Reviewerly/exHarmony huggingface dataset:
Ironic Strategies of Postmodern Art Games

Postmodernism has left a significant number of strategies to imitate art. They all rely on the game, offering to “play art”. The way of existing in Postmodernism in the form of the game is possible due to the broad interpretation of the concept of the “game” in modern aesthetics. The game principle in the works of Baudrillard, Lyotard, Debord and Gadamer is absolutised as the basis of the social interactions within which art is created. The methodology of the postmodern games is worked out and at the same time turns into arbitrariness in science, everyday life and social life, depriving them of certainty and consistency, making the principle of process the most important quality of any activity, including artistic one. In the postmodern game, hyperreal, carnival and marginal features are outlined. It is the basis of imitation, combinatorics, pastiche and intertextuality as the text production strategy. As a result, a playing text appears, erasing any differences and hierarchies. The game appropriates the place of the sacred and parodying, declining and desecration become the leading forms of creativity. The game turns into a masquerade, acquiring performative-spectacular forms, performing the function of returning the past to the present, preserving the lost tradition. The game within the cultural framework defined the field of the unserious. The game that has swallowed up culture, deprives it of its seriousness, puts it beyond morality, making evil and death a thing of the past. The game allows Postmodernism to overcome the finality of art. Within the framework of the total playful nature of Postmodernism, the new avant-garde technologies of irony, nihilism, labyrinth, chaos, creativity, authorship, and representation are filled with new content
On Estimate of Malaysian Mortality Rates Using Interpolation Methods

Life table is a table that shows mortality experience of a nation. However, in Malaysia, the information in this table is provided in the five-year age groups (abridged) instead of every one-year age. Hence, this study aims to estimate the one-year age mortality rates from the abridged mortality rates using several interpolation methods. We applied Kostaki method and the Akima spline method to five sets of Malaysian group mortality rates ranging from period of 2012 to 2016. The results were then compared with the one-year mortality rates. We found that the method by Akima is the best method for the Malaysian mortality experience as it gives the least minimum of sum of square errors. The method does not only provide a good fit but also, shows a smooth mortality curve.
A Comparative Study of High-Recall Real-Time Semantic Segmentation Based on Swift Factorized Network

Semantic Segmentation (SS) is the task to assign a semantic label to each pixel of the observed images, which is of crucial significance for autonomous vehicles, navigation assistance systems for the visually impaired, and augmented reality devices. However, there is still a long way for SS to be put into practice as there are two essential challenges that need to be addressed: efficiency and evaluation criterions for practical application. For specific application scenarios, different criterions need to be adopted. Recall rate is an important criterion for many tasks like autonomous vehicles. For autonomous vehicles, we need to focus on the detection of the traffic objects like cars, buses, and pedestrians, which should be detected with high recall rates. In other words, it is preferable to detect it wrongly than miss it, because the other traffic objects will be dangerous if the algorithm miss them and segment them as safe roadways. In this paper, our main goal is to explore possible methods to attain high recall rate. Firstly, we propose a real-time SS network named Swift Factorized Network (SFN). The proposed network is adapted from SwiftNet, whose structure is a typical U-shape structure with lateral connections. Inspired by ERFNet and Global convolution Networks (GCNet), we propose two different blocks to enlarge valid receptive field. They do not take up too much calculation resources, but significantly enhance the performance compared with the baseline network. Secondly, we explore three ways to achieve higher recall rate, i.e. loss function, classifier and decision rules. We perform a comprehensive set of experiments on state-of-the-art datasets including CamVid and Cityscapes. We demonstrate that our SS convolutional neural networks reach excellent performance. Furthermore, we make a detailed analysis and comparison of the three proposed methods on the promotion of recall rate.
Design and Analysis of Erp Integration for Pari Fish Leather Smes in Yogyakarta Using Web Service Technology

Small and Medium Enterprises (SMEs) usually have limited markets and raw materials, so they cannot do mass production. The purpose of Enterprise Resource Planning (ERP) in Jogjakarta Pari Fish Leather SMEs is to maintain consumers and increase the effectiveness of using raw materials by integrating data on the production (factory) and marketing (shop / branch) sections using the SOAP method and REST web service. The results achieved are data integration between the marketing and manufacturing departments using SOAP and REST Web Service, as well as an ERP information and monitoring system that displays functions such as: sales charts, best-selling products, buyer's charts, and buyer's regional charts.
RANCANG BANGUN APLIKASI PENGENALAN WAJAH UNTUK PASSWORD FILE DENGAN METODE JARINGAN SYARAF TIRUAN

Data security for computer users is increasingly becoming a concern because it is increasingly vulnerable to illegal access even though the file has been protected with a password. This is possible with the increasing number of applications aimed at hacking owner protection. Artificial neural network that was appointed in this study is one part of computer vision, which in this study is intended to make computers able to "see" through a webcam and recognize that face has access rights to the selected file. So that computers can distinguish facial images, it needs to be trained by applying the back propagation method. The reason for choosing facial recognition is because each person has a different face, so that it can be a more effective security key than conventional methods of making or accessing files that are on a computer.
Realia as a Media to Improve the EFL Learnersâ€™ Achievements in Descriptive Writing

This study was aimed at investigating the improvement of EFL learnersâ€™ achievement in writing descriptive paragraph in Indonesia. Therefore, the moethodology conducted in this study was action research. The techniques of collecting data were documentation (writing test) and questionnaire for quantitative data, and observation and interview for the qualitative data. The result shows that the score mean of EFL learners was improved from cycle 1 to cycle 2 which was 66.28 to 87.00 respectively. Furthermore, the result of questionnaire shows that most of EFL learners were very interested and enjoying learning writing by using realia. Moreover, from the observation and interview, EFL learners said that realia is very good and enjoyable to improve their achievements in descriptive writing. Therefore, relia should be implemented to teach English in Indonesia not only to teaching writing but also to teach other skills such as listening, speaking and reading
A Novel Multiple Classifier Generation and Combination Framework Based on Fuzzy Clustering and Individualized Ensemble Construction

Multiple classifier system (MCS) has become a successful alternative for improving classification performance. However, studies have shown inconsistent results for different MCSs, and it is often difficult to predict which MCS algorithm works the best on a particular problem. We believe that the two crucial steps of MCS - base classifier generation and multiple classifier combination, need to be designed coordinately to produce robust results. In this work, we show that for different testing instances, better classifiers may be trained from different subdomains of training instances including, for example, neighboring instances of the testing instance, or even instances far away from the testing instance. To utilize this intuition, we propose Individualized Classifier Ensemble (ICE). ICE groups training data into overlapping clusters, builds a classifier for each cluster, and then associates each training instance to the top-performing models while taking into account model types and frequency. In testing, ICE finds the k most similar training instances for a testing instance, then predicts class label of the testing instance by averaging the prediction from models associated with these training instances. Evaluation results on 49 benchmarks show that ICE has a stable improvement on a significant proportion of datasets over existing MCS methods. ICE provides a novel choice of utilizing internal patterns among instances to improve classification, and can be easily combined with various classification models and applied to many application domains.
On the Sensorial of Imagination

The senses appear on the boundary between the inside and the outside of human experience. They are like an open door through which light, impres-sions, and air enter, are exchanged, and merge with the light, impressions, and air that were there before, exchanging inner and outer. While perception and sensoriality appear to be experiences that happen in real time, the sensorial can and most of the time does inhabit imagination in very precise ways. Sensorial imagination is related to human experience, to sensorial remembrance, and is based on sensorial knowledge. It happens in the present, is sustained by the sensorial knowledge of the past, and projects itself by way of sensorially driven expectations towards the future. Our sensorial imagination leads our interpret-ations, expectations, and actions. Offering background information as well as projecting expectation, it is an important cue for understanding the world, for creating synaesthetic associations, and for making connections between our experience and knowledge and those of others. Sensorial imagination has for a long time been linked mainly to vis-ual imagination. The word imagination even contains a visual reference to “image.” However, not all our experiences are related to the visual. Just start to think about everyday experiences like the wind, water, a dark country path, a hidden heap of rubbish: all these experiences are sensorial in different modal-ities: haptic, olfactive, auditory, motoric. In the context of art, and specifically music, this multi-sensory presence—involving a synaesthetic approach—is of uttermost importance. A musician sees a score, hears the music, has a haptic and motoric sense of how to interact with both, and can anticipate this in an imaginary multi-sensorial mode.
Creating the immersive world of BioWare's Anthem

The savage world of Anthem is volatile, lush, expansive, and full of unexpected characters. Bringing these aspects to life in a real-time (30fps) interactive environment presented a wealth of challenging problems for BioWare's technical artists and rendering engineers. These developers work with content creators to bridge art and technology through creative problem solving in areas such as performance/runtime, shaders, and artist tools. This retrospective panel will highlight some of the team's work, alongside reflections on innovation, distributed collaboration/coordination, and the successes and challenges of creating a new IP for the world to enjoy.
Optimum Parameter Estimation of Tone Mapping Operators by Natural Image Statistics

In this paper, we propose a method that optimizes the parameters of tone mapping operators by compressing the dynamic range of HDR images using natural image statistics.First, a prior probability model of a natural image is constructed for color natural images based on a generalized Gaussian distribution.Then, an LDR image is generated by converting the HDR image using the tone mapping operator. Next, we generate a normalized histogram of the LDR image using a discrete wavelet transformation.Finally, the optimal parameters of the tone mapping operator are estimated by minimizing the Kullbuck--Leibler divergence of the probability density function and the normalized histogram.Using these parameters, it is possible to generate an LDR image that closely resembles the natural image.
Semi Supervised Phrase Localization in a Bidirectional Caption-Image Retrieval Framework

We introduce a novel deep neural network architecture that links visual regions to corresponding textual segments including phrases and words. To accomplish this task, our architecture makes use of the rich semantic information available in a joint embedding space of multi-modal data. From this joint embedding space, we extract the associative localization maps that develop naturally, without explicitly providing supervision during training for the localization task. The joint space is learned using a bidirectional ranking objective that is optimized using a $N$-Pair loss formulation. This training mechanism demonstrates the idea that localization information is learned inherently while optimizing a Bidirectional Retrieval objective. The model's retrieval and localization performance is evaluated on MSCOCO and Flickr30K Entities datasets. This architecture outperforms the state of the art results in the semi-supervised phrase localization setting.
The Role of Non-monotonic Reasoning in Future Development of Artificial Intelligence (Dagstuhl Perspectives Workshop 19072)

This report documents the program and the outcomes of Dagstuhl Perspectives Workshop 19072 Role of Non-monotonic Reasoning in Future Development of Artificial Intelligence. The workshop brought together researchers both from core topics and peripheral areas of non-monotonic reasoning (NMR), but also attracted researchers from other scientific domains in which recent developments have shown an increased relevance of NMR topics. The overall goal of this workshop was to reshape NMR as a core methodology for artificial intelligence being able to meet present and future challenges. Participants of this workshop discussed in what shape NMR would be useful for future AI, and how NMR can be developed for those requirements. The workshop started with brief survey talks and had some technical talks on central topics of NMR afterwards. These were followed by working groups on core aspects of NMR and potential links with learning. On the last day of the seminar, each working group presented their ideas and future plans. The workshop closed with a plenary discussion on the future of NMR.
PENGARUH STRATEGI INDEX CARD MATCH BERBANTUAN MEDIA KARTU BERGAMBAR TERHADAP HASIL BELAJAR SISWA KELAS V SDN PANDEAN LAMPER 05 SEMARANG

This study motivated by the problems experienced by Pandean Lamper 05 Elementary School Semarang that classroom learning is still monotonous teacher-centered. The focus of the problem to be examined is is there an influence of the Index Card Match strategy assisted with pictorial card media on the learning outcomes of fifth grade students of Pandean Lamper 05 SDN Semarang. This study uses a quantitative approach in the form of a quasi pre-experimental design withtypes one-group pretest-posttest. The population in this study were all VA students of Pandean Lamper 05 Elementary School Semarang. The sample taken consisted of VA classes totaling 38 students using saturated sampling techniques. Data collection methods used were interviews, documentation, test methods (pretest-posttest), and observation sheets. The instrument in this study is a test in the form of multiple choice questions that have been tested for validity, reliability, degree of difficulty, and differentiation. The results of this study can be concluded that thestrategy is Index Card Match assisted bycard media pictorialtowards the learning outcomes of class V students of Pandean Lamper 05 Elementary School Semarang
Random Directional Attack for Fooling Deep Neural Networks

Deep neural networks (DNNs) have been widely used in many fields such as images processing, speech recognition; however, they are vulnerable to adversarial examples, and this is a security issue worthy of attention. Because the training process of DNNs converge the loss by updating the weights along the gradient descent direction, many gradient-based methods attempt to destroy the DNN model by adding perturbations in the gradient direction. Unfortunately, as the model is nonlinear in most cases, the addition of perturbations in the gradient direction does not necessarily increase loss. Thus, we propose a random directed attack (RDA) for generating adversarial examples in this paper. Rather than limiting the gradient direction to generate an attack, RDA searches the attack direction based on hill climbing and uses multiple strategies to avoid local optima that cause attack failure. Compared with state-of-the-art gradient-based methods, the attack performance of RDA is very competitive. Moreover, RDA can attack without any internal knowledge of the model, and its performance under black-box attack is similar to that of the white-box attack in most cases, which is difficult to achieve using existing gradient-based attack methods.
SISTEM PREDIKSI PENGGUNAAN LISTRIK PELANGGAN DI PT.PLN (PERSERO) RAYON LAMONGAN AREA BOJONEGORO DENGAN METODE TRIPLE EXPONENTIAL SMOOTHING (BROWN)

Electricity is one of the means of fulfilling the needs of human life which is very important in this era. Excessive use of electricity will have an impact on the high use of electricity kWh. The process of recording kWh on the customer meter is carried out by officers from PLN who routinely visit the customer's homes once a month. The meter recording clerk cannot record when the customer's house cannot be reached resulting in empty customer kWh data. Prediction System Using Electricity Customers at PT. PLN Lamongan aims to determine the amount of electricity usage kWh of the customer for the next period. This research uses the Triple Exponential Smoothing method (Brown). The calculation is done on 10 different customers with 24 data, namely the use of electric kWh per period from January 2015 to December 2016 with 9 different alpha values, namely alpha 0.1 - 0.9 and uses a reference of 3 months, 6 months and 12 months before. Prediction results will be compared with the actual data of kWh to determine the failure value or error value in predictions using mean absolute deviaton (MAD) and mean absolute percentage error (MAPE). From the third average forecasting test analysis, it produces an average MAPE value of 3 months reference with an average value of 2.922%, 6 months reference with an average value of 3.092% and a 12-month reference with an average value of 4.175%. The smallest MAPE, which is a test using a 6-month reference, produces a value of 1.886% with alpha 0.1.
Self-Adaptive 2D-3D Ensemble of Fully Convolutional Networks for Medical Image Segmentation

Segmentation is a critical step in medical image analysis. Fully Convolutional Networks (FCNs) have emerged as powerful segmentation models achieving state-of-the-art results in various medical image datasets. Network architectures are usually designed manually for a specific segmentation task so applying them to other medical datasets requires extensive experience and time. Moreover, the segmentation requires handling large volumetric data that results in big and complex architectures. Recently, methods that automatically design neural networks for medical image segmentation have been presented; however, most approaches either do not fully consider volumetric information or do not optimize the size of the network. In this paper, we propose a novel self-adaptive 2D-3D ensemble of FCNs for medical image segmentation that incorporates volumetric information and optimizes both the model's performance and size. The model is composed of an ensemble of a 2D FCN that extracts intra-slice information, and a 3D FCN that exploits inter-slice information. The architectures of the 2D and 3D FCNs are automatically adapted to a medical image dataset using a multiobjective evolutionary based algorithm that minimizes both the segmentation error and number of parameters in the network. The proposed 2D-3D FCN ensemble was tested on the task of prostate segmentation on the image dataset from the PROMISE12 Grand Challenge. The resulting network is ranked in the top 10 submissions, surpassing the performance of other automatically-designed architectures while being considerably smaller in size.
Automation of Deformation Monitoring Techniques and Integration with Prediction Modelling

In 1965, the Department of Geodesy and Geomatics Engineering (formerly, Department of Surveying Engineering) at the University of New Brunswick initiated a specialization in engineering and mining ...
Probing ferroelectric materials with machine learning techniques

Researchers have developed and made public a machine learning analysis describing the structure and behavior of different classes of materials.
Media Informasi Ragam Jenis Makanan Tradisional Khas Lombok Berbasis Mobile

Lombok has a variety of traditional Lombok foods that must be preserved. Traditional Lombok foods are one of the characteristics and treasures of culture owned by Indonesia, not only in terms of taste, form, and variety, but also from diverse colors. Lombok has considerable tourism potential, as one of the centers of culinary tourism destinations. However, promotional media and information are still limited, so this research want to introduce or promote traditional Lombok foods with easily and quickly by utilizing mobile-based technology. Making information media on various of traditional foods of Lombok with 2 languages, Indonesian and English. While the method used in multimedia development was Luther Sutopo. The steps are Concept, Design, Material Collecting, Assembly, Testing, Distribution. The tools used in making these mobile applications are Adobe flash and Adobe Photoshop. The results of this research is a Media Application for Variety of Traditional Foods of Lombok
ЦИФРОВАЯ ТРАНСФОРМАЦИЯ УНИВЕРСИТЕТОВ: ЗАМЕТКИ О ГЛОБАЛЬНОЙ КОНФЕРЕНЦИИ ПО ТЕХНОЛОГИЯМ В ОБРАЗОВАНИИ EDCRUNCH URAL

Overview. The article discusses universities’ of digital transformation issues, which were discussed at the EdCrunch Ural global conference on technology in education held on April 24–26, 2019 at the Ural Federal University (Yekaterinburg). There are observed the opinions and positions of the conference participants on the general concept of a digital university, on new forms and models of student learning, on the transformation of the teacher’s role, on the creation and effective use of digital educational content, and on new approaches to university management.