"I'm" Lost in Translation: Pronoun Missteps in Crowdsourced Data Sets

As virtual assistants continue to be taken up globally, there is an ever-greater need for these speech-based systems to communicate naturally in a variety of languages. Crowdsourcing initiatives have focused on multilingual translation of big, open data sets for use in natural language processing (NLP). Yet, language translation is often not one-to-one, and biases can trickle in. In this late-breaking work, we focus on the case of pronouns translated between English and Japanese in the crowdsourced Tatoeba database. We found that masculine pronoun biases were present overall, even though plurality in language was accounted for in other ways. Importantly, we detected biases in the translation process that reflect nuanced reactions to the presence of feminine, neutral, and/or non-binary pronouns. We raise the issue of translation bias for pronouns and offer a practical solution to embed plurality in NLP data sets.Comment: 6 page

Exoskeleton for the Mind: Exploring Strategies Against Misinformation with a Metacognitive Agent

Misinformation is a global problem in modern social media platforms with few solutions known to be effective. Social media platforms have offered tools to raise awareness of information, but these are closed systems that have not been empirically evaluated. Others have developed novel tools and strategies, but most have been studied out of context using static stimuli, researcher prompts, or low fidelity prototypes. We offer a new anti-misinformation agent grounded in theories of metacognition that was evaluated within Twitter. We report on a pilot study (n=17) and multi-part experimental study (n=57, n=49) where participants experienced three versions of the agent, each deploying a different strategy. We found that no single strategy was superior over the control. We also confirmed the necessity of transparency and clarity about the agent's underlying logic, as well as concerns about repeated exposure to misinformation and lack of user engagement.Comment: Pages 209-22

Analysis of Causal Relationships for Nutrient Removal of Activated Sludge Process Based on Structural Equation Modeling Approaches

The removal process of activated sludge in sewage treatment plants is very nonlinear, and removal performance has a complex causal relationship depending on environmental factors, influent load, and operating factors. In this study, how causal relationships are expressed in collected data was identified by structural equation modeling. First, path modeling was attempted as a preliminary step in structural equation model (SEM) construction and, as a result, the nutrient-removal mechanism could not be sufficiently represented as a direct causal relationship between measured variables. However, as a result of the deduced SEMs for effluent total nitrogen (T-N) and total phosphorus (T–P) concentrations, accompanied by exploratory factor analysis to extract latent variables, a causal network was formed that describes the direct or indirect effect of the latent factors and measured variables. Hereby, this study suggests that it is possible to construct an SEM explaining the nutrient-removal mechanism of the activated-sludge process with latent variables. Moreover, nonlinear features embedded in the mechanism could be represented by SEM, which is a model based on linearity, by including causal relations and variables that were not derived by path analysis. This attempt to model the direct and indirect causalities of the process could enhance the understanding of the process, and help decision making such as changing the driving conditions that would be required

Muscle Synergy and Musculoskeletal Model-Based Continuous Multi-Dimensional Estimation of Wrist and Hand Motions

In this study, seven-channel electromyography signal-based two-dimensional wrist joint movement estimation with and without handgrip motions was carried out. Electromyography signals were analyzed using the synergy-based linear regression model and musculoskeletal model; they were subsequently compared with respect to single and combined wrist joint movements and handgrip. Using each one of wrist motion and grip trial as a training set, the synergy-based linear regression model exhibited a statistically significant performance with 0.7891 ± 0.0844 Pearson correlation coefficient (r) value in two-dimensional wrist motion estimation compared with 0.7608 ±  0.1037 r value of the musculoskeletal model. Estimates on the grip force produced 0.8463 ± 0.0503 r value with 0.2559 ± 0.1397 normalized root-mean-square error of the wrist motion range. This continuous wrist and handgrip estimation can be considered when electromyography-based multi-dimensional input signals in the prosthesis, virtual interface, and rehabilitation are needed

Discriminating between Terminal- and Non-Terminal Respiratory Unit-Type Lung Adenocarcinoma Based on MicroRNA Profiles.

Lung adenocarcinomas can be classified into terminal respiratory unit (TRU) and non-TRU types. We previously reported that non-TRU-type adenocarcinoma has unique clinical and morphological features as compared to the TRU type. Here we investigated whether micro (mi)RNA expression profiles can be used to distinguish between these two subtypes of lung adenocarcinoma. The expression of 1205 human and 144 human viral miRNAs was analyzed in TRU- and non-TRU-type lung adenocarcinoma samples (n = 4 each) by microarray. Results were validated by quantitative real-time (qRT-)PCR and in situ hybridization. A comparison of miRNA profiles revealed 29 miRNAs that were differentially expressed between TRU- and non-TRU adenocarcinoma types. Specifically, hsa-miR-494 and ebv-miR-BART19 were up regulated by > 5-fold, whereas hsa-miR-551b was down regulated by > 5-fold in the non-TRU relative to the TRU type. The miRNA signature was confirmed by qRT-PCR analysis using an independent set of paired adenocarcinoma (non-TRU-type, n = 21 and TRU-type, n = 12) and normal tissue samples. Non-TRU samples showed increased expression of miR-494 (p = 0.033) and ebv-miR-BART19 (p = 0.001) as compared to TRU-type samples. Both miRNAs were weakly expressed in the TRU type but strongly expressed in the non-TRU type. Neither subtype showed miR-551b expression. TRU- and non-TRU-type adenocarcinomas have distinct miRNA expression profiles, suggesting that tumorigenesis in lung adenocarcinoma occur via different pathways