Miracle-dz/UniFAR-FTUs huggingface dataset:
Definition of Fault-proneness at Early Phase in Object-Oriented Development

To analyze the complexity of object-oriented software, several metrics have been proposed. Among them, Chi-damber and Kemerer’s metrics are well-known ones as object-oriented metrics. Also, the effectiveness has been empirically evaluated from the viewpoints of estimating the fault-proneness of object-oriented software. In the evaluations, their metrics were applied to not design specification but the source code because some of them measure an inner complexity of a class, and such information cannot be obtained until the algorithm and structure of the class are determined at the end of design phase. However, the estimation of the fault-proneness should be done in the early phase to effectively allocate effort for fixing the faults. This paper proposes a new method to estimate the fault-proneness of the class in the early phase, using several complexity metrics for object-oriented software. In the proposed method, we introduce four check-points into the analysis / design / implementation phase, and estimate the fault-prone classes using the applicable metrics at each checkpoint.
Clustering Algorithms For High Dimensional Data – A Survey Of Issues And Existing Approaches

- Clustering is the most prominent data mining technique used for grouping the data into clusters based on distance measures. With the advent growth of high dimensional data such as microarray gene expression data, and grouping high dimensional data into clusters will encounter the similarity between the objects in the full dimensional space is often invalid because it contains different types of data. The process of grouping into high dimensional data into clusters is not accurate and perhaps not up to the level of expectation when the dimension of the dataset is high. It is now focusing tremendous attention towards research and development. The performance issues of the data clustering in high dimensional data it is necessary to study issues like dimensionality reduction, redundancy elimination, subspace clustering, co-clustering and data labeling for clusters are to analyzed and improved. In this paper, we presented a brief comparison of the existing algorithms that were mainly focusing at clustering on high dimensional data.
Rates of hearing loss in primary school children in Australia: A systematic review

Objective: To systematically review literature reports of the rates of hearing impairment in primary school children in Australia. Methods: A search of five electronic databases yielded three studies that had used follow-up diagnostic hearing assessment to examine hearing impairment in primary school children in Australia. Results: The rate of hearing impairment in primary school children in Australia was estimated to lie between 3.4 and 12.8%. The rates of different types of hearing impairment were estimated as follows: conductive hearing impairment between 2.6 and 7.1%, sensorineural hearing impairment between 0 and 1.19%, and mixed hearing impairment between 0.1 and 4.0%. All estimated rates were affected by the use of different criteria to define the presence of hearing impairment. Conclusions: The rates of hearing impairment in primary school children in Australia were higher than those reported in other high- income countries, mostly as a result of a higher rate of conductive hearing impairment. The present study's suggestion of higher rates of conductive hearing impairment in primary school children in Australia warrants further consideration as such impairments can be detected and managed using existing health resources.
LEARNING CURVE WITH A NEW PRIMARY TKA IMPLANT: A WORLDWIDE PERSPECTIVE WITH MORE THAN 2000 PATIENTS

With the introduction of new technology in orthopaedics, surgeons must balance anticipated benefits in patient outcomes with challenges or complications associated with surgical learning curve for the technology. The purpose of this study was to determine whether surgeon learning curve with a new multi-radius primary TKA system and instruments designed to improve surgical team ease would impact clinical outcomes, surgical time, and complications. From November 2012 to July 2015, 2369 primary TKAs were prospectively enrolled in two multicentre studies across 50 sites in 14 countries with a new knee system (NEW-TKA) evenly balanced across four configurations: cruciate retaining or posterior stabilised with either fixed bearing or rotating platform (CRFB, CRRP, PSFB, PSRP). 2128 knees had a Mean (SD) surgical time for NEW-TKA learning curve cases was 79.1 (24.3) minutes, which reduced thereafter to 73.6 (24.3) (p=0.002). Beyond 10 cases, there was a continued reduction in NEW-TKA surgical time (R-Squared = 0.031). After 10 cases, surgical time was on par with the mean (SD) 71.9 (21.6) for CA-TKA (p=0.078). PROM outcomes of the first 10 learning curve cases for NEW-TKA were not statistically different from later cases at less than 1 year or later when adjusted for relevant covariates including configuration, patient demographics, pre-op functional status, and time post-op (p-values > 0.01). PROM outcomes for NEW-TKA vs. CA-TKA under the same covariate adjustments showed a trend favoring KOOS ADL, Symptoms, and Sport and Recreation subscores at minimum 1 year (p-values The introduction of new implants into the market place needs to have adequate data to support that they are safe and effective. Except for a minor increase in surgical time during the first 10 patients, this study found that surgeon learning curve with this new primary TKA system does not adversely affect patient short term outcomes and complication rates.
Educational Data Mining: An Exploration of students' Academic performance Through Association Rule Mining set

: Data Mining is a technology to explore data, analyse the data and finally discovering patterns from large data repository. Currently there is growing interest in data mining and educational systems leading to the development of educational data mining to monitor the progress of student’s academic performance. The knowledge is hidden among the educational data set and it is extractable through data mining techniques. This paper discusses the capabilities of data mining techniques in context of school education system to monitor the progression of academic performance of students for the purpose of making an effective decision by the academic planners. The entire process is composed of different phases that begin with the collection of the educational data from relevant sources. After data collection phase, pre-processing of dataset is done to prepare the data for execution and remove all anomalies. Further, the implementation of powerful association rule mining is done for analysing student’s result data in order to discover the association rules based on educational dataset. Finally the paper is concluded with the research outcomes for different cases of association rule sets viz. mining from multiple variables and mining from single variable.
Implementation of Low Power and Memory Efficient 2D FIR Filter Architecture

 Abstract: A memory efficient design is analyzed to derive a low power-area-delay two dimensional (2D) Finite Impulse Response (FIR) filter architecture. The parallel processing concept is introduced in the fully direct-form 2D FIR filter. Due to this, memory reuse is carried out, and it reduces the overall storage memory of the FIR filter. The non-separable 2D FIR filter structure is designed and implemented with block size L and filter length N. The high speed and power efficient multipliers and optimized Carry Look Ahead (CLA) adders are used in the arithmetic module of the FIR filter and a pipelined adder unit is used for the final computation of the filter output. The switch level modification in the logic gates is proposed to reduce the area, power and delay of the adders. This proposed architecture is represented in HDL code and validation is carried out in CADENCE environment using NC Simulator and RTL Compiler synthesis tool. The area, power and delay reports are generated and compared with existing memory efficient 2D FIR filter hardware structures. The power is reduced to 44% and delay is reduced by 20% using Modified CLA (MCLA) adders and pipelining in the design.
A Brief Survey on Different Contrast Enhancement Techniques

Contrast enhancement is used to either increase the contrast of an image with low dynamic range or to bring out image details that would otherwise be hidden. Generally, the contrast enhanced image looks better than the original image. The contrast variations in an image vary with different aspects like illumination, color, scene etc. So many approaches are developed to increase the contrast of an image with abnormal contrast levels. This paper outlines a brief survey over different contrast enhancement approaches. Based on the domain the contrast enhancement takes place, all the earlier approaches are classified as spatial domain and transform domain approaches. Further based on the fashion, they are categorized as local contrast enhancement and global contrast enhancement approaches. All the possible constraints with contrast enhancement techniques is represented here.
An efficient fairness between parties for contract signing using OTP

— Contract signing protocol is a new way of interacting two parties online though communication devices. Since communication is done though internet or media so authentication is required between these parties to check the contract is done between right parties or not. The main aim of contract signing is fairness between parties. Although there are various techniques implemented to provide authentication and fairness between these parties such as by R. Song, but these techniques contains some issues, hence a new and efficient technique is implemented here to provide prevention from various attacks and fairness between parties.
A Novel MPPT Method for PV Arrays Based on Modified Bat Algorithm with Partial Shading Capability

The power–voltage curve of photovoltaic arrays has one global peak and one or more local peak under partially shaded conditions. The conventional maximum power point tracking algorithms such as Perturb&Observe are converge to the first peak. Therefore they may not find global maximum power point. However; soft computing methods such as Bat Algorithm may find it. This paper proposes a dual algorithm search method that consist of modified Bat Algorithm and Perturb&Observe algorithm. The standard Bat Algorithm has been modified by adding a similar feature to tabu list of Tabu Search Algorithm. In search process, firstly, modified Bat Algorithm is used to determine the area of global peak, then Perturb&Observe is replaced to track the maximum power point in the area of global peak. If the power varies greatly and rapidly, the search process starts again. Standard Bat Algorithm, Perturb&Observe and the proposed method are simulated under standard test condition and partially shaded conditions. The simulations show that performans of the proposed method is superior to both the standard Bat Algorithm and Perturb&Observe.
One-to-Many Face Recognition with Bilinear CNNs: A Survey

 Abstract — Face recognition from the real data, capture images, sensor images and database images is challenging problem due to the wide variation of face appearances, illumination effect and the complexity of the image background. Face recognition is one of the most effective and relevant applications of image processing and biometric systems. In this paper we are discussing the face recognition methods, algorithms proposed by many researchers using artificial neural networks (ANN) which have been used in the field of image processing and pattern recognition. How ANN will used for the face recognition system and how it is effective than another methods will also discuss in this paper. There are many ANN proposed methods which give overview face recognition using ANN. Therefore, this research includes a general review of face detection studies and systems which based on different ANN approaches and algorithms. The strengths and limitations of these literature studies and systems were included, and also the performance analysis of different ANN approach and algorithm is analyzing in this research study. In the past two decades, Shape detection has been proven as the most interesting research field. We present a novel approach to measuring the similarities between shapes and exploit it for object recognition. The measurement of similarity preceded by solving correspondences between points on two shapes as well as by using the correspondences to estimate an aligning transform. In this paper, we are going to describe some important aspects of face detection, which are very much useful in many applications like face recognition, facial expression recognition, face tracking, facial feature extraction, gender classification, identification system, document control and access control, clustering, biometric science, human computer interaction (HCI) system, digital cosmetics and many more. Before that, I would like to focus on some well-known face detection techniques and then some feature detection methods because until and unless we extract the important features (eyes, nose, mouth) from a face we won’t be able to uniquely identify the person properly. The dissimilarity between the two shapes is computed as some of matching error between corresponding points. We treat recognition in a nearest neighbor classification framework as the problem of finding the stored prototype shape that is maximally similar to that in the image
Personal Happiness in Relation to Culture

Studies have reported that happiness is meaningfully related to cultural factors such as collectivism and individualism (I/C) and can vary between I/C countries [1]. Not only can country’s I/C orientation effect the levels of happiness, but also personal preference for a particular cultural orientation. However, the relationship between happiness, country’s orientation, and personal cultural orientation has not been clearly described. Therefore, the current study examined if an individual would be more likely to experience happiness when individual’s individual and collectivism orientation matched with society by comparing an individualistic country (the U.S.) and collectivistic country (India). Method: 120 American sample and 132 Indian sample were collected through Amazon Mechanical Turk. All of the participants took four surveys: Demographic, Individualism and Collectivism Scale, Orientation to Happiness, and Revised Life Orientation Test. Results: Both personal tendencies predicted personal happiness, yet personal tendency of individualism predicted personal happiness more strongly with the U.S. sample. On the other hand, personal tendency of collectivism predicted personal happiness with the Indian sample while personal tendency of individualism did not. Also, personal levels of optimism were controlled with both samples, but it was not related to personal happiness with Indian sample. Conclusions: The results showed there were meaningful associations between cultural personal tendencies and personal happiness with the U.S. sample and the Indian sample.
ISOLATION AND IDENTIFICATION OF ENDOPHYTIC FUNGI WITH ANTIMICROBIAL ACTIVITIES FROM THE LEAVES OF ELAEOCARPUS

: Endophyte fungi reside in the tissues beneath the epidermal cell layers and live within a plant for at least part of its life without causing apparent diseases. Eight morphologically distinct endophytic fungi were selected from the medicinal plants Elaeocarpus sphaericus coded as ELSPF 1 to 4 and Myristica fragrans coded as MYFRF 1 to 4. The highest colonization frequencies (CF %) were found to be 12.69%, and 8.33% for ELSPF 1 and MYFRF 2. Internal transcribed spacer- polymerase chain was performed for molecular identification and ~600 bp amplified products were obtained. The sequences were analysed by BLAST and the phylogenetic tree was constructed with neighbour joining method. The fungi were cultured on Potato Dextrose broth for production metabolites. The metabolites were extracted with ethyl acetate and constituent phytochemicals were analyzed. The crude extracts were tested for antimicrobial activity by agar well diffusion method. The test organisms used for the antimicrobial activity studies were E. coli , Staphylococcus aureus and Klebliella pneumonia . The ethyl acetate extracts of all endophytic fungi showed antimicrobial activity except MYFRF-1 against E. coli , Staphylococcus aureus , Klebliella pneumonia .
Towards a next generation ofscientific c omputing in the Cloud

More than ever, designing new types of highly scalable data intensivecomputing is needed to qualify the new generation of scientific computing and analytics effectively perform complex tasks on massive amounts of data such as clustering, matrix computation, data mini ng, information extraction & etc. MapReduce, put forward by Google ,is a well-known modelfor programming commodity computer clusters to perform large
International Journal of Advance Research in Computer Science and Management Studies

In current era of highly volatile business environment organizations are facing emerging challenges in form of acquisition and optimization of human resource. Being valuable and scarce capabilities, human resources are considered as a source of sustainable competitive advantage. The success of an organization depends upon several factors but the most crucial factor that affects the organization performance is its employee. Human resources play an integral role in achieving an innovative and high-quality product/ service. The present study is an attempt to examine and analyse the impact of human resource management practices on job satisfaction of private sector banking employees. In the study, the estimated regression model identified that the HRM practices like Training, Performance Appraisal, Team Work and Compensation has significant impact on job satisfaction. On the other hand Employee Participation has no significant impact on job satisfaction of the employees of HDFC banks.
PERFORMANCE IMPLICATIONS OF CLINICAL PHARMACIST INFORMATION ON GESTATIONAL DIABETES MELLITUS AT A TEACHING HOSPITAL IN SOUTHERN INDIA

Context: Gestational diabetes mellitus (GDM) early diagnosis of disease can reduce the morbidity and mortality by prevention of maternal and fetal complications. Aims: To assess the impact of clinical pharmacist knowledge on GDM mothers. Setting Tertiary care teaching hospital of North Karnataka, India. Methods and Materials: A randomized controlled crosssectional study was carried out for a period of 4 years. Pregnant women with GDM were divided into control (C) and interventional (I) groups. Group C patients received only physicians’ counselling whereas, group I patients received both clinical pharmacist counselling and physicians’ counselling. Both the groups were screened for blood glucose levels at baseline and follow-up. Results: A total of 500 (group C, n=250 and group I, n=250) mothers diagnosed to have GDM participated in the study and randomized into two groups. Initially the knowledge of GDM among pregnant women was poor. After the clinical pharmacist counselling, knowledge was enhanced, which prompts better education and use of new methods such as phemplets, audio visual methods and flipcharts. Conclusions: The results of this study showed noteworthy changes in knowledge after clinical pharmacist structured counselling. INTRODUCTION: Gestational diabetes mellitus (GDM) is one of the major health issues and the most common metabolic disorders in pregnant women characterized by glucose intolerance of varying degree with onset or first recognition during pregnancy 1, 2 . In India, the prevalence of GDM varies widely, depending on the population studied and the diagnostic test employed, may range from approximately 4% to 40% of all pregnancies 3 . Moreover, Indian women are 11 times more vulnerable to GDM, compared to Caucasians 4 .
Combining Ability for Sunflower Yield Contributing Characters and Oil Content over Different Water Supply Environments

Combining ability estimate is essential for selection of suitable parents for hybridization and identification of promising drought tolerant hybrids in breeding program. The present study aimed to select sunflower parents with good general combining ability (GCA) and crosses with best specific combining ability (SCA) effects under different levels of water supply environments. Seven sunflower inbred lines were crossed in 7x7 half diallel. The resultant 21 hybrids were evaluated along with their parents for yield contributing characters and oil content under three levels of water supply. The analysis of variance for combining ability showed that GCA variance was higher in magnitude than that of SCA for the studied characters, indicating the preponderance of additive gene action. The GCA effects of the parents revealed that the inbred lines L350, L460, L990 and L770 proved to be good combiners for seed yield, while the parents L38,L990 and L235 were found to be promising general combiners for oil % content. Inbred lines L38, L11 and L235, are good, candidates for drought tolerance. L350 is a good combiner for seed yield, while L38 proved to be good combiner for, oil content and drought tolerance. On The basis Of SCA, the cross L38 x L350 was identified as promising seed yield and oil content. [A.H. Salem and M.A. Ali. Combining Ability for Sunflower Yield Contributing Characters and Oil Content over Different Water Supply Environments. J Am Sci 2012;8(9):227-233]. (ISSN: 1545-1003). http://www.jofamericanscience. org. 34
Computer Science and Software Engineering

— In this work, we first used binary particle swarm optimization (BPSO) for Gabor Phase features selection and then we used Euclidean distance matching or Fuzzy Support Vector Machines (FSVM) for classification to improve performance of face recognition. Results have been compared based on different space reduction algorithms namely PCA, LDA, KPCA and KFA. Results on ORL database show the robustness of the proposed approach and the effectiveness of FSVM for face recognition.
Mindfulness Based Interventions for Addictions: What is Next?

Although mindfulness based interventions have recently received attention as efficacious approaches for treatment of addictive disorders, exactly how mindfulness works, for whom, and for how long are not precisely delineated. An improved understanding of these key factors needs to include physiological as well as cognitive and psychological assessments of mechanisms of action to tailor more effective treatments and achieve longer term impacts on addictive behaviors. Mindfulness is defined as a way of paying attention: on purpose, in the present moment, and non-judgmentally [1]. In the context of Mindfulness Based Relapse Prevention (MBRP), the key contribution of mindfulness practices is “. to develop awareness and acceptance of thoughts, feelings, . and to utilize these mindfulness skills as an effective coping strategy in the face of high-risk [drinking] situations” [2]. Although physiological response is not directly specified in this definition, I propose that the physiology of mindfulness may be one of the most salient factors impacting effectiveness.
Implementation of Symmetric Encryption Algorithms

Cryptography considered being the most vital component in information security because it is responsible for securing all information passed through networked computers. The discussions in this paper include an overview of cryptography and symmetric encryption. This paper also discusses some of the algorithms used in our research. This paper aims to design an application that consist of some symmetric encryption algorithms which allow users to encrypt and decrypt different size of files, also the application can be used as a test field to compare between different symmetric algorithms. Keywords: Cryptography, symmetric, encryption