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Language identification ("LI") is the problem of determining the natural language that a document or part thereof is written in. Automatic LI has been extensively researched for over fifty years. Today, LI is a key part of many text processing pipelines,...
The goal of this work is to investigate the impact of missing values in clustering joint categorical social sequences. Identifying patterns in sociodemographic longitudinal data is important in a number of social science settings. However, performing ...
The kind of causal inference seen in natural human thought can be "algorithmitized" to help produce human-level machine intelligence.
Researchers from across the social and computer sciences are increasingly using machine learning to study and address global development challenges. This article examines the burgeoning field of machine learning for the developing world (ML4D). First, ...
Tracing 20 years of progress in making machines hear our emotions based on speech signal properties.
In this article, we present a distributed algorithm for allocating resources to tasks in multiagent systems, one that adapts well to dynamic task arrivals where new work arises at short notice. Our algorithm is designed to leverage preemption if it is ...
We propose in this article a new approach to robot cognitive control based on a stimulus-response framework that models both a robot’s stimuli and the robot’s decision to switch tasks in response to or inhibit the stimuli. In an autonomous system, we ...
Many areas of computer science require answering questions about reachability in compactly described discrete transition systems. Answering such questions effectively requires techniques to be able to do so without building the entire system. In ...
This research investigates the use of Artificial Neural Networks (ANNs) to predict first year student retention rates. Based on a significant body of previous research, this work expands on previous attempts to predict student outcomes using machine-...
Item-to-item recommendation -- when the most similar items sought to the actual item -- is an important recommendation scenario in practical recommender systems. One way to solve this task is to use the similarity between item feature vectors of ...
Speaking using unconstrained natural language is an intuitive and flexible way for humans to interact with robots. Understanding this kind of linguistic input is challenging because diverse words and phrases must be mapped into structures that the robot ...
Allomorphic variation, or form variation among morphs with the same meaning, is a stumbling block to morphological induction (MI). To address this problem, we present a hybrid approach that uses a small amount of linguistic knowledge in the form of ...
Emotional speech synthesis is traditionally achieved using time-pitch manipulation of the synthesized acoustic waveform. Rule-based approaches rely on rules that describe the behavior of the pitch frequency along time to generate time-pitch values. ...
An interesting problem related to geometric constraint solving is the choice of the "good" solution. The suitability and effectiveness of genetic algorithms applied to this problem has been demonstrated but their performance depends on the values ...
The processes and representations used to generate the behavior of expressive virtual characters are a valuable and largely untapped resource for helping those characters make sense of the world around them. In this paper, we present Max T. Mouse, an ...
Scene-Driver is a software toolkit for the reuse of broadcast animation content to provide new engaging experiences for children. It has been developed and tested using content from the children's television series "Tiny Planets". Scene-Driver can be ...
We propose a new declarative planning language, called K, which is based on principles and methods of logic programming. In this language, transitions between states of knowledge can be described, rather than transitions between completely described ...
Recent advances in cooperative knowledge-based systems (CKBS) offer significant promise for intelligent interaction between multiple AI systems for solving larger, more complex problems. In this paper, we propose a logical, qualitative problem-solving ...