Comparing How a Chatbot References User Utterances from Previous Chatting Sessions: An Investigation of Users' Privacy Concerns and Perceptions

Chatbots are capable of remembering and referencing previous conversations, but does this enhance user engagement or infringe on privacy? To explore this trade-off, we investigated the format of how a chatbot references previous conversations with a user and its effects on a user's perceptions and privacy concerns. In a three-week longitudinal between-subjects study, 169 participants talked about their dental flossing habits to a chatbot that either, (1-None): did not explicitly reference previous user utterances, (2-Verbatim): referenced previous utterances verbatim, or (3-Paraphrase): used paraphrases to reference previous utterances. Participants perceived Verbatim and Paraphrase chatbots as more intelligent and engaging. However, the Verbatim chatbot also raised privacy concerns with participants. To gain insights as to why people prefer certain conditions or had privacy concerns, we conducted semi-structured interviews with 15 participants. We discuss implications from our findings that can help designers choose an appropriate format to reference previous user utterances and inform in the design of longitudinal dialogue scripting.Comment: 10 pages, 3 figures, to be published in Proceedings of the 11th International Conference on Human-Agent Interaction (ACM HAI'23

Supporting Acute Appendicitis Diagnosis: A Pre-Clustering-Based Classification Technique

Service quality and cost containment represent two critical challenges in healthcare management. Toward that end, acute appendicitis, a common surgical condition, is important and requires timely, accurate diagnosis. The diverse and atypical symptoms make such diagnoses difficult, thus resulting in increased morbidity and negative appendectomy. While prior research has recognized the use of classification analysis to support acute appendicitis diagnosis, the skewed distribution of the cases pertaining to positive or negative acute appendicitis has significantly constrained the effectiveness of the existing classification techniques. In this study, we develop a pre-clustering-based classification (PCC) technique to address the skewed distribution problem common to acute appendicitis diagnosis. We empirically evaluate the proposed PCC technique with 574 clinical cases of positive and negative acute appendicitis obtained from a tertiary medical center in Taiwan. Our evaluation includes tradition support vector machine, a prevalent resampling classification technique, Alvarado scoring system, and a multi-classifier committee for performance benchmark purposes. Our results show the PCC technique more effective and less biased than the benchmark techniques, without favoring the positive or negative class

Probable Secondary Infections in Households of SARS Patients in Hong Kong

Although severe acute respiratory syndrome (SARS) is highly infectious in clinical settings, SARS has not been well examined in household settings. The household and household member attack rates were calculated for 1,214 SARS case-patients and their household members, stratified by two phases of the epidemic. A case-control analysis identified risk factors for secondary infection. Secondary infection occurred in 14.9% (22.1% versus 11% in earlier and later phases) of all households and 8% (11.7% versus 5.9% in the earlier and later phases) of all household members. Healthcare workers’ households were less likely to be affected. Risk factors from the multivariate analysis included at-home duration before hospitalization, hospital visitation to the SARS patient (and mask use during the visit), and frequency of close contact. SARS transmission at the household level was not negligible in Hong Kong. Transmission rates may be greatly reduced with precautionary measures taken by household members of SARS patients

Automatic Defect Detection for TFT-LCD Array Process Using Quasiconformal Kernel Support Vector Data Description

Defect detection has been considered an efficient way to increase the yield rate of panels in thin film transistor liquid crystal display (TFT-LCD) manufacturing. In this study we focus on the array process since it is the first and key process in TFT-LCD manufacturing. Various defects occur in the array process, and some of them could cause great damage to the LCD panels. Thus, how to design a method that can robustly detect defects from the images captured from the surface of LCD panels has become crucial. Previously, support vector data description (SVDD) has been successfully applied to LCD defect detection. However, its generalization performance is limited. In this paper, we propose a novel one-class machine learning method, called quasiconformal kernel SVDD (QK-SVDD) to address this issue. The QK-SVDD can significantly improve generalization performance of the traditional SVDD by introducing the quasiconformal transformation into a predefined kernel. Experimental results, carried out on real LCD images provided by an LCD manufacturer in Taiwan, indicate that the proposed QK-SVDD not only obtains a high defect detection rate of 96%, but also greatly improves generalization performance of SVDD. The improvement has shown to be over 30%. In addition, results also show that the QK-SVDD defect detector is able to accomplish the task of defect detection on an LCD image within 60 ms

Electrooxidation of glucose by binder-free bimetallic Pd1Ptx/graphene aerogel/nickel foam composite electrodes with low metal loading in basic medium

Many 2D graphene-based catalysts for electrooxidation of glucose involved the use of binders and toxic reducing agents in the preparation of the electrodes, which potentially causes the masking of original activity of the electrocatalysts. In this study, a green method was developed to prepare binder-free 3D graphene aerogel/nickel foam electrodes in which bimetallic Pd-Pt NP alloy with different at% ratios were loaded on 3D graphene aerogel. The influence of Pd/Pt ratio (at%: 1:2.9, 1:1.31, 1:1.03), glucose concentration (30 mM, 75 mM, 300 mM, 500 mM) and NaOH concentration (0.1 M, 1 M) on electrooxidation of glucose were investigated. The catalytic activity of the electrodes was enhanced with increasing the Pd/Pt ratio from 1:2.9 to 1:1.03, and changing the NaOH/glucose concentration from 75 mM glucose/0.1 M NaOH to 300 mM glucose/1 M NaOH. The Pd1Pt1.03/GA/NF electrode achieved a high current density of 388.59 A g−1 under the 300 mM glucose/1 M NaOH condition. The stability of the electrodes was also evaluated over 1000 cycles. This study demonstrated that the Pd1Pt1.03/GA/NF electrode could be used as an anodic electrode in glucose-based fuel cells

Improvement of Statistical Typhoon Rainfall Forecasting with ANN-Based Southwest Monsoon Enhancement

Typhoon Morakot 2009, with significant southwest monsoon flow, produced a record-breaking rainfall of 2361 mm in 48 hours. This study hopes to improve a statistical typhoon rainfall forecasting method used over the mountain region of Taiwan via an artificial neural network based southwest monsoon enhancement (ANNSME) model. Rainfall data collected at two mountain weather stations, ALiShan and YuShan, are analyzed to establish the relation to the southwest monsoon moisture flux which is calculated at a designated sea area southwest of Taiwan. The results show that the moisture flux, with southwest monsoon flow, transported water vapor during the landfall periods of Typhoons Mindulle, Bilis, Fungwong, Kalmaegi, Haitaing and Morakot. Based on the moisture flux, a linear regression is used to identify an effective value of moisture flux as the threshold flux which can enhance mountain rainfall in southwestern Taiwan. In particular, a feedforward neural network (FNN) is applied to estimate the residuals from the linear model to the differences between simulated rainfalls by a typhoon rainfall climatology model (TRCM) and observations. Consequently, the ANNSME model integrates the effective moisture flux, linear rainfall model and the FNN for residuals. Even with very limited training cases, our results indicate that the ANNSME model is robust and suitable for improvement of TRCM rainfall prediction. The improved prediction of the total rainfall and of the multiple rainfall peaks is important for emergency operation

Association of anticardiolipin, antiphosphatidylserine, anti-β2 glycoprotein I, and antiphosphatidylcholine autoantibodies with canine immune thrombocytopenia

β2GPI expression and identification. (PDF 159 kb

Whole Body Screening Using High-Temperature Superconducting MR Volume Resonators: Mice Studies

High temperature superconducting (HTS) surface resonators have been used as a low loss RF receiver resonator for improving magnetic resonance imaging image quality. However, the application of HTS surface resonators is significantly limited by their filling factor. To maximize the filling factor, it is desirable to have the RF resonator wrapped around the sample so that more nuclear magnetic dipoles can contribute to the signal. In this study, a whole new Bi2Sr2Ca2Cu2O3 (Bi-2223) superconducting saddle resonator (width of 5 cm and length of 8 cm) was designed for the magnetic resonance image of a mouse's whole body in Bruker 3 T MRI system. The experiment was conducted with a professionally-made copper saddle resonator and a Bi-2223 saddle resonator to show the difference. Signal-to-noise ratio (SNR) with the HTS saddle resonator at 77 K was 2.1 and 2 folds higher than that of the copper saddle resonator at 300 K for a phantom and an in-vivo mice whole body imaging. Testing results were in accordance with predicted ones, and the difference between the predicted SNR gains and measured SNR gains were 2.4%∼2.7%. In summary, with this HTS saddle system, a mouse's whole body can be imaged in one scan and could reach a high SNR due to a 2 folds SNR gain over the professionally-made prototype of copper saddle resonator at 300 K. The use of HTS saddle resonator not only improves SNR but also enables a mouse's whole body screen in one scan

Mechanisms of neurodegeneration in a preclinical autosomal dominant retinitis pigmentosa knock-in model with a RhoD190N mutation

D190N, a missense mutation in rhodopsin, causes photoreceptor degeneration in patients with autosomal dominant retinitis pigmentosa (adRP). Two competing hypotheses have been developed to explain why D190N rod photoreceptors degenerate: (a) defective rhodopsin trafficking prevents proteins from correctly exiting the endoplasmic reticulum, leading to their accumulation, with deleterious effects or (b) elevated mutant rhodopsin expression and unabated signaling causes excitotoxicity. A knock-in D190N mouse model was engineered to delineate the mechanism of pathogenesis. Wild type (wt) and mutant rhodopsin appeared correctly localized in rod outer segments of D190N heterozygotes. Moreover, the rhodopsin glycosylation state in the mutants appeared similar to that in wt mice. Thus, it seems plausible that the injurious effect of the heterozygous mutation is not related to mistrafficking of the protein, but rather from constitutive rhodopsin activity and a greater propensity for chromophore isomerization even in the absence of light.We greatly appreciate the assistance of the members of the Bernard & Shirlee Brown Glaucoma laboratory, especially to Chun-Wei Hsu for technical support. SHT is a Burroughs-Wellcome Program in Biomedical Sciences Fellow, and is also supported by the Charles E. Culpeper-Partnership for Cures 07-CS3, Crowley Research Fund, Schneeweiss Stem Cell Fund, New York State N09G-302, Foundation Fighting Blindness [TA-NMT-0116-0692- COLU] (Owings Mills, MD), TS080017 from US Department of Defense, NIH Grants [P30EY019007, R01EY018213, R01EY024698, R01EY026682, R21AG050437], Research to Prevent Blindness (New York, NY), and Joel Hoffmann Scholarship. CSL is the Homer McK. Rees Scholar. JSP is a BEST2016 awardee (BEST/ 2016/030, Conselleria de Educación, Investigación, Cultura y Deporte; Generalitat Valenciana) and his research is supported by a Prometeo Grant (PROMETEO/2016/094; Conselleria de Educación, Investigación, Cultura y Deporte; Generalitat Valenciana) and by internal funds from Universidad Católica de Valencia San Vicente Mártir (2018-128-001). VBM is supported by NIH Grants K08EY020530, R01EY016822, The Doris Duke Charitable Foundation Grant #2013103, and Research to Prevent Blindness (New York, NY); GV is supported by NIH Grants [F30EYE027986 and T32GM007337].Author manuscriptMedicin