One, two, or many mechanisms? The brain's processing of complex words

The heated debate over whether there is only a single mechanism or two mechanisms for morphology has diverted valuable research energy away from the more critical questions about the neural computations involved in the comprehension and production of morphologically complex forms. Cognitive neuroscience data implicate many brain areas. All extant models, whether they rely on a connectionist network or espouse two mechanisms, are too underspecified to explain why more than a few brain areas differ in their activity during the processing of regular and irregular forms. No one doubts that the brain treats regular and irregular words differently, but brain data indicate that a simplistic account will not do. It is time for us to search for the critical factors free from theoretical blinders.

Identification of the Emerging Issues in Recycled Fiber Processing

The objectives of this research work “Identification of the Emerging Issues in Recycled Fiber processing” are discovering of emerging research issues and presenting of new approaches to identify promising research themes in recovered paper application and production. The projected approach consists of identifying technological problems often encountered in wastepaper preparation processes and also improving the quality of recovered paper and increasing its proportion in the composition of paper and board. The source of information for the problem retrieval is scientific publications in which waste paper application and production were discussed. The study has exploited several research methods to understand the changes related to utilization of recovered paper. The all assembled data was carefully studied and categorized by applying software called RefViz and CiteSpace. Suggestions were made on the various classes of these problems that need further investigation in order to propose an emerging research trends in recovered paper.

Customer knowledge processing in key account management

The ability of the supplier firm to generate and utilise customer-specific knowledge has attracted increasing attention in the academic literature during the last decade. It has been argued the customer knowledge should treated as a strategic asset the same as any other intangible assets. Yet, at the same time it has been shown that the management of customer-specific knowledge is challenging in practice, and that many firms are better at acquiring customer knowledge than at making use of it. This study examines customer knowledge processing in the context of key account management in large industrial firms. This focus was chosen because key accounts are demanding and complex. It is not unusual for a single key account relationship to constitute a complex web of relationships between the supplier and the key account – thus easily leading to the dispersion of customer-specific knowledge in the supplier firm. Although the importance of customer-specific knowledge generation has been widely acknowledged in the literature, surprisingly little attention has been paid to the processes through which firms generate, disseminate and use such knowledge internally for enhancing the relationships with their major, strategically important key account customers. This thesis consists of two parts. The first part comprises a theoretical overview and draws together the main findings of the study, whereas the second part consists of five complementary empirical research papers based on survey data gathered from large industrial firms in Finland. The findings suggest that the management of customer knowledge generated about and form key accounts is a three-dimensional process consisting of acquisition, dissemination and utilization. It could be concluded from the results that customer-specific knowledge is a strategic asset because the supplier’s customer knowledge processing activities have a positive effect on supplier’s key account performance. Moreover, in examining the determinants of each phase separately the study identifies a number of intra-organisational factors that facilitate the process in supplier firms. The main contribution of the thesis lies in linking the concept of customer knowledge processing to the previous literature on key account management. Moreover, given than this literature is mainly conceptual or case-based, a further contribution is to examine its consequences and determinants based on quantitative empirical data.

Use of data mining and spectral profiles to differentiate condition after harvest of coffee plants

This study aimed at identifying different conditions of coffee plants after harvesting period, using data mining and spectral behavior profiles from Hyperion/EO1 sensor. The Hyperion image, with spatial resolution of 30 m, was acquired in August 28th, 2008, at the end of the coffee harvest season in the studied area. For pre-processing imaging, atmospheric and signal/noise effect corrections were carried out using Flaash and MNF (Minimum Noise Fraction Transform) algorithms, respectively. Spectral behavior profiles (38) of different coffee varieties were generated from 150 Hyperion bands. The spectral behavior profiles were analyzed by Expectation-Maximization (EM) algorithm considering 2; 3; 4 and 5 clusters. T-test with 5% of significance was used to verify the similarity among the wavelength cluster means. The results demonstrated that it is possible to separate five different clusters, which were comprised by different coffee crop conditions making possible to improve future intervention actions.

Environmental adaptation of the source of the subbasin of Rico Stream, Monte Alto - SP, Brazil

The aim of this study was to define the photographic patterns that represent the use and occupation of the landcover of the "spring" of the Rico Stream subbasin, located at Monte Alto, state of São Paulo (SP), Brazil, for environmental adaptation regarding the Brazilian Forest Law. The mapping was performed using remote sensing techniques and visual interpretation of the World View image, followed by the digitalization of the net of drainage and vegetation (natural and agricultural) at the AutoCad software with documents and field work. The study area has 2141.53 ha and the results demonstrated that the main crop is sugarcane with 546.34 ha, followed by 251.22 ha of pastures, 191.71 ha of perennial crops, 57.31 ha of Eucalyptus and 49.52 ha of onion, confirming the advance of sugarcane culture in the region. The region has 375.04 ha of areas of permanent preservation (APPs), and of this area it was found that only 72.17 ha (19.24%) has arboreal vegetation or natural forest, and 302.87 ha of these areas need to be enriched and reforested with native vegetation from the region, according to the current legislation. The data of the area enable future proposals of models for environmental adaptation to the microbasin according to the current environmental legislation.

Data management as a part of performance management: Case study about production reporting development project

Because of the increased availability of different kind of business intelligence technologies and tools it can be easy to fall in illusion that new technologies will automatically solve the problems of data management and reporting of the company. The management is not only about management of technology but also the management of processes and people. This thesis is focusing more into traditional data management and performance management of production processes which both can be seen as a requirement for long lasting development. Also some of the operative BI solutions are considered in the ideal state of reporting system. The objectives of this study are to examine what requirements effective performance management of production processes have for data management and reporting of the company and to see how they are effecting on the efficiency of it. The research is executed as a theoretical literary research about the subjects and as a qualitative case study about reporting development project of Finnsugar Ltd. The case study is examined through theoretical frameworks and by the active participant observation. To get a better picture about the ideal state of reporting system simple investment calculations are performed. According to the results of the research, requirements for effective performance management of production processes are automation in the collection of data, integration of operative databases, usage of efficient data management technologies like ETL (Extract, Transform, Load) processes, data warehouse (DW) and Online Analytical Processing (OLAP) and efficient management of processes, data and roles.

An Open Source, DDI-based Curation System for Social Science Data

Poster at Open Repositories 2014, Helsinki, Finland, June 9-13, 2014

Scheduling dynamic dataflow graphs with model checking

With the shift towards many-core computer architectures, dataflow programming has been proposed as one potential solution for producing software that scales to a varying number of processor cores. Programming for parallel architectures is considered difficult as the current popular programming languages are inherently sequential and introducing parallelism is typically up to the programmer. Dataflow, however, is inherently parallel, describing an application as a directed graph, where nodes represent calculations and edges represent a data dependency in form of a queue. These queues are the only allowed communication between the nodes, making the dependencies between the nodes explicit and thereby also the parallelism. Once a node have the su cient inputs available, the node can, independently of any other node, perform calculations, consume inputs, and produce outputs. Data ow models have existed for several decades and have become popular for describing signal processing applications as the graph representation is a very natural representation within this eld. Digital lters are typically described with boxes and arrows also in textbooks. Data ow is also becoming more interesting in other domains, and in principle, any application working on an information stream ts the dataflow paradigm. Such applications are, among others, network protocols, cryptography, and multimedia applications. As an example, the MPEG group standardized a dataflow language called RVC-CAL to be use within reconfigurable video coding. Describing a video coder as a data ow network instead of with conventional programming languages, makes the coder more readable as it describes how the video dataflows through the different coding tools. While dataflow provides an intuitive representation for many applications, it also introduces some new problems that need to be solved in order for data ow to be more widely used. The explicit parallelism of a dataflow program is descriptive and enables an improved utilization of available processing units, however, the independent nodes also implies that some kind of scheduling is required. The need for efficient scheduling becomes even more evident when the number of nodes is larger than the number of processing units and several nodes are running concurrently on one processor core. There exist several data ow models of computation, with different trade-offs between expressiveness and analyzability. These vary from rather restricted but statically schedulable, with minimal scheduling overhead, to dynamic where each ring requires a ring rule to evaluated. The model used in this work, namely RVC-CAL, is a very expressive language, and in the general case it requires dynamic scheduling, however, the strong encapsulation of dataflow nodes enables analysis and the scheduling overhead can be reduced by using quasi-static, or piecewise static, scheduling techniques. The scheduling problem is concerned with nding the few scheduling decisions that must be run-time, while most decisions are pre-calculated. The result is then an, as small as possible, set of static schedules that are dynamically scheduled. To identify these dynamic decisions and to find the concrete schedules, this thesis shows how quasi-static scheduling can be represented as a model checking problem. This involves identifying the relevant information to generate a minimal but complete model to be used for model checking. The model must describe everything that may affect scheduling of the application while omitting everything else in order to avoid state space explosion. This kind of simplification is necessary to make the state space analysis feasible. For the model checker to nd the actual schedules, a set of scheduling strategies are de ned which are able to produce quasi-static schedulers for a wide range of applications. The results of this work show that actor composition with quasi-static scheduling can be used to transform data ow programs to t many different computer architecture with different type and number of cores. This in turn, enables dataflow to provide a more platform independent representation as one application can be fitted to a specific processor architecture without changing the actual program representation. Instead, the program representation is in the context of design space exploration optimized by the development tools to fit the target platform. This work focuses on representing the dataflow scheduling problem as a model checking problem and is implemented as part of a compiler infrastructure. The thesis also presents experimental results as evidence of the usefulness of the approach.

Spatial variations at different observational scales and the seasonal distributions of stream macroalgae in a Brazilian subtropical region

We examined the relationships between environmental variations in lotic ecosystems with the seasonal dynamics of macroalgae communities at different spatial scales: drainage basin of two rivers (Rio das Pedras and Rio Marrecas), shading (open and shaded stream segments), mesohabitat (riffles and pools), and microhabitats. Data collections were made on a monthly basis between January and December/2007. A total of 16 taxa were encountered (13 species and 3 vegetative groups). All of the biotic parameters (richness, abundance, diversity, equitability, and dominance) were found to be highly variable at all of the spatial scales evaluated. On the other hand, abiotic variables demonstrated differences only at mesohabitat (in terms of current velocity) and shaded habitat (in terms of irradiance) scales. The seasonality of the macroalgae community structure was strongly influenced by microhabitat variables (current velocity, substrate H', and irradiance), demonstrating their importance over time and at different scales. Regional variables (temperature, oxygen saturation, specific conductance, pH, and turbidity) were found to have little influence on the temporal dynamics of the macroalgae communities evaluated.

Estimated frequency of B-chromosomes and population density of Astyanax scabripinnis paranae in a small stream

A study was made of the frequency of B-chromosomes and the population density of Astyanax scabripinnis paranae (Pisces, Characidae, Tetragonopterinae) from three stretches of the Cascatinha stream (Botucatu, SP). In the first stretch the population was estimated to be about 212 individuals and among 35 karyotyped specimens, 23 carried one macro B-chromosome; in the second stretch the population was estimated to be about 650 individuals and among 20 specimens karyotyped, two possessed one macro B-chromosome; in the third stretch the population was estimated to be about 107 individuals and among 10 specimens karyotyped, one carried one macro B-chromosome. A significant difference was observed in the frequency of macro B-chromosomes in females (57%) and males (8.7%) (P = 0.0001). These data suggest that the B-chromosome frequency and the populational density are not directly related. The hypothesis of the existence of some adaptive effect conferred by the B-chromosomes to the specimens from the first stretch of the Cascatinha stream is presented and discussed

Autonomic processing of the cardiovascular reflexes in the nucleus tractus solitarii

The nucleus tractus solitarii (NTS) receives afferent projections from the arterial baroreceptors, carotid chemoreceptors and cardiopulmonary receptors and as a function of this information produces autonomic adjustments in order to maintain arterial blood pressure within a narrow range of variation. The activation of each of these cardiovascular afferents produces a specific autonomic response by the excitation of neuronal projections from the NTS to the ventrolateral areas of the medulla (nucleus ambiguus, caudal and rostral ventrolateral medulla). The neurotransmitters at the NTS level as well as the excitatory amino acid (EAA) receptors involved in the processing of the autonomic responses in the NTS, although extensively studied, remain to be completely elucidated. In the present review we discuss the role of the EAA L-glutamate and its different receptor subtypes in the processing of the cardiovascular reflexes in the NTS. The data presented in this review related to the neurotransmission in the NTS are based on experimental evidence obtained in our laboratory in unanesthetized rats. The two major conclusions of the present review are that a) the excitation of the cardiovagal component by cardiovascular reflex activation (chemo- and Bezold-Jarisch reflexes) or by L-glutamate microinjection into the NTS is mediated by N-methyl-D-aspartate (NMDA) receptors, and b) the sympatho-excitatory component of the chemoreflex and the pressor response to L-glutamate microinjected into the NTS are not affected by an NMDA receptor antagonist, suggesting that the sympatho-excitatory component of these responses is mediated by non-NMDA receptors.

Algorithmic Techniques in Gene Expression Processing. From Imputation to Visualization

The amount of biological data has grown exponentially in recent decades. Modern biotechnologies, such as microarrays and next-generation sequencing, are capable to produce massive amounts of biomedical data in a single experiment. As the amount of the data is rapidly growing there is an urgent need for reliable computational methods for analyzing and visualizing it. This thesis addresses this need by studying how to efficiently and reliably analyze and visualize high-dimensional data, especially that obtained from gene expression microarray experiments. First, we will study the ways to improve the quality of microarray data by replacing (imputing) the missing data entries with the estimated values for these entries. Missing value imputation is a method which is commonly used to make the original incomplete data complete, thus making it easier to be analyzed with statistical and computational methods. Our novel approach was to use curated external biological information as a guide for the missing value imputation. Secondly, we studied the effect of missing value imputation on the downstream data analysis methods like clustering. We compared multiple recent imputation algorithms against 8 publicly available microarray data sets. It was observed that the missing value imputation indeed is a rational way to improve the quality of biological data. The research revealed differences between the clustering results obtained with different imputation methods. On most data sets, the simple and fast k-NN imputation was good enough, but there were also needs for more advanced imputation methods, such as Bayesian Principal Component Algorithm (BPCA). Finally, we studied the visualization of biological network data. Biological interaction networks are examples of the outcome of multiple biological experiments such as using the gene microarray techniques. Such networks are typically very large and highly connected, thus there is a need for fast algorithms for producing visually pleasant layouts. A computationally efficient way to produce layouts of large biological interaction networks was developed. The algorithm uses multilevel optimization within the regular force directed graph layout algorithm.

Critical infrastructure protection against cyber threats

A postgraduate seminar series with a title Critical Infrastructure Protection against Cyber Threats held at the Department of Military Technology of the National Defence University in the fall of 2013 and 2014. This book is a collection of some of talks that were presented in the seminar. The papers address origin of critical infrastructure protection, wargaming cyberwar in critical infrastructure defence, cyber-target categorization, supervisory control and data acquisition systems vulnerabilities, electric power as critical infrastructure, improving situational awareness of critical infrastructure and trust based situation awareness in high security cloud environment. This set of papers tries to give some insight to current issues of the network-centric critical infrastructure protection. The seminar has always made a publication of the papers but this has been an internal publication of the Finnish Defence Forces and has not hindered publication of the papers in international conferences. Publication of these papers in peer reviewed conferences has indeed been always the goal of the seminar, since it teaches writing conference level papers. We still hope that an internal publication in the department series is useful to the Finnish Defence Forces by offering an easy access to these papers.

Developing purchase invoice processing through workflow automation

The aim of this master’s thesis is to research and analyze how purchase invoice processing can be automated and streamlined in a system renewal project. The impacts of workflow automation on invoice handling are studied by means of time, cost and quality aspects. Purchase invoice processing has a lot of potential for automation because of its labor-intensive and repetitive nature. As a case study combining both qualitative and quantitative methods, the topic is approached from a business process management point of view. The current process was first explored through interviews and workshop meetings to create a holistic understanding of the process at hand. Requirements for process streamlining were then researched focusing on specified vendors and their purchase invoices, which helped to identify the critical factors for successful invoice automation. To optimize the flow from invoice receipt to approval for payment, the invoice receiving process was outsourced and the automation functionalities of the new system utilized in invoice handling. The quality of invoice data and the need of simple structured purchase order (PO) invoices were emphasized in the system testing phase. Hence, consolidated invoices containing references to multiple PO or blanket release numbers should be simplified in order to use automated PO matching. With non-PO invoices, it is important to receive the buyer reference details in an applicable invoice data field so that automation rules could be created to route invoices to a review and approval flow. In the beginning of the project, invoice processing was seen ineffective both time- and cost-wise, and it required a lot of manual labor to carry out all tasks. In accordance with testing results, it was estimated that over half of the invoices could be automated within a year after system implementation. Processing times could be reduced remarkably, which would then result savings up to 40 % in annual processing costs. Due to several advancements in the purchase invoice process, business process quality could also be perceived as improved.

Local Binary Patterns in Focal-Plane Processing. Analysis and Applications

Feature extraction is the part of pattern recognition, where the sensor data is transformed into a more suitable form for the machine to interpret. The purpose of this step is also to reduce the amount of information passed to the next stages of the system, and to preserve the essential information in the view of discriminating the data into different classes. For instance, in the case of image analysis the actual image intensities are vulnerable to various environmental effects, such as lighting changes and the feature extraction can be used as means for detecting features, which are invariant to certain types of illumination changes. Finally, classification tries to make decisions based on the previously transformed data. The main focus of this thesis is on developing new methods for the embedded feature extraction based on local non-parametric image descriptors. Also, feature analysis is carried out for the selected image features. Low-level Local Binary Pattern (LBP) based features are in a main role in the analysis. In the embedded domain, the pattern recognition system must usually meet strict performance constraints, such as high speed, compact size and low power consumption. The characteristics of the final system can be seen as a trade-off between these metrics, which is largely affected by the decisions made during the implementation phase. The implementation alternatives of the LBP based feature extraction are explored in the embedded domain in the context of focal-plane vision processors. In particular, the thesis demonstrates the LBP extraction with MIPA4k massively parallel focal-plane processor IC. Also higher level processing is incorporated to this framework, by means of a framework for implementing a single chip face recognition system. Furthermore, a new method for determining optical flow based on LBPs, designed in particular to the embedded domain is presented. Inspired by some of the principles observed through the feature analysis of the Local Binary Patterns, an extension to the well known non-parametric rank transform is proposed, and its performance is evaluated in face recognition experiments with a standard dataset. Finally, an a priori model where the LBPs are seen as combinations of n-tuples is also presented