Network On Network for Tabular Data Classification in Real-world Applications

Tabular data is the most common data format adopted by our customers ranging from retail, finance to E-commerce, and tabular data classification plays an essential role to their businesses. In this paper, we present Network On Network (NON), a practical tabular data classification model based on deep neural network to provide accurate predictions. Various deep methods have been proposed and promising progress has been made. However, most of them use operations like neural network and factorization machines to fuse the embeddings of different features directly, and linearly combine the outputs of those operations to get the final prediction. As a result, the intra-field information and the non-linear interactions between those operations (e.g. neural network and factorization machines) are ignored. Intra-field information is the information that features inside each field belong to the same field. NON is proposed to take full advantage of intra-field information and non-linear interactions. It consists of three components: field-wise network at the bottom to capture the intra-field information, across field network in the middle to choose suitable operations data-drivenly, and operation fusion network on the top to fuse outputs of the chosen operations deeply. Extensive experiments on six real-world datasets demonstrate NON can outperform the state-of-the-art models significantly. Furthermore, both qualitative and quantitative study of the features in the embedding space show NON can capture intra-field information effectively

Expression of WRKY and MYB genes during infection with powdery mildew in cucumber (Cucumis sativus L)

The expression change pattern of three transcription genes (WRKY30, WRKY6 and MYB) in two cucumber lines with different powdery mildew resistance (resistant line ‘JIN5-508’ and susceptible line ‘D8’) were investigated during the infection process with powdery mildew using real-time quantitative polymerase chain reaction (RT-PCR). Gene expression analysis during different time points revealed that the expression ratio of WRKY30 was 10.08585 in D8 and 5.117667 in JIN5-508, respectively, and for WRKY6, the expression ratio was 5.396152 in D8 and 3.787322 in JIN5-508, respectively, and for MYB, the expression ratio was 14.17324 in D8 and 10.70195 in JIN5-508, respectively. Additionally, the time point of the highest relative expression ratio for the three genes was different in the two cucumber lines according to their resistance to powdery mildew, whereas the susceptible line D8 was earlier than the resistant line JIN5-508 in responding to the powdery mildew infection. We suggest that the three genes’ expressions induced by powdery mildew pathogen is related to the disease resistance, and the response of susceptible line is earlier and higher than the resistant line, which may have interactions between the three genes and other resistant genes.Key words: Cucumber, powdery mildew, gene expression pattern

Victim Detection and Localization in Emergencies

Detecting and locating victims in emergency scenarios comprise one of the most powerful tools to save lives. Fast actions are crucial for victims because time is running against them. Radio devices are currently omnipresent within the physical proximity of most people and allow locating buried victims in catastrophic scenarios. In this work, we present the benefits of using WiFi Fine Time Measurement (FTM), Ultra-Wide Band (UWB), and fusion technologies to locate victims under rubble. Integrating WiFi FTM and UWB in a drone may cover vast areas in a short time. Moreover, the detection capacity of WiFi and UWB for finding individuals is also compared. These findings are then used to propose a method for detecting and locating victims in disaster scenarios.This work was performed in the framework of the Horizon 2020 project LOCUS (Grant Agreement Number 871249), receiving funds from the European Union. This work was also partially funded by Junta de Andalucia (Project PY18-4647:PENTA)

Time-dependent KPI generation based on Copula

New generations of mobile networks are developed to serve the increasing user and devices connected to the networks. However, the management of these networks has a need of automation, due to the also growing complexity. Self-Organizing Network (SON) was conceived to fulfil the automation of network management, within which troubleshooting is located under Self-Healing (SH). The current tendency is the use of Artificial Intelligence (AI) algorithms that are trained using Machine Learning (ML). This training requires a considerable amount of data. Anyway, the reluctance of operators to sharing their data with the research community causes a scarcity of data representing degradations that can be used for the development and training of ML algorithms. In this paper a method to solve this data sample limitation is proposed. In the first place, the method divides the data into time categories to create models which preserve the time characteristics. Afterwards, it applies statistical copulas to adapt the models into new ones maintaining statistical relationships. Finally, the method returns synthetic data that can be an input for ML. As an example, the data from a real mobile network is processed.I Plan Propio de Investigación y Transferencia de la Universidad de Málag

Fusion of LTE and UWB ranges for trilateration

High precise indoor positioning is the spotlight for the new mobile generation 5G. Ultra-Wide Band (UWB) technology stands out as the creditable preference for locating the user in indoor scenarios. The principal limitation of this technology appears in the coverage area that reaches a few tens of meters. In our case of study, we have simulated a conceivable real environment with UWB and Long Term Evolution (LTE) base stations for positioning users. In this scenario, users have been tracked by an Extended Kalman Filter (EKF), a memory state filter to predict the movement of the user that improves the performance of the system. In regions that receivers only track isolated UWB stations we make use of this information in order to improve the location provided by mobile networks. Essentially, when performing trilateration using the data offered by LTE, we also include the data of UWB in case that this information do not serve to position by itself. In this manner, the coverage area by at least one UWB station augments and accuracy of the system improves in those regions where only LTE previously provided location.Financiado por la Unión Europea en el marco del acuerdo de subvención Horizonte 2020 (Grant 871249, LOCUS)

UWB and WiFi characterization for localization in construction sites

High-precision location is becoming a necessity in the future Industry 4.0 applications that will come up in the near future. However, the construction sector remains particularly obsolete in the adoption of Industry 4.0 applications. In this work we study the accuracy and penetration capacity of two technologies that are expected to deal with future high-precision location services such as Ultra Wide Band (UWB) and WiFi Fine Time Measurement (FTM).For this, a measurement campaign has been done in a construction environment, where UWB and WiFi-FTM setups have been deployed. The performance of UWB and WiFi-FTM have been compared with a prior set of indoors measurements. Moreover, the impact of fusion of location technologies has been assesed to measure the potential improvements in the construction scenario.This work has been carried out through the I plan Propio de Investigación y Transferencia y Divulgación Científica by University of Malaga and the Junta de Andalucía under the UMA-CEIATECH-12 TEDES-5G grant agreement. Moreover, this work has been performed in the framework of the Horizon 2020 project LOCUS (grant agreement number 871249), receiving funds from the European Comission. In addition, we would like to thank the company ACR for providing us access to a real construction environment. Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Localización de usuarios con coordenadas polares.

Currently, the increase of location aware services and network management has driven the demand for user location estimation schemes, although it is not usually available to operators. Moreover, commercial networks have limited access to specific user related metrics. In general, solutions with Machine Learning (ML) have reached high precisions, but only in a trained scenario, and with difficulties in predicting unseen areas. The approach proposed here solves the above limitation by a reference coordinate conversion, to obtain relative polar positions which create scenario agnostic models, and whose performance is demonstrated using a dataset recollected from a commercial mobile network.Ministerio de Asuntos Económicos y Transformación Digital y la Unión Europea - NextGenerationEU, en el marco del Plan de Recuperación, Transformación y Resiliencia y el Mecanismo de Recuperación y Resiliencia bajo el proyecto MAORI. Además, también está parcialmente financiado por la Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech., a través de II Plan Propio de Investigación y Transferencia y por el proyecto “Desarrollo de casos de uso para el diseño, optimización y dimensionado de redes móviles – Líneas B1 y D1” (Ref. 8.06/5.59.5705-3 IDEA)