Reviewerly/exHarmony huggingface dataset:
FontRNN: Generating Large‐scale Chinese Fonts via Recurrent Neural Network

Abstract Despite the recent impressive development of deep neural networks, using deep learning based methods to generate large‐scale Chinese fonts is still a rather challenging task due to the huge number of intricate Chinese glyphs, e.g., the official standard Chinese charset GB18030‐2000 consists of 27,533 Chinese characters. Until now, most existing models for this task adopt Convolutional Neural Networks (CNNs) to generate bitmap images of Chinese characters due to CNN based models' remarkable success in various applications. However, CNN based models focus more on image‐level features while usually ignore stroke order information when writing characters. Instead, we treat Chinese characters as sequences of points (i.e., writing trajectories) and propose to handle this task via an effective Recurrent Neural Network (RNN) model with monotonic attention mechanism, which can learn from as few as hundreds of training samples and then synthesize glyphs for remaining thousands of characters in the same style. Experimental results show that our proposed FontRNN can be used for synthesizing large‐scale Chinese fonts as well as generating realistic Chinese handwritings efficiently.
A Novel Siamese-Based Approach for Scene Change Detection With Applications to Obstructed Routes in Hazardous Environments

The demand for automatic scene change detection has massively increased in the last decades due to its importance regarding safety and security issues. Although deep learning techniques have provided significant enhancements in the field, such methods must learn which object belongs to the foreground or background beforehand. In this article, we propose an approach that employs siamese U-Nets to address the task of change detection, such that the model learns to perform semantic segmentation using background reference frames only. Therefore, any object that comes up into the scene defines a change. The experimental results show the robustness of the proposed model over the well-known public dataset CDNet2014. Additionally, we also consider a private dataset called “PetrobrasROUTES,” which comprises obstruction or abandoned objects in escape routes in hazardous environments. Moreover, the experiments show that the proposed approach is more robust to noise and illumination changes.
Visualization and Explainable Machine Learning for Efficient Manufacturing and System Operations

Abstract To enable Industry 4.0 successfully, there is a need to build a resilient automation system that can quickly recover after having been attacked or robustly sustain continued operations while being threatened, enable an automated monitoring evolution via various sensor channels in real time, and use advanced machine learning and data analytics to formulate strategies to mitigate and eliminate faults, threats, and malicious attacks. It is envisioned that if we can develop an intelligent model that (a) represents a meaningful, realistic environment and complex entity containing manufacturing Internet of Things interdependent and independent properties that are stepping-stones of the cyber kill chain or precursors of the onset of cyberattacks; (b) can learn and predict potential errors and formulate offense/defense strategies and healing solutions; (c) can enable cognitive ability and human-in-the-loop analytics in real time; and (d) can facilitate system behavior changes to disrupt the attack cascade, then the hosting system can learn how to neutralize threats and attacks and self-repair infected or damaged links autonomously. In this article, our preliminary work presents a visual analytics framework and technique for situational awareness, including autonomously monitoring, diagnosing, and prognosticating the state of cyber-physical systems. Our approach, presented in this article, relies on visual characterizations of multivariate time series and real-time predictive analytics to highlight potential faults, threats, and malicious attacks. To validate the usefulness of our approach, we demonstrate the developed technique using various aviation datasets obtained from the Prognostics Center of Excellence at the National Aeronautics and Space Administration Ames.
Towards Interpretable Object Detection by Unfolding Latent Structures

This paper first proposes a method of formulating model interpretability in visual understanding tasks based on the idea of unfolding latent structures. It then presents a case study in object detection using popular two-stage region-based convolutional network (i.e., R-CNN) detection systems. The proposed method focuses on weakly-supervised extractive rationale generation, that is learning to unfold latent discriminative part configurations of object instances automatically and simultaneously in detection without using any supervision for part configurations. It utilizes a top-down hierarchical and compositional grammar model embedded in a directed acyclic AND-OR Graph (AOG) to explore and unfold the space of latent part configurations of regions of interest (RoIs). It presents an AOGParsing operator that seamlessly integrates with the RoIPooling/RoIAlign operator widely used in R-CNN and is trained end-to-end. In object detection, a bounding box is interpreted by the best parse tree derived from the AOG on-the-fly, which is treated as the qualitatively extractive rationale generated for interpreting detection. In experiments, Faster R-CNN is used to test the proposed method on the PASCAL VOC 2007 and the COCO 2017 object detection datasets. The experimental results show that the proposed method can compute promising latent structures without hurting the performance. The code and pretrained models are available at https://github.com/iVMCL/iRCNN.
HSE-Voting: A secure high-efficiency electronic voting scheme based on homomorphic signcryption

In most electronic voting schemes, a voter signs his/her ballot using a digital signature algorithm before submission to ensure that the ballot is generated by himself/herself, which results that the workload of the verification process in ballot tallying increases with the number of voters and candidates. In this paper we present a new electronic voting scheme, HSE-voting, to lighten the workload of tallying. The cryptographic approach of this scheme is homomorphic signcryption, whose signature homomorphism enables that the number of signature verifications depends on the number of candidates only, and the encryption homomorphism keeps ballots from being decrypted during the tallying process. Moreover, for voters, the encryption and signature of ballots can be completed in one step using the homomorphic signcryption. Finally, this paper gives proofs for the privacy of voters and the security and verifiability of voting, and provides a comparative analysis with the latest scheme.
Flexible QoS-aware services composition for service computing environments

The rapid development of service computing environments has led to an increase in the number of functionally equivalent services that differ according to their QoS (Quality of Service) attributes values. Accordingly, a challenging issue in the context of services composition is how to select the most appropriate candidate services for each user's requirements such that the obtained composite service satisfies the user's non-functional requirements in terms of global QoS constraints. In this paper, a Flexible QoS-aware Services Composition (FQSC) algorithm is proposed to increase the composition feasibility and reduce the composition time, while the composition optimality is maintained as high as possible. The proposed algorithm deals with the limitations of Pareto dominance-based and QoS decomposition-based services composition approaches. Indeed, the number of compositions increases exponentially with the number of services and QoS attributes in the case of Pareto dominance-based approaches, whereas the QoS decomposition-based approaches can lead to a decrease in the composition feasibility when the user's global QoS constraints are highly restrictive. The simulation results show that the proposed approach is effective compared to other services composition approaches in terms of composition time, optimality and search space.
Assessment of the invariance and discriminant power of morphological features under geometric transformations for breast tumor classification

Computer-aided diagnosis (CAD) systems are intended to assist specialists in the interpretation of images aiming to support clinical conduct. In breast tumor classification, CAD systems involve a feature extraction stage, in which morphological features are used to describe the tumor shape. Such features are expected to satisfy at least two conditions: (1) discriminant to distinguish between benign and malignant tumors, and (2) invariant to geometric transformations. Herein, 39 morphological features were evaluated in terms of invariance and discriminant power for breast tumor classification. Morphological features were divided into region-based features, for describing the irregularity of the tumor shape, and boundary-based features, for measuring the anfractuosity of the tumor margin. Also, two datasets were considered in the experiments: 2054 breast ultrasound images and 892 mammographies. From both datasets, synthetic data augmentation was performed to obtain distinct combinations of rotation and scaling of breast tumors, from which morphological features were calculated. The linear discriminant analysis was used to classify breast tumors in benign and malignant classes. The area under the ROC curve (AUC) quantified the discriminant power of every morphological feature, whereas the relative difference (RD) between AUC values measured the invariance to geometric transformations. For indicating adequate performance, AUC and RD should tend toward unity and zero, respectively. For both datasets, the convexity was the most discriminant feature that reached AUC > 0.81 with RD<1×10−2, while the most invariant feature was the roundness that attained RD<1×10−3 with AUC < 0.72. Additionally, for each dataset, the most discriminant and invariant features were combined for performing tumor classification. For mammography, it was achieved accuracy (ACC) of 0.76, sensitivity (SEN) of 0.76, and specificity (SPE) of 0.84, whereas for breast ultrasound the results were ACC=0.88, SEN=0.81, and SPE=0.91. In general, region-based features are more discriminant and invariant than boundary-based features. Moreover, it was observed that an invariant feature is not necessarily a discriminant feature; hence, a balance between invariance and discriminant power should be attained for breast tumor classification.
Knowledge Transferability Between the Speech Data of Persons With Dysarthria Speaking Different Languages for Dysarthric Speech Recognition

In this paper, we present an end-to-end speech recognition system for Japanese persons with articulation disorders resulting from athetoid cerebral palsy. Because their utterance is often unstable or unclear, speech recognition systems struggle to recognize their speech. Recent deep learning-based approaches have exhibited promising performance. However, these approaches require a large amount of training data, and it is difficult to collect sufficient data from such dysarthric people. This paper proposes a transfer learning method that transfers two types of knowledge corresponding to the different datasets: the language-dependent (phonetic and linguistic) characteristic of unimpaired speech and the language-independent characteristic of dysarthric speech. The former is obtained from Japanese non-dysarthric speech data, and the latter is obtained from non-Japanese dysarthric speech data. In the proposed method, we pre-train a model using Japanese non-dysarthric speech and non-Japanese dysarthric speech, and thereafter, we fine-tune the model using the target Japanese dysarthric speech. To handle the speech data of the two different languages in one model, we employ language-specific decoder modules. Experimental results indicate that our proposed approach can significantly improve speech recognition performance compared with other approaches that do not use additional speech data.
Fuzzy Membership Functional Analysis for Nonparametric Deep Models of Image Features

The application of fuzzy theory to deep learning is limited,1) under the realm of deep neural networks, 2) to the parametric form of modeling, and 3) relying on gradient-descent-based numerical algorithms for optimization because of lack of analytical solutions. This article fills this gap by providing an analytical nonparametric deep modeling solution based on the mathematical analysis of membership functions assigned to model variables. The nonparametric approach is based on the concept of representing the unknown mappings (between input and output variables) through a fuzzy set with Student-t type membership function, such that the dimension of membership function increases with an increasing data size. This concept of function representation is referred to as Student-t fuzzy-mapping in this article. The most significant feature of this article is to analytically derive the mathematical expressions for membership functions (which quantify uncertainties regarding the values of variables) using variational optimization such that the degree-of-belongingness of given data to the considered datamodel is maximized. This article focuses on the modeling of image features, where a layer of the deep-model first projects the feature vector onto a lower dimensional subspace, and then construct the output feature vector through Student-t fuzzy-mappings. Numerous image classification experiments are provided to support the proposed approach.
Connecting domain-specific features to source code: towards the automatization of dashboard generation

Dashboards are useful tools for generating knowledge and support decision-making processes, but the extended use of technologies and the increasingly available data asks for user-friendly tools that allow any user profile to exploit their data. Building tailored dashboards for any potential user profile would involve several resources and long development times, taking into account that dashboards can be framed in very different contexts that should be studied during the design processes to provide practical tools. This situation leads to the necessity of searching for methodologies that could accelerate these processes. The software product line paradigm is one recurrent method that can decrease the time-to-market of products by reusing generic core assets that can be tuned or configured to meet specific requirements. However, although this paradigm can solve issues regarding development times, the configuration of the dashboard is still a complex challenge; users’ goals, datasets, and context must be thoroughly studied to obtain a dashboard that fulfills the users’ necessities and that fosters insight delivery. This paper outlines the benefits and a potential approach to automatically configuring information dashboards by leveraging domain commonalities and code templates. The main goal is to test the functionality of a workflow that can connect external algorithms, such as artificial intelligence algorithms, to infer dashboard features and feed a generator based on the software product line paradigm.
ResumeGAN

Nowadays, it is popular to utilize online recruitment services for talent recruitment and job recommendation. Given the vast amounts of online talent profiles and job-posts, it is labor-intensive and exhausted for recruiters to manually select only a few potential candidates for further consideration, and also nontrivial for talents to find the most matched job positions. Recently, some deep learning-based approaches are developed to automatically matching the talent resumes and job requirements, and have achieved encouraging performance. In this paper, we propose a novel framework that targets the same task, but integrate different types of information in a more sophisticated way and introduce adversarial learning to learn more expressive representation. In addition, we build a dataset for model evaluation and the effectiveness of our framework is demonstrated by extensive experiments.
Novel calibration method for camera array in spherical arrangement

A camera array placed in a spherical arrangement facilitates a new paradigm for future interactive visual applications, and the calibration of the cameras is a crucial task for such implementation. It is difficult to apply previous calibration methods to this special camera system because opposite features can commonly be found among the opposing cameras used in this system. In this case, a part-by-part calibration may fail in terms of error accumulation. In this paper, we propose an opposite feature based camera calibration method for a camera array in a spherical arrangement. An adaptive scheme is designed on a cube with opposite colour patterns and optimisations in handling the above problem of opposite features being invisible to cameras at opposite positions, based upon which, with our method, a common visible feature point is not required in advance. By applying a practical camera array in a spherical arrangement, and using 3D modelling, the superiority of the proposed method was verified through computer simulations. The results demonstrate that the proposed calibration method can contribute to the imaging system and future visual applications.
Control of Air Free-Cooled Data Centers in Tropics via Deep Reinforcement Learning

Air free-cooled data centers (DCs) have not existed in the tropical zone due to the unique challenges of year-round high ambient temperature and relative humidity (RH). The increasing availability of servers that can tolerate higher temperatures and RH due to the regulatory bodies' prompts to raise DC temperature setpoints sheds light upon the feasibility of air free-cooled DCs in tropics. This paper studies the problem of controlling the temperature and RH of the air supplied to the servers in a free-cooled tropical DC below certain thresholds to maintain servers' computing performance and reliability. To achieve the goal, a portion of the hot air generated by the servers is recirculated and mixed with the fresh outside air to adjust the RH of the supply air. To address the complex psychrometric dynamics, we apply deep reinforcement learning to learn the control policy that aims at minimizing the energy used for moving air and on-demand cooling. Extensive evaluation based on real data traces collected from an air free-cooled testbed and comparisons with hysteresis-based and model-predictive control approaches show the superior performance of our solution.
A Bayesian Optimization Framework for Neural Network Compression

Neural network compression is an important step for deploying neural networks where speed is of high importance, or on devices with limited memory. It is necessary to tune compression parameters in order to achieve the desired trade-off between size and performance. This is often done by optimizing the loss on a validation set of data, which should be large enough to approximate the true risk and therefore yield sufficient generalization ability. However, using a full validation set can be computationally expensive. In this work, we develop a general Bayesian optimization framework for optimizing functions that are computed based on U-statistics. We propagate Gaussian uncertainties from the statistics through the Bayesian optimization framework yielding a method that gives a probabilistic approximation certificate of the result. We then apply this to parameter selection in neural network compression. Compression objectives that can be written as U-statistics are typically based on empirical risk and knowledge distillation for deep discriminative models. We demonstrate our method on VGG and ResNet models, and the resulting system can find optimal compression parameters for relatively high-dimensional parametrizations in a matter of minutes on a standard desktop machine, orders of magnitude faster than competing methods.
Random forest for breast cancer prediction

One of cancer that commonly causes most of the death is breast cancer. According to WHO data published in 2017, breast cancer deaths in Indonesia reached 21,287 or 1.27 % of total deaths. Delay in knowing of the condition of breast cancer in women with breast cancer, results in increased mortality, poor prognosis, and decreased survival rates, which are also associated with lower awareness of breast cancer, and also recommended non-adherence to screening. In this paper, we propose a random forest for breast cancer prediction. Random forest is one of many classification techniques, and it is an algorithm for big data classification. Random forest classification is applied to cancer microarray data to achieve a more accurate and reliable classification performance. The accuracy in this paper is 100 %.
Dynamic auto-weighted multi-view co-clustering

To exploit the complementary information of multi-view data, many weighted multi-view clustering methods have been proposed and have demonstrated impressive performance. However, most of these methods learn the view weights by introducing additional parameters, which can not be easily obtained in practice. Moreover, they all simply apply the learned weights on the original feature representation of each view, which may deteriorate the clustering performance in the case of high-dimensional data with redundancy and noise. In this paper, we extend information bottleneck co-clustering into a multi-view framework and propose a novel dynamic auto-weighted multi-view co-clustering algorithm to learn a group of weights for views with no need for extra weight parameters. By defining the new concept of the discrimination-compression rate, we quantify the importance of each view by evaluating the discriminativeness of the compact features (i.e., feature-wise clusters) of the views. Unlike existing weighted methods that impose weights on the original feature representations of multiple views, we apply the learned weights on the discriminative ones, which can reduce the negative impact of noisy features in high-dimensional data. To solve the optimization problem, a new two-step sequential method is designed. Experimental results on several datasets show the advantages of the proposed algorithm. To our knowledge, this is the first work incorporating weighting scheme into multi-view co-clustering framework.