Interpretable Aspect-Aware Capsule Network for Peer Review Based Citation Count Prediction

Citation count prediction is an important task for estimating the future impact of research papers. Most of the existing works utilize the information extracted from the paper itself. In this article, we focus on how to utilize another kind of useful data signal (i.e., peer review text) to improve both the performance and interpretability of the prediction models. Specially, we propose a novel aspect-aware capsule network for citation count prediction based on review text. It contains two major capsule layers, namely the feature capsule layer and the aspect capsule layer, with two different routing approaches, respectively. Feature capsules encode the local semantics from review sentences as the input of aspect capsule layer, whereas aspect capsules aim to capture high-level semantic features that will be served as final representations for prediction. Besides the predictive capacity, we also enhance the model interpretability with two strategies. First, we use the topic distribution of the review text to guide the learning of aspect capsules so that each aspect capsule can represent a specific aspect in the review. Then, we use the learned aspect capsules to generate readable text for explaining the predicted citation count. Extensive experiments on two real-world datasets have demonstrated the effectiveness of the proposed model in both performance and interpretability.

Monitoring System of Vibroacoustic Parameters of a Working Zone

This article discusses the safety problems of the use of aviation technology associated with the influence of operational vibration of aircraft. The topical issue of timely detection and prevention of a dangerous state of critical machines and mechanisms is analyzed. Modern means of measuring vibration parameters, principles of measurement, as well as characteristics of the sensitive element of the measuring transducer, are considered. The block diagram and operation algorithm of the proposed system for monitoring vibroacoustic parameters, which is built on the basis of a piezoelectric transducer, is presented. This system can measure the parameters of noise and vibration and analyze the measured data, signal about exceeding the permissible ranges for human work, display the measured data. The advantage of the proposed system is the connection of the measuring channels with the mainboard using the Bluetooth module, which allows the sensors to measure noise and vibration to be placed in any part of the working area.

Visible Light Wireless Data Center Links with Distinct Beam Configurations

Visible light communication (VLC) is being explored as one promising approach to enable wireless data centers (WDC). Up to now, the visible light wireless data center links are still limited to the conventional Lambertian beam paradigm. The potential coverage gain relevant to the optical beam space is waiting for sufficient investigation. For addressing this issue, in this paper, the dynamic optical beam based WDC coverage enhancement scheme is introduced, and for each transmitter, the best candidate asymmetrical optical beam is selected to load the data signal. Numerical evaluation shows that, compared with the conventional static beam configuration, up to 6.76 dB peak signal to noise ratio (SNR) gain and 4.46 dB average SNR gain could be provided by the proposed dynamic beam scheme. Moreover, this SNR dynamic range is reduced to 36.65 dB while the counterpart of the static non-Lambertian beam configuration is up to 44.78 dB.

Digital filters windowing for data transmission enhancement in communication channel

In this search, an important methodology has been presented for communicated information rectification utilizing advanced channel windowing approach. The modern data communication technologies are ensured with numerous challenges because of their unpredictability and arrangement. Various digital transmission topologies in 4G can't fulfill the requirements in future arrangements, therefore, alternative multicarrier modulation (MCM) becoming the nominated approaches among all other data transmission techniques. Wherein prototype filter configuration is a fundamental system based on which the synthesis and analysis filters are derived. This paper presents a complete review on the ongoing advances of finite impulse response (FIR) filter plan procedures in MCM based correspondence frameworks. Initially, the essential issues are tried, taking into consideration the presentation of available data signal applicants and the FIR filter design concept. At that point the techniques for FIR filter configuration are summed up in subtleties and are center around the accompanying three group’s recurrence testing strategies, windowing based strategies and advancement-based techniques. At last, the exhibitions of different FIR structure strategies are assessed and measured by power spectral density (PSD) and bit error rate (BER), and variable MCM plots as well as their potential prototype filters are examined.

Optical SNR estimation using machine learning

A technique for the estimation of an optical signal-to-noise ratio (OSNR) using machine learning algorithms has been proposed. The algorithms are trained with parameters derived from eye-diagram via simulation in 10 Gb/s On-Off Keying (OOK) nonreturn-to-zero (NRZ) data signal. The performance of different machine learning (ML) techniques namely, multiple linear regression, random forest, and K-nearest neighbor (K-NN) for OSNR estimation in terms of mean square error and R-squared value has been compared. The proposed methods may be useful for intelligent signal analysis in a test instrument and to monitor optical performance.

Particle Classification through the Analysis of the Forward Scattered Signal in Optical Tweezers

The ability to select, isolate, and manipulate micron-sized particles or small clusters has made optical tweezers one of the emergent tools for modern biotechnology. In conventional setups, the classification of the trapped specimen is usually achieved through the acquired image, the scattered signal, or additional information such as Raman spectroscopy. In this work, we propose a solution that uses the temporal data signal from the scattering process of the trapping laser, acquired with a quadrant photodetector. Our methodology rests on a pre-processing strategy that combines Fourier transform and principal component analysis to reduce the dimension of the data and perform relevant feature extraction. Testing a wide range of standard machine learning algorithms, it is shown that this methodology allows achieving accuracy performances around 90%, validating the concept of using the temporal dynamics of the scattering signal for the classification task. Achieved with 500 millisecond signals and leveraging on methods of low computational footprint, the results presented pave the way for the deployment of alternative and faster classification methodologies in optical trapping technologies.

Classification of EEG signal using EACA based approach at SSVEP-BCI

The brain-computer-interfaces (BCI) can also be referred towards a mindmachine interface that can provide a non-muscular communication channel in between the computer device and human brain. To measure the brain activity, electroencephalography (EEG) has been widely utilized in the applications of BCI to work system in real-time. It has been analyzed that the identification probability performed with other methodologies do not provide optimal classification accuracy. Therefore, it is required to focus on the process of feature extraction to achieve maximum classification accuracy. In this paper, a novel process of data-driven spatial has been proposed to improve the detection of steady state visually evoked potentials (SSVEPs) at BCI. Here, EACA has been proposed, which can develop the reproducibility of SSVEP across many trails. Further this can be utilized to improve the SSVEP from a noisy data signal by eliminating the activities of EEG background. In the simulation process, the SSVEP dataset recorded from given 11 subjects are considered. To validate the performance, the state-of-art method is considered to compare with the EDCA based proposed approach.

Artificial intelligence based optical performance monitoring

In this paper, a technique for optical performance monitoring (OPM) using deep learning-based artificial neural network (ANN) is implemented. ANN is trained with parameters derived from eye-diagram for the identification of optical signal to noise ratio (OSNR), chromatic dispersion (CD) and polarisation mode dispersion (PMD) simultaneously and independently in a 10 Gb/s system with non-return-to-zero (NRZ) on-off keying (OOK) data signal. ANN-based OPM confirms that the proposed approach can provide reliable estimated results. The mean squared errors for OSNR, CD and differential group delay (DGD) are found to be 4.6071 dB, 0.0417 ps/nm/km and 0.0016 ps/km, respectively. The proposed technique may be utilized in analyzing the signals of future heterogeneous optical communication networks intelligently.