152 research outputs found
Conversational homes
As devices proliferate, the ability for us to interact with them in an intuitive and meaningful way becomes increasingly challenging. In this paper we take the typical home as an experimental environment to investigate the challenges and potential solutions arising from ever-increasing device proliferation and complexity. We show a potential solution based on conversational interactions between âthingsâ in the environment where those things can be either machine devices or human users. Our key innovation is the use of a Controlled Natural Language (CNL) technology as the underpinning information representation language for both machine and human agents, enabling humans and machines to trivially âreadâ the information being exchanged. The core CNL is augmented with a conversational protocol enabling different speech acts to be exchanged within the system. This conversational layer enables key contextual information to be conveyed, as well as providing a mechanism for translation from the core CNL to other forms, such as device specific API requests, or more easily consumable human representations. Our goal is to show that a single, uniform language can support machine- machine, machine-human, human-machine and human-human interaction in a dynamic environment that is able to rapidly evolve to accommodate new devices and capabilities as they are encountered
Inferring interestingness in online social networks
Information sharing and user-generated content on the Internet has given rise to the increased presence of uninteresting and ânoisyâ information in media streams on many online social networks. Although there is a lot of âinterestingâ information also shared amongst users, the noise increases the cognitive burden in terms of the usersâ abilities to identify what is interesting and may increase the chance of missing content that is useful or important. Additionally, users on such platforms are generally limited to receiving information only from those that they are directly linked to on the social graph, meaning that users exist within distinct content âbubblesâ, further limiting the chance of receiving interesting and relevant information from outside of the immediate social circle. In this thesis, Twitter is used as a platform for researching methods for deriving âinterestingnessâ through popularity as given by the mechanism of retweeting, which allows information to be propagated further between users on Twitterâs social graph. Retweet behaviours are studied, and features; such as those surrounding Tweet audience, information redundancy, and propagation depth through path-length, are uncovered to help relate retweet action to the underlying social graph and the communities it represents. This culminates in research into a methodology for assigning scores to Tweets based on their âqualityâ, which is validated and shown to perform well in various situations
Bowel sounds identification and migrating motor complex detection with low-cost piezoelectric acoustic sensing device
Interpretation of bowel sounds (BS) provides a convenient and non-invasive technique to aid in the diagnosis of gastrointestinal (GI) conditions. However, the approachâs potential is limited by variation between BS and their irregular occurrence. A short, manual auscultation is sufficient to aid in diagnosis of only a few conditions. A longer recording has the potential to unlock additional understanding of GI physiology and clinical utility. In this paper, a low-cost and straightforward piezoelectric acoustic sensing device was designed and used for long BS recordings. The migrating motor complex (MMC) cycle was detected using this device and the sound index as the biomarker for MMC phases. This cycle of recurring motility is typically measured using expensive and invasive equipment. We also used our recordings to develop an improved categorization system for BS. Five different types of BS were extracted: the single burst, multiple bursts, continuous random sound, harmonic sound, and their combination. Their acoustic characteristics and distribution are described. The quantities of different BS during two-hour recordings varied considerably from person to person, while the proportions of different types were consistent. The sensing devices provide a useful tool for MMC detection and study of GI physiology and function
SHERLOCK: Experimental evaluation of a conversational agent for mobile information tasks
AbstractâControlled Natural Language (CNL) has great potential to support human-machine interaction (HMI) because it provides an information representation that is both human readable and machine processable.We investigated the effectiveness of a CNL-based conversational interface for HMI in a behavioural experiment called Simple Human Experiment Regarding Locally Observed Collective Knowledge (SHERLOCK). In SHERLOCK, individuals acted in groups to discover and report information to the machine using natural language (NL), which the machine then processed into CNL. The machine fused responses from different users to form a common operating picture, a dashboard showing the level of agreement for distinct information. To obtain information to add to this dashboard, users explored the real world in a simulated crowd-sourced sensing scenario. This scenario represented a simplified, controlled analogue for tactical intelligence (i.e., direct intelligence of the environment), which is key for rapidly planning military, law enforcement, and emergency operations. Overall, despite close to zero training, 74% of the users inputted NL that was machine interpretable and addressed the assigned tasks. An experimental manipulation aimed to increase user-machine interaction, however, did not improve performance as hypothesised. Nevertheless, results indicate the conversational interface may be effective in assisting humans with collection and fusion of information in a crowd-sourcing context
Sentinel: a co-designed platform for semantic enrichment of social media streams
We introduce the Sentinel platform that supports semantic enrichment of streamed social media data for the purposes of situational understanding. The platform is the result of a codesign effort between computing and social scientists, iteratively developed through a series of pilot studies. The platform is founded upon a knowledge-based approach, in which input streams (channels) are characterized by spatial and terminological parameters, collected media is preprocessed to identify significant terms (signals), and data are tagged (framed) in relation to an ontology. Interpretation of processed media is framed in terms of the 5W framework (who, what, when, where, and why). The platform is designed to be open to the incorporation of new processing modules, building on the knowledge-based elements (channels, signals, and framing ontology) and accessible via a set of user-facing apps. We present the conceptual architecture for the platform, discuss the design and implementation challenges of the underlying streamprocessing system, and present a number of apps developed in the context of the pilot studies, highlighting the strengths and importance of the codesign approach and indicating promising areas for future research
Retweeting beyond expectation: Inferring interestingness in Twitter
Online social networks such as Twitter have emerged as an important mechanism for individuals to share information and post user generated content. However, filtering interesting content from the large volume of messages received through Twitter places a significant cognitive burden on users. Motivated by this problem, we develop a new automated mechanism to detect personalised interestingness, and investigate this for Twitter. Instead of undertaking semantic content analysis and matching of tweets, our approach considers the human response to content, in terms of whether the content is sufficiently stimulating to get repeatedly chosen by users for forwarding (retweeting). This approach involves machine learning against features that are relevant to a particular user and their network, to obtain an expected level of retweeting for a user and a tweet. Tweets observed to be above this expected level are classified as interesting. We implement the approach in Twitter and evaluate it using comparative human tweet assessment in two forms: through aggregated assessment using Mechanical Turk, and through a web-based experiment for Twitter users. The results provide confidence that the approach is effective in identifying the more interesting tweets from a userâs timeline. This has important implications for reduction of cognitive burden: the results show that timelines can be considerably shortened while maintaining a high degree of confidence that more interesting tweets will be retained. In conclusion we discuss how the technique could be applied to mitigate possible filter bubble effects
The Impact of Lab4 Probiotic Supplementation in a 90-Day Study in Wistar Rats
The anti-inflammatory and cholesterol lowering capabilities of probiotic bacteria highlight them as potential prophylactics against chronic inflammatory diseases, particularly cardiovascular disease. Previous studies in silico, in vitro, and in vivo suggest that the Lab4 probiotic consortium may harbour such capabilities and in the current study, we assessed plasma levels of cytokines/chemokines, short chain fatty acids and lipids and faecal levels of bile acids in a subpopulation of healthy Wistar rats included in 90-day repeat dose oral toxicity study. In the rats receiving Lab4, circulating levels of pro-inflammatory interleukin-6, tumour necrosis factor-Îą and keratinocyte chemoattractant/growth regulated oncogene were significantly lower compared to the control group demonstrating a systemic anti-inflammatory effect. These changes occurred alongside significant reductions in plasma low density lipoprotein cholesterol and increases in faecal bile acid excretion implying the ability to lower circulating cholesterol via the deconjugation of intestinal bile acids. Correlative analysis identified significant associations between plasma tumour necrosis factor-Îą and the plasma total cholesterol:high density lipoprotein cholesterol ratio and faecal levels of bifidobacteria in the Lab4 rats. Together, these data highlight Lab4 supplementation as a holistic approach to CVD prevention and encourages further studies in humans
Automation bias with a conversational interface: user confirmation of misparsed information
We investigate automation bias for confirming
erroneous information with a conversational interface.
Participants in our studies used a conversational interface to
report information in a simulated intelligence, surveillance, and
reconnaissance (ISR) task. In the task, for flexibility and ease of
use, participants reported information to the conversational
agent in natural language. Then, the conversational agent
interpreted the userâs reports in a human- and machine-readable
language. Next, participants could accept or reject the agentâs
interpretation. Misparses occur when the agent incorrectly
interprets the report and the user erroneously accepts it. We
hypothesize that the misparses naturally occur in the experiment
due to automation bias and complacency because the agent
interpretation was generally correct (92%). These errors indicate
some users were unable to maintain situation awareness using
the conversational interface. Our results illustrate concerns for
implementing a flexible conversational interface in safety critical
environments (e.g., military, emergency operations)
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