Hand movement data acquisition is used in many engineering applications ranging from the analysis of gestures to the biomedical sciences. Glove-based systems represent one of the most important efforts aimed at acquiring hand movement data. While they have been around for over three decades, they keep attracting the interest of researchers from increasingly diverse fields. This paper surveys such glove systems and their applications. It also analyzes the characteristics of the devices, provides a road map of the evolution of the technology, and discusses limitations of current technology and trends at the frontiers of research. A foremost goal of this paper is to provide readers who are new to the area with a basis for understanding glove systems technology and how it can be applied, while offering specialists an updated picture of the breadth of applications in several engineering and biomedical sciences areas.

A Survey of Glove-Based Systems and Their Applications

Collaborative filtering (CF) is the most successful approach for personalized product or service recommendations Neighborhood based collaborative filtering is an important class of CF, which is simple, intuitive and efficient product recommender system widely used in commercial domain Typically, neighborhood-based CF uses a similarity measure for finding similar users to an active user or similar products on which she rated Traditional similarity measures utilize ratings of only co-rated items while computing similarity between a pair of users Therefore, these measures are not suitable in a sparse data In this paper, we propose a similarity measure for neighborhood based CF, which uses all ratings made by a pair of users Proposed measure finds importance of each pair of rated items by exploiting Bhattacharyya similarity To show effectiveness of the measure, we compared performances of neighborhood based CFs using state-of-the-art similarity measures with the proposed measured based CF Recommendation results on a set of real data show that proposed measure based CF outperforms existing measures based CFs in various evaluation metrics

A new similarity measure using Bhattacharyya coefficient for collaborative filtering in sparse data

Existing clustering approaches are usually restricted to crisp clustering, where objects just belong to one cluster; meanwhile there are some applications where objects could belong to more than one cluster. In addition, existing clustering approaches usually analyze static datasets in which objects are kept unchanged after being processed; however many practical datasets are dynamically modified which means some previously learned patterns have to be updated accordingly. In this paper, we propose a new tree-based incremental overlapping clustering method using the three-way decision theory. The tree is constructed from representative points introduced by this paper, which can enhance the relevance of the search result. The overlapping cluster is represented by the three-way decision with interval sets, and the three-way decision strategies are designed to updating the clustering when the data increases. Furthermore, the proposed method can determine the number of clusters during the processing. The experimental results show that it can identifies clusters of arbitrary shapes and does not sacrifice the computing time, and more results of comparison experiments show that the performance of proposed method is better than the compared algorithms in most of cases.

A tree-based incremental overlapping clustering method using the three-way decision theory

In selected embodiments a recommendation generator builds a network of interrelationships between venues, reviewers and users based on their attributes and reviewer and user reviews of the venues. Each interrelationship or link may be positive or negative and may accumulate with other links (or anti-links) to provide nodal links the strength of which are based on commonality of attributes among the linked nodes and/or common preferences that one node, such as a reviewer, expresses for other nodes, such as venues. The links may be first order (based on a direct relationship between, for instance, a reviewer and a venue) or higher order (based on, for instance, the fact that two venue are both liked by a given reviewer). The recommendation engine in certain embodiments determines recommended venues based on user attributes and venue preferences by aggregating the link matrices and determining the venues which are most strongly coupled to the user.

Systems and methods for providing recommendations based on collaborative and/or content-based nodal interrelationships

An efficient recommendation generation using relevant Jaccard similarity

A few dedicated training simulator applications exist that mix realistic interaction devices-like real cockpits in flight simulators-with virtual environment (VE) components. Dedicated virtual reality (VR) systems have been utilized also in astronaut training. However there are no detailed descriptions of projection wall VR systems and related interaction techniques for astronaut assembly training in zero gravity conditions. Back projection technology tends to have certain advantages over head mounted displays including less simulation sickness and less restricted user movement. A prototype was built to evaluate the usefulness of projection technology VEs and interaction techniques for astronaut training. This was achieved by first constructing a PC cluster-based general purpose VE software and hardware platform. This platform was used to implement a testing prototype for astronaut assembly sequence training. An interaction tool battery was designed for the purposes of viewpoint control and object handling. A selected training task was implemented as a case study for further analysis based on laptop usage in the Fluid Science Laboratory (FSL) inside the Columbus module in the International Space Station (ISS). User tests were conducted on the usability of the prototype for the intended training purpose. The results seem to indicate that projection technology-based VE systems and suitably selected interaction techniques can be successfully utilized in zero gravity training operations.

Multimodal astronaut virtual training prototype

Collaborative Filtering (CF) is one of the most successful approaches for personalized product recommendations. Neighborhood based collaborative filtering is an important class of CF, which is simple and efficient product recommender system widely used in commercial domain. However, neighborhood based CF suffers from user cold-start problem. This problem becomes severe when neighborhood based CF is used in sparse rating data. In this paper, we propose an effective approach for similarity measure to address user cold-start problem in sparse rating dataset. Our proposed approach can find neighbors in the absence of co-rated items unlike existing measures. To show the effectiveness of this measure under cold-start scenario, we experimented with real rating datasets. Experimental results show that our approach based CF outperforms state-of-the art measures based CFs for cold-start problem.

Exploiting Bhattacharyya Similarity Measure to Diminish User Cold-Start Problem in Sparse Data

Recommendation system based on the actions of a group of users, and not requiring prior metadata, is provided. The recommendation is not limited to from user-to-item (e.g. media recommendation) type; e.g. from user-to-user (e.g. social networking), from item-to-item (e.g. “see also” in internet shopping) and item-to-user (e.g. advertizing) recommendations are supported. The solution utilizes a set of identifiable tokens, associated with each entity, an entity being either a user or an item. Whenever there is an interaction between entities, either existing token sets are updated or new token sets are created such that after an interaction each interacting entity is associated with a token set which resembles more than before the token set(s) associated with the other interacting entity. Recommendation can be produced by searching best matching token sets on the user-to-item, user-to-user, from item-to-item or item-to-user basis.

Method and apparatus for a recommendation system based on token exchange

Clustering has been recognized as one of the important tasks in data mining. One important class of clustering is distance based method. To reduce the computational and storage burden of the classical clustering methods, many distance based hybrid clustering methods have been proposed. However, these methods are not suitable for cluster analysis in dynamic environment where underlying data distribution and subsequently clustering structures change over time. In this paper, we propose a distance based incremental clustering method, which can find arbitrary shaped clusters in fast changing dynamic scenarios. Our proposed method is based on recently proposed al-SL method, which can successfully be applied to large static datasets. In the incremental version of the al-SL (termed as IncrementalSL), we exploit important characteristics of al-SL method to handle frequent updates of patterns to the given dataset. The IncrementalSL method can produce exactly same clustering results as produced by the al-SL method. To show the effectiveness of the IncrementalSL in dynamically changing database, we experimented with one synthetic and one real world datasets.

/pdf/distance-based-incremental-clustering-for-mining-clusters-of-3osxscndd1.pdf

Raimo Launonen

Papers

A new similarity measure using Bhattacharyya coefficient for collaborative filtering in sparse data

Multimodal astronaut virtual training prototype

Exploiting Bhattacharyya Similarity Measure to Diminish User Cold-Start Problem in Sparse Data

Method and apparatus for a recommendation system based on token exchange

Distance based Incremental Clustering for Mining Clusters of Arbitrary Shapes