Reviewerly/exHarmony huggingface dataset:
Multimodel Facial Makeup Translation with Generative Adversarial Networks

Automatic apply/remove makeup is a more challenging task than traditional style transfer, there are specific makeup styles in different facial areas, such as eye shadow, lipstick, and foundation. We propose a method of makeup transfer, which can achieve a makeup-oriented synthesis under different faces without pairs of images, and output different styles of makeup automatically. Specifically, owing to makeup transfer is more than a simple global transformation, we will use the separation training method, not only save the identity of the face, but also retain the characteristics of the specific part. Our framework is based on generative adversarial networks, adding face segmentation algorithms and Poisson blending techniques in the synthesis, make it possible to smoothly blend faces from different parts. Then we can complete makeup and makeup removal between different faces.
Performance Trend Prediction Analysis of Key Component of Aircraft Hydraulic System

As one of the key components of the aircraft hydraulic system, the aircraft hydraulic pump plays an important role in providing mechanical power to the aircraft hydraulic system and even the entire aircraft. Aiming at the problem that the complex structure and harsh working environment make it difficult to accurately analyze its performance trend, a performance trend prediction analysis method based on particle swarm optimization algorithm is proposed to analysis of the trend of aircraft hydraulic pump performance changes Firstly, the performance characterization parameter of a certain type of aircraft hydraulic pump(the raw data of oil return flow)is analyzed and the ELM trend prediction model of aircraft hydraulic pump oil return flow is designed. In order to further improve the ELM trend prediction accuracy of hydraulic pump oil return flow, particle swarm is adopted. The Particle Swarm Optimization (PSO) algorithm is used to optimize the ELM trend prediction model of oil return flow of the aircraft hydraulic pump, and the PSO-ELM prediction model of the aircraft hydraulic pump performance trend is obtained. The PSO-ELM prediction model is used to predict the oil return flow value of aircraft hydraulic pump. The results show that the prediction accuracy of PSO-ELM prediction model of aircraft hydraulic pump performance is better than that of ELM prediction model, which verifies the effectiveness of particle swarm optimization algorithm and provides theoretical guidance for predicting and analyzing the performance trend of key components of aircraft hydraulic system.
Drinking Gesture Recognition from Poorly Annotated Data: A Case Study

In this chapter we present a case study on drinking gesture recognition from a dataset annotated by Experience Sampling (ES). The dataset contains 8825 “sensor events” and users reported 1808 “drink events” through experience sampling. We first show that the annotations obtained through ES do not reflect accurately true drinking events. We present then how we maximise the value of this dataset through two approaches aiming at improving the quality of the annotations post-hoc. Based on the work presented in Ciliberto et al. (2018), we extend the application of template-matching (Warping Longest Common Subsequence) to multiple sensor channels in order to spot a subset of events which are highly likely to be drinking gestures. We then propose an unsupervised approach which can perform drinking gesture recognition by combining K-Means clustering with WLCSS. Experimental results verify the effectiveness of the proposed method, with an improvement of the F1 score by 16% compared to standard K-Means using Euclidean distance.
Infrastructure Facilities for Unmanned Vehicles

The article considers the technological aspects of creating infrastructure facilities for organization of movement of unmanned vehicles on the roads. The features of operation of cars with an autonomous control system inRussiaand other regions of the world are analyzed. Basing on the study, the authors offer a fundamentally new approach for solving a problem of recognizing objects along the route of unmanned vehicles.The study suggests installation of stationary points that are interconnected in a single network and exchange data with the cloud storage. The effectiveness of existing and alternative systems is evaluated.
Axiomatic Kernels on Graphs for Support Vector Machines

We solve the problem of classification on graphs by generating a similarity matrix from a graph with virtual edges created using predefined rules. The rules are defined based on axioms for similarity spaces. Virtual edges are generated by solving the problem of computing paths with maximal fixed length. We perform experiments by using the similarity matrix as a kernel matrix in support vector machines (SVM). We consider two versions of SVM: for inductive and transductive learning. The experiments show that virtual edges reduce the number of support vectors. When comparing to kernels on graphs, the SVM method with virtual edges is faster while preserving similar generalization performance.
Dots and Boxes Interactive Interface Implementation and Algorithm Improvement

According to the rules of the 2018 Chinese University Computer Game Contest, a checkerboard interface that can realize the standardized game play is written. The interface program is written in the Pyqt5 framework. The Dots and Boxes algorithm was improved in the early stage, and the rules were imported to make the algorithm more reasonable in the early stage. In the 2018 Chinese University Computer Game Competition, the third-class results were obtained.
Reforms of Russian Education and Their Impact on the Quality of Labor Force Training

The article assesses the consequences of reforming the vocational education system in Russia and its impact on the quality of graduate training. The need for changes in the education system is due to the transformation of the economy and the development of a knowledge society. The paper analyzes the main results of the reforms carried out since the beginning of the 200s. The conclusion is made that the reform of the education system allowed students to adapt more flexibly to the labor market conditions. It is noted that at present there is a gap between the real availability of professional personnel and their competencies and the needs of the economy. The article emphasizes that the introduction of two-level training for most humanitarian and natural sciences turned out to be optimal, but for a number of technical specialties negatively affected the quality of training. The authors suggest areas for further reform of the vocational education system in accordance with the requirements of the labor market.
Effectively Representing Short Text via the Improved Semantic Feature Space Mapping

Short text representation (STR) has attracted increasing interests recently with the rapid growth of Web and social media data existing in short text form. In this paper, we present a new method using an improved semantic feature space mapping to effectively represent short texts. Firstly, semantic clustering of terms is performed based on statistical analysis and word2vec, and the semantic feature space can then be represented via the cluster center. Then, the context information of terms is integrated with the semantic feature space, based on which three improved similarity calculation methods are established. Thereafter the text mapping matrix is constructed for short text representation learning. Experiments on both Chinese and English test collections show that the proposed method can well reflect the semantic information of short texts and represent the short texts reasonably and effectively.
DSRNet

It is important to reduce the computation complexity while maintaining the accuracy of convolution neural networks. We deem it is possible to further reduce the network complexity while ensuring the accuracy. In this paper, we propose a novel feature extraction network called DSRNet which is lightweight but effective. DSRNet follows the basic ideas of stacking modules and short connection, introduces Depthwise Separable convolution and utilizes the Dilated convolution. We conducted comprehensive experiments on CIFAR10, CIFAR100 and STL10 datasets, and the results showed the DSRNet has great performance improvement in terms of accuracy and speed.
On-board Rapid Planning for Booster Trajectory of Multistage Rocket Based on SQP and Surrogate Model

Based on the Sequential Quadratic Programming (SQP), this paper proposes an on-board rapid planning guidance for the boost phase of multistage rockets. Considering the time-consuming of the SQP algorithm, a surrogate model is built in order to reduce the times of iteration. Firstly, according to the multistage rocket model and traditional SQP algorithm, an initial trajectory is achieved by flight trajectory optimization. Secondly, considering the varieties of constraints and aerodynamics deviation, a polynomial surrogate model is built for calculating the approximate guidance command in every planning period. Finally, improved SQP algorithm is used for on-board trajectory planning with the approximate guidance command. Numerical simulation results show that surrogate model can effectively reduce the time consuming of SQP algorithm and on-board trajectory planning problems with multiple constraints and uncertain aerodynamic coefficient can be solved by the improved SQP effectively.
Referring Expression Comprehension via Co-attention and Visual Context

As a research hotspot of multimodal media analysis, referring expression comprehension locates the referred object region in an image by mapping a natural language. Though the localizing accuracy of similar objects is often distorted by the presence or absence of supporting objects in the referring expression, we propose a referring expression comprehension method via co-attention and visual context. For lacking supporting objects in referring expression, we propose co-attention to enhance the attention on attributes for the subject module. For existing supporting objects, we introduce visual context to explore the latent link between the candidate object and its supporters. Experiments on three datasets RefCOCO, RefCOCO+, and RefCOCOg, show that our approach outperforms published approaches by a considerable margin.
Adaptive- $$L_2$$ L 2 Batch Neural Gas

Neural Gas (NG) algorithms aim to find optimal data representations based on feature vectors. Unlike SOM, NG algorithms take into consideration the dissimilarities between prototypes in the original input space and not on a grid defined in advance. It has been successfully applied in vector quantization and clustering. However, conventional NG algorithms implicitly assume that the variables have the same importance in the clustering task. Nevertheless, some variables may be irrelevant and, among the important ones, some may be more or less important than others to the clustering task. This paper provides an adaptive batch NG algorithm that, in comparison with the traditional batch NG algorithm, has an additional step where it automatically computes the importance of the variables in the clustering task. Experiments with synthetic and real datasets show the usefulness of the proposed adaptive NG algorithm.
Development of University Scientific Knowledge Ontological Model

An ontology is a link between objects of knowledge and a connecting bridge between various steps of Knowledge Processes. Ontology development is an important aspect of knowledge management solution support (KM-solutions). In this paper, we consider a university scientific knowledge ontological model which is one of the knowledge management systems tools. The main functions of the university scientific knowledge ontology are given. The main classes, properties and relations of ontology for maintaining the knowledge base of educational resources are described.
Image Classification via Hierarchical Dictionary Learning

Recently lots of dictionary learning methods have been presented to deal with the issue of image classification, there is still substantial room for improvement. To further improve the performance, a novel Fisher discriminative criterion based structured dictionary learning method is here introduced which contains the main two components. Firstly, category-unique dictionary for all categories in different levels are learned to obtain the specific information of each category, and category-shared dictionary between all categories are learned in different levels to obtain the common information across different categories; secondly, a discriminative term in terms of Fisher discrimination criterion is here jointly utilized to enhance the discrimination of category-unique dictionary and category-shared dictionary. Experiment results on public available databases are conducted to demonstrate the superiority of the proposed method compared with the state-of-the-art dictionary learning methods.
Distortion calculation and removal for an off-axis and wide angle camera

For off-axis and wide angle systems, the calculation, calibration and removal of distortion effects from the images are often challenging tasks. Specific procedures have been implemented to assess and remove the distortion from the images acquired by the OSIRIS imaging instrument on-board the Rosetta ESA mission. OSIRIS consisted in a narrow and a wide angle camera. The Wide Angle Camera (WAC) is an off-axis, unobstructed and wide FoV (i.e. about 12°x12°) optical system. It has a peculiar optical configuration, and due to the off-axis design the camera presents a high level of intrinsic distortion, with the major component being anamorphism. The distortion has been estimated theoretically via raytracing during the design phase, then measured on-ground and inflight during the calibration campaigns. To obtain correct undistorted images, a distortion removal procedure has been implemented. The first step of the process has been to remove from the images the theoretical distortion. Then the distortion correction procedure has been refined using on-ground and in-flight calibration measurements. This work describes in detail the development of the procedure adopted to define, calculate and remove the distortion from the WAC images.
การสร้างหนังสืออิเล็กทรอนิกส์ เรื่อง “เทคโนโลยีสารสนเทศและการสื่อสาร” สาหรับนักเรียนชั้นประถมศึกษาปีที่ 5

The research aimed to (1) developing an electronic books on information and communication technology for for grade 5 students to meet the criteria by 80/80 (2) to study of student satisfaction towards usage Electronic books on information and communication technology. The sample group used in this research is 26 grade 5 students at Ban Tha Si Khai School Bueng Kan Province, 2 nd semester, academic year 2018, and using simple random methods by using the classroom as a random unit in all 2 rooms. The research instrument used in this research consisted of (1) an electronic books on information and communication technology (2) Learning achievement tests and (3) The Assessment of satisfaction statistics used in data analysis are mean of finding percentage. Standard deviation and T-test. The results showed that (1) E-books had efficiency value 81.73 / 82.98 according to the criteria set 80/80 (2) the mean of the Student’s satisfactions on electronic books at ( = 4.18)  and standard deviation. (SD = 0.72) at a high level.
An AI Application in Wing Chun Wooden Dummy Techniques

Recently AI has gained a remarkable public attention globally and its development is still accelerating. AI can help a lot in many traditional industries. In this paper, we are taking our first step to try to apply AI technology into one of the traditional areas - Wing Chun. Wing Chun is one of the styles of Chinese traditional martial arts (Kung Fu). Wooden dummy techniques are very important in Wing Chun. The traditional wooden dummy tool is superb that it can help a human to practice many different techniques of Wing Chun against one single tool. Now with the power of AI, it is possible to improve the wooden dummy to be even more helpful for the practicing person to master Wing Chun techniques.
InteracDiff

We present InteracDiff, a prototype of a UX visualization tool that helps address the challenge of interpreting and understanding UX data after testing and evaluation sessions. We describe the development and implementation of our tool and follow with the summary findings of a study. This study was conducted with twelve novice users to examine the added value of our solution, compared to (standard) representations of user experience data. The preliminary results indicate, that participants outside the scientific community could take advantage of a more playful way to digest collected UX data.
Analysis identification of hub airport of Flight Training Network

Identifying the most important hub airport of complex network formed during the long- time flight training possesses effective solution and obvious potential to alleviate resource tension improve training ability and strengthen safety management. The flight training network(FTN) was innovatively constructed employing the airports and training course via the complex network framework, and its fundamental scale-free network characteristic was analyzed. The FTN data was simulated based on the classic network topological node analysis and an improved method based on the key assessment index correlation. The main results are as follows: degree-value or betweenness-value can effectively identify the hub airport while clustering-value showing no obvious rules, moreover, the correction between node's betweenness and degree can identify the complete list of hub airports. The simulation results show high consistence with actual situation, which can serve future guidance and direction in hub airport application in actual flight training.
Virtual Cryptopgraphic Technique and Bit-plane Segmentation Stegnegography for Security in Bioinformatics and Biomedical Applications

The security techniques are very important in all modern applications since most of the applications of science and engineering are using digital media and processing. There are sevral security techniques for protection of various types of data, such as biometrics, cryptograpy, watermarking etc. This paper discussses about cryptographic methods only and virtual cryptopgraphy has been implemented successfuly with considerable improvement in signal quality.The importance of the virtual cryptography is empahsized that can be used to protect and secure various types of data invloved in bioinformatics and biomedical applications. The bit-plane segmentation stagenography enhances the security and also helps in better ptrotection of data
Consideration of Socium as Mechanical System with Stream Control

Abstract Mathematical modeling of socium as mechanical system with the material components determined by parts of system with qualitatively excellent complexes of properties concerning production, storage and consumption of production is considered. Existence of objectively operating feedback in the representing system is established. The mathematical model formed in work appears the nonlinear system of the ordinary differential equations and is far-reaching expansion of classical models Lotka and Volterra. The mathematical analysis shows existence of a fixed point in phase space of solutions of system and gives for her analytical expressions. Results of private numerical modeling of the offered model are presented. Results of modeling show manifestations of difficult oscillatory dynamics of phase trajectories of the solution of system and serve as an essential argument of an explanation of real oscillatory processes in the history of social systems.
CONSTITUTIVE ASPECTS OF JOINT ACTIVITY: PRERQUISITES AND CONTRADICTIONS OF REALIZING COOPERATIVE RELATIONS

The article examines the dominant paradigm and current research state of the theoretical and practical dimension of cooperative behavior based on the notion of “collective intentionality” scrutinized within the framework of analytic social philosophy. Accordingly, the goal was to elaborate foundational normative, socio-ontological and psychological aspects of the debate around the underlying nature of the mechanisms of content, modality, and subjects of shared praxeological undertakings, outline major points of agreement, controversy and contention in positions and arguments of the principal sides and their proponents.
Application of Multi-Attribute Rating Matrix in KNN Collaborative Filtering

this evaluation algorithm based on Attribute Rating Matrix is inefficient in the case of cold-start. The Application of Multi-Attribute Rating Matrix can solve the problem effectively. The situations are analyzed to create their attribute-tables. The ratings are mapped to the relevant attributes generate a User Attribute-Item Attribute Rating Matrix (UAIARM). After UAIARM is simplified, MARM will be created. When a new item enters into this system, the attributes of new item and MARM are matched to find the N items with the highest match degrees as the target of the new items or the recommended items. Experiment results validate the improved algorithm based on MARM is efficient.
Automated Formal Analysis of Side-Channel Attacks on Probabilistic Systems

The security guarantees of even theoretically-secure systems can be undermined by the presence of side channels in their implementations. We present Sch-imp, a probabilistic imperative language for side channel analysis containing primitives for identifying secret and publicly-observable data, and in which resource consumption is modelled at the function level. We provide a semantics for Sch-imp programs in terms of discrete-time Markov chains. Building on this, we propose automated techniques to detect worst-case attack strategies for correctly deducing a program’s secret information from its outputs and resource consumption, based on verification of partially-observable Markov decision processes. We implement this in a tool and show how it can be used to quantify the severity of worst-case side-channel attacks against a selection of systems, including anonymity networks, covert communication channels and modular arithmetic implementations used for public-key cryptography.
The Effect of Camera Perspectives on Locomotion Accuracy in Virtual Reality

This paper investigates the effect of commonly used camera perspectives (First-person perspective, over the shoulder, behind the back and from top to bottom) on locomotion efficiency in virtual reality (VR). To test this research objective in a user study, a virtual test environment was implemented in which a ball can be maneuvered using four different camera perspectives. We analyzed the time taken to complete four different paths and the offset between the path taken and the optimal one (path accuracy) displayed on the ground. The study results show that the "first person perspective" and "behind the back" perspectives allow more precise maneuvering in VR, while no difference in speed was found. We defined guidelines that can help game designers and VR developers to create VR applications with high accuracy needs. Our findings are relevant, especially to decide which perspectives are best for specific applications that require high accuracy, such as remote surgery.