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The Ocean Observatories Initiative (OOI) consists of seven research sites collecting ocean, seafloor, and meteorological data in the world's oceans, extending from the Irminger Sea to the Southern Ocean, and the East and West coasts of the United States. The Ocean Observatories Data Evaluation Team is part of the Cyberinfrastructure group at Rutgers University, tasked with reviewing the oceanographic...
The Ocean Observatories Initiative (OOI) is a project funded by the National Science Foundation which provides over 100,000 data products. OOI Cyberinfrastructure takes a two-pronged approach to data quality control: system level and human-in-the-loop. With system level, the system runs datasets through a series of six algorithms: global range, local range, stuck value, gradient, trend, and spike...
Accurate estimation of detection/classification performance for sidescan sonar systems in Mine Counter-Measure (MCM) applications is important for informing mission tactics and adapting autonomous behaviors. The approach presented in this paper assumes that detection/classification performance can be estimated solely from historical data collected from similar surveys. This paper introduces an algorithm...
This paper demonstrates the process of adapting publicly available repositories for the MOOS-IvP middleware to efficiently create and solve problems for collaborative marine autonomy. The problem introduced in this paper addresses a collaborative approach for autonomous environmental anomaly detection, bounding, and containment. An environmental anomaly may be an oil spill, radioactive contamination,...
Most systems that support speculative parallelization, like hardware transactional memory (HTM), do not support nested parallelism. This sacrifices substantial parallelism and precludes composing parallel algorithms. And the few HTMs that do support nested parallelism focus on parallelizing at the coarsest (shallowest) levels, incurring large overheads that squander most of their potential. We present...
This paper presents a method that embeds stability-sensitive filter in the temporal matching kernel with explicit feature maps (TE). The added filter improves the robustness of TE to noise for content-based video retrieval. Originally TE embeds temporal information of frame descriptors by using explicit feature mapping in a fixed length video vector by using a temporal invariant match kernel. TE matches...
It is usual for a consumer to search a product based on its category and go to related kind of shop to buy a product, e.g. food in supermarket, a pencil from a stationary shop and etc. While it is not uncommon nowadays for a shop to sell various categories of goods at the same time, like a newspaper stand do sell toys, an accessory shop has stationary. However, consumer may not easily notice and purchase...
The subsequence-matching operation applied to motion capture data searches in long motion sequences to locate their parts that are similar to a query example. An effective and efficient implementation of such operation is valuable to increase reusability and findability of expensively recorded data in the past. This demonstration paper builds on recent advances in the field of motion-data processing...
Kotenseki is a collection of classical and ancient Japanese literature. It is comprised of image books that express Japanese stories by using comic drawings of different characters, such as humans, nature, and animals. To effectively store them for posterity, a search system is important. We propose an efficient CBIR system to assist the users in easily accessing the information and have an enjoyable...
With the rapid growth of online content consumption, knowing end-users and having actionable content insights has become extremely important for any online content provider. Insights from user segment identification could help in developing a content recommendation as well as new content acquisition. For advertisers, identifying segments could assist in designing ad campaigns with greater target accuracy...
The task of object tracking in rectangular videos has been addressed in recent years by many researchers, where each method tries to propose a solution for a special challenge. Handling a variety of challenging situation of object tracking in 360-degree videos is still an unsolved problem and needs to be more considered. In the real world, the challenging situations include moving camera, high-resolution...
To maintain the high level of security, many organizations use Deep Packet Inspection (DPI) firewalls to filter anomaly traffic coming into their networks. However, a DPI firewall with a large volume of traffic can lead to a high packet drop rate, high delay, and the poor network throughput. One possible way to relieve the firewall workload is to deploy multiple firewalls and select only suspicious...
Facial micro-expression refers to split-second muscle changes in the face, indicating that a person is either consciously or unconsciously suppressing their true emotions and even mental health. Therefore, micro-expression recognition attracts increasing research efforts in both fields of psychology and computer vision. Existing research on micro-expression recognition has mainly used hand-crafted...
A large number of long non-coding RNAs (lncRNAs) have been identified over the past decades. Accumulating evidence proves that lncRNAs play key roles in various biological processes. However, the majority of the lncRNAs have not been functionally characterized. The annotation of lncRNA functions has become an area of focus in the fields of biology and bioinformatics. In this paper, we develop a global...
The collaborations of the diseases might be the key to understand the mechanism of the diseases since it is difficult to detect the role of complex genes and micro RNA in diseases. With the rapid development of technology, several metabolites of many kinds of diseases could be obtained by the advanced machines. Some diseases are related to several metabolites, and some metabolites have strong relationship...
The increasing adoption of health information technologies in the United States accelerates their potential to facilitate beneficial studies that combine large, complex data sets from multiple sources. The process of de-identification, by which identifiers are removed from the health information, mitigates privacy risks to individuals and thereby supports the secondary use of data for comparative...
Drug-target interaction identification is of highly importance in drug research and development. The traditional experimental paradigm is costly, while the previous in silico prediction paradigm remains a challenge because of diversified data production platforms and data scarcity. In this paper, we modeled drug-target interaction prediction as a binary classification task based on transcriptome data...
While high-throughput single cell technologies enable in depth examination of specific cell subsets, these experiments lack the context of these subsets in other cell types and diseases. We compared novel dendritic and monocyte signatures from single cell RNAseq with bulk transcriptome of immune cells to show that the gene signatures for the novel cell subsets are also up-regulated in functionally...
Classification models have proven useful for predicting clinical interventions and patient outcomes. One of the key issues that affect the predictive ability of supervised learning frameworks in the healthcare scenario is imbalance in data sets. In addition, non-uniform data collection processes in clinical scenarios lead to poor quality data sets. We designed a novel approach to predict Intensive...
As the diagnosis of lung cancer, lung mass for the diagnosis of the disease is meaningful, chest radiography has low price, low radiation, popularity and other characteristics, it is a significant attempt for the location of chest masses on chest radiography using deep learning method. In this paper we have established a labeled lung mass database, and presented a state of the art deep learning methodology...
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