28,248 research outputs found
Integrating Fuzzy Decisioning Models With Relational Database Constructs
Human learning and classification is a nebulous area in computer science. Classic decisioning problems can be solved given enough time and computational power, but discrete algorithms cannot easily solve fuzzy problems. Fuzzy decisioning can resolve more real-world fuzzy problems, but existing algorithms are often slow, cumbersome and unable to give responses within a reasonable timeframe to anything other than predetermined, small dataset problems. We have developed a database-integrated highly scalable solution to training and using fuzzy decision models on large datasets. The Fuzzy Decision Tree algorithm is the integration of the Quinlan ID3 decision-tree algorithm together with fuzzy set theory and fuzzy logic. In existing research, when applied to the microRNA prediction problem, Fuzzy Decision Tree outperformed other machine learning algorithms including Random Forest, C4.5, SVM and Knn. In this research, we propose that the effectiveness with which large dataset fuzzy decisions can be resolved via the Fuzzy Decision Tree algorithm is significantly improved when using a relational database as the storage unit for the fuzzy ID3 objects, versus traditional storage objects. Furthermore, it is demonstrated that pre-processing certain pieces of the decisioning within the database layer can lead to much swifter membership determinations, especially on Big Data datasets. The proposed algorithm uses the concepts inherent to databases: separated schemas, indexing, partitioning, pipe-and-filter transformations, preprocessing data, materialized and regular views, etc., to present a model with a potential to learn from itself. Further, this work presents a general application model to re-architect Big Data applications in order to efficiently present decisioned results: lowering the volume of data being handled by the application itself, and significantly decreasing response wait times while allowing the flexibility and permanence of a standard relational SQL database, supplying optimal user satisfaction in today\u27s Data Analytics world. We experimentally demonstrate the effectiveness of our approach
Task-specific Word Identification from Short Texts Using a Convolutional Neural Network
Task-specific word identification aims to choose the task-related words that
best describe a short text. Existing approaches require well-defined seed words
or lexical dictionaries (e.g., WordNet), which are often unavailable for many
applications such as social discrimination detection and fake review detection.
However, we often have a set of labeled short texts where each short text has a
task-related class label, e.g., discriminatory or non-discriminatory, specified
by users or learned by classification algorithms. In this paper, we focus on
identifying task-specific words and phrases from short texts by exploiting
their class labels rather than using seed words or lexical dictionaries. We
consider the task-specific word and phrase identification as feature learning.
We train a convolutional neural network over a set of labeled texts and use
score vectors to localize the task-specific words and phrases. Experimental
results on sentiment word identification show that our approach significantly
outperforms existing methods. We further conduct two case studies to show the
effectiveness of our approach. One case study on a crawled tweets dataset
demonstrates that our approach can successfully capture the
discrimination-related words/phrases. The other case study on fake review
detection shows that our approach can identify the fake-review words/phrases.Comment: accepted by Intelligent Data Analysis, an International Journa
The Online Laboratory: Conducting Experiments in a Real Labor Market
Online labor markets have great potential as platforms for conducting
experiments, as they provide immediate access to a large and diverse subject
pool and allow researchers to conduct randomized controlled trials. We argue
that online experiments can be just as valid---both internally and
externally---as laboratory and field experiments, while requiring far less
money and time to design and to conduct. In this paper, we first describe the
benefits of conducting experiments in online labor markets; we then use one
such market to replicate three classic experiments and confirm their results.
We confirm that subjects (1) reverse decisions in response to how a
decision-problem is framed, (2) have pro-social preferences (value payoffs to
others positively), and (3) respond to priming by altering their choices. We
also conduct a labor supply field experiment in which we confirm that workers
have upward sloping labor supply curves. In addition to reporting these
results, we discuss the unique threats to validity in an online setting and
propose methods for coping with these threats. We also discuss the external
validity of results from online domains and explain why online results can have
external validity equal to or even better than that of traditional methods,
depending on the research question. We conclude with our views on the potential
role that online experiments can play within the social sciences, and then
recommend software development priorities and best practices
Automated Influence and the Challenge of Cognitive Security
Advances in AI are powering increasingly precise and widespread computational propaganda, posing serious threats to national security. The military and intelligence communities are starting to discuss ways to engage in this space, but the path forward is still unclear. These developments raise pressing ethical questions, about which existing ethics frameworks are silent. Understanding these challenges through the lens of “cognitive security,” we argue, offers a promising approach
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