Reviewerly/exHarmony huggingface dataset:
Social extended reality—use case entities on property life cycle

Virtual reality (VR), augmented reality (AR) and mixed reality (MR) are collectively referred to as extended reality (XR). In recent years, the hardware and software of said technologies have developed to the point where multiuser experiences have been enabled; thus, the conversation surrounding social extended reality (SXR) has begun. SXR––or collaborative extended reality—has many possible use cases in a property’s life cycle, and the architecture, engineering, and construction (AEC) industry can be its forefront user. The AEC industry’s work assignments include visuality and multiple stakeholders; thus, the SXR’s level of applicability is high throughout the industry. Despite these possibilities, we need a more structured understanding of SXR use cases to aid in its deployment. The current research article examines and identifies the five key entities from those use cases: involving stakeholders in decision making, effective teamwork, remote assistance, training and simulation and sales and marketing; it also recognises the greatest potential entities for utilisation from a value-creation standpoint and considers the transferability of these entities to other business sectors. Qualitative empirical data were collected in twenty-one semi-structured interviews and three focus groups with professionals from the AEC industry. The focus groups and semi-structured interviews surveyed SXR’s use cases in the property life cycle, examined the situation of companies’ invocation of the technology and contemplated the benefits and obstacles of using SXR.
Synthesizing Informative Training Samples with GAN

Remarkable progress has been achieved in synthesizing photo-realistic images with generative adversarial networks (GANs). Recently, GANs are utilized as the training sample generator when obtaining or storing real training data is expensive even infeasible. However, traditional GANs generated images are not as informative as the real training samples when being used to train deep neural networks. In this paper, we propose a novel method to synthesize Informative Training samples with GAN (IT-GAN). Specifically, we freeze a pre-trained GAN model and learn the informative latent vectors that correspond to informative training samples. The synthesized images are required to preserve information for training deep neural networks rather than visual reality or fidelity. Experiments verify that the deep neural networks can learn faster and achieve better performance when being trained with our IT-GAN generated images. We also show that our method is a promising solution to dataset condensation problem.
Neural Network-Based Ultra-High-Definition Video Live Streaming Optimization Algorithm

Online live streaming has been widely used in distant teaching, online live shopping, and so on. Particularly, online teaching live streaming breaks the time and space boundary of teaching and has better interactivity, which is a new distant education mode. As a new online sales model, online live shopping promotes the rapid development of Internet economy. However, the quality of live video affects the user experience. This paper studies the optimization algorithm of ultra-high-definition live streaming, focusing on superresolution technology. Convolutional neural network (CNN) is a multilayer artificial neural network designed to process two-dimensional input data. It takes advantage of CNN in image processing. This paper proposes an image superresolution algorithm based on hybrid dilated convolution and Laplacian pyramid. By mixing the dilated convolution module, the receptive field of the network can be improved more effectively to obtain more context information so that the high-frequency features of the image can be extracted more effectively. Experiment was running on Set5, Set14, Urban100, and BSD100 datasets, and the results reveal that the proposed algorithm outperforms baselines with respect to peak signal to noise ratio (PSNR), structural similarity index measurement (SSIM), and image quality.
On the issue of the formation of pedagogues-defectologists’ professional qualities in the conditions of distance learning

The article presents the characteristics of the distance learning as the modern version of teaching students of a pedagogic university; we list the main distance educational technologies; we reveal the essence and features of using e-learning environment "IOS Moodle" and learning management system “Mirapolis LMS”; we analyse the possibilities provided by “Mirapolis LMS”. It is indicated that professional pedagogic competency is by a number of authors considered to be a community of theoretical and practical readiness of students to implement pedagogic activities. The essential characteristics of competency are commented on by the results of a survey of students about the possibilities of using distance learning to form important professional qualities of teachers-defectologists; the results of a survey of students of Defectology Faculty on the possibility of effective use of distance learning for the formation of a number of their special professional competences are presented; the results of a survey of students about the formed skills to plan correctional and developmental work taking into account the structure of the violation are of interest as well as the current state and potential capabilities of disabled persons and carrying out psychological, medical and pedagogic support of such people on issues of education, upbringing, development and their social adaptation. The article lists the main difficulties of organising distance learning for students – defectologists and the directions of solving the identified problems.
Driven Gaussian quantum walks

Quantum walks function as essential means to implement quantum simulators, allowing one to study complex and often directly inaccessible quantum processes in controllable systems. In this contribution, the notion of a driven Gaussian quantum walk is introduced. In contrast to typically considered quantum walks in optical settings, we describe the operation of the walk in terms of a nonlinear map rather than a unitary operation, e.g., by replacing a beam-splitter-type coin with a two-mode squeezer, being a process that is controlled and driven by a pump field. This opens previously unattainable possibilities for quantum walks that include nonlinear elements as core components of their operation, vastly extending their range of applications. A full framework for driven Gaussian quantum walks is developed, including methods to dynamically characterize nonlinear, quantum, and quantum-nonlinear effects. Moreover, driven Gaussian quantum walks are compared with their classically interfering and linear counterparts, which are based on classical coherence of light rather than quantum superpositions. In particular, the generation and boost of highly multimode entanglement, squeezing, and other quantum effects are studied over the duration of the nonlinear walk. Importantly, we prove the quantumness of the evolution itself, regardless of the input state. A scheme for an experimental realization is proposed. Furthermore, nonlinear properties of driven Gaussian quantum walks are explored, such as amplification that leads to an ever increasing number of correlated quantum particles, constituting a source of new walkers during the walk. Therefore, a concept for quantum walks is proposed that leads to -- and even produces -- directly accessible quantum phenomena, and that renders the quantum simulation of nonlinear processes possible.
A Comprehensive Survey on Trustworthy Graph Neural Networks: Privacy, Robustness, Fairness, and Explainability

Graph Neural Networks (GNNs) have made rapid developments in the recent years. Due to their great ability in modeling graph-structured data, GNNs are vastly used in various applications, including high-stakes scenarios such as financial analysis, traffic predictions, and drug discovery. Despite their great potential in benefiting humans in the real world, recent study shows that GNNs can leak private information, are vulnerable to adversarial attacks, can inherit and magnify societal bias from training data and lack interpretability, which have risk of causing unintentional harm to the users and society. For example, existing works demonstrate that attackers can fool the GNNs to give the outcome they desire with unnoticeable perturbation on training graph. GNNs trained on social networks may embed the discrimination in their decision process, strengthening the undesirable societal bias. Consequently, trustworthy GNNs in various aspects are emerging to prevent the harm from GNN models and increase the users' trust in GNNs. In this paper, we give a comprehensive survey of GNNs in the computational aspects of privacy, robustness, fairness, and explainability. For each aspect, we give the taxonomy of the related methods and formulate the general frameworks for the multiple categories of trustworthy GNNs. We also discuss the future research directions of each aspect and connections between these aspects to help achieve trustworthiness.
Performance Evaluation System Based on Online Monitoring of Internet of Things

Research on performance evaluation system based on online monitoring of Internet of things technology, taking medical imaging equipment as an example. Build the performance management platform of the Internet of things through four aspects: data acquisition, network architecture, CPU, and display output. Select 100 medical imaging devices in clinical use in the hospital and divide them into the control group (50 sets) and the observation group (50 sets) according to different management modes. The control group adopts conventional performance management mode, and the observation group implements Internet of things performance management mode. Compare the differences of equipment operation performance, cost-effectiveness, and service effect between the two groups. The experimental results showed that the equipment utilization rate, operation rate, inspection schedule balance, and technician schedule balance of the observation group were better than those of the control group, and the difference was statistically significant ( <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mi>P</mi> </math> &lt; 0.05). The quarterly cost-benefit increase of x-ray examination equipment, CT equipment, Mr equipment, interventional radiotherapy equipment, color ultrasound instrument, and nuclear medicine equipment in the observation group was higher than that in the control group ( <math xmlns="http://www.w3.org/1998/Math/MathML" id="M2"> <mi>P</mi> </math> &lt; 0.05). The satisfaction of imaging doctors, technicians, engineers, and patients with the equipment service quality of the observation group was higher than that of the control group; the difference was statistically significant ( <math xmlns="http://www.w3.org/1998/Math/MathML" id="M3"> <mi>P</mi> </math> &lt; 0.05). It is proved that the Internet of things performance management platform of medical imaging equipment can solve the problems of untimely information collection and unscientific scheduling during equipment operation, realize the real-time display and statistics of equipment operation performance, and improve the operation efficiency and service level of medical imaging equipment.
Computer-Assisted Quantitative Analysis of Skeletal Muscles of Snowboarding Parallel Giant Slalom Athletes after Exercise Based on Artificial Intelligence and Complex Networks

The snowboarding project has the characteristics of high risk and high technical level. The current publicity level is not high, and the number of participants is also very limited. Another potential advantage medal breakthrough project that is expected to be achieved in the Winter Olympics has received a lot of attention, creating favorable opportunities for the promotion and development of this project in China. The event requires good special physical support, skeletal muscle contraction is the body to produce motor function, and special physical training and recovery are key factors for athletes to obtain excellent results in the competition. This article is aimed at performing ultrasonic quantitative analysis on the skeletal muscles of skiers after exercise based on artificial intelligence and complex networks and at studying the skeletal muscle conditions of snowboarders after exercise, so as to provide a certain theoretical basis for coaches in future scientific training. Based on a large amount of literature, this paper uses variational optical flow calculation and split Bregman method to solve the typical HS model, L1-L2 model, and L1-high-order model, respectively, and uses the motion estimation method to describe the movement of muscles. An experiment was designed to collect ultrasound images of the gastrocnemius and quadriceps muscles during contraction. In addition, a motion target positioning algorithm was used to obtain some motion parameters, which provided direct help for athletes in rationally arranging training load and scientific training. The experimental results in this paper show that the muscle motion features extracted from the ultrasound sequence images can quantitatively express a lot of important information about the skeletal muscle motion form and function and have potential practical application value. And the different invariants of each type of ski trajectory vary greatly, floating between 1.5429 and 7.6759.
Effects of Pre-Task Planning and Source Texts on Korean EFL College Learners’ Summary Writing

This study aims to investigate to what extent pre-task planning and the source text type(genre) affect Korean English as a Foreign Language (EFL) college learners' summary writings in terms of lexical, sentential, and discourse-level features.A total of 120 summary writings of cause/effect expository texts and argumentative texts in the different modes of planning were collected and analyzed using a computational assessment tool, Coh-Metrix.The results show that the participants' summary writings in the different planning conditions and text types were statistically different according to their lexical-level (the mean word length, word frequency, imageability, concreteness, the third person pronouns), sentential-level (the mean sentence length, causal connectives, temporal connectives, noun density, Flesch-Reading Ease (FRE), Flesch-Kincaid Grade Level (FKGL)), and discourse-level (typetoken ratio, LSA cosines for all and adjacent sentences) features.This study provides some pedagogical implications for teaching English summary writings of various planning contexts and source text types.
Back to the Future: Bidirectional Information Decoupling Network for Multi-turn Dialogue Modeling

Multi-turn dialogue modeling as a challenging branch of natural language understanding (NLU), aims to build representations for machines to understand human dialogues, which provides a solid foundation for multiple downstream tasks. Recent studies of dialogue modeling commonly employ pre-trained language models (PrLMs) to encode the dialogue history as successive tokens, which is insufficient in capturing the temporal characteristics of dialogues. Therefore, we propose Bidirectional Information Decoupling Network (BiDeN) as a universal dialogue encoder, which explicitly incorporates both the past and future contexts and can be generalized to a wide range of dialogue-related tasks. Experimental results on datasets of different downstream tasks demonstrate the universality and effectiveness of our BiDeN.
Immunophenotyping Investigation in Acute and Chronic Leukemias as a Gold Standard Test

Immunophenotyping of leukemias and other hematological neoplasms has become one of the most relevant clinical applications of flow cytometry which remains an indispensable tool for the diagnosis, classification, staging as well as hematologic malignancies monitoring. It can provide quantitative data on a number of cell parameters for a large number of cells in the region of cells thousands per second which making it a powerful diagnostic tool. If immunophenotyping is more sensitive significantly, specific and objective than morphological studying, the latter remains the gold standard criterion for creating and establishing the blast percentage in presentation and post treatment together. Moreover, it is important that interpreters of flow cytometric data have the phenotypes exact knowledge of diverse normal cell populations, which can be one of the best tool in recognize deviations from normal as well as are able to discuss the potential clinical significance and knowing look of the flow cytometric findings.
A Study on Synergistic Development of Innovative Public Management and Economic Growth Based on Big Data

Since the twenty-first century, the Internet of Things technology has developed rapidly, and its impact on China’s economy and society is far-reaching, and its significance is also far-reaching. This makes more and more scholars start to pay attention to the related issues of the Internet of Things and economic development. The Internet of Things provides a superior information platform for various economic entities, enabling economic exchanges to transcend the boundaries of time and space. The IoT generates a lot of big data, and it is not easy to get valuable insights from the huge amount of data in various formats, ensuring that sensors are working properly and data is transmitted securely and processed efficiently. In order to study the role of the Internet of Things in promoting economic growth, clarify the status of the Internet of Things in the national economy and explore public management innovation as a change in actual social relations. This paper analyzes the application of the Internet of Things technology in public management. In order to deal with the massive multisource heterogeneous data involved in public management, this paper proposes a resource optimization scheduling algorithm for differentiated distributed storage systems and focuses on the analysis of China’s economic growth in recent years. Through experiments, it is found that from 2014 to 2020, China’s economy will continue to maintain rapid growth, with a GDP of 101,5986 billion yuan. Since 2014, the number and quality of R&amp;D personnel in China have increased significantly. In 2020, due to the resumption of growth in corporate profits in the second half of the year, the cumulative decline in revenue will result in a decline in domestic value-added tax by 8.9% and corporate income tax by 2.4%.
Local body shape control of an articulated mobile robot and an application for recovery from a stuck state

This paper proposes a method for an articulated mobile robot to change its local body shape in a complex environment. By coexisting a heuristic approach which is easy to design a motion in a complex three-dimensional environment and a model-based approach which can utilize the redundancy of the robot, the robot achieves propulsion in a complex environment and the local body shape control simultaneously. A part of the robot's body shape is changed based on a kinematic model, whereas the rest of it is controlled to follow the original shape or maintain its original position based on a heuristic method. As an application of the proposed method, the recovery motion from a stuck state is proposed. The robot is recovered from a stuck state by controlling the part of the robot that is in collision with an obstacle to move it away from the obstacle. The effectiveness of the proposed method is verified by experiments on an actual robot.
Video Action Detection: Analysing Limitations and Challenges

Beyond possessing large enough size to feed data hungry machines (eg, transformers), what attributes measure the quality of a dataset? Assuming that the definitions of such attributes do exist, how do we quantify among their relative existences? Our work attempts to explore these questions for video action detection. The task aims to spatio-temporally localize an actor and assign a relevant action class. We first analyze the existing datasets on video action detection and discuss their limitations. Next, we propose a new dataset, Multi Actor Multi Action (MAMA) which overcomes these limitations and is more suitable for real world applications. In addition, we perform a biasness study which analyzes a key property differentiating videos from static images: the temporal aspect. This reveals if the actions in these datasets really need the motion information of an actor, or whether they predict the occurrence of an action even by looking at a single frame. Finally, we investigate the widely held assumptions on the importance of temporal ordering: is temporal ordering important for detecting these actions? Such extreme experiments show existence of biases which have managed to creep into existing methods inspite of careful modeling.
Generalized Triangular Dynamical System: An Algebraic System for Constructing Cryptographic Permutations over Finite Fields

In recent years a new class of symmetric-key primitives over $\mathbb{F}_p$ that are essential to Multi-Party Computation and Zero-Knowledge Proofs based protocols have emerged. Towards improving the efficiency of such primitives, a number of new block ciphers and hash functions over $\mathbb{F}_p$ were proposed. These new primitives also showed that following alternative design strategies to the classical Substitution-Permutation Network (SPN) and Feistel Networks leads to more efficient cipher and hash function designs over $\mathbb{F}_p$ specifically for large odd primes $p$. In view of these efforts, in this work we build an \emph{algebraic framework} that allows the systematic exploration of viable and efficient design strategies for constructing symmetric-key (iterative) permutations over $\mathbb{F}_p$. We first identify iterative polynomial dynamical systems over finite fields as the central building block of almost all block cipher design strategies. We propose a generalized triangular polynomial dynamical system (GTDS), and based on the GTDS we provide a generic definition of an iterative (keyed) permutation over $\mathbb{F}_p^n$. Our GTDS-based generic definition is able to describe the three most well-known design strategies, namely SPNs, Feistel networks and Lai--Massey. Consequently, the block ciphers that are constructed following these design strategies can also be instantiated from our generic definition. Moreover, we find that the recently proposed \texttt{Griffin} design, which neither follows the Feistel nor the SPN design, can be described using the generic GTDS-based definition. We also show that a new generalized Lai--Massey construction can be instantiated from the GTDS-based definition. We further provide generic analysis of the GTDS including an upper bound on the differential uniformity and the correlation.