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Existing approaches to time series classification can be grouped into shape-based (numeric) and structure-based (symbolic). Shape-based techniques use the raw numeric time series with Euclidean or Dynamic Time Warping distance and a 1-Nearest Neighbor classifier. They are accurate, but computationally intensive. Structure-based methods discretize the raw data into symbolic representations, then extract...
Technology evolution has raised serious reliability considerations, as transistor dimensions shrink and modern microprocessors become denser and more vulnerable to faults. Reliability studies have proposed a plethora of methodologies for assessing system vulnerability which, however, highly rely on traditional reliability metrics that solely express failure rate over time. Although Failures In Time...
Whilst there are many attributes that need to be considered for cloud service selection, performance remains one of the most crucial aspects. Thus, we argue for a transparent cloud provider comparison framework in this study. We initiate the development of TCloud: a transparent framework for public cloud service comparison. Our framework helps prospective cloud users to decipher public cloud benchmarking...
Efficiently exploiting thread level parallelism from new multicore systems has been challenging for software developers. While blindly increasing the number of threads may lead to performance gains, it can also result in disproportionate increase in energy consumption. For this reason, rightly choosing the number of threads is essential to reach the best compromise between both. However, such task...
Systems-on-a-chip (SoC) represents a rupture on the traditional HPC infrastructure and started to be adopted together OS-level virtualization to provide services in Fog and Edge computing scenarios. In this work, we analyzed the performance of OS-level virtualization solutions - Linux Containers (LXC) and Docker - for HPC activities running in SoC systems in order to discover how OS-level virtualization...
Current DRAM based memory systems face the scalability challenges in terms of storage density, power, and cost. Hybrid memory architecture composed of emerging Non-Volatile Memory (NVM) and DRAM is a promising approach to large-capacity and energy-efficient main memory. However, hybrid memory systems pose a new challenge to on-chip cache management due to the asymmetrical penalty of memory access...
Community structure is a feature that reveals the internal organization of the network. The problem of community detection i.e., identifying the densely connected nodes in a network is an important task. There are many methods that can be employed to find communities. We have identified and analyzed seven of the methods of community detection which includes both pioneer methods and state of the art...
Reliability to soft errors is an increasingly important issue as technology continues to shrink. In this paper, we show that applications exhibit different reliability characteristics on big, high-performance cores versus small, power-efficient cores, and that there is significant opportunity to improve system reliability through reliability-aware scheduling on heterogeneous multicore processors....
Exploring the design space of the memory hierarchy requires the use of effective methodologies, tools, and models to evaluate different parameter values. Reuse distance is of one of the locality models used in the design exploration and permits analytical cache miss estimation, program characterization, and synthetic trace generation. Unfortunately, the reuse distance is limited to a single locality...
In this paper, we analyze the temporal performance of multi-robot system which are performing a foraging object task. The coordination between different robotic agents was inspired by the ant colony optimization algorithms (ACO). The objective was to prospect about parameters influence on foraging objects time. In this work, we consider three possible influencing parameters including robotic group...
Assessing and comparing computer systems under changing contexts is becoming crucial due to the dynamic characteristics of modern computing environments. This is especially relevant for database management systems, as the behavior of the DBMS when immersed in today's volatile environments is determinant for the success of a multitude of commercial, industrial and scientific endeavors. This paper presents...
Significant progress has been made in recent years using computer programs recognizing objects in images. Meanwhile, many cameras are embedded in battery-powered systems (such as mobile phones, wearable devices, and drones) and energy efficiency is essential. Even though many research papers have been published on the topics related to low power and image recognition, there does not exist a common...
Cache hierarchies have long been utilized to minimize the latency of main memory accesses by caching frequently used data closer to the processor. Significant research has been done to identify the most crucial metrics of cache performance. Though the majority of research focuses on measuring cache hit rates and data movement as the major cache performance metrics, cache utilization can be equally...
This paper presents a novel deep architecture for saliency prediction. Current state of the art models for saliency prediction employ Fully Convolutional networks that perform a non-linear combination of features extracted from the last convolutional layer to predict saliency maps. We propose an architecture which, instead, combines features extracted at different levels of a Convolutional Neural...
In this work, we propose a metric adaptation method for set-based face verification and evaluate it on the newly released IARPA Janus Benchmark A (IJB-A) dataset and its extended version, the Janus Challenging Set 2 (CS2). A template-specific metric is trained to adaptively learn the discriminative information in test templates and the negative training set, which contains subjects that are mutually...
While prior evaluation methodologies for data-science research have focused on efficient and effective teamwork on independent data science problems within given fields [1], this paper argues that an enriched notion of evaluation-driven research (EDR) supports methodologies and effective solutions to data-science problems across multiple fields. We adopt the view that progress in data-science research...
The massive amounts of data processed by information systems raise the importance of detailed database performance analysis. Column-oriented data stores are becoming increasingly popular in big data appliances. This paper identifies database performance factors on the basis of empirical studies on a custom implementation. To summarize the research, a simple performance mathematical model has been...
High throughput is of particular interest in data center and HPC networks. Although myriad network topologies have been proposed, a broad head-to-head comparison across topologies and across traffic patterns is absent, and the right way to compare worst-case throughput performance is a subtle problem. In this paper, we develop a framework to benchmark the throughput of network topologies, using a...
In this paper we introduce a novel, dense, system-on-chip many-core Lenovo NeXtScale System® server based on the Cavium THUNDERX® ARMv8 processor that was designed for performance, energy efficiency and programmability. THUNDERX processor was designed to scale up to 96 cores in a cache coherent, shared memory architecture. Furthermore, this hardware system has a power interface board (PIB) that measures...
With the growing amount of documents in the search index of information retrieval systems, the problem of ranking documents becomes crucial. The modern state of the problem leads to the point where machine learning becomes the most efficient way to optimize the ranking function. In this article investigated ranking function in information retrieval systems (IRS) and learning to rank problem. During...
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