A Service Oriented Framework for Analysing Social Network Activities

AbstractAnalysing and monitoring Social Networking activities raise multiple challenges for the evolution of Service Oriented Systems Engineering. This is particularly evident for event detection in social networks and, more in general, for large-scale Social Analytics, which require continuous processing of data. In this paper we present a service oriented framework exploring effective ways to leverage the opportunities coming from innovations and evolutions in computational power, storage, and infrastructures, with particular focus on modern architectures including in-memory database technology, in-database computation, massive parallel processing, Open Data Services, and scalability with multi-node clusters in Cloud. A prototype of this system was experimented in the contest of a specific kind of social event, an art exhibition of sculptures, where the system collected and analyzed in real-time the tweets issued in an entire region, including exhibition sites, and continuously updated analytical dashboards placed in one of the exhibition rooms

A Deep Learning Approach for Predicting Two-dimensional Soil Consolidation Using Physics-Informed Neural Networks (PINN)

Soil consolidation is closely related to seepage, stability, and settlement of geotechnical buildings and foundations, and directly affects the use and safety of superstructures. Nowadays, the unidirectional consolidation theory of soils is widely used in certain conditions and approximate calculations. The multi-directional theory of soil consolidation is more reasonable than the unidirectional theory in practical applications, but it is much more complicated in terms of index determination and solution. To address the above problem, in this paper, we propose a deep learning method using physics-informed neural networks (PINN) to predict the excess pore water pressure of two-dimensional soil consolidation. In the proposed method, (1) a fully connected neural network is constructed, (2) the computational domain, partial differential equation (PDE), and constraints are defined to generate data for model training, and (3) the PDE of two-dimensional soil consolidation and the model of the neural network is connected to reduce the loss of the model. The effectiveness of the proposed method is verified by comparison with the numerical solution of PDE for two-dimensional consolidation. Using this method, the excess pore water pressure could be predicted simply and efficiently. In addition, the method was applied to predict the soil excess pore water pressure in the foundation in a real case at Tianjin port, China. The proposed deep learning approach can be used to investigate the large and complex multi-directional soil consolidation.Comment: 23 page

An Efficient Deep Learning Approach Using Improved Generative Adversarial Networks for Incomplete Information Completion of Self-driving

Autonomous driving is the key technology of intelligent logistics in Industrial Internet of Things (IIoT). In autonomous driving, the appearance of incomplete point clouds losing geometric and semantic information is inevitable owing to limitations of occlusion, sensor resolution, and viewing angle when the Light Detection And Ranging (LiDAR) is applied. The emergence of incomplete point clouds, especially incomplete vehicle point clouds, would lead to the reduction of the accuracy of autonomous driving vehicles in object detection, traffic alert, and collision avoidance. Existing point cloud completion networks, such as Point Fractal Network (PF-Net), focus on the accuracy of point cloud completion, without considering the efficiency of inference process, which makes it difficult for them to be deployed for vehicle point cloud repair in autonomous driving. To address the above problem, in this paper, we propose an efficient deep learning approach to repair incomplete vehicle point cloud accurately and efficiently in autonomous driving. In the proposed method, an efficient downsampling algorithm combining incremental sampling and one-time sampling is presented to improves the inference speed of the PF-Net based on Generative Adversarial Network (GAN). To evaluate the performance of the proposed method, a real dataset is used, and an autonomous driving scene is created, where three incomplete vehicle point clouds with 5 different sizes are set for three autonomous driving situations. The improved PF-Net can achieve the speedups of over 19x with almost the same accuracy when compared to the original PF-Net. Experimental results demonstrate that the improved PF-Net can be applied to efficiently complete vehicle point clouds in autonomous driving.Comment: 10 figures, 4 table

The Internet of Things supporting the Cultural Heritage domain: analysis, design and implementation of a smart framework enhancing the smartness of cultural spaces

Nowadays embedded systems have reached a great level of maturity and diffusion thanks to their small size, low power consumption, large connectivity and variety of application in everyday contexts. These systems, if properly structured and configured, can signifi- cantly increase the smartness of the environments where they are deployed, monitoring and continuously collecting data to be processed and elaborated. In this perspective, the Internet of Things (IoT) paradigm supports the transition from a closed world, in which an object is characterized by a descriptor, to an open world, in which objects interact with the surrounding environment, because they have become ”intelligent”. Accordingly, not only people will be connected to the internet, but objects such as cars, fridges, televisions, water management systems, buildings, monuments and so on will be connected as well. The Cultural Heritage represents a worldwide resource of inestimable value, attracting millions of visitors every year to monuments, museums and art exhi- bitions. Fundamental aspects of this resource to be investigated are its promotion and people enjoyment. Indeed, to achieve an enjoyment of a cultural space that is attractive and sustainable, it is necessary to realize ubiquitous and multimedia solutions for users’ interaction to enrich their visiting experience and improve the knowledge transmission process of a cultural site. The main target of this PhD Thesis is the study of the IoT paradigm, devoted to the design of a smart framework supporting the fruition, enjoyment and tutelage of the Cultural Heritage domain. In order to assess the proposed approach, a real case study is presented and discussed. In detail, it represents the deployment of our framework during an art exhibition, named The Beauty or the Truth within the Monumental Complex of San Domenico Maggiore, Naples (Italy). Following the Internet of Things paradigm, the proposed intelligent framework relies on the integration of a Sensor Network of Smart Objects with Wi-Fi and Bluetooth Low Energy technologies to identify, locate and support users. In this way technology can become a mediator between visitors and fruition, an instrument of connection between people, objects, and spaces to create new social, economic and cultural opportunities

remarks on a computational estimator for the barrier option pricing in an iot scenario

Abstract The importance of derivatives in financial markets has known an exponential growth in the last decades, especially in risk management and speculation fields: this explains researchers' interest in answering questions about this kind of contracts. In particular, in this paper we restrict our attention on European vanilla and barrier options, and we propose a statistical procedure to solve efficiently the problem of determining the no arbitrage price of this type of derivatives in an IoT context: starting form an Internet of Things (IoT) data flow, an IoT system takes information from several sources and stores it into a suitable database; this information is used in our estimation problem. Our scheme is based on some strong assumptions about the market model, in particular the completeness of the market, the log-normality of the underlying asset with a constant volatility. We conclude this paper with an application of our framework to a real case

Synthesis and Pharmacological Evaluation of a Novel Peptide Based on Anemonia sulcata BDS-I Toxin as a New KV3.4 Inhibitor Exerting a Neuroprotective Effect Against Amyloid-β Peptide

There is increasing evidence that the fast-inactivating potassium current IA, encoded by KV3. 4 channels, plays an important role in Alzheimer's Disease (AD), since the neurotoxic β-amyloid peptide1-42 (Aβ1-42) increases the IA current triggering apoptotic processes. The specific inhibition of KV3.4 by the marine toxin extracted from Anemonia sulcata, named blood depressing substance-I (BDS-I), reverts the Aβ peptide-induced cell death. The aim of the present study was to identify the smallest fragments of BDS-I, obtained by peptide synthesis, able to inhibit KV3.4 currents. For this purpose, whole-cell patch clamp technique was used to evaluate the effects of BDS-I fragments on KV3.4 currents in CHO cells heterologously expressing KV3.4. We found that BDS-I[1-8] fragment, containing the N-terminal octapeptide sequence of full length BDS-I, was able to inhibit KV3.4 currents in a concentration dependent manner, whereas the scrambled sequence of BDS-I[1-8] and all the other fragments obtained from BDS-I full length were ineffective. As we demonstrated in a previous study, BDS-I full length is able to counteract Aβ1-42-induced enhancement of KV3.4 activity, preventing Aβ1-42-induced caspase-3 activation and the abnormal nuclear morphology in NGF-differentiated PC-12 cells. Similarly to BDS-I, we found that BDS-I[1-8] blocking KV3.4 currents prevented Aβ1-42-induced caspase-3 activation and apoptotic processes. Collectively, these results suggest that BDS-I[1-8] could represent a lead compound to be developed as a new drug targeting KV3.4 channels

DeepVATS : Deep Visual Analytics for time series

The field of Deep Visual Analytics (DVA) has recently arisen from the idea of developing Visual Interactive Systems supported by deep learning, in order to provide them with large-scale data processing capabilities and to unify their implementation across different data and domains. In this paper we present DeepVATS, an open-source tool that brings the field of DVA into time series data. DeepVATS trains, in a self-supervised way, a masked time series autoencoder that reconstructs patches of a time series, and projects the knowledge contained in the embeddings of that model in an interactive plot, from which time series patterns and anomalies emerge and can be easily spotted. The tool includes a back-end for data processing pipeline and model training, as well as a front-end with an interactive user interface. We report on results that validate the utility of DeepVATS, running experiments on both synthetic and real datasets. The code is publicly available on https://github.com/vrodriguezf/deepvats