473 research outputs found
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
- …