Reviewerly/exHarmony huggingface dataset:
An online reliable local trajectory planner for quadrotors

Purpose This paper aims to propose an online local trajectory planner for safe and fast trajectory generation that combines the jerk-limited trajectory (JLT) generation algorithm and the particle swarm optimization (PSO) algorithm. A trajectory switching algorithm is proposed to improve the trajectory tracking performance. The proposed system generates smooth and safe flight trajectories online for quadrotors. Design/methodology/approach First, the PSO algorithm method can obtain the optimal set of target points near the path points obtained by the global path searching. The JLT generation algorithm generates multiple trajectories from the current position to the target points that conform to the kinetic constraints. Then, the generated multiple trajectories are evaluated to pick the obstacle-free trajectory with the least cost. A trajectory switching strategy is proposed to switch the unmanned aerial vehicle (UAV) to a new trajectory before the UAV reaches the last hovering state of the current trajectory, so that the UAV can fly smoothly and quickly. Findings The feasibility of the designed system is validated through online flight experiments in indoor environments with obstacles. Practical implications The proposed trajectory planning system is integrated into a quadrotor platform. It is easily implementable onboard and computationally efficient. Originality/value The proposed local planner for trajectory generation and evaluation combines PSO and JLT generation algorithms. The proposed method can provide a collision-free and continuous trajectory, significantly reducing the required computing resources. The PSO algorithm locally searches for feasible target points near the global waypoint obtained by the global path search. The JLT generation algorithm generates trajectories from the current state toward each point contained by the target point set. The proposed trajectory switching strategy can avoid unnecessary hovering states in flight and ensure a continuous and safe flight trajectory. It is especially suitable for micro quadrotors with a small payload and limited onboard computing power.
Effectiveness of Using the Quizzz Application in Islamic Religious Education

Since the Covid-19 pandemic, in human life there have been many influences in the field of education—both in the form of systems and characters, as well as the learning media used. Every time there is a change, it is sure that there will be an innovation regarding learning media which is, of course, IT-based. This research was conducted to determine the effectiveness of the Quiziz application media in Islamic Religious Education subjects in improving student learning outcomes at SMA Negeri 7 Malang. This research uses descriptive quantitative methods, with data collected through observation, documentation and questionnaires. Data sourced from this study were taken from students and students of class X SMA Negeri 7 Malang. Based on the results of research conducted by researchers, it can be concluded that: (1) learning media using the Quizizz application can be applied in teaching and learning activities because the Quizizz application is an interactive and fun application in which there are various features such as quizzes, surveys, games and discussions. (2) in its application, learning media using the Quizizz application is quite liked by students. (3) from the results of this study, the researcher feels that learning media using the Quizizz application is very effective and can increase the motivation and desire of students to understand and explore the subject matter given by the teacher, especially Islamic Religious Education subjects.
MASAC-based confrontation game method of UAV clusters

With the improvement in the intelligence level of UAVs and the development of cluster control technology, the intelligent decision-making method of UAV cluster confrontation will become the key technology of UAV combat in the future. The UAV cluster confrontation learning environment is of complex characteristics of high dimension, non-linearity, incomplete information, continuous action space, and so on. Recently, artificial intelligence technology represented by deep learning and reinforcement learning has made a great breakthrough. Deep reinforcement learning has shown a great ability to solve intelligent decision-making problems in complex environments. In this paper, enlightened by the multi-agent centralized training distributed execution framework and the idea of maximum policy entropy, we propose a deep reinforcement learning method of the multi-agent soft actor-critic (MASAC) with incomplete information. A game model for UAV cluster confrontation based on multi-agent deep reinforcement learning is established, and a continuous space multiple UAV combat environment is constructed. Simulation experiments are performed on the asymmetric confrontation of UAV clusters with red and blue teams. The experimental results show that MASAC outperforms the existing popular multi-agent deep reinforcement learning methods, making the game players converge to a higher return equilibrium point of the game. Moreover, the convergence of MASAC is investigated and analyzed extensively, and the results show that MASAC is of good convergence and stability, ensuring the practicability of MASAC in intelligent decision-making regarding UAV cluster confrontation.
Phase Calibration of the Parametric Gate in the Superconducting Circuits

The quantum gate, as the foundation of quantum computation, depends on the architecture of the quantum chip, especially in superconducting systems. The parametric modulation to the flux bias is an important protocol to realize the multi‐qubit gate, which has some intrinsic advantages. Similar to other two‐qubit operations, parametric gates leave the extra phase in single‐qubit space, which is required to be corrected. In this article, the relation between the phases of the modulated pulse and the applied microwaves is experimentally characterized. By adjusting the envelope shape and phase of the applied pulses, the extra phase of the one qubit generated by the two‐qubit gate is suppressed via a simple and fast Ramsey‐like experiment. The tomography results demonstrate that this protocol is an effective calibrating method for future large‐scale quantum circuit manipulation.
Performance Evaluation of Novel Random Biased-Genetic Algorithm (NRB-GA): A Hybrid Load Balancing Algorithm in a Cloud Computing Environment

A novel random biased-genetic algorithm (NRB-GA) load-balancing algorithm that exhibits the characteristics of both genetic algorithms and biased random algorithms is designed and developed to improve the processing time and response time metrics of the cloud computing environment. The NRB-GA is designed to discover a virtual machine with fewer loads by applying a genetic algorithm with a fitness function that is inversely proportional to the average load over a period of time for each virtual machine and with biased parent selection to maximize the fitness values of offspring. The developed NRB-GA load-balancing algorithm is evaluated by analysing its performance for various simulated scenarios in a cloud computing environment with different user bases and data center configurations. The analysis of the experimental results of NRB-GA indicates that the average response time is reduced by 27.22%, 21.15%, and 22.34%, and the processing time is reduced by 25.73%, 16.14%, and 18.82% for one, two, and three data centers, respectively. It is evident that the proposed NRB-GA algorithm for load balancing outperforms other existing algorithms significantly.
Financial Constraints, Productivity, and Investment: Evidence from Lithuania

This paper studies the relation between firms' access to finance, labor productivity and investment using Lithuanian firm-level data from 2000-2018.To do so, we construct a measure of financial constraints.We estimate that, given firm characteristics, removing these constraints can improve average productivity and investment of firms in Lithuania by 0.51 percent and 7.2 percent, respectively.Our results further suggest that policies targeting firm age and size together will be more effective in mitigating the impact of financial constraints as the relationship between firm age and size with financial constraints exhibits nonlinearities.
Open Domain Multi-document Summarization: A Comprehensive Study of Model Brittleness under Retrieval

Multi-document summarization (MDS) assumes a set of topic-related documents are provided as input. In practice, this document set is not always available; it would need to be retrieved given an information need, i.e. a question or topic statement, a setting we dub "open-domain" MDS. We study this more challenging setting by formalizing the task and bootstrapping it using existing datasets, retrievers and summarizers. Via extensive automatic and human evaluation, we determine: (1) state-of-the-art summarizers suffer large reductions in performance when applied to open-domain MDS, (2) additional training in the open-domain setting can reduce this sensitivity to imperfect retrieval, and (3) summarizers are insensitive to the retrieval of duplicate documents and the order of retrieved documents, but highly sensitive to other errors, like the retrieval of irrelevant documents. Based on our results, we provide practical guidelines to enable future work on open-domain MDS, e.g. how to choose the number of retrieved documents to summarize. Our results suggest that new retrieval and summarization methods and annotated resources for training and evaluation are necessary for further progress in the open-domain setting.
An Indian Currency Recognition Model for Assisting Visually Impaired Individuals

Visually impaired persons find it extremely difficult to perform cash transactions in outdoor environments. For assisting the visually challenged individuals, a YOLOv5 based deep neural network was designed to detect image based currency denominations. Thereby aid in completing the authentic transaction. The robust model was trained for images with currency notes in different backgrounds, multiple sides of the currency notes presented, notes around cluttered objects, notes near reflective surfaces, and blurred images of the currency notes. An annotated and augmented dataset of around 10,000 original images was created for developing the model. A pre-processing step to rescale all the images to 224 × 224 was applied to standardize the input to the neural network, and generalize the model for different platforms including single board computer and smartphones. The trained model showcased an average denomination recognition accuracy of 92.71% for an altogether different dataset. The trained model was deployed on Raspberry-Pi and Smartphone independently, and the outcome to detect the currency denomination from the image was successfully demonstrated. The model showcased adequate performance on different platforms, leading to the exploration of several other assistive applications based on the currency recognition model to improve the standard of living for visually challenged individuals.
RDA: Reciprocal Distribution Alignment for Robust Semi-supervised Learning

In this work, we propose Reciprocal Distribution Alignment (RDA) to address semi-supervised learning (SSL), which is a hyperparameter-free framework that is independent of confidence threshold and works with both the matched (conventionally) and the mismatched class distributions. Distribution mismatch is an often overlooked but more general SSL scenario where the labeled and the unlabeled data do not fall into the identical class distribution. This may lead to the model not exploiting the labeled data reliably and drastically degrade the performance of SSL methods, which could not be rescued by the traditional distribution alignment. In RDA, we enforce a reciprocal alignment on the distributions of the predictions from two classifiers predicting pseudo-labels and complementary labels on the unlabeled data. These two distributions, carrying complementary information, could be utilized to regularize each other without any prior of class distribution. Moreover, we theoretically show that RDA maximizes the input-output mutual information. Our approach achieves promising performance in SSL under a variety of scenarios of mismatched distributions, as well as the conventional matched SSL setting. Our code is available at: https://github.com/NJUyued/RDA4RobustSSL.
Review on Metaverse's Market Development

Metaverse is a new type of Internet application and social form that integrates a variety of new technologies. It provides an immersive experience based on extended reality technology, generates a mirror of the real world based on digital twin technology, and builds an economic system based on blockchain technology. From the perspective of the development of the metaverse industry, this paper first discusses the development background of the metaverse industry, explores the concept and economic connotation of the metaverse, outlines the background and opportunities for the development of the metaverse industry, and comprehensively discusses and analyzes the various aspects of the metaverse industry in theory. The reshaping effect of the metaverse industry development, at the same time, summarizes the three stages of the metaverse industry development and the characteristics of each stage, and discusses with the life cycle theory. Finally, the evolution prospect of the metaverse industry development is described and given. policy suggestion.
CGUN-2A: Deep Graph Convolutional Network via Contrastive Learning for Large-Scale Zero-Shot Image Classification

Taxonomy illustrates that natural creatures can be classified with a hierarchy. The connections between species are explicit and objective and can be organized into a knowledge graph (KG). It is a challenging task to mine features of known categories from KG and to reason on unknown categories. Graph Convolutional Network (GCN) has recently been viewed as a potential approach to zero-shot learning. GCN enables knowledge transfer by sharing the statistical strength of nodes in the graph. More layers of graph convolution are stacked in order to aggregate the hierarchical information in the KG. However, the Laplacian over-smoothing problem will be severe as the number of GCN layers deepens, which leads the features between nodes toward a tendency to be similar and degrade the performance of zero-shot image classification tasks. We consider two parts to mitigate the Laplacian over-smoothing problem, namely reducing the invalid node aggregation and improving the discriminability among nodes in the deep graph network. We propose a top-k graph pooling method based on the self-attention mechanism to control specific node aggregation, and we introduce a dual structural symmetric knowledge graph additionally to enhance the representation of nodes in the latent space. Finally, we apply these new concepts to the recently widely used contrastive learning framework and propose a novel Contrastive Graph U-Net with two Attention-based graph pooling (Att-gPool) layers, CGUN-2A, which explicitly alleviates the Laplacian over-smoothing problem. To evaluate the performance of the method on complex real-world scenes, we test it on the large-scale zero-shot image classification dataset. Extensive experiments show the positive effect of allowing nodes to perform specific aggregation, as well as homogeneous graph comparison, in our deep graph network. We show how it significantly boosts zero-shot image classification performance. The Hit@1 accuracy is 17.5% relatively higher than the baseline model on the ImageNet21K dataset.
Study on Denoising Microseismic Signal Based on Autoencoder Convolutional Neural Networks

As a kind of dynamic real-time monitoring technology, microseismic monitoring technology has been widely used for rockburst warning. Due to the complexity of the actual monitoring environment, the monitoring signals often contain different types of noise, affecting the earning warning of rockburst. In this study, an Autoencoder Convolutional Neural Network denoising model based on deep learning has been proposed to denoising of the complex signals. The unsupervised adaptive training method is used to train the model, which only needs to set its initial parameters. The importance of an enhanced training dataset is illustrated by the comparison experiment. The results indicate that the training and verification shows well performance during training. The denoising efficiency of the proposed model is studied by the denoising of the synthetic noise-containing signals. Furthermore, the dataset from the water conveyance tunnel in the Hanjiang-to-Weihe River water diversion project (HJ-Project) in Shaanxi Province is taken as an engineering example to evolute the performance of the proposed model for practical project. The denoising performance of the model is analysed through the visual denoising results and evaluation index. The model can effectively denoise the complex noised signal which separate it into pure microseismic signal and noise signal, and improve the signal-to-noise ratio, which is benefit for arrive-time picking and source locating then improve the performance of early warning of rockburst.
Import substitution: Regulative acts and library reality

Various aspects of import substitution strategy introduced in Russia in response to the Western sanctions are examined. The aim was to analyze regulative and legislative acts related to the library policy and activities. The key documents are named; the principles of import substitution are reviewed: the registers of software and databases of the Eurasian Economic Union member states are listed; the alternatives to blocked Internet-based resources and services are suggested. The libraries encounter certain difficulties in their transferring to domestic software. The reason is that there is the vast array of programs for MS Windows and the lack of the similar national software. You cannot replace MS Windows in all library computers in the single step, so for some time you will have to use diverse OS. The author suggests and examines four options to solve the problem: through designing crossplatform software; platform-independent software, WINE compatibility layer to run existing Windows programs in domestic OS, or using terminal access to the software. The majority of specialized library software, in particular automated information library systems (ALIS) designed by Russian providers are either the client-server software for MS Windows or domestic OS via WINE compatibility layer, or the platform-independent software. The author concludes that designing platform-independent software based on web-technologies and cloud software, in particular, is the most promising vector of development.
Industry's cry for tools that support large-scale refactoring

Software refactoring plays an important role in software engineering. Developers often turn to refactoring when they want to restructure software to improve its quality without changing its external behavior. Compared to small-scale (floss) refactoring, many refactoring efforts are much larger, requiring entire teams and months of effort, and the role of tools in these efforts is not as well studied. This short paper introduces an industry survey that we conducted. Results from 107 developers demonstrate that projects commonly go through multiple large-scale refactorings, each of which requires considerable effort. While there is often a desire to refactor, other business concerns such as developing new features often take higher priority. Our study finds that developers use several categories of tools to support large-scale refactoring and rely more heavily on general-purpose tools like IDEs than on tools designed specifically to support refactoring. Tool support varies across the different activities (spanning communication, reasoning, and technical activities), with some particularly challenging activities seeing little use of tools in practice. Our study demonstrates a clear need for better large-scale refactoring tools.
CDNeRF: A Multi-modal Feature Guided Neural Radiance Fields

We present CDNeRF, a simple yet powerful learning framework that creates novel view synthesis by reconstructing neural radiance fields from a single view RGB image. Novel view synthesis by neural radiance fields has achieved great improvement with the development of deep learning. However, how to make the method generic across scenes has always been a challenging task. A good idea is to introduce 2D image features as prior knowledge for adaptive modeling, yet RGB features (C) lack geometry and 3D spacial information. To compensate, we introduce depth features into the model. Our method uses a variant depth estimation network to extract depth features (D) without the need for additional input. In addition, we also introduce the transformer module to effectively fuse the multi-modal features of RGB and depth. Extensive experiments are carried out on two categories specific benchmarks (i.e., Chair, Car) and two category agnostic benchmarks (i.e., ShapeNet, DTU). The results demonstrate that our CDNeRF outperforms the previous methods, and achieves state-of-the-art neural rendering performance.
Dozer

Software developers frequently use the system shell to perform configuration management tasks. Unfortunately, the shell does not scale well to large systems, and configuration management tools like Ansible are more difficult to learn. We address this problem with Dozer, a technique to help developers push their shell commands into Ansible task definitions. It operates by tracing and comparing system calls to find Ansible modules with similar behaviors to shell commands, then generating and validating migrations to find the task which produces the most similar changes to the system. Dozer is syntax agnostic, which should allow it to generalize to other configuration management platforms. We evaluate Dozer using datasets from open source configuration scripts.
An Approach to Rating Prediction for Personality Recommendation via Attention Mechanism and Denoising Autoencoder

Rich personalized information, trust and distruct, which is fully utilized can improve the quality of rating prediction for personality recommendation. In this work, we propose a novel model of Attention Mechanism and Denoising Autoencoder Recommender, namely AMDAERec, to explore the nonlinear features between users who have diversity relationships and items which are interacted with these users. We also compare this model with eight state-of-the-art personality recommendation algorithms based on the Epinions dataset. Experimental results show that our approach can make full use of users' trust or distrust relationship information and capture the nonlinear features between users and items. Finally, we effectively improve the accuracy of rating prediction.
Semantic Aspects of the Experts’ Communication Problem in Relation to the Conceptual Design of Complex Systems

This article discusses several world-wide problems in the communication between the experts, such as harmonization of conceptual apparatus and translation between traditional languages as major barriers for effective and rapid complex systems’ design process. The authors of the article state that in modern, especially post-covid world has been formed the conditions that encourage the development of a new planned language of international communication for experts, which, on one hand, would help to overcome the problems of translation, coordination of the conceptual apparatus, raising the effectiveness of communication between experts, and on the other hand, to make a breakthrough in the evolution of software development. The authors consider the initiative in the creation of such a language may come from experts and the business environment, especially from the global innovative IT companies, for which such language would greatly simplify the writing of software as would replace or simultaneously play a role the programming language.