Speech emotion recognition (SER) has been a complex and difficult task for a long time due to emotional complexity. In this paper, we propose a multitask deep learning approach based on cascaded attention network and self-adaption loss for SER. First, non-personalized features are extracted to represent the process of emotion change while reducing external variables' influence. Second, to highlight salient speech emotion features, a cascade attention network is proposed, where spatial temporal attention can effectively locate the regions of speech that express emotion, while self-attention reduces the dependence on external information. Finally, the influence brought by the differences in gender and human perception of external information is alleviated by using a multitask learning strategy, where a self-adaption loss is introduced to determine the weights of different tasks dynamically. Experimental results on IEMOCAP dataset demonstrate that our method gains an absolute improvement of 1.97% and 0.91% over state-of-the-art strategies in terms of weighted accuracy (WA) and unweighted accuracy (UA), respectively.

This paper proposes a scheme that reduces residual self-interference significantly in the analog-circuit domain on wireless full-duplex relay systems. Full-duplex relay systems utilize the same time and frequency resources for transmission and reception at the relay node to improve spectral efficiency. Our proposed scheme measures multiple responses of the feedback path by changing the direction of the main beam of the transmitter at the relay, and then selecting the optimal direction that minimizes the residual self-interference. Analytical residual self-interference is derived as the criterion to select the optimal direction. In addition, this paper considers the target of residual self-interference power before the analog-to-digital converter (ADC) dependent on the dynamic range in the analog-circuit domain. Analytical probability that the residual interference exceeds the target is derived to help in determining the number of measured responses of the feedback path. Computer simulations validate the analytical results, and show that in particular, the proposed scheme with ten candidates improves the residual self-interference by approximately 6dB at the probability of 0.01 that the residual self-interference exceeds target power compared with a conventional scheme with the feedback path modeled as Rayleigh fading.

To protect user privacy and establish self-information control rights, service providers must notify users of their privacy policies and obtain their consent in advance. The frameworks that impose these requirements are mandatory. Although originally designed to protect user privacy, obtaining user consent in advance has become a mere formality. These problems are induced by the gap between service providers' privacy policies, which prioritize the observance of laws and guidelines, and user expectations which are to easily understand how their data will be handled. To reduce this gap, we construct a tool supporting users in reading privacy policies in Japanese. We designed the tool to present users with separate unique expressions containing relevant information to improve the display format of the privacy policy and render it more comprehensive for Japanese users. To accurately extract the unique expressions from privacy policies, we created training data for machine learning for the constructed tool. The constructed tool provides a summary of privacy policies for users to help them understand the policies of interest. Subsequently, we assess the effectiveness of the constructed tool in experiments and follow-up questionnaires. Our findings reveal that the constructed tool enhances the users' subjective understanding of the services they read about and their awareness of the related risks. We expect that the developed tool will help users better understand the privacy policy content and and make educated decisions based on their understanding of how service providers intend to use their personal data.

Cancellation of self interference (SI) is an important technology in order for wireless communication system devices to perform full-duplex communication. In this paper, we propose a novel self-interference cancellation using null beamforming to be applied entire IEEE 802.11 frame including the legacy part for full-duplex wireless communication on Cooperative MIMO (Multiple Input Multiple Output). We evaluate the SI cancellation amount by the proposed method using a field programmable gate array (FPGA) and software defined radio (SDR), and show the experimental results. In the experiment, it is confirmed that the amount of SI cancellation by the proposed method was at least 18dB. The SI cancellation amount can be further potentiated with more accurate CSI (channel state information) by increasing the transmission power. It is shown that SI can be suppressed whole frame which includes legacy preamble part. The proposed method can be applied to next generation wireless communication standards as well.

Aiming at the problem of non-line of sight (NLOS) signal recognition for Ultra Wide Band (UWB) positioning, we utilize the concepts of Neural Network Clustering and Neural Network Pattern Recognition. We propose a classification algorithm based on self-organizing feature mapping (SOM) neural network batch processing, and a recognition algorithm based on convolutional neural network (CNN). By assigning different weights to learning, training and testing parts in the data set of UWB location signals with given known patterns, a strong NLOS signal recognizer is trained to minimize the recognition error rate. Finally, the proposed NLOS signal recognition algorithm is verified using data sets from real scenarios. The test results show that the proposed algorithm can solve the problem of UWB NLOS signal recognition under strong signal interference. The simulation results illustrate that the proposed algorithm is significantly more effective compared with other algorithms.

The characteristics of antenna array, like sensor location, gain and phase response are rarely perfectly known in realistic situations. Location errors usually have a serious impact on the DOA (direction of arrival) estimation. In this paper, a novel array location calibration method of MUSIC (multiple signal classification) algorithm based on the virtual interpolated array is proposed. First, the paper introduces the antenna array positioning scheme. Then, the self-calibration algorithm of FIR-Winner filter based on virtual interpolation array is derived, and its application restriction are also analyzed. Finally, by simulating the different location errors of antenna array, the effectiveness of the proposed method is validated.

The road space rationing (RSR) method regulates a period in which a user group can make telephone calls in order to decrease the call attempt rate and induce calling parties to shorten their calls during disaster congestion. This paper investigates what settings of this indirect control induce more self-restraint and how the settings change calling parties' behavior using experimental psychology. Our experiments revealed that the length of the regulated period differently affected calling parties' behavior (call duration and call attempt rate) and indicated that the 60-min RSR method (i.e., 10 six-min periods) is the most effective setting against disaster congestion.

Self-review is essential to improving presentation, particularly for novice/unskilled researchers. In general, they could record a video of their presentation, and then check it out for self-review. However, they would be quite uncomfortable due to their appearance and voice in the video. They also struggle with in-depth self-review. To address these issues, we designed a presentation avatar that reproduces presentation made by researchers. The presentation avatar intends to increase self-awareness through self-reviewing. We also designed a checklist to aid in a detailed self-review, which includes points to be reviewed. This paper also demonstrates presentation avatar systems that use a virtual character and a robot, to allow novice/unskilled researchers as learners to self-review their own presentation using the checklist. The results of case studies with the systems indicate that the presentation avatar systems have the potential to promote self-review. In particular, we found that robot avatar promoted engagement in self-reviewing presentation.

Cow behavior monitoring is critical for understanding the current state of cow welfare and developing an effective planning strategy for pasture management, such as early detection of disease and estrus. One of the most powerful and cost-effective methods is a neural-network-based monitoring system that analyzes time series data from inertial sensors attached to cows. For this method, a significant challenge is to improve the quality and quantity of teaching data in the development of neural network models, which requires us to collect data that can cover various realistic conditions and assign labels to them. As a result, the cost of data collection is significantly high. This work proposes a data augmentation method to solve two major quality problems in the collection process of teaching data. One is the difficulty and randomicity of teaching data acquisition and the other is the sensor position changes during actual operation. The proposed method can computationally emulate different rotating states of the collar-type sensor device from the measured acceleration data. Furthermore, it generates data for actions that occur less frequently. The verification results showed significantly higher estimation performance with an average accuracy of over 98% for five main behaviors (feeding, walking, drinking, rumination, and resting) based on learning with long short-term memory (LSTM) network. Compared with the estimation performance without data augmentation, which was insufficient with a minimum of 60.48%, the recognition rate was improved by 2.52-37.05pt for various behaviors. In addition, comparison of different rotation intervals was investigated and a 30-degree increment was selected based on the accuracy performances analysis. In conclusion, the proposed data expansion method can improve the accuracy in cow behavior estimation by a neural network model. Moreover, it contributes to a significant reduction of the teaching data collection cost for machine learning and opens many opportunities for new research.

In a wireless ad hoc network (MANET), nodes can form a centerless, self-organizing, multi-hop dynamic network without any centralized control function, while hidden and exposed terminals seriously affect the network performance. Meanwhile, the wireless network node is evolving from single communication function to jointly providing a self precise positioning function, especially in indoor environments where GPS cannot work well. However, the existing medium access control (MAC) protocols only deal with collision control for data transmission without positioning function. In this paper, we propose a MAC protocol based on 802.11 standard to enable a node with self-positioning function, which is further used to solve hidden and exposed terminal problems. The location of a network node is obtained through exchanging of MAC frames jointly using a time of arrival (TOA) algorithm. Then, nodes use the location information to calculate the interference range, and judge whether they can transmit concurrently. Simulation shows that the positioning function of the proposed MAC protocol works well, and the corresponding MAC protocol can achieve higher throughput, lower average end-to-end delay and lower packet loss rate than that without self-localization function.

Visual Question Answering (VQA) is multi-task research that requires simultaneous processing of vision and text. Recent research on the VQA models employ a co-attention mechanism to build a model between the context and the image. However, the features of questions and the modeling of the image region force irrelevant information to be calculated in the model, thus affecting the performance. This paper proposes a novel dual self-guided attention with sparse question networks (DSSQN) to address this issue. The aim is to avoid having irrelevant information calculated into the model when modeling the internal dependencies on both the question and image. Simultaneously, it overcomes the coarse interaction between sparse question features and image features. First, the sparse question self-attention (SQSA) unit in the encoder calculates the feature with the highest weight. From the self-attention learning of question words, the question features of larger weights are reserved. Secondly, sparse question features are utilized to guide the focus on image features to obtain fine-grained image features, and to also prevent irrelevant information from being calculated into the model. A dual self-guided attention (DSGA) unit is designed to improve modal interaction between questions and images. Third, the sparse question self-attention of the parameter δ is optimized to select these question-related object regions. Our experiments with VQA 2.0 benchmark datasets demonstrate that DSSQN outperforms the state-of-the-art methods. For example, the accuracy of our proposed model on the test-dev and test-std is 71.03% and 71.37%, respectively. In addition, we show through visualization results that our model can pay more attention to important features than other advanced models. At the same time, we also hope that it can promote the development of VQA in the field of artificial intelligence (AI).

It is known that quasi-cyclic (QC) codes over the finite field Fq correspond to certain Fq[x]-modules. A QC code C is specified by a generator polynomial matrix G whose rows generate C as an Fq[x]-module. The reversed code of C, denoted by R, is the code obtained by reversing all codewords of C while the dual code of C is denoted by C⊥. We call C reversible, self-orthogonal, and self-dual if R = C, C⊥ ⊇ C, and C⊥ = C, respectively. In this study, for a given C, we find an explicit formula for a generator polynomial matrix of R. A necessary and sufficient condition for C to be reversible is derived from this formula. In addition, we reveal the relations among C, R, and C⊥. Specifically, we give conditions on G corresponding to C⊥ ⊇ R, C⊥ ⊆ R, and C = R = C⊥. As an application, we employ these theoretical results to the construction of QC codes with best parameters. Computer search is used to show that there exist various binary reversible self-orthogonal QC codes that achieve the upper bounds on the minimum distance of linear codes.

We propose an attention mechanism in deep learning networks for gender recognition using the gaze distribution of human observers when they judge the gender of people in pedestrian images. Prevalent attention mechanisms spatially compute the correlation among values of all cells in an input feature map to calculate attention weights. If a large bias in the background of pedestrian images (e.g., test samples and training samples containing different backgrounds) is present, the attention weights learned using the prevalent attention mechanisms are affected by the bias, which in turn reduces the accuracy of gender recognition. To avoid this problem, we incorporate an attention mechanism called gaze-guided self-attention (GSA) that is inspired by human visual attention. Our method assigns spatially suitable attention weights to each input feature map using the gaze distribution of human observers. In particular, GSA yields promising results even when using training samples with the background bias. The results of experiments on publicly available datasets confirm that our GSA, using the gaze distribution, is more accurate in gender recognition than currently available attention-based methods in the case of background bias between training and test samples.

We propose a self-stabilizing leader election protocol on directed rings in the model of population protocols. Given an upper bound N on the population size n, the proposed protocol elects a unique leader within O(nN) expected steps starting from any configuration and uses O(N) states. This convergence time is optimal if a given upper bound N is asymptotically tight, i.e., N=O(n).

In this paper, we present an efficient and robust method for estimating Homography matrix for soccer field registration between a captured camera image and a soccer field model. The presented method first detects reliable field lines from the camera image through clustering. Constructing a novel directional feature of the intersection points of the lines in both the camera image and the model, the presented method then finds matching pairs of these points between the image and the model. Finally, Homography matrix estimations and validations are performed using the obtained matching pairs, which can reduce the required number of Homography matrix calculations. Our presented method uses possible intersection points outside image for the point matching. This effectively improves robustness and accuracy of Homography estimation as demonstrated in experimental results.

Heterogeneous networks (HetNets) are emerging as an inevitable method to tackle the capacity crunch of the cellular networks. Due to the complicated network environment and a large number of configured parameters, coverage and capacity optimization (CCO) is a challenging issue in heterogeneous cellular networks. By combining the self-optimizing algorithm for radio frequency (RF) parameters with the power control mechanism of small cells, the CCO problem of self-organizing network is addressed in this paper. First, the optimization of RF parameters is solved based on reinforcement learning (RL), where the base station is modeled as an agent that can learn effective strategies to control the tunable parameters by interacting with the surrounding environment. Second, the small cell can autonomously change the state of wireless transmission by comparing its distance from the user equipment with the virtual cell size. Simulation results show that the proposed algorithm can achieve better performance on user throughput compared to different conventional methods.

Relation extraction is one of the key basic tasks in natural language processing in which distant supervision is widely used for obtaining large-scale labeled data without expensive labor cost. However, the automatically generated data contains massive noise because of the wrong labeling problem in distant supervision. To address this problem, the existing research work mainly focuses on removing sentence-level noise with various sentence selection strategies, which however could be incompetent for disposing word-level noise. In this paper, we propose a novel neural framework considering both intra-sentence and inter-sentence relevance to deal with word-level and sentence-level noise from distant supervision, which is denoted as Sentence-Related Gated Piecewise Convolutional Neural Networks (SR-GPCNN). Specifically, 1) a gate mechanism with multi-head self-attention is adopted to reduce word-level noise inside sentences; 2) a soft-label strategy is utilized to alleviate wrong-labeling propagation problem; and 3) a sentence-related selection model is designed to filter sentence-level noise further. The extensive experimental results on NYT dataset demonstrate that our approach filters word-level and sentence-level noise effectively, thus significantly outperforms all the baseline models in terms of both AUC and top-n precision metrics.

Offset quadrature amplitude modulation based orthogonal frequency division multiplexing (OFDM/OQAM) is a promising multi-carrier modulation technique to achieve a low-sidelobe spectrum while maintaining orthogonality among subcarriers. However, a major shortcoming of OFDM/OQAM systems is the high peak-to-average power ratio (PAPR) of the transmit signal. To resolve the high-PAPR issue of traditional OFDM, a self-synchronized-scrambler-based selected-mapping has been investigated, where the transmit sequence is scrambled to reduce PAPR. In this method, the receiver must use a descrambler to recover the original data. However, the descrambling process leads to error propagation, which degrades the bit error rate (BER). As described herein, a partial scrambling overlapped selected mapping (PS-OSLM) scheme is proposed for PAPR reduction of OFDM/OQAM signals, where candidate sequences are generated using partial scrambling of original data. The best candidate, the one that minimizes the peak amplitude within multiple OFDM/OQAM symbols, is selected. In the proposed method, an overlap search algorithm for SLM is applied to reduce the PAPR of OFDM/OQAM signals. Numerical results demonstrate that our PS-OSLM proposal achieves better BER than full-scrambling overlapped SLM (FS-OSLM) in OFDM/OQAM systems while maintaining almost equivalent PAPR reduction capability as FS-OSLM and better PAPR than SLM without overlap search. Additionally, we derive a theoretical lower bound expression for OFDM/OQAM with PS-OSLM, and clarify the effectiveness of the proposed scheme.

In this paper, we consider interference suppression for a full-duplex (FD) multiuser system based on single-carrier transmission in frequency-selective channels where a FD base-station (BS) simultaneously communicates with half-duplex (HD) uplink and downlink mobile users. We propose a design method for time-domain filtering where the filters in the BS transmitter suppress inter-symbol interference (ISI) and downlink inter-user interference (IUI); those in the BS receiver, self-interference, ISI, and uplink IUI; and those in the downlink mobile users, co-channel interference (CCI) without the channel state information of the CCI channels. Simulation results indicate that the FD system based on the proposed method outperforms the conventional HD system and FD system based on multicarrier transmission.

As characterizing videos simultaneously from spatial and temporal cues have been shown crucial for video processing, with the shortage of temporal information of soft assignment, the vector of locally aggregated descriptor (VLAD) should be considered as a suboptimal framework for learning the spatio-temporal video representation. With the development of attention mechanisms in natural language processing, in this work, we present a novel model with VLAD following spatio-temporal self-attention operations, named spatio-temporal self-attention weighted VLAD (ST-SAWVLAD). In particular, sequential convolutional feature maps extracted from two modalities i.e., RGB and Flow are receptively fed into the self-attention module to learn soft spatio-temporal assignments parameters, which enabling aggregate not only detailed spatial information but also fine motion information from successive video frames. In experiments, we evaluate ST-SAWVLAD by using competitive action recognition datasets, UCF101 and HMDB51, the results shcoutstanding performance. The source code is available at:https://github.com/badstones/st-sawvlad.