The study proposes a personalised session-based recommender system that embeds items by using Word2Vec and sequentially updates the session and user embeddings with the hierarchicalization and aggregation of item embeddings. To process a recommendation request, the system constructs a real-time user embedding that considers users’ general preferences and sequential behaviour to handle short-term changes in user preferences with a low computational cost. The system performance was experimentally evaluated in terms of the accuracy, diversity, and novelty of the ranking of recommended items and the training and prediction times of the system for three different datasets. The results of these evaluations were then compared with those of the five baseline systems. According to the evaluation experiment, the proposed system achieved a relatively high recommendation accuracy compared with baseline systems and the diversity and novelty scores of the proposed system did not fall below 90% for any dataset. Furthermore, the training times of the Word2Vec-based systems, including the proposed system, were shorter than those of FPMC and GRU4Rec. The evaluation results suggest that the proposed recommender system succeeds in keeping the computational cost for training low while maintaining high-level recommendation accuracy, diversity, and novelty.

This paper presents a deep network based on unrolling the diffusion process with the morphological Laplacian. The diffusion process is an iterative algorithm that can solve the diffusion equation and represents time evolution with Laplacian. The diffusion process is applied to smoothing of images and has been extended with non-linear operators for various image processing tasks. In this study, we introduce the morphological Laplacian to the basic diffusion process and unwrap to deep networks. The morphological filters are non-linear operators with parameters that are referred to as structuring elements. The discrete Laplacian can be approximated with the morphological filters without multiplications. Owing to the non-linearity of the morphological filter with trainable structuring elements, the training uses error back propagation and the network of the morphology can be adapted to specific image processing applications. We introduce two extensions of the morphological Laplacian for deep networks. Since the morphological filters are realized with addition, max, and min, the error caused by the limited bit-length is not amplified. Consequently, the morphological parts of the network are implemented in unsigned 8-bit integer with single instruction multiple data set (SIMD) to achieve fast computation on small devices. We applied the proposed network to image completion and Gaussian denoising. The results and computational time are compared with other denoising algorithm and deep networks.

This paper proposes a novel random access identifier (RAID)-linked receiver beamforming method for time division duplex (TDD)-based random access. When the number of receiver antennas at the base station is large in a massive multiple-input multiple-output (MIMO) scenario, the channel estimation accuracy per receiver antenna at the base station receiver is degraded due to the limited received signal power per antenna from the user terminal. This results in degradation in the receiver beamforming (BF) or antenna diversity combining and active RAID detection. The purpose of the proposed method is to achieve accurate active RAID detection and channel estimation with a reasonable level of computational complexity at the base station receiver. In the proposed method, a unique receiver BF vector applied at the base station is linked to each of the M RAIDs prepared by the system. The user terminal selects an appropriate pair comprising a receiver BF vector and a RAID in advance based on the channel estimation results in the downlink assuming channel reciprocity in a TDD system. Therefore, per-receiver antenna channel estimation for receiver BF is not necessary in the proposed method. Furthermore, in order to utilize fully the knowledge of the channel at the user transmitter, we propose applying transmitter filtering (TF) to the proposed method for effective channel shortening in order to increase the orthogonal preambles for active RAID detection and channel estimation prepared for each RAID. Computer simulation results show that the proposed method greatly improves the accuracy of active RAID detection and channel estimation. This results in lower error rates than that for the conventional method performing channel estimation at each antenna in a massive MIMO environment.

This paper presents a deep network based on morphological filters for Gaussian denoising. The morphological filters can be applied with only addition, max, and min functions and require few computational resources. Therefore, the proposed network is suitable for implementation using a small microprocessor. Each layer of the proposed network consists of a top-hat transform, which extracts small peaks and valleys of noise components from the input image. Noise components are iteratively reduced in each layer by subtracting the noise components from the input image. In this paper, the extensions of opening and closing are introduced as linear combinations of the morphological filters for the top-hat transform of this deep network. Multiplications are only required for the linear combination of the morphological filters in the proposed network. Because almost all parameters of the network are structuring elements of the morphological filters, the feature maps and parameters can be represented in short bit-length integer form, which is suitable for implementation with single instructions, multiple data (SIMD) instructions. Denoising examples show that the proposed network obtains denoising results comparable to those of BM3D [1] without linear convolutions and with approximately one tenth the number of parameters of a full-scale deep convolutional neural network [2]. Moreover, the computational time of the proposed method using SIMD instructions of a microprocessor is also presented.

The prediction of the malfunction timing of wind turbines is essential for maintaining the high profitability of the wind power generation industry. Studies have been conducted on machine learning methods that use condition monitoring system data, such as vibration data, and supervisory control and data acquisition (SCADA) data to detect and predict anomalies in wind turbines automatically. Autoencoder-based techniques that use unsupervised learning where the anomaly pattern is unknown have attracted significant interest in the area of anomaly detection and prediction. In particular, vibration data are considered useful because they include the changes that occur in the early stages of a malfunction. However, when autoencoder-based techniques are applied for prediction purposes, in the training process it is difficult to distinguish the difference between operating and non-operating condition data, which leads to the degradation of the prediction performance. In this letter, we propose a method in which both vibration data and SCADA data are utilized to improve the prediction performance, namely, a method that uses a power curve composed of active power and wind speed. We evaluated the method's performance using vibration and SCADA data obtained from an actual wind farm.

Predicting the malfunction timing of wind turbines is essential for maintaining the high profitability of the wind power generation business. Machine learning methods have been studied using condition monitoring system data, such as vibration data, and supervisory control and data acquisition (SCADA) data, to detect and predict anomalies in wind turbines automatically. Autoencoder-based techniques have attracted significant interest in the detection or prediction of anomalies through unsupervised learning, in which the anomaly pattern is unknown. Although autoencoder-based techniques have been proven to detect anomalies effectively using relatively stable SCADA data, they perform poorly in the case of deteriorated SCADA data. In this letter, we propose a power-curve filtering method, which is a preprocessing technique used before the application of an autoencoder-based technique, to mitigate the dirtiness of SCADA data and improve the prediction performance of wind turbine degradation. We have evaluated its performance using SCADA data obtained from a real wind-farm.

To find the exact items from the massive patent resources for users is a matter of great urgency. Although the recommender systems have shot this problem to a certain extent, there are still some challenging problems, such as tracking user interests and improving the recommendation quality when the rating matrix is extremely sparse. In this paper, we propose a novel method called Collaborative Filtering Auto-Encoder for the top-N recommendation. This method employs Auto-Encoders to extract the item's features, converts a high-dimensional sparse vector into a low-dimensional dense vector, and then uses the dense vector for similarity calculation. At the same time, to make the recommendation list closer to the user's recent interests, we divide the recommendation weight into time-based and recent similarity-based weights. In fact, the proposed method is an improved, item-based collaborative filtering model with more flexible components. Experimental results show that the method consistently outperforms state-of-the-art top-N recommendation methods by a significant margin on standard evaluation metrics.

Multikernel adaptive filtering is an attractive nonlinear approach to online estimation/tracking tasks. Despite its potential advantages over its single-kernel counterpart, a use of inappropriately weighted kernels may result in a negligible performance gain. In this paper, we propose an efficient recursive kernel weighting technique for multikernel adaptive filtering to activate all the kernels. The proposed weights equalize the convergence rates of all the corresponding partial coefficient errors. The proposed weights are implemented via a certain metric design based on the weighting matrix. Numerical examples show, for synthetic and multiple real datasets, that the proposed technique exhibits a better performance than the manually-tuned kernel weights, and that it significantly outperforms the online multiple kernel regression algorithm.

This paper proposes a parallel peak cancellation (PC) process for the computational complexity-efficient algorithm called PC with a channel-null constraint (PCCNC) in the adaptive peak-to-average power ratio (PAPR) reduction method using the null space in a multiple-input multiple-output (MIMO) channel for MIMO-orthogonal frequency division multiplexing (OFDM) signals. By simultaneously adding multiple PC signals to the time-domain transmission signal vector, the required number of iterations of the iterative algorithm is effectively reduced along with the PAPR. We implement a constraint in which the PC signal is transmitted only to the null space in the MIMO channel by beamforming (BF). By doing so the data streams do not experience interference from the PC signal on the receiver side. Since the fast Fourier transform (FFT) and inverse FFT (IFFT) operations at each iteration are not required unlike the previous algorithm and thanks to the newly introduced parallel processing approach, the enhanced PCCNC algorithm reduces the required total computational complexity and number of iterations compared to the previous algorithms while achieving the same throughput-vs.-PAPR performance.

In Internet applications, when users search for information, the search engines invariably return some invalid webpages that do not contain valid information. These invalid webpages interfere with the users' access to useful information, affect the efficiency of users' information query and occupy Internet resources. Accurate and fast filtering of invalid webpages can purify the Internet environment and provide convenience for netizens. This paper proposes an invalid webpage filtering model (HAIF) based on deep learning and hierarchical attention mechanism. HAIF improves the semantic and sequence information representation of webpage text by concatenating lexical-level embeddings and paragraph-level embeddings. HAIF introduces hierarchical attention mechanism to optimize the extraction of text sequence features and webpage tag features. Among them, the local-level attention layer optimizes the local information in the plain text. By concatenating the input embeddings and the feature matrix after local-level attention calculation, it enriches the representation of information. The tag-level attention layer introduces webpage structural feature information on the attention calculation of different HTML tags, so that HAIF is better applicable to the Internet resource field. In order to evaluate the effectiveness of HAIF in filtering invalid pages, we conducted various experiments. Experimental results demonstrate that, compared with other baseline models, HAIF has improved to various degrees on various evaluation criteria.

Nowadays recommender systems (RS) keep drawing attention from academia, and collaborative filtering (CF) is the most successful technique for building RS. To overcome the inherent limitation, which is referred to as data sparsity in CF, various solutions are proposed to incorporate additional social information into recommendation processes, such as trust networks. However, existing methods suffer from multi-source data integration (i.e., fusion of social information and ratings), which is the basis for similarity calculation of user preferences. To this end, we propose a social collaborative filtering method based on novel trust metrics. Firstly, we use Graph Convolutional Networks (GCNs) to learn the associations between social information and user ratings while considering the underlying social network structures. Secondly, we measure the direct-trust values between neighbors by representing multi-source data as user ratings on popular items, and then calculate the indirect-trust values based on trust propagations. Thirdly, we employ all trust values to create a social regularization in user-item rating matrix factorization in order to avoid overfittings. The experiments on real datasets show that our approach outperforms the other state-of-the-art methods on usage of multi-source data to alleviate data sparsity.

This paper proposes a computational complexity-reduced algorithm for an adaptive peak-to-average power ratio (PAPR) reduction method previously developed by members of our research group that uses the null space in a multiple-input multiple-output (MIMO) channel for MIMO-orthogonal frequency division multiplexing (OFDM) signals. The proposed algorithm is an extension of the peak cancellation (PC) signal-based method that has been mainly investigated for per-antenna PAPR reduction. This method adds the PC signal, which is designed so that the out-of-band radiation is removed/reduced, directly to the time-domain transmission signal at each antenna. The proposed method, referred to as PCCNC (PC with channel-null constraint), performs vector-level signal processing in the PC signal generation so that the PC signal is transmitted only to the null space in the MIMO channel. We investigate three methods to control the beamforming (BF) vector in the PC signal, which is a key factor in determining the achievable PAPR performance of the algorithm. Computer simulation results show that the proposed PCCNC achieves approximately the same throughput-vs.-PAPR performance as the previous method while dramatically reducing the required computational cost.

Collaborative filtering (CF) is one of the most popular approaches to building Recommender systems (RS) and has been extensively implemented in many online applications. But it still suffers from the new user cold start problem that users have only a small number of items interaction or purchase records in the system, resulting in poor recommendation performance. Thus, we design a new similarity model which can fully utilize the limited rating information of cold users. We first construct a new metric, Popularity-Mean Squared Difference, considering the influence of popular items, average difference between two user's common ratings and non-numerical information of ratings. Moreover, the second new metric, Singularity-Difference, presents the deviation degree of favor to items between two users. It considers the distribution of the similarity degree of co-ratings between two users as weight to adjust the deviation degree. Finally, we take account of user's personal rating preferences through introducing the mean and variance of user ratings. Experiment results based on three real-life datasets of MovieLens, Epinions and Netflix demonstrate that the proposed model outperforms seven popular similarity methods in terms of MAE, precision, recall and F1-Measure under new user cold start condition.

In this letter, a polarization filtering based transmission (PFBT) scheme is proposed to enhance the spectrum efficiency in wireless communications. In such scheme, the information is divided into several parts and each is conveyed by a polarized signal with a unique polarization state (PS). Then, the polarized signals are added up and transmitted by the dual-polarized antenna. At the receiver side, the oblique projection polarization filters (OPPFs) are adopted to separate each polarized signal. Thus, they can be demodulated separately. We mainly focus on the construction methods of the OPPF matrix when the number of the separate parts is 2 and 3 and evaluate the performance in terms of the capacity and the bit error rate. In addition, we also discuss the probability of the signal separation when the number of the separate parts is equal or greater than 4. Theoretical results and simulation results demonstrate the performance of the proposed scheme.

This letter proposes a novel speech enhancement system based on the ‘L’ shaped triple-microphone. The modified coherence-based algorithm and the first-order differential beamforming are combined to filter the spatial distributed noise. The experimental results reveal that the proposed algorithm achieves significant performance in spatial filtering under different noise scenarios.

We prove generalization error bounds of classes of low-rank matrices with some norm constraints for collaborative filtering tasks. Our bounds are tighter, compared to known bounds using rank or the related quantity only, by taking the additional L1 and L∞ constraints into account. Also, we show that our bounds on the Rademacher complexity of the classes are optimal.

We consider online linear optimization over symmetric positive semi-definite matrices, which has various applications including the online collaborative filtering. The problem is formulated as a repeated game between the algorithm and the adversary, where in each round t the algorithm and the adversary choose matrices Xt and Lt, respectively, and then the algorithm suffers a loss given by the Frobenius inner product of Xt and Lt. The goal of the algorithm is to minimize the cumulative loss. We can employ a standard framework called Follow the Regularized Leader (FTRL) for designing algorithms, where we need to choose an appropriate regularization function to obtain a good performance guarantee. We show that the log-determinant regularization works better than other popular regularization functions in the case where the loss matrices Lt are all sparse. Using this property, we show that our algorithm achieves an optimal performance guarantee for the online collaborative filtering. The technical contribution of the paper is to develop a new technique of deriving performance bounds by exploiting the property of strong convexity of the log-determinant with respect to the loss matrices, while in the previous analysis the strong convexity is defined with respect to a norm. Intuitively, skipping the norm analysis results in the improved bound. Moreover, we apply our method to online linear optimization over vectors and show that the FTRL with the Burg entropy regularizer, which is the analogue of the log-determinant regularizer in the vector case, works well.

This paper proposes to pre-compute approximate normal distribution functions and store them in textures such that real-time applications can process complex specular surfaces simply by sampling the textures. The proposed method is compatible with the GPU pipeline-based algorithms, and rendering is completed at real time. The experimental results show that the features of complex specular surfaces, such as the glinty appearance of leather and metallic flakes, are successfully reproduced.

Wireless Sensor Networks (WSNs) are randomly deployed in a hostile environment and left unattended. These networks are composed of small auto mouse sensor devices which can monitor target information and send it to the Base Station (BS) for action. The sensor nodes can easily be compromised by an adversary and the compromised nodes can be used to inject false vote or false report attacks. To counter these two kinds of attacks, the Probabilistic Voting-based Filtering Scheme (PVFS) was proposed by Li and Wu, which consists of three phases; 1) Key Initialization and assignment, 2) Report generation, and 3) En-route filtering. This scheme can be a successful countermeasure against these attacks, however, when one or more nodes are compromised, the re-distribution of keys is not handled. Therefore, after a sensor node or Cluster Head (CH) is compromised, the detection power and effectiveness of PVFS is reduced. This also results in adverse effects on the sensor network's lifetime. In this paper, we propose a Fuzzy Rule-based Key Redistribution Method (FRKM) to address the limitations of the PVFS. The experimental results confirm the effectiveness of the proposed method by improving the detection power by up to 13.75% when the key-redistribution period is not fixed. Moreover, the proposed method achieves an energy improvement of up to 9.2% over PVFS.

Based on the known binary and quaternary zero correlation zone (ZCZ) sequence sets, a class of 16-QAM sequence sets with ZCZ is presented, where the term “QAM sequence” means the sequence over the quadrature amplitude modulation (QAM) constellation. The sequence sets obtained by this method achieve an expansion in the number of 16-QAM sequence sets with ZCZ. The proposed sequence sets can be applied to quasi-synchronous code division multiple access (QS-CDMA) systems to eliminate the multiple access interference (MAI) and multipath interference (MPI) and improve the transmission data rate (TDR).