Signal Appropriation of Explicit HIV Status Disclosure Fields in Sex-Social Apps used by Gay and Bisexual Men

HIV status disclosure fields in online sex-social applications ("apps") are designed to help increase awareness, reduce stigma, and promote sexual health. Public disclosure could also help those diagnosed relate to others with similar statuses to feel less isolated. However, in our interview study (n=28) with HIV positive and negative men who have sex with men (MSM), we found some users preferred to keep their status private, especially when disclosure could stigmatise and disadvantage them, or risk revealing their status to someone they knew offline in a different context. How do users manage these tensions between health, stigma, and privacy? We analysed our interview data using signalling theory as a conceptual framework and identify participants developing 'signal appropriation' strategies, helping them manage the disclosure of their HIV status. Additionally, we propose a set of design considerations that explore the use of signals in the design of sensitive disclosure fields

Poor power quality is a major barrier to providing optimal care in special neonatal care units (SNCU) in Central India [version 1; peer review: 2 approved]

Background: Approximately 25% of all neonatal deaths worldwide occur in India. The Indian Government has established Special Neonatal Care Units (SNCUs) in district and sub-district level hospitals to reduce neonatal mortality, but mortality rates have stagnated. Reasons include lack of personnel and training and sub-optimal quality of care. The role of medical equipment is critical for the care of babies, but its role in improving neonatal outcomes has not been well studied.  Methods: In a qualitative study, we conducted seven focus group discussions with SNCU nurses and pediatric residents and thirty-five key informant interviews and with pediatricians, residents, nurses, annual equipment maintenance contractors, equipment manufacturers, and Ministry of Health personnel in Maharashtra between December 2019 and November 2020. The goal of the study was to understand challenges to SNCU care. In this paper, we focus on current gaps and future needs for SNCU equipment, quality of the power supply, and use of SNCU equipment. Results: Respondents described a range of issues but highlighted poor power quality as an important cause of equipment malfunction. Other concerns were lack of timely repair that resulted in needed equipment being unavailable for neonatal care. Participants recommended procuring uninterrupted power supply (UPS) to protect equipment, improving quality/durability of equipment to withstand constant use, ensuring regular proactive maintenance for SNCU equipment, and conducting local power audits to discern and address the causes of power fluctuations. Conclusions: Poor power quality and its negative impact on equipment function are major unaddressed concerns of those responsible for the care and safety of babies in SNCUs in Central India. Further research on the power supply and protection of neonatal equipment is needed to determine a cost-effective way to improve access to supportive care in SNCUs and desired improvements in neonatal mortality rates

Fixing Faults with Confidence

This paper presents Confidence, a tool for identifying and addressing faults in wireless sensing systems. Confidence pinpoints potential sensor and network faults in real time, allowing users to validate unexpected data and address any failures in the field. By introducing a well defined, low-dimension feature space, and functions to map sensor data into this space, we are able to achieve fault detection and diagnosis with relatively simple mechanisms such as outlier detection. Users can directly modify system outcomes by altering a classification label in instances when Confidence's automated algorithm draws the wrong inference. This label is applied to all similar points in the feature space, enabling Confidence to learn from user interaction in the field. This abstraction for incorporating user knowledge provides a lightweight and easy-to-understand interface for the user, while limiting user burden and reducing the required a priori environmental knowledge. Confidence has performed well on real-world deployments, including one deployment of 130 sensors, replayed datasets, and network simulations. Confidence accurately detects and diagnoses at least 90% of all data, and user interaction improves it's performance

Fixing Faults with Confidence

This paper presents Confidence, a tool for identifying and addressing faults in wireless sensing systems. Confidence pinpoints potential sensor and network faults in real time, allowing users to validate unexpected data and address any failures in the field. By introducing a well defined, low-dimension feature space, and functions to map sensor data into this space, we are able to achieve fault detection and diagnosis with relatively simple mechanisms such as outlier detection. Users can directly modify system outcomes by altering a classification label in instances when Confidence's automated algorithm draws the wrong inference. This label is applied to all similar points in the feature space, enabling Confidence to learn from user interaction in the field. This abstraction for incorporating user knowledge provides a lightweight and easy-to-understand interface for the user, while limiting user burden and reducing the required a priori environmental knowledge. Confidence has performed well on real-world deployments, including one deployment of 130 sensors, replayed datasets, and network simulations. Confidence accurately detects and diagnoses at least 90% of all data, and user interaction improves it's performance

Fixing Faults in Wireless Sensing Systems with Confidence

This paper presents Confidence, a tool for identifying and addressing faults in wireless sensing systems. Confidence pinpoints potential sensor and network faults in real time, allowing users to validate unexpected data and address any failures in the field. By introducing a well defined, low-dimension feature space, and functions to map sensor data into this space, we are able to achieve fault detection and diagnosis with relatively simple mechanisms such as outlier detection. Users can directly modify system outcomes by altering a classification label in instances when Confidence's automated algorithm draws the wrong inference. This label is applied to all similar points in the feature space, enabling Confidence to learn from user interaction in the field. This abstraction for incorporating user knowledge provides a lightweight and easy- to-understand interface for the user, while limiting user bur- den and reducing the required a priori environmental knowledge. Confidence has performed well on real-world deployments, including one deployment of 130 sensors, replayed datasets, and network simulations. Confidence accurately detects and diagnoses at least 90% of all data, and user interaction improves it's performance

Towards a Debugging System for Sensor Networks

Due to their resource constraints and tight physical coupling, sensor networks afford limited visibility into an application's behavior. As a result it is often difficult to debug issues that arise during development and deployment. Existing techniques for fault management focus on fault tolerance or detection; before we can detect anomalous behavior in sensor networks, we need first to identify what simple metrics can be used to infer system health and correct behavior. We propose metrics and events that enable system health inferences, and present a preliminary design of Sympathy, a debugging tool for pre- and post-deployment sensor networks. Sympathy will contain mechanisms for collecting system performance metrics with minimal memory overhead; mechanisms for recognizing application-defined events based on these metrics; and a system for collecting events in their spatiotemporal context. The Sympathy system will help programmers draw correlations between seemingly unrelated, distributed events, and produce graphs that highlight those correlations. As an example, we describe how we used a preliminary version of Sympathy to help debug a complex application, Tiny Diffusion

A Platform for Collaborative Acoustic Signal Processing

In this paper, we present a platform for collaborative acoustic signal processing, and demonstrate its use with an example application. Our platform is built upon the Stargate Linux-based microserver, and supports synchronized multi-channel acoustic data acquisition. We implement a dataflow-like staged event-driven programming model within the Emstar software framework that simplifies the development of collaborative processing applications. Unlike previous dataflow systems that emphasize real-time constraints, our framework emphasizes collaborative processing across nodes in a distributed system connected by an energy-conserving wireless network with non-deterministic message latency. In our model, an application is constructed by wiring together multiple stages, where each stage is implemented by an EmStar module. The modular approach simplifies development by isolating errors to specific stages, and enables run-time systemreconfigurability by allowing users to swap out implementations of individual stages, and to reconfigure the dataflow at run time