Reviewerly/exHarmony huggingface dataset:
Weakly supervised facial expression recognition via transferred DAL-CNN and active incremental learning

In recent years, facial expression recognition (FER) has becoming a growing topic in computer vision with promising applications on virtual reality and human–robot interaction. Due to the influence of illumination, individual differences, attitude variation, etc., facial expression recognition with robust accuracy in complex environment is still an unsolved problem. Meanwhile, with the wide use of social communication, massive data are uploaded to the Internet; the effective utilization of those data is still a challenge due to noisy label phenomenon in the study of FER. To resolve the above-mentioned problems, firstly, a double active layer-based CNN is established to recognize the facial expression with high accuracy by learning robust and discriminative features from the data, which could enhance the robustness of network. Secondly, an active incremental learning method was utilized to tackle the problem of using Internet data. During the training phase, a two-stage transfer learning method is explored to transfer the relative information from face recognition to FER task to alleviate the inadequate training data in deep convolution network. Besides, in order to make better use of facial expression data from Web site and further improve the FER accuracy, Unconstrained Facial Expression Database from Web site database is built in this paper. Extensive experiments performed on two public facial expression recognition databases FER 2013 and SFEW 2.0 have demonstrated that the proposed scheme outperforms the state-of-the-art methods, which could achieve 67.08% and 51.90%, respectively.
Text-to-Speech Synthesis

The recent progress of text-to-speech synthesis (TTS) technology has allowed computers to read any written text aloud with voice that is artificial but almost indistinguishable from real human speech. Such improvement in the quality of synthetic speech has expanded the application of the TTS technology. This chapter will explain the mechanism of a state-of-the-art TTS system after a brief introduction to some conventional speech synthesis methods with their advantages and weaknesses. The TTS system consists of two main components: text analysis and speech signal generation, both of which will be detailed in individual sections. The text analysis section will describe what kinds of linguistic features need to be extracted from text, and then present one of the latest studies at NICT from the forefront of TTS research. In this study, linguistic features are automatically extracted from plain text by applying an advanced deep learning technique. The later sections will detail a state-of-the-art speech signal generation using deep neural networks, and then introduce a pioneering study that has lately been conducted at NICT, where leading-edge deep neural networks that directly generate speech waveforms are combined with subband decomposition signal processing to enable rapid generation of human-sounding high-quality speech.
A Hybrid Method for Sentiment Analysis of Election Related Tweets

Political sentiment analysis using social media content has recently attracted significant interest. This paper focuses on analyzing tweets in Greek regarding the recent European Elections. A hybrid method that combines Greek lexicons and classification methods is presented. A probabilistic classification model, that predicts the sentiment of tweets, is combined with hashtag-based filtering. Based on the predictions, an analysis is implemented, that shows how the public sentiment was affected by specific events during the pre-election period.
Transfer Learning via Minimizing the Performance Gap Between Domains

We propose a new principle for transfer learning, based on a straightforward intuition: if two domains are similar to each other, the model trained on one domain should also perform well on the other domain, and vice versa. To formalize this intuition, we define the performance gap as a measure of the discrepancy between the source and target domains. We derive generalization bounds for the instance weighting approach to transfer learning, showing that the performance gap can be viewed as an algorithm-dependent regularizer, which controls the model complexity. Our theoretical analysis provides new insight into transfer learning and motivates a set of general, principled rules for designing new instance weighting schemes for transfer learning. These rules lead to gapBoost, a novel and principled boosting approach for transfer learning. Our experimental evaluation on benchmark data sets shows that gapBoost significantly outperforms previous boosting-based transfer learning algorithms.
A Visionary Model on Blockchain-based Accountability for Secure and Collaborative Digital Twin Environments

With the recent advancements in blockchain technology, it has become obvious that this technology is not just for crypto-currencies but instead can be used as a decentralized tool for better accountability. Blockchain has the capacity to transform the industries disruptively as it presents new features like smart contracts, tokenization of content, eliminating counterfeit products, supply chain improvement, digital twins, and end-to-end security. This paper presents a blockchain-based model for distributed and collaborative digital twin environments which is becoming indispensable in new “Any 4.0” era. The proposed model includes a public or private authority in the digital twin ecosystem providing non-repudiation of blockchain transactions, holistic security and privacy preservation. The proposed technique is based on the “X-by-design” and “X-as-a-service” principles which can be discussed as a novel model for better security, accountability and integrity in decentralized mechanisms. In order to elucidate, two case studies are described where the digital twin operations, stakeholders' activities and regarding transactions are stored on a blockchain.
Blind Image Quality Assessment of Natural Distorted Image Based on Generative Adversarial Networks

Most existing image quality assessment (IQA) methods focus on improving the performance of synthetic distorted images.Although these methods perform well on the synthetic distorted IQA database, once they are applied to the natural distorted database, the performance will severely decrease.In this work, we propose a blind image quality assessment based on generative adversarial network (BIQA-GAN) with its advantages of self-generating samples and self-feedback training to improve network performance.Three different BIQA-GAN models are designed according to the target domain of the generator.Comprehensive experiments on popular benchmarks show that our proposed method significantly outperforms the previous state-of-the-art methods for authentically distorted images, which also has good performances on synthetic distorted benchmarks.
Technical Debt's State of Practice on Stack Overflow

Context: Previous work have investigated the industry's state of practice on technical debt (TD) by surveying invited practitioners. However, these studies have included only practitioners with a sound understanding of TD, which might incur biases in their findings. One way to reduce this bias is to consider Q&A platforms, such as Stack Overflow (SO), because of containing practitioners with different roles and levels of TD knowledge. Therefore, the discussions on SO can broaden the perception of the state of practice on TD in the industry. Goal: To investigate how TD concepts have been discussed by SO's practitioners. Method: First, we employed data mining techniques to identify TD-related discussions on SO. Next, based on significance and popularity metrics, we considered 195 discussions to be further investigated. For each discussion, we analyzed its contents to identify types of TD followed by activities, strategies, and tools used in its management. Results: We found that code, infrastructure, and architecture debt are the most discussed types of TD on SO. Similarly, TD identification and payment are the most discussed activities. Regarding tools and strategies for TD management, SonarQube and refactoring have been mostly mentioned by practitioners. Conclusion: Our findings may indicate new research directions, as well as expanding the empirical knowledge on how the industry has handled TD.
Histomorphometric Evaluation of Critical-Sized Bone Defects Using Osteomeasure and Aperio Image Analysis Systems

Most histological evaluations of critical-sized bone defects are limited to the analysis of a few regions of interest at a time. Manual and semiautomated histomorphometric approaches often have intra- and interobserver subjectivity, as well as variability in image analysis methods. Moreover, the production of large image data sets makes histological assessment and histomorphometric analysis labor intensive and time consuming. Herein, we tested and compared two image segmentation methods: thresholding (automated) and region-based (manual) modes, for quantifying complete image sets across entire critical-sized bone defects, using the widely used Osteomeasure system and the freely downloadable Aperio Image Scope software. A comparison of bone histomorphometric data showed strong agreement between the automated segmentation mode of the Osteomeasure software with the manual segmentation mode of Aperio Image Scope analysis (bone formation R2 = 0.9615 and fibrous tissue formation R2 = 0.8734). These results indicate that Aperio is capable of handling large histological images, with excellent speed performance in producing highly consistent histomorphometric evaluations compared with the Osteomeasure image analysis system. The statistical evaluation of these two major bone parameters demonstrated that Aperio Image Scope is as capable as Osteomeasure. This study developed a protocol to improve the quality of results and reduce analysis time, while also promoting the standardization of image analysis protocols for the histomorphometric analysis of critical-sized bone defect samples. Impact Statement Despite bone tissue engineering innovations increasing over the last decade, histomorphometric analysis of large bone defects used to study such approaches continues to pose a challenge for pathological assessment. This is due to the resulting large image data set, and the lack of a gold standard image analysis protocol to quantify histological outcomes. Herein, we present a standardized protocol for the image analysis of critical-sized bone defect samples stained with Goldner's Trichrome using the Osteomeasure and Aperio Image Scope image analysis systems. The results were critically examined to determine their reproducibility and accuracy for analyzing large bone defects.
Neural and rule-based Finnish NLP models—expectations, experiments and experiences

In this article I take a critical look at some recent results in the field of neural language modeling of Finnish in terms of popular shared tasks.One novel point of view I present is comparing the neural methods' results to traditional rule-based systems for the given tasks, since most of the shared tasks have concentrated on the supervised learning concept.The shared task results I re-evaluate, are morphological regeneration by SIGMORPHON 2016, universal dependency parsing by CONLL-2018 and a machine translation application that imitates WMT 2018 for German instead of English.The Uralic language used throughout is Finnish.I use out of the box, best performing neural systems and rule-based systems and evaluate their results. TiivistelmäTässä artikkelissa tarkastelemme joitain hiljattaisia tuloksia niinkutsutuissa shared task -kilpailuissa suomen kielen hemroverkkomallien osalta.Yksi tämän artikkelin kontribuutioista on hermoverkkomallien tuottamien tulosten vertailu perinteisiin sääntöpohjaisiin kielimallituloksiin, sillä shared task -kisailut pääosin keskittyvät täysin tai osittain hallitsemattomien mallien oppimisen konseptiin.
State-only Imitation with Transition Dynamics Mismatch

Imitation Learning (IL) is a popular paradigm for training agents to achieve complicated goals by leveraging expert behavior, rather than dealing with the hardships of designing a correct reward function. With the environment modeled as a Markov Decision Process (MDP), most of the existing IL algorithms are contingent on the availability of expert demonstrations in the same MDP as the one in which a new imitator policy is to be learned. This is uncharacteristic of many real-life scenarios where discrepancies between the expert and the imitator MDPs are common, especially in the transition dynamics function. Furthermore, obtaining expert actions may be costly or infeasible, making the recent trend towards state-only IL (where expert demonstrations constitute only states or observations) ever so promising. Building on recent adversarial imitation approaches that are motivated by the idea of divergence minimization, we present a new state-only IL algorithm in this paper. It divides the overall optimization objective into two sub-problems by introducing an indirection step, and solves the sub-problems iteratively. We show that our algorithm is particularly effective when there is a transition dynamics mismatch between the expert and imitator MDPs, while the baseline IL methods suffer from performance degradation. To analyze this, we construct several interesting MDPs by modifying the configuration parameters for the MuJoCo locomotion tasks from OpenAI Gym.
Adversarially Robust Representations with Smooth Encoders

This paper studies the undesired phenomena of over-sensitivity of representations learned by deep networks to semantically-irrelevant changes in data. We identify a cause for this shortcoming in the classical Variational Auto-encoder (VAE) objective, the evidence lower bound (ELBO). We show that the ELBO fails to control the behaviour of the encoder out of the support of the empirical data distribution and this behaviour of the VAE can lead to extreme errors in the learned representation. This is a key hurdle in the effective use of representations for data-efficient learning and transfer. To address this problem, we propose to augment the data with specifications that enforce insensitivity of the representation with respect to families of transformations. To incorporate these specifications, we propose a regularization method that is based on a selection mechanism that creates a fictive data point by explicitly perturbing an observed true data point. For certain choices of parameters, our formulation naturally leads to the minimization of the entropy regularized Wasserstein distance between representations. We illustrate our approach on standard datasets and experimentally show that significant improvements in the downstream adversarial accuracy can be achieved by learning robust representations completely in an unsupervised manner, without a reference to a particular downstream task and without a costly supervised adversarial training procedure.
Style Data Augmentation for Robust Segmentation of Multi-modality Cardiac MRI

We propose a data augmentation method to improve the segmentation accuracy of the convolutional neural network on multi-modality cardiac magnetic resonance (CMR) dataset. The strategy aims to reduce over-fitting of the network toward any specific intensity or contrast of the training images by introducing diversity in these two aspects. The style data augmentation (SDA) strategy increases the size of the training dataset by using multiple image processing functions including adaptive histogram equalisation, Laplacian transformation, Sobel edge detection, intensity inversion and histogram matching. For the segmentation task, we developed the thresholded connection layer network (TCL-Net), a minimalist rendition of the U-Net architecture, which is designed to reduce convergence and computation time. We integrate the dual U-Net strategy to increase the resolution of the 3D segmentation target. Utilising these approaches on a multi-modality dataset, with SSFP and T2 weighted images as training and LGE as validation, we achieve 90% and 96% validation Dice coefficient for endocardium and epicardium segmentations. This result can be interpreted as a proof of concept for a generalised segmentation network that is robust to the quality or modality of the input images. When testing with our mono-centric LGE image dataset, the SDA method also improves the performance of the epicardium segmentation, with an increase from 87% to 90% for the single network segmentation.
SMiRL: Surprise Minimizing RL in Dynamic Environments

Every living organism struggles against disruptive environmental forces to carve out and maintain an orderly niche. We propose that such a struggle to achieve and preserve order might offer a principle for the emergence of useful behaviors in artificial agents. We formalize this idea into an unsupervised reinforcement learning method called surprise minimizing reinforcement learning (SMiRL). SMiRL alternates between learning a density model to evaluate the surprise of a stimulus, and improving the policy to seek more predictable stimuli. The policy seeks out stable and repeatable situations that counteract the environment's prevailing sources of entropy. This might include avoiding other hostile agents, or finding a stable, balanced pose for a bipedal robot in the face of disturbance forces. We demonstrate that our surprise minimizing agents can successfully play Tetris, Doom, control a humanoid to avoid falls, and navigate to escape enemies in a maze without any task-specific reward supervision. We further show that SMiRL can be used together with standard task rewards to accelerate reward-driven learning.
Embedding a Color Watermark into DC coefficients of DCT from Digital Images

In this paper, a secure watermarking framework based on Discrete Cosine Transform (DCT) for embedding a color watermark into digital images that provides robustness against JPEG compression and some other image processing attacks is presented. In the proposed algorithm, the RGB components of the color image have been separated, and then each component is selected to embed a color watermark. The images R, G and B are divided in 8×8 non-overlapped pixels blocks and each block is transformed to frequency domain using DCT. Next, with DC coefficient of each block a new block of 8×8 DC coefficients are arranged and a second DCT is applied. Later, Quantization Index Modulation (QIM) Dither Modulation (DM) is used as the embedding method to insert the color watermark into the 64 resultant coefficients. Meantime, the color watermark is transformed to YCbCr color space and a codification 4:2:0 is used to reduce the quantity of bits to represent it. The proposed watermarking scheme has demonstrated high resistance against various types of attacks in comparison with other state-of-art algorithms.
How Does Disagreement Benefit Co-teaching?

Learning with noisy labels is one of the hottest problems in weakly-supervised learning. Based on memorization effects of deep neural networks, training on small-loss instances becomes very promising for handling noisy labels. This fosters the state-of-the-art approach that cross-trains two deep neural networks using the small-loss trick. However, with the increase of epochs, two networks converge to a consensus and Co-teaching reduces to the self-training MentorNet. To tackle this issue, we propose a robust learning paradigm called Co-teaching+, which bridges the Update by Disagreement strategy with the original Co-teaching. First, two networks feed forward and predict all data, but keep prediction disagreement data only. Then, among such disagreement data, each network selects its small-loss data, but back propagates the small-loss data from its peer network and updates its own parameters. Empirical results on benchmark datasets demonstrate that Co-teaching+ is much superior to many state-of-the-art methods in the robustness of trained models.
Doctor2Vec: Dynamic Doctor Representation Learning for Clinical Trial Recruitment

Massive electronic health records (EHRs) enable the success of learning accurate patient representations to support various predictive health applications. In contrast, doctor representation was not well studied despite that doctors play pivotal roles in healthcare. How to construct the right doctor representations? How to use doctor representation to solve important health analytic problems? In this work, we study the problem on clinical trial recruitment, which is about identifying the right doctors to help conduct the trials based on the trial description and patient EHR data of those doctors. We propose Doctor2Vec which simultaneously learns 1) doctor representations from EHR data and 2) trial representations from the description and categorical information about the trials. In particular, Doctor2Vec utilizes a dynamic memory network where the doctor's experience with patients are stored in the memory bank and the network will dynamically assign weights based on the trial representation via an attention mechanism. Validated on large real-world trials and EHR data including 2,609 trials, 25K doctors and 430K patients, Doctor2Vec demonstrated improved performance over the best baseline by up to 8.7% in PR-AUC. We also demonstrated that the Doctor2Vec embedding can be transferred to benefit data insufficiency settings including trial recruitment in less populated/newly explored country with 13.7% improvement or for rare diseases with 8.1% improvement in PR-AUC.
Performance comparative study of machine learning algorithms for automobile insurance fraud detection

Nowadays, fraud is a major problem facing the insurance industry that Big Data and Machine Learning are trying to solve. This paper deals with the evaluation of the effectiveness and the verifiability of the best-known machine learning algorithms for fraud prediction. We adopted the supervised method applied to automobile data claims of an anonymous insurance company. We aim to propose an approach that improves the relevance of the results of artificial intelligence. The study has demonstrated that RandomForest works better among all algorithms compared.
Transductive Ensemble Learning for Neural Machine Translation

Ensemble learning, which aggregates multiple diverse models for inference, is a common practice to improve the accuracy of machine learning tasks. However, it has been observed that the conventional ensemble methods only bring marginal improvement for neural machine translation (NMT) when individual models are strong or there are a large number of individual models. In this paper, we study how to effectively aggregate multiple NMT models under the transductive setting where the source sentences of the test set are known. We propose a simple yet effective approach named transductive ensemble learning (TEL), in which we use all individual models to translate the source test set into the target language space and then finetune a strong model on the translated synthetic corpus. We conduct extensive experiments on different settings (with/without monolingual data) and different language pairs (English↔{German, Finnish}). The results show that our approach boosts strong individual models with significant improvement and benefits a lot from more individual models. Specifically, we achieve the state-of-the-art performances on the WMT2016-2018 English↔German translations.