC and telephone gets charged persistently and remotely without the requirement for connecting anything to. In this paper, an inductive wireless power charging system to charge a laptop at 19.5V, 2.3A is proposed. An ac-ac resonant converter is adopted at the front end instead of a dc link. An LLC-SS is employed to reduce the power loss during transmission. The main challenge in WPT system lies in designing efficient transmitter and receiver coils and maintaining a constant voltage at the load for varied distance between the coils. A PI controller is adapted to mitigate the effects of variation in mutual coupling. Resonant coupling will improve the system efficiency and permit the power transfer at kilohertz frequency range.
Bernstein global optimization approach for distributed optimal power flow problem incorporating emission costs

This paper proposes a distributed architecture for solving the optimal power flow (OPF) problem i n power systems operation. Specifically, a centralized solution approach (to solve single optimization problem) presented in the literature is posed as solving different local optimization problems. The Bernstein global optimization approach is used to solve these local optimization problems in a co-ordinated and distributed fashion. The proposed approach is implemented on a typical 3-bus power system, wherein the cost of CO2 emissions is also considered. The overall results obtained are satisfactory.
Evaluation of 2 0 , 4 0-dihydroxy-3 , 4 , 5-trimethoxychalcone as antimitotic agent that induces mitotic catastrophe in MCF-7 breast cancer cells

20,40-Dihydroxy-3,4,5-trimethoxychalcone disrupts spindle organization. 20,40-Dihydroxy-3,4,5-trimethoxychalcone induces sustained mitotic arrest. 20,40-Dihydroxy-3,4,5-trimethoxychalcone activates the spindle checkpoint. 20,40-Dihydroxy-3,4,5-trimethoxychalcone results in mitotic catastro-
Optimal coordinated bidding of a profit-maximizing EV aggregator under uncertainty

An aggregator acts as a middleman between the small customers and the system operator (SO) offering a mutually beneficial agreement to trade electric power, where each market player (system operator, aggregator and electric vehicle (EV owner) has its own economic incentives. The EV aggregator aims to maximize its profit while trading energy and providing balancing power in wholesale markets. This paper develops a stochastic and dynamic mixed integer linear program (SD-MILP) for optimal coordinated bidding of an EV aggregator to maximize its profit from participating in competitive day-ahead and real-time markets. Under uncertain day-ahead and real-time market prices as well as fleet mobility, the proposed SD-MILP model finds optimal EV charging/discharging plans for every EV. The degradation costs of EV batteries are modeled. To reflect the continuous clearing nature of the real-time market, rolling planning is applied which allows re-forecasting and re-dispatching. The proposed SD-MILP is used to derive a bidding curve of an aggregator managing 1000 EVs.
Face verification based on deep reconstruction network

Face verification is a difficult problem for the variations of illumination, expression and pose. Inspired by the denoising auto-encoder, a new deep learning algorithm named deep reconstruction network (DRN) is proposed to improve the face verification accuracy under these variations. In this proposed DRN, the input face containing variations is fed to the encoder and the target of the decoder is to reconstruct its reference version. With this object, the DRN models the input face as the reference face contaminated by the illumination, pose and expression, and thus the extracted facial features are insensitive to the three variations. Experimental results on the CMP-PIE dataset demonstrate that the DRN has good capability to reconstruct the reference face, and the facial features extracted by DRN have good robustness to obtain high face verification accuracy.
Educational robotics: A teaching and learning experience in children with disorders of the autistic spectrum

The autistic spectrum disorder of high cognitive functioning is a chronic dysfunction that presents communication and attention problems in educational processes. The development of computational tools that combine hardware and software supports this type of difficulty. This article shows the development and implementation of an educational tool, based on robotics and augmented reality, that integrates Hardware and Software to become an alternative for the development of communication skills.
Longitudinal Behavior Planning with Maneuver Prediction for Urban Driving

One of the remaining challenges in the development of intelligent vehicles is the topic of behavior planning in urban scenarios. Based on perception of the environment around the intelligent vehicle, driving behavior has to be optimized to achieve a comfortable driving experience without sacrificing safety. This work covers the development of a novel longitudinal behavior planning method for an intelligent passenger vehicle in urban scenarios. A multi-layer situation analysis architecture consisting of localization, maneuver prediction and trajectory prediction is used to predict traffic object trajectories on urban intersections and roundabouts. A universal model predictive controller is developed to determine the optimal longitudinal acceleration behavior of the intelligent vehicle online in a variety of urban scenarios, based on the predicted trajectories. Real-time simulations show that the behavior planning method is able to maintain safe driving conditions with reasonable comfort during scenarios with varying traffic maneuvers.
Low Overhead Warning Flip-Flop Based on Charge Sharing for Timing Slack Monitoring

Timing error predictors have a strong potential to reduce the worst case timing margins by monitoring timing slack of a design. However, these timing error predictors incur substantial amount of silicon area and power which limit the overall benefits in the system level. This paper presents a low overhead warning flip-flop (FF), which predicts setup time violations. It consists of a delay buffer and a warning generator along with a conventional master-slave FF. Low overhead FF can be designed by exploiting the concept of charge sharing to implement the warning generator. As the warning generator requires only seven transistors to predict the timing violation, the proposed warning FF occupies 30% less area and consumes 27% less power compared to the state-of-the-art timing error predictors. A test chip is fabricated using the proposed FF in a 130-nm CMOS technology to verify the functionality of the proposed warning FF in dynamic voltage and frequency scaling applications. Measurement results from the test chip show that a performance improvement of 44% can be achieved at a supply voltage of 0.9 V by employing the proposed technique compared to the worst case design. For a typical chip, the power consumption can be reduced by 36% compared to the worst case design.
Long short-term memory based recurrent neural networks for collaborative filtering

Consuming behaviors of users form sequences ordered by time intuitively. Long Short-Term Memory Based Recurrent Neural Networks(LSTM), which are special kind of Recurrent Neural Networks, are ideal for modeling sequences. In this work, we propose a LSTM based model called CF-LSTM which can model the consuming sequences of users for Collaborative Filtering(CF). To effectively train the CF-LSTM model, we propose the step-combine technique, which processes k ratings at a time step and solves the long sequences problem of ratings. To improve the performance of CF-LSTM, we extend our model with ordinal cost by considering the ordinary nature of users' ratings. Finally, we compare our model with state-of-the-art methods in the metrics of accuracy, novelty and diversity. Extensive experiment results show that CF-LSTM provides highly accurate, novel and diverse recommendations, which outperforms state-of-the-art methods.