Miracle-dz/UniFAR-FTUs huggingface dataset:
Multi-period Optimal Portfolio Decision with Transaction Costs and HARA Utility Function

Portfolio selection problem is one of the core research fields in modern financial management. While considering the transaction costs in the long term investment makes the portfolio selection problems more complex than there are no transaction costs. In this paper, the general multi-period investment problems with HARA utility function and proportional transaction costs are investigated. By using the dynamic programming method, the indirect utility function is defined for solving the portfolio selection problem. The optimal strategies and the boundary of the no-transaction region are obtained in the explicit form. And the procedure for solving the original portfolio selection problem is given. Numerical example shows the feasibility and effectiveness of the method provided in this paper.
A DETAILED STUDY AND ANALYSIS OF OCR USING MATLAB

This paper presents detailed review in the field of Optical Character Recognition. Various techniques are determine that have been proposed to realize the center of character recognition in an optical character recognition system. Even though, sufficient studies and papers are describes the techniques for converting textual content from a paper document into machine readable form. Optical character recognition is a process where the computer understands automatically the image of handwritten script and transfer into classify character. This material use as a guide and update for readers working in the Character Recognition area. Selection of a relevant feature extraction method is probably the single most important factor in achieving high character recognition with much better accuracy in character recognition systems without any variation. Character recognition techniques associate a symbolic identity with the image of character. In a typical OCR systems input characters are digitized by an optical scanner. Each character is then located and segmented, and the resulting character image is fed into a pre-processor for noise reduction and normalization. Certain characteristics are the extracted from the character for classification. The feature extraction is critical and many different techniques exist, each having its strengths and weaknesses. After classification the identified characters are grouped to reconstruct the original symbol strings, and context may then be applied to detect and correct errors.
Inferences on Operating Characteristics of the Queue in General Earlang Queuing System Model

Various queues system have been analysed in respect of their characteristics using the corresponding probability mass function (p.m.f.). Traffic intensity define as the ratio of the arrival rate to the service rate is an important concept. This ratio is also known as availability ratio in reliability theory. Various inference procedures on the queue characteristics are discussed in the present paper. Keywords--Likelihood Estimator, UMVUE, Bayes Estimate, Prior and Posterior distribution, Queue’s characteristics
MR2P: A mutually reinforced relevance propagation model for query-focused multi-document summarization

Query-focused multi-document summarization aims to create a compressed summary biased to a given query. This paper presents a mutually reinforced relevance propagation (MR2P) approach to this summarization task. Experiments are conducted on the DUC 2005 and DUC 2006 data sets and the ROUGE evaluation results demonstrate the advantages of the proposed approach
The Process Young Mothers in New Mexico Undergo to Achieve the Healthy People 2020 Objective of Exclusive Breastfeeding for Three Months

Breastfeeding is an important and relatively affordable health promotion and disease prevention activity with positive health outcomes for both the women who breastfeed and the infants who receive human milk as their first source of nutrition. As these benefits are dose dependent, the duration of breastfeeding and exclusivity of breastfeeding are important in the development of these protective effects. The purpose of this study is to understand the process younger mothers, ages 18 to 24 years, undergo to be able to sustain breastfeeding for longer than most women their age breastfeed. Ten women, ages 21 to 24 years, participated in individual, in-depth interviews in this grounded theory study. Following the iterative process of data collection and analysis, focus of the interviews changed as the data analysis guided the data collection process. Data underwent open, focused, axial, and theoretical/selective coding. The analysis process resulted in the development of a middle-range, descriptive theory grounded in the data. Prolonged Breastfeeding Through Fierce Determination succinctly explains the central phenomenon, fierce determination, and the relationship it shares with the other concepts identified as important contributing factors as the women moved through their breastfeeding journeys. Facing and Deciding to Overcome Obstacles, influenced most heavily by fierce determination, exerted its influence upon the outcome of the theory, “Trucking Along”, an in vivo code provided by Participant 10, which captured her ongoing breastfeeding even in the face of obstacles. Normalized Infant Feeding Method contributed to the participants’ fierce determination. Availability of Support positively impacted the participants’ ability to face and overcome their breastfeeding obstacles. Personal Agency had a reciprocal relationship with
Biofilm detection amongst Extended Spectrum Beta Lactamase (ESBL) and Metallo Beta Lactamase (MBL) producing clinical isolates of Acinetobacter baumannii

Acinetobacter baumannii has emerged as one of the most troublesome pathogen responsible for various infections especially in the intensive care units, accounting for almost 10% of nosocomial infections. Over the recent years Acinetobacter baumannii has become a red alert pathogen as a result of their profundity in developing multidrug (MDR) and pan drug resistance (PDR). Moreover, they have remarkable ability to acquire resistant determinant genes encoding for enzymes like extended spectrum beta lactamase (ESBL) and metallo beta lactamase (MBL), which make it resistant to extended spectrum cephalosporins and carbapenems. Patients infected with XDR or MDR strains act as reservoir of resistant organism and can further spread these lethal strains to community and health care worker. 2
Can IMERG Precipitation Estimates Suitably Replace TMPA Products For Brazil?

Knowledge of the spatial and temporal distribution of precipitation is of key importance for planning a wide range of socio-economic activities such as agriculture, livestock grazing, energy generation, etc. The availability of accurate and consistent precipitation data is then paramount for the proper assessment of such activities. However, traditional rain gauge measurements are relatively scarce and poorly distributed over the surface of the globe, particularly over remote areas or in developing countries. In the last three decades, satellite-derived precipitation estimate products have been developed using multi-satellites and multisensors. Such products provide an effective way of estimating precipitation data in areas where measurements are scarce, such as deserts [1], forests [2] and oceans [3]. Accordingly, they have been widely used in research and applications worldwide [410]. The first approaches to employ remote sensing techniques for estimating precipitation were performed during the 70s. During that time, satellite images were not digitalized. Barret [11] was one of the pioneers in developing a method for estimating monthly precipitation using the visible channel. This method was called “cloud index”, and was later improved by Follansbee [12], which allowed for estimating the daily precipitation rate. Almost two decades later, at the end of 1997, the Tropical Rainfall Measuring Mission (TRMM) [13] was launched jointly by NASA (National Aeronautics and Space Administration) and JAXA (Japan Aerospace Exploration Agency), aiming to improve precipitation estimates in tropical and subtropical regions. One of the most successful products generated from this mission is the MultiSatellite Precipitation Analysis research version (TMPA) and the real-time version of the same product (TMPA-RT). The TMPA algorithm [4,5,14,15], besides combining precipitation estimates derived from several satellites, is also able to incorporate observed precipitation data [16]. Over the last decade, those retrievals have been improved greatly with different versions of those products. For this study, the latest version of TMPA launched in 2012 (version 7 or TRMM_V7 hereafter) will be used. The TMPA product has been used in scientific research and operational activities which lead to outstanding socio-economic gains, such as studies of extreme precipitation events [17-19], forecasting of natural disasters [20,21], water resources management planning [22], performance of numerical models [23-26], among others. Due to this fact, TMPA validation studies have been performed in several regions of the globe, and those results show great agreement between TMPA products and surface data [27-32]. Nevertheless, particularly over Brazilian territory, systematic bias are still observed for some precipitation regimes associated with shallow convection systems observed near the coast of northeastern Brazil (underestimation of precipitation) [33,34], while in Southern Brazil (close to the border with Argentina and Volume 2 Issue 3 2018
Camera Shake Removal With Multiple Images Via Weighted Fourier Burst Accumulation

— Blur introduced in an image from camera shake is mostly due to the 3D rotation of the camera. This results in a blur kernel which is non uniform throughout the image. Hence each image in the burst is blurred differently. Various experiments were done to find the deblurred image either with single image or with multiple image. In this paper we analyze multiple image approaches, which capture and combine multiple frames in order to make deblurring more robust and tractable. If the photographer takes many images known as burst, we show that a clear and sharp image can be obtained by combining these multiple images. Also for this work the blurring kernel is unknown (blind) and also it is not found. The methodology used here is Fourier Burst Accumulation which performs a weighted average in Fourier Domain where the weights depend on Fourier spectrum magnitude. In simple words the method can be generalized as Align and Average procedure. Experiments with real camera data and extensive comparisons, show that the proposed burst accumulation algorithm achieves results faster.
Effect of Supplier and Customer Collaboration on Product Innovativeness: A Focus on Small and Medium-Sized Enterprises (SMES) in Kenya

This study investigated the effect of supplier and customers’ collaboration on product innovativeness in manufacturing SMEs. Using a cross sectional survey design with a sample size of 196, a standard multiple linear regression (MLR) was performed between product innovativeness as the dependent variable and the dimensions of supplier and customer collaboration, as independent variables. The results of the supplier collaboration regression indicated that the predictors explained 45.1% of the variance (R 2 =.477, Adj R 2 =.451), F (6, 119) = 18.115, p < . 001; t=1.995. ; that of customer collaboration indicated that the predictors explained 38.8% of the variance (R 2 = .388, Adj R 2 = .358), F (6, 119) = 12.597, p < . 001; t=6.441. Understanding customer needs is the only dimension that significantly contributed positively to predicting PI (B = 1.169, p= .056) t=1.930. It is concluded that supplier dimensions of win-win relationships, deep suppliers trust, expertise from suppliers and support in product launch will positively predict PI. Similarly, customer collaboration dimension of deep understanding of customer needs will positively predict PI. The researchers recommend the setting up of SMEs support policies that promote collaborations in research for purposes of sharing information / accessing the diverse knowledge base on new product design, development and production.
ANT-LOAD: A Proficient Meta-Heuristic Cloud Load Balancing

The Cloud computing is an embryonic as an innovative hypothesis of gigantic distributed calculation. Load balancing, the main trial in cloud computing, requires to allocate the vibrant workload uniformly across all of the machines. Burden balancing leads to a high user satisfaction and resource utilization ratio by confirming a proficient and fair allocating of all of the resources. Burden Balancing additionally supports ranking users by applying suitable method for scheduling. This paper concludes the counseled algorithm, Ant colony optimization, to resolve the setback of burden on the nodes in the cloud web, making the nodes burden free to work. This paper displays the drawbacks of Genetic Algorithm are resolved employing ACO for balancing the burden in the cloud network.
Efficient Weighted Frequent Patterns Mining over Evolving Dataset

--Weighted frequent pattern mining is suggested to find out more important frequent pattern by considering different weights of each item. Weighted Frequent Patterns are generated in weight ascending and frequency descending order by using prefix tree structure. These generated weighted frequent patterns are applied to maximal frequent item set mining algorithm. Maximal frequent pattern mining can reduces the number of frequent patterns and keep sufficient result information. In this paper, we proposed an efficient algorithm to mine maximal weighted frequent pattern mining over data streams. A new efficient data structure i.e. prefix tree and conditional tree structure is used to dynamically maintain the information of transactions. Here, three information mining strategies (i.e. Incremental, Interactive and Maximal) are presented. The detail of the algorithms is also discussed. Our study has submitted an application to the Electronic shop Market Basket Analysis. Experimental studies are performed to evaluate the good effectiveness of our algorithm.
Screening for Antibacterial activity of Bacteria isolated from Marine sediment

Though ocean comprises 70% of the earth surface area, marine bacteria of this habitat are not fully explored for their bioactive compounds. The notable abilities of marine bacteria to produce chemically unique bioactive molecules are supported by characterization of a wealth of intriguing new structures through the application of classical screening and isolation techniques (Gulder et al., 2009). Discovery of antibiotic lead molecules through screening of microbial secondary metabolites is becoming progressively significant. Microorganisms have been reported to produce around 23,000 bioactive secondary metabolites (Shinn et al., 2013) with a wide range of industrially valuable activities, such as antibacterial, antifungal, antimalarial, cytotoxic, anticancer etc., Bacillus genus is a key antibiotic resource and being observed as a promising starting point in the hunt for new inhibitory substances as it is proficient of producing enormous antibacterial products. Over 800 antibiotic metabolites, such as gramicidin, bacitracin and polymyxin B have been reported from different species of Bacillus sp (Xiaoyong Zhang et al., 2010). Several compounds of clinical value are being assayed in vitro to control the growth of pathogenic microbes. Several non-ribosomal compounds, cyclic lipopeptidides of iturin group and macrolactones such as surfactins, fengycins and plipastatins have also identified from Bacillus through multistep mechanisms including the selection and condensation of amino acid residues (Stein et al., 2005; Urdaci et al., 2004). Subtilosin International Journal of Current Microbiology and Applied Sciences ISSN: 2319-7706 Special Issue-5 (October-2017) pp. 37-44 Journal homepage: http://www.ijcmas. com
TEXTURE CLASSIFICATION BASED ON NEURAL NETWORK AND WAVELET TRANSFORM

: This paper describes a method for classifying textured images using neural networks and discrete wavelet transform. In this method a multiresolution analysis is applied to textured images to extract a set of intelligible features. These extracted features, in the form of DWT coefficient matrices, are used as inputs to four different multilayer perceptron (MLP) Neural Networks and classified. Generalization performance is improved when a locally connected, weight-sharing network topology is utilized, thus drastically decreasing the number of free parameters during training. This method takes advantages of the quasi-periodic nature of the textured images. A novel voting network scheme is also employed to achieve a system classification result from the four networks. The efficiency of the algorithm is demonstrated using real-world textured images. The proposed technique was verified using four classes of visually similar textured images, with a successful classification rate as high as 99.5%.
Survey of Context-Aware Systems-Challenges in development of Context-Aware Applications

Context-awareness has attracted increasing attention in computing and communication communities since it allows automatic adaptation of devices, systems, and applications to user's context change. The context is the information related to the situation of an entity such as the present status of people, places, things and devices in the environment. An entity is a person, device, place, or object relevant to the interaction between a user and an application, such as location, time, activities, and services . Context awareness allows for customization or creation of the application to match the preferences of the individual user, based on current context such as enterprise environment or home network . Currently context has been considered as part of a process in which users are involved, hence specifying and developing context models are needed to support context-aware applications to (a) adapt interfaces, (b) tailor the set of application-relevant data, (c) increase the precision of information retrieval (d) discover services (e) make the user interaction implicit(f) build smart environments. Context related to human factors is structured into three categories: (a) information on the user (b) the user’s social environment (c) the user’s tasks. Likewise, context related to physical environment is structured into three categories: (a) location, (b) infrastructure, and (c) physical conditions Context-aware systems are able to adapt their operations to the current context without explicit user intervention thus aim at increasing usability and effectiveness by taking environmental context into account.
An Informal Method of Village Mapping Using Edge Detection Technique& ISRO-BHUVAN Software

: It is always necessary to have map of any village. If anyone wants to find out any local area, school, government talati office/panchayat office, internal roads, society, hospital or medical practitioner’s clinic in village then map is very helpful tool to navigate. Nowadays, it is very necessary to have navigator in a mobile phone to find the highway, a complete roadmap to reach from source to the destination. As we know, mainly there are two navigators available in India. 1) Google earth for global world and 2) ISRO BHUVAN specifically for India. In this paper we have used ISRO BHUVAN to navigate and find area of a small village called Gopalpura, near Anand, Gujarat, India. There are many views available in the BHUVAN software like Base admin, Hybrid, Satellite, Terrain, 2D and 3D etc. we have used Base admin, Hybrid and satellite view of ISRO-BHUVAN software, to map Gopalpura. We have filtered base admin view of Gopalpura map through Canny Edge detection technique. Resulted image shown is edge detected image of Gopalpura map derived.
Integration of Linear Goal Programming and Fuzzy VIKOR Method for Marketing Strategy Selection: A Case Study

: The objective of this paper is to present a new methodology for selecting of marketing strategy by integration of Linear Goal Programming model and Fuzzy VIKOR. LGP method is used to determine the fuzzy weights of criteria and Fuzzy VIKOR aims to rank strategies. We apply the integrated approach in real case to demonstrate the application of the proposed method.
Bovine Trypanosomosis and Glossina Flies Density in and Around Chelo Settlement Areas of Didesa District of Buno Bedele Zone, Western Oromia

The study was conducted in Chelo settlement area of Didesa district to estimate prevalence of the parasite (Trypanosomosis) and identify Tsetse fly species involved. Out of the total of 384 local breeds of cattle examined, 27 animals were found positive for trypanosomosis. The overall trypanosomosis prevalence was 7.03% which is composed of (n=5) 1.3%, (n=11) 2.87%, (n=7) 1.82% and (n=4) 1.04% in Doyo, Chelo, Cheti and Other area respectively was recorded and peak prevalence was observed in Chelo (n=11) 2.87%. A total of 2551 tsetse and other biting flies were caught during the study period. Out of these, Glossina accounts n=2333 (91.45%) and other biting flies includes n=218 (9.34%). The apparent fly density was found to be Flay/Trap/Day (FTD)=13.89 for Glossina species with the only identified species was Glossina tachinoides and Flay/Trap/Day (FTD)=1.23 was accounted for other biting flies. Finally, despite the continued interventions were applied on tsetse and trypanosomosis control in the study area, significant numbers of Glossina tachinoides were caught FTD=13.89 with 7.03% of overall prevalence of trypanosomosis.
An Approach of Secure Face recognition using Linear discriminant analysis in Network

— Our aim is to apply Linear Discriminant Analysis (LDA) to face recognition which is based on a linear projection from the image space to a low dimensional space by maximizing the between class scatter and minimizing the within-class scatter. LDA allows objective evaluation of the significance of visual information in different features of the face for identifying the human face. The LDA also provides us with a small set of features that carry the most relevant information for classification purposes. LDA method overcomes the limitation of Principle Component Analysis method by applying the linear discriminant criterion. This criterion tries to maximize the ratio of determinant of the between-class scatter matrix of the projected samples to the determinant of the within-class scatter matrix of the projected samples. Linear discriminant groups the images of the same class and separate images of different classes
Partial characterization and optimization of alkaline amaylase production by Bacillus circulans from Lonar Lake

In India the Lonar Crater Lake, popularly called as the Lonar Soda Lake is situated in the Buldhana District of the Maharashtra State, which is occupied by saline water and harbors various unidentified, unique haloalkaliphilic bacterial species which produces thermo-alkaliphilic enzyme. The present study deals with isolation, production and partial characterization of amylase producing bacterial culture isolated from the alkaline Lonar Lake. Total thirty seven bacterial cultures were isolated by using different enrichment media. Out of thirty seven, six bacterial strains were selected on the basis of their amylolytic activity and then studied their phenotypic and biochemical characters. The strain CW2 (2) was selected for 16S rDNA sequencing and production, partial characterizations of amylase on the basis of their maximum amylolytic activity. Phylogenetic analysis based on 16S rDNA gene sequences indicated that strain CW2 (2) was affiliated to phylum Firmicutes with genera Bacillus. Newly isolated Bacillus circulans CW2 (2) strain was found to produce alkaline amylase at optimum pH 10 and optimum temperature was found at 50°C. The best substrate concentration 1250 μg/ mL and enzyme concentration 3500 μg/ mL was observed. The present studies indicated that the isolated Bacillus circulans CW2 (2) have ability to produced amylase which can be exploited for biotechnological potential.