A Word Sense-Oriented User Interface for Interactive Multilingual Text Retrieval

In this paper we present an interface for supporting a user in an interactive cross-language search process using semantic classes. In order to enable users to access multilingual information, different problems have to be solved: disambiguating and translating the query words, as well as categorizing and presenting the results appropriately. Therefore, we first give a brief introduction to word sense disambiguation, cross-language text retrieval and document categorization and finally describe recent achievements of our research towards an interactive multilingual retrieval system. We focus especially on the problem of browsing and navigation of the different word senses in one source and possibly several target languages. In the last part of the paper, we discuss the developed user interface and its functionalities in more detail

Collaborative Knowledge Acquisition and Explorationin Technology Search

This article is about technology search as an example of a knowledge acquisition task in industry. Technology search is about finding technology related information in structured as well as unstructured sources. This information is needed to support optimal decision making in business processes. There are new opptortunities for technology search and challenges that need to be addressed. This article outlines some of these challenges and presents two concepts to address them in a search system

A Systematic Comparison of Music Similarity Adaptation Approaches

In order to support individual user perspectives and different retrieval tasks, music similarity can no longer be considered as a static element of Music Information Retrieval (MIR) systems. Various approaches have been proposed recently that allow dynamic adaptation of music similarity measures. This paper provides a systematic comparison of algorithms for metric learning and higher-level facet distance weighting on the MagnaTagATune dataset. A crossvalidation variant taking into account clip availability is presented. Applied on user generated similarity data, its effect on adaptation performance is analyzed. Special attention is paid to the amount of training data necessary for making similarity predictions on unknown data, the number of model parameters and the amount of information available about the music itself. 1

Interaction with Interconnected Data in Participatory Processes

This paper proposes a conceptual graphical user interface for the interaction with interconnected data in participatory processes that play an important role for future smart cities. The presented idea is based on identifying important tasks for data exploration and data editing. The data to consider is structured, semi-structured or unstructured and of different facets. For example, participatory processes like planning and decision processes involve text, time and spatial data. In other words, the handling of the data is a complex endeavor in terms of representation and interaction. In this respect, we utilize and describe a graph-based data model that properly reflects the connected data

Extended Parallel Corpus for Amharic-English Machine Translation

This paper describes the acquisition, preprocessing, segmentation, and alignment of an Amharic-English parallel corpus. It will be useful for machine translation of an under-resourced language, Amharic. The corpus is larger than previously compiled corpora; it is released for research purposes. We trained neural machine translation and phrase-based statistical machine translation models using the corpus. In the automatic evaluation, neural machine translation models outperform phrase-based statistical machine translation models.Comment: Accepted to 2nd AfricanNLP workshop at EACL 202

Acoustic inter- and intra-room similarity based on room acoustic parameters

This paper shows various approaches for determining acoustic (dis-)similarity based on room acoustic parameter values derived from real measurements. The similarity is calculated across different room configurations and/or between different microphone-loudspeaker positions within the same room configuration. We compare supervised (LDA, Random Forrest) and unsupervised techniques (PCA, SPPA) and pre-selected visualizations in terms of their ability to exhibit inter- and intra-room (dis-)similarities. The data set generated comprises spatially high-resolution room impulse responses obtained from multiple source-receiver positions within a room configuration. The room acoustics are varied by introducing active walls and geometries accounting for specific room configurations. The results show that the separation of room configurations primarily relies on specific acoustic parameters, with the reverberation time playing an important role. Within a given room configuration, the acoustic parameters excluding the reverberation time mainly capture the orientation and distance between the source and receiver

Oxygen-Free Compound Casting of Aluminum and Copper in a Silane-Doped Inert Gas Atmosphere: A New Approach to Increase Thermal Conductivity

Novel aluminum-copper compound castings devoid of oxide layers at the interface between the joining partners were developed in order to increase the thermal conductivity of the hybrid component. Due to the natural oxide layers of both aluminum and copper, metallurgical bonds between such bi-metal castings cannot be easily achieved in conventional processes. However, in an atmosphere comparable to extreme high vacuum created by using silane-doped inert gas, metallurgical bonds between the active surfaces of both aluminum and copper can be realized without additional coatings or fluxes. An intermetallic was created between aluminum and copper. Thus, very high thermal conductivities could be obtained for these hybrid castings, exceeding those of conventionally joined samples considerably. The intermetallic phase seams emerging between the joining partners were investigated using scanning electron microscopy and X-ray diffraction. The reduction of casting temperatures resulted in narrower intermetallic phase seams and these in turn in a much lower contact resistance between the two joining partners. This effect can be utilized for increasing the heat transfer capabilities of compound casting components employed for cooling heat sources such as high-power light-emitting diodes

Exploration of Interpretability Techniques for Deep COVID-19 Classification using Chest X-ray Images

The outbreak of COVID-19 has shocked the entire world with its fairly rapid spread and has challenged different sectors. One of the most effective ways to limit its spread is the early and accurate diagnosis of infected patients. Medical imaging such as X-ray and Computed Tomography (CT) combined with the potential of Artificial Intelligence (AI) plays an essential role in supporting the medical staff in the diagnosis process. Thereby, the use of five different deep learning models (ResNet18, ResNet34, InceptionV3, InceptionResNetV2, and DenseNet161) and their Ensemble have been used in this paper, to classify COVID-19, pneumoni{\ae} and healthy subjects using Chest X-Ray. Multi-label classification was performed to predict multiple pathologies for each patient, if present. Foremost, the interpretability of each of the networks was thoroughly studied using techniques like occlusion, saliency, input X gradient, guided backpropagation, integrated gradients, and DeepLIFT. The mean Micro-F1 score of the models for COVID-19 classifications ranges from 0.66 to 0.875, and is 0.89 for the Ensemble of the network models. The qualitative results depicted the ResNets to be the most interpretable model