Reviewerly/exHarmony huggingface dataset:
Improving tree-based neural machine translation with dynamic lexicalized dependency encoding

Tree-to-sequence neural machine translation models have proven to be effective in learning the semantic representations from the exploited syntactic structure. Despite their success, tree-to-sequence models have two major issues: (1) the embeddings of constituents at the higher tree levels tend to contribute less in translation; and (2) using a single set of model parameters is difficult to fully capture the syntactic and semantic richness of linguistic phrases. To address the first problem, we proposed a lexicalized dependency model, in which the source-side lexical representations are learned in a head-dependent fashion following a dependency graph. Since the number of dependents is variable, we proposed a variant recurrent neural network (RNN) to jointly consider the long-distance dependencies and the sequential information of words. Concerning the second problem, we adopt a latent vector to dynamically condition the parameters for the composition of each node representation. Experimental results reveal that the proposed model significantly outperforms the recently proposed tree-based methods in English–Chinese and English–German translation tasks with even far fewer parameters.
BarcodeTree: Scalable Comparison of Multiple Hierarchies

We propose BarcodeTree (BCT), a novel visualization technique for comparing topological structures and node attribute values of multiple trees. BCT can provide an overview of one hundred shallow and stable trees simultaneously, without aggregating individual nodes. Each BCT is shown within a single row using a style similar to a barcode, allowing trees to be stacked vertically with matching nodes aligned horizontally to ease comparison and maintain space efficiency. We design several visual cues and interactive techniques to help users understand the topological structure and compare trees. In an experiment comparing two variants of BCT with icicle plots, the results suggest that BCTs make it easier to visually compare trees by reducing the vertical distance between different trees. We also present two case studies involving a dataset of hundreds of trees to demonstrate BCT's utility.
Channel Decoding with Quantum Approximate Optimization Algorithm

Motivated by the recent advancement of quantum processors, we investigate quantum approximate optimization algorithm (QAOA) to employ quasi-maximum-likelihood (ML) decoding of classical channel codes. QAOA is a hybrid quantum-classical variational algorithm, which is advantageous for the near-term noisy intermediate-scale quantum (NISQ) devices, where the fidelity of quantum gates is limited by noise and de-coherence. We first describe how to construct Ising Hamiltonian model to realize quasi-ML decoding with QAOA. For level-1 QAOA, we derive the systematic way to generate theoretical expressions of cost expectation for arbitrary binary linear codes. Focusing on [7], [4] Hamming code as an example, we analyze the impact of the degree distribution in associated generator matrix on the quantum decoding performance. The excellent performance of higher-level QAOA decoding is verified when Pauli rotation angles are optimized through meta-heuristic variational quantum eigensolver (VQE). Furthermore, we demonstrate the QAOA decoding performance in a real quantum device.
Fraud Detection using Machine Learning in e-Commerce

The volume of internet users is increasingly causing transactions on e-commerce to increase as well.We observe the quantity of fraud on online transactions is increasing too.Fraud prevention in e-commerce shall be developed using machine learning, this work to analyze the suitable machine learning algorithm, the algorithm to be used is the Decision Tree, Naïve Bayes, Random Forest, and Neural Network.Data to be used is still unbalance.Synthetic Minority Over-sampling Technique (SMOTE) process is to be used to create balance data.Result of evaluation using confusion matrix achieve the highest accuracy of the neural network by 96 percent, random forest is 95 percent, Naïve Bayes is 95 percent, and Decision tree is 91 percent.Synthetic Minority Over-sampling Technique (SMOTE) is able to increase the average of F1-Score from 67.9 percent to 94.5 percent and the average of G-Mean from 73.5 percent to 84.6 percent.
Hexagonal Convolutional Neural Networks for Hexagonal Grids

Hexagonal grids use a hierarchical subdivision tessellation to cover the entire plane or sphere. Due to the 6-fold rotational symmetry, hexagonal grids have some advantages (e.g. isoperimetry, equidistant neighbors, and uniform connectivity) over quadrangular and triangular girds, which makes them suitable to tackle tasks of geospatial information processing and intelligent decision-making. In this paper, we first introduce some applications based on the hexagonal grids. Then, we introduce the planer and spherical hexagonal grids and analyze the group representations for them, we review geometric deep learning, some Convolutional Neural Networks (CNNs) for hexagonal grids, and group-based equivariant convolution. Next in importance, we propose the HexagonNet for hexagonal grids, and define a new convolution operator and pooling operator. Finally, in order to evaluate the effectiveness of the proposed HexagonNet, we perform experiments on two tasks: aerial scene classification on the aerial image dataset (AID), and 3D shape classification on the ModelNet40 dataset. The experimental results verify the practical applicability of the HexagonNet given some fixed parameter budgets.
The Adaptation of Students to the Academic Environment in University

The aim of this research is related to the issue of the adaptation of the students to academic life and to the analysis of the difficulties faced by students when adapting to their new status. Considering these, we initiated a study that addressed the adaptation process in relation to the reasons and the ability to resist stress.This study had 146 participants, students from the Polytechnic University of Bucharest, who were asked a set of questions on the subject.The research has shown that many students face problems related to integration and adaptation to the new context of life. As a result, a large number of them considered dropping out of university. Also, one-third of interviewees believe that teachers did not contribute in accommodating students to the academic life. Only 10% of students have sought help from a psychotherapist, although many of them have considered this option.In the conclusions of this paper, solutions have been proposed to help the development and the maintenance of skills meant to help those involved in the process of adaptation. These solutions have been based on the development of relational-psychological and neuro-physiological abilities. With respect to relational-psychological solutions, we relied on the stimulation of creative capacity and, indirectly, supporting the manifestation of intellectual potential. When discussing the development of neuro-physiological abilities, we mainly referred to the development of the capacity to relax.We believe that this research can be a valuable information source, the results of which can be integrated into the administrative and educational endeavours of several categories of beneficiaries or decision-makers.
Absolute Human Pose Estimation with Depth Prediction Network

The common approach to 3D human pose estimation is predicting the body joint coordinates relative to the hip. This works well for a single person but is insufficient in the case of multiple interacting people. Methods predicting absolute coordinates first estimate a root-relative pose then calculate the translation via a secondary optimization task. We propose a neural network that predicts joints in a camera centered coordinate system instead of a root-relative one. Unlike previous methods, our network works in a single step without any post-processing. Our network beats previous methods on the MuPoTS-3D dataset and achieves state-of-the-art results.
Multiple Security Certification System between Blockchain Based Terminal and Internet of Things Device: Implication for Open Innovation

As the number of Internet of Things (IoT) devices increases, services expand and illegal hacking and infringement methods become more sophisticated, an effective solution for blockchain technology is required as a fundamental solution to security threats.In this paper, we develop the security module of an IoT device based on blockchain technology that blocks hacking and information infringement and forms a multi-security blockchain system between the IoT device and the user device and we develop a user application.We contribute to addressing the security threats faced by IoT application services by developing a new method.In particular, we present some schemes for the development of a multi-security certification system based on blockchain for IoT security.
Easy Web API Development with SPARQL Transformer

In a document-based world as the one of Web APIs, the triple-based output of SPARQL endpoints can be a barrier for developers who want to integrate Linked Data in their applications. A different JSON output can be obtained with SPARQL Transformer, which relies on a single JSON object for defining which data should be extracted from the endpoint and which shape should they assume. We propose a new approach that amounts to merge SPARQL bindings on the base of identifiers and the integration in the grlc API framework to create new bridges between the Web of Data and the Web of applications.
An efficient pattern growth approach for mining fault tolerant frequent itemsets

Mining fault tolerant (FT) frequent itemsets from transactional databases are computationally more expensive than mining exact matching frequent itemsets. Previous algorithms mine FT frequent itemsets using Apriori heuristic. Apriori-like algorithms generate exponential number of candidate itemsets including the itemsets that do not exist in the database. These algorithms require multiple scans of database for counting the support of candidate FT itemsets. In this paper we present a novel algorithm, which mines FT frequent itemsets using frequent pattern growth approach (FT-PatternGrowth). FT-PatternGrowth adopts a divide-and-conquer technique and recursively projects transactional database into a set of smaller projected transactional databases and mines FT frequent itemsets in each projected database by exploring only locally frequent items. This mines the complete set of FT frequent itemsets and substantially reduces those candidate itemsets that do not exist in the database. FT-PatternGrowth stores the transactional database in a highly condensed much smaller data structure called frequent pattern tree (FP-tree). The support of candidate itemsets are counted directly from the FP-tree without scanning the original database multiple times. This improves the processing speed of algorithm. Our experiments on benchmark databases indicates mining FT frequent itemsets using FT-PatternGrowth is highly efficient than Apriori-like algorithms.
Nonlocality, quantum correlations, and violations of classical realism in the dynamics of two noninteracting quantum walkers

That quantum correlations can be generated over time between the spin and the position of a quantum walker is indisputable. The creation of bipartite entanglement has also been reported for two-walker systems. In this scenario, however, since the global state lies in a fourpartite Hilbert space, the question arises as to whether genuine multipartite entanglement may develop in time. Also, since the spatial degrees of freedom can be viewed as a noisy channel for the two-spin part, one may wonder how other nonclassical aspects, such as Bell nonlocality, Einstein-Podolsky-Rosen steering, quantum discord, and symmetrical quantum discord, evolve in time during the walk. The lack of analytical and numerical evidences which would allow one to address these questions is possibly due to the usual computational difficulties associated with the recursive nature of quantum walks. Here, we work around this issue by introducing a simplified Gaussian model which proves to be very accurate within a given domain and powerful for analytical studies. Then, for an instance involving two noninteracting quantum walkers, whose spins start in the singlet state, we quantify the aforementioned nonclassical features as a function of time, and evaluate violations of both realism and related aspects of locality. In addition, we analyze situations in which the initial two-spin state is affected by white noise. The typical scenario found is such that while genuine fourpartite entanglement increases over time, all the investigated nonclassical features vanish (suddenly or asymptotically) except realism-based nonlocality. Moreover, realism is prevented for all finite times. Our findings open perspectives for the understanding of the dynamics of quantum resources in quantum walks.
A Cloud Detection Algorithm with Reduction of Sunlight Interference in Ground-Based Sky Images

Cloud detection for ground-based sky images has attracted much attention in cloud-related fields. In this paper, we proposed a cloud detection algorithm that reduced the sunlight interference in the image. The solar location method was introduced to track the sun in the image used for feature calculation, which was suitable for the case where the camera could not be calibrated. Following this, the adjustable red green difference (ARGD) feature using red and green channels was proposed. The red weight in the feature was determined by the layering region division, which classified the degree of sunlight interference in the image, and the sky state, which discriminated whether there was sunlight interference in the image. Finally, a fixed zero threshold was applied to feature images in order to obtain the cloud detection results. Experimental results showed that the proposed algorithm performs better than the other algorithms and can effectively reduce the sunlight interference.
Cross-Modal Dual Learning for Sentence-to-Video Generation

Automatic content generation has become an attractive while challenging topic in the past decade. Generating videos from sentences particularly poses great challenges to the multimedia community due to its multi-modal characteristics in essence, e.g., difficulties in semantic alignment, and the temporal dependencies in video contents. Existing works resort to Variational AutoEncoder (VAE) or Generative Adversary Network (GAN) for generating videos given sentences, which may suffer from either blurry generated videos or unstable training processes as well as difficulties in converging to optimal solutions. In this paper, we propose a cross-modal dual learning (CMDL) algorithm to tackle the challenges in sentence-to-video generation and address the weaknesses in existing works. The proposed CMDL model adopts a dual learning mechanism to simultaneously learn the bidirectional mappings between sentences and videos such that it is able to generate realistic videos which maintain semantic consistencies with their corresponding textual descriptions. By further capturing both global and contextual structures, CMDL employs a multi-scale sentence-to-visual encoder to produce more sequentially consistent and plausible videos. Extensive experiments on various datasets validate the advantages of our proposed CMDL model against several state-of-the-art benchmarks both visually and quantitatively.
A Novel Firefly Algorithm for Scheduling Bag-of-Tasks Applications Under Budget Constraints on Hybrid Clouds

This paper aims at scheduling bag-of-tasks (BoT) applications under budget constraints on hybrid clouds for minimizing the makespan. To solve this NP-hard problem, we propose a novel firefly algorithm (NFA) in which the evaluation of a firefly consists of two steps: (1) mapping a firefly to a scheduling solution (a task sequence); (2) calculating the solution's objective (its corresponding makespan). In the first step, different from the well-known ranked-order value (ROV) rule, we propose a distance-based mapping operator that relies on the distance between a firefly and the brightest one to determine the mapping relationship between a firefly and a solution. We use a probability model in which solutions corresponding to fireflies closer to the brightest one would have higher probabilities to inherit tasks from the current best solution. In this manner, these solutions can inherit more “good genes” hidden inside the current best solution to evolve into more high-quality solutions. In the second step, we employ an effective heuristic to evaluate solution objectives. We further develop a composite heuristic to generate the initial best solution, providing the proposed NFA with a good start. We also establish a new movement scheme such that fireflies distant from the brightest one can explore a wide range in the search space, whereas fireflies nearby the brightest one can search in a small neighborhood. Experimental results show that, by employing the above-mentioned strategies, NFA outperforms the standard firefly algorithm and the existing best algorithm, in terms of scheduling effectiveness and computational efficiency. Specifically, the distance-based mapping operator is verified to be both more effective and more efficient than the ROV rule. The composite heuristic is capable of generating a good initial solution, leading to the high quality of the final schedule. The movement scheme can further reduce the makespan of BoT applications.
Erasing-based Attention Learning for Visual Question Answering

Attention learning for visual question answering remains a challenging task, where most existing methods treat the attention and the non-attention parts in isolation. In this paper, we propose to enforce the correlation between the attention and the non-attention parts as a constraint for attention learning. We first adopt an attention-guided erasing scheme to obtain the attention and the non-attention parts respectively, and then learn to separate the attention and the non-attention parts by an appropriate distance margin in a feature embedding space. Furthermore, we associate a typical classification loss with the above distance constraint to learn a more discriminative attention map for answer prediction. The proposed approach does not introduce extra model parameters or inference complexity, and can be combined with any attention-based models. Extensive ablation experiments validate the effectiveness of our method, and new state-of-the-art or competitive results on four publicly available datasets are achieved.
Post Text Processing of Chinese Speech Recognition Based on Bidirectional LSTM Networks and CRF

With the rapid development of Internet of Things Technology, speech recognition has been applied more and more widely. Chinese Speech Recognition is a complex process. In the process of speech-to-text conversion, due to the influence of dialect, environmental noise, and context, the accuracy of speech-to-text in multi-round dialogues and specific contexts is still not high. After the general speech recognition technology, the text after speech recognition can be detected and corrected in the specific context, which is helpful to improve the robustness of text comprehension and is a beneficial supplement to the speech recognition technology. In this paper, a text processing model after Chinese Speech Recognition is proposed, which combines a bidirectional long short-term memory (LSTM) network with a conditional random field (CRF) model. The task is divided into two stages: text error detection and text error correction. In this paper, a bidirectional long short-term memory (Bi-LSTM) network and conditional random field are used in two stages of text error detection and text error correction respectively. Through verification and system test on the SIGHAN 2013 Chinese Spelling Check (CSC) dataset, the experimental results show that the model can effectively improve the accuracy of text after speech recognition.
Ensemble learning for software fault prediction problem with imbalanced data

Fault prediction problem has a crucial role in the software development process because it contributes to reducing defects and assisting the testing process towards fault-free software components. &lt;span lang="EN-US"&gt;Therefore, there are a lot of efforts aiming to address this type of issues, in which static code characteristics are usually adopted to construct fault classification models. &lt;/span&gt; One of the challenging problems influencing the performance of predictive classifiers is the high imbalance among patterns belonging to different classes. This paper aims to integrate the sampling techniques and common classification techniques to form a useful ensemble model for the software defect prediction problem. The empirical results conducted on the benchmark datasets of software projects have shown the promising performance of our proposal in comparison with individual classifiers.
End-to-End Conditional GAN-based Architectures for Image Colourisation

In this work recent advances in conditional adversarial networks are investigated to develop an end-to-end architecture based on Convolutional Neural Networks (CNNs) to directly map realistic colours to an input greyscale image. Observing that existing colourisation methods sometimes exhibit a lack of colourfulness, this paper proposes a method to improve colourisation results. In particular, the method uses Generative Adversarial Neural Networks (GANs) and focuses on improvement of training stability to enable better generalisation in large multi-class image datasets. Additionally, the integration of instance and batch normalisation layers in both generator and discriminator is introduced to the popular U-Net architecture, boosting the network capabilities to generalise the style changes of the content. The method has been tested using the ILSVRC 2012 dataset, achieving improved automatic colourisation results compared to other methods based on GANs.
Abstractive Timeline Summarization

Timeline summarization (TLS) automatically identifies key dates of major events and provides short descriptions of what happened on these dates.Previous approaches to TLS have focused on extractive methods.In contrast, we suggest an abstractive timeline summarization system.Our system is entirely unsupervised, which makes it especially suited to TLS where there are very few gold summaries available for training of supervised systems.In addition, we present the first abstractive oracle experiments for TLS.Our system outperforms extractive competitors in terms of ROUGE when the number of input documents is high and the output requires strong compression.In these cases, our oracle experiments confirm that our approach also has a higher upper bound for ROUGE scores than extractive methods.A study with human judges shows that our abstractive system also produces output that is easy to read and understand.
A Comparison of Machine Learning Approaches for Detecting Misogynistic Speech in Urban Dictionary

Recent moves to consider misogyny as a hate crime have refocused efforts for owners of web properties to detect and remove misogynistic speech. This paper considers the use of deep learning techniques for detection of misogyny in Urban Dictionary, a crowdsourced online dictionary for slang words and phrases. We compare the performance of two deep learning techniques, Bi-LSTM and Bi-GRU, to detect misogynistic speech with the performance of more conventional machine learning techniques, logistic regression, Naive-Bayes classification, and Random Forest classification. We find that both deep learning techniques examined have greater accuracy in detecting misogyny in the Urban Dictionary than the other techniques examined.