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The rapid advances of transportation infrastructure have led to a dramatic increase in the demand for smart systems capable of monitoring traffic and street safety. Fundamental to these applications are a community-based evaluation platform and benchmark for object detection and multi-object tracking. To this end, we organize the AVSS2017 Challenge on Advanced Traffic Monitoring, in conjunction with...
The present paper deals with online convex optimization involving adversarial loss functions and adversarial constraints, where the constraints are revealed after making decisions, and can be tolerable to instantaneous violations but must be satisfied in the long term. Performance of an online algorithm in this setting is assessed by: i) the difference of its losses relative to the best dynamic solution...
Network infrastructures are in jeopardy of suffering nowadays since a number of attacks have been developed and grown up enormously. In order to get rid of such security threats, a defense mechanism is much sought-after. This paper proposes an improved model of intrusion detection by using two-level classifier ensemble. The proposed model is made up of a PSO-based feature selection technique and a...
Resilience test and evaluation is an important technology to evaluate system resilience and verify whether the value of system resilience meets the designated requirements before the system is used. In this study, a resilience test and evaluation process is proposed for given disturbances. The system resilience is defined by the ratio of the integral of the normalized system performance within its...
A major cyber-security concern to date for webservers are Distributed Denial of Service (DDoS) attacks. Previously we proposed a novel overlay-based method consisting of distributed network of public servers (PS) for preparation, and access nodes (AN) for actual communication. The AN's performance is evaluated under difficult to detect HTTP(S)-DDoS attacks. Yet, attackers may attempt service denial...
Image retargeting techniques that adjust images into different sizes have attracted much attention recently. Objective quality assessment (OQA) of image retargeting results is often desired to automatically select the best results. Existing OQA methods output an absolute score for each retargeted image and use these scores to compare different results. Observing that it is challenging even for human...
This paper presents a novel large-scale dataset and comprehensive baselines for end-to-end pedestrian detection and person recognition in raw video frames. Our baselines address three issues: the performance of various combinations of detectors and recognizers, mechanisms for pedestrian detection to help improve overall re-identification (re-ID) accuracy and assessing the effectiveness of different...
Matching local image descriptors is a key step in many computer vision applications. For more than a decade, hand-crafted descriptors such as SIFT have been used for this task. Recently, multiple new descriptors learned from data have been proposed and shown to improve on SIFT in terms of discriminative power. This paper is dedicated to an extensive experimental evaluation of learned local features...
Hyperspectral image classification, an astonishing tool to distinguish the land covers in remote sensed hyperspectral images, has been investigated by multiple disciplines such as geoscience, environmental science, mathematics, and computer vision. Following early machine learning (e.g., support vector machines and neural networks) and feature extraction theories (e.g., principal component analysis),...
Cloud environments are criticized for their volatility in performance aspects, making it extremely difficult for time- critical applications owners to perform the decisive step for migration and owners of SaaS to present performance vs cost tradeoffs to their customers when acting as IaaS customers. The aim of this work is to present an architectural approach based on which a)IaaS providers may enhance...
In the past, spectrum-based fault localization (SBFL) techniques have been developed to pinpoint a fault location in a program given a set of failing and successful test executions. Most of the algorithms use similarity coefficients and have only been evaluated on established but small benchmark programs from the Software-artifact Infrastructure Repository (SIR). In this paper, we evaluate the feasibility...
In this paper, we present a new benchmark (Menpo benchmark) for facial landmark localisation and summarise the results of the recent competition, so-called Menpo Challenge, run in conjunction to CVPR 2017. The Menpo benchmark, contrary to the previous benchmarks such as 300-W and 300-VW, contains facial images both in (nearly) frontal, as well as in profile pose (annotated with a different markup...
In the past decade, research in person re-identification (re-id) has exploded due to its broad use in security and surveillance applications. Issues such as inter-camera viewpoint, illumination and pose variations make it an extremely difficult problem. Consequently, many algorithms have been proposed to tackle these issues. To validate the efficacy of re-id algorithms, numerous benchmarking datasets...
Semantic instance segmentation remains a challenge. We propose to tackle the problem with a discriminative loss function, operating at pixel level, that encourages a convolutional network to produce a representation of the image that can easily be clustered into instances with a simple post-processing step. Our approach of combining an offthe- shelf network with a principled loss function inspired...
This paper presents the results of the depth estimation challenge for dense light fields, which took place at the second workshop on Light Fields for Computer Vision (LF4CV) in conjunction with CVPR 2017. The challenge consisted of submission to a recent benchmark [7], which allows a thorough performance analysis. While individual results are readily available on the benchmark web page http://www...
The current design drivers for multi-cores, namely performance per watt, scalability and flexibility, make the Networks-on-Chip (NoCs) the de-facto on-chip interconnect. State of the art NoCs can exploit heterogeneous solutions and complex DVFS techniques to fulfill also the variability of the application requirements. Relevant showstoppers to the design of a truly flexible NoC fitting all the possible...
Cloud computing is becoming increasingly pervasive and is being adopted even for high performance computing and mission critical applications. As cloud computing extends its usage, understanding of its performance becomes more important. In this paper, we present the system performance using Amazon EC2, representing a large public cloud platform, and OpenStack, representing the most popular open-source...
Apache Spark provides numerous configuration settings that can be tuned to improve the performance of specific applications running on the platform. However, due to its multi-stage execution model and high interactive complexity across nodes, it is nontrivial to understand how/why a specific setting influences the execution flow and performance. To address this challenge, we develop an execution model-driven...
To avoid the lock-in problem in service-oriented software, existing interface-decoupling mechanisms focus on identifying high-level service mappings, which are not necessarily applicable on translating actual data. Based on the fundamental data-translation process, its successful outcome is guaranteed if mappings are low-level, i.e. they satisfy schema constraints. The problem is that if similar services...
The outsourcing of elaboration of data streams requires that a service provider collects and stores data on behalf of a company that does not have enough resources to sustain the efforts related to the management of such data streams. If a company does not trust the service provider, then it has to check the validity of the answers when querying the data store, since the results may be not reliable...
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