Reviewerly/exHarmony huggingface dataset:
Normalized cross correlation image stitching algorithm based on minimum spanning tree

This paper presents a normalized cross correlation optimization algorithm based on minimum spanning tree. This method smoothes the original image and generates normalized correlation matrix for the smoothing image. According to the generated normalized cross-correlation matrix, the maximum value and corresponding index of each row and column (one point in each image relative to all corresponding points in another image) are obtained; according to the result, feature points are matched, and the minimum spanning tree is used in the matching process to plan the fastest traversal route. Two groups of pictures are used to calculate the feature point detection time, the matching time of feature points and the total matching time. Experiments show that the improved algorithm can save 2.72s and 0.53s, 2.68s and 0.56s compared with the traditional normalized cross-correlation algorithm and the optimized cross-correlation algorithm without minimum spanning tree. Experiments show that the proposed algorithm has higher accuracy and convenience, cost-effective and easy to implement.
Despeckling of ultrasound images using directionally decimated wavelet packets with adaptive clustering

Two-dimensional transforms are extensively used for speckle noise reduction (despeckling) in ultrasound images. This work proposes a double filter bank structure consisting of a discrete wavelet packet transform (DWPT) and directional filter bank (DFB) along with a fuzzy-based clustering technique to despeckle ultrasound images. Wavelet packet transform can efficiently reject noises based on grey scale relational thresholding and DFB can efficiently preserve edge information. In this study, instead of conventional thresholding methods, fuzzy-based clustering techniques are applied for noise rejection. The algorithm provides a consistent improvement over the competing state-of-the-art speckle reduction algorithms due to the improved ability to preserve geometric features while rejecting speckle noise adaptively. The authors claim is validated by applying a number of clinical images with performance indices such as peak signal to noise ratio, mean structural similarity, signal to mean square error, speckle signal to noise ratio, speckle suppression index and edge preservation index.
On PSPACE generation of a solution set of a word equation and its applications

There are three approaches to solve satisfiability problem for word equations: Makanin's algorithm, Plandowski's algorithm, Jez's algorithm. Until now there was no extension of Plandowski's algorithm to do the same. We fill this gap. We present extension of Plandowski's algorithm which generates a representation of all solutions of a word equation. The representation is a finite graph. This allows to decide in PSPACE whether a solution set of a word equation is finite. Our approach is generalized to solve in PSPACE two problems dealing with expressible relations, namely the emptiness of the relation and finiteness of the relation.
Prototype System Design of Mooring Buoy for Seafloor Observation and Construction of Its Communication Link

Zhang, S.; Yang, W.; Xin, Y.; Wang, R.; Li, C.; Cai, D., and Gao H., 2018. Prototype system design of mooring buoy for seafloor observation and construction of its communication link. In: Liu, Z.L. and Mi, C. (eds.), Advances in Sustainable Port and Ocean Engineering. Journal of Coastal Research, Special Issue No. 83, pp. 41–49. Coconut Creek (Florida), ISSN 0749-0208.A prototype system of mooring buoy for ocean seafloor observation is constructed, that, ocean buoy and underwater junction box are combined together with an Electro-Optical-Mechanical (EOM) cable. So, the seafloor observation data can transmit to the sea surface buoy through EOM cable, and finally, the data transmit to the shore through satellite and wireless communication. On the other side, sea surface buoy offers power with the wind and solar panel to underwater junction box. Compared to the traditional sea surface observational buoy, we redesign the communication model, so as to transmit the seafloor data to shore in the real time mode. The transmit communication is constructed as "Seafloor observation (underwater junction box) – Ethernet (underwater junction box) – Ethernet/Optical Switch – EOM cable – Optical/Ethernet Switch – Ethernet (buoy) – Beidou satellite/wireless communication (buoy)". According to the energy transmission property of EOM cable and the power supply demand of the seafloor, the wind/solar controller system and DC-DC convert modular is designed. We give the detail design and test result. In the test, we transmit the sea surface video and seafloor video in real time mode through wireless communication link. The reliability of the communication link is verified, and the feasibility of launch and recovery process is verified.
A Generic Data Mining Model for Software Cost Estimation Based on Novel Input Selection Procedure

It is always preferable for any estimation model to be inclusive as accuracy in estimation models inherently lie with their inclusiveness. Software cost estimation is the prediction of development effort and time required to develop a software project and being predictive in nature, it demands for inclusiveness, which will accordingly bring the accuracy in it. In this study, a generic model for software cost estimation using an input selection procedure is proposed. The proposed model brings inclusiveness into the already available data mining techniques of software cost estimation by sensitively choosing a subset of highly relevant project attributes and ignoring the less relevant ones. In this article, a diverse set of data mining techniques for software cost estimation are considered. All these techniques are experimented on five data sets before and after passed through the proposed procedure. The obtained results showed that newly generated techniques after being passed through the proposed procedure offer accurate results up in the way of efficiency in software cost estimation.
A novel neurodynamic reaction-diffusion model for solving linear variational inequality problems and its application

In this paper, we present a new delayed projection neural network with reaction-diffusion terms for solving linear variational inequality problems. The proposed neural network possesses a simple one-layer structure. By employing the differential inequality technique and constructing a new Lyapunov–Krasovskii functional, we derive some novel sufficient conditions ensuring the globally exponential stability. These conditions are dependent on diffusions and the monotonicity assumption is unnecessary. Furthermore, the considered neural network can solve quadratic programming problems. Finally, several applicable examples are provided to illustrate the satisfactory performance of the proposed neural network.
Classification and Retrieval of Images Based on Extensive Context and Content Feature Set

Background: This paper renders a classification and retrieval of image achievements in the search area of image retrieval, especially content-based image retrieval, an area that has been very active and successful in the past few years. Objective: Primarily the features extracted established on the bag of visual words (BOW) can be arranged by utilizing Scaling Invariant Feature Transform (SIFT) and developed K-Means clustering method. Methods: The texture is extracted for a developed multi-texton method by our study. Our retrieval process consists of two stages such as retrieval and classification. The images will be classified established on the features by applying k- Nearest Neighbor (kNN) algorithm. This will separate the images into various classes in order to develop the precision and recall rate initially. Results: After the classification of images, the similar images are retrieved from the relevant class as per the afforded query image.
Bilateral structure based matrix regression classification for face recognition

Robust sparse representation is a well-known method in computer vision. Many sparse representation models have been proposed and perform well in face recognition. Most of them use transformed images of one dimensional vector, and such implementation ignores structural information between features. To make use of this structural information, this paper presents a novel model for face recognition. Unlike traditional sparse regression measuring differences between test sample and predicted response by vector norm, our model uses matrix norm, l2,1, to calculate residual. Instead of treating each pixel independently, the residual of a pixel is connected with all other pixels from the same line and column by means of double direction l2,1-norm. Moreover, we incorporate the specific class information into the representation model by introducing group-sparse regularizer. And then, we use the alternating direction method of multipliers approach to optimize proposed model. The extensive experiments have been conducted to evaluate the proposed methods by comparing with many sparse representation based techniques.
Fully abstract module compilation

We give a translation suitable for compilation of modern module calculi supporting sealing, generativity, translucent signatures, applicative functors, higher-order functors and/or first-class modules. Ours is the first module-compilation translation with a dynamic correctness theorem. The theorem states that the translation produces target terms that are contextually equivalent to the source, in an appropriate sense. A corollary of the theorem is that the translation is fully abstract. Consequently, the translation preserves all abstraction present in the source. In passing, we also show that modules are a definitional extension of the underlying core language. All of our proofs are formalized in Coq.
A new birthday-type algorithm for attacking the fresh re-keying countermeasure

The fresh re-keying scheme is a countermeasure designed to protect low-cost devices against side-channel attacks. In this paper, we present a new birthday-type attack based on a refined reduction to Ring-LPN with a reducible polynomial. Compared with the previous research, our algorithm significantly reduces the time complexity in the 128-bit leakage model—with an SNR equal to 8.21 and at most 220 traces, for instance, the key can be recovered using 241.96 bit-operations.
Emerging data encryption methods applicable to Energy Internet

Information exchange through the communication network is an indispensable part of the Energy Internet. Any intentional or unintentional disclosure of stored and transmitted confidential data may endanger the privacy of entities or the entire system. Cyber-security's activities can help system and its agents to protect their privacy against adversaries and promote the level of competition in a market. In this chapter, some major cryptography methods and their advantages, disadvantages,and possible applications to the Energy Internet are investigated.
Detecting Work Zones in SHRP2 NDS Videos Using Deep Learning Based Computer Vision

Naturalistic drivingAbodo, Franklin studies seek to perform the observations of human driver behavior in the variety of environmental conditions necessary to analyze, understand, and predict that behavior using statisticalRittmuller, Robert and physical models. The second Strategic Highway Research Program (SHRP2) funds a number of transportation safety-related projects including its primary effort, the Naturalistic Driving Study (NDS), and an effortSumner, Brian supplementary to the NDS, the Roadway Information Database (RID). This work seeks to expand the range of answerable research questions that researchers might pose to the NDS and RIDBerthaume, Andrew databases. Specifically, we present the SHRP2 NDS Video Analytics (SNVA) software application, which extracts information from NDS-instrumented vehicles’ forward-facing camera footage and efficiently integrates that information into the RID, tying the video content to geolocations and other trip attributes. Of particular interest to researchers and other stakeholders in the integration of the work zone, traffic signal state, and weather information. The version of SNVA introduced here focuses on work zone detection, the highest priority. The ability to automate the discovery and cataloging of this information, and to do so quickly, is especially important given the two petabytes (2PB) size of the NDS video data set.
CAPTAIN: Comprehensive Composition Assistance for Photo Taking

Many people are interested in taking astonishing photos and sharing them with others. Emerging high-tech hardware and software facilitate the ubiquitousness and functionality of digital photography. Because composition matters in photography, researchers have leveraged some common composition techniques, such as the rule of thirds and the perspective-related techniques, in providing photo-taking assistance. However, composition techniques developed by professionals are far more diverse than well-documented techniques can cover. We present a new approach to leverage the underexplored photography ideas, which are virtually unlimited, diverse, and correlated. We propose a comprehensive fork-join framework, named CAPTAIN ( C omposition A ssistance for P hoto Ta k in g), to guide a photographer with a variety of photography ideas. The framework consists of a few components: integrated object detection, photo genre classification, artistic pose clustering, and personalized aesthetics-aware image retrieval. CAPTAIN is backed by a large managed dataset crawled from a Website with ideas from photography enthusiasts and professionals. The work proposes steps to decompose a given amateurish shot into composition ingredients and compose them to bring the photographer a list of useful and related ideas. The work addresses personal preferences for composition by presenting a user-specified preference list of photography ideas. We have conducted many experiments on the newly proposed components and reported findings. A user study demonstrates that the work is useful to those taking photos.
Adversarial Learning of Label Dependency: A Novel Framework for Multi-class Classification

Recent work has shown that exploiting relations between labels improves the performance of multi-label classification. We propose a novel framework based on generative adversarial networks (GANs) to model label dependency. The discriminator learns to model label dependency by discriminating real and generated label sets. To fool the discriminator, the classifier, or generator, learns to generate label sets with dependencies close to real data. Extensive experiments and comparisons on two large-scale image classification benchmark datasets (MS-COCO and NUS-WIDE) show that the discriminator improves generalization ability for different kinds of models.
A Conceptual Model for Cloud-Based E-Training in Nursing Education

In specific fields, such as e-training in nursing, involving computer-intensive training scenarios, there is an increased demand to deliver training services to a larger number of learners, and with it, the need for cloud services. However, to date there has been a lack of a formalized framework relating to the use of cloud computing for on-demand interactive e-training resources in nursing education. To this end, this chapter formalizes a conceptual framework for a cloud-based e-training system in nursing education. The conceptualization takes into consideration nursing e-training system requirements, with a focus on applying cloud computing technologies to ensure the dynamic scalability of virtual distributed services and computing power while maintaining QoS and security.
Fuzzy classification boundaries against adversarial network attacks

Adversarial machine learning copes with the development of methods to prevent machine learning algorithms from being misled by malicious users. This field is especially relevant for applications where machine learning lies in the core of security systems. In the field of network security, adversarial samples are actually novel network attacks or old attacks with tuned properties. This paper proposes to blur classification boundaries in order to enhance machine learning robustness and improve the detection of adversarial samples that exploit learning weaknesses. We test this concept by an experimental setup with network traffic in which linear decision trees are wrapped by a one-class-membership scoring algorithm. We benchmark our proposal with plain linear decision trees and fuzzy decision trees. Results show that evasive attacks (i.e., false negatives) tend to be ranked with low class-membership levels, meaning that they are located in zones close to classification thresholds. In addition, classification performances improve when membership scores are added as new features. Using fuzzy class boundaries is highly consistent with the interpretation of many network traffic features used for malware detection; moreover, it prevents network attackers from exploiting classification boundaries as attack objectives.
A Trust Verification Architecture with Hardware Root for Secure Clouds

Cloud security has become a vital issue within thousands of inter-connected servers in clouds, as malicious attacks or discovered vulnerabilities may spread more rapidly than ever. Based on the opinion that hardware is more secure and trustworthy, a trust platform module (TPM) is used as an external chip to ensure the trust verification, while it's unsuitable as virtual machine (VM) migration, hybrid servers, distributed storage with a low performance. So, we design a novel cloud architecture with a special physical server named as the trust verification server (TVS) to provide trust services according to the TPM specification, then the servers in the cloud can use TVS remotely as a high-performance TPM chip. In this paper, we design the TVS with accelerator hardware, upgrade the cloud architecture with an additional certificate authority (CA) server, and use TVS with a non-interference trust measurement model. The experiments show that the TVS can work efficiently with huge performance improvements at more than 100 times compared with the use of TPM in the cloud. This can be used to solve the complex cloud security problems such as VM sprawl and VM escape.
Comparative analysis of semantic frameworks in healthcare

Challenges in healthcare are multidisciplinary, requiring data from various disciplines such as biology, geography, pharmaceutical science, and bioscience. Such diversified complexity represents a challenge in developing call support systems for managing data coming from totally different origins. In looking for a solution, this chapter presents varied multiagent systems such as AOIS, MASE, MET4, Karthika, and Cancer Search Engine, which support the sharing of information among dynamic communities of users connected through the internet. The concepts of the Semantic Web and software agents are introduced for retrieving meaningful information and achieving automation in the discovery of information from the composition of data. Any multiagent system works generally on three parameters: (1) Filtering of information coming from various origins; (2) Pushing for new information in the system based on user's interest; (3) Privacy and security issues in terms of users accessing data from the system. We demonstrate existing multiagent systems in this chapter, giving pros and cons and also illustrating a prototyped semantic multiagent system to enhance performance in information retrieval in a run-time decision-making support system.
Effect of dissipation and dipole–dipole interplay on Hilbert–Schmidt distance and Bell’s inequality correlations of two qubits interacting with two-mode cavity field

An analytical description is obtained for a system composed of two two-level atoms (qubits) coupled via dipole–dipole interplay by Su(1, 1) and Su(2) Lie group. Both qubits interact with two-mode open cavity through a non-degenerate two-photon process. We explore the influence of the dissipation rate on the non-classical correlation (NCC), based on the Hilbert–Schmidt distance and Bell's inequality correlations. The robustness of the generated NCCs depend not only on the dipole–dipole interplay but also on the dissipation rate and the initial coherence intensity. The NCCs can be enhanced for some selected values of the dipole–dipole interaction.
Adversarial learning for viewpoints invariant 3D human pose estimation

2D pose estimation have achieve remarkable performance with deep convolutional neural networks. However 3D pose estimation is current constrained by the limited datasets of 3D annotations. Meanwhile most annotated images are captured using Motion Capture system in lab or certain studio, which has large variations with large-scale monocular 2D pose datasets. We propose an adversarial learning framework, which can learn invariant human pose latent from 3D annotated datasets to optimize the estimation of monocular images with only 2D annotations. However there is large difference in observation coordinates between 2D datasets and 3D datasets, and this viewpoints issue should be separated from invariant pose latent. We add a viewpoints invariant module to automatically regulate observation viewpoints for generated 3D pose, which transforming the generated pose to more suitable observation in the 3D datasets. Our method achieve competitive results on both 2D and 3D benchmarks.
Microvibration Measurement Based on Multichannel Projector-Camera System

A noncontact rapid microvibration measurement method is proposed based on multichannel projector-camera system and phase shift profilometry. The multichannel characteristics of color image can be used to calculate vibration information dynamically by collecting only one piece of image. The waveform within a frequency range from 0.1 to 2 Hz is measured accurately by means of this vibration measurement technology, and minimum vibration amplitude is about 163 um. The system can be achieved by a standard low cost commercial charge coupled device camera with 30 frames/s.
Strategy research based on chess shapes for Tibetan JIU computer game

JIU is a variant of traditional Tibetan chess which is mainly played in Tibetan Qiang Autonomous Prefecture of Ngawa, China. The JIU game process is divided into two sequential stages: preparation and battle. The layout of stones during the preparation stage strongly influences gameplay in the batt le stage. Compared with Go, computer game research on JIU is almost non-existent. We collected data for 300 JIU games recorded in Tibetan Qiang Autonomous Prefecture of Ngawa. By analyzing the data, several important strategic shapes were extracted: triangles, trinities, diagonals and squares. A chess-shape pattern-matching method based on matrices was developed to recognize these shapes. Offensive and defensive strategies based on these shapes were proposed for the preparation stage, being ranked in terms of priority. Moving and capturing strategies based on these shapes were proposed for the battle stage. Human players of various skill levels were invited to play against the software, applying the proposed strategies, to evaluate its performance. The win rates of the software were 2%, 25% and 60%, against top, intermediate, and beginner players. These results demonstrate that the JIU game software using the proposed strategy performs slightly above the beginner level.
Quantum Control at the Boundary

We present aBalmaseda, A. scheme for controlling the state of a quantum system byPérez-Pardo, J. M. modifying the boundary conditions. This constitutes an infinite-dimensional control problem. We provide conditions for the existence of solutions of the dynamics and prove that this system is approximately controllable.