Reviewerly/exHarmony huggingface dataset:
Bidirectional Discrete Matrix Factorization Hashing for Image Search

Unsupervised image hashing has recently gained significant momentum due to the scarcity of reliable supervision knowledge, such as class labels and pairwise relationship. Previous unsupervised methods heavily rely on constructing sufficiently large affinity matrix for exploring the geometric structure of data. Nevertheless, due to lack of adequately preserving the intrinsic information of original visual data, satisfactory performance can hardly be achieved. In this article, we propose a novel approach, called bidirectional discrete matrix factorization hashing (BDMFH), which alternates two mutually promoted processes of 1) learning binary codes from data and 2) recovering data from the binary codes. In particular, we design the inverse factorization model, which enforces the learned binary codes inheriting intrinsic structure from the original visual data. Moreover, we develop an efficient discrete optimization algorithm for the proposed BDMFH. Comprehensive experimental results on three large-scale benchmark datasets show that the proposed BDMFH not only significantly outperforms the state-of-the-arts but also provides the satisfactory computational efficiency.
From Unilateral to Bilateral Learning: Detecting Mammogram Masses with Contrasted Bilateral Network

The comparison of bilateral mammogram images is important for finding masses especially in dense breasts. However, most existing mammogram mass detection algorithms only considered unilateral image. In this paper, we propose a deep model called contrasted bilateral network (CBN) to take bilateral information into consideration. In CBN, Mask R-CNN is used as a basic framework, upon which two major modules are developed to exploit the bilateral information: distortion insensitive comparison module and logic guided bilateral module. The former one is designed to be robust to nonrigid distortion of bilateral registration, while the latter one integrates the bilateral domain knowledge of radiologist. Experimental results on DDSM dataset demonstrate that the proposed algorithm achieves the state-of-the-art performance.
A deep learning technique for imputing missing healthcare data

Missing data is a frequent occurrence in medical and health datasets. The analysis of datasets with missing data can lead to loss in statistical power or biased results. We address this issue with a novel deep learning technique to impute missing values in health data. Our method extends upon an autoencoder to derive a deep learning architecture that can learn the hidden representations of data even when data is perturbed by missing values (noise). Our model is constructed with overcomplete representation and trained with denoising regularization. This allows the latent/hidden layers of our model to effectively extract the relationships between different variables; these relationships are then used to reconstruct missing values. Our contributions include a new loss function designed to avoid local optima, and this helps the model to learn the real distribution of variables in the dataset. We evaluate our method in comparison with other well-established imputation strategies (mean, median imputation, SVD, KNN, matrix factorization and soft impute) on 48,350 Linked Birth/Infant Death Cohort Data records. Our experiments demonstrate that our method achieved lower imputation mean squared error (MSE=0.00988) compared with other imputation methods (with MSE ranging from 0.02 to 0.08). When assessing the imputation quality using the imputed data for prediction tasks, our experiments show that the data imputed by our method yielded better results (F1=70.37%) compared with other imputation methods (ranging from 66 to 69%).
Prediction of Breast Cancer from Imbalance Respect Using Cluster-Based Undersampling Method

To overcome the two-class imbalanced problem existing in the diagnosis of breast cancer, a hybrid of K-means and Boosted C5.0 (K-Boosted C5.0) is proposed which is based on undersampling. K-means is utilized to select the informative samples near the boundary. During the training phase, the K-means algorithm clusters the majority and minority instances and selects a similar number of instances from each cluster. Boosted C5.0 is then used as the classifier. As there is one different instance selection factor via clustering that encourages the diversity of the training subspace in K-Boosted C5.0, it would be a great advantage to get better performance. To test the performance of the new hybrid classifier, it is implemented on 12 small-scale and 2 large-scale datasets, which are the often used datasets in class imbalanced learning. The extensive experimental results show that our proposed hybrid method outperforms most of the competitive algorithms in terms of Matthews' correlation coefficient (MCC) and accuracy indices. It can be a good alternative to the well-known machine learning methods.
Two ethical concerns about the use of persuasive technology for vulnerable people

Abstract Persuasive technologies for health‐related behaviour change give rise to ethical concerns. As of yet, no study has explicitly attended to ethical concerns arising with the design and use of these technologies for vulnerable people. This is striking because these technologies are designed to help people change their attitudes or behaviours, which is particularly valuable for vulnerable people. Vulnerability is a complex concept that is both an ontological condition of our humanity and highly context‐specific. Using the Mackenzie, Rogers and Dodds’ taxonomy of vulnerability, this paper identifies (a) the wrongs or harms to which a person is vulnerable, (b) the source of this vulnerability, and (c) the safeguards needed in response. Two ethical concerns with the design of persuasive technology for vulnerable people are discussed: the concerns of taking into account users' interests and their autonomy.
Joint Inference for Aspect-level Sentiment Analysis by Deep Neural Networks and Linguistic Hints

The state-of-the-art techniques for aspect-level sentiment analysis focused on feature modeling using a variety of deep neural networks (DNN). Unfortunately, their performance may still fall short of expectation in real scenarios due to the semantic complexity of natural languages. Motivated by the observation that many linguistic hints (e.g., sentiment words and shift words) are reliable polarity indicators, we propose a joint framework, SenHint, which can seamlessly integrate the output of deep neural networks and the implications of linguistic hints in a unified model based on Markov logic network (MLN). SenHint leverages the linguistic hints for multiple purposes: (1) to identify the easy instances, whose polarities can be automatically determined by the machine with high accuracy; (2) to capture the influence of sentiment words on aspect polarities; (2) to capture the implicit relations between aspect polarities. We present the required techniques for extracting linguistic hints, encoding their implications as well as the output of DNN into the unified model, and joint inference. Finally, we have empirically evaluated the performance of SenHint on both English and Chinese benchmark datasets. Our extensive experiments have shown that compared to the state-of-the-art DNN techniques, SenHint can effectively improve polarity detection accuracy by considerable margins.
Weakly-Supervised Learning of Category-Specific 3D Object Shapes

Category-specific 3D object shape models have greatly boosted the recent advances in object detection, recognition and segmentation. However, even the most advanced approach for learning 3D object shapes still requires heavy manual annotations on large-scale 2D images. Such annotations include object categories, object keypoints, and figure-ground segmentation for the instances in each image. In particular, annotating figure-ground segmentation is unbearably labor-intensive and time-consuming. To address this problem, this paper devotes to learn category-specific 3D shape models under weak supervision, where only object categories and keypoints are required to be manually annotated on the training 2D images. By exploring the underlying relationship between two tasks: object segmentation and category-specific 3D shape reconstruction, we propose a novel weakly-supervised learning framework to jointly address these two tasks and combine them to boost the final performance of the learned 3D shape models. Moreover, learning without using figure-ground segmentation leads to ambiguous solutions. To this end, we develop the confidence weighting schemes in the viewpoint estimation and 3D shape learning procedure. These schemes effectively reduce the confusion caused by the noisy data and thus increase the chances for recovering more reliable 3D object shapes. Comprehensive experiments on the challenging PASCAL VOC benchmark show that our framework achieves comparable performance with the state-of-the-art methods that use expensive manual segmentation-level annotations. In addition, our experiments also demonstrate that our 3D shape models improve object segmentation performance.
An Automated Live Forensic and Postmortem Analysis Tool for Bitcoin on Windows Systems

Bitcoin is popular not only with consumers, but also with cybercriminals (e.g., in ransomware and online extortion, and commercial online child exploitation). Given the potential of Bitcoin to be involved in a criminal investigation, the need to have an up-to-date and in-depth understanding on the forensic acquisition and analysis of Bitcoins is crucial. However, there has been limited forensic research of Bitcoin in the literature. The general focus of existing research is on postmortem analysis of specific locations (e.g. wallets on mobile devices), rather than a forensic approach that combines live data forensics and postmortem analysis to facilitate the identification, acquisition, and analysis of forensic traces relating to the use of Bitcoins on a system. Hence, the latter is the focus of this paper where we present an open source tool for live forensic and postmortem analysing automatically. Using this open source tool, we describe a list of target artifacts that can be obtained from a forensic investigation of popular Bitcoin clients and Web Wallets on different web browsers installed on Windows 7 and Windows 10 platforms.
Extra-detection-free monoscopic deflectometry for the in situ measurement of freeform specular surfaces

Extra detections of feature points conducted by a third-party instrument are indispensable in the monoscopic deflectometry to specify the surface position, but the measuring capability can be limited because most freeform surfaces do not contain any distinguishable and detectable feature points. In this Letter, the behaviors of the position error and form error of the measured surface are discriminated by reverse ray-tracing. A novel iterative optimization strategy is developed to position the workpiece for the in situ deflectometric measurement so that extra detections can be avoided. It is demonstrated that this method can achieve parallel measuring accuracy with conventional deflectometry, but the measuring efficiency and range of freeform surfaces can be greatly improved. A hollow off-axis parabolic mirror is measured, and its departure from the interferometric results is 139.85&#x00A0;nm root mean square.
Low-Resource Parsing with Crosslingual Contextualized Representations

Despite advances in dependency parsing, languages with small treebanks still present challenges.We assess recent approaches to multilingual contextual word representations (CWRs), and compare them for crosslingual transfer from a language with a large treebank to a language with a small or nonexistent treebank, by sharing parameters between languages in the parser itself.We experiment with a diverse selection of languages in both simulated and truly low-resource scenarios, and show that multilingual CWRs greatly facilitate low-resource dependency parsing even without crosslingual supervision such as dictionaries or parallel text.Furthermore, we examine the non-contextual part of the learned language models (which we call a "decontextual probe") to demonstrate that polyglot language models better encode crosslingual lexical correspondence compared to aligned monolingual language models.
Using Clinical Notes with Time Series Data for ICU Management

Monitoring patients in ICU is a challenging and high-cost task.Hence, predicting the condition of patients during their ICU stay can help provide better acute care and plan the hospital's resources.There has been continuous progress in machine learning research for ICU management, and most of this work has focused on using time series signals recorded by ICU instruments.In our work, we show that adding clinical notes as another modality improves the performance of the model for three benchmark tasks: in-hospital mortality prediction, modeling decompensation, and length of stay forecasting that play an important role in ICU management.While the time-series data is measured at regular intervals, doctor notes are charted at irregular times, making it challenging to model them together.We propose a method to model them jointly, achieving considerable improvement across benchmark tasks over baseline time-series model.Our implementation can be found at https://github.com/kaggarwal/ClinicalNotesICU.
Feasibility of Using Rice Leaves Hyperspectral Data to Estimate CaCl2-extractable Concentrations of Heavy Metals in Agricultural Soil

Abstract Heavy metals contamination is a serious problem of China. It is necessary to estimate bioavailability concentrations of heavy metals in agricultural soil for keeping the food security and human health. This study aimed to use hyperspectral data of rice ( Oryza sativa ) leaves as an indicator to retrieve the CaCl 2 -extractable concentrations of heavy metals in agricultural soil. Twenty-one rice samples, soil samples and reflectance spectra of rice leaves were collected, respectively. The potential relations between hyperspectral data and CaCl 2 -extractable heavy metals (E-HM) were explored. The partial least-squares regression (PLSR) method with leave-one-out cross-validation has been used to predict concentrations of CaCl 2 -extractable cadmium (E-Cd) and concentrations of CaCl 2 -extractable lead (E-Pb) in farmland soil. The results showed that the concentrations of E-Cd in soil had significant correlation with concentrations of Cd in rice leaves; the number of bands associated with E-Cd was more than that of E-Pb. Four indices (normalized difference vegetation index (NDVI), carotenoid reflectance index (CRI), photochemical reflectance index 2 (PRI2), normalized pigments chlorophyll ratio index (NPCI)) were significant ( P &lt; 0.05) and negatively related to the E-Cd concentrations. The PLSR model of E-Cd concentrations performed better than the PLSR model of E-Pb concentrations, which with R 2 = 0.592 and RMSE = 0.046. We conclude that if the rice was sensitive to E-HM and/or the crop was stressed by the E-HM, the hyperspectral data of field rice leaves hold potentials in estimating concentration of E-HM in farmland soil. Therefore, this method provides a new insight to monitoring the E-HM content in agricultural soil.
Battlefield Image Situational Awareness Application Based on Deep Learning

With the rapid development of information technology, it has become an important topic to construct a situational awareness system that can independently mine data and information as well as perceive environmental situations by using deep learning. First, this article introduced the structure of convolutional neural networks (CNN) and You Only Look Once (YOLO) model. Then, it analyzed the structure and function of battlefield situational awareness system, and concluded that: in the whole situational awareness system, the discovery, category, and location analysis of situational elements, namely object target, is the foundation and key to realize the function. On this basis, this article establishes a battlefield situational awareness model based on the YOLO model. Finally, five common objects on the battlefield (helicopter gunship, missile, tank, soldier and gun) are classified and located, respectively. The YOLO model based on CNN is used to process the input image, and then the position, category, and corresponding confidence probability of all objects in the image are obtained directly, which realizes end-to-end learning, greatly improves the speed of target detection, and lays a foundation for assessing the battlefield situation.
Meta-Seg: A Generalized Meta-Learning Framework for Multi-Class Few-Shot Semantic Segmentation

Semantic segmentation performs pixel-wise classification for given images, which can be widely used in autonomous driving, robotics, medical diagnostics and etc.The recent advanced approaches have witnessed rapid progress in semantic segmentation.However, these supervised learning based methods rely heavily on large-scale datasets to acquire strong generalizing ability, such that they are coupled with some constraints.Firstly, human annotation of pixel-level segmentation masks is laborious and timeconsuming, which causes relatively expensive training data and make it hard to deal with urgent tasks in dynamic environment.Secondly, the outstanding performance of the above data-hungry methods will decrease with few available training examples.In order to overcome the limitations of the supervised learning semantic segmentation methods, this paper proposes a generalized meta-learning framework, named Meta-Seg.It consists of a meta-learner and a base-learner.Specifically, the meta-learner learns a good initialization and a parameter update strategy from a distribution of few-shot semantic segmentation tasks.The baselearner can be any semantic segmentation models theoretically and can implement fast adaptation (that is updating parameters with few iterations) under the guidance of the meta-learner.In this work, the successful semantic segmentation model FCN8s is integrated into Meta-Seg.Experiments on the famous few-shot semantic segmentation dataset PASCAL5 i prove Meta-Seg is a promising framework for few-shot semantic segmentation.Besides, this method can provide with reference for the relevant researches of meta-learning semantic segmentation.
Small, Highly Accurate Quantum Processor for Intermediate-Depth Quantum Simulations

Analog quantum simulation is widely considered a step on the path to fault tolerant quantum computation. With current noisy hardware, the accuracy of an analog simulator will degrade after just a few time steps, especially when simulating complex systems likely to exhibit quantum chaos. Here we describe a quantum simulator based on the combined electron-nuclear spins of individual Cs atoms, and its use to run high fidelity simulations of three different model Hamiltonians for >100 time steps. While not scalable to exponentially large Hilbert spaces, it provides the accuracy and programmability required to explore the interplay between dynamics, imperfections, and accuracy in quantum simulation.
Automatic Analysis of Facial Expressions Based on Deep Covariance Trajectories

In this article, we propose a new approach for facial expression recognition (FER) using deep covariance descriptors. The solution is based on the idea of encoding local and global deep convolutional neural network (DCNN) features extracted from still images, in compact local and global covariance descriptors. The space geometry of the covariance matrices is that of symmetric positive definite (SPD) matrices. By conducting the classification of static facial expressions using a support vector machine (SVM) with a valid Gaussian kernel on the SPD manifold, we show that deep covariance descriptors are more effective than the standard classification with fully connected layers and softmax. Besides, we propose a completely new and original solution to model the temporal dynamic of facial expressions as deep trajectories on the SPD manifold. As an extension of the classification pipeline of covariance descriptors, we apply SVM with valid positive definite kernels derived from global alignment for deep covariance trajectories classification. By performing extensive experiments on the Oulu-CASIA, CK+, static facial expression in the wild (SFEW), and acted facial expressions in the wild (AFEW) data sets, we show that both the proposed static and dynamic approaches achieve the state-of-the-art performance for FER outperforming many recent approaches.
Active Data Replica Recovery for Quality-Assurance Big Data Analysis in IC-IoT

QoS-aware big data analysis is critical in Information-Centric Internet of Things (IC-IoT) system to support various applications like smart city, smart grid, smart health, intelligent transportation systems, and so on. The employment of non-volatile memory (NVM) in cloud or edge system provides good opportunity to improve quality of data analysis tasks. However, we have to face the data recovery problem led by NVM failure due to the limited write endurance. In this paper, we investigate the data recovery problem for QoS guarantee and system robustness, followed by proposing a rarity-aware data recovery algorithm. The core idea is to establish the rarity indicator to evaluate the replica distribution and service requirement comprehensively. With this idea, we give the lost replicas with distinguishing priority and eliminate the unnecessary replicas. Then, the data replicas are recovered stage by stage to guarantee QoS and provide system robustness. From our extensive experiments and simulations, it is shown that the proposed algorithm has significant performance improvement on QoS and robustness than the traditional direct data recovery method. Besides, the algorithm gives an acceptable data recovery time.
On the Role of Scene Graphs in Image Captioning

Scene graphs represent semantic information in images, which can help image captioning system to produce more descriptive outputs versus using only the image as context. Recent captioning approaches rely on ad-hoc approaches to obtain graphs for images. However, those graphs introduce noise and it is unclear the effect of parser errors on captioning accuracy. In this work, we investigate to what extent scene graphs can help image captioning. Our results show that a state-of-the-art scene graph parser can boost performance almost as much as the ground truth graphs, showing that the bottleneck currently resides more on the captioning models than on the performance of the scene graph parser.
ETNLP: A Visual-Aided Systematic Approach to Select Pre-Trained Embeddings for a Downstream Task

Given many recent advanced embedding models, selecting pre-trained word embedding (a.k.a., word representation) models best fit for a specific downstream task is non-trivial.In this paper, we propose a systematic approach, called ETNLP, for extracting, evaluating, and visualizing multiple sets of pre-trained word embeddings to determine which embeddings should be used in a downstream task.We demonstrate the effectiveness of the proposed approach on our pre-trained word embedding models in Vietnamese to select which models are suitable for a named entity recognition (NER) task.Specifically, we create a large Vietnamese word analogy list to evaluate and select the pre-trained embedding models for the task.We then utilize the selected embeddings for the NER task and achieve the new state-of-the-art results on the task benchmark dataset.We also apply the approach to another downstream task of privacy-guaranteed embedding selection, and show that it helps users quickly select the most suitable embeddings.In addition, we create an open-source system using the proposed systematic approach to facilitate similar studies on other NLP tasks.