Reviewerly/exHarmony huggingface dataset:
Visual question answering algorithm based on image caption

A number of recent works have proposed image caption models for Visual Question Answering(VQA) that explain the answer. However, we think these models do not effectively combine the two important textual information about image caption and problem, and these methods are lack of attention on image and problem information. In addition, we believe that richer text information can improve the accuracy of answer prediction. In this paper we propose a VQA algorithm based on image caption. The model uses the CNN and LSTM algorithms and the collaborative attention mechanism to generate a picture caption related to the problem information, and then combines the two text information on the image description and the question to obtain an answer and output the picture description. Our model is compared with the mainstream algorithms on MSCOCO-VQA and VQA-V2. The experimental results show that the proposed algorithm can predict answer more accurately.
Method of Fast Video Inpainting

during transfer of video through communication channels, there may be distortions and loss of images that affect the quality of the services provided by various video services. The paper proposes a method of inpainting based on the use of statistical redundancy. The method is based on the approximation of video by a multidimensional Markov process with discrete arguments and prediction of pixels of the lost frame block at a known neighborhood and transition probability matrix. There are given the mathematical model and simulation results confirming the efficiency of the method. The proposed method does not require computational procedures for its implementation and can be used on-line due to the possibility of parallel processing.
Determination of Quality Seed Coffee Use Method of Fuzzy Logic

Coffee is one of Indonesian plantation yields whose selling value is quite high. Particularly in Gayo highland, this plant IS the main incomes for the farmers. There are two kinds of coffee plant raised on namely Arabica and Robusta. This study aims to explain application of fuzzy logic method using tsukamoto method. The result of this study shows that accuracy level can reach up to 30%. The accuracy for each coffee beans quality can be explained as: 80%Arabica grade 1, 0% Arabica grade 2, 0% Arabica grade 3, 100% robusta grade 1, 0% robusta grade 2, and 0% robusta grade 3.
CyberArts 2017: International Compendium: Prix Ars Electronica - Computer Animations/Film/VFX - Hybrid Art - Digital Music & Sound Art - u19 – CREATE YOUR WORLD : STARTS Prize '17 - Grand Prize of the European Commission honoring Innovation in Technology, Industry and Society stimulated by the Arts

The catalogue CyberArts 2017. Prix Ars Electronica edited by Gerfried Stocker, Christine Schopf and Hannes Leopoldseder contains texts by the winning artists, statements by the jury and illustrations of the works honored for the 38th edition of the Festival Prix Ars Electronica 2017. The Festival was dedicated to the Artificial Intelligence and the ‘other I’. We live in a world signed by uncertainty, not only in technology, but also in political and economic terms. What do humans have to do t...
A Super-Resolution Reconstruction Algorithm Based on Learning Improvement

In view of the problem of edge blurring and slow reconstruction speed in the existing super-resolution reconstruction of learning-based images, this paper proposes an improvement to the original learning-based reconstruction algorithm and applies Markov random fields to image super-resolution. In the rate reconstruction, during the dictionary training phase, training image blocks are randomly selected, and the texture part of the image is learned and reconstructed. The bicubic interpolation method is used to enlarge the image structure part and color information, the final reconstructed image and the interpolation-amplified image are merged. That is the final result. The experimental results show that the peak signal-to-noise ratio (PSNR) is used to objectively evaluate the image reconstruction effect, and it is concluded that the algorithm of this paper reconstructs the image better, and the reconstruction time also has a certain increase.
ECGAN: Image Translation with Multi-scale Relativistic Average Discriminator

Image-to-image translation aims to transform an image of one source domain to another target domain, which can be applied to many specific tasks, such as image restoration and style transfer. As one of the most popular frameworks, CycleGAN with cycle consistency loss can transform between different domains with unpaired training data, which greatly expands its application efficiency. However, due to its under-constrained mapping and dissatisfactory network design, the results of CycleGAN are sometimes unnatural and unreal. In some cases, there are visible artifacts in the synthetic images. In this paper, we propose an Enhanced CycleGAN (ECGAN) framework with multi-scale relativistic average discriminator, which integrates the loss function design and network structure optimization to make the generated images have more natural textures and fewer unwanted artifacts. In the evaluation, besides using quantitative full reference image quality assessment metrics (such as PSNR, SSIM), we also conduct an evaluation on the Frechet Inception distance (FID), which are more consistent with human subjective assessment according to natural, realistic and diverse. Experiments on two benchmark datasets, including CMP facades and CUHK face datasets, show that the proposed ECGAN framework outperforms the state-of-the-art methods in both quantitative and qualitative evaluation.
An Analysis of the Cultural Characteristics of Elementary School Classrooms Based on Delphi technique

The purpose of this study is to analyze what characteristics of elementary school classroom culture are to foster creativity, to gain implications for the direction and content of creativity education in elementary school, and to enable teachers to diagnose their own classroom culture. For this purpose, three Delphi surveys were conducted on experts, including creativity and creativity education, and elementary and secondary school teachers with expertise in creating classroom culture. In the first Delphi survey, the appearance and characteristics of creative elementary school classrooms were investigated through open questions and classified into large, medium and small areas through categorization work. In the second Delphi survey, items corresponding to small areas were asked to respond to their validity and importance with the characteristics of the elementary school classroom culture for fostering creativity, and the Content Validity Ration(CVR) was calculated according to the criteria of each small area, while integration, deletion, re-examination, and completion were made for large, medium, and small areas based on the subjective responses of experts. In the third Delphi survey, 55 experts were asked to determine whether and how important the small area items were, and 11 large areas, 24 medium and 193 small areas were finally selected as elementary school classroom cultural characteristics for fostering creativity. The third Delphi survey found a total of 11items with a Content Validity Ration(CVR) 1.0, which all experts said was valid and important, which was considered the most important and core item in the study. The 11 key items related to teaching methods(class strategy), teachers(capacity, attitudes), parents(value), educational philosophy (attitude toward students) and school/class culture(creating atmosphere) were also found to be related to the characteristics of school culture, including teaching methods related to teachers, parents
Multi-Agent System and Classification Algorithms Applied for eHealth in Order to Support the Referral of Post-operative Patients

The need to perform accurate and timely diagnoses in cases involving patients in a post-operative situation is one of the challenges involved in the area of health care. According to studies in some countries, patients are concerned about spending more time in the hospital after surgery. In this sense, we tried to verify how the implementation of solutions that use IT devices and techniques could improve this process of diagnosis. Considering those, this article proposes a multi-agent system architecture that uses, among other techniques, IoT devices, machine learning algorithms and the XMPP protocol with the purpose of determining the best referral to post-operative patients based on medical information. The results obtained showed an accuracy of almost 90% in the evaluated cases, evidencing the possibility of the use and evolution of the IT solution that was developed.
Scheduling Feature Selection for Data-driven Job Shop Scheduling System Using Improved Firefly Algorithm Optimization

This study proposes an improved version of the Firefly Algorithm (FA) for scheduling feature selection optimization in data-driven job shop scheduling system. The proposed FA variant employs a dynamic step strategy with the elite individual dipartition in firefly population to improve the optimization ability of FA. Selecting essential schedule feature data from production system attributes data based on various production requirements to construct data-driven job shop scheduling system is a critical issue because of the existence of irrelevant and redundant attributes in job shop production system, by selecting the important attributes as schedule features, robust performance can be expected in data-driven job shop scheduling system. A wrapped scheduling feature selection approach based on the proposed FA variant and extreme learning machine (ELM) is presented for ELM-based job shop scheduling system. The feasibility and effectiveness of the proposed scheduling feature selection approach have been verified via a practical job shop scheduling case.
VALIDATING CORE CONCEPTS OF CYBERSECURITY CONTROL USING COMMONLY ACCEPTED STANDARDS

Expert models specify repeatable actions that are designed to ensure an organization’s resources are utilized for maximum effectiveness. Therefore, a commonly accepted expert model can serve as the basis for defining and enforcing a satisfactory degree of cybersecurity for any organization. In that respect then, expert models serve as a fixed point of reference for instituting specific cybersecurity control. This paper discusses the evolution of cybersecurity expert models, core knowledge elements, and the areas of agreement between the common expert models.
Wasserstein Reinforcement Learning.

We propose behavior-driven optimization via Wasserstein distances (WDs) toimprove several classes of state-of-the-art reinforcement learning (RL)algorithms. We show that WD regularizers acting on appropriate policyembeddings efficiently incorporate behavioral characteristics into policyoptimization. We demonstrate that they improve Evolution Strategy methods byencouraging more efficient exploration, can be applied in imitation learningand to speed up training of Trust Region Policy Optimization methods. Since theexact computation of WDs is expensive, we develop approximate algorithms basedon the combination of different methods: dual formulation of the optimaltransport problem, alternating optimization and random feature maps, toeffectively replace exact WD computations in the RL tasks considered. Weprovide theoretical analysis of our algorithms and exhaustive empiricalevaluation in a variety of RL settings.
Incremental Bit-Quads Count in Tree of Shapes

Bit-quads are $$2\times 2$$ binary patterns which are counted within a binary image and can be used to compute attributes. Based on previous works which proposed an efficient algorithm to count bit-quads in component trees, in this paper, we discuss how we can count these patterns in tree of shapes by presenting two approaches. In the first one, we show how counting quads in component trees can be used to count them in tree of shapes by using the depth of the node as the value of pixels in a larger and interpolated image representation (used in an algorithm for constructing tree of shapes). In the second approach, we propose a novel algorithm which uses this larger image representation, but, the resulting quad counts are for the input image. In this way, our approach gives exactly the counts for the original image. We also provide experimental results showing that our algorithm is much faster than the non-incremental naive approach.
An Incremental Scheme with Weight Pruning to Train Deep Neural Network

Deep neural networks have present state-of-the-art results in many different machine learning tasks. In the traditional machine learning task, we train models on the formerly prepared dataset. However, in the real-world scenarios, training data are always collected in an incremental manner, in which new samples and new classes will be added to the training data gradually. Since the traditional training method with stochastic gradient descent will suffer from catastrophic forgetting problem when training on the new data set, in this paper, we proposed a new scheme to train deep neural networks incrementally. We first train the deep model on the original dataset with a weight-pruning manner, then on the newly added training data, we train the former pruned weights while remaining the former trained core-part weights unchanged. Experiments on MNIST demonstrated that our method is efficient and can even get better performance than training from scratch on the whole dataset in the traditional manner.
A Keyword Search Approach for Semantic Web Data

More and more RDF datasets are available on the web. These datasets can be queried using the SPARQL language; to do so, one must be familiar with the query language itself, but also with the content of the dataset in terms of resources and properties in order to formulate the queries. Keyword search is an alternative way to query RDF data. In this paper, we present a keyword search approach which uses online lexical databases to bridge the terminological gap between the keywords and the dataset when searching for matching elements in the dataset. We formulate the problem of aggregating the matching elements as a Steiner tree problem and we adapt Kruskal's algorithm to provide a solution. We also propose a ranking approach if several answers are found for a given query. We have performed some experiments on the DBpedia and the AIFB datasets to illustrate the effectiveness of our approach.
Maximal Codes for Finite Chain Rings

Coding theory is one of the bases of the information theory, this behind has experienced a spectacular evolution exploiting what has been achieved in the codes by the use of the concept of algebra. So to ensure a good transfer of information, it is better to use good codes by using a few aspects which represents an advantage. In this work, we will talk about the maximal codes on finite rings and exactly we will give the form of these codes for finite chain rings, this notion of maximality allows to show that all codes form classes what can server in decoding.
Acoustic Evaluation of the Air-Conditioning Unit in the Room

Abstract This paper deals with an acoustic simulation, evaluation and measurement of an indoor air-conditioning unit commonly used in hotel rooms. The unit is situated in the plaster board ceiling of a hotel room above the entrance of the hotel room. The first step was to create a model of the real indoor air-conditioning unit from the catalogue data of a manufacturer, then built the model for the simulation of the internal sound propagation in the hotel room. The second step was to mock-up the real hotel room. The goal of the paper is to compare the calculated data from the simulations with the measured data in-situ.
Coarse-graining in retrodictive quantum state tomography

Quantum state tomography often operates in the highly idealised scenario of assuming perfect measurements. The errors implied by such an approach are entwined with other imperfections relating to the information processing protocol or application of interest. We consider the problem of retrodicting the quantum state of a system, existing prior to the application of random but known phase errors, allowing those errors to be separated and removed. The continuously random nature of the errors implies that there is only one click per measurement outcome -- a feature having a drastically adverse effect on data-processing times. We provide a thorough analysis of coarse-graining under various reconstruction algorithms, finding dramatic increases in speed for only modest sacrifices in fidelity.
On the Influence of Interval Normalization in IVOVO Fuzzy Multi-class Classifier

IVOVO stands for Inverval-Valued One-Vs-One and is the combination of IVTURS fuzzy classifier and the One-Vs-One strategy. This method is designed to improve the performance of IVTURS in multi-class problems, by dividing the original problem into simpler binary ones. The key issue with IVTURS is that interval-valued confidence degrees for each class are returned and, consequently, they have to be normalized for applying a One-Vs-One strategy. However, there is no consensus on which normalization method should be used with intervals. In IVOVO, the normalization method based on the upper bounds was considered as it maintains the admissible order between intervals and also the proportion of ignorance, but no further study was developed. In this work, we aim to extend this analysis considering several normalizations in the literature. We will study both their main theoretical properties and empirical performance in the final results of IVOVO.
Dot pattern generation using thick sinusoidal phase grating under Gaussian beam illumination

Far-field dot pattern generation is analyzed for a Gaussian beam source that illuminates a sinusoidal phase grating which is placed at a certain distance behind the beam waist. We obtain a bigger field of view with more numbers of points using a Gaussian beam in comparison to a plane wave illumination because of the initial curvature of the Gaussian beam. Light propagation modeling through the sinusoidal grating is carried out using different approximations for a thin and thick phase grating. Using thin element approximation (TEA), the complex field is carried out with low computational effort and accuracy. We compare TEA with more accurate methods, such as FFT-BPM and FDTD methods. For thin phase gratings, TEA can be used but for thick gratings, FDTD method is the only valid option. For thick phase gratings, the effect of reflection from phase grating on the field modulation increases and we use FDTD method to find the correct far field pattern distribution.
EAGER

Image understanding is a fundamental task for many multimedia and computer vision applications, such as self-driving, multimedia retrieval, and augmented reality, etc. In this paper, we demonstrate that edge detection could aid image understanding tasks such as semantic segmentation, optical flow estimation, and object proposal generation. Based on our recent research efforts on edge detection, we develop a robust and efficient Edge-Aided imaGe undERstanding system named as EAGER. EAGER is built on a compact and efficient edge detection module, which is constructed with a bi-directional cascade network, multi-scale feature enhancement, and layer-specific training supervision, respectively. Based on detected edges, EAGER achieves accurate semantic segment, optical flow estimation, as well as object bounding-box proposal generation for user-uploaded images and videos.
A Coherence Model for Sentence Ordering

Text generation applications such as machine translation and automatic summarization require an additional post-processing step to enhance readability and coherence of output texts. In this work, we identify a set of coherence features from different levels of discourse analysis. Features have either positive or negative input to the output coherence. We propose a new model that combines these features to produce more coherent summaries for our target application: extractive summarization. The model use a genetic algorithm to search for a better ordering of the extracted sentences to form output summaries. Experimentations on two datasets using an automatic coherence assessment measure show promising results.
The Chirp Function Revisited: A Uniqueness Conjecture for Chirplet Modulation

The chirp function (a unit amplitude quadratic phase-only function with linear frequency modulation) is well known and is used in a wide range of applications including radar, digital communications, information coding and hiding and many other forms of signal and image processing. This is because the chirp provides an optimal solution to the problem of retrieving information from low energy signals with a low Signal-to-Noise Ratio (SNR). The chirp function also occurs in the solution to many mathematical models used to describe the propagation and scattering of waves (in the Fresnel zone), in quantum mechanics (the quantum shutter problem) and optical fiber communications to name but a few. In a ‘systems and signals' context, the chirp function is accepted to be unique in that no other function has properties which yield such an optimal solution to the problem of extracting information from noise. In this context, we revisit the chirp function, consider a theorem and conjecture that attempt to quantify its unique properties through an analysis of its Fourier transform and re-establish the principles associated with the chirplet transform for functions of compact support. We then consider the principles of chirplet modulation for the transmission and reconstruction of bit-streams from signals with a low SNRs and show how this approach can be used to secure chirplet modulated signals using the prime number factorisation of a semi-prime derived from the value of the bandwidth of a communications channel.
Chapter 10 Introduction to Chinese Knowledge Graphs and their Applications

With the continuous development of intelligent technologies, knowledge graph, the backbone of artificial intelligence, has attracted much attention from both academic and industrial communities due to its powerful capability of knowledge representation and reasoning. Besides, knowledge graph has been widely applied in different kinds of applications, such as semantic search, question answering, knowledge management, and so on. In recent years, knowledge graph techniques in China are also developing rapidly and different Chinese knowledge graphs have been built to support various applications. Under the background of “One Belt One Road (OBOR)” initiative, cooperating with the countries along OBOR on studying knowledge graph techniques and applications will greatly promote the development of artificial intelligence. At the same time, the accumulated experience of China on developing knowledge graph is also a good reference. Thus, in this chapter, the authors mainly introduce the development of Chinese knowledge graphs and their applications. The authors first describe the background of OBOR, and then introduce the concept of knowledge graph and three typical Chinese knowledge graphs, including Zhishi.me, CN-DBpedia, and XLORE. Finally, the authors demonstrate several applications of Chinese knowledge graphs.
Proper Choice of Control Parameters for CoDE Algorithm

An adaptive variant of CoDE algorithm uses three couples of settings of two control parameters. These combinations provide well performance when solving a various type of optimisation problems. The aim of the paper is to replace original values of control parameters in CoDE to achieve better efficiency in real-world problems. Two different variants of enhanced CoDE algorithm are proposed and compared with the original CoDE variant. The new combinations of F and CR parameters are selected from results provided in a preliminary study where 441 various combinations of these parameters were evaluated. The results show that newly proposed CoDE variants (CoDE $$_\mathrm {FCR1}$$ and CoDE $$_\mathrm {FCR2}$$ ) perform better than the original CoDE in most of 22 real-world problems.