Reviewerly/exHarmony huggingface dataset:
Construction of functional data analysis modeling strategy for global solar radiation prediction: application of cross-station paradigm

To support initiatives for global emissions targets set by the United Nations Framework Convention on climate change, sustainable extraction of usable power from freely-available global solar radiation as a renewable energy resource requires accurate estimation and forecasting models for solar energy. Understanding the Global Solar Radiation (GSR) pattern is highly significant for determining the solar energy in any particular environment. The current study develops a new mathematical model based on the concept of Functional Data Analysis (FDA) to predict daily-scale GSR in the Burkina Faso region of West Africa. Eight meteorological stations are adopted to examine the proposed predictive model. The modeling procedure of the regression FDA is performed using two different internal parameter tuning approaches including Generalized Cross-Validation (GCV) and Generalized Bayesian Information Criteria (GBIC). The modeling procedure is established based on a cross-station paradigm wherein the climatological variables of six stations are used to predict GSR at two targeted meteorological stations. The performance of the proposed method is compared with the panel data regression model. Based on various statistical metrics, the applied FDA model attained convincing absolute error measures and best goodness of fit compared with the observed measured GSR. In quantitative evaluation, the predictions of GSR at the Ouahigouya and Dori stations attained correlation coefficients of R = 0.84 and 0.90 using the FDA model, respectively. All in all, the FDA model introduced a reliable alternative modeling strategy for global solar radiation prediction over the Burkina Faso region with accurate line fit predictions.
Variational quantum algorithms for dimensionality reduction and classification

In this work, we present a quantum neighborhood preserving embedding and a quantum local discriminant embedding for dimensionality reduction and classification. We demonstrate that these two algorithms have an exponential speedup over their respectively classical counterparts. Along the way, we propose a variational quantum generalized eigenvalue solver that finds the generalized eigenvalues and eigenstates of a matrix pencil $(\mathcal{G},\mathcal{S})$. As a proof of principle, we implement our algorithm to solve ${2}^{5}\ifmmode\times\else\texttimes\fi{}{2}^{5}$ generalized eigenvalue problems. Finally, our results offer two optional outputs with quantum or classical form, which can be directly applied in another quantum or classical machine learning process.
A Novel Clustering Validity Function of FCM Clustering Algorithm

Cluster analysis refers to the process of grouping a collection of physical or abstract objects into multiple classes of similar objects.Determining the optimal classification number of a data set is the key to the clustering problem, that is to say whether the data set can be effectively partitioned.Cluster validity study is a process of establishing clustering effectiveness indicators, evaluating clustering quality and determining the optimal number of clusters.A validity function of fuzzy C-means (FCM) clustering algorithm is proposed by adopting the division of intra-class compactness and inter-class separation, whose minimum represents the best clustering.Then, the proposed validity function on FCM clustering algorithm is compared with the known typical validity functions by carrying out simulation experiments to compare the related clustering performance.Three data sets are adopted to carry out FCM clustering, which includes three classical data sets, two artificial data sets and six real data sets in UCI database.Simulation experimental results show that the proposed validity function can effectively partition the data set.
Generation of Multimodal Justification Using Visual Word Constraint Model for Explainable Computer-Aided Diagnosis

The ambiguity of the decision-making process has been pointed out as the main obstacle to practically applying the deep learning-based method in spite of its outstanding performance. Interpretability can guarantee the confidence of the deep learning system, therefore it is particularly important in the medical field. In this study, a novel deep network is proposed to explain the diagnostic decision with visual pointing map and diagnostic sentence justifying result simultaneously. To increase the accuracy of sentence generation, a visual word constraint model is devised in training justification generator. To verify the proposed method, comparative experiments were conducted on the problem of the diagnosis of breast masses. Experimental results demonstrated that the proposed deep network can explain diagnosis more accurately with various textual justifications.
TextGraphs 2019 Shared Task on Multi-Hop Inference for Explanation Regeneration

While automated question answering systems are increasingly able to retrieve answers to natural language questions, their ability to generate detailed human-readable explanations for their answers is still quite limited.The Shared Task on Multi-Hop Inference for Explanation Regeneration tasks participants with regenerating detailed gold explanations for standardized elementary science exam questions by selecting facts from a knowledge base of semistructured tables.Each explanation contains between 1 and 16 interconnected facts that form an "explanation graph" spanning core scientific knowledge and detailed world knowledge.It is expected that successfully combining these facts to generate detailed explanations will require advancing methods in multihop inference and information combination, and will make use of the supervised training data provided by the WorldTree explanation corpus.The top-performing system achieved a mean average precision (MAP) of 0.56, substantially advancing the state-of-the-art over a baseline information retrieval model.Detailed extended analyses of all submitted systems showed large relative improvements in accessing the most challenging multi-hop inference problems, while absolute performance remains low, highlighting the difficulty of generating detailed explanations through multihop reasoning.
HIT-SCIR at MRP 2019: A Unified Pipeline for Meaning Representation Parsing via Efficient Training and Effective Encoding

This paper describes our system (HIT-SCIR) for the CoNLL 2019 shared task: Cross-Framework Meaning Representation Parsing.We extended the basic transition-based parser with two improvements: a) Efficient Training by realizing stack LSTM parallel training; b) Effective Encoding via adopting deep contextualized word embeddings BERT (Devlin et al., 2019).Generally, we proposed a unified pipeline to meaning representation parsing, including framework-specific transitionbased parsers, BERT-enhanced word representation, and post-processing.In the final evaluation, our system was ranked first according to ALL-F1 (86.2%) and especially ranked first in UCCA framework (81.67%).
A Case Study on Combining ASR and Visual Features for Generating Instructional Video Captions

Instructional videos get high-traffic on video sharing platforms, and prior work suggests that providing time-stamped, subtask annotations (e.g., "heat the oil in the pan") improves user experiences.However, current automatic annotation methods based on visual features alone perform only slightly better than constant prediction.Taking cues from prior work, we show that we can improve performance significantly by considering automatic speech recognition (ASR) tokens as input.Furthermore, jointly modeling ASR tokens and visual features results in higher performance compared to training individually on either modality.We find that unstated background information is better explained by visual features, whereas fine-grained distinctions (e.g., "add oil" vs. "add olive oil") are disambiguated more easily via ASR tokens.
Fine-Grained Image Retrieval via Piecewise Cross Entropy loss

Fine-Grained Image Retrieval is an important problem in computer vision. It is more challenging than the task of content-based image retrieval because it has small diversity within the different classes but large diversity in the same class. Recently, the cross entropy loss can be utilized to make Convolutional Neural Network (CNN) generate distinguish feature for Fine-Grained Image Retrieval, and it can obtain further improvement with some extra operations, such as Normalize-Scale layer. In this paper, we propose a variant of the cross entropy loss, named Piecewise Cross Entropy loss function, for enhancing model generalization and promoting the retrieval performance. Besides, the Piecewise Cross Entropy loss is easy to implement. We evaluate the performance of the proposed scheme on two standard fine-grained retrieval benchmarks, and obtain significant improvements over the state-of-the-art, with 11.8% and 3.3% over the previous work on CARS196 and CUB-200-2011, respectively.
An Exploration of Data Augmentation and Sampling Techniques for Domain-Agnostic Question Answering

To produce a domain-agnostic question answering model for the Machine Reading Question Answering (MRQA) 2019 Shared Task, we investigate the relative benefits of large pretrained language models, various data sampling strategies, as well as query and context paraphrases generated by back-translation.We find a simple negative sampling technique to be particularly effective, even though it is typically used for datasets that include unanswerable questions, such as SQuAD 2.0.When applied in conjunction with per-domain sampling, our XLNet (Yang et al., 2019)-based submission achieved the second best Exact Match and F1 in the MRQA leaderboard competition.
Elderly Fall Detection with an Accelerometer Using Lightweight Neural Networks

Falls have been one of the main threats to people’s health, especially for the elderly. Detecting falls in time can prevent the long lying time, which is extremely fatal. This paper intends to show the efficacy of detecting falls using a wearable accelerometer. In the past decade, the fall detection problem has been extensively studied. However, since the hardware resources of wearable devices are limited, designing highly accurate embeddable models with feasible computational cost remains an open research problem. In this paper, different types of shallow and lightweight neural networks, including supervised and unsupervised models are explored to improve the fall detection results. Experiment results on a large open dataset show that the lightweight neural networks proposed have obtained much better results than machine learning methods used in previous work. Moreover, the storage and computation requirements of these lightweight models are only a few hundredths of deep neural networks in literature. In tested lightweight neural networks, the best one is proved to be the supervised convolutional neural network (CNN) that can achieve an accuracy beyond 99.9% with only 441 parameters. Its storage and computation requirements are only 1.2 KB and 0.008 MFLOPs, which make it more suitable to be implemented in wearable devices with restricted memory size and computation power.
Large-Scale, Diverse, Paraphrastic Bitexts via Sampling and Clustering

Producing diverse paraphrases of a sentence is a challenging task.Natural paraphrase corpora are scarce and limited, while existing large-scale resources are automatically generated via back-translation and rely on beam search, which tends to lack diversity.We describe PARABANK 2, a new resource that contains multiple diverse sentential paraphrases, produced from a bilingual corpus using negative constraints, inference sampling, and clustering.We show that PARABANK 2 significantly surpasses prior work in both lexical and syntactic diversity while being meaningpreserving, as measured by human judgments and standardized metrics.Further, we illustrate how such paraphrastic resources may be used to refine contextualized encoders, leading to improvements in downstream tasks.
Facial expression recognition in JAFFE and KDEF Datasets using histogram of oriented gradients and support vector machine

Abstract This paper presents the used of histogram of oriented gradient (HOG) for facial expression recognition using support vector machine (SVM). In this work, the facial expression images are firstly preprocessed by face detection and cropped images. Then, HOG method is adopted as feature extraction on facial image. The ability of HOG to preserve the local information and orientation density distribution in facial images suitable as shape descriptor for facial expression. It divides the image into cell or patch that has magnitude and orientations. The extracted HOG was then concatenated into histogram bin to form one feature vector before feed into SVM classifier. Both JAFFE and KDEF datasets were employed to evaluate the performance of proposed method. Based on results, the average recognition rates of JAFFE and KDEF datasets are 76.19% and 80.95% respectively. The results show that the performance of expression surprise has outperformed compared to others expression while expression fear contributes the lowest recognition rate. Thus, utilization of HOG features with SVM classifier have shown the promising results in recognizing facial expression.
Keywords extraction with deep neural network model

Keywords can express the main content of an article or a sentence. Keywords extraction is a critical issue in many Natural Language Processing (NLP) applications and can improve the performance of many NLP systems. The traditional methods of keywords extraction are based on machine learning or graph model. The performance of these methods is influenced by the feature selection and the manually defined rules. In recent years, with the emergence of deep learning technology, learning features automatically with the deep learning algorithm can improve the performance of many tasks. In this paper, we propose a deep neural network model for the task of keywords extraction. We make two extensions on the basis of traditional LSTM model. First, to better utilize both the historic and following contextual information of the given target word, we propose a target center-based LSTM model (TC-LSTM), which learns to encode the target word by considering its contextual information. Second, on the basis of TC-LSTM model, we apply the self-attention mechanism, which enables our model has an ability to focus on informative parts of the associated text. In addition, we also introduce a two-stage training method, which takes advantage of large-scale pseudo training data. Experimental results show the advantage of our method, our model beats all the baseline systems all across the board. And also, the two-stage training method is of great significance for improving the effectiveness of the model.
Information flow versus divisibility for qubit evolution

We study the relation between lack of information backflow and completely positive divisibility for noninvertible qubit dynamical maps. Recently, these two concepts were shown to be fully equivalent for the so-called image nonincreasing dynamical maps. Here we show that this equivalence is universal for any qubit dynamical map. A key ingredient in our proof is the observation that there does not exist a completely positive and trace-preserving projector onto a three-dimensional subspace spanned by qubit density operators. Our analysis is illustrated by several examples of qubit evolution, including dynamical maps which are not image nonincreasing.
Application of image retrieval based on convolutional neural networks and Hu invariant moment algorithm in computer telecommunications

Image recognition and retrieval is an important application field of digital image and it has a wide range of application scenarios in the computer telecommunications. Convolutional neural networks (CNN) is a kind of feed-forward neural networks which includes convolution calculation and which has a deep structure and it is one of the typical algorithms of deep learning. In recent years, it has become a highly efficient recognition algorithm which has been widely applied in such fields as pattern recognition and image processing. Its characteristics include few training parameters and strong adaptability. On the other hand, Hu invariant moment algorithm-a conventional algorithm, is also extensively used in various image processing fields due to its simple calculation and high efficiency. This paper analyzes the research background and significance of both CNN and Hu invariant moment algorithm, and introduces their research status. Besides, it also analyzes the results of color-, distance- and weight-based Hu invariant moment algorithm, and compares it with CNN to serve as a theoretical support for better achieving image classification, recognition and retrieval technology.
An Improved Grey Wolf Optimization Algorithm Based Task Scheduling in Cloud Computing Environment

The demand for massive computing power and storage space has been escalating in various fields and in order to satisfy this need a new technology known as cloud computing is introduced. The capability of providing these services effectively and economically has made cloud computing technology more popular. With the advent of virtualization, IT services being offered have started to shift to cloud computing. Virtualization had paved way for resource availability in an inexhaustible manner. As Cloud Computing is still at its unrefined form and to derive its full potential more analysis is needed. The way in which resources and tasks get allocated in cloud environment requires more analysis. This in turn accounts for the Quality of Services (QoS) of the services offered by cloud service providers. This paper proposes to simulate the Performance-Cost Grey Wolf Optimization (PCGWO) algorithm based to achieve optimization in the process of allocation of resources and tasks in cloud computing domain using CloudSim toolkit. The main purpose is to lower both the processing time and cost in accordance to objective function. The superiority of proposed technique is evident from the simulation results that show a comprehensive reduction in task completion time and cost. Also using this technique more no. of tasks can be efficiently completed within the deadline. Thus the results indicate that in accordance to performance the PCGWO method fares better than existing algorithms
Multi-Label Causal Feature Selection

Multi-label feature selection has received considerable attentions during the past decade. However, existing algorithms do not attempt to uncover the underlying causal mechanism, and individually solve different types of variable relationships, ignoring the mutual effects between them. Furthermore, these algorithms lack of interpretability, which can only select features for all labels, but cannot explain the correlation between a selected feature and a certain label. To address these problems, in this paper, we theoretically study the causal relationships in multi-label data, and propose a novel Markov blanket based multi-label causal feature selection (MB-MCF) algorithm. MB-MCF mines the causal mechanism of labels and features first, to obtain a complete representation of information about labels. Based on the causal relationships, MB-MCF then selects predictive features and simultaneously distinguishes common features shared by multiple labels and label-specific features owned by single labels. Experiments on real-world data sets validate that MB-MCF could automatically determine the number of selected features and simultaneously achieve the best performance compared with state-of-the-art methods. An experiment in Emotions data set further demonstrates the interpretability of MB-MCF.
DeepWAF: Detecting Web Attacks Based on CNN and LSTM Models

The increasing popularity of web applications makes the web a main venue for attackers engaging in a myriad of cybercrimes. With large quantities of information processing and sharing by web applications, the situation for web attack detection or prevention becomes increasingly severe. We present a prototype implementation called DeepWAF to detect web attacks based on deep learning techniques. We systematically discuss the approach for effective use of the currently popular CNN and LSTM models, and their combinational models CNN-LSTM and LSTM-CNN. The experimental results on the dataset of HTTP DATASET CSIC 2010 demonstrate that our proposed four types of detection models all achieve satisfactory results, with the detection rate of approximately 95% and the false alarm rate of approximately 2%. We also carried out case studies to analyze the causes of false negatives and false positives, which can be used for further improvements. Our work further illustrates that machine learning has a promising application prospect in the field of web attack detection.
Exploiting Spatial Invariance for Scalable Unsupervised Object Tracking

The ability to detect and track objects in the visual world is a crucial skill for any intelligent agent, as it is a necessary precursor to any object-level reasoning process. Moreover, it is important that agents learn to track objects without supervision (i.e. without access to annotated training videos) since this will allow agents to begin operating in new environments with minimal human assistance. The task of learning to discover and track objects in videos, which we call unsupervised object tracking, has grown in prominence in recent years; however, most architectures that address it still struggle to deal with large scenes containing many objects. In the current work, we propose an architecture that scales well to the large-scene, many-object setting by employing spatially invariant computations (convolutions and spatial attention) and representations (a spatially local object specification scheme). In a series of experiments, we demonstrate a number of attractive features of our architecture; most notably, that it outperforms competing methods at tracking objects in cluttered scenes with many objects, and that it can generalize well to videos that are larger and/or contain more objects than videos encountered during training.
Privacy Enhanced Multimodal Neural Representations for Emotion Recognition

Many mobile applications and virtual conversational agents now aim to recognize and adapt to emotions. To enable this, data are transmitted from users' devices and stored on central servers. Yet, these data contain sensitive information that could be used by mobile applications without user's consent or, maliciously, by an eavesdropping adversary. In this work, we show how multimodal representations trained for a primary task, here emotion recognition, can unintentionally leak demographic information, which could override a selected opt-out option by the user. We analyze how this leakage differs in representations obtained from textual, acoustic, and multimodal data. We use an adversarial learning paradigm to unlearn the private information present in a representation and investigate the effect of varying the strength of the adversarial component on the primary task and on the privacy metric, defined here as the inability of an attacker to predict specific demographic information. We evaluate this paradigm on multiple datasets and show that we can improve the privacy metric while not significantly impacting the performance on the primary task. To the best of our knowledge, this is the first work to analyze how the privacy metric differs across modalities and how multiple privacy concerns can be tackled while still maintaining performance on emotion recognition.