NAIS: Neural Attentive Item Similarity Model for Recommendation

Item-to-item collaborative filtering (aka. item-based CF) has been long used for building recommender systems in industrial settings, owing to its interpretability and efficiency in real-time personalization. It builds a user's profile as her historically interacted items, recommending new items that are similar to the user's profile. As such, the key to an item-based CF method is in the estimation of item similarities. Early approaches use statistical measures such as cosine similarity and Pearson coefficient to estimate item similarities, which are less accurate since they lack tailored optimization for the recommendation task. In recent years, several works attempt to learn item similarities from data, by expressing the similarity as an underlying model and estimating model parameters by optimizing a recommendation-aware objective function. While extensive efforts have been made to use shallow linear models for learning item similarities, there has been relatively less work exploring nonlinear neural network models for item-based CF. In this work, we propose a neural network model named Neural Attentive Item Similarity model (NAIS) for item-based CF. The key to our design of NAIS is an attention network, which is capable of distinguishing which historical items in a user profile are more important for a prediction. Compared to the state-of-the-art item-based CF method Factored Item Similarity Model (FISM), our NAIS has stronger representation power with only a few additional parameters brought by the attention network. Extensive experiments on two public benchmarks demonstrate the effectiveness of NAIS. This work is the first attempt that designs neural network models for item-based CF, opening up new research possibilities for future developments of neural recommender systems

Optimized complex power quality classifier using one vs. rest support vector machine

Nowadays, power quality issues are becoming a significant research topic because of the increasing inclusion of very sensitive devices and considerable renewable energy sources. In general, most of the previous power quality classification techniques focused on single power quality events and did not include an optimal feature selection process. This paper presents a classification system that employs Wavelet Transform and the RMS profile to extract the main features of the measured waveforms containing either single or complex disturbances. A data mining process is designed to select the optimal set of features that better describes each disturbance present in the waveform. Support Vector Machine binary classifiers organized in a ?One Vs Rest? architecture are individually optimized to classify single and complex disturbances. The parameters that rule the performance of each binary classifier are also individually adjusted using a grid search algorithm that helps them achieve optimal performance. This specialized process significantly improves the total classification accuracy. Several single and complex disturbances were simulated in order to train and test the algorithm. The results show that the classifier is capable of identifying >99% of single disturbances and >97% of complex disturbances.Fil: de Yong, David Marcelo. Universidad Nacional de Río Cuarto. Facultad de Ingeniería. Departamento de Electricidad y Electrónica; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba; ArgentinaFil: Bhowmik, Sudipto. Nexant Inc; Estados UnidosFil: Magnago, Fernando. Universidad Nacional de Río Cuarto. Facultad de Ingeniería. Departamento de Electricidad y Electrónica; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba; Argentin

P and M class phasor measurement unit algorithms using adaptive cascaded filters

The new standard C37.118.1 lays down strict performance limits for phasor measurement units (PMUs) under steady-state and dynamic conditions. Reference algorithms are also presented for the P (performance) and M (measurement) class PMUs. In this paper, the performance of these algorithms is analysed during some key signal scenarios, particularly those of off-nominal frequency, frequency ramps, and harmonic contamination. While it is found that total vector error (TVE) accuracy is relatively easy to achieve, the reference algorithm is not able to achieve a useful ROCOF (rate of change of frequency) accuracy. Instead, this paper presents alternative algorithms for P and M class PMUs which use adaptive filtering techniques in real time at up to 10 kHz sample rates, allowing consistent accuracy to be maintained across a ±33% frequency range. ROCOF errors can be reduced by factors of >40 for P class and >100 for M class devices

Electromagnetic compatibility aware design and testing of intermodulation distortion under multiple co-located sources illumination

Current electromagnetic immunity tests mainly rely on single-frequency sources. However, the evolution of electronic systems leads to miniaturisation and low-cost solutions, in which filters are omitted in front of active non-linear components, also for efficiency reasons. As a result, intermodulation products may leak into the band of operation. The authors propose a comprehensive strategy consisting of design and test methodologies to evaluate in-the-band leakage of out-of-band undesired components, using multiple-tone excitation and relying on an anechoic chamber as test facility. The aim of this study is to demonstrate that an anechoic chamber together with a dual-source network analyser represents an optimal facility to investigate signal integrity issues owing to leakage of intermodulation products

On-Disk Data Processing: Issues and Future Directions

In this paper, we present a survey of "on-disk" data processing (ODDP). ODDP, which is a form of near-data processing, refers to the computing arrangement where the secondary storage drives have the data processing capability. Proposed ODDP schemes vary widely in terms of the data processing capability, target applications, architecture and the kind of storage drive employed. Some ODDP schemes provide only a specific but heavily used operation like sort whereas some provide a full range of operations. Recently, with the advent of Solid State Drives, powerful and extensive ODDP solutions have been proposed. In this paper, we present a thorough review of architectures developed for different on-disk processing approaches along with current and future challenges and also identify the future directions which ODDP can take.Comment: 24 pages, 17 Figures, 3 Table