Miracle-dz/UniFAR-FTUs huggingface dataset:
Geo-Social K-Cover Group queries for collaborative spatial computing

In this paper, we study a new type of Geo-Social K-Cover Group (GSKCG) queries that, given a set of query points and a social network, retrieves a minimum user group in which each user is socially related to at least k other users and the users' associated regions (e.g., familiar regions or service regions) can jointly cover all the query points. Albeit its practical usefulness, the GSKCG query problem is NP-hard. We consequently explore a set of effective pruning strategies to derive an efficient algorithm for finding the optimal solution. Moreover, we design a novel index structure tailored to our problem to further accelerate query processing. Extensive experiments demonstrate that our algorithm achieves desirable performance on real-life datasets.
Earning Management : Evidence From Indonesia

The purpose of this study was to test whether the absence of a definition of earnings management as something accidental, one statement of decision, omission of a material fact, or because of the process of accounting data that is misleading when considered with all the information that is available, thus causing the reader to replace judgment or decision. Data in this study were drawn from a database BEI with the sample period from 2004 to 2014 PT Kalbe Farma Tbk. As for the data analysis method used is multiple linear regression analysis. From research conducted concluded that the absence of earnings management practices in the years 2002-2014 at PT Kalbe Farma Tbk. However, research conducted by the authors has limitations in the form of research data is used only for 10 years, for companies that sampled only 1. The implication at the time of future research may sample of companies that will be examined could be even more.
Effectiveness of Neurofeedback Associated with Cognitive Rehabilitation Therapy on Children with Attention Defect Hyperactivity Disorder (ADHD)

Objectives: Neurofeedback is known as a modern therapy for hyperactive children, nevertheless it is not a complete therapy for these patients. The goal of this therapy is to create a complete therapy for ADHD children, so neurofeedback is used with cognitive rehabilitation therapy to make more integrated therapy. Method: this is an experimental study. 30 subjects were selected through random selection from women who referred to 5 clinics of Tehran city, and they were placed into 2 experimental groups and 1 control group. Groups are matched in age, sexuality and economic situation. One group just received neurofeedback into 10 sessions, another group received neurofeedback plus cognitive rehabilitation therapy at the same during, and control group received no therapy or they leaved the therapy at the first steps. Integrated visual and auditory test (IVA) was used as pretest and posttest, to measure two factors (attention and impulsivity) in ADHD children. Cognitive rehabilitation therapy protocol is based on work of Nejati (1391). Data evaluated by dependent T test and analysis of covariance. Results: according to findings, there is meaningful difference between all groups in attention and impulsivity factors. The group who received neurofeedback plus cognitive rehabilitation therapy showed more improvement in attention and impulsivity factors. Also there was a meaningful difference between neurofeedback group and control group in attention factor. Conclusion: neurofeedback is supposed as a facilitator therapy to improve attention factor in ADHD children, but not as a complete therapy, especially about performance factors. Also, it is better to use a complementary therapy such as cognitive rehabilitation therapy that focuses more on performance factors. It leads to more positive effects on impulsivity or even hyperactivity.
Analyzing Low Bit Rate Image Compression Using Filters and Pre Filtering

: In this paper we analyze the performance of B-spline filters, pre filtering and bit-plane coding for low bit rate image compression. The B-spline filters exhibit higher energy concentration in the lower frequency band than other known filters. This property can be exploited for very low bit rate image coding where the higher frequency bands might be very coarsely quantized. The relative information in the low frequency bands of the of the image can be increased by employ of the pre processing technique and bit-plane coding. Averaging technique is employed for image smoothening at low bit rates and bit-plane coding helps removing noise by discarding LSB. In this paper we analyze five techniques for image compression. It is found to be B-spline with one level of decomposition and preprocessing (along with bit-plane coding) gives the appropriate results as compared to the other techniques.
POTENTIAL BENEFITS OF AGROFORESTRY PRACTICES ON LIVELIHOOD AND ENVIRONMENT OF FARMERS IN ODEDA LOCAL GOVERNMENT, AREA OF OGUN STATE, NIGERIA

Trees are beneficial in nature, they do not only offer environmental services but they also offer tangible products such as food, timber, fodder, medicine, and fuelwood. This paper assessed potential benefits of agroforestry practices on livelihood and environment of farmers in Odeda Local Government, Area of Ogun State, Nigeria. Multistage sampling procedure was used for the selection of respondents. Structured questionnaires were used to collect information from two hundred and ten farmers selected in the area. Descriptive statistical techniques such as the frequency table, bar chart, and Pearson correlation analysis were respectively used to analysed the data. Predominantly (97.1%) engaged in scattered trees on cropland and improved fallow (81.0%). Scattered trees on cropland showed significant (P<0.05) negative relationship, while improved fallow showed no significance (P>0.05), negative and positive correlation with the agroforestry benefits. Farmers prefer cultivation of arable crops to tree planting and animal rearing, and it was concluded that participation of farmers in agroforestry practices is low. It is therefore, recommended that farmers awareness should be on benefits they can enjoy when practice agroforestry.
A Study on Performance Enhancement of Heat Exchanger in Thermoelectric Generator using CFD

According to the energy balance of the combustion engine, efficiency of internal combustion engine is around 30%, remaining energy is wasted as exhaust gas, which has a high percentage of loss 40%, and rest are wasted in lubrication, cooling water and radiation. In order to increase the efficiency of engine lot of researchers try to recover waste energy from the exhaust gas, among that thermoelectric generator (TEG), plays an important role in recovering waste energy from the exhaust gas. Thermoelectric generator efficiency depends on the shape of heat exchanger, the size of the fins and material of the thermoelectric element. In this paper, four different configurations of heat exchange modeled and compared through Computational Fluid Dynamics (CFD) software. They are Fishbone shape, accordion shape, and serial plate and empty cavity heat exchangers. The Serial plate has better heat transfer rate than Fishbone, accordion and empty cavity heat exchanger.
A Review of Improving Software Quality using Machine Learning Algorithms

: Software is a process and maintains continuous change to improve the functionality and effectiveness of the software quality. During the life cycle of software various problems arises like advanced planning, well documentation and proper process control. This problem may result in not achieving the software quality as desired. With respect to competition in the market it is necessary to remove this problem with the help of software engineering. In this paper software prediction model used to control the classes of software which are often to change. Machine learning algorithms are used for predicting software.
A Decomposition Structure for Image Denoising Algorithms

: We consider an image decomposition model that provides a novel framework for image denoising. The model computes the components of the image to be processed in a moving frame that encodes its local geometry (directions of gradients and level lines). Then, the strategy we develop is to denoise the components of the image in the moving frame in order to preserve its local geometry, which would have been more affected if processing the image directly. Experiments on a whole image database tested with several denoising methods show that this framework can provide better results than denoising the image directly, both in terms of Peak signal-to-noise ratio and Structural similarity index metrics.
The Impact of Brand Ambassador on the Cosmetic Consumers: A Case Study in the Sivakasi Region

Normally, a popular figure would be chosen as a brand ambassador for the company in order of promotional and marketing activities. Celebrities are getting popularity especially through mass communication Medias, who would normally be chosen as brand ambassador with the hope that public can be drawn to attention bit easily for the messages that they deliver. Thus, by transferring or captivating their image values for attention gaining, likeable and impactful to convince and perhaps improve sales and profit. However, it is noticed that on reality there exist very minimum research for the impact of ambassadors’ endorsements on consumers. This is the thrust for framing the problem of studying the influence of brand ambassador on the cosmetic consumers in the Sivakasi region. A total of 60 respondents have finally been considered to survey. Statistical techniques have been adopted to ensure the reliability of data and for analysis of the data. The analysis revealed that the brand ambassadors make significant impact on adolescence in choosing the cosmetic product, however for long standing the quality of the product play a vital role. Further analysis revealed that irrespective of income of several age groups did not disturb the purchasing power of cosmetic products. Keywords— Brand ambassador, Chi-square test, Cosmetic items, Garrett’s ranking.
Divide and Conquer Solution to Class-Imbalance Problem in Classification pattern

— Large dataset and class imbalanced distribution of samples across the data classes are intrinsic propertied of the problems to be faced in the applications like bioinformatics, network security and text mining. The class imbalanced problem appears in the dataset, classification categories are not represented with approximately equal number of instances. In this paper, we have explored the solution to the problem of imbalanced representations of the classes in the dataset. In this method, instance selection is applied concurrently to the small class-balanced subsets of the training data. Then, subsets are combined based on the voting score calculated from the optimized pair of thresholds of minority and majority classes. We used support vector machine (SVM) and kNN classifier to perform the experiments on the dataset for analyzing the performance of proposed algorithm. On comparison, it is observe that proposed algorithm outperform the random sampling method. Further, proposed algorithm has linear computational complexity and can be easily be implemented using parallelism to have real-time performance.
Fast and Effective Spatial Clustering Using Multi-Start Particle Swarm Optimization Technique

—Increase in digitalization has led to an increase in the data available for digital processing. Such data are termed as rich data, as they depict direct real time data. Difficulty arises when trying to process such data due to their inconsistent nature. This paper presents a density based clustering technique that can be used to identify arbitrary shaped clusters in data. The advantage of this approach is that it requires no external input to identify the range threshold. Particle Swarm Optimization is used as the selection technique to identify nodes belonging to a cluster. A multi-start variant of the Particle Swarm Optimization technique is used, which parallelizes the entire clustering process making it faster and more efficient. Experiments were conducted and it was identified that the current approach exhibits faster clustering process with better efficiency.
A Review Paper on Object Detection for Improve the Classification Accuracy and Robustness using different Techniques

Object detection is a computer technology that connected to image processing and computer vision that deal with detecting instance objects of certain class in digital images and videos. Object detection is a challenging problem in vision based computer applications. It is used to identifying that whether in scene or image object is been there or not. In this review paper, we are going to present different techniques and methods for detecting or recognizing object with various benefits like efficiency, accuracy, robustness etc.
Survey on content Based Video Retrieval

Content based video retrieval and analysis is the one of the most important and recent research area in the Image processing domain. Video retrieval is a wide spectrum of promising applications, motivating the researchers from worldwide. Due to the availability of increased internet bandwidth and transfer media, the multimedia objects usage is widely used. The number of videos is being uploaded through various sources. It requires some kind of retrieval technique which will aid users from tracking videos relatively similar manually. In this paper represents an overview of the general methods used in content based video retrieval. It focuses on the different methods for video structure analysis, including key frame extraction, shot segmentation, scene segmentation, feature extraction, video annotation and video retrieval method.
A Survey on Fisher Criterion based Genetic Algorithm for Feature Selection Method

With the presence of large amount of data in the database, retrieval process based on feature selection method became a major research issue in multimedia community because of its computational complexity and memory efficiency. In this paper, the combination of Fisher Criterion based Genetic Algorithm to find the fittest feature subset from the feature vector were surveyed. Different forms of hashing technique, the genetic algorithm improves the computational speed of estimating the fittest solution. The most consequently used extraction methods were GIST, SIFT and Bag-of-words to extract the feature vector from the images. By applying the Fisher criterion algorithm the fittest solutions were obtained and reduces the dimensionality of the image. The Extensive experiments were mostly carried out in Matlab using four commonly used datasets as MIR Flickr, CIFAR-10, NUS-Wide and SIFT-1M.
A Fuzzy Data Envelopment Analysis model to evaluate the Tunisian banks efficiency

— Data Envelopment Analysis (DEA) is widely applied in evaluating the efficiency of banks with crisp inputs and outputs. However, in real-world problems inputs and outputs are often imprecise and vague. In this paper, we develop first a method based on arithmetic operations to solve fuzzy data envelopment analysis models (FDEA). The method transforms the FDEA model into linear programming problem which gives crisp efficiency scores. Then we propose a methodology to evaluate the performance of commercial Tunisian banks in terms of several financial and non-financial data. FDEA is used to calculate the efficiency score of each bank. The results show that, in a competitive environment, non-financial inputs and outputs should be taken into account in order to obtain credible and realistic efficiency scores.
Forecasting Fuzzy Delphi and Hybrid intelligent system for ERP Architecture through the Scientific Private Cloud

To enhance the competitive advantage in a constantly changing environment, the manager of a company must make the right decision at the right time based on the information at hand. The Enterprise Resource Planning System (ERP) integrates the management of internal and external information across the organization (finance / accounting, manufacturing, sales and service, customer relationship management, etc.). How to use the information resources of the ERP and how to exercise effective information resources are currently pressing issues. This research proposes an intelligent hybrid sales forecasting system based on Fuzzy Delphi Method, fuzzy clustering and Back-propagation (BP) Neural Networks with adaptive learning rate in ERP architecture (Delphi-FCBPN-ERP). We utilize SPC (Scientific Private Cloud) was to reduce the time computation of the proposed model. This cloud computing platform will allow improved the execution time of parallel neural networks proposed in our model. Experimental results show that the proposed approach is superior then the traditional approaches.
MENINGOCOCCAL PRE-VACCINATION SURVEY ON CARRIAGE OF NEISSERIA SPECIES

Introduction and aims: the carriage rate concurrency of N. meningitidis serogroups and N. lactamica in oropharynx of volunteers preparing for military service before vaccination are remained unknown. The current study aimed to investigate the frequency of N. meningitidis serogroups and N. lactamica in the oropharynx of young adults. Materials and Methods: A total of 300 oropharyngeal swab samples of the young healthy adults were studied during August 2014 to September 2015. Swabs were plated onto enriched selective media. Gram-negative and oxidasepositive diplococci were phenotypically, genetically and biochemically identified. The PCR products were subjected sequencing in order to confirm the accuracy of the results. Results: Among 300 young healthy adults with the mean age of 24 years, identified: 25 N. meningitidis strains (8.3%.) which highest frequency was belonging to serogroup C with 12 cases (4%). in 121 cases (40.3%) the N. lactamica was isolated from oropharynx of the subjects. Analyses of the results indicate that in 154 cases (51.3%), none of the mentioned neisseria had not isolated from the orophrynx. Copyright © 2016, Mohammad Reza Atae. This is an open access article distributed under the creative commons attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. ARTICLE INFO Article History: Received 27th October, 2016 Received in revised form 12th November, 2016 Accepted 16th December, 2016 Published online 30th December, 2016
Pixel Value Graphical Password Scheme: Compatibility of K-means Clustering Algorithm as Pixel Value Password Fault Tolerance Mechanism

In September 2018, the patent for pixel value graphical password scheme was granted in Malaysia. The graphical password scheme was designed to reduce the complexity of previously developed graphical password scheme where a user only requires to load their personal image as password instead of complex graphical challenge during authentication. As the guardian of digital access, Pixel Value Access Control was highly invincible from password pixel forgery attack where a little bit different pixel value derived from loaded image will deny the access. Only the original enrolled image from a registered user can be recognized by Pixel Value Access Control to authenticate the respective username. That fact makes the graphical password scheme is a trusted access control mechanism but, on the other hand, it makes users bound with the only original password pixel image file. Thus, Pixel Value Access Control need to be installed the pixel value fault tolerance mechanism where it could allow users to acquire their password pixel image file from various storage media. The clustering technique was suggested to solve this issue where it allows an altered pixel password being recognized as the same group of the original pixel password. There are number of clustering algorithms developed for various purposed and application of digital image clustering. K-Means algorithm is one the partition-based clustering algorithm that found to be the simplest and fastest clustering algorithm as suggested by many researchers. This paper is mainly to exhibit the selection of K-Means clustering algorithm became the crucial algorithm for Pixel Value Access Control password pixel fault tolerance algorithm. Background of this topic was briefly explained in introduction section, the implementation of K-Means algorithm as Pixel Value Access Control fault tolerance was elaborate in section 2 and followed by validation of the implementation in section 3. At the end of this paper, there is conclusion for this study and followed by list of references.
Realization of Self driving Car with Collision Avoidance

There are many driver assistance systems which can make driving safer and comfortable. Moving one step further we can change the driving process to fully autonomous, so that the time utilization and can be done in a magnificient manner. That is the system doesn’t need human interface in driving and people can occupy their time by doing other things while on road. We aim to implement such a fully automated driving system. for the movement of the car, two DC motors are chosen and the motor gives the forward and reverse motion to the car. For the controlling purpose the Arduino developer model PROTON and the main controller chip of At Mega 8L is utilized in this . We have chosen the sensors for observing the surroundings and the environment. The Ultrasonic sensors for measuring the minimum difference between the car and the nearest object. By completion we obtained82% of efficiency
LyellÃ¢ÂÂs Syndrome: Effectiveness of a Diagnostic Management and EarlyTreatment

Lyell’s syndrome or toxic epidermal necrolysis is a bullous dermatosis, of etiology, very often medicated. It is an acute necrosis of the epidermis, with a clinical appearance of extensive burns. At this table are associated mucosal damage and, frequently, a multiorgan making poor prognosis. We report in this paper a case of toxic epidermal necrolysis, which illustrates the importance of early treatment in an intensive care unit, whose foundations are based on fluid and electrolyte intake, infection prevention and its treatment with appropriate antibiotics.
Design and Implementation of Pipelined Floating Point Fast Fourier Transform Processor

There are several methodologies and techniques that already offer hardware and software solutions for computing Fast Fourier Transform (FFT), which have advantages for specific applications. These solutions are developed for running in several platforms, such as GPU, DSP, FPGA and ASIC and they are usually described in C/C++ language or HDL. Implementation intended for reconfigurable logic is usually described in HDL, such as VHDL or Verilog. Different FFT algorithms have been proposed to exploit certain signal properties to improve the trade-off between computation time and hardware requirements. For years the common practice for implementing FFTs has been to run them on a digital signal processor (DSP). But as FPGAs have evolved and have begun to accommodate function specific computing cores, FPGAs are beginning to displace DSP as the optimum FFT solution. In this project the implementation of FFT radix 2 algorithm design of floating point FFT in FPGA is discussed. The VHDL Design was tested using Modelsim measure its performance in term of speed and scalability and also the processor is synthesized on an FPGA to measure the performance parameters such as maximum operating frequency, area and power consumption. Synthesis tool is Xilinx ISE Design Suit 10.1 and FPGA is Sparten III.