Reviewerly/exHarmony huggingface dataset:
Vertical intent prediction approach based on Doc2vec and convolutional neural networks for improving vertical selection in aggregated search

Vertical selection is the task of selecting the most relevant verticals to a given query in order to improve the diversity and quality of web search results. This task requires not only predicting relevant verticals but also these verticals must be those the user expects to be relevant for his particular information need. Most existing works focused on using traditional machine learning techniques to combine multiple types of features for selecting several relevant verticals. Although these techniques are very efficient, handling vertical selection with high accuracy is still a challenging research task. In this paper, we propose an approach for improving vertical selection in order to satisfy the user vertical intent and reduce user’s browsing time and efforts. First, it generates query embeddings vectors using the doc2vec algorithm that preserves syntactic and semantic information within each query. Secondly, this vector will be used as input to a convolutional neural network model for increasing the representation of the query with multiple levels of abstraction including rich semantic information and then creating a global summarization of the query features. We demonstrate the effectiveness of our approach through comprehensive experimentation using various datasets. Our experimental findings show that our system achieves significant accuracy. Further, it realizes accurate predictions on new unseen data.
A case of study about overfitting in multiclass classifiers using Convolutional Neural Networks

Convolutional Neural Networks (CNNs) have achieved much success mainly in areas of computational vision, such as image recognition, classification, object segmentation, and more. The learning process of this type of network generally requires large volumes of data, commonly high-resolution images, and the adjustment of a large number of parameters. The lack of control over the learning process of the model can lead to various problems. One of them is overfitting, which leads the network to a situation where it loses generality, making incorrect forecasts in the presence of new data. Another very common problem is its speed of convergence, which depends on the parameterization of the network: selection of the number of filters per layer, number of convolution layers, and more, where a fine adjustment is very important to avoid excessive computational costs. Understanding the origins of these problems and the ways to prevent them from happening is essential for a successful design. In this paper, we analyze these problems by designing a multiclass classifier among ten categories of images from the Caltech 256 dataset, based on the metrics of accuracy, precision, recall, and loss. To do so, python 3.6, TensorFlow and Keras libraries were used on an RTX 2060 GPU.
Pelatihan Augmented Reality (AR) Untuk Meningkatkan Keterampilan Guru

The development of information technology in the world is currently experiencing very rapid development, especially in the field of mobile phones making it easier for people to get information through smartphones. This development also plays a role in the development of learning media. One of the development of learning media that is currently still new is learning media using Augmented Reality (AR). but the teacher of SMKN 1 Karanganyar does not know Augmented Reality so they do not have the skills to make Augmented Reality as an alternative learning media. Therefore it is needed Augmented Reality (AR) technology training for learning media. Community service activities have a goal that teachers can understand Augmented Reality (AR) and have the skills to make learning media using augmented reality. Community service uses a monological and dialogical approach. This augmented reality training activity was participated by 21 productive teachers from SMK N 1 Karanganyar. After the training, 95% of productive teachers of SMK N 1 Karanganyar have improved their skills so they already know about Augmented Reality (AR) and how to make Augmented Reality (AR) as an alternative learning media.
Methods for early recognition of OFDM data

A technique of early recognition (recovery) of data transmitted using OFDM technology by an incompletely received signal is considered. Theoretically, this approach is able to increase the speed of information transfer, as well as the resistance of the de-encoder to the loss of part of the transmitted signal. The article proposes a mathematical formulation of the OFDM signal early recognition problem, and also discusses several methods for solving it: a regularization method, an iterative method based on the fast Fourier transform, a gradient method based on learning, and an inverse operator method. The possibility of simultaneously using several methods to improve the accuracy of information recovery is considered. The results of numerical experiments presented in this work confirm the practical potential of the proposed approach.
A noncontact full-field flatness measuring system based on fringe projection

Flatness is one of the most important properties for quality control of manufacturing mechanical parts. The most widely applied coordinate measuring machine techniques of measuring flatness error are ineffective to collect abundant sample points, and the probes must contact the tested surface. Existing noncontact optical techniques are not full-field measurement and their devices are complex. This paper presents a simple but noncontact full-field flatness measuring system based on fringe projection profilometry technique. The designed device projects fringe patterns onto the tested surface, and by calculating the phase of modulated fringe images and calibrating the phase-height mapping relationship, the full-field surface sample points are acquired. Polarizers are applied to eliminate intense highlight of the measured surfaces. Optimization algorithm is introduced to determine the reference ideal plane and flatness error is then calculated. Several experiments are conducted to demonstrate that the proposed flatness measuring system can be applied to general mechanical parts, and it has high precision and high repeatability.
DIABLO: Dictionary-based attention block for deep metric learning

Recent breakthroughs in representation learning of unseen classes and examples have been made in deep metric learning by training at the same time the image representations and a corresponding metric with deep networks. Recent contributions mostly address the training part (loss functions, sampling strategies, etc.), while a few works focus on improving the discriminative power of the image representation. In this paper, we propose DIABLO, a dictionary-based attention method for image embedding. DIABLO produces richer representations by aggregating only visually-related features together while being easier to train than other attention-based methods in deep metric learning. This is experimentally confirmed on four deep metric learning datasets (Cub-200-2011, Cars-196, Stanford Online Products, and In-Shop Clothes Retrieval) for which DIABLO shows state-of-the-art performances.
Blind identification of network protocols based on improved Apriori algorithm

Abstract With the development and application of network technology and private protocols, more and more unknown protocols are found in communication networks. Analysis and identification of unknown protocols has become an important and difficult problem in obtaining valuable information from data. Based on the traditional Apriori algorithm, a reduced bit string algorithm and a weight compression algorithm are proposed to identify the unknown protocols according to the characteristics of network unknown protocol data in this paper. These proposed algorithms make use of location information, matrix compression, bit string and weight compression to gradually improve the identification accuracy and the processing efficiency of unknown protocols. Experiments on simulation data and actual data demonstrate the performance of the improved algorithms in terms of identification accuracy and time efficiency.
Enhancing Decision-making Systems with Relevant Patient Information by Leveraging Clinical Notes

Hospitalised patients suffering from secondary illnesses that require daily medication typically need personalised treatment.Although clinical guidelines were designed considering those circumstances, existing decision-support features fail in assimilating detailed relevant patient information, which opens up opportunities for systems capable of performing a real-time evaluation of such data against existing knowledge and providing recommendations during clinical treatments.In this paper, we present a proposal for a new feature to integrate with electronic health record (EHR) systems that enriches the health treatment process by automatically extracting information from patient medical notes and aggregating it in clinical protocols.Our goal is to leverage the historical component of the patient trajectory to improve clinical decision support systems performance.
Transfer learning of ImageNet Object Classification Challenge features to image aesthetic binary classification

The aesthetic classification of photographies is a problem of separating aesthetically pleasing images from not pleasing images using algorithms that describe and evaluate both emotional and technical factors. Since the mass adoption of deep convolutional neural network (DCNN) models for image classification problems different DCNN architectures have been developed due to its overall better performance, pushing the boundaries of the state-of-the-art performance of the image classification further. This paper evaluates how architectures and features that were primarily developed for the ImageNet Object Classification Challenge perform when analyzed under the aesthetic scope. A high level transfer learning model composed of a DCNN layer and a top layer that behaves as a linear SVM is proposed and seven different DCNN architectures are trained using it. Scenarios with just transfer learning and with fine tuning are evaluated and a model using the ResNet-Inception V2 architecture is proposed, which achieves results better than current state-of-the-art for the experiment conditions used.
Tranformasi Manajemen Universitas menggunakan Framework Cobit 5 di Era Industri 4.0

The Industrial Revolution 4.0 has brought the consequences of increasing demands for accountability, transparency for all organization. This is made about a change in the organizational design paradigm. In this RI 4.0, adactive power and fast responsive are the key to success achieving the organization's vision and mission. . It is a challenge for universities to adjust various problems in this era of disruption. The transformation of educational organizations is one of alternative in order to realize that is oriented to public services and to prepare competent human resources in their fields. This research offers a solution for universities to find out how far the university is prepared to face this era. The research used a descriptive method with a qualitative approach. By using purposive sampling and determined data source by snowball, the results showed that the transformation of universities in Riau had to immediately adapt of demands in RI 4.0. The IT management level process showed that it was still in level 2, which is the managed process level. These processes were still at level 1 and level 2 so they need to be increased to a minimum at level 3 (Established). It is recommended to improve the management of IT services and the application of information security management become more effective, increasing the adaptive power of the industrial revolution 4.0
SPEECH ACTS IN CORTEZ’S POLITICAL CAMPAIGN ADVERTISEMENT 2018

The study explores the role of language in the communication and interpretation of intentions by analyzing the narration of Alexandria Ocasio-Cortez’s political advertisement in Congressional Campaign in 2018. Hence, the study focuses on the pragmatic functions of locution, illocutionary and perlocutionary acts of the speeches. This study was conducted using the qualitative descriptive method. The findings show that the overall relative frequency percentages for the speech acts in Ocasio-Cortez’s 2018 political advertisement are: assertive 68%, directive 23%, commissive 4.5%, and declarative 4.5%. The results reflects that Ocasio-Cortez relied more on sentences that performed assertive acts than other speech acts since she wanted to introduce to the public who she is as an individual and what she is capable of as a future representative, and she also offered some relatable facts and situations that can appeal to the working class public to show that she’s on their sides. Using directive acts, she included herself as a part of the society and shows that she wants to make a change together with them, not on her own. This is a different approach to political advertisement since most of them rely more on the commissive acts by promising a better future for the society. This different strategy is something fresh that eventually played a part in her win in the 2018 Congressional District Election.Keywords:speech acts theory; pragmatics; political discourse; language and politics; political language.
The Design of SOA-based Applications for Electronic Medical Records in DKI Jakarta

The Hospital is public health care institutions.The services and actions will affect the patient's condition and comfort.all of that needs support from information systems that support business processes.The Hospital require Information systems that support for daily activity.Consider to Applying Enterprise Application Integration (EAI) is to connect various databases from other systems.Then, using EAI based on SOA, data sharing can be done in real time and unlimited business processes between applications and data sources that are interconnected.
Análisis del uso de la robótica en un Centro de Infantil y Primaria

The use of technologies has an impact on society and therefore on education, so analyzing the educational use of Learning and Communication Technologies, it is essential to know their development in the different educational stages, their learning and its use at different times and emphasizing the implementation of the use of robotics and digital tools. This analysis consists of two differentiated parts in an analysis of the use of robotics, benefits and disadvantages and an investigation to know the use, training and implication of the digital tools of the centers. In the conclusions, it can be observed that the educational centers and the analysis of robotics in a specific context do not exceed the proposed goals and ends. So the analysis of the improvements will be essential, for the correct implementation of robotics in Andalusia.
Breakdown film script using parsing algorithm

Breakdown script is a breakdown of the scenario into parts that describe each detail of the scene for shooting. The scenario is broken down into more detailed parts using the parsing algorithm. The film script used is a script in Bahasa Indonesia. The process starts from the film script file/scenario in FBX format uploaded to the website then is solved using a parsing algorithm into film elements such as cast members, extras, props, costumes, makeup, vehicles, stunts, special effects, music and sound. The results of this breakdown into sheets according to film elements. The purpose of this research is to produce breakdown sheets from film scripts according to film elements. The parsing algorithm test results showed the correct results of 12 scenes out of 19 scenes.
Transformation Methodology of Binary Executables to Run on Bare Machines

This paper identifies challenges involved in the transformation of binary executables to run on bare machines such as PCs. It also addresses why we want to transform binary executables to run on bare machines. Text processing applications such as “vi,” “word,” and “notepad” are chosen to illustrate the need for transformation because these editors are the most commonly used across many operating system platforms, including Windows and Linux. They have much functionality in common to provide a general text processing application. Why not consolidate these standard functions and develop a generic text processing application? How do you make these editors without any platform dependencies? Transforming these applications to run on bare PCs or bare machines by using source or a binary level transformation will address these challenges. A binary transformation methodology described here lays the groundwork for further research in this area and provides some insight into the transformation process.
Gambaran Kecerdasan Emosional Mahasiswa Prodi D-III Kebidanan dan Program Studi Sarjana Terapan Berlanjut Pendidikan Profesi Bidan

Background: Psychological theory reveals that emotional intelligence influences human behavior. One of them is student behavior in the learning process will be influenced by the emotional intelligence he has. With good emotional intelligence, students tend to be more responsible in carrying out their roles as students. But in fact, not many teaching staff at the Educational Institution pay attention to the teori about the importance of emotional intelligence, whereas if emotional intelligence is developed in an educational institution it will optimize student responsibility, behavior and learning achievement. Aims study to describe the emotional intelligence of students of the Department of Midwifery Diploma Program and Continued Applied Bachelor Study Program Professional Education Midwife Professional Program in the Midwifery Department of the Polytechnic of the Ministry of Health Surakarta. The results of this study are expected to be used as an additional reference and input to teaching staff, especially in the Surakarta Health Polytechnic and Education Institutions in general to develop emotional intelligence in order to increase student responsibility, behavior and learning achievement. Methods: This type of research is descriptive. Univariate data analysis uses frequency distribution. Results: The level of emotional intelligence in midwifery DIII program is quite good 98.8% and good 1.2%. The level of emotional intelligence in midwifery applied bachelor is quite good 52.7%, good 44.9%, and very good 2.7%. Conclusion: Most midwifery students have good emotional intelligence.
Mobile Game Based Chemical Bond Learning Application

&lt;p&gt;Mobile game technology is growing rapidly and this is directly proportional to the increasing number of mobile games in circulation. The growing market of the mobile game industry is not accompanied by the number of games that have educative content in them. The challenging, fun, and addictive nature of the game has a negative impact if no educational content is provided. Therefore, educational content needs to be present in a game so that the game can have a positive impact on the players. This research makes a chemical educational game to study chemical bonding material that aims as an interactive learning media for students and the general public. This game is made based on mobile with 2D characters consisting of 5 levels. Level 1 contains material for salt compounds, level 2 contains material for acid compounds, level 3, 4 and 5 contains material for basic compounds. The results of this game trial show that all the features of this game application run well, can be run on several smartphones, and most users from 20 audiences stated that this game is comfortable and easy to use.&lt;/p&gt;
Blowfish Implementation on SoloEncrypt Security Model for User Data Protection Prior to Cloud Storage

Smartphones' data security has become increasingly important in the advancement of mobile technologies.Users today use smartphones as means of communication, sending and receiving messages, browsing the internet as well as storing sensitive information.Due to these functionalities smartphones today are preferred targets of attacks.Hence, the foremost goal of this work is to provide security for user data on smartphones by the implementation of Blowfish encryption algorithm prior to cloud storage.Blowfish encryption algorithm is a popular and well tested cryptographic algorithm to provide security for user data in mobile cloud computing.Further, there is the need for the development of a mobile application to provide the needed security on smartphones using this existing algorithm.This paper therefore proposed a Secure App using java + xml which was implemented on SoloEncrypt security model.Three Android devices were used to carry out the experiment; Samsung Galaxy On7 Prime, Samsung Galaxy J2 Pro and Samsung Galaxy J7 Nxt.Encryption time and decryption time were parameters used for evaluating the encryption algorithm which are among the standardized metrics approved by the National Institute of Standards and Technology (NIST).The experimental results revealed that the algorithm encrypts with better speed and it takes less time to decrypt when compared with related work.
Faces extracted from Time Magazine 1923-2014

We present metadata of labeled faces extracted from a Time magazine archive that contains 3,389 issues ranging from 1923 to 2012. The data we are publishing consists of three subsets: Dataset 1) the gender labels and image characteristics for each of the 327,322 faces that were automatically-extracted from the entire Time archive, Dataset 2) a subset of 8,789 faces from a sample of 100 issues that were labeled by Amazon Mechanical Turk (AMT) workers according to ten dimensions (including gender) and used as training data to produce Dataset 1, and Dataset 3) the raw data collected from the AMT workers before being processed to produce Dataset 2.
A Survey on Essential Strategies for Avoiding Cloud Data Leaks

As of late, there has been a tremendous development from putting away information the customary route, as the cloud has developed and turned into the better answer and alternative for organizations and associations alike.Notwithstanding, this has additionally prompted a development in digital culprits and information breaks now that somebody can get to touchy archives from their lounge room love seat.In that capacity, cloud information spills have turned into a very regular issue for organizations.More news has examined approaches to counteract breaks, and you can discover a plenty of articles that discuss about huge and little organizations alike being assaulted.Therefore, knowing how to keep a cloud information spill is the initial phase in protecting your business and data.In this paper there are a couple of the best and most straightforward methodologies to maintain a strategic distance from cloud information spills.
The Problem Of The Educational Value Of Information

The difference between the notion of information and the notion of knowledge grounded in the difference between quantitative and qualitative characteristics. Knowledge is a qualitative characteristic of information in terms of its correspondence to truth (in accordance with its truth-value). It seems reasonable that Theorists of information society introduce a notion "society of knowledge" as new, next step in the development of postindustrial society. In this regard education, today is a process of producing, mastering and transferring knowledge (scientific knowledge primarily) which has a deep relation with truth conditions. The idea of information society, as well as a society of knowledge with its pedagogical generative practices are the products of objectification of scientific rationality. Therefore, the evaluating of such ideas in terms of truth is correlated with defining them in the perspective of their belonging to either «naturally origin» or «artificially created» phenomenon of socio-cultural creativity. In this regard, the problem of the educational value of information appears to be deeply related to the series of questions of the nature of scientific knowledge, condition its truth, epistemological significance as well as methodological, conceptual and linguistic means (resources) of scientific rationality.