6,986 research outputs found
Speech Emotion Diarization: Which Emotion Appears When?
Speech Emotion Recognition (SER) typically relies on utterance-level
solutions. However, emotions conveyed through speech should be considered as
discrete speech events with definite temporal boundaries, rather than
attributes of the entire utterance. To reflect the fine-grained nature of
speech emotions, we propose a new task: Speech Emotion Diarization (SED). Just
as Speaker Diarization answers the question of "Who speaks when?", Speech
Emotion Diarization answers the question of "Which emotion appears when?". To
facilitate the evaluation of the performance and establish a common benchmark
for researchers, we introduce the Zaion Emotion Dataset (ZED), an openly
accessible speech emotion dataset that includes non-acted emotions recorded in
real-life conditions, along with manually-annotated boundaries of emotion
segments within the utterance. We provide competitive baselines and open-source
the code and the pre-trained models
TURNING THE CORNER: IMPROVING LAW ENFORCEMENT PERCEPTION THROUGH MEDIA
Over the last several years, law enforcement’s image in the United States has been tarnished by the unlawful acts of a number of bad actors. These actions have negatively influenced the perception of law enforcement, particularly within some minority communities located in metropolitan cities nationwide. To strengthen its public profile, law enforcement is exploring methods to improve its image. This thesis investigates how law enforcement can develop positive social media exposure to improve police–community relations in the current social climate. Using qualitative and quantitative research, this thesis examined the different methods the Metropolitan Police Department of Washington, DC, the Boston Police Department, and the Portland Police Bureau used to develop positive social media exposure on Twitter directly before and after the murder of George Floyd on May 25, 2020. To assess the departments’ methods, this research analyzed their respective tweets by their frequency, method of posting, and the number of reactions they generated within this timeframe. As a result, this thesis finds that nationwide protests created significant exposure opportunities for law enforcement. This thesis concludes that to improve its image, law enforcement should continuously promote positive messaging on Twitter by highlighting positive work, conveying solidarity with the community, and exhibiting a willingness to work with the public.Civilian, State GovtApproved for public release. Distribution is unlimited
Generating Efficient Training Data via LLM-based Attribute Manipulation
In this paper, we propose a novel method, Chain-of-Thoughts Attribute
Manipulation (CoTAM), to guide few-shot learning by carefully crafted data from
Large Language Models (LLMs). The main idea is to create data with changes only
in the attribute targeted by the task. Inspired by facial attribute
manipulation, our approach generates label-switched data by leveraging LLMs to
manipulate task-specific attributes and reconstruct new sentences in a
controlled manner. Instead of conventional latent representation controlling,
we implement chain-of-thoughts decomposition and reconstruction to adapt the
procedure to LLMs. Extensive results on text classification and other tasks
verify the advantage of CoTAM over other LLM-based text generation methods with
the same number of training examples. Analysis visualizes the attribute
manipulation effectiveness of CoTAM and presents the potential of LLM-guided
learning with even less supervision
An empirical investigation of the relationship between integration, dynamic capabilities and performance in supply chains
This research aimed to develop an empirical understanding of the relationships between integration,
dynamic capabilities and performance in the supply chain domain, based on which, two conceptual
frameworks were constructed to advance the field. The core motivation for the research was that, at
the stage of writing the thesis, the combined relationship between the three concepts had not yet
been examined, although their interrelationships have been studied individually.
To achieve this aim, deductive and inductive reasoning logics were utilised to guide the qualitative
study, which was undertaken via multiple case studies to investigate lines of enquiry that would
address the research questions formulated. This is consistent with the author’s philosophical
adoption of the ontology of relativism and the epistemology of constructionism, which was considered
appropriate to address the research questions. Empirical data and evidence were collected, and
various triangulation techniques were employed to ensure their credibility. Some key features of
grounded theory coding techniques were drawn upon for data coding and analysis, generating two
levels of findings. These revealed that whilst integration and dynamic capabilities were crucial in
improving performance, the performance also informed the former. This reflects a cyclical and
iterative approach rather than one purely based on linearity. Adopting a holistic approach towards
the relationship was key in producing complementary strategies that can deliver sustainable supply
chain performance.
The research makes theoretical, methodological and practical contributions to the field of supply
chain management. The theoretical contribution includes the development of two emerging
conceptual frameworks at the micro and macro levels. The former provides greater specificity, as it
allows meta-analytic evaluation of the three concepts and their dimensions, providing a detailed
insight into their correlations. The latter gives a holistic view of their relationships and how they are
connected, reflecting a middle-range theory that bridges theory and practice. The methodological
contribution lies in presenting models that address gaps associated with the inconsistent use of
terminologies in philosophical assumptions, and lack of rigor in deploying case study research
methods. In terms of its practical contribution, this research offers insights that practitioners could
adopt to enhance their performance. They can do so without necessarily having to forgo certain
desired outcomes using targeted integrative strategies and drawing on their dynamic capabilities
Fairness Testing: A Comprehensive Survey and Analysis of Trends
Unfair behaviors of Machine Learning (ML) software have garnered increasing
attention and concern among software engineers. To tackle this issue, extensive
research has been dedicated to conducting fairness testing of ML software, and
this paper offers a comprehensive survey of existing studies in this field. We
collect 100 papers and organize them based on the testing workflow (i.e., how
to test) and testing components (i.e., what to test). Furthermore, we analyze
the research focus, trends, and promising directions in the realm of fairness
testing. We also identify widely-adopted datasets and open-source tools for
fairness testing
Facilitating prosociality through technology: Design to promote digital volunteerism
Volunteerism covers many activities involving no financial rewards for volunteers but which contribute
to the common good. There is existing work in designing technology for volunteerism in HumanComputer Interaction (HCI) and related disciplines that focuses on motivation to improve
performance, but it does not account for volunteer wellbeing. Here, I investigate digital volunteerism
in three case studies with a focus on volunteer motivation, engagement, and wellbeing. My research
involved volunteers and others in the volunteering context to generate recommendations for a
volunteer-centric design for digital volunteerism. The thesis has three aims:
1. To investigate motivational aspects critical for enhancing digital volunteers’ experiences
2. To identify digital platform attributes linked to volunteer wellbeing
3. To create guidelines for effectively supporting volunteer engagement in digital volunteering
platforms
In the first case study I investigate the design of a chat widget for volunteers working in an
organisation with a view to develop a design that improves their workflow and wellbeing. The second
case study investigates the needs, motivations, and wellbeing of volunteers who help medical
students improve their medical communication skills. An initial mixed-methods study was followed by
an experiment comparing two design strategies to improve volunteer relatedness; an important
indicator of wellbeing. The third case study looks into volunteer needs, experiences, motivations, and
wellbeing with a focus on volunteer identity and meaning-making on a science-based research
platform. I then analyse my findings from these case studies using the lens of care ethics to derive
critical insights for design.
The key contributions of this thesis are design strategies and critical insights, and a volunteer-centric
design framework to enhance the motivation, wellbeing and engagement of digital volunteers
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Student Citizens: Whiteness, Inequality, and Social Reproduction in Marketized Music Education
Music education policy and administration attempts to shape the musical sensibilities of young people. Yet the logics of music education from a socioeconomic standpoint are inadequately understood. This dissertation focuses on the relationship between music education nonprofits and public schools and on the public and private policies that have shaped the formation and perpetuation of these relationships. I analyze the logics of policy documents alongside the discourses and narratives of private organizations that support music education within the specific contexts of New Jersey, a state that mandates music education access for all students, and the COVID-19 pandemic, which has exacerbated societal inequalities, to illuminate how policy makers and administrators shape student experiences in the proto-democratic space of the classroom.
I use policy analysis and institutional ethnography, approaching data primarily through the lenses of neoliberal critiques of marketization, critical whiteness studies, and analyses of the intersection of class and race, which I outline in chapter one. I also consider the design of music education programs within the theoretical framework of culturally relevant pedagogy. Education systems are adapting to shifting racial discourses as schools continue to construct citizens within racialized and classed hierarchies. Music historically has been invoked in the construction of societal stratifications to mark ethnic and cultural boundaries.
In chapter two, I examine these narratives that have shaped the formation of music education in the United States as a culturally hegemonizing force and persist in debates around the purpose of music education in under-resourced schools that mainly serve students from minoritized communities. Music education remains a site at which policy makers, administrators, educators, and community members negotiate the role of culture in shaping new citizens. State music education policy in New Jersey specifically struggles to support the progressive vision it professes as it continues to suggest a strongly hegemonic curriculum and perpetually underfunds music programs in schools.
Within this context, the third chapter considers how funders and advocacy groups are so frequently focused on short-term funding needs that they persistently struggle to address systemic issues in music education, such as issues with administrations that do not represent the communities being served, colonial content and pedagogy, and unsustainable funding solutions. As such, the limited services and non-democratic leadership of privately funded music education programs in public schools reinforce the role of public schools as gate-keepers of exclusionary citizenship norms. At the same time, privatization has also opened opportunities for non-normative, anti-oppressive forms of music pedagogy to enter public schools. In the fourth chapter, I investigate how, though their very existence reinforces the marketizing trends that rank and exclude, some nonprofits do attempt to serve students in culturally relevant ways within this environment, and can even work in ways that support publicly funded programs.
Altogether, my research provides insight into the role that the privatization of public spaces within neoliberalism plays in the formation and reproduction of classed and raced citizens, as policy makers, funders, and program administrators determine which young people are given access to which forms of education
Ensemble Machine Learning Model Generalizability and its Application to Indirect Tool Condition Monitoring
A practical, accurate, robust, and generalizable system for monitoring tool condition during a machining process would enable advancements in manufacturing process automation, cost reduction, and efficiency improvement. Previously proposed systems using various individual machine learning (ML) models and other analysis techniques have struggled with low generalizability to new machining and environmental conditions, as well as a common reliance on expensive or intrusive sensory equipment which hinders their industry adoption. While ensemble ML techniques offer significant advantages over individual models in terms of performance, overfitting reduction, and generalizability improvement, they have only begun to see limited applications within the field of tool condition monitoring (TCM).
To address the research gaps which currently surround TCM system generalizability and optimal ensemble model configuration for this application, nine ML model types, including five heterogeneous and homogeneous ensemble models, are employed for tool wear classification. Sound, spindle power, and axial load signals are utilized through the sensor fusion of practical external and internal machine sensors. This original experimental process data is collected through tool wear experiments using a variety of machining conditions. Four feature selection methods and multiple tool wear classification resolution values are compared for this application, and the performance of the ML models is compared across metrics including k-fold cross validation and leave-one-group-out cross validation. The generalizability of the models to data from unseen experiments and machining conditions is evaluated, and a method of improving the generalizability levels using noisy training data is examined. T-tests are used to measure the significance of model performance differences. The extra-trees ensemble ML method, which had never before been applied to signal-based TCM, shows the best performance of the nine models.M.S
Science and Innovations for Food Systems Transformation
This Open Access book compiles the findings of the Scientific Group of the United Nations Food Systems Summit 2021 and its research partners. The Scientific Group was an independent group of 28 food systems scientists from all over the world with a mandate from the Deputy Secretary-General of the United Nations. The chapters provide science- and research-based, state-of-the-art, solution-oriented knowledge and evidence to inform the transformation of contemporary food systems in order to achieve more sustainable, equitable and resilient systems
Measuring the Severity of Depression from Text using Graph Representation Learning
The common practice of psychology in measuring the severity of a patient's depressive symptoms is based on an interactive conversation between a clinician and the patient. In this dissertation, we focus on predicting a score representing the severity of depression from such a text. We first present a generic graph neural network (GNN) to automatically rate severity using patient transcripts. We also test a few sequence-based deep models in the same task. We then propose a novel form for node attributes within a GNN-based model that captures node-specific embedding for every word in the vocabulary. This provides a global representation of each node, coupled with node-level updates according to associations between words in a transcript. Furthermore, we evaluate the performance of our GNN-based model on a Twitter sentiment dataset to classify three different sentiments and on Alzheimer's data to differentiate Alzheimer’s disease from healthy individuals respectively. In addition to applying the GNN model to learn a prediction model from the text, we provide post-hoc explanations of the model's decisions for all three tasks using the model's gradients
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