Reviewerly/exHarmony huggingface dataset:
A Data-Driven Approach for Direct and Global Component Separation from a Single Image

The radiance captured by camera is often under influence of both direct and global illumination from complex environment. Though separating them is highly desired, existing methods require strict capture restriction such as modulated active light. Here, we propose the first method to infer both components from a single image without any hardware restriction. Our method is a novel generative adversarial network (GAN) based networks which imposes prior physics knowledge to force a physics plausible component separation. We also present the first component separation dataset which comprises of 100 scenes with their direct and global components. In the experiments, our method has achieved satisfactory performance on our own testing set and images in public dataset. Finally, we illustrate an interesting application of editing realistic images through the separated components.
Prediksi Angka Partisipasi Sekolah dengan Fungsi Pelatihan Gradient Descent With Momentum &amp; Adaptive LR

&lt;p class="Abstract"&gt;School Participation Rate (APS) is known as one of the indicators of the success of the development of educational services in regions both Province, Regency or City in Indonesia. The higher the value of the School Participation Rate, then the area is considered successful in providing access to education services. The purpose of this study is to predict School Participation Rates based on Provinces in Indonesia from Aceh to Papua. The prediction algorithm used is the backpropagation algorithm using the gradient descent with momentum &amp;amp; adaptive LR (traingdx) training function. Traingdx is a network training function that updates weight values and biases based on gradient descent momentum and adaptive learning levels. Usually, the backpropagation algorithm uses the gradient descent backpropagation (traingd) function, but in this study, the training function used is using gradient descent with momentum &amp;amp; adaptive LR (traingdx). The data used in this study data on School Participation Figures for each province in Indonesia in 2011-2017 aged 19-24 years were taken from the Indonesian Central Bureau of Statistics (BPS). The reason for choosing this age range is because at this age is one of the factors that determine the success of education in a country, especially Indonesia. This study uses 3 network architecture models, namely: 5-5-1, 5-15-1 and 5-25-1. Of the 3 models, the best model is 5-5-1 with an iteration of 130, the accuracy of 94% and MSE 0,0008708473. This model is then used to predict School Participation Rates in each province in Indonesia over the next 3 years (2018-2020). These results are expected to help the Indonesian government to further increase scholarships and improve the quality of education in the future..&lt;/p&gt;&lt;p&gt; &lt;/p&gt;&lt;p class="IndexTerms"&gt;&lt;strong&gt;Keywords&lt;/strong&gt;: Prediction, APS, Backpropagation, Traingdx.&lt;/p&gt;
A Comparative Study of Performance and Security Issues of Public Key Cryptography and Symmetric Key Cryptography in Reversible Data Hiding

Security of data is the main aspect to be considered in the digital network. Data transmission can be made secure by performing reversible data hiding in images. Here the data can be hidden and transmitted inside a host image. Security to the image can be provided by various algorithms like symmetric key algorithm and public key algorithms. This paper provides a comparative study of AES and RSA algorithms for image encryption and reversible data hiding. Data embedding in both cases is done by histogram shifting method. The RSA algorithm can be used for encrypting the image to provide higher security but consumes more time whereas the security of image in AES algorithm is comparatively small but consumes only small amount of time for both encryption and decryption.
One Novel Word Segmentation Method Based on N-Shortest Path in Vietnamese

Automatic word segmentation of Vietnamese is the primary step in Vietnamese text information processing, which would be an important support for cross-language information processing tasks in China and Vietnam. Since the Vietnamese language is an isolating language with tones, each syllable can not only form a word individually, but also create a new word by combining with left and/or right syllables. Therefore, automatic word segmentation of Vietnamese cannot be simply based on spaces. This paper takes automatic word segmentation of the Vietnamese language as the research object. First, it makes a rough segmentation of Vietnamese sentences with the N-shortest path model. Then, syllables in each sentence are abstracted into a directed acyclic graph. Finally, the Vietnamese word segmentation is obtained by calculating the shortest path with the help of the BEMS marking system. The results show that the proposed algorithm achieves a satisfactory performance in Vietnamese word segmentation.
Liquid-VR - Wetness Sensations for Immersive Virtual Reality Experiences

We propose Liquid-VR, a system that simulates wetness and liquid sensations for the whole body by providing a combination of thermal and vibrotactile stimuli from the user’s face and across the upper body to the lower body. A head mounted display with thermal and vibrotactile modules simulates the wetness sensations on the face with the displayed visuals. These sensations are carried through out the body by using an array of vibrotactile modules placed on the collarbones and the feet actuated to simulate moving phantom sensations. We demonstrate this experience through virtual reality application scenarios such as being in a shower or being in side a glass of liquid.
Resource-dependent contextual planning in AmI

When we consider ambient intelligence (AmI) environments, contextual planning for multiple agents requires efficient management of resources in order to deploy multi-agent plans. We propose a contextual planning framework to be used by agents in such environments. The proposed framework covers the entire cycle from modeling, multi-agent contextual planning, and deployment of generated plans while ensuring concurrent access and sharing of physical resources. For efficient and effective management of resources, the framework includes a resource management layer. This layer works as a service that provides the virtualization and registration of environments and devices informing the resources availability. The added value of this framework resides in formalizing resource-dependent contextual states in order to allow existing physical resources to be properly perceived as part of the context. Consequently, it allows for plan feasibility to be verified according to any eventual resources requirements related to the deployed plans. The paper goes on to present a proof of concept implementing a scenario based on real-world conditions. In that, we extrapolate the internal execution and monitoring mechanisms that should be required in the case of the framework practical application.
Constructing a japanese sign language database and developing its viewer

Sign language is a means of communication used by the hearing impaired, and is a unique, interactive natural language with a grammatical system that differs from spoken languages. Research on sign language in the fields of engineering and linguistics, however, has been behind. One of the reasons is lack of a database available to any researcher.
Video Frame Interpolation via Cyclic Fine-Tuning and Asymmetric Reverse Flow

The objective in video frame interpolation is to predict additional in-between frames in a video while retaining natural motion and good visual quality. In this work, we use a convolutional neural network (CNN) that takes two frames as input and predicts two optical flows with pixelwise weights. The flows are from an unknown in-between frame to the input frames. The input frames are warped with the predicted flows, multiplied by the predicted weights, and added to form the in-between frame. We also propose a new strategy to improve the performance of video frame interpolation models: we reconstruct the original frames using the learned model by reusing the predicted frames as input for the model. This is used during inference to fine-tune the model so that it predicts the best possible frames. Our model outperforms the publicly available state-of-the-art methods on multiple datasets.
User Identification in the Process of Web Usage Data Preprocessing

If we are talking about user behavior analytics, we have to understand what the main source of valuable information is. One of these sources is definitely a web server. There are multiple places where we can extract the necessary data. The most common ways are to search for these data in access log, error log, custom log files of web server, proxy server log file, web browser log, browser cookies etc. A web server log is in its default form known as a Common Log File (W3C, 1995) and keeps information about IP address; date and time of visit; ac-cessed and referenced resource. There are standardized methodologies which contain several steps leading to extract new knowledge from provided data. Usu-ally, the first step is in each one of them to identify users, users’ sessions, page views, and clickstreams. This process is called pre-processing. Main goal of this stage is to receive unprocessed web server log file as input and after processing outputs meaningful representations which can be used in next phase. In this pa-per, we describe in detail user session identification which can be considered as most important part of data pre-processing. Our paper aims to compare the us-er/session identification using the STT with the identification of user/session us-ing cookies. This comparison was performed concerning the quality of the se-quential rules generated, i.e., a comparison was made regarding generation useful, trivial and inexplicable rules.
Classifying Image Sequences with the Markov Chain Structure and Matrix Normal Distributions

We consider the problem of classifying image sequences to several classes. Such problems arise in numerous applications, e.g., when a task to be completed requires that all sub-tasks are properly executed. In order to derive realistic classifiers for such complicated problems, we assume that images in the sequence form a Markov chain, while the conditional probability density function of transitions has the matrix normal distribution, i.e., it has the covariance matrix being the Kronecker product of inter-rows and inter-columns covariance matrices. Under these assumptions we derive the Bayes classifier for image sequences and its empirical version that is based on applying the plug-in rule. We also provide interpretable versions of such classifiers at the expense of additional assumptions. The proposed classifier is tested on the sequence of images from the laboratory experiments of detecting stages of an additive manufacturing process. Finally, we state conclusions and (partial) explanations on why the problem of classifying sequences of images is (much) more difficult than that of classifying individual images.
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 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 show here that arithmetic coding may produce an output that is identical to that of Huffman.
Stochastic Variance Reduction for Deep Q-learning

Recent advances in deep reinforcement learning have achieved human-level performance on a variety of real-world applications. However, the current algorithms still suffer from poor gradient estimation with excessive variance, resulting in unstable training and poor sample efficiency. In our paper, we proposed an innovative optimization strategy by utilizing stochastic variance reduced gradient (SVRG) techniques. With extensive experiments on Atari domain, our method outperforms the deep q-learning baselines on 18 out of 20 games.
Implementation of a

Thomas Ruprecht, Tobias Denkinger. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). 2019.
bayesvl: Visually Learning the Graphical Structure of Bayesian Networks and Performing MCMC with 'Stan'

This User Guide is written following the logic that aims to enable users to acquire Bayesian computation skills through examples using real data. Therefore, users are advised to perform MCMC computations very early on by repeating the R code provided in the file simulation_example.R. The performing of the MCMC computing code given in the file will require users' computers to meet technical requirements and to follow the algorithmic logic. Also, a critical component apart from installing bayesvl itself is rstan. Users are strongly advised to install relevant packages for successfully performing the MCMC problem, as specified in the notes contained in our example file.
An Efficient Algorithm for Recommender System Using Kernel Mapping Techniques

Recommender Systems is a system that helps users to find good stuff like movies, books etc. The user gives ratings to items and it is a system that predicts these ratings. User rates those items that are of their interest. Two types of techniques are used by the recommender systems for recommendations-content based filtering (CBF) and collaborative filtering (CF). Both techniques have their own pros and cons. The most common problems with CF are a cold star, scalability, and sparsity. Also, collaborative filtering needs a large amount of data. We will propose a solution using Kernel Mapping Recommender (KMR) to resolve the recommendation issues like a cold star, scalability, and sparsity the main goal of proposed work is to find dynamic, efficient and effective recommendation algorithm that can be successfully used to make a recommendation to users'. Several heuristic algorithms have been anticipated that merge different categories of KMR for improving correctness and removal of problems linked with a predictable recommender system. The proposed system is checked on movie datasets that are available online and then standardized with KMR. In conditions of accuracy, precision, recall, F1 measure, and ROC metrics; the results expose that the proposed algorithm is quite precise mainly under cold-start and sparse situations.
Capsule Based Image Synthesis for Interior Design Effect Rendering

Effect rendering that renders 3D model to 2D images with various coloring and lighting effects, is an important step in home interior design. Traditional way of manual rendering using professional software is very labor intensive and time consuming. In this paper, we present a novel capsule based conditional generative adversarial network that can automatically synthesize an indoor image with realistic and aesthetically pleasing rendering effect from a given plain image rendered without any effects from a interior designed 3D model. By adapting capsule blocks in both generator and discriminator and a novel multi-way loss function inside discriminator, our framework is able to generate more realistic rendering effect at both detail and global levels. In addition, a novel line preservation loss is introduced not only to help preserve the properties that are independent of lighting effect, but also improves the lighting effect along those lines. We apply our technique on a dataset specially prepared for interior design effect rendering and systematically compare our approach with multiple state-of-the-art methods.
A Generator and Corrector of Parametric Questions in Hard Copy

The demand and use of automatic test generators for exams and competitions have both been increasing. But the handiness offered by these generators brings a difficulty in the process of creating databanks of questions: from a group of questions their different versions are still made for each question separately. Of course, this manual task turns out to be lengthy and costly. This problem proved to be effectively solved by generators of parametric questions. Our present work introduces an innovative and accessible approach that consists of an open platform to generate and correct parametric questions. To the best of our knowledge the platform presented here is the first one totally devoted to exams in hard copy that also includes automatic correction. Teachers and professors of any institution that registers in our platform can share question databanks among them.
Cervical Cancer Detection Using Single Cell and Multiple Cell Histopathology Images

Cervical cancer is the second most common cancer in females in India. A Pap smear screening is most efficient and prominent to detect the abnormality in cells. Pap smear test is time-consuming and sometimes gives the wrong result by human experts. In India, a shortage of pathologist is there in rural areas. Automated systems using image processing and machine learning techniques help the pathologist to take correct decisions. In this paper, two data sets are generated from one pathologist center. The first data set contains 300 single cells and the second contains 50 multiple cell images for the validation of work. In a single cell, nucleus and cytoplasm both are extracted from the cell, but in multiple cells, only the nuclei are extracted due to overlapping of cells. Edges have been enhanced by sharpening function, and the multi-threshold values and morphological operations have been used for the segmentation of cell. Shape-based features have extracted from a multiple cell and single cell images. Support Vector Machine (SVM) and Artificial Neural Network (ANN) is applied to improve the performance of classification using 10 fold cross-validation.
Designing User Interfaces

Every app needs a user interface. Unlike user interfaces for desktop operating systems like Windows or macOS that can display multiple windows on a screen at a time, iOS apps typically display a single window (called a view) that fills the entire screen at a time. Also unlike Windows or macOS user interfaces that users can manipulate using a keyboard or a mouse, users must be able to manipulate iOS apps solely through a touch screen although they can also be controlled through an optional keyboard as well. In iOS apps, a user interface consists of
Randomized Controlled Trial of Kindergarten Students Using Literacy Technology

Randomized controlled trials in education are necessary to keep pace with the evidence-based practices demanded by schools and the nation. In this study, kindergarten students in a school district in Indiana were randomized on the class level: The experimental condition consisted of students utilizing a computer-adaptive reading program for 15 minutes per day, 5 days per week over the course of the 2017–2018 school year; the control condition consisted of students receiving traditional, teacher-directed literacy instruction for the same amount of time. Thus, overall exposure to literacy instruction was the same for both groups. At the end of the school year, students randomized to the experimental condition outperformed their control counterparts on two different literacy assessments, indicating that early literacy instruction in the format of computer-adaptive reading programs may be beneficial to kindergarten students. Experimental students also outperformed their control counterparts across demographics, demonstrating a benefit from the computer-adaptive program for all students.
Impact of applying social responsibility accounting on companies' financial performance

This research aims to examine the impact of applying social responsibility accounting measured through (environment protection, community service, and human resources) on the financial performance of companies measured by Earning per Shear (EPS) and Return on Assets (ROA).The study population consisted of service companies listed in Amman stock Exchange.To realize the research objectives, analytical descriptive approach was adopted.For testing hypotheses, Multiple Regression Analysis was used.Results showed that there is an application of social responsibility accounting of the selected activities by the Jordanian companies listed in listed in Amman stock Exchange.In addition, there was a statistically significant impact at the level of (α ≤ 0.05) for social responsibility accounting whether combined and separately on the financial performance being measured by the EPS and the return on assets in the service companies listed on the Amman Stock Exchange.Among the recommendations was to increase the awareness of the companies 'administrations about the effects and implications of social activities due to their impact on their financial performance.Officials of the Amman Stock Exchange and other concerned parties have been urged to promote the supervision on companies' commitment to disclose their social activities and to encourage companies to follow the same approach.
TobiiGlassesPySuite: an open-source suite for using the Tobii Pro Glasses 2 in eye-tracking studies

In this paper we present the TobiiGlassesPySuite, an open-source suite we implemented for using the Tobii Pro Glasses 2 wearable eye-tracker in custom eye-tracking studies. We provide a platform-independent solution for controlling the device and for managing the recordings. The software consists of Python modules, integrated into a single package, accompanied by sample scripts and recordings. The proposed solution aims at providing additional methods with respect to the manufacturer's software, for allowing the users to exploit more the device's capabilities and the existing software. Our suite is available for download from the repository indicated in the paper and usable according to the terms of the GNU GPL v3.0 license.
Novel Secured Image Scrambling Technique Using Chaotic Sequence Shuffling and Pixel Value Modification Through Random Grid Map and Its Performance Analysis

Images are scrambled generally for different purposes like information hiding and communicating data in secret manner so that even unauthenticated persons hack the data not be able to understand as it is not true data. A novel and secured image scrambling technique is being proposed here in which the original image values are shuffled through a specific chaotic sequence order which will act as key and pixel values which are also manipulated through created random grid map which acts as another key. Person with both correct keys can only unscramble the image to reveal the original image. This method is implemented on different bitmap and grayscale images. Performance of proposed algorithm is also done by performing certain tests and it is found to be proposed method which is giving very convincing, and scrambled image is not at all perceptible to the human eye.