BigEAR: Inferring the Ambient and Emotional Correlates from Smartphone-based Acoustic Big Data

This paper presents a novel BigEAR big data framework that employs psychological audio processing chain (PAPC) to process smartphone-based acoustic big data collected when the user performs social conversations in naturalistic scenarios. The overarching goal of BigEAR is to identify moods of the wearer from various activities such as laughing, singing, crying, arguing, and sighing. These annotations are based on ground truth relevant for psychologists who intend to monitor/infer the social context of individuals coping with breast cancer. We pursued a case study on couples coping with breast cancer to know how the conversations affect emotional and social well being. In the state-of-the-art methods, psychologists and their team have to hear the audio recordings for making these inferences by subjective evaluations that not only are time-consuming and costly, but also demand manual data coding for thousands of audio files. The BigEAR framework automates the audio analysis. We computed the accuracy of BigEAR with respect to the ground truth obtained from a human rater. Our approach yielded overall average accuracy of 88.76% on real-world data from couples coping with breast cancer.Comment: 6 pages, 10 equations, 1 Table, 5 Figures, IEEE International Workshop on Big Data Analytics for Smart and Connected Health 2016, June 27, 2016, Washington DC, US

SmartEAR: Smartwatch-based Unsupervised Learning for Multi-modal Signal Analysis in Opportunistic Sensing Framework

Wrist-bands such as smartwatches have become an unobtrusive interface for collecting physiological and contextual data from users. Smartwatches are being used for smart healthcare, telecare, and wellness monitoring. In this paper, we used data collected from the AnEAR framework leveraging smartwatches to gather and store physiological data from patients in naturalistic settings. This data included temperature, galvanic skin response (GSR), acceleration, and heart rate (HR). In particular, we focused on HR and acceleration, as these two modalities are often correlated. Since the data was unlabeled we relied on unsupervised learning for multi-modal signal analysis. We propose using k-means clustering, GMM clustering, and Self-Organizing maps based on Neural Networks for group the multi-modal data into homogeneous clusters. This strategy helped in discovering latent structures in our data

Hot ductility and deformation behavior of C-Mn/Nb-microalloyed steel related to cracking during continuous casting

Hot ductility studies have been performed on C-Mn and C-Mn-Nb steels with an approach to simulate the effect of cooling conditions experienced by steel in secondary cooling zone during continuous casting. Thermal oscillations prior to tensile straining deteriorate hot ductility of steel by deepening and widening the hot ductility trough. C-Mn steels are found to exhibit ductility troughs in three distinct zones whereas C-Mn-Nb steel shows drop in ductility only at low temperature in the vicinity of ferrite transformation temperatures. Start of ferrite transformation in steels causes yield ratio to increase while work hardening rates and strength coefficient decrease with decrease in test temperature in presence of thermal oscillation prior to tensile testing. Inhibition of recrystallization due to build-up of AlN particles along with the presence of MnS particles in structure and low work hardening rates causes embrittlement of steel in austenitic range. Alloying elements enhancing work hardening rates in austenitic range can be promoted to improve hot ductility. The presence of low melting phase saturated with impurities along the austenitic grain boundaries causes intergranular fracture at high temperature in C-Mn steels

Predicting Marine Teleost Responses to Ocean Warming and Pollution

Ocean warming and pollution are two detrimental anthropogenic factors causing rapid marine ecosystem degradation recorded in the past decades. These factors alter the marine environment intolerable for many marine species, forcing them to either adapt or shift their contemporary habitat ranges to reduce the extinction risk embedded with environmental degradation. Estimating marine species’ habitat range shifts, and their potential for developing adaptive mechanisms are critical for ecosystem conservation and management, human health risk assessment, and climate change vulnerability assessments. Given that, for the first chapter of this thesis, we focused on developing a species distribution model (SDM) integrating marine species temperature-sensitive physiological factor, into a bioclimate model to better predict future habitat patterns with warming. We integrated two omics datasets for the second and third chapters to determine the potential transcriptomic and epigenomic mechanisms underlying marine species’ evolved resistance to extreme pollution. We tested the new model to predict the future (the 2050s and 2080s) habitat ranges of the highly eurythermal intertidal minnow, Atlantic killifish (Fundulus heteroclitus), as a best-case scenario. Our SDM predicts complex and diverse habitat patterns for Atlantic killifish, including habitat fragmentation, migration between adjacent populations, and range contractions but no poleward range expansion. Our model predictions are quite unique compared to existing SDMs, mainly with the integration of thermal physiology into the model. The molecular analysis in the second and third chapters posited the repeated desenstivity of the Aryl Hydrocarbon Receptor (AHR) pathway regulated through the downregulation of the ahr2 gene. ahr2 gene intron hypermethylation was also detected in a Polycyclic Aromatic Hydrocarbons (PAHs)-resistant killifish population, a potential novel molecular mechanism underlying killifish rapid adaptations to PAHs toxicity. Reduced lipid metabolism and mitochondrial respiration were also identified as other key molecular processes underlying the evolved PAHs resistance in Atlantic killifish. Overall, the chapters of this thesis demonstrate the importance of integrating ectotherm physiology into SDMs to better predict their future habitat range shift patterns with ocean warming and the necessity of integrating different omics data to uncover the complex patterns of molecular mechanisms underlying marine organisms’ evolved resistance to ubiquitous aquatic pollution

FIT A Fog Computing Device for Speech TeleTreatments

There is an increasing demand for smart fogcomputing gateways as the size of cloud data is growing. This paper presents a Fog computing interface (FIT) for processing clinical speech data. FIT builds upon our previous work on EchoWear, a wearable technology that validated the use of smartwatches for collecting clinical speech data from patients with Parkinson's disease (PD). The fog interface is a low-power embedded system that acts as a smart interface between the smartwatch and the cloud. It collects, stores, and processes the speech data before sending speech features to secure cloud storage. We developed and validated a working prototype of FIT that enabled remote processing of clinical speech data to get speech clinical features such as loudness, short-time energy, zero-crossing rate, and spectral centroid. We used speech data from six patients with PD in their homes for validating FIT. Our results showed the efficacy of FIT as a Fog interface to translate the clinical speech processing chain (CLIP) from a cloud-based backend to a fog-based smart gateway.Comment: 3 pages, 5 figures, 1 table, 2nd IEEE International Conference on Smart Computing SMARTCOMP 2016, Missouri, USA, 201

Swine finishing room air infiltration quantification-modelling and use in ventilation system design

Modern day swine production buildings used in the Midwestern region (severe winters and moderately hot summers) of the US are typically ventilated using negative pressure mechanical ventilation systems (VSs). The design of VSs for livestock buildings is not a simple task due to complexities of air flow behavior, varying outside climate, and environmental requirements of animals. In addition, air infiltration (AI)-an integral part of negative pressure VSs, makes the design process increasingly complex. Due to AI, VSs perform in a non-optimum manner and are responsible for the unsatisfactory environment for animals. Optimal performance of VSs for livestock rooms (LRs), such as swine finishing rooms (SFRs), can be achieved by maintaining proper pressure difference (PD) across the rooms. Furthermore, during cold weather periods, design ventilation rates (DVRs) are minimum for LRs and, in some cases, dominated by AI rates of the room. Low required DVRs along with high potential AI rates makes it impossible to maintain a desired pressure difference (minimum 10 Pa) across a LR/SFR. In this study, the AI rates of Midwestern style SFRs were quantified, modelled, and a procedure was developed for active use of AI in the design of the VSs. The AI rates were quantified for the whole room and room components. The power law equations and multiple linear equations (MLR) were used for modelling the AI data. The standard (sea level) total air infiltration (It) of Midwestern style SFRs was 5.96 ñ1.49 ACH (air changes per hour) at 20 Pa, and at the same 20 Pa PD, the standard curtain, fan, and other (excluding curtain and fan AI) AI rates were 1.49 ñ1.00 ACH (about 25% of It), 1.52 ñ1.38 ACH (about 26% of It), and 2.90 ñ1.42 ACH (about 49% of It), respectively. The MLR models developed in this study found superior over the power law equations and can be used to predict AI rates of similar SFRs. Furthermore, the novel design procedure (NDP) introduced in this study found useful for designing ventilation systems (VSs) of LRs/SFRs with active use of AI data and can be used in digital control of LRs/SFRs

A Speaker Diarization System for Studying Peer-Led Team Learning Groups

Peer-led team learning (PLTL) is a model for teaching STEM courses where small student groups meet periodically to collaboratively discuss coursework. Automatic analysis of PLTL sessions would help education researchers to get insight into how learning outcomes are impacted by individual participation, group behavior, team dynamics, etc.. Towards this, speech and language technology can help, and speaker diarization technology will lay the foundation for analysis. In this study, a new corpus is established called CRSS-PLTL, that contains speech data from 5 PLTL teams over a semester (10 sessions per team with 5-to-8 participants in each team). In CRSS-PLTL, every participant wears a LENA device (portable audio recorder) that provides multiple audio recordings of the event. Our proposed solution is unsupervised and contains a new online speaker change detection algorithm, termed G 3 algorithm in conjunction with Hausdorff-distance based clustering to provide improved detection accuracy. Additionally, we also exploit cross channel information to refine our diarization hypothesis. The proposed system provides good improvements in diarization error rate (DER) over the baseline LIUM system. We also present higher level analysis such as the number of conversational turns taken in a session, and speaking-time duration (participation) for each speaker.Comment: 5 Pages, 2 Figures, 2 Tables, Proceedings of INTERSPEECH 2016, San Francisco, US