Reviewerly/exHarmony huggingface dataset:
Performance Evaluation of the Semantic Web Reasoners

The major benefit of working on Ontology Web Language (OWL) is its ability to define semantics such that the information becomes more valuable. To realize the full power of semantics, it is essential to integrate a reasoning engine to it. The software codes that perform inferences are often referred to as reasoning engines or reasoners. The reasoners can be classified into categories: tableau based and rule based reasoners. The rule based reasoners combines the assertions with a set of logical rules to infer new knowledge chunks. The Jena framework offers several ways to integrate rule based reasoners programmatically. The operation is similar to creating a more advanced model from a simpler one. The objective of this paper is to list and classify the reasoners according to OWL 2 profiles thereafter the focus of this study is to develop a model which evaluate the performance of Semantic Web Reasoner based on few parameters.
Data Mining / Live Scoring – A Live Acoustic Algorithmic Composition Based on Twitter

Data Mining / Live Scoring is a live algorithmic composition for a six member acoustic ensemble. The algorithm receives feed from the popular social platform Twitter, sonifying the tweets it receives by writing a score for each of the six musicians. The concept behind this performance is to create a musical composition which can serve as a soundtrack for Twitter, depending on what is written by the users. Based on the sentiment of the tweets and a musical library written especially for this project, the algorithm attempts to create a score which reflects the mood on Twitter in an abstract way. This project was commissioned by the Onassis Cultural Centre in Athens, and was presented there twice in April 2019. It was realized for the ARTéfacts Ensemble.
Problems of data protection competence formation

the analysis of the peculiarities of the formation of professional competence, pedagogical conditions and the model of training specialists for data protection, as well as the formation of deontologically oriented thinking has been carried out.It has been determined that deontology for specialists in data protection should penetrate as far as possible all classroom and non-auditorium classes.The pedagogical model, in addition to the content part, must have an activity component, which permits the formation of competence as a sequence of definite steps.An institution that prepares specialists for data protection is not bound to interrupt communications with their graduates, helping them in professional adaptation and professional development in the form of, for example, additional training, participate in the periodic certification of specialists in their profile, the results of which make changes to the content, methods its formation and practical realization.
Perancangan Sistem Deteksi Isyarat BISINDO Dengan Metode Adaptive Neuro-Fuzzy Inference System (ANFIS)

Deafness is a condition where an individual's hearing cannot function normally. So, sign language was created which was used as a solution to the problem. In Indonesia, the sign languages that are known are SIBI (Indonesian Sign Language System) and BISINDO (Indonesian Sign Language). Although SIBI has been recognized by the Indonesian government, in its use it is less desirable. This research was conducted to identify empty hand signals. Where it will help the user naturally without additional assistance. Experiments carried out using a dataset that was demonstrated by 1 display. In the process, the characteristics of the hand are taken using the Histogram Oriented Gradient (HOG) method. Whereas to separate it from the background image, color segmentation is used. The results of the process are then taken to classify. The classification process uses the Adaptive Neuro-Fuzzy Inference System method. The results of the tests carried out resulted in an accuracy of 78.31%. The problem is done.
Research on Speech Codec with Compression using Legendre Polynomials

Speech Codecs have been developed in several forms over the past 30 years and are used in Cellular digital telephony. Based on the throat excitation pulses and mouth cavity filter model, all the methods aim to encode the speech samples without much loss of clarity. The complexity of the methods using codebook searching, filtering and matrix inversion, all need heavy computations, leading to timing constraints. After describing a tutorial elucidating the essentials of the present methods, a different direct method of encoding, suggested by the spherical harmonic oscillator functions of Legendre, is introduced. Its performance and simplicity are such that the same may find its applications soon.
Smart Student Intelligent System

In the era fast growing technology it is necessary for all the educationist to keep more focus on the students because they are the backbones of our nation. In this regard even though there are many systems that make the notation and analysis of students as the one that contribute to the student. This paper presents a consequent approach to measure the problem solving skills which imparts few of life skills, technical skills and intelligent skills which are basically needed in this generation to seek jobs. There must be a system to calculate the intellectual abilities of a student because intellectuality shows a rapid transformation on the evolution of mind sets of students. This paper represents an elaborative approach to estimate the levels of students and categorize them based on their mind sets. It is necessary for all the students in order to acquire a skill set and in order to sustain the skill their intellectuality matters.
Content-independent orientation detection with histogram of optimized local binary pattern

This thesis is primarily concerned with the introduction of a new approach to the general problem of automatic image orientation detection. Inspired by the local binary pattern (LBP), a luminance, rotation and scale invariant and content-independent algorithm is proposed, namely: Histogram of Optimized Local Binary Pattern (HOOPLBP). Whilst the proposed approach is essentially generic, the core application considered in this study is human face orientation detection. To detect the face orientation, a general face model is trained using the HOOPLBP feature. The experiment show a very impressive result. Integrating this result with other face related techniques will facilitate some applications. To this end, this thesis propose a hybrid face detection system. Specifically, the new system aims to detect both upright and tilted human face in digital images. In the scheme, several face related algorithms are integrated to achieve difference tasks in different stages. In addition, two modified systems are used in this thesis to detect faces in both grayscale images and color images. The HOOPLBP is a new and robust method in automatic image orientation detection. It can be improved by other techniques and also can be used in many other fields. The future work is also included in the thesis.
Personalized decision making for QOS-based web service selection

In order to choose from a list of functionally similar services, users often need to make their decisions based on multiple QoS criteria they require on the target service. In this process, different users may follow different decision making strategies, some are compensatory and some are non-compensatory. Most of the current QoS-based service selection systems do not consider these decision strategies in the ranking process, which we believe are crucial for generating accurate ranking results for individual users. In this thesis, we propose a decision strategy based service ranking model. Furthermore, considering that different users follow different strategies in different contexts at different times, we apply a learning to rank algorithm to learn a personalized ranking model for individual users based on how they select services in the past. Our experiment result shows the effectiveness of the proposed approach.
Emotion Categorization from Video-Frame Images Using a Novel Sequential Voting Technique

Emotion categorization can be the process of identifying different emotions in humans based on their facial expressions. It requires time and sometimes it is hard for human classifiers to agree with each other about an emotion category of a facial expression. However, machine learning classifiers have done well in classifying different emotions and have widely been used in recent years to facilitate the task of emotion categorization. Much research on emotion video databases uses a few frames from when emotion is expressed at peak to classify emotion, which might not give a good classification accuracy when predicting frames where the emotion is less intense. In this paper, using the CK+ emotion dataset as an example, we use more frames to analyze emotion from mid and peak frame images and compared our results to a method using fewer peak frames. Furthermore, we propose an approach based on sequential voting and apply it to more frames of the CK+ database. Our approach resulted in up to 85.9% accuracy for the mid frames and overall accuracy of 96.5% for the CK+ database compared with the accuracy of 73.4% and 93.8% from existing techniques.
A homomorphic range searching scheme for sensitive data in internet of things

With the popularisation and development of the internet of things, big data and cloud computing, the search of data in cloud-based internet of things becomes a hot research topic. However, the sensitive data, such as the medical data collected by wearable devices, is inevitable to be stored in the cloud server. Homomorphic encryption has the ability to calculate the ciphertext without decryption. We separate the calculating and the decryption into different security domains to preserve the privacy of sensitive data, so the original plaintext would not be exposed in the cloud. Hence, we can compare two ciphertexts to get the difference of them in a privacy preserving way. In order to accelerate the search process of range query, we build an encrypted self-balancing binary index tree. Based on oblivious RAM, the searching scheme can hide the access patterns of the node of tree. The actual nodes and logic relation of tree are stored on different servers. A sample implementation of the proposed scheme is given, and the experimental results and analysis are presented to illustrate the scheme's effectiveness and security.
Optimization Algorithms based Task Scheduling Method for Cloud Computing Environment

There are various enhancements in the world of technology. Among that Cloud computing delivers numerous amenities over the Internet. It employs data centers which comprise hardware and software provision for loading, servers, and systems. The primary reason for the popularity of Cloud computing is consistent performance, economical operation, prompt accessibility, rapid scaling and much more. The chief cause for concern in cloud computing are the errors that happen either in the software or the hardware and energy consumption on a large scale. The clients pay only for resources utilized by them and assets which are accessible during the computing in a cloud setting. In the environment of cloud computing, Task scheduling is significant concepts which can be used to minimize the energy and time spent. The algorithms in Task scheduling might employ various measures toward dispense preference to subtasks that may generate many schedules to the divergent computing structure. Moreover, consumption of energy could be dissimilar for every source which is allocated to a job. This present research explores that the PSO-CA based energy aware task scheduling method can predict with the aim to enhance the resource distribution.
Prediction Models for the Diagnosis of Asthma

Asthma is popular with substantial patient morbidity and costs for healthcare.We performed a systematic literature review to identify different algorithms used in asthma diagnostic systems.In this paper we reviewed the 35 papers based on asthma diagonosis system and we identified that the algorithms used in these papers gives how much accuracy.We read different prediction models designed to help diagnostics determine clinicians to evaluate patients with asthma-suggesting symptoms in primary care or similar settings I.INTRODUCTION Asthma nowadays is a commonly occurring disorder at any age that is a concern for public health worldwide.It is a chronic respiratory inflammatory condition which can be particularly responsive to a number of stimuli.The asthma patient is affected by attacks including coughing, dyspnea and wheezing.Sounds created during respiration can be a good source of lung health information.Any characteristic changes in normal lung sounds may involve an infection that is likely to enter the lung.Each disease type is different and its sound signature, pitch, amplitude, frequency, length and so on will detect this variation.To improve asthma treatment and performance, having an correct diagnosis of asthma is fundamental.However, in children and adults, asthma is commonly misdiagnosed and over-and under-diagnosed.Over-diagnosis leads to insufficient treatment and avoidable morbidity and mortality while under-diagnosis leads to expensive, potentially dangerous treatment and needless health care.It is difficult to reliably diagnose asthma.Asthma is a disorder that involves multiple genotypes, endotypes and phenotypes.There is no gold standard to affirm or deny diagnosis categorically.Therefore, Asthma is the clinical diagnosis, but the particular sensitivities / specificity of individual symptoms , signs and tests are low.In comparing guidance between national and international guidelines and in commentary aimed at minimising confusion for clinicians, ambiguity about the correct combination of clinical properties and tests for asthma diagnosis are expressed.A clinical prediction model can be used to estimate the likelihood of outcomes using an algorithm based on datadriven, which incorporates at least two predictors, such as clinic history components, physical tests, test results and/or treatment response.Models for clinical forecasts will help health practitioners evaluate the chance of a diagnosis, improve collaborative decision making and help patients Subtypes stratification.Because most diagnosis of asthma occur in specialist settings where health conditions generally arise undifferentially and the evaluation frequently relies on probabilities, it may be important to enhance the precision of the diagnisation of asthma by reviewing available clinical evidence and directing further measures.We aimed to define, compare and synthesise current models of clinical prediction aiming at promoting asthma diagnoses in children and adults with symptoms of primary care asthma or similar conditions.
Fuzzy Thesauri Recommendation System For Web 2.0 Social networks

In the information age with billions of documents available on the Internet, searching among these documents has become quite a challenge for researchers. Since most of the search methods are based on terms within the documents, identifying the relationship between the terms has always been important in the eld of Information Retrieval. Using term relations in query expansion techniques is one of the most commonly used and successful approaches that are being used in order to help users nd what they need. In this study a fuzzy set based methodology is exploited for the retrieval and analysis of data available in Web2.0 social networking sites. The documents in each server or node are used for building a knowledge-base that will be employed by the Recommendation System in order to provide domain speci c suggestions, based on the friendship network in social networking sites. The results of the study show that the proposed methodology is reasonably scalable and can be employed on social networking sites.
A Relative Examination on Clustering Techniques: Agglomerative, K-Means, Affinity Propagation and DBSCAN

Clustering is a procedure of grouping a collection of certain objects into a relevant sub-group. Each sub-group is called as a cluster, which guides users to comprehend the collections in a data set. It is an unsupervised learning technique where each dispute of this type deals with discovering a structure during the accumulation of unlabeled data. Statistics, Pattern Recognition, Machine learning are some of the active research in the theme of Clustering techniques. A Large and Multivariate database is built upon excellent data mining tools in the analysis of clustering. Many types of clustering techniques are— Hierarchical, Partitioning, Density–based, Model based, Grid–based, and Soft-Computing techniques. In this paper a comparative study is done on Agglomerative Hierarchical, K-Means, Affinity Propagation and DBSCAN Clustering and its Techniques.
Techniques for Extracting Region of Interest in Breast Cancer

The main aim of the project is to develop an automatic system which detects the stages and Region of Interest (ROI) in Breast images. Breast cancer in females is one of the main causes for deaths among women. Finding at early stage of breast cancer which helps in the treatment and recovery rates. Discovering region of interest for breast in the breast images is a demanding problem. Retrieving of breast tumors region and the pectoral muscle is a key step in the process of CAD.
An Optimal Algorithm to Improve Resource Utilization in Cloud Data Centre

Cloud Computing Plays significant role in all type of business due to it features of measured service. so 89% of business are moving toward cloud that increases more number of data centers. Because of this there is more carbon dioxide emission.utilizing data centres for the efficient usage of client requests is major issue in the growing scenario amongst cloud users. Waiting for the data in queue for a long period of time increases the delay to obtain specific data. Our system focuses on reducing the delay of users, increasing the bandwidth thus giving an optimized system in cloud using software defined network. The software defined network has a central control over the entire system which efficiently manages the client requests. We have used the genetic load balancing algorithm to process the client requests in an orderly manner to enhance the data distribution between the various users of cloud.
Restructuring the Academic Library: Teams-based Management and the Merger of Interlibrary Loans with Circulation and Reserve

The Ryerson University Library has recently adopted a teams-based management model in order to ameliorate growing pressures on service points consistent with increased demand for interlibrary loans, growing circulation activity, stagnant staffing levels, and a larger influx of students as a result of the “double cohort”. The opportunity to redesign the entrance of the Library has allowed the Library to plan for a merger of Interlibrary Loan with Circulation and Reserve. The background to this merger and library goals are discussed.
Tool of Automated System Armoured Scaffold to Rank Requirements through AHP

Requirement Engineering is really significant phase in software development life cycle. Construction of software and its functionalities is entirelygrounded on the requirements elicited for the project[6]. In this paper, we propose a tool to prioritize the requirements only with AHP bearing in mind effortless implementation for large Scale Application, Precision of result and Stakeholder’s Contribution. The tool is developed in Java and SQL. This work principallyfocused on applying AHP for larger projects. The proposed framework has been assessed through an exploratory case study that has fixed number of requirements and the status after the arrival of new requirements to the priority list. This is to know about the certainty of the projected framework, which has been conducted in a software firm. Then the tool was developed for the framework and used by the company to check for the certainty of result. The deployment of the tool and the result obtained from the effort are presented.
METHODOLOGICAL BACKGROUND AND FOUNDATIONS OF THE NONMATERIAL THEORY OF THE PSYCHE

The problem of the psyche and consciousness has been the most mysterious one for a few thousand years and is still unresolved. It has been almost forgotten that Aristotle considered human psyche a structure that is not bound to the body. This idea did not persist, though. It was Hippocrates who ruined it and declared a different concept, which prevailed for many centuries, that the brain is a repository of all mental processes. Even such a genius as Rene Descartes took Hippocrates’s idea for granted and spent many months in attempts to find memory and emotions in gyrus and ventricles of the brain. This path – the search of material structures of the psyche – was followed by I.M. Sechenov, I.P.Pavlov and many others. Later, many other mistaken ideas were born, declared new and revolutionary ones and died prematurely. However, not only ideas died, but also patients, who were treated by methods developed on the basis of these hypotheses. The author formulates the idea of the brain as the biological interface and proves a non-material theory of the psyche, which is a discovery that requires a change in basic paradigms of human sciences.
Implementation of Model-View-ViewModel (MVVM) Architecture Pattern in the Sistem Informasi Akademik UNIDA Gontor Mobile Application

Software architecture primarily tuned for moderating the rising software complexities and changes. Model-View-ViewModel (MVVM) is a software architectural pattern that facilitates a separation of development of the graphical user interface from the development of the back-end logic (the data model). University of Darussalam Gontor (UNIDA Gontor) is having an Academic System called Sistem Informasi Akademik (SIAKAD) which supports the management of its college’s data administration. Currently, the SIAKAD UNIDA Gontor’s performance is still far from optimal, mainly because some of its features aren’t user-friendly yet, especially when the SIAKAD UNIDA Gontor accessed from the mobile devices. Therefore, this research aims to implement the MVVM architecture pattern in the SIAKAD UNIDA Gontor mobile application, to increase its user-friendly aspect, especially for Android device users. This research is carried out with Waterfall development method, using Kotlin programming language and utilising Android Jetpack. Results from the app testing with Black Box method show that the application is running well and have no error. Next, results from the questionnaire distributed to the users (students, lecturers and BAAK staffs) show that the application run well and satisfying. From those trial results, show that the implementation of MVVM on SIAKAD UNIDA Gontor Android application has been successful according to the scenario and ready to be applied in the even semester of 2019/2020 campus’ academic year and so on (https://play.google.com/store/apps/details?id=com.amoled.sidago). Further research and development, can be directed to the addition of new features and can support another mobile operating system, such as iOS, Tizen, Harmony OS, etc.
Suspense of Korean Family Ethic Drama

When we were moved by the warmth of Korean ethic family drama, we began to fall into the feeling that these dramas were so real just as our daily life.But we tend to overlook the fact that the dramas which have been elaborated by authors are just a mirror of daily life.Suspense narrative, contrast narrative, and coincidence narrative are three main narrative techniques of Korean family drama.As an important narrative technique of Korean family ethic drama, suspense is a narrative skill organizing the plot and making dramas to be more charming.