Robust clustering of multi-type relational data via a heterogeneous manifold ensemble

Currently, many of the processing techniques for the location-based queries provide information of a single type of spatial objects, based on their spatial closeness to the query object. However, in real-life applications the user may be interested in obtaining information about different types of objects, in terms of their quality, cost, and neighboring relationship. We term the different types of objects with better quality and closer to each other the Neighboring skyline set (or NS set). Three new types of location-based queries, the Distance-based neighboring skyline query (Dist-NS query), the Cost-based neighboring skyline query (Cost-NS query), and the Budget-based neighboring skyline query (BGT-NS query), are presented to determine the NS sets according to user’s specific requirement. A R-tree-based index, the $$R^{a,c}$$-tree, is first designed to manage each type of objects with their locations, attributes, and costs. Then, a simultaneous traversal of the $$R^{a,c}$$-trees built on different types of objects is employed with several pruning criteria to prune the non-qualifying object sets as early as possible, so as to improve the query performance. Extensive experiments using the synthetic dataset demonstrate the efficiency and the effectiveness of the proposed algorithms.

Heterogeneous information networks have drawn much attention in recent years due to their significant applications, such as text mining, e-commerce, social networks, and bioinformatics. Clustering different types of objects simultaneously based upon not only their relations of the same type, but also the relations between different types of objects can improve the clustering quality mutually. In this paper, we propose a general model, in which both the homogeneous and heterogeneous relations are considered simultaneously, to describe the structure of the heterogeneous information networks and devise a novel parametric free multi-type overlapped clustering approach. In this model, different types of relations between different types of objects are represented by a group of matrices. In this way, we transfer the multi-type clustering problem into the information compression problem. Subsequently, greedy search approaches, which aim at describing the group of relational matrices with least bits, are proposed. Moreover, by discovering the discriminative clusters among different types of objects, we devise effective parameter-free strategies to discover either overlapping or non-overlapping structure among different types of clusters. Extensive experiments on real-world and synthetic data sets demonstrate our methods are effective and efficient.

This paper investigates the effects of encumbrance (holding different types of objects while using mobile devices) to understand the interaction difficulties that it causes. An experiment was conducted where participants performed a target acquisition task on a touchscreen mobile phone while carrying different types of bags and boxes. Mobility was also evaluated since people carry items from one place to another. Motion capture hardware was used to track hand and arm postures to examine how holding the different types of objects caused excessive movement and instability therefore resulting in performance to decline. The results showed encumbrance and mobility caused target accuracy to decrease although input while holding the box under the non-dominant arm was more accurate and exerted quicker targeting times than holding no objects. Encumbrance affected the dominant hand more than the non-dominant hand as targeting error significantly increased and caused greater hand instability. The issues caused by encumbrance suggest the topic requires more attention from researchers and users would benefit greatly if better interaction techniques and applications are developed to counteract the problems.

In an object oriented system, different types of objects may have one or more attributes in common. For example different types of objects in a library system might be loanable or catalogued. The first part of the paper introduces a distinction between object types and attribute types, showing that there are advantages in treating them as separate constructs of an object oriented programming language. Then generalized attribute types, (e.g. synchronized, journalized, protected) are discussed. It is shown how implementations involving a technique called bracket routines can lead to clearly modeled and cleanly implemented programs. It is then shown how the bracket technique can be effectively used to introduce protection and security concepts into a system. Among the clear advantages of this technique is that it results in better reusability of code.

There are various kinds of social media information, including different types of objects and relations among these objects, in music social communities such as Last.fm and Pandora. This information is valuable for music recommendation. However, there are two main challenges to exploit this rich social media information: (a) There are many different types of objects and relations in music social communities, which makes it difficult to develop a unified framework taking into account all objects and relations. (b) In these communities, some relations are much more sophisticated than pairwise relation, and thus cannot be simply modeled by a graph. We propose a novel music recommendation algorithm by using both multiple kinds of social media information and music acoustic-based content. Instead of graph, we use hypergraph to model the various objects and relations, and consider music recommendation as a ranking problem on this hypergraph. While an edge of an ordinary graph connects only two objects, a hyperedge represents a set of objects. In this way, hypergraph can be naturally used to model high-order relations.

Heterogeneous object co-clustering has become an important research topic in data mining. In early years of this research, people mainly worked on two types of heterogeneous data (denoted by pair-wise co-clustering); while recently more and more attention was paid to multiple types of heterogeneous data (denoted by high- order co-clustering). In this paper, we studied the high- order co-clustering of objects with star-structured interrelationship, i.e., there is a central type of objects that connects the other types of objects. Actually, this case could be a very good model for many real-world applications, such as the co-clustering of Web images, their low-level visual features, and the surrounding text. We used a tripartite graph to represent the interrelationships among different objects, and proposed a consistent information theory which generates an effective algorithm to obtain the co-clusters of different types of objects. Experiments on a Web image show that our proposed algorithm is a better choice compared with previous work on heterogeneous object co-clustering.

Recently, many computational models have been proposed to simulate visual cognition process. For example, the hierarchical Max-Pooling (HMAX) model was proposed according to the hierarchical and bottom-up structure of V1 to V4 in the ventral pathway of primate visual cortex, which could achieve position- and scale-tolerant recognition. In our previous work, we have introduced memory and association into the HMAX model to simulate visual cognition process. In this paper, we improve our theoretical framework by mimicking a more elaborate structure and function of the primate visual cortex. We will mainly focus on the new formation of memory and association in visual processing under different circumstances as well as preliminary cognition and active adjustment in the inferior temporal cortex, which are absent in the HMAX model. The main contributions of this paper are: 1) in the memory and association part, we apply deep convolutional neural networks to extract various episodic features of the objects since people use different features for object recognition. Moreover, to achieve a fast and robust recognition in the retrieval and association process, different types of features are stored in separated clusters and the feature binding of the same object is stimulated in a loop discharge manner and 2) in the preliminary cognition and active adjustment part, we introduce preliminary cognition to classify different types of objects since distinct neural circuits in a human brain are used for identification of various types of objects. Furthermore, active cognition adjustment of occlusion and orientation is implemented to the model to mimic the top-down effect in human cognition process. Finally, our model is evaluated on two face databases CAS-PEAL-R1 and AR. The results demonstrate that our model exhibits its efficiency on visual recognition process with much lower memory storage requirement and a better performance compared with the traditional purely computational methods.

The problem of routing between two types of objects is considered. Two variants of the problem statement are considered: with a single point of collection and without it. An important aspect of the routes is the mandatory alternation of different types of objects. A mathematical model of the problem with boolean variables is proposed for each statement. A meaningful statement can be represented as the task of collecting haystacks in trucks afield. Two algorithms are developed to solve this problem. The first one is based on the “greedy” strategies, and the second one is based on the approach of Ant Colony Optimization (ACO) algorithms. To configure the parameters of the second algorithm it is carried out the computational experiment. The results of the experiment are provided. In conclusion, the main results of the work are presented.

The SiamFC target tracking algorithm has attracted extensive attention because of its good balance between speed and performance, but the tracking effect of the SiamFC algorithm is not satisfactory in complex background scenes. When SiamFC algorithm uses deep semantic features for tracking, it has good recognition ability for different types of objects, but it has insufficient discrimination for the same types of objects. Therefore, we propose an effective anti-interference module to improve the discrimination ability of the algorithm. The anti-interference module uses another feature extraction network to extract the features of the candidate target images generated by the SiamFC main network. In addition, we set up the feature vector set to save the feature vectors of the tracking target and the template image. Finally, the tracking target is selected by calculating the minimum cosine distance between the feature vector of the candidate target and the vector in the feature vector set. A large number of experiments show that our anti-interference module can effectively improve the performance of SiamFC algorithm, and the performance of this algorithm can be comparable to the popular algorithms.

With the rapid development of online social media, online shopping sites and cyber-physical systems, heterogeneous information networks have become increasingly popular and content-rich over time. In many cases, such networks contain multiple types of objects and links, as well as different kinds of attributes. The clustering of these objects can provide useful insights in many applications. However, the clustering of such networks can be challenging since (a) the attribute values of objects are often incomplete, which implies that an object may carry only partial attributes or even no attributes to correctly label itself; and (b) the links of different types may carry different kinds of semantic meanings, and it is a difficult task to determine the nature of their relative importance in helping the clustering for a given purpose. In this paper, we address these challenges by proposing a model-based clustering algorithm. We design a probabilistic model which clusters the objects of different types into a common hidden space, by using a user-specified set of attributes, as well as the links from different relations. The strengths of different types of links are automatically learned, and are determined by the given purpose of clustering. An iterative algorithm is designed for solving the clustering problem, in which the strengths of different types of links and the quality of clustering results mutually enhance each other. Our experimental results on real and synthetic data sets demonstrate the effectiveness and efficiency of the algorithm.

Functional topography of a distributed neural system for spatial and nonspatial information maintenance in working memory

We developed growth method allowing covering of different types of objects, such as surgical instruments, tissues, masks, etc. with the atomic layer deposition method using oxide thin layers as antimicrobial coatings. These layers are deposited at low temperature (usually below 100°C), which allows for the covering of various substrates, including the soft tissue paper material or surgical instruments, as well as, synthetic and organic materials. The elaborated method of coating on various kinds of surfaces with metal oxide layers can be very helpful in reducing the energy and water inputs to processes designed to eliminate micro-organisms (sterilization), especially in the food industry, production technology and production of animal feed. Potential applications can also be found in medicine, veterinary medicine and the broader health care.

Soft robot grippers have been investigated for handling different types of objects and have been applied in various applications. However, the fabrication of soft robots usually involves molding and demolding processes and it is time consuming. In this paper, we propose a pneumatic shell gripper which can be easily fabricated without demolding process and its thickness was minimized for the purpose of packaging multiple cucumbers simultaneously. The shell gripper includes multiple gripping units and each unit consists of two flat fingers configured in a parallel pattern. Each finger has a hybrid structure combining a rigid shell and a soft membrane. The membrane can be pneumatically inflated and therefore can be used to grasp objects. Inflation tests on the hybrid finger showed that the new fabrication method can provide similar inflated deformation to the fingers compared with the conventional method. Moreover, the new method yields less individual differences among fingers due to the less manual process. Durability was tested for the new fabrication method and the finger is not ruptured after 100,000 circles. Finally, tests on packaging multiple cucumbers were conducted and results demonstrated the ability and advantages of the shell gripper.

Video retrieval based on object discovery

This paper presents a description of the difficulties for automatic optimized scheduling for the Space Surveillance Network (SSN) and then describes a design and algorithm to overcome those difficulties. These difficulties includes a large number of objects to track, which requires an even larger number of observations to schedule, different types of observation tasks, different regimes of space (near earth and deep space) and different types of requirements for the different types of objects (e.g., some have frequent visit requirements). The solution must therefore be similarly multifaceted with different algorithms appropriate for the different types of regimes while still being combined into a single whole.