Asynchronous Remote Medical Consultation for Ghana

Computer-mediated communication systems can be used to bridge the gap between doctors in underserved regions with local shortages of medical expertise and medical specialists worldwide. To this end, we describe the design of a prototype remote consultation system intended to provide the social, institutional and infrastructural context for sustained, self-organizing growth of a globally-distributed Ghanaian medical community. The design is grounded in an iterative design process that included two rounds of extended design fieldwork throughout Ghana and draws on three key design principles (social networks as a framework on which to build incentives within a self-organizing network; optional and incremental integration with existing referral mechanisms; and a weakly-connected, distributed architecture that allows for a highly interactive, responsive system despite failures in connectivity). We discuss initial experiences from an ongoing trial deployment in southern Ghana.Comment: 10 page

Best Practices for Notification Studies for Security and Privacy Issues on the Internet

Researchers help operators of vulnerable and non-compliant internet services by individually notifying them about security and privacy issues uncovered in their research. To improve efficiency and effectiveness of such efforts, dedicated notification studies are imperative. As of today, there is no comprehensive documentation of pitfalls and best practices for conducting such notification studies, which limits validity of results and impedes reproducibility. Drawing on our experience with such studies and guidance from related work, we present a set of guidelines and practical recommendations, including initial data collection, sending of notifications, interacting with the recipients, and publishing the results. We note that future studies can especially benefit from extensive planning and automation of crucial processes, i.e., activities that take place well before the first notifications are sent.Comment: Accepted to the 3rd International Workshop on Information Security Methodology and Replication Studies (IWSMR '21), colocated with ARES '2

Are Code Examples on an Online Q&A Forum Reliable?

Programmers often consult an online Q&A forum such as Stack Overflow to learn new APIs. This paper presents an empirical study on the prevalence and severity of API misuse on Stack Overflow. To reduce manual assessment effort, we design ExampleCheck, an API usage mining framework that extracts patterns from over 380K Java repositories on GitHub and subsequently reports potential API usage violations in Stack Overflow posts. We analyze 217,818 Stack Overflow posts using ExampleCheck and find that 31% may have potential API usage violations that could produce unexpected behavior such as program crashes and resource leaks. Such API misuse is caused by three main reasons---missing control constructs, missing or incorrect order of API calls, and incorrect guard conditions. Even the posts that are accepted as correct answers or upvoted by other programmers are not necessarily more reliable than other posts in terms of API misuse. This study result calls for a new approach to augment Stack Overflow with alternative API usage details that are not typically shown in curated examples

A Review of the Enviro-Net Project

Ecosystems monitoring is essential to properly understand their development and the effects of events, both climatological and anthropological in nature. The amount of data used in these assessments is increasing at very high rates. This is due to increasing availability of sensing systems and the development of new techniques to analyze sensor data. The Enviro-Net Project encompasses several of such sensor system deployments across five countries in the Americas. These deployments use a few different ground-based sensor systems, installed at different heights monitoring the conditions in tropical dry forests over long periods of time. This paper presents our experience in deploying and maintaining these systems, retrieving and pre-processing the data, and describes the Web portal developed to help with data management, visualization and analysis.Comment: v2: 29 pages, 5 figures, reflects changes addressing reviewers' comments v1: 38 pages, 8 figure

AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation Framework

This technical report presents AutoGen, a new framework that enables development of LLM applications using multiple agents that can converse with each other to solve tasks. AutoGen agents are customizable, conversable, and seamlessly allow human participation. They can operate in various modes that employ combinations of LLMs, human inputs, and tools. AutoGen's design offers multiple advantages: a) it gracefully navigates the strong but imperfect generation and reasoning abilities of these LLMs; b) it leverages human understanding and intelligence, while providing valuable automation through conversations between agents; c) it simplifies and unifies the implementation of complex LLM workflows as automated agent chats. We provide many diverse examples of how developers can easily use AutoGen to effectively solve tasks or build applications, ranging from coding, mathematics, operations research, entertainment, online decision-making, question answering, etc.Comment: 28 page

Collective IT artifacts: Toward Inclusive Crisis Infrastructures

This paper investigates a previously overlooked phenomenon in crisis response information systems, namely inclusive crisis infrastructure. By expanding the well-acknowledged infrastructure concept with alternatives to understand the nature and scope of inclusive crisis infrastructures, this paper contributes to closing the gap between theory and practice by raising some research questions critical to the study of inclusive crisis infrastructures. The emerging literature on crisis response information systems suggests that external sourcing of information increasingly influences crisis response operations. To contribute to this discourse, the paper draws on Pipek and Wulf’s (2009) definition of work infrastructures and Palen and Liu’s (2007) conceptualization of peer-to-peer communications to develop a better understanding of the crisis response arena as a whole. In doing so, this paper goes beyond the emphasis on event-based technologies that currently dominate the crisis response information systems literature and instead argues why crisis infrastructures need to be both inward-looking and accommodating to technological and social outcomes parallel to formal response contexts. The novel conceptualization captures the fact that the crisis context contains collections of collective IT artifacts that are not aligned or related but that are, for autonomy reasons, interlinked to crisis organizations’ current IT infrastructure and may be of great value to such organizations if infrastructure capability options are considered

Autonomous Acquisition of Natural Situated Communication

An important part of human intelligence, both historically and operationally, is our ability to communicate. We learn how to communicate, and maintain our communicative skills, in a society of communicators – a highly effective way to reach and maintain proficiency in this complex skill. Principles that might allow artificial agents to learn language this way are in completely known at present – the multi-dimensional nature of socio-communicative skills are beyond every machine learning framework so far proposed. Our work begins to address the challenge of proposing a way for observation-based machine learning of natural language and communication. Our framework can learn complex communicative skills with minimal up-front knowledge. The system learns by incrementally producing predictive models of causal relationships in observed data, guided by goal-inference and reasoning using forward-inverse models. We present results from two experiments where our S1 agent learns human communication by observing two humans interacting in a realtime TV-style interview, using multimodal communicative gesture and situated language to talk about recycling of various materials and objects. S1 can learn multimodal complex language and multimodal communicative acts, a vocabulary of 100 words forming natural sentences with relatively complex sentence structure, including manual deictic reference and anaphora. S1 is seeded only with high-level information about goals of the interviewer and interviewee, and a small ontology; no grammar or other information is provided to S1 a priori. The agent learns the pragmatics, semantics, and syntax of complex utterances spoken and gestures from scratch, by observing the humans compare and contrast the cost and pollution related to recycling aluminum cans, glass bottles, newspaper, plastic, and wood. After 20 hours of observation S1 can perform an unscripted TV interview with a human, in the same style, without making mistakes