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Although social anxiety and depression are common, they are often under diagnosed and under treated, in part due to difficulties identifying and accessing individuals in need of services. Current assessments rely on client self-report and clinician judgment, which are vulnerable to social desirability and other subjective biases. Identifying objective, non-burdensome markers of these mental health problems, such as features of speech, could help advance assessment, prevention, and treatment approaches. Prior research examining speech detection methods has focused on fully supervised learning approaches employing strongly labeled data. However, strong labeling of individuals high in symptoms or state affect in speech audio data is impractical, in part because it is not possible to identify with high confidence which regions of a long speech indicate the person’s symptoms or affective state. We propose a weakly supervised learning framework for detecting social anxiety and depression from long audio clips. Specifically, we present a novel feature modeling technique named NN2Vec that identies and exploits the inherent relationship between speakers’ vocal states and symptoms/affective states. Detecting speakers high in social anxiety or depression symptoms using NN2Vec features achieves F-1 scores 17% and 13% higher than those of the best available baselines. In addition, we present a new multiple instance learning adaptation of a BLSTM classifier, named BLSTM-MIL. Our novel framework of using NN2Vec features with the BLSTM-MIL classifier achieves F-1 scores of 90.1% and 85.44% in detecting speakers high in social anxiety and depression symptoms. 

An integrated urban transportation system usually consists of multiple transport modes that have complementary characteristics of capacities, speeds, and costs, facilitating smooth passenger transfers according to planned schedules. However, such an integration is not designed to operate under disruptive events, e.g., a signal failure at a subway station or a breakdown of a bus, which have rippling effects on passenger demand and significantly increase delays. To address these disruptive events, current solutions mainly rely on a substitute service to transport passengers from and to affected areas using adhoc schedules. To fully utilize heterogeneous transportation systems under disruptive events, we design a service called eRoute based on a hierarchical receding horizon control framework to automatically reroute, reschedule, and reallocate multi-mode transportation systems based on real-time and predicted demand and supply. Focusing on an integration of subway and bus, we implement and evaluate eRoute with large datasets including (i) a bus system with 13,000 buses, (ii) a subway system with 127 subway stations, (iii) an automatic fare collection system with a total of 16,840 readers and 8 million card users from a metropolitan city. The data-driven evaluation results show that our solution improves the ratio of served passengers (RSP) by up to 11.5 times and reduces the average traveling time by up to 82.1% compared with existing solutions. 

Research in the area of internet-of-things, cyber physical- systems, and smart health often employ sensor systems at residences for continuous monitoring. Such research oriented residential monitoring systems (RRMSs) usually face two major challenges, long-term reliable operation management and validation of system functionality with minimal human effort. Targeting these two challenges, this paper describes a monitor of monitoring systems with ground-truth validation capabilities, M2G. It consists of two subsystems, the Monitor2 system and the Ground-truth validation system. The Monitor2 system encapsulates a flexible set of general-purpose components to monitor the operation and connectivity of heterogeneous sensor devices (e.g. smart watches, smart phones, microphones, beacons, etc.), a local base-station, as well as a cloud server. It provides a user-friendly interface and supports different types of RRMSs in various contexts. The system also features a ground truth validation system to support obtaining ground truth in the field. Additionally, customized alerts can be sent to remote administrators and other personnel to report any dysfunction or inaccuracy of the system in real time. M2G is applied to three very different case studies: the M2FED system which monitors family eating dynamics, an in-home wireless sensing system for monitoring nighttime agitation, and the BESI system which monitors behavioral and environmental parameters to predict health events and to provide interventions. The results indicate that M2G is a comprehensive system that (i) requires small cost in time and effort to adapt to an existing RRMS, (ii) provides reliable data collection and reduction in data loss by detecting faults in real-time, and (iii) provides a convenient and timely ground truth validation facility.