1,720,961 research outputs found
Modeling Mood Polarity and Declaration Occurrence by Neural Temporal Point Processes
No full textNeural point processes provide the flexibility needed to deal with time series of heterogeneous nature within the robust framework of point processes. This aspect is of particular relevance when dealing with real-world data, mixing generative processes characterized by radically different distributions and sampling. This brief discusses a neural point process approach for health and behavioral data, comprising both sparse events coming from user subjective declarations as well as fast-flowing time series from wearable sensors. We propose and empirically validate different neural architectures and we assess the effect of including input sources of different nature. The empirical analysis is built on the top of a challenging original dataset, never published before, and collected as part of a real-world experiment in an uncontrolled setting. Results show the potential of neural point processes both in terms of predicting the next event type as well as in predicting the time to next user interaction
Towards resource-aware dialogue systems and sentiment analysis
No full textIn the past few years, the use of transformer-based models has experienced increasing popularity as new state-of-the-art performance was achieved in several natural language processing tasks. As these models are often extremely large, however, their use for applications within embedded devices may not be feasible. This thesis looks at two specific applications, Dialogue Systems and Sentiment Analysis. These offer great potential to enhance user experience, but at the same time, when running on embedded devices, cannot make use of the same models and algorithms designed for server-based execution, due to factors such as reduced memory capacity and limited computational power. Novel solutions that are resource- and user-aware are therefore needed.
Dialogue Systems: Research on building dialogue systems able to engage in natural sounding conversation with humans has attracted increasing attention in recent years. This has led to the rise of commercial conversational agents such as Google Home, Alexa and Siri situated on embedded devices, that enable users to interface with a wide range of underlying functionalities in a natural and seamless manner. However, in part due to memory and computational power constraints,
these systems necessitate to either be placed on, or initiate frequent communication with, a server in order to process the users’ queries. When placed on embedded systems, this communication may act as a bottleneck, resulting in delays as well as in the halt of the system should the network connection be lost or unavailable. Moreover, despite the rise of generative models such as ChatGPT, retrieval-based dialogue systems remain a promising approach due to their ability to deliver syntactically rich and informative responses while allowing for greater control on the responses that the model can provide, which may be critical in some applications. This thesis proposes a new framework for hardware-aware retrieval-based dialogue systems based on the Dual-Encoder architecture, coupled with a clustering method to group candidates pertaining to a same conversation, that reduces storage capacity and computational power requirements.
Sentiment Analysis: The availability of new datasets and deep learning techniques have led to a surge of effort directed towards sentiment analysis research. However, little attention has been given to the development of models that are not only accurate, but also suitable for user-specific use or geared towards resource-constrained devices. State-of-the-art models often have tens of millions of parameters which make it unfeasible to deploy such solutions on devices characterized by limited memory and computational power. This work explores the concept of software-hardware co-design and propose a methodical procedure to select the most desirable model taking into consideration application constraints described in terms of memory and latency. In doing so, it shows how fully utilizing the feature extraction capabilities of large pre-trained language models can close the gap
between the fine-tuning of such models and their frozen counterpart.
Additionally, an empirical study of the training and deployment of emotion recognition models at the edge is proposed. The feasibility and merit of the proposed solution is shown by deploying it on two classes of edge devices.Doctor of Philosoph
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Audio-Augmented Dialogue Systems
No full textResearch on building dialogue systems able to converse with humans naturally has recently attracted a lot of attention. Most work on this area assumes text-based conversation, where the user message is modeled as a sequence of words in a vocabulary. Real-world human conversation, in contrast, involves other modalities, such as voice, facial expression and body language, which in certain scenarios can have a significant influence on the conversation.
In this work, we explore the impact of incorporating the audio features of the user message into the dialogue system. Specifically, we first design an auxiliary response classification task to refine raw audio features. Then we use word-level modality fusion to incorporate the audio features as additional context in our main generative model. Experiments show that our audio-augmented model outperforms the audio-free counterpart on perplexity, response diversity and human evaluation
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
koamabayili/VECTRON-author-checklist: VECTRON author checklist
No full textWe have done our best to complete the author checklist relating to the use of animals in the hut study. Note that the objective for the hut study was to evaluate the IRS treatment applications for residual efficacy against Anopheles mosquitoes, including the local An. coluzzii mosquito population. Cows were only used to attract mosquitoes into the huts and no tests were carried out directly on the cows. The author checklist is intended for use with studies where experiments are carried out on animals, which is why we have had such difficulty in completing this for the hut study, as many of the questions do not relate to how the cows were used
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