Reviewerly/exHarmony huggingface dataset:
A Novel Feature Representation for Prediction of Global Horizontal Irradiance Using a Bidirectional Model

Complex weather conditions—in particular clouds—leads to uncertainty in photovoltaic (PV) systems, which makes solar energy prediction very difficult. Currently, in the renewable energy domain, deep-learning-based sequence models have reported better results compared to state-of-the-art machine-learning models. There are quite a few choices of deep-learning architectures, among which Bidirectional Gated Recurrent Unit (BGRU) has apparently not been used earlier in the solar energy domain. In this paper, BGRU was used with a new augmented and bidirectional feature representation. The used BGRU network is more generalized as it can handle unequal lengths of forward and backward context. The proposed model produced 59.21%, 37.47%, and 76.80% better prediction accuracy compared to traditional sequence-based, bidirectional models, and some of the established states-of-the-art models. The testbed considered for evaluation of the model is far more comprehensive and reliable considering the variability in the climatic zones and seasons, as compared to some of the recent studies in India.
The Common Greenhouse Ontology: An Ontology Describing Components, Properties, and Measurements inside the Greenhouse

Modern greenhouses have systems that continuously measure the properties of greenhouses and their crops. These measurements cannot be queried together without linking the relevant data. In this paper, we introduce the Common Greenhouse Ontology, a standard for sharing data on greenhouses and their measurable components. The ontology was created with domain experts and incorporates existing ontologies, SOSA and OM. It was evaluated using competency questions and SPARQL queries. The results of the evaluation show that the Common Greenhouse Ontology is an innovative solution for data interoperability and standardization, and an enabler for advanced data science techniques over larger databases.
Empowering Communities in Marginalized Surroundings: Opportunities from Collaborative Community-Managed Electrification and Advanced ICT

Marginalized communities in Africa present a challenge for sustainable and equal development. Opportunities for breaking out from poverty are scarce. Electrification has been seen to act as an important enabler for improving livelihood in such surroundings. However, the activities and services that are enabled by electricity are often the desired outcomes. Aspects of special importance relate to improved earnings: business development and entrepreneurial work. Such activities are usually supported with advanced ICT. However, the introduction and operation of such systems requires substantial resources. In this paper, we explore collaborative practices that may lower these resource demands. Accordingly, we present a case study conducted in an African marginalized community showing how collaborative introduction and operation of electricity and advanced ICT infrastructure were sustained by the local community members. We also show the processes that were followed in engaging ICTs. The study is based on a research and piloting project that seek ways to establish a community-centered ICT infrastructure for electricity and Internet connectivity enabling the members of the marginalized community to get access to digital services. We argue that the experimented infra-structure will give the community an opportunity to improve their livelihoods by using the Internet and access to information for eLearning, e-government services, and a wide market for selling local products. Importantly however, our findings include the collaborative remote support for the deployment and usage of the infrastructure improving sustainability. Such a new approach can be considered of importance given that traditional electrification and digitalization projects often degrade or even cease to exist soon after handing over everything to the community.
Challenges of Big Data and Vehicle Data

In this short paper, we outline the challenges of Big Data processing for vehicle data, considering different use cases as well as different locations. On an architectural level, we show the benefits and possible limitations of the different approaches for in-vehicle processing, edge computing as well as cloud processing. We also discuss different approaches for Big Data Processing, including batch processing as well as distributed stream processing.
Multiple Regression and K-Nearest-Neighbor Based Algorithm for Estimating Missing Values within Sensor

The missing of sensor data is unavoidable when sensors are using to monitor in a system. This missing has very big effecting on the applications of sensor itself. When missing occurred, the best method is to estimate a value as closest as possible to replace it. An algorithm, named KMRA (K-Nearest-Neighbor on Multiple Regression Algorithm), builds on K nearest neighbor and multiple regression is introduced in this paper. In the process of estimation, it considers both spatial correlations from its neighbor sensor and temporal correlations from its own time serials. After computed these two correlations, the algorithm combines them into a unified estimation to be the monitored value. As algorithm KMRA counted both spatial and temporal correlations, it reaches a high efficiency and practicability in performance. The examination results show that KMRA in this paper can estimate the missing data precisely.
Insulator Fault Recognizing via Modified Faster R-CNN Using UAV Data

Insulator fault recognition is a common hidden danger that affects the normal operation of the power system. In recent years, UAV aerial images have been widely used to identify defects in insulators. In order to solve the problem of low efficiency caused by manual inspection and traditional image defect detection methods, this paper proposed a modified Faster R-CNN model to improve the accuracy of model detection and reduce the amount of model parameters. Based on the traditional Faster R-CNN detection framework, the proposed method selected ResNet- 50 to replace VGGNet-16 as the feature extraction networks. The database used 781 labeled UAV aerial insulator images, and they are divided into training set, validation set and test set according to the ratio of 6:2:2. The mAP of the modified feature network model reached 62.41%, and the network model parameter size was 25.26M. Compared with the original framework with VGGNet-16 as the feature network, the mAP increased by 3.43% and the parameter amount was compressed by 4.41 times. The results show that the improved algorithm reduces the missed detection rate and false detection rate, and on the basis of improving the recognition accuracy, it can greatly reduce the amount of network parameters. And it can better meet the needs of lightweight network structure in actual scenarios.
Research and Development of Entity Extraction Based on Information Extraction

Information extraction is mainly to extract the required specific facts from a large number of documents. That is to say, it is the process of automatically extracting unstructured information and converting it into structured data. This paper reviews the development of information extraction, introduces the research of named entity recognition, relationship extraction, event extraction and open information extraction. At the same time, it introduces data sets and application fields used in information extraction in detail.
Using a Smartphone to Teach Physics Experiment Online and Offline

More and more universities and even high schools are moving to online instruction as a cost-effective way to reach more students with fewer resources. Teachers and students are already proficient in using mobile learning software. To make up for the problem that students cannot conduct experiments due to venues, experimental equipment and other reasons during online classes. The online live class is combined with many sensors of the mobile phone around and the software is integrated into a mini-DIS laboratory to carry out Synchronous experiment teaching mode.
Modeling and Sectional Compensation of Temperature Error of FOG Based on BOA-GBDT

According to the requirements of temperature compensation for real-time and accuracy, a method of using Bayesian algorithm to optimize the gradient boosting tree regression is proposed to establish the temperature error compensation model of fiber optic gyroscope, and it adopts the method of real-time acquisition of temperature change rate with multiple data windows to meet the requirements of online compensation and model input. The fiber optic gyroscope is placed in a temperature box to perform a temperature change test of −40-60°C to obtain measured data. The temperature and temperature change rate are used as input, and Bayesian algorithm optimization gradient lifting tree regression modeling and temperature rising and falling segment modeling are performed respectively. The comparative experiment results show that the proposed model achieves the best compensation effect. Through the compensation comparison test, it is verified that the proposed model has good compensation ability and generalization ability for non-training data.
Using the Hopfield Neural Network to Select a Behaviour Strategy for the Group of Mobile Robots

Abstract The use of the Hopfield neural network for the task distribution problem solving in teams of mobile robots performing monosyllabic operations in a single workspace is considered. The study is a continuation of earlier works in which the same problem was solved by the authors using other heuristic algorithms – swarm and genetic. This article presents the problem statement and the model of the working space, distinguishes the goals of robotic operation. The quality indicator is the total distance traveled by each of the robots in the group. To enable the original problem to be solved using the Hopfield neural network, a graph representation of the Hopfield is made by switching from the VRP to the TSP problem. The results of computational experiments confirming the effectiveness of the chosen approach for choosing a strategy of behavior of a group of mobile robots are shown.
Latency of Trading Transactions in Brokered IoT Data Marketplace in Ethereum

Trading data as valuable assets has become a trend. Data such as IoT data constitute a new source of profit for their owners and they have become tradeable assets in data marketplaces. In this work, we present the technical implementation and evaluation of our marketplace model, which was introduced in a previous work. The marketplace supports the trading of streaming data, from offering data for sale and negotiating trade agreements, to automating trade obligations and fulfilment. It ends with payment settlement and the scoring of trade parties. We show that by using Ethereum blockchain technology and Ethereum smart contracts in particular, we can offer a trade-off between the gas price paid for transaction confirmation and the transaction latency, the time taken for an Ethereum network to process and confirm our marketplace transactions. We experimentally assess such trade-offs on a testbed using our marketplace implementation as a DApp.
The Challenges of Cross-Document Coreference Resolution for Email

Long-form conversations such as email are an important source of information for knowledge capture. For tasks such as knowledge graph construction, conversational search, and entity linking, being able to resolve entities from across documents is important. Building on recent work on within document coreference resolution for email, we study for the first time a cross-document formulation of the problem. Our results show that the current state-of-the-art deep learning models for general cross-document coreference resolution are insufficient for email conversations. Our experiments show that the general task is challenging and, importantly for knowledge intensive tasks, coreference resolution models that only treat entity mentions perform worse. Based on these results, we outline the work needed to address this challenging task.
DataQuest: An Approach to Automatically Extract Dataset Mentions from Scientific Papers

The rapid growth of scientific literature is presenting several challenges for the search and discovery of research artifacts. Datasets are the backbone of scientific experiments. It is crucial to locate the datasets used or generated by previous research as building suitable datasets is costly in terms of time, money, and human labor. Hence automated mechanisms to aid the search and discovery of datasets from scientific publications can aid reproducibility and reusability of these valuable scientific artifacts. Here in this work, utilizing the next sentence prediction capability of language models, we show that a BERT-based entity recognition model with POS aware embedding can be effectively used to address this problem. Our investigation shows that identifying sentences containing dataset mentions in the first place proves critical to the task. Our method outperforms earlier ones and achieves an F1 score of 56.2 in extracting dataset mentions from research papers on a popular corpus of social science publications. We make our codes available at https://github.com/sandeep82945/data_discovery.
Real-time Vehicle Detection and Tracking Based on YOLOv3 Pruning Model

Real-time detection and tracking of vehicles is very important in the field of automatic driving. In this paper, a YOLOv3 network based on pruning algorithm is proposed to solve the problem of real-time vehicle detection. Through reducing the number of channels and layers in the backbone network, the computation of the model is reduced. And thus, the rate of detection is greatly increased. On the basis of vehicle detection, the real-time tracking of multiple vehicle targets is completed by using Kalman filter algorithm for prediction and Hungarian algorithm for data association. The experimental results show that compared with the original YOLOv3 network, the model size is compressed by 95% to 11.25 MB and the detection rate is doubled to 128.1 frames/s while the average accuracy is basically unchanged. The detection and tracking frame rate of the whole algorithm is 18fps, and the recall is 98.5%. The algorithm also has strong robustness on complex traffic roads, and can basically realize real-time detection and tracking of road vehicles.
Interactive mapping and geospatial analysis with Leafmap and Jupyter

Leafmap is a Python package for interactive mapping and geospatial analysis with minimal coding in a Jupyter environment. It is built upon several open-source packages, such as ipyleaflet and kepler.gl (for creating interactive maps), WhiteboxTools (for analyzing geospatial data), and ipywidgets (for designing interactive graphical user interface). Leafmap provides many convenient functions for loading and visualizing geospatial data with only one line of code. Users can also use the interactive user interface to load geospatial data without coding. Anyone with a web browser and Internet connection can use leafmap to perform geospatial analysis and data visualization in the cloud with minimal coding. This workshop will introduce the key features of leafmap for interactive mapping and geospatial analysis in a Jupyter environment. Attendees will learn how to leverage open-source Python packages and free cloud computing platforms for geospatial analysis and data visualization.
An Architecture for Collaborative Terrain Sketching with Mobile Devices

3D terrains used in digital animations and videogames are typically created by several collaborators with a single-user application, which constrains them to update the shared terrain from their PCs, using a turn-taking strategy. Moreover, collaborators have to visualize the terrain through 2D views, confusing novice users when conceiving its shape in 3D. In this article, we describe an architecture for collaborative applications, which allow co-located users to sketch a terrain using their mobile devices concurrently. Two interaction modes are supplied: the standard one and an augmented reality-based mode, which helps collaborators understand the 3D terrain shape. Using the painting with brushesparadigm, users can modify the terrain while visualizing its shape evolution through the camera of their devices. Work coordination is promoted by enriching the 3D space with each collaborator's avatar, which provides awareness information about identity, location, and current action. We implemented a collaborative application from this architecture that was tested by groups of users, who assessed its hedonic and pragmatic qualities in both interaction modes and compared them with the qualities of a similar Web terrain editor. The results showed that the augmented reality mode of our prototype was considered more attractive and usable by the participants.
Object Tracking Algorithm of Fully-Convolutional Siamese Networks Using the Templates with Suppressed Background Information

The current visual object tracking algorithm of Fully-Convolutional Siamese Networks (SiamFC) has good performance of accuracy and frame rate. However, when tracking an object moving in a scene with complex background, the templates applied in SiamFC tend to introduce the excessive background information that may cause interference to the target. In this paper, a method of suppressing the background information in templates is proposed to cope with this problem. On one hand, it reduces the introduction of background information by using adaptive aspect ratio when making templates. On the other hand, it decreases the impact of background information on matching results through Gaussian weighting after the templates inevitably introduce background information. The effectiveness of the proposed method has been experimentally validated without loss of real-time performance. In the comparison experiments, the proposed algorithm has improved the area under curve (AUC) of success plots by 9.07% and 13.31% on OTB2013 dataset and OTB50 dataset, respectively, compared with the original SiamFC under the complex background interference scenarios.
General Model-Agnostic Transfer Learning for Natural Degradation Image Enhancement

In order to obtain high-quality images in natural conditions, natural degradation image enhancement has been a research hotspot in recent years. In this paper, we present a transfer learning approach for multiple types of natural degradation image enhancement. We propose to create a common source domain for various natural degradations and perform the transfer learning for every specific natural degradation individually. By reusing the general enhancement model, we can circumvent the scarcity of training dataset and the computation-intensive training process for deep learning methods. In the experiment, we transfer the general model to three target tasks: raining image enhancement, snowing image enhancement and underwater image enhancement. With the finetuning of only 5 epochs, the enhancement models have been able to outperform several state-of-the-art methods that designed for specific task.
Review Literature Performance: Quality of Service from Internet of Things for Transportation System

The Internet of Things (IoT) is one of the technologies that are becoming a trend nowadays. Needs and implementation develop very fast. These conditions encourage increased infrastructure requirement and good data management. The number of points connected to IoT increases rapidly, as does the quantity and quality of data sent. IoT is a technology that connects objects (things) or devices in a digital communication. Is a development from the previous internet connection that connects only computer devices. In a Scale-up transportation system, it can occur due to an increase in connected vehicles, the amount of data sent, the area or range of vehicle mobility that is increasingly widespread. Data generated by the IoT system will be widely used by various parties according to their interests. Data increase will result in a decrease in data quality in terms of data source trust, data security from improper changes and timely and targeted data distribution. The expected result of the research is an IoT system in transportation that is able to produce valid, accurate and up to date data. The use of these data further improves the performance of the transportation system. Transportation is one area that has the potential to benefit from IoT. But unique and challenging characteristics require a guarantee of the quality of the IoT. The flow of information on transportation is needed to improve services for users. Utilization of IoT in transportation will provide major benefits such as ease of getting vehicle position information, accuracy in getting information on engine conditions and in the future leading to transportation automation. If the quality of the data flow is not guaranteed, the implementation of IoT will pose a potential hazard.
Exploration of React Native Framework in designing a Rule-Based Application for healthy lifestyle education

Researches indicates the implementation of hybrid applications is more profitable for mobile application development solutions. Hybrid application combine the advantages of web and native application. React Native is a hybrid framework for developing mobile applications. React Native framework can create within two platform applications by compiling the code written in React. The utilization of React Native in a rule-based application can build a solution for healthy lifestyle education. The aim of this study is to build a rule-based application for healthy reminder in daily activities. By developing an application in React Native, the study will design a comprehensive mobile application that make users easy to use. Moreover, this study will explore and construct an application that guide the user to maintain their daily health. To develop the application, author uses waterfall methodology. Before building the application, a systematic survey was conducted to gain relevant data from the users and also to invent a rule-based that will be the way of thinking of the application design. The results indicate that React Native framework can be utilized in building a reminder application about healthy lifestyle education. This study built a mobile application product that has good performance and helps users change the lifestyle in order to improve the quality of a healthy lifestyle.
The concept of using augmented reality technology to present interactive calligraphic objects

The use of augmented reality is a great opportunity to make exhibitions more engaging to the user. This paper concerns an application of augmented reality on mobile applications for exhibitions in museums. The goal is to make by museums more interactive, and engaging for visitors. Augmented reality (AR) is nowadays used extensively in various fields of cultural heritage. The current work presents an application of the AR technology in calligraphy. The proposed methodology presents calligraphic art objects using collected information, taking into account the peculiarities of tracking approaches and means of implementation. The work is formulated across various following scenarios of using augmented reality technologies in museum. The different scenarios are explained in detail and can be used in order to increase public attention to virtual exhibitions and to the field of calligraphy.