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In this paper, we describe KOSHIK, an end-to-end framework to process the unstructured natural language content of multilingual documents. We used the Hadoop distributed computing infrastructure to build this framework as it enables KOSHIK to easily scale by adding inexpensive commodity hardware. We designed an annotation model that allows the processing algorithms to incrementally add layers of a
Inthispaper,wedescribeanovelsystemthatidentifiesrelationsbetweentheobjectsextractedfromanimage. We started from the idea that in addition to the geometric and visual properties of the image objects, we could exploit lexical and semantic information from the text accompanying the image. As experimental set up, we gathered a corpus of images from Wikipedia as well as their associated articles. We ext
Most question answering systems feature a step to predict an expected answer type given a question. Li and Roth \cite{li2002learning} proposed an oft-cited taxonomy to the categorize the answer types as well as an annotated data set. While offering a framework compatible with supervised learning, this method builds on a fixed and rigid model that has to be updated when the question-answering domai
The massive amounts of text data made available through the Google Books digitization project have inspired a new field of big-data textual research. Named culturomics, this field has attracted the attention of a growing number of scholars over recent years. However, initial studies based on these data have been criticized for not referring to relevant work in linguistics and language technology.
This paper describes a set of methods to link entities across images and text. As a corpus, we used a data set of images, where each image is commented by a short caption and where the regions in the images are manually segmented and labeled with a category. We extracted the entity mentions from the captions and we computed a semantic similarity between the mentions and the region labels. We also
Things such as organizations, persons, or locations are ubiquitous in all texts circulating on the internet, particularly in the news, forum posts, and social media. Today, there is more written material than any single person can read through during a typical lifespan. Automatic systems can help us amplify our abilities to find relevant information, where, ideally, a system would learn knowledge
Established approaches to data aggregation in wirelesssensor networks (WSNs) do not cover the variety of new usecases developing with the advent of the Internet of Things. In particular,the current push towards fog computing, in which control,computation, and storage are moved to nodes close to the networkedge, induces a need to collect data at multiple sinks, ratherthan the single sink typically
This paper considers the problem of recovering either a low rank matrix or a sparse vector from observations of linear combinations of the vector or matrix elements. Recent methods replace the non-convex regularization with ℓ1 or nuclear norm relaxations. It is well known that this approach recovers near optimal solutions if a so called restricted isometry property (RIP) holds. On the other hand i
The class of information set decoding algorithms is the best known way of decoding general codes, i.e. codes that admit no special structure, in the Hamming metric. Stern's algorithm is the origin of the most efficient algorithms in this class. In this paper we consider the same decoding problem but for a channel with soft information. We give a version of Stern's algorithm for a channel with soft
While educational research (EdR) and the Scholarship of Teaching and Learning (SoTL) are overlapping fields there remains considerable friction between the two. Shulman, (2011, p. 5), recounts a situation when an EdR colleague accused him “of contributing to the bastardization of the field by encouraging faculty members who were never trained to conduct educational or social science research to en
We develop fixed-point algorithms for the approximation of structured matrices with rank penalties. In particular we use these fixed-point algorithms for making approximations by sums of exponentials, i.e., frequency estimation. For the basic formulation of the fixed-point algorithm we show that it converges to the solution of a related minimization problem, namely the one obtained by replacing th
Popular Abstract in Swedish Denna avhandling behandlar olika aspekter av den svenska föräldraförsäkringen. Avhandlingen inleds med ett översiktligt kapitel i vilket föräldraförsäkringens historia samt nyttjandet av föräldraförsäkringen under perioden 1993-2005 beskrivs. Därefter följer två studier som behandlar tillfällig föräldraledighet (för vård av sjukt barn) och en studie som behandlar föräldThis thesis contains an introductory chapter and three essays, all considering different aspects of the Swedish parental insurance. In the first two essays, different aspects of temporary parental leave (leave from work to take care of a sick child) are investigated, whereas the third essay considers parental leave (leave from work to take care of a new-born child). In the first essay, On the Sha
The market of connected devices, IoT devices in particular, is hotter than ever. Today, lightweight IoT devices are used in several sectors, such as smart cities, smart homes, healthcare, and the manufacturing industry.IoT solutions help increase productivity by predictive maintenance and resource management in the industry. Devices with voice interfaces are spreading rapidly in the home automatio
This paper addresses the need for secure storage in virtualized services in the cloud. To this purpose, we evaluate the security properties of Intel's Software Guard Extensions (SGX) technology, which provides hardware protection for general applications, for securing virtual Hardware Security Modules (vHSM). In order for the analysis to be comparable with analyses of physical HSMs, the evaluation
A key enabler for Industry 4.0 is Fifth Generation Wireless Specifications (5G), within which network slicing is a promising technique to ensure customized quality of service for specific end-user groups in industrial scenarios. Massive Multiple Input Multiple Output (MIMO) plays a significant role in 5G but network slicing for massive MIMO has not yet been addressed. In this paper, we propose a n
It is well known that load balancing in data centers can lead to unnecessary energy usage if all servers are kept active. Usingdynamic server provisioning, the number of servers that serve requests can be reduced by turning off idle servers and thereby savingenergy. However, such a scheme, usually increases the risk of instability of server queues. In this work, we analyze the trade-offbetween ene
Side-channel attacks on cryptographic algorithms targets the implementation of the algorithm. Information can leak from the implementation in several different ways and, in this paper, electromagnetic radiation from an FPGA is considered. We examine to which extent key information from an AES implementation can be deduced using a low-end oscilloscope. Moreover, we examine how the antenna's distanc
Grain-128AEAD is a stream cipher supporting authenticated encryptionwith associated data, and it is currently in round 2 of the NIST lightweight cryptostandardization process. In this paper we present and benchmark software implementations of the cipher, targeting constrained processors. The processors chosen arethe 8-bit (AVR) and 16-bit (MSP) processors used in the FELICS-AEAD framework.Both hig
In the implementation of post-quantum primitives, it is well known that all computations that handle secret information need to be implemented to run in constant time. Using the Fujisaki-Okamoto transformation or any of its different variants, a CPA-secure primitive can be converted into an IND-CCA secure KEM. In this paper we show that although the transformation does not handle secret informatio
The Learning with Errors (LWE) problem is one of the main mathematical foundations of post-quantum cryptography. One of the main groups of algorithms for solving LWE is the Blum-Kalai-Wasserman (BKW) algorithm. This paper presents new improvements for BKW-style algorithms for solving LWE instances. We target minimum concrete complexity and we introduce a new reduction step where we partially reduc
