Reviewerly/exHarmony huggingface dataset:
Context-Specific Heterogeneous Graph Convolutional Network for Implicit Sentiment Analysis

Sentiment analysis has attracted considerable attention in recent years. In particular, implicit sentiment analysis is a more challenging problem due to the lack of sentiment words. It requires us to combine contextual information and precisely understand the emotion changing process. Graph convolutional network (GCN) techniques have been widely applied for sentiment analysis since they are capable of learning from complex structures and preserving global information. However, these models either only focus on extracting features from a single sentence and ignore the context semantic background or only consider the textual information and overlook the phrase dependency when constructing the graph. To address these problems, we propose a new context-specific heterogeneous graph convolutional network (CsHGCN) framework that can combine all context representations. It has a complete context that reflects the information on documents more comprehensively. It has a dependency structure that obtains token-token semantic acquisition more accurately. The experimental results on a Chinese implicit sentiment dataset show that our proposed model can effectively identify the target sentiment of sentences, and visualization of the attention layers further demonstrates that the model selects qualitatively informative tokens and sentences.
An Efficient Certificateless Aggregate Signature Scheme for Blockchain-Based Medical Cyber Physical Systems

Different from the traditional healthcare field, Medical Cyber Physical Systems (MCPS) rely more on wireless wearable devices and medical applications to provide better medical services. The secure storage and sharing of medical data are facing great challenges. Blockchain technology with decentralization, security, credibility and tamper-proof is an effective way to solve this problem. However, capacity limitation is one of the main reasons affecting the improvement of blockchain performance. Certificateless aggregation signature schemes can greatly tackle the difficulty of blockchain expansion. In this paper, we describe a two-layer system model in which medical records are stored off-blockchain and shared on-blockchain. Furthermore, a multi-trapdoor hash function is proposed. Based on the proposed multi-trapdoor hash function, we present a certificateless aggregate signature scheme for blockchain-based MCPS. The purpose is to realize the authentication of related medical staffs, medical equipment, and medical apps, ensure the integrity of medical records, and support the secure storage and sharing of medical information. The proposed scheme is highly computationally efficient because it does not use bilinear maps and exponential operations. Many certificateless aggregate signature schemes without bilinear maps in Internet of things (IoT) have been proposed in recent years, but they are not applied to the medical field, and they do not consider the security requirements of medical data. The proposed scheme in this paper has high computing and storage efficiency, while meeting the security requirements in MCPS.
A Hematologist-Level Deep Learning Algorithm (BMSNet) for Assessing the Morphologies of Single Nuclear Balls in Bone Marrow Smears: Algorithm Development

Bone marrow aspiration and biopsy remain the gold standard for the diagnosis of hematological diseases despite the development of flow cytometry (FCM) and molecular and gene analyses. However, the interpretation of the results is laborious and operator dependent. Furthermore, the obtained results exhibit inter- and intravariations among specialists. Therefore, it is important to develop a more objective and automated analysis system. Several deep learning models have been developed and applied in medical image analysis but not in the field of hematological histology, especially for bone marrow smear applications.The aim of this study was to develop a deep learning model (BMSNet) for assisting hematologists in the interpretation of bone marrow smears for faster diagnosis and disease monitoring.From January 1, 2016, to December 31, 2018, 122 bone marrow smears were photographed and divided into a development cohort (N=42), a validation cohort (N=70), and a competition cohort (N=10). The development cohort included 17,319 annotated cells from 291 high-resolution photos. In total, 20 photos were taken for each patient in the validation cohort and the competition cohort. This study included eight annotation categories: erythroid, blasts, myeloid, lymphoid, plasma cells, monocyte, megakaryocyte, and unable to identify. BMSNet is a convolutional neural network with the YOLO v3 architecture, which detects and classifies single cells in a single model. Six visiting staff members participated in a human-machine competition, and the results from the FCM were regarded as the ground truth.In the development cohort, according to 6-fold cross-validation, the average precision of the bounding box prediction without consideration of the classification is 67.4%. After removing the bounding box prediction error, the precision and recall of BMSNet were similar to those of the hematologists in most categories. In detecting more than 5% of blasts in the validation cohort, the area under the curve (AUC) of BMSNet (0.948) was higher than the AUC of the hematologists (0.929) but lower than the AUC of the pathologists (0.985). In detecting more than 20% of blasts, the AUCs of the hematologists (0.981) and pathologists (0.980) were similar and were higher than the AUC of BMSNet (0.942). Further analysis showed that the performance difference could be attributed to the myelodysplastic syndrome cases. In the competition cohort, the mean value of the correlations between BMSNet and FCM was 0.960, and the mean values of the correlations between the visiting staff and FCM ranged between 0.952 and 0.990.Our deep learning model can assist hematologists in interpreting bone marrow smears by facilitating and accelerating the detection of hematopoietic cells. However, a detailed morphological interpretation still requires trained hematologists.
Multiview Concept Learning Via Deep Matrix Factorization

Multiview representation learning (MVRL) leverages information from multiple views to obtain a common representation summarizing the consistency and complementarity in multiview data. Most previous matrix factorization-based MVRL methods are shallow models that neglect the complex hierarchical information. The recently proposed deep multiview factorization models cannot explicitly capture consistency and complementarity in multiview data. We present the deep multiview concept learning (DMCL) method, which hierarchically factorizes the multiview data, and tries to explicitly model consistent and complementary information and capture semantic structures at the highest abstraction level. We explore two variants of the DMCL framework, DMCL-L and DMCL-N, with respectively linear/nonlinear transformations between adjacent layers. We propose two block coordinate descent-based optimization methods for DMCL-L and DMCL-N. We verify the effectiveness of DMCL on three real-world data sets for both clustering and classification tasks.
Virtual reality: physiological and behavioral mechanisms to increase individual pain tolerance limits

Abstract Immersive virtual reality (VR) consists of immersion in artificial environments through the use of real-time render technologies and the latest generation devices. The users feel just as immersed as they would feel in an everyday life situation, and this sense of presence seems to have therapeutic potentials. However, the VR mechanisms remain only partially known. This study is novel in that, for the first time in VR research, appropriate controls for VR contexts, immersive characteristics (ie, control VR), and multifaceted objective and subjective outcomes were included in a within-subject study design conducted on healthy participants. Participants received heat thermal stimulations to determine how VR can increase individual heat-pain tolerance limits (primary outcome) measured in degrees Celsius and seconds while recording concurrent autonomic responses. We also assessed changes in pain unpleasantness, mood, situational anxiety, and level of enjoyment (secondary outcomes). The VR induced a net gain in heat-pain tolerance limits that was paralleled by an increase of the parasympathetic responses. VR improved mood, situational anxiety, and pain unpleasantness when participants perceived the context as enjoyable, but these changes did not influence the increases in pain tolerance limits. Distraction increased pain tolerance limits but did not induce such mood and physiological changes. Immersive VR has been anecdotally applied to improve acute symptoms in contexts such as battlefield, emergency, and operating rooms. This study provides a mechanistic framework for VR as a low-risk, nonpharmacological intervention, which regulates autonomic, affective (mood and situational anxiety), and evaluative (subjective pain and enjoyment ratings) responses associated with acute pain.
Least Loss: A simplified filter method for feature selection

Identifying the relevant set of features in a dataset is an important part of data analytics. Discarding significant variables or keeping irrelevant variables has significant effects on the performance of the learning algorithm during knowledge discovery. In this paper, a feature selection method called Least Loss (L2) is proposed that significantly reduces the dimensionality of data by disposing weakly correlated variables in a robust manner without diminishing the predictive performance of classifiers. The proposed method is based on quantifying the similarity between the observed and expected probabilities and generating scores for each independent variable, which makes it simple and intuitive. The evaluation of the proposed method was done by comparing its performance against Information Gain (IG) and Chi Square (CHI) feature selection methods on 27 different datasets modeled using a probabilistic classifier. The results reveal that L2 is highly competitive with respect to error rate, precision, and recall measures while substantially reducing the number of selected variables in the datasets. Our study would be of high interest to data analysts, scholars and domain experts who deal with applications that include large numbers of features using statistical analysis methods.
Deep memory network with Bi-LSTM for personalized context-aware citation recommendation

The explosive growth of data leads researchers to waste time and energy to search for papers they need. Context-aware citation recommendation aims to solve this problem by analyzing a citation context and provides a list of recommended papers. In this paper, we propose a context-aware citation recommendation model based on end to end memory network. The model learns the representations of papers and citation contexts respectively based on bidirectional long short-term memory (Bi-LSTM). In particular, we jointly integrate author information and citation relationship in the distributed vector representations of citation contexts and papers. Then calculates the continuous relevance between them based on a computational multilayers memory network. We also conduct experiments on three real-world datasets to evaluate the performance of our model.
AliCoCo: Alibaba E-commerce Cognitive Concept Net

One of the ultimate goals of e-commerce platforms is to satisfy various shopping needs for their customers. Much efforts are devoted to creating taxonomies or ontologies in e-commerce towards this goal. However, user needs in e-commerce are still not well defined, and none of the existing ontologies has the enough depth and breadth for universal user needs understanding. The semantic gap in-between prevents shopping experience from being more intelligent. In this paper, we propose to construct a large-scale E-commerce Cognitive Concept Net named "AliCoCo", which is practiced in Alibaba, the largest Chinese e-commerce platform in the world. We formally define user needs in e-commerce, then conceptualize them as nodes in the net. We present details on how AliCoCo is constructed semi-automatically and its successful, ongoing and potential applications in e-commerce.
Aspect Sentiment Classification with Document-level Sentiment Preference Modeling

In the literature, existing studies always consider Aspect Sentiment Classification (ASC) as an independent sentence-level classification problem aspect by aspect, which largely ignore the document-level sentiment preference information, though obviously such information is crucial for alleviating the information deficiency problem in ASC. In this paper, we explore two kinds of sentiment preference information inside a document, i.e., contextual sentiment consistency w.r.t. the same aspect (namely intra-aspect sentiment consistency) and contextual sentiment tendency w.r.t. all the related aspects (namely inter-aspect sentiment tendency). On the basis, we propose a Cooperative Graph Attention Networks (CoGAN) approach for cooperatively learning the aspect-related sentence representation. Specifically, two graph attention networks are leveraged to model above two kinds of document-level sentiment preference information respectively, followed by an interactive mechanism to integrate the two-fold preference. Detailed evaluation demonstrates the great advantage of the proposed approach to ASC over the state-of-the-art baselines. This justifies the importance of the document-level sentiment preference information to ASC and the effectiveness of our approach capturing such information.
LogiQA: A Challenge Dataset for Machine Reading Comprehension with Logical Reasoning

Machine reading is a fundamental task for testing the capability of natural language understand- ing, which is closely related to human cognition in many aspects. With the rising of deep learning techniques, algorithmic models rival human performances on simple QA, and thus increasingly challenging machine reading datasets have been proposed. Though various challenges such as evidence integration and commonsense knowledge have been integrated, one of the fundamental capabilities in human reading, namely logical reasoning, is not fully investigated. We build a comprehensive dataset, named LogiQA, which is sourced from expert-written questions for testing human Logical reasoning. It consists of 8,678 QA instances, covering multiple types of deductive reasoning. Results show that state-of-the-art neural models perform by far worse than human ceiling. Our dataset can also serve as a benchmark for reinvestigating logical AI under the deep learning NLP setting. The dataset is freely available at https://github.com/lgw863/LogiQA-dataset.
Defending and Harnessing the Bit-Flip Based Adversarial Weight Attack

Recently, a new paradigm of the adversarial attack on the quantized neural network weights has attracted great attention, namely, the Bit-Flip based adversarial weight attack, aka. Bit-Flip Attack (BFA). BFA has shown extraordinary attacking ability, where the adversary can malfunction a quantized Deep Neural Network (DNN) as a random guess, through malicious bit-flips on a small set of vulnerable weight bits (e.g., 13 out of 93 millions bits of 8-bit quantized ResNet-18). However, there are no effective defensive methods to enhance the fault-tolerance capability of DNN against such BFA. In this work, we conduct comprehensive investigations on BFA and propose to leverage binarization-aware training and its relaxation - piece-wise clustering as simple and effective countermeasures to BFA. The experiments show that, for BFA to achieve the identical prediction accuracy degradation (e.g., below 11% on CIFAR-10), it requires 19.3× and 480.1× more effective malicious bit-flips on ResNet-20 and VGG-11 respectively, compared to defend-free counterparts.
Improving Image Captioning with Better Use of Caption

Image captioning is a multimodal problem that has drawn extensive attention in both the natural language processing and computer vision community.In this paper, we present a novel image captioning architecture to better explore semantics available in captions and leverage that to enhance both image representation and caption generation.Our models first construct caption-guided visual relationship graphs that introduce beneficial inductive bias using weakly supervised multi-instance learning.The representation is then enhanced with neighbouring and contextual nodes with their textual and visual features.During generation, the model further incorporates visual relationships using multi-task learning for jointly predicting word and object/predicate tag sequences.We perform extensive experiments on the MSCOCO dataset, showing that the proposed framework significantly outperforms the baselines, resulting in the state-of-the-art performance under a wide range of evaluation metrics.The code of our paper has been made publicly available.
Attention as Relation: Learning Supervised Multi-head Self-Attention for Relation Extraction

Joint entity and relation extraction is critical for many natural language processing (NLP) tasks, which has attracted increasing research interest. However, it is still faced with the challenges of identifying the overlapping relation triplets along with the entire entity boundary and detecting the multi-type relations. In this paper, we propose an attention-based joint model, which mainly contains an entity extraction module and a relation detection module, to address the challenges. The key of our model is devising a supervised multi-head self-attention mechanism as the relation detection module to learn the token-level correlation for each relation type separately. With the attention mechanism, our model can effectively identify overlapping relations and flexibly predict the relation type with its corresponding intensity. To verify the effectiveness of our model, we conduct comprehensive experiments on two benchmark datasets. The experimental results demonstrate that our model achieves state-of-the-art performances.
X-LineNet: Detecting Aircraft in Remote Sensing Images by a Pair of Intersecting Line Segments

Motivated by the development of deep convolution neural networks (DCNNs), aircraft detection has gained tremendous progress. State-of-the-art DCNN-based detectors mainly belong to top-down approaches, which enumerate massive potential locations of aircraft with the form of rectangular regions, and then identify whether they are objects or not. Compared with these top-down detectors, this article shows that aircraft detection via a type of bottom-up method can have better performances in the era of deep learning. In this article, we propose a novel bottom-up detector named X-LineNet. It formulates the aircraft detection task as prediction and clustering of paired intersecting line segments inside each target. Aircraft detection is then a purely appearance-based line segments estimation problem, without any rectangular regions classification or implicit features learning. With simple postprocessing, X-LineNet can simultaneously provide multiple representation forms of the detection result: the horizontal bounding box, the oriented bounding box, and the pentagonal mask. The pentagonal mask is a more accurate representation form of aircraft which has less redundancy than that of a rectangular box. Experiments show that X-LineNet outperforms prevalent top-down and region-based detectors on UCAS-AOD, NWPU VHR-10, and DIOR public data sets in the field of aircraft detection.
Joint Modelling of Emotion and Abusive Language Detection

The rise of online communication platforms has been accompanied by some undesirable effects, such as the proliferation of aggressive and abusive behaviour online.Aiming to tackle this problem, the natural language processing (NLP) community has experimented with a range of techniques for abuse detection.While achieving substantial success, these methods have so far only focused on modelling the linguistic properties of the comments and the online communities of users, disregarding the emotional state of the users and how this might affect their language.The latter is, however, inextricably linked to abusive behaviour.In this paper, we present the first joint model of emotion and abusive language detection, experimenting in a multi-task learning framework that allows one task to inform the other.Our results demonstrate that incorporating affective features leads to significant improvements in abuse detection performance across datasets.
Contextual and Non-Contextual Word Embeddings: an in-depth Linguistic Investigation

In this paper we present a comparison between the linguistic knowledge encoded in the internal representations of a contextual Language Model (BERT) and a contextual-independent one (Word2vec).We use a wide set of probing tasks, each of which corresponds to a distinct sentence-level feature extracted from different levels of linguistic annotation.We show that, although BERT is capable of understanding the full context of each word in an input sequence, the implicit knowledge encoded in its aggregated sentence representations is still comparable to that of a contextualindependent model.We also find that BERT is able to encode sentence-level properties even within single-word embeddings, obtaining comparable or even superior results than those obtained with sentence representations.
GAIA: A Fine-grained Multimedia Knowledge Extraction System

We present the first comprehensive, open source multimedia knowledge extraction system that takes a massive stream of unstructured, heterogeneous multimedia data from various sources and languages as input, and creates a coherent, structured knowledge base, indexing entities, relations, and events, following a rich, fine-grained ontology.Our system, GAIA 1 , enables seamless search of complex graph queries, and retrieves multimedia evidence including text, images and videos.GAIA achieves top performance at the recent NIST TAC SM-KBP2019 evaluation 2 .The system is publicly available at GitHub 3 and DockerHub 4 , with complete documentation 5 .