Reviewerly/exHarmony huggingface dataset:
No-Reference Stereoscopic Image Quality Assessment Based on Local to Global Feature Regression

In this paper, we propose a two-channel deep convolutional neural network (DCNN) through local to global regression for no-reference (NR) stereoscopic images quality assessment (SIQA). Firstly, in most deep learning based methods, they use the given subjective mean opinion score (MOS) or the differential MOS (DMOS) value to adjust the network parameters. But it is unreasonable, especially for the asymmetrical distortion stereoscopic image. To alleviate the problem, we propose to use feature similarity index (FSIM) to provide pseudo labels for the left and right view respectively, named local regression, so that the left and right channel are trained better. Then, we use the given DMOS to finetune the locally trained model parameters, named global regression. So we achieve an end-to-end network to measure the stereoscopic image quality. The experimental results show that the proposed method is superior to other existing SIQA methods.
An active contour model based on hybrid energy and fisher criterion for image segmentation

Image segmentation (Lin et al. 2018, Shoulin et al. 2018) is the basic link of image processing and machine vision, so it is very important to study this content. Because the image can be interfere...
On the Correlation of CNN Performance and Hardware Metrics for Visual Inference on a Low-Cost CPU-based Platform

While providing the same functionality, the various Deep Learning software frameworks available these days do not provide similar performance when running the same network model on a particular hardware platform. On the contrary, we show that the different coding techniques and underlying acceleration libraries have a great impact on the instantaneous throughput and CPU utilization when carrying out the same inference with Caffe, OpenCV, TensorFlow and Caffe2 on an ARM Cortex-A53 multi-core processor. Direct modelling of this dissimilar performance is not practical, mainly because of the complexity and rapid evolution of the toolchains. Alternatively, we examine how the hardware resources are distinctly exploited by the frameworks. We demonstrate that there is a strong correlation between inference performance - including power consumption - and critical parameters associated with memory usage and instruction flow control. This identified correlation is a preliminary step for the development of a simple empirical model. The objective is to facilitate selection and further performance tuning among the ever-growing zoo of deep neural networks and frameworks, as well as the exploration of new network architectures.
Troubleshooting in the histology laboratory

Humans and machines both have an inherent error rate. As long as this is true, sub-optimal events will occur in the histology laboratory. The best approach to troubleshooting and remedying these events is to (1) understand the various theories of action behind histology procedures and stains then (2) apply a problem-solving mentality to develop a corrective action. These theories and problem solving strategies are presented in this review article.
Enhanced pooling method for convolutional neural networks based on optimal search theory

To obtain the best pooling effect and higher accuracy in image recognition, an improved method based on optimal search theory for the pooling layer of convolutional neural networks (CNNs) is proposed. The purpose is to solve the problems of the traditional pooling method, namely that it is too simplistic and it is difficult to extract effective features. The basic principle and network structure of CNN are introduced in the study. A new optimum-pooling method is proposed, and the authors study how to obtain the maximum probability to detect the target function under the constrained condition. Comparison experiments of different pooling methods are performed on three widely used datasets: LFW, CIFAR-10, and ImageNet. The experimental results show that the proposed method has the characteristics of more effective feature extraction and wide adaptability, and leads to higher accuracy and lower error rate in image recognition.
Face Recognition Using Dynamic Features Extracted from Smile Videos

Biometric systems measure and analyze the physical and behavioral characteristics of individuals. Facial biometry has become one of the most preferred biometric methods in recent years due to the fact that it can be used in applications, which do not require the cooperation of the user. In facial recognition systems, which use images recorded under controlled conditions, the use of physical properties is sufficient for face recognition. However, physical features may not be sufficient for face recognition using images recorded under challenging conditions. In such cases, behavioral characteristics of the face may provide additional information. In this study, we extract dynamic features of the face from expressive face videos and use them for face recognition. Experimental results on the UvA-NEMO smile database, which contains 400 subjects, show that dynamic face features carry identity-related information and can be used for face recognition.
Application of Augmented Reality Technology in Industrial Design

Augmented reality is an interactive experience of a real-world environment where the objects that reside in the real-world are augmented by computer-generated perceptual information, sometimes across multiple sensory modalities, including visual, auditory, and haptic. With the full integration of augmented reality technology and industrial design activities, new and breakthrough changes can be brought to industrial design. This paper takes augmented reality technology as the research object, expounds the development status of augmented reality technology, the related content of augmented reality technology, and the application related problems of augmented reality technology in industrial design. Finally, it describes the application strategy and direction of augmented reality technology.
Reading Pegmatites: Part 5—What Pollucite Says

THIS IS THE FIFTH OF SEVERAL ARTICLES THAT ARE MEANT TO INFORM MINERAL MINERS, COLLECTORS, AND AMATEUR SCIENTISTS ABOUT THE GEOLOGIC ENVIRONMENTS OF THE MINERALS THEY COLLECT. In these articles, th...
Use of short message service for monitoring Zika-related behaviors in four Latin American countries: lessons learned from the field

Effective response to widespread epidemics relies in part on rapidly changing information from affected communities as individuals react to emergency response efforts. The short message service (SMS) approach was used to monitor knowledge, prevention, and behavior during an outbreak of Zika cases in Honduras, El Salvador, the Dominican Republic, and Guatemala in 2017. SMS methods supported collection of monitoring data at a time of heightened reliance on communication via mobile phones, and when the use of phones for epidemic-related communication was amplified. SMS methods were used to collect monitoring data on knowledge of Zika risk and prevention, and behaviors to prevent Zika infection.Serial SMS surveys were conducted over a seven-month period to capture changing community perceptions of risk and behaviors to prevent Zika during the epidemic. This article discusses the development of the SMS surveys, from planning and designing tools to collecting the data, and the unique experiences encountered during survey implementation.Special considerations for the effective use of SMS for data collection are described, including market penetration of mobile phones, predominant phone types in use, preparation of communities and the target audience, options for tailoring questionnaires to the available technology, relationships with telecommunication companies, and supporting participants' ability to complete questionnaires. The article discusses issues related to the development of the questionnaires, context-specific challenges before and during implementation of the surveys, and creative strategies to overcome barriers. Lessons learned, and recommendations to increase opportunities for a successful SMS survey, are included.Understanding the social and economic context of mobile phone use in the target area and careful crafting of SMS questionnaires can produce useful data that may rival information captured through other approaches, such as Knowledge, Attitudes and Practices (KAP) surveys. Despite the challenges experienced, there is room for more consistent and widespread use of SMS for data collection in Latin America.
Enhancing Big Data Security using Elliptic Curve Cryptography

Withgrowing times and technology, and the data related to it is increasing on daily basis and so is the daunting task to manage it. The present solution to this problem i.e our present databases, are not the long-term solutions. These data volumes need to be stored safely and retrieved safely to use. This paper presents an overview of security issues for big data. Big Data encompasses data configuration, distribution and analysis of the data that overcome the drawbacks of traditional data processing technology. Big data manages, stores and acquires data in a speedy and cost-effective manner with the help of tools, technologies and frameworks.
Enhancing PowerFactory Dynamic Models with Python for Rapid Prototyping

DIgSILENT PowerFactory is among the most widely adopted power system analysis tools in research and industry. It provides a comprehensive library of device models and it allows users to define their own. Models for dynamic simulation can be defined in the DIgSILENT Simulation Language (DSL). When the functionality of DSL is insufficient, new DSL functions can be defined in C or C++. However, C and C++ can be challenging for inexperienced programmers. Furthermore, every time the C or C++ code is modified, it needs to be recompiled and PowerFactory needs to be restarted for the changes to take effect, which slows down the workflow, model development, and inhibits rapid prototyping. In this paper we present an open source library that allows users to call Python functions and methods from DSL with minimal effort. Python is a powerful and much easier to use language than C or C++. Additionally, Python programs do not need to be compiled. Furthermore, with this library PowerFactory does not need to be restarted every time the Python code is changed. To illustrate what can be accomplished with our library we present three example use cases related to load modeling, co-simulation, and fault detection based on machine learning. The examples show that it becomes straightforward to enhance DSL with Python and that sophisticated models can be produced with reduced effort using popular open source Python libraries. As a consequence, PowerFactory users gain access to enhanced modeling capabilities and user-friendliness, and a more speedy workflow, which is beneficial for rapid prototyping.
Multiple Attention Networks with Temporal Convolution for Machine Reading Comprehension

Machine reading comprehension (MRC) is an important branch of natural language processing (NLP). In this paper, a new neural structure called multiple attention network with temporal convolution (MATC-net) is proposed for reading comprehension style question answering, which aims to answer questions from a given passage. The main contribution and originality of MATC-net are as follows: 1) a novel deep bidirectional language model is employed to produce better word-level embedding vectors. 2) the convoluteional architectures and bidirectional long short-term memory are combined for the comprehension text encoding process. 3) the proposed multiple attention mechanisms are designed to capture the complete information and exploit it in its counterpart layer by layer. Experimental verification is conducted on Stanford Question Answering Dataset (SQuAD). The results clearly show that our model achieves improvements on previous public reading comprehension.
Denoising of Pulsar Signal Using Wavelet Transform

This paper investigates the algorithm of denoising of pulsar signals using wavelet transform. The signal processing includes three major stages: wavelet transform, threshold processing, and inverse wavelet transform. The proposed algorithm is tested using the real signal from pulsar B0329+54.
An Augmented Reality Tool to Detect Design Discrepancies: A Comparison Test with Traditional Methods

Augmented Reality (AR) is an innovation accelerator for Industry 4.0 that supports the digitalization and improves the efficiency of the industrial sector by providing powerful tools able to enhance the workers' visual perception by combining the real world view with computer-generated data. In this context, the paper presents a new AR tool and an exploratory test in order to examine how well it supports the user's tasks for the detection of design discrepancies. In particular, the test aims to evaluate the effectiveness and efficiency of the proposed tool and how it compares to other instruments traditionally adopted for this end, such as technical drawings and CAD systems. The experimental findings show that the proposed AR tool presents similar results with the other instruments in term of effectiveness and very encouraging results about its efficiency.
A CONTRIBUTION TO THE THEORETICAL STRUCTURE FOR COGNITIVE DYNAMIC CAPABILITY

ABSTRACT Purpose: The study seeks to contribute to the theoretical structure of cognitive dynamic capability. Originality/value: The study provides a review of the sources and characteristics of cognitive management capabilities, points out their specific dimensions of analysis, and explores the research possibilities associated with approaches such as strategic changes and the performance of the firm. Design/methodology/approach: This work uses the concept of mental models, understood as simplified knowledge structures or cognitive representations about the functioning of the business environment to expose the logical chain of theoretical reflections and formulation of propositions. Findings: The main contributions of this study are: 1. the reformulation of the concept of cognitive dynamic capability; 2. the inclusion of the concepts of controlled and automatic mental processing in the proposed model; 3. the inclusion of the variables experience, time, team confidence and complexity; and 4. the formulation of five propositions to support future research. We suggest an opportunity for research on the relationship between cognitive management capabilities and organizational dynamic capabilities, and their joint contribution to strategic changes in business models and firm performance.
HELIOS2018: RoboCup 2018 Soccer Simulation 2D League Champion

The RoboCup Soccer Simulation 2D League is the oldest of the RoboCup competitions. The 2D soccer simulator enables to teams of simulated autonomous agents to play a game of soccer with realistic rules and sophisticated game play. This paper introduces the RoboCup 2018 Soccer Simulation 2D League champion team, HELIOS2018, a united team from Fukuoka University and Osaka Prefecture University. The overview of the team's two recent research themes is also described. The first one is the method of online search of cooperative behavior and several approaches to acquire the appropriate evaluation functions. The second one is an opponent analysis in order to adopt the team strategy to the current opponent team online.
Controls on and prospectivity mapping of volcanic-type uranium mineralization in the Pucheng district, NW Fujian, China

A number of volcanic-type uranium deposits occur in Pucheng district, Fujian province, China. Structural controls and volcanic rocks clearly explain the uranium mineralization in this area. To explore the subtle spatial associations of uranium occurrences with certain geological features, these uranium deposits were subjected to L–function, Fry, and fractal analyses. The results reveal that (1) these deposits are clustered, have a bifractal distribution, and are spatially parallel to the NE-striking faults, (2) the regional-scale NE-striking, and prospect-scale NE-, and NW-striking faults exhibit significant spatial associations with uranium deposits as illustrated by the Fry models; (3) intersections of the faults with larger permeability and connectivity promote the mineralization process, and (4) the spatial complexities of structures and lithological contacts, quantified by the fractal dimension, form strong and positive correlations with uranium occurrences and sizes. Finally, by integrating nine spatial evidential layers representing the controlling factors for the occurrence of mineralization, the artificial neural network model was applied to map mineral prospectivity for the uranium mineralization. The receiver operating characteristics (ROC) curve and the under the ROC curve (AUC) were utilized to measure the performance of the prospectivity models. The model is highly capable of mapping uranium prospectivity because the AUC is greater than 0.5 and close to 1. The prospectivity mapping confirmed that there is significant potential for uranium mineralization in the study area, which opens up new avenues for further explorations on uranium deposits in Pucheng district.
Literature Explorer: effective retrieval of scientific documents through nonparametric thematic topic detection

Scientific researchers are facing a rapidly growing volume of literatures nowadays. While these publications offer rich and valuable information, the scale of the datasets makes it difficult for the researchers to manage and search for desired information efficiently. Literature Explorer is a new interactive visual analytics suite that facilitates the access to desired scientific literatures through mining and interactive visualisation. We propose a novel topic mining method that is able to uncover "thematic topics" from a scientific corpus. These thematic topics have an explicit semantic association to the research themes that are commonly used by human researchers in scientific fields, and hence are human interpretable. They also contribute to effective document retrieval. The visual analytics suite consists of a set of visual components that are closely coupled with the underlying thematic topic detection to support interactive document retrieval. The visual components are adequately integrated under the design rationale and goals. Evaluation results are given in both objective measurements and subjective terms through expert assessments. Comparisons are also made against the outcomes from the traditional topic modelling methods.
A Continuous Integration-Based Framework for Software Management

Automated software-installation tools such as EasyBuild and Spack have enabled HPC support staff to more quickly deploy software environments for HPC. However, to provide reliable user-facing software environments, any change made to the environments should be recorded and tested. In this paper, we will describe a continuous integration-based framework for software management in use at the Ohio Supercomputer Center (OSC). In this framework, we rely on homegrown tools for automated software build and installation and the ReFrame framework to setup the testing system which can be triggered on commit to a local Gitlab repository. We will document the workflow and explain how we can deploy the software environment quickly and confidently using these tools.
Identifying Features of Android Apps from Execution Traces

Understanding a program and the features it provides is essential for a number of software engineering tasks, including refactoring, debugging, and debloating. Unfortunately, program understanding and feature identification are also extremely challenging and time consuming activities. To support developers when they perform these activities, we propose FeatureFinder, an approach that aims to identify and understand the features of a program by analyzing its executions. Specifically, we defined our approach for Android apps, given their widespread use. Given an app, FeatureFinder generates traces that capture different properties of the app executions through instrumentation. It then leverages the user events in the trace to split the trace into segments, and clusters these segments based on their characteristics, using a classifier. Each identified cluster indicates a feature exercised in the execution. Finally, FeatureFinder suitably labels each identified cluster, so as to provide a human-readable description of the corresponding feature. We performed a case study in which we used FeatureFinder to identify features in two executions of the K-9 MAIL app. In the study, FeatureFinder was able to correctly identify 6 of the 11 manually identified features, which we believe is an encouraging result and motivates further research.
Solving Arithmetic Word Problems by Object Oriented Modeling and Query-Based Information Processing

The paper presents an Object Oriented Analysis and Design (OOAD) approach to modeling, reasoning and a database query based approach to processing and solving addition-subtraction (Add-Sub) type arithmetic Mathematical Word Problems (MWP) of elementary school level. The system identifies and extracts the key entities in a word problem like owners, items and their attributes and quantities, verbs, from all the input sentences, using a rule based Information Extraction (IE) approach based on Semantic Role Labeling (SRL) technique. These information are then stored in predefined templates which are further modeled to represent an MWP in the object-oriented paradigm and processed using query based approach to generate the answer. These kind of applications are based on Natural Language Processing (NLP), Natural Language Understanding (NLU) and Artificial Intelligence (AI), and can be used as intelligent dynamic mathematical tutoring tools as part of E-Learning systems, Learning Management Systems, on-line education, etc. The proposed object oriented mathematical word problem solver can solve arithmetic MWPs involving only addition-subtraction operations and it has produced an accuracy of 94.35% on a subset of the AI2 arithmetic questions dataset.
Excitation and depression of coherent state of the simple harmonic oscillator

Othman and Yevick [Int. J. Theor. Phys. 57, 2293 (2018)] introduced a new class of states defined as “near” coherent states attached to the simple harmonic oscillator. Such states can be expressed as superposition of a standard coherent state and a derivative state, which are neither completely quantum nor completely classical. Here, we introduce photon-added (-depleted) near coherent states [PA(D)NCS] through “m” times application of creation (annihilation) operators â†(â) to the near coherent state. A general analysis of nonclassical properties of the PA(D)NCS, such as sub-Poissonian statistics and squeezing effect, is given analytically and numerically in the context of the conventional quantum optics. We also derive the Wigner distribution function of the PA(D)NCS over phase space which may bear negative values, which is a good indication of their nonclassical properties. Finally, an experimental procedure for generating the PA(D)NCSs is established.