Reviewerly/exHarmony huggingface dataset:
English noun+noun syntactic structures in English for specific purposes and their Serbian equivalents

The purpose of this study is to investigate if there is a structure agreement between syntactic structures consisting of two nouns [N + N] in English for Occupational Health and Safety and their Serbian equivalents. After narrowing down the results of the research to approximately five thousand examples and ordering them by frequency of appearance in the corpus, a sample of about one hundred and seventy items was extracted and translated into Serbian. The analysis shows that there are three main types of Serbian translation equivalents but none of them are structurally identical to English noun + noun structures.
Methods for Identifying Architectural Debt: A Systematic Mapping Study

Technical debt in general refers to suboptimal decisions the practitioners make during software development that achieve short-term goals at the expense of long-term quality concerns. Architecture technical debt is a subset of technical debt, when software practitioners make wrong or sub-optimal decisions related to the architecture of the software. Identifying such architecture technical debt plays a crucial role in software quality. In the last decade, there were several methods proposed to identify architecture debts in the literature.In this study, we conduct a systematic literature review of methods that identify architecture technical debt by inspecting 28 primary studies published from 2011 to 2020. Based on the outcomes of our review: (1) design rule space and traceability graphs are the dominant techniques; (2) despite the increase of automated techniques in identifying architecture debt, pure manual methods using expert opinion is still popular; (3) majority of the approaches use code/version history to mine archictural technical debt; (4) the field is getting increasingly more attraction in the last five years.
Control of Spin-1/2 Quantum System Using Lyapunov Methods to Achieve Quantum NOT Gate Operation

Theoretical study on the control of spin-1/2 quantum system is an important matter for the development of quantum control theory. Lyapunov methods play vital role in quantum control because of their concise mathematical rigor. Previously, using the Lyapunov methods, it has been shown that signum function control law can be employed to control spin-1/2 quantum system to achieve NOT quantum gate operation. This paper demonstrates that other forms of control law can be used to control spin-1/2 quantum system to achieve the same NOT quantum gate operation. Two examples of those other forms are provided, namely hyperbolic tangent (tanh) function and proportional multiplier. It is also explained why signum function is not the only function that can be used to control spin-1/2 quantum system. This paper also shows the advantage of using fully-differentiable control law instead of signum function, which is not fully-differentiable. Control law design and stability analysis are conducted using Lyapunov methods. The numerical simulation results show that tanh and proportional multiplier function can also control spin-1/2 quantum system and achieve the NOT quantum gate operation.
Secret Image Sharing Based on Chinese Remainder Theorem over a Polynomial Ring

Secret image sharing denotes a technique for apportioning secret images into $$ n $$ shadow images and preserving in $$ n $$ participants as the extension of secret sharing on images. Compared to Shamir’s scheme-based secret image sharing, Chinese remainder theorem-based secret imaging has the advantages as lossless recovery, no further encryption, and low recovery computation complexity. However, traditional secret sharing schemes based on the Chinese remainder theorem have shortcomings including no $$ \left( {t, n} \right) $$ threshold, lossy recovery, ignoring image features or further encryption. In this paper, a $$ \left( {t, n} \right) $$ secret image sharing scheme is suggested based on Chinese remainder theorem over a polynomial ring realizing no further encryption and lossless recovery and making the pixel values of shadow images randomly distribute $$ \left[ {0, 255} \right] $$ . The effectiveness of the suggested scheme will be demonstrated by experiments and analyses later.
Digital Image Forgery Detection using Graph Fourier Transform

This paper proposes a novel method using graph based features for detecting copy move forgery done in digital images. For extracting the features of the image patches the graph Fourier transform are used. A graph can represent data in different applications with a generic data structure. A graph different from classical transforms like DCT and DWT in the sense that it can represent signals on irregular and high dimensional domain. A graph can fit to local characteristics through edges for regular signals like images. Graph Fourier transform extracts the frequency interpretation for signals on graph. To reduce the processing time of feature matching patch match algorithm is used which is a fast approximate nearest neighbor search algorithm. The experiments conducted on the GRIP database shows that the proposed method is highly accurate and are robust to rotation and scaling.
Improving Particle Swarm Optimization of Its Convergence

There are few controllable parameters in the particle swarm optimization algorithm and its value is fixed, it has some problems such as premature convergence and falling into local optimal solutions. In this paper, an improved particle swarm optimization algorithm is proposed. By dynamically adjusting the inertia weight and introducing a behavior learning factor to update the particle's velocity vector and the particle has more movement diversity in each iteration. Through the demonstration of experimental data, compared with the random weight particle swarm optimization algorithm and the linear decreasing weight particle swarm optimization algorithm, the improved particle swarm optimization algorithm effectively accelerates the convergence speed of the algorithm, improves the optimization performance of the algorithm, and has good convergence and optimization results, the number of convergence iterations is reduced by at least 38.8%, and the accuracy of the convergence accuracy of the optimal solution and the average solution all is improved by at least 28.9%.
Multi-reward Based Reinforcement Learning for Neural Machine Translation

Reinforcement learning (RL) has made remarkable progress in neural machine translation (NMT). However, it exists the problems with uneven sampling distribution, sparse rewards and high variance in training phase. Therefore, we propose a multi-reward reinforcement learning training strategy to decouple action selection and value estimation. Meanwhile, our method combines with language model rewards to jointly optimize model parameters. In addition, we add Gumbel noise in sampling to obtain more effective semantic information. To verify the robustness of our method, we not only conducted experiments on large corpora, but also performed on low-resource languages. Experimental results show that our work is superior to the baselines in WMT14 English-German, LDC2014 Chinese-English and CWMT2018 Mongolian-Chinese tasks, which fully certificates the effectiveness of our method.
Accurate Classification for HPC Applications Concerning Traffic Matrix Visual Patterns

The following topics are dealt with: telecommunication traffic; quality of service; cloud computing; Internet of Things; telecommunication network topology; software defined networking; optimisation; 5G mobile communication; and virtualisation.
Blockchain Based End-to-End Tracking System for Distributed IoT Intelligence Application Security Enhancement

IoT devices provide a rich data source that is not available in the past, which is valuable for a wide range of intelligence applications, especially deep neural network (DNN) applications that are data-thirsty. An established DNN model provides useful analysis results that can improve the operation of IoT systems in turn. The progress in distributed/federated DNN training further unleashes the potential of integration of IoT and intelligence applications. When a large number of IoT devices are deployed in different physical locations, distributed training allows training modules to be deployed to multiple edge data centers that are close to the IoT devices to reduce the latency and movement of large amounts of data. In practice, these IoT devices and edge data centers are usually owned and managed by different parties, who do not fully trust each other or have conflicting interests. It is hard to coordinate them to provide end-to-end integrity protection of the DNN construction and application with classical security enhancement tools. For example, one party may share an incomplete data set with others, or contribute a modified sub DNN model to manipulate the aggregated model and affect the decision-making process. To mitigate this risk, we propose a novel blockchain based end-to-end integrity protection scheme for DNN applications integrated with an IoT system in the edge computing environment. The protection system leverages a set of cryptography primitives to build a blockchain adapted for edge computing that is scalable to handle a large number of IoT devices. The customized blockchain is integrated with a distributed/federated DNN to offer integrity and authenticity protection services.
Forecasting chaotic events and the prediction of a rare cognitive ability

People often live and work in chaotic environments, and thus need to forecast and control what will happen next. The management of chaos is an apparently rare skill, and it would be valuable to identify and develop this skill in the workforce. Untrained undergraduates (N = 147) forecasted number series from four chaotic attractors of varying levels of complexity. They contributed measurements of 16PF personality traits, general intelligence, field independence, and divergent thinking. The results indicated that field independence and personality traits associated with the creative personality profile were the most frequent correlates of performance on forecasting one to four steps into the future. It should be possible to adapt the experimental results to personnel selection and placement decisions that require the search for talent for forecasting.
Translation lookaside buffer management

This paper focuses on the Translation Lookaside Buffer (TLB) management as part of memory management. TLB is an associative cache of the advanced processors, which reduces the overhead of the virtual to physical address translations. We consider challenges related to the design of the TLB management subsystem of the OS kernel on the example of the IA-32 platform and propose a simple model of complete and consistent policy of TLB management. This model can be used as a foundation for memory management subsystems design and verification.
Improving Sentence Classification by Multilingual Data Augmentation and Consensus Learning

Neural network based models have achieved impressive results on the sentence classification task. However, most of previous work focuses on designing more sophisticated network or effective learning paradigms on monolingual data, which often suffers from insufficient discriminative knowledge for classification. In this paper, we investigate to improve sentence classification by multilingual data augmentation and consensus learning. Comparing to previous methods, our model can make use of multilingual data generated by machine translation and mine their language-share and language-specific knowledge for better representation and classification. We evaluate our model using English (i.e., source language) and Chinese (i.e., target language) data on several sentence classification tasks. Very positive classification performance can be achieved by our proposed model.
Detecting GAN-synthesized faces based on deep alignment network

Generative adversary networks (GAN) have recently led to highly realistic synthesized image. For the current GAN-synthesized faces detection methods exist false prediction if the real faces with angles or occlusion. This paper proposes a GAN-synthesized faces detection method based on Deep Alignment Network (DAN), which improve prediction accuracy of real faces by makes the locations of facial landmark points more precise. Our method first uses DAN to obtain the locations of facial landmark points of real and synthesized faces; then the landmark points are converted into feature vectors by principal component analysis (PCA); finally, input feature vectors to the constructed Support Vector Machine (SVM)classifier for training. Experimental results show that our method achieves better performance than other method under face with angles or occlusion.
Interactive Attention Encoder Network with Local Context Features for Aspect-Level Sentiment Analysis

The sentiment analysis of the data collected by Internet of Things devices has been widely concerned by researchers. Aspect sentiment analysis is a fine-grained task of sentiment analysis, which aims to identify the sentiment polarity of specific aspects in a given context. Most of the previous studies have modelled context and aspect terms using Recurrent Neural Network (RNN) and attention mechanisms. However, the RNN model is difficult to process in parallel, taking into account only the global context features, not the correlation between the sentiment polarity and the local context. To address this issue, this paper proposes an Interactive Attention Encoder Network Model with Local Context Features (IAEN-LCF) for identifying aspect-level sentiment polarity. First, the word embedding and aspect term embedding are pre-trained by Bidirectional Encoder Representations from Transformers (BERT). Secondly, the attention-over-attention (AOA) module in the machine reading comprehension task is applied to the attentional encoder network, and a network model named Interactive Attention Encoder (IAEN) is proposed to extract global context features. By setting a fixed text window, the local context features are captured in a dynamic weighted manner. Finally, the performance of the proposed model is verified in three public data sets. Experimental results show that the proposed model can outperform state-of-the-art methods in aspect sentiment analysis tasks.
Crime Detection using Data Mining Techniques

As crime rates keep spiraling each day, new challenges are faced by law enforcement agencies. They have to keep their on the lookout for any signs criminal activity. The law enforcement agencies should therefore be able to predict such increase or decrees or trends in crime. Such as theft, Killing. Crime that may occur in a particular area in a particular month, year, any timespan. Data mining is defined as a process of discovering hidden valuable knowledge by analyzing large amounts of data, which is stored in databases or data warehouse, using various data mining techniques such as machine learning, artificial intelligence, statistical. Many algorithms for data mining approach to help detect the crimes patterns. Data Collection, Data Preprocessing Phase, Data Filtering, Linier Regression. Wekasoft are used for collection of data analyzing. Visualization finally get results. The advantage of using this tool is that clustering will be performed automatically.
Learning Rotation Adaptive Correlation Filters in Robust Visual Object Tracking

Visual object tracking is one of the major challenges in the field of computer vision. Correlation Filter (CF) trackers are one of the most widely used categories in tracking. Though numerous tracking algorithms based on CFs are available today, most of them fail to efficiently detect the object in an unconstrained environment with dynamically changing object appearance. In order to tackle such challenges, the existing strategies often rely on a particular set of algorithms. Here, we propose a robust framework that offers the provision to incorporate illumination and rotation invariance in the standard Discriminative Correlation Filter (DCF) formulation. We also supervise the detection stage of DCF trackers by eliminating false positives in the convolution response map. Further, we demonstrate the impact of displacement consistency on CF trackers. The generality and efficiency of the proposed framework is illustrated by integrating our contributions into two state-of-the-art CF trackers: SRDCF and ECO. As per the comprehensive experiments on the VOT2016 dataset, our top trackers show substantial improvement of 14.7% and 6.41% in robustness, 11.4% and 1.71% in Average Expected Overlap (AEO) over the baseline SRDCF and ECO, respectively.
Document Image Binarization Using U-Net

In this paper, we propose an algorithm to binarize the degraded document images. We incorporate U-Net for the task at hand. We model document image binarization as a classification problem wherein we generate an image which is a result of classification of each pixel as text or background. Optimizing the cross entropy loss function, we translate the input degraded image to the corresponding binarized image. Our approach of using U-Net ensures low level feature transfer from the input degraded image to the output binarized image and thus it is better than using a simple convolution neural network. Our method tries to obtain the desired results with suitable generalization. The results obtained are significantly better than the state-of-the-art techniques and the approach is simpler than other deep learning approaches for document image binarization.
3DWofE: An open-source software package for three-dimensional weights of evidence modeling

The shortage of outcropping ore deposits and the inefficiency of traditional mineral exploration methods for discovering deep-seated deposits have increased the necessity of developing three-dimensional modeling methods for in-depth exploration. The 3DWofE is a Python-based open-source software package that provides the tools required for three-dimensional modeling of concealed ore bodies by developing a data-driven prospectivity modeling method called weights of evidence. This software enables users to create three types of models which include posterior probability, uncertainty, and studentized posterior probability. These models can guide exploration geologists through identifying potential regions of target mineralization in depth.
Emphasizing Similar Feature Representations to Defend Against Adversarial Attacks

Deep Neural Networks (DNNs) are vulnerable to adversarial perturbations of the input data. For DNNs to be deployed in critical applications, they have to be made robust to such perturbations. In this work, we test an existing strategy and propose a new strategy based on autoencoders to defend DNNs against adversarial attacks. The first strategy is based on Contractive AutoEncoder (CAE). The second strategy is a Siamese network based AutoEncoder (SAE). Both CAE and SAE emphasize on similar feature representation for both the original sample and its adversarial counterpart. CAE reconstructs original sample from the adversarial input by imposing that the norm of the Jacobian of latent representation with respect to input should be minimum. SAE works with the pair of original sample and the adversarial input simultaneously, enforcing similarity in their representations at the level of layer granularity. We demonstrate the superlative performance of both the strategies in the presence of three popular attacks - The Fast Gradient Sign Method (FGSM), DeepFool (DF) and Universal Adversarial Perturbations (UAP). In particular, the proposed methods reduce the effectiveness of adversarial attacks by increasing the accuracy from 7% to 97% on the powerful DeepFool attack on MNIST dataset and from 18% to 67% on Universal Adversarial Perturbation on Fashion-MNIST dataset. We also show the superior performance of our methods over a recent method using Denoising AutoEncoder (DAE) (The PyTorch implementation of all the codes used in this work is made available at https://github.com/akhilesh-pandey/SAE ).
Coastal zone image dehazing network based on feature fusion and adversarial training

The quality of the images collected by the coastal zone video surveillance equipment is seriously degraded due to the sea fog, which directly affects the analysis of the image. Therefore, the study of the costal image dehazing method is of great significance to the related research of the coastal zone. Costal image has the characteristics of large sky area and monotonous color. The traditional method based on atmospheric scattering physics model is not suitable for this kind of image for block effect and color distortion. In this paper, we introduce the generative adversarial mechanism into sea fog image defogging, and propose a coastal image dehazing network based on it. The proposed model includes a generative network and a discriminative model, and is trained by adversarial mechanism. The generative model is composed of multi-scale feature extraction module and residual connection module. The discriminative network consists of two subnetworks of receptive field of different sizes.
Localization and Estimation of Bending and Twisting Loads Using Neural Networks

In this paper a neural network based modeling approach is proposed for localization and estimation of loads acting on aircraft wings from full field depth measurements. These measurements can be provided by a multitude of sensors such as depth cameras. Depth cameras have many advantages over other intensity sensors in that they can work in low light conditions and they are invariant to texture and color changes. First, an autoencoder is proposed to extract maximum informative data from the depth images and encode them at a much smaller dimension. Next, to develop the models for localization and estimation of loads, supervised multinomial classification and logistic regression networks are proposed, where the encoded depth features are utilized as input in both networks. The performance of the proposed method is validated on a composite wing subject to concentrated and distributed loads, during which the proposed methods for localization and estimation of loads achieved high accuracies of 90.6% and 90.5%, respectively.
A High-Recall Membership Inference Attack Based on Confidence-Thresholding Method with Relaxed Assumption

Membership inference attack (MIA) aims to infer whether a given data sample is in the target training dataset or not, which poses a severe privacy risk in particular data-sensitive fields like the military, national security department, as well as enterprise. Observing that a model generated from adversarial training is more vulnerable against MIA, a novel attack method based on confidence-thresholding was proposed by Song et al. recently. However, it is not a straightforward work to deploy such an attack into real-world application scenarios, since shadow training and redundant assumptions are prerequisites. To address the above issues, in this paper, we propose an improved confidence-thresholding method with relaxed assumption, evaluating the prediction accuracy as the threshold. Our attack can be released without using shadow training and an additional dataset. Instead of collecting an additional dataset, attackers use their target data records, which are needed to be inferred about membership, to achieve MIA. As a result, our proposed attack against robust model has an overwhelming advantage on model recall with fewer accuracy and precision loss. Extensive experiments are conducted on real-world data, i.e., Yale Face, and the results show that our proposed MIA attack is effective and feasible.