Application of Big Data Analysis to Agricultural Production, Agricultural Product Marketing, and Influencing Factors in Intelligent Agriculture

Agricultural Internet of things (AIoT) promotes the modernization of traditional agricultural production and marketing model. However, the existing time series prediction methods for agricultural production and agricultural product (AP) marketing cannot adapt well to most real-world scenarios, failing to realize multistep forecast of production and AP marketing data. To solve the problem, this paper explores the big data analysis of agricultural production, AP marketing, and influencing factors in intelligent agriculture. To realize long-, and short-term predictions, a small-sample time series model was set up for AIoT production, and a big-sample time series model was constructed for AP marketing. The data fusion algorithm based on Kalman filter (KF) was adopted to fuse the massive multi-source AP marketing data. The proposed strategy was proved valid through experiments

A systematic literature review

Albuquerque, V., Dias, M. S., & Bacao, F. (2021). Machine learning approaches to bike-sharing systems: A systematic literature review. ISPRS International Journal of Geo-Information, 10(2), 1-25. [62]. https://doi.org/10.3390/ijgi10020062Cities are moving towards new mobility strategies to tackle smart cities’ challenges such as carbon emission reduction, urban transport multimodality and mitigation of pandemic hazards, emphasising on the implementation of shared modes, such as bike-sharing systems. This paper poses a research question and introduces a corresponding systematic literature review, focusing on machine learning techniques’ contributions applied to bike-sharing systems to improve cities’ mobility. The preferred reporting items for systematic reviews and meta-analyses (PRISMA) method was adopted to identify specific factors that influence bike-sharing systems, resulting in an analysis of 35 papers published between 2015 and 2019, creating an outline for future research. By means of systematic literature review and bibliometric analysis, machine learning algorithms were identified in two groups: classification and prediction.publishersversionpublishe

Automated Detection of COVID-19 using Chest X-Ray Images and CT Scans through Multilayer- Spatial Convolutional Neural Networks

The novel coronavirus-2019 (Covid-19), a contagious disease became a pandemic and has caused overwhelming effects on the human lives and world economy. The detection of the contagious disease is vital to avert further spread and to promptly treat the infected people. The need of automated scientific assisting diagnostic methods to identify Covid-19 in the infected people has increased since less accurate automated diagnostic methods are available. Recent studies based on the radiology imaging suggested that the imaging patterns on X-ray images and Computed Tomography (CT) scans contain leading information about Covid-19 and is considered as a potential automated diagnosis method. Machine learning and deep learning techniques combined with radiology imaging can be helpful for accurate detection of the disease. A deep learning approach based on the multilayer-Spatial Convolutional Neural Network for automatic detection of Covid-19 using chest X-ray images and CT scans is proposed in this paper. The proposed model, named as the Multilayer Spatial Covid Convolutional Neural Network (MSCovCNN), provides an automated accurate diagnostics for Covid-19 detection. The proposed model showed 93.63% detection accuracy and 97.88% AUC (Area Under Curve) for chest x-ray images and 91.44% detection accuracy and 95.92% AUC for chest CT scans, respectively. We have used 5-tiered 2D-CNN frameworks followed by the Artificial Neural Network (ANN) and softmax classifier. In the CNN each convolution layer is followed by an activation function and a Maxpooling layer. The proposed model can be used to assist the radiologists in detecting the Covid-19 and confirming their initial screening

Dynamic Demand Forecast and Assignment Model for Bike-and-Ride System

Bike-and-Ride (B&R) has long been considered as an effective way to deal with urbanization-related issues such as traffic congestion, emissions, equality, etc. Although there are some studies focused on the B&R demand forecast, the influencing factors from previous studies have been excluded from those forecasting methods. To fill this gap, this paper proposes a new B&R demand forecast model considering the influencing factors as dynamic rather than fixed ones to reach higher forecasting accuracy. This model is tested in a theoretical network to validate the feasibility and effectiveness and the results show that the generalised cost does have an effect on the demand for the B&R system.</p

Interactions naturelles en réalité virtuelle~: impact sur la charge cognitive

National audienceMany of the virtual reality (VR) interaction devices available to the general public rely on the use of controllers. However, theses ones generate some usability constraints. Current availability of new gestural devices provides a more "natural" way to interact in VR, i.e. more intuitive, facilitating learning and especially minimizing cognitive load. However, this last one is rarely taken into account in the literature on VR design and evaluation. In order to fill this gap, we propose to evaluate, within a comparative study, the respective impact of 2 interaction paradigms on the cognitive load and performance of two different user populations (experienced vs. novice): gestural interaction using Leap Motion\textregistered (test group) and more traditional interaction using gamepad controllers (control group). Initial results indicate significantly higher cognitive load and significantly lower performance during gestural interaction with the Leap Motion\textregistered than during interaction with the controllers. These results highlight technical limitations related to Leap Motion\textregistered and the need to improve technically these devices to obtain a robust technology.Une grande partie des dispositifs d'interaction en réalité virtuelle (VR) accessibles au grand public repose sur l'usage des contrôleurs. Or, ces derniers génèrent certaines contraintes d'utilisabilité. La récente disponibilité de dispositifs d'interaction gestuelle permet d'avoir des interactions plus " naturelles " pour l'utilisateur, c'est-à-dire plus intuitives, permettant de faciliter l'apprentissage et surtout de minimiser la charge cognitive ; or ce dernier trait est peu pris en compte dans la littérature en conception/évaluation VR. Afin de pallier ce manque, nous proposons d'évaluer au sein d'une étude comparative, l'impact respectif de 2 paradigmes d'interaction sur la charge cognitive et les performances de deux populations d'utilisateurs différentes (expérimentée vs. novice)~: les interactions gestuelles à l'aide du Leap Motion\textregistered (groupe test) et les interactions plus classiques à l'aide de contrôleurs de mouvement de type gamepad (groupe contrôle). Les premiers résultats indiquent une charge cognitive significativement plus élevée et des performances significativement moindres lors de l'interaction gestuelle avec le Leap Motion\textregistered que lors de l'interaction avec les contrôleurs et ce, pour les 2 types de population. Ces résultats mettent en évidence les limites techniques liées au Leap Motion\textregistered ainsi que la nécessité d'améliorer techniquement ces dispositifs avant de pouvoir aboutir à une technologie robuste

Recommendation system using autoencoders

The magnitude of the daily explosion of high volumes of data has led to the emergence of the Big Data paradigm. The ever-increasing amount of information available on the Internet makes it increasingly difficult for individuals to find what they need quickly and easily. Recommendation systems have appeared as a solution to overcome this problem. Collaborative filtering is widely used in this type of systems, but high dimensions and data sparsity are always a main problem. With the idea of deep learning gaining more importance, several works have emerged to improve this type of filtering. In this article, a product recommendation system is proposed where an autoencoder based on a collaborative filtering method is employed. A comparison of this model with the Singular Value Decomposition is made and presented in the results section. Our experiment shows a very low Root Mean Squared Error (RMSE) value, considering that the recommendations presented to the users are in line with their interests and are not affected by the data sparsity problem as the datasets are very sparse, 0.996. The results are quite promising achieving an RMSE value of 0.029 in the first dataset and 0.010 in the second one.This research has been supported by FCT—Fundação para a Ciência e Tecnologia within the R&D UnitsProject Scope: UIDB/00319/202

Smart rogaining for computer science orientation

In this paper, we address the problem of designing new formats of computer science orientation activities to be offered during high school students internships in Computer Science Bachelor degrees. In order to cover a wide range of computer science topics as well to deal with soft skills and gender gap issues, we propose a teamwork format, called smart rogaining, that offer engaging introductory activities to prospective students in a series of checkpoints dislocated along the different stages of a rogaine. The format is supported by a smart mobile and web application. Our proposal is aimed at stimulating the interest of participants in different areas of computer science and at improving digital and soft skills of participants and, as a side effect, of staff members (instructors and university students). In the paper, we introduce the proposed format and discuss our experience in the editions organized at the University of Genoa before the COVID-19 pandemic (2019 and 2020 waves)

Smart rogaining for computer science orientation

In this paper, we address the problem of designing new formats of computer science orientation activities to be offered during high school students internships in Computer Science Bachelor degrees. In order to cover a wide range of computer science topics as well to deal with soft skills and gender gap issues, we propose a teamwork format, called smart rogaining, that offer engaging introductory activities to prospective students in a series of checkpoints dislocated along the different stages of a rogaine. The format is supported by a smart mobile and web application. Our proposal is aimed at stimulating the interest of participants in different areas of computer science and at improving digital and soft skills of participants and, as a side effect, of staff members (instructors and university students). In the paper, we introduce the proposed format and discuss our experience in the editions organized at the University of Genoa before the COVID-19 pandemic (2019 and 2020 waves)

MAPPING IS CURRICULUM RESEARCH AREAS: A SYSTEMATIC LITERATURE REVIEW FROM 2010 TO 2019

Research on IS curriculum addresses many important aspects related to IS curriculum planning: sharing of good curriculum planning practices, reviewing and recommending contents for IS curriculum, and identifying graduates’ competency needs. A bit surprisingly, however, there is no systematic literature review on IS curriculum research, increasing the possibility that knowledge does not accumulate, or reach intended beneficiaries. In this paper, we present results of a systematic literature review of IS curriculum research from 2010 to 2019. In total, 204 articles are downloaded from Scopus, AIS eLibrary, and ACM digital library. In addition to providing an overview of research demographics, we classify the articles first into three broad categories (planning process, curriculum contents, competency requirements), and secondly to more specific classes within each category. For IS curriculum researchers, the results assist in identifying prior research in different areas, thus promoting accumulation of research knowledge. For IS faculty, the paper provides an overview of IS curriculum related studies and a possibility to identify papers based on their immediate curriculum design needs and interests

Particle Filter-Based Prediction for Anomaly Detection in Automatic Surveillance

Automatic surveillance of abnormal events is a major unsolved problem in city management. By successful implementation of automatic surveillance of abnormal events, a significant amount of human resources in video monitoring can be economized. One solution to this application is computer vision technology. This approach utilizes an image processing algorithm to extract specific features and then uses discriminator algorithms to give an alert. In this paper, we propose to apply a particle filter-based algorithm to feature series extracted from videos in order to give alerts when abnormal events occur. The whole process consists of feature series generation and particle filter tracking. To represent the features of a video, an L2-norm extractor is designed based on the optical flow. Then, the particle filter keeps track of these feature series. The occurrence of abnormal events will cause the shift of feature series and a large error in PF tracking. This, in turn, will allow computers to understand and define the occurrences of anomalies. Experiments on UMN dataset show that our algorithm reaches 90% accuracy in frame-level detection