Reviewerly/exHarmony huggingface dataset:
Fine-Grained Scene-Graph-to-Image Model Based on SAGAN

Text-to-image has become one of the most active research fields in recent years. Although current technology can generate complex images with multiple objects, how to generate detailed images with multiple objects still faces enormous challenges. In this paper, we propose a model driven by the attention mechanism to generate multi-object images from scene graphs. In the proposed model we introduce self-attention network into the mask regression network to overcome the limitation of the local receptive field of convolution Generative Adversarial Networks. Self-attention network can extract the long-range dependencies in an image, so the proposed model can generate image with more details. In addition, our model improves the stability of image generation and accelerates the process of image generation by gradually increasing the resolution of the cascading refinement network. Experiments on the Visual Genome and COCO-Stuff datasets show that the proposed model can generate more detailed images.
An improved human-object interaction detection method based on short-term memory selection network

Human-object interaction (HOI) detection task is defined as inferring all the &lt; human, verb, object &gt; triplets in the image, which helps computers to obtain a more comprehensive understanding of the visual scene. Most existing HOI detection methods focus on instance local features, and rarely consider the information from backgrounds. Our core idea is that the relationship between human, object and other backgrounds contains important cues to facilitate HOI detection. According to the short-term memory selection (STMS) mechanism, we regard the interaction relationship as the result of human and object stimulating the union area, and simulate the stimulation process by the recurrent neural network. The features in the union area of human and object are taken as the input of RNN, human and object are the two inputs of RNN, and the output is the representation of the interaction relationship. Combined with the visual features and spatial features of instances, a multi-stream network is utilized to detect HOIs in the image. Experiments on V-COCO and HICO-DET show that the proposed model achieves better performance, verifying the effectiveness of our method.
Loaning Decision for Electric Vehicles under Uncertain Electricity Price in the Blockchain Internet of Energy

This paper discusses an uncertain loaning decision problem for electric vehicles in the blockchain Internet of energy. Blockchain guarantees the security and privacy of transactions. In view of the uncertainty of electricity price, the scenario method is introduced. A new Stackelberg game model between multi plug-in hybrid electric vehicles and a loan bank is established. For each player, through the optimal solution of the loaning decision problem, the decision of each electric vehicle and the bank is obtained. Differing from the deterministic decision problem, a robust decision problem is proposed for the uncertainty of electricity price. In particular, in the loaning decision for each plug-in hybrid vehicle, a two scenario subset robust optimization method is used. A relaxation algorithm is proposed to solve the robust decision problem. The equilibrium of robust Stackelberg game model is achieved at the optimal loaning amount of all electric vehicles and the optimal loaning rate of the bank. Finally, an experiment was conducted to test the established game model in three different instances. The effectiveness and advantages of the established game model were verified by the computational results.
Role Workflow Model Applied to Multi Node Fog Computing System

Abstract A role task scheduling method for fog computing system based on behavior flow is proposed. As traditional computing has shift from face data to the edge of the distributed computing, task scheduling method by establishing the role behavior flow system, the role of the resource node cooperation behaviors definition and role of organization behavior, and set up from center to edge the role behavior of flow pattern, enables the compute nodes according to their respective role behavior line classification processing. Therefore, it can effectively solve the problems of slow service response, high power consumption and frequent task interruption caused by large data volume and redundant operation in the traditional cloud computing system.
A multi-function app for supporting reading and writing ability – teachers’ perspective

This study investigates if a mobile text-to- speech accommodation device can compensate for reading difficulties in an applied school set- ting. Twenty-six special education teachers and their stud ...
Research on Construct Scenario-Response Emergency Model of Dam Break

Abstract Scientific prediction and prevention of unexpected events are the key issues in emergency treatment. To tackle this problem, according to the common characteristics of dams collapsing, environmental pollution and other unexpected events with energy release, based on the idea of “Scenario-Response” and by constructing the physical model of the dam-break event. To analyzing the various reasons and the principle of disaster of the emergencies, and constructing mathematical intelligent prediction model based on the analysis of the factors in physical model, then analyzing the degree of control caused by the disaster factors through the mathematical intelligent prediction model, at last, proposed a prediction calculation formula for the emergency. Extensive simulation on the Jingjiang section of the Yangtze River realized the scientifically prediction method of dam-break event, and well solved the problem of the prediction and prevention scientifically for the emergency with energy release.
Inter Cloud Interoperability Use Cases and Gaps in Corresponding Standards

Growing use of cloud computing is leading to Anything-as-a-Service (XaaS). The economics of cloud computing, supported by advancements in associated technologies have made it a more viable IT solution for various businesses. To meet this increasing demand, Cloud Service Providers (CSPs) now need to build elasticity; to meet customer requirements which are beyond their own resources and may have to be met by securely collaborating with other CSPs. Leveraging cloud concepts and technologies to establish a cloud of clouds, can take us to the next level of cloud computing, with enormous potential. However, due to vast range of cloud services, use cases for inter cloud interoperability can also be substantial. This paper ascertains all possible use cases of inter cloud collaboration, and their corresponding interoperability standards and protocols. Some research has already been done to achieve interoperability between clouds by setting architectural principles and standards based on use cases. However different authors have formulated inter cloud use cases in different ways. This paper highlights use cases for which appropriate inter cloud interoperability standards or protocols are yet to be established. Large number of proprietary standards has made development of common standards a challenge. Measures for security and privacy of user data need to be designed to give users control over their data. This includes trust mechanism as user data or services shift from one cloud to another. Other areas identified as lacking to have common inter cloud standards/ protocols include exchange of pay load, VM mobility, IP mobility and resource management. Challenges to achieve inter cloud interoperability have been identified and gaps in existing standards have been underlined to suggest future research areas. The concept in nutshell is to accrue cloud benefits between various heterogeneous clouds to achieve a possible cloud of clouds.
Modern virtual laboratory for aggregate and assembly works using VR technologies

Abstract Today in the production sector all industries are trying to reduce costs while improving product quality. This is a quite ambitious purpose. However, current advances in technology provide such a necessary step towards making significant improvements in the standardization and general practice of complex processes. Making steps simpler, using different visual cues and a quality assurance system, a virtual reality system can provide greater flexibility in production without any additional risk of making mistakes. Its unique feature is the ability to simplify the diverse complex tasks associated with modern assembly and production processes, reduce risks and bring to life products that are part of the new 21st century.
Relation Extraction Based on Relation Label Constraints

Knowledge graphs have a significant role in promoting natural language processing tasks, and they have received substantial attention. The relation extractor is a key step in the construction of a knowledge graph, so it is important to improve its performance. However, previous works are mainly based on the pipeline method, which rarely address the problem of overlapping triplets. In addition, the literature does not consider models in which the correlation between relation pairs is addressed, which limits their accuracy. In this paper, we propose a new model called Relation Extraction Based On Relation Label Constraints(RRC) that is based on relation matrix constraints. The subject is extracted in our model in the first step; then, the relation and object are extracted based on the subject information. Each relation is regarded as a vector to assist in the extraction of the relation and object; the vector is used to consider the correlation between the relation vectors. This is used as a constraint to optimize the relation vector. Experiments on two public datasets, NYT and WebNLG, show that this method can perform well.
Ant Colony Algorithm

Ant colony optimization (ACO) algorithm, models the real-life behavior of ants to solve optimization problems. Information provided in this chapter include: path navigation of ants, foraging pattern of ants by depositing pheromones during exploration and ACO model development. ACO algorithm has been applied to routing in telecommunication networks using TSP. The best route was [1 2 5 3 4 1], corresponding to a minimum cost of 52.
Sampling-Based Path Planning in Heterogeneous Dimensionality-Reduced Spaces

Many sampling strategies often consider the goal and obstacle population to bias/restrict the search area, and they however become less effective when the robot has many degrees of freedom. This paper explores the nonhomogeneous restriction imposed by the obstacles and presents an improved SBP approach enhanced by heterogeneous dimensionality reduction of the full configuration space. Based on the projection residual, a new Dirichlet process (DP) mixture model is proposed to capture a number of Dimensionality-Reduced Spaces (DRSs), which offer the planning spaces with fewer dimensions than its single-DRS counterpart. Then, the sampling and planning procedures are unified with a proposed transversality condition, connecting sampled nodes across DRSs. At last, a quadratic programming is formulated and quickly solved to map the found path in DRSs to an output path in the full configuration space. Numerical simulations on path planning problems of a high-dimensional Intervention Autonomous Underwater Vehicle (I-AUV) have been conducted, showing the feasibility and efficiency of the proposed method.
Analysis of Time Series of Statistical Air Traffic Data

Commercial ADS-B data providers deliver massive amounts of continuous data for aircraft traffic worldwide. They are therefore an ideal source for statistical analysis of traffic flows and anomaly detection. By clearly identifying the aircraft using the ICAO code, unique trajectories are obtained worldwide that allow the pattern-of-life of each aircraft to be understood. Furthermore, with modern machine learning technologies and parallel processing frameworks such as Spark, heat maps can be generated and areas of interest, e.g. airports, can be found, which in turn are a starting point for further analysis. The investigation of the change over time of these statistics is part of this paper, as well as the detection of anomalies in the statistics with the usage of an autoencoder deep learning network.
Bucketing and information flow analysis for provable timing attack mitigation

This paper investigates the effect of bucketing in security against timing-channel attacks. Bucketing is a technique proposed to mitigate timing-channel attacks by restricting a system’s outputs to only occur at designated time intervals, and has the effect of reducing the possible timing-channel observations to a small number of possibilities. However, there is little formal analysis on when and to what degree bucketing is effective against timing-channel attacks. In this paper, we show that bucketing is in general insufficient to ensure security. Then, we present two conditions that can be used to ensure security of systems against adaptive timing-channel attacks. The first is a general condition that ensures that the security of a system decreases only by a limited degree by allowing timing-channel observations, whereas the second condition ensures that the system would satisfy the first condition when bucketing is applied and hence becomes secure against timing-channel attacks. A main benefit of the conditions is that they allow separation of concerns whereby the security of the regular channel can be proven independently of concerns of side-channel information leakage, and certain conditions are placed on the side channel to guarantee the security of the whole system. Further, we show that the bucketing technique can be applied compositionally in conjunction with the constant-time-implementation technique to increase their applicability. While we instantiate our contributions to timing channel and bucketing, many of the results are actually quite general and are applicable to any side channels and techniques that reduce the number of possible observations on the channel. It is interesting to note that our results make non-trivial (and somewhat unconventional) uses of ideas from information flow research such as channel capacity and refinement order relation.
AdversarialQR Revisited: Improving the Adversarial Efficacy

At present, deep learning and convolutional neural networks are currently two of the fastest rising trends as the tool to perform a multitude of tasks such as image classification and computer vision. However, vulnerabilities in such networks can be exploited through input modification, leading to negative consequences to its users. This research aims to demonstrate an adversarial attack method that can hide its attack from human intuition in the form of a QR code, an entity that is most likely to conceal the attack from human acknowledgment due to its widespread use at the current time. A methodology was developed to demonstrate the QR-embedded adversarial patch creation process and attack existing CNN image classification models. Experiments were also performed to investigate trade-offs in different patch shapes and find the patch’s optimal color adjustment to improve scannability while retaining acceptable adversarial efficacy.
Squeeze Criterion GANs: Double Adversarial Learning Method

Generative adversarial networks (GANs) have attracted much attention since it is able to effective learn from an unknown real distribution. However, the instability of the training process greatly affects the quality of the generated images. To address this problem, the network structure-based, loss-based variant model and some training techniques are proposed. Unfortunately, there are some problems with the above methods, such as the limited effect of stabilizing the training process, the complex mathematical derivation, and the lack of universality of training techniques for different tasks. To this end, we propose a novel squeeze criterion GANs. In this method, we design a pseudo real module to synthesize adversarial sample and the double identity discriminator is designed. Then, the generated image and adversarial sample, as well as the generated image and real image form double adversarial learning. Through double adversarial learning, it forms a squeeze criterion to stabilize the training process of generator and discriminator. Finally, experimental results show that the proposed method has well portability and stabilizes the training process of existing GANs, and improves the quality of generated images.
Web Browser Extension User-Script XSS Vulnerabilities

Browser extensions have by and large become a normal and accepted omnipresent feature within modern browsers. However, since their inception, browser extensions have remained under scrutiny for opening vulnerabilities for users. While a large amount of effort has been dedicated to patching such issues as they arise, including the implementation of extension sandboxes and explicit permissions, issues remain within the browser extension ecosystem through user-scripts. User-scripts, or micro-script extensions hosted by a top-level extension, are largely unregulated but inherit the permissions of the top-level application manager, which popularly includes extensions such as Greasemonkey, Tampermonkey, or xStyle. While most user-scripts are docile and serve a specific beneficial functionality, due to their inherently open nature and the unregulated ecosystem, they are easy for malicious parties to exploit. Common attacks through this method involve hijacking of DOM elements to execute malicious javascript and/or XSS attacks, although other more advanced attacks can be deployed as well. User-scripts have not received much attention, and this vulnerability has persisted despite attempts to make browser extensions more secure. This ongoing vulnerability remains an unknown threat to many users who employ user-scripts, and circumvents security mechanisms otherwise put in place by browsers. This paper discusses this extension derivative vulnerability as it pertains to current browser security paradigms.
Improved convex set projection algorithm for Image sequence reconstruction

The convex set projection (POCS) algorithm based on set theory has the advantages of flexible observation model, simple implementation principle and easy introduction of prior information of images. It has become an important method to solve the problem of super-resolution reconstruction of sequence images. In this paper, the projection reconstruction algorithm of convex set is studied in detail. In view of the shortcomings of traditional POCS algorithm, a series of improvements are made: the weight function is constructed at the edge pixels of the image to eliminate the edge ringing effect in POCS reconstruction image; the iterative step size of relaxation projection operator is adaptively selected to improve the image restoration quality and improve the computational efficiency. The experimental results show that the improved POCS algorithm can obtain better reconstruction effect.
Comparison between the morphological analyzers based on graph and the one based on surface patterns

In this article, we will present the different approaches that we have developed in recent years for the morphological analysis of Arabic words: the surface pattern-based approach and the graph-based approach. To make these analyzers, we have developed several databases either to learn these approaches or test them.
Algorithm for Automatic Description of Historical Series of Forecast Error in Electrical Power Grid

The EU-FP7 iTesla project developed a toolbox that assesses dynamic security of large electric power systems in the presence of forecast uncertainties. In particular, one module extracts plausible realizations of the stochastic variables (power injections of RES Renewable Energy Sources, load power absorptions). It is built upon historical data series of hourly forecasts and realizations of the stochastic variables at HV (High-Voltage) nodes in the French transmission grid. Data reveal a large diversity of forecast error distributions: characterizing them allows to adapt the module to the data, improving the results. The algorithm here presented is aimed to automatically classify all the forecast error variables and to cluster them into smoother variables. The main steps of the algorithm are filtering of the variables with too many missing data or too low variance, outliers detection by two methods (Chebyshev inequality, quantile method), separation of unimodal variables from multimodal ones by exploiting a peak counting algorithm, Gaussian mixtures, comparison with asymmetrical distributions, multimodality index, clustering of the multimodal variables whose sum is unimodal, comparing two alternative algorithms (the former based on hierarchical clusterization, accounting for correlation and geographical closeness, and the latter on the identification of the same initial characters in the identification codes).
Heatmap-Aware Low-Cost Design to Resist Adversarial Attacks: Work-in-Progress

It is a challenging task to resist adversarial attacks due to the imperceptibility of adversarial examples. The passive defense method is developed based on a series of input transformations and has achieved a promising result, which however suffers from a high computation cost. In this paper, we design a new heatmap-aware method to defend adversarial attacks, leading to a significant decrease in the time cost. To be specific, we compute the classification importance from each part of the input to obtain the heatmap of the data, and the key areas of classification are extracted according to the heatmap. A series of transformations are applied to the key areas of the classification, which reduces the amount of data to be processed and thus reduces the time cost. A set of preliminary experiments are conducted to testify the effectiveness of the proposed approach.
Image Inpainting as a Quadratic Programming Task

Digital image inpainting is an image restoration technique used to replace missing, damaged, or deteriorated parts of images. Classical textual and structural, as well as deep neural network learning-based, methods have been proposed for inpainting digital images originating from various sources. In this paper we propose a method that falls within the realm of classical methods. The inpainting task is formulated as a quadratic programming problem, and two solution methods, unsupervised and supervised, are described. The proposed methods are experimentally evaluated on the different classes of images. We analyze and discuss the results and identify possible future research directions.
Semantic Classification of Scientific Sentence Pair Using Recurrent Neural Network

One development of Natural Language Processing is the semantic classification of sentences and documents. The challenge is finding relationships between words and between documents through a computational model. The development of machine learning makes it possible to try out various possibilities that provide classification capabilities. This paper proposes the semantic classification of sentence pairs using Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM). Each couple of sentences is turned into vectors using Word2Vec. Experiments carried out using CBOW and Skip-Gram to get the best combination. The results are obtained that word embedding using CBOW produces better than Skip-Gram, although it is still around 5%. However, CBOW slows slightly at the beginning of iteration but is stable towards convergence. Classification of all six classes, namely Equivalent, Similar, Specific, No Alignment, Related, and Opposite. As a result of the unbalanced data set, the retraining was conducted by eliminating a few classes member from the data set, thus providing an accuracy of 73 % for non-training data. The results showed that the Adam model gave a faster convergence at the start of training compared to the SGD model, and AdaDelta, which was built, gave 75% better accuracy with an F1-Score of 67%.