Statistical models for the analysis of short user-generated documents: author identification for conversational documents

In recent years short user-generated documents have been gaining popularity on the Internet and attention in the research communities. This kind of documents are generated by users of the various online services: platforms for instant messaging communication, for real-time status posting, for discussing and for writing reviews. Each of these services allows users to generate written texts with particular properties and which might require specific algorithms for being analysed. In this dissertation we are presenting our work which aims at analysing this kind of documents. We conducted qualitative and quantitative studies to identify the properties that might allow for characterising them. We compared the properties of these documents with the properties of standard documents employed in the literature, such as newspaper articles, and defined a set of characteristics that are distinctive of the documents generated online. We also observed two classes within the online user-generated documents: the conversational documents and those involving group discussions. We later focused on the class of conversational documents, that are short and spontaneous. We created a novel collection of real conversational documents retrieved online (e.g. Internet Relay Chat) and distributed it as part of an international competition (PAN @ CLEF'12). The competition was about author characterisation, which is one of the possible studies of authorship attribution documented in the literature. Another field of study is authorship identification, that became our main topic of research. We approached the authorship identification problem in its closed-class variant. For each problem we employed documents from the collection we released and from a collection of Twitter messages, as representative of conversational or short user-generated documents. We proved the unsuitability of standard authorship identification techniques for conversational documents and proposed novel methods capable of reaching better accuracy rates. As opposed to standard methods that worked well only for few authors, the proposed technique allowed for reaching significant results even for hundreds of users

A modular, deep learning-based holistic intent sensing system tested with Parkinson’s disease patients and controls

People living with mobility-limiting conditions such as Parkinson’s disease can struggle to physically complete intended tasks. Intent-sensing technology can measure and even predict these intended tasks, such that assistive technology could help a user to safely complete them. In prior research, algorithmic systems have been proposed, developed and tested for measuring user intent through a Probabilistic Sensor Network, allowing multiple sensors to be dynamically combined in a modular fashion. A time-segmented deep-learning system has also been presented to predict intent continuously. This study combines these principles, and so proposes, develops and tests a novel algorithm for multi-modal intent sensing, combining measurements from IMU sensors with those from a microphone and interpreting the outputs using time-segmented deep learning. It is tested on a new data set consisting of a mix of non-disabled control volunteers and participants with Parkinson’s disease, and used to classify three activities of daily living as quickly and accurately as possible. Results showed intent could be determined with an accuracy of 97.4% within 0.5 s of inception of the idea to act, which subsequently improved monotonically to a maximum of 99.9918% over the course of the activity. This evidence supports the conclusion that intent sensing is viable as a potential input for assistive medical devices

A modular, deep learning-based holistic intent sensing system tested with Parkinson’s disease patients and controls

Copyright \ua9 2023 Russell, Inches, Carroll and Bergmann.People living with mobility-limiting conditions such as Parkinson’s disease can struggle to physically complete intended tasks. Intent-sensing technology can measure and even predict these intended tasks, such that assistive technology could help a user to safely complete them. In prior research, algorithmic systems have been proposed, developed and tested for measuring user intent through a Probabilistic Sensor Network, allowing multiple sensors to be dynamically combined in a modular fashion. A time-segmented deep-learning system has also been presented to predict intent continuously. This study combines these principles, and so proposes, develops and tests a novel algorithm for multi-modal intent sensing, combining measurements from IMU sensors with those from a microphone and interpreting the outputs using time-segmented deep learning. It is tested on a new data set consisting of a mix of non-disabled control volunteers and participants with Parkinson’s disease, and used to classify three activities of daily living as quickly and accurately as possible. Results showed intent could be determined with an accuracy of 97.4% within 0.5 s of inception of the idea to act, which subsequently improved monotonically to a maximum of 99.9918% over the course of the activity. This evidence supports the conclusion that intent sensing is viable as a potential input for assistive medical devices

Height-dependence of the temporal variability of wind speed : a multiscale approach

1 online resource (31 pages) : illustrationsIncludes abstract.Includes bibliographical references (page 31).Climate change and diminishing fossil fuel reserves have contributed to the increasing need for alternative renewable energy resources. Wind power is particularly attractive as it is both renewable and abundant. However, the spatial and temporal variability of wind makes power production intermittent, which affects the feasibility of large-scale implementation. Using statistical moments and multiscale analysis, this project intends to characterize wind speed variability as a function of height and to deepen wind variability understanding. Detrended Fluctuation Analysis (DFA) is a multiscale analysis method which is capable of assessing time series variability based on the scaling relationship between time scale and the average size of the fluctuations in the time series, thereby taking into consideration the temporal succession of time series values. By applying the coefficient of variation and DFA through three consecutive years and at 6 successive heights, a relationship can be identified between wind speed variability and height. This study found that wind speed variability consistently decreases with height up to a certain height. Beyond this height, wind speed variability was found to decrease at a more gradual rate or not at all. This was confirmed both through statistical moments and multiscale analysis. The outcomes of this project have implications for the methodology used to assess potential locations of wind turbines, as well as for studies regarding turbine design

Sudden hearing loss in sarcoidosis: otoneurological study and neuroradiological correlates

Sarcoidosis is an inflammatory multisystem disorder of unknown cause. Approximately 5-7% of patients manifest symptoms of central nervous system involvement, or neurosarcoidosis. Cranial neuropathy usually entails facial nerve palsy and optic neuritis. Sudden hearing loss has been reported in fewer than 20 cases. Herewith, two new cases of sudden hearing loss due to probable neurosarcoidosis are reported, each having a quite different clinical course. In one case, unilateral sudden hearing loss and facial palsy were the presenting symptoms of systemic sarcoidosis, while in the second, unilateral sudden deafness occurred despite ongoing immunosuppressive treatment for systemic sarcoidosis

Magnetic Resonance Imaging Confirmed Olfactory Bulb Reduction in Long COVID-19: Literature Review and Case Series

An altered sense of smell and taste was recognized as one of the most characteristic symptoms of coronavirus infection disease (COVID-19). Despite most patients experiencing a complete functional resolution, there is a 21.3% prevalence of persistent alteration at 12 months after infection. To date, magnetic resonance imaging (MRI) findings in these patients have been variable and not clearly defined. We aimed to clarify radiological alterations of olfactory pathways in patients with long COVID-19 characterized by olfactory dysfunction. A comprehensive review of the English literature was performed by analyzing relevant papers about this topic. A case series was presented: all patients underwent complete otorhinolaryngology evaluation including the Sniffin’ Sticks battery test. A previous diagnosis of SARS-CoV-2 infection was confirmed by positive swabs. The MRIs were acquired using a 3.0T MR scanner with a standardized protocol for olfactory tract analysis. Images were first analysed by a dedicated neuroradiologist and subsequently reviewed and compared with the previous available MRIs. The review of the literature retrieved 25 studies; most cases of olfactory dysfunction more than 3 months after SARS-CoV-2 infection showed olfactory bulb (OB) reduction. Patients in the personal case series had asymmetry and a reduction in the volume of the OB. This evidence was strengthened by the comparison with a previous MRI, where the OBs were normal. The results preliminarily confirmed OB reduction in cases of long COVID-19 with an altered sense of smell. Further studies are needed to clarify the epidemiology, pathophysiology and prognosis

Overview of the Author Profiling Task at PAN 2013

[EN] This overview presents the framework and results for the Author Profiling task at PAN 2013. We describe in detail the corpus and its characteristics, and the evaluation framework we used to measure the participants performance to solve the problem of identifying age and gender from anonymous texts. Finally, the approaches of the 21 participants and their results are described.The author profiling task @PAN-2013 was an activity of the WIQ-EI IRSES project (Grant No. 269180) within the FP 7 Marie Curie People Framework of the European Commission. We want to thank the Forensic Lab of the Universitat Pompeu Fabra Barcelona for sponsoring the award for the winner team. The work of the first author was partially funded by Autoritas Consulting SA and by Ministerio de Economía y Competitividad de España under grant ECOPORTUNITY IPT-2012-1220-430000. The work of the second author was in the framework the DIANA-APPLICATIONS-Finding Hidden Knowledge in Texts: Applications (TIN2012-38603-C02-01) project, and the VLC/CAMPUS Microcluster on Multimodal Interaction in Intelligent Systems. The work of fifth author was funded in part by the Swiss National Science Foundation (SNF) project "Mining Conversational Content for Topic Modelling and Author Identification (ChatMiner)" under grant number 200021_130208.Rangel, F.; Rosso, P.; Koppel, M.; Stamatatos, E.; Inches, G. (2013). Overview of the Author Profiling Task at PAN 2013. CLEF Conference on Multilingual and Multimodal Information Access Evaluation. 352-365. http://hdl.handle.net/10251/46636S35236