Miracle-dz/UniFAR-FTUs huggingface dataset:
A Conceptual Framework for Digital Library Software Attributes Relationship-base Prioritization in attaining Sustainable Developments Targets

Sustainability is the focus on balancing the people essential needs with the economy and natural resources. It will improve the world we live in by increasing our ability to drive profits and create sustainable value. On-going actions have been implemented to address the sustainable issues via information and communication technology (ICT). Unfortunately, previous research in ICT sustainability area focus more on designing and developing new hardware devices and software technologies rather than of recognizing regulatory framework and standards specifically in the present use of software application and limitation on time, budget and people. As understanding about being sustainable is that the relationship of any activities we practices with sustainable aspects, this paper, thus, aims to focus on relationship-base prioritization of software attributes with emphasizing on its relationship with the stakeholders’ sustainable development targets (SDTs) in a digital library area as an example. This paper involves extensive reviews and classifications of the literature in constructing the conceptual framework. The methodology begins with identifying a clear study topic, then, the extensive articles review on the peer-reviewed and well-known scientific journals. This is followed with identifying information accordingly to their theme and generates the conceptual framework. The proposed conceptual framework consists of three main components – Digital Library Institutional, Digital Library Attributes (Software SDTs) and Relationship-base Prioritization. The future empirical research shall be considered to employ this conceptual framework in order to understand the existence of relationship in the phenomenon under study.
Photodetector Based on All-inorganic Perovskite Quantum Dots with Ring Electrode

It is proposed that the vertical type photodetectors based on all-inorganic perovskite quantum dots (QDs) with the Au ring electrode in the structure of indium tin oxide (ITO)/ zinc oxide (ZnO)/ CsPbBr3 QDs/ Au. The incident light from the Au ring electrode side can be harvested directly by CsPbBr3 QD film and the photon-induced carriers are separated by the oblique electrode filed between the Au ring electrode and ITO electrode. As the high carrier mobility and long diffusion length of CsPbBr3 QDs, photodetectors have high on/off ratio and low operation voltage. The effect of ring electrode size on performance of photodetector is researched and the smallest active device area has the highest on/off ratio. The photodetectors with solution-processing method have feasibility on the flexible substrate.
Trusted Follower Factors That Influence Purchase Intention In Social Commerce

Social commerce is an e-commerce platform where online shopper can shop and purchase online through social networking platforms such as Facebook, Instagram, and Twitter. There are many factors that influence online shopper to make a purchase through social commerce and one of the factors is follower. The follower is a social commerce user that can communicate with other follower through like, comment, and share another follower’s post. There are many types of follower in social commerce, such as trusted follower and fake follower. The trusted follower can influence online shoppers’ trust to purchase in social commerce. However, usually online shoppers do not know which follower is the trusted follower. Therefore, the aim of this paper is to identify the trusted follower factors that can influence purchase intention in social commerce. From the literature review, the trusted follower factors are identified, which is self-presentation, selfconfidence, social interaction, self-expressive and social sharing. Then, a questionnaire has been developed and distributed to the 33 experts who have the knowledge and experience regarding purchasing in social commerce. Based on the data received from the questionnaire, TOPSIS method has been applied in order to rank the trusted follower factors. The final rank shows that self-presentation is receiving the highest rank, followed by social sharing, self-confidence, social interaction and self-expressive. From the rank of trusted follower factors, we can recognize which one of the follower factors is the most influence factor that can influence purchase intention in social commerce.
Probabilistic Modeling of Plug-in Electric Vehicles Charging from Fast Charging Stations

In this paper, a probabilistic charging demand profile of plug-in battery electric vehicles charging from a fast charging station is presented. Different vehicles' models are utilized, such as the Chevrolet Bolt, the Tesla Model S, and the Nissan Leaf. Two different parameters are considered, the daily distance travelled, and the battery state-of-charge. The Markov Chain Monte Carlo, relying on Metropolis-Hastings sampler, is utilized to estimate the aforementioned parameters based on exponential and Weibull distributions. The convergence of the algorithm is assessed based on the Gelman-Rubin approach. Distributions for the parameter of daily distance travelled were selected by comparing the data of daily mileage driven, corresponding to three types of electric vehicles collected in a mixed and marine climate zone, with theoretical empirical cumulative distributions calculated from seven standard distribution functions. The battery state-of-charge depends on the parameter of daily distance travelled and estimated by taking the difference between the battery total range and the daily distance traveled. The results have shown that the best distribution is exponential for Chevrolet Bolt and Nissan Leaf whereas Weibull represents the best distribution for Tesla Model S. The best distribution has been determined by calculating the Sum of Squares Error.
Convergence of Blockchain and Artificial Intelligence to Decentralize Healthcare Systems

Owing to enlarged digital data obtainability and artificial intelligence (AI) progressions, there are quite a few occasions that can be reconnoitered in healthcare. Deep learning (DL) and inductive transfer practices are turning healthcare data— such as phantasmagorias and videotapes—into powerful data sources for predictive analytics. At the present time, patients fail to have entree to his/her individual medicinal records and hang around ignorant of data importance or prominence. This paper directs to offer a gestalt of AI and blockchain and exhibit a roadmap for a blockchain-assisted decentralized bionetwork of private healthcare data to expediate new methodologies to drug discovery and precautionary healthcare. A protected and crystal-clear disseminated marketplace of personal data by means of blockchain and DL technology will circumvent challenges faced by authorities of a given healthcare system and restore custody all across private records that includes medicinal documents back to humans. It also proposes a novel type of utility cryptotoken named LifeCoin, which can be produced through the stationing of data on the blockchain-assisted open market to streamline transactions and expedite inventive reward schemes.
Protein Secondary Structure Prediction Using Ensemble of LSTM Neural Networks

The prediction of protein structure plays an important role in understanding protein structure composition and protein biological function, which is an important part of protein structure prediction. The characteristics of biological information carried by protein residues are the prerequisites for predicting protein secondary structure. After the position specific evolution matrix is adopted to encode a residue, each residue can be represented as a two-dimensional data plane. In this paper, the Long and Short Memory Network(LSTM) is used as a training method, the length and width of the two-dimensional data plane can be respectively used as the time dimension of an LSTM to design two LSTM models, and then the two models can be combined to design a third LSTM model. Finally, the three models were integrated with the ensemble method, and the Q3 result obtained by the ensemble method in the CB513 protein dataset was 77.9.
Simulation Study of a Novel Snapback Free Reverse-Conducting SOI-LIGBT With Embedded P-Type Schottky Barrier Diode

In this article, a novel reverse-conducting lateral insulated gate bipolar transistor (RC-LIGBT) with embedded diode and p-type Schottky Barrier Diode (p-SBD) is proposed. The two diodes are connected in series through a floating electrode, which provides a current path for carriers in reverse-conducting mode. Compared with the Separated Shorted-Anode RCLIGBT (SSA-RC-LIGBT), the proposed structure not only eliminates the snapback voltage (<inline-formula> <tex-math notation="LaTeX">$\Delta {V}_{\text {SB}}$ </tex-math></inline-formula>) but also avoids the waste of device area. Therefore, the superior reverse recovery characteristics and excellent tradeoff relationship between ON-state voltage (<inline-formula> <tex-math notation="LaTeX">${V}_{ \mathrm{\scriptscriptstyle ON}}$ </tex-math></inline-formula>) and turn-off loss (<inline-formula> <tex-math notation="LaTeX">${E}_{ \mathrm{\scriptscriptstyle OFF}}$ </tex-math></inline-formula>) are obtained. The reverse recovery charge of the proposed RC-LIGBT shows 43.9% and 63.2% reduction compared with those of the SSA-RC-LIGBT with <inline-formula> <tex-math notation="LaTeX">${L}_{\text {B}}$ </tex-math></inline-formula>(distance between the p<sup>+</sup> collector and the shorted n<sup>+</sup> collector) being 34 and <inline-formula> <tex-math notation="LaTeX">$64~\mu \text{m}$ </tex-math></inline-formula>, respectively. The turn-off loss of the proposed RC-LIGBT at <inline-formula> <tex-math notation="LaTeX">${V}_{ \mathrm{\scriptscriptstyle ON}} = {2.6}$ </tex-math></inline-formula> V is reduced by 68.2% and 87.1% compared with those of the SSA-RC-LIGBT with <inline-formula> <tex-math notation="LaTeX">$\sf \Delta {V}_{\text {SB}} = {0.48}$ </tex-math></inline-formula> V and <inline-formula> <tex-math notation="LaTeX">$\sf \Delta {V}_{\text {SB}} = {0.17}$ </tex-math></inline-formula> V, respectively. Moreover, the proposed RC-LIGBT has a self-adjusted collector injection efficiency under different temperatures to dramatically improve the Short Circuit Safe Operation Area (SCSOA).
Supervised Brain Network Learning Based on Deep Recurrent Neural Networks

Task-based functional magnetic resonance imaging (tfMRI) is a widely used neuroimaging technique in exploring brain networks and functions associated with cognitive behaviors. Traditionally, the general linear model (GLM) is the most popular method in tfMRI data analysis due to its simpleness and robustness. This model-driven method adopts a canonical hemodynamic response function (HRF) and its various derivatives to construct regressors in the design matrix and estimate changes in the tfMRI data. However, a possible limitation of current model-driven methods is that the HRF is fixed and non-adaptive which may overlook other diverse and concurrent brain networks. In order to overcome these limitations, we proposed a novel hybrid framework, supervised brain network learning based on deep recurrent neural networks (SUDRNN), to reconstruct the diverse and concurrent functional brain networks. Specifically, this hybrid framework first takes advantage of the great capacity of deep recurrent neural networks (DRNN) in modeling sequential data to learn the diverse regressors from real tfMRI data. After that, it utilizes the effective supervised dictionary learning (SDL) method to reconstruct both the task-related functional brain networks and other latent brain networks simultaneously. Extensive experiment results on different tfMRI datasets from Human connectome project (HCP) demonstrated the superiority of the proposed framework.
Stability and Properties of the Solid Solutions Based on Carbides, Borides and MAX Phases

A plausible mechanism of stabilization or destabilization of the solid solutions (alloys) based on transition metal (TM) carbides, borides and MAX phases is suggested using the results of first-principles calculations of the Ti<inf>1-x</inf>Hf<inf>x</inf>C, Ti<inf>1-x</inf>Ta<inf>x</inf>C, Ti<inf>1-x</inf>Zr<inf>x</inf>C, Ba<inf>1-x</inf>La<inf>x</inf>B<inf>6</inf>, and Ti<inf>2</inf>Al<inf>1-x</inf>Si<inf>x</inf>C alloys. It was established that the alloying of the end members with different valence electron concentrations (VEC) leads to a change of the occupancy of the metal band. As a result, the mixing energy of the solid solutions is negative, i.e. the alloys are stable relatively the end members. On the contrary, the solid solutions are unstable relatively the end members with the same VECs. The stable alloys exhibit a positive deviation of the Vickers hardness from linearity.
High Orbital Spacecraft Fast Positioning Algorithm Assisted by Inter-Satellite Links

Traditional algorithm of the transmission time milliseconds reconstruction for GNSS signal receiver has a problem that when the receiver's approximate position is unknown, the calculating amount increases sharply, so that traditional algorithm is inapplicable on this condition. A fast positioning algorithm of navigation receiver assisted by inter-satellite link (ISL) is proposed in this paper by utilizing the high-speed data transmission and ranging function of ISL. To ensure that when the receiver position is completely unknown, this algorithm can calculate receiver’s outline position by us of the ISL’s time-division signal to complete ranging and the ISL’s high speed data transmitting signal to acquire the satellites’ positions of GNSS constellation. Firstly, ISL ranging results with complete signal transmission time is made use of to finish rough positioning using. Secondly, take advantage of the approximate position to resolute integer millisecond ambiguity of navigation signal. Finally, by using the pseudorange of fast reconstruction of all visible, satellites precise positioning is achieved. Verified by simulation with the CE-5T flight experiment parameters, the GPS ephemeris parameters and ISL’s signals, fast positioning can be achieved on the full part of the high orbital spacecraft operating on the transfer section of earth-moon orbiter. Compared with the original downlink navigation signal, the first positioning time can be reduced by about 1/6.
Restrictions of the Measurement System and a Patient When Using Visually Evoked Potentials

The article covers methods of visually evoked potentials selection, problems and restrictions on usage of methods for evoked potentials definition, restrictions imposed on a patient. The authors unravel relevance of the humanmachine neurocomputer interaction tasks, provide a description of the main approaches and basic training and management methods, denote pain points and requirements in this field, inter alia when developing and attempting to advance efficiency of the robot system management methods. Current work presents comparison of the main selection methods of well-established visually evoked potentials based on The Minimum energy combination method, Canonical correlation analysis and Multivariate synchronization index method. The result of the authors’ research is recommendations on usage of any given methods of visually evoked potentials selection subject to terms and conditions of usage, restrictions imposed on the methods and these imposed on patients when it comes to usage of neurocomputer interfaces; the values obtained are of a considerable practical importance because they could be used for neurocomputer interface forming up on the basis of visually evoked potentials and could be used in future for developing of the management theory of robot system of various purpose and for realization of decisions on human-machine interaction arrangement within restricted practical tasks.
Single Crossbar Array of Memristors With Bipolar Inputs for Neuromorphic Image Recognition

In this paper, we propose a new crossbar architecture of memristors with bipolar inputs for an image recognition application. The performance of the proposed crossbar array with bipolar inputs is based on the simplified Exclusive-NOR function to measure the similarity between the input pattern and the stored patterns. In the proposed architecture, only one crossbar array is used instead of two crossbar arrays. The number of memristors in the proposed architecture is reduced by 50%, when compared to the complementary and the twin architectures. The proposed crossbar architecture with bipolar inputs consumes 16.7% and 7.2% less power than do the complementary and twin architectures. In addition, using only one crossbar array in the proposed architecture can improve the fault tolerance of the crossbar circuit. When 10% of memristors are defective, the proposed crossbar architecture shows a recognition rate improved by 5%, 7% and 4% over that of the memristor binarized neural network, the complementary architecture of the memristor crossbar and the twin architecture of the memristor crossbar when recognizing 10 images.
Automated Hand X-Ray Based Gender Classification and Bone Age Assessment Using Convolutional Neural Network

Bone Age Assessment (BAA) is a medical approach to predict the growth in any individual and for this gender the classification has immense importance in medical research and forensics. To the best of our knowledge we have introduced a novel framework, which classifies the gender and predict the age of that individual by using a single left-hand radiograph. Deep Convolutional Neural Network (DCNN) as a method of learning and predicting the results gave us the accuracy of 79.6% for gender classification and for age classification we have achieved MAD 0.50 years and RMS 0.67 years. We have studied the methods of transfer learning and trained our dataset with VGG-16 model to find the optimal solution.
Improved Compressed Sensing-Based Joint User and Symbol Detection for Media-Based Modulation-Enabled Massive Machine- Type Communications

Media-Based Modulation (MBM) is regarded as a promising technique for future massive machine-type communications (mMTC) due to its high energy/spectral efficiency, good error performance and low-complexity radio frequency hardware implementation. In this paper, we consider both sparsity nature of user activity and sparsity nature of MBM signals in the uplink MBM-enabled mMTC system. According to the static user activation or the dynamic user activation in a coherent time, we classify the transmission schemes into two types and propose corresponding improved compressive sensing (CS)-based joint user identification and data detection with/without prior information of channel state information (CSI). The simulation results demonstrate the performance advantages of our proposed algorithms over the state-of-the-art CS-based user detection methods or CS-based symbol detection methods and evaluate the performance with different system parameters.
Analysis of a Transmitter System for IEEE 802.15.4a UWB Standard

Ultra-Wide Band (UWB) is emerging as a promising technique in wireless communication by employing the narrow pulses that consumes less power when compared to the narrowband signal. In this paper, the all-digital IR ultra-wideband (UWB) transmitter block is proposed. UWB Transmitter is modeled using time-interleaved architecture using monopulse signal, 1st order gaussian pulse and 2nd order gaussian pulse using MATLAB. simulated. The proposed IR UWB transmitter uses tunable Delay units is Simulated using Cadence virtuoso in a 180nm CMOS Technology. UWB signal Simulated fall under the UWB Standards of EIRP limit −41.3dbm and has a pulse width of 0.56ns with an amplitude of 500mV(pp) at Vcntr of 2.4V
Air Quality Prediction Of Data Log By Machine Learning

The air quality monitoring system measures various air pollutants in various locations to maintain good air quality. It is the burning issue in the present scenario. Air is contaminated by the arrival of dangerous gases into the climate from the industries, vehicular emissions, etc. Nowadays, air pollution has reached critical levels and the air pollution level in many major cities has crossed the air quality index value as set by the government. It has a major impact on the health of the human. With the advancement in technology of machine learning, it is now possible to predict the pollutants based on the past data. In this paper we are introducing a device that can continue that can take present pollutants and with the help of past pollutants, we are running an algorithm based on the machine learning to predict the future data of pollutants. The sensed data is saved inside the Excel sheet for further evaluation. These sensors are used on the Arduino Uno platform to collect the pollutant data.
On the Problem of Developing Software and Hardware Instruments for the Social Robot

The present work reviews the procedure of transfer of various objects by the robot in response to human verbal request. It features major aspects involved in designing of the required image of the gripping device of the social robot, aimed at performing a large range of tasks for human assistance. It also contains the description of algorithmic instruments ensuring formulation of tasks for the robot based on patient request.
Face Drawing by KUKA 6 Axis Robot Manipulator

This article describes the algorithm of points extraction from the picture processed by a canny edge detector that will be turned in the curves using the cubic spline interpolation. Here will be presented several examples, including code. Furthermore, this paper will describe steps of creating the system, which will depict on a sheet of paper a picture taken from a web-camera. The system enables a webcam from which will be taken snapshot, the convenient program with the graphical user interface, including algorithms, whose parameters can be changed, TCP server, webcam control, observation of the drawing process and logging. The main drawing component of the system is KUKA KR3 R540. KR3 R540 is robotic system with a manipulator that will take commands to draw a result image.
A novel chaotic dragonfly algorithm based on sine-cosine mechanism for optimization design

Dragonfly algorithm is a novel meta-heuristic algorithm, which solves the problem by simulating the biological behavior of dragonfly’s predation and migration. There are also some common problems of meta-heuristic algorithms, such as slow convergence speed and entrapment in local optima. To improve the performance of dragonfly algorithm, this study combines singer chaos and sine-cosine mechanism to reinforce the original dragonfly algorithm. Singer chaos is used to optimize the population initialization of the algorithm, and the sine-cosine mechanism is applied to modify the iterative updating formula of the position. The performance of the proposed approach is examined by eight numerical benchmark functions. From the optimal solution and standard deviation of the experimental data, it can be seen that the convergence accuracy and stability of the proposed dragonfly algorithm are significantly strengthened. Besides, SC-DA is applied to optimize the design of cantilever beam problem with good performance.
Nature Inspired Meta-heuristic Optimization Algorithms Capitalized

Nature is an incredible and gigantic wellspring of inspiration for taking care of intense and complex issues in software engineering since it shows very shifted, dynamic, hearty, perplexing and enchanting phenomenon. Nature propelled calculations are meta heuristics that ridicules the nature for taking care of advancement issues opening another age in retribution. For the past years, several inquire about efforts have been engaged right now. As yet being youngster and the outcomes being shocking, augments the degree and reasonability of Bio Inspired Algorithms (BIAs) observing new territories of utilization and more projections in computing. This paper delineates the study on key meta-heuristic calculations. The writing overview is controlled by the control parameters, administrators and its highlights.