Miracle-dz/UniFAR-FTUs huggingface dataset:
A-DSP: An Adaptive Join Algorithm for Dynamic Data Stream on Cloud System

The join operations, including both equi and non-equi joins, are essential to the complex data analytics in the big data era. However, they are not inherently supported by existing <italic>DSPEs</italic> (<underline>D</underline>istributed <underline>S</underline>tream <underline>P</underline>rocessing <underline>E</underline>ngines). The state-of-the-art join solutions on DSPEs rely on either complicated routing strategies or resource-inefficient processing structures, which are susceptible to dynamic workload, especially when the <italic>DSPEs</italic> face various join predicate operations and skewed data distribution. In this paper, we propose a new cost-effective stream join framework, named <italic>A-DSP</italic> (<underline>A</underline>daptive <underline>D</underline>imensional <underline>S</underline>pace <underline>P</underline>rocessing), which enhances the adaptability of real-time join model and minimizes the resource used over the dynamic workloads. Our proposal includes: 1) a join model generation algorithm devised to adaptively switch between different join schemes so as to minimize the number of processing task required; 2) a load-balancing mechanism which maximizes the processing throughput; and 3) a lightweight algorithm designed for cutting down unnecessary migration cost. Extensive experiments are conducted to compare our proposal against state-of-the-art solutions on both benchmark and real-world workloads. The experimental results verify the effectiveness of our method, especially on reducing the operational cost under pay-as-you-go pricing scheme.
Disentangling stigma to find entry points for intervention.

HIV-related stigma and resulting discrimination is a key barrier to all HIV activities from prevention to care and treatment. A recent study investigating the causes manifestations and consequences of HIV-related stigma and resulting discrimination in Ethiopia Tanzania and Zambia unravelled the complexities of stigma and was used to develop interventions. The causes and consequences of stigma found are similar across all the countries study sites as well as in a recent study in Vietnam. AIDS-related stigma and resulting discrimination are deeply intertwined with social values fears around sex and death and gender and social inequity. The same complexity also provides many entry points for stigma-reduction interventions. Key reasons for why stigma persists are a lack of in-depth knowledge of HIV/AIDS which allows fears of casual transmission to endure; strong norms about ‘improper and "proper" sex and its association with HIV; and a lack of recognition of stigmatising attitudes and behaviour. The data shows how widespread stigma impedes programmatic efforts for HIV prevention testing and disclosure as well as care support and treatment. For example stigma leads to reluctance to be tested for HIV and limited disclosure of positive test results while condom use or discussing safe sex are considered indications of HIV infection or immoral behaviours. Similarly available care and support is accompanied by judgmental attitudes and isolating behaviour which can result in PLWHA delaying care until absolutely necessary. (excerpt)
Catálogo de arquitecturas de software y tácticas arquitectónicas para contextos de big data

El presente documento presenta un catálogo de arquitecturas de software y tácticas arquitectónicas aplicables en contextos de big data. En la primera parte se describen las arquitecturas de software utilizando un esquema que presenta sus escenarios de uso y principales componentes. A partir de estas descripciones se identificaron tácticas arquitectónicas comunes aplicadas en contextos de big data. Se presenta una descripción de cada táctica arquitectónica y su estrategia de resolución de los atributos de calidad afectados. Palabras clave: big data, arquitectura de software, tácticas arquitectónicas. Documento de Investigación, Nro.15, setiembre 2019. Universidad ORT Uruguay. Facultad de Ingeniería. ISSN 1688-8707.
Wind turbine health monitoring based on accelerometer data

A structural health monitoring (SHM) system verifies the mechanical state of a structure toensure its proper functioning and determines whether it needs some kind of maintenance. Thus, SHMfor wind turbines (WT) in remote locations, as offshore, is crucial. Offshore wind farms are increasinglyrealized in water depths beyond 30 meters, where lattice support structures are an interesting optionto withstand the severe environmental actions. In particular jackets appear to be a highly competitivesubstructure type with a wide range of applicability, from approximately 25 to 70 meters water depth.With no doubt, structural damage is a significant issue in these structures. Unlike on-shore structures oreven shallow water structures, the access for regular monitoring and repair is not an easy option, in termsof both the cost and the accessibility. In this work, a methodology for the detection and classification ofstructural damages in offshore jacket-type WT is stated. The proposed method relies on the paradigmthat any damage in the structure produces changes in the vibrational response. However, it is assumedthat the only available excitation of the WT is the wind turbulence, so the input excitation is assumedto be unknown. Therefore, using only accelerometer information, a data driven approach for damagedetection is developed. The scheme of the proposed method can be summarized in the following steps:(i) the wind excitation is simulated as a Gaussian white noise and the data coming from the WT iscollected using a set of accelerometers; (ii) the raw data is arranged in matrix form and pre-processedusing mean-centered group-scaling; (iii) principal component analysis (PCA) is selected as a technique toreduce the dimensionality of the data and the computing time of the next step; finally, (iv) the quadratic-kernel support vector machine (SVM) is used as a classifier. The 5-fold cross-validation technique isemployed to estimate the overall accuracy and to avoid overfitting. In order to experimentally validatethe proposed approach, the damage detection strategy is applied to different types of predefined damagein a small-scale structure —an experimental laboratory tower modeling an offshore-fixed jacked-typewind turbine—. The results that have been obtained for these predefined damages are included anddiscussed to demonstrate the reliability of the proposed approach.
Electricity markets operation planning with risk-averse agents : Stochastic decomposition and equilibrium

The growing penetration of renewable energy sources in electricity systems requires adapting operation models to face the inherent variability and uncertainty of wind or solar generation. In addition, the volatility of fuel prices (such as natural gas) or the uncertainty of the hydraulic natural inflows requires to take into account all these sources of uncertainty within the operation planning of the generation system. Thus, stochastic optimization techniques have been widely used in this context. From the point of view of the system operation, the introduction of wind and solar generation in the mix has forced conventional generators to be subject to more demanding schedules from the technical point of view, increasing for example the number of start-up and shutdown decisions during the week, or having to face more pronounced ramps. From the point of view of the market, all these technical issues are transferred to the market prices that are subject to greater volatility. This thesis focuses on the problem of risk management using the Conditional Value at Risk (CVaR) as a coherent risk measure. The thesis presents a novel iterative method that can be used by a market agent to optimize its operating decisions in the short term when the uncertainty is characterized by a set of random variable scenarios. The thesis analyses how it is possible to decompose the problem of risk management by means of Lagrangian Relaxation techniques and Benders decomposition, and shows that the proposed iterative algorithm (Iterative-CVaR) converges to the same solution as under the direct optimization setting. The algorithm is applied to two typical problems faced by agents: 1) optimization of the operation of a combined cycle power plant (CCGT) that has to cope with the volatility in the spot market price to build the supply curve for the futures market, and 2) strategic unit-commitment model. In a second part of the thesis the problem of market equilibrium is studied to model the interaction between several generating companies with mixed generation portfolios (thermal, hydraulic and renewable). The thesis analyses how the Nash equilibrium solution is modified at different risk-aversion level of the risk of the agents. In particular, the thesis studies how the management of hydroelectric reservoirs is modified along the annual horizon when agents are risk-averse, and it is compared with the risk-neutral solution that coincides with a centralized planning when the objective is the minimization expected operational cost.
Developments in MCMC diagnostics and sparse Bayesian learning models

This dissertation consists of three research articles on the topic of Markov chain Monte Carlo (MCMC) diagnostics and sparse Bayesian learning models. The first article consists of MCMC diagnostic tools based on Kullback Leibler (KL) divergence and smoothing methods. These tools can assess the joint convergence of multiple variables and detect non convergence when MCMC chains get stuck at a particular mode of a multi modal stationary distribution. Further, in case of non convergence of multiple MCMC chains, the visualization tool can be used to investigate reasons for non convergence. The second article deals with assessing posterior propriety of some sparse Bayesian learning models. Relevance Vector Machine (RVM) is a popular sparse Bayesian learning model that assumes improper prior over its hyperparameters. We prove that this improper prior leads to an improper posterior. Further, we also provide necessary and sufficient conditions for posterior propriety of RVM. Additionally, we also prove the posterior impropriety of some Bayesian learning models that have a prior structure similar to that of RVM. In the third article, we propose to replace multiple penalties of RVM with a single penalty. The new model is named as single penalty relevance vector machine (SPRVM) and is analyzed using a semi Bayesian approach. The SPRVM allows for computation of Monte Carlo standard errors since we prove the geometric ergodicity of its associated Gibbs sampler. Such a Monte Carlo standard error cannot be computed in the case of RVM since the rate of convergence of its associated Gibbs sampler is not known. Thus, through these three articles we hope to make valuable additions to the literature of MCMC diagnostics and sparse Bayesian learning models.
A Deep Learning Framework for Malware Classification

In this article, the authors propose a deep learning framework for malware classification. There has been a huge increase in the volume of malware in recent years which poses serious security threats to financial institutions, businesses, and individuals. In order to combat the proliferation of malware, new strategies are essential to quickly identify and classify malware samples. Nowadays, machine learning approaches are becoming popular for malware classification. However, most of these approaches are based on shallow learning algorithms (e.g. SVM). Recently, convolutional neural networks (CNNs), a deep learning approach, have shown superior performance compared to traditional learning algorithms, especially in tasks such as image classification. Inspired by this, the authors propose a CNN-based architecture to classify malware samples. They convert malware binaries to grayscale images and subsequently train a CNN for classification. Experiments on two challenging malware classification datasets, namely Malimg and Microsoft, demonstrate that their method outperforms competing state-of-the-art algorithms.
Classification of Myoelectric Signal using Spectrogram Based Window Selection

This paper presents a study of the classification of myoelectric signal using spectrogram with different window sizes. The electromyography (EMG) signals of 40 hand movement types are collected from 10 subjects through NinaPro database. By employing spectrogram, the EMG signals are represented in time-frequency representation. Ten features are extracted from spectrogram for performance evaluation. In this study, two classifiers namely support vector machine (SVM) and linear discriminate analysis (LDA) are used to evaluate the performance of spectrogram features in the classification of EMG signals. To determine the best window size in spectrogram, three different Hanning window sizes are examined. The experimental results indicate that by applying spectrogram with optimize window size and LDA, the highest mean classification accuracy of 91.29% is obtained.
Smart Aquarium Berbasis IoT

Current technological advances facilitate communication with each other, exchange information between information sources with users or search for information using facilities from the internet. Especially in the electronics sector which has now started a phase or generation of IoT. I have negative water for fish and plants in the aquarium that is fish can die because of the water in the aquarium. To complete this final project, you must reactivate the aquarium water in the aquarium by supporting it with IoT, as a substitute it means that the water in the aquarium must be replaced immediately. The method used in completing this thesis is experimental, by asking for turbidity sensors that are put into clear water. The hardware used is ESP32 as a microcontroller that sends sensor data to the database, turbidity sensor as an air turbidity sensor, Asus A455l laptop and the software used is Arduino IDE as a media for making source code to be uploaded to ESP32, Thingspeak.com as a platform to store data transferred by ESP32. After testing the sensor, the next step is to upload data from ESP32 to Thingspeak.com. From the attempt to upload data 10 times, no attempt failed in uploading data. The conclusion is uploading data to Thingspeak.com has a percentage of 100%, sensor testing can run as expected, and all hardware and software can be used properly.
A Shared Bus Profiling Scheme for Smart Cities Based on Heterogeneous Mobile Crowdsourced Data

Mobile crowdsourcing (MCS), as an effective and crucial technique of Industrial Internet of Things, is enabling smart city initiatives in the real world. It aims at incorporating the intelligence of dynamic crowds to collect and compute decentralized ubiquitous sensing data that can be used to solve major urbanization problems such as traffic congestion. The shared bus, as a neotype transportation mode, aims at improving the resource utilization rate and maintaining the advantages of convenience and economy. In this article, we provide a scheme to profile shared buses through heterogeneous mobile crowdsourced data (TRProfiling). First, we design an MCS-based shared bus data generation and collection solution to overcome the aforementioned data scarcity issue. Then, we propose a travel profiling to profile resident travel and design a method called multiconstraint evolution algorithm to optimize the routes. Experimental results demonstrate that TRProfiling has an excellent performance in satisfying passengers’ travel requirements.
Congestion control in wireless sensor networks

Sensor networks are being increasingly deployed for surveillance and monitoring applications. These networks might suffer from severe congestion as soon as the target events are detected. Congestion control during this period is important not only to improve the overall throughput but also to lengthen the network lifetime by saving the scarce energy wasted due to congestion. While the traditional traffic control schemes that throttle the incoming traffic can effectively alleviate congestion, it does not increase the end-to-end throughput called accuracy level observed by the deployed application. In addition, throttling important data packets during this period can essentially nullify the purpose of sensor networks. In this thesis, I first attempt to formally define the resource control framework that adjusts the resource provisioning at the hotspot nodes. For accurate and timely congestion detection, I propose a new congestion detection mechanism that quantifies the congestion level accurately and energy-efficiently at both a node-level and a flow-level. Then I applied a resource control approach to control the detected congestion. In an effort to find the optimal resource control policy under the fidelity and energy constraints, I design a resource increase and decrease algorithm called Early Increase/Early Decrease (EIED) that tries to adjust the effective channel capacity quickly to suit the incoming traffic volume in an energy-efficient manner. The resource availability is controlled by changing the routing topology based on the EIED algorithm. To estimate the end-to-end capacity of different topologies, a capacity analysis model is formulated as a graph coloring problem. Finally, a topology-aware resource adaptation (TARA) protocol is designed that adjusts the resource provisioning in a distributed manner based on the perceived congestion level and projected resource availability of the targeted routing topology that has been learned from the capacity analysis model. Detailed simulation results have shown that TARA can energy-efficiently absorb the incoming traffic load and the performance of TARA is very close to that of an ideal offline resource control algorithm in terms of both fidelity requirement and energy efficiency.
Nonlinear Analysis: Hybrid Systems

In this paper, we propose an optimization-based design technique for constrained control and bounded-error state estimation for affine systems in the presence of intermittent measurements. We treat the affine system as a switched system where the measurement equation switches between two modes based on whether a measurement exists or is missing, and model potential missing data patterns with a finite-length language that constrains the feasible mode sequences. Then, we introduce a novel property, equalized recovery, that generalizes the equalized performance property and that allows us to tolerate missing observations. By utilizing Q -parametrization, we show that a finite horizon optimal estimator/controller can be constructed using time-based and prefixbased approaches, where the latter implicitly estimates the specific missing data pattern (i.e., mode sequence), within the given language, according to the prefix observed so far. We illustrate with numerical examples that the proposed approaches can provide desirable performance guarantees. © 2020 Elsevier Ltd. All rights reserved.
A Case of Dengue Encephalitis with Intracerebral Hemorrhage

1. Epidemiology Unit, Ministry of Health, Sri Lanka, 2009. Available from: http://www.epid. gov.lk/pdf/Guidelines%20on%20clinical%20management%20%20DFDHF2009.pdf[last accessed on 2010 May 25]. 2. WHO. Dengue: Guidelines for diagnosis, treatment and control. World Health Organization; 2009.3. Kularatne SA. Survey on the management of dengue infection in Sri Lanka: Opinions of physicians and pediatricians. Southeast Asian J Trop Med Public Health 2005;36:1198-200.4. Rajapakse S, Ranasinghe C, Rodrigo C. Corticosteroid therapy in dengue infection- opinions of junior doctors. J Glob Infect Dis 2010;2:199-200.
Uma Taxonomia para Descrever e Caracterizar Estratégias de Mitigação de Ataques DDoS em Ambientes IoT Usando Tecnologias SDN

A Internet das Coisas têm atraído significativa atenção da comunidade de Tecnologia da Informação e Comunicação (TIC) pela perspectiva de objetos do cotidiano coletando dados do ambiente e transmitindo-os através da Internet para processamento a posteriori, sem que haja necessidade de interação humano-computador. Embora a IoT represente uma poderosa plataforma para criação de novos produtos e serviços que irão beneficiar uma ampla variedade de verticais (\textit{e-health}, V2X, \textit{smart homes}, etc), existem diversas preocupações de segurança que precisam ser remediadas para sua implementação adequada, como as vulnerabilidades explorados por agentes maliciosos para assumir o controle de dispositivos e desferir ataques DDoS em larga escala. Por conseguinte, pesquisas foram iniciadas para desenvolver soluções visando a detecção e contenção de ataques DDoS baseados em IoT, fazendo uso de tecnologias emergentes como o paradigma de Redes Definidas por Software (SDN). Neste sentido, o presente trabalho apresenta uma taxonomia para descrever e caracterizar o corpo de soluções SDN contra ataques DDoS em cenários da IoT.
BiGeo: A Foundational PaaS Framework for Efficient Storage, Visualization, Management, Analysis, Service, and Migration of Geospatial Big Data - A Case Study of Sichuan Province, China

With the rapid development of big data, numerous industries have turned their focus from information research and construction to big data technologies. Earth science and geographic information systems industries are highly information-intensive, and thus there is an urgent need to study and integrate big data technologies to improve their level of information. However, there is a large gap between existing big data and traditional geographic information technologies. Owing to certain characteristics, it is difficult to quickly and easily apply big data to geographic information technologies. Through the research, development, and application practices achieved in recent years, we have gradually developed a common geospatial big data solution. Based on the formation of a set of geospatial big data frameworks, a complete geospatial big data platform system called BiGeo was developed. Through the management and analysis of massive amounts of spatial data from Sichuan Province, China, the basic framework of this platform can be better utilized to meet our needs. This paper summarizes the design, implementation, and experimental experience of BiGeo, which provides a new type of solution to the research and construction of geospatial big data.
Analysis of Users Behaviour in Structured E-Commerce Websites using Web Usage Mining Techniques

: With the fast improvement of Internet in India, the industry's/company business model or plan of action has changed. At present, extraordinary procedure has been made in web based business stage for its benefit and exchange quick. Rivalry for clients is the key factor for online business in the inexorably furious challenge. If we can grasp the requirements of customer and targeted business activities is developed then we can provide trading mode in convenient way and a wide selection for customers on website and provides better retain of customers for e-commerce business. One of the key approaches to this issue is web data mining techniques. We can obtain the behaviour of user by viewing the browsing behaviour of customers on websites, analysis it and then finding out solution to it. With this sellers will identifies the needs of customers and gives personalized in accordance with preferences of customers and then competitive advantage can be taken. In this paper we propose to use methodology and tool to improve accuracy of interpretation of user’s behaviours. Also we propose the usage of Temporal Logic and model checking techniques for structured e-commerce websites. We perform the analysis of the usage of e-commerce websites and to find out customers’ complex behavioural patterns with the help of checking temporal logic formulas describing such behaviours against the log model.