O.S.S. Operation RYPE: Cutting the Nordland Rail Line in Occupied Norway at Two Points in the North Töndelag Area, April, 1945

This thesis investigates the conduct of unconventional warfare as performed by one of the many teams of allied sabateurs parachuted deep behind enemy lines in German-occupied countries during World War II. The team studied as an example of this type of warfare was the Office of Strategic Services Norwegian Special Operations Group (O. S. S. NORSO Group) in Operation RYPE. Its mission was to disrupt German troop movements in Norway. Its effectivemess will be judged tactically and also in the strategic environment of the Allied war effort, of which it was a part

Authentication: From Passwords to Biometrics: An implementation of a speaker recognition system on Android

We implement a biometric authentication system on the Android platform, which is based on text-dependent speaker recognition. The Android version used in the application is Android 4.0. The application makes use of the Modular Audio Recognition Framework, from which many of the algorithms are adapted in the processes of preprocessing and feature extraction. In addition, we employ the Dynamic Time Warping (DTW) algorithm for the comparison of different voice features. A training procedure is implemented, using the DTW algorithm to align features. Furthermore, we introduce personal thresholds, based on which the performance for each individual user can be further optimized.We have carried out several tests in order to evaluate the performance of the developed system. The tests are performed on 16 persons, with in total 240 voice samples, of which 15 samples are from each person. As a result, for authentication, one of the optimal trade-offs of the False Acceptance Rate (FAR) and False Rejection Rate (FRR) achieved by the system is shown to be 13% and 12%, respectively. For identification, the system could identify the user correctly with a rate of 81%. Our results show that one can actually improve the system performance in terms of FAR and FRR significantly, through using the training procedure and the personal thresholds

FPGA virtualization layer for non-deterministic state machines

In this thesis a virtual layer for running self-cloning state machines on FPGAs has been developed. The goal has been to connect software with hardware resources, and to make partial reconfigurability more available. Previous work has been done on defining self-cloning state machines that can run on an FPGA, but was not tested with partial runtime reconfiguration. A framework for reconfiguration has been used in this thesis, which had previous shown some difficulties regarding synchronous designs. Specifications for the virtual layer were defined, and the different modules constructed. The virtual layer was implemented on a Virtex-4 FPGA, with an embedded PorwerPC microprocessor running a Linux operating system. The virtual layer gives software application an interface for defining state machines, which will be mapped to the FPGA and executed. The modules on the FPGA are separated into two parts, one reconfigurable region and one static region. The static region contains a back-end that handles the control of the NFSM and communication with the processor. The reconfigurable region contains the NFSM, which is divided into several clones. The clones can be inserted or removed by using partial runtime reconfiguration. Many difficulties were experienced when trying to implement the virtual layer with support for partial runtime reconfiguration. The tool support was lacking and the space on the FPGA became a problem. Only one clone could be fitted on the FPGA. Therefore the verification of the system was divided in two. A state machine with four clones was tested and verified. The virtual layer was able to take input from software and map this into a functional self-cloning state machine. Some limitations had to be put on the system to make it possible to implement. A second test was performed with partial runtime reconfiguration to show that clones could be added or removed from the design at runtime. The test was successful, but could only be done with one active clone. The limitations of the Virtex-4 platform can be avoided by implementing the virtual layer on a more state of the art FPGA. The system defined in this thesis should work on any FPGA, but will require a lot of work, especially porting of the framework for reconfiguration

Does context matter? Examining robbery reporting in a high crime country

Most empirical studies that examine why individuals report property crimes to the police have focused on Global North countries where crime rates are low. This study is situated in the most violent area of the world, Latin America, and examines Peru, which has the highest robbery victimization rate in the Americas. This article examines the applicability of theories of crime reporting in this Global South context using a large sample and multilevel modeling. We find that trust in the police has no impact on the reporting of the robbery of one’s cellphone, purse or wallet. The theories of rational choice and Black’s stratification of law provide strong explanations for the reporting of robbery of these personal items. Individuals of higher social status and those who reside in districts with low levels of social disadvantage are more likely to report, as well as those who have experienced violent victimization

A System for Conversational Case-Based Reasoning in Multiple-Disease Medical Diagnosis

In this thesis, we develop a model that uses Conversational Case-Based Reasoning (CCBR) in order to help physicians diagnose patients. To be able to process the vast amount of information embedded in the domain of general medicine, we introduce a divide and conquer approach. By focusing on small, well-defined sub-domains of medicine, we are able to capture specific knowledge from each of them. Together the sub-domains form our understanding of the medical domain, and we argue that this approach is more sound than to reason from the entire domain at the same time. We adopt a set of existing approaches to the CCBR process to fit our needs. By testing these algorithms on real life data and analysing the results, we are able to identify strengths and weaknesses for each of them. By studying different dialogue management techniques embedded in CCBR, we are able to introduce targeted measures to increase the performance of these algorithms. At the same time, we are able to increase their flexibility, enabling them to take on domains that they previously did not support. We also introduce different dialogue inference techniques to our system, and demonstrate that this has the potential to further increase the performance of our system. To bind the different sub-domains together we introduce an architecture that includes a stack of CCBR dialogues. This enables our system to explore multiple areas of medicine within the same session, increasing the probability of finding the correct diagnosis. For each sub-domain the system can choose from the set of CCBR algorithms included in the system, and find the one that maximises the performance in that particular domain. To be able to determine which dialogues to add to this stack we introduce a meta-level dialogue. This dialogue is added on top of the other dialogues and presents the user with a set of general questions in an effort to identify the most relevant sub-domains to explore

Why are property crimes reported to the police? An empirical assessment for Peru

Peru has not only one of the highest victimization rates in Latin America (24%), but also the lowest rate of reported crimes (15%) (Latinobarómetro, 2016) The purpose of this study was to identify the characteristics of the crimes and the characteristics of the individuals that predict the decision of reporting four different property crimes (burglary, auto theft, auto parts theft, and motorcycle theft). To this end, a seven-year survey of households at the national level in Peru was used (2010-2016). The estimations were made using multilevel mixed effects logistics regression, in order to control for characteristics of the environment that also influence the decision to report. Use of a weapon by the criminal is the factor that most increases the probability of reporting any of the four assessed crimes. Repeated victimization is also an important predictor, although for a lower number of crimes. Trust in the police is not associated to the decision to report. This is the first study to quantitatively analyze the factors that affect the decision to report in Peru. Its results are useful for a better understanding of the low reporting rates in the country

Elecciones en Estados Unidos: el reflejo de una sociedad dividida

Las elecciones presidenciales del país norteamericano se llevarán a cabo el próximo martes 3 de noviembre. En estas se enfrentarán Joe Biden del partido demócrata yDonald Trump del partido republicano

A System for Conversational Case-Based Reasoning in Multiple-Disease Medical Diagnosis

In this thesis, we develop a model that uses Conversational Case-Based Reasoning (CCBR) in order to help physicians diagnose patients. To be able to process the vast amount of information embedded in the domain of general medicine, we introduce a divide and conquer approach. By focusing on small, well-defined sub-domains of medicine, we are able to capture specific knowledge from each of them. Together the sub-domains form our understanding of the medical domain, and we argue that this approach is more sound than to reason from the entire domain at the same time.We adopt a set of existing approaches to the CCBR process to fit our needs. By testing these algorithms on real life data and analysing the results, we are able to identify strengths and weaknesses for each of them. By studying different dialogue management techniques embedded in CCBR, we are able to introduce targeted measures to increase the performance of these algorithms. At the same time, we are able to increase their flexibility, enabling them to take on domains that they previously did not support. We also introduce different dialogue inference techniques to our system, and demonstrate that this has the potential to further increase the performance of our system.To bind the different sub-domains together we introduce an architecture that includes a stack of CCBR dialogues. This enables our system to explore multiple areas of medicine within the same session, increasing the probability of finding the correct diagnosis. For each sub-domain the system can choose from the set of CCBR algorithms included in the system, and find the one that maximises the performance in that particular domain. To be able to determine which dialogues to add to this stack we introduce a meta-level dialogue. This dialogue is added on top of the other dialogues and presents the user with a set of general questions in an effort to identify the most relevant sub-domains to explore