Authorship Attribution Using a Neural Network Language Model

In practice, training language models for individual authors is often expensive because of limited data resources. In such cases, Neural Network Language Models (NNLMs), generally outperform the traditional non-parametric N-gram models. Here we investigate the performance of a feed-forward NNLM on an authorship attribution problem, with moderate author set size and relatively limited data. We also consider how the text topics impact performance. Compared with a well-constructed N-gram baseline method with Kneser-Ney smoothing, the proposed method achieves nearly 2:5% reduction in perplexity and increases author classification accuracy by 3:43% on average, given as few as 5 test sentences. The performance is very competitive with the state of the art in terms of accuracy and demand on test data. The source code, preprocessed datasets, a detailed description of the methodology and results are available at https://github.com/zge/authorship-attribution.Comment: Proceedings of the 30th AAAI Conference on Artificial Intelligence (AAAI'16

Occupancy estimation in smart buildings using audio-processing techniques

In the past few years, several case studies have illustrated that the use of occupancy information in buildings leads to energy-efficient and low-cost HVAC operation. The widely presented techniques for occupancy estimation include temperature, humidity, CO2 concentration, image camera, motion sensor and passive infrared (PIR) sensor. So far little studies have been reported in literature to utilize audio and speech processing as indoor occupancy prediction technique. With rapid advances of audio and speech processing technologies, nowadays it is more feasible and attractive to integrate audio-based signal processing component into smart buildings. In this work, we propose to utilize audio processing techniques (i.e., speaker recognition and background audio energy estimation) to estimate room occupancy (i.e., the number of people inside a room). Theoretical analysis and simulation results demonstrate the accuracy and effectiveness of this proposed occupancy estimation technique. Based on the occupancy estimation, smart buildings will adjust the thermostat setups and HVAC operations, thus, achieving greater quality of service and drastic cost savings

Refining Wi-Fi Based Indoor Localization with Li-Fi Assisted Model Calibration in Smart Buildings

In recent years, there has been an increasing number of information technologies utilized in buildings to advance the idea of "smart buildings". Among various potential techniques, the use of Wi-Fi based indoor positioning allows to locate and track smartphone users inside a building, therefore, location-aware intelligent solutions can be applied to control and of building operations. These location-aware indoor services (e.g., path finding, internet of things, location based advertising) demand real-time accurate indoor localization, which is a key issue to guarantee high quality of service in smart buildings. This paper presents a new Wi-Fi based indoor localization technique that achieves significantly improvement of indoor positioning accuracy with the help of Li-Fi assisted coefficient calibration. The proposed technique leverages indoor existing Li-Fi lighting and Wi-Fi infrastructure, and results in a cost-effective and user-convenient indoor accurate localization framework. In this work, experimental study and measurements are conducted to verify the performance of the proposed idea. The results substantiate the concept of refining Wi-Fi based indoor localization with Li-Fi assisted computation calibration.Comment: International Conference on Computing in Civil and Building Engineering (ICCCBE) 201

Excellent Silicon Surface Passivation Achieved by Industrial Inductively Coupled Plasma Deposited Hydrogenated Intrinsic Amorphous Silicon Suboxide

We present an alternative method of depositing a high-quality passivation film for heterojunction silicon wafer solar cells, in this paper. The deposition of hydrogenated intrinsic amorphous silicon suboxide is accomplished by decomposing hydrogen, silane, and carbon dioxide in an industrial remote inductively coupled plasma platform. Through the investigation on CO2 partial pressure and process temperature, excellent surface passivation quality and optical properties are achieved. It is found that the hydrogen content in the film is much higher than what is commonly reported in intrinsic amorphous silicon due to oxygen incorporation. The observed slow depletion of hydrogen with increasing temperature greatly enhances its process window as well. The effective lifetime of symmetrically passivated samples under the optimal condition exceeds 4.7 ms on planar n-type Czochralski silicon wafers with a resistivity of 1 Ωcm, which is equivalent to an effective surface recombination velocity of less than 1.7 cms−1 and an implied open-circuit voltage (Voc) of 741 mV. A comparison with several high quality passivation schemes for solar cells reveals that the developed inductively coupled plasma deposited films show excellent passivation quality. The excellent optical property and resistance to degradation make it an excellent substitute for industrial heterojunction silicon solar cell production