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What would have happened if I did not have any code smell? This is an interesting question that no previous study, to the best of our knowledge, has tried to answer. In this paper, we present a method for implementing a what-if scenario analysis estimating the number of defective files in the absence of smells. Our industrial case study shows that 20% of the total defective files were likely avoidable...
In Infrastructure as a Service clouds, customers lease virtual resources (e.g., CPU, memory, network) offered by cloud providers, paying for the allocated capacity of resources, regardless of their effective use. In this context, it is in the interest of the customers to reserve resources with sufficient capacity so that their applications achieve good performance while, at the same time, minimizing...
Advances of IoT have enabled users to use intelligent services from surrounding smart objects. As the needs of preventing the misusage of personal information increases, the estimation of the trustworthiness of interaction counterparts before interaction is important to prevent potential dangers. Existing trust estimation solutions are based on Social IoT, which is hard to adopt in reality. Another...
A guidance problem with the impact time constraint is considered under nonlinear kinematics. The guidance design makes the range to the stationary target behave as a quartic polynomial in time. The analytical availability of the polynomial coefficients provides a feasible basis. Additionally, another important feature of the proposed impact time guidance law is that it does not rely on a time to go...
Optimum Bid price estimation is crucial for Amazon Elastic Compute Cloud (EC2) consumers if they want to secure uninterrupted access to Spot instances at reduced costs. We recently reported that Bid price estimation is an implicit function of seasonal components and extreme spikes in the Spot price history. In this paper we apply time series forecasting to further substantiate this claim. In particular,...
The jackknife resampling procedure is a technique to reduce the bias of a statistic. As with other resampling techniques, the jackknife procedure is motivated by and is well understood in the i.i.d. regime. However, analysis of the procedure when samples have memory is limited, and is predominantly restricted to cases with strong mixing or memory constraints. In this paper, we analyze a natural jackknife...
In this paper, we propose a node density estimation method in intermittently connected mobile ad-hoc networks. We consider situations where mobile nodes do not know location information about their positions. Under the situations, the proposed method estimates the node density using sparsely deployed access points. These access points broadcast inspection packets by mobile nodes relaying. After the...
Consumers can realize significant cost savings by procuring resources from computational spot markets such as Amazon Elastic Compute Cloud (EC2) Spot Instances. They can take advantage of the price differentials across time slots, regions, and instance types to minimize the total cost of running their applications on the cloud. However, Spot markets are inherently volatile and dynamic, as a consequence...
In recent years, there has been a tremendous increase in the popularity of event-based social networks which allow social and physical interactions among their members. One major challenge for their members is the difficulty of searching events that meet their preferences from a large number of upcoming events. To tackle this challenge, we propose a personalized event recommendation framework called...
The previous goal of having a one-size-fits-all software cost (and schedule) estimation model is no longer achievable. Sources of wide variation in the nature of software development and evolution processes, products, properties, and personnel (PPPPs) require a variety of estimation models and methods best fitting their situations. This Technical Briefing will provide a short history of pattern-breaking...
This paper proposes a method that estimatesconsumer family-structure attributesby focusing on purchasing behavior. The method generatesa relevance model for each product type between family-structure attributes and purchasing histories beforehand based on a consumer panel survey. Random Forest, a machine learning method, is employed to generate the model. The proposed method facilitates provisioning...
As IoT devices become prevalent in our daily lives, estimation of their trustworthiness plays an important role for privacy protection complementary to security solutions. Existing trust estimation solutions are based on SIoT whose full social network is not likely to be observable in a public space or do not reflect situation-dependent dynamism of trust. In this paper, we propose a new trust estimation...
We consider stochastic optimization problems in decentralized settings, where a network of agents aims to learn decision variables which are optimal in terms of a global objective which depends on possibly heterogeneous streaming observations received at each node. Consensus optimization techniques implicitly operate on the hypothesis that each node aims to learn a common parameter vector, which is...
There has been a growing effort in studying the distributed optimization problem over a network. The objective is to optimize a global function formed by a sum of local functions, using only local computation and communication. Literature has developed consensus-based distributed (sub)gradient descent (DGD) methods and has shown that they have the same convergence rate O(log t/√t) as the centralized...
This paper presents a work for imaging the moving targets and estimating their velocities with better accuracies using the Digital Elevation Map (DEM) in urban environments under multi-channel Synthetic Aperture Radar (SAR) scenarios. Given the received phase histories pre-processed by channel balancing techniques, we employ Ground Moving Target Indicator (GMTI) methods to detect the moving targets,...
Open Source Software (OSS) has become a major model for software development today. In OSS, software is developed by individuals that contribute their time and experience to the project of their preference using shared repositories. The success of each project (or repository) depends on factors such as increasing their workforce [7], and allowing its contributors to find an appropriate task.
In this paper we focus on the problem of pedestrian detection in low visibility conditions, with infrared cameras. Widely applied, tracking is essential for driving assistance applications, providing support for removing false positives and forcing the detection of border line true positives. We propose a multiple feature and temporal based pedestrian detector for far-infrared images. Our model benefits...
User location is crucial in understanding the dynamics of user activities, especially in relating their online and offline aspects. However, users' social media activities, such as tweets sent, do not always reveal their location. We consider the problem of estimating geo-tags for tweets and develop a comprehensive approach that incorporates textual content, the user's personalized behavior, and the...
We have been developing and researching chat robot for elderly people. This paper proposed category estimation for voice based chat robot system. Proposed method estimates a category of the user's utterance sentence. Category or topic estimation have been studied by many researchers, however voice base chat system needs a quick response. Therefore our system is simple method just using keyword match...
In this paper, we consider the generation of inverse synthetic aperture radar (ISAR) image of a target using polynomial phase signal (PPS) modeling of time histories. For a target under complex motion, the time histories in the backscatter signal with time-varying Doppler frequency shifts are modeled as multicomponent PPS. We propose the convolution of scaled high-order instantaneous moments to enable...
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