Reviewerly/exHarmony huggingface dataset:
Differential privacy under dependent tuples—the case of genomic privacy

Abstract Motivation The rapid progress in genome sequencing has led to high availability of genomic data. Studying these data can greatly help answer the key questions about disease associations and our evolution. However, due to growing privacy concerns about the sensitive information of participants, accessing key results and data of genomic studies (such as genome-wide association studies) is restricted to only trusted individuals. On the other hand, paving the way to biomedical breakthroughs and discoveries requires granting open access to genomic datasets. Privacy-preserving mechanisms can be a solution for granting wider access to such data while protecting their owners. In particular, there has been growing interest in applying the concept of differential privacy (DP) while sharing summary statistics about genomic data. DP provides a mathematically rigorous approach to prevent the risk of membership inference while sharing statistical information about a dataset. However, DP does not consider the dependence between tuples in the dataset, which may degrade the privacy guarantees offered by the DP. Results In this work, focusing on genomic datasets, we show this drawback of the DP and we propose techniques to mitigate it. First, using a real-world genomic dataset, we demonstrate the feasibility of an inference attack on differentially private query results by utilizing the correlations between the entries in the dataset. The results show the scale of vulnerability when we have dependent tuples in the dataset. We show that the adversary can infer sensitive genomic data about a user from the differentially private results of a query by exploiting the correlations between the genomes of family members. Second, we propose a mechanism for privacy-preserving sharing of statistics from genomic datasets to attain privacy guarantees while taking into consideration the dependence between tuples. By evaluating our mechanism on different genomic datasets, we empirically demonstrate that our proposed mechanism can achieve up to 50% better privacy than traditional DP-based solutions. Availability and implementation https://github.com/nourmadhoun/Differential-privacy-genomic-inference-attack. Supplementary information Supplementary data are available at Bioinformatics online.
Generalizing Question Answering System with Pre-trained Language Model Fine-tuning

With a large number of datasets being released and new techniques being proposed, Question answering (QA) systems have witnessed great breakthroughs in reading comprehension (RC)tasks. However, most existing methods focus on improving in-domain performance, leaving open the research question of how these mod-els and techniques can generalize to out-of-domain and unseen RC tasks. To enhance the generalization ability, we propose a multi-task learning framework that learns the shared representation across different tasks. Our model is built on top of a large pre-trained language model, such as XLNet, and then fine-tuned on multiple RC datasets. Experimental results show the effectiveness of our methods, with an average Exact Match score of 56.59 and an average F1 score of 68.98, which significantly improves the BERT-Large baseline by8.39 and 7.22, respectively
Evolutionary extreme learning machine with sparse cost matrix for imbalanced learning

Extreme learning machine is a popular machine learning technique for single hidden layer feed-forward neural network. However, due to the assumption of equal misclassification cost, the conventional extreme learning machine fails to properly learn the characteristics of the data with skewed category distribution. In this paper, to enhance the representation of few-shot cases, we break down that assumption by assigning penalty factors to different classes, and minimizing the cumulative classification cost. To this end, a case-weighting extreme learning machine is developed on a sparse cost matrix with a diagonal form. To be more actionable, we formulate a multi-objective optimization with respect to penalty factors, and optimize this problem using an evolutionary algorithm combined with an error bound model. By doing so, this proposed method is developed into an adaptive cost-sensitive learning, which is guided by the relation between the generalization ability and the case-weighting factors. In a broad experimental study, our method achieves competitive results on benchmark and real-world datasets for software bug reports identification.
No-reference screen content image quality assessment based on multi-region features

Unlike natural image captured with cameras, screen content image (SCI) is a composite image including textual and pictorial regions. The different characteristics lead to many difficulties in image quality assessment (IQA). Most existing models based on the convolutional neural network (CNN) divide large SCIs into image patches to increase training samples for CNN training. This brings two problems: (1) a single image patch can not represent the quality of the entire image, especially in IQA of SCI; (2) SCI patches of an entire image degraded by the same distortion type and strength may have drastically different quality. In addition, these models adopt the mean square error (MSE) between the predicted quality and the subjective differential mean opinion score (DMOS) to train the CNN, without considering quality ranking between different SCIs. In this paper, we propose a novel no-reference (NR) IQA model based on the convolutional neural network (CNN). The contributions of our algorithm can be concluded as follows: (1) considering a large difference exists in different regions in a SCI, the pseudo global features generated with multi-region local features are utilized for quality evaluation, which better reflect image quality than local features of each image patch; (2) the noise classification task is used as an auxiliary task which aids the quality score prediction task to improve the representation ability; (3) the Siamese networks are used to predict the quality scores of two different SCIs, and a new ranking loss is proposed to rank the predicted scores, aiming to enhance the ability of the model to rank image in terms of quality. Experimental results verify that our model outperforms all test NR IQA methods and full-reference (FR) IQA methods on the screen content image quality assessment database (SIQAD).
Deep Attentive Ranking Networks for Learning to Order Sentences

We present an attention-based ranking framework for learning to order sentences given a paragraph. Our framework is built on a bidirectional sentence encoder and a self-attention based transformer network to obtain an input order invariant representation of paragraphs. Moreover, it allows seamless training using a variety of ranking based loss functions, such as pointwise, pairwise, and listwise ranking. We apply our framework on two tasks: Sentence Ordering and Order Discrimination. Our framework outperforms various state-of-the-art methods on these tasks on a variety of evaluation metrics. We also show that it achieves better results when using pairwise and listwise ranking losses, rather than the pointwise ranking loss, which suggests that incorporating relative positions of two or more sentences in the loss function contributes to better learning.
GraLSP: Graph Neural Networks with Local Structural Patterns

It is not until recently that graph neural networks (GNNs) are adopted to perform graph representation learning, among which, those based on the aggregation of features within the neighborhood of a node achieved great success. However, despite such achievements, GNNs illustrate defects in identifying some common structural patterns which, unfortunately, play significant roles in various network phenomena. In this paper, we propose GraLSP, a GNN framework which explicitly incorporates local structural patterns into the neighborhood aggregation through random anonymous walks. Specifically, we capture local graph structures via random anonymous walks, powerful and flexible tools that represent structural patterns. The walks are then fed into the feature aggregation, where we design various mechanisms to address the impact of structural features, including adaptive receptive radius, attention and amplification. In addition, we design objectives that capture similarities between structures and are optimized jointly with node proximity objectives. With the adequate leverage of structural patterns, our model is able to outperform competitive counterparts in various prediction tasks in multiple datasets.
Importance-Aware Semantic Segmentation in Self-Driving with Discrete Wasserstein Training

Semantic segmentation (SS) is an important perception manner for self-driving cars and robotics, which classifies each pixel into a pre-determined class. The widely-used cross entropy (CE) loss-based deep networks has achieved significant progress w.r.t. the mean Intersection-over Union (mIoU). However, the cross entropy loss can not take the different importance of each class in an self-driving system into account. For example, pedestrians in the image should be much more important than the surrounding buildings when make a decisions in the driving, so their segmentation results are expected to be as accurate as possible. In this paper, we propose to incorporate the importance-aware inter-class correlation in a Wasserstein training framework by configuring its ground distance matrix. The ground distance matrix can be pre-defined following a priori in a specific task, and the previous importance-ignored methods can be the particular cases. From an optimization perspective, we also extend our ground metric to a linear, convex or concave increasing function w.r.t. pre-defined ground distance. We evaluate our method on CamVid and Cityscapes datasets with different backbones (SegNet, ENet, FCN and Deeplab) in a plug and play fashion. In our extenssive experiments, Wasserstein loss demonstrates superior segmentation performance on the predefined critical classes for safe-driving.
Experimental Data Analysis of College English Teaching based on Computer Multimedia Technology

As the scale, shape and function of engineering construction projects become more and more complex, the design of building systems has become more and more complex.In order to help students better acquire course resource information, effectively demonstrate the teaching process, and help students understand the curriculum and learn effectively, this paper designs an Auto CAD network architecture design teaching system.We analyze and compare the teaching model based on the network architecture design teaching system, and introduce the cluster analysis to the course resource management.An Auto CAD model was constructed through collaborative design among professionals based on Auto CAD technology.We analyze and compare the characteristics of traditional 2D design and inter-professional collaborative design based on Auto CAD technology, and select appropriate 3D simulation software for inter-professional collaborative design.The teaching of Auto CAD architectural design course proposed in this paper has certain innovation and practicability, and puts forward some constructive suggestions on the solution of the problem.
The impact of moral intelligence on green purchase intention

In this article, the impacts that moral intelligence has on green purchasing intentions in Jordan was investigated based on three moral theories (Utilitarianism, Deontology, Virtue Ethics), as well as the theory of Planned Behavior.Furthermore, four key areas of moral intelligence (compassion, forgiveness, responsibility and integrity) were discussed.A questionnaire was used to obtain the necessary primary data from 191 customers in Jordan.To analyze the results, partial least squares structural equation modeling was carried out.It was concluded that the four key aspects of moral intelligence (compassion, forgiveness, responsibility and integrity) positively impact green purchasing intentions.This research has practical significance in the fields of green marketing and moral intelligence, especially with regard to the dimensions of compassion, forgiveness, responsibility and integrity.These dimensions can thus serve as a guide for improving customers' green purchase intentions in future.Moreover, the research is important for investigations into individual and company-based environmental sustainability.
OPMP: An Omnidirectional Pyramid Mask Proposal Network for Arbitrary-Shape Scene Text Detection

Scene text detection methods have achieved significant progresses. However, stack-omnidirectional text dilemma, under-segmentation of very close text words, and over-segmentation of arbitrary-shape long text lines, are still main challenges. Motivated by these problems, we proposed a two stage method called omnidirectional pyramid mask proposal text detector (OPMP). OPMP removes anchor mechanism that requires heuristic non-maximum suppress processing. Instead, it uses an effective pyramid lengthwise and sidewise residual sequence modeling method to produce arbitrary-shape proposals. To accurately extract the features of text shape, OPMP enhances the backbone layers by a multiple arbitrary-shape fitting mechanism. Finally, a multi-grain text classification module is proposed, which reclassifies each text region robustly. Comprehensive ablation studies demonstrate the effectiveness of each proposed component. In addition, experiments on various benchmarks, including ICDAR2015, MLT, MSRA-TD500, CTW1500, and Total-text, show that our method outperforms previous state-of-the-art methods.
Sentiment Analysis in a Forensic Timeline With Deep Learning

A forensic investigator creates a timeline from a forensic disk image after an occurrence of a security incident. This procedure aims to acquire the time for all events identified from the investigated artifacts. An investigator usually looks for events of interest by manually searching the timeline. One of the sources from which to build a timeline is log files, and these events are often found in log messages. In this paper, we propose a sentiment analysis technique to automatically extract events of interest from log messages in the forensic timeline. We use a deep learning technique with a context and content attention model to identify aspect terms and the corresponding sentiments in the forensic timeline. Terms with negative sentiments indicate events of interest and are highlighted in the timeline. Therefore, the investigator can quickly examine the events and other activities recorded within the surrounding time frame. Experimental results on four public forensic case studies show that the proposed method achieves 98.43% and 99.64% for the F1 score and accuracy, respectively.
A Kernel Extreme Learning Machines Algorithm for Node Localization in Wireless Sensor Networks

Node localization is one of the promising research issues in Wireless Sensor Networks (WSNs). A novel node localization algorithm termed Kernel Extreme Learning Machines based on Hop-count Quantization (KELM-HQ) is proposed. The proposed algorithm employs the real number hop-counts between anchors and unknown nodes as the training inputs and the locations of the anchors as the training targets for KELM training. The proposed method also employs the real number hop-counts between unknown nodes as the test samples to compute the locations of unknown nodes by the trained KELM. Simulation results demonstrate that the proposed KELM-HQ algorithm improves the accuracy of node localization and it outperforms state-of-the-arts localization methods.
Bitcoin pricing: impact of attractiveness variables

Abstract The research seeks to contribute to Bitcoin pricing analysis based on the dynamics between variables of attractiveness and the value of the digital currency. Using the error correction model, the relationship between the price of the virtual currency, Bitcoin, and the number of Google searches that used the terms bitcoin , bitcoin crash and crisis between December 2012 and February 2018 is analyzed. The study also applied the same analysis to prices of Bitcoin denominated in different sovereign currencies traded during the same period. The Johansen (J Econ Dyn Control 12:231-254, 1988) test demonstrates that the price and number of searches on Google for the first two terms are cointegrated. This research indicates that there are strong short-term and long-term dynamics among attractiveness factors, suggesting that an increase in worldwide interest in Bitcoin is usually preceded by a price increase. In contrast, an increase in market mistrust over a collapse of the currency, as measured by the term bitcoin crash , is followed by a fall in price. Intense world economic crisis events appear to have a strong impact on interest in the virtual currency. This study demonstrates that during a worldwide crisis Bitcoin becomes an alternative investment, increasing its price. Based on it, bitcoin may be used as a safe haven by the financial market and its intrinsic characteristics might help the investors and governments to find new mechanisms to deal with monetary transactions.
Feature Selection Using Different Transfer Functions for Binary Bat Algorithm

The selection feature is an important and fundamental step in the preprocessing of many classification and machine learning problems. The feature selection (FS) method is used to reduce the amount of data used and to create high-probability of classification accuracy (CA) based on fewer features by deleting irrelevant data that often reason confusion for the classifiers. In this work, bat algorithm (BA), which is a new metaheuristic rule, is applied as a wrapper type of FS technique. Six different types of BA (BA-S and BA-V) are proposed, where apiece used a transfer function (TF) to map the solutions from continuous space to the discrete space. The results of the experiment show that the features that use the BA-V methods (that is, the V-shaped transfer function) have proven effective and efficient in selecting subsets of features with high classification accuracy.
Synthesizing barrier certificates using neural networks

This paper presents an approach of safety verification based on neural networks for continuous dynamical systems which are modeled as a system of ordinary differential equations. We adopt the deductive verification methods based on barrier certificates. These are functions over the states of the dynamical system with certain constraints the existence of which entails the safety of the system under consideration. We propose to represent the barrier function by neural networks and provide a comprehensive synthesis framework. In particular, we devise a new type of activation functions, i.e., Bent-ReLU, for the neural networks; we provide sampling based approaches to generate training sets and formulate the loss functions for neural network training which can capture the essence of barrier certificate; we also present practical methods to check a learnt candidate barrier certificate against the criteria of barrier certificates as a formal guarantee. We implement our approaches via proof-of-concept experiments with encouraging results.
Visual Analytics for Dimension Reduction and Cluster Analysis of High Dimensional Electronic Health Records

Recent advancement in EHR-based (Electronic Health Record) systems has resulted in producing data at an unprecedented rate. The complex, growing, and high-dimensional data available in EHRs creates great opportunities for machine learning techniques such as clustering. Cluster analysis often requires dimension reduction to achieve efficient processing time and mitigate the curse of dimensionality. Given a wide range of techniques for dimension reduction and cluster analysis, it is not straightforward to identify which combination of techniques from both families leads to the desired result. The ability to derive useful and precise insights from EHRs requires a deeper understanding of the data, intermediary results, configuration parameters, and analysis processes. Although these tasks are often tackled separately in existing studies, we present a visual analytics (VA) system, called Visual Analytics for Cluster Analysis and Dimension Reduction of High Dimensional Electronic Health Records (VALENCIA), to address the challenges of high-dimensional EHRs in a single system. VALENCIA brings a wide range of cluster analysis and dimension reduction techniques, integrate them seamlessly, and make them accessible to users through interactive visualizations. It offers a balanced distribution of processing load between users and the system to facilitate the performance of high-level cognitive tasks in such a way that would be difficult without the aid of a VA system. Through a real case study, we have demonstrated how VALENCIA can be used to analyze the healthcare administrative dataset stored at ICES. This research also highlights what needs to be considered in the future when developing VA systems that are designed to derive deep and novel insights into EHRs.
Adversarial Multi-view Networks for Activity Recognition

Human activity recognition (HAR) plays an irreplaceable role in various applications and has been a prosperous research topic for years. Recent studies show significant progress in feature extraction (i.e., data representation) using deep learning techniques. However, they face significant challenges in capturing multi-modal spatial-temporal patterns from the sensory data, and they commonly overlook the variants between subjects. We propose a Discriminative Adversarial MUlti-view Network (DAMUN) to address the above issues in sensor-based HAR. We first design a multi-view feature extractor to obtain representations of sensory data streams from temporal, spatial, and spatio-temporal views using convolutional networks. Then, we fuse the multi-view representations into a robust joint representation through a trainable Hadamard fusion module, and finally employ a Siamese adversarial network architecture to decrease the variants between the representations of different subjects. We have conducted extensive experiments under an iterative left-one-subject-out setting on three real-world datasets and demonstrated both the effectiveness and robustness of our approach.
Extreme Learning Machine-Based Ensemble Transfer Learning for Hyperspectral Image Classification

Although the extreme learning machine (ELM) has been successfully applied to hyperspectral image (HSI) classification, the development of the ELM is restricted by insufficient training data.In this article, we propose a novel extreme learning machine-based ensemble transfer learning algorithm for hyperspectral image classification named TL-ELM.TL-ELM not only retains the input weights and hidden biases of the ELM learned from the target domain, but also utilizes instances in the source domain to iteratively adjust the output weights of the ELM, which are used as the weights of the training models, and then ensembles the training models with their weights for the final classification.In experiments, we choose different regions in northern Italy, namely, Pavia University and Pavia Centre, as the source dataset and target dataset, respectively, and through a comparison with other transfer learning algorithms, we demonstrate that our proposed TL-ELM algorithm is superior on HSI classification tasks with only a few labeled data points in the target domain.Furthermore, we set Pavia University as the source dataset and Pavia Centre as the target dataset to demonstrate that our proposed method can effectively transfer useful instances between different HSIs.