Reviewerly/exHarmony huggingface dataset:
A Standalone Vision Device to Recognize Facial Landmarks and Smile in Real Time Using Raspberry Pi and Sensor

In current scenario of technological advancement, human-machine association is becoming sought after and machine needs to comprehend human emotions and feelings. The productivity of an exercise can be improved to a considerable extent, if a machine can distinguish human feelings by understanding the human conduct. Feelings can comprehend by content, vocal, verbal and outward appearances. The major deciding factor in the identification of human emotions is Facial expression. Working with facial images and emotion is real time is a big task. It is also found that confined amount of work has been done in this field. In this paper, we propose a technique for facial landmark detection and feature extraction which is the most crucial prerequisite for emotion recognition system by capturing the facial images in real time. The proposed system is divided into three tightly coupled stages of face detection, landmark detection and feature extraction. This is done by HOG and Linear SVM-based face detector using dlib and OpenCV. The curiosity of our proposed strategy lies in the execution stage. Raspberry Pi III, B+ and a normal exactness of 99.9% is accomplished at ongoing. This paper can be proved as the basis of real time emotion recognition in majority of applications.
Differential Evolution for Large-Scale Clustering

Clustering is the task of organizing data instances into groups based on the similarity between them.It p lays an essential role in knowledge discovery and data min ing, ranging fro m preprocessing step to the final goal o f the task.Evo lutionary algorith ms (EAs) based clustering methods have developed with the intention of enhancing the effectiveness and accurateness of clustering.The huge amount of data emerging by the progress of technology have become data clustering as a challenging task and as an attractive attention for the use of EAs based approaches.Differential Evolut ion (DE), an in stance of EAs, has been explo ited to discover the best solution for clustering problems.It has become a successful solution to produce more compact clusters than other traditional clustering techniques.This paper presents a parallel differential evolution algorith m on Spark framewo rk to facilitate huge amount of data clustering.Experimentations were conducted on some frequently used UCI mach ine learn ing datasets.The results have presented that the proposed approach is effective and comparable to existin g algorithms.
The Effect of Self-voice Recording Technique on Oral Performance of EFL learners in Electronic Learning

The recent COVID-19 pandemic has negatively affected each sector in society. Likewise, even the education system has been affected in many countries as governments across the globe have temporarily closed all educational institutes for curbing the spread of this virus. With the cancellation of all face-to-face classes, the teachers have resorted to online classes and online learning, thereby allowing the students to continue their education. However, this transition from the traditional face-to-face classes to online classes has given rise to many challenges for the teachers, students, school administrators and even the leaders. The teachers have been compelled to design new tools and techniques that can help the students learn and understand their lessons. These tools also help the teachers cooperate with the students like their interaction during the face-to-face classes. Hence, in this study, the researchers have used a self-voice recording technique for determining its effect on the speaking skills of the students along with their perspectives. For this purpose, they interviewed 100 University students studying in an English conversation class. All data were collected using a questionnaire, or through class observation, where the learners carried out self-voice tasks. Some significant results noted in this study were - Firstly, the students showed a lot of interest in electronic learning and using self-voice techniques, even though many of them had never experienced it before. Secondly, the students displayed a positive perspective and were interested in using the self-voice recording techniques for e-learning. Thirdly, the students noted that the self-voice recording technique offered them numerous chances to put forth their point, which allowed them to enjoy their interactions. However, they asked for more corrective feedback. Lastly, it was observed that the self-voice recording technique was very effective in e-learning as it improved the speaking skills of the students. After reviewing the findings of the study, the researchers have offered some suggestions in this study.
Transediting Trump’s inaugural speech in Italian daily newspapers

The inaugural speech of the new President of The United States is one of those rare cases in news translation in which it is possible to identify a Source Text (ST) and compare it to several Target Texts (TTs). This paper focuses on what was reported in eight Italian national daily newspapers concerning the inaugural address of Donald Trump in 2017 on the day following his inaugural address. It provides an overview of the different choices and levels of translation visibility that were observed in the selected newspapers, observing full translations, paratexts and migrated segments embedded in articles describing the inaugural. Through a focus on the ST-TT comparison – employing Selective Appropriation (Valdeón, 2008) and Munday’s Evaluation paradigm (2012) – this investigation shows how the comparative analysis of translations can also be employed to enrich the textual analysis of the inaugural itself. Ideological translation choices are explored, focusing in particular on a case of omission in the right-wing Libero – which openly appreciated the election of Trump – and a case of addition in the communist Il Manifesto – which openly deplored Trump.
Collective writing

This is the second text in the series collectively written by members of the Editors Collective (https://editorscollective.org.nz/), which comprises a series of individual and collaborative reflections upon the experience of contributing to the previous and first text written by the Editors Collective: ‘Towards a Philosophy of Academic Publishing.’ In the article, contributors reflect upon their experience of collective writing and summarize the main themes and challenges. They show that the act of collective writing disturbs the existing systems of academic knowledge creation, and link these disturbances to the age of the digital reason. They conclude that the collaborative and collective action is a thing of learning-by-doing, and that collective writing seems to offer a possible way forward from the co-opting of academic activities by economics. Through detaching knowledge creation from economy, collaborative and collective writing address the problem of forming new collective intelligences.
An Accuracy Examination of OCR Tools

In this research paper, the authors have aimed to do a comparative study of optical character recognition using different open source OCR tools. Optical character recognition (OCR) method has been used in extracting the text from images. OCR has various applications which include extracting text from any document or image or involves just for reading and processing the text available in digital form. The accuracy of OCR can be dependent on text segmentation and pre-processing algorithms. Sometimes it is difficult to retrieve text from the image because of different size, style, orientation, a complex background of image etc. From vehicle number plate the authors tried to extract vehicle number by using various OCR tools like Tesseract, GOCR, Ocrad and Tensor flow. The authors in this research paper have tried to diagnose the best possible method for optical character recognition and have provided with a comparative analysis of their accuracy
Implementation of Selection Sort Algorithm in Various Programming Languages

Sorting algorithm deals with the arrangement of alphanumeric data in static order.It plays an important role in he field of data science.Selection sort is one of the simplest and efficient algorithms which can be applied for the huge number of elements it works like by giving list of unsorted information, the calculation which breaks into two partitions.One section has all the sorted information and another section has all the staying unsorted information.The calculation rehashes itself, by finding the smallest component inside the rundown of unsorted information and swapping it with the furthest left component, in the end setting everything straight information.This research presents the implementation of selection sort using C/C++, Python, and Rust and measured the time complexity.After experiment, we have collected the results in terms of running time, and analyzed the outcomes.It was observed that python language has very small amount of line of code, and it also consumes less storage and fast running time then other two languages.
Major Courses Selection using Three Layer Factors: A Recommender System

Data mining is one of the significant territories. Since numerous understudies will face difficulties and disarray while picking their significant courses in their graduation and even in the wake of picking their real few are in danger of bombing in it because of less intrigue and endeavors, so we should have a model to discover their interests and help them to pick their real seminars all alone. This methodology proposes three-layered representation which could be adjusted consecutively for the profound established arrangement. The recommended model would be utilized to early identify students who may be in danger because of miss coordinate determinations of the major course’s groupings. The essential target incorporates the disclosure of examples, wherein the students can choose or lessen danger of failure of their major courses
The Legal Development of Public Control in Education and Science

The article analyzes subjects and forms of public control in science and education, the author singles out among such subjects the Public Chamber of the Russian Federation and public chambers of the constituent entities of the Russian Federation, public councils under federal executive authorities and public councils for the independent evaluation of quality of educational activities of organizations as well as public organizations and public supervisors. The following public control forms are characteristic of education: pedagogical examination of bills; independent evaluation of quality of education; creation of ratings in education; public accreditation of educational institutions; professional and public accreditation of educational programs; monitoring in the system of education; participation in management of educational institutions.
Reducing Cloud Storage Space Consumption using PRCR

Data reliability constantly exists to be the important concern in dealing with the storage of data in distributed environment. For data reliability to be guaranteed, the replication approaches commonly used are in need of large amount of storage space which in turn leads to heavy cost for storage. The utmost need in data reliability requirement is the reduction of cloud storage space, In this paper, a comprehensive data reliability strategy is proposed so as to minimize the storage needed in the cloud and at the same time fulfilling the requirement of data reliability. The proposed model is based on Proactive Replica Checking for Reliability (PRCR ) technique. The proposed method needs only a less amount of replication for ensuring data reliability which in turn leads to a more cost effective approach. Our proposed method also reduces the consumption of storage of data on the cloud
Web service selection based on QoS and user profile

The web services are from different sources, heterogeneous, and of large volume. The user is in a crucial situation to select the best web services. The web service selection process aims to discovery the desired web services; as it allows to select the best web services to users' query. In particular, various web services have the same functionalities, so we need another factor to select the desired web services, which is the quality of service (QoS). The QoS has an important role in the web service selection process, it aims to classify the web service that have same functionality. This paper focuses on different concepts of the QoS. We present a new approach that is composed by two services; its role is primarily the best web service selection in relation with users' query and profile. In our approach, a better knowledge of user behavior is important because users can participate in research design and construction. The experiment shows that our method can accurately recommend the needed web services in a faster time.
Solar Power-Operated Microcontroller-Based Earthquake Detector with Automatic Alarm System

This study shows the development of an earthquake detector unit system using Arduino Mega and ADXL335 accelerometer.The alarm system will be triggered and will give a sound when the microcontroller-based earthquake detector detects a ground motion of specified intensity levels.A solar panel system is also integrated to the unit to provide its own generated electric current to supply power to the whole system.Having a solar power-operated earthquake detector with automatic alarm system will help in raising awareness about the occurrence of earthquakes to minimize the number of physical harms to humans and accidents.
Correction to: Changes of Affective States in Intelligent Tutoring System to Improve Feedbacks Through Low-Cost and Open Electroencephalogram and Facial Expression

The original version of the chapter was inadvertently published without incorporating the author’s proof corrections. The chapter has now been corrected and approved by the author.
RFID Technology Based Library Management System

The present invention demonstrates the designing of an automated library system which is based on RFID Technology. The system helps in reducing staff requirement, increasing the efficiency of the management, reducing cost, increasing accuracy and security of the management. In the system RFID technology is explained as an application for library management system which is extremely helpful to implement such an automated library management system. The system includes 3 layers for operation wherein these are a hardware system, a software system, and ware layer. The functioning of each and every module is described in the projected paper. The project is implemented for university/college/school libraries. The system explains whole functioning starting from and ending to circulation of books.
Augmentation of Classifier Accuracy through Implication of Feature Selection for Breast Cancer Prediction

Breast Cancer Examination and Prediction are great provocations to the researchers in the medical applications. Breast Cancer Examination distinguishes benign from malignant breast lumps, Breast Cancer Prediction has great deal in foretelling when Breast Cancer is expected to reoccur in patients that have had their cancers excised. Feature Selection is considered to be the preliminary step used in process to find best subsets of attributes. In this paper authors confer about the performance of five classifiers Sequential minimal optimization (SMO), Multilayer Perceptrons, Kstar, Decision Table and Random Forest with and without feature selection. The results manifest that after implying two feature selection techniques such as Correlation based and information based with ranker algorithm there is an augmentation in the accuracy rate of the classifier. It has been observed that after through implication feature selection techniques accuracy of the classifiers such as SMO, Multilayer Perceptrons, Kstar, Decision Trees, and Random Forest are enhanced.
An Enhanced Unsupervised Fuzzy Expectation Maximization Clustering for Deduplication of Records in Big data

The main issue while handling records in data warehouse or cloud storage is the presence of duplicate records which may unnecessarily test the storage capacity and computation complexity. This is an issue while integrating various databases. This paper focuses on discovering records, entirely and partly replicated, before storing them in cloud storage. This work converts whole content of data to numeric values for applying deduplication using radix method. Fuzzy Expectation Maximization (FEM) is used to cluster the numerals, so that the time taken for comparison between records is reduced. To discover and eliminate the duplicate records, this paper used divided-and-conquer-algorithm to match records among intra-clusters, which further enhances the performance of the model. The simulation results have proved that the performance of the proposed model achieves higher detection rate of duplicate records.
Different again

One of the open questions in the literature on again-type elements (such as English again and German wieder ‘again’) is how to derive their different readings. Specifically, we can differentiate between a repetitive reading (e.g., some activity happens that has happened before) and a restitutive reading (e.g., some activity that has not happened before restores an earlier state). One controversial question is whether these two readings involve lexical ambiguity of ‘again’ (the lexical ambiguity analysis) or whether they can be derived from one lexical entry of ‘again’ by assuming different structural configurations (the scope analysis). We investigate Kutchi Gujarati pacho ‘again’ and show that this again-type element has, in fact, three readings, which are best accounted for by combining both the lexical ambiguity analysis and the scope analysis within the same language. Moreover, Kutchi Gujarati is a language in which word order closely reflects information structure. This allows us to investigate the information-structural effects that are associated with different readings of pacho ‘again’, such as the “all-given” status of utterances with repetitive pacho, and the corresponding surface word orders. BibTeX info
Intensification of Lempel-ZIV-Welch Algorithm

There is a necessity to reduce the consumption of exclusive resources. This is achieved using data compression. The data compression is one well known technique which can reduce the file size. A plethora of data compression algorithms are available which provides compression in various ratios. LZW is one of the powerful widely used algorithms. This paper attempts to propose and apply some enhancements to LZW, hence comes out with an efficient lossless text compression scheme that can compress a given file at better compression ratio. The paper proposes three approaches which practically enhances the original algorithm. These approaches try to gain better compression ratio. In approach1, it exploits the notion of using existing string code with odd code for a newly encounter string which is reverse of existing. In approach2 it uses a choice of code length for the current compression, so avoiding the problem of dictionary overflow. In approach3 it appends some selective set of frequently encountered string patterns. So the intensified LZW method provides better compression ratio with the inclusion of the above features.
A hybrid pre-joined service network in graph database and memory for service composition

To make full use of the large space and persistency provided by a database and the fast processing speed of main memory, we propose a hybrid pre-joined service network method combining in-graph-database and in-memory calculations. This method firstly stores services' information and combinations in a graph database. Then, for a user's request, it retrieves candidate services from the database and constructs a service network in memory. After that, services are picked and composed to fulfil an optimal solution in memory. We assess the performance of our method by conducting experiments and comparing the approach with other database-based methods. Experiment results indicate the efficiency of this method in comparison, and it can always find solutions and lead to higher users' satisfaction.
Efficient Moving Vehicle Detection Algorithm for Various Traffic Conditions

Many computer vision applications needs to detect moving object from an input video sequences. The main applications of this are traffic monitoring, visual surveillance, people tracking and security etc. Among these, traffic monitoring is one of the most difficult tasks in real time video processing. Many algorithms are introduced to monitor traffic accurately. But most of the cases, the detection accuracy is very less and the detection time is higher which makes the algorithms are not suitable for real time applications. In this paper, a new technique to detect moving vehicle efficiently using Modified Gaussian Mixture Model and Modified Blob Detection techniques is proposed. The modified Gaussian Mixture model generates the background from overall probability of the complete data set and by calculating the required step size from the frame differences. The modified Blob Analysis is then used to classify proper moving objects. The simulation results shows that the method accurately detect the target.
The Impact of Using YouTube Videos to Promote Students’ Performance in Speaking Skill: A Case Study of a School in Libya

The main aim of this study is to examine the impact of applying YouTube to promote students’ performance in speaking skill in Kufrah secondary schools. The nature of this problem guided to choose the quasi-experimental research as a research design for this study. To conduct the experiment, the writer chose AL Ahfad Al Mokhtar School as the target population. The writer randomly selected 50 students from the study area. The oral test was the instrument of this study. The study adopts descriptive analytical method to analyse the collected data. The hypotheses were tested by using the t- test. From the analysis of the data, the results proved that, the performance of students taught using YouTube was significantly higher than that of those taught traditional method. Based on the results, recommendations were made on the need to apply YouTube Videos in EFL teaching and learning in various learning institutions in Libya. Finally, the study suggests that YouTube can promote students’ performance in speaking skill.
Supervised Word Sense Disambiguation using Decision Tree

Semantic processing is an essential task in natural language processing. In semantic processing it has observed that some words have more than one meaning. Multiple meanings of a word create serious problems to linguists which produces ambiguity in sentence. Word Sense Disambiguation is one of the main challenges in natural language processing which is present in almost all the languages. By existing knowledge and experience human can certainly disambiguate the words but for machine it is difficult task. In the proposed work, we are resolving the ambiguity of all open class word in English sentence and translating it to the Hindi sentence. We have used decision tree as a classifier. For improving the speed of translation we have used the concept of translation memory.