Reviewerly/exHarmony huggingface dataset:
Classification of Pap smear images for Cervical cancer using Convolutional Neural Network

Classification of Pap smear images for cervical cancer consists of two types namely, normal and abnormal cancerous cells. The dataset involves 7 sets of classes of cancerous images which have 3 sets of normal cancerous images and 4 sets of abnormal cancerous images. The proposed work performs two stages of classification. The first stage of the work is classifying the data as normal or abnormal cancerous cells. In the second stage of the work, the class of the cancer as normal columnar, normal intermediate, normal superficial, light dysplasia, moderate dysplasia, severe dysplasia and carcinoma_in_situ are classified. The proposed work gives good results for classifying images for 3 sets of classes and 4 sets of classes for normal cells and is able to classify and detect normal and abnormal cell accurately.
Gradient boosting machine assisted approximate Bayesian computation for uncertainty analysis of rainfall-runoff model parameters

Bayesian inference is a well-regarded approach for diagnostic model evaluation that is often applied to hydrological models to constrain parameters and estimate uncertainty within a statistical framework.Typically, Bayesian uncertainty analysis is carried out using the Generalised Likelihood Uncertainty Estimation (GLUE) or Markov Chain Monte Carlo (MCMC) sampling.Approximate Bayesian Computation (ABC) is alternative set of likelihood-free Bayesian methods that have been gathering interest in many fields including astrophysics, population genetics and biology.The main appeal of ABC is that is the requirement for a formal likelihood function is replaced by one or more summary statistics that compare the simulated model to the observed data.ABC works in situations where an analytical likelihood function is either unavailable or intractable.Instead of evaluating the likelihood function, ABC only has to be able to sample from the likelihood function in an empirical fashion.This broadens the class of problems to which statistical inference can be applied.In practice, the appeal of the ABC method is limited somewhat due to its requirement for a large number of model evaluations.Because ABC is essentially a rejection sampling method, when the overlap between the prior and the posterior is poor, the sampling efficiency can be very low and it may be necessary for hundreds of thousands or even millions of model evaluations to be run to collect an appropriate number of accepted samples to construct a statistically informative posterior.If the model runtime is significant, ABC rejection sampling can easily be rendered impractical.In this paper a hybrid method is developed that serves to retain the flexibility of ABC while drastically reducing the computational effort required.The first component of the hybrid approach is to employ Sequential Monte Carlo sampling (SMC) to improve the sampling efficiency and reduce the total number of samples required.Secondly, the primitive model is replaced by a surrogate model that can accurately reproduce the results of the original model at a fraction of the computational cost.In this case, XGBoost, a gradient boosted regression tree machine learning algorithm, is used to construct the surrogate models.Employed together, SMC-ABC and XGBoost trained surrogate models offer an accurate and efficient framework for model parameter inference and uncertainty analysis.As a demonstration, the proposed method is applied to a four parameter GR4J distributed rainfall runoff model to estimate marginal model parameter probability density functions for parameter identification and uncertainty analysis.
Product Review Sentiment Analysis by Artificial Neural Network Algorithm

Buying and selling and marketing goods and services are now done online. The online store provides facilities that enable its customers to provide review related products offered. The number of reviews received by the store, online sometimes does not allow the store online to analyze one by one. Thus, it takes the help of machines to assist in the analysis of such sentiments. Analysis of the sentiments of the review the product is done to help the shop get a general overview related to the level of consumer satisfaction. In this study, the ANN algorithm will be used to analyze sentiment for review. A product ANN algorithm used because it can provide high accuracy performance. This research resulted in a reasonably high accuracy performance is 88.2%.
Logical Methodology

Absract. The regulatory documents of the Ministry of Higher Education and Science set the task of forming competencies of students. Competences are understood as knowledge and skills. Knowledge is the world outlook component of science, and therefore the corresponding academic discipline, and skills are its methodological component. The world outlook refers to the system of views on the subject area of science, and the methodology is the system of intangible means of cognition and practical activities developed in this science and presented in the relevant academic discipline. It is noted that the subject of study in logic is not only the particular structure of thoughts and processes of thinking (forms of thought and processes of thinking), but also the types and characteristics of the original components of complex thoughts and processes of thinking, such as signs and terms. In the article on the example of logic the author's concept of the methodological component of the academic discipline is described. Attention is drawn to the importance of using in the process of cognition methodological standpoints created on the basis of world outlook, as well as to the need to take into account the types of knowledge - empirical and theoretical knowledge, problem, hypothesis, theory. Some other methodological tools of logic are given, for example, principles, technology. More well-known methodological tools (methods, strategies, tactics, etc.) are not considered. It is assumed that the article will cause interest among the representatives of other philosophical, and not only philosophical, academic disciplines, and a discussion of the problem of ideological and methodological components of these disciplines will continue.
Healthcare Smartwatch for Monitoring Elderly

According to the data provided by the Census in 2011, around 15 million elderly live alone in India. An NGO from Delhi named Agewell Foundation performed a survey on 10,000 elderly individuals and concluded that every fourth elderly that is 23.44% of the people who responded were living alone [1]. They also reported that around 88.5% felt the need of healthcare monitoring to ensure their independence. A lot of research work is going on to come up with a perfect solution to solve this problem and there are many computing devices which have been invented which solve the problem partially faced by elderly people. But none of them provide a solution which is convenient to use and which covers all aspects of the problem ranging from medicine reminders for elderly to proper maintaining of health records and it’s timely reporting to the doctor and family members even during emergencies such as a cardiac arrest. This paper proposes a neat solution to help monitor the daily reports of the elderly by automatically sending the data [12] (real-time [10][15] heart rate and current location measured by smartwatch; blood pressure measured by machine; sugar level measured by machine) to their family via android application and also provide 1st degree detection of cardiac arrest and prediction of heart problems up to some accuracy. It will provide medication [9] reminders to the elderly by three vibrations after an interval of 5 minutes during one course of medication and will also provide assistance during emergencies by sending an SOS call to the family or doctor. The smartwatch uses microcontroller ATmega328, SIM800L GSM module [11][14], RSI 0.96 inch 4 Pin OLED display module, HC-06 Bluetooth module, small LiPo battery, mini 1020 flat vibration motor, pulse sensor, GPS NEO 6M module [11], micro USB, button. This solution will not only help the elderly in rightly monitoring [8] their daily data [12] but also inform their family with their daily reports and any unusual changes. The smart watch acts as a care taker of the elderly people and it will be useful to the elderly who stay alone most of the time and it can even be used by others for proper maintaining of their health records, for medicine reminders and assistance during emergency conditions.
Financial indicators of corporate social responsibility in Nigeria: a binary choice analysis

Using multivariate binary choice models, this study investigates the effect of financial indicators on the practice of corporate social responsibility (CSR) in Nigeria. The indicators include return on equity, asset size, and revenue growth. Results of both linear probability and logistic models show that return on equity and asset size increase the likelihood of CSR practice. Sales growth has a negative effect. Compared to other metrics, firms with a large asset investment exhibit the highest likelihood of investing in CSR. Non-parametric tests confirm the positive linkage between CSR and asset size. These findings suggest that large firms, irrespective of their financial conditions, are more likely than other firms to invest in social initiatives. An implication for civil society might be to employ moral suasion to encourage financially strong firms, irrespective of size, to embrace CSR as an important means to boost their public image and long run performance.
Leveraging ACBSP Collaboration to Promote Articulation for Seamless Transfer

Programmatic accreditation through the Accreditation Council for Business Schools and Programs (ACBSP) provides academic consistency and fosters mutual respect among members. When two ACBSP institutions enter into an articulation agreement, the students benefit from seamless, continued education from a two-year to four-year degree completion. Two such institutions, the Community College of Baltimore County, Baltimore, MD, and Peirce College, Philadelphia, PA share their experience as a way to encourage ACBSP programmatic accreditation and the collaboration of other ACBSP accredited programs.
Extensive Image Enhancement Techniques for Efficient Micro Classification in Detection of Mammography Masses

Digital mammography used for detecting and suspecting the tumors from mammogram images and it is most demanding research in current era. It infers the field of image processing [14]so that key tasks of image processing are useful for radiologists in the medical diagnosis of mammogram images. Image processing play a key role in the diagnosis of either tumors or small masses from mammography images, in which pre-processing or enhancing techniques are required for smooth micro classification analysis. In this paper, proposed technique is presented for improving the quality of mammogram images as well as better classification of mammogram images that helpful for presenting the effective diagnosis for detecting the tumors in digital mammography. In the experimental study, an efficiency of proposed technique is demonstrated with respect to several performance parameters from benchmarked mammogram images.
Hybrid CNN Classification for Sentiment Analysis under Deep Learning

Sentiment Analysis (SA) is a popular field in Natural Language Processing (NLP) which focuses on the human emotions by analyzing the lexical and syntactic features. This paper presents an efficient method to find and extract the strong emotions for the sentiment classification using the proposed hybrid Convolutional Neural Networks - Global Vectors - Complex Sentence Searching - ABstract Noun Searching (CNN-GloVe-CSS-ABNS) model. The strong emotions are mostly found in the abstract nouns than the adjectives and adverbs present in the sentences. This research aims in extracting the complex sentences with abstract nouns for the sentiment classification from the twitter data. To extract the complex sentences, the proposed Complex Sentence Searching (CSS) algorithm was used. On the other hand, another proposed algorithm named, ABstract Noun Searching (ABNS) algorithm was used for identifying the abstract nouns in the sentences based on their position in the sentences. The results of this study presents that the proposed CNN-GloVe-CSS-ABNS model outperforms the other proposed models as well as the existing models, by producing an of accuracy 94.87 per cent in sentiment classification.
Enhanced Unsupervised Image Generation using GAN based Convolutional Nets

Generative Adversarial Networks (GANs) use deep learning methods like neural nets for generative modeling. Neural style transferring of images and facial character generation of anime images are previously implemented by applying GAN methods but were not successful in giving a promising output. In this work, Image Processing is applied on the datasets in the mode along with the training of GAN system. The problem of applying GAN to generate specific images is addressed by using a clean and problem specific dataset for anime facial character generation. Modeling is done by applying Convolutional Neural Nets, GANs empirically. Neural style transfer, Automatic Anime characters are generated with high-resolution, and this model tackles the limitations by progressively increasing the resolution of both generated images and structural conditions during training. This model can be used to develop unique anime characters or the image generated can be used as inspiration by artists and graphic designers, can be used as filters in famous apps such as snapchat for style transferring. With different evaluations and result analysis, it is observed that this model is a stable and high-quality model.
Implementation of Parallelized K-means and K-Medoids++ Clustering Algorithms on Hadoop Map Reduce Framework

The electronic information from online newspapers, journals, conference proceedings website pages and emails are growing rapidly which are generating huge amount of data. Data grouping has been gotten impressive consideration in numerous applications. The size of data is raised exponentially due to the advancement of innovation and development, makes clustering of vast size of information, a challenging issue. With the end goal to manage the issue, numerous scientists endeavor to outline productive parallel clustering representations to be needed in algorithms of hadoop. In this paper, we show the implementation of parallelized K-Means and parallelized K-Medoids algorithms for clustering an large data objects file based on MapReduce for grouping huge information. The proposed algorithms combines initialization algorithm with Map Reduce framework to reduce the number of iterations and it can scale well with the commodity hardware as the efficient process for large dataset processing. The outcome of this paper shows the implementation of each algorithms.
The Effectiveness of Posters as a Learning Media to Improve Student Learning Quality

The aim of the study is to identify the importance of poster as a learning media that can be utilized in learning activities. It also aims to explore the learning outcomes of students who use poster as learning media. In Indonesia, high school students are not allowed to carry a mobile phone, while the school on the other hand, does not provide enough computers for all students. The school computer laboratory could only cater for students need interchangeably. Therefore, teachers in schools choose other media that they think are effective, such as poster media. The poster media was seemed very relevant and appropriate to improve the quality of learning and student achievement. This study involved both qualitative and quantitative research method through Four-D model (Define, Design, Development, and Dissemination) that used for the learning material development. The data was collected through interview with two experts and experimental research design among two groups of students. The qualitative data were analyzed and the experimental data was tested using T-test statistical analysis. The learning outcomes with poster as learning media showed the mean values of the control and experimental groups were, respectively, 60.97 and 85.86. It indicated the difference in learning outcomes of 24.89. The produced poster was also assessed by media experts and curriculum experts. This study recommended that poster is very feasible and relevant to be utilized as a form of learning media in improving quality of learning and engagement with students.
Evolution of Concentric Fuzzy Hypergraph for Inclusive Decision Making

Fuzzy hypergraphs plays a pivotal role in reaching concrete resolutions at times when the circumstances of making decisions is characterized by uncertainty. Decision making is an integrated process involving the dominance of cognition in the nature of decision making environment and the associated factors. The process of making managerial decisions centres on state of affairs, which can be tackled with the formulation of Concentric Fuzzy Hypergraph, an extended concept of fuzzy hypergraph to arrive at compatible and feasible pronouncements. The notion of Concentric Fuzzy Hypergraph is introduced and a comprehensive decision making model with the integration of Fuzzy Cognitive Maps is proposed. This research work outlines the conceptual framework of Concentric Fuzzy hypergraph and presents a comparative analysis of concentric fuzzy hypergraphic approach with Fuzzy Cognitive maps over fuzzy hypergraphic approach to justify the efficiency of the former. The idea of concentric fuzzy hypergraph will certainly open new vistas in the field of decision making.
An Efficient Watermarking and Key Generation Technique using DWT Algorithm in Three-Dimensional Image

Discrete Wavelet Transform is the algorithm which can be used to increase the contrast of an image for better visual quality of an image. The histogram value for original image with highest bins is taken for embedding the data into an image to perform the histogram equalization for repeating the process simultaneously. Information can be embedded into the source image with some bit value, for recovering the original image without any loss of the pixels. DWT is the first algorithm which has achieved the image contrast enhancement accurately. This approach maintained the original visual quality of an image even though themessage bits are embedded into the contrast-enhanced images. The proposed work with an original watermarking scheme based on the least significant bit technique. As a substitute of embedding the data into a simple image as watermarking, least significant bitmethod by utilizing the three wavelets transform is applied in the proposed system in order to enhance the embedding technique using spatial domain. For security, the Huffman coding has used to secure the data embedded into a host image, which can convert the secret message sequence into bit sequence for least significant bit operation. DWT can analyze the signal at multiple resolutions and it can divide the image into two types of quadrants as high and low-frequency quadrants. Here dividing an image into low and high it makes the information to hide.
Analisa dan Perancangan Sistem Informasi Pembayaran Air HIPPAM Desa Leran Berbasis Web

The HIPPAM of Leran Desa is a village-owned company that works to provide clean water for the people of Leran Village. The process of recording meters and water payments for HIPPAM still uses a manual system (notebook and simple application on Microsoft Excel), so that errors often occur such as inaccurate data and not responding to excel documents. Not only that, but the payment process takes a lot of time so it is less efficient. For this reason, a web-based payment system is needed that can solve these problems and simplify the process of payment for water from the Leran village HIPPAM, so that the resulting data are minimal errors and reduce long queues in the payment process, this system can be used by customers to view bill payments, and filing a complaint if there is a problem in the HIPPAM water either about the water meter or service. This research consists of analysis and design, while the methodology used to develop the system is SDLC (system development life cycle). The results of this research are in the form of a system design and analysis document that will be used to develop this HIPPAM payment system. The analysis document contains functional requirements analysis and non-functional requirements analysis, for the design document there is DFD design or data flow diagrams and user interface design.
An Application and Performance Evaluation of Twin Extreme Learning Machine Classifier for Intrusion Detection

Network along with Security is most significant in the digitalized environment. It is necessary to secure data from hackers and intruders. A strategy involved in protection of information from hackers will be termed as Intrusion Detection System (IDS).By taking into nature of attack or the usual conduct of user, investigation along with forecasting activities of the clients will be performed by mentioned system.Variousstrategies are utilized for the intrusion detection system. For the purpose of identification of hacking activity, utilization of machine learning based approach might be considered as novel strategy.In this paper, for identification of the hacking activity will be carried out by Twin Extreme Learning Machines (TELM).Employing the concept of Twin Support Vector Machine with the fundamental structure of Extreme Learning Machine is considered in the establishment of Twin Extreme Learning Machine (TELM).Also, its performance and accuracy are compared with the other intrusion detection techniques.
POnline: An Online Pupil Annotation Tool Employing Crowd-sourcing and Engagement Mechanisms

Pupil center and pupil contour are two of the most important features in the eye-image used for video-based eye-tracking. Well annotated databases are needed in order to allow benchmarking of the available- and new pupil detection and gaze estimation algorithms. Unfortunately, creation of such a data set is costly and requires a lot of efforts, including manual work of the annotators. In addition, reliability of manual annotations is hard to establish with a low number of annotators. In order to facilitate progress of the gaze tracking algorithm research, we created an online pupil annotation tool that engages many users to interact through gamification and allows utilization of the crowd power to create reliable annotations \cite{artstein2005bias}. We describe the tool and the mechanisms employed, and report results on the annotation of a publicly available data set. Finally, we demonstrate an example utilization of the new high-quality annotation on a comparison of two state-of-the-art pupil center algorithms.