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Gaming Disorder: A Contemporary Ampliative Account
Diagnosing gaming disorder requires that mental health practitioners weigh in patient's values in an appropriate symmetry with an evidence-based approach to deal with patient's issues. Nevertheless, to date, the science of gaming disorder tends to overemphasize psychopharmacological and therapeutic intervention, while the importance of values is ignored to the periphery. We argue in this paper, for gaming disorder formalization to be rigid, the science of gaming disorder needs further research and its scientific basis be established with an improved data of the players at the local level
The Nature of Novelty Detection
Sentence level novelty detection aims at reducing redundant sentences from a
sentence list. In the task, sentences appearing later in the list with no new
meanings are eliminated. Aiming at a better accuracy for detecting redundancy,
this paper reveals the nature of the novelty detection task currently
overlooked by the Novelty community Novelty as a combination of the partial
overlap (PO, two sentences sharing common facts) and complete overlap (CO, the
first sentence covers all the facts of the second sentence) relations. By
formalizing novelty detection as a combination of the two relations between
sentences, new viewpoints toward techniques dealing with Novelty are proposed.
Among the methods discussed, the similarity, overlap, pool and language
modeling approaches are commonly used. Furthermore, a novel approach, selected
pool method is provided, which is immediate following the nature of the task.
Experimental results obtained on all the three currently available novelty
datasets showed that selected pool is significantly better or no worse than the
current methods. Knowledge about the nature of the task also affects the
evaluation methodologies. We propose new evaluation measures for Novelty
according to the nature of the task, as well as possible directions for future
study.Comment: This paper pointed out the future direction for novelty detection
research. 37 pages, double spaced versio
Abelian networks II. Halting on all inputs
Abelian networks are systems of communicating automata satisfying a local
commutativity condition. We show that a finite irreducible abelian network
halts on all inputs if and only if all eigenvalues of its production matrix lie
in the open unit disk.Comment: Supersedes sections 5 and 6 of arXiv:1309.3445v1. To appear in
Selecta Mathematic
Theory, data, and formulation: The unusual case of david laitin
In two influential articles David Laitin laid out a tripartite method for comparative politics and for social science more generally (Laitin 2002, 2003). The three methods that Laitin advocated were Formal Theory, Quantitative Analysis, and Narrative. In this paper I take issue with Laitin’s categorization scheme for the methods, and I consider the criteria and constraints on choosing methods
Classically-Controlled Quantum Computation
Quantum computations usually take place under the control of the classical
world. We introduce a Classically-controlled Quantum Turing Machine (CQTM)
which is a Turing Machine (TM) with a quantum tape for acting on quantum data,
and a classical transition function for a formalized classical control. In
CQTM, unitary transformations and measurements are allowed. We show that any
classical TM is simulated by a CQTM without loss of efficiency. The gap between
classical and quantum computations, already pointed out in the framework of
measurement-based quantum computation is confirmed. To appreciate the
similarity of programming classical TM and CQTM, examples are given.Comment: 20 page
Thinking outside the graph: scholarly knowledge graph construction leveraging natural language processing
Despite improved digital access to scholarly knowledge in recent decades, scholarly communication remains exclusively document-based.
The document-oriented workflows in science publication have reached the limits of adequacy as highlighted by recent discussions on the increasing proliferation of scientific literature, the deficiency of peer-review and the reproducibility crisis.
In this form, scientific knowledge remains locked in representations that are inadequate for machine processing.
As long as scholarly communication remains in this form, we cannot take advantage of all the advancements taking place in machine learning and natural language processing techniques.
Such techniques would facilitate the transformation from pure text based into (semi-)structured semantic descriptions that are interlinked in a collection of big federated graphs.
We are in dire need for a new age of semantically enabled infrastructure adept at storing, manipulating, and querying scholarly knowledge.
Equally important is a suite of machine assistance tools designed to populate, curate, and explore the resulting scholarly knowledge graph.
In this thesis, we address the issue of constructing a scholarly knowledge graph using natural language processing techniques.
First, we tackle the issue of developing a scholarly knowledge graph for structured scholarly communication, that can be populated and constructed automatically.
We co-design and co-implement the Open Research Knowledge Graph (ORKG), an infrastructure capable of modeling, storing, and automatically curating scholarly communications.
Then, we propose a method to automatically extract information into knowledge graphs.
With Plumber, we create a framework to dynamically compose open information extraction pipelines based on the input text.
Such pipelines are composed from community-created information extraction components in an effort to consolidate individual research contributions under one umbrella.
We further present MORTY as a more targeted approach that leverages automatic text summarization to create from the scholarly article's text structured summaries containing all required information.
In contrast to the pipeline approach, MORTY only extracts the information it is instructed to, making it a more valuable tool for various curation and contribution use cases.
Moreover, we study the problem of knowledge graph completion.
exBERT is able to perform knowledge graph completion tasks such as relation and entity prediction tasks on scholarly knowledge graphs by means of textual triple classification.
Lastly, we use the structured descriptions collected from manual and automated sources alike with a question answering approach that builds on the machine-actionable descriptions in the ORKG.
We propose JarvisQA, a question answering interface operating on tabular views of scholarly knowledge graphs i.e., ORKG comparisons.
JarvisQA is able to answer a variety of natural language questions, and retrieve complex answers on pre-selected sub-graphs.
These contributions are key in the broader agenda of studying the feasibility of natural language processing methods on scholarly knowledge graphs, and lays the foundation of which methods can be used on which cases.
Our work indicates what are the challenges and issues with automatically constructing scholarly knowledge graphs, and opens up future research directions
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