Assessing State-of-the-Art Sentiment Models on State-of-the-Art Sentiment Datasets

There has been a good amount of progress in sentiment analysis over the past 10 years, including the proposal of new methods and the creation of benchmark datasets. In some papers, however, there is a tendency to compare models only on one or two datasets, either because of time restraints or because the model is tailored to a specific task. Accordingly, it is hard to understand how well a certain model generalizes across different tasks and datasets. In this paper, we contribute to this situation by comparing several models on six different benchmarks, which belong to different domains and additionally have different levels of granularity (binary, 3-class, 4-class and 5-class). We show that Bi-LSTMs perform well across datasets and that both LSTMs and Bi-LSTMs are particularly good at fine-grained sentiment tasks (i. e., with more than two classes). Incorporating sentiment information into word embeddings during training gives good results for datasets that are lexically similar to the training data. With our experiments, we contribute to a better understanding of the performance of different model architectures on different data sets. Consequently, we detect novel state-of-the-art results on the SenTube datasets.Comment: Presented at WASSA 201

Bilingual Sentiment Embeddings: Joint Projection of Sentiment Across Languages

Sentiment analysis in low-resource languages suffers from a lack of annotated corpora to estimate high-performing models. Machine translation and bilingual word embeddings provide some relief through cross-lingual sentiment approaches. However, they either require large amounts of parallel data or do not sufficiently capture sentiment information. We introduce Bilingual Sentiment Embeddings (BLSE), which jointly represent sentiment information in a source and target language. This model only requires a small bilingual lexicon, a source-language corpus annotated for sentiment, and monolingual word embeddings for each language. We perform experiments on three language combinations (Spanish, Catalan, Basque) for sentence-level cross-lingual sentiment classification and find that our model significantly outperforms state-of-the-art methods on four out of six experimental setups, as well as capturing complementary information to machine translation. Our analysis of the resulting embedding space provides evidence that it represents sentiment information in the resource-poor target language without any annotated data in that language.Comment: Accepted to ACL 2018 (Long Papers

Volume and macroscopic scalar curvature

We prove the macroscopic cousins of three conjectures: (1) a conjectural bound of the simplicial volume of a Riemannian manifold in the presence of a lower scalar curvature bound, (2) the conjecture that rationally essential manifolds do not admit metrics of positive scalar curvature, (3) a conjectural bound of ℓ$^{2}$-Betti numbers of aspherical Riemannian manifolds in the presence of a lower scalar curvature bound. The macroscopic cousin is the statement one obtains by replacing a lower scalar curvature bound by an upper bound on the volumes of 1-balls in the universal cover

Volume and macroscopic scalar curvature

We prove the macroscopic cousins of three conjectures: 1) a conjectural bound of the simplicial volume of a Riemannian manifold in the presence of a lower scalar curvature bound, 2) the conjecture that rationally essential manifolds do not admit metrics of positive scalar curvature, 3) a conjectural bound of $L^2$-Betti numbers of aspherical Riemannian manifolds in the presence of a lower scalar curvature bound. The macroscopic cousin is the statement one obtains by replacing a lower scalar curvature bound by an upper bound on the volumes of $1$-balls in the universal cover.Comment: 48 pages; added a statement about integral foliated simplicial volume in the introduction and made minor corrections; to be published in GAF

Cloudifying Desktops – A Taxonomy for Desktop Virtualization

Compared to traditional desktops, the implementation of desktop virtualization can leverage cost reductions and enable desktop access via mobile devices. Consequently, researchers and practitioners increasingly focus on virtualized desktops and Desktop as a Service (DaaS). However, a consistent definition for these technologies and the related delivery models does not exist yet. Therefore, we conducted a literature analysis which revealed that optimized resource allocation and performant DaaS infrastructures are the primary topics in research. Afterward, we developed a taxonomy to categorize extant virtual desktop delivery models and propose a holistic definition as theoretical framework for DaaS

DIGITAL FORMATIVE LEARNING ASSESSMENT TOOL – TOWARDS HELPING STUDENTS TO TAKE OWNERSHIP OF THEIR LEARNING

Over the last years, the number of students has constantly risen while the number of lecturers remained steady. To the consequence are large-scale classes with often hundreds of students. Large-scale classes have didactical challenges such as providing effective feedback for the students’ learning success. This is in particular problematic, since feedback belongs to the most influential factors for the student learn-ing success. In order to overcome the challenges of providing feedback in large-scale classes, we suggest using an IT-based solution we label digital formative learning assessment tool (DFLAT). In this research-in-progress paper, we will show the development of this tool by using the method of action design research (ADR). More precisely, we will concentrate on the first part from the requirements gathering to the alpha-version. In order to collect the requirements, we conducted expert interviews with lecturers and students and also derived requirements from scientific literature. Based on the requirements, we will define the key design elements of the first version of DFLAT. The next steps in our research are then the intervention and evaluation of our alpha-version in a large-scale lecture. With our completed research, we aim to contribute to literature by developing a theory of design and action for providing individualized feedback for students in large-scale classes

Influence of rectal prolapse on the asymmetry of the anal sphincter in patients with anal incontinence

BACKGROUND: Anal sphincter defects have been shown to increase pressure asymmetry within the anal canal in patients with fecal incontinence. However, this correlation is far from perfect, and other factors may play a role. The goal of this study was to assess the impact of rectal prolapse on anal pressure asymmetry in patients with anal incontinence. METHODS: 44 patients, (42 women, mean age: 64 (11) years), complaining of anal incontinence, underwent anal vector manometry, endo-anal ultrasonography (to assess sphincter defects) and pelvic viscerogram (for the diagnosis of rectal prolapse). Resting and squeeze anal pressures, and anal asymmetry index at rest and during voluntary squeeze were determined by vector manometry. RESULTS: Ultrasonography identified 19 anal sphincter defects; there were 9 cases of overt rectal prolapse, and 14 other cases revealed by pelvic viscerogram (recto-anal intussuception). Patients with rectal prolapse had a significantly higher anal sphincter asymmetry index at rest, whether patients with anal sphincter defects were included in the analysis or not (30 (3) % versus 20 (2) %, p < 0.005). Among patients without rectal prolapse, a higher anal sphincter asymmetry index during squeezing was found in patients with anal sphincter defects (27 (2) % versus 19 (2) %, p < 0.03). CONCLUSIONS: In anal incontinent patients, anal asymmetry index may be increased in case of anal sphincter defect and/or rectal prolapse. In the absence of anal sphincter defect at ultrasonogaphy, an increased anal asymmetry index at rest may point to the presence of a rectal prolapse