Reviewerly/exHarmony huggingface dataset:
Facial Expression Recognition using SVM With CNN and Handcrafted Features

The facial expression recognition system is playing vital role in many organizations, institutes, shopping malls to know about their stakeholders’ need and mind set. It comes under the broad category of computer vision. Facial expression can easily explain the true intention of a person without any kind of conversation. The main objective of this work is to improve the performance of facial expression recognition in the benchmark datasets like CK+, JAFFE. In order to achieve the needed accuracy metrics, the convolution neural network was constructed to extract the facial expression features automatically and combined with the handcrafted features extracted using Histogram of Gradients (HoG) and Local Binary Pattern (LBP) methods. Linear Support Vector Machine (SVM) is built to predict the emotions using the combined features. The proposed method produces promising results as compared to the recent work in [1].This is mainly needed in the working environment, shopping malls and other public places to effectively understand the likeliness of the stakeholders at that moment.
A Systematic Access Through Machine Learning Methods For Expectation In Malady Related Qualities

There are Many learning strategies that are been identified with distinguish infection based related qualities. At the early, they as a rule moved toward this issue as a parallel arrangement, where preparing set is involved examples. Examples developed sickness qualities, whereas negative examples are there mining which are not known to be connected with contaminations. This is the essential of the twofold deals based diagrams, since the negative arranging set ought to be true non-infection qualities; regardless, advancement of this set is on a very basic level unfeasible in biomedical inspects. Therefore, to reduce this delicacy, insightfully sensible social gathering based techniques have been proposed. For example, unary outline strategy subject to one-class SVM framework was proposed by grabbing from fundamentally positive models. Also, there mining set may contain cloud torment qualities; as such, semi-formed methodologies, for example, twofold semi-controlled &amp; positive &amp; unlabeled (PU) learning blueprints have been proposed. Specifically, PU learning frameworks, which increase from both known suffering qualities &amp; there mining attributes, were appeared to beat others. In these examinations, information sources are commonly tended to by vectorial plan for cemented classifiers, while they are in bit frameworks for unary &amp; PU learning ones. The bit based information blend is reasonable for information with various sorts &amp; it has the majority of the stores of being uncalled for or the relationship subject to various information diagrams. In like manner, in this examination, we looked accumulating structures for the ailment quality figure dependent on vectorial delineation of tests. The spread outcome demonstrated that the unary strategy structure, which joins both thickness &amp; class likelihood estimation approachs, accomplished the best execution, where as it is most recognizably stunning for the one-class SVM-based technique. fascinatingly, execution of a best twofold outline framework is in each rational sense misty with that of uneven SVM-based PU learning &amp; twofold semidirected hoarding strategy they are altogether improved.
Service Now Based Course Registration System

In the past few decades, Course Registration for a particular student in an university is a very difficult task. It consumes lot of time and man power to complete the task manually. This is because of the increase in the number of students in the university every year. So to reduce all the effort we implemented this project using a cloud based platform called Servicenow. The main theme of the project Course Registration System is to develop a registration system that will completely automate the process of a new student registration in an university.The system will be cloud based and will have two implementations i.e client side(Student) and server side(Admin). The server side implementation can be accessed over the university only when the student starts registration for the particular course.Johnson and Manning (2010) explicit that the two biggest variations between registering on-line and mailing in your work square measures time and technology. It will take time once users ought to fill in the form manually, and then submit it in other places. Rather than taking time, technology has helped us to create the registration procedure into the next level. You will be able to notice additional data regarding the courses you wish to acquire and in the same time fill up the form, pay the fees, etc. The management that receives registration data most probably will process the information in same system, so by using online course registration and management system, we can save time.
Development of Saraiki WordNet by Mapping of Word Senses: A Corpus-based Approach

This paper aimed to develop the Saraiki WordNet. Saraiki is one of the regional languages spoken in Pakistan and has a unique history of its own. Saraiki language is remarkably similar to two languages, namely Punjabi and Sindhi. Saraiki has different dialects and each dialect is representative of the region where it is spoken. This paper used the Urdu WordNet (Zafar, Mahmood, Shams &amp; Hussain, 2014) as the basis for the formation of Saraiki WordNet. Urdu WordNet (Zafar et al., 2014) was created by UET Lahore and is based on Princeton WordNet (Miller, 1990). Dictionaries or lughats and literary sources, such as poetry, fiction, as well as non-literary sources, such as newspapers of Saraiki language, were used to extract data. Additionally, Urdu word senses were mapped onto Saraiki word senses. The method used in this study was mapping, while the expansion approach was used in the mapping process. This study may aid in creating bilingual dictionaries (of Saraiki and Urdu?) in the future.&#x0D; Keywords: expand approach, mapping, Saraiki language, WordNet
Evaluasi Sistem Elektronik Rekam Medis di Rumah Sakit Primasatya Husada Citra Surabaya

One of the obligations of the hospital is carrying out medical records. The implementation of medicalrecords at the Primasatya Husada Citra Surabaya Hospital has used an electronic system. The applicationis an Electronic Medical Record (ERM) with a website-based that runs since September 1, 2018. SinceSeptember 1, 2018, until now there has never been an evaluation of the application. This study aims toevaluate ERM using the Human Organization Technology (HOT) -Fit Model evaluation method. This typeof research is qualitative research with data collection methods using interviews, observation, anddocumentation. The results of the study indicated that the implementation of ERM has been going well,socialization and training have been provided by organizations, the presentation of data are not right, andcomputer facilities are still lacking. The researcher's suggestion for ERM at PHC Surabaya Hospital is totest the reliability and security of the ERM system, add one computing device, and improve thepresentation of data by the system to improve the quality of the ERM system.
Automatic Generation of Exercises for Second Language Learning from Parallel Corpus Data

Creating language learning exercises is a time-consuming task and made -up sample sentences frequently lack authenticity.Authentic samples can be obtained from corpora, but it is necessary to identify material that is suitable for language learners.Parallel corpora of written text consist of translated material.Comparing the text in one language with its translation into another (known) language makes the structure accessible to the learner.However, the correspondence of words between the two languages is more important.By carefully selecting well-suited parallel sentences, a learner can explore the target language in a guided way.We present an approach to generate a novel type of language learning exercise from a large parallel corpus based on movie subtitles.The size of the corpus allows for defining selective criteria, favoring precision over recall.It is a non-trivial task to give reliable feedback to automatically generated exercises.ICALL literature often deals with fill-inthe-blanks exercises or multiple-choice questions, which allow for very limited answer options.Our proposed exercise is a special case of sentence reconstruction on bilingual sentence pairs.It combines two elements which have proven to be effective for language learning: a gamified approach, to awaken the students' competitive desire, and the identification of syntactic structures and vocabulary use, to improve language sensitivity.This article presents the methods used to select example pairs and to implement a prototype.
Naive Bayes Classifier Optimization on Sentiment Analysis of Hotel Reviews

Feature extraction plays an important role in the sentiment analysis process, especially of text data. The Naive Bayes Classifier performs well on low feature dimensions. However, the accuracy provided is not optimal. To acquire optimal machine learning model, information gain method, evolutionary algorithm, and swarm intelligent algorithm are applied. The objective of this study is to determine the performance of the Particle Swarm Optimization (PSO) to optimize the Naive Bayes Classifier. Vectorization of words is carried out using TF-IDF. In order to produce high PSO performance, the PSO-NBC model is tested with several parameters, namely the number of particles (k = 3), setting of the number of iterations and inertia weight, individual intelligence coefficient (c1 = 1), and social intelligence coefficient (c2 = 2). Inert weight is calculated using the formulation (w = 0.5+ Rand ([- 1,1])). In conclusion, PSO is able to solve the problem space of text-based sentiment analysis. PSO is able to optimize the accuracy of Naive Bayes at a value of 89% to 91.76%. PSO performance is determined by the parameters used, especially the number of particles, the number of iterations, and the weight of inertia. A large number of particles accompanied by an increase in inertia weight can increase accuracy. The number of particles 20-30 has reached the optimal accuracy.
Pattern Recognition using Convolutional Neural Network for Handwritten Gujarati Numerals

Day-to-day technology is going in no time, digital acknowledgement are taking part immensely and providing additional scope to perform scrutiny in CNN techniques. Recognition of Gujarati transcribed numeral is difficult compared to different western numerals. However, several analyzers have provided real time answer for transcribed Gujarati numerals. This paper represents acknowledgement of transcribed Gujarati digits which enhances Convolutional Neural Network. Current analysis offers several solutions on Gujarati handwritten documents analysis and reasonable accuracy for concerning transcribed digit recognition.
Early Detection of Sepsis Using Feature Selection, Feature Extraction, and Neural Network Classification

Introduction: This work represents an entry to the 2019 PhysioNET Computing in Cardiology Challenge.Algorithm: Using the supplied biomedical data, we reduce the original 40 features to 10 principal components.One additional feature is generated from a quick Sequential Organ Failure Assessment (qSOFA) score.These 11 features are then fed into a deep neural network classifier implemented in Tensorflow.The features associated with each hour are analyzed independently.A sigmoid function is used for the activation functions, RMSprop for the optimizer functions, and categorical cross entropy for the loss function.We have found that this setup works best for our current method and leads to the highest accuracy with minimal loss.Results: By testing our algorithm on a subset of the given dataset we achieved a validation score of 0.038.The official score received from the competition (under the team name "Whitaker's Warriors") was 0.022 placing us 65th out of the 78 scored entries.Conclusions: Achieving a positive utility score of 0.022 shows our method of combining PCA with a quick SOFA score and classifying with a neural network is a promising approach.More work in the future could be done to increase the accuracy of the model by adding additional features to the input of the classifier and adjusting the parameters of the neural network.
A Novel Technique for Moving Object Detection &amp; Tracking with Optical Flow

A novel technique for tracking for tracking of objects in moving state is proposed to done by optical flow analysis which provide accurate results. In this Optical flow analysis, the velocity vectors of the pixels are obtained. As a result, velocity field is analyzed for the frame sequences which are considered and described for short duration of the video. This method doesn’t need to refer the previous frames which reduces the processing time and can be applicable for real time events. This novel algorithm produces better results with repeatability, uniqueness and reliability, compared to previous techniques. This method can be able to perform faster computation and comparison. This can be achieved by performing image convolutions for integral images followed by technical operations in the descriptors and detectors. Finally these methods can be simplified to the needed requirements. This leads to a package of better quality detection followed by description and recognition steps. The paper encircles a brief explanation of the algorithm and the results of simulation are also described in detail.
Geochemical Data

The central thrust of this book is that geochemical data can be used to identify and interpret geological processes in igneous, metamorphic and sedimentary rocks. This chapter categorises geochemical data into major element oxides, trace elements, radiogenic isotopes and stable isotopes. The text discusses the main processes which control the chemical composition of planetary bodies, which operate in igneous and metamorphic rocks and at the Earth’s surface and describes the main analytical methods currently in use. These include the methods of X-ray fluorescence, mass spectrometry and inductively coupled plasma mass spectrometry for both whole-rock analysis and in situ micro-analysis. Sampling protocols are briefly described and the choice of a suitable analytical method is discussed. Potential sources of error in geochemical analysis are identified and discussed.
Determinants of Team Climate and Team Trust on Team Performance-An Extensive Examination

Purpose: Team Climate is foreseen as a variable that enhances the organizations effectiveness, if the criterion has been satisfied under particular working conditions. It is said that team climate can be viewed under perceptions that are shared under a proximal working team. The study focuses on the conceptual framework of Team climate that enhances the organizations effectiveness what are the predictors to be thrown limelight to improve the same. Team climate and Leadership has been viewed as implicitly inter-related variables as a warm conducive work climate would lead to an effective leadership quality which also extensively affects the two prime attributes such as knowledge-sharing and innovative Work behavior of an employee. Design / Methodology/ Approach: The focus of this study is to assess the determinants of theTeam Climate on Work Teams. The data for the study was collected from the software developers from various IT organizations. The team climate inventory (TCI) used in the study also has to tested for its impact on the Work Teams. The sample data was obtained from five major IT organizations in Chennai and Bangalore region. Selected organizations mainly comprises of the Software Developer’s Team under the Self Managed Team base. The data used for the study is empirical though some of the base studies was found to contain information form appropriate sources. The basic tools used for analysing the data were IBM SPSS Statistics20 and multiple regressions were performed using AMOS Graphics for running the Structured Equation Modeling Technique (SEM). Scale development: The Team Climate Inventory(TCI) which uses the five point Agreement scale contains four dimensions namely Vision(12),Task Orientation(7), Support for Innovation (8) Participative Safety(7) which accounts to nearly 34 items. The scale was developed by Neil.R.Anderson and Michael A.West (1994). Findings and Implications: The research findings about the study done on the determinants of team climate shows that the factor loadings pertaining to the Confirmatory Factor Analysis (CFA) using AMOS Graphics (SEM) has been proved to be have a good impact and thereby it is to be implied that there prevails a conducive team climate in the organization which serves as a limelight for a best level of Knowledge sharing behaviour and Innovative work behaviour among employees in the organization. Conclusion: Thus it has been found from the research findings and observations that a good Team climate among the work teams would enhance the Team Trust as observed from the meta analytical studies, that in turn would boost the Knowledge sharing and the Innovative Work Behaviour amongst Teams to a greater appreciable level.
Hybrid Fuzzy Knowledge Based Prediction Model for the Software Development and Maintenance Quality in Software Engineering Approach

The main arena of Software Engineering development with ood design, development, coding, testing, implementation, deployment of the software, finally maintaining the software with good functionality. For the development of software many organizations are investing more and more budget in their revenue. Software Engineering development has several categories of data presented in software engineering such as Graphical User Interface, Usage graphs, writing text, realities and images. Significant information be able to be obtained from this composite data by well recognized data mining techniques such as association, classification, clustering etc. By discovery hidden patterns by data mining software engineering data is made illegal. Software Engineering development has many objectives in software engineering such as Code and Design optimization, Project documentation, Development cost estimation etc. Variety of significant data mining method in each phase of software development life cycle supports in realizing these objectives proficiently and the failure rate of software is decreased. . This paper focused a new hybrid model like combination of Fuzzy Logic and knowledge management offers a significant method for developing models for software quality prediction. This research paper explains about exercise of estimate and valuation at a particular organization by developments and represents the outcomes attained with a fuzzy based classification and knowledge model for the fuzzy knowledge management predication for the quality of software engineering Approach. This result illustrate that the significance of Average Error Evaluation Efficiency observed and used in fuzzy logic is lesser than Average Error Evaluation Efficiency used in another regression multiple regression; while the value of prediction is higher value that other prediction models is used before. Thus Results demonstrate that Hybrid fuzzy knowledge management predication for the quality of software engineering can be used as alternative for predicting the Software Development and Maintenance Quality (SDMQ).
Rating Prediction based on Social Sentiment from Textual Reviews

In recent years, we've witnessed a flourish of review websites.It presents an excellent chance to share our viewpoints for numerous merchandise we have a tendency to purchase.However, we have a tendency to face the data overloading drawback.A way to mine valuable data from reviews to grasp a user's preferences associated build and correct recommendation is crucial.Ancient recommender systems (RS) take into account some factors, like user's purchase records, product class, and geographic location.During this work, we have a tendency to propose a sentiment-based rating prediction methodology (RPS) to enhance prediction accuracy in recommender systems.Firstly, we have a tendency to propose a social user sentimental measuring approach and calculate every user's sentiment on items/products.Secondly, we have a tendency to not solely take into account a user's own sentimental attributes however additionally take social sentimental influence into thought.Then, we have a tendency to take into account product name, which might be inferred by the sentimental distributions of a user set that replicate customers' comprehensive analysis.At last, we have a tendency to fuse 3 factors-user sentiment similarity, social sentimental influence, and associated item's name similarity into our recommender system to form a correct rating prediction.We have a tendency to conduct a performance analysis of the 3 sentimental factors on a real-world dataset collected from Yelp.Our experimental results show the sentiment will well characterize user preferences that facilitate to enhance the advice performance.
Going Beyond Readability Formula: How Do Titles Contribute to the Readability of Essays?

The belief that genre specific reading provides numerous benefits for apprenticing writers is something we in the field of writing education value as an underlying constant.Accepting this, writing center directors stock their self-access library shelves with a variety of composition texts, to include rhetorics (and the essays therein).To select these materials, readability formulae (e.g., the Lexile Readability Formula) are often employed.However, such formulae only measure two of the many features that make up the readability of an essay (i.e., semantic, syntactic).Other important features such as the title are not considered.To address this, this article reports the results of a sequential, mixed-methods study conducted in an Asian postsecondary setting.The study found that titles influence readability both as (a) a primary (i.e., an isolated feature) and (b) a conjoined feature (i.e., consisting of two or more associated entities where the second impacts the first).The article also makes a recommendation for teachers, writing center staff, and the publishing industry that readability formulae be administered in a hybrid fashion to explore additional features such as the title when considering the difficulty of exemplars.
Chatbot Enrichment

In this chapter, we will describe some advanced use cases that leverage the capabilities of Google Dialogflow. We have already discussed fulfillment and using the webhook service to integrate with third-party APIs, so in this chapter we will focus on chatbot personality, sentiment analysis, events, and integration with third-party messengers such as Facebook. We will also look at the Google knowledge base and implement a document search feature in Google Dialogflow.
Blockchain based Certificate Issuing System using Smart Contracts

Nowadays everything seems to be original and it's being herculean task to identify which is not real. It may be a currency for people or valued currency for students such as certificates. A lot of fraudulent parties have made money by encouraging the duplicate certificates in society. As a result, low talented or inefficient people are getting more and they are being responsible for the degradation of any nation’s value. A blockchain-based certificate is a prime solution for the above problem. In this work, would like to discuss the functioning of a smart contract in favor of Issuing, verifying, and revoking the certificates through gas value[9] deductions. Thus our certificate can provide the added assurances of evidence of origin in a transparent manner using.