Miracle-dz/UniFAR-FTUs huggingface dataset:
Novel Recursive Kernel Construction for Polar Codes with Practical Codeword Lengths

The basic approach introduced by Arikan in [1] to polarize equal capacity channels to unequal capacities, can be used to design only codewords of length $N=2^{n}$, which is clearly a major limitation when codewords of length $N\neq 2^{n}$ are required. In order to systematically construct polarization kernels of size 3 and higher, a recursive construction technique is developed in this paper, that describes each kernel as a concatenation of smaller kernels. Optimally designing and selecting kernels over the underlying system parameters is based on the evolution of z-parameter values (i.e. effect of channel polarization) of the bit channels, which are used to define a design metric $\zeta$. The complex process of optimizing kernel designs, which depends on a wide range of parameters particularly the coderate, is simplified by this metric and its effectiveness is validated by comparative error rate performance.
Regression Analysis of Factors Influencing Volume of Households' Savings in the V4 Countries

In the Visegrad Four countries (V4) as well as all across the Euro area the household savings rate declined to a level of 12,8% in 2014, despite the fact that the real household income per capita in the Euro area increased by 0,2 %. Aim of the submitted article concerns the empirical analysis of the relationship between savings and selected macroeconomic factors. The analysis provides a complete view to assess developments in household savings in the Visegrad Four countries within the period 2005–2014 and subsequently assess the impact of selected macroeconomic factors using multivariate regression analysis. Analysis solves linear relationship between savings as a dependent variable, and independent variables such as disposable income, gross domestic product, unemployment rate and inflation rate. Results of the analysis confirmed that a statistically significant model parameters and the most important determinant with strongly positive impact in all the surveyed countries was the disposable income. DOI: 10.5901/mjss.2016. v7n1p213
Research on SQL Injection Detection Model Based on CNN

SQL injection attack has been listed as one of the top ten network application risks by the open web application security project (OWASP) for five consecutive years, and has always been the focus of network security research. In this paper, a SQL injection detection model based on Convolutional neural network (CNN) is proposed. The model is divided into model training stage and classification detection stage. The key parts are the word vector model and CNN model. Firstly, the SQL injection data set is pre-processed, word2vec, word vectorization and other steps to get the vectorized data, and then input to the CNN model for model training and classification detection in turn. The method proposed in this paper realizes the purpose of SQL injection detection, and provides a theoretical reference for researchers and database security maintainers of relevant departments in academic circles, which has a certain application value.
CIBECS: Consumer Input Based Electric Vehicle Charge Scheduling for a Residential Home

Electrical utility companies offer dynamic electricity pricing to limit peak demand of residential homes to provide charging for the fast-growing Electric Vehicle (EV) fleet. Charging EV at off-peak hours is economical for a user; however, scheduling brings the possibility of an undercharged EV at the time of use. The user has the best knowledge about his driving schedule, so including his input about target charge level and available charging time is an effective way to avoid such discomfort. To this end, this work proposes a Consumer Input Based Electric Vehicle Charge Scheduling (CIBECS) for a residential home. CIBECS takes consumer input, electricity price, and load forecasts to propose an adaptive scheduling technique. Moreover, we utilize an artificial neural network, particularly an LSTM network, to predict highly volatile residential loads. Experiments show our model's superior performance in minimizing electricity cost compared to existing approaches.
Novel model of a AlGaN/GaN high electron mobility transistor based on an artificial neural network

In this paper we present a novel approach to modeling AlGaN/GaN high electron mobility transistor (HEMT) with an artificial neural network (ANN). The AlGaN/GaN HEMT device structure and its fabrication process are described. The circuit-based Neuro-space mapping (neuro-SM) technique is studied in detail. The EEHEMT model is implemented according to the measurement results of the designed device, which serves as a coarse model. An ANN is proposed to model AlGaN/GaN HEMT based on the coarse model. Its optimization is performed. The simulation results from the model are compared with the measurement results. It is shown that the simulation results obtained from the ANN model of AlGaN/GaN HEMT are more accurate than those obtained from the EEHEMT model.
A 10b 42MS/s SAR ADC with Power Efficient Design

A 10b 42MS/s power-efficient successive-approximation-register (SAR) analog-to-digital converter (ADC) is presented in this paper. The ADC structure is optimized for lower power consumption. For this purpose, an accuracy-enhanced DAC switching method and a comparator that can dynamically adjust current to save energy are introduced. The linearity of SAR ADCs can be improved without adding capacitors or calibration logic in this way. In 130nm CMOS process, simulation results show the ADC achieves a SNDR of 60dB for Nyquist input and consumes $510 \mu \mathrm{W}$ under a 1.2V power supply.
Full-Duplex Efficient Channel Codes for Residual Self-Interference/Quantization Noise Cancellation

Full-Duplex (FD) systems are became very attractive technique for 5G and beyond transmissions by offering higher spectral efficiency. The implementation of a real FD system can be a challenging task due to the analog and residual Self-Interference (SI) and some imperfections introduced by analog components such as quantization error of Digital-to-Analog/Analog-to-Digital Converters (DAC/ADC). This paper investigates in the digital domain different channel coding schemes to compensate the residual SI and quantization noise cancellation process in case of Single Input Single Output (SISO) FD transmission. The promising channel coding schemes from the Third Generation Partnership Project (3GPP) such as 5G Quasi-Cyclic Low Density Check (QC-LDPC), 5G Polar Codes and LTE Turbo codes have been considered. Several numerical simulations are performed to evaluate the Bit Error Rate (BER) performance.
INTERACTIVE WRITING AND DEBUGGING OF BAYESIAN PROBABILISTIC PROGRAMS

In systems for Bayesian probabilistic programming such as BLOG, a user defines the generative model, presents the observations, and specifies the queries of interest. The program is then executed to obtain the desired results: posterior distributions over one or more queries. Obtaining results for a different query requires re-running the entire program. However, neither the data nor the generative model are changed by the new query, so performing inference in this case is inefficient. We show how to eliminate this inefficiency while simultaneously facilitating a more fluid exploration of the probabilistic model specified by the program. Our system is an implementation of BLOG [Milch et al., 2005] that allows users to interact with the full posterior distribution. This system is also the first interactive debugger for probabilistic programs. One internal result of the execution of a BLOG program is the creation of a posterior distribution over possible worlds. Each possible world is a model structure of first-order logic that can be constructed from the given generative process [Milch et al., 2005]. We propose a system that exposes the posterior distribution of worlds to the user, providing the ability to query any element of the full posterior. When the worlds are restricted to ancestors of the observations, they are complete worlds, but removing this restriction may create partial instantiations (some variables may not be generated). Once the user takes control of the process, they may create and evaluate any number of queries. If the user provides a query that includes objects not yet instantiated, the system can instantiate that part of the world while paying only the cost of forward inference. For the case when a joint probability distribution that can be represented as a Bayesian network, both backward inference and forward inference may be necessary to estimate a posterior distribution. Backward inference refers to the task of inferring the distribution of a set of variables given all their descendents, which is required to infer the value of an observation’s ancestors. In contrast, forward inference refers to computing the posterior distribution of a set of variables given observations of all their ancestors. This difference is relevant because backward inference is usually the more costly inference mechanism. Our proposed system highlights this as it runs backward inference once and required forward inferences are executed in a lazily.
Application of the Random Forest Kernel in Nonparametric Regression Model with Spherical Variables

In this paper, we apply the random forest kernel to nonparametric regression model with spherical and linear variables. Validate the new model with simulated data and actual airfoil noise data. Comparing with the Gaussian kernel and the linear-circular kernel, the experimental results show that the random forest kernel has stable performance and fast computation speed, and the random forest kernel has a better fitting effect in high dimension.
Estimation under the matrix variate elliptical model

The problem of estimation within the matrix variate elliptical model is addressed. In this paper a subjective Bayesian approach is followed to derive new estimators for the parameters of the matrix variate elliptical model by assuming the previously intractable normal-Wishart prior. These new estimators are compared to the estimators derived under a normal-inverse Wishart prior as well as the objective Jeffreys’ prior which results in the maximum likelihood estimators, using different measures. A valuable contribution is the development of algorithms for the simulation of the posterior distributions of the matrix variate parameters with emphasis on the new proposed estimators. A simulation study as well as Fisher’s Iris data set are used to illustrate the novelty of these new estimators and to investigate the accuracy gained by assuming the normal-Wishart prior.
Corpus Processing Frame Extraction Frame Projection Statistics Calculation Lucene RDF Triples

The extraction of semantic propositions has proven instrumental in applications like IBM Watson (Ferrucci, 2012) and in Google’s knowledge graph (Singhal, 2012). One of the core components of IBM Watson is the PRISMATIC knowledge base consisting of one billion propositions extracted from the English version of Wikipedia and the New York Times (Fan et al., 2010). However, extracting the propositions from the English version of Wikipedia is a time-consuming process. In practice, this task requires multiple machines and a computation distribution involving a good deal of system technicalities. In this paper, we describe REFRACTIVE, an open-source tool to extract propositions from a parsed corpus based on the Hadoop variant of MapReduce. While the complete process consists of a parsing part and an extraction part, we focus here on the extraction from the parsed corpus and we hope this tool will help computational linguists speed up the development of applications.
An End-to-End Deep Learning System for Hop Classification

Automatic classification of plant species is a very challenging and widely studied problem in the literature. Distinguishing different varieties within the same species is an even more challenging task although less explored. Nevertheless, for some species distinguishing the varieties within the species can be of paramount importance.Hops, a plant widely used in beer production, has over 250 cataloged varieties. Although the varieties have similar appearances, their chemical components, which influence the aroma and flavor of the drink, are quite heterogeneous. Therefore, it is important for producers to distinguish which variety the plant belongs to in a simple manner.In this work, an end-to-end deep learning system is proposed to automate the task of hop classification. Five architectures are proposed and evaluated with an uncontrolled environment dataset that includes 12 varieties of hops on 1592 images, from three different cell phone cameras. The best architecture automatically detects the hop leaves on the image and performs the classification using the information of up to 10 leaves. The method achieved an accuracy of 95.69% with an inference time of 672ms. To reach such figures, state-of-the-art convolutional blocks were explored along with data augmentation techniques. Our results show that the system is robust and has a low computational cost.
Position Control of Magnetic Levitation Transfer System by Pitch Angle

Magnetic levitation transfer systems are useful for transfer tools in clean rooms and positioning control systems with high precision because of frictionless characteristics. In this paper, the new method is proposed which is a sensorless position. At first, the magnetic levitation is performed by state feedback control with a disturbance observer for each of six axes of the movement of a levitated vehicle. The position of the vehicle is then estimated as the disturbance term of a disturbance observer for a pitch angle which is one of the control axes for the magnetic levitation. In addition, the positioning force is generated by the pitch angle control which gives a tilt to the levitated vehicle so that it generates the horizontal component of force.
Additively Manufactured Customized Implant for Severe Acetabular Bone Defect – A Case Report

The advent of metal-based Additive Manufacturing (AM) processes opened new perspectives in improving the treatment of acetabular bone defects by using patient-tailored implants. These implants have the advantage of better complying with severe and difficult cases as AM technology can produce topologically optimized geometries with complex-shaped supports for fixation, ingrowth surfaces and on-demand sizes, more easily than when using conventional manufacturing technologies. This paper reports a clinical case where such an implant was successfully used for the pelvis reconstruction of a male patient who had undergone a long series of revision surgeries at his left hip, following a car accident. The patient computer tomography (CT) data was used not only for designing the implant, but also in the preoperative planning stage for more clearly identifying the positions of the broken screws from a previous implant and investigating the quality of the bone for the new implant fixation. Moreover, 3D reconstructed pelvis models based on two series of CT scans at one-year difference (delay caused by covid-19 pandemic) were compared for evaluating if acetabular defect changes occurred from the time of the AM implant production to the time of the surgery.
Optimal Estimation for Continuous-Time Markov Jump Systems with Multiplicative Noise

This paper investigates the optimal estimation for continuous-time Markov jump linear (MJL) systems with multiplicative noises, where the multiplicative noise is with diagonal matrix form. Firstly, the complete mean square method is used to design the optimal MJL filter and the gain is obtained by solving the differential Ricatti equation. Secondly, the stable optimal filter is designed. Finally, a numerical example is given to show the efficiency of the method.
Control of $N$-Connected Current-Programmed Independent-Input Series-Output Boost DC-DC Converters

This work is concerned with the closed-loop control of a modular current-programmed independent-input series-output (IISO) boost dc-dc converter operating in the continuous conduction mode. Each module is supplied from an independent power source and has its own current and voltage feedback loops. A Matlab/Simulink-based averaged small-signal model is developed to study the module control-to-output voltage frequency responses with current loops closed and voltage loops open. Based on these responses the model is updated with a type-2 compensated error amplifier in the voltage loop of each module. The compensator is designed for the maximum number of modules to ensure system stability when lower number of modules is used. Closed-loop responses due to step changes in load current and module input voltage show equal load-voltage sharing among the modules. All model predictions are validated using cycle-by-cycle simulations.
Information scientist: Technological and managerial skill requirement in 21st century: An overview

Information Scientists in today's atmosphere are designated and deemed as Information Technologists. Information Scientists particularly work for Information processing with the help of computing technologies. An information scientist mainly deals with information processing activities. The main objective of an information scientist may differ depending upon the nature, place and type of information. The technologists who often stand for Information Scientists are no other than information technologists, Chief Information Officer, Chief Technological Officer In this article various facets of an information scientist have been discussed with the contemporary technologies, communication management skills with special reference to the need of information scientists in the 21st century. First time in India (as per our major abstract and index survey) we describe this matter and contemporary issues related to information scientists.
Over-the-Air Testing of Impulsive Frequency Shift Keying Modulation

Code division multiple access has been used in military communications due to its low probability of detection and interception. However, with the shift towards the wideband regime due to the crowded frequency spectrum, the high cost of obtaining channel state information in this regime impedes the usage of code division multiple access. Impulsive frequency shift keying is a modulation scheme that performs well in the wideband regime without channel state information, and has potential for low probability of intercept systems due to it being based on frequency shift keying. In this work, we demonstrate the concept of impulsive frequency shift keying with over the air testing. We consider the performance of impulsive frequency shift keying in Rayleigh fading channels with both log-normal path loss and shadowing to facilitate the development of a link budget for the transmitter and receiver design. Its performance is investigated in the real world by implementing the transmitter and receiver using software-defined radios and performing over the air tests in a suburban environment by fixing the transmitter and attaching the receiver to a moving vehicle. We achieve a data rate of 0.9 kb/s, which is sufficient for text communications, and show that the symbol error rate for discretized signal-to-noise ratio bins follows a similar trend to the probability of a symbol error in the simulations.
Econophysics of cryptocurrency crashes: a systematic review

Cryptocurrencies refer to a type of digital asset that uses distributed ledger, or blockchain technology to enable a secure transaction. Like other financial assets, they show signs of complex systems built from a large number of nonlinearly interacting constituents, which exhibits collective behavior and, due to an exchange of energy or information with the environment, can easily modify its internal structure and patterns of activity. We review the econophysics analysis methods and models adopted in or invented for financial time series and their subtle properties, which are applicable to time series in other disciplines. Quantitative measures of complexity have been proposed, classified, and adapted to the cryptocurrency market. Their behavior in the face of critical events and known cryptocurrency market crashes has been analyzed. It has been shown that most of these measures behave characteristically in the periods preceding the critical event. Therefore, it is possible to build indicators-precursors of crisis phenomena in the cryptocurrency market.
BlogosphereExplorer: Opens a window to the blogosphere

For Web Science, we think that blog is a very important part. BlogosphereExplorer is an on going project that exploits the power of blogosphere, the pioneer of Web 2.0 era that advocates spirits of open mind, reason, sharing, participation and collaboration. We launched this project and hope that it can be a practicable blog searching and mining service for individuals, commercial organizations and the governments or regulation organizations. We think that the multiple properties mining and spatiotemporal evolution analysis will make BlogosphereExplorer more useful in some aspects than even some commercial blog search engines such as Technorati。 In this paper, we present the functionality of the BlogosphereExplorer in design, and present architecture of the system, and also discuss some special implementation considerations in related components of the system.
ALFABETO: Supporting COVID-19 Hospital Admissions with Bayesian Networks

The ongoing pandemics of coronavirus disease has accelerated the implementation of machine learning methods (ML) to support clinical decisions. Within this context, we present the ALFABETO project, whose aim is to aid clinicians during COVID-19 patients hospital admission through the application of ML approaches exploiting clinical and chest x-ray features. Yet, non linear ML classifiers are often perceived as not easily interpretable by users, thus hampering trust in ML predictions. Moreover, these ML models, such as Neural Networks or Random Forest, are not able to include pre-exisisting knowledge about a specific domain and are not designed to find causal relationships between variables. For these reasons, we wanted to investigate if Bayesian Networks were able to properly describe the hospital admission decision process. Bayesian Networks are probabilistic graphical models representing a set of variables and their conditional dependencies. The network structure was derived both from existing medical knowledge and from patients data collected during the first wave of the pandemic. While being explainable, we show that the Bayesian network has similar performance when compared to a less explainable ML model and that was able to generalize well across COVID-19 waves. © 2021 Copyright for this paper by its authors.
The use of genetic algorithms for multicriteria optimization of the oil and gas enterprises financial stability

The article considers the problem of optimizing the financial condition of oil and gas companies. The offered methods of optimization of a financial condition by scientists from different countries are investigated. It is determined that the financial condition of the enterprise depends on the effectiveness of the risk management system of enterprises. It is proved that the enterprises of the oil and gas complex need to develop a system for risk management to ensure the appropriate financial condition. The financial condition is estimated according to the system of certain financial indicators, the integrated indicator of financial condition assessment is constructed using the method of taxonomy. According to the results of the calculation of the integrated indicator, it is concluded that this indicator does not have a stable trend. On the basis of the conducted researches it is offered to carry out optimization of an integral indicator of a financial condition with use of genetic algorithm in the Matlab environment. Based on the obtained results, recommendations of the management of the researched enterprises on increase of management efficiency are given.