Reviewerly/exHarmony huggingface dataset:
Argumentative explanations for interactive recommendations

A significant challenge for recommender systems (RSs), and in fact for AI systems in general, is the systematic definition of explanations for outputs in such a way that both the explanations and the systems themselves are able to adapt to their human users' needs. In this paper we propose an RS hosting a vast repertoire of explanations, which are customisable to users in their content and format, and thus able to adapt to users' explanatory requirements, while being reasonably effective (proven empirically). Our RS is built on a graphical chassis, allowing the extraction of argumentation scaffolding, from which diverse and varied argumentative explanations for recommendations can be obtained. These recommendations are interactive because they can be questioned by users and they support adaptive feedback mechanisms designed to allow the RS to self-improve (proven theoretically). Finally, we undertake user studies in which we vary the characteristics of the argumentative explanations, showing users' general preferences for more information, but also that their tastes are diverse, thus highlighting the need for our adaptable RS.
Neutral Cross-Entropy Loss Based Unsupervised Domain Adaptation for Semantic Segmentation

The generalization performance for semantic segmentation remains a major challenge when the data distributions between the source and target domain mismatch. Unsupervised domain adaptation (UDA) approaches are proposed to mitigate the problem above, among which entropy-minimization-based methods have gained more and more attention. However, the methods merely follow the cluster assumption sharpening the prediction distribution, thus have limited performance improvement. Without additional priors, the entropy loss can easily over-sharpen the prediction distribution, which brings noisy information into the learning process. On the other hand, the gradient of the entropy loss is strongly biased toward easy samples, also leading to limited generalization advances. In this paper, we firstly propose a pixel-level consistency regularization method, which introduces the smoothness prior to the UDA problem. Furthermore, we propose the neutral cross-entropy loss based on the consistency regularization, and reveal that its internal neutralization mechanism mitigates the over-sharpness of entropy minimization via the flatness effect of consistency regularization. We also demonstrate that the gradient bias toward easy samples is inherently tackled via the neutral cross-entropy loss. The experiments show that the proposed method has outperformed state-of-the-art methods in two synthetic-to-real experiments, only using the lightweight network.
GemNN: Gating-enhanced Multi-task Neural Networks with Feature Interaction Learning for CTR Prediction

Deep neural network (DNN) models have been widely used for click-through rate (CTR) prediction in online advertising. The training framework typically consists of embedding layers and multi-layer perceptions (MLP). At Baidu Search Ads (a.k.a. Phoenix Nest), the new generation of CTR training platform has become PaddleBox, a GPU-based parameter server system. In this paper, we present Baidu's recently updated CTR training framework, called Gating-enhanced Multi-task Neural Networks (GemNN). In particular, we develop a neural network based multi-task learning model to predict CTR in a coarse-to-fine manner, which gradually reduces ad candidates and allows parameter sharing from upstream tasks to downstream tasks to improve the training efficiency. Also, we introduce a gating mechanism between embedding layers and MLP to learn feature interactions and control the information flow fed to MLP layers. We have launched our solution in Baidu PaddleBox platform and observed considerable improvements in both offline and online evaluations. It is now part of the current production~system.
Generative adversarial networks for imputing missing data for big data clinical research

Missing data is a pervasive problem in clinical research. Generative adversarial imputation nets (GAIN), a novel machine learning data imputation approach, has the potential to substitute missing data accurately and efficiently but has not yet been evaluated in empirical big clinical datasets.This study aimed to evaluate the accuracy of GAIN in imputing missing values in large real-world clinical datasets with mixed-type variables. The computation efficiency of GAIN was also evaluated. The performance of GAIN was compared with other commonly used methods, MICE and missForest.Two real world clinical datasets were used. The first was that of a cohort study on the long-term outcomes of patients with diabetes (50,000 complete cases), and the second was of a cohort study on the effectiveness of a risk assessment and management programme for patients with hypertension (10,000 complete cases). Missing data (missing at random) to independent variables were simulated at different missingness rates (20, 50%). The normalized root mean square error (NRMSE) between imputed values and real values for continuous variables and the proportion of falsely classified (PFC) for categorical variables were used to measure imputation accuracy. Computation time per imputation for each method was recorded. The differences in accuracy of different imputation methods were compared using ANOVA or non-parametric test.Both missForest and GAIN were more accurate than MICE. GAIN showed similar accuracy as missForest when the simulated missingness rate was 20%, but was more accurate when the simulated missingness rate was 50%. GAIN was the most accurate for the imputation of skewed continuous and imbalanced categorical variables at both missingness rates. GAIN had a much higher computation speed (32 min on PC) comparing to that of missForest (1300 min) when the sample size is 50,000.GAIN showed better accuracy as an imputation method for missing data in large real-world clinical datasets compared to MICE and missForest, and was more resistant to high missingness rate (50%). The high computation speed is an added advantage of GAIN in big clinical data research. It holds potential as an accurate and efficient method for missing data imputation in future big data clinical research.ClinicalTrials.gov ID: NCT03299010 ; Unique Protocol ID: HKUCTR-2232.
On the diversity of multi-head attention

Multi-head attention is appealing for the ability to jointly attend to information from different representation subspaces at different positions. In this work, we propose two approaches to better exploit such diversity for multi-head attention, which are complementary to each other. First, we introduce a disagreement regularization to explicitly encourage the diversity among multiple attention heads. Specifically, we propose three types of disagreement regularization, which respectively encourage the subspace, the attended positions, and the output representation associated with each attention head to be different from other heads. Second, we propose to better capture the diverse information distributed in the extracted partial-representations with the routing-by-agreement algorithm. The routing algorithm iteratively updates the proportion of how much a part (i.e. the distinct information learned from a specific subspace) should be assigned to a whole (i.e. the final output representation), based on the agreement between parts and wholes. Experimental results on the machine translation, sentence encoding and logical inference tasks demonstrate the effectiveness and universality of the proposed approaches, which indicate the necessity of better exploiting the diversity for multi-head attention. While the two strategies individually boost performance, combining them together can further improve the model performance.
Time-optimal Qubit mapping

Rapid progress in the physical implementation of quantum computers gave birth to multiple recent quantum machines implemented with superconducting technology. In these NISQ machines, each qubit is physically connected to a bounded number of neighbors. This limitation prevents most quantum programs from being directly executed on quantum devices. A compiler is required for converting a quantum program to a hardware-compliant circuit, in particular, making each two-qubit gate executable by mapping the two logical qubits to two physical qubits with a link between them. To solve this problem, existing studies focus on inserting SWAP gates to dynamically remap logical qubits to physical qubits. However, most of the schemes lack the consideration of time-optimality of generated quantum circuits, or are achieving time-optimality with certain constraints. In this work, we propose a theoretically time-optimal SWAP insertion scheme for the qubit mapping problem. Our model can also be extended to practical heuristic algorithms. We present exact analysis results by using our model for quantum programs with recurring execution patterns. We have for the first time discovered an optimal qubit mapping pattern for quantum fourier transformation (QFT) on 2D nearest neighbor architecture. We also present a scalable extension of our theoretical model that can be used to solve qubit mapping for large quantum circuits.
Edge-preserving MRI image synthesis via adversarial network with iterative multi-scale fusion

Magnetic resonance imaging (MRI) is a major imaging technique for studying neuroanatomy. By applying different pulse sequences and parameters, different modalities can be generated regarding the same anatomical structure, which can provide complementary information for diagnosis. However, limited by the scanning time and related cost, multiple different modalities are often not available for the same patient in clinic. Recently, many methods have been proposed for cross-modality MRI synthesis, but most of them only consider pixel-level differences between the synthetic and ground-truth images, ignoring the edge information, which is critical to provide clinical information. In this paper, we propose a novel edge-preserving MRI image synthesis method with iterative multi-scale feature fusion based generative adversarial network (EP_IMF-GAN). Particularly, the generator consists of a shared encoder and two specific decoders to carry out different tasks: 1) a primary task aiming to generate the target modality and 2) an auxiliary task aiming to generate the corresponding edge image of target modality. We assume that infusing the auxiliary edge image generation task can help preserve edge information and learn better latent representation features through the shared encoder. Meanwhile, an iterative multi-scale fusion module is embedded in the primary decoder to fuse supplementary information of feature maps at different scales, thereby further improving quality of the synthesized target modality. Experiments on the BRATS dataset indicate that our proposed method is superior to the state-of-the-art image synthesis approaches in both qualitative and quantitative measures. Ablation study further validates the effectiveness of the proposed components.
Dilated MultiResUNet: Dilated multiresidual blocks network based on U-Net for biomedical image segmentation

U-net is a classical and high-efficiency network, which achieves better performance than other end-to-end networks in Biomedical Image Segmentation with fewer training images. However, the feature details of images are often missing out of pooling layers and the contour of small objects cannot be totally reconstructed by the up-sampling layers of U-Net. To reduce the loss of important features, we propose a Dilated MultiResUNet network to improve the performance of end-to-end image segmentation based on U-Net, Res2Net, MultiResUNet, Dilated Residual Networks and Squeeze-and-Excitation Networks. We use Improved Multi Block, Res Block, and Dilated Multi Block to substitute for common convolutional operation. Besides, we design two special networks of Dilated MultiResUNet with various combination of blocks to improve up-sampling and concatenation operation of U-Net. We evaluate the proposed models in terms of typical evaluation indexes on four biomedical datasets, i.e. ISBI2015, INBreast, blood vessel and psoriasis dataset. The experimental results show that the two Dilated MultiResUNet networks have superior accuracy and achieve better generalization performance only with 59.7 % and 63.3 % number of parameters of U-Net model respectively.
Method Of Text Summarization Using Lsa And Sentence Based Topic Modelling With Bert

Document summarization is one such task of the natural language processing which deals with the long textual data to make its concise and fluent summaries that contains all of document relevant information. The Branch of NLP that deals with it, is automatic text summarizer. Automatic text summarizer does the task of converting the long textual document into short fluent summaries. There are generally two ways of summarizing text using automatic text summarizer, first is using extractive text summarizer and another abstractive text summarizer. This paper has demonstrated an experiment in contrast with the extractive text summarizer for summarizing the text. On the other hand topic modelling is a NLP task that extracts the relevant topic from the textual document. One such method is Latent semantic Analysis (LSA) using truncated SVD which extracts all the relevant topics from the text. This paper has demonstrated the experiment in which the proposed research work will be summarizing the long textual document using LSA topic modelling along with TFIDF keyword extractor for each sentence in a text document and also using BERT encoder model for encoding the sentences from textual document in order to retrieve the positional embedding of topics word vectors. The algorithm proposed algorithm in this paper is able to achieve the score greater than that of text summarization using Latent Dirichlet Allocation (LDA) topic modelling.
TREC Deep Learning Track: Reusable Test Collections in the Large Data Regime

The TREC Deep Learning (DL) Track studies ad hoc search in the large data regime, meaning that a large set of human-labeled training data is available. Results so far indicate that the best models with large data may be deep neural networks. This paper supports the reuse of the TREC DL test collections in three ways. First we describe the data sets in detail, documenting clearly and in one place some details that are otherwise scattered in track guidelines, overview papers and in our associated MS MARCO leaderboard pages. We intend this description to make it easy for newcomers to use the TREC DL data. Second, because there is some risk of iteration and selection bias when reusing a data set, we describe the best practices for writing a paper using TREC DL data, without overfitting. We provide some illustrative analysis. Finally we address a number of issues around the TREC DL data, including an analysis of reusability.
A Survey on Future of Augmented Reality with AI in Education

Augmented reality now has reached the stage in real time simulations which needed and contemplated. In the year 2020 and forthcoming years to 2030 augmented reality is going to play a lead role in most of industries, which includes military, education, medical, manufacturing industries, training and remote assistant, navigation, and gaming to name a few. This all-new entire article brings out the broad meaning of augmented reality sorted into a detailed survey of how it's going to be a cornerstone of the new education system and how this can used in other sectors too. The core contribution of this entire article is the discussion of recent studies on represents of the present state of field, which seek to inspire educators to improve mixed reality experiences and to carry out further more research in favor of interactive learning environments.
Fully convolutional line parsing

We present a one-stage F ully C onvolutional Li ne P arsing network (F-Clip) that detects line segments from images. The proposed network is very simple and flexible with variations that gracefully trade off between speed and accuracy for different applications. F-Clip detects line segments in an end-to-end fashion by predicting each line’s center position, length, and angle. We further customize the design of convolution kernels of our fully convolutional network to effectively exploit the statistical priors of the distribution of line angles in real image datasets. We conduct extensive experiments and show that our method achieves a significantly better trade-off between efficiency and accuracy, resulting in a real-time line detector at up to 73 FPS on a single GPU. Such inference speed makes our method readily applicable to real-time tasks without compromising any accuracy of previous methods. Moreover, when equipped with a performance-improving backbone network, F-Clip is able to significantly outperform all state-of-the-art line detectors on accuracy at a similar or even higher frame rate. In other word, under same inference speed, F-Clip always achieving best accuracy compare with other methods. Source code https://github.com/Delay-Xili/F-Clip .
Hierarchical Pressure Data Recovery for Pipeline Network via Generative Adversarial Networks

In the real-time status monitoring of pipeline network, incomplete pressure data are unavoidable due to some device or communication errors. To solve this problem, a hierarchical data recovery method based on generative adversarial networks (GANs) is proposed in this article. First, a hierarchical data recovery framework is proposed to handle different numbers of incomplete data due to the structure of the semicentral pipeline network. Second, a joint attention module is presented to capture both interior nature and correlation relationships of multivariate pressure series and further guarantee the consistency of pressure data. Third, the macromicrodual discriminators are proposed to evaluate the recovery result through the combination of the local and global variation in temporal and spatial dependencies. Based on the novel structures, the proposed model is able to recover incomplete data with abnormal fluctuation values, unreasonable fixed values, or missing values. Finally, under a series of data recovery experiments, the efficiency of the proposed method is evaluated. Experimental results demonstrate that the proposed method is a practical way to ensure data recovery performance in the pipeline network. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —Status monitoring based on pressure data is of great importance for safe and efficient operation in a pipeline network. However, due to unexpected situations, the appearance of incomplete pressure data affects the subsequent data processing and status analysis, resulting in an incorrect decision. In this article, a deep learning-based method is proposed to recover the incomplete data. With the help of the spatiotemporal dependencies of multivariate pressure series, the proposed method can recover different numbers of incomplete data through the no-missing part of pressure data. The experiment results show that the proposed method is better than the similar data recovery methods through three different evaluation metrics. In the future, we will address the data recovery problem without the complete data pairs in the training process.
Modeling personalized cognition of customers in online shopping

Online reviews play an important role in online shopping. Previous studies on Multi-Criteria Decision Making (MCDM) based on customer reviews focused too much on sentiment words in text reviews but ignored the personalized semantics of linguistic terms. In addition, only considering the qualitative information of products/services is not enough to simulate the purchase behaviors of customers given that the customers are also concerned with quantitative parameters. To bridge these research gaps, this study models the personalized cognition of customers on both quantitative and qualitative information, and proposes an MCDM framework for online shopping. Firstly, we determine the personalized semantics of linguistic terms through the emotional consistency between star ratings and text reviews. Afterwards, we investigate the "psychological intensity" based on Weber-Fechner's law to determine the utilities of quantitative parameters. A utility-based translation method is then developed to express both quantitative parameters and text reviews as probabilistic linguistic term sets. The unified information is further aggregated to represent the performance of products/services. The applicability of the proposed method is illustrated by a case study of television selection from Amazon.com. The results demonstrate that the personalized cognition has an influence on the judgments of products/services.
QuCloud: A New Qubit Mapping Mechanism for Multi-programming Quantum Computing in Cloud Environment

For a specific quantum chip, multi-programming improves overall throughput and resource utilization. Previous studies on mapping multiple programs often lead to resource under-utilization, high error rate, and low fidelity. This paper proposes QuCloud, a new approach for mapping quantum programs in the cloud environment. We have three new designs in QuCloud. (1) We leverage the community detection technique to partition physical qubits among concurrent quantum programs, avoiding the waste of robust resources. (2) We design X-SWAP scheme that enables inter-program SWAPs and prioritizes SWAPs associated with critical gates to reduce the SWAP overheads. (3) We propose a compilation task scheduler that schedules concurrent quantum programs to be compiled and executed based on estimated fidelity for the best practice. We evaluate our work on publicly available quantum computer IBMQ16 and a simulated quantum chip IBMQ50. Our work outperforms the state-of-the-art work for multi-programming on fidelity and compilation overheads by 9.7% and 11.6%, respectively.
Electronic Gadget Rating Model Using Feature Based Summarization

Nowadays things assessed by customers in the wake of purchasing the thing is growing rapidly. For a notable thing, the amount of reviews can be unlimited. Nevertheless, with respect to new thing the amount of reviews about the thing is less. From this time forward people won't be exceptionally enchanted to buy that thing. Additionally, it saves a huge load of exertion in favour of novel thing towards benchmark among the beforehand active challenger. Thus significant weakness meant for merchants in favour of novel new thing. The standard target is to help the convenient organization with improving the segment of the adaptable reliant on the consumer's information. This helps the new thing shippers with picking up reputation for the idea of the thing. Hence a site has been made so that there will be a correspondence between the customers, the convenient association and the association. The segment based framework is showed up in kind of diagram for each mobile phone subject to clients input. This makes the association improve thing subject to customer necessities. It moreover shows the graphical depiction of the customers input and besides show the amount of customers who has bought the thing from the chairman. This suggestion is significant for the association selling another thing and besides urges them to make their thing according to customer requirements then enhance their selling.