Reviewerly/exHarmony huggingface dataset:
A General Approach to Multimodal Document Quality Assessment

&#x0D; &#x0D; &#x0D; The perceived quality of a document is affected by various factors, including grammat- icality, readability, stylistics, and expertise depth, making the task of document quality assessment a complex one. In this paper, we explore this task in the context of assessing the quality of Wikipedia articles and academic papers. Observing that the visual rendering of a document can capture implicit quality indicators that are not present in the document text — such as images, font choices, and visual layout — we propose a joint model that combines the text content with a visual rendering of the document for document qual- ity assessment. Our joint model achieves state-of-the-art results over five datasets in two domains (Wikipedia and academic papers), which demonstrates the complementarity of textual and visual features, and the general applicability of our model. To examine what kinds of features our model has learned, we further train our model in a multi-task learning setting, where document quality assessment is the primary task and feature learning is an auxiliary task. Experimental results show that visual embeddings are better at learning structural features while textual embeddings are better at learning readability scores, which further verifies the complementarity of visual and textual features.&#x0D; &#x0D; &#x0D;
Preconditioned Gradient Descent Algorithm for Inverse Filtering on Spatially Distributed Networks

Graph filters and their inverses have been widely used in denoising, smoothing, sampling, interpolating and learning. Implementation of an inverse filtering procedure on spatially distributed networks (SDNs) is a remarkable challenge, as each agent on an SDN is equipped with a data processing subsystem with limited capacity and a communication subsystem with confined range due to engineering limitations. In this letter, we introduce a preconditioned gradient descent algorithm to implement the inverse filtering procedure associated with a graph filter having small geodesic-width. The proposed algorithm converges exponentially, and it can be implemented at vertex level and applied to time-varying inverse filtering on SDNs.
EGAN: Non-uniform image deblurring based on edge adversarial mechanism and partial weight sharing network

In this paper, we propose a solution to transform spatially variant blurry images into the photo-realistic sharp manifold. Image deblurring task is valuable and challenging in computer vision. However, existing learning-based methods cannot produce images with clear edges and fine details, which exhibit significant challenges for generated-based loss functions used in existing methods. Instead of only designing architectures and loss functions for generators, we propose a generative adversarial network (GAN) framework based on an edge adversarial mechanism and a partial weight sharing network. In order to propel the entire network to learn image edges information consciously, we propose an edge reconstruction loss function and an edge adversarial loss function to restrict the generator and the discriminator respectively. We further introduce a partial weight sharing structure, the sharp features from clean images encourage the recovery of image details of deblurred images. The proposed partial weight sharing structure improves image details effectively. Experimental results show that our method is able to generate photo-realistic sharp images from real-world blurring images and outperforms state-of-the-art methods.
Improved anti-noise attack ability of image encryption algorithm using de-noising technique

Information security is considered as one of the important issues in the information age used to preserve the secret information through out transmissions in practical applications. With regard to image encryption, a lot of schemes related to information security were applied. Such approaches might be categorized into 2 domains; domain frequency and domain spatial. The presented work develops an encryption technique on the basis of conventional watermarking system with the use of singular value decomposition (SVD), discrete cosine transform (DCT), and discrete wavelet transform (DWT) together, the suggested DWT-DCT-SVD method has high robustness in comparison to the other conventional approaches and enhanced approach for having high robustness against Gaussian noise attacks with using denoising approach according to DWT. MSE in addition to the peak signal-to-noise ratio (PSNR) specified the performance measures which are the base of this study’s results, as they are showing that the algorithm utilized in this study has high robustness against Gaussian noise attacks.
A Deep Reinforced Model for Zero-Shot Cross-Lingual Summarization with Bilingual Semantic Similarity Rewards

Cross-lingual text summarization aims at generating a document summary in one language given input in another language.It is a practically important but under-explored task, primarily due to the dearth of available data.Existing methods resort to machine translation to synthesize training data, but such pipeline approaches suffer from error propagation.In this work, we propose an end-to-end cross-lingual text summarization model.The model uses reinforcement learning to directly optimize a bilingual semantic similarity metric between the summaries generated in a target language and gold summaries in a source language.We also introduce techniques to pre-train the model leveraging monolingual summarization and machine translation objectives.Experimental results in both English-Chinese and English-German cross-lingual summarization settings demonstrate the effectiveness of our methods.In addition, we find that reinforcement learning models with bilingual semantic similarity as rewards generate more fluent sentences than strong baselines. 1
Can Humor Prediction Datasets be used for Humor Generation? Humorous Headline Generation via Style Transfer

Understanding and identifying humor has been increasingly popular, as seen by the number of datasets created to study humor.However, one area of humor research, humor generation, has remained a difficult task, with machine generated jokes failing to match humancreated humor.As many humor prediction datasets claim to aid in generative tasks, we examine whether these claims are true.We focus our experiments on the most popular dataset, included in the 2020 SemEval's Task 7, and teach our model to take normal text and "translate" it into humorous text.We evaluate our model compared to humorous human generated headlines, finding that our model is preferred equally in A/B testing with the human edited versions, a strong success for humor generation, and is preferred over an intelligent random baseline 72% of the time.We also show that our model is assumed to be human written comparable with that of the human edited headlines and is significantly better than random, indicating that this dataset does indeed provide potential for future humor generation systems.
Survey on Text Classification

Now a day there is rapid growth of the World Wide Web. The work of instinctive classification of documents is a main process for organizing the information and knowledge discovery. The classification of e-documents, online bulletin, blogs, e-mails and digital libraries need text mining, machine learning and natural language processing procedures to develop significant information. Therefore, proper classification and information detection from these assets is a fundamental field for study. Text classification is significant study topic in the area of text mining, where the documents are classified with supervised information. In this paper various text representation schemes and learning classifiers such as Naïve Bayes and Decision Tree algorithms are described with illustration for predefined classes. This present approaches are compared and distinguished based on quality assurance parameters.
Automatically Ranked Russian Paraphrase Corpus for Text Generation

The article is focused on automatic development and ranking of a large corpus for Russian paraphrase generation which proves to be the first corpus of such type in Russian computational linguistics.Existing manually annotated paraphrase datasets for Russian are limited to small-sized ParaPhraser corpus and ParaPlag which are suitable for a set of NLP tasks, such as paraphrase and plagiarism detection, sentence similarity and relatedness estimation, etc. Due to size restrictions, these datasets can hardly be applied in end-to-end text generation solutions.Meanwhile, paraphrase generation requires a large amount of training data.In our study we propose a solution to the problem: we collect, rank and evaluate a new publicly available headline paraphrase corpus (ParaPhraser Plus), and then perform text generation experiments with manual evaluation on automatically ranked corpora using the Universal Transformer architecture.
Demonstration of Blockchain-based IoT Devices Anonymous Access Network Using Zero-knowledge Proof

We propose a blockchain-based IoT devices anonymous access system using zero-knowledge proof in cloud-based radio over optical fiber networks (C-RoFN) to prevent device sensitive information from being exposed to massive IoT devices.
SEMANTIC APPROACH TO SMART CONTRACT VERIFICATION

Vulnerabilities of smart contract are certainly one of the limiting factors for wider adoption of blockchain technology. Smart contracts written in Solidity language are considered due to common adoption of the Ethereum blockchain platform. Despite its popularity, the semantics of the language is not completely documented and relies on implicit mechanisms not publicly available and as such vulnerable to possible attacks. In addition, creating formal semantics for the higher-level language provides support to veriﬁcation mechanisms. In this paper, a novel approach to smart contact verification is presented that uses ontologies in order to leverage semantic annotations of the smart contract source code combined with semantic representation of domain-specific aspects. The following aspects of smart contracts, apart from source code are taken into consideration for verification: business logic, domain knowledge, run-time state changes and expert knowledge about vulnerabilities. Main advantages of the proposed verification approach are platform independence and extendability.
AntsOMG: A Framework Aiming to Automate Creativity and Intelligent Behavior with a Showcase on Cantus Firmus Composition and Style Development

Creative behavior is one of the most fascinating areas in intelligence. The development of specific styles is the most characteristic feature of creative behavior. All important creators, such as Picasso and Beethoven, have their own distinctive styles that even non-professional art lovers can easily recognize. Hence, in the present work, attempting to achieve cantus firmus composition and style development as well as inspired by the behavior of natural ants and the mechanism of ant colony optimization (ACO), this paper firstly proposes a meta-framework, called ants on multiple graphs (AntsOMG), mainly for roughly modeling creation activities and then presents an implementation derived from AntsOMG for composing cantus firmi, one of the essential genres in music. Although the mechanism in ACO is adopted for simulating ant behavior, AntsOMG is not designed as an optimization framework. Implementations can be built upon AntsOMG in order to automate creation behavior and realize autonomous development on different subjects in various disciplines. In particular, an implementation for composing cantus firmi is shown in this paper as a demonstration. Ants walk on multiple graphs to form certain trails that are composed of the interaction among the graph topology, the cost on edges, and the concentration of pheromone. The resultant graphs with the distribution of pheromone can be interpreted as a representation of cantus firmus style developed autonomously. Our obtained results indicate that the proposal has an intriguing effect, because significantly different styles may be autonomously developed from an identical initial configuration in separate runs, and cantus firmi of a certain style can be created in batch simply by using the corresponding outcome. The contribution of this paper is twofold. First, the presented implementation is immediately applicable to the creation of cantus firmi and possibly other music genres with slight modifications. Second, AntsOMG, as a meta-framework, may be employed for other kinds of autonomous development with appropriate implementations.
Relation Extraction with BERT-based Pre-trained Model

Distant supervision relation extraction is an effective method to extract the real relation between entities from unstructured corpus. However, affected by the hypothesis of distant supervision mechanism, relation extraction model often faces the disturbance of mislabeled data and noise samples. In order to alleviate the above problems and improve the performance, we propose a relation extraction framework based on Bert-based pre-trained models, Bert for Relation Extraction (BRE). BRE uses BERT as feature extractor and loads pre-trained parameters for fine-tuning. It integrates external semantic knowledge with entity relation knowledge in specific tasks to improve the performance of classifier. In addition, we designed position enhanced CNN module and time-decay selective attention mechanism for BRE to bridge the semantic gap between external knowledge and relation knowledge, and alleviate the problem of mislabeling and noise in the multi-instance learning mode. We conducted experiments on NYT-10 and GIDS datasets, and the results show that BRE achieves the best performance.
Auto-Weighted Incomplete Multi-View Clustering

Nowadays, multi-view clustering has attracted more and more attention, which provides a way to partition multi-view data into their corresponding clusters.Previous studies assume that each data instance appears in all views.However, in real-world applications, it is common that each view may contain some missing data instances, resulting in incomplete multi-view data.To address the incomplete multi-view clustering problem, we will propose an auto-weighted incomplete multi-view clustering method in this paper, which learns a common representation of the instances and an affinity matrix of the learned representation simultaneously in a unified framework.Learning the affinity matrix of the representation guides to learn a more discriminative and compact consensus representation for clustering.Moreover, by considering the impact of the significance of different views, an adaptive weighting strategy is designed to measure the importance of each view.An efficient iterative algorithm is proposed to optimize the objective function.Experimental results on various real-world datasets show that the proposed method can improve the clustering performance in comparison with the state-of-the-art methods in most cases.
Simultaneous paraphrasing and translation by fine-tuning Transformer models

This paper describes the third place submission to the shared task on simultaneous translation and paraphrasing for language education at the 4th workshop on Neural Generation and Translation (WNGT) for ACL 2020. The final system leverages pre-trained translation models and uses a Transformer architecture combined with an oversampling strategy to achieve a competitive performance. This system significantly outperforms the baseline on Hungarian (27% absolute improvement in Weighted Macro F1 score) and Portuguese (33% absolute improvement) languages.
Multiple agreement in Southern Italian dialects

This paper is dedicated to Rita, a senior sister linguist for us.We have known Rita's name since our very first year of classes, as her paper on control was part of the syllabus of our introduction to linguistics at Ca' Foscari, 1980-81, taught by Guglielmo Cinque.Reading and trying to understand that paper was probably our first experience of theoretical argumentation; and knowing that it was by an Italian, and a young woman, was certainly of stimulus for us to entertain our own professional career.1 We report the data as they are found in the literature.The transcription of the examples will therefore not be consistent throughout the paper.2
Virtual Machine Consolidation with Minimization of Migration Thrashing for Cloud Data Centers

Cloud data centers consume huge amount of electrical energy bringing about in high operating costs and carbon dioxide emissions. Virtual machine (VM) consolidation utilizes live migration of virtual machines (VMs) to transfer a VM among physical servers in order to improve the utilization of resources and energy efficiency in cloud data centers. Most of the current VM consolidation approaches tend to aggressive-migrate for some types of applications such as large capacity application such as speech recognition, image processing, and decision support systems. These approaches generate a high migration thrashing because VMs are consolidated to servers according to VM’s instant resource usage without considering their overall and long-term utilization. The proposed approach, dynamic consolidation with minimization of migration thrashing (DCMMT) which prioritizes VM with high capacity, significantly reduces migration thrashing and the number of migrations to ensure service-level agreement (SLA) since it keeps VMs likely to suffer from migration thrashing in the same physical servers instead of migrating. We have performed experiments using real workload traces compared to existing aggressive-migration-based solutions; through simulations, we show that our approach improves migration thrashing metric by about 28%, number of migrations metric by about 21%, and SLAV metric by about 19%.
Order from chaos in quantum walks on cyclic graphs

It has been shown classically that combining two chaotic random walks can yield an ordered (periodic) walk. Our aim in this paper is to find a quantum analog for this rather counterintuitive result. We study the chaotic and periodic nature of cyclic quantum walks and focus on a unique situation wherein a periodic quantum walk on 3-cycle graph is generated via a deterministic combination of two chaotic quantum walks on the same graph. We extend our results to even numbered cyclic graphs, specifically a 4-cycle graph too. Our results will be relevant in quantum cryptography and quantum chaos control.
The role of organizational culture, knowledge sharing and job satisfaction in higher education

This study aims to examine the influence of organizational culture on job satisfaction through knowledge sharing.The sample in this study totaled 259 lecturers, 50 groups of study programs at 27 universities.Hypothesis testing uses Hierarchical linear modeling (HLM).The results show that organizational culture had a significant positive effect on knowledge sharing, knowledge sharing had a significant positive effect on job satisfaction, and organizational culture had a positive and significant effect on satisfaction directly or indirectly.
A robust W-state encoding for linear quantum optics

Error-detection and correction are necessary prerequisites for any scalable quantum computing architecture. Given the inevitability of unwanted physical noise in quantum systems and the propensity for errors to spread as computations proceed, computational outcomes can become substantially corrupted. This observation applies regardless of the choice of physical implementation. In the context of photonic quantum information processing, there has recently been much interest in<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow class="MJX-TeXAtom-ORD"><mml:mtext class="MJX-tex-mathit" mathvariant="italic">passive</mml:mtext></mml:mrow></mml:math>linear optics quantum computing, which includes boson-sampling, as this model eliminates the highly-challenging requirements for feed-forward via fast, active control. That is, these systems are<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow class="MJX-TeXAtom-ORD"><mml:mtext class="MJX-tex-mathit" mathvariant="italic">passive</mml:mtext></mml:mrow></mml:math>by definition. In usual scenarios, error detection and correction techniques are inherently<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow class="MJX-TeXAtom-ORD"><mml:mtext class="MJX-tex-mathit" mathvariant="italic">active</mml:mtext></mml:mrow></mml:math>, making them incompatible with this model, arousing suspicion that physical error processes may be an insurmountable obstacle. Here we explore a photonic error-detection technique, based on W-state encoding of photonic qubits, which is entirely passive, based on post-selection, and compatible with these near-term photonic architectures of interest. We show that this W-state redundant encoding techniques enables the suppression of dephasing noise on photonic qubits via simple fan-out style operations, implemented by optical Fourier transform networks, which can be readily realised today. The protocol effectively maps dephasing noise into heralding failures, with zero failure probability in the ideal no-noise limit. We present our scheme in the context of a single photonic qubit passing through a noisy communication or quantum memory channel, which has not been generalised to the more general context of full quantum computation.
Improved Linear Convergence of Training CNNs With Generalizability Guarantees: A One-Hidden-Layer Case

We analyze the learning problem of one-hidden-layer nonoverlapping convolutional neural networks with the rectified linear unit (ReLU) activation function from the perspective of model estimation. The training outputs are assumed to be generated by the neural network with the unknown ground-truth parameters plus some additive noise, and the objective is to estimate the model parameters by minimizing a nonconvex squared loss function of the training data. Assuming that the training set contains a finite number of samples generated from the Gaussian distribution, we prove that the accelerated gradient descent (GD) algorithm with a proper initialization converges to the ground-truth parameters (up to the noise level) with a linear rate even though the learning problem is nonconvex. Moreover, the convergence rate is proved to be faster than the vanilla GD. The initialization can be achieved by the existing tensor initialization method. In contrast to the existing works that assume an infinite number of samples, we theoretically establish the sample complexity of the required number of training samples. Although the neural network considered here is not deep, this is the first work to show that accelerated GD algorithms can find the global optimizer of the nonconvex learning problem of neural networks. This is also the first work that characterizes the sample complexity of gradient-based methods in learning convolutional neural networks with the nonsmooth ReLU activation function. This work also provides the tightest bound so far of the estimation error with respect to the output noise.