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With the development of deep learning, the Super-Resolution (SR) reconstruction of microscopic images has improved significantly. However, the scarcity of microscopic images for training, the underutilization of hierarchical features in original Low-Resolution (LR) images, and the high-frequency noise unrelated with the image structure generated during the reconstruction process are still challenges in the Single Image Super-Resolution (SISR) field. Faced with these issues, we first collected sufficient microscopic images through Motic, a company engaged in the design and production of optical and digital microscopes, to establish a dataset. Secondly, we proposed a Residual Dense Attention Generative Adversarial Network (RDAGAN). The network comprises a generator, an image discriminator, and a feature discriminator. The generator includes a Residual Dense Block (RDB) and a Convolutional Block Attention Module (CBAM), focusing on extracting the hierarchical features of the original LR image. Simultaneously, the added feature discriminator enables the network to generate high-frequency features pertinent to the image's structure. Finally, we conducted experimental analysis and compared our model with six classic models. Compared with the best model, our model improved PSNR and SSIM by about 1.5 dB and 0.2, respectively. [ABSTRACT FROM AUTHOR]

Recently, polar code has attracted the attention of many scholars and has been developed as a code technology in coded‐cooperative communication. We propose a polar code scheme based on Plotkin structure and quasi‐uniform punching (PC‐QUP). Then we apply the PC‐QUP to coded‐cooperative scenario and built to a new coded‐cooperative scheme, which is called PCC‐QUP scheme. The coded‐cooperative scheme based on polar code is studied on the aspects of codeword construction and performance optimization. Further, we apply the proposed schemes to space–time block coding (STBC) to explore the performance of the scheme. Monte Carlo simulation results show that the proposed cooperative PCC‐QUP‐STBC scheme can obtain a lower bit error ratio (BER) than its corresponding noncooperative scheme. [ABSTRACT FROM AUTHOR]

The emergence of massive open online courses not only changes the ecology of higher education, but also facilitates a blending learning paradigm, also known as small private online courses (SPOCs). In order to understand how college students interact with an SPOC platform, this study collects their online behaviors for a semester and adopts a lag sequential analysis approach to identify significant transitions between interactions with content, peers, and instructors. Regarding content, after entering courses, the students tend to access learning resources. Besides, the transitions between learning resources and personal performance are significantly interconnected to each other. Regarding peers, the interaction with classmates was mainly connected to the access of assignments and performance. Regarding instructors, the interaction with teachers was minor but connected to all other behaviors. In addition, the results also show that students' online behavioral patterns in SPOCs may change over time. The implications of the findings for SPOCs research are discussed in this paper.

In a MOOC environment, each student's interaction with the course content is a crucial clue for learning analytics, which offers an opportunity to record learner activity of unprecedented scale. In online learning, the educators and the administrators need to get informed with students' learning states since the performance of unsupervised learning style is difficult to control. Learning analytics considered as a key process is to provide students and educators with evidence-based, analytical and contextual outcomes in a way of making sense of their learning engagements. In this conceptual framework, this manuscript per the authors intends to adopt sequential analysis method to exploit students' learning behavior patterns in Cloud classroom (an online course platform based on MOOC). Moreover, this research also compares the behavioral patterns of four grade levels in a university, with the purpose of finding the most key behavioral patterns of each grade group.

Due to high retention rates, small private online course (SPOC) has become increasingly popular among universities. However, existing analyses of learning behavioral patterns in SPOC remain extremely lacking. This present study conducts an empirical analysis on the behavioral patterns of 12,517 undergraduates engaging in a college's SPOC platform, called StarC. In this study, the authors collected and summarized the learning behaviors generated from these learners during 348 days of observation. They further coded the behaviors and extracted the two-step lag sequences in learning processes of individuals. The frequency analysis and sequential analysis were subsequently adopted to discover the distributions and frequency transition patterns of the two-step behavioral sequence in StarC. Besides, grade similarities and differences were computed and analyzed in terms of behavioral patterns. With these results, the potential and inadequacies of the learning platform are discussed, and some suggestions are offered for future work on the study and development of SPOCs.

Nowadays, Massive Open Online Courses (MOOCs) have obtained a rapid development and drawn much attention from the areas of learning analytics and artificial intelligence. There are lots of unstructured data being generated in online reviews area. The learning behavioral data become more and more diverse, and they prompt the emergence of big data in education. To mine useful information from these data, we need to use educational data mining and learning analysis technique to study the learning feelings and discussed topics among learners. This paper aims to mine and analyze topic information hidden in the unstructured reviews data in MOOCs; a novel author topic model based on an unsupervised learning idea is proposed to extract learning topics for each learner. According to the experimental results, we will analyze and focus on interests of learners, which facilitates further personalized course recommendation and improves the quality of online courses.

Using the source residual redundancy in a joint source and channel coded modulation (JSCCM) system to achieve a shaping gain has proven to be a good solution for probabilistic amplitude shaping. In a JSCCM system, rate-compatible source codes are essential for adapting to dynamically changing source probabilities. However, conventional rate-compatible source codes are not appropriate for JSCCM systems, since they may yield a loss in the shaping gain. Moreover, a code design that depends solely on the source decoding threshold appears to be ill suited, thus complicating the search for good source codes. In this paper, we propose a rate-compatible family of source codes for JSCCM systems based on an achievable system rate analysis and the source protograph extrinsic information transfer (PEXIT) algorithm. The proposed codes not only have good source decoding thresholds but also obtain shaping gains over the whole considered range of source probabilities. Numerical results show that the proposed rate-compatible family of source codes can significantly improve the bit-error-rate (BER) performance of the JSCCM system. [ABSTRACT FROM AUTHOR]

By using the residual source redundancy to achieve the shaping gain, a joint source-channel coded modulation (JSCCM) system has been proposed as a new solution for probabilistic amplitude shaping (PAS). However, the source and channel codes in the JSCCM system should be designed specifically for a given source probability to ensure optimal PAS performance, which is undesirable for systems with dynamically changing source probabilities. In this paper, we propose a new shaping scheme by optimizing the bit-labeling of the JSCCM system. Instead of the conventional fixed labeling, the proposed bit-labelings are adaptively designed according to the source probability and the source code. Since it is simple to switch between different labelings according to the source probability and the source code, the proposed design can be considered as a promising low complexity alternative to obtain the shaping gain for sources with different probabilities. Numerical results show that the proposed bit-labelings can significantly improve the bit-error rate (BER) performance of the JSCCM system. [ABSTRACT FROM AUTHOR]

In this paper, a joint group shuffled scheduling decoding (JGSSD) algorithm for a joint source-channel coding (JSCC) scheme based on double low-density parity-check (D-LDPC) codes is presented. The proposed algorithm considers the D-LDPC coding structure as a whole and applies shuffled scheduling to each group; the grouping relies on the types or the length of the variable nodes (VNs). By comparison, the conventional shuffled scheduling decoding algorithm can be regarded as a special case of this proposed algorithm. A novel joint extrinsic information transfer (JEXIT) algorithm for the D-LDPC codes system with the JGSSD algorithm is proposed, by which the source and channel decoding are calculated with different grouping strategies to analyze the effects of the grouping strategy. Simulation results and comparisons verify the superiority of the JGSSD algorithm, which can adaptively trade off the decoding performance, complexity and latency. [ABSTRACT FROM AUTHOR]

Although the joint source-channel coding (JSCC) system based on double protograph low-density parity-check (DP-LDPC) codes has been shown to possess excellent error performance over additive white Gaussian noise (AWGN) channels, it cannot perform well over one-dimensional inter-symbol-interference (OD-ISI) magnetic recording channels. In this study, a new JSCC system with a three-stage serially concatenated framework of Turbo equalization is firstly proposed for OD-ISI magnetic recording channels. Then, a modified joint protograph extrinsic information transfer (M-JPEXIT) algorithm is put forward to analyze the convergence-performance of the proposed system. By applying the M-JPEXIT algorithm, the channel codes are redesigned for this system to improve the error performance. Both the M-JPEXIT analysis and the bit-error-rate (BER) simulation results show the performance improvement of the proposed channel codes, especially in the water-fall region. [ABSTRACT FROM AUTHOR]

Considering the high compression requirements of transmission, lossy block codes are particularly concerned due to their good compression performance and simple implementation. This paper investigates and analyzes the distortion rate performance of protograph LDPC (P-LDPC) codes for Bernoulli sources over AWGN channels. We first analytically establish the connection between the parity check matrix of a P-LDPC code and the extra distortion caused by the noisy channels. It was found that the additional distortion related to channel noise increases with the rising total degree of a parity check matrix. Further, two design algorithms are proposed for optimizing lossy multirate P-LDPC codes, considering the effect of noisy channels. Finally, simulation results demonstrate the robustness of the optimized P-LDPC codes over noisy channels. [ABSTRACT FROM AUTHOR]

Taxonomy merging is an important work to provide a uniform schema for several heterogeneous taxonomies. Previous studies primarily focus on merging two taxonomies in a specific domain, while the merging of multiple taxonomies has been neglected. This article proposes a taxonomy merging approach to automatically merge multiple source taxonomies into a target taxonomy in an asymmetric manner. The approach adopts a strategy of breaking up the whole into parts to decrease the complexity of merging multiple taxonomies and employs a block-based method to reduce the scale of measuring semantic relations between concept pairs. In addition, for the problem of multiple inheritance, a method of topical coverage is proposed. Experiments conducted on synthetic and real-world scenarios indicate that the proposed merging approach is feasible and effective to merge multiple taxonomies. In particular, the proposed approach works well in the aspects of limiting the semantic redundancy and establishing high-quality hierarchical relations between concepts. [ABSTRACT FROM AUTHOR]

The joint base matrix ${{{{B}}_{{\mathrm{J}}}}}$ of the joint source-channel coding (JSCC) system based on double protograph low-density parity-check (DP-LDPC) codes consists of four components, namely, the source code ${{{{B}}_{{\mathrm{s}}}}}$ , the channel code ${{{{B}}_{{\mathrm{c}}}}}$ , the type-1 connection edge ${{{{B}}_{{ \textit {L1}}}}}$ and the type-2 connection edge ${{{{B}}_{{ \textit {L2}}}}}$ , each having a non-negligible influence on the system performance. Different from the traditional component-specific design approach, we propose a joint design and optimization algorithm based on the idea of multi-objective differential evolution (MODE). Specifically, we consider the optimization of the DP-LDPC JSCC system through joint design of three components ${{{{B}}_{{\mathrm{s}}}}}$ , ${{{{B}}_{{\mathrm{c}}}}}$ , ${{{{B}}_{{ \textit {L1}}}}}$ and all four components ${{{{B}}_{{\mathrm{s}}}}}$ , ${{{{B}}_{{\mathrm{c}}}}}$ , ${{{\mathbf{B}}_{{ \textit {L1}}}}}$ , ${{{{B}}_{{ \textit {L2}}}}}$ , respectively. The proposed algorithm has low search complexity due to the reduction in size and element value of base matrices. The joint protograph extrinsic information transfer (JPEXIT) analyses and the simulation results demonstrate that the resulting JSCC system is free from a high error floor, requires fewer number of iterations for reaching the same bit error rate (BER) and achieves significant coding gains as compared to the state-of-the-art. Our DP-LDPC JSCC system is also shown to outperform its separation-based counterpart by a wide margin. [ABSTRACT FROM AUTHOR]

This paper presents a heuristic algorithm for solving a specific NP-hard 2D rectangular packing problem in which a rectangle called central rectangle is required to be placed in the center of the final layout, and the aspect ratio of the container is also required to be in a given range. The key component of the proposed algorithm is a greedy constructive procedure, according to which, the rectangles are packed into the container one by one and each rectangle is packed into the container by an angle-occupying placement with maximum fit degree. The proposed algorithm is evaluated on two groups of 35 well-known benchmark instances. Computational results disclose that the proposed algorithm outperforms the previous algorithm for the packing problem. For the first group of test instances, solutions with average filling rate 99.31% can be obtained; for the real-world layout problem in the second group, the filling rate of the solution is 94.75%. [ABSTRACT FROM AUTHOR]

Although joint source-channel coding (JSCC) based on double protograph low-density parity-check (LDPC) codes has been shown to be an excellent solution for wireless communication, the protograph LDPC code for source coding needs to be optimized to improve the error-floor performance. In this letter, first, it is found that optimized protograph LDPC codes for channel coding are not optimal for source compression, which indicates that the source codes in the JSCC systems need to be designed specifically. Second, a rate-1/2 source code is designed by using the source decoding threshold as evaluation criteria. Finally, low rate source codes are designed by code lengthening. The protograph extrinsic information transfer analysis and the bit error rate simulation have shown that the proposed source codes obtain higher source decoding thresholds than conventional codes, which extend the range of compressible source entropies and improve the error-floor performance in the JSCC system. [ABSTRACT FROM PUBLISHER]

The emergence of massive open online courses not only changes the ecology of higher education, but also facilitates a blending learning paradigm, also known as small private online courses (SPOCs). In order to understand how college students interact with an SPOC platform, this study collects their online behaviors for a semester and adopts a lag sequential analysis approach to identify significant transitions between interactions with content, peers, and instructors. Regarding content, after entering courses, the students tend to access learning resources. Besides, the transitions between learning resources and personal performance are significantly interconnected to each other. Regarding peers, the interaction with classmates was mainly connected to the access of assignments and performance. Regarding instructors, the interaction with teachers was minor but connected to all other behaviors. In addition, the results also show that students’ online behavioral patterns in SPOCs may change over time. The implications of the findings for SPOCs research are discussed in this paper. [ABSTRACT FROM PUBLISHER]

Sentiment recognition of online course reviews is valuable to understand emotions and feelings of learners. Nowadays, an increasing number of course reviews are being generated with the emergence of Massive Open Online Courses (MOOCs), which offers teachers a chance to analyze the opinions of learners and improve teaching strategies. However, the unstructured data contain large amounts of redundant features, which will significantly impact the performance of machine learning. To select effective emotional features, we adopt a multi-swarm particle swarm optimization (MSPSO) method, which generates multi diverse particle swarms on several cross training subsets. These swarms are utilized to find the best features by the F -Measure fitness function. The experimental results on the real-life dataset show that MSPSO can effectively reduce redundancy of text features and capture discriminative features. Compared with conventional feature selection methods, MSPSO can gain the better performance when selecting the same dimensions. Besides, the result of a user survey indicates that 72.19% of subjects approve of the usability of the recognition results and effectiveness of the feature selection. [ABSTRACT FROM AUTHOR]

• A novelty infrared imaging technology for facial express recognition with convolution neural network. • Discrete beamlet transform regularization is designed to retain infrared spectral structures. • The split Bregman iteration algorithm is introduced to optimize the proposed DBTR-BR model. • The proposed model is applied for infrared facial expression recognition problem in Oulu-CASIA dataset. Facial expression recognition is an important research topic in the field of human-machine interaction. Infrared imaging technology has illustrated good potential in the applications of facial expression recognition and computer vision. But the low-resolution spectral data has limited its applications, such as band overlap and random noises. To address the problems, a rapid blind restoration model with discrete beamlet transforms regularization is presented to reconstruct the infrared spectrum. To compare the sparsity between the observed infrared spectrum and ground-truth one in frequency domain, the discrete beamlet transforms is applied to analyze the different of their coefficients distributions. We propose an IR spectral deconvolution model with the sparsity coefficients regularization by L 0 -norm. We execute the proposed algorithm on the simulated and actual IR spectrum data, and the results demonstrate that can effectively suppress the Poisson noises and retain infrared spectral structure. The high-resolution IR spectrum can raise the recognition rate of facial expression classification task. [ABSTRACT FROM AUTHOR]

With the continuous development of educational informatization, more and more emerging technologies are applied in teaching activities. These technologies provide massive and multidimensional information for teaching research, but at the same time, the information obtained by teachers and students presents an explosive increase. Extracting the core content of the class record text through text summarization technology to generate concise class minutes can significantly improve the efficiency of teachers and students to obtain information. This article proposes a hybrid-view class minutes automatic generation model (HVCMM). The HVCMM model uses a multilevel encoding strategy to encode the long text of the input class records to avoid memory overflow in the calculation after the long text is input into the single-level encoder. The HVCMM model uses the method of coreference resolution and adds role vectors to solve the problem that the excessive number of participants in the class may lead to confusion about the referential logic. Machine learning algorithms are used to analyze the topic and section of the sentence to capture structural information. We test the HVCMM model on the Chinese class minutes dataset (CCM) and the augmented multiparty interaction (AMI) dataset, and the results show that the HVCMM model outperforms other baseline models on the ROUGE metric. With the help of the HVCMM model, teachers can improve the efficiency of reflection after class and improve the teaching level. Students can review the key content to strengthen their understanding of what they have learned with the help of the class minutes automatically generated by the model.

An FTIR spectrometer often suffers from common problems of band overlap and Poisson noises. In this paper, we show that the issue of infrared (IR) spectrum degradation can be considered as a maximum a posterior (MAP) problem and solved by minimized a cost function that includes a likelihood term and two prior terms. In the MAP framework, the likelihood probability density function (PDF) is constructed based on the observed Poisson noise model. A fitted distribution of curvelet transform coefficient is used as spectral prior PDF, and the instrument response function (IRF) prior is described based on a Gauss-Markov function. Moreover, the split Bregman iteration method is employed to solve the resulting minimization problem, which highly reduces the computational load. As a result, the Poisson noises are perfectly removed, while the spectral structure information is well preserved. The novelty of the proposed method lies in its ability to estimate the IRF and latent spectrum in a joint framework, thus eliminating the degradation effects to a large extent. The reconstructed IR spectrum is more convenient for extracting the spectral feature and interpreting the unknown chemical or biological materials.

Raman spectroscopy often suffers from the problems of band overlap and random noise. In this work, we develop a nonlocal low-rank regularization (NLR) approach toward exploiting structured sparsity and explore its applications in Raman spectral deconvolution. Motivated by the observation that the rank of a ground-truth spectrum matrix is lower than that of the observed spectrum, a Raman spectral deconvolution model is formulated in our method to regularize the rank of the observed spectrum by total variation regularization. Then, an effective optimization algorithm is described to solve this model, which alternates between the instrument broadening function and latent spectrum until convergence. In addition to conceptual simplicity, the proposed method has achieved highly competent objective performance compared to several state-of-the-art methods in Raman spectrum deconvolution tasks. The restored Raman spectra are more suitable for extracting spectral features and recognizing the unknown materials or targets.

Band overlap and random noise are a serious problem in infrared spectra, especially for aging spectrometers. In this paper, we have presented a simple method for spectrum restoration. The proposed method is based on local operations, and involves sparse decompositions of each spectrum piece under an evolving overcomplete dictionary, and a simple averaging calculation. The content of the dictionary is of prime importance for the deconvolution process. Quantitative assessments of this technique on simulated and real spectra show significant improvements over the state-of-the-art methods. The proposed method can almost eliminate the effects of instrument aging. The features of these deconvoluted infrared spectra are more easily extracted, aiding the interpretation of unknown chemical mixtures.

Laser instruments often suffer from the problem of baseline drift and random noise, which greatly degrade spectral quality. In this article, we propose a variation model that combines baseline correction and denoising. First, to guide the baseline estimation, morphological operations are adopted to extract the characteristics of the degraded spectrum. Second, to suppress noise in both the spectrum and baseline, Tikhonov regularization is introduced. Moreover, we describe an efficient optimization scheme that alternates between the latent spectrum estimation and the baseline correction until convergence. The major novel aspect of the proposed algorithms is the estimation of a smooth spectrum and removal of the baseline simultaneously. Results of a comparison with state-of-the-art methods demonstrate that the proposed method outperforms them in both qualitative and quantitative assessments.

Spectroscopic data often suffer from common problems of band overlap and noise. This paper presents a maximum a posteriori (MAP)-based algorithm for the band overlap problem. In the MAP framework, the likelihood probability density function (PDF) is constructed with Gaussian noise assumed, and the prior PDF is constructed with adaptive total variation (ATV) regularization. The split Bregman iteration algorithm is employed to optimize the ATV spectral deconvolution model and accelerate the speed of the spectral deconvolution. The main advantage of this algorithm is that it can obtain peak structure information as well as suppress noise simultaneity. Simulated and real spectra experiments manifest that this algorithm can satisfactorily recover the overlap peaks as well as suppress noise and are robust to the regularization parameter.