Reviewerly/exHarmony huggingface dataset:
Simulation Environment of Evaluation and Optimization for Moving Target Defense

In this paper, a simulation environment for evaluating the effectiveness and performance cost of moving target defense (MTD) is presented. This simulation environment is based on SimPy, which is a process-based discrete-event simulation framework worked on standard Python. In our MTD simulation environment, we develop a typical topology to contain different deployments of moving target (MT) techniques. Meanwhile, processes of attackers, defenders, services, and users are analyzed and modeled in the simulation environment, which also provides an extendable interface for MT technologies in the future. In the case study, we quantify the effectiveness and performance of MTD in the proposed simulation environment under several typical scenarios, none-MTD deployment, high-security strategy, high-performance strategy. The average attacking time, average processed requests, and average request's processing time are used as metrics to assess different MTD strategies. Results show that our simulation environment provides an efficient and intuitionistic method for evaluating the effectiveness and performance of MTD and helping to choose a more optimal strategy set for MTD.
Vision-based Fast-terminal Sliding Mode Super Twisting Controller for Autonomous Landing of a Quadrotor on a Static Platform

This paper proposes a vision-based sliding mode control technique for autonomous landing of a quadrotor over the static platform. The proposed vision algorithm estimates the quadrotor's position relative to an ArUco marker placed on a static platform using an on-board monocular camera. The relative position is provided as an input to a Fast-terminal Sliding Mode Super Twisting Controller (FTSMSTC) which ensures finite time convergence of the relative position between the landing pad marker and the quadrotor. In addition, the proposed controller attenuates chattering phenomena and guarantees robustness towards bounded external disturbances and modelling uncertainties. The proposed vision-based control scheme is implemented using numerical simulations and validated in real-time on the DJI Matrice 100.
Performance Analysis of Differential Evolution Algorithm Variants in Solving Image Segmentation

Image segmentation is an activity of dividing an image into multiple segments. Thresholding is a typical step for analyzing image, recognizing the pattern, and computer vision. Threshold value can be calculated using histogram as well as using Gaussian mixture model. but those threshold values are not the exact solution to do the image segmentation. To overcome this problem and to find the exact threshold value, differential evolution algorithm is applied. Differential evolution is considered to be meta-heuristic search and useful in solving optimization problems. DE algorithms can be applied to process Image Segmentation by viewing it as an optimization problem. In this paper, Different Differential evolution (DE) algorithms are used to perform the image segmentation and their performance is compared in solving image segmentation. Both 2 class and 3-class segmentation is applied and the algorithm performance is analyzed. Experimental results shows that DE/best/1/bin algorithm out performs than the other variants of DE algorithms
Application of Mixed Reality Based on Hololens in Nuclear Power Engineering

With the continuous development of computer hardware and software technology, mixed reality has been emerged. It has been applied in many industries. However, the use in nuclear power engineering is little. The current design data of nuclear power mainly uses 2D drawings and 3D models, which is not conducive to collaborative design and design verification. In the maintenance process of Nuclear Power Plant, the staff has to carry the manual, and can’t get real-time guidance. Teaching methods of technology and skill for staffs are a little boring and inefficient. It is possible to solve these problems by using mixed reality with Hololens. The sharing feature of Hololens is helpful to collaborative design. Rich holographic images and easy interactions of Hololens help maintenance workers get tasks done faster. The training system of HPR1000 is developed, which provides the nuclear power staffs with the content of HPR1000 plant layout and key equipments in the form of holographic images.
Multi-Dilation Network for Crowd Counting

With the growth of urban population, crowd analysis has become an important and necessary task in the field of computer vision. The goal of crowd counting, which is a subfield of crowd analysis, is to count the number of people in an image or a zone of a picture. Due to the problems like heavy occlusions, perspective and luminous intensity variations, it is still extremely challenging to achieve crowd counting. Recent state-of-the-art approaches are mainly designed with convolutional neural networks to generate density maps. In this work, Multi-Dilation Network (MDNet) is proposed to solve the problem of crowd counting in congested scenes. The MDNet is made up of two parts: a VGG-16 based front end for feature extraction and a back end containing multi-dilation blocks to generate density maps. Especially, a multi-dilation block has four branches which are used to collect features in different sizes. By using dilated convolutional operations, the multi-dilation block could obtain various features while the maximum kernel size is still 3 x 3. The experiments on two challenging crowd counting datasets, UCF_CC_50 and ShanghaiTech, have shown that the proposed MDNet achieves better performances than other state-of-the-art methods, with a lower mean absolute error and mean squared error. Comparing to the network with multi-scale blocks which adopt larger kernels to extract features, MDNet still gains competitive performances with fewer model parameters.
Interactive Anime Sketch Colorization with Style Consistency via a Deep Residual Neural Network

Anime line sketch colorization is to fill a variety of colors the anime sketch, to make it colorful and diverse. The coloring problem is not a new research direction in the field of deep learning technology. Because of coloring of the anime sketch does not have fixed color and we can't take texture or shadow as reference, so it is difficult to learn and have a certain standard to determine whether it is correct or not. After generative adversarial networks (GANs) was proposed, some used GANs to do coloring research, achieved some result, but the coloring effect is limited. This study proposes a method use deep residual network, and adding discriminator to network, that expect the color of colored images can consistent with the desired color by the user and can achieve good coloring results.
Burst‐aware virtual machine migration for improving performance in the cloud

Summary Burst is a common pattern in the user's requirements, which suddenly increases the workload of virtual machines (VMs) and reduces the performance and energy efficiency of cloud computing systems (CCS). Virtualization technology with the ability to migrate VMs attempts to solve this problem. By migration, VMs can be dynamically consolidated to the users' requests. Burst temporarily increases the workload. Ignoring this issue will lead to incorrect decisions regarding the migration of VMs. It increases the number of migrations and Service Level Agreement Violations (SLAVs) due to overload. This may cause waste of resources, increase in energy consumption, and misplaced VMs. Therefore, a burst‐aware method for these issues is proposed in this paper. The method consists of two algorithms: one for determining the migration time and the other for the placement of VMs. We use the PlanetLab real dataset and CloudSim simulator to evaluate the performance of the proposed method. The results confirm the advantages of the method regarding performance compared to benchmark methods.
Residue-to-Binary Converter for the New RNS Moduli Set $\{2^{2n}-2, \ 2^{n}-1, \ 2^{n}+1\}$

Even moduli of the 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sup> -2 form have been recently proposed in the design of various digital systems that utilize the Residue Number System (RNS), as an alternative to the dominating modulo channel of 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sup> . Although efficient arithmetic units and residue generators have been proposed for the 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sup> -2 channel, the residue-to-binary conversion for moduli sets that incorporate this channel still remains a challenging task. In this work, we propose a reverse converter for the novel 3-moduli set {2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2n</sup> -2,2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sup> -1,2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sup> +1} that can be easily derived based on the well-known New Chinese Remainder Theorem-I (New CRT-I). Performance evaluation based on experimental CMOS VLSI results for various bit-lengths reveals a significant reduction in conversion delay and integration area of up to 49% and 27% respectively, when compared with reverse converters of similar moduli sets.
Unsupervised Graph Representation Learning With Variable Heat Kernel

Graph representation learning aims to learn a low-dimension latent representation of nodes, and the learned representation is used for downstream graph analysis tasks. However, most of the existing graph embedding models focus on how to aggregate all the neighborhood node features to encode the semantic information into the representation and neglect the global structural features of the node such as community structure and centrality. In the paper, we propose a novel unsupervised graph representation learning method (VHKRep), where a variable heat kernel is designed to better capture implicit global features via heat diffusion with the different time scale and generate the robust node representation. We conduct extensive experiment on three real-world datasets for node classification and link prediction tasks. Compared with the state-of-the-art seven models, the experimental results demonstrate the effectiveness of our proposed method on both node classification and link prediction tasks.
Approaching Natural Conversation Chatbots by Interactive Dialogue Modelling &amp; Microsoft LUIS

In this paper, we preset guidelines on how to model a dialogue-based conversation with a chatbot build on Microsoft conversational services. Therefore, we created a free framework which manages dialogues and user sessions. For this purpose, we use a systematic classification of the input and intents. Especially, we used Microsoft Language Understanding Intelligent Service (LUIS) for intent extraction. LUIS provides a platform to extract the user need by calculating the intent probability. Unfortunately, LUIS does not provide dialogue management. For that reason, developers need to create own dialogue behavior in Visual Studio project. Therefore, we extracted and use it as a free guideline for the community and other platform developers.
Formation of the main sulfide zone at Unki Mine, Shurugwi Subchamber of the Great Dyke, Zimbabwe: Constraints from petrography and sulfide compositions

Abstract The major platinum group element (PGE) occurrence in the Great Dyke of Zimbabwe, the main sulfide zone, is a tabular stratabound layer hosted in pyroxenites, and it is broadly similar in form throughout the length of the Great Dyke. We conducted a petrographic and sulfide composition study on a sulfide-enriched zone from the contact of the mafic sequence–ultramafic sequence through the main sulfide zone at Unki Mine in the Shurugwi Subchamber to its underlying footwall rocks to place some constraints on the origin of the rocks. Pyrrhotite, pentlandite, chalcopyrite, and pyrite are the base metal sulfides that were encountered during the study. Pyrrhotite, pentlandite, and chalcopyrite typically occurred as inclusions in both primary (orthopyroxene, plagioclase, and clinopyroxene) and secondary (amphibole and chlorite) silicate phases, whereas pyrite was observed in only three samples, where it occurred in association with pyrrhotite. The concentrations of PGEs in the base metal sulfides were nearly all at or below minimum detection limits. The intercumulus nature of some of these sulfides in the investigated sequence suggests that they were likely formed during the crystallization history of these rocks. The occurrence of pyrite, which we interpret to be an alteration phase, suggests that a late-stage event, likely formed during hydrothermal alteration, helped to concentrate the mineralization at Unki Mine. In some cases, however, these sulfides occur partially surrounding some chromite and silicate phases. Thus, some sulfides in the Unki Mine area were likely formed early in the crystallization history of the Great Dyke, whereas others were formed late during hydrothermal processes. Low concentrations of PGEs such as platinum (Pt), palladium (Pd), and rhodium (Rh) in base metal sulfides imply that the PGEs in the main sulfide zone and Unki Mine are hosted either in silicates and/or platinum group minerals. Very low Co contents in pentlandites in the rocks under investigation are interpreted to imply that very limited Fe substitution by Co, and also of Ni by Co, occurred. Broadly comparable trends, with minor variations of Fe in pyrrhotite, of Co and Ni in pentlandite, and of Cu in chalcopyrite, for example, likely reflect magmatic processes. The concentrations of these metals in base metal sulfides vary sympathetically, indicating that their original magmatic signatures were subsequently affected by hydrothermal fluids. The spiked pattern displayed by the variations in the percent modal proportions of the base metal sulfides across the entire investigated stratigraphic section is interpreted to reflect remobilization of the sulfides during hydrothermal alteration. Depletions in some elements, which occur near the base and at the top of the investigated succession, are likely a result of this hydrothermal alteration.
Research on Pseudo-Random Characteristics of New Random Components

With the maturity of modern cryptography theory and research, the security of traditional stream cipher systems is increasingly vulnerable to various attacks. In this regard, people began to work on various new pseudo-random sequence generation algorithms. The Linear Feedback Shift Register (LFSR) is the basic component of the most commonly used keystream generators, but because of its constant coefficients, it is weak against algebraic attacks, rational approximation algorithms, and Berlekamp-Massey (B-M) algorithm attacks. Thus we propose a method of automatically changing the coefficients to improve the linear feedback shift register. It is proved by theory that the sequence generated by the new variable coefficient linear feedback shift register has better pseudo-randomness and the security is greatly improved.
Trust Degree Calculation Method Based on Trust Blockchain Node

Due to the diversity and mobility of blockchain network nodes and the decentralized nature of blockchain networks, traditional trust value evaluation indicators cannot be directly used. In order to obtain trusted nodes, a trustworthiness calculation method based on trust blockchain nodes is proposed. Different from the traditional P2P network trust value calculation, the trust blockchain not only acquires the working state of the node, but also collects the special behavior information of the node, and calculates the joining time by synthesizing the trust value generated by the node transaction and the trust value generated by the node behavior. After the attenuation factor is comprehensively evaluated, the trusted nodes are selected to effectively ensure the security of the blockchain network environment, while reducing the average transaction delay and increasing the block rate.
Deep Semantic Feature Matching Using Confidential Correspondence Consistency

This work aims to establish visual correspondences between a pair of images depicting objects of the same semantic category. It encounters many challenges such as non-overlapping of scenes or objects, background clutter, and large intra-class variation. Existing methods handle this task with handcrafted features, which cannot effectively fit the correlations between non-overlapping images. Besides, additional training or information may be implemented into the learned features. In this paper, we propose a novel approach for semantic correspondence, which is based on deep feature representation, geometric and semantic associations between intra-class objects, and hierarchical matching selection according to the convolutional feature pyramid. Firstly, we construct the initial correspondence by developing a sparse feature matching model on the coarsest feature level, which enforces the nearest-neighbor searching under semantic and geometric consistency constraints. Further, a narrowing strategy is proposed and employed from the coarsest to the finest feature level, which hierarchically refine and optimize the correspondence. The results illustrate that this approach achieves competitive performance on the public datasets for semantic correspondence.
InSocialNet: Interactive visual analytics for role—event videos

Abstract Role–event videos are rich in information but challenging to be understood at the story level. The social roles and behavior patterns of characters largely depend on the interactions among characters and the background events. Understanding them requires analysis of the video contents for a long duration, which is beyond the ability of current algorithms designed for analyzing short-time dynamics. In this paper, we propose InSocialNet, an interactive video analytics tool for analyzing the contents of role–event videos. It automatically and dynamically constructs social networks from role–event videos making use of face and expression recognition, and provides a visual interface for interactive analysis of video contents. Together with social network analysis at the back end, InSocialNet supports users to investigate characters, their relationships, social roles, factions, and events in the input video. We conduct case studies to demonstrate the effectiveness of InSocialNet in assisting the harvest of rich information from role–event videos. We believe the current prototype implementation can be extended to applications beyond movie analysis, e.g., social psychology experiments to help understand crowd social behaviors.
CartoonRenderer: An Instance-Based Multi-style Cartoon Image Translator

Instance based photo cartoonization is one of the challenging image stylization tasks which aim at transforming realistic photos into cartoon style images while preserving the semantic contents of the photos. State-of-the-art Deep Neural Networks (DNNs) methods still fail to produce satisfactory results with input photos in the wild, especially for photos which have high contrast and full of rich textures. This is due to that: cartoon style images tend to have smooth color regions and emphasized edges which are contradict to realistic photos which require clear semantic contents, i.e., textures, shapes etc. Previous methods have difficulty in satisfying cartoon style textures and preserving semantic contents at the same time. In this work, we propose a novel "CartoonRenderer" framework which utilizing a single trained model to generate multiple cartoon styles. In a nutshell, our method maps photo into a feature model and renders the feature model back into image space. In particular, cartoonization is achieved by conducting some transformation manipulation in the feature space with our proposed Soft-AdaIN. Extensive experimental results show our method produces higher quality cartoon style images than prior arts, with accurate semantic content preservation. In addition, due to the decoupling of whole generating process into "Modeling-Coordinating-Rendering" parts, our method could easily process higher resolution photos, which is intractable for existing methods.
Design of affinity-aware encoding by embedding graph centrality for graph classification

Deep learning methods for graph classification are critical for graph data mining. Recently, graph convolutional networks (GCNs) have been able to achieve state-of-the-art node classification. A typical process for GCNs includes two iterative steps: node feature encoding and message passing. While the former encodes each graph node independently via the uniform encoding function, the latter updates the features of each node by weighted aggregation of the features of neighboring nodes, where the weights are generated by predefined or learned graph Laplacian. However, their accuracy deteriorates for graph classification tasks because the uniform encoding function encodes all the node features involved. In this study, we propose a novel affinity-aware encoding for graph classification. In our model, we implement a separate encoding function for the neighboring nodes of each node for updating the node features, where the nodes are arranged in the order of affinity values, such as graph centrality, in order to determine the correspondence between an encoding function and a specific neighboring node. Our separate encoding function performs non-Euclidean neighboring encoding for each node by weight sharing, which enables message passing. We also develop two variants based on our separate encoding function: the graph centrality-convolutional neural network (C-CNN) and the graph centrality-graph convolutional network (C-GCN). The former performs the separate encoding function on graph data directly by the function of message passing. The latter combines the separate encoding function with the normalized graph Laplacian implemented on the graph data. Experiments demonstrate that the results obtained by our models are consistent with those obtained by classical convolutional neural networks (CNNs) on the MNIST dataset, and they outperform existing GCNs on the 20NEWS, Reuters8, and Reuters52 datasets. We also apply our two variants to online car-hailing service data for traffic congestion recognition. Our methods show state-of-the-art results compared with GCNs.
A Novel Reversible DSG Gate and Its Quantum Implementation

Continued progress of miniaturization is a big boom in the electronics industry, but as per Moore’s statement, this rapidly changing technology cannot sustain forever as efforts made by researchers are becoming critical on atomic levels. A more elusive challenge is to delay forever by searching for an alternative technology. Reversible computing is one technology to continue Moore’s law by making a lossless system having no energy loss and no information loss. The roots of the said technology were laid many decades back, but it actually came into the interest in many researches only almost last decade back. Researchers have proposed almost all digital circuits by investigating reversible logic-based gates, and optimization revolves around making garbage-free reversible computing and low quantum cost. To optimize quantum cost of any reversible circuit, it is important to know the quantum cost of all reversible logic gates used in the designed architecture. To calculate the quantum cost of a reversible logic gate, a methodology is required to draw its quantum implementation and its further optimization. In this paper, a novel reversible DSG gate is proposed and its quantum implementation is done to seek its benefits in designing a high functionality reversible logic based arithmetic and logic unit. The quantum implementation and calculated cost of the proposed DSG gate are verified using the RCViewer+ tool.