To Normalize, or Not to Normalize: The Impact of Normalization on Part-of-Speech Tagging

Does normalization help Part-of-Speech (POS) tagging accuracy on noisy, non-canonical data? To the best of our knowledge, little is known on the actual impact of normalization in a real-world scenario, where gold error detection is not available. We investigate the effect of automatic normalization on POS tagging of tweets. We also compare normalization to strategies that leverage large amounts of unlabeled data kept in its raw form. Our results show that normalization helps, but does not add consistently beyond just word embedding layer initialization. The latter approach yields a tagging model that is competitive with a Twitter state-of-the-art tagger.Comment: In WNUT 201

An audio-based sports video segmentation and event detection algorithm

In this paper, we present an audio-based event detection algorithm shown to be effective when applied to Soccer video. The main benefit of this approach is the ability to recognise patterns that display high levels of crowd response correlated to key events. The soundtrack from a Soccer sequence is first parameterised using Mel-frequency Cepstral coefficients. It is then segmented into homogenous components using a windowing algorithm with a decision process based on Bayesian model selection. This decision process eliminated the need for defining a heuristic set of rules for segmentation. Each audio segment is then labelled using a series of Hidden Markov model (HMM) classifiers, each a representation of one of 6 predefined semantic content classes found in Soccer video. Exciting events are identified as those segments belonging to a crowd cheering class. Experimentation indicated that the algorithm was more effective for classifying crowd response when compared to traditional model-based segmentation and classification techniques

E-QED: Electrical Bug Localization During Post-Silicon Validation Enabled by Quick Error Detection and Formal Methods

During post-silicon validation, manufactured integrated circuits are extensively tested in actual system environments to detect design bugs. Bug localization involves identification of a bug trace (a sequence of inputs that activates and detects the bug) and a hardware design block where the bug is located. Existing bug localization practices during post-silicon validation are mostly manual and ad hoc, and, hence, extremely expensive and time consuming. This is particularly true for subtle electrical bugs caused by unexpected interactions between a design and its electrical state. We present E-QED, a new approach that automatically localizes electrical bugs during post-silicon validation. Our results on the OpenSPARC T2, an open-source 500-million-transistor multicore chip design, demonstrate the effectiveness and practicality of E-QED: starting with a failed post-silicon test, in a few hours (9 hours on average) we can automatically narrow the location of the bug to (the fan-in logic cone of) a handful of candidate flip-flops (18 flip-flops on average for a design with ~ 1 Million flip-flops) and also obtain the corresponding bug trace. The area impact of E-QED is ~2.5%. In contrast, deter-mining this same information might take weeks (or even months) of mostly manual work using traditional approaches

Two-tier channel estimation aided near-capacity MIMO transceivers relying on norm-based joint transmit and receive antenna selection

We propose a norm-based joint transmit and receive antenna selection (NBJTRAS) aided near-capacity multiple-input multiple-output (MIMO) system relying on the assistance of a novel two-tier channel estimation scheme. Specifically, a rough estimate of the full MIMO channel is first generated using a low-complexity, low-training-overhead minimum mean square error based channel estimator, which relies on reusing a modest number of radio frequency (RF) chains. NBJTRAS is then carried out based on this initial full MIMO channel estimate. The NBJTRAS aided MIMO system is capable of significantly outperforming conventional MIMO systems equipped with the same modest number of RF chains, while dispensing with the idealised simplifying assumption of having perfectly known channel state information (CSI). Moreover, the initial subset channel estimate associated with the selected subset MIMO channel matrix is then used for activating a powerful semi-blind joint channel estimation and turbo detector-decoder, in which the channel estimate is refined by a novel block-of-bits selection based soft-decision aided channel estimator (BBSB-SDACE) embedded in the iterative detection and decoding process. The joint channel estimation and turbo detection-decoding scheme operating with the aid of the proposed BBSB-SDACE channel estimator is capable of approaching the performance of the near-capacity maximumlikelihood (ML) turbo transceiver associated with perfect CSI. This is achieved without increasing the complexity of the ML turbo detection and decoding process

Correcting menu usability problems with sound

Future human-computer interfaces will use more than just graphical output to display information. In this paper we suggest that sound and graphics together can be used to improve interaction. We describe an experiment to improve the usability of standard graphical menus by the addition of sound. One common difficulty is slipping off a menu item by mistake when trying to select it. One of the causes of this is insufficient feedback. We designed and experimentally evaluated a new set of menus with much more salient audio feedback to solve this problem. The results from the experiment showed a significant reduction in the subjective effort required to use the new sonically-enhanced menus along with significantly reduced error recovery times. A significantly larger number of errors were also corrected with sound

Real-time human ambulation, activity, and physiological monitoring:taxonomy of issues, techniques, applications, challenges and limitations

Automated methods of real-time, unobtrusive, human ambulation, activity, and wellness monitoring and data analysis using various algorithmic techniques have been subjects of intense research. The general aim is to devise effective means of addressing the demands of assisted living, rehabilitation, and clinical observation and assessment through sensor-based monitoring. The research studies have resulted in a large amount of literature. This paper presents a holistic articulation of the research studies and offers comprehensive insights along four main axes: distribution of existing studies; monitoring device framework and sensor types; data collection, processing and analysis; and applications, limitations and challenges. The aim is to present a systematic and most complete study of literature in the area in order to identify research gaps and prioritize future research directions

Enhancing Energy Minimization Framework for Scene Text Recognition with Top-Down Cues

Recognizing scene text is a challenging problem, even more so than the recognition of scanned documents. This problem has gained significant attention from the computer vision community in recent years, and several methods based on energy minimization frameworks and deep learning approaches have been proposed. In this work, we focus on the energy minimization framework and propose a model that exploits both bottom-up and top-down cues for recognizing cropped words extracted from street images. The bottom-up cues are derived from individual character detections from an image. We build a conditional random field model on these detections to jointly model the strength of the detections and the interactions between them. These interactions are top-down cues obtained from a lexicon-based prior, i.e., language statistics. The optimal word represented by the text image is obtained by minimizing the energy function corresponding to the random field model. We evaluate our proposed algorithm extensively on a number of cropped scene text benchmark datasets, namely Street View Text, ICDAR 2003, 2011 and 2013 datasets, and IIIT 5K-word, and show better performance than comparable methods. We perform a rigorous analysis of all the steps in our approach and analyze the results. We also show that state-of-the-art convolutional neural network features can be integrated in our framework to further improve the recognition performance

Review of Face Detection Systems Based Artificial Neural Networks Algorithms

Face detection is one of the most relevant applications of image processing and biometric systems. Artificial neural networks (ANN) have been used in the field of image processing and pattern recognition. There is lack of literature surveys which give overview about the studies and researches related to the using of ANN in face detection. Therefore, this research includes a general review of face detection studies and systems which based on different ANN approaches and algorithms. The strengths and limitations of these literature studies and systems were included also.Comment: 16 pages, 12 figures, 1 table, IJMA Journa