Reviewerly/exHarmony huggingface dataset:
A Survey On Auto-Image Colorization Using Deep Learning Techniques With User Proposition

Abstract An approach based on deep learning for automatic colorization of image with optional user-guided hints. The system maps a gray-scale image, along with, user hints” (selected colors) to an output colorization with a Convolution Neural Network (CNN). Previous approaches have relied heavily on user input which results in non-real-time desaturated outputs. The network takes user edits by fusing low-level information of source with high-level information, learned from large-scale data. Some networks are trained on a large data set to eliminate this dependency. The image colorization systems find their applications in astronomical photography, CCTV footage, electron microscopy, etc. The various approaches combine color data from large data sets and user inputs provide a model for accurate and efficient colorization of grey-scale images.
Homogeneous Space as Media for the Inductive Selection of Separating Features for the Construction of Classification Rules

This paper deals with problems which require the reconstruction of structures of multi-dimensional dependencies from data. From a mathematical point of view these problems belong to the most complicated problems of artificial intelligence, such as reconstruction of the structures of multi-dimensional regressions or difference equations. In classification we meet similar problems when we have to select the space of classification features. Here we consider a special problem of supervisor-based classification that can be solved by the classification method “Alpha-procedure”. This problem consists in the following: Normally, the construction of the separating rule is performed during a training phase, where a supervisor defines the belonging of objects to classes by using a training set of data that can be small. But obviously the rich (partly subconscious) experience of the supervisor which is not described quantitatively somehow influences his decision. This may concern the importance, the uselessness, or even the harmfulness of the features. By this reason, the construction of the separation rule directly in the Euclidian data space leads to instability of that rule in certain cases. The paper explains why the Alpha-procedure, that performs an inductive construction of the separating rule in the homogeneous Lorentz space, allows a stable classification of new objects in the application phase without supervisor. It also shows, from the point of view of group transformations and their invariants, the difference between the mathematical apparatus for the search of the decision rule in a fixed feature space and in a space that is constructed by selecting features.
iFR: A New Framework for Real-Time Face Recognition with Deep Learning

This paper investigates the performance of the real-time face recognition system with machine learning, as well as the performance of each haarcascade classifiers that based on accuracy and speed. We employ the subset of machine learning called deep learning to the real-time face recognition system as the deep face recognition technique has improved the state-of-the-art performance. This system uses a pre-trained model named FaceNet and employs triplet loss technique to impose a margin between every pair of faces from the same person to other faces. In other words, it minimizes the distance between the anchor and the positive from the same identity and maximizes the distance between the anchor and the negative from different identity. Furthermore, to further investigate the performance of the system, we implement the Tensorflow framework to improve the system performance by the usage of Graphics Processing Unit (GPU). This system uses Labeled Faces in Wild (LFW) dataset as the benchmark to test the performance of the face recognition system. Apart from that, we conduct a preliminary experiment to evaluate the performance of haarcascade classifiers so that we can choose the best classifier in term of accuracy and speed. Haarcascade_frontalface_default.xml (FD) exhibits best performance compared to haarcascade_frontalface_alt.xml (FA) and haarcascade_frontalface_alt2.xml (FA2) with accurate number of faces detected and shortest average time taken to detect faces.
Deep Learning Framework for Detection of an Illicit Drug Abuser Using Facial Image

The detection of an illicit drug abuser by analyzing the subject’s facial image has been an active topic in the field of machine learning research. The big question here is up to what extent and with what accuracy can a computer model help us to identify if a person is an illicit drug abuser only by analyzing the subject’s facial image. The main objective of this paper is to propose a framework which can identify an illicit drug abuser just by giving an image of the subject as an input. The paper proposes a framework which relies on Deep Convolutional Neural Network (DCNN) in combination with Support Vector Machine (SVM) classifier for detecting an illicit drug abuser’s face. We have created dataset consisting of 221 illicit drug abusers’ facial images which present various expressions, aging effects, and orientations. We have taken random 221 non-abusers’ facial images from available dataset named as, Labeled Faces in the Wild (LFW). The experiments are performed using both datasets to attain the objective. The proposed model can predict if the person in an image is an illicit drug abuser or not with an accuracy of 98.5%. The final results show the importance of the proposed model by comparing the accuracies obtained in the experiments performed.
Super-resolution for remote sensing images via dual-domain network learning

Super-resolution (SR), which aims at recovering a high-resolution (HR) image from single or sequential low-resolution (LR) images, is a widely used technology in image processing. In the field of image SR, convolutional neural networks (CNNs) have attracted increasing attention because of their high-quality performance. However, most CNN-based methods treat each channel-wise feature equally, which lacks discriminative learning ability across feature channels. Furthermore, many methods neglect to fully use information of each convolutional layer. To resolve these problems, we propose a remote sensing image SR method named dense channel attention network (DCAN). In our DCAN, a sequence of residual dense channel attention blocks (RDCABs) is cascaded with a densely connected structure. In each RDCAB, we make full use of the information from all convolution layers via densely connected convolutional layers. In addition, RDCABs utilize the channel attention mechanism to adaptively recalibrate channel-wise feature responses by explicitly modeling the interdependencies between the channels. In addition, our DCAN can make full use of the hierarchical features by densely connecting each RDCAB. Finally, to further improve the SR performance, the proposed DCAN is learned in both the pixel and wavelet domains, and a fusion layer is used to fuse the outputs of these two domains. Extensive quantitative and qualitative evaluations verify the superiority of our proposed method over several state-of-the-art methods.
Staging of breast cancer based on the area of the primary tumour

Abstract Breast cancer is one of the most common and alarming diseases for women. With early detection and diagnosis, the chances of successful treatment and survival would improve. The diagnosis includes classification or staging of the breast cancer, which plays an important role in the prognosis of the disease hence determining the best treatment for the disease. Breast cancer staging is a way of describing the size of a cancer and how far it has grown. One of the staging numbering systems is the Tumour-Node-Metastasis (TNM) system, which categories cancer into several stages. Most types of cancer have four stages, numbered from I to IV. Normally, staging is determined by doctors by examining the pathology information which describes the spreading area of the cancer. In this study, a series of computer algorithms is applied to produce the area information of the cancerous region. The methodology consists of four phases which are image gathering; image pre-processing using Median filter and PCA; image segmentation using FCM; and staging using the area of the primary tumour as a representation of size in the TNM system. From thirty-five randomly selected mammography images of Malaysian women with malignant tumour, 14.3% is in stage I, 5.7% is in stage II and 80% in stage III. Experimental results also shows that 96.4% accuracy is obtained for stage III but higher errors occur at the boundaries of the staging scale.
Colored Ronchi pattern for fringe projection profilometry

We developed a color Ronchi pattern, a new form of fringe pattern, that can effectively used in fringe projection profilometry. The pattern is formed such that color channels red, green and blue are in separate spatial domains. From a single-shot image of the structured pattern, we are able to reconstruct the surface profile of a distorted paint canvas using both Fourier transform profilometry (FTP) and phase shifting profilometry (PSP). From the three surface reconstructions obtained using FTP, tiny artifacts in the form of ripples are observed. These artifacts are significantly reduced when the surface measurements in FTP are averaged. Experimental results have shown that both FTP and PSP successfully reconstructed the profile of a distorted paint canvas with a measurement difference between 10.00% to 15.00% from the actual displacement.
How can a plenoptic camera be used as a metrology tool?

In this paper, we propose a method for the structured-light field (SLF) 3D measurement, involving ray calibration and phase mapping. The ray calibration is carried out to determine each light ray with metric spatio-angular parameters. Base on the ray parametric equation, the phase mapping in the SLF is developed so that spatial coordinates could be directly mapped from phase-encoding information. Then, a calibration strategy is designed to determine the mapping coefficients for each light ray, achieving high-efficiency SLF 3D reconstruction.
A whirlwind introduction to computer graphics

Provide a background for the amazing things you will see at SIGGRAPH Asia 2019
Diffusion Maps Particle Filter

In this paper, we propose a new nonparametric filtering framework combining manifold learning and particle filtering. Diffusion maps, a nonparametric manifold learning method, is applied to obtain a parametric state-space model, inferring the state coordinates, their dynamics, as well as the function that links the state to the noisy observations, in a purely data-driven manner. Then, based on the inferred parametric model, a particle filter is devised, facilitating the processing of high-dimensional noisy observations without rigid prior model assumptions. We demonstrate the performance of the proposed approach in a simulation of a challenging tracking problem with noisy observations and a hidden model.
SS-GANs: Text-to-Image via Stage by Stage Generative Adversarial Networks

Realistic text-to-image synthesis has achieved great improvements in recent years. However, most work ignores the relationship between low and high resolution and prefers to adopt identical module in different stages. It is obviously inappropriate because the differences in various generation stages are huge. Therefore, we propose a novel structure of network named SS-GANs, in which specific modules are added in different stages to satisfy the unique requirements. In addition, we also explore an effective training way named coordinated train and a simple negative sample selection mechanism. Lastly, we train our model on Oxford-102 dataset, which outperforms the state-of-the-art models.
Innovative potential of modern lecturer

The article is analyzed the factors contributing to and impeding the introduction of pedagogical innovations; characterized anti-innovation barriers – external (social, organizational, methodological, logistical) and internal (propensity to conformism, anxiety, self-doubt, negative self-perception, low self-esteem, rigidity of thinking and other). It was emphasized that the effectiveness of implementation pedagogical innovations depends primarily on the lecturer, his innovative potential. The results of diagnosing the innovative potential of teachers involved in the training of future social workers are also presented. By results of diagnostics, it’s established that the vast majority of educators have a high level of innovation in spite of availability different external anti-innovation barriers. Suggested the activities to promote further development the innovative potential of teachers and teaching staff of the department as a whole.
Copyright and managements

This chapter describes various items in the literature, as represented by the Bibliography at the end. However, the uninitiated - taken as those entering the information professions, since existing professionals should already know something of copyright - may like to start with a concise paper on the basics. Copyright law tries to be even-handed both to rights owners and users, allowing the former to benefit from their work and the latter to make appropriate use of it, especially for the good of society. A ‘balancing act’ may not be found witty by commercial publishers but describes the result. The law gives exclusive rights to the first owners of copyright in respect of particular actions. ‘Research or private study’ happens to be one of the fair dealing purposes as well as the authorised purpose of library copying.
Effect of RMS models (reading, mind mapping and sharing) based on e-learning to the learning outcomes of students in the teaching and learning subject

Abstract This study aims to determine the effectiveness of the RMS model (Reading, Mind Mapping and Sharing) based on e-learning in the subject of teaching and learning on student results. This study was a quasi-experimental type. The sampling technique was carried out through purposive sampling technique. The study population was the entire class of students of Faculty of Teacher Training and Education Universitas Papua second semester of the 2017-2018 academic year who contracted teaching and learning subjects. The sample in this study were 11 students of the Department of Physics Education and 19 students of the Department of Mathematics Education so that 30 students. To measure and find out the student learning outcomes the tests were conducted using multiple-choice questions totalling 20 questions. The results of the analysis show that there were significant differences in the average student learning outcomes before and after learning using the e-learning based RMS model as evidenced by the acquisition of sig values &lt;0.05. The effectiveness of the RMS model based on e-learning can be seen from the effect size value of 1,976 which is in the high category. So it can be concluded that physics learning through an e-learning based RMS model was effectively applied to student learning outcomes in teaching and learning subjects.
Sentimental Analysis Using Convolution Neutral Network Through Word to Vector Embedding for Patients Dataset

Sentimental analysis involves the incorporation of working of a structure that explores sentiments seen in blog sections, comments, or tweet relating to a thing or point. In this proposed research, the authors have used speech recognition for collecting the opinions from patients relating to health care. The mental condition of a patient in respect of the health care has been identified through sentimental analysis based on the convolutional neural network using some convolutional filter (1D) in word2vec logistic synopsis. It features implicit or explicit determination of the patient's strength based on sentiments through results. It helps the physician in taking necessary action. Accuracy of the result is of help in the recommendation to the patient by the physicians and health care centers.
Secure Data Integrity in Cloud Storage with Multi-level Hash Indexing Technique

A growing technology which offers storage, compute and network resources over the internet on a pay-for-use basis can be named as cloud computing. Cloud Storage is one of computing and service model in which the system allows you to store, manage, maintained and backed up remotely over a network and made available to users to use. Data integrity implies that the accuracy, validity of data over its Life cycle has to be maintained and any violations are to be detected like if data is loss, altered or compromised. User money and time can be saved by using cloud services. So user loses control over the data as there are maintained, stored by the cloud and it can be hacked, tampered by the attacker. Now integrity of data has to be maintained in a cloud where data is present in cloud. Here the aim is to provide data integrity for which the data will be stored in the cloud by user. Here paper says, we need to have an auditing technique which must be efficient and check the data integrity using Third party auditor (TPA), the auditing schemes uses Advanced Encryption Standard (AES) and Secure hash algorithm for encryption and to generate verification meta data respectively and using multi-level hash indexing technique to eliminate duplicate files, reduce storage consumption of file which is stored in the cloud.
The Regulation of Human Germline Genome Modification in Spain

This chapter offers a paramount analysis of Spain’s regulatory framework for human gene editing. This is a particularly complex task, due to several reasons. First, Spain not only ratified the Oviedo Convention, which rules gene editing, but has also developed a national legal framework that sometimes contradicts it. Second, many competencies related to monitoring and control have been developed at the regional level to “autonomous communities” (Comunidades Autónomas). In general, the Spanish response to the issues posed by human gene editing can be summarized like this: somatic gene editing is perfectly acceptable, once the corresponding administrative steps are taken. Germline gene editing, instead, is illegal provided that it affects someone's descendants. The legal stature of basic science involving germline gene editing remains unclear. The final decision should be adopted in a case-by-case analysis by corresponding ethics committees.
Stochastic multi-agent planning with partial state models

People who observe a multi-agent team can often provide valuable information to the agents based on their superior cognitive abilities to interpret sequences of observations and assess the overall situation. The knowledge they possess is often difficult to be fully represent using a formal model such as DEC-POMDP. To deal with this, we propose an extension of the DEC-POMDP that allows states to be partially specified and benefit from expert knowledge, while preserving the partial observability and decentralized operation of the agents. In particular, we present an algorithm for computing policies based on history samples that include human labeled data in the form of reward reshaping. We also consider ways to minimize the burden on human experts during the labeling phase. The results offer the first approach to incorporating human knowledge in such complex multi-agent settings. We demonstrate the benefits of our approach using a disaster recovery scenario, comparing it to several baseline approaches.
Clustering Mixed Datasets by Using Similarity Features

Clustering datasets consisting of numeric and nominal features is a challenging task as there are different similarity measures for numeric and nominal features. In the present paper, we propose a method to transform a mixed dataset to a numeric dataset. This method uses a similarity measure for mixed datasets and a randomly selected set of the data objects form the given mixed dataset and generate numeric similarity features. A clustering algorithm for pure numeric datasets is then applied on the newly generated numeric dataset to produce clusters. A comparative study with the other clustering algorithms demonstrated the superior performance of the proposed clustering approach.
Research on Decentralized Music Sharing Model Based on Consortium Blockchain

To solve the problem of low transparency of digital copyright and insufficient data storage in current traditional music industry, we propose a decentralized music sharing model based on blockchain technology and InterPlanetary File System (IPFS). Blockchain technology has the characteristics of decentralization, non-tampering and traceability, thus we build the Ethereum consortium blockchain in the application environment of digital music copyright. Compared to the PoW algorithm, we use the PoA consensus algorithm suitable for the Ethereum consortium blockchain scenario to avoid hashrate mining and reduce the consensus time of the whole network. IPFS is combined to realize distributed and reliable storage of massive music data. In the paper, the business process of decentralized music sharing model is proposed, and personalized access control strategy is designed to set access permission, and smart contracts is used to ensure the reliability and transparency of transactions between users. The experimental analysis and testing of blockchain transaction processing efficiency is also conducted. The results show that when out-of-block time is 100 s, the throughput of system reaches 324 pens per second which is far higher than PoW algorithm, and has high operating efficiency.
Macromanagement and Strategy Classification in Real-Time Strategy Games

We have done some research on the macro-management modeling and strategy classification in the RTS(real-time strategy) game StarCraft. Similar to the real military battlefield, simulation in the virtual environment could do favor to modeling, realize and predict the opponent strategy which could lead us to the counter strategy. Firstly we collect several typical replays played by professional player, then we do some preprocess and extract the key features to readable data, finally, we use HMM-KNN to do the strategy classification.
Influence of Methods and Means of Informatics on the Professionalism of the Teacher

The aim is to identify the degree of infl uence of methods and means of Informatics on the professional activities of the teachers. Research methodology. Methodological approaches are used: systemic, informational, sociocultural, personal. Methods. Analysis of psychological, pedagogical and special literature, materials of discussion platforms of conferences, systematization, generalization. Research problem. On the basis of the analysis of psychological and pedagogical literature to reveal the infl uence of information and communication technologies on the content of professional and pedagogical activity as a component of professional competence of the teacher. Results. The study noted a number of objective and subjective factors that have a significant impact on the ways of the teacher, the organization of training in the information and educational environment (IEE) and the content of the components of professional and pedagogical activity. Thus, the need to purposefully prepare future teachers to work in such an environment is not in doubt. Conclusion. In the article it is noted that the qualitative impact of information and communication technologies (ICT) in education, their role and importance to full-fl edged professional activity of the modern teacher will be meaningful only when ICTs are having a concrete impact on the content of the components of his professional and pedagogical activity, as ICT become key elements in improving the educational system.