Reviewerly/exHarmony huggingface dataset:
Region-of-interest Attentive Heteromodal Variational Encoder-Decoder for Segmentation with Missing Modalities

The use of multimodal images generally improves segmentation. However, complete multimodal datasets are often unavailable due to clinical constraints. To address this problem, we propose a novel multimodal segmentation framework that is robust to missing modalities by using a region-of-interest (ROI) attentive modality completion. We use ROI attentive skip connection to focus on segmentation-related regions and a joint discriminator that combines tumor ROI attentive images and segmentation probability maps to learn segmentation-relevant shared latent representations. Our method is validated in the brain tumor segmentation challenge dataset of 285 cases for the three regions of the complete tumor, tumor core, and enhancing tumor. It is also validated on the ischemic stroke lesion segmentation challenge dataset with 28 cases of infarction lesions. Our method outperforms state-of-the-art methods in robust multimodal segmentation, achieving an average Dice of 84.15 $$\%$$ , 75.59 $$\%$$ , and 54.90 $$\%$$ for the three types of brain tumor regions, respectively, and 48.29 $$\%$$ for stroke lesions. Our method can improve the clinical workflow that requires multimodal images.
Revisiting Unsupervised Domain Adaptation Models: A Smoothness Perspective

Unsupervised Domain Adaptation (UDA) aims to leverage the labeled source data and unlabeled target data to generalize better in the target domain. UDA methods utilize better domain alignment or carefully-designed regularizations to increase the discriminability of target features. However, most methods focus on directly increasing the distance between cluster centers of target features, i.e., enlarging inter-class variance, which intuitively increases the discriminability of target features and is easy to implement. However, due to intra-class variance optimization being under-explored, there are still some samples of the same class are prone to be classified into several classes. To handle this problem, we aim to equip UDA methods with the high smoothness constraint. We first define the model's smoothness as the predictions similarity within each class, and propose a simple yet effective technique LeCo (impLicit smoothness Constraint) to promote the smoothness. We construct the weak and strong "views" of each target sample and enforce the model predictions of these two views to be consistent. Besides, a new uncertainty measure named Instance Class Confusion conditions the consistency is proposed to guarantee the transferability. LeCo implicitly reduces the model sensitivity to perturbations for target samples and guarantees smaller intra-class variance. Extensive experiments show that the proposed technique improves various baseline approaches by a large margin, and helps yield comparable results to the state-of-the-arts on four public datasets. Our codes are publicly available at https://github.com/Wang-Xiaodong1899/LeCo_UDA .
OntoSenseNet: A Verb-Centric Ontological Resource for Indian Languages

Following approaches for understanding lexical meaning developed by Yāska, Patanjali and Bhartrihari from Indian linguistic traditions and extending approaches developed by Leibniz and Brentano in the modern times, a framework of formal ontology of language was developed. This framework proposes that meaning of words are in-formed by intrinsic and extrinsic ontological structures. The paper aims to capture such intrinsic and extrinsic meanings of words for two major Indian languages, namely, Hindi and Telugu. Parts-of-speech have been rendered into sense-types and sense-classes. Using them we have developed a gold-standard annotated lexical resource to support semantic understanding of a language. The resource has collection of Hindi and Telugu lexicons, which has been manually annotated by native speakers of the languages following our annotation guidelines. Further, the resource was utilised to derive adverbial sense-class distribution of verbs and kāraka-verb sense-type distribution. Different corpora (news, novels) were compared using verb sense-types distribution. Word Embedding was used as an aid for the enrichment of the resource. This is a work in progress that aims at lexical coverage of language extensively.
Multi-scale Wavelet Transformer for Face Forgery Detection

Currently, many face forgery detection methods aggregate spatial and frequency features to enhance the generalization ability and gain promising performance under the cross-dataset scenario. However, these methods only leverage one level frequency information which limits their expressive ability. To overcome these limitations, we propose a multi-scale wavelet transformer framework for face forgery detection. Specifically, to take full advantage of the multi-scale and multi-frequency wavelet representation, we gradually aggregate the multi-scale wavelet representation at different stages of the backbone network. To better fuse the frequency feature with the spatial features, frequency-based spatial attention is designed to guide the spatial feature extractor to concentrate more on forgery traces. Meanwhile, cross-modality attention is proposed to fuse the frequency features with the spatial features. These two attention modules are calculated through a unified transformer block for efficiency. A wide variety of experiments demonstrate that the proposed method is efficient and effective for both within and cross datasets.
Bridging the Resource Gap in Cross-Lingual Embedding Space

The mapping based methods for inducing a cross-lingual embedding space involves learning a linear mapping from the individual monolingual embedding spaces to a shared semantic space where English is often chosen as the hub language. Such methods are based on the orthogonal assumption. Resource limitations and typological distance from English often result in a deviation from this assumption and subsequently poor performance for the low-resource languages. In this research, we will present a method for identifying optimal bridge languages to achieve better mapping for the low-resource languages in the cross-lingual embedding space. We also report Bilingual Induction Task (BLI) performances for the shared semantic space achieved using different cross-lingual signals.
A Differentiable Distance Approximation for Fairer Image Classification

Naïvely trained AI models can be heavily biased. This can be particularly problematic when the biases involve legally or morally protected attributes such as ethnic background, age or gender. Existing solutions to this problem come at the cost of extra computation, unstable adversarial optimisation or have losses on the feature space structure that are disconnected from fairness measures and only loosely generalise to fairness. In this work we propose a differentiable approximation of the variance of demographics, a metric that can be used to measure the bias, or unfairness, in an AI model. Our approximation can be optimised alongside the regular training objective which eliminates the need for any extra models during training and directly improves the fairness of the regularised models. We demonstrate that our approach improves the fairness of AI models in varied task and dataset scenarios, whilst still maintaining a high level of classification accuracy. Code is available at https://bitbucket.org/nelliottrosa/base_fairness .
Tasks Management Using Modern Devices

Nowadays the people need tools that will help in their work and businesses. For this purpose, mobile applications have been developed for almost all domains, in order to have a quick access to information. The architecture and the design of a mobile application is very important. When looking for the best mobile application architecture, the developers must choose between MVC, MVP and MVVM models. As aprove of concept we implemented a mobile application for task management that uses the MVC model, the most popular in the mobile app development. Generally, task management tools help the people to work efficiently and to organize their tasks. The aim of this paper is to analyze the advantages of using Java ME, XML, Firebase and its services in mobile applications development. The conclusion is that these technologies are suitable for mobile task management applications implementation.
Sparse Word Representation for RNN Language Models on Cellphones

Language models are a key component of input methods, because they provide good suggestions for the next candidate input word given previous context. Recurrent neural network (RNN) language models are the state-of-the-art language models, but they are notorious for their large size and computation cost. A main source of parameters and computation of RNN language models is embedding matrices. In this paper, we propose a sparse representation-based method to compress embedding matrices and reduce both the size and computation of the models. We conduct experiments on the PTB dataset and also test its performance on cellphones to illustrate its effectiveness.
Advanced Text Mining Methods for Bilingual Lexicon Extraction from Speciliazed Comparable Corpora

Recent works rely on comparable corpora to extract efficient bilingual lexicon. Most of approaches in the litterature for bilingual lexicon extraction are based on context vectors (CV). These approaches suffer from noisy vectors that affect their accuracy. This paper presents new approaches which relies on some advanced text mining methods to extract association rules between terms (AR) and extend them to contextual meta-rules (MR). In this respect, we propose to extract bilingual lexicons by deploying standard context vectors, association rules and contextual meta-rules. These proposed approaches utilize correlations between co-occurrence patterns across language. An experimental validation conducted on a specialized comparable corpora, highlights a significant improvement of bilingual lexicon based on MR compared to the standard approach.
Classifiers for Yelp-Reviews Based on GMDH-Algorithms

Yelp is one of the most popular international web resources about products and services that provide users with useful information on local businesses and helps the business owners to make their business more attractive for the users. The Yelp dataset consists of attributes for describing the business, reviews in free text form and numeric star ratings out of 5. The utility of such a dataset has provoked dozens of publications related to classifiers of ratings, which used various smart tools of opinion mining. Unlike them, in this paper we propose to use simpler approaches, namely: (a) selection of descriptors based on term specificity, and (b) formation of classifiers with these descriptors based on inductive modeling. The latter is implemented by the well-known tool GMDH Shell, where GMDH stands for Group Method of Data Handling. This method allows us to build models with high noise immunity. We compare 96 prediction models with identified descriptors by combining various variants: (i) preprocessing with data transformation and balancing classes, (ii) algorithms of classification; and (iii) post processing with ensembling. Instead of the typical 5- star classification we consider combined classes reflecting a more practical view on purchase of goods or development of business. The experiments refer to the most popular categories of business: restaurants and shopping. To evaluate the quality of classifiers we consider the results of predecessors, and we also introduce the so-called defensible accuracy. With this comparison the results presented in the paper prove to be promising.
Connectives with Both Arguments External: A Survey on Czech

Determining a relative position of a discourse connective and the two arguments (text segments) it connects is an important part of a full discourse parsing task. This paper investigates discourse connectives whose position in a text deviates from the usual setting – namely connectives that occur in neither of the two arguments – and as such present a challenge for discourse parsers. We find syntactic patterns for this phenomenon and describe it linguistically on the basis of Czech discourse-annotated corpus material, with the aim to facilitate an automatic detection of such connectives and a correct localization of their arguments.
Class Concentration with Twin Variational Autoencoders for Unsupervised Cross-Modal Hashing

Multi-modal deep hash learning is arguably one of the most commonly used unsupervised methods in cross-modal retrieval tasks. Most existing deep hashing methods focus on maintaining similarity information in the hash code learning step. Although accurate and compact binary representations are learned, these methods fail to encourage discriminative learning of features. In this paper, we propose a new method called Class Concentrated Variational auto-encoder (CCTV) to learn discriminative hash codes. The novelty of CCTV lies in two aspects. First, the proposed method focuses on the concentration of the mean vector of latent features. Based on the assumption that the features in the shared latent space produce multivariate Gaussian, CCTV updates the mean vectors and the cluster centroids of the latent features at the same time by minimizing the class concentration loss, so as to narrow the distance between the cluster centroids and the mean vectors, and further make the concentration more compact. Secondly, under the constraint of raw similarity information, CCTV is different from previous works, it uses the mean vector of latent features as the representation of the images to reduce the influence of variance, and then embeds them in the Hamming space. Our experimental evaluation on four multimedia benchmarks shows a significant improvement over the state-of-the-art methods. Code is available at: https://github.com/theusernamealreadyexists/CCTV .
Towards Social Context Summarization with Convolutional Neural Networks

This paper studies Web document summarization by exploiting social information. The motivation comes from the fact that social context of a Web document provides additional information to enrich the content of sentences. To take advantage of such information, we design a model based on Convolutional Neural Networks. Unlike traditional ones, our model enhances representation of sentences by mutually combining internal and social information. The model learns to rank sentences and user posts and then extracts top ranked ones as a summary. Experimental results on three datasets in two languages confirm the efficiency of our model in summarizing single documents.
Unmasking Bias in News

We present experiments on detecting hyperpartisanship in news using a ‘masking’ method that allows us to assess the role of style vs. content for the task at hand. Our results corroborate previous research on this task in that topic related features yield better results than stylistic ones. We additionally show that competitive results can be achieved by simply including higher-length n-grams, which suggests the need to develop more challenging datasets and tasks that address implicit and more subtle forms of bias.
Cross-Framework Evaluation for Portuguese POS Taggers and Parsers

This work compares POS and parsing systems for the Portuguese language. We analyse available features, tagsets, and compare the results of POS tagging, and syntactic structure identification by means of both intrinsic and extrinsic evaluation methods. For such, we use in this work well-known metric for parser evaluation such as bracket cross, leaf ancestor for intrinsic evaluation, as well as the application of such parsers to the task of noun phrase identification, for extrinsic evaluation. The comparison proposed in this work takes into account the different linguistic theories and frameworks each parser subscribes to, but it is not dependent of any particular one.
Augmenting Softmax Information for Selective Classification with Out-of-Distribution Data

Detecting out-of-distribution (OOD) data is a task that is receiving an increasing amount of research attention in the domain of deep learning for computer vision. However, the performance of detection methods is generally evaluated on the task in isolation, rather than also considering potential downstream tasks in tandem. In this work, we examine selective classification in the presence of OOD data (SCOD). That is to say, the motivation for detecting OOD samples is to reject them so their impact on the quality of predictions is reduced. We show under this task specification, that existing post-hoc methods perform quite differently compared to when evaluated only on OOD detection. This is because it is no longer an issue to conflate in-distribution (ID) data with OOD data if the ID data is going to be misclassified. However, the conflation within ID data of correct and incorrect predictions becomes undesirable. We also propose a novel method for SCOD, Softmax Information Retaining Combination (SIRC), that augments softmax-based confidence scores with feature-agnostic information such that their ability to identify OOD samples is improved without sacrificing separation between correct and incorrect ID predictions. Experiments on a wide variety of ImageNet-scale datasets and convolutional neural network architectures show that SIRC is able to consistently match or outperform the baseline for SCOD, whilst existing OOD detection methods fail to do so. Code is available at https://github.com/Guoxoug/SIRC .
Two-Phased Dynamic Language Model: Improved LM for Automated Language Translation

We discuss the importance of domain specific language model in statistical machine translation system. Both the structures and phrase selection are not the same for different domains. So, the language model trained with the general domain data or other domain data can not provide better accuracy. Moreover, there may have some specific focus in different texts of the same domain. Hence, the language model trained with data from the default domain may not yield significant output. In this paper, we learn our system dynamically based on the better matches with the input text. Instead of directly selecting pre-trained language model we prepare the prioritized language model according to the situation. The proposed model is evaluated for Hindi-English translation. It shows a significant improvement on the translated output in terms of the BLEU score. Our evaluation shows that automated domain adoption to predict better language model improves the translation quality.
Using Reviewer Information to Improve Performance of Low-Quality Review Detection

The drastic increase of user-generated contents has exhibited a rich source for mining opinions. Unfortunately, the quality of user-generated content varies significantly from excellent to meaningless, which by general estimation, causes a great deal of difficulty in mining-related applications. In the field of low-quality review detection, many previous approaches have individually detected low-quality reviews by using the intrinsic features of the review. However, no systematic study measuring the significance of reviewer information for detecting low-quality reviews has been previously done. In this paper, the importance of reviewer information when predicting review quality is studied and how to exploit it to build low-quality review detection models is determined. The experimental results on two different domains show that reviewer information does matter when modeling and predicting the quality of reviews. It is also shown that significant performance improvements can be achieved if the reviewer information is integrated with the intrinsic features of the reviews. These findings are of the essence in solving the low-quality review detection problem and in developing review-based opinion mining applications.
Feature Decoupled Knowledge Distillation via Spatial Pyramid Pooling

Knowledge distillation (KD) is an effective and widely used technique of model compression which enables the deployment of deep networks in low-memory or fast-execution scenarios. Feature-based knowledge distillation is an important component of KD which leverages intermediate layers to supervise the training procedure of a student network. Nevertheless, the potential mismatch of intermediate layers may be counterproductive in the training procedure. In this paper, we propose a novel distillation framework, termed Decoupled Spatial Pyramid Pooling Knowledge Distillation, to distinguish the importance of regions in feature maps. Specifically, we reveal that (1) spatial pyramid pooling is an outstanding method to define the knowledge and (2) the lower activation regions in feature maps play a more important role in KD. Our experiments on CIFAR-100 and Tiny-ImageNet achieve state-of-the-art results.
Few-shot Adaptive Object Detection with Cross-Domain CutMix

In object detection, data amount and cost are a trade-off, and collecting a large amount of data in a specific domain is labor-intensive. Therefore, existing large-scale datasets are used for pre-training. However, conventional transfer learning and domain adaptation cannot bridge the domain gap when the target domain differs significantly from the source domain. We propose a data synthesis method that can solve the large domain gap problem. In this method, a part of the target image is pasted onto the source image, and the position of the pasted region is aligned by utilizing the information of the object bounding box. In addition, we introduce adversarial learning to discriminate whether the original or the pasted regions. The proposed method trains on a large number of source images and a few target domain images. The proposed method achieves higher accuracy than conventional methods in a very different domain problem setting, where RGB images are the source domain, and thermal infrared images are the target domain. Similarly, the proposed method achieves higher accuracy in the cases of simulation images to real images.
QS-Craft: Learning to Quantize, Scrabble and Craft for Conditional Human Motion Animation

This paper studies the task of conditional Human Motion Animation (cHMA). Given a source image and a driving video, the model should animate the new frame sequence, in which the person in the source image should perform a similar motion as the pose sequence from the driving video. Despite the success of Generative Adversarial Network (GANs) methods in image and video synthesis, it is still very challenging to conduct cHMA due to the difficulty in efficiently utilizing the conditional guided information such as images or poses, and generating images of good visual quality. To this end, this paper proposes a novel model of learning to Quantize, Scrabble, and Craft (QS-Craft) for conditional human motion animation. The key novelties come from the newly introduced three key steps: quantize, scrabble and craft. Particularly, our QS-Craft employs transformer in its structure to utilize the attention architectures. The guided information is represented as a pose coordinate sequence extracted from the driving videos. Extensive experiments on human motion datasets validate the efficacy of our model.
TriMix: A General Framework for Medical Image Segmentation from Limited Supervision

We present a general framework for medical image segmentation from limited supervision, reducing the reliance on fully and densely labeled data. Our method is simple, jointly trains triple diverse models, and adopts a mix augmentation scheme, and thus is called TriMix. TriMix imposes consistency under a more challenging perturbation, i.e., combining data augmentation and model diversity on the tri-training framework. This straightforward strategy enables TriMix to serve as a strong and general learner learning from limited supervision using different kinds of imperfect labels. We conduct extensive experiments to show TriMix’s generic purpose for semi- and weakly-supervised segmentation tasks. Compared to task-specific state-of-the-arts, TriMix achieves competitive performance and sometimes surpasses them by a large margin. The code is available at https://github.com/MoriLabNU/TriMix .