Filetype
In this paper we discuss AI in industrial robotics. In automatic control, computer vision and optimization, ma- chine learning and data mining algorithms are widely used. However, cognition enabling mechanisms, such as high-level logic and symbolic reasoning, are still limited. This is not due to the lack of available algorithms, rather the bottleneck is knowledge representation, acquisition and t
Task-level industrial robot programming is a mundane, error-prone activity requiring expertise and skill. Since humans easily communicate with natural language (NL), it may be attractive to use speech or text as instruction means for robots. However, there has to be a substantial amount of knowledge in the system to translate the high-level language instructions to executable robot programs. In th
The role of time-dependent freezing of ice nucleating particles (INPs) is evaluated with the “Aerosol–Cloud” (AC) model in 1) deep convection observed over Oklahoma during the Midlatitude Continental Convective Cloud Experiment (MC3E), 2) orographic clouds observed over North California during the Atmospheric Radiation Measurement (ARM) Cloud Aerosol Precipitation Experiment (ACAPEX), and 3) super
To train well-performing generalizing neural networks, sufficiently large and diverse datasets are needed. Collecting data while adhering to privacy legislation becomes increasingly difficult and annotating these large datasets is both a resource-heavy and time-consuming task. An approach to overcome these difficulties is to use synthetic data since it is inherently scalable and can be automatical
High resolution radio based positioning and tracking is a key enabler for new or improved cellular services. In this work, we are aiming to track user movements with accuracy down to centimeters using standard cellular bandwidths of 20-40 MHz. The goal is achieved by using phase information from the multi-path components (MPCs) of the radio channels. First, an extended Kalman filter (EKF) is used
Video data and the tools for automated analysis have a great potential to be used in road traffic research, particularly road safety. In this project a video dataset is built and made public so that researchers can evaluate their algorithms on it. The dataset focuses on the traffic research applications (data from real research projects) and provides recordings of the traffic scenes, meta-data, ca
In this paper, we describe how we use a named entity disambiguation module to merge entities in a question answering system. The question answering system uses a baseline passage retrieval component that extracts paragraphs from the Swedish version of Wikipedia. The passages are indexed and ranked using the Lucene platform. Prior to the indexing, we carried out a recognition and disambiguation of
Cardinality and rank functions are ideal ways of regularizing under-determined linear systems, but optimization of the resulting formulations is made difficult since both these penalties are non-convex and discontinuous. The most common remedy is to instead use the ℓ1- and nuclear norms. While these are convex and can therefore be reliably optimized, they suffer from a shrinking bias that degrades
Semantic role labeling has become a key module of many language processing applications. To build an unrestricted semantic role labeler, the first step is to develop a comprehensive proposition bank. However, building such a bank is a costly enterprise, which has only been achieved for a handful of languages. In this paper, we describe a technique to build proposition banks for new languages using
Several studies have shown the value of using proximity data to understand the social context of users. To simplify the use of social context in application development we have developed Proximates, a social context engine for mobile phones. It scans nearby Bluetooth peers to determine what devices are in proximity. We map Bluetooth MAC ids to user identities on existing social networks which then
Sparse sums of Lorentzians can give good approximations to functions consisting of linear combination of piecewise continuous functions. To each Lorentzian, two parameters are assigned: translation and scale. These parameters can be found by using a method for complex frequency detection in the frequency domain. This method is based on an alternating projection scheme between Hankel matrices and f
This paper introduces two new methods of registering 2D point sets over rigid transformations when the registration error is based on a robust loss function. In contrast to previous work, our methods are guaranteed to compute the optimal transformation, and at the same time, the worst-case running times are bounded by a low-degree polynomial in the number of correspondences. In practical terms, th
We consider sequences of points obtained by projecting a given point B=B (0) back and forth between two manifolds and , and give conditions guaranteeing that the sequence converges to a limit . Our motivation is the study of algorithms based on finding the limit of such sequences, which have proved useful in a number of areas. The intersection is typically a set with desirable properties but for w
Spectral estimation is an important classical problem that has received considerable attention in the signal processing literature. In this contribution, we propose a novel method for estimating the parameters of sums of complex exponentials embedded in additive noise from regularly or irregularly spaced samples. The method relies on Kronecker's theorem for Hankel operators, which enables us to fo
When robots are working in dynamic environments, close to humans lacking extensive knowledge of robotics, there is a strong need to simplify the user interaction and make the system execute as autonomously as possible, as long as it is feasible. For industrial robots working side-by-side with humans in manufacturing industry, AI systems are necessary to lower the demand on programming time and sys
In this paper, we introduce a method to use written natural language instructions to program assembly tasks for industrial robots. In our application, we used a state-of-the-art semantic and syntactic parser together with semantically rich world and skill descriptions to create highlevel symbolic task sequences. From these sequences, we generated executable code for both virtual and physical robot
The concept of culturomics was born out of the availability of massive amounts of textual data and the interest to make sense of cultural and language phenomena over time. Thus far however, culturomics has only made use of, and shown the great potential of, statistical methods. In this paper, we present a vision for a knowledge-based culturomics that complements traditional culturomics. We discuss
Radio based localization and tracking usually require multiple receivers/transmitters or a known floor plan. This paper presents a method for anchor free indoor positioning based on single antenna ultra wideband (UWB) measurements. By using time of arrival information from multipath propagation components stemming from scatterers with different, but unknown, positions we estimate the movement of t
In this paper, we describe a passage retrieval component for a questioning answering system and we evaluate its performance on Swedish documents. We used a corpus of questions and answers transcribed from the Swedish board game Kvitt eller dubbelt and, as source for the passages, we used the articles of the Swedish version of Wikipedia. We show that Wikipedia is a suitable knowledge source to answ
