University of Passau

About: University of Passau is a education organization based out in Passau, Bayern, Germany. It is known for research contribution in the topics: Computer science & Context (language use). The organization has 1543 authors who have published 4763 publications receiving 93338 citations.

Contextual Bidirectional Long Short-Term Memory Recurrent Neural Network Language Models: A Generative Approach to Sentiment Analysis.

Abstract: Traditional learning-based approaches to sentiment analysis of written text use the concept of bag-of-words or bag-of-n-grams, where a document is viewed as a set of terms or short combinations of terms disregarding grammar rules or word order. Novel approaches de-emphasize this concept and view the problem as a sequence classification problem. In this context, recurrent neural networks (RNNs) have achieved significant success. The idea is to use RNNs as discriminative binary classifiers to predict a positive or negative sentiment label at every word position then perform a type of pooling to get a sentence-level polarity. Here, we investigate a novel generative approach in which a separate probability distribution is estimated for every sentiment using language models (LMs) based on long short-term memory (LSTM) RNNs. We introduce a novel type of LM using a modified version of bidirectional LSTM (BLSTM) called contextual BLSTM (cBLSTM), where the probability of a word is estimated based on its full left and right contexts. Our approach is compared with a BLSTM binary classifier. Significant improvements are observed in classifying the IMDB movie review dataset. Further improvements are achieved via model combination.

A large step random walk for minimizing total weighted tardiness in a job shop

Abstract: We consider a job shop with m machines. There are n jobs and each job has a specified sequence to be processed by the machines. Job j has release date rj, due date dj, weight wj and processing time pij on machine i (1,…, m). The objective is to minimize the total weighted tardiness of the n jobs. We describe and analyse a large step random walk which uses different neighbourhood sizes depending on whether the algorithm performs a small step or a large step. The small step consists of iterative improvement while the large step consists of a metropolis algorithm. Computational testing of the large step random walk on 66 instances with 10 jobs and 10 machines shows that the large step random walk achieves better results for the given problem structure compared to an existing shifting bottleneck algorithm. We further show results for large instances with up to 50 jobs and 15 machines. Copyright © 2000 John Wiley & Sons, Ltd.

Competition on software verification

Abstract: This report describes the definitions, rules, setup, procedure, and results of the 1st International Competition on Software Verification. The verification community has performed competitions in various areas in the past, and SV-COMP'12 is the first competition of verification tools that take software programs as input and run a fully automatic verification of a given safety property. This year's competition is organized as a satellite event of the International Conference on Tools and Algorithms for the Construction and Analysis of Systems (TACAS).