Reviewerly/exHarmony huggingface dataset:
Dual-Path Deep Fusion Network for Face Image Hallucination

Along with the performance improvement of deep-learning-based face hallucination methods, various face priors (facial shape, facial landmark heatmaps, or parsing maps) have been used to describe holistic and partial facial features, making the cost of generating super-resolved face images expensive and laborious. To deal with this problem, we present a simple yet effective dual-path deep fusion network (DPDFN) for face image super-resolution (SR) without requiring additional face prior, which learns the global facial shape and local facial components through two individual branches. The proposed DPDFN is composed of three components: a global memory subnetwork (GMN), a local reinforcement subnetwork (LRN), and a fusion and reconstruction module (FRM). In particular, GMN characterize the holistic facial shape by employing recurrent dense residual learning to excavate wide-range context across spatial series. Meanwhile, LRN is committed to learning local facial components, which focuses on the patch-wise mapping relations between low-resolution (LR) and high-resolution (HR) space on local regions rather than the entire image. Furthermore, by aggregating the global and local facial information from the preceding dual-path subnetworks, FRM can generate the corresponding high-quality face image. Experimental results of face hallucination on public face data sets and face recognition on real-world data sets (VGGface and SCFace) show the superiority both on visual effect and objective indicators over the previous state-of-the-art methods.
Solving the Sampling Problem of the Sycamore Quantum Circuits

We study the problem of generating independent samples from the output distribution of Google's Sycamore quantum circuits with a target fidelity, which is believed to be beyond the reach of classical supercomputers and has been used to demonstrate quantum supremacy. We propose a method to classically solve this problem by contracting the corresponding tensor network just once, and is massively more efficient than existing methods in generating a large number of uncorrelated samples with a target fidelity. For the Sycamore quantum supremacy circuit with 53 qubits and 20 cycles, we have generated 1×10^{6} uncorrelated bitstrings s which are sampled from a distribution P[over ^](s)=|ψ[over ^](s)|^{2}, where the approximate state ψ[over ^] has fidelity F≈0.0037. The whole computation has cost about 15 h on a computational cluster with 512 GPUs. The obtained 1×10^{6} samples, the contraction code and contraction order are made public. If our algorithm could be implemented with high efficiency on a modern supercomputer with ExaFLOPS performance, we estimate that ideally, the simulation would cost a few dozens of seconds, which is faster than Google's quantum hardware.
When Algorithmic Predictions Use Human-Generated Data: A Bias-Aware Classification Algorithm for Breast Cancer Diagnosis

When algorithms use data generated by human beings, they inherit the errors stemming from human biases, which likely diminishes their performance. We examine the design and value of a bias-aware linear classification algorithm that accounts for bias in input data, using breast cancer diagnosis as our specific setting. In this context, a referring physician makes a follow-up recommendation to a patient based on two inputs: the patient’s clinical-risk information and the radiologist’s mammogram assessment. Critically, the radiologist’s assessment could be biased by the clinical-risk information, which in turn can negatively affect the referring physician’s performance. Thus, a bias-aware algorithm has the potential to be of significant value if integrated into a clinical decision support system used by the referring physician. We develop and show that a bias-aware algorithm can eliminate the adverse impact of bias if the error in the mammogram assessment due to radiologist’s bias has no variance. On the other hand, in the presence of error variance, the adverse impact of bias can be mitigated, but not eliminated, by the bias-aware algorithm. The bias-aware algorithm assigns less (more) weight to the clinical-risk information (radiologist’s mammogram assessment) when the mean error increases (decreases), but the reverse happens when the error variance increases. Using point estimates obtained from mammography practice and the medical literature, we show that the bias-aware algorithm can significantly improve the expected patient life years or the accuracy of decisions based on mammography. The online appendix is available at https://doi.org/10.1287/isre.2018.0789 .
Failure prediction using machine learning in a virtualised HPC system and application

Failure is an increasingly important issue in high performance computing and cloud systems. As large-scale systems continue to grow in scale and complexity, mitigating the impact of failure and providing accurate predictions with sufficient lead time remains a challenging research problem. Traditional existing fault-tolerance strategies such as regular check-pointing and replication are not adequate because of the emerging complexities of high performance computing systems. This necessitates the importance of having an effective as well as proactive failure management approach in place aimed at minimizing the effect of failure within the system. With the advent of machine learning techniques, the ability to learn from past information to predict future pattern of behaviours makes it possible to predict potential system failure more accurately. Thus, in this paper, we explore the predictive abilities of machine learning by applying a number of algorithms to improve the accuracy of failure prediction. We have developed a failure prediction model using time series and machine learning, and performed comparison based tests on the prediction accuracy. The primary algorithms we considered are the support vector machine (SVM), random forest (RF), k-nearest neighbors (KNN), classification and regression trees (CART) and linear discriminant analysis (LDA). Experimental results indicates that the average prediction accuracy of our model using SVM when predicting failure is 90% accurate and effective compared to other algorithms. This finding implies that our method can effectively predict all possible future system and application failures within the system.
Structure-Preserved Unsupervised Domain Adaptation

Domain adaptation has been a primal approach to addressing the issues by lack of labels in many data mining tasks. Although considerable efforts have been devoted to domain adaptation with promising results, most existing work learns a classifier on a source domain and then predicts the labels for target data, where only the instances near the boundary determine the hyperplane and the whole structure information is ignored. Moreover, little work has been done regarding to multi-source domain adaptation. To that end, we develop a novel unsupervised domain adaptation framework, which ensures the whole structure of source domains is preserved to guide the target structure learning in a semi-supervised clustering fashion. To our knowledge, this is the first time when the domain adaptation problem is re-formulated as a semi-supervised clustering problem with target labels as missing values. Furthermore, by introducing an augmented matrix, a non-trivial solution is designed, which can be exactly mapped into a K-means-like optimization problem with modified distance function and update rule for centroids in an efficient way. Extensive experiments on several widely-used databases show the substantial improvements of our proposed approach over the state-of-the-art methods.
Fast spectral clustering learning with hierarchical bipartite graph for large-scale data

Spectral clustering (SC) is drawing more and more attention due to its effectiveness in unsupervised learning. However, all of these methods still have limitations. First, the method is not suitable for large-scale problems due to its high computational complexity. Second, the neighborhood weighted graph is constructed by the Gaussian kernel, meaning that more work is required to tune the heat-kernel parameter. In order to overcome these issues, we propose a novel spectral clustering based on hierarchical bipartite graph (SCHBG) approach by exploring multiple-layer anchors with a pyramid-style structure. First, the proposed algorithm constructs a hierarchical bipartite graph, and then performs spectral analysis on the graph. As a result, the computational complexity can be largely reduced. Furthermore, we adopt a parameter-free yet effective neighbor assignment strategy to construct the similarity matrix, which avoids the need to tune the heat-kernel parameter. Finally, the algorithm is able to deal with the out-of-sample problem for large-scale data and its computational complexity is significantly reduced. Experiments demonstrate the efficiency and effectiveness of the proposed SCHBG algorithm. Results show that the SCHBG approach can achieve good clustering accuracy (76%) on an 8-million datasets. Furthermore, owing to the use of the bipartite graph, the algorithm can reduce the time cost for out-of-sample situations with almost the same clustering accuracy as for large sizes of data.
Robust Object Tracking Using Manifold Regularized Convolutional Neural Networks

In visual tracking, usually only a small number of samples are labeled, and most existing deep learning based trackers ignore abundant unlabeled samples that could provide additional information for deep trackers to boost their tracking performance. An intuitive way to explain unlabeled data is to incorporate manifold regularization into the common classification loss functions, but the high computational cost may prohibit those deep trackers from practical applications. To overcome this issue, we propose a two-stage approach to a deep tracker that takes into account both labeled and unlabeled samples. The annotation of unlabeled samples is propagated from its labeled neighbors first by exploring the manifold space that these samples are assumed to lie in. Then, we refine it by training a deep convolutional neural network using both labeled and unlabeled data in a supervised manner. Online visual tracking is further carried out under the framework of particle filters with the presented manifold regularized deep model being updated every few frames. Experimental results on different tracking datasets demonstrate that our tracker outperforms most existing tracking approaches. The source code and results are available at: https://github.com/shenjianbing/MRCNNTracking.
Exploiting GPU and cluster parallelism in single scan frequent itemset mining

This paper considers discovering frequent itemsets in transactional databases and addresses the time complexity problem by using high performance computing (HPC). Three HPC versions of the Single Scan (SS) algorithm are proposed. The first one (GSS) implements SS on a GPU (Graphics Processing Unit) architecture using an efficient mapping between thread blocks and the input data. The second approach (CSS) implements SS on a cluster architecture by scheduling independent jobs to workers in a cluster. The third, (CGSS) accelerates the frequent itemset mining process by using multiple cluster nodes equipped with GPUs. Moreover, three partitioning strategies are proposed to reduce GPU thread divergence and cluster load imbalance. Results show that CGSS outperforms SS, GSS, and CSS in terms of speedup. Specifically, CGSS provides up to a 350 times speedup for low minimum support values on large datasets. GCSS demonstrably outperforms the state-of-the-art HPC-based algorithms on big databases.
IoT-NUMS: Evaluating NUMS Elliptic Curve Cryptography for IoT Platforms

In 2015, NIST held a workshop calling for new candidates for the next generation of elliptic curves to replace the almost two-decade old NIST curves. Nothing Upon My Sleeves (NUMS) curves are among the potential candidates presented in the workshop. Here, we present the first implementation of the NUMS256, NUMS379, and NUMS384 curves on two types of embedded devices. The implementations, which exhibit regular, constant-time execution to protect against timing and simple side-channel attacks, set new speed records and advance the state-of-the-art of curve-based (without endomorphism) scalar multiplication on 8-bit AVR and 32-bit ARM11 microcontrollers. For example, our NUMS256 implementation computes a scalar multiplication in ~1.4 million cycles on a low-power 32-bit ARM11 microcontroller using mixed C and assembly language. These results demonstrate the potential of deploying IoT-NUMS on constrained and low-power applications such as protocols for the Internet of Things.
Multiple Coordinated Views at Large Displays for Multiple Users: Empirical Findings on User Behavior, Movements, and Distances

Interactive wall-sized displays benefit data visualization. Due to their sheer display size, they make it possible to show large amounts of data in multiple coordinated views (MCV) and facilitate collaborative data analysis. In this work, we propose a set of important design considerations and contribute a fundamental input vocabulary and interaction mapping for MCV functionality. We also developed a fully functional application with more than 45 coordinated views visualizing a real-world, multivariate data set of crime activities, which we used in a comprehensive qualitative user study investigating how pairs of users behave. Most importantly, we found that flexible movement is essential and-depending on user goals-is connected to collaboration, perception, and interaction. Therefore, we argue that for future systems interaction from the distance is required and needs good support. We show that our consistent design for both direct touch at the large display and distant interaction using mobile phones enables the seamless exploration of large-scale MCV at wall-sized displays. Our MCV application builds on design aspects such as simplicity, flexibility, and visual consistency and, therefore, supports realistic workflows. We believe that in the future, many visual data analysis scenarios will benefit from wall-sized displays presenting numerous coordinated visualizations, for which our findings provide a valuable foundation.
Visual Analytics for Topic Model Optimization based on User-Steerable Speculative Execution

To effectively assess the potential consequences of human interventions in model-driven analytics systems, we establish the concept of speculative execution as a visual analytics paradigm for creating user-steerable preview mechanisms. This paper presents an explainable, mixed-initiative topic modeling framework that integrates speculative execution into the algorithmic decision-making process. Our approach visualizes the model-space of our novel incremental hierarchical topic modeling algorithm, unveiling its inner-workings. We support the active incorporation of the user's domain knowledge in every step through explicit model manipulation interactions. In addition, users can initialize the model with expected topic seeds, the backbone priors. For a more targeted optimization, the modeling process automatically triggers a speculative execution of various optimization strategies, and requests feedback whenever the measured model quality deteriorates. Users compare the proposed optimizations to the current model state and preview their effect on the next model iterations, before applying one of them. This supervised human-in-the-Ioop process targets maximum improvement for minimum feedback and has proven to be effective in three independent studies that confirm topic model quality improvements.
A novel combinatorial merge-split approach for automatic clustering using imperialist competitive algorithm

Cluster analysis has a wide application in many areas, including pattern recognition, information retrieval, and image processing. In most real-world clustering problems, the number of clusters must be predetermined. Automatic clustering is a promising solution for this challenge which automatically determines the number and structure of clusters in data. In recent years, the evolutionary algorithm due to their search mechanisms has been popular in solving automatic clustering problems. Imperialist Competitive Algorithm (ICA) is a successful evolutionary algorithm. In this paper, for the first time, Imperialist Competitive Algorithm (ICA) is used for solving automatic clustering problems, called “the automatic clustering using ICA (AC-ICA)”. In the proposed algorithm, in order to increase the exploration ability, the movement of colonies toward the imperialist was changed at the assimilation step. A new method has been provided for changing the number of clusters by combining random and homogeneity based merge-split approach. Furthermore, an efficient method based on density has been proposed for reinitializing empty cluster centers. To use AC-ICA in automatic clustering, the initialization and imperialist competition steps were changed. Based on changes in these two steps, a framework was provided for changing different types of ICA to solve automatic clustering problems. Then, the basic ICA and its three recently developed types, were changed by this framework and their performances in automatic clustering were compared to AC-ICA. The examinations were done on six synthetic and ten real word data sets. The comparison of the proposed algorithm's results with basic ICA, its three recently developed types and several state-of-art automatic clustering methods, shows AC-ICA's superiority in terms of the speed of convergence to the optimal solution and quality of the obtained solution. We also applied our algorithm to a real world application (i.e., face recognition) and the achieved results were acceptable.
Statistical Model-Based Detector via Texture Weight Map: Application in Re-Sampling Authentication

The problem of authenticating a re-sampled image has been investigated over many years. Currently, however, little research proposes a statistical model-based test, resulting in that statistical performance of the resampling detector could not be completely analyzed. To fill the gap, we utilize a parametric model to expose the traces of resampling forgery, which is described with the distribution of residual noise. Afterward, we propose a statistical model describing the residual noise from a resampled image. Then, the detection problem is cast into the framework of hypothesis testing theory. By considering the image content with designing a texture weight map, two types of statistical detectors are established. In an ideal context in which all distribution parameters are perfectly known, the likelihood ratio test (LRT) is presented and its performance is theoretically established. An upper bound of the detection power can be successfully obtained from the statistical performance of an LRT. For practical use, when the distribution parameters are not known, a generalized LRT with three different maps based on estimation of parameters is established. Numerical results on simulated data and real natural images highlight the relevance of our proposed approach.
Automatic Depression Analysis Using Dynamic Facial Appearance Descriptor and Dirichlet Process Fisher Encoding

Depression causes mood disorders with noticeable problems in day-to-day activities. Current methods of assessing depression depend almost entirely on clinical interviews or questionnaires. They lack systematic and efficient ways of incorporating behavioral observations that are strong indicators of a psychological disorder. To help clinicians effectively and efficiently diagnose depression severity, automated systems, using objective and quantifiable data for depression assessment, are being developed. This paper presents a framework toward estimating a clinical depression-specific score, namely the Beck Depression Inventory-II (BDI-II) score, based on the analysis of facial expressions features. To extract facial dynamic features, we propose a novel dynamic feature descriptor denoted as median robust local binary patterns from three orthogonal planes (MRLBP-TOP), which can capture both the microstructure and macrostructure of facial appearance and dynamics. To aggregate the MRLBP-TOP over an image sequence, we propose a variant to the Fisher vector (FV) encoding scheme, denoted as the Dirichlet process FV (DPFV). DPFV adopts Dirichlet process Gaussian mixture models (DPGMM) to automatically learn the number of GMM mixtures and model parameters. Experimental results on the AVEC2013 and AVEC2014 depression databases have demonstrated the effectiveness of the proposed method.
Multi-label Arabic text categorization: A benchmark and baseline comparison of multi-label learning algorithms

Multi-label text categorization refers to the problem of assigning each document to a subset of categories by means of multi-label learning algorithms. Unlike English and most other languages, the unavailability of Arabic benchmark datasets prevents evaluating multi-label learning algorithms for Arabic text categorization. As a result, only a few recent studies have dealt with multi-label Arabic text categorization on non-benchmark and inaccessible datasets. Therefore, this work aims to promote multi-label Arabic text categorization through (a) introducing “RTAnews”, a new benchmark dataset of multi-label Arabic news articles for text categorization and other supervised learning tasks. The benchmark is publicly available in several formats compatible with the existing multi-label learning tools, such as MEKA and Mulan. (b) Conducting an extensive comparison of most of the well-known multi-label learning algorithms for Arabic text categorization in order to have baseline results and show the effectiveness of these algorithms for Arabic text categorization on RTAnews. The evaluation involves four multi-label transformation-based algorithms: Binary Relevance, Classifier Chains, Calibrated Ranking by Pairwise Comparison and Label Powerset, with three base learners (Support Vector Machine, k-Nearest-Neighbors and Random Forest); and four adaptation-based algorithms (Multi-label kNN, Instance-Based Learning by Logistic Regression Multi-label, Binary Relevance kNN and RFBoost). The reported baseline results show that both RFBoost and Label Powerset with Support Vector Machine as base learner outperformed other compared algorithms. Results also demonstrated that adaptation-based algorithms are faster than transformation-based algorithms.
Optimization of DCP dehazing algorithm through sky segmentation

The Dark Channel Prior dehazing algorithm degrades scenes of the sky when attempting to dehaze them, ruining their quality by trying to find a solid scene that isn&#x0027;t present. Due to this, a simple algorithm to identify sky regions is proposed and applied in order to improve its output. The main procedure described in this paper is to replace the original image&#x0027;s sky region into the DCP-processed image. The result is a dehazed image through the DCP algorithm that has a natural looking sky region.
Analysis of the Application of Feedback Filtering and Seq2Seq Model in English Grammar

Natural language processing (NLP) technology is widely used in grammatical error correction, but its error correction logic is complex and fault-tolerant, which leads to low accuracy. With the progress of deep learning and big data analysis technology, a new method is proposed in the technical means of English grammar error correction. This paper proposes a deep learning model-based feedback grammar error correction method, which can effectively improve the accuracy and tolerance of grammar error correction. Firstly, the Seq2Seq model with attention mechanism is proposed, and then the feedback filtering model is integrated, so that the existing errors or inefficient grammars can be corrected again, thus improving the efficiency of the model. Through a large number of text detection, the model proposed in this paper has high execution efficiency and application ability and can widely meet the needs of English translation and grammar correction.