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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...
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...
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...
This article examines the problems of distance learning and one of their possible solution. An adaptive learning system is described that allows to provide an individual approach when passing a course of study. An example of the response of an adaptive learning system to the actions of students while performing tasks and its proposal for a level change is given.
Parkinson's disease is a debilitating and chronic disease of the nervous system. Traditional Chinese Medicine (TCM) is a new way for diagnosing Parkinson, and the data of Chinese Medicine for diagnosing Parkinson is a multi-label data set. Considering that the symptoms as the labels in Parkinson data set always have correlations with each other, we can facilitate the multi-label learning process by...
In recent years, remarkable breakthrough has been achieved in person re-identification (Re-ID). However most methods are only tested in the closed-world setting where the probe person is assumed to be one of the gallery people. In this paper, we tackle a more realistic problem, open-world Re-ID, which requires to find out whether the probe person is among the gallery or not, and if so, who he is....
We study the problem of single-image depth estimation for images in the wild. We collect human annotated surface normals and use them to help train a neural network that directly predicts pixel-wise depth. We propose two novel loss functions for training with surface normal annotations. Experiments on NYU Depth, KITTI, and our own dataset demonstrate that our approach can significantly improve the...
The intensive annotation cost and the rich but unlabeled data contained in videos motivate us to propose an unsupervised video-based person re-identification (re-ID) method. We start from two assumptions: 1) different video tracklets typically contain different persons, given that the tracklets are taken at distinct places or with long intervals; 2) within each tracklet, the frames are mostly of the...
What is the right way to reason about human activities? What directions forward are most promising? In this work, we analyze the current state of human activity understanding in videos. The goal of this paper is to examine datasets, evaluation metrics, algorithms, and potential future directions. We look at the qualitative attributes that define activities such as pose variability, brevity, and density...
Automatic image aesthetics rating has received a growing interest with the recent breakthrough in deep learning. Although many studies exist for learning a generic or universal aesthetics model, investigation of aesthetics models incorporating individual user’s preference is quite limited. We address this personalized aesthetics problem by showing that individual’s aesthetic preferences exhibit strong...
While metric learning is important for Person reidentification (RE-ID), a significant problem in visual surveillance for cross-view pedestrian matching, existing metric models for RE-ID are mostly based on supervised learning that requires quantities of labeled samples in all pairs of camera views for training. However, this limits their scalabilities to realistic applications, in which a large amount...
In this empirical study we develop forecasting models for electricity demand using publicly available data and three models based on machine learning algorithms. It compares accuracy of these models using different evaluation metrics. The data consist of several measurements and observations related to the electricity market in Turkey from 2011 to 2016. It is available in different time granularities...
Segment Routing (SR) can be used as a traffic engineering strategy to counteract increasing loads on networks like Internet Service Provider (ISP) backbones. Many SR approaches, however, optimize traffic flows that were measured in the past. This paper introduces a new tunnel training architecture. It aims to show that the results of these strategies can still be beneficial for routing new traffic...
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...
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...
In the era of Internet and electronic devices bullying shifted its place from schools and backyards into the cyberspace; it is now known as Cyberbullying. Children of the Arab countries are suffering from cyberbullying same as children worldwide. Thus concerns from cyberbullying are elevating. A lot of research is done for the purpose of handling this situation. The current research is focusing on...
Given the wide skill gap prevalent in the developing nations today, providing vocational education to the unskilled promises to improve the quality of life of people. Vocational skills are practical competencies and the necessary underpinning knowledge that helps a person acquire gainful employment in the labor market. This paper focuses on a surface mount hand soldering technique which is a vocational...
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...
Riding on the waves of deep neural networks, deep metric learning has achieved promising results in various tasks by using triplet network or Siamese network. Though the basic goal of making images from the same category closer than the ones from different categories is intuitive, it is hard to optimize the objective directly due to the quadratic or cubic sample size. Hard example mining is widely...
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