Reviewerly/exHarmony huggingface dataset:
Developing Products Update-Alert System for E-Commerce Websites Users using Html Data and Web Scraping Technique

Websites are regarded as domains of limitless information which anyone and everyone can access. The new trend of technology has shaped the way we do and manage our businesses. Today, advancements in Internet technology has given rise to the proliferation of e-commerce websites. This, in turn made the activities and lifestyles of marketers/vendors, retailers and consumers (collectively regarded as users in this paper) easier as it provides convenient platforms to sale/order items through the internet. Unfortunately, these desirable benefits are not without drawbacks as these platforms require that the users spend a lot of time and efforts searching for best product deals, products updates and offers on ecommerce websites. Furthermore, they need to filter and compare search results by themselves which takes a lot of time and there are chances of ambiguous results. In this paper, we applied web crawling and scraping methods on an e-commerce website to obtain HTML data for identifying products updates based on the current time. These HTML data are preprocessed to extract details of the products such as name, price, post date and time, etc. to serve as useful information for users.
Zero-Shot Information Extraction to Enhance a Knowledge Graph Describing Silk Textiles

The knowledge of the European silk textile production is a typical case for which the information collected is heterogeneous, spread across many museums and sparse since rarely complete.Knowledge Graphs for this cultural heritage domain, when being developed with appropriate ontologies and vocabularies, enable to integrate and reconcile this diverse information.However, many of these original museum records still have some metadata gaps.In this paper, we present a zero-shot learning approach that leverages the Concept-Net common sense knowledge graph to predict categorical metadata informing about the silk objects production.We compared the performance of our approach with traditional supervised deep learning-based methods that do require training data.We demonstrate promising and competitive performance for similar datasets and circumstances and the ability to predict sometimes more fine-grained information.
A Finer-grain Universal Dialogue Semantic Structures based Model For Abstractive Dialogue Summarization

Although abstractive summarization models have achieved impressive results on document summarization tasks, their performance on dialogue modeling is much less satisfactory due to the crude and straight methods for dialogue encoding.To address this question, we propose a novel end-to-end Transformer-based model FinDS for abstractive dialogue summarization that leverages Finer-grain universal Dialogue semantic Structures to model dialogue and generates better summaries.Experiments on the SAMsum dataset show that FinDS outperforms various dialogue summarization approaches and achieves new stateof-the-art (SOTA) ROUGE results.Finally, we apply FinDS to a more complex scenario, showing the robustness of our model.We also release our source code 1 * The first two authors contributed equally.Weiran Xu
GRI: General Reinforced Imitation and Its Application to Vision-Based Autonomous Driving

Deep reinforcement learning (DRL) has been demonstrated to be effective for several complex decision-making applications, such as autonomous driving and robotics. However, DRL is notoriously limited by its high sample complexity and its lack of stability. Prior knowledge, e.g., as expert demonstrations, is often available but challenging to leverage to mitigate these issues. In this paper, we propose General Reinforced Imitation (GRI), a novel method which combines benefits from exploration and expert data and is straightforward to implement over any off-policy RL algorithm. We make one simplifying hypothesis: expert demonstrations can be seen as perfect data whose underlying policy gets a constant high reward. Based on this assumption, GRI introduces the notion of offline demonstration agent. This agent sends expert data which are processed both concurrently and indistinguishably with the experiences coming from the online RL exploration agent. We show that our approach enables major improvements on camera-based autonomous driving in urban environments. We further validate the GRI method on Mujoco continuous control tasks with different off-policy RL algorithms. Our method ranked first on the CARLA Leaderboard and outperforms World on Rails, the previous state-of-the-art method, by 17%.
Language-agnostic Representation from Multilingual Sentence Encoders for Cross-lingual Similarity Estimation

We propose a method to distil languageagnostic meaning embedding using a multilingual sentence encoder.By removing languagespecific information from the original embedding, we retrieve an embedding that fully represents the meaning of the sentence.The proposed method relies only on parallel corpora without any human annotations.Our meaning embedding allows for efficient cross-lingual sentence similarity estimation using a simple cosine similarity calculation.Experimental results of both the quality estimation of machine translation and cross-lingual semantic textual similarity tasks reveal that our method consistently outperforms the strong baselines using the original multilingual embeddings.The method also consistently improves the performance of any pre-trained multilingual sentence encoder, even in low-resource language pairs, where only tens of thousands of parallel sentence pairs are available. 1
Have You Seen That Number? Investigating Extrapolation in Question Answering Models

Numerical reasoning in machine reading comprehension (MRC) has shown drastic improvements over the past few years.While the previous models for numerical MRC are able to interpolate the learned numerical reasoning capabilities, it is not clear whether they can perform just as well on numbers unseen in the training dataset.Our work rigorously tests state-of-the-art models on DROP, a numerical MRC dataset, to see if they can handle passages that contain out-of-range numbers.One of the key findings is that the models fail to extrapolate to unseen numbers.Presenting numbers as digit-by-digit input to the model, we also propose the E-digit number form that alleviates the lack of extrapolation in models and reveals the need to treat numbers differently from regular words in the text.Our work provides a valuable insight into the numerical MRC models and the way to represent number forms in MRC.* Equal contribution.As of the census of 2000, there were 49,927 people, 18,009 households, and 12,192 families residing in the county.The population density was 48 people per square mile (19/km²).As of the census of 2000, there were 4,992,700 people, 1,800,900 households, and 1,219,200 families residing in the county.The population density was 4,800 people per square mile (19/km²)."How many people, households, and families reside in the county according to the 2000 census?"
A flexible closed-loop (fcl) pid and dynamic fuzzy logic + pid controllers for optimization of dc motor

For electrical machines and power circuits to operate at high performance, it is important to control them at the optimum point. So, in this study, a flexible closed-loop (FCL) PID and PID FUZZY study is presented. Different from other studies, different controls can be activated for different positions and durations within the total operating time of the motor. At the same time, by applying four inputs for fuzzification in the designed fuzzy logic control section, 3 different outputs are provided to the PID control in three defuzzification sections. So, Dynamic Fuzzy logic interactivity is ensured with PID on load. This, unlike other PID FUZZY controllers, provides a more effective application of error and error change to the system. In the second chapter, DC motors and controls are explained. In the third chapter, designed controls and dc motor applications are made. The results obtained in practice reveal the effectiveness of the proposed system after compared with conventional P, PI and PID.
NB-MLM: Efficient Domain Adaptation of Masked Language Models for Sentiment Analysis

While Masked Language Models (MLM) are pre-trained on massive datasets, the additional training with the MLM objective on domain or task-specific data before fine-tuning for the final task is known to improve the final performance.This is usually referred to as the domain or task adaptation step.However, unlike the initial pre-training, this step is performed for each domain or task individually and is still rather slow, requiring several GPU days compared to several GPU hours required for the final task fine-tuning.We argue that the standard MLM objective leads to inefficiency when it is used for the adaptation step because it mostly learns to predict the most frequent words, which are not necessarily related to a final task.We propose a technique for more efficient adaptation that focuses on predicting words with large weights of the Naive Bayes classifier trained for the task at hand, which are likely more relevant than the most frequent words.The proposed method provides faster adaptation and better final performance for sentiment analysis compared to the standard approach.
Efficient Application of Tensor Core Units for Convolving Images

Tensor Core Units (TCUs) are a domain-specific architecture capable of executing small Matrix Multiply-Accumulates (MMAs) in a single clock cycle, showing significant performance improvements over other optimized implementations. When Convolutional Neural Networks (CNNs) are accelerated using TCUs, the layout of the input image is transformed to allow large amounts of filters to be applied to an image using a single large matrix-matrix multiplication. However, there are applications in other domains that only require a small number of filters. To accommodate such applications, we first show the inappropriateness of this standard technique of transforming the data layout. Subsequently, we propose an approach that uses TCUs to convolve one filter with an image. Further, we introduce several optimizations of this method. Finally, we evaluate the performance of our approach and its optimizations by comparing it to code generated using a state-of-the-art image processing language.
Fine-grained Temporal Relation Extraction with Ordered-Neuron LSTM and Graph Convolutional Networks

Fine-grained temporal relation extraction (FineTempRel) aims to recognize the durations and timeline of event mentions in text.A missing part in the current deep learning models for FineTempRel is their failure to exploit the syntactic structures of the input sentences to enrich the representation vectors.In this work, we propose to fill this gap by introducing novel methods to integrate the syntactic structures into the deep learning models for FineTempRel.The proposed model focuses on two types of syntactic information from the dependency trees, i.e., the syntax-based importance scores for representation learning of the words and the syntactic connections to identify important context words for the event mentions.We also present two novel techniques to facilitate the knowledge transfer between the subtasks of FineTempRel, leading to a novel model with the state-of-the-art performance for this task.
PLATO-KAG: Unsupervised Knowledge-Grounded Conversation via Joint Modeling

Large-scale conversation models are turning to leveraging external knowledge to improve the factual accuracy in response generation.Considering the infeasibility to annotate the external knowledge for large-scale dialogue corpora, it is desirable to learn the knowledge selection and response generation in an unsupervised manner.In this paper, we propose PLATO-KAG (Knowledge-Augmented Generation), an unsupervised learning approach for end-to-end knowledge-grounded conversation modeling.For each dialogue context, the top-k relevant knowledge elements are selected and then employed in knowledgegrounded response generation.The two components of knowledge selection and response generation are optimized jointly and effectively under a balanced objective.Experimental results on two publicly available datasets validate the superiority of PLATO-KAG.
Optimizing Hopfield Neural Network for Super-Resolution Mapping

Remote sensing is a potential source of information of land covers on the surface of the Earth. Different types of remote sensing images offer different spatial resolution quality. High resolution images contain rich information, but they are expensive, while low resolution image are less detail but they are cheap. Super-resolution mapping (SRM) technique is used to enhance the spatial resolution of the low resolution image in order to produce land cover mapping with high accuracy. The mapping technique is crucial to differentiate land cover classes. Hopfield neural network (HNN) is a popular approach in SRM. Currently, numerical implementation of HNN uses ordinary differential equation (ODE) calculated with traditional Euler method. Although producing satisfactory accuracy, Euler method is considered slow especially when dealing with large data like remote sensing image. Therefore, in this paper several advanced numerical methods are applied to the formulation of the ODE in SRM in order to speed up the iterative procedure of SRM. These methods are an improved Euler, Runge-Kutta, and Adams-Moulton. Four classes of land covers such as vegetation, water bodies, roads, and buildings are used in this work. Results of traditional Euler produces mapping accuracy of 85.18% computed in 1000 iterations within 220-1020 seconds. Improved Euler method produces accuracy of 86.63% computed in a range of 60-620 iterations within 20-500 seconds. Runge-Kutta method produces accuracy of 86.63% computed in a range of 70-600 iterations within 20-400 seconds. Adams-Moulton method produces accuracy of 86.64% in a range of 40-320 iterations within 10-150 seconds.
Deep Ensembles Based on Stochastic Activations for Semantic Segmentation

Semantic segmentation is a very popular topic in modern computer vision, and it has applications in many fields. Researchers have proposed a variety of architectures for semantic image segmentation. The most common ones exploit an encoder–decoder structure that aims to capture the semantics of the image and its low-level features. The encoder uses convolutional layers, in general with a stride larger than one, to extract the features, while the decoder recreates the image by upsampling and using skip connections with the first layers. The objective of this study is to propose a method for creating an ensemble of CNNs by enhancing diversity among networks with different activation functions. In this work, we use DeepLabV3+ as an architecture to test the effectiveness of creating an ensemble of networks by randomly changing the activation functions inside the network multiple times. We also use different backbone networks in our DeepLabV3+ to validate our findings. A comprehensive evaluation of the proposed approach is conducted across two different image segmentation problems: the first is from the medical field, i.e., polyp segmentation for early detection of colorectal cancer, and the second is skin detection for several different applications, including face detection, hand gesture recognition, and many others. As to the first problem, we manage to reach a Dice coefficient of 0.888, and a mean intersection over union (mIoU) of 0.825, in the competitive Kvasir-SEG dataset. The high performance of the proposed ensemble is confirmed in skin detection, where the proposed approach is ranked first concerning other state-of-the-art approaches (including HarDNet) in a large set of testing datasets.
IR like a SIR: Sense-enhanced Information Retrieval for Multiple Languages

With the advent of contextualized embeddings, attention towards neural ranking approaches for Information Retrieval increased considerably.However, two aspects have remained largely neglected: i) queries usually consist of few keywords only, which increases ambiguity and makes their contextualization harder, and ii) performing neural ranking on non-English documents is still cumbersome due to shortage of labeled datasets.In this paper we present SIR (Sense-enhanced Information Retrieval) to mitigate both problems by leveraging word sense information.At the core of our approach lies a novel multilingual query expansion mechanism based on Word Sense Disambiguation that provides sense definitions as additional semantic information for the query.Importantly, we use senses as a bridge across languages, thus allowing our model to perform considerably better than its supervised and unsupervised alternatives across French, German, Italian and Spanish languages on several CLEF benchmarks, while being trained on English Robust04 data only.We release SIR at https://github.com/SapienzaNLP/sir.
Real-time binaural room modelling for augmented reality applications

This paper proposes and evaluates an integrated method for real-time, head-tracked, 3D binaural audio with synthetic reverberation.Virtual vector base amplitude panning is used to position the sound source and spatialize outputs from a scattering delay network reverb algorithm running in parallel.A unique feature of this approach is its realization of interactive auralization using vector base amplitude panning and a scattering delay network, within acceptable levels of latency, at low computational cost.The rendering model also allows direct parameterization of room geometry and absorption characteristics.Varying levels of reverb complexity can be implemented, and these were evaluated against two distinct aspects of perceived sonic immersion.Outcomes from the evaluation provide benchmarks for how the approach could be deployed adaptively, to balance three real-time spatial audio objectives of envelopment, naturalness, and efficiency, within contrasting physical spaces.1. node azimuth/elevation points were confirmed
Modeling Human Sentence Processing with Left-Corner Recurrent Neural Network Grammars

In computational linguistics, it has been shown that hierarchical structures make language models (LMs) more human-like.However, the previous literature has been agnostic about a parsing strategy of the hierarchical models.In this paper, we investigated whether hierarchical structures make LMs more human-like, and if so, which parsing strategy is most cognitively plausible.In order to address this question, we evaluated three LMs against human reading times in Japanese with head-final leftbranching structures: Long Short-Term Memory (LSTM) as a sequential model and Recurrent Neural Network Grammars (RNNGs) with top-down and left-corner parsing strategies as hierarchical models.Our computational modeling demonstrated that left-corner RNNGs outperformed top-down RNNGs and LSTM, suggesting that hierarchical and leftcorner architectures are more cognitively plausible than top-down or sequential architectures.In addition, the relationships between the cognitive plausibility and (i) perplexity, (ii) parsing, and (iii) beam size will also be discussed. 1
Classification of Leaves Based on the Shape of Leaves Using Convolutional Neural Network Methods

One part of the tree, namely the leaves, which grow on the branches, has several types of leaves consisting of 4 shapes, ranging from circular shapes, elongated shapes, and some even have a finger shape. Often we mistake the shapes of these leaves. This study discusses the classification of leaves based the shape of the leaf bones using the Convolutional Neural Network, which is used to classify data that has been labeled using one of the methods, namely supervised learning. The purpose of this method is to classify a variable into the variables that have been listed. The goal is to classify leaves based on leaf shape to implement a Convolutional Neural Network algorithm model for leaf classification based on bone shape, which will produce an accuracy value. Accuracy values are obtained from conducting experiments at the training and trial stages. So it can be concluded using the epochs parameter of 30 and a batch size of 128, using ReLU and Softmax activations. The results obtained for the accuracy value for training are 98.52%, while the validation is 89.06%.
MS-Mentions: Consistently Annotating Entity Mentions in Materials Science Procedural Text

Material science synthesis procedures are a promising domain for scientific NLP, as proper modeling of these recipes could provide insight into new ways of creating materials.However, a fundamental challenge in building information extraction models for material science synthesis procedures is getting accurate labels for the materials, operations, and other entities of those procedures.We present a new corpus of entity mention annotations over 595 Material Science synthesis procedural texts (157,488 tokens), which greatly expands the training data available for the Named Entity Recognition task.We outline a new label inventory designed to provide consistent annotations and a new annotation approach intended to maximize the consistency and annotation speed of domain experts.Inter-annotator agreement studies and baseline models trained upon the data suggest that the corpus provides high-quality annotations of these mention types.This corpus helps lay a foundation for future high-quality modeling of synthesis procedures.
Interoperability between Real and Virtual Environments Connected by a GAN for the Path-Planning Problem

Path planning is a fundamental issue in robotic systems because it requires coordination between the environment and an agent. The path-planning generator is composed of two modules: perception and planning. The first module scans the environment to determine the location, detect obstacles, estimate objects in motion, and build the planner module’s restrictions. On the other hand, the second module controls the flight of the system. This process is computationally expensive and requires adequate performance to avoid accidents. For this reason, we propose a novel solution to improve conventional robotic systems’ functions, such as systems having a small-capacity battery, a restricted size, and a limited number of sensors, using fewer elements. A navigation dataset was generated through a virtual simulator and a generative adversarial network to connect the virtual and real environments under an end-to-end approach. Furthermore, three path generators were analyzed using deep-learning solutions: a deep convolutional neural network, hierarchical clustering, and an auto-encoder. Since the path generators share a characteristic vector, transfer learning approaches complex problems by using solutions with fewer features, minimizing the costs and optimizing the resources of conventional system architectures, thus improving the limitations with respect to the implementation in embedded devices. Finally, a visualizer applying augmented reality was used to display the path generated by the proposed system.
DEEP: DEnoising Entity Pre-training for Neural Machine Translation

It has been shown that machine translation models usually generate poor translations for named entities that are infrequent in the training corpus.Earlier named entity translation methods mainly focus on phonetic transliteration, which ignores the sentence context for translation and is limited in domain and language coverage.To address this limitation, we propose DEEP, a DEnoising Entity Pretraining method that leverages large amounts of monolingual data and a knowledge base to improve named entity translation accuracy within sentences.Besides, we investigate a multi-task learning strategy that finetunes a pre-trained neural machine translation model on both entity-augmented monolingual data and parallel data to further improve entity translation.Experimental results on three language pairs demonstrate that DEEP results in significant improvements over strong denoising autoencoding baselines, with a gain of up to 1.3 BLEU and up to 9.2 entity accuracy points for English-Russian translation. 1