The success of Amazon Mechanical Turk (MTurk) as an online research platform has come at a price: MTurk has suffered from slowing rates of population replenishment, and growing participant non-naivety. Recently, a number of alternative platforms have emerged, offering capabilities similar to MTurk but providing access to new and more naïve populations. After surveying several options, we empirically examined two such platforms, CrowdFlower (CF) and Prolific Academic (ProA). In two studies, we found that participants on both platforms were more naïve and less dishonest compared to MTurk participants. Across the three platforms, CF provided the best response rate, but CF participants failed more attention-check questions and did not reproduce known effects replicated on ProA and MTurk. Moreover, ProA participants produced data quality that was higher than CF's and comparable to MTurk's. ProA and CF participants were also much more diverse than participants from MTurk.

This study sought to determine if time spent engaging in online gaming, interpersonal communication skills, and introvert personality traits are predictors of an individual’s likelihood of experiencing symptoms of social anxiety. A sample of 128 participants (82 males and 46 females) completed measures of demographics, interpersonal communication skills, problematic online gaming, social anxiety, and introversion. Participants were recruited through Amazon Mechanical Turk. There were significant correlations among social anxiety and interpersonal communication skills, problematic online gaming, and introversion. There was no significant correlation among social anxiety and time spent playing Massively Multiplayer Online Role-Playing Games (MMORPG), a specific form of online video game. It was also found that interpersonal communication skills, problematic online gaming, and introversion were all significant predictors of social anxiety. However, time spent playing MMORPGs was not a significant predictor of social anxiety.

This dissertation investigates whether transitivity distinctions are manifest in the phonetics of linguistic and paralinguistic manual actions, namely lexical verbs and classifier constructions in American Sign Language (ASL) and gestures produced by hearing non-signers without speech (i.e., pantomime). A positive result would indicate that grammatical features are (a) transparent and (b) may thus arise from non-linguistic sources, here the visual-praxic domain. Given previous literature, we predict that transitivity is transparent in pantomime and classifier constructions, but opaque in lexical verbs. <div><br></div><div>We first collected judgments from hearing non-signers who classed pantomimes, classifier constructions, and ASL lexical verbs as unergative, unaccusative, transitive, or ditransitive. We found that non-signers consistently judged items across all three stimulus types, suggesting that there is transitivity-related information in the signed signal. </div><div><br></div><div>We then asked whether non-signers’ judging ability has its roots in a top-down or bottom-up strategy. A top-down strategy might entail guessing the meaning of the sign or pantomime and then using the guessed meaning to assess/guess its transitivity. A bottom-up strategy entails using one or more meaningful phonetic features available in the formation of the signal to judge an item. We predicted that both strategies would be available in classing pantomimes and classifier constructions, but that transitivity information would only be available top-down in lexical verbs, given that the former are argued to be more imagistic generally than lexical verbs. Further, each strategy makes a different prediction with respect to the internal representation xv of signs and pantomimes. The top-down strategy would suggest signs and pantomimes are unanalyzable wholes, whereas the bottom-up strategy would suggest the same are compositional. </div><div><br></div><div>For the top-down analysis, we correlated lexical iconicity score and a measure of the degree to which non-signers ‘agreed’ on the transitivity of an item. We found that lexical iconicity only weakly predicts non-signer judgments of transitivity, on average explaining 10-20% of the variance for each stimulus class. However, we note that this is the only strategy available for lexical verbs. </div><div><br></div><div>For the bottom-up analysis, we annotate our stimuli for phonetic and phonological features known to be relevant to transitivity and/ or event semantics in sign languages. We then apply a text classification model to try to predict transitivity from these features. As expected, our classifiers achieved stably high accuracy for pantomimes and classifier constructions, but only chance accuracy for lexical verbs. </div><div><br></div><div>Taken together, the top-down and bottom-up analyses were able to predict nonsigner transitivity judgments for the pantomimes and classifier constructions, with the bottom-up analysis providing a stronger, more convincing result. For lexical verbs, only the top-down analysis was relevant and it performed weakly, providing little explanatory power. When interpreting these results, we look to the semantics of the stimuli to explain the observed differences between classes: pantomimes and classifier constructions both encode events of motion and manipulation (by human hands), the transitivity of which may be encoded using a limited set of strategies. By contrast, lexical verbs denote a multitude of event types, with properties of those events (and not necessarily their transitivity) being preferentially encoded compared to the encoding of transitivity. That is, the resolution of transitivity is a much more difficult problem when looking at lexical verbs. </div><div><br></div><div>This dissertation contributes to the growing body of literature that appreciates how linguistic and paralinguistic forms may be both (para)linguistic and iconic at the same time. It further helps disentangle at least two different types of iconicities (lexical vs. structural), which may be selectively active in some signs or constructions xvi but not others. We also argue from our results that pantomimes are not holistic units, but instead combine elements of form and meaning in an analogous way to classifier constructions. Finally, this work also contributes to the discussion of how Language could have evolved in the species from a gesture-first perspective: The ‘understanding’ of others’ object-directed (i.e. transitive) manual actions becomes communicative.</div>

No description available.

In pay-as-you-go data integration, feedback can inform the regeneration of different aspects of a data integration system, and as a result, helps to improve the system's quality. However, feedback could be expensive as the amount of feedback required to annotate all the possible integration artefacts is potentially big in contexts where the budget can be limited. Also, feedback could be used in different ways. Feedback of different types and in different orders could have different effects in the quality of the integration. Some feedback types could give rise to more benefit than others. There is a need to develop techniques to collect feedback effectively. Previous efforts have explored the benefit of feedback in one aspect of the integration. However, the contributions have not considered the benefit of different feedback types in a single integration task. We have investigated the annotation of mapping results using crowdsourcing, and implementing techniques for reliability. The results indicate that precision estimates derived from crowdsourcing improve rapidly, suggesting that crowdsourcing can be used as a cost-effective source of feedback. We propose an approach to maximize the improvement of data integration systems given a budget for feedback. Our approach takes into account the annotation of schema matchings, mapping results and pairs of candidate record duplicates. We define a feedback plan, which indicates the type of feedback to collect, the amount of feedback to collect and the order in which different types of feedback are collected. We defined a fitness function and a genetic algorithm to search for the most cost-effective feedback plans. We implemented a framework to test the application of feedback plans and measure the improvement of different data integration systems. In the framework, we use a greedy algorithm for the selection of mappings. We designed quality measures to estimate the quality of a dataspace after the application of a feedback plan. For the evaluation of our approach, we propose a method to generate synthetic data scenarios. We evaluate our approach in scenarios with different characteristics. The results showed that the generated feedback plans achieved higher quality values than the randomly generated feedback plans in several scenarios.

PERFORMANCE OF DEPENDENT WORK THROUGH DIGITAL PLATFORMS ABSTRACT This thesis discusses the issue of work performance through digital platforms, especially regarding whether it is possible to classify such activity as "dependent work", particularly with regard to the laws of the Czech Republic. The thesis responds to the current legal "vacuum" surrounding the performance of work through digital platforms and the legal status of persons who perform such activity (referred to in the thesis as workers). The mentioned legal "vacuum" stems primarily from the absence of sufficient legal regulation in the given area and the "unconventionality" of such a way of performing work. Digital platforms describe their workers as independent entrepreneurs, and this practice is quietly tolerated by the Czech Republic (e. g. administrative authorities). However, there can be more than reasonable doubts about the veracity of such proclamation. Therefore, the thesis firstly analyzes the characteristics and conditions of dependent work according to the laws of the Czech Republic, which are then applied to the reality of the performance of work through digital platforms. Due to the number of their types, as well as the various systems that the platforms implement, three representatives were chosen for the purposes of this thesis,...

Educational Digital Libraries (DLs) are complex information systems which are designed to support individuals' information needs and information seeking behavior. To have a broad impact on the communities in education and to serve for a long period, DLs need to structure and organize the resources in a way that facilitates the dissemination and the reuse of resources. Such a digital library should meet defined quality dimensions in the 5S (Societies, Scenarios, Spaces, Structures, Streams) framework - including completeness, consistency, efficiency, extensibility, and reliability - to ensure that a good quality DL is built. In this research, we addressed both external and internal quality aspects of DLs. For internal qualities, we focused on completeness and consistency of the collection, catalog, and repository. We developed an application pipeline to acquire user-generated computing-related resources from YouTube and SlideShare for an educational DL. We applied machine learning techniques to transfer what we learned from the ACM Digital Library dataset. We built classifiers to catalog resources according to the ACM Computing Classification System from the two new domains that were evaluated using Amazon Mechanical Turk. For external qualities, we focused on efficiency, scalability, and reliability in DL services. We proposed cloud-based designs and applications to ensure and improve these qualities in DL services using cloud computing. The experimental results show that our proposed methods are promising for enhancing and enriching an educational digital library. This work received support from ACM, as well as the National Science Foundation under Grant Numbers DUE-0836940, DUE-0937863, and DUE-0840719, and IMLS LG-71-16-0037-16. / Ph. D.

Essenslieferungen, Fahrdienste oder etwa die Erledigung von Kleinstaufträgen sog. „Microjobs“, besonders seit der COVID-19-Pandemie erfolgt diese Arbeit immer häufiger unter Vermittlung digitaler Plattformen. Diese Untersuchung eröffnet den Blick auf eine in der Vergangenheit gänzlich unbekannte Form der Arbeitsorganisation, bei der einer vermeintlichen Autonomie der Plattformarbeiter ein Intermediär gegenübersteht, der seine zentrale Position zur Steuerung und Kontrolle ebendieser Plattformarbeiter nutzt und dennoch das Vorliegen eines Arbeitsverhältnisses in der Regel vehement bestreitet. Die Dissertation arbeitet zunächst die theoretischen Grundlagen und wirtschaftlichen Hintergründe derartiger Plattformarbeit heraus. Im zweiten Schritt erfolgt auf Basis von Fallbeispielen – Clickworker, Deliveroo, Upwork – erstmals eine umfassende steuerrechtliche Einordnung des Phänomens Plattformarbeit. Hierbei werden die wesentlichen Besonderheiten im Vergleich zu tradierten Arbeitsverhältnissen, insbesondere die Steuerung der Plattformarbeiter mittels algorithmusbasierter Methoden der Verhaltensökonomie, und deren Auswirkungen auf die steuerliche Einordnung ausführlich beleuchtet. Es kann nachgewiesen werden, dass abhängig von der Art der zu erledigenden Aufgaben vom Intermediär ein Anreizsystem geschaffen werden muss, welches eine indirekte Steuerung des Plattformarbeiters zum Ziel und den Bezug von Einkünften aus nichtselbständiger Arbeit zur Folge hat. Abschließend werden für die ermittelten Unzulänglichkeiten der tradierten steuerlichen Abgrenzungskriterien Lösungsvorschläge entwickelt, etwa eine Beweislastregelung zu Gunsten der Plattformarbeiter. Auf Grund des Querschnittcharakters des Themas schafft die Arbeit zugleich interessante Ansatzpunkte für andere Rechtsgebiete, beispielsweise das Arbeits- oder Vertragsrecht. Stand des Werkes ist Juli 2020.