Promoting Bright Patterns

User experience designers are facing increasing scrutiny and criticism for creating harmful technologies, leading to a pushback against unethical design practices. While clear-cut harmful practices such as dark patterns have received attention, trends towards automation, personalization, and recommendation present more ambiguous ethical challenges. To address potential harm in these "gray" instances, we propose the concept of "bright patterns" - persuasive design solutions that prioritize user goals and well-being over their desires and business objectives. The ambition of this paper is threefold: to define the term "bright patterns", to provide examples of such patterns, and to advocate for the adoption of bright patterns through policymaking.Comment: For associated website, see https://brightpatterns.org/. Published to the CHI '23 Workshop: Designing Technology and Policy Simultaneousl

Neural Networks for cost estimating in project management

This thesis considers the application of neural networks in cost estimating in project management and whether they lead to more accurate estimates. It strikes two areas of research, namely neural networks and project management; an introductory chapter on both subjects is included. The statistical problem of parametric cost estimating is described and an explanation of the general principles is given. The Multi-Layer Perceptron with the Backpropagation learning algorithm is determined to be the most appropriate network and a selection of available software programs is reviewed. A Multi-Layer Perceptron neural model is used to determine one of the most important cost estimating relationships of the PRICE model. A comparison of the outputs of the neural network and the PRICE model shows that the Backpropagation algorithm is able to find the underlying estimating relationships used by PRJCE. To investigate whether other underlying functions can be found with artificial intelligence methods, other input parameters are selected and the costs generated by the PRICE model and by the neural network are compared with each other. Further experiments were undertaken in order to improve the performance of the neural network. The neural networks were applied to real data. and their output compared with the PRICE model. The processes of achieving better results are analogous to those used for the artificial data. A neural network was created which performs better than the PRICE model in terms of the accuracy of the estimates produced. The results are discussed and the collection of significant and accurate information and then deciding on which type of network is the best network to be used are identified as the major problems in the application of artificial intelligence for cost estimation in project management. The limitations and restrictions of the implementation of neural networks are examined and the scope and topics of further research are suggested

Drug Candidate Discovery: Targeting Bacterial Topoisomerase I Enzymes for Novel Antibiotic Leads

Multi-drug resistance in bacterial pathogens has become a global health crisis. Each year, millions of people worldwide are infected with bacterial strains that are resistant to currently available antibiotics. Diseases such as tuberculosis, pneumonia, and gonorrhea have become increasingly more difficult to treat. It is essential that novel drugs and cellular targets be identified in order to treat this resistance. Bacterial topoisomerase IA is a novel drug target that is essential for cellular growth. As it has never been targeted by existing antibiotics, it is an attractive target. Topoisomerase IA is responsible for relieving torsional strain on DNA by relaxing supercoiled DNA following processes such as replication and transcription. The aim of this study is to find novel compounds that can be developed as leads for antibiotics targeting bacterial type IA topoisomerase. Various approaches were used in order to screen thousands of compounds against bacterial type IA topoisomerases, including mixture-based screening and virtual screening. In the mixture-based screen, scaffold mixtures were tested against the M. tuberculosis topoisomerase I enzyme and subsequently optimized for maximum potency and selectivity. The optimized compounds were effective at inhibiting the enzyme at low micromolar concentrations, as well as killing the tuberculosis bacteria. In a virtual screen, libraries with hundreds of thousands of compounds were screened against the E. coli and M. tuberculosis topoisomerase I crystal structures in order to find new classes of drugs. The top hits were effective at inhibiting the enzymes, as well as preventing the growth of M. smegmatis cells in the presence of efflux pump inhibitors. Organometallic complexes containing Cu(II) or Co(III) were tested as well against various topoisomerases in order to determine their selectivity. We discovered a poison for human type II topoisomerase that has utility as an anticancer agent, as it killed even very resistant cell lines of breast and colon cancer. The Co(III) complexes were found to inhibit the bacterial topoisomerase I very selectively over other topoisomerases. The various methods of drug discovery utilized here have been successful at identifying new classes of compounds that may be further developed into antibiotic drugs that specifically target bacterial type IA topoisomerases

Content-Based Weak Supervision for Ad-Hoc Re-Ranking

One challenge with neural ranking is the need for a large amount of manually-labeled relevance judgments for training. In contrast with prior work, we examine the use of weak supervision sources for training that yield pseudo query-document pairs that already exhibit relevance (e.g., newswire headline-content pairs and encyclopedic heading-paragraph pairs). We also propose filtering techniques to eliminate training samples that are too far out of domain using two techniques: a heuristic-based approach and novel supervised filter that re-purposes a neural ranker. Using several leading neural ranking architectures and multiple weak supervision datasets, we show that these sources of training pairs are effective on their own (outperforming prior weak supervision techniques), and that filtering can further improve performance.Comment: SIGIR 2019 (short paper

Die tunesische Verfassung zwischen demokratischem Anspruch und Verfassungsrealität

Die einstige Euphorie auf eine Demokratisierung der Staaten des „Arabischen Frühlings“ ist nach den jüngsten Entwicklungen in Libyen oder Ägypten getrübt. Einzig Tunesien gilt nach wie vor als hoffnungsvoller Kandidat für eine erfolgreiche demokratische Konsolidierung. Verstärkt wird dieser Enthusiasmus durch die Verabschiedung der neuen Verfassung im Januar 2014, die erstmals und einzigartig im arabischen Kontext, Menschen-, Freiheits- und Grundrechte gewährt, sowie die Gleichstellung der Geschlechter sichert. Fraglich ist jedoch, ob die Ratifizierung einer –zumindest formal betrachtet – demokratischen Verfassung auch zur Entwicklung einer demokratischen politischen Gesellschaft führt, die für die Beseitigung autoritärer und hybrider Strukturen notwendig ist. Um also Aussagen zum demokratischen Potential der tunesischen Verfassung machen zu können, müssen sowohl die Verfassungsrealität als auch ihre gesellschaftlichen und politischen Bedingungen hinterfragt werden

Towards Prototyping Driverless Vehicle Behaviors, City Design, and Policies Simultaneously

Autonomous Vehicles (AVs) can potentially improve urban living by reducing accidents, increasing transportation accessibility and equity, and decreasing emissions. Realizing these promises requires the innovations of AV driving behaviors, city plans and infrastructure, and traffic and transportation policies to join forces. However, the complex interdependencies among AV, city, and policy design issues can hinder their innovation. We argue the path towards better AV cities is not a process of matching city designs and policies with AVs' technological innovations, but a process of iterative prototyping of all three simultaneously: Innovations can happen step-wise as the knot of AV, city, and policy design loosens and tightens, unwinds and reties. In this paper, we ask: How can innovators innovate AVs, city environments, and policies simultaneously and productively toward better AV cities? The paper has two parts. First, we map out the interconnections among the many AV, city, and policy design decisions, based on a literature review spanning HCI/HRI, transportation science, urban studies, law and policy, operations research, economy, and philosophy. This map can help innovators identify design constraints and opportunities across the traditional AV/city/policy design disciplinary bounds. Second, we review the respective methods for AV, city, and policy design, and identify key barriers in combining them: (1) Organizational barriers to AV-city-policy design collaboration, (2) computational barriers to multi-granularity AV-city-policy simulation, and (3) different assumptions and goals in joint AV-city-policy optimization. We discuss two broad approaches that can potentially address these challenges, namely, "low-fidelity integrative City-AV-Policy Simulation (iCAPS)" and "participatory design optimization".Comment: Published to the CHI '23 Workshop: Designing Technology and Policy Simultaneousl

VR Job Interview Using a Gender-Swapped Avatar

Virtual Reality (VR) has emerged as a potential solution for mitigating bias in a job interview by hiding the applicants' demographic features. The current study examines the use of a gender-swapped avatar in a virtual job interview that affects the applicants' perceptions and their performance evaluated by recruiters. With a mixed-method approach, we first conducted a lab experiment (N=8) exploring how using a gender-swapped avatar in a virtual job interview impacts perceived anxiety, confidence, competence, and ability to perform. Then, a semi-structured interview investigated the participants' VR interview experiences using an avatar. Our findings suggest that using gender-swapped avatars may reduce the anxiety that job applicants will experience during the interview. Also, the affinity diagram produced seven key themes highlighting the advantages and limitations of VR as an interview platform. These findings contribute to the emerging field of VR-based recruitment and have practical implications for promoting diversity and inclusion in the hiring process.Comment: CSCW 2022 Poster

Same but Different: Distant Supervision for Predicting and Understanding Entity Linking Difficulty

Entity Linking (EL) is the task of automatically identifying entity mentions in a piece of text and resolving them to a corresponding entity in a reference knowledge base like Wikipedia. There is a large number of EL tools available for different types of documents and domains, yet EL remains a challenging task where the lack of precision on particularly ambiguous mentions often spoils the usefulness of automated disambiguation results in real applications. A priori approximations of the difficulty to link a particular entity mention can facilitate flagging of critical cases as part of semi-automated EL systems, while detecting latent factors that affect the EL performance, like corpus-specific features, can provide insights on how to improve a system based on the special characteristics of the underlying corpus. In this paper, we first introduce a consensus-based method to generate difficulty labels for entity mentions on arbitrary corpora. The difficulty labels are then exploited as training data for a supervised classification task able to predict the EL difficulty of entity mentions using a variety of features. Experiments over a corpus of news articles show that EL difficulty can be estimated with high accuracy, revealing also latent features that affect EL performance. Finally, evaluation results demonstrate the effectiveness of the proposed method to inform semi-automated EL pipelines.Comment: Preprint of paper accepted for publication in the 34th ACM/SIGAPP Symposium On Applied Computing (SAC 2019