An Analysis of Wi-Fi Based Indoor Positioning AccuracyThe increasing demand for location based services inside buildings has made indoor positioning a significant research topic. This study deals with indoor positioning using the Wireless Ethernet IEEE 802.11 (Wireless Fidelity, Wi-Fi) standard that has a distinct advantage of low cost over other indoor wireless technologies. The aim of this study is to examine several aspects of location fingerprinting based indoor positioning that affect positioning accuracy. Overall, the positioning accuracy achieved in the performed experiments is 2.0 to 2.5 meters.

The task of learning useful models from available data is common in virtually all fields of science, engineering, and finance. The goal of the learning task is to estimate unknown (input, output) dependency (or model) from training data (consisting of a finite number of samples) with good prediction (generalization) capabilities for future (test) data (Cherkassky & Mulier, 2007; Hastie et al., 2003). One of the specific learning tasks is regression-estimating an unknown real-valued function. The process of regression model learning is also called regression modelling or regression model building. Many practical regression modelling methods use basis function representation-these are also called dictionary methods (Friedman, 1994; Cherkassky & Mulier, 2007; Hastie et al., 2003), where a particular type of chosen basis functions constitutes a "dictionary". Further distinction is then made between non-adaptive methods and adaptive (also called flexible) methods. The most widely used form of basis function expansions is polynomial of a fixed degree. If a model always includes a fixed (predetermined) set of basis functions (i.e. they are not adapted to training data), the modelling method is considered non-adaptive (Cherkassky & Mulier, 2007; Hastie et al., 2003). Using adaptive modelling methods however the basis functions themselves are adapted to data (by employing some kind of search mechanism). This includes methods where the restriction of fixed polynomial degree is removed and the model's degree now becomes another parameter to fit. Adaptive methods use a very wide dictionary of candidate basis functions and can, in principle, approximate any continuous function with a pre-specified accuracy. This is also known as the universal approximation property (Kolmogorov & Fomin, 1975, Cherkassky & Mulier, 2007). However, in polynomial regression the increase in the model's degree leads to exponential growth of the number of basis functions in the model (Cherkassky & Mulier, 2007; Hastie et al., 2003). With finite training data, the number of basis functions along with the number of model's parameters (coefficients) quickly exceeds the number of data samples, making model's parameter estimation impossible.

Detection of local text reuse is central to a variety of applications, including plagiarism detection, origin detection, and information flow analysis. This paper evaluates and compares effectiveness of fingerprint selection algorithms for the source retrieval stage of local text reuse detection. In total, six algorithms are compared – Every p-th, 0 mod p, Winnowing, Hailstorm, Frequency-biased Winnowing (FBW), as well as the proposed modified version of FBW (MFBW).Most of the previously published studies in local text reuse detection are based on datasets having either artificially generated, long-sized, or unobfuscated text reuse. In this study, to evaluate performance of the algorithms, a new dataset has been built containing real text reuse cases from Bachelor and Master Theses (written in English in the field of computer science) where about half of the cases involve less than 1 % of document text while about two-thirds of the cases involve paraphrasing.In the performed experiments, the overall best detection quality is reached by Winnowing, 0 mod p, and MFBW. The proposed MFBW algorithm is a considerable improvement over FBW and becomes one of the best performing algorithms.The software developed for this study is freely available at the author’s website http://www.cs.rtu.lv/jekabsons/.

Social networking sites such as Facebook, Twitter and VKontakte, online stores such as eBay, Amazon and Alibaba as well as many other websites allow users to share their thoughts with their peers. Often those thoughts contain not only factual information, but also users’ opinion and feelings. This subjective information may be extracted using sentiment analysis methods, which are currently a topic of active research. Most studies are carried out on the basis of texts written in English, while other languages are being less researched. The present survey focuses on research conducted on the sentiment analysis for the Latvian and Russian languages.

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