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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...
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...
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...
The recent advancement of web-scale digital advertising saw a paradigm shift from the conventional focus of digital advertisement distribution towards integrating digital processes and methodologies and forming a seamless workflow of advertisement design, production, distribution, and effectiveness monitoring. In this work, we implemented a computational framework for the predictive analysis of the...
Comparing images to recommend items from an image-inventory is a subject of continued interest. Added with the scalability of deep-learning architectures the once 'manual' job of hand-crafting features have been largely alleviated, and images can be compared according to features generated from a deep convolutional neural network. In this paper, we compare distance metrics (and divergences) to rank...
Class imbalance exists in many applications of bioinformatics and biomedicine, while dimension reduction in the feature space is often needed when building prediction models on a dataset. When the above two issues need to be considered simultaneously for skewed/imbalanced datasets, practitioners and researchers in machine learning may raise the following question: should feature selection be conducted...
The number of triangles in a graph is useful to deduce a plethora of important features of the network that the graph is modeling. However, finding the exact value of this number is computationally expensive. Hence, a number of approximation algorithms based on random sampling of edges, or wedges (adjacent edge pairs) have been proposed for estimating this value. We argue that for large sparse graphs...
Within the context of road estimation, the present paper addresses the problem of the fusion of several sources with different reliabilities. Thereby, reliability represents a higher-level uncertainty. This problem arises in automated driving and ADAS due to changing environmental conditions, e.g., road type or visibility of lane markings. Thus, we present an online sensor reliability assessment and...
Currently, open source projects receive various kinds of issues daily, because of the extreme openness of Issue Tracking System (ITS) in GitHub. ITS is a labor-intensive and time-consuming task of issue categorization for project managers. However, a contributor is only required a short textual abstract to report an issue in GitHub. Thus, most traditional classification approaches based on detailed...
Given the soaring amount of data being generated daily, graph mining tasks are becoming increasingly challenging, leading to tremendous demand for summarization techniques. Feature selection is a representative approach that simplifies a dataset by choosing features that are relevant to a specific task, such as classification, prediction, and anomaly detection. Although it can be viewed as a way to...
Person re-identification is a challenge in video-based surveillance where the goal is to identify the same person in different camera views. In recent years, many algorithms have been proposed that approach this problem by designing suitable feature representations for images of persons or by training appropriate distance metrics that learn to distinguish between images of different persons. Aggregating...
In this study, we apply machine learning algorithms to predict technical failures that can be encountered in Oracle databases and related services. In order to train machine learning algorithms, data from log files are collected hourly from Oracle database systems and labeled with two classes; normal or abnormal. We use several data science approaches to preprocess and transform the input data from...
Face verification approaches aim at determining whether two given faces are from the same person. This scenario has several applications, such as information security, forensics, surveillance and smart cards. Several works extract features independently from each face image, i.e., any sort of relation between the two faces is not modeled a priori to either training or classification stages. In this...
Mobile phones equipped with a monocular camera and an inertial measurement unit (IMU) are ideal platforms for augmented reality (AR) applications, but the lack of direct metric distance measurement and the existence of aggressive motions pose significant challenges on the localization of the AR device. In this work, we propose a tightly-coupled, optimization-based, monocular visual-inertial state...
In this letter, a novel no reference image quality metric is developed, a set of ten features are extracted from each distorted image, then Relevance Vector Machine algorithm (RVM) is utilized to learn the mapping between the combined features and human opinion scores, experiments are conducted on the LIVE databases. The performance of the proposed metric is compared with some existing NR metrics...
Software fault prediction is one of the significant stages in the software testing process. At this stage, the probability of fault occurrence is predicted based on the documented information of the software systems that are already tested. Using this prior knowledge, developers and testing teams can better manage the testing process. There are many efforts in the field of machine learning to solve...
While strong progress has been made in image captioning recently, machine and human captions are still quite distinct. This is primarily due to the deficiencies in the generated word distribution, vocabulary size, and strong bias in the generators towards frequent captions. Furthermore, humans – rightfully so – generate multiple, diverse captions, due to the inherent ambiguity in the captioning task...
With current high performance scientific computing workflows, data are typically recorded at regular intervals spaced several hundred time steps apart. Data are not saved at every time step to prevent excessive memory usage and because data I/O is often a bottleneck in the workflow. However, in many dynamical systems, events of interest occur locally in space and time. In these cases, a global data...
Omnidirectional imaging, also known as 360° and spherical imaging, records all 360° of a scene from a specific spatial position, thus offering the user the capability to enjoy three rotational degrees of freedom (3-DoF). To offer a good quality of experience, omnidirectional imaging requires very high bitrates as high spatial resolution are a must and, ideally, also high frame rates. Due to the lack...
Recommender systems (RS) performance largely depends on diverse types of input that characterize users' preference in the form of both explicit and implicit feedbacks. An explicit feedback is stated directly by an explicit input from users regarding their interest in some options of services or products. Such feedback, however, is not always available. On the other hand, an implicit feedback, which...
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