Sorry, you need to enable JavaScript to visit this website.

In this paper, we examine the problem of modeling overdispersed frequency vectors that are naturally generated by several machine learning and computer vision applications.

In this paper, we examine the problem of modeling overdispersed frequency vectors that are naturally generated by several machine learning and computer vision applications.

When modelling the stable baseline component of a riometer voltage series, the degradation of statistical performance can be significant if either the data are noisy or the underlying stochastic process is highly nonstationary. It is desirable to explore models which balance the high degree of stability of a quiet-day curve with low computation time. This paper introduces a multitaper method for generating quiet-day curves. A novel metric is introduced for determining the overlap fraction in a section-overlap model of the stable baseline component.

Elderly people commonly face health problems related to their sedentary life. Thus, their physical strength, mental capability, and motor skills are decreasing. Moreover, overweight and physical problems are becoming a serious health problem around the world. On the other hand, they suffer from the social isolation that directly affects their physical and mental health. Gamification for elderly people emerges to motivate them to exercise and socialize with their peers, through social interaction on mobile devices.

Pathological Hand Tremor (PHT) is one of the most prevalent symptoms of some neurological movement disorders such as Parkinson’s Disease (PD) and Essential Tremor (ET). Characterization, estimation, and extraction of PHT is a crucial requirement for assistive and robotic rehabilitation technologies that aim to counteract or resist PHT as an input noise to the system. In general, research in the literature on the topic of PHT removal can be categorized into two major categories, namely, classic and data-driven methods.

Learning representations of nodes in a low dimensional space is a crucial task with many interesting applications in network analysis, including link prediction and node classification. Two popular approaches for this problem include matrix factorization and random walk-based models. In this paper, we aim to bring together the best of both worlds, towards learning latent node representations. In particular, we propose a weighted matrix factorization model which encodes random walk-based information about the nodes of the graph.

Alpha matting is an important topic in areas of computer vision. It has various applications, such as virtual reality, digital image and video editing, and image synthesis. Conventional approaches for alpha matting do not perform well when they encounter complicated background or when foreground and background color distributions overlap. It is also difficult to extract alpha matte accurately when the foreground objects are semi-transparent or hairy.