Abstract: Creating sessions in scientific conferences consists in grouping papers with common topics taking into account the size restrictions imposed by the conference schedule. Therefore, this problem can be considered as semi-supervised clustering of documents based on their content. This paper aims to propose modifications in traditional clustering algorithms to incorporate size constraints in each cluster. Specifically, two new algorithms are proposed to semi-supervised clustering, based on: binary integer linear programming with cannot-link constraints and a variation of the K-Medoids algorithm, respectively. The applicability of the proposed semi-supervised clustering methods is illustrated by addressing the problem of automatic configuration of conference schedules by clustering articles by similarity. We include experiments, applying the new techniques, over real conferences datasets: ICMLA-2014, AAAI-2013 and AAAI-2014. The results of these experiments show that the new methods are able to solve practical and real problems.

Abstract: Exploiting multi-core architectures is a way to tackle the CPU time consumption when solving SATisfiability (SAT) problems. Portfolio is one of the main techniques that implements this principle. It consists in making several solvers competing, on the same problem, and the winner will be the first that answers. In this work, we improved this technique by using a learning schema, namely the Exploration- Exploitation using Exponential weight (EXP3), that allows smart resource allocations. Our contribution is adapted to situations where we have to solve a bench of SAT instances issued from one or several sequence of problems. Our experiments show that our approach achieves good results.

Abstract: Knowledge resolution is the task of clustering knowledge mentions, e.g., entity and relation mentions into several disjoint groups with each group representing a unique entity or relation. Such resolution is a central step in constructing high-quality knowledge graph from unstructured text. Previous research has tackled this problem by making use of various textual and structural features from a semantic dictionary or a knowledge graph. This may lead to poor performance on knowledge mentions with poor or not well-known contexts. In addition, it is also limited by the coverage of the semantic dictionary or knowledge graph. In this work, we propose ETransR, a method which automatically learns entity and relation feature representations in continuous vector spaces, in order to measure the semantic relatedness of knowledge mentions for knowledge resolution. Experimental results on two benchmark datasets show that our proposed method delivers significant improvements compared with the state-of-the-art baselines on the task of knowledge resolution.

Abstract: 3D textile model plays an important role in textile engineering. However, not much work focus on high-quality 3D textile reconstruction. The texture is also limited by photography methods in 3D scanning. This paper presents a novel framework of reconstructing a high-quality 3D textile model with a synthesized texture. Firstly, a pipeline of 3D textile processing is proposed to obtain a better 3D model based on KinectFusion. Then, convolutional neural networks (CNN) is used to synthesize a new texture. To our best knowledge, this is the first paper combining 3D textile reconstruction and texture synthesis. Experimental results show that our method can conveniently obtain high-quality 3D textile models and realistic textures.

Abstract: Cloud computing has emerged as an optimal option for almost all computational problems today. Using cloud services, customers and providers come to terms of usage conditions defined in Service Agreement Layer (SLA), which specifies acceptable Quality of Service (QoS) metric levels. From the view of cloud-based software developers, their application-level SLA must be mapped to provided virtual resource-level SLA. Hence, one of the important challenges in clouds today is to improve QoS of computing resources. In this direction, there are many studies dealing with the problem by bringing forward prediction consumption models. However, the SLA violation evaluation for these prediction models still has been received less attentions. In this paper, we focus on developing a comprehensive autoscaling solution for clouds based on forecasting resource consumption in advance and validating prediction-based scaling decisions. Our prediction model takes all advantages of fuzzy approach, genetic algorithm and neural network to process historical monitoring time series data. After that the scaling decisions are validated and adapted through evaluating SLA violations. Our solution is tested on real workload data generated from Google data center. The achieved results show significant efficiency and feasibility of our model.