Reviewerly/exHarmony huggingface dataset:
Investigating Significant Factors Influencing Response Time of Customer Complaint Based on Analytics Method

The purpose of this study is to investigate significant factors that influenced duration of solving financial institutions’ customer complaint. Using raw customer complaint dataset from Consumer Financial Protection Bureau (CFPB) website, it was found that many of sub-categories are not well organized. Thus, it is important to proceed with data cleaning and data preparation steps before any analysis been performed. In this study, Artificial Neural Network (ANN) had been chosen since it can deal with non-linear relationship by using sigmoid function. Further to this, it was found that Product, Company response and Issues are the significant factors that are more likely to be solved more than one day. The use of this analysis can be particularly beneficial for related financial party that might need to assist their customer in future.
Deteksi Konten Gereflekter pada Cerita Anak Menggunakan Naïve Bayes Classifier

Textbooks and storybooks are the ones used as a source of knowledge. When children read a book, they will try to interpret each word and sentence in it. However, it will be a problem if the book contains vulgar words and indecent sentences. For children at the elementary school level, it is not allowed. For this research, we called that content as gereflekter content. Based on these problems, this research was conducted by building a system to detect gereflekter content in the text of the child's stories that were used as a data set. A system is built by using Naïve Bayes Classifier (NBC) and then evaluated in two scenarios using accuracy, precision, and recall metrics because the characteristics of the data set are imbalanced with the amount of data in the negative class are greater than the data in the positive class. From evaluation results, test scenario produced a high average precision of 99.01%, whereas the recall value has an average of above 50%. From these two values, it can be concluded that the model built by the system has not detected the class properly, but highly trusted when it does.
Peningkatan Keterampilan Menulis dengan Menggunakan Layanan Informasi Berbasis Contextual Teaching and Learning (CTL)

Very important learning skills mastered to support the achievement of a learner, especially students who are already at a high level as well as the learning patterns demanded independence and maximum sincerity to be able to complete the study properly and on time. One of the skills learned are very important for a student namely writing skills. Student condition at the present time most have yet to master the technique of writing optimally, so that it could lead to underprivileged students write a scientific paper. This article is trying to improve student understanding of the writing skills by using a information service based Contextual Teaching and Learning (CTL). The sampling method using simple random sampling. The research sample is student counseling, amounting to 22 people. Analysis of the results using percentages and statistical tests using SPSS with the using Wilcoxon Signed Rank Test. The research found that the information services based Contextual Teaching and Learning (CTL) can significantly improve the writing skills of students with significant value of 0.005.
StegoCrypt: Combining steganography and cryptography

The utilization of pictures to conceal data is a highlight which leaves little uncertainty in a watcher's psyche. Utilizing any medium to shroud data alludes to a strategy called steganography. When we utilize a picture as a medium then that technique is called picture steganography. The most renowned technique to now is the Least Significant Bit Algorithm (LSB). In that technique, the least critical piece of every pixel is taken and data is covered up in that. This anyway is effectively brittle. Consequently an option and increasingly secure arrangement is given. First the information is scrambled utilizing the Blowfish calculation. Next an inventive technique is displayed. This encoded square is broken down to 'n' littler squares and 'n' pictures are picked indiscriminately what's more, each picture is made to conceal a square of the encoded information. To keep up the right arrangement of hinders a hash table is kept up. This is then encoded utilizing LSB to another picture called the hashing picture. This hashing picture is sent alongside the 'n' different pictures. To separate the information out, first the hash picture is acquired and utilizing this the scrambled square is reassembled and after that unique information is gotten by unscrambling.The utilization of pictures to conceal data is a highlight which leaves little uncertainty in a watcher's psyche. Utilizing any medium to shroud data alludes to a strategy called steganography. When we utilize a picture as a medium then that technique is called picture steganography. The most renowned technique to now is the Least Significant Bit Algorithm (LSB). In that technique, the least critical piece of every pixel is taken and data is covered up in that. This anyway is effectively brittle. Consequently an option and increasingly secure arrangement is given. First the information is scrambled utilizing the Blowfish calculation. Next an inventive technique is displayed. This encoded square is broken down to 'n' littler squares and 'n' pictures are picked indiscriminately what's more, each picture is made to conceal a square of the encoded information. To keep up the right arrangement of hinders a hash table is kept up. This is then encoded utilizing LSB to another picture called the hashing picture. This hashing picture is sent alongside the 'n' different pictures. To separate the information out, first the hash picture is acquired and utilizing this the scrambled square is reassembled and after that unique information is gotten by unscrambling.
SISTEM PENGOLAHAN DATA ABSENSI KARYAWAN STMIK KHARISMA MAKASSAR

The purpose of this research is to design a website that can retrieve attendance data from the fingerprnit machine and then save it to the database and then display it to the website. The problem that arises is, to retrieve data from a fingerprint machine, the computer is first installed an application provided by the fingerprint machine provider company to be able to retrieve data from the fingerprint machine. The author makes a website so that the process of retrieving data from the fingerprint machine can be done by accessing the website. Websites created by authors can only be accessed in the same network. The author describes the system processes that are running by using a use case diagram. Authors use activity diagrams to describe system workflows in applications. To describe the structure of a web application, the author uses the Entity Relationship Diagram. The author uses the blackbox method for testing applications. The results of this study the author managed to design and create a website that can retrieve data from the fingerprint machine.
PELATIHAN PENULISAN KARYA ILMIAH BAGI GURU SD DI KECAMATAN DUREN SAWIT DAN PULOGADUNG

Professional teachers are required to have abilities in various aspects, not only have competence in learning, also teachers are required to be able to write scientific papers, this is a must. For some teachers, scientific papers is considered difficult work, and as a result scientific papers still becomes a barrier in many ways. The problem that is happening right now is elementary school teachers in Duren Sawit and Pulogadung Subdistricts are still constrained in making scientific papers, especially classroom action research with the following conditions: (1) The teacher does not have the knowledge in conducting classroom action research; (2) The teacher does not yet have the skills in conducting classroom action research; (3) The teacher is not accustomed to doing classroom action research. To overcome these problems, then do the following things; (1) Provide training to teachers through writing scientific papers (CAR) training; (2) Provide reinforcement and direct experience for the practice of conducting CAR. (3) Providing assistance to teachers in the writing scientific papers (CAR. Training result show that; (1) The teacher knows the procedures for writing scientific papers; (2) Workshop participants are able to make a draft classroom action research.
Voiceprint-Based Biometric Template Identifications

Performance improvement of the speaker recognizers using the traditional methods like signal processing, has hit a dead end. Speech researchers are therefore now focusing on other techniques and processes to supplement the traditional methods to reduce the gap in communication interfaces between humans and machines. Voiceprint based biometric identifications is evolving as a new technique. Phonetic distance measurement is one such evolving technique and the cutting edge researchers are inspired to work on this technique to circumvent and overcome the above problem. This chapter covers a new speaker recognition model based on the pronunciation variability. The pronunciation variability is used to identify the voiceprint of the speakers. The Kullback-Leibler divergence relative entropy criterion is used for the speaker identification and verification. An adaptation model is designed for the unsupervised dynamic adaptation of the new pronunciation variants. The multi-layered code book memory using the modified vector quantization technique is designed to keep the word confusability low and ensures efficient retrieval of the pronunciation variants. The confusion matrix and performance metrics are used for performance evaluation of pronunciation classifier. The pronunciation classification error rate, OOV error rate and word error rate are used for evaluation.
Deep learning networks to segment and detect breast terminal duct lobular units, acini, and adipose tissue: a step toward the automated analysis of lobular involution as a marker for breast cancer risk

Background: Terminal duct lobular unit (TDLU) involution is the physiological process whereby Type 2 and 3 lobules revert to Type 1 after child-bearing years. TDLU involution (quantitatively assessed by TDLU count per mm2, TDLU span, and acini count per TDLU) is inversely associated with breast cancer risk. The manual assessment of involution is time-consuming and subjective, making it impractical to perform on large epidemiological studies. Deep learning algorithms such as convolutional neural networks (CNNs) could be utilized for rapid and automated assessment of TDLU involution. We designed two CNNs to segment TDLUs and detect acini as the first step toward large-scale assessment of TDLU involution, and a third CNN to segment adipose tissue. Design: Whole slide images (WSIs; n=50) were obtained from the Nurses’ Health Study Incident Benign Breast Disease Study. For each WSI, TDLUs, acini, and adipose tissue were annotated within a region of interest comprising approximately 10% of the total tissue area. In order to assess involution in histologically normal breast parenchyma only, TDLUs with proliferative or metaplastic changes were excluded from manual evaluation. CNNs were engineered to recognize TDLUs, acini, and adipose tissue using 60% of the WSIs for training, 20% as a test set, and 20% for validation. F1 and Dice scores were calculated as accuracy measures to compare CNN segmentation to manual assessment. Results: Our CNNs detected acini, segmented TDLUs, and segmented adipose tissue with accuracy measures of 0.73, 0.84, and 0.86, respectively. Two primary causes of discordance with manual assessment were identified: 1) complex clustering of TDLUs where our CNN had difficulty predicting TDLU boundaries and 2) acini with proliferative or metaplastic changes which our CNN frequently detected as acini but which were intentionally excluded from manual annotation. Conclusion: We have developed a series of deep learning networks to segment and detect TDLUs, acini, and adipose tissue on WSIs. With accuracy measures of >0.7, our CNNs are sufficiently robust to be integrated into a computational pipeline for automated assessment of the quantitative features of TDLU involution, and will be further refined to address sources of discordance with manual assessment. This is the first step toward the large-scale quantification of TDLU involution which, when applied to patient samples, could be used to better determine the breast cancer risk associated with lobule type and degree of involution.
Contradictions And Meanings Of Modern Vocational Education: Research Results

The article is devoted to the problem of value-semantic foundations of vocational education as significant components of professionalization in the conditions of social transformation. Based on the results of the study, the authors reveal contradictions in the formation and development of values among the younger generation. On the back of diagnostics, the article discusses the specificity of value representations of students, university teachers, school teachers. The results of the study confirm that university teachers do not believe in the development of the student as a citizen and do not work on it. Also, by graduation from the university, students experience a crisis in acceptance of the values of the professional community, that is, they are not only unprepared to form a system of values in other people, but they themselves have not understood their system of values. At the same time, school teachers recognize that they need additional knowledge for development of schoolchildrens values. Consequently, to increase the level of professional identity of university graduates, the process of forming the values of the professional pedagogical community among students should be accelerated, and the special role in this aspect belongs to the university teacher. To solve this problem, systematic organization of the process of lifelong education using the whole arsenal of tools of the competency-based approach is necessary.
YOLOv3-Tesseract Model for Improved Intelligent form Recognition

Background: YOLOv3-Tesseract is widely used for the intelligent form recognition because it exhibits several attractive properties. It is important to improve the accuracy and efficiency of the optical character recognition. Methods: The YOLOv3 exhibits the classification advantages for the object detection. Tesseract can effectively recognize regular characters in the field of the optical character recognition. In this study, a YOLOv3 and Tesseract-based model of improved intelligent form recognition is proposed. Results: First, YOLOv3 is trained to detect the position of the text in the table and to subsequently segment text blocks. Second, Tesseract is used to individually detect text blocks and combine YOLOv3 and Tesseract to achieve the goal of table character recognition. Conclusion: Based on the Tianchi big data, experimental simulation is used to demonstrate the proposed method. The YOLOv3-Tesseract model is trained and tested to effectively accomplish the recognition task.
Luminance mapping of light sources using ray sampling and compression

In this paper, a ray sampling method for calculating the luminance maps of light sources and compressing the ray files is proposed. The approach can directly derive luminance maps ofa light source from the ray file. The Fibonacci lattice model and the Kriging techniques are used to reconstruct the rays and to interpolate the luminous flux values of the new rays from the original ray data, respectively. A planar LED is used for the verification experiments. Luminance maps are simulated via sampling a ray file of the LED, and compared with actually measured luminance maps. A good agreement is found by high NCC and low NRMSE values as wellas the visual comparisons,and indicates the effectiveness of the proposed method. In addition, the ray emission experiments are performed,and the experimental results preliminarily validate the ability of the proposed sampling method in terms of compressing ray data.
Se, con i new media, è lecito sperare in un mondo umano

This paper focuses on the risks and opportunities associated with the way people use new media. In this context, we propose that more attention must be paid to language in order to create new dynamics within the humanities.
Survey on Clustering Techniques

Clustering is a common technique for statistical data analysis, which is used in many fields, including machine learning, data mining, pattern recognition, image analysis and bioinformatics. In this journal the clustering is applied to the image data. The feature values are taken, and the final solution depends upon, these values on which the categorization is done. The complexities for the different methods are also defined here. Clustering algorithms can be classified into partition-based algorithms, hierarchical based algorithms, density-based algorithms and grid-based algorithms. This journal paper focuses on a keen study of different clustering algorithms in data mining. A brief overview of various clustering algorithms is discussed.
User Experience of Wearable Infant Sleep Monitoring System for Medical Research and Diagnostics

A large proportion of infants have frequent sleep-related issues, and some of them may also have an underlying medical adversity, such as preterm birth or neonatal infections. While infant sleep is currently only studied in hospitals using labor-intensive polysomnography measurements, there would be a clear need for recording in patient’s home as well. Here, we describe a newly developed mobile wearable system designed for such purpose, consisting of pull-up pants, with integrated motion sensor and textile ECG-sensors. The paper presents our preliminary findings of product user’s experience; a parent, a nurse and a doctor.
PARTY NEEDS AND SERVICES: AN ONLINE ORDERING SYSTEM WITH SMS NOTIFICATION

We all know that today in our current generation we were facing a technological advancement in the different field of our industries. That’s why a lot of Schools, Companies, and Businesses who is our top beneficiaries of this integrated technologies aimed to achieve different kinds of advanced systems to improve the old ways of their method of processing and management. Businesses were trying to connect to the internet to promote their products and services by creating online marketing techniques that nowadays trend. Online Ordering System and can handle the online inquiry. However, we decided to upgrade and include SMS Notification that provides information and status of their transaction since the use of cell phone and Internet are nowadays trend.
ESL learners’ vocabulary strategy use and learning perception

Vocabulary is an important aspect of second language acquisition. It plays a vital role in language learning. However, it has become the highest challenge that ESL learners faced in the process of language learning. This can be specifically focused on second language learning. In the need of high vocabulary in the second language, vocabulary learning strategies are suggested to be introduced among ESL learners. This enlightens of vocabulary learning strategies among ESL learners believed to have an improvement in vocabulary learning. This study employed a qualitative research design. It is conducted in need of identifying strategies used by learners. This study examined a number of strategies used by pupils from schools located in suburban areas. Besides, the interest of pupils towards vocabulary learning also examined in this study. Furthermore, this research was conducted to test whether there is any significant difference between male and female ESL learners in vocabulary learning perception. A set-off adopted version questionnaire was given to 100 samples which consist of 50 males and 50 females to gather the results of this study. The samples for this study were selected based on a random sampling method. The findings of the study showed that both male and female students have positive perceptions towards vocabulary learning and they preferred social strategy highly in their vocabulary learning.
Convolutional neural network hyperparameters designed for highest object recognition accuracy in satellite imagery

Geospatial data follows Moore's law. On the back of satellite hardware improvements and reduced costs of rocket launch there were more nanosatellites deployed to Lower Earth Orbit (LEO) in 2018 than in the previous 10 years combined allowing exponential satellite imagery data growth. Machine Learning tools enable us to process high resolution, multi-spectral satellite imagery data to recognize objects at large scale and generates insights with practical industry applications. It enables us to calculate global oil reserves, track tanker ships, forecast wheat yields or estimate retail revenue based on the car traffic, all exceptionally valuable financial information. In this paper, we research various computer vision techniques to develop and optimal technique for this unique type of the dataset: multi-spectral satellite imagery.
Reckless VII: Wife of Journalist Slain in Cartel-Linked Killing Targeted with NSO Group’s Spyware

Citizen lab would like to thank Griselda Triana for consenting to share this casewith the collaborating organizations, especially Article 19, and with the public.We are also grateful to the many other targets and victims of Pegasus for havingshared the cases on which our continuing work is based.Special thanks to the teams at R3D, SocialTic, and Article19 for their careful andimportant investigative work. We would like to especially thank and highlight thecontribution of Luis Fernando Garcia and his colleagues at R3D, and Article19 fortheir coordination in this particular case.Thanks to the whole Citizen Lab team, especially Miles Kenyon, Adam Senft, andMari Zhou for graphical assistance.Thanks to Amnesty International and Access Now for assistance in earlier phasesof the investigation.
Can Blockchain Solve the Hold-Up Problem for Shared Databases?

Consortium and industry-wide databases can help enterprises transact more efficiently, reduce redundancies in recording and transmitting information, and help identify profitable patterns and trends. Despite the fact that consortium and industry-wide databases are technologically feasible and can provide tremendous value, in practice they often do not succeed. One cause of this is an economic issue called the hold-up problem. The hold-up problem occurs when organizations are reluctant to make investments in tools or skills in order to join a collaboration, because those investments make them vulnerable to opportunism by collaboration partners. Blockchain can provide the economic structure for organizations to enter shared databases without fear of being victim to the hold-up problem. In this whitepaper, we illustrate through case studies the underlying economic drivers of the hold-up problem, levers by which it can be reduced, and the path forward for complementary research in technological standards and consortium governance.
Planning the trajectory of the 3RRR manipulator

The manipulator provides a gain in terms of production rate compared to conventional serial manipulators. The trajectory planning made it possible to optimize the performance of the parallel 3RRR manipulator in its workspace. This planning was done on the octave software, based on the modeling of the manipulator using the inverse geometry method. Which allowed to determine the workspace of the robot. The working space of a parallel robot therefore depends on the different independent kinematic chains that connect the base to the effector (mobile platform).