Reviewerly/exHarmony huggingface dataset:
Diverse and Informative Dialogue Generation with Context-Specific Commonsense Knowledge Awareness

Generative dialogue systems tend to produce generic responses, which often leads to boring conversations.For alleviating this issue, Recent studies proposed to retrieve and introduce knowledge facts from knowledge graphs.While this paradigm works to a certain extent, it usually retrieves knowledge facts only based on the entity word itself, without considering the specific dialogue context.Thus, the introduction of the context-irrelevant knowledge facts can impact the quality of generations.To this end, this paper proposes a novel commonsense knowledge-aware dialogue generation model, ConKADI.We design a Felicitous Fact mechanism to help the model focus on the knowledge facts that are highly relevant to the context; furthermore, two techniques, Context-Knowledge Fusion and Flexible Mode Fusion are proposed to facilitate the integration of the knowledge in the ConKADI.We collect and build a large-scale Chinese dataset aligned with the commonsense knowledge for dialogue generation.Extensive evaluations over both an open-released English dataset and our Chinese dataset demonstrate that our approach ConKADI outperforms the state-of-the-art approach CCM, in most experiments.
4D Association Graph for Realtime Multi-Person Motion Capture Using Multiple Video Cameras

his paper contributes a novel realtime multi-person motion capture algorithm using multiview video inputs. Due to the heavy occlusions and closely interacting motions in each view, joint optimization on the multiview images and multiple temporal frames is indispensable, which brings up the essential challenge of realtime efficiency. To this end, for the first time, we unify per-view parsing, cross-view matching, and temporal tracking into a single optimization framework, i.e., a 4D association graph that each dimension (image space, viewpoint and time) can be treated equally and simultaneously. To solve the 4D association graph efficiently, we further contribute the idea of 4D limb bundle parsing based on heuristic searching, followed with limb bundle assembling by proposing a bundle Kruskal's algorithm. Our method enables a realtime motion capture system running at 30fps using 5 cameras on a 5-person scene. Benefiting from the unified parsing, matching and tracking constraints, our method is robust to noisy detection due to severe occlusions and close interacting motions, and achieves high-quality online pose reconstruction quality. The proposed method outperforms state-of-the-art methods quantitatively without using high-level appearance information.
Wearable heart rate monitoring intelligent sports bracelet based on Internet of things

Aiming at the shortcomings of traditional health monitoring equipment, such as large size, not easy to carry, this paper proposes a wearable heart rate monitoring intelligent sports bracelet monitoring system based on the Internet of things, which is used to monitor the user's changes in the human heart rate during sports. Through the Internet of things technology, ZigBee wireless sensor, Bluetooth and other communication technologies, data will be transmitted to personal terminal PC or only mobile phone for real-time monitoring, storage and analysis. The Internet of things communication network technology is used to transmit the data to the monitoring terminal platform. After the processing and analysis of the data, the abnormal data will be given an alarm in time to realize the monitoring of the heart rate health status of mobile personnel in the process of movement.
An efficient RRT cache method in dynamic environments for path planning

This paper is concentrated on path planning for robots working in a dynamic environment to satisfy real-time needs. An efficient bias-goal factor RRT (EBG-RRT), which is multiple-query sampling-based replanning algorithm, is proposed with rapid response and high success rate. Specifically, a relay node method is proposed to get a position where the robot and dynamic obstacles will be no-collision and help robots to move without suspended. Based on the relay node method, Connection strategy performs minimal modifications to maintain the interrupted path. In order to overcome the short of Waypoint Cache method, an efficient and optimal Waypoint Cache (EOWC) method is proposed to make use of potential cache information and find an optimal path to repair. The EOWC method is combined with the BG-RRT algorithm according to the iterative characteristics. Finally, the EBG-RRT algorithm is verified on ROS with Aubo-i5 manipulator. Simulation results provide the EBG-RRT algorithm is outperformed both in static and dynamic environments.
Cloud computing simulators: A comprehensive review

Nowadays, cloud computing is an emerging technology due to virtualization and providing low price services on pay-as per-use basis. One of the main challenges in this regard is how to evaluate different models of the cloud resources usage and the ability of cloud systems based on QoS constraints. Experimentation in a real environment is very difficult and expensive. Therefore, many works pay much attention on designing cloud simulation frameworks that only cover a subset of the different components. Consequently, choosing the right tools to use needs a deep comparative analysis of available simulators based on different features. In this paper, we present a comparative analysis of 33 tools for cloud environments. This review will enable the readers to compare the prominent simulators in terms of the supported model, architecture, and high-level features. Subsequently, it provides recommendations regarding the choice of the most suitable tool for researchers, providers, and managers of cloud environment. Eight common simulators are appraised in a practical way based on various scenarios in order to evaluate their performance. Finally, it addresses the open issues and future challenges for further research.
Auto loan fraud detection using dominance-based rough set approach versus machine learning methods

Financial fraud is escalating as financial services and operations grow. Despite preventive actions and security measures deployed to mitigate financial fraud, fraudsters are learning and finding new ways to get around fraud prevention systems, thereby, challenging quantitative techniques and predictive models. Thus, new techniques must be explored and tested so the insights obtained from the analysis may be used to support more accurate fraud prediction and the development of fraud prevention systems which have additional checks to mitigate suspicious events. Auto loan is a significant financial product not yet explored in the literature, unlike the misuse of credit cards, for instance. Given the recent increase in fraudulent transactions concerning auto loan applications, this paper tests a new data set for auto loan applications using a technique not yet explored for financial fraud prediction, namely the Dominance-based Rough Set Balanced Rule Ensemble (DRSA-BRE), and after comparing it with other techniques traditionally used for predicting financial fraud, finds that the proposed approach has several advantages over the traditional ones.
Local Self-Attention over Long Text for Efficient Document Retrieval

Neural networks, particularly Transformer-based architectures, have achieved significant performance improvements on several retrieval benchmarks. When the items being retrieved are documents, the time and memory cost of employing Transformers over a full sequence of document terms can be prohibitive. A popular strategy involves considering only the first n terms of the document. This can, however, result in a biased system that under retrieves longer documents. In this work, we propose a local self-attention which considers a moving window over the document terms and for each term attends only to other terms in the same window. This local attention incurs a fraction of the compute and memory cost of attention over the whole document. The windowed approach also leads to more compact packing of padded documents in minibatches resulting in additional savings. We also employ a learned saturation function and a two-staged pooling strategy to identify relevant regions of the document. The Transformer-Kernel pooling model with these changes can efficiently elicit relevance information from documents with thousands of tokens. We benchmark our proposed modifications on the document ranking task from the TREC 2019 Deep Learning track and observe significant improvements in retrieval quality as well as increased retrieval of longer documents at moderate increase in compute and memory costs.
Synthesis of diagnostic quality cancer pathology images by generative adversarial networks

Abstract Deep learning‐based computer vision methods have recently made remarkable breakthroughs in the analysis and classification of cancer pathology images. However, there has been relatively little investigation of the utility of deep neural networks to synthesize medical images. In this study, we evaluated the efficacy of generative adversarial networks to synthesize high‐resolution pathology images of 10 histological types of cancer, including five cancer types from The Cancer Genome Atlas and the five major histological subtypes of ovarian carcinoma. The quality of these images was assessed using a comprehensive survey of board‐certified pathologists ( n = 9) and pathology trainees ( n = 6). Our results show that the real and synthetic images are classified by histotype with comparable accuracies and the synthetic images are visually indistinguishable from real images. Furthermore, we trained deep convolutional neural networks to diagnose the different cancer types and determined that the synthetic images perform as well as additional real images when used to supplement a small training set. These findings have important applications in proficiency testing of medical practitioners and quality assurance in clinical laboratories. Furthermore, training of computer‐aided diagnostic systems can benefit from synthetic images where labeled datasets are limited (e.g. rare cancers). We have created a publicly available website where clinicians and researchers can attempt questions from the image survey ( http://gan.aimlab.ca/ ). © 2020 Pathological Society of Great Britain and Ireland. Published by John Wiley &amp; Sons, Ltd.
Enhancing Performance of Deep Learning Models with different Data Augmentation Techniques: A Survey

Deep convolutional neural networks have shown impressive results on different computer vision tasks. Nowadays machines are fed by new artificial intelligence techniques which makes them intelligent enough to cognize the visual world better than humans. These computer vision algorithms rely heavily on large data sets. Having a large training data set plays a very crucial role in the performance of deep convolutional neural networks. We can enhance the performance of the model by augmenting the data of the image. Data augmentation is a set of techniques that are used to increase the size and quality of the image with label preserving transformations. This survey paper focuses on different data augmentation techniques based on data warping and oversampling. In addition to data augmentation techniques, this paper gives a brief discussion on different solutions of reducing the overfitting problem.
Continual Prototype Evolution: Learning Online from Non-Stationary Data Streams

Attaining prototypical features to represent class distributions is well established in representation learning. However, learning prototypes online from streaming data proves a challenging endeavor as they rapidly become outdated, caused by an ever-changing parameter space during the learning process. Additionally, continual learning does not assume the data stream to be stationary, typically resulting in catastrophic forgetting of previous knowledge. As a first, we introduce a system addressing both problems, where prototypes evolve continually in a shared latent space, enabling learning and prediction at any point in time. To facilitate learning, a novel objective function synchronizes the latent space with the continually evolving prototypes. In contrast to the major body of work in continual learning, data streams are processed in an online fashion without task information and can be highly imbalanced, for which we propose an efficient memory scheme. As an additional contribution, we propose the learner-evaluator framework that i) generalizes existing paradigms in continual learning, ii) introduces data incremental learning, and iii) models the bridge between continual learning and concept drift. We obtain state-of-the-art performance by a significant margin on eight benchmarks, including three highly imbalanced data streams. Code is publicly available. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>
Unsupervised Quality Estimation for Neural Machine Translation

Quality Estimation (QE) is an important component in making Machine Translation (MT) useful in real-world applications, as it is aimed to inform the user on the quality of the MT output at test time. Existing approaches require large amounts of expert annotated data, computation, and time for training. As an alternative, we devise an unsupervised approach to QE where no training or access to additional resources besides the MT system itself is required. Different from most of the current work that treats the MT system as a black box, we explore useful information that can be extracted from the MT system as a by-product of translation. By utilizing methods for uncertainty quantification, we achieve very good correlation with human judgments of quality, rivaling state-of-the-art supervised QE models. To evaluate our approach we collect the first dataset that enables work on both black-box and glass-box approaches to QE.
Blockchain-Based Supply Chain Management: Understanding the Determinants of Adoption in the Context of Organizations

This study investigates the importance of the determinants affecting the adoption and usage of blockchain-based SCM systems in the context of organizations. Hence, an SLR method was followed to uncover critical determinants in the literature. Then, a research model, including 14 key determinants, was developed based on the TOE Framework. Subsequently, the AHP method was applied to rank the adoption determinants. The findings reveal that environment-related determinants are more critical than technology-related or organization-related determinants.
Invariance, Causality and Robustness

We discuss recent work for causal inference and predictive robustness in a unifying way. The key idea relies on a notion of probabilistic invariance or stability: it opens up new insights for formulating causality as a certain risk minimization problem with a corresponding notion of robustness. The invariance itself can be estimated from general heterogeneous or perturbation data which frequently occur with nowadays data collection. The novel methodology is potentially useful in many applications, offering more robustness and better "causal-oriented" interpretation than machine learning or estimation in standard regression or classification frameworks.
Heterogeneous Graph Attention Network for Unsupervised Multiple-Target Domain Adaptation

Domain adaptation, which transfers the knowledge from label-rich source domain to unlabeled target domains, is a challenging task in machine learning. The prior domain adaptation methods focus on pairwise adaptation assumption with a single source and a single target domain, while little work concerns the scenario of one source domain and multiple target domains. Applying pairwise adaptation methods to this setting may be suboptimal, as they fail to consider the semantic association among multiple target domains. In this work we propose a deep semantic information propagation approach in the novel context of multiple unlabeled target domains and one labeled source domain. Our model aims to learn a unified subspace common for all domains with a heterogeneous graph attention network, where the transductive ability of the graph attention network can conduct semantic propagation of the related samples among multiple domains. In particular, the attention mechanism is applied to optimize the relationships of multiple domain samples for better semantic transfer. Then, the pseudo labels of the target domains predicted by the graph attention network are utilized to learn domain-invariant representations by aligning labeled source centroid and pseudo-labeled target centroid. We test our approach on four challenging public datasets, and it outperforms several popular domain adaptation methods.
Privacy-Preserving Traffic Management: A Blockchain and Zero-Knowledge Proof Inspired Approach

Incorporation of connected vehicle (CV) data into real-time traffic management systems presents a host of new challenges resulting from the current lack of data integrity and data privacy in traffic networks. Over the past few years, blockchain technologies have been inspiring extensive innovations in the transportation field. However, due to the transparency property, sensitive data stored on the blockchain would be accessible to anyone, resulting in a lack of privacy. In this paper, we propose a decentralized and location-aware architecture to address the data integrity along with the privacy-preserving issues in blockchain-based traffic management systems. Our proposed architecture integrates with permissioned and modular blockchain network and non-interactive zero-knowledge range proof (ZKRP) protocol. We develop the prototype system on the Hyperledger Fabric platform and Hyperledger Ursa cryptographic library. The performance results show that our approach is effective and feasible for real-time traffic management while preserving the data privacy requirements.
Contact tracing with digital assistance in Taiwan’s COVID-19 outbreak response

<h2>Abstract</h2><h3>Aim</h3> Comprehensive case investigation and contact tracing are crucial to prevent community spread of COVID-19. We demonstrated a utility of using traditional contact tracing measures supplemented with symptom tracking and contact management system to assist public health workers with high efficiency. <h3>Methods</h3> A centralized contact tracing system was developed to support data linkage, cross-jurisdictional coordination, and follow-up of contacts' health status. We illustrated the process of how digital tools support contact tracing and management of COVID-19 cases and measured the timeliness from case detection to contact monitoring to evaluate system performance. <h3>Results</h3> Among the 8051 close contacts of the 487 confirmed cases (16.5 close contacts/case, 95% CI [13.9–19.1]), the median elapsed time from last exposure to quarantine was three days (IQR 1–5). By implementing the approach of self-reporting using automatic text-messages and web-app, the percentage of health status updates from self-reporting increased from 22.5% to 61.5%. The high proportion of secondary cases detected via contact tracing (88%) might reduce the R0 to under one and minimize the impact of local transmission in the community. <h3>Conclusion</h3> Comprehensive contact tracing and management with complementary technology would still be a pillar of strategies for containing outbreaks during de-escalation or early in the next wave of COVID-19 pandemic.
A Multimodality Fusion Deep Neural Network and Safety Test Strategy for Intelligent Vehicles

Multimodality fusion based on deep neural networks (DNN) is a significant method for intelligent vehicles. The special characteristics of DNN lead to the issue of AI safety and safety test. In this paper, we firstly propose a multimodality fusion framework called Integrated Multimodality Fusion Deep Neural Network (IMF-DNN), which can flexibly accomplish both object detection and end-to-end driving policy for prediction of steering angle and speed. Then, we propose a DNN safety test strategy, which systematically analyzes DNN's robustness and generalization ability in large amounts of diverse driving environment conditions. The test in this paper is based on our IMF-DNN model and the strategy can be widely used for other DNNs. Finally, the experiment analysis is performed on KITTI for object detection and the dateset DBNet for end-to-end tasks. The results show the superior accuracy of the proposed IMF-DNN model and the test strategy's potential ability to improve the robustness and generalization of autonomous vehicle deep learning model. Code is available at https://github.com/ennisnie/IMF-DNN.
Object-Centric Stereo Matching for 3D Object Detection

Safe autonomous driving requires reliable 3D object detection-determining the 6 DoF pose and dimensions of objects of interest. Using stereo cameras to solve this task is a cost-effective alternative to the widely used LiDAR sensor. The current state-of-the-art for stereo 3D object detection takes the existing PSMNet stereo matching network, with no modifications, and converts the estimated disparities into a 3D point cloud, and feeds this point cloud into a LiDAR-based 3D object detector. The issue with existing stereo matching networks is that they are designed for disparity estimation, not 3D object detection; the shape and accuracy of object point clouds are not the focus. Stereo matching networks commonly suffer from inaccurate depth estimates at object boundaries, which we define as streaking, because background and foreground points are jointly estimated. Existing networks also penalize disparity instead of the estimated position of object point clouds in their loss functions. We propose a novel 2D box association and object-centric stereo matching method that only estimates the disparities of the objects of interest to address these two issues. Our method achieves state-of-the-art results on the KITTI 3D and BEV benchmarks.
DevOps and software quality: A systematic mapping

Quality pressure is one of the factors affecting processes for software development in its various stages. DevOps is one of the proposed solutions to such pressure. The primary focus of DevOps is to increase the deployment speed, frequency and quality. DevOps is a mixture of different developments and operations to its multitudinous ramifications in software development industries, DevOps have attracted the interest of many researchers. There are considerable literature surveys on this critical innovation in software development, yet, little attention has been given to DevOps impact on software quality. This research is aimed at analyzing the implications of DevOps features on software quality. DevOps can also be referred to a change in organization cultures aimed at removal of gaps between the development and operations of an organization. The adoption of DevOps in an organization provides many benefits including quality but also brings challenges to an organization. This study presents systematic mapping of the impact of DevOps on software quality. The results of this study provide a better understanding of DevOps on software quality for both professionals and researchers working in this area. The study shows research was mainly focused in automation, culture, continuous delivery, fast feedback of DevOps. There is need of further research in many areas of DevOps (for instance: measurement, development of metrics of different stages to assess its performance, culture, practices toward ensuring quality assurance, and quality factors such as usability, efficiency, software maintainability and portability).