Reviewerly/exHarmony huggingface dataset:
QuesNet

Understanding learning materials (e.g. test questions) is a crucial issue in online learning systems, which can promote many applications in education domain. Unfortunately, many supervised approaches suffer from the problem of scarce human labeled data, whereas abundant unlabeled resources are highly underutilized. To alleviate this problem, an effective solution is to use pre-trained representations for question understanding. However, existing pre-training methods in NLP area are infeasible to learn test question representations due to several domain-specific characteristics in education. First, questions usually comprise of heterogeneous data including content text, images and side information. Second, there exists both basic linguistic information as well as domain logic and knowledge. To this end, in this paper, we propose a novel pre-training method, namely QuesNet, for comprehensively learning question representations. Specifically, we first design a unified framework to aggregate question information with its heterogeneous inputs into a comprehensive vector. Then we propose a two-level hierarchical pre-training algorithm to learn better understanding of test questions in an unsupervised way. Here, a novel holed language model objective is developed to extract low-level linguistic features, and a domain-oriented objective is proposed to learn high-level logic and knowledge. Moreover, we show that QuesNet has good capability of being fine-tuned in many question-based tasks. We conduct extensive experiments on large-scale real-world question data, where the experimental results clearly demonstrate the effectiveness of QuesNet for question understanding as well as its superior applicability.
Improving Collaborative Metric Learning with Efficient Negative Sampling

Distance metric learning based on triplet loss has been applied with success in a wide range of applications such as face recognition, image retrieval, speaker change detection and recently recommendation with the Collaborative Metric Learning (CML) model. However, as we show in this article, CML requires large batches to work reasonably well because of a too simplistic uniform negative sampling strategy for selecting triplets. Due to memory limitations, this makes it difficult to scale in high-dimensional scenarios. To alleviate this problem, we propose here a 2-stage negative sampling strategy which finds triplets that are highly informative for learning. Our strategy allows CML to work effectively in terms of accuracy and popularity bias, even when the batch size is an order of magnitude smaller than what would be needed with the default uniform sampling. We demonstrate the suitability of the proposed strategy for recommendation and exhibit consistent positive results across various datasets.
Relative entropy and catalytic relative majorization

Given two pairs of quantum states, a fundamental question in the resource theory of asymmetric distinguishability is whether there exists a quantum channel converting one pair to the other. In this work, we reframe this question in such a way that a catalyst can be used to help perform the transformation, with the only constraint on the catalyst being that its reduced state is returned unchanged, so that it can be used again to assist a future transformation. What we find here, for the special case in which the states in a given pair are commuting, and thus quasiclassical, is that this catalytic transformation can be performed if and only if the relative entropy of one pair of states is larger than that of the other pair. This result endows the relative entropy with a fundamental operational meaning that goes beyond its traditional interpretation in the setting of independent and identical resources. Our finding thus has an immediate application and interpretation in the resource theory of asymmetric distinguishability, and we expect it to find application in other domains.Received 11 May 2020Accepted 1 September 2020DOI:https://doi.org/10.1103/PhysRevResearch.2.033455Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasResource theoriesQuantum Information
Neural Machine Translation System of Indic Languages - An Attention based Approach

Neural machine translation (NMT) is a recent and effective technique which led to remarkable improvements in comparison of conventional machine translation techniques. Proposed neural machine translation model developed for the Gujarati language contains encoder-decoder with attention mechanism. In India, almost all the languages are originated from their ancestral language - Sanskrit. They are having inevitable similarities including lexical and named entity similarity. Translating into Indic languages is always be a challenging task. In this paper, we have presented the neural machine translation system (NMT) that can efficiently translate Indic languages like Hindi and Gujarati that together covers more than 58.49 percentage of total speakers in the country. We have compared the performance of our NMT model with automatic evaluation matrices such as BLEU, perplexity and TER matrix. The comparison of our network with Google translate is also presented where it outperformed with a margin of 6 BLEU score on English-Gujarati translation.
Methods for Measuring Magnetic Flux Crosstalk between Tunable Transmons

In the gate model of quantum computing, a program is typically decomposed into a sequence of 1- and 2-qubit gates that are realized as control pulses acting on the system. A key requirement for a scalable control system is that the qubits are addressable - that control pulses act only on the targeted qubits. The presence of control crosstalk makes this addressability requirement difficult to meet. In order to provide metrics that can drive requirements for decreasing crosstalk, we present three measurements that directly quantify the DC and AC flux crosstalk present between tunable transmons, with sensitivities as fine as 0.001%. We develop the theory to connect AC flux crosstalk measures to the infidelity of a parametrically activated two-qubit gate. We employ quantum process tomography in the presence of crosstalk to provide an empirical study of the effects of crosstalk on two-qubit gate fidelity.
Analysis and synthesis of feature map for kernel-based quantum classifier

A method for analyzing the feature map for the kernel-based quantum classifier is developed; that is, we give a general formula for computing a lower bound of the exact training accuracy, which helps us to see whether the selected feature map is suitable for linearly separating the dataset. We show a proof of concept demonstration of this method for a class of 2-qubit classifier, with several 2-dimensional datasets. Also, a synthesis method, which combines different kernels to construct a better-performing feature map in a lager feature space, is presented.
Building a Bridge: A Method for Image-Text Sarcasm Detection Without Pretraining on Image-Text Data

Sarcasm detection in social media with text and image is becoming more challenging.Previous works of image-text sarcasm detection were mainly to fuse the summaries of text and image: different sub-models read the text and image respectively to get the summaries, and fuses the summaries.Recently, some multimodal models based on the architecture of BERT are proposed such as ViLBERT.However, they can only be pretrained on the imagetext data.In this paper, we propose an imagetext model for sarcasm detection using the pretrained BERT and ResNet without any further pretraining.BERT and ResNet have been pretrained on much larger text or image data than image-text data.We connect the vector spaces of BERT and ResNet to utilize more data.We use the pretrained Multi-Head Attention of BERT to model the text and image.Besides, we propose a 2D-Intra-Attention to extract the relationships between words and images.In experiments, our model outperforms the state-of-the-art model.
On Passivity, Reinforcement Learning, and Higher Order Learning in Multiagent Finite Games

In this article, we propose a passivity-based methodology for the analysis and design of reinforcement learning dynamics and algorithms in multiagent finite games. Starting from a known, first-order reinforcement learning scheme, we show that convergence to a Nash distribution can be attained in a broader class of games than previously considered in the literature - namely, in games characterized by the monotonicity property of their (negative) payoff vectors. We further exploit passivity techniques to design a class of higher order learning schemes that preserve the convergence properties of their first-order counterparts. Moreover, we show that the higher order schemes improve upon the rate of convergence and can even achieve convergence where the first-order scheme fails. We demonstrate these properties through numerical simulations for several representative games.
A fast human action recognition network based on spatio-temporal features

Artificial intelligence models are widely used in the field of human activity recognition, and human action recognition is an important aspect of human activity recognition. The core of human action recognition is to understand the temporal relationship between video frames. Almost all state-of-the-art methods of human action recognition in videos use optical flow. However, traditional local optical flow estimation methods areexpensive and not trained end-to-end. In this paper, we propose a fast network for human action recognition. Our purpose is to improve the efficiency of optical flow feature extraction and explore the fusion method of spatio-temporal features. For spatio-temporal features, our method combines spatial features and temporal features into fusion features. In addition, we propose CNN with OFF instead of the VGG16 network, which is used to process optical flow features to obtain abundant features. Our model only needs RGB inputs to get the state-of-the-art accuracy of 91.5% on UCF-101, 67.9% on HMDB51, 83.3% on MSR Daily Activity3D, and 91.25% on Florence 3D action, respectively. Compared with most state-of-the-art video action recognition models, our proposed model can effectively improve the accuracy of human action recognition.
Significance, relevance and explainability in the machine learning age: an econometrics and financial data science perspective

(2021). Significance, relevance and explainability in the machine learning age: an econometrics and financial data science perspective. The European Journal of Finance: Vol. 27, Recent Developments in Econometrics and Financial Data Science, pp. 1-7.
Face Video Retrieval Based on the Deep CNN With RBF Loss

This paper presents a novel framework to extract highly compact and discriminative features for face video retrieval tasks using the deep convolutional neural network (CNN). The face video retrieval task is to find the videos containing the face of a specific person from a database with a face image or a face video of the same person as a query. A key challenge is to extract discriminative features with small storage space from face videos with large intra-class variations caused by different angle, illumination, and facial expression. In recent years, the CNN-based binary hashing and metric learning methods showed notable progress in image/video retrieval tasks. However, the existing CNN-based binary hashing and metric learning have limitations in terms of inevitable information loss and storage inefficiency, respectively. To cope with these problems, the proposed framework consists of two parts: first, a novel loss function using a radial basis function kernel (RBF Loss) is introduced to train a neural network to generate compact and discriminative high-level features, and secondly, an optimized quantization using a logistic function (Logistic Quantization) is suggested to convert a real-valued feature to a 1-byte integer with the minimum information loss. Through the face video retrieval experiments on a challenging TV series data set (ICT-TV), it is demonstrated that the proposed framework outperforms the existing state-of-the-art feature extraction methods. Furthermore, the effectiveness of RBF loss was also demonstrated through the image classification and retrieval experiments on the CIFAR-10 and Fashion-MNIST data sets with LeNet-5.
Sketching Image Gist: Human-Mimetic Hierarchical Scene Graph Generation

Scene graph aims to faithfully reveal humans’ perception of image content. When humans analyze a scene, they usually prefer to describe image gist first, namely major objects and key relations in a scene graph. This humans’ inherent perceptive habit implies that there exists a hierarchical structure about humans’ preference during the scene parsing procedure. Therefore, we argue that a desirable scene graph should be also hierarchically constructed, and introduce a new scheme for modeling scene graph. Concretely, a scene is represented by a human-mimetic Hierarchical Entity Tree (HET) consisting of a series of image regions. To generate a scene graph based on HET, we parse HET with a Hybrid Long Short-Term Memory (Hybrid-LSTM) which specifically encodes hierarchy and siblings context to capture the structured information embedded in HET. To further prioritize key relations in the scene graph, we devise a Relation Ranking Module (RRM) to dynamically adjust their rankings by learning to capture humans’ subjective perceptive habits from objective entity saliency and size. Experiments indicate that our method not only achieves state-of-the-art performances for scene graph generation, but also is expert in mining image-specific relations which play a great role in serving downstream tasks.
MINI-Net: Multiple Instance Ranking Network for Video Highlight Detection

We address the weakly supervised video highlight detection problem for learning to detect segments that are more attractive in training videos given their video event label but without expensive supervision of manually annotating highlight segments. While manually averting localizing highlight segments, weakly supervised modeling is challenging, as a video in our daily life could contain highlight segments with multiple event types, e.g., skiing and surfing. In this work, we propose casting weakly supervised video highlight detection modeling for a given specific event as a multiple instance ranking network (MINI-Net) learning. We consider each video as a bag of segments, and therefore, the proposed MINI-Net learns to enforce a higher highlight score for a positive bag that contains highlight segments of a specific event than those for negative bags that are irrelevant. In particular, we form a max-max ranking loss to acquire a reliable relative comparison between the most likely positive segment instance and the hardest negative segment instance. With this max-max ranking loss, our MINI-Net effectively leverages all segment information to acquire a more distinct video feature representation for localizing the highlight segments of a specific event in a video. The extensive experimental results on three challenging public benchmarks clearly validate the efficacy of our multiple instance ranking approach for solving the problem.
Batch recommendation of experts to questions in community-based question-answering with a sailfish optimizer

To facilitate question-answering in community-based question-answering (CQA), this paper proposes an approach for the batch recommendation of answerers by optimizing the utilization of expert resources. First, questions and experts are modeled with a biterm topic model (BTM). Next, the answered questions are clustered based on a novel discrete sailfish optimizer (SFO) with a genetic algorithm (GA), and the topic distribution is obtained. Then, experts are ranked in each cluster based on activeness, recency, and professionalism. Considering the limited number of experts, to ensure that core questions are answered and to avoid repeated answers to similar or duplicate questions, coverage, answerability and the consumption of expert resources are taken as objects to be optimized. This scenario is formulated as a multiobjective optimization problem and is addressed by the proposed novel binary multiobjective SFO (MOSFO) with a GA. The solution of the model includes not only the selected questions to be answered but also the matching between the questions and experts. The proposed approach is evaluated with a real dataset, and the experimental results show that the proposed approach is feasible and has superior performance to the question-priority method, the expert-priority method and other swarm intelligence (SI) methods. This study is the first to make batch recommendations, providing a new idea and extending research on expert recommendation. Additionally, the approach can be used practically to improve the satisfaction of the knowledge needs of users by improving the answerability of high-coverage questions.
Unsupervised deep hashing with node representation for image retrieval

Supervised graph convolution network (GCN) based hashing algorithms have achieved good results by recognizing images according to the relationships between objects, but they are hard to be applied to label-free scenarios. Besides, most existing unsupervised deep hashing algorithms neglect the relationships between different samples and thus fail to achieve high precision. To address this problem, we propose NRDH, an unsupervised Deep Hashing method with Node Representation for image retrieval, which adopts unsupervised GCN to integrate the relationships between samples into image visual features. NRDH consists of node representation learning stage and hash function learning stage. In the first stage, we treat each image as a node of a graph and design GCN-based AutoEncoder, which can integrate the relationships between samples into node representation. In the second stage, we use above node representations to guide the network and help learn the hash function to fast achieve an end-to-end hash model to generate semantic hash codes. Extensive experiments on CIFAR-10, MS-COCO and FLICKR25K show NRDH can achieve higher performance and outperform the state-of-the-art unsupervised deep hashing methods.
Adaptive Shape based Interactive Approach to Segmentation for Nodule in Lung CT Scans

In lung cancer diagnosis, growth of pulmonary nodule should be detected perfectly. Mostly watershed segmentation methods play a very important role in lung CT images to detect their growth. But this method detection will be ineffective in terms of energy function and speed as well. The proposed modified graph-cut technique is providing the good performing result in the speed and accuracy of the process than the conservative graph cut methods. Also, this research paper is proposed adaptive shape based interactive approach to segmentation for lung CT scan image and provide a more efficient. This proposed algorithm is proving that the energy function of the system is lesser than old methods. In this research paper, applying shape-based technique in segmentation technique has been proposed and proved for better accuracy with low energy function.
Interactive Classification of Whole-Slide Imaging Data for Cancer Researchers

Abstract Whole-slide histology images contain information that is valuable for clinical and basic science investigations of cancer but extracting quantitative measurements from these images is challenging for researchers who are not image analysis specialists. In this article, we describe HistomicsML2, a software tool for learn-by-example training of machine learning classifiers for histologic patterns in whole-slide images. This tool improves training efficiency and classifier performance by guiding users to the most informative training examples for labeling and can be used to develop classifiers for prospective application or as a rapid annotation tool that is adaptable to different cancer types. HistomicsML2 runs as a containerized server application that provides web-based user interfaces for classifier training, validation, exporting inference results, and collaborative review, and that can be deployed on GPU servers or cloud platforms. We demonstrate the utility of this tool by using it to classify tumor-infiltrating lymphocytes in breast carcinoma and cutaneous melanoma. Significance: An interactive machine learning tool for analyzing digital pathology images enables cancer researchers to apply this tool to measure histologic patterns for clinical and basic science studies.
Time Series Domain Adaptation via Sparse Associative Structure Alignment

Domain adaptation on time series data is an important but challenging task. Most of the existing works in this area are based on the learning of the domain-invariant representation of the data with the help of restrictions like MMD. However, such extraction of the domain-invariant representation is a non-trivial task for time series data, due to the complex dependence among the timestamps. In detail, in the fully dependent time series, a small change of the time lags or the offsets may lead to difficulty in the domain invariant extraction. Fortunately, the stability of the causality inspired us to explore the domain invariant structure of the data. To reduce the difficulty in the discovery of causal structure, we relax it to the sparse associative structure and propose a novel sparse associative structure alignment model for domain adaptation. First, we generate the segment set to exclude the obstacle of offsets. Second, the intra-variables and inter-variables sparse attention mechanisms are devised to extract associative structure time-series data with considering time lags. Finally, the associative structure alignment is used to guide the transfer of knowledge from the source domain to the target one. Experimental studies not only verify the good performance of our methods on three real-world datasets but also provide some insightful discoveries on the transferred knowledge.
Extractive Multi-Document Arabic Text Summarization Using Evolutionary Multi-Objective Optimization With K-Medoid Clustering

The increasing usage of the Internet and social networks has produced a significant amount of online textual data.These online textual data led to information overload and redundancy.It is important to eliminate the information redundancy and preserve the time required for reading these online textual data.Thus, there is a persistent need for an automatic text summarization system, which extract the relevant and salient information from a collection of documents, that sharing the same or related topics.Then, presenting this extracted information in a condensed form to preserve the main topics.This paper proposes an automatic, generic, and extractive Arabic multi-document summarization system.The proposed system employs the clustering-based and evolutionary multi-objective optimization methods.The clustering-based method discovers the main topics in the text, while the evolutionary multi-objective optimization method optimizes three objectives based on coverage, diversity/redundancy, and relevancy.The performance of the proposed system is evaluated using TAC 2011 and DUC 2002 datasets.The experimental results are compared using ROUGE evaluation measure.The obtained results showed the effectiveness of the proposed system compared to other peer systems.The proposed system outperformed other peer systems for all ROUGE metrics using TAC 2011.We achieved an F-measure of 38.9%, 17.7%, 35.4%, and 15.8% for Rouge-1, Rouge-2, Rouge-L, and Rouge-SU4, respectively.In addition, the proposed system with DUC 2002 dataset achieved an F-measure of 47.1%, 23.7%, 47.1%, 20.4% for Rouge-1, Rouge-2, Rouge-L, and Rouge-SU4, respectively.