Automatic summarization systems provide an effective solution to today's unprecedented growth of textual data. For real-world tasks, such as data mining and information retrieval, the factual correctness of generated summary is critical. However, existing models usually focus on improving the informativeness rather than optimizing factual correctness. In this work, we present a Fact-Aware Reinforced

Being one of the most common empirical regularities, the Zipf’s law for word frequencies is a power law relation between word frequencies and frequency ranks of words. We quantitatively study semantic uncertainty of words through non-point distribution-based word embeddings and reveal the Zipfian regularities. Uncertainty of a word can increase due to polysemy, the word having “broad” meaning (such

With the increasing number of open-source software projects, code plagiarism has become one of the threats to the software industry. However, current research on code copyright protection mostly focuses on the approach for code plagiarism detection, failing to fundamentally solve the problem of copyright confirmation and protection. This paper proposes a blockchain-based code copyright management system

The domain feature retrieval has potential applications in text summarization. However, it is challenging to mine domain features from the user reviews. In this paper, a novel Domain Feature Miner (DOFM) is designed by (i) formulating the feature mining problem as a clustering problem and (ii) engaging three newly conceived empirical observations such as frequency count, grouping semantics, and distributional

Blockchain technologies and distributed ledgers enable the design and implementation of trustable data logging systems that can be used by multiple parties to produce a non-repudiable database. The case of Internet of Vehicles may greatly benefit of such a possibility to track the chain of responsibility in case of accidents or damages due to bad or omitted maintenance, improving the safety of circulation

In this paper, we study the recurrence coefficients of a deformed Hermite polynomials orthogonal with respect to the weight w(x;t,α):=e−x2|x−t|α(A+B⋅𝜃(x−t)),x∈(−∞,∞), where α>−1,A≥0,A+B≥0 and t∈ℝ. It is an extension of Chen and Feigin [J. Phys. A., Math. Gen. 39 (2006) 12381–12393]. By using the ladder operator technique, we show that the recurrence coefficients satisfy a particular Painlevé IV equation

We study the limiting distribution of a pair counting statistics of the form ∑1≤i≠j≤Nf(LN(𝜃i−𝜃j)) for the circular β-ensemble (CβE) of random matrices for sufficiently smooth test function f and LN=O(N). For β=2 and LN=N our results are inspired by a classical result of Montgomery on pair correlation of zeros of Riemann zeta function.

Subordination is the basis of the analytic approach to free additive and multiplicative convolution. We extend this approach to a more general setting and prove that the conditional expectation 𝔼φ(z−X−f(X)Yf∗(X))−1|X for free random variables X,Y and a Borel function f is a resolvent again. This result allows the explicit calculation of the distribution of noncommutative polynomials of the form X+f(X)Yf∗(X)

Presents a listing of the editorial board, board of governors, current staff, committee members, and/or society editors for this issue of the publication.

The aim of this work is to develop a fully-distributed algorithmic framework for training graph convolutional networks (GCNs). The proposed method is able to exploit the meaningful relational structure of the input data, which are collected by a set of agents that communicate over a sparse network topology. After formulating the centralized GCN training problem, we first show how to make inference

Consider a robust multiple-input single-output downlink beamforming optimization problem in a frequency division duplexing system. The base station (BS) sends training signals to the users, and every user estimates the channel coefficients, quantizes the gain and the direction of the estimated channel and sends them back to the BS. Suppose that the channel state information at the transmitter is imperfectly

Traffic flow prediction is a challenging task while most existing works are faced with two main problems in extracting complicated intrinsic and extrinsic features. In terms of intrinsic features, current methods don’t fully exploit different functions of short-term neighboring and long-term periodic temporal patterns. As for extrinsic features, recent works mainly employ hand-crafted fusion strategies

Previous works about linguistic steganography such as synonym substitution and sampling-based methods usually manipulate observed symbols explicitly to conceal secret information, which may give rise to security risks. In this letter, in order to preclude straightforward operation on observed symbols, we explored generation-based linguistic steganography in latent space by means of encoding secret

On-grid based direction-of-arrival (DOA) estimation methods rely on the resolution of a difficult group-sparse optimization problem that involves the $\ell _{2,0}$ pseudo-norm. In this work, we show that an exact relaxation of this problem can be obtained by replacing the $\ell _{2,0}$ term with a group minimax concave penalty with suitable parameters. This relaxation is more amenable to non-convex

In fluoroscopic imaging, we can acquire x-ray image sequences using a flat-panel dynamic radiography detector. Because of the charge trapping at photodiodes, lag signals occur at the sequentially acquired images and produce lag artifacts. To design low-lag detectors, accurately measuring an amount of the lag signal is important. In the standard of IEC62220-1-3, a lag correction factor (LCF) is measured

Most existing deep learning based binaural speaker separation systems focus on producing a monaural estimate for each of the target speakers, and thus do not preserve the interaural cues, which are crucial for human listeners to perform sound localization and lateralization. In this study, we address talker-independent binaural speaker separation with interaural cues preserved in the estimated binaural

Geometric information of scenes available with four-dimensional (4-D) light fields (LFs) paves the way for post-capture refocusing. Light field refocusing methods proposed so far have been limited to a single planar or a volumetric region of a scene. In this letter, we demonstrate simultaneous refocusing of multiple volumetric regions in LFs. To this end, we employ a 4-D sparse finite-extent impulse

The coprime array owns a sparse array structure, which can effectively alleviate mutual coupling, while the holes existing in the difference co-array(DCA) greatly reduce the number of uniform degrees of freedom(uDOFs). In this letter, we propose a new coprime array structure called hole-free coprime array (HFCA) by carefully assembling the subarrays, and the resulting HFCA can generate a hole-free

Suitable techniques for reading out large arrays of metallic magnetic calorimeters are very challenging and demand optimal performance in each system stage to preserve intrinsic fast signal rise time, excellent energy resolution, large energy dynamic range, and highly linear detector response. A multiplexer based on superconducting quantum interference device is the key component in achieving this

Light field saliency detection can leverage the rich visual features of light field(LF) to highlight the salient regions, but existing CNN-based saliency detection methods are specifically designed for RGB image, not for light field. To tackle this problem, a three-stream cross-modal feature aggregation network is proposed for 4D light field saliency detection. To fully utilize the rich visual features

Time difference of arrival (TDOA) estimation, which often serves as the fundamental step for a source localization or a beamforming system, has a significant practical importance in a wide spectrum of applications. To deal with reverberation, the TDOA estimation problem is often transformed into one of identifying the relative acoustic impulse responses. This letter presents a method to efficiently

Aircraft landmark detection (ALD) aims at detecting the keypoints of aircraft, which can serve as an important role for subsequent applications such as fine-grained aircraft recognition. In ALD, the physical size discrepancy between different kinds of aircraft may lead to inconsistent landmark structure, which significantly harms landmark detection results. In this letter, we take advantage of the

Glaucoma is a chronic eye disease, causing damage to the optic nerve; it may cause permanent vision loss. The conventional instrument methods for glaucoma detection are manual and time-consuming. Many approaches have recently been proposed for automatic glaucoma classification using retinal fundus images. However, none of the existing methods can efficiently use for early-stage glaucoma detection.

In recently years, some useful unsupervised video object segmentation methods that emphasize the common information in videos have been proposed. Despite the effectiveness of these methods, they ignore the information from the shallow layers of the network and thus fail to segment the details of the objects. To address this problem, we propose a multi-attention network for unsupervised video object

In this letter, we propose a novel estimator for the phase parameters of sinusoidal frequency-modulated (SFM) signals. First, we developed a simple function to perform phase modulation on the original SFM signal to obtain a new modulated signal. Second, we derived the amplitude spectrum (AS) of the modulated signal and proved that the AS exhibited peaks periodically. Third, we obtained the parameter

This letter addresses the problem of domain adaptation for the task of music source separation. Using datasets from two different domains, we compare the performance of a deep learning-based harmonic-percussive source separation model under different training scenarios, including supervised joint training using data from both domains and pre-training in one domain with fine-tuning in another. We propose

Different features describe different aspects of the object. Individually tailoring proper features for visual tracking is crucial to obtain high performance. In this letter, we propose a hybrid cascade filter to fuse handcrafted and deep features for exploiting their strengths. We complement the deep representation with handcrafted features to achieve better localization accuracy, as well as build

In complex electromagnetic environments, the challenge of signal sorting task for multi-function radars (MFRs) with various work modes has arisen. The previous methods are prone to cause the so-called “increasing batch” problem, which means that the work modes of one MFR may be sorted into multiple emitters. In this letter, a MFR signal sorting method based on complex network is proposed to tackle

To assist the clinical diagnosis and treatment of speech dysarthria, automatic dysarthric speech detection techniques providing reliable and cost-effective assessment are indispensable. Based on clinical evidence on spectro-temporal distortions associated with dysarthric speech, we propose to automatically discriminate between healthy and dysarthric speakers exploiting spectro-temporal subspaces of

Reverberation impairs not only the speech quality, but also intelligibility. The weighted-prediction-error (WPE) method, which estimates the late reverberation component based on a multichannel linear predictor, is by far one of the most effective algorithms for dereverberation. Generally, the WPE prediction filter in every short-time-Fourier-transform (STFT) subband has to be long enough to estimate

Blind identification of encoders has received increasing attention in recent years. In this letter, we focus on LDPC coded communication systems and study the problem of blind identification over a candidate set. We propose a blind identification method based on a two-stage maximum a posteriori probability estimation. The first stage measures the parity-check relationship between the received vectors

With a sponsored content plan on the Internet market, a content provider (CP) negotiates with the Internet service providers (ISPs) on behalf of the end-users to remove the network subscription fees. In this work, we have studied the impact of data sponsoring plans on the decision-making strategies of the ISPs and the CPs in the telecommunications market. We develop game-theoretic models to study the

It is increasingly popular that platforms integrate various services into mobile applications due to the high usage and convenience of mobile devices, many of which demand high computational capacities and energy, such as cryptocurrency services based on blockchain. However, it is hard for mobile devices to run these services due to the limited storage and computational capacity. In this paper, the

The purpose of this study is to improve the gain and directivity of the microstrip patch antenna by means of metamaterial (MTM) structures. As it is known, antennas have power densities in a certain direction, and the radiation curves of the antennas are shaped by orienting them according to this power density. Based on this feature of the antennas, it is aimed to improve the gain and directivity of

Inspired by the role of therapist-patient relationship in fostering behaviour change, agent-human relationship has been an active research area. This trusted relationship could be a result of the agent’s behavioural cues or the content it delivers that shows its knowledge. However, the impact of the resulting relationship using the various strategies on behaviour change is understudied. In this paper

Presents the 2020 subject/author index for this publication.

Presents the table of contents for this issue of the publication.

Presents the table of contents for this issue of the publication.

Presents the table of contents for this issue of the publication.

Presents the table of contents for this issue of the publication.

Presents a listing of the editorial board, board of governors, current staff, committee members, and/or society editors for this issue of the publication.

Presents the table of contents for this issue of the publication.

Advertisement.

Presents a listing of the editorial board, board of governors, current staff, committee members, and/or society editors for this issue of the publication.

Advertisement.

Advertisement.

Advertisement.

Advertisement.

Although realizations of transfer functions are a standard topic in textbooks about linear systems theory, this article focuses on several points that are not widely treated. First, it is shown that, for a given dynamics matrix A, the smallest number of sensors and actuators that can be used to form a minimal realization is determined by properties of the Jordan form of A. This leads to the notions

Advertisement.

Advertisement.

Advertisement.

Presents information on the 32nd Chinese Control and Decision Conference.

Presents a listing of the editorial board, board of governors, current staff, committee members, and/or society editors for this issue of the publication.

Estimating the optimal number of clusters (NC) is pivotal in cluster analysis. From the viewpoint of sample geometry, a novel internal clustering validity index, which is termed the between-within cluster (BWC) index, is designed in this paper. Moreover, a method is proposed to estimate the optimal NC. The BWC index improves the well-known Silhouette index. BWC validates the clustering results from

Visual process monitoring allows operators to identify and diagnose faults intuitively and quickly. The performance of visual process monitoring depends on the quality of extracted features and the performance of the visual model to visualize these features. In this study, we propose a deep model for feature extraction. First, a stacked auto-encoder is used to obtain the feature representation of the

The surveillance and forecast of newly confirmed cases are important to mobilize medical resources and facilitate policymaking during a public health emergency. Digital surveillance using data available online has increasingly become a trend with the advancement of the Internet. In this study, we assessed the predictive value of multiple online medical behavioral data, including online medical consultation

The most important challenge of the IoT is how to manage it. The presence of different technologies rendered IoT management much more complicated. DMTF has collected the management parameters in five FCAPS capacities. Regarding FCAPS, Fault Tolerance Capacity should be viewed as the first parameter among the top management characteristics. The objective of the present study is providing a new hybrid

In this paper, we propose a cross-correlation algorithm for correction of common phase error (CPE) in orthogonal frequency division multiplexing (OFDM) systems with high implementation efficiency. CPE resulting from the impairment of orthogonality among subcarriers is due to phase noise. It leads to the offset of demodulated data and increases the bit error rate (BER) of the receivers. As a result

The use of Internet of things in health care is a major breakthrough as it can help us save a lot of lives that can be prevented because of prolonged commute distance to the hospital. We have improvised on pre-existing models to create this model. We were successfully able to achieve results on a small scale by transmitting relays of data over a Wi-Fi network. Our model will help reduce the travel