Reviewerly/exHarmony huggingface dataset:
Impact of Monetary Policies on the Exchange Rate and Global Trade Evidence from Ghana

The impact of monetary policies and their implementation by the exchange rate covered the economic condition of Ghana. The social inclusion and conversion factors change the implemented policies of nations, where the real price, trade, technology, a price rate, and price level of ratio take an important part of growth. The reform of the financial sector favors the free-floating of the exchange rate and global trade under the premise of flexible exchange rates. The tragedy of country growth and exchange rate toward a trajectory of growth with the growth-enhancing effect through social inclusion, conversion factors, price level ratio, exchange rate, merchant rate, export, and trade services. The research study is based on the secondary study and social inclusion equity indicators with public resources, building human resources and social protection for economic development has determined. Different evidence and trade indicators classify the monetary policies. The significant influence of growth and internal policies has affected trade and exchange rates with growth and reserve policies. The results have computed by linear regression and it proved that social inclusion and alternative conversion factors impact on global trade and create short term binary relationships.
A general construction of Reed-Solomon codes based on generalized discrete Fourier transform

In this paper, we employ the concept of the Generalized Discrete Fourier Transform, which in turn relies on the Hasse derivative of polynomials, to give a general construction of Reed-Solomon codes over Galois fields of characteristic not necessarily co-prime with the length of the code. The constructed linear codes enjoy nice algebraic properties just as the classic one.
An Analysis on Ensemble Classifiers in Ensemble Classification Problems

Day to Day the amount of data was increasing rapidly. Due to analyzing the huge amount of data various technologies are also introduced. Traditional data mining approaches can be used to perform data analysis through classification algorithms. In data mining a single classifier can be used to perform data analysis. Sometimes, multiple or combined classifier can also be used to perform data analysis. But, the performance of ensemble classifier is better than single classifier. Based on improved accuracy the various number of ensemble classifiers are introduced. Now, this paper can reviews various ensemble classifiers based on their accuracy.
Adaptive Depth Guided Image Completion for Structure and Texture Synthesis

The problem of synthesis of missing image parts represents an interesting and challenging area of image processing and computer vision with significant potential. This thesis, focuses on an adaptive depth-guided image completion method that addresses the image completion problem using information contained in the rest of the image. The completion process is separated into structure and texture synthesis. A method is first introduced for completing the respective depth map through the use of a diffusion-based operation, preserving global image structure within the unknown region. Building upon the state of the art exemplar based inpainting technique of Barnes et al., we complete the target (unknown) region by matching to and blending source patches drawn from the rest of the image, using the reconstructed depth information to guide the completion process. Secondly, for each target patch, we formulate an adaptive patch size determination as an optimization problem that minimizes an objective function involving local image gradient magnitude and orientations. An extension to the coherence- based objective function introduced by Wexler et al. is then introduced, which not only encourages coherence of the respective target region with respect to the source region in colour but also in depth. We further consider the variance between patches in the SSD criteria for preventing error accumulation and propagation. Experimental results show that our method can provide a significant improvement to patch-based image completion algorithms shown by PSNR and SSIM calculations as well as a qualitative subjective study.
Prediction of Software Design Defect using Enhanced Machine Learning Techniques

Prediction of software detection is most widely used in many software projects and this will improve the software quality, reducing the cost of the software project. It is very important for the developers to check every package and code files within the project. There are two classifiers that are present in the Software Package Defect (SPD) prediction that can be divided as Defect–prone and not-defect-prone modules. In this paper, the merging of Cost-Sensitive Variance Score (CSVS), Cost-Sensitive craniologist Score (CSLS) and Cost-Sensitive Constraint Score (CSCS). The comparitive analysis can be shown in between the three algorithms and also individually.
Extraction of Character Regions through Machine Learning and Filtering

Characters in images are able to provide main information of the image. Therefore, it is important to analyze various kinds of image data and accurately extract the characters in images. This study proposes a new method of excluding background regions and accurately detecting character regions from input images with the uses of MCT features and Adaboost algorithm. The proposed method first extracts candidate character regions from input images with the uses of MCT features and Adaboost algorithm. It then excludes non-character regions and detects real character regions from the extracted candidate regions with the use of geometrical features. In the experiment of this study, the proposed method more robustly detected character regions from various input color images than a conventional method. For performance comparison, this study compared the method based on existing texture analysis and the proposed method. In this study, to qualitatively evaluate the performance of the proposed method of extracting license plate regions, the accuracy measure was defined. The measure is used to show the ratio of the accurately extracted character regions to all character regions of an image. The conventional method using the frequency factor-based texture information had many errors of character region detection, since it failed to execute binarization of background and character regions properly. On contrary, the proposed method made use of MCT features and Adaboost algorithm, effectively filtered candidate regions with the use of geometrical features, so that it detected character regions more accurately. The proposed character detection method is expected to be usefully applied to the fields of pattern recognition and image processing, such as store sign recognition and license plate recognition.
Robust Image Labeling Using Conditional Random Fields

Object recognition has become a central topic in computer vision applications such as image search, robotics and vehicle safety systems. However, it is a challenging task due to the limited discriminative power of low-level visual features in describing the considerably diverse range of high-level visual semantics of objects. Semantic gap between low-level visual features and high-level concepts are a bottleneck in most systems. New content analysis models need to be developed to bridge the semantic gap. In this thesis, algorithms based on conditional random fields (CRF) from the class of probabilistic graphical models are developed to tackle the problem of multiclass image labeling for object recognition. Image labeling assigns a specific semantic category from a predefined set of object classes to each pixel in the image. By well capturing spatial interactions of visual concepts, CRF modeling has proved to be a successful tool for image labeling. This thesis proposes novel approaches to empowering the CRF modeling for robust image labeling. Our primary contributions are twofold. To better represent feature distributions of CRF potentials, new feature functions based on generalized Gaussian mixture models (GGMM) are designed and their efficacy is investigated. Due to its shape parameter, GGMM can provide a proper fit to multi-modal and skewed distribution of data in nature images. The new model proves more successful than Gaussian and Laplacian mixture models. It also outperforms a deep neural network model on Corel imageset by 1% accuracy. Further in this thesis, we apply scene level contextual information to integrate global visual semantics of the image with pixel-wise dense inference of fully-connected CRF to preserve small objects of foreground classes and to make dense inference robust to initial misclassifications of the unary classifier. Proposed inference algorithm factorizes the joint probability of labeling configuration and image scene type to obtain prediction update equations for labeling individual image pixels and also the overall scene type of the image. The proposed context-based dense CRF model outperforms conventional dense CRF model by about 2% in terms of labeling accuracy on MSRC imageset and by 4% on SIFT Flow imageset. Also, the proposed model obtains the highest scene classification rate of 86% on MSRC dataset.
Identifikasi Penyakit Tanaman Ubi Kayu Berdasarkan Citra Daun Menggunakan Metode Probabilistic Neural Network (PNN)

Cassava or better known as cassava is one of the staples of rice which is popular in Indonesia. Cassava plants can flourish in almost all regions of Indonesia. However, cassava is a plant that is susceptible to plant disease, which attacks the disease resulting in a decrease in the amount of productivity of tubers produced by cassava plants. The application of identifying cassava disease based on leaf image is expected to be useful as a support for cassava farming in easily detecting cassava disease, so that it can be dealt with more quickly. This study uses the Gray Level Co-occurrence Matrix (GLCM) method as an extraction feature and the Probabilistic Neural Network (PNN) method for identification processes. Based on the results of tests on 6 types of cassava leaf images, obtained an accuracy of 83.33%.
DWT-DCT Based copyright Protection In Ycbcr Color Space

Digital image watermarking is powerful technique which provide ownership protection and copyright protection. In this paper, a novel watermarking technique based on Discrete Cosine Transform (DCT) and Discrete Wavelet Transform (DWT) is presented. YCbCr color model is used for watermark embedding and extraction because of its close resemblance to human visual system. Single level DWT is applied to Luma Component of YCbCr color cover image and then DCT coefficients are taken for watermark embedding process. DCT is applied block by block of size . Binary watermark is scrambled using Arnold transform with k iterations to achieve robustness. Proposed method has been evaluated by many performance evaluation measures such as Peak Signal to Noise Ratio (PSNR), Normalized Correlation (NC) and Computational time. Various watermark attacks are also applied against proposed method, result shows that superiority over other methods.
Toward a Unified Framework for the Heterogeneous Cloud Utilizing Bytecode Containers

&lt;p&gt;As we approach the limits of Moore’s law the Cloud computing landscape is becoming ever more heterogeneous in order to extract more performance from available resources. Meanwhile, the container-based cloud is of growing importance as a lightweight way to deploy applications. A unified heterogeneous systems framework for use with container-based applications in the heterogeneous cloud is required. We present a bytecode-based framework and it’s implementation called Man O’ War, which allows for the creation of novel, portable LLVM bitcode-based containers for use in the heterogeneous cloud. Containers in Man O’ War enabled systems can be efficiently specialized for the available hardware within the Cloud and expand the frontiers for optimization in heterogeneous cloud environments. We demonstrate that a framework utilizing portable bytecode-based containers eases optimizations such as heterogeneous scaling which have the potential to improve resource utilization and significantly lower costs for users of the public cloud.&lt;/p&gt;
An optimal broker design for inter-cloud systems

In the next generation of Cloud computing systems, it is expected that multiple Cloud Service Providers (CSPs) will cooperate together to advertise their services and prices to their end users, which may choose the one that best meets their budgetary and technical needs. Despite this benefit of having multiple CSPs to select from, several issues may arise. For instance, how does an IT entrepreneur select a CSP to offload his/her service request? How does the underlying Inter-Cloud system handle this service request? To address these questions, this thesis proposes a novel Optimal Cloud Broker design for Inter-Cloud Systems in the form of a Semi-Markov Decision Process (SMDP) based model. Under the long-run expected average cost criterion, the optimal policy is derived, which aim at maximizing the overall virtual machine utilization while giving the end users the best possible prices. The effectiveness of the proposed Broker design is validated by numerical results.
The analysis of MCQs in a newly developed reading comprehension ability test: a study on Yemeni undergraduate EFL learners

Studies have shown that the best way to test the students' ability in reading comprehension is the multiple-choice questions (MCQs) for its validity and reliability. This paper tries to assess items and test quality in order to explore the relationship between difficulty index (p-value) and discrimination indices (DIs) with distractor efficiency (DE). The study was conducted among 134 second year Yemeni EFL students in Sana'a University, Yemen. Twenty MCQs analysed for p-value, DI and DE. Results indicated that the mean p-value and DI were 61.92 ± 25.1% and 0.31 ± 0.27, respectively. DI was noted to be maximum at p value range between 40% and 60%. Combining the two indices, 19 items could be called 'good' having a p-value from 20% to 90%, as well as a DI ≥ 0.40. Overall 75% items had two non-functional distractors (NFDs), while 20% items had three functional distractors and 5% had only one functional distractor.
SINTESIS TiO2 TERDOPING Bi3+ DAN UJI AKTIVITAS FOTOKATALISIS ANTIBAKTERI E.coli DENGAN BANTUAN SINAR TAMPAK (SYNTHESIS OF Bi3+ DOPED TiO2 AND PHOTOCATALYSIS ACTIVITY TEST OF E.coli ANTIBACTERIA UNDER VISIBLE IRRADIATION)

The synthesis of Bi3+ doped TiO2 was carried out using the sol-gel method with the precursor solution of titanium tetraisopropoxide (TTiP). Bi3+ cation dopant is done to reduce the energy of the TiO2 band gap so that it can be active in the visible light region. The synthesized Bi(III) –TiO2 photocatalyst was characterized using DRS UV-Vis, FTIR, and EDX. The results of the DRS UV-Vis analysis showed that 1% Bi(III) –TiO2 photocatalyst had a bandgap energy of 2.79 eV or equivalent to a wavelength of 469.97 nm. Infrared spectra show that there is a unique absorption at the wave number 956,56 cm-1 which is the absorption of the vibration Bi-O-Ti group. Photocatalyst Bi(III) -TiO2 was then tested for antibacterial activity against E. coli in polluted water. The activity test was carried out using the turbidimetric method by UV-vis spectrophotometry. The results of the activity test for 120 minutes with the help of visible light showed that the photocatalyst Bi(III) –TiO21% had the best antibacterial activity. The decrease in the number of E. coli in the water reached 48.54%.
Voice from Within: Student Teachers’ Perception on Factor Influencing Willingness to Speak English in Language Classroom at English Education Study Program of UIN Raden Fatah Palembang

The need for good communication skill in English has become a demand that continues to increase. One frustration is that they have spent years studying English, but they still cannot speak it. Therefore in this case, it is necessary to know what are the things that influence willingness of the students to speak English in the language class. This study investigated the student teachers’ perception on factor influencing willingness to speak English in language classroom at English Education Study Program of UIN Raden Fatah Palembang. I used qualitative study with case study design. The participants of this study were nine students of English Education Study Program of UIN Raden Fatah Palembang in the academic year of 2016/2017. The participants were chosen by using purposeful sampling technique. I employed interview technique to collect the data. The result showed that a number of factors contributed to student teachers’ willingness to speak. These factors included motivation, selfbeliefs’, collocutor, and object discussion.
Data Deduplication for Efficient Storage on Cloud using Fog Computing Paradigm

Cloud services have taken the IT world by storm by making its services available to everyone over large geographic area. With the increasing amount of data generate every minute it has become increasing difficult to manage resources and the storage. Thus, data compression techniques like data de duplication that aims at executing the redundancy of data and forming chunks of data that can be stored on a distributed system can be proved to a logistic solution. But when it comes to cloud problems like security has always been a major issue. In order to eliminate these challenges, we need to implement a layer of fog computing they would deal with the shortcomings of cloud computing and at the same time present a filtration front before the incoming data.
Face Recognition using Fast GA

Face Identification System using a fast genetic algorithm computation (FGA) is presented. FGA is used to compute and search the face in a database. The objective of the work is to make a face identification system which can recognize face from a given image or any other image streaming system like webcam. The system also has to detect the face from a system accurately in order to identify the face accurately. The image can be captured either from a proposed webcam or a captured JPEG or PNG image or any other data source. The system needs training with adequate sample images to perform this operation. Training the generic system plays a vital role in identifying the face in an image. A tolerance is identified as a limit to the genetic algorithm which acts as a terminal condition to the evolution. A unique encoding is used which stores the facial features of a human face into numeric string which can be stored and searched with much ease thereby decreasing the search and computational time. Template matching technique is applied to identify the face in a big picture. Generation of an Eigen face is obtained by the stage a mathematical practice called PCA. Eigen Features is also computed such that the measurement of facial metrics is done using nodal point measurement.
The Performance of Challenges Faced by SME in Managing Real-Time Information in the Inventory Management Process

Managing real-time information is an important task for any organization regardless of the size. This is because real-time information is used as the basis for an organization to make decisions that will affect the business if the information obtained is inaccurate, slow and outdated. For SMEs, this is a great challenge as it faces capital constraints and outdated technological applications. Thus, the purpose of this study to examine the challenges faced by SMEs in managing the real-time information to their inventory management and the impact on the overall business performance. A qualitative method was adopted where the in-depth interview was using to extract the information. Based on the saturation principle, the respondent was selected specifically from the SMEs in food manufacturer based in Malacca Halal Hub area. The finding of this study has supported the previous study on the challenges and issue of real-time information facing by the SMEs. A notable finding in this research is that SMEs is employing a skilled worker to manage the absence of an information system to manage real-time information and still capable to generate a profit from the business. This study extended the previous finding and offer an opportunity for further exploration in this area.
Optimizing Replica Migration in Cloud Data Centers

With the wide task of massive scale Internet affiliations and epic information, the cloud has changed into the perfect condition to fulfill the continually making storage request, by flawlessness of its indisputably monster inspiration driving restriction, high accessibility and snappier access time. In this novel condition, information replication has been touted as a total reaction for improve information accessibility and decreasing access time. Regardless, replica plan structures for the most part need to move and make vast information replicas after some time between and inside server farms, understanding a gigantic overhead to the degree system weight and accessibility. Cloud Storage Providers (CSPs) offer geographically information stores giving a couple of storage classes various costs. A colossal issue looking by cloud clients is the course by which to Abuse these storage instructions to serve an software program with a length switching fantastic development preserving up be executed on its subjects at any price price. This rate incorporates personal price (i.e., storage, Put and Get costs) and capacity migration price (i.E., kind out fee). To cope with this issue, we at the beginning suggest the best isolates calculation that usage dynamic and direct programming techniques with the supposition of on hand careful records of remarkable weight on articles. To enhance such charges, we on the start suggest the appropriate affiliation that use dynamic and direct programming structures with the supposition that the the relaxation of the burden on articles is notion early. We aside from advise a light-weight heuristic framework, brought on from a mistook estimation for the Set Covering Problem, which does no longer make any supposition at the article staying rule artwork. This device collectively alternatives object replicas region, object replicas migration times, and redirection of Get (read) sport plans to cope with replicas with the goal that the money related fee of facts storage the heap up is revived even as the consumer noticed nation of no interest is fulfilled. We chart the practicality of the proposed light-weight calculation regarding cost spare resources by techniques for wide redirections utilizing Clouds test system and searches for after from Twitter..
A New Method to Estimate Skew Angle in Printed and Historical Document Images

In this paper, a new approach to assess the skew angle for scanned/printed documents and historical document images has been proposed. This is substantial for an automatic document processing system (as text and image segmentation) to avert errors in auxiliary stages. The proposed tactic is based on the statistical analysis of the slope of the connected lines in the document. The proposed technique detects skew and corrects it by initial letter (X1, Y1+200) from left margin of the resized (800X800) image and (X1+200, Y1) from top margin. Final letter (X2, Y2-200) and (X2-200, Y2) were chosen from right and bottom margins of the same image. The skew angle estimation is done for standard skewed dataset and effective correction of the same is performed with minimum errors.