Reviewerly/exHarmony huggingface dataset:
Iterative Domain-Repaired Back-Translation

In this paper, we focus on the domain-specific translation with low resources, where indomain parallel corpora are scarce or nonexistent.One common and effective strategy for this case is exploiting in-domain monolingual data with the back-translation method.However, the synthetic parallel data is very noisy because they are generated by imperfect out-of-domain systems, resulting in the poor performance of domain adaptation.To address this issue, we propose a novel iterative domain-repaired back-translation framework, which introduces the Domain-Repair (DR) model to refine translations in synthetic bilingual data.To this end, we construct corresponding data for the DR model training by round-trip translating the monolingual sentences, and then design the unified training framework to optimize paired DR and NMT models jointly.Experiments on adapting NMT models between specific domains and from the general domain to specific domains demonstrate the effectiveness of our proposed approach, achieving 15.79 and 4.47 BLEU improvements on average over unadapted models and back-translation. 1
Automatic Curriculum Learning through Value Disagreement

Continually solving new, unsolved tasks is the key to learning diverse behaviors. Through reinforcement learning (RL), we have made massive strides towards solving tasks that have a single goal. However, in the multi-task domain, where an agent needs to reach multiple goals, the choice of training goals can largely affect sample efficiency. When biological agents learn, there is often an organized and meaningful order to which learning happens. Inspired by this, we propose setting up an automatic curriculum for goals that the agent needs to solve. Our key insight is that if we can sample goals at the frontier of the set of goals that an agent is able to reach, it will provide a significantly stronger learning signal compared to randomly sampled goals. To operationalize this idea, we introduce a goal proposal module that prioritizes goals that maximize the epistemic uncertainty of the Q-function of the policy. This simple technique samples goals that are neither too hard nor too easy for the agent to solve, hence enabling continual improvement. We evaluate our method across 13 multi-goal robotic tasks and 5 navigation tasks, and demonstrate performance gains over current state-of-the-art methods.
mgsohrab at WNUT 2020 Shared Task-1: Neural Exhaustive Approach for Entity and Relation Recognition Over Wet Lab Protocols

We present a neural exhaustive approach that addresses named entity recognition (NER) and relation recognition (RE), for the entity and relation recognition over the wet-lab protocols shared task.We introduce BERT-based neural exhaustive approach that enumerates all possible spans as potential entity mentions and classifies them into entity types or no entity with deep neural networks to address NER.To solve relation extraction task, based on the NER predictions or given gold mentions we create all possible trigger-argument pairs and classify them into relation types or no relation.In NER task, we achieved 76.60% in terms of F-score as third rank system among the participated systems.In relation extraction task, we achieved 80.46% in terms of F-score as the top system in the relation extraction or recognition task.Besides we compare our model based on the wet lab protocols corpus (WLPC) with the WLPC baseline and dynamic graph-based information extraction (DyGIE) systems.
Bio-medical analysis of breast cancer risk detection based on deep neural network

Breast tumour remains a most important reason of cancer fatality among women globally and most of them pass away due to delayed diagnosis. But premature recognition and anticipation can significantly diminish the chances of death. Risk detection of breast cancer is one of the major research areas in bioinformatics. Various experiments have been conceded to examine the fundamental grounds of breast tumour. Alternatively, it has already been verified that early diagnosis of tumour can give the longer survival chance to a patient. This paper aims at finding an efficient set of features for breast tumour prediction using deep learning approaches called restricted Boltzmann machine (RBM). The proposed framework diagnoses and analyses breast tumour patient's data with the help of deep neural network (DNN) classifier using the Wisconsin dataset of UCI machine learning repository and, thereafter assesses their performance in terms of measures like accuracy, precision, recall, F-measure, etc.
Grey Wolf Optimizer

GWO algorithm is a swarm or population-based meta-heuristic technique developed based on motivation from the hunting pattern of the Grey Wolves (GW). In this study, the model was implemented using MATLAB 2020. Thirty (30) search agents were considered and the maximum number of iterations was set to 1000. The best solution, best optimal value and objective function are presented in the study. GWO algorithm is considered useful for solving complex optimization problem.
HENIN: Learning Heterogeneous Neural Interaction Networks for Explainable Cyberbullying Detection on Social Media

In the computational detection of cyberbullying, existing work largely focused on building generic classifiers that rely exclusively on text analysis of social media sessions.Despite their empirical success, we argue that a critical missing piece is the model explainability, i.e., why a particular piece of media session is detected as cyberbullying.In this paper, therefore, we propose a novel deep model, HEterogeneous Neural Interaction Networks (HENIN), for explainable cyberbullying detection.HENIN contains the following components: a comment encoder, a post-comment co-attention sub-network, and session-session and post-post interaction extractors.Extensive experiments conducted on real datasets exhibit not only the promising performance of HENIN, but also highlight evidential comments so that one can understand why a media session is identified as cyberbullying.
ZEST: Zero-shot Learning from Text Descriptions using Textual Similarity and Visual Summarization

We study the problem of recognizing visual entities from the textual descriptions of their classes.Specifically, given birds' images with free-text descriptions of their species, we learn to classify images of previously-unseen species based on specie descriptions.This setup has been studied in the vision community under the name zero-shot learning from text, focusing on learning to transfer knowledge about visual aspects of birds from seen classes to previously-unseen ones.Here, we suggest focusing on the textual description and distilling from the description the most relevant information to effectively match visual features to the parts of the text that discuss them.Specifically, (1) we propose to leverage the similarity between species, reflected in the similarity between text descriptions of the species.(2) we derive visual summaries of the texts, i.e., extractive summaries that focus on the visual features that tend to be reflected in images.We propose a simple attention-based model augmented with the similarity and visual summaries components.Our empirical results consistently and significantly outperform the state-of-the-art on the largest benchmarks for text-based zero-shot learning, illustrating the critical importance of texts for zero-shot image-recognition.
Quantum measurement optimization by decomposition of measurements into extremals

Abstract Using the convex structure of positive operator value measurements and several quantities used in quantum metrology, such as quantum Fisher information or the quantum Van Trees information, we present an efficient numerical method to find the best strategy allowed by quantum mechanics to estimate a parameter. This method explores extremal measurements thus providing a significant advantage over previously used methods. We exemplify the method for different cost functions in a qubit and in a harmonic oscillator and find a strong numerical advantage when the desired target error is sufficiently small.
Towards Interpreting BERT for Reading Comprehension Based QA

BERT and its variants have achieved stateof-the-art performance in various NLP tasks.Since then, various works have been proposed to analyze the linguistic information being captured in BERT.However, the current works do not provide an insight into how BERT is able to achieve near human-level performance on the task of Reading Comprehension based Question Answering.In this work, we attempt to interpret BERT for RCQA.Since BERT layers do not have predefined roles, we define a layer's role or functionality using Integrated Gradients.Based on the defined roles, we perform a preliminary analysis across all layers.We observed that the initial layers focus on query-passage interaction, whereas later layers focus more on contextual understanding and enhancing the answer prediction.Specifically for quantifier questions (how much/how many), we notice that BERT focuses on confusing words (i.e., on other numerical quantities in the passage) in the later layers, but still manages to predict the answer correctly.The fine-tuning and analysis scripts will be publicly available at https://github.com/iitmnlp/BERT-
Defining and Learning Refined Temporal Relations in the Clinical Narrative

We present refinements over existing temporal relation annotations in the Electronic Medical Record clinical narrative.We refined the THYME corpus annotations to more faithfully represent nuanced temporality and nuanced temporal-coreferential relations.The main contributions are in re-defining CONTAINS and OVERLAP relations into CONTAINS, CONTAINS-SUBEVENT, OVERLAP and NOTED-ON.We demonstrate that these refinements lead to substantial gains in learnability for state-of-the-art transformer models as compared to previously reported results on the original THYME corpus.We thus establish a baseline for the automatic extraction of these refined temporal relations.Although our study is done on clinical narrative, we believe it addresses far-reaching challenges that are corpus-and domain-agnostic.
Robustness to Modification with Shared Words in Paraphrase Identification

Revealing the robustness issues of natural language processing models and improving their robustness is important to their performance under difficult situations.In this paper, we study the robustness of paraphrase identification models from a new perspective -via modification with shared words, and we show that the models have significant robustness issues when facing such modifications.To modify an example consisting of a sentence pair, we either replace some words shared by both sentences or introduce new shared words.We aim to construct a valid new example such that a target model makes a wrong prediction.To find a modification solution, we use beam search constrained by heuristic rules, and we leverage a BERT masked language model for generating substitution words compatible with the context.Experiments show that the performance of the target models has a dramatic drop on the modified examples, thereby revealing the robustness issue.We also show that adversarial training can mitigate this issue.
Adaptive Feature Selection With Reinforcement Learning for Skeleton-Based Action Recognition

Skeleton-based action recognition has attracted extensive attention recently in the computer vision community. Previous studies, especially GCN-based methods, have presented remarkable improvements for this task. However, in existing GCN-based methods, global average pooling is applied to the extracted features before the classifier. This may hurt the recognition performance since it neglects the fact that not all features are equally important in the temporal dimension. To tackle this issue, in this article, we propose a feature selection network (FSN) with actor-critic reinforcement learning. Given the extracted feature sequence, FSN learns to adaptively select the most representative features and discard the ambiguous features for action recognition. In addition, conventional graph convolution is a local operation, it cannot fully capture the non-local joint dependencies that could be vital to recognize the action. Thus, we also propose a generalized graph generation module to capture latent dependencies and further propose a generalized graph convolution network (GGCN). The GGCN and FSN are combined in a three-stream recognition framework, in which different types of information from skeleton data are further fused to improve the recognition accuracy. Extensive experiments demonstrate that the proposed FSN is a flexible and effective module that can cooperate with any existing GCN-based framework to enhance the recognition accuracy, the proposed GGCN can extract richer skeleton features for skeleton-based action recognition, and our method achieves superior performance over several public datasets, e.g. 95.7 top-1 accuracy on NTU-RGB+D, 86.7 top-1 accuracy on NTU-RGB+D 120, etc.
Contract Discovery: Dataset and a Few-Shot Semantic Retrieval Challenge with Competitive Baselines

We propose a new shared task of semantic retrieval from legal texts, in which a so-called contract discovery is to be performed-where legal clauses are extracted from documents, given a few examples of similar clauses from other legal acts.The task differs substantially from conventional NLI and shared tasks on legal information extraction (e.g., one has to identify text span instead of a single document, page, or paragraph).The specification of the proposed task is followed by an evaluation of multiple solutions within the unified framework proposed for this branch of methods.It is shown that state-of-the-art pretrained encoders fail to provide satisfactory results on the task proposed.In contrast, Language Model-based solutions perform better, especially when unsupervised fine-tuning is applied.Besides the ablation studies, we addressed questions regarding detection accuracy for relevant text fragments depending on the number of examples available.In addition to the dataset and reference results, LMs specialized in the legal domain were made publicly available.
Blockchain Hash, the Missing Axis of the Accounts to Settle the Triple Entry Bookkeeping System

The bookkeeping accounting method recognized all over the world is based on the so-called double entry, with which in each account (defined T-Account for its shape) there are two sections, Debit and Credit. These two sections take on different meanings depending on the nature of each account. The double entry is an exceptional consolidated accounting method that dates back to the fifteenth century, however it has highlighted limits in terms of auditing. The single entry did not take into account the cross relations between the accounts, reporting only on the journal. The double entry has overcome this limit by introducing the ledger, but a cross-check of the mirrored transactions recorded by the other companies is still missing. The introduction of blockchain technology could now offer a new opportunity to ensure further auditing control. The transition from double entry to triple entry is therefore only a matter of time. The blockchain itself is borrowing the word "ledger" from accounting and now it is time for accounting to introduce a third leg (another axis) to the T-Accounts, that can be turned into a new X-shaped version of accounts. This third axis can be used to include the unique identifier of each transaction represented by the Hash.
Packet Vision: a convolutional neural network approach for network traffic classification

Network traffic classification can improve the management and network service offer, taking into account the kind of application. The future network architectures, mainly mobile networks, foresee intelligent mechanisms in their architectural frameworks to deliver application-aware network requirements.The potential of convolutional neural networks capabilities, widely exploited in several contexts, can be used in network traffic classification. Thus, it is necessary to develop methods based on the content of packets which can transform them into a suitable input for CNN technologies. Hence, we implemented and evaluated the Packet Vision, a method capable of building images from packets raw-data, considering both header and payload. Our approach surpasses those found in the state-of-the-art, considering classification performance and regarding the fully-packet structure characteristic, delivering security and privacy by transforming the raw-data packet into images. Besides, we built a dataset with four traffic classes and evaluated three CNNs architectures, considering performance and the exploitation of training from scratch, fine-tuning and hyperparameter optimization. Experiments showcase applicability and suitability when combining Packet Vision with CNNs, which seemed to be a promising approach to deliver outstanding performance in the classification of network traffic.
Neural Graph for Personalized Tag Recommendation

Traditional personalized tag recommendation methods cannot guarantee that the coupling relationships hidden in the interactions among entities are effectively encoded in the process of learning the representations of entities, resulting in insufficient expressive capacity for characterizing the preferences or attributes of entities. In this article, we first propose a graph neural networks boosted personalized tag recommendation model, namely NGTR, which integrates the graph neural networks into the pairwise interaction tensor factorization model. Specifically, we exploit the graph neural networks to capture the coupling relationships and integrate the coupling relationships into the learning of representations of entities by transmitting and assembling the representations of neighbors along the interaction graphs. In addition, we also propose a light graph neural networks boosted personalized tag recommendation model, namely LNGTR. Different from NGTR, our proposed LNGTR model removes feature transformation and nonlinear activation components as well as adopts the weighted sum of the embeddings learned at all layers as the final embedding. Experimental results on real world datasets show that our proposed personalized tag recommendation models outperform the traditional tag recommendation methods.
Succinct and Adaptively Secure ABE for ABP from k-Lin

We present succinct and adaptively secure attribute-based encryption (ABE) schemes for arithmetic branching programs, based on k-Lin in pairing groups. Our key-policy ABE scheme has ciphertexts of constant size, independent of the length of the attributes, and our ciphertext-policy ABE scheme has secret keys of constant size. Our schemes improve upon the recent succinct ABE schemes in [Tomida and Attrapadung, ePrint ’20], which only handles Boolean formulae. All other prior succinct ABE schemes either achieve only selective security or rely on q-type assumptions. Our schemes are obtained through a general and modular approach that combines a public-key inner product functional encryption satisfying a new security notion called gradual simulation security and an information-theoretic randomized encoding scheme called arithmetic key garbling scheme.
Perceptual Quality of Light Field Images and Impact of Visualization Techniques

Light Field imaging provides a wide range of interactive features, such as view point changing and refocusing, and it has been developing as a solution for six degree of freedom applications. Many ongoing efforts, specifically related to pre/post processing, compression, and rendering, have been devoted to the development of this technology. To benchmark processing algorithms, the knowledge of degraded or improved quality level is important. However, the definition of validated subjective and objective quality assessment methods is still an open and challenging issue. In this scenario, this contribution deals with: the impact of visualization techniques on Light Filed image Quality of Experience, a visualization technique recommended for subjective quality assessment, perceptual visual impact of compression and noise artifacts, performance analysis of 2D image quality measures applied to Light Field images, and a Light Field image quality dataset.
A Different Approach on Automated Use Case Diagram Semantic Assessment

The use case diagram is one of the diagrams commonly taught in colleges of computer science. Assessment of use case diagrams is often an obstacle for a teacher in the learning process. It is due to the interpersonal and intrapersonal problems of the teacher in assessing. Interpersonal problems are caused by the absence of an assessment standard among teachers. Intrapersonal problems are caused by the inconsistency of a teacher in assessing many diagrams of student answers. This research aims to create a semantic use case diagram automatic assessment method. Semantic assessment is divided into two kinds, namely property and relationship. All information used is a label translated from the XMI document. Similarity assessment between labels used cosine similarity, employing WuPalmer to perform WordNet searches. The results showed that the proposed method had a substantial agreement with the teacher as an expert; however, a teacher tends to look at property information rather than relationship information to assess use case diagrams.
Memebusters at SemEval-2020 Task 8: Feature Fusion Model for Sentiment Analysis on Memes Using Transfer Learning

In this paper, we describe our deep learning system used for SemEval 2020 Task 8: Memotion analysis.We participated in all the subtasks i.e Subtask A: Sentiment classification, Subtask B: Humor classification, and Subtask C: Scales of semantic classes.Similar multimodal architecture was used for each subtask.The proposed architecture makes use of transfer learning for images and text feature extraction.The extracted features are then fused together using stacked bidirectional Long Short Term Memory (LSTM) and Gated Recurrent Unit (GRU) model with attention mechanism for final predictions.We also propose a single model for predicting semantic classes (Subtask B) as well as their scales (Subtask C) by branching the final output of the post LSTM dense layers.Our model was ranked 5 in Subtask B and ranked 8 in Subtask C and performed nicely in Subtask A on the leader board.Our system makes use of transfer learning for feature extraction and fusion of image and text features for predictions.