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Increasingly, trust has played a crucial role in the security of an IoT system from its inception to the end of its lifecycle. A device has to earn some level of trust even before it is authenticated for admission to the system. Furthermore, once the device is admitted to the system, it may behave maliciously over time; hence its behavior must be evaluated constantly in the form of trust to ensure...
Software Defined Networking (SDN) technology enables us to configure switches in a network flexibly. Previously, distributed SDN control methods have been discussed to improve their scalability and robustness. Distributed placement of controllers reduces computation load of each controller and backing up each other enhances robustness. However, these techniques do not include an emergency measure...
Mobile edge computing (MEC) provides a promising way to bring enhanced computing capabilities in proximity to user equipments (UEs). Various previous works have been done and they usually focus on how to segment the computation intensive task and how to offload the task to edge of the network with plenty of computation capacity. However, few of them consider integrating MEC with small cell networks...
In recent years, software-defined networking (SDN) has been adopted in practice, such as the fast-growing market of software-defined wide area networks (SD-WAN). Intent, an emerging concept for SDN, aims to provide a simple interface for operators to manage the network. Under the framework of intent, an application submits a request by specifying the objective only, and the SDN controller decides...
Crowded streets are a major problem in large cities. A large part of the problem stems from drivers seeking on-street parking. Cities such as San Francisco, Los Angeles and Seattle have tackled this problem with smart parking systems that aim to maintain the on-street parking occupancy rates around a target level, thus ensuring that empty spots are spread across the city rather than clustered in a...
Middleboxes present new requirements that need to be integrated with traffic engineering applications that are already complex and consider myriad factors (e.g., routing, QoS, load-balancing). While it is possible to revisit traffic engineering algorithms to explicitly integrate middleboxes, such an approach is not compositional. Existing efforts at compositional SDN application development do not...
Understanding the mobile user flow flux patterns is important for numerous applications. Much of the mobility modeling studies have focused on a user-centric approach for outdoors movement. In this study, we take a location-centric approach for indoor mobility to analyze and characterize region relationships as pertains to user flow. Our study is trace-driven, as we use extensive measurements from...
Given the mice-elephant phenomenon (where 80% of flows in number contribute to only 20% of traffic in volume) of TCP traffic, TCP's slow-start algorithm plays an important role in today's Internet. Designed to slowly probe the network for available bandwidth, the current standard mandates TCP flows to start with an initial window (IW) size of at most ten segments. Given the speed of the today's networks,...
Traditional internet architecture is challenged and it cannot satisfy the growing demand of todays networks. Information-centric Networks (ICN) is regarding as a promising replacement to meet this trend. The main characteristic in ICN is Content Store (CS), which is used to enable users to retrieve data from nearby nodes instead of remote server. However, with the limited storage capacity of routers,...
A set of instructions for data flow virtual machine is researched. An experimental virtual machine for data flow paradigm is presented for demonstration of computational model. Backus-Naur form of input file for data flow virtual machine is described. A definition of data flow processing in virtual machine is proposed. Some simple data flow computation examples are described.
For i-vector model, normalization approach is Probabilistic linear discriminant analysis and has a significant performance for verification of speaker. However it requires a huge development data which cost a lot in many cases. Unsupervised adaption method is a possible approach, which use unlabeled data to adapt PLDA scattering matrices to the target domain. In this paper, ‘local training’ approach...
Location Model is a classification model that capable to deal with mixtures of binary and continuous variables simultaneously. The binary variables create segmentation in the groups called cells whilst the continuous variables measure the differences between groups based on information inside the cells. It is important to note that location model is biased and even impossible to be constructed when...
In this work, we introduce a Deep Neural Network model for automated software categorization. The model is ableto form high-level concepts from low-level code tokens andto distinguish important features such as API calls and identifiersin order to support software categorization. Our empirical evaluationshows that DNN outperformed other machine learning approacheswith 15.9–36.4% higher accuracy in...
In this paper, the variational iteration algorithm-II for generalized diffusion problems is theoretically constructed. Then, the sufficient conditions that ensure convergence of variational iteration method applied to this type of two-point boundary value problems are established with an error analysis. Some nonlinear diffusion problems are investigated to elucidate the convergence concept and the...
A key open question in the area of software modeling is which costs and benefits it brings to software development and maintenance. For answering this question, better empirical studies into software modeling are needed. In this paper I focus on what I believe are the pitfalls in- and prospects for such types of studies. This paper is an abstract for an invited keynote at the Modeling in Software...
In recent years, Convolutional Neural Networks (CNNs) have shown great performance not only in image classification and image recognition tasks but also several tasks of computer vision. A lot of models which have different number of layers and depths, have been proposed. In this work, locations of leopards are tried to be identified by deep neural networks. To accomplish this task, two different...
The ACC will offer workshops addressing current and future topics in automatic control from experts in academia, national laboratories, and industry. The workshops at ACC 2017 will take place prior to the conference on Monday, May 22 and Tuesday, May 23, 2017.
The existing software release planning models aim to find a subset of software requirements with the highest value on the assumption that the value of a selected subset of requirements equals to the Accumulated Value (AV) of that subset. This assumption however, does not hold due to the Value-related Dependencies among software requirements. To address this, we have formulated an integer programming...
Supercomputing systems are used everywhere today. Starting with their humble beginnings as tools for large scientific simulations, supercomputing systems are used in almost all aspects of our daily lives today from interactions with friends and family to transportation, education, and the general improvement in the quality of our lives. As we move from the current generation of supercomputers to the...
This study aims to provide an overview on the intersection and interaction between architecture, urban modeling, planning fields and computer vision field. The reflection of the methods and approaches of fields such as visual recognition, natural language processing, data mining and data visualization onto architecture and urban studies are investigated and potentials of inter/transdisciplinary encounters...
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