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Focusing on the history of prehistoric archaeology in the 20th century, this papers shows (1) that statistical multidimensional analyses were carried out by a new kind of actors who challenged the previous common language shared by prehistorians. This fundamental change was important, considering that (2) language is a fundamental point for the epistemology of archaeology. However, a comparison of...
Traditional methods of system verification call for the repeated exercise of that system in its intended environment to give confidence that it will operate as designed. Autonomous systems, however, are systems designed for sophisticated operation in stochastic environments that specifically handle the unexpected; testing all possible operational scenarios for such systems is intractably complex....
In this paper we present an autonomous detection approach for airborne surveillance in maritime scenarios. This approach is robust to sun glare, waves and scale variation. Additionally, we introduce a new metric to evaluate detection and tracking results that is more adequate for these scenarios. The proposed detection method is evaluated using videos from different monitoring missions and its results...
Currently, the main application of commercially available three-dimensional forward looking sonar (3D FLS) technology is for real-time vessel navigation. Using 3D FLS technology, the vessel operator can detect not only the range and bearing to a navigational hazard, but also the depth of the hazard in the water column. However, 3D FLS is itself a nascent technology for which many exciting applications...
MAESTRO is a project of the European Ambient Assisted Living Programme which aims at enhancing the quality of life and whenever possible the autonomy of seniors. The project objective is to develop a web-based ICT platform that will enable any user, either customer, producer or prescribing party of self monitoring devices, the possibility to evaluate their relevance, effectiveness and performance...
In nowadays, as the development of digital photographic technology, video files grow rapidly, there is a great demand for automatic video semantic analysis in many scenes, such as video semantic understanding, content-based analysis, video retrieval. Shot boundary detection is a key basic technology and first step for video analysis. However, recent methods are time consuming and performs bad in the...
The increasing demand of mobile applications has brought large amount of mobile traffic. To meet users' requirements for high-quality video delivery, it is an urgent task to provide fair-quality video delivery for various users and situations. In this paper, we evaluate QoS and QoE characteristics and validate QoE unfriendliness in heterogeneous DASH contents distributions to provide QoE-fair video...
Account sharing is a significant problem for online recommender systems to generate accurate personalized recommendations. To solve this problem, one not only has to identify whether an account is shared, but also needs to recognize the different users sharing that account. However, to generate relevant, personalized recommendations, the particular user under a shared account has to be correctly identified...
Video summarization is an important multimedia task for applications such as video indexing and retrieval, video surveillance, human-computer interaction and video "storyboarding". In this paper, we present a new approach for automatic summarization of video collections that leverages a structured minimum-risk classifier and efficient submodular inference. To test the accuracy of the predicted...
The newly emerged Software Defined Networks (SDN) can apply to Wide Area Network (WAN) for network control and management. To achieve this, a logically centralized but a physically distributed control architecture is normally required. This type of control architecture consists of multiple controllers. For maintaining the global consistency of the network, controller placement is a key issue in a...
This paper presents a two-pass clustering technique for orientation-invariant text line clustering in a language-independent text localization problem based on the connected component analysis (CCA) approach. Instead of doing a single-pass cluster in the conventional way, the proposed technique firstly explores nearby objects around the candidate components. By setting up the global constraints with...
Stochastic Gradient Descent (SGD) is the method of choice for large scale problems, most notably in deep learning. Recent studies target improving convergence and speed of the SGD algorithm. In this paper, we equip the SGD algorithm and its advanced versions with an intriguing feature, namely handling constrained problems. Constraints such as orthogonality are pervasive in learning theory. Nevertheless...
In this paper, a unified deep convolutional architecture is proposed to address the problems in the person re-identification task. The proposed method adaptively learns the discriminative deep mid-level features of a person and constructs the correspondence features between an image pair in a data-driven manner. The previous Siamese structure deep learning approaches focus only on pair-wise matching...
Left ventricle(LV) segmentation is a prerequisite step of evaluation of LV structure and function, which plays an important role in the diagnosis and treatment of cardiovascular diseases. In this paper, we propose a method to segment endocardium and epicardium of LV using convolution neural network combined with active contour model and tensor voting. A fully convolution neural network (FCN) named...
Cloud users have little visibility into the performance characteristics and utilization of the physical machines underpinning the virtualized cloud resources they use. This uncertainty forces users and researchers to reverse engineer the inner workings of cloud systems in order to understand and optimize the conditions their applications operate. At Massachusetts Open Cloud (MOC), as a public cloud...
Social surveys have been used by researchers and policy makers as an essential tool for understanding social and political activities in society. Social media has introduced a new way of capturing data from large numbers of people. Unlike surveys, social media deliver data more rapidly and cheaply. In this paper, we aim to rapidly identify socio-political activity in South Africa using proxy data...
The large adoption of Twitter during electioneering has created a valuable opportunity to monitor political deliberation nationwide. Recent work has analyzed online attention to forecast elections results addressing some limitations of opinion polling. However, the reproducibility of such methods remains a challenge given that most of them rely on the number of political parties or candidates mentions...
Network centrality reflects node importance in networks, which is a challenging problem in social network analysis. Based on Fuzzy Set and MYCIN theory, this paper proposes a novel node centrality measuring method and models n-monkeys dataset, where n is 20. Initially, we created monkeys relationship graph and generated relationship matrix based on the monkeys' encountering times in a specific time...
Analyzing job hopping behavior is important for the understanding of job preference and career progression of working individuals. When analyzed at the workforce population level, job hop analysis helps to gain insights of talent flow and organization competition. Traditionally, surveys are conducted on job seekers and employers to study job behavior. While surveys are good at getting direct user...
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