Reviewerly/exHarmony huggingface dataset:
A study on the influence mechanism of CBDC on monetary policy: An analysis based on e-CNY

This paper attempts to introduce central bank digital currency (CBDC) into the analysis framework of monetary policy, and studies the influence mechanism of e-CNY, central bank digital currency in China, on the monetary policy of the central bank from the aspects of money demand, money supply and monetary policy transmission mechanism. The research finds that e-CNY will have significant impact on monetary policy: (1) E-CNY will change the structure of money demand, speed up currency circulation, make central bank reserves more controllable and money supply more intelligent; (2) E-CNY will increase the volatility and expansion effect of currency multiplier to a certain extent; (3) E-CNY will dredge the transmission channel of monetary policy so as to improve the transmission effect of existing monetary policy tools. At the same time, based on the organic combination with structural monetary policy tools, it will achieve precise implementation of medium-term lending facilities (MLF), pledged supplementary lending (PSL), and it may bring new monetary policy tools. (4) E-CNY will make the intermediate target of monetary policy more controllable and reliable, and have a positive impact on the target of monetary policy through the smooth transmission of monetary policy channels. Therefore, it is necessary to strengthen the research on CBDC, give full play to the positive role of e-CNY in monetary policy, and improve the effectiveness of monetary policy.
A Review of Collaborative Virtual Reality Systems for the Architecture, Engineering, and Construction Industry

In this paper, we focus on interdisciplinary collaboration using intuitive virtual reality interfaces and building information models in the architecture, engineering, and construction industries. These systems have been a topic of research and development for the past ten years; however, there is still no widely open standard format, related software platform, or guidelines that are sufficiently mature; the complexity of such systems is very high. We review existing state-of-the-art interdisciplinary collaborative virtual reality systems, proposing solutions and standards. Thirteen state-of-the-art systems are reviewed and compared to illustrate emerging trends and insufficiencies. It is found that these systems differ significantly with respect to drawing capabilities, photorealism, construction simulation, and interdisciplinary communication. We discover trends in user interfaces that could be evolved to better standards, and provide future guidelines to developers. Combining the best aspects of existing systems, we provide a blueprint for an ideal system that combines the most advanced features for collaborative design.
Finding Nonrigid Tiny Person With Densely Cropped and Local Attention Object Detector Networks in Low-Altitude Aerial Images

Finding tiny persons under the drone vision was, is and remains to be an integral and challenging task. Unmanned Aerial Vehicles (UAVs) with high-speed, low-altitude, and multi-perspective flight bring about violently various scales of objects, which burdens the optimization of models. Moreover, the detection performance of densely and faintly discernible person characteristics is far less than that of large objects in high-resolution aerial images. In this paper, we introduce the image cropping strategy and attention mechanism based on YOLOv5 to address small person detection in the optimized VisDrone2019 dataset. Specifically, we propose a Densely Cropped and Local Attention of object detector Network (DCLANet), which is inspired by the observation that less area occupied by small objects should be fully focused and relatively magnified in the original image. DCLANet assembled Density Map Guided Object Detection (DMNet) in Aerial Images and You Only Look Twice (YOLT): Rapid Multi-Scale Object Detection In Satellite Imagery to crop images upon training and testing stage, meanwhile, added Bottleneck Attention Mechanism (BAM) to YOLOv5 baseline framework, which more focus on person objects other than irrelevant categories. To achieve further improvement of DCLANet, we also provide bags of useful strategies: data augmentation, label fusion, category filtering and hyperparameter evolution. Extensive experiments on the VisDrone2019 show that DCLANet achieves state-of-the-art performance, the detection result of person category <formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex>$A P^{\rm{val}}$</tex></formula> @0.5 is 50.04% with test-dev subset, which is substantially better than the previous SOTA method(DPNetV3) by 12.01%. In addition, on our optimized VisDrone2019 dataset, <formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex>$A P^{\rm{val}}$</tex></formula> @0.5 and A P test@0.5 obtained 74.95%, 62.18%, respectively. Compared to YOLOv5, DCLANet improves 3.8% or so, which is encouraging and competitve.
Quality of Experience in Telemeetings and Videoconferencing: A Comprehensive Survey

Telemeetings such as audiovisual conferences or virtual meetings play an increasingly important role in our professional and private lives. For that reason, system developers and service providers will strive for an optimal experience for the user, while at the same time optimizing technical and financial resources. This leads to the discipline of Quality of Experience (QoE), an active field originating from the telecommunication and multimedia engineering domains, that strives for understanding, measuring, and designing the quality experience with multimedia technology. This paper provides the reader with an entry point to the large and still growing field of QoE of telemeetings, by taking a holistic perspective, considering both technical and non-technical aspects, and by focusing on current and near-future services. Addressing both researchers and practitioners, the paper first provides a comprehensive survey of factors and processes that contribute to the QoE of telemeetings, followed by an overview of relevant state-of-the-art methods for QoE assessment. To embed this knowledge into recent technology developments, the paper continues with an overview of current trends, focusing on the field of eXtended Reality (XR) applications for communication purposes. Given the complexity of telemeeting QoE and the current trends, new challenges for a QoE assessment of telemeetings are identified. To overcome these challenges, the paper presents a novel Profile Template for characterizing telemeetings from the holistic perspective endorsed in this paper.
CKMIB: Construction of Key Agreement Protocol for Cloud Medical Infrastructure Using Blockchain

In the traditional medical healthcare system, each medical facility is responsible for preserving its own records.Sharing such records with another medical establishment is difficult for them.To tackle this challenge, the traditional medical system leverages internet technology to transform into a modern electronic system.In electronic healthcare systems, managing the security and privacy of patient data becomes a major issue.As an alternative, the healthcare sector might use blockchain technology to exchange digitised healthcare data.Blockchain technology is characterised by anonymity, decentralisation, and immutability.It is hard to keep all electronic healthcare data on blockchain due to the expense and volume.Cloud computing is the best solution for storing this type of data and resolving problems like these.To address these concerns, we offer a blockchain-based key agreement protocol for cloud medical network systems that enhances privacy and security.We demonstrate a formal and informal security analysis of the proposed protocol that shows that the proposed protocol is both secure and communicative.We provide security verification of the proposed protocol by using the AVISPA software tool against man in the middle attack and replay attack.Finally, we compute the computation and communication costs of the proposed protocol and other existing protocols, the proposed protocol has less computation and communication costs than other existing protocols in the electronic healthcare system.
Dilated transformer: residual axial attention for breast ultrasound image segmentation

The segmentation of breast ultrasound (US) images has been a challenging task, mainly due to limited data and the inherent image characteristics involved, such as low contrast and speckle noise. Although convolutional neural network-based (CNN-based) methods have made significant progress over the past decade, they lack the ability to model long-range interactions. Recently, the transformer method has been successfully applied to the tasks of computer vision. It has a strong ability to capture distant interactions. However, most transformer-based methods with excellent performance rely on pre-training on large datasets, making it infeasible to directly apply them to medical images analysis, especially that of breast US images with limited high-quality labels. Therefore, it is of great significance to find a robust and efficient transformer-based method for use on small breast US image datasets.We developed a dilated transformer (DT) method which mainly uses the proposed residual axial attention layers to build encoder blocks and the introduced dilation module (DM) to further increase the receptive field. We evaluated the proposed method on 2 breast US image datasets using the 5-fold cross-validation method. Dataset A was a public dataset with 562 images, while dataset B was a private dataset with 878 images. Ground truth (GT) was delineated by 2 radiologists with more than 5 years of experience. The evaluation was followed by related ablation experiments.The DT was found to be comparable with the state-of-the-art (SOTA) CNN-based method and outperformed the related transformer-based method, medical transformer (MT), on both datasets. Especially on dataset B, the DT outperformed the MT on metrics of Jaccard index (JI) and Dice similarity coefficient (DSC) by 2.67% and 4.68%, respectively. Meanwhile, when compared with Unet, the DT improved JI and DSC by 4.89% and 4.66%, respectively. Moreover, the results of the ablation experiments showed that each add-on part of the DT is important and contributes to the segmentation accuracy.The proposed transformer-based method could achieve advanced segmentation performance on different small breast US image datasets without pretraining.
MotionMixer: MLP-based 3D Human Body Pose Forecasting

In this work, we present MotionMixer, an efficient 3D human body pose forecasting model based solely on multi-layer perceptrons (MLPs). MotionMixer learns the spatial-temporal 3D body pose dependencies by sequentially mixing both modalities. Given a stacked sequence of 3D body poses, a spatial-MLP extracts fine-grained spatial dependencies of the body joints. The interaction of the body joints over time is then modelled by a temporal MLP. The spatial-temporal mixed features are finally aggregated and decoded to obtain the future motion. To calibrate the influence of each time step in the pose sequence, we make use of squeeze-and-excitation (SE) blocks. We evaluate our approach on Human3.6M, AMASS, and 3DPW datasets using the standard evaluation protocols. For all evaluations, we demonstrate state-of-the-art performance, while having a model with a smaller number of parameters. Our code is available at: https://github.com/MotionMLP/MotionMixer.
Incorporating Registration, Reputation, and Incentivization Into the NFT Ecosystem

Non-Fungible Tokens (NFTs) have recently received immense popularity in the digital art industry. An NFT represents ownership of a unique item that is stored on the blockchain and cannot be changed, replaced, and copied. The current NFT ecosystem falls short in trust features and is prone to illegitimate users, threats, and vulnerabilities. In this paper, we propose a blockchain-based solution for the NFT ecosystem that incorporates registration of the participating actors, involves a decentralized reputation system, provides incentives to its users through rewards, and penalizes misconduct. Our system design is built to ensure trust and credibility in the NFT ecosystem. The proposed solution leverages blockchain&#x2019;s intrinsic security features such as transparency, tamper-proof logs, data integrity, accountability, and non-repudiation. We use the decentralized storage of the InterPlanetary File System (IPFS) to store the NFTs&#x2019; metadata, whereas their hash is stored on the chain. We present algorithms along with their implementation, testing, and validation details. We demonstrate how our solution, as well as smart contract code, is secure enough against common security threats and attacks. We make our smart contract code publicly available on the GitHub repository.
Unconstrained face mask and face-hand interaction datasets: building a computer vision system to help prevent the transmission of COVID-19

Health organizations advise social distancing, wearing face mask, and avoiding touching face to prevent the spread of coronavirus. Based on these protective measures, we developed a computer vision system to help prevent the transmission of COVID-19. Specifically, the developed system performs face mask detection, face-hand interaction detection, and measures social distance. To train and evaluate the developed system, we collected and annotated images that represent face mask usage and face-hand interaction in the real world. Besides assessing the performance of the developed system on our own datasets, we also tested it on existing datasets in the literature without performing any adaptation on them. In addition, we proposed a module to track social distance between people. Experimental results indicate that our datasets represent the real-world's diversity well. The proposed system achieved very high performance and generalization capacity for face mask usage detection, face-hand interaction detection, and measuring social distance in a real-world scenario on unseen data. The datasets are available at https://github.com/iremeyiokur/COVID-19-Preventions-Control-System.
Overlapped grouping measurement: A unified framework for measuring quantum states

Quantum algorithms designed for realistic quantum many-body systems, such as chemistry and materials, usually require a large number of measurements of the Hamiltonian. Exploiting different ideas, such as {importance sampling,} observable compatibility, or classical shadows of quantum states, different advanced measurement schemes have been proposed to greatly reduce the large measurement cost. Yet, the underline cost reduction mechanisms seem distinct from each other, and how to systematically find the optimal scheme remains a critical challenge. Here, we address this challenge by proposing a unified framework of quantum measurements, incorporating advanced measurement methods as special cases. Our framework allows us to introduce a general scheme~ -- ~overlapped grouping measurement, which simultaneously exploits the advantages of most existing methods. An intuitive understanding of the scheme is to partition the measurements into overlapped groups with each one consisting of compatible measurements. We provide explicit grouping strategies and numerically verify its performance for different molecular Hamiltonians with up to 16 qubits. Our numerical result shows significant improvements over existing schemes. Our work paves the way for efficient quantum measurement and fast quantum processing with current and near-term quantum devices.
Demonstration of universal control between non-interacting qubits using the Quantum Zeno effect

Abstract The Zeno effect occurs in quantum systems when a very strong measurement is applied, which can alter the dynamics in non-trivial ways. Despite being dissipative, the dynamics stay coherent within any degenerate subspaces of the measurement. Here we show that such a measurement can turn a single-qubit operation into a two- or multi-qubit entangling gate, even in a non-interacting system. We demonstrate this gate between two effectively non-interacting transmon qubits. Our Zeno gate works by imparting a geometric phase on the system, conditioned on it lying within a particular non-local subspace. These results show how universality can be generated not only by coherent interactions as is typically employed in quantum information platforms, but also by Zeno measurements.
Learning Rich Representation of Keyphrases from Text

In this work, we explore how to train taskspecific language models aimed towards learning rich representation of keyphrases from text documents.We experiment with different masking strategies for pre-training transformer language models (LMs) in discriminative as well as generative settings.In the discriminative setting, we introduce a new pre-training objective -Keyphrase Boundary Infilling with Replacement (KBIR), showing large gains in performance (upto 8.16 points in F1) over SOTA, when the LM pre-trained using KBIR is fine-tuned for the task of keyphrase extraction.In the generative setting, we introduce a new pre-training setup for BART -Key-BART, that reproduces the keyphrases related to the input text in the CatSeq format, instead of the denoised original input.This also led to gains in performance (upto 4.33 points in F1@M) over SOTA for keyphrase generation.Additionally, we also fine-tune the pre-trained language models on named entity recognition (NER), question answering (QA), relation extraction (RE), abstractive summarization and achieve comparable performance with that of the SOTA, showing that learning rich representation of keyphrases is indeed beneficial for many other fundamental NLP tasks.
A generalized framework for elliptic curves based PRNG and its utilization in image encryption

In the last decade, Elliptic Curves (ECs) have shown their efficacy as a safe fundamental component in encryption systems, mainly when used in Pseudorandom Number Generator (PRNG) design. This paper proposes a framework for designing EC-based PRNG and maps recent PRNG design techniques into the framework, classifying them as iterative and non-iterative. Furthermore, a PRNG is designed based on the framework and verified using the National Institute of Standards and Technology (NIST) statistical test suite. The PRNG is then utilized in an image encryption system where statistical measures, differential attack measures, the NIST statistical test suite, and system key sensitivity analysis are used to demonstrate the system's security. The results are good and promising as compared with other related work.
Challenges and Perspectives for Clinical Applications of Immersive and Non-Immersive Virtual Reality

The development of rehabilitative technologies able to increase the intensity and the amount of time for daily treatment as well as the patients' motivation and interest is a high-priority area of scientific research [...].
Distributed Hybrid CPU and GPU training for Graph Neural Networks on Billion-Scale Heterogeneous Graphs

Graph neural networks (GNN) have shown great success in learn- ing from graph-structured data. They are widely used in various applications, such as recommendation, fraud detection, and search. In these domains, the graphs are typically large and heterogeneous, containing many millions or billions of vertices and edges of different types. To tackle this challenge, we develop DistDGLv2, a system that extends DistDGL for training GNNs on massive heterogeneous graphs in a mini-batch fashion, using distributed hybrid CPU/GPU training. DistDGLv2 places graph data in distributed CPU memory and performs mini-batch computation in GPUs. For ease of use, DistDGLv2 adopts API compatible with Deep Graph Library (DGL)'s mini-batch training and heterogeneous graph API, which enables distributed training with almost no code modification. To ensure model accuracy, DistDGLv2 follows a synchronous training approach and allows ego-networks forming mini-batches to include non-local vertices. To ensure data locality and load balancing, DistDGLv2 partitions heterogeneous graphs by using a multi-level partitioning algorithm with min-edge cut and multiple balancing constraints. DistDGLv2 deploys an asynchronous mini- batch generation pipeline that makes computation and data access asynchronous to fully utilize all hardware (CPU, GPU, network, PCIe). We demonstrate DistDGLv2 on various GNN workloads. Our results show that DistDGLv2 achieves 2 - 3x speedup over DistDGL and 18× speedup over Euler. It takes only 5 - 10 seconds to complete an epoch on graphs with hundreds of millions of vertices on a cluster with 64 GPUs.
Efficient Dual-Branch Bottleneck Networks of Semantic Segmentation Based on CCD Camera

This paper investigates a novel Efficient Dual-branch Bottleneck Network (EDBNet) to perform real-time semantic segmentation tasks on mobile robot systems based on CCD camera. To remedy the non-linear connection between the input and the output, a small-scale and shallow module called the Efficient Dual-branch Bottleneck (EDB) module is established. The EDB unit consists of two branches with different dilation rates, and each branch widens the non-linear layers. This module helps to simultaneously extract local and situational information while maintaining a minimal set of parameters. Moreover, the EDBNet, which is built on the EDB unit, is intended to enhance accuracy, inference speed, and parameter flexibility. It employs dilated convolution with a high dilation rate to increase the receptive field and three downsampling procedures to maintain feature maps with superior spatial resolution. Additionally, the EDBNet uses effective convolutions and compresses the network layer to reduce computational complexity, which is an efficient technique to capture a great deal of information while keeping a rapid computing speed. Finally, using the CamVid and Cityscapes datasets, we obtain Mean Intersection over Union (MIoU) results of 68.58 percent and 71.21 percent, respectively, with just 1.03 million parameters and faster performance on a single GTX 1070Ti card. These results also demonstrate the effectiveness of the practical mobile robot system.
Incoming Metaverses: Digital Mirrors for Urban Planning

The planning process has been, arguably, slow to adapt and adopt new technologies: It is perhaps only now that it is starting to move into a more digitally focused era. Yet, it is not the current thinking around the digital that is going to change planning; it is the emerging metaverse. It is a change on the near horizon that is there but is currently largely unseen in the urban planning profession. The metaverse is, at first sight, a mirror to the current world, a digital twin, but it is more than this: It is an inhabited mirror world where the physical dimensions and rules of time and space do not necessarily apply. Operating across scales, from the change of use of a building up to a local plan and onwards to the scale of future cities, these emerging metaverses will exist either directly within computational space or emerge into our physical space via augmented reality. With economic systems operating via blockchain technology and the ability to instigate aspects of planning law, interspaced with design fiction type scenarios, they represent a new tool kit for the urban planner, spatial, economic, and social. We explore these emerging spaces, taking a look at their origins and how the use of game engines have allowed participation and design to become part of the workflow of these 3D spaces. Via a series of examples, we look at the current state of the art, explore the short term future, and speculate on digital planning using these incoming metaverses 10 years from now.