Reviewerly/exHarmony huggingface dataset:
LibGuides: Quick Kit for SCC Faculty Pivoting to Online: Free e-textbooks during COVID-19

Many academic and technical publishers are making textbooks and other learning resources free during this time. Some platforms and instructions are listed here. Contact a librarian if you can't easily find what you need.
ANALISIS PENGGUNA XL AXIATA SELAMA PANDEMI COVID-19 DENGAN MENGGUNAKAN TEKNIK LINEAR REGRESSION

2020 is a very disturbing year for the whole world because in 2020 there is a deadly virus, the virus is called Coronavirus 2 or Covid-19. In Indonesia to tackle the virus using the PSBB method, where everyone must provide a minimum distance of 1 meter, and public places such as supermarkets, offices, transportation, schools, etc. that have been mentioned must be closed or reduce crowds. Under these circumstances, employees are required to work from home as well as for students to undertake bold learning. To carry out these activities, it is necessary to have the internet to access work and school assignments, so that providers benefit greatly from the Covid-19 pandemic. Providers are companies that provide internet services to users, such as XL Axiata. During the Covid-19 pandemic, XL Axiata provider predicts future XL Axiata users, whether they want to increase or decrease and from these results, it can be seen how many devices have exceeded capacity, so the device needs to be upgraded. To make such an increase, XL Axiata needs to conduct an analysis of existing data. Data processing used data mining methods and the technique used is Linear Regression. By processing data using Linear Regression results in future predictions, the West Jakarta Kodya has increased per week by an average of 0.12%, the East Jakarta Kodya has decreased per week by an average of 0.27%, the Central Jakarta Kodya experiences the decline per week by an average of 2.31%, the South Jakarta Kodya experienced an increase per week by an average of 1.17%, and in the North Jakarta Kodya, it decreased per week by an average of 0.12%.
Parallelization of the Apriori Algorithm for the Search of Frequent Elements

There is a wide variety of techniques that increase application performance by alleviating one or more of the most important problems with today's processors. In this work, the execution time, speedup and efficiency of the linear Apriori algorithm are shown as well as parallel with the use of OpenMP. By identifying the frequent elements of transactional databases, in processing 5 thousand records the time improves in 42,078 seconds of the algorithm with openMP compared to the sequential algorithm, in the execution 8 processor cores were used.
Face Mask Detection System

In these tough times of Pandemic (COVID -19) situation, wouldn’t it be worthy to do something related to face detection to ensure our safety?Face Detection has evolved popularly in Image processing and Computer Vision. Face detection is more relevant today because it not only used on images but also in video applications like real time surveillance and face detection in videos. High accuracy image classification is possible now with the advancements of Convolutional networks.Our aim is to design a Binary Face Classifier which can detect any face present in the frame regardless of its alignment. The purpose of the research is to identify whether the user is wearing a mask or not? It alerts the user if not wearing the mask, and the recorded images are sent to the registered mail-id. The Pandemic Preventer (mask detection system) runs automatically and enforces the wearing of the mask.The face mask detector system can work on any existing cameras without the installation of any new cameras.The Face Mask Detection System can be used at office premises to ensure that employees are maintaining safety standards at work. It monitors employees without masks and alert them to wear a mask. It can also be used in schools as well as in colleges, ensuring safety of the students.Post COVID-19 it can also be put to application in various fields of work.
Domain adaptation in Natural Language Processing

Domain adaptation has received much attention in the past decade. It has been shown that domain knowledge is paramount for building successful Natural Language Processing (NLP) applications.To investigate the domain adaptation problem, we conduct several experiments from different perspectives. First, we automatically adapt sentiment dictionaries for predicting the financial outcomes “excess return” and “volatility”. In these experiments, we compare manual adaptation of the domain-general dictionary with automatic adaptation, and manual adaptation with a combination consisting of first manual, then automatic adaptation. We demonstrate that automatic adaptation performs better than manual adaptation, namely the automatically adapted sentiment dictionary outperforms the previous state of the art in predicting excess return and volatility. Furthermore, we perform qualitative and quantitative analyses finding that annotation based on an expert’s a priori belief about a word’s meaning is error-prone – the meaning of a word can only be recognized in the context that it appears in.Second, we develop the temporal transfer learning approach to account for the language change in social media. The language of social media is changing rapidly – new words appear in the vocabulary, and new trends are constantly emerging. Temporal transfer-learning allows us to model these temporal dynamics in the document collection. We show that this method significantly improves the prediction of movie sales from discussions on social media forums. In particular, we illustrate the success of parameter transfer, the importance of textual information for financial prediction, and show that temporal transfer learning can capture temporal trends in the data by focusing on those features that are relevant in a particular time step, i.e., we obtain more robust models preventing overfitting.Third, we compare the performance of various domain adaptation models in low-resource settings, i.e., when there is a lack of large amounts of high-quality training data. This is an important issue in computational linguistics since the success of NLP applications primarily depends on the availability of training data. In real-world scenarios, the data is often too restricted and specialized. In our experiments, we evaluate different domain adaptation methods under these assumptions and find the most appropriate techniques for such a low-data problem. Furthermore, we discuss the conditions under which one approach substantially outperforms the other.Finally, we summarize our work on domain adaptation in NLP and discuss possible future work topics.
Measuring The quality of services provided to Customers in Jeddah Municipality: قياس جودة الخِدْمات المُقدَّمة للمُراجعين في أمانة مُحافظة جِدَّة

The study aimed to measuring the quality of the services provided by the secretariat of Jeddah's Governate using the gap between perceptions and expectations (SERVQUAL). The descriptive (analytical) approach was used, to achieve the objectives of the study; data were collected using a questionnaire consisting of five main dimensions (concrete, reliability, response, assurance, empathy), the community of the study consists of the reviewers of the Secretariat of Jeddah, where the study was applied to a randomly selected sample, and questionnaires were distributed to them with a total of (325) questionnaires valid for analysis. The results of the study showed that there is a gap in the quality of services provided by the Secretariat of Jeddah, since the actual quality of service is below the expected quality level. There are also statistically significant differences between opinions of the sample members on the quality of services expected and actual in each dimension (reliability, response, assurance, empathy), as well as the overall degree of quality of services expected and actual according to gender variable. In the light of the findings, the study reached recommendations based on the (SERVQUAL) scale; the most important of them was the promotion of the focusing culture on reviewers through the attention of the complaints, the identification of their desires and determine them periodically through the process of continuous evaluation; to ensure improvement of the quality of municipal services.
The effect of speed humps on pavement conditions in local streets

Speed humps is one of the traffic calming devices beside speed bumps, speed tables, roundabouts, transverse rumble strips, optical speed bars, textured pavement, and cat-eye reflectors. Main function of these devices is to control and calm the speed of vehicles such as cars, lorries and motorcycles so that they were driven at suitable speed, through critical areas or local streets. It also can be acknowledged as a raised pavement which is spanning across roadway to force drivers to reduce the speed of their vehicles. By slowing down their vehicles, more time could be given to the pedestrians to safely cross the road. The objective of this research is to identify surface pavements conditions within speed humps area and to determine the most suitable type of road hump to be build based on the traffic volumes at the study area. In order to achieve the objectives, traffic counting were carried out during peak hours at 5 speed humps along Sri Damai Road (C181). The traffic volume for this study area is in range of 2,000-5,000 vehicles per day. From this study, it was found out that the size and characteristics of hump located at the study area are totally different than Public Work Department’s standard specification in terms of height and length of hump. The best speed hump that is suitable to be used at this local road is sinusoidal with 4 meter in length and 100 mm of height. The defect that usually occur before and after the hump is crocodile crack, transverse crack, block cracking, pothole and depression by divided it into three main conditions which is less severe, medium severe and badly severe. It is hope that there outcomes from this thesis could help the Public Work Department to plan appropriate measure for surface improvement at humps located at local roads.
Time, defeasible logic and belief revision: Pathways to legal dynamics

In order to properly model norm change in the law, temporal aspects of legal dynamics must be considered. Since there exist several time-based features of law that should be studied, we discuss two interesting approaches: one based on defeasible logic and the other based on belief revision. Each of these makes use of one of the two classic forms of reasoning about time: point-based and interval-based. Both formalisms provide the necessary logical infrastructure to address the characterization of complex behaviour of legal dynamics.
Significance of using new technologies in the education area

The comprehensive development of today’s scientific and technological progress, which is closely connected with the automation and computerization of production in transition, updating the content and teaching methods, improving the quality of knowledge among students through mastering the fundamentals of science and maximizing the approximation of knowledge to world standards, is a state-level problem. The solution to the above problems is associated with the material base of the school, the compilation of teaching aids on a scientific and methodological basis, the organization of teacher work by teachers on the basis of new technologies, as well as the pedagogical unity of the school’s leadership, the ability to organize the educational process with modern requirements. The development of science and technological progress, the beginning of the civilized world, the change in society, led to the emergence of great opportunities for updating through textbooks, the content of knowledge. Each teacher is obliged to prepare an educated, business-oriented, having a conscience and human qualities, culturally improved and comprehensively prepared generation, immensely loving his homeland. To do this, the teacher must clearly organize, using a comprehensive plan with various techniques and methods in the lessons, his scientific and methodological level of the disciplines taught and ultimately achieve the desired results. At this time, highly-quoted new technologies that improve the educational process are being successfully introduced into the school’s practice.
Nivel de desarrollo de la competencia mediática en centros educativos de Ecuador

espanolEste trabajo valora el nivel de desarrollo de la competencia mediatica de los ninos y ninas de educacion inicial y basica entre las edades de 4 a 8 anos de instituciones educativas de Ecuador. Para ello se aplico el instrumento que toma como referencia las seis dimensiones propuestas por Ferres (2007). Los resultados muestran que los ninos y ninas han desarrollado un nivel alto de competencia con respecto al lenguaje, a la estetica y a la recepcion e interaccion, es decir, identifican y utilizan codigos mediaticos para comunicarse correctamente, identifican la innovacion, la originalidad, el estilo y las tendencias audiovisuales e interpretan los mensajes y la informacion de manera adecuada. Por otra parte, alcanzan un nivel de desarrollo de la competencia medio y bajo con respecto a la tecnologia, produccion y difusion e ideologia y valores, de tal forma que el manejo de las herramientas tecnologicas es limitado y las utilizan principalmente para el entretenimiento, sin ninguna aplicacion de la normativa y regulacion de los medios. Finalmente, los ninos y ninas tienen dificultades para detectar informacion fiable. EnglishThis work assesses the level of media competence development of children aged between 4 and 8 years who are in initial and basic levels in educational institutions in Ecuador. For this, an instrument based on the 6 dimensions proposed by Ferres (2007) was applied. The results show that children have developed a high level of competence with respect to language, aesthetics and reception and interaction, that is, they identify and use media codes to communicate correctly, they identify innovation, originality, style and audiovisual trends and interpret messages and information appropriately. On the other hand, they reach a level of development of medium and low competence with respect to technology, production and dissemination, as well as ideology and values, so that the management of technological tools is limited and they are used mainly for entertainment without any norm application nor media regulation. Finally, children have difficulty discerning reliable information.
Human Action Detection and Recognition by Using Deep Convolutional Neural Network

This paper explains a deep learning method to detect and recognize human actions. In this, a camera is used to monitor the human actions. A deep convolutional neural network is used for the image or video obtained from the camera. At first segmentation is performed followed by several processing techniques. After this the human actions are detected and recognized by using deep convolutional neural network. Finally, the recognized action is send to a particular person by using ARDUINO. It is developed mainly for two applications: 1. neural network for elder people or active and assisted living system for smart home, 2. Smart gesture identification. This project is implemented using MATLAB simulation and ARDUINO.
Research Guides: Introduction to Systematic Reviews: Protocols & Guidelines

The purpose of this guide is to facilitate collaboration between Stanford students, faculty and staff, and Lane Librarians on knowledge synthesis projects, such as systematic reviews; provide guidance documents for conducting knowledge synthesis; and high
Menumbuhkan Minat Melanjutkan Studi Ke Perguruan Tinggi Siswa Sekolah Menengah Kejuruan (SMK) Perpajakan

Through the Community Service Program (PKM) to create an additional learning platform by providing explanations or seminars and motivation to be precise is motivation leadership about how to generate potential from within students and about how to motivate students to continue their education. at University. It is hoped that a good level of education and adequate skills can improve the quality of human resources so that development can run well too.The method of implementing this activity is by lecturing. The result of this activity is the increasing interest of students of SMK Taxation Riau to continue their higher education, this can be seen from the results of the post test and pre test given to the participants of the activity.
The Hypothalamus–Pituitary (HPT) Set Point Theory

In recent years, a considerable amount of effort has been devoted, both in industry and academia, towards the behavioral modeling, evaluation and prediction of the hypothalamus pituitary thyroid system. Thyroid Systems Engineering targets an optimal treatment of people suffering from thyroid hormone disorders. The content is motivated by in-depth observations of such patients whose rich data supported the theoretical framework arising from formal mathematical reasoning, guided by the nature of thyroid physiology. Leveraging on the insights emerging from the unique combination of an electrical engineer working with a clinical thyroidologist, and both being scientists skilled in mathematics, the authors introduce this new discipline and field of scientific investigation aptly designated as Thyroid Systems Engineering. Readers will discover that mathematics can indeed model the behavior of the hypothalamus-pituitary-thyroid (HPT) axis. Focused on modeling, each of the eighteen chapters gives the reader a notion of the application of relevant mathematics to pertinent issues encountered in mainstream thyroidology. Many cellular processes resemble the flux of variables and states in a complex multi-parameter space through time analogous to current flow in electrical networks. It is then logical to apply the principles and physical laws of electrodynamics, electrical network theory, control systems theory and signal theory to many of the biological phenomena encountered in endocrinology. Such an approach is used liberally throughout the book and successfully yields elegant solutions to a number of models presented within. This book can serve as a reference to mathematical modeling in other aspects of endocrine physiology, and as the starting point for a fundamental course in medical modeling. It will appeal to postgraduates in electrical engineering, academic physicians and biomedical researchers. Further, readers equipped with advanced calculus, electrical network theory, control theory and signal theory should be able to follow the mathematical expositions that describe thyrotropic control. They represent a new discipline based on mathematical modeling in physiology applicable to medical diagnostics, measurement and treatment to cooperate in the clinical team and realize an optimized treatment for patients.
Applying data synthesis for longitudinal business data across three countries

Data on businesses collected by statistical agencies are challenging to protect. Many businesses have unique characteristics, and distributions of employment, sales, and profits are highly skewed. Attackers wishing to conduct identification attacks often have access to much more information than for any individual. As a consequence, most disclosure avoidance mechanisms fail to strike an acceptable balance between usefulness and confidentiality protection. Detailed aggregate statistics by geography or detailed industry classes are rare, public-use microdata on businesses are virtually inexistant, and access to confidential microdata can be burdensome. Synthetic microdata have been proposed as a secure mechanism to publish microdata, as part of a broader discussion of how to provide broader access to such data sets to researchers. In this article, we document an experiment to create analytically valid synthetic data, using the exact same model and methods previously employed for the United States, for data from two different countries: Canada (LEAP) and Germany (BHP). We assess utility and protection, and provide an assessment of the feasibility of extending such an approach in a cost-effective way to other data.
Homogeneity hypothesis in discriminant analysis

One of the most important properties of a machine learning algorithm is its ability to generalize results of learning on finite training sets.This property is based on the compactness hypothesis stating that objects of the same class in the feature space, as a rule, are located closer to each other than to objects of other classes.The compactness hypothesis has a geometric nature and uses the concept of proximity in the feature space, which is most often expressed in terms of a metric.Meanwhile, this hypothesis does not fully take into account the probabilistic nature of the features.It is quite suitable for data with unimodal distributions that have a compact support, but in a general case it may not hold leading to incorrect generalizations.In the paper, an alternative approach is described in which the homogeneity hypothesis is used instead of the compactness hypothesis.Within the framework of this approach, objects are called homogeneous, if their features follow identical distributions.We propose as measures of homogeneity the Petunin's p-statistics and its versions, which is highly efficient in recognizing both disjoint and significantly overlapping samples that violate the compactness assumption.This approach has a rigorous mathematical foundation and high efficiency in practical applications.
The Drivers of Sustainable Cyber Service Offer in the Government with an Emphasis on Maintaining Security Using Artificial Intelligence

Purpose: Due to the increasing development of information technology, researchers estimate that in the near future, organizational structures will act hastily for fear of backwardness. Without sufficient attention to the security dimensions, they ignore the need for intelligent security simply by emphasizing cyberization and allocating large costs for preparing the technical infrastructure. In this regard, by observing the security dimensions, our research tries to identify and prioritize the drivers that have the most ability to provide cyber services.Method: Our research is descriptive-analytical. The data collection is done theoretically in accordance with library studies; its tool is analytically questionnaire and data analysis is conducted by SPSS and Mic-Mac software.Findings: Considering the security dimensions according to experts, we identified 12 drivers with the highest potential to provide cyber services and prioritized them in 4 areas. Next, by considering the two parameters of action and reaction, we explored the relationships between the drivers. Finally, we tried to bring the system closer to stability by prescribing an appropriate procedure.Conclusion: According to the results of the research, in order to provide cyber services, the government should consider the degree of the organization's action and reaction and avoid making sporadic decisions that do not have a specific priority. In the realization of its cyber services, it should also pay special attention to the security dimension.