Reviewerly/exHarmony huggingface dataset:
Efektivitas Teknik Evaluasi Pembelajaran Menggunakan Aplikasi QR Code Terhadap Kemampuan Berpikir Kreatif Matematis Dan Kemandirian Belajar Siswa

Utilization of informasi technologi in particular a QR Code in education is still lacking. therefore researchers want to use the QR Code as an evaluation technique in mathematics. During this evaluation technique used by the teacher is a normal evaluation technique, while students seem to expect more help from their peers in working on math problems, because also students are less creative and less independent in working on math problems. In this study researchers used qr code as an evaluation technique in linear program material. The statement of the problem is whether students whose evaluation techniques use the application of QR code mathematical creative thinking skills and learning independence are better than students whose evaluation techniques do not use the qr code application. The number of students in class x office administration degree in smk indonesia bandung highway 2018/2019 is 34 students in the experimental class and 34 students in the control class. Based on the results of the study it can be concluded that students who use the qr code evaluation technique creative thinking ability and independence students who use ordinary evaluation techniques.
Development of a Mobile Learning System for Nurses' Cultural Competency Training.

We developed a mobile learning system that provides cultural competency training courses for nurses at a university hospital in South Korea using the system development life cycle. The contents and functions of the system were identified from the literature review, expert's recommendations, and the users' requirements. An algorithm, database, and user interface were designed. The system was implemented using the Newin Touchclass authoring tool. We evaluated and modified the system based on heuristics evaluation.
Las TIC y la práctica experimental: una revisión bibliográfica

espanolEl presente trabajo tiene por objeto hacer una revision cuantitativa, no exhaustiva, de investigaciones hechas desde el ano 2012/2013 a la fecha, sobre temas referidos a Tecnologias de la Informacion y la Comunicacion en general, y Simulaciones y Laboratorios Remotos, de manera particular, presentes en revistas electronicas de consulta habitual en el ambito de la investigacion en Ensenanza de la Ciencias Naturales y la Tecnologia. De su relevamiento y analisis, surge que es mucho lo que se habla sobre las TIC pero es verdaderamente poco lo que se investiga de manera sistematica y continua, hecho que se traduce en una baja tasa de publicacion de trabajos vinculados a esta problematica. EnglishThe purpose of this work is to make a quantitative, not exhaustive, review of research done from 2012/2013 to date, on issues related to Information and Communication Technologies in general, and Remote Simulations and Laboratories, in particular, present in electronic journals of habitual consultation in the field of research in Teaching of Natural Sciences and Technology. From its survey and analysis, it appears that there is a lot of talk about ICTs but there is really little that is systematically and continuously investigated, a fact that translates into a low rate of publication of works related to this problem.
There and Back Again: Cross-Lingual Transfer Learning for Event Detection

In this contribution we in- vestigate the generalisation abilities of a pre-trained multilingual Language Model, namely Multilingual BERT, in different transfer learning scenarios for event de- tection and classification for Italian and English. Our results show that zero-shot models have satisfying, although not opti- mal, performances in both languages (av- erage F1 higher than 60 for event detec- tion vs. average F1 ranging between 40 and 50 for event classification). We also show that adding extra fine-tuning data of the evaluation language is not simply ben- eficial but results in better models when compared to the corresponding non zero- shot transfer ones, achieving highly com- petitive results when compared to state-of- the-art systems.
One-bit Sigma-Delta modulation on the circle

Manifold models in data analysis and signal processing have become more prominent in recent years. In this paper, we will look at one of the main tasks of modern signal processing, namely, at analog-to-digital (A/D) conversion in connection with a simple manifold model — the circle. We will focus on Sigma-Delta modulation which is a popular method for A/D conversion of bandlimited signals that employs coarse quantization coupled with oversampling. Classical Sigma-Delta schemes provide mismatches and large errors at the initialization point if the signal to be converted is defined on the circle. In this paper, our goal is to get around these problems for Sigma-Delta schemes. Our results show how to design an update for the first and the second order schemes based on the reconstruction error analysis such that for the updated scheme the reconstruction error is improved.
A Hausdorff Stable Method for Finding Singularities with Application to the Intersections of Sampled Manifolds

We will present a family of novel methods for feature detection and image restoration which have a very clear geometrical interpretation, though the application areas, however, are not only limited to these ones. Our methods rely on the idea of realizing a close smooth approximation of the digital image or of a modified image which creates the singularity at the feature of interest. By close smooth approximation we mean that given the input function, our transformation outputs a smooth function which coincides with the input function in the neighbourhood where the function is smooth. As a result, one then by difference gets a neighbourhood of the singularity. With this respect, we could term them as geometric based methods for singularity detection. By such transformation, we are able to develop multi-scale, parametrised methods for identifying singularities in functions. These tools can then be used, via a numerical implementation, to detect features in images or data (e.g. edges, corner points, blobs, etc.), remove noise from images, identify intersections between surfaces, etc, and thus produce new geometric techniques for image processing, feature extraction and geometric interrogation.
Exploration of ML data models: Part 2

This chapter is a continuation of the discussions and demonstrations of basic ML data models that started in the previous chapter. I presented ten data models at the beginning of the previous chapter, and these were
A Study of Job Satisfaction of Academicians

Every management face challenges daily, but there are some challenges which are customary to every management. One from such common problems confronted by the management while managing employees (teachers) include - Job satisfaction &amp; Employee Loyalty. The invaluable resource that any institute possesses is its work force; indeed, an employee’s longer work experience at the same corporate enhances his worth. The primary qualities a teacher possesses include being introspective, being cooperative, being directive and being expressive. A syllabus which is effective and a curriculum that is well planned is only fruitful with availability of teachers who are meticulous in their duties. Knowledge alone cannot be the basis for gauging the ability of a teacher. There are other factors too like whether the teacher is comfortable in handling the profession that impacts the effectiveness of the system. With many research works having taken place in this zone with multiple organizations, what we lack is a distinct research on satisfaction at job which needs to be taken up.Hence, we conducted a research where we took a sample size containing 50 teachers and performed the survey on the premise of systematic sampling. The method followed in the process of acquiring and assembly of data was structured questionnaire method.
Analisis Performansi Image Registration Pair Mode Berbasis Sparse Representation

Abstrak Pengukuran kesamaan atau similarity measure adalah hal penting dalam image registration. Dalam penelitian kali ini penulis mengukur kesamaan dari dua buah gambar yang salah satunya sudah diregistrasikan dimana gambar pertama menjadi groundtruth. Pengukuran kesamaan telah banyak diteliti sebelumnya dengan banyak metode dengan hasil yang baik, tetapi masih ditemukan beberapa celah dimana pengukuran kesamaan tidak bisa diterapkan di semua kondisi. Sparse Representation (SR) adalah salah satu metode dalam pengukuran kesamaan di image registration dimana metode ini menghitung melalui indeks sparsness dari gambar. Keunggulan dari metode SR ini adalah akurasi dari kemiripan/kesamaan dari gambar masukan yang bisa terhitung dengan baik. Metode SR ini juga cukup kuat dalam menangani gambar dalam intensitas distorsi yang besar, yang banyak terdapat dalam gambar medis, gambar jarak yang jauh yang disebabkan perbedaan modalitas akuisisi dan kondisi iluminasi. Hasil yang didapatkan dalam penelitian ini antara lain nilai Root Mean Square Error (RMSE) dengan nilai terbaik sebesar 39,5825, nilai Peak-Signal to NoiseRatio (PSNR) dengan nilai terbaik 16,18 dB, nilai Structural Similarity Index (SSIM) dengan nilai terbaik 0,8318, nilai Correlation Coefficient (CC) dengan nilai terbaik 0,732, dan nilai Coherence dengan nilai terbaik 0,268. Kata Kunci: image registration, sparse representation, gambar panchromatic, gambar multispectral, pengukuran kesamaan. Abstract Similarity measure is an important thing in image registration. In this study the authors measures the similarity of the two images, one of which was registered where the first image became the groundtruth. Similarity measure have been studied previously with many methods with good results, but there are still some gaps where similarity measurements cannot be applied in all conditions. Sparse Representation (SR) is one method in measuring similarity in image registration where this method counts the sparsenessâ€™ index of images. The advantage of this SR method is the accuracy of the similarities of the input images that can be counted well. The SR method is also strong enough to handle images in a large intensity of distortion, which is often found in medical images, long distance imagescaused by differences in acquisition modalities and illumination conditions. The results obtained in this study include the value of Root Mean Square Error (RMSE) with the best value is 39,5825 the value of Peak Signal to NoiseRatio (PSNR) with the best value is 16,16 dB, the value of the Structural Similarity Index (SSIM) is 0,8313, the value of Correlation Coefficient (CC) with the best value is 0,732, and the value of Coherence with the best value is 0,268. Keywords : image registration, sparse representation, panchromatic image, multispectral image, similarity measure
Intelligent Location of Data Center Linkage Path Under Non-Outage Maintenance

According to the analysis of problems such as inaccurate intelligent positioning of data center linkage path, an intelligent positioning method of data center linkage path under non-outage maintenance is proposed. The optimal path in data center linkage structure is selected by combining neural network technology, and the safety coefficient in path nodes is calculated, so as to strengthen the safety coefficient of target path, check positioning errors, and finally realize the optimal selection and intelligent positioning of data center linkage path. Finally, experiments prove that the intelligent positioning technology of data center linkage path under non-outage maintenance is greatly improved compared with the traditional positioning technology.
Regularizing Neural Networks via Stochastic Branch Layers

We introduce a novel stochastic regularization technique for deep neural networks, which decomposes a layer into multiple branches with different parameters and merges stochastically sampled combinations of the outputs from the branches during training. Since the factorized branches can collapse into a single branch through a linear operation, inference requires no additional complexity compared to the ordinary layers. The proposed regularization method, referred to as StochasticBranch, is applicable to any linear layers such as fully-connected or convolution layers. The proposed regularizer allows the model to explore diverse regions of the model parameter space via multiple combinations of branches to find better local minima. An extensive set of experiments shows that our method effectively regularizes networks and further improves the generalization performance when used together with other existing regularization techniques.
Top-K search scheme on encrypted data in cloud

&lt;span&gt;A Secure and Effective Multi-keyword Ranked Search Scheme on Encrypted Cloud Data. Cloud computing is providing people a very good knowledge on all the popular and relevant domains which they need in their daily life. For this, all the people who act as Data Owners must possess some knowledge on Cloud should be provided with more information so that it will help them to make the cloud maintenance and administration easy. And most important concern these days is privacy. Some sensitive data exposed in the cloud these days have security issues. So, sensitive information ought to be encrypted earlier before making the data externalized for confidentiality, which makes some keyword-based information retrieval methods outdated. But this has some other problems like the usage of this information becomes difficult and also all the ancient algorithms developed for performing search on these data are not so efficient now because of the encryption done to help data from breaches. In this project, we try to investigate the multi- keyword top-k search problem for encryption against privacy breaks and to establish an economical and secure resolution to the present drawback. we have a tendency to construct a special tree-based index structure and style a random traversal formula, which makes even identical question to supply totally different visiting ways on the index, and may additionally maintain the accuracy of queries unchanged below stronger privacy. For this purpose, we take the help of vector area models and TFIDF. The KNN set of rules are used to develop this approach.&lt;/span&gt;
Instance segmentation by using mask R-CNN based on feature fusion of RGB and depth images

The instance segmentation for obstacle detection based on machine vision and deep learning is quite important for autonomous driving system. In this paper, a method using the Mask R-CNN based on feature fusion of RGB and depth images for instance segmentation is proposed. It extracts the features of depth image by designing a two-layer NiN network, and uses convolution to realize the feature fusion and dimension reduction of RGB image and depth image. The edge texture in depth image can improve the accuracy of boundary frame positioning. Experimental results on typical benchmark dataset demonstrates the effectiveness of the proposed method, which can improve the segmentation accuracy by 4% and the recall rate by 2%.
A Novel Intersecting Cortical Model Based Adaptive Parameter Setting and Segmentation Method for Waveform Images

Recently the field of signal processing advanced a lot with the advent of enabling computational solutions and signals perceived in the form of an image are being interpreted and analyzed with ease. Waveforms generated by signal acquisitions modules can be analyzed as an image or their corresponding numerical functions to extract the scrambled pertinent information. As to date, there is no significant work done to automatically set parameters and segment waveform image considering its unique characteristic. In this study, we have proposed automatic parameters setting and segmentation method based on Intersecting Cortical Mode (ICM) which enables maximum entropy gain from waveform images. We used the ICM as a base and developed a novel method which is natural for waveform images. Characteristics of pixel values used to deduce corresponding segmentation parameters. The experiment is done on UCI-5000-Waveform database generator (version 2) dataset, images captured from the oscilloscope and Berkeley segmentation dataset and benchmark. Our method outperformed for subjective and objective evaluations also had shown the consistent assignment of parameter values. This paper has bridged the gap of analyzing waveform images with the state of the art novel automatic method, minimized loss of pertinent signals and paved the way to unstructured data manipulation in signal processing.
Extracting Components from Thai-Official Documents using Image Processing and Machine Learning Techniques

This work proposes a technique for classifying Thai-official documents and extracting the document components (titles, official id, dates, from, attachments, references, others). The classification is performed using a two-stage approach, i.e. (i) initial classification and (ii) refinement process. The classification is carried out on the specific regions (the headers) of the documents – as different types of the documents contain different information in the header sections. The results in the classification process are then refined in a post-processing procedure, as to improve the classification results. Detecting and exacting the components of the documents are carried out by applying a mathematics morphological operation method, detecting a set of the component candidates. The false candidate rejection is proceeded using a classification-based technique, i.e. Random Forest and CNN. The results of the experiments show that the proposed technique produces promising results for both the document classification and component extraction.
The Flouting of Grice’s Conversational Maxims in Indonesian and American Vlogs.

width: 0px; > Keywords: Grice’s conversational maxims, conversational implicature, flouting conversational maxim, vlog. Grice proposed a rule called cooperative principle as a way to create an effective conversation. The principle is elaborated into conversational maxims. The conversational maxims are not always obeyed by speakers of a conversation. One of the ways to disobey the maxim is by flouting. The study aims to investigate the flouting of Grice’s conversational maxim in Indonesian ad American vlogs. The study tries to find the differences of flouting done by Indonesian and American vloggers. The vlogs used as the data sources should have more than one speaker in order to get the data in the form of conversation. The vlogs taken were also in similar duration. The data are obtained from ten Indonesian vlogs and ten American vlogs. The obtained data were analyzed and categorized based on four Grice’s conversational maxims. The data obtained reveals that in Indonesian vlogs, the most frequently flouted maxim is the maxim of manner, followed by the maxim of relevance, quantity, and quality. In American vlogs, the researcher found that the most often flouted maxim in the vloggers’ conversation is the maxim of manner, followed by the maxim of quality, quantity, and relevance. The findings of the study show that the maxim of manner occupies the first position as the most often flouted maxim in Indonesian vlogs. It is followed by the maxim of relevance, quantity, and quality. The most often flouted maxim in American vlogs is the maxim of manner, followed by the maxim of quantity, quality, and relevance. The statistics of the finding suggests that there are no big differences between the flouts made by Indonesians and Americans. The findings then reveal that both Indonesians and Americans also sometimes flout more than one maxim at the same time. Based on the findings and discussion, there are several conclusions that could be noted. It can be concluded that both Indonesians and Americans regularly flout Grice’s conversational maxims. This means that Grice’s cooperative principle is not always obeyed all the time by participants of a conversation. Also, the study reveals that although Indonesians and Americans speak two different languages, their ways of applying Grice’s cooperative principle is similar.
Learning to Sample Hard Instances for Graph Algorithms.

Hard instances, which require a long time for a specific algorithm to solve, help (1) analyze the algorithm for accelerating it and (2) build a good benchmark for evaluating the performance of algorithms. There exist several efforts for automatic generation of hard instances. For example, evolutionary algorithms have been utilized to generate hard instances. However, they generate only finite number of hard instances. The merit of such methods is limited because it is difficult to extract meaningful patterns from small number of instances. We seek for a probabilistic generator of hard instances. Once the generative distribution of hard instances is obtained, we can sample a variety of hard instances to build a benchmark, and we can extract meaningful patterns of hard instances from sampled instances. The existing methods for modeling the hard instance distribution rely on parameters or rules that are found by domain experts; however, they are specific to the problem. Hence, it is challenging to model the distribution for general cases. In this paper, we focus on graph problems. We propose HiSampler, the hard instance sampler, to model the hard instance distribution of graph algorithms. HiSampler makes it possible to obtain the distribution of hard instances without hand-engineered features. To the best of our knowledge, this is the first method to learn the distribution of hard instances using machine learning. Through experiments, we demonstrate that our proposed method can generate instances that are a few to several orders of magnitude harder than the random-based approach in many settings. In particular, our method outperforms rule-based algorithms in the 3-coloring problem.
Development of Finger Scanner Integrated Absention Web Application in Astra Manufacturing Polytechnic

Astra Manufacturing Polytechnic is educational institution under the auspices of PT Astra International (Astra Group) which has total of approximately 700 students. Industrial culture is adopted by applying discipline in the form of attendance. Students who do not arrive on time will be charged Minus Hours. Students do attendance with attendance cards and recapitulated by each study program admin and takes 700 minutes (all study programs) every week to produce minus hours as calculation of student errors. Many students are cheating in the attendance process. Because attendance cards can be deposited to other students. To overcome this, the attendance method was replaced by initially using machine and attendance card with finger scanner and integrated finger scanner integrase application with sql server database. So hopefully cheating and recapitulation time can be eliminated. In the implementation of this research extreme programming is used as software development methodology. After implementation of the application is able to eliminate 700 minutes and eliminate the use of attendance cards.