Reviewerly/exHarmony huggingface dataset:
The application framework of big data technology during the COVID-19 pandemic in China

Abstract Big data has been reported widely to facilitate epidemic prevention and control in health care during the coronavirus disease 2019 (COVID-19) pandemic. However, there is still a lack of practical experience in applying it to hospital prevention and control. This study is devoted to the practical experience of design and implementation as well as the preliminary results of an innovative big data-driven COVID-19 risk personnel screening management system in a hospital. Our screening system integrates data sources in four dimensions, which includes Health Quick Response (QR) code, abroad travelling history, transportation close contact personnel and key surveillance personnel. Its screening targets cover all patients, care partner and staff who come to the hospital. As of November 2021, nearly 690 000 people and 5.79 million person-time had used automated COVID-19 risk screening and monitoring. A total of 10 376 person-time (0.18%) with abnormal QR code were identified, 242 person-time with abroad travelling history were identified, 925 person-time were marked based on the data of key surveillance personnel, no transportation history personnel been reported and no COVID-19 nosocomial infection occurred in the hospital. Through the application of this system, the hospital's expenditure on manpower and material resources for epidemic prevention and control has also been significantly reduced. Collectively, this study has proved to be an effective and efficient model for the use of digital health technology in response to the COVID-19 pandemic. Based on the data from multiple sources, this system has an irreplaceable role in identifying close contacts or suspicious person, and can significantly reduce the social burden caused by COVID-19, especially the human resources and economic costs of hospital prevention and control. It may provide guidance for clinical epidemic prevention and control in hospitals, as well as for future public health emergencies.
Things not Written in Text: Exploring Spatial Commonsense from Visual Signals

Spatial commonsense, the knowledge about spatial position and relationship between objects (like the relative size of a lion and a girl, and the position of a boy relative to a bicycle when cycling), is an important part of commonsense knowledge.Although pretrained language models (PLMs) succeed in many NLP tasks, they are shown to be ineffective in spatial commonsense reasoning.Starting from the observation that images are more likely to exhibit spatial commonsense than texts, we explore whether models with visual signals learn more spatial commonsense than text-based PLMs.We propose a spatial commonsense benchmark that focuses on the relative scales of objects, and the positional relationship between people and objects under different actions.We probe PLMs and models with visual signals, including visionlanguage pretrained models and image synthesis models, on this benchmark, and find that image synthesis models are more capable of learning accurate and consistent spatial knowledge than other models.The spatial knowledge from image synthesis models also helps in natural language understanding tasks that require spatial commonsense.Code and
Universal-dephasing-noise injection via Schrödinger-wave autoregressive moving-average models

We present and validate a method for noise injection of arbitrary spectra in quantum circuits that can be applied to any system capable of executing arbitrary single qubit rotations, including cloud-based quantum processors. As the consequences of temporally correlated noise on the performance of quantum algorithms are not well understood, the capability to engineer and inject such noise in quantum systems is paramount. To date, noise injection capabilities have been limited and highly platform specific, requiring low-level access to control hardware. We experimentally validate our method by comparing to a direct hardware-based noise-injection scheme. Using a combination of quantum noise spectroscopy and classical signal analysis, we show that the two approaches agree. These results showcase a highly versatile method for noise injection that can be utilized by theoretical and experimental researchers to verify, evaluate, and improve quantum characterization protocols and quantum algorithms for sensing and computing.
Interacting With New York City Data by HoloLens Through Remote Rendering

In the digital era, extended reality (XR) is considered the next frontier. However, XR systems are computationally intensive, and they must be implemented within strict latency constraints. Thus, XR devices with finite computing resources are limited in terms of quality of experience (QoE) they can offer, particularly in the cases of big 3D data. This problem can be effectively addressed by offloading the highly intensive rendering tasks to a remote server. Therefore, we proposed a remote rendering enabled XR system that presents the 3D city model of New York City on the Microsoft HoloLens. Experimental results indicate that remote rendering outperforms local rendering for the New York City model with significant improvement in average QoE by at least 21%. In addition, we clarified the network traffic pattern in the proposed XR system developed under the OpenXR standard.
DAG-Based Smart Contract for Dynamic 6G Wireless EVs Charging System

This study proposes a novel secure and lightweight charging scheduling framework approach to provide the customization of edge computing services and personalized edge computing services for Moving EVs (MEVs) and Parking EVs (PEVs) in 6G heterogeneous Vehicular Networks (HetVNETs). A DAG-based smart-contract well-protected edge computing structural design is specially designed in the scheme by taking the wireless charging models into account including the attributes of 6G smart devices, where each vehicle network infrastructure copes with some more EVs in parking station wireless zone (PEVs) to make computing services whole through a wireless task. Each communication network handles a group of Electric Vehicles (EVs) in the parking zone to fulfill Cloud Computing Services (CCS) in wireless charging tasks. With this architecture, the collaborative computing resource allocation (CCRA) algorithm is developed to assist each network computing and communication hardware and software systems in deciding a customized service strategy (CSS) to fulfill the fast wireless charging and secure easy access of MEVs based on the widely available computational complexity solution held by various network infrastructures.
Quo Vadis, Explainability? – A Research Roadmap for Explainability Engineering

[Context and motivation] In our modern society, software systems are highly integrated into our daily life. Quality aspects such as ethics, fairness, and transparency have been discussed as essential for trustworthy software systems and explainability has been identified as a means to achieve all of these three in systems. [Question/problem] Like other quality aspects, explainability must be discovered and treated during the design of those systems. Although explainability has become a hot topic in several communities from different areas of knowledge, there is only little research on systematic explainability engineering. Yet, methods and techniques from requirements and software engineering would add a lot of value to the explainability research. [Principal ideas/results] As a first step to explore this research landscape, we held an interdisciplinary workshop to collect ideas from different communities and to discuss open research questions. In a subsequent working group, we further analyzed and structured the results of this workshop to identify the most important research questions. As a result, we now present a research roadmap for explainable systems. [Contribution] With our research roadmap we aim to advance the software and requirements engineering methods and techniques for explainable systems and to attract research on the most urgent open questions.
Blockchain Enabled Emperor Penguin Optimizer Based Encryption Technique for Secure Image Management System

In recent years, the electronic sharing of digital images faces a major threat to security, as the existing image transmission infrastructure is mainly based on the trust of third parties. At the same time, the available solutions are placed on the cloud based centralized data center, which is expensive, requires large storage area, and security issues regarding the transmission of data over the network. So, it is needed to develop an image management system which enables sharing and storing of digital images effectively. This paper develops novel multiple share creation schemes with block technology for secure image management (MSCCBT-SIM) systems. The MSCCBT-SIM model allows the user to create consensus with no dependencies on central authorities. It involves an MSC which involves share creation and share encryption using emperor penguin optimizer based ElGamal public key cryptosystem (EPO-EPKC). In addition, the blockchain is used as a distributed data storage mechanism to generate a ledger for permitting access to the user and prevent third party access to the encrypted shares. The application of blockchain technology and MSC techniques helps to achieve decentralization, highly reliable, inexpensive, and secure transmission and storage of digital images. In order to validate the effective performance of the MSCCBT-SIM model, a series of simulations take place and investigated the results interms of different measures. The experimental results ensured the better performance of the MSCCBT-SIM model with the superior PSNR of 57.09 dB whereas the HOCE-ECC, GO-ECC, PSO-ECC, and CS-ECC methods offered a lower PSNR of 53.08 dB, 52.91 dB, 52.65 dB, and 50.51 dB respectively.
Auditable blockchain voting system - the blockchain technology toward the electronic voting process

There exist many different electronic voting solutions and each has its own set of advantages and disadvantages. Most of the existing systems suffer from inadequate transparency and auditability. However, recently introduced blockchain technology may provide a solution to these problems. In this paper, auditable blockchain voting system (ABVS) is presented. It combines existing voting approach and combines it with blockchain technology to create a supervised and remote internet voting system, which is transparent and audit capable. The paper describes the system's processes, components, model and results of initial testing.
A 1036 TOp/s/W, 12.2 mW, 2.72 μJ/Inference All Digital TNN Accelerator in 22 nm FDX Technology for TinyML Applications

Tiny Machine Learning (TinyML) applications impose μJ/Inference constraints, with maximum power consumption of a few tens of mW. It is extremely challenging to meet these requirement at a reasonable accuracy level. In this work, we address this challenge with a flexible, fully digital Ternary Neural Network (TNN) accelerator in a RISC-V-based SoC. The design achieves 2.72 μJ/Inference, 12.2 mW, 3200 Inferences/sec at 0.5 V for a non-trivial 9-layer, 96 channels-per-layer network with CIFAR-10 accuracy of 86 %. The peak energy efficiency is 1036 TOp/s/W, outperforming the state-of-the-art in silicon-proven TinyML accelerators by 1.67x.
Complementarity between success probability and coherence in Grover search algorithm

Abstract Coherence plays a very important role in Grover search algorithm (GSA). In this paper, we define the normalization coherence <?CDATA $\mathds{N}(C)$ ?> , where C is a coherence measurement. By virtue of the constraint of large N and Shannon's maximum entropy principle, a surprising complementary relationship between the coherence and the success probability of GSA is obtained. Namely, <?CDATA $P_s(t)+\mathds{N}(C(t))\simeq 1$ ?> , where C is in terms of the relative entropy of coherence and l 1 norm of coherence, t is the number of the search iterations in GSA. Moreover, the equation holds no matter either in ideal or noisy environments. Considering the number of qubits is limited in the recent noisy intermediate-scale quantum (NISQ) era, some exact numerical calculation experiments are presented for different database sizes N with different types of noises. The results show that the complementarity between the success probability and the coherence almost always holds. This work provides a new perspective to improve the success probability by manipulating its complementary coherence, and vice versa. It has an excellent potential for helping quantum algorithms design in the NISQ era.
Residual-Concatenate Neural Network with Deep Regularization Layers for Binary Classification

Many complex Deep Learning models are used with different variations for various prognostication tasks. The higher learning parameters not necessarily ensure great accuracy. This can be solved by considering changes in very deep models with many regularization based techniques. In this paper we train a deep neural network that uses many regularization layers with residual and concatenation process for best fit with Polycystic Ovary Syndrome Diagnosis prognostication. The network was built with improvements from every step of failure to meet the needs of the data and achieves an accuracy of 99.3% seamlessly.
A New Adaptive High-Degree Unscented Kalman Filter with Unknown Process Noise

Vehicle state, including location and motion information, plays an essential role on the Internet of Vehicles (IoV). Accurately obtaining the system state information is the premise of realizing precise control. However, the statistics of system process noise are often unknown due to the complex physical process. It is challenging to estimate the system state when the process noise statistics are unknown. This paper proposes a new adaptive high-degree unscented Kalman filter based on the improved Sage–Husa algorithm. First, the traditional Sage–Husa algorithm is improved using a high-degree unscented transform. A noise estimator suitable for the high-degree unscented Kalman filter is obtained to estimate the statistics of the unknown process noise. Then, an adaptive high-degree unscented Kalman filter is designed to improve the accuracy and stability of the state estimation system. Finally, the target tracking simulation results verify the proposed algorithm’s effectiveness.
FreeKD

Knowledge distillation (KD) has demonstrated its effectiveness to boost the performance of graph neural networks (GNNs), where its goal is to distill knowledge from a deeper teacher GNN into a shallower student GNN. However, it is actually difficult to train a satisfactory teacher GNN due to the well-known over-parametrized and over-smoothing issues, leading to invalid knowledge transfer in practical applications. In this paper, we propose the first Free-direction Knowledge Distillation framework via Reinforcement learning for GNNs, called FreeKD, which is no longer required to provide a deeper well-optimized teacher GNN. The core idea of our work is to collaboratively build two shallower GNNs in an effort to exchange knowledge between them via reinforcement learning in a hierarchical way. As we observe that one typical GNN model often has better and worse performances at different nodes during training, we devise a dynamic and free-direction knowledge transfer strategy that consists of two levels of actions: 1) node-level action determines the directions of knowledge transfer between the corresponding nodes of two networks; and then 2) structure-level action determines which of the local structures generated by the node-level actions to be propagated. In essence, our FreeKD is a general and principled framework which can be naturally compatible with GNNs of different architectures. Extensive experiments on five benchmark datasets demonstrate our FreeKD outperforms two base GNNs in a large margin, and shows its efficacy to various GNNs. More surprisingly, our FreeKD has comparable or even better performance than traditional KD algorithms that distill knowledge from a deeper and stronger teacher GNN.
Graph-Based Multi-Robot Path Finding and Planning

Planning collision-free paths for multiple robots is important for real-world multi-robot systems and has been studied as an optimization problem on graphs, called multi-agent path finding (MAPF). This review surveys different categories of classic and state-of-the-art MAPF algorithms and different research attempts to tackle the challenges of generalizing MAPF techniques to real-world scenarios. Solving MAPF problems optimally is computationally challenging. Recent advances have resulted in MAPF algorithms that can compute collision-free paths for hundreds of robots and thousands of navigation tasks in seconds of runtime. Many variants of MAPF have been formalized to adapt MAPF techniques to different real-world requirements, such as considerations of robot kinematics, online optimization for real-time systems, and the integration of task assignment and path planning. Algorithmic techniques for MAPF problems have addressed important aspects of several multi-robot applications, including automated warehouse fulfillment and sortation, automated train scheduling, and navigation of non-holonomic robots and quadcopters. This showcases their potential for real-world applications of large-scale multi-robot systems.
A user-guided personalization methodology to facilitate new smart home occupancy

Abstract Smart homes are becoming increasingly popular in providing people with the services they desire. Activity recognition is a fundamental task to provide personalised home facilities. Many promising approaches are being used for activity recognition; one of them is data-driven. It has some fascinating features and advantages. However, there are drawbacks such as the lack of ability to providing home automation from the day one due to the limited data available. In this paper, we propose an approach, called READY (useR-guided nEw smart home ADaptation sYstem) for developing a personalised automation system that provides the user with smart home services the moment they move into their new house. The system development process was strongly user-centred, involving users in every step of the system’s design. Later, the user-guided transfer learning approach was introduced that uses an old smart home data set to enhance the existing smart home service with user contributions. Finally, the proposed approach and designed system were tested and validated in the smart lab that showed promising results.
What makes a scene? Fast scene categorization as a function of global scene information at different resolutions.

The ability to quickly and accurately categorize our environment into meaningful scene categories has been linked to the fast processing of global scene properties. Here, we causally tested this claim by reducing scene images to different sets of global scene properties with varying degrees of spatial resolution. In Experiment 1, human observers reached above-chance categorization accuracy for most stimulus conditions, indicating that images reduced to global scene properties indeed allow for correct categorization. In Experiment 2, we demonstrated that these features can be extracted from only 30 ms of stimulus presentation. However, accuracy in both experiments was far below performance on unmanipulated images and differed strongly between conditions. Images with higher resolutions yielded better performance than those with lower resolutions. In Experiment 3, we related this performance advantage to the identification of single objects in stimuli with higher resolutions which supports the notion that object information is used for scene categorization. Taken together, we show that global scene properties are useful but not sufficient for fast scene categorization. Instead, localized information is crucial in this process, presumably because it conveys object identities. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
VolcanoML: speeding up end-to-end AutoML via scalable search space decomposition

End-to-end AutoML has attracted intensive interests from both academia and industry which automatically searches for ML pipelines in a space induced by feature engineering, algorithm/model selection, and hyper-parameter tuning. Existing AutoML systems, however, suffer from scalability issues when applying to application domains with large, high-dimensional search spaces. We present VolcanoML, a scalable and extensible framework that facilitates systematic exploration of large AutoML search spaces. VolcanoML introduces and implements basic building blocks, which decompose a large search space into smaller ones, and allows users to utilize these building blocks to compose an execution plan for the AutoML problem at hand. VolcanoML further supports a Volcano-style execution model—akin to the one supported by modern database systems—to execute the plan constructed. Our evaluation demonstrates that, not only does VolcanoML raise the level of expressiveness for search space decomposition in AutoML, it also leads to actual findings of decomposition strategies that are significantly more efficient than the ones employed by state-of-the-art AutoML systems such as auto-sklearn.
KTN: Knowledge Transfer Network for Learning Multiperson 2D-3D Correspondences

Human densepose estimation, aiming at establishing dense correspondences between 2D pixels of human body and 3D human body template, is a key technique in enabling machines to have an understanding of people in images. It still poses several challenges due to practical scenarios where real-world scenes are complex and only partial annotations are available, leading to incompelete or false estimations. In this work, we present a novel framework to detect the densepose of multiple people in an image. The proposed method, which we refer to Knowledge Transfer Network (KTN), tackles two main problems: 1) how to refine image representation for alleviating incomplete estimations, and 2) how to reduce false estimation caused by the low-quality training labels (i.e., limited annotations and class-imbalance labels). Unlike existing works directly propagating the pyramidal features of regions for densepose estimation, the KTN uses a refinement of pyramidal representation, where it simultaneously maintains feature resolution and suppresses background pixels, and this strategy results in a substantial increase in accuracy. Moreover, the KTN enhances the ability of 3D based body parsing with external knowledges, where it casts 2D based body parsers trained from sufficient annotations as a 3D based body parser through a structural body knowledge graph. In this way, it significantly reduces the adverse effects caused by the low-quality annotations. The effectiveness of KTN is demonstrated by its superior performance to the state-of-the-art methods on DensePose-COCO dataset. Extensive ablation studies and experimental results on representative tasks (e.g., human body segmentation, human part segmentation and keypoints detection) and two popular densepose estimation pipelines (i.e., RCNN and fully-convolutional frameworks), further indicate the generalizability of the proposed method.