The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
This paper describes concepts and required ability for a new inspection robot to locomote on H-shaped structures. And we present a concept model satisfied the concepts and explain the motion of the robot on the structure. In addition, the paper discusses feasibility of the robot through a numerical simulation based on specification of an available servomotor.
As many diseases are known to be related to microbes, interests in statistical methods for Microbiome-Wide Association Studies (MWAS) are also increasing. In this respect, we systematically investigate the properties of statistical methods for MWAS and compare their performances using simulation data generated from Human Microbiome Project data. We first assessed the type I error rates of eight commonly...
Understanding epigenetic changes across various conditions is a fundamental problem to epigenome annotation. With more high-throughput epigenomic data available, computational methods have been developed to quantify various types of epigenetic modification signals, to compare epigenetic marks between different conditions and to understand the functional consequences of epigenetic changes. However,...
We present a fast range search algorithm, which greatly reduces unnecessary distance computations, based on a technique to prune redundant distance computations. Theoretical and experimental analysis have shown that the proposed algorithm significantly improves the original k-D tree based algorithm, which runs in O(log(n)) time either in low dimension or the searching range is small. In the case where...
Inverse classification is the process of manipulating an instance such that it is more likely to conform to a specific class. Past methods that address such a problem have shortcomings. Greedy methods make changes that are overly radical, often relying on data that is strictly discrete. Other methods rely on certain data points, the presence of which cannot be guaranteed. In this paper we propose...
Online data provide a way to monitor how users behave in social systems like social networks and online games, and understand which features turn an ordinary individual into a successful one. Here, we propose to study individual performance and success in Multiplayer Online Battle Arena (MOBA) games. Our purpose is to identify those behaviors and playing styles that are characteristic of players with...
In this paper, we propose a very simple method for learning relationships between events by accounting for the spatial or temporal sequence of occurrence of the events. The underlying idea behind our proposed method is that for certain data processing application, such as data collected from retail shoppers, relational access to data is more useful and immediately informative than sequential access...
Identifying meaningful signal buried in noise is a problem of interest arising in diverse scenarios of data-driven modeling. We present here a theoretical framework for exploiting intrinsic geometry in data that resists noise corruption, and might be identifiable under severe obfuscation. Our approach is based on uncovering a valid complete inner product on the space of ergodic stationary finite valued...
Agent-based modeling is a paradigm of modeling dynamic systems of interacting agents that are individually governed by specified behavioral rules. Training a model of such agents to produce an emergent behavior by specification of the emergent (as opposed to agent) behavior is easier from a demonstration perspective. Without the involvement of manual behavior specification via code or reliance on...
This paper presents a temporal pattern mining method for medical data. It modifies the mining algorithms proposed by Batal et al. to incorporate with ranged relations. Experimental results demonstrate that the proposed method could generate frequent patterns with abstracted time ranges embedded in their temporal relations.
Following the trend of big data, the business value of data is becoming a hot research field in recent years. The novel concept of Data Jacket introduced by Ohsawa et al. solved the difficult problem of data transactions due to the particular characteristic of data, i.e. the safeguarding privacy. In order to make sure the mechanism of the market of data, there are some researchers proposed a gamified...
In this paper, we will first explain the FS (familiarity and strangeness) model as a requirement for attracting people's attention and bringing about analogical thinking. After introducing the idea of shikake (triggers for behavior change) and its requirements, we propose the inclusion of the FS model as an attribute of MoDAT in order to encourage MoDAT participants to come up with new shikake ideas.
Performing statistical inference on collections of graphs is of import to many disciplines. Graph embedding, in which the vertices of a graph are mapped to vectors in a low-dimensional Euclidean space, has gained traction as a basic tool for graph analysis. Here we describe an omnibus embedding in which multiple graphs on the same vertex set are jointly embedded into a single space with a distinct...
Stochastic principal component analysis (SPCA) has become a popular dimensionality reduction strategy for large, high-dimensional datasets. We derive a simplified algorithm, called Lazy SPCA, which has reduced computational complexity and is better suited for large-scale distributed computation. We prove that SPCA and Lazy SPCA find the same approximations to the principal subspace, and that the pairwise...
The Krylov subspace based information retrieval (IR) approach has been shown to provide comparable accuracy to latent semantic indexing (LSI), while providing some computational advantages. Recently, in the area of numerical linear algebra, attention has been drawn to the block Krylov subspace methods, which are shown to be more efficient than the classic Krylov subspace methods in solving linear...
Networks (i.e., graphs) appears in many high-impact applications. Often these networks are collected from different sources, at different times, at different granularities. In this talk, I will present our recent work on mining such multiple networks. First, we will present several new data models, whose key idea is to leverage networks as context to connect different data sets or different data mining...
In this paper we propose a new method to anonymize (share relevant and detailed information while not naming names) and protect data sets (minimize the utility loss) based on Factor Analysis. The method basically consists of obtaining the factors, which are uncorrelated, protecting them and undoing the transformation in order to get interpretable protected variables. We first show how to proceed when...
Data Jacket (DJ) is a technique for sharing information about data and for considering the potential value of datasets, with the data itself hidden, by describing the summary of data in natural language. In DJs, variables are described by variable labels (VLs), which are the names/meanings of variables. In the previous study, the matrix-based method for inferring VLs in DJs whose VLs are unknown,...
To collect and explicate meaningful knowledge of a community, we propose an Activity Model based on structured knowledge. The following issues arise related to the model development: (a) difficulties in capturing activities; (b) difficulty of acquiring knowledge; and (c) difficulty in optimizing the activities to newly adopted technologies. Therefore, we are developing technologies that use on-site...
In recent years, predicting future hot events in online social networks is becoming increasingly meaningful in marketing, advertisement, and recommendation systems to support companies' strategy making. Currently, most prediction models require long-term observations over the event or depend a lot on other features which are expensive to extract. However, at the early stage of an event, the temporal...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.