Learning from Noisy Label Distributions

In this paper, we consider a novel machine learning problem, that is, learning a classifier from noisy label distributions. In this problem, each instance with a feature vector belongs to at least one group. Then, instead of the true label of each instance, we observe the label distribution of the instances associated with a group, where the label distribution is distorted by an unknown noise. Our goals are to (1) estimate the true label of each instance, and (2) learn a classifier that predicts the true label of a new instance. We propose a probabilistic model that considers true label distributions of groups and parameters that represent the noise as hidden variables. The model can be learned based on a variational Bayesian method. In numerical experiments, we show that the proposed model outperforms existing methods in terms of the estimation of the true labels of instances.Comment: Accepted in ICANN201

Communications interface for wireless communications headset

A universal interface adapter circuit interfaces, for example, a wireless communications headset with any type of communications system, including those that require push-to-talk (PTT) signaling. The interface adapter is comprised of several main components, including an RF signaling receiver, a microcontroller and associated circuitry for decoding and processing the received signals, and programmable impedance matching and line interfacing circuitry for interfacing a wireless communications headset system base to a communications system. A signaling transmitter, which is preferably portable (e.g., handheld), is employed by the wireless headset user to send signals to the signaling receiver. In an embodiment of the invention directed specifically to push-to-talk (PTT) signaling, the wireless headset user presses a button on the signaling transmitter when they wish to speak. This sends a signal to the microcontroller which decodes the signal and recognizes the signal as being a PTT request. In response, the microcontroller generates a control signal that closes a switch to complete a voice connection between the headset system base and the communications system so that the user can communicate with the communications system. With this arrangement, the wireless headset can be interfaced to any communications system that requires PTT signaling, without modification of the headset device. In addition, the interface adapter can also be configured to respond to or deliver any other types of signals, such as dual-tone-multiple-frequency (DTMF) tones, and on/off hook signals. The present invention is also scalable, and permits multiple wireless users to operate independently in the same environment through use of a plurality of the interface adapters

Noise-Canceling Helmet Audio System

A prototype helmet audio system has been developed to improve voice communication for the wearer in a noisy environment. The system was originally intended to be used in a space suit, wherein noise generated by airflow of the spacesuit life-support system can make it difficult for remote listeners to understand the astronaut s speech and can interfere with the astronaut s attempt to issue vocal commands to a voice-controlled robot. The system could be adapted to terrestrial use in helmets of protective suits that are typically worn in noisy settings: examples include biohazard, fire, rescue, and diving suits. The system (see figure) includes an array of microphones and small loudspeakers mounted at fixed positions in a helmet, amplifiers and signal-routing circuitry, and a commercial digital signal processor (DSP). Notwithstanding the fixed positions of the microphones and loudspeakers, the system can accommodate itself to any normal motion of the wearer s head within the helmet. The system operates in conjunction with a radio transceiver. An audio signal arriving via the transceiver intended to be heard by the wearer is adjusted in volume and otherwise conditioned and sent to the loudspeakers. The wearer s speech is collected by the microphones, the outputs of which are logically combined (phased) so as to form a microphone- array directional sensitivity pattern that discriminates in favor of sounds coming from vicinity of the wearer s mouth and against sounds coming from elsewhere. In the DSP, digitized samples of the microphone outputs are processed to filter out airflow noise and to eliminate feedback from the loudspeakers to the microphones. The resulting conditioned version of the wearer s speech signal is sent to the transceiver

Leaders or Followers? A Temporal Analysis of Tweets from IRA Trolls

The Internet Research Agency (IRA) influences online political conversations in the United States, exacerbating existing partisan divides and sowing discord. In this paper we investigate the IRA's communication strategies by analyzing trending terms on Twitter to identify cases in which the IRA leads or follows other users. Our analysis focuses on over 38M tweets posted between 2016 and 2017 from IRA users (n=3,613), journalists (n=976), members of Congress (n=526), and politically engaged users from the general public (n=71,128). We find that the IRA tends to lead on topics related to the 2016 election, race, and entertainment, suggesting that these are areas both of strategic importance as well having the highest potential impact. Furthermore, we identify topics where the IRA has been relatively ineffective, such as tweets on military, political scandals, and violent attacks. Despite many tweets on these topics, the IRA rarely leads the conversation and thus has little opportunity to influence it. We offer our proposed methodology as a way to track the strategic choices of future influence operations in real-time.Comment: ICWSM 202

Effective balancing error and user effort in interactive handwriting recognition

This is the author’s version of a work that was accepted for publication in Pattern Recognition Letters. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Pattern Recognition Letters, Volume 37, 1 February 2014, Pages 135–142 DOI 10.1016/j.patrec.2013.03.010[EN] Transcription of handwritten text documents is an expensive and time-consuming task. Unfortunately, the accuracy of current state-of-the-art handwriting recognition systems cannot guarantee fully-automatic high quality transcriptions, so we need to revert to the computer assisted approach. Although this approach reduces the user effort needed to transcribe a given document, the transcription of handwriting text documents still requires complete manual supervision. An especially appealing scenario is the interactive transcription of handwriting documents, in which the user defines the amount of errors that can be tolerated in the final transcribed document. Under this scenario, the transcription of a handwriting text document could be obtained efficiently, supervising only a certain number of incorrectly recognised words. In this work, we develop a new method for predicting the error rate in a block of automatically recognised words, and estimate how much effort is required to correct a transcription to a certain user-defined error rate. The proposed method is included in an interactive approach to transcribing handwritten text documents, which efficiently employs user interactions by means of active and semi-supervised learning techniques, along with a hypothesis recomputation algorithm based on constrained Viterbi search. Transcription results, in terms of trade-off between user effort and transcription accuracy, are reported for two real handwritten documents, and prove the effectiveness of the proposed approach.The research leading to these results has received funding from the European Union Seventh Framework Programme (FP7/2007-2013) under Grant agreement No 287755 (transLectures). Also supported by the EC (FEDER, FSE), the Spanish Government (MICINN, MITyC, "Plan E", under grants MIPRCV "Consolider Ingenio 2010", MITTRAL (TIN2009-14633-C03-01), iTrans2 (TIN2009-14511), and FPU (AP2007-02867), and the Generalitat Valenciana (Grants Prometeo/2009/014 and GV/2010/067). Special thanks to Jesus Andres for his fruitful discussions.Serrano Martinez Santos, N.; Civera Saiz, J.; Sanchis Navarro, JA.; Juan Císcar, A. (2014). Effective balancing error and user effort in interactive handwriting recognition. Pattern Recognition Letters. 37(1):135-142. https://doi.org/10.1016/j.patrec.2013.03.010S13514237