Reviewerly/exHarmony huggingface dataset:
SuperDepth: Self-Supervised, Super-Resolved Monocular Depth Estimation

Recent techniques in self-supervised monocular depth estimation are approaching the performance of supervised methods, but operate in low resolution only. We show that high resolution is key towards high-fidelity self-supervised monocular depth prediction. Inspired by recent deep learning methods for Single-Image Super-Resolution, we propose a subpixel convolutional layer extension for depth super-resolution that accurately synthesizes high-resolution disparities from their corresponding low-resolution convolutional features. In addition, we introduce a differentiable flip-augmentation layer that accurately fuses predictions from the image and its horizontally flipped version, reducing the effect of left and right shadow regions generated in the disparity map due to occlusions. Both contributions provide significant performance gains over the state-of-the-art in self-supervised depth and pose estimation on the public KITTI benchmark. A video of our approach can be found at https://youtu.be/jKNgBeBMx0I.
Syntax-Aware Aspect Level Sentiment Classification with Graph Attention Networks

Aspect level sentiment classication aims to identify the sentiment expressed towards an aspect given a context sentence.Previous neural network based methods largely ignore the syntax structure in one sentence.In this paper, we propose a novel target-dependent graph attention network (TD-GAT) for aspect level sentiment classification, which explicitly utilizes the dependency relationship among words.Using the dependency graph, it propagates sentiment features directly from the syntactic context of an aspect target.In our experiments, we show our method outperforms multiple baselines with GloVe embeddings.We also demonstrate that using BERT representations further substantially boosts the performance.
A Comparative Analysis of Blockchain Architecture and its Applications: Problems and Recommendations

In the past few years, the implementation of blockchain technology for various applications has been widely discussed in the research community and the industry. There are sufficient number of articles that discuss the possibility of applying blockchain technology in various areas, such as, healthcare, IoT, and business. However, in this article, we present a comparative analysis of core blockchain architecture, its fundamental concepts, and its applications in three major areas: the Internet-of-Things (IoT), healthcare, business and vehicular industry. For each area, we discuss in detail, challenges and solutions that have been proposed from the research community and industry. This research studies also presented the complete ecosystem of blockchain of all the papers we reviewed and summarized. Moreover, analysis is performed of various blockchain platforms, their consensus models, and applications. Finally, we discuss key aspects that are required for the widespread future adoption of blockchain technology in these major areas.
On Safeguarding Privacy and Security in the Framework of Federated Learning

Motivated by the advancing computational capacity of wireless end-user equipment (UE), as well as the increasing concerns about sharing private data, a new machine learning (ML) paradigm has emerged, namely federated learning (FL). Specifically, FL allows a decoupling of data provision at UEs and ML model aggregation at a central unit. By training model locally, FL is capable of avoiding direct data leakage from the UEs, thereby preserving privacy and security to some extent. However, even if raw data are not disclosed from UEs, an individual's private information can still be extracted by some recently discovered attacks against the FL architecture. In this work, we analyze the privacy and security issues in FL, and discuss several challenges to preserving privacy and security when designing FL systems. In addition, we provide extensive simulation results to showcase the discussed issues and possible solutions.
Zero-Shot Entity Linking by Reading Entity Descriptions

We present the zero-shot entity linking task, where mentions must be linked to unseen entities without in-domain labeled data.The goal is to enable robust transfer to highly specialized domains, and so no metadata or alias tables are assumed.In this setting, entities are only identified by text descriptions, and models must rely strictly on language understanding to resolve the new entities.First, we show that strong reading comprehension models pre-trained on large unlabeled data can be used to generalize to unseen entities.Second, we propose a simple and effective adaptive pre-training strategy, which we term domainadaptive pre-training (DAP), to address the domain shift problem associated with linking unseen entities in a new domain.We present experiments on a new dataset that we construct for this task and show that DAP improves over strong pre-training baselines, including BERT.
Training of quantum circuits on a hybrid quantum computer

We train generative modeling circuits on a quantum hybrid computer showing an optimization strategy and a resource trade-off.
Explainable artificial intelligence models using real-world electronic health record data: a systematic scoping review

Abstract Objective To conduct a systematic scoping review of explainable artificial intelligence (XAI) models that use real-world electronic health record data, categorize these techniques according to different biomedical applications, identify gaps of current studies, and suggest future research directions. Materials and Methods We searched MEDLINE, IEEE Xplore, and the Association for Computing Machinery (ACM) Digital Library to identify relevant papers published between January 1, 2009 and May 1, 2019. We summarized these studies based on the year of publication, prediction tasks, machine learning algorithm, dataset(s) used to build the models, the scope, category, and evaluation of the XAI methods. We further assessed the reproducibility of the studies in terms of the availability of data and code and discussed open issues and challenges. Results Forty-two articles were included in this review. We reported the research trend and most-studied diseases. We grouped XAI methods into 5 categories: knowledge distillation and rule extraction (N = 13), intrinsically interpretable models (N = 9), data dimensionality reduction (N = 8), attention mechanism (N = 7), and feature interaction and importance (N = 5). Discussion XAI evaluation is an open issue that requires a deeper focus in the case of medical applications. We also discuss the importance of reproducibility of research work in this field, as well as the challenges and opportunities of XAI from 2 medical professionals’ point of view. Conclusion Based on our review, we found that XAI evaluation in medicine has not been adequately and formally practiced. Reproducibility remains a critical concern. Ample opportunities exist to advance XAI research in medicine.
Boosting methods for multi-class imbalanced data classification: an experimental review

Abstract Since canonical machine learning algorithms assume that the dataset has equal number of samples in each class, binary classification became a very challenging task to discriminate the minority class samples efficiently in imbalanced datasets. For this reason, researchers have been paid attention and have proposed many methods to deal with this problem, which can be broadly categorized into data level and algorithm level. Besides, multi-class imbalanced learning is much harder than binary one and is still an open problem. Boosting algorithms are a class of ensemble learning methods in machine learning that improves the performance of separate base learners by combining them into a composite whole. This paper’s aim is to review the most significant published boosting techniques on multi-class imbalanced datasets. A thorough empirical comparison is conducted to analyze the performance of binary and multi-class boosting algorithms on various multi-class imbalanced datasets. In addition, based on the obtained results for performance evaluation metrics and a recently proposed criteria for comparing metrics, the selected metrics are compared to determine a suitable performance metric for multi-class imbalanced datasets. The experimental studies show that the CatBoost and LogitBoost algorithms are superior to other boosting algorithms on multi-class imbalanced conventional and big datasets, respectively. Furthermore, the MMCC is a better evaluation metric than the MAUC and G-mean in multi-class imbalanced data domains.
Cross-Domain Correspondence Learning for Exemplar-Based Image Translation

We present a general framework for exemplar-based image translation, which synthesizes a photo-realistic image from the input in a distinct domain (e.g., semantic segmentation mask, or edge map, or pose keypoints), given an exemplar image. The output has the style (e.g., color, texture) in consistency with the semantically corresponding objects in the exemplar. We propose to jointly learn the cross-domain correspondence and the image translation, where both tasks facilitate each other and thus can be learned with weak supervision. The images from distinct domains are first aligned to an intermediate domain where dense correspondence is established. Then, the network synthesizes images based on the appearance of semantically corresponding patches in the exemplar. We demonstrate the effectiveness of our approach in several image translation tasks. Our method is superior to state-of-the-art methods in terms of image quality significantly, with the image style faithful to the exemplar with semantic consistency. Moreover, we show the utility of our method for several applications.
A Survey on Transfer Learning for Multiagent Reinforcement Learning Systems

&#x0D; &#x0D; &#x0D; Multiagent Reinforcement Learning (RL) solves complex tasks that require coordination with other agents through autonomous exploration of the environment. However, learning a complex task from scratch is impractical due to the huge sample complexity of RL algorithms. For this reason, reusing knowledge that can come from previous experience or other agents is indispensable to scale up multiagent RL algorithms. This survey provides a unifying view of the literature on knowledge reuse in multiagent RL. We define a taxonomy of solutions for the general knowledge reuse problem, providing a comprehensive discussion of recent progress on knowledge reuse in Multiagent Systems (MAS) and of techniques for knowledge reuse across agents (that may be actuating in a shared environment or not). We aim at encouraging the community to work towards reusing all the knowledge sources available in a MAS. For that, we provide an in-depth discussion of current lines of research and open questions.&#x0D; &#x0D; &#x0D;
Imaging and fusing time series for wearable sensor-based human activity recognition

To facilitate data-driven and informed decision making, a novel deep neural network architecture for human activity recognition based on multiple sensor data is proposed in this work. Specifically, the proposed architecture encodes the time series of sensor data as images (i.e., encoding one time series into a two-channel image), and leverages these transformed images to retain the necessary features for human activity recognition. In other words, based on imaging time series, wearable sensor-based human activity recognition can be realized by using computer vision techniques for image recognition. In particular, to enable heterogeneous sensor data to be trained cooperatively, a fusion residual network is adopted by fusing two networks and training heterogeneous data with pixel-wise correspondence. Moreover, different layers of deep residual networks are used to deal with dataset size differences. The proposed architecture is then extensively evaluated on two human activity recognition datasets (i.e., HHAR dataset and MHEALTH dataset), which comprise various heterogeneous mobile device sensor combinations (i.e., acceleration, angular velocity, and magnetic field orientation). The findings demonstrate that our proposed approach outperforms other competing approaches, in terms of accuracy rate and F1-value.
Learning Depth with Convolutional Spatial Propagation Network

In this paper, we propose the convolutional spatial propagation network (CSPN) and demonstrate its effectiveness for various depth estimation tasks. CSPN is a simple and efficient linear propagation model, where the propagation is performed with a manner of recurrent convolutional operations, in which the affinity among neighboring pixels is learned through a deep convolutional neural network (CNN). Compare to the previous state-of-the-art (SOTA) linear propagation model, i.e., spatial propagation networks (SPN), CSPN is 2 to 5× faster in practice. We concatenate CSPN and its variants to SOTA depth estimation networks, which significantly improve the depth accuracy. Specifically, we apply CSPN to two depth estimation problems: depth completion and stereo matching, in which we design modules which adapts the original 2D CSPN to embed sparse depth samples during the propagation, operate with 3D convolution and be synergistic with spatial pyramid pooling. In our experiments, we show that all these modules contribute to the final performance. For the task of depth completion, our method reduce the depth error over 30 percent in the NYU v2 and KITTI datasets. For the task of stereo matching, our method currently ranks 1st on both the KITTI Stereo 2012 and 2015 benchmarks.
Hello, It’s GPT-2 - How Can I Help You? Towards the Use of Pretrained Language Models for Task-Oriented Dialogue Systems

Data scarcity is a long-standing and crucial challenge that hinders quick development of task-oriented dialogue systems across multiple domains: task-oriented dialogue models are expected to learn grammar, syntax, dialogue reasoning, decision making, and language generation from absurdly small amounts of taskspecific data.In this paper, we demonstrate that recent progress in language modeling pretraining and transfer learning shows promise to overcome this problem.We propose a taskoriented dialogue model that operates solely on text input: it effectively bypasses explicit policy and language generation modules.Building on top of the TransferTransfo framework (Wolf et al., 2019) and generative model pre-training (Radford et al., 2019), we validate the approach on complex multi-domain task-oriented dialogues from the MultiWOZ dataset.Our automatic and human evaluations show that the proposed model is on par with a strong task-specific neural baseline.In the long run, our approach holds promise to mitigate the data scarcity problem, and to support the construction of more engaging and more eloquent task-oriented conversational agents.
Energy and Policy Considerations for Modern Deep Learning Research

The field of artificial intelligence has experienced a dramatic methodological shift towards large neural networks trained on plentiful data. This shift has been fueled by recent advances in hardware and techniques enabling remarkable levels of computation, resulting in impressive advances in AI across many applications. However, the massive computation required to obtain these exciting results is costly both financially, due to the price of specialized hardware and electricity or cloud compute time, and to the environment, as a result of non-renewable energy used to fuel modern tensor processing hardware. In a paper published this year at ACL, we brought this issue to the attention of NLP researchers by quantifying the approximate financial and environmental costs of training and tuning neural network models for NLP (Strubell, Ganesh, and McCallum 2019). In this extended abstract, we briefly summarize our findings in NLP, incorporating updated estimates and broader information from recent related publications, and provide actionable recommendations to reduce costs and improve equity in the machine learning and artificial intelligence community.
The Effect of Blockchain Technology on Supply Chain Sustainability Performances

Improving supply chain sustainability is an essential part of achieving the UN’s sustainable goals. Digitalization, such as blockchain technology, shows the potential to reshape supply chain management. Using distributed ledger technology, the blockchain platform provides a digital system and database to record the transactions along the supply chain. This decentralized database of transactions brings transparency, reliability, traceability, and efficiency to the supply chain management. This paper focuses on such novel blockchain-based supply chain management and its sustainability performances in the areas of environmental protection, social equity, and governance efficiency. Using a systematic literature review and two case studies, we evaluate whether the three sustainability indicators can be improved indirectly along supply chains based on blockchain technology. Our study shows that blockchain technology has the potential to improve supply chain sustainability performance, and we expect blockchain technology to rise in popularity in supply chain management.
YOLOv4-5D: An Effective and Efficient Object Detector for Autonomous Driving

The use of object detection algorithms has become extremely important in autonomous vehicles. Object detection at high accuracy and a fast inference speed is essential for safe autonomous driving. Therefore, the balance between the effectiveness and efficiency of the object detector must be considered. This article proposes a one-stage object detection framework for improving the detection accuracy while supporting a true real-time operation based on the YOLOv4. The backbone network in the proposed framework is the CSPDarknet53_dcn(P). The last output layer in the CSPDarknet53 is replaced with deformable convolution to improve the detection accuracy. In order to perform feature fusion, a new feature fusion module PAN++ is designed and five scales detection layers are used to improve the detection accuracy of small objects. In addition, this article proposes an optimized network pruning algorithm to solve the problem that the real-time performance of the algorithm cannot be satisfied due to the limited computing resources of the vehicle-mounted computing platform. The method of sparse scaling factor is used to improve the existing channel pruning algorithm. Compared to the YOLOv4, the YOLOV4-5D improves the mean average precision by 4.23% on the BDD data sets and 1.68% on the KITTI data sets. Finally, by pruning the model, the inference speed of YOLOV4-5D is increased 31.3% and the memory is only 98.1 MB when the detection accuracy is almost unchanged. Nevertheless, the proposed algorithm is capable of real-time detection at faster than 66 frames/s (fps) and shows higher accuracy than the previous approaches with a similar fps.
Blockchain Applications for Healthcare Data Management

Objectives This pilot study aimed to provide an overview of the potential for blockchain technology in the healthcare system. The review covers technological topics from storing medical records in blockchains through patient personal data ownership and mobile apps for patient outreach. Methods We performed a preliminary survey to fill the gap that exists between purely technically focused manuscripts about blockchains, on the one hand, and the literature that is mostly concerned with marketing discussions about their expected economic impact on the other hand. Results The findings show that new digital platforms based on blockchains are emerging to enabling fast, simple, and seamless interaction between data providers, including patients themselves. Conclusions We provide a conceptual understanding of the technical foundations of the potential for blockchain technology in healthcare, which is necessary to understand specific blockchain applications, evaluate business cases such as blockchain startups, or follow the discussion about its expected economic impacts. Keywords: Health Information Interoperability, Information Storage and Retrieval, Genomics, Artificial Intelligence, mHealth
Fast density peak clustering for large scale data based on kNN

Density Peak (DPeak) clustering algorithm is not applicable for large scale data, due to two quantities, i.e, ρ and δ, are both obtained by brute force algorithm with complexity O(n2). Thus, a simple but fast DPeak, namely FastDPeak,1 is proposed, which runs in about O(nlog(n)) expected time in the intrinsic dimensionality. It replaces density with kNN-density, which is computed by fast kNN algorithm such as cover tree, yielding huge improvement for density computations. Based on kNN-density, local density peaks and non-local density peaks are identified, and a fast algorithm, which uses two different strategies to compute δ for them, is also proposed with complexity O(n). Experimental results show that FastDPeak is effective and outperforms other variants of DPeak.
Where to Go Next: Modeling Long- and Short-Term User Preferences for Point-of-Interest Recommendation

Point-of-Interest (POI) recommendation has been a trending research topic as it generates personalized suggestions on facilities for users from a large number of candidate venues. Since users' check-in records can be viewed as a long sequence, methods based on recurrent neural networks (RNNs) have recently shown promising applicability for this task. However, existing RNN-based methods either neglect users' long-term preferences or overlook the geographical relations among recently visited POIs when modeling users' short-term preferences, thus making the recommendation results unreliable. To address the above limitations, we propose a novel method named Long- and Short-Term Preference Modeling (LSTPM) for next-POI recommendation. In particular, the proposed model consists of a nonlocal network for long-term preference modeling and a geo-dilated RNN for short-term preference learning. Extensive experiments on two real-world datasets demonstrate that our model yields significant improvements over the state-of-the-art methods.