Internet of Things (IoT) devices such as connected sensors are increasingly being used in the public sector, often deployed and collecting data in public spaces. A theme commonly seen in the rhetoric surrounding public space IoT initiatives is empowerment, and these deployments are broadly perceived as beneficial by policy makers. However, such technology presents new governance challenges. It is important to ask who is empowered and who benefits, and we must ensure that such technological interventions follow democratic principles and are trusted by citizens. In this paper, we investigate how risk, transparency, and data governance require careful consideration in this domain, describing work which investigates how these combine to form components of trusted IoT ecosystems. This includes an overview of the landscape of public space IoT deployments, consideration of how they may often be subsumed in idealized smart city focused rhetoric, and discussion of how methodologies such as design fiction in community settings can uncover potential risks and concerns. Our findings suggest that agency, value and intent associated with IoT systems are key components that must be made transparent, particularly when multiple actors and stakeholders are involved. We suggest that good governance requires consideration of these systems in their entirety, throughout the full planning, implementation, and evaluation process, and in consultation with multiple stakeholders who are impacted, including the public. To achieve this effectively, we argue for transparency at the device and system level, which may require legislative change.

Social relationships are important among persons experiencing homelessness, but there is little research on changes in social networks among persons moving into permanent supportive housing (PSH). Using data collected as part of a longitudinal study of 405 adults (aged 39+) moving into PSH, this study describes network upheaval during this critical time of transition. Interviews conducted prior to and after three months of living in PSH assessed individual-level (demographics, homelessness history, health, and mental health) and social network characteristics, including network size and composition (demographics, relationship type, and social support). Interviewers utilized network member characteristics to assess whether network members were new or sustained between baseline and three months post-housing. Multilevel logistic regression models assessed characteristics of network members associated with being newly gained or persisting in networks three months after PSH move-in. Results show only one-third of social networks were retained during the transition to PSH, and veterans, African Americans, and other racial/ethnic minorities, and those living in scattered site housing, were more likely to experience network disruption. Relatives, romantic partners, and service providers were most likely to be retained after move-in. Some network change was moderated by tie strength, including the retention of street-met persons. Implications are discussed.

An academic makerspace, home to tools and people dedicated to facilitating and inspiring a making culture, is characterized by openness, creativity, learning, design, and community. This nontraditional learning environment has found an immense increase in popularity and investment in the last decade. Further, makerspaces have been shown to be highly gendered, privileging men's and masculine understandings of making. The spike in popularity warrants deeper analysis, examining the value of these spaces for women and if learning is occurring in these spaces, specifically at higher education institutions. We implemented a phenomenologically based interviewing process to capture the making experiences of 20 women students, recruited through purposive and snowball sampling. By eliciting the narratives of women students, we captured how making, designing, and creating evolved through gendered experiences in the university makerspace. Each interview was transcribed and resulted in around 868 pages of single-spaced text transcriptions. The data were analyzed through multiple cycles of open and axial coding for common themes and patterns, where makerspaces create a culture of learning, facilitate students’ design journey, and form a laboratory for creativity. These themes forwarded the creation of a learning model that showcases how design and learning interact in the makerspace. This work demonstrates that women students are engaging learning and inspiration; developing confidence and resilience; and learning how to work with others and collaborate.

This paper investigates the processes involved when newly hired employees need to simultaneously build up and mobilize personal network ties during their organizational socialization. It focuses on the quality of ties at an early formative stage, characterized by the lack of a tie history between actors. Social capital theory would suggest that such nascent ties do not offer optimal channels for the kind and volume of resources that newcomers (need to) rely on during socialization. To better understand how this apparent mismatch between tie quality and resource needs is handled from an ego-centered perspective, the paper analyzes personal network data from 24 newcomers in nine organizations, using an adapted form of Qualitative Structural Analysis. Three tie-level qualities are found to explain how the lack of tie history may be alleviated, circumvented, or compensated. They comprise (a) variants of openness experienced with stronger ties, (b) perceptions of a lowered threshold towards weaker ties, and (c) sources of legitimacy regarding latent ties. Based on these findings, the paper presents an integrated conceptual model to clarify how nascent ties offer channels for network resources during socialization and discusses the need for further research on the role of specific moderators for the investigated processes.

We investigate the usage of semantic information for morphological segmentation since words that are derived from each other will remain semantically related. We use mathematical models such as maximum likelihood estimate (MLE) and maximum a posteriori estimate (MAP) by incorporating semantic information obtained from dense word vector representations. Our approach does not require any annotated data which make it fully unsupervised and require only a small amount of raw data together with pretrained word embeddings for training purposes. The results show that using dense vector representations helps in morphological segmentation especially for low-resource languages. We present results for Turkish, English, and German. Our semantic MLE model outperforms other unsupervised models for Turkish language. Our proposed models could be also used for any other low-resource language with concatenative morphology.

Alleviating pain is good and abandoning hope is bad. We instinctively understand how words like alleviate and abandon affect the polarity of a phrase, inverting or weakening it. When these words are content words, such as verbs, nouns, and adjectives, we refer to them as polarity shifters. Shifters are a frequent occurrence in human language and an important part of successfully modeling negation in sentiment analysis; yet research on negation modeling has focused almost exclusively on a small handful of closed-class negation words, such as not, no, and without. A major reason for this is that shifters are far more lexically diverse than negation words, but no resources exist to help identify them. We seek to remedy this lack of shifter resources by introducing a large lexicon of polarity shifters that covers English verbs, nouns, and adjectives. Creating the lexicon entirely by hand would be prohibitively expensive. Instead, we develop a bootstrapping approach that combines automatic classification with human verification to ensure the high quality of our lexicon while reducing annotation costs by over 70%. Our approach leverages a number of linguistic insights; while some features are based on textual patterns, others use semantic resources or syntactic relatedness. The created lexicon is evaluated both on a polarity shifter gold standard and on a polarity classification task.

Type I/II interferons (IFNα,β/IFNɣ) are cytokines that activate signal-transducer-and-activator-of-transcription-1 (STAT1). The STAT1 N-terminal domain (NTD) mediates dimerization and cooperative DNA-binding. The STAT1 DNA-binding domain (DBD) confers sequence-specific DNA-recognition. STAT1 has been connected to growth inhibition, replication stress and DNA-damage. We investigated how STAT1 and NTD/DBD mutants thereof affect fibrosarcoma cells. STAT1 and indicated mutants do not affect proliferation of resting and IFNα-treated cells as well as checkpoint kinase signaling, and phosphorylation of the tumor-suppressive transcription factor p53 ensuing ɣ-irradiation. Of the STAT1 reconstituted U3A cells those with STAT1 NTD mutants accumulate the highest levels of the replication stress/DNA-damage marker S139-phosphorylated histone H2AX (ɣH2AX). This is similarly seen with a STAT1 NTD/DBD double mutant, indicating transcription-independent effects. Furthermore, U3A cells with STAT1 NTD mutants are most susceptible to apoptotic DNA fragmentation and cleavage of the DNA repair protein PARP1. These data provide novel insights into the relevance of the STAT1 NTD.