Reviewerly/exHarmony huggingface dataset:
Exploring the Professional Development and Improvement Strategies of College Teachers’ Skills in the Twenty-First Century in the Era of Covid-19

Covid-19, which suddenly broke out in 2019 and has been popular all over the world for more than 3 years, has brought unprecedented changes to human beings. Before that, colleges and universities have already need to change and innovate their curricula and teaching to teach “twenty-first century skills” for students, which are problem-solving and critical thinking, creativity and innovation, intercultural understanding, communications, information, media literacy, computing and ICT literacy, responsibility and leadership, accountability and productivity, self-direction and initiative, adaptability and flexibility. In this situation, TPD (teacher professional development) evokes teachers to satisfy students needs for education in the twenty-first century and in the era of Covid-19 even post-Covid-19, which increases the urgency and necessity. Through the systematic analysis of the relevant research results, summarized strategies of TPD in the twenty-first century Covid-19 and post-Covid-19 era: evaluation of TPD needs, cultivating skills of the twenty-first century, integrating ICT instruction, peer coaching, creating the positive culture of campus, building collaboration, initiative learning, embedding the core values, sustainable professional development, research projects and training teachers’ emergency capabilities, to meet teachers’ learning and practice, and to address challenges that have existed and may arise in future.
Automatic medical specialty classification based on patients’ description of their symptoms

In China, patients usually determine their medical specialty before they register the corresponding specialists in the hospitals. This process usually requires a lot of medical knowledge for the patients. As a result, many patients do not register the correct specialty for the first time if they do not receive help from the hospitals. In this study, we try to automatically direct the patients to the appropriate specialty based on the symptoms they described. As far as we know, this is the first study to solve the problem. We propose a neural network-based model based on a hybrid model integrated with an attention mechanism. To prove the actual effect of this hybrid model, we utilized a data set of more than 40,000 items, including eight departments, such as Otorhinolaryngology, Pediatrics, and other common departments. The experiment results show that the hybrid model achieves more than 93.5% accuracy and has a high generalization capacity, which is superior to traditional classification models.
A high presence traditional crafts experience system that combines multicultural architectural styles with Japanese culture

Traditional crafts such as tategu (door and window fittings), kimono (clothes), and shikki (lacquerware) are regarded as important items for daily life in Japan; however, in recent years, the industry that manufactures and sells these products has experienced various problems and has continued to decline. Conversely, overseas demand for traditional crafts has been gradually increasing with the increase in foreign visitors to Japan in recent years. For these reasons, there is a need in the traditional crafts industry to provide information and promote traditional crafts to overseas customers. Accordingly, in this study, we implement a high presence traditional crafts experience system using virtual reality technology. Our proposed system provides users with a highly realistic virtual space experience using a head-mounted display and a data glove. In addition, multiple users can share the space via the network. Further, we consider the promotion of traditional crafts to overseas customers by combining multicultural architectural styles and Japanese culture.
How Deep Neural Networks Understand Motion? Toward Interpretable Motion Modeling by Leveraging the Relative Change in Position

Motion understanding plays an important role in video-based cross-media analysis and multiple knowledge representation learning. This paper discusses physical motion recognition and prediction by deep neural networks (DNNs), such as convolutional neural networks and recurrent neural networks. In physics, motion is the relative change in position with respect to time. To ablate the moving object and the background where the motion happens, we focus on an ideal scenario where a point moves in a plane. As the first contribution, we evaluate a few popular DNN architectures from video research on the relative position change modeling. Experiment results and conclusions can be insightful in action recognition and video prediction. As the second contribution, we propose a vector network (VecNet) to model the relative change in position. VecNet considers the motion in a short interval as a vector. Meanwhile, VecNet can move a point to the corresponding position given a vector representation. To obtain the representation of the motion for a long time, we use a long short-term memory (LSTM) to aggregate or predict vector representations over time. The resulting VecNet+LSTM approach is able to effectively support both recognition and prediction, proving that modeling relative position change is necessary for motion recognition and makes motion prediction easier.
Towards optimal virtual machine placement methods in cloud environments

The rapid growth of cloud services for hosting applications in the scientific, commercial, web, and social networks has led to enormous growth in the number of large-scale data centers. By shifting the costs of data center maintenance, hardware, and software from customers to service providers using a pay-as-you-go policy, service providers and customers are benefited. On the other hand, the massive growth of data centers has been accompanied by challenges that have limited the boundaries of this technology. Thus, researchers in this field tend to focus on eliminating these limitations. Since virtualization is at the core of cloud computing, allocating Virtual Machines (VMs) to physical hosts in the Infrastructure as a Service layer (IaaS) is one of the most significant challenges. Nonetheless, the VM allocation problem is a combinatorial optimization problem that is known to be NP-Hard. In this paper, we presented a comprehensive analysis of virtual machine placement problem and outlined different approaches to solving it. This paper aims to provide insight into the challenges and issues for recent virtual machine placement strategies. The current study aims to comprehensively classify the physical resource allocation for VMs by overviewing available trends.
CD-GAN: Commonsense-Driven Generative Adversarial Network with Hierarchical Refinement for Text-to-Image Synthesis

Synthesizing vivid images with descriptive texts is gradually emerging as a frontier cross-domain generation task. However, it is obviously inadequate to generate the high-quality image with one single sentence accurately due to the information asymmetry between modalities, which needs external knowledge to balance the process. Moreover, the limited description of the entities in the sentence cannot guarantee the semantic consistency between text and generated image, causing the deficiency of details in foreground and background. Here, we propose a commonsense-driven generative adversarial network to generate photo-realistic images depending on entity-related commonsense knowledge. Commonsense-driven generative adversarial network contains 2 key commonsense-based modules: (a) Entity semantic augment is designed to enhance entity semantics with common sense for abating the information asymmetry, and (b) adaptive entity refinement is used to generate the high-resolution image guided by various commonsense knowledges in multistage for keeping text-image consistency. We demonstrated extensive synthetic cases on the wildly used CUB-birds (Caltech-UCSD Birds-200-2011) dataset, where our model achieves the competitive results compared to the other state-of-the-art models.
In Search of Basement Indicators from Street View Imagery Data: An Investigation of Data Sources and Analysis Strategies

Street view imagery databases such as Google Street View, Mapillary, and Karta View provide great spatial and temporal coverage for many cities globally. Those data, when coupled with appropriate computer vision algorithms, can provide an effective means to analyse aspects of the urban environment at scale. As an effort to enhance current practices in urban flood risk assessment, this project investigates a potential use of street view imagery data to identify building features that indicate buildings' vulnerability to flooding (e.g., basements and semi-basements). In particular, this paper discusses (1) building features indicating the presence of basement structures, (2) available imagery data sources capturing those features, and (3) computer vision algorithms capable of automatically detecting the features of interest. The paper also reviews existing methods for reconstructing geometry representations of the extracted features from images and potential approaches to account for data quality issues. Preliminary experiments were conducted, which confirmed the usability of the freely available Mapillary images for detecting basement railings as an example type of basement features, as well as geolocating the features.
Novel Features and Neighborhood Complexity Measures for Multiclass Classification of Hybrid Data

The present capabilities for collecting and storing all kinds of data exceed the collective ability to analyze, summarize, and extract knowledge from this data. Knowledge management aims to automatically organize a systematic process of learning. Most meta-learning strategies are based on determining data characteristics, usually by computing data complexity measures. Such measures describe data characteristics related to size, shape, density, and other factors. However, most of the data complexity measures in the literature assume the classification problem is binary (just two decision classes), and that the data is numeric and has no missing values. The main contribution of this paper is that we extend four data complexity measures to overcome these drawbacks for characterizing multiclass, hybrid, and incomplete supervised data. We change the formulation of Feature-based measures by maintaining the essence of the original measures, and we use a maximum similarity graph-based approach for designing Neighborhood measures. We also use ordering weighting average operators to avoid biases in the proposed measures. We included the proposed measures in the EPIC software for computational availability, and we computed the measures for publicly available multiclass hybrid and incomplete datasets. In addition, the performance of the proposed measures was analyzed, and we can confirm that they solve some of the biases of previous ones and are capable of natively handling mixed, incomplete, and multiclass data without any preprocessing needed.
Application and Research of the White Light Interferometer

Abstract Surface topography measurement is the micro topography measurement of samples to be tested, which plays an important role in measuring white light interferometer. Taking the B3-shaped penetration test block for nondestructive testing and the profilometer standard ball for length measurement as the main research objects. The depth and width of crack defects in the penetration test block are measured and analyzed, the radius of the spherical groove on the reverse side of the penetration test block is measured and analyzed, and the spherical radius of the profilometer standard ball is measured and analyzed. The measurement results show that there is little difference between the radius measurement of the penetration test block and the theoretical value. The measurement of crack width and depth will contribute to the qualitative analysis of the crack, and the measurement of the profilometer standard ball provides a non-contact measurement means.
Smart water chain: Immutable, distributed and decentralized water transaction ledgers

Abstract Blockchain is a transactional data storage system where data can be stored reliably without the need for a central database or trusted authority. The data can be anything like financial transactions, supply chain processes or medical records. It is similar to a classical database but uses a decentralized ledger and allowing each participant in the network to have their own copy of the ledger and be able to see all transactions. Data stored in the distributed ledger can only be read or written, not deleted or updated unlike traditional central database. Reliable data is essential for the water industry for information about the current status of any water system, to build trust between stakeholders at all scales, and for effective forecasting and future scenarios by reducing uncertainty. The aim of this study is to examine the potential of using blockchain-based algorithms (smart contracts, chain codes, decentralised identifiers etc.) for the water industry on the edge of digital transformation, and to propose water-related data processing system architectures for different water quantity-quality based scenarios.
Research on Staff Training and Education Strategy of Highway Passenger Transport Enterprises with the High-Quality Development

The highway passenger transport business is facing the competitive challenge of declining passenger volume and transportation proportion, and is also facing in a good opportunity for the high-quality development of building a strong transportation country. By analyzing the characteristics of high-quality transformation and development of highway passenger transport and the characteristics of talent demand, and analyzing the important role of compound talents, innovative talents and collaborative talents in the transformation and development of highway passenger transport, combined with the theory of enterprise human resources education and training, this paper puts forward the Trinity training and education content system of “knowledge + skills + literacy” for highway passenger transport enterprise talents, and makes it clear that knowledge, skills and quality are divided by training platform and industry experts and internal trainers are three different subjects to undertake. Finally, the implementation path of implementing and cultivating the education system is elaborated in detail.
FIImap: Fast Incremental Inflate Mapping for Autonomous MAV Navigation

Three-dimensional mapping is an essential component of autonomous Micro Aerial Vehicle (MAV) navigation. The paper focuses on the 3D spatial representation method of MAV to overcome the collision problem caused by soft constraints, control error, and planning with the center of mass by inflating the occupancy grid map. A fast incremental inflated map construction method is proposed, which reduces the time-consumption caused by the increase of map range and inflated size. The method focuses on areas of the map that occupied state changes and introduces two arrays that record newly appearing and disappearing obstacles. Then, a series of breadth-first search algorithms are used to traverse the parts of the inflated map that need local modification to update the inflated map. Moreover, a sliding map model is designed based on the MAV position, which is suitable for large-range autonomous flight. The effectiveness of the proposed approach is verified with simulated and actual flight data. The proposed method takes about 3 ms to construct the inflated map with a local update range of 16 m × 16 m × 6 m.
RGCLN: Relational Graph Convolutional Ladder-Shaped Networks for Signed Network Clustering

Node embeddings are increasingly used in various analysis tasks of networks due to their excellent dimensional compression and feature representation capabilities. However, most researchers’ priorities have always been link prediction, which leads to signed network clustering being under-explored. Therefore, we propose an asymmetric ladder-shaped architecture called RGCLN based on multi-relational graph convolution that can fuse deep node features to generate node representations with great representational power. RGCLN adopts a deep framework to capture and convey information instead of using the common method in signed networks—balance theory. In addition, RGCLN adds a size constraint to the loss function to prevent image-like overfitting during the unsupervised learning process. Based on the node features learned by this end-to-end trained model, RGCLN performs community detection in a large number of real-world networks and generative networks, and the results indicate that our model has an advantage over state-of-the-art network embedding algorithms.
AKTIVITAS PERCOBAAN MENANAM HIDROPONIK BERBASIS PENDEKATAN HEUTAGOGY BAGI SISWA SEKOLAH DASAR

Student learning activities carried out online during the Covid-19 pandemic, in addition to providing convenience, also impact the increasingly limited practice of activity-based learning oriented to produce products/works. Overcoming these obstacles, among others, can be facilitated through simple experimental practice activities to plant hydroponic plants to facilitate independent learning, creativity, and collaboration of students with parents. This community service activity was packaged through online training through the Zoom web meeting platform for three sessions, including training material presentation sessions, independent practice of hydroponic planting experiments with the wick system technique, and reflection and follow-up plans. This activity was attended by Elementary School (SD) students and their assistants spread across three Cities/Regencies in East Java, including Surabaya City and Sidoarjo Regency. Participants in this activity are urban communities with limited land, so they are technically suitable for hydroponic planting activities. The experimental practice phase was carried out starting from seeding to nurseries by utilizing training packages in starts and hydroponic planting assistance modules. During the training preparation, implementation and evaluation, participants showed high enthusiasm. Based on the reflection and follow-up of this activity, it is constructive to develop independent learning and collaboration between students and parents. It can be an alternative activity to be applied at home in the surrounding environment and school during the pandemic.&#x0D;
A type language for distributed reactive components governed by communication protocols

Component based software engineering (CBSE) is a methodology that aims to design and build software systems by assembling together reusable and loosely coupled components. Applying CBSE in a distributed setting is appealing but challenging: distributed applications require different remote components to interact following a well-defined protocol. In this paper we consider a model for message passing component-based systems where components are assembled together with a protocol, and are reactive to messages in a flexible way. We propose a type language that allows capturing component reactive behaviour and checking its compatibility with the protocol. Moreover, we show the correspondence of component and type behaviours, which entails a progress property for components.
WFLTree: A Spanning Tree Construction for Federated Learning in Wireless Networks

Nowadays, more and more federated learning algorithms have been implemented in edge computing, to provide various customized services for mobile users, which has strongly supported the rapid development of edge intelligence. However, most of them are designed relying on the reliable device-to-device communications, which is not a realistic assumption in the wireless environment. This paper considers a realistic aggregation problem for federated learning in a single-hop wireless network, in which the parameters of machine learning models are aggregated from the learning agents to a parameter server via a wireless channel with physical interference constraint. Assuming that all the learning agents and the parameter server are within a distance [Formula: see text] from each other, we show that it is possible to construct a spanning tree to connect all the learning agents to the parameter server for federated learning within [Formula: see text] time steps. After the spanning tree is constructed, it only takes [Formula: see text] time steps to aggregate all the training parameters from the learning agents to the parameter server. Thus, the server can update its machine learning model once according to the aggregated results. Theoretical analyses and numerical simulations are conducted to show the performance of our algorithm.
A Jointly Guided Deep Network for Fine-Grained Cross-Modal Remote Sensing Text–Image Retrieval

Remote sensing (RS) cross-modal text–image retrieval has great application value in many fields such as military and civilian. Existing methods utilize the deep network to project the images and texts into a common space and measure the similarity. However, the majority of those methods only utilize the inter-modality information between different modalities, which ignores the rich semantic information within the specific modality. In addition, due to the complexity of the RS images, there exists a lot of interference relation information within the extracted representation from the original features. In this paper, we propose a jointly guided deep network for fine-grained cross-modal RS text–image retrieval. First, we capture the fine-grained semantic information within the specific modality and then guide the learning of another modality of representation, which can make full use of the intra- and inter-modality information. Second, to filter out the interference information within the representation extracted from the two modalities of data, we propose an interference filtration module based on the gated mechanism. According to our experimental results, significant improvements in terms of retrieval tasks can be achieved compared with state-of-the-art algorithms. The source code is available at https://github.com/CQULab/JGDN .
Multi-State Measurement Processing with Factorized Stochastic Cloning

A stochastic cloning framework is presented for processing multi-state measurements within a factorized, consider/neglect extended Kalman filter. A multi-state measurement depends on two or more temporal instances of the same state, which breaks traditional assumptions of measurement models in Kalman filtering. Alterations to previous stochastic cloning developments for practical usage are discussed. An example of multi-state measurement dependence is presented from visual odometry. Performance of the stochastic cloning approach is illustrated in simulation for processing the visual odometry direction-of-motion measurement during a lunar descent-to-landing scenario. Monte Carlo analysis is used to demonstrate statistical consistency of the Kalman filter with the stochastic cloning augmentation.
Sensor Based Motion Planner for Small Solar System Body Exploration

Next-generation small solar system body exploration missions intend to use flying concepts to increase surface mobility and scientific return. However, detailed local maps are only partially available for such missions, if at all, and hence, cannot be used for motion planning. This paper proposes a sensor-based terrain following algorithm that does not require maps to address scenario-specific challenges. Instead, the terrain-following approach allows the vehicle to actively adapt its flight path to the sensed terrain, thus allowing flights in unknown terrain. Trajectories are generated by sampling continuous motion primitives and checking for collisions within the sensor field of view, ensuring that feasible and collision-free trajectories are generated. Furthermore, a height estimation algorithm is proposed, which is crucial for the terrain following capabilities. Finally, the presented approach has a low computational effort which is critical for space applications. The proposed algorithm is demonstrated in simulation for functional verification.