Reviewerly/exHarmony huggingface dataset:
Intelligent Segmentation Medical Assistance System for MRI Images of Osteosarcoma in Developing Countries

Osteosarcoma is the most common primary malignant bone tumor in children and adolescents. It has a high degree of malignancy and a poor prognosis in developing countries. The doctor manually explained that magnetic resonance imaging (MRI) suffers from subjectivity and fatigue limitations. In addition, the structure, shape, and position of osteosarcoma are complicated, and there is a lot of noise in MRI images. Directly inputting the original data set into the automatic segmentation system will bring noise and cause the model’s segmentation accuracy to decrease. Therefore, this paper proposes an osteosarcoma MRI image segmentation system based on a deep convolution neural network, which solves the overfitting problem caused by noisy data and improves the generalization performance of the model. Firstly, we use Mean Teacher to optimize the data set. The noise data is put into the second round of training of the model to improve the robustness of the model. Then, we segment the image using a deep separable U-shaped network (SepUNet) and conditional random field (CRF). SepUnet can segment lesion regions of different sizes at multiple scales; CRF further optimizes the boundary. Finally, this article calculates the area of the tumor area, which provides a more intuitive reference for assisting doctors in diagnosis. More than 80000 MRI images of osteosarcoma from three hospitals in China were tested. The results show that the proposed method guarantees the balance of speed, accuracy, and cost under the premise of improving accuracy.
Understanding the challenges of immersive technology use in the architecture and construction industry: A systematic review

Despite the increasing scholarly attention being given to immersive technology applications in the architecture and construction industry, very few studies have explored the key challenges associated with their usage, with no aggregation of findings or knowledge. To bridge this gap and gain a better understanding of the state-of-the-art immersive technology application in the architecture and construction sector, this study reviews and synthesises the existing research evidence through a systematic review. Based on rigorous inclusion and exclusion criteria, 51 eligible articles published between 2010 and 2019 (inclusive) were selected for the final review. Predicted upon a wide range of scholarly journals, this study develops a generic taxonomy consisting of various dimensions. The results revealed nine (9) critical challenges which were further ranked in the following order: Infrastructure; Algorithm Development; Interoperability; General Health and Safety; Virtual Content Modelling; Cost; Skills Availability; Multi-Sensory Limitations; and Ethical Issues.
An enhanced binary Rat Swarm Optimizer based on local-best concepts of PSO and collaborative crossover operators for feature selection

In this paper, an enhanced binary version of the Rat Swarm Optimizer (RSO) is proposed to deal with Feature Selection (FS) problems. FS is an important data reduction step in data mining which finds the most representative features from the entire data. Many FS-based swarm intelligence algorithms have been used to tackle FS. However, the door is still open for further investigations since no FS method gives cutting-edge results for all cases. In this paper, a recent swarm intelligence metaheuristic method called RSO which is inspired by the social and hunting behavior of a group of rats is enhanced and explored for FS problems. The binary enhanced RSO is built based on three successive modifications: i) an S-shape transfer function is used to develop binary RSO algorithms; ii) the local search paradigm of particle swarm optimization is used with the iterative loop of RSO to boost its local exploitation; iii) three crossover mechanisms are used and controlled by a switch probability to improve the diversity. Based on these enhancements, three versions of RSO are produced, referred to as Binary RSO (BRSO), Binary Enhanced RSO (BERSO), and Binary Enhanced RSO with Crossover operators (BERSOC). To assess the performance of these versions, a benchmark of 24 datasets from various domains is used. The proposed methods are assessed concerning the fitness value, number of selected features, classification accuracy, specificity, sensitivity, and computational time. The best performance is achieved by BERSOC followed by BERSO and then BRSO. These proposed versions are comparatively assessed against 25 well-regarded metaheuristic methods and five filter-based approaches. The obtained results underline their superiority by producing new best results for some datasets.
EDDI: Efficient Dynamic Discovery of High-Value Information with Partial VAE

Many real-life decision making situations allow further relevant information to be acquired at a specific cost, for example, in assessing the health status of a patient we may decide to take additional measurements such as diagnostic tests or imaging scans before making a final assessment. Acquiring more relevant information enables better decision making, but may be costly. How can we trade off the desire to make good decisions by acquiring further information with the cost of performing that acquisition? To this end, we propose a principled framework, named EDDI (Efficient Dynamic Discovery of high-value Information), based on the theory of Bayesian experimental design. In KDDI, we propose a novel partial variational autoencoder (Partial VAE) to predict missing data entries problematically given any subset of the observed ones, and combine it with an acquisition function that maximizes expected information gain on a set of target variables. We show cost reduction at the same decision quality and improved decision quality at the same cost in multiple machine learning benchmarks and two real-world health-care applications.
Mixture Models for Diverse Machine Translation: Tricks of the Trade

Mixture models trained via EM are among the simplest, most widely used and well understood latent variable models in the machine learning literature. Surprisingly, these models have been hardly explored in text generation applications such as machine translation. In principle, they provide a latent variable to control generation and produce a diverse set of hypotheses. In practice, however, mixture models are prone to degeneracies---often only one component gets trained or the latent variable is simply ignored. We find that disabling dropout noise in responsibility computation is critical to successful training. In addition, the design choices of parameterization, prior distribution, hard versus soft EM and online versus offline assignment can dramatically affect model performance. We develop an evaluation protocol to assess both quality and diversity of generations against multiple references, and provide an extensive empirical study of several mixture model variants. Our analysis shows that certain types of mixture models are more robust and offer the best trade-off between translation quality and diversity compared to variational models and diverse decoding approaches.\footnote{Code to reproduce the results in this paper is available at \url{this https URL}}
Supervised Domain Adaptation for Automatic Sub-cortical Brain Structure Segmentation with Minimal User Interaction

Abstract In recent years, some convolutional neural networks (CNNs) have been proposed to segment sub-cortical brain structures from magnetic resonance images (MRIs). Although these methods provide accurate segmentation, there is a reproducibility issue regarding segmenting MRI volumes from different image domains – e.g., differences in protocol, scanner, and intensity profile. Thus, the network must be retrained from scratch to perform similarly in different imaging domains, limiting the applicability of such methods in clinical settings. In this paper, we employ the transfer learning strategy to solve the domain shift problem. We reduced the number of training images by leveraging the knowledge obtained by a pretrained network, and improved the training speed by reducing the number of trainable parameters of the CNN. We tested our method on two publicly available datasets – MICCAI 2012 and IBSR – and compared them with a commonly used approach: FIRST. Our method showed similar results to those obtained by a fully trained CNN, and our method used a remarkably smaller number of images from the target domain. Moreover, training the network with only one image from MICCAI 2012 and three images from IBSR datasets was sufficient to significantly outperform FIRST with (p &lt; 0.001) and (p &lt; 0.05), respectively.
An ensemble of neural models for nested adverse drug events and medication extraction with subwords

Abstract Objective This article describes an ensembling system to automatically extract adverse drug events and drug related entities from clinical narratives, which was developed for the 2018 n2c2 Shared Task Track 2. Materials and Methods We designed a neural model to tackle both nested (entities embedded in other entities) and polysemous entities (entities annotated with multiple semantic types) based on MIMIC III discharge summaries. To better represent rare and unknown words in entities, we further tokenized the MIMIC III data set by splitting the words into finer-grained subwords. We finally combined all the models to boost the performance. Additionally, we implemented a featured-based conditional random field model and created an ensemble to combine its predictions with those of the neural model. Results Our method achieved 92.78% lenient micro F1-score, with 95.99% lenient precision, and 89.79% lenient recall, respectively. Experimental results showed that combining the predictions of either multiple models, or of a single model with different settings can improve performance. Discussion Analysis of the development set showed that our neural models can detect more informative text regions than feature-based conditional random field models. Furthermore, most entity types significantly benefit from subword representation, which also allows us to extract sparse entities, especially nested entities. Conclusion The overall results have demonstrated that the ensemble method can accurately recognize entities, including nested and polysemous entities. Additionally, our method can recognize sparse entities by reconsidering the clinical narratives at a finer-grained subword level, rather than at the word level.
Pseudonym Management Through Blockchain: Cost-Efficient Privacy Preservation on Intelligent Transportation Systems

Research into the established area of the intelligent transportation system is evolving into the Internet of Vehicles, a fast-moving research area, fuelled in part by rapid changes based on cyberphysical systems.It needs to be recognized that existing vehicular communication systems are susceptible to privacy vulnerabilities which require addressing.A practical challenge is that many vehicular communication applications and services make use of basic safety messages that contain the identity of the vehicle, location, and other personal data.A popular way of dealing with this privacy issue is to utilize a pseudonym change scheme to protect the vehicle's identity and location.However, many such schemes suffer that the cost grows and the certificate management difficulty raises with the number of pseudonyms generated and stored, casting doubt of the economic feasibility of that approach.We propose a decentralized blockchain-based solution for pseudonym management that overcomes these limitations.This scheme consists of pseudonym distribution and a shuffle operation, allowing the reuse of existing pseudonyms to different vehicles.The results reported here, including those from our simulations, demonstrate that the proposed scheme can reuse existing pseudonyms and achieve a better degree of anonymity at a lower cost than existing schemes.
Visual Tracking via Dynamic Graph Learning

Existing visual tracking methods usually localize a target object with a bounding box, in which the performance of the foreground object trackers or detectors is often affected by the inclusion of background clutter. To handle this problem, we learn a patch-based graph representation for visual tracking. The tracked object is modeled by with a graph by taking a set of non-overlapping image patches as nodes, in which the weight of each node indicates how likely it belongs to the foreground and edges are weighted for indicating the appearance compatibility of two neighboring nodes. This graph is dynamically learned and applied in object tracking and model updating. During the tracking process, the proposed algorithm performs three main steps in each frame. First, the graph is initialized by assigning binary weights of some image patches to indicate the object and background patches according to the predicted bounding box. Second, the graph is optimized to refine the patch weights by using a novel alternating direction method of multipliers. Third, the object feature representation is updated by imposing the weights of patches on the extracted image features. The object location is predicted by maximizing the classification score in the structured support vector machine. Extensive experiments show that the proposed tracking algorithm performs well against the state-of-the-art methods on large-scale benchmark datasets.
Perspectives on the Gap Between the Software Industry and the Software Engineering Education

The gap between the software industry and software engineering education was first mentioned three decades ago, in 1989. Since then, its existence has been regularly reported on and solutions to close it have been proposed. However, after thirty years this gap resists all efforts for closure. In this study we assert that the gap between industry and academia exists for several reasons that are related and intertwined. To take a broader look at the problem from the perspective of all related entities, we (i) provide a detailed overview of the profession and identify the entities, (ii) extract the causes that stem from these entities and discuss what each entity should do, (iii) report and analyze the results of a questionnaire that has been conducted with students and recent graduates, (iv) emphasize the highlights of the interviews conducted with students, recent graduates and academics, (v) and compile a list of skills that are sought by the industry by analyzing the software engineering job advertisements. We further contribute to finding solutions by considering all entities involved, which provides an opportunity to access all of them, so that each can find out what they can do to acknowledge and narrow the gap. Our study concludes that the gap requires constant attention and hard work for all of the entities involved, and therefore all should be on the lookout for new technologies, learn to embrace the changes and adapt to them, so that the gap is kept at a minimum.
Large Memory Layers with Product Keys

This paper introduces a structured memory which can be easily integrated into a neural network. The memory is very large by design and significantly increases the capacity of the architecture, by up to a billion parameters with a negligible computational overhead. Its design and access pattern is based on product keys, which enable fast and exact nearest neighbor search. The ability to increase the number of parameters while keeping the same computational budget lets the overall system strike a better trade-off between prediction accuracy and computation efficiency both at training and test time. This memory layer allows us to tackle very large scale language modeling tasks. In our experiments we consider a dataset with up to 30 billion words, and we plug our memory layer in a state-of-the-art transformer-based architecture. In particular, we found that a memory augmented model with only 12 layers outperforms a baseline transformer model with 24 layers, while being twice faster at inference time. We release our code for reproducibility purposes.
Glyce: Glyph-vectors for Chinese Character Representations

It is intuitive that NLP tasks for logographic languages like Chinese should benefit from the use of the glyph information in those languages. However, due to the lack of rich pictographic evidence in glyphs and the weak generalization ability of standard computer vision models on character data, an effective way to utilize the glyph information remains to be found. In this paper, we address this gap by presenting Glyce, the glyph-vectors for Chinese character representations. We make three major innovations: (1) We use historical Chinese scripts (e.g., bronzeware script, seal script, traditional Chinese, etc) to enrich the pictographic evidence in characters; (2) We design CNN structures (called tianzege-CNN) tailored to Chinese character image processing; and (3) We use image-classification as an auxiliary task in a multi-task learning setup to increase the model's ability to generalize. We show that glyph-based models are able to consistently outperform word/char ID-based models in a wide range of Chinese NLP tasks. When combing with BERT, we are able to set new state-of-the-art results for a variety of Chinese NLP tasks, including language modeling, tagging (NER, CWS, POS), sentence pair classification (BQ, LCQMC, XNLI, NLPCC-DBQA), single sentence classification tasks (ChnSentiCorp, the Fudan corpus, iFeng), dependency parsing, and semantic role labeling. For example, the proposed model achieves an F1 score of 81.6 on the OntoNotes dataset of NER, +1.5 over BERT; it achieves an almost perfect accuracy of 99.8\% on the the Fudan corpus for text classification.
Learning to Predict Layout-to-image Conditional Convolutions for Semantic Image Synthesis

Semantic image synthesis aims at generating photorealistic images from semantic layouts. Previous approaches with conditional generative adversarial networks (GAN) show state-of-the-art performance on this task, which either feed the semantic label maps as inputs to the generator, or use them to modulate the activations in normalization layers via affine transformations. We argue that convolutional kernels in the generator should be aware of the distinct semantic labels at different locations when generating images. In order to better exploit the semantic layout for the image generator, we propose to predict convolutional kernels conditioned on the semantic label map to generate the intermediate feature maps from the noise maps and eventually generate the images. Moreover, we propose a feature pyramid semantics-embedding discriminator, which is more effective in enhancing fine details and semantic alignments between the generated images and the input semantic layouts than previous multi-scale discriminators. We achieve state-of-the-art results on both quantitative metrics and subjective evaluation on various semantic segmentation datasets, demonstrating the effectiveness of our approach.
A Transfer-Based Additive LS-SVM Classifier for Handling Missing Data

The performance of a classifier might greatly deteriorate due to missing data.Many different techniques to handle this problem have been developed.In this work, we solve the problem of missing data using a novel transfer learning perspective and show that when additive LS-SVM is adopted, model transfer learning can be used to enhance classification performance on incomplete training datasets.A novel transferbased additive LS-SVM classifier is accordingly proposed.This method also simultaneously determines the influence of classification errors caused by each incomplete sample using a fast leave-one-out cross validation strategy, as an alternative way to clean the training data to further improve data quality.The proposed method has been applied to seven public datasets.The experimental results indicate that the proposed method achieves at least comparable, if not better, performance than case deletion, mean imputation, and k-nearest neighbor imputation methods, followed by the standard LS-SVM and SVM classifiers.Moreover, a case study on a community healthcare dataset using the proposed method is presented in detail, which particularly highlights the contributions and benefits of the proposed method to this real world application.
Few-shot Text Classification with Distributional Signatures

In this paper, we explore meta-learning for few-shot text classification. Meta-learning has shown strong performance in computer vision, where low-level patterns are transferable across learning tasks. However, directly applying this approach to text is challenging–lexical features highly informative for one task maybe insignificant for another. Thus, rather than learning solely from words, our model also leverages their distributional signatures, which encode pertinent word occurrence patterns. Our model is trained within a meta-learning framework to map these signatures into attention scores, which are then used to weight the lexical representations of words. We demonstrate that our model consistently outperforms prototypical networks learned on lexical knowledge (Snell et al., 2017) in both few-shot text classification and relation classification by a significant margin across six benchmark datasets (19.96% on average in 1-shot classification).
Knowing what's coming: Anticipatory audio cues can mitigate motion sickness

Being able to anticipate upcoming motion is known to potentially mitigate sickness resulting from provocative motion. We investigated whether auditory cues could increase anticipation and subsequently reduce motion sickness. Participants (N = 20) were exposed on a sled on a rail track to two 15-min conditions. Both were identical in terms of motion, being composed of the same repeated 9 m fore-aft displacements, with a semi-random timing of pauses and direction. The auditory cues were either 1) informative on the timing and direction of the upcoming motion, or 2) non-informative. Illness ratings were recorded at 1-min intervals using a 11-point scale. After exposure, average illness ratings were significantly lower for the condition that contained informative auditory cues, as compared to the condition without informative cues. This knowledge, i.e. that auditory signals can improve anticipation to motion, could be of importance in reducing carsickness in domains such as that of autonomous vehicles.
Quantum in the Cloud: Application Potentials and Research Opportunities

Quantum computers are becoming real, and they have the inherent potential to significantly impact many application domains. We sketch the basics about programming quantum computers, showing that quantum programs are typically hybrid consisting of a mixture of classical parts and quantum parts. With the advent of quantum computers in the cloud, the cloud is a fine environment for performing quantum programs. The tool chain available for creating and running such programs is sketched. As an exemplary problem we discuss efforts to implement quantum programs that are hardware independent. A use case from machine learning is outlined. Finally, a collaborative platform for solving problems with quantum computers that is currently under construction is presented.
Sequence-Level Mixed Sample Data Augmentation

Despite their empirical success, neural networks still have difficulty capturing compositional aspects of natural language.This work proposes a simple data augmentation approach to encourage compositional behavior in neural models for sequence-to-sequence problems.Our approach, SeqMix, creates new synthetic examples by softly combining input/output sequences from the training set.We connect this approach to existing techniques such as SwitchOut (Wang et al., 2018) and word dropout (Sennrich et al., 2016), and show that these techniques are all approximating variants of a single objective.SeqMix consistently yields approximately 1.0 BLEU improvement on five different translation datasets over strong Transformer baselines.On tasks that require strong compositional generalization such as SCAN and semantic parsing, Se-qMix also offers further improvements.