Reviewerly/exHarmony huggingface dataset:
XSS Detection Technology Based on LSTM-Attention

Cross-site scripting (XSS) is one of the main threats of Web applications, which has great harm. How to effectively detect and defend against XSS attacks has become more and more important. Due to the malicious obfuscation of attack codes and the gradual increase in number, the traditional XSS detection methods have some defects such as poor recognition of malicious attack codes, inadequate feature extraction and low efficiency. Therefore, we present a novel approach to detect XSS attacks based on the attention mechanism of Long Short-Term Memory (LSTM) recurrent neural network. First of all, the data need to be preprocessed, we used decoding technology to restore the XSS codes to the unencoded state for improving the readability of the code, then we used word2vec to extract XSS payload features and map them to feature vectors. And then, we improved the LSTM model by adding attention mechanism, the LSTM-Attention detection model was designed to train and test the data. We used the ability of LSTM model to extract context-related features for deep learning, the added attention mechanism made the model extract more effective features. Finally, we used the classifier to classify the abstract features. Experimental results show that the proposed XSS detection model based on LSTM-Attention achieves a precision rate of 99.3% and a recall rate of 98.2% in the actually collected dataset. Compared with traditional machine learning methods and other deep learning methods, this method can more effectively identify XSS attacks.
Configuration smells in continuous delivery pipelines: a linter and a six-month study on GitLab

An effective and efficient application of Continuous Integration (CI) and Delivery (CD) requires software projects to follow certain principles and good practices. Configuring such a CI/CD pipeline is challenging and error-prone. Therefore, automated linters have been proposed to detect errors in the pipeline. While existing linters identify syntactic errors, detect security vulnerabilities or misuse of the features provided by build servers, they do not support developers that want to prevent common misconfigurations of a CD pipeline that potentially violate CD principles (“CD smells”). To this end, we propose CD-Linter, a semantic linter that can automatically identify four different smells in pipeline configuration files. We have evaluated our approach through a large-scale and long-term study that consists of (i) monitoring 145 issues (opened in as many open-source projects) over a period of 6 months, (ii) manually validating the detection precision and recall on a representative sample of issues, and (iii) assessing the magnitude of the observed smells on 5,312 open-source projects on GitLab. Our results show that CD smells are accepted and fixed by most of the developers and our linter achieves a precision of 87% and a recall of 94%. Those smells can be frequently observed in the wild, as 31% of projects with long configurations are affected by at least one smell.
Deep Learning Versus Traditional Solutions for Group Trajectory Outliers

This article introduces a new model to identify a group of trajectory outliers from a large trajectory database and proposes several algorithms. These can be split into three categories: 1) algorithms based on data mining and knowledge discovery, which study the different correlations among the trajectory data and identify the group of abnormal trajectories from the knowledge extracted; 2) algorithms based on machine learning and computational intelligence methods, which use the ensemble learning and metaheuristics to find the group of trajectory outliers; and 3) an algorithm exploring the convolution deep neural network that learns the different features of historical data to determine the group of trajectory outliers. Experiments on different trajectory databases have been carried out to investigate the proposed algorithms. The results show that the deep learning solution outperforms data mining, machine learning, and computational intelligence solutions, as well as state-of-the-art solutions in terms of runtime and accuracy performance.
Optical Character Recognition for English Handwritten Text Using Recurrent Neural Network

Manually written Text Recognition is an innovation that is genuinely necessary right now of today.Before appropriate execution of this innovation we have depended on composing writings with own may leads to some mistakes. It is hard to maintain safely and gathering that information with effectiveness.Difficult work is required so as to keep up appropriate association of the data.Recurrent neural networkis utilized to discover the arrangement of character.Today we have OCRs effectively accessible for the English language.We can discover OCRs for formal texted English also yet OCRs for written by hand content are uncommon.Furthermore,those which are accessible don't have a goodaccuracy.We expect to make such an OCR which gives us an impressiverecognition exactness for manually written Text using recurrent neural network.The proposed model is implemented using Conda, used with TensorflowFramework. The purpose of Recurrent neural network is to improve accuracy.
EMOGI

Modern analytics and recommendation systems are increasingly based on graph data that capture the relations between entities being analyzed. Practical graphs come in huge sizes, offer massive parallelism, and are stored in sparse-matrix formats such as compressed sparse row (CSR). To exploit the massive parallelism, developers are increasingly interested in using GPUs for graph traversal. However, due to their sizes, graphs often do not fit into the GPU memory. Prior works have either used input data pre-processing/partitioning or unified virtual memory (UVM) to migrate chunks of data from the host memory to the GPU memory. However, the large, multi-dimensional, and sparse nature of graph data presents a major challenge to these schemes and results in significant amplification of data movement and reduced effective data throughput. In this work, we propose EMOGI, an alternative approach to traverse graphs that do not fit in GPU memory using direct cache-line-sized access to data stored in host memory. This paper addresses the open question of whether a sufficiently large number of overlapping cache-line-sized accesses can be sustained to 1) tolerate the long latency to host memory, 2) fully utilize the available bandwidth, and 3) achieve favorable execution performance. We analyze the data access patterns of several graph traversal applications in GPU over PCIe using an FPGA to understand the cause of poor external bandwidth utilization. By carefully coalescing and aligning external memory requests, we show that we can minimize the number of PCIe transactions and nearly fully utilize the PCIe bandwidth with direct cache-line accesses to the host memory. EMOGI achieves 2.60X speedup on average compared to the optimized UVM implementations in various graph traversal applications. We also show that EMOGI scales better than a UVM-based solution when the system uses higher bandwidth interconnects such as PCIe 4.0.
An efficient identity-based proxy signcryption using lattice

Signcryption combines the functionalities of digital signature and encryption in a single step, which promises to increase the efficiency and the confidentiality of on-line transactions. Identity-based proxy signcryption integrates the functions of identity-based cryptography, proxy signature and signcryption. However, identity-based proxy signcryption constructed on the discrete logarithm and the integer factoring problems cannot defeat quantum attack. Therefore, the paper proposes a lattice-based identity-based proxy signcryption since there are none efficient quantum algorithms to attack lattice-based cryptography. The constructed scheme has the properties of both signcryption and proxy signature, i.e., the constructed scheme has ind-cca2 security, suf-cma security, strong identifiability, strong undeniability and key dependence properties. Comparing with the known identity-based proxy signcryption, the proposed scheme needs smaller computation complexity, smaller secret key size and smaller signature tuple size. Besides, the proposed scheme does not depend on the public key infrastructure since it is an identity-based cryptography.
Procedure Planning in Instructional Videos

In this paper, we study the problem of procedure planning in instructional videos, which can be seen as a step towards enabling autonomous agents to plan for complex tasks in everyday settings such as cooking. Given the current visual observation of the world and a visual goal, we ask the question “What actions need to be taken in order to achieve the goal?”. The key technical challenge is to learn structured and plannable state and action spaces directly from unstructured videos. We address this challenge by proposing Dual Dynamics Networks (DDN), a framework that explicitly leverages the structured priors imposed by the conjugate relationships between states and actions in a learned plannable latent space. We evaluate our method on real-world instructional videos. Our experiments show that DDN learns plannable representations that lead to better planning performance compared to existing planning approaches and neural network policies.
Segmentation of Nuclei in Histopathology images using Fully Convolutional Deep Neural Architecture

Nuclei segmentation is an initial step in the automated analysis of digitized microscopic images. This paper focuses on utilizing the LinkNET-34 architecture for semantic segmentation of nuclei from the H&E stained breast cancer histopathology images. The segmentation process is implemented in two stages where in the first stage the H&E stained images are pre-processed to reduce the variance caused because of staining the microscopic images and scanning the slides. During the second stage the preprocessed images are given as input to the LinkNET network which consists of both down-sampling and up-sampling layers. The network is trained using a set of WSI patches released during the Data Science bowl 2018 competition. The performance of the deep learning model is evaluated based on the segmentation accuracy measured using the Dice Coefficient.
Double Targeted Universal Adversarial Perturbations

Despite their impressive performance, deep neural networks (DNNs) are widely known to be vulnerable to adversarial attacks, which makes it challenging for them to be deployed in security-sensitive applications, such as autonomous driving. Image-dependent perturbations can fool a network for one specific image, while universal adversarial perturbations are capable of fooling a network for samples from all classes without selection. We introduce a double targeted universal adversarial perturbations (DT-UAPs) to bridge the gap between the instance-discriminative image-dependent perturbations and the generic universal perturbations. This universal perturbation attacks one targeted source class to sink class, while having a limited adversarial effect on other non-targeted source classes, for avoiding raising suspicions. Targeting the source and sink class simultaneously, we term it double targeted attack (DTA). This provides an attacker with the freedom to perform precise attacks on a DNN model while raising little suspicion. We show the effectiveness of the proposed DTA algorithm on a wide range of datasets and also demonstrate its potential as a physical attack (Code: https://github.com/phibenz/double-targeted-uap.pytorch).
An advanced interpretable Fuzzy Neural Network model based on uni-nullneuron constructed from n-uninorms

This paper formulates a fuzzy logic neuron that uses n-uninorms to construct uni-nullneurons. A fuzzy neural network (FNN) composed of these neurons is easy to operate with nullnorms and uninorms at different times, which results in high accuracy of the model outputs and increases the flexibility in connecting the rule antecedents (enabling AND and OR connections within one rule). This, in turn, may allow experts/operators to extract more knowledge from data. The FNN uses a one-versus-rest classifier learning scheme for multi-class classification problems, where neuron activation levels construct the (indicator) regression matrix; this results in a non-linear regression by indicator, which can resolve the inherent class masking problem apparent in the linear case. We propose a specific neuron-selection strategy in the learning stage that applies Lasso to bootstrap replications in order to ensure that the rule base is as compact as possible and induced by a low number of neurons. To evaluate the new neuron acting in FNNs, we performed pattern classification, and regression tests. Compared with traditional FNN in the literature, our variant showed improved model accuracies for several high-dimensional real-world datasets in binary and multi-class classification and regression problems. Combined with the ability to generate human-readable rules, this offers the ability to generate parsimonious responses with a high degree of confidence.
MW-ACGAN: Generating Multiscale High-Resolution SAR Images for Ship Detection

In high-resolution Synthetic Aperture Radar (SAR) ship detection, the number of SAR samples seriously affects the performance of the algorithms based on deep learning. In this paper, aiming at the application requirements of high-resolution ship detection in small samples, a high-resolution SAR ship detection method combining an improved sample generation network, Multiscale Wasserstein Auxiliary Classifier Generative Adversarial Networks (MW-ACGAN) and the Yolo v3 network is proposed. Firstly, the multi-scale Wasserstein distance and gradient penalty loss are used to improve the original Auxiliary Classifier Generative Adversarial Networks (ACGAN), so that the improved network can stably generate high-resolution SAR ship images. Secondly, the multi-scale loss term is added to the network, so the multi-scale image output layers are added, and multi-scale SAR ship images can be generated. Then, the original ship data set and the generated data are combined into a composite data set to train the Yolo v3 target detection network, so as to solve the problem of low detection accuracy under small sample data set. The experimental results of Gaofen-3 (GF-3) 3 m SAR data show that the MW-ACGAN network can generate multi-scale and multi-class ship slices, and the confidence level of ResNet18 is higher than that of ACGAN network, with an average score of 0.91. The detection results of Yolo v3 network model show that the detection accuracy trained by the composite data set is as high as 94%, which is far better than that trained only by the original SAR data set. These results show that our method can make the best use of the original data set, improve the accuracy of ship detection.
Learning to Distort Images Using Generative Adversarial Networks

Modeling image and video distortions is an important, but difficult problem of great consequence to numerous and diverse image processing and computer vision applications. While many statistical models have been proposed to synthesize different types of image noise, real-world distortions are far more difficult to emulate. Toward advancing progress on this interesting problem, we consider distortion generation as an image-to-image transformation problem, and solve it via a data-driven approach. Specifically, we use a conditional generative adversarial network (cGAN) which we train to learn four kinds of realistic distortions. We experimentally demonstrate that the learned model can produce the perceptual characteristics of several types of distortion.
A Research Agenda for Mixed Reality in Automated Vehicles

With the increasing knowledge advancements and availability of mixed reality (MR), the number of its purposes and applications in vehicles rises. Mixed reality may help to increase road safety, support more immersive (non-) driving related activities, and finally enhance driving and passenger experience. MR may also be the enabling technology to increase trust and acceptance in automated vehicles and therefore help on the transition towards automated driving. Further, automated driving extends use cases of virtual reality and other immersive technologies. However, there are still a number of challenges with the use of mixed reality when applied in vehicles, and also several human factors issues need to be solved. This paper aims at presenting a research agenda for using mixed reality technology for automotive user interfaces (UIs) by identifying opportunities and challenges.
Multimodal deep fusion for image question answering

Multimodal fusion plays a key role in Image Question Answering (IQA). However, most of the current algorithms are insufficient to fuse multiple relations implied in multimodalities which are vital for predicting correct answers. In this paper, we design an effective Multimodal Deep Fusion Network (MDFNet) to achieve fine-grained multimodal fusion. Specifically, we propose Graph Reasoning and Fusion Layer (GRFL) to reason complex spatial and semantic relations between visual objects and fuse these two kinds of relations adaptively. This fusion strategy allows different relations make different contribution guided by the reasoning step. Then a Multimodal Deep Fusion Network is built based on stacking several GRFLs, to achieve sufficient multimodal fusion. Quantitative and qualitative experiments conducted on popular benchmarks including VQA v2 and GQA reveal the effectiveness of DMFNet. Our best single model achieves 71.19% overall accuracy on VQA v2 dataset, and 57.05% accuracy on GQA dataset.
Method towards reconstructing collaborative business processes with cloud services using evolutionary deep Q-learning

Service-oriented architecture (SOA) is a significant framework that enables intelligent information systems to offer business process-based services, namely Business Process as a Service (BPaaS). Cloud service-based business model reconstruction within and across enterprises has become an important issue to obtain competitive advantages. Finding appropriate service component is a critical phase in enterprise collaboration, to improve the quality and correlations among collaborative service-providers. Existed methods for business processes reconstruction have not systematically and fully considered the quality correlations in finer-grain, i.e. task-level or activity-level, and temporal performance in a process model optimization. Moreover, the existed approaches might fail to work in an uncertain cloud environment where the quality parameters are unknown in advance. Q-learning has proven its worth in an uncertain cloud environment. However, advances in Q-learning are challenges to leverage in collaborative business process reconstruction. The reconstruction algorithms based on Q-learning suffered from the two core drawbacks: lack of effective exploration and extremely slow convergence property. A hybrid Evolutionary Deep Q-Learning-based BPaaS reconstruction algorithm, named as EDQL-BPR, is proposed by leveraging Particle Swarm Optimization to improve Deep Q-Learning algorithm for systematically optimizing collaborative business processes reconstruction. In order to verify the effectiveness of the proposed algorithm, an annotated transition system has been developed supporting for all possible behaviors state convention from heterogeneous initial states to the target with several collaborative information at activity level, and encoding annotated BP representation matrix automatically. Then, an autonomous three-layer framework has been built to facilitate business process discovery and service reconstruction. In this framework, our optimal collaborative services composition is treated as a multi-objective constraint optimization problem via Evolutionary Deep Q-learning with updated strategy of Q-value, and each annotated component launch a service discovery by service agent. Extensive evaluation results show that EDQL-BPR can outperform several representative business process reconstructions in terms of model optimal convergence, QoS optimality, effectiveness and efficiency under heterogeneous service selection workloads in an uncertain Cloud environment.
Development and validation of the teachers’ digital learning identity survey

Research emphasizes teachers’ attitudes and methods towards classroom digital literacy (DL) integration with minimal studies accenting teachers’ attitudes towards personal DL use. Specifically, recognizing how teachers use DL to learn (i.e., their digital learning identity (DLI) — the identity developed from perceived DL competence). The Digital Learning Identity Survey (DLIS) was created to assist teachers in self-identification and recognition of their learning identity related to DL. This study investigates the reliability and validity of the DLIS with pre-service teachers using exploratory and confirmatory factor analyses. A correlation analysis was conducted to determine if survey items correlated logically and aligned with existing theory, including elements needed for digital identity development – DL, self-regulated learning, and motivation. Results found aspects of the DLIS validly measure DLI.
Is using deep learning frameworks free?

Developers of deep learning applications (shortened as application developers) commonly use deep learning frameworks in their projects. However, due to time pressure, market competition, and cost reduction, developers of deep learning frameworks (shortened as framework developers) often have to sacrifice software quality to satisfy a shorter completion time. This practice leads to technical debt in deep learning frameworks, which results in the increasing burden to both the application developers and the framework developers in future development.
Optical and digital double color-image encryption algorithm using 3D chaotic map and 2D-multiple parameter fractional discrete cosine transform

This paper proposes a new encryption and decryption method for optical and digital double color-images using 2D-multiple parameter fractional discrete cosine transform (MPFrDCT) and 3D-logistic chaotic map (3D-LCM). The double color-images to be encrypted are converted to their indexed formats by extracting their color maps. A 3D-LCM is used for generate a keystream and this keystream is used in scrambling the indexed images. The second scrambled image is normalized and then multiplied by a random phase mask (RPM) generated by 3D-LCM. The first scrambled image and the second modified image are treated as amplitude and phase of a complex signal and this complex signal is encrypted using 2D-MPFrDCT. The Encrypted image is obtained after separating the amplitude and phase parts of this signal. To enhance security, a substitution method is applied in the intermediate image using another keystream generated by 3D-LCM. The decryption method is the same but in reverse order. We have also given a setup for the encryption and decryption of optical double images using this method. The keyspace for the proposed method is also discussed in this paper. The experimental results and the security analysis of the proposed method are given to validate the feasibility and robustness of the proposed method. The statistical analysis like histogram, correlation, entropy analysis, and randomness test confirm the robustness of the proposed method against statistical attacks. The experimental results also show that the proposed method is resistant to a brute-force attack. The proposed method has the ability to resist the occlusion attack, differential attack, and Gaussian random noise attack.