Digitally Inspired Thinking: Can Social Media Lead to Deep Learning in Higher Education?

In this study, students from a variety of disciplines, who were enrolled in six courses that incorporate the use of social media, were surveyed to evaluate their perception of how the integration of social-media tools supports deep approaches to learning. Students reported that social media supports deep learning both directly and indirectly, makes learning easier, promotes long-term retention of content, and fosters a more engaging and enjoyable learning environment. These findings suggest that integration of social media into college courses can support deep approaches to learning.     

Spinoza,Deep Ecology and Education Informed by a (Post)human Sensibility

Abstract

This article explores the influence of Spinozism on the deep ecology movement (DEM) and on new materialism. It questions the stance of supporters of the DEM because their ecosophies unwittingly anthropomorphise the more-than-human-world. It suggests that instead of humanising the ‘natural’ world, morality should be naturalised, that is, that the object of human expression of ethics should be the more-than-human world. Moreover, the article discusses Deleuze’s Spinozism that informs new materialism and argues that stripping the human of its ontological privilege does not deprive the human animal from its ethico-normative distinctiveness. Implications of the discussion for an education aimed at cultivating (post)human sensibilities are explored.     

Deep lingual arterial chemoembolization of tongue carcinoma with microcapsuled anticancer drug

Objective: Microcapsule chemoembolism is a promising treatment of tumors. We describe a deep lingual arterial embolization of tongue carcinoma with microcapsuled carboplatinum. Methods: Lingual artery cast specimens from cadavers were microscopically examined, and 78 patients with tongue cancer were recruited and treated with the deep lingual arterial embolization therapy. Results: Microcapsule embolism occurred approximately at the fifth or sixth level of the deep lingual artery branches. The five-year survival rate was 88.5% (69 out of 78), and the ten-year survival rate 52.6% (41 out of 78). Conclusion: The deep lingual arterial embolization of tongue carcinoma with microcapsuled carboplatinum is an effective therapy to treat carcinoma in mid-margin or mid-body of the tongue.     

Slow thinking and deep learning: Tversky and Kahneman's taxi cabs

This article is based on classroom application of a problem story constructed by Amos Tversky in the 1970s. His intention was to evaluate human beings' intuitions about statistical inference. The problem was revisited by his colleague, the Nobel Prize winner Daniel Kahneman. The aim of this article is to show how popular science textbooks can serve as a source for rich classroom activity, with a little care in the implementation by teachers. Kahneman describes the problem as ‘standard’ and answers using a fixed point number. I describe how I have encouraged my students to challenge the certainty of this assertion by identifying ambiguities that are left unexplained in the story. This way, I claim to stimulate individuals to indeed move towards Thinking, Fast and Slow, the title of Kahneman's book.     

马里亚纳海沟Challenger Deep的岩石圈流变结构与动力学分析

马里亚纳海沟是西太平洋板块边缘沟-弧-盆体系构造演化的关键地区,其南端的Challenger Deep不仅是地球表面最深点,也是马里亚纳海沟、马里亚纳岛弧、马里亚纳海槽、西马里亚纳洋脊和帕里西维拉海盆的构造汇聚点。开展岩石流变结构与动力学过程研究对于认识Challenger Deep的形成演化具有重要的科学意义。利用综合地球物理资料,通过对重、磁数据的计算分析,研究马里亚纳沟-弧-槽-盆系统的等效黏滞系数和岩石圈强度等流变学特征。利用地震资料,勾绘海沟之下贝尼奥夫带随深度变化的特征以及陡变形态。计算结果表明：对应马里亚纳海沟-岛弧-海槽系统,自由空气重力异常向东凸出,形成弧型异常区;区内异常表现为串珠状线性特征,异常值中间高,两侧低。不同深度岩石圈累积强度比值表明,海沟南北两侧地壳上硬下软,海沟中部地壳上软下硬。在给定应变速率条件下计算的等效黏滞系数东高西低,说明西侧构造体地壳比东侧构造体地壳更容易变形。Challenger Deep岩石圈强度较大,等效黏滞系数较高,具有上硬下软的流变学特征,为板块俯冲在该区的弯曲、撕裂与快速翻转提供了重要条件。地震与重力剖面分析表明,Challenger Deep处的岩石圈累积应力强度和有效粘滞系数条件,可以使马里亚纳海沟俯冲带在重力作用下弯曲、开裂,或部分向南翻转、变陡。     

A deep learning-based expert finding method to retrieve agile software teams from CQAs

Currently, many software companies are looking to assemble a team of experts who can collaboratively carry out an assigned project in an agile manner. The most ideal members for an agile team are T-shaped experts, who not only have expertise in one skill-area but also have general knowledge in a number of related skill-areas. Existing related methods have only used some heuristic non-machine learning models to form an agile team from candidates, while machine learning has been successful in similar tasks. In addition, they have only used the number of candidates’ documents in various skill-areas as a resource to estimate the candidates’ T-shaped knowledge to work in an agile team, while the content of their documents is also very important. To this end, we propose a multi-step method that rectifies the drawbacks mentioned. In this method, we first pick out the best possible candidates using a state-of-the-art model, then we re-estimate their relevant knowledge for working in the team with the help of a deep learning model, which uses the content of the candidates’ posts on StackOverflow. Finally, we select the best possible members for the given agile team from among these candidates using an integer linear programming model. We perform our experiments on two large datasets C# and Java, which comprise 2,217,366 and 2,320,883 posts from StackOverflow, respectively. On datasets C# and Java, our method selects, respectively, 68.6% and 55.2% of the agile team members from among T-shaped experts, while the best baseline method only selects, respectively, 49.1% and 40.2% of the agile team members from among T-shaped experts. In addition, the results show that our method outperforms the best baseline method by 8.1% and 11.4% in terms of F-measure on datasets C# and Java, respectively.     

VAD-assisted multitask transformer framework for emotion recognition and intensity prediction on suicide notes

Detecting suicidal tendencies and preventing suicides is an important social goal. The rise and continuance of emotion, the emotion category, and the intensity of the emotion are important clues about suicidal tendencies. The three determinants of emotion, viz. Valence, Arousal, and Dominance (VAD) can help determine a person’s exact emotion(s) and its intensity. This paper introduces an end-to-end VAD-assisted transformer-based multi-task network for detecting emotion (primary task) and its intensity (auxiliary task) in suicide notes. As part of this research, we expand the utility of the emotion-annotated benchmark dataset of suicide notes, CEASE-v2.0, by annotating all its sentences with emotion intensity labels. Empirical results show that our multi-task method performs better than the corresponding single-task systems, with the best attained overall Mean Recall (MR) of 65.25% on the emotion task. On a similar task, we improved MR by 8.78% over the existing state-of-the-art system. We evaluated our approach on three benchmark datasets for three different tasks. We observed that the introduced method consistently outperformed existing state-of-the-art approaches on the studied datasets, demonstrating its capacity to generalize to other downstream correlated tasks. We qualitatively examined our model’s output by comparing it to the labeling of a psychiatrist.     

Evaluating the faithfulness of saliency maps in explaining deep learning models using realistic perturbations

Deep Learning has reached human-level performance in several medical tasks including classification of histopathological images. Continuous effort has been made at finding effective strategies to interpret these types of models, among them saliency maps, which depict the weights of the pixels on the classification as an heatmap of intensity values, have been by far the most used for image classification. However, there is a lack of tools for the systematic evaluation of saliency maps, and existing works introduce non-natural noise such as random or uniform values. To address this issue, we propose an approach to evaluate the faithfulness of the saliency maps by introducing natural perturbations in the image, based on oppose-class substitution, and studying their impact on evaluation metrics adapted from saliency models. We validate the proposed approach on a breast cancer metastases detection dataset PatchCamelyon with 327,680 patches of histopathological images of sentinel lymph node sections. Results show that GradCAM, Guided-GradCAM and gradient-based saliency map methods are sensitive to natural perturbations and correlate to the presence of tumor evidence in the image. Overall, this approach proves to be a solution for the validation of saliency map methods without introducing confounding variables and shows potential for application on other medical imaging tasks.     

Forecasting movements of stock time series based on hidden state guided deep learning approach

Stock movement forecasting is usually formalized as a sequence prediction task based on time series data. Recently, more and more deep learning models are used to fit the dynamic stock time series with good nonlinear mapping ability, but not much of them attempt to unveil a market system’s internal dynamics. For instance, the driving force (state) behind the stock rise may be the company’s good profitability or concept marketing, and it is helpful to judge the future trend of the stock. To address this issue, we regard the explored pattern as an organic component of the hidden mechanism. Considering the effective hidden state discovery ability of the Hidden Markov Model (HMM), we aim to integrate it into the training process of the deep learning model. Specifically, we propose a deep learning framework called Hidden Markov Model-Attentive LSTM (HMM-ALSTM) to model stock time series data, which guides the hidden state learning of deep learning methods via the market’s pattern (learned by HMM) that generates time series data. What is more, a large number of experiments on 6 real-world data sets and 13 stock prediction baselines for predicting stock movement and return rate are implemented. Our proposed HMM-ALSTM achieves an average 10% improvement on all data sets compared to the best baseline.     

RoSAS: Deep semi-supervised anomaly detection with contamination-resilient continuous supervision

Semi-supervised anomaly detection methods leverage a few anomaly examples to yield drastically improved performance compared to unsupervised models. However, they still suffer from two limitations: 1) unlabeled anomalies (i.e., anomaly contamination) may mislead the learning process when all the unlabeled data are employed as inliers for model training; 2) only discrete supervision information (such as binary or ordinal data labels) is exploited, which leads to suboptimal learning of anomaly scores that essentially take on a continuous distribution. Therefore, this paper proposes a novel semi-supervised anomaly detection method, which devises contamination-resilient continuous supervisory signals. Specifically, we propose a mass interpolation method to diffuse the abnormality of labeled anomalies, thereby creating new data samples labeled with continuous abnormal degrees. Meanwhile, the contaminated area can be covered by new data samples generated via combinations of data with correct labels. A feature learning-based objective is added to serve as an optimization constraint to regularize the network and further enhance the robustness w.r.t. anomaly contamination. Extensive experiments on 11 real-world datasets show that our approach significantly outperforms state-of-the-art competitors by 20%–30% in AUC-PR and obtains more robust and superior performance in settings with different anomaly contamination levels and varying numbers of labeled anomalies.