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Assessment of Reference Architectures and Reference Models for Ambient Assisted Living Systems: Results of a Systematic Literature Review
No full textThe innovation and development of software systems in the Ambient Assisted Living (AAL) domain have brought huge challenges for academia and software industry as well. Despite the existence of architectural models that can be used as references to build AAL systems, their selection for new AAL projects is a difficult task. In this work, the authors present the state of the art on Reference Architectures (RA) and Reference Models (RM) found through the conduction of a systematic literature review. The authors identified, analyzed, and assessed 24 existing RA&RM for AAL domain, and, as result, the authors spotted interesting research directions that should be further explored to improve existing and future RA&RM and software systems for that domain
Detecting DDoS Attacks Using Polyscale Analysis and Deep Learning
No full textDistributed denial-of-service (DDoS) attacks are serious threats to the availability of a smart grid infrastructure services because they can cause massive blackouts. This study describes an anomaly detection method for improving the detection rate of a DDoS attack in a smart grid. This improvement was achieved by increasing the classification of the training and testing phases in a convolutional neural network (CNN). A full version of the variance fractal dimension trajectory (VFDTv2) was used to extract inherent features from the stochastic fractal input data. A discrete wavelet transform (DWT) was applied to the input data and the VFDTv2 to extract significant distinguishing features during data pre-processing. A support vector machine (SVM) was used for data post-processing. The implementation detected the DDoS attack with 87.35% accuracy
Trust, Perceived Benefit, and Purchase Intention in C2C E-Commerce: An Empirical Examination in China
No full textIt is a class research question about how trust and perceived benefit affect consumers' purchase intentions. This research examines the relationship in a very different context: consumer-to-consumer (C2C) e-commerce in China. Specifically, this research empirically assesses the differences in effect size due to the change of context. First, a theoretical model linking trust, perceived benefit, and their antecedents to purchase intention is developed upon the literature. Then the model is evaluated using empirical data collected at Taobao, the largest C2C e-commerce website in China. Partial least squares based structural equation modeling (PLS-SEM) results strongly support the model and research hypotheses. A developing country context can indeed affect the strength of effect. These results contribute to the literature in that they provide new insights toward a more in-depth theoretical understanding. Meanwhile, they can also provide useful guidance for managers
An Unreliable Batch Arrival Retrial Queueing System With Bernoulli Vacation Schedule and Linear Repeated Attempts: Unreliable Retrial System With Bernoulli Schedule
No full textThis article deals with the steady-state behavior of an MX/G/1 retrial queue with the Bernoulli vacation schedule and unreliable server, under linear retrial policy. Breakdowns can occur randomly at any instant while the server is providing service to the customers. Further, the concept of Bernoulli admission mechanism is introduced. This model generalizes both the classical MX/G/1 retrial queue with unreliable server as well as the MX/G/1 retrial queue with the Bernoulli vacation model. The authors carry out an extensive analysis of this model. Namely, the embedded Markov chain, the stationary distribution of the number of units in the orbit, and the state of the server are studied. Some important performance measures and reliability indices of this model are obtained. Finally, numerical illustrations are provided and sensitivity analyses on some of the system parameters are conducted
Fenômica: A Computer Vision System for High-Throughput Phenotyping
No full textComputer vision and image processing procedures could obtain crop data frequently and precisely, such as vegetation indexes, and correlating them with other variables, like biomass and crop yield. This work presents the development of a computer vision system for high-throughput phenotyping, considering three solutions: an image capture software linked to a low-cost appliance; an image-processing program for feature extraction; and a web application for results' presentation. As a case study, we used normalized difference vegetation index (NDVI) data from a wheat crop experiment of the Brazilian Agricultural Research Corporation. Regression analysis showed that NDVI explains 98.9, 92.8, and 88.2% of the variability found in the biomass values for crop plots with 82, 150, and 200 kg of N ha1 fertilizer applications, respectively. As a result, NDVI generated by our system presented a strong correlation with the biomass, showing a way to specify a new yield prediction model from the beginning of the crop
Concept Drift Detection in Data Stream Clustering and its Application on Weather Data
No full textThis article presents a stream mining framework to cluster the data stream and monitor its evolution. Even though concept drift is expected to be present in data streams, explicit drift detection is rarely done in stream clustering algorithms. The proposed framework is capable of explicit concept drift detection and cluster evolution analysis. Concept drift is caused by the changes in data distribution over time. Relationship between concept drift and the occurrence of physical events has been studied by applying the framework on the weather data stream. Experiments led to the conclusion that the concept drift accompanied by a change in the number of clusters indicates a significant weather event. This kind of online monitoring and its results can be utilized in weather forecasting systems in various ways. Weather data streams produced by automatic weather stations (AWS) are used to conduct this study
Technology Addictions and Technostress: An Examination of the U.S. and China
No full textIn today's technology-centric world, people are becoming increasingly dependent on the Internet. The most common use of the Internet is through social media, which is used to communicate, share, collaborate, and connect. However, continued usage of a hedonic system can be linked with compulsion or addiction. Since problematic usage/behaviors can lead to negative outcomes, this study aims to determine differential effects of Internet and social media addictions on social media-related technostress. This is examined in two different cultures: The U.S. and China. The results support the association between the Internet and social media addictions with increases in social media-related technostress. Additionally, these effects are moderated by culture. Implications for research and practice are discussed along with future directions for this stream
An Integrated Structural Equation Model of eHealth Behavioral Intention
No full texteHealth offers promising tools and services to manage and improve the quality of health as well as the potential to provide accessible health information all over the world. The relatively low adoption rates among eHealth users motivates us to develop an integrated model to explain the learning process and provide essential antecedents of eHealth behavioral intention. The integrated model is empirically tested by using different structural equation modeling (SEM) methods, including partial least squares SEM (PLS-SEM), PLSc, and covariance-based SEM (CB-SEM). The model successfully explains the learning process and provides essential antecedents of eHealth behavioral intention. The findings support the interplay of social, cognitive, and personal factors that impact 18-30-year-old users' learning process related to eHealth behavioral intention. The results empirically show that these three types of SEM techniques provide consistent results with respect to path coefficients and coefficients of determination. The findings indicate that CB-SEM and PLS-SEM provide adverse consequences of interaction-term path coefficients
High Performance Fault Tolerant Resource Scheduling in Computational Grid Environment
No full textVirtual resources team up to create a computational grid, which is used in computation-intensive problem solving. A majority of these problems require high performance resources to compute and generate results, making grid computation another type of high performance computing. The optimization in computational grids relates to resource utilization which in turn is achieved by the proper distribution of loads among participating resources. This research takes up an adaptive resource ranking approach, and improves the effectiveness of NDFS algorithm by scheduling jobs in those ranked resources, thereby increasing the number of job deadlines met and service quality agreements met. Moreover, resource failure is taken care of by introducing a partial backup approach. The benchmark codes of Fast Fourier Transform and Matrix Multiplication are executed in a real test bed of a computational grid, set up by Globus Toolkit 5.2 for the justification of propositions made in this article
Critical Condition Detection Using Lion Hunting Optimizer and SVM Classifier in a Healthcare WBAN
No full textA timely critical condition detection and early notification are two essential requirements in a healthcare wireless body area network for the correct treatment of patients. However, most of the systems have limited capabilities and so could not detect the exact condition in a precise time interval. In addition to these it needs a reduction in the false alert rate, as issuing alerts for the deviation in each incoming packet increases the false alert rate and these false alerts consume more network resources. In order to fulfill the above-mentioned requirements, a dynamic alert system has been designed in this regard to make it more efficient, also, a new kind of hybridization approach is being introduced to it with the additive support of a nature-inspired optimization strategy named Lion Hunting and a machine-learning technique called support vector machine. The simulation is done using a network simulator NS-2.35, and the proposed alerting system outperforms others