In this study, a novel confidence indexing algorithm is proposed to minimize human labor in controlling the reliability of automatically extracted synsets from a non-machine-readable monolingual dictionary. Contemporary Turkish Dictionary of Turkish Language Association is used as the monolingual dictionary data. First, the synonym relations are extracted by traditional text processing methods from dictionary definitions and a graph is prepared in Lemma-Sense network architecture. After each synonym relation is labeled by a proper confidence index, synonym pairs with desired confidence indexes are analyzed to detect synsets with a spanning tree-based method. This approach can label synsets with one of three cumulative confidence levels (CL-1, CL-2, and CL-3). According to the confidence levels, synsets are compared with KeNet which is the only open access Turkish Wordnet. Consequently, while most matches with the synsets of KeNet is determined in CL-1 and CL-2 confidence levels, the synsets determined at CL-3 level reveal errors in the dictionary definitions. This novel approach does not find only the reliability of automatically detected synsets, but it can also point out errors of detected synsets from the dictionary.

We introduced a new classifier named Learning Word-vector Quantization (LWQ) to solve morphological ambiguities in Turkish, which is an agglutinative language. First, a new and morphologically annotated corpus, and then its datasets are prepared with a series of processes. According to datasets, LWQ finds optimal word-vectors positions by moving them in the Euclidean space. LWQ does morphological disambiguation in two steps: First, it defines all solution candidates of an ambiguous word using a morphological analyzer; second, it chooses the best candidate according to its total distances to neighbor words that are not ambiguous. To show LWQ's performance, we have conducted many tests on the corpus by considering the consistency of classification. In the experiments, we achieve 98.4% correct classification ratio to choose correct parse output, which is an excellent level for the literature.

Preparing a reliable lexical semantic network by experts requires a great investment and a long time. Although there are many computer-based studies for Turkish, there is no reliable and complete semantic network yet. Therefore, in this study, we present a semantic network automatically generated from a Turkish dictionary with a validation method for the reliability of the extracted relations. In this network, synonym relations between word senses are extracted with text patterns, then they are validated by using semantic relations obtained from definition sentences in the dictionary. In order to show its success, the proposed network is compared to one of the literature studies. As a result, this study emphasizes that the reliability of semantic relations is more valuable than the number of relations found.

One of the important characteristics of emerging multicores/manycores is the existence of “shared on-chip caches,” through which different threads/processes can share data (help each other) or displace each other's data (hurt each other). Most of current commercial multicore systems on the market have on-chip cache hierarchies with multiple layers (typically, in the form of L1, L2 and L3, the last two being either fully or partially shared). In the context of database workloads, exploiting full potential of these caches can be critical. Motivated by this observation, our main contribution in this work is to present and experimentally evaluate a cache hierarchy-aware query mapping scheme targeting workloads that consist of batch queries to be executed on emerging multicores. Our proposed scheme distributes a given batch of queries across the cores of a target multicore architecture based on the affinity relations among the queries. The primary goal behind this scheme is to maximize the utilization of the underlying on-chip cache hierarchy while keeping the load nearly balanced across domain affinities. Each domain affinity in this context corresponds to a cache structure bounded by a particular level of the cache hierarchy. A graph partitioning-based method is employed to distribute queries across cores, and an integer linear programming (ILP) formulation is used to address locality and load balancing concerns. We evaluate our scheme using the TPC-H benchmarks on an Intel Xeon based multicore. Our solution achieves up to 25 percent improvement in individual query execution times and 15-19 percent improvement in throughput over the default Linux-based process scheduler.

Lemmatization of the words is an important preprocess for Natural Language Processing (NLP) studies. Especially in language applications (such as part of speech tagging, spell-checking, and document clustering), selection of the right lemma with morphological features can provide better results. In this study, we present a new hybrid approach for Turkish inflected words by using morphological similarity based graph models which is recently getting popular in lemmatization. For this aim, a novel similarity function for Turkish is developed to connect the similar word forms. The proposed model is trained and tested by a double-checked Turkish lemmatization dataset. Then, empirical results are compared with ones of Zemberek which is the most used Turkish lemmatization tool.

Currently, many Brain Computer Interfaces (BCI) classifiers output point estimates of user intent which make it difficult to incorporate context prior information or assign a principled confidence measurement to a decision. We propose a Bayesian framework to extend current Steady State Visually Evoked Potential (SSVEP) classifiers to a maximum a posteriori (MAP) classifiers by using a Kernel Density Estimate (KDE) to learn the distribution of features conditioned on stimulation class. To demonstrate our framework we extend Canonical Correlation Analysis (CCA) and Power Spectral Density (PSD) style methods. Traditionally, in either example, the class is estimated as the class associated with the maximum feature. Our framework increases performance by relaxing the assumption that a stimulation class's sample often maximizes its class-associated feature. Further, by leveraging the KDE, we present a method which estimates the performance of a classifier under different stimulation frequency sets. Using this, we optimize the selection of stimulation frequencies from those present in a training set.

The RSVP Keyboard™ is a brain-computer interface (BCI)-based typing system for people with severe physical disabilities, specifically those with locked-in syndrome (LIS). It uses signals from an electroencephalogram (EEG) combined with information from an n-gram language model to select letters to be typed. One characteristic of the system as currently configured is that it does not keep track of past EEG observations, i.e., observations of user intent made while the user was in a different part of a typed message. We present a principled approach for taking all past observations into account, and show that this method results in a 20% increase in simulated typing speed under a variety of conditions on realistic stimuli. We also show that this method allows for a principled and improved estimate of the probability of the backspace symbol, by which mis-typed symbols are corrected. Finally, we demonstrate the utility of automatically typing likely letters in certain contexts, a technique that achieves increased typing speed under our new method, though not under the baseline approach.

Humans need communication. The desire to communicate remains one of the primary issues for people with locked-in syndrome (LIS). While many assistive and augmentative communication systems that use various physiological signals are available commercially, the need is not satisfactorily met. Brain interfaces, in particular, those that utilize event related potentials (ERP) in electroencephalography (EEG) to detect the intent of a person noninvasively, are emerging as a promising communication interface to meet this need where existing options are insufficient. Existing brain interfaces for typing use many repetitions of the visual stimuli in order to increase accuracy at the cost of speed. However, speed is also crucial and is an integral portion of peer-to-peer communication; a message that is not delivered timely often looses its importance. Consequently, we utilize rapid serial visual presentation (RSVP) in conjunction with language models in order to assist letter selection during the brain-typing process with the final goal of developing a system that achieves high accuracy and speed simultaneously. This paper presents initial results from the RSVP Keyboard system that is under development. These initial results on healthy and locked-in subjects show that single-trial or few-trial accurate letter selection may be possible with the RSVP Keyboard paradigm.

Visually evoked potentials have attracted great attention in the last two decades for the purpose of brain computer interface design. Visually evoked P300 response is a major signal of interest that has been widely studied. Steady state visual evoked potentials that occur in response to periodically flickering visual stimuli have been primarily investigated as an alternative. There also exists some work on the use of an m-sequence and its shifted versions to induce responses that are primarily in the visual cortex but are not periodic. In this paper, we study the use of multiple m-sequences for intent discrimination in the brain interface, as opposed to a single m-sequence whose shifted versions are to be discriminated from each other. Specifically we used four different m-sequences of length 31. Our main goal is to study if the bit presentation rate of the m-sequences have an impact on classification accuracy and speed. In this initial study, where we compared two basic classifier schemes using EEG data acquired with 15Hz and 30Hz bit presentation rates, our results are mixed; while on one subject, we got promising results indicating bit presentation rate could be increased without decrease in classification accuracy; thus leading to a faster decision-rate in the brain interface, on our second subject, this conclusion is not supported. Further detailed experimental studies as well as signal processing methodology design, especially for information fusion across EEG channels, will be conducted to investigate this question further.

As a response to the challenge of providing high-quality healthcare services with reasonable costs while the elderly population and the associated chronic diseases increase, SAPHIRE architecture provides an intelligent healthcare monitoring architecture. The monitoring of patients is achieved through a clinical decision support system based on clinical guidelines. SAPHIRE provides the necessary interoperability layers to access the patient's vital signs from wireless medical sensors and the electronic healthcare records of the patient in order to exploit them in the decision process seamlessly. This architecture is presented through two pilot applications: one for the bedside monitoring of cardiac patients at hospitals, and the other for homecare monitoring of the cardiac patients rehabilitated after a revascularisation therapy.