Reviewerly/exHarmony huggingface dataset:
Towards Robust Visual Information Extraction in Real World: New Dataset and Novel Solution

Visual Information Extraction (VIE) has attracted considerable attention recently owing to its various advanced applications such as document understanding, automatic marking and intelligent education. Most existing works decoupled this problem into several independent sub-tasks of text spotting (text detection and recognition) and information extraction, which completely ignored the high correlation among them during optimization. In this paper, we propose a robust Visual Information Extraction System (VIES) towards real-world scenarios, which is an unified end-to-end trainable framework for simultaneous text detection, recognition and information extraction by taking a single document image as input and outputting the structured information. Specifically, the information extraction branch collects abundant visual and semantic representations from text spotting for multimodal feature fusion and conversely, provides higher-level semantic clues to contribute to the optimization of text spotting. Moreover, regarding the shortage of public benchmarks, we construct a fully-annotated dataset called EPHOIE (https://github.com/HCIILAB/EPHOIE), which is the first Chinese benchmark for both text spotting and visual information extraction. EPHOIE consists of 1,494 images of examination paper head with complex layouts and background, including a total of 15,771 Chinese handwritten or printed text instances. Compared with the state-of-the-art methods, our VIES shows significant superior performance on the EPHOIE dataset and achieves a 9.01% F-score gain on the widely used SROIE dataset under the end-to-end scenario.
New robust method for image copyright protection using histogram features and Sine Cosine Algorithm

• Introducing a new application of the Sine Cosine Algorithm (SCA) in the field of image analysis. • Introducing a novel robust method for copyright protection of grayscale images based on SCA and the histogram descriptor. • Providing numerical experiments to demonstrate the efficiency and superiority of the proposed method. Zero-watermarking methods are widely used for efficient copyright protection of digital images. These methods have the ability to withstand both common image processing attacks and some geometric attacks. However, they cannot effectively resist the complex image attacks such as translation, cropping, combined geometric attacks, UnZign, etc. For this purpose, we propose in this work a novel robust zero-watermarking method that can effectively resist several complex image attacks. The proposed method involves the use of the histogram descriptor to generate a secret sequence of binary values from a user-selected Region of Interest (ROI). In order to check the intellectual property rights, the Sine Cosine Algorithm (SCA) is used for detecting the ROI in the attacked image. Then, the computed histogram of this ROI is binarized with a secret method. Next, the resulting sequence of binary values is compared to the original one generated from the original ROI. If there is a high similarity between these binary sequences, the grayscale image copyrights are validated. The simulation results show that the proposed method is not only resistant to geometric attacks (rotation, scaling) and to common image attacks (JPEG compression, filtering, etc.), but it is also robust against the most complex image attacks (cropping, translation, combined geometric attacks, etc.). Furthermore, the results of the comparisons carried out in terms of robustness against different types of attacks prove the superiority of our method over other similar recent zero-watermarking methods.
Structured Graph Convolutional Networks with Stochastic Masks for Recommender Systems

Graph Convolutional Networks (GCNs) are powerful for collaborative filtering. The key component of GCNs is to explore neighborhood aggregation mechanisms to extract high-level representations of users and items. However, real-world user-item graphs are often incomplete and noisy. Aggregating misleading neighborhood information may lead to sub-optimal performance if GCNs are not regularized properly. Also, the real-world user-item graphs are often sparse and low rank. These two intrinsic graph properties are widely used in shallow matrix completion models, but far less studied in graph neural models. Here we propose Structured Graph Convolutional Networks (SGCNs) to enhance the performance of GCNs by exploiting graph structural properties of sparsity and low rank. To achieve sparsity, we attach each layer of a GCN with a trainable stochastic binary mask to prune noisy and insignificant edges, resulting in a clean and sparsified graph. To preserve its low-rank property, the nuclear norm regularization is applied. We jointly learn the parameters of stochastic binary masks and original GCNs by solving a stochastic binary optimization problem. An unbiased gradient estimator is further proposed to better backpropagate the gradients of binary variables. Experimental results demonstrate that SGCNs achieve better performance compared with the state-of-the-art GCNs.
Multiple-element joint detection for Aspect-Based Sentiment Analysis

Aspect-Based Sentiment Analysis (ABSA) is a fine-grained sentiment analysis task, which aims to detect target-aspect-sentiment elements in a sentence. Most of the existing research work distinguished the sentiment for aspects or targets independently, ignoring the corresponding relation between the targets and the aspects. However, such a corresponding relation is significant for the accurate prediction of fine-grained sentiment polarity. In this paper, we propose a novel end-to-end multiple-element joint detection model (MEJD), which effectively extracts all (target, aspect, sentiment) triples from a sentence. Our model utilizes BERT to obtain the initial embedding vector from the aspect–sentence joint input and applies bidirectional long short-term memory to model aspect and sentence representations. We then employ a graph convolutional network with attention mechanisms to capture the dependency relationship between aspect and sentence. We evaluate our approach on two restaurant datasets of SemEval 2015 Task 12 and SemEval 2016 Task 5. Experiment results show that our model achieves state-of-the-art performance in extracting (target, aspect, sentiment) triples. Moreover, the model also has good performance on multiple subtasks of target-aspect-sentiment detection.
WHAT IS DIGITAL TRANSFORMATION?

Digital Transformation is a type of Business Transformation that is driven by emerging technologies. The potential for top line growth and bottom line savings of a Digital Transformation Program exceeds that of other types of Transformation initiatives. At its core, Digital Transformation is driven by the tangible shift in the role of the technology within an organization. No longer as purely a support function that enables business processes, technology is now capable of much more. Technology now allows for new, innovative business models (e.g. XaaS), drives sales growth, and can even be a source of Competitive Advantage. There are numerous emerging technology trends that drive Digital Transformation. Notable ones include Social Media, Mobility, Internet of Things (IoT), Cybersecurity, Big Data & Analytics, Cloud Computing, Robotic Process Automation (RPA), Artificial Intelligence (namely Machine Learning), Blockchain, and so forth. The applicability of technology is largely dependent on the industry and organization. With these technology trends, businesses are armed with the capability to fully digitize, transform, and grow their organizations. These technologies, in fact, mobilize 8 levers to Digital Transformation--spanning both Growth (business drivers) and Operational Improvement (business enablers).
PP-Rec: News Recommendation with Personalized User Interest and Time-aware News Popularity

Personalized news recommendation methods are widely used in online news services.These methods usually recommend news based on the matching between news content and user interest inferred from historical behaviors.However, these methods usually have difficulties in making accurate recommendations to cold-start users, and tend to recommend similar news with those users have read.In general, popular news usually contain important information and can attract users with different interests.Besides, they are usually diverse in content and topic.Thus, in this paper we propose to incorporate news popularity information to alleviate the cold-start and diversity problems for personalized news recommendation.In our method, the ranking score for recommending a candidate news to a target user is the combination of a personalized matching score and a news popularity score.The former is used to capture the personalized user interest in news.The latter is used to measure timeaware popularity of candidate news, which is predicted based on news content, recency, and real-time CTR using a unified framework.Besides, we propose a popularity-aware user encoder to eliminate the popularity bias in user behaviors for accurate interest modeling.Experiments on two real-world datasets show our method can effectively improve the accuracy and diversity for news recommendation.
Mind Your Outliers! Investigating the Negative Impact of Outliers on Active Learning for Visual Question Answering

Active learning promises to alleviate the massive data needs of supervised machine learning: it has successfully improved sample efficiency by an order of magnitude on traditional tasks like topic classification and object recognition.However, we uncover a striking contrast to this promise: across 5 models and 4 datasets on the task of visual question answering, a wide variety of active learning approaches fail to outperform random selection.To understand this discrepancy, we profile 8 active learning methods on a per-example basis, and identify the problem as collective outliers -groups of examples that active learning methods prefer to acquire but models fail to learn (e.g., questions that ask about text in images or require external knowledge).Through systematic ablation experiments and qualitative visualizations, we verify that collective outliers are a general phenomenon responsible for degrading pool-based active learning.Notably, we show that active learning sample efficiency increases significantly as the number of collective outliers in the active learning pool decreases.We conclude with a discussion and prescriptive recommendations for mitigating the effects of these outliers in future work.
U-BERT: Pre-training User Representations for Improved Recommendation

Learning user representation is a critical task for recommendation systems as it can encode user preference for personalized services. User representation is generally learned from behavior data, such as clicking interactions and review comments. However, for less popular domains, the behavior data is insufficient to learn precise user representations. To deal with this problem, a natural thought is to leverage content-rich domains to complement user representations. Inspired by the recent success of BERT in NLP, we propose a novel pre-training and fine-tuning based approach U-BERT. Different from typical BERT applications, U-BERT is customized for recommendation and utilizes different frameworks in pre-training and fine-tuning. In pre-training, U-BERT focuses on content-rich domains and introduces a user encoder and a review encoder to model users' behaviors. Two pre-training strategies are proposed to learn the general user representations; In fine-tuning, U-BERT focuses on the target content-insufficient domains. In addition to the user and review encoders inherited from the pre-training stage, U-BERT further introduces an item encoder to model item representations. Besides, a review co-matching layer is proposed to capture more semantic interactions between the reviews of the user and item. Finally, U-BERT combines user representations, item representations and review interaction information to improve recommendation performance. Experiments on six benchmark datasets from different domains demonstrate the state-of-the-art performance of U-BERT.
Source-Free Open Compound Domain Adaptation in Semantic Segmentation

In this work, we introduce a new concept, named source-free open compound domain adaptation (SF-OCDA), and study it in semantic segmentation. SF-OCDA is more challenging than the traditional domain adaptation but it is more practical. It jointly considers (1) the issues of data privacy and data storage and (2) the scenario of multiple target domains and unseen open domains. In SF-OCDA, only the source pre-trained model and the target data are available to learn the target model. The model is evaluated on the samples from the target and unseen open domains. To solve this problem, we present an effective framework by separating the training process into two stages: (1) pre-training a generalized source model and (2) adapting a target model with self-supervised learning. In our framework, we propose the Cross-Patch Style Swap (CPSS) to diversify samples with various patch styles in the feature-level, which can benefit the training of both stages. First, CPSS can significantly improve the generalization ability of the source model, providing more accurate pseudo-labels for the latter stage. Second, CPSS can reduce the influence of noisy pseudo-labels and also avoid the model overfitting to the target domain during self-supervised learning, consistently boosting the performance on the target and open domains. Experiments demonstrate that our method produces state-of-the-art results on the C-Driving dataset. Furthermore, our model also achieves the leading performance on CityScapes for domain generalization.
I See Dead µops: Leaking Secrets via Intel/AMD Micro-Op Caches

Modern Intel, AMD, and ARM processors translate complex instructions into simpler internal micro-ops that are then cached in a dedicated on-chip structure called the micro-op cache. This work presents an in-depth characterization study of the micro-op cache, reverse-engineering many undocumented features, and further describes attacks that exploit the micro-op cache as a timing channel to transmit secret information. In particular, this paper describes three attacks – (1) a same thread cross-domain attack that leaks secrets across the user-kernel boundary, (2) a cross-SMT thread attack that transmits secrets across two SMT threads via the micro-op cache, and (3) transient execution attacks that have the ability to leak an unauthorized secret accessed along a misspeculated path, even before the transient instruction is dispatched to execution, breaking several existing invisible speculation and fencing-based solutions that mitigate Spectre.
Dynamic Modeling Cross-Modal Interactions in Two-Phase Prediction for Entity-Relation Extraction

Joint extraction of entities and their relations benefits from the close interaction between named entities and their relation information. Therefore, how to effectively model such cross-modal interactions is critical for the final performance. Previous works have used simple methods, such as label-feature concatenation, to perform coarse-grained semantic fusion among cross-modal instances but fail to capture fine-grained correlations over token and label spaces, resulting in insufficient interactions. In this article, we propose a dynamic cross-modal attention network (CMAN) for joint entity and relation extraction. The network is carefully constructed by stacking multiple attention units in depth to dynamic model dense interactions over token-label spaces, in which two basic attention units and a novel two-phase prediction are proposed to explicitly capture fine-grained correlations across different modalities (e.g., token-to-token and label-to-token). Experiment results on the CoNLL04 dataset show that our model obtains state-of-the-art results by achieving 91.72% F1 on entity recognition and 73.46% F1 on relation classification. In the ADE and DREC datasets, our model surpasses existing approaches by more than 2.1% and 2.54% F1 on relation classification. Extensive analyses further confirm the effectiveness of our approach.
Pseudo-loss Confidence Metric for Semi-supervised Few-shot Learning

Semi-supervised few-shot learning is developed to train a classifier that can adapt to new tasks with limited labeled data and a fixed quantity of unlabeled data. Most semi-supervised few-shot learning methods select pseudo-labeled data of unlabeled set by task-specific confidence estimation. This work presents a task-unified confidence estimation approach for semi-supervised few-shot learning, named pseudo-loss confidence metric (PLCM). It measures the data credibility by the loss distribution of pseudo-labels, which is synthetical considered multi-tasks. Specifically, pseudo-labeled data of different tasks are mapped to a unified metric space by mean of the pseudo-loss model, making it possible to learn the prior pseudo-loss distribution. Then, confidence of pseudo-labeled data is estimated according to the distribution component confidence of its pseudo-loss. Thus highly reliable pseudo-labeled data are selected to strengthen the classifier. Moreover, to overcome the pseudo-loss distribution shift and improve the effectiveness of classifier, we advance the multi-step training strategy coordinated with the class balance measures of class-apart selection and class weight. Experimental results on four popular benchmark datasets demonstrate that the proposed approach can effectively select pseudo-labeled data and achieve the state-of-the-art performance.
Image Denoising Using Non-Local Means (NLM) Approach in Magnetic Resonance (MR) Imaging: A Systematic Review

The non-local means (NLM) noise reduction algorithm is well known as an excellent technique for removing noise from a magnetic resonance (MR) image to improve the diagnostic accuracy. In this study, we undertook a systematic review to determine the effectiveness of the NLM noise reduction algorithm in MR imaging. A systematic literature search was conducted of three databases of publications dating from January 2000 to March 2020; of the 82 publications reviewed, 25 were included in this study. The subjects were categorized into four major frameworks and analyzed for each research result. Research in NLM noise reduction for MR images has been increasing worldwide; however, it was found to have slightly decreased since 2016. It was found that the NLM technique was most frequently used on brain images taken using the general MR imaging technique; these were most frequently performed during simultaneous real and simulated experimental studies. In particular, comparison parameters were frequently used to evaluate the effectiveness of the algorithm on MR images. The ultimate goal is to provide an accurate method for the diagnosis of disease, and our conclusion is that the NLM noise reduction algorithm is a promising method of achieving this goal.
Survey and Evaluation of Causal Discovery Methods for Time Series

We introduce in this survey the major concepts, models, and algorithms proposed so far to infer causal relations from observational time series, a task usually referred to as causal discovery in time series. To do so, after a description of the underlying concepts and modelling assumptions, we present different methods according to the family of approaches they belong to: Granger causality, constraint-based approaches, noise-based approaches, score-based approaches, logic-based approaches, topology-based approaches, and difference-based approaches. We then evaluate several representative methods to illustrate the behaviour of different families of approaches. This illustration is conducted on both artificial and real datasets, with different characteristics. The main conclusions one can draw from this survey is that causal discovery in times series is an active research field in which new methods (in every family of approaches) are regularly proposed, and that no family or method stands out in all situations. Indeed, they all rely on assumptions that may or may not be appropriate for a particular dataset.
Hybrid leader based optimization: a new stochastic optimization algorithm for solving optimization applications

Abstract In this paper, a new optimization algorithm called hybrid leader-based optimization (HLBO) is introduced that is applicable in optimization challenges. The main idea of HLBO is to guide the algorithm population under the guidance of a hybrid leader. The stages of HLBO are modeled mathematically in two phases of exploration and exploitation. The efficiency of HLBO in optimization is tested by finding solutions to twenty-three standard benchmark functions of different types of unimodal and multimodal. The optimization results of unimodal functions indicate the high exploitation ability of HLBO in local search for better convergence to global optimal, while the optimization results of multimodal functions show the high exploration ability of HLBO in global search to accurately scan different areas of search space. In addition, the performance of HLBO on solving IEEE CEC 2017 benchmark functions including thirty objective functions is evaluated. The optimization results show the efficiency of HLBO in handling complex objective functions. The quality of the results obtained from HLBO is compared with the results of ten well-known algorithms. The simulation results show the superiority of HLBO in convergence to the global solution as well as the passage of optimally localized areas of the search space compared to ten competing algorithms. In addition, the implementation of HLBO on four engineering design issues demonstrates the applicability of HLBO in real-world problem solving.
BiSyn-GAT+: Bi-Syntax Aware Graph Attention Network for Aspect-based Sentiment Analysis

Aspect-based sentiment analysis (ABSA) is a fine-grained sentiment analysis task that aims to align aspects and corresponding sentiments for aspect-specific sentiment polarity inference.It is challenging because a sentence may contain multiple aspects or complicated (e.g., conditional, coordinating, or adversative) relations.Recently, exploiting dependency syntax information with graph neural networks has been the most popular trend.Despite its success, methods that heavily rely on the dependency tree pose challenges in accurately modeling the alignment of the aspects and their words indicative of sentiment, since the dependency tree may provide noisy signals of unrelated associations (e.g., the "conj" relation between "great" and "dreadful" in Figure 2).In this paper, to alleviate this problem, we propose a Bi-Syntax aware Graph Attention Network (BiSyn-GAT+).Specifically, BiSyn-GAT+ fully exploits the syntax information (e.g., phrase segmentation and hierarchical structure) of the constituent tree of a sentence to model the sentiment-aware context of every single aspect (called intracontext) and the sentiment relations across aspects (called inter-context) for learning.Experiments on four benchmark datasets demonstrate that BiSyn-GAT+ outperforms the stateof-the-art methods consistently.