bluuebunny/crossref_metadata_2025 huggingface dataset:
ELABORATION AND VALIDATION OF AN EDUCATIONAL VIDEO FOR THE PREVENTION OF SEXUAL VIOLENCE IN ADOLESCENTS
Authors: Souza Valesca Patriota de , Perrelli Jaqueline Galdino Albuquerque , Brandão Neto Waldemar , Pereira Maria Beatriz Ferreira Leite de Oliveira , Guedes Tatiane Gomes , Monteiro Estela Maria Leite Meirelles
Published: July 2022

ABSTRACT Objective: to evaluate the elaboration and validation process of an educational video for the prevention of sexual violence in school-age adolescents. Method: this is a methodological study, developed from September 2018 to February 2021 in Recife-PE. Production of the educational video was carried out according to the methodological path proposed by Kindem and Musberg. The video was submitted to content validation and face evaluation by expert judges and school-age adolescents, respectively. In the data analysis, absolute frequency, Content Validity Index and binomial test were used. Results: in content validation, agreement was satisfactory and the mean validity index for all video items was 0.92. In the face evaluation, the adolescents considered the video understandable, with a minimum agreement level of 92.3%. Conclusion: the educational video elaborated was considered by the judges as valid to contribute to the knowledge and attitude of school-age adolescents regarding the prevention of sexual violence.
Evaluation of two stereophotogrametry software for 3D reconstruction of virtual facial models
Authors: CHAVES Lorena Basilio , BARBOSA Taliane Lima , CASAGRANDE Caroline Pelagio Maués , ALENCAR David Silveira , CAPELLI JR Jonas , CARVALHO Felipe de Assis Ribeiro
Published: July 2022

ABSTRACT Objective: The present study aimed to evaluate the accuracy of 3D facial soft tissue virtual models produced by two photogrammetry softwares (AgiSoft Photoscan and 3DF Zephyr Free), when compared to those created by cone beam computed tomography (CBCT). Methods: Ten patients were submitted to two sequences of photographs performed with a DSLR camera (with and without the aid of a ring flash) and CBCT scans. Each photo series for each patient was processed with the softwares, and at the end, five models of each patient were generated: 1) CBCT, 2) AAL (Agisoft Ambient Light), 3) AFL (Agisoft Flash Light), 4) ZAL (Zephyr Ambient Light), and 5) ZFL (Zephyr Flash Light). Color coded maps and root-mean-square (RMS) distances were used to compare the photogrammetry models to the CBCT ones. Results: One sample t-test showed significant differences between all methods versus CBCT. The worst results were seen in the ZAL group (discrepancies up to 5.17mm), while the best results were produced by AAL group (discrepancies up to 2.11mm). Conclusions: It can be concluded that this type of virtual facial models are reasonably accurate, although not perfect, and considering its lower biological and financial cost, they may play an important role in specific situations.
Intercenter comparison of slow and rapid maxillary expansion in unilateral complete cleft lip and palate
Authors: AYUB Priscila Vaz , GARIB Daniela Gamba , EBRAHIM Hussain , POLIDO José , BLASCA Wanderléia , YEN Stephen
Published: July 2022

ABSTRACT Objective: The aim of this study was to compare the occlusal changes of rapid maxillary expansion (RME) and slow maxillary expansion (SME) in patients with unilateral complete cleft lip and palate (UCLP), by means of digital dental models. Methods: Group RME was composed by 22 patients (13 males and 9 females), with mean age of 9.9 years, treated with rapid maxillary expansion with Hyrax appliance in Center 1. Group SME was composed by 29 patients (14 females and 15 males), with mean age of 10.7 years, treated with slow maxillary expansion with quad-helix appliance in Center 2. Digital dental models of the maxillary dental arch were obtained immediately pre-expansion (T1) and 6-month post-expansion (T2). Transversal distances, arch perimeter, arch length, palatal depth, palatal volume and posterior tooth inclination were digitally measured. Interphase and intergroup comparisons were performed with paired t-test and independent t-test, respectively. Results: Intercanine expansion was 4 to 5mm in both groups, and increase in the intercanine distance was similar for both groups. RME group showed a greater increase in arch distances at the region of permanent premolar and molars, compared to SME group. Arch perimeter increase was greater for RME group, compared to SME. No differences were found for arch length, palatal depth, palatal volume and posterior tooth buccal tipping. Conclusion: RME and SME produced similar dentoalveolar outcomes, with greater amount of expansion on RME group.
Effect of 8% arginine toothpaste on Streptococcus mutans in patients undergoing fixed orthodontic treatment: randomized controlled trial
Authors: RAZEGHIAN-JAHROMI Iman , BABANOURI Neda , EBRAHIMI Zahra , NAJAFI Hooman Zarif , SARBAZ Maryam , MONTAZERI-NAJAFABADY Nima
Published: July 2022

ABSTRACT Objective: To assess the effect of toothpaste containing 8% arginine on Streptococcus mutans (S. mutans) in dental plaque around orthodontic brackets, and to draw a comparison with a regular fluoride toothpaste. Trial design: A single-center, parallel-arm, triple-blind, randomized controlled trial was conducted. Methods: The clinical trial was conducted at the Orthodontic Clinic, School of Dentistry, Shiraz University of Medical Sciences, Shiraz, Iran. Seventy-two patients (age range: 15-30 years) who required fixed orthodontic treatment were recruited and randomly assigned to arginine and fluoride groups. Randomization was performed using RANDOM.ORG online software, and the participants were divided into two parallel groups, with a 1:1 allocation ratio. Patients were requested to brush their teeth twice daily for 30 days with an experimental toothpaste. Plaque sampling was performed at two intervals, namely at the beginning of the study (T0) and 30 days later (T1). Real-time PCR was used to assess plaque samples in terms of the number of S. mutans surrounding stainless steel brackets in orthodontic patients. A triple-blind design was employed. Results: The baseline characteristics (age, sex, and the relative number of S. mutans) between the groups were similar (p>0.05). Only the arginine group showed a significant decrease in the relative number of bacteria between T0 and T1 (p=0.02). Conclusion: Arginine is an important prebiotic agent in maintaining healthy oral biofilms, and prevent dental caries during fixed orthodontic treatments. Trial registration: The trial was registered at the Iranian Registry of Clinical Trials (IRCT20181121041713N2), https://en.irct.ir/user/trial/42409/view.
Change in crown inclination accompanying initial tooth alignment with round archwires
Authors: MONTASSER Mona A. , KEILIG Ludger , BOURAUEL Christoph
Published: July 2022

ABSTRACT Objective: To evaluate, in-vitro, the change in crown inclination that occurs during orthodontic leveling and alignment using different archwire-bracket-ligation combinations. Materials and Methods: Four archwire types were tested: (1) 0.012-in stainless steel and (2) 0.0155-in stainless steel multi-stranded, (3) 0.012-in nitinol Orthonol® and (4) 0.012-in nitinol Thermalloy®. Combinations with five types of 0.022-in slot orthodontic brackets were tested: SmartClipTM and Time3® self-ligating brackets, Mini-Taurus® and Victory SeriesTM conventional brackets, and Synergy® conventional-low friction bracket. Conventional brackets were ligated with both stainless steel and elastomeric ligatures. The simulated malocclusion comprised 2.0mm gingival and 2.0mm labial displacements of a maxillary right central incisor. Rotation around the Y-axis (representing labio-palatal inclination) was measured for the different archwire-bracket-ligation combinations. Results: The largest rotation was measured whith Orthonol® and Thermalloy® wires when combined with SmartClipTM brackets (8.07±0.24º and 8.06±0.26º, respectively) and with Synergy® brackets ligated with stainless steel ligatures (8.03±0.49º and 8.0±0.37º, respectively). The lower rotation was recorded when Thermalloy®, multi-stranded, and Orthonol® wires were ligated with elastomeric rings to Mini-Taurus® brackets (1.53±0.18º, 1.65± 0.23º and 1.70±0.28º, respectively) and to Victory SeriesTM brackets (1.68± 0.78º, 2.92± 1.40º and 1.74±0.46º, respectively). Conclusions: All archwire-bracket-ligation combinations produced lingual crown inclination; however, lower changes were observed when the conventional brackets were ligated with elastomeric rings. The multi-stranded archwire produced less rotation with nearly every bracket-ligation combination, compared to the other archwires. The effect of the archwire-bracket-ligation combination on tooth inclination during leveling and alignment should be considered during planning treatment mechanics.
Information and Communications Technology in Dentistry: an informative and educational approach for patients with fixed orthodontic appliances
Authors: ASSIS Marina Araújo Leite , TAVARES Lílian Dayse Fróes , BERNARDINO Aline Pereira , ROCHA Breno Amaral , ABREU Lucas Guimarães , OLIVEIRA Dauro Douglas , PITHON Matheus Melo , SOARES Rodrigo Villamarim
Published: July 2022

ABSTRACT Objective: To develop and make available, at no cost to the user, Information and Communications Technology (ICT) tools for Dentistry, providing dental information and advice geared toward patients undergoing orthodontic treatment with fixed appliances. Material and Methods: A Dentistry-based content that contemplated information and advice concerning orthodontic treatment with fixed appliances was elaborated. The materials, which included instructions on oral hygiene and treatment strategies when faced with possible complications, were evaluated and validated by specialists, whose assessments reached a 85% approval. From the validated content, products using four distinct ICT tools were formulated. Results: The following technological products were developed: a program for community radios, three blog posts, four educational and informative videos, and a smartphone application - using texts, as well as images and videos. These ICT tools, geared toward patients wearing fixed orthodontic appliances, were made available by internet at no cost to the user, and the number of accesses is already expressive. Conclusion: These technological-scientific tools, developed and provided freely to the population, can aid patients during their treatment with fixed orthodontic appliances, contributing to the dissemination of reliable information, and clarifying doubts that may arise during orthodontic therapy. These free ICT tools serve to facilitate access to scientific knowledge, thereby favoring social inclusion, bearing in mind that this educational and informative material was offered in a simple and accessible manner to the general population.
The dimension of health care in Health Promotion: notes on the approach to care,
Authors: Carvalho Fabio , Akerman Marco , Cohen Simone
Published: July 2022

Abstract The aim of this text is to explore the interfaces between Health Promotion (HP), health care and the care process. It starts from the assumption that the dimension of health care is essential for the theoretical-practical construction of HP, so that it can contribute to better health conditions. This is a qualitative research, a consultation was carried out with managers and municipal workers of Health Primary Care, using an electronic form, which had 215 respondents; and 13 interviews with experts, so called members of the Working Group of the Brazilian Association of Collective Health; also a categorical-thematic analysis was performed. As a result, stands out that: there is not necessarily a dichotomy between HP, , clinical care; the way in which care is produced becomes relevant since considering HP principles and guidelines is necessary, thus care would be related to HP even though the perspective is the recovery of health in cases of illness. Thus, defending the importance of the health care dimension is not to ratify the hegemony of health care over other components of care process, and considering individual needs alongside the social dynamics and the context in which people live is essential.
THE PREVALENCE OF LOW BACK PAIN IN NURSES AT A UNIVERSITY HOSPITAL IN THE EASTERN AREA OF SÃO PAULO
Authors: GUEDES FELIPE RAMALHO , NAVARRO FERNANDA MINUTTI , NAKAO RODRIGO YUITI , FRANCO ISABELA PAGLIARO , RODRIGUES LUIZ CLAUDIO LACERDA
Published: July 2022

ABSTRACT Objective: To evaluate the prevalence of low back pain in nurses at a university hospital in São Paulo and establish a relationship with social aspects. Methods: A cross-sectional study was carried out, through the application of a questionnaire containing social questions(weight, age, height, work sector, working hours, physical activity, presence and frequency of low back pain) in addition to the Oswestry questionnaire. Results: One hundred fifty-three nurses participated in the study. Of these, 92.30% of the women and 73.91% of the men presented low back pain, with a third classifying the pain as sporadic. In relation to BMI, pain is lower in those who are underweight (60%) and higher among those who are overweight (96.77%). Most of the sample was sedentary (66%), and of these, 96% had low back pain. There was no difference in the comparison by working hours, in relation to work sector, pain was more present in the following sectors: coordination (100%); children’s ward (92%); adult emergency room (90%) and adult ICU (31%). Thirty nurses worked double shifts, and of these, 90% reported low back pain, while among those who worked only at the university hospital, 89.4% reported pain. In relation to working hours, the longer the working day, the greater the pain. In the function assessment (Oswestry), 99 participants obtained a value of up to 30% disability. Conclusion: Based on the results of this work, it is concluded that there is a high prevalence of low back pain in nurses at the Hospital Universitário; however, it was not possible to determine a direct risk factor associated with this high prevalence. Level of Evidence IV; Cross-sectional study.
Learning Unseen Emotions from Gestures via Semantically-Conditioned Zero-Shot Perception with Adversarial Autoencoders
Authors: Banerjee Abhishek , Bhattacharya Uttaran , Bera Aniket
Published: July 2022

We present a novel generalized zero-shot algorithm to recognize perceived emotions from gestures. Our task is to map gestures to novel emotion categories not encountered in training. We introduce an adversarial autoencoder-based representation learning that correlates 3D motion-captured gesture sequences with the vectorized representation of the natural-language perceived emotion terms using word2vec embeddings. The language-semantic embedding provides a representation of the emotion label space, and we leverage this underlying distribution to map the gesture sequences to the appropriate categorical emotion labels. We train our method using a combination of gestures annotated with known emotion terms and gestures not annotated with any emotions. We evaluate our method on the MPI Emotional Body Expressions Database (EBEDB) and obtain an accuracy of 58.43%. We see an improvement in performance compared to current state-of-the-art algorithms for generalized zero-shot learning by an absolute 25-27%. We also demonstrate our approach on publicly available online videos and movie scenes, where the actors' pose has been extracted and map to their respective emotive states.
Handwritten Mathematical Expression Recognition via Attention Aggregation Based Bi-directional Mutual Learning
Authors: Bian Xiaohang , Qin Bo , Xin Xiaozhe , Li Jianwu , Su Xuefeng , Wang Yanfeng
Published: July 2022

Handwritten mathematical expression recognition aims to automatically generate LaTeX sequences from given images. Currently, attention-based encoder-decoder models are widely used in this task. They typically generate target sequences in a left-to-right (L2R) manner, leaving the right-to-left (R2L) contexts unexploited. In this paper, we propose an Attention aggregation based Bi-directional Mutual learning Network (ABM) which consists of one shared encoder and two parallel inverse decoders (L2R and R2L). The two decoders are enhanced via mutual distillation, which involves one-to-one knowledge transfer at each training step, making full use of the complementary information from two inverse directions. Moreover, in order to deal with mathematical symbols in diverse scales, an Attention Aggregation Module (AAM) is proposed to effectively integrate multi-scale coverage attentions. Notably, in the inference phase, given that the model already learns knowledge from two inverse directions, we only use the L2R branch for inference, keeping the original parameter size and inference speed. Extensive experiments demonstrate that our proposed approach achieves the recognition accuracy of 56.85 % on CROHME 2014, 52.92 % on CROHME 2016, and 53.96 % on CROHME 2019 without data augmentation and model ensembling, substantially outperforming the state-of-the-art methods. The source code is available in https://github.com/XH-B/ABM.
Proximal PanNet: A Model-Based Deep Network for Pansharpening
Authors: Cao Xiangyong , Chen Yang , Cao Wenfei
Published: July 2022

Recently, deep learning techniques have been extensively studied for pansharpening, which aims to generate a high resolution multispectral (HRMS) image by fusing a low resolution multispectral (LRMS) image with a high resolution panchromatic (PAN) image. However, existing deep learning-based pansharpening methods directly learn the mapping from LRMS and PAN to HRMS. These network architectures always lack sufficient interpretability, which limits further performance improvements. To alleviate this issue, we propose a novel deep network for pansharpening by combining the model-based methodology with the deep learning method. Firstly, we build an observation model for pansharpening using the convolutional sparse coding (CSC) technique and design a proximal gradient algorithm to solve this model. Secondly, we unfold the iterative algorithm into a deep network, dubbed as Proximal PanNet, by learning the proximal operators using convolutional neural networks. Finally, all the learnable modules can be automatically learned in an end-to-end manner. Experimental results on some benchmark datasets show that our network performs better than other advanced methods both quantitatively and qualitatively.
DeTarNet: Decoupling Translation and Rotation by Siamese Network for Point Cloud Registration
Authors: Chen Zhi , Yang Fan , Tao Wenbing
Published: July 2022

Point cloud registration is a fundamental step for many tasks. In this paper, we propose a neural network named DetarNet to decouple the translation t and rotation R, so as to overcome the performance degradation due to their mutual interference in point cloud registration. First, a Siamese Network based Progressive and Coherent Feature Drift (PCFD) module is proposed to align the source and target points in high-dimensional feature space, and accurately recover translation from the alignment process. Then we propose a Consensus Encoding Unit (CEU) to construct more distinguishable features for a set of putative correspondences. After that, a Spatial and Channel Attention (SCA) block is adopted to build a classification network for finding good correspondences. Finally, the rotation is obtained by Singular Value Decomposition (SVD). In this way, the proposed network decouples the estimation of translation and rotation, resulting in better performance for both of them. Experimental results demonstrate that the proposed DetarNet improves registration performance on both indoor and outdoor scenes. Our code will be available in https://github.com/ZhiChen902/DetarNet.
Model Doctor: A Simple Gradient Aggregation Strategy for Diagnosing and Treating CNN Classifiers
Authors: Feng Zunlei , Hu Jiacong , Wu Sai , Yu XiaoTian , Song Jie , Song Mingli
Published: July 2022

Recently, Convolutional Neural Network (CNN) has achieved excellent performance in the classification task. It is widely known that CNN is deemed as a 'blackbox', which is hard for understanding the prediction mechanism and debugging the wrong prediction. Some model debugging and explanation works are developed for solving the above drawbacks. However, those methods focus on explanation and diagnosing possible causes for model prediction, based on which the researchers handle the following optimization of models manually. In this paper, we propose the first completely automatic model diagnosing and treating tool, termed as Model Doctor. Based on two discoveries that 1) each category is only correlated with sparse and specific convolution kernels, and 2) adversarial samples are isolated while normal samples are successive in the feature space, a simple aggregate gradient constraint is devised for effectively diagnosing and optimizing CNN classifiers. The aggregate gradient strategy is a versatile module for mainstream CNN classifiers. Extensive experiments demonstrate that the proposed Model Doctor applies to all existing CNN classifiers, and improves the accuracy of 16 mainstream CNN classifiers by 1%~5%.
DOC2PPT: Automatic Presentation Slides Generation from Scientific Documents
Authors: Fu Tsu-Jui , Wang William Yang , McDuff Daniel , Song Yale
Published: July 2022

Creating presentation materials requires complex multimodal reasoning skills to summarize key concepts and arrange them in a logical and visually pleasing manner. Can machines learn to emulate this laborious process? We present a novel task and approach for document-to-slide generation. Solving this involves document summarization, image and text retrieval, slide structure and layout prediction to arrange key elements in a form suitable for presentation. We propose a hierarchical sequence-to-sequence approach to tackle our task in an end-to-end manner. Our approach exploits the inherent structures within documents and slides and incorporates paraphrasing and layout prediction modules to generate slides. To help accelerate research in this domain, we release a dataset about 6K paired documents and slide decks used in our experiments. We show that our approach outperforms strong baselines and produces slides with rich content and aligned imagery.
Unsupervised Underwater Image Restoration: From a Homology Perspective
Authors: Fu Zhenqi , Lin Huangxing , Yang Yan , Chai Shu , Sun Liyan , Huang Yue , Ding Xinghao
Published: July 2022

Underwater images suffer from degradation due to light scattering and absorption. It remains challenging to restore such degraded images using deep neural networks since real-world paired data is scarcely available while synthetic paired data cannot approximate real-world data perfectly. In this paper, we propose an UnSupervised Underwater Image Restoration method (USUIR) by leveraging the homology property between a raw underwater image and a re-degraded image. Specifically, USUIR first estimates three latent components of the raw underwater image, i.e., the global background light, the transmission map, and the scene radiance (the clean image). Then, a re-degraded image is generated by randomly mixing up the estimated scene radiance and the raw underwater image. We demonstrate that imposing a homology constraint between the raw underwater image and the re-degraded image is equivalent to minimizing the restoration error and hence can be used for the unsupervised restoration. Extensive experiments show that USUIR achieves promising performance in both inference time and restoration quality.
Playing Lottery Tickets with Vision and Language
Authors: Gan Zhe , Chen Yen-Chun , Li Linjie , Chen Tianlong , Cheng Yu , Wang Shuohang , Liu Jingjing , Wang Lijuan , Liu Zicheng
Published: July 2022

Large-scale pre-training has recently revolutionized vision-and-language (VL) research. Models such as LXMERT and UNITER have significantly lifted the state of the art over a wide range of VL tasks. However, the large number of parameters in such models hinders their application in practice. In parallel, work on the lottery ticket hypothesis (LTH) has shown that deep neural networks contain small matching subnetworks that can achieve on par or even better performance than the dense networks when trained in isolation. In this work, we perform the first empirical study to assess whether such trainable subnetworks also exist in pre-trained VL models. We use UNITER as the main testbed (also test on LXMERT and ViLT), and consolidate 7 representative VL tasks for experiments, including visual question answering, visual commonsense reasoning, visual entailment, referring expression comprehension, image-text retrieval, GQA, and NLVR2. Through comprehensive analysis, we summarize our main findings as follows. (i) It is difficult to find subnetworks that strictly match the performance of the full model. However, we can find relaxed winning tickets at 50%-70% sparsity that maintain 99% of the full accuracy. (ii) Subnetworks found by task-specific pruning transfer reasonably well to the other tasks, while those found on the pre-training tasks at 60%/70% sparsity transfer universally, matching 98%/96% of the full accuracy on average over all the tasks. (iii) Besides UNITER, other models such as LXMERT and ViLT can also play lottery tickets. However, the highest sparsity we can achieve for ViLT is far lower than LXMERT and UNITER (30% vs. 70%). (iv) LTH also remains relevant when using other training methods (e.g., adversarial training).
Towards Light-Weight and Real-Time Line Segment Detection
Authors: Gu Geonmo , Ko Byungsoo , Go SeoungHyun , Lee Sung-Hyun , Lee Jingeun , Shin Minchul
Published: July 2022

Previous deep learning-based line segment detection (LSD) suffers from the immense model size and high computational cost for line prediction. This constrains them from real-time inference on computationally restricted environments. In this paper, we propose a real-time and light-weight line segment detector for resource-constrained environments named Mobile LSD (M-LSD). We design an extremely efficient LSD architecture by minimizing the backbone network and removing the typical multi-module process for line prediction found in previous methods. To maintain competitive performance with a light-weight network, we present novel training schemes: Segments of Line segment (SoL) augmentation, matching and geometric loss. SoL augmentation splits a line segment into multiple subparts, which are used to provide auxiliary line data during the training process. Moreover, the matching and geometric loss allow a model to capture additional geometric cues. Compared with TP-LSD-Lite, previously the best real-time LSD method, our model (M-LSD-tiny) achieves competitive performance with 2.5% of model size and an increase of 130.5% in inference speed on GPU. Furthermore, our model runs at 56.8 FPS and 48.6 FPS on the latest Android and iPhone mobile devices, respectively. To the best of our knowledge, this is the first real-time deep LSD available on mobile devices.
Convolutional Neural Network Compression through Generalized Kronecker Product Decomposition
Authors: Hameed Marawan Gamal Abdel , Tahaei Marzieh S. , Mosleh Ali , Partovi Nia Vahid
Published: July 2022

Modern Convolutional Neural Network (CNN) architectures, despite their superiority in solving various problems, are generally too large to be deployed on resource constrained edge devices. In this paper, we reduce memory usage and floating-point operations required by convolutional layers in CNNs. We compress these layers by generalizing the Kronecker Product Decomposition to apply to multidimensional tensors, leading to the Generalized Kronecker Product Decomposition (GKPD). Our approach yields a plug-and-play module that can be used as a drop-in replacement for any convolutional layer. Experimental results for image classification on CIFAR-10 and ImageNet datasets using ResNet, MobileNetv2 and SeNet architectures substantiate the effectiveness of our proposed approach. We find that GKPD outperforms state-of-the-art decomposition methods including Tensor-Train and Tensor-Ring as well as other relevant compression methods such as pruning and knowledge distillation.
Generalizable Person Re-identification via Self-Supervised Batch Norm Test-Time Adaption
Authors: Han Ke , Si Chenyang , Huang Yan , Wang Liang , Tan Tieniu
Published: July 2022

In this paper, we investigate the generalization problem of person re-identification (re-id), whose major challenge is the distribution shift on an unseen domain. As an important tool of regularizing the distribution, batch normalization (BN) has been widely used in existing methods. However, they neglect that BN is severely biased to the training domain and inevitably suffers the performance drop if directly generalized without being updated. To tackle this issue, we propose Batch Norm Test-time Adaption (BNTA), a novel re-id framework that applies the self-supervised strategy to update BN parameters adaptively. Specifically, BNTA quickly explores the domain-aware information within unlabeled target data before inference, and accordingly modulates the feature distribution normalized by BN to adapt to the target domain. This is accomplished by two designed self-supervised auxiliary tasks, namely part positioning and part nearest neighbor matching, which help the model mine the domain-aware information with respect to the structure and identity of body parts, respectively. To demonstrate the effectiveness of our method, we conduct extensive experiments on three re-id datasets and confirm the superior performance to the state-of-the-art methods.
MODNet: Real-Time Trimap-Free Portrait Matting via Objective Decomposition
Authors: Ke Zhanghan , Sun Jiayu , Li Kaican , Yan Qiong , Lau Rynson W.H.
Published: July 2022

Existing portrait matting methods either require auxiliary inputs that are costly to obtain or involve multiple stages that are computationally expensive, making them less suitable for real-time applications. In this work, we present a light-weight matting objective decomposition network (MODNet) for portrait matting in real-time with a single input image. The key idea behind our efficient design is by optimizing a series of sub-objectives simultaneously via explicit constraints. In addition, MODNet includes two novel techniques for improving model efficiency and robustness. First, an Efficient Atrous Spatial Pyramid Pooling (e-ASPP) module is introduced to fuse multi-scale features for semantic estimation. Second, a self-supervised sub-objectives consistency (SOC) strategy is proposed to adapt MODNet to real-world data to address the domain shift problem common to trimap-free methods. MODNet is easy to be trained in an end-to-end manner. It is much faster than contemporaneous methods and runs at 67 frames per second on a 1080Ti GPU. Experiments show that MODNet outperforms prior trimap-free methods by a large margin on both Adobe Matting Dataset and a carefully designed photographic portrait matting (PPM-100) benchmark proposed by us. Further, MODNet achieves remarkable results on daily photos and videos.
Pinpointing Fine-Grained Relationships between Hateful Tweets and Replies
Authors: Albanyan Abdullah , Blanco Eduardo
Published: July 2022

Recent studies in the hate and counter hate domain have provided the grounds for investigating how to detect this pervasive content in social media. These studies mostly work with synthetic replies to hateful content written by annotators on demand rather than replies written by real users. We argue that working with naturally occurring replies to hateful content is key to study the problem. Building on this motivation, we create a corpus of 5,652 hateful tweets and replies. We analyze their fine-grained relationships by indicating whether the reply (a) is hate or counter hate speech, (b) provides a justification, (c) attacks the author of the tweet, and (d) adds additional hate. We also present linguistic insights into the language people use depending on these fine-grained relationships. Experimental results show improvements (a) taking into account the hateful tweet in addition to the reply and (b) pretraining with related tasks.
Enhanced Story Comprehension for Large Language Models through Dynamic Document-Based Knowledge Graphs
Authors: Andrus Berkeley R , Nasiri Yeganeh , Cui Shilong , Cullen Benjamin , Fulda Nancy
Published: July 2022

Large transformer-based language models have achieved incredible success at various tasks which require narrative comprehension, including story completion, answering questions about stories, and generating stories ex nihilo. However, due to the limitations of finite context windows, these language models struggle to produce or understand stories longer than several thousand tokens. In order to mitigate the document length limitations that come with finite context windows, we introduce a novel architecture that augments story processing with an external dynamic knowledge graph. In contrast to static commonsense knowledge graphs which hold information about the real world, these dynamic knowledge graphs reflect facts extracted from the story being processed. Our architecture uses these knowledge graphs to create information-rich prompts which better facilitate story comprehension than prompts composed only of story text. We apply our architecture to the tasks of question answering and story completion. To complement this line of research, we introduce two long-form question answering tasks, LF-SQuAD and LF-QUOREF, in which the document length exceeds the size of the language model's context window, and introduce a story completion evaluation method that bypasses the stochastic nature of language model generation. We demonstrate broad improvement over typical prompt formulation methods for both question answering and story completion using GPT-2, GPT-3 and XLNet.
ContrastNet: A Contrastive Learning Framework for Few-Shot Text Classification
Authors: Chen Junfan , Zhang Richong , Mao Yongyi , Xu Jie
Published: July 2022

Few-shot text classification has recently been promoted by the meta-learning paradigm which aims to identify target classes with knowledge transferred from source classes with sets of small tasks named episodes. Despite their success, existing works building their meta-learner based on Prototypical Networks are unsatisfactory in learning discriminative text representations between similar classes, which may lead to contradictions during label prediction. In addition, the task-level and instance-level overfitting problems in few-shot text classification caused by a few training examples are not sufficiently tackled. In this work, we propose a contrastive learning framework named ContrastNet to tackle both discriminative representation and overfitting problems in few-shot text classification. ContrastNet learns to pull closer text representations belonging to the same class and push away text representations belonging to different classes, while simultaneously introducing unsupervised contrastive regularization at both task-level and instance-level to prevent overfitting. Experiments on 8 few-shot text classification datasets show that ContrastNet outperforms the current state-of-the-art models.
Zero-Shot Commonsense Question Answering with Cloze Translation and Consistency Optimization
Authors: Dou Zi-Yi , Peng Nanyun
Published: July 2022

Commonsense question answering (CQA) aims to test if models can answer questions regarding commonsense knowledge that everyone knows. Prior works that incorporate external knowledge bases have shown promising results, but knowledge bases are expensive to construct and are often limited to a fixed set of relations. In this paper, we instead focus on better utilizing the implicit knowledge stored in pre-trained language models. While researchers have found that the knowledge embedded in pre-trained language models can be extracted by having them fill in the blanks of carefully designed prompts for relation extraction and text classification, it remains unclear if we can adopt this paradigm in CQA where the inputs and outputs take much more flexible forms. To this end, we investigate four translation methods that can translate natural questions into cloze-style sentences to better solicit commonsense knowledge from language models, including a syntactic-based model, an unsupervised neural model, and two supervised neural models. In addition, to combine the different translation methods, we propose to encourage consistency among model predictions on different translated questions with unlabeled data. We demonstrate the effectiveness of our methods on three CQA datasets in zero-shot settings. We show that our methods are complementary to a knowledge base improved model, and combining them can lead to state-of-the-art zero-shot performance. Analyses also reveal distinct characteristics of the different cloze translation methods and provide insights on why combining them can lead to great improvements. Code/dataset is available at https://github.com/PlusLabNLP/zero_shot_cqa.
ISEEQ: Information Seeking Question Generation Using Dynamic Meta-Information Retrieval and Knowledge Graphs
Authors: Gaur Manas , Gunaratna Kalpa , Srinivasan Vijay , Jin Hongxia
Published: July 2022

Conversational Information Seeking (CIS) is a relatively new research area within conversational AI that attempts to seek information from end-users in order to understand and satisfy the users' needs. If realized, such a CIS system has far-reaching benefits in the real world; for example, CIS systems can assist clinicians in pre-screening or triaging patients in healthcare. A key open sub-problem in CIS that remains unaddressed in the literature is generating Information Seeking Questions (ISQs) based on a short initial query from the end-user. To address this open problem, we propose Information SEEking Question generator (ISEEQ), a novel approach for generating ISQs from just a short user query, given a large text corpus relevant to the user query. Firstly, ISEEQ uses a knowledge graph to enrich the user query. Secondly, ISEEQ uses the knowledge-enriched query to retrieve relevant context passages to ask coherent ISQs adhering to a conceptual flow. Thirdly, ISEEQ introduces a new deep generative-adversarial reinforcement learning-based approach for generating ISQs. We show that ISEEQ can generate high-quality ISQs to promote the development of CIS agents. ISEEQ significantly outperforms comparable baselines on five ISQ evaluation metrics across four datasets having user queries from diverse domains. Further, we argue that ISEEQ is transferable across domains for generating ISQs, as it shows the acceptable performance when trained and tested on different pairs of domains. A qualitative human evaluation confirms that ISEEQ generated ISQs are comparable in quality to human-generated questions, and it outperformed the best comparable baseline.
Block-Skim: Efficient Question Answering for Transformer
Authors: Guan Yue , Li Zhengyi , Lin Zhouhan , Zhu Yuhao , Leng Jingwen , Guo Minyi
Published: July 2022

Transformer models have achieved promising results on natural language processing (NLP) tasks including extractive question answering (QA). Common Transformer encoders used in NLP tasks process the hidden states of all input tokens in the context paragraph throughout all layers. However, different from other tasks such as sequence classification, answering the raised question does not necessarily need all the tokens in the context paragraph. Following this motivation, we propose Block-skim, which learns to skim unnecessary context in higher hidden layers to improve and accelerate the Transformer performance. The key idea of Block-Skim is to identify the context that must be further processed and those that could be safely discarded early on during inference. Critically, we find that such information could be sufficiently derived from the self-attention weights inside the Transformer model. We further prune the hidden states corresponding to the unnecessary positions early in lower layers, achieving significant inference-time speedup. To our surprise, we observe that models pruned in this way outperform their full-size counterparts. Block-Skim improves QA models' accuracy on different datasets and achieves 3 times speedup on BERT-base model.
XLM-K: Improving Cross-Lingual Language Model Pre-training with Multilingual Knowledge
Authors: Jiang Xiaoze , Liang Yaobo , Chen Weizhu , Duan Nan
Published: July 2022

Cross-lingual pre-training has achieved great successes using monolingual and bilingual plain text corpora. However, most pre-trained models neglect multilingual knowledge, which is language agnostic but comprises abundant cross-lingual structure alignment. In this paper, we propose XLM-K, a cross-lingual language model incorporating multilingual knowledge in pre-training. XLM-K augments existing multilingual pre-training with two knowledge tasks, namely Masked Entity Prediction Task and Object Entailment Task. We evaluate XLM-K on MLQA, NER and XNLI. Experimental results clearly demonstrate significant improvements over existing multilingual language models. The results on MLQA and NER exhibit the superiority of XLM-K in knowledge related tasks. The success in XNLI shows a better cross-lingual transferability obtained in XLM-K. What is more, we provide a detailed probing analysis to confirm the desired knowledge captured in our pre-training regimen. The code is available at https://github.com/microsoft/Unicoder/tree/master/pretraining/xlmk.
Search and Learn: Improving Semantic Coverage for Data-to-Text Generation
Authors: Jolly Shailza , Zhang Zi Xuan , Dengel Andreas , Mou Lili
Published: July 2022

Data-to-text generation systems aim to generate text descriptions based on input data (often represented in the tabular form). A typical system uses huge training samples for learning the correspondence between tables and texts. However, large training sets are expensive to obtain, limiting the applicability of these approaches in real-world scenarios. In this work, we focus on few-shot data-to-text generation. We observe that, while fine-tuned pretrained language models may generate plausible sentences, they suffer from the low semantic coverage problem in the few-shot setting. In other words, important input slots tend to be missing in the generated text. To this end, we propose a search-and-learning approach that leverages pretrained language models but inserts the missing slots to improve the semantic coverage. We further finetune our system based on the search results to smooth out the search noise, yielding better-quality text and improving inference efficiency to a large extent. Experiments show that our model achieves high performance on E2E and WikiBio datasets. Especially, we cover 98.35% of input slots on E2E, largely alleviating the low coverage problem.
Braid: Weaving Symbolic and Neural Knowledge into Coherent Logical Explanations
Authors: Kalyanpur Aditya , Breloff Tom , Ferrucci David A
Published: July 2022

Traditional symbolic reasoning engines, while attractive for their precision and explicability, have a few major drawbacks: the use of brittle inference procedures that rely on exact matching (unification) of logical terms, an inability to deal with uncertainty, and the need for a precompiled rule-base of knowledge (the “knowledge acquisition” problem). To address these issues, we devise a novel logical reasoner called Braid, that supports probabilistic rules, and uses the notion of custom unification functions and dynamic rule generation to overcome the brittle matching and knowledge-gap problem prevalent in traditional reasoners. In this paper, we describe the reasoning algorithms used in Braid, and their implementation in a distributed task-based framework that builds proof/explanation graphs for an input query. We use a simple QA example from a children’s story to motivate Braid’s design and explain how the various components work together to produce a coherent logical explanation. Finally, we evaluate Braid on the ROC Story Cloze test and achieve close to state-of-the-art results while providing frame-based explanations.
DiffSinger: Singing Voice Synthesis via Shallow Diffusion Mechanism
Authors: Liu Jinglin , Li Chengxi , Ren Yi , Chen Feiyang , Zhao Zhou
Published: July 2022

Singing voice synthesis (SVS) systems are built to synthesize high-quality and expressive singing voice, in which the acoustic model generates the acoustic features (e.g., mel-spectrogram) given a music score. Previous singing acoustic models adopt a simple loss (e.g., L1 and L2) or generative adversarial network (GAN) to reconstruct the acoustic features, while they suffer from over-smoothing and unstable training issues respectively, which hinder the naturalness of synthesized singing.
In this work, we propose DiffSinger, an acoustic model for SVS based on the diffusion probabilistic model. DiffSinger is a parameterized Markov chain that iteratively converts the noise into mel-spectrogram conditioned on the music score. By implicitly optimizing variational bound, DiffSinger can be stably trained and generate realistic outputs.
To further improve the voice quality and speed up inference, we introduce a shallow diffusion mechanism to make better use of the prior knowledge learned by the simple loss. Specifically, DiffSinger starts generation at a shallow step smaller than the total number of diffusion steps, according to the intersection of the diffusion trajectories of the ground-truth mel-spectrogram and the one predicted by a simple mel-spectrogram decoder. Besides, we propose boundary prediction methods to locate the intersection and determine the shallow step adaptively.
The evaluations conducted on a Chinese singing dataset demonstrate that DiffSinger outperforms state-of-the-art SVS work. Extensional experiments also prove the generalization of our methods on text-to-speech task (DiffSpeech). Audio samples: https://diffsinger.github.io. Codes: https://github.com/MoonInTheRiver/DiffSinger.
Improving Biomedical Information Retrieval with Neural Retrievers
Authors: Luo Man , Mitra Arindam , Gokhale Tejas , Baral Chitta
Published: July 2022

Information retrieval (IR) is essential in search engines and dialogue systems as well as natural language processing tasks such as open-domain question answering. IR serve an important function in the biomedical domain, where content and sources of scientific knowledge may evolve rapidly. Although neural retrievers have surpassed traditional IR approaches such as TF-IDF and BM25 in standard open-domain question answering tasks, they are still found lacking in the biomedical domain. In this paper, we seek to improve information retrieval (IR) using neural retrievers (NR) in the biomedical domain, and achieve this goal using a three-pronged approach. First, to tackle the relative lack of data in the biomedical domain, we propose a template-based question generation method that can be leveraged to train neural retriever models. Second, we develop two novel pre-training tasks that are closely aligned to the downstream task of information retrieval. Third, we introduce the ``Poly-DPR'' model which encodes each context into multiple context vectors. Extensive experiments and analysis on the BioASQ challenge suggest that our proposed method leads to large gains over existing neural approaches and beats BM25 in the small-corpus setting. We show that BM25 and our method can complement each other, and a simple hybrid model leads to further gains in the large corpus setting.