bluuebunny/crossref_metadata_2025_split huggingface dataset:
Role of programmatic advertising on effective digital promotion strategy: A conceptual framework
Authors: Uday Kiran K , Arumugam Thangaraja 
Published: January 2021

Abstract
Today“s world of digital advances technology has created major impact on the effective, efficiency and increase performance to companies with improved information and create more connective with customers and clients, and also streamline the process of various activities. As today advertising spending has involved in more of applicable and dependable, automation process of digital ads selling along with its effective campaigning implementation has an important role in supporting to the online marketing this growth is known as “programmatic Advertising.” The programmatic advertising as converted the traditional method of execution of ads which are involved in the ad stock field has normally used by company“s publicity. The digital automation advertising has been very important tool which creates efficient and effective than manual process. The earlier pattern of advertising observation process as seen gradually decline in the advertising promotion method and also has brought less margin with more cost for the ad companies in the field of advertisement which has forced the companies to adopt the innovative technology and systems for the competitive world. This paper describes the theoretical and conceptual framework of programmatic advertising and explores how the programmatic advertising as an important tool will help the advertisers and ad agencies in developing effective digital promotion strategies.
UVM methodology based functional Verification of SPI Protocol
Authors: Kulkarni Aman , Sakthivel S M 
Published: January 2021

Abstract
The scalability and complexity nature of the integrated circuit design makes the verification process more complicated and time-consuming. Therefore, in the present modern-day SOC’s there is a strong need for verification architectures with increased reusability and easy accessibility. The UVM methodology-based verification architecture with reusable components is one of the widely accepted test bench architectures for carrying out such functional verification. This paper presents a UVM methodology based functional verification of the SPI protocol core with a dedicated architecture. First the SPI core is modeled using Verilog RTL. Then using the reusable components in UVM + System Verilog environment, the SPI core is verified under two modes such as i) SPI communication with wishbone interface and ii) SPI Master-Slave communication.
Design of Low Power SRAM Cell Using Adiabatic Logic
Authors: Penugonda Ramya sri , Ravi V 
Published: January 2021

Abstract
In VLSI system design power consumption and energy dissipation are become more important. To optimize the power consumption we are using adiabatic logic which is having capability of reusing power. In this paper we proposed novel 7T SRAM cell using MCPL adiabatic logic. We compared the performance of energy dissipation and power consumption of conventional 7T SRAM and adiabatic SRAM. Furthermore, the conventional 6T,7T SRAM cell and the designs which are related are designed using FINFET devices to achieve low power.7T SRAM is more stable compared to 6T SRAM but due to excess transistor in 7T SRAM the power consumption will be more. SRAM cells are designed in cadence EDA environment and layouts for these circuits are carried out in cadence Assura tool.
Energy Efficient Adiabatic Logic Circuit for Improve Security in DPA Resistant RFID
Authors: Vaishiba A G , Durai Dr. Ananiah 
Published: January 2021

Abstract
A Differential Power Analysis (DPA) attack is an exploit which overcomes the hardware and software security by analyzing the correlation between electricity usage of a chip in a device and the encryption key. These attacks are non-invasive, leading an intruder to crack a system without leaving any trace. CMOS Technology is prone to DPA attack because of higher power dissipation. A DPA resistant adiabatic technique has been analyzed which has lower power dissipation when compared to the CMOS technology. The conventional Positive Feedback Adiabatic Logic (PFAL) suffers from certain non-adiabatic energy loss. Hence, Energy Efficient Secure Positive Feedback Adiabatic Logic (EE-SPFAL) is proposed which reduces the non-uniform power consumption, preventing information leakage thus securing the system from DPA attacks. Thus the proposed EE-SPFAL is used to design logical circuits such as buffer, OR-XNOR and AND-NAND gate. It is proved that the information leakage in the form of current consumption takes place in PFAL buffer whereas current consumption traces in EE-SPFAL circuits are uniform, depicting the ability of the proposed logic family to resist DPA attack. The simulation was done using Cadence virtuoso 180 nm technologies.
Blast Fragmentation Analysis using Image Processing
Authors: Kumar Ujjwal , Shaik Ayesha 
Published: January 2021

Abstract
This paper proposes an image enhancement algorithm for rock particle images during blast fragmentation. Image enhancement of rock piles plays an important role in rock size distribution. To measure the rock size, the original images are not of good quality. So our method is used to enhance the image to help the rock size distribution. The proposed method is implemented in Matlab software.
An Innovative Approach for Secured Smart Office and Home System using IoT
Authors: Shashank Apurva , Vincent Rajiv 
Published: January 2021

Abstract
As technology grows every day, everybody wants to use smart devices (phones, lights, appliances, etc.). Nowadays we find that many houses and offices are neither safe nor smart. To overcome this problem, we have proposed a smart and secure office and home system using IoT. The proposed system contains several components, where a single microcontroller will connect and control several components. Our system will sense the environment and will detect movement and also will distinguish whether a human has moved in or out of the sensors. Secondly, when light falls on its resistance decreases and as it senses darkness its resistance becomes high. Next, our system will sense the blockage level in the housing pipelines and give information about the exact blockage point. Finally, it will control the gardening system. This system will monitor and use all the data from the sensors and principally facilitate physically challenged persons to manage all of their home and office devices from one place, and simultaneously saves power as well.
The influence of Ag in TiO2/NGP composites as a high performance photocatalyst under UV and visible light irradiation
Authors: Fauzian M , Taufik A , Saleh R 
Published: January 2021

Abstract
Ag/TiO2 nanocomposites were synthesized using a sol-gel method, and Ag/TiO2/NGP composites were synthesized using a co-precipitation method. X-ray diffraction (XRD), UV-Vis absorbance, Raman spectometry, and thermogravimetric analysis (TGA) were used for the characterization of prepared samples. The photocatalytic performance of all prepared samples was tested by removing methylene blue (MB) dyes. Photocatalytic activity using UV and visible light irradiation. The photocatalytic performance of all Ag/TiO2/NGP composites was much higher than pure TiO2 and TiO2/NGP composites. The enhanced photocatalytic ability is predominantly due to the properties of NGP and the surface plasmon resonance (SPR) of Ag nanoparticles (NP). The NGP acted as an electron transporter and acceptor in composites and inhibited electron–hole recombination. The Ag NPs acted as an electron trap, making the separation of photo-generated electron–hole pairs easier. The best condition for the photocatalytic activity of Ag/TiO2/NGP composites was pH 13. Holes were the major radicals involved in the photocatalytic process of Ag/TiO2/NGP composites.
The parameter estimation of logistic regression with maximum likelihood method and score function modification
Authors: Febrianti R , Widyaningsih Y , Soemartojo S 
Published: January 2021

Abstract
The maximum likelihood parameter estimation method with Newton Raphson iteration is used in general to estimate the parameters of the logistic regression model. Parameter estimation using the maximum likelihood method cannot be used if the sample size and proportion of successful events are small, since the iteration process will not yield a convergent result. Therefore, the maximum likelihood method cannot be used to estimate the parameters. One way to resolve this un-convergence problem is using the score function modification. This modification is used to obtain the parameters estimate of logistic regression model. An example of parameter estimation, using maximum likelihood method with small sample size and proportion of successful events equals 0.1, showed that the iteration process is not convergent. This non-convergence can be solved with modifications on a score function. Modification on score function is to change a score function, a matrix of the first derivative of the log likelihood function, to the first derivative matrix itself minus multiplication of information matrix and biased vector. The modification of the score function can quickly yield values of parameter estimates, especially when the sample sizes are larger, and convergence was reached before the 10th iteration.
The accuracy of transfer learning using neural network method for sentiment analysis problem on Indonesian tweets
Authors: Augustizhafira A N , Murfi H , Ardaneswari G 
Published: January 2021

Abstract
In this paper, sentiment analysis is applied to one social media called Twitter. Sentiment analysis is categorized as a classification problem that can be solved using one of machine learning methods, namely Neural Network. If machine learning is applied, it is necessary to rebuilt the model from scratch using new training data that requires manual labelling process. Hence, it is better to apply other learning besides machine learning, such as transfer learning. The simulation in this research yielded an accuracy of transfer learning using Neural Network which will be tested by N-grams (bigram and trigram) feature and one of feature selection method, namely Extra-Trees Classifier. The highest value of transfer learning accuracy is obtained when one hidden layer, 250 neurons on hidden layer, and tanh activation function are used. The use of feature selection method in simulation can also improve the transfer learning performance, so that the accuracy value is higher than the one that does not use feature selection method.
Model of transmission of three types Leprosy Disease with treatment
Authors: Martahi R L , Tasman H 
Published: January 2021

Abstract
Leprosy disease is caused by infection of Mycobacterium leprae that affects the skin and nerve. The treatment duration for leprosy takes 6 to 24 months with multi-drug therapy. In this article, a 6-d mathematical model for transmission of three types leprosy disease with treatment was constructed. Analysis and numerical simulation were done to explore the existence and stability of equilibrium points and basic reproduction number R0. The local stability of endemic equilibrium point was done by numerical simulation. Numerical simulation also confirmed that the disease-free equilibrium is locally asymptotically stable when R0 < 1 and the endemic equilibrium point locally asymptotically stable when R0 > 1. Numerical simulation was also done to show the sensitivity of R0 with respect to some contact parameters. The result showed that the change of contact rate between susceptible people and lepromatous type infected hosts has more effect to change the value of R0. The reduction of contact rate between susceptible people and lepromatous type infected hosts is more effective to control prevention leprosy disease compared with tuberculoid and borderline types.
Classification of the likelihood of Indonesian Facebook users in spreading hoaxes using Support Vector Machine (SVM)
Authors: Rampisela T V , Andarlia H T , Rustam Z 
Published: January 2021

Abstract
Social media is the most commonly accessed internet content by Indonesian netizens; 97.4 % Indonesians access social media while using the internet. In 2016, Facebook was the most commonly used social media format for Indonesian internet users. While many are benefited by the features offered by Facebook, many also use Facebook for things that they are not supposed to, such as sharing hoaxes, either intentionally or unintentionally. This clearly puts Facebook users at a disadvantage, considering the increasing trend of hoax-sharing nowadays. Therefore, this research aims to prevent the spread of hoax through Facebook by analyzing the pattern of how people use Facebook. This pattern is obtained from a survey on 200 samples that use Facebook, chosen by purposive sampling. Using the Support Vector Machine method, an application of the collaboration between mathematics and computer science, the acquired data is used to predict whether or not someone has the potential of spreading hoaxes. Simulation results show that the average of the prediction accuracy of this binary classification problem is 86 percent. Hence, it is hoped that Facebook could prevent the sharing of hoaxes by making use of the results from this research.
Analysis of factors that affect the empathy level of mathematics FMIPA UI students
Authors: Syarif M A , Setiadi R , Soemartojo S M 
Published: January 2021

Abstract
Mathematics students of FMIPA UI are required to have responsibility in behaving accordance to the society norms, highly intellectual, set a good example to the society, and have a high sense of empathy. Empathy causes students have feeling, understanding, and caring for others. Therefore, empathy has to be developed. Empathy has four dimensions namely perspective taking, fantasy, empathic concern, and personal distress. The purpose of this study is finding factors that affect the empathy level of Mathematics FMIPA UI students. If the factors can be found, so the empathy level can be increased. Population in this research is Mathematics FMIPA UI students in generation of 2012–2016 with active status of academic year 2017/2018. The number of all students is 513 people. The data were collected by distributing questionnaires to 170 mathematics students of FMIPA UI who have been selected as sample. The sampling method used here is Simple Random Sampling (SRS). The main method that is used in this study is Partial Least Square (PLS). From the data analysis, it is found that the factor that give most affect on Mathematics FMIPA UI students’ empathy level is fantasy. Other factors that also affect empathy level of Mathematics FMIPA UI students are perspective taking, empathic concern, personal distress, parenting patterns, and gender.
Varying intercept regression model
Authors: Rosalia  , Abdullah S , Nurrohmah S 
Published: January 2021

Abstract
Varying intercept model is a regression model that is applied to nested data, which consists of several groups and each group contains several individual observations. Several characteristics are often found in nested data, namely, the variance between groups and individual observations from the same group are correlated. By considering errors in two different levels, the individual and group levels, the varying intercept model is more suitable than the linear regression model in nested data because varying intercept model accommodates those characteristics. In this thesis, we discussed the varying intercept model both with and without a predictor variable. The varying intercept model consists of several parameters that must be estimated, namely, regression coefficients and variance components. There is also a random effect, which is a group effect and also a random variable. The regression coefficients are estimated using Generalized Least Squares (GLS) and Maximum Likelihood (ML) via the Expectation-Maximization (EM) algorithm. The random effect in varying intercept model is predicted using Best Linear Unbiased Prediction (BLUP). On the other side, the variance components in varying intercept model are estimated using Maximum Likelihood via Expectation-Maximization (EM) Algorithm. In this thesis, we used a simulation to analyze the effect of the standard deviation of the error components in the varying intercept model and the effect of the number of individual observations in each group toward the standard deviation of the error components. The simulation results show that if the standard deviation of the error component in the individual level is greater than the standard deviation of the error component in the group level, then the classifications of individual observations into several groups should be ignored. On the other hand, if the standard deviation of the error component in the individual level is not greater than the standard deviation of the error component in the group level, then the classifications of individual observations into several groups should not be ignored. The simulation results also show that the number of individual observations in each group is not associated with the standard deviation of the error components.
The GSp criterion on subset selection method for regression in the presence of outliers and multicollinearity
Authors: Nabila A , Widyaningsih Y , Nurrohmah S 
Published: January 2021

Abstract
Linear regression models a relation between a response variable with regressor variables and produce regression model. Ordinary least squares (OLS) method is often utilized to estimate parameters of linear regression model. OLS method will produce the best linear unbiased estimates when all the assumptions are met. The “best” means the estimate is unbiased and has the minimum variance. However, in real data, those assumptions are often violated, such as the multicollinearity and outliers. Multicollinearity will produce a large variance of the regression parameters, while outliers will cause a biased estimate. Jackknife ridge M-estimator is recommended to be implemented in the model when these problems are present. Jackknife ridge M-estimator is a parameter estimation method of the regression with robust property, which makes robust to outliers, and ridge method is utilized to overcome multicollinearity. Whereas the jackknife method is utilized to reduce the bias from the result of the ridge method. In multiple linear regression, regression model could possibly contain many candidates for regressor variables. Subset selection method is utilized to select some of those regressors that best predicts the response variable. The selection criterion that can be utilized on the subset selection method when outlier and multicollinearity are present is the GSp criterion. The reason for this is because the GSp criterion is based on jackknife ridge M-estimator, which is able to solve the problem of outlier and multicollinearity. Furthermore, GSp criterion is implemented to obtain the best model on predicting IQ based on several predictors. It was found that IQ could be predicted by only doing 3 personality test which is test 1, test 3 and test 5 instead of all 5 different personality test.
Linear regression model as an approach to analyze spatio temporal data to know the influence factors of the number of crimes in North Sumatera
Authors: Manurung E N , Widyaningsih Y , Aminah S 
Published: January 2021

Abstract
Crimes in Indonesia tend to increase every year, both in variation and in number. Crimes in this study include murder, torture, rape, theft, fraud, and other cases that make people feel insecure. This study aims to know what factors affect the number of criminal acts in the Province of North Sumatera and determine the influence of time and location in 33 districts/cities in North Sumatra. This study uses secondary data collected by POLRI, BPS, and Dinsos in North Sumatera Province in 2013, 2014, 2015 and 2016. The independent variables used are the number of unemployed people (X1), the number of poor people (X2), population density (X3), human development index (X4), the number of places of worship (X5), the economic growth rate (X6) and the number of families with a social psychological problem (X7). The linear regression model is used as an approach to analyze. The results of this study show that the independent variables significantly influence the number of crimes acts in North Sumatera Province. The independent variables are unemployment (X1), the number of poor (X2), population density (X3), and human development index (X4). There is a significant location influence on the occurrence of the number of criminal acts in 33 districts/cities of North Sumatera Province. Based on the descriptive statistics, Sibolga, Tebing Tinggi, Padangsidempuan, Tanjungbalai, Samosir, and Gunungsitoli are the six districts with the highest proportions in criminal actions, where those districts are all cities.
Discrete Weibull-geometric distribution
Authors: Mabel Y O , Novita M , Nurrohmah S 
Published: January 2021

Abstract
The failure-time distribution is used to describe the life of a device, material, or structure. One of the most commonly used distributions to analyze failure-time data is Weibull distribution. The Weibull distribution is very flexible for modeling varying types of failure rates data with constant, increasing and decreasing shapes, but cannot be used for modeling data with unimodal failure rates. Because of this, we developed the Weibull distribution using compounding method to produce a Weibull-Geometric distribution. The Weibull-Geometric distribution is useful for modeling failure rates data with a unimodal shape. In studies of failure, time to failure is frequently measured in the number of cycles to failure or shocks to failure and become a discrete random variable. Hence, this paper discusses the formation of distribution that more appropriate to modeling discrete failure data. The discrete distribution is obtained by discretizing the continuous Weibull-Geometric distribution. The discretization is carried out by maintaining one of characteristics of the continuous distribution, that is, its survival function. The result distribution, called Discrete Weibull-Geometric distribution (DWG) has unimodal failure rates, right skew, and more appropriate at modeling discrete failure data. At the end of this paper, the Discrete Weibull-Geometric distribution is used to illustrate the real dataset and show that DWG distribution is the appropriate model.
The community structure of epiphytons on Utricularia sp. and Hydrilla sp. plants at Situ Alam FMIPA Universitas Indonesia, Depok, West Java
Authors: Zuardi S H , Wardhana W 
Published: January 2021

Abstract
Water plants will affect abundance and diversity of epiphytons in the community structure. The purpose of this research is to know the difference between the community structure of epiphytons on Utricularia sp. and Hydrilla sp., also periphytic planktons at Situ Alam FMIPA UI. Abundance and diversity of epiphytons community are the main discussion in this research. Research was carried out from March 2018 until May 2018. Using the purposive sampling method, samples were taken from Utricularia sp. and Hydrilla sp. Each sample was taken three times, one repetition, once every 2 weeks. Research showed that 67 genera, including 1 planktonic genus were found on the object glass. Utricularia sp. had higher abundance of epiphytons (84609 cells/mL) than Hydrilla sp. (74392 cells/mL). Both diversity indices are categorized as low diversity (H’ < 2.302). Different diversities were found between epiphytons on both plants (P < 0.05), but there was no difference between abundance of epiphytons on both plants (P > 0.05).