Humanism is a violence typical of the beasts. Philosozing with a Hammer, using Levinas and Derrida, the Measure of the Human and the Human as a measure

Levinas’s reflections arose as a critique of traditional philosophy which, since it was based on presence and identity, leads to the exclusion of the other. Instead of an onto-logical thought the Lithuanian proposes that the ipseity of the human being be constituted by alterity, and that it be so ethically, because the subject is sub-ject, that is, that which upholds, responsibility. In an attempt to take the obligatory attention to the otherness of the other even further, Derrida would develop a radical critique of the Levinasian posture. Deconstruction of every trace of ipseity and sovereignty in the relationship with the other, the reading that we have done of the work of Derrida opts for a no definable understanding of the human. That is why every de-limitation of an ethical field as a properly human implies a brutal violence that the levinasian humanism of the other tried to exceed.

Gedragsmatige en Emotionele Betrokkenheid in de Mbo Opleidingen Allround Operationeel Technicus en Koopvaardij Officier Alle Schepen Bij het Scheepvaart en Transport College

Rikhof, O. (2016).Gedragsmatige en Emotionele Betrokkenheid in de Mbo Opleidingen Allround Operationeel Technicus en Koopvaardij Officier Alle Schepen Bij het Scheepvaart en Transport College. Mei, 26, 2016, Heerlen, Nederland: Open Universiteit

De controller als Businesspartner. Hoe beïnvloeden persoonskenmerken de ontwikkeling naar "Partner in Business"binnen de afdeling Financiën bij de provincie Gelderland?

Bij de provincie Gelderland is per 01-01-2009 een reorganisatie doorgevoerd waarbij o.a. de ondersteunende disciplines zoals Financiën zijn samengevoegd binnen één centrale afdeling. Binnen de afdeling Financiën is een team ‘Business control’ gevormd waarin de Financieel adviseurs zijn ondergebracht. Daarbij is vanaf 2009, in verschillende termen en bewoordingen, steeds de ambitie uitgesproken om van de “Financieel expert” naar “Partner in business” te groeien. De ervaring tot dusver laat zien dat deze ambitie in de praktijk moeizaam te realiseren is. Dit onderzoek richt zich op verschillende facetten die samenhangen met bovengenoemde ambitie. Daarbij richt het onderzoek zich vooral op de vraag wat de invloed daarbij is van de persoonskenmerken van de medewerkers. De onderzoeksvraag is: Hoe beïnvloeden de persoonskenmerken de ontwikkeling naar “Partner in Business” bij het team Business control binnen de afdeling Financiën bij de provincie Gelderland? Uit het literatuuronderzoek blijkt dat New Public Management (NPM) een belangrijke externe ontwikkelingen is voor de publieke sector. NPM heeft als doel om publieke organisaties meer resultaatgericht, meer gecoördineerd en efficiënter te laten werken. Bij NPM gaat het o.a. om begrippen als resultaatgerichtheid, output en efficiency. Aangezien de controller het management adviseert bij het efficiënt realiseren van de organisatiedoelstellingen is NPM van invloed op de (rol van) de controller. Een verandering in de rol van de controller is ook het gevolg van de veranderingen in de financiële functie. Uit diverse onderzoeken blijkt dat de controllersfunctie zich ontwikkelt van een administratieve, ten behoeve van het top-management controlerende functie, naar een beslissingsondersteunende functie voor alle geledingen van het management. Conijn et al. (2005) beschrijven de ontwikkeling in de financiële functie aan de hand van een denkmodel met daarin vier fasen met de bijbehorende archetypes Scorekeeper, Financial controller, Managementcontroller en Businesspartner. Naast deze ontwikkelingen zijn ook persoonsgerelateerde factoren van invloed op de rol van een controller binnen een organisatie. Vanuit de organisatiepsychologie worden de persoonlijke eigenschappen van mensen dikwijls in vijf verschillende dimensies gevat, ook wel ‘the big five’ genoemd. Het big five factor model gaat ervan uit dat elk persoon in meer of mindere mate de volgende vijf persoonlijke dimensies heeft: Extraversie, Meegaandheid, Zorgvuldigheid, Openheid en Emotionele stabiliteit. De situatie bij de provincie Gelderland is onderzocht aan de hand van een enquête. De enquête is uitgezet bij de 28 Financieel adviseurs met 17 representatieve respondenten. Hieruit blijkt dat de Financieel adviseurs bij de provincie Gelderland voornamelijk activiteiten verrichten die horen bij de rol van Financial controller en in mindere mate die van respectievelijk Managementcontroller, Businesspartner en Scorekeeper. Daarbij beschikken de Managementcontrollers en de Businesspartners meer over de persoonskenmerken Extraversie, Openheid en Emotionele stabiliteit dan de Scorekeepers en Financial controllers. De Scorekeepers beschikken juist het minst over deze drie persoonskenmerken ten opzichte van de andere drie typen controllers. Voor wat betreft de persoonskenmerken Zorgvuldigheid en Meegaandheid laten de resultaten van de enquête geen eenduidig beeld zien in de relatie tot de typen controllers die de Financieel adviseurs vervullen. Op basis van dit onderzoek en met inachtneming van het aantal van 17 respondenten bij de enquête, lijkt er een relatie te zijn tussen de persoonskenmerken van controllers en de rol die zij als controller vervullen. De rol van Businesspartner vraagt blijkbaar om een hoge mate van Extraversie, Openheid en Emotionele stabiliteit. Voor de provincie Gelderland betekent dit concreet dat bij de gewenste ontwikkeling van Financieel expert naar Partner in business rekening gehouden moet worden met de persoonskenmerken van de Financieel adviseurs. Hierdoor kan er een goede aansluiting tot stand worden gebracht tussen de controller als persoon en zijn/haar controllersrol binnen de organisatie. Kortom; de juiste persoon op de juiste plaats.

Stichting Brein: op het scherp van de snede

In de scriptie “Stichting Brein: Op het scherp van de snede” is onderzocht in hoeverre de private opsporing door stichting Brein kan bijdragen aan de strafrechtelijke handhaving van de Auteurswet. Brein is een organisatie die voor haar aangeslotenen uit de entertainmentindustrie de strijd aangaat met intellectuele eigendomsfraude. Brein richt zich vooral op piraterij: illegale dvd’s en cd’s en grootschalig delen via internet. Haar werkzaamheden zijn het best te omschrijven als private opsporing. Het juridisch kader hiervan is beperkt: stichting Brein valt niet onder een wet zoals de WPBR. Medewerkers van Brein zijn in wezen burgers die aan opsporing doen. Tijdens de opsporing wordt intensief samengewerkt met politie en FIOD. Het door elkaar lopen van private en publieke opsporing kan vragen oproepen over de monopolie van opsporing en vervolging van het OM. Brein is altijd afhankelijk van OM: het OM kan alleen bij grootschalige piraterij, georganiseerde criminaliteit én als civielrechtelijk optreden tekort schiet, kiezen voor een strafrechtelijke vervolging. De jurisprudentie toont aan dat de private opsporing van stichting Brein goed kan bijdragen aan de strafrechtelijke handhaving van de Auteurswet, zolang er sprake is van een goed opererend OM. Als het OM faalt, staat stichting Brein, ondanks de gedegen eigen private opsporing en hoog gewaardeerde expertise, met lege handen.

ASSESSMENT VAN VOLWASSEN STATE-HECHTING

Volgens de hechtingstheorie komt de band die een kind met zijn of haar primaire verzorgers heeft gehad, op latere leeftijd tot uiting in veilige dan wel onveilige hechtingspatronen of –stijlen binnen de affectieve relaties met nabije anderen. Onveilig gehechte individuen hanteren specifieke hechtingsstrategieën in reactie op nonresponsiviteit of insensitiviteit van nabije anderen, zoals een geïntensiveerd (angstig) zoeken naar steun of bescherming, ofwel de vermijding van intimiteit waarbij de pogingen om steun of bescherming te verkrijgen, volledig worden opgeven. Hechtingsrepresentaties werden lange tijd gezien als vrij stabiele constructen (trait-hechting). Inmiddels is echter bewezen dat hechting veranderlijk kan zijn in de tijd en over verschillende soorten relaties heen (state-hechting). Individuen blijken meer aanpasbaar te zijn voor wat betreft de hechtingsstijlen en –strategieën binnen nauwe verbintenissen dan vroeger werd gedacht. In onderzoek is een verband geconstateerd tussen onveilige hechtingsstijlen en –strategieën en psychopathologie. Voor het vaststellen van de (dominante) volwassen hechtingsstijl binnen intieme partnerrelaties en in relaties met anderen ‘in het algemeen’, worden in de praktijk veelal hechtingsvragenlijsten ingezet. Met kennis van en inzicht in de hechtingsstijl en –strategieën van cliënten en met een goed hechtingsinstrument om deze te meten, kan de therapeut gerichte interventies toepassen om cliënten te begeleiden van meer onveilige hechtingspatronen en ineffectieve hechtingsstrategieën naar veilige hechting en effectieve hechtingsstrategieën binnen relaties. Met dit onderzoek werd getracht een idiografische (persoonsbeschrijvende) state-hechtingslijst voor de psychologiepraktijk te ontwikkelen. Daartoe werden twee instrumenten op interne structuur en psychometrische kwaliteiten onderzocht, namelijk de State Adult Attachment Measure (SAAM; Gillath et al., 2009) en de Hechtingslijst (HL-48; Van Geel et al., 2011). Een externe validatie werd gedaan met een hexagon-analyse (Van Geel, 2011) door projectie van de twee instrumenten en (een Nederlandse vertaling van) de Relationship Questionnaire van Bartholomew en Horowitz (1991; RQ-NL; Emmelkamp, 2011) tegen de achtergrond van de zes affectieve prototypen van de Zelfconfrontatiemethode (ZKM; Hermans & Hermans-Jansen, 1995; Hermans, Hermans-Jansen & Van Gilst, 1985).

Machine learning classification of surgical pathology reports and chunk recognition for information extraction noise reduction

Background and aims: Machine learning techniques for the text mining of cancer-related clinical documents have not been sufficiently explored. Here some techniques are presented for the pre-processing of free-text breast cancer pathology reports, with the aim of facilitating the extraction of information relevant to cancer staging.

Materials and methods: The first technique was implemented using the freely available software RapidMiner to classify the reports according to their general layout: ‘semi-structured’ and ‘unstructured’. The second technique was developed using the open source language engineering framework GATE and aimed at the prediction of chunks of the report text containing information pertaining to the cancer morphology, the tumour size, its hormone receptor status and the number of positive nodes. The classifiers were trained and tested respectively on sets of 635 and 163 manually classified or annotated reports, from the Northern Ireland Cancer Registry.

Results: The best result of 99.4% accuracy – which included only one semi-structured report predicted as unstructured – was produced by the layout classifier with the k nearest algorithm, using the binary term occurrence word vector type with stopword filter and pruning. For chunk recognition, the best results were found using the PAUM algorithm with the same parameters for all cases, except for the prediction of chunks containing cancer morphology. For semi-structured reports the performance ranged from 0.97 to 0.94 and from 0.92 to 0.83 in precision and recall, while for unstructured reports performance ranged from 0.91 to 0.64 and from 0.68 to 0.41 in precision and recall. Poor results were found when the classifier was trained on semi-structured reports but tested on unstructured.

Conclusions: These results show that it is possible and beneficial to predict the layout of reports and that the accuracy of prediction of which segments of a report may contain certain information is sensitive to the report layout and the type of information sought.

Joint Analysis of Multiple Algorithms and Performance Measures

There has been an increasing interest in the development of new methods using Pareto optimality to deal with multi-objective criteria (for example, accuracy and time complexity). Once one has developed an approach to a problem of interest, the problem is then how to compare it with the state of art. In machine learning, algorithms are typically evaluated by comparing their performance on different data sets by means of statistical tests. Standard tests used for this purpose are able to consider jointly neither performance measures nor multiple competitors at once. The aim of this paper is to resolve these issues by developing statistical procedures that are able to account for multiple competing measures at the same time and to compare multiple algorithms altogether. In particular, we develop two tests: a frequentist procedure based on the generalized likelihood-ratio test and a Bayesian procedure based on a multinomial-Dirichlet conjugate model. We further extend them by discovering conditional independences among measures to reduce the number of parameters of such models, as usually the number of studied cases is very reduced in such comparisons. Data from a comparison among general purpose classifiers is used to show a practical application of our tests.

Fast classification in incrementally growing spaces

[EN]The classification speed of state-of-the-art classifiers such as SVM is an important aspect to be considered for emerging applications and domains such as data mining and human-computer interaction. Usually, a test-time speed increase in SVMs is achieved by somehow reducing the number of support vectors, which allows a faster evaluation of the decision function. In this paper a novel approach is described for fast classification in a PCA+SVM scenario. In the proposed approach, classification of an unseen sample is performed incrementally in increasingly larger feature spaces. As soon as the classification confidence is above a threshold the process stops and the class label is retrieved...

The High-Redshift Quasar Luminosity Function from Multi-Epoch Imaging Surveys

Thesis (Ph.D.)--University of Washington, 2016-08

IntelliChair: a non-intrusive sitting posture and sitting activity recognition system

Current Ambient Intelligence and Intelligent Environment research focuses on the interpretation of a subject’s behaviour at the activity level by logging the Activity of Daily Living (ADL) such as eating, cooking, etc. In general, the sensors employed (e.g. PIR sensors, contact sensors) provide low resolution information. Meanwhile, the expansion of ubiquitous computing allows researchers to gather additional information from different types of sensor which is possible to improve activity analysis. Based on the previous research about sitting posture detection, this research attempts to further analyses human sitting activity. The aim of this research is to use non-intrusive low cost pressure sensor embedded chair system to recognize a subject’s activity by using their detected postures. There are three steps for this research, the first step is to find a hardware solution for low cost sitting posture detection, second step is to find a suitable strategy of sitting posture detection and the last step is to correlate the time-ordered sitting posture sequences with sitting activity. The author initiated a prototype type of sensing system called IntelliChair for sitting posture detection. Two experiments are proceeded in order to determine the hardware architecture of IntelliChair system. The prototype looks at the sensor selection and integration of various sensor and indicates the best for a low cost, non-intrusive system. Subsequently, this research implements signal process theory to explore the frequency feature of sitting posture, for the purpose of determining a suitable sampling rate for IntelliChair system. For second and third step, ten subjects are recruited for the sitting posture data and sitting activity data collection. The former dataset is collected byasking subjects to perform certain pre-defined sitting postures on IntelliChair and it is used for posture recognition experiment. The latter dataset is collected by asking the subjects to perform their normal sitting activity routine on IntelliChair for four hours, and the dataset is used for activity modelling and recognition experiment. For the posture recognition experiment, two Support Vector Machine (SVM) based classifiers are trained (one for spine postures and the other one for leg postures), and their performance evaluated. Hidden Markov Model is utilized for sitting activity modelling and recognition in order to establish the selected sitting activities from sitting posture sequences.2. After experimenting with possible sensors, Force Sensing Resistor (FSR) is selected as the pressure sensing unit for IntelliChair. Eight FSRs are mounted on the seat and back of a chair to gather haptic (i.e., touch-based) posture information. Furthermore, the research explores the possibility of using alternative non-intrusive sensing technology (i.e. vision based Kinect Sensor from Microsoft) and find out the Kinect sensor is not reliable for sitting posture detection due to the joint drifting problem. A suitable sampling rate for IntelliChair is determined according to the experiment result which is 6 Hz. The posture classification performance shows that the SVM based classifier is robust to “familiar” subject data (accuracy is 99.8% with spine postures and 99.9% with leg postures). When dealing with “unfamiliar” subject data, the accuracy is 80.7% for spine posture classification and 42.3% for leg posture classification. The result of activity recognition achieves 41.27% accuracy among four selected activities (i.e. relax, play game, working with PC and watching video). The result of this thesis shows that different individual body characteristics and sitting habits influence both sitting posture and sitting activity recognition. In this case, it suggests that IntelliChair is suitable for individual usage but a training stage is required.

Anomaly Detection in Noisy Images

Finding rare events in multidimensional data is an important detection problem that has applications in many fields, such as risk estimation in insurance industry, finance, flood prediction, medical diagnosis, quality assurance, security, or safety in transportation. The occurrence of such anomalies is so infrequent that there is usually not enough training data to learn an accurate statistical model of the anomaly class. In some cases, such events may have never been observed, so the only information that is available is a set of normal samples and an assumed pairwise similarity function. Such metric may only be known up to a certain number of unspecified parameters, which would either need to be learned from training data, or fixed by a domain expert. Sometimes, the anomalous condition may be formulated algebraically, such as a measure exceeding a predefined threshold, but nuisance variables may complicate the estimation of such a measure. Change detection methods used in time series analysis are not easily extendable to the multidimensional case, where discontinuities are not localized to a single point. On the other hand, in higher dimensions, data exhibits more complex interdependencies, and there is redundancy that could be exploited to adaptively model the normal data. In the first part of this dissertation, we review the theoretical framework for anomaly detection in images and previous anomaly detection work done in the context of crack detection and detection of anomalous components in railway tracks. In the second part, we propose new anomaly detection algorithms. The fact that curvilinear discontinuities in images are sparse with respect to the frame of shearlets, allows us to pose this anomaly detection problem as basis pursuit optimization. Therefore, we pose the problem of detecting curvilinear anomalies in noisy textured images as a blind source separation problem under sparsity constraints, and propose an iterative shrinkage algorithm to solve it. Taking advantage of the parallel nature of this algorithm, we describe how this method can be accelerated using graphical processing units (GPU). Then, we propose a new method for finding defective components on railway tracks using cameras mounted on a train. We describe how to extract features and use a combination of classifiers to solve this problem. Then, we scale anomaly detection to bigger datasets with complex interdependencies. We show that the anomaly detection problem naturally fits in the multitask learning framework. The first task consists of learning a compact representation of the good samples, while the second task consists of learning the anomaly detector. Using deep convolutional neural networks, we show that it is possible to train a deep model with a limited number of anomalous examples. In sequential detection problems, the presence of time-variant nuisance parameters affect the detection performance. In the last part of this dissertation, we present a method for adaptively estimating the threshold of sequential detectors using Extreme Value Theory on a Bayesian framework. Finally, conclusions on the results obtained are provided, followed by a discussion of possible future work.

Multiresolution analysis (discrete wavelet transform) through Daubechies family for emotion recognition in speech

We propose a study of the mathematical properties of voice as an audio signal -- This work includes signals in which the channel conditions are not ideal for emotion recognition -- Multiresolution analysis- discrete wavelet transform – was performed through the use of Daubechies Wavelet Family (Db1-Haar, Db6, Db8, Db10) allowing the decomposition of the initial audio signal into sets of coefficients on which a set of features was extracted and analyzed statistically in order to differentiate emotional states -- ANNs proved to be a system that allows an appropriate classification of such states -- This study shows that the extracted features using wavelet decomposition are enough to analyze and extract emotional content in audio signals presenting a high accuracy rate in classification of emotional states without the need to use other kinds of classical frequency-time features -- Accordingly, this paper seeks to characterize mathematically the six basic emotions in humans: boredom, disgust, happiness, anxiety, anger and sadness, also included the neutrality, for a total of seven states to identify

Devaluation, competitiveness and new business formation in emerging countries

In September 2010, Brazil’s Finance Minister, Guido Mantega, used the term “currency war” with reference to monetary policies implemented by different countries to generate an artificial devaluation of their currency and achieve a cheaper, more competitive domestic economy that may be attractive to foreign investors. Similar cases have been documented since the 1930s Great Depression, when several countries abandoned the gold standard as backing for their currencies. More recently, a large-scale asset purchase by Japan’s Central Bank in 2013 was singled out as a strategy aimed at generating devaluation of the yen. This research uses statistics of new business formation density reported by Doing Business for 30 emerging countries in the period 2004-2011 to evaluate the impact of devaluation measured by the behavior of the real effective exchange rate (REER) on the rate of new business formation (NBF). It is determined how variables associated with competitiveness affect the relationship between devaluation and business formation. Results show that devaluation has a positive effect on NBF in the short term, which gets diluted in the long term. Countries with greater competitiveness have less dependence on devaluation to increase the number of businesses.

Export Behavior and Board Independence in Colombian Family Firms: The Reverse Causality Relationship

In the context of greater market liberalization in Latin America, one issue that merits greater attention for empirical investigation is the international expansion of family-owned business. Specifically, the relationship between export behavior, family control and board composition in the Latin American context is absent in the literature. Using a large and unique database from Colombian firms (33,249 firms in the period of 2008 to 2013), we provide insightful information on the determinants of export behavior of family firms in emerging markets. Our empirical test confirms an endogenous relation between boards’ composition (specifically the presence of independent members) and export behavior in family firms. Firms with a higher participation of independent board members are more likely to exhibit higher levels of exports. A "virtuous cycle" was also detected whereby the introduction of independent members on the board can be expected to boost export behavior, which in turn will encourage the increase of independent members on the board of private firms.

Leveraging Multiple Features for Image Retrieval and Matching

The goal of image retrieval and matching is to find and locate object instances in images from a large-scale image database. While visual features are abundant, how to combine them to improve performance by individual features remains a challenging task. In this work, we focus on leveraging multiple features for accurate and efficient image retrieval and matching. We first propose two graph-based approaches to rerank initially retrieved images for generic image retrieval. In the graph, vertices are images while edges are similarities between image pairs. Our first approach employs a mixture Markov model based on a random walk model on multiple graphs to fuse graphs. We introduce a probabilistic model to compute the importance of each feature for graph fusion under a naive Bayesian formulation, which requires statistics of similarities from a manually labeled dataset containing irrelevant images. To reduce human labeling, we further propose a fully unsupervised reranking algorithm based on a submodular objective function that can be efficiently optimized by greedy algorithm. By maximizing an information gain term over the graph, our submodular function favors a subset of database images that are similar to query images and resemble each other. The function also exploits the rank relationships of images from multiple ranked lists obtained by different features. We then study a more well-defined application, person re-identification, where the database contains labeled images of human bodies captured by multiple cameras. Re-identifications from multiple cameras are regarded as related tasks to exploit shared information. We apply a novel multi-task learning algorithm using both low level features and attributes. A low rank attribute embedding is joint learned within the multi-task learning formulation to embed original binary attributes to a continuous attribute space, where incorrect and incomplete attributes are rectified and recovered. To locate objects in images, we design an object detector based on object proposals and deep convolutional neural networks (CNN) in view of the emergence of deep networks. We improve a Fast RCNN framework and investigate two new strategies to detect objects accurately and efficiently: scale-dependent pooling (SDP) and cascaded rejection classifiers (CRC). The SDP improves detection accuracy by exploiting appropriate convolutional features depending on the scale of input object proposals. The CRC effectively utilizes convolutional features and greatly eliminates negative proposals in a cascaded manner, while maintaining a high recall for true objects. The two strategies together improve the detection accuracy and reduce the computational cost.