Search results for "Base"
showing 10 items of 8362 documents
Real-time recognition of personal routes using instance-based learning
2011
Predicting routes is a critical enabler for many new location-based applications and services, such as warning drivers about congestion- or accident-risky areas. Hybrid vehicles can also utilize the route prediction for optimizing their charging and discharging phases. In this paper, a new lightweight route recognition approach using instance-based learning is introduced. In this approach, the current route is compared in real-time against the route instances observed in past, and the most similar route is selected. In order to assess the similarity between the routes, a similarity measure based on the longest common subsequence (LCSS) is employed, and an algorithm for incrementally evaluat…
Making group processes explicit to student
2014
This article considers student learning about group work in the context of project courses where student groups work under realistic expectations. Based on the literature, justice is explicated as a group work concept and regarded as a professional skill that can be practiced. Preliminary student feedback on teaching through continuous discussions on justice are presented together with teacher experiences.
Automatic dynamic texture segmentation using local descriptors and optical flow
2012
A dynamic texture (DT) is an extension of the texture to the temporal domain. How to segment a DT is a challenging problem. In this paper, we address the problem of segmenting a DT into disjoint regions. A DT might be different from its spatial mode (i.e., appearance) and/or temporal mode (i.e., motion field). To this end, we develop a framework based on the appearance and motion modes. For the appearance mode, we use a new local spatial texture descriptor to describe the spatial mode of the DT; for the motion mode, we use the optical flow and the local temporal texture descriptor to represent the temporal variations of the DT. In addition, for the optical flow, we use the histogram of orie…
An Approach for Network Outage Detection from Drive-Testing Databases
2012
A data-mining framework for analyzing a cellular network drive testing database is described in this paper. The presented method is designed to detect sleeping base stations, network outage, and change of the dominance areas in a cognitive and self-organizing manner. The essence of the method is to find similarities between periodical network measurements and previously known outage data. For this purpose, diffusion maps dimensionality reduction and nearest neighbor data classification methods are utilized. The method is cognitive because it requires training data for the outage detection. In addition, the method is autonomous because it uses minimization of drive testing (MDT) functionalit…
Context-aware data caching for 5G heterogeneous small cells networks
2016
In this work, we investigate the problem of context-aware data caching in the heterogeneous small cell networks (HSCNs) to provide satisfactory to the end-users in reducing the service latency. In particular, we explore the storage capability of base stations (BSs) in HSCNs and propose a data caching model consists of edge caching elements (CAEs), small cell base stations (SBSs), and macro cell BS (MBS). Then, we concentrate on how to efficiently match the data contents to the different cache entities in order to minimize the overall system service latency. We model it as a distributed college admission (CA) stable matching problem and tackle this issue by utilizing contextual information t…
Towards Computer-based Exams in CS1
2017
Even though IDEs are often a central tool when learning to program in CS1, many teachers still lean on paper-based exams. In this study, we examine the “test mode effect” in CS1 exams using the Rainfall problem. The test mode was two-phased. Half of the participants started working on the problem with pen and paper, while the other half had access to an IDE. After submitting their solution, all students could rework their solution on an IDE. The experiment was repeated twice during subsequent course instances. The results were mixed. From the marking perspective, there was no statistically significant difference resulting from the mode. However, the students starting with the paper-based pa…
Transformation of the Forest-based Bioeconomy by Embracing Digital Solutions
2017
This paper attempts to explore a new insight to both industrialized and growing economies by demonstrating a digital-driven creative disruption in the forest-based bioeconomy which is beginning to replace its conventional and narrow concept of a forest-blinded economy. Notwithstanding the potential broad cross-sectoral benefits to both industrialized and growing economies, natural environments and locality constraints and the incessant challenge of distance have impeded balanced development of this economy. However, driven by digital solutions the economy has taken big steps forward in recent years. Digitalization has enabled real-time end-to-end supply chain visibility, improved delivery a…
Modelling Recurrent Events for Improving Online Change Detection
2016
The task of online change point detection in sensor data streams is often complicated due to presence of noise that can be mistaken for real changes and therefore affecting performance of change detectors. Most of the existing change detection methods assume that changes are independent from each other and occur at random in time. In this paper we study how performance of detectors can be improved in case of recurrent changes. We analytically demonstrate under which conditions and for how long recurrence information is useful for improving the detection accuracy. We propose a simple computationally efficient message passing procedure for calculating a predictive probability distribution of …
An Efficient Network Log Anomaly Detection System Using Random Projection Dimensionality Reduction
2014
Network traffic is increasing all the time and network services are becoming more complex and vulnerable. To protect these networks, intrusion detection systems are used. Signature-based intrusion detection cannot find previously unknown attacks, which is why anomaly detection is needed. However, many new systems are slow and complicated. We propose a log anomaly detection framework which aims to facilitate quick anomaly detection and also provide visualizations of the network traffic structure. The system preprocesses network logs into a numerical data matrix, reduces the dimensionality of this matrix using random projection and uses Mahalanobis distance to find outliers and calculate an a…
Data mining framework for random access failure detection in LTE networks
2014
Sleeping cell problem is a particular type of cell degradation. There are various software and hardware reasons that might cause such kind of cell outage. In this study a cell becomes sleeping because of Random Access Channel (RACH) failure. This kind of network problem can appear due to misconfiguration, excessive load or software/firmware problem at the Base Station (BS). In practice such failure might cause network performance degradation, which is hardly traceable by an operator. In this paper we present a data mining based framework for the detection of problematic cells. In its core is the analysis of event sequences reported by a User Equipment (UE) to a serving BS. The choice of N i…