False Identity Detection Using Complex Sentences

The use of faked identities is a current issue for both physical and online security. In this paper, we test the differences between subjects who report their true identity and the ones who give fake identity responding to control, simple, and complex questions. Asking complex questions is a new procedure for increasing liars' cognitive load, which is presented in this paper for the first time. The experiment consisted in an identity verification task, during which response time and errors were collected. Twenty participants were instructed to lie about their identity, whereas the other 20 were asked to respond truthfully. Different machine learning (ML) models were trained, reaching an accuracy level around 90-95% in distinguishing liars from truth tellers based on error rate and response time. Then, to evaluate the generalization and replicability of these models, a new sample of 10 participants were tested and classified, obtaining an accuracy between 80 and 90%. In short, results indicate that liars may be efficiently distinguished from truth tellers on the basis of their response times and errors to complex questions, with an adequate generalization accuracy of the classification models

Moderate and Non-Arbitrary Sentencing Without Guidelines: The German Experience

Sverige står inför omfattande renoveringsbehov med anledning av det stora antalet hushåll som byggts från 1960-talet och framåt. Läckage blir ett större problem med fler slitna och ålderstigna rör. För att åtgärda rör används främst två metoder: stambyte och relining. Med utgångspunkt i att Sverige står inför relativt stora klimatpolitiska utmaningar, syftar detta projekt till att analysera och kvantifiera hur energianvändning, koldioxidekvivalenta utsläpp och materialåtgång skiljer sig åt mellan metoderna. Rapporten bryter ned metoderna i tre delsystem: materialframställning, transporter och utförandeprocess. Data- och informationsinsamling har skett genom semistrukturerade intervjuer, databaser, publicerade rapporter och webbaserade källor. I rapporten baseras analysen på ett fiktivt badrum som bedöms vara representativt för svenska hushåll. Två modeller presenteras i studien. Den första betraktar relining och stambyte som två isolerade händelser. Ett stambyte medför dock ofta en badrumsrenovering vars behov sällan sammanfaller med behovet av rörbyte. Det medför att svårigheter i renoveringsplaneringen kan uppstå. Modell 2 tar hänsyn till detta förhållande och analyserar hur ett stambyte kan belastas, beroende på hur stor andel av badrummets tekniska livslängd som går förlorad. I studien behandlas de två vanligaste rörmaterialen, gjutjärn och PVC. Resultaten visar att ett stambyte exklusive badrumsrenovering innebär 85 procent högre energianvändning och 192 procent mer koldioxidekvivalenta utsläpp jämfört med relining om gjutjärnsrör byts ut. Motsvarande resultat för PVC-rör är 61 respektive 142 procent. När hänsyn tas till badrumsrenovering blir motsvarande resultat upp till 468 procent mer energianvändning och 683 procent högre koldioxidekvivalenta utsläpp. De två huvudsakliga slutsatserna är att relining innebär mindre miljöpåverkan jämfört med ett stambyte och det är framförallt materialframställningen som orsakar miljöbelastningen för båda metoderna. Det beror på miljöbelastande produktion av reliningmaterial och stor materialåtgång vid ett stambyte.Sweden is facing an extensive need for renovation of drainage systems following the large expansion in real estate during the 1960s. Old and damaged pipes are causing increasing problems with leakages to which there are two main solutions: replacement or relining. In the light of anthropogenic climate change and the emission goals set by the Swedish government, the aim of this study is to analyze and quantify how the two methods compare in terms of energy usage, carbon dioxide equivalent emissions and material usage. The report divides each method into three subsystems: material production, transports and execution. Information and data were gathered through semi-structured interviews as well as obtained from databases, published reports and web-based sources. The analysis was based on a fictive bathroom, which was assumed to be representative for Swedish households. Two models are presented in this study: the first model isolates the replacement of pipes from the rest of the renovation process and compares it with relining. The replacement method is however usually followed by a bathroom renovation, the need of which rarely coincides with the need for pipe replacement. This might cause complications in renovation planning. The second model includes this aspect in the analysis and burdens the replacement method with a certain amount of environmental impact depending on the lost amount of technical life span. The two most common pipe materials, cast iron and PVC, were analyzed in the study. The results show that replacement of pipes excluding bathroom renovation causes 85 percent more energy usage and 192 percent more carbon dioxide equivalent emissions compared to relining when cast iron pipes are installed. Corresponding results with installed PVC pipes are 61 percent and 142 percent. When the bathroom renovation is included in the analysis the numbers increase to up to 468 percent more energy usage and 683 percent higher carbon dioxide equivalent emissions. Two primary conclusions can be drawn from the study: relining has less environmental impact compared to replacement of pipes and it is mainly the production of materials that creates this impact for both methods. It is due to high environmental load in material production for relining and the sheer weight of material with the replacement method

Acquiring Word-Meaning Mappings for Natural Language Interfaces

This paper focuses on a system, WOLFIE (WOrd Learning From Interpreted Examples), that acquires a semantic lexicon from a corpus of sentences paired with semantic representations. The lexicon learned consists of phrases paired with meaning representations. WOLFIE is part of an integrated system that learns to transform sentences into representations such as logical database queries. Experimental results are presented demonstrating WOLFIE's ability to learn useful lexicons for a database interface in four different natural languages. The usefulness of the lexicons learned by WOLFIE are compared to those acquired by a similar system, with results favorable to WOLFIE. A second set of experiments demonstrates WOLFIE's ability to scale to larger and more difficult, albeit artificially generated, corpora. In natural language acquisition, it is difficult to gather the annotated data needed for supervised learning; however, unannotated data is fairly plentiful. Active learning methods attempt to select for annotation and training only the most informative examples, and therefore are potentially very useful in natural language applications. However, most results to date for active learning have only considered standard classification tasks. To reduce annotation effort while maintaining accuracy, we apply active learning to semantic lexicons. We show that active learning can significantly reduce the number of annotated examples required to achieve a given level of performance