Miracle-dz/UniFAR-FTUs huggingface dataset:
Relationship Between Training and Employee Productivity in Organization: A Partial Least Square (PLS-SEM) Approach

Many manufacturing organizations focus only on technical enhancement of machines and adopting latest technical knowhow and superior raw material to produce goods and offer improvements, ignoring the need of providing training, particularly to their technical staff involved in production process in order to improve their productivity. This study attempts to find a relationship between training and employee productivity. A mixed methods approach, both qualitative and quantitative, was used to carry out this study. A questionnaire consisting of 30 statements on the impact of general training on employee productivity was given to respondents. The data was analyzed with Smart PLS software. Taking a step further, a few statements were also added in the questionnaire about strategic skills in order to add value to this study. The gap analysis method was adopted to identify the questionnaire items and was validated for its content by experts. Complementary to the questionnaires, on‐site visits were made to collect data thorough interviews, documentation and observations. The study shows that both general skills and strategic skills have a positive relationship with employee productivity. The practical implication would be in the form of guidelines for training experts and HR mangers ensuring a mix of general and specific (strategic) training to impact the productivity. The limitations of this research were cost, time and lack of training and learning environment in the sampled organization. This study is a contribution to the domain of training which strives to find its positive relationship with employee productivity. Key word s : strategic training, input and output, learning environment
Modeling and simulation to monitor the integration of palm fuel oil ash on compressive strength of concrete pavement influenced by variation of water cement ratios

This model predicts the compressive strength of concrete pavement modified with palm oil fuel as partial replacement for cement. The study monitors the strength development of concrete pavement varying with different percentage of [POFA]. The study express the output of the modifier from graphical representation, where optimum strength were observed at 5% at curing age between [7, and 28days]. The study has observed that the modifier applied as partial replacement of cement experienced decrease in strength as the percentage of [POFA] dosage increase, these condition were observed from the graphical representation such that gradual decrease were experienced between [10-20%]. The declined in strength development from variation of water cement ratio were experienced between [0.40-0.50], the influences from variation of these mixed proportion were also monitored, these conditions were reflected on the output results from the designed mix, the developed model were subjected to simulation, these values were compared with pone et al 2018, where the early strength from 2.5-5% 0f [POFA] were also in agreement with partial replacement of silica fume that also experienced early attained strength, between [2.5-5%] variations from concrete pavement porosity were observed from the heterogeneity of the strength at different water cement ratios, including variation of compaction and placement of the materials. Keywords: modeling, palm oil fuel ash, compressive strength, water cement ratios
Design and Fabrication of a Motorized Tricycle for Physically Challenged Persons

To improve the existing indigenous model design and fabrication of a motorized tricycle for physically challenged persons was carried out. The focus is laid on the simplicity in design, high performance, easy maintenance and safety at very reasonable price.Most of the components are chosen keeping in mind the easy availability and reliability. This paper provides the details of components used, and designing parameters taken in consideration while designing the tricycle. This tricycle is very efficiently designed and can be proved as a better replacement for the indigenous models used by the handicapped keeping in mind the factors such as safety, cost & performance.
A regularized fuzzy support vector machine

By considering the role of different samples, fuzzy support vector machine is presented based on support vector machine. However, it ignores the structural information of each class samples. In this paper, structural information of the samples is introduced to fuzzy support vector machine and obtained a structural fuzzy support vector machine model. It is converted to dual problem with quadratic programming using Lagrange method. To solve this dual problem obtains the fuzzy support vector machine classifier. Experimental results in selected datasets demonstrate the effectiveness of the proposed method.
Integration of the Data Distribution Service middleware protocol in an SCADA system

: SCADA systems are widely spread in automation, monitoring and control systems for industrial processes. These systems are used to develop the factories of the future. Currently, the most SCADA systems allow data sharing only within a local network. With the development of the Internet of Things concept, it was started to be used in the systems for the factories of the future. One of the most powerful middleware systems used for the Internet of Things is these based on the DDS middleware systems. In this paper, it is presented the integration of the DDS middleware based systems within an SCADA system in order to enable the Internet of Things concept.
A Survey of Fast File Sharing System in Network

— The Phenomenal survey for file sharing in network is done based on some of the characteristics like speed and security. Perhaps, our protection loom not only considers the shrinking the size of network traffic that wishes to survive traced but also investigate to get better sustainability of the structure. File deduplication is a procedure for abolish replica photocopy of file
Design of Multi-Machine Power System Stabilizers using Gravitational Search Algorithm

Power system stabilizers (PSS) are used to generate supplementary control signals for the excitation system to damp electromechanical oscillations. This paper presents an approach based on the law of gravity and mass interactions called Gravitational Search Algorithm (GSA) fortuning the parameters of PSSs in a multi-machine power system.These stabilizers are tuned simultaneously to shift the lightly damped and undammed electromechanical modes of all plants to a prescribed zone in the s-plane. A multi objective problem is formulated to optimize a composite set of objective functions comprising the damping factor, and the damping ratio of the lightly damped electromechanical modes. The performance of the proposed PSS under different disturbances, loading conditions, and system configurations is investigated on New England 10-machine, 39bus power system. Non-linear time domain simulation results are presented under wide range of operating conditions and disturbances at different locations to show the effectiveness of the proposed GSA based PSS and their ability to provide efficient damping of low frequency oscillations.
A Review: Multi-Dimensional Space-Time Multilevel Codes using Rician Fading

This paper reviews multi-dimensional space-time multilevel codes (ST-MLCs). Basic construction methods, including conventional STCs. The spacetime multilevel encoders partition a dimensional signaling space, which spans all transmit antennas. Complexity of detection/ decoding can be reduced by multi-dimensional partitioning. Space time multistage decoder for the proposed ST-MLCs is reviewed. The complexity of soft decision decoding to be significantly reduced by comparing a single level approach. In addition, significant performance gains are obtained over a single level approach.
Study and Analysis of Copy-Move Forgery Detection in Digital Image: A Review

- In today's digital age, Digital image forgery becomes a common information falsification trend. This is generally done due to the largely available contemporary editing software and superior digital cameras. Authenticity of images becomes a more imperative issue while transferring data from one place to another place. Trustworthiness of photograph has a significant role in many areas - forensic investigation, criminal investigation, surveillance system, intelligent system, medical imaging and Journalism. Digital Image forensics finds the authenticity of the images. Different techniques are used to create forgery in the digital image. More regular type of digital image forgery is copy move forgery in which a part of an image itself is one copied and pasted into another location of the same image to conceal or alter the meaning. Post processing operations like resizing, blurring, rotation, JPEG compression etc has been done which makes digital image forgery detection difficult and hence an efficient approach is needed to detect the forgery into digital images. Copy Move forgery detection technique is grouped into two methods: Block based and Key Point based. In this paper, we have presented the various block based copy move forgery detection techniques. Image forensics is a fast growing research field and promises a convincing improvement in forgery detection.
Face Recognition based on Gabor Wavelet and Backpropagation Neural Network

—Face recognition is an efficient biometric technique which automatically identifies the face of an individual from adatabase of images. This paper proposes a face recognition technique using Gabor wavelet and Backpropagation Neural Network. Although there are so many existing methods, the illumination changes, out of plane rotations and occlusions still remain as challenging problems. In the proposed method, Gabor wavelet coefficients are used for creating feature vector due to its representative capability of the primary visual cortex of Human Visual System. The method also uses Principal Component Analysis for dimensionality reduction. The reduced feature vector is used as the input of the classifier, the Backpropagation neural network. Face recognition has many applications in a variety of fields such as access control, authentication and public surveillance.
Market Basket Data Analysis Using A Novel Genetic Frequent Itemset MiningAlgorithm by Partitional Clustering Approach

features of the data in the database are designated by the descriptive mining tasks. Predictions are inferred from the current data by the Predictive mining tasks. In recent trends, data mining plays a vital role for framing association rules among the massive collection of itemsets. Frequent itemsets are produced from large data sets by utilizing association rule mining, takes ample of time to �gure out all the frequent itemsets. By utilizing the Genetic Algorithm (GA) we improved the results of association rule mining. Genetic Algorithms are powerful and widely applicable stochastic search and optimization methods based on the concepts of natural selection and natural evaluation. On the whole, the objective of this study is to observe all the frequent itemsets and also generate the association rules in a short span of time from the large datasets by using the proposed genetic algorithm, GFISM. In this study, Apriori algorithm and genetic algorithms have been used to identify the techniques for association rule mining. The performance measurement and complexities of algorithms have also been presented. The combination of Soft computing techniques with existing association rule mining yields fast results. In this study ARM algorithm with genetic approach has been analyzed. A k-itemset that consists of k items from I, is frequent if it occurs in the Transaction(T) not less than s times, where s is a user-specied minimum support threshold and s <= n. In 1975, John Holland developed the Genetic Algorithm at the University of Michigan. Genetic Algorithm is an adaptive heuristic search algorithm based on the development of natural selection and genetics. This
A Global Nearest-Neighbour Depth Learning Based Automatic 2D to 3D image and Video Conversion

: - Despite a significant growth in the last few years, the availability of 3D content is still dwarfed by that of its 2D counterpart. In order to close this gap, many 2D-to-3D image and video conversion methods have been proposed. Methods involving human operators have been most successful but also time-consuming and costly. Automatic methods, that typically make use of a deterministic 3D scene model, have not yet achieved the same level of quality for they rely on assumptions that are often violated in practice. The proposed work is to present a new method based on the radically different approach of learning the 2D-to-3D conversion from examples. It is based on locally estimating the entire depth map of a query image directly from a repository of 3D images (image depth pairs or stereo pairs) using a nearest-neighbour regression type idea
An Empirical Analysis of Impact of Macroeconomic Variables on Indian Stock Market

This research paper, using monthly returns of macroeconomic variables, Nifty, and stock price from 3 sectors, examines the impact of macroeconomic determinants on Nifty and banking sector stocks from May 2009 to July 2018. The paper also analyses the granger cause between macroeconomic variables and Nifty; macroeconomic variables and Indian banking sector stocks. This paper also extends the research work in finding out the impact before and during Narendra Modi government. Johansen’s co-integration and granger causality tests were applied for this research work. The results of Johansen’s co-integration proved that there is a long relation between selected macroeconomic factors, i.e. bank rate, repo rate, and reverse repo rate, and Indian stock market and also on banking sector share price. There is granger cause before Modi Government and during Modi Government. It is concluded that, a positive significant relationship exists between macroeconomic determinants and Indian stock market.
Keyword-Based Search Mechanism for Data Stored in Cloud

: Peoples now a day’s rate of storing their personal and professional data on the cloud increasing rapidly. Data is getting transferred to remote area in larger chunks without knowing whether the server on which data is transferred is a trusted server or not. There is possibility of losing its contents in large size of data. In order to maintain privacy of documents which is being transferred on cloud environment, encryption of the document should be made before outsourcing it to the cloud. But after placing encrypted documents on cloud retrieving of the data is a tedious work. In order to retrieve the data on cloud computing there is so many techniques available. Among them keyword enabled search of the encrypted data is one of the most efficient and popular technique. In most of the case single keyword based technique are used. Because of its limitation not full fills the all it need. To increase efficiency and rapidness in searching multi-key word search can be applied. This paper represents a survey on a secure search scheme handled by single-keyword or multi-keyword ranked search over encrypted cloud data
Examine the Role of Spiritual Intelligence on Optimism and Psychological Well-Being of Teachers in City Taibad of Iran

The present study aims to investigate the role of spiritual intelligence on optimism and psychological wellbeing of teachers. The research design was correlative descriptive and included all the teachers in city Taibad, and 310 of them were selected by multistage random sampling. For data collection the questionnaire spiritual intelligence of Abdollah Zadeh et al. in 2008, Shearer and Carver’s direct orientation test of life in 1985 and Psychological well-being scale of Ryff in 1989 were used. Data analysis using Pearson correlation and regression analysis showed there was a significant relationship between the spiritual intelligence and its components and optimism and psychological well-being at level (P<0.01). Based on these results, providing support factors to enhance the spiritual intelligence can leads to optimism and psychological well-being.
QUOTIENT DIFFERENCE BASED IDEAL MOTIONPREDICTION FOR VIRTUAL SCENE

Objectives: Smooth dense depth for virtual reality is essential for stable and accurate identification of object present in implicit scenes. Methods/Statistical Analysis: Every point in real world is computed from Difference between left and right image by using sum of absolute difference algorithm. Apparent motion for a virtual world is calculated from differential methods. Findings: In this paper we have computed motion field for virtual scene from stereoscopic image. Consequent Images of dynamic environment are captured at different times for a scene. Applications/Improvements : Advance sophisticated assistance for Robots require more details about virtual scene in current scenario. This method allows Resilent discernment of dense motion field present in the scene.
A REVIEW ON MIMO OFDM SYSTEM UNDER CHANNEL ESTIMATION

Orthogonal Frequency Division Multiplexing (OFDM) is a technique of encryption digital information on multiple carrier frequencies. it's one of the modulation techniques which are multi-carrier, provides a high spectral efficiency, immunity to the frequency selective weakening channels, power efficiency and multipath delay unfold tolerance. it's a lot of advantageous over alternative technologies. although its benefits it's some obstacle additionally. The high peak-to-average ratio is the main obstacle that causes non-linearity at the receiving finish. One in all the main disadvantages in OFDM communication is the high peak-to-average power ratio (PAPR). in this paper we discuss regarding the PAPR within the OFDM system, its impact and a few techniques name which may be used to reduce the PAPR according to our need. This approach has the potential of reducing the PAPR of the OFDM without affecting the bandwidth efficiency of the system and the Bit Error Rate (BER) performance.
Human Robot Cooperative System Based on Non-invasive Brain Computer Interface

: In this paper a human robot cooperative approach to reliable planning and execution is presented. We provide a comprehensive review of the system which represents limit types of user's intention patterns based on EEG signals with insufficient decoding accuracy and time delay. BCI’s provide direct communication and control between the human brain and physical devices by translating different patterns of brain activity in to commands in real time. With these commands the human robot system (or mobile robot) can be controlled. The pattern of interaction between the neurons in brain is represented as thoughts and emotional states. This pattern will change according to the human thoughts which in turn produce different electrical waves. In this paper a brain wave sensor is used for sensing the signals in the brain and it is converted to control commands which further transmitted to the robot module for processing. By using the entire system, the human robot can be moved as per the human thoughts which can serve as a powerful aid for severely disabled persons in their daily life especially to help them move voluntarily. We conclude this paper with a discussion of the current challenges and future research directions.
MISSING VALUE ESTIMATION OF EPISTATIC MINIARRAY PROFILING DATA BY KERNEL PCA REGRESSION ENSEMBLE APPROACH

: Missing data imputation is a key issue in learning from incomplete data. Various techniques have been developed with great success on dealing with missing values in data sets with heterogeneous attributes (their independent attributes are of different types) referred to as imputing mixed-attribute data sets. Epistatic miniarray profiling (E-MAP) is a powerful tool for analyzing gene functions and their biological relevance. However, E-MAP data suffers from large proportion of missing values, which often results in misleading and biased analysis results. This paper studies a new setting of missing data imputation, a novel ensemble approach EMDI based on the high-level diversity to impute missing values that consists of two global (kernel principal component analysis regression) and four local base estimators. The performance of the proposed KPCAR impute algorithm is compared with state-of-the-art linear regression methods, i.e., Bayesian principal component analysis imputation (BPCA). The KPCAR impute outperforms the L-Simpute when the missing percentage increases. The performance of the KPCA impute is similar to that of the BPCA imputation. Therefore, it is an effective and promising algorithm in estimating missing values for DNA microarray profiles.
Fungicide Alternatives for Controlling Cantaloupe Root rot Incidence under Plastic Houses Conditions

319 Abstract Evaluating the efficacy of different plant resistance inducers and/or bio-agents treatments against root diseases incidence revealed that soil drench with treatments, mixture of [Humic & Folic acids + Furfural + Trichoderma harzianum] and the bio-agent [Furfural + Trichoderma harzianum] resulted in highly reduction in the incidence of root rot disease of cantaloupe grown under plastic house conditions comparing with fungicides treatment. The obtained results revealed that cantaloupe seedlings grown in plastic house showed 4.6 and 5.8% root rot incidence at treatments of mixtures of [Humic & Folic acids + Furfural + Thricoderma harziamum] and [Furfural + Trichoderma harzianum], respectively. Meanwhile root rot disease infection of cantaloupe seedlings was recorded as 12.4% in control (fungicide treatment). It could be conclude that, antagonistic fungal and bacterial bio-agents and/or furfural, Humic & Folic acid are the effective agents capable of protecting cantaloupe plants against root rot pathogens under plastic house conditions.
MANAGING FINANCIAL RESOURCES: A NEVER ENDING CHALLENGE TO OVERSEAS FILIPINO

Working abroad is considered by most as a hard earned money especially to the Overseas Filipino Workers (OFW). Some were successful in their long years of working while others were still suffering after settling back home. Based on this premise, the objective of this study is to examine whether the OFWs utilized planning and managing their financial resources while still working in the foreign land. It will also assess the experiences of OFWs regarding their utilization of earnings in terms of savings and spending and their responses in periods of uncertainties. The study employed the qualitative research and utilized the purposive sampling to solicit the ideas and opinions from respondents. Moreover, focus group discussion were presented to highlight the openness of the responses from the ten (10) respondents. Findings showed that, there is a need of the OFWs exposure with any trainings relative to financial management, financial literacy and others. Financial discipline is also necessary to manage the resources effectively. Also, realization should be reached that, government should respond to these needs with employment of trainings and programs to address these needs.