Reviewerly/exHarmony huggingface dataset:
Blotch Detection in Archive Films Based on Visual Saliency Map

Degradations frequently occur in archive films that symbolize the historical and cultural heritage of a nation. In this study, the problem of detection blotches commonly encountered in archive films is handled. Here, a block-based blotch detection method is proposed based on a visual saliency map. The visual saliency map reveals prominent areas in an input frame and thus enables more accurate results in the blotch detection. A simple and effective visual saliency map method is taken into consideration in order to reduce computational complexity for the detection phase. After the visual saliency maps of the given frames are obtained, blotch regions are estimated by considered spatiotemporal patches—without the requirement for motion estimation—around the saliency pixels, which are subjected to a prethresholding process. Experimental results show that the proposed block-based blotch detection method provides a significant advantage with reducing false alarm rates over HOG feature (Yous and Serir, 2017), LBP feature (Yous and Serir, 2017), and regions-matching (Yous and Serir, 2016) methods presented in recent years.
On the Randomness of Compressed Data

It seems reasonable to expect from a good compression method that its output should not be further compressible, because it should behave essentially like random data. We investigate this premise for a variety of known lossless compression techniques, and find that, surprisingly, there is much variability in the randomness, depending on the chosen method. Arithmetic coding seems to produce perfectly random output, whereas that of Huffman or Ziv-Lempel coding still contains many dependencies. In particular, the output of Huffman coding has already been proven to be random under certain conditions, and we present evidence here that arithmetic coding may produce an output that is identical to that of Huffman.
Institutionalized Algorithmic Enforcement—The Pros and Cons of the EU Approach to UGC Platform Liability

Commission held the view that the safe harbour for hosting allowed UGC platforms to generate income without sharing profits with producers of creative content. 14 The value gap argument was born.copyright legislation the template for the DSM Directive that has now entered into force sought to render the liability shield inapplicable when it came to copyrighted works. 15 The underlying strategy was simple: deprived of the safe harbour for hosting and exposed to direct liability for infringing user uploads, platform providers would have to embark on UGC licensing and filtering.With the erosion of the legal certainty resulting from the traditional liability privilege, a platform provider seeking to avoid liability risks would enter into agreements with copyright owners.The initial Commission proposal already contemplated that these agreements with right holders would bring filtering obligations in their wake.The Commission 16 Algorithmic enforcement measures, thus, played a central role in the EU copyright reform agenda from the outset.The value gap argument and the intention to generate new revenue streams for copyright owners served as a vehicle to present content filtering obligations as a necessary evil that had to be accepted. II. NEW LICENSING AND FILTERING OBLIGATIONSDespite the early focus on algorithmic enforcement, an unequivocal filtering obligation is sought in vain in the legislative text that evolved from 14 Communication from the Commission to the European Parliament, the Council, the European Economic and Social Committee and the Committee of the Regions: Towards a Modern, More European Copyright Framework, at 9 10, COM (2015) 266 final (Dec.9, 2015)
UTAUT: Analysis of Usage Level of Android Applications as Learning Media in Indonesian Educational Institutions

This study aims to analyze the application of android applications as a medium of mathematics learning in Indonesian educational institutions using the Unified Theory of Acceptance and Use of Technology (UTAUT) method both among teachers in delivering mathematical material and students in experimenting or completing school assignments. The research method used was descriptive quantitative with a four option Likert Scale questionnaire as a research instrument. Surveys were deployed using the Google Form-based Black Box system throughout Indonesia through Social Media. Based on the results of data analysis obtained information that the application of android applications as a learning media for mathematics in Indonesia is still below 50%. The effect of performance expectancy, effort expectancy, and social influence on behavioral intention is illustrated by the regression equation Y1 while facilitating the regression equation Y2 represents conditions and behavioral intention towards use behavior.
The digital Earth Observation Librarian: a data mining approach for large satellite images archives

Throughout the years, various Earth Observation (EO) satellites have generated huge amounts of data. The extraction of latent information in the data repositories is not a trivial task. New methodologies and tools, being capable of handling the size, complexity and variety of data, are required. Data scientists require support for the data manipulation, labeling and information extraction processes. This paper presents our Earth Observation Image Librarian (EOLib), a modular software framework which offers innovative image data mining capabilities for TerraSAR-X and EO image data, in general. The main goal of EOLib is to reduce the time needed to bring information to end-users from Payload Ground Segments (PGS). EOLib is composed of several modules which offer functionalities such as data ingestion, feature extraction from SAR (Synthetic Aperture Radar) data, meta-data extraction, semantic definition of the image content through machine learning and data mining methods, advanced querying of the image archives based on content, meta-data and semantic categories, as well as 3-D visualization of the processed images. EOLib is operated by DLR’s (German Aerospace Center’s) Multi-Mission Payload Ground Segment of its Remote Sensing Data Center at Oberpfaffenhofen, Germany.
Local feature extraction based facial emotion recognition: a survey

Notwithstanding the recent technological advancement, the identification of facial and emotional expressions is still one of the greatest challenges scientists have ever faced. Generally, the human face is identified as a composition made up of textures arranged in micro-patterns. Currently, there has been a tremendous increase in the use of local binary pattern based texture algorithms which have invariably been identified to being essential in the completion of a variety of tasks and in the extraction of essential attributes from an image. Over the years, lots of LBP variants have been literally reviewed. However, what is left is a thorough and comprehensive analysis of their independent performance. This research work aims at filling this gap by performing a large-scale performance evaluation of 46 recent state-of-the-art LBP variants for facial expression recognition. Extensive experimental results on the well-known challenging and benchmark KDEF, JAFFE, CK and MUG databases taken under different facial expression conditions, indicate that a number of evaluated state-of-the-art LBP-like methods achieve promising results, which are better or competitive than several recent state-of-the-art facial recognition systems. Recognition rates of 100%, 98.57%, 95.92% and 100% have been reached for CK, JAFFE, KDEF and MUG databases, respectively.
Load Balancing in Cloud Computing Using Hungarian Algorithm

Cloud computing is a highly popular computing paradigm providing on-demand resources with high reliability and availability.The user requests are fulfilled by providing a virtual machine with the requested configuration.However, with the ever-increasing load on the cloud resources, the need for optimal resource utilization of the cloud resources has become the need of the hour.Load balancing has been identified as one of the possible ways to improve resource utilization in the cloud and the current state-of-the-art algorithms indicate the numerous attempts made to find the approximate solution for this NP-hard problem.In this work, we have focused on evaluating the efficiency of the Hungarian algorithm for load distribution in the cloud and compared its performance with First-come-first-serve (FCFS).The simulations were carried out in CloudSim and show remarkable improvement in various performance parameters.Finish time of a given task schedule was reduced by 41% and average execution time was reduced by 13% in the Hungarian algorithm when compared with FCFS.The simulations were carried out under different workload conditions to validate our results.
Gender Recognition and Age Approximation using Deep Learning Techniques

Age and gender that are the two key facial attributes, play a foundational role in social interactions, making age and gender estimation from one face image a crucial task in intelligent applications, like access control, human-computer interaction, enforcement, marketing intelligence and visual surveillance.The basic aim of this paper is to develop an algorithm that estimates age and gender of a person correctly.One of the most widely used techniques is haar cascade.In this paper we propose a model which can predict the gender of a person with the assistance of Haar Cascade.The model trained the classifier with different male and female images as positive and negative images.Different facial features are extracted.With the assistance of Haar Cascade classifier will determine whether the input image is male or female.We made use of Deep-Convolution neural network.It works efficiently even with limited data.For the age approximation task, the paper makes use of caffedeep learning framework.Caffe provides expressive architecture, extensible code.Caffe can process over 60M photos per day.This makes it one of the fastest convent implementation available.
Incorporating multiple cluster centers for multi-label learning

Multi-label learning deals with the problem that each instance is associated with multiple labels simultaneously. Most of the existing approaches aim to improve the performance of multi-label learning by exploiting label correlations. Although the data augmentation technique is widely used in many machine learning tasks, it is still unclear whether data augmentation is helpful to multi-label learning. In this article, we propose to leverage the data augmentation technique to improve the performance of multi-label learning. Specifically, we first propose a novel data augmentation approach that performs clustering on the real examples and treats the cluster centers as virtual examples, and these virtual examples naturally embody the local label correlations and label importances. Then, motivated by the cluster assumption that examples in the same cluster should have the same label, we propose a novel regularization term to bridge the gap between the real examples and virtual examples, which can promote the local smoothness of the learning function. Extensive experimental results on a number of real-world multi-label datasets clearly demonstrate that our proposed approach outperforms the state-of-the-art counterparts.
AUTOC-AR: A Car Design and Specification as a Work Safety Guide Based on Augmented Reality Technology

The development of Augmented Reality (AR) technology until now continues to increase. Utilization of AR has been used in various aspects of life, including aspects of education, is no exception for automotive engineering education. In recent years, a variety of ideas and the latest innovations about automotive by utilizing AR technology began to boom, especially in the area of car design aimed at car production companies. At the car production stage, human resources skilled in understanding the design and specifications of car features are required. The seeds of educated human resources start from vocational students in automotive engineering expertise programs. This study aims to develop and implement an application called AUTOC-AR that functions to help and facilitate students in learning automotive engineering skills in vocational schools and supporting safety in the workplace. The research methodology consisted of a literature review and excavation of problems and needs, solution recommendations, application development, testing, results and discussion, conclusions and future work. The Extreme Programming (XP) model was used as a development method. Marker-based tracking was used as a detection approach. As many as 25 students as end-users were involved to use AUTOC-AR. The result is that all features in the AUTOC-AR application function properly based on the expected specifications. Non-functional testing has been carried out by adopting a user experience approach with a final average value of 4.83 with a percentage of 96.6%.
Using a Hybrid of Interval Type-2 RFCMAC and Bilateral Filter for Satellite Image Dehazing

With increasing advancement of science and technology, remote sensing satellite imaging does not only monitor the Earth’s surface environment instantly and accurately but also helps to prevent destruction from inevitable disasters. The changing weather, e.g., cloudiness or haze formed from atmospheric suspended particles, results in low contrast satellite images, and partial information about Earth’s surface is lost. Therefore, this study proposes an effective dehazing method for one single satellite image, aiming to enhance the image contrast and filter out the region covered with haze, so as to reveal the lost information. First, the initial transmission map of the image is estimated using an Interval Type-2 Recurrent Fuzzy Cerebellar Model Articulation Controller (IT2RFCMAC) model. For the halo and color oversaturation resulted from the processing procedure, a bilateral filter and quadratic function nonlinear conversion are used in turn to refine the initial transmission map. For the estimation of atmospheric light, the first 1% brightest region is used as the color vector of atmospheric light. Finally, the refined transmission map and atmospheric light are used as the parameters for reconstructing the image. The experimental results show that the proposed satellite image dehazing method has good performance in the visibility detail and color contrast of the reconstructed image. In order to further validate the effectiveness of the proposed method, visual assessment and quantitative evaluation were implemented, respectively, and compared with the methods proposed by relevant scholars. The visual assessment and quantitative evaluation analysis demonstrated good results of the proposed approach.
Pemodelan Unified Modeling Language Sistem Informasi Enterprise Resource Planning

Modeling is one of the techniques used to describe how information systems will be created and produced. Modeling itself can be used as a reference in the process of developing information systems to suit user needs. One modeling technique that is often used is to use a unified modeling language (UML). Modeling techniques with UML are able to describe various features of information systems. for this reason, in this research a model for the PTPN VII enterprise resource planning (ERP) information system is used as an initial step in fulfilling information management needs. the use of modeling itself is an initial implementation of information technology and information systems in a corporate environment, especially PTPN VII. The results of modeling have been done using three types of diagrams in UML as a reference in the development of structure diagrams created using class diagrams, behavior diagrams created using use case diagrams, and interaction diagrams created using activity diagrams. The results of the modeling have also been implemented in the form of a prototype of the information system so as to provide a clear picture for interested parties if the development of enterprise resource planning (ERP) information systems, especially in the PTPN VII environment. The information system prototype can also describe the initial forms for stakeholders if changes are to be made
An Image Denoising Method Based on BM4D and GAN in 3D Shearlet Domain

To overcome the disadvantages of the traditional block-matching-based image denoising method, an image denoising method based on block matching with 4D filtering (BM4D) in the 3D shearlet transform domain and a generative adversarial network is proposed. Firstly, the contaminated images are decomposed to get the shearlet coefficients; then, an improved 3D block-matching algorithm is proposed in the hard threshold and wiener filtering stage to get the latent clean images; the final clean images can be obtained by training the latent clean images via a generative adversarial network (GAN).Taking the peak signal-to-noise ratio (PSNR), structural similarity (SSIM for short) of image, and edge-preserving index (EPI for short) as the evaluation criteria, experimental results demonstrate that the proposed method can not only effectively remove image noise in high noisy environment, but also effectively improve the visual effect of the images.
Barcode detection and decoding method based on deep learning

Traditional Barcode detection methods are susceptible to environment. It is very difficult to implement bar code detection in complex backgrounds and on-site environments. Barcode detection is mainly divided into two processes of positioning and data decoding. In this paper, a bar code detection method based on deep learning is proposed. The method is based on the single-shot mutibox detector (SSD) method, which can meet the requirements of high speed and high precision. In this paper, the SSD method is used to detect the position of the barcode, and then the image processing method is used to segment the barcode. Then the morphological method and the barcode correction method are used to complete the decoding of the barcode data. The test results on the actual data set show that our method can stably and quickly complete the process of positioning and decoding the barcode.
Development of Blog-Based Audio Visual Learning Media to Improve Student Learning Interests in Money and Banking Topic

This research aims to produce audio-based learning media for blogs on the subjects of money and banking. This study used research and development methods with 4D Thiagarajan. There are four stages in Thiagarajan's 4D model; the initial needs analysis stage (define), the prototype (design) stage, the development stage based on expert advice and the field trial (develope), and the publication stage (disseminate). The data analysis technique used was quantitative descriptive technique. The results of research and development carried out in the form of audio-based learning media blog on economic subjects of money &amp; banking material which contains a homepage, learning tools, learning materials, videos, LKPD, assignments, commentary features and social media. The audio-visual media based on money &amp; banking material developed was very good ratings from media experts with an average score of 4 (after revision) and a material expert of 3.6 (scale 4). Students also gave a good response with an overall average score of 3.6, and an increase in student interest in learning with a difference of greater than 1 is 28.63%.
XGBDeepFM for CTR Predictions in Mobile Advertising Benefits from Ad Context

The problem of click-through rate (CTR) prediction in mobile advertising is one of the most informative metrics used in mobile business activities, such as profit evaluation and resource management. In mobile advertising, CTR prediction is essential but challenging due to data sparsity. Moreover, existing methods often have difficulty in capturing the different orders of feature interactions simultaneously. In this study, a method was developed to obtain accurate CTR prediction by incorporating contextual features and feature interactions. We initially use extreme gradient boosting (XGBoost) as a feature engineering phase to select highly significant features. The selected features are mobile contextual attributes including time contextual, geography contextual, and other contextual attributes (e.g., weather condition) in actual mobile advertising situations. Our model, XGBoost deep factorization machine- (FM-) supported neutral network (XGBDeepFM), combines the power of XGBoost for feature selection, FM for two-order cross feature interaction, and the deep neural network for high-order feature learning in a united architecture. In a mobile advertising condition, our methods lead to significantly accurate CTR prediction in “wide and deep” type of model. In comparison with existing models, many experiments on commercial datasets show that the XGBDeepFM model has better value of area under curve and improves the effectiveness and efficiency of CTR prediction for mobile advertising.
DATA MINING FOR PREDICTING THE AMOUNT OF COFFEE PRODUCTION USING CRISP-DM METHOD

The production of coffee plantations has become the leading plantation commodity with the export value of the fourth rank after oil palm, rubber and coconut. The number of coffee needs for export every year always increases, therefore it is necessary to predict the yield of coffee plants to estimate planting and anticipation that will be done so as to achieve the target. Coffee plant productivity is influenced by internal and external factors, namely the quality of the plant itself, soil, altitude and climate. The method used in this study is the CRISP-DM method and multiple linear regression algorithm to predict the amount of coffee production and determine the relationship between the variables. The steps taken are business understanding, data understanding, data preparation, modeling and evaluation. The data set that is used as many as 170 data after going through the data preparation stage produced 150 data with 5 attributes in the table. With calculations using tools, the coefficient of determination is 91.96%. That the variation in the value of the production of coffee plants is influenced by independent variables, namely the area of ​​plantations, rainfall, air pressure and solar radiation by 91.96% and 8.04% influenced by other variables not measured in this study. The results of the evaluation and validation of predictions produce good accuracy with an RMSE value of 0.3477.
Lazy or Dyslexic: A Multisensory Approach to Face English Language Learning Difficulties

An investigation was conducted to help weak academic English learners in a public high school in Colombia, as they seemed to be facing a learning specific difficulty called dyslexia. A focus group of ten students from ninth and tenth grade was the beneficiaries of the design, implementation, and assessment of five multisensory activities to help students decrease their struggles while learning the foreign language (English). For the present action research, five activities were applied during two academic terms (six months) where students were taught verbs, grammar rules, question words, and minimal pairs to help them do better while reading. Outcomes showed that low academic students tend to have a better performance when teachers target multisensory activities to assist them in their learning process related to grammar within the English sessions. Color-coded activities help low achieving students to exercise and remember more easily as senses are engaged while learning, reading exercises are better approached if their workload is split into smaller quantities compare to regular learners.