Reviewerly/exHarmony huggingface dataset:
Progressive Transmission of Medical Images via a Bank of Generative Adversarial Networks

The healthcare sector is currently undergoing a major transformation due to the recent advances in deep learning and artificial intelligence. Despite a significant breakthrough in medical imaging and diagnosis, there are still many open issues and undeveloped applications in the healthcare domain. In particular, transmission of a large volume of medical images proves to be a challenging and time-consuming problem, and yet no prior studies have investigated the use of deep neural networks towards this task. The purpose of this paper is to introduce and develop a deep-learning approach for the efficient transmission of medical images, with a particular interest in the progressive coding of bit-planes. We establish a connection between bit-plane synthesis and image-to-image translation and propose a two-step pipeline for progressive image transmission. First, a bank of generative adversarial networks is trained for predicting bit-planes in a top-down manner, and then prediction residuals are encoded with a tailored adaptive lossless compression algorithm. Experimental results validate the effectiveness of the network bank for generating an accurate low-order bit-plane from high-order bit-planes and demonstrate an advantage of the tailored compression algorithm over conventional arithmetic coding for this special type of prediction residuals in terms of compression ratio.
Using Image Augmentation in Marketing

Content has been the cornerstone of advertising and digital marketing since these domains came into existence. Visual content further influences the prospects in taking interest into the respective product according to studies. In this research, augmentation methods such as generative adversarial networks and neural style transfer are used to manipulate the existing data and the visuals, especially facial features, to achieve image dataset expansion. The method is effective in producing hyper-realistic images and enhancing facial appearances of the models posing for brands.
A Study on the improvement of Computation, Communication and Security in Garbled Circuits

By providing security and availability for information at the same time, secure multi-party computation (SMPC) has great prospects in cloud computing and mobile computing. Garbled circuits are the earliest solution to achieve SMPC as well as one of the most widely used solutions. Improving the performance of all aspects of garbled circuits and promoting the practical application of SMPC have always been the focus of researchers. This article introduces the research progress and achievement of garbled circuits. By focusing on three aspects of computation, communication and security, we take a deep analysis of the main technology of garbled circuits, which have optimized and improved the performance to varying degrees.
Transfer Optimistic Outcome-based Learning for Mature Behavior of Machine in Deep Learning

This paper focuses on experiences in every experiment in an optimistic manner as an input to the machine to train and create a Deep learning mechanism with previous optimistic outcome-based learning based on an evaluation by Bayes Theorem instead of hierarchical representation only. Here, we want to suggest that it is not favorable to depend on data only. Instead of focusing on data only to train the machine every time past experiences must be counted as outcomes. These outcomes, further transfer to the machine along with new data can change the approach of a machine to learn, and especially in Deep learning to train the model will be more affirmative and its hierarchical representation gains a sense of previous experiences. This paper focuses on experiences in every experiment in an optimistic manner as an input to a machine to train and create a Deep learning mechanism instead of hierarchical representation only. Also, these experiences must be optimistic in sense of often realistic, linear and high dimension. Knowledge-based on such optimistic experiences has a scientific value. We can use the Bayes formula repeatedly to increase correctness.
Video Fluency Prediction Based on Network Features Using Deep Learning

With the explosively increasing video traffic, ensuring the smooth playback of a video has been a challenging problem especially in the fifth generation (5G) mobile communication system. To improve the quality of experience (QoE) of a video playback, the real-time prediction of the video stuck can be a help. In this paper, we firstly select eight features from different layers to reflect the quality of video playback. Then, two models, long and short term memory (LSTM)-based Prediction Model and Gated recurrent unit(GRU)-based Prediction Model, are proposed to predict the stuck state of playback. Finally, to evaluate the effectiveness of the two proposed prediction models, we present the simulation results of accuracy and loss of the two models. Besides, comparison between traditional methods and the proposed one are provided with performance gain in terms of the accuracy, recall, confusion matrix as well as F1-score.
Anomaly Detection in Text Data Sets using Character-Level Representation

Abstract This paper proposes a character-level representation of unsupervised text data sets for anomaly detection problems. An empirical examination of the character-level text representation was conducted to demonstrate the ability to separate outlying and normal records using an ensemble of multiple classic numerical anomaly classifiers. Experimental results obtained on two different data sets confirmed the applicability of the developed unsupervised model to detect outlying instances in various real-world scenarios, providing the opportunity to quickly assess a large amount of textual data in terms of information consistency and conformity without knowledge of the data content itself.
Relevant Text Extraction Using Enhanced Artificial Neural Network Fuzzy Inference System

Identifying the security level of a private record is an indispensable task for associations to ensure the secret data represented. Different rules and systems are being applied by human specialists. Expanding the number of private data in associations are making it hard to order all the reports cautiously with human exertion. In the present advanced period, it turns into a test for users of the web to discover explicit data on the web. Many online records are recovered, and it is difficult to process all the recovered data. The programmed text summary is a procedure that recognizes the significant focuses from all the related archives to deliver a compact outline. Enormous datasets surround covered patterns which pass on significant information about the dataset. Information mining research manages the extraction of helpful and important data from such huge datasets. The procedure of information extraction can be seen as exploration and examination of huge amounts of information, via programmed or self-loader mean, to find significant examples and rules. These days, text acknowledgement has become a significant developing zone in handling images and extraction of data. In this proposed work, an efficient text extraction method enhanced artificial neural network-based fuzzy inference system (EANN-FIS) is introduced for relevant text extraction including hidden layers. The proposed method is compared with the traditional methods and the results show that the proposed method is exhibiting better performance in terms of accuracy and speed.
Effectively Incorporating Knowledge in Open-Domain Dialogue Generation

Dialogue generation is one of the most important parts in the dialogue system. Generating useful and informative responses in conversation has become a research hotspot. Previous work has proved that incorporating external knowledge is conducive to generating meaningful responses. But how to make full use of the existing information to select the most appropriate knowledge is a current research difficulty. In this paper, we propose a dialogue generation model with a lightweight knowledge routing module to sample knowledge needed for the conversation. In the knowledge routing module, we not only use the interactive information between the knowledge and the dialogue context utterance, but also the interactive information between each piece of knowledge for knowledge selection. Subsequently, the selected knowledge is incorporated into the dialogue generation model to generate responses. The experimental results show that our model tends to generate more meaningful and informative responses compared with baseline models.
An integrated assessment of the quality of teaching Mathematics in school

This article presents a diagnostic criteria-based methodology for a systematic and complex assessment of the quality of teaching Mathematics in school. This methodology may help assess the prospects of applied approaches to teaching Mathematics as well as monitor it and describe in terms of ensuring the high quality of general education in Mathematics. We used Kolmogorov’s “convolution of qualities” as a basis for developing this methodology for the systematic and complex diagnostics of mathematical education in school. This methodology is used to assess the quality and optimization of complex objects in mechanics, chemical industry, economics, and higher education. The suggested integral assessment (systematic and complex diagnostics) was successfully applied to evaluate the quality of teaching Mathematics in schools of the Volgograd region. We defined three levels of mathematical education quality: discrete (minimal and restricted level), fragmentary (average functional level), and integrated and comprehensive (a rather high level).
Study of groin structures effectiveness for against abrasion in Padang Beach

Abstract This study was conducted to evaluating the performance of existing coastal protection structure to restrain the rate of erosion as a result of the influence of hydrodynamics process. Condition that reviewed was in form of distance shoreline as simulation results to initial reference line before. STWAVE module used to determine wave height at near shore as transformation of deep water wave height that determined before from SMB method. One Line Model was conducted to get shoreline change in each time step calculation. Model consists of two conditions, with the absence of coastal structures and with structure respectively. The simulation result show that potential erosion occurs at Batang Arau river mouth with no protection structure. Large erosion predicted around 59.04 meters from the shoreline position early. With coastal protection, model result obtained sedimentation of 7.33 meters coastline from its initial position. This occurs at a distance of 475 meters from the boundary domain. Modeling results also showed erosion is 18.39 meters at a distance of 250 m from the boundary domain. This is likely due to the limited modelling conditions that without reviewing of littoral transport direction that occurred in perpendicular to the coast. Result study show that the presence of a groins in Padang coasts looks already quite effective to maintain the shoreline of the littoral transport direction, however the installation of groins seemingly did not give significant meaning in the addition of the coastline.
Feature Edge-Detail Preservation of Random-Valued Impulse Noise in Images

In this paper, we put forth a progressive, decision-based, two-phase image denoising algorithm for eliminating random-valued impulse noise from images. The manner in which this algorithm deals with noise is a completely pristine method when compared to the other existing image denoising algorithms. In the primary phase, the noise is dealt at a coarse level; in other words, the noisy pixels that are easily differentiable from the neighborhood are eliminated. In the secondary phase, fine-level image denoising is performed. In other words, the left-over fine scale noise in the detected corrupted pixels of the first phase, which cannot be straightforwardly differentiated from the surrounding pixels, is eliminated. In both the phases, separate mechanisms were followed to eliminate noise in the interior regions and edge regions. Hence, the algorithm is edge-detail preserving. Images with very high noise levels, in other words, with 70% noisy pixels were restored successfully. Speaking in terms of quantitative significant measures, the restored images in most cases were better than those of the other existing filters.
Modeling Human Decision Making with an Abstract Dynamic Argumentation Framework

Human-beings everyday decisions are often based on arguments and counter-arguments, and Argumentation has shown –within the research field of Artificial Intelligence– to be an appropriate way to advocate for a choice, given its explanatory power. For human beings it worth having a decision making approach in which one can better understand the underpinnings of the evaluation. In this work we show the adequacy of an abstract dynamic argumentation framework to model the Dictator Game by emulating the answers contained in a survey we conducted. The Dictator Game is a well-known problem belonging to the field of experimental economic studies related to human decision making.
Multi-level expression guided attention network for referring expression comprehension

Referring expression comprehension is a task of identifying a text-related object or region in a given image by a natural language expression. In this task, it is essential to understand the expression sentence in multi-aspect and adapt it to region representations for generating the discriminative information. Unfortunately, previous approaches usually focus on the important words or phrases in the expression using self-attention mechanisms, which causes that they may fail to distinguish the target region from others, especially the similar regions. To address this problem, we propose a novel model, termed Multi-level Expression Guided Attention network (MEGA-Net). It contains a multi-level visual attention schema guided by the expression representations in different levels, i.e., sentence-level, word-level and phrase-level, which allows generating the discriminative region features and helps to locate the related regions accurately. In addition, to distinguish the similar regions, we design a two-stage structure, where we first select top-K candidate regions according to their matching scores in the first stage, then we apply an object comparison attention mechanism to learn the difference between the candidates for matching the target region. We evaluate the proposed approach on three popular benchmark datasets and the experimental results demonstrate that our model performs against state-of-the-art methods.
Mobile Learning Strategy of Chinese Modern History Classroom Based on Streaming Media

In view of the shortcomings of traditional teaching methods of online education courses, a mobile learning platform of modern Chinese history based on streaming media is developed. The system uses Struts 2 and springy framework in J2EE technology specification, and uses Reds as streaming media server. In the system architecture design, following the principle of J2EE technology, it completes the system running environment architecture and application architecture design. Then, according to the layered architecture design idea, each module of the system is coded, and the function, performance and compatibility of the system are tested and analyzed. After practical deployment and tests, the platform shows a good interface and video real-time synchronous transmission ability, which can better meet the needs of mobile learning, and realize the mobile learning of micro video course based on Android platform.
The Use of VR Simulations in Nuclear Physics Education at the University Level

The use of virtual reality (VR) in the education of nuclear physics students, at the university level, is very rare. This paper aims to present the result of a nuclear physics exercise designed to compare real-world exercise ability (mass of Deuteron’s experimental setup available at the Faculty of Physics) with simulated exercise in VR. Students perform all the functions virtually at the human, atom and subatomic particles scale, in a radiation-free environment and time-saving manner. This research is beneficial for universities that do not have the appropriate equipment to perform nuclear physics exercises.
Review of domestic information chain research based on Bibliometrics

Information has become an important means of production, and the information chain has been formed in various fields. Based on the papers related to information chain in CNKI journal database, this paper analyzes by using bibliometric method and citespacev. The number of papers began to rise steadily from 2003 to 2007, which was a turning point, reached the peak and began to enter the stage of steady development. The research mainly focuses on some information consulting service institutions, but there is no clear academic leader. The research focuses on supply chain, value chain, industrial chain, logistics chain, information ecosystem.
Adversarial feature refinement for cross-view action recognition

Apparent motion information of an action may vary dramatically from one view to another, making transfer of knowledge across views a core challenge of action recognition. Recent times have seen the use of large scale datasets to compensate for this lack in generalization, and in fact most state-of-the-art methods today require large amounts of training data and have high computational cost while training. We propose a novel technique leveraging pre-trained features refined to minimize the view-related information through adversarial training inspired by domain adaptation methods. Our method is able to recognize actions from unfamiliar viewpoints and works effectively on substantially less training data than the ones necessary to train state-of-the-art cross-view methods with exceptional results.
The Communication and Translation of Data and Analytics-Driven Insights

This chapter explores and develops a framework to more effectively develop and utilize data and analytics-driven insights within a business. The key components of the framework are first derived from how the business, the analyst, and the computer communicate with each other. This results in clear distinctions between the language of the business and the language of the computer that positions the analyst within a conceptual chiasm. This positioning means that the analyst must first and foremost play the role of translator. The implications of this positioning within the framework arguably creates a more consistent conceptual model with more explanatory power than is typically found in popular business blogs and articles on the topic. As a result, potential strategies are developed from the framework to enable more successful data and analytics efforts, hiring practices, and communication strategies.
Cryptanalytic attacks on a chaos-based image encrypting cryptosystem

Multimedia security is of utmost important in this era of science and technology. The social networks in the internet witness huge number of digital images which need to be secured. Though lot of image encryption algorithms have been proposed by the scientific community, not all are worthy. In this aspect, a recently published image encryption algorithm is cryptanalyzed, and its weaknesses are revealed. The algorithm employs chaotic map to permute the pixels of the image row-wise and column-wise. After this, sequences from a neural network are generated for substitution in the image. This algorithm can be broken by a chosen plaintext attack, revealing the key. Apart from this, few changes in the algorithm are proposed to strengthen it, however none can really strengthen it without major modifications.
Measuring Human Eye Tolerance Towards the Digital Typography Sharpness on a High-Density Pixel Computer Screen: An Indicative Study

Technology hardware companies are increasingly competing to offer enhanced screen quality which is no longer a variable of size alone but also dependent on the density of pixels. Screens with high density pixels can display smaller size of graphics while still maintaining its details. High density pixel increases screen affordance to display more variants of font types which was not possible in low density pixels screen due to loss of details. Access to higher comfort levels for reading on-screen content has acquired a significance equivalent to availability of content on-screen. This paper aims and attempts to measure the human eye tolerance levels towards digital typography sharpness on a high-density pixel screen. The experiment was conducted using a group of fifty participants exposed to a group of texts with 9 degrees different levels of sharpness which was achieved using Gaussian blur method. Repeated experimental design was used to test the effects of human eye tolerance on text sharpness on high density screen. The results of the experiments undertaken in the study indicate that the human eye tolerates sharpness levels to some degree and the Serif font group tends to decrease the human eye tolerance towards sharpness while the Sans-Serif font group tends to increase the human eye tolerance of graphic sharpness.
Method of structural functional-value modeling of a complex system with a mixed combination of subsystems

The improved method of structural functional-value analysis of a complex system with a mixed combination of subsystems in an analytical approximation of the value dependences on the level of functional suitability is proposed. The minimization of the value of a complex system under the condition it fulfills its functional purpose at a given level is proposed to be implemented by the method of Lagrange multipliers. The application of the developed method allows checking the possibility of the monitoring system to perform its functional tasks with a given level of perfection as well as the identification of the opportunities for structural and parametric simplification of the system. This method is adapted for use at different levels of a priori uncertainty of the input data and can be useful at all stages of a complex system existence: development, operation, and disposal. In addition, it can be used to study low formalized and informalized complex systems.
Sequential Representation of Suffix Trie: An Empirical Evaluation

A suffix trie is a full-text index that can efficiently answer queries in a text database. However, this index uses much more space than the text itself. In practice, a suffix trie requires from 10 to 20 times the size of T. In this work, we present an exhaustive experimental evaluation of a sequential representation of suffix trie (\({ST}_{\!s}\)) consisting of storing each component of a suffix trie in a separated array. We analyze count and locate time, construction time, and space usage for three versions of \({ST}_{\!s}\): the original one and two later improvements.
Minimum Structural Transformation in Parallel Prefix Adders and its Application to Search-Based Optimization

Parallel prefix adder is a type of adder design which has enough structural flexibility on parallelism of carry propagation, and provides us a wide spread of adder structures. This paper proposes the minimum structural transformation of Parallel Prefix Tree, a core of Parallel Prefix Adder, and its application to the structural optimization of Parallel Prefix Adder based on search methods such as Simulated Annealing Algorithm. When we design a solution space for a search-based optimization, (1) containment of the optimum solution, (2) reachability between solutions, and (3) smoothness of a solution space are keys for the success of search-based optimization. The solution space defined with the proposed minimum structural transformation satisfies (1) and (2), and (3) as well in a certain proper level. The smoothness of the solution space defined by the minimum structural transformation will contribute to easy escape from local optima during a search-based optimization with some hill-climb mechanism, which is verified through experiment.