Reviewerly/exHarmony huggingface dataset:
Moral Foundations Twitter Corpus: A Collection of 35k Tweets Annotated for Moral Sentiment

Research has shown that accounting for moral sentiment in natural language can yield insight into a variety of on- and off-line phenomena such as message diffusion, protest dynamics, and social distancing. However, measuring moral sentiment in natural language is challenging, and the difficulty of this task is exacerbated by the limited availability of annotated data. To address this issue, we introduce the Moral Foundations Twitter Corpus, a collection of 35,108 tweets that have been curated from seven distinct domains of discourse and hand annotated by at least three trained annotators for 10 categories of moral sentiment. To facilitate investigations of annotator response dynamics, we also provide psychological and demographic metadata for each annotator. Finally, we report moral sentiment classification baselines for this corpus using a range of popular methodologies.
Systematic Review of Privacy-Preserving Distributed Machine Learning From Federated Databases in Health Care

Big data for health care is one of the potential solutions to deal with the numerous challenges of health care, such as rising cost, aging population, precision medicine, universal health coverage, and the increase of noncommunicable diseases. However, data centralization for big data raises privacy and regulatory concerns.Covered topics include (1) an introduction to privacy of patient data and distributed learning as a potential solution to preserving these data, a description of the legal context for patient data research, and a definition of machine/deep learning concepts; (2) a presentation of the adopted review protocol; (3) a presentation of the search results; and (4) a discussion of the findings, limitations of the review, and future perspectives.Distributed learning from federated databases makes data centralization unnecessary. Distributed algorithms iteratively analyze separate databases, essentially sharing research questions and answers between databases instead of sharing the data. In other words, one can learn from separate and isolated datasets without patient data ever leaving the individual clinical institutes.Distributed learning promises great potential to facilitate big data for medical application, in particular for international consortiums. Our purpose is to review the major implementations of distributed learning in health care.
A Two-Timescale Duplex Neurodynamic Approach to Mixed-Integer Optimization

This article presents a two-timescale duplex neurodynamic approach to mixed-integer optimization, based on a biconvex optimization problem reformulation with additional bilinear equality or inequality constraints. The proposed approach employs two recurrent neural networks operating concurrently at two timescales. In addition, particle swarm optimization is used to update the initial neuronal states iteratively to escape from local minima toward better initial states. In spite of its minimal system complexity, the approach is proven to be almost surely convergent to optimal solutions. Its superior performance is substantiated via solving five benchmark problems.
Hotel selection driven by online textual reviews: Applying a semantic partitioned sentiment dictionary and evidence theory

Browsing online reviews before selecting a satisfactory hotel has become a trend. Multiple criteria decision making models are powerful tools to provide competitive guidance. The first research gap motivating this study is that online textual reviews perform well in describing abundant perceptions and sentiments hidden in texts, while customer ratings used in existing models ignore them. Moreover, the existing sentiment analysis and hotel selection approaches have limited capacity in differentiating sentiment degrees, expressing natural languages, conveying comprehensive hotel descriptions and managing conflicting attitudes of different tourists. To narrow these gaps, a novel hotel selection model driven by online textual reviews on TripAdvisor.com is constructed. A semantic mapping function and the method of building this dictionary are proposed. Moreover, an evidence theory-based fusion method is proposed, which can guarantee the reliability of the results. Finally, the proposed model is tested in a case study and in robustness and comparative analyses.
Location-Relative Attention Mechanisms for Robust Long-Form Speech Synthesis

Despite the ability to produce human-level speech for in-domain text, attention-based end-to-end text-to-speech (TTS) systems suffer from text alignment failures that increase in frequency for out-of-domain text. We show that these failures can be addressed using simple location-relative attention mechanisms that do away with content-based query/key comparisons. We compare two families of attention mechanisms: location-relative GMM-based mechanisms and additive energy-based mechanisms. We suggest simple modifications to GMM-based attention that allow it to align quickly and consistently during training, and introduce a new location-relative attention mechanism to the additive energy-based family, called Dynamic Convolution Attention (DCA). We compare the various mechanisms in terms of alignment speed and consistency during training, naturalness, and ability to generalize to long utterances, and conclude that GMM attention and DCA can generalize to very long utterances, while preserving naturalness for shorter, in-domain utterances.
Adversarial Transfer Learning for Deep Learning Based Automatic Modulation Classification

Automatic modulation classification facilitates many important signal processing applications. Recently, deep learning models have been adopted in modulation recognition, which outperform traditional machine learning techniques based on hand-crafted features. However, automatic modulation classification is still challenging due to the following reasons. Existing deep learning methods are only applicable to the data of the same distribution. In practical scenarios, data distribution is varying with sampling frequency, thus domains with different sampling rates are formed. Besides, it is difficult to construct large-scale well-annotated datasets for all domains of interest. We define the domain with sufficient data as the source domain, while the domain with insufficient data as the target domain. Obviously, the classification model performs weakly in the target domain. To address these challenges, we propose an adversarial transfer learning architecture (ATLA), incorporating adversarial training and knowledge transfer in a unified way. Adversarial training performs an asymmetric mapping between domains and reduces the domain shift. Knowledge transfer is used to mine prior knowledge from the source domain. Experimental results demonstrate that the proposed ATLA substantially boosts the performance of the target model, which outperforms the existing parameter-transfer approach. With half of the training data reduced, the target model achieves competitive recognition accuracy to supervised learning. With one-tenth of training data, the promoted accuracy is up to 17.3% points.
A Bayesian Game Based Vehicle-to-Vehicle Electricity Trading Scheme for Blockchain-Enabled Internet of Vehicles

With ever increasing people's awareness of low carbon and environmental protection, electric vehicles are gradually gaining wide popularity. However, the driving endurance of the electric vehicle is the biggest shortage that hinders the fully acceptance of this new vehicle technology. To deal with this shortage, this paper proposed a vehicle-to-vehicle (V2V) electricity trading scheme based on Bayesian game pricing in blockchain-enabled Internet of vehicles (BIoV). Specifically, the Bayesian game is adopted for pricing in the distributed BIoV with incomplete information sharing. The optimal pricing under the linear strategic equilibrium has been obtained which maximizes the utilities of both sides of electricity transaction. The transaction volume is determined from the formulated convex problem that maximizes the social welfare. Then, the pricing game is implemented by the dedicated smart contract. Blockchain guarantees its trustworthiness, security, and reliability. Finally, the experimental results show that referring to the benchmark of static game with complete information, the proposed Bayesian game with incomplete information can achieve approximate satisfaction of users. The degree of approximation can reach to 98% when the pricing ranges of buyers and sellers are close. Moreover, the proposed scheme has great advantages over the static game with complete information in terms of communication overhead and timeliness in the decentralized IoVs.
BISHARE: Exploring Bidirectional Interactions Between Smartphones and Head-Mounted Augmented Reality

In pursuit of a future where HMD devices can be used in tandem with smartphones and other smart devices, we present BISHARE, a design space of cross-device interactions between smartphones and ARHMDs. Our design space is unique in that it is bidirectional in nature, as it examines how both the HMD can be used to enhance smartphone tasks, and how the smartphone can be used to enhance HMD tasks. We then present an interactive prototype that enables cross-device interactions across the proposed design space. A 12-participant user study demonstrates the promise of the design space and provides insights, observations, and guidance for the future.
Novice-AI Music Co-Creation via AI-Steering Tools for Deep Generative Models

While generative deep neural networks (DNNs) have demonstrated their capacity for creating novel musical compositions, less attention has been paid to the challenges and potential of co-creating with these musical AIs, especially for novices. In a needfinding study with a widely used, interactive musical AI, we found that the AI can overwhelm users with the amount of musical content it generates, and frustrate them with its non-deterministic output. To better match co-creation needs, we developed AI-steering tools, consisting of Voice Lanes that restrict content generation to particular voices; Example-Based Sliders to control the similarity of generated content to an existing example; Semantic Sliders to nudge music generation in high-level directions (happy/sad, conventional/surprising); and Multiple Alternatives of generated content to audition and choose from. In a summative study (N=21), we discovered the tools not only increased users' trust, control, comprehension, and sense of collaboration with the AI, but also contributed to a greater sense of self-efficacy and ownership of the composition relative to the AI.
"Alexa is a Toy": Exploring Older Adults' Reasons for Using, Limiting, and Abandoning Echo

Intelligent voice assistants (IVAs) have the potential to support older adults' independent living. However, despite a growing body of research focusing on IVA use, we know little about why older adults become IVA non-users. This paper examines the reasons older adults use, limit, and abandon IVAs (i.e., Amazon Echo) in their homes. We conducted eight focus groups, with 38 older adults residing in a Life Plan Community. Thirty-six participants owned an Echo for at least a year, and two were considering adoption. Over time, most participants became non-users due to their difficulty finding valuable uses, beliefs associated with ability and IVA use, or challenges with use in shared spaces. However, we also found that participants saw the potential for future IVA support. We contribute a better understanding of the reasons older adults do not engage with IVAs and how IVAs might better support aging and independent living in the future.
Multilabel feature selection using ML-ReliefF and neighborhood mutual information for multilabel neighborhood decision systems

Feature selection as an essential preprocessing step in multilabel classification has been widely researched. Due to the diversity and complexity of multilabel datasets, some feature selection methods are unstable and yield low predictive accuracy. To address these issues, this paper presents a novel multilabel feature selection method using multilabel ReliefF (ML-ReliefF) and neighborhood mutual information in multilabel neighborhood decision systems. First, to solve the problem of the few available randomly selected samples when searching the nearest samples in ReliefF, the coefficient of difference and the average distance among the nearest similar and heterogeneous samples are introduced to evaluate the differences among the samples, and then the average differences among the similar or heterogeneous samples are defined. Using the Jaccard correlation coefficient, a new formula for updating feature weights is presented. Second, the margin of the sample is studied to granulate all samples under each label, and the concept of the neighborhood is given. By combining algebra with information views, some neighborhood entropy-based uncertainty measures for multilabel classification are investigated, and new neighborhood mutual information is proposed. Furthermore, an optimization objective function is constructed to evaluate the candidate features in multilabel neighborhood decision systems, all the properties are discussed, and the relationships of these measures are established. Finally, an improved ML-ReliefF algorithm is designed for preliminarily eliminating unrelated features to decrease the computational complexity for multilabel classification, and a heuristic forward multilabel feature selection algorithm is developed to remove redundant features and improve classification performance. Experimental results conducted on thirteen multilabel datasets to verify the effectiveness of the proposed algorithms in multilabel neighborhood decision systems are compared with representative methods.
Generalized Zero-Shot Learning via Over-Complete Distribution

A well trained and generalized deep neural network (DNN) should be robust to both seen and unseen classes. However, the performance of most of the existing supervised DNN algorithms degrade for classes which are unseen in the training set. To learn a discriminative classifier which yields good performance in Zero-Shot Learning (ZSL) settings, we propose to generate an Over-Complete Distribution (OCD) using Conditional Variational Autoencoder (CVAE) of both seen and unseen classes. In order to enforce the separability between classes and reduce the class scatter, we propose the use of Online Batch Triplet Loss (OBTL) and Center Loss (CL) on the generated OCD. The effectiveness of the framework is evaluated using both Zero-Shot Learning and Generalized Zero-Shot Learning protocols on three publicly available benchmark databases, SUN, CUB and AWA2. The results show that generating over-complete distributions and enforcing the classifier to learn a transform function from overlapping to non-overlapping distributions can improve the performance on both seen and unseen classes.
ViewAL: Active Learning With Viewpoint Entropy for Semantic Segmentation

We propose ViewAL, a novel active learning strategy for semantic segmentation that exploits viewpoint consistency in multi-view datasets. Our core idea is that inconsistencies in model predictions across viewpoints provide a very reliable measure of uncertainty and encourage the model to perform well irrespective of the viewpoint under which objects are observed. To incorporate this uncertainty measure, we introduce a new viewpoint entropy formulation, which is the basis of our active learning strategy. In addition, we propose uncertainty computations on a superpixel level, which exploits inherently localized signal in the segmentation task, directly lowering the annotation costs. This combination of viewpoint entropy and the use of superpixels allows to efficiently select samples that are highly informative for improving the network. We demonstrate that our proposed active learning strategy not only yields the best-performing models for the same amount of required labeled data, but also significantly reduces labeling effort. For instance, our method achieves 95% of maximum achievable network performance using only 7%, 17%, and 24% labeled data on SceneNet-RGBD, ScanNet, and Matterport3D, respectively. On these datasets, the best state-of-the-art method achieves the same performance with 14%, 27% and 33% labeled data. Finally, we demonstrate that labeling using superpixels yields the same quality of ground-truth compared to labeling whole images, but requires 25% less time.
Reliable Weighted Optimal Transport for Unsupervised Domain Adaptation

Recently, extensive researches have been proposed to address the UDA problem, which aims to learn transferrable models for the unlabeled target domain. Among them, the optimal transport is a promising metric to align the representations of the source and target domains. However, most existing works based on optimal transport ignore the intra-domain structure, only achieving coarse pair-wise matching. The target samples distributed near the edge of the clusters, or far from their corresponding class centers are easily to be misclassified by the decision boundary learned from the source domain. In this paper, we present Reliable Weighted Optimal Transport (RWOT) for unsupervised domain adaptation, including novel Shrinking Subspace Reliability (SSR) and weighted optimal transport strategy. Specifically, SSR exploits spatial prototypical information and intra-domain structure to dynamically measure the sample-level domain discrepancy across domains. Besides, the weighted optimal transport strategy based on SSR is exploited to achieve the precise-pair-wise optimal transport procedure, which reduces negative transfer brought by the samples near decision boundaries in the target domain. RWOT also equips with the discriminative centroid clustering exploitation strategy to learn transfer features. A thorough evaluation shows that RWOT outperforms existing state-of-the-art method on standard domain adaptation benchmarks.
TSNet: Three-Stream Self-Attention Network for RGB-D Indoor Semantic Segmentation

This article proposes a three-stream self-attention network (TSNet) for indoor semantic segmentation comprising two asymmetric input streams (asymmetric encoder structure) and a cross-modal distillation stream with a self-attention module. The two asymmetric input streams are ResNet34 for the red-green-blue (RGB) stream and VGGNet16 for the depth stream. Accompanying the RGB and depth streams, a cross-modal distillation stream with a self-attention module extracts new RGB plus depth features in each level in the bottom-up path. In addition, while using bilinear upsampling to recover the spatial resolution of the feature map, we incorporated the feature information of both the RGB flow and the depth flow through the self-attention module. We constructed the NYU Depth V2 dataset to evaluate the TSNet and achieved results comparable to those of current state-of-the-art methods.
Explainability as a non-functional requirement: challenges and recommendations

Abstract Software systems are becoming increasingly complex. Their ubiquitous presence makes users more dependent on their correctness in many aspects of daily life. As a result, there is a growing need to make software systems and their decisions more comprehensible, with more transparency in software-based decision making. Transparency is therefore becoming increasingly important as a non-functional requirement. However, the abstract quality aspect of transparency needs to be better understood and related to mechanisms that can foster it. The integration of explanations into software has often been discussed as a solution to mitigate system opacity. Yet, an important first step is to understand user requirements in terms of explainable software behavior: Are users really interested in software transparency and are explanations considered an appropriate way to achieve it? We conducted a survey with 107 end users to assess their opinion on the current level of transparency in software systems and what they consider to be the main advantages and disadvantages of embedded explanations. We assess the relationship between explanations and transparency and analyze its potential impact on software quality. As explainability has become an important issue, researchers and professionals have been discussing how to deal with it in practice. While there are differences of opinion on the need for built-in explanations, understanding this concept and its impact on software is a key step for requirements engineering. Based on our research results and on the study of existing literature, we offer recommendations for the elicitation and analysis of explainability and discuss strategies for the practice.
RefactoringMiner 2.0

Refactoring detection is crucial for a variety of applications and tasks: (i) empirical studies about code evolution, (ii) tools for library API migration, (iii) code reviews and change comprehension. However, recent research has questioned the accuracy of the state-of-the-art refactoring mining tools, which poses threats to the reliability of the detected refactorings. Moreover, the majority of refactoring mining tools depend on code similarity thresholds. Finding universal threshold values that can work well for all projects, regardless of their architectural style, application domain, and development practices is extremely challenging. Therefore, in a previous work [N. Tsantalis, M. Mansouri, L. M. Eshkevari, D. Mazinanian, and D. Dig, Accurate and efficient refactoring detection in commit history, in 40th International Conference on Software Engineering, 2018, pp. 483–494], we introduced the first refactoring mining tool that does not require any code similarity thresholds to operate. In this work, we extend our tool to support low-level refactorings that take place within the body of methods. To evaluate our tool, we created one of the most accurate, complete, and representative refactoring oracles to date, including 7,226 true instances for 40 different refactoring types detected by one (minimum) up to six (maximum) different tools, and validated by one up to four refactoring experts. Our evaluation showed that our approach achieves the highest average precision (99.6 percent) and recall (94 percent) among all competitive tools, and on median is 2.6 times faster than the second faster competitive tool.
Machine Learning for Clinical Outcome Prediction

Clinical decision-making in healthcare is already being influenced by predictions or recommendations made by data-driven machines. Numerous machine learning applications have appeared in the latest clinical literature, especially for outcome prediction models, with outcomes ranging from mortality and cardiac arrest to acute kidney injury and arrhythmia. In this review article, we summarize the state-of-the-art in related works covering data processing, inference, and model evaluation, in the context of outcome prediction models developed using data extracted from electronic health records. We also discuss limitations of prominent modeling assumptions and highlight opportunities for future research.