A Nonlinear Technical Indicator Selection Approach for Stock Markets. Application to the Chinese Stock Market

In this paper we present a combinatorial nonlinear technical indicator approach for the identification of appropriate combinations of stock technical indicators as inputs in non-linear models. This approach is illustrated with the example of Chinese stock indexes and 35 different stock technical indicators using neural networks as the chosen non-linear method. Stock market technical indicators can generate contradictory signals regarding the future performance of the stock analyzed. Furthermore, some non-linear methods, such as neural networks, can have poor generalization power when dealing with problems of high dimensionality due to the issue of local minima. Therefore, non-linear approaches that can identify appropriate combinations of input variables are of clear importance. It will be shown that the proposed approach, when using neural networks as classifiers, generates error rates lower than those using directly neural networks without dimensionality reduction. It will also be shown that merely increasing the number of neurons does not increase the accuracy. The approach proposed in this article is illustrated with an application to the stock market using neural networks but it could be applied to other fields and it can also be used with other non-linear techniques such as for instance support vector machines.Ministerio de Ciencia e Innovación PID2019-106212RB-C41

GABAA Receptor Deficits Predict Recovery in Patients With Disorders of Consciousness: A Preliminary Multimodal [11C]Flumazenil PET and fMRI Study

Disorders of consciousness (DoC)—that is, unresponsive wakefulness syndrome/vegetative state and minimally conscious state—are debilitating conditions for which no reliable markers of consciousness recovery have yet been identified. Evidence points to the GABAergic system being altered in DoC, making it a potential target as such a marker

Household wealth proxies for socio-economic inequality policy studies in China

In China, one percent of the richest population holds more than one-third of the wealth, while the poorest 25% shares no more than two percent of the total. The country’s rapid economic development has resulted in increasing socio-economic disparities, and a rapidly deteriorating environment. This puts the Chinese citizens, especially the most vulnerable and deprived socio-economic status (SES) groups, at high risks of environmental inequality (EI). In most SES-based EI studies conducted in China, household wealth has often been overlooked, though it potentially serves a good economic indicator to capture the socio-economic effect of environmental change in China. Nevertheless, existing SES databases in China are of low spatial resolution and are insufficient to support fine-grained EI studies at the intra-city level in China. The core research challenge is to develop a representative household wealth proxy in high-spatial resolution for China. This study highlights the research gaps and proposes a new household wealth proxy, which integrates both fine-grained data/features such as daytime satellite imagery and easily accessible wealth indicators such as house prices. We also capitalize on everyday economic activity data retrieved from personal mobile phones and online transaction/social platforms in the composition of our wealth proxy to achieve a higher accuracy in estimating household wealth at fine-grained resolution via machine learning. Finally, we summarize the challenges in improving both the quality and the availability of Chinese socio-economic datasets, while protecting personal privacy and information security during the data collection process for household wealth proxy development in China

Towards Next Generation Teaching, Learning, and Context-Aware Applications for Higher Education: A Review on Blockchain, IoT, Fog and Edge Computing Enabled Smart Campuses and Universities

[Abstract] Smart campuses and smart universities make use of IT infrastructure that is similar to the one required by smart cities, which take advantage of Internet of Things (IoT) and cloud computing solutions to monitor and actuate on the multiple systems of a university. As a consequence, smart campuses and universities need to provide connectivity to IoT nodes and gateways, and deploy architectures that allow for offering not only a good communications range through the latest wireless and wired technologies, but also reduced energy consumption to maximize IoT node battery life. In addition, such architectures have to consider the use of technologies like blockchain, which are able to deliver accountability, transparency, cyber-security and redundancy to the processes and data managed by a university. This article reviews the state of the start on the application of the latest key technologies for the development of smart campuses and universities. After defining the essential characteristics of a smart campus/university, the latest communications architectures and technologies are detailed and the most relevant smart campus deployments are analyzed. Moreover, the use of blockchain in higher education applications is studied. Therefore, this article provides useful guidelines to the university planners, IoT vendors and developers that will be responsible for creating the next generation of smart campuses and universities.Xunta de Galicia; ED431C 2016-045Xunta de Galicia; ED431G/01Agencia Estatal de Investigación de España; TEC2016-75067-C4-1-

Geographical queries reformulation using a parallel association rules generator to build spatial taxonomies

Geographical queries need a special process of reformulation by information retrieval systems (IRS) due to their specificities and hierarchical structure. This fact is ignored by most of web search engines. In this paper, we propose an automatic approach for building a spatial taxonomy, that models’ the notion of adjacency that will be used in the reformulation of the spatial part of a geographical query. This approach exploits the documents that are in top of the retrieved list when submitting a spatial entity, which is composed of a spatial relation and a noun of a city. Then, a transactional database is constructed, considering each document extracted as a transaction that contains the nouns of the cities sharing the country of the submitted query’s city. The algorithm frequent pattern growth (FP-growth) is applied to this database in his parallel version (parallel FP-growth: PFP) in order to generate association rules, that will form the country’s taxonomy in a Big Data context. Experiments has been conducted on Spark and their results show that query reformulation using the taxonomy constructed based on our proposed approach improves the precision and the effectiveness of the IRS

Design and experimental validation of a LoRaWAN fog computing based architecture for IoT enabled smart campus applications

A smart campus is an intelligent infrastructure where smart sensors and actuators collaborate to collect information and interact with the machines, tools, and users of a university campus. As in a smart city, a smart campus represents a challenging scenario for Internet of Things (IoT) networks, especially in terms of cost, coverage, availability, latency, power consumption, and scalability. The technologies employed so far to cope with such a scenario are not yet able to manage simultaneously all the previously mentioned demanding requirements. Nevertheless, recent paradigms such as fog computing, which extends cloud computing to the edge of a network, make possible low-latency and location-aware IoT applications. Moreover, technologies such as Low-Power Wide-Area Networks (LPWANs) have emerged as a promising solution to provide low-cost and low-power consumption connectivity to nodes spread throughout a wide area. Specifically, the Long-Range Wide-Area Network (LoRaWAN) standard is one of the most recent developments, receiving attention both from industry and academia. In this article, the use of a LoRaWAN fog computing-based architecture is proposed for providing connectivity to IoT nodes deployed in a campus of the University of A Coruña (UDC), Spain. To validate the proposed system, the smart campus has been recreated realistically through an in-house developed 3D Ray-Launching radio-planning simulator that is able to take into consideration even small details, such as traffic lights, vehicles, people, buildings, urban furniture, or vegetation. The developed tool can provide accurate radio propagation estimations within the smart campus scenario in terms of coverage, capacity, and energy efficiency of the network. The results obtained with the planning simulator can then be compared with empirical measurements to assess the operating conditions and the system accuracy. Specifically, this article presents experiments that show the accurate results obtained by the planning simulator in the largest scenario ever built for it (a campus that covers an area of 26,000 m2), which are corroborated with empirical measurements. Then, how the tool can be used to design the deployment of LoRaWAN infrastructure for three smart campus outdoor applications is explained: a mobility pattern detection system, a smart irrigation solution, and a smart traffic-monitoring deployment. Consequently, the presented results provide guidelines to smart campus designers and developers, and for easing LoRaWAN network deployment and research in other smart campuses and large environments such as smart cities.This work has been funded by the Xunta de Galicia (ED431C 2016-045, ED431G/01), the Agencia Estatal de Investigación of Spain (TEC2016-75067-C4-1-R) and ERDF funds of the EU (AEI/FEDER, UE)

A Survey of Forex and Stock Price Prediction Using Deep Learning

The prediction of stock and foreign exchange (Forex) had always been a hot and profitable area of study. Deep learning application had proven to yields better accuracy and return in the field of financial prediction and forecasting. In this survey we selected papers from the DBLP database for comparison and analysis. We classified papers according to different deep learning methods, which included: Convolutional neural network (CNN), Long Short-Term Memory (LSTM), Deep neural network (DNN), Recurrent Neural Network (RNN), Reinforcement Learning, and other deep learning methods such as HAN, NLP, and Wavenet. Furthermore, this paper reviewed the dataset, variable, model, and results of each article. The survey presented the results through the most used performance metrics: RMSE, MAPE, MAE, MSE, accuracy, Sharpe ratio, and return rate. We identified that recent models that combined LSTM with other methods, for example, DNN, are widely researched. Reinforcement learning and other deep learning method yielded great returns and performances. We conclude that in recent years the trend of using deep-learning based method for financial modeling is exponentially rising