Reviewerly/exHarmony huggingface dataset:
The Influences of the Desert Photovoltaic Power Station on Local Climate and Environment: A Case Study in Dunhuang Photovoltaic Industrial Park, Dunhuang City, China in 2019

Based on the meteorological observation data of air temperature, surface temperature and albedo data retrieved from remote sensing images inside and outside the photovoltaic station, as well as the measured soil moisture content and bulk density at different locations of the photovoltaic power station in 2019, the impact of large-scale desert photovoltaic power plants on climate and environment was studied. The results show that air temperature, surface temperature and albedo inside the photovoltaic power station are lower than those outside the station, which are obvious in winter and not obvious in summer. Therefore, the photovoltaic power station is a cold source and an energy sink. The soil moisture content under and between the photovoltaic arrays is larger than other sampling points, and the soil bulk density gradually decreases with the distance from the center of the photovoltaic power station. Therefore, future plans for desert photovoltaic power station construction should take into account the impacts on local climate and environment.
Consistency-Aware Map Generation at Multiple Zoom Levels Using Aerial Image

The multilevel tiled map service is widely used and serves as a kind of digital infrastructure. These map tiles are usually rendered from vector data, whose update needs to walk or drive with professional equipment to check every point of interest. This leads to inconvenience and expensive cost in timely updating maps. Compared with vector data, aerial images are much easier and cheaper to obtain. In this article, we propose a novel multilevel map (MLM) generation framework that can automatically generate accurate and consistent maps with multiple zoom levels from aerial images. It consists of a level-aware map generator and a consistency-aware map generator. The level-aware map generator is able to generate accurate initial maps with realistic details for each zoom level. The consistency-aware map generator regards the initial maps at each zoom level as a sequence and builds the connection between them, so as to guarantee content consistency between maps at different zoom levels. Furthermore, we collect a large-scale high-quality dataset called MLM for map generation at multiple zoom levels. Experiments on our MLM dataset show that our method outperforms the previous state-of-the-art map generation methods on both comprehensive quantitative metrics and perceptual quality.
ICDVAE: An Effective Self-supervised Reinforcement Learning Method for behavior decisions of Non-player Characters in Metaverse Games

Reinforcement Learning (RL) is widely recognized as a powerful approach to constructing intelligent NPCs. Unlike general games, metaverse games tend to build up the larger, more complex, and more diverse virtual scenes with scores of various NPCs. In this case, it is challenging and hig-cost to design the reward functions for large-scale RL-based NPCs. To solve this challenge, we propose an effective distribution-aware curiosity mechanism termed Intrinsic Curiosity-Driven Variational Auto-Encoder (ICDVAE). It constructs the state distribution to rep-resent novelty by integrating the Intrinsic Curiosity Mechanism (ICM) and Generative Model (GM). In addition, we develop two effective techniques termed Inverse Dynamic Mechanism (IDM) and Variational Encoder Bottleneck (VEB) to extract the ego-motion feature and prevent overfitting. The empirical results show that our algorithm is more effective and robust than baselines on Atari games.
Stochastic Neighbor Embedding Based on Faure Sequence Initialized Chimp Optimization Algorithm

Stochastic neighbor embedding (SNE) is a nonlinear dimensionality reduction method of classical manifold learning in the field of artificial intelligence (AI). The main idea of SNE is to pursue the homeomorphism and isomorphism of the probability space for data points in both high-dimensional space and low-dimensional space. In SNE, Kullback-Leibler (KL) divergence is usually used to measure the approximation between two probability distributions, and it is usually solved by gradient descent method (GD) or stochastic gradient descent method (SGD). However, gradient dependent methods has the following shortcomings: easy to fall into the local minimum trap; the closer to the optimal value, the more possibility to appear sawtooth effect. In order to overcome the above shortcomings, in this paper, a Faure sequence initialized chimp optimization algorithm (FSChOA) has been proposed, which belongs to swarm intelligence algorithm and is also a derivative free optimization algorithm. By experimental comparison, it can be concluded that FSChOA is has more advantages in solving KL divergence for SNE.
Multi-receptive Field Aggregation Network for single image deraining

Image deraining is a significant problem that ensures the visual quality of images to prompt computer vision systems. However, due to the insufficiency of captured rain streaks features and global information, current image deraining methods often face the issues of rain streaks remaining and image blurring. In this paper, we propose a Multi-receptive Field Aggregation Network (MRFAN) to restore a cleaner rain-free image. Specifically, we construct a Multi-receptive Field Feature Extraction Block (MFEB) to capture rain features with different receptive fields. In MFEB, we design a Self-supervised Block (SSB) and an Aggregation Block (AGB). SSB can make the network adaptively focus on the critical rain features and rain-covered areas. AGB effectively aggregates and redistributes the multi-scale features to help the network simulate rain streaks better. Experiments show that our method achieves better results on both synthetic datasets and real-world rainy images.
What Makes Team[s] Work? A Study of Team Characteristics in Software Engineering Projects

Teaming is a core component in practically all professional software engineering careers, and as such, is a key skill taught in many undergraduate Computer Science programs. However, not all teams manage to work together effectively, and in education, this can deprive some students of successful teaming experiences. In this work, we seek to gain insights into the characteristics of successful and unsuccessful undergraduate student teams in a software engineering course. We conduct semi-structured interviews with 18 students who have recently completed a team-based software engineering course to understand how they worked together, what challenges they faced, and how they tried to overcome these challenges. Our results show that common problems include communicating, setting and holding to deadlines, and effectively identifying tasks and their relative difficulty. Additionally, we find that self-reflection on what is working and not working or external motivators such as grades help some, but not all, teams overcome these challenges. Finally, we conclude with recommendations for educators on successful behaviours to steer teams towards, and recommendations for researchers on future work to better understand challenges that teams face.
Designing obstacle’s map of an unknown place using autonomous drone navigation and web services

Purpose Drones are used in several purposes including examining areas, mapping surroundings and rescue mission operations. During these tasks, they could encounter compound surroundings having multiple obstacles, acute edges and deadlocks. The purpose of this paper is to propose an obstacle dodging technique required to move the drones autonomously and generate the obstacle's map of an unknown place dynamically. Design/methodology/approach Therefore, an obstacle dodging technique is essentially required to move autonomously. The automaton of drones requires complicated vision sensors and a high computing force. During this research, a methodology that uses two basic ultrasonic-oriented proximity sensors placed at the center of the drone and applies neural control using synaptic plasticity for dynamic obstacle avoidance is proposed. The two-neuron intermittent system has been established by neural control. The synaptic plasticity is used to find turning angles from different viewpoints with immediate remembrance, so it helps in decision-making for a drone. Hence, the automaton will be able to travel around and modify its angle of turning for escaping objects during the route in unknown surroundings with narrow junctions and dead ends. Furthermore, wherever an obstacle is detected during the route, the coordinate information is communicated using RESTful Web service to an android app and an obstacle map is generated according to the information sent by the drone. In this research, the drone is successfully designed and automated and an obstacle map using the V-REP simulation environment is generated. Findings Simulation results show that the drone effectively moves and turns around the obstacles and the experiment of using web services with the drone is also successful in generating the obstacle's map dynamically. Originality/value The obstacle map generated by autonomous drone is useful in many applications such as examining fields, mapping surroundings and rescue mission operations.
The third party will make a difference — A study on the impact of dyadic and triadic social situations on the relationship with a voice-based personal agent

As commercial voice-based personal agents (VPAs) like Google Assistant increasingly penetrate people’s private social habitats, sometimes involving more than one user, these social situations are gaining importance for how people define the human-machine relationship (HMR). The paper contributes to the understanding of the situation’s impact on HMR on a theoretical and methodological level. First, Georg Simmel’s theory on the “Quantitative determination of the group” is applied to the HMR. A 2x1 between-subjects quasi-experiment (N = 100) contrasted the defined HMR in dyadic social situations (one human interacting with the Google Assistant) to the defined HMR in triadic social situations (two humans interacting with the Google Assistant). Second, the method of central tendency analysis was extended by the more robust and informative comparison of distributions and quantiles using the two-sample Kolmogorov–Smirnov test and the shift function. The results show that the triadic situation, compared to the dyadic one, led to a more confounded categorization of the VPA’s subjecthood in terms of self-similarity, while simultaneously strengthening a definition of the relationship that resembled those of a business relation through lowered intimacy and feedback, mainly grounded in a more realistic definition of the agent’s inability to understand affects. In contrast to Simmel’s inter-human theory the relationship’s dimension of reciprocity and commitment remained unaffected by the situation. The paper discusses how these effects and non-effects of the triad could be explained by drawing on Simmel as well as peculiarities of HMR and methodology. Finally, it offers preliminary hypotheses about the situation’s implications for the HMR and outlines avenues for future research. • If the interaction is good, the first human perceives a higher self-similarity with the artificial agent when the second human is present. • When a second human is present, the first human estimates the artificial agent’s affect understanding at a more realistic level than in one-on-one situations with the agent. • The definition of reciprocity and interdependence were not affected by the quantity of the situation’s constellation. • If the artificial agent performs poorly, the presence of the second human decreases the perception of mutual attention and message understanding between the first human and the agent. • If the first human has already established a relationship with the artificial agent, the second human may destabilize this relationship by increasing the feeling of distance between the first human and the agent. • Methodological contribution: the analysis of distributions and quantiles allows for more robust and meaningful results than the analysis of central tendencies.
Yakubovich's method of recursive objective inequalities in machine learning

“Stripe” algorithm was designed by V.A. Yakubovich as an algorithm for linear classification model training. The basic approach is to reduce loss minimization problem to solving a system of infinite number of inequalities. The present paper considers various forms of this reduction as well as its practical applicability. Obtained algorithms are experimentally compared with traditional linear models such as logistic regression and linear regression trained using stochastic gradient descent. Simulation results show that the “Stripe” algorithm possesses fast convergence and in particular suitable well in the paradigm of online machine learning.
Decision support for healthcare cyber security

The pandemic has demonstrated that healthcare systems are prime targets for attackers. Finding an optimal security control set is a constant challenge for health organizations, where cost is a major consideration. The purpose of this paper is to demonstrate a healthcare cost optimization system as well as a case study based on two IT setup configurations that have been evaluated by medical experts as well as IT experts. These configurations would aid in conveying the complexity of the decision parameters and demonstrating how CySecTool handles this difficulty. In the study, 64 different security controls were linked to 70 vulnerabilities that could occur at any level of a hospital system dealing with both internal and external attacks/risks. The study also includes a novel visualization scheme that allows for the observation of vulnerabilities and also their subcategories based on Microsoft's STRIDE categorization.
Uneven wrapped phase pattern denoising using a deep neural network

The wrapped phase patterns obtained from an object composed of different materials have uneven gray values. In this paper, we improve the dilated-blocks-based deep convolution neural network (DBDNet) and build a new dataset for restoring the uneven gray values of uneven wrapped phase patterns as well as eliminating the speckle noise. In our method, we improve the structure of dilated blocks in DBDNet to enhance the ability of obtaining full scales of gray values and speckle noise information in the uneven phase patterns. We use the combined MS_SSIM+L1 loss function to improve the denoising and restoration performance of our method. We compare three representative networks ResNet-based, ADNet, and BRDNet in denoising with our proposed method. We test the three compared methods and our method on one group of computer-simulated and one group of experimentally obtained uneven noisy wrapped phase patterns from a dynamic measurement. We also conduct the ablation experiments on the improved model structure and the combined loss function used in our method. The denoising performance has been evaluated quantitatively and qualitatively. The denoising results demonstrate that our proposed method can reduce high speckle noise, restore the uneven gray values of wrapped phase patterns, and get better results than the compared methods.
A document analysis deep learning regression model for initial coin offerings success prediction

Initial coin offerings (ICOs) provide an early-stage financing method for blockchain-based ventures. During the ICO process, whitepapers are important not only as promotional material through which ventures can demonstrate the technical and financial project details but also as references for investors. Persuasion theory and the related literature suggest that the presentation and order of information have a significant impact on the attitude of the audience. Therefore, in addition to projects' metadata features, we construct a document analysis deep regression model (DADRM) to innovatively extract deep text and layout features from whitepapers. Based on a real-life dataset, we conduct a comparative study to assess the effectiveness of the proposed framework in predicting ICO success in terms of the funding amount. The empirical results show that our model that both extracts text content and retains the original 2D structure of the document can significantly reduce prediction error. Based on our proposed model, both ICO platforms and investors can prejudge the funding amount of cryptocurrency projects and mitigate information asymmetry. Additionally, this study demonstrates that both what is written in the business document and how the document is presented affect investor decisions.
QUBE – Towards Quantum Key Distribution with Small Satellites

Quantum Key Distribution (QKD) in space will be integral for future quan-tum networks. The space mission QUBE will test novel integrated optics components in a three-unit CubeSat demonstrating an economic route for global-scale QKD.
Effects of Refraction Model on Binocular Visuotactile Sensing of 3-D Deformation

This article presents a binocular visuotactile sensor (BVTS) and its model for reconstructing the 3-D deformation due to mechanical contact on its elastomer surface. The model explicitly accounts for the refraction effects of the elastomer on the depth map reconstructed from the imaged markers. To help gain physical insights into the refraction effects on the depth map reconstruction and experimentally analyze the effectiveness of the model-based BVTS, a prototype BVTS has been developed for experimental investigation. The refractive indexes of the elastomer, the acrylics, and their combination are experimentally determined from the model-based reconstruction and verified with that measured by a spectroscopic ellipsometer; the differences are within 3%. As a basis for evaluation, the reconstructed depth maps are compared with that of the two other commonly used practices, stereovision (SV) calibration without considering refraction and integration camera model that lumps the refraction effects with the SV parameters during calibration. By explicitly modeling the refraction physics using ray tracing, the model-based BVTS faithfully reconstructs the points on the actual height and is capable of dynamically tracking small torsional displacements with balls of different radii (3–15 mm) with a depth error below 4%.
COVIBOT: A Smart Chatbot for Assistance and E-Awareness during COVID-19 Pandemic

The coronavirus pandemic has spread over the past two years in our highly connected and information-dense society. Nonetheless, disseminating accurate and up-to-date information on the spread of this pandemic remains a challenge. In this context, opting for a solution based on conversational artificial intelligence, also known under the name of the chatbot, is proving to be an unavoidable solution, especially since it has already shown its effectiveness in fighting the coronavirus crisis in several countries. This work proposes to design and implement a smart chatbot on the theme of COVID-19, called COVIBOT, which will be useful in the context of Saudi Arabia. COVIBOT is a generative-based contextual chatbot, which is built using machine learning APIs that are offered by the cloud-based Azure Cognitive Services. Two versions of COVIBOT are offered: English and Arabic versions. Use cases of COVIBOT are tested and validated using a scenario-based approach.
Robust Dynamic Surface Control of Unmanned Aerial Vehicles with Constrained inputs and Unmodelled Dynamics

The approach used in this work is the synthesis of an effective control strategy to deal with uncertainties in the unmanned aerial vehicle's (UAV) characteristics, as well as the theoretical derivation of an adequate theory to overcome the saturation effect. The robust dynamic surface control technique begins by segmenting the unmanned aerial vehicle dynamics into two loops: attitude and position. Then, based on the attitude and position error variables, which comprise attitude angles and position variables, appropriate dynamic surface variables are developed for each loop. The unmodeled dynamics are studied while taking their respective topological features into consideration. The research study's contributions include a novel control approach that accounts for the impact of nonlinear saturation in the input as well as unmodeled dynamics seen in UAV dynamics, resulting in rapid, precise, and reliable attitude variable stabilization. According to the results of this study, the suggested control approach eliminates high frequency oscillations, takes minimal control effort, and provides quick convergence to the appropriate operational setpoint.
Standardization of Financial Blockchain: Technologies, Challenges, and Future

Blockchain technology has developed rapidly in recent years. Especially, in China, it is included in country’s new information infrastructure. As one of the important digital technologies for China’s digital economy, blockchain has many applications in the financial industry, and the development of financial blockchain standards has also been widely carried out. The four major international standardization organizations and China’s financial blockchain standardization have achieved preliminary results, but there are still some problems in the financial blockchain at present. The next step can be carried out from four aspects: building a standard system, strengthening the supervision of financial blockchain, participating in international standardization affairs and training of standardized talents.
Synchronization of Real and Virtual Processes in the Context of the Industrial Revolution 4.0

In the context of the functioning and evolution of modern information society, processes that are actually implemented in the economy, public life and, recently, even in politics, in a certain way significantly correlate with the totality of information processes in numerous social networks and Internet communities. Information relationships between objects, systems and processes of any nature are quite logical to interpret and analyze as multidimensional information connections and interactions implemented within the framework of coordination of actions aimed at successful achievement of the hierarchy of global and local goals. This coordination is to provide some minimum level of uncertainty regarding the determination of a number of optimal scenarios for the development of relevant events in the short and long term. The intensive exchange of information contributes to such successful coordination, which, as a rule, is due to the need to adjust the goals of the functioning of objects and their likely behavior. An important aspect, in this case, is the reliability and objectivity of the information generated in the virtual space, on the basis of which management decisions are made. In particular, it is necessary to identify and stop the so-called «fakes». Mutual coordination of the actions of objects and systems, based on a compromise of their interests, can be interpreted as a targeted synchronization of the relevant processes and the set of actions implemented in the virtual and real world.