Reviewerly/exHarmony huggingface dataset:
Neural Language Modeling for Contextualized Temporal Graph Generation

This paper presents the first study on using large-scale pre-trained language models for automated generation of an event-level temporal graph for a document.Despite the huge success of neural pre-training methods in NLP tasks, its potential for temporal reasoning over event graphs has not been sufficiently explored.Part of the reason is the difficulty in obtaining large training corpora with humanannotated events and temporal links.We address this challenge by using existing IE/NLP tools to automatically generate a large quantity (89,000) of system-produced document-graph pairs, and propose a novel formulation of the contextualized graph generation problem as a sequence-to-sequence mapping task.These strategies enable us to leverage and fine-tune pre-trained language models on the systeminduced training data for the graph generation task.Our experiments show that our approach is highly effective in generating structurally and semantically valid graphs.Further, evaluation on a challenging hand-labeled, out-ofdomain corpus shows that our method outperforms the closest existing method by a large margin on several metrics.We also show a downstream application of our approach by adapting it to answer open-ended temporal questions in a reading comprehension setting. 1
Progressive Graph Convolutional Networks for Semi-Supervised Node Classification

Graph convolutional networks have been successful in addressing graph-based tasks such as semi-supervised node classification.Existing methods use a network structure defined by the user based on experimentation with fixed number of layers and neurons per layer and employ a layer-wise propagation rule to obtain the node embeddings.Designing an automatic process to define a problem-dependant architecture for graph convolutional networks can greatly help to reduce the need for manual design of the structure of the model in the training process.In this paper, we propose a method to automatically build compact and task-specific graph convolutional networks.Experimental results on widely used publicly available datasets show that the proposed method outperforms related methods based on convolutional graph networks in terms of classification performance and network compactness.
Towards Enhancing Spectrum Sensing: Signal Classification Using Autoencoders

The demand for technologies relying on the radio spectrum, such as mobile communications and IoT, has been growing exponentially.As a consequence, providing access to the radio spectrum is becoming increasingly more important.The ever-growing wireless traffic and the increasing scarcity of available spectrum warrants efficient management of the radio spectrum.At the same time, machine learning (ML) is becoming ubiquitous and has found applications in many fields for its ability to identify patterns and assist with decision-making processes.Recently, machine learning algorithms have been used to address challenges in the wireless communications domain, such as radio spectrum sensing, and have shown better performance than traditional sensing methods, such as energy detection.Spectrum sensing, a method for detecting and identifying different wireless signals being transmitted in the same band of the radio spectrum, is crucial for improving dynamic spectrum sharing, which has the potential to enhance sharing and coexistence of different wireless technologies in the same frequency band and ultimately improve spectrum efficiency.To this end, this research evaluates different types of autoencoders, such as deep, variational and Long Short-Term Memory (LSTM) autoencoders, to identify and differentiate between LTE and Wi-Fi transmissions.The goal is to investigate the performance of the different types of autoencoders on an I/Q dataset consisting of LTE and a combination of Wi-Fi signals (IEEE 802.11ax and IEEE 802.11ac) for the classification task in terms of complexity, precision, and recall to identify the best algorithm.Our models have achieved up to 99.9% precision and 88.1% recall for this classification task.Additionally, with a shortest training time of approximately 47 seconds, the models are suitable for online learning and deployment in a dynamic RF environment.
Improving Cross-Modal Alignment in Vision Language Navigation via Syntactic Information

Vision language navigation is the task that requires an agent to navigate through a 3D environment based on natural language instructions.One key challenge in this task is to ground instructions with the current visual information that the agent perceives.Most of the existing work employs soft attention over individual words to locate the instruction required for the next action.However, different words have different functions in a sentence (e.g., modifiers convey attributes, verbs convey actions).Syntax information like dependencies and phrase structures can aid the agent to locate important parts of the instruction.Hence, in this paper, we propose a navigation agent that utilizes syntax information derived from a dependency tree to enhance alignment between the instruction and the current visual scenes.Empirically, our agent outperforms the baseline model that does not use syntax information on the Room-to-Room dataset, especially in the unseen environment.Besides, our agent achieves the new state-of-the-art on Room-Across-Room dataset, which contains instructions in 3 languages (English, Hindi, and Telugu).We also show that our agent is better at aligning instructions with the current visual information via qualitative visualizations. 1
Non-Parametric Few-Shot Learning for Word Sense Disambiguation

Word sense disambiguation (WSD) is a longstanding problem in natural language processing.One significant challenge in supervised all-words WSD is to classify among senses for a majority of words that lie in the longtail distribution.For instance, 84% of the annotated words have less than 10 examples in the SemCor training data.This issue is more pronounced as the imbalance occurs in both word and sense distributions.In this work, we propose MetricWSD, a non-parametric few-shot learning approach to mitigate this data imbalance issue.By learning to compute distances among the senses of a given word through episodic training, MetricWSD transfers knowledge (a learned metric space) from high-frequency words to infrequent ones.MetricWSD constructs the training episodes tailored to word frequencies and explicitly addresses the problem of the skewed distribution, as opposed to mixing all the words trained with parametric models in previous work.Without resorting to any lexical resources, MetricWSD obtains strong performance against parametric alternatives, achieving a 75.1 F1 score on the unified WSD evaluation benchmark (Raganato et al., 2017b).Our analysis further validates that infrequent words and senses enjoy significant improvement. 1
Automatic Story Generation: Challenges and Attempts

Automated storytelling has long captured the attention of researchers for the ubiquity of narratives in everyday life.The best humancrafted stories exhibit coherent plot, strong characters, and adherence to genres, attributes that current states-of-the-art still struggle to produce, even using transformer architectures.In this paper, we analyze works in story generation that utilize machine learning approaches to (1) address story generation controllability, (2) incorporate commonsense knowledge, (3) infer reasonable character actions and (4) generate creative language.
paht_nlp @ MEDIQA 2021: Multi-grained Query Focused Multi-Answer Summarization

In this article, we describe our systems for the MEDIQA 2021 Shared Tasks.First, we will describe our method for the second task, Multi-Answer Summarization (MAS).For extractive summarization, two series of methods are applied.The first one follows Xu and Lapata (2020).First a RoBERTa model is first applied to give a local ranking of the candidate sentences.Then a Markov Chain model is applied to evaluate the sentences globally.The second method applies cross-sentence contextualization to improve the local ranking and discard the global ranking step.Our methods achieve the 1st Place in the MAS task.For the question summarization (QS) and radiology report summarization (RRS) tasks, we explore how end-to-end pre-trained seq2seq model perform.A series of tricks for improving the fine-tuning performances are validated.
Analysis and evaluation of document-oriented structures

Document-oriented bases allow high flexibility in data representation which facilitates a rapid development of applications and enables many possibilities for data structuring. Unfortunately, in many cases, due to this flexibility and the absence of data modelling, the choice of a data representation is neglected by developers leading to many issues on several aspects of the document base and application quality; e.g., memory print, data redundancy, readability and maintainability. We aim at facilitating the study of data structuring alternatives and providing objective metrics to better reveal the advantages and disadvantages of a structure with respect to user needs. The main contributions of our approach are twofold. First of all, the semi-automatic generation of many suitable alternatives for data structuring given an initial UML model. Second, the automatic computation of structural metrics, allowing a comparison of the alternatives for JSON-compatible schema abstraction. These metrics reflect the complexity of the structure and are intended to be used in decision criteria for schema analysis and design process. This work capitalises on experiences with MongoDB, XML and software complexity metrics. The paper presents the schema generation and the metrics together with a validation scenario where we discuss how to use the results in a schema recommendation perspective.
A Non-Linear Structural Probe

Probes are models devised to investigate the encoding of knowledge-e.g.syntactic structure-in contextual representations.Probes are often designed for simplicity, which has led to restrictions on probe design that may not allow for the full exploitation of the structure of encoded information; one such restriction is linearity.We examine the case of a structural probe (Hewitt and Manning, 2019), which aims to investigate the encoding of syntactic structure in contextual representations through learning only linear transformations.By observing that the structural probe learns a metric, we are able to kernelize it and develop a novel non-linear variant with an identical number of parameters.We test on 6 languages and find that the radialbasis function (RBF) kernel, in conjunction with regularization, achieves a statistically significant improvement over the baseline in all languages-implying that at least part of the syntactic knowledge is encoded non-linearly.We conclude by discussing how the RBF kernel resembles BERT's self-attention layers and speculate that this resemblance leads to the RBF-based probe's stronger performance.
Automatic Guided Vehicle Global Path Planning Considering Multi-objective Optimization and Speed Control

Over the past few decades, as the main tool of intelligent material transportation, automatic guided vehicles (AGVs) have been widely used in modern production systems, logistics, transportation, industry, and commerce to further improve productivity, reduce labor costs, raise energy efficiency, and enhance safety.Path planning is a key issue in the field of AGVs to ensure that they do not collide with obstacles during movement and reach the destination as fast as possible to complete the assigned task.We propose two different and crucial operating environments in this paper.More specifically, in a static environment, a multi-objective mathematical model is established with the shortest path and the maximum smoothness, and the improved Levy random quantum particle swarm optimization (LRQPSO) algorithm is used to solve the proposed model and screen the AGV's driving path.In a dynamic environment, an inductive steering algorithm (ISA) that considers the movement of obstacles is proposed to make the AGV avoid obstacles rationally.By combining the steering characteristics of the two environments, AGV speed control rules are set and applied to the steering process in complex environments to ensure that the AGV can travel more smoothly and quickly.Simulation results show that the proposed method can ensure the obstacle avoidance and flexible steering of an AGV, and improve the driving speed and work efficiency in the two environments.In addition, compared with the conventional algorithm, the smoothness, operation speed, and work efficiency of the AGV are significantly increased using the improved LRQPSO algorithm and ISA.
TPA Auditing to Enhance the Privacy and Security in Cloud Systems

Over the last decade, many enterprises around the world migrating from traditional infrastructure to cloud resources in order to cut down operational and capital expenditure. With cloud computing, huge amount of data transactions is communicated between cloud consumers and cloud service providers. However, this cloud computing enables surplus security challenges associated to unauthorized access and data breaches. We proposed in this paper a trusted third-party auditor (TPA) model which uses lightweight cryptographic system and lightweight hashing technique to ensure data security and data integrity to audit the cloud users outsourced data from cloud service providers. With our proposed system, we solve the concern of data reliability using data correctness and verification analysis and error recovery analysis. The time complexity of our proposed system is less as compared with other TPA model. Our proposed system also shows resistance against various known cryptanalytic attacks, the performance and extensive compression technique of our proposed system are probably secure and highly proficient.
Image Authentication Based on Watermarking Approach: Review

Digital image authentication techniques have recently gained a lot of attention due to their importance to a large number of military and medical applications, banks, and institutions, which require a high level of security. Generally, digital images are transmitted over insecure media, such as the Internet and computer networks of various kinds. The Internet has become one of the basic pillars of life and a solution to many of the problems left by the coronavirus. As a result, images must be protected from attempts to alter their content that might affect important decision-making. An image authentication (IA) system is a solution to this difficult problem. In the previous literature, several methods have been proposed to protect the authenticity of an image. Digital image watermark is a strategy to ensure the reliability, resilience, intellectual property, and validity of multimedia documents. Digital media, such as images, audio, and video, can hide content. Watermarking of a digital image is a mechanism by which the watermark is embedded in multimedia and the image of the watermark is retrieved or identified in a multimedia entity. This paper reviews IA techniques, watermark embedding techniques, tamper detection methods and discusses the performance of the techniques, the pros and cons of each technique, and the proposed methods for improving the performance of watermark techniques.
Non-Markovian dynamics under time-translation symmetry

A dynamical symmetry is employed to determine the structure of the quantum non-Markovian time-local master equation. Such a structure is composed from two components: scalar kinetic coefficients and the standard quantum Markovian operator form. The kinetic coefficients are generally time-dependent and incorporate information on the kinematics and memory effects, while the operators manifest the dynamical symmetry. Specifically, we focus on time-translation symmetric dynamics, where the Lindblad jump operators constitute the eigenoperators of the free dynamics. This symmetry is motivated by thermodynamic microscopic considerations, where strict energy conservation between system and environment imposes the time-translation symmetry. The construction is generalized to other symmetries, and to driven quantum systems. The formalism is illustrated by three exactly solvable non-Markovian models, where the exact reduced description exhibits a dynamical symmetric structure. The formal structure of the master equation leads to a first principle calculation of the exact kinetic coefficients. This opens the possibility to simulate in a modular fashion non-Markovian dynamics.
A Novel Method for Food Market Regulation by Emotional Tendencies Predictions from Food Reviews Based on Blockchain and SAEs

As a part of food safety research, researches on food transactions safety has attracted increasing attention recently. Food choice is an important factor affecting food transactions safety: It can reflect consumer preferences and provide a basis for market regulation. Therefore, this paper proposes a food market regulation method based on blockchain and a deep learning model: Stacked autoencoders (SAEs). Blockchain is used to ensure the fairness of transactions and achieve transparency within the transaction process, thereby reducing the complexity of the trading environment. In order to enhance the usability, relevant Web pages have been developed to make it more friendly and conduct a security analysis for using blockchain. Consumers’ reviews after the transactions are finished can be used to train SAEs in order to perform emotional tendencies predictions. Compared with different advanced models for predictions, the test results show that SAEs have a better performance. Furthermore, in order to provide a basis for the formulation of regulation strategies and its related policies, case studies of different traders and commodities have also been conducted, proving the effectiveness of the proposed method.
YOLO-Highway: An Improved Highway Center Marking Detection Model for Unmanned Aerial Vehicle Autonomous Flight

Automatic visual navigation flight of an unmanned aerial vehicle (UAV) plays an important role in the highway maintenance field. Automatic highway center marking detection is the most important part of the visual navigation flight of a UAV. In this study, the UAV-viewed highway data are collected from the UAV perspective. This paper proposes a model named the YOLO-Highway that uses an improved form of the You Only Look Once (YOLO) model to enhance the real-time detection of highway marking problems. The proposed model is mainly designed by optimizing the network structure and the loss function of the original YOLOv3 model. The proposed model is verified by the experiments using the highway center marking dataset, and the results show that the average precision (AP) of the trained model is 82.79%, and the frames per second (FPS) is 25.71 f/s. In comparison with the original YOLOv3 model, the detection accuracy of the proposed model is improved by 7.05%, and its speed is improved by 5.29 f/s. Moreover, the proposed model had stronger environmental adaptability and better detection precision and speed than the original model in complex highway scenarios. The experimental results show that the proposed YOLO-Highway model can accurately detect the highway center markings in real-time and has high robustness to changes in different environmental conditions. Therefore, the YOLO-Highway model can meet the real-time requirements of the highway center marking detection.
Frustratingly Easy Edit-based Linguistic Steganography with a Masked Language Model

With advances in neural language models, the focus of linguistic steganography has shifted from edit-based approaches to generationbased ones.While the latter's payload capacity is impressive, generating genuine-looking texts remains challenging.In this paper, we revisit edit-based linguistic steganography, with the idea that a masked language model offers an off-the-shelf solution.The proposed method eliminates painstaking rule construction and has a high payload capacity for an edit-based model.It is also shown to be more secure against automatic detection than a generation-based method while offering better control of the security/payload capacity tradeoff.
Improving Tree-Structured Decoder Training for Code Generation via Mutual Learning

Code generation aims to automatically generate a piece of code given an input natural language utterance. Currently, among dominant models, it is treated as a sequence-to-tree task, where a decoder outputs a sequence of actions corresponding to the pre-order traversal of an Abstract Syntax Tree. However, such a decoder only exploits the pre-order traversal based preceding actions, which are insufficient to ensure correct action predictions. In this paper, we first throughly analyze the context modeling difference between neural code generation models with different traversals based decodings (preorder traversal vs breadth-first traversal), and then propose to introduce a mutual learning framework to jointly train these models. Under this framework, we continuously enhance both two models via mutual distillation, which involves synchronous executions of two one-to-one knowledge transfers at each training step. More specifically, we alternately choose one model as the student and the other as its teacher, and require the student to fit the training data and the action prediction distributions of its teacher. By doing so, both models can fully absorb the knowledge from each other and thus could be improved simultaneously. Experimental results and in-depth analysis on several benchmark datasets demonstrate the effectiveness of our approach. We release our code at https://github.com/DeepLearnXMU/CGML.
Mobile Remote Content Feed Editing in Content Management System

Mobile content editing in a content management system is the way and manner in which web or electronic content is edited, added, removed, or modified. The internet is the network of affiliated websites, while a website is an interconnection between web pages online. This work explains how the websites are created and authored using the HTML method and the various web authoring tools. It presents the reader with the benefits of using web authoring in web building.
Pre-Exposure Cybersickness Assessment Within a Chronic Pain Population in Virtual Reality

Virtual Reality (VR) is being increasingly explored as an adjunctive therapy for distraction from symptoms of chronic pain. However, using VR often causes cybersickness; a condition with symptoms similar to those of motion and simulator sickness. Cybersickness is commonly assessed using self-report questionnaires, such as the Simulator Sickness Questionnaire (SSQ), and is traditionally conducted post-exposure. It’s usually safe to assume a zero baseline of cybersickness as participants are not anticipated to be exhibiting any sickness symptoms pre-exposure. However, amongst populations such as chronic pain patients, it’s not unusual to experience symptoms of their condition or medication which could have a confounding influence on cybersickness symptom reporting. Therefore, in population groups where illness and medication use is common, assuming baseline is not necessarily desirable. This study aimed to investigate cybersickness baseline recordings amongst a chronic pain population, and highlights how deviations from an assumed baseline may incorrectly infer adverse effects arising from VR exposure. A repeated measures study design was used, in which twelve participants were assessed pre and post VR exposure via SSQ. Significant differences were found between actual and assumed pre-exposure baseline scores. Furthermore, we found significant differences between actual and assumed increases in cybersickness scores from baseline to post exposure. This study highlights that clinical sub-populations cannot be assumed to have a zero baseline SSQ score, and this should be taken into consideration when evaluating the usability of VR systems or interventions for participants from different demographics.