Reviewerly/exHarmony huggingface dataset:
Mask-Guided Style Transfer Network for Purifying Real Images

Recently, the progress of learning-by-synthesis has proposed a training model for synthetic images, which can effectively reduce the cost of human and material resources. However, due to the different distribution of synthetic images compared with real images, the desired performance cannot be achieved. To solve this problem, the previous method learned a model to improve the realism of the synthetic images. Different from the previous methods, this paper try to purify real image by extracting discriminative and robust features to convert outdoor real images to indoor synthetic images. In this paper, we first introduce the segmentation masks to construct RGB-mask pairs as inputs, then we design a mask-guided style transfer network to learn style features separately from the attention and bkgd(background) regions and learn content features from full and attention region. Moreover, we propose a novel region-level task-guided loss to restrain the features learnt from style and content. Experiments were performed using mixed studies (qualitative and quantitative) methods to demonstrate the possibility of purifying real images in complex directions. We evaluate the proposed method on various public datasets, including LPW, COCO and MPIIGaze. Experimental results show that the proposed method is effective and achieves the state-of-the-art results.
The Reliability Characteristics of the Data Processing Centers Cooling Systems

This paper provides an overview of the main types of data processing center cooling systems and methods for assessing reliability indicators. The methodology for estimating reliability characteristics of the cooling system components is created. The proposed methodology differs from common methods by including reliability calculation of cooling system mechanical parts which increases the accuracy of reliability indicators prediction. The standard failure rate calculation models are analyzed and complemented with the correction coefficients, that were absent earlier in them. The resulting methodology is applicable at various stages of data center design, as well as in conditions where there is only a minimum amount of data about the object being created.
RepLab: An Evaluation Campaign for Online Monitoring Systems

Over a period of 3 years, RepLab was a CLEF initiative where computer scientists and online reputation experts worked together to identify and formalize the computational challenges in the area of online reputation monitoring. Two main results emerged from RepLab: a community of researchers engaged in the problem, and an extensive Twitter test collection comprising more than half a million expert annotations, which cover many relevant tasks in the field of online reputation: named entity resolution, topic detection and tracking, reputational alerts identification, reputational polarity, author profiling, opinion makers identification and reputational dimension classification. It has probably been one of the CLEF labs with a larger set of expert annotations provided to participants in a single year, and one of the labs where the target user community has been more actively engaged in the evaluation campaign. Here we summarize the design and results of the Replab campaigns, and also report on research that has built on RepLab datasets after completion of the 3-year competition cycle.
Research on Neural Style Transfer Algorithm

Researchers have successfully applied the convolutional neural network (CNN) to style transfer. Since then, Neural Style Transfer (NST) has received widespread attention in both scientific and industrial fields. Researchers in the field of machine vision are constantly proposing ways to optimize image style migration. This paper aims to summarize the history of style transfer before and after the rise of CNN, classify the existing classical and improved algorithms, and compare the results of some of them. Finally, after this study, we put forward some suggestions on the development trend of image style transfer.
Multivariate Bounded Asymmetric Gaussian Mixture Model

In this chapter, bounded asymmetric Gaussian mixture model (BAGMM) is proposed. In the described model, parameter estimation is performed by maximization of log-likelihood via expectation–maximization (EM) and Newton–Raphson algorithm. This model is applied to several applications for data clustering. As a first step, to validate our model, we have chosen spambase dataset for clustering spam and non-spam emails. Another application selected for validation of our algorithm is object data clustering and we have used two popular datasets (Caltech 101 and Corel) in this task. Finally we have performed clustering on texture data and VisTex dataset is employed for this task. In order to evaluate the clustering, in all abovementioned applications, several performance metrics are employed and experimental results are further compared in similar settings with asymmetric Gaussian mixture model (AGMM). From the experiments and results in all applications, it is examined that BAGMM has outperformed AGMM in the clustering task.
GENERAL PEDAGOGY IN THE MODERN SCIENTIFIC DISCOURSE

The article deals with the essence and content of the concept of general pedagogy in the context of itscorrelation with the multiplicity of knowledge about education, its development and improvement of the conceptual and terminological apparatus of modern pedagogical research. It has been emphasized that general pedagogy is understood as science and as a scientific discipline, and the definition of general pedagogy, as a rule, includes both aspects. The article concludes that general pedagogy is the core of pedagogical science; it forms its basis, which at the substantive-essential level distinguishes pedagogical science from all other branches of scientific knowledge. General pedagogy studies and discloses key pedagogical categories, notions, concepts, paradigms, approaches, laws, patterns, principles according to which any pedagogical phenomenon or pedagogical process are opened out, develops a methodology for building pedagogical science and conducting pedagogical research, methods and methodology for evaluating the effectiveness of pedagogical phenomena and processes, criteria of the scientific and pedagogical activity effectiveness, analysis of general pedagogical and philosophical doctrines. The actual problems of general pedagogy are the methodology of pedagogical research, problems of the upbringing and socialization of children and youth, history of pedagogy, comparative pedagogy, problems of managing educational institutions and strategies for their development. The modernity of the solution of the indicated problems is determined by the completeness of the account of the conditions in which people find themselves today: the globalized world, technological challenges, expansion of the media space and its impact on human consciousness, a crisis of values, problems of intercultural communication, and the like. The study of these complex problems requires a new methodology of pedagogical research at the level of multi- and interdisciplinarity. The development of such a methodology is an urgent need for modern pedagogical science and practice.
The optimization of bibliographic information resource integration of digital library

The integration of bibliographic information resource of digital library can increase retrieval speed and make sharing easy. With the current method, knowledge chain is used to integrate the information resources of the digital library to realize the integration of its bibliography. Therefore, this paper proposes a method for bibliographic information resource integration based on Service-Oriented Architecture (SOA). With content relevance and attribute correlation, the information resources are related, and by using SOA, the information resource integration framework of digital library bibliography is formed and the information resource retrieval platform is constructed. The experiment proves that this method can improve the information resource utilization efficiency of digital library.
Recommendation System for Cross-Platform Mobile Development Framework

Mobile application developers, aimming to make their applications available to the largest number of platforms, are increasingly seeking to optimize how to develop mobile applications. Creating the same specific application for each platform demands greater financial, time and labor investment. In order to reduce these problems, mobile development frameworks for multiplatforms have emerged, making possible to create multi-platform applications from the same source code. However, a new issue arises for the developer - the plurality and diversity of these frameworks, each one with objectives and characteristics that sometimes make them closer and in other cases farther. In addition, there is a demand for multi-purpose applications and work teams with different training profiles. For these reasons, choosing a framework is not trivial. This paper presents an approach that indicates which multi-platform application development framework is most suitable for a given project, implemented as a knowledge-based recommendation system that considered the critical factors of a given project.
VR system to simulate tightrope walking with a standalone VR headset and slack rails

Slack Rails, which are made of foam rubber, can offer a simulated experience of using slack lines. In this study, we developed a prototype system and its content to enable tightrope walking between skyscrapers with a VR headset. An evaluation experiment was conducted, and results show that it can make VR tightrope walking more realistic, difficult, and suitable for training.
Augmented Chair: Exploring the Sittable Chair in Immersive Virtual Reality for Seamless Interaction

Virtual reality (VR) has been a promising technique to provide an immersive experience. Comparing to traditional multimedia, when users want to take a rest or change the position such as sitting down, the chair might not be sittable because of the inconsistency between the physical and the virtual chair. In this work, we utilized a tracker attached to a physical chair and conducted a user study to explore the sitting behavior when they interact with different forms of the virtual chair. Results indicate that visualizing each part of the chair could provide different information and affect trust and preference.
The Intelligent Early Warning System for the Gale-Induced Transmission Tower Fault Probability

Gale is one of the important reasons causing transmission tower collapse. Therefore, precisely prediction and early warning of the gale which may cause the transmission tower fall down can decrease the loss to power grid at utmost. In this paper, non-linear stepwise regression (NLSR) method is used to forecast gale in the power grid. To avoid the single statistical method errors, the back-propagation (BP) artificial neural network is used to optimize the results intelligently. Further, we consider the gale-induced tower failure rate, and couple it to the forecast model. Finally, we establish an intelligent gale-induced transmission tower fault warning system to predict the probability of accident. The simulated result shows that the accuracy of the model in predicting gale-induced transmissions tower fault probability is good.
Comparison of Exponentially Decreasing Vs. Polynomially Decreasing Objective Functions for Making Quantum Circuits Nearest Neighbour Compliant

Many proposed architectures for quantum computing involve arranging qubits in a lattice, and then using swap gates to move the quantum states between qubits to enable any pair of qubits to interact with each other. Since swap gates perform no useful computation, they can be viewed as an overhead whose number should be minimised in efficient quantum programs. The location of the swap gates in a quantum circuit are chosen by minimising an objective function characterising the structure of the quantum circuit. Those which currently offer the lowest swap cost reduce the significance of each gate to the objective function relative to its position in the circuit. The criterion that describes how quickly the significance must decrease. In this paper, we show that to satisfy this criterion, it is necessary and sufficient for the decay to be exponential. However, when applying this new objective function, there is no significant improvement over the old function, with most of the benchmark circuits having less than 5% improvement. Only three circuits had more than 10% improvement in swap cost, and these circuits were all less than 100 gates long. The three longest circuits in the benchmark suite had less than 1% change in swap cost. There is also a significant penalty in run time, in one case reaching 265.48%.
The Role of Knowledge Management to Enhancing Public Services in Dubai

Abstract This study aims to highlight the importance of knowledge management and the factors influencing its implementation within the public sector organizations in Dubai. This study provides a clear understanding of the concept of knowledge management in the public sector and the ability of these sectors to collect and preserve knowledge, knowledge management. This requires the development of knowledge to review and strengthen their strategies to collect document and disseminate their workforce knowledge in order to achieve better institutional performance and to ensure that the knowledge of outstanding expertise and competencies remains within the institution even after leaving their sources for the organizations. Data were collected by distributing questionnaires to 215 systematic randomly sampled respondents from seven public departments. Data collected were analyzed by using descriptive and inferential statistics. It was found that all the independent variables were significantly related to organizational performances. The multiple regressions analysis showed that all the predictor variables contributed 55.5% towards the organizational performances of the public departments in Dubai. The research implication and suggestion were discussed at the end of the paper.
Actionness-pooled Deep-convolutional Descriptor for fine-grained action recognition

Recognition of general actions has witnessed great success in recent years. However, the existing general action representations cannot work well to recognize fine-grained actions, which usually share high similarities in both appearance and motion pattern. To solve this problem, we introduce the visual attention mechanism into the proposed descriptor, termed Actionness-pooled Deep-convolutional Descriptor (ADD). Instead of pooling features uniformly from the entire video, we aggregate features in sub-regions that are more likely to contain actions according to actionness maps. This endows ADD with the superior capability of capturing the subtle differences between fine-grained actions. We conduct experiments on HIT Dances dataset, one of the few existing datasets for fine-grained action analysis. Quantitative results have demonstrated that ADD remarkably outperforms traditional CNN-based representations. Extensive experiments on two general action benchmarks, JHMDB and UCF101, have additionally proved that combining ADD with end-to-end ConvNet can further boost the recognition performance. Besides, taking advantage of ADD, we reveal the sparsity characteristic existing in actions and point out a potential direction to design more effective action analysis models by extracting both representative and discriminative action patterns.
A comparative study of different metaheuristic optimization algorithms using standard test functions

The role of metaheuristic optimization algorithms in the analysis of real world optimization problems is significantly increasing against traditional optimization methods. But these algorithms possesses the limitation that they are highly problem dependent. The selection of an optimization algorithm for a specific application can be validated using standard test functions. A comparative study of three metaheuristic algorithms, Differential Evolution (DE), Particle Swarm Optimization (PSO) and Artificial Bee Colony (ABC) Algorithm using standard test functions is presented in this paper. Standard test functions which are very much similar to real world optimization problems are used for the performance comparison of optimization algorithms.
Application of Deep Learning Techniques to Video QoE Prediction in Smartphones

This paper evaluates the use of deep learning prediction models for assessing the quality of experience of video applications using input data from a single measuring point at the smartphone. Three architectures for deep neural networks, single task fully connected (FC), multitask FC and convolutional, have been implemented. A set of four relevant metrics recommended by ITU-T models has been considered as outputs for the prediction models. The amount of data received by the device, a parameter which can be easily extracted from the majority of smartphones, is used as input to the machine learning algorithms. For the convolutional neural model, the amount of transmitted data is also used as input. The predictions models have been applied to evaluate YouTube applications on smartphones, but could be generalized to other services. The overall achieved performance on the test set is around 90%, a very promising result as the generation of new labelled datasets is quite feasible at low cost thanks to the fully automated test setup developed.
Measuring Similarity Between Data Structures for Detecting Plagiarized Source Codes

A program consists of data structures and algorithms. However, most studies, up to now, for detecting plagiarism of source codes are suggesting lopsided analyses considering only the algorithms (or instructions) of the source codes. This paper introduces a method for measuring the similarity between data structures for detecting plagiarized source codes. The proposed method was experimented with test sets including plagiarized source codes. The experimental result shows that the similarities among the data structures of plagiarized source codes are high degree as expected. This result implies that the similarity on data structures, along with the similarity on algorithms, is also one of the main factors to the decrease false alarms by lowering the threshold for the plagiarism.
A Probabilistic Algorithm to Predict Missing Facts from Knowledge Graphs

Knowledge Graph, as the name says, is a way to represent knowledge using a directed graph structure (nodes and edges). However, such graphs are often incomplete or contain a considerable amount of wrong facts. This work presents ProA: a probabilistic algorithm to predict missing facts from Knowledge Graphs based on the probability distribution over paths between entities. Compared to current state-of-the-art approaches, ProA has the following advantages: simplicity as it considers only the topological structure of a knowledge graph, good performance as it does not require any complex calculations, and readiness as it has no other requirement but the graph itself.
Probability based selective encryption scheme for fast encryption of medical images

Hiding data using encryption is the major solution to deliver security for data transmitted over a connection amongst any pair of nodes, however by adopting selective encryption, the speed and security can be provided by encoding only a specified portion of the data bit. Visual and multimedia data protections such as images require special design considerations for use in medical applications. This research focuses on choosing an significant part of the medical image to hide the secret data and that can be achieved efficiently by selecting parts of the image that are more used in the normal mode. When the process of encryption is complete, the secured and encrypted data is transmitted to ensure a safe transport and delivery over the public network. This paper intends to propose a novel approach to encrypt data in medical images using probability equation for random selection of blocks for encryption. The background idea with respect to the proposed effort is to encrypt only fragment of the image for encryption using AES algorithm instead of encrypting the whole image. By doing th is we can save computation power, time and speed than using the traditional approach.
Performance Evaluation of Visual Object Detection for Moving Vehicle

With the rapid development of intelligent vehicle technology, the evaluation of the perception algorithms is crucial for safe driving of intelligent vehicles. This paper proposes a novel evaluation method for visual object detection of intelligent vehicles. The traditional evaluation methods are based on each frame of the video and treat all the objects equally. Distinguished from that, the proposed method applies the length of driving trajectory to evaluate the detection algorithm. Besides, the proposed method brings about the road region constraint, weight of safety and weight of early detection. The road region constraint ensures that only the objects relevant to intelligent vehicles are taken into account. The weight of safety for each object is determined based on its degree of relevance of the safe driving. The weight of early detection is set to evaluate whether the objects are detected in time or not. In experiments, the proposed evaluation method can reflect the relatively true detection ability of intelligent vehicles compared to the traditional methods.
Augmented Reality Interfaces for Semi-Autonomous Drones

With recent advances in hardware technology, drones are increasingly present in research activities outside of robotics, performing a multitude of tasks such as image capture and sample collection. However, user interfaces for task-oriented drones have not kept pace with hardware breakthroughs. Current planning and control interfaces for drones are not intuitive and often place a large cognitive burden on those who are not highly trained in their use. This paper proposes a course of research that seeks to leverage natural user interfaces in augmented reality (AR) for controlling drones completing task-oriented objectives.
Study on construction schedule optimization of bored pile based on genetic algorithm

Abstract The site construction management of traditional filling pile has some disadvantages such as redundancy of data, difficulty in finding data, low working efficiency and poor timeliness. Thus, combined with the actual needs of filling pile construction management, based on the Java language, the mobile phone client of filling pile construction information management system was developed, and the mobile client system framework, operating process and functional framework were designed and illustrated. The software provided an advanced management method for the filling pile construction, it had great value to guarantee the construction progress and improve the information level of construction management.
An Approach for LPF Table Computation

In this article, we introduce a new solution for the Longest Previous Factor (LPF) table computation. The LPF table is the table that stores the maximal length of factors re-occurring at each position of a string and this table is useful for text compression. The LPF table has the important role for computational biology, data compression and string algorithms. In this paper, we present an approach to compute the LPF table of a string from its suffix heap. The algorithm runs in linear time with linear memory space.