Reviewerly/exHarmony huggingface dataset:
Health-BlockEdge: Blockchain-Edge Framework for Reliable Low-Latency Digital Healthcare Applications

The rapid evolution of technology allows the healthcare sector to adopt intelligent, context-aware, secure, and ubiquitous healthcare services. Together with the global trend of an aging population, it has become highly important to propose value-creating, yet cost-efficient digital solutions for healthcare systems. These solutions should provide effective means of healthcare services in both the hospital and home care scenarios. In this paper, we focused on the latter case, where the goal was to provide easy-to-use, reliable, and secure remote monitoring and aid for elderly persons at their home. We proposed a framework to integrate the capabilities of edge computing and blockchain technology to address some of the key requirements of smart remote healthcare systems, such as long operating times, low cost, resilience to network problems, security, and trust in highly dynamic network conditions. In order to assess the feasibility of our approach, we evaluated the performance of our framework in terms of latency, power consumption, network utilization, and computational load, compared to a scenario where no blockchain was used.
A New Anti-Quantum Proxy Blind Signature for Blockchain-Enabled Internet of Things

Blockchain technology has become a research hotspot in recent years with the prominent characteristics as public, distributed and decentration. And blockchain-enabled internet of things (BIoT) has a tendency to make a revolutionary change for the internet of things (IoT) which requires distributed trustless consensus. However, the scalability and security issues become particularly important with the dramatically increasing number of IoT devices. Especially, with the development of quantum computing, many extant cryptographic algorithms applied in blockchain or BIoT systems are vulnerable to the quantum attacks. In this paper, an anti-quantum proxy blind signature scheme based on the lattice cryptography has been proposed, which can provide user anonymity and untraceability in the distributed applications of BIoT. Then, the security proof of the proposed scheme can derive that it is secure in random oracle model, and the efficiency analysis can indicate it is efficient than other similar literatures.
Mixed Assessment of Virtual Serious Games Applied in Architectural and Urban Design Education

The creation and usage of serious games on virtual reality (VR) and/or interactive platforms for the teaching of architecture, construction, urban planning, and other derived areas, such as security and risk prevention, require design processes, studies, and research that lead to further consolidation expansion. In that sense, this paper presents two main aims developed: the improvement of a virtual navigation system through the results of previous user studies and mixed research (quantitative and qualitative) improved based on the user perception for educational and professional uses. The VR system used is based on Unreal Engine programming of the HTC Vive sensor. This study is related to the GAME4City 3.0 and a broader project focused on gamified visualization and its educational uses in architectural and urban projects. The results reflect great interest, good usability, and high motivation for further usage for all types of users. However, an apparent resistance to deepen its use continues to be perceived in academia. Based on the research results, weak points of educational gamified systems have been identified, and the main differences and needs in user profiles’ function. With these data, progress regarding implementing this kind of system at the teaching and professional levels must be pursued.
Do we still need physical monitors? An evaluation of the usability of AR virtual monitors for productivity work

Physical monitors require space, lack flexibility, and can become expensive and less portable in large setups. Virtual monitors, on the other hand, can minimize those problems, but may be subject to technological limitations such as lower resolution and field of view. We investigate the impacts of using virtual monitors displayed on a current state-of-the-art augmented reality headset for conducting productivity work. We conducted a user study that compared physical monitors, virtual monitors, and a hybrid combination of both in terms of performance, accuracy, comfort, focus, preference, and confidence. Results show that virtual monitors are a feasible approach for performing serious productivity work, albeit currently constrained by technical limitations that lead to inferior usability and performance compared to physical monitors. We also discovered that, with current technology, the hybrid condition was a better tradeoff between the familiarity and trustworthiness of physical monitors and the extra space provided by virtual monitors. We conclude by expressing the opportunity for designing strategies for mixing virtual and physical monitors into novel hybrid interfaces.
Projecting Your View Attentively: Monocular Road Scene Layout Estimation via Cross-view Transformation

HD map reconstruction is crucial for autonomous driving. LiDAR-based methods are limited due to the deployed expensive sensors and time-consuming computation. Camera-based methods usually need to separately perform road segmentation and view transformation, which often causes distortion and the absence of content. To push the limits of the technology, we present a novel framework that enables reconstructing a local map formed by road layout and vehicle occupancy in the bird’s-eye view given a front-view monocular image only. In particular, we propose a cross-view transformation module, which takes the constraint of cycle consistency between views into account and makes full use of their correlation to strengthen the view transformation and scene understanding. Considering the relationship between vehicles and roads, we also design a context-aware discriminator to further refine the results. Experiments on public benchmarks show that our method achieves the state-of-the-art performance in the tasks of road layout estimation and vehicle occupancy estimation. Especially for the latter task, our model outperforms all competitors by a large margin. Furthermore, our model runs at 35 FPS on a single GPU, which is efficient and applicable for real-time panorama HD map reconstruction.
A Graph Attention Spatio-temporal Convolutional Network for 3D Human Pose Estimation in Video

Spatio-temporal information is key to resolve occlusion and depth ambiguity in 3D human pose estimation. Previous methods have focused on either temporal contexts or local-to-global architectures that embed fixed-length spatiotemporal information. To date, there have not been effective proposals to simultaneously and flexibly capture varying spatiotemporal sequences and effectively achieves real-time 3D human pose estimation. In this work, we improve the learning of kinematic constraints in the human skeleton: posture, local kinematic connections, and symmetry by modeling local and global spatial information via attention mechanisms. To adapt to single- and multi-frame estimation, the dilated temporal model is employed to process varying skeleton sequences. Also, importantly, we carefully design the interleaving of spatial semantics with temporal dependencies to achieve a synergistic effect. To this end, we propose a simple yet effective graph attention spatio-temporal convolutional network (GAST-Net) that comprises of interleaved temporal convolutional and graph attention blocks. Experiments on two challenging benchmark datasets (Human3.6M and HumanEva-I) and YouTube videos demonstrate that our approach effectively mitigates depth ambiguity and self-occlusion, generalizes to half upper body estimation, and achieves competitive performance on 2D-to-3D video pose estimation. Code, video, and supplementary information is available at: http://www.juanrojas.net/gast/
Social Robots in Education for Long-Term Human-Robot Interaction : Socially Supportive Behaviour of Robotic Tutor for Creating Robo-Tangible Learning Environment in a Guided Discovery Learning Interaction

The article discusses the possible applications of social robots in education, the technological and pedagogical challenges they possess, and the ways in which they may affect learning outcomes, concentrating only on robots intended to assist students in learning via social interaction. Author has highlighted three significant research problems : 1) Does robot tutors help improve students’ learning outcomes? 2) Does appearance and behaviour of robots have a significant role to play on academic engagement of learners? 3) What could be the potential roles of a robot in an educational setting? A statistical meta-analysis of previously published research articles is used to substantiate author’s claims. The larger aim of this article is to provide effective groundwork for future research by describing the expected outcomes of using social robots to offer education and identifying potential research areas for further inquiry.
Bridging pre-trained models and downstream tasks for source code understanding

With the great success of pre-trained models, the pretrain-then-finetune paradigm has been widely adopted on downstream tasks for source code understanding. However, compared to costly training a large-scale model from scratch, how to effectively adapt pre-trained models to a new task has not been fully explored. In this paper, we propose an approach to bridge pre-trained models and code-related tasks. We exploit semantic-preserving transformation to enrich downstream data diversity, and help pre-trained models learn semantic features invariant to these semantically equivalent transformations. Further, we introduce curriculum learning to organize the transformed data in an easy-to-hard manner to fine-tune existing pre-trained models.
A survey on providing customer and public administration based services using AI: chatbot

A chatbot is emerged as an effective tool to address the user queries in automated, most appropriate and accurate way. Depending upon the complexity of the subject domain, researchers are employing variety of soft-computing techniques to make the chatbot user-friendly. It is observed that chatbots have flooded the globe with wide range of services including ordering foods, suggesting products, advising for insurance policies, providing customer support, giving financial assistance, schedule meetings etc. However, public administration based services wherein chatbot intervention influence the most, is not explored yet. This paper discuses about artificial intelligence based chatbots including their applications, challenges, architecture and models. It also talks about evolution of chatbots starting from Turing Test and Rule-based chatbots to advanced Artificial Intelligence based Chatbots (AI-Chatbots). AI-Chatbots are providing much kind of services, which this paper outlines into two main aspects including customer based services and public administration based services. The purpose of this survey is to understand and explore the possibility of customer & public administration services based chatbot. The survey demonstrates that there exist an immense potential in the AI assisted chatbot system for providing customer services and providing better governance in public administration services.
Backbone is All Your Need: A Simplified Architecture for Visual Object Tracking

Exploiting a general-purpose neural architecture to replace hand-wired designs or inductive biases has recently drawn extensive interest. However, existing tracking approaches rely on customized sub-modules and need prior knowledge for architecture selection, hindering the development of tracking in a more general system. This paper presents a Simplified Tracking architecture (SimTrack) by leveraging a transformer backbone for joint feature extraction and interaction. Unlike existing Siamese trackers, we serialize the input images and concatenate them directly before the one-branch backbone. Feature interaction in the backbone helps to remove well-designed interaction modules and produce a more efficient and effective framework. To reduce the information loss from down-sampling in vision transformers, we further propose a foveal window strategy, providing more diverse input patches with acceptable computational costs. Our SimTrack improves the baseline with 2.5%/2.6% AUC gains on LaSOT/TNL2K and gets results competitive with other specialized tracking algorithms without bells and whistles. The source codes are available at https://github.com/LPXTT/SimTrack .
Towards An End-to-End Framework for Flow-Guided Video Inpainting

Optical flow, which captures motion information across frames, is exploited in recent video inpainting methods through propagating pixels along its trajectories. However, the hand-crafted flow-based processes in these methods are applied separately to form the whole inpainting pipeline. Thus, these methods are less efficient and rely heavily on the intermediate results from earlier stages. In this paper, we propose an End-to-End framework for Flow-Guided Video Inpainting (E <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> FGVI) through elaborately designed three trainable modules, namely, flow completion, feature propagation, and content hallucination modules. The three modules correspond with the three stages of previous flow-based methods but can be Jointly optimized, leading to a more efficient and effective inpainting process. Experimental results demonstrate that the proposed method outperforms state-of-the-art methods both qualitatively and quantitatively and shows promising efficiency. The code is available at https://github.com/MCG-NKU/E2FGVI.
GRAPHOIDS: Graph-Based Logic for Reasoning about Relevance Relations <i>Or</i> When Would <b> <i>x</i> </b> Tell You More about <b> <i>y</i> </b> If You Already Know <b> <i>z</i> </b> ?

chapter Share on GRAPHOIDS: Graph-Based Logic for Reasoning about Relevance Relations OrWhen Would x Tell You More about y If You Already Know z? Authors: Judea Pearl Search about this author , Azaria Paz Search about this author Authors Info & Claims Probabilistic and Causal Inference: The Works of Judea PearlFebruary 2022 Pages 189–200https://doi.org/10.1145/3501714.3501729Online:04 March 2022Publication History 0citation5DownloadsMetricsTotal Citations0Total Downloads5Last 12 Months5Last 6 weeks2 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my Alerts New Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Recognition of surrounding environment from electric wheelchair videos based on modified YOLOv2

Currently, the aging population is growing in Japan, and the needs for the utilization of welfare equipment are consequently increasing. The electric wheelchair, which is a convenient transportation tool, has rapidly become popular. However, many accidents have occurred when using electric wheelchairs, and the dangers of driving have been noted. Therefore, there is a need to improve accident factors, reduce accidents and improve the convenience of electric wheelchairs by using automation. Environmental recognition is the key technology for developing autonomous electric wheelchairs. Environmental recognition includes self-position estimation, the recognition of sidewalks, crosswalks and traffic lights, and moving object predictions. To solve these problems, this paper develops a system for detecting sidewalks, crosswalks and traffic lights. We develop the object recognition methods using a modified YOLOv2, which is an object detection algorithm that applies convolutional neural networks (CNNs). We detect the object through YOLOv2 and perform processing steps, such as unnecessary bounding box deletion and interpolation. The experimental results demonstrate that the average AUC of the detection rate is 0.587.
Comprehensive Analysis of Deep Learning-Based Vehicle Detection in Aerial Images

Vehicle detection in aerial images is a crucial image processing step for many applications such as screening of large areas as used for surveillance, reconnaissance, or rescue tasks. In recent years, several deep learning-based frameworks have been proposed for object detection. However, these detectors were developed for data sets that considerably differ from aerial images. In this paper, we systematically investigate the potential of fast R-CNN and faster R-CNN for aerial images, which achieve top performing results on common detection benchmark data sets. Therefore, the applicability of eight state-of-the-art object proposal methods used to generate a set of candidate regions and of both detectors is examined. Relevant adaptations to account for the characteristics of the aerial images are provided. To overcome the shortcomings of the original approach in the case of handling small instances, we further propose our own networks that clearly outperform state-of-the-art methods for vehicle detection in aerial images. Furthermore, we analyze the impact of the different adaptations with respect to various ground sampling distances to provide a guideline for detecting small objects in aerial images. All experiments are performed on two publicly available data sets to account for differing characteristics such as varying object sizes, number of objects per image, and varying backgrounds.
Multi‐stream 3D CNN structure for human action recognition trained by limited data

Here, the authors proposed a solution to improve the training performance in limited training data case for human action recognition. The authors proposed three different convolutional neural network (CNN) architectures for this purpose. At first, the authors generated four different channels of information by optical flows and gradients in the horizontal and vertical directions from each frame to apply to three‐dimensional (3D) CNNs. Then, the authors proposed three architectures, which are single‐stream, two‐stream, and four‐stream 3D CNNs. In the single‐stream model, the authors applied four channels of information from each frame to a single stream. In the two‐stream architecture, the authors applied optical flow‐ x and optical flow‐ y into one stream and gradient‐ x and gradient‐ y to another stream. In the four‐stream architecture, the authors applied each one of the information channels to four separate streams. Evaluating the architectures in an action recognition system, the system was assessed on IXMAS, a data set which has been recorded simultaneously by five cameras. The authors showed that the results of four‐stream architecture were better than other architectures, achieving 87.5, 91.66, 91.11, 88.05, and 81.94% recognition rates for cameras 0–4, respectively, using four‐stream structure (88.05% recognition rate in average).
Hostile Blockchain Takeovers (Short Paper)

Most research modelling Bitcoin-style decentralised consensus protocols has assumed profit-motivated participants. Complementary to this analysis, we revisit the notion of attackers with an extrinsic motivation to disrupt the consensus process (Goldfinger attacks). We outline several routes for obtaining a majority of decision-making power in the consensus protocol (a hostile takeover). Our analysis suggests several fundamental differences between proof-of-work and proof-of-stake systems in the face of such an adversary.
What You Say and How You Say it: Joint Modeling of Topics and Discourse in Microblog Conversations

This paper presents an unsupervised framework for jointly modeling topic content and discourse behavior in microblog conversations. Concretely, we propose a neural model to discover word clusters indicating what a conversation concerns (i.e., topics) and those reflecting how participants voice their opinions (i.e., discourse). 1 Extensive experiments show that our model can yield both coherent topics and meaningful discourse behavior. Further study shows that our topic and discourse representations can benefit the classification of microblog messages, especially when they are jointly trained with the classifier. Our data sets and code are available at: http://github.com/zengjichuan/Topic_Disc .
Python‐based multi‐agent platform for application on power grids

Concerns about the future energy needs challenge energy experts to present advanced power grids making them smarter. Smart grids employ digital technology and are based on highly collaborative and responsive decision-making strategies. Current trends to control and manage their operation lead to the use of multi-agent system (MAS) technology. This paper aims to present the design, specification, and application of the Python Agent DEvelopment (PADE) framework, an open source platform implemented in Python language and conceived for the implementation of multi-agent systems on power systems. PADE is compliant with specifications of the Foundation for Intelligent Physical Agents and eases the development of solutions to power systems based on MAS. For demonstration of the effectiveness of PADE, an automatic restoration system MAS-based is presented. A test bed that encompasses agents embedded on computation devices is used to demonstrate the practical implementation of a PADE-based multi-agent systems in a power distribution network.
Affinity Lens

Despite the availability of software to support Affinity Diagramming (AD), practitioners still largely favor physical sticky-notes. Physical notes are easy to set-up, can be moved around in space and offer flexibility when clustering un-structured data. However, when working with mixed data sources such as surveys, designers often trade off the physicality of notes for analytical power. We propose AffinityLens, a mobile-based augmented reality (AR) application for Data-Assisted Affinity Diagramming (DAAD). Our application provides just-in-time quantitative insights overlaid on physical notes. Affinity Lens uses several different types of AR overlays (called lenses) to help users find specific notes, cluster information, and summarize insights from clusters. Through a formative study of AD users, we developed design principles for data-assisted AD and an initial collection of lenses. Based on our prototype, we find that Affinity Lens supports easy switching between qualitative and quantitative 'views' of data, without surrendering the lightweight benefits of existing AD practice.