Predictive Validation of Interaction Terms in PLS-SEM

The use of interaction terms in partial least squares structural equation modeling (PLS-SEM) risks overfitting models to small samples and producing poor out-of-sample generalizability. But the added complexity of interactions in PLS-SEM is not captured by in-sample fit metrics, and we propose that interaction terms in PLS-SEM should be assessed by out-of-sample methods and metrics. However, out-of-sample predictive methods like PLSpredict do not yet account for interaction terms. We start by providing a formal procedure for generating out-of-sample predictions from such models. We then empirically demonstrate that interactions produce far higher Type I error than that expected by researchers, and that out-of-sample predictive metrics indeed offer more accurate assessment of the validity of interaction terms for PLS-SEM. We also show that two-stage estimation of interactions is superior to other popular methods of operationalizing interactions in PLS-SEM, when the generalizability of interactions is of concern

Reimagining the Journal Editorial Process: An AI-Augmented Versus an AI-Driven Future

The editorial process at our leading information systems journals has been pivotal in shaping and growing our field. But this process has grown long in the tooth and is increasingly frustrating and challenging its various stakeholders: editors, reviewers, and authors. The sudden and explosive spread of AI tools, including advances in language models, make them a tempting fit in our efforts to ease and advance the editorial process. But we must carefully consider how the goals and methods of AI tools fit with the core purpose of the editorial process. We present a thought experiment exploring the implications of two distinct futures for the information systems powering today’s journal editorial process: an AI-augmented and an AI-driven one. The AI-augmented scenario envisions systems providing algorithmic predictions and recommendations to enhance human decision-making, offering enhanced efficiency while maintaining human judgment and accountability. However, it also requires debate over algorithm transparency, appropriate machine learning methods, and data privacy and security. The AI-driven scenario, meanwhile, imagines a fully autonomous and iterative AI. While potentially even more efficient, this future risks failing to align with academic values and norms, perpetuating data biases, and neglecting the important social bonds and community practices embedded in and strengthened by the human-led editorial process. We consider and contrast the two scenarios in terms of their usefulness and dangers to authors, reviewers, editors, and publishers. We conclude by cautioning against the lure of an AI-driven, metric-focused approach, advocating instead for a future where AI serves as a tool to augment human capacity and strengthen the quality of academic discourse. But more broadly, this thought experiment allows us to distill what the editorial process is about: the building of a premier research community instead of chasing metrics and efficiency. It is up to us to guard these values

Learning Statistical Models for Annotating Proteins with Function Information using Biomedical Text

<p>Abstract</p> <p>Background</p> <p>The BioCreative text mining evaluation investigated the application of text mining methods to the task of automatically extracting information from text in biomedical research articles. We participated in Task 2 of the evaluation. For this task, we built a system to automatically annotate a given protein with codes from the Gene Ontology (GO) using the text of an article from the biomedical literature as evidence.</p> <p>Methods</p> <p>Our system relies on simple statistical analyses of the full text article provided. We learn <it>n</it>-gram models for each GO code using statistical methods and use these models to hypothesize annotations. We also learn a set of Naïve Bayes models that identify textual clues of possible connections between the given protein and a hypothesized annotation. These models are used to filter and rank the predictions of the <it>n</it>-gram models.</p> <p>Results</p> <p>We report experiments evaluating the utility of various components of our system on a set of data held out during development, and experiments evaluating the utility of external data sources that we used to learn our models. Finally, we report our evaluation results from the BioCreative organizers.</p> <p>Conclusion</p> <p>We observe that, on the test data, our system performs quite well relative to the other systems submitted to the evaluation. From other experiments on the held-out data, we observe that (i) the Naïve Bayes models were effective in filtering and ranking the initially hypothesized annotations, and (ii) our learned models were significantly more accurate when external data sources were used during learning.</p

Topical ciprofloxacin induced ocular toxicity: case report

Ciprofloxacin is a commonly used fluoroquinolone group of antimicrobial which is used for treating infective conditions like community acquired pneumonia and urinary tract infections. A patient of cataract was treated with ciprofloxacin eye drop as her pre-operative medication. She presented after four days with itching and redness in her right eye with swelling of the peri-orbital skin. We report this rare case where topical application of ciprofloxacin was responsible for the ocular symptoms

Evaluation of the effect of Cyclic Load on the Microgap at the Implant-Cast Abutment Interface of Screw-Retained Implant Supported Restorations.

BACKGROUND : Comparative literature on the effect of cyclic loading, on the microgap at the implant-cast abutment interface of screw-retained implant supported restoration using base metal alloys are inadequately documented. MATERIALS AND METHODS : Ten implant-supported cast abutment-restorations each were fabricated using Nickel-Chromium (Ni-Cr) (Group I) and Cobalt- Chromium (Co-Cr) alloy (Group II). All twenty samples were subjected to cyclic loading of 0-109N, for 1,50,000 cycles, simulating 6 months of function. Scanning electron microscopic measurements of the implant-cast abutment interface were made both before and after cyclic loading. The results were analyzed using Paired ‘t’ and Independent ‘t’ test. RESULTS : Cyclic loading resulted in the reduction of the microgap at the implant-cast abutment interface for both Ni-Cr and Co-Cr test samples. The respective differences between the mean pre and post cyclic load microgap measurements for both groups were statistically insignificant (p–value > 0.05). Whereas, the mean microgap values after cyclic loading were significantly lower than those values before cyclic loading for both Ni-Cr and Co-Cr samples (p-value < 0.05). CONCLUSION : Simulated functional loading on a maxillary anterior screwretained implant-supported restoration led to a decrease in the microgap at the implant-cast abutment interface

Reactive Supervision: A New Method for Collecting Sarcasm Data

Sarcasm detection is an important task in affective computing, requiring large amounts of labeled data. We introduce reactive supervision, a novel data collection method that utilizes the dynamics of online conversations to overcome the limitations of existing data collection techniques. We use the new method to create and release a first-of-its-kind large dataset of tweets with sarcasm perspective labels and new contextual features. The dataset is expected to advance sarcasm detection research. Our method can be adapted to other affective computing domains, thus opening up new research opportunities.Comment: 7 pages, 2 figures, 8 tables. To be published in Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020

Clearing-Up the Black Box: Personalization Transparency and Regulatory Focus in Recommendation Systems

Recommendation systems automate most of our decision process to facilitate a final decision: They learn from our past behavior, filter our choices, and present a subset of alternatives to us. Consequently, organizations have paid much attention to refine the accuracy of recommendations to match users’ needs. However, increasing evidence and research calls warn against unilaterally focusing on the system without considering the users’ trade-offs. Simply choosing from a curated set of options might deprive users from a thorough understanding of their preferences; or even deny them the unexpected discoveries resulting from their own decision efforts. We expect to learn how users perceive the recommendation system to understand recommendations–personalization transparency–and how their decision-making orientation affect their choice of unfamiliar recommendations–regulatory focus. We propose two studies to fill these gaps. First, we will further explore other factors affecting users’ perceptions of the recommendation process by interviewing and observing people using Netflix. Using a confirmatory controlled experiment, we will validate our resulting model which, for now, hypothesizes the interaction between the above constructs to enhance users’ adherence to recommendation. The spirit of this research is our strong expectation that recommendation systems will enjoy stronger acceptance if designed to reciprocate the faith users put in them, by compensating users for this loss of decision-making. More generally, we hope to contribute to our initial understanding of why we are willing to delegate daily decision-making tasks to intelligent services, and allow them to take greater control of our decisions