Region-Aware Portrait Retouching with Sparse Interactive Guidance

Portrait retouching aims to improve the aesthetic quality of input portrait photos and especially requires human-region priority. \pink{The deep learning-based methods largely elevate the retouching efficiency and provide promising retouched results. However, existing portrait retouching methods focus on automatic retouching, which treats all human-regions equally and ignores users' preferences for specific individuals,} thus suffering from limited flexibility in interactive scenarios. In this work, we emphasize the importance of users' intents and explore the interactive portrait retouching task. Specifically, we propose a region-aware retouching framework with two branches: an automatic branch and an interactive branch. \pink{The automatic branch involves an encoding-decoding process, which searches region candidates and performs automatic region-aware retouching without user guidance. The interactive branch encodes sparse user guidance into a priority condition vector and modulates latent features with a region selection module to further emphasize the user-specified regions. Experimental results show that our interactive branch effectively captures users' intents and generalizes well to unseen scenes with sparse user guidance, while our automatic branch also outperforms the state-of-the-art retouching methods due to improved region-awareness.

CFUI: Collaborative Filtering With Unlabeled Items

As opposed to Web search, social tagging can be considered an alternative technique tapping into the wisdom of the crowd for organizing and discovering information on the Web. Effective tag-based recommendation of information items is critical to the success of this social information discovery mechanism. Over the past few years, there have been a growing number of studies aiming at improving the item recommendation quality of collaborative filtering (CF) methods by leveraging tagging information. However, a critical problem that often severely undermines the performance of tag-based CF methods, i.e., sparsity of user-item and user-tag interactions, is still yet to be adequately addressed. In this paper, we propose a novel learning framework, which deals with this data sparsity problem by making effective use of unlabeled items and propagating users’ preference information between the item space and the tag space. Empirical evaluation using real-world tagging data demonstrates the utility of the proposed framework

Collaborative Filtering in Social Tagging Systems Based on Joint Item-Tag Recommendations

Tapping into the wisdom of the crowd, social tagging can be considered an alternative mechanism - as opposed to Web search - for organizing and discovering information on the Web. Effective tag-based recommendation of information items, such as Web resources, is a critical aspect of this social information discovery mechanism. A precise understanding of the information structure of social tagging systems lies at the core of an effective tag-based recommendation method. While most of the existing research either implicitly or explicitly assumes a simple tripartite graph structure for this purpose, we propose a comprehensive information structure to capture all types of co-occurrence information in the tagging data. Based on the proposed information structure, we further propose a unified user profiling scheme to make full use of all available information. Finally, supported by our proposed user profile, we propose a novel framework for collaborative filtering in social tagging systems. In our proposed framework, we first generate joint item-tag recommendations, with tags indicating topical interests of users in target items. These joint recommendations are then refined by the wisdom from the crowd and projected to the item space for final item recommendations. Evaluation using three real-world datasets shows that our proposed recommendation approach significantly outperformed state-of-the-art approaches

Two NHX-type transporters from Helianthus tuberosus improve the tolerance of rice to salinity and nutrient deficiency stress.

The NHX-type cation/H+ transporters in plants have been shown to mediate Na+ (K+ )/H+ exchange for salinity tolerance and K+ homoeostasis. In this study, we identified and characterized two NHX homologues, HtNHX1 and HtNHX2 from an infertile and salinity tolerant species Helianthus tuberosus (cv. Nanyu No. 1). HtNHX1 and HtNHX2 share identical 5'- and 3'-UTR and coding regions, except for a 342-bp segment encoding 114 amino acids (L272 to Q385 ) which is absent in HtNHX2. Both hydroponics and soil culture experiments showed that the expression of HtNHX1 or HtNHX2 improved the rice tolerance to salinity. Expression of HtNHX2, but not HtNHX1, increased rice grain yield, harvest index, total nutrient uptake under K+ -limited salt-stress or general nutrient deficiency conditions. The results provide a novel insight into NHX function in plant mineral nutrition

Contrastive Domain Adaptation for Early Misinformation Detection: A Case Study on COVID-19

Despite recent progress in improving the performance of misinformation detection systems, classifying misinformation in an unseen domain remains an elusive challenge. To address this issue, a common approach is to introduce a domain critic and encourage domain-invariant input features. However, early misinformation often demonstrates both conditional and label shifts against existing misinformation data (e.g., class imbalance in COVID-19 datasets), rendering such methods less effective for detecting early misinformation. In this paper, we propose contrastive adaptation network for early misinformation detection (CANMD). Specifically, we leverage pseudo labeling to generate high-confidence target examples for joint training with source data. We additionally design a label correction component to estimate and correct the label shifts (i.e., class priors) between the source and target domains. Moreover, a contrastive adaptation loss is integrated in the objective function to reduce the intra-class discrepancy and enlarge the inter-class discrepancy. As such, the adapted model learns corrected class priors and an invariant conditional distribution across both domains for improved estimation of the target data distribution. To demonstrate the effectiveness of the proposed CANMD, we study the case of COVID-19 early misinformation detection and perform extensive experiments using multiple real-world datasets. The results suggest that CANMD can effectively adapt misinformation detection systems to the unseen COVID-19 target domain with significant improvements compared to the state-of-the-art baselines.Comment: Accepted to CIKM 202

Domain Adaptation for Question Answering via Question Classification

Question answering (QA) has demonstrated impressive progress in answering questions from customized domains. Nevertheless, domain adaptation remains one of the most elusive challenges for QA systems, especially when QA systems are trained in a source domain but deployed in a different target domain. In this work, we investigate the potential benefits of question classification for QA domain adaptation. We propose a novel framework: Question Classification for Question Answering (QC4QA). Specifically, a question classifier is adopted to assign question classes to both the source and target data. Then, we perform joint training in a self-supervised fashion via pseudo-labeling. For optimization, inter-domain discrepancy between the source and target domain is reduced via maximum mean discrepancy (MMD) distance. We additionally minimize intra-class discrepancy among QA samples of the same question class for fine-grained adaptation performance. To the best of our knowledge, this is the first work in QA domain adaptation to leverage question classification with self-supervised adaptation. We demonstrate the effectiveness of the proposed QC4QA with consistent improvements against the state-of-the-art baselines on multiple datasets.Comment: Accepted to COLING 202

Defending Substitution-Based Profile Pollution Attacks on Sequential Recommenders

While sequential recommender systems achieve significant improvements on capturing user dynamics, we argue that sequential recommenders are vulnerable against substitution-based profile pollution attacks. To demonstrate our hypothesis, we propose a substitution-based adversarial attack algorithm, which modifies the input sequence by selecting certain vulnerable elements and substituting them with adversarial items. In both untargeted and targeted attack scenarios, we observe significant performance deterioration using the proposed profile pollution algorithm. Motivated by such observations, we design an efficient adversarial defense method called Dirichlet neighborhood sampling. Specifically, we sample item embeddings from a convex hull constructed by multi-hop neighbors to replace the original items in input sequences. During sampling, a Dirichlet distribution is used to approximate the probability distribution in the neighborhood such that the recommender learns to combat local perturbations. Additionally, we design an adversarial training method tailored for sequential recommender systems. In particular, we represent selected items with one-hot encodings and perform gradient ascent on the encodings to search for the worst case linear combination of item embeddings in training. As such, the embedding function learns robust item representations and the trained recommender is resistant to test-time adversarial examples. Extensive experiments show the effectiveness of both our attack and defense methods, which consistently outperform baselines by a significant margin across model architectures and datasets.Comment: Accepted to RecSys 202

Zero- and Few-Shot Event Detection via Prompt-Based Meta Learning

With emerging online topics as a source for numerous new events, detecting unseen / rare event types presents an elusive challenge for existing event detection methods, where only limited data access is provided for training. To address the data scarcity problem in event detection, we propose MetaEvent, a meta learning-based framework for zero- and few-shot event detection. Specifically, we sample training tasks from existing event types and perform meta training to search for optimal parameters that quickly adapt to unseen tasks. In our framework, we propose to use the cloze-based prompt and a trigger-aware soft verbalizer to efficiently project output to unseen event types. Moreover, we design a contrastive meta objective based on maximum mean discrepancy (MMD) to learn class-separating features. As such, the proposed MetaEvent can perform zero-shot event detection by mapping features to event types without any prior knowledge. In our experiments, we demonstrate the effectiveness of MetaEvent in both zero-shot and few-shot scenarios, where the proposed method achieves state-of-the-art performance in extensive experiments on benchmark datasets FewEvent and MAVEN.Comment: Accepted to ACL 202

Karar Ağacı Algoritması Kullanılarak Çin Topraklarındaki Orta Dereceli Okul Öğrencilerine İlişkin Jeo-Uzamsal Düşünme Yeteneğinin Tahmin Edilmesi

Predicting secondary school students' geospatial thinking ability can provide targeted guidance for teachers. To date, few scholars have focused on predicting students’ geospatial thinking ability. In this paper, we address this gap by constructing a prediction model based on the decision tree algorithm, to predict the geospatial thinking ability of secondary school students. A total of 1029 secondary school students were surveyed using the Spatial Thinking Ability Test, the Students' Geography Learning Status Questionnaire, and the Middle Students Motivation Test. Our model indicates that geospatial thinking ability can be predicted by nine factors, in order of importance: academic achievement in geography, geography learning strategy, geography classroom environment, gender, learning initiative, learning goals, extra-curricular time spent learning geography, ego-enhancement drive, and interest in learning geography. The model accuracy is 81.25%. Specifically, our study is the first to predict geospatial thinking ability. It provides a tool for teachers that can help them identify and predict students' geospatial thinking ability, which is conducive to designing better teaching plans and making adjustments to the curriculum.Orta dereceli okul öğrencilerinin jeo-uzamsal düşünme yeteneklerinin tahmin edilmesi öğretmenler için hedefe yönelik rehberlik sağlayabilir. Şimdiye kadar az sayıda bilim insanı, öğrencilerin jeo-uzamsal düşünme yeteneklerinin tahmin edilmesine odaklanmıştır. Bu makalede, orta dereceli okul öğrencilerinin jeo-uzamsal düşünme yeteneklerinin tahmin edilmesi amacıyla karar ağacı algoritmasına dayanan bir tahmin modeli oluşturarak bu boşluğu doldurmayı amaçlıyoruz. Uzamsal Düşünme Yeteneği Testi, Öğrencilerin Coğrafya Öğrenimi Durumu Anketi ve Orta Dereceli Okul Öğrencileri Motivasyon Testi kullanılarak toplam 1029 orta dereceli okul öğrencisine anket uygulanmıştır. Modelimiz, jeo-uzamsal düşünme yeteneğinin dokuz etmenle tahmin edilebileceğine işaret etmektedir. Önem sırasına göre bu etmenler; coğrafya dersindeki akademik başarı, coğrafya öğrenimi stratejisi, coğrafya sınıf ortamı, cinsiyet, öğrenme inisiyatifi, öğrenme hedefleri, coğrafya öğreniminde harcanan müfredat harici zaman, benlik geliştirme dürtüsü ve coğrafya öğrenimine ilgi şeklindedir. Model doğruluk oranı %81,25’tir. Özellikle, çalışmamız jeo-uzamsal düşünme yeteneğinin tahmin edilmesine yönelik ilk çalışmadır. Öğretmenlere öğrencilerin jeo-uzamsal düşünme yeteneklerini saptamalarına ve tahmin etmelerine yardımcı olabilecek bir araç sunan çalışmamız böylelikle daha iyi eğitim planları hazırlanmasında ve müfredatta düzenlemeler yapılmasında fayda sağlayacaktır

Unsupervised Domain Adaptation for COVID-19 Information Service with Contrastive Adversarial Domain Mixup

In the real-world application of COVID-19 misinformation detection, a fundamental challenge is the lack of the labeled COVID data to enable supervised end-to-end training of the models, especially at the early stage of the pandemic. To address this challenge, we propose an unsupervised domain adaptation framework using contrastive learning and adversarial domain mixup to transfer the knowledge from an existing source data domain to the target COVID-19 data domain. In particular, to bridge the gap between the source domain and the target domain, our method reduces a radial basis function (RBF) based discrepancy between these two domains. Moreover, we leverage the power of domain adversarial examples to establish an intermediate domain mixup, where the latent representations of the input text from both domains could be mixed during the training process. Extensive experiments on multiple real-world datasets suggest that our method can effectively adapt misinformation detection systems to the unseen COVID-19 target domain with significant improvements compared to the state-of-the-art baselines