10,117 research outputs found
Inside Job: Diagnosing Bluetooth Lower Layers Using Off-the-Shelf Devices
Bluetooth is among the dominant standards for wireless short-range
communication with multi-billion Bluetooth devices shipped each year. Basic
Bluetooth analysis inside consumer hardware such as smartphones can be
accomplished observing the Host Controller Interface (HCI) between the
operating system's driver and the Bluetooth chip. However, the HCI does not
provide insights to tasks running inside a Bluetooth chip or Link Layer (LL)
packets exchanged over the air. As of today, consumer hardware internal
behavior can only be observed with external, and often expensive tools, that
need to be present during initial device pairing. In this paper, we leverage
standard smartphones for on-device Bluetooth analysis and reverse engineer a
diagnostic protocol that resides inside Broadcom chips. Diagnostic features
include sniffing lower layers such as LL for Classic Bluetooth and Bluetooth
Low Energy (BLE), transmission and reception statistics, test mode, and memory
peek and poke
Designing Chatbots for Crises: A Case Study Contrasting Potential and Reality
Chatbots are becoming ubiquitous technologies, and their popularity and adoption are rapidly spreading. The potential of chatbots in engaging people with digital services is fully recognised. However, the reputation of this technology with regards to usefulness and real impact remains rather questionable. Studies that evaluate how people perceive and utilise chatbots are generally lacking. During the last Kenyan elections, we deployed a chatbot on Facebook Messenger to help people submit reports of violence and misconduct experienced in the polling stations. Even though the chatbot was visited by more than 3,000 times, there was a clear mismatch between the users’ perception of the technology and its design. In this paper, we analyse the user interactions and content generated through this application and discuss the challenges and directions for designing more effective chatbots
The Pros and Cons of Using Machine Learning and Interpretable Machine Learning Methods in psychiatry detection applications, specifically depression disorder: A Brief Review
The COVID-19 pandemic has forced many people to limit their social
activities, which has resulted in a rise in mental illnesses, particularly
depression. To diagnose these illnesses with accuracy and speed, and prevent
severe outcomes such as suicide, the use of machine learning has become
increasingly important. Additionally, to provide precise and understandable
diagnoses for better treatment, AI scientists and researchers must develop
interpretable AI-based solutions. This article provides an overview of relevant
articles in the field of machine learning and interpretable AI, which helps to
understand the advantages and disadvantages of using AI in psychiatry disorder
detection applications.Comment: 12 page
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