Reviewerly/exHarmony huggingface dataset:
Investigating on Incorporating Pretrained and Learnable Speaker Representations for Multi-Speaker Multi-Style Text-to-Speech

The few-shot multi-speaker multi-style voice cloning task is to synthesize utterances with voice and speaking style similar to a reference speaker given only a few reference samples. In this work, we investigate different speaker representations and proposed to integrate pretrained and learnable speaker representations. Among different types of embeddings, the embedding pretrained by voice conversion achieves the best performance. The FastSpeech 2 model combined with both pretrained and learnable speaker representations shows great generalization ability on few-shot speakers and achieved 2nd place in the one-shot track of the ICASSP 2021 M2VoC challenge.
Concept of multistage discrete fourier transform without performing multiplications

Abstract The issues related to the development and research of digital methods and algorithms for the discrete Fourier transform, which do not require algorithmic multiplication operations, are considered. The aim of the research is to develop and formalize the concept of multistage discrete Fourier transform of complex signals only by algebraic addition of their time samples. The research used the methods of mathematical and hardware-software modeling of the operation of computing algorithms for digital signal processing on programmable logic integrated circuits. The concept of a multistep discrete Fourier transforms without performing multiplications is described. The corresponding formulas for digital signal processing are given. The results of the study showed and confirmed the possibility of this Fourier transform of complex signals only by algebraic addition of their time samples.
Designing Conversational Agents: A Self-Determination Theory Approach

Bringing positive experiences to users is one of the key goals when designing conversational agents (CAs). Yet we still lack an understanding of users' underlying needs to achieve positive experiences and how to support them in design. This research first applies Self-Determination Theory in an interview study to explore how users' needs of competence, autonomy and relatedness could be supported or undermined in CA experiences. Ten guidelines are then derived from the interview findings. The key findings demonstrate that: competence is affected by users' knowledge of the CA capabilities and effectiveness of the conversation; autonomy is influenced by flexibility of the conversation, personalisation of the experiences, and control over user data; regarding relatedness, users still have concerns over integrating social features into CAs. The guidelines recommend how to inform users about the system capabilities, design effective and socially appropriate conversations, and support increased system intelligence, customisation, and data transparency.
A Hybrid Approach for Human Activity Recognition with Support Vector Machine and 1D Convolutional Neural Network

The Human Activity Recognition (HAR) is a pattern recognition task that learns to identify human physical activities recorded by different sensor modalities. The application areas include human behavior analysis, ambient assistive living, surveillance-based security, gesture recognition, and context-aware computing. The HAR remains challenging as the sensor data is noisy in nature and the activity signal varies from person to person. To recognize different types of activity with a single classifier is often error-prone. To mitigate this problem, we introduced an adaptive human activity recognition model. We present a two-stage learning process to recognize human activity recorded using a waist-mounted accelerometer and gyroscope sensor. In the first step, we classify activity into static and moving, using a Random Forest (RF) binary classifier. In the second step, we adopt a Support Vector Machine (SVM) to identify individual static activity and 1D Convolutional Neural Network (CNN)-based deep learning model for individual moving activity recognition. This makes our approach more robust and adaptive. The static activity has less frequency variation in features compared to dynamic activity waveforms for CNN to learn. On the other hand, SVM demonstrated superior performance to recognize static activities but performs poorly on moving, complex, and uncertain activity recognition. Our method is similarly robust to different motion intensity and can also capture the variation of the same activity effectively. In our hybrid model, the CNN captures local dependencies of activity signals as well as preserves the scale invariance. We achieved 97.71% overall accuracy on six activity classes of widely accepted benchmark UCI-HAR dataset.
Trait Based Trustworthiness Assessment in Human-Agent Collaboration Using Multi-Layer Fuzzy Inference Approach

Trust is an essential requirement for effective Human-Agent interaction as artificial agents are becoming part of human society in a social context.To blend into our society and maximize their acceptability and reliability, artificial agents need to adapt to the complexity of their surroundings, like humans.This adaptation should come through knowing whom to trust by evaluating the trustworthiness of its human mate.It is therefore required to build cognitive agents with trust models that may allow them to trust humans the same way a human trusts other humans keeping under consideration all factors influencing the human agent trust mechanism.Several antecedents within the cognitive system itself and the surroundings dynamically influence the trust mechanism.Personality, as a trusted antecedent has been found to have a substantial impact in predicting human interactor's trustworthiness that critically assists trust decision making.Current research, therefore, aims to infuse characteristics of respective humans as the antecedent of the human agent trust process.This is accomplished by incorporating into the trust model the agent's capability to perceive the personality traits of the human interactor.The current work is focused on introducing a trustworthiness assessment model (TAMFIS) based on fuzzy inference to assess human's trustworthiness towards artificial agents by exploring the human's personality traits that predict trustworthiness.The artificial agent could develop its character towards its human collaborators that will help it in effective interactions.The testing of the proposed architecture is carried out using Dempster Shafer Theory of belief and estimation.It is anticipated that the proposed trust model will effectively evaluate the trustworthiness of human collaborators and develop a more reliable human-agent trust relationship.
Understanding Code Reuse in Smart Contracts

Smart contracts are programs that automatically execute on the blockchain system such as Ethereum. Everybody can write and deploy smart contracts on Ethereum, which causes a large collection of similar contracts via code reuse. In practice, code reuse in smart contract may amplify severe threats like security attacks, resource waste, etc. In this paper, we conduct an empirical study of code reuse in smart contracts for understanding the code reuse practice in the smart contract ecosystem. We first collect 146,452 open-source smart contract projects from Ethereum and then perform a detailed analysis. We first study how often the smart contract projects reuse and then we identify the top reused smart contracts and analyze how the developers revise smart contracts during reuse. Our research suggests that the code reuse in smart contract is quite frequent because about 26% contract code blocks are reused and the average time of reuse is 14.6. And the top reused contracts are almost all related to ERC20 token, which reveals that the current smart contract ecosystem is relatively homogenous. At last, we summarize 7 common types of code revision in smart contracts.
CDeC-Net: Composite Deformable Cascade Network for Table Detection in Document Images

Localizing page elements/objects such as tables, figures, equations, etc. is the primary step in extracting information from document images. We propose a novel end-to-end trainable deep network, (cnec-xet) for detecting tables present in the documents. The proposed network consists of a multistage extension of Mask R-CNN with a dual backbone having deformable convolution for detecting tables varying in scale with high detection accuracy at higher IoU threshold. We empirically evaluate CDeC-Net on the publicly available benchmark datasets with extensive experiments. Our solution has three important properties: (i) a single trained model CDeC-Net <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">‡</sup> that performs well across all the popular benchmark datasets; (ii) we report excellent performances across multiple, including higher, thresholds of IoU; (iii) by following the same protocol of the recent papers for each of the benchmarks, we consistently demonstrate the superior quantitative performance. Our code and models are publicly available at https://github.com/mdv3101/CDeCNet for enabling reproducibility of the results.
AI as Social Glue: Uncovering the Roles of Deep Generative AI during Social Music Composition

Recent advances in deep generative neural networks have made it possible for artificial intelligence to actively collaborate with human beings in co-creating novel content (e.g. music, art). While substantial research focuses on (individual) human-AI collaborations, comparatively less research examines how AI can play a role in human-human collaborations during co-creation. In a qualitative lab study, we observed 30 participants (15 pairs) compose a musical phrase in pairs, both with and without AI. Our findings reveal that AI may play important roles in influencing human social dynamics during creativity, including: 1) implicitly seeding a common ground at the start of collaboration, 2) acting as a psychological safety net in creative risk-taking, 3) providing a force for group progress, 4) mitigating interpersonal stalling and friction, and 5) altering users' collaborative and creative roles. This work contributes to the future of generative AI in social creativity by providing implications for how AI could enrich, impede, or alter creative social dynamics in the years to come.
Deep Learning-Based Classification of News Texts Using Doc2Vec Model

The rapid increment in internet usage has also resulted in bulk gerenation of text data . Therefore, investigation of new techniques for automatic classification of textual content is needed as manually managing unstructured text is challenging. The main objective of text classification is to train a model such that it should place an unseen text into correct category. In this study, text classification was performed using the Doc2vec word embedding method on the Turkish Text Classification 3600 (TTC-3600) dataset consisting of Turkish news texts and the BBC-News dataset consisting of English news texts. As the classification method, deep learning-based CNN and traditional machine learning classification methods Gauss Naive Bayes (GNB), Random Forest (RF), Naive Bayes (NB) and Support Vector Machine (SVM) are used. In the proposed model, the highest result was obtained as 94.17% in the Turkish dataset and 96.41% in the English dataset in the classification made with CNN.
Unsupervised Domain Adaptation for Speech Recognition via Uncertainty Driven Self-Training

The performance of automatic speech recognition (ASR) systems typically degrades significantly when the training and test data domains are mismatched. In this paper, we show that self-training (ST) combined with an uncertainty-based pseudo-label filtering approach can be effectively used for domain adaptation. We propose DUST, a dropout-based uncertainty-driven self-training technique which uses agreement between multiple predictions of an ASR system obtained for different dropout settings to measure the model’s uncertainty about its prediction. DUST excludes pseudo-labeled data with high uncertainties from the training, which leads to substantially improved ASR results compared to ST without filtering, and accelerates the training time due to a reduced training data set. Domain adaptation experiments using WSJ as a source domain and TED-LIUM 3 as well as SWITCHBOARD as the target domains show that up to 80% of the performance of a system trained on ground-truth data can be recovered.
A three-way clustering approach for novelty detection

Novelty detection aims to identify novel instances in the test data that differ in some respect from the normal instances in the training data. Novel instances may be defined and interpreted in different ways. We consider a specific interpretation where novel instances are instances from unknown classes which are not seen during the training phase. This is also sometimes referred to as open world classification or open set recognition. A key challenge in this scenario is to design approaches that effectively classify normal instances and reject the classification of novel instances. Three-way decisions may be realized as a useful strategy to deal with this challenge. It provides provision for deferring the decisions of classifying objects whenever the available evidence is not enough. The deferred cases may be realized as novel or unknown since their classification results are not known and not available. Three-way clustering is an important three-way decision model which can be used for the classification of objects by considering classes as clusters in the data. In this paper, we introduce a three-way clustering based algorithm called reduction and elevation based three-way clustering for open world classification or RE3OWC. A three-way cluster consists of a pair of core and support sets. The RE3OWC uses the operations of reduction and elevation to define the core and support of a three-way cluster. The two sets lead to the three regions of inside, partial and outside corresponding to a cluster. The three regions provide the realization of three-way decisions and are used to identify instances from the unknown classes. Experimental results on datasets of 20 Newsgroups and Amazon reviews suggest improvements in commonly and widely used F1 measure by up to 2.3% and 6.5%, respectively, in comparisons to some of the best known available approaches of DOC, cbsSVM, openMax and others, for identifying instances from unknown classes.
Leveraging Artificial Intelligence to Improve Chronic Disease Care: Methods and Application to Pharmacotherapy Decision Support for Type-2 Diabetes Mellitus

Artificial intelligence (AI), including predictive analytics, has great potential to improve the care of common chronic conditions with high morbidity and mortality. However, there are still many challenges to achieving this vision. The goal of this project was to develop and apply methods for enhancing chronic disease care using AI.Using a dataset of 27,904 patients with diabetes, an analytical method was developed and validated for generating a treatment pathway graph which consists of models that predict the likelihood of alternate treatment strategies achieving care goals. An AI-driven clinical decision support system (CDSS) integrated with the electronic health record (EHR) was developed by encapsulating the prediction models in an OpenCDS Web service module and delivering the model outputs through a SMART on FHIR (Substitutable Medical Applications and Reusable Technologies on Fast Healthcare Interoperability Resources) web-based dashboard. This CDSS enables clinicians and patients to review relevant patient parameters, select treatment goals, and review alternate treatment strategies based on prediction results.The proposed analytical method outperformed previous machine-learning algorithms on prediction accuracy. The CDSS was successfully integrated with the Epic EHR at the University of Utah.A predictive analytics-based CDSS was developed and successfully integrated with the EHR through standards-based interoperability frameworks. The approach used could potentially be applied to many other chronic conditions to bring AI-driven CDSS to the point of care.
MAP-Net: SAR and Optical Image Matching via Image-Based Convolutional Network With Attention Mechanism and Spatial Pyramid Aggregated Pooling

The complementarity of synthetic aperture radar (SAR) and optical images allows remote sensing observations to “see” unprecedented discoveries. Image matching plays a fundamental role in the fusion and application of SAR and optical images. However, both the geometric imaging pattern and the physical radiation mechanism of these two sensors are significantly different, so that the images show complex geometric distortion and nonlinear radiation differences. This phenomenon brings great challenges to image matching, which neither the handcrafted descriptors nor the deep learning-based methods have adequately addressed. In this article, a novel image-based matching method for SAR to optical images via an image-based convolutional network with spatial pyramid aggregated pooling (SPAP) and an attention mechanism is proposed, namely MAP-Net. The original image is embedded through the convolutional neural network to generate the feature map. Through the information extraction and abstraction of the original imagery, the embedded features containing the high-level semantic information are more robust to the geometric distortion and radiation variation among the different modal images, which is beneficial to the matching of cross-modal images. The adoption of the SPAP module makes the network more capable of integrating global and local contextual information. The attention block weights the dense features generated from the network to extract the key features that are invariant, distinguishable, repeatable, and suitable for the image matching task. In the experiments, five sets of multisource and multiresolution SAR and optical images with wide and varied ground coverage were used to evaluate the accuracy of MAP-Net, compared to both handcrafted and deep learning-based methods. The experimental results show that the MAP-Net method is superior to the current state-of-the-art image matching methods for SAR to optical images.
Pixel-Wise Wasserstein Autoencoder for Highly Generative Dehazing

We propose a highly generative dehazing method based on pixel-wise Wasserstein autoencoders. In contrast to existing dehazing methods based on generative adversarial networks, our method can produce a variety of dehazed images with different styles. It significantly improves the dehazing accuracy via pixel-wise matching from hazy to dehazed images through 2-dimensional latent tensors of the Wasserstein autoencoder. In addition, we present an advanced feature fusion technique to deliver rich information to the latent space. For style transfer, we introduce a mapping function that transforms existing latent spaces to new ones. Thus, our method can produce highly generative haze-free images with various tones, illuminations, and moods, which induces several interesting applications, including low-light enhancement, daytime dehazing, nighttime dehazing, and underwater image enhancement. Experimental results demonstrate that our method quantitatively outperforms existing state-of-the-art methods for synthetic and real-world datasets, and simultaneously generates highly generative haze-free images, which are qualitatively diverse.
Early Acceptability of a Mobile App for Contact Tracing During the COVID-19 Pandemic in France: National Web-Based Survey

Several countries have implemented mobile apps in an attempt to trace close contacts of patients with COVID-19 and, in turn, reduce the spread of SARS-CoV-2. However, the effectiveness of this approach depends on the adherence of a large segment of the population.The aims of this study were to evaluate the acceptability of a COVID-19 contact tracing mobile app among the French population and to investigate the barriers to its use.The Health Literacy Survey 2019 questioned 1003 people in France during the COVID-19 pandemic on the basis of quota sampling. The survey collected sociodemographic characteristics and health literacy data, as well as information on participants' communication with caregivers, trust in institutions, and COVID-19 knowledge and preventive behaviors. The acceptability of a mobile app for contact tracing was measured by a single question, the responses to which were grouped into three modalities: app-supporting, app-willing, and app-reluctant. Multinomial logistic regression analysis was performed to identify the factors associated with the acceptability of a mobile app during the COVID-19 pandemic.Only 19.2% (193/1003) of all participants were app-supporting, whereas half of them (504/1003, 50.3%) were reluctant. The factors associated with willingness or support toward the contact tracing app included lower financial deprivation (app-willing: adjusted odds ratio [aOR] 0.8, 95% CI 0.69-0.93; app-supporting: aOR 0.7, 95% CI 0.58-0.84) and higher perceived usefulness of using a mobile app to send completed health questionnaires to doctors (app-willing: aOR 2.3, 95% CI 1.70-3.26; app-supporting: aOR 3.1, 95% CI 2.04-4.82). Furthermore, the likelihood of supporting the mobile app increased with age over 60 years (aOR 1.9, 95% CI 1.13-3.22), trust in political representatives (aOR 2.7, 95% CI 1.72-4.23), feeling concerned about the pandemic situation (aOR 2.2, 95% CI 1.47-3.32), and knowledge about the transmission of COVID-19 (aOR 2.0, 95% CI 1.39-2.96).The most socioeconomically precarious people, who are at a higher risk of SARS-CoV-2 infection, are also the most reluctant to using a contact tracing mobile app. Therefore, optimal adherence can only be effective with a targeted discourse on public health benefits to adopt such an app, which should be combined with a reduction in inequalities by acting on structural determinants.
A Location-Sensitive Local Prototype Network For Few-Shot Medical Image Segmentation

Despite the tremendous success of deep neural networks in medical image segmentation, they typically require a large amount of costly, expert-level annotated data. Few-shot segmentation approaches address this issue by learning to transfer knowledge from limited quantities of labeled examples. Incorporating appropriate prior knowledge is critical in designing high-performance few-shot segmentation algorithms. Since strong spatial priors exist in many medical imaging modalities, we propose a prototype-based method-namely, the location-sensitive local prototype network-that leverages spatial priors to perform few-shot medical image segmentation. Our approach divides the difficult problem of segmenting the entire image with global prototypes into easily solvable subproblems of local region segmentation with local prototypes. For organ segmentation experiments on the VISCERAL CT image dataset, our method outperforms the state-of-the-art approaches by 10% in the mean Dice coefficient. Extensive ablation studies demonstrate the substantial benefits of incorporating spatial information and confirm the effectiveness of our approach.
Recursive Contour-Saliency Blending Network for Accurate Salient Object Detection

Contour information plays a vital role in salient object detection. However, excessive false positives remain in predictions from existing contour-based models due to insufficient contour-saliency fusion. In this work, we designed a network for better edge quality in salient object detection. We proposed a contour-saliency blending module to exchange information between contour and saliency. We adopted recursive CNN to increase contour-saliency fusion while keeping the total trainable parameters the same. Furthermore, we designed a stage-wise feature extraction module to help the model pick up the most helpful features from previous intermediate saliency predictions. Besides, we proposed two new loss functions, namely Dual Confinement Loss and Confidence Loss, for our model to generate better boundary predictions. Evaluation results on five common benchmark datasets reveal that our model achieves competitive state-of-the-art performance.