1,663 research outputs found
ZOE: A cloud-less dialog-enabled continuous sensing wearable exploiting heterogeneous computation
The wearable revolution, as a mass-market phenomenon, has finally
arrived. As a result, the question of how wearables should evolve
over the next 5 to 10 years is assuming an increasing level of societal
and commercial importance. A range of open design and
system questions are emerging, for instance: How can wearables
shift from being largely health and fitness focused to tracking a
wider range of life events? What will become the dominant methods
through which users interact with wearables and consume the
data collected? Are wearables destined to be cloud and/or smartphone
dependent for their operation?
Towards building the critical mass of understanding and experience
necessary to tackle such questions, we have designed and
implemented ZOE – a match-box sized (49g) collar- or lapel-worn
sensor that pushes the boundary of wearables in an important set of
new directions. First, ZOE aims to perform multiple deep sensor
inferences that span key aspects of everyday life (viz. personal, social
and place information) on continuously sensed data; while also
offering this data not only within conventional analytics but also
through a speech dialog system that is able to answer impromptu
casual questions from users. (Am I more stressed this week than
normal?) Crucially, and unlike other rich-sensing or dialog supporting
wearables, ZOE achieves this without cloud or smartphone
support – this has important side-effects for privacy since all user
information can remain on the device. Second, ZOE incorporates
the latest innovations in system-on-a-chip technology together with
a custom daughter-board to realize a three-tier low-power processor
hierarchy. We pair this hardware design with software techniques
that manage system latency while still allowing ZOE to remain energy
efficient (with a typical lifespan of 30 hours), despite its high
sensing workload, small form-factor, and need to remain responsive to user dialog requests.This work was supported by Microsoft Research through its PhD
Scholarship Program. We would also like to thank the anonymous
reviewers and our shepherd, Jeremy Gummeson, for helping us improve
the paper.This is the author accepted manuscript. The final version is available from ACM at http://dl.acm.org/citation.cfm?doid=2742647.2742672
Development of customized conversational interfaces with Deep Learning techniques
This Bachelor’s thesis will cover the end-to-end process of developing a personalized conversational interface for a specific domain, using Deep Learning techniques. In particular, it will focus on the study of the Dialog Manager module, which is in charge of deciding the next system response based on the current dialog state. AlthoughthereisplentyofliteratureaboutMachineLearningappliedtotheconstruction of dialog management models, there is very little reference to the utilization of Deep Learning for such task. As a result, this work analyzes the improvement that deep neural networks can bring to accuracy. Several models are created with TensorFlow, and comparisons are made with traditional Machine Learning solutions. Results show that Deep Learning is not the most recommended approach for this type of problems, yet further research is suggested for more complex datasets. After this, one of the Deep Learning models, based on a train scheduling domain, is used for the implementation of the dialog manager inside a real spoken dialog system. To integrate the rest of required components of such technology (automatic speech recognizer, natural language understanding module and text-to-speech service), a modern framework is used: DialogFlow. With this platform, a complete chatbot is built in the form of an assistant in the train scheduling domain. Evaluationof thespoken dialogsystemwith real users generatesavery positivefeedback, demonstrating that a Deep Learning based dialog manager is a valid solution in commercial conversational interfaces.IngenierÃa Informátic
Exploring the Compositional Generalization in Context Dependent Text-to-SQL Parsing
In the context-dependent Text-to-SQL task, the generated SQL statements are
refined iteratively based on the user input utterance from each interaction.
The input text from each interaction can be viewed as component modifications
to the previous SQL statements, which could be further extracted as the
modification patterns. Since these modification patterns could also be combined
with other SQL statements, the models are supposed to have the compositional
generalization to these novel combinations. This work is the first exploration
of compositional generalization in context-dependent Text-to-SQL scenarios. To
facilitate related studies, we constructed two challenging benchmarks named
\textsc{CoSQL-CG} and \textsc{SParC-CG} by recombining the modification
patterns and existing SQL statements. The following experiments show that all
current models struggle on our proposed benchmarks. Furthermore, we found that
better aligning the previous SQL statements with the input utterance could give
models better compositional generalization ability. Based on these
observations, we propose a method named \texttt{p-align} to improve the
compositional generalization of Text-to-SQL models. Further experiments
validate the effectiveness of our method. Source code and data are available.Comment: Accepted to ACL 2023 (Findings), Long Paper, 11 page
A Digital Practice Tool for Chemical Resonance
Digital practice tools support online learning in math, language, computer science, and other subjects, but practice with problems whose answers are not well represented by text or quantities is underrepresented in the digital learning ecosystem beyond multiple-choice questions. This thesis project explored an alternative to multiple choice practice problems in organic chemistry that does not rely on a molecule drawing interface. This project included development and evaluation of a proof-of-concept digital practice tool for chemical resonance problems. Results of a utility study strongly suggest that the practice tool could fill a learning niche within organic chemistry practice as part of a larger integrated learning environment. The study supported the idea that the digital practice tool and others like it can meet different needs for certain learners, such as reinforcing concepts visually, allowing learners to pace themselves, encouraging learners, and providing immediate feedback. Lastly, this project identified generalizable design challenges for similar practice tools, including the need for a known deployment context, curating content for diverse learner backgrounds, and managing appropriate difficulty for diverse learner backgrounds and needs
Using Haptic Virtual Reality to Increase Learning Gains and Construct Knowledge of Unobservable Phenomena
This project is designed to be a compilation of ten haptic virtual reality labs using the software zSpace. The labs will follow the NYS Living Environment Standards as well as the Next Generation Science Standards for living environment as well as physical/general science topics for middle school students. The project will be a list of available laboratories along with their appropriate fit into the curriculum and a description of how they fit New York State curriculum standards for the appropriate discipline. The goal of these laboratory assignments is to increase learning gains in students by allowing them to experience scientific phenomena that can often be unrelatable and unobservable
Strong-AI Autoepistemic Robots Build on Intensional First Order Logic
Neuro-symbolic AI attempts to integrate neural and symbolic architectures in
a manner that addresses strengths and weaknesses of each, in a complementary
fashion, in order to support robust strong AI capable of reasoning, learning,
and cognitive modeling. In this paper we consider the intensional First Order
Logic (IFOL) as a symbolic architecture of modern robots, able to use natural
languages to communicate with humans and to reason about their own knowledge
with self-reference and abstraction language property.
We intend to obtain the grounding of robot's language by experience of how it
uses its neuronal architectures and hence by associating this experience with
the mining (sense) of non-defined language concepts (particulars/individuals
and universals) in PRP (Properties/Relations/Propositions) theory of IFOL.\\ We
consider the robot's four-levels knowledge structure: The syntax level of
particular natural language (Italian, French, etc..), two universal language
levels: its semantic logic structure (based on virtual predicates of FOL and
logic connectives), and its corresponding conceptual PRP structure level which
universally represents the composite mining of FOL formulae grounded on the
last robot's neuro-system level.
Finally, we provide the general method how to implement in IFOL (by using the
abstracted terms) different kinds of modal logic operators and their deductive
axioms: we present a particular example of robots autoepistemic deduction
capabilities by introduction of the special temporal predicate and
deductive axioms for it: reflexive, positive introspection and distributive
axiom.Comment: 25 pages, 2 figure
Technological expectations and global politics:Three waves of enthusiasm in non-governmental remote sensing
Media, industry and academia frequently depict the commercialization of satellite imagery as geospatial revolution with transformational effects on global politics. In doing so, they follow an understanding that isolates technology from politics. While this division is still prevalent in International Relations, recent scholarship has promoted the intricate relationship of technology with politics as socio-material. Adding to this literature, I draw on the sociology of expectations to propose an alternative reading of non-governmental remote sensing. For this purpose, the notion of techno-political barriers is introduced to trace controversies about technological expectations of satellite imagery. Based on expert interviews and document analysis, I identify three waves of enthusiasm, which are characterized by particularly salient expectations and techno-political barriers. The first wave is fuelled by an enthusiasm about the general benefits of visual transparency as opposed to Cold War secrecy. The second wave turns towards non-governmental imagery intelligence for human security. In the third wave satellite imagery joins multiple data streams to support political and business decisions. Taken together, the three-waves model distorts the linear understanding of a revolutionary development but reveals the political and controversial nature of the ongoing commercialization of satellite imagery. As a part of this, non-governmental remote sensing has experienced a focus shift from visual transparency towards geospatial big data. Moreover, the three waves model highlights the persistence of expectations and techno-political barriers in the non-governmental sector with important implications for policymaking and the global impact of commercial satellite imagery
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