Reviewerly/exHarmony huggingface dataset:
Fast Blind Decontouring Network

Contouring artifacts usually appear in large and smooth flat areas, which are caused by many widely used processes such as bit-depth expansion, compression, image sharpening and contrast enhancement. Unfortunately, recent decontouring methods were mainly designed for specific and non-blind degradations, which significantly reduces the generalization ability of these methods when applied to complex and various real-world false contours. Therefore, this paper explores the blind decontouring problem by proposing a blind decontouring network (BDCN). Instead of directly training a decontouring network with mixed degradations, the proposed model consists of two independent modules, i.e., a flat region detection module (FDM) and a decontouring module (DCM). The FDM is designed to extract flat region masks robust to various false contours, which can preserve texture details from global smoothing. Then, the task of DCM becomes simply smoothing different contouring artifacts. Both the FDM and DCM are designed with a lightweight architecture and reparameterization strategy. Experimental results on both synthetic and real-world contouring artifacts demonstrate the effectiveness and generalization of the proposed method.
Optimizing Multidocument Summarization by Blending Reinforcement Learning Policies

We consider extractive summarization within a cluster of related texts (multidocument summarization). Unlike single-document summarization, redundancy is particularly important because sentences across related documents might convey overlapping information. Thus, sentence extraction in such a setting is difficult because one will need to determine which pieces of information are relevant while avoiding unnecessary repetitiveness. To solve this difficult problem, we propose a novel reinforcement learning-based method Policy Blending with maximal marginal relevance and Reinforcement Learning ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">PoBRL</i> ) for solving multidocument summarization. PoBRL jointly optimizes over the following objectives necessary for a high-quality summary: importance, relevance, and length. Our strategy decouples this multiobjective optimization into different subproblems that can be solved individually by reinforcement learning. Utilizing PoBRL, we then blend each learned policies to produce a summary that is a concise and a complete representation of the original input. Our empirical analysis shows high performance on several multidocument datasets. Human evaluation also shows that our method produces high-quality output.
Memento

When we looked at the Command design pattern, we noted that recording a list of every single change theoretically allows you to roll back the system to any point in time – after all, you've kept a record of all the modifications.
Gender inequalities in rural labour markets: the role of corporate social responsibility in Niger Delta, Nigeria

Purpose The purpose of this paper is to critically examine the multinational oil companies’ (MOCs) corporate social responsibility (CSR) initiatives in Nigeria. Its special focus is to investigate the impact of the global memorandum of understanding (GMoU) on women’s involvement in rural labour market in the Niger Delta region of Nigeria. Design/methodology/approach This paper adopts a survey research technique, aimed at gathering information from a representative sample of the population, as it is essentially cross-sectional, describing and interpreting the current situation. A total of 768 respondents were sampled across the rural areas of the Niger Delta region. Findings The results from the use of a combined propensity score matching and logit model indicate that CSR of the MOCs using GMoU model has recorded little but significant success in improving women’s participation in the labour market by freeing women’s time through labour-saving technologies and the provision of public services, raising women’s capital through education, eliminating discriminatory employment practices and capitalizing on public works programmes. Practical implications This suggest that the underlining causes of gender inequality in rural labour markets are institutional, including both social norms and the structure of labour market organizations, and can be holistically tackled through CSR programmes, government policies and building the strength of women in labour organizations. Social implications This implies that reducing rural poverty requires not just the barriers to women’s participation in decent employment but also aiming for a policy that helps to change people’s perceptions of what is possible, beneficial and fair; fosters cooperative action; and strengthens women’s bargaining power in the work place, the home and the market place. Originality/value This research contributes to the gender debate in agriculture from a CSR perspective in developing countries and rationale for demands for social project by host communities. It concludes that business has an obligation to help in solving problems of public concern.
Research on Improved Random Forest Algorithm for Highly Unbalanced Data

Abstract To address the problems of low prediction performance of minority class samples due to highly unbalanced data and improved prediction performance of minority class but decreased prediction performance of majority class after balanced training set, an improved algorithm combining feature fusion and random forests quantile classifier is proposed. The algorithm obtains multiple data subsets by feature segmentation through sliding windows, and then inputs the obtained data subsets into the quantile random forest for cross-validation to obtain multiple class probability vectors for each sample, and finally splices the obtained class probability vectors with the original data set as new features to form a new data set, and uses the new combination of features to better represent the relationship between minority and majority classes, thus improve the overall classification performance of the classifier. Experiments on the KEEL dataset show that the improved algorithm improves the overall sample classification performance for unbalanced data compared to other algorithms.
FAIAD: Feature Adaptive-based Image Anomaly Detection

Abstract Image abnormality detection is a hot research topic in the field of data mining, and it has great application value in the fields of industrial appearance defect detection and medical image analysis. To address the problem of poor performance of anomaly detection models caused by incomplete feature extraction, we propose a feature-adaptive image anomaly detection model. FAIAD first trains the initial feature extraction model by pre-training the model. Then introduce feature adaptation methods to improve image feature extraction performance. The last step is to calculate the accuracy of image anomaly detection. In order to explore the feature extraction effects of different neural networks, this paper designs three kinds of backbone network comparison experiments. Experimenting on both Cifar-10 and Fashion-MNIST datasets, the accuracy of our model improved by 3.5% and 2.3%, respectively, compared to the baseline model. The experimental results show that combining pre-trained models with feature adaptation methods can effectively improve the performance of anomaly detection models.
A State Transition Based Cache Side Channel Attack Model

Abstract Over the last decades, the Cache Side Channel Attack (CSCA) seriously threatens the security of user information, which repeatedly highlights the security of cache. Even though the secure cache design can mitigate or defend against this attack, the high design period and tap-out cost reduce the development of the specific designs. Therefore, many researchers focus on proposing a secure cache model to analyse the CSCA and simplify the secure design. Nowadays, the cache model can be divided into 1) Indicator analysis model; 2) Cache verify model; 3) Cache abstract model. However, most of these models focus on the establishment phase of CSCA, regardless of the usage phase of CSCA. On the other hand, the granularity of the cache model is either the entire cache or a single cache line, which can’t describe the cache behaviour in CSCA clearly. To overcome these limitations, we proposed a novel cache model based on state transition and gave a formal specification of CSCA by this model in this work. Firstly, we summarize 11 basic states of the cache set and point to the target state in the process of CSCA. Then we describe the CSCA from three aspects: pre-attack cost, state transition cost, and detection accuracy, which gives a more comprehensive analysis of CSCA.
Inducing a Malay Lexicon from an Unlabelled Dataset Using Word Embeddings

The Malay language in Malaysia is commonly mixed with English and slang words especially when observing social media text. In the past, researchers would develop Malay-English lexicons by translating widely accepted English lexicons and carrying over the labels. However, translation may produce words that have no sentiment or is rarely used in its translated form. In this paper, an end-to-end framework involving a two-phase sentiment classification method is proposed. The first phase presents a method where a domain-specific lexicon is induced from word embeddings trained on an unlabelled dataset. It was found that the induced lexicon-based classifier outperformed general and translated lexicons. However, lexicon-based methods still face low recall issues when documents have sentiment words not found in the lexicon. This can be mitigated by the second phase of the framework where a supervised classifier is trained on a filtered output of the induced lexicon-based method to produce a model that can classify new and unseen documents. This framework was shown to be applicable to any Malay or English unlabelled dataset through results with a f1-score of 0.81 on the MALAYA Twitter dataset and 0.82 on the IMDB movie review dataset.
Adaptation of HMIs According to Users’ Feelings Based on Multi-agent Systems

There is always a component of the field of Human-Machine Interactions (HMI) which constantly tries to provide solutions to the problems relating to adaptation, in order to move towards more ergonomic approaches and more flexible and adaptive tools. It is in this context that this research work is taking place, which will consider an adaptation of User Interfaces (UIs) guided by the results of the analysis of user feelings and this, by adopting a new approach based on Multi-Agent System (MAS) and Deep Learning. To implement this approach, a system admitting a first component as a dynamic Django web application and a second component which corresponds to a SPADE Multi-Agent System, has been produced and tested and has shown effective and interesting experimental results.
A Novel Type-Based Genetic Algorithm for Extractive Summarization

Automatic text summarization has been an growingly important task since a huge amount of textual information needs to be processed on the Internet. Genetic Algorithm (GA) is an efficient approach for extractive text summarization, which aims to find out the best summary with an optimized fitness function through the evolution of generations. This paper proposes a novel extractive summarization method using GA with two types of individuals based on their internal chromosomal structure. Each individual may have one or two full chromosomes, where a chromosome represents a candidate summary. In this type-based GA, good summaries are better kept through generations, the mutation more likely happens with more flexible strategies and prominent summaries are more likely found in the solution space. The mutation can occur in two levels: off-springs can be obtained by changing their parents' type or flipping some genes, i.e. multi-point, in their parents' chromosomes. Our proposed approach has been experimented on DUC2001, DUC2002 and CNN/DailyMail datasets, outperforming all other extractive state-of-the-art methods by all three Rouge points. Indeed, the Rouge-1 and Rouge-L scores considerably improve from 10% to 20%, while the Rouge-2 has the highest performance.
Evolution of Prioritized $$\mathcal{E}\mathcal{L}$$ Ontologies

In this paper, we place ourselves in the context of the evolution of $$\mathcal{E}\mathcal{L}$$ ontologies when a new piece of information that can be uncertain is available. The $$\mathcal{E}\mathcal{L}$$ is a tractable family of lightweight description logics that underlay the OWL2 EL profile. To take into consideration the weights attached to the axioms reflecting their reliability or credibility, we propose an extension of the $$\mathcal{E}\mathcal{L}$$ family within the possibility theory setting. This theory provides a natural framework to deal with the ordinal scale. We show how to induce a prioritized $$\mathcal{E}\mathcal{L}$$ ontology in case we start with standard ontology,i.e., axioms without weights. The explored method exploits conflict statistical analysis between axioms to induce a vector space. Finally, we propose polynomial syntactic algorithms for the evolution process while preserving the consistency of the ontology.
Face Authentication from Masked Face Images Using Deep Learning on Periocular Biometrics

Nowadays, identity theft is an alarming issue with the growth of e-commerce and online services. Moreover, due to the Covid-19 pandemic, society has been pushed towards the usage of masks for people to safely interact with one another. It is hard to recognize a person if the face is mostly covered, even more so to artificial intelligence who have more difficulty identifying a masked individual. To further protect personal information and to develop a secure information system, more comprehensive bio-metric approaches are required. The currently used facial recognition systems are using biometrics such as periocular regions, iris, face, skin tone and racial information etc. In this paper, we apply a deep learning-based authentication approach using periocular biometric information to enhance the performance of the facial recognition system. We used the Real-World Masked Face Dataset (RMFD) and other datasets to develop our system. We implemented some experiments using CNN model on the periocular region information of the images. Hence, we developed a system that can recognize a person from only using a small region of face, which in this case is the periocular information including both eyes and eyebrows region. There is only a focus on the periocular region with our model in the view of the fact that the periocular region of the face is the main reliable source of information we can get while a person is wearing a face mask.
Relation-Level Vector Representation for Relation Extraction and Classification on Specialized Data

During this last decade, word embeddings models learning continuous vector representations of words have been established and integrated in several applications of Natural Language Processing (NLP). These models have been subsequently extended to learn representations of other textual objects such as word senses/definitions, fragments of textual documents or even whole texts. In this paper, we focus on the creation of continuous vector representations for relations. We propose a model where vectors embeddings of relations are deduced from (multi-)words embeddings. The training of these representations is carried out from a business corpus referring to several specialized domains such as health, justice, urbanism, or elections. The quality of these representations is evaluated on the task of identifying/classifying lexical-semantic relations from texts with binary classifiers (Machine Learning techniques). This task consists in classifying from a relation embedding in input, the relation type of this representation. The obtained results are good and surpass the performances for a recent one state-of-the-art system dedicated to the creation of relations representations.
Improving Neural Machine Translation by Efficiently Incorporating Syntactic Templates

In the success of Transformer architecture in Neural Machine Translation, integrating linguistic features into the traditional systems gains a huge interest in both research and practice. With less increase in computational cost as well as improving the quality of translation, we propose an abstract template integration model to intensify the structural information in source language from syntactic tree. Besides, the previous works have not considered the effect of the template generating mechanism, while this is an essential component of template-based translation. In this work, we investigate various template generating methods and propose two prominent abstract template generation techniques based on the POS information. Together with the strength of Transformer, our proposed approach allows to effectively incorporate and extract the linguistics features to enrich the information in encoding phase. Experiments on several benchmarks prove that our approaches achieve competitive results against the competitive baselines with less effort in training time. Furthermore, our results reflect that syntactic information is the rich fertile ground to have benefited greatly in neural machine translation. Our code is available at https://github.com/phuongnm-bkhn/multisources-trans-nmt .
Hospital Readmission Prediction via Personalized Feature Learning and Embedding: A Novel Deep Learning Framework

Hospital readmissions are frequent and costly events. Early risk prediction can lead to more effective resource planning and utilization. This paper presents a deep learning framework for predicting the risk of 30-day all-cause readmission given a patient journey dataset. The problem is posed as a binary classification. A novel personalized self-adaptive feature learning and embedding strategy is applied to learn the representations of patient journeys. We first introduce a Variable Attention module to capture the interdependencies of clinical features and generate attention feature representations. We then place a convolutional neural network (CNN) on the generated feature representations to estimate outcome probabilities. Demographic features, including sex and age, are then incorporated into a personalized representation used for adaptively fixing the output of CNN by modifying the network loss function. We successfully predict 30-day all-cause risk-of-readmission with area-under-receiver-operating-curve (AUROC) ranging between 0.838 to 0.858 and overall maximum accuracy of 77.34%.
A Survey of Pretrained Embeddings for Japanese Legal Representation

Pretrained embeddings have proven effective in legal problems in English. Even so, working well in one language does not guarantee that these models have an advantage in other languages. Understanding the characteristics of these models in a particular language helps us to make more accurate decisions when choosing technology for problems in that language. This paper provides an analytical perspective on pretrained embeddings in the legal field in Japanese. These models are measured on quantitative numbers as well as visualized in terms of their ability to represent Japanese legal terms. With such contributions, this paper may be useful to researchers and engineers who are building Japanese legal embeddings.
Forensic Analysis of Text and Messages in Smartphones by a Unification Rosetta Stone Procedure

In this paper we introduce an innovative application of translation techniques applied to the problem of forensics analysis of smartphones. This analysis has the specific objective of determining which messages (either text or vocal), transmitted from and received by a specific device, seized for forensic analysis, may contain data that are relevant in a criminal investigation. The problems that make this analysis difficult are three: (1) the content could be written in a language that is not spoken by the analyst, (2) the number of messages actually containing pertinent and relevant traits is a small percentage on a potentially quite large space and (3) texts could be rather noisy in terms of content, for they could contain emoticons, language loans, and slang terms (beyond the fact that they could also be written in obscure languages such as specific dialects or languages spoken by small communities). We adopt a machine translation approach by providing an algorithm that takes messages of a smartphone as input, and processes them to a target language in an innovative way. We then show that the application is effective when applied to a set of real world cases, demonstrating a performance increase in terms of accuracy that could exceed 30 $$\%$$ when compared to traditional approaches.
Improvement of Text Image Super-Resolution Benefiting Multi-task Learning

Text image super-resolution is a pre-processing of scene text recognition, which aims to improve the visual quality of text from low-resolution images. However, existing super-resolution (SR) models designed for general images have difficulty in recovering text from low-resolution images in real scenes. There are several reasons for this, including the fact that the models do not consider text-specific properties and that the background is not important for text images SR. In this paper, we propose a multi-task learning model for reconstruction and SR termed TRSRT using a transformer for text images. Compared to the super-resolution model, the reconstruction model is better at denoising and tends to have structural information about the text. Focusing on this point, the proposed method utilizes these properties of the reconstructed model to the SR model through the transformer. In addition, we attempt to acquire a text-specific model by training with three loss functions including feature-driven loss using a text recognizer. Experimental results on TextZoom show that the proposed method achieves performance comparable to state-of-the-art methods and prove the advantages of multi-task learning.
Hybrid Deep Neural Networks for Industrial Text Scoring

Academic scoring is mainly explored through the pedagogical fields of Automated Essay Scoring (AES) and Short Answer Scoring (SAS), but text scoring in other domains has received limited attention. This paper focuses on industrial text scoring, namely the processing and adherence checking of long annual reports based on regulatory requirements. To lay the foundations for non-academic scoring, a pioneering corpus of annual reports from companies is scraped, segmented into sections, and domain experts score relevant sections based on adherence. Subsequently, deep neural non-hierarchical attention-based LSTMs, hierarchical attention networks and longformer-based models are refined and evaluated. Since the longformer outperformed LSTM-based models, we embed it into a hybrid scoring framework that employs lexicon and named entity features, with rubric injection via word-level attention, culminating in a Kappa score of 0.9670 and 0.820 in both our corpora, respectively. Though scoring is fundamentally subjective, our proposed models show significant results when navigating thin rubric boundaries and handling adversarial responses. As our work proposes a novel industrial text scoring engine, we hope to validate our framework using more official documentation based on a broader range of regulatory practices.