Interleaving is used for error-correcting on a bursty noisy channel. Given a graph describing the topology of the channel, we label the vertices of so that each label-set is sufficiently sparse. Interleaving scheme corrects for any error burst of size at most ; it is a labeling where the distance between any two vertices in the same label-set is at least . We consider interleaving schemes on in...

Many problems in computer science can be represented by a graph and reducedto a graph clustering or k-way partitioning problem. In the classical definition,a graph consists of nodes and edges which usually connect exactly two nodes.Hypergraphs are a generalization of graphs, where every edge can connect anarbitrary number of nodes. Recent results suggest that some problems i...

Graph classification is an important data mining task, and various graph kernel methods have been proposed recently for this task. These methods have proven to be effective, but they tend to have high computational overhead. In this paper, we propose an alternative approach to graph classification that is based on feature-vectors constructed from different global topological attributes, as well...

The typical way for relation extraction is fine-tuning large pre-trained language models on task-specific datasets, then selecting the label with highest probability of output distribution as final prediction. However, usage Top-k prediction set a given sample commonly overlooked. In this paper, we first reveal that contains useful information predicting correct label. To effectively utilizes s...

Visual data such as images and videos contain a rich source of structured semantic labels as well as a wide range of interacting components. Visual content could be assigned with fine-grained labels describing major components, coarse-grained labels depicting high level abstractions, or a set of labels revealing attributes. Such categorization over different, interacting layers of labels evince...

The efficiency of graph-based semi-supervised algorithms depends on the graph of instances on which they are applied. The instances are often in a vectorial form before a graph linking them is built. The construction of the graph relies on a metric over the vectorial space that help define the weight of the connection between entities. The classic choice for this metric is usually a distance me...

In this paper, we present a novel probabilistic label enhancement model to tackle multi-label image classification problem. Recognizing multiple objects in images is a challenging problem due to label sparsity, appearance variations of the objects and occlusions. We propose to tackle these difficulties from a novel perspective by constructing auxiliary labels in the output space. Our idea is to...

Deciding whether a collection of unrooted trees is compatible is a fundamental problem in phylogenetics. Two different graph-theoretic characterizations of tree compatibility have recently been proposed. In one of these, tree compatibility is characterized in terms of the existence of a specific kind of triangulation in a structure known as the display graph. An alternative characterization exp...