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Background The optimal treatment volume for Glioblastoma multiforme (GBM) is still a subject of debate worldwide. The current study was aimed to determine the distances between recurring tumors and the edge of primary lesions, and thereby provide evidence for accurate target area delineation. Methods Between October 2007 and March 2019, 68 recurrent patients with GBM were included in our study. We measured the distance from the initial tumor to the recurrent lesion of GBM patients by expanding the initial gross tumor volume (GTV) to overlap the center of recurrent lesion, with the help of the Pinnacle Treatment Planning System. Results Recurrences were local in 47(69.1%) patients, distant in 12(17.7%) patients, and both in 9(13.2%) patients. Factors significantly influencing local recurrence were age ( P  = 0.049), sex ( P  = 0.049), and the size of peritumoral edema ( P  = 0.00). A total number of 91 recurrent tumors were analyzed. All local recurrences occurred within 2 cm and 94.8% (55/58) occurred within 1 cm of the original GTV based on T1 enhanced imaging. All local recurrences occurred within 1.5 cm and 98.3%(57/58) occurred within 0.5 cm of the original GTV based on T2-FLAIR imaging. 90.9% (30/33) and 81.8% (27/33) distant recurrences occurred >3 cm of T1 enhanced and T2-Flair primary tumor margins, respectively. Conclusions The 1 cm margin from T1 enhanced lesions and 0.5 cm margin from T2-Flair abnormal lesions could cover 94.8 and 98.3% local recurrences respectively, which deserves further prospective study as a limited but effective target area.

Conscious perception of the emotional valence of faces has been proposed to involve top-down and bottom-up information processing. Yet, the underlying neuronal mechanisms of these two processes and the implementation of their cooperation is still unclear. According to the global workspace model, higher level cognitive processing of visual emotional stimuli relies on both bottom-up and top-down processing. Using masking stimuli in a visual backward masking paradigm with delays at the perceptual threshold, at which stimuli can only partly be detected, suggests that only top-down processing differs between correctly and incorrectly perceived stimuli, while bottom-up visual processing is not compromised and comparable for both conditions. Providing visual stimulation near the perceptual threshold in the backward masking paradigm thus enabled us to compare differences in top-down modulation of the visual information of correctly and incorrectly recognized facial emotions in 12 healthy individuals using magnetoencephalography (MEG). For correctly recognized facial emotions, we found a right-hemispheric fronto-parietal network oscillating in the high-beta and low-gamma band and exerting top-down control as determined by the causality measure of phase slope index (PSI). In contrast, incorrect recognition was associated with enhanced coupling in the gamma band between left frontal and right parietal regions. Our results indicate that the perception of emotional face stimuli relies on the right-hemispheric dominance of synchronized fronto-parietal gamma-band activity.

While the recent emergence of a large number of online course resources has made life more convenient for many people, it has also caused information overload. According to a user’s situation and behavior, course recommendation systems can recommend courses of interest to the user, so that the user can quickly sift through a massive amount of information to find courses that meet his or her needs. This paper provide a systematic review of deep learning-based recommendation systems in e-learning environments. Firstly, the concept of recommendation systems is introduced in e-learning environments, and present a comprehensive survey and classification of deep learning techniques for course recommendation. And then, a detailed analysis of existing recommendation system is conducted based on the collected literature, and an overall course recommendation system framework is presented. Subsequently, this artical main focus is on multilayer perceptual machines, recurrent neural networks, convolutional neural networks, neural attention mechanisms, and deep reinforcement learning-based recommendation, and summarize the existing research on the use of the five techniques mentioned above in e-learning environments. The last section discusses seven flaws in the current recommendation systems used in e-learning environments and identify opportunities for future research.

Although online learning platforms are gradually becoming commonplace in modern society, learners’ high dropout rates and serious academic performance require more attention within the virtual learning environment (VLE). This study aims to predict students’ performance in a specific course as it is continuously running, using the statistic personal biographical information and sequential behavior data with VLE. To achieve this goal, a novel recurrent neural network (RNN)-gated recurrent unit (GRU) joint neural network is proposed to fit both static and sequential data, where the data completion mechanism is also adopted to fill the missing stream data. To incorporate the sequential relationship of learning data, three kinds of time-series deep neural network algorithms: simple RNN, GRU, and LSTM are first taken into consideration as baseline models. Their performances are compared in identifying at-risk students. Experimental results on Open University Learning Analytics Dataset (OULAD) show that simple methods like GRU and simple RNN have better results than the relatively complex LSTM model. The results also reveal that different models have different peak performance time, which results in the proposed joint model that achieves over 80% prediction accuracy of at-risk students at the end of the semester.

Background Biomedical named entity recognition(BNER) is a crucial initial step of information extraction in biomedical domain. The task is typically modeled as a sequence labeling problem. Various machine learning algorithms, such as Conditional Random Fields (CRFs), have been successfully used for this task. However, these state-of-the-art BNER systems largely depend on hand-crafted features. Results We present a recurrent neural network (RNN) framework based on word embeddings and character representation. On top of the neural network architecture, we use a CRF layer to jointly decode labels for the whole sentence. In our approach, contextual information from both directions and long-range dependencies in the sequence, which is useful for this task, can be well modeled by bidirectional variation and long short-term memory (LSTM) unit, respectively. Although our models use word embeddings and character embeddings as the only features, the bidirectional LSTM-RNN (BLSTM-RNN) model achieves state-of-the-art performance — 86.55% F1 on BioCreative II gene mention (GM) corpus and 73.79% F1 on JNLPBA 2004 corpus. Conclusions Our neural network architecture can be successfully used for BNER without any manual feature engineering. Experimental results show that domain-specific pre-trained word embeddings and character-level representation can improve the performance of the LSTM-RNN models. On the GM corpus, we achieve comparable performance compared with other systems using complex hand-crafted features. Considering the JNLPBA corpus, our model achieves the best results, outperforming the previously top performing systems. The source code of our method is freely available under GPL at https://github.com/lvchen1989/BNER .

The COVID-19 pandemic has impelled the majority of schools and universities around the world to switch to remote teaching. One of the greatest challenges in online education is preserving the academic integrity of student assessments. The lack of direct supervision by instructors during final examinations poses a significant risk of academic misconduct. In this paper, we propose a new approach to detecting potential cases of cheating on the final exam using machine learning techniques. We treat the issue of identifying the potential cases of cheating as an outlier detection problem. We use students’ continuous assessment results to identify abnormal scores on the final exam. However, unlike a standard outlier detection task in machine learning, the student assessment data requires us to consider its sequential nature. We address this issue by applying recurrent neural networks together with anomaly detection algorithms. Numerical experiments on a range of datasets show that the proposed method achieves a remarkably high level of accuracy in detecting cases of cheating on the exam. We believe that the proposed method would be an effective tool for academics and administrators interested in preserving the academic integrity of course assessments.

Recurrent neural networks (RNNs) enable the production and processing of time-dependent signals such as those involved in movement or working memory. Classic gradient-based algorithms for training RNNs have been available for decades, but are inconsistent with biological features of the brain, such as causality and locality. We derive an approximation to gradient-based learning that comports with these constraints by requiring synaptic weight updates to depend only on local information about pre- and postsynaptic activities, in addition to a random feedback projection of the RNN output error. In addition to providing mathematical arguments for the effectiveness of the new learning rule, we show through simulations that it can be used to train an RNN to perform a variety of tasks. Finally, to overcome the difficulty of training over very large numbers of timesteps, we propose an augmented circuit architecture that allows the RNN to concatenate short-duration patterns into longer sequences.

Emotions are an integral part of human interactions and are significant factors in determining user satisfaction or customer opinion. speech emotion recognition (SER) modules also play an important role in the development of human–computer interaction (HCI) applications. A tremendous number of SER systems have been developed over the last decades. Attention-based deep neural networks (DNNs) have been shown as suitable tools for mining information that is unevenly time distributed in multimedia content. The attention mechanism has been recently incorporated in DNN architectures to emphasise also emotional salient information. This paper provides a review of the recent development in SER and also examines the impact of various attention mechanisms on SER performance. Overall comparison of the system accuracies is performed on a widely used IEMOCAP benchmark database.

Recently, interest in e-learning has increased rapidly owing to the lockdowns imposed by COVID-19. A major disadvantage of e-learning is the difficulty in maintaining concentration because of the limited interaction between teachers and students. The objective of this paper is to develop a methodology to predict e-learners’ concentration by applying recurrent neural network models to eye gaze and facial landmark data extracted from e-learners’ video data. One hundred eighty-four video data of ninety-two e-learners were obtained, and their frame data were extracted using the OpenFace 2.0 toolkit. Recurrent neural networks, long short-term memory, and gated recurrent units were utilized to predict the concentration of e-learners. A set of comparative experiments was conducted. As a result, gated recurrent units exhibited the best performance. The main contribution of this paper is to present a methodology to predict e-learners’ concentration in a natural e-learning environment.

Programming education has recently received increased attention due to growing demand for programming and information technology skills. However, a lack of teaching materials and human resources presents a major challenge to meeting this demand. One way to compensate for a shortage of trained teachers is to use machine learning techniques to assist learners. This article proposes a learning path recommendation system that applies a recurrent neural network to a learner's ability chart, which displays the learner's scores. In brief, a learning path is constructed from a learner's submission history using a trial-and-error process, and the learner's ability chart is used as an indicator of their current knowledge. An approach for constructing a learning path recommendation system using ability charts and its implementation based on a sequential prediction model and a recurrent neural network, are presented. Experimental evaluation is conducted with data from an e-learning system.