Reviewerly/exHarmony huggingface dataset:
PROSES PEMBONGKARAN COIL TMBP MV. NAGATO V.078 OLEH PERUSAHAAN BONGKAR MUAT PT. MERAK JAYA ASRI DI PELABUHAN CIGADING 1 BANTEN

The main discussion in the writing of this Paper is about the process of discharging TMBP coil by a loading and unloading company PT. Merak Jaya Asri in Banten Cigading Port. There are some background problems that must be resolved in this paper, namely how the process of discharging the TMBP coil, the tools used, the parties involved, and documents for the completion of the discharge. The company is engaged in loading and unloading which includes stevedoring, cargodoring, receiving and delivery activities. The purpose of writing this paper is to find out how the TMBP Coil stevedoring activities from the ship to the dock, what tools are used, who are the parties involved and what documents are made in the discharge process. Based on data collection conducted during land practice (PRADA) the author uses several data collection methods including methods of observation, documentation, interviews and literature. The results show that the TMBP Coil discharge process will run smoothly and in accordance with predetermined estimates if there is no waiting truck, weather factors and all crew and other relevant parties can be responsible for their respective duties. Keywords: TMBP coil, Stevedoring, Discharge
Advanced Filter Based Machine Learning Models on Clinical Databases for Outlier Detection

Feature selection approaches are used to improve the efficiency of the clinical databases in the machine learning classification. Since, most of the conventional feature selection and classification approaches are difficult to handle high dimensionality for pattern evaluation. Also these models are difficult to filter noise on different heterogeneous features. In this work, a hybrid data transformation and outlier detection methods are developed on the clinical databases to improve the classification accuracy. Experimental results show that the present model has better accuracy in evaluating the accuracy than the conventional models on clinical databases.
Individual Interaction Styles: Evidence from a Spoken Chat Corpus

here is increasing interest in modeling style choices in dialog, for example for enabling dialog systems to adapt to their users. It is commonly assumed that each user has his or her own stable characteristics, but for interaction style the truth of this assumption has not been well examined. I investigated using a vector-space model of interaction styles, derived from the Switchboard corpus of telephone conversations and a broad set of prosodic-behavior features. While most individuals exhibited interaction style tendencies, these were generally far from stable, with a predictive model based on individual tendencies outperforming a speaker-independent model by only 3.6%. The tendencies were somewhat stronger for some speakers, including generally males, and for some dimensions of variation.
Adversarial Domain Adaptation Enhanced via Self-training

Deep learning models trained on large number of labeled samples improve the accuracy of many tasks of computer vision. In addition to this, since collecting and labeling vast amount of samples in various domains is difficult, it is important to develop adaptable models to different domains. In unsupervised domain adaptation, given data of labeled samples on source domain, our goal is to learn a classifier which performs well for both the samples on source domain and unlabeled samples on target domain. Although recent adversarial domain adaptation methods made impressive progress, training the classifier on source samples hinders the classifier from perfectly generalizing to the target samples. To this end, we propose an adversarial domain adaptation method enhanced via self-training to overcome the generalization problems of adversarial domain adaptation methods. In order to perform self-training, pseudo labels are assigned to the samples on target domain to learn more generalized representations for target domain. The experimental results on benchmark domain adaptation dataset, VisDA-2017, show that our proposed method significantly improves and outperforms the base method exploited in this work.
A Deep Learning-Based IoT Implementation for Detection of Patients’ Falls in Hospitals

Falls in hospitalized patients are a major problem for patient safety. Accidental falls are one of the most common incidents reported in hospitals. Thanks to the advances in technology, smart solutions can be developed for hospital environments as well as in all areas of life. Wearable devices, context-aware or computer vision-based systems can be designed to detect patients who fall in hospital. Internet of Things (IoT) can also be placed on wearable health products, and gathered sensors data is processed and analyzed with Machine Learning (ML) and Deep Learning (DL) algorithms. Furthermore, some DL algorithms such as LSTM are also applied to the analysis of time-series data. In this study, to minimize damage caused by falls, we’ve proposed a model that can achieve real-time fall detection by applying LSTM based deep learning technique on IoT sensor data. In result of the study, falling detection has been realized with 98% F1-score. Moreover, a mobile application has been successfully developed to inform caregivers about patients’ fall.
ENT 530 Social Media Portfolio / Alia Nur Hazirah Amirudin

The assignment is the study on how to run a business by using the social media. I have chosen a business to be run by me which is a business of selling scarves for women. By doing this business, I have learned on how to promote our products by using social media which is Facebook. Facebook is a social networking and e­-commerce site that makes it a strategic platform for entrepreneurs to market their products/services. While doing this assignment, I have learned on how to create sale postings by using teaser, and hard sell. Actually, an entrepreneur has to be proficient when closing sales using effective soft sell to answers customers' enquiries. Furthermore, I have learned on how to attract our customers and introduce our product to the customers. We need to set our customers target in order to make sure that our business running smoothly. We must know what customers want. We also need to know how to handle the upcoming problems.
Deep Learning Modeling Using Normal Mammograms for Predicting Breast Cancer

Breast cancer is the most dangerous leading cause of cancer-related death among women in the world. Internet of things (IoT) connected device that can help detect breast cancer earlier. Mammograms are the main way to detect breast cancer. But it is difficult to detect the cancer cell in the breast tissue. Because it has more tissue and less fat, the digitized mammography images are used for analyzing the abnormal areas of density, mass, and calcification that indicate the presence of cancer. In cases where adequate labeled data is available, deep learning approaches show considerable progress, and many advanced deep learning approaches have been proposed that have shown efficiency in the last few years in various modalities of computer vision and medical imaging. The small size of the medical image dataset also lowers the efficiency and robustness of computer-aided detection and/or diagnosis (CAD) systems based on deep learning. This research aims to build and test a new hybrid deep learning-based CAD method to predict the probability of a breast lesion found on a mammogram being malignant in an attempt to overcome this technological challenge. In this method, a deep convolutional neural network (CNN) will first be pre-trained using the ImageNet dataset. This work aims to develop and test a new CAD method based on hybrid transfer learning for mammographic mass classification. By combining the initial mammogram images with their morphological and texture variations, it is possible to produce more pseudo-color images to be added with information. Additional filters are used to remove unrelated transferred CNN characteristics on the network.
Perception of learning mediated by information technologies and communicate in higher education

The use of information and communication technologies (ICT) has become an engine for transforming education around the world. Due to the Covid-19 health crisis, educational institutions have increased the use of ICT to guarantee quality education and face student problems; therefore, the purpose of the research was to analyze the perception of learning mediated by the use of ICT in the training of university students. The methodological approach used was quantitative, the data was contrasted through a descriptive study, where a previously validated questionnaire was applied to 146 students from different careers of the Universidad Técnica Particular de Loja with a closed assessment Likert type (1-5). The results obtained showed that the perception of ICT-mediated learning by students is positive and they contribute to learning in a significant way.
Analysis of Smooth Heaps and Slim Heaps

The smooth heap is a recently introduced self-adjusting heap [Kozma, Saranurak, 2018] similar to the pairing heap [Fredman, Sedgewick, Sleator, Tarjan, 1986]. The smooth heap was obtained as a heap-counterpart of Greedy BST, a binary search tree updating strategy conjectured to be instance-optimal [Lucas, 1988], [Munro, 2000]. Several adaptive properties of smooth heaps follow from this connection; moreover, the smooth heap itself has been conjectured to be instance-optimal within a certain class of heaps. Nevertheless, no general analysis of smooth heaps has existed until now, the only previous analysis showing that, when used in sorting mode (n insertions followed by n delete-min operations), smooth heaps sort n numbers in O(nlg n) time.In this paper we describe a simpler variant of the smooth heap we call the slim heap. We give a new, self-contained analysis of smooth heaps and slim heaps in unrestricted operation, obtaining amortized bounds that match the best bounds known for self-adjusting heaps. Previous experimental work has found the pairing heap to dominate other data structures in this class in various settings. Our tests show that smooth heaps and slim heaps are competitive with pairing heaps, outperforming them in some cases, while being comparably easy to implement.
‘Voice Mind’ / Nur Adilah Syafiqah Adiman

Nowadays, people are busy with their life such as busy workers and busy housewives. They need to manage their time to serve the best for their life especially for those who have married. Therefore, our company offers Voice Mind that provides a functional tool for human beings especially for people with an active lifestyle and people who love music. Our team will ensure that users can feel the experience while using this Voice Mind. In addition, it offers a reasonable price for Voice Mind so that middle income people can afford to buy this product. The target market for my product is individuals who love music and people who have active lifestyles. People who love music will care about the product’s design and the function. This is because design is the main element where product could stick longer at the ears and probability to fall is low. These people are also concerned about the function where the Voice Mind are able to do anything other than hearing music. People will like it when they can reduce surrounding distractions without being taken out and at the same time able to hear music clearly.
Obstacles Avoidance of Wheeled Mobile Robot by Using Modified Artificial Bee Colony Optimization

This work presents the local static and movable obstacles avoidance of WMR by using modified artificial bee colony (MABC) optimization. In this work, two developed equations have been adopted to overcome the disadvantages of the ABC optimization. In the first modification, a new equation for representing the limit parameter of ABC optimization has been used to make the algorithm faster to reach the solutions. While in the second modification, a new equation has been applied to enhance the exploitation process of the algorithm. MABC optimization is used to generate an optimum short, fast and safe path followed by WMR between the start and target points with presence of static and movable with circular shapes obstacles in the environment. Three scenarios are presented in this work. In the first and second scenarios, a comparison was made between the results of the adopted MABC and the classical ABC. In the first scenario, there are six static obstacles in the environment and in the second scenario, there are two movable obstacles. While in the third scenario, there is a comparison between the results of the adopted MABC optimization with other algorithm. MATLAB software is used to run the simulation results as well as to check the validation of the adopted algorithm. The simulation results indicate that the performance of MABC is better than the performance of the other algorithms by about 5% and the WMR made a successful avoidance with minimum distance and minimum computation time and this is an indication of how well the MABC algorithm is working.
PERANCANGAN ULANG VISUAL APLIKASI MOBILE SISTEM PERINGATAN BANJIR DKI JAKARTA

s research was conducted to redesign the visual application of the DKIJakarta Flood Warning System specifically for the Capital City area. Thisapplication is not a creation for the community in the flood and providesinformation about the flood. A feature that can also assist users inmonitoring future and current flooding. The right way in the design elementsis good for users in the finished application so that the application isfinished. Selection of typography, color scheme to layout so that it will bemore maximal and possible in the information.
Binary Finite Field Extensions for Diffusion Matrices over the Finite Field F2m

In this paper, a new software tool has been developed that computes the corresponding m× m binary matrix over the finite field F2 of each element which is defined over 2m F (where 3 = m = 8 ) generated by different primitive irreducible polynomials. This extension process is necessary for the optimization of XOR (exclusive OR) counts of diffusion matrices whose elements are defined over the finite field, which are used especially in the diffusion layers of block ciphers. Therefore, the corresponding binary matrices given in this study can be used directly for the construction of new diffusion matrices.
An Image Quality Improvement Method Through Generative Adversarial Networks in Cloud Edge Integration

In this paper, a method is proposed to improve the image quality of clients based on the information of the residual images stored in the edge. A large amount of image information is stored in the cloud, whose image quality can meet the requirements of users in most cases, but for individual high-value targets, a higher image quality is needed. With the compressed information of the residual images stored at the edge, the quality of client images can be effectively improved. In this paper, firstly, an original image is compressed by JPEG with different quality factors and subtracted to obtain the residual images, which are then compressed by a proposed generative adversarial network, and the 1/100 one-dimensional data is stored in the edge. Secondly, when users receive a basic image, the quality of the final image is improved by decompressing the residual data. The compressed data can be appropriately removed and the image quality can be improved according to different needs. The experimental results show that, compared with the original compression method, through the cloud edge client collaboration method, not only can the image quality be improved, but the amount of data stored and transmitted in the cloud can also be effectively reduced, fully reflecting the advantages of on-demand service.
Leveraging rapid scene perception in attentional learning

Author(s): Adema, Juliana D.; Tang, Shuran; Alizadeh Saghati, Nahal; Mack, Michael L. | Abstract: In addition to saliency and goal-based factors, a scene’s semantic content has been shown to guide attention in visual search tasks. Here, we ask if this rapidly available guidance signal can be leveraged to learn new attentional strategies. In a variant of the scene preview paradigm (Castelhano a Heaven, 2010), participants searched for targets embedded in real-world scenes with target locations linked to scene gist. We found that activating gist with scene previews significantly increased search efficiency over time in a manner consistent with formal theories of skill acquisition. We combine VGG16 and EBRW to provide a biologically inspired account of the gist preview advantage and its effects on learning in gist-guided attention. Preliminary model results suggest that, when preview information is useful, stimulus features may amplify the similarities and differences between exemplars.
REFLECTING THE USE OF MOBILE APPLICATIONS TO FACILITATE LANGUAGE TEACHING AND LEARNING AMID COVID-19: A SYSTEMATIC LITERATURE REVIEW

REFLECTING THE USE OF MOBILE APPLICATIONS TO FACILITATE LANGUAGE TEACHING AND LEARNING AMID COVID-19: A SYSTEMATIC LITERATURE REVIEW
Estrategias pedagógicas para el fortalecimiento de la convivencia y la democracia: retos pedagógicos por la contingencia sanitaria covid-19

espanolLa presente investigacion analiza las formas de articular aspectos relacionados con la educacion, la convivencia y la democracia, partiendo de las multiples limitantes pedagogicas, generadas por la contingencia sanitaria del COVID-19. Para tal fin, se hizo un estudio al interior de las Instituciones Educativas, de Barranquilla del Departamento del Atlantico, Colombia, para determinar el aporte de las guias didacticas de aprendizaje, la implementacion del enfoque de aulas invertidas, los modelos educativos hibridos, como estrategias que promueven plurales formas de aprendizaje, la democracia y la sana convivencia. El estudio se sustento en el tipo de investigacion cualitativa, basado en el paradigma interpretativo, con un enfoque racionalista-deductivo. Los resultados indicaron que el empleo de las estrategias pedagogicas mencionadas y los recursos que facilitan las tecnologias de la comunicacion, con formas epistemicas dialogicas, inducen el fortalecimiento de la educacion, la convivencia y la democracia, asi como el pensamiento critico compartido, buscando promover educar para la ruptura de imposiciones en beneficio de sociedades democraticas como mejores lugares de convivencia. EnglishThis research analyses ways of articulating aspects related to education, coexistence and democracy, based on the multiple pedagogical constraints generated by the health contingency of COVID-19. To this end, a study was made within the Educational Institutions, barranquilla of the Department of the Atlantic, Colombia, to determine the contribution of the Didactic Learning Guides, the implementation of the Inverted Classrooms approach, hybrid educational models, as strategies that promote plural forms of learning, democracy and healthy coexistence. The study was based on the type of qualitative research, based on the interpretive paradigm, with a rationalist-deductive approach. The results indicated that the use of the aforementioned pedagogical strategies and the resources that facilitate communication technologies, with dialogical epistemic forms, induce the strengthening of education, coexistence and democracy, as well as shared critical thinking, seeking to promote education for the breakdown of impositions for the benefit of democratic societies as better places of coexistence.
PROSPECTIVE TEACHERS’ OPINION ON COMPUTER PROGRAMMING IN PRIMARY EDUCATION

In this paper, author discusses the importance and the need for teaching and learning computer programming in primary education. Due to the increasing use of automation, robots and artificial intelligence, the needs for occupations in the labour market are changing - some existing occupations are being discontinued, and others are being created. According to the information from various sources, it is expected that today's primary school students, when they grow up, will work in the occupations that do not yet exist today. Various predictions say that it will be between 33%, over 65%, and up to 85% of new occupations. Today's primary education students should be prepared for such future labour market by introducing them early in their education to the technologies they are likely to encounter in their future workplace.If machines (robots and computers) will perform heavy and repetitive tasks, and a range of other tasks that can be automated or controlled by artificial intelligence, then the people are left with tasks such as designing, building, operating, monitoring, and maintaining such machines, at least until they themselves are designed, built, and maintained. One of the activities that is increasingly present in today’s occupations, and with a very high probability very much in the future, is the programming of computers to perform tasks that can be automated.Author presents the results of the research of the primary education prospective teachers' opinion on computer programming in the primary education of the Republic of Croatia. The aim of this research is to find out their interest on computer programming and whether they think that the knowledge about computer programming is important for today’s primary education students.The research hypotheses are:Hypothesis 1: Primary education prospective teachers would like to learn more about computer programming.Hypothesis 2: Primary education prospective teachers believe that computer programming is important for today’s primary education students in relation to their future occupations.The survey participants were 109 (106 female and 3 male) primary education prospective teachers - the students at the University of Zagreb’s Faculty of Teacher Education. The data were collected in January and February 2020. using the anonymous online Google Forms questionnaire prepared by the author. Statistical analysis (including One Sample t-test) was performed using the free and open source statistical software GNU PSPP, version 1.0.1. (https://www.gnu.org/software/pspp/).The research results show that on the scale of 1 to 5 (1-poor to 5-excellent), the primary education prospective teachers self-evaluate their current knowledge of computer programming as M=2,52.On the Likert scale (1-strongly disagree to 5-strongly agree) they would like to learn more about computer programming (M=3,70, t=6,61, sig. < 0,01), and their opinion is that computer programming is important for today’s primary education students in relation to their future occupations (M=3,72, t=7,65, sig. < 0,01). Both hypotheses are confirmed.
FX Infrastructure Complaint Management System

The FX Infrastructure Complaint Management System is a web application which focuses on an activity or function, which is based on management of infrastructure of institutions. In this web application user select faulty accessories and report it to the admin with particular complaint. It maintains a database of all the information that are recorded and received. It helps the college to maintain all the accessories safely. The admin get this information, Admin can perform quick action to repair infrastructure of college. Also according to students and teachers complaint he quickly know the location of accessories as classroom, cantine, library, bathroom etc. This web application is mainly focus on the maintenance of college infrastructure management.